Analysis of healthy and tumour DNA methylation distributions in kidney-renal-clear-cell-carcinoma using Kullback-Leibler and Jensen-Shannon distance measures

Quantifying information of intracellular signaling: progress with machine learning

Cells convey information about their extracellular environment to their core functional machineries. Studying the capacity of intracellular signaling pathways to transmit information addresses fundamental questions about living systems. Here, we review how information-theoretic approaches have been used to quantify information transmission by signaling pathways that are functionally pleiotropic and subject to molecular stochasticity. We describe how recent advances in machine learning have been leveraged to address the challenges of complex temporal trajectory datasets and how these have contributed to our understanding of how cells employ temporal coding to appropriately adapt to environmental perturbations.

Concepts and Applications of Information Theory to Immuno-Oncology

Recent successes of immune-modulating therapies for cancer have stimulated research on information flow within the immune system and, in turn, clinical applications of concepts from information theory. Through information theory, one can describe and formalize, in a mathematically rigorous fashion, the function of interconnected components of the immune system in health and disease. Specifically, using concepts including entropy, mutual information, and channel capacity, one can quantify the storage, transmission, encoding, and flow of information within and between cellular components of the immune system on multiple temporal and spatial scales. To understand, at the quantitative level, immune signaling function and dysfunction in cancer, we present a methodology-oriented review of information-theoretic treatment of biochemical signal transduction and transmission coupled with mathematical modeling.

DNA hypermethylation of SFRP2 influences the pathology of rheumatoid arthritis through the canonical Wnt signaling in model rats

In this work, the expression of secreted frizzled related protein 2 (SFRP2) in rheumatoid arthritis (RA) model rats and the mechanisms of SFRP2 on the RA pathogenesis were investigated. Data suggested that SFRP2 was significantly down-regulated in RA model rats compared with normal control, and overexpression of SFRP2 suppressed the RA pathogenesis and the canonical Wnt signaling in fibroblast-like synovial cells (FLS) from RA model rats, whereas knockdown of SFRP2 got an opposite observation. Interestingly, 5-azadC treatment up-regulated the SFRP2 expression, inhibited the FLS proliferation, suppressed the expression of IL-6 and IL-8 and the fibronectin production, suggesting that the decreased SFRP2 in RA model rats was due to the DNA methylation. Furthermore, DNMT1 knockdown up-regulated the SFRP2 expression, DNMT1 overexpression inhibited the SFRP2, and the quantitative methylation-specific PCR (qMSP) confirmed that the DNMT1 has direct methylation roles for the SFRP2 promoter, leading to a regulation of FLS proliferation and fibronectin expression in RA model rats. In addition, up-regulated MeCP2 was involved in the SFRP2 regulation and the pathogenesis of RA model rats, and MeCP2 and DNMT1 have synergistic inhibition roles in the SFRP2 expression. Combination of DNMT1 and DNA methylation may be a promising treatment strategy for individuals with RA in which SFRP2 is down-regulated.