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Islam SMR, Ahmed R, Sharmen F, Hossain MM, Chakma K, Tanni AA, Akash MAA, Hossain ME, Chowdhury MSN, Siddiki AMAMZ, Hossain A, Mandal SC, Crandall KA, Rahnavard A, Sharifuzzaman SM, Mannan A. Genome sequence of white spot syndrome virus (WSSV) infecting cultured black tiger shrimp ( Penaeus monodon) in Bangladesh. Microbiol Resour Announc 2024; 13:e0121123. [PMID: 38501780 PMCID: PMC11008216 DOI: 10.1128/mra.01211-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 03/08/2024] [Indexed: 03/20/2024] Open
Abstract
The white spot syndrome virus (WSSV) is a causative agent of white spot disease (WSD) in crustaceans, especially in cultivated black tiger shrimp (Penaeus monodon), leading to significant economic losses in the aquaculture sector. The present study describes four whole genome sequences of WSSV obtained from coastal regions of Bangladesh.
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Affiliation(s)
- S. M. Rafiqul Islam
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chattogram, Bangladesh
- Next-generation Sequencing, Research and Innovation Laboratory Chittagong (NRICh), Biotechnology Research and Innovation Centre (BRIC), University of Chittagong, Chattogram, Bangladesh
| | - Robel Ahmed
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chattogram, Bangladesh
- Next-generation Sequencing, Research and Innovation Laboratory Chittagong (NRICh), Biotechnology Research and Innovation Centre (BRIC), University of Chittagong, Chattogram, Bangladesh
| | - Farjana Sharmen
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chattogram, Bangladesh
- Next-generation Sequencing, Research and Innovation Laboratory Chittagong (NRICh), Biotechnology Research and Innovation Centre (BRIC), University of Chittagong, Chattogram, Bangladesh
| | - Md. Mobarok Hossain
- International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B), Dhaka, Bangladesh
| | - Kallyan Chakma
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chattogram, Bangladesh
- Next-generation Sequencing, Research and Innovation Laboratory Chittagong (NRICh), Biotechnology Research and Innovation Centre (BRIC), University of Chittagong, Chattogram, Bangladesh
| | - Afroza Akter Tanni
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chattogram, Bangladesh
- Next-generation Sequencing, Research and Innovation Laboratory Chittagong (NRICh), Biotechnology Research and Innovation Centre (BRIC), University of Chittagong, Chattogram, Bangladesh
| | - Md. Ashikur Alim Akash
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chattogram, Bangladesh
- Next-generation Sequencing, Research and Innovation Laboratory Chittagong (NRICh), Biotechnology Research and Innovation Centre (BRIC), University of Chittagong, Chattogram, Bangladesh
| | - Mohammad Enayet Hossain
- International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B), Dhaka, Bangladesh
| | | | - AMAM Zonaed Siddiki
- Department of Pathology and Parasitology, Genomics Research Group, Chattogram Veterinary and Animal Sciences University, Chattogram, Bangladesh
| | - Anwar Hossain
- Department of Fisheries, Aquaculture Genomics Laboratory, University of Dhaka, Dhaka, Bangladesh
| | - Shankar C. Mandal
- Department of Fisheries, Aquaculture Genomics Laboratory, University of Dhaka, Dhaka, Bangladesh
| | - Keith A. Crandall
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia, USA
| | - Ali Rahnavard
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia, USA
| | - SM Sharifuzzaman
- Institute of Marine Sciences, University of Chittagong, Chattogram, Bangladesh
| | - Adnan Mannan
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chattogram, Bangladesh
- Next-generation Sequencing, Research and Innovation Laboratory Chittagong (NRICh), Biotechnology Research and Innovation Centre (BRIC), University of Chittagong, Chattogram, Bangladesh
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2
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Buendia P, Fernandez K, Raley C, Rahnavard A, Crandall KA, Castro JG. Hospital antimicrobial stewardship: profiling the oral microbiome after exposure to COVID-19 and antibiotics. Front Microbiol 2024; 15:1346762. [PMID: 38476940 PMCID: PMC10927822 DOI: 10.3389/fmicb.2024.1346762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/22/2024] [Indexed: 03/14/2024] Open
Abstract
Introduction During the COVID-19 Delta variant surge, the CLAIRE cross-sectional study sampled saliva from 120 hospitalized patients, 116 of whom had a positive COVID-19 PCR test. Patients received antibiotics upon admission due to possible secondary bacterial infections, with patients at risk of sepsis receiving broad-spectrum antibiotics (BSA). Methods The saliva samples were analyzed with shotgun DNA metagenomics and respiratory RNA virome sequencing. Medical records for the period of hospitalization were obtained for all patients. Once hospitalization outcomes were known, patients were classified based on their COVID-19 disease severity and the antibiotics they received. Results Our study reveals that BSA regimens differentially impacted the human salivary microbiome and disease progression. 12 patients died and all of them received BSA. Significant associations were found between the composition of the COVID-19 saliva microbiome and BSA use, between SARS-CoV-2 genome coverage and severity of disease. We also found significant associations between the non-bacterial microbiome and severity of disease, with Candida albicans detected most frequently in critical patients. For patients who did not receive BSA before saliva sampling, our study suggests Staphylococcus aureus as a potential risk factor for sepsis. Discussion Our results indicate that the course of the infection may be explained by both monitoring antibiotic treatment and profiling a patient's salivary microbiome, establishing a compelling link between microbiome and the specific antibiotic type and timing of treatment. This approach can aid with emergency room triage and inpatient management but also requires a better understanding of and access to narrow-spectrum agents that target pathogenic bacteria.
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Affiliation(s)
| | | | - Castle Raley
- The George Washington University Genomics Core, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
| | - Ali Rahnavard
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
| | - Keith A. Crandall
- The George Washington University Genomics Core, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
| | - Jose Guillermo Castro
- Division of Infectious Diseases, Leonard M. Miller School of Medicine, University of Miami, Miami, FL, United States
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3
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Rahnavard A, Chatterjee R, Wen H, Gaylord C, Mugusi S, Klatt KC, Smith ER. Molecular epidemiology of pregnancy using omics data: advances, success stories, and challenges. J Transl Med 2024; 22:106. [PMID: 38279125 PMCID: PMC10821542 DOI: 10.1186/s12967-024-04876-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 12/26/2023] [Indexed: 01/28/2024] Open
Abstract
Multi-omics approaches have been successfully applied to investigate pregnancy and health outcomes at a molecular and genetic level in several studies. As omics technologies advance, research areas are open to study further. Here we discuss overall trends and examples of successfully using omics technologies and techniques (e.g., genomics, proteomics, metabolomics, and metagenomics) to investigate the molecular epidemiology of pregnancy. In addition, we outline omics applications and study characteristics of pregnancy for understanding fundamental biology, causal health, and physiological relationships, risk and prediction modeling, diagnostics, and correlations.
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Affiliation(s)
- Ali Rahnavard
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.
| | - Ranojoy Chatterjee
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Hui Wen
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Clark Gaylord
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Sabina Mugusi
- Department of Clinical Pharmacology, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Kevin C Klatt
- Nutritional Sciences & Toxicology, University of California, Berkeley, CA, 94720, USA
| | - Emily R Smith
- Department of Global Health, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.
- Department of Exercise and Nutrition Sciences, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.
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4
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Fuller H, Zhu Y, Nicholas J, Chatelaine HA, Drzymalla EM, Sarvestani AK, Julián-Serrano S, Tahir UA, Sinnott-Armstrong N, Raffield LM, Rahnavard A, Hua X, Shutta KH, Darst BF. Author Correction: Metabolomic epidemiology offers insights into disease aetiology. Nat Metab 2024; 6:187. [PMID: 38191668 DOI: 10.1038/s42255-023-00967-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Affiliation(s)
- Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jayna Nicholas
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Haley A Chatelaine
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Emily M Drzymalla
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Afrand K Sarvestani
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | | | - Usman A Tahir
- Department of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Xinwei Hua
- Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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5
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Fuller H, Zhu Y, Nicholas J, Chatelaine HA, Drzymalla EM, Sarvestani AK, Julián-Serrano S, Tahir UA, Sinnott-Armstrong N, Raffield LM, Rahnavard A, Hua X, Shutta KH, Darst BF. Metabolomic epidemiology offers insights into disease aetiology. Nat Metab 2023; 5:1656-1672. [PMID: 37872285 DOI: 10.1038/s42255-023-00903-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/06/2023] [Indexed: 10/25/2023]
Abstract
Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.
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Affiliation(s)
- Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jayna Nicholas
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Haley A Chatelaine
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Emily M Drzymalla
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Afrand K Sarvestani
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | | | - Usman A Tahir
- Department of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Xinwei Hua
- Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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Lee J, Wellenstein K, Rahnavard A, Nelson AT, Holter MM, Cummings B, Yeliseyev V, Castoldi A, Clish CB, Bry L, Siegel D, Kahn BB. Beneficial metabolic effects of PAHSAs depend on the gut microbiota in diet-induced obese mice. bioRxiv 2023:2023.09.28.558803. [PMID: 37808673 PMCID: PMC10557726 DOI: 10.1101/2023.09.28.558803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Dietary lipids play an essential role in regulating the function of the gut microbiota and gastrointestinal tract, and these luminal interactions contribute to mediating host metabolism. PAHSAs are a class of lipids with anti-diabetic and anti-inflammatory properties, but whether the gut microbiota contributes to their beneficial effects on host metabolism is unknown. Here, we report that treating high fat diet (HFD)-fed germ-free mice with PAHSAs does not improve insulin sensitivity. However, transfer of feces from PAHSA-treated, but not Vehicle-treated, chow-fed mice increases insulin-sensitivity in HFD-fed germ free mice. Thus, the gut microbiota is necessary for and can transmit the insulin-sensitizing effects of PAHSAs in HFD-fed germ-free mice. Functional analyses of the cecal metagenome and lipidome of PAHSA-treated mice identified multiple lipid species that associate with the gut commensal Bacteroides thetaiotaomicron ( Bt ) and with insulin sensitivity resulting from PAHSA treatment. Bt supplementation in HFD-fed female mice prevented weight gain, reduced adiposity, improved glucose tolerance, fortified the colonic mucus barrier and reduced systemic inflammation versus chow-fed controls, effects that were not observed in HFD-fed male mice. Furthermore, ovariectomy partially reversed the beneficial Bt effects on host metabolism, indicating a role for sex hormones in mediating probiotic effects. Altogether, these studies highlight the fact that lipids can modulate the gut microbiota resulting in improvement in host metabolism and that PAHSA-induced changes in the microbiota result in at least some of their insulin-sensitizing effects in female mice.
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7
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Joshi AD, Rahnavard A, Kachroo P, Mendez KM, Lawrence W, Julián-Serrano S, Hua X, Fuller H, Sinnott-Armstrong N, Tabung FK, Shutta KH, Raffield LM, Darst BF. An epidemiological introduction to human metabolomic investigations. Trends Endocrinol Metab 2023; 34:505-525. [PMID: 37468430 PMCID: PMC10527234 DOI: 10.1016/j.tem.2023.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023]
Abstract
Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.
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Affiliation(s)
- Amit D Joshi
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kevin M Mendez
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Wayne Lawrence
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sachelly Julián-Serrano
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Public Health, University of Massachusetts Lowell, Lowell, MA, USA
| | - Xinwei Hua
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA; Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Nasa Sinnott-Armstrong
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Fred K Tabung
- The Ohio State University College of Medicine and Comprehensive Cancer Center, Columbus, OH, USA
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
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8
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Rahnavard A. Omics correlation for efficient network construction. Nat Comput Sci 2023; 3:285-286. [PMID: 38177931 DOI: 10.1038/s43588-023-00436-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Affiliation(s)
- Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington DC, USA.
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9
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Bustamante JM, Dawson T, Loeffler C, Marfori Z, Marchesi JR, Mullish BH, Thompson CC, Crandall KA, Rahnavard A, Allegretti JR, Cummings BP. Impact of Fecal Microbiota Transplantation on Gut Bacterial Bile Acid Metabolism in Humans. Nutrients 2022; 14:5200. [PMID: 36558359 PMCID: PMC9785599 DOI: 10.3390/nu14245200] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/29/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Fecal microbiota transplantation (FMT) is a promising therapeutic modality for the treatment and prevention of metabolic disease. We previously conducted a double-blind, randomized, placebo-controlled pilot trial of FMT in obese metabolically healthy patients in which we found that FMT enhanced gut bacterial bile acid metabolism and delayed the development of impaired glucose tolerance relative to the placebo control group. Therefore, we conducted a secondary analysis of fecal samples collected from these patients to assess the potential gut microbial species contributing to the effect of FMT to improve metabolic health and increase gut bacterial bile acid metabolism. Fecal samples collected at baseline and after 4 weeks of FMT or placebo treatment underwent shotgun metagenomic analysis. Ultra-high-performance liquid chromatography-mass spectrometry was used to profile fecal bile acids. FMT-enriched bacteria that have been implicated in gut bile acid metabolism included Desulfovibrio fairfieldensis and Clostridium hylemonae. To identify candidate bacteria involved in gut microbial bile acid metabolism, we assessed correlations between bacterial species abundance and bile acid profile, with a focus on bile acid products of gut bacterial metabolism. Bacteroides ovatus and Phocaeicola dorei were positively correlated with unconjugated bile acids. Bifidobacterium adolescentis, Collinsella aerofaciens, and Faecalibacterium prausnitzii were positively correlated with secondary bile acids. Together, these data identify several candidate bacteria that may contribute to the metabolic benefits of FMT and gut bacterial bile acid metabolism that requires further functional validation.
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Affiliation(s)
- Jessica-Miranda Bustamante
- Department of Surgery, School of Medicine, Center for Alimentary and Metabolic Science, University of California, Sacramento, CA 95817, USA
| | - Tyson Dawson
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Caitlin Loeffler
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Zara Marfori
- Department of Surgery, School of Medicine, Center for Alimentary and Metabolic Science, University of California, Sacramento, CA 95817, USA
| | - Julian R. Marchesi
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, St. Mary’s Hospital Campus, Imperial College London, London W2 1NY, UK
| | - Benjamin H. Mullish
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, St. Mary’s Hospital Campus, Imperial College London, London W2 1NY, UK
| | - Christopher C. Thompson
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Keith A. Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Jessica R. Allegretti
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Bethany P. Cummings
- Department of Surgery, School of Medicine, Center for Alimentary and Metabolic Science, University of California, Sacramento, CA 95817, USA
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10
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Mallick H, Chatterjee S, Chowdhury S, Chatterjee S, Rahnavard A, Hicks SC. Differential expression of single-cell RNA-seq data using Tweedie models. Stat Med 2022; 41:3492-3510. [PMID: 35656596 PMCID: PMC9288986 DOI: 10.1002/sim.9430] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 12/13/2022]
Abstract
The performance of computational methods and software to identify differentially expressed features in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq expression features. To model the technological variability in cross-platform scRNA-seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA-seq expression profiles across experimental platforms induced by platform- and gene-specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate- and droplet-based scRNA-seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open-source software (R/Bioconductor package) is available at https://github.com/himelmallick/Tweedieverse.
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Affiliation(s)
- Himel Mallick
- Biostatistics and Research Decision Sciences, Merck &
Co., Inc., Rahway, NJ 07065, USA
| | - Suvo Chatterjee
- Epidemiology Branch, Division of Intramural Population
Health Research, Eunice Kennedy Shriver National Institute of Child
Health and Human Development, National Institutes of Health, Bethesda, MD 20892,
USA
| | - Shrabanti Chowdhury
- Department of Genetics and Genomic Sciences and Icahn
Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount
Sinai, New York, NY 10029, USA
| | - Saptarshi Chatterjee
- Department of Statistics, Data and Analytics, Eli Lilly
& Company, Indianapolis, IN 46225, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of
Biostatistics and Bioinformatics, Milken Institute School of Public Health, The
George Washington University, Washington, DC 20052, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School
of Public Health, Baltimore, MD 21205, USA
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11
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Ghazi AR, Sucipto K, Rahnavard A, Franzosa EA, McIver LJ, Lloyd-Price J, Schwager E, Weingart G, Moon YS, Morgan XC, Waldron L, Huttenhower C. High-sensitivity pattern discovery in large, paired multiomic datasets. Bioinformatics 2022; 38:i378-i385. [PMID: 35758795 PMCID: PMC9235493 DOI: 10.1093/bioinformatics/btac232] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Modern biological screens yield enormous numbers of measurements, and identifying and interpreting statistically significant associations among features are essential. In experiments featuring multiple high-dimensional datasets collected from the same set of samples, it is useful to identify groups of associated features between the datasets in a way that provides high statistical power and false discovery rate (FDR) control. RESULTS Here, we present a novel hierarchical framework, HAllA (Hierarchical All-against-All association testing), for structured association discovery between paired high-dimensional datasets. HAllA efficiently integrates hierarchical hypothesis testing with FDR correction to reveal significant linear and non-linear block-wise relationships among continuous and/or categorical data. We optimized and evaluated HAllA using heterogeneous synthetic datasets of known association structure, where HAllA outperformed all-against-all and other block-testing approaches across a range of common similarity measures. We then applied HAllA to a series of real-world multiomics datasets, revealing new associations between gene expression and host immune activity, the microbiome and host transcriptome, metabolomic profiling and human health phenotypes. AVAILABILITY AND IMPLEMENTATION An open-source implementation of HAllA is freely available at http://huttenhower.sph.harvard.edu/halla along with documentation, demo datasets and a user group. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andrew R Ghazi
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Kathleen Sucipto
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Ali Rahnavard
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Eric A Franzosa
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Lauren J McIver
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jason Lloyd-Price
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Emma Schwager
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - George Weingart
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Yo Sup Moon
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Xochitl C Morgan
- Department of Microbiology and Immunology, University of Otago, Dunedin 9016, New Zealand
| | - Levi Waldron
- Department of Epidemiology and Biostatistics, City University of New York Graduate School of Public Health and Health Policy, New York City, NY 10035, USA
| | - Curtis Huttenhower
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
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12
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Zhang Y, Bhosle A, Bae S, McIver LJ, Pishchany G, Accorsi EK, Thompson KN, Arze C, Wang Y, Subramanian A, Kearney SM, Pawluk A, Plichta DR, Rahnavard A, Shafquat A, Xavier RJ, Vlamakis H, Garrett WS, Krueger A, Huttenhower C, Franzosa EA. Discovery of bioactive microbial gene products in inflammatory bowel disease. Nature 2022; 606:754-760. [PMID: 35614211 PMCID: PMC9913614 DOI: 10.1038/s41586-022-04648-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 03/15/2022] [Indexed: 01/26/2023]
Abstract
Microbial communities and their associated bioactive compounds1-3 are often disrupted in conditions such as the inflammatory bowel diseases (IBD)4. However, even in well-characterized environments (for example, the human gastrointestinal tract), more than one-third of microbial proteins are uncharacterized and often expected to be bioactive5-7. Here we systematically identified more than 340,000 protein families as potentially bioactive with respect to gut inflammation during IBD, about half of which have not to our knowledge been functionally characterized previously on the basis of homology or experiment. To validate prioritized microbial proteins, we used a combination of metagenomics, metatranscriptomics and metaproteomics to provide evidence of bioactivity for a subset of proteins that are involved in host and microbial cell-cell communication in the microbiome; for example, proteins associated with adherence or invasion processes, and extracellular von Willebrand-like factors. Predictions from high-throughput data were validated using targeted experiments that revealed the differential immunogenicity of prioritized Enterobacteriaceae pilins and the contribution of homologues of von Willebrand factors to the formation of Bacteroides biofilms in a manner dependent on mucin levels. This methodology, which we term MetaWIBELE (workflow to identify novel bioactive elements in the microbiome), is generalizable to other environmental communities and human phenotypes. The prioritized results provide thousands of candidate microbial proteins that are likely to interact with the host immune system in IBD, thus expanding our understanding of potentially bioactive gene products in chronic disease states and offering a rational compendium of possible therapeutic compounds and targets.
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Affiliation(s)
- Yancong Zhang
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Amrisha Bhosle
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Sena Bae
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Lauren J. McIver
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Gleb Pishchany
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Emma K. Accorsi
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Kelsey N. Thompson
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Cesar Arze
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Ya Wang
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Ayshwarya Subramanian
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Sean M. Kearney
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - April Pawluk
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Damian R. Plichta
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ali Rahnavard
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Afrah Shafquat
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Ramnik J. Xavier
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA,Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hera Vlamakis
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wendy S. Garrett
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Andy Krueger
- Takeda Pharmaceutical Company Limited, Cambridge, MA, USA
| | - Curtis Huttenhower
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. .,Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA. .,Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Eric A. Franzosa
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA,These authors jointly supervised this work: Curtis Huttenhower & Eric A. Franzosa
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13
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Chow JH, Rahnavard A, Gomberg-Maitland M, Chatterjee R, Patodi P, Yamane DP, Levine AR, Davison D, Hawkins K, Jackson AM, Quintana MT, Lankford AS, Keneally RJ, Al-Mashat M, Fisher D, Williams J, Berger JS, Mazzeffi MA, Crandall KA. Association of Early Aspirin Use With In-Hospital Mortality in Patients With Moderate COVID-19. JAMA Netw Open 2022; 5:e223890. [PMID: 35323950 PMCID: PMC8948531 DOI: 10.1001/jamanetworkopen.2022.3890] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 02/02/2022] [Indexed: 01/08/2023] Open
Abstract
Importance Prior observational studies suggest that aspirin use may be associated with reduced mortality in high-risk hospitalized patients with COVID-19, but aspirin's efficacy in patients with moderate COVID-19 is not well studied. Objective To assess whether early aspirin use is associated with lower odds of in-hospital mortality in patients with moderate COVID-19. Design, Setting, and Participants Observational cohort study of 112 269 hospitalized patients with moderate COVID-19, enrolled from January 1, 2020, through September 10, 2021, at 64 health systems in the United States participating in the National Institute of Health's National COVID Cohort Collaborative (N3C). Exposure Aspirin use within the first day of hospitalization. Main Outcome and Measures The primary outcome was 28-day in-hospital mortality, and secondary outcomes were pulmonary embolism and deep vein thrombosis. Odds of in-hospital mortality were calculated using marginal structural Cox and logistic regression models. Inverse probability of treatment weighting was used to reduce bias from confounding and balance characteristics between groups. Results Among the 2 446 650 COVID-19-positive patients who were screened, 189 287 were hospitalized and 112 269 met study inclusion. For the full cohort, Median age was 63 years (IQR, 47-74 years); 16.1% of patients were African American, 3.8% were Asian, 52.7% were White, 5.0% were of other races and ethnicities, 22.4% were of unknown race and ethnicity. In-hospital mortality occurred in 10.9% of patients. After inverse probability treatment weighting, 28-day in-hospital mortality was significantly lower in those who received aspirin (10.2% vs 11.8%; odds ratio [OR], 0.85; 95% CI, 0.79-0.92; P < .001). The rate of pulmonary embolism, but not deep vein thrombosis, was also significantly lower in patients who received aspirin (1.0% vs 1.4%; OR, 0.71; 95% CI, 0.56-0.90; P = .004). Patients who received early aspirin did not have higher rates of gastrointestinal hemorrhage (0.8% aspirin vs 0.7% no aspirin; OR, 1.04; 95% CI, 0.82-1.33; P = .72), cerebral hemorrhage (0.6% aspirin vs 0.4% no aspirin; OR, 1.32; 95% CI, 0.92-1.88; P = .13), or blood transfusion (2.7% aspirin vs 2.3% no aspirin; OR, 1.14; 95% CI, 0.99-1.32; P = .06). The composite of hemorrhagic complications did not occur more often in those receiving aspirin (3.7% aspirin vs 3.2% no aspirin; OR, 1.13; 95% CI, 1.00-1.28; P = .054). Subgroups who appeared to benefit the most included patients older than 60 years (61-80 years: OR, 0.79; 95% CI, 0.72-0.87; P < .001; >80 years: OR, 0.79; 95% CI, 0.69-0.91; P < .001) and patients with comorbidities (1 comorbidity: 6.4% vs 9.2%; OR, 0.68; 95% CI, 0.55-0.83; P < .001; 2 comorbidities: 10.5% vs 12.8%; OR, 0.80; 95% CI, 0.69-0.93; P = .003; 3 comorbidities: 13.8% vs 17.0%, OR, 0.78; 95% CI, 0.68-0.89; P < .001; >3 comorbidities: 17.0% vs 21.6%; OR, 0.74; 95% CI, 0.66-0.84; P < .001). Conclusions and Relevance In this cohort study of US adults hospitalized with moderate COVID-19, early aspirin use was associated with lower odds of 28-day in-hospital mortality. A randomized clinical trial that includes diverse patients with moderate COVID-19 is warranted to adequately evaluate aspirin's efficacy in patients with high-risk conditions.
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Affiliation(s)
- Jonathan H. Chow
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Ali Rahnavard
- George Washington University Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, Washington, DC
| | - Mardi Gomberg-Maitland
- Division of Cardiology, Department of Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Ranojoy Chatterjee
- George Washington University Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, Washington, DC
| | - Pranay Patodi
- George Washington University Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, Washington, DC
| | - David P. Yamane
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
- Department of Emergency Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Andrea R. Levine
- Division of Pulmonary and Critical Care, Department of Medicine, University of Maryland School of Medicine, Baltimore
| | - Danielle Davison
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Katrina Hawkins
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Amanda M. Jackson
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Walter Reed National Military Medical Center, Bethesda, Maryland
| | - Megan T. Quintana
- Department of Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Allison S. Lankford
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Maryland School of Medicine, Baltimore
- Division of Critical Care Medicine, Department of Surgery, R. Adams Cowley Shock Trauma Center, Baltimore, Maryland
| | - Ryan J. Keneally
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Mustafa Al-Mashat
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Daniel Fisher
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Jeffrey Williams
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Jeffrey S. Berger
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Michael A. Mazzeffi
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Keith A. Crandall
- George Washington University Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, Washington, DC
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14
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Rahnavard A, Dawson T, Clement R, Stearrett N, Pérez-Losada M, Crandall KA. Epidemiological associations with genomic variation in SARS-CoV-2. Sci Rep 2021; 11:23023. [PMID: 34837008 PMCID: PMC8626494 DOI: 10.1038/s41598-021-02548-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 11/16/2021] [Indexed: 11/24/2022] Open
Abstract
SARS-CoV-2 (CoV) is the etiological agent of the COVID-19 pandemic and evolves to evade both host immune systems and intervention strategies. We divided the CoV genome into 29 constituent regions and applied novel analytical approaches to identify associations between CoV genomic features and epidemiological metadata. Our results show that nonstructural protein 3 (nsp3) and Spike protein (S) have the highest variation and greatest correlation with the viral whole-genome variation. S protein variation is correlated with nsp3, nsp6, and 3′-to-5′ exonuclease variation. Country of origin and time since the start of the pandemic were the most influential metadata associated with genomic variation, while host sex and age were the least influential. We define a novel statistic—coherence—and show its utility in identifying geographic regions (populations) with unusually high (many new variants) or low (isolated) viral phylogenetic diversity. Interestingly, at both global and regional scales, we identify geographic locations with high coherence neighboring regions of low coherence; this emphasizes the utility of this metric to inform public health measures for disease spread. Our results provide a direction to prioritize genes associated with outcome predictors (e.g., health, therapeutic, and vaccine outcomes) and to improve DNA tests for predicting disease status.
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Affiliation(s)
- Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.
| | - Tyson Dawson
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Rebecca Clement
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Nathaniel Stearrett
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Marcos Pérez-Losada
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.,CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, Vairão, Portugal
| | - Keith A Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
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15
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Mallick H, Rahnavard A, McIver LJ, Ma S, Zhang Y, Nguyen LH, Tickle TL, Weingart G, Ren B, Schwager EH, Chatterjee S, Thompson KN, Wilkinson JE, Subramanian A, Lu Y, Waldron L, Paulson JN, Franzosa EA, Bravo HC, Huttenhower C. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol 2021; 17:e1009442. [PMID: 34784344 PMCID: PMC8714082 DOI: 10.1371/journal.pcbi.1009442] [Citation(s) in RCA: 560] [Impact Index Per Article: 186.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 12/28/2021] [Accepted: 09/09/2021] [Indexed: 12/13/2022] Open
Abstract
It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.
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Affiliation(s)
- Himel Mallick
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington DC, United States of America
| | - Lauren J. McIver
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Siyuan Ma
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Yancong Zhang
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Long H. Nguyen
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Timothy L. Tickle
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - George Weingart
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Boyu Ren
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Emma H. Schwager
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Suvo Chatterjee
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Kelsey N. Thompson
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Jeremy E. Wilkinson
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Ayshwarya Subramanian
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Yiren Lu
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Levi Waldron
- Department of Epidemiology and Biostatistics, CUNY School of Public Health, New York City, New York, United States of America
| | - Joseph N. Paulson
- Department of Biostatistics, Product Development, Genentech, Inc., South San Francisco, California, United States of America
| | - Eric A. Franzosa
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Hector Corrada Bravo
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
| | - Curtis Huttenhower
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
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16
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Mallick H, Rahnavard A, McIver LJ, Ma S, Zhang Y, Nguyen LH, Tickle TL, Weingart G, Ren B, Schwager EH, Chatterjee S, Thompson KN, Wilkinson JE, Subramanian A, Lu Y, Waldron L, Paulson JN, Franzosa EA, Bravo HC, Huttenhower C. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol 2021. [PMID: 34784344 DOI: 10.1101/2021.01.20.427420v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2023] Open
Abstract
It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.
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Affiliation(s)
- Himel Mallick
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington DC, United States of America
| | - Lauren J McIver
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Siyuan Ma
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Yancong Zhang
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Long H Nguyen
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Timothy L Tickle
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - George Weingart
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Boyu Ren
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Emma H Schwager
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Suvo Chatterjee
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Kelsey N Thompson
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Jeremy E Wilkinson
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Ayshwarya Subramanian
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Yiren Lu
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Levi Waldron
- Department of Epidemiology and Biostatistics, CUNY School of Public Health, New York City, New York, United States of America
| | - Joseph N Paulson
- Department of Biostatistics, Product Development, Genentech, Inc., South San Francisco, California, United States of America
| | - Eric A Franzosa
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Hector Corrada Bravo
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
| | - Curtis Huttenhower
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
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17
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Smith ER, He S, Klatt KC, Barberio MD, Rahnavard A, Azad N, Brandt C, Harker B, Hogan E, Kucherlapaty P, Moradian D, Gernand AD, Ahmadzia HK. Limited data exist to inform our basic understanding of micronutrient requirements in pregnancy. Sci Adv 2021; 7:eabj8016. [PMID: 34678054 PMCID: PMC8535830 DOI: 10.1126/sciadv.abj8016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/02/2021] [Indexed: 06/13/2023]
Abstract
Women and pregnant people have historically been underrepresented in research; this may extend to the basic research informing nutrient reference values, such as the United States’ and Canada’s Dietary Reference Intakes (DRIs). After screening the DRI reports for 23 micronutrients, we extracted metadata from 704 studies. Women were excluded in 23% of studies, and they accounted for a smaller proportion of the sample size (29%). Pregnant or lactating people were included in 17% of the studies. Studies that used rigorous design elements, such as controlled feeding and stable isotope studies, were the most likely to include men only. The majority of studies (>90%) did not report race and ethnicity. Although nutrient reference values are intended for use in the general population, we find that the basic science informing these values may not be generalizable. We call urgently upon funders and researchers to address fundamental gaps in knowledge with high-quality research.
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Affiliation(s)
- Emily R. Smith
- Department of Global Health, The Milken Institute School of Public Health, The George Washington University, Washington , DC 20052 USA
- Department of Exercise and Nutrition Sciences, The Milken Institute School of Public Health, The George Washington University, Washington, DC 20052 USA
| | - Siran He
- Department of Global Health, The Milken Institute School of Public Health, The George Washington University, Washington , DC 20052 USA
- Department of Exercise and Nutrition Sciences, The Milken Institute School of Public Health, The George Washington University, Washington, DC 20052 USA
| | - Kevin C. Klatt
- USDA Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Matthew D. Barberio
- Department of Exercise and Nutrition Sciences, The Milken Institute School of Public Health, The George Washington University, Washington, DC 20052 USA
| | - Ali Rahnavard
- Computational Biology Institute, Departments of Biostatistics and Bioinformatics, The Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Negeena Azad
- Department of Exercise and Nutrition Sciences, The Milken Institute School of Public Health, The George Washington University, Washington, DC 20052 USA
| | - Carolyn Brandt
- Department of Exercise and Nutrition Sciences, The Milken Institute School of Public Health, The George Washington University, Washington, DC 20052 USA
| | - Bethany Harker
- Department of Global Health, The Milken Institute School of Public Health, The George Washington University, Washington , DC 20052 USA
| | - Emily Hogan
- Department of Exercise and Nutrition Sciences, The Milken Institute School of Public Health, The George Washington University, Washington, DC 20052 USA
| | - Padmini Kucherlapaty
- Department of Global Health, The Milken Institute School of Public Health, The George Washington University, Washington , DC 20052 USA
| | - Dina Moradian
- Department of Exercise and Nutrition Sciences, The Milken Institute School of Public Health, The George Washington University, Washington, DC 20052 USA
| | - Alison D. Gernand
- Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA 16801, USA
| | - Homa K. Ahmadzia
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, The George Washington University School of Medicine and Health Sciences, Washington, DC 20052, USA
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18
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Nikookar E, Naderi E, Rahnavard A. Cervical Cancer Prediction by Merging Features of Different Colposcopic Images and Using Ensemble Classifier. J Med Signals Sens 2021; 11:67-78. [PMID: 34268095 PMCID: PMC8253312 DOI: 10.4103/jmss.jmss_16_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 03/15/2020] [Accepted: 05/02/2020] [Indexed: 11/04/2022]
Abstract
Background Cervical cancer is a significant cause of cancer mortality in women, particularly in low-income countries. In regular cervical screening methods, such as colposcopy, an image is taken from the cervix of a patient. The particular image can be used by computer-aided diagnosis (CAD) systems that are trained using artificial intelligence algorithms to predict the possibility of cervical cancer. Artificial intelligence models had been highlighted in a number of cervical cancer studies. However, there are a limited number of studies that investigate the simultaneous use of three colposcopic screening modalities including Greenlight, Hinselmann, and Schiller. Methods We propose a cervical cancer predictor model which incorporates the result of different classification algorithms and ensemble classifiers. Our approach merges features of different colposcopic images of a patient. The feature vector of each image includes semantic medical features, subjective judgments, and a consensus. The class label of each sample is calculated using an aggregation function on expert judgments and consensuses. Results We investigated different aggregation strategies to find the best formula for aggregation function and then we evaluated our method using the quality assessment of digital colposcopies dataset, and our approach performance with 96% of sensitivity and 94% of specificity values yields a significant improvement in the field. Conclusion Our model can be used as a supportive clinical decision-making strategy by giving more reliable information to the clinical decision makers. Our proposed model also is more applicable in cervical cancer CAD systems compared to the available methods.
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Affiliation(s)
- Elham Nikookar
- Department of Computer Engineering, Faculty of Engineering, Shiahd Chamran University of Ahvaz, Ahvaz, Iran
| | - Ebrahim Naderi
- Department of Computer Engineering, University of Applied Science and Technology, Ahvaz, Iran
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington D.C., United States
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19
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Rahnavard A, Chatterjee S, Sayoldin B, Crandall KA, Tekola-Ayele F, Mallick H. Omics community detection using multi-resolution clustering. Bioinformatics 2021; 37:3588-3594. [PMID: 33974004 PMCID: PMC8545346 DOI: 10.1093/bioinformatics/btab317] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/23/2021] [Accepted: 04/26/2021] [Indexed: 12/26/2022] Open
Abstract
MOTIVATION The discovery of biologically interpretable and clinically actionable communities in heterogeneous omics data is a necessary first step towards deriving mechanistic insights into complex biological phenomena. Here we present a novel clustering approach, omeClust, for community detection in omics profiles by simultaneously incorporating similarities among measurements and the overall complex structure of the data. RESULTS We show that omeClust outperforms published methods in inferring the true community structure as measured by both sensitivity and misclassification rate on simulated datasets. We further validated omeClust in diverse, multiple omics datasets, revealing new communities and functionally related groups in microbial strains, cell line gene expression patterns, and fetal genomic variation. We also derived enrichment scores attributable to putatively meaningful biological factors in these datasets that can serve as hypothesis generators facilitating new sets of testable hypotheses. AVAILABILITY omeClust is open-source software, and the implementation is available online at http://github.com/omicsEye/omeClust. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Suvo Chatterjee
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA
| | - Bahar Sayoldin
- School of Systems Biology, George Mason University, Fairfax, VA 22030, USA
| | - Keith A Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Fasil Tekola-Ayele
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA
| | - Himel Mallick
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
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20
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Stearrett N, Dawson T, Rahnavard A, Bachali P, Bendall ML, Zeng C, Caricchio R, Pérez-Losada M, Grammer AC, Lipsky PE, Crandall KA. Expression of Human Endogenous Retroviruses in Systemic Lupus Erythematosus: Multiomic Integration With Gene Expression. Front Immunol 2021; 12:661437. [PMID: 33986751 PMCID: PMC8112243 DOI: 10.3389/fimmu.2021.661437] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 04/12/2021] [Indexed: 11/20/2022] Open
Abstract
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by the production of autoantibodies predominantly to nuclear material. Many aspects of disease pathology are mediated by the deposition of nucleic acid containing immune complexes, which also induce the type 1interferon response, a characteristic feature of SLE. Notably, SLE is remarkably heterogeneous, with a variety of organs involved in different individuals, who also show variation in disease severity related to their ancestries. Here, we probed one potential contribution to disease heterogeneity as well as a possible source of immunoreactive nucleic acids by exploring the expression of human endogenous retroviruses (HERVs). We investigated the expression of HERVs in SLE and their potential relationship to SLE features and the expression of biochemical pathways, including the interferon gene signature (IGS). Towards this goal, we analyzed available and new RNA-Seq data from two independent whole blood studies using Telescope. We identified 481 locus specific HERV encoding regions that are differentially expressed between case and control individuals with only 14% overlap of differentially expressed HERVs between these two datasets. We identified significant differences between differentially expressed HERVs and non-differentially expressed HERVs between the two datasets. We also characterized the host differentially expressed genes and tested their association with the differentially expressed HERVs. We found that differentially expressed HERVs were significantly more physically proximal to host differentially expressed genes than non-differentially expressed HERVs. Finally, we capitalized on locus specific resolution of HERV mapping to identify key molecular pathways impacted by differential HERV expression in people with SLE.
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Affiliation(s)
- Nathaniel Stearrett
- Computational Biology Institute, George Washington University, Washington, DC, United States
| | - Tyson Dawson
- Computational Biology Institute, George Washington University, Washington, DC, United States
| | - Ali Rahnavard
- Computational Biology Institute, George Washington University, Washington, DC, United States
- Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC, United States
| | - Prathyusha Bachali
- RILITE Research Institute and AMPEL BioSolutions, Charlottesville, VA, United States
| | - Matthew L. Bendall
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Chen Zeng
- Department of Physics, The George Washington University, Washington, DC, United States
| | - Roberto Caricchio
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA, United States
| | - Marcos Pérez-Losada
- Computational Biology Institute, George Washington University, Washington, DC, United States
- Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC, United States
- CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal
| | - Amrie C. Grammer
- RILITE Research Institute and AMPEL BioSolutions, Charlottesville, VA, United States
| | - Peter E. Lipsky
- RILITE Research Institute and AMPEL BioSolutions, Charlottesville, VA, United States
| | - Keith A. Crandall
- Computational Biology Institute, George Washington University, Washington, DC, United States
- Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC, United States
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21
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He S, Klatt KC, Rahnavard A, Barberio MD, Gernand AD, Smith ER. Protocol for meta-research on the evidence informing micronutrient dietary reference intakes for pregnant and lactating women. Gates Open Res 2020; 4:171. [PMID: 33629039 PMCID: PMC7876347 DOI: 10.12688/gatesopenres.13199.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/06/2020] [Indexed: 01/28/2023] Open
Abstract
Nutrient reference values are important parameters that guide nutrition and public health work globally. Micronutrient requirements during the peri-conception period are generally increased, which is essential in ensuring maternal, fetal, and neonatal health. Nevertheless, the current dietary reference intakes (DRIs) may be limited in terms of the methods used and the populations included, particularly the DRIs for pregnancy and lactation. In this proposed review, we will examine the methods (rigor of design, utilization of molecular methods, and presence of modern methods) and the population (inclusion of women, and in particular, pregnant and lactating people) in the studies used to inform the current DRIs. We will apply meta-science methods to this review, which involves formally reviewing the current evidence, and identifying opportunities to improve how we fund, perform, evaluate, and incorporate nutrition science into public health programs for better outcomes.
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Affiliation(s)
- Siran He
- Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Kevin C Klatt
- USDA Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Ali Rahnavard
- Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Matthew D Barberio
- Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | | | - Emily R Smith
- Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
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22
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Thingholm LB, Rühlemann MC, Koch M, Fuqua B, Laucke G, Boehm R, Bang C, Franzosa EA, Hübenthal M, Rahnavard A, Frost F, Lloyd-Price J, Schirmer M, Lusis AJ, Vulpe CD, Lerch MM, Homuth G, Kacprowski T, Schmidt CO, Nöthlings U, Karlsen TH, Lieb W, Laudes M, Franke A, Huttenhower C. Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition. Cell Host Microbe 2019; 26:252-264.e10. [PMID: 31399369 DOI: 10.1016/j.chom.2019.07.004] [Citation(s) in RCA: 231] [Impact Index Per Article: 46.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 05/17/2019] [Accepted: 07/16/2019] [Indexed: 02/07/2023]
Abstract
Obesity and type 2 diabetes (T2D) are metabolic disorders that are linked to microbiome alterations. However, their co-occurrence poses challenges in disentangling microbial features unique to each condition. We analyzed gut microbiomes of lean non-diabetic (n = 633), obese non-diabetic (n = 494), and obese individuals with T2D (n = 153) from German population and metabolic disease cohorts. Microbial taxonomic and functional profiles were analyzed along with medical histories, serum metabolomics, biometrics, and dietary data. Obesity was associated with alterations in microbiome composition, individual taxa, and functions with notable changes in Akkermansia, Faecalibacterium, Oscillibacter, and Alistipes, as well as in serum metabolites that correlated with gut microbial patterns. However, microbiome associations were modest for T2D, with nominal increases in Escherichia/Shigella. Medications, including antihypertensives and antidiabetics, along with dietary supplements including iron, were significantly associated with microbiome variation. These results differentiate microbial components of these interrelated metabolic diseases and identify dietary and medication exposures to consider in future studies.
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Affiliation(s)
- Louise B Thingholm
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany
| | - Malte C Rühlemann
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany
| | - Manja Koch
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Brie Fuqua
- Department of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Guido Laucke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany
| | - Ruwen Boehm
- Institute of Experimental and Clinical Pharmacology, University Hospital Schleswig-Holstein, 24105 Kiel, Germany
| | - Corinna Bang
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany
| | - Eric A Franzosa
- Biostatistics Department, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; The Broad Institute of MIT and Harvard, Cambridge, MA 02115, USA
| | - Matthias Hübenthal
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany; Department of Dermatology, Venereology and Allergy, University Hospital, Schleswig-Holstein, 24105 Kiel, Germany
| | - Ali Rahnavard
- Biostatistics Department, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; The Broad Institute of MIT and Harvard, Cambridge, MA 02115, USA
| | - Fabian Frost
- Department of Medicine A, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Jason Lloyd-Price
- Biostatistics Department, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; The Broad Institute of MIT and Harvard, Cambridge, MA 02115, USA
| | - Melanie Schirmer
- Biostatistics Department, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; The Broad Institute of MIT and Harvard, Cambridge, MA 02115, USA
| | - Aldons J Lusis
- Department of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Chris D Vulpe
- College of Veterinary Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Markus M Lerch
- Department of Medicine A, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Georg Homuth
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Tim Kacprowski
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, 17475 Greifswald, Germany; Research Group on Computational Systems Medicine, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Weihenstephan, Technical University of Munich, Freising-Weihenstephan 85354, Germany
| | - Carsten O Schmidt
- Institute for Community Medicine SHIP-KEF, University Medicine Greifswald, Greifswald 17475, Germany
| | - Ute Nöthlings
- Department of Nutrition and Food Sciences, Nutritional Epidemiology, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
| | - Tom H Karlsen
- Norwegian PSC Research Center, Department of Transplantation Medicine and Research Institute of Internal Medicine, Division of Surgery, Inflammatory Medicine and Transplantation, Oslo University Hospital, Rikshospitalet, 0372 Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0315 Oslo, Norway
| | - Wolfgang Lieb
- Institute of Epidemiology, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany
| | - Matthias Laudes
- Department of Internal Medicine I, University Hospital Schleswig-Holstein, 24105 Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany.
| | - Curtis Huttenhower
- Biostatistics Department, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; The Broad Institute of MIT and Harvard, Cambridge, MA 02115, USA
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23
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Abstract
In the present study, the antinociceptive effect of cholecystokinin receptor agonists in the hot-plate test in mice has been evaluated. Subcutaneous administration of cholecystokinin octapeptide (cholecystokinin-8; 0.001, 0.005, 0.01, 0.05, and 0.1 mg/kg), unsulfated cholecystokinin octapeptide (cholecystokinin-8U; 0.1 mg/kg) or caerulein (0.25 mg/kg) produced antinociception. Administration of the cholecystokinin tetrapeptide (cholecystokinin-4; 0.25, 0.5 and 1.0 mg/kg) had no effect in the hot-plate test. Subcutaneous injection of the selective cholecystokinin receptor antagonists, MK-329 (0.125, 0.25 and 0.5 mg/kg) or L-365,260 (0.125, 0.25 and 0.5 mg/kg), produced no antinociceptive response. When the animals were pretreated with the cholecystokinin receptor antagonists or naloxone (0.5 and 1 mg/kg), a significant decrease in the antinociceptive response induced by cholecystokinin-8 and caerulein was obtained. The results indicate that single administration of cholecystokinin receptor agonists could produce an antinociceptive effect which is probably mediated via cholecystokinin receptors. With respect to the results obtained from morphine and naloxone administration, it is concluded that there may be an interaction between cholecystokinin and opiate mechanisms.
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Affiliation(s)
- M Rezayat
- Department of Pharmacology, School of Medicine, Tehran University of Medical Sciences, Iran
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