Human pluripotent stem cell-derived acinar/ductal organoids generate human pancreas upon orthotopic transplantation and allow disease modelling

Objective The generation of acinar and ductal cells from human pluripotent stem cells (PSCs) is a poorly studied process, although various diseases arise from this compartment. Design We designed a straightforward approach to direct human PSCs towards pancreatic organoids resembling acinar and ductal progeny. Results Extensive phenotyping of the organoids not only shows the appropriate marker profile but also ultrastructural, global gene expression and functional hallmarks of the human pancreas in the dish. Upon orthotopic transplantation into immunodeficient mice, these organoids form normal pancreatic ducts and acinar tissue resembling fetal human pancreas without evidence of tumour formation or transformation. Finally, we implemented this unique phenotyping tool as a model to study the pancreatic facets of cystic fibrosis (CF). For the first time, we provide evidence that in vitro, but also in our xenograft transplantation assay, pancreatic commitment occurs generally unhindered in CF. Importantly, cystic fibrosis transmembrane conductance regulator (CFTR) activation in mutated pancreatic organoids not only mirrors the CF phenotype in functional assays but also at a global expression level. We also conducted a scalable proof-of-concept screen in CF pancreatic organoids using a set of CFTR correctors and activators, and established an mRNA-mediated gene therapy approach in CF organoids. Conclusions Taken together, our platform provides novel opportunities to model pancreatic disease and development, screen for disease-rescuing agents and to test therapeutic procedures.This study was funded by the Deutsche Forschungsgemeinschaft (DFG, K.L. 2544/1-1 and 1-2), the Forschungskern SyStaR to AK, BIU (Böhringer Ingelheim Ulm to AK), the Fritz-Thyssen Foundation (Az. 10.15.2.040), the German Cancer Aid (111879) and the Else-Kröner-Fresenius-Stiftung (2011_A200). AK is indebted to the Baden-Württemberg Stiftung for the financial support of this research project by the Eliteprogramme for Postdocs. AK is also an Else-Kröner-Fresenius Memorial Fellow. LP is supported by a research fellowship of the Else-Kröner-Fresenius-Stiftung. MH was supported by the International Graduate School in Molecular Medicine and the Bausteinprogramme (L.SBN. 110), Ulm University. MM is supported by a grant of Ulm University (Baustein for Senior Clinician Scientists). IGC is funded by the Interdisciplinary Center for Clinical Research (IZKF Aachen) and Start Program, RWTH Aachen University Medical School, Aachen, German

Plasma and Muscle Myostatin in Relation to Type 2 Diabetes

OBJECTIVE: Myostatin is a secreted growth factor expressed in skeletal muscle tissue, which negatively regulates skeletal muscle mass. Recent animal studies suggest a role for myostatin in insulin resistance. We evaluated the possible metabolic role of myostatin in patients with type 2 diabetes and healthy controls. DESIGN: 76 patients with type 2 diabetes and 92 control subjects were included in the study. They were matched for age, gender and BMI. Plasma samples and biopsies from the vastus lateralis muscle were obtained to assess plasma myostatin and expression of myostatin in skeletal muscle. RESULTS: Patients with type 2 diabetes had higher fasting glucose (8.9 versus 5.1 mmol/L, P<0.001), plasma insulin (68.2 versus 47.2 pmol/L, P<0.002) and HOMA2-IR (1.6 versus 0.9, P<0.0001) when compared to controls. Patients with type 2 diabetes had 1.4 (P<0.01) higher levels of muscle myostatin mRNA content than the control subjects. Plasma myostatin concentrations did not differ between patients with type 2 diabetes and controls. In healthy controls, muscle myostatin mRNA correlated with HOMA2-IR (r = 0.30, P<0.01), plasma IL-6 (r = 0.34, P<0.05) and VO2 max (r = -0.26, P<0.05), however, no correlations were observed in patients with type 2 diabetes. CONCLUSIONS: This study supports the idea that myostatin may have a negative effect on metabolism. However, the metabolic effect of myostatin appears to be overruled by other factors in patients with type 2 diabetes

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)