bioRxivSystems BiologyPreprint
Gene Co-expression Connectivity Predicts Gene Targets Underlying High Ionic Liquid Tolerance in Yarrowia lipolytica
Caleb Walker, Seunghyun RyuiD, Sergio GarciaiD, David Dooley, Brian J. Mendoza, Cong T. TrinhiD
Abstract
Microbial tolerance to organic solvents such as ionic liquids (ILs) is a robust phenotype beneficial for novel biotransformation. While most microbes become inhibited in 1%-5% (v/v) IL (e.g., 1-ethyl-3-methylimidazolium acetate), we engineered a robust Yarrowia lipolytica (YlCW001) that tolerates a record high of 18% (v/v) IL via adaptive laboratory evolution. Yet, genotypes conferring high IL tolerance in YlCW001 remain to be discovered. Using dynamic RNA-Seq data, we shed light on the underlying cellular processes that Y. lipolytica responds to IL. We introduced Gene Co-expression Connectivity (GeCCo) to discover genotypes conferring desirable phenotypes that might not be found by the conventional differential expression (DE) approaches. GeCCo selects genes based on their number of co-expressed genes in a sub-network of upregulated genes by the target phenotype. We experimentally validated GeCCo by reversely engineering high IL-tolerant phenotype in wildtype Y. lipolytica. We found that gene targets selected by both DE and GeCCo exhibited the best statistical chance at increasing IL tolerance when individually overexpressed. Remarkably, the best combination of dual-overexpressed genes were genes selected by GeCCo alone. This non-intuitive combination of genes, BRN1 and OYE2, are involved in guiding/regulating mitotic cell division, chromatin segregation/condensation, microtubule and cytoskeletal organization, and Golgi vesicle transport.
- organism: yeast
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 0
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