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Correlation between conservation of gene location and gene expression similarity. ( A ) Boxplots of expression divergences among nonessential orthologs with highly conserved and divergent genomic neighborhoods (*** P < 10 À 5 ). ( B ) Same as A for essential genes. Interestingly, the trend is
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Genomic analyses have shown that adjacent genes are often coexpressed. However, it remains unclear whether the observed coexpression is a result of functional organization or a consequence of adjacent active chromatin or transcriptional read-through, which may be free of selective biases. Here, we compare temporal expression profiles of one-to-one...
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... significant gene- expression divergence is readily detected even among the earliest embryonically transcribed genes (cluster 2). For exam- ple, genes nhr-7, ins-1, and tra-3 each shows a different profile in C. briggsae (Fig. 2). Similar results were obtained when the analysis was initiated with C. briggsae generated clusters (Supplemental Fig. ...
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... in expression. Thus, we asked whether gene expression conservation between orthologs correlates with changes to gene neighborhoods. For this we compared for each ortholog pair the gene neighborhoods (five genes both upstream and downstream) and looked for conservation. We defined two gene sets: (1) those with a high level of ortholog conservation (Fig. 5C); and (2) genes with no ortholog neighbors in common (Fig. 5D). For nonessential genes, orthologs with different neighborhoods show significantly more expression divergence than those with conserved order (P < 10 À5 , Fig. 5A). Interestingly, the trend is opposite for essential genes (P < 0.05, Fig. 5B), indicating that natural ...
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... between orthologs correlates with changes to gene neighborhoods. For this we compared for each ortholog pair the gene neighborhoods (five genes both upstream and downstream) and looked for conservation. We defined two gene sets: (1) those with a high level of ortholog conservation (Fig. 5C); and (2) genes with no ortholog neighbors in common (Fig. 5D). For nonessential genes, orthologs with different neighborhoods show significantly more expression divergence than those with conserved order (P < 10 À5 , Fig. 5A). Interestingly, the trend is opposite for essential genes (P < 0.05, Fig. 5B), indicating that natural selection acts to constrain gene expression divergence among essential ...
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... downstream) and looked for conservation. We defined two gene sets: (1) those with a high level of ortholog conservation (Fig. 5C); and (2) genes with no ortholog neighbors in common (Fig. 5D). For nonessential genes, orthologs with different neighborhoods show significantly more expression divergence than those with conserved order (P < 10 À5 , Fig. 5A). Interestingly, the trend is opposite for essential genes (P < 0.05, Fig. 5B), indicating that natural selection acts to constrain gene expression divergence among essential genes, in- dependent of gene neighbor ...
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... with a high level of ortholog conservation (Fig. 5C); and (2) genes with no ortholog neighbors in common (Fig. 5D). For nonessential genes, orthologs with different neighborhoods show significantly more expression divergence than those with conserved order (P < 10 À5 , Fig. 5A). Interestingly, the trend is opposite for essential genes (P < 0.05, Fig. 5B), indicating that natural selection acts to constrain gene expression divergence among essential genes, in- dependent of gene neighbor ...
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... for the essential orthologs (P < 10 À3 , Supplemental Fig. S11). This suggests that changes to the promoter sequence are concomitant with changes Table S3. to gene neighborhoods, perhaps representing an adaptation to the local DNA composition. Since expression similarity for essential orthologs remained high irrespective of genomic neighborhood (Fig. 5), promoter divergence does not necessarily lead to gene expression evolution. Together, these results show that, irre- spective of gene expression divergences, promoters evolve signif- icantly when the genomic location of the gene changes and sug- gest that the gene expression changes correlated with genomic rearrangements may be ...
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Citations
... For instance, subsequent to the discovery of oncogenic mutations in the P5314 gene, extensive investigations have been conducted to examine this gene, primarily within the realm of cancer research. Consequently, the association between P53 and different types of cancer has been shown, highlighting its pivotal position within the network of disease-related genes [11,12]. interactions among various factors involved in the development and progression of complex diseases. ...
Bioinformatics is a field that combines computational methods with biology, explicitly focusing on macromolecules. It uses informatics techniques to analyze and structure vast information about these molecules. The data above have been generated by extensive molecular biology initiatives, including but not limited to large-scale projects involving genome sequencing, gene expression analysis, and investigations into genomics, proteomics, and protein-protein interactions. Precision healthcare programs are being widely implemented globally to enhance individualized patient care. The detection and management of genetic illnesses have seen notable advancements, primarily due to the growing availability of affordable and accurate sequencing data. The data above are derived from extensive molecular biology initiatives, including but not limited to large-scale programs focused on genome sequencing, gene expression analysis, genomics analysis, proteomics analysis, and protein-protein interaction study. In recent years, the field of bioinformatics has witnessed notable progressions. The progress in sequencing technology has resulted in a substantial augmentation in the quantity of genetic data produced. As a result, the establishment of approaches that can efficiently and promptly evaluate this data poses a substantial obstacle in the realm of bioinformatics research. This study aims to present a succinct summary of the most recent advancements in bioinformatics approaches that can be used in the domain of personalized medicine. To effectively leverage the potential of personalized medicine, it is imperative to embrace four innovative techniques. This paper will explore the obstacles and potential solutions related to the identification of predictive genetic biomarkers and genetic variations in patients. Moreover, it will tackle the anticipated future bioinformatics issues within this domain.
... Since spr-5 encodes a 10 of 21 Peroxisomal FA degradation genes were most significantly coexpressed in dataset 132, which measured gene expression in precisely staged embryos during the first quarter of embryonic development (Fig 6C). The time course included a stage of exclusively maternal transcripts (four-cell), the transition to zygotic transcription (28-cell), and the presumptive commitment to the major cell fates (55-, 95-, and 190-cell stages;Yanai & Hunter, 2009). Peroxisomal FA degradation genes were lowly expressed in four-cell embryos and their expression increased in later embryonic stages, which may reflect a change in carbon source for energy and biomass generation prior to hatching and feeding. ...
Metabolism is controlled to ensure organismal development and homeostasis. Several mechanisms regulate metabolism, including allosteric control and transcriptional regulation of metabolic enzymes and transporters. So far, metabolism regulation has mostly been described for individual genes and pathways, and the extent of transcriptional regulation of the entire metabolic network remains largely unknown. Here, we find that three-quarters of all metabolic genes are transcriptionally regulated in the nematode Caenorhabditis elegans. We find that many annotated metabolic pathways are coexpressed, and we use gene expression data and the iCEL1314 metabolic network model to define coregulated subpathways in an unbiased manner. Using a large gene expression compendium, we determine the conditions where subpathways exhibit strong coexpression. Finally, we develop "WormClust," a web application that enables a gene-by-gene query of genes to view their association with metabolic (sub)-pathways. Overall, this study sheds light on the ubiquity of transcriptional regulation of metabolism and provides a blueprint for similar studies in other organisms, including humans.
... Because DUF34 is conserved across bacteria, archaea, and most eukaryotes, and as physical clustering was appropriate for only two of three superkingdoms [177], coexpression (top 300 co-expressed, CoXPresDb; Data Table S9, sheets d.1-d.10) and coregulation databases (ProteomeHD; Data Table S10, a) were consulted to identify trends in putative functional associations of eukaryotic DUF34 family members shared with those observed through preceding analyses with bacterial and archaeal family members. Interestingly, a number of genes directly involved in iron homeostasis and Fe-S cluster biogenesis were observed to occur in most eukaryotic organisms surveyed (Data Table S9; Figure S12). ...
... Because DUF34 is conserved across bacteria, archaea, and most eukaryotes, and as physical clustering was appropriate for only two of three superkingdoms [177], co-expression (top 300 co-expressed, CoXPresDb; Data Table S9, sheets d.1-d.10) and coregulation databases (ProteomeHD; Data Table S10, a) were consulted to identify trends in putative Figure 6. Metal ion-binding of proteins encoded in representative Bacterial and Archaeal operons. ...
Members of the DUF34 (domain of unknown function 34) family, also known as the NIF3 protein superfamily, are ubiquitous across superkingdoms. Proteins of this family have been widely annotated as “GTP cyclohydrolase I type 2” through electronic propagation based on one study. Here, the annotation status of this protein family was examined through a comprehensive literature review and integrative bioinformatic analyses that revealed varied pleiotropic associations and phenotypes. This analysis combined with functional complementation studies strongly challenges the current annotation and suggests that DUF34 family members may serve as metal ion insertases, chaperones, or metallocofactor maturases. This general molecular function could explain how DUF34 subgroups participate in highly diversified pathways such as cell differentiation, metal ion homeostasis, pathogen virulence, redox, and universal stress responses.
... Because DUF34 is conserved across bacteria, archaea and most eukaryotes, and as physical clustering was appropriate for only two of three superkingdoms [170], coexpression (top 300 co-expressed, CoXPresDb; Data Table 9, sheets d.1-d.10) and coregulation databases (ProteomeHD; Data Table 10, a) were consulted to identify trends in putative functional associations of eukaryotic DUF34 family members shared with those observed through preceding analyses with bacterial and archaeal family members. Interestingly, a number of genes directly involved in iron homeostasis and Fe-S cluster biogenesis were observed to occur in most eukaryotic organisms surveyed (Data Table 9; Figure S11). ...
Members of the DUF34 (domain of unknown function 34) family, also known as the NIF3 protein superfamily, are ubiquitous across superkingdoms. Proteins of this family have been widely annotated as “GTP cyclohydrolase I type 2” through electronic propagation based on one study. Here, the annotation status of this protein family was examined through comprehensive literature review and integrative bioinformatic analyses that revealed varied pleiotropic associations and phenotypes. This analysis combined with functional complementation studies strongly challenges the current annotation and suggests that DUF34 family members may serve as metal ion insertases, chaperones, or metallocofactor maturases. This general molecular function could explain how DUF34 subgroups participate in highly diversified pathways such as cell differentiation, metal ion homeostasis, pathogen virulence, redox and universal stress responses.
... The evolutionary "tinkering" of regulatory systems through GRN divergence can facilitate the evolution of phenotypic diversity and rapid adaptation [19]. Various mechanisms underlie these events, including horizontal gene transfer and regulatory reorganization in bacteria [38]; gene duplication in fungi [39]; cis-regulatory expression divergence in flies [40]; variable gene co-expression in worms [41]; dynamic rewiring of TFs in plant leaf shape [11]; coding and non-coding evolution in stickleback fish [42]; alternative splicing [43], and differential rate of gene expression evolution shaped by various selective pressures [44,45] in mammals. However, since very little is known about the combined effect of some of these mechanisms; in-depth analyses of regulatory network evolution can shed light on the key contributing mechanisms associated with phenotypic effect across ecologically diverse species in a phylogeny. ...
... While it appears that cichlids utilize an array of regulatory mechanisms that are also shown to drive phenotypic diversity in other organisms [11,[39][40][41][42]58], we provide experimental support of selected TF-TG rewiring events in regulatory regions of genes associated with adaptive traits in cichlids [18]. This is further supported by large-scale genotyping studies of the predicted sites in radiating cichlid species [20]. ...
Background
Seminal studies of vertebrate protein evolution speculated that gene regulatory changes can drive anatomical innovations. However, very little is known about gene regulatory network (GRN) evolution associated with phenotypic effect across ecologically diverse species. Here we use a novel approach for comparative GRN analysis in vertebrate species to study GRN evolution in representative species of the most striking examples of adaptive radiations, the East African cichlids. We previously demonstrated how the explosive phenotypic diversification of East African cichlids can be attributed to diverse molecular mechanisms, including accelerated regulatory sequence evolution and gene expression divergence.
Results
To investigate these mechanisms across species at a genome-wide scale, we develop a novel computational pipeline that predicts regulators for co-extant and ancestral co-expression modules along a phylogeny, and candidate regulatory regions associated with traits under selection in cichlids. As a case study, we apply our approach to a well-studied adaptive trait—the visual system—for which we report striking cases of network rewiring for visual opsin genes, identify discrete regulatory variants, and investigate their association with cichlid visual system evolution. In regulatory regions of visual opsin genes, in vitro assays confirm that transcription factor binding site mutations disrupt regulatory edges across species and segregate according to lake species phylogeny and ecology, suggesting GRN rewiring in radiating cichlids.
Conclusions
Our approach reveals numerous novel potential candidate regulators and regulatory regions across cichlid genomes, including some novel and some previously reported associations to known adaptive evolutionary traits.
... In support of that, modifying the expression of a single, differentiation-promoting gene during organism development can give rise to novel body morphologies (Warren et al. 1994;Prud'homme et al. 2006). Although transcription programs of closely related species are in large conserved (Gilad et al. 2006;Paris et al. 2013;Wong et al. 2015), understanding the basic mechanisms of regulatory variation is of considerable interest (Yvert et al. 2003;Wittkopp et al. 2004Wittkopp et al. , 2008Landry et al. 2005;Tirosh et al. 2006Tirosh et al. , 2009Yanai and Hunter 2009;McManus et al. 2010;Goncalves et al. 2012;Shi et al. 2012;Combs et al. 2018). ...
... This ability to independently control the level and regulation of gene transcription implies that both can be subject to evolution. Previous studies that compared gene expression between related species considered only a few conditions and therefore could only address variation in expression levels (Yvert et al. 2003;Wittkopp et al. 2004Wittkopp et al. , 2008Landry et al. 2005;Tirosh et al. 2006Tirosh et al. , 2009Yanai and Hunter 2009;McManus et al. 2010;Goncalves et al. 2012;Shi et al. 2012;Combs et al. 2018). Whether this variation results from changes in condition-specific regulation or from differences in the capacity of the general transcriptional machinery remain unresolved. ...
Changes in gene expression drive novel phenotypes, raising interest in how gene expression evolves. In contrast to the static genome, cells modulate gene expression in response to changing environments. Previous comparative studies focused on specific conditions, describing interspecies variation in expression levels, but providing limited information about variation across different conditions. To close this gap, we profiled mRNA levels of two related yeast species in hundreds of conditions and used coexpression analysis to distinguish variation in the dynamic pattern of gene expression from variation in expression levels. The majority of genes whose expression varied between the species maintained a conserved dynamic pattern. Cases of diverged dynamic pattern correspond to genes that were induced under distinct subsets of conditions in the two species. Profiling the interspecific hybrid allowed us to distinguish between genes with predominantly cis- or trans-regulatory variation. We find that trans-varying alleles are dominantly inherited, and that cis-variations are often complemented by variations in trans Based on these results, we suggest that gene expression diverges primarily through changes in expression levels, but does not alter the pattern by which these levels are dynamically regulated.
... In support of that, modifying the expression of a single, differentiation-promoting gene during organism development can give rise to novel body morphologies (Prud'homme et al., 2006;Warren et al., 1994). Genomic studies revealed that closely related species differ massively in orthologous gene expression, emphasizing the susceptibility of gene expression for rapid divergence (Combs et al., 2018;Gilad et al., 2006;Goncalves et al., 2012;Landry et al., 2005;McManus et al., 2010;Shi et al., 2012;Tirosh et al., 2006Tirosh et al., , 2009Wittkopp et al., 2004Wittkopp et al., , 2008Yanai and Hunter, 2009;Yvert et al., 2003). ...
... Previous studies described inter-species variations in gene expression when compared at individual conditions (Combs et al., 2018;Gilad et al., 2006;Goncalves et al., 2012;Landry et al., 2005;McManus et al., 2010;Shi et al., 2012;Tirosh et al., 2006Tirosh et al., , 2009Wittkopp et al., 2004Wittkopp et al., , 2008Yanai and Hunter, 2009). We extended the analysis by generating a large collection of comparative transcription profiles surveying a wide array of conditions. ...
Changes in gene expression drive novel phenotypes, raising interest in how gene expression evolves. In contrast to the static genome, cells regulate gene expression to accommodate changing conditions. Previous comparative studies focused on specific conditions, describing inter-species variation in expression levels, but providing limited information about variations in gene regulation. To close this gap, we profiled gene expression of related yeast species in hundreds of conditions, and used co-expression analysis to distinguish variations in transcription regulation from variations in expression levels or environmental perception. The majority of genes whose expression varied between the species maintained a conserved transcriptional regulation. Profiling the interspecific hybrid provided insights into the basis of variations, showed that trans-varying alleles interact dominantly, and revealed complementation of cis-variations by variations in trans. Our data suggests that gene expression diverges primarily through changes in promoter strength that do not alter gene positioning within the transcription network.
... No. Those night science explorations were not only fun; together, we explored a region of the night that each of us came back to often in the future, drawing ideas for project proposals and eventually papers [4][5][6][7][8][9]. In that sense, night science never fails to be productive-we always broaden and reshape our thinking and its horizons. ...
... I conducted comparative clustering analyses to identify statistical differences in timecourse data (79,80). By clustering Sp regulatory orthologs into six distinct clusters and forcing assignment of each Et ortholog into all clusters, cluster membership scores and clustering similarities for each ortholog were acquired (SI Appendix, Tables S3-S5). ...
Significance
Sea urchins (echinoids) consist of two subclasses, cidaroids and euechinoids. Research on gene regulatory networks (GRNs) in the early development of three euechinoids indicates that little appreciable change has occurred to their linkages since they diverged ∼90 million years ago (mya). I asked whether this conservation extends to all echinoids. I systematically analyzed the spatiotemporal expression and function of regulatory genes segregating euechinoid ectoderm and mesoderm in a cidaroid. I report marked divergence of GRN architecture in early embryonic specification of the oral–aboral axis in echinoids. Although I found evidence for diverged regulation of both mesodermal and ectodermal genes, comparative analyses indicated that, since these two clades diverged 268 mya, mesodermal GRNs have undergone significantly more alterations than ectodermal GRNs.
... Numerous studies have implicated regulatory variation in generating the phenotypic diversity of both unicellular and multicellular organisms within populations and between species. Regulatory divergence between species has been studied across many kingdoms of life, including bacteria (McAdams et al., 2004), fungi (Gasch et al., 2004;Thompson et al., 2013;Tirosh et al., 2006Tirosh et al., , 2011Wohlbach et al., 2009), flies (Bradley et al., 2010;Jimenez-Guri et al., 2013;Paris et al., 2013;Prud'homme et al., 2007;Wittkopp et al., 2008), worms (Yanai and Hunter, 2009), plants (Ichihashi et al., 2014), fish (Brawand et al., 2014;Chan et al., 2010;Jones et al., 2012) and mammals (Barbosa-Morais et al., 2012;Brawand et al., 2011;Khaitovich et al., 2006;Merkin et al., 2012;Necsulea and Kaessmann, 2014). In fact, the genetic variants associated with many diseases and phenotypes identified in higher organisms most often map to non-coding regulatory regions (Maurano et al., 2012;Zhang and Lupski, 2015) and studies are beginning to show how this variation is mechanistically associated with gene expression changes that underlie physiological differences. ...
The rise of sequence information across different yeast species and strains is driving an increasing number of studies in the emerging field of genomics to associate polymorphic variants - mRNA abundance and phenotypic differences between individuals. Here, we gathered evidence from recent studies covering several layers that define the genotype-phenotype gap such as: mRNA abundance, allele-specific expression and translation efficiency to demonstrate how genetic variants co-evolve and define an individual's genome. Moreover, we exposed several antecedents where inter- and intra-specific studies led to opposite conclusions, likely due to genetic divergence. Future studies in this area will benefit from the access to a massive array of well annotated genomes and new sequencing technologies, which will allow the fine breakdown of the complex layers that delineate the genotype-phenotype map.This article is protected by copyright. All rights reserved.
