Gene regulatory networks present a wide variety of dynamical responses to intrinsic and extrinsic perturbations. Arguably, one of the most important of such coordinated responses is the one of amplification cascades, in which activation of a few key-responsive transcription factors (termed master regulators, MRs) lead to a large series of transcriptional activation events. This is so since master regulators are transcription factors controlling the expression of other transcription factor molecules and so on. MRs hold a central position related to transcriptional dynamics and control of gene regulatory networks and are often involved in complex feedback and feedforward loops inducing non-trivial dynamics. Recent studies have pointed out to the myocyte enhancing factor 2C (MEF2C, also known as MADS box transcription enhancer factor 2, polypeptide C) as being one of such master regulators involved in the pathogenesis of primary breast cancer. In this work, we perform an integrative genomic analysis of the transcriptional regulation activity of MEF2C and its target genes to evaluate to what extent are these molecules inducing collective responses leading to gene expression deregulation and carcinogenesis. We also analyzed a number of induced dynamic responses, in particular those associated with transcriptional bursts, and nonlinear cascading to evaluate the influence they may have in malignant phenotypes and cancer.
In recent times whole-genome gene expression analysis has turned out to be a highly important tool to study the coordinated function of a very large number of genes within their corresponding cellular environment, especially in relation to phenotypic diversity and disease. A wide variety of methods of quantitative analysis have been developed to cope with high throughput data sets generated by gene expression profiling experiments. Due to the complexity associated with transcriptomics, specially in the case of gene regulation phenomena, most of these methods are of a probabilistic or statistical nature. Even if these methods have reached a central status in the development of an integrative, systematic understanding of the associated biological processes, they very rarely constitute a concrete guide to the actual physicochemical mechanisms behind biological function and the role of these methods is more on a hypotheses generating line. An important improvement could be done with the development of a thermodynamic theory for gene expression and transcriptional regulation that will build the foundations for a proper integration of the vast amount of molecular biophysical data and could lead, in the future, to a systemic view of genetic transcription and regulation. . * ehernandez@inmegen.gob.mx
Background: Zika virus, which is widely spread and infects humans through the bites of Aedes albopictus and Aedes aegypti female mosquitoes, represents a serious global health issue. Objective: The objective of the present study is to computationally characterize Zika virus polyproteins (UniProt Name: PRO_0000443018 [residues 1-3423], PRO_0000445659 [residues 1-3423] and PRO_0000435828 [residues 1-3419]) and their envelope proteins using their physico-chemical properties. Methods: To achieve this, the Polarity Index Method (PIM) profile and the Protein Intrinsic Disorder Predisposition (PIDP) profile of 3 main groups of proteins were evaluated: structural proteins extracted from specific Databases, Zika virus polyproteins, and their envelope proteins (E) extracted from UniProt Database. Once the PIM profile of the Zika virus envelope proteins (E) was obtained and since the Zika virus polyproteins were also identified with this profile, the proteins defined as “reviewed proteins” extracted from the UniProt Database were searched for the similar PIM profile. Finally, the difference between the PIM profiles of the Zika virus polyproteins and their envelope proteins (E) was tested using 2 non-parametric statistical tests. Results: It was found and tested that the PIM profile is an efficient discriminant that allows obtaining a “computational fingerprint” of each Zika virus polyprotein from its envelope protein (E). Conclusion: PIM profile represents a computational tool, which can be used to effectively discover Zika virus polyproteins from Databases, from their envelope proteins (E) sequences.
Background: The Brugada syndrome (BrS) is a heart rhythm condition that is commonly associated with a strong predisposition for sudden cardiac death. Malignant ventricular arrhythmias could occur secondary to the dysfunction of the cardiac sodium voltage-gated Na(v)1.5 channel (SCN5A). Objective: This study aimed to perform a multiparametric computational analysis of the physicochemical properties of SCN5A mutants associated with BrS using a set of bioinformatics tools. Methods: In-house algorithms were calibrated to calculate, in a double-blind test, the Polarity Index Method (PIM) profile and protein intrinsic disorder predisposition (PIDP) profile of each sequence, and computer programs specialized in the genomic analysis were used. Results: Specific regularities in the charge/polarity and PIDP profile of the SCN5A mutant proteins enabled the re-creation of the taxonomy, allowing us to propose a bioinformatics method that takes advantage of the PIM profile to identify this group of proteins from their sequence. Conclusion: Bioinformatics programs could reproduce characteristic PIM and PIDP profiles of the BrS-related SCN5A mutant proteins. This information can contribute to a better understanding of these altered proteins.
Background: From the existing knowledge of viruses, those infecting plants and bacteria and affecting animals are particularly interesting. This is because such viruses have an ability to vertically transmit to other species, including humans, and therefore could represent a public health issue of significant proportions. Objective: This study aims to bioinformatically characterize the proteins from the DNA and RNA viruses capable of infecting plants and bacteria, and affecting animals, of which there is some evidence of contact with human beings. It follows up on our previous Polanco et al., [1] “Characterization of Proteins from Putative Human DNA and RNA Viruses. Current Proteomics, 2022 19(1), 65-82 DOI: 10.2174/1570164618666210212123850”. Methods: The Polarity Index Method profile (PIM), intrinsic disorder predisposition (IDPD) profiles, and a Markov chains analysis of three DNA-viruses protein sequences and four RNA-viruses protein sequences that infect plants and bacteria and affect animals, extracted from the UniProt database, were calculated using a set of in-house computational programs. Results: Computational runs carried out in this work reveal relevant regularities at the level of the viral proteins' charge/polarity and IDPD profiles. These results enable the re-creation of the taxonomy known for the DNA- and RNA-virus protein sequences. In addition, an analysis of the entire set of proteins qualified as "reviewed" in the UniProt database was carried out for each protein viral group to discover proteins with similar PIM profiles. A significant number of proteins with such charge/polarity profiles were found. Conclusion: The bioinformatics results obtained at the level of the amino acid sequences, generated important information that contributes to the understanding of these protein groups.
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