Magbubah Essack

The peptide hormone leptin regulates food intake, body mass, and reproductive function and plays a role in fetal growth, proinflammatory immune responses, angiogenesis and lipolysis. Leptin is a product of the obese ( ob ) gene and, following synthesis and secretion from fat cells in white adipose tissue, binds to and activates its cognate receptor, the leptin receptor (LEP-R). LEP-R distribution facilitates leptin’s pleiotropic effects, playing a crucial role in regulating body mass via a negative feedback mechanism between adipose tissue and the hypothalamus. Leptin resistance is characterized by reduced satiety, over-consumption of nutrients, and increased total body mass. Often this leads to obesity, which reduces the effectiveness of using exogenous leptin as a therapeutic agent. Thus, combining leptin therapies with leptin sensitizers may help overcome such resistance and, consequently, obesity. This review examines recent data obtained from human and animal studies related to leptin, its role in obesity, and its usefulness in obesity treatment.

The FANTOM4 study identified transcriptional start sites active during proliferation arrest and differentiation of the human monocytic cell line THP-1. Systematic knockdown of 52 transcription factors provide support for their model in which a complex transcriptional network regulates the differentiation process. Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth arrest and differentiation. Modeling the expression dynamics in terms of predicted cis-regulatory sites, we identified the key transcription regulators, their time-dependent activities and target genes. Systematic siRNA knockdown of 52 transcription factors confirmed the roles of individual factors in the regulatory network. Our results indicate that cellular states are constrained by complex networks involving both positive and negative regulatory interactions among substantial numbers of transcription factors and that no single transcription factor is both necessary and sufficient to drive the differentiation process.

Even though type 2 diabetes mellitus (T2DM) represents a worldwide chronic health issue that affects about 462 million people, specific underlying determinants of insulin resistance (IR) and impaired insulin secretion are still unknown. There is growing evidence that chronic subclinical inflammation is a triggering factor in the origin of T2DM. Increased C-reactive protein (CRP) levels have been linked to excess body weight since adipocytes produce tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6), which are pivotal factors for CRP stimulation. Furthermore, it is known that hepatocytes produce relatively low rates of CRP in physiological conditions compared to T2DM patients, in which elevated levels of inflammatory markers are reported, including CRP. CRP also participates in endothelial dysfunction, the production of vasodilators, and vascular remodeling, and increased CRP level is closely associated with vascular system pathology and metabolic syndrome. In addition, insulin-based therapies may alter CRP levels in T2DM. Therefore, determining and clarifying the underlying CRP mechanism of T2DM is imperative for novel preventive and diagnostic procedures. Overall, CRP is one of the possible targets for T2DM progression and understanding the connection between insulin and inflammation may be helpful in clinical treatment and prevention approaches.

Knowing metastasis is the primary cause of cancer-related deaths, incentivized research directed towards unraveling the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications, and is now being used to predict the onset of metastasis to improve diagnostics and disease therapies. In this regard, predicting metastasis onset has also been explored using artificial intelligence approaches that are machine learning, and more recently, deep learning-based. This review summarizes the different machine learning and deep learning-based metastasis prediction methods developed to date. We also detail the different types of molecular data used to build the models and the critical signatures derived from the different methods. We further highlight the challenges associated with using machine learning and deep learning methods, and provide suggestions to improve the predictive performance of such methods.

More people die from cardiovascular diseases (CVD) than from any other cause. Cardiovascular complications are thought to arise from enhanced levels of free radicals causing impaired "redox homeostasis," which represents the interplay between oxidative stress (OS) and reductive stress (RS). In this review, we compile several experimental research findings that show sustained shifts towards OS will alter the homeostatic redox mechanism to cause cardiovascular complications, as well as findings that show a prolonged antioxidant state or RS can similarly lead to such cardiovascular complications. This experimental evidence is specifically focused on the role of glutathione, the most abundant antioxidant in the heart, in a redox homeostatic mechanism that has been shifted towards OS or RS. This may lead to impairment of cellular signaling mechanisms and elevated pools of proteotoxicity associated with cardiac dysfunction.