Do essential genes evolve slowly?

The identification of essential cellular genes is critical for validating drug targets

Accurately identifying biological targets is crucial for advancing treatment options. Essential genes, vital for cell or organism survival, hold promise as potential drug targets in disease treatment. Although many studies have sought to identify essential genes as therapeutic targets in medicine and bioinformatics, systematic reviews on their relationship with drug targets are relatively rare. This work presents a comprehensive analysis to aid in identifying essential genes as potential targets for drug discovery, encompassing their relevance, identification methods, successful case studies, and challenges. This work will facilitate the identification of essential genes as therapeutic targets, thereby boosting new drug development.

Moderating the neutralist-selectionist debate: exactly which propositions are we debating, and which arguments are valid?

Half a century after its foundation, the neutral theory of molecular evolution continues to attract controversy. The debate has been hampered by the coexistence of different interpretations of the core proposition of the neutral theory, the 'neutral mutation-random drift' hypothesis. In this review, we trace the origins of these ambiguities and suggest potential solutions. We highlight the difference between the original, the revised and the nearly neutral hypothesis, and re-emphasise that none of them equates to the null hypothesis of strict neutrality. We distinguish the neutral hypothesis of protein evolution, the main focus of the ongoing debate, from the neutral hypotheses of genomic and functional DNA evolution, which for many species are generally accepted. We advocate a further distinction between a narrow and an extended neutral hypothesis (of which the latter posits that random non-conservative amino acid substitutions can cause non-ecological phenotypic divergence), and we discuss the implications for evolutionary biology beyond the domain of molecular evolution. We furthermore point out that the debate has widened from its initial focus on point mutations, and also concerns the fitness effects of large-scale mutations, which can alter the dosage of genes and regulatory sequences. We evaluate the validity of neutralist and selectionist arguments and find that the tested predictions, apart from being sensitive to violation of underlying assumptions, are often derived from the null hypothesis of strict neutrality, or equally consistent with the opposing selectionist hypothesis, except when assuming molecular panselectionism. Our review aims to facilitate a constructive neutralist-selectionist debate, and thereby to contribute to answering a key question of evolutionary biology: what proportions of amino acid and nucleotide substitutions and polymorphisms are adaptive?

What Has Genomics Taught An Evolutionary Biologist?

Genomics, an interdisciplinary field of biology on the structure, function, and evolution of genomes, has revolutionized many subdisciplines of life sciences, including my field of evolutionary biology, by supplying huge data, bringing high-throughput technologies, and offering a new approach to biology. In this review, I describe what I have learned from genomics and highlight the fundamental knowledge and mechanistic insights gained. I focus on three broad topics that are central to evolutionary biology and beyond-variation, interaction, and selection-and use primarily my own research and study subjects as examples. In the next decade or two, I expect that the most important contributions of genomics to evolutionary biology will be to provide genome sequences of nearly all known species on Earth, facilitate high-throughput phenotyping of natural variants and systematically constructed mutants for mapping genotype-phenotype-fitness landscapes, and assist the determination of causality in evolutionary processes using experimental evolution.

Shared Patterns of Gene Expression and Protein Evolution Associated with Adaptation to Desert Environments in Rodents

Desert specialization has arisen multiple times across rodents and is often associated with a suite of convergent phenotypes, including modification of the kidneys to mitigate water loss. However, the extent to which phenotypic convergence in desert rodents is mirrored at the molecular level is unknown. Here, we sequenced kidney mRNA and assembled transcriptomes for three pairs of rodent species to search for shared differences in gene expression and amino acid sequence associated with adaptation to deserts. We conducted phylogenetically independent comparisons between a desert specialist and a non-desert relative in three families representing ∼70 million years of evolution. Overall, patterns of gene expression faithfully recapitulated the phylogeny of these six taxa providing a strong evolutionary signal in levels of mRNA abundance. We also found that 8.6% of all genes showed shared patterns of expression divergence between desert and non-desert taxa, much of which likely reflects convergent evolution, and representing more than expected by chance under a model of independent gene evolution. In addition to these shared changes, we observed many species-pair-specific changes in gene expression indicating that instances of adaptation to deserts include a combination of unique and shared changes. Patterns of protein evolution revealed a small number of genes showing evidence of positive selection, the majority of which did not show shared changes in gene expression. Overall, our results suggest that convergent changes in gene regulation play an important role in the complex trait of desert adaptation in rodents.

Accurate Prediction of Human Essential Proteins Using Ensemble Deep Learning

Essential proteins are considered the foundation of life as they are indispensable for the survival of living organisms. Computational methods for essential protein discovery provide a fast way to identify essential proteins. But most of them heavily rely on various biological information, especially protein-protein interaction networks, which limits their practical applications. With the rapid development of high-throughput sequencing technology, sequencing data has become the most accessible biological data. However, using only protein sequence information to predict essential proteins has limited accuracy. In this paper, we propose EP-EDL, an ensemble deep learning model using only protein sequence information to predict human essential proteins. EP-EDL integrates multiple classifiers to alleviate the class imbalance problem and to improve prediction accuracy and robustness. In each base classifier, we employ multi-scale text convolutional neural networks to extract useful features from protein sequence feature matrices with evolutionary information. Our computational results show that EP-EDL outperforms the state-of-the-art sequence-based methods. Furthermore, EP-EDL provides a more practical and flexible way for biologists to accurately predict essential proteins. The source code and datasets can be downloaded from https://github.com/CSUBioGroup/EP-EDL.

Global genomic and proteomic analysis indicates co-evolution of Neisseria species and with their human host

Neisseria, a genus from the beta-proteobacteria class, is of potential clinical importance. This genus contains both pathogenic and commensal strains. Gonorrhea and meningitis are two major diseases caused by pathogens belonging to this genus. With the increased use of antimicrobial agents against these pathogens they have evolved the antimicrobial resistance capacity making these diseases nearly untreatable. The set of anti-bacterial resistance genes (resistome) and genes associated with signal processing (secretomes) are crucial for the host-microbial interaction. With the virtue of whole-genome sequences and computational biology, it is now possible to study the genomic and proteomic riddles of Neisseria along with their comprehensive evolutionary and metabolic profiling. We have studied relative synonymous codon usage, amino acid usage, reverse ecology, comparative genomics, evolutionary analysis and pathogen-host (Neisseria-human) interaction through bioinformatics analysis. Our analysis revealed the co-evolution of Neisseria genomes with the human host. Moreover, the co-occurrence of Neisseria and humans has been supported through reverse ecology analysis. A differential pattern of the evolutionary rate of resistomes and secretomes was evident among the pathogenic and commensal strains. Comparative genomics supported the presence of virulent genes in both pathogenic and commensal strains of the select genus. Our analysis also indicated a transition from commensal to pathogenic Neisseria strains through the long run of evolution.

Comparative genome analysis among Variovorax species and genome guided aromatic compound degradation analysis emphasizing 4-hydroxybenzoate degradation in Variovorax sp. PAMC26660

Background

While the genus Variovorax is known for its aromatic compound metabolism, no detailed study of the peripheral and central pathways of aromatic compound degradation has yet been reported. Variovorax sp. PAMC26660 is a lichen-associated bacterium isolated from Antarctica. The work presents the genome-based elucidation of peripheral and central catabolic pathways of aromatic compound degradation genes in Variovorax sp. PAMC26660. Additionally, the accessory, core and unique genes were identified among Variovorax species using the pan genome analysis tool. A detailed analysis of the genes related to xenobiotic metabolism revealed the potential roles of Variovorax sp. PAMC26660 and other species in bioremediation.

Results

TYGS analysis, dDDH, phylogenetic placement and average nucleotide identity (ANI) analysis identified the strain as Variovorax sp. Cell morphology was assessed using scanning electron microscopy (SEM). On analysis of the core, accessory, and unique genes, xenobiotic metabolism accounted only for the accessory and unique genes. On detailed analysis of the aromatic compound catabolic genes, peripheral pathway related to 4-hydroxybenzoate (4-HB) degradation was found among all species while phenylacetate and tyrosine degradation pathways were present in most of the species including PAMC26660. Likewise, central catabolic pathways, like protocatechuate, gentisate, homogentisate, and phenylacetyl-CoA, were also present. The peripheral pathway for 4-HB degradation was functionally tested using PAMC26660, which resulted in the growth using it as a sole source of carbon.

Conclusions

Computational tools for genome and pan genome analysis are important to understand the behavior of an organism. Xenobiotic metabolism-related genes, that only account for the accessory and unique genes infer evolution through events like lateral gene transfer, mutation and gene rearrangement. 4-HB, an aromatic compound present among lichen species is utilized by lichen-associated Variovorax sp. PAMC26660 as the sole source of carbon. The strain holds genes and pathways for its utilization. Overall, this study outlines the importance of Variovorax in bioremediation and presents the genomic information of the species.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08589-3.

Population-genomic analysis identifies a low rate of global adaptive fixation in the proteins of the cyclical parthenogen Daphnia magna

Daphnia are well-established ecological and evolutionary models, and the interaction between D. magna and its microparasites is widely considered a paragon of the host-parasite coevolutionary process. Like other well-studied arthropods such as Drosophila melanogaster and Anopheles gambiae, D. magna is a small, widespread, and abundant species that is therefore expected to display a large long-term population size and high rates of adaptive protein evolution. However, unlike these other species, D. magna is cyclically asexual and lives in a highly structured environment (ponds and lakes) with moderate levels of dispersal, both of which are predicted to impact upon long-term effective population size and adaptive protein evolution. To investigate patterns of adaptive protein fixation, we produced the complete coding genomes of 36 D. magna clones sampled from across the European range (Western Palaearctic), along with draft sequences for the close relatives D. similis and D. lumholtzi, used as outgroups. We analyzed genome-wide patterns of adaptive fixation, with a particular focus on genes that have an a priori expectation of high rates, such as those likely to mediate immune responses, RNA interference against viruses and transposable elements, and those with a strongly male-biased expression pattern. We find that, as expected, D. magna displays high levels of diversity and that this is highly structured among populations. However, compared with Drosophila, we find that D. magna proteins appear to have a high proportion of weakly deleterious variants and do not show evidence of pervasive adaptive fixation across its entire range. This is true of the genome as a whole, and also of putative ‘arms race’ genes that often show elevated levels of adaptive substitution in other species. In addition to the likely impact of extensive, and previously documented, local adaptation, we speculate that these findings may reflect reduced efficacy of selection associated with cyclical asexual reproduction.

Determination of the Amino Acid Recruitment Order in Early Life by Genome-Wide Analysis of Amino Acid Usage Bias

The mechanisms shaping the amino acids recruitment pattern into the proteins in the early life history presently remains a huge mystery. In this study, we conducted genome-wide analyses of amino acids usage and genetic codons structure in 7270 species across three domains of life. The carried-out analyses evidenced ubiquitous usage bias of amino acids that were likely independent from codon usage bias. Taking advantage of codon usage bias, we performed pseudotime analysis to re-determine the chronological order of the species emergence, which inspired a new species relationship by tracing the imprint of codon usage evolution. Furthermore, the multidimensional data integration showed that the amino acids A, D, E, G, L, P, R, S, T and V might be the first recruited into the last universal common ancestry (LUCA) proteins. The data analysis also indicated that the remaining amino acids most probably were gradually incorporated into proteogenesis process in the course of two long-timescale parallel evolutionary routes: I→F→Y→C→M→W and K→N→Q→H. This study provides new insight into the origin of life, particularly in terms of the basic protein composition of early life. Our work provides crucial information that will help in a further understanding of protein structure and function in relation to their evolutionary history.

OUP accepted manuscript

The rate of amino acid substitution has been shown to be correlated to a number of factors including the rate of recombination, the age of the gene, the length of the protein, mean expression level, and gene function. However, the extent to which these correlations are due to adaptive and nonadaptive evolution has not been studied in detail, at least not in hominids. We find that the rate of adaptive evolution is significantly positively correlated to the rate of recombination, protein length and gene expression level, and negatively correlated to gene age. These correlations remain significant when each factor is controlled for in turn, except when controlling for expression in an analysis of protein length; and they also generally remain significant when biased gene conversion is taken into account. However, the positive correlations could be an artifact of population size contraction. We also find that the rate of nonadaptive evolution is negatively correlated to each factor, and all these correlations survive controlling for each other and biased gene conversion. Finally, we examine the effect of gene function on rates of adaptive and nonadaptive evolution; we confirm that virus-interacting proteins (VIPs) have higher rates of adaptive and lower rates of nonadaptive evolution, but we also demonstrate that there is significant variation in the rate of adaptive and nonadaptive evolution between GO categories when removing VIPs. We estimate that the VIP/non-VIP axis explains about 5–8 fold more of the variance in evolutionary rate than GO categories.

Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications

The availability of high-quality genomes and advances in functional genomics have enabled large-scale studies of essential genes in model eukaryotes, including the 'elegant worm' (Caenorhabditis elegans; Nematoda) and the 'vinegar fly' (Drosophila melanogaster; Arthropoda). However, this is not the case for other, much less-studied organisms, such as socioeconomically important parasites, for which functional genomic platforms usually do not exist. Thus, there is a need to develop innovative techniques or approaches for the prediction, identification and investigation of essential genes. A key approach that could enable the prediction of such genes is machine learning (ML). Here, we undertake an historical review of experimental and computational approaches employed for the characterisation of essential genes in eukaryotes, with a particular focus on model ecdysozoans (C. elegans and D. melanogaster), and discuss the possible applicability of ML-approaches to organisms such as socioeconomically important parasites. We highlight some recent results showing that high-performance ML, combined with feature engineering, allows a reliable prediction of essential genes from extensive, publicly available 'omic data sets, with major potential to prioritise such genes (with statistical confidence) for subsequent functional genomic validation. These findings could 'open the door' to fundamental and applied research areas. Evidence of some commonality in the essential gene-complement between these two organisms indicates that an ML-engineering approach could find broader applicability to ecdysozoans such as parasitic nematodes or arthropods, provided that suitably large and informative data sets become/are available for proper feature engineering, and for the robust training and validation of algorithms. This area warrants detailed exploration to, for example, facilitate the identification and characterisation of essential molecules as novel targets for drugs and vaccines against parasitic diseases. This focus is particularly important, given the substantial impact that such diseases have worldwide, and the current challenges associated with their prevention and control and with drug resistance in parasite populations.

An Overexpression Experiment Does Not Support the Hypothesis That Avoidance of Toxicity Determines the Rate of Protein Evolution

The misfolding avoidance hypothesis postulates that sequence mutations render proteins cytotoxic and therefore the higher the gene expression, the stronger the operation of selection against substitutions. This translates into prediction that relative toxicity of extant proteins is higher for those evolving faster. In the present experiment, we selected pairs of yeast genes which were paralogous but evolving at different rates. We expressed them artificially to high levels. We expected that toxicity would be higher for ones bearing more mutations, especially that overcrowding should rather exacerbate than reverse the already existing differences in misfolding rates. We did find that the applied mode of overexpression caused a considerable decrease in fitness and that the decrease was proportional to the amount of excessive protein. However, it was not higher for proteins which are normally expressed at lower levels (and have less conserved sequence). This result was obtained consistently, regardless whether the rate of growth or ability to compete in common cultures was used as a proxy for fitness. In additional experiments, we applied factors that reduce accuracy of translation or enhance structural instability of proteins. It did not change a consistent pattern of independence between the fitness cost caused by overexpression of a protein and the rate of its sequence evolution.

Abundance Imparts Evolutionary Constraints of Similar Magnitude on the Buried, Surface, and Disordered Regions of Proteins

An understanding of the forces shaping protein conservation is key, both for the fundamental knowledge it represents and to allow for optimal use of evolutionary information in practical applications. Sequence conservation is typically examined at one of two levels. The first is a residue-level, where intra-protein differences are analyzed and the second is a protein-level, where inter-protein differences are studied. At a residue level, we know that solvent-accessibility is a prime determinant of conservation. By inverting this logic, we inferred that disordered regions are slightly more solvent-accessible on average than the most exposed surface residues in domains. By integrating abundance information with evolutionary data within and across proteins, we confirmed a previously reported strong surface-core association in the evolution of structured regions, but we found a comparatively weak association between disordered and structured regions. The facts that disordered and structured regions experience different structural constraints and evolve independently provide a unique setup to examine an outstanding question: why is a protein’s abundance the main determinant of its sequence conservation? Indeed, any structural or biophysical property linked to the abundance-conservation relationship should increase the relative conservation of regions concerned with that property (e.g., disordered residues with mis-interactions, domain residues with misfolding). Surprisingly, however, we found the conservation of disordered and structured regions to increase in equal proportion with abundance. This observation implies that either abundance-related constraints are structure-independent, or multiple constraints apply to different regions and perfectly balance each other.

Bacterial Protein Interaction Networks: Connectivity is Ruled by Gene Conservation, Essentiality and Function

BACKGROUND

Protein-protein interaction (PPI) networks are the backbone of all processes in living cells. In this work, we relate conservation, essentiality and functional repertoire of a gene to the connectivity k (i.e. the number of interactions, links) of the corresponding protein in the PPI network.

METHODS

On a set of 42 bacterial genomes of different sizes, and with reasonably separated evolutionary trajectories, we investigate three issues: i) whether the distribution of connectivities changes between PPI subnetworks of essential and nonessential genes; ii) how gene conservation, measured both by the evolutionary retention index (ERI) and by evolutionary pressures, is related to the connectivity of the corresponding protein; iii) how PPI connectivities are modulated by evolutionary and functional relationships, as represented by the Clusters of Orthologous Genes (COGs).

RESULTS

We show that conservation, essentiality and functional specialisation of genes constrain the connectivity of the corresponding proteins in bacterial PPI networks. In particular, we isolated a core of highly connected proteins (connectivities k≥40), which is ubiquitous among the species considered here, though mostly visible in the degree distributions of bacteria with small genomes (less than 1000 genes).

CONCLUSION

The genes that support this highly connected core are conserved, essential and, in most cases, belong to the COG cluster J, related to ribosomal functions and the processing of genetic information.

The Relationship between the Misfolding Avoidance Hypothesis and Protein Evolutionary Rates in the Light of Empirical Evidence

For more than a decade, the misfolding avoidance hypothesis (MAH) and related theories have dominated evolutionary discussions aimed at explaining the variance of the molecular clock across cellular proteins. In this study, we use various experimental data to further investigate the consistency of the MAH predictions with empirical evidence. We also critically discuss experimental results that motivated the MAH development and that are often viewed as evidence of its major contribution to the variability of protein evolutionary rates. We demonstrate, in Escherichia coli and Homo sapiens, the lack of a substantial negative correlation between protein evolutionary rates and Gibbs free energies of unfolding, a direct measure of protein stability. We then analyze multiple new genome-scale data sets characterizing protein aggregation and interaction propensities, the properties that are likely optimized in evolution to alleviate deleterious effects associated with toxic protein misfolding and misinteractions. Our results demonstrate that the propensity of proteins to aggregate, the fraction of charged amino acids, and protein stickiness do correlate with protein abundances. Nevertheless, across multiple organisms and various data sets we do not observe substantial correlations between proteins’ aggregation- and stability-related properties and evolutionary rates. Therefore, diverse empirical data support the conclusion that the MAH and similar hypotheses do not play a major role in mediating a strong negative correlation between protein expression and the molecular clock, and thus in explaining the variability of evolutionary rates across cellular proteins.

Richard Dickerson, Molecular Clocks, and Rates of Protein Evolution

Proteins approximately behave as molecular clocks, accumulating amino acid replacements at a more or less constant rate. Nonetheless, each protein displays a characteristic rate of evolution: whereas some proteins remain largely unaltered over large periods of time, others can rapidly accumulate amino acid replacements. An article by Richard Dickerson, published in the first issue of the Journal of Molecular Evolution (J Mol Evol 1:26-45, 1971), described the first analysis in which the rates of evolution of many proteins were compared, and the differences were interpreted in the light of their function. When comparing the sequences of fibrinopeptides, hemoglobin, and cytochrome c of different species, he observed a linear relationship between the number of amino acid replacements and divergence time. Remarkably, fibrinopeptides had evolved fast, cytochrome c had evolved slowly, and hemoglobin exhibited an intermediate rate of evolution. As the Journal of Molecular Evolution celebrates its 50th anniversary, I highlight this landmark article and reflect on its impact on the field of Molecular Evolution.

Eutherian-Specific Gene TRIML2 Attenuates Inflammation in the Evolution of Placentation

Evolution of highly invasive placentation in the stem lineage of eutherians and subsequent extension of pregnancy set eutherians apart from other mammals, that is, marsupials with short-lived placentas, and oviparous monotremes. Recent studies suggest that eutherian implantation evolved from marsupial attachment reaction, an inflammatory process induced by the direct contact of fetal placenta with maternal endometrium after the breakdown of the shell coat, and shortly before the onset of parturition. Unique to eutherians, a dramatic downregulation of inflammation after implantation prevents the onset of premature parturition, and is critical for the maintenance of gestation. This downregulation likely involved evolutionary changes on maternal as well as fetal/placental side. Tripartite-motif family-like2 (TRIML2) only exists in eutherian genomes and shows preferential expression in preimplantation embryos, and trophoblast-derived structures, such as chorion and placental disc. Comparative genomic evidence supports that TRIML2 originated from a gene duplication event in the stem lineage of Eutheria that also gave rise to eutherian TRIML1. Compared with TRIML1, TRIML2 lost the catalytic RING domain of E3 ligase. However, only TRIML2 is induced in human choriocarcinoma cell line JEG3 with poly(I:C) treatment to simulate inflammation during viral infection. Its knockdown increases the production of proinflammatory cytokines and reduces trophoblast survival during poly(I:C) stimulation, while its overexpression reduces proinflammatory cytokine production, supporting TRIML2’s role as a regulatory inhibitor of the inflammatory pathways in trophoblasts. TRIML2’s potential virus-interacting PRY/SPRY domain shows significant signature of selection, suggesting its contribution to the evolution of eutherian-specific inflammation regulation during placentation.

Demystifying "drop-outs" in single-cell UMI data

Many existing pipelines for scRNA-seq data apply pre-processing steps such as normalization or imputation to account for excessive zeros or "drop-outs." Here, we extensively analyze diverse UMI data sets to show that clustering should be the foremost step of the workflow. We observe that most drop-outs disappear once cell-type heterogeneity is resolved, while imputing or normalizing heterogeneous data can introduce unwanted noise. We propose a novel framework HIPPO (Heterogeneity-Inspired Pre-Processing tOol) that leverages zero proportions to explain cellular heterogeneity and integrates feature selection with iterative clustering. HIPPO leads to downstream analysis with greater flexibility and interpretability compared to alternatives.

Survival of the cheapest: how proteome cost minimization drives evolution

Darwin's theory of evolution emphasized that positive selection of functional proficiency provides the fitness that ultimately determines the structure of life, a view that has dominated biochemical thinking of enzymes as perfectly optimized for their specific functions. The 20th-century modern synthesis, structural biology, and the central dogma explained the machinery of evolution, and nearly neutral theory explained how selection competes with random fixation dynamics that produce molecular clocks essential e.g. for dating evolutionary histories. However, quantitative proteomics revealed that selection pressures not relating to optimal function play much larger roles than previously thought, acting perhaps most importantly via protein expression levels. This paper first summarizes recent progress in the 21st century toward recovering this universal selection pressure. Then, the paper argues that proteome cost minimization is the dominant, underlying 'non-function' selection pressure controlling most of the evolution of already functionally adapted living systems. A theory of proteome cost minimization is described and argued to have consequences for understanding evolutionary trade-offs, aging, cancer, and neurodegenerative protein-misfolding diseases.

Aligning functional network constraint to evolutionary outcomes

BACKGROUND

Functional constraint through genomic architecture is suggested to be an important dimension of genome evolution, but quantitative evidence for this idea is rare. In this contribution, existing evidence and discussions on genomic architecture as constraint for convergent evolution, rapid adaptation, and genic adaptation are summarized into alternative, testable hypotheses. Network architecture statistics from protein-protein interaction networks are then used to calculate differences in evolutionary outcomes on the example of genomic evolution in yeast, and the results are used to evaluate statistical support for these longstanding hypotheses.

RESULTS

A discriminant function analysis lent statistical support to classifying the yeast interactome into hub, intermediate and peripheral nodes based on network neighborhood connectivity, betweenness centrality, and average shortest path length. Quantitative support for the existence of genomic architecture as a mechanistic basis for evolutionary constraint is then revealed through utilizing these statistical parameters of the protein-protein interaction network in combination with estimators of protein evolution.

CONCLUSIONS

As functional genetic networks are becoming increasingly available, it will now be possible to evaluate functional genetic network constraint against variables describing complex phenotypes and environments, for better understanding of commonly observed deterministic patterns of evolution in non-model organisms. The hypothesis framework and methodological approach outlined herein may help to quantify the extrinsic versus intrinsic dimensions of evolutionary constraint, and result in a better understanding of how fast, effectively, or deterministically organisms adapt.

Molecular Chaperones Accelerate the Evolution of Their Protein Clients in Yeast

Protein stability is a major constraint on protein evolution. Molecular chaperones, also known as heat-shock proteins, can relax this constraint and promote protein evolution by diminishing the deleterious effect of mutations on protein stability and folding. This effect, however, has only been stablished for a few chaperones. Here, we use a comprehensive chaperone–protein interaction network to study the effect of all yeast chaperones on the evolution of their protein substrates, that is, their clients. In particular, we analyze how yeast chaperones affect the evolutionary rates of their clients at two very different evolutionary time scales. We first study the effect of chaperone-mediated folding on protein evolution over the evolutionary divergence of Saccharomyces cerevisiae and S. paradoxus. We then test whether yeast chaperones have left a similar signature on the patterns of standing genetic variation found in modern wild and domesticated strains of S. cerevisiae. We find that genes encoding chaperone clients have diverged faster than genes encoding non-client proteins when controlling for their number of protein–protein interactions. We also find that genes encoding client proteins have accumulated more intraspecific genetic diversity than those encoding non-client proteins. In a number of multivariate analyses, controlling by other well-known factors that affect protein evolution, we find that chaperone dependence explains the largest fraction of the observed variance in the rate of evolution at both evolutionary time scales. Chaperones affecting rates of protein evolution mostly belong to two major chaperone families: Hsp70s and Hsp90s. Our analyses show that protein chaperones, by virtue of their ability to buffer destabilizing mutations and their role in modulating protein genotype–phenotype maps, have a considerable accelerating effect on protein evolution.

Identification, molecular characterization and functional analysis of interleukin (IL)-2 and IL-2like (IL-2L) cytokines in sea bass (Dicentrarchus labrax L.)

In mammals, interleukin (IL)-2, initially known as a T-cell grow factor, is an immunomodulatory cytokine involved in the proliferation of T cells upon antigen activation. In bony fish, some IL-2 orthologs have been identified, but, recently, an additional IL-2like (IL-2L) gene has been found. In this paper, we report the presence of these two divergent IL-2 isoforms in sea bass (Dicentrarchus labrax L.). Genomic analyses revealed that they originated from a gene duplication event, as happened in most percomorphs. These two IL-2 paralogs show differences in the amino acid sequence and in the exon 4 size, and these features could be an indication that they bind preferentially to different specific IL-2 receptors. Sea bass IL-2 paralogs are highly expressed in gut and spleen, which are tissues and organs involved in fish T cell immune functions, and the two cytokines could be up-regulated by both PHA stimulation and vaccination with a bacterial vaccine, with IL-2L being more inducible. To investigate the functional activities of sea bass IL-2 and IL-2L we produced the corresponding recombinant molecules in E. coli and used them to in vitro stimulate HK and spleen leukocytes. IL-2L is able to up-regulate the expression of markers related to different T cell subsets (Th1, Th2 and Th17) and to Treg cells in HK, whereas it has little effect in spleen. IL-2 is not active on these markers in HK, but shows an effect on Th1 markers in spleen. Finally, the stimulation with recombinant IL-2 and IL-2L is also able to induce in vitro proliferation of HK- and spleen-derived leukocytes. In conclusion, we have demonstrated that sea bass possess two IL-2 paralogs that likely have an important role in regulating T cell development in this species and that show distinct bioactivities.

Genes Encoding Teleost Fish Ligands and Associated Receptors Remained in Duplicate More Frequently than the Rest of the Genome

Signaling through ligand/receptor interactions is a widespread mechanism across all living taxa. During evolution, however, there has been a diversification in multigene families and changes in their interaction patterns. Among the events that led to the creation of new genes is the whole-genome duplication, which made possible some major innovations. Teleost fishes descended from a common ancestor which underwent one such whole-genome duplication.

In our study, we investigated the effect of complete genome duplication on the evolution of ligand–receptor pairs in teleosts. We selected ten teleost species and used bioinformatics programs and phylogenetic tools in order to study the evolution of the human ligands and receptors that have orthologous genes in fishes, as well as the rest of the fish genomes.

We established that since the complete duplication of the fish genomes, the conservation in duplicate copy of ligand and receptor genes is higher than expected. However, the ligand/receptor pair partners did not necessarily evolve in the same way, and a lot of situations occurred in which one of the partners returned in singleton copy when the other one was maintained in duplicate. This suggests that changes in interaction partners may have taken place during the evolution of teleosts. Moreover, the fate of the ligands and receptor coding genes is partly congruent with the phylogeny of teleosts. However, some incongruences can be observed. We suggest that these incongruences are correlated to the environment.

Comparative analyses of sperm DNA methylomes among human, mouse and cattle provide insights into epigenomic evolution and complex traits

Sperm DNA methylation is crucial for fertility and viability of offspring but epigenome evolution in mammals is largely understudied. By comparing sperm DNA methylomes and large-scale genome-wide association study (GWAS) signals between human and cattle, we aimed to examine the DNA methylome evolution and its associations with complex phenotypes in mammals. Our analysis revealed that genes with conserved non-methylated promoters (e.g., ANKS1A and WNT7A) among human and cattle were involved in common system and embryo development, and enriched for GWAS signals of body conformation traits in both species, while genes with conserved hypermethylated promoters (e.g., TCAP and CD80) were engaged in immune responses and highlighted by immune-related traits. On the other hand, genes with human-specific hypomethylated promoters (e.g., FOXP2 and HYDIN) were engaged in neuron system development and enriched for GWAS signals of brain-related traits, while genes with cattle-specific hypomethylated promoters (e.g., LDHB and DGAT2) mainly participated in lipid storage and metabolism. We validated our findings using sperm-retained nucleosome, preimplantation transcriptome, and adult tissue transcriptome data, as well as sequence evolutionary features, including motif binding sites, mutation rates, recombination rates and evolution signatures. In conclusion, our results demonstrate important roles of epigenome evolution in shaping the genetic architecture underlying complex phenotypes, hence enhance signal prioritization in GWAS and provide valuable information for human neurological disorders and livestock genetic improvement.

The Evolutionary Traceability of a Protein

Orthologs document the evolution of genes and metabolic capacities encoded in extant and ancient genomes. However, the similarity between orthologs decays with time, and ultimately it becomes insufficient to infer common ancestry. This leaves ancient gene set reconstructions incomplete and distorted to an unknown extent. Here we introduce the “evolutionary traceability” as a measure that quantifies, for each protein, the evolutionary distance beyond which the sensitivity of the ortholog search becomes limiting. Using yeast, we show that genes that were thought to date back to the last universal common ancestor are of high traceability. Their functions mostly involve catalysis, ion transport, and ribonucleoprotein complex assembly. In turn, the fraction of yeast genes whose traceability is not sufficient to infer their presence in last universal common ancestor is enriched for regulatory functions. Computing the traceabilities of genes that have been experimentally characterized as being essential for a self-replicating cell reveals that many of the genes that lack orthologs outside bacteria have low traceability. This leaves open whether their orthologs in the eukaryotic and archaeal domains have been overlooked. Looking at the example of REC8, a protein essential for chromosome cohesion, we demonstrate how a traceability-informed adjustment of the search sensitivity identifies hitherto missed orthologs in the fast-evolving microsporidia. Taken together, the evolutionary traceability helps to differentiate between true absence and nondetection of orthologs, and thus improves our understanding about the evolutionary conservation of functional protein networks. “protTrace,” a software tool for computing evolutionary traceability, is freely available at https://github.com/BIONF/protTrace.git; last accessed February 10, 2019.

Faster Evolving Primate Genes Are More Likely to Duplicate

An attractive and long-standing hypothesis regarding the evolution of genes after duplication posits that the duplication event creates new evolutionary possibilities by releasing a copy of the gene from constraint. Apparent support was found in numerous analyses, particularly, the observation of higher rates of evolution in duplicated as compared with singleton genes. Could it, instead, be that more duplicable genes (owing to mutation, fixation, or retention biases) are intrinsically faster evolving? To uncouple the measurement of rates of evolution from the determination of duplicate or singleton status, we measure the rates of evolution in singleton genes in outgroup primate lineages but classify these genes as to whether they have duplicated or not in a crown group of great apes. We find that rates of evolution are higher in duplicable genes prior to the duplication event. In part this is owing to a negative correlation between coding sequence length and rate of evolution, coupled with a bias toward smaller genes being more duplicable. The effect is masked by difference in expression rate between duplicable genes and singletons. Additionally, in contradiction to the classical assumption, we find no convincing evidence for an increase in dN/dS after duplication, nor for rate asymmetry between duplicates. We conclude that high rates of evolution of duplicated genes are not solely a consequence of the duplication event, but are rather a predictor of duplicability. These results are consistent with a model in which successful gene duplication events in mammals are skewed toward events of minimal phenotypic impact.

The variations of TRBV genes usages in the peripheral blood of a healthy population are associated with their evolution and single nucleotide polymorphisms

T cell receptors (TCRs) are a class of T cell surface molecules that recognize the antigen-derived peptides presented by the major histocompatibility complex (MHC) and are able to trigger a series of immune responses. TCRs are important members of the adaptive immune system that arose in the jawed fish 500 million years ago. T cell receptor beta variable (TRBV) genes have been widely used to characterize TCR repertoires. Studying the evolution of TRBV may help us to better understand the adaptive immune system. To investigate TRBV evolution and its impacts on the usages of TRBV genes in human populations, we compared the TRBV genes and their homologous sequences among humans, mouse, rhesus and chimpanzee, analyzed the single-nucleotide polymorphisms (SNPs) located at TRBV loci, and sequenced TCR repertoires in the peripheral blood of 97 healthy donors. We found that functional TRBVs are more evolutionarily conserved but possess more SNPs in human populations than do nonfunctional (pseudo) TRBVs. Based on the conservation levels in the four species, we classified the functional TRBVs into 2 groups: old (conserved between mouse and humans) and new (conserved only in primates). The new TRBVs evolve faster and possess more SNPs than the old TRBVs. The variations in TRBV genes frequencies in the peripheral blood of healthy donors are negatively correlated with SNP density. These observations suggest that TRBV usages may be influenced by TCR-MHC co-evolution.

Metabolic Determinants of Enzyme Evolution in a Genome-Scale Bacterial Metabolic Network

Different genes and proteins evolve at very different rates. To identify the factors that explain these differences is an important aspect of research in molecular evolution. One such factor is the role a protein plays in a large molecular network. Here, we analyze the evolutionary rates of enzyme-coding genes in the genome-scale metabolic network of Escherichia coli to find the evolutionary constraints imposed by the structure and function of this complex metabolic system. Central and highly connected enzymes appear to evolve more slowly than less connected enzymes, but we find that they do so as a by-product of their high abundance, and not because of their position in the metabolic network. In contrast, enzymes catalyzing reactions with high metabolic flux-high substrate to product conversion rates-evolve slowly even after we account for their abundance. Moreover, enzymes catalyzing reactions that are difficult to by-pass through alternative pathways, such that they are essential in many different genetic backgrounds, also evolve more slowly. Our analyses show that an enzyme's role in the function of a metabolic network affects its evolution more than its place in the network's structure. They highlight the value of a system-level perspective for studies of molecular evolution.

Evaluating the Potential for Resistance Development to Antimicrobial Blue Light (at 405 nm) in Gram-Negative Bacteria: In vitro and in vivo Studies

Antimicrobial resistance is a threat to public health that requires our immediate attention. With increasing numbers of microbes that are becoming resistant to routinely used antimicrobials, it is vital that we look to other, non-traditional therapies for the treatment of infections. Antimicrobial blue light (aBL) is an innovative approach that has demonstrated efficacy for the inactivation of an array of microbial pathogens. In the present study, we investigated the potential for resistance development to aBL in Gram-negative pathogenic bacteria by carrying out multiple aBL exposures on bacteria. In the first aBL exposure, clinical isolates of Pseudomonas aeruginosa, Acinetobacter baumannii, and uropathogenic Escherichia coli [10⁷ colony forming units/mL (CFU/mL)] were irradiated in phosphate-buffered saline with aBL at 405 nm until a >99.99% reduction in bacterial viability was achieved. Irradiation was then repeated for each bacterial species over 20 cycles of aBL exposure. The potential for resistance development to aBL was also investigated in vivo, in superficial mouse wounds infected with a bioluminescent strain of P. aeruginosa (PAO1; 10⁸ CFU) and irradiated with a sub-curative radiant exposures of 108 or 216 J/cm² aBL over 5 cycles of treatment (over 5 days) prior to bacterial isolation from the animal tissue. PAO1 isolated from infected tissue were treated with aBL at 216 J/cm², in vitro, in parallel with unexposed PAO1 or PAO1 isolates from mouse wound infections not treated with aBL. No statistically significant correlation was found between the aBL-susceptibility of bacteria in vitro and the number of cycles of aBL exposure any bacterial species (P ≥ 0.26). In addition, serial exposure of infected mouse wounds to aBL did not result in any change in the susceptibility to aBL of PAO1 (P = 0.97). In conclusion, it is unlikely that sequential exposure to aBL will result in aBL-resistance in Gram-negative bacteria. Also, multiple aBL treatments may potentially be administered to an infected wound without resistance development becoming a concern.

Reduced intrinsic DNA curvature leads to increased mutation rate

BACKGROUND

Mutation rates vary across the genome. Many trans factors that influence mutation rates have been identified, as have specific sequence motifs at the 1-7-bp scale, but cis elements remain poorly characterized. The lack of understanding regarding why different sequences have different mutation rates hampers our ability to identify positive selection in evolution and to identify driver mutations in tumorigenesis.

RESULTS

Here, we use a combination of synthetic genes and sequences of thousands of isolated yeast colonies to show that intrinsic DNA curvature is a major cis determinant of mutation rate. Mutation rate negatively correlates with DNA curvature within genes, and a 10% decrease in curvature results in a 70% increase in mutation rate. Consistently, both yeast and humans accumulate mutations in regions with small curvature. We further show that this effect is due to differences in the intrinsic mutation rate, likely due to differences in mutagen sensitivity and not due to differences in the local activity of DNA repair.

CONCLUSIONS

Our study establishes a framework for understanding the cis properties of DNA sequence in modulating the local mutation rate and identifies a novel causal source of non-uniform mutation rates across the genome.

Adaptive genetic variation at three loci in South African vervet monkeys (Chlorocebus pygerythrus) and the role of selection within primates

Vervet monkeys (Chlorocebus pygerythrus) are one of the most widely distributed non-human primate species found in South Africa. They occur across all the South African provinces, inhabiting a large variety of habitats. These habitats vary sufficiently that it can be assumed that various factors such as pathogen diversity could influence populations in different ways. In turn, these factors could lead to varied levels of selection at specific fitness linked loci. The Toll-like receptor (TLR) gene family, which play an integral role in vertebrate innate immunity, is a group of fitness linked loci which has been the focus of much research. In this study, we assessed the level of genetic variation at partial sequences of two TLR loci (TLR4 and 7) and a reproductively linked gene, acrosin (ACR), across the different habitat types within the vervet monkey distribution range. Gene variation and selection estimates were also made among 11-21 primate species. Low levels of genetic variation for all three gene regions were observed within vervet monkeys, with only two polymorphic sites identified for TLR4, three sites for TLR7 and one site for ACR. TLR7 variation was positively correlated with high mean annual rainfall, which was linked to increased pathogen abundance. The observed genetic variation at TLR4 might have been influenced by numerous factors including pathogens and climatic conditions. The ACR exonic regions showed no variation in vervet monkeys, which could point to the occurrence of a selective sweep. The TLR4 and TLR7 results for the among primate analyses was mostly in line with previous studies, indicating a higher rate of evolution for TLR4. Within primates, ACR coding regions also showed signs of positive selection, which was congruent with previous reports on mammals. Important additional information to the already existing vervet monkey knowledge base was gained from this study, which can guide future research projects on this highly researched taxon as well as help conservation agencies with future management planning involving possible translocations of this species.

Nearly neutral evolution in IFNL3 gene retains the immune function to detect and clear the viral infection in HCV

IFNL3 gene plays a crucial role in immune defense against viruses. It induces the interferon stimulated genes (ISGs) with antiviral properties by activating the JAK-STAT pathway. In this study, we investigated the evolutionary force involved in shaping the IFNL3 gene to perform its downstream function as a regulatory gene in HCV clearance. We have selected 25 IFNL3 coding sequences with human gene as a reference sequence and constructed a phylogeny. Furthermore, rate of variation, substitution saturation test, phylogenetic informativeness and differential selection were also analysed. The codon evolution result suggests that nearly neutral mutation is the key pattern in shaping the IFNL3 evolution. The results were validated by subjecting the human IFNL3 protein variants to that of the native through a molecular dynamics simulation study. The molecular dynamics simulation clearly depicts the negative impact on the reported variants in human IFNL3 protein. However, these detrimental mutations (R157Q and R157W) were shown to be negatively selected in the evolutionary study of the mammals. Hence, the variation revealed a mild impact on the IFNL3 function and may be removed from the population through negative selection due to its high functional constraints. In a nutshell, our study may contribute the overall evidence in phylotyping and structural transformation that takes place in the non-synonymous substitutions of IFNL3 protein. Substantially, our obtained theoretical knowledge will lay the path to extend the experimental validation in HCV clearance.

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network

The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein–protein interaction data set and the human signal transduction network—a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets.

Essentiality, conservation, evolutionary pressure and codon bias in bacterial genomes

Essential genes constitute the core of genes which cannot be mutated too much nor lost along the evolutionary history of a species. Natural selection is expected to be stricter on essential genes and on conserved (highly shared) genes, than on genes that are either nonessential or peculiar to a single or a few species. In order to further assess this expectation, we study here how essentiality of a gene is connected with its degree of conservation among several unrelated bacterial species, each one characterised by its own codon usage bias. Confirming previous results on E. coli, we show the existence of a universal exponential relation between gene essentiality and conservation in bacteria. Moreover, we show that, within each bacterial genome, there are at least two groups of functionally distinct genes, characterised by different levels of conservation and codon bias: i) a core of essential genes, mainly related to cellular information processing; ii) a set of less conserved nonessential genes with prevalent functions related to metabolism. In particular, the genes in the first group are more retained among species, are subject to a stronger purifying conservative selection and display a more limited repertoire of synonymous codons. The core of essential genes is close to the minimal bacterial genome, which is in the focus of recent studies in synthetic biology, though we confirm that orthologs of genes that are essential in one species are not necessarily essential in other species. We also list a set of highly shared genes which, reasonably, could constitute a reservoir of targets for new anti-microbial drugs.

Correlates of evolutionary rates in the murine sperm proteome

Background

Protein-coding genes expressed in sperm evolve at different rates. To gain deeper insight into the factors underlying this heterogeneity we examined the relative importance of a diverse set of previously described rate correlates in determining the evolution of murine sperm proteins.

Results

Using partial rank correlations we detected several major rate indicators: Phyletic gene age, numbers of protein-protein interactions, and survival essentiality emerged as particularly important rate correlates in murine sperm proteins. Tissue specificity, numbers of paralogs, and untranslated region lengths also correlate significantly with sperm genes’ evolutionary rates, albeit to a lesser extent. Multifunctionality, coding sequence or average intron lengths, and mean expression level have insignificant or virtually no independent effects on evolutionary rates in murine sperm genes. Gene ontology enrichment analyses of three equally sized murine sperm protein groups classified based on their evolutionary rates indicate strongest sperm-specific functional specialization in the most quickly evolving gene class.

Conclusions

We propose a model according to which slowly evolving murine sperm proteins tend to be constrained by factors such as survival essentiality, network connectivity, and/or broad expression. In contrast, evolutionary change may arise especially in less constrained sperm proteins, which might, moreover, be prone to specialize to reproduction-related functions. Our results should be taken into account in future studies on rate variations of reproductive genes.

Electronic supplementary material

The online version of this article (10.1186/s12862-018-1157-6) contains supplementary material, which is available to authorized users.

Disulfide Bonds Enable Accelerated Protein Evolution

The different proteins of any proteome evolve at enormously different rates. What factors contribute to this variability, and to what extent, is still a largely open question. We hypothesized that disulfide bonds, by increasing protein stability, should make proteins' structures relatively independent of their amino acid sequences, thus acting as buffers of deleterious mutations and enabling accelerated sequence evolution. In agreement with this hypothesis, we observed that membrane proteins with disulfide bonds evolved 88% faster than those without disulfide bonds, and that extracellular proteins with disulfide bonds evolved 49% faster than those without disulfide bonds. In addition, genes encoding proteins with disulfide bonds exhibit an increased likelihood of showing signatures of positive selection. Multivariate analyses indicate that the trend is independent of a number of potentially confounding factors. The effect, however, is not observed among the longest proteins, which can become stabilized by mechanisms other than disulfide bonds.

Evolutionary rate heterogeneity between multi- and single-interface hubs across human housekeeping and tissue-specific protein interaction network: Insights from proteins' and its partners' properties

Integrating gene expression into protein-protein interaction network (PPIN) leads to the construction of tissue-specific (TS) and housekeeping (HK) sub-networks, with distinctive TS- and HK-hubs. All such hub proteins are divided into multi-interface (MI) hubs and single-interface (SI) hubs, where MI hubs evolve slower than SI hubs. Here we explored the evolutionary rate difference between MI and SI proteins within TS- and HK-PPIN and observed that this difference is present only in TS, but not in HK-class. Next, we explored whether proteins' own properties or its partners' properties are more influential in such evolutionary discrepancy. Statistical analyses revealed that this evolutionary rate correlates negatively with protein's own properties like expression level, miRNA count, conformational diversity and functional properties and with its partners' properties like protein disorder and tissue expression similarity. Moreover, partial correlation and regression analysis revealed that both proteins' and its partners' properties have independent effects on protein evolutionary rate.

Emerging and evolving concepts in gene essentiality

Gene essentiality is a founding concept of genetics with important implications in both fundamental and applied research. Multiple screens have been performed over the years in bacteria, yeasts, animals and more recently in human cells to identify essential genes. A mounting body of evidence suggests that gene essentiality, rather than being a static and binary property, is both context dependent and evolvable in all kingdoms of life. This concept of a non-absolute nature of gene essentiality changes our fundamental understanding of essential biological processes and could directly affect future treatment strategies for cancer and infectious diseases.

High rate of adaptation of mammalian proteins that interact with Plasmodium and related parasites

Plasmodium parasites, along with their Piroplasm relatives, have caused malaria-like illnesses in terrestrial mammals for millions of years. Several Plasmodium-protective alleles have recently evolved in human populations, but little is known about host adaptation to blood parasites over deeper evolutionary timescales. In this work, we analyze mammalian adaptation in ~500 Plasmodium- or Piroplasm- interacting proteins (PPIPs) manually curated from the scientific literature. We show that (i) PPIPs are enriched for both immune functions and pleiotropy with other pathogens, and (ii) the rate of adaptation across mammals is significantly elevated in PPIPs, compared to carefully matched control proteins. PPIPs with high pathogen pleiotropy show the strongest signatures of adaptation, but this pattern is fully explained by their immune enrichment. Several pieces of evidence suggest that blood parasites specifically have imposed selection on PPIPs. First, even non-immune PPIPs that lack interactions with other pathogens have adapted at twice the rate of matched controls. Second, PPIP adaptation is linked to high expression in the liver, a critical organ in the parasite life cycle. Finally, our detailed investigation of alpha-spectrin, a major red blood cell membrane protein, shows that domains with particularly high rates of adaptation are those known to interact specifically with P. falciparum. Overall, we show that host proteins that interact with Plasmodium and Piroplasm parasites have experienced elevated rates of adaptation across mammals, and provide evidence that some of this adaptation has likely been driven by blood parasites.

Genome-wide mapping of large deletions and their population-genetic properties in dairy cattle

Large genomic deletions are potential candidate for loss-of-function, which could be lethal as homozygote. Analysing whole genome data of 175 cattle, we report 8,480 large deletions (199 bp–773 KB) with an overall false discovery rate of 8.8%; 82% of which are novel compared with deletions in the dbVar database. Breakpoint sequence analyses revealed that majority (24 of 29 tested) of the deletions contain microhomology/homology at breakpoint, and therefore, most likely generated by microhomology-mediated end joining. We observed higher differentiation among breeds for deletions in some genic-regions, such as ABCA12, TTC1, VWA3B, TSHR, DST/BPAG1, and CD1D. The genes overlapping deletions are on average evolutionarily less conserved compared with known mouse lethal genes (P-value = 2.3 × 10⁻⁶). We report 167 natural gene knockouts in cattle that are apparently nonessential as live homozygote individuals are observed. These genes are functionally enriched for immunoglobulin domains, olfactory receptors, and MHC classes (FDR = 2.06 × 10⁻²², 2.06 × 10⁻²², 7.01 × 10⁻⁶, respectively). We also demonstrate that deletions are enriched for health and fertility related quantitative trait loci (2-and 1.5-fold enrichment, Fisher’s P-value = 8.91 × 10⁻¹⁰ and 7.4 × 10⁻¹¹, respectively). Finally, we identified and confirmed the breakpoint of a ∼525 KB deletion on Chr23:12,291,761-12,817,087 (overlapping BTBD9, GLO1 and DNAH8), causing stillbirth in Nordic Red Cattle.

Secreted Proteins Defy the Expression Level-Evolutionary Rate Anticorrelation

The rates of evolution of the proteins of any organism vary across orders of magnitude. A primary factor influencing rates of protein evolution is expression. A strong negative correlation between expression levels and evolutionary rates (the so-called E-R anticorrelation) has been observed in virtually all studied organisms. This effect is currently attributed to the abundance-dependent fitness costs of misfolding and unspecific protein-protein interactions, among other factors. Secreted proteins are folded in the endoplasmic reticulum, a compartment where chaperones, folding catalysts, and stringent quality control mechanisms promote their correct folding and may reduce the fitness costs of misfolding. In addition, confinement of secreted proteins to the extracellular space may reduce misinteractions and their deleterious effects. We hypothesize that each of these factors (the secretory pathway quality control and extracellular location) may reduce the strength of the E-R anticorrelation. Indeed, here we show that among human proteins that are secreted to the extracellular space, rates of evolution do not correlate with protein abundances. This trend is robust to controlling for several potentially confounding factors and is also observed when analyzing protein abundance data for 6 human tissues. In addition, analysis of mRNA abundance data for 32 human tissues shows that the E-R correlation is always less negative, and sometimes nonsignificant, in secreted proteins. Similar observations were made in Caenorhabditis elegans and in Escherichia coli, and to a lesser extent in Drosophila melanogaster, Saccharomyces cerevisiae and Arabidopsis thaliana. Our observations contribute to understand the causes of the E-R anticorrelation.

An Essential Regulatory System Originating from Polygenic Transcriptional Rewiring of PhoP-PhoQ of Xanthomonas campestris

How essential, regulatory genes originate and evolve is intriguing because mutations of these genes not only lead to lethality in organisms, but also have pleiotropic effects since they control the expression of multiple downstream genes. Therefore, the evolution of essential, regulatory genes is not only determined by genetic variations of their own sequences, but also by the biological function of downstream genes and molecular mechanisms of regulation. To understand the origin of essential, regulatory genes, experimental dissection of the complete regulatory cascade is needed. Here, we provide genetic evidences to reveal that PhoP-PhoQ is an essential two-component signal transduction system in the gram-negative bacterium Xanthomonas campestris, but that its orthologs in other bacteria belonging to Proteobacteria are nonessential. Mutational, biochemical, and chromatin immunoprecipitation together with high-throughput sequencing analyses revealed that phoP and phoQ of X. campestris and its close relative Pseudomonas aeruginosa are replaceable, and that the consensus binding motifs of the transcription factor PhoP are also highly conserved. PhoP _Xcc in X. campestris regulates the transcription of a number of essential, structural genes by directly binding to cis-regulatory elements (CREs); however, these CREs are lacking in the orthologous essential, structural genes in P. aeruginosa, and thus the regulatory relationships between PhoP _Pae and these downstream essential genes are disassociated. Our findings suggested that the recruitment of regulatory proteins by critical structural genes via transcription factor-CRE rewiring is a driving force in the origin and functional divergence of essential, regulatory genes.

Effects of different kinds of essentiality on sequence evolution of human testis proteins

We asked if essentiality for either fertility or viability differentially affects sequence evolution of human testis proteins. Based on murine knockout data, we classified a set of 965 proteins expressed in human seminiferous tubules into three categories: proteins essential for prepubertal survival (“lethality proteins”), associated with male sub- or infertility (“male sub-/infertility proteins”), and nonessential proteins. In our testis protein dataset, lethality genes evolved significantly slower than nonessential and male sub-/infertility genes, which is in line with other authors’ findings. Using tissue specificity, connectivity in the protein-protein interaction (PPI) network, and multifunctionality as proxies for evolutionary constraints, we found that of the three categories, proteins linked to male sub- or infertility are least constrained. Lethality proteins, on the other hand, are characterized by broad expression, many PPI partners, and high multifunctionality, all of which points to strong evolutionary constraints. We conclude that compared with lethality proteins, those linked to male sub- or infertility are nonetheless indispensable, but evolve under more relaxed constraints. Finally, adaptive evolution in response to postmating sexual selection could further accelerate evolutionary rates of male sub- or infertility proteins expressed in human testis. These findings may become useful for in silico detection of human sub-/infertility genes.

Neurogenomics and the role of a large mutational target on rapid behavioral change

BACKGROUND

Behavior, while complex and dynamic, is among the most diverse, derived, and rapidly evolving traits in animals. The highly labile nature of heritable behavioral change is observed in such evolutionary phenomena as the emergence of converged behaviors in domesticated animals, the rapid evolution of preferences, and the routine development of ethological isolation between diverging populations and species. In fact, it is believed that nervous system development and its potential to evolve a seemingly infinite array of behavioral innovations played a major role in the successful diversification of metazoans, including our own human lineage. However, unlike other rapidly evolving functional systems such as sperm-egg interactions and immune defense, the genetic basis of rapid behavioral change remains elusive.

PRESENTATION OF THE HYPOTHESIS

Here we propose that the rapid divergence and widespread novelty of innate and adaptive behavior is primarily a function of its genomic architecture. Specifically, we hypothesize that the broad diversity of behavioral phenotypes present at micro- and macroevolutionary scales is promoted by a disproportionately large mutational target of neurogenic genes. We present evidence that these large neuro-behavioral targets are significant and ubiquitous in animal genomes and suggest that behavior's novelty and rapid emergence are driven by a number of factors including more selection on a larger pool of variants, a greater role of phenotypic plasticity, and/or unique molecular features present in large genes. We briefly discuss the origins of these large neurogenic genes, as they relate to the remarkable diversity of metazoan behaviors, and highlight key consequences on both behavioral traits and neurogenic disease across, respectively, evolutionary and ontogenetic time scales.

TESTING THE HYPOTHESIS

Current approaches to studying the genetic mechanisms underlying rapid phenotypic change primarily focus on identifying signatures of Darwinian selection in protein-coding regions. In contrast, the large mutational target hypothesis places genomic architecture and a larger allelic pool at the forefront of rapid evolutionary change, particularly in genetic systems that are polygenic and regulatory in nature. Genomic data from brain and neural tissues in mammals as well as a preliminary survey of neurogenic genes from comparative genomic data support this hypothesis while rejecting both positive and relaxed selection on proteins or higher mutation rates. In mammals and invertebrates, neurogenic genes harbor larger protein-coding regions and possess a richer regulatory repertoire of miRNA targets and transcription factor binding sites. Overall, neurogenic genes cover a disproportionately large genomic fraction, providing a sizeable substrate for evolutionary, genetic, and molecular mechanisms to act upon. Readily available comparative and functional genomic data provide unexplored opportunities to test whether a distinct neurogenomic architecture can promote rapid behavioral change via several mechanisms unique to large genes, and which components of this large footprint are uniquely metazoan.

IMPLICATIONS OF THE HYPOTHESIS

The large mutational target hypothesis highlights the eminent roles of mutation and functional genomic architecture in generating rapid developmental and evolutionary change. It has broad implications on our understanding of the genetics of complex adaptive traits such as behavior by focusing on the importance of mutational input, from SNPs to alternative transcripts to transposable elements, on driving evolutionary rates of functional systems. Such functional divergence has important implications in promoting behavioral isolation across short- and long-term timescales. Due to genome-scaled polygenic adaptation, the large target effect also contributes to our inability to identify adapted behavioral candidate genes. The presence of large neurogenic genes, particularly in the mammalian brain and other neural tissues, further offers emerging insight into the etiology of neurodevelopmental and neurodegenerative diseases. The well-known correlation between neurological spectrum disorders in children and paternal age may simply be a direct result of aging fathers accumulating mutations across these large neurodevelopmental genes. The large mutational target hypothesis can also explain the rapid evolution of other functional systems covering a large genomic fraction such as male fertility and its preferential association with hybrid male sterility among closely related taxa. Overall, a focus on mutational potential may increase our power in understanding the genetic basis of complex phenotypes such as behavior while filling a general gap in understanding their evolution.

Essentiality Is a Strong Determinant of Protein Rates of Evolution during Mutation Accumulation Experiments in Escherichia coli

The Neutral Theory of Molecular Evolution is considered the most powerful theory to understand the evolutionary behavior of proteins. One of the main predictions of this theory is that essential proteins should evolve slower than dispensable ones owing to increased selective constraints. Comparison of genomes of different species, however, has revealed only small differences between the rates of evolution of essential and nonessential proteins. In some analyses, these differences vanish once confounding factors are controlled for, whereas in other cases essentiality seems to have an independent, albeit small, effect. It has been argued that comparing relatively distant genomes may entail a number of limitations. For instance, many of the genes that are dispensable in controlled lab conditions may be essential in some of the conditions faced in nature. Moreover, essentiality can change during evolution, and rates of protein evolution are simultaneously shaped by a variety of factors, whose individual effects are difficult to isolate. Here, we conducted two parallel mutation accumulation experiments in Escherichia coli, during 5,500–5,750 generations, and compared the genomes at different points of the experiments. Our approach (a short-term experiment, under highly controlled conditions) enabled us to overcome many of the limitations of previous studies. We observed that essential proteins evolved substantially slower than nonessential ones during our experiments. Strikingly, rates of protein evolution were only moderately affected by expression level and protein length.

Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein's rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.

Model parameters of molecular evolution explain genomic correlations

One long-standing research focus in evolutionary genomics is trying to resolve how biological variables (expression, essentiality, protein-protein interaction, structural stability, etc.) determine the rate of protein evolution. While these studies have considerably deepened our understanding of molecular evolution, many issues remain unsolved. In this opinion article, after having a brief survey of literatures, we establish relationships between model parameters of molecular evolution and genomic variables, based on which, most-observed genomic correlations and confounds can be explained by model parameter combinations under different conditions, which include the strength of stabilizing selection, mutational variance, expression sufficiency, gene pleiotropy, as well as the effective population size. We suggest that the problem to discern biological variable(s) that may determine the rate of protein evolution can be tackled at two levels. The first level, as discussed here, is to demonstrate how the model of molecular evolution can predict potential genomic correlations under various conditions. And the second level is to estimate genome-wide variations of model parameters (or combinations) that help to identify canonical biological variables that may underlie the rate variation among genes that ranges up to at least three magnitudes.

Comparative Proteomics of Mouse Tears and Saliva: Evidence from Large Protein Families for Functional Adaptation

We produced a tear proteome of the genome mouse, C57BL/6, that contained 139 different protein identifications: 110 from a two-dimensional (2D) gel with subsequent trypsin digestion, 19 from a one-dimensional (1D) gel with subsequent trypsin digestion and ten from a 1D gel with subsequent Asp-N digestion. We compared this tear proteome with a C57BL/6 mouse saliva proteome produced previously. Sixteen of the 139 tear proteins are shared between the two proteomes, including six proteins that combat microbial growth. Among the 123 other tear proteins, were members of four large protein families that have no counterparts in humans: Androgen-binding proteins (ABPs) with different members expressed in the two proteomes, Exocrine secreted peptides (ESPs) expressed exclusively in the tear proteome, major urinary proteins (MUPs) expressed in one or both proteomes and the mouse-specific Kallikreins (subfamily b KLKs) expressed exclusively in the saliva proteome. All four families have members with suggested roles in mouse communication, which may influence some aspect of reproductive behavior. We discuss this in the context of functional adaptation involving tear and saliva proteins in the secretions of mouse lacrimal and salivary glands, respectively.