Molecular crowding defines a common origin for the Warburg effect in proliferating cells and the lactate threshold in muscle physiology

Aerobic glycolysis is a seemingly wasteful mode of ATP production that is seen both in rapidly proliferating mammalian cells and highly active contracting muscles, but whether there is a common origin for its presence in these widely different systems is unknown. To study this issue, here we develop a model of human central metabolism that incorporates a solvent capacity constraint of metabolic enzymes and mitochondria, accounting for their occupied volume densities, while assuming glucose and/or fatty acid utilization. The model demonstrates that activation of aerobic glycolysis is favored above a threshold metabolic rate in both rapidly proliferating cells and heavily contracting muscles, because it provides higher ATP yield per volume density than mitochondrial oxidative phosphorylation. In the case of muscle physiology, the model also predicts that before the lactate switch, fatty acid oxidation increases, reaches a maximum, and then decreases to zero with concomitant increase in glucose utilization, in agreement with the empirical evidence. These results are further corroborated by a larger scale model, including biosynthesis of major cell biomass components. The larger scale model also predicts that in proliferating cells the lactate switch is accompanied by activation of glutaminolysis, another distinctive feature of the Warburg effect. In conclusion, intracellular molecular crowding is a fundamental constraint for cell metabolism in both rapidly proliferating- and non-proliferating cells with high metabolic demand. Addition of this constraint to metabolic flux balance models can explain several observations of mammalian cell metabolism under steady state conditions.     

Comparative Genome-Scale Metabolic Reconstruction and Flux Balance Analysis of Multiple Staphylococcus aureus Genomes Identify Novel Antimicrobial Drug Targets

Mortality due to multidrug-resistant Staphylococcus aureus infection is predicted to surpass that of human immunodeficiency virus/AIDS in the United States. Despite the various treatment options for S. aureus infections, it remains a major hospital- and community-acquired opportunistic pathogen. With the emergence of multidrug-resistant S. aureus strains, there is an urgent need for the discovery of new antimicrobial drug targets in the organism. To this end, we reconstructed the metabolic networks of multidrug-resistant S. aureus strains using genome annotation, functional-pathway analysis, and comparative genomic approaches, followed by flux balance analysis-based in silico single and double gene deletion experiments. We identified 70 single enzymes and 54 pairs of enzymes whose corresponding metabolic reactions are predicted to be unconditionally essential for growth. Of these, 44 single enzymes and 10 enzyme pairs proved to be common to all 13 S. aureus strains, including many that had not been previously identified as being essential for growth by gene deletion experiments in S. aureus. We thus conclude that metabolic reconstruction and in silico analyses of multiple strains of the same bacterial species provide a novel approach for potential antibiotic target identification.Staphylococcus aureus is a major hospital/community-acquired opportunistic pathogen. It causes bacteremia, pneumonia, endocarditis, meningitis, and toxic-shock syndrome in adult humans; skin lesions, impetigo, and abscesses in children; and mastitis in cattle (7, 22, 27). In general, S. aureus infections are treated with β-lactam antibiotics, sulfa drugs, tetracycline, and clindamycin. However, drug-resistant strains, such as methicillin-resistant S. aureus (MRSA) and vancomycin-resistant S. aureus (VRSA), have emerged from both hospital and community infections in recent years. To date, only one new drug candidate, platensimycin, has been found to be effective against some strains of MRSA and VRSA (30). A recent meta-analysis suggested that mortality due to multidrug-resistant S. aureus in the United States may exceed that from human immunodeficiency virus infections and AIDS (19). This has resulted in a renewed interest in identifying new targets and molecules effective against multidrug-resistant strains of bacteria, and S. aureus in particular.Based on whole-genome sequence comparisons, S. aureus strains can be divided into three divergent groups arising from a common lineage (11). Significant sequence variations between animal and human S. aureus strains have also been identified (15). Though many virulence and drug resistance markers have been studied, the cause of the continuous emergence of multidrug-resistant strains remains elusive, as the resistance phenotype is not attributable to a few studied genes. Combining the data from multilocus sequence typing, microarray analysis, sequence relationships, homologous recombination, and phages of S. aureus, two major groups of clonal strains have been identified (11). A similar conclusion was reached when the S. aureus Newman genome sequence was compared to those of 11 other S. aureus strains (3). These studies not only confirm the clonality of the genome, but also reveal that nearly 20% of the sequence variations are due to prophages and pathogenicity islands.In order to further refine a generic antimicrobial drug target identification scheme (2), we performed metabolic reconstructions of multidrug-resistant and sensitive strains of S. aureus. This was feasible, as the genome sequences of 13 S. aureus strains are now available. They include strain N315 (a MRSA strain), Mu50 (a VRSA strain), JH9 (a vancomycin-nonsusceptible MRSA strain), JH1 (a vancomycin-susceptible, hospital-acquired MRSA strain), COL (a hospital-acquired MRSA strain), 252 (a hospital-acquired MRSA strain), USA 300 (a community-acquired MRSA strain), MW2 (a community-acquired MRSA strain), and RF122 (a bovine mastitis strain).Previous efforts in the metabolic reconstruction and subsequent flux balance analysis (FBA) of a single S. aureus strain (N315) provided valuable but limited insight into the metabolic capabilities of the bacterium (4, 14). Using this strain (20), Becker and Palsson predicted 518 metabolic reactions and 571 metabolites based on a limited set of genes (enzymes) (4). Their study also identified the components of minimal growth medium for S. aureus. Of the six required amino acids, only four (l-alanine, l-arginine, l-proline, and l-glycine) were common to both experimental and computational studies. Glucose (carbon source), phosphate, sulfate, nicotinamide, and thiamine were both experimentally utilized and computationally verified. However, other substrates, such as the nucleosides cytidine and uridine, were predicted not to be required in their metabolic model. A second genome-scale reconstruction of the same strain based on the KEGG ligand database was carried out and yielded 774 metabolic reactions catalyzed by 394 unique enzymes (13). Heinemann et al. (14) also validated their reconstruction using published experimental data and further defined a biomass composition for S. aureus.To reconcile the results of these two previous reconstructions and to address the differences in the metabolic capabilities of various S. aureus strains, we employed comprehensive genomic and metabolic reconstruction methodologies using the ERGO bioinformatics suite (24). This approach enabled us to identify the functional pathways, metabolic reactions, and transport reactions of several sequenced strains of S. aureus. The identified metabolic pathways and their individual reactions were systematically compared with those archived in the KEGG ligand database (17). FBA of such reconstructed metabolic networks have allowed in silico single and double gene deletion experiments, e.g., in Escherichia coli (2, 8, 23, 29). The application of these methods has led us to the identification of single enzymes and synthetic enzyme pairs that are unconditionally required for the growth (biomass production) of all S. aureus strains.     

ProbFAST: Probabilistic Functional Analysis System Tool

Background  

The post-genomic era has brought new challenges regarding the understanding of the organization and function of the human genome. Many of these challenges are centered on the meaning of differential gene regulation under distinct biological conditions and can be performed by analyzing the Multiple Differential Expression (MDE) of genes associated with normal and abnormal biological processes. Currently MDE analyses are limited to usual methods of differential expression initially designed for paired analysis.     

Catabolic efficiency of aerobic glycolysis: The Warburg effect revisited

Background  

Cancer cells simultaneously exhibit glycolysis with lactate secretion and mitochondrial respiration even in the presence of oxygen, a phenomenon known as the Warburg effect. The maintenance of this mixed metabolic phenotype is seemingly counterintuitive given that aerobic glycolysis is far less efficient in terms of ATP yield per moles of glucose than mitochondrial respiration.     

BayGO: Bayesian analysis of ontology term enrichment in microarray data

Background  

The search for enriched (aka over-represented or enhanced) ontology terms in a list of genes obtained from microarray experiments is becoming a standard procedure for a system-level analysis. This procedure tries to summarize the information focussing on classification designs such as Gene Ontology, KEGG pathways, and so on, instead of focussing on individual genes. Although it is well known in statistics that association and significance are distinct concepts, only the former approach has been used to deal with the ontology term enrichment problem.     

Identifying Ligand Binding Conformations of the β2-Adrenergic Receptor by Using Its Agonists as Computational Probes

Recently available G-protein coupled receptor (GPCR) structures and biophysical studies suggest that the difference between the effects of various agonists and antagonists cannot be explained by single structures alone, but rather that the conformational ensembles of the proteins need to be considered. Here we use an elastic network model-guided molecular dynamics simulation protocol to generate an ensemble of conformers of a prototypical GPCR, β₂-adrenergic receptor (β₂AR). The resulting conformers are clustered into groups based on the conformations of the ligand binding site, and distinct conformers from each group are assessed for their binding to known agonists of β₂AR. We show that the select ligands bind preferentially to different predicted conformers of β₂AR, and identify a role of β₂AR extracellular region as an allosteric binding site for larger drugs such as salmeterol. Thus, drugs and ligands can be used as “computational probes” to systematically identify protein conformers with likely biological significance.