Sedoheptulose Kinase SHPK Expression in Glioblastoma: Emerging Role of the Nonoxidative Pentose Phosphate Pathway in Tumor Proliferation

Identification of crucial inflammaging related risk factors in multiple sclerosis

Background

Multiple sclerosis (MS) is an immune-mediated disease characterized by inflammatory demyelinating lesions in the central nervous system. Studies have shown that the inflammation is vital to both the onset and progression of MS, where aging plays a key role in it. However, the potential mechanisms on how aging-related inflammation (inflammaging) promotes MS have not been fully understood. Therefore, there is an urgent need to integrate the underlying mechanisms between inflammaging and MS, where meaningful prediction models are needed.

Methods

First, both aging and disease models were developed using machine learning methods, respectively. Then, an integrated inflammaging model was used to identify relative risk factors, by identifying essential "aging-inflammation-disease" triples. Finally, a series of bioinformatics analyses (including network analysis, enrichment analysis, sensitivity analysis, and pan-cancer analysis) were further used to explore the potential mechanisms between inflammaging and MS.

Results

A series of risk factors were identified, such as the protein homeostasis, cellular homeostasis, neurodevelopment and energy metabolism. The inflammaging indices were further validated in different cancer types. Therefore, various risk factors were integrated, and even both the theories of inflammaging and immunosenescence were further confirmed.

Conclusion

In conclusion, our study systematically investigated the potential relationships between inflammaging and MS through a series of computational approaches, and could present a novel thought for other aging-related diseases.

Uncovering hidden cancer self-dependencies through analysis of shRNA-level dependency scores

Large-scale short hairpin RNA (shRNA) screens on well-characterized human cancer cell lines have been widely used to identify novel cancer dependencies. However, the off-target effects of shRNA reagents pose a significant challenge in the analysis of these screens. To mitigate these off-target effects, various approaches have been proposed that aggregate different shRNA viability scores targeting a gene into a single gene-level viability score. Most computational methods for discovering cancer dependencies rely on these gene-level scores. In this paper, we propose a computational method, named NBDep, to find cancer self-dependencies by directly analyzing shRNA-level dependency scores instead of gene-level scores. The NBDep algorithm begins by removing known batch effects of the shRNAs and selecting a subset of concordant shRNAs for each gene. It then uses negative binomial random effects models to statistically assess the dependency between genetic alterations and the viabilities of cell lines by incorporating all shRNA dependency scores of each gene into the model. We applied NBDep to the shRNA dependency scores available at Project DRIVE, which covers 26 different types of cancer. The proposed method identified more well-known and putative cancer genes compared to alternative gene-level approaches in pan-cancer and cancer-specific analyses. Additionally, we demonstrated that NBDep controls type-I error and outperforms statistical tests based on gene-level scores in simulation studies.

Dexamethasone effects on the expression and content of glycosylated components of mouse brain tissue

Introduction. Glucocorticoids are actively used in the treatment of various diseases, however their long-term use leads to numerous negative side-effects, the molecular mechanisms of which remain poorly understood.

Aim. Study of the short-term (1–10 days) effects of various doses of dexamethasone (Dex) (0,1–10 mg/kg) on the expression of the glucocorticoid receptor (GR, Nr3c1), core proteins of main proteoglycans and heparan sulfate metabolism-involved genes, as well as the content of carbohydrate macromolecules of glycosaminoglycans in the brain tissue of experimental animals.

Materials and methods. In the study, C57Bl/6 mice were used. The expression of GR, proteoglycan core proteins and heparan sulfate metabolism-involved genes was determined by real-time polymerase chain reaction with reverse transcription. The content and localization of GR protein molecule were studied by Western blot and immunohistochemical analysis, and the glycosaminoglycan content was determined by dot-blot analysis and Alcian Blue staining.

Results. It was shown that a single Dex administration leads to fast (1–3 days) short-term activation of GR expression (+1.5 times, p <0.05), proteoglycan’s genes (syndecan-3, Sdc3; perlecan, Hspg2; phosphacan, Ptprz1; neurocan, Ncan; +2–3-fold; p <0.05) and heparan sulfate-metabolism-involved genes (Ndst1, Glce, Hs2st1, Hs6st1, Sulf1 / 2; +1.5–2-fold; p <0.05) in the mouse brain, with a return to control values by 7–10 days after Dex administration. At the same time, the effect of Dex on carbohydrate macromolecules of glycosaminoglycans was more delayed and stable, increasing the content of low-sulfated glycosaminoglycans in the brain tissue in a dose-dependent manner starting from day 1 after Dex administration. Highly-sulfated glycosaminoglycans showed more delayed response to Dex administration, and an increase in their content was observed only at higher doses (2.5 and 10 mg/kg) and only on 7–10 days after its administration, apparently, mainly due to an increase in heparan sulfate content.

Conclusion. In general, the effect of a single injection of Dex on the transcriptional activity of GR, proteoglycan core proteins and heparan sulfate metabolism-involved genes were short-termed, and the genes expression quickly returned to the normal levels. However, even a single use of Dex significantly increased the content of total as well as highly sulfated glycosaminoglycans in the mouse brain tissue, which can lead to the changes in the composition and structure of the brain tissue, as well as its functional characteristics.

Different Approaches to Study Molecular Blueprint and Biological Behavior of Brain Tumors: Editorial to the Special Issue "Advances in Molecular Genetics of Brain Tumors"

Cancer remains one of the leading causes of mortality worldwide [...].