Hans V. Westerhoff

A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations

A large proportion of the 6,000 genes present in the genome of Saccharomyces cerevisiae, and of those sequenced in other organisms, encode proteins of unknown function. Many of these genes are “silent,” that is, they show no overt phenotype, in terms of growth rate or other fluxes, when they are deleted from the genome. We demonstrate how the intracellular concentrations of metabolites can reveal phenotypes for proteins active in metabolic regulation. Quantification of the change of several metabolite concentrations relative to the concentration change of one selected metabolite can reveal the site of action, in the metabolic network, of a silent gene. In the same way, comprehensive analyses of metabolite concentrations in mutants, providing “metabolic snapshots,” can reveal functions when snapshots from strains deleted for unstudied genes are compared to those deleted for known genes. This approach to functional analysis, using comparative metabolomics, we call FANCY—an abbreviation for functional analysis by co-responses in yeast.

A Systems Biology Approach to Deciphering the Etiology of Steatosis Employing Patient-Derived Dermal Fibroblasts and iPS Cells

Non-alcoholic fatty liver disease comprises a broad spectrum of disease states ranging from simple steatosis to non-alcoholic steatohepatitis. As a result of increases in the prevalences of obesity, insulin resistance, and hyperlipidemia, the number of people with hepatic steatosis continues to increase. Differences in susceptibility to steatohepatitis and its progression to cirrhosis have been attributed to a complex interplay of genetic and external factors all addressing the intracellular network. Increase in sugar or refined carbohydrate consumption results in an increase of insulin and insulin resistance that can lead to the accumulation of fat in the liver. Here we demonstrate how a multidisciplinary approach encompassing cellular reprogramming, transcriptomics, proteomics, metabolomics, modeling, network reconstruction, and data management can be employed to unveil the mechanisms underlying the progression of steatosis. Proteomics revealed reduced AKT/mTOR signaling in fibroblasts derived from steatosis patients and further establishes that the insulin-resistant phenotype is present not only in insulin-metabolizing central organs, e.g., the liver, but is also manifested in skin fibroblasts. Transcriptome data enabled the generation of a regulatory network based on the transcription factor SREBF1, linked to a metabolic network of glycerolipid, and fatty acid biosynthesis including the downstream transcriptional targets of SREBF1 which include LIPIN1 (LPIN) and low density lipoprotein receptor. Glutathione metabolism was among the pathways enriched in steatosis patients in comparison to healthy controls. By using a model of the glutathione pathway we predict a significant increase in the flux through glutathione synthesis as both gamma-glutamylcysteine synthetase and glutathione synthetase have an increased flux. We anticipate that a larger cohort of patients and matched controls will confirm our preliminary findings presented here.

The evolution of molecular biology into systems biology

Systems analysis has historically been performed in many areas of biology, including ecology, developmental biology and immunology. More recently, the genomics revolution has catapulted molecular biology into the realm of systems biology. In unicellular organisms and well-defined cell lines of higher organisms, systems approaches are making definitive strides toward scientific understanding and biotechnological applications. We argue here that two distinct lines of inquiry in molecular biology have converged to form contemporary systems biology.

Untangling the wires: A strategy to trace functional interactions in signaling and gene networks

Emerging technologies have enabled the acquisition of large genomics and proteomics data sets. However, current methodologies for analysis do not permit interpretation of the data in ways that unravel cellular networking. We propose a quantitative method for determining functional interactions in cellular signaling and gene networks. It can be used to explore cell systems at a mechanistic level or applied within a “modular” framework, which dramatically decreases the number of variables to be assayed. This method is based on a mathematical derivation that demonstrates how the topology and strength of network connections can be retrieved from experimentally measured network responses to successive perturbations of all modules. Importantly, our analysis can reveal functional interactions even when the components of the system are not all known. Under these circumstances, some connections retrieved by the analysis will not be direct but correspond to the interaction routes through unidentified elements. The method is tested and illustrated by using computer-generated responses of a modeled mitogen-activated protein kinase cascade and gene network.

Transcriptome meets metabolome: hierarchical and metabolic regulation of the glycolytic pathway

The fact that information flows from DNA to RNA to protein to function suggests that regulation is ‘hierarchical’, i.e. dominated by regulation of gene expression. In the case of dominant regulation at the metabolic level, however, there is no quantitative relationship between mRNA levels and function. We here develop a method to quantitate the relative contributions of metabolic and hierarchical regulation. Applying this method to the glycolytic flux in three species of parasitic protists, we conclude that it is rarely regulated by gene expression alone. This casts strong doubts on whether transcriptome and proteome analysis suffices to assess biological function.

Glycolysis in bloodstream form Trypanosoma brucei can be understood in terms of the kinetics of the glycolytic enzymes.

In trypanosomes the first part of glycolysis takes place in specialized microbodies, the glycosomes. Most glycolytic enzymes of Trypanosoma brucei have been purified and characterized kinetically. In this paper a mathematical model of glycolysis in the bloodstream form of this organism is developed on the basis of all available kinetic data. The fluxes and the cytosolic metabolite concentrations as predicted by the model were in accordance with available data as measured in non-growing trypanosomes, both under aerobic and under anaerobic conditions. The model also reproduced the inhibition of anaerobic glycolysis by glycerol, although the amount of glycerol needed to inhibit glycolysis completely was lower than experimentally determined. At low extracellular glucose concentrations the intracellular glucose concentration remained very low, and only at 5 mM of extracellular glucose, free glucose started to accumulate intracellularly, in close agreement with experimental observations. This biphasic relation could be related to the large difference between the affinities of the glucose transporter and hexokinase for intracellular glucose. The calculated intraglycosomal metabolite concentrations demonstrated that enzymes that have been shown to be near-equilibrium in the cytosol must work far from equilibrium in the glycosome in order to maintain the high glycolytic flux in the latter.

The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels

Metabolic fluxes may be regulated “hierarchically,” e.g., by changes of gene expression that adjust enzyme capacities (Vmax) and/or “metabolically” by interactions of enzymes with substrates, products, or allosteric effectors. In the present study, a method is developed to dissect the hierarchical regulation into contributions by transcription, translation, protein degradation, and posttranslational modification. The method was applied to the regulation of fluxes through individual glycolytic enzymes when the yeast Saccharomyces cerevisiae was confronted with the absence of oxygen and the presence of benzoic acid depleting its ATP. Metabolic regulation largely contributed to the ≈10-fold change in flux through the glycolytic enzymes. This contribution varied from 50 to 80%, depending on the glycolytic step and the cultivation condition tested. Within the 50–20% hierarchical regulation of fluxes, transcription played a minor role, whereas regulation of protein synthesis or degradation was the most important. These also contributed to 75–100% of the regulation of protein levels.

An alternative PII protein in the regulation of glutamine synthetase in Escherichia coli

The PII protein has been considered pivotal to the dual cascade regulating ammonia assimilation through glutamine synthetase activity. Here we show that PII, encoded by the glnB gene, is not always essential; for instance upon ammonia deprivation of a glnB deletion strain, glutamine synthetase can be deadenylylated as effectively as in the wild‐type strain. We describe a new operon, glnK amtB, which encodes a homologue of PII and a putative ammonia transporter. We cloned and overexpressed glnK and found that the expressed protein had almost the same molecular weight as PII, reacted with polyclonal PII antibody, and was 67% identical in terms of amino acid sequence with Escherichia coli PII. Like PII, purified GlnK can activate the adenylylation of glutamine synthetase in vitro, and, in vivo, the GlnK protein is uridylylated in a glnD‐dependent fashion. Unlike PII, however, the expression of glnK depends on the presence of UTase, nitrogen regulator I (NRI), and absence of ammonia. Because of a NRI and a σN (σ54) RNA polymerase‐binding consensus sequence upstream from the glnK gene, this suggests that glnK is regulated through the NRI/NRII two‐component regulatory system. Indeed, in cells grown in the presence of ammonia, glutamine synthetase deadenylylation upon ammonia depletion depended on PII. Possible regulatory implications of this conditional redundancy of PII are discussed.

Cytosolic triglycerides and oxidative stress in central obesity: the missing link between excessive atherosclerosis, endothelial dysfunction, and β-cell failure?

Central obesity is increasingly recognized as a risk factor for atherosclerosis and type 2 diabetes mellitus. Here we present a hypothesis that may explain the excess atherosclerosis, endothelial dysfunction and progressive beta-cell failure. Central obesity is associated with increased cytosolic triglyceride stores in non-adipose tissues such as muscles, liver and pancreatic beta-cells. A high cytosolic triglyceride content is accompanied by elevated concentrations of cytosolic long-chain acyl-CoA esters, the metabolically active form of fatty acids. These esters inhibit mitochondrial adenine nucleotide translocators, resulting in an intramitochondrial ADP deficiency. In vitro, such ADP deficiency is a potent stimulator of mitochondrial oxygen free radical production, and we assume that this mechanism is also active in vivo. The decline of organ function with normal ageing is thought to be due, at least partly, to a continuous low-grade mitochondrial oxygen free radical production. In tissues containing increased cytosolic triglyceride stores this process will be accelerated. Tissues with a high-energy demand or poor free radical scavenging capacity, such as pancreatic beta-cells, are likely to be more susceptible to this process. This is how we explain their gradual dysfunctioning in central obesity. Likewise we propose that the enhanced production of oxygen free radicals in endothelial cells, or vascular smooth muscle cells, leads to the increased subendothelial oxidation of LDL and atherosclerosis, as well as to the endothelial dysfunction and microalbuminuria.

Control of MAPK signalling: from complexity to what really matters.

Oncogenesis results from changes in kinetics or in abundance of proteins in signal transduction networks. Recently, it was shown that control of signalling cannot reside in a single gene product, and might well be dispersed over many components. Which of the reactions in these complex networks are most important, and how can the existing molecular information be used to understand why particular genes are oncogenes whereas others are not? We implement a new method to help address such questions. We apply control analysis to a detailed kinetic model of the epidermal growth factor-induced mitogen-activated protein kinase network. We determine the control of each reaction with respect to three biologically relevant characteristics of the output of this network: the amplitude, duration and integrated output of the transient phosphorylation of extracellular signal-regulated kinase (ERK). We confirm that control is distributed, but far from randomly: a small proportion of reactions substantially control signalling. In particular, the activity of Raf is in control of all characteristics of the transient profile of ERK phosphorylation, which may clarify why Raf is an oncogene. Most reactions that really matter for one signalling characteristic are also important for the other characteristics. Our analysis also predicts the effects of mutations and changes in gene expression.