Detection of viruses by innate immune sensors induces protective antiviral immunity. The viral DNA sensor cyclic GMP-AMP synthase (cGAS) is necessary for detection of HIV by human dendritic cells and macrophages. However, synthesis of HIV DNA during infection is not sufficient for immune activation. The capsid protein, which associates with viral DNA, has a pivotal role in enabling cGAS-mediated immune activation. We now find that NONO is an essential sensor of the HIV capsid in the nucleus. NONO protein directly binds capsid with higher affinity for weakly pathogenic HIV-2 than highly pathogenic HIV-1. Upon infection, NONO is essential for cGAS activation by HIV and cGAS association with HIV DNA in the nucleus. NONO recognizes a conserved region in HIV capsid with limited tolerance for escape mutations. Detection of nuclear viral capsid by NONO to promote DNA sensing by cGAS reveals an innate strategy to achieve distinction of viruses from self in the nucleus.

Adaptive evolution has shaped major biological processes. Finding the protein-coding genes and the sites that have been subjected to adaptation during evolutionary time is a major endeavor. However, very few methods fully automate the identification of positively selected genes, and widespread sources of genetic innovations such as gene duplication and recombination are absent from most pipelines. Here, we developed DGINN, a highly-flexible and public pipeline to Detect Genetic INNovations and adaptive evolution in protein-coding genes. DGINN automates, from a gene's sequence, all steps of the evolutionary analyses necessary to detect the aforementioned innovations, including the search for homologs in databases, assignation of orthology groups, identification of duplication and recombination events, as well as detection of positive selection using five methods to increase precision and ranking of genes when a large panel is analyzed. DGINN was validated on nineteen genes with previously-characterized evolutionary histories in primates, including some engaged in host-pathogen arms-races. Our results confirm and also expand results from the literature, including novel findings on the Guanylate-binding protein family, GBPs. This establishes DGINN as an efficient tool to automatically detect genetic innovations and adaptive evolution in diverse datasets, from the user's gene of interest to a large gene list in any species range.

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Adaptive evolution has shaped major biological processes. Finding the protein-coding genes and the sites that have been subjected to adaptation during evolutionary time is a major endeavor. However, very few methods fully automate the identification of positively selected genes, and widespread sources of genetic innovations as gene duplication and recombination are absent from most pipelines. Here, we developed DGINN, a highly-flexible and public pipeline to Detect Genetic INNovations and adaptive evolution in protein-coding genes. DGINN automates, from a gene's sequence, all steps of the evolutionary analyses necessary to detect the aforementioned innovations, including the search for homologues in databases, assignation of orthology groups, identification of duplication and recombination events, as well as detection of positive selection using five different methods to increase precision and ranking of genes when a large panel is analyzed. DGINN was validated on nineteen genes with previously-characterized evolutionary histories in primates, including some engaged in host-pathogen arms-races. The results obtained with DGINN confirm and also expand results from the literature, establishing DGINN as an efficient tool to automatically detect genetic innovations and adaptive evolution in diverse datasets, from the user's gene of interest to a large gene list in any species range. Running Title: DGINN, an automated pipeline to Detect Genetic INNovations

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.

We performed a prospective cohort study of 311 outpatients with non-severe COVID-19 (187 women, median age 39 years). Of the 214 (68.8%) who completed the 6-week follow-up questionnaire, 115 (53.7%) had recovered. Others mostly reported dyspnea (n=86, 40.2%), weight loss (n=83, 38.8%), sleep disorders (n=68, 31.8%), and anxiety (n=56, 26.2%). Of those who developed ageusia and anosmia, these symptoms were still present at week 6 in, respectively, 11/111 (9.9%), and 19/114 (16.7%). Chest CT scan and lung function tests found no explanation in the most disabled patients (n=23). This study confirms the high prevalence of persistent symptoms after non-severe COVID-19.

Background Enterococcus faecalis infective endocarditis (EFIE) are characterized by a higher frequency of relapses than other infective endocarditis. The role of the treatment on their occurrence remains poorly understood. The aim of this study was to investigate whether the antibiotic regimen could impact the risk of relapse in EFIE. Materials This was a multicenter retrospective study of patients diagnosed with definite EFIE between 2015 and 2019 in 14 French hospitals. The primary endpoint was the occurrence of relapses within the year following endocarditis diagnosis. As death was a competing risk for relapse, Fine & Gray models were used for studying risk factors and impact of treatment. Results Of the 279 patients included, 83 (29.7%) received the amoxicillin-gentamicin (A-G) combination, 114 (40.9%) amoxicillin-ceftriaxone (A-C), 63 (22.6%) A-G and A-C (A-G/A-C) sequentially, 9 (3.2%) amoxicillin (A), and 10 received other treatments. One-year-relapse rate was 9.3% (26 patients). Relapse occurred after a median delay of 107 days from EFIE diagnosis; 6 occurred after 6 months, and 6 were diagnosed by blood cultures in asymptomatic patients. In multivariate analysis, surgery during treatment was a protective factor against one-year relapse and death. The cumulative incidence of relapse one year after endocarditis was 46.2% for patients treated with amoxicillin, 13.4% with A-G, 14.7% with A-C, and 4.3% with A-G/A-C (P≥.05 in multivariate analysis). Conclusion Relapses after treatment of EFIE are frequent, frequently asymptomatic, and may occur more than 6 months after the initial episode.

The COVID-19 pandemic is caused by SARS-CoV-2, a novel coronavirus that spilled 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. Here, 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 important 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 functional adaptations involved in SARS-CoV-2 infection in bats and primates, critically enlightening modern genetic determinants of virus susceptibility and severity.