Evolutionary Rate Covariation Research Articles

Identification of a longevity gene through evolutionary rate covariation of insect mito-nuclear genomes.

Oxidative phosphorylation, essential for energy metabolism and linked to the regulation of longevity, involves mitochondrial and nuclear genes. The functions of these genes and their evolutionary rate covariation (ERC) have been extensively studied, but little is known about whether other nuclear genes not targeted to mitochondria evolutionarily and functionally interact with mitochondrial genes. Here we systematically examined the ERC of mitochondrial and nuclear benchmarking universal single-copy ortholog (BUSCO) genes from 472 insects, identifying 75 non-mitochondria-targeted nuclear genes. We found that the uncharacterized gene CG11837-a putative ortholog of human DIMT1-regulates insect lifespan, as its knockdown reduces median lifespan in five diverse insect species and Caenorhabditis elegans, whereas its overexpression extends median lifespans in fruit flies and C. elegans and enhances oxidative phosphorylation gene activity. Additionally, DIMT1 overexpression protects human cells from cellular senescence. Together, these data provide insights into the ERC of mito-nuclear genes and suggest that CG11837 may regulate longevity across animals.

Evolutionary rate covariation is a reliable predictor of co-functional interactions but not necessarily physical interactions.

Co-functional proteins tend to have rates of evolution that covary over time. This correlation between evolutionary rates can be measured over the branches of a phylogenetic tree through methods such as evolutionary rate covariation (ERC), and then used to construct gene networks by the identification of proteins with functional interactions. The cause of this correlation has been hypothesized to result from both compensatory coevolution at physical interfaces and nonphysical forces such as shared changes in selective pressure. This study explores whether coevolution due to compensatory mutations has a measurable effect on the ERC signal. We examined the difference in ERC signal between physically interacting protein domains within complexes compared to domains of the same proteins that do not physically interact. We found no generalizable relationship between physical interaction and high ERC, although a few complexes ranked physical interactions higher than nonphysical interactions. Therefore, we conclude that coevolution due to physical interaction is weak, but present in the signal captured by ERC, and we hypothesize that the stronger signal instead comes from selective pressures on the protein as a whole and maintenance of the general function.

Evolutionary rate covariation is a reliable predictor of co-functional interactions but not necessarily physical interactions

Co-functional proteins tend to have rates of evolution that covary over time. This correlation between evolutionary rates can be measured over the branches of a phylogenetic tree through methods such as evolutionary rate covariation (ERC), and then used to construct gene networks by the identification of proteins with functional interactions. The cause of this correlation has been hypothesized to result from both compensatory coevolution at physical interfaces and nonphysical forces such as shared changes in selective pressure. This study explores whether coevolution due to compensatory mutations has a measurable effect on the ERC signal. We examined the difference in ERC signal between physically interacting protein domains within complexes compared to domains of the same proteins that do not physically interact. We found no generalizable relationship between physical interaction and high ERC, although a few complexes ranked physical interactions higher than nonphysical interactions. Therefore, we conclude that coevolution due to physical interaction is weak, but present in the signal captured by ERC, and we hypothesize that the stronger signal instead comes from selective pressures on the protein as a whole and maintenance of the general function.

Genomic Analysis of Plastid-Nuclear Interactions and Differential Evolution Rates in Coevolved Genes across Juglandaceae Species.

The interaction between the nuclear and chloroplast genomes in plants is crucial for preserving essential cellular functions in the face of varying rates of mutation, levels of selection, and modes of transmission. Despite this, identifying nuclear genes that coevolve with chloroplast genomes at a genome-wide level has remained a challenge. In this study, we conducted an evolutionary rate covariation analysis to identify candidate nuclear genes coevolving with chloroplast genomes in Juglandaceae. Our analysis was based on 4,894 orthologous nuclear genes and 76 genes across seven chloroplast partitions in nine Juglandaceae species. Our results indicated that 1,369 (27.97%) of the nuclear genes demonstrated signatures of coevolution, with the Ycf1/2 partition yielding the largest number of hits (765) and the ClpP1 partition yielding the fewest (13). These hits were found to be significantly enriched in biological processes related to leaf development, photoperiodism, and response to abiotic stress. Among the seven partitions, AccD, ClpP1, MatK, and RNA polymerase partitions and their respective hits exhibited a narrow range, characterized by dN/dS values below 1. In contrast, the Ribosomal, Photosynthesis, Ycf1/2 partitions and their corresponding hits, displayed a broader range of dN/dS values, with certain values exceeding 1. Our findings highlight the differences in the number of candidate nuclear genes coevolving with the seven chloroplast partitions in Juglandaceae species and the correlation between the evolution rates of these genes and their corresponding chloroplast partitions.

Proteomics, phylogenetics, and co‐expression analyses indicate novel interactions in the plastid CLP chaperone‐protease system

The chloroplast CLPC1 chaperone delivers and unfolds substrates to the stromal CLPPRT protease complex for degradation. We previously used an in vivo trapping approach to identify a dozen interactors with CLPC1 in Arabidopsis thaliana.This was achieved by expressing a STREPII‐tagged copy of CLPC1 mutated in its Walker B domains (CLPC1‐TRAP) followed by affinity purification and mass spectrometry. To screen for additional interactors, we carried out a far more sensitive and comprehensive in vivo protein trapping analysis. This identified 59 highly enriched CLPC1 protein interactors, in particular proteins in families of unknown functions (DUF760, DUF179, DUF3143, UVR‐DUF151, HugZ/DUF2470), as well as the UVR domain proteins EXE1 and EXE2 implicated in singlet oxygen damage and signaling. Phylogenetic and functional domain analyses identified other members of these families. These protein families appear to localize (nearly) exclusively to plastids. Several of these DUF proteins are of very low abundance as determined through the Arabidopsis PeptideAtlas http://www.peptideatlas.org/builds/arabidopsis/ showing that enrichment in the CLPC1‐TRAP was extremely selective. Evolutionary rate covariation indicated that the HugZ/DUF2470 family co‐evolved with the plastid CLP machinery suggesting functional and/or physical interactions. mRNA‐based co‐expression networks showed that all 12 CLP protease subunits tightly co‐expressed as a single cluster with deep connections to DUF760‐3. Co‐expression modules for other trapped proteins suggested specific functions in biological processes, e.g. UVR2 and UVR3 were associated with extra‐plastidic degradation, whereas DUF760‐6 is likely involved in senescence. This study provides a strong foundation for discovery of substrate selection by the chloroplast CLP protease system.

Proteomics, phylogenetics, and coexpression analyses indicate novel interactions in the plastid CLP chaperone-protease system

The chloroplast chaperone CLPC1 unfolds and delivers substrates to the stromal CLPPRT protease complex for degradation. We previously used an in vivo trapping approach to identify interactors with CLPC1 in Arabidopsis thaliana by expressing a STREPII-tagged copy of CLPC1 mutated in its Walker B domains (CLPC1-TRAP) followed by affinity purification and mass spectrometry. To create a larger pool of candidate substrates, adaptors, or regulators, we carried out a far more sensitive and comprehensive in vivo protein trapping analysis. We identified 59 highly enriched CLPC1 protein interactors, in particular proteins belonging to families of unknown functions (DUF760, DUF179, DUF3143, UVR-DUF151, HugZ/DUF2470), as well as the UVR domain proteins EXE1 and EXE2 implicated in singlet oxygen damage and signaling. Phylogenetic and functional domain analyses identified other members of these families that appear to localize (nearly) exclusively to plastids. In addition, several of these DUF proteins are of very low abundance as determined through the Arabidopsis PeptideAtlas http://www.peptideatlas.org/builds/arabidopsis/ showing that enrichment in the CLPC1-TRAP was extremely selective. Evolutionary rate covariation indicated that the HugZ/DUF2470 family coevolved with the plastid CLP machinery suggesting functional and/or physical interactions. Finally, mRNA-based coexpression networks showed that all 12 CLP protease subunits tightly coexpressed as a single cluster with deep connections to DUF760-3. Coexpression modules for other trapped proteins suggested specific functions in biological processes, e.g., UVR2 and UVR3 were associated with extraplastidic degradation, whereas DUF760-6 is likely involved in senescence. This study provides a strong foundation for discovery of substrate selection by the chloroplast CLP protease system.

Evolutionary rate covariation identifies SLC30A9 (ZnT9) as a mitochondrial zinc transporter.

Recent advances in genome sequencing have led to the identification of new ion and metabolite transporters, many of which have not been characterized. Due to the variety of subcellular localizations, cargo and transport mechanisms, such characterization is a daunting task, and predictive approaches focused on the functional context of transporters are very much needed. Here we present a case for identifying a transporter localization using evolutionary rate covariation (ERC), a computational approach based on pairwise correlations of amino acid sequence evolutionary rates across the mammalian phylogeny. As a case study, we find that poorly characterized transporter SLC30A9 (ZnT9) coevolves with several components of the mitochondrial oxidative phosphorylation chain, suggesting mitochondrial localization. We confirmed this computational finding experimentally using recombinant human SLC30A9. SLC30A9 loss caused zinc mishandling in the mitochondria, suggesting that under normal conditions it acts as a zinc exporter. We therefore propose that ERC can be used to predict the functional context of novel transporters and other poorly characterized proteins.

Rapid evolution of mammalian APLP1 as a synaptic adhesion molecule

Amyloid precursor protein (APP) family members are involved in essential neuronal development including neurite outgrowth, neuronal migration and maturation of synapse and neuromuscular junction. Among the APP gene family members, amyloid precursor-like protein 1 (APLP1) is selectively expressed in neurons and has specialized functions during synaptogenesis. Although a potential role for APLP1 in neuronal evolution has been indicated, its precise evolutionary and functional contributions are unknown. This study shows the molecular evolution of the vertebrate APP family based on phylogenetic analysis, while contrasting the evolutionary differences within the APP family. Phylogenetic analysis showed 15 times higher substitution rate that is driven by positive selection at the stem branch of the mammalian APLP1, resulting in dissimilar protein sequences compared to APP/APLP2. Docking simulation identified one positively selected site in APLP1 that alters the heparin-binding site, which could affect its function, and dimerization rate. Furthermore, the evolutionary rate covariation between the mammalian APP family and synaptic adhesion molecules (SAMs) was confirmed, indicating that only APLP1 has evolved to gain synaptic adhesion property. Overall, our results suggest that the enhanced synaptogenesis property of APLP1 as one of the SAMs may have played a role in mammalian brain evolution.

α‐Arrestin regulation of protein trafficking: Using evolutionary rate covariation to define protein trafficking regulatory networks

α-Arrestins, a novel class of protein trafficking adaptors, help cells survive environmental changes by controlling membrane protein trafficking. One hurdle to understanding α-arrestins is that few α-arrestin-cargo pairs have been identified. It is technically challenging to identify membrane cargos trafficked by a-arrestins due to their transient associations and their biochemical nature. To identify α-arrestin-regulated cargos, we used Evolutionary Rate Covariation (ERC), which employs sequence-based signatures to identify genes with similar evolutionary histories. We compared ERC values for α-arrestins with proteins across 18 yeast species. Among the top co-evolving proteins were those previously defined as α-arrestin cargos. We are determining if membrane proteins, not yet identified as α-arrestin cargos, with the highest ERC values (>0.5), interact with α-arrestins by assessing their localization and relative protein abundances in wild-type cells versus those lacking the specific α-arrestins. Fluorescence intensity was used to quantify the abundance and/or subcellular distribution of GFP tagged proteins. Statistically significant changes in GFP abundance or localization between wild-type cells and those lacking α-arrestins demonstrate dependence on these protein trafficking adaptors. This makes them good candidates as new α-arrestin-dependent cargos, which we are confirming by co-localization, bimolecular fluorescence complementation, and biochemical approaches. Using ERC and our fluorescence imaging pipeline, we have quantitatively confirmed that 36 integral membrane proteins, previously unassociated with α-arrestins, display α-arrestin-dependent localization changes. This greatly expands the repertoire of α-arrestin cargo within cells, and raises important functional implications for this family. In conclusion, the ERC approach is a powerful new tool that is able to define protein trafficking regulatory networks, which will undoubtedly be of interest to the cell biology community.

Genome-wide signatures of plastid-nuclear coevolution point to repeated perturbations of plastid proteostasis systems across angiosperms.

Nuclear and plastid (chloroplast) genomes experience different mutation rates, levels of selection, and transmission modes, yet key cellular functions depend on their coordinated interactions. Functionally related proteins often show correlated changes in rates of sequence evolution across a phylogeny [evolutionary rate covariation (ERC)], offering a means to detect previously unidentified suites of coevolving and cofunctional genes. We performed phylogenomic analyses across angiosperm diversity, scanning the nuclear genome for genes that exhibit ERC with plastid genes. As expected, the strongest hits were highly enriched for genes encoding plastid-targeted proteins, providing evidence that cytonuclear interactions affect rates of molecular evolution at genome-wide scales. Many identified nuclear genes functioned in post-transcriptional regulation and the maintenance of protein homeostasis (proteostasis), including protein translation (in both the plastid and cytosol), import, quality control, and turnover. We also identified nuclear genes that exhibit strong signatures of coevolution with the plastid genome, but their encoded proteins lack organellar-targeting annotations, making them candidates for having previously undescribed roles in plastids. In sum, our genome-wide analyses reveal that plastid-nuclear coevolution extends beyond the intimate molecular interactions within chloroplast enzyme complexes and may be driven by frequent rewiring of the machinery responsible for maintenance of plastid proteostasis in angiosperms.

A Drosophila screen identifies NKCC1 as a modifier of NGLY1 deficiency.

N-Glycanase 1 (NGLY1) is a cytoplasmic deglycosylating enzyme. Loss-of-function mutations in the NGLY1 gene cause NGLY1 deficiency, which is characterized by developmental delay, seizures, and a lack of sweat and tears. To model the phenotypic variability observed among patients, we crossed a Drosophila model of NGLY1 deficiency onto a panel of genetically diverse strains. The resulting progeny showed a phenotypic spectrum from 0 to 100% lethality. Association analysis on the lethality phenotype, as well as an evolutionary rate covariation analysis, generated lists of modifying genes, providing insight into NGLY1 function and disease. The top association hit was Ncc69 (human NKCC1/2), a conserved ion transporter. Analyses in NGLY1-/- mouse cells demonstrated that NKCC1 has an altered average molecular weight and reduced function. The misregulation of this ion transporter may explain the observed defects in secretory epithelium function in NGLY1 deficiency patients.

Evolutionary rate covariation analysis of E-cadherin identifies Raskol as a regulator of cell adhesion and actin dynamics in Drosophila.

The adherens junction couples the actin cytoskeletons of neighboring cells to provide the foundation for multicellular organization. The core of the adherens junction is the cadherin-catenin complex that arose early in the evolution of multicellularity to link actin to intercellular adhesions. Over time, evolutionary pressures have shaped the signaling and mechanical functions of the adherens junction to meet specific developmental and physiological demands. Evolutionary rate covariation (ERC) identifies proteins with correlated fluctuations in evolutionary rate that can reflect shared selective pressures and functions. Here we use ERC to identify proteins with evolutionary histories similar to the Drosophila E-cadherin (DE-cad) ortholog. Core adherens junction components α-catenin and p120-catenin displayed positive ERC correlations with DE-cad, indicating that they evolved under similar selective pressures during evolution between Drosophila species. Further analysis of the DE-cad ERC profile revealed a collection of proteins not previously associated with DE-cad function or cadherin-mediated adhesion. We then analyzed the function of a subset of ERC-identified candidates by RNAi during border cell (BC) migration and identified novel genes that function to regulate DE-cad. Among these, we found that the gene CG42684, which encodes a putative GTPase activating protein (GAP), regulates BC migration and adhesion. We named CG42684 raskol (“to split” in Russian) and show that it regulates DE-cad levels and actin protrusions in BCs. We propose that Raskol functions with DE-cad to restrict Ras/Rho signaling and help guide BC migration. Our results demonstrate that a coordinated selective pressure has shaped the adherens junction and this can be leveraged to identify novel components of the complexes and signaling pathways that regulate cadherin-mediated adhesion.

Evolution of DNMT2 in drosophilids: Evidence for positive and purifying selection and insights into new protein (pathways) interactions.

The DNA methyltransferase 2 (DNMT2) protein is the most conserved member of the DNA methyltransferase family. Nevertheless, its substrate specificity is still controversial and elusive. The genomic role and determinants of DNA methylation are poorly understood in invertebrates, and several mechanisms and associations are suggested. In Drosophila, the only known DNMT gene is Dnmt2. Here we present our findings from a wide search for Dnmt2 homologs in 68 species of Drosophilidae. We investigated its molecular evolution, and in our phylogenetic analyses the main clades of Drosophilidae species were recovered. We tested whether the Dnmt2 has evolved neutrally or under positive selection along the subgenera Drosophila and Sophophora and investigated positive selection in relation to several physicochemical properties. Despite of a major selective constraint on Dnmt2, we detected six sites under positive selection. Regarding the DNMT2 protein, 12 sites under positive-destabilizing selection were found, which suggests a selection that favors structural and functional shifts in the protein. The search for new potential protein partners with DNMT2 revealed 15 proteins with high evolutionary rate covariation (ERC), indicating a plurality of DNMT2 functions in different pathways. These events might represent signs of molecular adaptation, with molecular peculiarities arising from the diversity of evolutionary histories experienced by drosophilids.

The Amino Acid Transporter JhI-21 Coevolves with Glutamate Receptors, Impacts NMJ Physiology, and Influences Locomotor Activity in Drosophila Larvae.

Changes in synaptic physiology underlie neuronal network plasticity and behavioral phenomena, which are adjusted during development. The Drosophila larval glutamatergic neuromuscular junction (NMJ) represents a powerful synaptic model to investigate factors impacting these processes. Amino acids such as glutamate have been shown to regulate Drosophila NMJ physiology by modulating the clustering of postsynaptic glutamate receptors and thereby regulating the strength of signal transmission from the motor neuron to the muscle cell. To identify amino acid transporters impacting glutmatergic signal transmission, we used Evolutionary Rate Covariation (ERC), a recently developed bioinformatic tool. Our screen identified ten proteins co-evolving with NMJ glutamate receptors. We selected one candidate transporter, the SLC7 (Solute Carrier) transporter family member JhI-21 (Juvenile hormone Inducible-21), which is expressed in Drosophila larval motor neurons. We show that JhI-21 suppresses postsynaptic muscle glutamate receptor abundance, and that JhI-21 expression in motor neurons regulates larval crawling behavior in a developmental stage-specific manner.

ERC analysis: web-based inference of gene function via evolutionary rate covariation.

The recent explosion of comparative genomics data presents an unprecedented opportunity to construct gene networks via the evolutionary rate covariation (ERC) signature. ERC is used to identify genes that experienced similar evolutionary histories, and thereby draws functional associations between them. The ERC Analysis website allows researchers to exploit genome-wide datasets to infer novel genes in any biological function and to explore deep evolutionary connections between distinct pathways and complexes. The website provides five analytical methods, graphical output, statistical support and access to an increasing number of taxonomic groups. Analyses and data at http://csb.pitt.edu/erc_analysis/ nclark@pitt.edu.

Evolutionary signatures amongst disease genes permit novel methods for gene prioritization and construction of informative gene-based networks.

Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC), is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting “disease map” network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks.

Evolutionary Rate Covariation Identifies New Members of a Protein Network Required for Drosophila melanogaster Female Post-Mating Responses

Seminal fluid proteins transferred from males to females during copulation are required for full fertility and can exert dramatic effects on female physiology and behavior. In Drosophila melanogaster, the seminal protein sex peptide (SP) affects mated females by increasing egg production and decreasing receptivity to courtship. These behavioral changes persist for several days because SP binds to sperm that are stored in the female. SP is then gradually released, allowing it to interact with its female-expressed receptor. The binding of SP to sperm requires five additional seminal proteins, which act together in a network. Hundreds of uncharacterized male and female proteins have been identified in this species, but individually screening each protein for network function would present a logistical challenge. To prioritize the screening of these proteins for involvement in the SP network, we used a comparative genomic method to identify candidate proteins whose evolutionary rates across the Drosophila phylogeny co-vary with those of the SP network proteins. Subsequent functional testing of 18 co-varying candidates by RNA interference identified three male seminal proteins and three female reproductive tract proteins that are each required for the long-term persistence of SP responses in females. Molecular genetic analysis showed the three new male proteins are required for the transfer of other network proteins to females and for SP to become bound to sperm that are stored in mated females. The three female proteins, in contrast, act downstream of SP binding and sperm storage. These findings expand the number of seminal proteins required for SP's actions in the female and show that multiple female proteins are necessary for the SP response. Furthermore, our functional analyses demonstrate that evolutionary rate covariation is a valuable predictive tool for identifying candidate members of interacting protein networks.

Evolutionary Rate Covariation in Meiotic Proteins Results from Fluctuating Evolutionary Pressure in Yeasts and Mammals

Evolutionary rates of functionally related proteins tend to change in parallel over evolutionary time. Such evolutionary rate covariation (ERC) is a sequence-based signature of coevolution and a potentially useful signature to infer functional relationships between proteins. One major hypothesis to explain ERC is that fluctuations in evolutionary pressure acting on entire pathways cause parallel rate changes for functionally related proteins. To explore this hypothesis we analyzed ERC within DNA mismatch repair (MMR) and meiosis proteins over phylogenies of 18 yeast species and 22 mammalian species. We identified a strong signature of ERC between eight yeast proteins involved in meiotic crossing over, which seems to have resulted from relaxation of constraint specifically in Candida glabrata. These and other meiotic proteins in C. glabrata showed marked rate acceleration, likely due to its apparently clonal reproductive strategy and the resulting infrequent use of meiotic proteins. This correlation between change of reproductive mode and change in constraint supports an evolutionary pressure origin for ERC. Moreover, we present evidence for similar relaxations of constraint in additional pathogenic yeast species. Mammalian MMR and meiosis proteins also showed statistically significant ERC; however, there was not strong ERC between crossover proteins, as observed in yeasts. Rather, mammals exhibited ERC in different pathways, such as piRNA-mediated defense against transposable elements. Overall, if fluctuation in evolutionary pressure is responsible for ERC, it could reveal functional relationships within entire protein pathways, regardless of whether they physically interact or not, so long as there was variation in constraint on that pathway.

Evolutionary rate covariation reveals shared functionality and coexpression of genes

Evolutionary rate covariation (ERC) is a phylogenetic signature that reflects the covariation of a pair of proteins over evolutionary time. ERC is typically elevated between interacting proteins and so is a promising signature to characterize molecular and functional interactions across the genome. ERC is often assumed to result from compensatory changes at interaction interfaces (i.e., intermolecular coevolution); however, its origin is still unclear and is likely to be complex. Here, we determine the biological factors responsible for ERC in a proteome-wide data set of 4459 proteins in 18 budding yeast species. We show that direct physical interaction is not required to produce ERC, because we observe strong correlations between noninteracting but cofunctional enzymes. We also demonstrate that ERC is uniformly distributed along the protein primary sequence, suggesting that intermolecular coevolution is not generally responsible for ERC between physically interacting proteins. Using multivariate analysis, we show that a pair of proteins is likely to exhibit ERC if they share a biological function or if their expression levels coevolve between species. Thus, ERC indicates shared function and coexpression of protein pairs and not necessarily coevolution between sites, as has been assumed in previous studies. This full interpretation of ERC now provides us with a powerful tool to assign uncharacterized proteins to functional groups and to determine the interconnectedness between entire genetic pathways.