Histone Demethylase Modulation: Epigenetic Strategy to Combat Cancer Progression

Epigenetic modifications are heritable, reversible changes in histones or the DNA that control gene functions, being exogenous to the genomic sequence itself. Human diseases, particularly cancer, are frequently connected to epigenetic dysregulations. One of them is histone methylation, which is a dynamically reversible and synchronously regulated process that orchestrates the three-dimensional epigenome, nuclear processes of transcription, DNA repair, cell cycle, and epigenetic functions, by adding or removing methylation groups to histones. Over the past few years, reversible histone methylation has become recognized as a crucial regulatory mechanism for the epigenome. With the development of numerous medications that target epigenetic regulators, epigenome-targeted therapy has been used in the treatment of malignancies and has shown meaningful therapeutic potential in preclinical and clinical trials. The present review focuses on the recent advances in our knowledge on the role of histone demethylases in tumor development and modulation, in emphasizing molecular mechanisms that control cancer cell progression. Finally, we emphasize current developments in the advent of new molecular inhibitors that target histone demethylases to regulate cancer progression.

GSA Central—A web platform to perform, learn, and discuss gene set analysis

Gene Set Analysis (GSA) is one of the most commonly used strategies to analyze omics data. Hundreds of GSA-related papers have been published, giving birth to a GSA field in Bioinformatics studies. However, as the field grows, it is becoming more difficult to obtain a clear view of all available methods, resources, and their quality. In this paper, we introduce a web platform called “GSA Central” which, as its name indicates, acts as a focal point to centralize GSA information and tools useful to beginners, average users, and experts in the GSA field. “GSA Central” contains five different resources: A Galaxy instance containing GSA tools (“Galaxy-GSA”), a portal to educational material (“GSA Classroom”), a comprehensive database of articles (“GSARefDB”), a set of benchmarking tools (“GSA BenchmarKING”), and a blog (“GSA Blog”). We expect that “GSA Central” will become a useful resource for users looking for introductory learning, state-of-the-art updates, method/tool selection guidelines and insights, tool usage, tool integration under a Galaxy environment, tool design, and tool validation/benchmarking. Moreover, we expect this kind of platform to become an example of a “thematic platform” containing all the resources that people in the field might need, an approach that could be extended to other bioinformatics topics or scientific fields.

Chromatin Hubs: A biological and computational outlook

This review discusses our current understanding of chromatin biology and bioinformatics under the unifying concept of “chromatin hubs.” The first part reviews the biology of chromatin hubs, including chromatin–chromatin interaction hubs, chromatin hubs at the nuclear periphery, hubs around macromolecules such as RNA polymerase or lncRNAs, and hubs around nuclear bodies such as the nucleolus or nuclear speckles. The second part reviews existing computational methods, including enhancer–promoter interaction prediction, network analysis, chromatin domain callers, transcription factory predictors, and multi-way interaction analysis. We introduce an integrated model that makes sense of the existing evidence. Understanding chromatin hubs may allow us (i) to explain long-unsolved biological questions such as interaction specificity and redundancy of mechanisms, (ii) to develop more realistic kinetic and functional predictions, and (iii) to explain the etiology of genomic disease.

Popularity and performance of bioinformatics software: the case of gene set analysis

Background

Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies.

Results

Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods (“GSA-BenchmarKING”). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods.

Conclusions

The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04124-5.

A priori, de novo mathematical exploration of gene expression mechanism via regression viewpoint with briefly cataloged modeling antiquity

Various algorithms have been devised to mathematically model the dynamic mechanism of the gene expression data. Gillespie’s stochastic simulation (GSSA) has been exceptionally primal for chemical reaction synthesis with future ameliorations. Several other mathematical techniques such as differential equations, thermodynamic models and Boolean models have been implemented to optimally and effectively represent the gene functioning. We present a novel mathematical framework of gene expression, undertaking the mathematical modeling of the transcription and translation phases, which is a detour from conventional modeling approaches. These subprocesses are inherent to every gene expression, which is implicitly an experimental outcome. As we foresee, there can be modeled a generality about some basal translation or transcription values that correspond to a particular assay.

An Indian eye to personalized medicine

Acknowledging the successful sequencing of the human genome and the valuable insights it has rendered, genetic drafting of non-human organisms can further enhance the understanding of modern biology. The price of sequencing technology has plummeted with time, and there is a noticeable enhancement in its implementation and recurrent usage. Sequenced genome information can be contained in a microarray chip, and then processed by a computer system for inferring analytics and predictions. Specifically, smart cards have been significantly applicable to assimilate and retrieve complex data, with ease and implicit mobility. Herein, we propose "The G-Card", a development with respect to the prevalent smart card, and an extension to the Electronic Health Record (EHR), that will hold the genome sequence of an individual, so that the medical practitioner can better investigate irregularities in a patient's health and hence recommend a precise prognosis.

Mining Gene Expression Data Focusing Cancer Therapeutics: A Digest

An understanding towards genetics and epigenetics is essential to cope up with the paradigm shift which is underway. Personalized medicine and gene therapy will confluence the days to come. This review highlights traditional approaches as well as current advancements in the analysis of the gene expression data from cancer perspective. Due to improvements in biometric instrumentation and automation, it has become easier to collect a lot of experimental data in molecular biology. Analysis of such data is extremely important as it leads to knowledge discovery that can be validated by experiments. Previously, the diagnosis of complex genetic diseases has conventionally been done based on the non-molecular characteristics like kind of tumor tissue, pathological characteristics, and clinical phase. The microarray data can be well accounted for high dimensional space and noise. Same were the reasons for ineffective and imprecise results. Several machine learning and data mining techniques are presently applied for identifying cancer using gene expression data. While differences in efficiency do exist, none of the well-established approaches is uniformly superior to others. The quality of algorithm is important, but is not in itself a guarantee of the quality of a specific data analysis.

New Hydroxylated Cationic Gemini Surfactants as Effective Corrosion Inhibitors for Mild Steel in Hydrochloric Acid Medium

Inhibitory action of hydroxylated cationic gemini surfactants on corrosion of mild steel [MS] in 1 M hydrochloric acid medium is studied by gravimetric, electrochemical polarization and electrochemical impedance spectroscopic (EIS) techniques. The results obtained reveal that these compounds are very efficient inhibitors. The inhibitory action of surfactants increases with the concentration and chain length. Concentration as low as 10−5 M of 16–4–16 MEA could show 99% inhibition whereas 12–4–12 MEA showed 93% inhibition at 10−4 M concentration. The inhibition efficiency versus concentration curves show an S-shape plot indicating adsorption of surfactants on metal surface as a three steps process. From Arrhenius plots corrosion rate of MS was observed to decrease at any given temperature with inhibitor concentration due to the increase in the degree of surface coverage. In contrast, at constant inhibitor concentration, the corrosion rate increases with temperature. EIS measurements show increase in the charge transfer resistance with the inhibitor concentration.

Postnatal survival and growth of mouse irradiated in utero with low dose

In order to investigate radiation risks associated with low dose and low dose-rates, pregnant Swiss albino mice were exposed to gamma rays, 0.80 Gy from a cobalt-60 source at two different dose-rates (0.0795 and 0.0012 Gy/min) on 18 day post conception. In females exposed to lower dose-rate (0.0012 Gy/min), litter size was found to be decreased, while those exposed to higher dose-rate (0.0795 Gy/min), it remained unaltered. In both groups, appearance of fur and development of complete fur were delayed, whereas gait was delayed only in higher dose-rate group. Male offspring exhibited a biphasic mode of weight loss, while female offspring after an initial weight loss at 1 week, displayed a continuous recovery, but could not attain the normal weight till 12 weeks of age. It appears that higher dose-rate is more effective in delaying the appearance of physiological markers and weight loss, while in terms of litter size lower dose-rate (0.0012 Gy/min) is more effective.