What is flux balance analysis?

matrix of stoichiometries-that consumes precursor metabolites at stoichiometries that simulate biomass production. The biomass reaction is based on experimental measurements of biomass components. This reaction is scaled so that the flux through it is equal to the exponential growth rate (µ) of the organism. Now that biomass is represented in the model, predicting the maximum growth rate can be accomplished by calculating the conditions that result in the maximum flux through the biomass reaction. In other cases, more than one reaction may contribute to the phenotype of interest. Mathematically, an 'objective function' is used to quantitatively define how much each reaction contributes to the phenotype. Taken together, the mathematical representations of the metabolic reactions and of the objective define a system of linear equations. In flux balance analysis, these equations are solved using linear programming Suppose we want to calculate the maximum aerobic growth of E. coli under the assumption that uptake of glucose, and not oxygen, is the limiting constraint on growth. This calculation can be performed using a published model of E. coli metabolism 12 . In addition to metabolic reactions and the biomass reaction discussed above, this model also includes reactions that represent glucose and oxygen uptake into the cell. The assumptions are mathematically represented by setting the maximum rate of glucose uptake to a physiologically realistic level (18.5 mmol The core feature of this representation is a tabulation, in the form of a numerical matrix, of the stoichiometric coefficients of each reaction Constraints are represented in two ways, as equations that balance reaction inputs and outputs and as inequalities that impose bounds on the system. The matrix of stoichiometries imposes flux (that is, mass) balance constraints on the system, ensuring that the total amount of any compound being produced must be equal to the total amount being consumed at steady state From constraints to optimizing a phenotype The next step in FBA is to define a phenotype in the form of a biological objective that is relevant to the problem being studied In this primer, we illustrate the principles behind FBA by applying it to predict the maximum growth rate of Escherichia coli in the presence and absence of oxygen. The principles outlined can be applied in many other contexts to analyze the phenotypes and capabilities of organisms with different environmental and genetic perturbations (a Supplementary Tutorial provides ten additional worked examples with figures and computer code). Flux balance analysis is based on constraints The first step in FBA is to mathematically represent metabolic reactions What is flux balance analysis? Jeffrey D Orth, Ines Thiele & Bernhard Ø Palsson Flux balance analysis is a mathematical approach for analyzing the flow of metabolites through a metabolic network. This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations

Huntington’s disease age at motor onset is modified by the tandem hexamer repeat in TCERG1

Huntington’s disease is caused by an expanded CAG tract in HTT. The length of the CAG tract accounts for over half the variance in age at onset of disease, and is influenced by other genetic factors, mostly implicating the DNA maintenance machinery. We examined a single nucleotide variant, rs79727797, on chromosome 5 in the TCERG1 gene, previously reported to be associated with Huntington’s disease and a quasi-tandem repeat (QTR) hexamer in exon 4 of TCERG1 with a central pure repeat. We developed a method for calling perfect and imperfect repeats from exome-sequencing data, and tested association between the QTR in TCERG1 and residual age at motor onset (after correcting for the effects of CAG length in the HTT gene) in 610 individuals with Huntington’s disease via regression analysis. We found a significant association between age at onset and the sum of the repeat lengths from both alleles of the QTR (p = 2.1 × 10−9), with each added repeat hexamer reducing age at onset by one year (95% confidence interval [0.7, 1.4]). This association explained that previously observed with rs79727797. The association with age at onset in the genome-wide association study is due to a QTR hexamer in TCERG1, translated to a glutamine/alanine tract in the protein. We could not distinguish whether this was due to cis-effects of the hexamer repeat on gene expression or of the encoded glutamine/alanine tract in the protein. These results motivate further study of the mechanisms by which TCERG1 modifies onset of HD

Negative Regulators of Insulin Signaling Revealed in a Genome-Wide Functional Screen

Type 2 diabetes develops due to a combination of insulin resistance and β-cell failure and current therapeutics aim at both of these underlying causes. Several negative regulators of insulin signaling are known and are the subject of drug discovery efforts. We sought to identify novel contributors to insulin resistance and hence potentially novel targets for therapeutic intervention.An arrayed cDNA library encoding 18,441 human transcripts was screened for inhibitors of insulin signaling and revealed known inhibitors and numerous potential novel regulators. The novel hits included proteins of various functional classes such as kinases, phosphatases, transcription factors, and GTPase associated proteins. A series of secondary assays confirmed the relevance of the primary screen hits to insulin signaling and provided further insight into their modes of action.Among the novel hits was PALD (KIAA1274, paladin), a previously uncharacterized protein that when overexpressed led to inhibition of insulin's ability to down regulate a FOXO1A-driven reporter gene, reduced upstream insulin-stimulated AKT phosphorylation, and decreased insulin receptor (IR) abundance. Conversely, knockdown of PALD gene expression resulted in increased IR abundance, enhanced insulin-stimulated AKT phosphorylation, and an improvement in insulin's ability to suppress FOXO1A-driven reporter gene activity. The present data demonstrate that the application of arrayed genome-wide screening technologies to insulin signaling is fruitful and is likely to reveal novel drug targets for insulin resistance and the metabolic syndrome

Huntington's disease pathogenesis: two sequential components

Historically, Huntington’s disease (HD; OMIM #143100) has played an important role in the enormous advances in human genetics seen over the past four decades. This familial neurodegenerative disorder involves variable onset followed by consistent worsening of characteristic abnormal movements along with cognitive decline and psychiatric disturbances. HD was the first autosomal disease for which the genetic defect was assigned to a position on the human chromosomes using only genetic linkage analysis with common DNA polymorphisms. This discovery set off a multitude of similar studies in other diseases, while the HD gene, later renamed HTT, and its vicinity in chromosome 4p16.3 then acted as a proving ground for development of technologies to clone and sequence genes based upon their genomic location, with the growing momentum of such advances fueling the Human Genome Project. The identification of the HD gene has not yet led to an effective treatment, but continued human genetic analysis of genotype-phenotype relationships in large HD subject populations, first at the HTT locus and subsequently genome-wide, has provided insights into pathogenesis that divide the course of the disease into two sequential, mechanistically distinct components

Genetic and functional analyses point to FAN1 as the source of multiple Huntington Disease modifier effects

A recent genome-wide association study of Huntington’s disease (HD) implicated genes involved in DNA maintenance processes as modifiers of onset, including multiple genome-wide significant signals in a chr15 region containing the DNA repair gene FAN1. Here, we have carried out detailed genetic, molecular and cellular investigation of the modifiers at this locus. We find that missense changes within or near the DNA binding domain (p.Arg507His and p.Arg377Trp) reduce FAN1's DNA binding activity and its capacity to rescue mitomycin C-induced cytotoxicity, accounting for two infrequent onset-hastening modifier signals. We also identified a third onset-hastening modifier signal whose mechanism of action remains uncertain but does not involve an amino acid change in FAN1. We present additional evidence that a frequent onset-delaying modifier signal does not alter FAN1 coding sequence but is associated with increased FAN1 mRNA expression in the cerebral cortex. Consistent with these findings and other cellular overexpression/suppression studies, knock out of FAN1 increased CAG repeat expansion in HD induced pluripotent stem cells. Together, these studies support the process of somatic CAG repeat expansion as a therapeutic target in HD, and clearly indicate that multiple genetic variations act by different means through FAN1 to influence HD onset in a manner that is largely additive, except in the rare circumstance that two onset-hastening alleles are present. Thus, an individual’s particular combination of FAN1 haplotypes may influence their suitability for HD clinical trials, particularly if the therapeutic agent aims to reduce CAG repeat instability

CAG repeat not polyglutamine length determines timing of Huntington’s disease onset

Variable, glutamine-encoding, CAA interruptions indicate that a property of the uninterrupted HTT CAG repeat sequence, distinct from the length of huntingtin’s polyglutamine segment, dictates the rate at which Huntington’s disease (HD) develops. The timing of onset shows no significant association with HTT cis-eQTLs but is influenced, sometimes in a sex-specific manner, by polymorphic variation at multiple DNA maintenance genes, suggesting that the special onset-determining property of the uninterrupted CAG repeat is a propensity for length instability that leads to its somatic expansion. Additional naturally occurring genetic modifier loci, defined by GWAS, may influence HD pathogenesis through other mechanisms. These findings have profound implications for the pathogenesis of HD and other repeat diseases and question the fundamental premise that polyglutamine length determines the rate of pathogenesis in the “polyglutamine disorders.

Posttranscriptional regulation of FAN1 by miR-124-3p at rs3512 underlies onset-delaying genetic modification in Huntington’s disease

Many Mendelian disorders, such as Huntington’s disease (HD) and spinocerebellar ataxias, arise from expansions of CAG trinucleotide repeats. Despite the clear genetic causes, additional genetic factors may influence the rate of those monogenic disorders. Notably, genome-wide association studies discovered somewhat expected modifiers, particularly mismatch repair genes involved in the CAG repeat instability, impacting age at onset of HD. Strikingly, FAN1 , previously unrelated to repeat instability, produced the strongest HD modification signals. Diverse FAN1 haplotypes independently modify HD, with rare genetic variants diminishing DNA binding or nuclease activity of the FAN1 protein, hastening HD onset. However, the mechanism behind the frequent and the most significant onset-delaying FAN1 haplotype lacking missense variations has remained elusive. Here, we illustrated that a microRNA acting on 3′-UTR (untranslated region) SNP rs3512, rather than transcriptional regulation, is responsible for the significant FAN1 expression quantitative trait loci signal and allelic imbalance in FAN1 messenger ribonucleic acid (mRNA), accounting for the most significant and frequent onset-delaying modifier haplotype in HD. Specifically, miR-124-3p selectively targets the reference allele at rs3512, diminishing the stability of FAN1 mRNA harboring that allele and consequently reducing its levels. Subsequent validation analyses, including the use of antagomir and 3′-UTR reporter vectors with swapped alleles, confirmed the specificity of miR-124-3p at rs3512. Together, these findings indicate that the alternative allele at rs3512 renders the FAN1 mRNA less susceptible to miR-124-3p-mediated posttranscriptional regulation, resulting in increased FAN1 levels and a subsequent delay in HD onset by mitigating CAG repeat instability

Modification of Huntington's disease by short tandem repeats

Expansions of glutamine-coding CAG trinucleotide repeats cause a number of neurodegenerative diseases, including Huntington's disease (HD) and several of the spinocerebellar ataxias (SCAs). In general, age-at-onset of the polyglutamine diseases is inversely correlated with the size of the respective inherited expanded CAG repeat. Expanded CAG repeats are also somatically unstable in certain tissues, and age-at-onset of HD corrected for individual HTT CAG repeat length (i.e., residual age-at-onset), is modified by repeat instability-related DNA maintenance/repair genes as demonstrated by recent genome-wide association studies (GWAS). Modification of one polyglutamine disease (e.g., HD) by the repeat length of another (e.g., ATXN3, CAG expansions in which cause SCA3) has also been hypothesized. Consequently, we determined whether age-at-onset in HD is modified by the CAG repeats of other polyglutamine disease genes. We found that the CAG measured repeat sizes of other polyglutamine disease genes were polymorphic in HD participants but did not influence HD age-at-onset. Additional analysis focusing specifically on ATXN3 in a larger sample set (n = 1,388) confirmed the lack of association between HD residual age-at-onset and ATXN3 CAG repeat length. Additionally, neither our HD onset modifier GWAS single nucleotide polymorphism (SNP) data nor imputed short tandem repeat (STR) data supported involvement of other polyglutamine disease genes in modifying HD. By contrast, our GWAS based on imputed STRs revealed significant modification signals for other genomic regions. Together, our STR GWAS show that modification of HD is associated with STRs that do not involve other polyglutamine disease-causing genes, refining the landscape of HD modification and highlighting the importance of rigorous data analysis, especially in genetic studies testing candidate modifiers

The immunobiology of primary sclerosing cholangitis

Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease histologically characterized by the presence of intrahepatic and/or extrahepatic biliary duct concentric, obliterative fibrosis, eventually leading to cirrhosis. Approximately 75% of patients with PSC have inflammatory bowel disease. The male predominance of PSC, the lack of a defined, pathogenic autoantigen, and the potential role of the innate immune system suggest that it may be due to dysregulation of immunity rather than a classic autoimmune disease. However, PSC is associated with several classic autoimmune diseases, and the strongest genetic link to PSC identified to date is with the human leukocyte antigen DRB01*03 haplotype. The precise immunopathogenesis of PSC is largely unknown but likely involves activation of the innate immune system by bacterial components delivered to the liver via the portal vein. Induction of adhesion molecules and chemokines leads to the recruitment of intestinal lymphocytes. Bile duct injury results from the sustained inflammation and production of inflammatory cytokines. Biliary strictures may cause further damage as a result of bile stasis and recurrent secondary bacterial cholangitis. Currently, there is no effective therapy for PSC and developing a rational therapeutic strategy demands a better understanding of the disease

Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions

AbstractBackgroundThe iJO1366 reconstruction of the metabolic network of Escherichia coli is one of the most complete and accurate metabolic reconstructions available for any organism. Still, because our knowledge of even well-studied model organisms such as this one is incomplete, this network reconstruction contains gaps and possible errors. There are a total of 208 blocked metabolites in iJO1366, representing gaps in the network.ResultsA new model improvement workflow was developed to compare model based phenotypic predictions to experimental data to fill gaps and correct errors. A Keio Collection based dataset of E. coli gene essentiality was obtained from literature data and compared to model predictions. The SMILEY algorithm was then used to predict the most likely missing reactions in the reconstructed network, adding reactions from a KEGG based universal set of metabolic reactions. The feasibility of these putative reactions was determined by comparing updated versions of the model to the experimental dataset, and genes were predicted for the most feasible reactions.ConclusionsNumerous improvements to the iJO1366 metabolic reconstruction were suggested by these analyses. Experiments were performed to verify several computational predictions, including a new mechanism for growth on myo-inositol. The other predictions made in this study should be experimentally verifiable by similar means. Validating all of the predictions made here represents a substantial but important undertaking