Vo2023 - M. maripaludis S2 constraint-based metabolic model
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ABSTRACT: Genome-scale metabolic model of Methanococcus maripaludis S2, including the updated biosynthetic pathways for NAD and thiamine and the salvage pathway for 5'-deoxyadenosine.
Project description:To uncover underlying mechanisms associated with failure of indoleamine 2, 3-dioxygenase 1 (IDO1) blockade in clinical trials, we conducted a pilot, window-of-opportunity clinical study testing the immunological and metabolic effects of the IDO1 inhibitor, epacadostat, in seventeen patients with newly diagnosed advanced high grade serous ovarian cancer prior to their standard tumor debulking surgery. Comprehensive immunologic, transcriptomic, and metabolomic characterization of the tumor microenvironment using baseline and post-treatment tissue biopsies revealed efficient blockade of the kynurenine pathway of tryptophan degradation. This blockade was accompanied by a metabolic adaptation that shunted tryptophan catabolism towards the serotonin pathway and elevated nicotinamide adenine dinucleotide (NAD)+ biosynthetic pathways, which was detrimental for T cell proliferation and function. Treatment of mice bearing IDO1 over-expressing ovarian tumors with the NAMPT inhibitor, FK866, did not improve tumor control by epacadostat. Because NAD+ metabolites could be ligands for purinergic receptors, we investigated the impact of blocking purinergic receptors in the presence of NAD+. We demonstrated that A2a and A2b, or the combination of A2a and A2b purinergic receptor antagonists rescued NAD+-mediated suppression of T cell proliferation, and the combination of IDO inhibition and A2a/A2b receptor blockade improved survival in the IDO1 over-expressing ovarian tumor bearing hosts. These findings unravel previously unrecognized downstream adaptive metabolic consequences of IDO1 blockade that may undermine efforts to induce tumor-specific T cell responses.
Project description:The goal of the microarray was to investigate the transcriptome changes induced by exogenous NAD+ in the wild-type Col-0 plants. Results showed that exogenous NAD+-induced dramatic transcriptional changes in Arabidopsis. Particularly, a large group of salicylic acid pathway genes including NPR1 and its traget genes were induced by NAD+, whereas the jasmonic acid/ethylene pathway defense marker gene PDF1.2 was inhibited by NAD+ treatment. In addition, a group of the pathogen-associated molecular pattern pathway genes were also induced by exogenous NAD+. These results indicate that exogenous NAD+ induces defense pathways against (hemi)biotrophic pathogens but suppresses defense against necrotrophs.
Project description:The goal of the microarray was to investigate the transcriptome changes induced by exogenous NAD+ in the wild-type Col-0 plants. Results showed that exogenous NAD+-induced dramatic transcriptional changes in Arabidopsis. Particularly, a large group of salicylic acid pathway genes including NPR1 and its traget genes were induced by NAD+, whereas the jasmonic acid/ethylene pathway defense marker gene PDF1.2 was inhibited by NAD+ treatment. In addition, a group of the pathogen-associated molecular pattern pathway genes were also induced by exogenous NAD+. These results indicate that exogenous NAD+ induces defense pathways against (hemi)biotrophic pathogens but suppresses defense against necrotrophs. Two to three replicates with leaves from 8-12 plants per sample were collected at 0, 4, and 24 hr after NAD+ treatment. Leaf tissues were collected as the control at 0 hr, and NAD+-treated leaf tissues were collected at 4 and 24 hr. After extraction, RNA concentration was determined on a NanoDrop Spectrophotometer (Thermofisher Scientific, Waltham, MA) and sample quality was assessed using the 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). Equal amount of RNA from the biological replicates were used for microarray analysis.
Project description:There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach Gene Expression and Metabolism Integrated for Network Inference (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10(-172)), significantly better than using gene expression alone. We applied GEMINI to create an integrated metabolic-regulatory network model for Saccharomyces cerevisiae involving 25,000 regulatory interactions controlling 1597 metabolic reactions. The model quantitatively predicts TF knockout phenotypes in new conditions (p-value = 10(-14)) and revealed potential condition-specific regulatory mechanisms. Our results suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to integrate and reconcile inconsistencies across different data-types. The algorithm and associated data are available at https://sourceforge.net/projects/gemini-data/
Project description:Chondrosarcomas represent the second most common primary bone malignancy. Despite the vulnerability of chondrosarcoma cells to nicotinamide adenine dinucleotide (NAD+) depletion, targeting the NAD+ synthesis pathway remains challenging due to broad implications in biological processes. Here, we establish SIRT1 as a central mediator reinforcing the dependency of chondrosarcoma cells on NAD+ metabolism via HIF-2α-mediated transcriptional reprogramming. SIRT1 knockdown abolishes aggressive phenotypes of chondrosarcomas in orthotopically transplanted tumors in mice. Chondrosarcoma cells thrive under glucose starvation by accumulating NAD+ and subsequently activating the SIRT1–HIF-2α axis. Decoupling this link via SIRT1 inhibition unleashes apoptosis and suppresses tumor progression in conjunction with chemotherapy. Unsupervised clustering analysis identifies a high-risk chondrosarcoma patient subgroup characterized by the upregulation of NAD+ biosynthesis genes. Finally, SIRT1 inhibition abolishes HIF-2α transcriptional activity and sensitizes chondrosarcoma cells to doxorubicin-induced cytotoxicity, irrespective of underlying pathways to accumulate intracellular NAD+. We provide system-level guidelines to develop therapeutic strategies for chondrosarcomas.
Project description:Cancer metabolism differs remarkably from the metabolism of healthy surrounding tissues, and it is extremely heterogeneous across cancer types. While these metabolic differences provide promising avenues for cancer treatments, much work remains to be done in understanding how metabolism is rewired in malignant tissues. To that end, constraint-based models provide a powerful computational tool for the study of metabolism at the genome scale. To generate meaningful predictions, however, these generalized human models must first be tailored for specific cell or tissue sub-types. Here we first present two improved algorithms for (1) the generation of these context-specific metabolic models based on omics data, and (2) Monte-Carlo sampling of the metabolic model ux space. By applying these methods to generate and analyze context-specific metabolic models of diverse solid cancer cell line data, and primary leukemia pediatric patient biopsies, we demonstrate how the methodology presented in this study can generate insights into the rewiring differences across solid tumors and blood cancers.
Project description:BackgroundConstraint-based models allow the calculation of the metabolic flux states that can be exhibited by cells, standing out as a powerful analytical tool, but they do not determine which of these are likely to be existing under given circumstances. Typical methods to perform these predictions are (a) flux balance analysis, which is based on the assumption that cell behaviour is optimal, and (b) metabolic flux analysis, which combines the model with experimental measurements.ResultsHerein we discuss a possibilistic framework to perform metabolic flux estimations using a constraint-based model and a set of measurements. The methodology is able to handle inconsistencies, by considering sensors errors and model imprecision, to provide rich and reliable flux estimations. The methodology can be cast as linear programming problems, able to handle thousands of variables with efficiency, so it is suitable to deal with large-scale networks. Moreover, the possibilistic estimation does not attempt necessarily to predict the actual fluxes with precision, but rather to exploit the available data--even if those are scarce--to distinguish possible from impossible flux states in a gradual way.ConclusionWe introduce a possibilistic framework for the estimation of metabolic fluxes, which is shown to be flexible, reliable, usable in scenarios lacking data and computationally efficient.
Project description:Clustering and correlation analysis techniques have become popular tools for the analysis of data produced by metabolomics experiments. The results obtained from these approaches provide an overview of the interactions between objects of interest. Often in these experiments, one is more interested in information about the nature of these relationships, e.g., cause-effect relationships, than in the actual strength of the interactions. Finding such relationships is of crucial importance as most biological processes can only be understood in this way. Bayesian networks allow representation of these cause-effect relationships among variables of interest in terms of whether and how they influence each other given that a third, possibly empty, group of variables is known. This technique also allows the incorporation of prior knowledge as established from the literature or from biologists. The representation as a directed graph of these relationship is highly intuitive and helps to understand these processes. This paper describes how constraint-based Bayesian networks can be applied to metabolomics data and can be used to uncover the important pathways which play a significant role in the ripening of fresh tomatoes. We also show here how this methods of reconstructing pathways is intuitive and performs better than classical techniques. Methods for learning Bayesian network models are powerful tools for the analysis of data of the magnitude as generated by metabolomics experiments. It allows one to model cause-effect relationships and helps in understanding the underlying processes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0166-2) contains supplementary material, which is available to authorized users.
Project description:BackgroundConstraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis.ResultsHere, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA).ConclusionFastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.