Claudins (CLDNs) are transmembrane proteins of tight junctions that regulate paracellular barriers to control the flow of molecules between cells, maintaining the cell-cell integrity. In pancreatic adenocarcinoma (PAAD), one of the deadliest types of cancer worldwide, the expression of some CLDNs is dysregulated suggesting its role in tumorigenesis, particularly in proliferation, migration, and invasion. Despite these mechanisms remaining unclear, CLDNs are promising molecular targets for diagnosis and therapy.
To explore the role of each CLDN in PAAD, a bioinformatics approach based on the TGCA database was used. The expression profile of CLDNs and their impact on PAAD patient survival were analyzed using GEPIA2. The correlation between the expression of CLDNs and PAAD infiltrates was assessed using TIMER2.0. A KEGG pathway and GO enrichment analysis for genes positively correlated with CLDN family genes were assessed using ShinyGO 0.76.2.
CLDN1, -2, -4, -5, -7, -11, -12, -15 and -18 showed overexpression in PAAD tissues when compared with normal tissues, while CLDN3 expression decreased. The overexpression of CLDN1, -4, and -7 has a negative impact on overall survival (OS), CLDN3 and -15 have a positive impact on OS, and CLDN3, -5, and -15 have a positive impact on disease-free survival (DFS).
The high expression of CLDN5, -11, and -15 are strongly correlated with endothelial cell response and B cells infiltration. CLDN5 and -11 are also strongly correlated with T CD8+ cells, macrophages, and monocytes. On other hand, higher expression of CLDN4, -7, and 18 are associated with stimulation of endothelial cells and lower infiltration of B cells and T CD8+ cells. CLDN7 and -12 are also negatively correlated with macrophages.
The enriched GO pathways for biological processes more common between CLDNs´ co-expressed genes comprise metabolic process, adhesion, immune response activation, biological regulation, and development processes. Further, the KEGG analysis showed that most of the CLDNs´ co-expressed genes were involved in metabolic, apoptosis, immune response pathways.
Overall, these results showed that CLDN3, -4, -5 and -7 can be potential targets for inhibitors, but not CLDN3. CLDN5 and -11 could contribute to the design of new immunotherapies.
Neonates are a vulnerable population with high mortality and morbidity rates. One of the leading causes of death in this group is their susceptibility to intracellular pathogens. CD4+ T lymphocytes are one of the main arms of cellular immunity, the immune branch against intracellular pathogens. CD4+ T lymphocytes enter the circulation as naive T cells, with a catabolic metabolic program. Naïve T cells use all nutrients to generate energy (ATP) by oxidative phosphorylation (OXPHOS). Naive CD4+ T cells are activated when they recognize its cognate antigen through their T Cell Receptor (TCR), when it’s presented by an Antigen Presenting Cell (APC), along with costimulatory signals like that of the receptor CD28. CD28 molecules cause the activation of signaling pathways like the PI3K/Akt/mTOR one, leading to an enhanced TCR-mediated signaling and augmented metabolism. These intracellular events cause metabolic reprogramming, changing the catabolic metabolism of the naive cell to an anabolic metabolism in the activated cell. In this new program, the metabolic rate increases, and the cell synthesizes all biomolecules necessary to fulfill the new functions of an effector CD4+ T cell. Previous work in our lab indicates an increased glycolytic rate and a low activation response of neonatal CD4+ T cell. To understand the relationship between the metabolism and the immune response of CD4+ T cells, we used a computational modeling approach aided by experimental data integration. Our current immunometobolic model of CD4+ T cell activation comprises 212 nodes, with 157 Boolean nodes (values 0 and 1) and 55 multivalued nodes (values 0, 1, 2 and 3). In the model, the nodes represent components of signaling pathways, like TCR and CD28, or components of the metabolic pathways, like metabolic enzymes and metabolites. Our dynamical analysis shows 17 possible stable states, 2 of these states represent a naive T cell, with the activation pathways off and a basal metabolic program; other 2 states represent anergic states, and the last 3 states represent an activated T cell, with the activation pathways on and metabolic reprogramming comprising high glycolysis and anabolic program. These results are in accordance with the metabolic reprogramming occurring during adult CD4+ T cell activation. We are currently exploring the effect of integrating the transcriptomic profile of neonatal CD4+ T cells in the activation response of these cells. We will also propose possible strategies to improve the neonatal CD4+ T cell immune response.