Increased HLA-G Expression in Tissue-Infiltrating Cells in Inflammatory Bowel Diseases

Soluble HLA-G (sHLA-G) measurement might be useful as an early diagnostic biomarker and screening test for gastric cancer

Gastric cancer (GC) is the fifth most frequent malignancy worldwide and has a high mortality rate related to late diagnosis. Although the gold standard for the GC diagnosis is endoscopy with biopsy, nonetheless, it is not cost-effective and is invasive for the patient. The Human leukocyte antigen G (HLA-G) molecule is a checkpoint of the immune response. Its overexpression in cancer is associated with immune evasion, metastasis, poor prognosis, and lower overall survival. We evaluate the plasma levels of soluble HLA-G, (sHLA-G) in patients with GC and benign gastric pathologies using an ELISA test. A higher concentration of sHLA-G in patients with GC than in those with benign pathologies, higher levels of plasma sHLA-G in women with GC compared with men and significant differences in the sHLA-G levels between the benign gastric pathologies evaluated, was our main findings. As no significant differences were found between the GC assessed stages in our study population, we suggest that sHLA-G is not an adequate marker for staging GC, but it does have diagnostic potential. In addition to providing information on the potential of sHLA-G as a diagnostic marker for GC, our study demonstrate that HLA-G molecules can be found in the membrane of exosomes, which highlights the need to perform studies with a larger number of samples to explore the functional implications of HLA-G positive exosomes in the context of gastric cancer, and to determine the clinical significance and possible applications of these findings in the development of non-invasive diagnostic methods.

Comparison between qPCR and RNA-seq reveals challenges of quantifying HLA expression

Human leukocyte antigen (HLA) class I and II loci are essential elements of innate and acquired immunity. Their functions include antigen presentation to T cells leading to cellular and humoral immune responses, and modulation of NK cells. Their exceptional influence on disease outcome has now been made clear by genome-wide association studies. The exons encoding the peptide-binding groove have been the main focus for determining HLA effects on disease susceptibility/pathogenesis. However, HLA expression levels have also been implicated in disease outcome, adding another dimension to the extreme diversity of HLA that impacts variability in immune responses across individuals. To estimate HLA expression, immunogenetic studies traditionally rely on quantitative PCR (qPCR). Adoption of alternative high-throughput technologies such as RNA-seq has been hampered by technical issues due to the extreme polymorphism at HLA genes. Recently, however, multiple bioinformatic methods have been developed to accurately estimate HLA expression from RNA-seq data. This opens an exciting opportunity to quantify HLA expression in large datasets but also brings questions on whether RNA-seq results are comparable to those by qPCR. In this study, we analyze three classes of expression data for HLA class I genes for a matched set of individuals: (a) RNA-seq, (b) qPCR, and (c) cell surface HLA-C expression. We observed a moderate correlation between expression estimates from qPCR and RNA-seq for HLA-A, -B, and -C (0.2 ≤ rho ≤ 0.53). We discuss technical and biological factors which need to be accounted for when comparing quantifications for different molecular phenotypes or using different techniques.

A primary cell-based in vitro model of the human small intestine reveals host olfactomedin 4 induction in response to Salmonella Typhimurium infection

Infection research largely relies on classical cell culture or mouse models. Despite having delivered invaluable insights into host-pathogen interactions, both have limitations in translating mechanistic principles to human pathologies. Alternatives can be derived from modern Tissue Engineering approaches, allowing the reconstruction of functional tissue models in vitro. Here, we combined a biological extracellular matrix with primary tissue-derived enteroids to establish an in vitro model of the human small intestinal epithelium exhibiting in vivo-like characteristics. Using the foodborne pathogen Salmonella enterica serovar Typhimurium, we demonstrated the applicability of our model to enteric infection research in the human context. Infection assays coupled to spatio-temporal readouts recapitulated the established key steps of epithelial infection by this pathogen in our model. Besides, we detected the upregulation of olfactomedin 4 in infected cells, a hitherto unrecognized aspect of the host response to Salmonella infection. Together, this primary human small intestinal tissue model fills the gap between simplistic cell culture and animal models of infection, and shall prove valuable in uncovering human-specific features of host-pathogen interplay.

HLA allele-specific expression: Methods, disease associations, and relevance in hematopoietic stem cell transplantation

Varying HLA allele-specific expression levels are associated with human diseases, such as graft versus host disease (GvHD) in hematopoietic stem cell transplantation (HSCT), cytotoxic T cell response and viral load in HIV infection, and the risk of Crohn’s disease. Only recently, RNA-based next generation sequencing (NGS) methodologies with accompanying bioinformatics tools have emerged to quantify HLA allele-specific expression replacing the quantitative PCR (qPCR) -based methods. These novel NGS approaches enable the systematic analysis of the HLA allele-specific expression changes between individuals and between normal and disease phenotypes. Additionally, analyzing HLA allele-specific expression and allele-specific expression loss provide important information for predicting efficacies of novel immune cell therapies. Here, we review available RNA sequencing-based approaches and computational tools for NGS to quantify HLA allele-specific expression. Moreover, we explore recent studies reporting disease associations with differential HLA expression. Finally, we discuss the role of allele-specific expression in HSCT and how considering the expression quantification in recipient-donor matching could improve the outcome of HSCT.