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      The vaginal microbiome and preterm birth

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      1 , 2 , 3 , 1 , 3 , 3 , 4 , 3 , 5 , 2 , 3 , 1 , 1 , 3 , 6 , 1 , 3 , 7 , 3 , 8 , 9 , 1 , 3 , 1 , 3 , 3 , 10 , 11 , 1 , 1 , 3 , 6 , 1 , 1 , 1 , 1 , 11 , 1 , 2 , 12 , 1 , 13 , 14 , 15 , 16 , 16 , 17 , 18 , 19 , 19 , 1 , 1 , 1 , 20 , 20 , 20 , 21 , 20 , 9 , 3 , 8 , 9 , 1 , 3 , 2 , 3 , 1 , 3 , 6 , *
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          Abstract

          The incidence of preterm birth exceeds 10% worldwide. There are significant disparities in the frequency of preterm birth among populations within countries, and women of African ancestry disproportionately bear the burden of risk in the United States. In the present study, we report a community resource that includes ‘omics’ data from approximately 12,000 samples as part of the integrative Human Microbiome Project. Longitudinal analyses of 16S ribosomal RNA, metagenomic, metatranscriptomic and cytokine profiles from 45 preterm and 90 term birth controls identified harbingers of preterm birth in this cohort of women predominantly of African ancestry. Women who delivered preterm exhibited significantly lower vaginal levels of Lactobacillus crispatus and higher levels of BVAB1, Sneathia amnii, TM7-H1, a group of Prevotella species and nine additional taxa. The first representative genomes of BVAB1 and TM7-H1 are described. Preterm-birth-associated taxa were correlated with proinflammatory cytokines in vaginal fluid. These findings highlight new opportunities for assessment of the risk of preterm birth.

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          mixOmics: An R package for ‘omics feature selection and multiple data integration

          The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.
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            A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis.

            We present a penalized matrix decomposition (PMD), a new framework for computing a rank-K approximation for a matrix. We approximate the matrix X as circumflexX = sigma(k=1)(K) d(k)u(k)v(k)(T), where d(k), u(k), and v(k) minimize the squared Frobenius norm of X - circumflexX, subject to penalties on u(k) and v(k). This results in a regularized version of the singular value decomposition. Of particular interest is the use of L(1)-penalties on u(k) and v(k), which yields a decomposition of X using sparse vectors. We show that when the PMD is applied using an L(1)-penalty on v(k) but not on u(k), a method for sparse principal components results. In fact, this yields an efficient algorithm for the "SCoTLASS" proposal (Jolliffe and others 2003) for obtaining sparse principal components. This method is demonstrated on a publicly available gene expression data set. We also establish connections between the SCoTLASS method for sparse principal component analysis and the method of Zou and others (2006). In addition, we show that when the PMD is applied to a cross-products matrix, it results in a method for penalized canonical correlation analysis (CCA). We apply this penalized CCA method to simulated data and to a genomic data set consisting of gene expression and DNA copy number measurements on the same set of samples.
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              The composition and stability of the vaginal microbiota of normal pregnant women is different from that of non-pregnant women

              Background This study was undertaken to characterize the vaginal microbiota throughout normal human pregnancy using sequence-based techniques. We compared the vaginal microbial composition of non-pregnant patients with a group of pregnant women who delivered at term. Results A retrospective case–control longitudinal study was designed and included non-pregnant women (n = 32) and pregnant women who delivered at term (38 to 42 weeks) without complications (n = 22). Serial samples of vaginal fluid were collected from both non-pregnant and pregnant patients. A 16S rRNA gene sequence-based survey was conducted using pyrosequencing to characterize the structure and stability of the vaginal microbiota. Linear mixed effects models and generalized estimating equations were used to identify the phylotypes whose relative abundance was different between the two study groups. The vaginal microbiota of normal pregnant women was different from that of non-pregnant women (higher abundance of Lactobacillus vaginalis, L. crispatus, L. gasseri and L. jensenii and lower abundance of 22 other phylotypes in pregnant women). Bacterial community state type (CST) IV-B or CST IV-A characterized by high relative abundance of species of genus Atopobium as well as the presence of Prevotella, Sneathia, Gardnerella, Ruminococcaceae, Parvimonas, Mobiluncus and other taxa previously shown to be associated with bacterial vaginosis were less frequent in normal pregnancy. The stability of the vaginal microbiota of pregnant women was higher than that of non-pregnant women; however, during normal pregnancy, bacterial communities shift almost exclusively from one CST dominated by Lactobacillus spp. to another CST dominated by Lactobacillus spp. Conclusion We report the first longitudinal study of the vaginal microbiota in normal pregnancy. Differences in the composition and stability of the microbial community between pregnant and non-pregnant women were observed. Lactobacillus spp. were the predominant members of the microbial community in normal pregnancy. These results can serve as the basis to study the relationship between the vaginal microbiome and adverse pregnancy outcomes.
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                Author and article information

                Journal
                9502015
                8791
                Nat Med
                Nat. Med.
                Nature medicine
                1078-8956
                1546-170X
                30 August 2019
                29 May 2019
                June 2019
                18 September 2019
                : 25
                : 6
                : 1012-1021
                Affiliations
                [1 ]Department of Microbiology and Immunology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.
                [2 ]Department of Obstetrics and Gynecology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.
                [3 ]Center for Microbiome Engineering and Data Analysis, Virginia Commonwealth University, Richmond, VA, USA.
                [4 ]Supply Chain Management and Analytics, School of Business, Virginia Commonwealth University, Richmond, VA, USA.
                [5 ]Department of Statistical Sciences and Operations Research, College of Humanities and Sciences, Virginia Commonwealth University, Richmond, VA, USA.
                [6 ]Department of Computer Science, College of Engineering, Virginia Commonwealth University, Richmond, VA, USA.
                [7 ]VCU Life Sciences, Virginia Commonwealth University, Richmond, VA, USA.
                [8 ]Division of Neonatal Medicine, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.
                [9 ]Department of Pediatrics, School of Medicine, Children’s Hospital of Richmond at Virginia Commonwealth University, Richmond, VA, USA.
                [10 ]Department of Chemical and Life Science Engineering, College of Engineering, Virginia Commonwealth University, Richmond, VA, USA.
                [11 ]Center for the Study of Biological Complexity, VCU Life Sciences, Virginia Commonwealth University, Richmond, VA, USA.
                [12 ]School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.
                [13 ]Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA.
                [14 ]Department of Women’s Health, Dell School of Medicine, University of Texas at Austin, Austin, TX, USA.
                [15 ]School of Nursing, University of Texas at Austin, Austin, TX, USA.
                [16 ]Department of Biostatistics, School of Public Health, West Virginia University, Morgantown, WV, USA.
                [17 ]Department of Mathematical Sciences, University of Montana, Missoula, MT, USA.
                [18 ]Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.
                [19 ]Pacific Biosciences, Menlo Park, CA, USA.
                [20 ]Global Alliance to Prevent Prematurity and Stillbirth, Seattle, WA, USA.
                [21 ]Department of Obstetrics & Gynecology, University of Washington, Seattle, WA, USA.
                Author notes

                Correspondence and requests for materials should be addressed to G.A.B.

                Author contributions

                J.M.F., K.K.J., J.F.S. and G.A.B. comprised the executive committee. J.M.F., M.G.S., J.P.B., D.J.E., T.J.A., P.H.G., B.H., L.E., A.L.G., K.D.H.-M., K.K.J., J.F.S. and G.A.B. designed the study. P.H.G., S.C.V., S.H.M., S.K.R., M.R.D., A.L.S., M.G.G., C.E.R., N.R.W., K.D.H.-M., J.F.S. and J.M.F. comprised the clinical team. M.G.S., B.H., V.L., A.M.L., S.D.M., R.A.D., A.V.M., J.L.B., K.K.J., J.M.F. and G.A.B. generated the 16S rRNA/genomics/metagenomics data. M.G.S., D.J.E., B.H., J.X., S.J., A.M.L., J.I.D., R.A.D., J.M.F. and K.D.H.-M. generated the cytokine data. J.M.F., M.G.S., H.I.P., L.E., A.L.G., N.R.J., N.U.S., S.P.B., V.N.K., X.V.O., A.V.M. and G.A.B. managed the bioinformatics, genomics and data. J.P.B., D.J.E., H.I.P., T.J.A., S.S.F., Y.A.B., M.L.W., S.V.H., E.M.J., E.S. and J.M.F. did the statistical analysis and modeling. J.K., Y.-C.T. and J.M.F. did the TM7-H1 genome sequencing. C.E.R., M.G.G., D.O.C. and A.L.S. comprised the GAPPS team.

                Author information
                http://orcid.org/0000-0001-5466-3000
                http://orcid.org/0000-0002-2168-5825
                http://orcid.org/0000-0002-9348-8740
                http://orcid.org/0000-0001-9577-4102
                http://orcid.org/0000-0002-2958-0278
                http://orcid.org/0000-0003-4621-3987
                Article
                NIHMS1048368
                10.1038/s41591-019-0450-2
                6750801
                31142849
                129c7cec-0f80-44f0-91b9-3540b2d63e5a

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