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Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases. Here, the authors present a method to build genetically personalised metabolic models across tissues to estimate individualised reaction fluxes. A fluxome-wide association study in UK Biobank identifies fluxes associated with metabolites and coronary artery disease.

In recent years, the central role of cell bioenergetics in regulating immune cell function and fate has been recognized, giving rise to the interest in immunometabolism, an area of research focused on the interaction between metabolic regulation and immune function. Thus, early metabolic changes associated with the polarization of macrophages into pro-inflammatory or pro-resolving cells under different stimuli have been characterized. Tumor-associated macrophages are among the most abundant cells in the tumor microenvironment; however, it exists an unmet need to study the effect of chemotherapeutics on macrophage immunometabolism. Here, we use a systems biology approach that integrates transcriptomics and metabolomics to unveil the immunometabolic effects of trabectedin (TRB) and lurbinectedin (LUR), two DNA-binding agents with proven antitumor activity. Our results show that TRB and LUR activate human macrophages toward a pro-inflammatory phenotype by inducing a specific metabolic rewiring program that includes ROS production, changes in the mitochondrial inner membrane potential, increased pentose phosphate pathway, lactate release, tricarboxylic acids (TCA) cycle, serine and methylglyoxal pathways in human macrophages. Glutamine, aspartate, histidine, and proline intracellular levels are also decreased, whereas oxygen consumption is reduced. The observed immunometabolic changes explain additional antitumor activities of these compounds and open new avenues to design therapeutic interventions that specifically target the immunometabolic landscape in the treatment of cancer.

Metabolic adaptations to complex perturbations, like the response to pharmacological treatments in multifactorial diseases such as cancer, can be described through measurements of part of the fluxes and concentrations at the systemic level and individual transporter and enzyme activities at the molecular level. In the framework of Metabolic Control Analysis (MCA), ensembles of linear constraints can be built integrating these measurements at both systemic and molecular levels, which are expressed as relative differences or changes produced in the metabolic adaptation. Here, combining MCA with Linear Programming, an efficient computational strategy is developed to infer additional non-measured changes at the molecular level that are required to satisfy these constraints. An application of this strategy is illustrated by using a set of fluxes, concentrations, and differentially expressed genes that characterize the response to cyclin-dependent kinases 4 and 6 inhibition in colon cancer cells. Decreases and increases in transporter and enzyme individual activities required to reprogram the measured changes in fluxes and concentrations are compared with down-regulated and up-regulated metabolic genes to unveil those that are key molecular drivers of the metabolic response.

Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are 13C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using 13C Metabolic Flux Analysis (13C MFA) to provide quantitative metabolic maps of flux distributions. However, 13C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious 13C MFA (p13CMFA), an approach that runs a secondary optimization in the 13C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with 13C data. As proof of concept, we demonstrate how p13CMFA can be used to estimate intracellular flux distributions from 13C measurements and transcriptomics data. We have implemented p13CMFA in Iso2Flux, our in-house developed isotopic steady-state 13C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2).

Existing immune signatures and tumor mutational burden have only modest predictive capacity for the efficacy of immune check point inhibitors. In this study, we developed an immune-metabolic signature suitable for personalized ICI therapies. A classifier using an immune-metabolic signature (IMMETCOLS) was developed on a training set of 77 metastatic colorectal cancer (mCRC) samples and validated on 4,200 tumors from the TCGA database belonging to 11 types. Here, we reveal that the IMMETCOLS signature classifies tumors into three distinct immune-metabolic clusters. Cluster 1 displays markers of enhanced glycolisis, hexosamine byosinthesis and epithelial-to-mesenchymal transition. On multivariate analysis, cluster 1 tumors were enriched in pro-immune signature but not in immunophenoscore and were associated with the poorest median survival. Its predicted tumor metabolic features suggest an acidic-lactate-rich tumor microenvironment (TME) geared to an immunosuppressive setting, enriched in fibroblasts. Cluster 2 displays features of gluconeogenesis ability, which is needed for glucose-independent survival and preferential use of alternative carbon sources, including glutamine and lipid uptake/β-oxidation. Its metabolic features suggest a hypoxic and hypoglycemic TME, associated with poor tumor-associated antigen presentation. Finally, cluster 3 is highly glycolytic but also has a solid mitochondrial function, with concomitant upregulation of glutamine and essential amino acid transporters and the pentose phosphate pathway leading to glucose exhaustion in the TME and immunosuppression. Together, these findings suggest that the IMMETCOLS signature provides a classifier of tumors from diverse origins, yielding three clusters with distinct immune-metabolic profiles, representing a new predictive tool for patient selection for specific immune-metabolic therapeutic approaches.

The liver performs many essential metabolic functions, which can be studied using computational models of hepatocytes. Here we present HepatoDyn, a highly detailed dynamic model of hepatocyte metabolism. HepatoDyn includes a large metabolic network, highly detailed kinetic laws, and is capable of dynamically simulating the redox and energy metabolism of hepatocytes. Furthermore, the model was coupled to the module for isotopic label propagation of the software package IsoDyn, allowing HepatoDyn to integrate data derived from 13C based experiments. As an example of dynamical simulations applied to hepatocytes, we studied the effects of high fructose concentrations on hepatocyte metabolism by integrating data from experiments in which rat hepatocytes were incubated with 20 mM glucose supplemented with either 3 mM or 20 mM fructose. These experiments showed that glycogen accumulation was significantly lower in hepatocytes incubated with medium supplemented with 20 mM fructose than in hepatocytes incubated with medium supplemented with 3 mM fructose. Through the integration of extracellular fluxes and 13C enrichment measurements, HepatoDyn predicted that this phenomenon can be attributed to a depletion of cytosolic ATP and phosphate induced by high fructose concentrations in the medium.

Abstract It has been recently suggested that a significant fraction of homomer protein–protein interfaces evolve neutrally, without contributing to function, due to a hydrophobic bias in missense mutations. However, the fraction of such gratuitous complexes is currently unknown. Here, we quantified the fraction of homodimers where multimerization is unlikely to contribute to their biochemical function. We show that: 1) ligand binding-site structure predicts whether a homomer is functional or not; the vast majority of homodimers with multichain binding-sites (MBS) are likely to be functional, while in homodimers with single-chain binding-sites (SBS) and small to medium interfaces, quaternary structure is unlikely to be functional in a significant fraction—35%, even up to 42%—of complexes; 2) the hydrophobicity of interfaces changes little with the strength of selection, and the amino acid composition of interfaces is shaped by the “hydrophobic ratchet” in both types, but they are not in a strict equilibrium with mutations; particularly cysteines are much more abundant in mutations than in interfaces or surfaces; 3) in MBS homomers, the interfaces are conserved, while in a high fraction of SBS homomers, the interface is not more conserved than the solvent-accessible surface; and 4) MBS homomer interfaces coevolve more strongly with ligand binding sites than the interfaces of SBS homomers, and MBS complexes have higher capacity to transfer information from ligands across the interfaces than SBS homomers, explaining the enrichment of allostery in the former.

Cuatro años antes, George H. W. Bush había usado esa frase («leed mis labios») para prometer que no subiría los impuestos, pero con ambas cámaras dominadas por los Demócratas, esa promesa se demostró difícil de sostener. Incluso en las presidenciales de los Estados Unidos más recientes, Hillary Clinton usó esta estructura cuando recopiló declaraciones ofensivas sobre las mujeres del ahora presidente. Sin embargo, lo que es relevante es que tanto lo uno como lo otro se ha democratizado a medida que la tecnología ha ido siendo cada vez más accesible para más gente con menos conocimientos técnicos. La abundancia de materiales audiovisuales, la universalización del acceso a las herramientas de edición y la atomización de los canales de difusión dibujan una escenario en el que va a ser materialmente imposible para un ciudadano de a pie discernir qué es real y qué no lo es sin tener que invertir en ello gran cantidad de tiempo o recursos.

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics 1 . Here we examine a large cohort (the INTERVAL study 2 ; n  = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n  = 3,175; Olink, n  = 4,822), plasma metabolomics (Metabolon HD4, n  = 8,153), serum metabolomics (Nightingale, n  = 37,359) and whole-blood Illumina RNA sequencing ( n  = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank 3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK–STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores. A machine learning approach is used to analyse multi-omics (proteomics, metabolomics and transcriptomics) data, producing genetic scores for more than 17,000 biomolecular traits in human blood, and identifying possible associations with disease.

BackgroundImmune check-point blockade (ICB) has shown clinical benefit in mismatch repair-deficient/microsatellite instability high metastatic colorectal cancer (mCRC) but not in mismatch repair-proficient/microsatellite stable patients. Cancer vaccines with autologous dendritic cells (ADC) could be a complementary therapeutic approach to ICB as this combination has the potential to achieve synergistic effects. MethodsThis was a Phase I/II multicentric study with translational sub-studies, to evaluate the safety, pharmacodynamics and anti-tumor effects of Avelumab plus ADC vaccine in heavily pre-treated MSS mCRC patients. Primary objective was to determine the maximum tolerated dose and the efficacy of the combination. The primary end-point was 40% progression-free survival at 6 months with a 2 Simon Stage. ResultsA total of 28 patients were screened and 19 pts were included. Combined therapy was safe and well tolerated. An interim analysis (Simon design first-stage) recommended early termination because only 2/19 (11%) patients were disease free at 6 months. Median PFS was 3.1 months [2.1–5.3 months] and overall survival was 12.2 months [3.2–23.2 months]. Stimulation of immune system was observed in vitro but not clinically. The evaluation of basal RNA-seq noted significant changes between pre and post-therapy liver biopsies related to lipid metabolism and transport, inflammation and oxidative stress pathways. ConclusionsThe combination of Avelumab plus ADC vaccine is safe and well tolerated but exhibited modest clinical activity. Our study describes, for the first-time, a de novo post-therapy metabolic rewiring, that could represent novel immunotherapy-induced tumor vulnerabilities.