Impaired Orthostatic Blood Pressure Recovery is associated with Unexplained and Injurious Falls

Background/Objectives: Cardiovascular disorders are recognised as important modifiable risk factors for falls. However the association between falls and orthostatic hypotension (OH) remains ambivalent, particularly because of poor measurement methods of previous studies. Our goal was to determine for the first time to what extent OH (and variants) are risk factors for incident falls, unexplained falls (UF), injurious falls (IF) and syncope using dynamic blood pressure (BP) measurements in a population study. Design: Nationally Representative Longitudinal Cohort Study - The Irish Longitudinal Study on Ageing (TILDA) – wave 1 (2009-2011) with 2 year follow-up at wave 2 (2012-2013). Setting: Community dwelling adults. Participants: 4127 participants were randomly sampled from the population of older adults aged ≥50 years resident in Ireland. Measurements: Continuous BP recordings measured during active stands were analysed. OH and variants (initial OH and impaired orthostatic BP stabilisation OH(40)) were defined using dynamic BP measurements. Associations with the number of falls, UF, IF and syncope reported two years later were assessed using negative binomial and modified Poisson regression. Results: Participants had a mean age 61.5(8.2) years (54.2% female). OH(40) was associated with increased relative risk of UF (RR:1.52 95%CI:1.03-2.26). OH was associated with all-cause falls (IRR:1.40 95%CI:1.01-1.96), UF(RR:1.81 95%CI:1.06-3.09), and IF(RR:1.58 95%CI:1.12-2.24). IOH was not associated with any outcome. Conclusion: With the exception of initial orthostatic hypotension, beat-to-beat measures of impaired orthostatic BP recovery (delayed or incomplete stabilisation) are independent risk factors for future falls, unexplained falls, and injurious falls

Genetically engineered frameshifted YopN-TyeA chimeras influence type III secretion system function in Yersinia pseudotuberculosis

Type III secretion is a tightly controlled virulence mechanism utilized by many gram negative bacteria to colonize their eukaryotic hosts. To infect their host, human pathogenic Yersinia spp. translocate protein toxins into the host cell cytosol through a preassembled Ysc-Yop type III secretion device. Several of the Ysc-Yop components are known for their roles in controlling substrate secretion and translocation. Particularly important in this role is the YopN and TyeA heterodimer. In this study, we confirm that Y. pseudotuberculosis naturally produce a 42 kDa YopN-TyeA hybrid protein as a result of a +1 frame shift near the 3 prime of yopN mRNA, as has been previously reported for the closely related Y. pestis. To assess the biological role of this YopN-TyeA hybrid in T3SS by Y. pseudotuberculosis, we used in cis site-directed mutagenesis to engineer bacteria to either produce predominately the YopN-TyeA hybrid by introducing +1 frame shifts to yopN after codon 278 or 287, or to produce only singular YopN and TyeA polypeptides by introducing yopN sequence from Y. enterocolitica, which is known not to produce the hybrid. Significantly, the engineered 42 kDa YopN-TyeA fusions were abundantly produced, stable, and were efficiently secreted by bacteria in vitro. Moreover, these bacteria could all maintain functionally competent needle structures and controlled Yops secretion in vitro. In the presence of host cells however, bacteria producing the most genetically altered hybrids (+1 frameshift after 278 codon) had diminished control of polarized Yop translocation. This corresponded to significant attenuation in competitive survival assays in orally infected mice, although not at all to the same extent as Yersinia lacking both YopN and TyeA proteins. Based on these studies with engineered polypeptides, most likely a naturally occurring YopN-TyeA hybrid protein has the potential to influence T3S control and activity when produced during Yersinia-host cell contact

One stop shop: backbones trees for important phytopathogenic genera: I (2014)

Many fungi are pathogenic on plants and cause significant damage in agriculture and forestry. They are also part of the natural ecosystem and may play a role in regulating plant numbers/density. Morphological identification and analysis of plant pathogenic fungi, while important, is often hampered by the scarcity of discriminatory taxonomic characters and the endophytic or inconspicuous nature of these fungi. Molecular (DNA sequence) data for plant pathogenic fungi have emerged as key information for diagnostic and classification studies, although hampered in part by non-standard laboratory practices and analytical methods. To facilitate current and future research, this study provides phylogenetic synopses for 25 groups of plant pathogenic fungi in the Ascomycota, Basidiomycota, Mucormycotina (Fungi), and Oomycota, using recent molecular data, up-to-date names, and the latest taxonomic insights. Lineage-specific laboratory protocols together with advice on their application, as well as general observations, are also provided. We hope to maintain updated backbone trees of these fungal lineages over time and to publish them jointly as new data emerge. Researchers of plant pathogenic fungi not covered by the present study are invited to join this future effort. Bipolaris, Botryosphaeriaceae, Botryosphaeria, Botrytis, Choanephora, Colletotrichum, Curvularia, Diaporthe, Diplodia, Dothiorella, Fusarium, Gilbertella, Lasiodiplodia, Mucor, Neofusicoccum, Pestalotiopsis, Phyllosticta, Phytophthora, Puccinia, Pyrenophora, Pythium, Rhizopus, Stagonosporopsis, Ustilago and Verticillium are dealt with in this paper

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)