key: cord-0274333-7idwxyqq authors: Bhalla, Prerna; Sridhar, Subasree; Kullu, Justin; Veerapaneni, Sriya; Sahoo, Swagatika; Bhatt, Nirav; Suraishkumar, GK title: Modeling Reactive Species Metabolism in Colorectal Cancer for Identifying Metabolic Targets and Devising Therapeutics date: 2022-05-04 journal: bioRxiv DOI: 10.1101/2022.05.03.490417 sha: 9097205c71358ac5908945ce9e64484db5d54c08 doc_id: 274333 cord_uid: 7idwxyqq Reactive species (RS) are known to play significant roles in cancer development as well as in treating or managing cancer. On the other hand, genome scale metabolic models are being used to understand cell metabolism in disease contexts including cancer, and also in planning strategies to handle diseases. Despite their crucial roles in cancers, the reactive species have not been adequately modeled in the genome scale metabolic models (GSMMs) when probing disease models for their metabolism or detection of drug targets. In this work, we have developed a module of reactive species reactions, which is scalable - it can be integrated with any human metabolic model as it is, or with any metabolic model with fine-tuning. When integrated with a cancer (colorectal cancer in this case) metabolic model, the RS module highlighted the deregulation occurring in important CRC pathways such as fatty acid metabolism, cholesterol metabolism, arachidonic acid and eicosanoid metabolism. We show that the RS module helps in better deciphering crucial metabolic targets for devising better therapeutics such as FDFT1, FADS2 and GUK1 by taking into account the effects mediated by reactive species during colorectal cancer progression. The results from this reactive species integrated CRC metabolic model reinforces ferroptosis as a potential target for colorectal cancer therapy. Integration of a context specific metabolic models built from Recon 3D Model and the RS module. RS module contains exclusive metabolites and reactions M RS and R RS . Few of the metabolites, m are common to the RS module and the 2 metabolic models RS-CRC, colon and RS-Colon. One thousand sample points for each reaction were drawn from the four models. Flux sampling [25] estimation was done using Cobrapy [26] 134 using the cplex solver. Two-sample Kolmogorov Smirnov Test (KS Test) in MATLAB 135 was used to differentiate the two flux samples of each reaction while comparing CRC vs. 136 RS-CRC and RS-Colon vs. RS-CRC and CRC vs. Colon models as well [27] . A 137 significance level threshold of 0.05 was used for filtering the deregulated reactions in the 138 three cases. All the deregulated reactions from FSr analysis were found to be 139 significantly different based on the KS test and these reactions were used for further 140 analysis. The results were compared with various serum metabolomic studies on colorectal cancer 152 to validate the findings [29, 30] . biomass, was carried out using fastfl [31] algorithm. This will be useful in predicting 158 suitable drug targets for CRC. The algorithm takes into account the gene protein network of reactions with GPR rules associated with them. The association between 163 genes for a reaction is given in the form of OR or AND rules depending on whether the 164 genes are the subunits of an enzyme or whether they are the isozymes of enzymes or act 165 synergistically in regulating a reaction. This attribute can be probed to identify 166 probable gene targets. Double gene/reaction pairs are those where the individual 167 gene/reaction in the pair is non-essential to the biomass growth, but the gene/reaction 168 when acting in pairs will be essential for the cell to grow, and deletion of the pair value is negative [32] . Potential gene targets obtained from our analyses were validated 184 using DEMETER log fold change values for the CRC cell line named HUTU-80. In summary, the computational analyses performed on the RS integrated models are 186 shown in Figure 2 . lipid peroxidation and also in tumour proliferation in colon [40] . The results are shown 290 in the Figure 6 291 As can be seen in the Table 6 , metabolic pathways associated with fatty acid and its 292 derivatives are have undergone significant deregulation in the RS-CRC model. can be observed in RS-CRC model [41] . Leukotriene B 4 formed from the 5-lipoxygenase 297 pathway is upregulated in the CRC tissue and is found to be overexpressed in the Fatty acid synthesis whereas ALOX12 is the risk gene [44] . Metabolism of arachidonic acid by ALOX12 312 produces 12-hydroxyeicosatetraenoic acid, which has been shown to increase reactive 313 oxygen species in CRC [41] . In spite of the burden from excessive RS species, CRC cells 314 survive by counteracting them with ferroptosis resisting genes, which is captured in our 315 analysis. Even though PUFA synthesis and breakdown happens significantly, MUFA 316 synthesis by FADS2 also occurs in RS-CRC model which is associated with increased 317 resistance to lipid peroxidation and ferroptosis [45] . Thus CRC seems to have protection 318 against ferroptosis mediated cell death, which can be targeted to inhibit CRC 319 development and it is illustarted in Figure 8 . PTGS2 is over-expression is correlated with a lower survival rate among CRC patients [41] Squalene monoxygenase (cholesterol synthesis) SQLE stimulates CRC cell proliferation by regulating the cell cycle and its overexpression is associated with poor prognosis in CRC patients in the primary stages [47] 1-Acylglycerol-3-phosphate O-acyltransferase (fatty acid metabolism) LPCAT1 stimulates CRC development by enhancing membrane biosynthesis and its a risky prognostic gene in CRC patients [48, 49] Linoleoyl-CoA desaturase (fatty acid synthesis) Upregulation of FADS2 promotes colon cancer proliferation and tumor growth and yet to be explored as CRC target [43] Carbonyl reductase (xenobiotics metabolism and antioxidant defense) Yet to be explored as CRC target and CBR1 overexpression enhances HCC survival by overcoming oxidative stress and cellular stress [50] Guanylate kinase 1a nd ribonucleotide reductase M1 and M2 (Purine metabolism) guanylate kinase 1 is yet to be explored as CRC target and is involved in recycling GMP and cyclic GMP and also involved in drug metabolism. Ribonucleotide reductase enzymes are associated with poor survival in CRC [51, 52] Phosphate cytidylyltransferase 1A, choline and phosphate cytidylyltransferase 1B, choline Yet to be explored as CRC target and has key roles in phosphatidylcholine biosynthesis and glycerophospholipid biosynthesis [53] 4 Discussion 377 Importance of modelling RS with CRC 378 RS, especially ROS, are responsible for increased oxidative stress inside a cell, which 379 exert proliferative effects in CRC propagation [54] . Therefore, the primary objective of 380 this study was to model the overall effect and implications of RS on cancer metabolism 381 to develop improved and novel therapeutic strategies. To do so, we developed a scalable 382 RS module using information from databases, and other extensive literature sources, the development of colon cancer [64] . Prostaglandin endoperoxide synthase 2 is the key 450 enzyme involved in inflammation in CRCs [64] . 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