The effects of mutations on gene expression and alternative splicing: a case study of EMS- induced heritable mutations in the microcrustacean Daphnia

Abstract

Understanding the relationship between mutations and their genomic and phenotypic consequences has been a longstanding goal of evolutionary biology. However, few studies have investigated the impact of mutations on gene expression and alternative splicing on the genome-wide scale. In this study, we aim to bridge this knowledge gap by utilizing whole-genome sequencing data and RNA sequencing data from 16 OP Daphnia mutant lines to investigate the effects of EMS-induced mutations on gene expression and alternative splicing. Using rigorous analyses of mutations, expression changes, and alternative splicing, we show that trans-effects are the major contributor to the variance in gene expression and alternative splicing between the wildtype and mutant lines, whereas cis mutations only affected a limited number of genes and do not always alter gene expression. Moreover, we show that there is a significant association between DE genes and exonic mutations, indicating that exonic mutations are an important driver of altered gene expression.

Full text

The effects of mutations on gene expression and alternative splicing: a case study of EMS-
induced heritable mutations in the microcrustacean Daphnia
Authors: Marelize Snyman1,2 and Sen Xu1,*
1Department of Biology, University of Texas at Arlington, Arlington, Texas USA 76019.
2Present address: Department of Dermatology, University of Texas Southwestern Medical
Center, Dallas, Texas USA 75235
*Correspondence author. 501 S Nedderman Dr., Arlington, Texas, USA 76019. Email:
sen.xu@uta.edu
Keywords: Daphnia, mutagenesis, ethyl methanesulfonate, gene expression, alternative splicing
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Abstract
Understanding the relationship between mutations and their genomic and phenotypic
consequences has been a longstanding goal of evolutionary biology. However, few studies have
investigated the impact of mutations on gene expression and alternative splicing on the genome-
wide scale. In this study, we aim to bridge this knowledge gap by utilizing whole-genome
sequencing data and RNA sequencing data from 16 OP Daphnia mutant lines to investigate the
effects of EMS-induced mutations on gene expression and alternative splicing. Using rigorous
analyses of mutations, expression changes, and alternative splicing, we show that trans-effects
are the major contributor to the variance in gene expression and alternative splicing between the
wildtype and mutant lines, whereas cis mutations only affected a limited number of genes and do
not always alter gene expression. Moreover, we show that there is a significant association
between DE genes and exonic mutations, indicating that exonic mutations are an important
driver of altered gene expression.
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Introduction
One of the fundamental goals of evolutionary biology is to understand the genomic and
phenotypic consequences of mutations. As mutations are rare, long-term mutation accumulation
(MA) experiments in a variety of different model organisms, e.g. Drosophila (Keightley et al.
2009, Krasovec 2021), Caenorhabditis (Denver et al. 2009), Arabidopsis (Ossowski et al. 2010)
and Daphnia (Bull et al. 2019, Flynn et al. 2017, Omilian et al. 2006, Xu et al. 2012) have been
instrumental in our understanding of mutational consequences. These MA experiments
minimized the power of selection on mutations and allow for the persistence of non-lethal
mutations in a nearly unbiased manner, offering insight into the genome-wide rates, spectrum,
and fitness effects of spontaneous mutations. The findings of these studies have yielded
important implications for our understanding of diseases, the origin of genetic diversity, and
molecular evolution in general.
To date, many MA studies have moved beyond simply measuring the mutation rate,
spectrum, and fitness effects on the organismal level. For example, MA studies have been
utilized to study transposable element mutation rates (Díaz-González and Domínguez 2020, Ho
et al. 2021), enzymatic activity (Aquadro et al. 1990, Harada 1995), methylation frequency
(Denkena et al. 2021, Jiang et al. 2014), phenotypic plasticity (Latta et al. 2015), and patterns of
genotype-environment interactions (Chu and Zhang 2021, Scheffer et al. 2022, Xu 2004).
However, few studies have examined the impacts of mutations on gene expression, which could
have dramatic phenotypic consequences (see below). Furthermore, while most MA experiments
focus on spontaneous mutations, few studies have paid attention to environmentally induced
mutations. As anthropogenic stressors such as chemical pollutants (e.g.,
Chernobyl disaster in
1986, Fukushima nuclear disaster in 2011) are impacting various ecosystems at a dramatically
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increased rate since the start of the Industrial Revolution (Landrigan et al. 2018), it has become
imperative to understand the genetic consequences of these mutations induced by environmental
agents such as chemical mutagens. In this study, we aim to bridge this knowledge gap by
investigating the impact that ethyl-methanesulfonate (EMS)-induced inheritable mutations exert
on genome-wide gene expression and alternative splicing.
Gene expression is a major link between an organism’s genotype and phenotype.
Numerous studies have demonstrated the critical role of altered gene expression in phenotypic
variation such as the beak morphology in Darwinian finches (Abzhanov et al. 2006), cold
adaptation in lizards (Josephs 2021), melanin production in Drosophila (Rebeiz et al. 2009),
body mass due to latitudinal adaptation and color pattern in mice (Mack et al. 2018, Manceau et
al. 2011), and adaptation to predation in Trinidadian guppies (Ghalambor et al. 2015).
Variation in gene expression can arise through cis and trans mutations. Cis mutations are
often located within a gene, or up- and downstream of a gene, affecting the interaction between
the gene and its regulators (e.g., promotors and enhancers). On the other hand, trans mutations
can be located anywhere within the genome (other than within or nearby the gene of interest) and
mediate the expression of genes through the use of e.g., transcription factors and long noncoding
RNAs (Albert et al. 2018, Lewis et al. 2014, Vande Zande et al. 2022). Trans mutations are
thought to be primarily responsible for the variation in gene expression within species since trans
factors represent a much larger mutational target and are thus more likely to arise than cis
mutations (Chen et al. 2015, Coolon et al. 2014, Gruber et al. 2012, Metzger et al. 2016, Rhoné
et al. 2017, Wittkopp et al. 2004). However, due to the pleiotropic effects of trans mutations on
expression (e.g., affecting multiple genes at the same time), trans mutations are often selected
against as populations adapt and diverge. With cis mutations mostly affecting the expression of
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specific genes, they survive selection more often than trans mutations. Therefore, cis mutations
rather than trans mutations are mostly found to be responsible for gene variation between
populations and between species (Coolon et al. 2015, Emerson et al. 2010, Schaefke et al. 2013).
These theories and empirical observations in natural populations lead us to expect that de
novo mutations (spontaneous or environmentally induced) upon arising in the genome should
exhibit stronger trans effects than cis effects. However, this hypothesis remains largely untested
due to previous work focusing on utilizing targeted mutagenesis to examine the effects of cis
mutations in a variety of different organisms (Hornung et al. 2012, Kwasnieski et al. 2012,
Maricque et al. 2017, Melnikov et al. 2012, Metzger et al. 2015, Patwardhan et al. 2009, Sharon
et al. 2012). We suggest that utilizing chemical mutagenesis throughout the genome provides an
important alternative approach to understanding how cis and trans mutations affect gene
expression on the genome-wide level.
Furthermore, a very much ignored aspect of mutations’ impact on gene expression
resides in alternative splicing. Alternative splicing is critically involved in the origin of
phenotypes, e.g., the flowering time of Arabidopsis (Macknight et al. 2002), seed development
in sunflowers (Smith et al. 2018), pigmentation of cichlid fishes (Terai et al. 2003), and
thermogenesis in mice (Vernia et al. 2016). With the involvement of trans-acting splicing factors
and cis-acting regulatory motifs, alternative splicing is often highly regulated and vulnerable to
mutations. One can imagine that point mutations can occur in both introns and exons, leading to
the alteration of existing splice sites or the generation of new ones, and affecting splicing
enhancers and silencers (Anna and Monika, 2018). However, it remains unclear to what extent
alternative splicing is affected by trans mutations.
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To examine the effects of mutations on gene expression and alternative splicing on a
genome-wide scale, we employed the chemical mutagen ethyl methanesulfonate (EMS) to
induce heritable mutations in the microcrustacean Daphnia. EMS induces DNA damage through
the alkylation of guanine, causing O6 ethylguanine to mispair with thymine instead of cytosine in
subsequent replications, resulting in EMS-induced mutations mainly consisting of G:C to A:T
transitions (Greene et al. 2003). EMS-induced mutations are randomly distributed throughout
the genome, enabling the generation of loss or gain of function mutants and weak nonlethal
alleles (Greene et al. 2003, Lee et al. 2003).
We have established an EMS mutagenesis protocol for Daphnia (Snyman et al. 2021),
where we showed that by exposing Daphnia to EMS, the base substitution rate reached 1.17×10-
6 and 1.75 ×10-6 per base per generation for 10mM and 25mM EMS concentrations, respectively
(Snyman et al. 2021). These EMS-induced mutation rates are greatly elevated compared to the
spontaneous base substitution rate estimates in Daphnia of 2.30×10−9 and 7.17 × 10-9 per base
per generation (Flynn et al. 2017, Keith et al. 2016). The EMS-induced mutations were also
randomly distributed in different types of genomic regions (e.g., exon, intron, intergenic
regions).
In this study, we selected an obligately parthenogenetic Daphnia isolate to examine the
impact of EMS-induced heritable mutations on gene expression and alternative splicing. A major
motivation for using an obligately parthenogenetic isolate is that we can avoid the maternal
effects imposed by exposure to EMS in the ancestral mutant as much as possible by examining
the asexual offspring of later generations that are genetically identical (albeit extremely rare
spontaneous mutations) to the ancestors. Daphnia typically reproduces by cyclical
parthenogenesis. Under favorable environmental conditions such as high food abundance and
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low population density, Daphnia females asexually produce diploid subitaneous eggs, giving rise
to genetically identical daughters (Figure 1). Under stressful environmental conditions (e.g.,
high population density, lack of food availability), females switch to sexual reproduction,
producing haploid eggs. Males also emerge in populations under environmental stress (hatched
from diploid subitaneous eggs), as sex is environmentally determined in Daphnia (Gorr et al.
2006, Tatarazako et al. 2003). Sexual reproduction generates dormant embryos deposited into a
protective case (i.e., ephippium), which can resume development once the environment becomes
favorable (Frisch et al. 2014). However, for obligately parthenogenetic Daphnia, females under
stress can asexually produce diploid resting eggs (Xu et al. 2022).
In this study, we examined the gene expression and alternative splicing mechanisms
functional in 16 Daphnia mutant lines derived from the same ancestor in comparison to the
wildtype using whole-genome DNA and RNA sequencing data. Our main goals were three-fold.
First, we investigated the alteration of gene expression (DS) and alternative splicing in the
mutant lines due to the impact of EMS-induced mutations. Second, we examined the
contribution of cis and trans mutations on differential gene expression and alternative splicing.
Third, we were interested in whether any genomic regions carrying cis mutations were
significantly associated with DE and AS.
Materials and Methods
Sampling and maintenance of isolates
An obligately parthenogenetic Daphnia isolate collected from a pond in the US was used in this
study. This isolate has been kept as a clonally reproducing line in artificial lake (Kilham et al.
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1998) water under a 16:8 (light: dark) cycle at 18°C and fed with the green algae, Scenedesmus
obliquus twice a week.
Generating Daphnia mutants using EMS
To generate Daphnia mutants with heritable mutations from the wildtype, we used EMS to
induce germline mutations following the established methodology in Daphnia (Snyman et al.
2021). Specifically, eight sexually mature Daphnia females (at the asexually reproducing stage
for subitaneous eggs) were exposed to 25 mM ethyl methanesulfonate for 4 hours to introduce
mutations into their oocytes. After exposure, these females were individually isolated (Figure 2).
We collected two progenies (G1s) in the first asexual brood from each of the eight exposed
females because each G1 was derived from an oocyte that was individually mutagenized, thus all
the G1s are genetically distinct. In total, we established 16 mutant lines.
To avoid maternal effect (i.e., from exposure to EMS) on gene expression in the
offspring, each of the mutant lines was propagated asexually for at least 3 generations to reach a
high enough animal density to induce the parthenogenetic production of resting eggs. 5-10
ephippia (each containing 2 resting eggs) were collected from each mutant line, and the
decapsulated resting eggs were hatched using the protocol established by Luu et al. 2020.
Briefly, the collected resting embryos were kept in the dark at 18°C for two weeks before being
placed under UV light to stimulate embryo development. If no sign of embryo development was
observed after five days, the resting embryos were placed back in the dark for 2 weeks and then
re-exposed to UV light until at least one resting embryo hatched per mutant line. The hatched
mutant offspring was then asexually propagated until a high enough animal density was reached
for DNA and RNA extraction.
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DNA extraction and whole-genome DNA sequencing
A total of 30-40 clonal offspring were collected for each mutant line for DNA extraction using a
CTAB (Cetyl Trimethyl Ammonium Bromide) method (Doyle and Doyle 1987). DNA quality
and concentration were assessed by electrophoresis on a 2% agarose gel and a Qubit 4.0
Fluorometer (Thermo Scientific, Waltham, MA, USA). DNA sequencing libraries were prepared
following standard MGI sequencing library protocol by the Beijing Genomics Institute (BGI,
Cambridge, MA, USA). All 16 mutant lines were sequenced on an MGI DNBseq platform with
150-bp paired-end reads, with a targeted sequencing coverage of 30X per line. The raw DNA
sequence data were deposited at NCBI SRA under PRJNA892919.
RNA extraction and transcriptomic sequencing
Experimental animals were maintained in the same environmental conditions i.e., 18

with a
16:8 light/dark cycle, and three replicates of 2 or 3-day old offspring were collected from the
wild-type (i.e., control), and each mutant line. RNA was extracted using the Promega SV Total
RNA Isolation kit (Madison, WI, USA) following the manufacturer’s instructions. RNA quality
was examined by electrophoresis on a 2% agarose gel, and RNA concentration was measured
using a Qubit 4.0 Fluorometer (Thermo Scientific, Waltham, MA, USA). RNA sequencing
libraries were prepared using the NEBNext Ultra II RNA Library Prep Kit for Illumina (Ipswich,
MA, USA). Transcriptomic sequencing was done by Novogene Corporation Inc. (Sacramento,
CA, USA) following standard Illumina sequencing protocol. Each library was sequenced on an
Illumina NovaSeq6000 platform with at least 20 million 150-bp paired-end reads. The raw RNA
sequence data were deposited at NCBI SRA underPRJNA892982.
Quality control and mapping
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Quality of the raw reads was examined using FastQC (Andrews 2010). No adapter contamination
was observed for the whole-genome sequencing data, thus further analysis was completed using
the raw reads. Our RNAseq dataset showed adapter contamination. To address this issue
Trimmomatic v.0.39 (Bolger et al. 2014) was used to perform adapter trimming and quality
filtering using the following parameters: ILLUMINACLIP
adapter.fasta:2:30:10:2:keepBothReads LEADING:3 TRAILING:3 MINLEN:36. The following
Illumina adapter sequences were trimmed, read 1:
AGATCGGAAGAGCACACGTCTGAACTCCAGTCA, read 2:
AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT. Lastly, reads were reassessed using
FastQC to confirm the removal of low-quality reads and adapter sequences.
Identification of EMS-induced heritable mutations
We identified heterozygous EMS-induced heritable mutations following the EMS mutagenesis
analysis protocol established by Snyman et al. (2021). Since these mutant lines are obligate
parthenogens (i.e., no segregation and no sexual reproduction) all EMS-induced mutations
should be in the heterozygous state, ignoring rare ameiotic gene conversions (Omilian et al
2006). We used the Burrows-Wheeler Alignment Tool BWA-MEM version 0.7.17 (Li and
Durbin 2009) with default parameters to align the whole-genome DNA sequencing raw reads of
each mutant line to the Daphnia pulicaria reference genome (Jackson et al. 2021). SAMtools (Li
et al. 2009) was used to remove reads mapped to multiple locations, and the MarkDuplicates
function of Picard tools (http://broadinstitute.github.io/picard/) was used to locate and tag PCR
duplicates. We used BCFtools (Li 2011) mpileup and call functions with default parameters to
generate genotype likelihoods and genotype calls in a VCF file. The following additional
FORMAT and INFO tags were added to the VCF file: AD (allelic depth), DP (number of high-
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quality bases), ADF (allelic depth on forward strand), and ADR (allelic depth on reverse strand).
Tentative EMS-induced mutations for all treatment lines were further filtered with BCFtools’
filter function to retain only single nucleotide polymorphisms (SNPs) with a sequencing depth
(DP) >= 10 and <= 60, quality score (QUAL) >= 20, and a distance of at least 50-bp from an
indel.
Furthermore, a custom python script (https://github.com/Marelize007/Functional_impact
_of_EMS-induced_mutations) was used to filter the tentative mutations. As described by
Snyman et al. (2021), the final set of mutations was identified using a consensus method.
Briefly, all genotype data from the 16 EMS mutant lines were added to one VCF file, and a
consensus genotype was established by majority rule. Out of the 16 mutant lines, at least 12
mutant lines had to agree to generate a consensus genotype call per site. This allowed for the
inclusion of mutation sites where up to 3 out of the 16 mutant lines had no genotype call due to
inadequate depth. A tentative mutation was identified if the mutant line showed a different
genotype than the consensus. All final mutations had to be supported by at least two forward and
two reverse reads to limit false positives due to allele drop and inadequate sequence coverage.
Differential expression analysis
The trimmed RNAseq reads were mapped to the D. pulicaria (Jackson et al. 2021) reference
genome utilizing the STAR aligner (Dobin et al. 2013) with default parameters. Reads mapped
to multiple locations in the genome were removed using SAMtools (Li et al. 2009), and raw
transcript counts were obtained for each sample using featureCounts (Liao et al. 2014).
Differential expression analysis was performed using DESeq2 v.1.34.0 (Love et al. 2014). First,
the regularized log (rlog) transformation function in DEseq2 was used to normalize the mapped
read counts. Next, differentially expressed genes were determined for each mutant line using the
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Wald negative binomial test with the design formula ~ genotype, where genotype represents
either the mutant or wild-type genotype. The Benjamini-Hochberg method (FDR < 0.05) was
used to adjust the p-values for multiple testing, and genes with a p-value <0.05 were considered
significantly differentially expressed. Differentially expressed genes were additionally filtered
according to fold change. Genes with a fold-change > 1.5 were considered upregulated, and < -
1.5 were considered downregulated in the mutant lines compared to the wildtype (i.e., control).
Mutation rate calculation
The formula, µ

=

m/n *l was used to calculate the per site per generation mutation rate for all
16 mutant lines, where m is the total number of mutations identified in each line, n is the total
number of genomic sites with a sequencing depth >=10, and <=60, QUAL >= 20, and where
each site is at least 50-bp from the nearest indel in each mutant line. Furthermore, l represents the
number of generations. To calculate the per gene per generation mutation rate, we used the
formula µg = mg/ng *1, where mg represents the total number of mutations detected in genic
regions (including UTRs, exons, and introns), ng represents the total number of genes analyzed in
each mutant line, and l represents one generation. The non-synonymous mutation rate was
calculated utilizing the same formula, except mg represented the number of non-synonymous
mutations per mutant line.
Annotation of EM-induced mutations
Functional annotation based on genomic locations and effect prediction of EMS-induced
heritable mutations were done using the cancer mode (-cancer) of SnpEff version 4.0 with
default parameters (Cingolani et al. 2012). This mode was utilized since it allowed direct
comparison between the EMS mutant genotypes and the wild type.
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Alternative splicing analysis
We used the tool rMATS v4.1.1 (Shen et al. 2014) to detect alternatively spliced (AS) events
using reads mapped to both exons and splice junctions. The following alternatively spliced
events were detected: skipped exon (SE), alternative 5’ splice site (A5SS), alternative 3’ splice
site (A3SS), retained intron (RI), and mutually exclusive exons (MXE). SE events take place
when an exon along with its flanking introns are spliced out, and A3SS and A5SS result when
different parts of exons are either included or excluded from the resulting transcript. During RI
events, introns are retained; during MXE events, only one of two exons are retained in the
resulting mRNA (Pohl et al. 2013, Wang et al. 2015). Alternatively spliced events had to be
supported by at least four uniquely mapped reads and have a minimum anchor length of 10nt.
Additionally, the Benjamini-Hochberg adjusted (FDR < 0.05) p-value had to be less than 0.05,
and the difference in exon inclusion level (
Δ
|
ψ
|) greater than 5% (Shen et al. 2014, Suresh et al.
2020).
Results
Whole-genome DNA and RNA sequencing data
A total of 16 EMS-induced mutant lines were whole-genome sequenced with 150-bp Illumina
paired-end reads (Supplementary Table S1). We obtained ~6GB of raw DNA sequence data for
each mutant line, containing an average of ~34 million reads (SD = 3.7) per sample. Read
trimming was not performed since the results from the FastQC analysis did not reveal any
adapter contamination. After removing PCR duplicates and reads mapped to multiple locations,
each mutant line had on average ~29 million (SD = 2.5) mapped reads with an average coverage
of 25 reads (SD = 2) per site.
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Furthermore, RNA sequencing data were obtained for three replicates per mutant line and
one wildtype (control) line, totaling 51 samples. Approximately 20 million (SD = 4.9 million)
150-bp paired-end reads were obtained per sample. After adapter trimming and quality control,
~99% of the reads were kept and mapped to the D. pulicaria reference genome (Jackson et al.
2021) with an average mapping rate of 95% (SD = 2.37%, Supplementary Table S2).
EMS-induced heritable mutation rate and spectrum
To identify EMS-induced mutations in these mutant lines, we used a rigorous bioinformatic EMS
mutation identification pipeline that a previous study tested with PCR mutation verification
(false positive rate < 0.05, Snyman et al. 2021). The number of EMS-induced heritable
heterozygous mutations in each mutant line ranged from 125 to 576 (median =152), translating
to a base substitution rate ranging from 1.51 to 6.98×10-6 (median= 1.84×10-6) per site per
generation. The number of mutations found within genic regions (including UTRs, exons, and
introns) ranged from 49 to 320 (median = 74) in each line, resulting in a mutation rate of 2.0×10-
3 to 1.3×10-2 (median =3.1×10-3) per gene per generation (Figures 3A and 3B, Supplementary
Table S3).
Across all 16 EMS-induced mutant lines the number of non-synonymous mutations
ranged from 21 to 140 (median = 29), resulting in a median non-synonymous mutation rate of
1.14 ×10-3 per gene per generation (Figure 3B). The number of synonymous mutations ranged
from 6 to 72 (median = 17, Supplementary Table S3) across mutant lines. Assuming a random
distribution of EMS-induced mutations, we expect to see a non-synonymous vs synonymous
mutation ratio of 3:1. However, in 6 of the mutant lines we observed a significant deviation (one
proportion Z test p-value < 0.05) from the expected 3:1 ratio (Figure 3 B, Graur and Li 2000).
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Lastly, in accordance with previous work on Daphnia (Snyman et al. 2021) and other
model organisms such as C. elegans (Flibotte et al. 2010) and D. melanogaster (Pastink et al.
1991), the majority of EMS-induced mutations across all 16 mutant lines were G:C to A:T
transitions (mean = 90%, SD = 4%), yielding a transition-transversion ratio greater than 5.06 for
all mutant lines (Figure 3C, Supplementary Table S4).
Differentially expressed genes in mutant lines
To assess how EMS-induced mutations altered the transcript abundance across the genome, we
examined the transcriptomic dataset derived from 16 mutant lines in comparison to the wildtype.
Our principal component analysis based on the normalized read counts (rlog transformation)
showed the tight clustering of replicates derived from the same mutant, suggesting strong overall
similarity among replicates of the same mutant background (Figure 4A). The top two principal
components (PC) were together responsible for >50% of the variance, with PC1 accounting for
32% and PC2 for 21%. It was not clear what biological factors these two principal components
potentially represent. Most likely, they jointly captured the variability between mutants, between
sibling mutant lines, and between replicates.
Our differential expression analysis contrasting each mutant line against the wildtype
revealed that the total number of differentially expressed (DE) genes ranged from 1176 to 6606
across all lines (p < 0.05 with FDR < 0.05), with a median of 3545. In nearly all mutant lines the
ratio of down- vs. upregulated genes was greater than 1 (1.05-3.63, Figure 4B, Supplementary
Table S5), demonstrating that downregulated genes were more abundant than upregulated genes.
Only one mutant line (RB1) showed a slight majority of upregulated genes with a ratio of 0.99.
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Next, we assessed the number of DE genes carrying an EMS-induced mutation as well as
DE genes free of a mutation. We defined that a gene carries an EMS-induced mutation if a
mutation was located 5kb upstream or downstream of the gene, in the 3’ UTR, 5’ UTR, introns,
or exons. The number of mutation-carrying DE genes in the mutant lines ranged from 5 (0.43%
of the total DE genes) to 423 (6.40%), with a median of 50.5 (Supplementary Table S5).
Furthermore, the ratio of down- vs. upregulated mutation-carrying DE genes ranged from 0.5 to
3, with mutant lines equally split between showing a majority of downregulated genes vs a
majority of upregulated genes (Figure 4B, Supplementary Table S5).
Except for lines RY-1 and RY-2 where ~6% of DE genes carried a mutation, the
remaining 14 lines all showed less than 2% of DE genes carrying a mutation (Figure 4C). On the
other hand, mutation-free DE genes in each mutant line ranged from 1171 (94.6%) to 6383
(99.6%) with a median of 3502 (Figure 4C). Therefore, with most DE genes being mutation-
free, it strongly suggests that trans-effects drive the transcriptional changes of the majority of DE
genes in these mutants and tend to result in more downregulation than upregulation, whereas cis-
effects may only influence a small proportion of genes where trans-effects cannot be excluded
from imposing their impacts. Lastly, cis mutations may not alter gene expression at all because
we identified many genes carrying a mutation that did not show any differential expression. Out
of the total number of genes carrying a mutation, the number of genes that were not differentially
expressed ranged from 215 (51.6%) to 1021 (77.3%) across the lines, with a median of 303.
Impact of EMS-induced mutations
Across all 16 mutant lines, the EMS-induced mutations were randomly distributed throughout
the genome (chi-squared test, all p values > 0.05), i.e., the proportion of mutations in a specific
type of genomic region does not exceed expectation based on random distribution of mutations.
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A total of 3047 (~31%) mutations were found in the 5kb downstream region (5kb downstream of
a gene), 3142 (~32%) in the 5kb upstream region (5kb upstream of a gene), 1068 (~11%) in
exons, 1762 (~18%) in intergenic regions, 538 (~5%) in introns, 58 (~0.6%) in splice sites , 144
(~1.5%) in 3’ UTR, and 147 (~1.5%) in 5’ UTR (Figure 4D, Supplementary Table S6).
Moreover, we analyzed the distribution of mutations located within differentially
expressed (DE) genes, and mutations located in non-DE genes across all 16 mutant lines. The
distribution of mutations in both categories of genes is highly similar. For the mutation-carrying
DE genes , a total of 650 (~34%) mutations were located in the 5kb downstream region, 698
(~37%) in the 5kb upstream region, 311 (~17%) in exons, 140 (~7%) in introns, 16 (~1%) in the
splice site region, 43 (~2%) in the 3’UTR, and 33 (~2%) in the 5’UTR (Figure 5A,
Supplementary Table S7). On the other hand, for the mutation-carrying non-DE genes, 2397
(~38%) in the 5kb downstream region, 2444 (~39%) in the 5kb upstream region, 757 (~12%) in
exons, 398 (~6%) in introns, 42 (~1%) in splice site region, 101 (~2%) in the 3’ UTR, and 114
(~2%) in the 5’ UTR region (Figure 5B, Supplementary Table S8).
The occurrence of mutation-carrying DE and non-DE genes presents an opportunity to
see whether differential expression is associated with EMS-induced mutations in a specific
genomic region. Using a multiple logistic regression model, we find that there was a significant
association (p-value = 0.028) between differential expression and mutations in exons. Our results
showed that for every mutation occurring in the exonic region of a gene, there is an increase of
0.73 in the log odds of that gene being differentially expressed, suggesting that exonic mutations
are an important driver of altered gene expression.
Moreover, we categorized the mutations in DE and non-DE genes as high impact,
moderate impact, low impact, or modifier variants. The mutations identified in DE genes
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consisted of 27 (1%) high impact variants (i.e., loss of a start codon, gain of a stop codon, splice
acceptor variant), 184 (10%) moderate impact variants (i.e., missense variants), 113 (6%) low
impact variants (i.e., splice region variant, synonymous variant, and variants producing a
premature start codon in the 5’ UTR region), and 1553 (83%) modifier variants (i.e., variants 5kb
upstream or downstream of a gene, variants within the 3’ UTR and 5’ UTR region, and variants
within introns (Supplementary Table S9).
Interestingly, for the high-impact variants, two genes carrying splice acceptor variants
were both downregulated, while two other genes carrying start-lost variants were both
upregulated. Out of 23 genes carrying stop-gained variants, only four were downregulated
whereas the remaining 19 were upregulated (Figure 6). From the genes carrying moderate
impact (i.e., missense) variants, low impact variants and modifier variants; 129, 83 and 765 were
upregulated, and 55, 36 and 793 were downregulated, respectively, suggesting moderate and low
impact variants tend to be more associated with upregulated expression than downregulated
expression (Supplementary Table S10).
On the other hand, mutations identified in non-DE genes have a similar distribution of the
impact categories, with 32 (~1%) high impact variants, 462 (~7%) moderate impact variants, 303
(~5%) low impact variants and 5441 (~87%) modifier variants (Supplementary Table S11 and
S12). It is interesting to note that even some high-impact variants such as loss of a start codon
and gain of a stop codon did not result in altered expression for the mutation carrying genes.
Alternative splicing
Alternatively spliced (AS) genes were identified by comparing all 16 EMS-induced mutant lines
to the wildtype. The number of alternatively spliced genes ranged from 212 to 627 across all
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mutant lines, with a median of 393 (Supplementary Table S13). The number of mutation-
carrying AS genes ranged from 2 (0.5% of the total AS genes) to 57 (9.6%) with a median of 12,
while that of mutation-free AS genes ranged from 202 (~90%) to 611 (~99%) across mutant
lines, with a median of 373 (Figure 7, Supplementary Table S14 and 15). The dominance of
mutation-free AS genes strongly suggests that the trans-effects caused by mutations are a major
driver of alternative splicing.
Overall, the AS genes consist of 665 (11%) A3SS events, 665 (11%) A5SS events, 1051
(17%) MXE events, 2027 (32%) RI events, and 1880 (30%) SE events (Supplementary Table
S13). Specifically, mutation-carrying AS genes consisted of the following events: 20 (~8%)
A3SS, 22 (~9%) A5SS, 48 (~19%) MXE, 55 (~22%) RI, and 104 (~42%) SE (Supplementary
Table S14). On the other hand, mutation-free AS events were as the follows: 645 (~11%) A3SS,
643 (~11%) A5SS, 1003 (~17%) MXE, 1972 (~33%) RI and 1776 (~29%) SE events
(Supplementary Table S15). No significant difference (chi-square test, p-value = 0.25) was
observed in the distribution of AS events between mutation-carrying and mutation-free AS
genes. Lastly, across all 16 EMS-induced mutant lines, a total of 44 genes were both AS and DE
(median=2). Of these genes, 13 were downregulated and 31 were upregulated (Supplementary
Table S14).
Impact and effect of genic EMS-induced mutations on alternative splicing
Mutations located in the alternatively spliced genes were further categorized according to their
impact, with 6 (~3%) being high impact, 14 (~7%) low impact, 20 (~10%) moderate impact, and
159 (~80%) modifier impact variants across all 16 EMS-induced mutant lines (Supplementary
Table S16). The high impact variants consisted of 1 (~0.5%) splice acceptor variant, 2 (~1%)
splice donor variant, and 3 (~2%) stop-gained variants. Moderate impact variants consisted of 20
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(~10%) missense variants, while low impact variants consisted of 1 (~0.5%) splice region variant
and 13 (~7%) synonymous variants. Modifier impact variants consisted of 1 (~0.5%) 3’ UTR
variant, 40 (~20%) downstream variants, 22 (~11%) intron variants, and 97 (~49%) upstream
variants across all mutant lines (Supplementary Table S17). All alternatively spliced genes
carried only one variant type, and only one gene from the P-1 mutant line carried 2 modifier
mutations located in an intron and the 5kb upstream region. Lastly, results from a logistics
regression showed that there was no significant association between AS event and variant type.
However, downstream variants (p-value = 0.080), missense variants (p-value 0.053) and
upstream variants (p-value = 0.078) were marginally significant, while all other variant types
have p-values > 0.99.
Discussion
How mutations alter genome-wide gene expression and alternative splicing has been
understudied. Only a limited number of studies have attempted to address this issue using
mutation-accumulation experiments in a small number of species including C. elegans, D.
melanogaster, S. cerevisiae, and zebrafish (Denver et al. 2005, Huang et al. 2016, Kim et al.
2007, Konrad et al. 2018, Landry et al. 2007, Rifkin et al. 2005, White et al. 2022, Zalts and
Yanai 2017). In this study, we examined the alterations in genome-wide gene expression and
alternative splicing patterns in EMS-induced mutants of the microcrustacean Daphnia to bridge
this knowledge gap. Furthermore, we dissected the contribution of cis and trans mutations to
differential gene expression (DE) and alternative splicing (AS).
Global gene expression changes due to EMS-induced mutations
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In our mutant lines the EMS-induced base substitution rate, per gene mutations rate, and non-
synonymous mutation rate were estimated to be (medians) 1.84×10-6 per site per generation,
3.1×10-3 per gene per generation and 1.14×10-3 per gene per generation respectively (Figure 3A,
B). These estimates are derived from a rigorous mutation filtering pipeline that has been
calibrated with PCR verification of mutations in a previous study (Snyman et al. 2021), with a
false discovery rate <0.05. These rate estimates are also consistent with our previous work
(Snyman et al. 2021), showing elevated mutation rates hundreds of times higher than the
spontaneous mutation rates in Daphnia (Bull et al. 2019, Flynn et al. 2017, Keith et al. 2016).
The collection of identified mutations in these mutant lines show the notable signature of EMS-
induced mutations, i.e., significant enrichment of G:C to A:T transitions (Figure 3C) and a
random distribution across the genome (Figure 4A, Greene et al. 2003).
Considering that EMS may cause acute gene expression changes in the exposed females
and the lingering maternal effects may alter gene expression in immediate offspring, we
measured gene expression in the clonal mutant lines several generations after the exposure. This
method ensures that the observed gene expression changes are most likely caused by the EMS-
induced mutations, rather than by maternal effects.
Our differential expression analyses on the mutant lines reveal that downregulated genes
are more abundant than upregulated genes in nearly all mutant lines, with the ratio of down- vs.
upregulated genes ranging between 1.05 and 3.63 (Figure 4B, Supplementary Table S5). It is
not clear whether mutations induced by other mutagens would cause a similar pattern as gene
expression changes in mutagen-induced mutant lines remains understudied. However, as we
discuss below the dominance of trans effects in gene expression changes, the higher abundance
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of downregulation is likely caused by trans-acting mutations mainly acting as silencers to reduce
transcription (Johnson et al. 2015).
Examining whether differentially expressed (DE) genes carry EMS-induced mutations
across our mutant lines unveiled a significant contribution of trans mutations to expression
changes. DE genes carrying EMS-induced mutations only accounted for a small portion of the
total (ranging from 0.43% to 6.40% across our mutant lines), while mutation-free DE genes
showed dominance (ranging from 94.6% to 99.6% across mutant lines). This pattern strongly
suggests that the pleiotropic effects of trans mutations are a major contributor to the variance in
gene expression between mutant and wildtype lines (Supplementary Table S5), while cis
mutations only play a relatively small role. The pleiotropic effects of trans mutations were
previously reported in D. melanogaster and C. elegans mutation accumulation lines, where a
relatively small number of trans-acting mutations with multiple downstream effects induced the
majority of the transcriptional differences (Denver et al. 2005, Huang et al. 2016). The dominant
pleiotropic effects of trans mutations are probably due to trans factors representing a larger
mutational target and are more likely to arise than cis mutations (Chen et al. 2015, Coolon et al.
2014, Gruber et al. 2012, Metzger et al. 2016, Rhoné et al. 2017, Wittkopp et al. 2004).
The presence of cis mutations in DE genes indicates that cis mutations may directly cause
gene expression changes. However, it cannot be ruled out that trans-effects also contribute to the
variance in gene expression of these genes. It is noteworthy that the presence of cis mutations
does not necessarily mean altered expression because 51.6% - 77.3% of the observed genes with
cis mutations do not show differential expression. However, our logistic regression analysis
provides a clear signal that exonic mutations are strongly associated with altered gene
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expression. Interestingly, it was observed in EMS mutagenized S. cerevisiae mutants that trans
regulatory mutations were also mostly located within the coding sequences (Duveau et al. 2021).
It is possible that some cis mutations do not directly impact gene expression. However,
the unchanged expression of genes with cis mutations may also be a result of complex
interactions between cis and trans effects, for which we do not have the capacity to further test in
this study. Furthermore, because the mutated sites are heterozygous for the mutation and
wildtype SNP allele, the invariance of total gene expression may have concealed the signature of
allele-specific expression changes caused by the EMS-induced mutations. It would be of great
interest for future studies to address whether and how mutations cause the changes in allele-
specific transcript abundance.
Impact of different classes of mutations on gene expression
Our analyses yielded insights into the distribution and impact of different classes of mutations on
gene expression. The distribution of mutations in DE and non-DE genes were highly similar with
the majority found in the 5kb upstream and downstream regions (Figure 5A and B,
Supplementary Table S7 and S8). Mutations identified in DE genes were mostly modifier
impact variants (i.e., variants 5kb upstream or downstream of a gene, variants within the 3’ UTR
and 5’ UTR region, and variants within introns) followed by moderate impact (i.e., missense
variants), low impact (i.e., splice region variant, synonymous variant, and variants producing a
premature start codon in the 5’ UTR region) and high impact (i.e., loss of a start codon, gain of a
stop codon, splice acceptor variant) variants (Supplementary Table S9).
Interestingly, our results also showed that moderate-impact (missense) and low-impact
variants tend to be associated with an upregulation of gene expression (129 upregulated genes vs
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55 downregulated genes for moderate impact, 83 upregulated vs 36 downregulated genes for low
impact, Figure 6). Missense variants are known to alter the amino acid sequence, and have been
shown to impact the interacting DNA-transcription factors, resulting in changed expression of
other genes (Ding et al. 2015). Low-impact variants including synonymous variants have also
been shown to influence gene expression by disrupting transcription and splicing, as well as
mRNA stability (Pagani et al. 2005, Presnyak et al. 2015, Stergachis et al. 2013, Wang et al.
2021).
High impact variants consisted of two downregulated splice acceptor variants, two
upregulated start-lost variants, and the upregulation of most stop-gained variants (Figure 6). The
downregulation of some stop-gained (nonsense) variants could indicate nonsense-mediated RNA
decay (NMD) is at play. NMD is a surveillance pathway that helps maintain RNA quality and
cellular homeostasis by detecting and eliminating transcripts containing premature stop codons
(Nickless et al. 2017). The upregulation of most stop-gained variants could indicate that these
genes escape NMD, that a stop-codon read-through occurs, or that transcriptional adaptation is
triggered causing an upregulation of the affected as well as related genes (El-Brolosy et al.
2019).
On the other hand, mutation-carrying non-DE genes showed a similar distribution to DE
genes. Interestingly, some high-impact mutations in these genes did not result in an altered
expression (Supplementary Table S11 and S12). It is plausible that some of these mutations are
false positives that slipped through our mutation identification pipeline, although the rate of false
positives should be < 5%. Another possibility is that our RNA-seq experimental design did not
provide enough experimental power to detect the expression changes of these genes. It should
also be noted that only one allele of the affected genes was mutated, which means that a
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functional wildtype allele still exists for the affected genes. Thus, it is possible that while the
mutated allele may have had reduced transcription, the wildtype allele likely experiences
enhanced transcription, resulting in unchanged expression of the gene in question.
Alternative splicing
Changes in pre-mRNA splicing are another important source of phenotypic variation.
Exon-intron boundaries consist of highly conserved splice site sequences that must be correctly
identified by the spliceosome to perform a splicing reaction. Additionally, splicing factors
interact with intronic and exonic sequences to determine how frequently an exon is included in
the final transcript (Wang and Burge 2008). If the splicing process is not correctly regulated,
premature stop codons and transcripts with an altered amino acid sequence can result.
Alternative splicing (AS) can result from both cis- and trans-regulatory mutations. Cis mutations
can interact with exonic or intronic splicing enhancers or silencers, whereas transmutations affect
splicing factors that can impact genes throughout the genome (Juan et al. 2014, Kornblihtt et al.
2013). To date, the impact of cis versus trans mutations on alternative splicing remains
understudied. Most studies have suggested that cis-regulatory (Ast 2004, Keren et al. 2010,
Thatcher et al. 2014) mutations may be the main contributor to alternative splicing. However, a
recent study raised the opposite view, suggesting that the pleiotropic effects of trans-mutations
may be the main culprit (Smith et al. 2018).
Our results are consistent with the latter view. Our analyses show that the number of
mutation-carrying AS genes ranged from 0.5% to 9.6% across mutant lines (Figure 7,
Supplementary Table S13). Most AS genes are free of EMS-induced mutations, (ranging from
90% to 99% of total AS genes), strongly suggesting that trans-effects are a major driver of
alternative splicing (Figure 7, Supplementary Table S14 and S15). Furthermore, our results
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showed that there was little overlap between differentially expressed and alternatively spliced
genes, a pattern also seen in Drosophila (Jakši
ć
and Schlötterer 2016), aphids (Grantham and
Brisson 2018), and salmonids (Jacobs and Elmer 2021).
Our logistics regression analysis showed that there was no significant association
between AS events and any variant type. However, downstream variants (p-value = 0.080),
missense variants (p-value 0.053) and upstream variants (p-value = 0.078) were marginally
significant, while all other variant types have p-values > 0.99. Mutations from these regions
warrant further investigation in future studies.
Implications for understanding the biological impacts of anthropogenic events
As we are preparing this manuscript, on February 3rd 2023, a train derailment accident in East
Palestine, Ohio, USA, resulted in the spill of > 100,000 gallons of vinyl chloride, a carcinogenic
mutagen. Accidents like this and many unreported, smaller-scale anthropogenic events occur at a
sharply increased rate since the Industrial Revolution. The epigenetic effects of some of these
environmental chemicals have been investigated (reviewed in Baccarelli and Bollati 2009, Goyal
et al. 2022, Hou et al. 2012). However, much work still needs to be done to determine the impact
of chemically induced mutations on gene expression and alternative splicing. Based on the
results from our study, mutations exerting trans effects can have a genome-wide impact by
altering the expression and splicing of thousands of genes (up to 99% of the total differentially
expressed and alternatively spliced genes respectively in Daphnia), while high impact and
moderate impact mutations can lead to phenotypic alterations by changing the amino acid
sequence. Understanding the consequences of these anthropogenic stressors are thus vital to
protecting and preserving the health of our ecosystems and ourselves.
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Conclusions
Our results show that trans effects are the major contributor to the variance in gene expression
and alternative splicing between the wildtype and EMS-induced mutant lines in Daphnia, while
cis mutations do not always alter total gene expression and only affected a small portion of
genes. Furthermore, our results showed a significant association between DE genes and exonic
mutations, indicating that exonic mutations are an important driver of altered gene expression.
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Acknowledgments
We thank A. Gamadia and M.T. Smith for their help with tissue collection and Trung Huynh for
his help with writing the Python scripts. Additionally, we would like to thank all Xu lab
members for their helpful discussions. This work is supported by NIH grant R35GM133730 and
NSF CAREER grant MCB-2042490 to SX.
Author Contributions
SX designed the study, and MS and SX wrote the manuscript. MS performed the tissue
collection, molecular work, and data analysis.
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Tables and Figures
Figure 1. The life cycle of obligately parthenogenetic Daphnia.
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Figure 2. Illustration of exposure method to generate EMS mutant lines.
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Figure 3. (A) The base substitution rates. (B) Non-synonymous and synonymous mutation rate, and non-synonymous vs synonymous
mutation ratio in all mutant lines. (C) The proportion of base substitutions caused by EMS-induced mutations.
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Figure 4. (A) PCA plot based on transcriptomic data of mutant lines. (B) Log2 fold change of differentially expressed (DE) genes
(red
dots) and DE genes with mutations (blue dots) with the ratio of down- vs. upregulated DE genes for each line. (C) Boxplot
illustrating
the proportions of differentially expressed (DE) genes with and without mutations. (D) Distribution of EMS-induced mutations in
different genomic regions.
red
ng
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Figure 5. (A) Distribution of EMS-induced mutations in DE genes. (B) Distribution of EMS-induced mutations in non-DE genes.
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Figure 6. Log2 fold-change of DE genes with high-impact and moderate-impact variants identified across all 16 mutant lines.
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Figure 7. Proportion of alternatively spliced (AS) genes with EMS-induced mutations and AS genes without mutations.
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