In Situ Fe and S isotope analyses in pyrite from the 3.2 Ga Mendon Formation (Barberton Greenstone Belt, South Africa): Evidence for early microbial iron reduction

International audienceOn the basis of phylogenetic studies and laboratory cultures, it has been proposed that the ability of microbes to metabolize iron has emerged prior to the Archaea/ Bacteria split. However, no unambiguous geochemical data supporting this claim have been put forward in rocks older than 2.7-2.5 giga years (Gyr). In the present work, we report in situ Fe and S isotope composition of pyrite from 3.28-to 3.26-Gyr-old cherts from the upper Mendon Formation, South Africa. We identified three populations of microscopic pyrites showing a wide range of Fe isotope compositions, which cluster around two δ 56 Fe values of −1.8‰ and +1‰. These three pyrite groups can also be distinguished based on the pyrite crystallinity and the S isotope mass-independent signatures. One pyrite group displays poorly crystallized pyrite minerals with positive Δ 33 S values > +3‰, while the other groups display more variable and closer to 0‰ Δ 33 S values with recrystallized pyrite rims. It is worth to note that all the pyrite groups display positive Δ 33 S values in the pyrite core and similar trace element compositions

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

Anticoronavirus Evaluation of Antimicrobial Diterpenoids: Application of New Ferruginol Analogues

The abietane diterpene (+)-ferruginol (1), like other natural and semisynthetic abietanes, is distinguished for its interesting pharmacological properties such as antimicrobial activity, including antiviral. In this study, selected C18-functionalized semisynthetic abietanes prepared from the commercially available (+)-dehydroabietylamine or methyl dehydroabietate were tested in vitro against human coronavirus 229E (HCoV-229E). As a result, a new ferruginol analogue caused a relevant reduction in virus titer as well as the inhibition of a cytopathic effect. A toxicity prediction based on in silico analysis was also performed as well as an estimation of bioavailability. This work demonstrates the antimicrobial and specifically antiviral activity of two tested compounds, making these molecules interesting for the development of new antivirals.The funds were granted by the Universitat Politècnica de Valencia (grant ADSIDEO AD 1902 to M.A.G.-C) and the Université de Lorraine and CNRS (M.V.). The project was partially supported by the Erasmus+ Programme of the European Union, Key Action 2: Strategic Partnerships, Project No. 2020-1-CZ01-KA203-078218

Soil functional operating range linked to microbial biodiversity and community composition using denitrifiers as model guild.

Soil microorganisms are key players in biogeochemical cycles. Yet, there is no consistent view on the significance of microbial biodiversity for soil ecosystem functioning. According to the insurance hypothesis, declines in ecosystem functioning due to reduced biodiversity are more likely to occur under fluctuating, extreme or rapidly changing environmental conditions. Here, we compare the functional operating range, a new concept defined as the complete range of environmental conditions under which soil microbial communities are able to maintain their functions, between four naturally assembled soil communities from a long-term fertilization experiment. A functional trait approach was adopted with denitrifiers involved in nitrogen cycling as our model soil community. Using short-term temperature and salt gradients, we show that the functional operating range was broader and process rates were higher when the soil community was phylogenetically more diverse. However, key bacterial genotypes played an important role for maintaining denitrification as an ecosystem functioning under certain conditions

pH- and glutathione-responsive release of curcumin from mesoporous silica nanoparticles coated using tannic acid-Fe(III) complex

International audienceA novel pH-and glutathione-responsive drug delivery system has been developed by deposition of tannic acid (TA)-Fe(III) complex on the surface of mesoporous silica nanoparticles (MSN). The coating was easily accomplished within 30 seconds by successive addition of iron chloride (FeCl3) and tannic acid in aqueous dispersion of MSN (e.g. MCM-41). A hydrophobic model drug, curcumin, showed sustainable drug release under physiological condition (pH 7.4), while a rapid curcumin release was triggered by lowering the pH to 6.0 or 4.5. Moreover, curcumin release could be controlled by adjusting the glutathione level, which accelerate the decomposition of TA-Fe(III) complex by competitive liganding. Therefore, these results would allow developing novel and simple pH-and glutathione-responsive drug delivery systems with potential applications such as in biomedicine

Disentangling the rhizosphere effect on nitrate reducers and denitrifiers: insight into the role of root exudates

International audienceTo determine to which extent root-derived carbon contributes to the effects of plants on nitrate reducers and denitrifiers, four solutions containing different proportions of sugar, organic acids and amino acids mimicking maize root exudates were added daily to soil microcosms at a concentration of 150 μg C g−1 of soil. Water-amended soils were used as controls. After 1 month, the size and structure of the nitrate reducer and denitrifier communities were analysed using the narG and napA, and the nirK, nirS and nosZ genes as molecular markers respectively. Addition of artificial root exudates (ARE) did not strongly affect the structure or the density of nitrate reducer and denitrifier communities whereas potential nitrate reductase and denitrification activities were stimulated by the addition of root exudates. An effect of ARE composition was also observed on N2O production with an N2O:(N2O + N2) ratio of 0.3 in microcosms amended with ARE containing 80% of sugar and of 1 in microcosms amended with ARE containing 40% of sugar. Our study indicated that ARE stimulated nitrate reduction or denitrification activity with increases in the range of those observed with the whole plant. Furthermore, we demonstrated that the composition of the ARE affected the nature of the end-product of denitrification and could thus have a putative impact on greenhouse gas emissions

Non-metric multidimensional scaling of phylogeny-based composition of denitrifying communities.

<p>The distance matrix was based on pairwise differences in the unique fraction of branches shared (UniFrac) for each soil community replicate in the maximum likelihood phylogentic tree of 400 <i>nos</i>Z gene sequences. Vectors indicate diversity metrics and modeled rate parameters that were significantly correlated with the ordination (<i>P</i><0.1): Chao richness index, Shannon’s diversity index (<i>H´</i>), phylogenetic diversity (PD), width of the modeled temperature gradient curve (<i>w</i>), denitrifiction rate at temperature optimum (<i>T_m</i>), salt concentration when denitrification rate was zero (SA0) and salt concentration at the initiation of rate inhibition (SAI). Stress was 12.0.</p

Venn diagram representing shared and unique restriction fragment length polymorphisms (RFLP) groups for denitrifying communities.

<p>The groups were detected by screening ∼100 <i>nosZ</i> gene clones retrived from soil communities A–C and J. Community A had 30, B had 34, C had 23 and J had 44 of the 64 groups in total.</p

Diversity indices of denitrifying bacterial communities determined from RFLP group membership using Chao and Shannon (<i>H</i>') metrics or by comparative sequence analysis for phylogenetic diversity (PD), net relatedness index (NRI), and nearest taxon index (NTI) based on <i>nosZ</i> gene clones from each soil community (n = 3).

<p>Values in parentheses indicate the lower and upper 95% confidence intervals <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051962#pone.0051962-Schloss1" target="_blank">[27]</a> and * indicate values significantly different from the null (<i>p</i><0.05).</p

Model parameters representing the functional operating range for potential denitrification rates in assembled soil communities under temperature and salt concentration gradients modeled using a Gaussian function and power equation, respectively.

<p>Mean values of three field replicates of the soil communities are shown with standard deviations (±SD). Values followed by the same letter indicate treatments without significant differences (<i>p</i><0.1).</p>†<p>Gaussian function: Denitrification rate = <i>V_max</i> * e^{–(<i>T-Tm</i>)²^{/(2*<i>w</i>²⁾ where <i>V_ma</i>_x = maximum denitrification rate, <i>T</i> = tested temperature, <i>T_m</i> = optimum temperature and <i>w</i> = measure of the width of the curve.}}</p>§<p>Power equation: Denitrification rate = <i>k</i>/√(<i>c</i>) - <i>a</i>, where <i>k</i> = slope, <i>c</i> = % salt concentration and <i>a</i> = intercept. From the equation, the salt concentration when denitrification begins to be inhibited (SAI) and when the rate reaches zero (SA0) were calculated.</p