MIR376A is a regulator of starvation-induced autophagy

Background: Autophagy is a vesicular trafficking process responsible for the degradation of long-lived, misfolded or abnormal proteins, as well as damaged or surplus organelles. Abnormalities of the autophagic activity may result in the accumulation of protein aggregates, organelle dysfunction, and autophagy disorders were associated with various diseases. Hence, mechanisms of autophagy regulation are under exploration. Methods: Over-expression of hsa-miR-376a1 (shortly MIR376A) was performed to evaluate its effects on autophagy. Autophagy-related targets of the miRNA were predicted using Microcosm Targets and MIRanda bioinformatics tools and experimentally validated. Endogenous miRNA was blocked using antagomirs and the effects on target expression and autophagy were analyzed. Luciferase tests were performed to confirm that 3’ UTR sequences in target genes were functional. Differential expression of MIR376A and the related MIR376B was compared using TaqMan quantitative PCR. Results: Here, we demonstrated that, a microRNA (miRNA) from the DlkI/Gtl2 gene cluster, MIR376A, played an important role in autophagy regulation. We showed that, amino acid and serum starvation-induced autophagy was blocked by MIR376A overexpression in MCF-7 and Huh-7 cells. MIR376A shared the same seed sequence and had overlapping targets with MIR376B, and similarly blocked the expression of key autophagy proteins ATG4C and BECN1 (Beclin 1). Indeed, 3’ UTR sequences in the mRNA of these autophagy proteins were responsive to MIR376A in luciferase assays. Antagomir tests showed that, endogenous MIR376A was participating to the control of ATG4C and BECN1 transcript and protein levels. Moreover, blockage of endogenous MIR376A accelerated starvation-induced autophagic activity. Interestingly, MIR376A and MIR376B levels were increased with different kinetics in response to starvation stress and tissue-specific level differences were also observed, pointing out to an overlapping but miRNA-specific biological role. Conclusions: Our findings underline the importance of miRNAs encoded by the DlkI/Gtl2 gene cluster in stress-response control mechanisms, and introduce MIR376A as a new regulator of autophagy

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Phosphoketolase activity in ruminal butyrivibrios

International audienc

Derivatized versus non-derivatized LC-MS/MS techniques for the analysis of estrogens and estrogen-like endocrine disruptors in human plasma

Bisphenols, parabens, alkylphenols and triclosan are anthropogenic substances with a phenolic group that have been introduced to the environment in recent decades. As they possess hormone-like effects, they have been termed endocrine disruptors (EDs), and can interfere with steroid pathways in organisms. To evaluate the potential impact of EDs on steroid biosynthesis and metabolism, sensitive and robust methods enabling the concurrent measurement of EDs and steroids in plasma are needed. Of crucial importance is the analysis of unconjugated EDs, which possess biological activity. The aim of the study was to develop and validate LC-MS/MS methods with and without a derivatization step for the analysis of unconjugated steroids (estrone-E1, estradiol-E2, estriol-E3, aldosterone-ALDO) and different groups of EDs (bisphenols, parabens, nonylphenol-NP and triclosan-TCS), and compare these methods on a set of 24 human plasma samples using Passing-Bablok regression analysis. Both methods were validated according to FDA and EMA guidelines. The method with dansyl chloride derivatization allowed 17 compounds to be measured: estrogens (E1, E2, E3), bisphenols (bisphenol A-BPA, BPS, BPF, BPAF, BPAP, BPZ, BPP), parabens (methylparaben-MP, ethylparaben-EP, propylparaben-PP, butylparaben-BP, benzylparaben-BenzylP), TCS and NP, with lower limits of quantification (LLOQs) between 4 and 125 pg/mL. The method without derivatization enabled 15 compounds to be analyzed: estrogens (E1, E2, E3), ALDO, bisphenols (BPA, BPS, BPF, BPAF, BPAP, BPZ), parabens (MP, EP, PP, BP, BenzylP) with LLOQs between 2 and 63 pg/mL, and NP and BPP in semiquantitative mode. Adding 6 mM ammonium fluoride post column into mobile phases in the method without derivatization achieved similar or even better LLOQs than the method with the derivatization step. The uniqueness of the methods lies in the simultaneous determination of different classes of unconjugated (bioactive) fraction of EDs together with selected steroids (estrogens + ALDO in the method without derivatization), which provides a useful tool for evaluating the relationships between EDs and steroid metabolism