The Progress of Multi-Omics Technologies: Determining Function in Lactic Acid Bacteria Using a Systems Level Approach
Author(s) -
Shane Thomas O’Donnell,
R. Paul Ross,
Catherine Stanton
Publication year - 2020
Publication title -
frontiers in microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.701
H-Index - 135
ISSN - 1664-302X
DOI - 10.3389/fmicb.2019.03084
Subject(s) - metagenomics , computational biology , omics , microbiome , decipher , biology , genomics , function (biology) , metabolomics , metaproteomics , proteomics , systems biology , genome , data science , microbiology and biotechnology , computer science , bioinformatics , genetics , gene
Lactic Acid Bacteria (LAB) have long been recognized as having a significant impact ranging from commercial to health domains. A vast amount of research has been carried out on these microbes, deciphering many of the pathways and components responsible for these desirable effects. However, a large proportion of this functional information has been derived from a reductionist approach working with pure culture strains. This provides limited insight into understanding the impact of LAB within intricate systems such as the gut microbiome or multi strain starter cultures. Whole genome sequencing of strains and shotgun metagenomics of entire systems are powerful techniques that are currently widely used to decipher function in microbes, but they also have their limitations. An available genome or metagenome can provide an image of what a strain or microbiome, respectively, is potentially capable of and the functions that they may carry out. A top-down, multi-omics approach has the power to resolve the functional potential of an ecosystem into an image of what is being expressed, translated and produced. With this image, it is possible to see the real functions that members of a system are performing and allow more accurate and impactful predictions of the effects of these microorganisms. This review will discuss how technological advances have the potential to increase the yield of information from genomics, transcriptomics, proteomics and metabolomics. The potential for integrated omics to resolve the role of LAB in complex systems will also be assessed. Finally, the current software approaches for managing these omics data sets will be discussed.
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