Computer-Aided Engineering of a Non-Phosphorylating Glyceraldehyde-3-Phosphate Dehydrogenase to Enable Cell-Free Biocatalysis

International audienceRedox cofactor utilization is one of the major barriers to the realization of efficient and cost-competitive cell-free biocatalysis, especially where multiple redox steps are concerned. The design of versatile, cofactor balanced modules for canonical metabolic pathways, such as glycolysis, is one route to overcoming such barriers. Here, we set up a computer-aided design framework to engineer the non-phosphorylating glyceraldehyde-3-phosphate dehydrogenase (GapN) from Streptococcus mutans for enabling an NADH linked efficient cell-free glycolytic pathway with a net zero ATP usage. This rational design approach combines molecular dynamics simulations with a multistate computational design method that allowed us to consider different conformational states encountered along the GapN enzyme catalytic cycle. In particular, the cofactor flip, characteristic of this enzyme family and occurring before product hydrolysis, was taken into account to redesign the cofactor binding pocket for NAD + utilization. While GapN exhibits only trace activity with NAD + , a ∼10,000-fold enhancement of this activity was achieved, corresponding to a recovery of ∼72% of the catalytic efficiency of the wild-type enzyme on NADP + , with a GapN enzyme harboring only 5 mutations

Characterization of the Biomass Degrading Enzyme GuxA from Acidothermus cellulolyticus

Microbial conversion of biomass relies on a complex combination of enzyme systems promoting synergy to overcome biomass recalcitrance. Some thermophilic bacteria have been shown to exhibit particularly high levels of cellulolytic activity, making them of particular interest for biomass conversion. These bacteria use varying combinations of CAZymes that vary in complexity from a single catalytic domain to large multi-modular and multi-functional architectures to deconstruct biomass. Since the discovery of CelA from Caldicellulosiruptor bescii which was identified as one of the most active cellulase so far identified, the search for efficient multi-modular and multi-functional CAZymes has intensified. One of these candidates, GuxA (previously Acel_0615), was recently shown to exhibit synergy with other CAZymes in C. bescii, leading to a dramatic increase in growth on biomass when expressed in this host. GuxA is a multi-modular and multi-functional enzyme from Acidothermus cellulolyticus whose catalytic domains include a xylanase/endoglucanase GH12 and an exoglucanase GH6, representing a unique combination of these two glycoside hydrolase families in a single CAZyme. These attributes make GuxA of particular interest as a potential candidate for thermophilic industrial enzyme preparations. Here, we present a more complete characterization of GuxA to understand the mechanism of its activity and substrate specificity. In addition, we demonstrate that GuxA exhibits high levels of synergism with E1, a companion endoglucanase from A. cellulolyticus. We also present a crystal structure of one of the GuxA domains and dissect the structural features that might contribute to its thermotolerance

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

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization 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