In silico model-guided identification of transcriptional regulator targets for efficient strain design

Abstract

Background: Cellular metabolism is tightly regulated by hard-wired multiple layers of biological processes to achieve robust and homeostatic states given the limited resources. As a result, even the most intuitive enzyme-centric metabolic engineering endeavours through the up-/down-regulation of multiple genes in biochemical pathways often deliver insignificant improvements in the product yield. In this regard, targeted engineering of transcriptional regulators (TRs) that control several metabolic functions in modular patterns is an interesting strategy. However, only a handful of in silico model-added techniques are available for identifying the TR manipulation candidates, thus limiting its strain design application.

Results: We developed hierarchical-Beneficial Regulatory Targeting (h-BeReTa) which employs a genome-scale metabolic model and transcriptional regulatory network (TRN) to identify the relevant TR targets suitable for strain improvement. We then applied this method to industrially relevant metabolites and cell factory hosts, Escherichia coli and Corynebacterium glutamicum. h-BeReTa suggested several promising TR targets, many of which have been validated through literature evidences. h-BeReTa considers the hierarchy of TRs in the TRN and also accounts for alternative metabolic pathways which may divert flux away from the product while identifying suitable metabolic fluxes, thereby performing superior in terms of global TR target identification.

Conclusions: In silico model-guided strain design framework, h-BeReTa, was presented for identifying transcriptional regulator targets. Its efficacy and applicability to microbial cell factories were successfully demonstrated via case studies involving two cell factory hosts, as such suggesting several intuitive targets for overproducing various value-added compounds.

Keywords: Constraint-based flux analysis; Genome-scale metabolic model; Model-guided strain design; Systems biology; Transcriptional regulator.

Fig. 1

Schematic workflow of h-BeReTa. a Acquisition of gene-expression data for producer and non-producer, processing TRN information, determination of nRS values. b Constraint-based flux analysis mediated determination of nGAPs for a desired product using GEM with necessary GPRs. c Calculation of the effect of TRs on product flux (TREs), and therefore global TREs using nRS, nGAP values in combination with TR-hierarchy information

Fig. 2

TR-hierarchy inferred from the E. coli regulatory network. Thirteen levels of TR–TR regulation were decoded from the TRN obtained from RegulonDB. Note that the self-regulating and loop forming TR–TR interactions are excluded from the TR-hierarchy to prevent gTREs from receiving unrealistically high values

Fig. 3

Different types of TR–TR interactions. Linear interactions represented by a, b, e and f, which result in finite gTREs were included in h-BeReTa. Interactions represented by c and d, which result in either zero or infinite gTREs, were excluded from the h-BeReTa analysis