Frontiers in MicrobiologyBioinformaticsPeer-reviewed journal article
Mining novel cis-regulatory elements from the emergent host Rhodosporidium toruloides using transcriptomic data
Luísa Czamanski NoraiD, Murilo Henrique Anzolini CassianoiD, Ítalo Paulino Santana, María‐Eugenia GuazzaroniiD, Rafael Silva‐RochaiD, Ricardo SilvaiD
Abstract
The demand for robust microbial cell factories that can produce valuable biomaterials while being resistant to stresses imposed by current bioprocesses is rapidly growing. R. toruloides is an emerging host that presents desirable features for bioproduction, since it can grow in a wide range of substrates and tolerate a variety of toxic compounds. In order to explore R. toruloides suitability for application as a cell factory in biorefineries, we sought to understand the transcriptional responses of this yeast when growing under experimental settings that simulated those used in biofuels-related industries. Thus, we performed RNA sequencing of the oleaginous, carotenogenic yeast in different contexts. The first ones were stress-related: two conditions of high temperature (37 {degrees}C and 42 {degrees}C) and two ethanol concentrations (2% and 4%), while the other was using the inexpensive and abundant sugarcane juice as substrate. Using transcriptomic data, differential expression and functional analysis were implemented to select differentially expressed genes and enriched pathways from each set-up. A reproducible bioinformatics workflow was developed for mining new regulatory elements. We then predicted, for the first time in this yeast, binding motifs for several transcription factors, including HAC1, ARG80, RPN4, ADR1, and DAL81. Most of the putative transcription factors uncovered here were involved in stress responses and found in the yeast genome. Our method for motif discovery provides a new realm of possibilities in the study of gene regulatory networks, not only for the emerging host R. toruloides, but for other organisms of biotechnological importance.
- organism: yeast
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 4
- FWCI
- 0.36
- Field percentile
- 46th percentile
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