Ben-Gurion University of the Negev The Jacob Blaustein Institutes for Desert Research

The Albert Katz International School for Desert Studies

Effect of heavy rainfall on desert soil bacterial community

composition and dynamics

Thesis submitted in partial fulfillment of the requirements for the degree of "Master

of Science"

By: Ani Azatyan

October, 2013

Ben-Gurion University of the Negev The Jacob Blaustein Institutes for Desert Research The Albert Katz International School for Desert Studies

Effect of heavy rainfall on desert soil bacterial community

composition and dynamics

Thesis submitted in partial fulfillment of the requirements for the degree of "Master

of Science"

By Ani Azatyan

Under the Supervision of Osnat Gillor

Department of Zuckerberg Institute for Water Research

Blaustein Institures for Desert Research

Ben Gurion University of the Negev

Author's Signature ……………….………………....… Date …………….

Approved by the Supervisor…………….……….….… Date …………….

Approved by the Director of the School ……………… Date ………….…

I

Abstract

The dogma in arid regions is that water strongly limits primary productivity and

therefore, arid ecosystems exhibit a pulse-dynamic response to rainfall, in which rain events

offer brief pulses of resource availability that can exert a strong influence on productivity

and function of plant and animal communities. However, the abundance, diversity and

structure of soil bacterial communities in arid ecosystems were seldom studied. It was

shown that soil bacteria in general are strongly influenced by soil temperature, moisture,

salinity, pH, or combinations of these parameters. In addition, seasonal variations of the

edaphic parameters often result in changes in the bacterial community structure, especially

in dry ecosystems. Investigations of temporal patterns of soil bacterial diversity in arid

environments, demonstrated that heavy rainfall is an important determinant of bacterial

activity variation, but overall changes in abundance and diversity were not elucidated. It is

not clear whether microorganisms in arid environments respond to rainfall events similarly

to macroorganisms. To answer this question the diversity and abundance of active bacterial

communities were studied in hot desert soil microcosms by closely following drought-rain-

drought cycles. The aim was to elucidate the bacterial response to the amount of rain (50

mm and 10 mm), the incubation temperature (25°C and 36°C), and diurnal cycles

(36/10°C) assuming that these parameters coalesce with the desiccation rate that will in turn

affect the bacterial community.

To that end, barren desert soil (directly below the crust) was collected and packed

into columns designed and constructed for the purpose of these experiments. Triplicate soil

columns were equipped with rain and drain simulators and were operated for one month

during each experiment. At constant intervals the soil was sampled from the columns for

II

physicochemical (including pH, salinity, water content, nitrite, nitrate and total carbon) and

bacterial analyses. The abundance, diversity and community composition of the bacterial

community were explored, with special focus on the dominant Actinobacteria phylum.

With an exception of water content, no major differences were observed in the

physicochemical parameters of the soil incubated under different rain regimes, temperature

or diurnal cycles. Interestingly, the biomass and diversity of the bacterial communities

including the Actinobacteria phylum, were unchanged under the various treatments.

However, the community composition was significantly altered within and between

experiments: the rain and temperature alterations yielded significantly different soil

bacterial communities, yet, diurnal cycles inflicted insignificant changes in the soil column

communities.

The obtained results suggest that unlike macroorganisms soil bacterial diversity and

abundance in hot desert environments are unaltered by hydration-desiccation cycles.

However, the community composition is markedly changed on a temporal scale following

rainfall and desiccation. Moreover, the community changes seem to be due to the amount of

rain and the desiccation temperature but not the diurnal cycles.

III

Acknowledgments

I want to express my deepest gratitude and appreciation to my supervisor Dr. Osnat

Gillor assisting and guiding me throughout my research project and the opportunities she

gave me to develop my skills and knowledge in molecular biology and environmental

microbiology. Thanks to Dr. Ines Soares for her generous support, and valuable advises. I

would like to thank also my student colleges and technicians in the Environmental

Hydrology and Microbiology Department for their help in the laboratory. My special

thanks to Lusine Ghazaryan for helping in the lab and introducing me to the technical skills

of molecular biology, and to Dr. Menachem Sklartz for his advises on statistical analyses.

I am thankful to Elena Morozovsky, secretary of ZIWR and Dorit Levin, assistant to

the AKIS Director, for their kind help and patience.

I would like to express my gratitude to the Albert Katz International School (AKIS)

for Desert Studies, and the Department of Environmental Hydrology and Microbiology, for

the financial support and for supplying me with the highest level of infrastructure and

technical facilities, and to everyone who helped me in the process of conducting my studies

and research.

And finally, my gratefulness to my family members and friends for giving me

strength and encouragement in each step of my life.

IV

Table of contents

Chapter 1. Introduction

1.1. Scientific background............................................................................................ 1

1.1.1. Bacterial activity and diversity in soil................................................................ 1

1.1.2. Bacterial diversity and abundance in arid soil.................................................... 2

1.1.3. Moisture effect on bacterial activity in arid soil................................................. 4

1.1.4. Temperature effect on bacterial growth and activity.......................................... 7

1.1.5. Actinobacteria..................................................................................................... 8

1.1.6. Firmicutes......................................................................................................... 10

Chapter 2. Research hypotheses and objectives................................................................ 12

Chapter 3. Materials and methods

3.1. Soil collecting from the field............................................................................... 13

3.2. Experimental setup.............................................................................................. 13

3.3. Soil sampling from the columns.......................................................................... 14

3.4. Soil physicochemical analyses............................................................................ 16

3.5. RNA extraction and cDNA synthesis.................................................................. 16

3.6. Bacterial abundance............................................................................................. 17

3.6.1. qPCR data analysis........................................................................................... 19

3.7. Community composition analysis……................................................................ 20

3.7.1. Terminal restriction fragment length polymorphism (T-RFLP) analysis......... 20

3.7.2. T-RFLP data analysis....................................................................................... 22

3.7.2.1. Shannon-Weaver species diversity index analysis........................................ 22

3.7.2.2. Stacked bar graphs......................................................................................... 23

V

3.7.2.3. Nonmetric multidimensional scaling............................................................. 23

3.7.2.4. Dissimilarity analysis..................................................................................... 25

Chapter 4. Results

4.1. Soil physicochemical characteristics during hydration-desiccation

experiments................................................................................................................. 26

4.2. Temporal dynamics of bacterial abundance during hydration-desiccation

experiments................................................................................................................. 29

4.3. Temporal dynamics of bacterial diversity during hydration-desiccation

experiments................................................................................................................. 31

4.4. Temporal dynamics in microbial community composition during hydration-

desiccation experiments.............................................................................................. 32

4.4.1. Bacterial community composition changes within experiments…………...... 32

4.4.2. Bacterial community composition changes between experiments ………...... 36

Chapter 5. Discussion

5.1. Soil physicochemical characteristics during hydration and desiccation.............. 42

5.2. Temporal dynamics of bacterial abundance during hydration and desiccation... 43

5.3. Temporal dynamics of bacterial diversity during hydration and desiccation...... 45

5.4. Temporal dynamics of bacterial community composition during hydration and

desiccation.................................................................................................................. 46

Chapter 6.

Conclusions................................................................................................................. 48

Future work................................................................................................................. 49

Chapter 7.

References....................................................................................................................50

VI

Supplementary data...................................................................................................65

Appendix 1. Recipe for RNAlater - RNA preservation medium..............................73

Appendix 2. Euclidean distance of the T-RFLP profiles of the samples..................74

VII

List of tables and figures

Table 1. Conditions applied to the soil columns and estimated average water loss during

the experiments ………….……................................................................................. 15

Table 2. Primers used in the study………….…................................................................ 18

Table 3. One-way ANOVA analysis of main soil physicochemical parameters upon

hydration-desiccation ………….…............................................................................ 26

Table 4. Main soil physicochemical parameters in all experiments.................................. 28

Table 5. Kruskal-Wallis non parametric test of the temporal changes in total abundance

of Bacteria, Actinobacteria and Firmicutes in soils under all treatments................... 30

Table 6. ANOVA test of the Shannon-Weaver diversity indices...................................... 31

Table 7. MRPP pairwise test between different experimental conditions......................... 38

Figure 1. Experimental setup............................................................................................. 15

Figure 2. Water content in soil columns incubated under different experimental

conditions.................................................................................................................... 29

Figure 3. Community size dynamics of Bacteria, Actinobacteria and Firmicutes upon

soil hydration and desiccation.................................................................................... 30

Figure 4. Shannon-Weaver diversity indices of Bacteria and Actinobacteria using

restriction enzymes TaqI and HapII, respectively...................................................... 32

Figure 5. Bacterial community composition dynamics within time in different

microcosms experiments.................................................................................................... 34

VIII

Figure 6. Actinobacterial community composition dynamics within time in different

microcosms

experiments................................................................................................................. 35

Figure 7. Non-metric multidimensional scaling (NMDS) ordinations of active Bacteria

communities upon hydration-desiccation................................................................... 37

Figure 8. Non-metric multidimensional scaling (NMDS) ordinations of active soil

Actinobacteria communities upon hydration and desiccation.................................... 40

Supplementary Table 1. MRPP pairwise test between the samples collected before and

after treatments........................................................................................................... 65

Supplementary Table 2. MRPP pairwise test between the different experimental

conditions.................................................................................................................... 65

Supplementary Figure 1. Soil WC (%), pH and EC (ds/m) at different sampling

depths.......................................................................................................................... 66

Supplementary Figure 2. Shannon-Weaver diversity indices of Bacteria and

Actinobacteria using restriction enzumes HpyCH4IV and HhaI, respectively.......... 67

Supplementary Figure 3. Non-metric multidimensional scaling (NMDS) ordinations of

bacterial and actinobacterial community changes before and after the rain events.... 68

Supplementary Figure 4. Bacterial community composition dynamics within time in

different microcosms experiments.............................................................................. 69

Supplementary Figure 5. Actinobacterial community composition dynamics within time

in different microcosms experiments.......................................................................... 70

Supplementary Figure 6. Non-metric multidimensional scaling (NMDS) ordinations of

active Bacteria communities upon soil hydration and desiccation............................. 71

IX

Supplementary Figure 7. Non-metric multidimensional scaling (NMDS) ordinations of

active Actinobacteria communities upon soil hydration and desiccation................... 72

X

List of abbreviations

ANOVA: Analysis of variance

cDNA: Complementary deoxyribonucleic acid

DDW: Double-distilled water

DNA: Desoxyribonucleic acid

DNTP: Deoxynucleotide triphosphate

EC: Electrical conductivity

HCL: Hydrogen chloride

KCL: Kalium chloride

LTER: Long term ecological research

MDS: Multidimensional scaling

MRPP: Multiresponse permutation procedure

NMDS: Nonmetric multidimensional scaling

OTU: Operational taxonomic unit

PCR: Polymerase chain reaction

PVC: Polyvinyl chloride

qPCR: Quantitative polymerase chain reaction

RDP: Ribosomal database

REs: Restriction enzymes

RNA: Ribosomal nucleic acid

SD: Standard deviation

SILVA: Comprehensive ribosomal RNA database

TC: Total carbon

T-RFLP: Terminal restriction fragment length polymorphism

T-RFs: Terminal restriction fragments

WC: Water content

ZIWR: Zuckerberg Institute for Water Research

1

Chapter 1. Introduction

1.1. Scientific background

1.1.1. Bacterial activity and diversity in soil

Prokaryotic organisms comprise a substantial proportion of Earth’s biota. Microbial

biomass carbon represents 0.6-1.1% of soil organic carbon and 1-20% of total plant

biomass carbon (Fierer et al., 2009). Prokaryotes contain about 10-fold more nitrogen and

phosphorus than plants, thus presenting the largest pool of macro-nutrients (Whitman et al.,

1998). The ability of prokaryotes to act as source-sink of nutrient cycling processes and as

regulators of organic matter transformations may determine many food webs structure and

function (Jordan et al., 1991; Vishnevetsky et al., 1997; Placella et al., 2012).

Bacterial diversity is of major interest due to the involvement of microorganisms in

biogeochemical cycles. Knowledge about bacterial community structure and diversity is

essential for understanding the relationship between environmental factors and the function

of ecosystems. The functional and genetic potential of microorganisms may exceed that of

higher organisms and provide a valuable source for novel products and technologies.

Despite their importance, at the present our knowledge is limited concerning the role of

bacteria in most ecosystems (Torsvik et al., 1996; Colwell, 1997; Hughes et al., 2001; Kirk

et al., 2004; Koeppel et al., 2013)

Soil is one of the most complex and ubiquitous habitats for microorganisms and is

considered to be a major reservoir of organic carbon. It has been estimated that the diversity

of prokaryotes in soil is orders of magnitude greater than in marine environments (Curtis et

al., 2002). Phylogenetic-based studies across different terrestrial biomes (from tropical

forest to desert) have shown that the general structure of bacterial communities was mostly

2

dominated by the same bacterial phyla - Acidobacteria, Actinobacteria, Proteobacteria and

Bacteroidetes (Fierer et al., 2009). Although these different biomes comprised similar

bacterial communities, the relative abundance of these groups varied, which could be

explained mostly by the soil pH (Fierer and Jackson, 2006; Fierer et al., 2009).

1.1.2. Bacterial diversity and abundance in arid soil

About a third of Earth's terrestrial environment is arid (mean annual precipitation <

250 mm), yet the bacterial communities of these ecosystems have not been thoroughly

studied. In the past decade studies conducted on the bacterial ecology of arid soil focused

on archiving soil bacteria, trying to determine what rules their diversity and how soil

bacterial communities are affected by specific changes and disturbances of the

environment. It was suggested that the structure of arid soil bacterial communities is

strongly influenced by soil temperature (Gestel et al., 2013), water availability (Williams

and Rice, 2007; Angel and Conrad, 2013; Placella et al., 2012; Barnard et al., 2013), pH

(Fierer and Jackson 2006; Lauber et al., 2009) or a combination of these parameters.

It was shown that bacterial community composition in arid soils is influenced by

fluctuations in temperature (Gestel et al., 2013), precipitation pulses (Huxman et al., 2004),

elevated UV radiation (Mattimore and Battista, 1996; Rainey et al., 2005) and nutrient

limitation (Placella et al., 2012; Gestel et al., 2013). Biotic factors, such as plant abundance

and grazing were also shown to have a role in shaping the composition of soil microbial

communities (Andrew et al., 2012). Yet, understanding of the factors that determine the

relationships between the bacterial community structure and the resources associated with

arid ecosystems is still limited (Zak et al., 2003; Bell et al., 2009). Moreover, in natural

environments these factors are interconnected and correlated, which in turn complicates the

3

uncoupling of these determinants. For example, Chihuahuan desert grassland soil bacterial

communities were responsive to rainfall, soil moisture pulses and subsequent nitrogen

availability during rain events (Bell et al., 2009). Several studies conducted in Israel arid,

semi-arid and Mediterranean sites revealed distinct spatial community clustering of

bacterial and archaeal domains, which abundance was correlated with soil water content

(Bachar et al., 2010; Angel et al., 2010). In semi-arid soils, bacterial and archaeal

community compositions were also affected by vegetation cover but not in arid and

Mediterranean soils (Angel et al., 2010).

Very few studies attempted to uncover the bacterial community structure of bulk

soils. In most of Israel soils Actinobacteria and Protebacteria are the most abundant, but in

arid soils Actinobacteria are dominant. Bulk soils in Israel’s Negev desert, like other arid

soils around the world, are dominated by the Actinobacteria that account for more than

45% of the total bacterial community (Bachar et al., 2010).

In the past, lack of adequate methods presented an obstacle to studies of soil bacterial

communities. The traditional cultivation methods in laboratory conditions studying

culturable strains provide little information on the ecological role and function of soil

bacteria since the vast majority (more than 99%) is unculturable or very difficult to culture

(Sait et al., 2002). In the past decade, molecular methods have become more common in

revealing the ecological characteristics of bacterial communities (Handelsman 2004; Fierer

et al., 2007; Placella et al., 2012; Angel and Conrad, 2013; Angel et al., 2013; Barnard et

al., 2013).

In order to understand the relationships between ecosystem function and bacterial

community structure one should accurately associate bacterial identity with its current

metabolic state. Bacterial studies commonly use the rRNA encoding gene for the

4

description of bacterial diversity in environmental samples, regardless of the

microorganisms’ metabolic state as soil extracted DNA is used as template for the

amplification of the genes. However, the rRNA itself could be used to portray the currently

active portion of microbes (Blazewicz et al., 2013; Barnard et al., 2013; Angel et al., 2013).

The use of the rRNA rather than DNA as template for amplification analysis has its

limitations: Dormant cells can contain high numbers of ribosomes synthesized to serve in

times when rapid response of the bacteria is required (Sukenik et al., 2012); The

relationship of rRNA concentration and growth rate are not always linearly correlated

(Worden and Binder, 2003); The presence of rRNA is indicative of potential protein

synthesis, but is not necessarily realized (Blazewicz et al., 2013); and lastly, rRNA is more

difficult to extract due to high degradability of the single strain rRNA molecule. Still,

analysis of bacterial community’s according to their RNA rather than DNA has potential

advantages as an indicator for characterization of its active members (Blazewicz et al.,

2013; Angel et al., 2013).

1.1.3. Moisture effect on bacterial activity in arid soil

In arid and semi-arid ecosystems plants net primary productivity depends strongly on

both total rainfall (Noy-Meir, 1973) and its variability (Le Houérou et al., 1988). Moreover,

these ecosystems can be affected by changes in the rainfall regime due to its interaction

with other limiting factors such as temperature, nutrient availability, soil texture and depth

(Seligman and van Keulen, 1989; Zaady, 2005). Moreover, plant species in hot desert

ecosystems often exhibit specific mechanisms that reduce vegetation in response to

draught, relying on seed banks to act during rain events (Harel et al., 2011) avoiding

5

demographic effects of reproductive failure (Evans and Cabin, 1995) or changing the

vegetation composition in time of abundance (Golodets et al., 2013).

It has been suggested that rainfall magnitude and frequency are likely to exert a

considerable influence on the physiological ecology and survivorship of biocrust mosses,

cyanobacteria and lichens (Pringoult and Garcia-Pichel, 2004; Coe et al., 2012; Rajeev et

al., 2013). Rainfall magnitude is typically a driver of resource availability; growth and

biomass increase in pulse-dynamic systems. Similarly, rainfall frequency is likely to

influence C balance because the timing of events is directly related to length of desiccation

period between events, which is probably related to recovery cost (Coe et al., 2012).

In microbial communities moisture is believed to be one of the main limiting factors

in arid soils, restraining growth to precipitation events (Waksman et al., 1931; Wen et al.,

2006; Placella et al., 2012; Barnard et al., 2013). Changes in soil water content were

suggested to impact the structure and physiology of soil bacterial communities by

influencing the osmotic potential (Kieft et al., 1987; Halverson et al., 2000; Placella et al.,

2012), nutrients transport and availability in the soil and cellular metabolism (Harris et al.,

1981; Williams et al., 2007), competitive interactions between microorganisms (Barnard et

al., 2013), and even by enhancing bacterial motility (Pringault et al., 2004; Rajeev et al.,

2013). Rain events in arid ecosystems may boost the metabolic activity of soil microbes

triggering mineralization processes that result in accumulation of nutrients, which

subsequently enhance the activity of soil macroorganisms (Huxman et al., 2004; Okoro et

al., 2009). In the Chihuahuan desert changes in soil moisture, coupled with changes in soil

temperatures and resource availability, were suggested to drive the soil-bacterial dynamics

(Bell et al., 2009). In this environment, drought and subsequent precipitation events

resulted in short-term changes in the abundance of the soil bacterial community, as

6

indicated by the slightly higher total bacterial and actinobacterial fatty acid methyl ester

levels detected in the winter compared to summer samples (Clark et al., 2009). The

diversity patterns of Bacteria in soil collected along Israel’s precipitation gradient (ranging

from 100 to 900 mm of annual rain) were constrained by precipitation and vegetation cover

(Angel et al., 2010), and the bacterial biomass in the arid soils was significantly lower

(Bachar et al., 2010).

A number of studies focused on the effects of moisture in desert soil crusts. Crusts

from the Las Bardenas Reales (Spain) desert region clearly showed evidence of

cyanobacterial migration (towards moisture) caused by change of water content, even in

dark conditions (Pringault et al., 2004). After a simulation of a rainfall event hydrating soil

crusts collected from different desert regions in the USA a rapid recovery of metabolism

was observed in cyanobacterial cells. Upon hydration of the crust biogeochemical changes

took place together with patterns of gene expression that were followed by bacterial exit

from dormancy (Brock et al., 1975; Rajeev et al., 2013).

The dynamics of the active bacterial communities (investigated by RNA-SIP) were

examined in biocrusts collected from arid and hyper-arid sites of the Negev Desert. The

crusts were incubated under dark-anoxic and light-oxic conditions after hydration with

heavy water (H218O). The results suggest that the biomass of the four major microbial

groups (Bacteria, Archaea, Cyanobacteria and Fungi) is unchanged, except for

Cyanobacteria in crust incubated under light conditions. However the community

composition changed significantly following hydration. Actinomicetales, a prominent (over

25% in relative abundance) bacterial component in dry crusts, collapsed to less than 1% of

the community following hydration (Angel and Conrad, 2013).

7

It has been suggested that Mediterranean soil bacteria can display anticipatory

strategies at seasonal scales: at the end of a summer dry-down period, the microbial

communities showed almost no measurable microbial activity (based on CO2 production),

yet, total extractable bacterial 16S rRNA was similar to that found after the first wet-up

event (Placella et al., 2012). On the other hand, soil bacteria show high resilience to the

drought conditions and different responses to wet up at the phyla and class level (Barnard et

al., 2013). Despite evidence indicating profound changes in bacterial diversity and

community composition in desert soil crusts following precipitation, to the best of my

knowledge, comprehensive studies monitoring changes in the active bacterial communities

and abundance in desert barren top soils during wet-dry cycles have not been conducted to

date.

1.1.4. Temperature effect on bacterial growth and activity

Another important factor controlling the activity of bacterial communities is

temperature; usually bacterial growth is enhanced at higher temperatures and inhibited at

low temperatures (Lloyd et al., 1994). The latter is mainly due to the inhibition of

enzymatic processes. However, high temperatures, above optimal level, are prone to cause

protein denaturation and cell death (Gestel et al., 2013). Thus, higher temperatures near or

above the optimum will result in an altered bacterial growth adapted to high temperatures,

while at the lower temperature the response will be less profound (Barcenas-Moreno et al.,

2009; Gestel et al., 2013).

Hot desert ecosystems with no or very poor vegetation coverage are known for high

soil temperatures, and wide annual and diurnal fluctuations. In such environments, even if

the mean annual temperature does not reach above optimum levels, the diurnal fluctuations

8

are large. The daytime summer temperatures can be very high in comparison with night

temperatures (Cable et al., 2011). Thus, sessile organisms living in these habitats should be

adapted not only to high temperatures but also to lower temperature, adjusting to wide

temperature range (Gestel et al., 2013).

Optimum soil respiration (linked to microbial growth) is usually found around 30°C

and is positively correlated to soil moisture, though these correlations are dependent on soil

type and the range of both temperature and soil moisture (Rosso et al., 1993; Qi et al.,

2002; Reichstein et al., 2002; Wen et al., 2006; Gestel et al., 2013). Temperature sensitivity

may be affected not only by soil moisture but also by other biotic and abiotic factors that

are spatially and temporally heterogeneous (Qi et al., 2002). At very high moisture, soil

particles are highly saturated with water and gas exchange between soil pores is limited,

which can lead to low concentration of oxygen and restrained aerobic respiration of the

microbial communities. Both dry and wet soil conditions can lower the sensitivity of

ecosystem respiration to temperature (Wen et al., 2006), and in soils where the water

content and availability are not in the optimal range, consideration of its effect on microbial

activity might be critical.

1.1.5. Actinobacteria

Actinobacteria comprise one of the largest phylum of the Bacterial domain,

characterized as Gram-positive Bacteria with a high guanine and cytosine content in their

DNA (Embley et al., 1994). They encompass a wide range of morphologies, from coccoid

to fragmenting hyphal forms with highly differentiated branched mycelium. Actinobacteria

may form spores in times of stress (Flärdh and Buttner, 2009; Swiercz et al., 2013).

Members of this group synthesize and excrete active extracellular secondary metabolites

9

(Bull et al., 2005; Fiedler et al., 2005) and are considered an unexhausted source for

bioactive products (e.g., antimicrobials, biopharmaceutins, agrichemicals and biocatalysts).

Although Actinobacteria have been studied extensively in the past, it is estimated that

only 3% of the natural-product potential of even the well-studied genus Streptomyces has

been realized (Watve et al., 2001), leaving ample opportunity for new discoveries. Bio-

discovery campaigns have been based on the premise that extreme environments, such as

deep seas and polar soils, are likely to contain novel microorganisms, which in turn may

produce novel metabolites. Several studies explored the abundance, diversity and

composition of biologically active natural products in Actinobacteria isolated from deep sea

hydrothermal vents (Feling et al., 2003; Bull et al., 2005), Antarctic soils (Lee et al., 2012)

and marine environments (Bull et al., 2005; Jensen et al., 2005; Lam, 2006).

Several surveys were reported to uncover bioactive natural products from

Actinobacteria isolated from the Atacama desert soil revealing high incidence of non-

ribosomal peptide synthases, indicative of the promising biotechnology opportunities of

these environments (Okoro et al., 2009). It was suggested that the relative abundance of

Actinobacteria in this soil increases at extreme drought conditions. Several studies have

shown Actinobacteria to be resilient to low levels of soil moisture and high temperatures

(Jiang et al., 1993; Zvyagintsev et al., 2007; Kurapova et al., 2012; Neilson et al., 2012;

Bull et al., 2005; Auche et al., 2013; Bull and Asenjo, 2013).

Desert soils present an under-researched biome in terms of its microbiotal secondary

metabolites, even more so in light of arid soil’s abundance and diversity of Actinobacteria.

For instance, this phylum represented 11.8% of the total bacterial diversity among the 48

genera of non-phototrophs detected in a study that combined molecular fingerprinting with

high throughput isolation to detect prominent bacterial communities in desert soil crusts

10

from the Colorado Plateau (Gundlapally et al., 2006). Recent bacterial community analyses

conducted in the Negev desert determined that the Actinobacteria consist over 45% of the

total bacterial community (Bachar et al., 2010). This phylum was found to be highly

prominent in other desert soils around the world: the Atacama desert where Actinobacteria,

comprise 94% of bacterial community (Connon et al., 2007). In the Tataouine (Chanal et

al., 2006), Arizona (Dunbar et al., 1999) and Australian (Holmes et al., 2000) deserts

Actinobacteria were also the dominating phylum.

Currently, the ecological function of Actinobacteria in arid ecosystems is unknown.

However, it may be inferred that the capacity of this phylum to form stress-resistant spores,

and to produce a wide array of natural extracellular metabolites, may give it a significant

competitive advantage. Actinobacteria may be able to out-compete their opponents by the

production of secondary metabolites. When resources are plentiful they may proliferate

undisturbed, yet once resources are depleted, they sporulate and thus persist, awaiting the

next rain event.

1.1.6. Firmicutes

The Firmicutes phylum mainly includes Gram-positive Bacteria with low GC

content. Many of them produce endospores, which are resistant to desiccation and survive

extreme conditions (Onyenwoke et al., 2004). Many microorganisms are capable of

resisting stress conditions such as temperature, desiccation, and antibiotics by entering

resting states or by forming spores (Roszak and Colwell, 1987; Onyenwoke et al., 2004). In

pure cultures of Bacillus, RNA is synthesized within minutes of spore transfer to favorable

conditions (Kennett and Sueoka, 1971). Bacterial dormancy is also thought to be important

in natural systems. Indeed, Firmicutes together with Actinobacteria were shown to be one

11

of the abundant groups in arid soils of Israel’s Negev desert consisting of about 10 and

45%, respectively in relative abundance (Bachar et al., 2010). Yet, in response to hydration

of dry crust samples from Israel’s Negev desert the relative abundance of Bacillales

increased sharply one day after hydration and decreased throughout the three subsequent

weeks of desiccation (Angel and Conrad, 2013).

In Mediterranean ecosystems increased activity of Bacilli was observed 3 to 24 hours

after wet-up, a timeframe that would have been sufficient for spore outgrowth (Placella et

al., 2012). In this study, Firmicutes were considered to be intermediate responders to

hydration, while Actinobactera responded rapidly with activity growth within 15 minutes to

1 hour. While in terms of C availability, Fierer et al. (2007) show that in the cross-site

study, neither Firmicutes nor Actinobacteria respond in any predictable manner to changes

in C availability.

A study conducted by Barnard et al. (2013) in California annual grasslands showed

that synthesis of Actinobacteria ribosomes was stimulated (from 62.5 to 82.9% relative

abundance) as summer dry-down progressed and slightly reduced (to 61.7%) after 2 hours

of rewetting, while the relative abundance of Firmicutes remained unchanged during dry-

down and wet up.

Spore forming strategy allows dormant cells to become potential seed banks and thus

contribute to the diversity and dynamics of communities in future generations. However,

very little is known about whether and to what extent dormancy influences the biodiversity

of bacterial communities (Jones and Lennon, 2010) and how they are affected by

environmental conditions, such as crowding, oxygen or temperature stress, and resource

limitation (Lewis, 2007).

12

Chapter 2. Research hypotheses and objectives

Given the accepted view that there exists a direct linkage between bursts of hydration

in arid environments and plants and animals’ growth, activity, structure and function, I

hypothesized that bacteria would follow the same patterns. In particular, I predicted that

arid soil bacterial communities would emulate plants pulse-dynamic response to rainfall

events exerting strongly on abundance and diversity. I further hypothesized that the rate of

desiccation would be influenced by rain amounts, temperature and diurnal cycles

questioning that all may play an important role in shaping the response of bacterial

biomass, diversity and community composition to the brief pulses of rain and the rate of

desiccation.

The aim of this study was to assess, under controlled laboratory conditions, the

dynamics of active (featuring the rRNA of the community) desert soil bacterial

communities during hydration-desiccation cycles, with special focus on Actinobacteria

(arid soil dominant bacterial phylum). To that end, continuous microcosoms experiments

were performed using bench-scale soil columns subjected to different intensities of rain,

different constant temperatures and night-day cycles. In particular, the current work was

aimed to assess the effects of these treatments on bacterial diversity, abundance and

community composition in arid soil.

13

Chapter 3. Materials and methods

3.1. Soil collecting from the field

The soil used in all experiments was collected from barren patches of an unmarked

plot at the long-term ecological research (LTER) station of Avdat (30°47' N, 34°46' E, 600-

700 m elevation). Eight randomly selected subsamples were taken from the top 5 cm of the

bulk soil (using ethanol-cleaned scoop), after the crust was removed. The soil samples were

collected into sterile bags (Whirl-Pack), transported to the Zuckerberg Institute for Water

Research (ZIWR) laboratory and kept at 4°C until homogenization (within 24 h of

sampling). Soil was homogenized by sieving trough an autoclave-sterilized sieve (2 mm

pore grid size), and 3.3 kg of the homogenate were packed in each of three replicate

columns (Figure 1).

3.2. Experimental setup

The experimental setup (Mishurov et al., 2008) consisted of three PVC (polyvinyl

chloride) cylinders (30 cm high, 10 cm in diameter) fitted at the bottom with a 4-8 m pore

size ceramic filter (Ace Glass Inc); an outlet tube at the base of the column led to a

continuously operating vacuum pump in order to simulate natural gravimetric forces in the

soil. Ten soil sampling ports were located around the perimeter of the top 12 cm of the

column (further details under 3.3). The sampling ports were fitted with Suba-Seal rubber

septa (Sigma-Aldrich).

To mimic rain events, a shower-like rain simulator was constructed (Figure 1A)

consisting of a PVC disk (10 cm in diameter) equipped with 21 syringe needles (0.4 x 13

mm) through which water dripped onto the soil surface by pumping (Gillson Minipuls 3

14

Peristaltic pump) double-distilled water (DDW) at a rate of 1.5 mL min-1. The volume of

water precipitating on the soil was 80 or 400 ml, corresponding to precipitation of 10 and

50 mm of rain, respectively according to the following formula:

𝐿 = (𝜋𝑟2) · 𝑚

where r is the radius of the soil column, 𝜋 = 3.14 and m is the precipitation amount in cm.

Following a single rain event, the columns were covered with aluminum foil to avoid

light penetration, so that photosynthetic microorganisms would be inhibited (Figure 1B),

and in incubators for 28 days under constant temperature of 25 or 36oC, or a day/night

cycle of 36/10oC (Table 1).

3.3. Soil sampling from the columns

The soil columns were sampled 0.5, 1.5 and 3 days intervals after the rain event, and

then once a week up to four weeks. Samples from the homogenate prior to packing into the

columns were considered as the baseline soil prior to treatment (time 0). Soil samples were

removed with a sterile spatula through the sampling ports (Figure 1A). At each sampling

time soil was collected from three levels of the column, at 4, 8 and 12 cm from the soil

surface. A total of 15-20 g of soil was removed from the sampling ports of each level and

mixed. For physicochemical analyses, the samples obtained from each level of the column

were combined and approximately 10 g of the mixture were used for all analyses, except

for TC determinations for which the three levels were combined. For molecular analyses, 7-

10 g soil from each level were suspended in RNA later (see Appendix 1) at 1:1 ratio, for

better stabilization of cellular RNA and stored at -80°C; prior to analysis, equal amounts of

the stored samples from the three different levels of each column were combined in one.

15

(A) (B)

Figure 1. (A) Soil column and rain simulator. 1- DDW; 2-peristaltic pump; 3- rain simulator; 4-

column; 5, 6 and 7- sampling ports at 4, 8 and 12 cm from the soil surface, respectively; 8- ceramic

filter (inside the column). (B) The three microcosm systems: 1, 2, 3- replicate columns; 4- water

trap; 5- vacuum pump.

Table 1. Conditions applied to the soil columns and estimated average water loss during the 28-day

experiments.

Experiment Temperature

(°C)

Rain event

(mm)

Field

collecting

date

Average

desiccation rate:

water loss per day

(%)

I 25 50 04.06.2012 3.7

II 25 10 15.07.2012 4.4

III 36 /10

(day / night) 50 14.09.2012 8.6

IV 36 50 19.10.2012 11.0

16

3.4. Soil physicochemical analyses

Soil physicochemical properties were determined according to standard methods

(SSSA, 1996) and are described in brief. Soil water content was determined by gravimetry.

Electrical conductivity (EC) and pH were measured with EC electrodes and pH meter,

respectively, in saturated soil extract. Nitrogen as nitrate and nitrite was determined in KCL

solution extract. Nitrite was determined by colorimetric method (HCL, sulfanilamide and

ethylenediamine dihydrochloride mix) using an Infinite M200, (Tecan) spectrophotometer.

Nitrate was measured by the second derivative method (APHA, 2005) using a BioMate 5

(Thermo Electron) spectrophotometer. Total carbon (TC) was measured in a CHNS/O

Elemental Analyzer (Flash 2000, Thermo).

Desiccation rates were calculated by the equation:

𝜔 = 𝜔0 × ℮−𝑘𝑡

where k is the desiccation rate, t is absolute temperature, and ω0 and ω are the initial and

the final water content, respectively (Kodikara et al., 2000). Here it is assumed that a)

desiccation follows a negative exponential curve, and b) desiccation rate is constant for a

given treatment.

3.5. RNA extraction and cDNA synthesis

Total nucleic acids were extracted by bead beating the soil in the presence of

phosphate buffer, 10% sodium dodecyl sulfate and phenol according to Angel et al. (2012)

(see http://www.nature.com/protocolexchange/protocols/2484 for the full protocol). The

obtained RNA was purified using RNA purification kit (Epicenter) according the

manufacturer’s instructions. The RNA was reverse-transcribed to cDNA using ImProm-

II™ Reverse Transcriptase (Promega) in the presence of Recombinant RNasin

17

Ribonuclease Inhibitor (Promega) following the manufacturer’s protocol; the resulting

cDNA was purified using PCR purification kit (Bioneer) and quantified

spectrophotometrically using Nanodrop.

3.6. Bacterial abundance

The abundance of the Bacteria, Actinobacteria and Firmicutes populations were

quantified using group specific qPCR assays targeting the 16S rRNA encoding gene. All

qPCR reactions were performed in an iCycler thermocycler equipped with a MyiQ

detection system (Bio-Rad) the data were processed using Bio-Rad CFX Manager 3.0

software (Bio-Rad). The quantifying assays were based on SYBR Green I quantification.

For all assays, standards containing known number of copies of the target gene were

used. Standards were serially diluted and used for construction of calibration curves for

each qPCR reaction plate. The standards used to determine the abundance were cloned

fragments of the 16S rRNA encoding genes obtained from amplifying and cloning the

DNA from Streptomices griseus to quantify the Bacteria and Actinobacteria. Amplified

fragments from Bacillus subtilis were used to quantify the Firmicutes. The primers used in

this study are detailed in Table 2.

18

Table 2. Primers used in this study.

Target Primer Sequence (5’-3’) Source

16S rRNA

(Bacteria)

S-D-Bact-0341-a-S-171 CCTACGGGAGGCAGCA(I)* Klindworth et

al. (2012)

S-*-Bact-0515-a-S-19 TTACCGCGGCTGCTGGCAC

S-D-Bact-0907-a-S-20 CCGTCAATTCMTTTGAGTTT

(I)*

16S rRNA

(Actinobacteria)

S-C-Act-235-a-201 CGCGGCCTATCAGCTTGTT

G

Stach et al.

(2003)

Act1200R TCRCCCCACCTTCCTCCG Bacchetti de

Gregoris et al.

(2011)

S-Bact-0515-a-S-19 TTACCGCGGCTGCTGGCAC Klindworth et

al. (2012)

Firmicutes 928F-Firm

TGAAACTYAAAGGAATTGA

CG

Bacchetti de

Gregoris et al.

(2011) 1040FirmR ACCATGCACCACCTGTC

1Primers used for amplification of T-RFLP-based amplicons were modified by the addition of inosine at the 3'

end in an attempt to broaden their target scope (Ben-Dov et al., 2006) and by the addition of FAM.

Preliminary PCR analysis were performed for all primer pairs used to insure the

specificity of the primers in polymerase chain reaction before performing the qPCR assays.

The primer pair used for amplifying Bacteria were S-D-Bact-0341-a-S-17 and S-*-Bact-

0515-a-S-19 (Klindworth et al., 2012). The primer pair used for amplifying Actinobacteria

were S-C-Act-235-a-20 and S-*-Bact-0515-a-S-19 (Stach et al., 2003). The primers used to

amplify the Firmicutes 16S rRNA were 928F-Firm and 1040FirmR (Bacchetti de Gregoris

et al., 2011). Primers were selected in accordance with RDP (http://rdp.cme.msu.edu/) and

SILVA (http://www.arb-silva.de/) databases.

19

For evaluating Bacteria and Actinobacteria each qPCR reaction contained the

following mixture: 10 μl of SYBR Absolute Blue qPCR Rox Mix (Thermo), 1 μl of 400

nM of each primer (Metabion), 5 μl of template cDNA and 3 μl of molecular grade water

(HyLab) assays. To quantify the Firmicutes population, slightly different reaction

conditions were used: duplicates of 25 μl were used for each qPCR reaction containing:

12.5 μl of SYBR Absolute Blue qPCR Rox Mix (Thermo), 0.5 μl of 250 nM of each primer

(Metabion), 5 μl of template and 6.5 μl of molecular grade water. Each reaction was

repeated at least twice.

Bacterial abundance estimation was performed under the following conditions: 95°C

for 15 min, followed by 35 cycles of 95°C for 10 sec, 60°C for 15 sec and 72°C for 30 sec

for extension. Actinobacteria abundance estimations conditions were: 95°C for 15 min,

followed by 35 cycles of 95°C for 45 sec, 63°C for 45sec and 72°C for 45 sec. Firmicutes

abundance estimation conditions were: 95°C for 15 min, followed by 35 cycles of 95°C for

10 sec, 65°C for 15 sec and 72°C for 45 sec. The reliability of quantification was evaluated

using a melting curve at 65-95°C.

3.6.1. qPCR data analysis

Temporal changes in abundance of Bacteria, Actinobacteria and Fermicutes

communities were assessed by ANOVA test with lm function in Stats package V.3.0.1. The

abundance measurements for all three communities were log-transformed to meet the

assumption of normality of the residuals and homogeneity of variances among groups

(defined as different time points). The assumptions of normality and homoscedasticity of

the residuals were tested by Shapiro-Wilks (Royston, 1995) and Bartlett tests (Bartlett,

1937) with shapiro.test and bartlett.test functions, respectively, in Stats package V.3.0.1.

20

Soil samples were independently taken from three replicate columns, thus the assumption

of independency of samples was not violated. Since after the data transformation the

assumption about the normality of the residuals was still violated, a non-parametric

equivalent Kruskal-Wallis test (Myles and Wolfe, 1973) was performed instead of ANOVA

in Stats package V.3.0.1 using kruskal.test function.

3.7. Community composition analysis

3.7.1. Terminal restriction fragment length polymorphism (T-RFLP) analysis

Bacteria and Actinobacteria community fingerprints of soil samples were obtained by

T-RFLP analysis. This method was first described by Liu et al. (1997) and is a high

throughput molecular technic frequently used for this purpose (Kirk et al., 2004). In the T-

RFLP method, fluorescent labeled PCR-amplicons of the target gene are generated and

subjected to a restriction reaction, normally using four-cutter restriction enzyme and

analyzed with a sequencer. Since the amplicons are labeled at their 5’ end, only the

terminal fragments of a restriction digests are detected by the sequencer (Tiedje et al.,

1999).

PCR amplification of the 16S rRNA-encoding gene using the cDNA as template was

performed following Angel et al. (2010). The primers that were used to target the 16S

rRNA encoding gene were selected in accordance with RDP (http://rdp.cme.msu.edu/) and

SILVA (http://www.arb-silva.de/) databases and are listed in Table 2.

The PCR reactions were carried in a thermocycler (Biometra) in triplicates to

minimize the reaction bias of the PCR. Each PCR mixture (50 μl) contained: 5 μl of Dream

Taq 10x buffer (Thermo), 2.5 mM MgCl2, 5 μl of bovine serum albumin solution (New

21

England Biolabs), 1000 nM of each PCR primer (HyLab) and 0.25 mM of each DNTP and

8 ng of cDNA as template.

The amplification reactions for Bacteria 16S rRNA encoding gene were carried under

the following conditions: 95°C for 5 min, followed by 30 cycles of 94°C for 45 sec, 45°C

for 45 sec and 72°C for 45 sec, and then 72°C for 10 min. For Actinobacteria the reactions

conditions were: 95°C for 5 min, followed by 30 cycles of 94°C for 45 sec, 58°C for 45

sec, 72°C for 1 min and 72°C for 45 sec. Aliquots of 5 μl of the PCR products were

visualized on 1% agarose gel (Sigma) using gel electrophoresis (Bio-Rad) to confirm

successful amplification.

Before the digestion with restriction enzymes, the amplified DNA samples were

combined and treated with Mung bean exonuclease (TaKara) according to the

manufacturer’s instructions, in order to eliminate the single-stranded amplicons that might

result in pseudo terminal restriction fragments (Egert et al., 2003). The purified PCR

products were digested with the restriction enzymes TaqI (TaKara) and HpyCH4IV (NEB)

for samples amplified using the bacterial primers S-D-Bact-0341-a-S-17-FAM and S-D-

Bact-0907-a-S-20 (Klindworth et al., 2012). The Actinobacteria rRNA-based amplicons

were digested with HhaI and HapII (TaKara) restriction enzymes for samples amplified

using primers S-C-Act-235-a-20-FAM and Act1200R (Table 2). Digestions were

performed according to the manufacturers’ instructions, and were followed by purification

using SigmaSpin™ Post-Reaction Clean-up Columns (Sigma). The samples were then

mixed with HiDi (Applied Biosystems) and MapMarker 1000 (Bioventures), and analyzed

with an ABI Prism® 3100 genetic analyzer (Applied Biosystems).

22

3.7.2. T-RFLP data analysis

Electropherograms were retrieved using Peak ScannerTM software V.1.0. (Applied

Biosystems). T-RFs with size below 50 bp and above 600 bp were removed due to the

ladder restrictions. Samples that had less than 18 (Bacteria) and 5 (Actinobacteria) such T-

RFs were removed from further statistical analyses. The true peaks were defined as those

with height standard deviation of more than 2 degrees from one another and T-RFLP

fragment sizes were rounded to the nearest integer; peak heights were expressed as

percentage of all the peaks present in a given sample.

3.7.2.1. Shannon-Weaver diversity index analysis

The bacterial community diversity at each time point within an experiment was

calculated using Shannon-Weaver (or Shannon-Wiener) diversity index (H’), which is a

measure of the amount of information (entropy) in the system and hence is a measure of the

difficulty in predicting the identity of the next individual sampled (Krebs, 1989).

H’ index was calculated using diversity function, Vegan package V.2.0.8. (Oksanen

et al., 2013) of the R Software V.3.0.1. H’ index takes into account both abundance and

evenness of species present in the given community and is expressed as:

𝐻’ = − ∑ 𝑝𝑖 (𝑙𝑜𝑔𝑏𝑝𝑖)

where pi is the proportional abundance of species i and b is the base of the logarithm. In this

study the H’ index was calculated taking b as 2.

One-way ANOVA test was performed to test the effect of time (having eight levels

as 0 (before the rain event) and 0.5, 1.5, 3, 7, 14, 21, 28 days after the single rain event) on

the Shannon-Weaver diversity index within each experiment. The test was performed with

23

lm function (Wilkinson et al., 1973; Chambers, 1992) of R programming environment

V.3.0.1. The assumptions of normality and homoscedasticity of the residuals were tested by

Shapiro-Wilks (Royston, 1995) and Bartlett (Bartlett, 1937) tests with shapiro.test and

bartlett.test functions, respectively, in Stats package V.3.0.1. Soil samples were

independently taken from three replicate columns, thus the assumption of independency of

samples was not violated.

Also, t-test (with t.test function in Stats package V.3.0.1.) pairwise test was

performed using each restriction enzyme separately to test the difference of bacterial and

actinobacterial communities’ diversity among experiments. The normality assumption was

checked by Shapiro-Wilks normality test and the equal variance assumption was tested

using var.test function, in Stats package V.3.0.1. Since the data violates the assumptions of

the t-test (mostly the normality assumption and in few cases the variance equality

assumption as well), the non-parametric analysis, Mann-Whitney test (Bauer, 1972), was

performed using wilcox.test function in Stats package V.3.0.1. of R software environment.

3.7.2.2. Stacked bar graphs

Stacked bars were portrayed to illustrate the bacterial community composition change

within each experiment, i.e. between time points of experiments. To that end barplot

function, Graphics package of the R Software V.3.0.1. was used (Murrell, 2005).

3.7.2.3. Nonmetric multidimensional scaling

Multivariate analysis was performed with metaMDS function, Vegan package

V.2.0.8. (Oksanen et al., 2013) of the R Software V.3.0.1. (http://www.r-project.org/).

Ordinations were generated with nonmetric multidimensional scaling (NMDS) (Taguchi et

24

al., 2004) using 500 iterations. In this method the similarity data is ordinated using an

iterative algorithm that takes the multidimensional data of a similarity matrix and presents

it in minimal dimensional space, typically two dimensions. The result of MDS ordination is

a map where the position of each sample is determined by its distance from all other points

analysed. Also, the NMDS plot can be rotated resulting the first axes to contain most of the

variance (Oksanen et al., 2013). Since MDS ordination is an iterative algorithm that

involves a ‘goodness of fit’ estimate, an important component of an MDS plot is a measure

of the goodness of fit of the final plot. In the case of an MDS ordination, the latter is termed

the ‘stress’ of the plot. A stress value greater than 0.2 indicates that the plot is close to

random, stress less than 0.2 indicates a useful 2 dimensional picture and less than 0.1

corresponds to an ideal ordination with no real prospect of misinterpretation (Clarke, 1993).

Significance of the environmental variables was assessed using Monte-Carlo

permutation against 999 random data sets. Differences between sample groups were

calculated by multi-response permutation procedure (MRPP) (Mielke et al., 1981) with

mrpp function, Vegan package V.2.0.8. (Oksanen et al., 2013) of the R Software V.3.0.1.

The MRPP test is based on the assumption that if two groups are different from each other,

the average within-group difference will be smaller than the average between-group

distance. The "effect size" of the difference between data groups is represented by the A-

statistic of the MRPP test (MRPP-A), while its significance is identified by the MRPP's P-

value. MRPP-A ranges from zero, meaning that data points are randomly distributed (i.e.

the two tested groups are homogenous), to one, meaning that data point distribution is

wholly determinate (i.e. the two groups are completely separate); therefore, we are able to

compare MRPP-A values obtained from different MRPP tests conducted with different data

sets.

25

3.7.2.4. Dissimilarity analysis

In NMDS analysis community dissimilarity was based on the Bray-Curtis index,

which includes the presence and relative abundance of T-RFs, which is defined as

𝐵𝐶𝑗𝑘 = 1 −∑ |𝑦𝑖𝑗 − 𝑦𝑖𝑘|𝑛

𝑖=1

∑ (𝑦𝑖𝑗 + 𝑦𝑖𝑘)𝑛𝑖=1

where 𝐵𝐶𝑗𝑘 is the Bray-Curtis dissimilarity between groups j and k, yij and yik are the height

of peak i in j and k groups, respectively, and n is the number of peaks summed over both

groups (Rees et al., 2004).

26

Chapter 4. Results

4.1. Soil physicochemical characteristics during hydration-desiccation

experiments

In each experiment the soil was sampled from three different depths of the columns

(4, 8 and 12 cm from the soil surface) and was tested for WC, EC, pH, NO2-N, NO3-N, and

TC. Then the results were analyzed to find whether samples collected concurrently from the

different depths were significantly different, using one-way ANOVA.

Table 3. One-way ANOVA analysis of water content (WC), pH, electrical conductivity (EC),

nitrate (NO3-N) and nitrite (NO2-N) measurements in soil samples collected from different depths

of the soil column (4, 8 and 12 cm) during the 28 days of hydration and desiccation. Assumptions of

the ANOVA test were not violated: independence of samples, normal distribution of the residuals

(Shapiro-Wilk test, p > 0.05), and homogeneity of variances among groups (Bartlett test, p > 0.05).

Rain: 50 mm

Temp: 25°C

Rain: 10 mm

Temp: 25°C

Rain: 50 mm

Temp: 36/10°C

Rain: 50 mm

Temp: 36°C

WC (%) F(2,21) = 0.02

p = 0.98

F(2,21) = 2.11

p = 0.15

F(2,21) = 0.15

p = 0.86

F(2,20) = 0.16

p = 0.85

pH F(2,21) = 0.48

p = 0.62

F(2,21) = 0.11

p = 0.8

F(2,21) = 0.52

p = 0.60

F(2,21) = 0.09

p = 0.92

EC (dS/m) F(2,21) = 0.13

p = 0.88

F(2,20) = 0.21

p = 0.82

F(2,21) = 0.09

p = 0.91

F(2,17) = 0.06

p = 0.94

NO2-N

(mg/kg soil)

F(2,21) = 0.02

p = 0.98

F(2,19) = 1.93

p = 0.17

F(2,12) = 0.16

p = 0.85

F(2,21) = 0.23

p = 0.79

NO3-N

(mg/kg soil)

F(2,18) = 0.13

p = 0.88

F(2,21) = 0.82

p = 0.46

F(2,12) = 0.42

p = 0.66

F(2,12) = 0.69

p = 0.52

*In the experiment where 10 mm of rain was applied, soil samples collected at 1.5 and 3 days from

the 4 cm depth were considered as outliers in order to meet the assumption of homogeneity of

variances.

The ANOVA results (Table 3) suggest that none of the physicochemical parameters

significantly differed (p > 0.05) due to column sampling level (Supplementary Figure 1).

27

Thus, the molecular analyses were performed on composites of the samples collected from

the three different depths considering the latter as biological replications. Moreover, as

there were no significant differences between the values of physicochemical parameters,

samples from the different depths of each soil column, collected at the same time point,

were pooled together. Thus, the results presented in Table 4 are the average measurements

of the three replicate columns ± SD (n = 9).

The results show that: (I) the simulated rain events had no marked effect on the soil

pH; the Negev soil is basic as it is rich in calcium carbonate (Brady and Weil, 2001), and

the pH values measured ranged between 7.8 and 8.5; (II) the EC of the soil was also

unaffected by the simulated rain and measures were all below 0.3 dS/m, with no major

differences between experiments; (III) measures of TC, nitrite and nitrate showed

fluctuations throughout the experiments (Table 4).

The initial water content in the soil was very low at 0.95% ± 0.21 and, as expected,

increased following rain simulation, to approximately 16 to 19% after 0.5 days with 50 mm

precipitation, and 5% after 10 mm (Table 4). The overall desiccation rate (Table 1) was

strongly dependent on the temperature, being much higher at 36°C than at 25°C (Figure 2,

Table 1). Another parameter that affected the overall desiccation rate was the amount of

water used for the rain simulation. With time, a decrease in water content was observed

under all the conditions tested, more pronounced during days 2 to 5, and more moderated

thereafter (Figure 2).

28

Table 4. Soil physicochemical parameters: water content (WC), pH, electrical conductivity (EC),

total carbon (TC), nitrite (NO2-N) and nitrate (NO3-N).

*NA- not available, **BD- below detection level.

29

4.2. Temporal dynamics of bacterial abundance during hydration-desiccation

experiments

Temporal changes in the abundance of Bacteria, Actinobacteria and Firmicutes were

assessed using amplification and detection of the 16S rRNA encoding gene by qPCR.

Figure 3 illustrates the results obtained in each experiment for Bacteria, Actinobacteria, and

Firmicutes, as well as the soil water content at each of the sampling times.

The results suggest that under all the conditions tested gene copies of Bacteria,

Actinobacteria and Firmicutes did not vary significantly with time (Figure 3; Table 5),

except for abundance of Firmicutes at 36°C temperature and 50 mm precipitation which

differed significantly with time (KW(6) = 0.19, p = 0.02) (Table 5).

Figure 2. Water content in the 4-12 cm

depth section of soil columns

incubated under different experimental

conditions. Values are the average of

independent replicates from combined

depths of the column for each time

point ± SD (n = 3).

30

Figure 3. Community size dynamics of Bacteria, Actinobacteria and Firmicutes upon hydration

and desiccation of soil columns. Values are average of independent biological replicates ± SD (n = 3

for Bacteria and Actinobacteria, and n = 2 for Firmicutes). Time 0 corresponds to soil prior to the

rain event.

Table 5. Kruskal-Wallis non parametric test of the temporal changes in total abundance of Bacteria,

Actinobacteria and Firmicutes in soil columns subjected to different treatments. Assumption of

independency of samples and homogeneity of variances among groups (Bartlett test, p > 0.05) were

not violated.

Rain: 50 mm

Temp: 25°C

Rain: 10 mm

Temp: 25°C

Rain: 50 mm

Temp: 36/10°C

Rain: 50 mm

Temp: 36°C

Bacteria KW(7) = 9.17

p = 0.24

KW(5) = 10.29

p = 0.07

KW(6) = 9.71

p = 0.13

KW(6) = 11.7

p = 0.07

Actinobacteria KW(7) = 6.84

p = 0.44

KW(6) = 12.35

p = 0.07

KW(5) = 12.00

p = 0.06

KW(6) = 13

p = 0.04

Firmicutes KW(7) = 14.4

p = 0.05

KW(7) = 9.77

p = 0.13

KW(6) = 11.79

p = 0.06

KW(6) = 0.19

p = 0.02

31

Although the overall abundance of the three bacterial groups was mostly stable

throughout each experiment, the higher hydration (50 mm rain) instigated a decrease in the

community size, which was detected at the first sampling time, 12 hours after the initial

hydration. This decrease was followed by an increase in abundance as the soil water

content decreased (Figure 3). Yet, when the amount of rain was low (i.e., 10 mm) the

changes in abundance were less pronounced (Figure 3B).

4.3. Temporal dynamics of bacterial diversity during hydration-desiccation

experiments

Shannon-Weaver diversity indices (H’) were calculated for Bacteria and

Actinobacteria for all samples collected during the experiments and for all the restriction

enzymes used (Figure 4). The community was diverse with estimated H’ values between 2

and 3, but only minor temporal changes were detected. The significance of the changes

observed was tested further using one-way ANOVA and was found to be insignificant both

for Bacteria (p > 0.05) and Actinobacteria (p > 0.05), indicating steady temporal diversity

which was unaffected by the hydration-desiccation events (Table 6).

Table 6. ANOVA test of the Shannon-Weaver diversity indices calculated for all soil samples of

each experiment. Assumptions of the ANOVA were not violated: independence of samples, normal

distribution of the residuals (Shapiro-Wilk test, p > 0.05) and homogeneity of variances among

groups (Bartlett test, p > 0.05).

Rain: 50 mm

Temp: 25°C

Rain: 10 mm

Temp: 25°C

Rain: 50 mm

Temp: 36/10°C

Rain: 50 mm

Temp: 36°C

Bacteria-

TaqI

F(7,17) = 0.93

p = 0.506

F(6,10) = 1.84

p = 0.188

F(6,13) = 0.32

p = 0.915

F(7,15) = 2.62

p = 0.056

Bacteria-

HpyCH4IV

F(6,16) = 1.00

p = 0.461

F(6,11) = 2.36

p = 0.103

F(6,12) = 0.52

p = 0.785

F(7,14) = 2.55

p = 0.064

Actinobacteria-

HapII

F(7,16) = 1.01

p = 0.458

F(6,13) = 1.20

p = 0.364

F(6,12) = 2. 783

p = 0.062

F(7,16) = 1.01

p = 0.458

Actinobacteria-

HhaI

F(7,14) = 0.77

p = 0.625

F(6,12) = 1.95

p = 0.153

F(5,9) = 0.62

p = 0.686

F(7,10) = 2.06

p = 0.145

32

The H’ values of the four experiments were plotted for each restriction enzyme

separately to detect the temporal dynamics of the diversity within and between the different

treatments. Little fluctuations but not major differences were observed between the

diversity of the Bacteria and Actinobacteria communities’ composition upon hydration and

desiccation of different experimental conditions (Figure 4; Supplementary Figure 2).

Figure 4. Shannon-Weaver diversity indices of (A) Bacteria using restriction enzyme TaqI and (B)

Actinobacteria, using restriction enzyme HapII

4.4. Temporal dynamics of bacterial community composition during hydration-

desiccation experiments

4.4.1. Bacterial community composition changes within experiments

NMDS analyses were performed to validate the evenness of the starting conditions.

To that end the community composition pattern of active bacterial and actinobacterial

fingerprints were compared between samples taken before and after the treatments were

applied. According to the NMDS graphs both Bacteria (Supplementary Figure 3A and B)

and Actinobacteria (Supplementary Figure 3C and D) communities were initially similar in

all experiments and changed following the applied treatments. The NMDS analyses were

33

confirmed by the MRPP tests for HpyCH4IV (A = 0.01758, p = 0.012) and HapII (A =

0.01088, p = 0.041) but not for TaqI (A = -2.295e-0.5, p = 0.442) and HhaI (A= 0.004511,

p = 0.182).

Stacked bar graphs of active Bacteria and Actinobacteria operational taxonomic units

(depicted by the TRFs) were plotted in order to demonstrate changes in their community

composition within each experiment (Figures 5 and 6; Supllementary Figures 4 and 5). As

mentioned above, the community composition of active bacterial and actinobacterial

fingerprints prior to the rain event mostly start with similar pattern in each of the

experiment and change throughout time after the rain event. In some cases (Figure 5B and

D; Figure 6B and D) the community composition at the end point resembled the initial

community (before hydration).

34

Figure 5. Bacterial community composition dynamics within time in different microcosms

experiments. (A) Rain 50 mm, Temp 25oC; (B) Rain 10 mm, Temp 25oC; (C) Rain 50 mm,

Temp 36/10oC; (D) Rain 50 mm, Temp 36oC. The segments represent abundance of each peak

(OTU) as percentage of all the peaks present in a given sample.

35

Figure 6. Actinobacterial community composition dynamics within time in different

microcosms experiments. (A) Rain 50 mm, Temp 25oC; (B) Rain 10 mm, Temp 25oC; (C) Rain

50 mm, Temp 36/10oC; (D) Rain 50 mm, Temp 36oC. The segments represent abundance of

each peak (OTU) as percentage of all the peaks present in a given sample.

36

4.4.2. Bacterial community composition changes between experiments

The fingerprint patterns of the Bacteria and Actinobacteria communities were

estimated using T-RFLP analysis. A fragment of the small subunit 16S rRNA encoding

gene was amplified and digested by two restriction enzymes for Bacteria and for

Actinobacteria. Figures 7 and 8 summarize the results obtained by digesting the amplicons

with the restriction enzymes TaqI for Bacteria and HhaII for Actinobacteria; the results for

the analyses conducted by digesting the amplicons with the restriction enzymes HpyCH4IV

for Bacteria and HhaI for Actinobacteria are presented in Supplementary Figures 6 and 7.

The statistical analyses of all T-RFs are presented in Table 7.

Non-metric multidimensional scaling (NMDS) analyses were performed to compare

the community composition pattern of the Bacteria and Actinobacteria communities under

the different environmental conditions. Pairs of experiments were compared for effects of

hydration intensity and temperature on the communities (Figures 7 and 8 for Bacteria and

Actinobacteria, respectively).

37

Figure 7. Non-metric multidimensional scaling (NMDS) ordinations of active Bacteria

communities in arid soil upon hydration and desiccation. The different set of conditions in the

microcosm experiments (see Table 1) are marked by color. Black- Rain 50 mm, Temp 25oC;

red- Rain 10 mm, Temp 25oC; green- Rain 50 mm, Temp 36/10oC; blue- Rain 50 mm, Temp

36oC. Axes represent distance with ideal (B, C) and almost ideal (A, D) ordination.

38

Table 7. MRPP pairwise test between different experimental conditions. P and A values are based

on Bray-Curtis distance measure of dissimilarity matrixes.

Temp: 25°C

Rain: 10 mm

vs. 50 mm

Rain: 50 mm

Temp: 25°C vs.

36/10°C

Rain: 50 mm

Temp: 25°C vs.

36°C

Rain: 50 mm

Temp: 36°C vs.

36/10°C

Bacteria-

TaqI

A** = 0.0723

p = 0.001

A** = 0.1167

p = 0.001

A* = 0.0383

p = 0.019

A = 0.0165

p = 0.055

Bacteria-

HpyCH4IV

A** = 0.0667

p = 0.001

A** = 0.0584

p = 0.001

A* = 0.0313

p = 0.009

A = 0.0141

p = 0.090

Actinobacteria-

HapII

A** = 0.0830

p = 0.001

A** = 0.0569

p = 0.003

A* = 0.0469

p = 0.005

A = 0.0162

p = 0.061

Actinobacteria-

HhaI

A* = 0.0830

p = 0.037

A** = 0.0571

p = 0.002

A* = 0.0388

p = 0.007

A* = 0.0290

p = 0.036

* Moderate significance (p < 0.05)

** High significance (p < 0.005)

The significance was based on MRPP analysis of active Bacteria and Actinobacteria

fingerprints, depending on the A-value that explains the separation level of compared

groups based on Bray-Curtis distance (Table 7). Very similar significances were obtained

also with MRPP test based on Euclidian distance (Supplementary Table 2; Appendix 2).

Bacteria fingerprint patterns were significantly different (Table 7) when soils were

differently hydrated but incubated at the same temperature (Figure 7A; Table 7). In the

soils treated with equivalent rain amounts (50 mm) significant differences were detected in

the Bacteria community composition patterns at different temperatures (Figure 7B and C;

Table 7). However, no significant differences were detected in the community composition

of Bacteria fingerprints due to the diurnal temperature cycle (Figure 7D; Table 7). The

higher A-values between the soil community patterns receiving 10 mm vs. 50 mm rain (A =

0.0723) and the ones in soils incubated at the different temperatures of 25°C and 36/10°C

(A = 0.1167) suggest a more significant separation between the compared community

patterns as shown in the cluster analyses (Figures 7A and B).

39

High significant differences were detected between the Actinobacteria fingerprint

patterns (Figure 8A; Table 7) when the soil microcosms were hydrated with 10 or 50 mm of

rain and incubated at the same temperature of 25°C. High significant differences were also

observed in soils receiving 50 mm rain and incubated at different temperatures of 25°C vs.

36/10°C (Figure 8B; Table 7). Lower but still significant differences were detected in soils

receiving 50 mm of rain but incubated at temperatures of 25°C vs. 36°C (Figure 8C; Table

7). Yet, no differences were detected when the microcosms were incubated with and

without the circadian cycle (Figure 8D; Table 7).

40

Figure 8. Non-metric multidimensional scaling (NMDS) ordinations of active Actinobacteria

communities in arid soil microcosms upon hydration and desiccation. The different set of

conditions in the microcosm experiments (see Table 1) are marked by color. Black- Rain 50

mm, Temp 25oC; red- Rain 10 mm, Temp 25oC; green- Rain 50 mm, Temp 36/10oC; blue- Rain

50 mm, Temp 36oC. Axes represent distance with almost ideal (A, B, C, D) ordination.

41

Chapter 5. Discussion

The factors shaping bacterial diversity and community composition in arid soils are

largely unknown. Surveys of soil bacteria community composition in general suggested

that pH (Lauber et al., 2008; Lauber et al., 2009; Fierer and Jackson, 2006) salinity

(Lozupone and Knight, 2007) and water content (Angel et al., 2010; Placella et al., 2012;

Barnard et al., 2013) shape the community composition, but would bacteria found in bulk

soils of arid environments obey the same rules? A spatial study of the bacterial composition

in arid and semi-arid soils suggested that precipitation plays an important role in the

community pattern (Angel et al., 2010). Moreover, a study comparing the patterns

portrayed by the total (DNA-based) and active (RNA-based) communities, suggested that

the latter better reflects fluctuation in the community than the former (Blazewitcz et al.,

2013). Could that suggest that in arid soils the main parameter shaping soil bacterial

community composition is precipitation?

The current work aimed to elucidate the effects of rain and desiccation rate on the

abundance and community composition of the soil bacteria in general, and of

Actinobacteria in particular. To this end, microcosms of desert soil were designed, in the

form of columns, which were constructed and packed with soil and subjected to different

amounts of rainfall and desiccation rate associated with temperature and diurnal cycle.

Subsamples from the microcosms’ soil were collected during the hydration-desiccation

cycle and the abundance, diversity and community composition of the soil bacterial

community as well as the physicochemical characteristics of the soil were monitored.

42

5.1. Soil physicochemical characteristics during hydration and desiccation

The Negev soil tested is not saline but it is basic and rich in calcium carbonate (Brady

and Weil, 2001). No major differences were observed in physicochemical parameters (pH,

salinity, total carbon, nitrite and nitrate) within and between experiments. None of the

tested physicochemical parameters were significantly affected by difference in sampling

depths at 4, 8 and 12 cm at the different conditions tested. Noticeable changes were

observed in water content throughout each experiment, especially with 50 mm initial rain

event. The dry soil sampled prior to the rain event had low water content (0.98% ± 0.21)

which increased following the rain simulation. The overall desiccation rate in each

experiment was strongly dependent on the temperature: it was much higher at 36°C than at

25°C (Table 1; Figure 2). Another parameter that controlled the overall desiccation rate was

the amount of water used for the rain simulation.

Thus, all physicochemical measurements in this study were in range of values that

may not be considered limiting to bacterial growth (Rietz and Haynes, 2003; Yuan et al.,

2007; Mavi et al., 2010). The fluctuations in the parameters values that were observed may

be attributed to stochasticity and probably have no effect on bacterial activity. The neutral

pH soils of deserts were shown to harbor higher number of bacterial taxa than the acidic

soils of tropical forests (Fierer and Jackson, 2006; Meklat et al., 2013). The pH values

measured in this study show little fluctuations and should have no effect on the bacterial

community.

Values of soil initial water content (prior to the rain event), pH, salinity, total carbon,

nitrite and nitrate are in line with previous studies conducted in the Avdat site at the Negev

43

desert (Angel et al., 2010; Bachar et al., 2010; Sher et al., 2013; Angel et al, 2013) (Tables

3 and 4).

5.2. Temporal dynamics of bacterial abundance during hydration and

desiccation

Under all the conditons tested the community sizes of Bacteria, Actinobacteria and

Fermicutes fluctuated slightly with time, but the changes were not significant throughout

each experiment (p > 0.05) (Figure 3; Table 5). This is in line with studies of Angel and

Conrad (2013), showing that hydration of the Negev soil crust did not affect the community

size of Bacteria, Archaea and Fungi throughout hydration-desiccation cycles. Only the

Firmicutes abundance significantly changed (p = 0.02) in soil incubated at 25°C and 36°C

and hydrated with 50 mm precipitation (Table 5). Although there were mostly no

significant differences throughout each experiment in the abundance of the three bacterial

groups following hydration with 50 mm rain, a slight decrease in the community size was

observed after the initial 12 hours, followed by a slight increase in abundance as the soil

water content decreased.

Extreme summer droughts, when soil water potentials commonly drop drastically

(Kieft et al., 1987), were suggested to cause a significant stress to soil microorganisms

(Potts, 1994) and were associated with minimal activity (Chou et al., 2008). Following a

hydration event and the sudden increase in water availability after the prolonged drought,

microbes were suggested to experience osmotic shock and die, serving as a potential

nutrient source for the cells that have passively equilibrated to the dry soil conditions and

survived (Kieft et al., 1987; Placella et al., 2012). Thus, the soil microbes that survived high

water potential conditions and accumulated organic and inorganic nutrients from dead cells

44

can now rapidly grow (Kieft et al., 1987). These observations are in line with our results:

the abundance of the Bacteria, Actinobacteria and Firmicutes had suddenly dropped during

the first 12 hours after the initial hydration (Figure 3), and then increased with the decrease

in soil water content. This pattern could also correlate with soil aeration. Once the soil is

over-hydrated it could inhibit aerobic microbes; later, desiccation leads to aeration of the

soil and oxygen is again accessible to the bacteria. Barnard et al. (2013) showed that in

California dry grasslands bacterial activity increased throughout the desiccation period.

Moreover, they could not detect significant changes in bacterial abundance 2 hours after

hydration, though it might have been too soon to detect any differences. However, when the

Actinobacteria activity was evaluated an increase in abundance was detected following the

desiccation and a reduction followed immediately (2 hours) after rewetting (Barnard et al.,

2013). It has been shown that these bacteria are able to grow under harsh dry conditions

(Goodfellow and Williams, 1983; Zvyagintsev et al., 2007). Actinobacetria may

accumulate ribosomes during the period of desiccation in anticipation for the next rain

event (Barnard et al., 2013). In contrast to relatively late response of Actinobacteria

described by Barnard et al. (2013), in the Mediterranean soil of California the relative

abundance of Actinobacteria was shown to increase rapidly within 15 minutes to 1 hour

after the hydration (Placella et al., 2012). The Firmicutes abundance did not change

throughout the desiccation period, albeit a slight decrease 2 hours after hydration (Barnard

et al., 2013). However, Placella et al. (2012) suggest that Firmicutes are intermediate

responders to soil hydration; increased activity of Bacilli was observed 3 to 24 hours after

hydration, a time which would have been sufficient for spore outgrowth. Concomitantly,

sequencing analysis of the arid and hyperarid soil crusts of the Negev desert suggested that

45

after one day of hydration the relative abundance of Bacilliales drastically increased and

then decreased over a period of 3 weeks (Angel and Conrad, 2013).

Here, we were unable to detect significant changes in total bacterial abundance

(Table 5; Figure 3), suggesting that although some taxa died, others grew, resulting in

reallocation of resources between bacterial response groups rather than an absolute increase

in bacterial biomass as was previously suggested (Placella et al., 2012).

5.3. Temporal dynamics of bacterial diversity during hydration and desiccation

Temporal fluctuations of soil Bacteria and Actinobacteria OUT’s diversities and their

relation to the experimental environmental factors (rain and temperature) were tested. The

Shannon-Weaver diversity index is a useful general diversity index that is influenced by

both species (OUT’s) richness and evenness, and is more sensitive to changes in abundance

of the rare groups (Hill et al., 2003).

The OTU diversity (H’) values obtained in our study are in the range of values

previously reported by Bachar et al. (2010) and Pereira e Silva et al. (2012) for arid soils.

No significant temporal changes were observed in the diversities of Bacteria nor in

Actinobacteria, in any of the experiments we had conducted (Figure 4). There were also no

noticeable changes in diversity between different experiments. The community composition

of Bacteria and Actinobacteria was experiencing temporal changes (Figures 7 and 8)

following changes in rain amount, incubation temperature and diurnal cycles, yet, the

diversity did not significantly change. This might indicate that there is a succession in the

community in contrast to the pulse-dynamics reported for desert plants during hydration-

desiccation cycles where plants wither and decline during desiccation and peak in biomass

and diversity following rainfall events (Coe et al., 2012). In the case of Bacteria and

46

Actinobacteria there is neither decline nor peak in OUT’s abundance or diversity following

the simulated rain events, which might suggest that the soil cannot support additional

species; as soon as one niche is vacated due to hydration another occupant immediately

takes its place.

5.4. Temporal dynamics of bacterial community composition during hydration

and desiccation

The NMDS analysis suggest that both bacteria and Actinobacteria communities

cluster differently before and after the applied treatments (Supplementary Figure 3) when

analyzed with the restriction enzymes HpyCH4IV and HapII. But when the comunity was

visualized following restriction with TaqI and HhaI no significantly differences were

detected. These might indicate that only members of the community differ due to the

applied treatments: HpyCH4IV and HapII spot these groups, while TaqI and HhaI cannot

point to members of the community that do not respond to the conditions employed in these

experiments. The identity or difference of said groups could be further elucidated by Next

Generation Sequencing type analysis.

Both the Bacteria and Actinobacteria community patterns showed temporal changes

in each of the experiments (Figures 5 and 6). NMDS analysis was performed to compare

the community composition of bacterial and actinobacterial communities following

hydration and desiccation in experiments which differed in desiccation rate resulting from

the amount of rain, temperature and diurnal cycles (Figures 7 and 8). According to the

results obtained the amount of rain significantly affected the community composition of

both Bacteria and Actinobacteria. The fingerprint patterns of both groups were also

significantly different when the soil columns were similarly hydrated but incubated under

47

different temperatures (Figures 7 and 8). Yet, no significant differences were found due to

diurnal temperature cycles.

Different studies conducted in arid and semi-arid environments have shown that

Bacteria, and especially Actinobacteria community patterns and their relative abundance

significantly change in response to hydration (Placella et al., 2012; Angel and Conrad,

2013; Angel et al., 2013; Barnard et al., 2013). As reported earlier, Actinobacteria are

among the groups having rapid response to hydration; their relative abundance increased

during desiccation and decreased 2 hours (Barnard et al., 2013) or 1 day (Angel and

Conrad, 2013) after the hydration. Alternatively, it was reported that Actinobacteria relative

abundance increases rapidly within 15 minutes to 1 hour after the hydration (Placella et al.,

2012). Our study suggests that the total abundance of Actinobacteria was unchanged during

hydration and desiccation, although their relative abundance may have changed.

Temperature directly affects water evaporation and thus the rate of soil desiccation,

yet diurnal cycles seem to have had marginal effect on desiccation. In arid environments

bacterial communities are well adapted to both high temperatures and diurnal fluctuations

in temperatures (Gestel et al., 2013). In this study temperature affected the overall

desiccation rate, especially in the first 2-5 days after the simulated rain event, when the

water content decreased faster at the higher temperature of 36°C. This is in agreement with

the results of Barnard et al. (2013) and indicates that the bacterial community composition

changes when a sudden rain event is applied on dry soil.

48

Chapter 6.

Conclusions

The current study assessed the effects of hydration-desiccation cycles on arid soil

bacterial community composition, diversity and abundance in controlled environments. The

aim of this study was to assess, under controlled laboratory conditions, the dynamics of

active (featuring the rRNA of the community) desert soil bacterial communities during

hydration-desiccation cycles, with special focus on Actinobacteria, the soil’s dominant

bacterial phylum. In addition we monitored some of the physicochemical features of the

soil including water content, pH, salinity, nitrate, nitrite and carbon content. We predicted

that arid soil bacterial communities would emulate plants pulse-dynamic response to

rainfall events exerting strong influence on abundance and diversity. The temporal changes

in arid soil Bacteria and Actinobacteria were monitored following the rain events and

different rates of desiccation.

None of the tested physicochemical parameters of the soil were significantly affected

by the different conditions tested, except for the water content. The bacterial and

actinobacterial diversity and abundance were also unaltered by hydration and desiccation

cycles, while the community composition was significantly changed after the rainfall.

Moreover, rain amount and incubation temperature altered the community, while diurnal

temperature cycles had no effect on the community composition.

The results suggest that the prediction that microorganisms respond to hydration

similarly to macroorganisms might not be valid. The bacterial community did not follow

the pulse-dynamic pattern but might have gone through succession-dynamics where

Bacteria and Actinobacteria died following the rain event, while other bacterial groups

49

grew, resulting in reallocation of resources between bacterial groups rather than an absolute

increase in bacterial biomass.

Future work

To test whether bacteria indeed go through succession in drought-rain-drought cycles,

high-throughput sequencing of the soil samples collected during the experiments should be

performed. The sequencing results will shed further light on the changes in the community

following hydration and desiccation.

50

Chapter 7.

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Supplementary Data

Supplementary Table 1. MRPP pairwise test between samples collected before and after

treatments. P and A values are based on either Bray-Curtis or Euclidean distance measures of the

respective dissimilarity matrixes.

MRPP values

Bray-Curtis distance Euclidean distance

Bacteria- TaqI A = 0.004511

p = 0.182

A = 0.007031

p = 0.12

Bacteria- HpyCH4IV A* = 0.01758

p = 0.012

A* = 0.01553

P = 0.014

Actinobacteria-

HapII

A* = 0.01088

p = 0.041

A*= 0.01046

p = 0.047

Actinobacteria- HhaI A = 0.004511

p = 0.182

A = 0.007031

p = 0.12

* Moderate significance (p < 0.05)

Supplementary Table 2. MRPP pairwise analyses between different experimental conditions. P

and A values are based on Euclidean distance measured of the respective dissimilarity matrixes.

Temp: 25°C

Rain: 10 mm

vs. 50 mm

Rain: 50 mm

Temp: 25°C vs.

36/10°C

Rain: 50 mm

Temp: 25°C vs.

36°C

Rain: 50 mm

Temp: 36°C vs.

36/10°C

Bacteria-

TaqI

A** = 0.0835

p = 0.001

A** = 0.0998

p = 0.001

A* = 0.0347

p = 0.036

A** = 0.0254

p = 0.006

Bacteria-

HpyCH4IV

A** = 0.0693

p = 0.001

A** = 0.0511

p = 0.001

A* = 0.0266

p = 0.007

A = 0.0112

p = 0.124

Actinobacteria-

HapII

A** = 0.0994

p = 0.001

A** = 0.0784

p = 0.001

A* = 0.0450

p = 0.005

A = 0.0104

p = 0.099

Actinobacteria-

HhaI

A* = 0.0181

p = 0.072

A* = 0.0477

p = 0.005

A* = 0.0253

p = 0.038

A = 0.0185

p = 0.083

* Moderate significance (p < 0.05)

** High significance (p < 0.005)

66

Supplementary Figure 1. Values of (A) soil water content (%), (B) pH and (C) EC (ds/m) at the

depths of 4, 8 and 12 cm depth, under the different conditions of temperature and rain amount.

67

Supplementary Figure 2. Shannon-Weaver diversity indices of (A) Bacteria using restriction

enzume HpyCH4IV and (B) Actinobacteria, using restriction enzyme HhaI.

68

Supplementary Figure 3. Non-metric multidimensional scaling (NMDS) ordinations of active

bacterial (A and B) and actinobacterial (C and D) communities in arid soil microcosms before

and after the rain event. The different experimental conditions are marked by colors defining

the grouping of the samples by time-factor. NT- samples with no treatment which were collected

before the rain event, i.e. samples from the 0-time point. AR- samples collected from all four

experiments and all three columns after the rain event for each of the restriction enzyme used.

Axes represent distance with almost ideal (A, B, C, D) ordination.

69

Supplementary Figure 4. Bacterial community composition dynamics within time in different

microcosms experiments. (A) Rain 50 mm, Temp 25oC; (B) Rain 10 mm, Temp 25oC; (C) Rain

50 mm, Temp 36/10oC; (D) Rain 50 mm, Temp 36oC. The segments represent abundance of

each peak (OTU) as percentage of all the peaks present in a given sample.

70

Supplementary Figure 3. Actinobacterial community composition dynamics within time in

different microcosms experiments. (A) Rain 50 mm, Temp 25oC; (B) Rain 10 mm, Temp 25oC;

(C) Rain 50 mm, Temp 36/10oC; (D) Rain 50 mm, Temp 36oC. The segments represent

abundance of each peak (OTU) as percentage of all the peaks present in a given sample.

Supplementary Figure 5. Actinobacterial community composition dynamics within time in

different microcosms experiments. (A) Rain 50 mm, Temp 25oC; (B) Rain 10 mm, Temp 25oC;

(C) Rain 50 mm, Temp 36/10oC; (D) Rain 50 mm, Temp 36oC. The segments represent

abundance of each peak (OTU) as percentage of all the peaks present in a given sample.

71

1

Supplementary Figure 6. Non-metric multidimensional scaling (NMDS) ordinations of active

Bacteria communities in arid soil microcosms upon hydration and desiccation. The different

experimental conditions (see Table 1) are marked by color, Black- Rain 50 mm, Temp 25oC;

red- Rain 10 mm, Temp 25oC; green- Rain 50 mm, Temp 36/10oC; blue- Rain 50 mm, Temp

36oC. Axes represent distance with almost ideal (A, B, C, D) ordination.

72

Supplementary Figure 7. Non-metric multidimensional scaling (NMDS) ordinations of active

Actinobacteria communities in arid soil microcosms upon hydration and desiccation. The

different experimental conditions (see Table 1) are marked by color: black- Rain 50 mm, Temp

25oC; red- Rain 10 mm, Temp 25oC; green- Rain 50 mm, Temp 36/10oC; blue- Rain 50 mm,

Temp 36oC. Axes represent distance with ideal (A, D) and almost ideal (B, C) ordination.

73

Appendix 1.

Recipe for RNAlater - RNA preservation medium

Overview: This buffer mimics the RNAlater that can be bought from Ambion. Both

DNA and RNA are stable at room temperature in this buffer.

Materials: EDTA disodium, dehydrate; sodium citrate trisodium salt, dehydrate;

ammonium sulfate; ultrapure water; H2SO4 to adjust the pH, if necessary.

Preparation: 40 ml 0.5 M EDTA, 25 ml 1M sodium citrate, 700 g ammonium sulfate

and 935 ml of sterile distilled water were combined and stirred on a hot plate stirrer on low

heat until the ammonium sulfate completely dissolved. The solution was allowed to cool

and its pH was adjusted to 5.2 using 1M H2SO4. The ready solution was transferred to a

screw top sterile bottle and stored either at room temperature or refrigerated.

To make 40 mL of 0.5M EDTA: 7.44 g EDTA were added to 40 mL ultrapure water

and pH was adjusted to 8.

To make 25 mL of 1 M sodium citrate: 5.88 g EDTA were added to 25 mL ultrapure

water.

Precautions: All the flasks and bottles used for preparing or storing the solutions were

sterilized and baked in the oven at 180°C for at least 3 hours.

74

Appendix 2.

Euclidean distance of the T-RFLP profiles of the samples.

The Euclidian distance (ED) between two groups (j and k) based on n variables

(number of peaks) is defined as

𝐸𝐷𝑗𝑘 = √ ∑(𝑥𝑖𝑗 − 𝑥𝑖𝑘)2

𝑛

𝑖=1

where 𝑥𝑖𝑗 and 𝑥𝑖𝑘 are the height of peak i in j and k groups, respectively (McGarigal et al.,

2000).