Bringing metabolomics data into the forefront of systems biology.
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Computational Biology↗
Biomedical subjects
Christophe H Schilling
Publications and source records attributed to Christophe H Schilling.
Use of constraint-based modeling for the prediction and validation of antimicrobial targets.
The overall process of antimicrobial drug discovery and development seems simple, to cure infectious disease by identifying suitable antibiotic drugs. However, this goal has been difficult to fulfill in recent years. Despite the promise of the high-throughput innovations sparked by the genomics revolution, discovery, and development of new antibiotics has lagged in recent years exacerbating the already serious problem of evolution of antibiotic resistance. Therefore, both new antimicrobials are desperately needed as are improvements to speed up or improve nearly all steps in the process of discovering novel antibiotics and bringing these to clinical use. Another product of the genomic revolution is the modeling of metabolism using computational methodologies. Genomic-scale networks of metabolic reactions based on stoichiometry, thermodynamics and other physico-chemical constraints that emulate microbial metabolism have been developed into valuable research tools in metabolic engineering and other fields. This constraint-based modeling is predictive in identifying critical reactions, metabolites, and genes in metabolism. This is extremely useful in determining and rationalizing cellular metabolic requirements. In turn, these methods can be used to predict potential metabolic targets for antimicrobial research especially if used to increase the confidence in prioritization of metabolic targets. The many different capacities of constraint-based modeling also enable prediction of cellular response to specific inhibitors such as antibiotics and this may, ultimately find a role in drug discovery and development. Herein, we describe the principles of metabolic modeling and how they might initially be applied to antimicrobial research.
Anti-Bacterial Agents↗
Catabolite repression and activation in Bacillus subtilis: dependency on CcpA, HPr, and HprK.
Previous studies have suggested that the transcription factor CcpA, as well as the coeffectors HPr and Crh, both phosphorylated by the HprK kinase/phosphorylase, are primary mediators of catabolite repression and catabolite activation in Bacillus subtilis. We here report whole transcriptome analyses that characterize glucose-dependent gene expression in wild-type cells and in isogenic mutants lacking CcpA, HprK, or the HprK phosphorylatable serine in HPr. Binding site identification revealed which genes are likely to be primarily or secondarily regulated by CcpA. Most genes subject to CcpA-dependent regulation are regulated fully by HprK and partially by serine-phosphorylated HPr [HPr(Ser-P)]. A positive linear correlation was noted between the dependencies of catabolite-repressible gene expression on CcpA and HprK, but no such relationship was observed for catabolite-activated genes, suggesting that large numbers of the latter genes are not regulated by the CcpA-HPr(Ser-P) complex. Many genes that mediate nitrogen or phosphorus metabolism as well as those that function in stress responses proved to be subject to CcpA-dependent glucose control. While nitrogen-metabolic genes may be subject to either glucose repression or activation, depending on the gene, almost all glucose-responsive phosphorus-metabolic genes exhibit activation while almost all glucose-responsive stress genes show repression. These responses are discussed from physiological standpoints. These studies expand our appreciation of CcpA-mediated catabolite control and provide insight into potential interregulon control mechanisms in gram-positive bacteria.
Adaptation, Physiological↗
Flux coupling analysis of genome-scale metabolic network reconstructions.
In this paper, we introduce the Flux Coupling Finder (FCF) framework for elucidating the topological and flux connectivity features of genome-scale metabolic networks. The framework is demonstrated on genome-scale metabolic reconstructions of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae. The analysis allows one to determine whether any two metabolic fluxes, v(1) and v(2), are (1) directionally coupled, if a non-zero flux for v(1) implies a non-zero flux for v(2) but not necessarily the reverse; (2) partially coupled, if a non-zero flux for v(1) implies a non-zero, though variable, flux for v(2) and vice versa; or (3) fully coupled, if a non-zero flux for v(1) implies not only a non-zero but also a fixed flux for v(2) and vice versa. Flux coupling analysis also enables the global identification of blocked reactions, which are all reactions incapable of carrying flux under a certain condition; equivalent knockouts, defined as the set of all possible reactions whose deletion forces the flux through a particular reaction to zero; and sets of affected reactions denoting all reactions whose fluxes are forced to zero if a particular reaction is deleted. The FCF approach thus provides a novel and versatile tool for aiding metabolic reconstructions and guiding genetic manipulations.
Aerobiosis↗
An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR).
BACKGROUND: Diverse datasets, including genomic, transcriptomic, proteomic and metabolomic data, are becoming readily available for specific organisms. There is currently a need to integrate these datasets within an in silico modeling framework. Constraint-based models of Escherichia coli K-12 MG1655 have been developed and used to study the bacterium's metabolism and phenotypic behavior. The most comprehensive E. coli model to date (E. coli iJE660a GSM) accounts for 660 genes and includes 627 unique biochemical reactions. RESULTS: An expanded genome-scale metabolic model of E. coli (iJR904 GSM/GPR) has been reconstructed which includes 904 genes and 931 unique biochemical reactions. The reactions in the expanded model are both elementally and charge balanced. Network gap analysis led to putative assignments for 55 open reading frames (ORFs). Gene to protein to reaction associations (GPR) are now directly included in the model. Comparisons between predictions made by iJR904 and iJE660a models show that they are generally similar but differ under certain circumstances. Analysis of genome-scale proton balancing shows how the flux of protons into and out of the medium is important for maximizing cellular growth. CONCLUSIONS: E. coli iJR904 has improved capabilities over iJE660a. iJR904 is a more complete and chemically accurate description of E. coli metabolism than iJE660a. Perhaps most importantly, iJR904 can be used for analyzing and integrating the diverse datasets. iJR904 will help to outline the genotype-phenotype relationship for E. coli K-12, as it can account for genomic, transcriptomic, proteomic and fluxomic data simultaneously.
Citric Acid Cycle↗
Genome-scale microbial in silico models: the constraints-based approach.
Genome sequencing and annotation has enabled the reconstruction of genome-scale metabolic networks. The phenotypic functions that these networks allow for can be defined and studied using constraints-based models and in silico simulation. Several useful predictions have been obtained from such in silico models, including substrate preference, consequences of gene deletions, optimal growth patterns, outcomes of adaptive evolution and shifts in expression profiles. The success rate of these predictions is typically in the order of 70-90% depending on the organism studied and the type of prediction being made. These results are useful as a basis for iterative model building and for several practical applications.
Animals↗
Genome-scale metabolic model of Helicobacter pylori 26695.
A genome-scale metabolic model of Helicobacter pylori 26695 was constructed from genome sequence annotation, biochemical, and physiological data. This represents an in silico model largely derived from genomic information for an organism for which there is substantially less biochemical information available relative to previously modeled organisms such as Escherichia coli. The reconstructed metabolic network contains 388 enzymatic and transport reactions and accounts for 291 open reading frames. Within the paradigm of constraint-based modeling, extreme-pathway analysis and flux balance analysis were used to explore the metabolic capabilities of the in silico model. General network properties were analyzed and compared to similar results previously generated for Haemophilus influenzae. A minimal medium required by the model to generate required biomass constituents was calculated, indicating the requirement of eight amino acids, six of which correspond to essential human amino acids. In addition a list of potential substrates capable of fulfilling the bulk carbon requirements of H. pylori were identified. A deletion study was performed wherein reactions and associated genes in central metabolism were deleted and their effects were simulated under a variety of substrate availability conditions, yielding a number of reactions that are deemed essential. Deletion results were compared to recently published in vitro essentiality determinations for 17 genes. The in silico model accurately predicted 10 of 17 deletion cases, with partial support for additional cases. Collectively, the results presented herein suggest an effective strategy of combining in silico modeling with experimental technologies to enhance biological discovery for less characterized organisms and their genomes.
Amino Acids↗