Journal of bioinformatics and systems biology : Open access最新文献

We show that the simple and multiple linear regression coefficients and the coefficient of determination R² computed from sampling distributions of the mean (with or without replacement) are equal to the regression coefficients and coefficient of determination computed with individual data. Moreover, the standard error of estimate is reduced by the square root of the group size for sampling distributions of the mean. The result has applications when formulating a distance measure between two genes in a hierarchical clustering algorithm. We show that the Pearson $R$ coefficient can measure how differential expression in one gene correlates with differential expression in a second gene.

Traumatic brain injury (TBI) is a leading cause of morbidity, disability, and mortality worldwide. Motor and cognitive deficits and emotional disturbances are long-term consequences of TBI. A lack of effective treatment for TBI-induced neural damage, functional impairments, and cognitive deficits makes it challenging in the recovery following TBI. One of the reasons may be the lack of knowledge underlying the complex pathophysiology of TBI and the regulatory factors involved in the cellular and molecular mechanisms of inflammation, neural regeneration, and injury repair. These mechanisms involve a change in the expression of various proteins encoded by genes whose expression is regulated by transcription factors (TFs) at the transcriptional level and microRNA (miRs) at the mRNA level. In this pilot study, we performed the RNA sequencing of injured tissues and non-injured tissues from the brain of Yucatan miniswine and analyzed the sequencing data for differentially expressed genes (DEGs) and the TFs and miRs regulating the expression of DEGs using in-silico analysis. We also compared the effect of the electromagnetic field (EMF) applied to the injured miniswine on the expression profile of various DEGs. The results of this pilot study revealed a few DEGs that were significantly upregulated in the injured brain tissue and the EMF stimulation showed effect on their expression profile.

Collagen (Col) types I and III are integral components in wound healing and tissue regeneration, influencing tissue development, homeostasis, and related pathologies. Col I and Col III expression changes during different stages of wound healing and understanding the regulation of collagen phenotype determination is crucial for unraveling the complexities of these processes. Transcription factors and microRNAs, directly and indirectly, play a critical role in regulating collagen expression, however, a comprehensive understanding of the factors regulating Col I and III phenotypes remains elusive. This critically analyzed published reports with focuses on various factors regulating the expression of Col I and Col III at the transcriptional and translational levels. We performed bioinformatics analysis with an input of proinflammatory mediators, growth factors, elastases, and matrix metalloproteinases and predicted transcription factors and microRNAs involved in the regulation of collagen expression. Network analysis revealed an interaction between genes, transcription factors, and microRNAs and provided a holistic view of the regulatory landscape governing collagen expression and unveils intricate interconnections. This analysis lays a founda-tional framework for guiding future research and therapeutic interventions to promote extracellular matrix remodeling, wound healing, and tissue regeneration after an injury by modulating collagen expression. In essence, this scientific groundwork offers a comprehensive exploration of the regulatory dynamics in collagen synthesis, serving as a valuable resource for advancing both basic research and clinical interventions in tissue repair.

Pipelines are a natural tool in bioinformatics applications. Virtually any meaningful processing of biological data involves the execution of multiple software tools, and this execution must be arranged in a coherent manner. Many tools for the building of pipelines were developed over time and used to facilitate work with increasing volume of bioinformatics data. Here we present a flexible and expandable framework for building pipelines, MXP, which we hope will find its own niche in bioinformatics applications. We developed MXP and tested it on various tasks in our organization, primarily for building pipelines for GWAS (Genome-Wide Association Studies) and post-GWAS analysis. It was proven to be sufficiently flexible and useful. MXP implements a number of novel features which, from our point of view, make it to be more suitable and more convenient for building bioinformatics pipelines.

Non-healing diabetic foot ulcer, a chronic inflammatory disease, is a sizable clinical and economic burden to healthcare systems around the world. Chronic inflammation plays a critical role in the nonhealing pattern due to the arrest of the cellular response during wound healing in the inflammatory phase without progressing to the proliferative and remodeling phase. Fibroblasts play a critical role in all three phases of wound healing. Activation of fibroblasts in the presence of cytokines results in the formation of myofibroblast that contributes to extracellular matrix formation. Additionally, few studies documented the presence of inflammatory, angiogenic, and angiostatic fibroblast subpopulation during wound healing. Various studies have discussed the role of transcription factors and microRNA in regulating the transdifferentiation of fibroblast to myofibroblast, however, what factors regulate the reprogramming of fibroblast to inflammatory, angiogenic, and angiostatic phenotypes have not been clearly addressed in the literature. This critical review article addresses the role of transcription factors and microRNAs in regulating fibroblast to myofibroblast transdifferentiation followed by the prediction of transcription factors and microRNAs, based on the bioinformatics analysis, in regulating transdifferentiation of fibroblasts to inflammatory, angiogenic, and angiostatic subtypes. The results of in-silico networking revealed multiple new transcription factors and microRNAs and their interaction with specific markers on other fibroblasts suggesting their role in the regulation of fibroblast reprogramming.