AMBRA1 levels predict resistance to MAPK inhibitors in melanoma.

Intrinsic and acquired resistance to mitogen-activated protein kinase inhibitors (MAPKi) in melanoma remains a major therapeutic challenge. Here, we show that the clinical development of resistance to MAPKi is associated with reduced tumor expression of the melanoma suppressor Autophagy and Beclin 1 Regulator 1 (AMBRA1) and that lower expression levels of AMBRA1 predict a poor response to MAPKi treatment. Functional analyses show that loss of AMBRA1 induces phenotype switching and orchestrates an extracellular signal-regulated kinase (ERK)-independent resistance mechanism by activating focal adhesion kinase 1 (FAK1). In both in vitro and in vivo settings, melanomas with low AMBRA1 expression exhibit intrinsic resistance to MAPKi therapy but higher sensitivity to FAK1 inhibition. Finally, we show that the rapid development of resistance in initially MAPKi-sensitive melanomas can be attributed to preexisting subclones characterized by low AMBRA1 expression and that cotreatment with MAPKi and FAK1 inhibitors (FAKi) effectively prevents the development of resistance in these tumors. In summary, our findings underscore the value of AMBRA1 expression for predicting melanoma response to MAPKi and supporting the therapeutic efficacy of FAKi to overcome MAPKi-induced resistance.

Metabolic modelling-based in silico drug target prediction identifies six novel repurposable drugs for melanoma.

Despite high initial response rates to targeted kinase inhibitors, the majority of patients suffering from metastatic melanoma present with high relapse rates, demanding for alternative therapeutic options. We have previously developed a drug repurposing workflow to identify metabolic drug targets that, if depleted, inhibit the growth of cancer cells without harming healthy tissues. In the current study, we have applied a refined version of the workflow to specifically predict both, common essential genes across various cancer types, and melanoma-specific essential genes that could potentially be used as drug targets for melanoma treatment. The in silico single gene deletion step was adapted to simulate the knock-out of all targets of a drug on an objective function such as growth or energy balance. Based on publicly available, and in-house, large-scale transcriptomic data metabolic models for melanoma were reconstructed enabling the prediction of 28 candidate drugs and estimating their respective efficacy. Twelve highly efficacious drugs with low half-maximal inhibitory concentration values for the treatment of other cancers, which are not yet approved for melanoma treatment, were used for in vitro validation using melanoma cell lines. Combination of the top 4 out of 6 promising candidate drugs with BRAF or MEK inhibitors, partially showed synergistic growth inhibition compared to individual BRAF/MEK inhibition. Hence, the repurposing of drugs may enable an increase in therapeutic options e.g., for non-responders or upon acquired resistance to conventional melanoma treatments.

Ambra1 modulates the tumor immune microenvironment and response to PD-1 blockade in melanoma.

BACKGROUND: Loss of Ambra1 (autophagy and beclin 1 regulator 1), a multifunctional scaffold protein, promotes the formation of nevi and contributes to several phases of melanoma development. The suppressive functions of Ambra1 in melanoma are mediated by negative regulation of cell proliferation and invasion; however, evidence suggests that loss of Ambra1 may also affect the melanoma microenvironment. Here, we investigate the possible impact of Ambra1 on antitumor immunity and response to immunotherapy. METHODS: This study was performed using an Ambra1-depleted BrafV600E /Pten-/ - genetically engineered mouse (GEM) model of melanoma, as well as GEM-derived allografts of BrafV600E /Pten-/ - and BrafV600E /Pten-/ -/Cdkn2a-/ - tumors with Ambra1 knockdown. The effects of Ambra1 loss on the tumor immune microenvironment (TIME) were analyzed using NanoString technology, multiplex immunohistochemistry, and flow cytometry. Transcriptome and CIBERSORT digital cytometry analyses of murine melanoma samples and human melanoma patients (The Cancer Genome Atlas) were applied to determine the immune cell populations in null or low-expressing AMBRA1 melanoma. The contribution of Ambra1 on T-cell migration was evaluated using a cytokine array and flow cytometry. Tumor growth kinetics and overall survival analysis in BrafV600E /Pten-/ -/Cdkn2a-/ - mice with Ambra1 knockdown were evaluated prior to and after administration of a programmed cell death protein-1 (PD-1) inhibitor. RESULTS: Loss of Ambra1 was associated with altered expression of a wide range of cytokines and chemokines as well as decreased infiltration of tumors by regulatory T cells, a subpopulation of T cells with potent immune-suppressive properties. These changes in TIME composition were associated with the autophagic function of Ambra1. In the BrafV600E /Pten-/ -/Cdkn2a-/ - model inherently resistant to immune checkpoint blockade, knockdown of Ambra1 led to accelerated tumor growth and reduced overall survival, but at the same time conferred sensitivity to anti-PD-1 treatment. CONCLUSIONS: This study shows that loss of Ambra1 affects the TIME and the antitumor immune response in melanoma, highlighting new functions of Ambra1 in the regulation of melanoma biology.

scFASTCORMICS: A Contextualization Algorithm to Reconstruct Metabolic Multi-Cell Population Models from Single-Cell RNAseq Data.

Tumours are composed of various cancer cell populations with different mutation profiles, phenotypes and metabolism that cause them to react to drugs in diverse manners. Increasing the resolution of metabolic models based on single-cell expression data will provide deeper insight into such metabolic differences and improve the predictive power of the models. scFASTCORMICS is a network contextualization algorithm that builds multi-cell population genome-scale models from single-cell RNAseq data. The models contain a subnetwork for each cell population in a tumour, allowing to capture metabolic variations between these clusters. The subnetworks are connected by a union compartment that permits to simulate metabolite exchanges between cell populations in the microenvironment. scFASTCORMICS uses Pareto optimization to simultaneously maximise the compactness, completeness and specificity of the reconstructed metabolic models. scFASTCORMICS is implemented in MATLAB and requires the installation of the COBRA toolbox, rFASTCORMICS and the IBM CPLEX solver.

Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases.

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition.

Drug Target Prediction Using Context-Specific Metabolic Models Reconstructed from rFASTCORMICS.

Metabolic modeling is a powerful computational tool to analyze metabolism. It has not only been used to identify metabolic rewiring strategies in cancer but also to predict drug targets and candidate drugs for repurposing. Here, we will elaborate on the reconstruction of context-specific metabolic models of cancer using rFASTCORMICS and the subsequent prediction of drugs for repurposing using our drug prediction workflow.

Bruceine D Identified as a Drug Candidate against Breast Cancer by a Novel Drug Selection Pipeline and Cell Viability Assay.

The multi-target effects of natural products allow us to fight complex diseases like cancer on multiple fronts. Unlike docking techniques, network-based approaches such as genome-scale metabolic modelling can capture multi-target effects. However, the incompleteness of natural product target information reduces the prediction accuracy of in silico gene knockout strategies. Here, we present a drug selection workflow based on context-specific genome-scale metabolic models, built from the expression data of cancer cells treated with natural products, to predict cell viability. The workflow comprises four steps: first, in silico single-drug and drug combination predictions; second, the assessment of the effects of natural products on cancer metabolism via the computation of a dissimilarity score between the treated and control models; third, the identification of natural products with similar effects to the approved drugs; and fourth, the identification of drugs with the predicted effects in pathways of interest, such as the androgen and estrogen pathway. Out of the initial 101 natural products, nine candidates were tested in a 2D cell viability assay. Bruceine D, emodin, and scutellarein showed a dose-dependent inhibition of MCF-7 and Hs 578T cell proliferation with IC50 values between 0.7 to 65 µM, depending on the drug and cell line. Bruceine D, extracted from Brucea javanica seeds, showed the highest potency.

DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling.

The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the virus replication within the host tissue. Making use of expression datasets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, ferroptosis, and pyrimidine metabolism. By in silico screening of Food and Drug Administration (FDA)-approved drugs on the putative disease-specific essential genes and gene pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 (https://github.com/sysbiolux/DCcov).

Towards the routine use of in silico screenings for drug discovery using metabolic modelling.

Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.

The Power of LC-MS Based Multiomics: Exploring Adipogenic Differentiation of Human Mesenchymal Stem/Stromal Cells.

The molecular study of fat cell development in the human body is essential for our understanding of obesity and related diseases. Mesenchymal stem/stromal cells (MSC) are the ideal source to study fat formation as they are the progenitors of adipocytes. In this work, we used human MSCs, received from surgery waste, and differentiated them into fat adipocytes. The combination of several layers of information coming from lipidomics, metabolomics and proteomics enabled network analysis of the biochemical pathways in adipogenesis. Simultaneous analysis of metabolites, lipids, and proteins in cell culture is challenging due to the compound's chemical difference, so most studies involve separate analysis with unimolecular strategies. In this study, we employed a multimolecular approach using a two-phase extraction to monitor the crosstalk between lipid metabolism and protein-based signaling in a single sample (~105 cells). We developed an innovative analytical workflow including standardization with in-house produced 13C isotopically labeled compounds, hyphenated high-end mass spectrometry (high-resolution Orbitrap MS), and chromatography (HILIC, RP) for simultaneous untargeted screening and targeted quantification. Metabolite and lipid concentrations ranged over three to four orders of magnitude and were detected down to the low fmol (absolute on column) level. Biological validation and data interpretation of the multiomics workflow was performed based on proteomics network reconstruction, metabolic modelling (MetaboAnalyst 4.0), and pathway analysis (OmicsNet). Comparing MSCs and adipocytes, we observed significant regulation of different metabolites and lipids such as triglycerides, gangliosides, and carnitine with 113 fully reprogrammed pathways. The observed changes are in accordance with literature findings dealing with adipogenic differentiation of MSC. These results are a proof of principle for the power of multimolecular extraction combined with orthogonal LC-MS assays and network construction. Considering the analytical and biological validation performed in this study, we conclude that the proposed multiomics workflow is ideally suited for comprehensive follow-up studies on adipogenesis and is fit for purpose for different applications with a high potential to understand the complex pathophysiology of diseases.

Integrated In Vitro and In Silico Modeling Delineates the Molecular Effects of a Synbiotic Regimen on Colorectal-Cancer-Derived Cells.

By modulating the human gut microbiome, prebiotics and probiotics (combinations of which are called synbiotics) may be used to treat diseases such as colorectal cancer (CRC). Methodological limitations have prevented determining the potential combinatorial mechanisms of action of such regimens. We expanded our HuMiX gut-on-a-chip model to co-culture CRC-derived epithelial cells with a model probiotic under a simulated prebiotic regimen, and we integrated the multi-omic results with in silico metabolic modeling. In contrast to individual prebiotic or probiotic treatments, the synbiotic regimen caused downregulation of genes involved in procarcinogenic pathways and drug resistance, and reduced levels of the oncometabolite lactate. Distinct ratios of organic and short-chain fatty acids were produced during the simulated regimens. Treatment of primary CRC-derived cells with a molecular cocktail reflecting the synbiotic regimen attenuated self-renewal capacity. Our integrated approach demonstrates the potential of modeling for rationally formulating synbiotics-based treatments in the future.

Identifying and targeting cancer-specific metabolism with network-based drug target prediction.

BACKGROUND: Metabolic rewiring allows cancer cells to sustain high proliferation rates. Thus, targeting only the cancer-specific cellular metabolism will safeguard healthy tissues. METHODS: We developed the very efficient FASTCORMICS RNA-seq workflow (rFASTCORMICS) to build 10,005 high-resolution metabolic models from the TCGA dataset to capture metabolic rewiring strategies in cancer cells. Colorectal cancer (CRC) was used as a test case for a repurposing workflow based on rFASTCORMICS. FINDINGS: Alternative pathways that are not required for proliferation or survival tend to be shut down and, therefore, tumours display cancer-specific essential genes that are significantly enriched for known drug targets. We identified naftifine, ketoconazole, and mimosine as new potential CRC drugs, which were experimentally validated. INTERPRETATION: The here presented rFASTCORMICS workflow successfully reconstructs a metabolic model based on RNA-seq data and successfully predicted drug targets and drugs not yet indicted for colorectal cancer. FUND: This study was supported by the University of Luxembourg (IRP grant scheme; R-AGR-0755-12), the Luxembourg National Research Fund (FNR PRIDE PRIDE15/10675146/CANBIO), the Fondation Cancer (Luxembourg), the European Union's Horizon2020 research and innovation programme under the Marie Sklodowska- Curie grant agreement No 642295 (MEL-PLEX), and the German Federal Ministry of Education and Research (BMBF) within the project MelanomSensitivity (BMBF/BM/7643621).

The FASTCORE Family: For the Fast Reconstruction of Compact Context-Specific Metabolic Networks Models.

The FASTCORE family is a family of algorithms that are mainly used to build context-specific models but can also be applied to other tasks such as gapfilling and consistency testing. The FASTCORE family has very low computational demands with running times that are several orders of magnitude lower than its main competitors. Furthermore, the models built by the FASTCORE family have a better resolution power (defined as the ability to capture metabolic variations between different tissues, cell types, or contexts) than models from other algorithms.

Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond.

Genome-scale metabolic network reconstructions provide a basis for the investigation of the metabolic properties of an organism. There are reconstructions available for multiple organisms, from prokaryotes to higher organisms and methods for the analysis of a reconstruction. One example is the use of flux balance analysis to improve the yields of a target chemical, which has been applied successfully. However, comparison of results between existing reconstructions and models presents a challenge because of the heterogeneity of the available reconstructions, for example, of standards for presenting gene-protein-reaction associations, nomenclature of metabolites and reactions or selection of protonation states. The lack of comparability for gene identifiers or model-specific reactions without annotated evidence often leads to the creation of a new model from scratch, as data cannot be properly matched otherwise. In this contribution, we propose to improve the predictive power of metabolic models by switching from gene-protein-reaction associations to transcript-isoform-reaction associations, thus taking advantage of the improvement of precision in gene expression measurements. To achieve this precision, we discuss available databases that can be used to retrieve this type of information and point at issues that can arise from their neglect. Further, we stress issues that arise from non-standardized building pipelines, like inconsistencies in protonation states. In addition, problems arising from the use of non-specific cofactors, e.g. artificial futile cycles, are discussed, and finally efforts of the metabolic modelling community to unify model reconstructions are highlighted.

Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network.

BACKGROUND: The reconstruction of context-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important in research and medicine. Current reconstruction methods suffer from high computational effort and arbitrary threshold setting. Moreover, understanding the underlying epigenetic regulation might allow the identification of putative intervention points within metabolic networks. Genes under high regulatory load from multiple enhancers or super-enhancers are known key genes for disease and cell identity. However, their role in regulation of metabolism and their placement within the metabolic networks has not been studied. METHODS: Here we present FASTCORMICS, a fast and robust workflow for the creation of high-quality metabolic models from transcriptomics data. FASTCORMICS is devoid of arbitrary parameter settings and due to its low computational demand allows cross-validation assays. Applying FASTCORMICS, we have generated models for 63 primary human cell types from microarray data, revealing significant differences in their metabolic networks. RESULTS: To understand the cell type-specific regulation of the alternative metabolic pathways we built multiple models during differentiation of primary human monocytes to macrophages and performed ChIP-Seq experiments for histone H3 K27 acetylation (H3K27ac) to map the active enhancers in macrophages. Focusing on the metabolic genes under high regulatory load from multiple enhancers or super-enhancers, we found these genes to show the most cell type-restricted and abundant expression profiles within their respective pathways. Importantly, the high regulatory load genes are associated to reactions enriched for transport reactions and other pathway entry points, suggesting that they are critical regulatory control points for cell type-specific metabolism. CONCLUSIONS: By integrating metabolic modelling and epigenomic analysis we have identified high regulatory load as a common feature of metabolic genes at pathway entry points such as transporters within the macrophage metabolic network. Analysis of these control points through further integration of metabolic and gene regulatory networks in various contexts could be beneficial in multiple fields from identification of disease intervention strategies to cellular reprogramming.

Fast reconstruction of compact context-specific metabolic network models.

Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms.