A critical assessment of Traditional Chinese Medicine databases as a source for drug discovery.

Traditional Chinese Medicine (TCM) has been used for thousands of years to treat human diseases. Recently, many databases have been devoted to studying TCM pharmacology. Most of these databases include information about the active ingredients of TCM herbs and their disease indications. These databases enable researchers to interrogate the mechanisms of action of TCM systematically. However, there is a need for comparative studies of these databases, as they are derived from various resources with different data processing methods. In this review, we provide a comprehensive analysis of the existing TCM databases. We found that the information complements each other by comparing herbs, ingredients, and herb-ingredient pairs in these databases. Therefore, data harmonization is vital to use all the available information fully. Moreover, different TCM databases may contain various annotation types for herbs or ingredients, notably for the chemical structure of ingredients, making it challenging to integrate data from them. We also highlight the latest TCM databases on symptoms or gene expressions, suggesting that using multi-omics data and advanced bioinformatics approaches may provide new insights for drug discovery in TCM. In summary, such a comparative study would help improve the understanding of data complexity that may ultimately motivate more efficient and more standardized strategies towards the digitalization of TCM.

Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling.

Acute myeloid leukemia (AML), a heterogeneous and aggressive blood cancer, does not respond well to single-drug therapy. A combination of drugs is required to effectively treat this disease. Computational models are critical for combination therapy discovery due to the tens of thousands of two-drug combinations, even with approved drugs. While predicting synergistic drugs is the focus of current methods, few consider drug efficacy and potential toxicity, which are crucial for treatment success. To find effective new drug candidates, we constructed a bipartite network using patient-derived tumor samples and drugs. The network is based on drug-response screening and summarizes all treatment response heterogeneity as drug response weights. This bipartite network is then projected onto the drug part, resulting in the drug similarity network. Distinct drug clusters were identified using community detection methods, each targeting different biological processes and pathways as revealed by enrichment and pathway analysis of the drugs' protein targets. Four drugs with the highest efficacy and lowest toxicity from each cluster were selected and tested for drug sensitivity using cell viability assays on various samples. Results show that ruxolitinib-ulixertinib and sapanisertib-LY3009120 are the most effective combinations with the least toxicity and the best synergistic effect on blast cells. These findings lay the foundation for personalized and successful AML therapies, ultimately leading to the development of drug combinations that can be used alongside standard first-line AML treatment.

Bipartite network models to design combination therapies in acute myeloid leukaemia.

Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.

The ENDS of assumptions: an online tool for the epistemic non-parametric drug-response scoring.

MOTIVATION: The drug sensitivity analysis is often elucidated from drug dose-response curves. These curves capture the degree of cell viability (or inhibition) over a range of induced drugs, often with parametric assumptions that are rarely validated. RESULTS: We present a class of non-parametric models for the curve fitting and scoring of drug dose-responses. To allow a more objective representation of the drug sensitivity, these epistemic models devoid of any parametric assumptions attached to the linear fit, allow the parallel indexing such as half-maximal inhibitory concentration and area under curve. Specifically, three non-parametric models including spline (npS), monotonic and Bayesian and the parametric logistic are implemented. Other indices including maximum effective dose and drug-response span gradient pertinent to the npS are also provided to facilitate the interpretation of the fit. The collection of these models is implemented in an online app, standing as useful resource for drug dose-response curve fitting and analysis. AVAILABILITY AND IMPLEMENTATION: The ENDS is freely available online at https://irscope.shinyapps.io/ENDS/ and source codes can be obtained from https://github.com/AmiryousefiLab/ENDS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A community challenge for a pancancer drug mechanism of action inference from perturbational profile data.

The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.

Efficacy of Persian medicine herbal formulations (capsules and decoction) compared to standard care in patients with COVID-19, a multicenter open-labeled, randomized, controlled clinical trial.

Persian medicine has recommended clinical experiences and proper herbal remedies for prevention and treatment of microbial infections and respiratory diseases. An open-label, randomized, controlled, multicenter trial was conducted at five hospitals in Tehran and Isfahan provinces of Iran on 358 hospitalized adult patients. A total of 174 patients received standard care and 184 received herbal remedies (polyherbal decoction every 8 hr and two herbal capsules every 12 hr) plus standard care for 7 days. The primary clinical endpoint was the duration of hospital stay, and secondary outcomes were clinical improvement of symptoms based on self-assessment questionnaire. Results demonstrated that these natural decoction and capsules treatment plus routine care significantly decreased duration of hospital dyspnea (3.291 day vs. 6.468 days), accelerated clinical improvement, and decreased symptoms such as dry cough, dyspnea, muscle pain, headache, fatigue, anorexia, chills, runny nose, sputum cough, and vertigo in the treatment group compared with standard-care group. Significant effects of these polyherbal formulations on improving the symptoms of COVID-19 could be incredibly promising for managing this pandemic with acceptable tolerability.

Metabolomic signature of amino acids in plasma of patients with non-segmental Vitiligo.

INTRODUCTION: Vitiligo pathogenesis is complicated, and several possibilities were suggested. However, it is well-known that the metabolism of pigments plays a significant role in the pathogenicity of the disease. OBJECTIVES: We explored the role of amino acids in vitiligo using targeted metabolomics. METHODS: The amino acid profile was studied in plasma using liquid chromatography. First, 22 amino acids were derivatized and precisely determined. Next, the concentrations of the amino acids and the molar ratios were calculated in 31 patients and 34 healthy individuals. RESULTS: The differential concentrations of amino acids were analyzed and eight amino acids, i.e., cysteine, arginine, lysine, ornithine, proline, glutamic acid, histidine, and glycine were observed differentially. The ratios of cysteine, glutamic acid, and proline increased significantly in Vitiligo patients, whereas arginine, lysine, ornithine, glycine, and histidine decreased significantly compared to healthy individuals. Considering the percentage of skin area, we also showed that glutamic acid significantly has a higher amount in patients with less than 25% involvement compared to others. Finally, cysteine and lysine are considered promising candidates for diagnosing and developing the disorder with high accuracy (0.96). CONCLUSION: The findings are consistent with the previously illustrated mechanism of Vitiligo, such as production deficiency in melanin and an increase in immune activity and oxidative stress. Furthermore, new evidence was provided by using amino acids profile toward the pathogenicity of the disorder.

Rigosertib elicits potent anti-tumor responses in colorectal cancer by inhibiting Ras signaling pathway.

BACKGROUND: The therapeutic potency of Rigosertib (RGS) in the treatment of the myelodysplastic syndrome has been investigated previously, but little is known about its mechanisms of action. METHODS: The present study integrates systems and molecular biology approaches to investigate the mechanisms of the anti-tumor effects of RGS, either alone or in combination with 5-FU in cellular and animal models of colorectal cancer (CRC). RESULTS: The effects of RGS were more pronounced in dedifferentiated CRC cell types, compared to cell types that were epithelial-like. RGS inhibited cell proliferation and cell cycle progression in a cell-type specific manner, and that was dependent on the presence of mutations in KRAS, or its down-stream effectors. RGS increased both early and late apoptosis, by regulating the expression of p53, BAX and MDM2 in tumor model. We also found that RGS induced cell senescence in tumor tissues by increasing ROS generation, and impairing oxidant/anti-oxidant balance. RGS also inhibited angiogenesis and metastatic behavior of CRC cells, by regulating the expression of CD31, E-cadherin, and matrix metalloproteinases-2 and 9. CONCLUSION: Our findings support the therapeutic potential of this potent RAS signaling inhibitor either alone or in combination with standard regimens for the management of patients with CRC.

Metformin inhibits polyphosphate-induced hyper-permeability and inflammation.

Circulating inflammatory factor inorganic polyphosphate (polyP) released from activated platelets could enhance factor XII and bradykinin resulted in increased capillary leakage and vascular permeability. PolyP induce inflammatory responses through mTOR pathway in endothelial cells, which is being reported in several diseases including atherosclerosis, thrombosis, sepsis, and cancer. Systems and molecular biology approaches were used to explore the regulatory role of the AMPK activator, metformin, on polyP-induced hyper-permeability in different organs in three different models of polyP-induced hyper-permeability including local, systemic short- and systemic long-term approaches in murine models. Our results showed that polyP disrupts endothelial barrier integrity in skin, liver, kidney, brain, heart, and lung in all three study models and metformin abrogates the disruptive effect of polyP. We also showed that activation of AMPK signaling pathway, regulation of oxidant/anti-oxidant balance, as well as decrease in inflammatory cell infiltration constitute a set of molecular mechanisms through which metformin elicits it's protective responses against polyP-induced hyper-permeability. These results support the clinical values of AMPK activators including the FDA-approved metformin in attenuating vascular damage in polyP-associated inflammatory diseases.

Downregulation of ITM2A Gene Expression in Macrophages of Patients with Ankylosing Spondylitis.

OBJECTIVES: Ankylosing spondylitis (AS) is a rheumatic disorder that is mostly determined by genetic and environmental factors. Given the known importance of macrophage in AS pathogenesis, we investigated the transcriptional profile of macrophage cells in the disease. METHODS AND RESULTS: Two approaches of differential expression and subsequently, weighted gene co-expression network analysis was utilized to analyze a publicly available microarray dataset of macrophages. Integral membrane protein 2A (ITM2A) was among the most significant genes with a decreased trend in the common results of both methods. In order to confirm the finding, the expression of ITM2A was evaluated in monocyte-derived (M2-like) and M1 macrophages obtained from 14 AS patients and 14 controls. Macrophages were differentiated from whole-blood separated monocytes by 7 days incubating with macrophage colony-stimulating factor and then macrophages specific markers were verified with the flow cytometer. M1 polarization was induced by IFN-γ and lipopolysaccharide. Finally, relative gene expression analysis by real-time polymerase chain reaction revealed a significant downregulation of the ITM2A gene in both M2 like and M1 macrophages of the AS group compared to the control. CONCLUSION: Since ITM2A plays a critical role in osteo- and chondrogenic cellular differentiation, our finding may provide new insights into AS pathogenesis.

IrGO: Iranian traditional medicine General Ontology and knowledge base.

BACKGROUND: Iranian traditional medicine, also known as Persian Medicine, is a holistic school of medicine with a long prolific history. It describes numerous concepts and the relationships between them. However, no unified language system has been proposed for the concepts of this medicine up to the present time. Considering the extensive terminology in the numerous textbooks written by the scholars over centuries, comprehending the totality of concepts is obviously a very challenging task. To resolve this issue, overcome the obstacles, and code the concepts in a reusable manner, constructing an ontology of the concepts of Iranian traditional medicine seems a necessity. CONSTRUCTION AND CONTENT: Makhzan al-Advieh, an encyclopedia of materia medica compiled by Mohammad Hossein Aghili Khorasani, was selected as the resource to create an ontology of the concepts used to describe medicinal substances. The steps followed to accomplish this task included (1) compiling the list of classes via examination of textbooks, and text mining the resource followed by manual review to ensure comprehensiveness of extracted terms; (2) arranging the classes in a taxonomy; (3) determining object and data properties; (4) specifying annotation properties including ID, labels (English and Persian), alternative terms, and definitions (English and Persian); (5) ontology evaluation. The ontology was created using Protégé with adherence to the principles of ontology development provided by the Open Biological and Biomedical Ontology (OBO) foundry. UTILITY AND DISCUSSION: The ontology was finalized with inclusion of 3521 classes, 15 properties, and 20,903 axioms in the Iranian traditional medicine General Ontology (IrGO) database, freely available at http://ir-go.net/ . An indented list and an interactive graph view using WebVOWL were used to visualize the ontology. All classes were linked to their instances in UNaProd database to create a knowledge base of ITM materia medica. CONCLUSION: We constructed an ontology-based knowledge base of ITM concepts in the domain of materia medica to help offer a shared and common understanding of this concept, enable reuse of the knowledge, and make the assumptions explicit. This ontology will aid Persian medicine practitioners in clinical decision-making to select drugs. Extending IrGO will bridge the gap between traditional and conventional schools of medicine, helping guide future research in the process of drug discovery.

Angiotensin receptor blocker Losartan inhibits tumor growth of colorectal cancer.

The renin-angiotensin system (RAS) is up-regulated in patients with colorectal cancer (CRC) and is reported to be associated with poor prognosis and chemo-resistance. Here we explored the therapeutic potential of targeting RAS in CRC using Losartan, an angiotensin receptor blocker. An integrative-systems biology approach was used to explore a proteome-level dataset of a gene signature that is modulated by Losartan. The anti-proliferative activity of Losartan was evaluated using 2- and 3-dimensional cell culture models. A xenograft model of colon cancer was used to investigate tumor growth with Losartan alone and in combination with 5-FU followed by histological staining (Hematoxylin & Eosin and Masson trichrome staining), biochemical analyses, gene expression analyses by RT-PCR, western blot/IHC, or MMP Gelatin Zymography studies. Effects on cell cycle and cell death were assessed by flow cytometry. Losartan inhibited cell growth and suppressed cell cycle progression, causing an increase in CRC cells in the G1 phase. Losartan significantly reduced tumor growth and enhanced tumor cell necrosis. An impact on the inflammatory response, including up-regulation of pro-inflammatory cytokines and chemokines in CRC cells are potential mechanisms that could partially explain Losartan's anti-proliferative effects. Moreover, metastasis and angiogenesis were reduced in Losartan-treated mice as observed by inhibited matrix metalloproteinase-2 and -9 activities and decreased tumor vasculature. These data demonstrate the therapeutic potential of combining chemotherapeutic regimens with Losartan to synergistically enhance its activity and target the renin-angiotensin system as a new approach in colorectal cancer treatment.

Network-based modeling of herb combinations in traditional Chinese medicine.

Traditional Chinese medicine (TCM) has been practiced for thousands of years for treating human diseases. In comparison to modern medicine, one of the advantages of TCM is the principle of herb compatibility, known as TCM formulae. A TCM formula usually consists of multiple herbs to achieve the maximum treatment effects, where their interactions are believed to elicit the therapeutic effects. Despite being a fundamental component of TCM, the rationale of combining specific herb combinations remains unclear. In this study, we proposed a network-based method to quantify the interactions in herb pairs. We constructed a protein-protein interaction network for a given herb pair by retrieving the associated ingredients and protein targets, and determined multiple network-based distances including the closest, shortest, center, kernel, and separation, both at the ingredient and at the target levels. We found that the frequently used herb pairs tend to have shorter distances compared to random herb pairs, suggesting that a therapeutic herb pair is more likely to affect neighboring proteins in the human interactome. Furthermore, we found that the center distance determined at the ingredient level improves the discrimination of top-frequent herb pairs from random herb pairs, suggesting the rationale of considering the topologically important ingredients for inferring the mechanisms of action of TCM. Taken together, we have provided a network pharmacology framework to quantify the degree of herb interactions, which shall help explore the space of herb combinations more effectively to identify the synergistic compound interactions based on network topology.

HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) versus adult T-cell leukemia/lymphoma (ATLL).

OBJECTIVES: Human T cell leukemia virus-1 (HTLV-1) infection may lead to one or both diseases including HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) or adult T cell leukemia lymphoma (ATLL). The complete interactions of the virus with host cells in both diseases is yet to be determined. This study aims to construct an interaction network for distinct signaling pathways in these diseases based on finding differentially expressed genes (DEGs) between HAM/TSP and ATLL. RESULTS: We identified 57 hub genes with higher criteria scores in the primary protein-protein interaction network (PPIN). The ontology-based enrichment analysis revealed following important terms: positive regulation of transcription from RNA polymerase II promoter, positive regulation of transcription from RNA polymerase II promoter involved in meiotic cell cycle and positive regulation of transcription from RNA polymerase II promoter by histone modification. The upregulated genes TNF, PIK3R1, HGF, NFKBIA, CTNNB1, ESR1, SMAD2, PPARG and downregulated genes VEGFA, TLR2, STAT3, TLR4, TP53, CHUK, SERPINE1, CREB1 and BRCA1 were commonly observed in all the three enriched terms in HAM/TSP vs. ATLL. The constructed interaction network was then visualized inside a mirrored map of signaling pathways for ATLL and HAM/TSP, so that the functions of hub genes were specified in both diseases.

Identification of Celecoxib-Targeted Proteins Using Label-Free Thermal Proteome Profiling on Rat Hippocampus.

Celecoxib, or Celebrex, a nonsteroidal anti-inflammatory drug, is one of the most common medicines for treating inflammatory diseases. Recently, it has been shown that celecoxib is associated with implications in complex diseases, such as Alzheimer disease and cancer as well as with cardiovascular risk assessment and toxicity, suggesting that celecoxib may affect multiple unknown targets. In this project, we detected targets of celecoxib within the nervous system using a label-free thermal proteome profiling method. First, proteins of the rat hippocampus were treated with multiple drug concentrations and temperatures. Next, we separated the soluble proteins from the denatured and sedimented total protein load by ultracentrifugation. Subsequently, the soluble proteins were analyzed by nano-liquid chromatography tandem mass spectrometry to determine the identity of the celecoxib-targeted proteins based on structural changes by thermal stability variation of targeted proteins toward higher solubility in the higher temperatures. In the analysis of the soluble protein extract at 67°C, 44 proteins were uniquely detected in drug-treated samples out of all 478 identified proteins at this temperature. Ras-associated binding protein 4a, 1 out of these 44 proteins, has previously been reported as one of the celecoxib off targets in the rat central nervous system. Furthermore, we provide more molecular details through biomedical enrichment analysis to explore the potential role of all detected proteins in the biologic systems. We show that the determined proteins play a role in the signaling pathways related to neurodegenerative disease-and cancer pathways. Finally, we fill out molecular supporting evidence for using celecoxib toward the drug-repurposing approach by exploring drug targets. SIGNIFICANCE STATEMENT: This study determined 44 off-target proteins of celecoxib, a nonsteroidal anti-inflammatory and one of the most common medicines for treating inflammatory diseases. It shows that these proteins play a role in the signaling pathways related to neurodegenerative disease and cancer pathways. Finally, the study provides molecular supporting evidence for using celecoxib toward the drug-repurposing approach by exploring drug targets.

Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine.

The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.

Can We Assume the Gene Expression Profile as a Proxy for Signaling Network Activity?

Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways.

UNaProd: A Universal Natural Product Database for Materia Medica of Iranian Traditional Medicine.

BACKGROUND: Iranian traditional medicine (ITM) is a holistic medical system that uses a wide range of medicinal substances to treat disease. Reorganization and standardization of the data on ITM concepts is a necessity for optimal use of this rich source. In an initial step towards this goal, we created a database of ITM materia medica. Main Body. Primarily based on Makhzan al-Advieh, which is the most recent encyclopedia of materia medica in ITM with the largest number of monographs, a database of natural medicinal substances was created using both text mining methods and manual editing. UNaProd, a Universal Natural Product database for materia medica of ITM, is currently host to 2696 monographs, from herbal to animal to mineral compounds in 16 diverse attributes such as origin and scientific name. Currently, systems biology, and more precisely systems medicine and pharmacology, can be an aid in providing rationalizations for many traditional medicines and elucidating a great deal of knowledge they can offer to guide future research in medicine. CONCLUSIONS: A database of materia medica is a stepping stone in creating a systems pharmacology platform of ITM that encompasses the relationships between the drugs, their targets, and diseases. UNaProd is hyperlinked to IrGO and CMAUP databases for Mizaj and molecular features, respectively, and it is freely available at http://jafarilab.com/unaprod/.

Rigosertib potently protects against colitis-associated intestinal fibrosis and inflammation by regulating PI3K/AKT and NF-κB signaling pathways.

AIMS: Rigosertib (RGS) is a PI3K inhibitor that exerts protective effects against tumor progression and cancer-related inflammation. This study was aimed to explore the regulatory effects of RGS on proliferative, pro-fibrotic and inflammatory factors in DSS- induced colitis mice model. MATERIALS AND METHODS: The present study integrates systems and molecular biology approaches to investigate the therapeutic potency of RGS in an experimental model of colitis specifically examining its effects on the PI3K/AKT and NF-κB signaling pathways. KEY FINDINGS: Analysis of time-resolved proteome profiling showed that PI3K-AKT inhibitors regulate expression of many proteins in all stages of inflammation, fibrogenesis and extracellular matrix remodeling. Consistent with our in-silico findings, RGS improved colitis disease activity as assessed by changes in body weight, degree of stool consistency, rectal bleeding and prolapse. RGS also reduced oxidative stress markers and colon histopathological score by decreasing inflammatory responses in colon tissues. Moreover, expression of pro-fibrotic and pro-inflammatory factors including Acta 2, Col 1a1, Col 1a2, IL-1ß, TNF-α, INF-γ, and MCP-1 were suppressed in the mice treated with RGS compared to the control group. The protective effects of RGS were mediated by inactivation of PI3K/AKT and NF-kB signaling pathways. SIGNIFICANCE: This study clearly demonstrates the anti-proliferative, anti-inflammatory and anti-fibrotic effects of RGS in colitis that may have implications for the treatment of colitis and colitis-associated cancer.