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      Dynamic molecular changes during the first week of human life follow a robust developmental trajectory

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      1 , 2 , 3 , 25 , 4 , 5 , 6 , 5 , 6 , 7 , 8 , 1 , 9 , 10 , 5 , 6 , 11 , 7 , 7 , 1 , 11 , 3 , 2 , 9 , 1 , 12 , 7 , 7 , 5 , 13 , 14 , 10 , 10 , 10 , 10 , 10 , 7 , 15 , 5 , 6 , 2 , 16 , 5 , 6 , 17 , 5 , 6 , The EPIC Consortium, 15 , 18 , 5 , 6 , 19 , 1 , 14 , 4 , 5 , 7 , 2 , 20 , 21 , 7 , 22 , 5 , 6 , 23 , , 3 , 9 , 25 ,
      Nature Communications
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          Abstract

          Systems biology can unravel complex biology but has not been extensively applied to human newborns, a group highly vulnerable to a wide range of diseases. We optimized methods to extract transcriptomic, proteomic, metabolomic, cytokine/chemokine, and single cell immune phenotyping data from <1 ml of blood, a volume readily obtained from newborns. Indexing to baseline and applying innovative integrative computational methods reveals dramatic changes along a remarkably stable developmental trajectory over the first week of life. This is most evident in changes of interferon and complement pathways, as well as neutrophil-associated signaling. Validated across two independent cohorts of newborns from West Africa and Australasia, a robust and common trajectory emerges, suggesting a purposeful rather than random developmental path. Systems biology and innovative data integration can provide fresh insights into the molecular ontogeny of the first week of life, a dynamic developmental phase that is key for health and disease.

          Abstract

          The first week of life impacts health for all of life, but the mechanisms are little-understood. Here the authors extract multi-omic data from small volumes of blood to study the dynamic molecular changes during the first week of life, revealing a robust developmental trajectory common to different populations.

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          Most cited references42

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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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              Stereotypic Immune System Development in Newborn Children

              Summary Epidemiological data suggest that early life exposures are key determinants of immune-mediated disease later in life. Young children are also particularly susceptible to infections, warranting more analyses of immune system development early in life. Such analyses mostly have been performed in mouse models or human cord blood samples, but these cannot account for the complex environmental exposures influencing human newborns after birth. Here, we performed longitudinal analyses in 100 newborn children, sampled up to 4 times during their first 3 months of life. From 100 μL of blood, we analyze the development of 58 immune cell populations by mass cytometry and 267 plasma proteins by immunoassays, uncovering drastic changes not predictable from cord blood measurements but following a stereotypic pattern. Preterm and term children differ at birth but converge onto a shared trajectory, seemingly driven by microbial interactions and hampered by early gut bacterial dysbiosis.
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                Author and article information

                Contributors
                ofer.levy@childrens.harvard.edu
                tkollm@mac.com
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                12 March 2019
                12 March 2019
                2019
                : 10
                : 1092
                Affiliations
                [1 ]ISNI 0000 0001 2288 9830, GRID grid.17091.3e, Department of Microbiology & Immunology, , University of British Columbia, ; 1365-2350 Health Sciences Mall, Vancouver,  BC V6T 1Z3 Canada
                [2 ]GRID grid.460559.b, PROOF Centre of Excellence, ; 10th Floor, 1190 Hornby Street, Vancouver, BC V6Z 2K5 Canada
                [3 ]ISNI 0000 0001 2288 9830, GRID grid.17091.3e, Department of Experimental Medicine, , University of British Columbia, ; 2775 Laurel Street, 10th Floor, Room 10117, Vancouver, BC V5Z 1M9 Canada
                [4 ]ISNI 0000 0004 0378 8438, GRID grid.2515.3, Department of Pathology, , Boston Children’s Hospital, ; BCH 3108, 300 Longwood Ave, Boston, MA 02115 USA
                [5 ]ISNI 0000 0004 0378 8438, GRID grid.2515.3, Precision Vaccines Program, Division of Infectious Diseases, , Boston Children’s Hospital, ; 300 Longwood Ave, BCH 3104, Boston, MA 02115 USA
                [6 ]ISNI 000000041936754X, GRID grid.38142.3c, Harvard Medical School, ; 25 Shattuck Street, Boston, MA 02115 USA
                [7 ]Vaccines & Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Boulevard, P.O. Box 273, Banjul, Gambia
                [8 ]ISNI 0000 0004 1936 973X, GRID grid.5252.0, Center for International Health, , Medical Center of the University of Munich (LMU), ; Munich, Germany
                [9 ]ISNI 0000 0001 2288 9830, GRID grid.17091.3e, Department of Pediatrics, BC Children’s Hospital, , University of British Columbia, ; Rm 2D19, 4480 Oak Street, Vancouver, BC V6H 3V4 Canada
                [10 ]ISNI 0000 0001 2288 2831, GRID grid.417153.5, Papua New Guinea Institute of Medical Research, ; Homate Street, Goroka, Eastern Highlands Province Papua New Guinea
                [11 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Statistical Genomics, School of Mathematics and Statistics, Melbourne Integrative Genomics, Centre for Systems Genomics, , The University of Melbourne, ; Building 184 Ground Floor, Parkville, VIC 3010 Australia
                [12 ]ISNI 0000 0001 0702 3000, GRID grid.248762.d, Graduate Program in Bioinformatics, , BCCA, ; 100−570 West 7th Avenue, Vancouver, BC V5Z 4S6 Canada
                [13 ]ISNI 0000 0004 1936 7910, GRID grid.1012.2, Division of Paediatrics, School of Medicine, University of Western Australia, ; 35 Stirling Highway, Nedlands, WA 6009 Australia
                [14 ]ISNI 0000 0004 1936 7910, GRID grid.1012.2, Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, , University of Western Australia Perth, ; 15 Hospital Avenue, Nedlands, WA 6009 Australia
                [15 ]ISNI 0000 0001 0702 3000, GRID grid.248762.d, BC Cancer Agency, ; 686 West Broadway, Suite 500, Vancouver, BC V5Z 1G1 Canada
                [16 ]ISNI 0000 0001 2288 9830, GRID grid.17091.3e, Department of Pathology and Laboratory Medicine, Faculty of Medicine, , University of British Columbia, ; Rm. G227–2211 Wesbrook Mall, Vancouver, BC V6T 2B5 Canada
                [17 ]ISNI 0000 0004 0378 8438, GRID grid.2515.3, Division of Newborn Medicine, , Boston Children’s Hospital, ; 300 Longwood Ave, BCH 3146, Boston, MA 02115 USA
                [18 ]ISNI 0000 0001 2288 9830, GRID grid.17091.3e, Department of Medical Genetics, , University of British Columbia, ; Vancouver, V6T1Z4 BC Canada
                [19 ]ISNI 0000 0004 0378 8438, GRID grid.2515.3, Center for Applied Pediatric Quality Analytics, , Boston Children’s Hospital, ; Boston, 02115 MA USA
                [20 ]ISNI 0000 0001 2288 9830, GRID grid.17091.3e, UBC Centre for Heart and Lung Innovation, ; Vancouver, V6T1Z4 BC Canada
                [21 ]ISNI 0000 0001 2288 9830, GRID grid.17091.3e, Department of Medicine, Division of Respiratory Medicine, , UBC, ; Vancouver, V6T1Z4 BC Canada
                [22 ]ISNI 0000 0004 0425 469X, GRID grid.8991.9, The Vaccine Centre, Faculty of Infectious and Tropical Diseases, , London School of Hygiene and Tropical Medicine, ; London, WC1E 7HT UK
                [23 ]GRID grid.66859.34, Broad Institute of MIT & Harvard, ; Cambridge, 02142 MA USA
                [24 ]ISNI 0000 0001 2348 0746, GRID grid.4989.c, Institute for Medical Immunology, Université libre de Bruxelles, ; Charleroi, Rue Adrienne Bolland 8, 6041 Gosselies, Belgium
                [25 ]ISNI 0000 0000 8828 1230, GRID grid.414659.b, Telethon Kids Institute, ; 100 Roberts Road, Subiaco, 6008 Australia
                [26 ]ISNI 0000 0001 0742 471X, GRID grid.5117.2, Present Address: Department of Health Science and Technology, , Aalborg University, ; Fredrik Bajers Vej 7 D2, 9220 Aalborg, Denmark
                [27 ]ISNI 0000 0001 2166 5843, GRID grid.265008.9, Present Address: Department of Pathology, Anatomy and Cell Biology, , Thomas Jefferson University, Jefferson Alumni Hall, ; 1020 Locust Street, Suite 279, Philadelphia, PA 19107 USA
                Author information
                http://orcid.org/0000-0003-3250-3246
                http://orcid.org/0000-0002-5687-3156
                http://orcid.org/0000-0003-4354-1731
                http://orcid.org/0000-0003-3923-1116
                http://orcid.org/0000-0002-1152-8156
                http://orcid.org/0000-0002-9302-6036
                http://orcid.org/0000-0002-7475-1646
                http://orcid.org/0000-0001-5465-3814
                http://orcid.org/0000-0003-0179-6648
                http://orcid.org/0000-0002-7908-1581
                http://orcid.org/0000-0002-5859-1945
                http://orcid.org/0000-0003-2403-9762
                Article
                8794
                10.1038/s41467-019-08794-x
                6414553
                30862783
                3d38a192-3c4e-4b20-9ec0-111eebb9f888
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 February 2018
                : 24 January 2019
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