Urinary tract infections trigger synucleinopathy via the innate immune response

Symptoms in the urogenital organs are common in multiple system atrophy (MSA), also in the years preceding the MSA diagnosis. It is unknown how MSA is triggered and these observations in prodromal MSA led us to hypothesize that synucleinopathy could be triggered by infection of the genitourinary tract causing ɑ-synuclein (ɑSyn) to aggregate in peripheral nerves innervating these organs. As a first proof that peripheral infections could act as a trigger in MSA, this study focused on lower urinary tract infections (UTIs), given the relevance and high frequency of UTIs in prodromal MSA, although other types of infection might also be important triggers of MSA. We performed an epidemiological nested-case control study in the Danish population showing that UTIs are associated with future diagnosis of MSA several years after infection and that it impacts risk in both men and women. Bacterial infection of the urinary bladder triggers synucleinopathy in mice and we propose a novel role of ɑSyn in the innate immune system response to bacteria. Urinary tract infection with uropathogenic E. coli results in the de novo aggregation of ɑSyn during neutrophil infiltration. During the infection, ɑSyn is released extracellularly from neutrophils as part of their extracellular traps. Injection of MSA aggregates into the urinary bladder leads to motor deficits and propagation of ɑSyn pathology to the central nervous system in mice overexpressing oligodendroglial ɑSyn. Repeated UTIs lead to progressive development of synucleinopathy with oligodendroglial involvement in vivo. Our results link bacterial infections with synucleinopathy and show that a host response to environmental triggers can result in ɑSyn pathology that bears semblance to MSA

Case-mix and the use of control charts in monitoring mortality rates after coronary artery bypass

BACKGROUND: There is debate about the role of crude mortality rates and case-mix adjusted mortality rates in monitoring the outcomes of treatment. In the context of quality improvement a key purpose of monitoring is to identify special cause variation as this type of variation should be investigated to identify possible causes. This paper investigates agreement between the identification of special cause variation in risk adjusted and observed hospital specific mortality rates after coronary artery bypass grafting in New York hospitals. METHODS: Coronary artery bypass grafting mortality rates between 1994 and 2003 were obtained from the New York State Department of Health's cardiovascular reports for 41 hospitals. Cross-sectional control charts of crude (observed) and risk adjusted mortality rates were produced for each year. Special cause variation was defined as a data point beyond the 99.9% probability limits: hospitals showing special cause variation were identified for each year. Longitudinal control charts of crude (observed) and risk adjusted mortality rates were produced for each hospital with data for all ten years (n = 27). Special cause variation was defined as a data point beyond 99.9% probability limits, two out of three consecutive data points beyond 95% probability limits (two standard deviations from the mean) or a run of five consecutive points on one side of the mean. Years showing special cause variation in mortality were identified for each hospital. Cohen's Kappa was calculated for agreement between special causes identified in crude and risk-adjusted control charts. RESULTS: In cross sectional analysis the Cohen's Kappa was 0.54 (95% confidence interval: 0.28 to 0.78), indicating moderate agreement between the crude and risk-adjusted control charts with sensitivity 0.4 (95% confidence interval 0.17–0.69) and specificity 0.98 (95% confidence interval: 0.95–0.99). In longitudinal analysis, the Cohen's Kappa was 0.61 (95% confidence interval: 0.39 to 0.83) indicating good agreement between the tests with sensitivity 0.63 (95% confidence interval: 0.39–0.82) and specificity 0.98 (95% confidence interval: 0.96 to 0.99). CONCLUSION: There is moderate-good agreement between signals of special cause variation between observed and risk-adjusted mortality. Analysis of observed hospital specific CABG mortality over time and with other hospitals appears to be useful in identifying special causes of variation. Case-mix adjustment may not be essential for longitudinal monitoring of outcomes using control charts

Opportunities and Challenges in Functional Genomics Research in Osteoporosis: Report From a Workshop Held by the Causes Working Group of the Osteoporosis and Bone Research Academy of the Royal Osteoporosis Society on October 5th 2020.

The discovery that sclerostin is the defective protein underlying the rare heritable bone mass disorder, sclerosteosis, ultimately led to development of anti-sclerostin antibodies as a new treatment for osteoporosis. In the era of large scale GWAS, many additional genetic signals associated with bone mass and related traits have since been reported. However, how best to interrogate these signals in order to identify the underlying gene responsible for these genetic associations, a prerequisite for identifying drug targets for further treatments, remains a challenge. The resources available for supporting functional genomics research continues to expand, exemplified by "multi-omics" database resources, with improved availability of datasets derived from bone tissues. These databases provide information about potential molecular mediators such as mRNA expression, protein expression, and DNA methylation levels, which can be interrogated to map genetic signals to specific genes based on identification of causal pathways between the genetic signal and the phenotype being studied. Functional evaluation of potential causative genes has been facilitated by characterization of the "osteocyte signature", by broad phenotyping of knockout mice with deletions of over 7,000 genes, in which more detailed skeletal phenotyping is currently being undertaken, and by development of zebrafish as a highly efficient additional in vivo model for functional studies of the skeleton. Looking to the future, this expanding repertoire of tools offers the hope of accurately defining the major genetic signals which contribute to osteoporosis. This may in turn lead to the identification of additional therapeutic targets, and ultimately new treatments for osteoporosis

Opportunities and Challenges in Functional Genomics Research in Osteoporosis:Report From a Workshop Held by the Causes Working Group of the Osteoporosis and Bone Research Academy of the Royal Osteoporosis Society on October 5th 2020

The discovery that sclerostin is the defective protein underlying the rare heritable bone mass disorder, sclerosteosis, ultimately led to development of anti-sclerostin antibodies as a new treatment for osteoporosis. In the era of large scale GWAS, many additional genetic signals associated with bone mass and related traits have since been reported. However, how best to interrogate these signals in order to identify the underlying gene responsible for these genetic associations, a prerequisite for identifying drug targets for further treatments, remains a challenge. The resources available for supporting functional genomics research continues to expand, exemplified by “multi-omics” database resources, with improved availability of datasets derived from bone tissues. These databases provide information about potential molecular mediators such as mRNA expression, protein expression, and DNA methylation levels, which can be interrogated to map genetic signals to specific genes based on identification of causal pathways between the genetic signal and the phenotype being studied. Functional evaluation of potential causative genes has been facilitated by characterization of the “osteocyte signature”, by broad phenotyping of knockout mice with deletions of over 7,000 genes, in which more detailed skeletal phenotyping is currently being undertaken, and by development of zebrafish as a highly efficient additional in vivo model for functional studies of the skeleton. Looking to the future, this expanding repertoire of tools offers the hope of accurately defining the major genetic signals which contribute to osteoporosis. This may in turn lead to the identification of additional therapeutic targets, and ultimately new treatments for osteoporosis

Cholangiocytes derived from human induced pluripotent stem cells for disease modeling and drug validation.

The study of biliary disease has been constrained by a lack of primary human cholangiocytes. Here we present an efficient, serum-free protocol for directed differentiation of human induced pluripotent stem cells into cholangiocyte-like cells (CLCs). CLCs show functional characteristics of cholangiocytes, including bile acids transfer, alkaline phosphatase activity, γ-glutamyl-transpeptidase activity and physiological responses to secretin, somatostatin and vascular endothelial growth factor. We use CLCs to model in vitro key features of Alagille syndrome, polycystic liver disease and cystic fibrosis (CF)-associated cholangiopathy. Furthermore, we use CLCs generated from healthy individuals and patients with polycystic liver disease to reproduce the effects of the drugs verapamil and octreotide, and we show that the experimental CF drug VX809 rescues the disease phenotype of CF cholangiopathy in vitro. Our differentiation protocol will facilitate the study of biological mechanisms controlling biliary development, as well as disease modeling and drug screening.This work was funded by ERC starting grant Relieve IMDs (L.V., N.H.), the Cambridge Hospitals National Institute for Health Research Biomedical Research Center (L.V., N.H., F.S.), the Evelyn trust (N.H.) and the EU Fp7 grant TissuGEN (M.CDB.). FS has been supported by an Addenbrooke’s Charitable Trust Clinical Research Training Fellowship and a joint MRC-Sparks Clinical Research Training Fellowship.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nbt.327

Reconstruction of the mouse extrahepatic biliary tree using primary human extrahepatic cholangiocyte organoids

Treatment of common bile duct disorders such as biliary atresia or ischaemic strictures is limited to liver transplantation or hepatojejunostomy due to the lack of suitable tissue for surgical reconstruction. Here, we report a novel method for the isolation and propagation of human cholangiocytes from the extrahepatic biliary tree and we explore the potential of bioengineered biliary tissue consisting of these extrahepatic cholangiocyte organoids (ECOs) and biodegradable scaffolds for transplantation and biliary reconstruction in vivo. ECOs closely correlate with primary cholangiocytes in terms of transcriptomic profile and functional properties (ALP, GGT). Following transplantation in immunocompromised mice ECOs self-organize into tubular structures expressing biliary markers (CK7). When seeded on biodegradable scaffolds, ECOs form tissue-like structures retaining biliary marker expression (CK7) and function (ALP, GGT). This bioengineered tissue can reconstruct the wall of the biliary tree (gallbladder) and rescue and extrahepatic biliary injury mouse model following transplantation. Furthermore, it can be fashioned into bioengineered ducts and replace the native common bile duct of immunocompromised mice, with no evidence of cholestasis or lumen occlusion up to one month after reconstruction. In conclusion, ECOs can successfully reconstruct the biliary tree following transplantation, providing proof-of-principle for organ regeneration using human primary cells expanded in vitro

Opportunities and Challenges in Functional Genomics Research in Osteoporosis: Report From a Workshop Held by the Causes Working Group of the Osteoporosis and Bone Research Academy of the Royal Osteoporosis Society on October 5th 2020

The discovery that sclerostin is the defective protein underlying the rare heritable bone mass disorder, sclerosteosis, ultimately led to development of anti-sclerostin antibodies as a new treatment for osteoporosis. In the era of large scale GWAS, many additional genetic signals associated with bone mass and related traits have since been reported. However, how best to interrogate these signals in order to identify the underlying gene responsible for these genetic associations, a prerequisite for identifying drug targets for further treatments, remains a challenge. The resources available for supporting functional genomics research continues to expand, exemplified by “multi-omics” database resources, with improved availability of datasets derived from bone tissues. These databases provide information about potential molecular mediators such as mRNA expression, protein expression, and DNA methylation levels, which can be interrogated to map genetic signals to specific genes based on identification of causal pathways between the genetic signal and the phenotype being studied. Functional evaluation of potential causative genes has been facilitated by characterization of the “osteocyte signature”, by broad phenotyping of knockout mice with deletions of over 7,000 genes, in which more detailed skeletal phenotyping is currently being undertaken, and by development of zebrafish as a highly efficient additional in vivo model for functional studies of the skeleton. Looking to the future, this expanding repertoire of tools offers the hope of accurately defining the major genetic signals which contribute to osteoporosis. This may in turn lead to the identification of additional therapeutic targets, and ultimately new treatments for osteoporosis

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