bims-maitce 2026-05-31 papers

Issue of 2026–05–31
two papers selected by
Andy E. Hogan, Maynooth University

Sci Rep. 2026 May 29.

Sorting endosomes play key roles in presentation of Mycobacterium tuberculosis-derived ligands to MAIT cells.

Allison E Tammen, Jessie C Peterson, Aneta Worley, David M Lewinsohn, Elham Karamooz.

The immune system has developed specialized mechanisms to recognize intracellular pathogens such as Mycobacterium tuberculosis (Mtb). Major Histocompatibility Complex Class I-Related protein 1 (MR1) is a conserved nonclassical antigen presenting molecule that presents ligands derived from microbial riboflavin synthesis to Mucosal Associated Invariant T (MAIT) cells. While endosomal trafficking facilitates MR1 antigen presentation during Mtb infection, the exact mechanisms by which MR1 loading of Mtb-derived ligands occurs are not known. We found that trafficking through sorting endosomes mediates MR1 antigen presentation during Mtb infection. Sorting endosomes utilize trafficking proteins such as Syntaxin (Stx) 6, Stx12, Stx16 and VAMP4. Prior work demonstrates the importance of VAMP4 for MR1 presentation during Mtb infection; we have found that Stx12 and Stx16 are also important. Interference with Stx12 or Stx16 via siRNA-mediated knockdown reduces MR1 antigen presentation of Mtb. Using RFP-tagged constructs, we found Stx16 co-localized more with MR1 vesicles compared to Stx12 in MR1-GFP expressing airway epithelial cells. Stx12 and Stx16 blockade increase MR1 surface stabilization and total expression, indicating that impaired endosomal trafficking hinders MR1 internalization. Together, these findings support a role for sorting endosomes in the selective sampling of the intracellular environment and MR1-mediated recognition of Mtb-infected cells.

Keywords: Mycobacterium tuberculosis ; Antigen presentation; Endosomal trafficking proteins; MAIT cells; MR1; Syntaxins

DOI: https://doi.org/10.1038/s41598-026-50843-1

Mol Ther Oncol. 2026 Jun 18. 34(2): 201218

An integrative transcriptomic analysis for prognosis and tumor microenvironment in HBV/HCV-associated hepatocellular carcinoma.

Fahimeh Safarnezhad Tameshkel, Niloufar Sadat Kalaki, Elham Karimi, Mohammad Hossein Razizadeh, Abed El Hasan Haidar, Ali Amiri, Mohammadreza Zaki, Mehdi Nikkhah, Mohammad Hadi Karbalaie Niya.

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide; molecular biomarkers that reflect hepatitis B virus (HBV)/hepatitis C virus (HCV)-associated tumor biology and predict prognosis or therapeutic vulnerabilities are needed. The research is a secondary analysis of public data (secondary data analysis) based on Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data to identify differentially expressed genes (DEGs) common to HBV-HCC, HCV-HCC, and non-viral HCC datasets. Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, protein-protein interaction (PPI) network analysis, and hub-gene selection were then performed. Differential expression, prognostic association, immune-infiltration, and drug-sensitivity correlations were also analyzed. Eighty common DEGs (73 upregulated, 7 downregulated) were identified. PPI topological analysis yielded 53 hub genes; five genes TOP2A, RACGAP1, ASPM, CENPF, and GPC3, were prioritized and validated as significantly upregulated in liver HCC and associated with poorer overall survival. Immune deconvolution showed consistent positive correlations between the hub genes and B cells and regulatory T cell subsets, and negative correlations with mucosal-associated invariant T (MAIT) cells, macrophages, and natural killer (NK)/monocyte signatures. Drug-gene correlation analysis revealed positive associations of TOP2A and RACGAP1 with sensitivity to mitogen-activated protein kinase inhibitors and negative correlations with several targeted inhibitors. The identified prognostically unfavorable hub genes in HBV/HCV-associated HCC are associated with an immunosuppressive microenvironment and with patterns of drug sensitivity.

Keywords: DEGs; PPI network; bioinformatics; differentially expressed genes; hepatocellular carcinoma; hub gene; molecular biomarker; prognosis; protein-protein interaction network; target-specific therapies; transcriptomic analysis; tumor biolog; viral hepatitis

DOI: https://doi.org/10.1016/j.omton.2026.201218