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Abstract
In periodontitis patients, dysbiosis of the oral microbiota is not only found at clinically diseased periodontal sites but also at clinically healthy periodontal sites, buccal mucosae, tongue, and saliva. The present study evaluated the safety and efficacy of an oral microbiota transplant (OMT) for the treatment of periodontitis in dogs. Eighteen systemically healthy beagle dogs with naturally occurring periodontitis were enrolled in the study and randomly assigned to a test or control group. A 4-y-old, periodontally healthy female beagle dog served as a universal OMT donor. To reduce periodontal inflammation, all dogs received full-mouth mechanical debridement of teeth and mucosae 2 wk before baseline. At baseline, full-mouth mechanical debridement was repeated and followed by adjunctive subgingival and oral irrigation with 0.1% NaOCl. Subsequently, test dogs were inoculated with an OMT from the healthy donor. No daily oral hygiene was performed after OMT transplantation. Adverse events were assessed throughout the observation period. Clinical examinations were performed and whole-mouth oral microbiota samples were collected at week 2, baseline, week 2, and week 12. The composition of oral microbiota samples was analyzed using high-throughput 16S ribosomal RNA gene amplicon sequencing followed by taxonomic assignment and downstream bioinformatic and statistical analyses. Results demonstrated that the intergroup difference in the primary outcome measure, probing pocket depth at week 12, was statistically insignificant. However, the single adjunctive OMT had an additional effect on the oral microbiota composition compared to the full-mouth mechanical and antimicrobial debridement alone. The OMT resulted in an "ecological shift" toward the composition of the donor microbiota, but this was transient in nature and was not observed at week 12. No local or systemic adverse events were observed throughout the study period. The results indicate that OMT may modulate the microbiota composition in dogs with naturally occurring periodontitis and can be applied safely.
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Affiliation(s)
- T. Beikler
- Department of Periodontics, Preventive and Restorative Dentistry, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - K. Bunte
- Department of Periodontics, Preventive and Restorative Dentistry, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Y. Chan
- Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | | | - S. Selbach
- The School of Dentistry, The University of Adelaide, Adelaide, Australia
| | - U. Peters
- Department of Periodontics, Preventive and Restorative Dentistry, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - R.M. Watt
- Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - T.F. Flemmig
- Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
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Bunte K, Smith DJ, Chappell MJ, Hassan-Smith ZK, Tomlinson JW, Arlt W, Tiňo P. Learning pharmacokinetic models for in vivo glucocorticoid activation. J Theor Biol 2018; 455:222-231. [PMID: 30048717 DOI: 10.1016/j.jtbi.2018.07.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [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: 10/23/2017] [Revised: 07/03/2018] [Accepted: 07/21/2018] [Indexed: 10/28/2022]
Abstract
To understand trends in individual responses to medication, one can take a purely data-driven machine learning approach, or alternatively apply pharmacokinetics combined with mixed-effects statistical modelling. To take advantage of the predictive power of machine learning and the explanatory power of pharmacokinetics, we propose a latent variable mixture model for learning clusters of pharmacokinetic models demonstrated on a clinical data set investigating 11β-hydroxysteroid dehydrogenase enzymes (11β-HSD) activity in healthy adults. The proposed strategy automatically constructs different population models that are not based on prior knowledge or experimental design, but result naturally as mixture component models of the global latent variable mixture model. We study the parameter of the underlying multi-compartment ordinary differential equation model via identifiability analysis on the observable measurements, which reveals the model is structurally locally identifiable. Further approximation with a perturbation technique enables efficient training of the proposed probabilistic latent variable mixture clustering technique using Estimation Maximization. The training on the clinical data results in 4 clusters reflecting the prednisone conversion rate over a period of 4 h based on venous blood samples taken at 20-min intervals. The learned clusters differ in prednisone absorption as well as prednisone/prednisolone conversion. In the discussion section we include a detailed investigation of the relationship of the pharmacokinetic parameters of the trained cluster models for possible or plausible physiological explanation and correlations analysis using additional phenotypic participant measurements.
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Affiliation(s)
- Kerstin Bunte
- School of Computer Science, The University of Birmingham, Birmingham B15 2TT, UK; Faculty of Science and Engineering, University of Groningen, P.O. Box 407, Groningen 9700 AK, Netherlands.
| | - David J Smith
- School of Mathematics, The University of Birmingham, Birmingham B15 2TT, UK; Institute of Metabolism and Systems Research, University of Birmingham, UK
| | | | - Zaki K Hassan-Smith
- Centre for Applied Biological and Exercise Science, Coventry University, Coventry, UK; Departments of Endocrinology and Acute Internal Medicine, Queen Elizabeth Hospital Birmingham, Birmingham B15 2TH, UK; Centre of Endocrinology, Diabetes and Metabolism, Queen Elizabeth Hospital Birmingham, Birmingham Health Partners, UK
| | - Jeremy W Tomlinson
- Oxford Centre for Diabetes, Endocrinology and Metabolism, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Wiebke Arlt
- Institute of Metabolism and Systems Research, University of Birmingham, UK; Centre of Endocrinology, Diabetes and Metabolism, Queen Elizabeth Hospital Birmingham, Birmingham Health Partners, UK
| | - Peter Tiňo
- School of Computer Science, The University of Birmingham, Birmingham B15 2TT, UK; Institute of Metabolism and Systems Research, University of Birmingham, UK
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7
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Gönen M, Weir BA, Cowley GS, Vazquez F, Guan Y, Jaiswal A, Karasuyama M, Uzunangelov V, Wang T, Tsherniak A, Howell S, Marbach D, Hoff B, Norman TC, Airola A, Bivol A, Bunte K, Carlin D, Chopra S, Deran A, Ellrott K, Gopalacharyulu P, Graim K, Kaski S, Khan SA, Newton Y, Ng S, Pahikkala T, Paull E, Sokolov A, Tang H, Tang J, Wennerberg K, Xie Y, Zhan X, Zhu F, Aittokallio T, Mamitsuka H, Stuart JM, Boehm JS, Root DE, Xiao G, Stolovitzky G, Hahn WC, Margolin AA. A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines. Cell Syst 2017; 5:485-497.e3. [PMID: 28988802 DOI: 10.1016/j.cels.2017.09.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [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: 09/30/2016] [Revised: 06/18/2017] [Accepted: 09/07/2017] [Indexed: 12/18/2022]
Abstract
We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.
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Affiliation(s)
- Mehmet Gönen
- Department of Industrial Engineering, College of Engineering, Koç University, İstanbul, Turkey; School of Medicine, Koç University, İstanbul, Turkey; Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | | | - Glenn S Cowley
- Genetic Perturbation Platform, The Broad Institute, Boston, MA, USA; Janssen R&D US, Spring House, PA, USA
| | - Francisca Vazquez
- Cancer Program, The Broad Institute, Boston, MA, USA; Dana-Farber Cancer Institute, Boston, MA, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Alok Jaiswal
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Masayuki Karasuyama
- Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan
| | - Vladislav Uzunangelov
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA
| | - Tao Wang
- Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Sara Howell
- Cancer Program, The Broad Institute, Boston, MA, USA; Brandeis University, Waltham, MA, USA
| | - Daniel Marbach
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | | | - Antti Airola
- Department of Information Technology, University of Turku, Turku, Finland
| | - Adrian Bivol
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA
| | - Kerstin Bunte
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland; School of Computer Science, The University of Birmingham, Birmingham, UK
| | - Daniel Carlin
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Sahil Chopra
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Alden Deran
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA
| | - Kyle Ellrott
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA; Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA
| | | | - Kiley Graim
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA
| | - Samuel Kaski
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland; Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Suleiman A Khan
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Yulia Newton
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA
| | - Sam Ng
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA
| | - Tapio Pahikkala
- Department of Information Technology, University of Turku, Turku, Finland
| | - Evan Paull
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA
| | - Artem Sokolov
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA
| | - Hao Tang
- Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jing Tang
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Krister Wennerberg
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Yang Xie
- Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Simons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Fan Zhu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | | | - Tero Aittokallio
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland; Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Hiroshi Mamitsuka
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan
| | - Joshua M Stuart
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA
| | - Jesse S Boehm
- Cancer Program, The Broad Institute, Boston, MA, USA
| | - David E Root
- Genetic Perturbation Platform, The Broad Institute, Boston, MA, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Simons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Gustavo Stolovitzky
- Computational Biology Center, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA; Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - William C Hahn
- Cancer Program, The Broad Institute, Boston, MA, USA; Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Adam A Margolin
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA; Computational Biology Program, Oregon Health & Science University, Portland, OR, USA.
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