Project 2. A systems-metabolism approach to identify mitochondria-dependent vulnerabilities in colorectal cancer
Co-Leads and Co-Investigators
Jason Papin
Harrison Distinguished Teaching Professor of Biomedical Engineering
Email Lab Website Twitter
Harrison Distinguished Teaching Professor of Biomedical Engineering
Email Lab Website Twitter
Research
Mutant KRAS is a potent oncogene that drives proliferation and adaptive cell-state changes in multiple cancer types. One direct consequence of active KRAS signaling is fragmentation of the normal mitochondrial network with concomitant decreases in oxidative phosphorylation and mitochondrial membrane potential. The impact of this organelle stress is unclear in colorectal cancer, where KRAS mutations are acquired late in the disease and only in about one-third of cases. Primary tumors develop amidst short-chain fatty acids (SCFAs) and other metabolites uniquely produced by the gut flora, creating carbon sources that may impact how mid-stage colorectal cancers (CRC) adapt to an acquired KRAS mutation. Non-obvious mechanisms exist at the systems level that may cause an even greater metabolic impairment than generic decreases in oxidative phosphorylation or mitochondrial membrane potential. The hypothesis of Project 2 is that mitochondrial fragmentation causes hyper-compartmentalization of key low-abundance metabolic enzymes that constrains how primary tumors evolve in the presence of SCFAs and colonize the liver where metabolic inputs are very different. The specific aims are to 1) curate a metabolic model of human CRC cells that incorporates the system-wide impact of mitochondrial fragmentation and the availability of microbe-derived SCFAs; 2) instantiate metabolic models of CRC with data characterizing in vivo metabolic states to assess impacts of gut microbiota metabolism and mitochondrial fragmentation; and 3) evaluate the impact of metabolic adaptations to mitochondrial organelle stress on CRC colonization and growth as liver metastases. Metabolic circuits isolated by mitochondrial fission could give rise to tumor cell biochemistry that is very different from the universal roadmap assumed in most standard genome-wide metabolic network reconstructions.
Related Publications
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Biomedical Image Registration; Berlin, Heidelberg: Springer Berlin Heidelberg. 2003; .
A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching.
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A general system for automatic biomedical image segmentation using intensity neighborhoods.
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A generative model of microtubule distributions, and indirect estimation of its parameters from fluorescence microscopy images.
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Cytometry Part A : the journal of the International Society for Analytical Cytology. 2010; 77(5):457-66.
A linear optimal transportation framework for quantifying and visualizing variations in sets of images.
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Adaptive resistance of melanoma cells to RAF inhibition via reversible induction of a slowly dividing de-differentiated state.
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Ann Surg Oncol. 2016; 23(6):1993-2000.
An optimal transportation approach for nuclear structure-based pathology.
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IEEE Trans Med Imaging. 2010; 30(3):621-31.
Automated Learning of Subcellular Variation among Punctate Protein Patterns and a Generative Model of Their Relation to Microtubules.
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CD47 Blockade as an Adjuvant Immunotherapy for Resectable Pancreatic Cancer.
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Cell Image Classification: A Comparative Overview.
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Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI.
Rohde GK, Barnett AS, Basser PJ, Marenco S, Pierpaoli C.
Magn Reson Med. 2004; 51(1):103-14.
Correction of motion artifact in cardiac optical mapping using image registration.
Rohde GK, Dawant BM, Lin SF.
IEEE Trans Biomed Eng. 2005; 52(2):338-41.
Cyclic Immunofluorescence (CycIF), A Highly Multiplexed Method for Single-cell Imaging.
Lin JR, Fallahi-Sichani M, Chen JY, Sorger PK.
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Detecting and segmenting cell nuclei in two-dimensional microscopy images.
Liu C, Shang F, Ozolek JA, Rohde GK.
J Pathol Inform. 2017; 7:42.
Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry.
Basu S, Kolouri S, Rohde GK.
Proc Natl Acad Sci U S A. 2014; 111(9):3448-53.
Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images.
Wang W, Ozolek JA, Rohde GK.
Cytometry Part A : the journal of the International Society for Analytical Cytology. 2010; 77(5):485-94.
Drp1 Promotes KRas-Driven Metabolic Changes to Drive Pancreatic Tumor Growth.
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Cell Rep. 2019; 28(7):1845-59 e5.
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Proceedings of the National Academy of Sciences. 2020; 117(40):24709.
Epigenetic modulation reveals differentiation state specificity of oncogene addiction.
Khaliq M, Manikkam M, Martinez ED, Fallahi-Sichani M.
Nat Commun. 2021; 12(1):1536.
Epithelium-Stroma Classification via Convolutional Neural Networks and Unsupervised Domain Adaptation in Histopathological Images.
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Erk2 phosphorylation of Drp1 promotes mitochondrial fission and MAPK-driven tumor growth.
Kashatus JA, Nascimento A, Myers LJ, Sher A, Byrne FL, Hoehn KL, Counter CM, Kashatus DF.
Mol Cell. 2015; 57(3):537-51.
Estimating microtubule distributions from 2D immunofluorescence microscopy images reveals differences among human cultured cell lines.
Li J, Shariff A, Wiking M, Lundberg E, Rohde GK, Murphy RF.
PLoS One. 2012; 7(11):e50292.
Functional integration of a metabolic network model and expression data without arbitrary thresholding.
Jensen PA, Papin JA.
Bioinformatics. 2010; 27(4):541-7.
Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes.
Rawls KD, Blais EM, Dougherty BV, Vinnakota KC, Pannala VR, Wallqvist A, Kolling GL, Papin JA.
Toxicol Sci. 2019; 172(2):279-91.
Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning.
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Cell Syst. 2020; 10(1):109-19 e3.
Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method.
Lin JR, Fallahi-Sichani M, Sorger PK.
Nat Commun. 2015; 6:8390.
Identifying functional metabolic shifts in heart failure with the integration of omics data and a heart-specific, genome-scale model.
Dougherty BV, Rawls KD, Kolling GL, Vinnakota KC, Wallqvist A, Papin JA.
Cell Rep. 2021; 34(10):108836.
Inferring Metabolic Mechanisms of Interaction within a Defined Gut Microbiota.
Medlock GL, Carey MA, McDuffie DG, Mundy MB, Giallourou N, Swann JR, Kolling GL, Papin JA.
Cell Syst. 2018; 7(3):245-57 e7.
Interpolation artifacts in sub-pixel image registration. IEEE Trans Image Process.
Rohde GK, Aldroubi A, Healy DM, Jr.
IEEE Trans Image Process. 2009; 18(2):333-45.
Joint modeling of cell and nuclear shape variation.
Johnson GR, Buck TE, Sullivan DP, Rohde GK, Murphy RF.
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Localizing and extracting filament distributions from microscopy images.
Basu S, Liu C, Rohde GK.
J Microsc. 2015; 258(1):13-23.
Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.
Biggs MB, Papin JA.
PLoS Comput Biol. 2017; 13(3):e1005413.
Mechanistic identification of biofluid metabolite changes as markers of acetaminophen-induced liver toxicity in rats.
Pannala VR, Vinnakota KC, Rawls KD, Estes SK, O'Brien TP, Printz RL, Papin JA, Reifman J, Shiota M, Young JD, Wallqvist A.
Toxicol Appl Pharmacol. 2019; 372:19-32.
Mechanistic models of microbial community metabolism.
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Medusa: Software to build and analyze ensembles of genome- scale metabolic network reconstructions.
Medlock GL, Moutinho TJ, Papin JA.
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MetDraw: automated visualization of genome-scale metabolic network reconstructions and high-throughput data.
Jensen PA, Papin JA.
Bioinformatics. 2014; 30(9):1327-8.
Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology.
Rohani A, Kashatus JA, Sessions DT, Sharmin S, Kashatus DF.
Sci Rep. 2020; 10(1):18941.
Mitochondrial control by DRP1 in brain tumor initiating cells.
Xie Q, Wu Q, Horbinski CM, Flavahan WA, Yang K, Zhou W, Dombrowski SM, Huang Z, Fang X, Shi Y, Ferguson AN, Kashatus DF, Bao S, Rich JN.
Nat Neurosci. 2015; 18(4):501-10.
Multi-channel registration of diffusion tensor images using directional information.
Rohde GK, Pajevic S, Pierpaoli C, editors.
2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No 04EX821). 2004; .
Opa1 and Drp1 reciprocally regulate cristae morphology, ETC function, and NAD+ regeneration in KRas-mutant lung adenocarcinoma.
Dane T. Sessions, Kee-Beom Kim, Jennifer A. Kashatus, Nikolas Churchill, Kwon-Sik Park, Marty W. Mayo, Hiromi Sesaki, David F. Kashatus.
Cell Reports. 2022; 41(11):111818.
Phenotype-based probabilistic analysis of heterogeneous responses to cancer drugs and their combination efficacy.
Comandante-Lou N, Khaliq M, Venkat D, Manikkam M, Fallahi-Sichani.
PLoS Comput Biol. 2020; 16(2):e1007688.
Radon Cumulative Distribution Transform Subspace Modeling for Image Classification.
Shifat-E-Rabbi M, Yin X, Rubaiyat AHM, Li S, Kolouri S, Aldroubi A, Nichols JM, Rohde GK.
Journal of Mathematical Imaging and Vision. 2021; 63(9):1185-203.
Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions.
Blais EM, Rawls KD, Dougherty BV, Li ZI, Kolling GL, Ye P, Wallqvist A, Papin JA.
Nat Commun. 2017; 8:14250.
Registration Methods for Quantitative Imaging [PhD]..
Rohde GK.
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Systematic analysis of BRAF(V600E) melanomas reveals a role for JNK/c-Jun pathway in adaptive resistance to drug-induced apoptosis.
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Mol Syst Biol. 2015; 11(3):797.
Systems-level metabolism of the altered Schaedler flora, a complete gut microbiota.
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TGIF transcription factors repress acetyl CoA metabolic gene expression and promote intestinal tumor growth.
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The adaptive bases algorithm for intensity-based nonrigid image registration.
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IEEE Trans Med Imaging. 2003; 22(11):1470-9.
The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations.
Keenan AB, Jenkins SL, Jagodnik KM, Koplev S, He E, Torre D, Wang Z, Dohlman AB, Silverstein MC, Lachmann A, Kuleshov MV, Ma'ayan A, Stathias V, Terryn R, Cooper D, Forlin M, Koleti A, Vidovic D, Chung C, Schurer SC, Vasiliauskas J, Pilarczyk M, Shamsaei.
Cell Syst. 2017; 6(1):13-24.
The Radon Cumulative Distribution Transform and Its Application to Image Classification.
Kolouri S, Park SR, Rohde GK.
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TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks.
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Toward the virtual cell: automated approaches to building models of subcellular organization “learned” from microscopy images.
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Bioessays. 2012; 34(9):791-9.
Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments.
Jenior ML, Moutinho TJ, Jr., Dougherty BV, Papin JA.
PLoS Comput Biol. 2020; 16(4):e1007099.
