Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica

Abstract

Complex phenotypes, such as lipid accumulation, result from cooperativity between regulators and the integration of multiscale information. However, the elucidation of such regulatory programs by experimental approaches may be challenging, particularly in context-specific conditions. In particular, we know very little about the regulators of lipid accumulation in the oleaginous yeast of industrial interest Yarrowia lipolytica. This lack of knowledge limits the development of this yeast as an industrial platform, due to the time-consuming and costly laboratory efforts required to design strains with the desired phenotypes. In this study, we aimed to identify context-specific regulators and mechanisms, to guide explorations of the regulation of lipid accumulation in Y. lipolytica. Using gene regulatory network inference, and considering the expression of 6539 genes over 26 time points from GSE35447 for biolipid production and a list of 151 transcription factors, we reconstructed a gene regulatory network comprising 111 transcription factors, 4451 target genes and 17048 regulatory interactions (YL-GRN-1) supported by evidence of protein-protein interactions. This study, based on network interrogation and wet laboratory validation (a) highlights the relevance of our proposed measure, the transcription factors influence, for identifying phases corresponding to changes in physiological state without prior knowledge (b) suggests new potential regulators and drivers of lipid accumulation and

Fig. 1

Heatmap of TF influence as a function of C/N ratio during a time-course experiment. Four main phases were identified on the basis of changes in influence pattern: phase I (t ±  = 123.67 h, C/N ratio = 7.89), phase II (t = 139.58 h, C/N ratio = 8.63), phase III (t = 157.58 h, C/N ratio = 20.41), and phase IV (t = 166.08 h, C/N ratio = 30.96). These phases are shown on the left in turquoise, yellow, purple and red, respectively. Negative and positive influences are indicated from blue to red, with color intensity proportional to the influence value. Time and C/N ratio are indicated on the right, as described by Ochoa-Estopier and Guillouet and in the GSE35447 data set

Fig. 2

Heterarchy—Cooperativity network for Yarrowia lipolytica (YL-CoRegNet-1) constructed from YL-GRN-1, which was inferred from our transcriptomic data set under nitrogen limitation, GSE35447. Nodes represent transcription factors (TFs), whereas gray edges indicate co-regulatory relationships. Red edges are co-regulatory relationships for which evidence of protein–protein interactions has been obtained. Node size and color represent the influence of the corresponding TFs during the onset of lipid accumulation (phase II). Red indicates a positive influence whereas blue indicates a negative influence. Color intensity and node size are proportional to the influence value

Fig. 3

Mean percentage differences in lipid accumulation profile of overexpressing TFs mutant relative to the wild type with their s.d. Differences were considered significant if there was a change of at least ± 10%. TF-overexpressing strains were selected on the basis of their ranks during phase I (a) and phase II (b)