gut microbiome in rats: effects of diet on …...gut microbiome in rats: effects of diet on...
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Gut microbiome in rats: Effects of
diet on community structure and
host-microbiome interactions
Heli Jaime Barron Pastor
A thesis submitted for the degree of DOCTOR OF PHILOSOPHY of
the Australian National University (May 2017)
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DECLARATIONExcept where otherwise indicated, this thesis is my original work.
Heli Jaime Barron Pastor
15 May 2017
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ACKNOWLEDGMENTS
Very big and warmest thanks to my beloved wife Yesica, and my children Melissa and
Alejandro for your support and endless love. Thank you because you helped and
supported me in every way possible. This thesis is dedicated to you, you have been my
inspiration throughout this project; thanks for always standing close to me. Surely in all
this time I have changed; but my love for you has not changed, even though I was far
away.
I am very much grateful to my supervisor, Professor David Gordon, whose support and
enthusiastic guidance was invaluable throughout this wonderful journey; thank you very
much for your incredible support and generosity. I would also like to thank Professor
David Cooper and Professor William Foley for their academic and technical support
during my research journey. Special thanks to Dr. Charles Hocart for his valuable
guidance in developing protocols for Short Chain Fatty Acid analysis.
I would also like to extend my gratitude to my lab mates, current and former colleagues
at the “Gordon Lab” for creating a nice working atmosphere. Special thanks to Belinda,
Alex, Angeline, Samantha, Nythia, Delia and Melissa for sharing lovely moments in the
lab. Thank you Kimmy for trying to keep me on track of the faith, for all our scientific
and also non-scientific discussions, for sharing laughs and for listening to me through
difficult times.
I wish to thank my kiwi friend Mamaria for our non-sense mutual advice of finding fun
and happiness and for sharing some lovely like-familiar times. My heartfelt gratitude is
also with my Australian friends of the suburb of Yarralumla and my housemate and
international friends for the happy multicultural dinners and other memorable moments.
The PhD was funded by the scholarship: “Becas de Excelencia Presidente de la
Republica”. I wish to express my gratitude to the Peruvian Government for giving me
this opportunity to do my PhD at the Australian National University.
May 2017
Heli Jaime Barron Pastor
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PREFACE
This thesis presents the studies carried out at the Research School of Biology, College
of Medicine, Biology and Environment, The Australian National University (ANU) during
the years 2013-2015 under the supervision of Professor David Gordon. Acting advisors
were Professor Paul Cooper and Professor William Foley.
The in vivo model experiments, microbiological and molecular analyses were carried
out at the Gordon Lab, Fibre analysis in Foley Lab and Chromium and Cobalt analysis
for gut transit time experiment at the Geology Department.
Gas Chromatography was developed and carried out in the Mass Spectrometric
Facility of ANU. High Throughput Sequencing was executed in the John Curtin School
of Medical Research.
The present thesis is submitted to fulfil the requirements for obtaining the degree of
PhD in Ecology, Evolution and Genetics.
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SUMMARY
Host-microbe interactions are now considered essential for maintaining host health. It
is known that short and long term dietary interventions influences the structure and
activity of gut bacterial communities. However, our understanding of the forces shaping
the gut microbiota is still limited and controversial, and most of the studies of the gut
microbiota use the microbiota from faeces as a proxy for the intestinal tract
populations. As such, the overarching aim of this thesis is to contribute to the
understanding of host-microbiome interactions using an animal model.
In this thesis I describe the effect of diet changes on microbial community structure and
host-microbiome interactions following 14 weeks on one of the three experimental
diets. The diets consisted of a basal diet low in fibre (LF); the basal diet together with
26 % cellulose; a difficult to ferment fibre (HF); and the basal diet together with 50%
dried cooked red kidney beans (B); a diet relatively high in easily fermentable fibre.
These diets were fed to 45, 21 day old female Wistar rats originating from 6 litters for
14 weeks.
Diet had little effect on rat growth rates or adult body mass. However, diet had
profound effects on gastro-intestinal morphology and dynamics. Caecum size was
smallest in animals fed the LF diet, and caecums were about 2x as large in animals fed
the B diet, while animals on the HF diet had intermediate-sized caecums. Food transit
times were slowest in animals on the B and LF diets and fastest in animals on the HF
diets. At the end of the diet experiment, colon and caecum contents were collected
when the animals were killed and short chain fatty acids, nitrogen, carbon, as well fibre
concentrations were determined. These data showed that the ‘chemical’ environment
of the hindgut varied substantially among animals fed the different diets.
E. coli diversity and dynamics were described by characterizing more than three
thousand isolates. E. coli diversity was low, and more than 97% of the isolates were
represent by three strains: one phylogroup B2 strain and two phylogroup B1 strains. A
decline of the frequency of the B2 strain in the animals fed on the bean diet was
observed.
The faecal microbiota was characterized when the animals were 21 days old, while
faecal, caecal and rectal microbial communities characterized at the end of the
experiment. 16S amplicon sequencing of the V4 region on the Ion Torrent platform
was the approach used to characterize the microbiota.
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Members of 23 microbial families were detected in communities of the animals before
and after 14 weeks on the experimental diets. At the start of the experiment there were
significant litter membership effects on the structure of the faecal microbial
communities. After 14 weeks on the experimental diets, both litter and diet explained a
significant amount of the variation in microbial community structure. There were
substantial differences in the microbial communities of the caecum and rectum and the
extent of these differences depended on diet and on the time taken for material to
move through the hindgut.
The outcomes of the present study make a contribution to our understanding of the
factors that shape gut microbial communities. Microbial characterization of faecal
samples is frequently used as proxy of gut microbiota. However, stool samples are
probably most likely representative of the microbial communities in the rectum than
other parts of the gastrointestinal tract. Indeed, the findings also throw doubt on the
value of faecal community characterization as a means to understand community
structure and function in the gastro-intestinal tract. Further, the results of these
experiments suggest that efforts attempting to achieve positive health outcomes
through diet manipulation may have limited success in general due to among individual
differences in microbial community composition, and in how these different
communities respond to dietary manipulation.
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LISTOFTABLES
CHAPTER1Table1.1Maintypeofcarbohydrates.......................................................................................................16CHAPTER2Table2.1Distributionofanimalperdietandlitter....................................................................................30Table2.2DietaryfibreandcarbonandnitrogencompositionofHighFibreDiet(HF),LowFibreDiet(LF)andFermentableFibre(B)..........................................................................................................................32CHAPTER3Table3.1ParameterestimationofGompertzmodelanalysisoffemaleWistarratsbydiet.....................49Table3.2Two-way(factorial)ANOVAdeterminingtheeffectoflitteranddietonmaximunbodymassparameteroffemaleWistarrats...............................................................................................................49Table3.3Estimationoffaecalcomposition(%)bydiettreatmentonfemaleWistarrats........................51Table3.4Estimationofcompositionofexperimentaldiets(%)fedtoexperimentalanimals...................52Table3.5Estimationofdigestibilityofdietarycomponents......................................................................52Table3.6Massvariation(g)ofgutcomponentsofanimalsfedondietsHF,LFandB.............................53Table3.7Effectoflitter,dietandbodymassondrymassongutcomponents.........................................53Table3.8ComparisonondietspairsofmeansusingTukey-KramerHSDforcaecumdrymassandcolondrymass.....................................................................................................................................................53Table3.9GuttransittimeparametersinhoursofparticulatemattermarkerandliquiddigestamarkerinWistarfemaleratsunderthethreeexperimentaldiettreatments............................................................55Table3.10ForegutretentionofCrtimebydiet.........................................................................................57Table3.11Matchedpairsreportonthedifferenceofliquidmarkerandparticulatemarker(CoRetention-CrRetention)............................................................................................................................................58Table3.12RelativeamountofshortchainfattycaidsobservedfromthecaecumcontentoffemaleWistarratsbythethreediettreatments...................................................................................................59Table3.13EffectofdietandlitterintheproductionofindividualSCFA....................................................59Table3.14ComparisonofallpairsofdietontheproductionofSCFA(usingTukey_KramerHSD)............61CHAPTER4Table4.1RelationshipbetweenpredominantE.coligenotypeanddietinthelastweekofdietaryintervention................................................................................................................................................71Table4.2DieteffectonEcoligenotypesinthelastweekofdietaryintervention.....................................71CHAPTER5Table5.1One-wayANOSIManalysisofcomparisonofthestructureofbacterialcommunitiesinthefaecesofWistarfemaleratsafter14weeksfedonHF,LFandBdiets......................................................90Table5.2FamilylevelabundancevariationamongdiettreatmentsinthefaecalmicrobiotaoffemaleWistarratsafter14weeksofbeingfedoneoftheexperimentsldiets......................................................91Table5.3One-wayANOSIMofgutmicrobialcommunitiesatfamilyleveloffemaleWistarrats.............95Table5.4One-wayANOSIMcomparisonofthedieteffectinthegutmicrobiotaoffemaleWistarrats..95Table5.5Atwo-wayPERMANOVAresultsofallcommunitycompositionofthethreegroups(HF,LFandB)inthecaecumoffemaleWistarrats......................................................................................................96Table5.6SIMPERanalysis(dissimilaritycontribution)ofpredominantBacteroidetesFamilybydietinthegutmicrobiotaoffemaleWistarrats.........................................................................................................98Table5.7SIMPERanalysis(dissimilaritycontribution)ofpredominantFirmicutesFamilybydietinthegutmicrobiotaoffemaleWistarrats...............................................................................................................99
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Table5.8SIMPERanalysis(dissmilaritycontribution)ofpredominantProteobacteriaFamilybydietinthegutmicrobiotaoffemaleWistarrats.................................................................................................101Table5.9Effectofdietandparticulatemarker(Cr)retentionontheshiftofbacterialcommunitieswhenmovingfromcaecumtocolon..................................................................................................................104Table5.10Effectofdietandhindgutretentiontimeofparticulatemarkerintheshiftofmicrobialcommunities(caecum-rectum)onfamilymembersofFirmicutes...........................................................109Table5.11Effectofdietandhindgutretentiontimeofparticulatemarkerintheshiftofmicrobialcommunities(caecum-colon)onfamilymembersofBacteroidetes........................................................110Table5.12Effectofdietandhindgutretentiontimeofparticulatemarkerintheshiftofmicrobialcommunities(caecum-colon)onfamilymembersofProteobacteria.......................................................111
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LISTOFFIGURES
CHAPTER 3 Figure3.1Compositionofalimentarycanalsofhumansandrats.............................................................46Figure3.2Gompertzexpressionaverageonbodymasschangesbydiet..................................................48Figure3.3Cobaltmeanconcentration(mg/gfaeces)&Chromiummeanconcentration(mg/gfaecesversustime(hours).....................................................................................................................................55Figure3.4Principalcomponentanalysisofshortchainfattyacidsprofilebydiet....................................60CHAPTER4Figure4.1E.colicelldensityperweekinfemaleWistarratsfedHFdiet..................................................70Figure4.2EcolicelldensityperweekinfemaleWistarratsfedLFdiet....................................................70Figure4.3EcolicelldensityperweekinfemaleWistarratsfedBdiet.....................................................70Figure4.4FrequencyofE.coliphylogeneticgroupsinfaecalsamplesofanimalsduringdietaryintervention................................................................................................................................................72Figure4.5Two-wayinteractionplotofleastsquareonEcolicelldensityandsexualmaturityoffemaleWistarrats..................................................................................................................................................74CHAPTER5Figure5.1Diversityofbacterialcommunitiesinfaecalbaseline,caecumandrectumoffemaleWistarrats.............................................................................................................................................................83Figure5.2Compositionoffaecalmicrobiota(baseline)offemaleWistarratsatPhylumlevel.................84Figure5.3RelativeabundanceofpredominantfamiliesinthefaecesoffemaleWistarrats,beforedietarytreatment(baseline)..................................................................................................................................85Figure5.4Non-metricmultidimensionalscaling(nMDS)plotofbacterialcommunitycompositioninthefaecesoftheanimalsbydietbeforestartingdietaryintervention(baseline).Inthisfigureandinthefollowingrelatedfigures,acolourcodeisusedtoidentifymicrobialcommunityofeachanimalbydiet:HFdiet=Green,LF=Blue,Bdiet=Red.....................................................................................................86Figure5.5Compositionoffaecalmicrobiota(afterdietaryintervention)offemaleWistarratsatPylumlevelbydiet................................................................................................................................................87Figure5.6RelativeabundanceofpredominantfamiliesintherectumoffemaleWistarratsafter14weeksondietaryintervention....................................................................................................................88Figure5.7Non-metricmultidimensionalscaling(nMDS)plotoftotalbacterialcommunitycompositionintherectumoffemaleWistarratsafter14weeksofdietaryintervention..................................................89Figure5.8CompositionofcaecummicrobiotaoffemaleWistarratsatPhylumlevelbydiet...................90Figure5.9RelativeabundanceofpredominantfamiliesinthecaecumoffemaleWistarratsafterHF,LFandBdietarytreatments...........................................................................................................................93Figure5.10Non-metricmultidimensionalscaling(nMDS)plotoftotalbacterialcommunitycompositioninthecaecum(afterdietaryintervention)offemaleWistarratsbydiet...................................................94Figure5.11Non-metricmultidimensionalscaling(nMDS)plotofcaecalandcolonicbacterialcommunititesoffemaleWistarratsafterdietaryintervention...............................................................102Figure5.12Caecal-colonicshiftofbacterialcommunitiesoffemaleWistarratsfedHF,LFandBdiets.103Figure5.13Caecum-colondistanceshiftandtotalgutretentiontime(Crparticulatemarker).)............104Figure5.14ChangesinRuminococcaceaeabudancewhilemovingfromcaecumtorectuminfemaleWistarrats................................................................................................................................................106Figure5.15ChangesinPeptostreptococcaceaeabundancewhilemovingfromceacumtorectuminfemaleWistarrats....................................................................................................................................107Figure5.16ChangesinClostridiaceaeabundancewhilemovingfromceaecumtorectuminfemaleWistarrats................................................................................................................................................107
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Figure5.17ChangesinAlcaligenaceaeabundancewhilemovingfromceaecumtocoloninfemaleWistarrats...........................................................................................................................................................112
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TABLEOFCONTENTS
DECLARATION............................................................................................................ii
ACKNOWLEDGMENTS...............................................................................................iii
PREFACE...................................................................................................................iv
SUMMARY.................................................................................................................v
LISTOFTABLES........................................................................................................vii
LISTOFFIGURES........................................................................................................ix
TABLEOFCONTENTS.................................................................................................xi
Chapter1INTRODUCTION.......................................................................................14Motivationofthestudyandoriginalcontribution............................................................14Effectofdietaryfibreongastrointestinalphysiologyandnutritionofmammals..............14Factorsthatinfluencemicrobialcommunitycompositioninthegastrointestinaltract.....18Interactionsbetweengutmicrobiotaandimmuneresponse,hostphysiology,andmetabolism......................................................................................................................21Characterizationofmicrobialcommunities......................................................................23Objectives........................................................................................................................25
Objectiveone......................................................................................................................25Objectivetwo......................................................................................................................26Objectivethree...................................................................................................................27Objectivefour.....................................................................................................................27
Chapter2MATERIALSANDMETHODS.....................................................................29Ethicsapproval.................................................................................................................29Studyanimalsandexperimentaldesign...........................................................................29Animalhusbandry............................................................................................................31Dietsandtreatments.......................................................................................................31Monitoringfoodconsumptionandfaecalproduction.......................................................32
Faecalproduction...............................................................................................................32Digestibility.........................................................................................................................32
MeanRetentionTime......................................................................................................33Preparationfaecalsamplesanddietsamplesforfibre,NitrogenandCarbonestimates...34Fibreestimates................................................................................................................34Methodsoffibreanalysis.................................................................................................34CarbonandNitrogencontent...........................................................................................36CharacterizationofE.coli.diversityanddynamics...........................................................37
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EnumerationofE.coli..........................................................................................................37GenotypingE.colistrains....................................................................................................37Clermontgenotyping..........................................................................................................38E.colistrainfingerprinting-ERICPCR................................................................................38
Bacterialcommunitycharacterization..............................................................................38Genomiclibraries................................................................................................................38QuantificationofDNAproducts,LibraryNormalizationandPooling,andHighThroughputSequencingAnalysis............................................................................................................39
Bioinformaticsanalysisofbacterialcommunities.............................................................39Killingtheanimalsandgutmorphologyanalysis..............................................................40Shortchainfattyacidanalysisofthecaecumcontent:GasChromatography–MassSpectrometry(GC-MS).....................................................................................................41
Determinationofshortchainfattyacids............................................................................41Chemicals............................................................................................................................41Extractionprocedure..........................................................................................................41GasChromatography-MassSpectrometry(GC-MS).........................................................42Validation............................................................................................................................42Linearityandsensitivity......................................................................................................43Optimizationoftheextractionprotocol.............................................................................43
StatisticalAnalyses...........................................................................................................43
Chapter3HOSTRESPONSE......................................................................................44Introduction.....................................................................................................................44Results.............................................................................................................................48
Bodymassandgrowthparameters....................................................................................48Foodconsumptionanddigestibility....................................................................................49Fibre,CarbonandNitrogenanalysis...................................................................................51Gutmorphologyanalysis....................................................................................................52Transittimeparameters.....................................................................................................54Shortchainfattyacidsinthecaecum.................................................................................58
Discussion........................................................................................................................62
Chapter4E.coliRESPONSE......................................................................................66Introduction.....................................................................................................................66Results.............................................................................................................................69
E.colicelldensity................................................................................................................69E.coligenotypinginrelationtodietandlitter...................................................................69
Discussion........................................................................................................................74
Chapter5EFFECTOFDIETONGUTBACTERIALCOMMUNITIES.................................76Introduction.....................................................................................................................76Methods..........................................................................................................................79Results.............................................................................................................................82
ComparisonI:EffectofChromiunandCobalt(usedfortransittimeexperiments)onmicrobialcommunitycomposition.....................................................................................82ComparisonII:Littereffectonmicrobialcommunitycompositionbeforeandafterdietaryintervention........................................................................................................................83
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ComparisonIII.DifferencesinCaecumandRectummicrobiotaatage17weeks(14weeksofdietaryintervention).......................................................................................................92ComparisonIV.Effectsofsiteanddietofguttransittimeexperimentinmicrobialcommunitystructureandshiftoncaecal-colonicmicrobialcommunities.....................101
Discussion......................................................................................................................113
CONCLUSIONSANDFUTUREPERSPECTIVES...........................................................120
REFERENCES:.........................................................................................................122
APPENDIX..............................................................................................................131DNAExtraction...............................................................................................................131ClermontGenotyping.....................................................................................................131ERIC-PCR........................................................................................................................132PREPARATIONOFGENOMICLIBRARIES..........................................................................133
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Chapter1 INTRODUCTION
Motivationofthestudyandoriginalcontribution
The human body can be considered as a super-organism, as prokaryote cells
outnumber host cells by a factor of ten (Sleator, 2010). However revisited analysis
sugests a more realistic ratio bacterial cells/human cells (B/H) closer to 1:1 until more
accurate estimations are availble (Sender, Fuchs, & Milo, 2016). Independenlty of the
values of B/H ratio of 1:1, 10:1 or 100:1, bacterial communities in and on the body
affect host function in many ways (Sleator, 2010). The extended definition of the
human microbiome includes different and highly dynamic ecosystems in the body
biogeography (Zhou et al., 2013). From all of these ecosystems, the gut microbiota, a
life partner for mammals, is particularly important for nutrition, detoxification, priming
the immune system and behavior. Consequently, human health is seen as a product of
services delivered by body ecosystems (Costello et al., 2009; Costello, Stagaman,
Dethlefsen, Bohannan, & Relman, 2012). Much of the work about gut microbiota in
vertebrates is described over short time frames or are cross-sectional analyses
describing static microbial communities. Moreover most of the studies of the gut
microbiota use faeces microbiota as proxy for the gastrointestinal tract microbial
populations.
It is known that dietary intervention influences microbial community structure in the gut.
Previous studies have reported the relationship between the variation of fibre content in
rats’ diet and the distribution of a single member (E. coli) of gut microbial community
(Herawati, 2006; Montagne, Pluske, & Hampson, 2003; O'Brien, 2005). However,
much uncertainty still exists about the relationship between the effects of fibre intake
changes on bacterial community and the current knowledge concerning interactions of
the host and its microbiome is still limited and controversial.
Effectofdietaryfibreongastrointestinalphysiologyandnutritionofmammals
Carbohydrates and Fibre
Carbohydrates are the main source of energy in human diets. Chemically,
carbohydrates include a variety of constituents such as polyhydroxy aldehydes,
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ketones, alcohols and acids, including their derivatives and polymers like starch and
other polysaccharides. Carbohydrates are chemically characterized based on
molecular size and the degree of polymerization (Englyst & Englyst, 2005). The
carbohydrates that are important for nutrition can be separated into two broad
categories: those digested and absorbed in the small intestine (glycaemic
carbohydrates) which are also called non-structural carbohydrates or non-fibrous
carbohydrates and those that pass to the large intestine to form a substrate for the
colonic micro flora, referred to as complex or non-starch carbohydrates (dietary fibre)
(Agostini, 2010).
Glycans are polymers of simple sugars connected by covalent bonds. The term
glycans is frequently used as synonymous of polysaccharides (Koropatkin, Cameron, &
Martens, 2012). Carbohydrates are defined as glycaemic when they are absorbed in
the small intestine and are available for metabolism; and are defined as non-glycaemic
when they enter the large intestine as substrate for bacterial fermentation (Englyst &
Englyst, 2005). Glycaemic carbohydrates are those that provide carbohydrates to body
cells in the form of glucose. The main glycaemic carbohydrates are glucose, fructose,
sucrose and lactose, malto-oligosaccharides and starch; they are easily hydrolysed by
enzymatic reactions and absorbed in the small intestine (Agostini, 2010; Lattimer &
Haub, 2010) (Table 1.1). The non-glycemic carbohydrates are partially or completely
fermented in the colon.
The influx of glycans, mainly from diet, is one of the main sources of energy for
bacteria in the gut (Koropatkin et al., 2012); this event is crucial in the coevolution of
host and its microbiota because it increases significantly the energy available from
food. In the large intestine the bacteria degrade polysaccharides and other glycol-
conjugates that come from the small intestine; the energy is recovered not only for the
use of bacteria but also a significant amount of Carbon is converted into short chain
fatty acids (Johansson, Larsson, & Hansson, 2011). The microbial ecology can be
greatly affected by fermentable carbohydrates either as substrates or by supplying
short chain fatty acids (Topping & Clifton, 2001).
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Table1.1Maintypeofcarbohydrates
Class (Degree of polymerization)
Sub-group Components Digestibility in small intestine *
Sugars (1-2) Monosaccharides Disaccharides
Glucose Galactose Fructose Sucrose Lactose Trehalose Maltose
+ + + +
+(-)** + +
Oligosaccharides (3-9)
Malto-oligo-saccharides Other oligosaccharides
Maltodextrines α-Galactosides (GOS) Fructo-oligosaccharides (FOS) Polydextrose Resistant dextrins
+ - - - -
Polyols Maltitol, sorbitol, sylitol, lactitol
+/-
Polysaccharides (>9)
Starch Non-starch polysaccharides
Amylose Amylopectin Modified starch Resistant starch Inulin Cellulose Hemicellulose Pectins Other hydrocolloids (gums, mucilages, β- glucans)
+(-) +(-)
- - - - - - -
Related substance
lignin -
• *Denotes digestibility in small intestine: + digestible, +(-) mainly digestible, +/- partly digestible, - non-digestible
• ** Lactose is poorly digested in individuals with low intestinal activity. (Adapted from Agostini, 2010)
Dietary fibre
Dietary fibre has distinct physiological effects compared to glycaemic carbohydrates.
Dietary fibre is the edible part of plants that is resistant to digestion and absorption in
the small intestine and requires bacterial fermentation for breakdown in the large
intestine. Dietary fibre, also called non-starch polysaccharide (NSP) is reflective in
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chemical analysis as Neutral Detergent Fibre (NDF) and Acid Detergent Fibre (ADF).
hemicellulose, cellulose and lignin are components of NDF, while cellulose and lignin
are components of ADF (Lattimer & Haub, 2010).
Non-starch polysaccharides can also be subdivided based on chemical, physical and
functional properties, into two general groups of soluble and insoluble fibre.
Accordingly, soluble fibre are those that dissolve in water forming viscous gels, bypass
the digestion in the small intestine and are easily fermented by microbiota of the large
intestine. On the other hand, insoluble fibres are not water soluble in the human
gastrointestinal tract, do not form gels and fermentation is severely limited (Lattimer &
Haub, 2010).
Dietary fibre and host response
Although the beneficial effects of fibre have been suggested for centuries, this topic
has been scientifically explored in the last 40 years. The first systematic mode of action
of fibre in human health in the gastrointestinal tract was expressed in terms of its
indigestibility; in the so called “roughage model”(Topping & Clifton, 2001).
The potential beneficial effects of dietary fibre are based on direct and indirect
evidence. A study revealed that weight gain is inversely associated with the intake of
high fibre, as whole-grain; this association is independent of other factors as body
mass at baseline or age (Liu et al., 2003). Other studies describe that dietary fibre
affect growth and body mass parameters in laboratory animals; Zhao et al. described
that there is a difference in body mass and visceral organ parameters in rats feeding
on low fibre and high content fibre. The same study revealed that the size of
gastrointestinal tract is increased in animals fed on a diet with high fibre content (X.
Zhao, Jorgensen, & Eggum, 1995).
Studies in humans revealed that populations consuming a diet containing high
unrefined cereals, as native East Africans, are at a lower risk of gastrointestinal
disorders like colorectal cancer, diverticular disease and constipation compared to
Europeans who ate a diet with a low content of such foods (Topping & Clifton, 2001).
A study conducted by O’Brien (2005) revealed that variation of dietary fibre intake from
1% to 26% in rats significantly affected digestibility and visceral mass. Thus, dietary
18
fibre affected the mass of the colon and stomach significantly. However, there was no
change in caecum and small intestine mass (O'Brien, 2005; O’Brien & Gordon, 2011).
A study compared the diet recommended by the American Diabetes Association (ADA)
versus high dietary fibre intake. The findings revealed that high intake of soluble fibre
improved glycemic control and insulinemia levels in patients with diabetes type 2
compared to the diet recommended by ADA. However, it remains unclear whether this
effect was due to the increase of soluble or insoluble fibre (Chandalia et al., 2000).
The level of dietary fibre is not the only important parameter for evaluating its beneficial
effects in digestion and absorption, but the type of fibre (fermentable vs. non-
fermentable) plays an important role in the gastrointestinal physiology (Wenk, 2001).
The beneficial effects of dietary fibre include the reduction in the foregut and increase
in the hindgut transit time, increase in production of short chain fatty acids and
stimulating regular peristalsis (Wenk, 2001).
Factorsthatinfluencemicrobialcommunitycompositioninthegastrointestinaltract
It has been described that microbiota are vertically inherited from mothers (Maria G
Dominguez-Bello et al., 2010) and the community composition is stable over time (Ley,
Peterson, & Gordon, 2006). However, as described in a culture based study, the
composition of gut microbiota in infants is determined by other factors that include
delivery mode (caesarean or vaginal), type of feeding (breastfeeding or formula),
gestational age, antecedents of antibiotic usage and hospitalization of the infant
(Penders et al., 2006).
There are at least 7 divisions of bacteria in the human gut. However, in humans as well
as in rodents, more than 95% of the bacterial community comprises only two divisions:
Firmicutes and Bacteroidetes (Ley, Peterson, et al., 2006). Members of phylum
Bacteroidetes and Firmicutes do not appear to grow outside of the gastrointestinal
ecosystem, yet it is possible for pathogenic Proteobacteria as Vibrio cholerae (Ley,
Peterson, et al., 2006) to proliferate in external environments.
The relationship between the members of gut microbiota and human host has been
traditionally described as commensal (one partner benefits and the other is
19
unaffected). However, from an evolutionary point of view the term mutualism is
preferred. In a mutualistic relationship both partners are favoured (Ley, Peterson, et
al., 2006). Ley et al. proposed that the diversity in the microbial community in the gut is
the result of coevolution between bacterial communities and their hosts.
Diversity of microbial communities in the gastrointestinal tract (GIT).
The human gastrointestinal tract harbours a diverse and complex bacterial community.
This diversity varies depending on the location of each region of the GIT.
Approximately, 700 species can be found in the mouth, in which the genus
Streptococcus, a member of the Firmicutes phylum, is quantitatively dominant (Aas,
Paster, Stokes, Olsen, & Dewhirst, 2005). There are 95 species that have been
detected in the oesophagus; most of them resembling species found in the oral cavity
(Pei et al., 2004). On the other hand, in the stomach the only resident specie is
Helicobacter pylori (Blaser, 1997); however, 16S rRNA analysis revealed 128 species
in this portion of the gastrointestinal tract, representing either transient or resident
strains (Bik et al., 2006). Moreover, in the small intestine the bacterial population is
higher in the portion near the colon (ileum), increasing the proportion of anaerobic
species. Finally, the bacterial population is greatly increased in the caecum and colon
with more than 800 species that represent nine bacteria and one archaeal phylum
(Ley, Peterson, et al., 2006)
Factors that influence diversity
- Method of colonization
In humans, the initial microbiota community of a new born comes from vagina and
faeces of the mother as this is the first exposure to the external microbial community
(Maria G Dominguez-Bello et al., 2010; Maria Gloria Dominguez-Bello, Blaser, Ley, &
Knight, 2011). Evidence suggests that the bacterial colonization of babies delivered by
caesarean section is altered compared to their vaginally delivered counterparts (Maria
G Dominguez-Bello et al., 2010; Ley, Peterson, et al., 2006). In contrast to vaginal
delivery, in caesarean section delivery, the lack of vaginal exposure results in the first
microbial communities resembling the mother’s skin microbiota (Maria G Dominguez-
Bello et al., 2010).
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- Diet
This factor is especially important in shaping the gut microbiota. Evidence shows that
there is a difference in faecal microbiota of infants who are breast-fed and those fed on
formula diets (Harmsen et al., 2000). According to a recent study, in formula fed
babies the predominant members in the gut microbiota are Bacteroides, Clostridium
and Lactobacillus, while in breast fed infants the microbiota was dominated by
members of Bifidobacteria (Fallani et al., 2010).
- Host genotype
Interindividual variation in the intestinal microbiota can be explained by the influence of
host genotype (Spor, Koren, & Ley, 2011). Host genotype has an important effect in
shaping microbial community in the gastrointestinal tract (Zoetendal, Akkermans,
Akkermans-van Vliet, de Visser, & de Vos, 2001).
Microbiota is highly variable from person to person (Costello et al., 2009) and there is
also variation in different body sites within the same host. There is evidence of
similarity in the microbiota of family members compared to unrelated individuals;
similar bacterial strains are found among members of the same family in humans and
other mammals (Song et al., 2013). Likewise, genetically related individuals have
similar microbiota regardless of whether they cohabitate or not (Turnbaugh et al.,
2009).
Another study demonstrated that the Major Histocompatibility Complex (MHC) plays an
important role in modulating the gut microbiota (Toivanen, Vaahtovuo, & Eerola, 2001).
Findings revealed that mice with similar background and different MHC have
significantly different faecal microbiota, confirming that MHC alone has a profound
effect in faecal microbiota in mice.
- Immune tolerance
The intestinal mucosa is highly adapted to an environment containing an enormous
amount of bacteria in the lower gastrointestinal tract. The organism is adapted to the
fact that systematic immune response is tolerant to this very active microbial population
in the gut (Macpherson, Geuking, & McCoy, 2005).
21
- Microbial interactions.
It has been proposed, based on the niche construction theory, that the gut microbiota
has the capacity to construct and modify their local environment (Day, Laland, &
Odling-Smee, 2003). Niche-construction is described as an evolutionary process
rather than as a product of natural selection. Based on this theory microbial
interactions can also determine the selection pressure for determining intestinal niches.
However, interactions between specific members of the gut microbiota in modifying
their own environment remain unexplored.
Interactionsbetweengutmicrobiotaandimmuneresponse,hostphysiology,andmetabolism
The mammalian immune system plays an important role in shaping microbial
communities; at the same time resident bacteria in the gut can shape host immunity
(Hooper, Littman, & Macpherson, 2012).
Effect of host immunity in gut microbiota
Despite the symbiotic relationship between the intestinal microbiota and mammalian
host, the close association of rich bacterial community in gut tissues represents
immense challenges to the point that if this complex and dynamic interaction is
disrupted it could represent serious health consequences for the host (Hooper et al.,
2012). The immune system has evolved adaptations to preserve this mutualistic
relationship between the host and its microbiota.
An important function of the immune system is to prevent the exposure of the host to
potential pathogenic bacteria. Stratification and compartmentalization are mechanisms
to prevent bacteria crossing the immunological barrier to the bloodstream in the host
gut (Hansson & Johansson, 2010; Hooper et al., 2012). This is particularly important
because the intestinal immune system faces enormous challenges compared to other
organs due to the high bacterial density in the lower gastrointestinal tract. Stratification
is a mechanism to minimize direct contact between intestinal bacteria and surface of
gastrointestinal cells and, compartmentalization is to limit and confine penetrant
bacteria limiting their exposure (Hooper et al., 2012). The inner mucus layer prevents a
direct contact between the great numbers of potential pathogenic commensal bacteria
22
and the epithelia. Several immune effectors function to minimize contact of bacteria
with intestinal epithelium (Vaishnava et al., 2011). Compartmentalization and
stratification of bacteria depend on antibacterial proteins and secreted immunoglobulin
produced by cells of the gut and immune system, limiting the penetration of bacteria
into the epithelia. For example, Reg IIIy is an antibacterial lectin involved in the
promotion of the mutualistic relationship between the host and its microbiota regulating
compartmentalization (Vaishnava et al., 2011). Dendritic cells as well as other immune
cells promote compartmentalization of gut bacteria. Another example is mucin
glycoproteins, a thick and viscous secretion produced by goblet cells; in this case, the
outer mucus layer contains a great amount of bacteria while the inner mucus is
resistant to bacterial colonization. The inner mucus layer prevents a direct contact of
the great numbers of potential pathogenic commensal bacteria with the epithelia
(Hansson & Johansson, 2010). Defects in this mucus layer can trigger colon
inflammation and ulcerative colitis.
Effect of gut microbiota in host immunity
Evidence supports the theory that developmental aspects of adaptive immune system
are influenced by gut microbiota. There is a beneficial partnership between symbiotic
bacteria and host immune system. It is described that the immune system has been
developed to protect from microbial pathogens, however a peaceful partnership
coexists with the vast and complex microbial community in the gut (Round &
Mazmanian, 2009). Members of the gut microbiota can induce inflammation and
disease under particular conditions, though some symbiotic bacteria can prevent this
situation. As suggested by a recent study, some symbiotic bacteria can present anti-
inflammatory properties. The same study highlights the importance that certain aspects
of human health depend on the status of the gut microbiota (Mazmanian, Round, &
Kasper, 2008).
Effect of gut microbiota in host physiology
The effects of gut microbiota on host physiology are far of merely biochemistry
changes. These effects include morphogenesis and organ development (Shin et al.,
2011). In addition to the effects in immune system and metabolic function, a recent
study revealed that the host intestinal microbiota plays an important role on behaviour,
cell proliferation and even brain neurochemistry and behaviour (Bercik et al., 2011).
23
Characterizationofmicrobialcommunities
Initial characterization of microbial communities has been started based on culture
methods developed more than a century ago (Dethlefsen, Eckburg, Bik, & Relman,
2006). These methods do not have enough sensitivity to identify most members of
bacterial communities in the gut ecosystem due to the fact that more than 85% of
bacterial are uncultivable. However, in the last 15 years, our knowledge on microbial
diversity in the gut ecosystem has been dramatically expanded by molecular
techniques using the 16S rRNA gene (Clarridge, 2004). Next generation sequencing is
dramatically increasing our understanding in microbial genomics, providing sufficient
sequencing data to evaluate micro-ecosystems.
Methods to analyse microbial communities
Historically, identification of bacteria was based on culture-based methods using
morphology of the colony, gram stain, carbohydrate fermentation and other
biochemical tests (Bertelli & Greub, 2013). However, besides being laborious, it has
been estimated that more than 80% of bacteria in the gut cannot be cultivated in the
laboratory (Eckburg et al., 2005).
Culture independent sequencing analysis of different variable regions of the 16S rRNA
gene has been proposed for taxonomic classification (Clarridge, 2004). Combined
variable regions V1-V3, V3-V4, V6-V9 achieved similar accuracy classification, while
V2 and V4 regions were the most accurate regions among individual regions (Claesson
et al., 2010). Access to sequencing platforms available and limitation in funding often
place a dilemma on deciding which fragment of the 16S rRNA gene should be selected
for sequencing (Clarridge, 2004).
Described in the 1970s, Sanger sequencing is a technology that uses inhibitors to
terminate newly synthesized chains at specific residues. 2',3'-dideoxy and
arabinonucleoside are analogues of the normal deoxynucleoside triphosphates, that
inhibit specific chain-termination of DNA polymerase (Sanger, Nicklen, & Coulson,
1977). After this event, Sanger sequencing rapidly became the gold standard for DNA
sequencing. It was in 2005, that the new high-throughput sequencing technologies
were commercially available and were referred to as ‘next generation sequencing’
24
technologies, replacing traditional and automated Sanger’s sequencing method
(Bertelli & Greub, 2013).
The most widely used platform for DNA sequencing from 1995 to 2005 was automated
sanger-sequencing technology. This technology used a chemical and enzyme based
approach developed by Sanger et al. in 1977, integrating capillary electrophoresis and
fluorescent detection. However, the limitation of this technology to detect rare mutation
and its prohibitive costs have anticipated the need for new generation sequencing
technologies (Strausberg, Levy, & Rogers, 2008). The new sequencing technology
employs sequencing by synthesis in a massively parallel format at a substantially lower
cost than automated sanger-sequencing technology. However, this new technology
has several limitations including shorter read lengths compared to those achieved by
automated Sanger technology (35 to 250 bp), that can be overcome using an
appropriate methodology.
The principle of Solexa Illumina sequencing is the use of reversible terminator
chemistry (Bentley et al., 2008). Essentially, single DNA molecules are attached to a
flat surface and amplified in situ and used as a template for the next step of synthetic
sequencing. Synthetic sequencing is performed with fluorescent terminator
deoxyribonucleotides. Images generated in the surface are used to generate the
sequences.
Ion torrent methodology, similar to Solexa Illumina sequencing, is also considered as
the second generation of high throughput sequencing. Their developers have
described this new technology as a low cost semiconductor manufacturing technique
for non-optical sequencing of genomes. In this platform, the sequences data are
obtained by directly detecting the ions produced by template directed DNA polymerase
synthesis in an ion chip. The ion chip allows parallel simultaneous detection in 1.2
million wells. In this platform the detection is based on changes of pH measurements
(Bentley et al., 2008; Rothberg et al., 2011). Changes of pH in the well are produced
when protons (H) are released. Protons are released when nucleotides are
incorporated in the growing DNA strands.
25
Objectives
Objectiveone
Potential health benefits of dietary fibre have been previously described. Dietary fibre
(DF) affects lower gastrointestinal tract environment. DF enhances the production of
short chain fatty acids in the colonic environment. Fibre intake can retain or accelerate
transit time in the gut; this event could affect bacterial establishment, particularly in the
caecum, affecting the digestive performance and short chain fatty acid production. The
rate and amount of short chain fatty acid production depend on the type and quantity of
fibre ingested and quantity and diversity of the microbiota present in the
gastrointestinal tract(Wong, de Souza, Kendall, Emam, & Jenkins, 2006). Several
epidemiological studies emphasized the effect of dietary fibre in gastrointestinal
dynamics and host metabolism (Brownlee, 2011; Lissner, Lindroos, & Sjöström, 1998).
However, the findings still remain controversial (Graff, Brinch, & Madsen, 2001;
Madsen, 1992).
The aim is to evaluate the factors that can influence host gastrointestinal dynamics
including gut transit time, caecum morphology, food consumption and digestibility,
short chain fatty acids production and Carbon an Nitrogen contents.
This objective was satisfied by estimating the fibre contents in the experimental diets
and animal faeces, estimating carbon and nitrogen contents in diets and faeces,
estimating the gut transit time and analysing the short chain fatty acids. Fibre content in
experimental diets and faecal pellets of the animals in the study was estimated using
gravimetric methods. Gut transit time estimation was performed using chromium and
cobalt-EDTA as markers for large particles and fluid digesta, respectively. Short chain
fatty acids in the caecum were analysed through a gas chromatography – mass
spectrometry (GC-MS) based protocol standardized as part of this research. Carbon
and Nitrogen were analysed through Mass Spectrometry techniques. Food
consumption and digestibility were also estimated. All results were combined to predict
changes in the gastrointestinal environment, body mass parameters and
gastrointestinal morphology.
26
Objectivetwo
Analysis of the variation of the number of CFU of E. coli strains as well as the
phylogroup classification based on the exposure to diet treatments have highlighted the
importance of diet on evaluation of the dynamics and persistence of E. coli in the gut
(Herawati, 2006; O’Brien & Gordon, 2011). Although other methods can be used to
gain an understanding of E. coli persistence and dynamics in the host, phylogroup
classification based on PCR techniques is a standardized method to compare E. coli
strains. A comparison of E. coli phylogroups of strains isolated from the rat populations
in this study was made based on the relationship of the different fibre contents and
different types of fibre (fermentable versus non-fermentable) in their diets. This type of
comparison also emphasizes the importance of age and host genetic background in
persistence of E. coli and how they may differ between different diet treatments, litters
and relative position in their cages.
The aim is to investigate the effect of fermentable and non-fermentable dietary fibre on
the diversity and dynamics of E. coli in the gut.
Specifically, the aim purposes is to use the information on E. coli enumeration and
classification of phylogroups to assess the persistence of this bacterium in the gut of
the host, based on diet composition and time spent feeding the animals in study.
To fulfil this first objective, faecal samples were collected from the animals in study for
microbiological and molecular analyses. Faecal samples were collected every week
and cultured on MacConkey agar. After quantifying the number of E. coli CFU per gram
of faecal matter, 12 colonies were randomly selected for phylogrouping. The number of
CFU per gram of faeces was enumerated weekly throughout the whole period of the
experiment. The phylogroup classification was conducted on almost 3000 E. coli
strains. The sampling was performed for 14 weeks. The same experiment was
conducted on E. coli isolates obtained from the gut contents sampled in the week the
animals were killed. The data was combined with information on the composition of the
three diets delivered to the animals to estimate the density and/or the variability of E.
coli density.
27
Objectivethree
Molecular profiling methods have provided unprecedented insights into the assessment
of the diversity within the gut microbiota community. There are several factors that can
affect the structure of these communities. Although some major features of mammalian
gut microbiota are conserved, especially related to genetic background and method of
birth, the gut microbiota can be altered according to the breeding conditions,
environmental conditions and diet. It is known that dietary intervention influences
microbial community structure in the gut
In this third objective, the aim was to evaluate the factors that can affect the diversity of
the microbiota community. Quality of dietary fibre and intake as well as selection of
animals from the same litter and sex, have been an important part of the experimental
design to prevent influence of intervening factors.
To fulfil all the objectives of this thesis, including the third objective, animals of the
same sex (females) and litter were selected. Gut microbiota diversity was analysed
using Ion torrent platform high throughput sequencing. 16s RNA sequencing results
were combined with fibre analysis to evaluate the temporary variation of the diversity of
the gut microbiota. Genomic libraries were analysed based on samples collected
before diet treatment, in the intermediate period under diet treatment, and at the end of
the experiment.
Objectivefour
Characterization of gut microbiota has been the major source of studies in the last ten
years to elucidate relationship with host physiology and related metabolic diseases.
However more of the studies describing the effect of diet in gut microbial community
diversity and host physiology are conducted using faeces as proxy of gastrointestinal
microbial communities and do not address the relationship with gastrointestinal
dynamics. Here we used custom diets that differ in the content of fermentable and non-
fermentable fibre to investigate these events.
The aim was to study the effect of feeding different types of fibre on differences in
caecal and colonic microbial communities and to investigate the interplay among
dietary fibre, gut microbiota, and gut dynamics in the host.. At the same time, another
28
related aim was to evaluate the interaction effects of diet, gastrointestinal environment
and dynamics on the quality of the microbial community through the gastrointestinal
tract. This fourth objective was fulfilled by characterizing caecal and colonic bacterial
communities and evaluating the differences in both environments. High throughput
sequencing based on the 16s RNA gene was conducted to characterize bacterial
communities in the caecum and in the colon before and after dietary treatments. The
information collected in all experiments was combined to explain variation in microbial
communities.
29
Chapter2 MATERIALSANDMETHODS
The methodologies used in this thesis were chosen to satisfy the objectives and
carefully balanced to fulfil the requirements of the overall project. All methods and
considerations are described in this chapter.
Ethicsapproval
The details of all experimental procedures in this research complied with the research
integrity guidelines of the Australian National University. With respect to the animal
experimentation and care of the animals under study, the experimentation protocol was
approved by the Animal Experiment Ethic Committee of the Australian National
University (Protocol number A2013/24).
Studyanimalsandexperimentaldesign
A total of 45, pathogen free, outbred (Wistar) rats were obtained from the Animal
Resources Centre, Western Australia. Subjects were 21 days old on arrival at the
laboratory. As soon as the animals arrived at the animal facility laboratory, they were
given one week to acclimatize with free access to commercial rat food and water ad
libitum prior to initiating the diet treatments. As we also wanted to assess the genetic
background of the animals, all animals were selected from 6 different litters. There was
no information about how many males and females were in each litter. However, this
study selected only females out of each litter. Of all the animals in the study, there
were 3 litters comprising 9 animals each, and 3 litters with 6 animals each. According
to the experimental design, each diet (low fibre, high fibre and beans) was randomly
assigned to the 45 animals while ensuring that each block had all three diets.
Experimental design.
Animals were randomly distributed in three racks. Diets also were randomly distributed
among the animals. Each animal in a block was from the same litter. There were six
litters. Each litter comprised 6 or 9 animals. Block 1, Block 6 and Block 11 were the
upper level while Block 5, Block 10 and Block 15 were the lower level of the rack,
respectively. Animals were randomly assigned to three different diet treatments: HF =
30
Diet High in non-fermentable fibre; LF= Diet Low in non-fermentable fibre; B = Diet
based in fermentable fibre. The experiment was designed according to the table 2.1.
Table2.1Distributionofanimalperdietandlitter
Rack Cage Diet Treatment Litter Animal ID
1
1 HF 2 1HF 2 B 2 1B 3 LF 2 1LF 4 HF 4 2HF 5 B 4 2B 6 LF 4 2LF 7 LF 6 3LF 8 HF 6 3HF 9 B 6 3B
10 B 1 4B 11 HF 1 4HF 12 LF 1 4LF 13 HF 5 5HF 14 LF 5 5LF 15 B 5 5B
2
16 B 5 6B 17 HF 5 6HF 18 LF 5 6LF 19 LF 4 7LF 20 B 4 7B 21 HF 4 7HF 22 B 2 8B 23 HF 2 8HF 24 LF 2 8LF 25 HF 3 9HF 26 LF 3 9LF 27 B 3 9B 28 HF 6 10HF 29 LF 6 10LF 30 B 6 10B
3
31 HF 5 11HF 32 LF 5 11LF 33 B 5 11B 34 B 2 12B 35 HF 2 12HF 36 LF 2 12LF 37 HF 4 13HF 38 B 4 13B 39 LF 4 13LF 40 HF 1 14HF 41 B 1 14B 42 LF 1 14LF 43 HF 3 15HF 44 B 3 15B 45 LF 3 15LF
31
Animalhusbandry
The animals were housed individually in a single room for 14 weeks in a standard rat
cage. No other animals were housed in the room. The room was maintained at 21 °C
with a 12 hour light/dark cycle and a relative humidity of 50 to 60%. Constant
ventilation was ensured to supply adequate oxygen and optimize airflow and
recirculation. To acclimatize, the animals were fed on a standard rat diet for one week,
followed by diet treatments that were assigned as mentioned before. All animals had
free access to water. In order to collect faecal pellets for microbiological and molecular
analysis, every week the animals were transferred to a clean cage with new bedding;
with a block of cages being changed per day. To determine the body mass, each
animal was weighed weekly, starting from block 1 and cage 1 to block 3 and cage 3 on
Monday; from block 4 and cage 1 to block 6 and cage 3 on Tuesday, and so on. Water
was provided ad libitum. The housing was under optimal hygienic conditions. Latex
gloves and lab coat were always used whenever the animals were handled.
Dietsandtreatments
There were three different diets: Low content of non-fermentable fibre (LF), High
content of non-fermentable fibre (HF) and fermentable fibre (B) One type of diet was
assigned to a group of 15 animals. Commercial Low Fibre Diet (LF), Hills prescription
diet i/d ® canine gastrointestinal health; Commercial High Fibre Diet (HF), Hills
prescription diet w/d® Canine Low Fat-Glucose Management-Gastrointestinal; and
fermentable fibre Kidney Beans Diet (B). Fermentable Kidney Bean Diet was prepared
by mixing Hills prescription diet i/d ® canine gastrointestinal health with ground dry red
kidney beans. For this purpose, cooked large kidney beans (Masterfood® brand) were
removed from the can, drained, soaked and dried at 50 °C for 4 days until a constant
weight was reached. Dry beans were ground, mixed 50/50% with ground dog food Hills
prescription diet i/d ® canine gastrointestinal health and pelleted again. Fermentable
fibre (B) pellets were prepared by Specialty Feed, a company based in Western
Australia, coded as SF13-112 Customer supplied raw material Batch 090582. Then the
so called Bean (B) diets were vacuum packed in 2 Kg bags and stored at -20 °C.
Dietary fibre components, carbon and nitrogen contents of experimental diets were
estimated and are shown in table 2.2.
32
Table2.2DietaryfibreandcarbonandnitrogencompositionofHighFibreDiet(HF),LowFibreDiet(LF)andFermentableFibre(B)
Composition (% Dry matter) HF LF B Neutral detergent fibre Acid detergent fibre Acid detergent lignin Carbon Nitrogen
15.86 8.71 0.53
46.91 5.61
5.16 1.56 0.28
46.35 4.20
6.72 2.89 0.27
46.07 4.38
Monitoringfoodconsumptionandfaecalproduction
Animals were fed with commercial rat food and water ad libitum for one week. Fresh
food of each type of diet according to the experimental design and fresh clean water
were provided daily between 7:00 and 9:00 a.m. for 13 weeks. Food consumption was
monitored one week before the end of the experiment. A fixed amount of diet was
weighed with 20% to 30% more food than the animals would eat weekly. The weekly
amount of food consumption was determined by the difference between the amount of
food provided and the food remaining.
𝐹𝑜𝑜𝑑 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛
= 𝐴𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑓𝑜𝑜𝑑 𝑔𝑖𝑣𝑒𝑛 − 𝐴𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑓𝑜𝑜𝑑 𝑙𝑒𝑓𝑡 𝑖𝑛 𝑡ℎ𝑒 𝑐𝑎𝑔𝑒
Faecalproduction
Faecal production was determined one week before the end of the experiment. This
information was used to estimate the faeces produced daily by a rat and to determine
the digestibility. Faecal production was estimated by removing all faeces produced by
the animal over the week 13 on diet treatment. Faecal pellets produced in the whole
week for each animal were collected and dried at 75 °C for digestibility and Carbon and
Nitrogen analysis.
Digestibility
The following formula (Herawati, 2006) was used to estimate the apparent dry matter
digestibility, which was calculated in the same period of time (one week).
33
𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑏𝑖𝑙𝑖𝑡𝑦 =𝐹𝑜𝑜𝑑 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 − 𝐹𝑎𝑒𝑐𝑒𝑠 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛
𝐹𝑜𝑜𝑑 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛
MeanRetentionTime
Retention time was determined at the end of the diet treatment experiment using the
methods described by Caton (1997) (Caton, 1997). Cobalt-Ethylene
Diamminetetraacetic Acid (Co-EDTA) and Chromium mordanted on cell wall
constituent (non-digestible fibre NDF) were used to determine retention time of fluid
and particulate matter, respectively. The pellet was prepared using 0.1 g of Co -EDTA,
0.2 g of NDF mordant with Chromium and 1.5 g of the respective ground diet, mixed
with 1 ml of egg white, then moulded using a pellet machine to form +/- 3 cm marker
pellet (1 cm diameter). Marker pellets were dried for 12 hours at room temperature and
kept at -20 °C until being delivered to the animals.
Animals were fasted for 12 hours, with only water ad libitum (starting at 8 p.m.) prior to
presenting them with the marker pellets (next day at 8 a.m.). All animals were allowed
to ingest the pellet or at least more than 75% of it, which took between 5 to 30 minutes.
Afterwards, the remainder of pellets was removed and all animals were provided with
their respective diets. All faeces produced by each animal was collected every 2 hours
for 52 hours. Each animal was transferred to a clean cage to collect faeces. The faeces
was stored in 15 ml Falcon® tubes and dried to a constant weight at 75 °C. Dried
faeces was weighed, ground and stored at -20 °C until further analysis.
Chemical digestion of the faecal samples was performed in a chemical fume hood.
Ground and weighed samples from each tube were soaked for 12 hours in a 100 ml
flask tube containing 10 ml of 1:1 mixture of 70% nitric acid and deionized water.
Subsequently, 10 ml of 70% nitric acid was added and heated until the liquid started
boiling and left to simmer for 30 minutes. Then, the flasks were removed from the hot
plate, allowed to cool and 5 ml of 70% nitric acid was added. After that, they were
returned to the hot plate and boiled vigorously until the volume reduction was about 5
ml. When it was cool, 10 ml of Hydrogen peroxide (33% w/w) was added and gently
heated until the volume reduced to approximately 5 ml. The content of each flask was
transferred to a 15 ml pre-labelled centrifuge tube and 3% nitric acid was added to
reach a final volume of 14 ml. The tubes were centrifuged at 4400 g for 30 minutes.
The supernatant was transferred to a pre-labelled 15 ml centrifuge tube and 3% nitric
34
acid was added to bring the volume to 15 ml. The samples were stored at room
temperature until being analysed at the laboratory of Geology Department. Elemental
Chromium and Cobalt were determined in each sample using Inductively Coupled
Plasma Atomic Emission Spectrometer (ICP-AES).
Preparationfaecalsamplesanddietsamplesforfibre,NitrogenandCarbonestimates
On the last week of the experiment, the total amount of faecal pellets produced in one
week by each animal was collected. Faecal samples and each diet were used to
estimate the content of Neutral Detergent Fibre (NDF), Acid Content Fibre (ADF) and
Acid Lignin Fibre. The amounts of Nitrogen and Carbon were also determined. Initially,
the samples were dried to a constant weight at 50 °C. Afterwards, they were ground in
a Cyclotec 1093 Sample Mill (Tecator ®, Sweden) using a 2 mm mesh. Subsequently,
NDF content, ADF, lignin, Nitrogen and Carbon content were estimated in duplicate by
the RSB Stable Isotope Laboratory (Dr Hilary Stuart-Williams).
Fibreestimates
Methodsoffibreanalysis
There are several analytical techniques for fibre analysis. Goering and Van Soet
developed protocols using detergents [acid detergent fibre (ADF), neutral detergent
fibre (NDF) and acid detergent lignin (ADL)]; these methods are proven to be good for
fibre analysis (de‐Oliveira et al., 2012).
In NDF method, a neutral detergent solution is used to dissolve the easily digested
pectins and plant cell constituents (sugars, proteins and lipids) (Ferreira & Mertens,
2007). The remaining residues are primary cellulose, hemicellulose and lignin. Heat
stable amylase is used to remove gelatinized starch (de‐Oliveira et al., 2012); poor
extraction occurs when there is no amylase in the reaction (Ferreira & Mertens, 2007).
The function of the detergent is to solubilize proteins and sodium sulphite helps to
remove nitrogenous. EDTA is used to chelate calcium and remove pectins. Non-fibrous
matter is removed by triethylene glycol (Ferreira & Mertens, 2007).
ADF is the residue obtained after boiling the sample in acid detergent solution, and
includes lignin, cellulose, silica and insoluble forms of nitrogen, excluding hemicellulose
35
(de‐Oliveira et al., 2012) (Jung, 1997). The ADF method uses the NDF method as pre-
treatment (Jung, 1997). ADL is considered not digestible and can be completely
recovered from faeces. Usually the procedure is performed after ADF determination.
Neutral Detergent Fibre (NDF; Filter Bag Technique) is the residue that remains after
digestion in a detergent solution and is predominantly hemicellulose, cellulose and
lignin. Acid Detergent Fibre (ADF; Filter Bag Technique) is the residue remaining after
digesting with H2SO4 and CTAB, in which cellulose and lignin are predominant. Acid
Detergent Lignin (ADL; Filter Bag Technique) is the organic residue that is left after
digesting with 24 N H2SO4. ADL analysis is usually performed after ADF determinations
and removes structural carbohydrates except lignin. Duplicate samples were analysed
for all methods.
Extraction of detergent fibre was performed using filter bags, and was based on a
process using a fibre analyser (Ankom 220, Ankom Technology Corp). Briefly, for NDF
and ADF, 2 litres of neutral detergent or acid detergent was poured into the fibre
analyser vessel (100 ml/bag of AD solution). Filter bags (F57, 25 µm, Ankom
Technology Corp) containing 0.5 g of the samples were placed in plastic trays, the lid
was sealed, the heat and agitation turned on for 75 and 60 minutes of extraction,
respectively. Following the extraction, the detergent solution was expelled and rinsed
three times for 5 minutes with 2 litres of water at 80-90 °C. Furthermore, only in NDF
analysis, 5 ml of heat-stable α-amylase was used for the first and second rinse. After
the last washing, the filter bags were removed from the analyser vessel and gently
pressed to remove water. Filter bags were placed in a beaker. Subsequently, enough
acetone was added to cover the bags, then soaked for 5 minutes. Afterwards, the bags
were removed and acetone residues were allowed to evaporate by air-drying. Finally,
the bags were dried in a forced-air oven at 102 °C for 8 hours before being weighed
(Ferreira & Mertens, 2007). The content of NDF and ADF was estimated using the
following formula.
% 𝑁𝐷𝐹 𝑎𝑠 − 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑏𝑎𝑠𝑖𝑠 =𝑊! − 𝑊!𝑥𝐶! 𝑥 100
𝑊!
Where: %NDF = Percentage of Neutral Detergent Fibre, W1 = Bag tare weight, W2 =
Sample weight, W3 = Dried weight of bag with fibre after extraction process and C1 =
Blank bag correction factor (final oven-dried weight divided by original weight)
36
%𝐴𝐷𝐹 𝑎𝑠 − 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑏𝑎𝑠𝑖𝑠 =(𝑊! − 𝑊!𝑥𝐶! ) 𝑥100
𝑊!
Where: %ADF = Percentage of Acid Detergent Fibre, W1= Bag tare weight, W2=
sample weight, W3= Dried weight of bag with fibre after extraction process and C1=
Blank bag correction factor (final oven-dried weight divided by original weight)
For estimating lignin content, ADF residue was used. Completely dry bags/samples
were placed in a 3 L beaker. For each 24 filter bags, approximately 250 ml of 72%
H2SO4 was added to cover the bags. An empty 2 L beaker was placed inside the 3 L
beaker to keep the bags submerged. Bags were agitated every 30 minutes for 3 hours.
Then, the H2SO4 was poured out and rinsed several times with warm H2O until a
neutral pH was reached. Subsequently, the samples were rinsed three times with
acetone to remove the water and placed in an oven at 105 °C for 4 hours.
The content of Acid Detergent Lignin was estimated using the following formula.
% 𝐴𝐷𝐿 𝑎𝑠 − 𝑖𝑠 𝑏𝑎𝑠𝑖𝑠 =𝑊! − 𝑊!𝑥𝐶! 𝑥 100
𝑊!
Where: %ADL = Percentage of Acid Detergent Lignin, W1 = Bag tare weight, W2 =
Sample weight, W3 = Dried weight of bag with fibre after extraction process and C1 =
Blank bag correction (final oven-dried weight divided by original weight).
CarbonandNitrogencontent
Dried and ground faecal samples for each animal prepared as mentioned before were
used to estimate the Carbon and Nitrogen percentage. The samples were analysed in
duplicate in the Stable Isotope Laboratory of the Research School of Biology, ANU (Dr
Hilary Stuart-Williams) by elemental analysis (EA) using the Dumas method to convert
the samples to CO2 and N2 gases before analysis by isotope ratio mass spectrometry
(irMS).
37
CharacterizationofE.coli.diversityanddynamics
EnumerationofE.coli
E. coli cell densities (number of E. coli CFU/g faeces) were determined for each
animal. This sampling was done for 14 weeks. To estimate the cell density, two fresh
faecal pellets from each animal were collected weekly early morning (8:00 – 9:00 a.m.)
by placing the animal in an empty clean cage until it defecated. It usually took around
15 minutes to produce the faecal pellets needed for the analysis. Samples were
processed immediately. The fresh faecal pellets were placed in a pre-weighed sterile
15 ml centrifuge tube containing 0.85% sterile saline. The faecal pellet was crushed
using a sterile glass rod and vortexed for 1 minute. The faecal suspension was serially
diluted 2 times in 10 fold steps. An aliquot of 100 µl of each dilution was spread onto a
MacConkey agar plate using a disposable sterile plastic spread bar. The MacConkey
agar plate was incubated 18 hours at 37 °C. The dilution and the number of colony
forming units (CFU) were recorded. Once faecal suspension had been sampled in
MacConkey agar, the tubes were dried at 75 °C for 96 hours. The dry mass of faeces
was obtained by subtracting the weight of the tube and the amount of NaCl required to
be present. CFU were converted to number of cells. Cell densities were expressed in
terms of the number of cells per gram of dry faeces.
GenotypingE.colistrains
Putative E. coli colonies were isolated from fresh faecal pellets diluted in sterile saline
solution by streaking onto MacConkey agar plates. Lactose positive colonies were then
tested on citrate agar plates during 18 hours at 37°C (Blyton, Banks, Peakall, &
Gordon, 2013). Putative identification of E. coli colonies was further confirmed by
Clermont PCR and REP-ERIC PCR (Leung, Mackereth, Tien, & Topp, 2004;
Versalovic, Koeuth, & Lupski, 1991). After identifying E. coli, 12 colonies were
randomly selected from the MacConkey agar plates and transferred to Luria Bertani
agar for temporary storage. Genotyping of bacterial strains was performed for each
animal, using the 12 E. coli CFU isolated from faecal pellets that were sampled before
starting diet treatment as well as on week 1, week 2, and on week 13 of diet treatment.
One CFU of E. coli isolated from the content of terminal ileum, caecum, caecum wash
38
as well as proximal and distal colon were also used for genotyping bacterial strains. A
faecal pellet was collected for the same purpose when the animal was killed.
Isolates were considered to be of the same E. coli strain when they showed the same
Clermont PCR Phylogenetic group (Blyton et al., 2013) and the same band pattern for
REP ERIC-PCR (Leung et al., 2004; Versalovic et al., 1991).
Clermontgenotyping
A PCR-based method was used to distinguish strains (Clermont, Christenson,
Denamur, & Gordon, 2013; Gordon, Stern, & Collignon, 2005). Genomic DNA was
extracted from 12 E. coli isolates from faecal samples of each animal collected on
week 0 (no diet treatment), week 1, week 2, and week 13 on diet. One CFU of E. coli
isolated from each compartment of the gastrointestinal tract content was also collected
during necropsy for genotyping.
E.colistrainfingerprinting-ERICPCR
ERIC-PCR fingerprinting analysis was used to distinguish E. coli strains. (Leung et al.,
2004; Versalovic et al., 1991). ERIC-PCR is a PCR based method and uses
Enterobacterial Repetitive Intergenic Consensus [ERIC] sequences that provide
unambiguous DNA fingerprinting of E. coli (Versalovic et al., 1991).
Bacterialcommunitycharacterization
All procedures for the bacterial community characterization were performed in a Class
II biosafety cabinet. High throughput sequencing analysis, using the Ion Torrent
Platform, was based on DNA libraries of target V4 region of 16S amplicon from
bacterial communities (table 2.6 and table 2.7).
Genomiclibraries
Total DNA extractions were performed on faecal pellet samples and caecum contents
to prepare 6 genomic libraries. A library was prepared with bacterial DNA extracted
from faecal samples collected before diet treatment (week 0). Another library was
39
prepared with DNA extracted from faecal content when animals were halfway towards
the finish of the diets treatment (week 7). Two libraries were prepared using DNA from
faecal samples collected before and after the transit time experiment (weeks 13 and 14
on diet). Finally, duplicate libraries were prepared from DNA extracted from caecum
content preserved in RNA later solution. The caecum contents used were those
collected immediately after the animal were sacrificed.
Faecal pellets and caecum contents were immediately frozen and stored at -80 °C.
DNA was extracted from 50 mg of faecal and caecal material using the Qiagen ®
Ministool kit, a silica membrane based purification kit according to the manufacturers
protocol. Hypervariable region 4 of the 16S rRNA gene (V4) was selected, as it has
been described as the best region for bacterial community characterization using
Illumina Hiseq platform (Caporaso et al., 2011; Caporaso et al., 2012; Qunfeng &
Claudia, 2012). The primers used were 515F and 806F primers of the V4 region of the
16S rRNA gene.
QuantificationofDNAproducts,LibraryNormalizationandPooling,andHighThroughputSequencingAnalysis.
Purified PCR product from the previous step was quantified using the Qubit® dsDNA
Assay Kit according to the instructions of the manufacturer. Afterwards, each PCR
product was normalized to 10 ng/µl of each DNA. The purified and pooled DNA
products were used for High Throughput Sequencing Analysis (HTSA). HTSA was
performed at the Biomolecular Resources Facilities at the John Curtin School of
Medical Research using the Miseq Illumina sequencing platform (Kozich, Westcott,
Baxter, Highlander, & Schloss, 2013).
Bioinformaticsanalysisofbacterialcommunities
16S rRNA gene sequences that were generated using the Illumina Miseq platform
were analysed using open source Mothur software. Phylotype, phylogenetic and OTU
analyses were performed. For each genomic library, bacterial and archaeal community
composition was obtained by taxonomic identity (Kozich et al., 2013; Whiteley et al.,
2012).
40
The generated sequences were processed using a combination of the standard
operating procedures for the Ion Torrent and 454 platforms using MOTHUR 1.32.1
software according to published methods,(Schloss et al., 2009)
(http://www.mothur.org/wiki/Ion_Torrent_sequence_analysis_using_Mothur) and on-
line 454 SOP (http://www.mothur.org/wiki/454_SOP). In brief, barcodes, primers and
barcodes, unwanted sequences (too short or too long: less than 200 base pairs, more
than 450 base pairs) more that 8 homopolymers were filtered out. In order to compare
the taxonomy between samples, alignment of sequences was carried out using the
SILVA reference database (http://www.mothur.org/wiki/Silva_reference_alignment).
Certain lineages that are not bacterial sequences (corresponding to mitochondria,
chloroplast, Archaea, Eukaryota and unknown were removed using MOTHUR.
Sequences were subsampled to 14041 sequences per sample, since this was the
number of sequences identified as fewest. The sequences were clustered into
operational taxonomy units (OTUs) at 50% cutoff and further assigned to phylotypes
from phylum to family level.
Killingtheanimalsandgutmorphologyanalysis
At the end of the diet treatment experiment (week 14), all animals were killed. Each
animal was randomly chosen and killed. All animals were euthanized by using CO2
asphyxiation and then placed on an examination table for dissection and organ
removal. The gastrointestinal tract was exposed and the digestive organs including
stomach, small intestine, caecum and colon were excised. During necropsy, the
caecum was removed and opened longitudinally. Caecum content was collected in 50
ml Falcon tubes and immediately stored in the -80 °C freezer. An aliquot of cecum
content was placed in RNA later solution for further analysis. After removing the
caecum content, gastrointestinal tract was rinsed with sterile saline. The contents of
the caecum, stomach and small intestine were removed by constantly flushing the
organ with sterile saline. The proximal colon and distal colon also were flushed with
sterile saline.
An aliquot of the content of terminal ileum, caecum, proximal colon and distal colon
was collected for E. coli genotyping and sampled in MacConkey agar. An aliquot of
ceacum content as well as of caecum wash were collected for the same purpose. The
wet mass of the stomach, small intestine, caecum and colon were recorded. Biopsies
41
of caecum, small intestine, stomach, proximal colon and distal colon were fixed in
Bouin fixative solution for further microscopic analysis. All remaining organs were
dried at 75 °C for 4 days. Dry mass of stomach, small intestine, caecum and colon
were recorded.
Shortchainfattyacidanalysisofthecaecumcontent:GasChromatography–MassSpectrometry(GC-MS)
Determinationofshortchainfattyacids
Fresh caecum content was collected in 50 ml Falcon ® tubes during the necropsy of
the animals. Samples were immediately placed in -80 °C freezer, and stored until
further analysis. Short chain fatty acids (SCFA) were analysed in the Mass
Spectrometry Facility of the Research School of Biology of the Australian National
University according to an optimized method developed as part of this research.
Chemicals
Acetic acid (C2), propionic acid (C3), isobutyric acid (i-C4), butyric acid (C4), isovaleric
acid (i-C5), caproic acid (C6), heptanoic acid (C7) and 2-ethylbutyric acid (used as
internal standard IS) were purchased from Sigma-Aldrich. 50 ml of aqueous solution of
each standard was prepared in the following concentrations: 400 mM for acetic acid,
propionic acid and n-butyric acid; 200 mM for n-valeric acid, i-valeric acid; 100 mM for
i-butyric acid; 50 mM for n-caproic acid and 15 mM for n-heptanoic acid. For internal
standard, 0.3 ml of 2- ethylbutyric acid was added to 50 ml of 12% formic acid. Each
component was stored at -20 °C until used. For the extraction procedure
dichloromethane was obtained from Merck (Darmstadt, Germany). Water was
deionized (> 18.2 MΩ.cm) by using a Millipore Q-system (Millipore, Bedford, MA, USA).
Extractionprocedure
Frozen caecum (100 +/-10 mg) was weighed out into an Eppendorf tube, diluted with
50 µL of 1 M HCl and the internal standard (IS) was added (100 µL, IS solution
consisting of 300 µL of 2-ethylbutyric acid dissolved in 50 mL water). The sample was
vortexed for 20 seconds and allowed to stand at room temperature for 20-30 minutes
42
following which it was centrifuged for 5 minutes (16.1 RCF) and 20 µL of the
supernatant was transferred to a new vial and this aqueous solution was further
extracted in an auto sampler vial (300 µL insert) with the same amount of
dichloromethane and centrifuged for 5 minutes at 16.1 RCF. Three independent
replicates were performed per sample.
GasChromatography-MassSpectrometry(GC-MS)
The GC-MS system consisted of a TRACETM Ultra gas chromatograph and Thermo
PolarisQTM mass spectrometer. Principal ions chromatogram acquisition was done
using XcalibuTM Data System Software. The mass spectrometer was operated in the
electron impact ionisation (EI) mode with ionisation energy of 70 eV. For each
component a characteristic single ion with the highest relative abundant m/z was
selected.
Samples were injected (0.2 µL injection volume) via an autosampler onto a fused-silica
capillary column (30 m x 0.25 mm id) coated with a Polyethylene Glycol bonded phase
(SGE Pty Ltd, Melbourne; BP21, film thickness 0.25 µm) which was eluted with He
(inlet pressure 15 psi) directly into the ion source of a Thermo Polaris Q GC/MS
(injection port 200 °C; interface 240 °C; source 250 °C). The column was temperature
programmed from 80 °C (hold 1 min.) to 100 °C at 20 °C/min and then to 180 °C (hold
1 min.) at 5° C/min.
The sample needle was washed consecutively with acetone. Every fourth sample was
followed by an injection of 12% formic acid. The short chain fatty acids (SCFAs) were
quantified against the internal standard, 2-ethylbutyric acid.
Validation
The stability of the components in storage was determined by injecting periodically,
over the course of one week, a standard mixture of acetic acid (C2), propionic acid (C3),
iso-butyric acid (i-C4), butyric acid (C4), iso-valeric acid (i-C5), caproic acid (C6),
heptanoic acid (C7) and 2-ethylbutyric acid (IS), which was stored at 4 oC.
43
Linearityandsensitivity
A standard curve of each short chain fatty acid of study were prepared using a mixture
of standards in aqueous solution, which were diluted. The internal standard was added
to each diluted standard mixture. The calibration curve was constructed by plotting the
ratio of (SCFA Area)/(IS Area) against (SCFA concentration)/(IS concentration). The
limits of detection (LOD) and quantification (LOQ) were obtained by injecting more
diluted standard solutions and calculating based on signal-to noise ratio (S/N) of 3 for
LOD and 10 for LOQ.
Optimizationoftheextractionprotocol
As acidification affects the efficiency of SCFA extraction, 3 different concentrations of
HCl were used; 2M, 1M and 0.5M. 1M was selected for the rest of the study due to the
ease in handling and as no further titration was needed to obtain a value of 2-3 pH.
After centrifugation, an aliquot (50 µL) of the supernatant of the aqueous and acidified
caecum sample was placed in the autosampler vial together with dichloromethane
(DCM, 20 µL). The vials were shaken and briefly centrifuged to separate the aqueous
(upper) and DCM (lower) layers. Samples for GC/MS analysis were taken from the
lower DCM layer.
The extraction protocol proposed in this work is very simple, fast and relatively
inexpensive with no additional equipment required for the sample preparation and
injection, requiring only 100 mg of caecum content.
StatisticalAnalyses
The statistical analyses were carried out using the software package JMP V11.00
(SAS Institute), software Past 3.3 (Hammer, 2001) and GUide to STatistical Analysis in
Microbial Ecology (GUSTA ME) (Buttigieg & Ramette, 2014).
44
Chapter3 HOSTRESPONSE
Introduction
Our knowledge of the effect of fibre on the host is largely based on empirical research
conducted in small mammals (X. Zhao et al., 1995), ruminants, and omnivores
(Castrillo, Vicente, & Guada, 2001) as well as a few studies in humans. Rats have
been proposed as experimental models for the fermentative breakdown of dietary fibre
and bulking capacity of dietary fibre in the human gut (Nyman, Asp, Cummings, &
Wiggins, 1986). The effect of five different sorts of dietary fibre on human
gastrointestinal transit time and other physiological properties has been described
using in vivo and in vitro experiments (Cherbut, Salvador, Barry, Doulay, & Delort-
Laval, 1991; Graff et al., 2001; Herawati, 2006; Madsen, 1992).
The laboratory rat is a necessary part of today’s biomedical investigations as it
represents a good experimental model for a number of aspects of human physiology
(DeSesso & Jacobson, 2001; Sengupta, 2013). The rat was first bred for scientific
purposes in the early 1900s at the Wistar institute in Philadelphia (Tomas, Langella, &
Cherbuy, 2012). Although the rat has proven to be a good model for much of human
biology (Krinke, 2000), there are significant differences that must be taken into
consideration when looking for correlations with human health (Roberts, Kwan, Evans,
& Haig, 2002; Sengupta, 2013).
Wistar and Sprague-Dawley are the most popular rats used in experiments. The
Wistar albino rat, a strain developed at the Wistar institute in 1906, is easy to handle;
however, aggressive behaviour can develop in mature males (Krinke, 2000). Genetic
differences, the developmental stage and gender may affect the nutritional
requirements of the animal. Specific free animals (SFE) are those free from specific
microorganisms and parasites and they are defined based on the negative screening
for known pathogens (Tomas et al., 2012).
The alimentary tract in rats is basically an open-ended epithelium-lined tube that
extends from the mouth to the anus (Figure 3.1). The mouth is the site at which the
digestions process starts and is the entry to the alimentary canal. The pharynx and
oesophagus are muscular structures that serve to transfer the digested material from
45
the mouth to the stomach (DeSesso & Jacobson, 2001). As in humans, rats have a
single-chambered stomach with two grossly discernible regions: the forestomach and
the glandular stomach (DeSesso & Jacobson, 2001). The small intestine is divided
into three differently sized portions: duodenum, jejunum and ileum. In humans, the
colon consists of ascending, transverse, descending and sigmoid sections and the
length varies from 90 to 150 cm, while in rats it is neither sacculated nor long (Kararli,
1995). The rat caecum is larger compared to that in humans who have a poorly
defined caecum, which is continuous with the colon. In rats, the caecum is a primary
site of microbial digestion (DeSesso & Jacobson, 2001). On average, the length of the
caecum in an adult rat varies from 50 to 70 mm and is approximately 10 mm in
diameter while the length of the colon and rectum ranges from 90 to 100 mm and 80
mm, respectively (Kararli, 1995). Absorption of water and electrolytes occurs in the
large intestine (DeSesso & Jacobson, 2001).
Transit time is the time taken for a bolus of food or chime to pass through a region of
the alimentary canal (DeSesso & Jacobson, 2001). In the mouth, the transit time is
determined by voluntary control of time spent chewing; once the bolus is passed to the
oesophagus (about 6 seconds), the transit time is controlled by peristalsis and gravity
(DeSesso & Jacobson, 2001).
Gastric emptying of the stomach is an important physiological event and can vary
depending on whether the animal is in a fed or unfed state. Motility in the unfed state
has several phases that are repeated every 2 hours in humans (also displayed
commonly in animals used in the laboratory); zero contraction in Phase 1, intermittent
contraction in phase 2 and high contraction in phase 3. Phase 3 lasts for 5 to 15
minutes. The size of the animal stomach is a limiting factor for the passage of non-
digestible particles into duodenum; thus large particles in the human stomach can be
retained for more than 12 hours while fluids can be rapidly released (Kararli, 1995).
46
Figure3.1Compositionofalimentarycanalsofhumans(A)andrats(B)
Adapted from DeSesso & Jacobson 2001 (DeSesso & Jacobson, 2001).
The time for the chyme to traverse the small intestine in rats is similar to that in
humans taking 3 to 4 hours (DeSesso & Jacobson, 2001). In the small intestine of
humans and rats the transit time for the proximal intestine is shorter than in the distal
intestine (Kararli, 1995). The transit time in the large intestine of rats is approximately
15 hours depending on many factors including diet, health status, age and fasting state
(DeSesso & Jacobson, 2001).
Short chain fatty acids (SCFA) are organic fatty acids with 1 (formic acid) to 6 atoms
(caproic acid) of carbon. These anions are the results of bacterial fermentation in the
gut. SCFA arise not only from the bacterial fermentation of polysaccharides and
oligosaccharides but also from proteins, peptides and glycoproteins (Wong et al.,
2006). The old idea that the colon was the site of salt and water absorption and
provided a mechanism for waste disposal has been largely superseded because of the
discovery of the role the human gastro-intestinal microbiota play in colonisation
resistance, immune-modulation, and their contribution to the nutrition of the host. Short
chain fatty acids are produced as by-products of fibre fermentation in the gut. They are
important anions in the colonic lumen and influence the function and morphology of the
gut (Scheppach, 1994). In this omics era, with the emergence of prebiotics and
47
probiotics, interest in short chain fatty acids (SCFA) has been rekindled with a
recognition of their value in improving the colonic and systemic health of humans
(Bapteste, Bicep, & Lopez, 2012; Wong et al., 2006; Zoetendal et al., 2001).
Important effects of Short Chain Fatty Acids (SCFA) include action on mucosal blood
flow, cellular differentiation, and ileocolonic motility (Scheppach, 1994). SCFA
contribute to normal large bowel function with action in the lumen, colonic vasculature
and musculature and through their metabolism by colonocytes. Butyrate has special
importance because it is thought to play an important role in maintaining the colonocyte
population (Scheppach, 1994; Topping & Clifton, 2001)..
In the colonic lumen, more than 95% of the SCFA produced by fermentation is rapidly
absorbed (Scheppach, 1994). The three major SCFAs (acetic, butyric and propionic)
stimulate “in vivo” cell proliferation and differentiation in the large and small intestine
and butyric acid in particular promotes reversion of neoplastic to non-neoplastic cells
(Guarner & Malagelada, 2003).
The aim in this chapter is to evaluate the factors influencing the host gastrointestinal
dynamics and morphology. To fulfill this aim the gut transit time, caecum morphology,
food consumption and digestibility, short chain fatty acids production and Carbon and
Nitrogen contents were estimated. Moreover, the data obtained was used to predict
changes in, body mass parameters and gastrointestinal dynamics and morphology
48
Results
Bodymassandgrowthparameters
To investigate the effects of the type of diet consumed, on the body mass of the
animals, regular weighing of all animals was made every week. This enabled an
investigation of the growth rates over a period of 14 weeks, the period during which the
respective diets were supplied.
A Gompertz model analysis was used to estimate growth curve parameters. This
model was chosen based on an earlier study (Herawati, 2006; Kurnianto, Shinjo, &
Suga, 1998). The time of diet treatment was used as the predictor of weight. The
mathematical expression of the Gompertz model is:
𝑊 𝑡 = 𝐴 ∗ 𝑒𝑥𝑝[−exp (−𝐺 ∗ 𝑡 − 𝐼 )]
Where: W(t) = Body weight (g) based on diet treatment on weeks
t = Time of diet treatment (weeks)
A = Asymptote (maximum body mass of the animal in g)
G = Growth rate (g/week)
I = Inflection point (rate of decline in the growth rate)
The time in weeks that each animal was exposed to the experimental diets was used
as the independent variable. Asymptote (A), growth rate (G) and inflection point (I) are
response variable parameters (Table 3.1).
Figure3.2Gompertzexpressionaverageonbodymasschangesbydiet
50100150200250300350400
Body
Mas
s (g
)
0 2 4 6 8 10 12 14Diet treatment
(on weeks)
LegendBHFLF
49
Exposure to the B and LF diets had similar value for predicting body mass in a
particular animal. Animals on these diets achieved higher body mass compared to
animals on the HF diet (Figure 3.2)
Table3.1ParameterestimationofGompertzmodelanalysisoffemaleWistarratsbydiet
Parameter Diet Estimate Std Error Asymptote HF 319.5 5.1
LF 347.3 6.2 B 342.2 5.6
Growth Rate
HF 0.26 0.02 LF 0.24 0.02 B 0.25 0.02
Inflection Point
HF 1.09 0.12 LF 1.39 0.11 B 1.21 0.11
Two-Factorial ANOVA was conducted to evaluate the effects of the two factors: diet
and litter on the predicted body mass of the rats (asymptote). Moreover, the interaction
effect between these factors was also evaluated to ascertain whether there is a
different effect of diet depending on litter, and alternatively to determine whether there
is a different effect of litter depending on diet (Table 3.2).
Table3.2Two-way(factorial)ANOVAdeterminingtheeffectoflitteranddietonmaximunbodymassparameteroffemaleWistarrats
Source DF F Ratio Prob > F Litter 5 10.36 <0.001* Diet 2 8.29 0.0016* Litter*Diet 10 2.62 0.226*
Foodconsumptionanddigestibility
Food consumption, fibre analysis, faecal production and digestibility.
The amount of food consumed and faeces produced by each animal was measured at
the end of the experiment (week 13). These estimations were used in several
analyses to investigate the effects of food consumption and faecal production on
variables such as digestibility, gut transit time, caecum mass, and cell density.
50
The following expression was used to estimate the digestibility
𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑏𝑖𝑙𝑖𝑡𝑦 =𝐹𝑜𝑜𝑑 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 − 𝐹𝑒𝑐𝑒𝑠 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛
𝐹𝑜𝑜𝑑 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛
Analysis of variance determining the effect diet on food consumption of female Wistar
rats.
There was a highly statistically significant difference in food consumption by diet as
determined by one-way ANOVA (F(2,42) = 18.65, p < .0001). To evaluate the
differences, a post hoc analysis was performed on the difference by using Tukey-
Kramer honest significant difference (HSD). Post hoc analysis at p <0.05 revealed that
there was a highly significant difference between HF and LF (p-value <.0001) on food
consumption; a highly significant difference between B and LF (p-value <.0001); and
no significant difference in food consumption between HF and B diets treatments. Raw
data about food consumption and faecal output is presented in the appendix.
Analysis of variance determining the effect of diet on faecal production by diet.
Faecal production was highly statistically significant different by diet as determined by
one-way ANOVA (F(2,42) = 129.71, p < .0001). Comparison of all pairs was carried
out using Tukey-Kramer honest significant post hoc analysis (HSD). Post hoc analysis
at p <0.05 revealed that there was a highly significant difference between HF and LF
(p-value <.0001); a highly significant difference between HF and B (p-value <.0001);
and a significant difference between B and LF diet treatments (p-value = 0.0050).
Analysis of variance determining the effect of diet on apparent food digestibility.
Digestibility coefficient comparison analysis was performed to evaluate the differences
between the three diets. There was a high statistically significant difference in
digestibility between the animals by diet as determined by one-way ANOVA (F (2,42) =
173.69, p < .0001). Post hoc Tukey-Kramer HSD analysis, at p <0.05, on the
difference, revealed highly significant differences between LF and HF (p-value <.0001);
highly significant differences between B and HF (p-value <.0001); and no significant
difference on apparent digestibility between LF and B (p-value = 0.1414).
51
Fibre,CarbonandNitrogenanalysis
At the end of the experiment, nitrogen, carbon and fibre concentrations in the faeces of
each animal were determined. Faecal samples collected during the last week of diet
treatment, were dried in a force–air oven at 75 °C for 4 days. After drying, the samples
were weighed and milled using a 1 mm screen. Food consumption was also measured
at the last week on diet treatment (week 14) to estimate the digestibility of different
diets and to estimate the faecal production by each animal. The estimation of food
consumption and faecal production by the animals was used in various analyses.
Carbon, nitrogen and fibre contents on ground faeces were estimated in duplicate for
each animal (table 3.3). Fibre content estimation analysis included neutral detergent
fibre (NDF), acid detergent fibre (ADF) and acid detergent lignin (ADL). Insoluble fibre
in faeces accounted for 28% in HF, 20% in LF and 18% in B, The nitrogen
concentration was high in diet B, representing an average of almost 6% of faecal mass
on average.
Table3.3Estimationoffaecalcomposition(%)bydiettreatmentonfemaleWistarrats
Faecal composition %
Diet treatment HF (n = 15) LF (n = 15) B (n = 15)
Mean ± SE Mean ± SE Mean ± SE N 2.14 ± 0.05 2.97 ± 0.06 5.76 ± 0.08 C 38.70 ± 0.52 35.19 ± 0.24 40.72 ± 0.12
NDF 28.15 ± 0.60 20.06 ± 0.44 18.44 ± 0.57 ADF 21.22 ± 0.57 11.11 ± 0.32 11.85 ± 0.40 ADL 0.89 ± 0.03 1.01 ± 0.06 1.37 ± 0.07
N = Nitrogen, C = Carbon, NDF = Neutral detergent fibre; ADF = Acid detergent fibre; ADL = Acid detergent lignin. HF = Diet High in non-fermentable fibre; LF= Diet Low in non-fermentable fibre; B = Diet based in Fermentable Fibre.
Similarly, carbon, nitrogen and fibre contents were estimated in duplicate for each type
of experimental diet (Table 3.4). In the HF diet, insoluble fibre accounted for almost
16% and acid detergent fibre for 8.7%.
Data on faecal production and composition collected in the last week of experiment
and data collected on food consumption were used in combination to determine the
digestibility of different diet constituents (Table 3.5).
52
Table3.4Estimationofcompositionofexperimentaldiets(%)fedtoexperimentalanimals
Diet composition %
Experimental diet HF LF B
N C
NDF
5.61 47.05 15.87
4.20 46.35 5.16
4.38 45.78 6.72
ADF 8.71 1.57 2.89 ADL 0.534 0.284 0.276
N = Nitrogen, C = Carbon, NDF = Neutral detergent fibre; ADF = Acid detergent fibre; ADL = Acid detergent lignin. HF = Diet High in non-fermentable fibre; LF= Diet Low in non-fermentable fibre; B = Diet based in Fermentable Fibre.
Apparent dry matter digestibility averaged at 75% in HF, 88% in LF and 86% in B. In
terms of fibre digestibility, the least digestible component in the three diet treatment
groups was Acid Detergent Lignin, while Neutral detergent fibre appeared to be the
most easily digested component among the three groups.
Table3.5Estimationofdigestibilityofdietarycomponents
Digestibility estimate
Diet treatment HF (n = 15) LF (n = 15) B (n = 15)
Mean ± SE Mean ± SE Mean ± SE Apparent dry
matter 74.87 ± 0.79 87.86 ± 0.37 86.38 ± 0.34
N 90.20 ± 0.24 91.69 ± 0.26 82.13 ± 0.44 C 78.76 ± 0.36 91.10 ± 0.23 87.96 ± 0.31
NDF 55.31 ± 1.90 52.57 ± 2.20 62.44 ± 1.73 ADF 38.54 ± 2.90 13.21 ± 4.47 43.92 ± 2.65 ADL 58.39 ± 1.75 56.95 ± 2.76 31.96 ± 4.39
N = Nitrogen, C = Carbon, NDF = Neutral detergent fibre; ADF = Acid detergent fibre; ADL = Acid detergent lignin. HF = Diet High in non-fermentable fibre; LF= Diet Low in non-fermentable fibre; B = Diet based in Fermentable Fibre.
Gutmorphologyanalysis
At the end of the experiment the animals were killed, necropsied, gastrointestinal
organs were removed and dry mass of stomach, small intestine, caecum and colon
was determined (Table 3.6). Animals on the B diet had the greatest caecum at the end
of dietary experiment, whilst animals on LF diet had the smallest caecum size.
53
Table3.6Massvariation(g)ofgutcomponentsofanimalsfedondietsHF,LFandB
Region Diet treatments
HF LF B Mean ± SE Mean + SE Mean + SE
Stomach 0.41 ± 0.01 0.39 ± 0.01 0.39 ± 0.02 Small intestine 1.93 ± 0.06 1.84 ± 0.12 1.93 ± 0.07
Caecum 0.21 ± 0.01 0.15 ± 0.02 0.27 ± 0.02 Colon 0.40 ± 0.03 0.28 ± 0.03 0.32 ± 0.02 Total gut 2.95 ± 0.10 2.65 ± 0.14 2.91 ± 0.11 Body mass 244 ± 4.4 254 ± 5.0 256 ± 4.8
Effect of litter, diet and their interactive effect on gut morphology
Two-factorial ANOVA was conducted to evaluate the effect of diet, litter and animal
body mass on gut morphology (Table 3.7).
Table3.7Effectoflitter,dietandbodymassondrymassongutcomponents
Effect Prob > F
Stomach Small intestine Caecum Colon Total Gut
Litter 0.83 0.15 0.87 0.08 0.1 Diet 0.61 0.02* <0.0001* 0.0031* 0.0007* Body mass <0.001* <0.001* 0.19 0.0251* <0.0001*
Tukey-Kramer honest significant difference (HSD) analysis comparisons for all pairs of
means were performed and the results are shown in table 3.8. The differences were
considered significant if p <0.05.
There was a highly statistically significant difference in caecum dry mass between B
and LF. Similarly, there was a statistically significant difference between HF and LF
diets. No significant difference was observed between B and HF diet treatments for
the same parameter. However, for the colon dry mass the differences of means were
only observed between Diet High in non-fermentable fibre and Diet Low in non-
fermentable fibre. No differences were observed in the other combinations.
Table3.8ComparisonondietspairsofmeansusingTukey-KramerHSDforcaecumdrymassandcolondrymass
Pair of means (diets)
Caecum dry mass (p-Value)
Colon dry mass (p-Value)
B - LF < 0.0001 * 0.5621
54
HF - LF 0.0457* 0.0222* B - HF 0.0525 0.2030
Diet treatments: HF = Diet High in non-fermentable fibre; LF= Diet Low in non-
fermentable fibre; B = Diet based in Fermentable Fibre.
Transittimeparameters
Gut transit time
A two-marker system (chromium mordant and cobalt-EDTA) was used in this study to
monitor both liquid and particulate fractions. Both markers were mixed with the
experimental diets and given to the animals in the last week of diet treatment trials. As
the chromium mordant fulfils most criteria to reflect the transit of particulate matter
through the gastrointestinal tract, it was used as the particulate marker. Cobalt-EDTA
is thought to reflect the movements of liquids through the gastrointestinal tract and
hence it was used as the liquid-digest marker (Hume, Morgan, & Kenagy, 1993; EI
Sakaguchi, Itoh, Uchida, & Horigome, 1987; Udén, Colucci, & Van Soest, 1980).
The mean retention time (average time spent by particulate and liquid matter in the
gastrointestinal tract) was estimated using the following equation.
𝑀𝑅𝑇 =𝑀!𝑥𝑇!𝑀!
Where Mi is the amount of marker in the ith sample and Ti is the time in hours when the
sample was collected. For calculating the average length of time that the marker spent
in the hindgut, the inverse of the slope of the line that describes the relationship
between the natural logarithm of the marker (concentration in mg/g) and the amount of
time elapsed after the marker achieved its maximum concentration in faeces, was
used.
The retention time of the particulate and liquid markers ranged from 10 – 25 hours
among all animals, whilst the retention time in the hindgut and foregut ranged from 2 –
14 hours and 2 – 20 hours, respectively. On average, particulate matter spent 2 hours
less in animals under HF diet treatment, whilst there was no significant difference
between LF and B diets (Table 3.9).
55
Figure3.3Cobaltmeanconcentration(mg/gfaeces)&Chromiummeanconcentration(mg/gfaecesversustime(hours)
Table3.9GuttransittimeparametersinhoursofparticulatemattermarkerandliquiddigestamarkerinWistarfemaleratsunderthethreeexperimentaldiettreatments
Diet Treatment
HF LF B
Mean ± SE Range Mean ± SE Range Mean ± SE Range
PARTICULATE
Total Gut 14.6 ± 0.6 10.8 - 19.2 17.9 ± 0.7 13.1 - 21.8 17.4 ± 0.8 13.3 - 24.6 Hindgut 6.6 ± 0.4 5.1 - 9.4 8.6 ± 0.4 6.4 - 12.2 9.2 ± 0.7 6.4 - 19.0 Foregut 8.0 ± 0.7 1.6 - 13.5 9.3 ± 0.6 4.5 - 12.1 8.2 ± 0.8 2.3 - 15.4
LIQUID
Total Gut 16.0 ± 0.6 10.9 -19.7 18.6 ± 0.7 13.7 - 22.6 17.9 ± 0.9 13.4 - 25.3
Hindgut 7.1 ± 0.4 5.4 -10.4 9.6 ± 0.5 7.8 - 14.7 8.3 ±0.5 2.2 - 10.5
Foregut 8.9 ± 0.6 3.7 -13.3 8.9 ± 0.7 4.5 - 12.8 9.6 ± 1.0 6.0 - 19.8
On average, particulate marker spent 2 hours less in animals under HF diet treatment,
whilst there was no significant difference between LF and B diets (table 3.10)
!
Cobalt Mean concentration (mg/g Faeces) & Chromium Mean concentration (mg/g Faeces) vs. Time hours
DietB HF LF
Cob
alt
Mea
n co
ncen
tratio
n (m
g/g
Faec
es) &
Chr
omiu
m M
ean
conc
entra
tion
(mg/
g Fa
eces
)
0
1
2
3
4
5
6
0 10 20 30 40 50 0 10 20 30 40 50 0 10 20 30 40 50Time hours
Cobalt Mean concentration (mg/g Faeces) & Chromium Mean concentration (mg/g Faeces) vs. Time hours
Cob
alt
Mea
n co
ncen
tratio
n (m
g/g
Faec
es) &
Chr
omiu
m M
ean
conc
entra
tion
(mg/
g Fa
eces
)
0
1
2
3
4
5
6
0 10 20 30 40 50 0 10 20 30 40 50 0 10 20 30 40 50Time hours
Smooth(Cobalt Mean concentration (mg/g Faeces))Smooth(Chromium Mean concentration (mg/g Faeces))Cobalt Mean concentration (mg/g Faeces)Chromium Mean concentration (mg/g Faeces)
56
ANALYSIS OF PARTICULATE MARKER (CHROMIUM)
There was a significant effect of diet on transit time of particulate matter marker
(Chromium) at the p<0.05 level for the three conditions [F(2, 42) = 6.37, p = 0.0038].
Post hoc comparisons using the Tukey-Kramer HSD test indicated that the mean score
for LF (M = 17.91, SD = 2.55) was significantly different from that for HF (M = 14.66,
SD = 2.19). Similarly, the mean score for B (M = 17.42 , SD = 3.23) was significantly
different from that of HF (M = 14.66 , SD = 2.19 ). However, the diet treatment LF (M =
17.92, SD = 2.55) did not significantly differ from treatment B. Taken together, these
results suggest that HF, B and LF do have an effect on particulate matter transit time.
Specifically, our results suggest that consumption of B and LF increase the transit time
compared to HF. However, it should be noted that HF decreases the transit time.
To evaluate the effect of diet on the differences of particulate matter on hindgut rate,
one way ANOVA was performed. There was a significant effect of diet on hindgut
passage rate of the particulate matter marker (chromium) at the p <0.05 level for the
three diet treatments [F(2, 42) = 11.53, p < .0001]. Post hoc comparisons using the
Tukey-Kramer HSD test indicated that the mean score of hindgut rate for HF (M = 0.16,
SD = 0.03) was significantly different from the B (M = 0.12, SE = 0.02). Similarly, the
mean sore for HF (M = 0.16 , SD = 0.03 ) was significantly different from the LF (M =
0.12 , SD = 0.02 ). However, the LF diet treatment (M = 0.12, SD = 0.02) did not
significantly differ from B treatment. Taken together, these results suggest that the
effect of the three diet treatments on chromium hindgut transit time was similar to
findings related to particulate matter transit time. Specifically, our results suggest that
consumption of B and LF decreases hindgut transit time. However, it should be noted
that HF increases the hindgut transit time
There was a significant effect of diet on chromium hindgut retention at the p <0.05 level
for the three conditions [F(2, 42) = 5.95, p = 0.0053]. Post hoc comparisons using the
Tukey-Kramer HSD test indicated that the mean score for B (M = 9.22, SD = 2.92)
was significantly different from the HF (M = 6.62, SD = 1.55). Similarly, the mean
score for LF (M = 8.57 , SD = 1.67) was significantly different from the HF (M = 6.62 ,
SD = 1.55 ). However, the B diet treatment (M = 9.22, SD = 2.92) did not significantly
differ from LF treatment. A one-way ANOVA yielded no significant differences between
groups in regard to foregut retention Cr, F (2, 42) = 0.947, ns.
57
ANALYSIS OF LIQUID MARKER (COBALT)
There was a significant effect of diet on transit time of the liquid marker (cobalt) at the
p<0.05 level for the three conditions [F(2, 42) = 3.28, p = 0.0475]. Post hoc
comparisons using the Tukey-Kramer HSD test indicated that the mean score for LF
(M = 18.56, SD = 2.62) was significantly different from that for HF (M = 16.02 , SD =
2.34 ). However, the B diet treatment did not significantly differ from HF treatment.
Similarly, there was no significant difference between LF and B diet treatments. Taken
together, these results suggest that HF, B and LF do have an effect on liquid marker
transit time. Specifically, our results suggest that the consumption of B and LF
increases Co retention. However, it should be noted that HF decreases liquid digest
transit time.
There was a significant effect of diet on cobalt hindgut retention at the p <0.05 level for
the three conditions [F(2, 42) = 6.87, p = 0.0026]. All pair post hoc comparisons using
the Tukey-Kramer HSD test indicated that the mean score for LF (M = 9.57, SD = 1.81)
was significantly different from that of the HF (M = 7.14, SD = 1.47). However, there
was no significant difference between LF and B diet treatments and a similar effect (no
significant difference) was observed between B and HF (Figure 3.10). These results
suggest that B and LF increase hindgut retention and HF reduces hindgut retention of
liquid digests.
One way analysis of variance of Co foregut retention by diet.
A one-way ANOVA analysis generated no significant differences between groups for
Co foregut retention F (2, 42) = 0.240, ns (Table 3.10).
Table3.10ForegutretentionofCrtimebydiet
Diet Mean SD
B 9.61 4.01
HF 8.88 2.51
LF 8.99 2.53
A paired t-test was performed to compare the mean gut transit time between
particulate and liquid markers by diet. Consequently in the HF diet there was a
significant difference in the results for particulate (M=14.65, SD=2.19) and liquid
(M=16.02, SD=2.34) markers; t(14))=-5.85, p<0.001. Likewise in LF diet there was a
58
significant difference in the results for particulate (M=17.19, SD=2.55) and liquid
(M=18.56, SD=2.62) markers; t(14))=-3.01, p=0.0094. Similar results were obtained
for diet B. There was a significant difference in the results for particulate (M=17.41,
SD=3.24) and liquid (M=17.93, SD=3.40) markers; t(14))=-4.56, p=0.0004
Matched pair analysis was conducted to compare the means of liquid and particulate
transit times. Paired t-test was used to correlate the responses (Table 3.11).
Table3.11Matchedpairsreportonthedifferenceofliquidmarkerandparticulatemarker(CoRetention-CrRetention)
Mean
difference
(hours)
DF t- ratio Prob > |t|
HF 1.37 14 5.845 < 0.0001*
LF 0.647 14 3.012 0.0094 *
B 0.517 14 4.56 0.0004*
The results reveal that, on average, the liquid marker spent 1.36 hours more than the
particulate marker in animals fed on the HF diet. Similarly, for the LF and B diets, the
liquid marker spent, on average, 0.64 and 0.51 hours more than the particulate marker
respectively. The smalls p-values (Prob > |t|) indicated that the differences were highly
statistically significant and not coincidental.
Shortchainfattyacidsinthecaecum
Caecum contents from each animal were collected at the end of the experiment, since
the caecum is the main site for microbial fermentation of dietary fibre in rats. These
samples were analysed to determine the content of short chain fatty acids present in
the hindgut. The concentrations of short chain fatty acids were determined in triplicate
and the relative amounts of short chain fatty acids are presented in table 3.12
59
Table3.12RelativeamountofshortchainfattycaidsobservedfromthecaecumcontentoffemaleWistarratsbythethreediettreatments
SCFA
Diet
HF LF B
Mean ± SE
(range)
Mean ± SE
(range)
Mean ± SE
(range)
Acetic 74.11 ± 2.45
(56.67 – 91.17)
75.39 ± 1.84
(65.14 – 88.0)
72.11 ± 1.98
(63.88-89.04)
Propionic 6.24 ± 0.52
(2.90 – 9.69)
9.69 ± 0.70
(4.80 – 13.79)
8.08 ± 0.59
(3.22 – 12.65)
i-butyric 0.46 ± 0.06
(0.17 – 1.15)
0.95 ± 0.06
(0.54 – 1.33)
0.34 ± 0.05
(0.11 – 0.81)
n-butyric 17.43 ± 1.72
(4.78 – 29.33)
11.86 ± 1.00
(5.41 – 17.47)
16.21 ± 1.43
(6.55 – 24.07)
i-valeric 0.18 ± 0.03
(0.03 – 0.43)
0.72 ± 0.07
(0.36- 1.31)
0.14 ± 0.02
(0.03 – 0.35)
n-valeric 0.87 ± 0.10
(0.24 – 1.70)
1.29 ± 0.11
(0.54 – 1.97)
0.85 ± 0.09
(0.23 – 1.64)
caproic 0.68 ± 0.09
(0.05 – 1.04)
0.10 ± 0.03
(0.02 – 0.40)
2.20 ± 0.36
(0.08 – 4.59)
heptanoic 0.02 ± 0.02
(0.00 – 0.33)
0.00 ± 0.00
(0.00 – 0.03)
0.08 ± 0.02
(0.00 – 0.21)
In order to determine how the response in SCFA profile differed with respect to the diet, a principal component analysis was conducted (Fig 3.4). Effect of diet and litter were evaluated in the production of individual SCFA using
factorial analysis (Table 3.13).
Table3.13EffectofdietandlitterintheproductionofindividualSCFA
Effect Prob > F Acetic Propionic i-butyric n-butyric i-valeric n-valeric caproic heptanoic
Litter 0.3949 0.4572 0.0172* 0.4754 0.4196 0.2743 0.0239* 0.7081
Diet 0.5846 0.0014* < .0001* 0.0477* <0.0001* 0.0065* < .0001* 0.0086*
Litter*Diet 0.5317 0.3667 0.6200 0.4268 0.8505 0.4623 0.0172 0.1117
60
A high statistically significant effect of diet is observed for the production of all
individual short chain fatty, with the exception of acetic acid production. There was no
interaction effect of diet and litter on the production of individual short chain fatty acids
in the caecum of the animals. However, litter effect was observed in the production of
i-butyric acid and caproic acid.
Figure3.4Principalcomponentanalysisofshortchainfattyacidsprofilebydiet
Fermentable fibre (B): Red dots; High content of non-fermentable fibre (HF) = Black dots; Low content of non-fermentable fibre (LF) = Blue dots.
One-way ANOVA analysis was used to evaluate and confirm the findings on the effect
of each type of diet on the productions of individual short chain fatty acids.
Acetic acid production did not show a statistically significant difference by diet as
determined by one-way ANOVA (F(2,42) = 0.62, p = 0.54). However, in all the other
cases, they were highly statistically significant difference by diet. Indeed, the effect of
diet observed for propionic acid (F(2,42) =8.06, p = 0.0011), i-butyric(F(2,42) = 33.57, p
-4
-2
0
2
4
Com
pone
nt 2
(29
.9 %
)
-4 -2 0 2 4Component 1 (47.4 %)
61
< .0001), n-butyric (F(2,42) = 4.27, p =0.0204) was significant. Similarly, high
statistically significant differences by diet were observed for i-valeric (F(2,42) = 50.38, p
< .0001 ) and n-valeric (F(2,42) = 6.02, p = 0.0050) acids. Analogous results were
obtained for caproic (F(2,42) = 25.75, p < .0001 ), and heptanoic (F(2,42) = 5.31, p =
0.0088 ) acids respectively.
Post hoc analysis at p < 0.05 was conducted for comparison of all pairs using Tukey-
Kramer HSD. Results are presented in the table 3.14.
Table3.14ComparisonofallpairsofdietontheproductionofSCFA(usingTukey_KramerHSD)
Pairs Prob > F Propionic i-butyric n-butyric i-valeric n-valeric caproic heptanoic
LF-HF 0.0007* < .0001* 0.0215 * < .0001 * 0.0150 * 0.1475 0.6827
B-HF 0.0946 0.3032 0.8151 0.7989 0.9863 < .0001 * 0.0653
LF-B 0.1581 < .0001* 0.0879 < .0001* 0.0100 * < .0001 * 0.0084 *
62
Discussion
The present study builds on previous research conducted in the Gordon laboratory,
and is intended to contribute to an understanding of the effects of diet modulation on
changes in the host and its gastrointestinal dynamics. Several previous studies in the
field have examined some aspects of the effect of diet on the dynamics of the gut
(Cherbut et al., 1991; Graff et al., 2001; Madsen, 1992). However, some aspects like
the interaction of diet, host characteristics, and environmental factors have been
moderately addressed. A few studies addressed this topic (Herawati, 2006; O'Brien,
2005; O’Brien & Gordon, 2011), and more research is needed to understand the effect
of dietary fibre in host dynamics.
Effect of fibre on body mass parameters
By conducting this research we found some new aspects of host response to fibre
dietary intake. First, there were differences in the body mass parameters depending
on the type of diet fed to the experimental animals. Second, similar differences were
observed in food consumption and digestibility and in the size of the caecum of
animals; indeed, the size of the caecum of animals under diet B was higher compared
to that observed in the animals under the other dietary treatments. Third, in terms of
gut transit time, there was a difference between particulate and liquid markers in
relation to the different dietary treatments. In O’Brien’s research (2005) there was no
difference in transit times between liquid and particulate markers. Herawati (2006)
found differences between liquid and particulate digests; one possible explanation of
this controversial result could be that in the O’Brien’s study there was variation in the
age of the animals selected (ranging from 27 to 76 days old at the start of the
experiment), since it was proposed that age can influence gut transit time (Graff et al.,
2001) and there were differences in diet compounds in both studies.
Results in this investigation are consistent with previous findings by O’Brien (2005).
According to O’Brien, the body mass of animals under 4% fibre (low content) was
higher compared to 18% (intermediate) and 26 % (high content) of non-fermentable
fibre. It should be taken into consideration that in that research the variable of interest
was the variation of the content of non-fermentable fibre.
63
On the other hand, in our study, there was no statistically significant difference
between low content of fermentable fibre (LF) and fermentable fibre (B) diets in the
prediction of Gompertz parameter; similar to findings obtained previously (Herawati,
2006). In Herawati’s (2006) study, that compared different amount of fementable fibre
in the diet, it was revealed that neither fermentable fibre nor low content of non-
fermentable fibre explained differences in body mass and other predicted parameters
as growth parameters.
Epidemiological studies in humans have emphasized the effects of dietary fibre in
obesity (Lissner et al., 1998), associating diet with obesity and related diseases. Fibre
intake in humans has been inversely associated with body mass. Those results are
consistent with the findings in this research. As described before, the body mass of
animals under LF and B diets was significantly higher compared to the animals fed on
HF. The genetic factors of the host in contributing towards obesity range from 5% to
25%; while environmental factors (as diet) play a major role in obesity (Kimm, 1995).
Impact of fibre on gut morphology
The results of this study show diverse levels of variation for the different gut
components among the three groups of animals. The total mass of the gastrointestinal
tract varied less than two-fold in LF treatment and less in HF and B diets treatments
among animals. Comparing the three groups, less variation was observed in the
stomach, whilst more variation was observed in the caecum mass (three-fold in HF,
six-fold in LF and two-fold in B). Similarly, the current study found litter effect on the
size of stomach and small intestine, but not of the caecum and colon. However, an
independent main effect of diet was observed in the size of caecum and colon. No
interaction effect of litter and diet was observed in stomach size.
When killing the animals, it became apparent that the size of the caecum of the
animals fed on fermentable fibre diet (B) was larger compared to the animals that
received the other diets. These observations were confirmed when the statistical
analysis of data was performed. The size of caecum was higher in the animals fed on
fermentable diet and high content of non- fermentable fibre. It is interesting to note that
there was a highly significant effect of diet on caecum size. There were differences
between the caecum size of B and LF and HF and LF. These results seem to be
partially inconsistent with those described by O’Brien who evaluated the effect of
64
different amounts of non-fermentable fibre in the diet. Four diets with varying contents
of non-fermentable fibre were used in O’Brien’s experiment. Surprisingly, in this study,
no differences were found between high content of non-fermentable fibre (HF) and
fermentable fibre (B). The effects of fermentable fibre on the size of the caecum are
consistent with the data obtained previously by Herawati (2006) who showed that the
caecum size increased as the content of fibre in the diet was increased. Litter effect on
the mass of gut and its morphology was concordant with previous findings. As
suggested previously, genetic background of the host affects the variation of gut
morphology.
Food consumption of animals fed on a high content of non-fermentable fibre (HF) and
fermentable fibre (B) was higher compared to those under low content of non-
fermentable fibre. However, fibre per se cannot stimulate cell proliferation in the lower
intestine; instead, it is the product of fermentation that induces the increase of intestinal
crypts cell production (Goodlad, Ratcliffe, Fordham, & Wright, 1989).
Effect of fibre on gut transit time
O’Brien (2005) revealed that there was no differences between liquid and particulate
matter regarding gut transit time in animals fed on non-fermentable fibre. In contrast to
earliest findings, we found differences in gut transit time between particulate and liquid
matter in animals on fed high content of non-fermentable fibre. Similarly, in this study
we found differences in the mean gut transit time between liquid and particulate matter
in LF and B diets. Results of this research regarding the effect of B in gut transit time
are consistent with data obtained by Herawati (2006) who also found differences in
liquid and particulate matter in animals fed on fermentable fibre.
The differences observed in transit time between particulate and liquid matter in this
study can be explained in terms of the size of the caecum. A voluminous caecum is an
adaption designed to retain the digesta for a longer time. Selective retention of the fluid
marker in the caecum maintains higher concentrations of bacteria and potentially more
fermentable fibre leading to a complete digestion of dry matter supporting a prevous
observations (Hume et al., 1993). Most of the small herbivors are caecum fermenters.
Similar to other small omnivors, the rat possesses a relatively well developed caecum.
A separation mechanism of larger fibre particles from smaller liquid content in the
65
hindgut of some small mammals has been described before (EI Sakaguchi, 2003) that
can explain the differences observed in this study. Prior studies in humans have noted
the importance of dietary fibre in gut transit time (Silk, Walters, Duncan, & Green,
2001; Wrick et al., 1983). These studies support the theory that dietary fibre can
provide faster passage of digesta compared to diets that are free or have low content
of fibre. This is in accord with the findings in this research.
Differences in short chain fatty acids analysis
Short chain fatty acids are the main end product of anaerobic bacteria fermentative
breakdown of dietary fibre; although minor amounts can be produced by degradation of
certain amino acids (Rasmussen, Holtug, & Mortensen, 1988). The most important
SCFAs in terms of concentration and effects on human colonic health are acetic,
propionic and butyric acid (Velázquez, Davies, Marett, Slavin, & Feirtag, 2000). It has
been proposed that the fermentative processes in the caecum of animals are similar to
the colonic metabolism in man (Cummings, 1981). However, results should be
cautiously interpreted when the intention is to apply these to humans.
Previous studies evaluating the effect of fermentable fibre observed inconsistent
results on short chain fatty acid production in the caecum (Herawati, 2006). No factor
was attributed to predict the variation in SCFAs (Herawati, 2006). This study set out
with the aim of assessing the effect of fibre intake in short chain fatty acid production in
the caecum. Principal component (PC) analysis clearly revealed that the differences in
SCFA production in the caecum could be attributed to the effect of diet. Again, based
on the discriminant analysis conducted, individuals can be differentiated and identified
by the feature (SCFA values and diet) they were separated
Given that the increased production of short chain fatty acids occurs from fermentable
fibre it could be expected that a diet rich in this type of fibre should have had a greater
effect on the size of the caecum (Sakata, 1987). However, in this research both the
high content of non-fermentable fibre and the fermentable fibre presented similar
effects on the size of the caecum. Nevertheless, it is not clear whether both types of
fibre have similar effects at cellular level. Microscopic studies have to be done to
elucidate these questions.
66
Chapter4 E.coliRESPONSE
Introduction
Escherichia coli, one of the best characterized bacteria models, is a Gram-negative,
non-sporulating and facultative anaerobic bacterium, of which the primary habitat is the
vertebrate gut of warm-blooded animals and reptiles (Walk, Alm, Calhoun, Mladonicky,
& Whittam, 2007); E. coli can be easily isolated in the laboratory. Although living in
symbiosis with the host, the ecological niche of Escherichia coli can fluctuate between
mutualism, commensalism (Berg, 1996), opportunistic and even specialized pathogen
(Kaper, Nataro, & Mobley, 2004; Tenaillon, Skurnik, Picard, & Denamur, 2010).
Ecological and evolutionary forces can shape strains and the strong selective pressure
in E. coli commensal strains may promote the emergence of virulence factors and
antibiotic resistance (Tenaillon et al., 2010). This facultative anaerobic bacterium is not
only found in the gastrointestinal tract of humans and warm blooded animal but
environmentally persistent strains occupy a secondary habitat outside the
gastrointestinal tract (Walk et al., 2007). Previous study suggested that a crude diet
interact with host characteristics affecting E. coli population dynamics in the lower
gastrointestinal tract(O’Brien & Gordon, 2011). In this chapter, rats as a model
organism are fed with different types of fibre (fermentable and non-fermentable) and
different ratio of fibre content to evaluate the effect on diversity and dynamics of E. coli
population and genetics.
The general facultative anaerobic nature of E. coli allows for easy cultivation in the
laboratory, facilitating research experiments. This ease made E. coli a popular tool in
biological research, with an extensive range of knowledge about tools adapted to study
this microorganism. Since 1940, several tools have been proposed for studying E. coli
population genetics. These tools include serotyping, multilocus enzyme electrophoresis
(MLEE), multilocus sequence typing (MLST) and phylogrouping based on multiplex
PCR (Tenaillon et al., 2010).
E. coli Serotyping, based on O, K, and H antigens was developed by Kauffman and
Orskov. The number all E. coli serotypes is more than 100,000; however the number of
frequent pathogenic serotypes is limited to two groups, one group associated with
67
diarrhoeal diseases and the other associated to extra-intestinal disease (Kauffmann,
1947; Ørskov & Ørskov, 1992).
Developed in the 1980s, MLEE, was initially used in eukaryotic population genetics.
This method was proposed to estimate the genetic diversity and structure in natural
bacteria population. In MLEE, the isolates are characterized by their relative
electrophoretic mobility and visualized on dendrograms based on the matrix of genetic
distance (Selander et al., 1986). In multilocus sequence typing, the analysis is based
on the same principles as MLEE; however, in MLST, the nucleotide sequence of
several genes is determined for each isolate, then sequences are analysed based on
nucleotide sequencing rather than electrophoretic mobility of their gene products
(Enright & Spratt, 1999).
Phylogrouping E. coli based on multiplex PCR was proposed by Clermont et al in 2000.
Since then the same research group improved the specificity of this tool in a new
quadruplex phylo-group assignment method. In this new technique, only typical E. coli
phenotype isolated will be screened (Clermont et al., 2013; Tenaillon et al., 2010). This
method allows an E. coli isolate to be assigned to one of the eight phylogroups and
other cryptic clades (II to V); these groups are A, B1, B2, C, D, E F and E. coli cryptic
clade.
The genetic diversity of E. coli strains is substantial, but there is no random distribution
of E. coli phylogroups (Gordon & Cowling, 2003; D. M. Gordon et al., 2005). A previous
study found an E. coli phylogroup that is most prevalent in mammals, less in birds and
infrequent in fish frogs and reptiles (Gordon & Cowling, 2003). Compared to other
bacteria populations, E. coli strains can easily establish a population in the host. The
relative abundance of E. coli phylogroups depends on several predictor factors
including host diet, climate and body mass (Gordon & Cowling, 2003).
It has been proposed that host characteristics such as diet, gut morphology and body
mass are important predictors of the distribution of E. coli phylogenetic groups (Gordon
& Cowling, 2003; Tenaillon et al., 2010).
In humans, E. coli can be commensal as part of the normal intestinal microbial flora
and/or the cause of several intestinal and extra intestinal infections (Picard et al.,
1999). In animals that posses a caecum, group B2 strains seem to be predominant.
68
Strains A and B1 can be recovered from any vertebrate and are considered
commensals (Gordon & Cowling, 2003); however, some of these strains can be
isolated under pathogenic conditions (Picard et al., 1999); whereas B2 and D are
restricted to endothermic vertebrates (Gordon & Cowling, 2003).
The prevalence of E. coli and its distribution among host individuals is influenced by
host factors as habitat, diet, gut morphology, and also body temperature (Gordon &
Cowling, 2003). The genotype of the dominant E. coli strain in a host is determined
partially by the sex and age of that host (D. M. Gordon et al., 2005); this study
suggests that there is an adaptive distribution of E. coli based on differences in habitats
in intestinal tracts of people. High variation of E. coli prevalence was found in mammals
and birds (0 to 100%). Body mass and diet, and climate, are predictors of presence of
E. coli in a host individual. Genotypes A, B1, B2 and D detected are not randomly
distributed. B1 and B2 were more prevalent followed by D and A. Main factors affecting
the distribution of A, B1, B2 and D strains were climate, host, diet and body mass.
However, the same study could not explain to what extent the residence time of E. coli
varies with diet and gut morphology and body mass (Gordon & Cowling, 2003).
The goal in this chapter was to investigate the effect of fermentable and non-
fermentable dietary fibre on the diversity and dynamics of E. coli in the gut. To this end,
over 3000 E. coli isolates from faecal samples of 45 animals collected during 14 weeks
were analyzed. I then assessed how the diet influenced the E. coli density and
genotype variation during the period of study.
69
Results
E.colicelldensity
To evaluate the effect of diet on E. coli cell density, faeces was weekly samples for 13
consecutive weeks. There was a fluctuation in the average cell density during the
experiment. E. coli cell densities did not vary over the course of the experiment in
animals fed a diet high in non-fermentable fibre. In animals fed either a low fibre diet or
one rich in soluble fibre, cell densities were initially higher than those observed in
animals on high fibre diet; after 3 - 4 weeks cell densities in these animals started to
decline until cell densities were similar in all animals regardless of diet. Visual
examination of the data indicated that the average E. coli cell density fluctuated during
the experiment, with a decline on the week 7 under diet tratement (Figure 4.1, 4.2 and
4.3).
E.coligenotypinginrelationtodietandlitter
Escherichia coli genotypes
To evaluate the effect of diet in E. coli diversity, 12 E. coli clones were randomly
selected from every animal for genetic analysis at week 0, week1, week 2, week 3, and
week 13 of diet treatments.
The E. coli strains were genotyped using a quadruplex PCR-based phylotyping method
to classify strains. ERIC-PCR fingerprinting analysis was used to distinguish E. coli
strains (Clermont et al., 2013; D. M. Gordon et al., 2005; Leung et al., 2004; Versalovic
et al., 1991). Isolates were considered to be of the same phylogenetic group if they
showed similar band patterns in both Clermont quadriplex PCR and ERIC-PCR. This
process yielded three E. coli genotypes representing over 97% of the >1500 isolates
characterized containing a predominant strain. In this study, predominant is defined as
the strain that represents more than 50% of isolates (Blyton et al., 2013). One
genotype (I) belonged to phylogroup B2, while the other two (II & III) were phylogroup
B1 strains. At Day 0 the B2 strain represented about 50% of the isolates and the two
B1 strains were equally abundant (Figure 4.4).
70
Figure4.1E.colicelldensityperweekinfemaleWistarratsfedHFdiet
Figure4.2EcolicelldensityperweekinfemaleWistarratsfedLFdiet
Figure4.3EcolicelldensityperweekinfemaleWistarratsfedBdiet
Mean(Cell density (log10 CFU/g faeces)) vs. Week
Cel
l den
sity
(log
10 C
FU/g
faec
es)
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
0 2 4 6 8 10 12 14Week
Mean(Cell density (log10 CFU/g faeces)) vs. Week
Cel
l den
sity
(log
10 C
FU/g
faec
es)
4.4
4.6
4.8
5.0
5.2
0 2 4 6 8 10 12 14Week
Mean(Cell density (log10 CFU/g faeces)) vs. Week
Cel
l den
sity
(log
10 C
FU/g
faec
es)
4.2
4.4
4.6
4.8
5.0
5.2
5.4
0 2 4 6 8 10 12 14Week
71
A three-predictor nominal logistic model was fitted to the data to test the hypothesis
that diet treatment, litter and initial E. coli genotype influence the predominance of
specific genotype at the end of dietary intervention.. In other words, the predominant E.
coli genotype in the last week of dietary intervention was used as the outcome variable,
and the predominant genotype before dietary treatment (baseline), litter, and diet as
predictor variables.
Table4.1RelationshipbetweenpredominantE.coligenotypeanddietinthelastweekofdietaryintervention
Source DF L-R ChiSquare Prob>ChiSq
Baseline 1 2.03 0.154
Diet 2 1.97 0.374
Litter 5 18.31 <0.005(*)
Nominal logistic model analysis using diet and E. coli genotype at baseline and each
experimental diet as predictors was also conducted. Analysis revealed that there was a
significant effect in HF diet; however, no significant effect was observed in LF and B
diets in the E. Coli genotype predition.
Table 4.2. Diet effect on E. coli genotypes in the last week of dietary intervention
Table4.2DieteffectonEcoligenotypesinthelastweekofdietaryintervention
Diet Prob>ChiSq
HF 0.022 (*)
LF 0.394
B 0.179
Finally, a statistically significant effect was found (p<0.05) when the analysis was
conducted using only diet and E. coli genotype at baseline as predictors.
72
Figure4.4FrequencyofE.coliphylogeneticgroupsinfaecalsamplesofanimalsduringdietaryintervention
0%
20%
40%
60%
80%
100%
0 1 2 13Weeksondiettreatment
FrequencyofE.coliphylogeneacgroupsunderLFdiet
GenotypeIII(B1)
GenotypeII(B1)
GenotypeI(B2)
0%
20%
40%
60%
80%
100%
0 1 2 13Weeksondiettreatment
FrequencyofE.coliphylogeneacgroupsunderHFdiet
GenotypeIII(B1)
GenotypeII(B1)
GenotypeI(B2)
0%
20%
40%
60%
80%
100%
0 1 2 13Weeksondiettreatment
FrequencyofE.coliphylogenacgroupsunderBdiet
GenotypeIII(B1)
GenotypeII(B1)
GenotypeI(B2)
73
The decline of E. coli cell density at week seven of dietary intervention could be
associated with sexual maturity. To address these questions more rigorously, further
analysis was performed to evaluate these differences in cell density before and after
age at pairing or mating. A Two-way factorial analysis of variance test was conducted
that examined the effect of diet and sexual maturity (categorical variables) and their
interaction with E. coli cell density (continuous response variable). The parameter
estimates of effects of each factor and the interaction showed that there was a
statistically significant interaction between the effects of diet and sexual maturity on E.
coli cell density, F (1, 2) = 4.07, p = .0175 (Figure 4.4). Simple main effects analysis
showed that both main effects which are diet and sexual maturity were significant,
indicating that the mean for sexually mature animals differed from the mean for
sexually non mature animals, F (1,2) = 10.22, p = .0015. Similarly not all the means for
the three diets were the same, F(1,2) = 8.31, p = 0.0001.
Moreover, analysis was conducted to evaluate the association of short chain fatty acid
profile en each animal with the predominat E. coli phylogroups at the end of the
experiment. Similarly, statistical analysis was performed to evaluate whether there is
effect of gut transit in the predominance of specific E. coli genotype. No associations
were found between predominant E. coli phylogroups and SCFA production and at the
end of the experiment and transit time experiments at p < 0.05.
74
Discussion
In general, cell density data reveals that over the course of the experiment there was
no variation of cell density among the animals under high fiber treatment. The effect of
fibre on E. coli cell density is higher in B and LF diets and less in HF diet. However,
weekly observation revealed changes in cell density on week 7 of dietary treatment;
this was more evident in animals under LF treatment (Figure 4.1-4.3).
Rat’s age at pairing or mating is between 8 to 10 weeks. Considering this, the
experimental animals could have reached sexual maturity at week 7 of dietary
intervention, since the animals were 21 days old when the dietary experiment started
These changes observed in cell density came shortly after the animals attained sexual
maturity, perhaps suggesting that the hormonal changes accompanying sexual
maturity may influence E. coli dynamics (Figure 4.5).
Fig 4.5. Two-way interaction plot of least square
Figure4.5Two-wayinteractionplotofleastsquareonEcolicelldensityandsexualmaturityoffemaleWistarrats
4.4
4.6
4.8
5
5.2
Cel
l den
sity
(log
10C
FU/g
faec
es) L
S M
eans
no yes
Sexual maturity
BHFLF
75
Diet effects were observed in E. coli phylogenetic groups. Indeed, once the animals
were placed on the experimental diets there was a significant decline in the frequency
of the B2 strain and a concomitant increase in the frequency of the two B1 strains on B
diet. Subsequently, in the animals fed the LF and HF diets there was no change in the
relative abundance of the three genotypes. However, the frequency of the B2 strain
continued to decline in the animals on the B diet (Figure 4.4).
Of all of the predictor variables presented in table 4.1, litter has the greatest influence
on the establishment of this bacterium in the lower gastrointestinal tract regardless of
diet and E. coli genotypes at baseline. In addition, there was an influence of the initial
E. coli genotype when it was independently examined.
The E. coli genotypes isolated from the animals at the end of the experiment clearly
derived from few sources, including the person who was taking care of the animals. In
addition, the few genotype variations observed in this study could be attributed to the
source and animal breeding conditions, specific pathogen free animals and controlled
facilities for animal husbandry. No external source of strains accounted for the
predominant genotypes shared among groups since no new genotype was observed at
the end of the experiment. Similarly to previous studies, it was found that the
differences on E. coli phylogroups were in response to changes in diet (Gordon &
Cowling, 2003; O’Brien & Gordon, 2011).
76
Chapter5 EFFECTOFDIETONGUTBACTERIALCOMMUNITIES
Introduction
Humans are now considered a supra-organism, because they possess a so called
extended genome. In this extended genome millions of bacterial genomes interact in
the human intestine in a complex symbiosis with host metabolism, physiology and
gene expression (Kinross, von Roon, Holmes, Darzi, & Nicholson, 2008). The human
gastrointestinal microbiota, which is considered a complex organ or ecosystem has
about 100 times as many unique genes (i.e. the human gut microbioma) compared to
the human genome.
The term “human supra-organism” was coined because individuals are a compilation of
microbial and human species. The microbiome must be characterized to understand
human physiological diversity (Turnbaugh et al., 2007). Intentional manipulation of our
microbiota can serve to optimize an individual’s physiology (Turnbaugh et al., 2007).
Most of these microorganisms have a profound impact in human nutrition and
physiology and are crucial for human life (Bapteste et al., 2012; Qin et al., 2010).
A question still not fully answered relates to the degree to which the human
microbiome is uniquely human. Lab members of the Washington University Genome
Sequencing Centre (WU-GSC) found a considerable similarity between human and
mouse distal microbiotas at the division level. As in humans, mice have as their most
abundant divisions, the Firmicutes and Bacteroidetes (J. I. Gordon et al., 2005).
The human microbiome consortium determined the largest reference set of human
microbiomes from healthy adult individuals. This study has generated more than 5000
microbial taxonomic unities based on 16S ribosomal RNA genes. This study promotes
future research which will foster benefits such as the application of probiotic and
prebiotics in human health (Consortium, 2012).
The human colonic microbiota comprises of many hundreds of bacterial species. The
human gastrointestinal tract harbours between 1013 to 1014 microorganisms. This
impressive bacterial content, which is more than 10 times the total number of cells
77
comprising the human body (Kurokawa et al., 2007), is turned over every three days
(Kinross et al., 2008). Bacteria population in the gastrointestinal environment, as well
in other ecosystems of the mammalian body co-exist with the host either as symbionts
of pathobionts, bringing beneficial or detrimental impacts on the health of the host
(Hasegawa & Inohara, 2014; McCracken & Lorenz, 2001).
In humans, there is a significant difference between the microbiota observed in adults,
children and unweaned infants (Kurokawa et al., 2007). The establishment and
maintenance of intestinal microbiota in humans can be influenced by diet, birth delivery
as well as microbe-microbe interactions and microbe-host interactions. A fetus was
thought to be sterile prior to birth, but recent evidence suggests that this may not be
the case (Jiménez et al., 2008). Regardless, after delivery the infant gut will be
colonized by bacteria from the surrounding environment. Subsequently such
colonization is based on exposure to the environment and food (McCracken & Lorenz,
2001). Although several factors such as mode of delivery and feeding type can affect
the infant intestinal microbiota in infants which is less complex than that of adults.
However, by two years of age, the community structure of the infant microbiota is
similar to that in the adult gut (Palmer, Bik, DiGiulio, Relman, & Brown, 2007).
Recent evidence indicates that when a child is born vaginally, the gut microbiome is
seeded by the mother (Dominguez-Bello et al., 2010). Other evidence show that
mothers share more microbes with their children than with unrelated children
(Yatsunenko et al., 2012) and that the microbial communities in family members are
more likely to be similar compared to unrelated individuals (Turnbaugh et al., 2009;
Moodley et al., 2009; Vaishampayan et al., 2010; Song et al., 2013; Schloss et al.,
2014). Similarly, several studies of mice also demonstrated significant maternal effects
on microbial community composition and diversity (Wen, L. et al. 2008 Benson, A. K. et
al 2010 Ley, R. E. et al. 2005).
Significant differences were found in the gut microbiota of individuals living in a West
African country and a European counterpart (De Filippo et al., 2010). Although the
geographic variation in human gut microbiota is driven by culturally based dietary
differences, these differences cannot be replicated through short-term diet
78
manipulation, suggesting that some members of the gut microbiota are non-randomly
distributed (Wu et al., 2014).
The impact of host diet on microbial community structure has been extensively studied.
Studies reveal that significant changes in the composition of gut microbial communities
are caused by short-term dietary intervention (Martínez, Kim, Duffy, Schlegel, & Walter,
2010) (De Filippo et al., 2010; Pérez-Cobas et al., 2015). However, once dietary
intervention ceases, the microbial community structure tends to return to its pre-
intervention state. Other studies suggest that the long-term impact of dietary
intervention on microbial community structure may be, at best, modest (Lawrence et al.
2014; Wu et al 2014).
Similar patterns appear in rodents and other mammals. In rodents for example, the gut
microbiota varies according to breeding conditions and diet. The intestinal microbiota in
rats during the suckling period has low diversity and most of the bacteria species are
derived from their mothers (Tomas et al., 2012) but diversification increases at weaning
and with the maturation of the immune system (Tomas et al., 2012). Most of the
bacteria species of the offspring are still detected when the rats have matured.
The effect of the host genetic background is particularly relevant in the gut microbiota
composition. Variation cannot be entirely reduced to a genetic component; the
environment plays an important role in host - gut microbiota interactions. Breeding
facilities, drugs and diet are also important modulators of the microbiota (Tomas et al.,
2012)
It was described that four ecological processes mediate microbial diversity in the gut:
environmental selection, historical contingency, stochastic factors and dispersal
limitation (Costello et al., 2012). While in environmental selection the host factors
favour the establishment and persistence of particular microbial taxa; in historical
contingency the differences in the time and order of microbial establishment impact
the community assemblage (Fukami, 2015). Moreover, in dispersal limitation the
presence or absence of particular microbial taxa is restricted by host population
structure and local environmental factors and this ecological process appears to play a
79
significant role in determining both microbial community structure and function
(Costello et al., 2012).
Many questions related to the role of the human microbiome in health and disease are
still unsolved, mostly because the studies directly conducted in humans are biased by
several factors that cannot be easily controlled. Such biases include 1) variability of
human genotypes, 2) inter-individual variation of bacterial species found in human gut,
3) recent and past exposure to diet and environment. Additionally there is strong
evidence that the composition of the human gastrointestinal microbiota is affected by
colonization history, aging effects, environmental factors and host genotype. Therefore,
the effects of diet cannot be clearly elucidated even in well-controlled laboratory
conditions due to all aspects of variability in humans (Kurokawa et al., 2007; Zoetendal
et al., 2001). However, some of these factors can be controlled using animal models,
with the potential of rational design for use in human studies. Recently methods of
studying the human microbiome using in vivo models of “humanized
animals”.(Gootenberg & Turnbaugh, 2011; Martin et al., 2007) have been developed. It
has been proposed that a metagenomic analysis will be useful in understanding the
roles of gut microbiota in health and evaluating the efficacy of prebiotics, probiotics and
functional food for modulating gut microbiota (Kurokawa et al., 2007).
This chapter describes the effects of varying dietary fibre on the composition of
bacterial communities in the caecum and colon of female Wistar rats. The objectives
were: 1) to study the impact of fermentable and non fermentable fibre on gut bacterial
communities, 2) to examine differences in bacterial community composition in the
caecum and colon, 3) to evaluate factors affecting the composition of the gut
microbiota.
Methods
The Animal Experimentation Ethics Committee of The Australian National University
approved the experiment. For this experiment, as well as for the whole thesis, 45
specific-pathogen-free, 21 days old Wistar rats from 6 different litters were sourced
from a single breeding facility. Individual animals were housed individually in a
80
covered cage ventilated with unfiltered air. Cages were changed weekly using sterile
bedding. Water and food were provided at libitum. At the end of the study, the animals
were killed by CO2 asphyxiation.
Three different diets were used in this experiment: low content of non fermentable fibre
(LF), high content of non fermentable fibre (HF) and beans based fermentable fibre (B).
The diets were selected on the basis of previous experiments and were chosen to
produce particular outcomes with regards to gastro-intestinal transit times and
morphology (Blyton, Herawati, O'Brien, & Gordon, 2015; O’Brien & Gordon, 2011).
Hills Pet prescription diet i/d ® canine (Hill’s Pet Nutrition, Inc) was used for the LF diet;
Hills prescription diet w/d® canine was used for the HF diet; and the B diet was
produced by mixing cooked red kidney beans (40% w/w) with Hills Pet prescription diet
i/d ® canine (60% w/w). To prepare the B diet, cooked large red kidney beans
(Masterfoods®) were drained, dried at 50 °C for 4 days. Both main diet ingredients
were ground, mixed, pelleted, vacuum packed and stored at -20 °C until required.
Faecal pellets were collected from each animal the day they arrived from the breeding
facility (3 weeks of age) and again (faecal pellets from the rectum) when the animals
were killed after being on the experimental diets for 14 weeks (17 weeks of age).
Caecum contents were collected when the animals were killed at the end of dietary
experiment. Caecum content and faecal pellets were immediately frozen and stored at
-80 C°. DNA was extracted from 50 mg of either faecal or caecal material using
Qiagen® Ministool kit according to the manufacturers protocol.
The V4 region of 16S rRNA (primers 515F and 806R) was selected to assess
community composition because of all the variable regions, the V4 region is the most
accurate classifier for taxonomic purposes (Qunfeng & Claudia, 2012). Samples were
randomized prior to PCR amplification and library preparation. Each 50 µl PCR
reaction contained 1 unit of HiFi Platinum Taq (Invitrogen™), 5 µl of 10X HiFi PCR
buffer, 0.2 mM of reverse primer, 0.2 mM of dNTP mix, 2 mM of MgSO4, polymerase
and 2 µl of DNA template (approximately 100 ng µl-1). Each PCR reaction also
contained 0.2 mM the forward primer that incorporated one of 48 unique barcodes and
the adaptor sequence. A blank tube was included using ultrapure water instead of
81
DNA template. The following conditions were used for PCR reactions: 180 s initial
denaturation step at 94°C, followed by 20 cycles of 15 s denaturation at 94°C, 30 s
annealing at 55°C and 60 s extension at 68°C. There was a final extension step of 600
s at 68°C.
The presence of an appropriate sized PCR product was confirmed using agarose gel
electrophoresis. The PCR products were purified by running the entire sample (48 µl)
on agarose gels and excising the bands using a sterile scalpel. The PCR product in
the gel slice was purified using Wizard® SV Gel and PCR Clean-Up System (Promega)
according manufacturer’s instructions. The purified DNA was quantified using Qubit®
dsDNA Assay Kit according to the instructions of the manufacturer. All samples were
normalised to 10 ng/µl of DNA per sample and the resulting library purified using the
Agencourt AMPure XP system according the manufacturer’s protocol.
Sequencing was carried out using the Ion PGM Ion Torrent platform using Ion 318
chips (ThermoFisher Scientific). Sequences were processed using Mothur (Schloss et
al., 2009) and classified using the SILVA database (Quast et al., 2012).
Non-metric multidimensional scaling analysis (nMDS) based on a Bray-Curtis similarity
metric on log-transformed count data (counts per family) was used to visualize the
similarity of the gut microbial communities of the animals by diet and by location
(caecum versus rectum). Statistical testing was carried out using Non-Parametric
Multiple Analysis of Variance (PERMANOVA) analysis (Bray–Curtis distance metric).
The relative contribution of each family of bacteria to the overall divergence between
treatment groups was assessed using Similarity Percentage analysis (SIMPER).
Bacterial communities composition comparison analysis was also conducted using
analysis of similarity (ANOSIM), an ANOVA-like hypothesis test. Statistical analyses
were undertaken using JMP V11 and Past (Hammer, 2001).
82
Results
ComparisonI:EffectofChromiunandCobalt(usedfortransittimeexperiments)onmicrobialcommunitycomposition.
Food transit times through the gastrointestinal tract are known to vary with host diet
and are associated with changes in E. coli population dynamics and the genetic
structure of E. coli populations in the rat (O’Brien & Gordon, 2011). Consequently,
transit time estimates were made as part of the experiments reported here. However,
it is not known if the cobalt and chromium based markers used to determine food
transit times, impact on microbial community composition. Therefore, the faecal
microbial communities were compared using faeces collected before and after the
transit time experiments were undertaken.
A two-way ANOSIM test with 9999 permutations and Bray-Curtis similarity index was
used to assess the degree of similarity in the microbiomes at the family level before
and after the transit time experiment. In the ANOSIM test, an R-value of “zero” means
that there is 100% of similarity, whereas R-value of “one” indicates complete
dissimilarity of groups. The two-way ANOSIM for transit time experiment effect
returned an R value of 0.005 indicating no significant difference (p = 0.0158 with α =
0.005) between groups. The diet effect returned an R value of 0.54392 indicating that
there was a diet effect (p = 0.0001 with α = 0.005) between groups. R-value indicates
that there was a high degree of similarity between bacterial community in the colon
before and after transit time experiment. This test accepted the null hypothesis that
there was no significant difference in total community structure at the level of family
sequences based on the sample type (before and after chromium and cobalt were fed
to the animals as part of transit time experiments), and also confirmed diet effect in
shaping microbial communities.
Assessments of changes in local diversity (alpha diversity) - Alpha diversity at family
level by site and diet
Differences in the level of diversity in microbial communities before and after rats fed
the three different experimental diets were determined by calculating the Shannon H
diversity indices (alpha index). The average number of sequences at family level per
83
sample of faeces (baseline), and caecum and rectum content after dietary experiment
per treatment group was used to calculate the index. The diversity of bacterial
communities, at family level, expressed as Shannon H Diversity Index in female Wistar
rats fed animal with three different type of diet is shown in figure 5.1.
Figure5.1Diversityofbacterialcommunitiesinfaecalbaseline,caecumandrectumoffemaleWistarrats
In general the diversity of the bacterial communities diversity were different from each
other when comparing baseline, caecum and rectum.
ComparisonII:Littereffectonmicrobialcommunitycompositionbeforeandafterdietaryintervention.
By intent, each litter used consisted of either 6 or 9 sisters and varying numbers of
brothers. The sisters from each litter were assigned, at random, to one of the three
diet treatments such that there were either 2 or 3 sisters from each litter assigned to
each diet treatment. There were 15 animals per diet treatment. The animals were
placed on their experimental diets when they arrived from the breeding facility.
The faecal microbiota of 45 female Wistar rats originating from 6 litter was
characterized before starting diet experiment (when animals were 21 days old), to
provide a baseline characterization. Then one of the three diet treatments was
randomly assigned to each animal in a litter. Characterization of microbial communities
0
1
2
HF LF B
Shan
nonHdiversity
inde
x
Diet
Baseline
Caecum
Rectum
84
preceding dietary intervention revealed that the Bacteroidetes phylum dominated
faecal microbial communities (30 – 70%), while members of the Proteobacteria
phylum represented about 6% of the communities. (Figure 5.2)
Figure5.2Compositionoffaecalmicrobiota(baseline)offemaleWistarratsatPhylumlevel
Non-metric Multi-Dimensional Scaling analysis based on a Bray-Curtis distance metric
was conducted to visualize the relationship among animals in their microbial
community composition (Figure 5.4). Non-Parametric Multiple Analysis of Variance
(PERMANOVA) based on a Bray-Curtis distance metric showed that litter membership
explained some of the among animal variation in microbial community composition
(F=3.07, p< 0.0001). As expected, diet explained none of the variation in microbial
community composition (F=0.50, p=0.833). Similarly, there was no interaction effect
between diet and litter membership (F=0.002, p=0.684).
Characterization of the initial microbial community (at family level) in faeces before
dietary intervention (baseline) is shown in figure 5.3.
0%
20%
40%
60%
80%
100%
B HF LF
Abun
dance
unclassified
Others
Proteobacteria
Firmicutes
Bacteroidetes
85
Figure5.3RelativeabundanceofpredominantfamiliesinthefaecesoffemaleWistarratsbylitter,beforedietarytreatment(baseline)
0%
20%
40%
60%
80%
100%
BHFLFB BHFLFLFBHFLFBHFHFLFB BHFLFLFBHFLF
1 2 3 4 5 6
Relahv
eab
unda
nce
Liier
Unclassified
Others
Verrucomicrobiaceae
Acanomycetales
Bifidobacteriales
Deferribacteraceae
Rhodospirillaceae
Enterobacteriaceae
Desulfovibrionaceae
Alcaligenaceae
Rikenellaceae
Porphyromonadaceae
Prevotellaceae
Bacteroidaceae
S24-7
Bacillaceae
Clostridiaceae
Erysipelotrichaceae
Peptostreptococcaceae
Family_XIII_Incertae_Sedis
Veillonellaceae
Lactobacillaceae
Ruminococcaceae
Lachnospiraceae
Firm
icutes
Proteo
bacteria
Bacteroide
tes
86
Figure5.4Non-metricmultidimensionalscaling(nMDS)plotofbacterialcommunitycompositioninthefaecesofthe animals by diet before starting dietary intervention (baseline). In this figure and in the following relatedfigures,acolourcodeisusedtoidentifymicrobialcommunityofeachanimalbydiet:HFdiet=Green,LF=Blue,Bdiet=Red.
The microbiota after dietary intervention
The faecal microbial community was assessed again when animals were 17 weeks old
(after 14 weeks on the experimental diets). There was a substantial decline of the
diversity of the faecal microbial communities after dietary intervention (Fig. 5.1). This
decline occurred irrespective of an animal’s diet during this period. The most obvious
difference was the decline in Bacteroidetes and concomitant increase in Firmicutes.
(Figure 5.5). An inverted Bacteroidetes to Firmicutes ratio was observed in the data of
faecal microbiota compared to those data obtained in baseline. The highest
contribution of Firmicutes was in the animals on the bean diet (68%), followed by those
87
on the high fibre diet (65%) and the contribution of Firmicutes to community
composition in the animals fed the low fibre diet was 59%. On the other hand, the
contribution of Bacteroidetes in faecal microbial community was 36% in LF, 33% in HF
and 28% in B.
Figure5.5Compositionoffaecalmicrobiota(afterdietary intervention)offemaleWistarratsatPylumlevelbydiet
Characterization of faecal microbial community at family level after dietary treatment
and relative abundance of predominant families is presented in figure 5.6
0%
20%
40%
60%
80%
100%
B HF LF
Abun
dance
unclassified
Others
Proteobacteria
Firmicutes
Bacteroidetes
88
Figure5.6RelativeabundanceofpredominantfamiliesintherectumoffemaleWistarratsafter14weeksondietaryintervention
0%
20%
40%
60%
80%
100%
. .
B HF LF
Relahv
eab
unda
nce
Diet
Unclassified
Others
Verrucomicrobiaceae
Acanomycetales
Bifidobacteriales
Deferribacteraceae
Rhodospirillaceae
Enterobacteriaceae
Desulfovibrionaceae
Alcaligenaceae
Rikenellaceae
Porphyromonadaceae
Prevotellaceae
Bacteroidaceae
S24-7
Bacillaceae
Clostridiaceae
Erysipelotrichaceae
Peptostreptococcaceae
Family_XIII_Incertae_Sedis
Veillonellaceae
Lactobacillaceae
Ruminococcaceae
Lachnospiraceae
Firm
icutes
Proteo
bacteria
Bacteroide
tes
89
NMDS analysis of the composition of faecal microbiota revealed that microbial
communities were clearly differentiated by diet (Figure 5.7).
Figure5.7Non-metricmultidimensionalscaling(nMDS)plotoftotalbacterialcommunitycompositionintherectumoffemaleWistarratsafter14weeksofdietaryintervention
ANOSIM test revealed high levels of dissimilarities among the gut microbiota in the
faeces of the three groups of rats after dietary intervention. The highest dissimilarity
was observed between high fibre (HF) and bean (B) diets, and the least difference
between high fibre and low fibre diet (LF) (Table 5.1).
90
Table5.1One-wayANOSIManalysisofcomparisonofthestructureofbacterialcommunitiesinthefaecesofWistarfemaleratsafter14weeksfedonHF,LFandBdiets
Effect p values (*)
Global effect R = 0.5045, p < 0.0001
HF vs. LF R = 0.3818, p < 0.0001
HF vs. B R = 0.6606, p <0.0001
LF vs. B R = 0.4882, P <0.0001
(*) values expressed as sequential Benferroni significance.
A two-way PERMANOVA revealed a significant effect of diet treatment in the among-
animal variation in microbial community composition (F(2,44) = 9.83, p < 0.0001).
Surprisingly, the litter effect observed prior to the dietary intervention remained
statistically significant (F(5,44)=1.32, p = 0.032) after long term dietary intervention.
There was no significant interaction between diet treatment and litter membership on
microbial community composition (F(10,44) = 0.07, p = 0.487).
Caecum microbial communities were characterized after dietary intervention. The
composition of caecum microbiota of the rats after dietary treatment (when animals
were 17 weeks old), at the Phylum level was different to that determined in rectum
samples. Differences were also observed in relation to diets fed to the animals (Figure
5.8).
Figure5.8CompositionofcaecummicrobiotaoffemaleWistarratsatPhylumlevelbydiet
0%
20%
40%
60%
80%
100%
B HF LF
Abun
dance
unclassified
Others
Proteobacteria
Firmicutes
Bacteroidetes
91
Table5.2FamilylevelabundancevariationamongdiettreatmentsinthefaecalmicrobiotaoffemaleWistarratsafter14weeksofbeingfedoneoftheexperimentsldiets
Taxon
Mean Log10 Abundance Week 3 Week 17
Baseline HF Diet
LF Diet
B Diet
All animals
FIRMICUTES Lachnospiraceae 3.21 3.63 3.53 3.65 3.6 Ruminococcaceae 3.14 3.22 3.22 3.23 3.22 Lactobacillaceae 1.81 1.14 1.4 1.32 1.29 Veillonellaceae 1.96 0.24 0.95 1.23 0.81 Family_XIII_Incertae_Sedis 1.47 1.46 1.58 1.22 1.42 Peptostreptococcaceae 2.08 1.27 1.67 0.18 1.04 Erysipelotrichaceae 1.75 0.83 1.38 1.34 1.19 Clostridiaceae 0.93 0.37 0.55 0.11 0.34 Bacillaceae 0.03 0.02 0 0.58 0.2 BACTEROIDETES S24-7 3.34 2.67 2.92 2.99 2.86 Bacteroidaceae 3.12 2.86 3.22 2.81 2.96 Prevotellaceae 2.77 2.8 2.07 2.68 2.52 Porphyromonadaceae 2.14 1.7 1.87 2.11 1.9 Rikenellaceae 1.43 1.3 1.67 1.77 1.58 PROTEOBACTERIA Alcaligenaceae 2.58 1.23 2.14 2.33 1.9 Desulfovibrionaceae 1.79 1.33 2.28 1.66 1.76 Enterobacteriaceae 0.35 0.65 0.63 0.64 0.64 Rhodospirillaceae 0.60 0.37 0.81 1.07 0.75 DEFERRIBACTERES Deferribacteraceae 0.85 1.27 0.52 0.35 0.71 ACTINOBACTERIA Bifidobacteriales 0.87 0.08 0 0 0.03 VERRUCOMICROBIA Verrucomicrobiaceae 0.49 0.27 0.32 0.30 0.30
92
ComparisonIII.DifferencesinCaecumandRectummicrobiotaatage17weeks(14weeksofdietaryintervention)
The most abundant phylum in the caecum in all groups was Firmicutes, followed by
Bacteroidetes and Proteobacteria. These phyla comprised more than 99% of the
caecal bacterial community.
In all caecum samples, regardless of dietary treatment, the abundance of
Bacteroidetes was less than that of Firmicutes. However, differences were observed in
the distribution of predominant phyla after feeding the different diets. Indeed, the
contribution of the predominant Phylum, Firmicutes, was highest in the HF group
(92%), followed by B group with 84% and least in the LF group (78%).
Differences in the contribution of the other phyla were also observed. The contribution
of Bacteroidetes was higher in LF (16%) followed by B (12%) and HF (7%). On the
other hand, the abundance of Protebacteria was highest in LF (6%), followed by B
(4%), and least in HF (1%).
93
Figure5.9RelativeabundanceofpredominantfamiliesinthecaecumoffemaleWistarratsafterHF,LFandBdietarytreatments
Non-metric multidimensional analysis (nMDS) conducted to visualize and compare
microbial communities in the caecum revealed that the shape of microbial communities
in the caecum was highly influenced by diet. Indeed, a distinct separation of
communities based on their experimental diets into groups of HF, LF, and B was
observed (Figure 5.10).
0%
20%
40%
60%
80%
100%
. .
B HF LF
Relahv
eab
unda
nce
Diet
Unclassified
Others
Verrucomicrobiaceae
Acanomycetales
Bifidobacteriales
Deferribacteraceae
Rhodospirillaceae
Enterobacteriaceae
Desulfovibrionaceae
Alcaligenaceae
Rikenellaceae
Porphyromonadaceae
Prevotellaceae
Bacteroidaceae
S24-7
Bacillaceae
Clostridiaceae
Erysipelotrichaceae
Peptostreptococcaceae
Family_XIII_Incertae_Sedis
Veillonellaceae
Lactobacillaceae
Ruminococcaceae
Lachnospiraceae
Firm
icutes
Proteo
bacteria
Bacteroide
tes
94
Figure5.10Non-metricmultidimensionalscaling(nMDS)plotoftotalbacterialcommunitycompositioninthecaecum(afterdietaryintervention)offemaleWistarratsbydiet.Microbialcommunityofthecaecumofeach
animalbydiet:highfibrediet(greendots),lowfibrediet(bluedots),beandiet(reddots).
Differences in total structure in the caecum and rectum after dietary treatment at the
taxonomic family level were evaluated using one-way analysis of similarity test
(ANOSIM) based on the Bray-Curtis similarity index. The degree of similarity between
pairwise bacterial community groups (caecum-rectum) were assessed using 9999
permutations. The statistical significance was determined using α = 0.005 between the
groups tested. The null hypothesis in this test is that there is no difference in total
community structure between the caecum and rectum at level of family sequences
based on the diet, among the three dietary groups after 14 weeks of treatment. R-value
reported by ANOSIM indicates that there was a high dissimilarity amongst groups
(Table 5.3).
95
Table5.3One-wayANOSIMofgutmicrobialcommunitiesatfamilyleveloffemaleWistarrats
Pairwise bacterial communities
Dietary treatment
HF LF B
R p R p R p
Caecum-rectum 0.618 <0.001 0.406 <0.001 0.534 <0.001
Table5.4One-wayANOSIMcomparisonofthedieteffectinthegutmicrobiotaoffemaleWistarrats
Pairwise bacterial communities based on diet
Caecum Colon
(after dietary treatment)
(after dietary treatment)
R p R p
HF-LF 0.819 <0.001 0.411 <0.001
LF-B 0.848 <0.001 0.579 <0.001
HF-B 0.986 <0.001 0.705 <0.001
A one-way ANOSIM rejected the null hypothesis that there was no difference between
caecum and rectum bacterial communities. Similarly, a one-way ANOSIM rejected the
null hypothesis that there was no significant difference in total community structure at
the level of family sequences after dietary treatment (HF, LF and B fed to the animals
during 14 weeks). Significance was determined using α = 0.005 for test between
groups (Table 5.4).
Effect of diet and litter in the composition of microbial communities in the caecum
A permutational analysis of variance (PERMANOVA) with 9999 permutations using the
Bray-Curtis similarity index was conducted for testing the effects of diet and litter on
96
bacterial composition in the caecum. A two-way PERMANOVA revealed that there was
an independent main effect of diet in the composition of microbial in the caecum.
Similarly an independent effect of litter in the composition on microbial community in
the caecum was observed. However, there was no litter and diet interaction effect
(Table 5.5).
Table5.5Atwo-wayPERMANOVAresultsofallcommunitycompositionofthethreegroups(HF,LFandB)inthecaecumoffemaleWistarrats
Source d.f. F p Litter 5 1.53 0.019
Diet 2 23.98 0.0001
Litter*Diet 10 -0.17 0.908
Significance level p = 0.05
Differences and similarities of bacterial community composition between caecum and
rectum
Similarity percentage analysis (SIMPER) is frequently used to answer questions like
how (di) similar are two or more communities. This analysis is used here to estimate
which taxa (family) contribute more to the differences amongst the microbial
communities in the caecum and rectum assessing the overall average and taxon-
specific dissimilarities driven by each diet. To undertake these comparisons the
members of the major phyla present in the animals were analysed separately.
SIMPER ANALYSIS ON PREDOMINANT BACTEROIDETES
Surprisingly, the similarity percentage analysis (SIMPER) for members of the
Bacteroidetes revealed an overall dissimilarity between caecal and rectal microbial
communities for animals fed high fibre diet of 61 %. The principal contributor to the
dissimilarity was Bacteroidaceae with 50,6%, followed by Prevotellaceae and S24-7
(Table 5.6 a).
The overall average dissimilarity for members of Bacteroidetes between the caecal and
rectal bacterial communities for animals on low fibre diet revealed an average
97
dissimilarity of 45%. Taxon-specific analysis of the contribution of dissimilarities
revealed that most of this difference was attributed to S24-7 and Bacteroidaceae; both
contributing with almost 90 % of the caecal and rectal dissimilarity (Table 5.6 b).
The similarity percentage analysis (SIMPER) for members of the Bacteroidetes
revealed that the average dissimilarity between the caecal and rectal communities for
animals of the bean diet was 38%. Most of this difference was due to the differences
due to the relative abundance of the families S24-7 and Bacteroidaceae (Table 5.6 c).
98
Table5.6SIMPERanalysis(dissimilaritycontribution)ofpredominantBacteroidetesFamilybydietinthegutmicrobiotaoffemaleWistarrats
SIMPER % Caecum vs Rectum
a. HF Diet
Taxon Contrib. Cum.
Bacteroidaceae 50.6 50.6
Prevotellaceae 28.65 79.25
S24-7 17.32 96.57
Porphyromonadaceae 1.921 98.49
Rikenellaceae 1.507 100
b. LF Diet
Bacteroidaceae 62.06 62.06
S24-7 27.21 89.27
Prevotellaceae 5.087 94.36
Rikenellaceae 3.061 97.42
Porphyromonadaceae 2.582 100
c. B Diet
S24-7 35.55 35.55
Bacteroidaceae 34.45 70
Prevotellaceae 21.87 91.87
Porphyromonadaceae 5.538 97.41
Rikenellaceae 2.59 100
99
Table5.7SIMPERanalysis(dissimilaritycontribution)ofpredominantFirmicutesFamilybydietinthegutmicrobiotaoffemaleWistarrats
SIMPER % Caecum vs Rectum
a. HF Diet
Taxon Contrib. Cum.
Lachnospiraceae 82.92 82.92
Ruminococcaceae 12.68 95.6
Peptostreptococcaceae 2.196 97.8
Lactobacillaceae 0.9963 98.79
Family_XIII_Incertae_Sedis 0.667 99.46
Erysipelotrichaceae 0.3422 99.8
Veillonellaceae 0.1962 100
b. LF Diet
Lachnospiraceae 74.93 74.93
Ruminococcaceae 14.28 89.22
Peptostreptococcaceae 5.811 95.03
Lactobacillaceae 1.52 96.55
Veillonellaceae 1.453 98
Erysipelotrichaceae 1.198 99.2
Family_XIII_Incertae_Sedis 0.8003 100
c. B Diet
Lachnospiraceae 70.46 70.46
Ruminococcaceae 24.63 95.09
Lactobacillaceae 2.017 97.11
Erysipelotrichaceae 1.973 99.08
Veillonellaceae 0.5901 99.67
Family_XIII_Incertae_Sedis 0.2748 99.95
Peptostreptococcaceae 0.05428 100
100
SIMPER ANALYSIS ON PREDOMINANT FIRMICUTES
The overall dissimilarity between caecum and colon in HF diet was 24.47%.
Taxon-specific dissimilarities analysis revealed that, on average, the contribution of
Lachnospiraceae and Ruminococcaceae were similar to those observed in B diet
(Table 5.7 a).
SIMPER analysis on animals in low fibre diet comparing caecal and rectal microbial
communities revealed an overall dissimilarity of 25 %. Taxon-specific dissimilarities
analysis revealed that Lachnospiraceae and Ruminococcaceae led the contribution on
the dissimilarities in caecum versus rectum comparison (Table 5.7 b).
SIMPER analysis, using the Bray-Curtis distance measure, for members of the
Firmicutes revealed that the overall average dissimilarity of bacterial communities of
caecum and rectum for the animals on the bean diet was 21%. Lachnospiraceae and
Ruminococcaceae contributed to more than 90 % of dissimilarities as it is observed in
table 5.7 c.
SIMPER ANALYSIS ON PREDOMINANT PROTEOBACTERIA
SIMPER analysis on predominant Proteobacteria family revealed an overall average
dissimilarity of bacterial communities of caecum versus rectum of 31%. The overall
average dissimilarity between the caecal and rectal bacterial communities for animals
in high fibre diet was 56 % and for animals in low fibre was 32% (Table 5.8).Taxon-
specific dissimilarity analysis revealed that the contribution of Desulfovibrionaceae and
Alcaligenaceae were similar (around 50% each) except in B diet (in which
Alcaligenaceae contributes in more than 75% of the dissimilarity between ceacum and
rectum microbial communities)
101
Table5.8SIMPERanalysis(dissmilaritycontribution)ofpredominantProteobacteriaFamilybydietinthegutmicrobiotaoffemaleWistarrats
SIMPER % Caecum vs Rectum
HF Diet
Taxon Contrib. Cum.
Desulfovibrionaceae 55 55
Alcaligenaceae 45 100
LF Diet
Alcaligenaceae 53 53
Desulfovibrionaceae 47 100
B Diet
Alcaligenaceae 76 76
Desulfovibrionaceae 24 100
ComparisonIV.Effectsofsiteanddietofguttransittimeexperimentinmicrobialcommunitystructureandshiftoncaecal-colonicmicrobialcommunities
Here, the effects of site (caecum versus colon) and diet on diversity on microbial
communities are evaluated. Two-way PERMANOVA with Bray-Curtis similarity index
using log transformed data of number of sequences at family level was used to analyse
differences on the bacterial community composition and to estimate variation due to
diet and location (caecum versus rectum environment). A highly statistical significant
effect of diet in shaping the microbial community (F(2,1)= 38.342, p < .0001) was
observed. Similarly there was a highly statistically significant effect of sample location
(caecum vs rectum) (F(2,1)=30.38, p < .0001). However there was no effect of diet –
location interaction (F=1.1671, p=0.29787).
Non-metric multidimensional analysis (nMDS) was conducted to visualize and compare
microbial communities between the caecum and colon at the end of the dietary
102
experiment. Each microbial community is represented based on location and their
experimental diets. Distinct separation of microbial communities based on location
Caecum (x) and colon (•), and their experimental diets (high fibre: green, low fibre:
blue, beans: red) were observed (Figure 5.11).
Figure 5.11 Non-metric multidimensional scaling (nMDS) plot of caecal and colonic bacterial communitites offemaleWistarratsafterdietary intervention.Eachthe45animal’scaecalmicrobiota issymbolizedbyc1-c45,andeachanimal’scolonicmicrobiotaissymbolizedbyr1-r45.Forexamplec1andr1denotecaecalandcolonicmicrobiotaofthesameindividual,(inthiscasetheanimalnumber1),andsoon.
Shift of caecal and colonic bacterial communities
A simple metric distance of bacterial communities in the caecum and in the colon for
each of the 45 rats was calculated using the data generated in nMDS (figure 5.11). In
103
this thesis this distance is called the caecal-colonic distance shift. The Pythagorean
formula was applied to calculate the so-called caecal- colonic distance shift based on
coordinate 1 and coordinate 2 of nMDS data (i.e. c1-r1, c13-r13 asd c23-r23 distances in
figure 5.12. The questions here are: can we explain the magnitude of this shift? And
what factors are influencing this shift? The hypothesis is that the extent of the shift
depends not only on the diet but also on retention time.
Figure5.12Caecal-colonicshiftofbacterialcommunitiesoffemaleWistarratsfedHF,LFandBdiets
The interaction effect of diet and gut retention time was estimated on the effect of the
diet and gut transit time in the shift of microbial communities when they are moving
from caecum to colon. Independent analysis was conducted for each marker
particulate (Cr) and liquid (Co).
Shift of microbial communities (caecal-colonic distance shift) and gut retention
(particulate marker)
A highly statistical significant effect of diet (p<.0001) on particulate marker retention
was observed. Likewise, there was an interaction effect of diet and gut retention time of
104
the particulate marker in the caecal – colonic shift of microbial communities (table
5.13).
Table5.9Effectofdietandparticulatemarker(Cr)retentionontheshiftofbacterialcommunitieswhenmovingfromcaecumtocolon
Source DF F Ratio Prob > F
DIET 2 17.4785 <.0001*
Cr Retention 1 4.5098 0.0401*
DIET*Cr Retention 2 6.5486 0.0035*
Analysis of variance (ANOVA) on the caecal-colonic distances yielded significant
variation among the three groups, (F(2,42) = 8.76, p<0.005). A post-hoc Tukey test
showed that the distance shift in group HF differed significantly from the other groups
at p < .05. Distances shift of B group was not significantly different from group LF
(Figure 5.13)
Figure5.13Caecum-colondistanceshiftandtotalgutretentiontime(Crparticulatemarker).Pearsonproduct-momentcorrelationassessingtherelationshipbetweenthetotalguttransittimeretentionandthecaecum-colondistanceshiftforeachdietarytreatment(highfibre=greenlineanddots,lowfibre=bluelineanddotsandbean
=readlineanddots)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Dis
tanc
e Sh
ift
10 12.5 15 17.5 20 22.5 25Cr Retention
105
Changes in specific members of gut microbiota when gut content is moving from
caecum to colon
The hindgut retention estimates the time taken for the gastrointestinal content to move
from caecum to colon. At this point the analysis focuses on evaluating the effects of
diet and hindgut retention time on the dynamics of specific members of the gut
microbiota.
Initially the differences of family counts between caecum and colon (log C – log R) was
calculated to estimate the shift in the count of specific family taxa while it is moving
from caecum to colon. A positive value indicates that a specific member of family taxa
is decreasing while it is moving from caecum to colon. A negative value indicates the
opposite, which means that the specific family taxon is increasing while it is moving
from caecum to colon. A value of zero or near zero indicated that there is no change. A
factorial analysis of variance was used to evaluate the effect of diet and hindgut
retention time on the difference in the number of sequence counts between the
caecum and rectum for each taxonomic family (Tables 5.13, 5.13 and 5.15).
Changes in thespecific family members of Firmicutes while the gut content is moving
from ceacum to colon are presented in figures 5.14 to 5.22 (the colour code is: green
line and dots for high fibre diet, blue line and dots for low fibre diet, and red line and
dots for bean diet).
106
Figure5.14ChangesinRuminococcaceaeabudancewhilemovingfromcaecumtorectuminfemaleWistarrats
Red line indicates that in animals with a short transit time that fed bean diet there is an
increase in Ruminococcaceae abundance while moving from caecum to rectum diet.
As transit time lengthens there is less of a change in numbers between caecum and
rectum. Blue line indicates that there is minor change in Ruminococcaceae abundance
in animals fed low fibre diet. Green line indicates that there is a decrease in
Ruminococcaceae abundance in animals whit short hindgut transit time fed high fibre
diet; however, there is an increase in Ruminococcaceae abundance as hind gut transit
time lengthens in the same group of animals.
-0.6-0.5-0.4-0.3-0.2-0.1
00.10.2
Rum
inoc
occa
ceae
(Log
10
Cae
cum
cou
nts-
Log1
0 R
ectu
m
coun
ts)
5 6 7 8 9 10 11 12 13Cr Hingut retention
107
Figure5.15ChangesinPeptostreptococcaceaeabundancewhilemovingfromceacumtorectuminfemaleWistarrats
Interaction effect of diet and transit time was observed in Peptostreptococcaceae. As
transit time lengthens there is an increase in Peptostreptococcaceae abundance in
animals under low fibre diet (blue dots and line), decrease in in animals under high
fibre diet (green dots and line) and no changes in beans diet (red dots and line).
Figure5.16ChangesinClostridiaceaeabundancewhilemovingfromceaecumtorectuminfemaleWistarrats.
-1.5-1
-0.50
0.51
1.52
Pept
ostre
ptoc
occa
ceae
(Log
10
Cae
cum
cou
nts-
Log
10
Rec
tum
cou
nts)
5 6 7 8 9 10 11 12 13Cr Hingut retention
-1
-0.5
0
0.5
1
Clo
strid
iace
ae (L
og10
Cae
cum
co
unts
- Log
10 R
ectu
m c
ount
s)
5 6 7 8 9 10 11 12 13Cr Hingut retention
108
Interaction effect of diet and transit time was observed in Clostridiaceae. As transit time
lengthens there is an increase in Clostridiaceae abundance in animals under low fibre
diet (blue dots and line), decrease in in animals under high fibre diet (green dots and
line) and minor changes in beans diet (red dots and line).
109
Table5.10Effectofdietandhindgutretentiontimeofparticulatemarkerintheshiftofmicrobialcommunities(caecum-rectum)onfamilymembersofFirmicutes
Table5.10
C
r Hin
dgut
DIE
T
EFFE
CT
(Pro
b >
F)
Mea
n C
-R (*
) Ta
xa
B
LF
HF
D
iet
Cr h
indg
ut
Inte
ract
ion
FIR
MIC
UTE
S
La
chon
ospi
race
ae
0.18
3 0.
214
0.24
2
ns
ns
ns
Rum
inoc
occa
ceae
-0
.177
-0
.061
-0
.111
0.01
1 0.
0311
0.
0012
La
ctob
acill
acea
e 0.
445
0.18
5 0.
175
ns
ns
ns
Ve
illon
ella
ceae
-0
.057
-0
.121
-0
.099
ns
ns
ns
Fam
ily_X
III_I
ncer
tae_
Sedi
s -0
.179
-0
.309
-0
.404
ns
ns
ns
Pept
ostr
epto
cocc
acea
e 0.
122
0.27
1 0.
177
ns
ns
0.
0066
Er
ysip
elot
richa
ceae
0.
228
0.32
9 -0
.005
ns
ns
ns
Clo
strid
iace
ae
-0.0
77
0.15
5 -0
.015
ns
ns
0.03
38
Bac
illac
eace
0.
662
0.07
2 <0
.000
1
<.00
01
ns
ns
(*)
Mea
n C
-R s
tand
s fo
r th
e di
ffere
nces
of
the
mea
ns o
f fa
mily
cou
nts
betw
een
caec
um a
nd c
olon
(log
C –
log
R)
to e
stim
ate
the
shift
in th
e co
unt
of s
peci
fic fa
mily
taxa
whi
le it
is m
ovin
g fro
m c
aecu
m to
col
on
110
Table5.11Effectofdietandhindgutretentiontimeofparticulatemarkerintheshiftofmicrobialcommunities(caecum-colon)onfamilymembersofBacteroidetes
Bacteroidetes
C
r Hin
dgut
DIE
T
EFFE
CT
(Pro
b >
F)
Mea
n C
-R
Taxa
B
LF
H
F
Die
t C
r hin
dgut
In
tera
ctio
n B
AC
TER
OID
ETES
S24-
7 -0
.258
-0
.322
-0
.472
ns
ns
ns
Bac
tero
idac
eae
-0.4
86
-0.3
76
-0.7
75
ns
ns
0.
0124
Pr
evot
ella
ceae
-0
.388
-0
.617
-0
.1
0.
0041
ns
ns
Po
rphy
rom
onad
acea
e -0
.324
-0
.2
-0.5
59
ns
ns
0.
0236
R
iken
ella
ceae
-0
.221
-0
.077
-0
.485
ns
ns
ns
Non
sig
nific
ant e
ffect
of h
indg
ut tr
ansi
t tim
e w
as o
bser
ved
in re
latio
n to
cha
nges
of s
peci
fic
fam
ily m
embe
rs o
f Bac
tero
idet
es w
hile
mov
ing
from
cae
cum
to c
olon
111
Table5.12Effectofdietandhindgutretentiontimeofparticulatemarkerintheshiftofmicrobialcommunities(caecum-colon)onfamilymembersofProteobacteria
Proteobacteria
C
r Hin
dgut
DIE
T
EFFE
CT
(Pro
b >
F)
Mea
n C
-R
Taxa
B
LF
H
F
Die
t C
r hin
dgut
In
tera
ctio
n PR
OTE
OB
AC
TER
IA
A
lcal
igen
acea
e -0
.088
0.
167
-0.3
11
0.
0401
ns
0.
0165
D
esul
fovi
brio
nace
ae
0.17
6 0.
025
-0.0
67
ns
ns
ns
En
tero
bact
eria
ceae
-0
.165
-0
.033
-0
.196
ns
ns
ns
Rho
dosp
irilla
ceae
0.
035
0.26
7 -0
.141
ns
ns
ns
112
Figure5.17ChangesinAlcaligenaceaeabundancewhilemovingfromceaecumtocoloninfemaleWistarrats
Diet effect was observed in Alcaligenaceae. There was no effect of hindgut transit time
in Alcaligenaceae abundance. However interaction effect of diet and transit time was
observed. As transit time lengthens there is an increase in Alcaligenaceae abundance
in animals under low fibre diet (blue dots and line), and decrease in in animals under
high fibre diet and beans diet (green dots and line, and red dots and line).
-1
-0.5
0
0.5
1
Alca
ligen
acea
e (L
og10
C
aecu
m c
ount
s-Lo
g10
Rec
tum
co
unts
)
5 6 7 8 9 10 11 12 13Cr Hingut retention
113
Discussion
Initially, the analysis was focused on whether the transit time experiment using
chromium and cobalt affected the gut microbiota. The community structure of bacterial
populations was not significantly affected. This gives confidence that subsequent
characterisations of microbial communities were not influenced by the transit time
experiments.
The relative abundance of the Firmicutes and Bacteroidetes at week 3 versus 17
weeks is the opposite to that reported in big mammals such as humans and pigs. In
these individuals, the members of the Bacteroidetes were more abundant than
members of the Firmicutes (Alain B. Pajarillo, Chae, P. Balolong, Bum Kim, & Kang,
2014; Arrieta, Stiemsma, Amenyogbe, Brown, & Finlay, 2014). The reasons for these
differences remain unclear, and diet does not appear to be linked with these changes,
since the decline in the Bacteroidetes was observed in most rats regardless of their
experimental diet.
At weaning (animals at 3 weeks age), litter membership explained a significant amount
of the observed microbial community composition variation among animals. Not
surprisingly, diet explained none of the variation in microbial community composition.
Although, litter effect was observed in the bacterial community before starting diet
treatment (baseline day 0), non-metric multidimensional analysis (NMDS), confirmed
the random distribution of experimental diets and therefore their correspondent
bacterial community at the time the experiment started.
This study has shown that litter plays an important role in shaping microbial
communities and intense diet manipulation cannot completely mask the impact of litter
on the structure of gut microbial communities. Several studies demonstrated litter
effects in microbial community composition. These effects have been confirmed in
other species, including mice (Benson et al., 2010) and ground squirrels (Stevenson,
Buck, & Duddleston, 2014). Studies in human twins revealed that the microbiotas are
more similar to each other compared to unrelated individuals (Goodrich et al., 2014).
Human studies also demonstrated that events occurring early in life, such as mode of
delivery, may have long-lasting effects on the composition of the gut microbiota (Maria
G Dominguez-Bello et al., 2010).
114
This study demonstrates that long term of dietary manipulation cannot eliminate the
litter membership differences in microbial communitites observed prior to treatment.
These results have clear implications for efforts that attempt to achieve positive health
outcomes through diet manipulation. The results suggest that the differences observed
among animals fed the same diet, but from different litters, may have consequences in
the bacterial community and composition and the host-microbiota interactions. The
litter effect observed in bacterial community composition after dietary intervention
maybe be translated to effect of community function. The outcomes may suggest that
efforts to enhance the health of humans and other animals through the use of
prebiotics may have limited success in general due to among individual differences in
the composition of their microbiotas and differences in how these microbiotas respond
to dietary manipulation.
These results also indicate a strong dietary effect in shaping gut microbial
communities. It has previously been demonstrated that there is a significant correlation
between dietary carbohydrate content and gut microbiota (G. D. Wu et al., 2011). In
this study, faecal samples collected from rats on fermentable fibre diet (beans) and
high content of non fermentable fibre (rich in cellulose) had significant changes at
Phylum and specific family levels, including higher contribution of Firmicutes relative to
the rats fed low fibre diet. On the other hand, the contribution of Bacteroidetes in faecal
microbial community was lower in bean diets compare to the high fibre and low fibre
diets. These results indicate that the cellulose content in the diet contributes to
microbiota composition given that cellulose is resistant to microbial fermentation.
Moreover changes in gut transit time related to cellulose consumption may lead to
changes in specific members of the microbiota as previously described (Kashyap et al.,
2013).
The relative abundances of six bacterial families varied substantially with diet. Indeed,
members of the Alcaligenaceae and Veillonellaceae were less abundant, and members
of the Deferribacteraceae more abundant in the faeces of animals on the high fibre diet
compared to the faecal microbial community of animals on other diets. Members of the
Desulfovibrionaceae were more abundant and members of the Prevotellaceae less
abundant in the faecal microbial communities of animals fed the low fibre diet. While
the Peptostreptoccaceae were under-represented in the faecal microbial communities
of animals on the non-fermentable fibre diet
115
Some changes observed in the gut microbiota of the experimental animals from
weaning through to adulthood showed similarities to previous studies, such as the
decline in Bifidobacteriales (Arrieta et al. 2014) and dominance of the Bacteroidaceae,
Lachnospiraceae, and Ruminococcaceae in the microbiota of adult animals (17
weeks). However the ratio of Firmicutes to Bacteroidetes at week 3 compared to 17
weeks is opposite to that reported in humans and pigs (Alain B. Pajarillo et al., 2014;
Arrieta et al., 2014). The reasons for these differences are unknown, but the results
suggest that the diet may not be the principal effector, as the decline in the
Bacteroidetes was observed in most rats regardless of their experimental diet.
The present work supports previous findings that have suggested diet is an important
factor for shaping the composition of gut microbiota. Previous studies found faster GI
transit in animals fed polysaccharide rich diet. Moreover a significant effect of diet in
gut function and microbiota composition was found (Kashyap et al., 2013). Similarly,
findings here support previous findings about the interplay of gut microbiota, dietary
intervention, genetic background of the host and the nervous system (Dey et al., 2015).
In this study, a linear relationship between transit time and caecum-colon distance shift
was observed only in HF diet for particulate matter; which could predict shift in bacterial
communities, as the higher the distance of caecal and colonic microbiota, the longest
Cr transit time in animal under HF diet. No associations were observed in the other
dietary treatments, or in the liquid marker. Moreover there were no associations with
hindgut transit time.
Great variability of gastrointestinal transit time in healthy humans was observed in
response to diet. Moreover, variations are observed also as an effect of diseases as
diarrhoea, constipation and irritable bowel syndrome. Nonetheless, the settings of the
present study was designed as a controlled experiment, there was little variation in the
gastrointestinal transit time among the animals on the same diet. However, clear
differences of gastrointestinal transit time between the dietary groups was found.
Most of the studies of the interaction between the gut microbiota and human health and
diseases focus on stool microbiota (Consortium, 2012; De Filippo et al., 2010; Palmer
et al., 2007; G. D. Wu et al., 2014). Although the use of faecal samples for
characterization of gut microbiota offers a great advance as an easy and non-invasive
116
method for collecting samples and easy storage (Fouhy et al., 2015; Lichtman,
Sonnenburg, & Elias, 2015; Tedjo et al., 2015). The results of this thesis show that
faecal microbiota do not represent gut microbial communities. Other authors have also
observed that the use of faecal microbiota as proxy of gut microbiota is not valid
(Alfano et al., 2015; Barker, Gillett, Polkinghorne, & Timms, 2013; Dey et al., 2015;
Durbán et al., 2011; Yasuda et al., 2015). In humans, it was proposed that faecal
microbiota cannot represent intestinal microbiota since faecal and colonic microbiota
communities from given individuals do not have a similar composition (Durbán et al.,
2011). Another study in human gut microbiota compared the microbial communities in
rectal mucosa and faeces; the findings revealed that bacterial communities in both site
samples cluster separately (Durbán et al., 2011). Differences in community structure
were also found in the caecal and colonic habitat in healthy chickens (Stanley, Geier,
Chen, Hughes, & Moore, 2015). However, a recent study found moderate correlation
between colon and faeces and high correlation between caecum and colon microbial
communities in a pig model (W. Zhao et al., 2015). Another study in Rhesus macaque
found a high correlation among all gastrointestinal biogeography microbiota, high
correlation for stool composition with the colonic lumen and mucosa, even moderate
correlation of between stool and ileal microbiota (Yasuda et al., 2015).
Results of caecum versus rectum microbial communities comparison are particularly
important because it has been suggested using faecal microbiota as proxy of gut
microbiota. Extrapolation of faecal samples as representation of gut microbiota should
be cautiously interpreted, as this study found that bacterial communities vary through
the gastrointestinal tract.
Up to now, a number of studies related to gut microbiota were characterized using
faecal samples (Ley, Turnbaugh, Klein, & Gordon, 2006; X. Wu et al., 2010), as it is
assumed that faecal samples can be representative of bacterial communities
throughout the lower gastrointestinal tract. Most of studies of the effect of gut
microbiota interactions on health and diseases in humans focus on stool microbiota
(Consortium, 2012; De Filippo et al., 2010; Palmer et al., 2007; G. D. Wu et al., 2014).
Although the use of faecal samples for characterization of gut microbiota offers a great
advance as an easy and non-invasive method for collecting samples and easy storage
(Fouhy et al., 2015; Lichtman et al., 2015; Tedjo et al., 2015), faecal microbiota do not
represent gut microbial communities as has been observed in the present study. The
most interesting finding in the present study was that the caecal microbiota differs from
117
colonic. Big differences were observed between caecal and colonic community
structure at all taxonomic level regardless of dietary treatment. Differences between
caecal and colonic microbiota were also observed among the three groups of animals.
Overall, the three main phyla, Firmicutes, Bacteroidetes and Proteobacteria dominated
both caecal and colonic microbial communities accounting for more than 95% of all
reading sequences.
Controversial results were found using faecal microbiota as proxy of gut microbiota
characterization (Alfano et al., 2015; Barker et al., 2013; Dey et al., 2015; Durbán et al.,
2011; Yasuda et al., 2015). Findings in this research support previous reports that
proposed that the use faecal microbiota cannot represent intestinal microbiota since
faecal and colonic microbiota communities from a given individuals do not have a
similar composition (Durbán et al., 2011).
Interaction of gut microbiota and gut dynamics is still poorly understood. The
mechanisms by which diet affect gut transit time and the differences between liquid
and particulate retention time have been described previously (Hume et al., 1993).
Moreover, the effect of diet and the interplay of gut microbiota and gastrointestinal
dynamics have recently been described in a short term dietary intervention (Dey et al.,
2015). In this study, it was found a shift on the microbial community structure while the
gut microbiota is moving from one habitat to another (caecum to the colon). The dietary
treatments might have contributed to the observed differences in community structure
and composition since clear diet-related clusters were also observed in three different
dietary treatments. The results described in this thesis demonstrated that diet modifies
gastrointestinal transit time. Specifically this part of the thesis revealed that the
microbiota changes with changes in the gastrointestinal transit time. It has been
described that compared to small intestine, bacteria reside for longer time in the colon
(Johansson et al., 2011). There are optimal conditions in the colon environment for
fast-growing bacteria so it is remarkable that the host is not taken over by the
microbiota residing there.
The decline in the abundance of a taxa when gut content is moving from caecum to
rectum is probably not due to death as the DNA probably does not disappear and
therefore will be extracted and sequenced. Rather the decline happens because other
118
species are replicating faster during the transition between caecum and rectum. It is
clear that as the pellet moves from ceacum to colon different taxa respond to the
change in different ways. Could we speculate that the quality of the DNA extracted
from two different habitats may explain the differences in the structure of bacterial
communities? I consider that it could be differences in the quantity of DNA extracted as
the colonic samples contain less water that the caecum and the DNA content is more
concentrated. However, it was demonstrated in a previous study that varying water
content in faecal samples does not affect the quality of DNA regardless of the type of
kit used or the nature of sample (Ariefdjohan, Savaiano, & Nakatsu, 2010). Moreover in
our study all sequence for each animal were randomly normalized to 10000 readings to
minimize variability among samples and site.
Interaction of diet and transit time affected the gut microbiota along the gastrointestinal
tract. These interactions varied depending on the section of the gastrointestinal tract.
The present work support previous findings that have suggested diet as an important
factor for shaping the gut microbiota. Moreover, faster gastrointestinal transit time in
animals fed a polysaccharide rich diet changed gut function and gut microbiota
composition (Kashyap et al., 2013). This study demonstrated that diet modifies
gastrointestinal transit time and affect differently the composition of microbiota in
dissimilar habitats of the lower gastrointestinal tract.
In this study changes in the hind gut transit time (while microbiota is moving from
ceacum to rectum) led to diet-related changes in specific families including an increase
in Ruminococcaceae in high fibre diet, increase in Bacillaceae in bean diet and
concomitant decrease of Ruminococcaceae in low fibre and bean diets. These results
suggest that these families have differentially adapted to changes related to gut transit
time and gut ecosystem.
Moreover increase in Prevotellaceae, Bacteroidaceae, Porphyromonadaceae, and
Alcaligenaceae in low fibre diet and concomitant decrease in high fibre diet suggest
that these families have distinct adaptations and their success in the complex gut
ecosystem are related to differences in bacteria growing rate and differences in
nutrition requirements
.
Our results confirm previous findings in humans suggesting that the microbial
119
communities in an individual varies along the gastrointestinal tract. Moreover, in our
study, colonic and caecal samples were collected at the same time to minimize
changes in microbial communities due to different sampling intervals.
120
CONCLUSIONSANDFUTUREPERSPECTIVES The findings in this thesis have significant implications for the understanding of the
effect of dietary fibre in host-microbiome interactions.
It has been shown that the competitive advantage of particular E. coli strains in the GI
tract varies with diet. The change observed in cell density came shortly after the rats
attained sexual maturity, perhaps suggesting that the hormonal changes
accompanying sexual maturity may influence E. coli dynamics. Further studies are
needed to confirm if the hormonal status of the host impacts bacterial dynamics in the
gut.
Modifying gut microbial by dietary factors, prebiotic and prebiotic have been observed
in humans and animal models. However, my research has also contributed to an
improved understanding of community structure and function in the gastrointestinal
tract. Unsurprisingly, faecal microbial communities are often used as a proxy for
characterizing gut microbial communities. In this it was demonstrated that profound
changes in community composition occur as the microbial communities move from the
caecum to the rectum and that the extent of change depends on not only host diet, but
on the rate at which material passes from the caecum to the rectum. These outcomes
throw doubt on the value of faecal community characterization as a proxy of gut
microbiota. Dietary treatment promotes growth of specific members of the gut
microbiota with significant changes at family level depending of the gut region.
The gut microbiota plays a critical role in the health of humans and other animals.
Findings support evidence for gut ecosystem manipulation. Our understanding of the
factors shaping the gut microbiota of animals is limited and largely based of descriptive
studies and experiments with humans. While the human studies have provided many
valuable insights, there are ethical limits to what can be accomplished using humans a
model system. This study has demonstrated, for the first time, that dispersal limitation
plays a significant role in shaping microbial communities and that the impact of
dispersal limitation can be seen even after exposing the gut microbiota to intense
selection via diet manipulation. The results have implications for efforts attempting to
achieve positive health outcomes through diet manipulation as wider implications for
efforts attempting to achieve positive health outcomes through diet manipulation are
observed.
121
Finally, the results of these experiments suggest that efforts to enhance the health of
humans and other animals through the use of prebiotics may have limited success in
general due to among individual differences in the composition of their microbiotas and
differences in how these microbiotas respond to dietary manipulation.
Future perspectives
Several different studies have looked at the interactions of diet and gut microbiota.
Findings are still unclear and controversial.
The following are the recommendations for further research, related to findings in this
thesis:
- It is clear that deep sequencing for taxonomic characterization of microbial
communities in not enough but that information on the interactions with the host
and environmental factors is also needed before animal studies can be
extrapolated to human health.
- Research is needed to determine the effects of functional food in the host.
Similar investigations into nutritional strategies of functional foods relatedness
to humans would also be valuable in the field of health and nutritional ecology.
- Research is needed to compare the gut microbiota composition in other parts of
the gut geography to determine whether the interaction of diet, host genetics
and specific members of gut microbiota can explain why some functional foods
are important for some populations, but not for others.
- Longitudinal studies are needed to evaluate short and long term impacts of
dietary interventions on gut microbiota.
- Large-scale epidemiological surveys in human and animal models and “humanized animals” are needed to link strategies for reshaping the gut microbiota in order to improve human health. Results of this surveys could be used in the field of personalized medicine.
122
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APPENDIX
DNA EXTRACTION
DNA extraction was performed in Eppendorf ® tubes using DNAzol® Invitrogen ®. For
this part of the experiment, a single small colony of E. coli was inoculated in 200 µl of
sterile Luria Bertani broth (LB) and incubated overnight with constant shaking (37 °C,
180 rpm). The samples were vortexed and 165 µl of culture was removed from each
tube, then spun in a micro-centrifuge for 1 minute at 14000 rpm. The supernatant was
removed using a 50 µl pipette and sterile STE buffer (5.84 g/L Tris, 0.372 g/L EDTA,
5.84 g/L NaCl, adjusted to pH8.8 with nHCl) was added. Each sample was mixed in a
vortex and 100 µl of DNAzol® was added and mixed for two minutes by inverting the
rack continuously; then 90 µl of 95% ethanol was added and mixed for one minute by
inverting the rack continuously. Samples were spun for 3 minutes at maximum speed
and the ethanol supernatant was poured out. This procedure was followed by the
addition of 500 µl of 70% ethanol, mixing 10 times by inverting the rack of tubes and
centrifuging for 1 minute at maximum speed. The ethanol supernatant was poured out
and the tubes were drained on paper towel and dried upside down in a rack in oven for
30 minutes. 103 µl of TE-NaOH (13 ml of TE Buffer and 780 µl of 1M NaOH), (TE
buffer: Tris 5.84 g/l, EDTA 0.372 g, pH adjusted to 8.1) was added to each tube.
Afterwards, the samples were placed in a 65 °C heating block for 30 minutes to
dissolve the DNA. Finally, the DNA samples were stored frozen at -20 °C for one
month to genotype. ThesameDNAextractionmethodwasusedforDNAextractionofE.coli
strains isolatedfromguttissuecontentscollectedwhentheanimalsweresacrificed.Forthis
purpose, the content of terminal ileum, caecum, caecumwash, proximal colon, distal colon
andfaecalpelletswerealsosampledinMacConkeyagar.Lactosepositivecolonieswereused
forputativeidentificationofE.colistrainsidentifiedasdescribedbefore.
CLERMONT GENOTYPING
PCR reactions were carried out in a 20 µl volume reaction mix containing 4 µl of 5X My
Taq Red Reaction Buffer (MyTaq® HS Red DNA Polymerase that contains dNTPs,
and MgCl2), 2 U of Taq polymerase (MyTaq HS Red), 2 µl of DNA (approximately 100
ng µl-1), and the appropriate primers. All primers used were previously diluted to a
concentration of 10 µM. The amounts of primers added in each PCR reaction were 0.8
µl for chuaA, yjaA and TspE4.C2 and 1.2 µl for arpA. The following are the PCR
132
reaction conditions: initial denaturation 5 min. at 94 °C, 30 cycles of 5 s at 94 °C and 25
s at 59 °C followed by a final extension step of 5 min at 72 °C. PCR products were
loaded in 1.5% agarose gels with Ethidium Bromide (2 µl of 1% Ethidium Bromide on
100 ml of agarose on Tris Borate EDTA buffer). Electrophoresis was carried out at 110
volts for 45 minutes. After electrophoresis the gels were photographed under UV light
using Gel Doc™ XR+ System. E. coli phylogroups were identified based on the chart
present in table 2.5.
Primer sequences and sizes of PCR products for quadruplex phylotyping method. Primer ID Target Primer Sequences PCR product (bp) chuA.1b chuA.2
chuA 5′-ATGGTACCGGACGAACCAAC-3′ 5′-TGCCGCCAGTACCAAAGACA-3′
288
yjaA.1b yjaA.2b
yjaA 5′-CAAACGTGAAGTGTCAGGAG-3′ 5′-AATGCGTTCCTCAACCTGTG-3′
211
TspE4C2.1b TspE4C2.2b
TspE4.C2
5′-CACTATTCGTAAGGTCATCC-3′ 5′-AGTTTATCGCTGCGGGTCGC-3′
152
AceK.f ArpA1.r
arpA 5′-AACGCTATTCGCCAGCTTGC-3′ 5′-TCTCCCCATACCGTACGCTA-3′
400
E. coli phylogroups based on PCR quadruplex
Genotype arpA chuA yjaA TspE4.C2 E.coli phylogroup
+ - - - A0 + - - + B1 - + - - F - + + - B2 - + + + B23 - + - + B2 (*) + - + - A1 or C (*) + + - - D or E (*) + + - + D or E (*) + + + - E or clade I (*)
Adapted from O.Clermont et al. 2013. (*) further test required to confirm.
ERIC- PCR
Each 20 µl PCR reaction contained 0.8 µl each of 2 opposing primers (10 µM
concentration), 4 µl of 5X My Taq Red Reaction Buffer (MyTaq® HS Red DNA
Polymerase that contains dNTPs, and MgCl2), 2 U of Taq polymerase (MyTaq HS Red)
and 2 µl of DNA (approximately 100 ng µl-1). Oligonucleotide sequences are as follows:
ERIC1R 5'-ATGTAAGCTCCTGGGGATTCAC-3' and ERIC2 5'-
AAGTAAGTGACTGGGGTGAGCG -3'. PCR amplifications were performed based on
the method currently used in the Gordon Lab. This method contains few modifications
on the Versalovic et al. (1991) basic protocol. The initial denaturation at 95 °C was
133
followed by 30 cycles of denaturation at 94 °C for 3 seconds, 92 °C for 30 seconds,
annealing at 50 °C for 1 minute, and extension at 65 °C for 8 minutes followed by a
single final extension at 65 °C for 8 minutes. For agarose gel electrophoresis PCR
products were loaded in 1.5% agarose gels with Ethidium Bromide (2 µl of 1%
Ethidium Bromide on 100 ml of agarose on Tris Borate EDTA buffer). Electrophoresis
was carried out at 80 volts for 150 minutes. Following electrophoresis the gels were
photographed under UV light using Gel Doc™ XR+ System.
PREPARATION OF GENOMIC LIBRARIES
DNA was extracted according to the instructions of the manufacturer, using Qiagen ®
Ministool kit, a silica membrane based purification kit. Each 50 µl PCR reaction
contained 5 µl of 10X HiFi PCR buffer, 0.2 mM of reverse primer, 0.2 mM of dNTP mix,
2 mM of MgSO4, 1 unit of HiFi Platinum Taq polymerase and 2 µl of DNA template
(approximately 100 ng ul-1). Barcoded as well as adaptor sequences forward primers,
which are designed to help the indentification of the sequences later in bioinformatics
analysis, were added to each PCR reaction well. A blank tube was included using
ultrapure water instead of DNA template.
The following conditions were used for PCR reactions: 3 min. of initial denaturation at
94 °C, followed by 20 cycles of 15 s denaturation at 94 °C, 30 s annealing at 55 °C and
60 s extension at 68 °C. Furthermore, a final extension step of 10 min. at 68 °C.
Each PCR product was verified by 1.5% agarose electrophoresis in TAE buffer at 120
volts for 30 minutes and photographed under UV. 2 µl of sample and 4 µl of loading
orange dye were loaded in the gel. Orange G was used as the loading dye buffer
(orange G buffer: 50% sucrose, 50 mM EDTA, orange G dye). The PCR products
appear on the gel between 300 and 400 bp. PCR reaction was repeated on those
samples that did not show any products.
Forward Primers used to amplify the V4 region on a HiFi PCR Reaction Ion Xpress
Barcode A Adapter Direction 1 Forward Primer (44bp tags + 18bp target)
1 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTAAGGTAACGATGTGCCAGCMGCCGCGGTAA-3'
2 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTAAGGAGAACGATGTGCCAGCMGCCGCGGTAA-3'
3 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGAAGAGGATTCGATGTGCCAGCMGCCGCGGTAA-3'
4 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTACCAAGATCGATGTGCCAGCMGCCGCGGTAA-3'
134
5 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCAGAAGGAACGATGTGCCAGCMGCCGCGGTAA-3'
6 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTGCAAGTTCGATGTGCCAGCMGCCGCGGTAA-3'
7 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTCGTGATTCGATGTGCCAGCMGCCGCGGTAA-3'
8 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTCCGATAACGATGTGCCAGCMGCCGCGGTAA-3'
9 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTGAGCGGAACGATGTGCCAGCMGCCGCGGTAA-3'
10 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTGACCGAACGATGTGCCAGCMGCCGCGGTAA-3'
11 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCCTCGAATCGATGTGCCAGCMGCCGCGGTAA-3'
12 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTAGGTGGTTCGATGTGCCAGCMGCCGCGGTAA-3'
13 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCTAACGGACGATGTGCCAGCMGCCGCGGTAA-3'
14 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTGGAGTGTCGATGTGCCAGCMGCCGCGGTAA-3'
15 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCTAGAGGTCGATGTGCCAGCMGCCGCGGTAA-3'
16 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCTGGATGACGATGTGCCAGCMGCCGCGGTAA-3'
17 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCTATTCGTCGATGTGCCAGCMGCCGCGGTAA-3'
18 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGAGGCAATTGCGATGTGCCAGCMGCCGCGGTAA-3'
19 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTAGTCGGACGATGTGCCAGCMGCCGCGGTAA-3'
20 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCAGATCCATCGATGTGCCAGCMGCCGCGGTAA-3'
21 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCGCAATTACGATGTGCCAGCMGCCGCGGTAA-3'
22 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTCGAGACGCGATGTGCCAGCMGCCGCGGTAA-3'
23 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTGCCACGAACGATGTGCCAGCMGCCGCGGTAA-3'
24 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGAACCTCATTCGATGTGCCAGCMGCCGCGGTAA-3'
25 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCCTGAGATACGATGTGCCAGCMGCCGCGGTAA-3'
26 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTACAACCTCGATGTGCCAGCMGCCGCGGTAA-3'
27 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGAACCATCCGCGATGTGCCAGCMGCCGCGGTAA-3'
28 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGATCCGGAATCGATGTGCCAGCMGCCGCGGTAA-3'
29 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCGACCACTCGATGTGCCAGCMGCCGCGGTAA-3'
30 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCGAGGTTATCGATGTGCCAGCMGCCGCGGTAA-3'
31 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCCAAGCTGCGATGTGCCAGCMGCCGCGGTAA-3'
32 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCTTACACACGATGTGCCAGCMGCCGCGGTAA-3'
33 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTCTCATTGAACGATGTGCCAGCMGCCGCGGTAA-3'
34 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCGCATCGTTCGATGTGCCAGCMGCCGCGGTAA-3'
35 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTAAGCCATTGTCGATGTGCCAGCMGCCGCGGTAA-3'
36 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGAAGGAATCGTCGATGTGCCAGCMGCCGCGGTAA-3'
37 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTTGAGAATGTCGATGTGCCAGCMGCCGCGGTAA-3'
93 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTTGTCCAATCGATGTGCCAGCMGCCGCGGTAA-3'
39 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTAACAATCGGCGATGTGCCAGCMGCCGCGGTAA-3'
40 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTGACATAATCGATGTGCCAGCMGCCGCGGTAA-3'
41 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTCCACTTCGCGATGTGCCAGCMGCCGCGGTAA-3'
42 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGAGCACGAATCGATGTGCCAGCMGCCGCGGTAA-3'
43 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTTGACACCGCGATGTGCCAGCMGCCGCGGTAA-3'
44 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTGGAGGCCAGCGATGTGCCAGCMGCCGCGGTAA-3'
45 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTGGAGCTTCCTCGATGTGCCAGCMGCCGCGGTAA-3'
Table. Forward primers (Continuation)
Ion Xpress
Barcode A Adapter Direction 1 Forward Primer (44bp tags + 18bp target)
46 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCAGTCCGAACGATGTGCCAGCMGCCGCGGTAA-3'
47 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTAAGGCAACCACGATGTGCCAGCMGCCGCGGTAA-3'
135
48 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTCTAAGAGACGATGTGCCAGCMGCCGCGGTAA-3'
49 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCCTAACATAACGATGTGCCAGCMGCCGCGGTAA-3'
50 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCGGACAATGGCGATGTGCCAGCMGCCGCGGTAA-3'
51 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTGAGCCTATTCGATGTGCCAGCMGCCGCGGTAA-3'
52 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCCGCATGGAACGATGTGCCAGCMGCCGCGGTAA-3'
53 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTGGCAATCCTCGATGTGCCAGCMGCCGCGGTAA-3'
54 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCCGGAGAATCGCGATGTGCCAGCMGCCGCGGTAA-3'
55 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCCACCTCCTCGATGTGCCAGCMGCCGCGGTAA-3'
56 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCAGCATTAATTCGATGTGCCAGCMGCCGCGGTAA-3'
57 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCTGGCAACGGCGATGTGCCAGCMGCCGCGGTAA-3'
58 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCCTAGAACACGATGTGCCAGCMGCCGCGGTAA-3'
59 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCCTTGATGTTCGATGTGCCAGCMGCCGCGGTAA-3'
60 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCTAGCTCTTCGATGTGCCAGCMGCCGCGGTAA-3'
61 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCACTCGGATCGATGTGCCAGCMGCCGCGGTAA-3'
62 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTCCTGCTTCACGATGTGCCAGCMGCCGCGGTAA-3'
63 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCCTTAGAGTTCGATGTGCCAGCMGCCGCGGTAA-3'
64 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTGAGTTCCGACGATGTGCCAGCMGCCGCGGTAA-3'
65 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCCTGGCACATCGATGTGCCAGCMGCCGCGGTAA-3'
66 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCCGCAATCATCGATGTGCCAGCMGCCGCGGTAA-3'
67 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTCCTACCAGTCGATGTGCCAGCMGCCGCGGTAA-3'
68 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCAAGAAGTTCGATGTGCCAGCMGCCGCGGTAA-3'
69 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTCAATTGGCGATGTGCCAGCMGCCGCGGTAA-3'
70 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCCTACTGGTCGATGTGCCAGCMGCCGCGGTAA-3'
71 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTGAGGCTCCGACGATGTGCCAGCMGCCGCGGTAA-3'
72 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCGAAGGCCACACGATGTGCCAGCMGCCGCGGTAA-3'
73 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCTGCCTGTCGATGTGCCAGCMGCCGCGGTAA-3'
74 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCGATCGGTTCGATGTGCCAGCMGCCGCGGTAA-3'
75 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCAGGAATACGATGTGCCAGCMGCCGCGGTAA-3'
76 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCGGAAGAACCTCGATGTGCCAGCMGCCGCGGTAA-3'
77 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCGAAGCGATTCGATGTGCCAGCMGCCGCGGTAA-3'
78 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCAGCCAATTCTCGATGTGCCAGCMGCCGCGGTAA-3'
79 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCCTGGTTGTCGATGTGCCAGCMGCCGCGGTAA-3'
80 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCGAAGGCAGGCGATGTGCCAGCMGCCGCGGTAA-3'
81 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCCTGCCATTCGCGATGTGCCAGCMGCCGCGGTAA-3'
82 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTGGCATCTCGATGTGCCAGCMGCCGCGGTAA-3'
83 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTAGGACATTCGATGTGCCAGCMGCCGCGGTAA-3'
84 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTTCCATAACGATGTGCCAGCMGCCGCGGTAA-3'
85 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCCAGCCTCAACGATGTGCCAGCMGCCGCGGTAA-3'
86 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTTGGTTATTCGATGTGCCAGCMGCCGCGGTAA-3'
87 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTGGCTGGACGATGTGCCAGCMGCCGCGGTAA-3'
88 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCCGAACACTTCGATGTGCCAGCMGCCGCGGTAA-3'
89 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCCTGAATCTCGATGTGCCAGCMGCCGCGGTAA-3'
90 5'-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTAACCACGGCGATGTGCCAGCMGCCGCGGTAA-3'
Reverse Primer used to amplify the V4 region on a HiFi PCR Reaction
P1 Adapter Direction 2 Reverse Primer (all same, 24bp tags + 19bp target)
5'-CCTCTCTATGGGCAGTCGGTGATGGACTACHVGGGTWTCTAAT-3'
136
Adapter A
Ion Xpress Barcode
Forward target sequence (V4 region of the 16S rRNA gene, position 515)
Adapter P1
Reverse target sequence (V4 region of the 16S rRNA gene, position 806)
For every PCR product, another electrophoresis was performed at 120 volts for 1 hour
using larger wells that may contain all the product of each PCR reaction (50 µl). The
band of interest of each sample was excised on a trans-illuminator plate using a sterile
scalpel blade each time. Afterwards, the gel slice that contains the band of interest
was carefully removed and placed on pre-weighed 1.5 ml Eppendorf® tubes. PCR
product in the gel slice was purified using Wizard ® SV Gel and PCR Clean-Up System
according to the instructions of the manufacturer.
137
Table:RawdataonfoodconsumptionandfaecalproductionbydietonfemaleWistarratsduringoneweek(13thweekofdietaryintervention)
AnimalNumber Litter Diet foodconsumptiong faecalproductiong
2 2 B 129 16.33
5 4 B 128 14.33
9 6 B 110 15.94
10 1 B 111 13.73
15 5 B 119 17.5
16 5 B 121 13.74
20 4 B 109 15.89
22 2 B 119 16.47
27 3 B 111 17.15
30 6 B 108 15.62
33 5 B 118 15
34 2 B 143 20.44
38 4 B 109 16.44
41 1 B 106 14.16
44 3 B 117 16.08
1 2 HF 124 30.53
4 4 HF 114 28.99
8 6 HF 111 28.04
11 1 HF 114 29.31
13 5 HF 107 26.89
17 5 HF 116 30.55
21 4 HF 118 29.65
23 2 HF 131 35.4
25 3 HF 116 28.66
28 6 HF 107 15.86
31 5 HF 129 33.16
35 2 HF 136 35.26
37 4 HF 118 32.74
40 1 HF 121 34.42
43 3 HF 113 28.25
3 2 LF 122 14.17
6 4 LF 91 9.9
7 6 LF 83 9.48
12 1 LF 97 9.48
138
14 5 LF 89 10.84
18 5 LF 110 13.82
19 4 LF 99 12.2
24 2 LF 115 15.27
26 3 LF 94 12.2
29 6 LF 92 9.01
32 5 LF 102 13.97
36 2 LF 99 13.84
39 4 LF 107 14.75
42 1 LF 95 12.72
45 3 LF 86 9.02
Table:SummaryonfoodconsumptionandfaecalproductionbydietonfemaleWistarratsduringoneweek(13thweekofdietaryintervention)
Foodconsumption
Faecaloutput
Bdiet 117.2 15.9HFDiet 118.3 29.8LFDiet 98.7 12.0
0
20
40
60
80
100
120
140
Bdiet HFDiet LFDiet
FoodConsumphonandfaecalproduchoningramsbydietduringoneweek(13thweek
ofdietarintervenhon)
Foodconsumpaon
Faecaloutput
139