Engineering BiologySystems BiologyPeer-reviewed journal article
Long Range Order and Short Range Disorder in Saccharomyces cerevisiae Biofilm
Abstract
AbstractBiofilm, a colony forming cooperative response of microorganisms under environmental stress, is a major concern for food safety, water safety and drug resistance. Most current works focus on controlling biofilm growth by targeting single genes. Here, we investigated transcriptome-wide expressions of the biofilm yeast Saccharomyces cerevisiae in wildtype, and 6 previously identified biofilm regulating overexpression strains (DIG1, SAN1, TOS8, ROF1, SFL1, HEK2). When tested across various statistical distributions, all transcriptome-wide data fitted well with lognormal distribution above TPM value of 5. Using this threshold as a low expression filter, Pearson auto-and cross-correlation reveal a strong transcriptome-wide invariance among all genotypes, which is also reflected by the random selection of 50 gene expressions. Focusing on the 50 highly expressed genes, however, they differ significantly between the genotypes. Principal components analysis (PCA) shows global similarity between DIG1, SAN1, ROF1, SFL1 and HEK2. Thus, although single overexpression strains may show significant favourable local and acute expression changes (short range disorder), the almost unperturbed global and collective structure between the genotypes indicate gradual adaptive response converging to original stable biofilm states (long range order). Hierarchical clustering and Gene Ontology show 11 groups of local (e.g. mitochondria processes, amine & nucleotide metabolic processes) and 6 groups of global (e.g. transcription, translation & cell cycle) processes for all genotypes. These data indicate that there is a strong global regulatory structure that keeps the overall biofilm stable in all investigated strains.
- organism: yeast
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 7
- FWCI
- 0.58
- Field percentile
- top 38%
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