1
|
Maurer JJ, Cheng Y, Pedroso A, Thompson KK, Akter S, Kwan T, Morota G, Kinstler S, Porwollik S, McClelland M, Escalante-Semerena JC, Lee MD. Peeling back the many layers of competitive exclusion. Front Microbiol 2024; 15:1342887. [PMID: 38591029 PMCID: PMC11000858 DOI: 10.3389/fmicb.2024.1342887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/19/2024] [Indexed: 04/10/2024] Open
Abstract
Baby chicks administered a fecal transplant from adult chickens are resistant to Salmonella colonization by competitive exclusion. A two-pronged approach was used to investigate the mechanism of this process. First, Salmonella response to an exclusive (Salmonella competitive exclusion product, Aviguard®) or permissive microbial community (chicken cecal contents from colonized birds containing 7.85 Log10Salmonella genomes/gram) was assessed ex vivo using a S. typhimurium reporter strain with fluorescent YFP and CFP gene fusions to rrn and hilA operon, respectively. Second, cecal transcriptome analysis was used to assess the cecal communities' response to Salmonella in chickens with low (≤5.85 Log10 genomes/g) or high (≥6.00 Log10 genomes/g) Salmonella colonization. The ex vivo experiment revealed a reduction in Salmonella growth and hilA expression following co-culture with the exclusive community. The exclusive community also repressed Salmonella's SPI-1 virulence genes and LPS modification, while the anti-virulence/inflammatory gene avrA was upregulated. Salmonella transcriptome analysis revealed significant metabolic disparities in Salmonella grown with the two different communities. Propanediol utilization and vitamin B12 synthesis were central to Salmonella metabolism co-cultured with either community, and mutations in propanediol and vitamin B12 metabolism altered Salmonella growth in the exclusive community. There were significant differences in the cecal community's stress response to Salmonella colonization. Cecal community transcripts indicated that antimicrobials were central to the type of stress response detected in the low Salmonella abundance community, suggesting antagonism involved in Salmonella exclusion. This study indicates complex community interactions that modulate Salmonella metabolism and pathogenic behavior and reduce growth through antagonism may be key to exclusion.
Collapse
Affiliation(s)
- John J. Maurer
- School of Animal Sciences, College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
| | - Ying Cheng
- Department of Population Health, University of Georgia, Athens, GA, United States
| | - Adriana Pedroso
- Department of Population Health, University of Georgia, Athens, GA, United States
| | - Kasey K. Thompson
- Department of Population Health, University of Georgia, Athens, GA, United States
| | - Shamima Akter
- Department of Biomedical Sciences and Pathobiology, College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
| | - Tiffany Kwan
- Department of Population Health, University of Georgia, Athens, GA, United States
| | - Gota Morota
- School of Animal Sciences, College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
| | - Sydney Kinstler
- School of Animal Sciences, College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
| | - Steffen Porwollik
- Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA, United States
| | - Michael McClelland
- Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA, United States
| | | | - Margie D. Lee
- Department of Biomedical Sciences and Pathobiology, College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
| |
Collapse
|
2
|
Sagris M, Vardas EP, Theofilis P, Antonopoulos AS, Oikonomou E, Tousoulis D. Atrial Fibrillation: Pathogenesis, Predisposing Factors, and Genetics. Int J Mol Sci 2021; 23:ijms23010006. [PMID: 35008432 PMCID: PMC8744894 DOI: 10.3390/ijms23010006] [Citation(s) in RCA: 99] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/07/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Atrial fibrillation (AF) is the most frequent arrhythmia managed in clinical practice, and it is linked to an increased risk of death, stroke, and peripheral embolism. The Global Burden of Disease shows that the estimated prevalence of AF is up to 33.5 million patients. So far, successful therapeutic techniques have been implemented, with a high health-care cost burden. As a result, identifying modifiable risk factors for AF and suitable preventive measures may play a significant role in enhancing community health and lowering health-care system expenditures. Several mechanisms, including electrical and structural remodeling of atrial tissue, have been proposed to contribute to the development of AF. This review article discusses the predisposing factors in AF including the different pathogenic mechanisms, sedentary lifestyle, and dietary habits, as well as the potential genetic burden.
Collapse
Affiliation(s)
- Marios Sagris
- 1st Cardiology Clinic, ‘Hippokration’ General Hospital, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, Greece; (E.P.V.); (P.T.); (A.S.A.); (E.O.); (D.T.)
- Correspondence: ; Tel.: +30-213-2088099; Fax: +30-213-2088676
| | - Emmanouil P. Vardas
- 1st Cardiology Clinic, ‘Hippokration’ General Hospital, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, Greece; (E.P.V.); (P.T.); (A.S.A.); (E.O.); (D.T.)
- Department of Cardiology, General Hospital of Athens “G. Gennimatas”, 11527 Athens, Greece
| | - Panagiotis Theofilis
- 1st Cardiology Clinic, ‘Hippokration’ General Hospital, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, Greece; (E.P.V.); (P.T.); (A.S.A.); (E.O.); (D.T.)
| | - Alexios S. Antonopoulos
- 1st Cardiology Clinic, ‘Hippokration’ General Hospital, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, Greece; (E.P.V.); (P.T.); (A.S.A.); (E.O.); (D.T.)
| | - Evangelos Oikonomou
- 1st Cardiology Clinic, ‘Hippokration’ General Hospital, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, Greece; (E.P.V.); (P.T.); (A.S.A.); (E.O.); (D.T.)
- 3rd Department of Cardiology, “Sotiria” Thoracic Diseases Hospital of Athens, University of Athens Medical School, 11527 Athens, Greece
| | - Dimitris Tousoulis
- 1st Cardiology Clinic, ‘Hippokration’ General Hospital, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, Greece; (E.P.V.); (P.T.); (A.S.A.); (E.O.); (D.T.)
| |
Collapse
|
3
|
Neves AL, Pereira Rodrigues P, Mulla A, Glampson B, Willis T, Darzi A, Mayer E. Using electronic health records to develop and validate a machine-learning tool to predict type 2 diabetes outcomes: a study protocol. BMJ Open 2021; 11:e046716. [PMID: 34330856 PMCID: PMC8327849 DOI: 10.1136/bmjopen-2020-046716] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION Type 2 diabetes mellitus (T2DM) is a major cause of blindness, kidney failure, myocardial infarction, stroke and lower limb amputation. We are still unable, however, to accurately predict or identify which patients are at a higher risk of deterioration. Most risk stratification tools do not account for novel factors such as sociodemographic determinants, self-management ability or access to healthcare. Additionally, most tools are based in clinical trials, with limited external generalisability. OBJECTIVE The aim of this work is to design and validate a machine learning-based tool to identify patients with T2DM at high risk of clinical deterioration, based on a comprehensive set of patient-level characteristics retrieved from a population health linked dataset. SAMPLE AND DESIGN Retrospective cohort study of patients with diagnosis of T2DM on 1 January 2015, with a 5-year follow-up. Anonymised electronic healthcare records from the Whole System Integrated Care (WSIC) database will be used. PRELIMINARY OUTCOMES Outcome variables of clinical deterioration will include retinopathy, chronic renal disease, myocardial infarction, stroke, peripheral arterial disease or death. Predictor variables will include sociodemographic and geographic data, patients' ability to self-manage disease, clinical and metabolic parameters and healthcare service usage. Prognostic models will be defined using multidependence Bayesian networks. The derivation cohort, comprising 80% of the patients, will be used to define the prognostic models. Model parameters will be internally validated by comparing the area under the receiver operating characteristic curve in the derivation cohort with those calculated from a leave-one-out and a 10 times twofold cross-validation. ETHICS AND DISSEMINATION The study has received approvals from the Information Governance Committee at the WSIC. Results will be made available to people with T2DM, their caregivers, the funders, diabetes care societies and other researchers.
Collapse
Affiliation(s)
- Ana Luisa Neves
- NIHR Imperial Patient Safety Translational Research Centre, Imperial College London, London, UK
- Center for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Pedro Pereira Rodrigues
- Center for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal
| | | | - Ben Glampson
- Imperial College Healthcare NHS Trust, London, UK
| | - Tony Willis
- North West London Diabetes Transformation Programme, North West London Health and Care Partnership, London, UK
| | - Ara Darzi
- NIHR Imperial Patient Safety Translational Research Centre, Imperial College London, London, UK
| | - Erik Mayer
- NIHR Imperial Patient Safety Translational Research Centre, Imperial College London, London, UK
| |
Collapse
|
4
|
Badawi A, Di Giuseppe G, Gupta A, Poirier A, Arora P. Bayesian network modelling study to identify factors influencing the risk of cardiovascular disease in Canadian adults with hepatitis C virus infection. BMJ Open 2020; 10:e035867. [PMID: 32371519 PMCID: PMC7228556 DOI: 10.1136/bmjopen-2019-035867] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
OBJECTIVES The present study evaluates the extent of association between hepatitis C virus (HCV) infection and cardiovascular disease (CVD) risk and identifies factors mediating this relationship using Bayesian network (BN) analysis. DESIGN AND SETTING A population-based cross-sectional survey in Canada. PARTICIPANTS Adults from the Canadian Health Measures Survey (n=10 115) aged 30 to 74 years. PRIMARY AND SECONDARY OUTCOME MEASURES The 10-year risk of CVD was determined using the Framingham Risk Score in HCV-positive and HCV-negative subjects. Using BN analysis, variables were modelled to calculate the probability of CVD risk in HCV infection. RESULTS When the BN is compiled, and no variable has been instantiated, 73%, 17% and 11% of the subjects had low, moderate and high 10-year CVD risk, respectively. The conditional probability of high CVD risk increased to 13.9%±1.6% (p<2.2×10-16) when the HCV variable is instantiated to 'Present' state and decreased to 8.6%±0.2% when HCV was instantiated to 'Absent' (p<2.2×10-16). HCV cases had 1.6-fold higher prevalence of high-CVD risk compared with non-infected individuals (p=0.038). Analysis of the effect modification of the HCV-CVD relationship (using median Kullback-Leibler divergence; DKL ) showed diabetes as a major effect modifier on the joint probability distribution of HCV infection and CVD risk (DKL =0.27, IQR: 0.26 to 0.27), followed by hypertension (0.24, IQR: 0.23 to 0.25), age (0.21, IQR: 0.10 to 0.38) and injection drug use (0.19, IQR: 0.06 to 0.59). CONCLUSIONS Exploring the relationship between HCV infection and CVD risk using BN modelling analysis revealed that the infection is associated with elevated CVD risk. A number of risk modifiers were identified to play a role in this relationship. Targeting these factors during the course of infection to reduce CVD risk should be studied further.
Collapse
Affiliation(s)
- Alaa Badawi
- Public Health Risk Sciences Division, Public Health Agency of Canada, Toronto, Ontario, Canada
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Giancarlo Di Giuseppe
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Pediatric Oncology Group of Ontario, Toronto, Ontario, Canada
| | - Alind Gupta
- Lighthouse Outcomes, Toronto, Ontario, Canada
| | - Abbey Poirier
- Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, Alberta, Canada
| | - Paul Arora
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
5
|
Heijman J, Dobrev D. Bayesian network analyses in atrial fibrillation - A path to better therapies? IJC HEART & VASCULATURE 2019; 22:210-211. [PMID: 30963097 PMCID: PMC6437288 DOI: 10.1016/j.ijcha.2019.02.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Despite several major innovations in atrial fibrillation (AF) management, including the improved detection of AF and advances in catheter-ablation-based rhythm control, AF remains a major health-care burden. Recent advances have enabled curation of increasingly large data sets, which, together with improvements in AF detection through screening and continuous rhythm monitoring, enable novel 'big data' approaches to better predict and classify AF. In this issue of the International Journal of Cardiology Heart & Vasculature, Drs. Ebana and Furakawa describe an approach to shed light on potential causal links between several risk factors and atrial arrhythmias from the superior vena cava using a Bayesian network analysis. This approach may be a relevant step from statistical association towards identification of causative mechanisms and together with experimental work and mechanistic computer models may help to establish tailored mechanism-based therapies for AF.
Collapse
Affiliation(s)
- Jordi Heijman
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Faculty of Health, Medicine, and Life Sciences, Maastricht University, Maastricht, the Netherlands
- Institute of Pharmacology, West German Heart and Vascular Center, University Duisburg-Essen, Essen, Germany
| | - Dobromir Dobrev
- Institute of Pharmacology, West German Heart and Vascular Center, University Duisburg-Essen, Essen, Germany
| |
Collapse
|