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Symul L, Jeganathan P, Costello EK, France M, Bloom SM, Kwon DS, Ravel J, Relman DA, Holmes S. Sub-communities of the vaginal microbiota in pregnant and non-pregnant women. Proc Biol Sci 2023; 290:20231461. [PMID: 38018105 PMCID: PMC10685114 DOI: 10.1098/rspb.2023.1461] [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: 06/28/2023] [Accepted: 10/30/2023] [Indexed: 11/30/2023] Open
Abstract
Diverse and non-Lactobacillus-dominated vaginal microbial communities are associated with adverse health outcomes such as preterm birth and the acquisition of sexually transmitted infections. Despite the importance of recognizing and understanding the key risk-associated features of these communities, their heterogeneous structure and properties remain ill-defined. Clustering approaches are commonly used to characterize vaginal communities, but they lack sensitivity and robustness in resolving substructures and revealing transitions between potential sub-communities. Here, we address this need with an approach based on mixed membership topic models. Using longitudinal data from cohorts of pregnant and non-pregnant study participants, we show that topic models more accurately describe sample composition, longitudinal changes, and better predict the loss of Lactobacillus dominance. We identify several non-Lactobacillus-dominated sub-communities common to both cohorts and independent of reproductive status. In non-pregnant individuals, we find that the menstrual cycle modulates transitions between and within sub-communities, as well as the concentrations of half of the cytokines and 18% of metabolites. Overall, our analyses based on mixed membership models reveal substructures of vaginal ecosystems which may have important clinical and biological associations.
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Affiliation(s)
- Laura Symul
- Department of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, CA 94305, USA
| | - Pratheepa Jeganathan
- Department of Mathematics and Statistics, McMaster University, 1280 Main Street, West Hamilton, Ontario, Canada L8S 4K1
| | - Elizabeth K. Costello
- Department of Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA
| | - Michael France
- Institute for Genome Sciences, University of Maryland School of Medicine, 670 W. Baltimore Street, Baltimore, MD 21201, USA
- Department of Microbiology and Immunology, University of Maryland School of Medicine, 685 West Baltimore Street, HSF-I Suite 380, Baltimore, MD 21201, USA
| | - Seth M. Bloom
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA
- Ragon Institute of MGH, MIT, and Harvard, 400 Technology Square, Cambridge, MA 02139, USA
| | - Douglas S. Kwon
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA
- Ragon Institute of MGH, MIT, and Harvard, 400 Technology Square, Cambridge, MA 02139, USA
| | - Jacques Ravel
- Institute for Genome Sciences, University of Maryland School of Medicine, 670 W. Baltimore Street, Baltimore, MD 21201, USA
- Department of Microbiology and Immunology, University of Maryland School of Medicine, 685 West Baltimore Street, HSF-I Suite 380, Baltimore, MD 21201, USA
| | - David A. Relman
- Department of Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA
- Department of Microbiology & Immunology, Stanford University School of Medicine, 299 Campus Drive, Stanford, CA 94305, USA
- Infectious Diseases Section, Veterans Affairs Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304, USA
| | - Susan Holmes
- Department of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, CA 94305, USA
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Cao KAL, Abadi AJ, Davis-Marcisak EF, Hsu L, Arora A, Coullomb A, Deshpande A, Feng Y, Jeganathan P, Loth M, Meng C, Mu W, Pancaldi V, Sankaran K, Righelli D, Singh A, Sodicoff JS, Stein-O'Brien GL, Subramanian A, Welch JD, You Y, Argelaguet R, Carey VJ, Dries R, Greene CS, Holmes S, Love MI, Ritchie ME, Yuan GC, Culhane AC, Fertig E. Author Correction: Community-wide hackathons to identify central themes in single-cell multi-omics. Genome Biol 2021; 22:246. [PMID: 34433496 PMCID: PMC8385897 DOI: 10.1186/s13059-021-02468-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Kim-Anh Lê Cao
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
| | - Al J Abadi
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Emily F Davis-Marcisak
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Lauren Hsu
- Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Arshi Arora
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alexis Coullomb
- Centre de Recherches en Cancérologie de Toulouse (INSERM), Université Paul Sabatier III, Toulouse, France
| | - Atul Deshpande
- Cancer Convergence Institute and Division of Quantitative Sciences, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yuzhou Feng
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | | | - Melanie Loth
- Cancer Convergence Institute and Division of Quantitative Sciences, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Chen Meng
- Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Wancen Mu
- Department of Biostatistics, UNC, Chapel Hill, NC, USA
| | - Vera Pancaldi
- Centre de Recherches en Cancérologie de Toulouse (INSERM), Université Paul Sabatier III, Toulouse, France
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Kris Sankaran
- Department of Statistics, University of Wisconsin, Madison, WI, USA
| | - Dario Righelli
- Department of Statistical Sciences, University of Padova, Padova, PD, Italy
| | - Amrit Singh
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- PROOF Centre of Excellence, Vancouver, BC, Canada
| | - Joshua S Sodicoff
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Genevieve L Stein-O'Brien
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Cancer Convergence Institute and Division of Quantitative Sciences, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | | | - Joshua D Welch
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Yue You
- Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, University of Melbourne, Melbourne, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, Australia
| | | | - Vincent J Carey
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ruben Dries
- Department of Hematology and Oncology, Boston Medical Center, Boston, MA, USA
- Department of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
- Center for Regenerative Medicine (CReM), Boston University, Boston, MA, USA
| | - Casey S Greene
- Center for Health AI and Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Susan Holmes
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Michael I Love
- Department of Biostatistics, UNC, Chapel Hill, NC, USA
- Department of Genetics, UNC, Chapel Hill, NC, USA
| | - Matthew E Ritchie
- Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, University of Melbourne, Melbourne, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, Australia
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aedin C Culhane
- Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Elana Fertig
- Cancer Convergence Institute and Division of Quantitative Sciences, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
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Lê Cao KA, Abadi AJ, Davis-Marcisak EF, Hsu L, Arora A, Coullomb A, Deshpande A, Feng Y, Jeganathan P, Loth M, Meng C, Mu W, Pancaldi V, Sankaran K, Righelli D, Singh A, Sodicoff JS, Stein-O’Brien GL, Subramanian A, Welch JD, You Y, Argelaguet R, Carey VJ, Dries R, Greene CS, Holmes S, Love MI, Ritchie ME, Yuan GC, Culhane AC, Fertig E. Community-wide hackathons to identify central themes in single-cell multi-omics. Genome Biol 2021; 22:220. [PMID: 34353350 PMCID: PMC8340473 DOI: 10.1186/s13059-021-02433-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Kim-Anh Lê Cao
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Al J. Abadi
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Emily F. Davis-Marcisak
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Lauren Hsu
- Data Science, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Genetics, UNC, Chapel Hill, NC USA
| | - Arshi Arora
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Alexis Coullomb
- Centre de Recherches en Cancérologie de Toulouse (INSERM), Université Paul Sabatier III, Toulouse, France
| | - Atul Deshpande
- Cancer Convergence Institute and Division of Quantitative Sciences, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Yuzhou Feng
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | | | - Melanie Loth
- Cancer Convergence Institute and Division of Quantitative Sciences, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Chen Meng
- Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Wancen Mu
- Department of Biostatistics, UNC, Chapel Hill, NC USA
| | - Vera Pancaldi
- Centre de Recherches en Cancérologie de Toulouse (INSERM), Université Paul Sabatier III, Toulouse, France
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Kris Sankaran
- Department of Statistics, University of Wisconsin, Madison, WI USA
| | - Dario Righelli
- Department of Statistical Sciences, University of Padova, Padova, PD Italy
| | - Amrit Singh
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC Canada
- PROOF Centre of Excellence, Vancouver, BC Canada
| | - Joshua S. Sodicoff
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI USA
| | - Genevieve L. Stein-O’Brien
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD USA
- Cancer Convergence Institute and Division of Quantitative Sciences, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD USA
| | | | - Joshua D. Welch
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI USA
| | - Yue You
- Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, University of Melbourne, Melbourne, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, Australia
| | | | - Vincent J. Carey
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Ruben Dries
- Department of Hematology and Oncology, Boston Medical Center, Boston, MA USA
- Department of Computational Biomedicine, Boston University School of Medicine, Boston, MA USA
- Center for Regenerative Medicine (CReM), Boston University, Boston, MA USA
| | - Casey S. Greene
- Center for Health AI and Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO USA
| | - Susan Holmes
- Department of Statistics, Stanford University, Stanford, CA USA
| | - Michael I. Love
- Department of Biostatistics, UNC, Chapel Hill, NC USA
- Department of Genetics, UNC, Chapel Hill, NC USA
| | - Matthew E. Ritchie
- Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, University of Melbourne, Melbourne, Australia
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, Australia
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Aedin C. Culhane
- Data Science, Dana-Farber Cancer Institute, Boston, MA USA
- Biostatistics, Harvard TH Chan School of Public Health, Boston, MA USA
| | - Elana Fertig
- Cancer Convergence Institute and Division of Quantitative Sciences, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering, Baltimore, MD USA
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Jeganathan P, Randrianampy NV, Paige RL, Trindade AA. An empirical saddlepoint approximation based method for smoothing survival functions under right censoring. CAN J STAT 2019. [DOI: 10.1002/cjs.11491] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | | | - Robert L. Paige
- Department of Mathematics & StatisticsMissouri University of Science and Technology Rolla MO 65409‐0020 U.S.A
| | - A. Alexandre Trindade
- Department of Mathematics & StatisticsTexas Tech University Lubbock TX 79409‐1042 U.S.A
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Jeganathan P, Mudappa D, Ananda Kumar M, Shankar Raman TR. Seasonal Variation in Wildlife Roadkills in Plantations and Tropical Rainforest in the Anamalai Hills, Western Ghats, India. CURR SCI INDIA 2018. [DOI: 10.18520/cs/v114/i03/619-626] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Cheng H, Strouts F, Sweeney TE, Briese T, Jeganathan P, Khadka V, Thair S, Popper S, Dalai S, Tan S, Hitchcock M, Multani A, Campen N, Yang S, Holmes SP, Lipkin WI, Khatri P, Relman DA. Integration of Next–Generation Sequencing, Viral Sequencing, and Host-Response Profiling for the Diagnosis of Acute Infections. Open Forum Infect Dis 2017. [PMCID: PMC5631976 DOI: 10.1093/ofid/ofx162.170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Background To guide treatment of infectious diseases, clinicians need sensitive, specific, and rapid diagnostics. We aim to incorporate complementary methods of microbial sequencing and host-response profiling to improve the diagnosis of patients at risk for acute infections. Methods We enrolled 200 adult patients with systemic inflammatory response syndrome (SIRS) at the Stanford Emergency Department. Physicians with specialty training in infectious diseases conducted retrospective two-physician chart review to establish likely admission diagnoses. Blood samples were tested with a previously described 18-gene host-response integrated antibiotics decision model (IADM) that distinguishes noninfectious SIRS, bacterial infections and viral infections. Plasma samples were tested with shotgun metagenomic next-generation sequencing (NGS) and viral sequencing with VirCapSeq. A novel statistical algorithm was developed to identify contaminant organism sequences in NGS data. Results The physician chart review classified 99 patients (49%) as infected, 69 (35%) possibly infected and 32 (16%) non-infected. Compared with chart review, the IADM distinguished bacterial from viral infections with an area under curve of 0.85 (95% confidence interval 0.77–0.93). NGS results to date confirmed positive blood cultures in seven of nine patients, with two of four blood culture-positive E. coli patients turning up negative on NGS due to E. coli contamination. NGS also confirmed positive cultures from other sites in two of six patients with negative blood cultures. Preliminary VirCapSeq data from 23 patients confirmed positive viral tests in five of six patients with Hepatitis C, BK Virus, Cytomegalovirus and Epstein–Barr Virus infections. VirCapSeq did not identify a causative agent in the plasma of 11 patients with confirmed respiratory viral infection and intestinal Norovirus infection, and six patients with idiopathic illness. Interestingly, VirCapSeq found viral reactivation in 8 of 12 immunocompromised patients. Conclusion The diagnosis of suspected infections may be enhanced by integrating host-response and microbial data alongside clinical judgment. Our results and large cohort lay the foundation to demonstrate the utility of this approach and in which patients these tools may be most useful. Disclosures T. E. Sweeney, Inflammatix, Inc: Employee and Shareholder, Salary; T. Briese, Roche: Columbia University has licensed VirCapSeq to Roche, Licensing agreement or royalty; W. I. Lipkin, Roche: Columbia University has licensed VirCapSeq to Roche., Licensing agreement or royalty; P. Khatri, Inflammatix, Inc.: Co-founder, Scientific Advisor and Shareholder, Licensing agreement or royalty and ownership stock; D. A. Relman, Karius: Consultant, Stock options; Arc Bio LLC: Consultant, Stock options
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Affiliation(s)
- Henry Cheng
- Bioengineering, Stanford University, Stanford, California
- Medicine, Microbiology and Immunology, Stanford University School of Medicine, Stanford, California
| | - Fiona Strouts
- Medicine, Microbiology and Immunology, Stanford University School of Medicine, Stanford, California
| | - Timothy E Sweeney
- Institute for Immunity, Transplantation, and Infections and Division of Biomedical Informatics, Department of Medicine, Stanford University, Stanford, California
| | - Thomas Briese
- Department of Epidemiology and Center for Infection and Immunity, Columbia University Mailman School of Public Health, New York, New York
| | | | - Veda Khadka
- Medicine, Microbiology and Immunology, Stanford University School of Medicine, Stanford, California
| | - Simone Thair
- Emergency Medicine, Stanford University Medical Center, Stanford, California
| | - Stephen Popper
- Medicine, Microbiology and Immunology, Stanford University School of Medicine, Stanford, California
| | - Sudeb Dalai
- Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California
| | - Susanna Tan
- Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California
| | - Matthew Hitchcock
- Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California
| | - Ashrit Multani
- Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California
| | - Natalie Campen
- Medicine, Microbiology and Immunology, Stanford University School of Medicine, Stanford, California
| | - Samuel Yang
- Emergency Medicine, Stanford University Medical Center, Stanford, California
| | | | - W Ian Lipkin
- Department of Epidemiology and Center for Infection and Immunity, Columbia University Mailman School of Public Health, New York, New York
| | - Purvesh Khatri
- Institute for Immunity, Transplantation, and Infections and Division of Biomedical Informatics, Department of Medicine, Stanford University, Stanford, California
| | - David A Relman
- Medicine, Microbiology and Immunology, Stanford University School of Medicine, Stanford, California
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Fukuyama J, Rumker L, Sankaran K, Jeganathan P, Dethlefsen L, Relman DA, Holmes SP. Multidomain analyses of a longitudinal human microbiome intestinal cleanout perturbation experiment. PLoS Comput Biol 2017; 13:e1005706. [PMID: 28821012 PMCID: PMC5576755 DOI: 10.1371/journal.pcbi.1005706] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [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: 03/13/2017] [Revised: 08/30/2017] [Accepted: 07/27/2017] [Indexed: 12/29/2022] Open
Abstract
Our work focuses on the stability, resilience, and response to perturbation of the bacterial communities in the human gut. Informative flash flood-like disturbances that eliminate most gastrointestinal biomass can be induced using a clinically-relevant iso-osmotic agent. We designed and executed such a disturbance in human volunteers using a dense longitudinal sampling scheme extending before and after induced diarrhea. This experiment has enabled a careful multidomain analysis of a controlled perturbation of the human gut microbiota with a new level of resolution. These new longitudinal multidomain data were analyzed using recently developed statistical methods that demonstrate improvements over current practices. By imposing sparsity constraints we have enhanced the interpretability of the analyses and by employing a new adaptive generalized principal components analysis, incorporated modulated phylogenetic information and enhanced interpretation through scoring of the portions of the tree most influenced by the perturbation. Our analyses leverage the taxa-sample duality in the data to show how the gut microbiota recovers following this perturbation. Through a holistic approach that integrates phylogenetic, metagenomic and abundance information, we elucidate patterns of taxonomic and functional change that characterize the community recovery process across individuals. We provide complete code and illustrations of new sparse statistical methods for high-dimensional, longitudinal multidomain data that provide greater interpretability than existing methods.
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Affiliation(s)
- Julia Fukuyama
- Statistics Department, Stanford University, Stanford, California, USA
| | - Laurie Rumker
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, California, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Kris Sankaran
- Statistics Department, Stanford University, Stanford, California, USA
| | | | - Les Dethlefsen
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, California, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - David A. Relman
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, California, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
- Infectious Diseases Section, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA
| | - Susan P. Holmes
- Statistics Department, Stanford University, Stanford, California, USA
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Akila A, Jeganathan P, Ponnuswamy S. Synthesis, conformational preferences and antimicrobial evaluation of N-piperazinoacetyl-r-2,c-6-diphenylpiperidin-4-ones. J Mol Struct 2016. [DOI: 10.1016/j.molstruc.2016.05.028] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Pérez-Torres A, Vera-Aguilera J, Sahaza JH, Vera-Aguilera C, Moreno-Aguilera E, Pulido-Camarillo E, Nuñez-Ochoa L, Jeganathan P. Hematological Effects, Serum, and Pulmonary Cytokine Profiles in a Melanoma Mouse Model Treated with GK1. Cancer Biother Radiopharm 2016; 30:247-54. [PMID: 26181852 DOI: 10.1089/cbr.2015.1835] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE In a previous study, we demonstrated the therapeutic efficacy of a subcutaneous injection of GK1 peptide in a melanoma mouse model, effectively increasing the mean survival time by 42.58%, delaying tumor growth, and increasing intratumoral necrosis compared with the control. As a first approach to investigate the anti-melanoma effect of GK1, this study was carried out to determine the hematological effects along with both serum and lung cytokine profiles in a melanoma lung metastatic model. MATERIALS AND METHODS Thirteen C57BL6 female mice were transfected in the lateral tail vein with 2×10(5) B16-F0 melanoma cells. After 7 days, mice were separated in two different groups and treatments were initiated (day 0): The GK1-treated group (seven mice) were injected every 5 days intravenously with GK1 (10 μg) in the lateral tail vein, and the control group (six mice) were injected every 5 days with intravenous saline solution. Blood samples were collected every 5 days from day 0; tumor samples were obtained for cytokine measurements on the day of sacrifice. RESULTS In the peripheral blood, mice treated with GK1 presented a statistically significant decrease in IFN-γ (p<0.05), and lymphocytes tended to be lower compared with the control mice (p=0.06). Lung metastatic analysis demonstrated a significant increase in IFN-γ and IL-12p70 (p<0.05); a significant decrease in IL-17, IL-4, IL-22, IL-23, and IL-12p40 (p<0.05); and a marginal decrease in IL-1β (p=0.07) compared with the control. DISCUSSION Our results suggest that an intratumoral increase of cytokines with antitumor activity along with an intratumoral decrease of cytokines with protumor activity could explain, in part, the anti-melanoma effects of GK1 in a lung metastatic melanoma mouse model. Further studies must be performed to elucidate the precise mechanisms of action for GK1 peptide against melanoma, and their eventual application in humans.
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Affiliation(s)
- Armando Pérez-Torres
- 1 Departamento de Biología Celular y Tisular, Facultad de Medicina, Universidad Nacional Autónoma de México , México City, México
| | | | - Jorge H Sahaza
- 3 Corporación para Investigaciones Biológicas (CIB) , Unidad de Micología Médica y Experimental, Medellín, Colombia
| | - Carlos Vera-Aguilera
- 1 Departamento de Biología Celular y Tisular, Facultad de Medicina, Universidad Nacional Autónoma de México , México City, México
| | - Eduardo Moreno-Aguilera
- 4 Servicio de Gastrocirugía, Hospital de Especialidades , Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, México DF, México
| | - Evelyn Pulido-Camarillo
- 1 Departamento de Biología Celular y Tisular, Facultad de Medicina, Universidad Nacional Autónoma de México , México City, México
| | - Luis Nuñez-Ochoa
- 5 Departamento de Patología Clínica/Oncología, Facultad de Medicina Veterinaria y Zootecnia, Universidad Nacional Autónoma de México , México City, México
| | - Pratheepa Jeganathan
- 6 Department of Mathematics and Statistics, Texas Tech University , Lubbock, Texas
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Ravichandran K, Ramesh P, Jeganathan P, Ponnuswamy S, Ponnuswamy MN. 1-Chloro-acetyl-3-isopropyl-r-2,c-6-diphenyl-piperidin-4-one. Acta Crystallogr Sect E Struct Rep Online 2010; 66:o276-7. [PMID: 21579713 PMCID: PMC2979709 DOI: 10.1107/s160053680905497x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2009] [Accepted: 12/21/2009] [Indexed: 11/11/2022]
Abstract
In the title compound, C(22)H(24)ClNO(2), the piperidine ring adopts a distorted boat conformation. The dihedral angle between the two phenyl rings is 83.2 (1)°. In the crystal, the mol-ecules are linked into chains running along the b axis by C-H⋯O hydrogen bonds. The Cl atom of the chloro-acetyl group is disordered over two positions with occupancies of 0.66 (2) and 0.34 (2).
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Jeganathan P. On the Strong Approximation of the Distributions of Estimators in Linear Stochastic Models, I and II: Stationary and Explosive AR Models. Ann Stat 1988. [DOI: 10.1214/aos/1176350962] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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