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Pauline Trinh

Bio: Pauline Trinh is an academic researcher from University of Washington. The author has contributed to research in topics: Microbiome & Heart transplantation. The author has an hindex of 12, co-authored 24 publications receiving 4562 citations. Previous affiliations of Pauline Trinh include University of California, Los Angeles & Columbia University.

Papers
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Journal ArticleDOI
Evan Bolyen1, Jai Ram Rideout1, Matthew R. Dillon1, Nicholas A. Bokulich1, Christian C. Abnet2, Gabriel A. Al-Ghalith3, Harriet Alexander4, Harriet Alexander5, Eric J. Alm6, Manimozhiyan Arumugam7, Francesco Asnicar8, Yang Bai9, Jordan E. Bisanz10, Kyle Bittinger11, Asker Daniel Brejnrod7, Colin J. Brislawn12, C. Titus Brown4, Benjamin J. Callahan13, Andrés Mauricio Caraballo-Rodríguez14, John Chase1, Emily K. Cope1, Ricardo Silva14, Christian Diener15, Pieter C. Dorrestein14, Gavin M. Douglas16, Daniel M. Durall17, Claire Duvallet6, Christian F. Edwardson, Madeleine Ernst18, Madeleine Ernst14, Mehrbod Estaki17, Jennifer Fouquier19, Julia M. Gauglitz14, Sean M. Gibbons20, Sean M. Gibbons15, Deanna L. Gibson17, Antonio Gonzalez14, Kestrel Gorlick1, Jiarong Guo21, Benjamin Hillmann3, Susan Holmes22, Hannes Holste14, Curtis Huttenhower23, Curtis Huttenhower24, Gavin A. Huttley25, Stefan Janssen26, Alan K. Jarmusch14, Lingjing Jiang14, Benjamin D. Kaehler27, Benjamin D. Kaehler25, Kyo Bin Kang14, Kyo Bin Kang28, Christopher R. Keefe1, Paul Keim1, Scott T. Kelley29, Dan Knights3, Irina Koester14, Tomasz Kosciolek14, Jorden Kreps1, Morgan G. I. Langille16, Joslynn S. Lee30, Ruth E. Ley31, Ruth E. Ley32, Yong-Xin Liu, Erikka Loftfield2, Catherine A. Lozupone19, Massoud Maher14, Clarisse Marotz14, Bryan D Martin20, Daniel McDonald14, Lauren J. McIver23, Lauren J. McIver24, Alexey V. Melnik14, Jessica L. Metcalf33, Sydney C. Morgan17, Jamie Morton14, Ahmad Turan Naimey1, Jose A. Navas-Molina14, Jose A. Navas-Molina34, Louis-Félix Nothias14, Stephanie B. Orchanian, Talima Pearson1, Samuel L. Peoples35, Samuel L. Peoples20, Daniel Petras14, Mary L. Preuss36, Elmar Pruesse19, Lasse Buur Rasmussen7, Adam R. Rivers37, Michael S. Robeson38, Patrick Rosenthal36, Nicola Segata8, Michael Shaffer19, Arron Shiffer1, Rashmi Sinha2, Se Jin Song14, John R. Spear39, Austin D. Swafford, Luke R. Thompson40, Luke R. Thompson41, Pedro J. Torres29, Pauline Trinh20, Anupriya Tripathi14, Peter J. Turnbaugh10, Sabah Ul-Hasan42, Justin J. J. van der Hooft43, Fernando Vargas, Yoshiki Vázquez-Baeza14, Emily Vogtmann2, Max von Hippel44, William A. Walters32, Yunhu Wan2, Mingxun Wang14, Jonathan Warren45, Kyle C. Weber37, Kyle C. Weber46, Charles H. D. Williamson1, Amy D. Willis20, Zhenjiang Zech Xu14, Jesse R. Zaneveld20, Yilong Zhang47, Qiyun Zhu14, Rob Knight14, J. Gregory Caporaso1 
TL;DR: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and R.K.P. and partial support was also provided by the following: grants NIH U54CA143925 and U54MD012388.
Abstract: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and 1565057 to R.K. Partial support was also provided by the following: grants NIH U54CA143925 (J.G.C. and T.P.) and U54MD012388 (J.G.C. and T.P.); grants from the Alfred P. Sloan Foundation (J.G.C. and R.K.); ERCSTG project MetaPG (N.S.); the Strategic Priority Research Program of the Chinese Academy of Sciences QYZDB-SSW-SMC021 (Y.B.); the Australian National Health and Medical Research Council APP1085372 (G.A.H., J.G.C., Von Bing Yap and R.K.); the Natural Sciences and Engineering Research Council (NSERC) to D.L.G.; and the State of Arizona Technology and Research Initiative Fund (TRIF), administered by the Arizona Board of Regents, through Northern Arizona University. All NCI coauthors were supported by the Intramural Research Program of the National Cancer Institute. S.M.G. and C. Diener were supported by the Washington Research Foundation Distinguished Investigator Award.

8,821 citations

Posted ContentDOI
Evan Bolyen1, Jai Ram Rideout1, Matthew R. Dillon1, Nicholas A. Bokulich1, Christian C. Abnet, Gabriel A. Al-Ghalith2, Harriet Alexander3, Harriet Alexander4, Eric J. Alm5, Manimozhiyan Arumugam6, Francesco Asnicar7, Yang Bai8, Jordan E. Bisanz9, Kyle Bittinger10, Asker Daniel Brejnrod6, Colin J. Brislawn11, C. Titus Brown4, Benjamin J. Callahan12, Andrés Mauricio Caraballo-Rodríguez13, John Chase1, Emily K. Cope1, Ricardo Silva13, Pieter C. Dorrestein13, Gavin M. Douglas14, Daniel M. Durall15, Claire Duvallet5, Christian F. Edwardson16, Madeleine Ernst13, Mehrbod Estaki15, Jennifer Fouquier17, Julia M. Gauglitz13, Deanna L. Gibson15, Antonio Gonzalez18, Kestrel Gorlick1, Jiarong Guo19, Benjamin Hillmann2, Susan Holmes20, Hannes Holste18, Curtis Huttenhower21, Curtis Huttenhower22, Gavin A. Huttley23, Stefan Janssen24, Alan K. Jarmusch13, Lingjing Jiang18, Benjamin D. Kaehler23, Kyo Bin Kang13, Kyo Bin Kang25, Christopher R. Keefe1, Paul Keim1, Scott T. Kelley26, Dan Knights2, Irina Koester13, Irina Koester18, Tomasz Kosciolek18, Jorden Kreps1, Morgan G. I. Langille14, Joslynn S. Lee27, Ruth E. Ley28, Ruth E. Ley29, Yong-Xin Liu8, Erikka Loftfield, Catherine A. Lozupone17, Massoud Maher18, Clarisse Marotz18, Bryan D Martin30, Daniel McDonald18, Lauren J. McIver21, Lauren J. McIver22, Alexey V. Melnik13, Jessica L. Metcalf31, Sydney C. Morgan15, Jamie Morton18, Ahmad Turan Naimey1, Jose A. Navas-Molina18, Jose A. Navas-Molina32, Louis-Félix Nothias13, Stephanie B. Orchanian18, Talima Pearson1, Samuel L. Peoples33, Samuel L. Peoples30, Daniel Petras13, Mary L. Preuss34, Elmar Pruesse17, Lasse Buur Rasmussen6, Adam R. Rivers35, Ii Michael S Robeson36, Patrick Rosenthal34, Nicola Segata7, Michael Shaffer17, Arron Shiffer1, Rashmi Sinha, Se Jin Song18, John R. Spear37, Austin D. Swafford18, Luke R. Thompson38, Luke R. Thompson39, Pedro J. Torres26, Pauline Trinh30, Anupriya Tripathi13, Anupriya Tripathi18, Peter J. Turnbaugh9, Sabah Ul-Hasan40, Justin J. J. van der Hooft41, Fernando Vargas18, Yoshiki Vázquez-Baeza18, Emily Vogtmann, Max von Hippel42, William A. Walters29, Yunhu Wan, Mingxun Wang13, Jonathan Warren43, Kyle C. Weber35, Kyle C. Weber44, Chase Hd Williamson1, Amy D. Willis30, Zhenjiang Zech Xu18, Jesse R. Zaneveld30, Yilong Zhang45, Rob Knight18, J. Gregory Caporaso1 
24 Oct 2018-PeerJ
TL;DR: QIIME 2 provides new features that will drive the next generation of microbiome research, including interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.
Abstract: We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

875 citations

Journal ArticleDOI
Evan Bolyen1, Jai Ram Rideout1, Matthew R. Dillon1, Nicholas A. Bokulich1, Christian C. Abnet2, Gabriel A. Al-Ghalith3, Harriet Alexander4, Harriet Alexander5, Eric J. Alm6, Manimozhiyan Arumugam7, Francesco Asnicar8, Yang Bai9, Jordan E. Bisanz10, Kyle Bittinger11, Asker Daniel Brejnrod7, Colin J. Brislawn12, C. Titus Brown4, Benjamin J. Callahan13, Andrés Mauricio Caraballo-Rodríguez14, John Chase1, Emily K. Cope1, Ricardo Silva14, Christian Diener15, Pieter C. Dorrestein14, Gavin M. Douglas16, Daniel M. Durall17, Claire Duvallet6, Christian F. Edwardson, Madeleine Ernst14, Madeleine Ernst18, Mehrbod Estaki17, Jennifer Fouquier19, Julia M. Gauglitz14, Sean M. Gibbons15, Sean M. Gibbons20, Deanna L. Gibson17, Antonio Gonzalez21, Kestrel Gorlick1, Jiarong Guo22, Benjamin Hillmann3, Susan Holmes23, Hannes Holste21, Curtis Huttenhower24, Curtis Huttenhower25, Gavin A. Huttley26, Stefan Janssen27, Alan K. Jarmusch14, Lingjing Jiang21, Benjamin D. Kaehler28, Benjamin D. Kaehler26, Kyo Bin Kang29, Kyo Bin Kang14, Christopher R. Keefe1, Paul Keim1, Scott T. Kelley30, Dan Knights3, Irina Koester21, Irina Koester14, Tomasz Kosciolek21, Jorden Kreps1, Morgan G. I. Langille16, Joslynn S. Lee31, Ruth E. Ley32, Ruth E. Ley33, Yong-Xin Liu, Erikka Loftfield2, Catherine A. Lozupone19, Massoud Maher21, Clarisse Marotz21, Bryan D Martin20, Daniel McDonald21, Lauren J. McIver25, Lauren J. McIver24, Alexey V. Melnik14, Jessica L. Metcalf34, Sydney C. Morgan17, Jamie Morton21, Ahmad Turan Naimey1, Jose A. Navas-Molina35, Jose A. Navas-Molina21, Louis-Félix Nothias14, Stephanie B. Orchanian, Talima Pearson1, Samuel L. Peoples20, Samuel L. Peoples36, Daniel Petras14, Mary L. Preuss37, Elmar Pruesse19, Lasse Buur Rasmussen7, Adam R. Rivers38, Michael S. Robeson39, Patrick Rosenthal37, Nicola Segata8, Michael Shaffer19, Arron Shiffer1, Rashmi Sinha2, Se Jin Song21, John R. Spear40, Austin D. Swafford, Luke R. Thompson41, Luke R. Thompson42, Pedro J. Torres30, Pauline Trinh20, Anupriya Tripathi14, Anupriya Tripathi21, Peter J. Turnbaugh10, Sabah Ul-Hasan43, Justin J. J. van der Hooft44, Fernando Vargas, Yoshiki Vázquez-Baeza21, Emily Vogtmann2, Max von Hippel45, William A. Walters32, Yunhu Wan2, Mingxun Wang14, Jonathan Warren46, Kyle C. Weber38, Kyle C. Weber47, Charles H. D. Williamson1, Amy D. Willis20, Zhenjiang Zech Xu21, Jesse R. Zaneveld20, Yilong Zhang48, Qiyun Zhu21, Rob Knight21, J. Gregory Caporaso1 
TL;DR: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Abstract: In the version of this article initially published, some reference citations were incorrect. The three references to Jupyter Notebooks should have cited Kluyver et al. instead of Gonzalez et al. The reference to Qiita should have cited Gonzalez et al. instead of Schloss et al. The reference to mothur should have cited Schloss et al. instead of McMurdie & Holmes. The reference to phyloseq should have cited McMurdie & Holmes instead of Huber et al. The reference to Bioconductor should have cited Huber et al. instead of Franzosa et al. And the reference to the biobakery suite should have cited Franzosa et al. instead of Kluyver et al. The errors have been corrected in the HTML and PDF versions of the article.

301 citations

Journal ArticleDOI
TL;DR: Evidence is identified that the environmental microbiome as well as the microbiome of animals in close contact can affect both the human microbiome and human health outcomes, which could lead to innovative interventions to prevent and manage a variety of human health and disease states.
Abstract: The One Health concept stresses the ecological relationships between human, animal, and environmental health. Much of the One Health literature to date has examined the transfer of pathogens from animals (e.g., emerging zoonoses) and the environment to humans. The recent rapid development of technology to perform high throughput DNA sequencing has expanded this view to include the study of entire microbial communities. Applying the One Health approach to the microbiome allows for consideration of both pathogenic and non-pathogenic microbial transfer between humans, animals, and the environment. We review recent research studies of such transmission, the molecular and statistical methods being used, and the implications of such microbiome relationships for human health. Our review identified evidence that the environmental microbiome as well as the microbiome of animals in close contact can affect both the human microbiome and human health outcomes. Such microbiome transfer can take place in the household as well as the workplace setting. Urbanization of built environments leads to changes in the environmental microbiome which could be a factor in human health. While affected by environmental exposures, the human microbiome also can modulate the response to environmental factors through effects on metabolic and immune function. Better understanding of these microbiome interactions between humans, animals, and the shared environment will require continued development of improved statistical and ecological modeling approaches. Such enhanced understanding could lead to innovative interventions to prevent and manage a variety of human health and disease states.

110 citations

Journal ArticleDOI
TL;DR: The assessment identified 20 known or suspected carcinogens that could be measured in future studies to advance exposure and risk assessments of cancer-causing agents and support the need for investigation into the relationship between UO&G development and risk of cancer in general and childhood leukemia in particular.

76 citations


Cited by
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Journal ArticleDOI
TL;DR: Some notable features of IQ-TREE version 2 are described and the key advantages over other software are highlighted.
Abstract: IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.

4,337 citations

Journal ArticleDOI
TL;DR: This protocol details MicrobiomeAnalyst, a user-friendly, web-based platform for comprehensive statistical, functional, and meta-analysis of microbiome data, a one-stop shop that enables microbiome researchers to thoroughly explore their preprocessed microbiome data via intuitive web interfaces.
Abstract: MicrobiomeAnalyst is an easy-to-use, web-based platform for comprehensive analysis of common data outputs generated from current microbiome studies. It enables researchers and clinicians with little or no bioinformatics training to explore a wide variety of well-established methods for microbiome data processing, statistical analysis, functional profiling and comparison with public datasets or known microbial signatures. MicrobiomeAnalyst currently contains four modules: Marker-gene Data Profiling (MDP), Shotgun Data Profiling (SDP), Projection with Public Data (PPD), and Taxon Set Enrichment Analysis (TSEA). This protocol will first introduce the MDP module by providing a step-wise description of how to prepare, process and normalize data; perform community profiling; identify important features; and conduct correlation and classification analysis. We will then demonstrate how to perform predictive functional profiling and introduce several unique features of the SDP module for functional analysis. The last two sections will describe the key steps involved in using the PPD and TSEA modules for meta-analysis and visual exploration of the results. In summary, MicrobiomeAnalyst offers a one-stop shop that enables microbiome researchers to thoroughly explore their preprocessed microbiome data via intuitive web interfaces. The complete protocol can be executed in ~70 min. This protocol details MicrobiomeAnalyst, a user-friendly, web-based platform for comprehensive statistical, functional, and meta-analysis of microbiome data.

823 citations

Journal ArticleDOI
TL;DR: A definition of microbiome is proposed based on the compact, clear, and comprehensive description of the term provided by Whipps et al. in 1988, amended with a set of novel recommendations considering the latest technological developments and research findings.
Abstract: The field of microbiome research has evolved rapidly over the past few decades and has become a topic of great scientific and public interest. As a result of this rapid growth in interest covering different fields, we are lacking a clear commonly agreed definition of the term “microbiome.” Moreover, a consensus on best practices in microbiome research is missing. Recently, a panel of international experts discussed the current gaps in the frame of the European-funded MicrobiomeSupport project. The meeting brought together about 40 leaders from diverse microbiome areas, while more than a hundred experts from all over the world took part in an online survey accompanying the workshop. This article excerpts the outcomes of the workshop and the corresponding online survey embedded in a short historical introduction and future outlook. We propose a definition of microbiome based on the compact, clear, and comprehensive description of the term provided by Whipps et al. in 1988, amended with a set of novel recommendations considering the latest technological developments and research findings. We clearly separate the terms microbiome and microbiota and provide a comprehensive discussion considering the composition of microbiota, the heterogeneity and dynamics of microbiomes in time and space, the stability and resilience of microbial networks, the definition of core microbiomes, and functionally relevant keystone species as well as co-evolutionary principles of microbe-host and inter-species interactions within the microbiome. These broad definitions together with the suggested unifying concepts will help to improve standardization of microbiome studies in the future, and could be the starting point for an integrated assessment of data resulting in a more rapid transfer of knowledge from basic science into practice. Furthermore, microbiome standards are important for solving new challenges associated with anthropogenic-driven changes in the field of planetary health, for which the understanding of microbiomes might play a key role.

733 citations

Journal ArticleDOI
TL;DR: Reviewing the recent literature and highlighting several molecular mechanisms by which ERs regulate the development or functional responses of innate immune cells may contribute to the reported sex differences in innate immune pathways.

660 citations

Journal ArticleDOI
05 Feb 2021-Science
TL;DR: Treatment with FMT was associated with favorable changes in immune cell infiltrates and gene expression profiles in both the gut lamina propria and the tumor microenvironment, which have implications for modulating the gut microbiota in cancer treatment.
Abstract: The gut microbiome has been shown to influence the response of tumors to anti-PD-1 (programmed cell death-1) immunotherapy in preclinical mouse models and observational patient cohorts. However, modulation of gut microbiota in cancer patients has not been investigated in clinical trials. In this study, we performed a phase 1 clinical trial to assess the safety and feasibility of fecal microbiota transplantation (FMT) and reinduction of anti-PD-1 immunotherapy in 10 patients with anti-PD-1-refractory metastatic melanoma. We observed clinical responses in three patients, including two partial responses and one complete response. Notably, treatment with FMT was associated with favorable changes in immune cell infiltrates and gene expression profiles in both the gut lamina propria and the tumor microenvironment. These early findings have implications for modulating the gut microbiota in cancer treatment.

609 citations