Evolutionary rate in the protein interaction network

Pleiotropy increases parallel selection signatures during adaptation from standing genetic variation

The phenomenon of parallel evolution, whereby similar genomic and phenotypic changes occur across replicated pairs of populations or species, is widely studied. Nevertheless, the determining factors of parallel evolution remain poorly understood. Theoretical studies have proposed that pleiotropy, the influence of a single gene on multiple traits, is an important factor. In order to gain a deeper insight into the role of pleiotropy for parallel evolution from standing genetic variation, we characterized the interplay between parallelism, polymorphism, and pleiotropy. The present study examined the parallel gene expression evolution in 10 replicated populations of Drosophila simulans, which were adapted from standing variation to the same new temperature regime. The data demonstrate that the parallel evolution of gene expression from standing genetic variation is positively correlated with the strength of pleiotropic effects. The ancestral variation in gene expression is, however, negatively correlated with parallelism. Given that pleiotropy is also negatively correlated with gene expression variation, we conducted a causal analysis to distinguish cause and correlation and evaluate the role of pleiotropy. The causal analysis indicated that both direct (causative) and indirect (correlational) effects of pleiotropy contribute to parallel evolution. The indirect effect is mediated by historic selective constraint in response to pleiotropy. This results in parallel selection responses due to the reduced standing variation of pleiotropic genes. The direct effect of pleiotropy is likely to reflect a genetic correlation among adaptive traits, which in turn gives rise to synergistic effects and higher parallelism.

A general principle of neuronal evolution reveals a human-accelerated neuron type potentially underlying the high prevalence of autism in humans

The remarkable ability of a single genome sequence to encode a diverse collection of distinct cell types, including the thousands of cell types found in the mammalian brain, is a key characteristic of multicellular life. While it has been observed that some cell types are far more evolutionarily conserved than others, the factors driving these differences in evolutionary rate remain unknown. Here, we hypothesized that highly abundant neuronal cell types may be under greater selective constraint than rarer neuronal types, leading to variation in their rates of evolution. To test this, we leveraged recently published cross-species single-nucleus RNA-sequencing datasets from three distinct regions of the mammalian neocortex. We found a strikingly consistent relationship where more abundant neuronal subtypes show greater gene expression conservation between species, which replicated across three independent datasets covering >106 neurons from six species. Based on this principle, we discovered that the most abundant type of neocortical neurons-layer 2/3 intratelencephalic excitatory neurons-has evolved exceptionally quickly in the human lineage compared to other apes. Surprisingly, this accelerated evolution was accompanied by the dramatic down-regulation of autism-associated genes, which was likely driven by polygenic positive selection specific to the human lineage. In sum, we introduce a general principle governing neuronal evolution and suggest that the exceptionally high prevalence of autism in humans may be a direct result of natural selection for lower expression of a suite of genes that conferred a fitness benefit to our ancestors while also rendering an abundant class of neurons more sensitive to perturbation.

Using residue interaction networks to understand protein function and evolution and to engineer new proteins

Residue interaction networks (RINs) provide graph-based representations of interaction networks within proteins, providing important insight into the factors driving protein structure, function, and stability relationships. There exists a wide range of tools with which to perform RIN analysis, taking into account different types of interactions, input (crystal structures, simulation trajectories, single proteins, or comparative analysis across proteins), as well as formats, including standalone software, web server, and a web application programming interface (API). In particular, the ability to perform comparative RIN analysis across protein families using "metaRINs" provides a valuable tool with which to dissect protein evolution. This, in turn, highlights hotspots to avoid (or target) for in vitro evolutionary studies, providing a powerful framework that can be exploited to engineer new proteins.

Number of human protein interactions correlates with structural, but not regulatory conservation of the respective genes

Introduction

The differential ratio of nonsynonymous to synonymous nucleotide substitutions (dN/dS) is a common measure of the rate of structural evolution in proteincoding genes. In addition, we recently suggested that the proportion of transposable elements in gene promoters that host functional genomic sites serves as a marker of the rate of regulatory evolution of genes. Such functional genomic regions may include transcription factor binding sites and modified histone binding loci.

Methods

Here, we constructed a model of the human interactome based on 600,136 documented molecular interactions and investigated the overall relationship between the number of interactions of each protein and the rate of structural and regulatory evolution of the corresponding genes.

Results

By evaluating a total of 4,505 human genes and 1,936 molecular pathways we found a general correlation between structural and regulatory evolution rate metrics (Spearman 0.08-0.16 and 0.25-0.37 for gene and pathway levels, respectively, p < 0.01). Further exploration revealed in the established human interactome model lack of correlation between the rate of gene regulatory evolution and the number of protein interactions on gene level, and weak negative correlation (∼0.15) on pathway level. We also found a statistically significant negative correlation between the rate of gene structural evolution and the number of protein interactions (Spearman -0.11 and -0.3 for gene and pathway levels, respectively, p < 0.01).

Discussion

Our result suggests stronger structural rather than regulatory conservation of genes whose protein products have multiple interaction partners.

Functional Optimization in Distinct Tissues and Conditions Constrains the Rate of Protein Evolution

Understanding the main determinants of protein evolution is a fundamental challenge in biology. Despite many decades of active research, the molecular and cellular mechanisms underlying the substantial variability of evolutionary rates across cellular proteins are not currently well understood. It also remains unclear how protein molecular function is optimized in the context of multicellular species and why many proteins, such as enzymes, are only moderately efficient on average. Our analysis of genomics and functional datasets reveals in multiple organisms a strong inverse relationship between the optimality of protein molecular function and the rate of protein evolution. Furthermore, we find that highly expressed proteins tend to be substantially more functionally optimized. These results suggest that cellular expression costs lead to more pronounced functional optimization of abundant proteins and that the purifying selection to maintain high levels of functional optimality significantly slows protein evolution. We observe that in multicellular species both the rate of protein evolution and the degree of protein functional efficiency are primarily affected by expression in several distinct cell types and tissues, specifically, in developed neurons with upregulated synaptic processes in animals and in young and fast-growing tissues in plants. Overall, our analysis reveals how various constraints from the molecular, cellular, and species' levels of biological organization jointly affect the rate of protein evolution and the level of protein functional adaptation.

Patterns of Fitness and Gene Expression Epistasis Generated by Beneficial Mutations in the rho and rpoB Genes of Escherichia coli during High-Temperature Adaptation

Epistasis is caused by genetic interactions among mutations that affect fitness. To characterize properties and potential mechanisms of epistasis, we engineered eight double mutants that combined mutations from the rho and rpoB genes of Escherichia coli. The two genes encode essential functions for transcription, and the mutations in each gene were chosen because they were beneficial for adaptation to thermal stress (42.2 °C). The double mutants exhibited patterns of fitness epistasis that included diminishing returns epistasis at 42.2 °C, stronger diminishing returns between mutations with larger beneficial effects and both negative and positive (sign) epistasis across environments (20.0 °C and 37.0 °C). By assessing gene expression between single and double mutants, we detected hundreds of genes with gene expression epistasis. Previous work postulated that highly connected hub genes in coexpression networks have low epistasis, but we found the opposite: hub genes had high epistasis values in both coexpression and protein-protein interaction networks. We hypothesized that elevated epistasis in hub genes reflected that they were enriched for targets of Rho termination but that was not the case. Altogether, gene expression and coexpression analyses revealed that thermal adaptation occurred in modules, through modulation of ribonucleotide biosynthetic processes and ribosome assembly, the attenuation of expression in genes related to heat shock and stress responses, and with an overall trend toward restoring gene expression toward the unstressed state.

Elaboration of the Homer1 recognition landscape reveals incomplete divergence of paralogous EVH1 domains

Short sequences that mediate interactions with modular binding domains are ubiquitous throughout eukaryotic proteomes. Networks of short linear motifs (SLiMs) and their corresponding binding domains orchestrate many cellular processes, and the low mutational barrier to evolving novel interactions provides a way for biological systems to rapidly sample selectable phenotypes. Mapping SLiM binding specificity and the rules that govern SLiM evolution is fundamental to uncovering the pathways regulated by these networks and developing the tools to manipulate them. We used high-throughput screening of the human proteome to identify sequences that bind to the Enabled/VASP homology 1 (EVH1) domain of the postsynaptic density scaffolding protein Homer1. This expanded our understanding of the determinants of Homer EVH1 binding preferences and defined a new motif that can facilitate the discovery of additional Homer-mediated interactions. Interestingly, the Homer1 EVH1 domain preferentially binds to sequences containing an N-terminally overlapping motif that is bound by the paralogous family of Ena/VASP actin polymerases, and many of these sequences can bind to EVH1 domains from both protein families. We provide evidence from orthologous EVH1 domains in pre-metazoan organisms that the overlap in human Ena/VASP and Homer binding preferences corresponds to an incomplete divergence from a common Ena/VASP ancestor. Given this overlap in binding profiles, promiscuous sequences that can be recognized by both families either achieve specificity through extrinsic regulatory strategies or may provide functional benefits via multi-specificity. This may explain why these paralogs incompletely diverged despite the accessibility of further diverged isoforms.

Wiring Between Close Nodes in Molecular Networks Evolves More Quickly Than Between Distant Nodes

As species diverge, a wide range of evolutionary processes lead to changes in protein-protein interaction (PPI) networks and metabolic networks. The rate at which molecular networks evolve is an important question in evolutionary biology. Previous empirical work has focused on interactomes from model organisms to calculate rewiring rates, but this is limited by the relatively small number of species and sparse nature of network data across species. We present a proxy for variation in network topology: variation in drug-drug interactions (DDIs), obtained by studying drug combinations (DCs) across taxa. Here, we propose the rate at which DDIs change across species as an estimate of the rate at which the underlying molecular network changes as species diverge. We computed the evolutionary rates of DDIs using previously published data from a high-throughput study in gram-negative bacteria. Using phylogenetic comparative methods, we found that DDIs diverge rapidly over short evolutionary time periods, but that divergence saturates over longer time periods. In parallel, we mapped drugs with known targets in PPI and cofunctional networks. We found that the targets of synergistic DDIs are closer in these networks than other types of DCs and that synergistic interactions have a higher evolutionary rate, meaning that nodes that are closer evolve at a faster rate. Future studies of network evolution may use DC data to gain larger-scale perspectives on the details of network evolution within and between species.

Re-examining Correlations Between Synonymous Codon Usage and Protein Bond Angles in Escherichia coli

Rosenberg AA, Marx A, Bronstein AM (Codon-specific Ramachandran plots show amino acid backbone conformation depends on identity of the translated codon. Nat Commun. 2022:13:2815) recently found a surprising correlation between synonymous codon usage and the dihedral bond angles of the resulting amino acid. However, their analysis did not account for the strongest known correlate of codon usage: gene expression. We re-examined the relationship between bond angles and codon usage by applying the approach of Rosenberg et al. to simulated protein-coding sequences that (i) have random codon usage, (ii) codon usage determined by mutation biases, and (iii) maintain the general relationship between codon usage and gene expression via the assumption of selection-mutation-drift equilibrium. We observed correlations between dihedral bond angle and codon usage when codon usage is entirely random, indicating possible conflation of noise with differences in bond angle distributions between synonymous codons. More relevant to the general analysis of codon usage patterns, we found surprisingly good agreement between the analysis of the real sequences and the analysis of sequences simulated assuming selection-mutation-drift equilibrium, with 91% of significant synonymous codon pairs detected in the former were also detected in the latter. We believe the correlation between codon usage and dihedral bond angles resulted from the variation in codon usage across genes due to the interplay between mutation bias, natural selection for translation efficiency, and gene expression, further underscoring these factors must be controlled for when looking for novel patterns related to codon usage.

Elaboration of the Homer1 Recognition Landscape Reveals Incomplete Divergence of Paralogous EVH1 Domains

Short sequences that mediate interactions with modular binding domains are ubiquitous throughout eukaryotic proteomes. Networks of Short Linear Motifs (SLiMs) and their corresponding binding domains orchestrate many cellular processes, and the low mutational barrier to evolving novel interactions provides a way for biological systems to rapidly sample selectable phenotypes. Mapping SLiM binding specificity and the rules that govern SLiM evolution is fundamental to uncovering the pathways regulated by these networks and developing the tools to manipulate them. We used high-throughput screening of the human proteome to identify sequences that bind to the Enabled/VASP homology 1 (EVH1) domain of the postsynaptic density scaffolding protein Homer1. In doing so, we expanded current understanding of the determinants of Homer EVH1 binding preferences and defined a new motif that can facilitate the discovery of additional Homer-mediated interactions. Interestingly, the Homer1 EVH1 domain preferentially binds to sequences containing an N-terminally overlapping motif that is bound by the paralogous family of Ena/VASP actin polymerases, and many of these sequences can bind to EVH1 domains from both protein families. We provide evidence from orthologous EVH1 domains in pre-metazoan organisms that the overlap in human Ena/VASP and Homer binding preferences corresponds to an incomplete divergence from a common Ena/VASP ancestor. Given this overlap in binding profiles, promiscuous sequences that can be recognized by both families either achieve specificity through extrinsic regulatory strategies or may provide functional benefits via multi-specificity. This may explain why these paralogs incompletely diverged despite the accessibility of further diverged isoforms.

Translation variation across genetic backgrounds reveals a post-transcriptional buffering signature in yeast

Gene expression is known to vary among individuals, and this variability can impact the phenotypic diversity observed in natural populations. While the transcriptome and proteome have been extensively studied, little is known about the translation process itself. Here, we therefore performed ribosome and transcriptomic profiling on a genetically and ecologically diverse set of natural isolates of the Saccharomyces cerevisiae yeast. Interestingly, we found that the Euclidean distances between each profile and the expression fold changes in each pairwise isolate comparison were higher at the transcriptomic level. This observation clearly indicates that the transcriptional variation observed in the different isolates is buffered through a phenomenon known as post-transcriptional buffering at the translation level. Furthermore, this phenomenon seemed to have a specific signature by preferentially affecting essential genes as well as genes involved in complex-forming proteins, and low transcribed genes. We also explored the translation of the S. cerevisiae pangenome and found that the accessory genes related to introgression events displayed similar transcription and translation levels as the core genome. By contrast, genes acquired through horizontal gene transfer events tended to be less efficiently translated. Together, our results highlight both the extent and signature of the post-transcriptional buffering.

Key interaction networks: Identifying evolutionarily conserved non-covalent interaction networks across protein families

Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A β-lactamases.

Hubs and Bottlenecks in Protein-Protein Interaction Networks

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

Essential proteins discovery based on dominance relationship and neighborhood similarity centrality

Essential proteins play a vital role in development and reproduction of cells. The identification of essential proteins helps to understand the basic survival of cells. Due to time-consuming, costly and inefficient with biological experimental methods for discovering essential proteins, computational methods have gained increasing attention. In the initial stage, essential proteins are mainly identified by the centralities based on protein-protein interaction (PPI) networks, which limit their identification rate due to many false positives in PPI networks. In this study, a purified PPI network is firstly introduced to reduce the impact of false positives in the PPI network. Secondly, by analyzing the similarity relationship between a protein and its neighbors in the PPI network, a new centrality called neighborhood similarity centrality (NSC) is proposed. Thirdly, based on the subcellular localization and orthologous data, the protein subcellular localization score and ortholog score are calculated, respectively. Fourthly, by analyzing a large number of methods based on multi-feature fusion, it is found that there is a special relationship among features, which is called dominance relationship, then, a novel model based on dominance relationship is proposed. Finally, NSC, subcellular localization score, and ortholog score are fused by the dominance relationship model, and a new method called NSO is proposed. In order to verify the performance of NSO, the seven representative methods (ION, NCCO, E_POC, SON, JDC, PeC, WDC) are compared on yeast datasets. The experimental results show that the NSO method has higher identification rate than other methods.

Deep mutational scanning reveals the functional constraints and evolutionary potential of the influenza A virus PB1 protein

IMPORTANCE

The influenza virus polymerase is important for adaptation to new hosts and, as a determinant of mutation rate, for the process of adaptation itself. We performed a deep mutational scan of the polymerase basic 1 (PB1) protein to gain insights into the structural and functional constraints on the influenza RNA-dependent RNA polymerase. We find that PB1 is highly constrained at specific sites that are only moderately predicted by the global structure or larger domain. We identified a number of beneficial mutations, many of which have been shown to be functionally important or observed in influenza virus' natural evolution. Overall, our atlas of PB1 mutations and their fitness impacts serves as an important resource for future studies of influenza replication and evolution.

pyScoMotif: discovery of similar 3D structural motifs across proteins

Motivation

The fast and accurate detection of similar geometrical arrangements of protein residues, known as 3D structural motifs, is highly relevant for many applications such as binding region and catalytic site detection, drug discovery and structure conservation analyses. With the recent publication of new protein structure prediction methods, the number of available protein structures is exploding, which makes efficient and easy-to-use tools for identifying 3D structural motifs essential.

Results

We present an open-source Python package that enables the search for both exact and mutated motifs with position-specific residue substitutions. The tool is efficient, flexible, accurate, and suitable to run both on computer clusters and personal laptops. Two successful applications of pyScoMotif for catalytic site identification are showcased.

Availability and implementation

The pyScoMotif package can be installed from the PyPI repository and is also available at https://github.com/3BioCompBio/pyScoMotif. It is free to use for non-commercial purposes.

Patterns and evolutionary consequences of pleiotropy

Pleiotropy refers to the phenomenon of one gene or one mutation affecting multiple phenotypic traits. While the concept of pleiotropy is as old as Mendelian genetics, functional genomics has finally allowed the first glimpses of the extent of pleiotropy for a large fraction of genes in a genome. After describing conceptual and operational difficulties in quantifying pleiotropy and the pros and cons of various methods for measuring pleiotropy, I review empirical data on pleiotropy, which generally show an L-shaped distribution of the degree of pleiotropy (i.e., the number of traits affected) with most genes having low pleiotropy. I then review the current understanding of the molecular basis of pleiotropy. The rest of the review discusses evolutionary consequences of pleiotropy, focusing on advances in topics including the cost of complexity, regulatory vs. coding evolution, environmental pleiotropy and adaptation, evolution of ageing and other seemingly harmful traits, and evolutionary resolution of pleiotropy.

Whole-Genome Sequencing Reveals That Regulatory and Low Pleiotropy Variants Underlie Local Adaptation to Environmental Variability in Purple Sea Urchins

AbstractOrganisms experience environments that vary across both space and time. Such environmental heterogeneity shapes standing genetic variation and may influence species' capacity to adapt to rapid environmental change. However, we know little about the kind of genetic variation that is involved in local adaptation to environmental variability. To address this gap, we sequenced the whole genomes of 140 purple sea urchins (Strongylocentrotus purpuratus) from seven populations that vary in their degree of pH variability. Despite no evidence of global population structure, we found a suite of single-nucleotide polymorphisms (SNPs) tightly correlated with local pH variability (outlier SNPs), which were overrepresented in regions putatively involved in gene regulation (long noncoding RNA and enhancers), supporting the idea that variation in regulatory regions is important for local adaptation to variability. In addition, outliers in genes were found to be (i) enriched for biomineralization and ion homeostasis functions related to low pH response, (ii) less central to the protein-protein interaction network, and (iii) underrepresented among genes highly expressed during early development. Taken together, these results suggest that loci that underlie local adaptation to pH variability in purple sea urchins fall in regions with potentially low pleiotropic effects (based on analyses involving regulatory regions, network centrality, and expression time) involved in low pH response (based on functional enrichment).

Role of genetic architecture in phenotypic plasticity

Phenotypic plasticity, the ability of an organism to display different phenotypes across environments, is widespread in nature. Plasticity aids survival in novel environments. Herein, we review studies from yeast that allow us to start uncovering the genetic architecture of phenotypic plasticity. Genetic variants and their interactions impact the phenotype in different environments, and distinct environments modulate the impact of genetic variants and their interactions on the phenotype. Because of this, certain hidden genetic variation is expressed in specific genetic and environmental backgrounds. A better understanding of the genetic mechanisms of phenotypic plasticity will help to determine short- and long-term responses to selection and how wide variation in disease manifestation occurs in human populations.

Speciation and development

Understanding general principles about the origin of species remains one of the foundational challenges in evolutionary biology. The genomic divergence between groups of individuals can spawn hybrid inviability and hybrid sterility, which presents a tantalizing developmental problem. Divergent developmental programs may yield either conserved or divergent phenotypes relative to ancestral traits, both of which can be responsible for reproductive isolation during the speciation process. The genetic mechanisms of developmental evolution involve cis- and trans-acting gene regulatory change, protein-protein interactions, genetic network structures, dosage, and epigenetic regulation, all of which also have roots in population genetic and molecular evolutionary processes. Toward the goal of demystifying Darwin's "mystery of mysteries," this review integrates microevolutionary concepts of genetic change with principles of organismal development, establishing explicit links between population genetic process and developmental mechanisms in the production of macroevolutionary pattern. This integration aims to establish a more unified view of speciation that binds process and mechanism.

Network medicine: an approach to complex kidney disease phenotypes

Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.

Chloride transporters controlling neuronal excitability

Synaptic inhibition plays a crucial role in regulating neuronal excitability, which is the foundation of nervous system function. This inhibition is largely mediated by the neurotransmitters GABA and glycine that activate Cl^--permeable ion channels, which means that the strength of inhibition depends on the Cl^- gradient across the membrane. In neurons, the Cl^- gradient is primarily mediated by two secondarily active cation-chloride cotransporters (CCCs), NKCC1 and KCC2. CCC-mediated regulation of the neuronal Cl^- gradient is critical for healthy brain function, as dysregulation of CCCs has emerged as a key mechanism underlying neurological disorders including epilepsy, neuropathic pain, and autism spectrum disorder. This review begins with an overview of neuronal chloride transporters before explaining the dependent relationship between these CCCs, Cl^- regulation, and inhibitory synaptic transmission. We then discuss the evidence for how CCCs can be regulated, including by activity and their protein interactions, which underlie inhibitory synaptic plasticity. For readers who may be interested in conducting experiments on CCCs and neuronal excitability, we have included a section on techniques for estimating and recording intracellular Cl^-, including their advantages and limitations. Although the focus of this review is on neurons, we also examine how Cl^- is regulated in glial cells, which in turn regulate neuronal excitability through the tight relationship between this nonneuronal cell type and synapses. Finally, we discuss the relatively extensive and growing literature on how CCC-mediated neuronal excitability contributes to neurological disorders.

The origin of genetic and metabolic systems: Evolutionary structuralinsights

DNA is derived from reverse transcription and its origin is related to reverse transcriptase, DNA polymerase and integrase. The gene structure originated from the evolution of the first RNA polymerase. Thus, an explanation of the origin of the genetic system must also explain the evolution of these enzymes. This paper proposes a polymer structure model, termed the stable complex evolution model, which explains the evolution of enzymes and functional molecules. Enzymes evolved their functions by forming locally tightly packed complexes with specific substrates. A metabolic reaction can therefore be considered to be the result of adaptive evolution in this way when a certain essential molecule is lacking in a cell. The evolution of the primitive genetic and metabolic systems was thus coordinated and synchronized. According to the stable complex model, almost all functional molecules establish binding affinity and specific recognition through complementary interactions, and functional molecules therefore have the nature of being auto-reactive. This is thermodynamically favorable and leads to functional duplication and self-organization. Therefore, it can be speculated that biological systems have a certain tendency to maintain functional stability or are influenced by an inherent selective power. The evolution of dormant bacteria may support this hypothesis, and inherent selectivity can be unified with natural selection at the molecular level.

Dissection of hubs and bottlenecks in a protein-protein interaction network

Analysis of degree centrality in conjunction with betweenness centrality of proteins in a human protein-protein interaction network revealed three categories of centrally important proteins: a) proteins with high degree and betweenness (hub-bottlenecks denoted as MX), b) proteins with high betweenness and low degree (non-hub-bottlenecks/pure bottlenecks denoted as PB) and c) proteins with high degree and low betweenness (hub-non-bottlenecks/pure hubs denoted as PH). When subjected to a detailed statistical analysis of their molecular-level properties, the proteins belonging to each of these categories were found to be associated with distinct canonical molecular properties, i.e., "molecular markers". The MX proteins are a) conformationally versatile, mainly comprising of essential proteins, b) the targets for interactions by the proteins of viral and bacterial pathogens, c) evolutionally constrained, involved in multiple pathways, enriched with disease genes and d) involved in the functions such as protein stabilization, phosphorylation, and mRNA slicing processes. PB proteins are a) enriched with extracellular and cancer-related proteins, b) enriched with the approved drug targets and c) involved in cell-cell signaling processes. Finally, PH are a) structurally versatile, b) enriched with essential proteins primarily involved in housekeeping processes (transcription and replication). The fact that the proteins belonging to these three categories form three distinct sets in terms of their molecular properties reveals the existence of trichotomy among hubs and bottlenecks, and this knowledge is of paramount importance while prioritizing protein targets for further studies such as drug design and disease association studies based on their network centrality values.

Unexpected absence of ribosomal protein genes from metagenome-assembled genomes

Metagenome-assembled genomes (MAGs) have revealed the hidden diversity and functions of uncultivated microbes, but their reconstruction from metagenomes remains a computationally difficult task. Repetitive or exogenous sequences, such as ribosomal RNA and horizontally transferred genes, are frequently absent from MAGs because of misassembly and binning errors. Here, we report that ribosomal protein genes are also often absent from MAGs, although they are neither repetitive nor exogenous. Comprehensive analyses of more than 190,000 MAGs revealed that these genes could be missing in more than 20-40% of near-complete (i.e., with completeness of 90% or higher) MAGs. While some uncultivated environmental microbes intrinsically lack some ribosomal protein genes, we found that this unexpected absence is largely due to special evolutionary patterns of codon usage bias in ribosomal protein genes and algorithmic characteristics of metagenomic binning, which is dependent on tetranucleotide frequencies of contigs. This problem reflects the microbial life-history strategy. Fast-growing microbes tend to have this difficulty, likely because of strong evolutionary pressures on ribosomal protein genes toward the efficient assembly of ribosomes. Our observations caution those who study genomics and phylogeny of uncultivated microbes, the diversity and evolution of microbial genes in the central dogma, and bioinformatics in metagenomics.

Cooperation loci are more pleiotropic than private loci in the bacterium Pseudomonas aeruginosa

Pleiotropy may affect the maintenance of cooperation by limiting cheater mutants if such mutants lose other important traits. If pleiotropy limits cheaters, selection may favor cooperation loci that are more pleiotropic. However, the same should not be true for private loci with functions unrelated to cooperation. Pleiotropy in cooperative loci has mostly been studied with single loci and has not been measured on a wide scale or compared to a suitable set of control loci with private functions. I remedy this gap by comparing genomic measures of pleiotropy in previously identified cooperative and private loci in Pseudomonas aeruginosa. I found that cooperative loci in P. aeruginosa tended to be more pleiotropic than private loci according to the number of protein-protein interactions, the number of gene ontology terms, and gene expression specificity. These results show that pleiotropy may be a general way to limit cheating and that cooperation may shape pleiotropy in the genome.

Structural Determinants of Yeast Protein-Protein Interaction Interface Evolution at the Residue Level

Interfaces of contact between proteins play important roles in determining the proper structure and function of protein-protein interactions (PPIs). Therefore, to fully understand PPIs, we need to better understand the evolutionary design principles of PPI interfaces. Previous studies have uncovered that interfacial sites are more evolutionarily conserved than other surface protein sites. Yet, little is known about the nature and relative importance of evolutionary constraints in PPI interfaces. Here, we explore constraints imposed by the structure of the microenvironment surrounding interfacial residues on residue evolutionary rate using a large dataset of over 700 structural models of baker's yeast PPIs. We find that interfacial residues are, on average, systematically more conserved than all other residues with a similar degree of total burial as measured by relative solvent accessibility (RSA). Besides, we find that RSA of the residue when the PPI is formed is a better predictor of interfacial residue evolutionary rate than RSA in the monomer state. Furthermore, we investigate four structure-based measures of residue interfacial involvement, including change in RSA upon binding (ΔRSA), number of residue-residue contacts across the interface, and distance from the center or the periphery of the interface. Integrated modeling for evolutionary rate prediction in interfaces shows that ΔRSA plays a dominant role among the four measures of interfacial involvement, with minor, but independent contributions from other measures. These results yield insight into the evolutionary design of interfaces, improving our understanding of the role that structure plays in the molecular evolution of PPIs at the residue level.

Phylogenies of the 16S rRNA gene and its hypervariable regions lack concordance with core genome phylogenies

BACKGROUND

The 16S rRNA gene is used extensively in bacterial phylogenetics, in species delineation, and now widely in microbiome studies. However, the gene suffers from intragenomic heterogeneity, and reports of recombination and an unreliable phylogenetic signal are accumulating. Here, we compare core gene phylogenies to phylogenies constructed using core gene concatenations to estimate the strength of signal for the 16S rRNA gene, its hypervariable regions, and all core genes at the intra- and inter-genus levels. Specifically, we perform four intra-genus analyses (Clostridium, n = 65; Legionella, n = 47; Staphylococcus, n = 36; and Campylobacter, n = 17) and one inter-genus analysis [41 core genera of the human gut microbiome (31 families, 17 orders, and 12 classes), n = 82].

RESULTS

At both taxonomic levels, the 16S rRNA gene was recombinant and subject to horizontal gene transfer. At the intra-genus level, the gene showed one of the lowest levels of concordance with the core genome phylogeny (50.7% average). Concordance for hypervariable regions was lower still, with entropy masking providing little to no benefit. A major factor influencing concordance was SNP count, which showed a positive logarithmic association. Using this relationship, we determined that 690 ± 110 SNPs were required for 80% concordance (average 16S rRNA gene SNP count was 254). We also found a wide range in 16S-23S-5S rRNA operon copy number among genomes (1-27). At the inter-genus level, concordance for the whole 16S rRNA gene was markedly higher (73.8% - 10th out of 49 loci); however, the most concordant hypervariable regions (V4, V3-V4, and V1-V2) ranked in the third quartile (62.5 to 60.0%).

CONCLUSIONS

Ramifications of a poor phylogenetic performance for the 16S rRNA gene are far reaching. For example, in addition to incorrect species/strain delineation and phylogenetic inference, it has the potential to confound community diversity metrics if phylogenetic information is incorporated - for example, with popular approaches such as Faith's phylogenetic diversity and UniFrac. Our results highlight the problematic nature of these approaches and their use (along with entropy masking) is discouraged. Lastly, the wide range in 16S rRNA gene copy number among genomes also has a strong potential to confound diversity metrics. Video Abstract.

Nickel-catalysed asymmetric hydrogenation of oximes

Chiral hydroxylamines are vital substances in bioscience and versatile subunits in the preparation of a variety of functional molecules. However, asymmetric and non-asymmetric synthetic approaches to these compounds are far from satisfactory. Although atom-economic metal-catalysed asymmetric hydrogenations have been studied for over 50 years, the asymmetric hydrogenation of oximes to the corresponding chiral hydroxylamines remains challenging because of the labile N-O bond and inert C=N bond. Here we report an environmentally friendly, earth-abundant, transition-metal nickel-catalysed asymmetric hydrogenation of oximes, affording the corresponding chiral hydroxylamines with up to 99% yield, 99% e.e. and with a substrate/catalyst ratio of 1,000. Computational results indicate that the weak interactions between the catalyst and substrate play crucial roles not only in the transition states, but also during the approach of the substrate to the catalyst, by selectively reducing the reaction barriers and thus improving the reaction efficiency and securing the generation of chirality.

Patterns of selection across gene regulatory networks

Gene regulatory networks (GRNs) are the core engine of organismal development. If we would like to understand the origin and diversification of phenotypes, it is necessary to consider the structure of GRNs in order to reconstruct the links between genetic mutations and phenotypic change. Much of the progress in evolutionary developmental biology, however, has occurred without a nuanced consideration of the evolution of functional relationships between genes, especially in the context of their broader network interactions. Characterizing and comparing GRNs across traits and species in a more detailed way will allow us to determine how network position influences what genes drive adaptive evolution. In this perspective paper, we consider the architecture of developmental GRNs and how positive selection strength may vary across a GRN. We then propose several testable models for these patterns of selection and experimental approaches to test these models.

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

Relating Translation Efficiency to Protein Networks Provides Evolutionary Insights in Shewanella and Its Implications for Extracellular Electron Transfer

Shewanella species are well-known for their extracellular electron transfer (EET) capacity, by which these microorganisms can transfer the electrons from intracellular environment to extracellular space for the reduction of the extracellular insoluble electron acceptors. Using a time-stamped data for the paired protein-mRNA, we investigate the impact of differential translation on the EET process of Shewanella oneidensis MR-1. Firstly, differentially translated proteins when O₂ levels are switched from high-O₂ to low-O₂ are identified by using a soft clustering method, 629 up-regulated translated proteins and 767 down-regulated translated proteins are considered to reflect the changes from inactivated to activated EET process. Then, we showed that the degrees of connectivity of differentially translated proteins were significantly larger than those of non-differentially translated proteins, and thereby these differentially translated proteins will be more important in the protein networks. After that, we networked these differentially translated proteins to construct the differentially translated sub-networks, and discussed the most important proteins that are involved in the EET process with the help of centralization analysis of these differentially translated networks. Furthermore, we also studied the differentially translated operonic genes. Taking together, this work searches the key proteins that potentially activated the EET process from a translational efficiency viewpoint.

Identification of the antibacterial mechanism of cryptotanshinone on methicillin-resistant Staphylococcus aureus using bioinformatics analysis

Cryptotanshinone (CT) is an extract from the traditional Chinese medicine Salvia miltiorrhiza, which inhibits the growth of methicillin-resistant Staphylococcus aureus (MRSA) in vitro. This study aims to determine the antibacterial mechanisms of CT by integrating bioinformatics analysis and microbiology assay. The microarray data of GSE13203 was retrieved from the Gene Expression Omnibus (GEO) database to screen the differentially expressed genes (DEGs) of S. aureus strains that were treated with CT treatment. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to identify the potential target of CT. Data mining on the microarray dataset indicated that pyruvate kinase (PK) might be involved in the antimicrobial activities of CT. The minimum inhibition concentrations (MICs) of CT or vancomycin against the MRSA strain ATCC43300 and seven other clinical strains were determined using the broth dilution method. The effects of CT on the activity of PK were further measured. In vitro tests verified that CT inhibited the growth of an MRSA reference strain and seven other clinical strains. CT hampered the activity of the PK of ATCC43300 and five clinical MRSA strains. CT might hinder bacterial energy metabolism by inhibiting the activity of PK.

Stable and Temporary Enzyme Complexes and Metabolons Involved in Energy and Redox Metabolism

Significance: Alongside well-characterized permanent multimeric enzymes and multienzyme complexes, relatively unstable transient enzyme-enzyme assemblies, including metabolons, provide an important mechanism for the regulation of energy and redox metabolism. Critical Issues: Despite the fact that enzyme-enzyme assemblies have been proposed for many decades and experimentally analyzed for at least 40 years, there are very few pathways for which unequivocal evidence for the presence of metabolite channeling, the most frequently evoked reason for their formation, has been provided. Further, in contrast to the stronger, permanent interactions for which a deep understanding of the subunit interface exists, the mechanism(s) underlying transient enzyme-enzyme interactions remain poorly studied. Recent Advances: The widespread adoption of proteomic and cell biological approaches to characterize protein-protein interaction is defining an ever-increasing number of enzyme-enzyme assemblies as well as enzyme-protein interactions that likely identify factors which stabilize such complexes. Moreover, the use of microfluidic technologies provided compelling support of a role for substrate-specific chemotaxis in complex assemblies. Future Directions: Embracing current and developing technologies should render the delineation of metabolons from other enzyme-enzyme complexes more facile. In parallel, attempts to confirm that the findings reported in microfluidic systems are, indeed, representative of the cellular situation will be critical to understanding the physiological circumstances requiring and evoking dynamic changes in the levels of the various transient enzyme-enzyme assemblies of the cell. Antioxid. Redox Signal. 35, 788-807.

Aspergillus fumigatus versus Genus Aspergillus: Conservation, Adaptive Evolution and Specific Virulence Genes

Aspergillus is an important fungal genus containing economically important species, as well as pathogenic species of animals and plants. Using eighteen fungal species of the genus Aspergillus, we conducted a comprehensive investigation of conserved genes and their evolution. This also allows us to investigate the selection pressure driving the adaptive evolution in the pathogenic species A. fumigatus. Among single-copy orthologs (SCOs) for A. fumigatus and the closely related species A. fischeri, we identified 122 versus 50 positively selected genes (PSGs), respectively. Moreover, twenty conserved genes of unknown function were established to be positively selected and thus important for adaption. A. fumigatus PSGs interacting with human host proteins show over-representation of adaptive, symbiosis-related, immunomodulatory and virulence-related pathways, such as the TGF-β pathway, insulin receptor signaling, IL1 pathway and interfering with phagosomal GTPase signaling. Additionally, among the virulence factor coding genes, secretory and membrane protein-coding genes in multi-copy gene families, 212 genes underwent positive selection and also suggest increased adaptation, such as fungal immune evasion mechanisms (aspf2), siderophore biosynthesis (sidD), fumarylalanine production (sidE), stress tolerance (atfA) and thermotolerance (sodA). These genes presumably contribute to host adaptation strategies. Genes for the biosynthesis of gliotoxin are shared among all the close relatives of A. fumigatus as an ancient defense mechanism. Positive selection plays a crucial role in the adaptive evolution of A. fumigatus. The genome-wide profile of PSGs provides valuable targets for further research on the mechanisms of immune evasion, antimycotic targeting and understanding fundamental virulence processes.

An Ebola, Neisseria and Trypanosoma human protein interaction census reveals a conserved human protein cluster targeted by various human pathogens

Filovirus ebolavirus (ZE; Zaire ebolavirus, Bundibugyo ebolavirus), Neisseria meningitidis (NM), and Trypanosoma brucei (Tb) are serious infectious pathogens, spanning viruses, bacteria and protists and all may target the blood and central nervous system during their life cycle. NM and Tb are extracellular pathogens while ZE is obligatory intracellular, targetting immune privileged sites. By using interactomics and comparative evolutionary analysis we studied whether conserved human proteins are targeted by these pathogens. We examined 2797 unique pathogen-targeted human proteins. The information derived from orthology searches of experimentally validated protein-protein interactions (PPIs) resulted both in unique and shared PPIs for each pathogen. Comparing and analyzing conserved and pathogen-specific infection pathways for NM, TB and ZE, we identified human proteins predicted to be targeted in at least two of the compared host-pathogen networks. However, four proteins were common to all three host-pathogen interactomes: the elongation factor 1-alpha 1 (EEF1A1), the SWI/SNF complex subunit SMARCC2 (matrix-associated actin-dependent regulator of chromatin subfamily C), the dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 1 (RPN1), and the tubulin beta-5 chain (TUBB). These four human proteins all are also involved in cytoskeleton and its regulation and are often addressed by various human pathogens. Specifically, we found (i) 56 human pathogenic bacteria and viruses that target these four proteins, (ii) the well researched new pandemic pathogen SARS-CoV-2 targets two of these four human proteins and (iii) nine human pathogenic fungi (yet another evolutionary distant organism group) target three of the conserved proteins by 130 high confidence interactions.

Variables Influencing Differences in Sequence Conservation in the Fission Yeast Schizosaccharomyces pombe

Which variables determine the constraints on gene sequence evolution is one of the most central questions in molecular evolution. In the fission yeast Schizosaccharomyces pombe, an important model organism, the variables influencing the rate of sequence evolution have yet to be determined. Previous studies in other single celled organisms have generally found gene expression levels to be most significant, with numerous other variables such as gene length and functional importance identified as having a smaller impact. Using publicly available data, we used partial least squares regression, principal components regression, and partial correlations to determine the variables most strongly associated with sequence evolution constraints. We identify centrality in the protein–protein interactions network, amino acid composition, and cellular location as the most important determinants of sequence conservation. However, each factor only explains a small amount of variance, and there are numerous variables having a significant or heterogeneous influence. Our models explain more than half of the variance in dN, raising the possibility that future refined models could quantify the role of stochastics in evolutionary rate variation.

An atlas of protein-protein interactions across mouse tissues

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

Ancestry analysis indicates two different sets of essential genes in eukaryotic model species

Essential genes are so-called because they are crucial for organism perpetuation. Those genes are usually related to essential functions to cellular metabolism or multicellular homeostasis. Deleterious alterations on essential genes produce a spectrum of phenotypes in multicellular organisms. The effects range from the impairment of the fertilization process, disruption of fetal development, to loss of reproductive capacity. Essential genes are described as more evolutionarily conserved than non-essential genes. However, there is no consensus about the relationship between gene essentiality and gene age. Here, we identified essential genes in five model eukaryotic species (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Drosophila melanogaster, Caenorhabditis elegans, and Mus musculus) and estimate their evolutionary ancestry and their network properties. We observed that essential genes, on average, are older than other genes in all species investigated. The relationship of network properties and gene essentiality convey with previous findings, showing essential genes as important nodes in biological networks. As expected, we also observed that essential orthologs shared by the five species evaluated here are old. However, all the species evaluated here have a specific set of young essential genes not shared among them. Additionally, these two groups of essential genes are involved with distinct biological functions, suggesting two sets of essential genes: (i) a set of old essential genes common to all the evaluated species, regulating basic cellular functions, and (ii) a set of young essential genes exclusive to each species, which perform specific essential functions in each species.

Variation in Pleiotropic Hub Gene Expression Is Associated with Interspecific Differences in Head Shape and Eye Size in Drosophila

Revealing the mechanisms underlying the breathtaking morphological diversity observed in nature is a major challenge in Biology. It has been established that recurrent mutations in hotspot genes cause the repeated evolution of morphological traits, such as body pigmentation or the gain and loss of structures. To date, however, it remains elusive whether hotspot genes contribute to natural variation in the size and shape of organs. As natural variation in head morphology is pervasive in Drosophila, we studied the molecular and developmental basis of differences in compound eye size and head shape in two closely related Drosophila species. We show differences in the progression of retinal differentiation between species and we applied comparative transcriptomics and chromatin accessibility data to identify the GATA transcription factor Pannier (Pnr) as central factor associated with these differences. Although the genetic manipulation of Pnr affected multiple aspects of dorsal head development, the effect of natural variation is restricted to a subset of the phenotypic space. We present data suggesting that this developmental constraint is caused by the coevolution of expression of pnr and its cofactor u-shaped (ush). We propose that natural variation in expression or function of highly connected developmental regulators with pleiotropic functions is a major driver for morphological evolution and we discuss implications on gene regulatory network evolution. In comparison to previous findings, our data strongly suggest that evolutionary hotspots are not the only contributors to the repeated evolution of eye size and head shape in Drosophila.

In vivo and in vitro human gene essentiality estimations capture contrasting functional constraints

Gene essentiality estimation is a popular empirical approach to link genotypes to phenotypes. In humans, essentiality is estimated based on loss-of-function (LoF) mutation intolerance, either from population exome sequencing (in vivo) data or CRISPR-based in vitro perturbation experiments. Both approaches identify genes presumed to have detrimental consequences on the organism upon mutation. Are these genes constrained by having key cellular/organismal roles? Do in vivo and in vitro estimations equally recover these constraints? Insights into these questions have important implications in generalizing observations from cell models and interpreting disease risk genes. To empirically address these questions, we integrate genome-scale datasets and compare structural, functional and evolutionary features of essential genes versus genes with extremely high mutational tolerance. We found that essentiality estimates do recover functional constraints. However, the organismal or cellular context of estimation leads to functionally contrasting properties underlying the constraint. Our results suggest that depletion of LoF mutations in human populations effectively captures organismal-level functional constraints not experimentally accessible through CRISPR-based screens. Finally, we identify a set of genes (OrgEssential), which are mutationally intolerant in vivo but highly tolerant in vitro. These genes drive observed functional constraint differences and have an unexpected preference for nervous system expression.

Universal Constraints on Protein Evolution in the Long-Term Evolution Experiment with Escherichia coli

Although it is well known that abundant proteins evolve slowly across the tree of life, there is little consensus for why this is true. Here, I report that abundant proteins evolve slowly in the hypermutator populations of Lenski's long-term evolution experiment with Escherichia coli (LTEE). Specifically, the density of all observed mutations per gene, as measured in metagenomic time series covering 60,000 generations of the LTEE, significantly anticorrelates with mRNA abundance, protein abundance, and degree of protein-protein interaction. The same pattern holds for nonsynonymous mutation density. However, synonymous mutation density, measured across the LTEE hypermutator populations, positively correlates with protein abundance. These results show that universal constraints on protein evolution are visible in data spanning three decades of experimental evolution. Therefore, it should be possible to design experiments to answer why abundant proteins evolve slowly.

Comprehensive Intrinsic Disorder Analysis of 6108 Viral Proteomes: From the Extent of Intrinsic Disorder Penetrance to Functional Annotation of Disordered Viral Proteins

Much of our understanding of proteins and proteomes comes from the traditional protein structure-function paradigm. However, in the last 2 decades, both computational and experimental studies have provided evidence that a large fraction of functional proteomes across different domains of life consists of intrinsically disordered proteins, thus triggering a quest to unravel and decipher protein intrinsic disorder. Unlike structured/ordered proteins, intrinsically disordered proteins/regions (IDPs/IDRs) do not possess a well-defined structure under physiological conditions and exist as highly dynamic conformational ensembles. In spite of this peculiarity, these proteins have crucial roles in cell signaling and regulation. To date, studies on the abundance and function of IDPs/IDRs in viruses are rather limited. To fill this gap, we carried out an extensive and thorough bioinformatics analysis of 283 000 proteins from 6108 reference viral proteomes. We analyzed protein intrinsic disorder from multiple perspectives, such as abundance of IDPs/IDRs across diverse virus types, their functional annotations, and subcellular localization in taxonomically divergent hosts. We show that the content of IDPs/IDRs in viral proteomes varies broadly as a function of virus genome types and taxonomically divergent hosts. We have combined the two most commonly used and accurate IDP predictors' results with charge-hydropathy (CH) versus cumulative distribution function (CDF) plots to categorize the viral proteins according to their IDR content and physicochemical properties. Mapping of gene ontology on the disorder content of viral proteins reveals that IDPs are primarily involved in key virus-host interactions and host antiviral immune response downregulation, which are reinforced by the post-translational modifications tied to disorder-enriched viral proteins. The present study offers detailed insights into the prevalence of the intrinsic disorder in viral proteomes and provides appealing targets for the design of novel therapeutics.

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.

Correlated Evolution of Large DNA Fragments in the 3D Genome of Arabidopsis thaliana

In eukaryotes, the three-dimensional (3D) conformation of the genome is far from random, and this nonrandom chromatin organization is strongly correlated with gene expression and protein function, which are two critical determinants of the selective constraints and evolutionary rates of genes. However, whether genes and other elements that are located close to each other in the 3D genome evolve in a coordinated way has not been investigated in any organism. To address this question, we constructed chromatin interaction networks (CINs) in Arabidopsis thaliana based on high-throughput chromosome conformation capture data and demonstrated that adjacent large DNA fragments in the CIN indeed exhibit more similar levels of polymorphism and evolutionary rates than random fragment pairs. Using simulations that account for the linear distance between fragments, we proved that the 3D chromosomal organization plays a role in the observed correlated evolution. Spatially interacting fragments also exhibit more similar mutation rates and functional constraints in both coding and noncoding regions than the random expectations, indicating that the correlated evolution between 3D neighbors is a result of combined evolutionary forces. A collection of 39 genomic and epigenomic features can explain much of the variance in genetic diversity and evolutionary rates across the genome. Moreover, features that have a greater effect on the evolution of regional sequences tend to show higher similarity between neighboring fragments in the CIN, suggesting a pivotal role of epigenetic modifications and chromatin organization in determining the correlated evolution of large DNA fragments in the 3D genome.

Comparative Gene Expression Analysis Reveals Mechanism of Pinus contorta Response to the Fungal Pathogen Dothistroma septosporum

Many conifers have distributions that span wide ranges in both biotic and abiotic conditions, but the basis of response to biotic stress has received much less attention than response to abiotic stress. In this study, we investigated the gene expression response of lodgepole pine (Pinus contorta) to attack by the fungal pathogen Dothistroma septosporum, which causes Dothistroma needle blight, a disease that has caused severe climate-related outbreaks in northwestern British Columbia. We inoculated tolerant and susceptible pines with two D. septosporum isolates and analyzed the differentially expressed genes (DEGs), differential exon usage, and coexpressed gene modules using RNA-sequencing data. We found a rapid and strong transcriptomic response in tolerant lodgepole pine samples inoculated with one D. septosporum isolate, and a late and weak response in susceptible samples inoculated with another isolate. We mapped 43 of the DEG- or gene module-identified genes to the reference plant-pathogen interaction pathway deposited in the Kyoto Encyclopedia of Genes and Genomes database. These genes are present in PAMP-triggered and effector-triggered immunity pathways. Genes comprising pathways and gene modules had signatures of strong selective constraint, while the highly expressed genes in tolerant samples appear to have been favored by selection to counterattack the pathogen. We identified candidate resistance genes that may respond to D. septosporum effectors. Taken together, our results show that gene expression response to D. septosporum infection in lodgepole pine varies both among tree genotypes and pathogen strains and involves both known candidate genes and a number of genes with previously unknown functions.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

CEGSO: Boosting Essential Proteins Prediction by Integrating Protein Complex, Gene Expression, Gene Ontology, Subcellular Localization and Orthology Information

Essential proteins are assumed to be an indispensable element in sustaining normal physiological function and crucial to drug design and disease diagnosis. The discovery of essential proteins is of great importance in revealing the molecular mechanisms and biological processes. Owing to the tedious biological experiment, many numerical methods have been developed to discover key proteins by mining the features of the high throughput data. Appropriate integration of differential biological information based on protein-protein interaction (PPI) network has been proven useful in predicting essential proteins. The main intention of this research is to provide a comprehensive study and a review on identifying essential proteins by integrating multi-source data and provide guidance for researchers. Detailed analysis and comparison of current essential protein prediction algorithms have been carried out and tested on benchmark PPI networks. In addition, based on the previous method TEGS (short for the network Topology, gene Expression, Gene ontology, and Subcellular localization), we improve the performance of predicting essential proteins by incorporating known protein complex information, the gene expression profile, Gene Ontology (GO) terms information, subcellular localization information, and protein's orthology data into the PPI network, named CEGSO. The simulation results show that CEGSO achieves more accurate and robust results than other compared methods under different test datasets with various evaluation measurements.

Network Modeling in Biology: Statistical Methods for Gene and Brain Networks

The rise of network data in many different domains has offered researchers new insight into the problem of modeling complex systems and propelled the development of numerous innovative statistical methodologies and computational tools. In this paper, we primarily focus on two types of biological networks, gene networks and brain networks, where statistical network modeling has found both fruitful and challenging applications. Unlike other network examples such as social networks where network edges can be directly observed, both gene and brain networks require careful estimation of edges using covariates as a first step. We provide a discussion on existing statistical and computational methods for edge esitimation and subsequent statistical inference problems in these two types of biological networks.