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Changes in the human gastrointestinal microbiome are associated with several diseases. To infer causality, experiments in representative models are essential, but widely used animal models exhibit limitations. Here we present a modular, microfluidics-based model (HuMiX, human-microbial crosstalk), which allows co-culture of human and microbial cells under conditions representative of the gastrointestinal human-microbe interface. We demonstrate the ability of HuMiX to recapitulate in vivo transcriptional, metabolic and immunological responses in human intestinal epithelial cells following their co-culture with the commensal Lactobacillus rhamnosus GG (LGG) grown under anaerobic conditions. In addition, we show that the co-culture of human epithelial cells with the obligate anaerobe Bacteroides caccae and LGG results in a transcriptional response, which is distinct from that of a co-culture solely comprising LGG. HuMiX facilitates investigations of host-microbe molecular interactions and provides insights into a range of fundamental research questions linking the gastrointestinal microbiome to human health and disease. ; We thank the scientists and technical staff of the Luxembourg Centre for Systems Biomedicine and Center for Applied Nanobioscience and Medicine, particularly Matthew Barrett and Brett Duane for their excellent technical assistance and engineering support. We are grateful to Francois Bernardin, Nathalie Nicot and Laurent Vallar for the microarray analysis; Aidos Baumuratov for imaging support; Linda Wampach for HuMiX illustrations; and Anna Heintz-Buschart for fruitful discussions. This work was supported by an ATTRACT programme grant (ATTRACT/A09/03), a CORE programme grant (CORE/11/BM/1186762), a European Union Joint Programming in Neurodegenerative Diseases grant (INTER/JPND/12/01) and a Proof-of-Concept grant (PoC-15/11014639) to P.W., Accompany Measures mobility grant (12/AM2c/05) to P.W. and P.S., an INTER mobility grant to P.S. (INTER/14/7516918), and an Aide a la Formation Recherche (AFR) postdoctoral grant (AFR/PDR 2013-1/BM/5821107) as well as a CORE programme grant (CORE/14/BM/8066232) to J.V.F., all funded by the Luxembourg National Research Fund (FNR). This work was further supported by a grant attributed to C.S.-D. by the 'Fondation Recherche sur le SIDA du Luxembourg'. Bioinformatics analyses presented in this paper were carried out in part using the HPC facilities of the University of Luxembourg (http://hpc.uni.lu). ; This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.

In the summer of 2021, European governments removed most NPIs after experiencing prolonged second and third waves of the COVID-19 pandemic. Most countries failed to achieve immunization rates high enough to avoid resurgence of the virus. Public health strategies for autumn and winter 2021 have ranged from countries aiming at low incidence by re-introducing NPIs to accepting high incidence levels. However, such high incidence strategies almost certainly lead to the very consequences that they seek to avoid: restrictions that harm people and economies. At high incidence, the important pandemic containment measure 'test-trace-isolate-support' becomes inefficient. At that point, the spread of SARS-CoV-2 and its numerous harmful consequences can likely only be controlled through restrictions. We argue that all European countries need to pursue a low incidence strategy in a coordinated manner. Such an endeavour can only be successful if it is built on open communication and trust.

In the summer of 2021, European governments removed most NPIs after experiencing prolonged second and third waves of the COVID-19 pandemic. Most countries failed to achieve immunization rates high enough to avoid resurgence of the virus. Public health strategies for autumn and winter 2021 have ranged from countries aiming at low incidence by re-introducing NPIs to accepting high incidence levels. However, such high incidence strategies almost certainly lead to the very consequences that they seek to avoid: restrictions that harm people and economies. At high incidence, the important pandemic containment measure 'test-trace-isolate-support' becomes inefficient. At that point, the spread of SARS-CoV-2 and its numerous harmful consequences can likely only be controlled through restrictions. We argue that all European countries need to pursue a low incidence strategy in a coordinated manner. Such an endeavour can only be successful if it is built on open communication and trust. ; peer-reviewed

In the summer of 2021, European governments removed most NPIs after experiencing prolonged second and third waves of the COVID-19 pandemic. Most countries failed to achieve immunization rates high enough to avoid resurgence of the virus. Public health strategies for autumn and winter 2021 have ranged from countries aiming at low incidence by re-introducing NPIs to accepting high incidence levels. However, such high incidence strategies almost certainly lead to the very consequences that they seek to avoid: restrictions that harm people and economies. At high incidence, the important pandemic containment measure 'test-trace-isolate-support' becomes inefficient. At that point, the spread of SARS-CoV-2 and its numerous harmful consequences can likely only be controlled through restrictions. We argue that all European countries need to pursue a low incidence strategy in a coordinated manner. Such an endeavour can only be successful if it is built on open communication and trust.

In the summer of 2021, European governments removed most NPIs after experiencing prolonged second and third waves of the COVID-19 pandemic. Most countries failed to achieve immunization rates high enough to avoid resurgence of the virus. Public health strategies for autumn and winter 2021 have ranged from countries aiming at low incidence by re-introducing NPIs to accepting high incidence levels. However, such high incidence strategies almost certainly lead to the very consequences that they seek to avoid: restrictions that harm people and economies. At high incidence, the important pandemic containment measure test-trace-isolate-support becomes inefficient. At that point, the spread of SARS-CoV-2 and its numerous harmful consequences can likely only be controlled through restrictions. We argue that all European countries need to pursue a low incidence strategy in a coordinated manner. Such an endeavour can only be successful if it is built on open communication and trust. Copyright (C) 2021 The Authors. Published by Elsevier Ltd. ; Funding Agencies|European UnionEuropean Commission [101016233]; Max Planck SocietyMax Planck SocietyFoundation CELLEX; "Netzwerk Universitatsmedizin" (NUM) project egePan [01KX2021]; Digital Society research program - Ministry of Culture and Science of the German State of North Rhine-Westphalia; University of Malta; Luxembourg National Research Fund (FNR) as part of the COVID-19 Fast-Track research project Cov-Screen [COVID-19/2020- 1/14715687]; European Research Council (ERC) under the European UnionEuropean Research Council (ERC) [863664]; Slovenian Research AgencySlovenian Research Agency - Slovenia [P1-0403, J1-2457]; University of Crete; Maastricht University; European CommissionEuropean CommissionEuropean Commission Joint Research Centre; UK Economic and Social Research Council (ESRC)UK Research & Innovation (UKRI)Economic & Social Research Council (ESRC) [ES/T014164/1]; UK Medical Research Council (MRC)UK Research & Innovation (UKRI)Medical Research Council UK ...

The Parkinson's Disease Digital Biomarker Challenge was funded by the Robert Wood Johnson Foundation and the Michael J. Fox Foundation. Data were contributed by users of the Parkinson mPower mobile application as part of the mPower study developed by Sage Bionetworks and described in Synapse [https://doi.org/10.7303/syn4993293].Resources and support for J.S. were provided by Elder Research, an AI and Data Science consulting agency. M.D. was supported by NIH NIGMS Bioinformatics Training Grant (5T32GM070449-12). J.F.D. was supported by a postdoctoral fellowship from the Canadian Institutes of Health Research. L.L.E. reports grants from the European Research Council ERC (677943), European Union's Horizon 2020 research and innovation programme (675395), Academy of Finland (296801, 304995, 310561 and 314443), and Sigrid Juselius Foundation, during the conduct of the study. EG1 acknowledges the funding support by the Fonds Nationale de la Recherche (FNR) Luxembourg, through the National Centre of Excellence in Research (NCER) on Parkinson's disease (I1R-BIC-PFN-15NCER), and as part of the grant project PD-Strat (INTER/11651464). M.K.J. was supported by Alfred Kordelin Foundation. J.J. is supported by UNSW Sydney Electronic Practice Based Research Network (ePBRN) and Translational Cancer Research Network (TCRN) programs. D.L. is supported in part by the University of Rochester CTSA award number UL1 TR002001 from the National Center for Advancing Translational Sciences of the National Institutes of Health. PS2 is supported by the Swiss National Science Foundation (SNSF) project No. 167302 within the National Research Program (NRP) 75 "Big Data". P.S. is an affiliated PhD fellow at the Max Planck ETH Center for Learning Systems. Y.G. is supported by NIH R35GM133346, NSF#1452656, Michael J. Fox Foundation #17373, American Parkinson Disease Association AWD007950. Cohen Veterans Bioscience contributed financial support to Early Signal Foundation's costs (U.R., C.E., and D.B.). ; Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95). ; Robert Wood Johnson Foundation (RWJF) Michael J. Fox Foundation NIH NIGMS Bioinformatics Training Grant 5T32GM070449-12 Canadian Institutes of Health Research (CIHR) European Research Council (ERC) European Commission 677943 European Union's Horizon 2020 research and innovation programme 675395 Academy of Finland 296801 304995 310561 314443 Sigrid Juselius Foundation Fonds Nationale de la Recherche (FNR) Luxembourg, through the National Centre of Excellence in Research (NCER) on Parkinson's disease I1R-BIC-PFN-15NCER project PD-Strat INTER/11651464 Alfred Kordelin Foundation UNSW Sydney Electronic Practice Based Research Network (ePBRN) program UNSW Translational Cancer Research Network (TCRN) program United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Center for Advancing Translational Sciences (NCATS) UL1 TR002001 Swiss National Science Foundation (SNSF) 167302 United States Department of Health & Human Services National Institutes of Health (NIH) - USA R35GM133346 National Science Foundation (NSF) 1452656 Michael J. Fox Foundation 17373 American Parkinson Disease Association AWD007950 Cohen Veterans Bioscience

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective. ; This study was supported by the Luxembourg National Research Fund (FNR) COVID-19 Fast-Track grant programme, grant COVID-19/2020-1/14715687/CovScreen (E. Glaab); European Commission, INFORE grant H2020-ICT-825070 (A. Montagud, M. Ponce de Leon, M. Vazques and A. Valencia); European Commission, PerMedCoE grant H2020-ICT-951773 (A. Montagud, M. Ponce de Leon, M. Vazques and A. Valencia) the Federal Ministry of Education and Research (BMBF, Germany) and the Baden-Württemberg Ministry of Science, the Excellence Strategy of the German Federal and State Governments (A. Renz); German Center for Infection Research (DZIF), grant no 8020708703 (A. Dräger); The Netherlands Organisation for Health Research and Development (ZonMw), grant no ...