Transcriptomic Module Discovery of Diarrhea-Predominant Irritable Bowel Syndrome: A Causal Network Inference Approach
Abstract
:1. Introduction
2. Results
2.1. Preprocessing and Dataset Selection
2.2. Differentially Expressed Genes (DEG) and Reactome Enrichment Analyses
2.3. Statistical Analysis
2.3.1. SEM-Based Gene Set Analysis (SEMgsa)
2.3.2. Analysis of the Active IBS-D Module
2.3.3. Gene Communities Detection
2.3.4. Characterization of the Gene Communities
3. Discussion
4. Materials and Methods
4.1. Dataset Description and Study Workflow
4.2. RNA-seq Pipeline and Dataset Quality Control Cut-Off
4.3. DEGs Analysis and Reactome Enrichment Analysis
4.4. Statistical Analysis
4.4.1. SEMgsa
4.4.2. Analysis of the Active IBS-D Module
4.4.3. Gene Communities Detection
4.4.4. Communities’ Characterization
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Drossman, D.A.; Hasler, W.L. Rome IV-Functional GI Disorders: Disorders of Gut-Brain Interaction. Gastroenterology 2016, 150, 1257–1261. [Google Scholar] [CrossRef] [PubMed]
- Vaiopoulou, A.; Karamanolis, G.; Psaltopoulou, T.; Karatzias, G.; Gazouli, M. Molecular basis of the irritable bowel syndrome. World J. Gastroenterol. 2014, 20, 376–383. [Google Scholar] [CrossRef] [PubMed]
- Mearin, F.; Lacy, B.E.; Chang, L.; Chey, W.D.; Lembo, A.J.; Simren, M.; Spiller, R. Bowel Disorders. Gastroenterology 2016, 150, 1393–1407.e5. [Google Scholar] [CrossRef]
- Longstreth, G.F.; Thompson, W.G.; Chey, W.D.; Houghton, L.A.; Mearin, F.; Spiller, R.C. Functional bowel disorders. Gastroenterology 2006, 130, 1480–1491. [Google Scholar] [CrossRef]
- Occhipinti, K.; Smith, J.W. Irritable bowel syndrome: A review and update. Clin. Colon Rectal Surg. 2012, 25, 46–52. [Google Scholar] [CrossRef]
- Devanarayana, N.M.; Rajindrajith, S.; Pathmeswaran, A.; Abegunasekara, C.; Gunawardena, N.K.; Benninga, M.A. Epidemiology of irritable bowel syndrome in children and adolescents in Asia. J. Pediatr. Gastroenterol. Nutr. 2015, 60, 792–798. [Google Scholar] [CrossRef]
- Hyams, J.S.; Di Lorenzo, C.; Saps, M.; Shulman, R.J.; Staiano, A.; van Tilburg, M. Functional Disorders: Children and Adolescents. Gastroenterology 2016, 150, 1456–1468.e2. [Google Scholar] [CrossRef]
- Oka, P.; Parr, H.; Barberio, B.; Black, C.J.; Savarino, E.V.; Ford, A.C. Global prevalence of irritable bowel syndrome according to Rome III or IV criteria: A systematic review and meta-analysis. Lancet Gastroenterol. Hepatol. 2020, 5, 908–917. [Google Scholar] [CrossRef]
- Tang, H.-Y.; Jiang, A.-J.; Wang, X.-Y.; Wang, H.; Guan, Y.-Y.; Li, F.; Shen, G.-M. Uncovering the pathophysiology of irritable bowel syndrome by exploring the gut-brain axis: A narrative review. Ann. Transl. Med. 2021, 9, 1187. [Google Scholar] [CrossRef]
- Cho, M.H.; McDonald, M.-L.N.; Zhou, X.; Mattheisen, M.; Castaldi, P.J.; Hersh, C.P.; DeMeo, D.L.; Sylvia, J.S.; Ziniti, J.; Laird, N.M.; et al. Risk loci for chronic obstructive pulmonary disease: A genome-wide association study and meta-analysis. Lancet Respir. Med. 2014, 2, 214–225. [Google Scholar] [CrossRef] [PubMed]
- Hasin, Y.; Seldin, M.; Lusis, A. Multi-omics approaches to disease. Genome Biol. 2017, 18, 83. [Google Scholar] [CrossRef] [PubMed]
- Mahmud, S.; Biswas, S.; Afrose, S.; Mita, M.A.; Hasan, R.; Shimu, M.S.S.; Paul, G.K.; Chung, S.; Saleh, A.; Alshehri, S.; et al. Use of Next-Generation Sequencing for Identifying Mitochondrial Disorders. Curr. Issues Mol. Biol. 2022, 44, 1127–1148. [Google Scholar] [CrossRef]
- James, S.C.; Fraser, K.; Young, W.; McNabb, W.C.; Roy, N.C. Gut Microbial Metabolites and Biochemical Pathways Involved in Irritable Bowel Syndrome: Effects of Diet and Nutrition on the Microbiome. J. Nutr. 2020, 150, 1012–1021. [Google Scholar] [CrossRef]
- Dothel, G.; Barbaro, M.R.; Di Vito, A.; Ravegnini, G.; Gorini, F.; Monesmith, S.; Coschina, E.; Benuzzi, E.; Fuschi, D.; Palombo, M.; et al. New insights into irritable bowel syndrome pathophysiological mechanisms: Contribution of epigenetics. J. Gastroenterol. 2023, 58, 605–621. [Google Scholar] [CrossRef] [PubMed]
- Edgar, R.; Domrachev, M.; Lash, A.E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002, 30, 207–210. [Google Scholar] [CrossRef] [PubMed]
- Thind, A.S.; Thind, A.S.; Monga, I.; Monga, I.; Thakur, P.K.; Thakur, P.K.; Kumari, P.; Kumari, P.; Dindhoria, K.; Dindhoria, K.; et al. Demystifying emerging bulk RNA-Seq applications: The application and utility of bioinformatic methodology. Brief. Bioinform. 2021, 22, bbab259. [Google Scholar] [CrossRef] [PubMed]
- Subramanian, A.; Tamayo, P.; Mootha, V.K.; Mukherjee, S.; Ebert, B.L.; Gillette, M.A.; Paulovich, A.; Pomeroy, S.L.; Golub, T.R.; Lander, E.S.; et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 2005, 102, 15545–15550. [Google Scholar] [CrossRef]
- Doniger, S.W.; Salomonis, N.; Dahlquist, K.D.; Vranizan, K.; Lawlor, S.C.; Conklin, B.R. MAPPFinder: Using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol. 2003, 4, R7. [Google Scholar] [CrossRef]
- Maere, S.; Heymans, K.; Kuiper, M. BiNGO: A Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks. Bioinformatics 2005, 21, 3448–3449. [Google Scholar] [CrossRef]
- Kroes, R.A.; Panksepp, J.; Burgdorf, J.; Otto, N.J.; Moskal, J.R. Modeling depression: Social dominance-submission gene expression patterns in rat neocortex. Neuroscience 2006, 137, 37–49. [Google Scholar] [CrossRef]
- Leonard, M.M.; Bai, Y.; Serena, G.; Nickerson, K.P.; Camhi, S.; Sturgeon, C.; Yan, S.; Fiorentino, M.R.; Katz, A.; Nath, B.; et al. RNA sequencing of intestinal mucosa reveals novel pathways functionally linked to celiac disease pathogenesis. PLoS ONE 2019, 14, e0215132. [Google Scholar] [CrossRef] [PubMed]
- Akond, Z.; Alam, M.; Mollah, M.N.H. Biomarker Identification from RNA-Seq Data using a Robust Statistical Approach. Bioinformation 2018, 14, 153–163. [Google Scholar] [CrossRef] [PubMed]
- Reactome Analysis Tool. Available online: https://reactome.org/PathwayBrowser/#TOOL=AT (accessed on 5 May 2024).
- Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28, 27–30. [Google Scholar] [CrossRef] [PubMed]
- Grassi, M.; Palluzzi, F.; Tarantino, B. SEMgraph: An R package for causal network inference of high-throughput data with structural equation models. Bioinformatics 2022, 38, 4829–4830. [Google Scholar] [CrossRef]
- Chen, W.; Liu, J.; He, S. Prior knowledge guided active modules identification: An integrated multi-objective approach. BMC Syst. Biol. 2017, 11, 8. [Google Scholar] [CrossRef]
- Cho, D.-Y.; Kim, Y.-A.; Przytycka, T.M. Chapter 5: Network biology approach to complex diseases. PLoS Comput. Biol. 2012, 8, e1002820. [Google Scholar] [CrossRef]
- Kim, Y.-A.; Przytycka, T.M. Bridging the Gap between Genotype and Phenotype via Network Approaches. Front. Genet. 2013, 3, 31660. [Google Scholar] [CrossRef]
- Ideker, T.; Ozier, O.; Schwikowski, B.; Siegel, A.F. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 2002, 18 (Suppl. S1), S233–S240. [Google Scholar] [CrossRef]
- Klammer, M.; Godl, K.; Tebbe, A.; Schaab, C. Identifying differentially regulated subnetworks from phosphoproteomic data. BMC Bioinform. 2010, 11, 351. [Google Scholar] [CrossRef]
- Boomsma, A. Book review of K. A. Bollen (1989). In Structural Equations with Latent Variables; John Wiley & Sons: Hoboken, NJ, USA, 2014; Volume 12, pp. 124–131. [Google Scholar]
- Dotolo, S.; Abate, R.E.; Roma, C.; Guido, D.; Preziosi, A.; Tropea, B.; Palluzzi, F.; Giacò, L.; Normanno, N. Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice. Biomedicines 2022, 10, 2074. [Google Scholar] [CrossRef] [PubMed]
- Kohestani, H.; Giuliani, A. Organization principles of biological networks: An explorative study. Biosystems 2016, 141, 31–39. [Google Scholar] [CrossRef] [PubMed]
- Chartrand, G.; Zhang, P. Chromatic Graph Theory; Introduction to Graphs; Chapman and Hall/CRC: New York, NY, USA, 2019; pp. 27–52. [Google Scholar]
- Barbara, G.; Cremon, C.; Bellini, M.; Corsetti, M.; Di Nardo, G.; Falangone, F.; Fuccio, L.; Galeazzi, F.; Iovino, P.; Sarnelli, G.; et al. Italian guidelines for the management of irritable bowel syndrome: Joint Consensus from the Italian Societies of: Gastroenterology and Endoscopy (SIGE), Neurogastroenterology and Motility (SINGEM), Hospital Gastroenterologists and Endoscopists (AIGO), Digestive Endoscopy (SIED), General Medicine (SIMG), Gastroenterology, Hepatology and Pediatric Nutrition (SIGENP) and Pediatrics (SIP). Dig. Liver Dis. 2023, 55, 187–207. [Google Scholar] [CrossRef] [PubMed]
- Kim, D. Methods of integrating data to uncover genotype—Phenotype interactions. Nat. Rev. Genet. 2015, 16, 85–97. [Google Scholar] [CrossRef]
- Liu, C.; Ma, Y.; Zhao, J.; Nussinov, R.; Zhang, Y.-C.; Cheng, F.; Zhang, Z.-K. Computational network biology: Data, models, and applications. Phys. Rep. 2020, 846, 1–66. [Google Scholar] [CrossRef]
- Grassi, M.; Tarantino, B. SEMtree: Tree-based structure learning methods with structural equation models. Bioinformatics 2023, 39, btad377. [Google Scholar] [CrossRef]
- Jain, U.; Otley, A.R.; Van Limbergen, J.; Stadnyk, A.W. The complement system in inflammatory bowel disease. Inflamm. Bowel Dis. 2014, 20, 1628–1637. [Google Scholar] [CrossRef]
- Andoh, A.; Fujiyama, Y.; Sumiyoshi, K.; Bamba, T. Local secretion of complement C3 in the exocrine pancreas: Ductal epithelial cells as a possible biosynthetic site. Gastroenterology 1996, 110, 1919–1925. [Google Scholar] [CrossRef] [PubMed]
- Dunkelberger, J.R.; Song, W.-C. Complement and its role in innate and adaptive immune responses. Cell Res. 2010, 20, 34–50. [Google Scholar] [CrossRef]
- McCullough, R.; McCullough, J. Deciphering the pathophysiology of irritable bowel syndrome and functional gastrointestinal disorders-an alternative model for pathogenesis: Cytokine controlled transepithelial multi-feedback loop. Transl. Gastroenterol. Hepatol. 2017, 2, 18. [Google Scholar] [CrossRef]
- Wouters, M.M.; Vicario, M.; Santos, J. The role of mast cells in functional GI disorders. Gut 2016, 65, 155–168. [Google Scholar] [CrossRef]
- Martin-Viñas, J.J.; Quigley, E.M.M. Immune response in irritable bowel syndrome: A systematic review of systemic and mucosal inflammatory mediators. J. Dig. Dis. 2016, 17, 572–581. [Google Scholar] [CrossRef] [PubMed]
- Shulman, R.J.; Devaraj, S.; Heitkemper, M. Activation of the Innate Immune System in Children With Irritable Bowel Syndrome Evidenced by Increased Fecal Human β-Defensin-2. Clin. Gastroenterol. Hepatol. 2021, 19, 2121–2127. [Google Scholar] [CrossRef] [PubMed]
- Yang, Y.; Jobin, C. Microbial imbalance and intestinal pathologies: Connections and contributions. Dis. Model. Mech. 2014, 7, 1131–1142. [Google Scholar] [CrossRef] [PubMed]
- Linsalata, M.; Riezzo, G.; Orlando, A.; D’attoma, B.; Prospero, L.; Ignazzi, A.; Losurdo, G.; Di Leo, A.; Giannelli, G.; Russo, F. The Role of Intestinal Barrier Function in Overweight Patients with IBS with Diarrhea Undergoing a Long-Term Low Fermentable Oligo-, Di-, and Monosaccharide and Polyol Diet. Nutrients 2023, 15, 4683. [Google Scholar] [CrossRef]
- Sina, C.; Kemper, C.; Derer, S. The intestinal complement system in inflammatory bowel disease: Shaping intestinal barrier function. Semin. Immunol. 2018, 37, 66–73. [Google Scholar] [CrossRef]
- Veres-Székely, A.; Szász, C.; Pap, D.; Szebeni, B.; Bokrossy, P.; Vannay, Á. Zonulin as a Potential Therapeutic Target in Microbiota-Gut-Brain Axis Disorders: Encouraging Results and Emerging Questions. Int. J. Mol. Sci. 2023, 24, 7548. [Google Scholar] [CrossRef]
- Kim, M.-Y.; Choi, S.-W. Dietary modulation of gut microbiota for the relief of irritable bowel syndrome. Nutr. Res. Pract. 2021, 15, 411–430. [Google Scholar] [CrossRef]
- Grasberger, H.; Gao, J.; Nagao-Kitamoto, H.; Kitamoto, S.; Zhang, M.; Kamada, N.; Eaton, K.A.; El-Zaatari, M.; Shreiner, A.B.; Merchant, J.L.; et al. Increased Expression of DUOX2 Is an Epithelial Response to Mucosal Dysbiosis Required for Immune Homeostasis in Mouse Intestine. Gastroenterology 2015, 149, 1849–1859. [Google Scholar] [CrossRef]
- Xiong, Z.; Zhu, X.; Geng, J.; Xu, Y.; Wu, R.; Li, C.; Fan, D.; Qin, X.; Du, Y.; Tian, Y.; et al. Intestinal Tuft-2 cells exert antimicrobial immunity via sensing bacterial metabolite N-undecanoylglycine. Immunity 2022, 55, 686–700.e7. [Google Scholar] [CrossRef]
- Kivelä, A.-J.; Kivelä, J.; Saarnio, J.; Parkkila, S. Carbonic anhydrases in normal gastrointestinal tract and gastrointestinal tumors. World J. Gastroenterol. 2005, 11, 155–163. [Google Scholar] [CrossRef]
- Lucarini, E.; Nocentini, A.; Bonardi, A.; Chiaramonte, N.; Parisio, C.; Micheli, L.; Toti, A.; Ferrara, V.; Carrino, D.; Pacini, A.; et al. Carbonic Anhydrase IV Selective Inhibitors Counteract the Development of Colitis-Associated Visceral Pain in Rats. Cells 2021, 10, 2540. [Google Scholar] [CrossRef] [PubMed]
- Cheng, Y.Y.; Yu, J.; Wong, Y.P.; Man, E.P.S.; To, K.F.; Jin, V.X.; Li, J.; Tao, Q.; Sung, J.J.Y.; Chan, F.K.L.; et al. Frequent epigenetic inactivation of secreted frizzled-related protein 2 (SFRP2) by promoter methylation in human gastric cancer. Br. J. Cancer 2007, 97, 895–901. [Google Scholar] [CrossRef]
- Suzuki, H.; Watkins, D.N.; Jair, K.-W.; E Schuebel, K.; Markowitz, S.D.; Chen, W.D.; Pretlow, T.P.; Yang, B.; Akiyama, Y.; van Engeland, M.; et al. Epigenetic inactivation of SFRP genes allows constitutive WNT signaling in colorectal cancer. Nat. Genet. 2004, 36, 417–422. [Google Scholar] [CrossRef]
- Kim, T.-O.; Han, Y.K.; Yi, J.M. Hypermethylated promoters of tumor suppressor genes were identified in Crohn’s disease patients. Intest. Res. 2020, 18, 297–305. [Google Scholar] [CrossRef] [PubMed]
- Al-Shamasi, A.-A.; Elkaffash, R.; Mohamed, M.; Rayan, M.; Al-Khater, D.; Gadeau, A.-P.; Ahmed, R.; Hasan, A.; Eldassouki, H.; Yalcin, H.C.; et al. Crosstalk between Sodium-Glucose Cotransporter Inhibitors and Sodium-Hydrogen Exchanger 1 and 3 in Cardiometabolic Diseases. Int. J. Mol. Sci. 2021, 22, 12677. [Google Scholar] [CrossRef] [PubMed]
- Nikolovska, K.; Seidler, U.E.; Stock, C. The Role of Plasma Membrane Sodium/Hydrogen Exchangers in Gastrointestinal Functions: Proliferation and Differentiation, Fluid/Electrolyte Transport and Barrier Integrity. Front. Physiol. 2022, 13, 899286. [Google Scholar] [CrossRef]
- Liu, Y.; Merrill, R.A.; Strack, S. A-Kinase Anchoring Protein 1: Emerging Roles in Regulating Mitochondrial Form and Function in Health and Disease. Cells 2020, 9, 298. [Google Scholar] [CrossRef]
- Khan, I.; Khan, K. Uncoupling of Carbonic Anhydrase from Na-H exchanger-1 in Experimental Colitis: A Possible Mechanistic Link with Na-H Exchanger. Biomolecules 2019, 9, 700. [Google Scholar] [CrossRef]
- Krishnan, D.; Liu, L.; Wiebe, S.A.; Casey, J.R.; Cordat, E.; Alexander, R.T. Carbonic anhydrase II binds to and increases the activity of the epithelial sodium-proton exchanger, NHE3. Am. J. Physiol. Renal Physiol. 2015, 309, F383–F392. [Google Scholar] [CrossRef]
- Cai, J.; Culley, M.K.; Zhao, Y.; Zhao, J. The role of ubiquitination and deubiquitination in the regulation of cell junctions. Protein Cell 2018, 9, 754–769. [Google Scholar] [CrossRef]
- Prospero, L.; Riezzo, G.; Linsalata, M.; Orlando, A.; D’Attoma, B.; Russo, F. Psychological and Gastrointestinal Symptoms of Patients with Irritable Bowel Syndrome Undergoing a Low-FODMAP Diet: The Role of the Intestinal Barrier. Nutrients 2021, 13, 2469. [Google Scholar] [CrossRef]
- Castelo-Branco, G.; Wagner, J.; Rodriguez, F.J.; Kele, J.; Sousa, K.; Rawal, N.; Pasolli, H.A.; Fuchs, E.; Kitajewski, J.; Arenas, E. Differential regulation of midbrain dopaminergic neuron development by Wnt-1, Wnt-3a, and Wnt-5a. Proc. Natl. Acad. Sci. USA 2003, 100, 12747–12752. [Google Scholar] [CrossRef]
- Mayer, E.A.; Ryu, H.J.; Bhatt, R.R. The neurobiology of irritable bowel syndrome. Mol. Psychiatry 2023, 28, 1451–1465. [Google Scholar] [CrossRef] [PubMed]
- C Babraham Bioinformatics. Available online: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 2 January 2024).
- Hart, S.N.; Therneau, T.M.; Zhang, Y.; Poland, G.A.; Kocher, J.-P. Calculating sample size estimates for RNA sequencing data. J. Comput. Biol. 2013, 20, 970–978. [Google Scholar] [CrossRef]
- Kukurba, K.R.; Montgomery, S.B. RNA Sequencing and Analysis. Cold Spring Harb. Protoc. 2015, 2015, 951–969. [Google Scholar] [CrossRef] [PubMed]
- Sourceforge. Available online: https://sourceforge.net/projects/bbmap/support (accessed on 2 January 2024).
- Babraham Bioinformatics. Available online: https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ (accessed on 2 January 2024).
- Dobin, A.; Davis, C.A.; Schlesinger, F.; Drenkow, J.; Zaleski, C.; Jha, S.; Batut, P.; Chaisson, M.; Gingeras, T.R. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 2013, 29, 15–21. [Google Scholar] [CrossRef] [PubMed]
- GENCODE. Available online: https://www.gencodegenes.org/human/release_35.html (accessed on 3 January 2024).
- Liao, Y.; Smyth, G.K.; Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014, 30, 923–930. [Google Scholar] [CrossRef]
- Jassal, B.; Matthews, L.; Viteri, G.; Gong, C.; Lorente, P.; Fabregat, A.; Sidiropoulos, K.; Cook, J.; Gillespie, M.; Haw, R.; et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2020, 48, D498–D503. [Google Scholar] [CrossRef]
- Fabregat, A.; Korninger, F.; Viteri, G.; Sidiropoulos, K.; Marin-Garcia, P.; Ping, P.; Wu, G.; Stein, L.; D’eustachio, P.; Hermjakob, H. Reactome graph database: Efficient access to complex pathway data. PLoS Comput. Biol. 2018, 14, e1005968. [Google Scholar] [CrossRef]
- Nguyen, T.; Mitrea, C.; Draghici, S. Network-Based Approaches for Pathway Level Analysis. Curr. Protoc. Bioinform. 2018, 61, 8.25.1–8.25.24. [Google Scholar] [CrossRef]
- Package ‘ggm’. Available online: https://CRAN.R-project.org/package=ggm (accessed on 3 January 2024).
- Rinaldo, A. Computing Maximum Likelihood Estimates in Log-Linear Models; Carnegie Mellon University: Pittsburgh, PA, USA, 2006. [Google Scholar]
- Zhao, T.; Roeder, K. The huge Package for High-dimensional Undirected Graph Estimation in R. J. Mach. Learn. Res. 2012, 13, 1059–1062. [Google Scholar] [PubMed]
- Fisher, R.A. Frequency Distribution of the Values of the Correlation Coefficient in Samples from an Indefinitely Large Population. Biometrika 1915, 10, 507–521. [Google Scholar] [CrossRef]
- Kou, L.; Markowsky, G.; Berman, L. A fast algorithm for Steiner trees. Acta Inform. 1981, 15, 141–145. [Google Scholar] [CrossRef]
- Pepe, D.; Grassi, M. Investigating perturbed pathway modules from gene expression data via structural equation models. BMC Bioinform. 2014, 15, 132. [Google Scholar] [CrossRef] [PubMed]
- Rondanelli, M.; Perna, S.; Alalwan, T.A.; Cazzola, R.; Gasparri, C.; Infantino, V.; Perdoni, F.; Iannello, G.; Pepe, D.; Guido, D. A structural equation model to assess the pathways of body adiposity and inflammation status on dysmetabolic biomarkers via red cell distribution width and mean corpuscular volume: A cross-sectional study in overweight and obese subjects. Lipids Health Dis. 2020, 19, 154. [Google Scholar] [CrossRef] [PubMed]
- Jankov, J. Confidence intervals for high-dimensional inverse covariance estimation. Electron. J. Stat. 2015, 9, 1205–1229. [Google Scholar] [CrossRef]
- Hu, L.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. A Multidiscip. J. 1999, 6, 1–55. [Google Scholar] [CrossRef]
- Ideker, T.; Krogan, N.J. Differential network biology. Mol. Syst. Biol. 2012, 8, 565. [Google Scholar] [CrossRef]
- Pons, P.; Latapy, M. Computing Communities in Large Networks Using Random Walks. In Computer and Information Sciences-ISCIS 2005: Proceedings of the 20th International Symposium, Istanbul, Turkey, 26–28 October 2005; Springer: Berlin/Heidelberg, Germany, 2005; pp. 284–293. [Google Scholar]
- Bioconductor. Available online: https://bioconductor.org/packages/release/data/annotation/html/hgu95av2.db.html (accessed on 4 May 2024).
- Kolberg, L.; Raudvere, U.; Kuzmin, I.; Adler, P.; Vilo, J.; Peterson, H. g:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update). Nucleic Acids Res. 2023, 51, W207–W212. [Google Scholar] [CrossRef] [PubMed]
Samples | Male | Female | Age (years) | BMI (kg/m2) | |
---|---|---|---|---|---|
Healthy | 18 | 5 | 13 | 23–59 | 27 |
IBS-D | 17 | 6 | 11 | 26–59 | 27 |
Gene_ID | ENSEMBL_ID | Gene Description | log2FC | p-Value |
---|---|---|---|---|
DUOX2 | ENSG00000140279 | dual oxidase 2 | 2.52 | 0 |
KLK12 | ENSG00000186474 | kallikrein related peptidase 12 | 1.38 | 0 |
SH2D6 | ENSG00000152292 | SH2 domain containing 6 | 1.26 | 0 |
MST1P2 | ENSG00000186301 | macrophage stimulating 1 pseudogene | −1.84 | 0 |
None | ENSG00000290851 | novel transcript | −2.09 | 0 |
RPL13P12 | ENSG00000215030 | ribosomal protein L13 pseudogene 12 | −2.8 | 0 |
HTR3C | ENSG00000178084 | 5-hydroxytryptamine receptor 3C | 1.39 | 0.0001 |
SH2D7 | ENSG00000183476 | SH2 domain containing 7 | 1.12 | 0.0001 |
HMX3 | ENSG00000188620 | H6 family homeobox 3 | 1.11 | 0.0001 |
RPSAP15 | ENSG00000237506 | ribosomal protein SA pseudogene 15 | 1.04 | 0.0001 |
ELFN2 | ENSG00000166897 | extracellular leucine-rich repeat and fibronectin type III domain containing 2 | 1.01 | 0.0003 |
AGAP7P | ENSG00000264204 | ArfGAP with GTPase domain, ankyrin repeat and PH domain 7, pseudogene | −1.91 | 0.0005 |
RPL29P4 | ENSG00000230202 | ribosomal protein L29 pseudogene 4 | 1.79 | 0.0006 |
None | ENSG00000277400 | None | 1.35 | 0.0006 |
DUOXA2 | ENSG00000140274 | dual oxidase maturation factor 2 | 2 | 0.0007 |
ACTG1P22 | ENSG00000271615 | actin gamma 1 pseudogene 22 | 1.16 | 0.0008 |
GTF2H2 | ENSG00000145736 | general transcription factor IIH subunit 2 | 1.03 | 0.0008 |
ATP12A | ENSG00000075673 | ATPase H+/K+ transporting non-gastric alpha2 subunit | −2.01 | 0.0011 |
HTR3E | ENSG00000186038 | 5-hydroxytryptamine receptor 3E | 1.16 | 0.0012 |
CHAT | ENSG00000070748 | choline O-acetyltransferase | 1.05 | 0.0014 |
MT1H | ENSG00000205358 | metallothionein 1H | −1.04 | 0.0014 |
HCG4B | ENSG00000227262 | HLA complex group 4B | −1.12 | 0.0016 |
PKD1P2 | ENSG00000227827 | polycystin 1, transient receptor potential channel interacting pseudogene 2 | −2.08 | 0.0017 |
GATD3 | ENSG00000160221 | glutamine amidotransferase class 1 domain containing 3 | 1.63 | 0.0021 |
IGHV2-70 | ENSG00000274576 | immunoglobulin heavy variable 2–70 | −1.23 | 0.0025 |
SLC13A3 | ENSG00000158296 | solute carrier family 13 member 3 | −1.02 | 0.0028 |
IGHV5-78 | ENSG00000211978 | immunoglobulin heavy variable 5–78 (pseudogene) | 1.4 | 0.003 |
ADGRF1 | ENSG00000153292 | adhesion G protein-coupled receptor F1 | 1.25 | 0.0032 |
IGKV2-29 | ENSG00000253998 | immunoglobulin kappa variable 2–29 | −1.93 | 0.0045 |
FAM230I | ENSG00000178248 | family with sequence similarity of 230 member, I | 1.32 | 0.0048 |
None | ENSG00000287188 | novel transcript, antisense to ANXA10 | 1.22 | 0.0057 |
ITPRID1 | ENSG00000180347 | ITPR interacting domain containing 1 | 1.02 | 0.0065 |
PI16 | ENSG00000164530 | peptidase inhibitor 16 | −1.17 | 0.0067 |
MGAM2 | ENSG00000257743 | maltase-glucoamylase 2 (putative) | 1.05 | 0.0078 |
None | ENSG00000230563 | novel transcript | 1.09 | 0.0111 |
CCNO | ENSG00000152669 | cyclin O | 1.04 | 0.0117 |
CHIT1 | ENSG00000133063 | chitinase 1 | 1.47 | 0.0136 |
AFF2 | ENSG00000155966 | ALF transcription elongation factor 2 | 1.28 | 0.0143 |
HERC2P3 | ENSG00000290376 | HERC2 pseudogene 3 | 1.12 | 0.0144 |
None | ENSG00000289911 | novel transcript, antisense to PTP4A1 | −1.06 | 0.0159 |
SFRP2 | ENSG00000145423 | secreted frizzled-related protein 2 | −1.01 | 0.0178 |
LGSN | ENSG00000146166 | lengsin, lens protein with glutamine synthetase domain | −1.67 | 0.0187 |
SULT2A1 | ENSG00000105398 | sulfotransferase family 2A member 1 | −2.18 | 0.0188 |
None | ENSG00000274767 | novel transcript, antisense CCL3L3 | −1.15 | 0.0212 |
PLA2G10BP | ENSG00000254609 | phospholipase A2 group XB, pseudogene | 1.49 | 0.0214 |
None | ENSG00000289810 | novel transcript | 1.3 | 0.0244 |
None | ENSG00000224114 | ribosomal protein S14 (RPS14) pseudogene | −1.31 | 0.0249 |
OLFM4 | ENSG00000102837 | olfactomedin 4 | 1.26 | 0.0255 |
CR2 | ENSG00000117322 | complement C3d receptor 2 | 1.29 | 0.0267 |
LTF | ENSG00000012223 | Lactotransferrin | 1.08 | 0.0315 |
None | ENSG00000213058 | ribosomal protein S14 (RPS14) pseudogene | −1.64 | 0.0328 |
HSD3B2 | ENSG00000203859 | hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 2 | 1.23 | 0.039 |
PWP2 | ENSG00000241945 | PWP2 small subunit processome component | 1.09 | 0.0406 |
SVOPL | ENSG00000157703 | SVOP like | −1 | 0.0481 |
Pathway | No. Nodes | No. DEGs | Perturbation Status | pNa | pNi | pval |
---|---|---|---|---|---|---|
Creation of C4 and C2 activators | 14 | 5 | Down | 0.999 | 0.000 | 0.001 |
Formation of the Early Elongation Complex | 33 | 2 | Up | 0.001 | 0.995 | 0.003 |
Classical antibody-mediated complement activation | 6 | 5 | Down | 1.000 | 0.002 | 0.004 |
Complement cascade | 57 | 8 | Down | 0.680 | 0.006 | 0.011 |
Regulation of complement cascade | 46 | 7 | Down | 0.694 | 0.007 | 0.014 |
Initial triggering of complement | 22 | 6 | Down | 0.611 | 0.008 | 0.016 |
Negative regulation of TCF-dependent signaling by WNT ligand antagonists | 15 | 4 | Down | 0.742 | 0.008 | 0.016 |
Digestion of dietary carbohydrate | 7 | 2 | Down | 0.370 | 0.144 | 0.288 |
FCGR activation | 12 | 2 | Down | 0.415 | 0.049 | 0.098 |
Role of phospholipids in phagocytosis | 24 | 0 | Down | 0.306 | 0.231 | 0.462 |
Thyroxine biosynthesis | 7 | 1 | Up | 0.230 | 0.600 | 0.460 |
Scavenging of heme from plasma | 12 | 0 | Down | 0.587 | 0.131 | 0.261 |
Role of LAT2/NTAL/LAB on calcium mobilization | 12 | 0 | Up | 0.298 | 0.372 | 0.597 |
FCERI-mediated Ca+2 mobilization | 30 | 1 | Up | 0.177 | 0.495 | 0.353 |
CD22-mediated BCR regulation | 5 | 0 | Up | 0.368 | 0.844 | 0.735 |
FCGR3A-mediated IL10 synthesis | 28 | 2 | Up | 0.321 | 0.323 | 0.642 |
Antigen activates B-Cell Receptor (BCR), leading to the generation of second messengers | 29 | 0 | Up | 0.414 | 0.590 | 0.827 |
Pathway | DEG 1 | DEG 2 | DEG 3 | DEG 4 | DEG 5 | DEG 6 | DEG 7 | DEG 8 |
---|---|---|---|---|---|---|---|---|
Creation of C4 and C2 activators | C1QC | C1QB | MASP1 | C1S | C1R | |||
Formation of the Early Elongation Complex | GTF2H2 | SUPT4H1 | ||||||
Classical antibody-mediated complement activation | C1QA | C1QC | C1QB | C1S | C1R | |||
Complement cascade | C1QC | C1QB | C4BPA | MASP1 | C6 | C1S | C1R | C3AR1 |
Regulation of complement cascade | C1QC | C1QB | C4BPA | C6 | C1S | C1R | C3AR1 | |
Initial triggering of complement | C1QC | C1QB | MASP1 | C4B | C1S | C1R | ||
Negative regulation of TCF-dependent signaling by WNT ligand antagonists | LRP5 | SFRP1 | LRP5 | KREMEN1 | ||||
Digestion of dietary carbohydrate | SI | MGAM | ||||||
FCGR activation | FGCR2A | CD247 | ||||||
Role of phospholipids in phagocytosis | ||||||||
Thyroxine biosynthesis | DUOX2 | |||||||
Scavenging of heme from plasma | ||||||||
Role of LAT2/NTAL/LAB on calcium mobilization | ||||||||
FCERI-mediated Ca+2 mobilization | LCP2 | |||||||
CD22-mediated BCR regulation | ||||||||
FCGR3A-mediated IL10 synthesis | FGCR2A | CD247 | ||||||
Antigen activates B-Cell Receptor (BCR), leading to the generation of second messengers |
Gene | Path | Gene | IBS-D Model | Control Model | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
β | p-Value | 95%CI inf | 95%CI Sup | A/NA | β | p-Value | 95%CI inf | 95%CI Sup | A/NA | |||
SLC9B1 | → | CA4 | 0.481 | 0.016 | 0.089 | 0.874 | A | −0.343 | 0.084 | −0.732 | 0.046 | NA |
LRP6 | → | CA4 | −0.251 | 0.21 | −0.644 | 0.141 | NA | −0.383 | 0.054 | −0.772 | 0.006 | NA |
MGAM | → | SLC5A1 | 0.023 | 0.926 | −0.453 | 0.498 | A | 0.386 | 0.076 | −0.041 | 0.812 | A |
UBC | → | SCNN1G | 0.819 | 0 | 0.546 | 1.092 | A | −0.068 | 0.772 | −0.529 | 0.393 | NA |
UBC | → | SFRP1 | −0.331 | 0.149 | −0.779 | 0.118 | NA | 0.078 | 0.74 | −0.383 | 0.539 | A |
CA4 | → | DUOX2 | 0.457 | 0.034 | 0.034 | 0.88 | A | 0.808 | 0 | 0.536 | 1.08 | A |
SLC9B1 | → | AKAP1 | −0.602 | 0 | −0.875 | −0.33 | NA | 0.227 | 0.167 | −0.095 | 0.548 | A |
SLC5A1 | → | AKAP1 | 0.093 | 0.468 | −0.159 | 0.345 | A | 0.619 | 0 | 0.28 | 0.959 | A |
SCNN1G | → | AKAP1 | −0.409 | 0.004 | −0.687 | −0.131 | NA | 0.304 | 0.085 | −0.042 | 0.649 | A |
SFRP1 | → | FZD1 | 0.533 | 0.009 | 0.131 | 0.935 | A | 0.356 | 0.106 | −0.076 | 0.787 | A |
Source | Path | Gene | b | p-Value | 95%CI inf | 95%CI sup | Up/Down |
---|---|---|---|---|---|---|---|
Group | → | SLC9B1 | −0.177 | 0.288 | −0.503 | 0.149 | down-expressed |
Group | → | LRP6 | 0.101 | 0.547 | −0.228 | 0.431 | upregulated |
Group | → | MGAM | 0.266 | 0.102 | −0.053 | 0.586 | upregulated |
Group | → | UBC | 0.271 | 0.096 | −0.048 | 0.589 | upregulated |
Group | → | CA4 | 0.474 | 0.001 | 0.192 | 0.755 | upregulated |
Group | → | SLC5A1 | 0.221 | 0.185 | −0.106 | 0.547 | upregulated |
Group | → | SCNN1G | −0.247 | 0.11 | −0.549 | 0.056 | down-expressed |
Group | → | SFRP1 | −0.405 | 0.009 | −0.71 | −0.1 | down-expressed |
Group | → | DUOX2 | 0.098 | 0.471 | −0.169 | 0.366 | upregulated |
Group | → | AKAP1 | −0.039 | 0.788 | −0.324 | 0.246 | down-expressed |
Group | → | FZD1 | −0.081 | 0.62 | −0.402 | 0.24 | down-expressed |
Gene | Path | Gene | db | p Value | 95%CI inf | 95%CI Sup | Up/Down |
---|---|---|---|---|---|---|---|
SLC9B1 | → | CA4 | 0.825 | 0.003 | 0.272 | 1.377 | upregulated |
LRP6 | → | CA4 | 0.132 | 0.640 | −0.421 | 0.684 | upregulated |
MGAM | → | SLC5A1 | −0.363 | 0.265 | −1.001 | 0.275 | downregulated |
UBC | → | SCNN1G | 0.887 | 0.001 | 0.352 | 1.423 | upregulated |
UBC | → | SFRP1 | −0.409 | 0.213 | −1.052 | 0.234 | downregulated |
CA4 | → | DUOX2 | −0.351 | 0.171 | −0.854 | 0.151 | downregulated |
SLC9B1 | → | AKAP1 | −0.829 | 0.000 | −1.250 | −0.407 | downregulated |
SLC5A1 | → | AKAP1 | −0.526 | 0.015 | −0.949 | −0.103 | downregulated |
SCNN1G | → | AKAP1 | −0.713 | 0.002 | −1.156 | −0.269 | downregulated |
SFRP1 | → | FZD1 | 0.178 | 0.555 | −0.412 | 0.768 | upregulated |
Community | Source | Term Name | Term id | adj_p-Value |
---|---|---|---|---|
1 | GO:BP | complement activation | GO:0006956 | 1.38 × 10−2 |
2 | GO:BP | immune response-regulating cell surface receptor signaling pathway | GO:0002768 | 2.48 × 10−4 |
GO:BP | regulation of immune response | GO:0050776 | 1.15 × 10−3 | |
GO:BP | regulation of immune system process | GO:0002682 | 1.15 × 10−3 | |
GO:BP | immune response-activating signaling pathway | GO:0002757 | 1.42 × 10−3 | |
GO:BP | immune response-regulating signaling pathway | GO:0002764 | 1.87 × 10−3 | |
GO:BP | activation of immune response | GO:0002253 | 2.72 × 10−3 | |
GO:BP | positive regulation of immune system process | GO:0002684 | 3.28 × 10−3 | |
GO:BP | immune system process | GO:0002376 | 3.82 × 10−3 | |
GO:BP | transmembrane receptor protein tyrosine kinase signaling pathway | GO:0007169 | 5.63 × 10−3 | |
3 | GO:BP | disaccharide catabolic process | GO:0046352 | 4.11 × 10−5 |
GO:BP | disaccharide metabolic process | GO:0005984 | 4.52 × 10−4 | |
GO:BP | oligosaccharide catabolic process | GO:0009313 | 7.19 × 10−4 | |
GO:BP | oligosaccharide metabolic process | GO:0009311 | 1.51 × 10−2 | |
4 | - | - | - | - |
5 | - | - | - | - |
6 | GO:BP | midbrain dopaminergic neuron differentiation | GO:1904948 | 7.82 × 10−7 |
GO:BP | dopaminergic neuron differentiation | GO:0071542 | 1.83 × 10−5 | |
GO:BP | canonical Wnt signaling pathway | GO:0060070 | 2.89 × 10−5 | |
GO:BP | Wnt signaling pathway involved in somitogenesis | GO:0090244 | 1.09 × 10−4 | |
GO:BP | Wnt signaling pathway | GO:0016055 | 1.40 × 10−4 | |
GO:BP | cell-cell signaling by wnt | GO:0198738 | 1.43 × 10−4 | |
GO:BP | midbrain development | GO:0030901 | 2.15 × 10−4 | |
GO:BP | negative regulation of canonical Wnt signaling pathway | GO:0090090 | 7.02 × 10−4 | |
GO:BP | planar cell polarity pathway involved in neural tube closure | GO:0090179 | 9.94 × 10−4 | |
GO:BP | regulation of establishment of planar polarity involved in neural tube closure | GO:0090178 | 1.19 × 10−3 | |
7 | GO:BP | visual system development | GO:0150063 | 3.51 × 10−4 |
GO:BP | sensory system development | GO:0048880 | 3.78 × 10−4 | |
GO:BP | tube morphogenesis | GO:0035239 | 4.28 × 10−4 | |
GO:BP | Norrin signaling pathway | GO:0110135 | 1.11 × 10−3 | |
GO:BP | tube development | GO:0035295 | 1.60 × 10−3 | |
GO:BP | extracellular matrix-cell signaling | GO:0035426 | 1.85 × 10−3 | |
GO:BP | Wnt signaling pathway, calcium modulating pathway | GO:0007223 | 1.85 × 10−3 | |
GO:BP | retinal blood vessel morphogenesis | GO:0061304 | 2.77 × 10−3 | |
GO:BP | blood vessel morphogenesis | GO:0048514 | 2.96 × 10−3 | |
GO:BP | cellular response to retinoic acid | GO:0071300 | 3.32 × 10−3 | |
8 | GO:BP | complement activation | GO:0006956 | 1.47 × 10−19 |
GO:BP | humoral immune response | GO:0006959 | 5.60 × 10−14 | |
GO:BP | complement activation, classical pathway | GO:0006958 | 9.30 × 10−12 | |
GO:BP | humoral immune response mediated by circulating immunoglobulin | GO:0002455 | 5.90 × 10−11 | |
GO:BP | immune effector process | GO:0002252 | 5.40 × 10−10 | |
GO:BP | positive regulation of immune response | GO:0050778 | 6.93 × 10−10 | |
GO:BP | regulation of immune response | GO:0050776 | 4.37 × 10−9 | |
GO:BP | innate immune response | GO:0045087 | 9.93 × 10−9 | |
GO:BP | positive regulation of immune system process | GO:0002684 | 2.19 × 10−8 | |
GO:BP | synapse pruning | GO:00998883 | 3.30 × 10−5 |
Communities | Source | Term_Name | Term_id | adj_p_Value |
---|---|---|---|---|
1 | - | - | - | - |
2 | GO:BP | sodium ion transmembrane transport | GO:0035725 | 3.20 × 10−2 |
GO:BP | maltose catabolic processes | GO:0090178 | 4.95 × 10−2 | |
GO:BP | starch metabolic processes | GO:0090177 | 4.95 × 10−2 | |
GO:BP | starch catabolic processes | GO:0090175 | 4.95 × 10−2 | |
GO:BP | dextrin catabolic processes | GO:1904948 | 4.95 × 10−2 | |
3 | GO:BP | planar cell polarity pathway involved in neural tube closure | GO:0090179 | 9.94 × 10−4 |
GO:BP | regulation of establishment of planar polarity involved in neural tube closure | GO:0090178 | 1.19 × 10−3 | |
GO:BP | establishment of planar polarity involved in neural tube closure | GO:0090177 | 1.41 × 10−3 | |
GO:BP | regulation of establishment of planar polarity | GO:0090175 | 1.90 × 10−3 | |
GO:BP | midbrain dopaminergic neuron differentiation | GO:1904948 | 1.90 × 10−3 | |
GO:BP | establishment of planar polarity of embryonic epithelium | GO:0042249 | 1.90 × 10−3 | |
GO:BP | establishment of tissue polarity | GO:0007164 | 1.27 × 10−2 | |
GO:BP | establishment of planar polarity | GO:0001736 | 1.27 × 10−2 | |
GO:BP | dopaminergic neuron differentiation | GO:0071542 | 1.48 × 10−2 | |
GO:BP | Wnt signaling pathway, planar cell polarity pathway | GO:0060071 | 2.21 × 10−2 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Guido, D.; Maqoud, F.; Aloisio, M.; Mallardi, D.; Ura, B.; Gualandi, N.; Cocca, M.; Russo, F. Transcriptomic Module Discovery of Diarrhea-Predominant Irritable Bowel Syndrome: A Causal Network Inference Approach. Int. J. Mol. Sci. 2024, 25, 9322. https://doi.org/10.3390/ijms25179322
Guido D, Maqoud F, Aloisio M, Mallardi D, Ura B, Gualandi N, Cocca M, Russo F. Transcriptomic Module Discovery of Diarrhea-Predominant Irritable Bowel Syndrome: A Causal Network Inference Approach. International Journal of Molecular Sciences. 2024; 25(17):9322. https://doi.org/10.3390/ijms25179322
Chicago/Turabian StyleGuido, Davide, Fatima Maqoud, Michelangelo Aloisio, Domenica Mallardi, Blendi Ura, Nicolò Gualandi, Massimiliano Cocca, and Francesco Russo. 2024. "Transcriptomic Module Discovery of Diarrhea-Predominant Irritable Bowel Syndrome: A Causal Network Inference Approach" International Journal of Molecular Sciences 25, no. 17: 9322. https://doi.org/10.3390/ijms25179322