Comparative genomics of the emerging human pathogen Photorhabdus asymbiotica with the insect pathogen Photorhabdus luminescens

<p>Abstract</p> <p>Background</p> <p>The Gram-negative bacterium <it>Photorhabdus asymbiotica </it>(Pa) has been recovered from human infections in both North America and Australia. Recently, Pa has been shown to have a nematode vector that can also infect insects, like its sister species the insect pathogen <it>P. luminescens </it>(Pl). To understand the relationship between pathogenicity to insects and humans in <it>Photorhabdus </it>we have sequenced the complete genome of Pa strain ATCC43949 from North America. This strain (formerly referred to as <it>Xenorhabdus luminescens </it>strain 2) was isolated in 1977 from the blood of an 80 year old female patient with endocarditis, in Maryland, USA. Here we compare the complete genome of Pa ATCC43949 with that of the previously sequenced insect pathogen <it>P. luminescens </it>strain TT01 which was isolated from its entomopathogenic nematode vector collected from soil in Trinidad and Tobago.</p> <p>Results</p> <p>We found that the human pathogen Pa had a smaller genome (5,064,808 bp) than that of the insect pathogen Pl (5,688,987 bp) but that each pathogen carries approximately one megabase of DNA that is unique to each strain. The reduced size of the Pa genome is associated with a smaller diversity in insecticidal genes such as those encoding the Toxin complexes (Tc's), Makes caterpillars floppy (Mcf) toxins and the <it>Photorhabdus </it>Virulence Cassettes (PVCs). The Pa genome, however, also shows the addition of a plasmid related to pMT1 from <it>Yersinia pestis </it>and several novel pathogenicity islands including a novel Type Three Secretion System (TTSS) encoding island. Together these data suggest that Pa may show virulence against man via the acquisition of the <it>pMT1</it>-like plasmid and specific effectors, such as SopB, that promote its persistence inside human macrophages. Interestingly the loss of insecticidal genes in Pa is not reflected by a loss of pathogenicity towards insects.</p> <p>Conclusion</p> <p>Our results suggest that North American isolates of Pa have acquired virulence against man via the acquisition of a plasmid and specific virulence factors with similarity to those shown to play roles in pathogenicity against humans in other bacteria.</p

The genome of the heartwater agent Ehrlichia ruminantium contains multiple tandem repeats of actively variable copy number

Heartwater, a tick-borne disease of domestic and wild ruminants, is caused by the intracellular rickettsia Ehrlichia ruminantium (previously known as Cowdria ruminantium ). It is a major constraint to livestock production throughout subSaharan Africa, and it threatens to invade the Americas, yet there is no immediate prospect of an effective vaccine. A shotgun genome sequencing project was undertaken in the expectation that access to the complete protein coding repertoire of the organism will facilitate the search for vaccine candidate genes. We report here the complete 1,516,355-bp sequence of the type strain, the stock derived from the South African Welgevonden isolate. Only 62% of the genome is predicted to be coding sequence, encoding 888 proteins and 41 stable RNA species. The most striking feature is the large number of tandemly repeated and duplicated sequences, some of continuously variable copy number, which contributes to the low proportion of coding sequence. These repeats have mediated numerous translocation and inversion events that have resulted in the duplication and truncation of some genes and have also given rise to new genes. There are 32 predicted pseudogenes, most of which are truncated fragments of genes associated with repeats. Rather then being the result of the reductive evolution seen in other intracellular bacteria, these pseudogenes appear to be the product of ongoing sequence duplication events

The DNA sequence of chromosome I of an African trypanosome: gene content, chromosome organisation, recombination and polymorphism

The African trypanosome, Trypanosoma brucei, causes sleeping sickness in humans in sub-Saharan Africa. Here we report the sequence and analysis of the 1.1 Mb chromosome I, which encodes approximately 400 predicted genes organised into directional clusters, of which more than 100 are located in the largest cluster of 250 kb. A 160-kb region consists primarily of three gene families of unknown function, one of which contains a hotspot for retroelement insertion. We also identify five novel gene families. Indeed, almost 20% of predicted genes are members of families. In some cases, tandemly arrayed genes are 99–100% identical, suggesting an active process of amplification and gene conversion. One end of the chromosome consists of a putative bloodstream-form variant surface glycoprotein (VSG) gene expression site that appears truncated and degenerate. The other chromosome end carries VSG and expression site-associated genes and pseudogenes over 50 kb of subtelomeric sequence where, unusually, the telomere-proximal VSG gene is oriented away from the telomere. Our analysis includes the cataloguing of minor genetic variations between the chromosome I homologues and an estimate of crossing-over frequency during genetic exchange. Genetic polymorphisms are exceptionally rare in sequences located within and around the strand-switches between several gene clusters

Sequence of Plasmodium falciparum chromosomes 1, 3-9 and 13.

Since the sequencing of the first two chromosomes of the malaria parasite, Plasmodium falciparum, there has been a concerted effort to sequence and assemble the entire genome of this organism. Here we report the sequence of chromosomes 1, 3-9 and 13 of P. falciparum clone 3D7--these chromosomes account for approximately 55% of the total genome. We describe the methods used to map, sequence and annotate these chromosomes. By comparing our assemblies with the optical map, we indicate the completeness of the resulting sequence. During annotation, we assign Gene Ontology terms to the predicted gene products, and observe clustering of some malaria-specific terms to specific chromosomes. We identify a highly conserved sequence element found in the intergenic region of internal var genes that is not associated with their telomeric counterparts

The genome of the African trypanosome Trypanosoma brucei

African trypanosomes cause human sleeping sickness and livestock trypanosomiasis in sub-Saharan Africa. We present the sequence and analysis of the 11 megabase-sized chromosomes of &lt;i&gt;Trypanosoma brucei&lt;/i&gt;. The 26-megabase genome contains 9068 predicted genes, including ~900 pseudogenes and ~1700 &lt;i&gt;T. brucei&lt;/i&gt;–specific genes. Large subtelomeric arrays contain an archive of 806 variant surface glycoprotein (VSG) genes used by the parasite to evade the mammalian immune system. Most VSG genes are pseudogenes, which may be used to generate expressed mosaic genes by ectopic recombination. Comparisons of the cytoskeleton and endocytic trafficking systems with those of humans and other eukaryotic organisms reveal major differences. A comparison of metabolic pathways encoded by the genomes of &lt;i&gt;T. brucei&lt;/i&gt;, &lt;i&gt;T. cruzi&lt;/i&gt;, and &lt;i&gt;Leishmania major&lt;/i&gt; reveals the least overall metabolic capability in &lt;i&gt;T. brucei&lt;/i&gt; and the greatest in &lt;i&gt;L. major&lt;/i&gt;. Horizontal transfer of genes of bacterial origin has contributed to some of the metabolic differences in these parasites, and a number of novel potential drug targets have been identified

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)

Genomic reconstruction of the SARS-CoV-2 epidemic in England

AbstractThe evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus leads to new variants that warrant timely epidemiological characterization. Here we use the dense genomic surveillance data generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 71 different lineages in each of 315 English local authorities between September 2020 and June 2021. This analysis reveals a series of subepidemics that peaked in early autumn 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. The Alpha variant grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown suppressed the Alpha variant and eliminated nearly all other lineages in early 2021. Yet a series of variants (most of which contained the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. However, by accounting for sustained introductions, we found that the transmissibility of these variants is unlikely to have exceeded the transmissibility of the Alpha variant. Finally, B.1.617.2/Delta was repeatedly introduced in England and grew rapidly in early summer 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on 26 June 2021.</jats:p