Role of Temperate Bacteriophage ϕ20617 on Streptococcus thermophilus DSM 20617T Autolysis and Biology

Streptococcus thermophilus DSM 20167T showed autolytic behavior when cultured in lactose- and sucrose-limited conditions. The amount of cell lysis induced was inversely related to the energetic status of the cells, as demonstrated by exposing cells to membrane-uncoupling and glycolysis inhibitors. Genome sequence analysis of strain DSM 20617T revealed the presence of a pac-type temperate bacteriophage, designated Φ20617, whose genomic organization and structure resemble those of temperate streptococcal bacteriophages. The prophage integrated at the 3′-end of the gene encoding the glycolytic enzyme enolase (eno), between eno and the lipoteichoic acid synthase-encoding gene ltaS, affecting their transcription. Comparative experiments conducted on the wild-type strain and a phage-cured derivative strain revealed that the cell-wall integrity of the lysogenic strain was compromised even in the absence of detectable cell lysis. More importantly, adhesion to solid surfaces and heat resistance were significantly higher in the lysogenic strain than in the phage-cured derivative. The characterization of the phenotype of a lysogenic S. thermophilus and its phage-cured derivative is relevant to understanding the ecological constraints that drive the stable association between a temperate phage and its bacterial host

On the Notion of Redundancy in Access Control Policies

The evolution of information systems sees an increasing need of flexible and sophisticated approaches for the automated detection of anomalies in security policies. One of these anomalies is redundancy, which may increase the total cost of management of the policies and may reduce the performance of access control mechanisms and of other anomaly detection techniques. We consider three approaches that can remove redundancy from access control policies, progressively reducing the number of authorizations in the policy itself. We show that several problems associated with redundancy are NP-hard. We propose exact solutions to two of these problems, namely the Minimum Policy Problem, which consists in computing the minimum policy that represents the behaviour of the system, and the Minimum Irreducible Policy Problem, consisting in computing the redundancy-free version of a policy with the smallest number of authorizations. Furthermore we propose heuristic solutions to those problems. We also present a comparison between the exact and heuristics solutions based on experiments that use policies derived from bibliographical databases

A Model-Driven Approach for Securing Software Architectures

Current IT systems consist usually of several components and services that communicate and exchange data over the Internet. They have security requirements that aim at avoiding information disclosure and at showing compliance with government regulations. In order to effectively handle the security management of complex IT systems, techniques are needed to help the security administrator in the design and configuration of the security architecture. We propose a model-driven security approach for the design and generation of concrete security configurations for software architectures. In our approach the system architect models the architecture of the system by means of UML class diagrams, and then the security administrator adds security requirements to the model by means of Security4UML, a UML profile. From the model enriched with security requirements, the concrete security configuration is derived in a semi-automated way. We present a tool that supports this model-driven approach, and a case study that involves a distributed multi-user meeting scheduler application

Chagas' disease and ageing: the coexistence of other chronic diseases with Chagas' disease in elderly patients Doença de Chagas e envelhecimento: a associação de outras enfermidades crônicas em pacientes idosos chagásicos

This study aimed to identify the main comorbidities in elderly chagasic patients treated in a reference service and identify possible associations between the clinical form of Chagas' disease and chronic diseases. Ninety patients aged 60 years-old or over were interviewed and their clinical diagnoses recorded. The study population profile was: women (55.6%); median age (67 years); married (51.1%); retired (73.3%); up to four years' education (64.4%); and earning less than two minimum wages (67.8%). The predominant forms of Chagas' disease were the cardiac (46.7%) and mixed forms (30%). There was a greater proportion of mild cardiac dysfunction (84.1%), frequently in association with megaesophagus. The mean number of concurrent diseases was 2.856 ± 1.845, and 33% of the patients had four or more comorbidities. The most frequent were systemic arterial hypertension (56.7%), osteoporosis (23.3%), osteoarthritis (21.2%) and dyslipidemia (20%). Positive correlations were verified between sex and comorbidities and between age group and comorbidities.<br>Este trabalho objetivou avaliar o perfil sociodemográfico e identificar as principais co-morbidades de idosos chagásicos, buscando associação entre forma clínica da doença de Chagas e enfermidades crônicas. Foi realizada entrevista e levantamento dos diagnósticos clínicos de 90 chagásicos com idade > 60 anos. Encontrou-se: mulheres (55,6%), mediana de 67 anos, casados (51,1%) e renda mensal inferior a dois salários-mínimos (67,8%). A forma clínica predominante foi a cardíaca (46,7%), seguida da mista (30%). Houve maior proporção de cardiopatia leve (84,1%), sendo frequente a associação com megaesôfago. Trinta e três por cento apresentavam quatro ou mais co-morbidades, dentre elas: hipertensão arterial (56,7%), osteoporose (23,3%), osteoartrite (21,2%) e dislipidemia (20%). Obteve-se correlação positiva entre gênero e co-morbidades, faixa etária e co-morbidades

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)