Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network

<p>Abstract</p> <p>Background</p> <p>Constraint-based flux analysis of metabolic network model quantifies the reaction flux distribution to characterize the state of cellular metabolism. However, metabolites are key players in the metabolic network and the current reaction-centric approach may not account for the effect of metabolite perturbation on the cellular physiology due to the inherent limitation in model formulation. Thus, it would be practical to incorporate the metabolite states into the model for the analysis of the network.</p> <p>Results</p> <p>Presented herein is a metabolite-centric approach of analyzing the metabolic network by including the turnover rate of metabolite, known as flux-sum, as key descriptive variable within the model formulation. By doing so, the effect of varying metabolite flux-sum on physiological change can be simulated by resorting to mixed integer linear programming. From the results, we could classify various metabolite types based on the flux-sum profile. Using the <it>i</it>AF1260 <it>in silico </it>metabolic model of <it>Escherichia coli</it>, we demonstrated that this novel concept complements the conventional reaction-centric analysis.</p> <p>Conclusions</p> <p>Metabolite flux-sum analysis elucidates the roles of metabolites in the network. In addition, this metabolite perturbation analysis identifies the key metabolites, implicating practical application which is achievable through metabolite flux-sum manipulation in the areas of biotechnology and biomedical research.</p

Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications.

Analysis of DNA methylation patterns relies increasingly on sequencing-based profiling methods. The four most frequently used sequencing-based technologies are the bisulfite-based methods MethylC-seq and reduced representation bisulfite sequencing (RRBS), and the enrichment-based techniques methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylated DNA binding domain sequencing (MBD-seq). We applied all four methods to biological replicates of human embryonic stem cells to assess their genome-wide CpG coverage, resolution, cost, concordance and the influence of CpG density and genomic context. The methylation levels assessed by the two bisulfite methods were concordant (their difference did not exceed a given threshold) for 82% for CpGs and 99% of the non-CpG cytosines. Using binary methylation calls, the two enrichment methods were 99% concordant and regions assessed by all four methods were 97% concordant. We combined MeDIP-seq with methylation-sensitive restriction enzyme (MRE-seq) sequencing for comprehensive methylome coverage at lower cost. This, along with RNA-seq and ChIP-seq of the ES cells enabled us to detect regions with allele-specific epigenetic states, identifying most known imprinted regions and new loci with monoallelic epigenetic marks and monoallelic expression

FASIMU: flexible software for flux-balance computation series in large metabolic networks

<p>Abstract</p> <p>Background</p> <p>Flux-balance analysis based on linear optimization is widely used to compute metabolic fluxes in large metabolic networks and gains increasingly importance in network curation and structural analysis. Thus, a computational tool flexible enough to realize a wide variety of FBA algorithms and able to handle batch series of flux-balance optimizations is of great benefit.</p> <p>Results</p> <p>We present FASIMU, a command line oriented software for the computation of flux distributions using a variety of the most common FBA algorithms, including the first available implementation of (i) weighted flux minimization, (ii) fitness maximization for partially inhibited enzymes, and (iii) of the concentration-based thermodynamic feasibility constraint. It allows batch computation with varying objectives and constraints suited for network pruning, leak analysis, flux-variability analysis, and systematic probing of metabolic objectives for network curation. Input and output supports SBML. FASIMU can work with free (lp_solve and GLPK) or commercial solvers (CPLEX, LINDO). A new plugin (faBiNA) for BiNA allows to conveniently visualize calculated flux distributions. The platform-independent program is an open-source project, freely available under GNU public license at <url>http://www.bioinformatics.org/fasimu</url> including manual, tutorial, and plugins.</p> <p>Conclusions</p> <p>We present a flux-balance optimization program whose main merits are the implementation of thermodynamics as a constraint, batch series of computations, free availability of sources, choice on various external solvers, and the flexibility on metabolic objectives and constraints.</p

Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets

<p>Abstract</p> <p>Background:</p> <p><it>Mycobacterium tuberculosis </it>continues to be a major pathogen in the third world, killing almost 2 million people a year by the most recent estimates. Even in industrialized countries, the emergence of multi-drug resistant (MDR) strains of tuberculosis hails the need to develop additional medications for treatment. Many of the drugs used for treatment of tuberculosis target metabolic enzymes. Genome-scale models can be used for analysis, discovery, and as hypothesis generating tools, which will hopefully assist the rational drug development process. These models need to be able to assimilate data from large datasets and analyze them.</p> <p>Results:</p> <p>We completed a bottom up reconstruction of the metabolic network of <it>Mycobacterium tuberculosis </it>H37Rv. This functional <it>in silico </it>bacterium, <it>iNJ</it>661, contains 661 genes and 939 reactions and can produce many of the complex compounds characteristic to tuberculosis, such as mycolic acids and mycocerosates. We grew this bacterium <it>in silico </it>on various media, analyzed the model in the context of multiple high-throughput data sets, and finally we analyzed the network in an 'unbiased' manner by calculating the Hard Coupled Reaction (HCR) sets, groups of reactions that are forced to operate in unison due to mass conservation and connectivity constraints.</p> <p>Conclusion:</p> <p>Although we observed growth rates comparable to experimental observations (doubling times ranging from about 12 to 24 hours) in different media, comparisons of gene essentiality with experimental data were less encouraging (generally about 55%). The reasons for the often conflicting results were multi-fold, including gene expression variability under different conditions and lack of complete biological knowledge. Some of the inconsistencies between <it>in vitro </it>and <it>in silico </it>or <it>in vivo </it>and <it>in silico </it>results highlight specific loci that are worth further experimental investigations. Finally, by considering the HCR sets in the context of known drug targets for tuberculosis treatment we proposed new alternative, but equivalent drug targets.</p

A Synthetic Adjuvant to Enhance and Expand Immune Responses to Influenza Vaccines

Safe, effective adjuvants that enhance vaccine potency, including induction of neutralizing Abs against a broad range of variant strains, is an important strategy for the development of seasonal influenza vaccines which can provide optimal protection, even during seasons when available vaccines are not well matched to circulating viruses. We investigated the safety and ability of Glucopyranosyl Lipid Adjuvant-Stable Emulsion (GLA-SE), a synthetic Toll-like receptor (TLR)4 agonist formulation, to adjuvant Fluzone® in mice and non-human primates. The GLA-SE adjuvanted Fluzone vaccine caused no adverse reactions, increased the induction of T helper type 1 (TH1)-biased cytokines such as IFNγ, TNF and IL-2, and broadened serological responses against drifted A/H1N1 and A/H3N2 influenza variants. These results suggest that synthetic TLR4 adjuvants can enhance the magnitude and quality of protective immunity induced by influenza vaccines

Behavioral Defects in Chaperone-Deficient Alzheimer's Disease Model Mice

Molecular chaperones protect cells from the deleterious effects of protein misfolding and aggregation. Neurotoxicity of amyloid-beta (Aβ) aggregates and their deposition in senile plaques are hallmarks of Alzheimer's disease (AD). We observed that the overall content of αB-crystallin, a small heat shock protein molecular chaperone, decreased in AD model mice in an age-dependent manner. We hypothesized that αB-crystallin protects cells against Aβ toxicity. To test this, we crossed αB-crystallin/HspB2 deficient (CRYAB-/-HSPB2-/-) mice with AD model transgenic mice expressing mutant human amyloid precursor protein. Transgenic and non-transgenic mice in chaperone-sufficient or deficient backgrounds were examined for representative behavioral paradigms for locomotion and memory network functions: (i) spatial orientation and locomotion was monitored by open field test; (ii) sequential organization and associative learning was monitored by fear conditioning; and (iii) evoked behavioral response was tested by hot plate method. Interestingly, αB-crystallin/HspB2 deficient transgenic mice were severely impaired in locomotion compared to each genetic model separately. Our results highlight a synergistic effect of combining chaperone deficiency in a transgenic mouse model for AD underscoring an important role for chaperones in protein misfolding diseases

Protein 3D Structure Computed from Evolutionary Sequence Variation

The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing