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bioRxiv2021

A Floxed exon (Flexon) approach to Cre-mediated conditional gene expression

Conditional gene expression allows for genes to be manipulated and lineages to be marked during development. In the established "lox-stop-lox" approach, Cre-mediated tissue-specific gene expression is achieved by excising the stop cassette, a lox-flanked translational stop that is inserted into the 5' untranslated region of a gene to halt its expression. Although lox-stop-lox has been successfully used in many experimental systems, the design of traditional stop cassettes also has common issues and limitations. Here, we describe the Floxed exon (Flexon), a stop cassette within an artificial exon that can be inserted flexibly into the coding region of any gene to cause premature termination of translation and nonsense-mediated decay of the mRNA. We demonstrate its efficacy in C. elegans by showing that, when promoters that cause weak and/or transient cell-specific expression are used to drive Cre in combination with a gfp(flexon) transgene, strong and sustained expression is obtained in specific lineages. We also describe several potential additional applications for using Flexon for developmental studies, including more precise control of gene expression using intersectional methods, tissue-specific protein degradation or RNAi, and generation of genetic mosaics. The Flexon approach should be feasible in any system where any site-specific recombination-based method may be applied. Summary statementThe Floxed exon (Flexon), a stop cassette that can be inserted flexibly into the coding region of any gene, facilitates Cre-mediated conditional gene expression.

genetics↗

bioRxiv2025

transfactor: Transcription factor activity estimation via probabilistic gene expression deconvolution

Gene expression is the primary modality being studied to differentiate between biological cells. Contemporary single-cell studies simultaneously measure genome-wide transcription levels for thousands of individual cells in a single experiment. While the characterization of cell population differences has often occurred through differential expression analysis, tiny effect sizes become statistically significant when thousands of cells are available for each population, compromising biological interpretation. Moreover, these large studies have spurred the development of methods to infer gene regulatory networks (GRNs) directly from the data, and GRN databases are becoming more comprehensive. In this work, we propose a statistical model for gene expression measures and an inference method that leverage GRNs to deconvolve transcription factor (TF) activity from gene expression, by probabilistically assigning mRNA molecules to TFs. This shifts the paradigm from investigating gene expression differences to regulatory differences at the level of TF activity, aiding interpretation and allowing prioritization of a limited number of TFs responsible for significant contributions to the observed gene expression differences. The inferred TF activities result in intuitive prioritization of TFs in terms of the (difference in) estimated number of molecules they produce, in contrast to other widely-used methods relying on arbitrary enrichment scores. Our model allows the incorporation of prior information on the regulatory potential between each TF and target gene through prior distributions, and is able to deal with both repressing and activating interactions. We compare our approach to other TF activity estimation methods using two simulation experiments and two case studies.

bioinformatics↗

bioRxiv2025

Global Analyses of Genomic and Epigenomic Influences on Gene Expression Reveals Serpina3n as a Major Regulator of Cardiac Gene Expression in Response to Catecholamine Challenge During Heart Failure

Heart failure arises from maladaptive remodeling driven by genetic and epigenetic networks. Using a systems genetics framework, we mapped how DNA variants and CpG methylation shape cardiac transcriptomes during beta adrenergic stress in the Hybrid Mouse Diversity Panel, a cohort of over 100 fully inbred mouse strains. Expression QTLs (eQTLs), methylation QTLs (mQTLs) and methylation-driven eQTLs (emQTLs) were generated from over 13k expressed genes and 200k hypervariable CpGs in left ventricles. We discovered hundreds of regulatory "hotspots" that control large portions of the genome, including several that regulate over 10% of the transcriptome and/or methylome. Approximately 16% of these hotspots overlapped with prior GWAS or EWAS signals. We focus on a hotspot on chromosome 12 and identify the serpine peptidase inhibitor Serpina3n, as the most likely driver gene in this hotspot. Experimental knockdown of Serpina3n in neonatal rat ventricular cardiomyocytes blunted hypertrophy induced by a variety of hypertrophic signals, while altering predicted target expression and modulating the activity of Nppa and Nppb. Together, these findings position Serpina3n as a major regulator of stress-responsive cardiac gene programs, highlighting how integration of genetic and epigenetic signals can pinpoint key drivers of heart failure. Key HighlightsO_LICross-omics hotspot analysis: We identified 286 eQTL, mQTL, and emQTL hotspots in the Hybrid Mouse Diversity Panel, including several hotspots that regulate over 10% of the cardiac transcriptome. C_LIO_LIIntegration with prior GWAS/EWAS: [~]16% of hotspots overlapped with previously reported heart failure hotspots, strengthening their biological relevance. C_LIO_LIDiscovery of Serpina3n as heart failure regulator: A hotspot on chromosome 12 linked to Serpina3n controlled 4-6% of all gene expression and CpG methylation changes in response to isoproterenol. C_LIO_LIFunctional validation: siRNA knockdown of Serpina3n in cardiomyocytes significantly blunted hypertrophy induced by isoproterenol, angiotensin II, and phenylephrine. C_LIO_LIPathway insights: Genes regulated by the Serpina3n hotspot were enriched for mitochondrial function, dilated cardiomyopathy, and hypertrophic signaling pathways, highlighting a mechanistic link to heart failure progression. C_LI

genetics↗

bioRxiv2023

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a framework based on a sparsely-connected autoencoder and a three-tier training that yields robust and interpretable GSAS. NetActivity was trained with 1,518 well-known gene sets and all GTEx samples, returning GSAS representative of the original transcriptome and assigning higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype.

bioinformatics↗

bioRxiv2026

Multi-level Analysis of Codon Usage Patterns Reveals Systematic Optimization of Oncogenic Gene Expression in Pancreatic Cancer

BackgroundCodon usage bias, the non-random usage of synonymous codons in coding sequences, represents a fundamental feature of genomic organization that has been largely understudied in cancer biology. Pancreatic ductal adenocarcinoma (PDAC), the predominant subtype of pancreatic cancer, is characterized by aggressive disease progression and limited therapeutic options, necessitating novel approaches to understand its molecular pathogenesis. Leveraging publicly available single-cell RNA sequencing data, we performed comprehensive codon usage analyses across different cellular populations in PDAC. ResultsEmploying a variety of computational codon usage indices uncovered the connections between cancer-specific cellular state features and codon usage signatures. Our findings reveal that malignant pancreatic cells express genes with significantly higher GC content, demonstrate preferential usage of optimal codons through increased frequency of preferred synonymous codons, and exhibit a marked preference for more cost-effective amino acids. Analysis of transcript-level bulk RNA-seq data from PDAC tumors revealed that these codon optimization patterns extend to alternative isoform usage, with highly expressed isoforms displaying increased codon optimality and enhanced mRNA stability. ConclusionThese codon usage-dependent adaptations operating at both gene expression and transcript isoform levels may enable malignant cells to enhance gene expression rates, potentially leading to increased translational efficiency and protein production. These insights into the codon usage landscape of PDAC may provide potential biomarkers for disease monitoring and treatment response prediction.

cancer biology↗

bioRxiv2017

Gene Expression Recovery For Single Cell RNA Sequencing

Rapid advances in massively parallel single cell RNA sequencing (scRNA-seq) is paving the way for high-resolution single cell profiling of biological samples. In most scRNA-seq studies, only a small fraction of the transcripts present in each cell are sequenced. The efficiency, that is, the proportion of transcripts in the cell that are sequenced, can be especially low in highly parallelized experiments where the number of reads allocated for each cell is small. This leads to unreliable quantification of lowly and moderately expressed genes, resulting in extremely sparse data and hindering downstream analysis. To address this challenge, we introduce SAVER (Single-cell Analysis Via Expression Recovery), an expression recovery method for scRNA-seq that borrows information across genes and cells to impute the zeros as well as to improve the expression estimates for all genes. We show, by comparison to RNA fluorescence in situ hybridization (FISH) and by data down-sampling experiments, that SAVER reliably recovers cell-specific gene expression concentrations, cross-cell gene expression distributions, and gene-to-gene and cell-to-cell correlations. This improves the power and accuracy of any downstream analysis involving genes with low to moderate expression.

genomics↗

bioRxiv2023

Human-specific lncRNAs contributed critically to human evolution by distinctly regulating gene expression

What genes and regulatory sequences critically differentiate modern humans from apes and archaic humans, which share highly similar genomes but show distinct phenotypes, has puzzled researchers for decades. Previous studies examined species-specific protein-coding genes and related regulatory sequences, revealing that birth, loss, and changes in these genes and sequences can drive speciation and evolution. However, investigations of species-specific lncRNA genes and related regulatory sequences, which regulate substantial genes, remain limited. We identified human-specific (HS) lncRNAs from GENCODE-annotated human lncRNAs, predicted their DNA binding domains (DBDs) and binding sites (DBSs), analyzed DBS sequences in modern humans (CEU, CHB, and YRI), archaic humans (Altai Neanderthals, Denisovans, and Vindija Neanderthals), and chimpanzees, and investigated how HS lncRNAs and their DBSs have influenced gene expression in archaic and modern humans. Our results suggest that these lncRNAs and DBSs have substantially reshaped gene expression. This reshaping has evolved continuously from archaic to modern humans, enabling humans to adapt to new environments and lifestyles, promoting brain evolution, and resulting in cross-population differences. The parallel analysis of gene expression in GTEx tissues by HS TFs and their DBSs indicates that HS lncRNAs have reshaped gene expression in the brain more significantly than HS TFs.

genomics↗

bioRxiv2023

Out-of-equilibrium gene expression fluctuations in presence of extrinsic noise

Cell-to-cell variability in protein concentrations is strongly affected by extrinsic noise, especially for highly expressed genes. Extrinsic noise can be due to fluctuations of several possible cellular factors connected to cell physiology and to the level of key enzymes in the expression process. However, how to identify the predominant sources of extrinsic noise in a biological system is still an open question. This work considers a general stochastic model of gene expression with extrinsic noise represented as colored fluctuations of the different model rates, and focuses on the out-of-equilibrium expression dynamics. Combining analytical calculations with stochastic simulations, we fully characterize how extrinsic noise shapes the protein variability during gene activation or inactivation, depending on the prevailing source of extrinsic variability, on its intensity and timescale. In particular, we show that qualitatively different noise profiles can be identified depending on which are the fluctuating parameters. This indicates an experimentally accessible way to pinpoint the dominant sources of extrinsic noise using time-coarse experiments. Author summaryGenetically identical cells living in the same environment may differ in their phenotypic traits. These differences originate from the inherent stochasticity in all cellular processes, starting from the basic process of gene expression. At this level, large part of the variability comes from cell-to-cell differences in the rates of the molecular reactions due to stochasticity in the level of key enzymes or in physiological parameters such as cell volume or growth rate. Which expression rates are predominantly affected by these so-called "extrinsic" fluctuations and how they impact the level of protein concentration are still open research questions. In this work, we tackle the protein fluctuation dynamics while approaching a steady state after gene activation or repression in presence of extrinsic noise. Our analytical results and simulations show the different consequences of alternative dominant sources of extrinsic noise, thus providing an experimentally-accessible way to distinguish them in specific systems.

systems biology↗

bioRxiv2025

Precise regulation of gene expression through transcriptional repression is essential for Plasmodium asexual blood stage development

Malaria is caused by the proliferation of Plasmodium parasites in the vertebrate host blood stream through repeated cycles of asexual multiplication inside erythrocytes. During these cycles, parasites dynamically change their transcriptome at each developmental step to express genes exactly when required; however, the mechanisms regulating these transcriptomic changes remain unclear. In this study, we revealed that the AP2-family transcription factor PbAP2-TR is essential for the asexual blood stage development of the rodent malaria parasite Plasmodium berghei, as a transcriptional repressor. Conditional knockout of pbap2-tr caused developmental arrest at the trophozoite stage, i.e., the cell growth phase of asexual blood stage development. Chromatin immunoprecipitation followed by high-throughput sequencing showed that PbAP2-TR binds to two different DNA motifs and targets genes that are downregulated towards late trophozoites, including ribosome biogenesis-related genes and host-modifying exported protein genes. The expression of these target genes was upregulated in pbap2-tr-knockout parasites, and introduction of mutations into the binding motifs increased the promoter activity of the target genes. These results indicate that PbAP2-TR establishes precise transcription peak patterns of its target genes by repressing their transcription during the trophozoite, thereby being essential for asexual blood stage development. Our data also suggest that PbAP2-TR induces transcriptional repression by recruiting a putative chromatin remodeler, PbMORC, as a co-factor.

microbiology↗

bioRxiv2021

A meta-analysis of areas of structural variation in grey matter in individuals with Autism Spectrum Disorder (ASD) in relation to gene expression of candidate ASD genes

Autism Spectrum Disorder (ASD) is a set of developmental pathologies with a strong genetic basis and high heritability. Although neuroimaging studies have indicated anatomical changes in grey matter (GM) morphometry, their associations with gene expression remain elusive. In the present study, we aim to understand how gene expression correlates with structural brain aberration in ASD and how it distributes in a functional network perspective. First, we performed an activation likelihood estimation (ALE) meta-analysis to determine GM alteration in the brain, then we selected genes from the SHANK, NRXN, NLGN family and MECP2, which have been implicated with ASD, particularly in regards to altered synaptic transmission. Gene expression maps were built. We then assessed the correlation between the gene expression maps and the GM alteration maps. We found that the default mode network regions were the most significantly correlated with gene expression of selected genes in both areas of GM decrease and increase. The dorsal attention and the cerebellar network regions are significantly correlated with ASD genes. Different networks, namely somatomotor, limbic and basal ganglia/thalamus network - were found in the increase; for each of these networks, however, only a few genes were significant. Our approach allowed to combine the well beaten path of genetic and brain imaging in a novel way, to specifically investigate the relation between gene expression and brain with structural damage, and individuate genes of interest for further investigation in specific functional networks.

neuroscience↗

bioRxiv2021

Local rather than global H3K27me3 dynamics associates with differential gene expression in Verticillium dahliae

Differential growth conditions typically trigger global transcriptional responses in filamentous fungi. Such fungal responses to environmental cues involve epigenetic regulation, including chemical histone modifications. It has been proposed that conditionally expressed genes, such as those that encode secondary metabolites but also effectors in pathogenic species, are often associated with a specific histone modification, lysine27 methylation of H3 (H3K27me3). However, thus far no analyses on the global H3K27me3 profiles have been reported under differential growth conditions in order to assess if H3K27me3 dynamics governs differential transcriptional. Using ChIP- and RNA-sequencing data from the plant pathogenic fungus Verticillium dahliae grown in three in vitro cultivation media, we now show that a substantial number of the identified H3K27me3 domains globally display stable profiles among these growth conditions. However, we do observe local quantitative differences in H3K27me3 ChIP-seq signal that associate with a subset of differentially transcribed genes between media. Comparing the in vitro results to expression during plant infection suggests that in planta-induced genes may require chromatin remodelling to achieve expression. Overall, our results demonstrate that some loci display H3K27me3 dynamics associated with concomitant transcriptional variation, but many differentially expressed genes are associated with stable H3K27me3 domains. Thus, we conclude that while H3K27me3 is required for transcriptional repression, it does not appear that transcriptional activation requires global erasure of H3K27me3. We propose that the H3K27me3 domains that do not undergo dynamic methylation may contribute to transcription through other mechanisms or may serve additional genomic regulatory functions.

microbiology↗

bioRxiv2026

SCG: Spatially Co-Expressed Gene Identification through Spatially Varying Networks

Spatial transcriptomics has enabled the advancement of gene expression analysis, yet spatial co-expression remains understudied. We introduce spatial covariance regression (SCR), a scalable Bayesian factor-model-based framework for estimation of spatially-resolved gene co-expression networks across tissue domains. These networks provide the spatial map of gene-gene correlations and enable the identification of spatially co-expressed genes (SCGs), which serve as potential prognostic biomarkers and therapeutic targets.

genomics↗

bioRxiv2024

Screening and identification of gene expression in large cohorts of clinical lung cancer samples unveils the major involvement of EZH2 and SOX2

Lung adenocarcinoma (LUAD), the primary subtype of Non-Small Cell Lung Cancer (NSCLC), accounts for 80% to 85% of cases. Due to suboptimal screening method, LUAD is often detected in late stage, leading to aggressive progression and poor outcomes. Therefore, early disease prognosis for the LUAD is high priority. In order to identify early detection biomarkers, we conducted a meta-analysis of mRNA expression TCGA and GTEx datasets from LUAD patients. A total of 795 differentially expressed genes (DEGs) were identified by exploring the Network-Analyst tool and utilizing combined effect size methods. DEGs refer to genes whose expression levels are significantly different (either higher or lower) compared to their normal baseline expression levels. KEGG pathway enrichment analysis highlighted the TNF signaling pathway as being prominently associated with these DEGs. Subsequently, using the MCODE and CytoHubba plugins in Cytoscape software, we filtered out the top 10 genes. Among these, SOX2 was the only gene exhibiting higher expression, while the others were downregulated. Consequently, our subsequent research focused on SOX2. Further transcription factor-gene network analysis revealed that enhancer of zeste homolog 2 (EZH2) is a significant partner of SOX2, potentially playing a crucial role in euchromatin-heterochromatin dynamics. Structure of SOX2 protein suggest that it is a non-druggable transcription factor, literature survey suggests the same; hence, we drove our focus to investigate on potential drug(s) targeting EZH2. Molecular docking analyses predicted most probable inhibitors of EZH2. We employed several predictive analysis tools and identified GSK343, as a promising inhibitor of EZH2.

genetics↗

bioRxiv2021

Simultaneous single cell measurements of intranuclear proteins and gene expression

Identifying gene regulatory targets of nuclear proteins in tissues remains a challenge. Here we describe intranuclear Cellular Indexing of Transcriptomes and Epitopes (inCITE-seq), a scalable method for measuring multiplexed intranuclear protein levels and the transcriptome in parallel in thousands of cells, enabling joint analysis of TF levels and gene expression in vivo. We apply inCITE-seq to characterize cell state-related changes upon pharmacological induction of neuronal activity in the mouse brain. Modeling gene expression as a linear combination of quantitative protein levels revealed the genome-wide effect of each TF and recovered known targets. Cell type-specific genes associated with each TF were co-expressed as distinct modules that each corresponded to positive or negative TF levels, showing that our approach can disentangle relative contributions of TFs to gene expression and add interpretability to gene networks. InCITE-seq can illuminate how combinations of nuclear proteins shape gene expression in native tissue contexts, with direct applications to solid or frozen tissues and clinical specimens.

genomics↗

bioRxiv2025

Investigating Cannabis sativa L. gene expression through housekeeping genes and gene coexpression networks

Cannabis sativa L. is a versatile crop with applications ranging from medicinal products to industrial materials. Despite growing interest in cannabis transcriptomics, comprehensive studies of gene expression in this species remain limited. Here, we explore cannabis transcriptomics through two complementary approaches: the identification and prioritization of housekeeping (HK) genes for qRT-PCR normalization and the construction of a genome-wide gene coexpression network (GCN) using publicly available RNA-Seq data. Based on expression stability criteria (i.e. log mean TPM [≥] 6, log variance < 0.3), we proposed the optimal HK genes and designed qRT-PCR primers for them. Primer specificity was verified in silico using the Jamaican Lion mother + Y genome. In parallel, to explore the non HK gene expression, we constructed a GCN comprising 32 coexpression modules, which was then analyzed for tissue-specific expression patterns, transcription factor (TF), and functional enrichment. Several modules were significantly correlated with cannabis tissues such as flowers and bast fibres and were linked to key processes, including photoperiod sensitivity, stress responses, and vegetative-to-reproductive phase transitions. Together, our results help understand the functions and maximize the use of HK genes in molecular biology and reveal coexpression modules that constitute critical genetic circuits underlying cannabis physiology.

plant biology↗

bioRxiv2026

Alternative splicing and gene expression repurpose ancestral genes through distinct evolutionary routes

Specialized tissues often evolve by repurposing ancestral genes. Tissue-specific alternative splicing (TS-AS) and tissue-specific gene expression (TS-GE) have been proposed as complementary mechanisms contributing to tissue specialization, but whether they provide broadly interchangeable solutions or instead follow distinct, constrained evolutionary routes remains unknown. To address this question, here we compare TS-AS and TS-GE across eight homologous tissues, 20 bilaterian species and 7,178 ancestral gene families spanning 700 million years. The two mechanisms were similarly prevalent and, together, they affected nearly three-quarters of ancestral gene families across our dataset. However, they targeted largely non-overlapping gene sets within species, with contrasting architectures and functions. TS-AS usually acts on longer, exon-rich genes and modulates intracellular machinery shared across tissues, whereas TS-GE preferentially evolves in duplicated genes and impacts tissue-defining extracellular and nuclear processes. Unexpectedly, although TS-GE was globally more conserved, neural-specific splicing gains outnumbered expression gains at the origins of vertebrates and insects, revealing a prominent contribution of TS-AS to early nervous system transcriptome diversification. Exon-level orthology reconstructions further uncovered extensive recurrent evolution of neural-specific exons in the same ancestral genes across the phylogeny. Importantly, deletion of two such microexons in Drosophila negatively impacted overall fitness and led to neurological-associated phenotypes such as reduced climbing or hyperactivity. Altogether, our findings reveal distinct but complementary evolutionary routes by which gene expression and alternative splicing repurpose ancestral genes, and highlight recurrent neural exon evolution as a functionally important contributor to nervous systems.

evolutionary biology↗

bioRxiv2024

Concerted neuron-astrocyte gene expression declines in aging and schizophrenia

Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a striking relationship between peoples cortical neurons and cortical astrocytes. We used single-nucleus RNA-seq to analyze the prefrontal cortex of 191 human donors ages 22-97 years, including healthy individuals and persons with schizophrenia. Latent-factor analysis of these data revealed that in persons whose cortical neurons more strongly expressed genes for synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the Synaptic Neuron- and-Astrocyte Program (SNAP). In schizophrenia and aging - two conditions that involve declines in cognitive flexibility and plasticity 1,2 - cells had divested from SNAP: astrocytes, glutamatergic (excitatory) neurons, and GABAergic (inhibitory) neurons all reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy persons of similar age, may underlie many aspects of normal human interindividual differences and be an important point of convergence for multiple kinds of pathophysiology.

neuroscience↗

bioRxiv2018

Osmoregulation in freshwaters: Gene expression in the gills of a Neotropical cichlid in contrasting pH and ionic environments

Freshwater habitats of the Neotropics exhibit a gradient from relatively neutral, ion-rich whitewater to acidic, ion-poor blackwater. Closely related species often show complementary distributions among ionic habitats, suggesting that adaptation to divergent osmoregulatory environments may be an important driver of Neotropical fish diversity. However, little is known about the evolutionary tradeoffs involved in ionoregulation across distinct freshwater environments. Here, we surveyed gill mRNA expression of Cichla ocellaris var. monoculus, a Neotropical cichlid, to examine cellular and physiological responses to experimental conditions mimicking whitewater and blackwater.\n\nGene ontology enrichment of expressed genes indicated that the gills were remodeled during both forms of environmental challenge, with changes biased towards the cellular membrane. We observed expression of signaling pathways from both the acute and extended response phases, including evidence that growth hormone (GH) may mediate osmoregulation in whitewater through paracrine expression of insulin-like growth factor I (IGF-I), but not through the GH receptor, which instead showed correlated up-regulation with the prolactin receptor and insulin-like growth factor II in blackwater.\n\nDifferential expression of genes related to paracellular tight junctions and transcellular ion transport showed responses similar to euryhaline fishes in fresh versus seawater, with some exceptions, suggesting that relaxed ion retention via the gills, possibly mediated by the GH/IGF-I axis, is a strong candidate for evolutionary modification in whitewater and blackwater endemic populations. In each osmoregulatory domain, we saw examples of contrasting differential expression of paralogs of genes that are single copy in most terrestrial vertebrates, indicating that adaption by fishes to diverse physicochemcial environments has capitalized on diversification of osmoregulatory gene families.

ecology↗