환경구배에따른미생물군집의...

이학박사학위논문

환경구배에 따른 미생물 군집의

메타지놈 구조 변화

Changes in Metagenome Structure of Microbial

Communities along Environmental Gradients

2018 년 2 월

서울대학교 대학원

생명과학부

송 호 경

Changes in Metagenome Structure

of Microbial Communities along

Environmental Gradients

Hokyung Song

Advisor: Professor Piotr Jablonski, Ph. D.

A Thesis Submitted for the Partial Fulfillment of the

Degree of Doctor of Science, Biological Sciences

February 2018

Graduate School of Biological Sciences

Seoul National University

i

Abstract

Ecology tackles the diversity and interactions of all life on

Earth. In recent years, it has become possible in ecology to obtain

radically new perspectives on the living world by using methods

based on nucleic acid sequencing. These methods, falling under the

categories of amplicon sequencing and shotgun metagenomic

sequencing, have revealed many hidden dimensions of microbial

ecology. Through amplicon sequencing techniques using key

taxonomic marker genes (usually rRNA genes), a vast natural

diversity of bacteria, archaea, fungi and micro-invertebrates has

been revealed from soil, sediment and waters. It has become

possible to test established ecological theories of diversity and

community assembly on microorganisms in fundamentally new ways.

Metagenomics, the direct sequencing of the collective genomes

found in a given environmental sample, has revealed important

insight into the potential functions of microbial communities from

various environments.

Studying how metagenome composition and diversity varies

along environmental gradients may improve the understanding of

the general principles of community and ecosystem structuring. A

culture system was set up using soil elutant to study the effects of

varying initial nutrient concentration, and time succession, on the

ii

community metagenome. A distinct set of nutrient-related or time-

related changes in the metagenome was found. For example, a high

nutrient (copiotrophic) strategy was associated with greater

abundance of genes related to cell division and cell cycle, while a

low nutrient (oligotrophic) strategy had greater abundance of genes

related to carbohydrate metabolism and virulence, disease and

defense. Time-related changes in the metagenome was also found,

revealing a distinct ‘r’-related strategy with greater abundance

of genes related to regulation and cell signaling, and a ‘K’

strategy rich in motility and chemotaxis-related genes. In terms of

diversity, both the richness of operational taxonomic unit and the

richness of species assignment of functional genes showed linear

correlations with functional gene richness, supporting the

hypothesis that greater taxonomic diversity is associated with

greater functional diversity, with possible implications for

ecosystem stability.

Aridity is also one of the most important factor for

structuring ecosystem. To see the effects of aridity stress on the

functional diversity of the soil biota, soil metagenomes along a

rainfall gradient across the eastern Tibetan Plateau was studied.

Lower mean annual precipitation was strongly associated with

reduced functional diversity of bacterial genes. It appears that

extreme environmental conditions associated with aridity constrain

iii

the diversity of functional strategies present in soil biota –

analogous to broad scale patterns found in plant functional diversity

along environmental gradients. In terms of specific functions, more

arid conditions were also associated with increased relative

abundance of dormancy related genes, and osmoprotectant-related

genes. Decreased relative abundance of antibiotic resistance and

virulence genes in more arid conditions suggests reduced intensity

of biotic interaction under extreme physiological conditions. These

trends parallel those seen in preliminary comparisons of

metagenomes across biomes.

In future studies, it will be interesting to elucidate changes

in metagenome structure of soil biota along different types of

environmental gradients, for example, pH, temperature, or soil

oxygen. Also, a study of metatranscriptomics may be required to

prove activity of functional genes in soil community. Development

of enrichment technology of prokaryotic mRNA would be a

prerequisite for achieving this goal.

Keyword: metagenome, functional diversity, copiotrophic,

oligotrophic, r-selection, K-selection, precipitation, aridity,

ecological stress

Student Number: 2016-30109

v

Table of Contents

Abstract .................................................................................................. i

Table of Contents ................................................................................. v

List of Figures.......................................................................................x

List of Tables .....................................................................................xvi

CHAPTER 1: Application of Metagenomics towards an Assessment

of Biodiversity in Microbial Ecology: An Introduction .....................1

1. 1. The impact of advanced sequencing technology on

microbial ecology............................................................................2

1. 1. 1. Discovery of the unseen majority: soil microorganisms.2

1. 1. 2. A general procedure for soil microbial community

analysis with amplicon sequencing .................................................4

1. 1. 3. Lack of linkage between microbial community structure

and function .......................................................................................8

1. 2. An assessment of functional traits of soil microorganisms

10

1. 2. 1. Diverse methods to describe functional traits .............. 10

1. 2. 2. Metagenomic approach for assessing microbial

functional traits .............................................................................. 11

1. 3. Research objectives ............................................................16

CHAPTER 2: Bacterial Strategies along Nutrient and Time

vi

Gradients, Revealed by Metagenomic Analysis of Laboratory

Microcosms..........................................................................................19

2. 1. Introduction ..........................................................................20

2. 2. Materials and Methods ........................................................27

Sample preparation........................................................................ 27

Sequencing and sequence processing ......................................... 28

Chemical analysis of culture material.......................................... 29

Statistical analysis ......................................................................... 30

2. 3. Result ....................................................................................32


Taxonomic and functional composition of communities ............ 34

Functional gene categories correlating with initial nutrient

concentration.................................................................................. 42

Functional gene categories correlating with incubation time ... 54

Taxonomic and functional diversity............................................. 61

Taxonomic annotation of functional genome .............................. 68

2. 4. Discussion.............................................................................73


Functional gene categories correlating with initial nutrient

concentration (hypothesis A)....................................................... 73

vii

Functional gene categories correlating with incubation time

(hypothesis B) ............................................................................... 75

Taxonomic and functional diversity (hypothesis C).................. 79

Taxonomic annotation of functional genome .............................. 80

Possible limitations of this study and suggestions for further

work................................................................................................. 81

2. 5. Concluding remarks.............................................................86

CHAPTER 3: Environmental Filtering of Bacterial Trait Diversity

along an Aridity Gradient...................................................................87

3. 1. Introduction ..........................................................................88

3. 2. Materials and Methods ........................................................95

Soil sampling and data collection ................................................. 95

Climatic data collection and physicochemical analysis.............. 97

DNA extraction, sequencing and sequence processing............. 99

Statistical analysis ....................................................................... 100

3. 3. Result ..................................................................................102

Functional gene composition along an aridity gradient ........... 102

Functional gene composition along nutrient gradient .............. 110

Functional gene diversity vs OTU diversity............................. 114

Functional gene diversity along aridity gradients.................... 116

viii

Taxonomic annotation of functional genes ............................... 123

3. 4. Discussion...........................................................................128

Hypothesis 1) Trait diversity will decrease along an aridity

gradient ......................................................................................... 128

Hypothesis 2) Genes related to cell division and dormancy will

increase along aridity gradient................................................... 129

Hypothesis 3) Stress response genes – including those relating

to osmoregulants - will be more abundant in higher aridity

gradients ....................................................................................... 130

Hypothesis 4) There will be less incidence of antibiotic

resistance and virulence genes in more arid conditions ......... 131

Hypothesis 5) Motility related genes will increase along aridity

gradient ......................................................................................... 132

Hypothesis 6) Functional gene composition along nutrient

concentration will follow those found in the microcosm (Chapter

2) ................................................................................................... 132

Possible limitations of the study ................................................ 133

3. 5. Concluding remarks...........................................................135

CHAPTER 4: General Conclusions .................................................137

4. 1. Changes in metagenome structure of microbial

communities along environmental gradients ...........................138

ix

4. 2. Future directions ...............................................................141

4. 2. 1. Study of various environmental gradients influencing

microbial community.................................................................... 141

4. 2. 2. Metatranscriptomics....................................................... 141

Publication..........................................................................................143

Manuscript in preparation ................................................................144

References.........................................................................................145

Appendix............................................................................................160

국문초록 (Abstract in Korean) ........................................................167

x

List of Figures

Figure 1. A general procedure for soil microbial community analysis

based on amplicon sequencing of microbial marker genes........... 7

Figure 2. A general procedure to assess microbial community

structure with shotgun metagenomic sequencing ...................... 15

Figure 3. Schematic diagram of the study ....................................... 18

Figure 4. Percentage of proteins with no known function against log

(initial nutrient concentration+5) transformed nutrient

concentration in metagenome (A). Percentage of functional genes

not assignable to known genera against log (initial nutrient

concentration+5) transformed nutrient concentration in

metagenome (B). Percentage of unclassified genera against log

(initial nutrient concentration+5) transformed nutrient

concentration in 16S rRNA amplicon data (Song et al., 2016) (C).

A quadratic regression line was applied only to significant results

(p<0.05). ................................................................................... 35

Figure 5. Percentage of proteins with no known function against

incubation time in metagenome (A). Percentage of functional

genes not assignable to known genera against incubation time (B).

Percentage of unclassified genera in the 16s amplicon data

against incubation time (Song et al., 2016) (C). Linear or

quadratic regression line was applied when significant (p<0.05)

xi

(N.B. only the line with the stronger regression is presented

when it fits more than one) ....................................................... 36

Figure 6. Species rarefaction curves with error bars for three

replicates. Sequence reads over 15,000 are not shown here since

the curve already reached saturation point in all samples. ........ 37

Figure 7. Subsystem Level 3 function rarefaction curves with error

bars for three replicates. Sequence reads over 15,000 are not

shown here since the curve already reached saturation point in

all samples................................................................................. 38

Figure 8. nMDS plots of the distance between sample data points

calculated based on the composition of species assignment of

functional genes (A) and functional gene profile (B)................. 39

Figure 9. Heatmap of Subsystem Level 1 genes which show strong

correlation with nutrient gradient. ............................................. 48

Figure 10. Heatmap of Subsystem Level 1 genes which show strong

correlation with incubation time................................................. 60

Figure 11. Richness and Shannon diversity of functional genes

against log (initial nutrient concentration+5) transformed

nutrient concentration. A quadratic regression line was applied

only to significant results (p<0.05) ........................................... 62


against incubation time. A quadratic regression line was applied

only to significant results (p<0.05) ........................................... 63

xii

Figure 13. Functional gene richness against richness of the species

assignments of functional genes (A: linear regression applied, B:

quadratic regression applied) and OTU richness (Song et al.

2016) (C: linear regression applied, D: quadratic regression

applied)...................................................................................... 64

Figure 14. Functional gene Shannon diversity against Shannon

diversity of species assignment of functional genes (A) and OTU

Shannon diversity (Song et al. 2015) (B).................................. 65

Figure 15. Beta diversity of functional genes calculated based on

Bray-Curtis dissimilarity between samples against log (initial

nutrient concentration+5) transformed nutrient concentration. A

quadratic regression line was applied only to significant results

(p<0.05) .................................................................................... 66


Bray-Curtis dissimilarity between samples against incubation

time. A linear regression line was applied only to significant

results (p<0.05) ........................................................................ 67

Figure 17. Taxonomic annotation of functional genes that have

positive correlation with nutrient concentration. Y-axis shows

the relative abundance of 20 most abundant taxa ...................... 69


negative correlation with nutrient concentration. Y-axis shows

the relative abundance of 20 most abundant taxa ...................... 70

xiii


positive correlation with incubation time. Y-axis shows the

relative abundance of 20 most abundant taxa ............................ 71


negative correlation with incubation time. Y-axis shows the

relative abundance of 20 most abundant taxa ............................ 72

Figure 21. Map of sample collection site ......................................... 96

Figure 22. RDA result showing sample distribution based on bacterial

functional gene composition along the environmental gradient in

Tibet. Vegetation types are indicated, with MAP of alpine

meadow>alpine steppe>desert steppe. MAP: mean annual

precipitation (mm), SM: soil moisture (g/g dried soil), SOC: soil

organic carbon (%), STP: soil total phosphorous (%). ............ 104

Figure 23. Heatmap of standardized relative abundance of Subsystem

Level 1 genes along mean annual precipitation (mm). Gene

categories that have positive/negative/no linear correlation with

mean annual precipitation was denoted by ‘(+)’/‘(-)’/‘(0)’

................................................................................................ 106

Figure 24. Heatmap of standardized relative abundance of Subsystem

Level 1 genes along soil moisture (g/g of dried soil). Gene

categories that have positive/negative/no linear correlation with

soil moisture was denoted by ‘(+)’/‘(-)’/‘(0)’.......... 107

Figure 25. Metagenome bacterial Subsystem Level 3 functional gene

xiv

richness against bacterial OTU richness (A). Metagenome

bacterial Subsystem Level 3 functional gene Shannon diversity

against bacterial OTU Shannon diversity (B). Linear regression

line was applied only when significant. Presented R-squared

value is adjusted R-squared value. ......................................... 115


richness against mean annual precipitation (A). Metagenome

bacterial Subsystem Level 3 functional gene Shannon diversity

against mean annual precipitation (B). Linear regression line was

applied. Presented R-squared value is adjusted R-squared value.

................................................................................................ 118

Figure 27. Bacterial OTU richness against mean annual precipitation

(A). Bacterial OTU Shannon diversity against mean annual

precipitation (B). Results not significant (P>0.05). ................. 119


richness against soil moisture (A). Metagenome bacterial

Subsystem Level 3 functional gene Shannon diversity against soil

moisture (B). ........................................................................... 120

Figure 29. Metagenome fungal Subsystem Level 3 functional gene

Shannon diversity against mean annual precipitation (A).

Metagenome archaeal Subsystem Level 3 functional gene

Shannon diversity against mean annual precipitation (B). Results

not significant (P>0.05)........................................................... 121

xv

Figure 30. Functional beta diversity in each vegetation type

calculated based on Bray-Curtis dissimilarity of Subsystem

Level 3 genes from group centroid. Alphabet denotes posthoc

test result of Tukey’s HSD.................................................... 122


negative correlation with precipitation gradients. Y-axis shows

the relative abundance of 20 most abundant taxa .................... 124


positive correlation with precipitation gradients. Y-axis shows

the relative abundance of 20 most abundant taxa .................... 126

xvi

List of Tables

Table 1. Chemical analysis of culture material after harvest (mean±

standard error).......................................................................... 33

Table 2. PERMANOVA test result presenting significant effect of

nutrient concentration, incubation time, and the interaction

between two on the composition of species assignment of

functional genes......................................................................... 40

Table 3. PERMANOVA test result presenting significant effect of

nutrient concentration, incubation time, and the interaction

between two on gene composition............................................. 41

Table 4. Subsystem Level 1 and Level 2 genes that have strong

(rho>0.5 or rho<-0.5) correlation with nutrient concentration

when controlling incubation time. Only the selected Subsystem

Level 2 genes that follow the same (whether positive or negative)

pattern with the Subsystem Level 1 category are shown. Partial

correlation results of “Clustering-based subsystem” and

“Miscellaneous” were not included. ....................................... 45

Table 5. Partial correlation result of cell markers suggested in Lauro

et al. (2009) against nutrient controlling incubation time. ......... 49

Table 6. Subsystem Level 1 and Level 2 genes that have strong

(rho>0.5 or rho<-0.5) correlation with incubation time when

controlling nutrient concentration. Only the selected Subsystem

xvii

Level 2 genes that follow the same (whether positive or negative)

pattern with the Subsystem Level 1 category are shown. Partial

correlation results of “Clustering-based subsystem” and

“Miscellaneous” were not included. ....................................... 56

Table 7. Forward selection of environmental variables, which best

explain variation in functional gene composition between samples.

A pseudo-F statistic from Monte Carlo permutation test was

derived in the same way as in ANOVA of the regression model

................................................................................................ 105

Table 8. Subsystem Level 3 genes belonging to stress response

category showing significant correlation with mean annual

precipitation............................................................................. 108

Table 9 Pearson correlation between Subsystem Level 1 genes and

soil total carbon (STC), soil organic carbon (SOC), soil total

phosphorous (STP), soil total nitrogen (STN) in the field study in

Tibetan plateau and Spearman correlation between Subsystem

Level 1 genes and initial nutrient concentration in the microcosm

study (Chapter 2). Significant positive correlations are

highlighted in green and significant negative correlations are

highlighted in red. .................................................................... 111

1

CHAPTER 1: Application of

Metagenomics towards an

Assessment of Biodiversity in

Microbial Ecology: An Introduction

2

1. 1. The impact of advanced sequencing

technology on microbial ecology

1. 1. 1. Discovery of the unseen majority: soil

microorganisms

Soil microbes are one of the most abundant taxonomic group

in terrestrial ecosystem. It has been estimated that microbial

biomass in a hectare of soil is over 1,000 kg∙C in grasslands and

forest sites and around 430 kg∙C in desert biome (Fierer et al.,

2009). These soil microorganisms play significant roles in shaping,

maintaining, or modifying environments surrounding them (Paul,

2014). They are involved in the key steps of nutrient cycles, such

as nitrogen fixation, denitrification, ammonia oxidation,

methanogenesis, carbon fixation, etc. (Fierer, 2017)

Although their role has been acknowledged for more than a

century, there was a limitation in studying microbial ecology in

natural ecosystems mainly because they are invisible to the naked

eyes. Prior to the development of culture-independent molecular

technologies, soil microbes could only be studied in detail by

culturing them under specific laboratory conditions. However, it had

been estimated that only about one percent or less of total soil

microbial organisms are culturable (Amann et al., 1995) thereby

3

limiting the scope of microbial ecology. Also, it is hard to guarantee

whether the observed characteristics in a culture system could also

be found in the natural ecosystem.

With the advent of sequencing technology, it has become

possible to disclose the unseen majority; soil microbes. The

biogeography of soil microbial community has been extensively

studied since early 2000s using next generation sequencing

(Cederlund et al., 2014; Fierer et al., 2007; Griffiths et al., 2011;

Lauber et al., 2009; Nemergut et al., 2011; Oliverio et al., 2017;

Pett-Ridge and Firestone, 2005; Rousk et al., 2010; Singh et al.,

2014; Sul et al., 2013; Tripathi et al., 2013; Tripathi et al., 2014).

Although there might be some exceptions, in most of these studies,

microbial community structure and diversity showed a predictable

pattern along environmental gradients. Among these factors, pH has

been reported as the primary abiotic factor influencing soil

microbial community structure and diversity followed by organic

carbon concentration, soil oxygen and redox potential, soil moisture,

and temperature.(Fierer, 2017; Lauber et al., 2009; Tripathi et al.,

2013).

4

1. 1. 2. A general procedure for soil microbial

community analysis with amplicon sequencing

As shown in Figure 1, analyzing soil microbial community

structure begins with collecting soil samples. Since soil environment

is extremely heterogeneous, it is hard to get a representative

sample of a soil of interest (Kirk et al., 2004; Vos et al., 2013).

There are several ways to avoid this problem, for example, by

sieving soils or by mixing point samples collected from a few

meters apart from each other.

DNA extraction follows after sample collection. In this step,

it is important to get a pure DNA and humic substances that inhibit

PCR amplification should be properly removed (Yeates et al., 1998).

In the PCR amplification step, 16S rRNA gene is generally

used as a target gene in bacterial community analysis. 16S rRNA

gene has several advantages as a phylogenetic marker. 16S rRNA

plays a fundamental role in survival and reproduction of bacterial

cells, so it is highly conserved and present in all bacterial cells.

This facilitates designing of a universal primer which targets the

whole taxa of interest. 16S rRNA also includes several variable

regions of which nucleotide sequence conversion speed generally

parallels that of phylogenetic divergence.

Traditional Sanger sequencing method requires separate

5

steps for sequencing (by applying chain-terminating

dideoxynucleotide), size separation (by electrophoresis), and

detection. Though it can produce long reads, Sanger sequencing is

time consuming and produce only one read per sample. Next

generation sequencing (NGS) methods can produce massive amount

of data in a relatively short time. There are many types of NGS

platforms exist. Iron Torrent uses semi-conductor chip sensing

hydrogen ion released from DNA when a nucleotide is incorporated.

Pyrosequencing machine use chemical reaction producing light

when pyrophosphate is released from DNA during polymerization

reaction. Illumina sequencing platforms detect fluorescent signal

from dye attached to each nucleotide. These various platforms

differ in read length, cost, error rate, throughput, and running time,

so it is important to compare and choose the proper machines which

qualifies. After PCR amplification, adaptors are ligated to each end

of the amplicons for library construction and after library

construction and quantification, we put samples to the sequencing

machine.

After sequencing, there are several steps required for

quality control. Programs such as FastQC, enable users to overview

the sequencing quality by checking metrics which has been

produced by sequencing machine. To begin with sequence

processing, adaptor, primer, and linker sequences should be

6

trimmed away. Also, low-quality sequences (which have

homopolymers or ambiguous sequences or have low quality scores)

and sequences that are too short considering the desired length

should be removed.

Once sequences pass pre-filtering steps, based on

sequence similarity, sequences are grouped into operational

taxonomic unit (OTU). There are two ways to pick OTUs. One is

alignment-based method and the other one is alignment-free

method such as uclust or CD-HIT. If most of the sequences can be

aligned with high accuracy to the alignment database, it is best to

use alignment-based methods. In both methods, chimeric

sequences, which is an artificial sequences generated during PCR

step should be removed.

The last step is microbial community analysis. This step

includes calculation of diversity indices such as Shannon diversity

or Simpson diversity. This step also includes ordination of

community data by calculating ecologically meaningful distances

(e.g. Bray-Curtis dissimilarity) or phylogenetic distances between

samples.

7

Figure 1. A general procedure for soil microbial community analysis

based on amplicon sequencing of microbial marker genes

8

1. 1. 3. Lack of linkage between microbial community

structure and function

With the advent of next generation sequencing technologies,

microbial community data has been accumulated with an unexpected

speed. It is now possible to understand the general pattern of how

microbial communities are assembled and how microbial diversity

correlates with specific environmental conditions. However, most of

the studies until now focused mainly on taxonomic information of

microbes and there exists a gap between the taxonomic information

and ecological functions of soil microorganisms.

Conventional niche theory suggests that no two species can

occupy the same niche in time and space or habitat. In this sense,

soil taxonomic diversity is important to understand overall

ecosystem productivity and resilience. In addition, it is important to

note that high taxonomic diversity is usually associated with high

ecosystem stability especially when there is no redundant function

between two groups of organisms dwelling in the same environment

(Cadotte et al., 2011; Hooper et al., 2002). However, in many cases,

several species can perform the same soil functions (functional

redundancy). Thus, measuring functional diversity could be

important for finding out the connection between biodiversity and

resilience.

10

1. 2. An assessment of functional traits of soil

microorganisms

1. 2. 1. Diverse methods to describe functional traits

Several methods have been developed for measuring

functional diversity (Petchey and Gaston, 2006). In most cases,

functional diversities are calculated based on collective traits

(Nunes et al., 2017; Petchey and Gaston, 2002, 2006). When

assessing functional diversity with functional or physiological traits,

it is important to choose the type of traits to be considered

(Petchey and Gaston, 2006). In the case of plants, life-cycle,

growth-form (e.g. shrub, bulb, and rosette), leaf type (e.g.

evergreen or semi-deciduous), seed mass, leaf N, fruit type, etc.

has been used (Flynn et al., 2009; Nunes et al., 2017; Petchey and

Gaston, 2002). In case of birds, weight, feeding guild, food type,

foraging location, foraging height, foraging habitat, etc. has been

used (Flynn et al., 2009; Petchey and Gaston, 2002; Sekercioglu,

2012). Similar to birds, in mammals, weight, feeding guild, food type,

activity, nesting, etc. has been used (Flynn et al., 2009; Petchey

and Gaston, 2002).

In soil microbiology, other types of methods have been

developed for measuring functional traits diversity for example,

11

assessment of catabolic potential of microbes (Degens and Harris,

1997; Torsvik and Øvreås, 2002). This type of method has been

developed mainly in culture system, by growing bacterial groups

with different type of nutrient supplied. However, these type of

analysis only focus on one subcategory of bacterial functions. There

should be a generalized methods to assess overall functions that

bacterial groups perform as one of the key member in the

ecosystem. Metagenomic profiling through shotgun sequencing can

provide a quantitative data of overall functional profile of microbes.

1. 2. 2. Metagenomic approach for assessing microbial

functional traits

The fundamental driving force of the functions that microbes

perform are the coded sequences in their DNA. In this sense, it is

natural to examine soil microbial functional diversity with functional

gene diversity. This type of approach has numerous advantages in

that the data produced from more than a million sequences are

highly quantitative and it makes it easy to compare results of

different studies.

Recent studies has started to apply shotgun metagenomic

sequencing to assess functional diversity. Burke et al. (2011)

studied algal-associated microbial communities using

12

metagenomics. They found that there was a high similarity (70%) in

functional gene composition between samples despite low similarity

(15%) in microbial species composition. Fierer et al. (2012) applied

metagenomics to assess taxonomic microbial community structure

and functional attributes of microorganisms in several different

biome. They found that several gene categories associated with

different type of biomes, for example, nutrient cycling related genes

were more abundant in mesic systems compared to desert systems.

Mendes et al. (2015) assessed functional diversity in relation to

disturbance using metagenomics and found that agricultural soil had

higher taxonomic and functional diversity than forest soil.

Figure 2 shows the basic procedure for assessing functional

attributes of microbes through shotgun metagenomic sequencing.

As in amplicon sequencing method described in Figure 1, it starts

with soil collection and DNA extraction. Unlike amplicon sequencing,

shotgun metagenomics doesn’t include amplification of marker gene.

Instead, the whole DNA extracted from soil are fragmented into

small pieces and sticky ends of DNAs are repaired for adaptor

ligation. Also, proper size of DNA should be selected in library

construction step.

After library construction and sequencing, DNA sequences

undergoes pre-filtering process as in amplicon sequencing. Using

programs such as FragGeneScan (specified for prokaryotic gene

13

calling), coding regions within the sequences are predicted. Before

annotation, proteins with high sequence similarity is clustered using

alignment-free clustering methods and a representative sequences

are BLAST against gene database for annotation.

There are several databases available for functional

annotation of genes such as GenBank (Benson et al., 2012), Cluster

of Orthologous Group (COG) (Tatusov et al., 2003), Kyoto

Encyclopedia of Genes and Genomes (KEGG) (Kanehisa, 2002), or

SEED (Overbeek et al., 2005). GenBank contains all publically

available DNA sequences and their product. COG gene database has

been constructed by finding orthologous groups in sets of complete

genome. KEGG database groups functional genes by cellular

pathways. SEED database use hierarchical way to organize gene

families into several levels of functions. These databases collect

functions of genes published based on experimental observation.

Functional genomics is the field in molecular biology which attempts

to describe gene functions and interactions. There are several ways

to prove gene functions and one example is introducing mutation to

find loss of function.

Once we get functional profile of genes through annotation,

we can calculate functional gene diversity. Besides, we can ordinate

samples by calculating dissimilarity between samples. Also, we can

regress functional categories against environmental factors to find

14

out relationships.

15

Figure 2. A general procedure to assess microbial community

structure with shotgun metagenomic sequencing

16

1. 3. Research objectives

One of the major aims of ecology is to be able to

characterize the adaptations which determine where and when each

type of organism occurs in nature (Ricklefs and Miller, 1999).

Certain combinations of features or behaviors occur predictably in

the context of certain environments and niches, and are known as

strategies (Grime, 1977; Southwood, 1977; Westoby et al., 2002).

Up until now, most of the studies on ecological strategies have

focused on macroorganisms such as plants or animals. There are

only few studies which studied microbial functions in relation to

their survival or reproductive strategies in association with

environmental conditions.

This study aims to explore the fundamental aspects of how

microbial community processes and function vary along

environmental gradients. We set hypotheses constructed based on

the studies of macrooganisms and aim to see whether the same

patterns are universally found in microbial communities. We expect

there would be specific functional gene categories correlating with

certain environmental gradients as a result of natural selection.

In the first study (Chapter 2), we constructed a laboratory

microcosm by culturing soil derived microorganisms, to study the

effects of varying initial nutrient concentration, and time succession,

17

on the community metagenome. In the second study (Chapter 3),

we set up a field study in Tibetan Plateau to understand how aridity

stress affect functional diversity of genes in the soil microbial

community.

18

Figure 3. Schematic diagram of the study

19

CHAPTER 2: Bacterial Strategies

along Nutrient and Time Gradients,

Revealed by Metagenomic Analysis of

Laboratory Microcosms

20

2. 1. Introduction

In plant and animal ecology, among the best characterized

and most widely recognized ecological strategies are the ‘r’ and ‘K’

strategies (MacArthur and Wilson, 1967). r strategists are

described as living in temporarily resource-rich environments, with

the potential to grow and mature rapidly, and produce large

numbers of young quickly, with poor ability to withstand sustained

competition or predation (Pianka, 1970). K strategists are

described as having the opposite of these characteristics, being

adapted to environments where resource supply rates are lower,

population densities are higher and competition between individuals

and between species is intense. r and K strategists have often been

described in relation to ecological succession, with r strategists

being abundant in early successional stages and K strategists

prevailing in late successional stages (Odum, 1969).

In microbial ecology, there has been little explicit discussion

of r or K strategies. Instead, attention has concentrated on the two

opposing microbial strategies of ‘oligotrophy’ (specialization to low

nutrient conditions) and ‘copiotrophy’ (specialization to high

nutrient conditions) (Koch, 2001). There has only been one

concerted attempt to define the differences between these two

strategies at the inferred functional level of cell physiology and

21

genome functions (Lauro et al., 2009). Lauro et al.’s study (2009),

while a valuable step forward, the inspiration for the present study,

involved selected examples of fully sequenced marine bacterial

species, and leaves open the need for broader testing in other

environments, and using other selection criteria.

There has been little discussion in microbial ecology of the

possible similarities and differences between the copiotrophy-

oligotrophy axis and the r – K axis of strategies. It seems, however,

that copiotrophy-oligotrophy is thought of in a static sense of

steady conditions of greater or lesser resource supply (since

population growth rate and stability is not generally discussed in the

context of this axis). The r-K axis as generally discussed in

ecology is considered as part of a time succession in which a

community develops from early conditions of sparse populations

and ready availability of resources, to later conditions of sustained

crowding and intense competition for resources.

Here we attempted an approach, which may help to elucidate

the functional strategies, which exist in the bacterial world.

Whereas field-based observations can accurately incorporate the

true complexity of nature, experimental microcosms can go some

way towards reducing the huge number of environmental factors

that vary simultaneously, and thus give further useful perspectives

(Jiang and Patel, 2008; Luckinbill, 1979). It is true that in a

22

culture-based system, there is the inevitable limitation that only a

subset of the species present in nature will grow in the system, and

that it can never precisely replicate the complexity of the natural

world. Nevertheless, the precise control of initial nutrient content,

temperature, etc. in culture systems may be useful in elucidating

general patterns that might easily be hidden within the vast

complexity of natural ecological systems.

Here, we tested for the existence of different strategies in

relation to nutrient and incubation time by combining precisely

controlled conditions, with an assessment of combined genome

capabilities. In this study, the distinctions ‘copiotrophy vs

oligotrophy’, and ‘r-strategy vs k-strategy’ have been used as a

relative form. For example, a function related to copiotrophy has

been defined as a functional gene for which relative abundances

increase along with the increase of initial nutrient concentration. We

predicted that the taxonomic assignment of functional genes and

functional gene structure of the community would differ in relation

to initial nutrient concentration and incubation time.

We tested the following predictions in relation to ecological

strategies:

A) What are the functional characteristics of oliogotrophy vs

copiotrophy amongst bacteria?

23

Hypothesis A-1) Cell division and cell cycle related genes will be

more abundant under copiotrophic conditions. It is clear that

amongst plants and corals, those growing in conditions of higher

rates of nutrient supply (and light supply, for photosynthesizers)

are innately able to grow faster and have higher potential rates of

tissue growth and extension, and more rapid life cycle completion to

reproduction (Grime, 1979; Huston, 1994). We hypothesized that,

as with larger organisms, at high nutrient concentrations bacterial

cells are able to acquire enough nutrient for their basic metabolism,

and will use supplementary nutrients for cellular

growth/reproduction.

Hypothesis A-2) Under copiotrophic conditions, genes that are

themselves related to promoting gene expression will be relatively

more abundant. In this case, we based our prediction on the

microbial literature in relation to copiotrophy. For example, rRNA

gene copy number has commonly been used as an indicator of

copiotrophy (Condon et al., 1995; Fegatella et al., 1998;

Klappenbach et al., 2000; Lauro et al., 2009). Our intention here

was to test this hypothesis under the more precisely controlled

conditions of a nutrient gradient in a culture system.

Hypothesis A-3) Nutrient accumulator genes will be more abundant

under oligotrophic conditions. Lower nutrient conditions will require

greater numbers of genes associated with accumulating nutrients.

24

Stress tolerator plants and lichens that grow under nutrient-poor

conditions are known to be very effective accumulators of nutrients

(Grime, 1979), and by extension we predicted empirical gene

function evidence for similar behavior by bacteria.

Hypothesis A-4) Dormancy and sporulation related genes will be

relatively more abundant in copiotrophic conditions. Here we were

testing for a functional gene abundance difference already predicted

by Fierer et al. (2007). Since copiotrophs cannot grow in nutrient

poor conditions, they are expected to survive in a dormant stage

until conditions improve (Fierer et al., 2007).

Hypothesis A-5) Functional gene abundance patterns will parallel

those found by Lauro et al. (2009). Using a very different approach,

based on classification of fully sequenced selected bacterial

genomes Lauro et al. (2009) compared species defined as either

oliogotrophic or copiotrophic strategists. Using this approach, they

arrived at an assemblage of functional genes, which may define the

copiotrophic strategy. We aimed to test the generality of the pattern

found by Lauro et al. (2009), by using a different approach – a

nutrient gradient in a controlled culture system.

B) What are the functional characteristics of r vs K selected

bacteria?

Hypothesis B-1) Cell division related genes will be more abundant

25

in the early successional stage. Cell division is an aspect of growth,

particularly in the prokaryotic world. Typically, in the ecology of

larger organisms, early successional species grow fast and give

many offspring, rapidly expanding their biomass (Bazzaz, 1979;

Connell and Slatyer, 1977; Odum, 1969). Thus, we expected this

pattern to hold true in bacterial succession, revealing itself in

greater relative abundance of genes related to rapid

growth/reproduction.

Hypothesis B-2) Genes related to cell-cell interactions will be

more abundant in the later successional stages. It is a generally

agreed concept in ecology that later successional ecosystems have

more intense and species-specific mutualistic and antagonistic

interactions (Morriën et al., 2017; Odum, 1969). We predicted

similar trends towards intensity of (either positive or negative)

organismic interactions in the late successional stage in the soil

bacterial systems we were studying.

Hypothesis B-3) Genes related to cell motility will be more

abundant in the early successional stage. The ‘r’ selected plant

species that thrive in early successional stages generally have high

seed dispersal ability (Huston and Smith, 1987; Peroni, 1994). Cell

motility, which functionally fulfils the same role as the seed

dispersal of ‘r’ selected plants, is important for a bacterial

population to reach newly created open patches or spaces of

26

environment that are relatively free of competition. Thus, genes for

cell motility will be more abundant in early successional microbes.

C) How does functional gene diversity relate to nutrient

concentration and incubation time?

Hypothesis C: Functional gene diversity will show a humpback

curve against initial nutrient concentration or incubation time,

resembling the pattern found in OTU diversity in the same

experimental system (Song et al., 2016).

In an earlier study on this same experimental system, Song et al.

(2016) found a ‘humpbacked’ pattern of bacterial OTU diversity in

relation to nutrient concentration, and time. We anticipated that

functional gene diversity would parallel OTU diversity, because

different taxa have different genetically based capabilities, and

coexistence of different taxa in itself will require separate

ecological niches, which are achieved partly by a range of different

cellular and metabolic functions. This taps into a wider debate of

whether functional diversity at the community level is necessarily a

correlate of taxonomic diversity (Griffiths and Philippot, 2013;

Huston, 1997; McCann, 2000; Tilman et al., 1997).

27

2. 2. Materials and Methods

Sample preparation

As described in Song et al. (2016), plates with different

nutrient concentrations were prepared. Tryptic Soy Broth was used

as a nutrient source, and agar was used as a solidifying agent. Agar

culture medium was prepared with original (nutrient A group), 10-

fold diluted (nutrient B group), 100-fold diluted (nutrient C group),

and 10,000-fold diluted (nutrient E group) Triptic Soy Broth (Difco)

concentrations. In the medium, cycloheximide (100㎍/ml) was

added to inhibit fungal growth. Sterile cellophane film was overlaid

on each culture plate to facilitate cell harvest.

Garden soil (0-5 c.m. depth) was collected from an

overgrown flowerbed on the campus of Seoul National University

(Song et al., 2016). 20 grams of soil was eluted to 190ml of 0.85%

NaCl. The sample underwent 10 min of blending followed by 30 min

of incubation in a shaking incubator at 24 ℃ at the speed of 250

r.p.m. We diluted soil solution 10-2 times, which was the

concentration that had the optimal density of derived colonies (Song

et al., 2016). We let soil particles settle for 6 hours before plating

while the water was kept chilled to 4℃ to prevent community

change. We carefully transferred supernatant into a new tube and

100 ㎕ of supernatant was spread on each prepared culture plate.

28

Plates were incubated at 25℃ for different lengths of time (Day 2,

7, 28, 56, and 84) before final sampling. Plates that showed any

fungal colonies on examination under a low power light microscope

were discarded (absence of fungi in the remaining sampled plates

was later confirmed from the analysis of metagenomes, see below,

which consist overwhelmingly of bacterial DNA). 5 plates were

pooled together and treated as one replicate. A total of 300 plates

(4 nutrient concentrations * 5 incubation time * 3 replicates for

each * 5 plates for each replicate) were used for further analysis.

DNA from the final samples was extracted using Genomic DNA

purification kit (Promega, Madison, Wisconsin, USA) following the

manufacturer’s guidelines. Extracted DNA was stored at -20℃ for

further analysis.

Sequencing and sequence processing

DNA samples were sent to Celemics (Celemics, Inc., Seoul,

Korea) for shotgun metagenomic sequencing using Illumina

HiSeq2500 sequencing platform. Sequences were uploaded to the

Metagenomics Rapid Annotation using Subsystem Technology

(MG-RAST) online server version 4.0 for taxonomic and functional

annotation (Meyer et al., 2008) under the project name “Effect of

nutrient concentration and successional stage on structure and

29

function of culturable bacteria” (project ID, 16343). The Refseq

database (Pruitt et al., 2007) was chosen for taxonomic annotation

of functional genes and the SEED database (Overbeek et al., 2005)

and the Clusters of Orthologous Groups (COG) database (Tatusov

et al., 2003) were used for functional annotation. Sequences were

annotated with default setting (maximum e-value cutoff of 10-5,

minimum percentage identity cutoff of 60%, and minimum alignment

length cutoff as 15 bp). Even though more than 99.5% of assigned

sequence reads were from bacteria, we filtered out non-bacterial

sequences for the statistical analysis.

Chemical analysis of culture material

Dissolved organic carbon (DOC) and dissolved total nitrogen

(DTN) left in the culture material after harvest was measured to

check how the nutrient treatment changed over time. Nutrient A and

C group, incubation times 2, 28 and 84 weeks were selected for the

analysis. Culture material, which had been kept after harvest at -20℃

was melted at room temperature and sent to NICEM (Korea) for

chemical analysis. Samples were filtered with hydrophilic

polyethersulfone filter and hydrophilic polypropylene filter

(Supor®-450 47mm 0.45㎛). DOC was measured using a total

organic carbon analyzer (TOC-L, SHIMADZU, Japan) and DTN was

30

measured with an AutoAnalyzer (SYNCA, BLTEC, Japan) following

standard methods provided by the American Public Health

Association (APHA), the American Water Works Association

(AWWA), and the Water Environment Federation (WEF) (Rice et

al., 2012).

Statistical analysis

To calculate taxonomic diversity, species level assignments

of functional genes from the metagenomes were used, and for

functional diversity, Subsystem Level 3 data were used. To

normalize the number of sequences in each sample, sequences were

subsampled based on the number of least abundant sequence reads

among all the samples (1,749,521 for species level assignment of

functional genes and 530,466 for Subsystem Level 3 function).

Rarefaction curves were drawn with subsampled sequence data. To

calculate taxonomic and functional distance between each sample,

the number of metagenome sequence assigned at species level and

Subsystem Level 3 in each group was square root transformed and

Bray-Curtis dissimilarity was calculated. To test the influence of

initial nutrient concentration and incubation time to the bacterial

community structure, permutational multivariate analysis of

variance (PERMANOVA) test were performed with 999

31

permutation using ‘adonis’ function in R package ‘vegan’.

To study the gene abundance differences of bacteria in

relation to initial nutrient concentration and incubation time, partial

Spearman correlation of initial nutrient concentration (or incubation

time) and relative abundance of Subsystem Level 1 and Level 2

functional gene categories, controlling for incubation time (or initial

nutrient concentration, and vice versa) was calculated using

‘pcor.test’ in R software ‘ppcor’ package. Partial correlation was

used since we aimed to discern the nutrient effect and time effect

independently and Spearman rank correlation was used since it

does not depend on normality or linearity. Relative abundance of

genes in each sample was calculated dividing the number of

sequences assigned to each category with the sum of sequences

successfully annotated to the gene database.

32

2. 3. Result


As shown in Table 1, dissolved organic carbon (DOC)

decreased over time, in both the nutrient A and nutrient C

treatments, which we analysed. However, the DOC of nutrient C

treatment never exceeded that of nutrient A, revealing the

continuing legacy of initial conditions in terms of nutrient loading of

the culture medium. Dissolved total nitrogen (DTN) decreased over

time in nutrient C group, but not in the nutrient A group. The scale

of the difference between nutrient A and C group was much greater

in the case of DTN.

33

Table 1. Chemical analysis of culture material after harvest (mean±standard error)

Incubation time

(days)

Initial nutrient

concentration

Dissolved organic carbon

(mg/L)

Dissolved total nitrogen

(mg/L)

2 A 6869.3±866.0 1838.9±86.8

2 C 2838.0±206.9 25.9±0.8

28 A 6627.3±606.1 2184.7±77.8

28 C 2194.3±14.4 16.4±0.8

84 A 5350.7±1015.5 2214.1±187.1

84 C 1108.9±79.4 11.2±0.0

34

Taxonomic and functional composition of communities

The total number of sequences per sample obtained after

quality control ranged from 1,962,970 to 4,641,543. The proportion

of proteins with no known function and functional genes not

assignable to known genera does not increase with increasing

nutrient concentration (Figure 4-A and B), but increases with

incubation time (Figure 5-A and B). 16S amplicon based results

show a similar pattern, unclassified genera being more abundant in

later successional stage (Figure 4C and Figure 5C). The rarefaction

curve of species level assignment of functional genes and

Subsystem Level 3 function had reached its saturation point in all

samples (Figure 6-7). The composition of species level assignment

of functional genes and functional profile showed a predictable

pattern with progression in either of two distinct directions related

to nutrient gradient and incubation time (Figure 8). The

permutational multivariate analysis of variance (PERMANOVA)

result shows that nutrient concentration, incubation time, and the

interaction between nutrient concentration and incubation time all

had significant influence in relation to community structure, both in

terms of the composition of species level assignment of functional

genes and the functional gene composition (Table 2 and Table 3).

35

Figure 4. Percentage of proteins with no known function against log

(initial nutrient concentration+5) transformed nutrient concentration

in metagenome (A). Percentage of functional genes not assignable to

known genera against log (initial nutrient concentration+5)

transformed nutrient concentration in metagenome (B). Percentage

of unclassified genera against log (initial nutrient concentration+5)

transformed nutrient concentration in 16S rRNA amplicon data (Song

et al., 2016) (C). A quadratic regression line was applied only to

significant results (p<0.05).

36

Figure 5. Percentage of proteins with no known function against

incubation time in metagenome (A). Percentage of functional genes

not assignable to known genera against incubation time (B).

Percentage of unclassified genera in the 16s amplicon data against

incubation time (Song et al., 2016) (C). Linear or quadratic

regression line was applied when significant (p<0.05) (N.B. only the

line with the stronger regression is presented when it fits more than

one)

37

Figure 6. Species rarefaction curves with error bars for three replicates. Sequence reads over 15,000 are

not shown here since the curve already reached saturation point in all samples.

38

Figure 7. Subsystem Level 3 function rarefaction curves with error bars for three replicates. Sequence

reads over 15,000 are not shown here since the curve already reached saturation point in all samples

39

Figure 8. nMDS plots of the distance between sample data points calculated based on the composition of

species assignment of functional genes (A) and functional gene profile (B).

40

Table 2. PERMANOVA test result presenting significant effect of nutrient concentration, incubation time,

and the interaction between two on the composition of species assignment of functional genes

d.f MS F. model R2 p-value

Day (incubation time) 4 0.603 25.40 0.267 0.001

Nutrient concentration 3 1.373 57.87 0.457 0.001

Day (incubation time) * Nutrient concentration 12 0.128 5.40 0.171 0.001

Residuals 40 0.024 0.105

Total 59 1

41

Table 3. PERMANOVA test result presenting significant effect of nutrient concentration, incubation time,

and the interaction between two on gene composition

d.f MS F. model R2 p-value

Day (incubation time) 4 0.051 19.30 0.267 0.001

Nutrient concentration 3 0.101 38.38 0.398 0.001

Day (incubation time) * Nutrient concentration 12 0.013 4.78 0.198 0.001

Residuals 40 0.003 0.138

Total 59 1

42

Functional gene categories correlating with initial

nutrient concentration

Table 4 shows the Subsystem Level 1 and Level 2 genes

that have strong (rho>0.5 or rho<-0.5) correlation with nutrient

concentration when controlling for incubation time. Only the

selected Subsystem Level 2 genes that follow the same (whether

positive or negative) pattern with the Subsystem Level 1 category

are shown. Partial correlation results of “Clustering-based

subsystem” and “Miscellaneous” were not included in the result

section and were not discussed because most of them are putative

genes, or genes for which functions are unclear. For Subsystem

Level 1 genes, we also drew a heatmap with Z-score transformed

relative abundance data (Figure 9).

As in Table 4, cell division and cell cycle related genes were

more abundant at high nutrient concentrations. Also, genes related

to RNA metabolism, especially RNA processing and modification

related genes, were relatively more abundant at higher nutrient

concentrations. Dormancy and sporulation related genes, especially,

spore DNA protection related genes, were more abundant in

copiotrophic conditions. Many different kinds of amino acid related

genes such as genes related to arginine, urea cycle, polyamines,

glutamine, glutamate, aspartate, asparagine, lysine, threonine,

43

methionine, and cysteine were also relatively more abundant at

higher nutrient concentrations.

By contrast, genes related to energy acquisition (e.g.

carbohydrate) or usage (e.g. respiration) were abundant in the low

nutrient treatments. Carbohydrate metabolism related genes had

negative correlations with initial nutrient concentration. This was

not restricted to one type of carbohydrate, but a broad range of

molecules including amino sugars, di- and oligosaccharides,

monosaccharides, and polysaccharides. Genes related to protein

metabolism, respiration and photosynthesis (although

photosynthesis itself would not have been directly selected for, as

the cultures were kept in the dark) which include genes related to

electron transfer reaction were also abundant in oligotrophic

condition. Having high abundance of genes related to virulence,

disease, interference competition and defense, especially the genes

providing resistance to antibiotics and toxic compounds, was also

one of the noticeable characteristics of more the oligotrophic/low

nutrient treatments.

We also attempted to annotate sequences based on the

COG gene database to compare our results with those of Lauro et al.

(2009) (Table 5). Note that COG2852 gene was not found in our

metagenomes (this gene was also minimally present in Lauro et al’s

(2009) result), and were thus excluded. When we set the rho cutoff

44

value higher than 0.5 or lower than -0.5, 28% (9 out of 32 genes)

of genes in our study matched the result found in Lauro et al.

(2009). The other genes did not show a strong correlation with

initial nutrient concentration, except for COG3710, which was the

only gene that showed the opposite trend from the pattern found by

Lauro et al. (2009).

45

Table 4. Subsystem Level 1 and Level 2 genes that have strong (rho>0.5 or rho<-0.5) correlation with

nutrient concentration when controlling incubation time. Only the selected Subsystem Level 2 genes that

follow the same (whether positive or negative) pattern with the Subsystem Level 1 category are shown.

Partial correlation results of “Clustering-based subsystem” and “Miscellaneous” were not included.

Subsystem Level 1 Subsystem Level 2

Averaged

relative

abundance

rhoTrophic

strategy

Amino Acids and Derivatives - 0.1015 0.803

Copiotroph

Arginine; urea cycle, polyamines 0.0174 0.794

Glutamine, glutamate, aspartate, asparagine;

ammonia assimilation0.0099 0.638

Lysine, threonine, methionine, and cysteine 0.0251 0.658

Cell Division and Cell Cycle - 0.0082 0.696

RNA Metabolism - 0.0326 0.619

RNA processing and modification 0.0237 0.648

46

Table 4. (Continued)

Subsystem Level 1 Subsystem Level 2Averaged relative

abundancerho

Trophic

strategy

Dormancy and Sporulation - 0.0029 0.593Copiotroph

Spore DNA protection 0.0001 0.800

Respiration - 0.0368 -0.828

Oligotroph

Electron accepting reactions 0.0105 -0.537

Electron donating reactions 0.0120 -0.846

Virulence, Disease and Defense - 0.0381 -0.795

Resistance to antibiotics and

toxic compounds0.0331 -0.798

Photosynthesis - 0.0003 -0.785

Electron transport and

photophosphorylation0.0000 -0.660

- 0.1248 -0.672

Aminosugars 0.0032 -0.588

Di- and oligosaccharides 0.0113 -0.667

Monosaccharides 0.0225 -0.683

47


Subsystem Level 1 Subsystem Level 2Averaged relative

abundancerho

Trophic

strategy

Carbohydrates Polysaccharides 0.0014 -0.830

Oligotroph

Protein Metabolism - 0.0633 -0.552

Protein folding 0.0065 -0.581

Protein processing and

modification0.0051 -0.658

48

Figure 9. Heatmap of Subsystem Level 1 genes which show strong correlation with nutrient gradient.

49

Table 5. Partial correlation result of cell markers suggested in Lauro et al. (2009) against nutrient

controlling incubation time.

Marker rho p-value Average relative

abundance (%)

Trophic

strategy

Trophic

strategy

(reference)

Cell motility -0.33 0.01 0.326 - Copiotroph

Signal transduction mechanisms -0.27 0.04 4.070 - Copiotroph

Defense mechanisms -0.03 0.84 2.397 - Copiotroph

Transcription 0.84 0.00 4.663 Copiotroph Copiotroph

Secondary metabolites biosynthesis,

transport and catabolism

-0.44 0.00 2.924 - Oligotroph

Lipid transport and metabolism 0.07 0.62 4.722 - Oligotroph

COG0110 (Acetyltransferase

(isoleucine patch superfamily))

0.43 0.00 0.045 - Copiotroph

COG0183 (Acetyl-CoA

acetyltransferase)

-0.28 0.03 0.366 - Oligotroph

50


Marker rho p-value Average

relative

abundance (%)

Trophic

strategy

Trophic

strategy

(reference)

COG0243 (Anaerobic dehydrogenases,

typically selenocysteine-containing)

0.15 0.27 0.271 - Copiotroph

COG0318 (Acyl-CoA synthetases

(AMP-forming)/AMP-acid ligases II)

-0.34 0.01 0466 - Oligotroph

COG0483 (Archaeal fructose-1,6-

bisphosphatase and related enzymes of

inositol monophosphatase family)

-0.47 0.00 0.106 - Oligotroph

COG0583 (Transcriptional regulator) 0.61 0.00 1.366 Copiotroph Copiotroph

COG0596 (Predicted hydrolases or

acyltransferases (alpha/beta hydrolase

superfamily))

-0.81 0.00 0.567 Oligotroph Oligotroph

COG0625 (Glutathione S-transferase) -0.59 0.00 0.323 Oligotroph Oligotroph

51



relative

abundance (%)

Trophic

strategy

Trophic

strategy

(reference)

COG0737 (5'-nucleotidase/2',3'-

cyclic phosphodiesterase and related

esterases)

-0.11 0.42 0.041 - Copiotroph

COG0814 (Amino acid permeases) 0.30 0.02 0.004 - Copiotroph

COG1024 (Enoyl-CoA

hydratase/carnithine racemase)

-0.3` 0.00 0.390 - Oligotroph

COG1028 (Dehydrogenases with

different specificities (related to

short-chain alcohol dehydrogenases))


COG1228 (Imidazolonepropionase and

related amidohydrolases)


52



relative

abundance (%)

Trophic

strategy

Trophic

strategy

(reference)

COG1263 (Phosphotransferase

system IIC components,

glucose/maltose/N-

acetylglucosamine-specific)

-0.29 0.03 0.031 - Copiotroph

COG1680 (Beta-lactamase class C

and other penicillin binding proteins)

-0.25 0.06 0.148 - Oligotroph

COG1804 (Predicted acyl-CoA

transferases/carnitine dehydratase)


COG1960 (Acyl-CoA

dehydrogenases)

0.23 0.09 0.944 - Oligotroph

COG2124 (Cytochrome P450) -0.66 0.00 0.051 Oligotroph Oligotroph

COG2200 (FOG: EAL domain) 0.13 0.31 0.117 - Copiotroph

53



relative

abundance (%)

Trophic

strategy

Trophic

strategy

(reference)

COG2207 (AraC-type DNA-binding

domain-containing proteins)

0.30 0.02 0.348 - Copiotroph

COG3293 (transposase and

inactivated derivatives)

0.32 0.01 0.007 - Oligotroph

COG3325 (Chitinase) -0.01 0.96 0.002 - Copiotroph

COG3386 (Gluconolactonase) -0.41 0.00 0.046 - Oligotroph

COG3710 (DNA-binding winged-

HTH domains)

-0.65 0.00 0.017 Oligotroph Copiotroph

COG3773 (Cell wall hydrolyses

involved in spore germination)

-0.46 0.00 0.028 - Oligotroph

COG3920 (Signal transduction

histidine kinase)


54

Functional gene categories correlating with incubation

time

Table 6 shows the Subsystem Level 1 and Level 2 genes

that have strong (rho>0.5 or rho<-0.5) correlation with incubation

time when controlling nutrient concentration. Only the selected

Subsystem Level 2 genes that follow the same (whether positive or

negative) pattern with the Subsystem Level 1 category are shown.

Partial correlation results of “Clustering-based subsystem” and

“Miscellaneous” were not included. For Subsystem Level 1 genes,

we also drew a heatmap with Z-score transformed relative

abundance data (Figure 10).

The regulation and cell signaling gene category was more

abundant in the early successional stage. In this gene category,

programmed cell death and toxin-antitoxin systems, quorum

sensing and biofilm formation related genes were abundant in the

early successional stage. The relative abundances of genes

associated with cell division and cell cycle decreased over time, but

the correlation (rho=-0.407, p=0.001) was weaker than for other

genes presented in Table 6. The motility related genes were more

abundant in late successional stages.

There were other genes that showed a significant

correlations which had not been predicted in our hypotheses. These

included genes related to sulfur metabolism, iron acquisition and

55

metabolism, potassium metabolism, metabolism of aromatic

compounds, amino acids and derivatives, and cell wall and capsule

for the early successional stage. For the late successional stage,

photosynthesis, protein metabolism, respiration, nucleosides and

nucleotides, and DNA metabolism were relatively more abundant.

56

Table 6. Subsystem Level 1 and Level 2 genes that have strong (rho>0.5 or rho<-0.5) correlation with

incubation time when controlling nutrient concentration. Only the selected Subsystem Level 2 genes that

follow the same (whether positive or negative) pattern with the Subsystem Level 1 category are shown.

Partial correlation results of “Clustering-based subsystem” and “Miscellaneous” were not included.


Averaged

relative

abundance

rho

Density

dependent

strategy

Sulfur Metabolism - 0.0171 -0.814

r-strategy

Inorganic sulfur assimilation 0.0043 -0.683

Organic sulfur assimilation 0.0090 -0.771

Iron acquisition and metabolism - 0.0268 -0.780

Siderophores 0.0053 -0.800

Regulation and Cell signaling - 0.0151 -0.773

Programmed Cell Death and

Toxin-antitoxin Systems0.0011 -0.610

Subsystem Level 1 Subsystem Level 2 Averaged rho Density

57


relative

abundance

dependent

strategy

Regulation and Cell signalingQuorum sensing and biofilm

formation0.0009 -0.751

r-strategy

Potassium metabolism - 0.0095 -0.730

Metabolism of Aromatic

Compounds- 0.0236 -0.694

Metabolism of central aromatic

intermediates0.0078 -0.550

Peripheral pathways for

catabolism of aromatic compounds0.0104 -0.810

Amino Acids and Derivatives - 0.1015 -0.605

Arginine; urea cycle, polyamines 0.0174 -0.515

Aromatic amino acids and

derivatives0.0139 -0.725

58



Averaged

relative

abundance

rho

Density

dependent

strategy

Amino Acids and

Derivatives

Glutamine, glutamate, aspartate,

asparagine; ammonia assimilation0.0099 -0.601

r-strategyProline and 4-hydroxyproline 0.0048 -0.843

Cell Wall and Capsule - 0.0367 -0.504

Gram-Negative cell wall components 0.0114 -0.616

Photosynthesis - 0.0003 0.821

K-strategy

Protein Metabolism - 0.0633 0.660

Protein biosynthesis 0.0386 0.544

Protein degradation 0.0110 0.848

Motility and Chemotaxis - 0.0150 0.640

Flagellar motility in Prokaryota0.0106 0.702

Respiration - 0.0368 0.592

Electron donating reactions 0.0120 0.654

59



Averaged

relative

abundance

rho

Density

dependent

strategy

Nucleosides and

Nucleotides- 0.0300 0.590

K-strategyDNA Metabolism - 0.0359 0.559

DNA replication 0.0087 0.812

60

Figure 10. Heatmap of Subsystem Level 1 genes which show strong correlation with incubation time

61

Taxonomic and functional diversity

Alpha diversity. In contrast to our expectation, neither functional

gene richness nor function gene diversity show a humpback pattern

against initial nutrient concentration (Figure 11). However,

functional gene richness showed a humpback pattern against

incubation time. (Figure 12). In terms of richness, functional gene

richness had a positive correlation with OTU richness (Song et al.,

2016) or the richness of species level assignment of functional

genes from metagenome (Figure 13). In the case of Shannon

diversity which indicates species evenness/relative abundances, as

opposed to richness, functional gene diversity had no correlation

with OTU diversity nor the diversity of species assignment of

functional genes (Figure 14).

Beta diversity. Beta diversity of functional genes showed a hump-

backed curve against initial nutrient concentration (Figure 15).

However, beta diversity of functional genes showed not so much

patterns against incubation time (Figure 16). Mantel test result

shows that the Bray-Curtis dissimilarity between samples

calculated based on taxonomic annotation of functional genes shows

significant correlation with the Bray-Curtis dissimilarity between

samples calculated based of functional genes. (Mantel static r:

0.9734, p=0.001)

62


against log (initial nutrient concentration+5) transformed nutrient

concentration. A quadratic regression line was applied only to

significant results (p<0.05)

63


against incubation time. A quadratic regression line was applied only

to significant results (p<0.05)

64

Figure 13. Functional gene richness against richness of the species

assignments of functional genes (A: linear regression applied, B:

quadratic regression applied) and OTU richness (Song et al. 2016)

(C: linear regression applied, D: quadratic regression applied).

65

Figure 14. Functional gene Shannon diversity against Shannon

diversity of species assignment of functional genes (A) and OTU

Shannon diversity (Song et al. 2015) (B).

66

Figure 15. Beta diversity of functional genes calculated based on Bray-Curtis dissimilarity between

samples against log (initial nutrient concentration+5) transformed nutrient concentration. A quadratic

regression line was applied only to significant results (p<0.05)

67


Bray-Curtis dissimilarity between samples against incubation time.

A linear regression line was applied only to significant results

(p<0.05)

68

Taxonomic annotation of functional genome

We analysed whether the taxonomic association of each

functional gene had strong correlations with nutrient concentration

or time, to see if they matched to particular genera of bacteria that

might be pinpointed as copiotrophic/oligotrophic or r/K strategists.

However, no matter what the function is, most of the gene

sequences derived from the same set of genera that were abundant

throughout all the samples (Figure 17-20), for example,

Acinetobacter, Pseudomonas, or Sphingomonas, such that we were

unable to identify any genera as being either clearly copiotrophic vs

oligotrophic, or r vs K strategists.

69

Figure 17. Taxonomic annotation of functional genes that have positive correlation with nutrient

concentration. Y-axis shows the relative abundance of 20 most abundant taxa

70

Figure 18. Taxonomic annotation of functional genes that have negative correlation with nutrient

concentration. Y-axis shows the relative abundance of 20 most abundant taxa

71

Figure 19. Taxonomic annotation of functional genes that have positive correlation with incubation time.

Y-axis shows the relative abundance of 20 most abundant taxa

72

Figure 20. Taxonomic annotation of functional genes that have negative correlation with incubation time.

Y-axis shows the relative abundance of 20 most abundant taxa

73

2. 4. Discussion


The overall result seems to confirm that as expected, there

is some decrease in nutrient availability over successional time, but

the initial nutrient concentration is much more important overall

than the change over time. The nutrient measured in the culture

medium might be derived from the leftover of initial nutrient, or

could be derived from recycling of nutrient from dead cells. One

important point is that nutrient A had only 2 to 5 times higher DOC

compared to nutrient C. Possibly, carbon in nutrient A was removed

quickly and sequestered into extra biomass.

Functional gene categories correlating with initial

nutrient concentration (hypothesis A)

As hypothesized, cell division, RNA metabolism and cell

cycle related genes were more abundant in high nutrient

concentrations. High abundance of the RNA related gene categories

implies the possibility of rapid growth being achieved by the more

active expression of overall genetic information encoded in the DNA.

This seems to fit with the view that a copiotrophic environment

selects for the functions of growth and reproduction at the expense

74

of other gene functions. Conversely, and as we had suggested, it

appears that lower nutrient conditions require greater abundances

of genes associated with obtaining and sequestering nutrient

sources such as carbohydrates. This prioritization of acquisition of

nutrients, in different forms and at lower concentrations, suggests

that indeed such functions are a fundamental part of the oligotrophic

strategy in bacteria. It seems that oligotrophic bacterial species also

have relatively high abundance of the genes related to respiration

and electron transfer reactions that are important for the utilization

of their acquired nutrients.

We also hypothesized that copiotrophs would show a

stronger tendency to contain dormancy and sporulation genes, since

they are not adapted to actively grow in nutrient poor conditions

(Fierer et al. 2007). The result supports a view that sporulation is

in particular a strategy used by copiotrophs, to shut down the

metabolism when nutrient supply decreases, and then wait until

nutrient supply improves at some time in the future.

There were also various nutrient concentration-related

patterns in other gene categories. For example, virulence, disease

and defense related genes turned out to be abundant in oligotrophic

conditions. The negative correlation of genes related to resistance

to antibiotics and toxic compounds (one of the subcategories of

virulence, disease and defense genes) with nutrient concentration

75

supports a view of the greater importance of interference

competition amongst bacterial species at lower concentrations of

nutrients (Vance, 1984).

Overall, the correlations of COG genes with nutrient gradient

in our results – at least those genes that show significant

correlations – do largely match the patterns noted by Lauro et al.

(2009) for copiotrophy vs oligotrophy (Table 5). This is even more

striking when one considers the very different basic design of the

two studies, and the fact that the target species and overall

community type belonged to two very different environments:

Lauro et al.’s study (2009) was on marine bacteria, whereas our

study was derived from soil. This strengthens the view that many

of the genes they pinpointed really are of importance in relation to

trophic strategy, and might be universally so – since they show

similar trends in such different environments.

Functional gene categories correlating with incubation

time (hypothesis B)

As expected, cell division and cell cycle related genes were

more abundant in the earlier successional stages, although the

correlation was weaker than for some other gene categories. This

seems to fit with the theory on ‘r’ strategists (Pianka, 1970) that

76

they are selected for rapid growth and reproduction to exploit initial

conditions of lower population densities.

However, contrary to our hypotheses, genes related to

regulation and cell signaling were more abundant at the earlier time

stages of succession in our culture experiments. A priori, the

typical view in ecology would be that later (K-strategy dominated)

successional stages include more biotic interaction (Morriën et al.,

2017; Odum, 1969), whether by mutualism, interference

competition or by specialized parasitism. In our bacterial culture

systems, this principle - based upon ecological observation of

larger organisms over longer periods - does not seem to hold true.

This appears to be an example where principles that apply to

macro-organisms do not apply to the microbial world. It is possible,

for example, that these genes play a role in a strategy of rapid

colonization (in what is largely a two-dimensional static

environment across the cellophane surface of our cultures) through

biofilm formation. Quorum sensing and biofilm formation related

genes were actually more abundant in the early successional stages.

In the early successional stages, biofilm formation might involve

collaborative interactions between members of the same and

different species for the capture of space or nutrients (Shapiro,

1998). Also important in initial capture of space or nutrients may be

activities preventing other cells from growing, for example, by

77

forming a barrier which can deny access of other cells to the

nutrient source, or by antibiotic production (Shapiro, 1998).

Within the general regulation and cell signaling category, we

likewise found that the subcategory of toxin-antitoxin related

genes was more abundant in early stages. These toxin-antitoxin

systems in bacterial species are used to provide genetic stability

when bacterial species divide (Hu et al., 2010). It might be

necessary to have high abundance of these genes for faster growing

bacteria, since they have very limited time for cell division and may

have more errors than slow growing bacteria. Another known

function of toxin-antitoxin system as a mean of programmed cell

death is to provide DNA through cell lysis as a structural

component of biofilm (Bayles, 2007). It is possible, then, that in the

early successional stage, the toxin-antitoxin system might be used

to facilitate biofilm formation genes were likewise more abundant in

the early successional stage.

Motility and chemotaxis genes, and especially flagella-

related genes, were less abundant in the early successional stages,

which also disagreed with one of our hypotheses. We had predicted

that just as r-selected macroorganisms have features promoting

mobility and rapid colonization (Huston and Smith, 1987; Peroni,

1994) these would also be abundant in the early succession stages

of microbial cultures. This might be due to a systematic difference

78

between culture system and the natural environment, whereby

pioneering species are required to have high motility. In setting up

our culture system, we spread soil elutant all across the culture

plates, so early colonizers might not have needed adaptations for

dispersal. By contrast, in aquatic systems the flagellum is a key

cellular structure for acquisition of nutrients, as there are

temporarily nutrient rich patches which bacteria can reach using

flagella and chemotaxis (Jens Efsen et al., 2002). However, in a

culture system such as ours, as colonies deplete nutrients from

around themselves, slower growing bacteria might require motility

to obtain nutrient from spots that have not yet been colonized.

Many other categories of genes showed a significant trend in

relation to incubation time. For example, genes related to minor

nutrients such as sulfur, iron, and potassium showed strong

negative correlations with incubation time. Iron is often a limiting

factor for growth, and such genes might be particularly necessary

to supply enough iron for rapid growth (Church et al., 2000). K-

strategists, in contrast, already have many growth limiting factors

other than iron to contend with, so might be subject to relatively

weak ecological selection for iron uptake compared to other factors.

Another interesting time-related trend is the strong

negative correlation of genes related to metabolism of aromatic

compounds with incubation time. Aromatic compounds might be

79

used as a nutrient source providing additional metabolic versatility

for r-strategists. Even though aromatics are not likely abundant in

our culture media, this may be an incidental legacy of the fact that

these are soil bacteria that normally live in aromatic rich (e.g.

tannin and lignin-rich) environments.

Taxonomic and functional diversity (hypothesis C)

Over the years, there has been much discussion of the

hypothesis that species diversity has a positive correlation with

stability, due to greater functional diversity (McCann, 2000; Wardle

et al., 1999). However, the observed results in terms of both

functional diversity and ecosystem stability vary greatly between

different studies, with some studies supporting the hypothesis of a

link to taxonomic diversity and others contradicting it (Cadotte et

al., 2011; Hooper and Vitousek, 1997; Mayfield et al., 2005;

Petchey and Gaston, 2006). One of the main reasons for this is that

there is no consensus about how best to define functional diversity

– and indeed it is often quite vaguely defined. Functional gene

diversity, based on the proportion of reads in different categories,

is highly quantitative data, and was defined in this fairly objective

way in our study. Gene functional diversity, in terms of functional

gene richness and relative abundances showed a clear linear

80

relationship with OTU richness/richness of species within our

metagenomes – empirically supporting the supposition that more

taxonomic diversity is associated with more functional diversity. We

did not test for aspects of ecosystem stability (McCann, 2000) or

efficiency/rates of ecosystem processes. It would be interesting in

any follow-up studies to test the relationship between stability,

species diversity and functional diversity in such culture systems. It

is also possible that strength of microbial interactions – which might

vary in complex ways that do not necessarily follow overall

diversity – confounds the expected diversity-stability pattern (Ho

et al., 2016).

Taxonomic annotation of functional genome

Previous study which used amplicon sequencing gave us

possible candidates for taxa previously categorized by us as

copiotrophic or oligotrophic, on the basis of their prevalence in

different nutrient treatments (Song et al., 2016). Beyond this, our

metagenome study presented here provides functional genes

associated with bacterial strategies in different nutrient

concentration and incubation time. The taxonomic annotation of the

functional genome shows a similar distribution pattern, particularly

in terms of the most abundant genera associated with each

81

functional gene category, (Figure 17-20) which implies that the

difference between samples in terms of functional gene composition

is strongly linked to the differences in relative abundance of these

most abundant genera. It is also possible that the differences in

functional composition between samples might be caused by the

combination of many minor genera that has been selected to their

functional characteristics. Finer phylogenetic resolution (species or

strain level) may be needed to capture differences in the

community composition between treatments.

Possible limitations of this study and suggestions for

further work

It is important to acknowledge that there are various

limitations in this study, which may limit the accuracy or relevance

of the results in terms of understanding bacterial community

ecology in nature:

1) This is an artificial system. Microcosms are by their nature

derived and simplified from natural systems. This simplification has

the advantage of allowing more precise control and manipulation of

conditions, but the risk of leaving out certain crucial aspects of

natural systems which may make their results less relevant to

nature. It is possible that microcosms, which modify and monitor

82

small quantities of soil (rather than soil elutant) would give a more

representative understanding of bacterial strategies in nature. It is

also important to realize that the types of nutrients added might

have an important effect on community structure. Also, the soil

system is only one of a huge variety of environments bacteria can

live in. In other environments, different strategies might apply –

although the overlap between the genes associated with copiotrophy

in our study and those found by Lauro et al. (2009) is reassuring,

and implies that we may be observing true general patterns in

bacterial ecology.

2) We did not study metatranscriptomes. Here we studied the

metagenome (DNA), rather than metatranscriptome (RNA) which

would provide a more direct picture of cellular activity. It is

possible that part of the community composition and functional gene

composition that we see is related to a ‘legacy’ of cells that are

already dead or inactive, derived from earlier successional stages

or from the initial soil elutant. This may be tending to ‘blur’ the

functional patterns observed. On the other hand, the metagenome

can give a picture of the whole range of potentially expressed genes

present, which may be relevant to the overall strategies of these

organisms in nature, whether or not they were being expressed at

the time of sampling.

3) Our data has been collected across a prescribed (though very

83

broad) range of initial nutrient concentrations and incubation times,

so the conclusions made in this study may only be applicable within

this range. It is possible that the gene abundance patterns would

differ outside of this range.

It is likely that the more nutrient-rich treatments we used

are much more copiotrophic than the average background level of

nutrients in soils, but do correspond to occasional, spatially isolated

and often brief, bursts of very high nutrients from rotting animal

carcasses, rotting rootlets, dung, rotting fruits etc. These highly

copiotrophic environments are likely to be ecologically important

enough to select specialized communities – even if strategists that

thrive in these environments must bide their time (perhaps as

dormant or low activity forms at lower abundance within the general

soil community) between such opportunities. These bursts may also

occur on a microscopic scale (e.g. the vicinity of a single dead

nematode), but to bacteria they would be high nutrient

environments nevertheless.

We did not do chemical analysis for the “nutrient E” sample,

which has the lowest nutrient concentration. From the dilutions, we

can deduce that DOC and DTN in nutrient E should be lower than

3mg/L which is similar to environmental samples (Jones and Willett,

2006; Michalzik et al., 2001) depending on the environment that is

used for comparison.

84

4) It is difficult to separate the effects of environmental selection

and phylogeny. If a particular phylogenetic group of bacteria is

particularly abundant in (for example) copiotrophic conditions, any

genes that are consistently associated with that group will tend to

be more abundant. These genes may be fundamental to the group’s

success as copiotrophs, or alternatively they might be ‘riders’

which have no particular significance. Instead, the true ‘strategy’

might lie with subtle differences in other groups of genes – perhaps

involving finely tuned qualitative differences in behavior of

expressed proteins rather than differences in gene abundances.

5) It is unclear whether the patterns observed are always due to

selection for a particular gene function, or selection against other

functions within the genome. Since metagenome composition

measures relative abundances, a gene may become more common

not because it is actively selected for, but by the relative absence

of other sets of genes, which are selected against. This somewhat

limits the confidence that can be placed in the conclusions.

6) Since the percentage of proteins with no known function was

greater in later successional stages, it is possible that there is a

trend of increasing diversity in gene functions and functional

composition with successional time that cannot easily be discerned

using existing databases.

Nevertheless, despite all these caveats we regard this study

85

as a useful early step towards identifying the key differences,

which can explain the differences in bacterial community

composition along nutrient and time gradients.

86

2. 5. Concluding remarks

Although based on a highly simplified system, the results of

this experiment identify sets of genes, which may be part of

distinguishable community-level strategies seen amongst bacteria,

in terms of the relative abundance of particular functional gene

types. In terms of the oligotrophic-copiotrophic axis, the gene

abundance patterns we found are close to those identified by Lauro

et al. (2009), and the agreement between the two studies (based on

quite different approaches) reinforces confidence that the functional

gene differences found have relevance in determining strategies.

We were also able to identify a second axis of functional gene

variation, distinct from the oligotrophic-copiotrophic one, that

relates to time and resembles the r-K axis found in successional

ecological systems of larger organisms. However, it is interesting

that the patterns of adaptations (e.g. for dispersal, or regulation and

cell signaling) do always not match those classically described for

macro-organisms. These results, while intriguing, are merely an

initial attempt at discerning and understanding the basis of bacterial

adaptations for survival. It is hoped that this study will provide a

spur for thought and further work on this important aspect of

microbial ecology.

87

CHAPTER 3: Environmental Filtering

of Bacterial Trait Diversity along an

Aridity Gradient

88

3. 1. Introduction

Soil metagenomes can potentially reveal patterns and

general rules of function of soil biota, and from this the general

ecological patterns of community structure and ecosystem function

(Fierer, 2017). The search for community traits in metagenomes is

based upon genes considered to code for these traits, rather than

observation of the traits themselves, which would often be difficult

to observe and quantify within the soil biota in the intact system.

Various metagenetic and metagenome studies in recent

years have tested whether hypotheses based on observation of

variation in community traits of larger organisms (such as trees,

corals or birds) also apply to the microbial world. For example, Choi

et al. (2017) tested for the existence of disturbance history related

gradients in ecological strategies, analogous to the r- and K

strategies seen in larger organisms (MacArthur and Wilson, 1967).

Tripathi et al. (2017) tested for certain traits against an aridity

gradient in Israel, making various predictions about how these traits

may be expected to vary in relation to a stress gradient.

However, the study by Tripathi et al. (2017) was limited in

scope, taking only four points along an aridity gradient and using

only 3 closely clustered replicates at each point along the gradient.

There was a need for a more thorough study comparing

89

metagenomes along an aridity gradient. We chose the Tibetan

Plateau because it presents a strong aridity gradient under similar

mean annual temperature conditions.

Our primary interest was in the effects of an environmental

stress gradient on the functional diversity of the soil biota. Theory

and observation of larger organisms, notably plants, has predicted

and observed broad scale gradients in the diversity of functional

traits along climatic stress gradients (Kleidon et al., 2009; Kleidon

and Mooney, 2000; Swenson et al., 2012). These authors suggested

that extreme physiological conditions and reduced energy budgets

for organisms constrain the number of different functional

strategies, which are viable in any given environment. There were

hints of this pattern in the earlier soil metagenome study of an

aridity gradient in Israel (Tripathi et al., 2017), where higher

aridity was associated with reduced functional trait diversity in the

metagenome.

In the Israel precipitation gradient study (Tripathi et al.,

2017), authors also made various predictions regarding specific

traits. They expected increased abundance of stress response gene

and genes related to sporulation in more arid conditions. Also, they

expected genes related to nutrient cycling will be more abundant in

less arid conditions and there would be more genes related to

metabolism of complex organic compounds. These showed the

90

observed patterns in some respects (genes related to sporulation,

nutrient cycling, and metabolism of complex organic compounds)

but not others (stress response gene).

Compared to the study by Tripathi et al. (2017), our present

study was based on a much greater number of samples, scattered

much more widely spatially and at a range of different precipitation

values. As such, it represents a more rigorous and convincing study

of how trait diversity and characteristics vary along climate

gradients.

In the present study, we aimed to test the following hypotheses

(from now on, ‘gradient’ will always be used as a gradient from low

to high):

1) Trait diversity will decrease along climate gradient towards

increasing aridity with less inter-site variability in functional

composition and reduced functional diversity will be closely

associated with reduced taxonomic diversity. Such trends have

been found in physiological or morphological characteristics of

larger organisms (Kleidon et al., 2009; Kleidon and Mooney, 2000;

Nunes et al., 2017) and in functional gene diversity in microbes

(Tripathi et al., 2017). Extreme conditions will require a large

proportion of an organism’s energy and other resources to be

diverted into homeostasis (e.g. against extreme osmotic conditions,

extreme pH, lack of water etc.) (Lauber et al., 2009; Nunes et al.,

91

2017). This may preclude other functional traits which could

otherwise enhance survival (e.g. through additional strategies of

nutrient acquisition, mutualism, or interference competition) or

allow occupation of a wider range of different types of niches. We

also expect inter-site variability will be lower in arid conditions due

to frequent wind transport homogenizing the microbial community.

2) Genes related to cell division and dormancy will increase along

an aridity gradient. Desert soil experiences a strikingly unstable

environment. Strong and frequent wind work as a mechanical

disturbance and there tends to be an extreme daily temperature

range – especially in high elevation deserts (Belnap and Gillette,

1998; Kemp et al., 1992; van Gestel et al., 2011).

Rainfall/meltwater events in desert are rare, with soils dry most of

the time, and when the soil is wetted organisms of all types have a

brief window of time in which to grow, feed and reproduce

(Schwinning and Sala, 2004). Therefore, it is expected that

bacterial cells will be adapted to a) divide fast when they encounter

moist and moderately warm conditions, and b) stay in a dormant

state under unfavorable conditions of aridity/high temperatures/low

temperatures. This will result in greater copy numbers within

genomes of genes related to cell division (for times of growth) or

dormancy (for times when growth is not favored).

92

3) Stress response genes – especially those relating to

osmoregulants - will be more abundant in environments that are

more arid. In plant ecology, stress is defined as “external

constraints which limit the rate of dry matter production of all or

part of the vegetation” (Grime, 2006). Desert environments are

often regarded as ‘stressful’ because of high disturbance rate

through wind erosion, high salinity, low moisture content, low

nutrient concentrations, and low primary productivity. In high

elevation deserts, extreme daily temperature range and high UV

light flux are also important. Many macro-organisms living in

desert are adapted to extreme conditions by mechanisms for

example, by producing osmoprotectants (Ashraf and Foolad, 2007)

such as insoluble forms of nitrogenous waste (Hadley, 1974).

Bacterial species also cope with extreme environments by

accumulating protectant solutes, or by producing large quantities of

chaperone proteins to stabilize cellular proteins (Csonka, 1989;

Schimel et al., 2007). We expected that bacterial genes for these

and other stress responses would be more abundant towards the

more arid end of the climate gradient we studied here.

4) There will be a lower frequency of antibiotic resistance and

virulence genes in more arid conditions due to the predominance of

abiotic selection. In ecology, it is generally considered that in

extreme environments, the physiological costs and functional

93

constraints on organisms leave relatively little of their resources

for interference competition (Grime, 2006; Pueyo et al., 2008).

Additionally, slow rates of growth and population increase in

extreme environments, relative to rates of disturbance, are seen as

resulting in less intense competition (Huston, 1979). Thus, we

anticipated that there would be relatively low abundances of genes

relating to biotic interaction – either for interference competition or

for purposes of forming mutualisms. Such genes would include, for

example, antibiotic resistance genes – being less frequent in the

metagenome because of reduced interference competition by

antibiotic production (Brook, 1999; Hibbing et al., 2010).

5) Motility related genes will decrease along aridity gradient. In

plant ecology, the average seed spatial dispersal rate of the

community decreases in more arid conditions, due to differences in

investment in dispersal traits in plants (Pueyo et al., 2008; Venable

et al., 2008). Favorable growing conditions are ephemeral and rare

in the desert landscape, and thus difficult to find, and as a result

plant seeds in deserts have evolved to stay close to the mother

plant and in a dormant state rather than disperse across the

landscape to find favorable conditions (Venable et al., 2008). In the

bacterial world, we expected the same principle to operate, in that

bacterial cells should allocate more towards genes related to

94

dormancy instead of motility, since it is better to await a return of

moist conditions than to spend resources on discovering new places.

6) Functional gene composition along nutrient concentration will

follow those found in the microcosm study (Chapter 2). Although

the two studies are very different from each other systematically

(one is from precisely controlled system and the other one is from

extremely heterogeneous environment with several environmental

factors co-varying), we expect that we will be able to find

consensus patterns in the two studies.

95

3. 2. Materials and Methods

Soil sampling and data collection

We collected soil sample from 29 grassland sites across in

Tibetan plateau (Figure 21). We sampled during the growing season

in July-August. In each site, seven randomly located 0-5cm depth

soil cores (each 5 cm diameter) were collected across a single 1

square meter quadrat (m2). The seven individual soil core samples

were placed in one bag and thoroughly mixed to homogenize them –

making one sample from which the DNA for a single metagenome

would be obtained.

96

Figure 21. Map of sample collection site

97

Climatic data collection and physicochemical analysis

Climatic condition of sampling sites and physicochemical

properties of the samples are shown in Appendix Table A1. We

collected mean annual temperature (MAT), mean annual

precipitation (MAP) data of each site from the National

Meteorological Bureau of China database (Jing et al., 2015).

Growing season (from April to August) temperature (GST) and

growing season precipitation (GSP) data were compiled from the

1950-2000 records of a global climate database (Hijmans et al.,

2005; Ma et al., 2010). Potential Evapotranspiration (PET) data

were collected from Global Aridity and PET database of CGIAR-

CSI (http://cigar-csi.org) (Yang et al., 2017a). Actual

evapotranspiration (AET) was determined by comparing potential

evapotranspiration with precipitation plus available water stored in

the soil as described in Major (1963) and Kreft and Jetz (2007).

Soil moisture (SM) was measured gravimetrically by drying

soils at 105℃ for ~10 hours. Soil pH was measured using a pH

meter (Thermo Orion-868) in a 1:5 ratio of fresh soil to deionized

water slurry. Soil total carbon (STC) and soil total nitrogen (STN)

were measured by combustion of the samples in a CHN elemental

analyzer (2400 Ⅱ CHN elemental analyzer, PerkinElmer, Boston,

MA, USA). Soil total phosphorous (STP) was measured by

98

molybdenum blue method in conjugation with ultraviolet-visible

spectrophotometer (UV-2550, Shimadzu, Kyoto, Japan). Soil

CaCO3 was measured using a volumetric calcimeter (Eijkelkamp,

Netherland). Soil organic carbon (SOC) was calculated by

subtracting soil CaCO3 (SCaCO3) from soil total carbon (STC). The

density of SOC, STN, STP, SCaCO3 were calculated in the top 0-

5cm soil.

Dissolved organic carbon (DOC), dissolved total nitrogen

(DTN), and soil mineral nitrogen were extracted by mixing 10

grams of fresh soil with 50 ml of 0.5 M K2SO4, shaking for an hour,

and vacuum filtering through glass fiber filters (Fisher G4, 1.2 ㎛

pore space). Ammonium nitrogen (NH4+-N) content were assessed

colourimetrically by automated segmented flow analysis

(Bran+Luebbe AAⅢ, Germany) using salicylate/dichloroisocyanuric

acid method. Nitrate nitrogen (NO3--N) content were assessed

using the same analysis with cadmium column/sulphanilamide

reduction method. DOC and DTN was assessed by TOC-TN

analyzer (Shimadzu, Kyoto, Japan). Dissolved organic nitrogen

(DOC) was calculated by subtracting NH4+-N and NO3

-—N from

DTN (Yang et al., 2017b).

To assess aboveground biomass, plant total carbon (PTC),

plant total nitrogen (PTN), and plant total phosphorous (PTP), plant

communities in each 1-m2 plot were surveyed and harvested before

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soil sampling (Jing et al., 2015). Plant materials were dried at 60℃

for ~12 hours and grinded. PTC, PTN, and PTP was analyzed with

the same method used for STC, STN, and STP. To assess

belowground biomass, 4-8 soil cores per 1-m2 plot (0-5 cm deep,

7 cm diameter) was collected. To calibrate root biomass, live:dead

(56:63) ratio was used.

DNA extraction, sequencing and sequence processing

DNA was extracted from 0.5 g of soil in each sample using a

FastDNA Spin kit (Bio 101, Carlsbad, CA, USA), following the

manufacturer’s instructions and stored at -40℃. Extracted DNA

samples were sent for shotgun metagenomic sequencing to

Celemics (Seoul, Korea) using Illumina HiSeq2500 sequencing

platform. Sequence data file was uploaded to MG-RAST

(metagenomics Rapid Annotation using Subsystem Technology)

server for quality control and for annotation (Meyer et al., 2008)

(reviewer access:

http://metagenomics.anl.gov/mgmain.html?mgpage=token&token=N

dd1oCi0lw4In9JuUQzlfjaXYDORTnEg2jEjWRjnsrYUaKcU6E). For

taxonomic annotation of functional genes, Refseq database was used

(Pruitt et al., 2007) and for functional annotation, SEED Subsystem

database was used (Overbeek et al., 2005). Sequences were

annotated with default setting (maximum e-value cutoff of 10-5,

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minimum % identity cutoff of 60%, and minimum alignment length

cutoff as 15 bp). About 97% of total sequences on average

comprised of bacterial sequences, so we focused only on bacterial

functional genes and removed sequences derived from other

organisms (archaea, viruses, and eukaryotes).

Statistical analysis

Samples were subsampled to the least sequence read

number for standardization. To find environmental variables that

explain variance in functional gene composition between samples,

we performed redundancy analysis (RDA) using Canoco ver. 5.

Subsystem Level 3 functions were square-root transformed and

environmental variables were forward selected. Monte Carlo

permutation test with 999 permutations were used to test

significance of the model. To visualize the correlation between z-

score transformed relative abundance of functional gene categories

and precipitation gradients, we drew heatmap using “pheatmap”

package in R. Gene categories were clustered by their Euclidean

distance with unweighted pair group method with arithmetic mean

(UPGMA) method. To find out linear relationship between

functional gene diversity and environmental factors and to generate

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figures, we used Sigmaplot ver. 10.0 from Systat Software, Inc.,

San Jose California USA (www.systatsoftware.com).

Functional diversity was calculated based on Subsystem

Level 3 functions. To compare functional diversity and taxonomic

diversity, bacterial community composition data was obtained from

Jing et al. (2015). To calculate beta diversity, subsampled read

numbers were square root transformed and Bray-Curtis

dissimilarity between each sample point was calculated. To compare

taxonomic and functional beta diversity, Mantel test was performed

by using “ mantel.test” function in R package “vegan” . To

compare functional beta diversity in different vegetation types

(alpine meadow, alpine steppe, and desert steppe), bacterial

functional gene composition data were square root transformed and

Bray-Curtis dissimilarity from group centroid was calculated.

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3. 3. Result

Functional gene composition along an aridity gradient

After quality control, the total number of sequence reads per

sample ranged from 1,046,994 to 2,274,876. The RDA result

showed that mean annual precipitation was the best predictor for

functional gene composition, alone explaining 26.7% of total

variation (Figure 22, Table 7). Heatmaps of standardized

Subsystem Level 1 gene relative abundance along mean annual

precipitation (Figure 23) and soil moisture (Figure 24) show gene

categories variously having significant positive, negative correlation

or no correlation. As aridity increases along the gradient, genes

related to cell division and cell cycling, dormancy and sporulation,

DNA metabolism, RNA metabolism, protein metabolism, nucleotides

and nucleosides, and etc. became relatively more abundant. Genes

related to nutrient cycling (sulfur metabolism, potassium

metabolism, nitrogen metabolism, iron acquisition and metabolism,

fatty acids) and metabolism of aromatic compounds became less

abundant. Motility gene, regulation and cell signaling gene and

secondary metabolism related genes also decreased. Since stress

response category can be subdivided to different type of stress, we

checked Subsystem Level 3 genes, which belongs to stress

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response category whether they have correlation with precipitation

gradients (Table 8).

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Figure 22. RDA result showing sample distribution based on bacterial

functional gene composition along the environmental gradient in Tibet.

Vegetation types are indicated, with MAP of alpine meadow>alpine

steppe>desert steppe. MAP: mean annual precipitation (mm), SM:

soil moisture (g/g dried soil), SOC: soil organic carbon (%), STP: soil

total phosphorous (%).

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Table 7. Forward selection of environmental variables, which best explain variation in functional gene

composition between samples. A pseudo-F statistic from Monte Carlo permutation test was derived in the

same way as in ANOVA of the regression model

Environmental variable Explains % pseudo-F P

Mean annual precipitation (MAP) 26.7 9.8 0.001

Soil moisture (SM) 6.9 2.7 0.003

Soil organic carbon (SOC) 7.4 3.1 0.002

Soil total phosphorous (STP) 4.1 1.8 0.018

Soil C:N ratio 4.0 1.8 0.019

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Figure 23. Heatmap of standardized relative abundance of Subsystem Level 1 genes along mean annual

precipitation (mm). Gene categories that have positive/negative/no linear correlation with mean annual

precipitation was denoted by ‘(+)’/‘(-)’/‘(0)’

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Figure 24. Heatmap of standardized relative abundance of Subsystem Level 1 genes along soil moisture

(g/g of dried soil). Gene categories that have positive/negative/no linear correlation with soil moisture was

denoted by ‘(+)’/‘(-)’/‘(0)’

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Table 8. Subsystem Level 3 genes belonging to stress response category showing significant correlation

with mean annual precipitation

Subsystem Level 2 Subsystem Level 3 p-value Correlation

coefficient

Acid stress Acid resistance mechanisms 0.000 0.739

Cold shock Cold shock, CspA family of proteins 0.027 -0.411

Detoxification Uptake of selenate and selenite 0.000 0.736

Heat shock Heat shock dnaK gene cluster extended 0.000 -0.700

NULL Bacterial hemoglobins 0.000 0.867

Carbon Starvation 0.007 0.488

Dimethylarginine metabolism 0.034 0.395

Flavohaemoglobin 0.024 0.419

Phage shock protein (psp) operon 0.000 -0.630

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Subsystem Level 2 Subsystem Level 3 p-value Correlation

coefficient

Osmotic stress Betaine biosynthesis from glycine 0.009 -0.474

Ectoine biosynthesis and regulation 0.001 -0.584

Osmoprotectant ABC transporter YehZYXW of

Enterobacteriales

0.010 -0.469

Osmoregulation 0.000 0.638

Osmotic stress cluster 0.044 0.377

Synthesis of osmoregulated periplasmic glucans 0.000 0.607

Oxidative stress Glutathione: Non-redox reactions 0.000 0.702

Glutathione: Redox cycle 0.030 0.403

Glutathione analogs: mycothiol 0.017 -0.441

Oxidative stress 0.002 0.561

Protection from Reactive Oxygen Species 0.002 0.555

Redox-dependent regulation of nucleus processes 0.002 -0.553

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Functional gene composition along nutrient gradient

To compare functional attributes changing along nutrient

gradients in this study with those found in the microcosm study

(Chapter 2) we tested correlation between Subsystem Level 1

functions and major nutrient components (soil total carbon, soil

organic carbon, soil total nitrogen, soil total phosphorous). As in

Table 9, several gene categories showed a different pattern in the

two studies. For example, relative abundance of carbohydrate

related genes showed negative correlation with nutrient

concentration in the microcosm study, but showed positive

correlation with nutrient concentration in the field study in Tibetan

plateau. Also, cell division and cell cycle related genes were

abundant in high nutrient condition in the microcosm study, but not

in the field study in Tibetan plateau. Genes related to motility and

chemotaxis, potassium metabolism, RNA metabolism, stress

response, virulence, disease and defense also showed an opposite

pattern.

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Table 9 Pearson correlation between Subsystem Level 1 genes and soil total carbon (STC), soil organic

carbon (SOC), soil total phosphorous (STP), soil total nitrogen (STN) in the field study in Tibetan plateau

and Spearman correlation between Subsystem Level 1 genes and initial nutrient concentration in the

microcosm study (Chapter 2). Significant positive correlations are highlighted in green and significant

negative correlations are highlighted in red.

Subsystem Level 1 genes STC SOC STP STN Microcosm

Amino Acids and Derivatives -0.287 -0.294 -0.238 -0.332 0.803

Carbohydrates 0.525 0.558 0.377 0.554 -0.672

Cell Division and Cell Cycle -0.772 -0.791 -0.409 -0.786 0.696

Cell Wall and Capsule 0.136 0.139 -0.052 0.138 0.251

Clustering-based subsystems -0.746 -0.773 -0.365 -0.752 0.551

Cofactors, Vitamins, Prosthetic Groups, Pigments -0.556 -0.557 -0.372 -0.582 0.188

DNA Metabolism -0.762 -0.785 -0.445 -0.787 0.213

Dormancy and Sporulation -0.352 -0.345 -0.154 -0.337 0.593

Fatty Acids, Lipids, and Isoprenoids 0.641 0.655 0.219 0.637 -0.226

Iron acquisition and metabolism 0.302 0.307 0.044 0.330 -0.025

Membrane Transport 0.238 0.214 0.318 0.259 0.138

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Subsystem Level 1 genes STC SOC STP STN Microcosm

Metabolism of Aromatic Compounds 0.762 0.798 0.326 0.767 0.347

Miscellaneous -0.384 -0.373 -0.202 -0.348 0.105

Motility and Chemotaxis 0.673 0.694 0.310 0.661 -0.404

Nitrogen Metabolism 0.634 0.652 0.356 0.662 0.217

Nucleosides and Nucleotides -0.797 -0.793 -0.518 -0.786 0.242

Phages, Prophages, Transposable elements, Plasmids -0.616 -0.645 -0.326 -0.598 -0.070

Phosphorus Metabolism 0.172 0.217 0.086 0.248 -0.136

Photosynthesis -0.231 -0.197 -0.322 -0.173 -0.785

Potassium metabolism 0.811 0.825 0.437 0.800 -0.406

Protein Metabolism -0.724 -0.760 -0.305 -0.743 -0.552

Regulation and Cell signaling 0.419 0.434 0.154 0.435 0.209

Respiration 0.505 0.482 0.352 0.465 -0.828

RNA Metabolism -0.735 -0.755 -0.578 -0.763 0.619

Secondary Metabolism 0.422 0.473 0.225 0.501 0.091

Stress Response 0.619 0.635 0.271 0.659 -0.326

Sulfur Metabolism 0.756 0.799 0.441 0.797 0.368

Virulence, Disease and Defense 0.704 0.720 0.411 0.716 -0.795

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Functional gene diversity vs OTU diversity

Alpha diversity. Functional gene richness increased as OTU

richness increase (Figure 25-A), but functional gene Shannon

diversity had no linear relationship with OTU Shannon diversity

(Figure 25-B).

Beta diversity. Taxonomic and functional gene beta diversity

between samples calculated based on Bray-Curtis dissimilarity

showed significant correlation. (Mantel test of 999 permutations,

Pearson correlation, Mantel static r=0.8376, p<0.001).

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Figure 25. Metagenome bacterial Subsystem Level 3 functional gene richness against bacterial OTU

richness (A). Metagenome bacterial Subsystem Level 3 functional gene Shannon diversity against

bacterial OTU Shannon diversity (B). Linear regression line was applied only when significant. Presented

R-squared value is adjusted R-squared value.

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Functional gene diversity along aridity gradients

Alpha diversity. Functional gene richness and Shannon diversity

decreased along aridity gradient (Figure 26). The correlation was

stronger than that between OTU richness/Shannon diversity

(Figure 27). In fact, neither bacterial OTU richness nor bacterial

Shannon diversity showed significant correlation with precipitation

gradient (0.05<p<0.1) (Figure 27). Functional gene richness nor

Shannon diversity had linear relationship with soil moisture (Figure

28). However, functional gene Shannon diversity and soil moisture

had strong rank based correlation (spearman correlation, rho=0.75,

p<0.0001) which are applicable to the dependent variables with

non-normal distribution. Although our study focuses on bacterial

sequences, for more information on the functional patterns in soil

biota, we also checked whether fungal or archaeal gene Shannon

diversity have correlation with mean annual precipitation (Figure

29). We found no significant correlation.

Beta diversity. We grouped samples by vegetation type (which is

broadly representative of sections of the aridity gradient) and

compared beta diversity calculated based on Bray-Curtis

dissimilarity of functional gene composition from group centroid. In

contrast to alpha diversity, beta diversity was greatest in desert

steppe samples, which had lowest average mean annual

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precipitation (127.87 mm) together with alpine meadow samples

(Figure 30). Thus, desert samples were on a local scale relatively

homogeneous, but at distance relatively heterogeneous.

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Figure 26. Metagenome bacterial Subsystem Level 3 functional gene richness against mean annual

precipitation (A). Metagenome bacterial Subsystem Level 3 functional gene Shannon diversity against

mean annual precipitation (B). Linear regression line was applied. Presented R-squared value is adjusted

R-squared value.

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Figure 27. Bacterial OTU richness against mean annual precipitation (A). Bacterial OTU Shannon diversity

against mean annual precipitation (B). Results not significant (P>0.05).

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Figure 28. Metagenome bacterial Subsystem Level 3 functional gene richness against soil moisture (A).

Metagenome bacterial Subsystem Level 3 functional gene Shannon diversity against soil moisture (B).

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Figure 29. Metagenome fungal Subsystem Level 3 functional gene Shannon diversity against mean annual

precipitation (A). Metagenome archaeal Subsystem Level 3 functional gene Shannon diversity against

mean annual precipitation (B). Results not significant (P>0.05).

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Figure 30. Functional beta diversity in each vegetation type

calculated based on Bray-Curtis dissimilarity of Subsystem Level 3

genes from group centroid. Alphabet denotes posthoc test result of

Tukey’s HSD.

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Taxonomic annotation of functional genes

To see if there are dominant taxonomic groups for each

gene category, we annotated each gene category using taxonomic

database. We found that most of Subsystem Level 1 genes that had

negative correlation with precipitation gradients belonged to

Conexibacter (Figure 31). Subsystem Level 1 genes that had

positive correlation with precipitation gradients belonged to

Mycobacterium, Rhodopseudomonas, Pseudomonas, Bradyrhizobium,

etc. (Figure 32). Overall, however, it was hard to spell out specific

taxonomic groups from each of the gene category since there were

huge overlap in the most abundant 20 genus of the taxonomic

annotation of functional genes (Figure 31-32).

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Figure 31. Taxonomic annotation of functional genes that have negative correlation with precipitation

gradients. Y-axis shows the relative abundance of 20 most abundant taxa

125

Figure 31. (Continued)

126

Figure 32. Taxonomic annotation of functional genes that have positive correlation with precipitation

gradients. Y-axis shows the relative abundance of 20 most abundant taxa

127

Figure 32. (Continued)

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3. 4. Discussion

Hypothesis 1) Trait diversity will decrease along an

aridity gradient

As hypothesized, functional gene diversity decreased along

the aridity gradient. By extension from gene diversity, it appears

that more arid conditions may indeed constrain the number of traits

that are viable (and hence retained in the community), as discussed

by Kleidon et al. (2009), Kleidon and Mooney (2000) for plants,

and Tripathi et al. (2017) for microbes. The relationship between

functional gene diversity and aridity was stronger than the

relationship between taxonomic diversity and aridity.

Intriguingly, functional beta diversity was paradoxically

greatest in the desert samples together with alpine meadow

samples. This may be a habitat heterogeneity effect, due to

stronger substrate rock effects in an arid environment where

weathering has exerted a weaker effect on soil chemistry. It might

also be due to an ‘oasis effect’ whereby locally more resource rich

environments produce greater patchiness in bacterial community

traits.

We found that functional gene richness also correlates with

OTU richness, but its relationship was much weaker than the effect

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of aridity. The OTU-richness/gene richness pattern was also

weaker in comparison to the strength of the OTU-richness

relationship seen in the microcosm study (Chapter 2). In the

microcosm study, we were dealing with a rapidly selected subset of

a single common community (one soil sample from which all the

culture treatments were derived), whereas in this study across the

Tibetan Plateau the samples are a product of long-term ecological

and evolutionary selection between different places. For this reason,

we suggest that the trend seen in our study of Tibet (gene

functional diversity independent of taxonomic diversity) is more

representative of broad scale community trends in nature.

Hypothesis 2) Genes related to cell division and

dormancy will increase along aridity gradient

Agreeing with our hypothesis, genes related to cell division

and dormancy increased along aridity gradient. This suggests

bacterial species living in extreme conditions allocate more space in

their genome towards awaiting and then rapidly exploiting more

favorable growing conditions. A similar pattern has been found in

cross-biome study – showing that desert soil metagenome samples

had more abundant cell division and dormancy genes compared to

non-desert samples (Fierer et al., 2012). Also, these two gene

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categories were most abundant in more arid regions in Tripathi et al.

(2017). The lower abundance of motility genes in more arid

conditions likewise parallels the findings of Tripathi et al. (2017),

and supports the idea that bacterial species may allocate their

energy and genome space towards dormancy rather than finding

new environments.

Hypothesis 3) Stress response genes – including

those relating to osmoregulants - will be more

abundant in higher aridity gradients

In contradiction with our expectations, overall stress

response gene relative abundance decreased along the aridity

gradient. This pattern was also found in Tripathi et al. (2017).

Within the overall category of stress response genes, this trend

was dominated by genes relating to “Synthesis of osmoregulated

periplasmic glucans” (Table 8), which are produced when there is

excessive water (Jean-Pierre and Jean-Marie, 2007). This might

then explain why moister climates were associated with greater

abundance of these genes, since average soil water content, and the

frequency of bursts of very high soil water content, is likely to be

greater. However, the subcategories of genes related to production

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of osmoprotectants such as choline and betaine uptake or betaine

biosynthesis (“Betaine biosynthesis from glycine”, “Choline and

betaine uptake and betaine biosynthesis”, “ectoine biosynthesis and

regulation”) were more abundant in more arid conditions, as would

be expected (Csonka, 1989; Schimel et al., 2007). Hence, it seems

to be partly a case of studying the correct subset of stress genes in

relation to aridity gradients.

Hypothesis 4) There will be less incidence of

antibiotic resistance and virulence genes in more arid

conditions

As predicted, there was lower relative abundance of genes

related to cell-cell interaction (regulation of cell signaling),

antibiotic resistance and virulence genes towards the more arid end

of the gradient. As aridity increases, cell density decreases (Clark

et al., 2009), and this may be expected to decrease selection for

competitive traits, by analogy with the stress-selected state of

Grime for plants (Grime, 2006). It is possible that resource-

consuming adaptations for survival of environmental stress may

also leave fewer resources available for such competitive traits.

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Hypothesis 5) Motility related genes will decrease

along aridity gradient

As we predicted by analogy with less investment in seed

mobility by desert plants (Pueyo et al., 2008; Venable et al., 2008),

bacterial motility genes were less abundant in arid conditions. This

suggests that for bacteria too, a more conservative strategy of

remaining in place in favorable microsites is ecologically selected in

the arid environment. The motility genes, which varied along the

aridity gradient, were mostly flagellar genes: motility in bacterial

world most often relates to flagellae, which represent an active

form of movement. It is also necessary to bear in mind that as

flagallae require water surrounding the cells, these might be of less

ecological benefit within a soil that is dry most of the time.

Hypothesis 6) Functional gene composition along

nutrient concentration will follow those found in the

microcosm (Chapter 2)

In contrast to our expectation, the field study in Tibetan

plateau and the microcosm study showed different pattern in the

functional composition along nutrient gradients. These differences

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seems to be caused by systematic differences between the two

studies. For example, the relative abundance of cell division and cell

cycle related genes were less abundant in low nutrient

concentration in culture sample, but not in the soil samples from

Tibetan Plateau. Low nutrient conditions are usually found in desert

samples from Tibetan Plateau (Appendix Table A1), which have

higher disturbance frequency than forest sites. It is possible that

disturbance intensity in the desert steppe of Tibetan Plateau have

stronger selective force than nutrient concentration for cell division

and cell cycle related genes. Motility related genes were more

abundant in high nutrient concentration in the microcosm study, but

opposite trend was found in the field study in Tibetan Plateau.

Moisture content could be more important for the selection of

motility related genes than nutrient.

Possible limitations of the study

The functional diversity as recognized here is based only on

gene diversity, which is not necessarily the same as phenotypic

functional diversity of the whole community. Some bacterial OTUs,

while present, may be relatively more active (Blazewicz et al., 2013)

- some gene function types may be misassigned by MG-RAST. It

would be informative to study soil catabolic diversity (Degens et al.,

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2001) to understand the full functional trait diversity of these soils

and how it varies.

It is possible that the functional profile of these soils is

significantly altered by a legacy of ‘old’ DNA from dead or lysed

cells (Carini et al., 2016; Fierer, 2017). Whether this old DNA

negates the relevance of what is seen is a moot point, as it

represents a time-integrated view of total community gene

diversity rather than a snapshot in time. In any case, it is important

to realize that old DNA would only be expected to add to the gene

diversity of the desert soils, where DNA may be preserved longer

due to slower turnover rates – yet the more arid soils in our

gradient still have lower functional gene diversity. Perhaps, without

the signal of old DNA, their gene diversity would be even lower

compared to that of moister climate soil.

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3. 5. Concluding remarks

In this study, we were able to identify several key trends in

soil bacterial functional gene composition along aridity gradients.

The most striking of these was a gradient in total functional gene

diversity. While the question remains of whether gene diversity as

measured by MG-RAST really translates into trait diversity in a

way that is analogous to that measured in plant communities

(Kleidon et al., 2009; Kleidon and Mooney, 2000; Swenson et al.,

2012), this trend is intriguing.

It is interesting that most of the trends we predicted on the

basis of functional traits based on plant ecology show up in the

bacterial world. It would be relevant also to attempt to measure trait

diversity along an aridity gradient more directly in soil bacteria,

perhaps through catabolic profiling (Degens et al., 2001) and other

measures of soil activity (Hector and Bagchi, 2007; Jing et al.,

2015). It is also interesting to consider whether the more arid soils

with low gene function diversity are less stable or less resilient

against perturbation.

It would also be interesting to study whether other apparent

‘stress’ gradients (e.g. gradients towards extreme salinity, extreme

heat, and high concentrations of chemical poisons) are associated

with reduced gene functional diversity towards the more extreme

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end of the gradient. Also, a systematic global comparison of

metagenomes along broader aridity gradients – expanding on fairly

small number of samples in the preliminary scale study by Fierer et

al. (2012) would be rewarding in understanding the true extent and

scale of these trends, especially if this trend actually differs on a

regional basis.

Our study did not concentrate on archaeal and fungal gene

functional diversity due to the far smaller numbers of reads

obtained, which obviated robust results. However, there are hints of

such a trend in fungi at least (Figure 29). Sequencing metagenomes

at greater depth would be interesting from the point of view of

examining functional diversity trends in these other organisms.

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CHAPTER 4: General Conclusions

138

4. 1. Changes in metagenome structure of

microbial communities along environmental

gradients

Nutrient concentration, time succession and aridity play a

significant role in structuring ecosystem. Our study aimed to see

the effects of nutrient concentration and incubation time on

functional gene composition in a culture system (Chapter 2), and

the effects of aridity stress on functional gene composition in

natural terrestrial ecosystem (Chapter 3). The results of this study

have implications for understanding microbial community ecology in

general, and will form the basis for more sophisticated

interpretation of patterns in community composition, soil function,

and biogeochemical fluxes.

In both of the studies, we were able to identify several gene

categories that have positive/negative correlations with the

environmental gradients that generally supports hypothesis based

on the study of macroorganisms, and previous studies on

microorganisms. These different gene-based strategies may help

to explain how and why so many bacterial OTUs coexist in nature,

and the functional principles dominating natural communities.

139

In this study, we also found that the correlation between

functional genes and nutrient concentration could be different in

culture system and in natural environment. The microcosm study

has an advantage in that other factors than nutrient could be

controlled enabling us to find the pure effect of nutrient on

functional composition of bacteria. As bacteria grow with incubation,

there might be some changes in environmental factors other than

nutrient. Even if there are, those changes would be negligible

compared to the initial variation in nutrient concentration.

Microcosm study, however, cannot represent the real

natural system. There are tremendous factors co-varying in natural

environment. The field study in Tibetan plateau has its significance

with this point of view. In most cases, moisturous conditions tend to

possess large pool of nutrients in natural environment. Moisturous

conditions promote plant growth eventually increasing soil nutrient

in general. So, it might be natural to find mixed pattern in here and

the result from the field study in Tibetan plateau could be more

believable than the result from the microcosm study when it comes

to realisticity. However, as mentioned earlier, since a lot of factors

co-vary in the natural environment, it is not clear whether the

correlation between precipitation gradient and relative abundance of

140

gene categories we found in the field study in Tibetan Plateau is a

direct cause-and-effect relationship.

In both of the studies, we found strong correlation between

functional gene richness and OTU richness, supporting the

hypothesis that greater taxonomic diversity is associated with

greater functional diversity, with possible implications for

ecosystem stability. However, this relationship could be highly

dependent on sample size. In the microcosm study, functional gene

richness tended to increase as OTU richness increased, but

somehow reached a plateau at the end. If there were more samples

with large number of OTUs, there could have been a clear evidence

of functional redundancy.

Some follow-up studies or experiments might be necessary

for further justification of findings. For example, qPCR (quantitative

PCR) of functional genes of interest will be helpful since absolute

count data can give a better understanding of the intact relationship

between environmental factors and functional genes.

141

4. 2. Future directions

4. 2. 1. Study of various environmental gradients

influencing microbial community

A range of environmental factors can influence microbial

community composition. Researchers reported that the most

important factor for shaping microbial community is pH, followed by

organic carbon quality and quantity, soil oxygen and redox status,

soil moisture availability, and nitrogen and phosphorous availability

(Fierer, 2017). In a future research, it will be interesting to

investigate the functional relationship of bacterial genes with those

factors, especially pH, which has been established to have the

greatest effect on structuring microbial community.

4. 2. 2. Metatranscriptomics

The metagenome derived from DNA sequences alone gives

an incomplete picture of the functional capabilities and activity of

soil community. Not all cells are active at any one time, and not all

genes in a cell are actively expressed. In addition, dead cells may

persist, with their DNA relatively intact, even though they are not in

any sense a functional part of the community. The short-lived

metatranscriptome derived from RNA, however, gives a clearer

142

picture of which genes are active at any one time. As such, it can

provide an additional perspective that compares the activity vs

inactivity patterns within the community.

There has been only few studies, which attempted to

elucidate the whole metatranscriptome. This is very hard to do this

type of study using current technology. Ribosomal RNA consists

over 90% of cellular RNA (Wilhelm and Landry, 2009). It is easy to

enrich mRNAs when we study eukaryotes since eukaryotic mRNAs

have poly A-tail (Zhao et al., 1999), which enables us to do PCR

with oligo-dT primer targeting mRNAs. Since prokaryotic mRNAs

are not polyadenylated, there has been several methods developed

to remove ribosomal RNA from bulk RNA (He et al., 2010; Pang et

al., 2004), but they are not efficient enough and expensive to be

used wildly. With the development of mRNA enrichment technology,

a new era will begin in microbial ecology that enables us to find

intact connection between microbial functional activity and

environmental conditions.

143

Publication

Ho-Kyung Song, Woojin Song, Mincheol Kim, Binu M. Tripathi,

Hyoki Kim, Piotr Jablonski, Jonathan M. Adams; Bacterial strategies

along nutrient and time gradients, revealed by metagenomic

analysis of laboratory microcosms, FEMS Microbiology Ecology,

Volume 93, Issue 10, 1 October 2017

144

Manuscript in preparation

Ho-Kyung Song, Yu Shi, Teng Yang, Jin-Sheng He, Hyoki Kim,

Piotr Jablonski, Haiyan Chu, Jonathan M. Adams; Environmental

filtering of bacterial trait diversity along an aridity gradient. In

preparation.

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Appendix

Table A1. Environmental variables listed by sample ID. Long,

longitude (°E); Lat, latitude (°N), Ele.m, elevation (m); MAT,

mean annual temperature (°C); GST, growing season temperature

(°C); MAP, mean annual precipitation (mm); GSP, growing season

precipitation(mm); PET, potential evapotranspiration (mm/year);

AET, actual evapotranspiration (mm/year); SM, soil moisture (g/g

of dried soil); pH, soil pH; STC, soil total carbon (%); SOC, soil

organic carbon (%); STN, soil total nitrogen (%); STP, soil total

phosphorous (%); CaCO3, soil CaCO3 (%); SSTC, soil carbon

density (g/m-2); SSOC, soil organic carbon density (g/m-2); SSTN,

soil total nitrogen density (g/m-2); SSTP, soil phosphorous density

(g/m-2); SCaCO3, soil CaCO3 density (g/m-2); SCNratio, soil C:N

ratio; SNPratio, soil N:P ratio; DOC, dissolved organic carbon

(mg/kg of dried soil); DTN, dissolved total nitrogen (mg/kg of dried

soil); DON, dissolved organic nitrogen (mg/kg of dried soil); NO3N,

nitrate nitrogen (mg/kg of dried soil); NH4N, ammonium nitrogen

(mg/kg of dried soil); TAN, total available nitrogen (mg/kg of dried

soil); SBD, soil bulk density (g/cm-3); AGB, above ground biomass

(g/m-2); BGB, root biomass at 5cm soil depth (g/m-2); PTC, percent

total carbon in aboveground biomass (%); PTN, percent total

nitrogen in aboveground biomass (%); PTP, percent total

161

phosphorous in aboveground biomass (%); PCNratio, C:N ratio in

aboveground biomass; PNPratio, N:P ratio in aboveground biomass;

Vege.type, vegetation type (AM: alpine meadow; AS: alpine steppe;

DS: desert steppe)

162

Table A1. (Continued)

ID Long Lat Ele.m MAT GST MAP GSP PET AET SM

T1 100.9 36.3 3282 4.7 13.5 321.3 245.6 495 337 0.15

T2 96.7 32.9 4258 3 9.9 488.9 356.4 428 428 1.1

T3 96.4 33.3 4284 0.4 8.4 484.9 346.5 388 388 0.51

T4 96 33.6 4234 -1 7.6 457.9 326.8 349 349 0.71

T5 96.2 34.1 4366 -2.2 7.2 418.3 289.4 345 345 0.53

T6 99.2 35.4 4162 -0.8 8.8 330.2 243.1 373 345 0.07

T7 99.5 35.4 4089 0 9.5 342.5 254.3 398 360 0.08

T8 101 36.9 3066 3.2 12.7 386.8 269.6 469 355 0.08

T9 99.6 37.1 3241 0.3 10.6 309 234.8 412 291 0.07

T10 98.9 36.9 3297 2.2 12 224.5 182 450 240 0.14

T11 98.4 37 3130 3.8 13 219.6 166.6 476 217 0.03

T12 98.4 37.3 3425 2.7 12.6 235.9 177 461 219 0.06

T13 97.1 37.4 2918 3.9 14.5 147.6 108.9 494 155 0.01

T14 95.9 37.5 3319 3.4 14.7 78.2 61.6 489 93 0

T15 95.5 37.4 3181 3 14.5 66.4 52.1 482 81 0.03

T16 100.5 35.6 3320 2 11.1 404.6 290.1 439 401 0.17

T17 94.5 35.9 3633 2.1 11.7 127.5 102.1 439 153 0.07

T18 93.9 35.5 4598 -1.2 8.6 205.4 153 359 202 0.05

T19 92.9 34.9 4555 -4.9 5.2 291.6 215.6 322 263 0.1

T20 92.1 33.7 4697 -3.4 5.8 336.6 261.9 332 323 0.13

T21 92 32.9 5228 -2.9 6 402.5 302.3 334 334 0.15

T22 91.7 32.2 4632 -2.3 6.4 436.1 326 337 337 0.05

T23 91.3 31.5 4664 -1.2 7.3 435.4 329.3 345 345 0.03

T24 91.5 30.6 4542 1.7 9.8 509.6 374.9 410 410 0.47

T25 90.6 30.2 4557 4.2 11.2 422.3 330.9 453 420 0.63

T26 100.8 34.9 3663 -0.3 9 493.6 347.6 389 389 0.6

T27 100.4 34.5 4250 0 8.2 523.2 363.9 386 386 0.68

T28 99 34.8 4505 -2.9 7 377.1 267.7 344 344 1.05

T29 98.5 34.9 4230 -3.5 6.5 343.7 244.7 339 338 0.09

163


ID pH STC SOC STN STP CaCO3 SSTC SSOC

T1 7.14 3.25 3.09 0.34 0.07 1.32 3245 3086.44

T2 6.97 15.22 15.21 1.3 0.12 0.08 15220 15210

T3 6.46 9.35 9.33 0.7 0.07 0.13 9345 9329.42

T4 7.22 11.41 11.39 0.88 0.09 0.19 11410 11387.07

T5 7.23 9.12 9.09 0.78 0.06 0.25 9120 9089.73

T6 8.4 3.08 2.07 0.16 0.06 8.46 3080 2065.12

T7 8.04 3.58 2.93 0.27 0.06 5.35 3575 2933.11

T8 8.35 3.87 3.27 0.33 0.07 4.95 3865 3270.46

T9 7.43 3.7 2.54 0.2 0.06 9.66 3695 2535.59

T10 7.91 3.34 2.12 0.19 0.07 10.13 3340 2124.49

T11 8.57 2.19 0.72 0.07 0.07 12.29 2190 715

T12 8.17 3.09 2.02 0.25 0.09 5.28 3085 2016.61

T13 8.43 2.81 0.97 0.07 0.07 15.31 2805 967.74

T14 8.66 1.85 0.66 0.02 0.06 9.92 1850 659.62

T15 7.92 1.12 0.25 0.03 0.04 7.27 1120 247.14

T16 7.8 5.09 4.85 0.45 0.08 1.96 5090 4854.52

T17 8.87 1.86 0.76 0.06 0.06 9.22 1863.33 757.53

T18 8.99 1.33 0.81 0.07 0.04 4.28 1325 811.77

T19 8.64 1.22 0.49 0.03 0.02 6.05 1215 489.34

T20 7.98 2.48 1.92 0.16 0.04 4.71 2480 1915.16

T21 7.61 2.18 2.18 0.16 0.05 0 2175 2175

T22 8.24 2.47 1.45 0.09 0.02 8.49 2465 1446.21

T23 7.27 2.14 2.14 0.2 0.04 0 2135 2135

T24 6.03 11.21 11.2 0.66 0.07 0.04 11205 11199.98

T25 8.06 7.33 7.2 0.54 0.07 1.07 7325 7197

T26 7.77 13.57 13.53 1.11 0.11 0.29 13565 13530.09

T27 6.81 9.55 9.54 0.76 0.09 0.12 9550 9535.04

T28 7.8 11.98 11 0.94 0.08 8.15 11980 11001.65

T29 9.15 2.37 1.19 0.07 0.05 9.77 2365 1192.66

164


ID SSTN SSTP SCaCO3 SCNratio SNPratio DOC DTN

T1 340 65.56 738.93 9.54 5.19 98.85 55.61

T2 1295 119.62 18.97 11.75 10.83 353.67 232.25

T3 695 73.66 45.34 13.45 9.44 610.07 109.57

T4 880 86.8 76.78 12.97 10.14 410.94 83.86

T5 775 63.9 88.53 11.77 12.13 423.81 69.35

T6 155 64.41 4378.51 19.87 2.41 112.95 21

T7 265 60.27 2651.61 13.49 4.4 201.74 23.82

T8 330 67.71 3020.83 11.71 4.87 148.65 16.83

T9 200 57.26 5215.11 18.48 3.49 259.34 17.81

T10 190 67.43 5913.64 17.58 2.82 232.48 62.13

T11 65 70.69 7553.41 33.69 0.92 97.27 10.72

T12 250 91.8 2742.92 12.34 2.72 202.72 28.16

T13 70 68.27 8710.38 40.07 1.03 327.28 39.96

T14 20 61.19 7346.49 75 0.33 311.91 12.11

T15 30 40.01 4938.94 37.33 0.75 175.43 13.24

T16 445 77.06 842.38 11.44 5.77 242.77 73.32

T17 56.67 61.11 6920.21 32.88 0.93 142.97 12.29

T18 70 42.85 2300.28 18.93 1.63 146.63 8.01

T19 30 21.18 4075.41 40.5 1.42 243.52 28.87

T20 160 40.68 2821.75 15.5 3.93 261.12 34.62

T21 160 46.71 0 13.59 3.43 190.71 20.06

T22 85 21.25 4578.88 29 4 151.68 12.48

T23 195 37.54 0 10.95 5.19 163.21 13.91

T24 660 73.43 16.88 16.98 8.99 896.4 63.22

T25 535 73.38 578.7 13.69 7.29 347.12 83.49

T26 1110 106.95 111.32 12.22 10.38 320.42 190.91

T27 755 88.88 32.78 12.65 8.49 78.01 120.91

T28 940 79.55 1767.97 12.74 11.82 84 98.56

T29 65 49.77 4969.57 36.38 1.31 36.27 9.05

165


ID DON NO3N NH4N TAN SBD AGB BGB PTC

T1 18.61 25.74 11.26 55.61 1.13 36.65 86.76 45.63

T2 101.65 106.1 24.51 232.25 0.45 63.91 1859.92 38.42

T3 66.77 22.92 19.88 109.57 0.69 67.49 1836.22 44.14

T4 49.69 5.03 29.14 83.86 0.77 44.45 1272.86 40.9

T5 40.31 16.3 12.74 69.35 0.93 78.02 746.5 38.16

T6 12.68 3.76 4.56 21 1.05 40.01 402.67 42.79

T7 10.09 8.8 4.94 23.82 0.99 28.49 428.44 44.24

T8 4.7 6.78 5.36 16.83 1.19 44.45 197.9 41.22

T9 6.29 5.63 5.89 17.81 1.09 110.07 106.52 45.61

T10 9.69 36.26 16.19 62.13 1.22 218.7 822.17 43.25

T11 0.99 1.69 8.04 10.72 1.23 87.08 197.32 38.92

T12 6.76 10.76 10.64 28.16 0.99 175 136.79 43.09

T13 14.12 16.65 9.19 39.96 1.25 187.12 14.37 42.96

T14 1.74 2.88 7.49 12.11 1.48 161.17 1.68 47.96

T15 3.57 1.05 8.61 13.24 1.44 59.83 1.87 41.05

T16 32.29 31.68 9.35 73.32 0.84 211.66 393.62 43.25

T17 1.27 4.01 7.01 12.29 1.59 105.74 19.96 41.5

T18 1.16 0.29 6.56 8.01 1.15 48.05 1268.63 37.6

T19 2.3 5.28 21.29 28.87 1.4 248.16 558.31 38.88

T20 8.15 17.85 8.62 34.62 1.21 118.79 824.27 35.42

T21 4.58 8.54 6.93 20.06 0.52 50.11 783.63 27.88

T22 3.18 2.32 6.97 12.48 1.22 66.13 2071.37 42.18

T23 6.17 0 7.74 13.91 1.16 54.71 3004.02 38

T24 38.13 0.33 24.75 63.22 0.88 41.39 2308.09 42.87

T25 15.66 55.54 12.28 83.49 1.08 283.09 819.99 44.33

T26 76.41 92.99 21.51 190.91 0.6 225.05 812.9 43.18

T27 31.64 62.4 26.87 120.91 0.63 123.85 575.16 42.73

T28 7.84 68.6 22.12 98.56 0.44 44.81 749.62 37.39

T29 3.02 0.5 5.53 9.05 1 54.13 155.5 36.21

166


ID PTN PTP PCNratio PNPratio Vege.type

T1 2.65 0.14 17.22 19.26 AS

T2 1.78 0.16 21.58 10.93 AM

T3 1.91 0.18 23.11 10.37 AM

T4 2.74 0.24 14.93 11.52 AM

T5 2.02 0.18 18.89 10.96 AM

T6 1.19 0.1 35.96 12.08 AS

T7 1.8 0.14 24.58 12.83 AS

T8 1.77 0.1 23.29 17.57 AS

T9 1.54 0.11 29.62 13.42 AS

T10 2.49 0.12 17.37 20.08 AS

T11 1.38 0.1 28.2 13.72 DS

T12 1.46 0.12 29.51 12.03 AS

T13 1.12 0.09 38.36 12.12 DS

T14 0.65 0.08 73.78 8.04 DS

T15 1.23 0.09 33.37 13.52 DS

T16 1.9 0.2 22.76 9.7 AM

T17 1.6 0.09 25.94 17.53 DS

T18 1.97 0.14 19.09 13.76 AS

T19 1.79 0.15 21.72 12.16 AM

T20 1.43 0.16 24.77 9.02 AS

T21 1.52 0.18 18.34 8.4 AM

T22 1.69 0.18 24.96 9.63 AM

T23 1.73 0.11 21.97 15.49 AM

T24 1.89 0.12 22.68 15.31 AM

T25 1.61 0.11 27.53 14 AM

T26 1.94 0.17 22.26 11.29 AM

T27 2.07 0.15 20.64 13.89 AM

T28 1.95 0.12 19.17 15.98 AM

T29 1.49 0.14 24.3 10.96 AS

167

국문초록 (Abstract in Korean)

생태학은 생물 간 상호작용 및 생물과 무생물적 요소 사이의 관

계에 대하여 연구하는 학문이다. 최근 몇 년간 핵산 시퀀싱(sequencing)

기술의 발달에 힘입어 우리는 생물권을 향한 새로운 시각을 가지게 되었

다. 앰플리콘(amplicon) 시퀀싱과 메타지놈(metagenome)으로 대두되

는 이러한 방법들은 미생물생태학 분야의 새로운 지평을 열어주었다. 분

류학적 표지 유전자(taxonomic marker gene)을 이용하는 앰플리콘 시

퀀싱 기술을 통해 박테리아, 고세균, 균 및 미소무척추동물이 토양, 퇴적

토, 물 등의 자연 환경에서 얼마나 다양하게 존재하는지를 알 수 있었다.

미생물의 다양성 및 군집 형성과 관련한 생태학적 이론들을 완전히 새로

운 방법으로 검증하는 것도 가능해졌다. 배양 비의존적으로 주어진 환경

에서의 유전자 군을 직접적으로 시퀀싱하는 메타지노믹스

(metagenomics)는 다양한 환경 속 미생물 군집의 잠재적인 기능들을

탐구하는 데에 중추적인 역할을 한다.

메타지놈의 구성과 다양성이 환경 구배에 따라 어떻게 변화하는

지 연구하는 것은 생태계 구조 및 군집 형성에 관한 일반적인 이론을 이

해하는데 도움을 줄 수 있다. 본 연구에서는 초기 양분 농도 및 배양 시

간에 따라 군집 메타지놈이 어떻게 변화하는지 알아보기 위해 토양 용출

액을 이용한 배양 실험을 설계했다. 기능성 메타지놈(functional

metagenome)의 구조는 양분 혹은 배양 시간에 따라 다르게 변화했다.

예를 들어, 고영양(copiotrophic) 조건에서는 세포 분열 및 세포 주기와

관련된 유전자가 많은 것이 확인되었고 빈영양(oligotrophic) 조건에서

168

는 탄수화물 대사 및 독성, 질병 및 방어에 관련한 유전자가 많았다. 배

양 시간과 관련한 변화도 확인할 수 있었는데, 배양 시간이 짧을수록

(r-전략) 조절 및 세포 신호 관련 유전자가 많았고 배양 시간이 길수록

(K-전략) 이동성 및 주화성(chemotaxis) 관련 유전자가 많았다. 다양

성 측면에서는 기능성 유전자(functional gene)의 풍부도(richness)가

기능성 유전자의 분류학적(taxonomic) 풍부도 및 조작 분류 단위

(operational taxonomic unit)의 풍부도와 선형 상관관계를 가지는 것을

확인할 수 있었고, 이는 분류학적 다양성이 높을수록 기능적 다양성이

높아 생태계의 안정성을 가져다 준다고 주장하는 이론들을 뒷받침해준다.

건조함의 정도는 생태계를 구성하는 데에 있어 매우 중요한 요

소 중 하나다. 건조함의 정도가 토양 생물군의 기능적 다양성에 어떤 영

향을 주는지 알아보기 위해 티베트 고원 동부지역에서 샘플을 채취하여

강수량에 따라 토양 메타지놈의 구조가 어떻게 달라지는지 연구했다. 연

구 결과, 연평균 강수량이 낮을수록 박테리아 기능성 유전자의 다양성이

감소하는 경향이 있다는 것을 알 수 있었다. 극한 건조 조건이 토양 생

물군의 기능성 유전자의 다양성을 제한하는 것으로 보이며, 이는 환경

구배에 따른 식물의 기능적 다양성 변화 양상과 흡사하다. 또한, 건조한

지역일수록 휴면 관련된 유전자와 삼투 억제자(osmoprotectant) 관련

유전자가 많이 있는 것도 알 수 있었다. 건조한 지역일수록 항생제 저항

과 관련된 유전자 및 독성 관련 유전자는 적었는데, 이는 극한 조건에서

는 생물 간 상호작용이 크지 않다는 것을 암시한다. 본 연구의 결과는

생물군계에 따른 메타지놈 구조를 비교한 기존 연구의 결과와 흡사하다.

향후에는 산도, 온도, 토양 내 산소 농도 등 다른 종류의 환경

169

구배에 따라 메타지놈 구조가 어떻게 변하는지에 대해 연구해 보는 것도

흥미로울 것으로 보인다. 또한, 메타트랜스크립토믹스

(metatranscriptomics)에 대한 연구를 추가적으로 진행한다면 기능적

활성도(activity)에 대한 보다 정확한 정보를 알 수 있을 것으로 보인다.

이러한 목적을 이루기 위해서는 원핵생물의 전령RNA(messenger RNA)

를 농축할 수 있는 기술 개발이 선행되어야 한다.

주요어: 메타지놈(metagenome), 기능적 다양성, 고영양(copiotrophic),

빈영양(oligotrophic), r-선택, K-선택, 강수량, 건조 정도(aridity), 생

태적 스트레스(ecological stress)

학번: 2016-30109

환경구배에따른미생물군집의...

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