Localisation and function of key axonemal microtubule inner proteins and dynein docking complex members reveal extensive diversity among vertebrate motile cilia

Vertebrate motile cilia are classified as (9+2) or (9+0), based on the presence or absence of the central pair apparatus, respectively. Cryogenic electron microscopy analyses of (9+2) cilia have uncovered an elaborate axonemal protein composition. The extent to which these features are conserved in (9+0) cilia remains unclear. CFAP53, a key axonemal filamentous microtubule inner protein (fMIP) and a centriolar satellites component, is essential for motility of (9+0), but not (9+2) cilia. Here, we show that in (9+2) cilia, CFAP53 functions redundantly with a paralogous fMIP, MNS1. MNS1 localises to ciliary axonemes, and combined loss of both proteins in zebrafish and mice caused severe outer dynein arm loss from (9+2) cilia, significantly affecting their motility. Using immunoprecipitation, we demonstrate that, whereas MNS1 can associate with itself and CFAP53, CFAP53 is unable to self-associate. We also show that additional axonemal dynein-interacting proteins, two outer dynein arm docking (ODAD) complex members, show differential localisation between types of motile cilia. Together, our findings clarify how paralogous fMIPs, CFAP53 and MNS1, function in regulating (9+2) versus (9+0) cilia motility, and further emphasise extensive structural diversity among these organelles.

Pairing interacting protein sequences using masked language modeling

Predicting which proteins interact together from amino acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments (MSAs), such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called Differentiable Pairing using Alignment-based Language Models (DiffPALM) that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids within protein chains. It also captures inter-chain coevolution, despite being trained on single-chain data. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolution-based pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. Starting from sequences paired by DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer. It also achieves competitive performance with using orthology-based pairing.

Bioinformatic Study of Possible Acute Regulation of Acid Secretion in the Stomach

The gastric H+,K+-ATPase is an integral membrane protein which derives energy from the hydrolysis of ATP to transport H+ ions from the parietal cells of the gastric mucosa into the stomach in exchange for K+ ions. It is responsible for the acidic environment of the stomach, which is essential for digestion. Acid secretion is regulated by the recruitment of the H+,K+-ATPase from intracellular stores into the plasma membrane on the ingestion of food. The similar amino acid sequences of the lysine-rich N-termini α-subunits of the H+,K+- and Na+,K+-ATPases, suggests similar acute regulation mechanisms, specifically, an electrostatic switch mechanism involving an interaction of the N-terminal tail with the surface of the surrounding membrane and a modulation of the interaction via regulatory phosphorylation by protein kinases. From a consideration of sequence alignment of the H+,K+-ATPase and an analysis of its coevolution with protein kinase C and kinases of the Src family, the evidence points towards a phosphorylation of tyrosine-7 of the N-terminus by either Lck or Yes in all vertebrates except cartilaginous fish. The results obtained will guide and focus future experimental research.

Recent advances in predicting and modeling protein-protein interactions

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins

Predicting protein-protein interactions from sequences is an important goal of computational biology. Various sources of information can be used to this end. Starting from the sequences of two interacting protein families, one can use phylogeny or residue coevolution to infer which paralogs are specific interaction partners within each species. We show that these two signals can be combined to improve the performance of the inference of interaction partners among paralogs. For this, we first align the sequence-similarity graphs of the two families through simulated annealing, yielding a robust partial pairing. We next use this partial pairing to seed a coevolution-based iterative pairing algorithm. This combined method improves performance over either separate method. The improvement obtained is striking in the difficult cases where the average number of paralogs per species is large or where the total number of sequences is modest.

Perfecting the Life Clock: The Journey from PTO to TTFL

The ubiquity of biological rhythms in life implies that it results from selection in the evolutionary process. The origin of the biological clock has two possible hypotheses: the selective pressure hypothesis of the oxidative stress cycle and the light evasion hypothesis. Moreover, the biological clock gives life higher adaptability. Two biological clock mechanisms have been discovered: the negative feedback loop of transcription-translation (TTFL) and the post-translational oscillation mechanism (PTO). The TTFL mechanism is the most classic and relatively conservative circadian clock oscillation mechanism, commonly found in eukaryotes. We have introduced the TTFL mechanism of the classical model organisms. However, the biological clock of prokaryotes is based on the PTO mechanism. The Peroxiredoxin (PRX or PRDX) protein-based PTO mechanism circadian clock widely existing in eukaryotic and prokaryotic life is considered a more conservative oscillation mechanism. The coexistence of the PTO and TTFL mechanisms in eukaryotes prompted us to explain the relationship between the two. Finally, we speculated that there might be a driving force for the evolution of the biological clock. The biological clock may have an evolutionary trend from the PTO mechanism to the TTFL mechanism, resulting from the evolution of organisms adapting to the environment.

Recent developments of sequence-based prediction of protein-protein interactions

The identification of protein-protein interactions (PPIs) can lead to a better understanding of cellular functions and biological processes of proteins and contribute to the design of drugs to target disease-causing PPIs. In addition, targeting host-pathogen PPIs is useful for elucidating infection mechanisms. Although several experimental methods have been used to identify PPIs, these methods can yet to draw complete PPI networks. Hence, computational techniques are increasingly required for the prediction of potential PPIs, which have never been seen experimentally. Recent high-performance sequence-based methods have contributed to the construction of PPI networks and the elucidation of pathogenetic mechanisms in specific diseases. However, the usefulness of these methods depends on the quality and quantity of training data of PPIs. In this brief review, we introduce currently available PPI databases and recent sequence-based methods for predicting PPIs. Also, we discuss key issues in this field and present future perspectives of the sequence-based PPI predictions.

Bioinformatic Analysis of Na+, K+-ATPase Regulation through Phosphorylation of the Alpha-Subunit N-Terminus

The Na⁺, K⁺-ATPase is an integral membrane protein which uses the energy of ATP hydrolysis to pump Na⁺ and K⁺ ions across the plasma membrane of all animal cells. It plays crucial roles in numerous physiological processes, such as cell volume regulation, nutrient reabsorption in the kidneys, nerve impulse transmission, and muscle contraction. Recent data suggest that it is regulated via an electrostatic switch mechanism involving the interaction of its lysine-rich N-terminus with the cytoplasmic surface of its surrounding lipid membrane, which can be modulated through the regulatory phosphorylation of the conserved serine and tyrosine residues on the protein's N-terminal tail. Prior data indicate that the kinases responsible for phosphorylation belong to the protein kinase C (PKC) and Src kinase families. To provide indications of which particular enzyme of these families might be responsible, we analysed them for evidence of coevolution via the mirror tree method, utilising coevolution as a marker for a functional interaction. The results obtained showed that the most likely kinase isoforms to interact with the Na⁺, K⁺-ATPase were the θ and η isoforms of PKC and the Src kinase itself. These theoretical results will guide the direction of future experimental studies.

Computational Docking Reveals Co-Evolution of C4 Carbon Delivery Enzymes in Diverse Plants

Proteins are modular functionalities regulating multiple cellular activities in prokaryotes and eukaryotes. As a consequence of higher plants adapting to arid and thermal conditions, C4 photosynthesis is the carbon fixation process involving multi-enzymes working in a coordinated fashion. However, how these enzymes interact with each other and whether they co-evolve in parallel to maintain interactions in different plants remain elusive to date. Here, we report our findings on the global protein co-evolution relationship and local dynamics of co-varying site shifts in key C4 photosynthetic enzymes. We found that in most of the selected key C4 photosynthetic enzymes, global pairwise co-evolution events exist to form functional couplings. Besides, protein-protein interactions between these enzymes may suggest their unknown functionalities in the carbon delivery process. For PEPC and PPCK regulation pairs, pocket formation at the interactive interface are not necessary for their function. This feature is distinct from another well-known regulation pair in C4 photosynthesis, namely, PPDK and PPDK-RP, where the pockets are necessary. Our findings facilitate the discovery of novel protein regulation types and contribute to expanding our knowledge about C4 photosynthesis.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences

Inferring protein-protein interactions from sequences is an important task in computational biology. Recent methods based on Direct Coupling Analysis (DCA) or Mutual Information (MI) allow to find interaction partners among paralogs of two protein families. Does successful inference mainly rely on correlations from structural contacts or from phylogeny, or both? Do these two types of signal combine constructively or hinder each other? To address these questions, we generate and analyze synthetic data produced using a minimal model that allows us to control the amounts of structural constraints and phylogeny. We show that correlations from these two sources combine constructively to increase the performance of partner inference by DCA or MI. Furthermore, signal from phylogeny can rescue partner inference when signal from contacts becomes less informative, including in the realistic case where inter-protein contacts are restricted to a small subset of sites. We also demonstrate that DCA-inferred couplings between non-contact pairs of sites improve partner inference in the presence of strong phylogeny, while deteriorating it otherwise. Moreover, restricting to non-contact pairs of sites preserves inference performance in the presence of strong phylogeny. In a natural data set, as well as in realistic synthetic data based on it, we find that non-contact pairs of sites contribute positively to partner inference performance, and that restricting to them preserves performance, evidencing an important role of phylogeny.

Mitochondrial Transcription of Entomopathogenic Fungi Reveals Evolutionary Aspects of Mitogenomes

Entomopathogenic fungi and more specifically genera Beauveria and Metarhizium have been exploited for the biological control of pests. Genome analyses are important to understand better their mode of action and thus, improve their efficacy against their hosts. Until now, the sequences of their mitochondrial genomes were studied, but not at the level of transcription. Except of yeasts and Neurospora crassa, whose mt gene transcription is well described, in all other Ascomycota, i.e., Pezizomycotina, related information is extremely scarce. In this work, mt transcription and key enzymes of this function were studied. RT-PCR experiments and Northern hybridizations reveal the transcriptional map of the mt genomes of B. bassiana and M. brunneum species. The mt genes are transcribed in six main transcripts and undergo post-transcriptional modifications to create single gene transcripts. Promoters were determined in both mt genomes with a comparative in silico analysis, including all known information from other fungal mt genomes. The promoter consensus sequence is 5'-ATAGTTATTAT-3' which is in accordance with the definition of the polycistronic transcripts determined with the experiments described above. Moreover, 5'-RACE experiments in the case of premature polycistronic transcript nad1-nad4-atp8-atp6 revealed the 5' end of the RNA transcript immediately after the in silico determined promoter, as also found in other fungal species. Since several conserved elements were retrieved from these analyses compared to the already known data from yeasts and N. crassa, the phylogenetic analyses of mt RNA polymerase (Rpo41) and its transcriptional factor (Mtf1) were performed in order to define their evolution. As expected, it was found that fungal Rpo41 originate from the respective polymerase of T7/T3 phages, while the ancestor of Mtf1 is of alpha-proteobacterial origin. Therefore, this study presents insights about the fidelity of the mt single-subunit phage-like RNA polymerase during transcription, since the correct identification of mt promoters from Rpo41 requires an ortholog to bacterial sigma factor, i.e., Mtf1. Thus, a previously proposed hypothesis of a phage infected alpha-proteobacterium as the endosymbiotic progenitor of mitochondrion is confirmed in this study and further upgraded by the co-evolution of the bacterial (Mtf1) and viral (Rpo41) originated components in one functional unit.

The S100A7 nuclear interactors in autoimmune diseases: a coevolutionary study in mammals

S100A7, a member of the S100A family of Ca²⁺-binding proteins, is considered a key effector in immune response. In particular, S100A7 dysregulation has been associated with several diseases, including autoimmune disorders. At the nuclear level, S100A7 interacts with several protein-binding partners which are involved in transcriptional regulation and DNA repair. By using the BioGRID and GAAD databases, S100A7 nuclear interactors with a putative involvement in autoimmune diseases were retrieved. We selected fatty acid-binding protein 5 (FABP5), autoimmune regulator (AIRE), cystic fibrosis transmembrane conductance regulator (CFTR), chromodomain helicase DNA-binding protein 4 (CHD4), epidermal growth factor receptor (EGFR), estrogen receptor 1 (ESR1), histone deacetylase 2 (HDAC2), v-myc avian myelocytomatosis viral oncogene homolog (MYC), protection of telomeres protein 1 (POT1), telomeric repeat-binding factor (NIMA-interacting) 1 (TERF1), telomeric repeat-binding factor 2 (TERF2), and Zic family member 1 (ZIC1). Linear correlation coefficients between interprotein distances were calculated with MirrorTree. Coevolution clusters were also identified with the use of a recent version of the Blocks in Sequences (BIS2) algorithm implemented in the BIS2Analyzer web server. Analysis of pair positions identified interprotein coevolving clusters between S100A7 and the binding partners CFTR and TERF1. Such findings could guide further analysis to better elucidate the function of S100A7 and its binding partners and to design drugs targeting for these molecules in autoimmune diseases.

The coevolution of large and small terminases of bacteriophages is a result of purifying selection leading to phenotypic stabilization

Genome packaging in many dsDNA phages requires a series of precisely coordinated actions of two phage-coded proteins, namely, large terminase (TerL) and small terminase (TerS) with DNA and ATP, and with each other. Despite the strict functional conservation, TerL and TerS homologs exhibit large sequence variations. We investigated the sequence variability across eight phage types and observed a coevolutionary framework wherein the genealogy of TerL homologs mirrored that of the corresponding TerS homologs. Furthermore, a high purifying selection observed (dN/dS«1) indicated strong structural constraints on both TerL and TerS, and identify coevolving residues in TerL and TerS of phage T4 and lambda. Using the highly coevolving (correlation coefficient of 0.99) TerL and TerS of phage N4, we show that their biochemical features are similar to the phylogenetically divergent phage λ terminases. We also demonstrate using the Surface Plasma Resonance (SPR) technique that phage N4 TerL transiently interacts with TerS.

Evidence of Adaptive Evolution in Wolbachia-Regulated Gene DNMT2 and Its Role in the Dipteran Immune Response and Pathogen Blocking

Eukaryotic nucleic acid methyltransferase (MTase) proteins are essential mediators of epigenetic and epitranscriptomic regulation. DNMT2 belongs to a large, conserved family of DNA MTases found in many organisms, including holometabolous insects such as fruit flies and mosquitoes, where it is the lone MTase. Interestingly, despite its nomenclature, DNMT2 is not a DNA MTase, but instead targets and methylates RNA species. A growing body of literature suggests that DNMT2 mediates the host immune response against a wide range of pathogens, including RNA viruses. Curiously, although DNMT2 is antiviral in Drosophila, its expression promotes virus replication in mosquito species. We, therefore, sought to understand the divergent regulation, function, and evolution of these orthologs. We describe the role of the Drosophila-specific host protein IPOD in regulating the expression and function of fruit fly DNMT2. Heterologous expression of these orthologs suggests that DNMT2's role as an antiviral is host-dependent, indicating a requirement for additional host-specific factors. Finally, we identify and describe potential evidence of positive selection at different times throughout DNMT2 evolution within dipteran insects. We identify specific codons within each ortholog that are under positive selection and find that they are restricted to four distinct protein domains, which likely influence substrate binding, target recognition, and adaptation of unique intermolecular interactions. Collectively, our findings highlight the evolution of DNMT2 in Dipteran insects and point to structural, regulatory, and functional differences between mosquito and fruit fly homologs.

Co-evolutionary landscape at the interface and non-interface regions of protein-protein interaction complexes

Proteins involved in interactions throughout the course of evolution tend to co-evolve and compensatory changes may occur in interacting proteins to maintain or refine such interactions. However, certain residue pair alterations may prove to be detrimental for functional interactions. Hence, determining co-evolutionary pairings that could be structurally or functionally relevant for maintaining the conservation of an inter-protein interaction is important. Inter-protein co-evolution analysis in several complexes utilizing multiple existing methodologies suggested that co-evolutionary pairings can occur in spatially proximal and distant regions in inter-protein interactions. Subsequently, the Co-Var (Correlated Variation) method based on mutual information and Bhattacharyya coefficient was developed, validated, and found to perform relatively better than CAPS and EV-complex. Interestingly, while applying the Co-Var measure and EV-complex program on a set of protein-protein interaction complexes, co-evolutionary pairings were obtained in interface and non-interface regions in protein complexes. The Co-Var approach involves determining high degree co-evolutionary pairings that include multiple co-evolutionary connections between particular co-evolved residue positions in one protein with multiple residue positions in the binding partner. Detailed analyses of high degree co-evolutionary pairings in protein-protein complexes involved in cancer metastasis suggested that most of the residue positions forming such co-evolutionary connections mainly occurred within functional domains of constituent proteins and substitution mutations were also common among these positions. The physiological relevance of these predictions suggested that Co-Var can predict residues that could be crucial for preserving functional protein-protein interactions. Finally, Co-Var web server (http://www.hpppi.iicb.res.in/ishi/covar/index.html) that implements this methodology identifies co-evolutionary pairings in intra and inter-protein interactions.

Whole Genome Level Analysis of the Wnt and DIX Gene Families in Mice and Their Coordination Relationship in Regulating Cardiac Hypertrophy

The Wnt signaling pathway is an evolutionarily conserved signaling pathway that plays essential roles in embryonic development, organogenesis, and many other biological activities. Both Wnt proteins and DIX proteins are important components of Wnt signaling. Systematic studies of Wnt and DIX families at the genome-wide level may provide a comprehensive landscape to elucidate their functions and demonstrate their relationships, but they are currently lacking. In this report, we describe the correlations between mouse Wnt and DIX genes in family expansion, molecular evolution, and expression levels in cardiac hypertrophy at the genome-wide scale. We observed that both the Wnt and DIX families underwent more expansion than the overall average in the evolutionarily early stage. In addition, mirrortree analyses suggested that Wnt and DIX were co-evolved protein families. Collectively, these results would help to elucidate the evolutionary characters of Wnt and DIX families and demonstrate their correlations in mediating cardiac hypertrophy.

The origin of SPA reveals the divergence and convergence of light signaling in Archaeplastida

Plants have evolved various photoreceptors to adapt to changing light environments, and photoreceptors can inactivate the large CONSTITUTIVE PHOTOMORPHOGENIC/DE-ETIOLATED/FUSCA (COP/DET/FUS) protein complex to release their repression of photoresponsive transcription factors. Here, we tracked the origin and evolution of COP/DET/FUS in Archaeplastida and found that most components of COP/DET/FUS were highly conserved. Intriguingly, the COP1-SUPPRESSOR OF PHYA-105 (SPA) protein originated in Chlorophyta but subsequently underwent a distinct evolutionary history in Viridiplantae. SPA experienced duplication events in the ancestors of specific clades after the colonization of land by plants and was divided into two clades (clades A and B) within euphyllophytes (ferns and seed plants). Our phylogenetic and experimental evidences support a new evolutionary model to clarify the divergence and convergence of light signaling during plant evolution.

The Phosphoprotein Synapsin Ia Regulates the Kinetics of Dense-Core Vesicle Release

Common fusion machinery mediates the Ca²⁺-dependent exocytosis of synaptic vesicles (SVs) and dense-core vesicles (DCVs). Previously, Synapsin Ia (Syn Ia) was found to localize to SVs, essential for mobilizing SVs to the plasma membrane through phosphorylation. However, whether (or how) the phosphoprotein Syn Ia plays a role in regulating DCV exocytosis remains unknown. To answer these questions, we measured the dynamics of DCV exocytosis by using single-vesicle amperometry in PC12 cells (derived from the pheochromocytoma of rats of unknown sex) overexpressing wild-type or phosphodeficient Syn Ia. We found that overexpression of phosphodeficient Syn Ia decreased the DCV secretion rate, specifically via residues previously shown to undergo calmodulin-dependent kinase (CaMK)-mediated phosphorylation (S9, S566, and S603). Moreover, the fusion pore kinetics during DCV exocytosis were found to be differentially regulated by Syn Ia and two phosphodeficient Syn Ia mutants (Syn Ia-S62A and Syn Ia-S9,566,603A). Kinetic analysis suggested that Syn Ia may regulate the closure and dilation of DCV fusion pores via these sites, implying the potential interactions of Syn Ia with certain DCV proteins involved in the regulation of fusion pore dynamics. Furthermore, we predicted the interaction of Syn Ia with several DCV proteins, including Synaptophysin (Syp) and soluble N-ethylmaleimide-sensitive factor attachment receptor (SNARE) proteins. By immunoprecipitation, we found that Syn Ia interacted with Syp via phosphorylation. Moreover, a proximity ligation assay (PLA) confirmed their phosphorylation-dependent, in situ interaction on DCVs. Together, these findings reveal a phosphorylation-mediated regulation of DCV exocytosis by Syn Ia.SIGNIFICANCE STATEMENT Although they exhibit distinct exocytosis dynamics upon stimulation, synaptic vesicles (SVs) and dense-core vesicles (DCVs) may undergo co-release in neurons and neuroendocrine cells through an undefined molecular mechanism. Synapsin Ia (Syn Ia) is known to recruit SVs to the plasma membrane via phosphorylation. Here, we examined whether Syn Ia also affects the dynamics of DCV exocytosis. We showed that Syn Ia regulates the DCV secretion rate and fusion pore kinetics during DCV exocytosis. Moreover, Syn Ia-mediated regulation of DCV exocytosis depends on phosphorylation. We further found that Syn Ia interacts with Synaptophysin (Syp) on DCVs in a phosphorylation-dependent manner. Thus, these results suggest that Syn Ia may regulate the release of DCVs via phosphorylation.

Interaction between matrix metalloproteinase-9 (MMP-9) and neutrophil gelatinase-associated lipocalin (NGAL): A recent evolutionary event in primates

Matrix metalloproteases are known to represent an early step in the evolution of the immune system. Similarly, neutrophil gelatinase-associated lipocalin is known to be a key effector in immune response. MMP-9 interacts with NGAL, but their interaction mechanisms remain unclear. Functional interaction between proteins is ensured by coevolution. Protein coevolution was inferred by calculating the linear correlation coefficients between inter-protein distance matrices using MirrorTree. Among examined mammal species, we found a robust signal of MMP-9/NGAL coevolution exclusively within Primates (R = 0.96, p < 1e-06). Owing to the high conservation of these proteins among Mammals, we chose to utilize a recent version of Blocks in Sequences (BIS2) algorithm implemented in BIS2Analyzer webserver. Coevolution clusters between the two proteins were identified in MMP-9 fibronectin and hemopexin domains. Our results suggest that MMP-9/NGAL interaction is a recent evolutionary acquisition in Primates. Furthermore, MMP-9 hemopexin domain would represent a promising target for drug design against these molecules.

New Insights into the Co-Occurrences of Glycoside Hydrolase Genes among Prokaryotic Genomes through Network Analysis

Glycoside hydrolase (GH) represents a crucial category of enzymes for carbohydrate utilization in most organisms. A series of glycoside hydrolase families (GHFs) have been classified, with relevant information deposited in the CAZy database. Statistical analysis indicated that most GHFs (134 out of 154) were prone to exist in bacteria rather than archaea, in terms of both occurrence frequencies and average gene numbers. Co-occurrence analysis suggested the existence of strong or moderate-strong correlations among 63 GHFs. A combination of network analysis by Gephi and functional classification among these GHFs demonstrated the presence of 12 functional categories (from group A to L), with which the corresponding microbial collections were subsequently labeled, respectively. Interestingly, a progressive enrichment of particular GHFs was found among several types of microbes, and type-L as well as type-E microbes were deemed as functional intensified species which formed during the microbial evolution process toward efficient decomposition of lignocellulose as well as pectin, respectively. Overall, integrating network analysis and enzymatic functional classification, we were able to provide a new angle of view for GHs from known prokaryotic genomes, and thus this study is likely to guide the selection of GHs and microbes for efficient biomass utilization.

Inter-protein residue covariation information unravels physically interacting protein dimers

BACKGROUND

Predicting physical interaction between proteins is one of the greatest challenges in computational biology. There are considerable various protein interactions and a huge number of protein sequences and synthetic peptides with unknown interacting counterparts. Most of co-evolutionary methods discover a combination of physical interplays and functional associations. However, there are only a handful of approaches which specifically infer physical interactions. Hybrid co-evolutionary methods exploit inter-protein residue coevolution to unravel specific physical interacting proteins. In this study, we introduce a hybrid co-evolutionary-based approach to predict physical interplays between pairs of protein families, starting from protein sequences only.

RESULTS

In the present analysis, pairs of multiple sequence alignments are constructed for each dimer and the covariation between residues in those pairs are calculated by CCMpred (Contacts from Correlated Mutations predicted) and three mutual information based approaches for ten accessible surface area threshold groups. Then, whole residue couplings between proteins of each dimer are unified into a single Frobenius norm value. Norms of residue contact matrices of all dimers in different accessible surface area thresholds are fed into support vector machine as single or multiple feature models. The results of training the classifiers by single features show no apparent different accuracies in distinct methods for different accessible surface area thresholds. Nevertheless, mutual information product and context likelihood of relatedness procedures may roughly have an overall higher and lower performances than other two methods for different accessible surface area cut-offs, respectively. The results also demonstrate that training support vector machine with multiple norm features for several accessible surface area thresholds leads to a considerable improvement of prediction performance. In this context, CCMpred roughly achieves an overall better performance than mutual information based approaches. The best accuracy, sensitivity, specificity, precision and negative predictive value for that method are 0.98, 1, 0.962, 0.96, and 0.962, respectively.

CONCLUSIONS

In this paper, by feeding norm values of protein dimers into support vector machines in different accessible surface area thresholds, we demonstrate that even small number of proteins in pairs of multiple alignments could allow one to accurately discriminate between positive and negative dimers.

The Evolutionary History of Ephs and Ephrins: Toward Multicellular Organisms

Eph receptor (Eph) and ephrin signaling regulate fundamental developmental processes through both forward and reverse signaling triggered upon cell–cell contact. In vertebrates, they are both classified into classes A and B, and some representatives have been identified in many metazoan groups, where their expression and functions have been well studied. We have extended previous phylogenetic analyses and examined the presence of Eph and ephrins in the tree of life to determine their origin and evolution. We have found that 1) premetazoan choanoflagellates may already have rudimental Eph/ephrin signaling as they have an Eph-/ephrin-like pair and homologs of downstream-signaling genes; 2) both forward- and reverse-downstream signaling might already occur in Porifera since sponges have most genes involved in these types of signaling; 3) the nonvertebrate metazoan Eph is a type-B receptor that can bind ephrins regardless of their membrane-anchoring structure, glycosylphosphatidylinositol, or transmembrane; 4) Eph/ephrin cross-class binding is specific to Gnathostomata; and 5) kinase-dead Eph receptors can be traced back to Gnathostomata. We conclude that Eph/ephrin signaling is of older origin than previously believed. We also examined the presence of protein domains associated with functional characteristics and the appearance and conservation of downstream-signaling pathways to understand the original and derived functions of Ephs and ephrins. We find that the evolutionary history of these gene families points to an ancestral function in cell–cell interactions that could contribute to the emergence of multicellularity and, in particular, to the required segregation of cell populations.

Statistical physics of interacting proteins: Impact of dataset size and quality assessed in synthetic sequences

Identifying protein-protein interactions is crucial for a systems-level understanding of the cell. Recently, algorithms based on inverse statistical physics, e.g., direct coupling analysis (DCA), have allowed to use evolutionarily related sequences to address two conceptually related inference tasks: finding pairs of interacting proteins and identifying pairs of residues which form contacts between interacting proteins. Here we address two underlying questions: How are the performances of both inference tasks related? How does performance depend on dataset size and the quality? To this end, we formalize both tasks using Ising models defined over stochastic block models, with individual blocks representing single proteins and interblock couplings protein-protein interactions; controlled synthetic sequence data are generated by Monte Carlo simulations. We show that DCA is able to address both inference tasks accurately when sufficiently large training sets of known interaction partners are available and that an iterative pairing algorithm allows to make predictions even without a training set. Noise in the training data deteriorates performance. In both tasks we find a quadratic scaling relating dataset quality and size that is consistent with noise adding in square-root fashion and signal adding linearly when increasing the dataset. This implies that it is generally good to incorporate more data even if their quality are imperfect, thereby shedding light on the empirically observed performance of DCA applied to natural protein sequences.

Comprehensive study of the genes involved in chlorophyll synthesis and degradation pathways in some monocot and dicot plant species

Chlorophyll (Chl) biosynthesis is one of the most important cellular processes essential for plant photosynthesis. Chl degradation pathway is also important catabolic process occurs during leaf senescence, fruit ripening and under biotic or abiotic stress conditions. Here we have systematically investigated the molecular evolution, gene structure, compositional analysis along with ENc plot, correspondence analysis and codon usage bias of the proteins and encoded genes involved in Chl metabolism from monocots and dicots. The gene and species specific phylogenetic trees using amino acid sequences showed clear clustering formation of the selected species based on monocots and dicots but not supported by 18S rRNA. Nucleotide composition of the encoding genes showed that average GC%, GC1%, GC2% and GC3% were higher in monocots. RSCU analysis depicts that genes from monocots for both pathways and genes for synthesis pathway from dicots only biased to G/C-ending synonymous codons but in degradation pathway most optimal codons (except UUG) in dicots biased to A/U-ending synonymous codons. We found strong evidence of episodic diversifying selection at several amino acid sites in all genes investigated. Conserved domain and gene structures were observed for the genes with varying lengths of introns and exons, involved in Chl metabolism along with some intronless genes within synthesis pathway. ENc and correspondence analyses suggested the mutational or selection constraint on the genes to shape the codon usage. These comprehensive studies may be helpful in further research in molecular phylogenetics and genomics and to better understand the evolutionary dynamics of Chl metabolic pathway.Communicated by Ramaswamy H. Sarma.

S100A7/Ran-binding protein 9 coevolution in mammals

S100A7 has been suggested to interact with Ran-binding protein 9. Both proteins are nowadays considered key effectors in immune response. Functional interaction between proteins is ensured by coevolution. The mechanisms of vertebrate coevolution between S100A7 and RanBP9 remain unclear. Several approaches for studying coevolution have been developed. Protein coevolution was inferred by calculating the linear correlation coefficients between inter-protein distance matrices using Mirrortree. We found an overall moderate correlation value (R = 0.53, p < 1e-06). Moreover, owing to the high conservation of RanBP9 protein among vertebrates, we chose to utilize a recent version of Blocks in Sequences (BIS2) algorithm implemented in BIS2Analyzer webserver. A coevolution cluster was identified between the two proteins (p < 8.10e-05). In conclusion, our coevolutionary analysis suggests that amino acid variations may modulate S100A7/RanBP9 interaction with potential pathogenic effects. Such findings could guide further analysis to better elucidate the function of S100A7 and RanBP9 and to design drugs targeting for these molecules in diseases.

Positive Selection in Bifidobacterium Genes Drives Species-Specific Host-Bacteria Communication

Bifidobacteria are commensal microorganisms that inhabit a wide range of hosts, including insects, birds and mammals. The mechanisms responsible for the adaptation of bifidobacteria to various hosts during the evolutionary process remain poorly understood. Previously, we reported that the species-specific PFNA gene cluster is present in the genomes of various species of the Bifidobacterium genus. The cluster contains signal transduction and adhesion genes that are presumably involved in the communication between bifidobacteria and their hosts. The genes in the PFNA cluster show high sequence divergence between bifidobacterial species, which may be indicative of rapid evolution that drives species-specific adaptation to the host organism. We used the maximum likelihood approach to detect positive selection in the PFNA genes. We tested for both pervasive and episodic positive selection to identify codons that experienced adaptive evolution in all and individual branches of the Bifidobacterium phylogenetic tree, respectively. Our results provide evidence that episodic positive selection has played an important role in the divergence process and molecular evolution of sequences of the species-specific PFNA genes in most bifidobacterial species. Moreover, we found the signatures of pervasive positive selection in the molecular evolution of the tgm gene in all branches of the Bifidobacterium phylogenetic tree. These results are consistent with the suggested role of PFNA gene cluster in the process of specific adaptation of bifidobacterial species to various hosts.

Phylogenetic correlations can suffice to infer protein partners from sequences

Determining which proteins interact together is crucial to a systems-level understanding of the cell. Recently, algorithms based on Direct Coupling Analysis (DCA) pairwise maximum-entropy models have allowed to identify interaction partners among paralogous proteins from sequence data. This success of DCA at predicting protein-protein interactions could be mainly based on its known ability to identify pairs of residues that are in contact in the three-dimensional structure of protein complexes and that coevolve to remain physicochemically complementary. However, interacting proteins possess similar evolutionary histories. What is the role of purely phylogenetic correlations in the performance of DCA-based methods to infer interaction partners? To address this question, we employ controlled synthetic data that only involve phylogeny and no interactions or contacts. We find that DCA accurately identifies the pairs of synthetic sequences that share evolutionary history. While phylogenetic correlations confound the identification of contacting residues by DCA, they are thus useful to predict interacting partners among paralogs. We find that DCA performs as well as phylogenetic methods to this end, and slightly better than them with large and accurate training sets. Employing DCA or phylogenetic methods within an Iterative Pairing Algorithm (IPA) allows to predict pairs of evolutionary partners without a training set. We further demonstrate the ability of these various methods to correctly predict pairings among real paralogous proteins with genome proximity but no known direct physical interaction, illustrating the importance of phylogenetic correlations in natural data. However, for physically interacting and strongly coevolving proteins, DCA and mutual information outperform phylogenetic methods. We finally discuss how to distinguish physically interacting proteins from proteins that only share a common evolutionary history.

Many biologically important protein-protein interactions are conserved over evolutionary time scales. This leads to two different signals that can be used to computationally predict interactions between protein families and to identify specific interaction partners. First, the shared evolutionary history leads to highly similar phylogenetic relationships between interacting proteins of the two families. Second, the need to keep the interaction surfaces of partner proteins biophysically compatible causes a correlated amino-acid usage of interface residues. Employing simulated data, we show that the shared history alone can be used to detect partner proteins. Similar accuracies are achieved by algorithms comparing phylogenetic relationships and by methods based on Direct Coupling Analysis (DCA), which are primarily known for their ability to detect the second type of signal. Using natural sequence data, we show that in cases with shared evolutionary history but without known physical interactions, both methods work with similar accuracy, while for some physically interacting systems, DCA and mutual information outperform phylogenetic methods. We propose methods allowing both to predict interactions between protein families and to find interacting partners among paralogs.

Mitochondrial lipid droplet formation as a detoxification mechanism to sequester and degrade excessive urothelial membranes

The apical surface of the terminally differentiated mammalian urothelial umbrella cell is mechanically stable and highly impermeable, in part due to its coverage by urothelial plaques consisting of 2D crystals of uroplakin particles. The mechanism for regulating the uroplakin/plaque level is unclear. We found that genetic ablation of the highly tissue-specific sorting nexin Snx31, which localizes to plaques lining the multivesicular bodies (MVBs) in urothelial umbrella cells, abolishes MVBs suggesting that Snx31 plays a role in stabilizing the MVB-associated plaques by allowing them to achieve a greater curvature. Strikingly, Snx31 ablation also induces a massive accumulation of uroplakin-containing mitochondria-derived lipid droplets (LDs), which mediate uroplakin degradation via autophagy/lipophagy, leading to the loss of apical and fusiform vesicle plaques. These results suggest that MVBs play an active role in suppressing the excessive/wasteful endocytic degradation of uroplakins. Failure of this suppression mechanism triggers the formation of mitochondrial LDs so that excessive uroplakin membranes can be sequestered and degraded. Because mitochondrial LD formation, which occurs at a low level in normal urothelium, can also be induced by disturbance in uroplakin polymerization due to individual uroplakin knockout and by arsenite, a bladder carcinogen, this pathway may represent an inducible, versatile urothelial detoxification mechanism.

Data for positive selection test and co-evolutionary analysis on mammalian cereblon

Cereblon (CRBN) is a substrate recognition subunit of the CRL4 E3 ubiquitin ligase complex, directly binding to specific substrates for poly-ubiquitination followed by proteasome-dependent degradation of proteins. Cellular CRBN is responsible for energy metabolism, ion-channel activation, and cellular stress response through binding to proteins related to the respective pathways. As CRBN binds to various proteins, the selective pressure at the interacting surface is expected to result in functional divergence. Here, we present two mammalian CRBN datasets of molecular evolutionary analyses. (1) The multiple sequence alignment data shows that positive selection occurred, determined with a dN/dS calculation. (2) Data on co-evolutionary analysis between vertebrate CRBN and related proteins are represented by calculating the correlation coefficient based on the comparison of phylogenetic trees. Co-evolutionary analysis shows the similarity of evolutionary traits of two proteins. Further molecular, functional interpretation of these analyses is explained in ‘Positive selection of Cereblon modified function including its E3 Ubiquitin Ligase activity and binding efficiency with AMPK’ (W. Onodera, T. Asahi, N. Sawamura, Positive selection of cereblon modified function including its E3 ubiquitin ligase activity and binding efficiency with AMPK. Mol Phylogenet Evol. (2019) 135:78-85. [1]).

BIS2Analyzer: a server for co-evolution analysis of conserved protein families

Along protein sequences, co-evolution analysis identifies residue pairs demonstrating either a specific co-adaptation, where changes in one of the residues are compensated by changes in the other during evolution or a less specific external force that affects the evolutionary rates of both residues in a similar magnitude. In both cases, independently of the underlying cause, co-evolutionary signatures within or between proteins serve as markers of physical interactions and/or functional relationships. Depending on the type of protein under study, the set of available homologous sequences may greatly differ in size and amino acid variability. BIS2Analyzer, openly accessible at http://www.lcqb.upmc.fr/BIS2Analyzer/, is a web server providing the online analysis of co-evolving amino-acid pairs in protein alignments, especially designed for vertebrate and viral protein families, which typically display a small number of highly similar sequences. It is based on BIS2, a re-implemented fast version of the co-evolution analysis tool Blocks in Sequences (BIS). BIS2Analyzer provides a rich and interactive graphical interface to ease biological interpretation of the results.

Positive selection of cereblon modified function including its E3 ubiquitin ligase activity and binding efficiency with AMPK

Cereblon (CRBN) is a substrate receptor for an E3 ubiquitin ligase that directly binds to target proteins resulting in cellular activities, such as energy metabolism, membrane potential regulation, and transcription factor degradation. Genetic mutations in human CRBN lead to intellectual disabilities. In addition, it draws pathological attention because direct binding with immunomodulatory drugs can cure multiple myeloma (MM) and lymphocytic leukemia. To further explore the function of CRBN, we focused on its molecular evolution. Since CRBN interacts directly with its substrates and is widely conserved in vertebrates, evolutionary study to identify the selective pressure on CRBN that occur during CRBN-substrate interaction is an effective approach to search for a novel active site. Using mammalian CRBN sequences, dN/dS analysis was conducted to detect positive selection. By multiple sequence alignment we found that the residue at position 366 was under positive selection. This residue is present in the substrate-binding domain of CRBN. Most mammals harbor cysteine at position 366, whereas rodents and chiroptera have serine at this site. Subsequently, we constructed a C366S human CRBN to confirm the potential of positive selection. Auto-ubiquitination activity occurs in E3 ubiquitin ligases, including CRBN, and increased in C366S CRBN, which lead to the conclusion that E3 ubiquitin ligase activity may have changed over the course of mammalian evolution. Furthermore, binding with AMP-activated protein kinase was augmented when the substitution was present, which is supported by coevolution analysis. These results suggest that the molecular evolution of CRBN occurred through codon-based positive selection, providing a new approach to investigate CRBN function.

TIC236 links the outer and inner membrane translocons of the chloroplast

The two-membrane envelope is a defining feature of chloroplasts. Chloroplasts evolved from a Gram-negative cyanobacterial endosymbiont. During evolution, genes of the endosymbiont have been transferred to the host nuclear genome. Most chloroplast proteins are synthesized in the cytosol as higher-molecular-mass preproteins with an N-terminal transit peptide. Preproteins are transported into chloroplasts by the TOC and TIC (translocons at the outer- and inner-envelope membranes of chloroplasts, respectively) machineries1,2, but how TOC and TIC are assembled together is unknown. Here we report the identification of the TIC component TIC236; TIC236 is an integral inner-membrane protein that projects a 230-kDa domain into the intermembrane space, which binds directly to the outer-membrane channel TOC75. The knockout mutation of TIC236 is embryonically lethal. In TIC236-knockdown mutants, a smaller amount of the inner-membrane channel TIC20 was associated with TOC75; the amount of TOC-TIC supercomplexes was also reduced. This resulted in a reduced import rate into the stroma, though outer-membrane protein insertion was unaffected. The size and the essential nature of TIC236 indicate that-unlike in mitochondria, in which the outer- and inner-membrane translocons exist as separate complexes and a supercomplex is only transiently assembled during preprotein translocation3,4-a long and stable protein bridge in the intermembrane space is required for protein translocation into chloroplasts. Furthermore, TIC236 and TOC75 are homologues of bacterial inner-membrane TamB5 and outer-membrane BamA, respectively. Our evolutionary analyses show that, similar to TOC75, TIC236 is preserved only in plants and has co-evolved with TOC75 throughout the plant lineage. This suggests that the backbone of the chloroplast protein-import machinery evolved from the bacterial TamB-BamA protein-secretion system.

ZomB is essential for flagellar motor reversals in Shewanella putrefaciens and Vibrio parahaemolyticus

The ability of most bacterial flagellar motors to reverse the direction of rotation is crucial for efficient chemotaxis. In Escherichia coli, motor reversals are mediated by binding of phosphorylated chemotaxis protein CheY to components of the flagellar rotor, FliM and FliN, which induces a conformational switch of the flagellar C-ring. Here, we show that for Shewanella putrefaciens, Vibrio parahaemolyticus and likely a number of other species an additional transmembrane protein, ZomB, is critically required for motor reversals as mutants lacking ZomB exclusively exhibit straightforward swimming also upon full phosphorylation or overproduction of CheY. ZomB is recruited to the cell poles by and is destabilized in the absence of the polar landmark protein HubP. ZomB also co-localizes to and may thus interact with the flagellar motor. The ΔzomB phenotype was suppressed by mutations in the very C-terminal region of FliM. We propose that the flagellar motors of Shewanella, Vibrio and numerous other species harboring orthologs to ZomB are locked in counterclockwise rotation and may require interaction with ZomB to enable the conformational switch required for motor reversals. Regulation of ZomB activity or abundance may provide these species with an additional means to modulate chemotaxis efficiency.

Algorithms for matching partially labelled sequence graphs

BACKGROUND

In order to find correlated pairs of positions between proteins, which are useful in predicting interactions, it is necessary to concatenate two large multiple sequence alignments such that the sequences that are joined together belong to those that interact in their species of origin. When each protein is unique then the species name is sufficient to guide this match, however, when there are multiple related sequences (paralogs) in each species then the pairing is more difficult. In bacteria a good guide can be gained from genome co-location as interacting proteins tend to be in a common operon but in eukaryotes this simple principle is not sufficient.

RESULTS

The methods developed in this paper take sets of paralogs for different proteins found in the same species and make a pairing based on their evolutionary distance relative to a set of other proteins that are unique and so have a known relationship (singletons). The former constitute a set of unlabelled nodes in a graph while the latter are labelled. Two variants were tested, one based on a phylogenetic tree of the sequences (the topology-based method) and a simpler, faster variant based only on the inter-sequence distances (the distance-based method). Over a set of test proteins, both gave good results, with the topology method performing slightly better.

CONCLUSIONS

The methods develop here still need refinement and augmentation from constraints other than the sequence data alone, such as known interactions from annotation and databases, or non-trivial relationships in genome location. With the ever growing numbers of eukaryotic genomes, it is hoped that the methods described here will open a route to the use of these data equal to the current success attained with bacterial sequences.

Applications of contact predictions to structural biology

Evolutionary pressure on residue interactions, intramolecular or intermolecular, that are important for protein structure or function can lead to covariance between the two positions. Recent methodological advances allow much more accurate contact predictions to be derived from this evolutionary covariance signal. The practical application of contact predictions has largely been confined to structural bioinformatics, yet, as this work seeks to demonstrate, the data can be of enormous value to the structural biologist working in X-ray crystallo-graphy, cryo-EM or NMR. Integrative structural bioinformatics packages such as Rosetta can already exploit contact predictions in a variety of ways. The contribution of contact predictions begins at construct design, where structural domains may need to be expressed separately and contact predictions can help to predict domain limits. Structure solution by molecular replacement (MR) benefits from contact predictions in diverse ways: in difficult cases, more accurate search models can be constructed using ab initio modelling when predictions are available, while intermolecular contact predictions can allow the construction of larger, oligomeric search models. Furthermore, MR using supersecondary motifs or large-scale screens against the PDB can exploit information, such as the parallel or antiparallel nature of any β-strand pairing in the target, that can be inferred from contact predictions. Contact information will be particularly valuable in the determination of lower resolution structures by helping to assign sequence register. In large complexes, contact information may allow the identity of a protein responsible for a certain region of density to be determined and then assist in the orientation of an available model within that density. In NMR, predicted contacts can provide long-range information to extend the upper size limit of the technique in a manner analogous but complementary to experimental methods. Finally, predicted contacts can distinguish between biologically relevant interfaces and mere lattice contacts in a final crystal structure, and have potential in the identification of functionally important regions and in foreseeing the consequences of mutations.

A coevolution analysis for identifying protein-protein interactions by Fourier transform

Protein-protein interactions (PPIs) play key roles in life processes, such as signal transduction, transcription regulations, and immune response, etc. Identification of PPIs enables better understanding of the functional networks within a cell. Common experimental methods for identifying PPIs are time consuming and expensive. However, recent developments in computational approaches for inferring PPIs from protein sequences based on coevolution theory avoid these problems. In the coevolution theory model, interacted proteins may show coevolutionary mutations and have similar phylogenetic trees. The existing coevolution methods depend on multiple sequence alignments (MSA); however, the MSA-based coevolution methods often produce high false positive interactions. In this paper, we present a computational method using an alignment-free approach to accurately detect PPIs and reduce false positives. In the method, protein sequences are numerically represented by biochemical properties of amino acids, which reflect the structural and functional differences of proteins. Fourier transform is applied to the numerical representation of protein sequences to capture the dissimilarities of protein sequences in biophysical context. The method is assessed for predicting PPIs in Ebola virus. The results indicate strong coevolution between the protein pairs (NP-VP24, NP-VP30, NP-VP40, VP24-VP30, VP24-VP40, and VP30-VP40). The method is also validated for PPIs in influenza and E.coli genomes. Since our method can reduce false positive and increase the specificity of PPI prediction, it offers an effective tool to understand mechanisms of disease pathogens and find potential targets for drug design. The Python programs in this study are available to public at URL (https://github.com/cyinbox/PPI).

Applying evolutionary genetics to developmental toxicology and risk assessment

Evolutionary thinking continues to challenge our views on health and disease. Yet, there is a communication gap between evolutionary biologists and toxicologists in recognizing the connections among developmental pathways, high-throughput screening, and birth defects in humans. To increase our capability in identifying potential developmental toxicants in humans, we propose to apply evolutionary genetics to improve the experimental design and data interpretation with various in vitro and whole-organism models. We review five molecular systems of stress response and update 18 consensual cell-cell signaling pathways that are the hallmark for early development, organogenesis, and differentiation; and revisit the principles of teratology in light of recent advances in high-throughput screening, big data techniques, and systems toxicology. Multiscale systems modeling plays an integral role in the evolutionary approach to cross-species extrapolation. Phylogenetic analysis and comparative bioinformatics are both valuable tools in identifying and validating the molecular initiating events that account for adverse developmental outcomes in humans. The discordance of susceptibility between test species and humans (ontogeny) reflects their differences in evolutionary history (phylogeny). This synthesis not only can lead to novel applications in developmental toxicity and risk assessment, but also can pave the way for applying an evo-devo perspective to the study of developmental origins of health and disease.

Proteomic and phylogenetic coevolution analyses of pM79 and pM92 identify interactions with RNA polymerase II and delineate the murine cytomegalovirus late transcription complex

The regulation of the late viral gene expression in betaherpesviruses is largely undefined. We have previously shown that the murine cytomegalovirus proteins pM79 and pM92 are required for late gene transcription. Here, we provide insight into the mechanism of pM79 and pM92 activity by determining their interaction partners during infection. Co-immunoprecipitation-coupled MS studies demonstrate that pM79 and pM92 interact with an array of cellular and viral proteins involved in transcription. Specifically, we identify RNA polymerase II as a cellular target for both pM79 and pM92. We use inter-protein coevolution analysis to show how pM79 and pM92 likely assemble into a late transcription complex composed of late transcription regulators pM49, pM87 and pM95. Combining proteomic methods with coevolution computational analysis provides novel insights into the relationship between pM79, pM92 and RNA polymerase II and allows the generation of a model of the multi-component viral complex that regulates late gene transcription.

Plasmodium malariae and P. ovale genomes provide insights into malaria parasite evolution

Elucidation of the evolutionary history and interrelatedness of Plasmodium species that infect humans has been hampered by a lack of genetic information for three human-infective species: P. malariae and two P. ovale species (P. o. curtisi and P. o. wallikeri). These species are prevalent across most regions in which malaria is endemic and are often undetectable by light microscopy, rendering their study in human populations difficult. The exact evolutionary relationship of these species to the other human-infective species has been contested. Using a new reference genome for P. malariae and a manually curated draft P. o. curtisi genome, we are now able to accurately place these species within the Plasmodium phylogeny. Sequencing of a P. malariae relative that infects chimpanzees reveals similar signatures of selection in the P. malariae lineage to another Plasmodium lineage shown to be capable of colonization of both human and chimpanzee hosts. Molecular dating suggests that these host adaptations occurred over similar evolutionary timescales. In addition to the core genome that is conserved between species, differences in gene content can be linked to their specific biology. The genome suggests that P. malariae expresses a family of heterodimeric proteins on its surface that have structural similarities to a protein crucial for invasion of red blood cells. The data presented here provide insight into the evolution of the Plasmodium genus as a whole.

Origin of the plant Tm-1-like gene via two independent horizontal transfer events and one gene fusion event

The Tomato mosaic virus (ToMV) resistance gene Tm-1 encodes a direct inhibitor of ToMV RNA replication to protect tomato from infection. The plant Tm-1-like (Tm-1L) protein is predicted to contain an uncharacterized N-terminal UPF0261 domain and a C-terminal TIM-barrel signal transduction (TBST) domain. Homologous searches revealed that proteins containing both of these two domains are mainly present in charophyte green algae and land plants but absent from glaucophytes, red algae and chlorophyte green algae. Although Tm-1 homologs are widely present in bacteria, archaea and fungi, UPF0261- and TBST-domain-containing proteins are generally encoded by different genes in these linages. A co-evolution analysis also suggested a putative interaction between UPF0261- and TBST-domain-containing proteins. Phylogenetic analyses based on homologs of these two domains revealed that plants have acquired UPF0261- and TBST-domain-encoding genes through two independent horizontal gene transfer (HGT) events before the origin of land plants from charophytes. Subsequently, gene fusion occurred between these two horizontally acquired genes and resulted in the origin of the Tm-1L gene in streptophytes. Our results demonstrate a novel evolutionary mechanism through which the recipient organism may acquire genes with functional interaction through two different HGT events and further fuse them into one functional gene.

Coevolution of RAC Small GTPases and their Regulators GEF Proteins

RAC proteins are small GTPases involved in important cellular processes in eukaryotes, and their deregulation may contribute to cancer. Activation of RAC proteins is regulated by DOCK and DBL protein families of guanine nucleotide exchange factors (GEFs). Although DOCK and DBL proteins act as GEFs on RAC proteins, DOCK and DBL family members are evolutionarily unrelated. To understand how DBL and DOCK families perform the same function on RAC proteins despite their unrelated primary structure, phylogenetic analyses of the RAC, DBL, and DOCK families were implemented, and interaction patterns that may suggest a coevolutionary process were searched. Interestingly, while RAC and DOCK proteins are very well conserved in humans and among eukaryotes, DBL proteins are highly divergent. Moreover, correlation analyses of the phylogenetic distances of RAC and GEF proteins and covariation analyses between residues in the interacting domains showed significant coevolution rates for both RAC–DOCK and RAC–DBL interactions.

Stimulation of Na(+),K(+)-ATPase Activity as a Possible Driving Force in Cholesterol Evolution

Cholesterol is exclusively produced by animals and is present in the plasma membrane of all animal cells. In contrast, the membranes of fungi and plants contain other sterols. To explain the exclusive preference of animal cells for cholesterol, we propose that cholesterol may have evolved to optimize the activity of a crucial protein found in the plasma membrane of all multicellular animals, namely the Na(+),K(+)-ATPase. To test this hypothesis, mirror tree and phylogenetic distribution analyses have been conducted of the Na(+),K(+)-ATPase and 3β-hydroxysterol Δ(24)-reductase (DHCR24), the last enzyme in the Bloch cholesterol biosynthetic pathway. The results obtained support the hypothesis of a co-evolution of the Na(+),K(+)-ATPase and DHCR24. The evolutionary correlation between DHCR24 and the Na(+),K(+)-ATPase was found to be stronger than between DHCR24 and any other membrane protein investigated. The results obtained, thus, also support the hypothesis that cholesterol evolved together with the Na(+),K(+)-ATPase in multicellular animals to support Na(+),K(+)-ATPase activity.

Izumo1 and Juno: the evolutionary origins and coevolution of essential sperm-egg binding partners

Reproductive proteins are among the most rapidly evolving classes of proteins. For a subset of these, rapid evolution is driven by positive Darwinian selection despite vital, well-conserved, reproductive functions. Izumo1 is the only essential sperm-egg fusion protein currently known on mammalian sperm, and its egg receptor (Juno; formerly Folr4) was recently discovered. Male knockout mice for Izumo1 and female knockout mice for Juno are both healthy but sterile. Here, both sperm-egg binding proteins are shown to be evolving under positive selection. Within mammals, coevolution of Izumo1 and Juno is also uncovered, suggesting that similar forces have shaped the evolutionary histories of these binding partners within Mammalia. Additionally, genomic analyses reveal an ancient origin for the Izumo gene family, initially reported as conserved exclusively in mammals. Newly identified Izumo1 orthologues could serve reproductive functions in birds, fish and reptiles. Surprisingly, these same analyses support Juno's presence in mammals alone, suggesting a recent mammalian-specific duplication and neofunctionalization of the ancestral folate receptor. Despite the indispensability of their reproductive interaction, and their apparent coevolution within Mammalia, this binding pair arose through strikingly different evolutionary forces.

Tex19 and Sectm1 concordant molecular phylogenies support co-evolution of both eutherian-specific genes

BACKGROUND

Transposable elements (TE) have attracted much attention since they shape the genome and contribute to species evolution. Organisms have evolved mechanisms to control TE activity. Testis expressed 19 (Tex19) represses TE expression in mouse testis and placenta. In the human and mouse genomes, Tex19 and Secreted and transmembrane 1 (Sectm1) are neighbors but are not homologs. Sectm1 is involved in immunity and its molecular phylogeny is unknown.

METHODS

Using multiple alignments of complete protein sequences (MACS), we inferred Tex19 and Sectm1 molecular phylogenies. Protein conserved regions were identified and folds were predicted. Finally, expression patterns were studied across tissues and species using RNA-seq public data and RT-PCR.

RESULTS

We present 2 high quality alignments of 58 Tex19 and 58 Sectm1 protein sequences from 48 organisms. First, both genes are eutherian-specific, i.e., exclusively present in mammals except monotremes (platypus) and marsupials. Second, Tex19 and Sectm1 have both duplicated in Sciurognathi and Bovidae while they have remained as single copy genes in all further placental mammals. Phylogenetic concordance between both genes was significant (p-value < 0.05) and supported co-evolution and functional relationship. At the protein level, Tex19 exhibits 3 conserved regions and 4 invariant cysteines. In particular, a CXXC motif is present in the N-terminal conserved region. Sectm1 exhibits 2 invariant cysteines and an Ig-like domain. Strikingly, Tex19 C-terminal conserved region was lost in Haplorrhini primates while a Sectm1 C-terminal extra domain was acquired. Finally, we have determined that Tex19 and Sectm1 expression levels anti-correlate across the testis of several primates (ρ = -0.72) which supports anti-regulation.

CONCLUSIONS

Tex19 and Sectm1 co-evolution and anti-regulated expressions support a strong functional relationship between both genes. Since Tex19 operates a control on TE and Sectm1 plays a role in immunity, Tex19 might suppress an immune response directed against cells that show TE activity in eutherian reproductive tissues.

I-COMS: Interprotein-COrrelated Mutations Server

Interprotein contact prediction using multiple sequence alignments (MSAs) is a useful approach to help detect protein–protein interfaces. Different computational methods have been developed in recent years as an approximation to solve this problem. However, as there are discrepancies in the results provided by them, there is still no consensus on which is the best performing methodology. To address this problem, I-COMS (interprotein COrrelated Mutations Server) is presented. I-COMS allows to estimate covariation between residues of different proteins by four different covariation methods. It provides a graphical and interactive output that helps compare results obtained using different methods. I-COMS automatically builds the required MSA for the calculation and produces a rich visualization of either intraprotein and/or interprotein covariating positions in a circos representation. Furthermore, comparison between any two methods is available as well as the overlap between any or all four methodologies. In addition, as a complementary source of information, a matrix visualization of the corresponding scores is made available and the density plot distribution of the inter, intra and inter+intra scores are calculated. Finally, all the results can be downloaded (including MSAs, scores and graphics) for comparison and visualization and/or for further analysis.

Functional bottlenecks for generation of HIV-1 intersubtype Env recombinants

Background

Intersubtype recombination is a powerful driving force for HIV evolution, impacting both HIV-1 diversity within an infected individual and within the global epidemic. This study examines if viral protein function/fitness is the major constraint shaping selection of recombination hotspots in replication-competent HIV-1 progeny. A better understanding of the interplay between viral protein structure-function and recombination may provide insights into both vaccine design and drug development.

Results

In vitro HIV-1 dual infections were used to recombine subtypes A and D isolates and examine breakpoints in the Env glycoproteins. The entire env genes of 21 A/D recombinants with breakpoints in gp120 were non-functional when cloned into the laboratory strain, NL4-3. Likewise, cloning of A/D gp120 coding regions also produced dead viruses with non-functional Envs. 4/9 replication-competent viruses with functional Env’s were obtained when just the V1-V5 regions of these same A/D recombinants (i.e. same A/D breakpoints as above) were cloned into NL4-3.

Conclusion

These findings on functional A/D Env recombinants combined with structural models of Env suggest a conserved interplay between the C1 domain with C5 domain of gp120 and extracellular domain of gp41. Models also reveal a co-evolution within C1, C5, and ecto-gp41 domains which might explain the paucity of intersubtype recombination in the gp120 V1-V5 regions, despite their hypervariability. At least HIV-1 A/D intersubtype recombination in gp120 may result in a C1 from one subtype incompatible with a C5/gp41 from another subtype.