There is general interest in understanding how animals adapt to new environments. What are the alleles that matter to positive selection and what sort of genes do they target? Since methods were developed to map and identify the genes harboring causative genetic variation, researchers have often isolated changes in the same gene in different populations or species (Wood et al., 2005; Martin and Orgogozo, 2013). Besides targeting specific genes, evolution can target classes of genes that share molecular features such as biochemical (e.g. chemoreceptor genes; Bachmanov and Beauchamp, 2007; Keller et al., 2007; Wisotsky et al., 2011; Lunde et al., 2012; McRae et al., 2012; McBride et al., 2014; Greene et al., 2016a; Greene et al., 2016b) or developmental function (e.g. master regulators of cell fate; Sucena et al., 2003; Colosimo et al., 2005; Chan et al., 2010; Yang et al., 2018). One molecular feature predicted to be important for evolution is the ability of genes to produce multiple protein isoforms. A single protein-coding gene can produce multiple isoforms using alternative transcription initiation and termination sites combined with alternative splicing between exons (Pan et al., 2008; Pal et al., 2011). Isoform-specific evolution is found throughout vertebrates, including recent evolution of transcript expression in primates (Barbosa-Morais et al., 2012; Merkin et al., 2012; Shabalina et al., 2014; Zhang et al., 2017). Whether the increase in transcriptomic diversity is important for adaptive evolution remains an important question, and only a few examples have shown how isoform evolution could be involved in phenotypic diversity (Mallarino et al., 2017).

The ability of a gene to produce multiple protein isoforms might also play a role in the genesis of new genes. Over long evolutionary timescales, gene duplication and diversification can create paralogous genes with different functions (Ohno, 1970; Innan and Kondrashov, 2010). One central mystery in this process is the order of these two events; do mutations that duplicate genes occur first or does functional diversification preclude the duplication event? One mechanism the latter route can happen through is by gene sharing, or the ability of a gene to create multiple protein products (or a single protein product) that have two or more distinct functions (Hughes, 1994). If each isoform acts in different tissues or plays distinct roles in biological processes, subsequent duplication mutations can result in the separation of these isoforms into two distinct genes.

As a model for understanding the genetic basis of adaptive evolution in an animal model, we use the small nematode Caenorhabditis elegans. Besides its genetic tractability, use of this organism allows the analysis of evolution at different timescales. For example, experimental evolution can be used to study evolutionary processes in controlled environments on the order of 10–1000 generations (Gray and Cutter, 2014; Teotónio et al., 2017; Penley et al., 2018; Chelo et al., 2019; Saxena et al., 2019; Wernick et al., 2019). For longer timescales, a growing number of isolated and sequenced Caenorhabditis species can be used to study genetic differences responsible for species-level differences (Ting et al., 2018; Yin et al., 2018; Bi et al., 2019; Stevens et al., 2019).

For understanding short-term adaptation, we study two laboratory strains of C. elegans, called N2 and LSJ2, which descended from a single hermaphrodite isolated in 1951 (Figure 1A). These two lineages split from genetically identical populations between 1957 and 1958 and evolved in two very different laboratory environments – N2 grew on agar plates seeded with E. coli bacteria and LSJ2 in liquid cultures containing liver and soy peptone extracts (McGrath et al., 2009; McGrath et al., 2011; Sterken et al., 2015). By the time permanent means of cryopreservation were developed, approximately 300–2000 generations had passed, and ~ 300 new mutations arose and fixed in one of the two lineages (McGrath et al., 2011). Despite their genetic similarity, substantial divergence has occurred between these strains in terms of phenotype and fitness, including a large number of developmental, behavioral, and reproductive traits. Use of these strains allow us to identify causal genetic variants responsible for phenotypic and fitness changes. To date, five de novo, causal genetic variants have been identified in either the N2 or LSJ2 lineage (de Bono and Bargmann, 1998; McGrath et al., 2009; Persson et al., 2009; McGrath et al., 2011; Duveau and Félix, 2012; Large et al., 2016; Large et al., 2017; Zhao et al., 2018).

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One of these mutations is an LSJ2-derived, 60 bp deletion at the 3’ end the nurf-1 gene that reduces growth rate, slows reproductive output, and prevents development into the dauer diapause state in response to ascaroside pheromones (Figure 1B) (Large et al., 2016). This genetic variant is beneficial in the LSJ2 liquid cultures in which it arose and fixed, but places animals at a disadvantage in the agar plate environments in which N2 evolved, an example of gene-environment interaction (Large et al., 2016). We proposed that nurf-1 is a regulator of life-history tradeoffs. Life history tradeoffs represent competing biological traits requiring large energetic investments, such as the tradeoff between energy required for reproduction versus the energy required for individual survival. The difference in fitness of this allele in the two laboratory environments is potentially determined by how the life-history tradeoffs map into reproductive success.

Studies of nurf-1 and its orthologs provide fundamental support for its role as a life history regulator. nurf-1 encodes an ortholog of mammalian BPTF, a subunit of the NURF chromatin remodeling complex (Barak et al., 2003) (Figure 1C). BPTF encodes a large protein containing a number of domains that facilitate recruitment of NURF to specific regions of the genome for chromatin remodeling (Alkhatib and Landry, 2011), including domains that interact with sequence-specific transcription factors and three PHDs and a bromodomain that facilitate interactions with modified nucleosomes (Li et al., 2006; Wysocka et al., 2006; Kwon et al., 2009; Ruthenburg et al., 2011). Through its DDT domain (Fyodorov and Kadonaga, 2002), BPTF cooperates with ISWI to slide nucleosomes along DNA, changing access of promoter regions to transcription factors that drive gene transcription. In mammals, BPTF regulates cellular differentiation and homeostasis of specific cell-types and tissues, including the distal visceral endoderm (Landry et al., 2008), ecoplacental cone (Goller et al., 2008), hematopoietic stem/progenitor cells (Xu et al., 2018), mammary stem cells (Frey et al., 2017), T-cells (Wu et al., 2016), and melanocytes (Koludrovic et al., 2015). In Drosophila, the ortholog to BPTF, NURF301, regulates the heat shock response, pupation, spermatogenesis, and innate immunity (Badenhorst et al., 2002; Badenhorst et al., 2005; Kwon et al., 2008; Kwon et al., 2009). Many of these traits can be viewed as life-history tradeoffs, e.g. large energetic investments in individual survival through the development of the immune system vs. energetic transfers to offspring in the placenta or mammary glands. The evolution of BPTF/NURF-1 function might also be relevant in human disease. Genetic alterations in BPTF have been reported in tumors, including gene amplification and point mutations (Buganim et al., 2010; Balbás-Martínez et al., 2013). In addition, BPTF has been shown to be required for the transcriptional activity of c-MYC, a major human oncogene (Richart et al., 2016).

In this paper, we continue our studies of the evolution of the N2/LSJ2 laboratory strains. We demonstrate that an independent, beneficial mutation in the nurf-1 gene was fixed in the N2 lineage, suggesting that nurf-1 is a preferred genetic target for laboratory adaptation. To understand why nurf-1 might be targeted, we explored the in vivo role in C. elegans development by taking advantage of CRISPR-Cas9 to test causal relationships that inform laboratory evolution and fitness effects. Our work suggests that the large, full-length isoform of nurf-1, primarily studied in mammals, is dispensable for development. Instead, two, largely non-overlapping isoforms are both essential for reproduction, having opposing effects on cellular differentiation of gametes into sperm or oocytes. Our results suggest that the ability of nurf-1 to regulate life history tradeoffs is the result of exquisite regulation of NURF function through the balance of two competing isoforms, reminiscent of the principle of Yin and Yang. Finally, we demonstrate that these two isoforms have split into separate genes in a clade of related nematodes, potentially resolving transcriptional and functional conflict between the Yin and Yang isoforms transcription and function. Our work demonstrates how evolution of isoforms can precede the origin of a new gene, supporting a role for gene sharing in the origin of functionally novel proteins.

nurf-1 produces multiple transcripts encoding multiple protein isoforms

Our results suggest that selection acted repeatedly on C. elegans nurf-1 during laboratory growth. The molecular nature of NURF-1, an essential subunit of the NURF chromatin remodeling complex, is surprising for a hotspot gene. In general, chromatin remodelers are thought of as ubiquitously expressed regulators with little variation in different cell types, akin to general function RNA polymerase proteins or ribosomes. Why would genetic perturbation of nurf-1 lead to increased fitness? One potential clue is the complexity of the nurf-1 locus. Previous cDNA analysis of nurf-1 identified four unique transcripts encoding four unique isoforms (Andersen et al., 2006), two of which have been shown to affect different phenotypes (summarized in Table 1).

Table 1

Name	Evidence		Size		Conservedc	Predicted biological role in C. elegansd	Other names
	Transcripta	Proteinb	aa	kD
nurf-1.a	N	-	2197	252	M,D	None	Full-length
nurf-1.b	C,N,I	W	1621	186	D	Reproduction, vulval development	N-terminal or NURF-1.A
nurf-1.d	C,N,I	W	816	92	-	Size, dauer, reproduction, axon guidance	C-terminal or NURF-1.C
nurf-1.f	C,N,I	W	581	58	-	None	NURF-1.E
nurf-1.q	N,I	-	243	36	-	None	-

1. ^a C indicates full-length cDNA have been isolated for this transcript, N indicates evidence from direct sequencing of RNA or cDNA using Oxford Nanopore reads support this transcript, and I indicates evidence from Illumina short read RNA-seq supports this transcript

   ^b W indicates evidence for the protein isoform was obtained using western blot

2. ^c M or D indicates an analogous isoform is described in mammals (mice or humans) or Drosophila, respectively

   ^dPredictions from Andersen et al. (2006), Large et al. (2016), or Mariani et al. (2016)

To identify other transcripts produced by nurf-1 and quantify the relative proportions of each that are produced, we analyzed previously published Illumina short-read (Brunquell et al., 2016) (isolated from synchronized L2 larval animals) and Oxford Nanopore long-read RNA sequencing reads (Roach et al., 2019) (isolated from mixed populations) (Figure 2—figure supplements 1–2). Our results support many of the conclusions of Andersen et al. (2006) but contain a few surprises. We identified five major transcripts (Figure 2A) - three previously isolated (nurf-1.b, nurf-1.d, and nurf-1.f) but also two newly identified (nurf-1.a and nurf-1.q) (mapping of transcript names used in Andersen et al. are listed in Table 1). nurf-1.a encodes a full-length 2197 amino acid isoform analogous to the primary isoform of BPTF in humans and NURF301 in Drosophila (Figure 1C). Despite the expectation that C. elegans would produce a similar protein, the Oxford Nanopore long-read data are the only evidence supporting its existence. The nurf-1.q transcript is predicted to produce a 243 amino acid unstructured protein. With the exception of the full-length nurf-1.a transcript, the overlap of these transcripts is quite minimal, resulting in predicted isoforms with unique protein domains and functions (Figure 2B).

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We quantified the relative expression of these five transcripts by either counting the number of Nanopore reads that matched the transcript or by using kallisto (Bray et al., 2016) to predict transcript abundance using Illumina short-read sequencing data (Figure 2C). These predictions qualitatively agreed in transcript ranking of expression strength (although quantitative variation in predictions were observed, reflective of the different technologies or developmental stages of the animals). Surprisingly, the newly described nurf-1.q transcript was the most highly expressed followed by the nurf-1.b transcript, and the nurf-1.a, nurf-1.d and nurf-1.f were expressed at similar lower levels.

Although each of the five major transcripts are transcribed, this result does not necessarily mean they are translated into stable protein products. To facilitate analysis of NURF-1 proteins, we used CRISPR-Cas9 to fuse two distinct epitope tags (HA and 3xFLAG tag) to the endogenous nurf-1 locus, just prior to the stop codons in the 16^th and 28^th exon, respectively (Figure 2A). Immunoblot analysis supported the expression of the B, D, and F isoforms, but not the A or Q isoforms (Figure 2D). Although larger proteins, such as the A isoform, can be difficult to transfer during immunoblots, the lack of a band matching the small Q isoform suggests the highly expressed nurf-1.q transcript is not translated into protein or the protein is rapidly degraded.

Based upon these results, we speculated that the intronic SNV, which we have shown regulates total fecundity and fitness in laboratory conditions (Figure 1), could specifically alter the expression level of the nurf-1.b transcript. However, analysis of all five nurf-1 transcript levels, using the previously described RNA-seq data on the N2 and ARL_{(intron,LSJ2>N2)} strains, did not reveal any significant expression differences (Figure 2—figure supplement 3). Potentially the effect of this SNV has cell-type specific effects in spermatocytes, however, our data, using RNA collected from the whole animal, does not allow us to test this hypothesis.

We also investigated whether similar isoforms could be expressed in human cells, using western blots on a small panel of human cancer cell lines. Interestingly, besides a band matching the expected size of the canonical full-length isoform, a number of additional bands were observed between 150–250 kD (Figure 2—figure supplement 4A). Using mass-spectrometry, we confirmed the presence of multiple BPTF peptides in the bands detected by western blotting (Figure 2—figure supplement 4), consistent with one or more of these bands representing novel BPTF isoforms. Potentially, these isoforms could play a role in cancer metastasis, although we provide no such evidence here. Despite the presence of these additional bands, the full-length version of BPTF is the most highly expressed isoform (Figure 2—figure supplement 4D), consistent with its importance in mammalian species.

The B and D isoforms are both essential for reproduction and the F isoform modifies the heat shock response

Genetic analysis of nurf-1 primarily relied on two deletion alleles, n4293 and n4295 (Figure 3A) (Andersen et al., 2006). The n4293 allele deletes the first exon and predicted transcriptional start site of the nurf-1.a and nurf-1.b transcripts. The n4295 allele deletes three exons of the nurf-1.a, nurf-1.d, and nurf.1.f transcripts that encode a C-terminal PHD domain (Figure 3—figure supplement 1) necessary for human BPTF function. Comparison of the phenotypes of the n4293 and n4295 homozygotes leads to the model that the B isoform is essential for reproduction and the A, D, and/or F isoforms have subtle effects on growth rate and reproductive rate (Table 1).

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To further delineate the biological role of each isoform, we used CRISPR-Cas9 to engineer nine stop codons in eight exons of the nurf-1 gene: the first, second (two positions), 7^th, 15^th, 18^th, 19^th, 23^rd, or 26^th exons (Figure 3A). The predicted effects of these stop codons on each major isoform are shown in Figure 2—figure supplement 4 and Table 2. Homozygous animals for each mutation were assayed for total brood size and growth rate. Analysis of the phenotypes of these mutants indicated that our working model was incorrect. Instead, we propose that both the B and D isoforms are essential for reproduction.

Table 2

Isoform	kah90	kah91	kah106	kah142	kah93	kah96	kah99	kah11	kah68	Length
NURF-1.A	5R	107P	147E	646A	1548G	1632T	1685Q, 1689P, 1693N		2056T	2197
NURF-1.B	5R	107P	147E	646A	1548G	-	-		-	1621
NURF-1.D	-	-	-		170G	254T	307Q, 311P, 315N		675T	816
NURF-1.F	-	-	-		-	-	-	3L	440T	581
NURF-1.Q	-	-	-		170G	-	-		-	243

As expected, engineering stop codons in the first, second, and 7^th exons greatly reduced fecundity, resulting in either sterility, or a mortal germline phenotype, initially reducing total brood size of animals, before eventually causing complete sterility after around three-to-five generations of homozygosity (Figure 3B and C). Although the qualitative phenotypes of these four alleles agreed, we observed interesting quantitative differences between them. The second stop codon in the second exon (kah106) and the stop codon in the 7^th exon (kah142) reduced growth and fecundity more than the first exon stop codon (kah90) or the first stop codon in second exon (kah91) (Figure 3B and C). We suspect this result indicates the presence of an internal ribosome entry site in the middle of the second exon at the 122^nd Methionine, causing the expression of two isoforms from a single transcript. The reduced severity of the first two stop codon alleles can be explained by their inability to affect the protein sequence of the second isoform. An alternative possibility is a difference in frequency of translational read-through of each stop codon, which are interpreted as sense codons at a low frequency (Jungreis et al., 2011).

Unexpectedly, engineering stop codons in the 18^th and 19^th exons also caused a mortal germline phenotype (kah96 and kah99) (Figure 3B). This result was surprising, because the n4295 allele, predicted to be a loss-of-function allele for the D and F isoforms due to the loss of the PHD and bromodomains, does not have a mortal germline phenotype. We excluded a number of potential explanations for this discrepancy. A suppressor for the n4295 allele could have fixed during the construction of this strain. However, the kah68 allele, which contains a stop codon within the n4295 deleted region, phenocopies the n4295 allele and not the kah96 and kah99 animals (Figure 3B and C, and Figure 3—figure supplement 2)). Another possibility is that the D isoform suppresses the F isoform; loss of both isoforms (in the n4295 background) is tolerated, but loss of just the D isoform (in the kah96 or kah99 backgrounds) allows the F isoform to prevent reproduction. However, we could exclude this possibility as the double mutant containing both the n4295 allele and the 18^th exon stop allele phenocopied the kah96 single mutant (Figure 3—figure supplement 3). Additionally, specific loss of the F isoform by the 23^rd exon stop allele (kah11) did not affect the phenotype of animals (Figure 3B and C). Our data suggest that, unlike human BPTF, the ability of NURF-1 to bind modified histones is not required for its function. We further confirmed this hypothesis by editing conserved residues in these the PHD and bromodomains necessary for recognition of the H3K4me3 and H4K16ac marks (Figure 3—figure supplement 1).

The most parsimonious explanation of our data is that either the A or D isoform is essential for reproduction in C. elegans. Compound heterozygote tests allowed us to distinguish between these possibilities, indicating that the D isoform is required for reproduction and wild-type growth rate, and the A isoform is dispensable for reproduction and development (Figure 4). We first verified that the kah93, kah96, and kah106 alleles were recessive by measuring the fecundity of heterozygous animals (Figure 4B). Next, we examined the fecundity of kah106/kah96 compound heterozygotes, which are predicted to lack only the A isoform, due to the production a single unaffected copy of the B isoform from the kah96 haplotype and a single unaffected copy of the D isoform from the kah106 haplotype. If the A isoform was essential for reproduction, we would expect these compound heterozygotes to be sterile or have severe defects in fecundity. However, these animals were indistinguishable from wild-type, suggesting that the full-length A isoform is not essential (Figure 4B). The kah106/kah93 compound heterozygotes showed similar results. These animals are predicted to encode one unaffected copy of the D isoform, one truncated copy of the B isoform, and zero unaffected copies of the A isoform. These animals were mostly wild-type, with a small reduction in total fecundity (Figure 4B). We interpret this to mean that the A isoform is not essential and the truncation of the B isoform slightly perturbs its function, causing a slight reduction in fecundity. Finally, we analyzed kah93/kah96 compound heterozygotes. These animals are predicted to encode zero wild-type copies of the D isoform, one wild-type copy of the B isoform, and zero wild-type copies of the A isoform. These animals were essentially sterile. Taken together, we conclude that the B and the D isoform are both essential for reproduction.

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To confirm that the D isoform is essential, we also created a transgenic strain containing an integrated construct driving a nurf-1.d cDNA from its endogenous promoter. This transgene could fully rescue the fecundity phenotype of the kah96 allele and partially rescue the fecundity phenotype of the kah93 allele (Figure 4C). This transgene could also rescue the reproductive timing and fecundity changes of the n4295 allele and the LSJ2-derived 60 bp deletion (kah3) (Figure 4C and Figure 4—figure supplement 1). As expected, this transgene could not rescue the kah106 allele, which creates a stop codon in the B isoform. These data further support a requirement of both the B and D isoforms for reproduction.

Although the F isoform does not seem to have an effect in normally developing animals, it is involved in the heat shock response. Multiple reports have demonstrated that nurf-1 is upregulated in response to heat shock (Brunquell et al., 2016; Li et al., 2016). By analyzing RNA-seq reads from these two papers, we found that the nurf-1.f transcript was specifically upregulated in both datasets, with increased coverage of the 23^rd exon as well as the 24^th through 28^th exons (Figure 4—figure supplement 2A and B). We confirmed that the increased transcription of the nurf-1.f transcript also increased NURF-1.F protein abundance (Figure 4—figure supplement 2C and D). Transcriptional analysis of strains lacking the F isoform indicated that the initial transcriptional response to heat shock was largely the same, but the long-term transcriptional response of a subset of genes was affected (Figure 4—figure supplement 2E–G). We conclude that the F isoform is specifically up-regulated by heat shock and plays a modulatory role in determining the long-term transcriptional response to heat shock.

The B and D isoforms have opposite effects on cell fate during gametogenesis

Although the B and D isoforms are both required for reproduction, the molecular mechanism that these isoforms operate through could be different. One possibility is that the long-form of NURF-1 has split into two subunits - both isoforms participate as part of the NURF complex, cooperating together to regulate reproduction. However, the D isoform might instead modify NURF activity by competing for binding with transcription factors or regions of the genome to which NURF is recruited. A third possibility is that the D isoform acts through a NURF-independent pathway.

To gain insights into the molecular nature of the D isoform, we decided to determine precisely how the B and D isoforms regulate reproduction, using three nurf-1 stop alleles (Figure 5A). For hermaphrodites to produce a fertilized egg, the gonads must produce both male and female gametes at different developmental times (Figure 5B). Initially, gametogenesis produces sperm, creating approximately 300 sperm at which point a permanent sperm-to-oocyte switch occurs. From this time, gametogenesis produces oocytes until the animal dies or the gonad ceases to function (Hubbard and Greenstein, 2005). A number of defects could cause sterility – inability to form gametes, inability to create sperm, inability to create oocytes, or defects in the sperm and/or oocyte function. We used DAPI staining to characterize the production of sperm and oocytes in three nurf-1 mutants (Figure 5C and D). We first tested kah106 mutants, which lack the B isoform (Figure 5A), for the ability to produce sperm. Compared with N2 animals, which create ~ 300 sperm per animal, the number of sperm produced by kah106 animals was greatly reduced, resulting in the production of only approximately 60 sperm (Figure 5D). These animals produced a normal number of oocytes, indicating that spermatogenesis seemed to be affected specifically (Figure 5E). We interpret these data as evidence that hermaphrodites that lack the NURF-1.B isoform spend less time in spermatogenesis before transitioning to oogenesis. We next tested kah96 mutants which lack the D isoform. These animals produced approximately 500 sperm and almost no oocytes (Figure 5C-E). We interpret these data as evidence that hermaphrodites that lack the D isoform are unable to transition from spermatogenesis to oogenesis. Finally, we performed similar experiments on kah93 mutants, which lack the D isoform and have a truncated B isoform. These animals showed an intermediate phenotype, with normal number of sperm but reduced number of oocytes (Figure 5D and E). The reduced activity of the B isoform due to its truncation potentially allows other factors to transition the animals to oogenesis, resulting in the milder defects found in the kah93 animals (Figure 3B).

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Although animals that lack either the B or D isoform are unable to reproduce, the cause of sterility is different at the cellular level. To further study the molecular effects of perturbing nurf-1 function, we transcriptionally profiled adult N2*, NIL_{(nurf-1,LSJ2>N2*)}, ARL_{(del, LSJ2, N2*)}, and LSJ2 animals, which contain various combinations of the N2 and LSJ2-derived nurf-1 mutations (Supplementary file 1). A multi-dimensional scaling plot indicated that the N2* and ARL_del replicates formed two unique clusters, and the LSJ2 and NIL_nurf-1 replicates largely overlapped in a third cluster (Figure 5—figure supplement 1A). The genetic variation surrounding the nurf-1 locus is responsible for the majority of transcriptional differences between adult LSJ2 and N2* animals, suggesting most of the fixed variants do not have a dramatic effect on transcription on N2-like growth conditions. Although the LSJ2-derived 60 bp deletion regulates transcription, additional genetic variation in the NIL_nurf-1 strain, presumably from the N2-derived intron variant, also regulates transcription in adult animals.

To study the effects of the 60 bp deletion and intron SNV on transcription, we focused on two comparisons: 1) the N2* vs ARL_{(del, LSJ2>N2*)}, which will identify transcriptional changes caused by the 60 bp deletion and 2) the NIL_{(nurf-1, LSJ2>N2*)} vs ARL_{(del, LSJ2>N2*)}, which will identify transcriptional changes caused by the intron SNV (as well as linked mutations in the NIL other than the 60 bp deletion). We expect that the latter comparison will mostly report the changes of the intron SNV, as it accounts for most of the fitness differences between the two strains. We observed a positive correlation between these two comparisons (Figure 5—figure supplement 1B). The most parsimonious explanation for this observation is that both the N2 and LSJ2-derived alleles in nurf-1 regulate the activity of a common molecular target, which is likely to be the NURF complex.

A duplication in a sister clade of Caenorhabditis species creates two separate nurf-1 genes

Previous work in C. briggsae characterized the role of nurf-1 in reproduction, including the isolation of nurf-1 cDNAs in this species (Chen et al., 2014). Interestingly, although transcripts matching the nurf-1.b and nurf-1.d were isolated from this species, they no longer shared any exons with each other, suggesting that they were expressed from two separate genes (Figure 6A). Further, spliced leader sequences to the 5’ end of both transcripts matched sl1 sequence, suggesting that these two genes were not expressed as a single operon (Blumenthal, 2012). We compared the gene products using BLAST and found that the shared exons in C. elegans had duplicated in C. briggsae, with one set of each retained in each of the new genes (Figure 6A). Short-read transcriptomics data for this species matched the cDNA analysis; we found evidence for transcripts matching nurf-1.b, nurf-1.d, and nurf-1.f (Figure 6—figure supplements 1–3). Unlike C. elegans, C. briggsae seemed to have lost both the nurf-1.a and nurf-1.q transcripts.

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Analysis of the nurf-1 gene structure within the context of the Caenorhabditis phylogeny suggested that the exon duplication and separation of nurf-1 into separate genes occurred at the base of a clade containing 11 described species, including C. brenneri and C. tribulationis (Figure 6B). We determined the nurf-1 gene structure in 22 of the 32 Caenorhabditis species with published genomes and transcriptomes (Kiontke et al., 2011; Stevens et al., 2019) (Figure 6—figure supplements 1–3). Like C. briggsae, the species in the brenneri/tribulationis clade express a transcript matching nurf-1.b from a single gene (which we call nurf-1–1). These species also express two transcripts matching nurf-1.d and nurf-1.f from a second gene, called nurf-1–2. Analysis of the spliced leader sequence of the 5’ end of the nurf-1.d transcript only identified sl1 sequence, consistent with separation of these genes into distinct transcriptional units. None of these species appears to express nurf-1.a or nurf-1.q transcripts (Figure 6—figure supplements 1–3). RNA-seq data for species outside of this clade (Figure 6—figure supplements 1–3) matched the transcription pattern of C. elegans, suggesting that these species express five major transcripts from a single nurf-1 gene: nurf-1.a, nurf-1.b, nurf-1.d, nurf-1.f, and nurf-1.q. These data suggest that the C. elegans transcript structure is ancestral.

Phylogenetic analysis of the duplicated ~ 200 amino acid sequence was used to evaluate different hypotheses surrounding the timing and number of duplication events. The analysis supported the model that the split of nurf-1 into two distinct genes happened once within the common ancestor of the brenneri/tribulationis clade (Figure 6C – additional possible trees shown in Figure 6—figure supplement 4). The topology recovered for the region of nurf-1 outside the duplication is congruent with the species tree (Figure 6—figure supplement 5). Interestingly, the rate of amino acid substitution in the duplicated region was accelerated in nurf-1–1 relative to nurf-1–2 (p<0.001; Welch's t-test) suggesting that this region experienced positive selection and/or relaxed selection after this duplication event occurred. Comparison of the synonymous vs. non-synonymous substitution rate in three closely-related species pairs was also consistent with an increase in the rate of protein evolution in the duplicated region following the separation of nurf-1 into independent genes (Table 3).

Table 3

		nurf-1–1 or nurf-1.b			nurf-1–2 or nurf-1.d
Sp. pair	Duplicationa	Dup. Reg.b	Otherc	Ratiod	Dup. Reg.b	Otherc	Ratiod
C. afra/ C. sulstoni	N	0.136e	0.121	1.1	0.116e	0.072	1.6
C. nigoni/ C. briggsae	Y	0.249	0.085	2.9	0.111	0.019	5.8
C. remanei/ C. latens	Y	0.295	0.121	2.4	0.177	0.048	3.7

1. ^a Duplication indicates whether the species pair contain the duplicated exons that create two nurf-1 genes

   ^b Dup. Reg. indicates dN/dS was calculated using the region of the alignment that contains the duplication

2. ^c Other indicates dN/dS was calculated using the region of the alignment that does not contain the duplication

   ^d Ratio was calculated by dividing the dN/dS value of the Dup. Reg. by the Other

3. ^e The dN/dS values for the nurf-1.b and nurf-1.d in the duplicated region were different due to the b transcript encoding two additional amino acids in the 14^th exon (before the M initiation codon in the d isoform) and the amino acids encoded by the 16^th alternatively spliced exon.

In this paper, we make use of CRISPR/Cas9-enabled gene editing to characterize the nurf-1 gene in C. elegans and then study the sequence and expression of nurf-1 orthologs in other Caenorhabditis species. The combination of genetics and evolutionary analysis allowed us to make a number of surprising observations. First, we show that an SNV in the 2^nd intron of nurf-1 that fixed in the N2 laboratory strain increases the fitness and fecundity of the N2 strain. Second, we show that the full-length isoform of nurf-1 has split into two essential, mostly non-overlapping isoforms with opposite effects on cell fate in differentiating gametes. Finally, we show that the B and D isoforms have split into separate genes in a subset of Caenorhabditis species. These data show that nurf-1 can be genetically perturbed to increase fitness of animals in new environments and has experienced long-term evolutionary changes that have split its function and regulation into two isoforms/genes (Figure 7A and B).

Figure 7

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Sequencing reads were uploaded to the SRA under PRJNA526473.

1. 1. Xu W
   2. Long L
   3. McGrath P

   (2019) NCBI Sequence Read Archive

   ID PRJNA526473. RNAseq of C. elegans under different genetic background and heat shock treatment to study the roles of different isoforms of nurf-1.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA526473

1. 1. Jian Li
   2. Laetitia Chauve
   3. Grace Phelps
   4. Renée M Brielmann
   5. Richard I Morimoto

   (2016) NCBI Sequence Read Archive

   ID PRJNA321853. RNA-seq analysis in C. elegans larval development and heat shock.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA321853
2. 1. Jessica Brunquell
   2. Stephanie Morris
   3. Yin Lu
   4. Feng Cheng
   5. Sandy D Westerheide

   (2016) NCBI Sequence Read Archive

   ID PRJNA311958. The genome-wide role of HSF-1 in the regulation of gene expression in Caenorhabditis elegans.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA311958/

1. 1. Alkhatib SG
   2. Landry JW

   (2011) The nucleosome remodeling factor

   FEBS Letters 585:3197–3207.

   https://doi.org/10.1016/j.febslet.2011.09.003

   • PubMed
   • Google Scholar
2. 1. Andersen EC
   2. Lu X
   3. Horvitz HR

   (2006) C. elegans ISWI and NURF301 antagonize an Rb-like pathway in the determination of multiple cell fates

   Development 133:2695–2704.

   https://doi.org/10.1242/dev.02444

   • PubMed
   • Google Scholar
3. 1. Arribere JA
   2. Bell RT
   3. Fu BX
   4. Artiles KL
   5. Hartman PS
   6. Fire AZ

   (2014) Efficient marker-free recovery of custom genetic modifications with CRISPR/Cas9 in Caenorhabditis elegans

   Genetics 198:837–846.

   https://doi.org/10.1534/genetics.114.169730

   • PubMed
   • Google Scholar
4. 1. Bachmanov AA
   2. Beauchamp GK

   (2007) Taste receptor genes

   Annual Review of Nutrition 27:389–414.

   https://doi.org/10.1146/annurev.nutr.26.061505.111329

   • PubMed
   • Google Scholar
5. 1. Badenhorst P
   2. Voas M
   3. Rebay I
   4. Wu C

   (2002) Biological functions of the ISWI chromatin remodeling complex NURF

   Genes & Development 16:3186–3198.

   https://doi.org/10.1101/gad.1032202

   • PubMed
   • Google Scholar
6. 1. Badenhorst P
   2. Xiao H
   3. Cherbas L
   4. Kwon SY
   5. Voas M
   6. Rebay I
   7. Cherbas P
   8. Wu C

   (2005) The Drosophila nucleosome remodeling factor NURF is required for Ecdysteroid signaling and metamorphosis

   Genes & Development 19:2540–2545.

   https://doi.org/10.1101/gad.1342605

   • PubMed
   • Google Scholar
7. 1. Balbás-Martínez C
   2. Sagrera A
   3. Carrillo-de-Santa-Pau E
   4. Earl J
   5. Márquez M
   6. Vazquez M
   7. Lapi E
   8. Castro-Giner F
   9. Beltran S
   10. Bayés M
   11. Carrato A
   12. Cigudosa JC
   13. Domínguez O
   14. Gut M
   15. Herranz J
   16. Juanpere N
   17. Kogevinas M
   18. Langa X
   19. López-Knowles E
   20. Lorente JA
   21. Lloreta J
   22. Pisano DG
   23. Richart L
   24. Rico D
   25. Salgado RN
   26. Tardón A
   27. Chanock S
   28. Heath S
   29. Valencia A
   30. Losada A
   31. Gut I
   32. Malats N
   33. Real FX

   (2013) Recurrent inactivation of STAG2 in bladder Cancer is not associated with aneuploidy

   Nature Genetics 45:1464–1469.

   https://doi.org/10.1038/ng.2799

   • PubMed
   • Google Scholar
8. 1. Barak O
   2. Lazzaro MA
   3. Lane WS
   4. Speicher DW
   5. Picketts DJ
   6. Shiekhattar R

   (2003) Isolation of human NURF: a regulator of engrailed gene expression

   The EMBO Journal 22:6089–6100.

   https://doi.org/10.1093/emboj/cdg582

   • PubMed
   • Google Scholar
9. 1. Barbosa-Morais NL
   2. Irimia M
   3. Pan Q
   4. Xiong HY
   5. Gueroussov S
   6. Lee LJ
   7. Slobodeniuc V
   8. Kutter C
   9. Watt S
   10. Colak R
   11. Kim T
   12. Misquitta-Ali CM
   13. Wilson MD
   14. Kim PM
   15. Odom DT
   16. Frey BJ
   17. Blencowe BJ

   (2012) The evolutionary landscape of alternative splicing in vertebrate species

   Science 338:1587–1593.

   https://doi.org/10.1126/science.1230612

   • PubMed
   • Google Scholar
10. 1. Berry FB
    2. Miura Y
    3. Mihara K
    4. Kaspar P
    5. Sakata N
    6. Hashimoto-Tamaoki T
    7. Tamaoki T

    (2001) Positive and negative regulation of myogenic differentiation of C2C12 cells by isoforms of the multiple homeodomain zinc finger transcription factor ATBF1

    Journal of Biological Chemistry 276:25057–25065.

    https://doi.org/10.1074/jbc.M010378200

    • PubMed
    • Google Scholar
11. 1. Bi Y
    2. Ren X
    3. Li R
    4. Ding Q
    5. Xie D
    6. Zhao Z

    (2019) Specific interactions between autosome and X chromosomes cause hybrid male sterility in Caenorhabditis species

    Genetics 212:801–813.

    https://doi.org/10.1534/genetics.119.302202

    • PubMed
    • Google Scholar
12. 1. Blumenthal T

    (2012) Trans-splicing and operons in C. elegans

    WormBook : The Online Review of C. Elegans Biology.

    https://doi.org/10.1895/wormbook.1.5.2

    • PubMed
    • Google Scholar
13. 1. Bowser R
    2. Giambrone A
    3. Davies P

    (1995) FAC1, a novel gene identified with the monoclonal antibody Alz50, is developmentally regulated in human brain

    Developmental Neuroscience 17:20–37.

    https://doi.org/10.1159/000111270

    • PubMed
    • Google Scholar
14. 1. Bray NL
    2. Pimentel H
    3. Melsted P
    4. Pachter L

    (2016) Near-optimal probabilistic RNA-seq quantification

    Nature Biotechnology 34:525–527.

    https://doi.org/10.1038/nbt.3519

    • PubMed
    • Google Scholar
15. 1. Brunquell J
    2. Morris S
    3. Lu Y
    4. Cheng F
    5. Westerheide SD

    (2016) The genome-wide role of HSF-1 in the regulation of gene expression in Caenorhabditis elegans

    BMC Genomics 17:559.

    https://doi.org/10.1186/s12864-016-2837-5

    • PubMed
    • Google Scholar
16. 1. Buganim Y
    2. Goldstein I
    3. Lipson D
    4. Milyavsky M
    5. Polak-Charcon S
    6. Mardoukh C
    7. Solomon H
    8. Kalo E
    9. Madar S
    10. Brosh R
    11. Perelman M
    12. Navon R
    13. Goldfinger N
    14. Barshack I
    15. Yakhini Z
    16. Rotter V

    (2010) A novel translocation breakpoint within the BPTF gene is associated with a pre-malignant phenotype

    PLOS ONE 5:e9657.

    https://doi.org/10.1371/journal.pone.0009657

    • PubMed
    • Google Scholar
17. 1. Cao J
    2. Packer JS
    3. Ramani V
    4. Cusanovich DA
    5. Huynh C
    6. Daza R
    7. Qiu X
    8. Lee C
    9. Furlan SN
    10. Steemers FJ
    11. Adey A
    12. Waterston RH
    13. Trapnell C
    14. Shendure J

    (2017) Comprehensive single-cell transcriptional profiling of a multicellular organism

    Science 357:661–667.

    https://doi.org/10.1126/science.aam8940

    • PubMed
    • Google Scholar
18. 1. Chan YF
    2. Marks ME
    3. Jones FC
    4. Villarreal G
    5. Shapiro MD
    6. Brady SD
    7. Southwick AM
    8. Absher DM
    9. Grimwood J
    10. Schmutz J
    11. Myers RM
    12. Petrov D
    13. Jónsson B
    14. Schluter D
    15. Bell MA
    16. Kingsley DM

    (2010) Adaptive evolution of pelvic reduction in sticklebacks by recurrent deletion of a Pitx1 enhancer

    Science 327:302–305.

    https://doi.org/10.1126/science.1182213

    • PubMed
    • Google Scholar
19. 1. Chelo IM
    2. Afonso B
    3. Carvalho S
    4. Theologidis I
    5. Goy C
    6. Pino-Querido A
    7. Proulx SR
    8. Teotónio H

    (2019) Partial selfing can reduce genetic loads while maintaining diversity during experimental evolution

    G3: Genes|Genomes|Genetics 9:2811–2821.

    https://doi.org/10.1534/g3.119.400239

    • Google Scholar
20. 1. Chen X
    2. Shen Y
    3. Ellis RE

    (2014) Dependence of the sperm/oocyte decision on the nucleosome remodeling factor complex was acquired during recent Caenorhabditis briggsae evolution

    Molecular Biology and Evolution 31:2573–2585.

    https://doi.org/10.1093/molbev/msu198

    • PubMed
    • Google Scholar
21. 1. Chojnacki S
    2. Cowley A
    3. Lee J
    4. Foix A
    5. Lopez R

    (2017) Programmatic access to bioinformatics tools from EMBL-EBI update: 2017

    Nucleic Acids Research 45:W550–W553.

    https://doi.org/10.1093/nar/gkx273

    • PubMed
    • Google Scholar
22. 1. Colosimo PF
    2. Hosemann KE
    3. Balabhadra S
    4. Villarreal G
    5. Dickson M
    6. Grimwood J
    7. Schmutz J
    8. Myers RM
    9. Schluter D
    10. Kingsley DM

    (2005) Widespread parallel evolution in sticklebacks by repeated fixation of ectodysplasin alleles

    Science 307:1928–1933.

    https://doi.org/10.1126/science.1107239

    • PubMed
    • Google Scholar
23. 1. Cutter AD

    (2004)

    Sperm-limited fecundity in Nematodes: how many sperm are enough?

    Evolution 58:651–655.

    • Google Scholar
24. 1. de Bono M
    2. Bargmann CI

    (1998) Natural variation in a neuropeptide Y receptor homolog modifies social behavior and food response in C. elegans

    Cell 94:679–689.

    https://doi.org/10.1016/S0092-8674(00)81609-8

    • PubMed
    • Google Scholar
25. 1. Des Marais DL
    2. Rausher MD

    (2008) Escape from adaptive conflict after duplication in an anthocyanin pathway gene

    Nature 454:762–765.

    https://doi.org/10.1038/nature07092

    • PubMed
    • Google Scholar
26. 1. Duveau F
    2. Félix MA

    (2012) Role of pleiotropy in the evolution of a cryptic developmental variation in Caenorhabditis elegans

    PLOS Biology 10:e1001230.

    https://doi.org/10.1371/journal.pbio.1001230

    • PubMed
    • Google Scholar
27. 1. Fierst JL
    2. Willis JH
    3. Thomas CG
    4. Wang W
    5. Reynolds RM
    6. Ahearne TE
    7. Cutter AD
    8. Phillips PC

    (2015) Reproductive mode and the evolution of genome size and structure in Caenorhabditis nematodes

    PLOS Genetics 11:e1005323.

    https://doi.org/10.1371/journal.pgen.1005323

    • PubMed
    • Google Scholar
28. 1. Frey WD
    2. Chaudhry A
    3. Slepicka PF
    4. Ouellette AM
    5. Kirberger SE
    6. Pomerantz WCK
    7. Hannon GJ
    8. Dos Santos CO

    (2017) BPTF maintains chromatin accessibility and the Self-Renewal capacity of mammary gland stem cells

    Stem Cell Reports 9:23–31.

    https://doi.org/10.1016/j.stemcr.2017.04.031

    • PubMed
    • Google Scholar
29. 1. Frøkjær-Jensen C

    (2015) Transposon-Assisted genetic engineering with Mos1-Mediated Single-Copy insertion (MosSCI)

    Methods in Molecular Biology 1327:49–58.

    https://doi.org/10.1007/978-1-4939-2842-2_5

    • PubMed
    • Google Scholar
30. 1. Fyodorov DV
    2. Kadonaga JT

    (2002) Binding of Acf1 to DNA involves a WAC motif and is important for ACF-mediated chromatin assembly

    Molecular and Cellular Biology 22:6344–6353.

    https://doi.org/10.1128/MCB.22.18.6344-6353.2002

    • PubMed
    • Google Scholar
31. 1. Goller T
    2. Vauti F
    3. Ramasamy S
    4. Arnold HH

    (2008) Transcriptional regulator BPTF/FAC1 is essential for trophoblast differentiation during early mouse development

    Molecular and Cellular Biology 28:6819–6827.

    https://doi.org/10.1128/MCB.01058-08

    • PubMed
    • Google Scholar
32. 1. Gray JC
    2. Cutter AD

    (2014) Mainstreaming Caenorhabditis elegans in experimental evolution

    Proceedings of the Royal Society B: Biological Sciences 281:20133055.

    https://doi.org/10.1098/rspb.2013.3055

    • Google Scholar
33. 1. Greene JS
    2. Brown M
    3. Dobosiewicz M
    4. Ishida IG
    5. Macosko EZ
    6. Zhang X
    7. Butcher RA
    8. Cline DJ
    9. McGrath PT
    10. Bargmann CI

    (2016a) Balancing selection shapes density-dependent foraging behaviour

    Nature 539:254–258.

    https://doi.org/10.1038/nature19848

    • PubMed
    • Google Scholar
34. 1. Greene JS
    2. Dobosiewicz M
    3. Butcher RA
    4. McGrath PT
    5. Bargmann CI

    (2016b) Regulatory changes in two chemoreceptor genes contribute to a Caenorhabditis elegans QTL for foraging behavior

    eLife 5:e21454.

    https://doi.org/10.7554/eLife.21454

    • PubMed
    • Google Scholar
35. 1. Hoang DT
    2. Chernomor O
    3. von Haeseler A
    4. Minh BQ
    5. Vinh LS

    (2018) UFBoot2: improving the ultrafast bootstrap approximation

    Molecular Biology and Evolution 35:518–522.

    https://doi.org/10.1093/molbev/msx281

    • PubMed
    • Google Scholar
36. 1. Hodgkin J
    2. Barnes TM

    (1991) More is not better: brood size and population growth in a self-fertilizing nematode

    Proceedings of the Royal Society B. Biological Sciences 246:19–24.

    https://doi.org/10.1098/rspb.1991.0119

    • PubMed
    • Google Scholar
37. 1. Hubbard EJ
    2. Greenstein D

    (2005) Introduction to the germ line

    WormBook.

    https://doi.org/10.1895/wormbook.1.18.1

    • PubMed
    • Google Scholar
38. 1. Hughes AL

    (1994) The evolution of functionally novel proteins after gene duplication

    Proceedings of the Royal Society B. Biological Sciences 256:119–124.

    https://doi.org/10.1098/rspb.1994.0058

    • PubMed
    • Google Scholar
39. 1. Innan H
    2. Kondrashov F

    (2010) The evolution of gene duplications: classifying and distinguishing between models

    Nature Reviews Genetics 11:97–108.

    https://doi.org/10.1038/nrg2689

    • PubMed
    • Google Scholar
40. 1. Jungreis I
    2. Lin MF
    3. Spokony R
    4. Chan CS
    5. Negre N
    6. Victorsen A
    7. White KP
    8. Kellis M

    (2011) Evidence of abundant stop codon readthrough in Drosophila and other metazoa

    Genome Research 21:2096–2113.

    https://doi.org/10.1101/gr.119974.110

    • PubMed
    • Google Scholar
41. 1. Kalyaanamoorthy S
    2. Minh BQ
    3. Wong TKF
    4. von Haeseler A
    5. Jermiin LS

    (2017) ModelFinder: fast model selection for accurate phylogenetic estimates

    Nature Methods 14:587–589.

    https://doi.org/10.1038/nmeth.4285

    • PubMed
    • Google Scholar
42. 1. Kanzaki N
    2. Tsai IJ
    3. Tanaka R
    4. Hunt VL
    5. Liu D
    6. Tsuyama K
    7. Maeda Y
    8. Namai S
    9. Kumagai R
    10. Tracey A
    11. Holroyd N
    12. Doyle SR
    13. Woodruff GC
    14. Murase K
    15. Kitazume H
    16. Chai C
    17. Akagi A
    18. Panda O
    19. Ke HM
    20. Schroeder FC
    21. Wang J
    22. Berriman M
    23. Sternberg PW
    24. Sugimoto A
    25. Kikuchi T

    (2018) Biology and genome of a newly discovered sibling species of Caenorhabditis elegans

    Nature Communications 9:3216.

    https://doi.org/10.1038/s41467-018-05712-5

    • PubMed
    • Google Scholar
43. 1. Katoh K
    2. Standley DM

    (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability

    Molecular Biology and Evolution 30:772–780.

    https://doi.org/10.1093/molbev/mst010

    • PubMed
    • Google Scholar
44. 1. Katz Y
    2. Wang ET
    3. Silterra J
    4. Schwartz S
    5. Wong B
    6. Thorvaldsdóttir H
    7. Robinson JT
    8. Mesirov JP
    9. Airoldi EM
    10. Burge CB

    (2015) Quantitative visualization of alternative exon expression from RNA-seq data

    Bioinformatics 31:2400–2402.

    https://doi.org/10.1093/bioinformatics/btv034

    • PubMed
    • Google Scholar
45. 1. Keller A
    2. Zhuang H
    3. Chi Q
    4. Vosshall LB
    5. Matsunami H

    (2007) Genetic variation in a human odorant receptor alters odour perception

    Nature 449:468–472.

    https://doi.org/10.1038/nature06162

    • PubMed
    • Google Scholar
46. 1. Kim D
    2. Langmead B
    3. Salzberg SL

    (2015) HISAT: a fast spliced aligner with low memory requirements

    Nature Methods 12:357–360.

    https://doi.org/10.1038/nmeth.3317

    • PubMed
    • Google Scholar
47. 1. Kiontke KC
    2. Félix MA
    3. Ailion M
    4. Rockman MV
    5. Braendle C
    6. Pénigault JB
    7. Fitch DH

    (2011) A phylogeny and molecular barcodes for Caenorhabditis, with numerous new species from rotting fruits

    BMC Evolutionary Biology 11:339.

    https://doi.org/10.1186/1471-2148-11-339

    • PubMed
    • Google Scholar
48. 1. Koludrovic D
    2. Laurette P
    3. Strub T
    4. Keime C
    5. Le Coz M
    6. Coassolo S
    7. Mengus G
    8. Larue L
    9. Davidson I

    (2015) Chromatin-Remodelling complex NURF is essential for differentiation of adult melanocyte stem cells

    PLOS Genetics 11:e1005555.

    https://doi.org/10.1371/journal.pgen.1005555

    • PubMed
    • Google Scholar
49. 1. Kwon SY
    2. Xiao H
    3. Glover BP
    4. Tjian R
    5. Wu C
    6. Badenhorst P

    (2008) The nucleosome remodeling factor (NURF) regulates genes involved in Drosophila innate immunity

    Developmental Biology 316:538–547.

    https://doi.org/10.1016/j.ydbio.2008.01.033

    • PubMed
    • Google Scholar
50. 1. Kwon SY
    2. Xiao H
    3. Wu C
    4. Badenhorst P

    (2009) Alternative splicing of NURF301 generates distinct NURF chromatin remodeling complexes with altered modified histone binding specificities

    PLOS Genetics 5:e1000574.

    https://doi.org/10.1371/journal.pgen.1000574

    • PubMed
    • Google Scholar
51. Preprint

    1. Lamelza P
    2. Young JM
    3. Noble LM
    4. Isakharov A
    5. Palanisamy M
    6. Rockman MV
    7. Malik HS
    8. Ailion M

    (2019) Cryptic asexual reproduction in Caenorhabditis nematodes revealed by interspecies hybridization

    bioRxiv.

    https://doi.org/10.1101/588152

    • Google Scholar
52. 1. Landry J
    2. Sharov AA
    3. Piao Y
    4. Sharova LV
    5. Xiao H
    6. Southon E
    7. Matta J
    8. Tessarollo L
    9. Zhang YE
    10. Ko MS
    11. Kuehn MR
    12. Yamaguchi TP
    13. Wu C

    (2008) Essential role of chromatin remodeling protein bptf in early mouse embryos and embryonic stem cells

    PLOS Genetics 4:e1000241.

    https://doi.org/10.1371/journal.pgen.1000241

    • PubMed
    • Google Scholar
53. 1. Landry JW
    2. Banerjee S
    3. Taylor B
    4. Aplan PD
    5. Singer A
    6. Wu C

    (2011) Chromatin remodeling complex NURF regulates thymocyte maturation

    Genes & Development 25:275–286.

    https://doi.org/10.1101/gad.2007311

    • PubMed
    • Google Scholar
54. 1. Large EE
    2. Xu W
    3. Zhao Y
    4. Brady SC
    5. Long L
    6. Butcher RA
    7. Andersen EC
    8. McGrath PT

    (2016) Selection on a subunit of the NURF chromatin remodeler modifies life history traits in a domesticated strain of Caenorhabditis elegans

    PLOS Genetics 12:e1006219.

    https://doi.org/10.1371/journal.pgen.1006219

    • PubMed
    • Google Scholar
55. 1. Large EE
    2. Padmanabhan R
    3. Watkins KL
    4. Campbell RF
    5. Xu W
    6. McGrath PT

    (2017) Modeling of a negative feedback mechanism explains antagonistic pleiotropy in reproduction in domesticated Caenorhabditis elegans strains

    PLOS Genetics 13:e1006769.

    https://doi.org/10.1371/journal.pgen.1006769

    • Google Scholar
56. 1. Letunic I
    2. Bork P

    (2016) Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees

    Nucleic Acids Research 44:W242–W245.

    https://doi.org/10.1093/nar/gkw290

    • PubMed
    • Google Scholar
57. 1. Li H
    2. Ilin S
    3. Wang W
    4. Duncan EM
    5. Wysocka J
    6. Allis CD
    7. Patel DJ

    (2006) Molecular basis for site-specific read-out of histone H3K4me3 by the BPTF PHD finger of NURF

    Nature 442:91–95.

    https://doi.org/10.1038/nature04802

    • PubMed
    • Google Scholar
58. 1. Li H
    2. Handsaker B
    3. Wysoker A
    4. Fennell T
    5. Ruan J
    6. Homer N
    7. Marth G
    8. Abecasis G
    9. Durbin R
    10. 1000 Genome Project Data Processing Subgroup

    (2009) The sequence alignment/Map format and SAMtools

    Bioinformatics 25:2078–2079.

    https://doi.org/10.1093/bioinformatics/btp352

    • Google Scholar
59. 1. Li J
    2. Chauve L
    3. Phelps G
    4. Brielmann RM
    5. Morimoto RI

    (2016) E2F coregulates an essential HSF developmental program that is distinct from the heat-shock response

    Genes & Development 30:2062–2075.

    https://doi.org/10.1101/gad.283317.116

    • PubMed
    • Google Scholar
60. 1. Lunde K
    2. Egelandsdal B
    3. Skuterud E
    4. Mainland JD
    5. Lea T
    6. Hersleth M
    7. Matsunami H

    (2012) Genetic variation of an odorant receptor OR7D4 and sensory perception of cooked meat containing androstenone

    PLOS ONE 7:e35259.

    https://doi.org/10.1371/journal.pone.0035259

    • PubMed
    • Google Scholar
61. 1. Mallarino R
    2. Linden TA
    3. Linnen CR
    4. Hoekstra HE

    (2017) The role of isoforms in the evolution of cryptic coloration in Peromyscus mice

    Molecular Ecology 26:245–258.

    https://doi.org/10.1111/mec.13663

    • PubMed
    • Google Scholar
62. 1. Mariani L
    2. Lussi YC
    3. Vandamme J
    4. Riveiro A
    5. Salcini AE

    (2016) The H3K4me3/2 histone demethylase RBR-2 controls axon guidance by repressing the actin-remodeling gene wsp-1

    Development 143:851–863.

    https://doi.org/10.1242/dev.132985

    • PubMed
    • Google Scholar
63. 1. Martin A
    2. Orgogozo V

    (2013) The loci of repeated evolution: a catalog of genetic hotspots of phenotypic variation

    Evolution 22:1235–1250.

    https://doi.org/10.1111/evo.12081

    • Google Scholar
64. 1. McBride CS
    2. Baier F
    3. Omondi AB
    4. Spitzer SA
    5. Lutomiah J
    6. Sang R
    7. Ignell R
    8. Vosshall LB

    (2014) Evolution of mosquito preference for humans linked to an odorant receptor

    Nature 515:222–227.

    https://doi.org/10.1038/nature13964

    • PubMed
    • Google Scholar
65. 1. McGinnis S
    2. Madden TL

    (2004) BLAST: at the core of a powerful and diverse set of sequence analysis tools

    Nucleic Acids Research 32:W20–W25.

    https://doi.org/10.1093/nar/gkh435

    • Google Scholar
66. 1. McGrath PT
    2. Rockman MV
    3. Zimmer M
    4. Jang H
    5. Macosko EZ
    6. Kruglyak L
    7. Bargmann CI

    (2009) Quantitative mapping of a digenic behavioral trait implicates globin variation in C. elegans sensory behaviors

    Neuron 61:692–699.

    https://doi.org/10.1016/j.neuron.2009.02.012

    • PubMed
    • Google Scholar
67. 1. McGrath PT
    2. Xu Y
    3. Ailion M
    4. Garrison JL
    5. Butcher RA
    6. Bargmann CI

    (2011) Parallel evolution of domesticated Caenorhabditis species targets pheromone receptor genes

    Nature 477:321–325.

    https://doi.org/10.1038/nature10378

    • PubMed
    • Google Scholar
68. 1. McRae JF
    2. Mainland JD
    3. Jaeger SR
    4. Adipietro KA
    5. Matsunami H
    6. Newcomb RD

    (2012) Genetic variation in the odorant receptor OR2J3 is associated with the ability to detect the “Grassy” Smelling Odor, cis-3-hexen-1-ol

    Chemical Senses 37:585–593.

    https://doi.org/10.1093/chemse/bjs049

    • Google Scholar
69. 1. Merkin J
    2. Russell C
    3. Chen P
    4. Burge CB

    (2012) Evolutionary dynamics of gene and isoform regulation in mammalian tissues

    Science 338:1593–1599.

    https://doi.org/10.1126/science.1228186

    • PubMed
    • Google Scholar
70. 1. Mortazavi A
    2. Schwarz EM
    3. Williams B
    4. Schaeffer L
    5. Antoshechkin I
    6. Wold BJ
    7. Sternberg PW

    (2010) Scaffolding a Caenorhabditis nematode genome with RNA-seq

    Genome Research 20:1740–1747.

    https://doi.org/10.1101/gr.111021.110

    • PubMed
    • Google Scholar
71. 1. Müller B
    2. Basler K

    (2000)

    The repressor and activator forms of Cubitus interruptus control Hedgehog target genes through common generic gli-binding sites

    Development 127:2999–3007.

    • PubMed
    • Google Scholar
72. 1. Nguyen L-T
    2. Schmidt HA
    3. von Haeseler A
    4. Minh BQ

    (2015) IQ-TREE: a fast and effective stochastic algorithm for estimating Maximum-Likelihood phylogenies

    Molecular Biology and Evolution 32:268–274.

    https://doi.org/10.1093/molbev/msu300

    • Google Scholar
73. Book

    1. Ohno S

    (1970)

    Evolution by Gene Duplication

    London, New York: Allen & Unwin; Springer-Verlag.

    • Google Scholar
74. 1. Paix A
    2. Folkmann A
    3. Rasoloson D
    4. Seydoux G

    (2015) High efficiency, Homology-Directed genome editing in Caenorhabditis elegans Using CRISPR-Cas9 Ribonucleoprotein Complexes

    Genetics 201:47–54.

    https://doi.org/10.1534/genetics.115.179382

    • PubMed
    • Google Scholar
75. 1. Pal S
    2. Gupta R
    3. Kim H
    4. Wickramasinghe P
    5. Baubet V
    6. Showe LC
    7. Dahmane N
    8. Davuluri RV

    (2011) Alternative transcription exceeds alternative splicing in generating the transcriptome diversity of cerebellar development

    Genome Research 21:1260–1272.

    https://doi.org/10.1101/gr.120535.111

    • PubMed
    • Google Scholar
76. 1. Pan Q
    2. Shai O
    3. Lee LJ
    4. Frey BJ
    5. Blencowe BJ

    (2008) Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing

    Nature Genetics 40:1413–1415.

    https://doi.org/10.1038/ng.259

    • PubMed
    • Google Scholar
77. 1. Penley MJ
    2. Greenberg AB
    3. Khalid A
    4. Namburar SR
    5. Morran LT

    (2018) No measurable fitness cost to experimentally evolved host defence in the Caenorhabditis elegans-Serratia marcescens host-parasite system

    Journal of Evolutionary Biology 31:1976–1981.

    https://doi.org/10.1111/jeb.13372

    • PubMed
    • Google Scholar
78. 1. Persson A
    2. Gross E
    3. Laurent P
    4. Busch KE
    5. Bretes H
    6. de Bono M

    (2009) Natural variation in a neural globin tunes oxygen sensing in wild Caenorhabditis elegans

    Nature 458:1030–1033.

    https://doi.org/10.1038/nature07820

    • PubMed
    • Google Scholar
79. 1. Prior H
    2. Jawad AK
    3. MacConnachie L
    4. Beg AA

    (2017) Highly efficient, rapid and Co-CRISPR-Independent genome editing in Caenorhabditis elegans

    G3: Genes|Genomes|Genetics 7:3693–3698.

    https://doi.org/10.1534/g3.117.300216

    • Google Scholar
80. 1. Richart L
    2. Carrillo-de Santa Pau E
    3. Río-Machín A
    4. de Andrés MP
    5. Cigudosa JC
    6. Lobo VJS
    7. Real FX

    (2016) BPTF is required for c-MYC transcriptional activity and in vivo tumorigenesis

    Nature Communications 7:10153.

    https://doi.org/10.1038/ncomms10153

    • PubMed
    • Google Scholar
81. Preprint

    1. Roach NP
    2. Sadowski N
    3. Alessi AF
    4. Timp W
    5. Taylor J
    6. Kim JK

    (2019) The full-length transcriptome of C. elegans using direct RNA sequencing

    bioRxiv.

    https://doi.org/10.1101/598763

    • Google Scholar
82. 1. Robinson JT
    2. Thorvaldsdóttir H
    3. Winckler W
    4. Guttman M
    5. Lander ES
    6. Getz G
    7. Mesirov JP

    (2011) Integrative genomics viewer

    Nature Biotechnology 29:24–26.

    https://doi.org/10.1038/nbt.1754

    • PubMed
    • Google Scholar
83. 1. Ruthenburg AJ
    2. Li H
    3. Milne TA
    4. Dewell S
    5. McGinty RK
    6. Yuen M
    7. Ueberheide B
    8. Dou Y
    9. Muir TW
    10. Patel DJ
    11. Allis CD

    (2011) Recognition of a mononucleosomal histone modification pattern by BPTF via multivalent interactions

    Cell 145:692–706.

    https://doi.org/10.1016/j.cell.2011.03.053

    • PubMed
    • Google Scholar
84. 1. Saxena AS
    2. Salomon MP
    3. Matsuba C
    4. Yeh SD
    5. Baer CF

    (2019) Evolution of the mutational process under relaxed selection in Caenorhabditis elegans

    Molecular Biology and Evolution 36:239–251.

    https://doi.org/10.1093/molbev/msy213

    • PubMed
    • Google Scholar
85. 1. Shabalina SA
    2. Ogurtsov AY
    3. Spiridonov NA
    4. Koonin EV

    (2014) Evolution at protein ends: major contribution of alternative transcription initiation and termination to the transcriptome and proteome diversity in mammals

    Nucleic Acids Research 42:7132–7144.

    https://doi.org/10.1093/nar/gku342

    • PubMed
    • Google Scholar
86. 1. Shevchenko A
    2. Tomas H
    3. Havlis J
    4. Olsen JV
    5. Mann M

    (2006) In-gel digestion for mass spectrometric characterization of proteins and proteomes

    Nature Protocols 1:2856–2860.

    https://doi.org/10.1038/nprot.2006.468

    • PubMed
    • Google Scholar
87. 1. Shimodaira H

    (2002) An approximately unbiased test of phylogenetic tree selection

    Systematic Biology 51:492–508.

    https://doi.org/10.1080/10635150290069913

    • PubMed
    • Google Scholar
88. 1. Slos D
    2. Sudhaus W
    3. Stevens L
    4. Bert W
    5. Blaxter M

    (2017) Caenorhabditis monodelphis sp. n.: defining the stem morphology and genomics of the genus Caenorhabditis

    BMC Zoology 2:4.

    https://doi.org/10.1186/s40850-017-0013-2

    • Google Scholar
89. 1. Stein LD
    2. Bao Z
    3. Blasiar D
    4. Blumenthal T
    5. Brent MR
    6. Chen N
    7. Chinwalla A
    8. Clarke L
    9. Clee C
    10. Coghlan A
    11. Coulson A
    12. D'Eustachio P
    13. Fitch DH
    14. Fulton LA
    15. Fulton RE
    16. Griffiths-Jones S
    17. Harris TW
    18. Hillier LW
    19. Kamath R
    20. Kuwabara PE
    21. Mardis ER
    22. Marra MA
    23. Miner TL
    24. Minx P
    25. Mullikin JC
    26. Plumb RW
    27. Rogers J
    28. Schein JE
    29. Sohrmann M
    30. Spieth J
    31. Stajich JE
    32. Wei C
    33. Willey D
    34. Wilson RK
    35. Durbin R
    36. Waterston RH

    (2003) The genome sequence of Caenorhabditis briggsae: a platform for comparative genomics

    PLOS Biology 1:e45.

    https://doi.org/10.1371/journal.pbio.0000045

    • PubMed
    • Google Scholar
90. 1. Sterken MG
    2. Snoek LB
    3. Kammenga JE
    4. Andersen EC

    (2015) The laboratory domestication of Caenorhabditis elegans

    Trends in Genetics 31:224–231.

    https://doi.org/10.1016/j.tig.2015.02.009

    • PubMed
    • Google Scholar
91. 1. Stevens L
    2. Félix MA
    3. Beltran T
    4. Braendle C
    5. Caurcel C
    6. Fausett S
    7. Fitch D
    8. Frézal L
    9. Gosse C
    10. Kaur T
    11. Kiontke K
    12. Newton MD
    13. Noble LM
    14. Richaud A
    15. Rockman MV
    16. Sudhaus W
    17. Blaxter M

    (2019) Comparative genomics of 10 new Caenorhabditis species

    Evolution Letters 3:217–236.

    https://doi.org/10.1002/evl3.110

    • PubMed
    • Google Scholar
92. 1. Sucena E
    2. Delon I
    3. Jones I
    4. Payre F
    5. Stern DL

    (2003) Regulatory evolution of shavenbaby/ovo underlies multiple cases of morphological parallelism

    Nature 424:935–938.

    https://doi.org/10.1038/nature01768

    • PubMed
    • Google Scholar
93. 1. Suyama M
    2. Torrents D
    3. Bork P

    (2006) PAL2NAL: robust conversion of protein sequence alignments into the corresponding Codon alignments

    Nucleic Acids Research 34:W609–W612.

    https://doi.org/10.1093/nar/gkl315

    • PubMed
    • Google Scholar
94. 1. Teotónio H
    2. Estes S
    3. Phillips PC
    4. Baer CF

    (2017) Experimental evolution with Caenorhabditis nematodes

    Genetics 206:691–716.

    https://doi.org/10.1534/genetics.115.186288

    • PubMed
    • Google Scholar
95. 1. Ting JJ
    2. Tsai CN
    3. Schalkowski R
    4. Cutter AD

    (2018) Genetic contributions to ectopic sperm cell migration in Caenorhabditis Nematodes

    G3: Genes|Genomics|Genetics 8:3891–3902.

    https://doi.org/10.1534/g3.118.200785

    • PubMed
    • Google Scholar
96. 1. Varet H
    2. Brillet-Guéguen L
    3. Coppée JY
    4. Dillies MA

    (2016) SARTools: a DESeq2- and EdgeR-Based R pipeline for comprehensive differential analysis of RNA-Seq data

    PLOS ONE 11:e0157022.

    https://doi.org/10.1371/journal.pone.0157022

    • PubMed
    • Google Scholar
97. 1. Wang C
    2. Xin X
    3. Xiang R
    4. Ramos FJ
    5. Liu M
    6. Lee HJ
    7. Chen H
    8. Mao X
    9. Kikani CK
    10. Liu F
    11. Dong LQ

    (2009) Yin-Yang regulation of adiponectin signaling by APPL isoforms in muscle cells

    Journal of Biological Chemistry 284:31608–31615.

    https://doi.org/10.1074/jbc.M109.010355

    • PubMed
    • Google Scholar
98. 1. Waterhouse AM
    2. Procter JB
    3. Martin DM
    4. Clamp M
    5. Barton GJ

    (2009) Jalview version 2--a multiple sequence alignment editor and analysis workbench

    Bioinformatics 25:1189–1191.

    https://doi.org/10.1093/bioinformatics/btp033

    • PubMed
    • Google Scholar
99. 1. Wernick RI
    2. Christy SF
    3. Howe DK
    4. Sullins JA
    5. Ramirez JF
    6. Sare M
    7. Penley MJ
    8. Morran LT
    9. Denver DR
    10. Estes S

    (2019) Sex and mitonuclear adaptation in experimental Caenorhabditis elegans Populations

    Genetics 211:1045–1058.

    https://doi.org/10.1534/genetics.119.301935

    • PubMed
    • Google Scholar
100. 1. Wisotsky Z
     2. Medina A
     3. Freeman E
     4. Dahanukar A

     (2011) Evolutionary differences in food preference rely on Gr64e, a receptor for glycerol

     Nature Neuroscience 14:1534–1541.

     https://doi.org/10.1038/nn.2944

     • PubMed
     • Google Scholar
101. 1. Wood TE
     2. Burke JM
     3. Rieseberg LH

     (2005) Parallel genotypic adaptation: when evolution repeats itself

     Genetica 123:157–170.

     https://doi.org/10.1007/s10709-003-2738-9

     • PubMed
     • Google Scholar
102. 1. Wu B
     2. Wang Y
     3. Wang C
     4. Wang GG
     5. Wu J
     6. Wan YY

     (2016) BPTF is essential for T cell homeostasis and function

     The Journal of Immunology 197:4325–4333.

     https://doi.org/10.4049/jimmunol.1600642

     • PubMed
     • Google Scholar
103. 1. Wysocka J
     2. Swigut T
     3. Xiao H
     4. Milne TA
     5. Kwon SY
     6. Landry J
     7. Kauer M
     8. Tackett AJ
     9. Chait BT
     10. Badenhorst P
     11. Wu C
     12. Allis CD

     (2006) A PHD finger of NURF couples histone H3 lysine 4 trimethylation with chromatin remodelling

     Nature 442:86–90.

     https://doi.org/10.1038/nature04815

     • PubMed
     • Google Scholar
104. 1. Xu B
     2. Cai L
     3. Butler JM
     4. Chen D
     5. Lu X
     6. Allison DF
     7. Lu R
     8. Rafii S
     9. Parker JS
     10. Zheng D
     11. Wang GG

     (2018) The chromatin remodeler BPTF activates a stemness Gene-Expression program essential for the maintenance of adult hematopoietic stem cells

     Stem Cell Reports 10:675–683.

     https://doi.org/10.1016/j.stemcr.2018.01.020

     • PubMed
     • Google Scholar
105. 1. Yang L
     2. Wang HN
     3. Hou XH
     4. Zou YP
     5. Han TS
     6. Niu XM
     7. Zhang J
     8. Zhao Z
     9. Todesco M
     10. Balasubramanian S
     11. Guo YL

     (2018) Parallel evolution of common allelic variants confers flowering diversity in Capsella rubella

     The Plant Cell 30:1322–1336.

     https://doi.org/10.1105/tpc.18.00124

     • PubMed
     • Google Scholar
106. 1. Yin D
     2. Schwarz EM
     3. Thomas CG
     4. Felde RL
     5. Korf IF
     6. Cutter AD
     7. Schartner CM
     8. Ralston EJ
     9. Meyer BJ
     10. Haag ES

     (2018) Rapid genome shrinkage in a self-fertile nematode reveals sperm competition proteins

     Science 359:55–61.

     https://doi.org/10.1126/science.aao0827

     • PubMed
     • Google Scholar
107. 1. Zhang SJ
     2. Wang C
     3. Yan S
     4. Fu A
     5. Luan X
     6. Li Y
     7. Sunny Shen Q
     8. Zhong X
     9. Chen JY
     10. Wang X
     11. Chin-Ming Tan B
     12. He A
     13. Li CY

     (2017) Isoform evolution in primates through independent combination of alternative RNA processing events

     Molecular Biology and Evolution 34:2453–2468.

     https://doi.org/10.1093/molbev/msx212

     • PubMed
     • Google Scholar
108. 1. Zhao Y
     2. Long L
     3. Xu W
     4. Campbell RF
     5. Large EE
     6. Greene JS
     7. McGrath PT

     (2018) Changes to social feeding behaviors are not sufficient for fitness gains of the Caenorhabditis elegans N2 reference strain

     eLife 7:e38675.

     https://doi.org/10.7554/eLife.38675

     • PubMed
     • Google Scholar
109. Preprint

     1. Zhao Y
     2. Wan J
     3. Biliya S
     4. Brady SC
     5. Lee D
     6. Andersen EC
     7. Vannberg FO
     8. Lu H
     9. McGrath PT

     (2019) A beneficial genomic rearrangement creates multiple versions of calcipressin in C. elegans

     bioRxiv.

     https://doi.org/10.1101/578088

     • Google Scholar

Author details

1. Wen Xu

   School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2085-7223

2. Lijiang Long

   1. School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States
   2. Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, United States

   Contribution

   Resources, Data curation, Formal analysis, Investigation, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9897-5900

3. Yuehui Zhao

   School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States

   Contribution

   Formal analysis, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9496-0023

4. Lewis Stevens

   Institute of Evolutionary Biology, Ashworth Laboratories, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6075-8273

5. Irene Felipe

   Epithelial Carcinogenesis Group, Spanish National Cancer Research Center-CNIO, Madrid, Spain

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

6. Javier Munoz

   Proteomics Unit-ProteoRed-ISCIII, Spanish National Cancer Research Center-CNIO, Madrid, Spain

   Contribution

   Data curation, Formal analysis, Validation, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3288-3496

7. Ronald E Ellis

   Department of Molecular Biology, Rowan University School of Osteopathic Medicine, Stratford, United States

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

8. Patrick T McGrath

   1. School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States
   2. Parker H. Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, United States
   3. School of Physics, Georgia Institute of Technology, Atlanta, United States

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   patrick.mcgrath@biology.gatech.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1598-3746

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