dynamics of bacterial endospores, and microbial activities...
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Dynamics of bacterial endospores, and microbial activities in soils and sediments Paulina Tamez-Hidalgo, PhD dissertation
Faculty of Bioscience, Department for Microbiology, Aarhus University March 2014
core
cortex
exosporium
Outer
membrane
coat
Inner
membrane
Transmission electron microscopy image of B. atrophaeus
spore. After Zhang et al., 2006.
Hans Røy, supervisor
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Abstract
Prokaryotes (Bacteria and Archaea) are essential for life on Earth. They catalyze unique and
necessary chemical transformations, which shape the biogeochemical cycles, develop stable and
labile pools of carbon (C) and nitrogen (N), produce essential molecules that multicellular organisms
live upon, and together they comprise the largest portion of life’s genetic diversity.
Prokaryotic communities face recurrent nutrient exhaustion periods that can be prolonged up
to centuries, until a season with nutrient deposition arrives. Thus, they have developed the ability to
lower their metabolism to the limit between being considered slow growers, dormant or even
immortal. Despite discussions about minimum energy requirements and metabolic categories, there
is a paraphyletic group of Bacteria (the Firmicutes or low-GC Gram positive) able to create the
toughest dormant forms on Earth, the endospores. Bacterial endospore is a great advantage to
persist in environments in spite of the physicochemical conditions. Bacterial endospores can
remained dormant for extraordinarily prolonged periods. Despite their apparent metabolic inactivity,
endospores are constantly monitoring the nutritional status of their surroundings. Thus, their ability
to react rapidly after an increased presence of monomeric low molecular weight compounds
(mLMW) compounds such as amino acids, purines and sugars is a remarkable characteristic.
Studies of endospores abundance and dynamics in the environment have been recently
enriched by the analytical quantification of dipicolinic acid (DPA). This endospore biomarker can be
quantified using common laboratory-based methods, such as liquid chromatography by measuring
the fluorescence of the Tb3+-DPA complex. As any other culture independent approach, this method
eliminates underestimation bias due to cultivation. The present investigation was focused on
bacterial endospores abundance in different environments, and the factors related. Abundance was
estimated through DPA quantification.
The overall results showed that endospore presence is intrinsically related with nutrient
limitation. The more nutrient exhausted the environment is, the higher the endospore numbers are
found. However, in marine sediments endospores decay rather than accumulate with time, which is
an indication of endospore settling from the overlying water and buried into the sediment.
Calculated half-life was in the order of hundreds of years. A considerable amount of endospores after
the sediment have reached thousands of years was also detected. This was interpreted as a
subpopulation able to persist over burial at larger time scales and being in a steady-state to the
relevant time scales. Predicted microbial turnover times were in the range of tenths to hundreds of
years, which is similar to the endospore half-life.
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Abstrakt
Prokaryoter ( Bakterier og Archaea ) er af afgørende betydning i livets planet. De katalyserer
unikke og nødvendige kemiske omdannelser, der former de biogeokemiske kredsløb, udvikle stabile
og labile puljer af kulstof (C) og kvælstof (N), producerer vigtige molekyler, flercellede organismer
lever på, og sammen udgør den største del af livets genetiske mangfoldighed.
Prokaryotiske samfund står over for tilbagevendende næringsstoffer udmattelse perioder,
der kan forlænges op til århundreder, indtil en sæson med næringsstof aflejring ankommer. Således
har de udviklet evnen til at sænke deres stofskifte til grænsen mellem betragtes langsom dyrkere,
hvilende eller udødelige. Trods diskussioner om minimumskrav til energi og metaboliske kategorier,
der er en paraphyletic gruppe af bakterier (de Firmicutes eller lavt-GC Gram positive) i stand til at
skabe de hårdeste hvilende former på jorden, endosporer. Bakteriel endospore er en stor fordel at
fortsætte i miljøer på trods af de fysisk-kemiske forhold. Bakterielle endosporer kan forblev hvilende
for ekstraordinært lange perioder. På trods af deres tilsyneladende metaboliske inaktivitet er
endosporer overvåger konstant ernæringstilstand deres omgivelser. Således deres evne til at reagere
hurtigt, når en øget forekomst af mLMW forbindelser, såsom aminosyrer, puriner og sukker er en
bemærkelsesværdig egenskab.
Undersøgelser af endosporer overflod og dynamik i miljøet er for nylig blevet beriget ved den
analytiske kvantificering af dipicolinsyre (DPA). Denne endospore biomarkør kan kvantificeres ved
hjælp af fælles laboratorie -baserede metoder, såsom væskekromatografi ved at måle fluorescensen
af Tb3 +-DPA kompleks. Som enhver anden kultur uafhængig tilgang , denne metode eliminerer
undervurdering bias som følge af dyrkning. Den aktuelle undersøgelse var fokuseret på bakterielle
endosporer overflod i forskellige miljøer, og de faktorer relateret. Overflod blev estimeret gennem
DPA kvantificering.
De overordnede resultater viste, at endospore tilstedeværelse er uløseligt forbundet med
næringssaltbegrænsning. Jo mere næringsstof udtømt miljøet er, jo højere endospore numre er
fundet. Men i marine sedimenter endosporer henfald i stedet akkumuleres med tiden, hvilket er en
indikation af endospore afregning fra det overliggende vand og begravet i sedimentet. Beregnet
halveringstiden var i størrelsesordenen hundreder af år. En betydelig mængde af endosporer efter
sedimentet har nået tusinder af år blev også opdaget. Dette blev fortolket som en delpopulation i
stand til at bestå over nedgravning på større tidsskalaer og at være i en stabil tilstand til det
relevante tidsrum. Forudsagte mikrobielle omsætningstal tider var i størrelsesordenen tiendedele for
hundreder af år, hvilket svarer til den endospore halveringstid.
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Acknowledgments
First of all, I want to thank the Mexican Ministry of Science and Technology in Mexico
(CONACyT) for giving the opportunity to study abroad and being part of Aarhus University. Thanks to
Per Nørberg for his support my first year.
Secondly, I would like to thank all people that were, and are part of the HPLC lab: Alice
Langerhuus, Kristoffer Pill, Drew Steen, Stephan Braun and Jonas Hovergaard. Your company, good
advices and technical assistance made my life in the PhD really pleasant. Special thanks to Lykke
Poulsen, your technical assistance, valuable work and long chats about nothing especial, babies,
concerts and bubble tea made this project cooler. Most of all many thanks to Bente A. Lomstein,
without your guidance and advices, this project would have never seen light.
Thirdly, I would like to thank Hans Røy for taking over supervision and withstanding the
toughest part of it with professionalism. Without your emotional support, and envision of my ideas,
the chapters would not have been finished the same way.
Fourthly, I thank all co-authors of the manuscripts for your scientific contribution, and for
helpful assistance during the writing phase: Bent T. Christensen, Mark A. Lever, Oscar Chiang,
Mathias Middelboe, Renato Salvatecci, Bente Lomstein and Hans Røy.
Also, I would like to thank all my friends and fellows at the department. You all are too many
to be mention. I spent very good times with each of you and I will treasure your friendship forever.
Finally, but not less importantly, this thesis is dedicated to my family. To my father, he has
always boosted my curiosity and supported my decisions. To my mother, she has always been there,
supporting everything I do without questioning. To my sister that has always been the compass and
the grounds to walk through. Also, I would like to thank my new family. To Torlak and Irene, you have
always been supportive and sometimes acted as a parent when I most needed it. To Ane, Rene and
little Victor, you have always being caring and fulfilled the missing gap of my family. To Tore, Ketil,
you and I have spent a lot of great time together. You have the ability to see things through,
especially when we have those cynical discussions about the world. Furthermore, I would like to
thank Leonor for being the best friend in the world and taking over Max when I had to go to the lab o
spent extra time at the university.
Especially, this thesis is dedicated to Ketil, the love of my life. You are my partner, my friend
and my direction. Many, many thanks, for all those nights you sat next to me to discuss the
manuscripts, when you spent your time read them and for all your proof-reading corrections,
suggestions and even checking my calculations. Without you Maxito would have missed his mom
tremendously. Maxito, you are my sunshine, this thesis is also dedicated to you.
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Preface
This document is the results of 3 years of intensive work, discussions and in the last part
writing. The work presented in this PhD dissertation, consisted in the study of dormancy in different
environments. Dormancy was investigated through the quantification of endospores and other
biogeochemical parameters that are possibly correlating with their presence in the different
environments studied. Furthermore, microbial activity was evaluated in deep sediments, and the
implications for an extant major non-sporulating dormant community were discussed. Three
environments were analized during this PhD project: endospores in soils (Chapter 1) and sediments
(Chapter 2), and activity of non-sporulating dormant cells in deep sediments (chapter 3).
The document is divided in a general introduction written as a monograph that comprises the
general subject and more particular topics that were involved in the development of the subsequent
chapters. Next there are chapters. Each of them is a manuscript draft to be submitted to peer-
reviewed scientific journals. The first two are in an almost finish stage being chapter one ready to be
submitted after the few last corrections are made. This is a story about the presence of bacterial
endospores and the environmental factors promoting them, in different arable soils as evaluated by
long-term experimental soil treatments. The name of this chapter is: The prokaryotic community and
its bacterial endospores in soil from three long-term agricultural experiments: effect of fertilization,
straw incorporation and soil type. The second chapter is close to completion and it will go through a
round of co-authorships revisions after the delivery of this thesis. The storyline behind this chapter is
the decay of endospores in the shallow subsurface biosphere. The name of the chapter is: The
dynamics of endospores in the subsurface of the Peru margin. Finally, the third chapter is a
manuscript draft in its first stage. This manuscript will go as well, through a round of co-authorship
revisions. This is a study about the microbial activities in the deep subsurface as evaluated in two
ways: 1) a recently developed model that estimates the speed at which the total organic carbon
(TOC) pool is oxidize and therefore its decrease with time. Based on that, the model estimates the
turnover times of biomass and necromass, 2) integrated total carbon oxidation rates based on the
observed TOC exponential decay. The name of this chapter is: Microbial activity rates of a Holocene-
Pleistocene biosphere. The main results of these three manuscripts are contained in a chapter,
subsequent to the introduction, entitled PhD synthesis. So the reader can have a familiar impression
of the following manuscripts when reading them.
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Contents
Abstract ............................................................................................................................................... 1
Abstrakt ............................................................................................................................................... 2
Acknowledgments ............................................................................................................................... 3
Preface ................................................................................................................................................ 4
Introduction ............................................................................................................................................ 8
Microorganisms, their role as organic matter mineralizers ................................................................ 8
Global estimations of prokaryotic cells ............................................................................................. 10
Dormancy in nature .......................................................................................................................... 11
Dormant or slow growers? ................................................................................................................ 13
Bacterial endospores, a conspicuous fraction of the dormant compartment .................................. 15
Endospore enumeration in the field ................................................................................................. 18
Longevity ........................................................................................................................................... 23
References ......................................................................................................................................... 27
PhD synthesis ........................................................................................................................................ 34
Chapter 1 ........................................................................................................................................... 35
Chapter 2 ........................................................................................................................................... 39
Chapter 3 ........................................................................................................................................... 41
Chapter 1 ............................................................................................................................................... 47
The prokaryotic community and its bacterial endospores in soil from three long-term agricultural
experiments: effect of fertilization, straw incorporation and soil type ............................................ 47
Abstract ............................................................................................................................................. 48
1. Introduction .............................................................................................................................. 49
2. Materials and Methods ................................................................................................................. 51
2.1. The experimental sites ............................................................................................................... 51
2.2. Soil collection and preparation for analysis ............................................................................... 52
2.3. Analyses for soil TOC and TN ...................................................................................................... 53
2.4. HPLC analysis of amino acids and amino sugars ........................................................................ 53
2.5. Analysis of dipicolinic acid (DPA) ................................................................................................ 54
2.6. Cell counts .................................................................................................................................. 54
2.7. DNA extraction ........................................................................................................................... 55
2.8. Quantitative PCR (qPCR) for 16S rRNA ....................................................................................... 56
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3. Results and Discussion .................................................................................................................. 58
3.1. Chemical analyses ...................................................................................................................... 58
3.2. The abundance of cells ............................................................................................................... 58
3.3. The abundance of endospores ................................................................................................... 59
3.4. Relative abundance of Bacteria and Archaea ............................................................................ 60
3.5. Relative proportions of Firmicutes and Bacteria ....................................................................... 61
3.6. Ratio of fungal to bacterial residues .......................................................................................... 61
3.7. Proportions of vegetative versus dormant organisms ............................................................... 61
3.8. Cell and endospore numbers and soil chemical parameters ..................................................... 63
3.9 Determinant factors of bacterial and endospore abundance ..................................................... 63
4. Conclusions.................................................................................................................................... 66
Acknowledgements ........................................................................................................................... 67
Figure legends ................................................................................................................................... 68
References ......................................................................................................................................... 69
Chapter 2 ............................................................................................................................................... 82
Manuscript: The dynamics of endospores in the subsurface of the Peru margin ............................ 82
Abstract ............................................................................................................................................. 83
1. Introduction .............................................................................................................................. 84
2. Materials and Methods ................................................................................................................. 86
2.1. Study site .................................................................................................................................... 86
2.2. Sampling and sample processing ............................................................................................... 86
2.3. Sediment dating by 210Pb ........................................................................................................... 87
2.4. Sediment dating by 14C ............................................................................................................... 87
2.5. Total organic carbon (TOC) ........................................................................................................ 88
2.6. Total hydrolizable amino acids (THAA) ...................................................................................... 88
2.7. Extraction and enumeration of prokaryotes .............................................................................. 88
2.8. Quantification of dipicolinic acid (DPA) and estimation of endospore numbers ...................... 89
3. Results ........................................................................................................................................... 90
4. Discussion ...................................................................................................................................... 92
4.1. Depth profiles of endospores ..................................................................................................... 92
Acknowledgements ........................................................................................................................... 93
References ......................................................................................................................................... 95
6. Figure legends ............................................................................................................................. 100
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Chapter 3 ............................................................................................................................................. 106
Microbial activity rates of a Holocene-Pleistocene biosphere ....................................................... 106
1. Introduction ............................................................................................................................ 107
2. Materials and Methods ............................................................................................................... 109
2.1. Study site .................................................................................................................................. 109
2.2. Sampling and sample processing ............................................................................................. 109
2.3. Radiocarbon measurements .................................................................................................... 110
2.4. Extraction and enumeration of prokaryotes ............................................................................ 111
2.5. Total organic carbon (TOC) and total nitrogen (TN) ................................................................ 111
2.6. Total hydrolysable amino acids (THAA) and total hydrolysable amino sugars (THAS) ............ 112
2.7. Analysis of diagenetic indicators .............................................................................................. 112
2.8. Stereochemical composition (D- L-amino acids isomers) ........................................................ 112
2.9. D:L-Amino acid modelling of bacterial activity ........................................................................ 113
3. Results ......................................................................................................................................... 115
3.1. The two sediment cores at the Peru margin, upwelling system .............................................. 115
3.2. Abundance of prokaryotic cells ................................................................................................ 115
3.3. Profiles of TOC, TN, THAA and THAS ........................................................................................ 115
3.4. Source of OM ........................................................................................................................... 117
3.5. D:L-Amino acid measurements ................................................................................................ 117
4. Discussion .................................................................................................................................... 118
4.1. Carbon oxidation under different scenarios ............................................................................ 119
4.2. Turnover times of biomass and necromass ............................................................................. 120
Acknowledgments ........................................................................................................................... 121
References ....................................................................................................................................... 122
Figure legends ................................................................................................................................. 127
Supplementary material .................................................................................................................. 135
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Introduction
Microorganisms, their role as organic matter mineralizers
Bacteria and Archaea, popularly simplified as prokaryotes, are an essential component of our
planet. What prokaryotes lack in size, they make up in numbers (Parkes et al., 1994; Whitman et al.,
1998; Torsvik and Øvreås, 2002; Kallmeyer et al., 2012). They catalyze unique and necessary chemical
transformations, which shape the biogeochemical cycles, develop stable and labile pools of carbon
(C) and nitrogen (N), produce essential molecules that multicellular organisms live upon, and
together they comprise the largest portion of life’s genetic diversity.
Mineralization of nutrients carried out by prokaryotes comprise a wide range of biochemical
processes ruled both by the accessibility of terminal electron acceptors and the energy yield from the
redox reaction involved in the process. By doing so, prokaryotes shape their own surrounding
environment in µm3 scale, helping to form
biogeochemical interfaces with soil and
sediment matrix (Canfield and Thamdrup,
2009; Totsche et al., 2010). Therefore
microbial communities are thought to be
the architects of soil (Rajendhran and
Gunasekaran, 2008), as they consume
inorganic compounds to construct
biomolecules needed to grow and excrete
inorganic waste compounds that are readily
used by plants. In the marine environment
including sediments, prokaryotes contribute
greatly to organic matter (OM)
transformation since CO2 is fixed by primary
production and enters the water column as
organic carbon molecules, defined as
organic matter (OM). Degradation of OM is
performed through microbial hydrolytic and
fermentative (e.g. Arndt et al., 2013)
processes transforming the majority of high
molecular weight compounds (HMW),
Figure 1. Molecular composition of organic matter, from sea water traps and sediment, sampled from the central pacific. Molecular uncharacterized refers to organic matter that has not been ascribed as amino acids, carbohydrates or lipids. Modified after Wakeham et al., 1997.
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consisting of biopolymers such as proteins and polysaccharides, to monomeric low molecular weight
compounds (mLMW) (Burdige and Zheng, 1998).While aerobic respiration mineralizes OM
completely to carbon dioxide via the citric acid cycle, mineralization of OM through anaerobic
respiration occurs via food chain. The initial breakdown occurs through extracellular and membrane-
bound hydrolytic enzymes produced by certain microorganisms. The hydrolytic products are then
consumed by fermenting and acetogenic bacteria that produce compounds such as acetate and
hydrogen. The terminal step in this anaerobic food chain involves the utilization of these latter
compounds by microorganisms that reduce sulfate and oxidized manganese/ iron or produce
methane (Arndt et al., 2013).
Studies carried out with environmental samples, have demonstrated that essential
biomolecules (i.e. amino acids, carbohydrates and lipids) are preferentially degraded compared to
bulk OM (Lee and Cronin, 1984; Henrich et al., 1984; Dauwe and Middelburg, 1998; Cowie and
Hedges, 1992; Dauwe et al., 1999; Keil et al., 2000). This is considered further In Chapter 3. The
decrease of total pools to half the concentration of organic carbon (TOC) and hydrolyzable amino
acids (THAA) were found to be in the order of 17 and 13 kyr, respectively. Thus, the fraction of
organic carbon present as molecular identifiable material (e.g. THAA) decreases while the molecular
uncharacterized fraction (bulk TOC) increases (Wakeham et al., 1997). For example, 85% of the
plankton is molecular identifiable whereas only 26% can be identified in deep sea sediments (Fig. 1).
In marine sediments, prokaryotes are responsible for most of the OM degradation. There is a
vast source of electron acceptors present in sediments that trigger hydrolytic and fermentative
respiratory processes (Fig. 2; Canfield and Thamdrup, 2009). Thus, as OM is deposited at the
sediment surface, degradation occurs and consequently prokaryotic cell production. As cells die,
their necromass becomes available for other cells, and then the original pool of OM will get gradually
substituted with local prokaryotic necromass. Organic material in soils, waters and sediments,
therefore, persist in transition of higher to lower degradation state. These materials comprised a mix
of recently formed plus older and less labile OM (Cowie and Hedges, 1994).
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Global estimations of prokaryotic cells
Many Prokaryotic abundance estimations are based on direct enumerations with DNA-binding
fluorescent dyes in the microscope (Weibauer et al., 1998; Parkes et al., 1994; Torsvik and Øvreås,
2002; Schippers et al., 2005; Kallmeyer et al., 2012; Chapter 1) or with the use of flow cytometry (e.g.
Glud and Middelboe, 2004; Duhamel and Jacquet, 2006; Czechowska et al., 2008; Glud et al., 2013;
Chapter 2 and Chapter 3).
The living fractions of bulk carbon and nitrogen pools, in terrestrial and oceanic subsurfaces,
are the single-celled community (Whitman et al., 1998; Fry et al., 2009; Lomstein et al., 2012). Global
estimations of prokaryotic cells are in the order > 2 1029 cells in soil and unconsolidated surfaces
(Table 1). This corresponds to a considerable amount of carbon (350-550 Pg of C), nitrogen (85-130
Pg of N) and phosphorus (9-14 Pg of P) (Whitman et al., 1998). As they comprise the majority of the
biota in such environments, and represent the largest pool of these chemical elements, their role in
organic matter decomposition, nutrient cycling and particle aggregation therefore, is essential.
In terms of energy supply, soil, and the terrestrial and marine subsurface become limited and
highly selective environments (Torsvik and Øvreås, 2002). In the deep marine sub-surfaces, total cell
Figure 2 Schematic view of the depth distribution, of commonly found electron acceptors in marine sediments. On the right, a cartoon reflecting the chemical zonations, which typically accompany the respiration processes on the left. After Canfield and Thamdrup, 2009.
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abundance decreases significantly with depth as the proportion of recalcitrant buried organic matter
increase (Parkes et al., 2000; Arndt et al., 2013; Chapter 3). Interestingly, the same trend is not found
in terrestrial sub-surfaces (Detmers et al., 2001; Fry et al., 2009).
Terrestrial soils harbor a major fraction of global microbial biomass (Table 1). Due to the
complexity of their matrix, these soils have highly compartmentalized microhabitats separated by
steep physicochemical gradients (Brözel et al; 2011; Maron et al., 2011). Microorganisms, which
inhabit these different microhabitats and gradients, contribute substantially to soil nutrient cycling,
water movement (Bisset et al., 2011) and aggregation (Miransari, 2011). In surface soils this natural
heterogeneity is disrupted by agricultural practices, e.g. the addition of fertilizers, tillage, and
rotation of different crops. This is reflected by the close similarities, found in the prokaryotic
diversity, across agricultural lands (Sessitsch et al., 2001; Ogilvie et al., 2008; Bisset et al., 2011;
Poulsen et al., 2013). Management promotes seasonal changes in the activity of the microbial
community (Girvan et al., 2004). With periods of increase microbial biomass and activity after
fertilization, and periods of nutrient exhaustion by the end of the harvest, showing decrease of
microbial biomass and activity. Thus, the question arises: Is microbial dormancy playing a role during
the starvation periods?
Table 1. Global estimations of single-celled organisms in soil and unconsolidated subsurface calculated as an extrapolation of cell concentration per gram of soil and the total area of the selected environment. Numbers in soil represent the grand total of soil from different environments which are an average of top 1m and top 1-8 m as described in Whitman et al., 1998.
Prokaryotes inhabiting Global estimations Reference
Top soil, < 8m 2.6 1029
Whitman et al., 1998
Terrestrial subsurface, > 8m 6 1029
Fry et al., 2009
Oceanic subseafloor 2.9 1029
Kallmeyer et al., 2012
Dormancy in nature
The term dormant has been defined as the state of low metabolic activity where cells are
unable to divide or to form a colony on an agar plate without a preceding resuscitation phase (Kell
and Young, 2000). Dormant cells can retain viability, but they need to undergo activation (e.g.
Mearls, et al., 2012). The extent of dormancy of microbial communities in natural environments has
undergone considerable debate (Kaprelyants et al., 1993; Kell and Young, 2000; Price and Sowers,
12
2004; Lennon and Jones, 2011; Jørgensen, 2011; Makarova et al., 2012). So far, we know that some
environments hold the majority of their prokaryotic cells in a dormant state (Fig. 3; Lennon and
Jones, 2011). For example, Luna et al., (2002) accounted 26-30% of living bacterial cells in coastal
sediments, from which only 4% were identified as actively growing. The percentage increased with
increasing sediment organic content. The number of dormant bacteria was estimated as the
difference between live bacterial counts and nucleoid-containing cells (actively growing cells).
Regardless the method employed, the results are consistent across environments, indicating that the
proportion of dormant cells is determined according to the environmental traits.
Although, a proportion of the cells ascribed as dormant, could be at the verge of dying, others
can still revive when favourable factors are met (Jones and Lennon, 2010), and after a period of
acclimation that includes repair of accumulated cell damage (Price and Sowers, 2004; Mearls et al.,
2012). According to Jones and Lennon, (2010), the ability to enter and successfully emerge from
dormancy had a strong, positive influence on species richness. Dormancy therefore can influence the
persistence of populations and has implications for community dynamics.
The environmental cues that make microorganisms shift from dormant periods to activation
periods are still unclear. It may be the lack of nutrients pushing cells to adopt survival strategies
(cannibalism, reducing size, become dormant, endospore formation). However, the results from
chapter 1 indicate a considerable number of endospores (> 2 107 endospores gdw-1) in soil samples
under all the examined agricultural regimes, with as well as without seasonal addition of nutrients.
The results from this investigation (chapter 1) were compared with findings from different
environments, including the deep subsurface, an environment which has severely restricted
nutritional inputs. The results are synthesized in Table 3, showing that there is a surprisingly small
range of endospore abundance regardless the environment.
In general, it is thought that growth is limited by energy (e.i. electron acceptors) and less by
the availability of C and N (e.g. Rothfuss et al., 1997; Morono et al., (2011). Nevertheless, survival is a
different corollary. In nutrient-limited environments (e.g. the deep biosphere), the issue of debate
might only be survival rather than growth. In chapter 3, it is observed that the microbial community
is able to persist over larger time scales (Fig. 2, chapter 3). However, as observed in all subsurface
biosphere studies, the abundance of cells does not increase. Instead, numbers decrease slowly,
following a power law (Parkes et al., 2000).
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Dormant or slow growers?
Subseafloor organisms are living at the verge of the minimum energy flux yet populations may
persist for millions of years (Jørgensen, 2011). Cells at the subseafloor display metabolic rates far
lower than cells in surface environments. Metabolic rate in soil, lake water, and seawater, is typically
in the range of 0.1 to 10 fmol C·cell−1·d−1, corresponding to 10−3 to 10−1 g C metabolized per gram cell
C per hour. The mean metabolic rate for deep subsurface bacteria is typically four orders of
magnitude lower: 10−5 to 10−3 fmol C·cell−1·d−1, corresponding to 10−7 to 10−5 g C·g−1 cell C·h−1
(Jørgensen, 2011). If one compares the definition of dormant cells (Kell and Young, 2000) with these
slow metabolic rates, there seem to be only a thin line dividing the concepts of dormant cells and
slow growers. Therefore, the question comes in mind: does a cell with detected maintenance
metabolic energy only should be considered as dormant? Of course this is hypothetical as this is not
yet possible to distinguish with our current laboratory techniques. Thus, if the answer is no, then,
what would differentiate a dormant from a dead cell with intact structures? Perhaps the best
approximation is the study of molecule motion inside the cell. In recently published research, the
motility of several macromolecular structures was screened inside microbial cells during differently
nutrition stages. Parry et al. (2014) discovered that cytoplasmic fluidity changes dramatically
between well fed and starved cells (Fig. 4). In the case of cell during severe nutrient limitation
conditions, all macromolecular structures studied were immobile during the time lapse those cells
Figure 3. Abundance of dormant cells in different environments. a) Percentages of cells that were found dormant determined by fluorescent in situ hydridization (FISH) or staining with 5-cyano-2,3-ditolyl tetrazolium (CTC) and compared with total cell counts with DAPI. b) Percentages of OTUs belonging to inactive cells determined by the ribosomal RNA to ribosomal DNA ratio, with the use of terminal restriction fragment length polymorphism (TRFLP). The data shown are for the mean the standard error of the mean. (Taken from Lennon and Jones, 2011)
14
were screened. The environmental implications for this bacterial physiology behaviour are still not
fully known. The authors portrayed as the strategy of non-sporulating dormant naked cells.
Price and Sowers, (2004), defined three metabolic rates of living cells, after gathering
extensive literature of microorganisms surviving in nutrient-poor ice and permafrost:
1) The metabolic rate necessary for growth. Refers to those cells encountering proper
conditions to repair accumulated cell damage and divide. A condition occurring sporadically in
nature, the frequency varies according to the environment.
2) The metabolic rate for maintenance. This is enough to maintain vital functions, but
inadequate for growth.
3) The rate for survival. Which makes the cell able to only repair molecular damage, thus it is
defined as dormant.
The latter category was found to be comparable with the rate of spontaneous molecular damage
(Price and Sowers, 2004). Thus cells can remain alive but dormant, in the sense of not growing but
repairing cell damage, over long periods of time.
Finally, slow-growers are what it is considered K-strategists (Fontaine et al., 2003; Janssen
2009), which are defined as organisms prepared for a slow but steady existence in nutrient-limiting
environments (Janssen, 2009). If there is abrupt flush of energy and substrates, K-strategist might
temporarily be overwhelmed by r-strategists which have formerly been present as dormant forms
Figure 4. The motility of macromolecular structures inside bacterial cells. Time-lapse montages of crescentin-GFP structures acquired under conditions of growth (M2G) and carbon source depletion. C. crescentus cells (CJW1265) were grown and imaged in M2G, a glucose-based medium (top). For carbon starvation, cells were washed into M2 buffer (lacking glucose) and incubated for 3 hr before imaging (bottom). Scale bar, 1 mm. (Taken from Parry et al., 2014).
15
(e.g. endospores). As r-strategists exhaust the nutrient pool, they will re-enter dormancy (Janssen,
2009). Catabolites and extra-cellular enzymes left behind, however, might be useful for K-strategists,
which continue their slow-but-steady life strategy (The priming effect; Fontaine et al., 2003).
Buerger et al., (2012) were able to test cells and endospores, viable but dormant, isolated from
soil and marine environments in a nutrient-rich medium. Their results showed a lack of response to
nutrients per se from all populations tested. Instead, they observed growth in a stochastic fashion.
The majority of the isolates developed colonies in a range of 40-200 days. However, re-growth only
took about 24-48 hours, even the isolates that required up to 200 days of initial incubation. This
observation was not a result of adaptative mutations. The likely explanation is that slow growth and
oligotrophy appears to be rarer than previously thought, wheras stochastic exiting from non-growing
state may be more common.
A closer simulatuion of a microbial community, should comprise a mix of cells at different
metabolically stages (Price and Sowers, 2004). In nutrient restricted environments dormancy would
signify the difference between survival and death and slow growing might as well be an ambiguous
interpretation. This discussion is taken further in the chapter 3.
Bacterial endospores, a conspicuous fraction of the dormant compartment
Bacterial endospores are some of the most conspicuous dormant structures representing
Earth’s most successful survival strategies of microorganisms, as they resist chemical, physical,
radiation and sterilization stresses (Nicholson et al., 2000). Endospores are metabolically inactive and
differ structurally from the parental vegetative cell. While in the core they contain the genomic
package to form a vegetative cell, the endospore is further covered by the spore cortex, the spore
coat and finally the exosporium (Fig. 5). They are formed when members of endospore-forming
Firmicutes (EFF) face unfavourable conditions (Slieman and Nicholson, 2001), such as starvation
(Lopes da Silva et al., 2005), viral attack (Makarova et al., 2012) or abrupt oxygen changes (Mearls et
al., 2012). Endospores can be spread via wind, water, living animal hosts, etc. This ability provides
them great advantages in colonizing, thriving and withstanding a wide range of environmental
conditions. Consequently, endospores are an important component of many natural microbial
communities (Nicholson, 2000).
16
Laboratory studies have shown
that endospore formation is an
elaborate and energy intensive process
that requires several hours to complete.
If during this period nutrients become
available, cells in the process of
sporulation would be at a competitive
disadvantage relative to cells not
committed to sporulation and hence
able to resume growth more rapidly.
Therefore, cells delay sporulation until
forced to do so by prolonged depletion
of nutrients (Lopes da Silva et al., 2005;
Lennon and Jones, 2011; Mearls et al.,
2012). However, microbial communities
in natural environments face nutrient
exhaustion that can be prolonged up months and years, until a season with nutrient deposition
arrives. Thus, endospore formation must be a great advantage for those able to do so.
Bacterial endospores can remain dormant for thousands of years (kyr) (Rothfuss et al., 1996;
Vreeland and Rosenzweig, 2002; Nicholson, 2003). Cano and Borucki (1995) may even have revived
endospores preserved for 25-40 million years (Ma) in amber, although their results are still
controversial (Willerslev et al., 2004). The longevity of the spores is facilitated by their multiple
coating-layers which allow them to resist peptidoglycan-lytic enzymes (Fig. 5). Also, they harbour
special acid soluble proteins (SASPs) that preserve the DNA (Setlow, 1992). Their ability to germinate
is dictated by the multiple receptors anchored to the exosporium, a glycoprotein layer sitting at the
exterior of the endospore (Fig. 5).
Despite their apparent metabolic inactivity, endospores are constantly monitoring the
nutritional status of their surroundings (Nicholson, 2000). Thus, their ability to react rapidly after an
increased presence of mLMW compounds such as amino acids, purines and sugars (Setlow, 2003) is a
remarkable characteristic. When such substrates bind to the endospore receptors, enzymatic activity
hydrolyses the peptidoglycan content of the endospore cortex, which triggers a biochemical chain
reaction that ultimately leads to vegetative outgrowth of the cell. The process of germination can be
divided into two stages before outgrowth occurs (Fig. 6.). During those two stages, most of the
Figure 5. TEM image of B. atrophaeus spores. The scattered fiber-like structure around the spore is the exosporium, a glycoprotein layer that can only be observed with ruthenium red stain. After Zhang et al., 2006.
core
cortex
exosporium
Outer
membrane
coat
Inner
membrane
17
endospore’s effort concentrate on the hydration of the core by replacement of H+, cations, Zn2+ and
2,6-Pyridinecarboxylic acid (dipicolinic acid, DPA) to increase the core pH and allow enzymatic
hydrolysis (Setlow, 2003).
Figure 6. Events in spore germination. After Setlow, 2003.
The endospore dehydration and the associated wet heat resistance are due to a calcium-
dipicolinic acid (DPA-Ca++) complex. DPA is an abundant compound residing within the core (5-15% of
endospore dry weight; Church and Halvorson, 1959). Powell (1953) showed for the first time that
DPA was secreted by B. megaterium after germination, and since then DPA has been isolated from
several spore-forming bacteria (Hindle and Hall, 1999). DPA is also linked to the maintenance of
dormancy and it is suggested to activate the spore DNA repair system (Slieman and Nicholson, 2001).
As free DPA is readily degraded under both oxic (Amador and Taylor, 1990) and anoxic conditions
(Seyfried and Schink, 1990), DPA may be used as a biomarker for the presence of bacterial
endospores (Hindle and Hall, 1999; Fichtel et al., 2007; Lomstein and Jørgensen, 2012) in
environmental complex matrixes. Until now few studies have investigated abundance and dynamics
of endospores using detection of DPA (Yung and Ponce, 2007; Fichtel et al., 2008; Ammann et al.,
2011; Lomstein et al., 2012; Langerhuus et al., 2012; Chapter 1 and Chapter 2).
18
Endospore enumeration in the field
Classical estimates of endospores abundance are based on cultivable viable endospore-
formers (Rothfuss et al., 1997; Yung et al., 2007; Logan and Halket, 2011; de Rezende et al., 2013).
Because there is a big spectrum of physiological diversity of endospore-forming bacteria, these
methods may all underestimate the actual in situ numbers of endospores. In addition, this approach
selects those EFF that produce large numbers of endospores and those producing endospores that
germinate most rapidly according to the growth media (Logan and Halket, 2011). Turnbull et al.
(2007) demonstrated that viable counts were lower than microscopic cell counts (10-95% lower) in
13 of 18 analysed strains, because a portion of the population either failed to germinate, were not
genuinely viable, or were viable but not cultivable.
Due to their metabolic diversity Bacillus and Clostridia are presumed to be important
contributors to the carbon, nitrogen and sulphur cycle (Mandic-Mulec and Prosser, 2011), and as
they are often isolated from soil, this is considered a natural reservoir (Kimble-Long and Madigan,
2001; Nicholson, 2002; Logan and Halket, 2011; Hong et al., 2009). However, their actual significance
might have been overestimated. A compilation from the literature shows that members from the
phyla Firmicutes are diversely abundant in soil and sediment environments (Table 2). Regardless the
method employed and their inherent biases, it seems to be that soil does not hold abundant
Firmicutes communities.
As mentioned above, only few studies of endospores abundances and dynamics in the
environment have been based on the analytical quantification of dipicolinic acid. This endospore
biomarker can be quantified using common laboratory-based methods, such as liquid
chromatography (Hindle and Hall, 1999), by measuring the fluorescence of the Tb3+-DPA complex. As
any other culture independent approach, this method eliminates underestimation bias due to
cultivation.
The Tb3+-DPA fluorescence methods usually have a limit of detection in the nM range (Hindle
and Hall, 1999; Fichtel et al., 2007; Ammann et al., 2011; Lomstein and Jørgensen, 2012) allowing
estimating low concentrations of bacterial endospores. Furthermore, by addition aluminium chloride,
one can reduce the interfering effects of the phosphates during fluorescence detection (Ammann et
al., 2011; Lomstein and Jørgensen, 2012). In this way, the determination of DPA via the fluorescence
of the Tb3+ seems a highly promising approach for investigations in natural samples.
19
Table 2. Literature compilation of the relative abundance of the phyla Firmicutes in soil and sediments.
Environment sampling depth (mbsf)
% Relative of
Firmicutesa
molecular marker
Technique Reference
Soil
Arable soils (long-term experimental field, Denmark)
top 0.05 0.2-1.7 Firmicutes-taxon specific primers/16S rRNA
qPCR Chapter 1
Arable soils (long-term experimental field, Denmark)
top 0.2 6 16S rRNA 455 Pyrosequencing
Poulsen et al., 2013
Arable soils (Texas High Plains region, USA)
top 0.05 15 16S rRNA 456 Pyrosequencing
Acosta-Martinez et al., 2008
Several soils (mini review) top 0.05 5 16S rRNA Clone libraries Janssen et al., 2006
Pairie Forest and Desert soils (USA) top 0.05 7 Firmicutes-taxon specific primers
qPCR, clone libraries
Fierer et al., 2005.
Soils from: a) 3 maize fields, Brazil,
b) 1 sugar cane field, Florida, USA,
c) 1 experimental field, Illinois, USA,
d) 1 boreal forest, Ontario, Canada
top 0.1 5 16S rRNA 456 Pyrosequencing
Roesch et al., 2007
Deep terrestrial subsurface (two depths)
31.9 & 133.5 20 16S rRNA Clone libraries, Isolates,PCR-DGGE for identification from enrichments
Fry et al., 2009
Sediments
Lake sediment (Geneva, Switzerland) top 0.09 2.6-59.4 16S rRNA 454 Pyrosequencing
Bueche et al., 2013
Soils and sediments from hypersaline lake, La Sal del Rey, Texas, USA
top 0.05 10 16S rRNA qPCR, clone libraries, sanger sequencing, 457 Pyrosequencing
Hollister et al., 2010
Marine sediments, Peru margin (ODP site 1229)
1-50 16-18 16S rRNA 454 Pyrosequencing
Biddle et al., 2008
Marine sediment, India top 0.1 40 50 16S rRNA Illumina sequencing
Aravindraja et al., 2013
Hydrothermal vent field Loki’s Castle at the Arctic Mid-Ocean Ridge, in the Norwegian-Greenland Sea
0.16-296 1 16S rRNA 454 Pyrosequencing
Jorgensen et al., 2012
20
Oceanic subsurface sediments (Peru margin)
> 1 46 16S rRNA Isolates, PCR-DGGE for identification
Batzke et al., 2007
Subseafloor oceanic crustb (rock chip
fragments)
oceanic crust > 280 mbsf
52 16S rRNA Clone libraries Orcutt et al., 2011
Subseafloor oceanic crustb (black rust
formed after seeping of hydrothermal fluids)
oceanic crust > 210 mbsf
86 16S rRNA Clone libraries Nakagawa et al., 2006
a Relative percentage over total gene numbers, clone libraries or culture collection
b Incubation chamber in a borehole at eastern flank of the Juan de Fuca Ridge
A number of studies employing DPA-quantification are summarized in Table 3. Overall, it
seems endospores abundance in the environment is surprisingly homogeneous in soils and marine
sediments (Table 3).
In chapter 1, endospore abundance was estimated in soil samples within an agricultural
experimental field. Three experiments were selected: a) Askov-LTE, that investigates the effect of
animal manure versus mineral fertilizers and doses rate on a four rotation crop, b) Askov-Maize, that
investigates the effect of selected soils from Denmark with different clay content on silage Maize
crops, c) Askov-Straw, that investigates different doses of straw incorporation to silage Maize crops.
Endospore abundances were found to be in the same order of magnitude in all selected treatments
(Table 3). The number of endospores per soil gram of dry weight increased significantly in soils that
are nutrient exhausted as they have not been fertilized for more than 100 years (Chapter 1). It was
concluded that endospore formation is not affected by common agricultural practices, as the C and N
availability are controlled by seasonal inputs of fertilizer or residue incorporation. Thus, it seems
there is a permanent pool of endospores in these managed environments, regardless the type of
practice.
Ammann et al., (2011) found the highest endospore concentrations in grassland soils (mean
2.4 108 endospores gdw-1), lower concentrations in forest soils (mean 4.6 107 endospore gdw-1)
and the lowest concentrations in freshwater sediments (mean 2.5 107 endospores gdw-1) (Table 3).
They conclude that endospore abundance was related to soil carbon-to-nitrogen ratio.
In marine sediments, the endospore abundance seems to decay with time, once they are
buried into the sediment. Endospores do not seem to have the capacity to germinate as they might
never encounter proper conditions before they accumulated enough damage and die. Chapter 2 was
focused on investigating endospore abundance in top sediments (0-30 cm). In five stations
distributed along a mud-slide in the Peru margin (Fig. 1, chapter 2), endospore numbers showed a
21
narrow variation with sediment depth (Table 3). The interpretations achieved in the chapter are
given in the next section.
Likewise, Langerhuus et al., (2012), showed that endospore abundances in marine sediments
from Aarhus Bay, Denmark, were in the range 4.5 x 106 to 1.5 x 107 cm-3. The numbers decline in the
first 40 cm (only in one station) and then remained relatively constant. However, below that (0.4-
10.9 mbsf), endospore numbers increased nearly double of what was found in the top 0-10 cm
interval.
Lomstein et al., (2012) showed that endospore numbers increased with depth in deep
sediment layers from the Peru Margin area. Moreover, Fichtel et al. (2008) investigated marine
sediments from a tidal flat in the Waden Sea, Germany. In these locations endospore numbers varied
considerably, thus abundance was related to lithology (e.i. highest numbers in organic-rich black mud
sediments and lowest in sandy sediments).
22
Table 3. Compiled data of endospore enumeration based on dipicolinic acid-Tb3+
complex detection, determined by reverse phase HPLC from environmental samples. For conversion of DPA concentration to endospore numbers, it was assumed 2.24 x 10
-16 mol-DPA endospore
-1 (Fichtel et al., 2007).
Endospores (DPA) gdw
-1 (x10
8) Std error (x10
8)
Chapter 3 (soil managed treatments)
Askov-LTE
LTE 0 0.61 0.07
1.5 AM 0.38 0.14
1.5 NPK 0.23 0.03
Askov-Maize
RON (18)a 0.25
ROS (14)
a 0.36
ASK (11)
a 0.41
LUN (5)
a 0.25
Askov-Straw
0 straw 0.28 0.05
8 straw 0.43
Fichtel et al., (2008)
North Sea, intertidal marine sediment cores NSN5 & NSN7
b
0.13
North Sea, intertidal marine sediment core NSN10
b
0.05
North Sea, intertidal marine sediment core JS11
b
0.2
Ammann et al., (2011)
Meadow 3.34 1.13
Meadow 2.70 0.50
Meadow 2.60 0.55
Meadow 1.79 0.41
Bank of river Glatt 1.67 0.28
Forest soil 0.58 0.18
Forest soil 0.50 0.09
Forest soil 0.30 0.26
Bank of canal 0.28 0.02
Sediment of canal 0.22 0.02
Langerhuus et al., (2012)
Aarhus bay, station M1c 0.1-0.05/0.15
Aarhus bay, station M5 0.13-0.1
Lomstein et al., (2012)
Peru margin, sediment core, site 1227d, 0.1-0.03
Chapter 2 Peru margin sediments, sites
G10d 0.32-0.11
G11d 0.21-0.10
G14d 0.11-0.04
G15d 0.31-0.19
Numbers in Askov experiment are given in average of soil treatment a. Percentage of clay per gdw
-1 soil
b. Highest concentration c. Top 0-40 cm/highest concentration below 40 cm d. Highest-lowest concentration
23
Longevity
The longevity of endospores is a matter of discussion. There is no doubt that endospores are
far more durable than naked cells, but the inquiry of how long they can last before they are no longer
able to germinate becomes problematic. Endospores encountered in natural complex samples are a
mix of different anaerobic, aerobic, hetero- and autotrophic organisms. Therefore, their ability to
withstand environmental changes will depend on the features of each species.
It is accepted that bacterial endospores can be found still viable after thousands of years (kyr)
(Vreeland and Rosenzweig, 2002; Nicholson, 2003) when they have been preserved in embedding
samples. Salt crystals (Vreeland et al., 2000), ice cores (Yung et al., 2007) deep sub-surface cores (de
Rezende et al., 2013; Rothfuss et al., 1997), and fossilized animals (Cano and Borucki, 1995) are
examples of that. The most surprising of them, is the revival of viable endospores of B. sphaericus
from the guts of bees trapped in a 25-40 Myr amber (Cano and Borucky, 1995) and from a 250 Myr
halite crystal (Vreeland et al., 2000), although their results are still controversial (Nicholson, 2003;
Willerslev et al., 2004). Moreover, numerous Bacillus spp. have been isolated from deep-subsurface
samples including buried marine sediments (Batzke et al., 2007), ice cores (Zhang et al., 2001;
Christner et al., 2003), buried paleosoils (Boone et al, 1995; Balkwill et al., 1997); buried lacustrine
sediments (Rothfuss et al., 1997), and oil fields (Cayol et al., 1995). Nevertheless, isolation of bacteria
from ancient materials continues to controversial, not only because the isolates are almost identical
to modern relatives, but also because the verification of their antiquity as the materials from where
they were isolated may be questionable (Maughan et al., 2002).
In chapter 2, the endospore abundance with sediment depth and age was analysed in marine
sediments from the Peru margin. As described before, it was found that endospore abundance was
influenced by the age of the sediment. Thus, the deeper the sediment, the older it is, and the fewer
endospores are found. In a closer inspection, it was realized that in all stations endospores decreased
exponentially with sediment age (Fig. 2; chapter 2). Based on this, two pools of endospore longevities
were found (chapter 2). The first pool, the labile endospore pool, have half-lives of 175 ( 51.7)
years. From the initial bulk of endospores roughly half the concentration will disappear within the
first two hundred years. The second pool, the refractory endospores, will endure burial far longer
before they disappear (Fig. 4; chapter 2). A somewhat steady-state scenario is hypothesized for the
latter pool (Fig. 7). The connotation for disappearance implies: a) certain number of endospores will
germinate after some probabilistic nutritional improvement event (e.g. encountering of mLMW
attached in clay particles, close proximity of recently dead microbial cell, mLMW catabolites found in
24
sulphate-methane transition zones); b) the rest of endospores will accumulate sufficient cell damage
and die. According to Yung et al., (1997), rejuvenation of endospore populations through
germination, repair, and sporulation cycles on the time scale of endospore longevity would yield
numbers constant with depth. Thus, there is relatively higher endospore dead rate than germination,
but still the endospore longevity (from the refractory pool) is large enough to allow detection
thousands of years.
Studies based on most probable numbers of viable endospore forming bacteria, have
reached similar conclusions. In lake sediments, Rothfuss et al. (1997) found that both endospores
from aerobic and anaerobic heterotrophic bacteria decreased exponentially with sediment depth
and were below detection limit after 4 m depth. The half-lives calculated for those endospores were
499-546 years.
More recently, endospores from thermophilic sulphate reducer bacteria were found in
marine sediments from cold waters, in Aarhus Bay, Denmark (de Rezende et al., 2013). Endospores
decreased exponentially with sediment depth, and as endospores of thermophilic sulphate reducers
do not have the ability to germinate in those environments, the results indicate that the endospores
slowly lose their viability within 250-440 years.
The endospore forming community was studied in a polar ice core from Greenland (Yung et
al., 2007), from a depth of 94 m estimated to be 295 years old. From the total endospore
concentrations (369 36 endospores per mL), 80% were viable endospores (e.i. able of
germination), indicating an endospore longevity older than the estimated age of the sample.
Figure 7. A steady state of endospore abundance in marine sediments from the Peru margin.
25
Culture experiments of EFF strains have shown that there is an endospore forming
subpopulation producing endospores already at the end of exponential/beginning of the steady-state
phase (e.g. Lopes da Silva et al., 2005). Then, as starvation increases, the number of endospores will
increase.
In general, members of Bacillus and Clostridia are considered opportunistic (r-strategists) due
to their versatile physiology and the relative ease with which they are isolated under laboratory
conditions. When resources become scarce, they re-enter to a dormant state (endospore formation).
This transition requires energy, and if it cannot be completed the cell will die, which could explain the
observations formerly described about a EFF sub-set sporulating when there is still some resources
to exploit.
Finally, the longevity of an endospore is determined by the time elapsing between its
formation and its accumulation of lethal cell damage (Nicholson, 2003). After analysing the kinetics
of thermal inactivation of endospores described in scientific reports, Nicholson, (2003), formulated a
model to describe survival probabilities of endospores in 25-40°C temperatures. He compared the D-
value (e. i. reduction of the population to a one tenth) versus thermal inactivation temperatures of
the compiled data and derived best fit lines that extrapolated the data to 0°C. According to his
theoretical findings, there might be different endospore longevities that can be divided in three
groups: group 1, those endospores that will be able to survive environments with temperatures of
25-40 °C range for 0.19 to 952 years; group 2, the mesophilic endospores that will be able the same
environmental temperature range for 571 years to 1.9 million years; group 3, the thermophilic
endospores with survival extrapolations that far exceeded credibility (1.9 billion to 1.9 trillion years).
Some interesting observations can be taken out from this theoretical analysis. First, endospore
survival will depend not only of the environmental characteristics but also on the intrinsic ability of
the endospore forming bacteria. And second, the span of endospore longevity is seriously long, thus
endospore revival from Myr old samples are not theoretically impossible.
Results from chapter 2, regarding the presence of labile and refractory endospore groups in
marine sediments from the Peru margin, are substantiated with this theoretical prediction of
different endospore longevities. Figure 8 explains graphical the results from the model obtained by
Nicholson, (2003). Assuming that endospore mortality is a probabilistic event, the obtained data was
used to calculate the time frame that a determined initial population (divided in the three groups)
will survive.
26
Figure 8. Probability distribution of endospore survival derived from the theoretical observations. On the X-axis is the size of the initial endospore population in log scale, on the Y-axis, the number of years expected for a single endospore to survive from the initial population. Hatched areas denote the survival probabilities for each group of endospores tested within the temperature range of 25-40 °C, indicated by the upper and lower boundaries of each hatched area, respectively (Taken from Nicholson, 2003).
27
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PhD synthesis
35
The present PhD project was focused on the study of bacterial endospore abundance in
selected environments, and activity of microbial cells in the deep biosphere. Bacterial endospore
numbers were compared with the numbers of Bacteria, Archaea and the phylum Firmicutes in
different arable soil treatments within a field station (chapter 1). Furthermore, the dynamics of
endospore numbers within marine sediments were analyzed and compared with microbial cells
(chapter 2). Finally, the activity of microbial cells was analyzed, in sediments as old as the late
Pleistocene, and contrasted with previous studies. The three chapters summarized the significant
results achieved during the project. They are presented in the form of scientific manuscripts.
The fundamental questions behind the project were:
a. What are the distributions of bacterial endospores in those selected environments?
b. Is bacterial endospore abundance dynamic or static on those selected environments?
c. What are the determinant factors for their presence and persistence?
d. How active are the microbial cells found in old sediments?
e. Is dormancy playing a role in those sediments?
Chapter 1
In this chapter, the main goal was to analyze the endospores abundance in arable lands. As
little is known about how anthropogenic soil management practices affect microbial communities
and their activities. Managed soils comprised near 38% of the total usable land on Earth (mean value
from the latest data collection, The World bank, 2014). Managed soils includes land defined by the
FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows
for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land
abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land
cultivated with crops that occupy the land for long periods and need not be replanted after each
36
harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit
trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent
pasture is land used for five or more years for forage, including natural and cultivated crops.
To be more specific, the study in chapter 1 examines the distribution of bacterial endospores
in differently long-term managed soils, by comparing the endospore numbers with total prokaryotic
community size as well as the fraction of likely endospore-forming bacteria (Firmicutes). The soils are
under specific managements for 24 to 118 years at Askov Experimental Station. Samples were from
three different experiments involving soil texture (5 to 18 % clay), fertilization (unfertilized, mineral
fertilizer, cattle manure) and straw disposal (no straw, 8 t straw ha-1). The microbial community was
characterized by the abundance of prokaryotic cells (direct cell counts), endospores (culture-
independent dipicolinic acid) and ratios of amino sugars (glucosamine:galactosamine GlcN:GalN, and
glucosamine:muramic acid GlcN:MA) indicative for the presence of bacteria or fungi. Bacteria,
Archaea and Firmicutes were characterized by domain- and phylum- specific DNA quantitative
polymerase chain reaction (qPCR).
The objective was to isolate and evaluate the effects of soil texture and individual
management elements on the structure of the soil prokaryotic community, which includes the
presence of Bacteria, Archaea, fungi and bacterial dormant forms (endospores).
The results demonstrated that while conventional agricultural practices have little effect on
microbial population size (Fig. 1). endospore numbers proportionally increase in long-term
unfertilized soils (LTE 0 treatment), suggesting that nutrient limitation-induced increased sporulation
on one hand. Addition of extra carbon residue however, stimulates bacterial growth (8 straw
treatment). As organic carbon incorporation might boost the heteroorganotrophic community.
37
Although, managed soils have been identified as the most abundant endospore reservoirs
(Ammann et al., 2011), the present study concluded that the main determinant factor for endospore
formation is nutrient availability. The prokaryotic community is significantly reduced with nutrient
exhaustion over-long periods (Askov-LTE), and significantly increased with additional amounts energy
resources (Askov-Maize). Nevertheless common soil management does not comprise long-extended
unfertilized and double addition of organic residues, that correspond to treatments LTE-0 and 8
straw, respectively. In general, endospores were not abundant in the investigated soils, where they
comprised less than 1% of the total bacterial community (as bacterial cells occupied the majority of
Figure 3. a) Total cell abundances estimated via DAPI cell counts, b) Endospores enumeration calculated as the concentration of DPA gdw-1 and an assumed content of DPA of 2.24 x 10
-16
mol endospore-1
38
the cell counts). Nevertheless, the Firmicutes:Endospores ratio indicate that the endospore-forming
community are as abundant as the endospores (Fig. 2). Further studies could address if the ratio of
vegetative cells versus endospores changes drastically with type of environment and with season.
This manuscript is in a final stage. It will submit it the highly impacted journal Soil Biology and
Biochemistry, after the manuscript has been formatted following the requirements of the journal.
Figure 4. a) Ratios of cell numbers versus endospores, b) Ratio of the calculated Firmicutes fraction from the total cells versus endospores.
39
Chapter 2
The main goal of this chapter was to investigate the presence of bacterial endospores in
marine sediments and their dynamics. Bacterial endospores are highly relevant in studies regarding
the deep biosphere. As they are tough-bacterial resting life stages for both dispersal to environments
and for surviving periods in which nutrients are not available. Marine sediments are characterized by
a constant settling of organic matter (OM) and other materials on the surface. Below that, vertical
biogeochemical zonations are caused by microbial degradation of organic material and chemical
diagenesis (Parkes et al., 2000; Canfield et al., 2009). Underneath the bioturbated surface layer,
transport of electron donors and dissolved organic material is generally restricted to molecular
diffusion (Orcutt et al., 2011; Arndt et al., 2013). As a consequence, the microorganisms are faced
with both constant limitations of energy supply and progressing burial in the sediment (Hoehler and
Jørgensen, 2013). Endospores may constitute a valuable survival state for nutrient-limited
microorganisms in such environment (Kaprelyants et al., 1993; Lennon and Jones, 2011), and it is well
known that bacterial endospores are present (Lomstein et al., 2012; Langerhuus et al., 2012) and
viable in marine sediments (de Rezende et al., 2013).
However, it is not known whether endospores are a result of particle settling or in situ
production and whether they fulfil a role down in the sediment. The aim of the present study was to
investigate the dynamics of the endospore community relative to the community of microbial cells
found in surface and subsurface sediments from the Peru Margin. Abundance was based on the
quantification of dipicolinic acid (DPA), a unique biomarker for the presence of endospores.
The overall results demonstrated endospores decay rather than accumulation with time (Fig.
3), which is an indication of endospore settling from the overlying water and buried into the
sediment. Calculated endospore half-lives (Table 1) are comparable with previous findings described
for specific viable endospore populations. However, a considerable amount of endospores, after the
sediment has reached thousands of years, was also detected. This was interpreted in several ways:
40
a) There is a subpopulation capable to persist over larger time scales.
b) Endospore decay, for example via spontaneous fruitless germination would have decrease
in response to time or environmental conditions, in the deeper sediment layers.
c) Finally, the endospores could have been generated in situ at a rate that balances the
decay.
Because the dynamics of the endospore community approach that of the vegetative community,
after the initial die off, this seems like a plausible explanation.
Figure 5. A) Concentration of endospores versus assigned age in a 5 m gravity core in station G10. Endospore numbers are based on DPA quantification and converted assuming 2.24 10
-16 mol DPA per endospore. The
lines represent the upper and lower 95% confidence interval of the fit in Figure 3, a. The stippled line represents the upper 95% confidence bound of the fit for MUC core with slowest decay rate. B) Concentration of total organic carbon (TOC) and the percentage of carbon bound in amino acids (%TAAC) in the same core as A. C) Cell numbers in the same core as A-B.
41
Table 4. Calculated endospores half-lives based on their exponential fit decay rates. Stations are organized according to water depth. Decay rates from the literature were obtained via viable endospores quantification.
Stations Water
depth (m) Half-life (yr)* Decay rate
Endospores
G10 313 205 (150-323) -0.0038
G10_gravity core 313 -
G15 743 332 (234-570) -0.0021
G11 1015 315 (233-490) -0.0021
Average 284
Other studies
Rothfuss et al., (1997) 115 499-546 -0.0013, -0.0025
de Rezende et al., (2013) 28 250-440 -0.002
*95% confidence interval
The results showed interesting assertions about the role of the endospores in the subsurface
biosphere. This manuscript is currently under revisions by each of the co-authors, meaning that there
will be some feedbacks before this draft is ready to be submitted. The journal of Limnology and
Oceanography has been suggested.
Chapter 3
The storyline of this chapter is still under discussion. The manuscript is presented as a scientific
publication draft. However, the part of the discussion is still in a first stage.
The main goal in this chapter was to elucidate the microbial activity in the subsurface
biosphere. Until now, it is not yet known the extent of metabolic activity in the microbial cells found
in the in sediment layers below the sea bed. Microbial activity is usually evaluated via models
focused on cells or biomass turnover times (D´Hondt et al., 2004; Schippers et al., 2005; Holmkvist et
al., 2011; Lomstein et al., 2012). The calculations are based on cell counts and by considering the
turnover rate of selected metabolic substrates, assumed to be the main electron donors, and the
turnover rate of the bulk sediment (Jørgensen, 2011). Previous studies showed bacterial turnover
times in the range from hundreds to thousands of years (Schippers et al., 2005; Jørgensen and
D’hondt, 2006). Also bacterial turnover times have been independently determined by the D:L-amino
42
acid modelling (Lomstein et al., 2012), that estimates the rate at which cells rework the amino acid
pool, and therefore estimates cells turnover times. This approach has found cells turnover times to
be in the order of tenths to hundreds of years in Holocene sediments (Langerhuus et al., 2012) and in
the order of hundreds to thousands of years in sediments as old as the Oligocene (Lomstein et al.,
2012).
The results showed that bacterial activities predicted from the model are not in accordance
with the observed TOC decay rates obtained by the exponential fit. The TOC decrease with age such
that the pool size decreases to the half at approximately every 17 kyr. To test how do the model
predictions fit the observed profiles of TOC, we compared the results from the model with the
observed TOC decay rate (-0.00004 yr-1; Fig. 4 b). In the sediment core from G10, the TOC pool
decrease from 20 to 300 cm depth (calculated ages 0.6-5.2 kyr) amounts to 1.1 mmol-C gdw-1.
Whereas the integrated rates, predicted in the model over the age of the sediment core, the total
carbon oxidation amounts to 0.1 mmol-C cm-3 sediment, or 0.34 mmol-C gdw-1 sediment. In the
sediment core from G14, the TOC pool decrease from the entire core (calculated ages 13-25.4 kyr)
amounts to 1.6 mmol-C gdw-1, and the integrated predicted rates from the model amounts to 0.13
mmol-C cm-3 or 0.07 mmol-C gdw-1 sediment. In both cases, the model predictions were lesser by 3.3
and 23.5 times in G10 and G14 respectively (Fig. 4 a). The differences between the model predictions
and the observed TOC decay trend were be interpreted as discordance in the assumption of the
steady-state of microbial cell numbers. Therefore, total carbon oxidation rates resulted smaller than
the actual observed TOC decrease.
Biomass turnover times estimated for both stations ranged from 124 to 712 and 54 to 294
years at G10 and G14 respectively (Fig. 5). The result was independent of whether Bacteria or
Archaea were assumed to dominate the community. The turnover times estimated in the present
study are far longer than generation times estimated for surficial nutrient-rich environments such as
43
soil, lake water or sea water (Jørgensen, 2011), and from results obtained by Schippers et al., (2005),
from the ODP Leg 201. However turnover times are similar to what Biddle et al. (2006) found in the
sulfate-methane transition zone in sediments from the Peru Margin, and to observations by
Langerhuus et al., (2012) from sediments from Aarhus bay, Denmark. Much longer turnover rates
have been found in even older sediment layers from the Peru margin (Lomstein et al., 2012).
Furthermore, the pool of necromass is far larger and cycles in a much slower span (Fig. 2).
0
5000
10000
15000
20000
25000
30000
1 10 100 1000
Ass
ign
ed a
ge (
yr B
P)
Total C-oxidation (nmol cm-3 yr-1) a
0 2000 4000 6000 8000 10000
0
5000
10000
15000
20000
25000
30000
TOC (µmol gdw-1
) b
Figure 6. a) D:L modelled carbon oxidation rates with sediment age in stations G10 and G14, b) comparison of D:L modelled carbon oxidation rates of 100% respiring bacterial cells, b) Total organic carbon (TOC). Its exponential fit is
shown: , R2 0.85.
44
0
5000
10000
15000
20000
25000
30000
1 E+0 1 E+2 1 E+4 1 E+6
Ass
ign
ed a
ge (
yr B
P)
Turnover time (yr)
Figure 7. D:L modeled turnover times of bacterial biomass and necromass with sediment age in stations G14 and G10. Open diamonds and squares biomass and necromass turnover at G10 respectively, close diamonds and squares biomass and necromass turnover times at G14 respectively.
45
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Lomstein, B. A., Langerhuus, A. T. D’Hondt, S. Jørgensen, B. B. Spivack, A. J. 2012. Endospore
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47
Chapter 1
The prokaryotic community and its bacterial endospores in soil from three
long-term agricultural experiments: effect of fertilization, straw
incorporation and soil type
For submission to Soil Biology and Biochemistry
Paulina Tamez-Hidalgoa, Bent T. Christensenb, Mark A. Levera,c, Bente Aa. Lomsteina,c
a Section for Microbiology, Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-
8000 Aarhus C, Denmark
b Department of Agroecology, Aarhus University, AU-Foulum, Blichers Allé 20, DK-8830 Tjele,
Denmark
c Center for Geomicrobiology, Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-
8000 Aarhus C, Denmark
48
Abstract
The effects of agricultural management on the structure of the soil microbial community
remain obscured. We examined the prokaryotic community in arable soils maintained under specific
managements for 24 to 118 years at Askov Experimental Station. Samples were from three different
experiments involving soil texture (5 to 18 % clay), fertilization (unfertilized, mineral fertilizer, cattle
manure) and straw disposal (no straw, 8 t straw ha-1). The microbial community was characterized by
the abundance of prokaryotic cells (direct cell counts), endospores (culture-independent dipicolinic
acid) and microbial source markers (glucosamine:galactosamine GlcN:GalN; glucosamine:muramic
acid GlcN:MA). Bacteria, Archaea and Firmicutes were characterized by domain- and phylum- specific
DNA quantitative polymerase chain reaction (qPCR).The nutrient-depleted unfertilized soil showed
reduced cell numbers and increased endospore abundance, indicating that the prokaryotic
community was subject to environmental stress, while straw incorporation had a positive effect on
the prokaryotic community. The differently textured soils displayed similar distributions for total cells
and endospores but proportions of Firmicutes were reduced in the less clayey soil. Animal manure
and mineral fertilizer had similar effect on the relative abundance of Firmicutes, but Firmicutes made
up a small fraction of the total bacterial community (0.2 to 1.7 %). Gene analyses showed a clear
dominance of Bacteria over Archaea, the last group accounting for 0.2 to 10.9 % of the gene pool.
Determinations of specific microbial biomarkers were less informative, but suggested a
predominance of fungal over bacterial residues in all soils. We conclude that the overall structure of
the prokaryotic community in long-term arable soils is little affected by conventional management.
49
1. Introduction
Maintaining soil quality is a critical challenge in modern agriculture (Schjønning et al., 2004).
The concept of soil quality embraces a range of physical, chemical and biological properties, which in
turn are defined by climate, soil parent material, land use, and soil management (e.g. crop rotation,
tillage, fertilization, and crop residue inputs). The interaction between soil management, microbial
community structure, and soil functions is extremely complex and remains a high-priority research
area (Brussard et al., 2007; Kibblewhite et al., 2008). Changes in land use (e.g. cultivation of native
soils and permanently vegetated soil) can lead to reduced microbial catabolic diversity (Degens et al.,
2000) and changes in community structure (Bissett et al., 2011). Using phospholipid fatty acid
analysis, Kaye et al. (2005) reported large effects of land use on the soil microbial biomass but minor
effects on the relative abundance of taxonomic groups. The impact of land use on the microbial
community may be related to changes in plant residue inputs (Zak et al., 2003; Paterson et al., 2011),
and soil properties such as texture and pH have been suggested as key factors for bacterial
community composition (Girvan et al., 2003; Johnson et al., 2003; Ulrich and Becker, 2006; Enwall et
al., 2007; Yin et al., 2010). Since soil management typically affects several soil properties
simultaneously, evaluation of the effect of individual management elements is most often impeded.
For instance, fertilization has generally been found to have little impact on the composition of the
soil bacterial community (Sessitsch et al., 2001; Ogilvie et al., 2008; Poulsen et al., 2013). Yet,
fertilization may induce indirect effects, e.g. on soil pH, which may mask the impact of fertilization
itself (Enwall et al., 2007). Soil management may promote seasonal changes in the activity of the
microbial community (Girvan et al., 2004), with periods of increased microbial biomass and activity
after residue disposal and nutrient additions and a decrease biomass and activity coinciding with
periods of nutrient limitations at the end of the growing season.
The decline in biomass and activity during periods of energy and nutrient depletion may cause
microorganisms to enter states of dormancy. Members of two classes of the phylum Firmicutes
50
(Bacillus, Clostridia) are able to enter a conspicuous state of dormancy termed endospore. The
endospore is able to withstand a wide range of environmental conditions for very long periods and
endospores are an important component of many natural microbial communities (Nicholson; 2002;
Hong et al., 2009). However, little is known on the abundance of endospore in agricultural soils and
how management provokes endospore formation (Ammann et al., 2011). Dipicolinic acid (DPA) is a
unique molecule found in bacterial endospores where it may account for of 5-15% of endospore dry
weight (Powell, 1953; Church and Halvorson, 1959), and DPA analyses have been applied to quantify
endospore abundance in complex natural matrices (Yung et al., 2007; Fichtel et al., 2008; Lomstein et
al., 2012; Langerhuus et al., 2012).
Our objectives were to examine effects of soil texture and individual management elements on
the structure of the soil prokaryotic community by using soil from experimental plots that had been
subject to long-term treatments with plant nutrients (unfertilized, mineral fertilizer, cattle slurry) and
straw disposal. All soils were under the same climate and were sampled at the end of the growing
season (September) to avoid any effects of recent nutrient inputs and straw disposal. The microbial
community was characterized by abundance of prokaryotic cells and endospores and by specific
microbial biomarkers. Endospore numbers were estimated by culture-independent quantification of
DPA and the total prokaryotic community was enumerated by direct cell counts. Bacteria, Archaea
and Firmicutes were characterized by domain- or phylum-specific DNA quantitative polymerase chain
reaction (qPCR). Ratios of vegetative versus dormant cells and ratios of endospore-formers versus
endospores were established and potential links between microbial biomarkers, cell numbers and
endospore abundance were examined.
51
2. Materials and Methods
2.1. The experimental sites
This study draws upon soil sampled in September 2012 in three long-term experiments at
Askov Experimental Station. The station is located in the South of Jutland (55°28'N, 09°07'E) and
experiences an annual precipitation of 862 mm and a mean annual temperature of 7.7 °C (1961-
1990). The soil pH ranges from 5.5 to 6.5 and is maintained within this range by occasional liming.
Soil from The Askov Long-Term Experiments on Animal Manure and Mineral Fertilizers (Askov-
LTE) was collected in the B3-field of the Lermarken site (Christensen et al., 2006). This site is a
terminal morainic deposit with 11 % clay and 13 % silt in the Ap-horizon. The Askov-LTE was initiated
in 1894 to test crop responses to different levels (0.5, 1, 1.5 and 2) of nitrogen, phosphorus and
potassium, added either in animal manure (AM) or in mineral fertilizers (NPK) and includes
unmanured/unfertilized plots as reference treatments (LTE-0). The experiment grows a four course
crop rotation of winter wheat (Triticum aestivum), silage maize (Zea mays), spring barley (Hordeum
vulgare) and mixed grass-clover crop used for cutting. Averaged across the rotation, 1 AM and 1 NPK
correspond to an annual input of 100 kg total-N ha-1, 19 kg P ha-1 and 87 kg K ha-1. For the present
study, soil was taken from the treatments LTE-0 (plots no. 322, 344, 351), 1.5 AM (plots no. 334, 341,
363), and 1.5 NPK (plots no. 342, 345, 362). When sampled, silage maize was grown in the B3-field.
Soil was also collected in the Askov Straw experiment (Askov-Straw), a field experiment with
different straw disposal initiated in 1981 on the O1-field next to the Askov-LTE and with the same soil
type (Thomsen and Christensen, 2004). Spring barley has been grown every year (except for 2000-
2002) and the soil is amended with mineral fertilizers (NPK). At harvest, the straw is baled and
removed from the field, leaving only the stubbles behind. Then 0, 4, 8 or 12 t ha-1 of straw are
returned and incorporated in main plots. In the present study, soil was sampled from the treatments
52
0 (plots no. 201, 606, 708) and 8 t ha-1 straw (plots no. 206, 308) given an annual dose of 100 kg N ha-
1, 14 kg P ha-1 and 48 kg K ha-1.
Finally, soil was sampled in a small-plot experiment where silage maize has been grown every
year since 1988 (Kristiansen et al., 2005). This experiment (termed Askov-Maize) involves four
different soil types collected in 1987 in Ap-horizons (0-20 cm soil) of field experiments at Rønhave
(RON; 18 % clay), Roskilde (ROS; 14 % clay), Askov (ASK; 11 % clay) and Lundgaard (LUN; 5 % clay).
The soils were transported to Askov Experimental Station, sieved to < 4 cm, and placed outdoors in
large open-ended cylinders (diameter 0.99 m; area 0.76 m-2; depth 0.5 m) inserted 45 cm into the
ground (Table 1). The maize crops receive an annual dose of mineral fertilizers corresponding to 170-
200 kg N ha-1, 36-40 kg P ha-1, and 190 kg K ha-1, and the maize biomass is whole-crop harvested in
mid-October leaving only 4 cm of stubbles. For the present study, soil was from LUN (plot no. 50),
ASK (plot no. 49), ROS (plots no. 33, 48) and RON (plots no. 46, 47).
2.2. Soil collection and preparation for analysis
Soil was sampled from the Ap-horizons by removing any surface plant residues and collecting
soil from the 0-5 cm layer in sterile bags (Whirl-Pak). Within each experimental plot three replicate
samples were collected and immediately stored under cold and dark conditions. In the laboratory,
the replicate samples were pooled and mixed in larger sterile bags and stored field-moist overnight
before subsamples (ca. 1.5 g) were taken for cell counts and DNA extraction. The remaining soil was
oven-dried at 60°C overnight, crushed and sieved to <2 mm, and stored. For cell counts, 1.5 g of field-
moist soil was suspended in 4% paraformaldehyde and left to fix overnight at 8°C. Samples were then
washed twice with phosphate buffered saline (PBS), re-dissolved in PBS-Methanol solution (1:1 v:v),
and stored at 8°C. Other 1.5 g field-moist subsamples were frozen at -20°C for subsequent DNA
extraction. Subsamples of dried soil were employed for determination of total organic carbon (TOC)
53
and nitrogen (TN), and for hydrolysable amino acids (THAA), amino sugars (THAS), muramic acid
(MA), and dipicolinic acid (DPA).
2.3. Analyses for soil TOC and TN
Soil TOC and TN contents were determined on dry (< 2 mm) samples. Any inorganic carbon
was removed by treating the soil with 5-6% sulfuric acid overnight. Samples were oven-dried
overnight at 70°C, and then for an hour at 105°C. Tin cups, treated with 1:1 hexane and acetone
solution and heated to 200°C, were used to load the samples into a Thermo Elemental Analyzer,
Flash EA 1112 HT. Empty tin cups were used as blanks. Blanks showed negligible TOC and TN. Fluor
powder (% N = 2.31, % C = 44.39) was used to build the standard curve.
2.4. HPLC analysis of amino acids and amino sugars
Freeze-dried soil samples (ca. 0.5 g) were hydrolysed in 10 mL 6 N HCl at 105°C for 24 h under
N2 and then placed in an ice bath to stop any further reactions. Hydrolyzates were stored at -20°C.
Subsamples of hydrolyzate (100 µL) were transferred to glass vials, dried under vacuum at 50°C, re-
dissolved in MilliQ water, and dried again. After re-suspension in 4 mL MilliQ water, the hydrolyzate
was passed through a 0.2 µm filter (Sartorius) and collected into polycarbonate picovials. Amino
acids (THAA) was determined by reverse-phase High Performance Liquid Chromatography (HPLC), of
fluorescent o-phthaldialdehyde (OPA)-derivatized products, using the method of Lindroth and
Mopper (1979) as detailed by Langerhuus et al. (2012). Individual amino acids were determined from
specific standard curves based on the amino acid standard solution AA-S-18 (Sigma–Aldrich)
amended with β-alanine (β-Ala), taurine (Tau) and ornithine (Orn). L-aminobutyric acid (L-Aba) was
added to standards and hydrolysed samples and used as internal standard since L-Aba was not
present in the original samples. The amino sugars glucosamine (GlcN) and galactosamine (GalN) were
54
measured in the same HPLC analytical run as THAA using previously established standard curves and
internal standards. Concentrations were corrected for losses during hydrolysis, which were 25.5%
and 21.6% for GlcN and GalN, respectively.
The amino sugar muramic acid (MA) was quantified in a separate HPLC analysis. The method
was as for THAA, except that ca. 0.5 g of freeze-dried soil samples were hydrolysed with 5 mL 3 N HCl
at 95 °C for 4 h under N2. Total hydrolysable amino sugar (THAS) is the sum of GlcN, GalN and MA.
2.5. Analysis of dipicolinic acid (DPA)
Analysis for DPA was used to estimate endospore abundances. Because free DPA is rapidly
decomposed in the soil (Fetzner, 1998), DPA was taken to originate from intact endospores only. DPA
was measured according to Lomstein and Jørgensen (2012) using reverse phase-HPLC after complex
formation with terbium (Te). In short, samples were prepared as described for MA with three
modifications: a) soil samples (ca. 0.5 g) were hydrolysed with 10 mL 3 N HCl at 95 °C for 4 h under
N2, b) two subsamples of each hydrolyzate (300 µL) were dried, re-dissolved in 300 µL Milli-Q, and
dried again, and c) the two subsamples were pooled and dissolved in 4 mL of 1 M luminescent grade
sodium acetate buffer with 80 µL of 2mM AlCl3 added in order to precipitate phosphates.
Subsamples were run as detailed by Lomstein and Jørgensen (2012). DPA concentrations were
determined from 4 to 5 point standard curves, obtained by adding standard to the sample to
compensate for matrix effects. To convert DPA concentrations to endospore numbers, 2.24 x 10-16
mol-DPA endospore-1 was assumed (Fichtel et al., 2007).
2.6. Cell counts
Total vegetative and recently dead but still intact cells were enumerated by direct visual
inspection using an epifluorescence microscope (Carl Zeiss, Axiovert 200 M) equipped with a Zeiss
55
Plan-Neofluar x 100 objective (Carl Zeiss, Germany). The extraction of cells followed Kallmeyer et al.
(2008) with two modifications. Firstly, dissolution of carbonate minerals was not implemented
because the soils were devoid of pedogenic carbonate. Since lime is added occasionally to keep soil
pH in the plots within the range 5.5 to 6.5, the need for removal of lime residues was tested by
incubating ca. 0.25 g soil with 0.5 mL of two acid buffers (Na-Acetate-acetic acid, 0.43 M, pH 4.6 and
PBS-Acetic acid 0.43 M, pH 5.5) for 2h. All soils showed negligible bubbling indicating that carbonates
were not present. Further, cell separation by density centrifugation involved a second sonication
step. When only one sonication was applied, total counts were ca. 5% of that found when applying
two sonication steps.
For counting, two dilutions of each sample were prepared (5x and 10x) and poured onto 0.22
µm black polycarbonate (GTBP, Millipore) filters with 0.45 µm cellulose acetate filters placed below
for support (Millipore). Filters were pre-wetted with autoclaved 1x PBS solution and dilutions were
added along with 5 mL of autoclaved 1x PBS solution and filtered by applying suction up to 15 cm Hg.
After filtration, cells were stained with DAPI-Citifluor:Vectashield solution (4:1 v:v). Filter membranes
were cut in halves, mounted on glass slides and the staining solution (< 10 µL) applied directly onto
each filter membrane. Membranes were kept in the freezer until they were counted under the
microscope. Cells were enumerated with a filter for DAPI. A minimum of 20 view fields were
enumerated per filter as recommended by Lunau et al. (2005).
2.7. DNA extraction
Genomic DNA was extracted from soil samples using bead beating (TissueLyzer LT, Qiagen) and
the FastDNA™ 2 mL spin kit for soil (MP Biomedicals, LLC) according to the manufacturer’s
instructions. After Fastprep and prior to DNA extraction, samples of 0.25 g (wet weight) soil were
subjected to enzymatic digestions. In brief, 50 µL of enzyme mixture 1 (1 µg mL-1 of Lysozyme, Lipase,
56
Pectinase and β-Glucuronidase) were added and incubated for 30 min at 37°C. Then 50 µL of enzyme
mixture 2 (1 µg mL-1 of Proteinase K, Protease and Pronase) was added and incubated again for 30
min at 37°C. Finally, 75 µL of sodium dodecyl sulfate (SDS) was added and incubated at 65°C for 2 h.
DNA concentrations were measured with a Qubit 2.0 fluorometer (Invitrogen). Following the
instructions provided by the manufacturer, 1 µL of sample was tested for double-stranded DNA
(dsDNA) and measured against instrument standards.
2.8. Quantitative PCR (qPCR) for 16S rRNA
Quantitative PCR was performed to quantify 16S rRNA gene numbers of Bacterial and Archaeal
domains and the phylum Firmicutes. Assays were performed on a Roche Light Cycler 480. 16S rRNA
universal primers were used to quantify total Bacteria, total Archaea and the phylum Firmicutes. For
Bacteria, the domain-specific primers Bac8Fmod (5´ -3´): AGAGTTTGATYMTGGCTCAG (Loy et al.,
2002; PMID: 12324358) and Bac338Rabc (5´ -3´): GCWGCCWCCCGTAGGWGT (Daims et al., 1999;
PMID: 10553296) were used. For Archaea, the domain-specific primers Arc 915F_mod (5´ -3´) AAT
TGG CGG GGG AGC AC (modified from Stahl and Amann (1991)) and Arc 1059R (5´ -3´) GCC ATG CAC
CWC CTC T (Yu et al., 2005) were used. The primers for the phylum Firmicutes were 928F-Firm (5´ -
3´): TGAAACTYAAAGGAATTGACG and 1040R-Firm (5´ -3´): ACCATGCACCACCTGTC (Bacchetti De
Gregoris et al., 2011). Each reaction mixture contained 2 μL of 3.1-41 ng DNA template, 10 μL Roche
Light cycler master mix containing SYBR-Green, 2 μL of 1 mg mL-1 bovine serum albumin, 1 μL of 50
μM solutions of each primer, and dH2O to complete 20 μL per reaction. Samples were analysed twice
at 1:10 and 1:100 dilutions for the detection of Bacteria, and at 1:5 and 1:10 dilutions for the
detection of Archaea and Firmicutes. Each sample dilution was run in triplicates. The qPCR protocol
included the following steps: 95°C polymerase activation for 5 min, followed by 45 PCR cycles for
Bacteria and Archaea, and 40 PCR cycles for Firmicutes. Individual PCR cycles consisted of
57
denaturation (95°C) for 30 s, annealing for 30 s at 55°C (Bacteria), 56°C (Archaea) and 61.5°C
(Firmicutes), elongation for 30 s at 72°C, and fluorescence measurement after 5 s at 80°C. Each PCR
was concluded with a stepwise melting curve from 95°C to 55°C for 1 min. Standard curves (6.4 x 101
– 6.4 x 108) were made in triplicate from pGEM-T plasmids (Promega) with Bacterial or Archaeal
16sRNA insert. Roche Light Cycler software was used to determine the threshold cycle (Ct) of each
reaction. In each run, the efficiency of each standard curve was 1.8 and Ct values of negative
controls were significantly below the lowest point in the standard curve, indicating minimal DNA
contamination. All amplifications were checked for specificity with dsDNA melt curves and any
exhibiting multiple products. Bacteria and Archaea have several ribosomal operons including several
16S rRNA genes in their genomes wherefore gene abundance cannot be used directly to estimate cell
abundance. Consequently, qPCR estimated gene copy numbers were divided with the average copy
number of 16S rRNA operons per genome. The average number of operons per genome is 4.2 for
Bacteria, 1.7 for Archaea domains and 6.78 for the phylum Firmicutes (Lee et al., 2009).
58
3. Results and Discussion
3.1. Chemical analyses
Table 1 shows the total concentrations of organic carbon (TOC), nitrogen (TN), hydrolysable
amino acids (THAA), amino sugars (THAS) and muramic acid (MA) in the three sets of soil. The
ranking of the soils from the Askov LTE was similar for all parameters, the lowest concentrations
being found in soil from LTE 0 and the highest in soil from the 1.5 AM treatment. Analyses of the
differently textured soils from the Askov-Maize experiment did not reveal any consistent trend
related to clay content, although the coarse sand soil (LUN) showed the smallest values. Straw
incorporation seemed to have little effect on TOC and TN but tended to increase THAA, THAS and MA
contents. Hydrolysable amino acids accounted for 8 - 14 % of soil TOC and 34 - 57 % of soil TN. In a
previous study of two Danish arable soils with annual straw incorporation, Christensen and Bech-
Andersen (1989) found 32 - 36 % of soil TN to be in hydrolysable amino acids with no effect of straw
incorporation.
3.2. The abundance of cells
Cell numbers were determined by microscopy using the fluorescent probe DAPI. Generally,
prokaryote numbers were 1 1010 cells gdw-1 (Fig. 1a). The cell numbers align with previous
studies using similar methods to detach cells from the soil matrix (Böckelmann et al., 2003; Portillo et
al., 2013). Poulsen et al. (2013) found cell numbers ranging from 2 to 4 109 cells gdw-1 in soils from
a 4-year old field trial testing effects of high rates of urban waste and cattle manure. One
conspicuous exception in the present study, was the unfertilized soil (LTE 0) which showed
considerably smaller cell numbers than any other soil. Fertilization and straw incorporation led to
significantly higher cell numbers than found in unfertilized soil and soil with straw removal,
respectively, when analysed by two-tailed t-tests (LTE 0 versus 1.5 AM, p = 0.04; LTE 0 versus 1.5
59
NPK, p = 0.02; 0 straw versus 8 t straw, p = 0.007). However, soils amended with mineral fertilizer
and animal manure maintained similar levels of prokaryotic cells (Fig. 1a), suggesting that the
nutrient source and the organic matter added with manure had little impact on prokaryote
abundance. This is in contrast to Poulsen et al. (2013) who found similar cell numbers in unfertilized
soil and soil receiving normal rates of mineral fertilizers while numbers were higher in soil taking
large loads of manure. The length of the treatment period was much longer in the soils examined
here, suggesting that the prokaryote community in arable soils exhibits considerable resilience
towards changes in nutrient management. The positive effect of straw incorporation agrees with
previous studies in which plant residue inputs were found to increase the microbial community (Zak
et al., 2003; Paterson el al., 2011).
3.3. The abundance of endospores
Endospore numbers estimated from DPA measurements were approximately three orders of
magnitude smaller than total cell numbers (Fig. 1b). Endospore numbers varied from to 6.1 107
gdw-1 soil and are in accordance with numbers found in forest soils and freshwater sediments (Table
2). Endospore abundance in grassland soils appears to be 5 to 10 times higher while marine
sediments show substantially lower numbers. However, the different endospore numbers recorded
in samples of different origin reflect the composition, the size as well as the environmental stress
imposed on the prokaryotic community. Significant differences in endospore abundance were
detected between LTE 0 and 1.5 NPK (p = 0.003) and between no straw and straw incorporation (p =
0.01). The lack of sufficient replicates prevents a proper statistical analysis of effects related to soil
type, but we observed no distinct trend related to soil texture.
60
3.4. Relative abundance of Bacteria and Archaea
Members of the prokaryotic community were ascribed to Bacteria, Archaea and to Firmicutes
by domain and phylum-specific DNA quantitative polymerase chain reaction (qPCR). To estimate the
proportions of each group, numbers of gene copies were converted to cell numbers as described in
Section 2.8. The sum of qPCR based cell estimates of Bacteria and Archaea averaged 2% of cells
enumerated by microscopy and DAPI. This may be due to a low extractability of soil DNA or
interference from dissolved soil organic substances during the extraction procedure. Although the
qPCR approach underestimates total cell numbers, we assume that the proportions of Bacteria and
Archaea remain representative for the soil community.
Figure 2a shows that most of the 16S rRNA genes were of bacterial origin (89.1-99.8%), while
Archaea accounted for a small fraction (0.2-10.9 %). This is in accordance with other studies (Bates et
al., 2011; Bengtson et al., 2012; Poulsen et al., 2013). The different soil types included in the Askov-
Maize experiment exhibited high and similar bacterial dominance (99.5-99.8%). These four soils are
under identical management and climate but differ in soil properties including texture (clay contents
range 5 to 18 %), suggesting that soil type is not a key factor determining the ratio between Bacteria
and Archaea. Previous studies based on soil from different sites have suggested that indigenous soil
properties rather than management are a key determinant of microbial community structure in
arable soils (Girvan et al., 2003; Johnson et al., 2003; Ulrich and Becker, 2006). However, effects of
soil characteristics, climatic conditions and management elements may be confounded in studies
based on soils retrieved from different locations.
The treatments without mineral fertilization (LTE 0 and 1.5 AM) showed the highest relative
archaeal abundance, suggesting that Archaea may be sensitive to inputs of mineral salts or becomes
overruled by fast growing bacterial species. Archaeal diversity appears to be sensitive to changes in
soil pH (Roesch et al., 2007; Nicol et al., 2008; Bengtson et al., 2012) and nitrogen availability (Gubry-
Rangin et al., 2010; Rasche et al., 2010; Pereira et al., 2012).
61
3.5. Relative proportions of Firmicutes and Bacteria
Numbers of vegetative endospore formers were estimated by qPCR specific for the 16S rRNA
genes of the phylum Firmicutes. Firmicutes accounted for a small fraction of the 16S rRNA total
bacterial community (0.2 to 1.7%; Fig. 2b). This corresponds well with previous studies (Fierer et al.,
2005; Roesch et al., 2007; Acosta-Martínez et al., 2008; Yin et al., 2010; Hollister et al., 2010),
although endospore-forming Bacilli and Clostridia has been found to comprise the majority of the
Firmicutes in a Danish arable soil under contrasting management (Poulsen et al., 2013). In the
present study at least three management related trends were identified: 1) Firmicutes gene copies
varied widely among differently textured soils, the proportion of Firmicutes increased with
decreasing clay content, 2) straw disposal had no effect the abundance of Firmicutes, and 3) the
nutrient source (manure or NPK) appears to have little effect on the proportion of Firmicutes.
3.6. Ratio of fungal to bacterial residues
According to Glaser et al. (2004), a GlcN:MA ratio 10 in the amino sugar pool of the
hydrolysable soil organic matter (SOM) is indicative of bacterial dominance over fungi, whereas
GalN:MA ratio 60 is associated with fungal cell walls. In the present study, the GlcN:MA ratio was
above 100 and the GalN:MA ratio above 40 (Table 1). Both source ratios suggest a predominance of
fungal over bacterial biomass residues in line with previous observations for cultivated soils (Zhang et
al., 1999; Glaser et al., 2004).
3.7. Proportions of vegetative versus dormant organisms
The ratio of total cell number to endospores was relatively similar across soils and treatments
with the notable exception of the LTE 0 (Fig. 3a). The smaller number of living plus recently dead cells
and the higher number of endospores may indicate that this long-term nutrient-depleted soil
62
harbours a prokaryotic community under environmental stress. The somewhat higher ratio for 1.5
NPK soil than for 1.5 AM soil remains unexplained, but does not support the notion of a pronounced
effect of animal manure on the soil bacterial community structure (Hartmann et al., 2006;
Esperschütz et al., 2007). Using a similar endospores and prokaryotic cell quantification approach,
Yung et al., (2007) found that endospores comprised 10% of the total prokaryotic cell numbers in a
295 year old ice core from Greenland. While Bueche et al., (2013) found that the endospore-forming
subset of the Firmicutes community represented 0.2 to 6.5% of the total Bacteria in Swiss lake
sediments. Those results were achieved by qPCR detection of the spo0A gene coding for sporulation
and confined to members of Bacilli and Clostridia and were compared to total Bacteria 16S rRNA
gene. In the present study, endospores accounted for 0.2-0.3% regardless of soil type and treatment,
the only exception being LTE 0 (1 %).
In the Askov-LTE, the ratio of vegetative Firmicutes to endospores ranged from 0.3 to 2.3 (Fig.
3b), with LTE 0 showing the lowest ratio and 1.5 NPK the highest. Firmicutes seem to be boosted
more by inorganic fertilization than by animal manure. When compared to the ratio between total
cell numbers and endospores (Fig. 3a), the ratio of Firmicutes to endospores (Fig. 3b) showed a more
distinct trend with declining soil clay content (from 6.2 for RON; 18 % clay, to around 1 for ASK; 11 %
and LUN; 5 % clay). Recalling the trend in the ratio of Bacteria:Firmicutes (Fig. 2b), the abundance of
Firmicutes appears to be linked to soil clay content. More sandy soils show diminished population
size and increased sporulation.
The ratio in Askov-Straw experiment was 2 in both treatments (Fig. 3b). Thus, it seems that
the population of Firmicutes is unaffected by long-term annual straw disposal, both in terms of
relative abundance and sporulation rate.
63
3.8. Cell and endospore numbers and soil chemical parameters
Generally the numbers of prokaryotic cells and endospores were only weakly related to the
proportions of hydrolysable amino acid-C and amino acid-N (Fig. 4). A Pearson product-moment
correlation coefficient was used to analyse all parameters within each experiment. Soils from the
Askov-LTE showed positive correlation of total cells versus contents of TOC (p = 0.036), TN (p =
0.041), THAA (p = 0.01) and MA (p = 0.006). Soil type and straw disposal, on the other hand, did not
show any significant correlations. One reason could be the small number of treatment replicates in
these two experiments.
Endospore numbers showed no correlation with the soil biogeochemical parameters although
endospores have receptors in the outer layer able to detect favourable changes in the environmental
conditions (Setlow, 2003). Endospore numbers showed a negative trend when plotted against
hydrolysable amino acid-C and -N (Fig. 4), whereas prokaryotic cells numbers seem to increase.
3.9 Determinant factors of bacterial and endospore abundance
In the present study, Askov-LTE showed highest endospore abundance in LTE-0 soils, which
have been nutrient exhausted for over 100 years. The use of animal manure and mineral fertilizers,
at the same nutrient rate, does not show any significant difference on the relative abundance of
Firmicutes (Fig. 2-b), but decreased the relative abundance of endospores (Fig. 3-b). Therefore,
bacterial sporulation seems to be promoted by starvation for nitrogen and phosphorous.
The Askov-Maize experiment, with soils of different origin and clay content, displayed a similar
distribution for total cells and endospores (Fig. 1 a-b), and the treatments where clay percentage is
highest and lowest showed similar numbers (RON and LUN). It should be noted that a relatively
stronger adhesion of cells and endospores to the clay particles in RON soils have biased the results
and masked a lower abundance in loamy sand soils (LUN). As a group, members of the Firmicutes in
64
the microbial community were highly affected by the clay content, with lower proportions of
Firmicutes at low clay contents, and an increased tendency to form endospores.
Treatments in Askov-Straw demonstrated that endospore abundances in this experiment are
closely correlated with prokaryotic cell abundance and that the increase of the prokaryotic
community depends upon energy availability (Fig. 1): The highest abundance was found in soils from
treatment 8 straw, where there is an excessive amount of carbon available indicating that the
bacterial community, but not the archaeal community (Fig. 2-a), grew as the energy resources
increased, followed by sporulation, of the endospore forming community, when the energy
resources become limited. The soils from both treatments received the same concentrations of
mineral fertilizers as a nitrogen and phosphorous source, and the difference in bacterial growth is
induced by the difference in energy availability.
For reference, other studies that were based on quantification of DPA are compared with the
results from the present study in Table 2. Ammann et al., (2011), found endospore hot-spots in soils
that correspond to grasslands and agricultural regimes and that are govern by energy (organic carbon
availability) rather than by nutrient limitation (nitrogen and phosphorous availability) as their C:N
ratio is 20. The terrestrial environment (Table 2) displays higher bacterial endospore numbers than
the marine sediment (Fichtel et al., 2008; Langerhuus et al., 2012; Lomstein et al., 2012). Perhaps this
is due to greater energy limitation as organic carbon resources are limited in marine sediments.
In summary, long-term absence of nutrient addition appears to decrease total prokaryotic
community size and increase the number of endospores (LTE 0 soils). Positive effects on the
prokaryotic abundance were observed in managed soils with excessive inputs of residue straw (8
straw) where the influential factor is energy availability. However, these two experimental
treatments are not common in agricultural practices. Thus, the overall results from the three
experiments showed that common soil management practices with different fertilization types,
residue incorporation and soil types appear to have little effect on the size of the prokaryotic
65
vegetative community as well as the dormant fraction because the prokaryotic community is not
nutrient-limited.
66
4. Conclusions
We conclude that the general structure of the prokaryotic community in long-term arable soils
is resilient to conventional management. Previous studies have also found that soil bacterial diversity
remains remarkably stable against changes in management (Sessitsch et al., 2001; Enwall et al., 2007;
Poulsen et al., 2013) whereas the initial cultivation of native soils and radical changes in land use has
been shown to affect bacterial and archaeal diversity (Buckley and Schmidt, 2003; Bissett et al.,
2011). Ogilvie et al. (2008) performed a phylogenetic and functional gene analyses on soils treated
with mineral fertilizer, farmyard manure or kept unfertilized for 160 years and found that the
bacterial community was relatively non-responsive to long-term balanced nutrient inputs. The
unfertilized soil from Askov-LTE was comparably high in endospore abundance and low in cell
numbers, showing that nutrient exhaustion could be a main factor in endospore formation. The
prokaryotic community was significantly reduced but only in extremely nutrient-depleted soil while
the relatively high rate of straw incorporation had a positive effect. Animal manure and mineral
fertilizer had a similar effect on the relative abundance of Firmicutes. Although endospores
comprised less than 1% of the total bacterial community (as bacterial cells occupied the majority of
the cell counts), the relation between Firmicutes and endospores abundance indicates that the
endospore-forming community are as abundant as the endospores. The differently textured soils
displayed similar distribution for total cells and endospores but proportions of Firmicutes were
reduced in the less clayey soils.
67
Acknowledgements
This work was supported by Mexican research council (CONACyT), grant number 213154. The
contribution of BTC and field experiments at Askov Experimental Station was financially supported by
the EU project SmartSOIL (Grant no. 289694). ). The research underlying this article has been co-
funded by the Danish National Research Foundation and from the European Research Council under
the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no
[294200].
68
Figure legends
Figure 1. a) Total cell abundances estimated via DAPI cell counts, b) Endospores enumeration
calculated as the concentration of DPA gdw-1 and an assumed content of DPA of 2.24 x 10-16 mol
endospore-1. Experiments are accommodated in the following order: Askov-LTE, Askov-Maize, Askov-
Straw.
Figure 2. a) Ratios of 16S rRNA bacterial gene copies gdw-1 versus 16S rRNA archaeal genes copies
gdw-1, b) Ratios of 16S rRNA bacterial gene copies gdw-1 versus 16S rRNA phylum Firmicutes gene
copies gdw-1.
Figure 3. a) Ratios of cell numbers versus endospores, b) Ratio of the assigned Firmicutes fraction
from the total cells versus endospores.
Figure 4. Total cells and endospores plotted against a-b) Amino acid-C and –N (e.i. the percentage of
acid hydrolyzable amino acid identifiable from the TOC and TN). Treatments from each experiment
have been assigned different pattern: LTE 0 (solid grey diamonds), 1.5 AM (open diamonds) and 1.5
NPK (solid black diamonds); all treatments from Askov-Maize (open squares); 0 straw (open circles)
and 8 straw (black circles).
69
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Table 1. Summary of the biogeochemical parameters measured in the soil samples.
Ratios
Treatments TN TOC THAA THAS MA Amino
acid-C (%) Amino
acid-N (%) GalN:MA GlcN:MA
mg N gdw-1
soil mg C gdw-1
soil mg AA’s
gdw-1 soil mg AS’s
gdw-1 soil µg C gdw-1
soil
Askov-LTE
LTE 0 0.8 (± 0.1) 9.6 (± 0.8) 1.8 (± 0.2) 1.2 (± 0.04) 11.4 (± 0.8) 8 (± 0.4) 34 (± 3.0) 40.7 110.6
1.5 AM 1.2 (± 0.2) 14.1 (± 2.9) 3.1 (± 0.5) 1.9 (± 0.2) 15.9 (± 3.2) 10 (± 1.4) 39 (± 4.9) 43.1 122.1
1.5 NPK 1.0 (± 0.2) 11.6 (± 2.1) 2.6 (± 0.4) 1.7 (± 0.2) 13.8 (± 1.5) 10 (± 1.2) 42 (± 6.4) 50.7 125.6
Askov-Maize
RON 1.6 16.8 3.6 2.4 24.6 9.5 34.8 35.8 100.2
ROS 1.3 14.9 3.1 1.8 17.2 9.3 36.5 48.1 102.4
ASK 1.5 20.4 4.5 2.7 25.1 9.8 45.8 45.1 106.5
LUN 0.7 7.9 2.5 1.5 9.9 14.1 57.4 66.8 151.2
Askov-Straw
0 straw 1.1 (± 0.05)
12.0 (± 0.6) 2.9 (± 0.1) 1.8 (± 0.05) 14.4 (± 0.8) 11 (± 1.0) 39.7 (± 3.2) 50.7 121.9
8 straw 1.1 11.4 3.3 2.2 20.0 12.6 46.3 39.5 108.1
Numbers are the average of measurements. Numbers in brackets represent the standard deviations
77
Table 2. Compiled data of endospore enumeration based on dipicolinic acid-Tb3+
complex detection, determined by reverse phase HPLC from environmental samples. For conversion of DPA concentration to endospore numbers, it was assumed 2.24 x 10
-16 mol-DPA endospore
-1 (Fichtel et al., 2007).
Endospores (DPA) gdw
-1 (x10
7) Std. error (x10
7)
Present study
Askov-LTE
LTE 0 6.1 0.7
1.5 AM 3.8 1.4
1.5 NPK 2.3 0.3
Askov-Maize
RON (18)a 2.5
ROS (14)
a 3.6
ASK (11)
a 4.1
LUN (5)
a 2.5
Askov-Straw
0 straw 2.8 0.5
8 straw 4.3
Fichtel et al., (2008)
North Sea, intertidal marine sediment cores NSN5 & NSN7
b
1.3
North Sea, intertidal marine sediment core NSN10
b
0.5
North Sea, intertidal marine sediment core JS11
b
2.0
Ammann et al., (2011)
Grassland Männedorf 1 33.4 11.3
Grassland Männedorf 2 27.0 5.0
Grassland Männedorf 3 26.0 5.5
Grassland Uerikon 17.9 4.1
Grassland Dübendorf 16.7 2.8
Forest soil Stäfa 1 5.8 1.8
Forest soil Stäfa 2 5.0 0.9
Forest soil Stäfa 3 3.0 2.6
Sediment River Glatt 1 2.8 0.2
Sediment River Glatt 2 2.2 0.2
Langerhuus et al., (2012)
Aarhus bay, station M1c 1-0.5/1.5
Aarhus bay, station M5 1.3-1
Lomstein et al., (2012)
Peru margin, sediment core, site 1227d, 1-0.3
Numbers in Askov experiment are given in average of soil treatment a. Percentage of clay per gdw
-1 soil
b. Highest concentration c. Top 0-40 cm/highest concentration below 40 cm
78
Figure 1.
0.0 E+0
2.0 E+9
4.0 E+9
6.0 E+9
8.0 E+9
1.0 E+10
1.2 E+10
1.4 E+10
1.6 E+10
1.8 E+10
2.0 E+10
Cel
l nu
mb
ers
gdw
-1
a
0 E+0
1 E+7
2 E+7
3 E+7
4 E+7
5 E+7
6 E+7
7 E+7
End
osp
ore
s g
dw
-1
LTE 0
1.5 AM
1.5 NPK
RON
ROS
ASK
LUN
0 straw
8 straw
b
Askov-LTE Askov-Maize Askov-Straw
79
Figure 2.
0
50
100
150
200
250
300
350
400
450
Bac
teri
a:A
rch
aea
0
50
100
150
200
250
300
350
Bac
teri
a:Fi
rmic
ute
s LTE 0
1.5 AM
1.5 NPK
RON
ROS
ASK
LUN
0 straw
8 straw
Askov-LTE Askov-Maize Askov-Straw
b
a
80
Figure 3.
0
100
200
300
400
500
600
Cel
l nu
mb
ers:
End
osp
ore
s
a
0
1
2
3
4
5
6
7
Firm
icu
tes
:En
do
spo
res
LTE 0
1.5 AM
1.5 NPK
RON
ROS
ASK
LUN
0 straw
8 straw
b
Askov-LTE Askov-Maize Askov-Straw
81
Figure 4.
0 E+0
5 E+9
1 E+10
2 E+10
2 E+10
3 E+10
0 20 40 60 800 E+0
5 E+9
1 E+10
2 E+10
2 E+10
3 E+10
0 5 10 15
Cel
l nu
mb
ers
gdw
-1
a
0 E+0
1 E+7
2 E+7
3 E+7
4 E+7
5 E+7
6 E+7
7 E+7
0 5 10 15
End
osp
ore
s gd
w-1
Amino acid-C (%)
0 E+0
1 E+7
2 E+7
3 E+7
4 E+7
5 E+7
6 E+7
7 E+7
0 20 40 60 80
Amino acid-N (%)
b
d c
82
Chapter 2
Manuscript: The dynamics of endospores in the subsurface of the Peru
margin
For submission to Limnology & Oceanography
Paulina Tamez-Hidalgoa, Thorbjørn J. Andersenb, Oscar Chiangc, Mathias Middelboec, and Bente Aa.
Lomsteinad and Hans Røyad.
aDepartment of Bioscience, Section for Microbiology, Aarhus University, Aarhus, Denmark
bInstitute of Geography, University of Copenhagen, Copenhagen, Denmark
cMarine Biological Section, University of Copenhagen, Helsingør, Denmark
dCenter for Geomicrobiology, Department of Bioscience, Aarhus University, Aarhus, Denmark,
83
Abstract
Bacterial endospores in the surface sediments of the Peru Margin decay with a half-life time in
the order of 300 years. Endospores in deeper layers, however, did not follow the rapid decay
rates found near the surface and their abundance remained nearly constant across sediment
layers ranging from 1000 to 8000 years old. Either there was a subpopulation capable to persist
over larger time scales or the endospores were generated in situ at a rate that balances the
decay. To replenish the endospore pool at the same rate as the decay, and thereby keeping the
endospore numbers constant in dynamic equilibrium, then each endospore-former cell must on
average form one endospore each 50-800 years. This is similar to the previous estimated
microbial cell turnover times for the deep biosphere.
84
1. Introduction
Endospores are long-lived survival stages formed by low GC, Gram positive bacteria from the
genera Bacillus and Clostridia in response to unfavorable conditions. There are examples of
endospores, thousands (Kennedy et al., 1994; Rothfuss et al., 1997; Nicholson et al., 2000) to millions
of years old that have been able to revive (Cano and Borucki, 1995; Vreeland et al., 2000).
Endospores are important for dispersion of bacteria between habitable niches and they function as
seed banks, preserving bacterial diversity during and after perturbations (Lennon and Jones, 2010).
Bacterial endospores have been found in large numbers in aquatic sediments (Rothfuss et al., 1997;
Hubert et al., 2010; de Rezende et al., 2013; Langerhuus et al., 2012; Lomstein et al., 2012), but it is
unclear what role they might fulfill.
The environmental conditions below the bioturbated zone in marine sediments are extremely
stabile and the availability of harvestable energy is steadily deteriorating as the sediment ages and
becomes buried (Middelburg 1989; Røy et al. 2012). There is, therefore, no obvious detrimental
condition that bacteria would need to bridge, in the endospore stage, in sediments unless they are
obligate oligotrophs. Dispersion of endospores within the sediment column is also an unlikely role of
the endospores due to the tight sediment matrix. Some bacterial endospores in permanently cold
marine sediments are clearly misplaced as their vegetative stages do not grow below 40°C (Isaksen et
al. 2006; Hubert et al., 2010; de Rezende et al. 2013). It is not unlikely that such incidental
sedimentation together with other fine grained organic matter is the common fate of all spores in
marine sediments, and that they play no role at all in the active community.
Most published estimates of endospore numbers in the environment have been based on
cultivable endospore-formers (Widdowson, 1967; Siala et al. 1974; Rothfuss et al., 1997; Hubert et
al., 2010; de Rezende et al., 2013), and because of the vast physiological diversity of endospore-
forming bacteria, the actual in situ numbers of endospores have most likely been underestimated
(Turnbull et al. 2007). Pyridine-2, 6-dicarboxylic acid (dipicolinic acid, DPA) comprises 5-15% of
85
endospore dry weight and it is found only in substantial amounts in bacterial endospores (Powell,
1953; Church and Halvorson, 1959). DPA can be quantified using common laboratory-based methods
such as liquid chromatography by measuring the fluorescence of a Tb3+-DPA complex (Hindle and
Hall, 1999), and a recently developed reverse-phase HPLC method for Tb3+-DPA complex
quantification has proven to be an effective and culture independent tool for investigating
endospore abundance in environmental samples (Fichtel et al., 2008; Ammann et al., 2011; Lomstein
et al., 2012; Langerhuus et al., 2012).
The aim of the present study was to investigate the dynamics of the endospore community
relative to the community of microbial cells found in surface and subsurface sediments from the Peru
Margin. Endospore numbers derived from the quantification of DPA in those analyzed sediments.
86
2. Materials and Methods
2.1. Study site
The study site was on the Peruvian continental shelf and slope (Fig. 1). The waters in this area
have the world’s most persistent wind-driven coastal upwelling, supporting intense primary
production ( 2700 mg C m-2 day-1; Lee and Cronin 1984; Chavez et al, 1996), which leads to high
sedimentation rates (Brodie and Kemp, 1994). The sediments consist of fine-grained, organic -rich
(Suess et al., 1986), diatom-rich laminated mud lenses with intervals of organic-poor and less
diatomaceous, structure-less or macroscopically bioturbated, silty muds (Krissek et al., 1980; Brodie
and Kemp, 1994). The high input of OM leads to intense oxygen consumption resulting in a
pronounced oxygen minimum zone (OMZ) (Wyrtki, 1962). During sampling in February 2007, the
OMZ was located between 50 to 500 m water depths and contained between 2 nmol L-1 and 3 µmol
L-1 of dissolved oxygen (Revsbech et al., 2009). This perennial OMZ maintains organic-rich sediments
deposited from the water column, and exclude bioturbation (Schrader, 1992).
2.2. Sampling and sample processing
The sampling was carried out during the Galathea 3 expedition in February 2007. Sediment
cores from 4 stations were recovered by multicoring (MUC) and gravity corer. The cores were
retrieved from water depths between 313 and 2367 m (Table 1). The cores were sectioned into the
following depth intervals: the upper 6 cm was sliced into 1 cm intervals and into 2 cm intervals at 6-
30 cm depth. A separate 1 mL sediment sample was taken from each sampled depth for enumeration
of vegetative cells and transferred to 2 mL cryogenic vials, fixed with 1 mL glutaraldehyde (2% final
concentration) and frozen at -20°C until further processing. The remaining sediment was transferred
into Nasco Whirl-Pack bags and frozen at -20°C immediately after sampling for later analysis of total
87
organic carbon (TOC), total hydrolizable amino acids (THAA) and dipicolinic acid (DPA). A 5 m gravity
core retrieve at site 10 was sampled in coarser resolution but otherwise treated identically.
2.3. Sediment dating by 210Pb
Sliced cores in the same intervals as previously described were analysed for the activity of
210Pb, 226Ra and 137Cs via gamma-spectrometry at the Gamma Dating Center, Institute of Geography,
University of Copenhagen. The measurements were carried out on a Canberra low-background
Germanium detector. 210Pb was measured via its gamma-peak at 46,5 keV, 226Ra via the
granddaughter 214Pb (peaks at 295 and 352 keV) and 137Cs via its peak at 661 keV. The cores showed
surface contents of unsupported 210Pb of about 400 - 1000 Bq kg-1, and the activity generally
decreased exponentially with depth although this decrease was not monotonous. The contents of
137Cs were generally below the detection limits. CRS-modeling has been applied on the profiles of
unsupported 210Pb using a modified method where the activity below the deepest sample is
calculated on the basis of a regression of unsupported 210Pb vs accumulated mass depth (Appleby,
2001). Each section of exponential decrease in unsupported 210Pb in each core was used to assign a
sedimentation velocity within that section and the combined results were used to construct age
models for each core. In each station, age models were extrapolated to cover the whole core, using
the deepest reliably determined sedimentation rate.
2.4. Sediment dating by 14C
The age structure of the gravity core was determined based on radiocarbon in the organic
fraction from a parallel core (G10_GC01) at the same station. The age model was transferred to core
G10_GC02, from the present study, by matching TOC profiles, and using the corresponding age to
calculate subsequent depths following the sedimentation rate from the linear fits.
88
2.5. Total organic carbon (TOC)
The TOC in the samples was determined after decarbonating 1 g of sediment samples with
H2SO3 (5-6 v/v %), air-drying and overnight drying at 105°C. The samples were weighted before and
after decarbonation to correct for loss of CO2. The analysis was performed in a Carlo Erba NA-1500
elemental analyzer. The concentrations were calculated from individual 4-10 point calibration curves
within the respective ranges. Flour containing 44.39% C and 2.31% N was used as standard for the
calibration curves.
2.6. Total hydrolizable amino acids (THAA)
THAA were processed and analyzed by high-performance liquid chromatography as described
in Lomstein et al. (2006), but the hydrolyzate was dissolved in 5 mL Milli-Q instead of the 4 mL Milli-
Q. In total, 18 amino acids were quantified: aspartic acid (Asp), glutamic acid (Glu), serine (Ser),
histidine (His), glycine (Gly), threonine (Thr), arginine (Arg), β-alanine (β-Ala), taurine (Tau), alanine
(Ala), γ-aminobutyric acid (γ-Aba), tyrosine (Tyr), valine (Val), phenylalanine (Phe), isoleucine (Ile),
leucine (Leu), ornithine (Orn) and lysine (Lys). The results are given as percentage of amino-acid
contained carbon relative to TOC, which is a recognized sensitive indicator of organic matter
diagenesis in marine sediments (Keil et al., 2000).
2.7. Extraction and enumeration of prokaryotes
The glutaraldehyde fixed sediment samples were transferred to sterile 50 mL Falcon tubes,
diluted in 4 mL autoclaved Milli-Q water, treated with sodium pyrophosphate (Na4P2O7; 5 mM final
concentration) and incubated in the dark at room temperature for 15 min. After the incubation, the
samples were sonicated (at 20 KHz; Branson, SLPe), two cycles of 30 s on ice, diluted in 30 mL
autoclaved Milli-Q water and then centrifuged for 10 min at 1000 RPM to separate the cells in the
89
supernatant from the mineral grains (Danovaro & Middelboe, 2010). The prokaryotes enumeration
was performed by flow cytometry (Bencton Dickinson FACSCanto II; equipped with a 488 nm Argon
laser). Prior the counting, 5 mL of extract were diluted in 490 mL TE-buffer (Tris-
ethylenediaminetetraacetic acid EDTA; pH 7.5), and stained with 5 mL of SYBR Green I (stock 1:200;
Molecular probes; Inc.) for 10 min at 80°C. Counts were calibrated using known densities of 2 and 3
mm fluorescent beads (Bencton Dickinson).
2.8. Quantification of dipicolinic acid (DPA) and estimation of endospore
numbers
Concentrations of the endospore specific compound DPA were determined by reverse phase-
HPLC after complexation with terbium according to Lomstein and Jørgensen (2012). In short, 0.5 g
sediment was hydrolysed with 10 mL 3 N HCl at 95 °C for 4 h under N2. Two subsamples of each
hydrolizate (ca. 0.3 mL) were dried, re-dissolved in 300 µL Milli-Q, and dried again. Immediately after
the two subsamples were combined and dissolved in 4 mL of 1 M luminescent grade sodium acetate
buffer, with 80 µL of 2mM AlCl3 added in order to precipitate phosphates. DPA concentrations were
determined from 4 to 5 point standard curves, obtained by addition of standard in the sample to
compensate for matrix effects. For conversion of DPA concentration to endospore numbers, it was
assumed that each endospore contained 2.24 x 10-16 mol-DPA (Fichtel et al., 2007).
90
3. Results
A total of 3 cores were analyzed for concentrations of DPA down to depths of between 24 and
30 cm (Table 1). In the analysis was also included a 500 cm gravity core retrieved from station G10. In
G16, the deepest station, DPA measurements were unsuccessful despite repeated attempts due to
low concentration of DPA and due to matrix effects in the HPLC.
The age of the sediment in different depths was estimated based on the 210Pb activity, a
method which is suitable for the time interval of about the past 120 years. All stations have
sedimentation rates between 1 and 2.5 mm yr-1 (Fig. 2). The 210Pb dating profiles of surface sediment
did not indicate that bioturbation or mixing had any significant effect on sediment profiles at any of
the stations.
The DPA analysis showed endospore abundances within the range 1 107 to 3.8 107
endospores gdw-1 in all short cores (Fig. 3, a-c). Overall, endospore abundance decreased with depth
and age in the sediments. The half-life of the endospore community in the upper 30 cm of each core
was estimated by fitting an exponential decay function to the spore abundance versus the sediment
assigned age (Table 1). The decay rates were in the order of 3-2 10-3 yr-1 in all short cores
corresponding to endospore half-life between 205-332 years (assuming no formation of new spores).
Total cell counts were performed on sediment cores from stations G15 and G16 (Fig. 4).
Abundance of cells at the sediment surface were two orders of magnitude above the number of
endospores, but decayed at a much faster rate (3-5 10-2 yr-1).
The gravity core from G10 was used to compare prokaryotic cells and endospore abundances
below 30 cm core depth (Fig. 5). In this core, the sediment dating was based on 14C (see section 2.4).
The abundance of vegetative cells down along the 5 m core followed a power function with a slope
near -1 as most other sites on the Peru margin and elsewhere (Whitman et al. 1998; Parkes et al.,
2000; Kallmeyer et al 2012). The exponential decay of endospores down core observed in the short
MUC cores were not sustained down along the 5m gravity core (Fig. 5, a), but instead the progression
91
of the endospore community became much closer coupled to the number of vegetative cells (Fig. 5,
c).
92
4. Discussion
4.1. Depth profiles of endospores
The studied sediments had uniform lithology (Fig. 3, d-f, Fig. 5, b), and with small deviations
both the endospore numbers and the TOC content decreased continuously down-core. The decay
rates of endospores, calculated in the top 30 cm of sediments from the Peru margin, are surprisingly
close to decay rates reported for thermophilic endospores, in permanently cold sediments, with a
half-life in the range 250-440 years (de Rezende et al., 2013). Similar values of 499-546 years have
also been found in limnic sediments (Rothfuss et al., 1997). This could indicate that the majority of
the endospores in the surface sediments of the Peruvian margin constitute a static community under
decay, just as the thermophilic endospores found in the arctic must be static because the vegetative
stages of those cells do not grow below 50 °C. Such endospores can apparently be dispersed over
great distances in the ocean and accumulate together with other fine grained materials in the
sediment (Müller et al., 2014). In the sediment, the static endospore community apparently decays
at a universal rate in the range of a few hundred years.
If the exponential decay, with a half-life of 300 years, had continued down through the
gravity core, then there should have been only 10 endospores gdw-1 left at 2.5 mbsf, which
corresponds to 4500 years old sediment. However, the measured number was six orders of
magnitude higher. This picture is not changed even if the upper 95% confidence bound to the fit with
the slowest decaying endospores is chosen as reference (Fig. 5, a, stippled line). The discrepancy
between the decay rate at the surface and the decay rate in the sub-surface can be explained by
several processes. One explanation could be the presence of a subpopulation of the endospores that
were orders of magnitude more resilient than the majority, so that no substantial decay had
occurred across 6000 years (Fig. 5). Another explanation could be if endospore decay, for example
via spontaneous fruitless germination (Epstein 2009; Buerger 2012), would decrease in response to
93
time or deprivation of nutritional conditions in the deeper sediment layers. Finally, the endospores
could be in dynamic equilibrium with new endospores generated in situ, deep below the sediment
surface at a rate that balances the decay. As the dynamics of the endospore community approach
that of the vegetative community, after the initial die off (Fig. 5, a-c), this seems like a plausible
explanation. The endospore forming bacteria belongs to either two out of the three classes of the
phylum Firmicutes (Bacilli and Clostridia). The Firmicutes fraction found in marine sediments
constitute in the range of 1-20 % of the total community (Biddle et al., 2008; Jorgensen et al., 2012).
Thus, we expect that the core section between 3 and 4 meters below seafloor (assigned age 6000-
8000 years) that contained 108 cells gdw-1 would contain 106 - 107 potentially endospore forming
Firmicutes per gdw-1. The same core section contains 6 106 endospores gdw-1. If those
endospores decay at the same rate as the endospores at the surface, there would be an annual loss
of 2 104 endospores gdw-1. To maintain the observed constant trend down-core, they would have
needed to be recruited from the vegetative Firmicutes pool (106 - 107 Firmicutes cells gdw-1). Thus,
despite the energetic costs that sporulation implicates, each potential endospore former would have
to form one new endospore every 50-800 years. This result corresponds well with the estimated
turnover times of cells in the deep biosphere (Lomstein et al. 2012; Hoehler and Jørgensen 2013).
Acknowledgements
This study was supported by the Mexican research council (CONACyT), PhD grant number
213154. The research has received funding from the European Research Council under the European
Union’s Seventh Framework Programme (FP/2007-2013)/ERC grant agreement no. 294200 and from
the Danish National Research Foundation. Sample collection was carried out during the Galathea 3
expedition under the auspice of the Danish Expedition Foundation. The master and crew on board
the Danish Navral vessel ”Vædderen are thanked for their help and hospitality during the cruise. We
94
thank Rikke O Holm, Lykke Poulsen and Preben G. Sørensen for technical support during the
laboratory analyses.
95
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Lomstein, B.A., Langehuus, A.T., D´Hondt, S.D., Jørgensen, B.B., and Spivack, A.J. 2012. Spore abundance, microbial growth and necromass turnover in deep subseafloor sediment. Nature 484: 101-104. Müller, A., de Rezende, J. R., Hubert, C., Kjeldsen, K. U., Lagkouvardos, I., Berry. D., Jørgensen, B.B., Loy, A. 2014. Endospores of thermophilic bacteria as tracers of microbial dispersal by ocean currents. ISME Journal in press. Middelburg, J. J. 1989. A simple rate model for organic-matter decomposition in marine-sediments. Geochimica and Cosmochimica Acta 53: 1577-1581. Nicholson, W. L., Munakata, N., Horneck, G., Melosh, H. J., Setlow., P. 2000. Resistance of Bacillus endospores to extreme terrestrial and extraterrestrial environments. Microbiology Molecular Biology Reviews 64: 548–572 Nicholson, W.L. 2003. Using thermal inactivation kinetics to calculate the probability of extreme spore longevity: Implications for paleomicrobiology and lithopanspermia. Origins of Life and Evolution of the Biosphere 33: 621-631. Parkes, R.J., Cragg, B.A., Wellsbury, P., 2000. Recent studies on bacterial populations and processes in subseafloor sediments: a review. Hydrogeology Review 8: 11–28. Powell, J.F. 1953. Isolation of dipicolinic acid (pyridine-2:6-dicarboxylic acid) from spores of Bacillus megatherium. Biochemical Journal 54: 210-211. Reinhardt, L., Kudrass, H.R., Lückge, A., Wiedicke, M., Wunderlich, J., and Wendt, G. 2002. High-resolution sediment echosounding off Peru: Late quaternary depositional sequences and sedimentary structures of a current-dominated shelf. Marine Geophysical Researches 23: 335-351. Revsbech, N.P., Larsen, L.H., Gundersen, J., Dalsgaard, T., Ulloa, O., and Thamdrup, B. 2009. Determination of ultra-low oxygen concentrations in oxygen minimum zones by the STOX sensor. Limnology and Oceanography: Methods 7: 371-381. Rothfuss, F., Bender, M., and Conrad, R. 1997. Survival and activity of bacteria in a deep, aged lake sediment (Lake constance). Microbial ecology 33: 69-77. Røy, H., J. Kallmeyer, R. R. Adhikari, R. Pockalny, B. B. Jørgensen, and S. D’hondt. 2012. Aerobic Microbial Respiration in 86-Million-Year-Old Deep-Sea Red Clay. Science 336: 922-925. Schrader, H. 1992. Comparison of Quaternary coastal upwelling proxies off central Peru. Marine Micropaleontology, 19: 29-47. Setlow, P. 2003. Spore germination. Current Opinion in Microbiology 6: 550-556. Siala, A., Hill, I. R., Gray, T. R. G. 1974. Populations of spore-forming bacteria in an acid forest soil, with special reference to Bacillus subtilis. Journal of General Microbiology 81: 183–190 Suess, E., Kulm, L.D. and Killingley, J.S., 1986. Coastal upwelling and a history of organic-rich mudstone deposition off Peru. In: J. Brooks and A.J. Fleet (Editors), Marine Petroleum Source Rocks. Special Publications of the Geological Society of London 26: 181-197.
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Table 1. Sampling site with positions of stations, water depth, core length and sediment location relative to water column and oxygen minimum zone (OMZ).
Station Station
code Position
Water depth
(m)
Bottom water temperature
(°C)
Core length (cm)
Location relative to
OMZ*
half-life of endospores
(yr)**
G10 G10_GC02 14°22.956´S 76°23.894´W
313 11.1 495 Within -
G10 G10_MUC04 14°23.010´S 76°24.069´W
313 11.1 30 Within 205
(150-323)
G15 G15_MUC26 13°52.236´S 76°48.349´W
743 5.4 30 Below 332
(234-570)
G11 G11_MUC15 14°14.604´S 76°36.217´W
1015 4.5 30 Below 315
(233-490)
G16 G16_BR11 14°16.576´S 76°47.603´W
2367 >4.5 25 Below no data
*based on Revsbech et al., (2009)
** with 95% confidence interval
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6. Figure legends
Figure 1. Chart of investigated area with bathymetry lines and sampling sites indicated. The map is based on Reinhardt et al., (2002). Figure 2. Age models of stations G10, G15 and G11 based on the 201Pb dating method. Figure 3. A-C) Concentration of endospores in MUC cores based on DPA analysis. The lines represent the best exponential fit to the data. The equation of the fit and the derived endospore half-life (T1/2) with 95% confidence interval is printed on each panel. D-F) Concentration of total organic carbon (TOC) and the percentage of carbon bound in amino acids (%TAAC) in the same cores as A-C. All cores were 30 cm long. Figure 4. Profiles of cells enumerations versus sediment assigned age. The exponential fits are
and in stations G15 and G16 respectively. Figure 5. A) Concentration of endospores versus assigned age in a 5 m gravity core in station G10. Endospore numbers are based on DPA quantification and converted assuming 2.24 10-16 mol DPA per endospore. The lines represent the upper and lower 95% confidence interval of the fit in Figure 3, a. The stippled line represents the upper 95% confidence bound of the fit for MUC core with slowest decay rate. B) Concentration of total organic carbon (TOC) and the percentage of carbon bound in amino acids (%TAAC) in the same core as A. C) Cell numbers in the same core as A-B.
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Figure 1
102
Figure 2
103
Figure 3
104
Figure 4
105
Figure 5
106
Chapter 3
Microbial activity rates of a Holocene-Pleistocene biosphere
Paulina Tamez-Hidalgoa, Renato Salvattecib, Oscar Chiangc, Mathias Middelboec
Hans Røya and Bente Aa. Lomsteina
aDepartment of Bioscience, Section for Microbiology, University of Aarhus, Aarhus, Denmark,
bInstitute of Geosciences, Kiel University, Kiel, Germany
cMarine Biological Section, University of Copenhagen, Helsingør, Denmark
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1. Introduction
The subsurface biosphere is constrained by energy limitation, constant burial and the
availability of labile organic carbon (Arndt et al., 2013). It is generally agreed that microbial cell
abundances decline steeply with depth following a power law (Parkes et al., 2000). Consequently, the
average carbon and energy flux per cell is highest near the sediment surface and drops off with
depth and age in the sediment (Lomstein et al., 2012).
Nevertheless, little is known regarding the development in microbial activity with depth and
age in sediments (Arndt et al., 2013). Prokaryotic activity may be evaluated employing models
focused on cells or biomass turnover times (D´Hondt et al., 2004; Schippers et al., 2005; Holmkvist et
al., 2011; Lomstein et al., 2012). The calculations are based on cell counts and on turnover rates of
selected metabolic substrates that are assumed to be the main electron donors, and the turnover
rate of the bulk sediment (Jørgensen, 2011). Previous studies found prokaryotic turnover times in the
range from hundreds to thousands of years (Schippers et al., 2005; Jørgensen and D’hondt, 2006).
Prokaryotic turnover times have also been independently determined by the D:L-amino acid
modeling (Lomstein et al., 2012), yielding estimates of the rate at which cells rework the amino acid
pool, and extrapolates to cells turnover times. This approach has found cell turnover times to be in
the order of tens to hundreds of years in Holocene sediments (Langerhuus et al., 2012) and in the
order of hundreds to thousands of years in sediments as old as the Oligocene (Lomstein et al., 2012).
According to these models, microbial populations in subsurface environments are adapted to
slow but steady existence under constant nutrient limitation, which would correspond to k-
strategists (Janssen, 2009). However, it is not known whether the cells are actually dividing or simply
turning over cell biomass without cell division (Arndt et al., 2013).
Recent studies have documented the presence of endospores in the subsurface (Lomstein et
al., 2012; Langerhuus et al., 2012; Tamez-Hidalgo et al., in revision), illustrating another possible
survival strategy. Populations forming endospores may represent r-strategists that grow at higher
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rates when conditions are suitable, and re-enter a dormant state before nutrients are exhausted
(Janssen, 2009).
The present study investigated the distribution of cells in sediments within the oxygen
minimum zone (OMZ) at the Peru Margin. Carbon oxidation rates, and biomass and necromass
turnover times are evaluated employing the D:L-amino acid model developed by Lomstein et al.,
(2012). The model results are compared to measurements of profiles of TOC in the sediment.
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2. Materials and Methods
2.1. Study site
The study site is situated on the Peruvian continental shelf and slope (average slope 4°) (Fig. 1).
The waters in this area have the world’s most persistent wind-driven coastal upwelling, supporting
intense primary production ( 2700 mg C m-2 day-1; Lee and Cronin 1984; Chavez et al, 1999), which
leads to high sedimentation rates (Brodie and Kemp, 1994). The sediments consist of fine-grained,
organic-carbon-rich (Suess et al., 1986), diatom-rich laminated mud lenses with intervals of paleo
organic-poor and less diatomaceous, structureless or macroscopically bioturbated silty muds (Krissek
et al., 1980; Brodie and Kemp, 1994). The high input of OM leads to increase oxygen consumption
resulting in an oxygen minimum zone (OMZ) (Wyrtki, 1962). During the Galathea 3 expedition, which
was carried out in February, 2007, the OMZ was located between 50 to 500 m water depths and
contained between 2 nmol L-1 and 3 µmol L-1 of dissolved oxygen (Revsbech et al., 2009). This
perennial OMZ maintains organic-rich sediments deposited from the water column, and exclude
bioturbation (Schrader, 1992). According to Suess et al., (1986) the upwelling is most intense in the
zones 7°-8°S, 11-12°S and 14°-16°S, which is the zone the sampling was carried out. The OM is mostly
of marine origin with high rates of bacterial remineralization (Niggeman and Schubert, 2006). The
study sites were located around 14°S at contrasting water depths (Table 1, Fig. 1).
2.2. Sampling and sample processing
The sampling was carried out during the Galathea 3 expedition in February 2007. Two gravity
cores were recovered. The cores were retrieved from water depths indicated on table 1. For
extraction and enumeration of prokaryotic cells, subsamples for prokaryotic abundance (1 mL) were
transferred to 2 mL cryogenic vials, fixed with 1 mL glutaraldehyde (2% final concentration) and
frozen at -20ºC until further processing. For biogeochemical analyses including total organic carbon
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(TOC), total nitrogen (TN), total hydrolizable amino acids (THAA), total hydrolizable amino sugars
(THAS) and dipicolinic acid (DPA), the cores were sectioned into the following depth intervals: the
upper 6 cm was sliced into 1 cm intervals and into 2 cm intervals at 6-30 cm depth. Each section was
transferred into a Nasco Whirl-Pak bags and frozen at -20°C immediately after sampling. The samples
were freeze-dried and homogenized by grinding in an agate mortar.
2.3. Radiocarbon measurements
Dating was based on the radiocarbon tracer method. Both stations where dated using
neighboring cores (G10-GC01 and G14GC05). Age models for cores in this study were possible by
matching TOC profiles, and using the corresponding age to calculate subsequent depths following the
sedimentation rate from the linear fits:
; R2 (Fig. 1). 14C-AMS were analyzed at relatively high resolution (about
each 20 cm of sediment depth and/or at interesting stratigraphic features). For the gravity core
G10-GC01, AMS and 32 radiocarbon measurements have been performed on the sedimentary
organic fraction, after acidification, at the LMC14 Laboratory Accelerator Mass Spectrometry
Facility (UMS 2572, Gif-sur-Yvette, France). For the gravity core G14-GC05, 30 radiocarbon ages
from the same source were determined in the Keck Carbon Cycle AMS facility of the University
of California at Irvine. 14C age calibration was done by applying an average local reservoir
correction of 200±100 years for the late Holocene (<4000 years BP; Gutiérrez et al., 2009, Ortlieb
et al. 2010) and 500±200 years for the early Holocene and Late Pleistocene (Ortlieb et al., 2010).
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2.4. Extraction and enumeration of prokaryotes
Sediment samples were transferred to a sterile 50 mL Falcon tube, diluted in 4 mL autoclaved
Milli-Q water, treated with sodium pyrophosphate (Na4P2O7; 5 mM final concentration) and
incubated in the dark at room temperature for 15 min. After the incubation, the samples were
sonicated (at 20 KHz; Branson, SLPe), two cycles of 30 s on ice, diluted in 30 mL autoclaved MilliQ
water and then, centrifuged for 10 min at 1000 RPM (Danovaro & Middelboe, 2010). The prokaryotes
enumeration was performed by flow cytometry (Bencton Dickinson FACSCanto II; equipped with a
488 nm Argon laser). Prior the counting, 5 mL of extract were diluted in 490 mL TE-buffer (Tris-
ethylenediaminetetraacetic acid EDTA; pH 7.5), and stained with 5 mL of SYBR Green I (stock 1:200;
Molecular probes; Inc.) for 10 min at 80ºC. Counts were calibrated using known densities of 2 and 3
mm fluorescent beads (Bencton Dickinson).
2.5. Total organic carbon (TOC) and total nitrogen (TN)
The TOC and TN in the samples were determined after decarbonating 1 g of sediment
samples with H2SO3 (5-6 w/w %), air-drying and overnight drying at 105°C. The samples were
weighted before and after decarbonating in order to correct for any losses or gains in the sample
weight due to loss of CO2 or weight gain due to the addition of acid. The analysis was performed in a
Carlo Erba NA-1500 elemental analyser. The concentrations were calculated from individual 4-10
point calibration curves within the respective ranges. Flour containing 44.39% C and 2.31% N was
used as standard for the calibration curves.
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2.6. Total hydrolysable amino acids (THAA) and total hydrolysable amino
sugars (THAS)
THAA and THAS were processed and analyzed by high-performance liquid chromatography as
described in Lomstein et al. (2006). The only exception was that the dried hydrolyzate was dissolved
in 5 mL MilliQ instead of the 4 mL MilliQ given in Lomstein et al. (2006). Blanks were prepared in the
same way as samples except that sediment was omitted. Blanks showed negligible concentration of
amino acids, from handling and reagents. In total, 18 amino acids were quantified: aspartic acid
(Asp), glutamic acid (Glu), serine (Ser), histidine (His), glycine (Gly), threonine (Thr), arginine (Arg), β-
alanine (β-Ala), taurine (Tau), alanine (Ala), γ-aminobutyric acid (γ-Aba), tyrosine (Tyr), valine (Val),
phenylalanine (Phe), isoleucine (Ile), leucine (Leu), ornithine (Orn) and lysine (Lys). Two amino sugars
were quantified in this order: galactosamine (GalN) and glucosamine (GlcN).
2.7. Analysis of diagenetic indicators
Amino acids are preferentially degraded from the bulk OM (Wakeham, 1997), as they are
protein building-block molecules. Thus, as diagenesis progress the amino acid pool will disappear
faster over the total OM. Thus, the contribution of amino acid carbon to the total organic carbon
%TAAC (Keil et al, 2000), was calculated taking into account the number of carbon and nitrogen atoms
in each individual amino acid relative to TOC.
2.8. Stereochemical composition (D- L-amino acids isomers)
D- and L-amino acid isomers of Asp, Glu, Ser and Ala were measured in aliquots of the
hydrolysate used for THAA analysis, following the HPLC method given in Mopper and Furton (1991),
with the modifications described in Guldberg et al. (2002) and Lomstein et al. (2009). The
concentrations of the isomers of the four amino acids were calculated from individual four-five point
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calibration curves of the respective amino acids. Blanks were prepared in the same way as samples
with the exception that sediment was omitted. Blanks showed negligible concentration of amino
acids from handling and reagents. The concentration of each amino acid and their respective isomers
were corrected for chemical racemization occurring in the liquid-phase acid hydrolysis as described
by Kaiser and Benner (2005). They found that the average percentages of D-enantiomers (
[ ( ] produced during acid hydrolysis of L-enantiomers were 4.4 % for Asp, 2.0 % for
Glu, 0.3 % for Ser and 1.2 % for Ala.
2.9. D:L-Amino acid modelling of bacterial activity
To estimate to what extent the microbial prokaryotic community is remineralizing the pool of
amino acid. The D:L-amino acid model developed by Lomstein et al., 2012) was applied. The
conceptual model and detailed explanation is found in supplementary material from Lomstein et al.,
(2012). The model conceptualizes the sedimentary amino acid pool (THAA) subdivided into three
compartments: amino acids present in vegetative cells, bacterial endospores and necromass. The
first two being comprised into biomass and necromass comprises the fraction that cannot be
accounted into biomass. The model estimates the speed at which the amino acid-carbon circulate
between the different compartments, for that, it assumes that only 11% of the necromass is
assimilated into biomass in each cycle (based on a growth yield of 11%; Heijnen and Van Dijken,
1992). It also assumes that bacterial biomass is in steady state meaning that necromass degradation
is equal to biomass production. Therefore 89% of the biomass produced must be mobilized from
uncharacterized TOC to maintain this steady state. The measured D:L-ratios can be used to model
bacterial activity when D:L-Asp ratios deviate from ratios that would otherwise be expected if
chemical racemization were the only source of D-Asp in the sediments, and when D:L-Asp ratios
deviate from ratios obtained from bacterial cultures. The model is typically performed under two
114
scenarios: a prokaryotic community dominated by Bacteria based on findings by Shippers et al.,
(2005), and prokaryotic community dominated by Archaea (90% Archaea and 10% Bacteria) as found
by Lipp et al., (2008).
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3. Results
3.1. The two sediment cores at the Peru margin, upwelling system
Even both gravity cores were nearly the same length, the core from station G10 contained
sediments far younger (up to ca.10 kyr) than the core from station G14 (ca. 11-25 kyr). In station G14
from the last 11 kyr were eroded by seismic events (Salvatteci et al., 2012). Although both stations
are located close each other (Table 1), in a seismically active mud-lens, the core from G14 belongs to
steeper zone than G10, thus being subject to seismic slides producing temporal gaps in the upslope
depositions. In the following, the data from both stations are therefore, discussed based on assigned
age (yr BP) rather than sediment depth.
3.2. Abundance of prokaryotic cells
A constant decline of prokaryotic cells was observed at both stations (Fig. 2). The numbers of
cells at the sediment-water interface were nearly two orders of magnitude higher in station G10 (1
109 9.7 107 cells gdw-1) than in station G14 (9 107 7.4 106 cells gdw-1). Sediments from
both cores pooled together followed a power-law function (Fig. 2), that correlates well with previous
descriptions (Parkes et al., 2000; Jørgensen et al., 2012).
3.3. Profiles of TOC, TN, THAA and THAS
Bulk pools of OM (TOC and TN) and identifiable pools of OM (THAA and THAS) decreased with
sediment age (Fig. 3). However, decrease followed an exponential trend with sediment age in TOC,
TN and THAA only. Calculated decay rates were 17,329 years for TOC (R2 0.85) and TN (R2 0.88)
respectively, and 11,552 years for THAA (R2 = 0.92; Fig. 3a-c). The pool of THAA decreases faster with
age than total pools of carbon and nitrogen. This corresponds well with previous observations
116
(Wakeham et al., 1997; Keil et al, 2000). The concentrations of THAS, on the other hand, seem to
follow a different pattern. Although, the data did not show any significant correlation, it was
observed the concentration decreased from 17 to 9.6 µmol gdw-1 and from 18.4 to 8.1 µmol gdw-1 in
the first 1000 years in G10 and G14 respectively. After that, a much slower decrease was observed
in G10, but G14 remained constant (Fig. 3, d). Thus, both sediments had a similar THAS concentration
in the top layers followed by a decrease. The presence of amino sugars reflects bacterial imprint
which can interpreted by OM remineralization process occurring at the water-sediment interface and
within the first layers of the sediment.
The C:N ratio in station G14 was constant with age ( 11), whereas in station G10, the ratio
varied from 9 to 11 (Fig. 1, suppl.). This is likely due to a gradual depletion of N diffusing upwards
(Grundmanis and Murray, 1977; Prokopenko et al., 2013). After 4000 years, G10 reached the same
ratio as G14 and remained constant, thus, both stations exhibited a straight line following age of the
sediment.
The range of THAA and THAS concentrations found in the present study, correspond well with
previous investigations (Henrichs et al., 1984; Niggemann and Schubert, 2006; Lomstein et al., 2009)
from the Peru margin.
Cell abundance was plotted against the contribution of amino acid carbon to the total organic
carbon (%TAAC; Fig. 4). This ratio has been suggested as a sensitive indicator of diagenesis of OM (Keil
et al., 2000). Cell abundances in station G10 have undergone a larger diagenetic span. In comparison,
the cells at G14 are sustained by far older and poorer materials (Fig. 4). Therefore, we hypothesized
that most of the cells found in G14 are in a dormant state rather than active, which explains their
steady abundance down-core.
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3.4. Source of OM
The source of OM was identified as been primarily of prokaryotic origin (Fig. 2, suppl.). The
ratios of glycine versus serine (Gly:Ser) and glucosamine versus galactosamine (GlcN:GalN) were
investigated. The Gly:Ser ratio showed a progressive increase with age in station G10. The range
varied from 2.1-3.4. In G14, on the other hand, the ratio varied from 2.7-3.7 but the trend was
scattered (Fig. 2, suppl.). A ratio around 2.3 indicates the replacement of the phytoplankton sources
by bacterial molecules due to OM reworking. Bacterial cells typically have a Gly:Ser ratio of 2.3,
while diatoms and phytoplankton have lower ratios, 0.7 and 1.0 respectively (Keil et al., 2000).
The ratio of GlcN:GalN has been shown to decrease as diagenesis proceeds (e.g Langerhuus et
al., 2012). At station G10, this ratio varied from 2.8 to 1.8 and at G14 from 1.7 to 1.0. GlcN:GalN
ratios below 8.1, indicate again a strong signature of bacterial reworking on sedimented OM.
Both ratios correspond well with previous investigations. The first one with marine sediments
studies (Lomstein et al., 2006; Lomstein et al., 2009; Langerhuus et al., 2012), and the latter with
sediment studies from rivers (Tremblay and Benner, 2009), lakes (Carstens et al., 2012), estuaries
(Unger et al., 2012; Khodse and Bhosle, 2013), sea and oceanic waters (Benner and Kaiser, 2003;
Davis and Benner, 2005) and marine sediments (Lomstein et al., 2009; Langerhuss et al., 2012).
3.5. D:L-Amino acid measurements
To apply the D:L-amino acid modelling, the occurrence of D-asp was analysed. The D-form was
found to increase relatively to its L-isomer as sediment became older (Fig. 3 a suppl.). In other terms,
measured D:L-Asp ratios ratios diverged from the D:L-Asp ratios found in bacterial cultures (0.086
mean ratio). Furthermore, total aspartic acid (D- & L-forms) was compared with THAA, and the
results indicated that aspartic acid harbours a similar reactivity as the bulk pool of THAA (Fig. 3 b,
suppl.). This central assumption of the D:L-amino acid model was thereby fulfilled.
118
4. Discussion
The model assumes that cell abundance is in a steady state (i.e. cell numbers are relatively
similar throughout the sediment profile). The implications for a subsurface microbial community in a
steady-state can be: 1) the community is actively engaged in respiration at a rate sufficient to
maintain the community size, but not to grow, corresponding to a community of K-strategists (e.g.
Janssen 2009); or 2) the majority of the community survive in a dormant state, maintaining a basal
metabolism for repairing cell-damage (Price and Sowers, 2004) and permitting slow transport of
protein and metabolites to allow reactivation once conditions turn favorable (Parry et al., 2014).
Buerger et al., (2012) were able to test soil and marine isolated cells and endospores, viable
but dormant, in a nutrient-rich medium. Their results showed a lack of response to nutrients per se
from all populations tested. Instead, they observed growth in a stochastic fashion. The majority of
the isolates developed colonies in a range of 40-200 days. However, colony re-growth only took
about 24-48 hours, even in the isolates that required up to 200 days of initial incubation. This
observation was not a result of adaptative mutations. The likely explanation, the authors claimed, is
that slow growth and oligotrophy appears to be rarer than previously thought, and that stochastic
exiting from non-growing state may be more common. A microbial community of cells at a different
metabolically stages may be a better representation of a natural microbial community (Price and
Sowers, 2004). In nutrient restricted environments, dormancy would signify the difference between
survival and death, and slow growth may be an ambiguous interpretation.
In this study, a steady-state situation in terms of microbial abundance was found in the
sediment core from station G14. However, at G10, cell numbers only fulfilled this assumption at an
interval between 20-300 cm. Therefore, the model was performed only in this section.
119
4.1. Carbon oxidation under different scenarios
The D:L-amino acid model predicts that the total carbon oxidation rates at the two stations
differed by an order of magnitude from each other (Fig. 5). However, results from both the tested
scenarios (100% bacterial and 90% archaeal dominance, see section 2.9), displayed overlapping
trends. Analogous results of overlapping scenarios are shown by Lomstein et al., (2012) and
Langerhuus et al., (2012). To avoid clutter, figure 5 is only displaying bacterial dominance.
The predicted carbon oxidation rate at station G10 ranged from 451.5 nmol cm-3 y-1 in the
upper part, to 122 nmol cm-3 y-1 in the lower part of the sediment interval. At station G14, on the
other hand, considerably lower rates were predicted, as carbon oxidation rates varied from 20 nmol
cm-3 y-1 in the top, to 4.9 nmol cm-3 y-1 in the lower part of the sediment. The values obtained from
G14 are similar to ranges obtained in sediments from Aarhus bay (Langerhuus et al., 2012).
The reason for the different carbon oxidation rates at the two stations may be that the
sediment layers modelled at G10 contains fresher OM than the layers modelled at G14. Thus it is
assumed that what is generated at the top will progressively decay or survive at the expense of the
OM that is buried, therefore carbon oxidation values decrease with sediment depth (Fig. 5).
According to the model, the TOC decrease with age such that the pool size decreases to the
half approximately every 17 kyr. To test how this fits the observed profiles of TOC, we compared the
results from the model with the observed TOC decay rate in the sediment profiles (-0.00004 yr-1; Fig.
3 a). As a note, the decay rate obtained from the exponential function did not change by plotting the
stations separately.
In the sediment core from G10, the decrease in TOC from 20 to 300 cm depth (calculated ages
0.6-5.2 kyr) amounts to 1.1 mmol-C gdw-1. For comparison, the rates predicted in the model were
integrated over the age of the sediment core, yielding a total predicted carbon oxidation of 0.1
mmol-C cm-3 sediment, or 0.34 mmol-C gdw-1 sediment. In the sediment core from G14, the decrease
in TOC from top to bottom in the core (calculated ages 13-25.4 kyr) amounts to 1.6 mmol-C gdw-1. In
120
contrast, the integrated predicted rates from the model amounts to 0.13 mmol-C cm-3 or 0.07 mmol-
C gdw-1 sediment. Thus, the model predicted an amount of carbon oxidation that was 3.3 and 23.5
times lower than the observed decrease in TOC at station G10 and G14, respectively. The differences
between the model predictions and the observed TOC decay trend merits further investigation.
4.2. Turnover times of biomass and necromass
Biomass turnover times estimated for both stations ranged from 124 to 712 and 54 to 294 years at
G10 and G14 respectively. The result was independent of whether Bacteria or Archaea were
assumed to dominate the community. The turnover times estimated in the present study are far
longer than generation times estimated for surficial nutrient-rich environments such as soil, lake
water or sea water (Jørgensen, 2011), and from results obtained by Schippers et al., (2005), from the
ODP Leg 201. However turnover times are similar to what Biddle et al. (2006) found in the sulfate-
methane transition zone in sediments from the Peru Margin, and to observations by Langerhuus et
al., (2012) from sediments from Aarhus bay, Denmark. Much longer turnover rates have been found
in even older sediment layers from the Peru margin (Lomstein et al., 2012).
The pool of necromass is far larger and cycles over a much longer time span (Fig. 6). This
implies that whether microbial cells persist in dormant or slow-but-active forms (K-strategists), there
is sufficient bulk OM to sustain the community. However, enzymatic hydrolysis becomes increasingly
challenging as the pool of OM becomes increasingly refractory (Zonneveld et al., 2010; Arndt et al.,
2013). One can hypothesize that members of a dormant community sporadically enter into an active
regime when they are temporarily exposed to labile molecules. For example, this could happen if OM
is released from silicate particles, or when a neighbouring cell lyses. Microbial associations are
important to optimize energy yields and metabolite exchange (Prokopenko et al., 2013), and a cluster
121
of microbial cells might persist over larger times by maintaining a dormant-like metabolism and
exchanging electron donors.
Acknowledgments
This study was supported by the Mexican research council (CONACyT), PhD grant number
213154. We are grateful to Rikke O. Holm and Lykke Poulsen for skilful technical assistance with HPLC
analyses. The master and the crew onboard the Danish Naval vessel “Vædderen” are thanked for
their help and hospitality. Sample collection was carried out during the Galathea 3 expedition under
the auspices of the Danish Expedition Foundation. This is Galathea 3 contribution Px. The research
underlying this article has been co-funded by the Danish National Research Foundation and from the
European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-
2013)/ERC grant agreement no [294200].
122
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Figure legends
Figure 1. Sediment profiles versus age model as based on the radio carbon tracer method.
Figure 2. Cell abundance profile of analyzed stations contrasted with sediment age. The plot is shown
in a log10 of cell numbers per gdw-1 versus log10 of the sediment assigned age in years. The y-axis
crosses in 1 105 to ease visualization of standard deviations. The pooled data follows a power
function ; R2=0.87.
Figure 3. a) Total organic carbon (TOC), b) Total nitrogen (TN), c) Total hydrolysable amino acids
(THAA) and d) amino sugars (THAS) versus sediment age. Exponential fits for TOC, TN and THAA
respectively: , R2 0.85; , R2 0.89;
, R2 0.92
Figure 4. Abundance of cells compared with OM diagenesis. The percentage of amino acid carbon to
the total organic carbon was used as diagenetic indicator.
Figure 5. a) D:L modelled carbon oxidation rates with sediment age in stations G10 and G14, b)
comparison of D:L modelled carbon oxidation rates of 100% respiring bacterial cells.
Figure 6. D:L modeled turnover times of bacterial biomass and necromass with sediment age.
128
Table 5. Gravity cores from the Galathea 3 expedition in the Peru margin.
Station Core Latitude Longitude Core length (cm) Water depth (m) Bottom water
Temp. (oC) Location relative to OMZ*
G10 GC02 14.22.956 S 076.23.894 W 495 313 11.10 Within
G14 GC06 14.23.516 S 076.25.507 W 539 390 8.63 Within
129
Figure 1.
G10 G14
0
5000
10000
15000
20000
25000
30000
0 100 200 300 400 500 600
iv
v
vi
iii
i
ii
Sediment depth (cm)
Cal
ibra
ted
age
(yr
BP
)
130
Figure 2.
G10 G14
1 E+5 1 E+6 1 E+7 1 E+8 1 E+9 1 E+10
1 E+0
1 E+1
1 E+2
1 E+3
1 E+4
1 E+5
cells gdw-1
assi
gned
age
(yr
BP
)
131
Figure 3.
G10 G14
0 50 100 150
0
5000
10000
15000
20000
25000
30000
THAA (µmol gdw-1
)
assi
gned
age
(yr
BP
)
0 2000 4000 6000 8000 10000
0
5000
10000
15000
20000
25000
30000
TOC (µmol gdw-1
)
assi
gned
age
(yr
BP
)
0 200 400 600 800 1000
0
5000
10000
15000
20000
25000
30000
TN (µmol gdw-1
)
0 5 10 15 20
0
5000
10000
15000
20000
25000
30000
THAS (µmol gdw-1
)
a b
c d
132
Figure 4.
%TAAC
1 E+6
1 E+7
1 E+8
1 E+9
1 E+10
0 2 4 6 8 10
Cel
ls g
dw
-1
%TAAC
G10 G14
133
Figure 5.
0
5000
10000
15000
20000
25000
30000
1 10 100 1000
Ass
ign
ed a
ge (
yr B
P)
Total C-oxidation (nmol cm-3 yr-1)
G10 G14
134
Figure 6.
G10 biomass G14 biomass G10 necromass G14 necromass
0
5000
10000
15000
20000
25000
30000
1 E+0 1 E+1 1 E+2 1 E+3 1 E+4 1 E+5 1 E+6
Ass
ign
ed a
ge (
yr B
P)
Turnover time (yr)
0
135
Supplementary material
136
Figure 1.
Figure 1. Profile of C:N ratio with sediment age.
0
5000
10000
15000
20000
25000
30000
0 2 4 6 8 10 12 14 16
Ass
ign
ed a
ge (
yr B
P)
C:N (mol mol-1)
G10 G14
137
Figure 2.
Figure 2. Accumulation of prokaryotic remains with age as judged by the Gly:Ser (a) and GlcN:GalN
(b) ratios.
0
5000
10000
15000
20000
25000
30000
0 1 2 3 4
GlcN:GalN
0
5000
10000
15000
20000
25000
30000
0 1 2 3 4
Ass
ign
ed a
ge (
yr B
P)
Gly:Ser
G10 G14
138
Figure 3.
Figure 3. D:L ratio of aspartic acid with sediment depth (a). Reactivity profile of aspartic acid as a
function of THAA (b).
y = 0.1094x - 0.3413 R² = 0.9696
0
2
4
6
8
10
12
14
16
0 50 100 150
asp
(µ
mo
l gd
w-1
)
THAA (µmol gdw-1)
b
0
5000
10000
15000
20000
25000
30000
0.00 0.10 0.20 0.30
Ass
ign
ed a
ge (
yr B
P)
Asp (D:L ratio) a
G10 G14