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The Progress of Multi-Omics Technologies: Determining Function in Lactic Acid Bacteria Using a Systems Level Approach

Authors
  • O’Donnell, Shane Thomas1, 2, 3
  • Ross, R. Paul1, 2, 3
  • Stanton, Catherine1, 3
  • 1 Teagasc Food Research Centre, Moorepark, Fermoy , (Ireland)
  • 2 Department of Microbiology, University College Cork – National University of Ireland, Cork , (Ireland)
  • 3 APC Microbiome Ireland, Cork , (Ireland)
Type
Published Article
Journal
Frontiers in Microbiology
Publisher
Frontiers Media SA
Publication Date
Jan 28, 2020
Volume
10
Identifiers
DOI: 10.3389/fmicb.2019.03084
PMID: 32047482
PMCID: PMC6997344
Source
PubMed Central
Keywords
License
Unknown

Abstract

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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