Special Issue "Microbial Communities in Health and Disease"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: closed (29 February 2020).

Special Issue Editors

Prof. Dr. Arul Jayaraman
E-Mail Website
Guest Editor
Artie McFerrin Department of Chemical Engineering, Texas A&M University, 3122 TAMU, College Station, TX 77845-3122, USA
Interests: molecular systems biotechnology, specifically on using integrated experimental and modeling approaches for investigating problems in human health and medicine; systems biology of cytokine signaling in inflammatory diseases; inter-kingdom signaling interactions between bacteria and human cells in GI tract infections; the development of microfluidic model systems for combinatorial drug screening and vascular tissue engineering
Prof. Dr. Kyongbum Lee
E-Mail Website
Guest Editor
Department of Chemical and Biological Engineering, Tufts University, Medford, MA 02155, USA
Interests: metabolic engineering; tissue engineering; systems biology
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Special Issue Information

Dear Colleagues,

The role of the microbiome in human health and disease is becoming increasingly important. Seminal studies have identified changes in the composition of the microbiota, as well in its functional output, in the context several diseases, including obesity and diabetes, cancer, autism, and inflammatory disorders. Advances in next-generation sequencing technologies for characterizing the composition of microbial communities and in metabolomic methods for identifying metabolites produced by the community have been instrumental in these advances; however, while correlational associations have been made, causal relationships between specific members of the microbial community and specific diseases are poorly understood. Systems biology tools and quantitative approaches can help better understand the role of the microbiota in maintaining homeostasis in their animal host as well as in the initiation and propagation of disease.

This Special Issue on “Microbial Communities in Health and Disease” seeks contributions on quantitative and experimental aspects of microbiome research. Topics of interest include, but are not limited to, the following:

  • Models for quantitative analysis of microbial communities associated with their animal host
  • A simulation of microbial communities in animal hosts using genome-scale modeling
  • Microbial community structure (composition)–function (metabolite output) relationships
  • Multi-omic data integration for describing microbial community function
  • Novel model-systems and tools for controlled studies of microbe–microbe and microbe–host interactions
  • Temporal and spatial dynamics of microbiota in animal hosts

In addition, a small number of critical reviews on molecular mechanisms mediating microbiome effects in the host are also invited.

Prof. Dr. Arul Jayaraman
Prof. Dr. Kyongbum Lee
Guest editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • microbiota composition
  • metabolomics
  • community dynamics
  • host-pathogens
  • temporal heterogeneity
  • spatial dynamics

Published Papers (3 papers)

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Article
Investigation of Virulence Genes Detected in Antimicrobial-Resistance Pathogens Isolates for Five Countries across the World
Processes 2020, 8(12), 1589; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8121589 - 02 Dec 2020
Cited by 1 | Viewed by 673
Abstract
A large portion of annual deaths worldwide are due to infections caused by disease-causing pathogens. These pathogens contain virulence genes, which encode mechanisms that facilitate infection and microbial survival in hosts. More recently, antimicrobial resistance (AMR) genes, also found in these pathogens, have [...] Read more.
A large portion of annual deaths worldwide are due to infections caused by disease-causing pathogens. These pathogens contain virulence genes, which encode mechanisms that facilitate infection and microbial survival in hosts. More recently, antimicrobial resistance (AMR) genes, also found in these pathogens, have become an increasingly large issue. While the National Center for Biotechnology Information (NCBI) Pathogen Detection Isolates Browser (NPDIB) database has been compiling genes involved in microbial virulence and antimicrobial resistance through isolate samples, few studies have identified the genes primarily responsible for virulence and compared them to those responsible for AMR. This study performed the first multivariate statistical analysis of the multidimensional NPDIB data to identify the major virulence genes from historical pathogen isolates for Australia, China, South Africa, UK, and US—the largely populated countries from five of the six major continents. The important virulence genes were then compared with the AMR genes to study whether there is correlation between their occurrences. Among the significant genes and pathogens associated with virulence, it was found that the genes fdeC, iha, iss, iutA, lpfA, sslE, ybtP, and ybtQ are shared amongst all five countries. The pathogens E. coli and Shigella, Salmonella enterica, and Klebsiella pneumoniae mostly contained these genes and were common among four of the five studied countries. Additionally, the trend of virulence was investigated by plotting historical occurrences of gene and pathogen frequency in the annual samples. These plots showed that the trends of E. coli and Shigella and Salmonella enterica were similar to the trends of certain virulence genes, confirming the two pathogens do indeed carry important virulence genes. While the virulence genes in the five countries are not significantly different, the US and the UK share the largest amount of important virulence genes. The plots from principal component analysis and hierarchical clustering show that the important virulence and AMR genes were not significantly correlated, with only few genes from both types of genes clustered into the same groups. Full article
(This article belongs to the Special Issue Microbial Communities in Health and Disease)
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Article
Predicting the Longitudinally and Radially Varying Gut Microbiota Composition using Multi-Scale Microbial Metabolic Modeling
Processes 2019, 7(7), 394; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7070394 - 26 Jun 2019
Cited by 8 | Viewed by 2043
Abstract
Background: The gut microbiota is a heterogeneous group of microbes that is spatially distributed along various sections of the intestines and across the mucosa and lumen in each section. Understanding the dynamics between the spatially differential microbial populations and the driving forces for [...] Read more.
Background: The gut microbiota is a heterogeneous group of microbes that is spatially distributed along various sections of the intestines and across the mucosa and lumen in each section. Understanding the dynamics between the spatially differential microbial populations and the driving forces for the observed spatial organization will provide valuable insights into important questions such as the nature of colonization of the infant gut and different types of inflammatory bowel disease localized in different regions of the intestines. However, in most studies, the microbiota is sampled only at a single site (often feces) or from a particular anatomical site of the intestines. Differential oxygen availability is putatively a key factor shaping the spatial organization. Results: To test this hypothesis, we constructed a community genome-scale metabolic model consisting of representative organisms for the major phyla present in the human gut microbiome. By solving step-wise optimization problems embedded in a dynamic framework to predict community metabolism and integrate the mucosally-adherent with the luminal microbiome between consecutive sections along the intestines, we were able to capture (i) the essential features of the spatially differential composition of obligate anaerobes vs. facultative anaerobes and aerobes determined experimentally, and (ii) the accumulation of microbial biomass in the lumen. Sensitivity analysis suggests that the spatial organization depends primarily on the oxygen-per-microbe availability in each region. Oxygen availability is reduced relative to the ~100-fold increase in mucosal microbial density along the intestines, causing the switch between aerobes and anaerobes. Conclusion: The proposed integrated dynamic framework is able to predict spatially differential gut microbiota composition using microbial genome-scale metabolic models and test hypotheses regarding the dynamics of the gut microbiota. It can potentially become a valuable tool for exploring therapeutic strategies for site-specific perturbation of the gut microbiota and the associated metabolic activities. Full article
(This article belongs to the Special Issue Microbial Communities in Health and Disease)
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Brief Report
Relationship Between MiRKAT and Coefficient of Determination in Similarity Matrix Regression
Processes 2019, 7(2), 79; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7020079 - 06 Feb 2019
Cited by 3 | Viewed by 1310
Abstract
The Microbiome Regression-based Kernel Association Test (MiRKAT) is widely used in testing for the association between microbiome compositions and an outcome of interest. The MiRKAT statistic is derived as a variance-component score test in a kernel machine regression-based generalized linear mixed model. In [...] Read more.
The Microbiome Regression-based Kernel Association Test (MiRKAT) is widely used in testing for the association between microbiome compositions and an outcome of interest. The MiRKAT statistic is derived as a variance-component score test in a kernel machine regression-based generalized linear mixed model. In this brief report, we show that the MiRKAT statistic is proportional to the R 2 (coefficient of determination) statistic in a similarity matrix regression, which characterizes the fraction of variability in outcome similarity, explained by microbiome similarity (up to a constant). Full article
(This article belongs to the Special Issue Microbial Communities in Health and Disease)
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