Dynamic Metabolite Profiling in an Archaeon Connects Transcriptional Regulation to Metabolic Consequences

Abstract

Previous work demonstrated that the TrmB transcription factor is responsible for regulating the expression of many enzyme-coding genes in the hypersaline-adapted archaeon Halobacterium salinarum via a direct interaction with a cis-regulatory sequence in their promoters. This interaction is abolished in the presence of glucose. Although much is known about the effects of TrmB at the transcriptional level, it remains unclear whether and to what extent changes in mRNA levels directly affect metabolite levels. In order to address this question, here we performed a high-resolution metabolite profiling time course during a change in nutrients using a combination of targeted and untargeted methods in wild-type and ΔtrmB strain backgrounds. We found that TrmB-mediated transcriptional changes resulted in widespread and significant changes to metabolite levels across the metabolic network. Additionally, the pattern of growth complementation using various purines suggests that the mis-regulation of gluconeogenesis in the ΔtrmB mutant strain in the absence of glucose results in low phosphoribosylpyrophosphate (PRPP) levels. We confirmed these low PRPP levels using a quantitative mass spectrometric technique and found that they are associated with a metabolic block in de novo purine synthesis, which is partially responsible for the growth defect of the ΔtrmB mutant strain in the absence of glucose. In conclusion, we show how transcriptional regulation of metabolism affects metabolite levels and ultimately, phenotypes.

Detecting differential growth of microbial populations with Gaussian process regression

“…We confirmed the known growth defect for ΔtrmB under standard conditions (Fig. 3B-D), which results from its function as a master regulator of metabolic pathways (Schmid et al 2009;Todor et al 2013Todor et al , 2014Todor et al , 2015. In contrast, the ΔasnC oxidative stress phenotype observed previously was not recapitulated here, likely because the growth defect of this mutant under standard conditions explains the difference in growth during stress ( Fig.…”

Detecting differential growth of microbial populations with Gaussian process regression

“…We 338 confirmed the known growth defect for ∆trmB under standard conditions (Fig. 3B-D), which results 339 from its function as a master regulator of metabolic pathways (Schmid et al 2009;Todor et al 2013;340 Todor et al 2014;Todor et al 2015). We combined data from this study with data from a previous study 341 (Sharma et al 2012) to confirm the susceptibility of the ∆rosR mutant to oxidative damage (Fig.…”

Conserved principles of transcriptional networks controlling metabolic flexibility in archaea

“…Recent metabolic modeling suggests that the TrmB TMnet functions as a metabolic switch between gluconeogenesis and glycolysis in Halobacterium salinarum [13] ( Figure 1B). When glucose is spiked into cultures growing on amino acids as a primary source of carbon and energy, transcription of enzyme-coding genes in gluconeogenic pathways is rapidly shut off, whereas glycolytic pathways are de-repressed [13,43]. This topology and switch-like dynamics closely resemble the general TMnet or GENSOR topology of bacteria.…”

“…This topology and switch-like dynamics closely resemble the general TMnet or GENSOR topology of bacteria. Also like the CRP/lac system, global control by the TrmB network is hierarchical: although TrmB functions alone as the sole TF required for the switch between central carbon metabolic pathways, TrmB also regulates downstream regulators to generate pulses of expression in peripheral pathways such as cofactor biosynthesis [5,13,43]. Another recently described example of a TMnet in archaea is the iron homeostasis regulatory network in H. salinarum [38,44].…”

“…For example, the E. coli lac operon is frequently used for modeling bacterial TMnets [40], usually using systems of ordinary differential equations (ODE) [51,52]. The TrmB TMnet is a model TMnet for archaea [13,43,53]. Although fine-grained ODE models of these systems are highly accurate in regard to quantitative explanation of how integrated GRNs and metabolic networks function over time [13,52], extensive knowledge of kinetic parameters for the network of interest is required [51].…”