Evidence for existence of an apoptosis-inducing BH3-only protein, sayonara, in Drosophila.

Cells need to sense stresses to initiate the execution of the dormant cell death program. Since the discovery of the first BH3-only protein Bad, BH3-only proteins have been recognized as indispensable stress sensors that induce apoptosis. BH3-only proteins have so far not been identified in Drosophila despite their importance in other organisms. Here, we identify the first Drosophila BH3-only protein and name it sayonara. Sayonara induces apoptosis in a BH3 motif-dependent manner and interacts genetically and biochemically with the BCL-2 homologous proteins, Buffy and Debcl. There is a positive feedback loop between Sayonara-mediated caspase activation and autophagy. The BH3 motif of sayonara phylogenetically appeared at the time of the ancestral gene duplication that led to the formation of Buffy and Debcl in the dipteran lineage. To our knowledge, this is the first identification of a bona fide BH3-only protein in Drosophila, thus providing a unique example of how cell death mechanisms can evolve both through time and across taxa.

Viruses traverse the human proteome through peptide interfaces that can be biomimetically leveraged for drug discovery.

We present a drug design strategy based on structural knowledge of protein-protein interfaces selected through virus-host coevolution and translated into highly potential small molecules. This approach is grounded on Vinland, the most comprehensive atlas of virus-human protein-protein interactions with annotation of interacting domains. From this inspiration, we identified small viral protein domains responsible for interaction with human proteins. These peptides form a library of new chemical entities used to screen for replication modulators of several pathogens. As a proof of concept, a peptide from a KSHV protein, identified as an inhibitor of influenza virus replication, was translated into a small molecule series with low nanomolar antiviral activity. By targeting the NEET proteins, these molecules turn out to be of therapeutic interest in a nonalcoholic steatohepatitis mouse model with kidney lesions. This study provides a biomimetic framework to design original chemistries targeting cellular proteins, with indications going far beyond infectious diseases.

Correction: Type B and type A influenza polymerases have evolved distinct binding interfaces to recruit the RNA polymerase II CTD.

[This corrects the article DOI: 10.1371/journal.ppat.1010328.].

Distinct evolutionary trajectories of SARS-CoV-2-interacting proteins in bats and primates identify important host determinants of COVID-19.

The coronavirus disease 19 (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a coronavirus that spilled over from the bat reservoir. Despite numerous clinical trials and vaccines, the burden remains immense, and the host determinants of SARS-CoV-2 susceptibility and COVID-19 severity remain largely unknown. Signatures of positive selection detected by comparative functional genetic analyses in primate and bat genomes can uncover important and specific adaptations that occurred at virus-host interfaces. We performed high-throughput evolutionary analyses of 334 SARS-CoV-2-interacting proteins to identify SARS-CoV adaptive loci and uncover functional differences between modern humans, primates, and bats. Using DGINN (Detection of Genetic INNovation), we identified 38 bat and 81 primate proteins with marks of positive selection. Seventeen genes, including the ACE2 receptor, present adaptive marks in both mammalian orders, suggesting common virus-host interfaces and past epidemics of coronaviruses shaping their genomes. Yet, 84 genes presented distinct adaptations in bats and primates. Notably, residues involved in ubiquitination and phosphorylation of the inflammatory RIPK1 have rapidly evolved in bats but not primates, suggesting different inflammation regulation versus humans. Furthermore, we discovered residues with typical virus-host arms race marks in primates, such as in the entry factor TMPRSS2 or the autophagy adaptor FYCO1, pointing to host-specific in vivo interfaces that may be drug targets. Finally, we found that FYCO1 sites under adaptation in primates are those associated with severe COVID-19, supporting their importance in pathogenesis and replication. Overall, we identified adaptations involved in SARS-CoV-2 infection in bats and primates, enlightening modern genetic determinants of virus susceptibility and severity.

Type B and type A influenza polymerases have evolved distinct binding interfaces to recruit the RNA polymerase II CTD.

During annual influenza epidemics, influenza B viruses (IBVs) co-circulate with influenza A viruses (IAVs), can become predominant and cause severe morbidity and mortality. Phylogenetic analyses suggest that IAVs (primarily avian viruses) and IBVs (primarily human viruses) have diverged over long time scales. Identifying their common and distinctive features is an effective approach to increase knowledge about the molecular details of influenza infection. The virus-encoded RNA-dependent RNA polymerases (FluPolB and FluPolA) are PB1-PB2-PA heterotrimers that perform transcription and replication of the viral genome in the nucleus of infected cells. Initiation of viral mRNA synthesis requires a direct association of FluPol with the host RNA polymerase II (RNAP II), in particular the repetitive C-terminal domain (CTD) of the major RNAP II subunit, to enable "cap-snatching" whereby 5'-capped oligomers derived from nascent RNAP II transcripts are pirated to prime viral transcription. Here, we present the first high-resolution co-crystal structure of FluPolB bound to a CTD mimicking peptide at a binding site crossing from PA to PB2. By performing structure-based mutagenesis of FluPolB and FluPolA followed by a systematic investigation of FluPol-CTD binding, FluPol activity and viral phenotype, we demonstrate that IBVs and IAVs have evolved distinct binding interfaces to recruit the RNAP II CTD, despite the CTD sequence being highly conserved across host species. We find that the PB2 627 subdomain, a major determinant of FluPol-host cell interactions and IAV host-range, is involved in CTD-binding for IBVs but not for IAVs, and we show that FluPolB and FluPolA bind to the host RNAP II independently of the CTD. Altogether, our results suggest that the CTD-binding modes of IAV and IBV may represent avian- and human-optimized binding modes, respectively, and that their divergent evolution was shaped by the broader interaction network between the FluPol and the host transcriptional machinery.

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.

Identifying genomic changes associated with insecticide resistance in the dengue mosquito Aedes aegypti by deep targeted sequencing.

The capacity of mosquitoes to resist insecticides threatens the control of diseases such as dengue and malaria. Until alternative control tools are implemented, characterizing resistance mechanisms is crucial for managing resistance in natural populations. Insecticide biodegradation by detoxification enzymes is a common resistance mechanism; however, the genomic changes underlying this mechanism have rarely been identified, precluding individual resistance genotyping. In particular, the role of copy number variations (CNVs) and polymorphisms of detoxification enzymes have never been investigated at the genome level, although they can represent robust markers of metabolic resistance. In this context, we combined target enrichment with high-throughput sequencing for conducting the first comprehensive screening of gene amplifications and polymorphisms associated with insecticide resistance in mosquitoes. More than 760 candidate genes were captured and deep sequenced in several populations of the dengue mosquito Ae. aegypti displaying distinct genetic backgrounds and contrasted resistance levels to the insecticide deltamethrin. CNV analysis identified 41 gene amplifications associated with resistance, most affecting cytochrome P450s overtranscribed in resistant populations. Polymorphism analysis detected more than 30,000 variants and strong selection footprints in specific genomic regions. Combining Bayesian and allele frequency filtering approaches identified 55 nonsynonymous variants strongly associated with resistance. Both CNVs and polymorphisms were conserved within regions but differed across continents, confirming that genomic changes underlying metabolic resistance to insecticides are not universal. By identifying novel DNA markers of insecticide resistance, this study opens the way for tracking down metabolic changes developed by mosquitoes to resist insecticides within and among populations.

Strand-specific transcriptomes of Enterohemorrhagic Escherichia coli in response to interactions with ground beef microbiota: interactions between microorganisms in raw meat.

BACKGROUND: Enterohemorrhagic Escherichia coli (EHEC) are zoonotic agents associated with outbreaks worldwide. Growth of EHEC strains in ground beef could be inhibited by background microbiota that is present initially at levels greater than that of the pathogen E. coli. However, how the microbiota outcompetes the pathogenic bacteria is unknown. Our objective was to identify metabolic pathways of EHEC that were altered by natural microbiota in order to improve our understanding of the mechanisms controlling the growth and survival of EHECs in ground beef. RESULTS: Based on 16S metagenomics analysis, we identified the microbial community structure in our beef samples which was an essential preliminary for subtractively analyzing the gene expression of the EHEC strains. Then, we applied strand-specific RNA-seq to investigate the effects of this microbiota on the global gene expression of EHEC O2621765 and O157EDL933 strains by comparison with their behavior in beef meat without microbiota. In strain O2621765, the expression of genes connected with nitrate metabolism and nitrite detoxification, DNA repair, iron and nickel acquisition and carbohydrate metabolism, and numerous genes involved in amino acid metabolism were down-regulated. Further, the observed repression of ftsL and murF, involved respectively in building the cytokinetic ring apparatus and in synthesizing the cytoplasmic precursor of cell wall peptidoglycan, might help to explain the microbiota's inhibitory effect on EHECs. For strain O157EDL933, the induced expression of the genes implicated in detoxification and the general stress response and the repressed expression of the peR gene, a gene negatively associated with the virulence phenotype, might be linked to the survival and virulence of O157:H7 in ground beef with microbiota. CONCLUSION: In the present study, we show how RNA-Seq coupled with a 16S metagenomics analysis can be used to identify the effects of a complex microbial community on relevant functions of an individual microbe within it. These findings add to our understanding of the behavior of EHECs in ground beef. By measuring transcriptional responses of EHEC, we could identify putative targets which may be useful to develop new strategies to limit their shedding in ground meat thus reducing the risk of human illnesses.

Sample size calculation in metabolic phenotyping studies.

The number of samples needed to identify significant effects is a key question in biomedical studies, with consequences on experimental designs, costs and potential discoveries. In metabolic phenotyping studies, sample size determination remains a complex step. This is due particularly to the multiple hypothesis-testing framework and the top-down hypothesis-free approach, with no a priori known metabolic target. Until now, there was no standard procedure available to address this purpose. In this review, we discuss sample size estimation procedures for metabolic phenotyping studies. We release an automated implementation of the Data-driven Sample size Determination (DSD) algorithm for MATLAB and GNU Octave. Original research concerning DSD was published elsewhere. DSD allows the determination of an optimized sample size in metabolic phenotyping studies. The procedure uses analytical data only from a small pilot cohort to generate an expanded data set. The statistical recoupling of variables procedure is used to identify metabolic variables, and their intensity distributions are estimated by Kernel smoothing or log-normal density fitting. Statistically significant metabolic variations are evaluated using the Benjamini-Yekutieli correction and processed for data sets of various sizes. Optimal sample size determination is achieved in a context of biomarker discovery (at least one statistically significant variation) or metabolic exploration (a maximum of statistically significant variations). DSD toolbox is encoded in MATLAB R2008A (Mathworks, Natick, MA) for Kernel and log-normal estimates, and in GNU Octave for log-normal estimates (Kernel density estimates are not robust enough in GNU octave). It is available at http://www.prabi.fr/redmine/projects/dsd/repository, with a tutorial at http://www.prabi.fr/redmine/projects/dsd/wiki.

VirHostNet 2.0: surfing on the web of virus/host molecular interactions data.

VirHostNet release 2.0 (http://virhostnet.prabi.fr) is a knowledgebase dedicated to the network-based exploration of virus-host protein-protein interactions. Since the previous VirhostNet release (2009), a second run of manual curation was performed to annotate the new torrent of high-throughput protein-protein interactions data from the literature. This resource is shared publicly, in PSI-MI TAB 2.5 format, using a PSICQUIC web service. The new interface of VirHostNet 2.0 is based on Cytoscape web library and provides a user-friendly access to the most complete and accurate resource of virus-virus and virus-host protein-protein interactions as well as their projection onto their corresponding host cell protein interaction networks. We hope that the VirHostNet 2.0 system will facilitate systems biology and gene-centered analysis of infectious diseases and will help to identify new molecular targets for antiviral drugs design. This resource will also continue to help worldwide scientists to improve our knowledge on molecular mechanisms involved in the antiviral response mediated by the cell and in the viral strategies selected by viruses to hijack the host immune system.