A Rapid, Scalable Method for the Isolation, Functional Study, and Analysis of Cell-Derived Extracellular Matrix

The extracellular matrix (ECM) is recognized as a diverse, dynamic, and complex environment that is involved in multiple cell-physiological and pathological processes. However, the isolation of ECM, from tissues or cell culture, is complicated by the insoluble and cross-linked nature of the assembled ECM and by the potential contamination of ECM extracts with cell surface and intracellular proteins. Here, we describe a method for use with cultured cells that is rapid and reliably removes cells to isolate a cell-derived ECM for downstream experimentation. Through use of this method, the isolated ECM and its components can be visualized by in situ immunofluorescence microscopy. The dynamics of specific ECM proteins can be tracked by tracing the deposition of a tagged protein using fluorescence microscopy, both before and after the removal of cells. Alternatively, the isolated ECM can be extracted for biochemical analysis, such as sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) and immunoblotting. At larger scales, a full proteomics analysis of the isolated ECM by mass spectrometry can be conducted. By conducting ECM isolation under sterile conditions, sterile ECM layers can be obtained for functional or phenotypic studies with any cell of interest. The method can be applied to any adherent cell type, is relatively easy to perform, and can be linked to a wide repertoire of experimental designs

Modulation of the extracellular matrix patterning of thrombospondins by actin dynamics and thrombospondin oligomer state

Thrombospondins (TSPs) are evolutionarily-conserved, secreted glycoproteins that interact with cell surfaces and extracellular matrix (ECM) and have complex roles in cell interactions. Unlike the structural components of the ECM that form networks or fibrils, TSPs are deposited into ECM as arrays of nanoscale puncta. The cellular and molecular mechanisms for the patterning of TSPs in ECM are poorly understood. In the present study, we investigated whether the mechanisms of TSP patterning in cell-derived ECM involves actin cytoskeletal pathways or TSP oligomer state. From tests of a suite of pharmacological inhibitors of small GTPases, actomyosin-based contractility, or actin microfilament integrity and dynamics, cytochalasin D and jasplakinolide treatment of cells were identified to result in altered ECM patterning of a model TSP1 trimer. The strong effect of cytochalasin D indicated that mechanisms controlling puncta patterning depend on global F-actin dynamics. Similar spatial changes were obtained with endogenous TSPs after cytochalasin D treatment, implicating physiological relevance. Under matched experimental conditions with ectopically-expressed TSPs, the magnitude of the effect was markedly lower for pentameric TSP5 and Drosophila TSP, than for trimeric TSP1 or dimeric Ciona TSPA. To distinguish between the variables of protein sequence or oligomer state, we generated novel, chimeric pentamers of TSP1. These proteins accumulated within ECM at higher levels than TSP1 trimers, yet the effect of cytochalasin D on the spatial distribution of puncta was reduced. These findings introduce a novel concept that F-actin dynamics modulate the patterning of TSPs in ECM and that TSP oligomer state is a key determinant of this process

Hydra Mesoglea Proteome Identifies Thrombospondin as a Conserved Component Active in Head Organizer Restriction

Thrombospondins (TSPs) are multidomain glycoproteins with complex matricellular functions in tissue homeostasis and remodeling. We describe a novel role of TSP as a Wnt signaling target in the basal eumetazoan Hydra. Proteome analysis identified Hydra magnipapillata TSP (HmTSP) as a major component of the cnidarian mesoglea. In general, the domain organization of cnidarian TSPs is related to the pentameric TSPs of bilaterians, and in phylogenetic analyses cnidarian TSPs formed a separate clade of high sequence diversity. HmTSP expression in polyps was restricted to the hypostomal tip and tentacle bases that harbor Wnt-regulated organizer tissues. In the hypostome, HmTSP- and Wnt3-expressing cells were identical or in close vicinity to each other, and regions of ectopic tentacle formation induced by pharmacological β-Catenin activation (Alsterpaullone) corresponded to foci of HmTSP expression. Chromatin immunoprecipitation (ChIP) confirmed binding of Hydra TCF to conserved elements in the HmTSP promotor region. Accordingly, β-Catenin knockdown by siRNAs reduced normal HmTSP expression at the head organizer. In contrast, knockdown of HmTSP expression led to increased numbers of ectopic organizers in Alsterpaullone-treated animals, indicating a negative regulatory function. Our data suggest an unexpected role for HmTSP as a feedback inhibitor of Wnt signaling during Hydra body axis patterning and maintenance

Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

Background: The risk of severe COVID-19 if an individual becomes infected is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 and how this varies between countries should inform the design of possible strategies to shield or vaccinate those at highest risk. Methods: We estimated the number of individuals at increased risk of severe disease (defined as those with at least one condition listed as “at increased risk of severe COVID-19” in current guidelines) by age (5-year age groups), sex, and country for 188 countries using prevalence data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 and UN population estimates for 2020. The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. To help interpretation of the degree of risk among those at increased risk, we also estimated the number of individuals at high risk (defined as those that would require hospital admission if infected) using age-specific infection–hospitalisation ratios for COVID-19 estimated for mainland China and making adjustments to reflect country-specific differences in the prevalence of underlying conditions and frailty. We assumed males were twice at likely as females to be at high risk. We also calculated the number of individuals without an underlying condition that could be considered at increased risk because of their age, using minimum ages from 50 to 70 years. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 95% confidence limits for country population size, disease prevalences, multimorbidity fractions, and infection–hospitalisation ratios, and plausible low and high estimates for the degree of clustering, informed by multimorbidity studies. Findings: We estimated that 1·7 billion (UI 1·0–2·4) people, comprising 22% (UI 15–28) of the global population, have at least one underlying condition that puts them at increased risk of severe COVID-19 if infected (ranging from <5% of those younger than 20 years to >66% of those aged 70 years or older). We estimated that 349 million (186–787) people (4% [3–9] of the global population) are at high risk of severe COVID-19 and would require hospital admission if infected (ranging from <1% of those younger than 20 years to approximately 20% of those aged 70 years or older). We estimated 6% (3–12) of males to be at high risk compared with 3% (2–7) of females. The share of the population at increased risk was highest in countries with older populations, African countries with high HIV/AIDS prevalence, and small island nations with high diabetes prevalence. Estimates of the number of individuals at increased risk were most sensitive to the prevalence of chronic kidney disease, diabetes, cardiovascular disease, and chronic respiratory disease. Interpretation: About one in five individuals worldwide could be at increased risk of severe COVID-19, should they become infected, due to underlying health conditions, but this risk varies considerably by age. Our estimates are uncertain, and focus on underlying conditions rather than other risk factors such as ethnicity, socioeconomic deprivation, and obesity, but provide a starting point for considering the number of individuals that might need to be shielded or vaccinated as the global pandemic unfolds. Funding: UK Department for International Development, Wellcome Trust, Health Data Research UK, Medical Research Council, and National Institute for Health Research

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

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

Analysis of Familial Hemophagocytic Lymphohistiocytosis type 4 (FHL-4) mutant proteins reveals that S-acylation is required for the function of syntaxin 11 in natural killer cells

Natural killer (NK) cell secretory lysosome exocytosis and cytotoxicity are impaired in familial hemophagocytic lymphohistiocytosis type 4 (FHL-4), a disorder caused by mutations in the gene encoding the SNARE protein syntaxin 11. We show that syntaxin 11 binds to SNAP23 in NK cells and that this interaction is reduced by FHL-4 truncation and frameshift mutation proteins that delete all or part of the SNARE domain of syntaxin 11. In contrast the FHL-4 mutant proteins bound to the Sec-1/Munc18-like (SM) protein Munc18-2. We demonstrate that the C-terminal cysteine rich region of syntaxin 11, which is deleted in the FHL-4 mutants, is S-acylated. This posttranslational modification is required for the membrane association of syntaxin 11 and for its polarization to the immunological synapse in NK cells conjugated to target cells. Moreover, we show that Munc18-2 is recruited by syntaxin 11 to intracellular membranes in resting NK cells and to the immunological synapse in activated NK cells. This recruitment of Munc18-2 is abolished by deletion of the C-terminal cysteine rich region of syntaxin 11. These results suggest a pivotal role for S-acylation in the function of syntaxin 11 in NK cells

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