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Temperature-Dependent Patterns of Gene Expression in Caenorhabditis brigssae

by

Stephanie Mark

A thesis submitted in conformity with the requirements for the degree of Master of Science

Department of Ecology and Evolutionary Biology University of Toronto

© Copyright by Stephanie Mark 2017

ii

Temperature-Dependent Patterns of Gene Expression in

Caenorhabditis briggsae

Stephanie Mark

Master of Science

Department of Ecology and Evolutionary Biology University of Toronto

2017

Abstract

Discerning the genetic basis of adaptive phenotypes is a fundamental problem in biology that

remains an open question. Studies using high-throughput sequencing methods of gene expression

have contributed greatly to our understanding of how genotype becomes phenotype by treating

gene expression as an intermediary phenotype, especially under variable environmental

conditions. Using whole genome high-throughput RNAseq data, I characterized the responses of

Temperate and Tropical genotypes of Caenorhabditis briggsae to chronic temperature stress.

These genotypes show evidence of local adaptation, suggesting that differences in their

responses to temperature may underlie adaptive phenotypes. I discovered that a large proportion

of genes show genotype-specific changes in gene expression in response to temperature

(genotype-by-temperature interactions), and that most of these genotype-specific responses occur

under heat stress. These results suggest that responses to cold stress and heat stress are

qualitatively different, and identify sets of genes that suggest further study into temperature-

adapted phenotypes.

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Acknowledgments

I would like to acknowledge the efforts of Joerg Weiss and Julie Claycomb in designing the

experiment and collecting the data on which my thesis work is based. I would also like to thank

past and present members of the Cutter Lab, especially Jeremy Gray, Richard Jovelin, Rebecca

Schalkowski, Janice Ting, Joanna Bundus, and Gregory Stegeman, for their advice and

encouragement. I am also extremely grateful for the help and guidance of two bioinformaticians,

Wei Wang and Ting Liu, for answering my many questions and mentoring me as a new

bioinformatician. I would also like to thank my supervisory committee, Nicholas Provart and

Stephen Wright for their constructive criticism and Helen Rodd for her assistance and kindness.

Finally, I would like to thank my advisor, Asher Cutter, for keeping me on track, for always

making time for my questions, and for giving me the opportunity to learn and grow while doing

research in his lab.

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Table of Contents

Acknowledgments.......................................................................................................................... iii

Table of Contents ........................................................................................................................... iv

List of Tables ................................................................................................................................. vi

List of Figures ............................................................................................................................... vii

Introduction ......................................................................................................................................1

How do genotypes give rise to phenotypes? ...............................................................................1

Using local adaptation to untangle environmental effects on phenotype ...................................2

Caenorhabditis briggsae as a model organism for GxE .............................................................3

Gene expression as a proxy for phenotype .................................................................................5

Exploring temperature-dependent patterns of gene expression in C. briggsae ..........................6

Methods............................................................................................................................................7

Experimental design and data provenance ..................................................................................7

Processing of raw data ................................................................................................................7

Mapping reads to the genome .....................................................................................................8

Quantifying expression: Counting reads ...................................................................................10

Exploratory data analysis of count data ....................................................................................11

Analysis for genes with differential expression ........................................................................13

Co-expression clustering of genes with similar expression profiles .........................................13

Gene Ontology analysis ............................................................................................................15

Heat shock proteins ...................................................................................................................16

Chromosomal domain analysis .................................................................................................16

Results ............................................................................................................................................17

Characterizing differential gene expression in response to genotype & temperature ...............17

Statistical analysis with limma...........................................................................................17

v

Differential expression in response to extreme temperature..............................................17

Defining co-expression clusters ................................................................................................19

Distribution of genes in modules .......................................................................................19

Genes with differential expression in modules and module eigengene expression ...........20

Analysis of heat shock protein genes ........................................................................................26

Differential expression of hsp genes ..................................................................................26

Clustering and expression patterns of hsp genes ...............................................................27

Analysis of differential expression across chromosomal domains ...........................................28

Chromosomal domain enrichment of differentially expressed genes ................................28

Chromosomal domain enrichment of genes for Representative Modules .........................28

Discussion ......................................................................................................................................30

Genes with genotype-by-environment interactions in gene expression ....................................30

Gene expression responses to chronic cold versus chronic heat stress .....................................32

Chromosomal domains and differentially expressed genes ......................................................35

Small RNAs and temperature-sensitive regulation of gene expression ....................................38

Conclusion ................................................................................................................................39

References ......................................................................................................................................92

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List of Tables

1. Table 1. Number of raw and cleaned reads in fastq files from Genome Quebec.

2. Table 2. Number and percentage of reads that mapped to unique locations (i.e. one

location in the genome) with STAR.

3. Table 3. Values for each soft threshold power that was tested.

4. Table 4. Table of G-test p-values for to test whether the proportion of differentially

expressed genes in a module differed significantly from genome-wide proportions

5. Table 5. G-test p-values from a test to determine whether the proportion of genes located

in chromosome arms or centres differed from expected proportions for each differential

expression group

6. Table 6. G-test p-values from a test to determine whether the proportion of genes located

in autosome arms or centres differed from expected proportions for each of the 22

modules identified by co-expression clustering

7. Table 7. Table of G-test p-values for tests of whether the proportions of genes on

autosomes and the X-chromosome were significantly different from expectations for each

differential expression group

8. Table 8. Table of G-test p-values for tests of whether the proportions of genes on

autosomes and the X-chromosome were significantly different from expectations for each

co-expression module

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List of Figures

Figure 1. Number of reads from fastq files for each data file……………………………………50

Figure 2. Distribution of intron lengths in C. briggsae reference genome………………………51

Figure 3. Ratio of average number of uniquely mapped reads in Tropical and Temperate Genotypes……………………………………………………………………………………..…52

Figure 4. Percentage of uniquely mapped reads with STAR per biological replicate……….......54

Figure 5. Reads counted by htseq-count per replicate…………………………………………...55

Figure 6. Multi-dimensional scaling plot (MDS) of filtered, normalized, and log transformed count data……………………………………………………………………………………...…56

Figure 7. Distributions of p-values from preliminary tests………………………………………57

Figure 8. Analysis for differential expression with edgeR versus limma………………………..58

Figure 9. Quantile-quantile plot for normalized, voom-transformed count data………………...59

Figure 10. Dendrogram and heatmap of normalized count data…………………………………60

Figure 11. Fit of scale-free topology generated by soft-thresholding powers…………………...61

Figure 12. Mean connectivity of soft-thresholding powers……………………………………...62

Figure 13. Dendrogram of initial 124 modules from co-expression clustering………………….64

Figure 14. Similarity heatmap of initial 124 modules from co-expression clustering…………..65

Figure 15. Dendrogram of merged co-expression clusters………………………………………66

Figure 16. Heatmap of merged co-expression clusters…………………………………………..67

Figure 17. Numbers of differentially expressed genes…………………………………………..68

Figure 18. Proportions of genes expressed under chronic cold versus heat stress………………69

Figure 19. Proportions of genes that increase or decrease expression under chronic cold versus heat stress………………………………………………………………………………………...70

Figure 20. Magnitude of change in expression under chronic cold versus heat stress…………..71

Figure 21. Distribution of differential expression groups within co-expression modules……….72

viii

Figure 22. Proportions of differentially expressed genes within co-expression modules….....…73

Figure 23. Module eigengene expression plots for Genotype modules………………………….74

Figure 24. Module eigengene expression plots for Temperature modules………………………75

Figure 25. Module eigengene expression plots for GxT modules……………………………….76

Figure 26. Module eigengene expression plots for genes with no differential expression………77

Figure 27. Proportion of differentially expressed genes in arm versus centre domains of autosomes………………………………………………………………………………………...81

Figure 28. Proportion of differentially expressed genes in arm versus centre domains of the X-chromosome……………………………………………………………………………………...82

Figure 29. Proportion of module genes in arm versus centre domains of autosomes…………...83

Figure 30. Proportion of module genes in arm versus centre domains of X-chromosome..…….84

Figure 31. Proportion of genes on autosomes and the X-chromsome for each differential expression group…………………………………………………………………………………87

Figure 32. Proportion of genes on autosomes and the X-chromsome for each co-expression module............................................................................................................................................88

Supplementary Figure 1.................................................................................................................89









Supplementary Figure 10...............................................................................................................98

Supplementary Figure 11...............................................................................................................99

1

Introduction

How do genotypes give rise to phenotypes?

Understanding the relationship between genotype and phenotype remains a fundamental

problem in biology. While much technological progress has been made in revealing the

sequences of genomes (Pruitt et al. 2014), the details of how DNA sequences give rise to

the different forms and functions of life largely remains an open question. This question

is of particular interest in evolutionary biology, which seeks to explain how adaptive

phenotypes arise from the information encoded in genomes as they get shaped by natural

selection. Many basic questions regarding the underlying genetic architecture of adaptive

phenotypes are the subject of active research (Grillo et al. 2013, Lasky et al. 2014, Chen

et al. 2015). For example, do mutations in certain functional regions of genes contribute

to adaptive phenotypes more than others? Even more basic questions include: What

proportion of the genome is involved in producing adaptive phenotypes? Are some

locations in the genome more likely to be involved in adaptive phenotypes than others?

Early research pointed to surprising patterns about the connection between genotype and

phenotype (Jacob and Monod 1961, Britten and Davidson 1969). Differences in

phenotype were initially assumed to result from proportional differences in genotype.

Contrary to the initial intuition that different species would have very divergent protein

coding sequences, there is a high degree of similarity between orthologous coding

sequences between species that differ in morphology, behaviour, and physiology (King

and Wilson 1975). This discovery led to the hypothesis that many differences between

species originate not from differences within genes themselves but rather arise from

differences in the timing, location, and levels to which genes are expressed (Britten and

Davidson 1971). Mutations in cis-regulatory controls have since been linked to

phenotypic variation in a wide variety of taxa, providing support for this gene regulation

model (Wray 2007). Proponents of this model have also argued that adaptation is most

likely to occur through mutations in cis-regulatory regions in particular (Wray et al. 2003,

Carroll 2005). Cis-regulatory regions are defined as the regions adjacent to a gene that

directly control its transcription, such as promoter sequences (Wray 2007). This argument

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for adaptation through cis-regulatory regions is primarily based on the assumption that

mutations in these regions should result in fewer pleiotropic effects (Carroll 2005, Wray

2007). For example, changes to protein-coding sequences could influence the function of

all proteins in the same network by altering the way they interact. Conversely, by

changing only spatial or temporal patterns of gene expression, cis-regulatory mutations

could alter phenotypes while avoiding potentially deleterious changes to interactions

between proteins (Wray 2007). However, given that there are other avenues for avoiding

pleiotropic effects such as gene duplication and alternative splicing, the cis-regulatory

model of adaptive evolution by itself likely only provides a partial explanation (Hoekstra

and Coyne 2007). Nevertheless, changes to cis-regulatory regions have been linked to

several forms of phenotypic variation, particularly in Drosophila (Massey and Wittkopp

2016). For example, 40 different loci have been linked to pigmentation variation to date

both within and between species and the vast majority of mutations that cause these

phenotypic differences occur in cis-regulatory regions. Furthermore, cis-regulatory

mutations seem to be more common in phenotype divergence between species than as

polymorphisms within species, which strongly suggests that the regulation of gene

expression plays a key role in adaptation and speciation (Coolon et al. 2014, Massey and

Wittkopp 2016).

Using local adaptation to untangle environmental effects on phenotype

The relationship between genotype and phenotype can be modulated further by the

environment (Gibson 2008). When subject to different environmental conditions, one

genotype can produce multiple phenotypes, resulting in phenotypic plasticity. This

capacity to generate multiple phenotypes can allow an organism to successfully navigate

the challenges of variable environments when that plasticity is adaptive (Pigliucci et al.

2006). For example, heat shock proteins (hsp) are upregulated in response to diverse

forms of stress, and to heat stress in particular (Lindquist 1986). Heat stress causes the

misfolding of outer membrane proteins, which act as a signal to initiate the cascade that

3

results in the production of more heat shock proteins (Walsh et al. 2003). Because heat

shock proteins can shield other proteins from the effects of heat stress, the regulation of

hsp gene expression can also influence functions that are related to fitness, such as

spermatogenesis (Sarge et al. 1994). The path from genotype to phenotype can therefore

be the result of the coordinated activity of many genes in many pathways simultaneously

responding to changing environmental conditions. To more fully understand how

genotypes generate phenotypes it is necessary to untangle the consequences of

environmental changes for different genes.

One way to isolate the genes involved in adaptive responses to the environment is to

study populations that have adapted to different local conditions (local adaptation). In

local adaptation, populations of the same species undergo divergent natural selection due

to differences in local environmental conditions (Kawecki and Ebert 2004). Responses to

divergent selection manifest as the “local” population having higher fitness than the

“foreign” population when tested in each other’s habitats. At the same time, these

populations are otherwise very similar because of gene flow and recent common

ancestry. Therefore, the differences between them, particularly with respect to

ecologically relevant traits, are especially likely to reflect selection for local conditions in

their respective habitats (Des Marais et al. 2013). By examining traits that show GxE in

locally adapted populations, we can identify the subset of genes that are potential

candidates for contributing to adaptive phenotypes (Thomas 2010).

Caenorhabditis briggsae as a model organism for GxE

Local adaptation and GxE are often studied in organisms whose range spans across

latitudes as habitats at different latitudes provide many opportunities for divergent natural

selection (Hurme et al. 1997, Stinchcombe et al. 2004, Gilchrist and Huey 2004,

Fournier-Level et al. 2011). One such organism is Caenorhabditis briggsae, a nematode

worm with a worldwide distribution and a close relative of the model organism

Caenorhabditis elegans. Like C. elegans, C. briggsae has a mature and highly contiguous

reference genome sequence, gene annotations, and genetic map (Stein et al. 2003, Hillier

4

et al. 2007, Ross et al. 2011). While temperature affects both C. elegans and C. briggsae

during development (Moss et al. 1997, Matsuba et al. 2013), only C. briggsae shows

evidence of local adaptation. Populations of C. briggsae show striking patterns of genetic

differentiation that mirror its geographic distribution (Cutter et al. 2006, Thomas et al.

2015). Populations from temperate latitudes, including Japan, Europe, and USA, are more

genetically similar to each other than populations from tropical latitudes including Africa,

Asia, and South America. C. briggsae isolates from these regions form two distinct

“Temperate” and “Tropical” clades in neighbour networks that make up the vast majority

of C. briggsae strains collected from nature (Cutter et al. 2006, Felix et al. 2013, Cutter

2015). This pattern suggests that ecological correlates of latitude could play a role in

shaping genetic divergence between Temperate and Tropical populations.

There is also strong experimental evidence for local adaptation in C. briggsae. When C.

briggsae strains of Temperate and Tropical populations are reared in the lab at a range of

temperatures, Temperate strains have higher lifetime fecundity at 14°C while Tropical

strains have higher lifetime fecundity at 30°C (Prasad et al. 2011). This pattern of strain-

specific fecundity responses to different temperatures demonstrates a genotype-by-

environment interaction that is consistent with local adaptation. This interaction also

points to temperature as an ecologically relevant factor that could be responsible for

divergent natural selection in this species. Furthermore, sperm number in C. briggsae is

reduced in response to heat stress whereas sperm number is not affected in C. elegans, a

species that does not show evidence of local adaptation (Poullet et al 2015). Strains of C.

briggsae from different latitudes also show greater within than between species

differences in thermotaxis (Stegeman et al. 2013). Finally, 20% of F2 offspring obtained

from crossing Temperate strains and the AF16 Tropical strain of C. briggsae show a

delay in development due to dysgenic interactions between maternal and zygotic loci,

showing the beginnings of reproductive isolation (Baird and Stonesifer 2012). Taken

together, this support for local adaptation combined with a high-quality genome assembly

that has been experimentally verified to the chromosome level makes C. briggsae an

excellent organism in which to study adaptive gene expression differences.

5

Gene expression as a proxy for phenotype

Many traits have plastic responses to changes in the environment and could therefore be

used to study GxE. However, using genome-wide gene expression to quantify responses

to the environment has several advantages. Firstly, the initial step in the process from

genotype to phenotype is transcription of a gene into mRNA. It is therefore

straightforward to infer which genes have a response to the environment by matching

mRNA sequences to the sequences of their genes of origin (Wang et al. 2009).

Furthermore, genome-wide measures of gene expression can identify genes that may

contribute to GxE without relying on prior expectations of gene function because

transcription of all genes can be captured at once (Marioni et al. 2008). The cost

effectiveness of high-throughput sequencing has also made RNA-seq studies a practical

way to obtain a vast amount of unbiased data to assess gene expression differences

among experimental treatments for the entire genome (Wang et al. 2009, Des Marais et

al. 2013).

Microarray and high-throughput sequencing techniques have contributed several

important insights about variation in plasticity and mechanisms of gene regulation by

measuring gene expression responses to the environment and to temperature in particular.

For example, by characterizing the plasticity of expression in response to temperature

change and linking expression patterns to regulatory architecture in D. melanogaster,

transcription factors and microRNAs were found to have opposing effects on gene

expression (Chen et al. 2015). Other investigations into expression plasticity across

latitudinal gradients in D. melanogaster revealed a “directionality” to plasticity such that

more genes were downregulated when reared in foreign temperature conditions than local

(Levine et al. 2010). Whole genome expression data were also used to quantify variance

in expression plasticity in response to environmental stress in A. thaliana, illustrating that

such variance has a key role in the genetic basis of local adaptation (Lasky et al. 2014).

6

Exploring temperature-dependent patterns of gene expression in C. briggsae

In order to characterize how organisms’ transcriptomes respond to temperature stress in

two locally adapted populations of C. briggsae, we performed a genome-wide survey of

differences in and patterns of gene expression. We analyse representative genotypes from

each of the Temperate and Tropical phylogeographic groups: one Temperate strain from

Okayama, Japan (HK104) and one Tropical strain from Ahmedabad, India (AF16). These

strains also provide the parental progenitors of a collection of recombinant inbred lines

(RILs) used in genetic mapping (Ross et al. 2011), making their gene expression

difference a valuable resource for future research in this study system. We reared

replicated pools of isogenic individuals from both Temperate and Tropical strains at 14,

20, and 30°C as treatments for differential expression analysis. These temperatures

encompass the range of temperatures for which strain-specific responses were observed

(Prasad et al. 2011). We then collected, sequenced and analysed mRNA of young adult

hermaphrodite animals to determine the effects that genotype, temperature, and the

interaction between genotype and temperature (“GxT”) had on the expression of all genes

as assayed by RNA-seq. We also performed co-expression clustering on gene expression

to reveal expression patterns across temperatures and to gain insight into the specific

effects that temperature exerts on different groups of genes. Finally, we examined the

genomic location of genes whose expression was significantly affected by genotype,

temperature, or GxT, as well as groups of genes with similar expression patterns.

7

Methods

Experimental design and data provenance

The raw data that were analysed in this project orginated from an experiment that was

designed by a former student in the Cutter Lab, Joerg Weiss. Joerg Weiss also isolated

the RNA and collected the data in collaboration with Julie Claycomb in the Department

of Molecular Genetics at the University of Toronto. Briefly, the experimental design that

they implemented to isolate and sequence mRNA is as follows.

In order to determine the genome-wide effect of different temperatures and different

genotypes on gene expression changes, hermaphrodites from two isogenic strains (AF16

= “Tropical” strain, HK104 = “Temperate” strain) were raised at 14°C, 20°C, and 30°C

from egg to adult. Prior to this experiment, previous generations of both genotypes had

been raised at 20°C. Individuals were synchronized to be at the same stage of

development using the standard C. elegans sodium hypochlorite (“bleaching”) protocol.

After reaching young adulthood, total RNA was isolated from each strain at each

temperature by mashing worms into a slurry, vortexing the slurry, adding 1-Bromo-3

Chloropropane, separating organic and aqueous phases, and then precipitating RNA with

a glycogen and isopropanol solution overnight. Each of the treatments had three

biological replicates, yielding a total of 18 samples. The mRNA was then separated from

rRNA and small RNA fractions.

The mRNA was then sequenced as 100 base-pair, single end reads using Illumina HiSeq

2000 at the Genome Quebec facility in Montreal, Quebec. mRNA from each of the 18

samples was sequenced across 2 lanes to control for lane effects (Fang and Cui 2011).

Processing of raw data

The number of reads per sample per lane in raw FASTQ files generated by Genome

Quebec ranged from 17.2 million to 36.7 million and had an average of 25.7 million

8

reads across all raw data files. The number of raw reads was similar across strains,

temperatures, biological replicates, and lanes, indicating roughly equal coverage across

all variables and that biases in sequencing depth were minimal (Table 1).

Cleaning and trimming of raw FASTQ files must be done in order to remove potential

artifacts of the sequencing process, such as adapter sequences. To identify and remove

the Illumina TruSeq 3 single-end adapters that are used in Illumina HiSeq 2000, I used

Trimmomatic 0.36 (Bolger et al. 2014). To ensure that any adapter sequences that

differed slightly due to technical sequencing variability could be identified, I chose a

seed-mismatch rate of 2 and a simple clip threshold of 10. Reads shorter than 60 base-

pairs were also discarded, and bases were trimmed from 5’ and 3’ ends if they had

phred33 scores lower than 3. After cleaning and trimming, over 90% of reads were kept

from each FASTQ file and the average number of reads in each of the 36 cleaned files

was 25.4 million reads (Figure 1).

Once these raw FASTQ files had been cleaned, I merged files that corresponded to the

same biological replicate but that had been sequenced in different lanes by concatenating

them into a single FASTQ file. This single file therefore represented the complete mRNA

data for one biological replicate (e.g. the first replicate of AF16 reared at 14°C).

Following this concatenation of files, the average number of reads in a biological

replicate (or “sample”) was 49.7 million reads and ranged from 33.3 million reads to 70.7

million reads. All subsequent analyses were performed on these 18 cleaned and merged

sample files.

Mapping reads to the genome

In order to identify which genes had been expressed as mRNA in each sample, read

sequences were aligned (or “mapped”) to the latest C. briggsae genome available on

wormbase.org (WS253), which is based on the AF16 (Tropical genotype) genome.

Mapping is an essential step in gene expression analysis as each mRNA transcript is cut

into smaller “reads” to increase the speed and depth with which genomic data can be

9

sequenced. The short length of the reads and the absence of introns in mRNA present

complications in identifying the locations of origin of reads in the genome, with many

bioinformatics mapping tools using different algorithms to address these challenges. I

evaluated several mapping approaches and settled on STAR as the best match of speed

and quality.

Because each read originated from a single location in the genome, one measure of

mapping efficacy is the proportion of reads in a sample for which a unique location in the

genome has been identified as the origin. I chose the software STAR (Dobin et al. 2013)

for mapping because of its ability to reliably identify unique locations of origin for a

large proportion of reads. As the STAR alignment algorithm is based on using the longest

matching sequence as an anchor from which splicing is inferred, it is important to specify

the number of mismatches that should be allowed between the read and the genome, as

well as the maximum intron size. To account for read sequence variability due to 1)

technical error in library preparation and sequencing and 2) intrinsic differences between

the Tropical and Temperate genotypes, a mismatch rate of 10 was selected. Although a

mismatch rate of 10 can be considered to be lax, I chose this parameter to minimize the

potential bias towards the Tropical genotype that could overestimate the effect of

differential expression due to genotype (Figure 3). The maximum intron size was set at

5000 base pairs which includes 99.6% of all intron annotations in the C. briggsae

reference genome (Figure 2).

Over 90% of the 894 million total reads mapped to unique locations in all samples except

for one, and the average number of reads per sample that mapped to unique locations was

45.9 million reads (Table 2). The third replicate of HK104 at 30°C was the only sample

with a relatively low proportion of reads that mapped uniquely to the genome (73.86%)

(Figure 4). However, because this sample nevertheless contained a substantial number of

reads (35.6 million), comparable to the number of reads in other samples, I retained this

sample in downstream analyses.

10

Quantifying expression: Counting reads

After locating the origin of each read in the genome, I counted the number of reads that

correspond to each gene to obtain a quantitative measure of expression for each gene.

Gene locations on each chromosome are defined by GFF/GTF (General Feature

Format/General Transfer Format) annotation files. As the publicly available annotation

file for C. briggsae from wormbase.org (version WS253) contains annotations for many

different types of genomic features that are not relevant to this analysis including

deprecated historical gene locations and BLAST matches, a custom annotation file was

parsed from it that consisted only of exons that were annotated on wormbase.org. The

number of reads that mapped to each exon was counted with htseq-count (Anders et al.

2014) and summed over all exons in a gene to give a measure of expression for each gene

in each sample.

Ambiguity in counting reads can arise when genes have alternative splicing isoforms that

consist of some of the same exons and when there are multiple genes that overlap in the

same location. Because it often is impossible to determine the isoform of origin of

individual reads, I neglected alternative splicing isoforms for this analysis such that

separate isoforms were treated as the same gene. To resolve the ambiguity of overlapping

genes, the “mode” parameter in htseq-count was set to “intersection-nonempty”. For

example, if Gene A and Gene B overlap in the genome annotation, this parameter would

count a read as coming from Gene A if the majority of the length of the read was located

Within Gene A as opposed to Gene B. However, if a read fell equally within Gene A and

Gene B, it was deemed ambiguous and not counted at all.

There were 23 267 genes in the genome annotation used for this analysis. Among all the

reads whose origin in the genome was successfully identified (mapped reads), 82-85% of

them were assigned successfully to a particular gene in all samples but one. Again, the

third replicate of HK104 at 30°C fared the worst in this analysis, with only 58% of reads

assigned to genes (Figure 5b). However, I continued to retain this replicate for

downstream analyses because the absolute number of reads assigned to genes (24.0

million) was comparable to other samples (Figure 5a). Among the reads that were not

assigned to genes, most of them (9% on average) could not be associated with any exon

11

or were counted in multiple locations (8% on average) and less than 0.1% were

ambiguous.

Exploratory data analysis of count data

Once I had obtained gene expression counts for genes in each sample, I performed

preliminary data analyses to better understand the overall quality of the data and to

evaluate the chances of being able to detect differential gene expression among samples.

To gauge the degree of similarity between samples and the consistency between

biological replicates in particular, I visualized the gene expression counts in each sample

in a Multi-Dimensional Scaling (MDS) plot. If samples are highly similar, they will have

similar values on both x- and y-axes and therefore cluster together in an MDS plot

(Nikolayeva and Robinson 2014). Most biological replicates showed strong similarity to

each other in an MDS plot (Figure 6). However, the biological replicates for 3

experimental combinations of genotype and temperature showed more variability among

replicates than the other treatments (AF16 at 14°C, HK104 at 20°C, and HK104 at 30°C).

In each of these three cases, one replicate appeared to be set apart while the other two

replicates clustered together well. Nevertheless, these replicates still grouped well with

other samples from the same genotype and so were retained in downstream analyses.

When looking for differential expression across a large number of genes, it is helpful to

perform preliminary tests to determine whether there is a strong enough signal in the data

set to detect significant effects of experimental variables of interest. If there is a strong

signal in the data set, the distribution of the p-values of these preliminary tests will be

heavily right-tailed and have a large peak for values very close to zero (Hung et al. 1997).

Distributions of p-values from preliminary t-tests for the effect of genotype and F-tests

for the effect of temperature on the count data both showed strong peaks near zero and

therefore confirmed that many genes within the data set would be identified as being

significantly differentially expressed (Figures 7a & 7b).

12

Gene expression quantified as counts of reads per gene do not follow a normal

distribution. Therefore, in order to perform statistical analyses for differential expression

of genes, it is necessary to either use statistical methods that apply a distribution that is

suitable for count data (e.g. negative binomial distribution) or to transform the count data

so that they closely approximate a normal distribution. While there are many statistical

packages for both approaches, I chose to transform the expression counts for this analysis

because one package in particular, limma and voom (Smyth 2005, Law et al. 2014) has

been shown to be better at controlling Type I error and detecting more true positives than

many popular software packages based on count distributions (Law et al. 2014). Limma

was also more conservative with my data than edgeR (Robinson et al. 2010) (Figure 8).

Because the power of limma depends on the data being normally distributed, it was

important to ensure that the count data were transformed such that they approximated a

normal distribution as closely as possible. Histograms and quantile-quantile plots (or “Q-

Q plots”) are effective ways to visually inspect the normality of data. Histograms of

normally distributed data should approximate a bell shape while data points in Q-Q plots

should fall on the 1-to-1 line. A common transformation to approximate normality with

count data is the log transformation. The histogram of log2 transformation of gene counts

began to approximate a bell shape but revealed a large number of genes with very few to

no counts. To remove genes with extremely low levels of expression I first changed gene

counts to counts per million based on library size using the “cpm” function in edgeR

(Robinson et al. 2010). Only genes with at least 1 cpm in 3 or more libraries (i.e. in one

biological replicate) were kept. There were 7068 genes that did not have at least 1 count-

per-million across at least 3 samples and were excluded from further analysis. Once

genes with low to no expression were removed from the data set, I applied the voom

transformation from the limma package to the remaining set of 16 199 genes. The Q-Q

plot showed that the data closely approximated a normal distribution after filtering and

voom-transformation (Figure 9).

13

Analysis for genes with differential expression

I tested the voom-transformed data for the remaining 16 199 genes for differential

expression using the package limma. Expression for each gene was fit to a linear model

(expression ~ strain + temperature + interaction) and tested. The intercept was set as

expression for the Tropical strain at 20°C. p-values were adjusted for multiple testing

using the Benjamini-Hochberg correction and the significance level was set at FDR=0.05

(Benjamini and Hochberg 1995).

In genes with a significant effect of temperature (either simple or with an interaction),

differential expression can be driven by exposure to cold, exposure to heat, or exposure to

both cold and heat. In order to distinguish which genes were responding to which

temperature conditions, I performed post-hoc tests on the individual temperature

coefficients for genes with a significant effect of temperature. Again, the Benjamini-

Hochberg correction was applied to p-values to maintain the false discovery rate (FDR)

at 0.05.

I then divided genes into categories based on whether they showed significant differential

expression due to genotype only (“genotype genes”), temperature only (“temperature

genes”), genotype and temperature independently (“G&T genes”), an interaction between

genotype and temperature (“GxT genes”), or no differential expression.

Co-expression clustering of genes with similar expression profiles

To illustrate the ways in which expression was changing in response to temperature in the

Temperate versus the Tropical genotype, I performed a co-expression clustering analysis

of gene expression using the Weighted Gene Correlation Network Analysis (WGCNA)

package (Langfelder and Horvath 2008). Co-expression clustering analysis identifies

genes with similar expression patterns across genotypes and temperatures to group them

together. This approach captures genes that change expression in similar ways even if

they are not identified as having individually statistically significant differential

expression. I therefore used co-expression clustering as a complementary approach to

differential expression testing to characterize gene activity in response to temperature.

14

Because WGCNA works best with normally distributed expression values, I again used

the same voom-transformed expression values for 16 199 filtered genes for this analysis

as for the differential expression analysis. Before performing co-expression analysis, I

determined whether the data were homogeneous or whether the data had a tendency to

cluster non-randomly (e.g. due to batch effects) by performing hierarchical clustering on

the samples. This procedure rejected batch effects but did identify genotype and

temperature as likely sources of heterogeneity in the data (Figure 10). Because the

experiment was designed to test these factors, they represent legitimate sources of

heterogeneity and contribute to biologically interesting expression similarities among

genes.

Because WGCNA defines cluster membership on a graded scale rather than an absolute

one, it is necessary to set a “soft-threshold” to define similarity. The soft threshold is

defined by the “soft-thresholding power”, the exponent that, when similarity values are

raised by it, results in the closest approximation to a scale-free network topology, the

topology that is assumed by WGCNA. To determine the best soft-thresholding power for

our data, I computed the fits (R2 correlation with a scale-free network topology) of a

range of values from 1 to 10, then even numbers from 12 to 42 and determined which

value resulted in the best fit (Table 3). The authors of WGCNA recommend using soft-

thresholding powers that result in fits whose R2 correlation with the theoretical scale-free

topology with the same power is at least 0.8 (Zhang and Horvath 2005). The best fit for

our data corresponded to a soft-thresholding power of 30 (R2 = 0.75) (Figure 11). This

lower than the recommended R2 value is likely the result of the biologically legitimate

heterogenity of our data. The soft-thresholding power of 30 also yielded an acceptable

level of mean connectivity (k = 115), which is again central to the assumptions of the

WGCNA model (Figure 12). I therefore set the soft-thresholding power to 30.

Initial clustering of genes using WGCNA resulted in 124 clusters or “modules” in which

all member genes showed similar patterns of expression (Figures 13 & 14). To

consolidate the number of modules further, I merged similar modules, defined as those

with a correlation of 0.75 or higher with each other. This procedure produced 23 modules

sorted by size (e.g. Module 1 contains the largest number of genes) (Figures 15 & 16).

15

One module was comprised of genes whose expression patterns were not similar to the

expression pattern of any other module and could not be grouped into a co-expression

cluster (Module 0, with 37 genes).

WGCNA generates a composite representative of the expression patterns of all the genes

in the module and is defined as the first principal component of each module called the

“module eigengene.” Overall patterns of gene expression for all genes in the same

module can be well-characterized by the expression pattern of the module eigengene

(Langfelder and Horvath, 2007). To visualize the expression profiles of each module, I

plotted the module eigengene expression values averaged across the three biological

replicates as a function of rearing temperature for each genotype separately.

Gene Ontology analysis

To generate hypotheses about the potential functions of modules comprised of genes that

showed similar expression patterns, I performed statistical overrepresentation tests of

Gene Ontology terms associated with gene lists of each module using PANTHER (Mi et

al. 2009). I tested the list of genes in each module against all four PANTHER lists

available for C. briggsae: PANTHER Pathways, PANTHER GO-slim Molecular

Function, PANTHER GO-slim Biological Process, and PANTHER GO-slim Cellular

Components. P-values were adjusted for multiple testing with the Bonferroni correction

and the significance level was set at p = 0.05.

After I obtained lists of significantly overrepresented GO terms for each module, I used

REVIGO (Supek et al. 2011) to simplify GO terms by combining terms with high

semantic similarity. I also used output from REVIGO to visualize the fold change of the

significant terms into tree maps such that terms that had a higher fold increase in

overrepresentation had a greater area in the tree map.

16

Heat shock proteins

Because I am investigating the effects of temperature on gene expression, I am especially

interested in understanding whether the heat shock protein genes were differentially

expressed in the experiment and what were their patterns of expression. I identified 27

heat shock protein genes in the C. briggsae genome by querying “hsp” on wormbase.org

and determined whether any of these showed significant differential expression in my

analysis with limma. I then assessed whether any of the significantly differentially

expressed heat shock protein genes were clustered into modules that had over 50%

differentially expressed genes or at least 1000 genes.

After genes with very low expression had been removed from the initial set of 23 267

genes, 24 heat shock protein genes remained for analysis.

Chromosomal domain analysis

Distinct recombination domains of C. briggsae chromosomes separate the tips, arms, and

centres of chromosomes (Ross et al. 2011). The centres of chromosomes have lower rates

of recombination and lower nucleotide polymorphism than the arms (Thomas et al.

2015). I therefore tested whether genes that showed differential expression occurred

preferrentially in a particular chromosomal domain. I counted the numbers of each kind

of differentially expressed gene (genotype genes, temperature genes, genotype and

temperature genes, GxT genes, non-differentially expressed genes, modules) in each

chromosomal domain and compared them to the number of genes found in centres and

arms for autosomes and the X chromosome separately because chromosomal

recombination domains are less distinct on the X chromosome. I performed G-tests to

determine whether the proportions of genes in arms versus centres in each group were

statistically different from genome-wide proportions.

17

Results

Characterizing differential gene expression in response to genotype & temperature

Statistical analysis with limma

Over half (54%) of all 16 199 genes that I tested for differential expression with limma

differed significantly in expression across genotypes, temperatures, or both. The majority

of genes that were differentially expressed had a significant interaction between genotype

and temperature (“GxT genes”), comprising 56% of the 8795 differentially expressed

genes. There were 1.3 times more genes with a significant interaction (n=4919) than

genes that were differentially expressed but that did not show an interaction (n=3876)

(Figure 17).

Among genes with differential expression but no interaction, genes with a significant

effect of temperature only (“temperature genes”, n=1987) outnumbered by 2.5-fold those

genes with a significant effect of genotype only (“genotype genes”, n=770). Finally, a

moderate number of genes had significant differential expression that resulted from the

independent effects of genotype and temperature (“G & T genes”, n= 1119). In other

words, 1987 + 1119 = 3106 genes overall showed a significant response to temperature

and 770 + 1119 = 1889 genes showed a significant response to genotype and none of

these genes showed a significant interaction effect (Figure 17).

Differential expression in response to extreme temperature

Among genes for whom a change in temperature caused differential expression,

responses to rearing under cold stress (14°C) differed from responses to rearing under

heat stress (30°C) relative to benign rearing conditions (20°C) in terms of the number of

genes involved, whether genes increased or decreased expression, and in terms of the

magnitude of expression change. For example, for the majority of genes with an effect of

temperature but no interaction (temperature genes and G & T genes), expression changed

significantly in response to rearing under cold stress (2308 out of 3106 genes, 74%)

18

whereas for GxT genes, the largest number of genes responded significantly to rearing

under heat stress only (2393 out of 4919 genes, 49%) (Figure 18).

However, when considering whether genes increased or decreased expression in response

to chronic cold stress or heat stress, genes with an effect of temperature and no

interaction were represented in similar proportions to GxT genes. There was a bias

towards genes that decreased expression as opposed to increased in response to cold

rearing, both for genes with an effect of temperature and GxT genes (Figure 19: 1.05

times more temperature genes decreased, 1.3 times more GxT genes decreased).

Similarly, in response to rearing under heat stress, there was a bias towards genes that

increased expression as opposed to decreased it for both genes with an effect of

temperature and for GxT genes (Figure 19: 6.8 times more temperature genes increased,

1.2 times more GxT genes increased). This suggests that, among all genes that respond

significantly to temperature in some way, more genes reduced their expression at cool

temperatures and more genes elevated their expression at hot temperatures.

In contrast, the magnitude of expression changes in response to rearing under cold stress

versus heat stress differed for those genes that showed GxT versus those that exhibited an

effect of temperature but no interaction (Figure 20). GxT genes showed no consistency in

the median fold change in expression between genes that were differentially expressed

under cold stress versus heat stress; the magnitude of expression change was different at

both temperature extremes. Specifically, for GxT genes that increased expression,

chronic heat stress caused a larger magnitude change in expression than chronic cold

stress. Conversely, for GxT genes that decreased expression, chronic cold stress caused a

larger magnitude change in expression than chronic heat stress. The median fold change

in expression of GxT genes that increased expression under heat stress was 8.57 while the

median fold change in expression of GxT genes that increased in under cold stress was

3.73. There was a similarly large discrepancy between magnitudes of expression change

under stress at both temperature extremes for GxT genes that decreased expression. For

GxT genes that decreased expression, the median fold change in response to cooling was

9.19 while the median fold change in response to heating was 6.50.

19

By contrast, the magnitude of expression changes in response to rearing under cold stress

versus heat stress differed for GxT genes versus those genes that showed an effect of

temperature but no interaction. For genes with an effect of temperature but no interaction,

the increased or decreased expression was similar under chronic exposure to both

temperature extremes. Specifically, gene expression levels showed a similar fold change

regardless of the direction of the temperature change (Figure 20). For instance, the

median fold change of genes that increased expression under cold stress was 6.06 while

the median fold change of genes that increased expression under heat stress was 6.50 for

genes with an effect of temperature but no interaction. Similarly, among these same

genes, the median fold change of genes that decreased expression in response to rearing

under cold stress was 3.03 while the median fold change of genes that decreased

expression under heat stress was 3.73.

Altogether, these results suggest that temperature shifts cause a wider range of expression

responses in terms of the magnitude of expression change for genes with a significant

interaction between genotype and temperature than for genes that show a simple

independent effect of temperature.

Defining co-expression clusters

Distribution of genes in modules

In order to characterize patterns of gene expression change in response to temperature in

both Temperate and Tropical strains, I clustered genes into “modules” based on similarity

of expression. Each gene was placed into one of 23 modules that were defined by co-

expression clustering.

Genes were not equally distributed among the 23 modules produced by WGCNA (Figure

21). The first six modules contained 75% of all 16 199 genes included in this analysis,

with each containing 1000 or more genes. Specifically, Modules 1 and 2 contributed 20%

and 18% of all genes, respectively, while Modules 3 to 6 had 8-10% each. Only three

modules contained fewer than 100 genes (Modules 21, 22, and 0). Module 0 represented

20

those 37 genes whose expression patterns were not sufficiently similar to any others to

cluster into a well-defined co-expression module.

Genes with differential expression in modules and module eigengene

expression

Cross-referencing module membership with lists of genes whose individual expression

differed significantly across treatments revealed that those genes with differential

expression were not equally distributed among modules (Figure 22). The proportion of

genes that were individually differentially expressed at FDR = 0.05 within each module

ranged from 6% (Module 13) to 84% (Module 15), with a mean proportion of 46%.

Within the six largest modules, Module 5 had the highest proportion of differentially

expressed genes (81%) followed by Modules 1 and 4 (each with 55%), Module 6 (47%),

Module 3 (45%) and Module 2 (38%) (Figure 22).

Although Modules 7 to 22 accounted for only 25% of all genes, several of these smaller

modules showed a striking concentration of genes that were differentially expressed due

to a specific experimental variable (Figure 22). For example, two modules, Module 10

and Module 7, contained the highest proportions of genotype genes that together

comprised 44% of all 770 genotype genes genome-wide. Similarly, two other modules,

Modules 15 and 12, consisted of over 50% temperature genes. However, these two

predominantly temperature gene modules accounted for just 12% of all 1987 temperature

genes. Finally, Modules 9, 14, 16, and 22 showed high proportions of GxT genes whereas

the remaining modules (8, 11, 13, 17, 18, 19, 20, 21) consisted primarily of genes with no

individually significant differential expression.

The expression profile of each module can be represented by its “module eigengene”,

which is defined as the first principal component of each module. In my analysis, module

eigengene plots also illustrate the trends of differentially expressed genes within each

module (Figures 23, 24, 25, 26). For instance, for modules with a high proportion of

genotype genes, the module eigengene expression for each genotype was distinct and did

21

not change across temperatures (Figure 23). Similarly, for modules with a high

proportion of temperature genes, module eigengene expression was very similar for each

genotype such that the genotype expression profiles overlapped and changed

synchronously across temperatures (Figure 24).

By contrast, modules with an abundance of non-differentially expressed genes did not

show distinct eigengene profiles (Figure 26), with relatively modest differences between

genotypes and temperatures, particularly in small modules (n < 1000). This lack of a

strong pattern in eigengenes of modules with many non-differentially expressed genes

likely reflected a preponderance of expression changes that were too slight to be

statistically distinguishable from random fluctuation on a per-gene basis. Therefore, for

the remainder of my analysis with co-expression clustering, I focused on the six largest

modules and the 8 modules with at least 50% genes that were identified as having

significantly different expression with limma (collectively referred to as “Representative

Modules”).

Among the 14 Representative Modules, the proportion of genotype genes, temperature

genes, G & T genes, GxT genes, and non-differentially expressed genes differed

significantly from genome-wide proportions for all modules (Table 4). Again, genotype

genes were especially common in two modules (Module7, Module 10), and together

contained nearly half of all genotype genes genome-wide. The 638 genes in Module 7

yielded an eigengene expression pattern of greater expression in the Temperate genotype

whereas Module 10 (338 genes) expression was characteristic of greater expression in the

Tropical (Figure 23). Significantly overrepresented GO terms with the highest fold

enrichment associated with Module 7 such as “GABA receptor activity” and

“acetylcholine receptor activity” suggested that some genes in this module were related to

nervous system function (Supplementary Figure 1). In contrast, GO terms associated with

Module 10 such as “regulation of liquid surface tension” and “homeostatic process”

suggested that many genes in Module 10 were involved in basic physiological processes

(Supplementary Figure 2).

22

The Representative Modules that showed a preponderance of temperature genes together

consisted of 35% of all temperature genes genome-wide (Modules 6, 12, and 15; Figure

24). Although Modules 12 and 15 only accounted for 12% of all temperature genes, they

each had a very high proportion of temperature genes and each of their module

eigengenes described expression patterns that were strongly characteristic of genes whose

response to temperature is not genotype-specific (Figure 24). For example, genes in

Module 12 (n = 245) had their lowest expression at 14°C and highest expression at 30°C

and there was little to no expression difference between genotypes. For genes in Module

12, the increase in expression between 14°C and 20°C was greater than the increase

between 20°C and 30°C, suggesting that genes in this module are strongly downregulated

at low temperatures (Figure 24). Only one GO term, “chromatin binding”, was

significantly overrepresented in Module 12.

Module 15 was similar to Module 12 in that genes in this module show their highest

expression at 30°C (Figure 24). However, expression for genes in Module 15 differed in

that expression at 14°C and 20°C was equally low; there was only a very slight decrease

in expression from 14°C to 20°C and the greatest increase in expression occurred

between 20°C and 30°C. This suggests that genes in Module 15 are strongly upregulated

when reared at under chronic heat stress but are not affected when reared under chronic

cold stress or benign temperatures. However, functions of genes in Module 15 were

unknown as it yielded no significantly overrepresented GO terms, providing little clue as

to whether these heat-sensitive genes act in related functional pathways.

Although 31% of genes in Module 6 were temperature genes, over half of the total

number of genes in this module were also non-differentially expressed, making its

module eigengene somewhat difficult to interpret (Figure 22). However, one general

trend in the Module 6 eigengene is that expression increased in both the Temperate and

Tropical genotypes as temperature increased (Figure 24). GO terms associated with

Module 6 suggest that genes in this module are used in mitochondrial activity

(“mitochondrial transport”, “mitochondrion organization”) and also in translation (“RNA

splicing”, “translation”, “mRNA binding”) (Supplementary Figure 3).

23

Two Representative Modules, Module 4 and Module 5, had a high proportion of G & T

genes that was also reflected in their module eigengene expression profiles (Figure 22).

These two modules accounted for over half (56%) of all G & T genes genome-wide.

Genes in Module 4 (n = 1592) had higher expression in the Temperate genotype at all

temperatures (Figure 24). Module 4 genes also had their highest expression at 14°C,

which decreased as the temperature reached 20°C. However, as temperatures increased to

30°C, there was no change in expression from 20°C. This expression pattern suggests that

genes in Module 4 exhibit elevated expression at cool temperatures only, and that they

are consistently more strongly expressed in the Temperate genotype than the Tropical.

GO terms associated with Module 4 suggest that several genes in this module are

involved in basic physiological processes, and in the processing of fats in particular

(“lipid transport”, “cholesterol metabolism”, “regulation of liquid surface tension”)

(Supplementary Figure 4).

Expression in Module 5 (n = 1390) was similar to that in Module 4 in that expression was

highest at 14°C (Figure 24). However, in Module 4, expression was higher in the

Tropical genotype at all temperatures as compared to the Temperate strain. While

expression decreased with a shift in temperature from 14°C to 20°C, as in Module 4,

expression rebounded by increasing again strongly from 20°C to 30°C. This pattern

suggests that genes in Module 5 increase expression at extreme temperatures, and that

expression is greater in Tropical than Temperate genotypes. GO terms associated with

Module 5 are mainly indicative of intracellular activity, and of “regulation of

carbohydrate metabolism”, “cellular component morphogenesis”, and “cell proliferation”

in particular, suggesting that genes in this module are involved in the regulation of

cellular activity (Supplementary Figure 5).

The remaining seven modules (1, 2, 3, 9, 14, 16, 22) were composed of a large proportion

of GxT genes, indicating that the majority of genes in these modules had genotype-

specific responses to temperature (Figure 22). These seven modules accounted for 69%

of all GxT genes genome-wide.

24

Four modules, Modules 1, 2, 3, and 16, were similar in that both genotypes responded to

temperature by changing expression in the same direction (e.g. increasing in response to

heat). However, the interaction between genotype and temperature arose from expression

changing at different rates in the different genotypes. Specifically, the Temperate

genotype showed more drastic expression changes in response to heat in particular. For

example, all genes in Module 1 (n = 3203) have their peak expression at benign

temperatures. While both the Temperate and the Tropical genotype decrease expression

under heat stress, the Temperate genotype does so at a much faster rate (Figure 25). Some

GO terms associated with Module 1 that had the highest fold enrichment included “DNA

repair”, “RNA polymerase II transcription”, “chromatin segregation”, and “double-

stranded DNA binding”, suggesting that many genes in Module 1 interact with DNA, and

particularly in the context of transcription (Supplementary Figure 6).

In contrast to Module 1, genes in Module 2 (n = 2863) had their lowest expression at

20°C and increased expression at extreme temperatures (Figure 25). Again, in Module 2,

the Temperate genotype changed expression more drastically than the Tropical genotype;

expression in the Tropical genotype was lower than the Temperate at all temperatures and

expression only increased slightly at extreme temperatures. Also, the increase in

expression in the Temperate genotype was more drastic from 20°C to 30°C than from

20°C to 14°C. This suggests that genes in Module 2 increase expression in response to

extreme temperatures, but only very slightly in the Tropical genotype. Conversely,

Module 2 genes in the Temperate genotype increase expression more strongly in response

to extreme temperatures, and to heat in particular. Similarly to Module 1, Module 2 had

enriched GO terms that were related to transcription (“RNA polymerase II transcription”)

but also “translation” and “muscle contraction” (Supplementary Figure 7).

Expression in Module 3 (n = 1609) was similar to that in Module 2 in that expression

increased in response to extreme temperatures in both genotypes. Expression was also

higher in the Temperate genotype than the Tropical at all temperatures in Module 3, and

expression increased more drastically in the Temperate genotype than the Tropical.

However, what distinguished Module 3 from Module 2 was that expression in the

Tropical genotype in Module 3 was more similar to the Temperate genotype in response

25

to cool temperatures (Figure 25). When temperatures shifted from 20°C to 14°C, both the

Temperate and Tropical genotypes increased expression at a similar rate. This suggests

that, for genes in Module 3, genotype-specific expression happens particularly in

response to heat. Some highly enriched GO terms associated with Module 3 were

“neuron-neuron synaptic transmission”, “neurological system process”, “voltage-gated

potassium channel activity”, and “acetylcholine receptor activity”, suggesting that many

genes in Module 3 are involved in nervous system processes (Supplementary Figure 8).

Module 16 (n = 168) was the last GxT module in which the genotype-by-temperature

interaction resulted from a difference in rate of expression change (Figure 25). As in the

previous three modules, the GxT interaction resulted primarily from a drastic expression

change in the Temperate genotype in response to heat. For example, although the

Tropical genotype had higher expression than the Temperate at 14°C and 20°C,

expression in both genotypes remained steady. Both genotypes increased expression from

20°C to 30°C, but the Temperate genotype increased expression much more strongly and

reached the same level of expression as the Tropical at 30°C. This suggests that genes in

Module 16 do not respond to cool temperatures, but increase expression in response to

heat, particularly in the Temperate genotype. However, possible common functions of

genes in Module 16 remain unknown as there were no significantly enriched GO terms

associated with these genes.

The remaining three Representative Modules with a high proportion of GxT genes,

Modules 9, 14, and 22, were similar in that genes within these modules changed

expression in opposite directions in different genotypes in response to temperature

(Figure 25). For instance, when temperatures cooled from 20°C to 14°C, genes in Module

9 (n = 562) increased expression in the Tropical genotype but decreased expression in the

Temperate genotype. The Tropical genotype had its highest expression at 14°C which

declined strongly at 20°C and decreased only slightly at 30°C. In contrast, the Temperate

genotype had its lowest expression at 14°C, then increased expression at 20°C and

decreased slightly at 30°C. This suggests that, at cool temperatures, genes in Module 9

have low expression in Temperate genotypes and high expression in Tropical genotypes

but that expression does not change much relative to benign conditions for either

26

genotype under chronic heat stress.. Many GO terms associated with Module 9 suggested

that these genes were involved in transcription (“regulation of transcription from RNA

polymerase II promoter”, “sequence-specific DNA binding transcription factor activity”,

“nucleic acid binding transcription factor activity”) and “biological regulation” was also

indicated (Supplementary Figure 9).

In Module 14 (n = 228), expression in the Tropical genotype peaked at 20°C while this

same temperature in the Temperate genotype caused expression to reach its lowest point

(Figure 25). In the Tropical genotype, expression decreased in response to extreme

temperatures while in the Temperate genotype, expression increased in response to

extreme temperatures such that the Temperate genotype had higher expression than the

Tropical at both 14°C and 30°C. This suggests that for genes in Module 14, Tropical and

Temperate genotypes have opposite responses to extreme temperatures.

This same opposing pattern of expression across temperatures for the two genotypes was

seen in Module 22 (n = 49). However, expression in Module 22 differed in that

expression in the Temperate genotype was highest at 14°C instead of 30°C as in Module

14 (Figure 25). Changes in expression at extreme temperatures were also more drastic in

Module 22 than in Module 14. This suggests that genes in Module 22 had a stronger

response to extreme temperatures, and that expression increased greatly in the Temperate

genotype in response to cold in particular. Both Modules 14 and 22 were enriched for GO

terms related to translation, translation initiation, and the ribosome, suggesting that genes

in these modules are responsible for the conversion of mRNA transcripts into functional

proteins (Supplementary Figures 10 & 11).

Analysis of heat shock protein genes

Differential expression of hsp genes

Of the 24 hsp genes that I tested for differential expression with limma, 8 showed

significant differential expression. Surprisingly, this proportion (33%) is significantly less

than the proportion of genes with differential expression genome-wide (54%) (G = 4.267,

27

df = 1, p-value = 0.039). Among the 8 significantly differentially expressed genes, there

was 1 genotype gene, 2 temperature genes, and 5 GxT genes, which is not significantly

different from the distribution of differential expression classes throughout the genome

(Fisher Exact Test, p-value = 0.35). This suggests that while there were fewer hsp genes

with differential expression than expected, no one type of differential expression (e.g. due

to temperature only) was predominant among hsp genes.

Clustering and expression patterns of hsp genes

Among the 24 hsp genes, 15 genes clustered into Representative Modules (Module 2: 3,

Module 3: 5, Module 4: 4, Module 5: 2, Module 7: 1). Of these 15 genes, 8 were in

predominantly GxT modules, 6 were in predominantly G & T, and 1 was in a

predominantly genotype module.

Given the module eigengenes of these Representative Modules, expression in 13 of these

15 genes was higher in the Temperate genotype than the Tropical. Four genes had their

highest expression at 14°C, 8 had their highest expression levels at 30°C, 2 genes

increased expression at both temperature extremes, and one gene (genotype gene) did not

change expression across temperatures. No genes were most highly expressed at 20°C,

suggesting that hsp genes that respond to temperature changes (either cooling or heating)

do so by increasing expression. Furthermore, many hsp genes were more highly

expressed in the Temperate genotype than the Tropical.

28

Analysis of differential expression across chromosomal domains

Chromosomal domains are characterized by distinct recombination rates in C. briggsae

(Ross et al. 2011). Arm regions of chromosomes experience high recombination rates

whereas centre regions have relatively low recombination rates. This pattern is true

across the autosomes, but the X chromosome has a less pronounced difference in

recombination rates between arms and the centre. Consequently, I tested whether

different groups of genes were located preferentially in the arms or centres of

chromosomes, and examined the autosomes and X chromosome separately. Among

groups of differentially expressed genes, Genotype genes and genes with no differential

expression were significantly enriched on the X chromosome whereas G&T and GxT

genes were enriched on autosomes (Figure 31). Among Representative Modules,

Modules 1, 5, 6, and 14 were significantly enriched on autosomes (whereas Modules 3, 4,

9, 10, and 12 were significantly enriched on the X chromosome (Figure 32).

Chromosomal domain enrichment of differentially expressed genes

Across autosomes, Genotype and GxT groups of differentially expressed genes were

significantly enriched in the arms by 1.22- and 1.04-fold, respectively. However, G&T

genes were enriched in centres by 1.15-fold. Temperature genes and genes with no

differential expression were not enriched in either domain (Figure 27, Table 5). By

contrast, no differentially expressed gene groups were enriched in either arm or centre

domains on the X chromosome (Figure 28).

Chromosomal domain enrichment of genes for Representative

Modules

Among the 22 co-expression modules (Module 0 was not included as membership in this

module was not based on expression similarity between genes), 14 modules had

29

significant enrichment in either arm or centre chromosomal domains on autosomes. Of

these 14 modules, 9 also were Representative Modules containing a large complement of

a particular class of differentially expressed genes (i.e. Temperature only, Genotype only,

GxT, G&T; Modules 1, 3, 6, 9, 10, 12, 14, 15, 16; Figure 29, Table 6). Among the 10

Representative Modules with significant chromosome domain enrichment on autosomes,

3 were enriched in the centres (Modules 3, 6,14), with 2 being predominantly GxT

modules (Modules 3, 14), the third module being comprised predominantly of

temperature genes (Module 6). The remaining 6 Representative Modules were enriched

in arm domains, corresponding to temperature (Modules 12 and 15), GxT (Modules 1, 9,

and 16), and genotype (Module 10) modules.

On the X chromosome, three co-expression modules were significantly enriched in either

arm or centre chromosomal domains, and all were Representative Modules (Modules 1,

7, 12, Figure 30). Modules 1 and 12 were enriched on the arms while Module 7 was

enriched in the centre. All three of these modules represented different differential

expression groups; Module 1 was a GxT module, Module 6 a Genotype module, and 12 a

Temperature module.

Overall, more modules showed enrichment in arms than in centres for both autosomes

and the X chromosome (Figure 32). All modules that consisted of primarily of Genotype

or Temperature genes were significantly enriched in either the arms or centre. Module 6,

a Temperature module, was enriched in the centres whereas Modules 12 and 15 were

enriched in the arms on autosomes. Module 12 was enriched in the arms on both

autosomes and the X chromosome. Similarly, all Genotype modules were enriched in

either the arms or centres. Module 10 was enriched in the arms of autosomes while

Module 7 was enriched in the centre of the X chromosome. Conversely, only 4 of the

GxT Representative Modules were significantly enriched in either arm or centre regions.

Among autosomes, two GxT modules were enriched in the arms and two were enriched

in the centres. Module 1 was significantly enriched in the arms on both autosomes and

the X chromosome. In terms of function, many GO terms associated with Representative

Modules with enrichment in centres were related to translation whereas this was not true

of modules with enrichment in the arms.

30

Discussion

I tested 16 199 genes for differential expression across three temperatures and between

two genotypes that derived from different latitudes of origin and discovered that over half

of all transcripts (54%) showed differential expression. The majority of differentially

expressed genes showed an interaction between genotype and temperature (4919 of

8795). To characterize the ways in which expression changes with temperature, and to

contrast these patterns between the Temperate and Tropical genotypes, I used co-

expression clustering to identify 22 distinct expression profiles, 14 of which I considered

their module eigengenes to be representative of all genes in the module. Finally, to

investigate the genomic architecture of genes whose expression is controlled by different

variables, I asked whether certain genes were preferentially located in certain regions of

chromosomes and discovered a consistent overrepresentation in arm domains of

genotype-dependent differential expression.

Genes with genotype-by-environment interactions in gene expression

Advances in sequencing technology have enabled the study of gene expression on a

genomic scale, making it possible to investigate the effects of genetics and environmental

conditions on whole organisms. Whole-genome expression studies also make it possible

to quantify genotype-by-environment interactions (GxE) and to shed light on the relative

proportion of genes that show genotype-specific responses to environmental variables.

Interaction effects on expression between genotype and environment can be very

common, with most studies showing over 20% of genes having GxE responses. However,

the proportion of genes with GxE effects tends to be variable. For example, 47% of yeast

transcripts showed strain-condition interactions in response to conditions with different

glucose concentrations (Smith and Kruglyak 2008). In contrast, 59% of genes in C.

elegans showed eQTL-by-environment interactions in response to changes in temperature

(Li et al 2006) and 21% of genes showed GxE in A. thaliana in response to soil drying

(DesMarais et al. 2012). Similarly, an investigation into the interactions between

31

temperature and geographic origin in Drosophila melanogaster identified 56 genes with

an interaction out of 1,760 assayed at an FDR of 0.10 (Levine et al. 2011). Results from

my study fall within the wide range seen in other investigations; 30% of all genes tested

showed GxE. However, there are many technical considerations that could affect the

proportion of genes with interactions. For example, some experimental designs include

many genotypes (DesMarais et al. 2012) or instead focus on contrasting two isogenic

genotypes, as was done in my study. Also, microarray experiments might provide

different results than transcriptomic studies (Marioni et al. 2008, Wang et al. 2009).

Comparing results between species may also be difficult due to inherent differences in

ecology that could influence gene expression. For instance, genes involved in

reproduction in an animal that is capable of self-fertilization could feasibly be expressed

in different proportions than genes with similar function in an animal that shows a high

degree of sexual conflict. Furthermore, different proportions of genes could be involved

in behavioural responses in animals compared to chemical responses in plants, making it

difficult to compare proportions of genes with GxE effects across species. Finally, even

within-species comparisons yield different proportions of GxE when testing different

environmental factors. In A. thaliana, for example, cold stress elicits more genotype-by-

environment interactions than drought stress (Lasky et al. 2014).

While numerous variables make it difficult to compare the proportions of interactions

observed, a common pattern among all previous studies investigating GxE in gene

expression is that interactions comprise a minority of genes among all genes that are

differentially expressed. Genes that are differentially expressed due to genotype or

environment generally outnumber those with significant interaction effects. By contrast,

in my study, the highest number of genes showed an interaction between genotype and

temperature, followed by genes that were differentially expressed due to temperature.

While part of this discrepancy may be due to true biological differences between study

organisms, it is again possible that experimental design and other technical factors might

contribute. For example, the environmental change from 14°C to 30°C may elicit a

greater response than the environmental difference between glucose concentrations for

yeast and could result in more power to detect interactions in my study. Furthermore,

gene expression was measured for single genotypes only in this study. When

32

investigating GxE in the context of local adaptation, it is common to measure expression

for ecotypes, often in natural habitats, that comprise several individuals that may not be

genetically identical (Des Marais et al. 2012). Expression for each ecotype could then

potentially be averaged across several genotypes, dampening stronger reactions and

decreasing the chance of detecting statistically significant interactions.

While averaging expression within ecotypes or across multiple strains (Smith and

Kruglyak 2008) likely gives a more realistic representation of overall patterns, focusing

on isogenic genotypes has the advantage of increasing resolution at the sequence level.

For example, it is possible to link SNPs directly to expression differences because

sequences are identical across all replicates within a genotype. This advantage could be

particularly useful in identifying the sequence changes that are associated with genes that

show an interaction between genotype and environment because it could allow us to

home in on the sequence differences that cause genes to behave differently in different

genotypic backgrounds.

Finally, many studies investigate GxE at only two environmental levels. In contrast, my

study measured expression at three different temperatures. This again could have the

effect of increasing the number of significant interactions identified in my analysis

because a gene could have an interaction at cold temperatures, hot temperatures, or at

both. Studies for which the environmental variable only has two levels have just one

opportunity for an interaction to manifest. If my analysis had only examined either hot or

cold stress, the number of significant interactions observed may well decrease and reflect

similar proportions to other studies of expression GxE.

Gene expression responses to chronic cold versus chronic heat stress

Although chronic cold stress and chronic heat stress are both induced by changes in

temperature, the challenges they pose to organisms are qualitatively different (Deutsch et

al. 2008, MacMillan and Sinclair 2011). This is especially true of ectotherms whose

33

internal temperature fluctuates with ambient temperature (Martin and Huey 2008). Traits

that vary with temperature often do so by following a characteristic thermal performance

curve that approximates the shape of a concave parabola where the peak of the curve

represents the optimal temperature for that trait (Angilletta 2009). While performance

declines when moving away from the optimal temperature in either direction, it does so at

different rates, prompting the hypothesis that cold stress affects organisms in different

ways than heat stress and operates through different mechanisms (Roitberg and Mangel

2016). If this is indeed the case, then I would expect to see very few genes that were

differentially expressed due to both very low and very high temperatures.

The results from my analysis provide support for the idea that cold stress and heat stress

elicit different responses, especially at the level of gene expression. Of the 3106 genes

that changed expression in response to temperature (with no interaction), fewer than 20%

responded to both cold and heat stress while nearly 80% responded to cold alone. This

result is consistent with the idea that one set of genes in particular responds to cold stress.

Interestingly, genes that had a genotype-specific response to temperature again had very

few genes that responded to both cold and heat, but in contrast to temperature genes, the

majority of GxT genes responded to heat alone, indicating that interactions happen more

frequently at increased temperatures. Taken together, these results suggest that most

genes whose expression changes in response to temperature respond in the same way

when temperatures decrease but have genotype-dependent responses when temperatures

increase. This pattern of expression was also borne out in module eigengene expression.

Modules 4, 5, and 6 represented the majority of temperature genes and Modules 4 and 6

in particular showed a change in expression only at decreasing temperatures. Similarly,

Modules 1, 2, and 3 comprised the majority of GxT genes and illustrated similar

expression levels between genotypes at decreased temperatures but disparate expression

levels at increased temperatures.

The differences in cold and heat response could be due to both mechanical and ecological

factors. For example, it has been suggested that as temperatures decrease from the

thermal optimum, performance is described by the Arrhenius relationship, indicating that

as temperatures cool, performance decreases simply because the reaction rates of

34

enzymes slow down (Brown et al. 2004, Roitberg and Mangel 2016). In contrast, high

temperatures disrupt diverse essential functions that affect performance in a complex

way. For example, exposure to high temperatures lowers the ability of mitochondria to

process oxygen in aquatic ectotherms (Portner 2010), may compromise immune function

in insects (Karl et al. 2011), and causes neuron death in D. melanogaster due to an

imbalance in ion homeostasis (Robertson and Money 2012). If it is true that lowering

temperatues simply slows temperature-dependent reactions whereas increasing

temperature results in complex instabilities, it could explain my observations of relatively

consistent responses to cold in both Temperate and Tropical phenotypes versus the varied

responses to heat.

In spite of Temperate and Tropical strains showing similar expression patterns under cold

stress and distinct patterns under heat stress, I observed phenotypic differences at both

temperature extremes, suggesting that expression is not simply the result of temperature-

dependent enzyme activity. At 14°C, the Temperate genotype has higher fecundity than

the Tropical genotype whereas at 30°C, the Tropical genotype is more fecund than the

Temperate (Prasad et al. 2011). This suggests that the relationship between gene

expression and phenotype at the organism level is not straightforward and that certain

genes influence fitness differently depending on the genotype in which it is expressed. A

possible explanation for this complex relationship is a scenario in which different alleles

fix in different populations because they are each beneficial in their local environments

but have either no effect or negative effects in the other, known as conditional neutrality

and antagonistic pleiotropy, respectively (Anderson et al. 2013). A meta analysis of QTL

studies in A. thaliana found that antagonistic pleiotropy underlies at least 60% of

instances of GxE (Des Marais et al. 2013), suggesting that antagonistic pleiotropy is very

common, and could be responsible for the pattern observed in our Temperate and

Tropical genotypes.

35

Chromosomal domains and differentially expressed genes

Given the well-described structure of C. briggsae chromosomes, I wanted to determine

whether there was a relationship between gene expression pattern and physical location

on chromosomes. Like C. elegans, chromosomes in C. briggsae lack centromeres and are

organized into clear domains that are defined by distinct rates of recombination

(Rockman and Kruglyak 2009, Ross et al. 2011). Centre domains have relatively low

rates of recombination while arm domains have much higher rates of recombination

(Cutter and Choi 2010). Arm domains also are the most genetically variable regions, in

terms of both functional and silent site diversity, whereas centre domains have the highest

gene density and lower polymorphism (Thomas et al. 2015). These patterns suggest that

if sequence differences drive differential expression, then the expression of genes that are

primarily located in chromosome centres is likely regulated by trans-acting factors.

Conversely, genes that are differentially expressed that are located in arm regions could

potentially be regulated in cis, given the higher polymorphism in these domains.

Furthermore, genes whose expression is more consistent across environments, or whose

expression changes in a similar manner across environments tend to be cis-regulated

(Smith and Kruglyak 2008) whereas genes whose expression is more variable across

different environments tend to be trans-regulated in C. elegans and in yeast (Li et al.

2006, Smith and Kruglyak 2008). Taken together, these observations suggest the

hypothesis that genes identified as having a significant effect of genotype are more likely

to be cis-regulated and located in arm domains. Additionally, I would expect to see most

Temperature genes in centre regions because they have similar responses in both

genotypes and therefore should have fewer cis-acting regulatory polymorphisms. Finally,

given that variable responses to the environment, especially those that constitute a change

in direction of expression between genotypes (i.e. increase in one and decrease in the

other), are regulated by trans-acting factors ((Li et al. 2006, Smith and Kruglyak 2008), I

would expect that most GxT genes would be located in the centres of chromosomes.

Consistent with this hypothesis, my analysis revealed that, on the whole, autosomal genes

that were differentially expressed due to genotype were overrepresented in arm domains

by almost 20%. Contrary to my expectations, GxT genes were also enriched in autosome

36

arm domains, albeit to a lesser extent (1.04-fold enrichment). Interestingly, G&T genes

were enriched in autosome centres.

The unexpected enrichment of GxT genes in autosome arms may be the result of

considering all GxT genes together, regardless of their expression patterns. For example,

whereas differential expression in Genotype genes can have only two patterns (i.e. higher

expression in Tropical than Temperate or vice versa), numerous distinct expression

profiles can each produce a significant interaction between genotype and temperature.

Indeed, when arm versus centre enrichment for GxT genes was examined for separate co-

expression modules the results can be better explained. For example, the observation that

the expression of cis-regulated genes tends to change less across environments whereas

expression in trans-regulated genes tends to change more across environments as well as

demonstrates the crossing reaction norms characteristic of GxE in local adaptation (Smith

and Kruglyak 2008, Kawecki and Ebert 2004). For instance, Modules 1, 9, and 16 were

enriched in autosome arms while Modules 3 and 14 were enriched in the centre. Module

eigengenes for the modules enriched in the arms could be interpreted as having regions in

which expression stays relatively consistent, a feature of cis-regulated genes (Smith and

Kruglyak 2008). For example, gene expression in Modules 1 and 16 does not change

drastically between 14°C and 20°C and expression in Module 9 is relatively unchanged

between 20°C and 30°C. Expression patterns between these temperatures are similar to

the expression profiles of Genotype genes, which were also enriched in arm domains. In

contrast, expression of module eigengenes of modules enriched in the centres shows

more drastic changes in expression, consistent with genes regulated in trans (Smith and

Kruglyak 2008). For instance, in Module 14, expression is opposite in the genotypes,

showing the most drastically different expression pattern between Temperate and

Tropical.

The enrichment of G&T genes in autosome centres can also be interpreted as conforming

to expectations. Although these genes have significant effects of Genotype and

Temperature on their expression, the effects of these factors are independent. When G&T

genes are considered as genes that are significantly differentially expressed due to

temperature, it is expected that they be primarily trans-regulated and consequently that

37

they be enriched in chromosome centres, where polymorphism is low. The difference in

expression between the genotypes that is maintained across temperatures could be caused

by a polymorphism in the promoter of a trans-acting factor that regulates expression in

the same way in both the Temperate and Tropical genotypes.

Less enrichment of gene groups on the X chromosome overall is unsurprising under the

assumption that differences in recombination rates drive differences in gene expression

and function. For example, genes that are specific to germline function are absent on the

X chromosome (Reinke et al. 2000). Gene density and recombination rates are both more

uniform across domains on the X chromosome (Hillier et al. 2007, Andersen et al. 2012).,

suggesting that differential expression groups would not be preferentially located in

either the arms or the centre. At the module level, three modules (1, 7, 12) were enriched

in X chromosome domains. However, Modules 1 and 12 show the same pattern of

enrichment in the autosomes, indicating that these two modules are overrepresented in

the arms in all chromosomes. Only Module 7, a Genotype module, was unique in being

enriched in the centre on the X. While it would be unexpected that a Genotype module be

enriched in the centre on an autosome, it is less surprising on the X chromosome where

the domains are less distinct.

My results are therefore consistent with the idea that higher nucleotide polymorphism

creates more functional variation in the form of genetic variation for gene expression.

More enrichment in distinct chromosomal domains was observed in the autosomes,

where regions of polymorphism are most pronounced. Given that these patterns of

enrichment are also consistent with expectations for genes that are regulated by cis- and

trans-acting factors, it would be interesting to further explore the question of gene

regulation with this dataset.

It would be possible to gain insight into the potential cis- and trans-regulation of these

differentially expressed genes by integrating data from my analysis with SNP

information. Using SNP variant data for the Temperate genotype (the C. briggsae

reference genome is based on the Tropical genotype), the SNP density could be

quantified for upstream regions of differentially expressed genes. If the expression of

38

genotype genes is primarily regulated in cis, then the density of SNPs in promoter regions

ought to be higher than expected. This information would be particularly interesting

given that the Temperate and Tropical genotypes are considered by some researchers to

represent the early stages of speciation (Baird and Stonesifer 2012, Abbott et al. 2013,

Chang et al. 2016). Interspecific differences in gene expression are caused primarily by

differences in cis-regulatory regions whereas intraspecies differences tend to be driven by

trans-regulatory regions (Wittkopp et al. 2008, Tirosh et al. 2009). If genotype genes

have higher SNP density in promoter regions, they could represent genes that are

contributing to expression differences between incipient species.

Small RNAs and temperature-sensitive regulation of gene expression

Small RNA RNA-seq data also were collected at the same time as the mRNA data used

in this study for the aim of shedding light on the mechanisms of gene expression

regulation in response to chronic temperature stress. Small RNAs are encoded within the

genome and bind to mRNA targets after being transcribed themselves (Claycomb 2012).

Typically, small RNAs silence their target genes by cleaving or binding to transcripts to

prevent subsequent translation into proteins. Certain small RNAs are also sensitive to

temperature, particularly those involved in the maintenance of fertility, such as Piwi-

interacting small RNAs (piRNAs) (Conine et al. 2009, Batista et al. 2008). piRNAs target

foreign genetic sequences such as transgenes and transposons in the germline during

development and are more active at increased temperatures in C. elegans (Batista et al.

2008, Lee et al. 2012). Similar piRNAs are found in many species within Caenorhabditis,

including C. briggsae (Shi et al. 2013, Tu et al. 2015). Given that most genotype-specific

responses are observed under heat stress, it is possible that post-transcriptional regulation

by piRNAs provide another pathway through which different expression patterns are

produced between the Temperate and Tropical genotypes. Future analyses that relate

small RNA expression to the patterns of mRNA expression revealed through this analysis

could help explain genotype-specific gene regulation, particularly under chronic heat

stress.

39

Conclusion

My analysis of temperature-dependent patterns of gene expression in Temperate and

Tropical populations of C. briggsae revealed several surprising results. For example, both

my differential expression and my co-expression clustering analyses showed that the

response to rearing under cold stress was qualitatively different from the response to

rearing under heat stress. A small proportion of genes responded to both cold stress and

heat stress whereas most Temperature genes responded to cold stress only and most GxT

genes responded to heat stress only. Genotype-specific responses to temperature were

also relatively common throughout the genome, occurring in 30% of all genes tested.

Visualization of module eigengenes of co-expression clusters corroborated my

differential expression results and revealed that the majority of genes that have GxT

responses showed genotype-specific expression only in response to heat stress.

Expression changed in the same direction for both Temperate and Tropical genotypes but

under heat stress, the expression change in the Temperate genotype was more drastic

when compared to that of the Tropical genotype. Finally, the enrichment of Genotype

genes and GxT genes in the more polymorphic arm domains of autosomal chromosomes

suggests that these genes tend to be regulated by cis-acting factors.

Results from this study point to the potential for future investigations. For example, given

that the Temperate and Tropical genotypes are considered by some to be undergoing

speciation and that most expression differences observed between species are cis-

regulated, it would be interesting to quantify the actual number of SNPs in regions

upstream of the differential expression groups to validate the results from analyses done

at the chromosome scale. In particular, genes with an effect of genotype that have a high

number of SNPs in upstream regions could be identified as likely being regulated in cis

and potentially contributing to expression differences between the incipient species. Such

genes may be considered candidate genes for investigations into the genome architecture

of speciation. Finally, the integration of the mRNA expression data with small RNA

expression data from the same experiment could shed light on patterns of post-

transcriptional regulation.

40

Table 1. Number of raw and cleaned reads in fastq files from Genome Quebec.

sample raw clean

AF14-1.1 17830362 17168256

AF14-1.2 17782202 17120594

AF14-2.1 26025046 25044050

AF14-2.2 26054157 25056610

AF14-3.1 29963016 29129264

AF14-3.2 29612363 28780806

AF20-1.1 31872995 30736051

AF20-1.2 31763308 30628482

AF20-2.1 34508074 33251105

AF20-2.2 34522693 33241950

AF20-3.1 24025150 23389721

AF20-3.2 23746489 23115267

AF30-1.1 36732913 35418582

AF30-1.2 36617640 35307045

AF30-2.1 19787947 19103142

AF30-2.2 19811944 19115062

AF30-3.1 18967224 18475211

AF30-3.2 18735493 18246983

HK14-1.1 28556436 27483161

HK14-1.2 28482204 27409768

HK14-2.1 24166460 23286124

HK14-2.2 24185031 23286128

HK14-3.1 26809455 26054377

HK14-3.2 26491330 25742077

HK20-1.1 17304708 16698014

HK20-1.2 17251430 16646977

HK20-2.1 27441709 26350515

HK20-2.2 27480811 26366445

HK20-3.1 22694619 22052191

HK20-3.2 22434965 21794800

HK30-1.1 29222885 28223148

HK30-1.2 29132626 28137121

HK30-2.1 22879967 22100957

HK30-2.2 22876949 22086416

HK30-3.1 24915269 24343611

HK30-3.2 24623445 24056254

41

Figure 1. Number of reads from fastq files for each data file (each biological replicate was sampled across 2 lanes) before and after cleaning with Trimmomatic. Replicates that begin with “AF” denote Tropical genotypes and “HK” denotes Temperate genotypes.

0

5

10

15

20

25

30

35

40

AF1

4-1

.1

AF1

4-1

.2

AF1

4-2

.1

AF1

4-2

.2

AF1

4-3

.1

AF1

4-3

.2

AF2

0-1

.1

AF2

0-1

.2

AF2

0-2

.1

AF2

0-2

.2

AF2

0-3

.1

AF2

0-3

.2

AF3

0-1

.1

AF3

0-1

.2

AF3

0-2

.1

AF3

0-2

.2

AF3

0-3

.1

AF3

0-3

.2

HK

14

-1.1

HK

14

-1.2

HK

14

-2.1

HK

14

-2.2

HK

14

-3.1

HK

14

-3.2

HK

20

-1.1

HK

20

-1.2

HK

20

-2.1

HK

20

-2.2

HK

20

-3.1

HK

20

-3.2

HK

30

-1.1

HK

30

-1.2

HK

30

-2.1

HK

30

-2.2

HK

30

-3.1

HK

30

-3.2

Re

ad

s (1

06)

raw

clean

42

Figure 2. Distribution of intron lengths in C. briggsae reference genome (WS253). Counts left of the red line represent 99% of all introns.

log2 intron length (bp)

Co

unt

(mill

ion

s)

0 5 10 15

0

1

2

3

4

5

43

Figure 3. Ratio of average number of uniquely mapped reads in Tropical and Temperate Genotypes. To minimize the bias towards reads mapped from the reference genotype, up to 10 mismatches were allowed per read.

1.06

1.08

1.1

1.12

1.14

1.16

1.18

0 1 2 3 4 5 6 7 8 9 10

Ra

tio

of

Tro

pic

al

to T

em

pe

rate

av

g.

un

iqu

e r

ea

ds

ma

pp

ed

Number of Mismatches

44

Table 2. Number and percentage of reads that mapped to unique locations (i.e. one location in the genome) with STAR.

Sample

Uniquely Mapped

Reads

% Uniquely Mapped

Reads

AF14-1 32007586 93.35%

AF14-2 47012080 93.84%

AF14-3 54523984 94.15%

AF20-1 57787850 94.17%

AF20-2 62122726 93.43%

AF20-3 43596994 93.75%

AF30-1 66295835 93.74%

AF30-2 35618842 93.20%

AF30-3 33087508 90.10%

HK14-1 51264144 93.39%

HK14-2 43616826 93.65%

HK14-3 48035023 92.74%

HK20-1 30262908 90.76%

HK20-2 49528582 93.95%

HK20-3 41332043 94.26%

HK30-1 51537189 91.44%

HK30-2 40748065 92.22%

HK30-3 35632338 73.62%

45

Figure 4. Percentage of uniquely mapped reads with STAR per biological replicate. Maximum mismatch rate was set at 10. “AF” denotes Tropical genotypes and “HK” denotes Temperate genotypes. Although sample HK30-3 had a lower proportion of uniquely mapped reads, the absolute number of uniquely mapped reads was comparable and so the sample was retained for downstream analysis.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% u

niq

ue

ly m

ap

pe

d r

ea

ds

Biological Replicate

46

a)

b)

Figure 5. a) Number of reads counted with htseq-count by biological replicate. “AF” = Tropical genotype, “HK” = Temperate genotype. b) Percentage of reads counted. Although the percentage of reads counted in HK30-3 was low, the sample was kept for downstream analysis because the number of reads counted was comparable.

0

10

20

30

40

50

60

70

80R

ea

ds

(x 1

06)

Biological Replicates

Not Counted

Counted

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% R

ea

ds

Biological Replicates

Not Counted

Counted

47

Figure 6. Multi-dimensional scaling plot (MDS) of filtered, normalized, and log transformed count data. Different colours represent experimental groups (ex. Temperate at 20°C). “AF” = Tropical genotype and “HK” = Temperate genotype. The x-axis represents the principal component with the largest proportion of variation and the y-axis represents the principal component with the second largest proportion of variation. Biological replicates that cluster together in the plot are more similar to each other. Samples that are close in space indicates consistency across replicates.

−4 −2 0 2 4

−2

0

2

4

Leading logFC dim 1

Le

ad

ing

lo

gF

C d

im 2

AF14−1AF14−2

AF14−3

AF20−1AF20−2

AF20−3

AF30−1AF30−2

AF30−3HK14−1HK14−2HK14−3

HK20−1HK20−2

HK20−3

HK30−1HK30−2

HK30−3

48

a)

b)

Figure 7. a) Distribution of p-values for t-tests for effect of strain, b) distribution of p-values for F-tests for effect of temperature. Heavily right-skewed distributions for both indicate that there is a good possibility of identifying significant effects for strain and temperature in many genes.

p−values

No.

of g

en

es (

10

00s)

0.0 0.2 0.4 0.6 0.8 1.0

0

1

2

3

4

5

6

7

p−values

No.

of g

en

es (

10

00

s)

0.0 0.2 0.4 0.6 0.8 1.0

0

1

2

3

4

5

6

7

49

Figure 8. Analysis for differential expression with edgeR (negative binomial distribution) versus limma (log-transformed counts distribution). edgeR = blue, limma = red. Limma is more conservative in all tests.

GxT G only T only G&T no DE

Differential Expression Group

No.

ge

ne

s (

10

00

s)

0

1

2

3

4

5

6

7

8

50

Figure 9. Quantile-quantile plot for normalized, voom-transformed count data shows that data approximate a normal distribution (represented by the red line) and are suitable for analysis with the limma package.

51

Figure 10. After filtering out genes with very low to no counts, TMM normalization for different library sizes, and log-transforming count data, a dendrogram reveals similarity of samples within strain and within temperature but not replicate. This suggests that the data are heterogeneous (i.e. from different sources) but for reasons of experimental design and not batch effects.

52

Figure 11. Analysis of soft-thresholding powers revealed 30 to be the power at which the scale-free fit is maximized (R2 = 0.75) and most closely approximates a scale-free network.

53

Figure 12. Analysis of a range of soft-thresholding powers revealed 30 to be the number at which the scale-free fit is maximized and that has a mean connectivity of at least 100 (k = 115).

54

Table 3. Values for each soft threshold power that was tested. Ideal powers have an

R-squared value close to 1, a slope close to -1, and a mean k (connectivity) over 100.

55

Figure 13. Clustering with WGCNA produced 124 modules based on expression similarity of 16 199 genes. One module (Module 0) contained 37 genes whose expression patterns were not sufficiently similar to be placed in any module. The soft thresholding power that determines similarity between genes was set at 30.

56

Figure 14. Clustering of 16 199 genes with WGCNA was also visualized as a heatmap in which red represents maximum similarity and blue no similarity. Large blocks of red indicate there is also similarity between clusters. Each colour on the x- or y-axis represents a co-expression module.

57

Figure 15. After merging modules with a <0.25 distance, 23 modules remained, including Module 0. The dendrogram and heatmap echo module similarity patterns across the initial 124 modules.

58

Figure 16. The heatmap of clustering of 16 199 genes with WGCNA after merging modules with a distance between them of less than 0.25. The clustering pattern of similar modules that was seen in the initial heatmap of 124 modules is retained after merging. Each colour on the x- or y-axis represents a co-expression module.

59

Figure 17. Differential expression analysis with limma showed that over half (54%) of all genes were significantly differentially expressed. Of these genes, the majority had significant interaction effects (GxT) (FDR= 0.05). “G&T” genes showed significant effects of genotype and temperature independently whereas “Genotype” genes and “Temperature” genes were significantly differentially expressed for those variables alone.

60

Figure 18. Looking at the proportion of differentially expressed genes that are expressed under cold stress versus heat stress reveals that the majority of genes with an effect of temperature respond to cold stress whereas most of the genes with an interaction respond to heat stress. G&T genes are included in this figure as Temperature genes because they have a significant effect of temperature and it is independent of its effect of genotype.

61

Figure 19. Looking at the proportion of genes that increase or decrease expression in response to either cold stress (light blue line) or heat stress (red line) shows that roughly equal proportions increase as decrease expression, especially for Temperature genes. Again, more Temperature genes (G&T and Temperature genes) respond to cold stress whereas more GxT genes respond to heat stress.

62

Figure 20. Constrasting the magnitude of expression change for genes with a significant independent effect of temperature (Temperature genes, G&T genes) under chronic cold stress (light blue line) and chronic heat stress (red line) shows that for genes with a significant independent effect of temperature, the magnitude of change in expression is similar under both types of extreme temperature stress. However, more variation is seen in the magnitude of expression change between cold stress and heat stress for genes with a significant interaction. For example, for GxT genes, the magnitude of expression increase is greater under heat stress than cold stress whereas for genes with a significant effect of temperature, the response is comparable at both temperatures.

63

Figure 21. Co-expression clustering of 16 199 genes by expression similarity with WGCNA resulted in 22 modules ordered by size. Module 0 is the module that contains genes whose expression patterns were not sufficiently similar to other genes to be placed in a module. Cross-referencing module membership with the results of my differential expression analysis revealed that differentially expressed genes are not distributed equally among the modules.

64

Figure 22. The 23 modules that resulted from co-expression clustering of 16 199 genes have different proportions of differentially expressed genes. Modules with > 1000 genes and or with > 50% differentially expressed genes were retained for further analysis.

65

Figure 23. Module eigengene plots of normalized, log2-transformed expression across temperature treatments for Modules 7 and 10, Representative Modules with a large proportion of genes that were differentially expressed due to Genotype, show that genes in Module 7 are expressed more in the Temperate genotype (blue) whereas genes in Module 10 are expressed more in the Tropical genotype (red).

66

Figure 24. Module eigengene plots of normalized, log2-transformed expression across temperature treatments for Modules 4, 5, 6, 12, and 15, Representative Modules with a large proportion of genes that were differentially expressed due to Temperature. Genes in Modules 6, 12, and 15 increase expression in response to rearing under heat stress. Genes in Modules 4 and 5, modules with a large proportion of G&T genes, increase expression in response to rearing under cold stress. Genes in Module 4 are expressed more in the Temperate genotype (blue) whereas genes in Module 5 are expressed more in the Tropical genotype (red).

67

Figure 25. Module eigengene plots of normalized, log2-transformed expression across temperature treatments for Modules 1,2,3,9,14,16, and 22, Representative Modules with a large proportion of genes that showed significant interactions between genotype and temperature. Genes in Modules 1, 2, 3, and 16 show similar patterns of increase and decrease in expression at each temperature, but the Temperate (blue) genotype changes expression more drastically when reared under chronic heat stress. A minority of genes from Modules 14 and 22 show opposite patterns of expression between Temperate and Tropical (red) genotypes.

68

Figure 26. Module eigengene plots of normalized, log2-transformed expression across temperature treatments for Modules with fewer than 1000 genes and less than 50% differentially expressed genes. Expression patterns in these non-Representative Modules are less distinct between Temperate (blue) and Tropical (red) genotypes and across temperatures. Crossing over of expression patterns between genotypes where the difference in slopes is not pronounced indicates low power to detect differential expression for genes in these modules.

69

Table 4. Table of G-test p-values for to test whether the proportion of differentially expressed genes in a module differed significantly from genome-wide proportions (p = 0.05, Bonferroni adjusted). All modules are significantly different except for Module 0 (membership in Module 0 is not based on expression similarity).

Module

G-test p-

value adj. p-value

0 0.006774373 0.155810568

1 2.69E-36 6.19E-35

2 3.67E-143 8.45E-142

3 2.06E-48 4.73E-47

4 1.66E-72 3.81E-71

5 1.34E-138 3.08E-137

6 8.79E-132 2.02E-130

7 1.39E-124 3.19E-123

8 9.32E-92 2.14E-90

9 3.47E-11 7.99E-10

10 3.52E-108 8.10E-107

11 5.35E-27 1.23E-25

12 1.24E-60 2.86E-59

13 8.84E-58 2.03E-56

14 1.24E-05 0.000284691

15 1.23E-46 2.83E-45

16 6.15E-08 1.41E-06

17 1.60E-08 3.68E-07

18 7.40E-17 1.70E-15

19 1.01E-21 2.33E-20

20 2.53E-08 5.81E-07

21 1.75E-14 4.01E-13

22 2.34E-08 5.39E-07

70

Table 5. G-test p-values from a test to determine whether the proportion of genes located in chromosome arms or centres differed from expected proportions for each differential expression group (p = 0.05, Bonferroni adjusted).

DE

group

G test p-

value adj. p-value

T only 0.144735537 0.723677687

G only 1.13E-07 5.66E-07

G&T 2.35E-05 0.00011734

GxT 0.005203834 0.026019169

noDE 0.132138705 0.660693526

71

Table 6. G-test p-values from a test to determine whether the proportion of genes located in autosome arms or centres differed from expected proportions for each of the 22 modules identified by co-expression clustering (FDR = 0.05, Benjamini-Hochberg correction).

Module

G test p-

value adj. p-value

1 0.012177946 0.022326235

2 0.544743083 0.630755149

3 0.009617524 0.021158554

4 0.152170221 0.209234054

5 0.768639623 0.805946524

6 6.28E-18 1.38E-16

7 0.995516473 0.995516473

8 1.37E-05 6.03E-05

9 0.02862504 0.044982206

10 1.86E-11 1.37E-10

11 0.116978603 0.171568618

12 8.24E-05 0.000258911

13 8.16E-14 8.98E-13

14 0.011301327 0.022326235

15 0.002961166 0.008143206

16 1.87E-05 6.85E-05

17 0.00545346 0.01333068

18 0.377099659 0.460899583

19 0.769312591 0.805946524

20 0.014375624 0.024327979

21 2.54E-09 1.40E-08

22 0.265232617 0.34324221

72

Figure 27. The proportion of genes in each differential expression category that are located in either arm or centre chromosomal domains on autosomes (chromosomes I – V). * indicates significant enrichment (p = 0.05, Bonferroni adjustment).

73

Figure 28. The proportion of genes in each differential expression category that are located in either arm or centre chromosomal domains on the X chromosome. No groups were significantly enriched in arms or the centre (p = 0.05, Bonferroni adjustment).

74

Figure 29. The proportion of genes in each of the Representative Modules that are located in either arm or centre chromosomal domains on the autosomes. Blue = Temperature Modules, red = Genotype Modules, purple = G&T modules, orange = GxT modules. * indicates significant enrichment (FDR = 0.05).

75

Figure 30. The proportion of genes in each of the Representative Modules that are located in either arm or centre chromosomal domains on the X-chromosome. Blue = Temperature Modules, red = Genotype Modules, purple = G&T modules, orange = GxT modules. * indicates significant enrichment (FDR = 0.05).

76

Table 7. Table of G-test p-values for tests of whether the proportions of genes on autosomes and the X-chromosome were significantly different from expectations for each differential expression group (p = 0.05, Bonferroni adjusted).

DE

group

G test p-

value adj. p-value

T only 0.766174012 1

G only 0.002842788 0.014213941

G&T 0.001210832 0.006054162

GxT 0.000236965 0.001184823

no DE 0.002134383 0.010671915

77

Table 8. Table of G-test p-values for tests of whether the proportions of genes on autosomes and the X-chromosome were significantly different from expectations for each co-expression module (FDR = 0.05, BH correction).

Module

G test p-

value adj. p-value

1 1.11E-36 8.17E-36

2 0.058403179 0.071381663

3 4.32E-17 1.58E-16

4 1.85E-38 2.04E-37

5 2.31E-30 1.27E-29

6 8.17E-39 1.80E-37

7 0.010162859 0.013973931

8 0.039370389 0.050949915

9 4.70E-28 2.07E-27

10 8.20E-05 0.000180384

11 0.002065337 0.003029161

12 1.05E-13 3.29E-13

13 0.000638395 0.001170391

14 0.001346515 0.002115951

15 0.285415181 0.313956699

16 0.121207929 0.140346023

17 1.96E-05 4.79E-05

18 0.000107935 0.000215869

19 0.415392993 0.435173612

20 0.000718097 0.001215242

21 1.09E-06 3.00E-06

22 0.758338434 0.758338434

78

Figure 31. The proportion of genes on autosomes and the X-chromsome for each differential expression group. The dotted red line indicates the expected proportion. * indicates significant enrichment on either the autosomes or the X-chromosome (p = 0.05, Bonferroni adjusted).

79

Figure 32. The proportion of genes on autosomes and the X-chromsome for each co-expression module. The dotted red line indicates the expected proportion. * indicates significant enrichment on either the autosomes or the X-chromosome (p = 0.05, Bonferroni adjusted). – under the module name indicates a Representative Module.

80

Supplementary Figure 1. Treemap generated with REVIGO from significantly overrepresented molecular function GO terms for Module 7 (Genotype module).

m7_mf

acetylcholine receptor activityGABA receptor activity GABA receptor activity

ligand−gated ion channel activity

nucleic acid binding

81

Supplementary Figure 2. Treemap generated with REVIGO from significantly overrepresented biological process GO terms for Module 10 (Genotype module).

m10_bp

metabolic

process

homeostatic processregulation of liquid surface tension

cell

communication

metabolism

regulation of liquid surface tension

82

Supplementary Figure 3. Treemap generated with REVIGO from significantly overrepresented biological process GO terms for Module 6 (Temperature module).

m6_bp

mitochondrial transport

protein localization

protein targeting

cellular component biogenesismitochondrion organization

nitrogen compound

metabolic process

primary

metabolic

process

nucleobase−containing

compound metabolic process

oxidative phosphorylation

protein metabolic process

RNA metabolic process

RNA splicing

rRNA metabolic process

translation

neurological

system

process

sensory

perception

of smell

single−multicellular

organism process

system

process

anatomical

structure

morphogenesis

behavior

biological

regulation

cell

communication

cellular component

organization or

biogenesis

developmental

process

immune system

process

metabolism

mitochondrial transport

mitochondrion organization

multicellular

organismal

process

nitrogen compound metabolism

response to

stimulus

RNA splicing

sulfur compound metabolism

system process

83

Supplementary Figure 4. Treemap generated with REVIGO from significantly overrepresented biological process GO terms for Module 4 (G&T module).

m4_bp

cellular amino acid

metabolic process

cholesterol

metabolic process

DNA metabolic process

lipid metabolic

process

proteolysis

cation transport

exocytosis

ion transport

lipid transport

protein transport

receptor−mediated endocytosis

transport

vesicle−mediated

transport

vitamin transport

anatomical structure

morphogenesis

cellular component

morphogenesis

ectoderm

developmentmesoderm development

organelle

organization

primary

metabolic

process

homeostatic process

regulation

of catalytic

activity

regulation of liquid

surface tension

regulation of molecular function

behavior

cell

communication

cell−cell signaling

synaptic transmission

sensory

perception

of smell


organism process

system

processvisual perception

biological adhesion

biological regulation

cell−matrix adhesion

cellular

process

cholesterol metabolism

developmental process

immune system process

lipid transport

localization

mesoderm development

metabolism

multicellular

organismal

process

primary

metabolism

regulation of liquid surface tension

response to external stimulus

response to stimulus

synaptic transmission

visual perception

84

Supplementary Figure 5. Treemap generated with REVIGO from significantly overrepresented biological process GO terms for Module 5 (G&T module).

m5_bp

carbohydrate

metabolic process

anatomical structure morphogenesis

cellular component morphogenesis

cellular component

organization

mitotic nuclear division

response

to stress

DNA

metabolic

process

mRNA processing


compound metabolic

process

phosphate−containing

compound metabolic processprotein metabolic process

protein phosphorylation

regulation of

transcription

from RNA

polymerase II

promoter

RNA metabolic

process

biosynthetic

process

primary

metabolic

process

glycogen metabolic process

polysaccharide metabolic process

regulation of carbohydrate metabolic process

neurological

system process


organism process

system

process

carbohydrate metabolism

cell proliferation

cellular component morphogenesis


immune responseimmune system process

mRNA processing

multicellular

organismal

processprimary metabolism

regulation of carbohydrate metabolism

response to stimulus

system process

85

Supplementary Figure 6. Treemap generated with REVIGO from significantly overrepresented biological process GO terms for Module 1 (GxT module).

m1_bp

cell communication

cell cycle

chromatin organization mitotic nuclear division

organelle organization

cellular protein

modification process

DNA metabolic processDNA repair



regulation of


compound

metabolic process

regulation of

transcription from RNA

polymerase II promoter

RNA metabolic process

RNA splicing



translation

nitrogen compound

metabolic process

primary metabolic process

anion

transportcation transport ion transport

nuclear transportRNA localization

muscle

contraction


organism process

system

process

visual

perception

biological

adhesion

biosynthesis

cell

adhesion

cell cycle

cellular component

organization or biogenesis

chromatin organization

chromosome segregation

DNA repair

ectoderm

development

immune system

process

metabolism

multicellular

organismal

process

nitrogen compound metabolism

RNA localization

synaptic

transmissionsystem process

86


m2_bp

anatomical

structure

morphogenesis

cellular

component

morphogenesis

cellular

component

organization

mitotic nuclear

divisioncation transport

ion transport

muscle contraction

single−multicellular organism process

system process

lipid metabolic processsteroid metabolic process cellular protein


DNA metabolic

processDNA repair



RNA splicing

rRNA

metabolic

processtranslation

cell cycle

cellular component

morphogenesis

cellular component

organization or biogenesis

cellular process


ion transportmulticellular organismal process

muscle contraction

primary metabolism

steroid metabolism

translation

87


m3_bp

anion transport

carbohydrate transport

cation transportendocytosis

extracellular transportion transport

transport

vesicle−mediated

transport

vitamin transport

cyclic nucleotide metabolic process

DNA

metabolic

process

regulation of

catalytic activity

regulation of phosphate

metabolic process

RNA metabolic

process

mesoderm

development

neurological

system process


organism processsystem process

visual perception

cell−cell signaling

neuron−neuron synaptic transmission

mRNA

processing

translation

anion transport

biological adhesion

biological regulation

cell communication

cellular process

cyclic nucleotide metabolism


ectoderm development

immune system

process

localizationmulticellular

organismal process

neurological system process

neuron−neuron synaptic transmission

regulation of

molecular function

response to

stimulus

single organismal

cell−cell adhesion

translation

88


m9_bp

cell communicationcell cycle

ectoderm developmentmesoderm development

cellular protein



compound metabolic process

regulation of

biological process

regulation of catalytic activity

regulation of molecular function

regulation of


compound

metabolic process

regulation of





transcription, DNA−templated

biological adhesion

biological regulationcell cycle

cell−matrix adhesion

cellular component movement

cellular process


mesoderm development

metabolism

primary

metabolism

regulation of transcription from RNA polymerase II promoter

sensory perception of sound

89


m14_bp

generation of precursor metabolites and energy protein metabolic process

regulation of translationtranslation

cellular component

biogenesis

metabolism

oxidative phosphorylation

primary metabolism

translation

90


m22_bp

protein metabolic processtranslation

metabolism

primary metabolism

translation

92

92

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