Extracting information from vehicle exteriors via soil and insect DNA

© 2017 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 12 month embargo from date of publication (Oct 2017) in accordance with the publisher’s archiving policyDNA present on the surface of a vehicle provides evidence of past journeys which may be of forensic value. We show here the type of information that can be economically sourced by arbitrarily primed PCR and next generation sequencing of the DNA present in crushed insects and dust adhering to the upper surfaces and mud adhering to the underbody of a vehicle

Crystalline cytochrome b₂

(1) It has been shown that the flavo-haemo-protein of cytochrome b2 must undergo modification before it will crystallise under the conditions of the first crystallisation step in the Appleby and Morton procedure. (2) Air drying or ageing of the yeast is necessary for the release of b2-DNA. Preparations of crystalline Type I cytochrome b2 from fresh, freeze-dried yeast contained less than the usual amounts of b2-DNA. (3) The protein and DNA of oxidised Type I cytochrome b2 have been shown to be largely dissociated when the enzyme is in solution. (4) The specificity of the association between the DNA component and the enzyme of crystalline Type I cytochrome b2 has been investigated by testing the ability of various nucleic acid preparations to form crystalline complexes with DNA-free (Type II) cytochrome b2. It was found that only double-stranded DNA molecules with a molecular weight of roughly 2 x 1O5 produced the square plate crystals that are characteristic of normal preparations of Type I cytochrome b2. High molecular weight DNA and single-stranded DNA, either native or denatured, produced either amorphous precipitates or various semi-crystalline forms. These effects were independent of the base composition of the samples used. PolyacryIate, a linear, non-cross linked, polyanion produced square plate crystals with Type II cytochrome b2. (5) The ability of b2-DNA to anneal extensively with all samples of labelled yeast RNA collected after centrifugation on a sucrose density gradient, even with RNA up to 10 times its own size, has been taken as proof that b2-DNA is a breakdown product of higher molecular weight yeast DNA. (6) The structure of the two crystalline forms of cytochrome b2 has been studied by electron microscopy. From the results, the approximate dimensions of a single enzyme molecule of molecular weight 170,000 were determined as 92 x 82 x 26 Ao. (7) Sections of the hexagonal bipyramid crystals (Type II cytochrome b2) at right angles to the c axis showed a regular hexagonal network with, apparently, one protein molecule forming the side of each hexagon. Sections parallel to the c axis showed that the empty, hexagonal tubes of protein ran right through the crystal. The structure deduced from the sections was in agreement with that observed in negatively stained, sonicated fragments of the same crystal type. (8) The flat,square plate crystals of Type I cytochrome b2 were seen as parallel rows of protein molecules arranged as layers which were stacked on top of each other to form the crystal. It appeared that alternate layers of protein were arranged at right angles to the ones in between. It has not been possible to obtain satisfactory side views of the structure of this crystal type, nor to locate visually the position of the DNA. However, a structure of these nucleoprotein crystals consistent with available data has been proposed. (9) A yeast protein solution has been prepared that had DNA-dependent RNA polymerase activity that was Actinomycin D sensitive. The enzyme appeared to be producing a hetero-polymer of ribonucleotides and some of its other properties have been briefly described.Thesis (Ph.D.) -- University of Adelaide, Dept. of Biochemistry, 196

P_METAGEN

P_METAGEN a zipped file of directories, software and some model data. Software is to analyse massed parallel sequencing results from arbitrary primed PCR products from soil or other metagenomes. The software is accompanied with a small set of HTML documents to explain it. For general public use compiled version is freeware

Microbial forensics

Burlington, US

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)