Hepatic insulin resistance and muscle insulin resistance are characterized by distinct postprandial plasma metabolite profiles: a cross-sectional study.

BACKGROUND: Tissue-specific insulin resistance (IR) predominantly in muscle (muscle IR) or liver (liver IR) has previously been linked to distinct fasting metabolite profiles, but postprandial metabolite profiles have not been investigated in tissue-specific IR yet. Given the importance of postprandial metabolic impairments in the pathophysiology of cardiometabolic diseases, we compared postprandial plasma metabolite profiles in response to a high-fat mixed meal between individuals with predominant muscle IR or liver IR. METHODS: This cross-sectional study included data from 214 women and men with BMI 25-40 kg/m2, aged 40-75 years, and with predominant muscle IR or liver IR. Tissue-specific IR was assessed using the muscle insulin sensitivity index (MISI) and hepatic insulin resistance index (HIRI), which were calculated from the glucose and insulin responses during a 7-point oral glucose tolerance test. Plasma samples were collected before (T = 0) and after (T = 30, 60, 120, 240 min) consumption of a high-fat mixed meal and 247 metabolite measures, including lipoproteins, cholesterol, triacylglycerol (TAG), ketone bodies, and amino acids, were quantified using nuclear magnetic resonance spectroscopy. Differences in postprandial plasma metabolite iAUCs between muscle and liver IR were tested using ANCOVA with adjustment for age, sex, center, BMI, and waist-to-hip ratio. P-values were adjusted for a false discovery rate (FDR) of 0.05 using the Benjamini-Hochberg method. RESULTS: Sixty-eight postprandial metabolite iAUCs were significantly different between liver and muscle IR. Liver IR was characterized by greater plasma iAUCs of large VLDL (p = 0.004), very large VLDL (p = 0.002), and medium-sized LDL particles (p = 0.026), and by greater iAUCs of TAG in small VLDL (p = 0.025), large VLDL (p = 0.003), very large VLDL (p = 0.002), all LDL subclasses (all p < 0.05), and small HDL particles (p = 0.011), compared to muscle IR. In liver IR, the postprandial plasma fatty acid (FA) profile consisted of a higher percentage of saturated FA (p = 0.013), and a lower percentage of polyunsaturated FA (p = 0.008), compared to muscle IR. CONCLUSION: People with muscle IR or liver IR have distinct postprandial plasma metabolite profiles, with more unfavorable postprandial metabolite responses in those with liver IR compared to muscle IR.

Analysis of high-dimensional metabolomics data with complex temporal dynamics using RM-ASCA.

The intricate dependency structure of biological "omics" data, particularly those originating from longitudinal intervention studies with frequently sampled repeated measurements renders the analysis of such data challenging. The high-dimensionality, inter-relatedness of multiple outcomes, and heterogeneity in the studied systems all add to the difficulty in deriving meaningful information. In addition, the subtle differences in dynamics often deemed meaningful in nutritional intervention studies can be particularly challenging to quantify. In this work we demonstrate the use of quantitative longitudinal models within the repeated-measures ANOVA simultaneous component analysis+ (RM-ASCA+) framework to capture the dynamics in frequently sampled longitudinal data with multivariate outcomes. We illustrate the use of linear mixed models with polynomial and spline basis expansion of the time variable within RM-ASCA+ in order to quantify non-linear dynamics in a simulation study as well as in a metabolomics data set. We show that the proposed approach presents a convenient and interpretable way to systematically quantify and summarize multivariate outcomes in longitudinal studies while accounting for proper within subject dependency structures.

Comparison of metabolic states using genome-scale metabolic models.

Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks.

Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge.

Plasma glucose and insulin responses following an oral glucose challenge are representative of glucose tolerance and insulin resistance, key indicators of type 2 diabetes mellitus pathophysiology. A large heterogeneity in individuals' challenge test responses has been shown to underlie the effectiveness of lifestyle intervention. Currently, this heterogeneity is overlooked due to a lack of methods to quantify the interconnected dynamics in the glucose and insulin time-courses. Here, a physiology-based mathematical model of the human glucose-insulin system is personalized to elucidate the heterogeneity in individuals' responses using a large population of overweight/obese individuals (n = 738) from the DIOGenes study. The personalized models are derived from population level models through a systematic parameter selection pipeline that may be generalized to other biological systems. The resulting personalized models showed a 4-5 fold decrease in discrepancy between measurements and model simulation compared to population level. The estimated model parameters capture relevant features of individuals' metabolic health such as gastric emptying, endogenous insulin secretion and insulin dependent glucose disposal into tissues, with the latter also showing a significant association with the Insulinogenic index and the Matsuda insulin sensitivity index, respectively.

Phenotypic clustering of dilated cardiomyopathy patients highlights important pathophysiological differences.

AIMS: The dilated cardiomyopathy (DCM) phenotype is the result of combined genetic and acquired triggers. Until now, clinical decision-making in DCM has mainly been based on ejection fraction (EF) and NYHA classification, not considering the DCM heterogenicity. The present study aimed to identify patient subgroups by phenotypic clustering integrating aetiologies, comorbidities, and cardiac function along cardiac transcript levels, to unveil pathophysiological differences between DCM subgroups. METHODS AND RESULTS: We included 795 consecutive DCM patients from the Maastricht Cardiomyopathy Registry who underwent in-depth phenotyping, comprising extensive clinical data on aetiology and comorbodities, imaging and endomyocardial biopsies. Four mutually exclusive and clinically distinct phenogroups (PG) were identified based upon unsupervised hierarchical clustering of principal components: [PG1] mild systolic dysfunction, [PG2] auto-immune, [PG3] genetic and arrhythmias, and [PG4] severe systolic dysfunction. RNA-sequencing of cardiac samples (n = 91) revealed a distinct underlying molecular profile per PG: pro-inflammatory (PG2, auto-immune), pro-fibrotic (PG3; arrhythmia), and metabolic (PG4, low EF) gene expression. Furthermore, event-free survival differed among the four phenogroups, also when corrected for well-known clinical predictors. Decision tree modelling identified four clinical parameters (auto-immune disease, EF, atrial fibrillation, and kidney function) by which every DCM patient from two independent DCM cohorts could be placed in one of the four phenogroups with corresponding outcome (n = 789; Spain, n = 352 and Italy, n = 437), showing a feasible applicability of the phenogrouping. CONCLUSION: The present study identified four different DCM phenogroups associated with significant differences in clinical presentation, underlying molecular profiles and outcome, paving the way for a more personalized treatment approach.

Genome-Wide Polyadenylation Maps Reveal Dynamic mRNA 3'-End Formation in the Failing Human Heart.

RATIONALE: Alternative cleavage and polyadenylation (APA) of mRNA represents a layer of gene regulation that to date has remained unexplored in the heart. This phenomenon may be relevant, as the positioning of the poly(A) tail in mRNAs influences the length of the 3'-untranslated region (UTR), a critical determinant of gene expression. OBJECTIVE: To investigate whether the 3'UTR length is regulated by APA in the human heart and whether this changes in the failing heart. METHODS AND RESULTS: We used 3'end RNA sequencing (e3'-Seq) to directly measure global patterns of APA in healthy and failing human heart specimens. By monitoring polyadenylation profiles in these hearts, we identified disease-specific APA signatures in numerous genes. Interestingly, many of the genes with shortened 3'UTRs in heart failure were enriched for functional groups such as RNA binding, whereas genes with longer 3'UTRs were enriched for cytoskeletal organization and actin binding. RNA sequencing in a larger series of human hearts revealed that these APA candidates are often differentially expressed in failing hearts, with an inverse correlation between 3'UTR length and the level of gene expression. Protein levels of the APA regulator, poly(A)-binding protein nuclear-1 were substantially downregulated in failing hearts. CONCLUSIONS: We provide genome-wide, high-resolution polyadenylation maps of the human heart and show that the 3'end formation of mRNA is dynamic in heart failure, suggesting that APA-mediated 3'UTR length modulation represents an additional layer of gene regulation in failing hearts.

A user-friendly workflow for analysis of Illumina gene expression bead array data available at the arrayanalysis.org portal.

BACKGROUND: Illumina whole-genome expression bead arrays are a widely used platform for transcriptomics. Most of the tools available for the analysis of the resulting data are not easily applicable by less experienced users. ArrayAnalysis.org provides researchers with an easy-to-use and comprehensive interface to the functionality of R and Bioconductor packages for microarray data analysis. As a modular open source project, it allows developers to contribute modules that provide support for additional types of data or extend workflows. RESULTS: To enable data analysis of Illumina bead arrays for a broad user community, we have developed a module for ArrayAnalysis.org that provides a free and user-friendly web interface for quality control and pre-processing for these arrays. This module can be used together with existing modules for statistical and pathway analysis to provide a full workflow for Illumina gene expression data analysis. The module accepts data exported from Illumina's GenomeStudio, and provides the user with quality control plots and normalized data. The outputs are directly linked to the existing statistics module of ArrayAnalysis.org, but can also be downloaded for further downstream analysis in third-party tools. CONCLUSIONS: The Illumina bead arrays analysis module is available at http://www.arrayanalysis.org . A user guide, a tutorial demonstrating the analysis of an example dataset, and R scripts are available. The module can be used as a starting point for statistical evaluation and pathway analysis provided on the website or to generate processed input data for a broad range of applications in life sciences research.

User-friendly solutions for microarray quality control and pre-processing on ArrayAnalysis.org.

Quality control (QC) is crucial for any scientific method producing data. Applying adequate QC introduces new challenges in the genomics field where large amounts of data are produced with complex technologies. For DNA microarrays, specific algorithms for QC and pre-processing including normalization have been developed by the scientific community, especially for expression chips of the Affymetrix platform. Many of these have been implemented in the statistical scripting language R and are available from the Bioconductor repository. However, application is hampered by lack of integrative tools that can be used by users of any experience level. To fill this gap, we developed a freely available tool for QC and pre-processing of Affymetrix gene expression results, extending, integrating and harmonizing functionality of Bioconductor packages. The tool can be easily accessed through a wizard-like web portal at http://www.arrayanalysis.org or downloaded for local use in R. The portal provides extensive documentation, including user guides, interpretation help with real output illustrations and detailed technical documentation. It assists newcomers to the field in performing state-of-the-art QC and pre-processing while offering data analysts an integral open-source package. Providing the scientific community with this easily accessible tool will allow improving data quality and reuse and adoption of standards.

Leveraging continuous glucose monitoring for personalized modeling of insulin-regulated glucose metabolism.

Continuous glucose monitoring (CGM) is a promising, minimally invasive alternative to plasma glucose measurements for calibrating physiology-based mathematical models of insulin-regulated glucose metabolism, reducing the reliance on in-clinic measurements. However, the use of CGM glucose, particularly in combination with insulin measurements, to develop personalized models of glucose regulation remains unexplored. Here, we simultaneously measured interstitial glucose concentrations using CGM as well as plasma glucose and insulin concentrations during an oral glucose tolerance test (OGTT) in individuals with overweight or obesity to calibrate personalized models of glucose-insulin dynamics. We compared the use of interstitial glucose with plasma glucose in model calibration, and evaluated the effects on model fit, identifiability, and model parameters' association with clinically relevant metabolic indicators. Models calibrated on both plasma and interstitial glucose resulted in good model fit, and the parameter estimates associated with metabolic indicators such as insulin sensitivity measures in both cases. Moreover, practical identifiability of model parameters was improved in models estimated on CGM glucose compared to plasma glucose. Together these results suggest that CGM glucose may be considered as a minimally invasive alternative to plasma glucose measurements in model calibration to quantify the dynamics of glucose regulation.

Elucidating the Corneal Endothelial Cell Proliferation Capacity through an Interspecies Transcriptome Comparison.

The regenerative capacity of corneal endothelial cells (CECs) differs between species; in bigger mammals, CECs are arrested in a non-proliferative state. Damage to these cells can compromise their function causing corneal opacity. Corneal transplantation is the current treatment for the recovery of clear eyesight, but the donor tissue demand is higher than the availability and there is a need to develop novel treatments. Interestingly, rabbit CECs retain a high proliferative profile and can repopulate the endothelium. There is a lack of fundamental knowledge to explain these differences. Gaining information on their transcriptomic variances could allow the identification of CEC proliferation drivers. In this study, human, sheep, and rabbit CECs are analyzed at the transcriptomic level. To understand the differences across each species, a pipeline for the analysis of pathways with different activities is generated. The results reveal that 52 pathways have different activity when comparing species with non-proliferative CECs (human and sheep) to species with proliferative CECs (rabbit). The results show that Notch and TGF-ß pathways have increased activity in species with non-proliferative CECs, which might be associated with their low proliferation. Overall, this study illustrates transcriptomic pathway-level differences that can provide leads to develop novel therapies to regenerate the corneal endothelium.

Clustering of Cardiac Transcriptome Profiles Reveals Unique: Subgroups of Dilated Cardiomyopathy Patients.

Dilated cardiomyopathy is a heterogeneous disease characterized by multiple genetic and environmental etiologies. The majority of patients are treated the same despite these differences. The cardiac transcriptome provides information on the patient's pathophysiology, which allows targeted therapy. Using clustering techniques on data from the genotype, phenotype, and cardiac transcriptome of patients with early- and end-stage dilated cardiomyopathy, more homogeneous patient subgroups are identified based on shared underlying pathophysiology. Distinct patient subgroups are identified based on differences in protein quality control, cardiac metabolism, cardiomyocyte function, and inflammatory pathways. The identified pathways have the potential to guide future treatment and individualize patient care.

Quantifying postprandial glucose responses using a hybrid modeling approach: Combining mechanistic and data-driven models in The Maastricht Study.

Computational models of human glucose homeostasis can provide insight into the physiological processes underlying the observed inter-individual variability in glucose regulation. Modelling approaches ranging from "bottom-up" mechanistic models to "top-down" data-driven techniques have been applied to untangle the complex interactions underlying progressive disturbances in glucose homeostasis. While both approaches offer distinct benefits, a combined approach taking the best of both worlds has yet to be explored. Here, we propose a sequential combination of a mechanistic and a data-driven modeling approach to quantify individuals' glucose and insulin responses to an oral glucose tolerance test, using cross sectional data from 2968 individuals from a large observational prospective population-based cohort, the Maastricht Study. The best predictive performance, measured by R2 and mean squared error of prediction, was achieved with personalized mechanistic models alone. The addition of a data-driven model did not improve predictive performance. The personalized mechanistic models consistently outperformed the data-driven and the combined model approaches, demonstrating the strength and suitability of bottom-up mechanistic models in describing the dynamic glucose and insulin response to oral glucose tolerance tests.

Cardiometabolic health improvements upon dietary intervention are driven by tissue-specific insulin resistance phenotype: A precision nutrition trial.

Precision nutrition based on metabolic phenotype may increase the effectiveness of interventions. In this proof-of-concept study, we investigated the effect of modulating dietary macronutrient composition according to muscle insulin-resistant (MIR) or liver insulin-resistant (LIR) phenotypes on cardiometabolic health. Women and men with MIR or LIR (n = 242, body mass index [BMI] 25-40 kg/m2, 40-75 years) were randomized to phenotype diet (PhenoDiet) group A or B and followed a 12-week high-monounsaturated fatty acid (HMUFA) diet or low-fat, high-protein, and high-fiber diet (LFHP) (PhenoDiet group A, MIR/HMUFA and LIR/LFHP; PhenoDiet group B, MIR/LFHP and LIR/HMUFA). PhenoDiet group B showed no significant improvements in the primary outcome disposition index, but greater improvements in insulin sensitivity, glucose homeostasis, serum triacylglycerol, and C-reactive protein compared with PhenoDiet group A were observed. We demonstrate that modulating macronutrient composition within the dietary guidelines based on tissue-specific insulin resistance (IR) phenotype enhances cardiometabolic health improvements. Clinicaltrials.gov registration: NCT03708419, CCMO registration NL63768.068.17.

An evaluation of two-channel ChIP-on-chip and DNA methylation microarray normalization strategies.

BACKGROUND: The combination of chromatin immunoprecipitation with two-channel microarray technology enables genome-wide mapping of binding sites of DNA-interacting proteins (ChIP-on-chip) or sites with methylated CpG di-nucleotides (DNA methylation microarray). These powerful tools are the gateway to understanding gene transcription regulation. Since the goals of such studies, the sample preparation procedures, the microarray content and study design are all different from transcriptomics microarrays, the data pre-processing strategies traditionally applied to transcriptomics microarrays may not be appropriate. Particularly, the main challenge of the normalization of "regulation microarrays" is (i) to make the data of individual microarrays quantitatively comparable and (ii) to keep the signals of the enriched probes, representing DNA sequences from the precipitate, as distinguishable as possible from the signals of the un-enriched probes, representing DNA sequences largely absent from the precipitate. RESULTS: We compare several widely used normalization approaches (VSN, LOWESS, quantile, T-quantile, Tukey's biweight scaling, Peng's method) applied to a selection of regulation microarray datasets, ranging from DNA methylation to transcription factor binding and histone modification studies. Through comparison of the data distributions of control probes and gene promoter probes before and after normalization, and assessment of the power to identify known enriched genomic regions after normalization, we demonstrate that there are clear differences in performance between normalization procedures. CONCLUSION: T-quantile normalization applied separately on the channels and Tukey's biweight scaling outperform other methods in terms of the conservation of enriched and un-enriched signal separation, as well as in identification of genomic regions known to be enriched. T-quantile normalization is preferable as it additionally improves comparability between microarrays. In contrast, popular normalization approaches like quantile, LOWESS, Peng's method and VSN normalization alter the data distributions of regulation microarrays to such an extent that using these approaches will impact the reliability of the downstream analysis substantially.

Hepatic Steatosis Contributes to the Development of Muscle Atrophy via Inter-Organ Crosstalk.

Individuals with hepatic steatosis often display several metabolic abnormalities including insulin resistance and muscle atrophy. Previously, we found that hepatic steatosis results in an altered hepatokine secretion profile, thereby inducing skeletal muscle insulin resistance via inter-organ crosstalk. In this study, we aimed to investigate whether the altered secretion profile in the state of hepatic steatosis also induces skeletal muscle atrophy via effects on muscle protein turnover. To investigate this, eight-week-old male C57BL/6J mice were fed a chow (4.5% fat) or a high-fat diet (HFD; 45% fat) for 12 weeks to induce hepatic steatosis, after which the livers were excised and cut into ~200-µm slices. Slices were cultured to collect secretion products (conditioned medium; CM). Differentiated L6-GLUT4myc myotubes were incubated with chow or HFD CM to measure glucose uptake. Differentiated C2C12 myotubes were incubated with chow or HFD CM to measure protein synthesis and breakdown, and gene expression via RNA sequencing. Furthermore, proteomics analysis was performed in chow and HFD CM. It was found that HFD CM caused insulin resistance in L6-GLUT4myc myotubes compared with chow CM, as indicated by a blunted insulin-stimulated increase in glucose uptake. Furthermore, protein breakdown was increased in C2C12 cells incubated with HFD CM, while there was no effect on protein synthesis. RNA profiling of C2C12 cells indicated that 197 genes were differentially expressed after incubation with HFD CM, compared with chow CM, and pathway analysis showed that pathways related to anatomical structure and function were enriched. Proteomics analysis of the CM showed that 32 proteins were differentially expressed in HFD CM compared with chow CM. Pathway enrichment analysis indicated that these proteins had important functions with respect to insulin-like growth factor transport and uptake, and affect post-translational processes, including protein folding, protein secretion and protein phosphorylation. In conclusion, the results of this study support the hypothesis that secretion products from the liver contribute to the development of muscle atrophy in individuals with hepatic steatosis.

EFMviz: A COBRA Toolbox extension to visualize Elementary Flux Modes in Genome-Scale Metabolic Models.

Elementary Flux Modes (EFMs) are a tool for constraint-based modeling and metabolic network analysis. However, systematic and automated visualization of EFMs, capable of integrating various data types is still a challenge. In this study, we developed an extension for the widely adopted COBRA Toolbox, EFMviz, for analysis and graphical visualization of EFMs as networks of reactions, metabolites and genes. The analysis workflow offers a platform for EFM visualization to improve EFM interpretability by connecting COBRA toolbox with the network analysis and visualization software Cytoscape. The biological applicability of EFMviz is demonstrated in two use cases on medium (Escherichia coli, iAF1260) and large (human, Recon 2.2) genome-scale metabolic models. EFMviz is open-source and integrated into COBRA Toolbox. The analysis workflows used for the two use cases are detailed in the two tutorials provided with EFMviz along with the data used in this study.

Stratifying cellular metabolism during weight loss: an interplay of metabolism, metabolic flexibility and inflammation.

Obesity is a global epidemic, contributing significantly to chronic non-communicable diseases, such as type 2 diabetes mellitus, cardiovascular diseases and metabolic syndrome. Metabolic flexibility, the ability of organisms to switch between metabolic substrates, is found to be impaired in obesity, possibly contributing to the development of chronic illnesses. Several studies have shown the improvement of metabolic flexibility after weight loss. In this study, we have mapped the cellular metabolism of the adipose tissue from a weight loss study to stratify the cellular metabolic processes and metabolic flexibility during weight loss. We have found that for a majority of the individuals, cellular metabolism was downregulated during weight loss, with gene expression of all major cellular metabolic processes (such as glycolysis, fatty acid ß-oxidation etc.) being lowered during weight loss and weight maintenance. Parallel to this, the gene expression of immune system related processes involving interferons and interleukins increased. Previously, studies have indicated both negative and positive effects of post-weight loss inflammation in the adipose tissue with regards to weight loss or obesity and its co-morbidities; however, mechanistic links need to be constructed in order to determine the effects further. Our study contributes towards this goal by mapping the changes in gene expression across the weight loss study and indicates possible cross-talk between cellular metabolism and inflammation.

Logical modelling reveals the PDC-PDK interaction as the regulatory switch driving metabolic flexibility at the cellular level.

BACKGROUND: Metabolic flexibility is the ability of an organism to switch between substrates for energy metabolism, in response to the changing nutritional state and needs of the organism. On the cellular level, metabolic flexibility revolves around the tricarboxylic acid cycle by switching acetyl coenzyme A production from glucose to fatty acids and vice versa. In this study, we modelled cellular metabolic flexibility by constructing a logical model connecting glycolysis, fatty acid oxidation, fatty acid synthesis and the tricarboxylic acid cycle, and then using network analysis to study the behaviours of the model. RESULTS: We observed that the substrate switching usually occurs through the inhibition of pyruvate dehydrogenase complex (PDC) by pyruvate dehydrogenase kinases (PDK), which moves the metabolism from glycolysis to fatty acid oxidation. Furthermore, we were able to verify four different regulatory models of PDK to contain known biological observations, leading to the biological plausibility of all four models across different cells and conditions. CONCLUSION: These results suggest that the cellular metabolic flexibility depends upon the PDC-PDK regulatory interaction as a key regulatory switch for changing metabolic substrates.

The public road to high-quality curated biological pathways.

Biological pathways are abstract and functional visual representations of existing biological knowledge. By mapping high-throughput data on these representations, changes and patterns in biological systems on the genetic, metabolic and protein level are instantly assessable. Many public domain repositories exist for storing biological pathways, each applying its own conventions and storage format. A pathway-based content review of these repositories reveals that none of them are comprehensive. To address this issue, we apply a general workflow to create curated biological pathways, in which we combine three content sources: public domain databases, literature and experts. In this workflow all content of a particular biological pathway is manually retrieved from biological pathway databases and literature, after which this content is compared, combined and subsequently curated by experts. From the curated content, new biological pathways can be created for a pathway analysis tool of choice and distributed among its user base. We applied this procedure to construct high-quality curated biological pathways involved in human fatty acid metabolism.