Summary
A number of TI Food and Nutrition projects are investigating the roles of
bacteria in optimising fermentation performance. Lines of research include:
• predicting product formation
• understanding bacterial behaviour in biofilms
• understanding the processes of spore formation and germination
• optimising survival under various conditions
• determining interactions between bacteria in complex microbial communities,
such as in the gastrointestinal tract, in the oral cavity and in complex
fermentations.
These projects share several approaches: bacterial genome sequencing, metabolic
and gene-regulation predictions based upon genome sequences, and correlating
gene content and regulatory potential to phenotypic measurements.
The platform activities occur within three work packages:
WP-1 focusses on sequence-based prediction of functional modules in bacteria.
These modules can consist of (parts of) metabolic pathways, regulatory units
and conserved gene clusters. Reconstruction methods include phylogenomic
analyses, comparative genomics, metabolic-pathway predictions, gene-regulatory
networks and transcriptional signatures derived from microarray or RNA-Seq
data. These modules will be reconstructed for model organisms and used to
understand and predict the presence or absence of modules (relating to
functionality or even phenotypes) in these bacteria and their (close)
relatives. Gene-regulatory network reconstruction methods are being setup and
applied to reconstruct species-wide networks for, amongst others, the species
Lactococcus lactis.
In WP2 a relational database will be established, for multiple strains and
species, to store their genome content, gene expression data and phenotype
measurements. The random forest-based correlation suite, PhenoLink, is suited
to determining biomarkers and, more specifically, genotype-phenotype
associations and will be integrated with the database. TI Food and Nutrition
wet-lab scientists will be able to interactively correlate (or link) phenotypic
measurements with, for example, gene content and gene expression. PhenoLink has
been adapted to directly determine non-linear correlations and establish a
visual representation of the associations in a very simple model. This
methodology is currently being applied to a wide range of TI Food and Nutrition
datasets.
In WP3 an externally-funded PhD student will further develop a gene-expression
analysis pipeline for RNA-Seq or (tiling) microarray data, based on defined
microbial consortia. This pipeline will allow the identification of ncRNAs,
sRNAs, 3'UTRs and will enable promoter mapping. Several TI Food and Nutrition
RNA-seq datasets are currently being analysed to specify the transcriptional
landscape of industrially-relevant bacterial strains.
The platform is fully staffed. A workshop on comparative genomics was given in
2012 and attended by a number of TI Food and Nutrition project members. In
2013, a platform workshop will be given on genotype-phenotype matching
.
TI Food and Nutrition Project Leader: Dr Sacha van Hijum
Time frame: 2011 – 2014