Tuning of Peroxiredoxin Catalysis for Various Physiological Roles

Peroxiredoxins (Prxs) make up an ancient family of enzymes that are the predominant peroxidases for nearly all organisms and play essential roles in reducing hydrogen peroxide, organic hydroperoxides, and peroxynitrite. Even between distantly related organisms, the core protein fold and key catalytic residues related to its cysteine-based catalytic mechanism have been retained. Given that these enzymes appeared early in biology, Prxs have experienced over a billion years of optimization for specific ecological niches. Although their basic enzymatic function remains the same, Prxs have diversified and are involved in roles such as protecting DNA against mutation, defending pathogens against host immune responses, suppressing tumor formation, and—for eukaryotes—helping regulate peroxide signaling via hyperoxidation of their catalytic Cys. Here, we review the current understanding of the physiological roles of Prxs by analyzing knockout and knockdown studies from ca. twenty-five different species. We also review what is known about the structural basis for the sensitivity of some eukaryotic Prxs to inactivation by hyperoxidation. In considering the physiological relevance of hyperoxidation, we explore the distribution across species of sulfiredoxin (Srx), the enzyme responsible for rescuing hyperoxidized Prxs. We unexpectedly find that among eukaryotes appearing to have a “sensitive” Prx isoform, some do not contain Srx. Also, as Prxs are suggested to be promising targets for drug design, we discuss the rationale behind recently proposed strategies for their selective inhibition

Peroxiredoxin Catalysis at Atomic Resolution

Peroxiredoxins (Prxs) are ubiquitous cysteine-based peroxidases that guard cells against oxidative damage, are virulence factors for pathogens, and are involved in eukaryotic redox regulatory pathways. We have analyzed catalytically active crystals to capture atomic resolution snapshots of a PrxQ-subfamily enzyme (from Xanthomonas campestris) proceeding through thiolate, sulfenate, and sulfinate species. These analyses provide structures of unprecedented accuracy for seeding theoretical studies, and show novel conformational intermediates giving insight into the reaction pathway. Based on a highly non-standard geometry seen for the sulfenate intermediate, we infer that the sulfenate formation itself can strongly promote local unfolding of the active site to enhance productive catalysis. Further, these structures reveal that preventing local unfolding, in this case via crystal contacts, results in facile hyperoxidative inactivation even for Prxs normally resistant to such inactivation. This supports previous proposals that conformation-specific inhibitors may be useful for achieving selective inhibition of Prxs that are drug targets

A bacterial inflammation sensor regulates c-di-GMP signaling, adhesion, and biofilm formation

The reactive oxygen species produced during inflammation through the neutrophilic respiratory burst play profound roles in combating bacterial pathogens and regulating the microbiota. Among these, the neutrophilic oxidant bleach, hypochlorous acid (HOCl), is the most prevalent and strongest oxidizer and kills bacteria through non-specific oxidation of proteins, lipids, and DNA. Thus, HOCl can be viewed as a host-specific cue that conveys important information about what bacterial physiology and lifestyle programs may be required for successful colonization. Nevertheless, bacteria that colonize animals face a molecular challenge in how to achieve highly selective detection of HOCl due to its reactive and transient nature and chemical similarity to more benign and non-host-specific oxidants like hydrogen peroxide (H2O2). Here, we report that in response to increasing HOCl levels E. coli regulates biofilm production via activation of the diguanylate cyclase DgcZ. We show the molecular mechanism of this activation to be specific oxidation of a conserved cysteine that coordinates the zinc of its regulatory chemoreceptor zinc-binding (CZB) domain, forming a zinc-cysteine redox switch 685-fold more sensitive to oxidation by HOCl over H2O2. Dissection of the signal transduction mechanism through quantum mechanics, molecular dynamics, and biochemical analyses reveal how the cysteine redox state alters the delicate equilibrium of competition for Zn++ between the CZB domain and other zinc binders to relay the presence of HOCl through activating the associated GGDEF domain to catalyze c-di-GMP. We find biofilm formation and HOCl-sensing in vivo to be regulated by the conserved cysteine, and point mutants that mimic oxidized CZB states increase production of the biofilm matrix polymer poly-N-acetylglucosamine and total biofilm. We observe CZB-regulated diguanylate cyclases and chemoreceptors in phyla in which host-associated bacteria are prevalent and are possessed by pathogens that manipulate host inflammation as part of their colonization strategy. A phylogenetic survey of all known CZB sequences shows these domains to be conserved and widespread across diverse phyla, suggesting CZB origin predates the bacterial last universal common ancestor. The ability of bacteria to use CZB protein domains to perceive and thwart the host neutrophilic respiratory burst has implications for understanding the mechanisms of diseases of chronic inflammation and gut dysbiosis

Peroxiredoxins: Guardians Against Oxidative Stress and Modulators of Peroxide Signaling

Peroxiredoxins (Prxs) are a ubiquitous family of cysteine-dependent peroxidase enzymes that play dominant roles in regulating peroxide levels within cells. These enzymes, often present at high levels and capable of rapidly clearing peroxides, display a remarkable array of variations in their oligomeric states and susceptibility to regulation by hyperoxidative inactivation and other post-translational modifications. Key conserved residues within the active site promote catalysis by stabilizing the transition state required for transferring the terminal oxygen of hydroperoxides to the active site (peroxidatic) cysteine residue. Extensive investigations continue to expand our understanding of the scope of their importance as well as the structures and forces at play within these critical defense and regulatory enzymes.Keywords: redox signaling, peroxidase, antioxidant enzyme, antioxidant defens

A bacterial inflammation sensor regulates c-di-GMP signaling, adhesion, and biofilm formation

Bacteria that colonize animals must overcome, or coexist, with the reactive oxygen species products of inflammation, a front-line defense of innate immunity. Among these is the neutrophilic oxidant bleach, hypochlorous acid (HOCl), a potent antimicrobial that plays a primary role in killing bacteria through nonspecific oxidation of proteins, lipids, and DNA. Here, we report that in response to increasing HOCl levels, Escherichia coli regulates biofilm production via activation of the diguanylate cyclase DgcZ. We identify the mechanism of DgcZ sensing of HOCl to be direct oxidation of its regulatory chemoreceptor zinc-binding (CZB) domain. Dissection of CZB signal transduction reveals that oxidation of the conserved zinc-binding cysteine controls CZB Zn2+ occupancy, which in turn regulates the catalysis of c-di-GMP by the associated GGDEF domain. We find DgcZ-dependent biofilm formation and HOCl sensing to be regulated in vivo by the conserved zinc-coordinating cysteine. Additionally, point mutants that mimic oxidized CZB states increase total biofilm. A survey of bacterial genomes reveals that many pathogenic bacteria that manipulate host inflammation as part of their colonization strategy possess CZB-regulated diguanylate cyclases and chemoreceptors. Our findings suggest that CZB domains are zinc-sensitive regulators that allow host-associated bacteria to perceive host inflammation through reactivity with HOCl

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

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