GATA-1 deficiency rescues trabecular but not cortical bone in OPG deficient mice

GATA-1(low/low) mice have an increase in megakaryocytes (MKs) and trabecular bone. The latter is thought to result from MKs directly stimulating osteoblastic bone formation while simultaneously inhibiting osteoclastogenesis. Osteoprotegerin (OPG) is known to inhibit osteoclastogenesis and OPG(-/-) mice have reduced trabecular and cortical bone due to increased osteoclastogenesis. Interestingly, GATA-1(low/low) mice have increased OPG levels. Here, we sought to determine whether GATA-1 knockdown in OPG(-/-) mice could rescue the observed osteoporotic bone phenotype. GATA-1(low/low) mice were bred with OPG(-/-) mice and bone phenotype assessed. GATA-1(low/low) × OPG(-/-) mice have increased cortical bone porosity, similar to OPG(-/-) mice. Both OPG(-/-) and GATA-1(low/low) × OPG(-/-) mice, were found to have increased osteoclasts localized to cortical bone, possibly producing the observed elevated porosity. Biomechanical assessment indicates that OPG(-/-) and GATA-1(low/low) × OPG(-/-) femurs are weaker and less stiff than C57BL/6 or GATA-1(low/low) femurs. Notably, GATA-1(low/low) × OPG(-/-) mice had trabecular bone parameters that were not different from C57BL/6 values, suggesting that GATA-1 deficiency can partially rescue the trabecular bone loss observed with OPG deficiency. The fact that GATA-1 deficiency appears to be able to partially rescue the trabecular, but not the cortical bone phenotype suggests that MKs can locally enhance trabecular bone volume, but that MK secreted factors cannot access cortical bone sufficiently to inhibit osteoclastogenesis or that OPG itself is required to inhibit osteoclastogenesis in cortical bone

Megakaryocytes Regulate Expression of Pyk2 Isoforms and Caspase-mediated Cleavage of Actin in Osteoblasts

The proliferation and differentiation of osteoblast (OB) precursors are essential for elaborating the bone-forming activity of mature OBs. However, the mechanisms regulating OB proliferation and function are largely unknown. We reported that OB proliferation is enhanced by megakaryocytes (MKs) via a process that is regulated in part by integrin signaling. The tyrosine kinase Pyk2 has been shown to regulate cell proliferation and survival in a variety of cells. Pyk2 is also activated by integrin signaling and regulates actin remodeling in bone-resorbing osteoclasts. In this study, we examined the role of Pyk2 and actin in the MK-mediated increase in OB proliferation. Calvarial OBs were cultured in the presence of MKs for various times, and Pyk2 signaling cascades in OBs were examined by Western blotting, subcellular fractionation, and microscopy. We found that MKs regulate the temporal expression of Pyk2 and its subcellular localization. We also found that MKs regulate the expression of two alternatively spliced isoforms of Pyk2 in OBs, which may regulate OB differentiation and proliferation. MKs also induced cytoskeletal reorganization in OBs, which was associated with the caspase-mediated cleavage of actin, an increase in focal adhesions, and the formation of apical membrane ruffles. Moreover, BrdU incorporation in MK-stimulated OBs was blocked by the actin-polymerizing agent, jasplakinolide. Collectively, our studies reveal that Pyk2 and actin play an important role in MK-regulated signaling cascades that control OB proliferation and may be important for therapeutic interventions aimed at increasing bone formation in metabolic diseases of the skeleton

C-Mpl Is Expressed on Osteoblasts and Osteoclasts and Is Important in Regulating Skeletal Homeostasis

C-Mpl is the receptor for thrombopoietin (TPO), the main megakaryocyte (MK) growth factor, and c-Mpl is believed to be expressed on cells of the hematopoietic lineage. As MKs have been shown to enhance bone formation, it may be expected that mice in which c-Mpl was globally knocked out (c-Mpl(-/-) mice) would have decreased bone mass because they have fewer MKs. Instead, c-Mpl(-/-) mice have a higher bone mass than WT controls. Using c-Mpl(-/-) mice we investigated the basis for this discrepancy and discovered that c-Mpl is expressed on both osteoblasts (OBs) and osteoclasts (OCs), an unexpected finding that prompted us to examine further how c-Mpl regulates bone. Static and dynamic bone histomorphometry parameters suggest that c-Mpl deficiency results in a net gain in bone volume with increases in OBs and OCs. In vitro, a higher percentage of c-Mpl(-/-) OBs were in active phases of the cell cycle, leading to an increased number of OBs. No difference in OB differentiation was observed in vitro as examined by real-time PCR and functional assays. In co-culture systems, which allow for the interaction between OBs and OC progenitors, c-Mpl(-/-) OBs enhanced osteoclastogenesis. Two of the major signaling pathways by which OBs regulate osteoclastogenesis, MCSF/OPG/RANKL and EphrinB2-EphB2/B4, were unaffected in c-Mpl(-/-) OBs. These data provide new findings for the role of MKs and c-Mpl expression in bone and may provide insight into the homeostatic regulation of bone mass as well as bone loss diseases such as osteoporosis

The loss of STAT3 in mature osteoclasts has detrimental effects on bone structure

Signal Transducer and Activator of Transcription 3 (STAT3) has recently been shown to be involved in bone development and has been implicated in bone diseases, such as Job’s Syndrome. Bone growth and changes have been known for many years to differ between sexes with male bones tending to have higher bone mass than female bones and older females tending to lose bone mass at faster rates than older males. Previous studies using conditional knock mice with Stat3 specifically deleted from the osteoblasts showed both sexes exhibited decreased bone mineral density (BMD) and strength. Using the Cre-Lox system with Cathepsin K promotor driving Cre to target the deletion of the Stat3 gene in mature osteoclasts (STAT3-cKO mice), we observed that 8-week old STAT3-cKO female femurs exhibited significantly lower BMD and bone mineral content (BMC) compared to littermate control (CN) females. There were no differences in BMD and BMC observed between male knock-out and male CN femurs. However, micro-computed tomography (μCT) analysis showed that both male and female STAT3-cKO mice had significant decreases in bone volume/tissue volume (BV/TV). Bone histomorphometry analysis of the distal femur, further revealed a decrease in bone formation rate and mineralizing surface/bone surface (MS/BS) with a significant decrease in osteoclast surface in female, but not male, STAT3-cKO mice. Profiling gene expression in an osteoclastic cell line with a knockdown of STAT3 showed an upregulation of a number of genes that are directly regulated by estrogen receptors. These data collectively suggest that regulation of STAT3 differs in male and female osteoclasts and that inactivation of STAT3 in osteoclasts affects bone turnover more in females than males, demonstrating the complicated nature of STAT3 signaling pathways in osteoclastogenesis. Drugs targeting the STAT3 pathway may be used for treatment of diseases such as Job’s Syndrome and osteoporosis

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

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

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). 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).S

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)