The B-Cell Specific Transcription Factor, Oct-2, Promotes Epstein-Barr Virus Latency by Inhibiting the Viral Immediate-Early Protein, BZLF1

The Epstein-Barr virus (EBV) latent-lytic switch is mediated by the BZLF1 immediate-early protein. EBV is normally latent in memory B cells, but cellular factors which promote viral latency specifically in B cells have not been identified. In this report, we demonstrate that the B-cell specific transcription factor, Oct-2, inhibits the function of the viral immediate-early protein, BZLF1, and prevents lytic viral reactivation. Co-transfected Oct-2 reduces the ability of BZLF1 to activate lytic gene expression in two different latently infected nasopharyngeal carcinoma cell lines. Furthermore, Oct-2 inhibits BZLF1 activation of lytic EBV promoters in reporter gene assays, and attenuates BZLF1 binding to lytic viral promoters in vivo. Oct-2 interacts directly with BZLF1, and this interaction requires the DNA-binding/dimerization domain of BZLF1 and the POU domain of Oct-2. An Oct-2 mutant (Δ262–302) deficient for interaction with BZLF1 is unable to inhibit BZLF1-mediated lytic reactivation. However, an Oct-2 mutant defective for DNA-binding (Q221A) retains the ability to inhibit BZLF1 transcriptional effects and DNA-binding. Importantly, shRNA-mediated knockdown of endogenous Oct-2 expression in several EBV-positive Burkitt lymphoma and lymphoblastoid cell lines increases the level of lytic EBV gene expression, while decreasing EBNA1 expression. Moreover, treatments which induce EBV lytic reactivation, such as anti-IgG cross-linking and chemical inducers, also decrease the level of Oct-2 protein expression at the transcriptional level. We conclude that Oct-2 potentiates establishment of EBV latency in B cells

THE MEMBRANE TOPOLOGY OF THE RHIZOBIUM-MELILOTI C4-DICARBOXYLATE PERMEASE (DCTA) AS DERIVED FROM PROTEIN FUSIONS WITH ESCHERICHIA-COLI K12 ALKALINE-PHOSPHATASE (PHOA) AND BETA-GALACTOSIDASE (LACZ)

Jording D, Pühler A. THE MEMBRANE TOPOLOGY OF THE RHIZOBIUM-MELILOTI C4-DICARBOXYLATE PERMEASE (DCTA) AS DERIVED FROM PROTEIN FUSIONS WITH ESCHERICHIA-COLI K12 ALKALINE-PHOSPHATASE (PHOA) AND BETA-GALACTOSIDASE (LACZ). MOLECULAR & GENERAL GENETICS. 1993;241-241(1-2):106-114.The Rhizobium meliloti dctA gene encodes the C4-dicarboxylate permease which mediates uptake of C4-dicarboxylates, both in free-living and symbiotic cells. Based on the hydrophobicity of the DctA protein, 12 putative membrane spanning regions were predicted. The membrane topology was further analysed by isolating in vivo fusions of DctA to Escherichia coli alkaline phosphatase (PhoA) and E. coli beta-galactosidase (LacZ). Of 10 different fusions 7 indicated a periplasmic and 3 a cytoplasmic location of the corresponding region of the DctA protein. From these data a two-dimensional model of DctA was constructed which comprised twelve transmembrane alpha-helices with the amino-terminus and the carboxy-terminus located in the cytoplasm. In addition, four conserved amino acid motifs present in many eukaryotic and prokaryotic transport proteins were observed

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)