The Evolution of Organellar Coat Complexes and Organization of the Eukaryotic Cell.

Eukaryotic cells possess a remarkably diverse range of organelles that provide compartmentalization for distinct cellular functions and are likely responsible for the remarkable success of these organisms. The origins and subsequent elaboration of these compartments represent a key aspect in the transition between prokaryotic and eukaryotic cellular forms. The protein machinery required to build, maintain, and define many membrane-bound compartments is encoded by several paralog families, including small GTPases, coiled-bundle proteins, and proteins with ß-propeller and α-solenoid secondary structures. Together these proteins provide the membrane coats and control systems to structure and coordinate the endomembrane system. Mechanistically and evolutionarily, they unite not only secretory and endocytic organelles but also the flagellum and nucleus. The ancient origins for these families have been revealed by recent findings, providing new perspectives on the deep evolutionary processes and relationships that underlie eukaryotic cell structure.

Regulation of alternative splicing through coupling with transcription and chromatin structure.

Alternative precursor messenger RNA (pre-mRNA) splicing plays a pivotal role in the flow of genetic information from DNA to proteins by expanding the coding capacity of genomes. Regulation of alternative splicing is as important as regulation of transcription to determine cell- and tissue-specific features, normal cell functioning, and responses of eukaryotic cells to external cues. Its importance is confirmed by the evolutionary conservation and diversification of alternative splicing and the fact that its deregulation causes hereditary disease and cancer. This review discusses the multiple layers of cotranscriptional regulation of alternative splicing in which chromatin structure, DNA methylation, histone marks, and nucleosome positioning play a fundamental role in providing a dynamic scaffold for interactions between the splicing and transcription machineries. We focus on evidence for how the kinetics of RNA polymerase II (RNAPII) elongation and the recruitment of splicing factors and adaptor proteins to chromatin components act in coordination to regulate alternative splicing.

The genome-wide signature of short-term temporal selection.

Despite evolutionary biology's obsession with natural selection, few studies have evaluated multigenerational series of patterns of selection on a genome-wide scale in natural populations. Here, we report on a 10-y population-genomic survey of the microcrustacean Daphnia pulex. The genome sequences of [Formula: see text]800 isolates provide insights into patterns of selection that cannot be obtained from long-term molecular-evolution studies, including the following: the pervasiveness of near quasi-neutrality across the genome (mean net selection coefficients near zero, but with significant temporal variance about the mean, and little evidence of positive covariance of selection across time intervals); the preponderance of weak positive selection operating on minor alleles; and a genome-wide distribution of numerous small linkage islands of observable selection influencing levels of nucleotide diversity. These results suggest that interannual fluctuating selection is a major determinant of standing levels of variation in natural populations, challenge the conventional paradigm for interpreting patterns of nucleotide diversity and divergence, and motivate the need for the further development of theoretical expressions for the interpretation of population-genomic data.

Why are whales big? Genes behind ocean giants.

Gigantism is prevalent in animals, but it has never reached more extreme levels than in aquatic mammals such as whales, dolphins, and porpoises. A new study by Silva et al. has uncovered five genes underlying this gigantism, a phenotype with important connections to aging and cancer suppression in long-lived animals.

Evolutionary algorithms simulating molecular evolution: a new field proposal.

The genetic blueprint for the essential functions of life is encoded in DNA, which is translated into proteins-the engines driving most of our metabolic processes. Recent advancements in genome sequencing have unveiled a vast diversity of protein families, but compared with the massive search space of all possible amino acid sequences, the set of known functional families is minimal. One could say nature has a limited protein "vocabulary." A major question for computational biologists, therefore, is whether this vocabulary can be expanded to include useful proteins that went extinct long ago or have never evolved (yet). By merging evolutionary algorithms, machine learning, and bioinformatics, we can develop highly customized "designer proteins." We dub the new subfield of computational evolution, which employs evolutionary algorithms with DNA string representations, biologically accurate molecular evolution, and bioinformatics-informed fitness functions, Evolutionary Algorithms Simulating Molecular Evolution.

Wheat Ym2 originated from Aegilops sharonensis and confers resistance to soil-borne Wheat yellow mosaic virus infection to the roots.

Wheat yellow mosaic virus (WYMV) is a pathogen transmitted into its host's roots by the soil-borne vector Polymyxa graminis. Ym1 and Ym2 genes protect the host from the significant yield losses caused by the virus, but the mechanistic basis of these resistance genes remains poorly understood. Here, it has been shown that Ym1 and Ym2 act within the root either by hindering the initial movement of WYMV from the vector into the root and/or by suppressing viral multiplication. A mechanical inoculation experiment on the leaf revealed that the presence of Ym1 reduced viral infection incidence, rather than viral titer, while that of Ym2 was ineffective in the leaf. To understand the basis of the root specificity of the Ym2 product, the gene was isolated from bread wheat using a positional cloning approach. The candidate gene encodes a CC-NBS-LRR protein and it correlated allelic variation with respect to its sequence with the host's disease response. Ym2 (B37500) and its paralog (B35800) are found in the near-relatives, respectively, Aegilops sharonensis and Aegilops speltoides (a close relative of the donor of bread wheat's B genome), while both sequences, in a concatenated state, are present in several accessions of the latter species. Structural diversity in Ym2 has been generated via translocation and recombination between the two genes and enhanced by the formation of a chimeric gene resulting from an intralocus recombination event. The analysis has revealed how the Ym2 region has evolved during the polyploidization events leading to the creation of cultivated wheat.

Ancient transcriptional regulators can easily evolve new pair-wise cooperativity.

Cells regulate gene expression by the specific binding of transcription regulators to cis-regulatory sequences. Pair-wise cooperativity between regulators-whereby two different regulators physically interact and bind DNA in a cooperative manner-is common and permits complex modes of gene regulation. Over evolutionary timescales, the formation of new combinations of regulators represents a major source of phenotypic novelty, facilitating new network structures. How functional, pair-wise cooperative interactions arise between regulators is poorly understood, despite the abundance of examples in extant species. Here, we explore a protein-protein interaction between two ancient transcriptional regulators-the homeodomain protein Matα2 and the MADS box protein Mcm1-that was gained approximately 200 million y ago in a clade of ascomycete yeasts that includes Saccharomyces cerevisiae. By combining deep mutational scanning with a functional selection for cooperative gene expression, we tested millions of possible alternative evolutionary solutions to this interaction interface. The artificially evolved, functional solutions are highly degenerate, with diverse amino acid chemistries permitted at all positions but with widespread epistasis limiting success. Nonetheless, approximately ~45% of the random sequences sampled function as well or better in controlling gene expression than the naturally evolved sequence. From these variants (which are unconstrained by historical contingency), we discern structural rules and epistatic constraints governing the emergence of cooperativity between these two transcriptional regulators. This work provides a mechanistic basis for long-standing observations of transcription network plasticity and highlights the importance of epistasis in the evolution of new protein-protein interactions.

Principles of metabolome conservation in animals.

Metabolite levels shape cellular physiology and disease susceptibility, yet the general principles governing metabolome evolution are largely unknown. Here, we introduce a measure of conservation of individual metabolite levels among related species. By analyzing multispecies tissue metabolome datasets in phylogenetically diverse mammals and fruit flies, we show that conservation varies extensively across metabolites. Three major functional properties, metabolite abundance, essentiality, and association with human diseases predict conservation, highlighting a striking parallel between the evolutionary forces driving metabolome and protein sequence conservation. Metabolic network simulations recapitulated these general patterns and revealed that abundant metabolites are highly conserved due to their strong coupling to key metabolic fluxes in the network. Finally, we show that biomarkers of metabolic diseases can be distinguished from other metabolites simply based on evolutionary conservation, without requiring any prior clinical knowledge. Overall, this study uncovers simple rules that govern metabolic evolution in animals and implies that most tissue metabolome differences between species are permitted, rather than favored by natural selection. More broadly, our work paves the way toward using evolutionary information to identify biomarkers, as well as to detect pathogenic metabolome alterations in individual patients.

Evolution of a novel regulatory mechanism of hypoxia inducible factor in hypoxia-tolerant electric fishes.

Hypoxia is a significant source of metabolic stress that activates many cellular pathways involved in cellular differentiation, proliferation, and cell death. Hypoxia is also a major component in many human diseases and a known driver of many cancers. Despite the challenges posed by hypoxia, there are animals that display impressive capacity to withstand lethal levels of hypoxia for prolonged periods of time and thus offer a gateway to a more comprehensive understanding of the hypoxic response in vertebrates. The weakly electric fish genus Brachyhypopomus inhabits some of the most challenging aquatic ecosystems in the world, with some species experiencing seasonal anoxia, thus providing a unique system to study the cellular and molecular mechanisms of hypoxia tolerance. In this study, we use closely related species of Brachyhypopomus that display a range of hypoxia tolerances to probe for the underlying molecular mechanisms via hypoxia inducible factors (HIFs)-transcription factors known to coordinate the cellular response to hypoxia in vertebrates. We find that HIF1⍺ from hypoxia tolerant Brachyhypopomus species displays higher transactivation in response to hypoxia than that of intolerant species, when overexpressed in live cells. Moreover, we identified two SUMO-interacting motifs near the oxygen-dependent degradation and transactivation domains of the HIF1⍺ protein that appear to boost transactivation of HIF1, regardless of the genetic background. Together with computational analyses of selection, this shows that evolution of HIF1⍺ are likely to underlie adaptations to hypoxia tolerance in Brachyhypopomus electric fishes, with changes in two SUMO-interacting motifs facilitating the mechanism of this tolerance.

Designing monomeric IFNγ: The significance of domain-swapped dimer structure in IFNγ immune responses.

Interferon (IFN) γ can initiate immune responses by inducing the expression of major histocompatibility complex molecules, suggesting its potential for cancer immunotherapy. However, it also has an immunosuppressive function that limits its application as a therapeutic agent. IFNγ has a characteristic domain-swapped dimer structure with two of the six α-helices exchanged with each other. As we hypothesized that the contrasting functions of IFNγ could be attributed to its unique domain-swapped structure, we designed monomeric IFNγ by transforming the domain-swapped dimer structure of WT IFNγ. We conjectured the evolution of this domain-swapped dimer and hypothesized that the current IFNγ structure emerged through shortening of the loop structure at the base of the swapped domain and the accumulation of hydrophobic amino acids at the newly generated interface during domain-swapping. We then designed and generated a stable monomeric IFNγ by retracing this evolutionary process, complementing the lost loop structure with a linker, and replacing the accumulated hydrophobic amino acids with hydrophilic ones. We determined that the designed variant was a monomer based on molecular size and number of epitopes and exhibited activity in cell-based assays. Notably, the monomeric IFNγ showed a qualitatively similar balance between immunostimulatory and immunosuppressive gene expression as WT IFNγ. This study demonstrates that the structural format of IFNγ affects the strength of its activity rather than regulating the fate of downstream gene expression.

Convergent mechanism underlying the acquisition of vertebrate scotopic vision.

High sensitivity of scotopic vision (vision in dim light conditions) is achieved by the rods' low background noise, which is attributed to a much lower thermal activation rate (kth) of rhodopsin compared with cone pigments. Frogs and nocturnal geckos uniquely possess atypical rods containing noncanonical cone pigments that exhibit low kth, mimicking rhodopsin. Here, we investigated the convergent mechanism underlying the low kth of rhodopsins and noncanonical cone pigments. Our biochemical analysis revealed that the kth of canonical cone pigments depends on their absorption maximum (λmax). However, rhodopsin and noncanonical cone pigments showed a substantially lower kth than predicted from the λmax dependency. Given that the λmax is inversely proportional to the activation energy of the pigments in the Hinshelwood distribution-based model, our findings suggest that rhodopsin and noncanonical cone pigments have convergently acquired low frequency of spontaneous-activation attempts, including thermal fluctuations of the protein moiety, in the molecular evolutionary processes from canonical cone pigments, which contributes to highly sensitive scotopic vision.

Klebsiella pneumoniae carbapenemase variant 44 acquires ceftazidime-avibactam resistance by altering the conformation of active-site loops.

Klebsiella pneumoniae carbapenemase 2 (KPC-2) is an important source of drug resistance as it can hydrolyze and inactivate virtually all ß-lactam antibiotics. KPC-2 is potently inhibited by avibactam via formation of a reversible carbamyl linkage of the inhibitor with the catalytic serine of the enzyme. However, the use of avibactam in combination with ceftazidime (CAZ-AVI) has led to the emergence of CAZ-AVI-resistant variants of KPC-2 in clinical settings. One such variant, KPC-44, bears a 15 amino acid duplication in one of the active-site loops (270-loop). Here, we show that the KPC-44 variant exhibits higher catalytic efficiency in hydrolyzing ceftazidime, lower efficiency toward imipenem and meropenem, and a similar efficiency in hydrolyzing ampicillin, than the WT KPC-2 enzyme. In addition, the KPC-44 variant enzyme exhibits 12-fold lower AVI carbamylation efficiency than the KPC-2 enzyme. An X-ray crystal structure of KPC-44 showed that the 15 amino acid duplication results in an extended and partially disordered 270-loop and also changes the conformation of the adjacent 240-loop, which in turn has altered interactions with the active-site omega loop. Furthermore, a structure of KPC-44 with avibactam revealed that formation of the covalent complex results in further disorder in the 270-loop, suggesting that rearrangement of the 270-loop of KPC-44 facilitates AVI carbamylation. These results suggest that the duplication of 15 amino acids in the KPC-44 enzyme leads to resistance to CAZ-AVI by modulating the stability and conformation of the 270-, 240-, and omega-loops.

Substitution Models of Protein Evolution with Selection on Enzymatic Activity.

Substitution models of evolution are necessary for diverse evolutionary analyses including phylogenetic tree and ancestral sequence reconstructions. At the protein level, empirical substitution models are traditionally used due to their simplicity, but they ignore the variability of substitution patterns among protein sites. Next, in order to improve the realism of the modeling of protein evolution, a series of structurally constrained substitution models were presented, but still they usually ignore constraints on the protein activity. Here, we present a substitution model of protein evolution with selection on both protein structure and enzymatic activity, and that can be applied to phylogenetics. In particular, the model considers the binding affinity of the enzyme-substrate complex as well as structural constraints that include the flexibility of structural flaps, hydrogen bonds, amino acids backbone radius of gyration, and solvent-accessible surface area that are quantified through molecular dynamics simulations. We applied the model to the HIV-1 protease and evaluated it by phylogenetic likelihood in comparison with the best-fitting empirical substitution model and a structurally constrained substitution model that ignores the enzymatic activity. We found that accounting for selection on the protein activity improves the fitting of the modeled functional regions with the real observations, especially in data with high molecular identity, which recommends considering constraints on the protein activity in the development of substitution models of evolution.

PhyloAln: A Convenient Reference-Based Tool to Align Sequences and High-Throughput Reads for Phylogeny and Evolution in the Omic Era.

The current trend in phylogenetic and evolutionary analyses predominantly relies on omic data. However, prior to core analyses, traditional methods typically involve intricate and time-consuming procedures, including assembly from high-throughput reads, decontamination, gene prediction, homology search, orthology assignment, multiple sequence alignment, and matrix trimming. Such processes significantly impede the efficiency of research when dealing with extensive data sets. In this study, we develop PhyloAln, a convenient reference-based tool capable of directly aligning high-throughput reads or complete sequences with existing alignments as a reference for phylogenetic and evolutionary analyses. Through testing with simulated data sets of species spanning the tree of life, PhyloAln demonstrates consistently robust performance compared with other reference-based tools across different data types, sequencing technologies, coverages, and species, with percent completeness and identity at least 50 percentage points higher in the alignments. Additionally, we validate the efficacy of PhyloAln in removing a minimum of 90% foreign and 70% cross-contamination issues, which are prevalent in sequencing data but often overlooked by other tools. Moreover, we showcase the broad applicability of PhyloAln by generating alignments (completeness mostly larger than 80%, identity larger than 90%) and reconstructing robust phylogenies using real data sets of transcriptomes of ladybird beetles, plastid genes of peppers, or ultraconserved elements of turtles. With these advantages, PhyloAln is expected to facilitate phylogenetic and evolutionary analyses in the omic era. The tool is accessible at https://github.com/huangyh45/PhyloAln.

The evolution of gene regulation on sex chromosomes.

Sex chromosomes have evolved repeatedly across the tree of life. Most work has focused on the loss of coding regions from sex-limited chromosomes through the accumulation of deleterious mutations. By comparison, less is known about how the regulatory landscape evolves. We review theories of how regulatory landscapes evolve on sex chromosomes and the overall impact they have on gametolog expression. We integrate empirical studies on sex chromosomes with theoretical work to synthesize how regulatory evolution could occur on sex chromosomes. Recent findings have revealed that downregulation of ancestral alleles is probably widespread on Y chromosomes and that regulatory evolution plays a key role in the evolution of sex chromosomes.

Toward a semi-supervised learning approach to phylogenetic estimation.

Models have always been central to inferring molecular evolution and to reconstructing phylogenetic trees. Their use typically involves the development of a mechanistic framework reflecting our understanding of the underlying biological processes, such as nucleotide substitu- tions, and the estimation of model parameters by maximum likelihood or Bayesian inference. However, deriving and optimizing the likelihood of the data is not always possible under complex evolutionary scenarios or even tractable for large datasets, often leading to unrealistic simplifying assumptions in the fitted models. To overcome this issue, we coupled stochastic simulations of genome evolution with a new supervised deep learning model to infer key parameters of molecular evolution. Our model is designed to directly analyze multiple sequence alignments and estimate per-site evolutionary rates and divergence, without requiring a known phylogenetic tree. The accuracy of our predictions matched that of likelihood-based phylogenetic inference, when rate heterogeneity followed a simple gamma distribution, but it strongly exceeded it under more complex patterns of rate variation, such as codon models. Our approach is highly scalable and can be efficiently applied to genomic data, as we showed on a dataset of 26 million nucleotides from the clownfish clade. Our simulations also showed that the integration of per-site rates obtained by deep learning within a Bayesian framework led to significantly more accu- rate phylogenetic inference, particularly with respect to the estimated branch lengths. We thus propose that future advancements in phylogenetic analysis will benefit from a semi-supervised learning approach that combines deep-learning estimation of substitution rates, which allows for more flexible models of rate variation, and probabilistic inference of the phylogenetic tree, which guarantees interpretability and a rigorous assessment of statistical support.

Mutation bias shapes the spectrum of adaptive substitutions.

Evolutionary adaptation often occurs by the fixation of beneficial mutations. This mode of adaptation can be characterized quantitatively by a spectrum of adaptive substitutions, i.e., a distribution for types of changes fixed in adaptation. Recent work establishes that the changes involved in adaptation reflect common types of mutations, raising the question of how strongly the mutation spectrum shapes the spectrum of adaptive substitutions. We address this question with a codon-based model for the spectrum of adaptive amino acid substitutions, applied to three large datasets covering thousands of amino acid changes identified in natural and experimental adaptation in Saccharomyces cerevisiae, Escherichia coli, and Mycobacterium tuberculosis Using species-specific mutation spectra based on prior knowledge, we find that the mutation spectrum has a proportional influence on the spectrum of adaptive substitutions in all three species. Indeed, we find that by inferring the mutation rates that best explain the spectrum of adaptive substitutions, we can accurately recover the species-specific mutation spectra. However, we also find that the predictive power of the model differs substantially between the three species. To better understand these differences, we use population simulations to explore the factors that influence how closely the spectrum of adaptive substitutions mirrors the mutation spectrum. The results show that the influence of the mutation spectrum decreases with increasing mutational supply ([Formula: see text]) and that predictive power is strongly affected by the number and diversity of beneficial mutations.

Layered evolution of gene expression in "superfast" muscles for courtship.

Identifying the molecular process of complex trait evolution is a core goal of biology. However, pinpointing the specific context and timing of trait-associated changes within the molecular evolutionary history of an organism remains an elusive goal. We study this topic by exploring the molecular basis of elaborate courtship evolution, which represents an extraordinary example of trait innovation. Within the behaviorally diverse radiation of Central and South American manakin birds, species from two separate lineages beat their wings together using specialized "superfast" muscles to generate a "snap" that helps attract mates. Here, we develop an empirical approach to analyze phylogenetic lineage-specific shifts in gene expression in the key snap-performing muscle and then integrate these findings with comparative transcriptomic sequence analysis. We find that rapid wing displays are associated with changes to a wide range of molecular processes that underlie extreme muscle performance, including changes to calcium trafficking, myocyte homeostasis and metabolism, and hormone action. We furthermore show that these changes occur gradually in a layered manner across the species history, wherein which ancestral genetic changes to many of these molecular systems are built upon by later species-specific shifts that likely finalized the process of display performance adaptation. Our study demonstrates the potential for combining phylogenetic modeling of tissue-specific gene expression shifts with phylogenetic analysis of lineage-specific sequence changes to reveal holistic evolutionary histories of complex traits.

Zymograph profiling reveals a divergent evolution of sirtuin that may originate from class III enzymes.

Sirtuins are a group of NAD+-dependent deacylases that conserved in three domains of life and comprehensively involved in the regulation of gene transcription, chromosome segregation, RNA splicing, apoptosis, and aging. Previous studies in mammalian cells have revealed that sirtuins not only exist as multiple copies, but also show distinct deacylase activities in addition to deacetylation. However, the understanding of sirtuin zymographs in other organisms with respect to molecular evolution remains at an early stage. Here, we systematically analyze the sirtuin activities in representative species from archaea, bacteria, and eukaryotes, using both the HPLC assay and a 7-amino-4-methylcoumarin-based fluorogenic method. Global profiling suggests that the deacylase activities of sirtuins could be divided into three categories and reveals undifferentiated zymographs of class III sirtuins, especially for those from bacteria and archaea. Nevertheless, initial differentiation of enzymatic activity was also observed for the class III sirtuins at both paralog and ortholog levels. Further phylogenetic analyses support a divergent evolution of sirtuin that may originate from class III sirtuins. Together, this work demonstrates a comprehensive panorama of sirtuin zymographs and provides new insights into the cellular specific regulation and molecular evolution of sirtuins.

Expression of the primate-specific LINC00473 RNA in mouse neurons promotes excitability and CREB-regulated transcription.

The LINC00473 (Lnc473) gene has previously been shown to be associated with cancer and psychiatric disorders. Its expression is elevated in several types of tumors and decreased in the brains of patients diagnosed with schizophrenia or major depression. In neurons, Lnc473 transcription is strongly responsive to synaptic activity, suggesting a role in adaptive, plasticity-related mechanisms. However, the function of Lnc473 is largely unknown. Here, using a recombinant adeno-associated viral vector, we introduced a primate-specific human Lnc473 RNA into mouse primary neurons. We show that this resulted in a transcriptomic shift comprising downregulation of epilepsy-associated genes and a rise in cAMP response element-binding protein (CREB) activity, which was driven by augmented CREB-regulated transcription coactivator 1 nuclear localization. Moreover, we demonstrate that ectopic Lnc473 expression increased neuronal excitability as well as network excitability. These findings suggest that primates may possess a lineage-specific activity-dependent modulator of CREB-regulated neuronal excitability.