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

Abstract

BackgroundCellular 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.ResultsWe 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.ConclusionsIn 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.Electronic supplementary materialThe online version of this article (10.1186/s12934-018-1015-7) contains supplementary material, which is available to authorized users.

Physiology and Transcriptional Analysis of (p)ppGpp-Related Regulatory Effects in Corynebacterium glutamicum

“…As a result, the corresponding processes could also have a massive influence on production processes. An in silico study on the identification of regulatory active targets for the optimization of production processes revealed the rel gene from C. glutamicum acting as a possible regulatory function for amino acid and lycopene production (Koduru et al, 2018).…”

Rational metabolic engineering of <i>Corynebacterium glutamicum</i> to create a producer of L-valine

“…Further progress in the creation of producer strains will involve a shift from studying the properties of a cell population to studying the properties of individual cells (Harst et al, 2017;Hemmerich et al, 2018;Pérez-García et al, 2018), as well as extensive application of computer modeling (Koduru et al, 2018) and using new knowledge about gene expression regulation (Dostálová et al, 2017;Shi et al, 2018;Zhang S. et al, 2018;Xu N. et al, 2019).…”

Recent Developments in Bioprocessing of Recombinant Proteins: Expression Hosts and Process Development

“…The industrially important strain engineering strategies utilized to increase both the quantity and quality of therapeutic products were discussed in another study (Castiñeiras et al, 2018). Another study described the use of hierarchical-Beneficial Regulatory Targeting (h-BeReTa) employing a genome-scale metabolic model and transcriptional regulatory network (TRN) to identify the relevant TR targets for strain improvement (Koduru et al, 2018). Translating heterologous proteins places a major burden on host cells, consuming expression resources and leading to slower cell growth and productivity.…”