The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

SignificanceThere is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection fatality rate (IFR) doubles with every 5 y of age from childhood onward. Circulating autoantibodies neutralizing IFN-α, IFN-ω, and/or IFN-β are found in ∼20% of deceased patients across age groups, and in ∼1% of individuals aged 4% of those >70 y old in the general population. With a sample of 1,261 unvaccinated deceased patients and 34,159 individuals of the general population sampled before the pandemic, we estimated both IFR and relative risk of death (RRD) across age groups for individuals carrying autoantibodies neutralizing type I IFNs, relative to noncarriers. The RRD associated with any combination of autoantibodies was higher in subjects under 70 y old. For autoantibodies neutralizing IFN-α2 or IFN-ω, the RRDs were 17.0 (95% CI: 11.7 to 24.7) and 5.8 (4.5 to 7.4) for individuals <70 y and ≥70 y old, respectively, whereas, for autoantibodies neutralizing both molecules, the RRDs were 188.3 (44.8 to 774.4) and 7.2 (5.0 to 10.3), respectively. In contrast, IFRs increased with age, ranging from 0.17% (0.12 to 0.31) for individuals <40 y old to 26.7% (20.3 to 35.2) for those ≥80 y old for autoantibodies neutralizing IFN-α2 or IFN-ω, and from 0.84% (0.31 to 8.28) to 40.5% (27.82 to 61.20) for autoantibodies neutralizing both. Autoantibodies against type I IFNs increase IFRs, and are associated with high RRDs, especially when neutralizing both IFN-α2 and IFN-ω. Remarkably, IFRs increase with age, whereas RRDs decrease with age. Autoimmunity to type I IFNs is a strong and common predictor of COVID-19 death.The Laboratory of Human Genetics of Infectious Diseases is supported by the Howard Hughes Medical Institute; The Rockefeller University; the St. Giles Foundation; the NIH (Grants R01AI088364 and R01AI163029); the National Center for Advancing Translational Sciences; NIH Clinical and Translational Science Awards program (Grant UL1 TR001866); a Fast Grant from Emergent Ventures; Mercatus Center at George Mason University; the Yale Center for Mendelian Genomics and the Genome Sequencing Program Coordinating Center funded by the National Human Genome Research Institute (Grants UM1HG006504 and U24HG008956); the Yale High Performance Computing Center (Grant S10OD018521); the Fisher Center for Alzheimer’s Research Foundation; the Meyer Foundation; the JPB Foundation; the French National Research Agency (ANR) under the “Investments for the Future” program (Grant ANR-10-IAHU-01); the Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence (Grant ANR-10-LABX-62-IBEID); the French Foundation for Medical Research (FRM) (Grant EQU201903007798); the French Agency for Research on AIDS and Viral hepatitis (ANRS) Nord-Sud (Grant ANRS-COV05); the ANR GENVIR (Grant ANR-20-CE93-003), AABIFNCOV (Grant ANR-20-CO11-0001), CNSVIRGEN (Grant ANR-19-CE15-0009-01), and GenMIS-C (Grant ANR-21-COVR-0039) projects; the Square Foundation; Grandir–Fonds de solidarité pour l’Enfance; the Fondation du Souffle; the SCOR Corporate Foundation for Science; The French Ministry of Higher Education, Research, and Innovation (Grant MESRI-COVID-19); Institut National de la Santé et de la Recherche Médicale (INSERM), REACTing-INSERM; and the University Paris Cité. P. Bastard was supported by the FRM (Award EA20170638020). P. Bastard., J.R., and T.L.V. were supported by the MD-PhD program of the Imagine Institute (with the support of Fondation Bettencourt Schueller). Work at the Neurometabolic Disease lab received funding from Centre for Biomedical Research on Rare Diseases (CIBERER) (Grant ACCI20-767) and the European Union's Horizon 2020 research and innovation program under grant agreement 824110 (EASI Genomics). Work in the Laboratory of Virology and Infectious Disease was supported by the NIH (Grants P01AI138398-S1, 2U19AI111825, and R01AI091707-10S1), a George Mason University Fast Grant, and the G. Harold and Leila Y. Mathers Charitable Foundation. The Infanta Leonor University Hospital supported the research of the Department of Internal Medicine and Allergology. The French COVID Cohort study group was sponsored by INSERM and supported by the REACTing consortium and by a grant from the French Ministry of Health (Grant PHRC 20-0424). The Cov-Contact Cohort was supported by the REACTing consortium, the French Ministry of Health, and the European Commission (Grant RECOVER WP 6). This work was also partly supported by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases and the National Institute of Dental and Craniofacial Research, NIH (Grants ZIA AI001270 to L.D.N. and 1ZIAAI001265 to H.C.S.). This program is supported by the Agence Nationale de la Recherche (Grant ANR-10-LABX-69-01). K.K.’s group was supported by the Estonian Research Council, through Grants PRG117 and PRG377. R.H. was supported by an Al Jalila Foundation Seed Grant (Grant AJF202019), Dubai, United Arab Emirates, and a COVID-19 research grant (Grant CoV19-0307) from the University of Sharjah, United Arab Emirates. S.G.T. is supported by Investigator and Program Grants awarded by the National Health and Medical Research Council of Australia and a University of New South Wales COVID Rapid Response Initiative Grant. L.I. reports funding from Regione Lombardia, Italy (project “Risposta immune in pazienti con COVID-19 e co-morbidità”). This research was partially supported by the Instituto de Salud Carlos III (Grant COV20/0968). J.R.H. reports funding from Biomedical Advanced Research and Development Authority (Grant HHSO10201600031C). S.O. reports funding from Research Program on Emerging and Re-emerging Infectious Diseases from Japan Agency for Medical Research and Development (Grant JP20fk0108531). G.G. was supported by the ANR Flash COVID-19 program and SARS-CoV-2 Program of the Faculty of Medicine from Sorbonne University iCOVID programs. The 3C Study was conducted under a partnership agreement between INSERM, Victor Segalen Bordeaux 2 University, and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study was also supported by the Caisse Nationale d’Assurance Maladie des Travailleurs Salariés, Direction générale de la Santé, Mutuelle Générale de l’Education Nationale, Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, and Ministry of Research–INSERM Program “Cohortes et collections de données biologiques.” S. Debette was supported by the University of Bordeaux Initiative of Excellence. P.K.G. reports funding from the National Cancer Institute, NIH, under Contract 75N91019D00024, Task Order 75N91021F00001. J.W. is supported by a Research Foundation - Flanders (FWO) Fundamental Clinical Mandate (Grant 1833317N). Sample processing at IrsiCaixa was possible thanks to the crowdfunding initiative YoMeCorono. Work at Vall d’Hebron was also partly supported by research funding from Instituto de Salud Carlos III Grant PI17/00660 cofinanced by the European Regional Development Fund (ERDF/FEDER). C.R.-G. and colleagues from the Canarian Health System Sequencing Hub were supported by the Instituto de Salud Carlos III (Grants COV20_01333 and COV20_01334), the Spanish Ministry for Science and Innovation (RTC-2017-6471-1; AEI/FEDER, European Union), Fundación DISA (Grants OA18/017 and OA20/024), and Cabildo Insular de Tenerife (Grants CGIEU0000219140 and “Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19”). T.H.M. was supported by grants from the Novo Nordisk Foundation (Grants NNF20OC0064890 and NNF21OC0067157). C.M.B. is supported by a Michael Smith Foundation for Health Research Health Professional-Investigator Award. P.Q.H. and L. Hammarström were funded by the European Union’s Horizon 2020 research and innovation program (Antibody Therapy Against Coronavirus consortium, Grant 101003650). Work at Y.-L.L.’s laboratory in the University of Hong Kong (HKU) was supported by the Society for the Relief of Disabled Children. MBBS/PhD study of D.L. in HKU was supported by the Croucher Foundation. J.L.F. was supported in part by the Evaluation-Orientation de la Coopération Scientifique (ECOS) Nord - Coopération Scientifique France-Colombie (ECOS-Nord/Columbian Administrative department of Science, Technology and Innovation [COLCIENCIAS]/Colombian Ministry of National Education [MEN]/Colombian Institute of Educational Credit and Technical Studies Abroad [ICETEX, Grant 806-2018] and Colciencias Contract 713-2016 [Code 111574455633]). A. Klocperk was, in part, supported by Grants NU20-05-00282 and NV18-05-00162 issued by the Czech Health Research Council and Ministry of Health, Czech Republic. L.P. was funded by Program Project COVID-19 OSR-UniSR and Ministero della Salute (Grant COVID-2020-12371617). I.M. is a Senior Clinical Investigator at the Research Foundation–Flanders and is supported by the CSL Behring Chair of Primary Immunodeficiencies (PID); by the Katholieke Universiteit Leuven C1 Grant C16/18/007; by a Flanders Institute for Biotechnology-Grand Challenges - PID grant; by the FWO Grants G0C8517N, G0B5120N, and G0E8420N; and by the Jeffrey Modell Foundation. I.M. has received funding under the European Union’s Horizon 2020 research and innovation program (Grant Agreement 948959). E.A. received funding from the Hellenic Foundation for Research and Innovation (Grant INTERFLU 1574). M. Vidigal received funding from the São Paulo Research Foundation (Grant 2020/09702-1) and JBS SA (Grant 69004). The NH-COVAIR study group consortium was supported by a grant from the Meath Foundation.Peer reviewe

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Impact of an antibiotic treatment on the intestinal microbiota

Le développement des méthodes de séquençage de nouvelle génération a permis d’approfondir les connaissances sur le rôle des communautés bactériennes commensales pour la santé de leur hôte, et l’impact négatif de la perturbation de leur équilibre. Les antibiotiques sont les principaux perturbateurs de cet équilibre, mais leur impact n’a pas été quantifié précisément.Nous avons quantifié la relation entre les concentrations fécales d’antibiotiques et la perturbation de la diversité bactérienne au sein du microbiote intestinal, et modélisé le lien entre la perte de diversité bactérienne et la probabilité de décès dans un modèle animal de colite à Clostridium difficile induite par les antibiotiques. Nous avons montré que l’indice de diversité de Shannon et la distance UniFac non pondérée étaient les indices de diversité qui étaient le plus prédictif du décès dans ce modèle d’infection.Chez des volontaires sains, nous avons développé un modèle mathématique semimécanistique de l’évolution de la diversité au sein du microbiote, mesurée par deux indices de diversité, après perturbation antibiotique, et quantifié la relation entre l’exposition individuelle plasmatique et fécale à un antibiotique, et son effet sur la perturbation de la diversité bactérienne au cours du temps. Nous avons également analysé le rôle de la voie d’élimination des antibiotiques pour la limitation de l’impact d’un antibiotique sur le microbiote. Ces travaux nous ont permis de montrer que le microbiote intestinal présente une grande sensibilité aux antibiotiques, et que la voie d’élimination ne semble de ce fait pas jouer un rôle prépondérant dans la perspective de limiter l’impact des antibiotiques sur le microbiote intestinal.The development of next generation sequencing broadened our knowledge on the role of commensal bacterial communities on their host’s health, and the negative impact of their disruption. Antibiotics are the main disrupting factor, but their impact has not been precisely quantified.We quantified the relationship between antibiotic fecal concentrations and the loss of bacterial diversity in the intestinal microbiota, and modelled the link between the loss of diversity and mortality in a hamster model of antibiotic-induced Clostridium difficile infection. We showed that the Shannon diversity index and the unweighted UniFrac distance are the 2 indices that best predict mortality in this model. In healthy volunteers, we developed a semi-mechanistic model of the evolution over time of bacterial diversity – measured by two indices – after an antibiotic perturbation, and quantified the relationship between antibiotic concentrations in plasma and feces and the loss of bacterial diversity in the intestinal microbiota. We also analyzed the role of the antibiotic elimination pathway in the reduction of their impact on the microbiota. In this work, we showed that the intestinal microbiota is highly susceptible to antibiotics, and that the elimination route doesn’t have a major role, in the perspective of limiting antibiotics’ impact on the intestinal microbiota

Impact d’une antibiothérapie sur le microbiote intestinal

The development of next generation sequencing broadened our knowledge on the role of commensal bacterial communities on their host’s health, and the negative impact of their disruption. Antibiotics are the main disrupting factor, but their impact has not been precisely quantified.We quantified the relationship between antibiotic fecal concentrations and the loss of bacterial diversity in the intestinal microbiota, and modelled the link between the loss of diversity and mortality in a hamster model of antibiotic-induced Clostridium difficile infection. We showed that the Shannon diversity index and the unweighted UniFrac distance are the 2 indices that best predict mortality in this model. In healthy volunteers, we developed a semi-mechanistic model of the evolution over time of bacterial diversity – measured by two indices – after an antibiotic perturbation, and quantified the relationship between antibiotic concentrations in plasma and feces and the loss of bacterial diversity in the intestinal microbiota. We also analyzed the role of the antibiotic elimination pathway in the reduction of their impact on the microbiota. In this work, we showed that the intestinal microbiota is highly susceptible to antibiotics, and that the elimination route doesn’t have a major role, in the perspective of limiting antibiotics’ impact on the intestinal microbiota.Le développement des méthodes de séquençage de nouvelle génération a permis d’approfondir les connaissances sur le rôle des communautés bactériennes commensales pour la santé de leur hôte, et l’impact négatif de la perturbation de leur équilibre. Les antibiotiques sont les principaux perturbateurs de cet équilibre, mais leur impact n’a pas été quantifié précisément.Nous avons quantifié la relation entre les concentrations fécales d’antibiotiques et la perturbation de la diversité bactérienne au sein du microbiote intestinal, et modélisé le lien entre la perte de diversité bactérienne et la probabilité de décès dans un modèle animal de colite à Clostridium difficile induite par les antibiotiques. Nous avons montré que l’indice de diversité de Shannon et la distance UniFac non pondérée étaient les indices de diversité qui étaient le plus prédictif du décès dans ce modèle d’infection.Chez des volontaires sains, nous avons développé un modèle mathématique semimécanistique de l’évolution de la diversité au sein du microbiote, mesurée par deux indices de diversité, après perturbation antibiotique, et quantifié la relation entre l’exposition individuelle plasmatique et fécale à un antibiotique, et son effet sur la perturbation de la diversité bactérienne au cours du temps. Nous avons également analysé le rôle de la voie d’élimination des antibiotiques pour la limitation de l’impact d’un antibiotique sur le microbiote. Ces travaux nous ont permis de montrer que le microbiote intestinal présente une grande sensibilité aux antibiotiques, et que la voie d’élimination ne semble de ce fait pas jouer un rôle prépondérant dans la perspective de limiter l’impact des antibiotiques sur le microbiote intestinal

Quinolones versus macrolides in the treatment of legionellosis: a systematic review and meta-analysis

International audienceBackgroundLegionellosis is a life-threatening disease. The clinical superiority of quinolones or macrolides for treating patients with legionellosis has not been established.MethodsWe performed a systematic review and meta-analysis of studies reporting data for comparison of quinolones versus macrolides in the treatment of proven legionellosis published from 01/01/1985 to 31/01/2013. We collected baseline aggregate patient characteristics. Studied outcomes included mortality, clinical cure, time to apyrexia, length of hospital-stay and occurrence of complication in each treatment group. Treatment effect was assessed using a Mantel-Haenszel random effects model. ResultsAmong 1005 abstracts reviewed, 12 studies were selected (n=879 patients). No randomized controlled trial (RCT) was available. Mean age was 58.3 years, 27.7% were women and Fine score was ≥4 in 35.8%. Among 253 patients with quinolone monotherapy, 10 died (4.0%). Among 211 patients with macrolide monotherapy, 23 died (10.9%). The pooled odds ratio of death when treated by a quinolone versus macrolide was 0.5 (95%CI=[0.2 – 1.3], n=8 studies, 464 patients). Length of stay was significantly lower in the quinolone monotherapy group. The difference was 3.0 days (95%CI=[0.7 – 5.3], p=0.001, n=3 studies, 263 patients). Both tests for heterogeneity were not significant (I2=0% for both, p=1). Other studied outcomes were not significantly different among treatment groups.Conclusion. Few clinical data on legionellosis treatment are available. This first meta-analysis showed a trend toward a lower mortality rate and a significant decrease in length of hospital-stay in patients receiving quinolone. These results must be confirmed by a randomized clinical trial

Impact of Antibiotic Gut Exposure on the Temporal Changes in Microbiome Diversity: Impact of moxifloxacin on gut microbiome

International audienceAlthough the global deleterious impact of antibiotics on the intestinal microbiota is well known, temporal changes in microbial diversity during and after an antibiotic treatment are still poorly characterized. We used plasma and fecal samples collected frequently during treatment and up to one month after from 22 healthy volunteers assigned to a 5-day treatment by moxifloxacin (n = 14) or no intervention (n = 8). Moxifloxacin concentrations were measured in both plasma and feces, and bacterial diversity was determined in feces by 16S rRNA gene profiling and quantified using the Shannon index and number of operational taxonomic units (OTUs). Nonlinear mixed effect models were used to relate drug pharmacokinetics and bacterial diversity over time. Moxifloxacin reduced bacterial diversity in a concentration-dependent manner, with a median maximal loss of 27.5% of the Shannon index (minimum [min], 17.5; maximum [max], 27.7) and 47.4% of the number of OTUs (min, 30.4; max, 48.3). As a consequence of both the long fecal half-life of moxifloxacin and the susceptibility of the gut microbiota to moxifloxacin, bacterial diversity indices did not return to their pretreatment levels until days 16 and 21, respectively. Finally, the model characterized the effect of moxifloxacin on bacterial diversity biomarkers and provides a novel framework for analyzing antibiotic effects on the intestinal microbiome

Impact of Antibiotic Gut Exposure on the Temporal Changes in Microbiome Diversity: Impact of moxifloxacin on gut microbiome

International audienceAlthough the global deleterious impact of antibiotics on the intestinal microbiota is well known, temporal changes in microbial diversity during and after an antibiotic treatment are still poorly characterized. We used plasma and fecal samples collected frequently during treatment and up to one month after from 22 healthy volunteers assigned to a 5-day treatment by moxifloxacin (n = 14) or no intervention (n = 8). Moxifloxacin concentrations were measured in both plasma and feces, and bacterial diversity was determined in feces by 16S rRNA gene profiling and quantified using the Shannon index and number of operational taxonomic units (OTUs). Nonlinear mixed effect models were used to relate drug pharmacokinetics and bacterial diversity over time. Moxifloxacin reduced bacterial diversity in a concentration-dependent manner, with a median maximal loss of 27.5% of the Shannon index (minimum [min], 17.5; maximum [max], 27.7) and 47.4% of the number of OTUs (min, 30.4; max, 48.3). As a consequence of both the long fecal half-life of moxifloxacin and the susceptibility of the gut microbiota to moxifloxacin, bacterial diversity indices did not return to their pretreatment levels until days 16 and 21, respectively. Finally, the model characterized the effect of moxifloxacin on bacterial diversity biomarkers and provides a novel framework for analyzing antibiotic effects on the intestinal microbiome

Unexpected activity of oral fosfomycin against resistant strains of Escherichia coli in murine pyelonephritis

International audienceFosfomycin tromethamine activity is well established for oral treatment of uncomplicated lower urinary tract infections, but little is known about its potential efficacy in pyelonephritis. Ascending pyelonephritis was induced in mice infected with 6 strains of Escherichia coli (fosfomycin MICs, 1 μg/ml to 256 μg/ml). The urine pH was 4.5 before infection and 5.5 to 6.0 during infection. Animals were treated for 24 h with fosfomycin (100 mg/kg of body weight subcutaneously every 4 h), and the CFU were enumerated in kidneys 24 h after the last fosfomycin injection. Peak (20.5 μg/ml at 1 h) and trough (3.5 μg/ml at 4 h) levels in plasma were comparable to those obtained in humans after an oral dose of 3 g. Fosfomycin treatment significantly reduced the bacterial loads in kidneys (3.65 log10 CFU/g [range, 1.83 to 7.03 log10 CFU/g] and 1.88 log10 CFU/g [range, 1.78 to 5.74 log10 CFU/g] in start-of-treatment control mice and treated mice, respectively; P < 10−6). However, this effect was not found to differ across the 6 study strains (P = 0.71) or between the 3 susceptible and the 3 resistant strains (P = 0.09). Three phenomena may contribute to explain this unexpected in vivo activity: (i) in mice, the fosfomycin kidney/plasma concentration ratio increased from 1 to 7.8 (95% confidence interval, 5.2, 10.4) within 24 h in vitro when the pH decreased to 5, (ii) the fosfomycin MICs for the 3 resistant strains (64 to 256 μg/ml) decreased into the susceptible range (16 to 32 μg/ml), and (iii) maximal growth rates significantly decreased for all strains and were the lowest in urine. These results suggest that local fosfomycin concentrations and physiological conditions may favor fosfomycin activity in pyelonephritis, even against resistant strains