Systematic alteration of in vitro metabolic environments reveals empirical growth relationships in cancer cell phenotypes.

Cancer cells, like microbes, live in complex metabolic environments. Recent evidence suggests that microbial behavior across metabolic environments is well described by simple empirical growth relationships, or growth laws. Do such empirical growth relationships also exist in cancer cells? To test this question, we develop a high-throughput approach to extract quantitative measurements of cancer cell behaviors in systematically altered metabolic environments. Using this approach, we examine relationships between growth and three frequently studied cancer phenotypes: drug-treatment survival, cell migration, and lactate overflow. Drug-treatment survival follows simple linear growth relationships, which differ quantitatively between chemotherapeutics and EGFR inhibition. Cell migration follows a weak grow-and-go growth relationship, with substantial deviation in some environments. Finally, lactate overflow is mostly decoupled from growth rate and is instead determined by the cells' ability to maintain high sugar uptake rates. Altogether, this work provides a quantitative approach for formulating empirical growth laws of cancer.

Quorum Sensing and Metabolic State of the Host Control Lysogeny-Lysis Switch of Bacteriophage T1.

Bacterial viruses, or bacteriophages, are highly abundant in the biosphere and have a major impact on microbial populations. Many examples of phage interactions with their hosts, including establishment of dormant lysogenic and active lytic states, have been characterized at the level of the individual cell. However, much less is known about the dependence of these interactions on host metabolism and signal exchange within bacterial communities. In this report, we describe a lysogenic state of the enterobacterial phage T1, previously known as a classical lytic phage, and characterize the underlying regulatory circuitry. We show that the transition from lysogeny to lysis depends on bacterial population density, perceived via interspecies autoinducer 2. Lysis is further controlled by the metabolic state of the cell, mediated by the cyclic-3',5'-AMP (cAMP) receptor protein (CRP) of the host. We hypothesize that such combinations of cell density and metabolic sensing may be common in phage-host interactions.IMPORTANCE The dynamics of microbial communities are heavily shaped by bacterium-bacteriophage interactions. But despite the apparent importance of bacteriophages, our understanding of the mechanisms controlling phage dynamics in bacterial populations, and particularly of the differences between the decisions that are made in the dormant lysogenic and active lytic states, remains limited. In this report, we show that enterobacterial phage T1, previously described as a lytic phage, is able to undergo lysogeny. We further demonstrate that the lysogeny-to-lysis decision occurs in response to changes in the density of the bacterial population, mediated by interspecies quorum-sensing signal AI-2, and in the metabolic state of the cell, mediated by cAMP receptor protein. We hypothesize that this strategy enables the phage to maximize its chances of self-amplification and spreading in bacterial population upon induction of the lytic cycle and that it might be common in phage-host interactions.

CRISPRi-Based Downregulation of Transcriptional Feedback Improves Growth and Metabolism of Arginine Overproducing E. coli.

Removing transcriptional feedback regulation of metabolic pathways is a classical approach to enhance overproduction of chemicals in microbes. However, disrupting transcriptional regulation can have broad physiological consequences that decrease cellular growth and productivity. Here, we compared downregulation and deletion of the transcriptional repressor ArgR in arginine overproducing Escherichia coli. Different levels of ArgR downregulation were achieved with CRISPR interference (CRISPRi) and resulted in 2-times higher growth rates compared to deletion of ArgR, while specific arginine production was similar (∼2 mmol gDW-1 h-1). Metabolomics and proteomics data revealed that poor growth of the ArgR deletion strain was caused by a limitation of pyrimidine nucleotide biosynthesis, because a 17-fold overexpression of ornithine carbamoyltransferase (ArgI) perturbed the arginine-pyrimidine branch point. These results demonstrate that overexpression of enzymes in an engineered pathway can impair metabolism of the host, especially in the case of branch-point enzymes. Thus, balancing enzyme levels is important to optimize industrial microbes, and CRISPRi of a transcription factor is a versatile tool for this purpose.

Allosteric Feedback Inhibition Enables Robust Amino Acid Biosynthesis in E. coli by Enforcing Enzyme Overabundance.

Microbes must ensure robust amino acid metabolism in the face of external and internal perturbations. This robustness is thought to emerge from regulatory interactions in metabolic and genetic networks. Here, we explored the consequences of removing allosteric feedback inhibition in seven amino acid biosynthesis pathways in Escherichia coli (arginine, histidine, tryptophan, leucine, isoleucine, threonine, and proline). Proteome data revealed that enzyme levels decreased in five of the seven dysregulated pathways. Despite that, flux through the dysregulated pathways was not limited, indicating that enzyme levels are higher than absolutely needed in wild-type cells. We showed that such enzyme overabundance renders the arginine, histidine, and tryptophan pathways robust against perturbations of gene expression, using a metabolic model and CRISPR interference experiments. The results suggested a sensitive interaction between allosteric feedback inhibition and enzyme-level regulation that ensures robust yet efficient biosynthesis of histidine, arginine, and tryptophan in E. coli.

Crosstalk between transcription and metabolism: how much enzyme is enough for a cell?

Cells employ various mechanisms for dynamic control of enzyme expression. An important mechanism is mutual feedback-or crosstalk-between transcription and metabolism. As recently suggested, enzyme levels are often much higher than absolutely needed to maintain metabolic flux. However, given the potential burden of high enzyme levels it seems likely that cells control enzyme expression to meet other cellular objectives. In this review, we discuss whether crosstalk between metabolism and transcription could inform cells about how much enzyme is optimal for various fitness aspects. Two major problems should be addressed in order to understand optimization of enzyme levels by crosstalk. First, mapping of metabolite-protein interactions will be crucial to obtain a better mechanistic understanding of crosstalk. Second, investigating cellular objectives that define optimal enzyme levels can reveal the functional relevance of crosstalk. We present recent studies that approach these problems, drawing from experimental transcript and metabolite data, and from theoretical network analyses. WIREs Syst Biol Med 2018, 10:e1396. doi: 10.1002/wsbm.1396 This article is categorized under: Biological Mechanisms > Metabolism Laboratory Methods and Technologies > Metabolomics Biological Mechanisms > Regulatory Biology.

Time-Optimized Isotope Ratio LC-MS/MS for High-Throughput Quantification of Primary Metabolites.

Cellular metabolite concentrations hold information on the function and regulation of metabolic networks. However, current methods to measure metabolites are either low-throughput or not quantitative. Here we optimized conditions for liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) for quantitative measurements of primary metabolites in 2 min runs. In addition, we tested hundreds of multiple reaction monitoring (MRM) assays for isotope ratio mass spectrometry of most metabolites in amino acid, nucleotide, cofactor, and central metabolism. To systematically score the quality of LC-MS/MS data, we used the correlation between signals in the 12C and 13C channel of a metabolite. Applying two optimized LC methods to bacterial cell extracts detected more than 200 metabolites with less than 20% variation between replicates. An exhaustive spike-in experiment with 79 metabolite standards demonstrated the high selectivity of the methods and revealed a few confounding effects such as in-source fragments. Generally, the methods are suited for samples that contain metabolites at final concentrations between 1 nM and 10 µM, and they are sufficiently robust to analyze samples with a high salt content.