Microbial "social networks"

Mitch Fernandez, Juan D. Riveros, Michael A Campos, Kalai Mathee, Giri Narasimhan

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Background: It is well understood that distinct communities of bacteria are present at different sites of the body, and that changes in the structure of these communities have strong implications for human health. Yet, challenges remain in understanding the complex interconnections between the bacterial taxa within these microbial communities and how they change during the progression of diseases. Many recent studies attempt to analyze the human microbiome using traditional ecological measures and cataloging differences in bacterial community membership. In this paper, we show how to push metagenomic analyses beyond mundane questions related to the bacterial taxonomic profiles that differentiate one sample from another. Methods: We develop tools and techniques that help us to investigate the nature of social interactions in microbial communities, and demonstrate ways of compactly capturing extensive information about these networks and visually conveying them in an effective manner. We define the concept of bacterial "social clubs", which are groups of taxa that tend to appear together in many samples. More importantly, we define the concept of "rival clubs", entire groups that tend to avoid occurring together in many samples. We show how to efficiently compute social clubs and rival clubs and demonstrate their utility with the help of examples including a smokers' dataset and a dataset from the Human Microbiome Project (HMP). Results: The tools developed provide a framework for analyzing relationships between bacterial taxa modeled as bacterial co-occurrence networks. The computational techniques also provide a framework for identifying clubs and rival clubs and for studying differences in the microbiomes (and their interactions) of two or more collections of samples. Conclusions: Microbial relationships are similar to those found in social networks. In this work, we assume that strong (positive or negative) tendencies to co-occur or co-infect is likely to have biological, physiological, or ecological significance, possibly as a result of cooperation or competition. As a consequence of the analysis, a variety of biological interpretations are conjectured. In the human microbiome context, the pattern of strength of interactions between bacterial taxa is unique to body site.

Original languageEnglish (US)
Article numberS6
JournalBMC Genomics
Volume16
Issue number11
DOIs
StatePublished - Nov 10 2015

Fingerprint

Microbiota
Social Support
Cataloging
Metagenomics
Information Services
Interpersonal Relations
Disease Progression
Bacteria
Health
Datasets

Keywords

  • Bacterial clubs
  • Club leader
  • Co-occurrence networks
  • Microbiome
  • Rival clubs

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

Cite this

Fernandez, M., Riveros, J. D., Campos, M. A., Mathee, K., & Narasimhan, G. (2015). Microbial "social networks". BMC Genomics, 16(11), [S6]. https://doi.org/10.1186/1471-2164-16-S11-S6

Microbial "social networks". / Fernandez, Mitch; Riveros, Juan D.; Campos, Michael A; Mathee, Kalai; Narasimhan, Giri.

In: BMC Genomics, Vol. 16, No. 11, S6, 10.11.2015.

Research output: Contribution to journalArticle

Fernandez, M, Riveros, JD, Campos, MA, Mathee, K & Narasimhan, G 2015, 'Microbial "social networks"', BMC Genomics, vol. 16, no. 11, S6. https://doi.org/10.1186/1471-2164-16-S11-S6
Fernandez M, Riveros JD, Campos MA, Mathee K, Narasimhan G. Microbial "social networks". BMC Genomics. 2015 Nov 10;16(11). S6. https://doi.org/10.1186/1471-2164-16-S11-S6
Fernandez, Mitch ; Riveros, Juan D. ; Campos, Michael A ; Mathee, Kalai ; Narasimhan, Giri. / Microbial "social networks". In: BMC Genomics. 2015 ; Vol. 16, No. 11.
@article{15d50832b1c1436abc27063eb0ecc51c,
title = "Microbial {"}social networks{"}",
abstract = "Background: It is well understood that distinct communities of bacteria are present at different sites of the body, and that changes in the structure of these communities have strong implications for human health. Yet, challenges remain in understanding the complex interconnections between the bacterial taxa within these microbial communities and how they change during the progression of diseases. Many recent studies attempt to analyze the human microbiome using traditional ecological measures and cataloging differences in bacterial community membership. In this paper, we show how to push metagenomic analyses beyond mundane questions related to the bacterial taxonomic profiles that differentiate one sample from another. Methods: We develop tools and techniques that help us to investigate the nature of social interactions in microbial communities, and demonstrate ways of compactly capturing extensive information about these networks and visually conveying them in an effective manner. We define the concept of bacterial {"}social clubs{"}, which are groups of taxa that tend to appear together in many samples. More importantly, we define the concept of {"}rival clubs{"}, entire groups that tend to avoid occurring together in many samples. We show how to efficiently compute social clubs and rival clubs and demonstrate their utility with the help of examples including a smokers' dataset and a dataset from the Human Microbiome Project (HMP). Results: The tools developed provide a framework for analyzing relationships between bacterial taxa modeled as bacterial co-occurrence networks. The computational techniques also provide a framework for identifying clubs and rival clubs and for studying differences in the microbiomes (and their interactions) of two or more collections of samples. Conclusions: Microbial relationships are similar to those found in social networks. In this work, we assume that strong (positive or negative) tendencies to co-occur or co-infect is likely to have biological, physiological, or ecological significance, possibly as a result of cooperation or competition. As a consequence of the analysis, a variety of biological interpretations are conjectured. In the human microbiome context, the pattern of strength of interactions between bacterial taxa is unique to body site.",
keywords = "Bacterial clubs, Club leader, Co-occurrence networks, Microbiome, Rival clubs",
author = "Mitch Fernandez and Riveros, {Juan D.} and Campos, {Michael A} and Kalai Mathee and Giri Narasimhan",
year = "2015",
month = "11",
day = "10",
doi = "10.1186/1471-2164-16-S11-S6",
language = "English (US)",
volume = "16",
journal = "BMC Genomics",
issn = "1471-2164",
publisher = "BioMed Central",
number = "11",

}

TY - JOUR

T1 - Microbial "social networks"

AU - Fernandez, Mitch

AU - Riveros, Juan D.

AU - Campos, Michael A

AU - Mathee, Kalai

AU - Narasimhan, Giri

PY - 2015/11/10

Y1 - 2015/11/10

N2 - Background: It is well understood that distinct communities of bacteria are present at different sites of the body, and that changes in the structure of these communities have strong implications for human health. Yet, challenges remain in understanding the complex interconnections between the bacterial taxa within these microbial communities and how they change during the progression of diseases. Many recent studies attempt to analyze the human microbiome using traditional ecological measures and cataloging differences in bacterial community membership. In this paper, we show how to push metagenomic analyses beyond mundane questions related to the bacterial taxonomic profiles that differentiate one sample from another. Methods: We develop tools and techniques that help us to investigate the nature of social interactions in microbial communities, and demonstrate ways of compactly capturing extensive information about these networks and visually conveying them in an effective manner. We define the concept of bacterial "social clubs", which are groups of taxa that tend to appear together in many samples. More importantly, we define the concept of "rival clubs", entire groups that tend to avoid occurring together in many samples. We show how to efficiently compute social clubs and rival clubs and demonstrate their utility with the help of examples including a smokers' dataset and a dataset from the Human Microbiome Project (HMP). Results: The tools developed provide a framework for analyzing relationships between bacterial taxa modeled as bacterial co-occurrence networks. The computational techniques also provide a framework for identifying clubs and rival clubs and for studying differences in the microbiomes (and their interactions) of two or more collections of samples. Conclusions: Microbial relationships are similar to those found in social networks. In this work, we assume that strong (positive or negative) tendencies to co-occur or co-infect is likely to have biological, physiological, or ecological significance, possibly as a result of cooperation or competition. As a consequence of the analysis, a variety of biological interpretations are conjectured. In the human microbiome context, the pattern of strength of interactions between bacterial taxa is unique to body site.

AB - Background: It is well understood that distinct communities of bacteria are present at different sites of the body, and that changes in the structure of these communities have strong implications for human health. Yet, challenges remain in understanding the complex interconnections between the bacterial taxa within these microbial communities and how they change during the progression of diseases. Many recent studies attempt to analyze the human microbiome using traditional ecological measures and cataloging differences in bacterial community membership. In this paper, we show how to push metagenomic analyses beyond mundane questions related to the bacterial taxonomic profiles that differentiate one sample from another. Methods: We develop tools and techniques that help us to investigate the nature of social interactions in microbial communities, and demonstrate ways of compactly capturing extensive information about these networks and visually conveying them in an effective manner. We define the concept of bacterial "social clubs", which are groups of taxa that tend to appear together in many samples. More importantly, we define the concept of "rival clubs", entire groups that tend to avoid occurring together in many samples. We show how to efficiently compute social clubs and rival clubs and demonstrate their utility with the help of examples including a smokers' dataset and a dataset from the Human Microbiome Project (HMP). Results: The tools developed provide a framework for analyzing relationships between bacterial taxa modeled as bacterial co-occurrence networks. The computational techniques also provide a framework for identifying clubs and rival clubs and for studying differences in the microbiomes (and their interactions) of two or more collections of samples. Conclusions: Microbial relationships are similar to those found in social networks. In this work, we assume that strong (positive or negative) tendencies to co-occur or co-infect is likely to have biological, physiological, or ecological significance, possibly as a result of cooperation or competition. As a consequence of the analysis, a variety of biological interpretations are conjectured. In the human microbiome context, the pattern of strength of interactions between bacterial taxa is unique to body site.

KW - Bacterial clubs

KW - Club leader

KW - Co-occurrence networks

KW - Microbiome

KW - Rival clubs

UR - http://www.scopus.com/inward/record.url?scp=84969529107&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84969529107&partnerID=8YFLogxK

U2 - 10.1186/1471-2164-16-S11-S6

DO - 10.1186/1471-2164-16-S11-S6

M3 - Article

C2 - 26576770

AN - SCOPUS:84969529107

VL - 16

JO - BMC Genomics

JF - BMC Genomics

SN - 1471-2164

IS - 11

M1 - S6

ER -