The regulation of tetraspanin 8 gene expression-A potential new mechanism in the pathogenesis of bipolar disorder

The role of Tetraspanins in digestive system tumor development: update and emerging evidence

Digestive system malignancies, including cancers of the esophagus, pancreas, stomach, liver, and colorectum, are the leading causes of cancer-related deaths worldwide due to their high morbidity and poor prognosis. The lack of effective early diagnosis methods is a significant factor contributing to the poor prognosis for these malignancies. Tetraspanins (Tspans) are a superfamily of 4-transmembrane proteins (TM4SF), classified as low-molecular-weight glycoproteins, with 33 Tspan family members identified in humans to date. They interact with other membrane proteins or TM4SF members to form a functional platform on the cytoplasmic membrane called Tspan-enriched microdomain and serve multiple functions including cell adhesion, migration, propagation and signal transduction. In this review, we summarize the various roles of Tspans in the progression of digestive system tumors and the underlying molecular mechanisms in recent years. Generally, the expression of CD9, CD151, Tspan1, Tspan5, Tspan8, Tspan12, Tspan15, and Tspan31 are upregulated, facilitating the migration and invasion of digestive system cancer cells. Conversely, Tspan7, CD82, CD63, Tspan7, and Tspan9 are downregulated, suppressing digestive system tumor cell metastasis. Furthermore, the connection between Tspans and the metastasis of malignant bone tumors is reviewed. We also summarize the potential role of Tspans as novel immunotherapy targets and as an approach to overcome drug resistance. Finally, we discuss the potential clinical value and therapeutic targets of Tspans in the treatments of digestive system malignancies and provide some guidance for future research.

Regional expression differences of SERT and TSPAN8 in hippocampus, cerebellum and cortex of wild-type young, adult and middle-aged rats

Changes in gene expression with aging are associated with a decline in physical and cognitive abilities. Here, we investigated the changes in mRNA and protein expression of TSPAN8 and SERT in the different parts of the brain for different age group rats. Our protein analysis revealed that aging mainly triggers SERT gene expression in the cerebellum and hippocampus, showing that an increase in mRNA expression correlates with protein expression. For TSPAN8, age-dependent protein increase was observed in the hippocampus and highest expression was observed for adult and middle-aged rats.

Constrained neuro fuzzy inference methodology for explainable personalised modelling with applications on gene expression data

Interpretable machine learning models for gene expression datasets are important for understanding the decision-making process of a classifier and gaining insights on the underlying molecular processes of genetic conditions. Interpretable models can potentially support early diagnosis before full disease manifestation. This is particularly important yet, challenging for mental health. We hypothesise this is due to extreme heterogeneity issues which may be overcome and explained by personalised modelling techniques. Thus far, most machine learning methods applied to gene expression datasets, including deep neural networks, lack personalised interpretability. This paper proposes a new methodology named personalised constrained neuro fuzzy inference (PCNFI) for learning personalised rules from high dimensional datasets which are structurally and semantically interpretable. Case studies on two mental health related datasets (schizophrenia and bipolar disorders) have shown that the relatively short and simple personalised fuzzy rules provided enhanced interpretability as well as better classification performance compared to other commonly used machine learning methods. Performance test on a cancer dataset also showed that PCNFI matches previous benchmarks. Insights from our approach also indicated the importance of two genes (ATRX and TSPAN2) as possible biomarkers for early differentiation of ultra-high risk, bipolar and healthy individuals. These genes are linked to cognitive ability and impulsive behaviour. Our findings suggest a significant starting point for further research into the biological role of cognitive and impulsivity-related differences. With potential applications across bio-medical research, the proposed PCNFI method is promising for diagnosis, prognosis, and the design of personalised treatment plans for better outcomes in the future.