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

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

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.

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

Acute Carpal Tunnel Syndrome: Early Nerve Decompression and Surgical Stabilization for Bony Wrist Trauma

Background:. We undertook this study to investigate the outcomes of surgical treatment for acute carpal tunnel syndrome following our protocol for concurrent nerve decompression and skeletal stabilization for bony wrist trauma to be undertaken within 48 hours. Methods:. We identified all patients treated at our trauma center following this protocol between January 1, 2014 and December 31, 2019. All patients were clinically reviewed at least 12 months after surgery and assessed using the Brief Michigan Hand Outcomes Questionnaire, the Boston Carpal Tunnel Questionnaire, and sensory assessment with Semmes-Weinstein monofilament testing. Results:. The study group was made up of 35 patients. Thirty-three patients were treated within 36 hours. Patients treated with our unit protocol for early surgery comprising nerve decompression and bony stabilization within 36 hours report excellent outcomes at medium term follow-up. Conclusions:. We propose that nerve decompression and bony surgical stabilization should be undertaken as soon as practically possible once the diagnosis is made. This is emergent treatment to protect and preserve nerve function. In our experience, the vast majority of patients were treated within 24 hours; however, where a short period of observation was required, excellent results were generally achieved when treatment was completed within 36 hours

Genome-scale network modeling

Genome-scale models have garnered considerable interest for their ability to elucidate cellular characteristics and lead to a better understanding of biological systems. Metabolic models in particular have been widely used to study complex metabolic pathways in order to better understand microbial systems and to design strategies for engineering various biotechnological applications. Similar to metabolic networks, transcriptional and signaling network models have also been reconstructed to elucidate regulatory interactions and to further understand the response of systems to various environmental stimuli. However, a true genome-scale model that integrates all these characteristics into one comprehensive model has not yet been constructed. For the time being, the existing network models have individually contributed to the knowledge of their respective fields and to our understanding of biological systems. In selected cases they have provided design strategies for systems wide engineering of metabolism. There have been several attempts to integrate these networks to realize the full potential of a complete cellular network model, although there is still room for further development. Here, we review the different network types and highlight their contributions to biotechnological applications via illustrative examples

Identification of symbol digit modality test score extremes in Huntington's disease

REGISTRY Investigators of the European Huntington's Disease Network and COHORT Investigators of the Huntington Study Group.Studying individuals with extreme phenotypes could facilitate the understanding of disease modification by genetic or environmental factors. Our aim was to identify Huntington's disease (HD) patients with extreme symbol digit modality test (SDMT) scores. We first examined in HD the contribution of cognitive measures of the Unified Huntington's Disease Rating Scale (UHDRS) in predicting clinical endpoints. The language-independent SDMT was used to identify patients performing very well or very poorly relative to their CAG and age cohort. We used data from REGISTRY and COHORT observational study participants (5,603 HD participants with CAG repeats above 39 with 13,868 visits) and of 1,006 healthy volunteers (with 2,241 visits), included to identify natural aging and education effects on cognitive measures. Separate Cox proportional hazards models with CAG, age at study entry, education, sex, UHDRS total motor score and cognitive (SDMT, verbal fluency, Stroop tests) scores as covariates were used to predict clinical endpoints. Quantile regression for longitudinal language-independent SDMT data was used for boundary (2.5% and 97.5% quantiles) estimation and extreme score analyses stratified by age, education, and CAG repeat length. Ten percent of HD participants had an extreme SDMT phenotype for at least one visit. In contrast, only about 3% of participants were consistent SDMT extremes at two or more visits. The thresholds for the one-visit and two-visit extremes can be used to classify existing and new individuals. The identification of these phenotype extremes can be useful in the search for disease modifiers.This work was in part funded by a grant from the EuropeanCommission under the 7th framework programme (RD-Connect, grantagreement number 305444)