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. 2025 May 24;15(5):e71453. doi: 10.1002/ece3.71453

Patterns of Gene Family Evolution and Selection Across Daphnia

Connor S Murray 1,2,✉, Alan O Bergland 1
PMCID: PMC12102780  PMID: 40416764

ABSTRACT

Gene family expansion underlies a host of biological innovations across the tree of life. Understanding why specific gene families expand or contract requires comparative genomic investigations clarifying further how species adapt in the wild. This study investigates the gene family change dynamics within several species of Daphnia, a group of freshwater microcrustaceans that are insightful model systems for evolutionary genetics' research. We employ comparative genomics approaches to understand the forces driving gene evolution and draw upon candidate gene families that change gene numbers across Daphnia. Our results suggest that genes related to stress responses and glycoproteins generally expand across taxa, and we investigate evolutionary hypotheses of adaptation that may underpin expansions. Through these analyses, we shed light on the interplay between gene expansions and selection within other ecologically relevant stress response gene families. While we show generalities in gene family turnover in genes related to stress response (i.e., DNA repair mechanisms), most gene family evolution is driven in a species‐specific manner. Additionally, while we show general trends toward positive selection within some expanding gene families, many genes are not under selection, highlighting the complexity of diversification and evolution within Daphnia. Our research enhances the understanding of individual gene family evolution within Daphnia and provides a case study of ecologically relevant genes prone to change.

Keywords: Daphnia, gene family evolution, positive selection, stress response

This study examines gene family dynamics within Daphnia species, using comparative genomics to explore adaptive expansion in gene families, particularly those related to stress response. Findings reveal general trends in gene turnover and selection, although most changes are species‐specific, highlighting complex evolutionary patterns.

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1. Introduction

A major goal in biology is to understand how adaptive evolution changes complex phenotypes (Lewontin 1974; Mayr 1963). Gene family expansion, mediated through the novel duplication of genes, is an influential process that provides species with the opportunity for biological innovation to occur (Hahn et al. 2007; Jordan et al. 2001). Neofunctionalization is the process wherein gene copies adopt new functions (Ohno 2013), while subfunctionalization is the process in which a paralogous gene retains a part of the progenitor's role (Lynch and Force 2000). Both neofunctionalization and subfunctionalization are ways in which gene duplications lead to diversification resulting from the fixation of beneficial amino acid substitutions (Lynch 2002). Gene family expansions have long been hypothesized as the product of adaptive evolution across the tree of life, from microbes to mammals (Huang, Jiang, et al. 2023; Huang, Lu, et al. 2023; Zhang et al. 2014; Hahn et al. 2007; Jordan et al. 2001; Lugli et al. 2017; Richter et al. 2018).

When developing hypotheses for studying gene family evolution, we often view gene content change as facilitating adaptation to environmental (abiotic and biotic) shifts. For instance, animals inhabiting extreme temperature regimes typically have an increased number of heat‐shock proteins (Chen et al. 2018; Zhang et al. 2012). In this way, gene family expansions have allowed taxa to adapt and survive during temperature fluctuations (Lindquist and Craig 1988). While heat‐shock proteins are known candidates of environmentally induced gene family expansions, opsins are another source of constant gene family fluctuations due to adaptation to various light environments and ultraviolet sensitivities (Novales Flamarique 2013), as well as chemosensory gene families (Peñalva‐Arana et al. 2009). Additionally, innate immune response proteins and DNA repair mechanisms are also common expansion targets (Teekas et al. 2022), yet lineage‐specific expansions are also expected due to specific adaptation to local environments (Lespinet et al. 2002). Overall, interpreting how gene family changes occur across related species is a worthwhile pursuit, especially for taxa prone to gene family turnover in response to environmental decay because it reveals a component of adaptation that changes many potential protein targets across an organism (Guijarro‐Clarke et al. 2020). By using comparative genomics, we can test hypotheses related to the timing of gene family diversification and lay the groundwork for better understanding adaptation within the context of both individual species and groups of taxa (Mendes et al. 2021).

In this work, we assess gene family evolution and infer the strength of selection acting upon Daphnia, a diverse genera of small freshwater Crustaceans that live in a range of habitats from ephemeral rain puddles to lakes and even manmade estuaries (Fryer 1991). Daphnia adaptively radiated roughly 200 million years ago (mya) and encompasses at least 121 species to date within the Daphniidea family. Subspecies and cryptic speciation are common within Daphnia, and so this number of species is likely an underestimate (Forró et al. 2008). One of the most studied taxa within Daphniidea is Daphnia pulex , a complex of cryptic species found across both North American and European ponds (Colbourne et al. 1998; Crease et al. 2012; Murray et al. 2024; Vergilino et al. 2011). The first Crustacean genome described was D. pulex (Colbourne et al. 2011) and subsequent studies showed that even within species' lineages of D. pulex show variability in the number of genes present (Brandon et al. 2017; Lynch et al. 2017).

Daphnia species show fluctuations in the spectrum of gene gain and loss in response to environmental cues, supporting the case that gene family change is an important evolutionary mechanism in these taxa (Hamza et al. 2023; Schurko et al. 2009; Zhang et al. 2021). The focal ponds that Daphnia inhabit will periodically go through environmental degradation and hypoxia (Paul et al. 1998). Occasionally, this hypoxia is driven by eutrophication caused by runoff of nutrients into freshwater systems (Ebert 2022; Frisch et al. 2014). While this environmental stress largely occurs in areas devoted to agriculture, it can nonetheless affect species across entire ranges, especially in areas prone to temperature fluctuations (Smith and Schindler 2009). One way in which Daphnia responds to eutrophication‐induced hypoxia is to increase the production of hemoglobin, which will aid oxygen binding (Fox et al. 1951). This increase in heme production results in a body‐wide red hue that is potentially conserved across Daphnia (Zeis 2020).

Daphnia are also notable because they have a unique reproductive mode of cyclical parthenogenesis whereby females have rounds of clonal reproduction followed by a sexual event triggered through environmental cues (Rouger et al. 2016). Cyclical parthenogenesis is common across the tree of life, yet we only have a limited understanding of the gene family pathways leading to this reproductive novelty, of which meiotic pathways are thought to be especially important (Schurko et al. 2009). Because some Daphnia express both asexual and sexual modes, their molecular machineries to accommodate these phenotypes must be conserved in the same genome, making them an attractive model to understand reproductive‐linked gene evolution (Lumer 1937; Zaffagnini and Sabelli 1972). Outside of reproductive mode variation, sperm morphology is highly polymorphic and is divergent in tail length between the Daphnia and Ctenodaphnia subgenera (Duneau et al. 2022). Sperm morphology differences motivate the question of which genes have expanded or contracted across Daphnia and Ctenodaphnia? And are there ecologically relevant gene families related to stress, immune responses, and reproduction that have evolved across Daphnia?

In this work, we survey gene family evolution across Daphnia, highlight the expanding gene families, and measure the strength of natural selection acting upon candidate genes. In this way, we test an overarching hypothesis that expanding gene families are also under positive selection. Our results show substantial gene content shifts across species and that stress response (DNA repair) and glycoprotein‐associated gene families largely fluctuate across Daphnia genomes. We detect positive selection within some of these overrepresented gene families, indicating a link between selection and gene content expansion among ecologically relevant genes.

2. Materials and Methods

2.1. Daphnia Whole‐Genome Dataset

Chromosome and scaffold‐level assemblies of seven species from the genus Daphnia were collected from the NCBI Genome search engine (https://www.ncbi.nlm.nih.gov/datasets/genome/) accessed in January 2025 (Kitts et al. 2016). We chose North American D. pulex (KAP4; RefSeq: GCF_021134715.1), European D. pulex (D84A; GenBank: GCA_023526725.1; Barnard‐Kubow et al. 2022), North American D. pulicaria (RefSeq: GCF_021234035.1; Wersebe et al. 2023), D. sinensis (GenBank: GCA_013167095.2, Jia et al. 2022), D. carinata (RefSeq: GCF_022539665.2), D. galeata (GenBank: GCA_030770115.1; Nickel et al. 2021), and D. magna (RefSeq: GCF_020631705.1) for analyses because they are the most complete species representatives and were annotated for protein‐coding genes. These genomes were the highest quality and newest available for each unique species. We also included Artemia franciscana (i.e., brine shrimp; GenBank: GCF_032884065.1) and Penaeus monodon (i.e., black tiger shrimp; GenBank: GCF_015228065.2) as Crustacean outgroups for our tree‐based analyses.

2.2. Computational Biology Pipeline

We implemented a nextflow v23.04.1 (Di Tommaso et al. 2017) pipeline (named nf‐GeneFamilyEvolution) to organize the “flow” of data between processes and implement reproducibility and accessibility across computational environments. Briefly, we set up the nextflow pipeline to take RefSeq/GenBank genome identifiers, download their genome and proteome from the NCBI, classify ortholog groups, run various software related to phylogenetic tree building, and perform gene family evolution analyses with minimal setup and editing (Figure S1A). The following sections describe the individual components that are incorporated in the pipeline.

2.3. Estimating Divergence‐Time Across Species

We used benchmark universal single copy orthologous (BUSCO) v5 (Manni et al. 2021) Arthropoda dataset on each genome assembly to acquire the BUSCO score and used 164 complete single‐copy genes for phylogenomic analyses of divergence dating across the Daphnia genera (Figure S1B). To do this, we extracted each amino acid alignment for each gene using seqkit faidx v2.2.0 (Shen et al. 2016) and aligned them using mafft –auto v7.505 (Katoh and Standley 2013). Then, we used clipkit‐m smart‐gap v2.1.1 (Steenwyk et al. 2020) to clip out regions with large gaps. After this, we concatenated all of the protein sequences together using seqkit concat v2.2.0 and used the mcmctree_tree_prep.py script (https://github.com/kfuku52/mcmctree_tree_prep) to create the necessary input files for MCMCtree v4.9e (Dos Reis and Yang 2019), which is part of the PAML package of software (Yang 2007). We assembled a gene‐tree using IQtree v2.2.0.3 with the modelfinder option (Kalyaanamoorthy et al. 2017).

We used several time‐calibration points from previous phylogenetic investigations in Daphnia. Specifically, we used D. carinata — D. magna (100.4–104.8 mya) in place of the subgenus Daphnia—Ctenodaphnia root comparison in Cornetti et al. (2019). Artemia and D. magna (365.1–492 mya), Artemia and Penaeus (275–541 mya), and North American D. pulex — D. magna (130–150 mya) were taken from Timetree5 (Kumar et al. 2022; Mathers et al. 2013), and D. magna — D. sinensis (21.5–22.4 mya) was from Cornetti et al. (2019). We used the MCMCtreeR v1.1 package in R to plot the 95% confidence interval of divergence estimates across taxa (Figure S1C; Puttick 2019). MCMCtree was run several times to ensure model convergence by showing minor deviations in the estimate of node divergence times and the mean time for each node.

2.4. Gene Family Evolution Analyses and Ontology Enrichment

We first identified and retained the longest transcript with an open‐reading frame in the sequence for each gene using the primary_transcript.py script within the OrthoFinder v2.5.5 tool set (Emms and Kelly 2019). We then classified orthologous genes between the FASTA format proteomes using OrthoFinder. After identifying the orthologous genes between the seven species, we annotated the phylogenetic hierarchical orthologous groups (HOGs) with the most common gene name by a majority vote using the annotate_orthogroups function in orthofinder‐tools (https://github.com/MrTomRod/orthofinder‐tools). We used the HMMER dataset and hmmer2go v0.18.2 functions (https://github.com/sestaton/HMMER2GO) to assign gene ontology (GO) terms to the HOGs for use in enrichment analyses. We performed quality control by hand for some single‐copy ortholog gene families identified in OrthoFinder by BLASTing each amino acid sequence against the NCBI database using blastp v2.13.0 (Sayers et al. 2022). This quality control step verified the GO term assignments and supported the translated function of each protein‐coding gene tested (N = 15).

CAFE5 v1.1 (Mendes et al. 2021) was used to estimate the expansions and contractions of gene families across the Daphnia proteomes. Before running CAFE5, we excluded any HOGs that had over 80 genes present within any one species and any genes that were exclusively present in only one species, according to best practices in the CAFE5 vignette. After this, we ran different models and found that the model with a varying gamma rate and root Poisson distribution (‐k3 ‐p) fits our dataset and converged. After this, we extracted the HOGs found to be significantly expanding or contracting within each species and used those genes as the foreground and each species' genome as the background with clusterProfiler v3.14.3 in R (Wu et al. 2021). We used REVIGO v1.8.1 as a semantic reduction tool to minimize GO term redundancy for any enriched terms (Supek et al. 2011) and performed Bonferroni–Holm multiple testing corrections on p‐values using p.adjust in R.

2.5. Hypothesis Testing of Positive Selection on Codon Sequences

To test for positive selection across gene families, we used hyphy v2.5 adaptive Branch‐Site Random Effects Likelihood (aBSREL; Kosakovsky Pond et al. 2020) on aligned codon FASTAs generated from OrthoFinder. aBSREL tests for positive selection occurring across branches of a tree by varying the rate of selection dN/dS (ω) across both sites and branches; in this way, it models both site‐ and branch‐level dN/dS heterogeneity (Smith et al. 2015). aBSREL fits a full adaptive model; a likelihood ratio test (LRT) is then performed at each branch and compares the full model to a null model where branches are not allowed to have rate classes of dN/dS > 1 (Kosakovsky Pond et al. 2020). As recommended by tutorials on aBSREL, we performed one test on each tree, comparing all leaf nodes (i.e., tips) in a pairwise manner (Spielman et al. 2019). We tested trees to understand the patterns of selection occurring on those that have potentially undergone neofunctionalization (Hou et al. 2013; Mulhair et al. 2023; Saad et al. 2018; Wang, Zhang, Yang, et al. 2023; Wang, Zhang, Wang, et al. 2023). We translated the amino acid alignments and corresponding nucleotide sequences with pal2nal.pl v14 (Suyama et al. 2006) and excluded any premature stop codons and gaps. We next aligned the sequences with mafft and visually inspected alignments for quality, and removed any alignments that had evidence of artificial frameshifts. We ran each translated codon FASTA independently with 10 cores and fixed the gene tree for usage in each aBSREL run. We read the aBSREL json files into R for further analysis and plotting. We also used the Datamonkey v2.0 webserver (Weaver et al. 2018) to export trees from the aBSREL models. We counted a gene family as being under significant positive selection where 1 < dN/dS (ω) < 10 and the multiple‐testing corrected p‐value < 0.05. We tested the odds ratio enrichment of the gene families that belong to the expanded genes identified from CAFE5 against the genes that are non‐fluctuating using a two‐tailed Fisher's exact test in R.

2.6. Statistics and Visualization in R

Statistical analyses were performed using R v4.3.1 (R Core Development Team 2013). We used the following R packages for general analysis and visualization: tidyverse v1.3.1 (Wickham et al. 2019), ggplot2 v3.3.5 (Villanueva and Chen 2019), ggtree v2.0.4 (Xu et al. 2022), patchwork v1.0.1 (Pedersen 2022), data.table v1.12.8 (Dowle and Srinivasan 2023), foreach v1.4.7, and doMC v1.3.5 (Daniel et al. 2022).

3. Results

3.1. Daphnia Genomes and the Gene Family Dataset

From the Daphnia genomes, we first identified 160,498 unique genes across the whole dataset after extracting the longest open reading frame per protein coding transcript. From these, Orthofinder identified 1,129 single‐copy orthologous gene families. Within the total gene dataset, Orthofinder found 13,784 hierarchical phylogenetic orthologous gene groups (HOGs). We use these 13,784 HOGs as input into CAFE5 to estimate the evolutionary rate of gene family gain and loss and to identify any gene groups that are evolving across the phylogeny. Below, we use this gene grouping information to expand our understanding of the phylogenetic relationship between taxa.

3.2. Phylogeny of the Represented Daphnia Genomes

We built a time‐calibrated phylogenetic tree to understand the relatedness of each Daphnia genome as well as create a phylogeny to serve in our gene family analyses (Figure S1C). We find that North American and European D. pulex diverged 12.5 mya (95% confidence intervals; 7.2, 18.2). Recent work highlights a split at 10 mya using BUSCO gene SNPs, well in range of our estimates (Murray et al. 2024). Previous estimates of the split time for North American and European D. pulex place it as early as 2–3 mya based on mitochondrial genes and assorted DNA loci (Colbourne et al. 1998; Crease et al. 2012). We also show that North American D. pulex and D. pulicaria diverged 5.6 mya (2.9, 8.5), an estimate higher than previous at 0.5–2 mya (Crease et al. 2012). We also refine the split between the Ctenodaphnia and Daphnia subgenera to 35.8 mya (27.3, 45.5) (Figure S1C).

3.3. Trends of Gene Family Evolution

We used CAFE5 to identify expanding and contracting gene families across our dataset (Figure 1; Mendes et al. 2021). The k3p CAFE5 model minimized the negative log‐likelihood value, so we are reporting its output. Our first finding is that D. magna has the largest expansion within its genome (N Genes = 1,054; Figure 1), while European D. pulex has the largest contraction (N Genes = 1,309; Figure 1). Also, across the dataset, there are more expansions than contractions when taking the average loss and gain across individual genomes. There are 673 individual genes being lost and 567 genes being gained. The range in gene gains (202–1,054) and loss (249–1,309) is wide for both classes. For the remainder of our work, we investigate the genes belonging to the 3,161 phylogenetic HOGs identified as significant candidates that are evolving across the species tree, as well as ecologically relevant gene families related to stress response that are tested below.

FIGURE 1.

FIGURE 1

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Gene family dynamics across Daphnia. Results of gene family evolution analyses across the phylogenetic tree from CAFE5. The blue colored numbers indicate the number of genes gained and the red indicate the number of genes lost within each node and terminal leaf. Icons were taken from PhyloPic.com.

Next, we were interested in understanding how gene expansions are related to function across species (Lespinet et al. 2002; Sánchez‐Gracia et al. 2009). We explicitly want to understand if there are common functions across all species that could be related to ecologically relevant phenotypes for Daphnids. We examine whether there is a generality across all species within our data by first measuring the enrichment of GO terms belonging to the expanding genes within each species' genome. We extracted the most common expanding genes in this dataset, and one intriguing pattern is that they are largely involved with glycolysis and glycoprotein biosynthetic processes (Figure 2), potentially linked with Daphnids ability to withstand periods of anoxia, food limitation, and stress response. Additionally, most Daphnids have terms related to double‐strand DNA repair enriched, except for D. magna and North American D. pulex , which may represent a general stress response to DNA damage across the genus (Figure 2). We see some general expansions for G‐protein processes and phosphorylation across species as well and have a notable expansion of several terms related to immune responses in D. magna , which is a well‐known system studied for resistance to pathogens (Ebert 2022).

FIGURE 2.

FIGURE 2

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Significantly expanding gene families and their gene ontology enrichment across species reveals an excess of stress response terms. The presence and absence data of the most enriched terms across species. The y‐axis is the enriched terms within each species. The red colored GO terms indicate any terms related to immune responses and the blue terms are general stress responses. All terms have been semantically reduced and condensed long descriptors.

We do not show any specific expansions of terms belonging to the Ctenodaphnia subgenera (e.g., D. carinata , D. magna , D. sinensis ) save for lipid modification; however, there are several terms that belong specifically to the Daphnia subgenera, including transition metal ion transport and sensory perception of touch among others (Figure 2). Yet, there is not a specific set of terms defining the difference between subgenera (Ctenodaphnia vs. Daphnia) for both contractions and expansions (Figures S2 and S3).

Outside of these examples from the enrichment data, there are many unique expanding and contracting GO terms found to be enriched in only one or two species (Figures S2 and S3). While we classify the enrichment of evolving gene families here, we test below whether these commonly enriched genes undergo selection across Daphnia genomes.

3.4. General Patterns of Positive Selection on Evolving Gene Families

We tested the hypothesis that expanding gene families undergo higher rates of selection by using hyphy v2.5 aBSREL (Kosakovsky Pond et al. 2020) on codon sequences from the relevant gene families identified from OrthoFinder. We excluded gene families that identified extreme values of dN/dS (ω) > 10 and classified a tip, or branch, as being under positive selection if both the dN/dS > 1 and the multiple testing adjusted p‐value < 0.05. We found that the expanded gene families have 6 of the 410 (1.5%) trees with positive selection compared to the non‐fluctuating class (32 in 5,044; 0.63%). A Fisher's exact test shows a positive odds ratio of 2.31 (95% CI: 0.96, 5.6) (two‐tailed Fisher's exact test; z = 1.87; p = 0.062), yet non‐significant.

3.5. Evolution of Stress Response Gene Families and Natural Selection in Daphnia

We investigated whether gene family expansions associated with stress response GO terms identified in Figure 2 are subject to positive selection using the aBSREL method. Specifically, we examined gene families involved in iron‐ion binding, heme production, and hemolymph processes due to their relevance in hypoxia adaptation and stress responses in Daphnia (Zeis et al. 2009). Initially, we focused on the hemoglobin‐1 gene family, which showed significant expansions within the transition metal‐ion binding and hemolymph coagulation GO terms (Figure 2) and is directly involved in stress response and heme production among Daphnia.

Positive selection was assessed using LRTs with an adjusted threshold of p < 0.05 after multiple testing corrections. Trees were rooted using available outgroups when present. Using aBSREL, we identified positive selection specifically in D. sinensis (p = 0.006; Figure 3A). Despite substantial expansions in D. galeata (N = 5) and D. pulicaria (N = 7), no selection was detected in these expanded lineages (Figure 3A). We further explored the hemolymph response by examining the 2‐oxoglutarate and iron‐dependent oxygenase JMJD4‐like protein family, noting expansions (N = 3) in D. magna , although without any detectable selection. This gene family remains single‐copy across all tested species and outgroups (including crustaceans Artemia and Penaeus), except D. magna , suggesting potential non‐selective functional diversification (Figure S4).

FIGURE 3.

FIGURE 3

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General stress response gene families undergoing positive selection and expansions. (A–D) The thickness of branches indicates the level of significance, and any tips that are significant are bolded. If a tip label is colored red, that indicates that a significant expansion occurred with the number of expanded genes in parentheses. The gene names are included as tip labels with the species name. The color of branches indicates the estimated dN/dS.

Signals of selection were observed in the hsc70‐interacting protein family within D. galeata (p = 0.046; Figure 3B), with expansions noted in both D. magna and the crustacean outgroup Penaeus. This indicates a broad evolutionary significance likely related to chaperone and heat shock protein function, which are widely conserved stress response components. Another related family, heat shock protein beta‐1‐like isoform X1, had expansions (N = 2) within D. magna , yet lacked detectable selection. Additionally, we explored genes related to physical defense responses in Daphnia, such as the chitin‐binding type‐2 domain‐containing protein family, which had expansions (N = 1) in European D. pulex , but showed no selection signals.

Within the DNA damage and repair pathways, the centrosomal protein of 164 kDa‐like family exhibited selection signals across multiple branches (five of nine terminal branches analyzed). Specifically, D. carinata demonstrated strong positive selection (p = 1.82 × 10−6; Figure 3C) with gene expansions (N = 2). Interestingly, selection signals (adjusted p < 0.05) were widespread throughout the genus except in D. sinensis , and although North American D. pulex also exhibited expansions, no selection signals were detected. This family is likely critical for DNA repair and chromosomal organization and has broadly expanded among Daphnia while absent in examined outgroups (Figure 3C).

Immune‐related pathways highlighted strong selection within the clotting factor G beta subunit‐like protein family specific to North American D. pulex (p = 6.93 × 10−12) and associated with expansions (N = 2; Figure 2). Similarly, putative defense protein 3, potentially important in pathogen defense, expanded within D. magna (N = 1) and showed positive selection specifically in D. galeata (p = 0.0032; Figure 3D).

Reproductive gene families also showed lineage‐specific dynamics, with a testes‐specific protein family expanding and undergoing strong positive selection exclusively in D. sinensis (p = 4.92 × 10−5). Notably, related orthologs were restricted to D. sinensis and D. magna , suggesting specialized reproductive roles.

Analysis of glycosylation and glycoprotein synthesis terms, significantly expanded across the genus (Figure 2), revealed that the glycoprotein‐N‐acetylgalactosamine 3‐beta‐galactosyltransferase family underwent positive selection in D. carinata (p = 9.3 × 10−5) without expansions, while D. pulicaria exhibited both selection (p = 2.13 × 10−5) and expansion (N = 1), illustrating species‐specific adaptive divergence (Figure S5).

Beyond specific expansions, general patterns of selection included structural and cytoskeletal GO terms related to heme production, where 6 of 133 genes (4.5%) showed selection signals. In defense and immune‐related GO terms, 2 of 66 genes (3%) had detectable selection. Among reproductive genes, 1 of 60 genes (1.7%) showed selection at terminal branches. Additionally, many GO terms remained under‐classified or uncharacterized through simple term matching and majority voting; among these under‐classified families, 39 of 1,676 genes (2.33%) exhibited positive selection. Collectively, our findings suggest that gene family expansions combined with lineage‐specific episodes of positive selection are likely crucial adaptive responses across stress response, immune defense, reproductive processes, DNA damage repair, and glycosylation pathways within the Daphnia genus.

4. Discussion

In this work, we describe which gene families have expanded or contracted across Daphnia. We find overrepresented GO terms related to stress response. We show evidence for elevated positive selection across the gene families identified as being expanded across species, roughly affecting 1.5% of the expanding genes. Overall, we overview the general patterns of gene family expansion and contraction and identify candidate families under both positive selection that ultimately help our understanding of the evolutionary dynamics occurring within Daphnia.

4.1. Evolutionary Dynamics of Gene Family Evolution in Daphnia

Daphnia are an interesting group of taxa to study from the perspective of gene family evolution. For instance, Daphnia have undergone adaptive radiations, have varying modes of asexual and sexual reproduction, and phenotypic plasticity occurring within and between populations related to predator defense and male production (Chin and Cristescu 2021; Hebert and Wilson 1994; Xu et al. 2013, 2015). Yet broadly speaking, Daphnia are algae grazers that make up an integral part of the biomass within freshwater systems, and as such, they may go through similar selection regimes related to predation (Schwartz 1984) and/or seasonal adaptation (Bergland et al. 2014; Winder et al. 2004). Our hypothesis was that Daphnia would have similar expansion relationships within their genomes related to these selective pressures in the wild. We show that there is a general trend toward expansions in metabolic (glycosylation and glycoprotein synthesis) and a number of stress responses in Daphnia, most notably DNA repair (Figure 2).

These results align in part with previous comparative analyses of Daphnia gene families. A comparative gene family analysis conducted by Zhang et al. (2021) using three Daphnia and several pancrustacean genomes revealed significant expansions that align with several enriched terms we identified. Particularly noteworthy were expansions related to methylation in D. pulicaria and North American D. pulex , as well as structural morphogenesis terms in D. carinata and North American D. pulex . Notably, Zhang et al. (2021) demonstrated that in the presence of fish kairomones, D. mitsukuri downregulates terms associated with heme production and iron binding while upregulating those linked to chemosensory and visual perception. Therefore, it is plausible that similar gene expression patterns could manifest in other Daphnia species in the presence of fish predators, a large selection pressure in ponds and lakes. Ye et al. (2017) conducted a gene evolution analysis of two Daphnia species, primarily uncovering terms related to chitin binding and oxidative stress processes. Additionally, heme production genes were shown to be highly variable in North American D. pulex and D. magna , in relation to their ability to tackle the issues of hypoxia in small ponds (Ye et al. 2017). This is interesting because Daphnia are known to adapt to hypoxic environments through several hemoglobin proteins (Fox et al. 1951; Kobayashi et al. 1994).

Additionally, glycosylation terms are likely composed of genes related to stress and defense response to other organisms. Previous experimental results of exposing D. magna to kairomones from Triops cancriformis (i.e., eastern tadpole shrimp) showed upregulation of chitin production and subsequent cuticle changes related to glycosylation (Otte et al. 2014). The Daphnia cuticle is composed of lipids and waxes, chitin, and glycosylated/unglycosylated proteins (Minelli et al. 2016). In an experimental exposure of a Chinese‐derived D. pulex to Microcystis, microbes known to produce toxic algae blooms, showed that D. pulex upregulates genes related to morphological change and glycoprotein synthesis (Huang, Jiang, et al. 2023; Huang, Lu, et al. 2023). D. galeata have similar responses to Microcystis and upregulate many overlapping terms with our expanding gene families like glycosylation and terms related to cuticle development (Kim et al. 2024). We show expansions in a glycoprotein synthesis family (Figure S5) and evidence for selection occurring in D. carinata and D. pulicaria . It could be likely that similar proteins are actively evolving and will likely reveal mechanisms that could be indicative of specific environmental or biotic responses.

We show overlap between our results and Ye et al. (2017), where we primarily observe expansions in glycosylation terms, a distinction possibly stemming from our utilization of a more extensive set of ortholog groups and inclusion of additional Daphnia species. This underscores the value of comparative genomics in elucidating key biological processes within Daphnia. This brings to light that comparative genomics in Daphnia has led to several interesting findings. The first Daphnia genome, D. pulex arenata, a subspecies of circumarctic D. pulex , had discovered over 30,000 unique genes (Colbourne et al. 2011). At the time, this number was over twice the amount in Drosophila melanogaster and humans. Upon reinvestigation, many are thought to be erroneous gene models due to fragmented draft genomes (Denton et al. 2014); however, some of these erroneous genes could nonetheless be describing evolutionarily significant events or splicing variants (Ye et al. 2017). We used the error prediction feature in CAFE5 (Han et al. 2013), which calculates a predicted influence of genome assembly error on the estimates of our gene expansion and contraction and found it to be 8%. This estimate of error is in range with other projects (Neale et al. 2017) and is similar to Drosophila genomes (Da Lage et al. 2019). Also, the gene family gain and loss rate (λ) across the phylogeny is λ = 0.0012, a similar estimate to projects in Drosophila (Da Lage et al. 2019; Hahn et al. 2007). Therefore, the genomes tested here tend to have similar evolutionary rates of gene gain and loss across Arthropods and Crustaceans. We chose to include high‐quality genomes available to minimize assembly bias, and we hope to include more in the future when created, especially those with chromosome‐level scaffolds.

5. Conclusion

Our study elucidates the gene family evolution of several members of Daphnia, and we provide supporting evidence that stress response genes are undergoing gene number evolution. We additionally show that some of these genes prone to expansions are also under positive selection, leading us to understand the gene diversification within Daphnia. Our study has important implications for continuing the work to elucidate the mechanisms that drive divergence across species, and we highlight the need to further validate how specific stress response genes are functional within species and populations of Daphnia (Genereux et al. 2020). Ultimately, although, we began the knowledge‐building process necessary to link gene evolution with function across an interesting group of taxa prone to rapid adaptation.

Author Contributions

Connor S. Murray: conceptualization (equal), data curation (lead), formal analysis (lead), investigation (lead), methodology (lead), project administration (equal), resources (equal), software (equal), supervision (equal), visualization (lead), writing – original draft (equal), writing – review and editing (equal). Alan O. Bergland: conceptualization (equal), formal analysis (equal), funding acquisition (equal), investigation (equal), methodology (equal), project administration (equal), supervision (equal), writing – review and editing (equal).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1. Computational biology pipeline and BUSCO species tree. (A) Nextflow pipeline showing the flow of data between processes starting with a list of RefSeq/GenBank identifiers. (B) BUSCO scores denoting the number of genes that are complete, single‐copy, duplicated, fragmented, missing, and the total across each of the genomes against the Arthropoda dataset. (C) Time‐calibrated phylogenetic tree of the whole‐genomes. The tree was built with the BUSCO genes that were complete and present within each genome. We ran each tree for 5 million Markov chain Monte Carlo generations.

ECE3-15-e71453-s004.pdf (335.9KB, pdf)

Figure S2. Upset plot of the enriched gene ontology terms for contracted gene families. The blue colored column denotes the GO terms shared by all species. We only show GO term combinations that appear at least twice and also remove outgroup comparisons. The insertion size (y‐axis of the above barplots) is the number of GO terms shared in each group by set combination. Set size (x‐axis of the side barplots) are the number of significant GO terms within each species. These terms are not semantically reduced.

ECE3-15-e71453-s002.pdf (449.6KB, pdf)

Figure S3. Upset plot of the enriched gene ontology terms for expanding gene families. The blue colored column denotes the GO terms shared by all species. We only show GO term combinations that appear at least twice and also remove outgroup comparisons. The insertion size (y‐axis of the above barplots) is the number of GO terms shared in each group by set combination. Set size (x‐axis of the side barplots) are the number of significant GO terms within each species. These terms are not semantically reduced.

ECE3-15-e71453-s003.pdf (436.5KB, pdf)

Figure S4. Heme response gene family with expansions, but no positive selection. The thickness of branches indicates the level of significance. If a tip label is colored red, that indicates that a significant expansion occurred with the number of expanded genes in parentheses. The gene names are included as tip labels with the species name. The color of branches indicates the estimated dN/dS.

ECE3-15-e71453-s001.pdf (28.6KB, pdf)

Figure S5. Glycoprotein synthesis gene family undergoing expansions and positive selection. The thickness of branches indicates the level of significance. If a tip label is colored red, that indicates that a significant expansion occurred with the number of expanded genes in parentheses. The gene names are included as tip labels with the species name. The color of branches indicates the estimated dN/dS.

ECE3-15-e71453-s005.pdf (33.2KB, pdf)

Acknowledgments

The authors wish to acknowledge members of the Bergland lab for their discussion and feedback related to the manuscript's development. The authors acknowledge Research Computing at the University of Virginia for providing computational resources and technical support that have contributed to the results reported within this publication. URL: https://rc.virginia.edu. We also thank the two anonymous reviewers who's suggestions greatly improved our work.

Funding: A.O.B. was supported by grants from the National Institutes of Health (R35 GM119686), and by start‐up funds provided by the University of Virginia. C.S.M. was supported by an Expand National Science Foundation Research Traineeship program at UVA.

Data Availability Statement

All scripts and data used in every analysis are deposited in our GitHub repository along with the nextflow pipeline: https://github.com/connor122721/nf‐GeneFamilyEvolution. All relevant data are deposited in data dryad at DOI: 10.5061/dryad.gqnk98t02.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1. Computational biology pipeline and BUSCO species tree. (A) Nextflow pipeline showing the flow of data between processes starting with a list of RefSeq/GenBank identifiers. (B) BUSCO scores denoting the number of genes that are complete, single‐copy, duplicated, fragmented, missing, and the total across each of the genomes against the Arthropoda dataset. (C) Time‐calibrated phylogenetic tree of the whole‐genomes. The tree was built with the BUSCO genes that were complete and present within each genome. We ran each tree for 5 million Markov chain Monte Carlo generations.

ECE3-15-e71453-s004.pdf (335.9KB, pdf)

Figure S2. Upset plot of the enriched gene ontology terms for contracted gene families. The blue colored column denotes the GO terms shared by all species. We only show GO term combinations that appear at least twice and also remove outgroup comparisons. The insertion size (y‐axis of the above barplots) is the number of GO terms shared in each group by set combination. Set size (x‐axis of the side barplots) are the number of significant GO terms within each species. These terms are not semantically reduced.

ECE3-15-e71453-s002.pdf (449.6KB, pdf)

Figure S3. Upset plot of the enriched gene ontology terms for expanding gene families. The blue colored column denotes the GO terms shared by all species. We only show GO term combinations that appear at least twice and also remove outgroup comparisons. The insertion size (y‐axis of the above barplots) is the number of GO terms shared in each group by set combination. Set size (x‐axis of the side barplots) are the number of significant GO terms within each species. These terms are not semantically reduced.

ECE3-15-e71453-s003.pdf (436.5KB, pdf)

Figure S4. Heme response gene family with expansions, but no positive selection. The thickness of branches indicates the level of significance. If a tip label is colored red, that indicates that a significant expansion occurred with the number of expanded genes in parentheses. The gene names are included as tip labels with the species name. The color of branches indicates the estimated dN/dS.

ECE3-15-e71453-s001.pdf (28.6KB, pdf)

Figure S5. Glycoprotein synthesis gene family undergoing expansions and positive selection. The thickness of branches indicates the level of significance. If a tip label is colored red, that indicates that a significant expansion occurred with the number of expanded genes in parentheses. The gene names are included as tip labels with the species name. The color of branches indicates the estimated dN/dS.

ECE3-15-e71453-s005.pdf (33.2KB, pdf)

Data Availability Statement

All scripts and data used in every analysis are deposited in our GitHub repository along with the nextflow pipeline: https://github.com/connor122721/nf‐GeneFamilyEvolution. All relevant data are deposited in data dryad at DOI: 10.5061/dryad.gqnk98t02.

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