Home chemical and microbial transitions across urbanization

McCall, Laura-Isobel; Callewaert, Chris; Zhu, Qiyun; Song, Se Jin; Bouslimani, Amina; Minich, Jeremiah J; Ernst, Madeleine; Ruiz-Calderon, Jean F.; Cavallin, Humberto; PEREIRA, Henrique S.; Novoselac, Atila; Hernandez, Jean Francois; Ríos, Rafael; Branch, OraLee H; Blaser, Martin J.; Paulino, Luciana C.; Dorrestein, Pieter C.; Knight, Rob; Dominguez-Bello, Maria G.

doi:10.1038/s41564-019-0593-4

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Home chemical and microbial transitions across urbanization

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Author(s): Laura-Isobel McCall ¹ ^, ² , Chris Callewaert ³ ^, ⁴ , Qiyun Zhu ³ , Se Jin Song ³ ^, ⁵ , Amina Bouslimani ⁶ ^, ⁷ , Jeremiah J. Minich ⁸ , Madeleine Ernst ⁶ ^, ⁷ , Jean F. Ruiz-Calderon ⁹ , Humberto Cavallin ¹⁰ , Henrique S. Pereira ¹¹ , Atila Novoselac ¹² , Jean Hernandez ¹³ , Rafael Rios ¹⁴ , OraLee H. Branch ¹⁵ ^, ¹⁶ , Martin J. Blaser ¹⁷ , Luciana C. Paulino ¹⁸ , Pieter C. Dorrestein ^* ^, ³ ^, ⁵ ^, ⁶ ^, ⁷ , Rob Knight ^* ^, ³ ^, ⁵ ^, ¹⁹ ^, ²⁰ , Maria G. Dominguez-Bello ^* ^, ²¹

Publication date (Electronic): 04 November 2019

Journal: Nature microbiology

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Abstract

Urbanization represents a profound shift in human behavior, with significant cultural and health-associated consequences ^{2,
3}. Here we investigate chemical and microbial characteristics of houses and their human occupants across an urbanization gradient in the Amazon rainforest, from a remote Peruvian Amerindian village to the Brazilian city of Manaus. Urbanization was associated with reduced microbial outdoor exposure, increased contact with housing materials, antimicrobials, and cleaning products, and increased exposure to chemical diversity. Urbanization degree correlated with changes in house bacterial and micro-eukaryotic community composition, increased house and skin fungal diversity, and increased relative abundance of human skin-associated fungi and bacteria in houses. Overall, our results indicate large-scale effects of urbanization on chemical and microbial exposures and on the human microbiota.

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Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

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The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

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SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.

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Author and article information

Journal

Journal ID (nlm-journal-id): 101674869

Journal ID (pubmed-jr-id): 44774

Journal ID (nlm-ta): Nat Microbiol

Journal ID (iso-abbrev): Nat Microbiol

Title: Nature microbiology

ISSN (Electronic): 2058-5276

Publication date Nihms-submitted: 18 September 2019

Publication date (Electronic): 04 November 2019

Publication date (Print): January 2020

Publication date PMC-release: 19 February 2021

Volume: 5

Issue: 1

Pages: 108-115

Affiliations

[1 ]Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA.

[2 ]Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK 73019, USA.

[3 ]Department of Pediatrics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.

[4 ]Center for Microbial Ecology and Technology, Ghent University, Coupure Links 653, 9000 Gent, Belgium.

[5 ]Center for Microbiome Innovation, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.

[6 ]Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.

[7 ]Collaborative Mass Spectrometry Innovation Center, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.

[8 ]Marine Biology Research Division, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA

[9 ]UPR-Medical Science Campus, Biochemistry Department, Main Bldg, Lab A646, San Juan, 00935, Puerto Rico.

[10 ]School of Architecture, University of Puerto Rico, Rio Piedras Campus, 00931, Puerto Rico.

[11 ]Center for Environmental Sciences, Federal University of Amazonas (UFAM), Av. Gal. Rodrigo Ramos, 6200, Manaus, AM 690800-900, Brazil.

[12 ]Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin, Austin, TX 78712-0273, USA.

[13 ]Department of Biology, University of Puerto Rico, Rio Piedras Campus, San Juan, PR 00931.

[14 ]Department of Environmental Sciences, University of Puerto Rico, Rio Piedras Campus, San Juan, PR 00931.

[15 ]Concordia University – Portland. College of Health and Human Services, Office of Research Integrity, Oregon 97211.

[16 ]Universidad Nacional de la Amazonia Peruana, Iquitos, Perú.

[17 ]Departments of Medicine and Microbiology, and the Human Microbiome Program, New York University Langone Medical Center, New York, NY 10016, USA.

[18 ]Center for Natural Sciences and Humanities, Federal University of ABC (UFABC), Av. dos Estados, 5001, Santo André, SP 09210-580, Brazil.

[19 ]Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.

[20 ]Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.

[21 ]Department of Biochemistry and Microbiology, Department of Anthropology, Institute for Food, Nutrition and Health, Rutgers University, New Brunswick, NJ 08901, USA

Author notes

[* ]Correspondence: Maria G. Dominguez-Bello ( mg.dominguezbello@ 123456rutgers.edu ), Rob Knight ( robknight@ 123456ucsd.edu ) and Pieter C. Dorrestein ( pdorrestein@ 123456ucsd.edu )

[†]

These authors contributed equally to this work.

Author contributions

MGDB, PCD and RK conceived and designed the study. MGDB, JFRC, HSP, JH, RR, OLB, MJB, LCP, AN, HC collected the samples and metadata. AB acquired LC-MS data. LIM led LC-MS data analysis. CC led taxonomy and metadata analysis. QZ led DNA data and multi-omics analysis. JJM performed qPCR. SJS, ME, HC, AN, AB, JJM provided additional contributions to data analysis. LIM, CC, QZ and MGDB wrote the manuscript with contributions from RK and PCD. All authors reviewed the final manuscript.

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Manuscript ID: NIHMS1540169

DOI: 10.1038/s41564-019-0593-4

PMC ID: 7895447

PubMed ID: 31686026

SO-VID: acaeb7df-39b4-42a3-945f-0ba16d79ccd0

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Home chemical and microbial transitions across urbanization

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Microbial Genomics

Most cited references 44

Cytoscape: a software environment for integrated models of biomolecular interaction networks.

The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

QIIME allows analysis of high-throughput community sequencing data.

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