Abstract
Type 2 diabetes (DM2) is an increasingly prevalent disease that challenges tuberculosis (TB) control strategies worldwide. It is significant that DM2 patients with poor glycemic control (PDM2) are prone to develo** tuberculosis. Furthermore, elucidating the molecular mechanisms that govern this susceptibility is imperative to address this problem. Therefore, a pilot transcriptomic study was performed. Human blood samples from healthy controls (CTRL, HbA1c < 6.5%), tuberculosis (TB), comorbidity TB-DM2, DM2 (HbA1c 6.5–8.9%), and PDM2 (HbA1c > 10%) groups (n = 4 each) were analyzed by differential expression using microarrays. We use a network strategy to identify potential molecular patterns linking the differentially expressed genes (DEGs) specific for TB-DM2 and PDM2 (p-value < 0.05, fold change > 2). We define OSM, PRKCD, and SOCS3 as key regulatory genes (KRGs) that modulate the immune system and related pathways. RT-qPCR assays confirmed upregulation of OSM, PRKCD, and SOCS3 genes (p < 0.05) in TB-DM2 patients (n = 18) compared to CTRL, DM2, PDM2, or TB groups (n = 17, 19, 15, and 9, respectively). Furthermore, OSM, PRKCD, and SOCS3 were associated with PDM2 susceptibility pathways toward TB-DM2 and formed a putative protein–protein interaction confirmed in STRING. Our results reveal potential molecular patterns where OSM, PRKCD, and SOCS3 are KRGs underlying the compromised immune response and susceptibility of patients with PDM2 to develop tuberculosis. Therefore, this work paved the way for fundamental research of new molecular targets in TB-DM2. Addressing their cellular implications, and the impact on the diagnosis, treatment, and clinical management of TB-DM2 could help improve the strategy to end tuberculosis for this vulnerable population.
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Data availability
The microarray files obtained here are available in the Gene Expression Omnibus (GEO) repository under the accession code GSE249102.
References
Alam A, Imam N, Ahmed MM et al (2019) Identification and classification of differentially expressed genes and network meta-analysis reveals potential molecular signatures associated with tuberculosis. Front Genet 10:1–20. https://doi.org/10.3389/fgene.2019.00932
Barreda NN, Arriaga MB, Aliaga JG et al (2020) Severe pulmonary radiological manifestations are associated with a distinct biochemical profile in blood of tuberculosis patients with dysglycemia. BMC Infect Dis 20:1–14. https://doi.org/10.1186/s12879-020-4843-0
Blankley S, Graham CM, Levin J et al (2016) A 380-gene meta-signature of active tuberculosis compared with healthy controls. Eur Respir J 47:1873–1876. https://doi.org/10.1183/13993003.02121-2015
Bloom CI, Graham CM, Berry MPR et al (2012) Detectable changes in the blood transcriptome are present after two weeks of antituberculosis therapy. PLoS ONE. https://doi.org/10.1371/journal.pone.0046191
Carow B, Reuschl AK, Gavier-Widén D et al (2013) Critical and independent role for SOCS3 in either myeloid or T cells in resistance to mycobacterium tuberculosis. PLoS Pathog. https://doi.org/10.1371/journal.ppat.1003442
Chai Q, Lu Z, Liu CH (2020) Host defense mechanisms against Mycobacterium tuberculosis. Cell Mol Life Sci 77:1859–1878. https://doi.org/10.1007/s00018-019-03353-5
Chen EY, Tan CM, Kou Y et al (2013) Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14:1–14. https://doi.org/10.1186/1471-2105-14-128
Chuanyou L, Shenjie T, Shengsheng L et al (2021) Identification of hub genes associated with diabetes mellitus and tuberculosis using bioinformatic analysis. Int J Gen Med 14:4061–4072. https://doi.org/10.2147/IJGM.S318071
Critchley JA, Carey IM, Harris T et al (2018) Glycemic control and risk of infections among people with type 1 or type 2 diabetes in a large primary care cohort study. Diabetes Care 41:2127–2135. https://doi.org/10.2337/dc18-0287
Croft D, Mundo AF, Haw R et al (2014) The reactome pathway knowledgebase. Nucleic Acids Res 42:472–477. https://doi.org/10.1093/nar/gkt1102
Duquesnes N, Lezoualc’h F, Crozatier B (2011) PKC-delta and PKC-epsilon: Foes of the same family or strangers? J Mol Cell Cardiol 51:665–673. https://doi.org/10.1016/j.yjmcc.2011.07.013
Eckold C, Kumar V, Weiner J et al (2021) Impact of intermediate hyperglycemia and diabetes on immune dysfunction in tuberculosis. Clin Infect Dis. https://doi.org/10.1093/cid/ciaa751
Eckold C, van Doorn CLR, Ruslami R et al (2023) Impaired resolution of blood transcriptomes through tuberculosis treatment with diabetes comorbidity. Clin Transl Med. https://doi.org/10.1002/ctm2.1375
Emanuelli B, Peraldi P, Filloux C et al (2000) SOCS-3 is an insulin-induced negative regulator of insulin signaling. J Biol Chem 275:15985–15991. https://doi.org/10.1074/jbc.275.21.15985
Gao Y, Zhao H, Wang P et al (2018) The roles of SOCS3 and STAT3 in bacterial infection and inflammatory diseases. Scand J Immunol 88:1–12. https://doi.org/10.1111/sji.12727
Geneva: World Health Organization; 2023. Global tuberculosis report 2023. Licence: CC BY-NC-SA 3.0 IGO. Cataloguing-in-Publication
Gil-Santana L, Almeida JL, Oliveira CAM et al (2016) Diabetes is associated with worse clinical presentation in tuberculosis patients from Brazil: a retrospective cohort study. PLoS ONE 11:1–13. https://doi.org/10.1371/journal.pone.0146876
Goren I, Kämpfer H, Müller E et al (2006) Oncostatin M expression is functionally connected to neutrophils in the early inflammatory phase of skin repair: Implications for normal and diabetes-impaired wounds. J Invest Dermatol 126:628–637. https://doi.org/10.1038/sj.jid.5700136
Harling K, Adankwah E, Güler A et al (2019) Constitutive STAT3 phosphorylation and IL-6/IL-10 co-expression are associated with impaired T-cell function in tuberculosis patients. Cell Mol Immunol 16:275–287. https://doi.org/10.1038/cmi.2018.5
Jain N, Zhang T, Kee WH et al (1999) Protein kinase C δ associates with and phosphorylates Stat3 in an interleukin-6-dependent manner. J Biol Chem 274:24392–24400. https://doi.org/10.1074/jbc.274.34.24392
Joshi-Tope G, Gillespie M, Vastrik I et al (2005) Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33:428–432. https://doi.org/10.1093/nar/gki072
Kanehisa M, Goto S, Sato Y et al (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 42:199–205. https://doi.org/10.1093/nar/gkt1076
Kelder T, Van Iersel MP, Hanspers K et al (2012) WikiPathways: building research communities on biological pathways. Nucleic Acids Res 40:1301–1307. https://doi.org/10.1093/nar/gkr1074
Kuleshov MV, Jones MR, Rouillard AD et al (2016) Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 44:W90–W97. https://doi.org/10.1093/nar/gkw377
Lam A, Prabhu R, Gross CM et al (2017) Role of apoptosis and autophagy in tuberculosis. Am J Physiol - Lung Cell Mol Physiol 313:L218–L229. https://doi.org/10.1152/ajplung.00162.2017
Lee PH, Fu H, Lai TC et al (2016a) Glycemic control and the risk of tuberculosis: a cohort study. PLoS Med 13:1–15. https://doi.org/10.1371/journal.pmed.1002072
Lee SW, Wu LSH, Huang GM et al (2016b) Gene expression profiling identifies candidate biomarkers for active and latent tuberculosis. BMC Bioinformatics 17:27–39. https://doi.org/10.1186/s12859-015-0848-x
Leisching GR (2018) Susceptibility to Tuberculosis is associated with PI3K-dependent increased mobilization of neutrophils. Front Immunol. https://doi.org/10.3389/fimmu.2018.01669
Li T, Wernersson R, Hansen RB et al (2017) A scored human protein–protein interaction network to catalyze genomic interpretation. Nat Methods 14:61–64
Lin Y, Bai Y, Zhang T et al (2020) Unfavourable treatment outcomes in tuberculosis patients with different vitamin D status and blood glucose levels in a programme setting in China. Trop Med Int Heal 25:373–379. https://doi.org/10.1111/tmi.13355
Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods 25:402–408. https://doi.org/10.1006/meth.2001.1262
López-Hernández Y, Lara-Ramírez EE, Salgado-Bustamante M et al (2019) Glycerophospholipid metabolism alterations in patients with type 2 diabetes mellitus and tuberculosis comorbidity. Arch Med Res 50:71–78. https://doi.org/10.1016/j.arcmed.2019.05.006
Miyake Y, Oh-hora M, Yamasaki S (2015) C-Type lectin receptor mcl facilitates mincle expression and signaling through complex formation. J Immunol 194:5366–5374. https://doi.org/10.4049/jimmunol.1402429
Nishimura D (2001) A View From the web biocarta. Biotech Softw Internet Rep 2:117–120
O’Kane CM, Elkington PT, Friedland JS (2008) Monocyte-dependent oncostatin M and TNF-α synergize to stimulate unopposed matrix metalloproteinase-1/3 secretion from human lung fibroblasts in tuberculosis. Eur J Immunol 38:1321–1330. https://doi.org/10.1002/eji.200737855
Ochoa-González FL, González-Curiel IE, Cervantes-Villagrana AR et al (2020) Innate immunity alterations in type 2 diabetes mellitus: understanding infection susceptibility. Curr Mol Med 21:318–331. https://doi.org/10.2174/1566524020999200831124534
Ogata H, Goto S, Sato K, et al (1999) KEGG: Kyoto Encyclopedia of Genes and Genomes. http://www.kegg.jp/ (accessed 2015). Nucleic Acids Res 27:29–34
Parihar SP, Ozturk M, Marakalala MJ et al (2018) Protein kinase C-delta (PKCδ), a marker of inflammation and tuberculosis disease progression in humans, is important for optimal macrophage killing effector functions and survival in mice. Mucosal Immunol 11:496–511. https://doi.org/10.1038/mi.2017.68
Prada-Medina CA, Fukutani KF, Kumar NP et al (2017) Systems immunology of diabetes-tuberculosis comorbidity reveals signatures of disease complications. Sci Rep 7:1–16. https://doi.org/10.1038/s41598-017-01767-4
Ravesloot-Chavez MM, Van Dis E, Stanley SA (2021) The innate immune response to mycobacterium tuberculosis infection. Annu Rev Immunol 39:611–637. https://doi.org/10.1146/annurev-immunol-093019-010426
Richards CD (2013) The enigmatic cytokine oncostatin m and roles in disease. ISRN Inflamm 2013:1–23. https://doi.org/10.1155/2013/512103
Ronacher K, van Crevel R, Critchley JA et al (2017) Defining a research agenda to address the converging epidemics of tuberculosis and diabetes: part 2: underlying biologic mechanisms. Chest 152:174–180. https://doi.org/10.1016/j.chest.2017.02.032
Rottenberg ME, Carow B (2014) SOCS3 and STAT3, major controllers of the outcome of infection with mycobacterium tuberculosis. Semin Immunol 26:518–532. https://doi.org/10.1016/j.smim.2014.10.004
Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303.metabolite
Simmons JD, Peterson GJ, Campo M et al (2020) Nicotinamide limits replication of mycobacterium tuberculosis and bacille calmette-guérin within macrophages. J Infect Dis 221:989–999. https://doi.org/10.1093/infdis/jiz541
Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. https://doi.org/10.2202/1544-6115.1027
Smyth DC, Kerr C, Richards CD (2006) Oncostatin m-induced il-6 expression in murine fibroblasts requires the activation of protein kinase cδ. J Immunol 177:8740–8747. https://doi.org/10.4049/jimmunol.177.12.8740
Smyth DC, Takenaka S, Yeung C, Richards CD (2015) Oncostatin M regulates osteogenic differentiation of murine adipose-derived mesenchymal progenitor cells through a PKCdelta-dependent mechanism. Cell Tissue Res 360:309–319. https://doi.org/10.1007/s00441-014-2099-y
Snel B, Lehmann G, Bork P, Huynen MA (2000) String: a web-server to retrieve and display the repeatedly occurring neighbourhood of a gene. Nucleic Acids Res 28:3442–3444. https://doi.org/10.1093/nar/28.18.3442
Ssekamatte P, Sande OJ, van Crevel R, Biraro IA (2023) Immunologic, metabolic and genetic impact of diabetes on tuberculosis susceptibility. Front Immunol 14:1–13. https://doi.org/10.3389/fimmu.2023.1122255
Stross C, Radtke S, Clahsen T et al (2006) Oncostatin M receptor-mediated signal transduction is negatively regulated by SOCS3 through a receptor tyrosine-independent mechanism. J Biol Chem 281:8458–8468. https://doi.org/10.1074/jbc.M511212200
Szklarczyk D, Gable AL, Nastou KC et al (2021) The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 49:D605–D612. https://doi.org/10.1093/nar/gkaa1074
van Doorn CLR, Eckold C, Ronacher K et al (2022) Transcriptional profiles predict treatment outcome in patients with tuberculosis and diabetes at diagnosis and at two weeks after initiation of anti-tuberculosis treatment. EBioMedicine 82:1
Wei C, Li J, Bumgarner RE (2004) Sample size for detecting differentially expressed genes in microarray experiments. BMC Genomics 5:1–10. https://doi.org/10.1186/1471-2164-5-87
World Health Organization (2021) Module 2: screening - systematic screening for tuberculosis disease. In: WHO consolidated guidelines on tuberculosis. Geneva, Switzerland pp 1–51
World Medical Association (2001) World medical association declaration of Helsinki. Bull World Heal Organ 79:373–374
Yamamoto K, Mizuguchi H, Tokashiki N et al (2017) Protein kinase C-δ signaling regulates glucagon secretion from pancreatic islets. J Med Investig 64:122–128. https://doi.org/10.2152/jmi.64.122
Zak DE, Penn-Nicholson A, Scriba TJ et al (2017) A blood RNA signature for tuberculosis disease risk: a prospective cohort study. Lancet 387:2312–2322. https://doi.org/10.1016/S0140-6736(15)01316-1
Acknowledgements
We thank Jesus Núñez Contreras from Unidad de Investigación Biomédica de Zacatecas for QFT quantification and PPD determinations, as well as Gerardo Martínez Aguilar from Unidad de Investigación Biomédica Durango. We want to thanks to epidemiologist and health personnel from UIBMZ and Unidad Médica Familiar 4 (IMSS, Zacatecas), UIBM Durango, Universidad Veracruzana at Xalapa, CIAE Monterrey Nuevo Leon and Hospital General de Subzona con Medicina Familiar 41 Huatulco Oaxaca, their support for the identification of diabetes and tuberculosis patients and in the whole blood sample collection.
Funding
This work was supported by the National Council of Humanities, Sciences and Technologies [CONAHCyT], under Grant [number: A1-S-48232]; [Instituto Mexicano del Seguro Social and CONACyT] provided fellowships for graduate studies to EJS under Grant [numbers: 2017–058 and 487638], JELR under Grant [numbers: 99348716 and 389725]; and [CONACyT] to JJOV under Grant [number: 487639].
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Elena Jaime-Sánchez: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data Curation, Writing, Original draft, Visualization. Edgar E. Lara-Ramírez: Methodology, Software, Formal analysis, Data Curation, Writing—Review & Editing; Juan Ernesto López-Ramos: Investigation, Resources, Data Curation, Writing—Review & Editing. Elsy Janeth Ramos-González: Formal analysis, Resources, Data Curation, Writing—Review & Editing. Ana Laura Cisneros-Méndez: Validation, Investigation. Juan José Oropeza-Valdez: Resources, Writing—Review & Editing. Roberto Zenteno-Cuevas: Resources, Writing—Review & Editing. Gerardo Martínez-Aguilar: Resources, Writing—Review & Editing. Yadira Bastian: Resources, Writing—Review & Editing. Julio Enrique Castañeda-Delgado: Resources, Writing—Review & Editing. Carmen Judith Serrano: Review & Editing. José Antonio Enciso-Moreno: Term, Conceptualization, Methodology, Resources, Writing—Review & Editing, Supervision, Project administration, Funding acquisition.
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This study was performed in line with the principles of the Declaration of Helsinki and its later amendments. Approval was granted by the National Committee for Scientific Research and Ethics of the Instituto Mexicano del Seguro Social (IMSS) (R-2013–785-001 and R-2018–785-118).
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Jaime-Sánchez, E., Lara-Ramírez, E.E., López-Ramos, J.E. et al. Potential molecular patterns for tuberculosis susceptibility in diabetic patients with poor glycaemic control: a pilot study. Mol Genet Genomics 299, 60 (2024). https://doi.org/10.1007/s00438-024-02139-0
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DOI: https://doi.org/10.1007/s00438-024-02139-0