Genetic Regulatory Networks of Apolipoproteins and Associated Medical Risks

Apolipoproteins (APO proteins) are the lipoprotein family proteins that play key roles in transporting lipoproteins all over the body. There are nearly more than twenty members reported in the APO protein family, among which the A, B, C, E, and L play major roles in contributing genetic risks to several disorders. Among these genetic risks, the single nucleotide polymorphisms (SNPs), involving the variation of single nucleotide base pairs, and their contributing polymorphisms play crucial roles in the apolipoprotein family and its concordant disease heterogeneity that have predominantly recurred through the years. In this review, we have contributed a handful of information on such genetic polymorphisms that include APOE, ApoA1/B ratio, and A1/C3/A4/A5 gene cluster-based population genetic studies carried throughout the world, to elaborately discuss the effects of various genetic polymorphisms in imparting various medical conditions, such as obesity, cardiovascular, stroke, Alzheimer's disease, diabetes, vascular complications, and other associated risks.

Inference of genetic regulatory networks with regulatory hubs using vector autoregressions and automatic relevance determination with model selections

The inference of genetic regulatory networks (GRNs) reveals how genes interact with each other. A few genes can regulate many genes as targets to control cell functions. We present new methods based on the order-1 vector autoregression (VAR1) for inferring GRNs from gene expression time series. The methods use the automatic relevance determination (ARD) to incorporate the regulatory hub structure into the estimation of VAR1 in a Bayesian framework. Several sparse approximation schemes are applied to the estimated regression weights or VAR1 model to generate the sparse weighted adjacency matrices representing the inferred GRNs. We apply the proposed and several widespread reference methods to infer GRNs with up to 100 genes using simulated, DREAM4 in silico and experimental E. coli gene expression time series. We show that the proposed methods are efficient on simulated hub GRNs and scale-free GRNs using short time series simulated by VAR1s and outperform reference methods on small-scale DREAM4 in silico GRNs and E. coli GRNs. They can utilize the known major regulatory hubs to improve the performance on larger DREAM4 in silico GRNs and E. coli GRNs. The impact of nonlinear time series data on the performance of proposed methods is discussed.

The impact of sources and sinks on the dynamics of homogeneous and heterogeneous boolean networks

Random Boolean networks, originally introduced as simplified models for the genetic regulatory networks, are abstract models widely applied for the study of the dynamical behaviors of self-organizing complex systems. In these networks, connectivity and the bias of the Boolean functions are the most important factors that can determine the behavioral regime of the systems. On the other hand, it has been found that topology and some structural elements of the networks such as the reciprocity, self-loops and source nodes, can have relevant effects on the dynamical properties of critical Boolean networks. In this paper, we study the impact of source and sink nodes on the dynamics of homogeneous and heterogeneous Boolean networks. Our research shows that an increase of the source nodes causes an exponentially growing of the different behaviors that the system can exhibit regardless of the network topology, while the amount of order seems to undergo modifications depending on the topology of the system. Indeed, with the increase of the source nodes the orderliness of the heterogeneous networks also increases, whereas it diminishes in the homogeneous ones. On the other hand, although the sink nodes seem not to have effects on the dynamic of the homogeneous networks, for the heterogeneous ones we have found that an increase of the sinks gives rise to an increasing of the order, although the different potential behaviors of the system remains approximately the same.

Molecular mechanisms governing circulating immune cell heterogeneity across different species revealed by single cell sequencing

Background: Immune cells play important roles in mediating immune response and host defense against invading pathogens. However, insights into the molecular mechanisms governing circulating immune cell diversity among multiple species are limited. Methods: In this study, we compared the single-cell transcriptomes of 77 957 immune cells from 12 species using single-cell RNA-sequencing (scRNA-seq). Distinct molecular profiles were characterized for different immune cell types, including T cells, B cells, natural killer cells, monocytes, and dendritic cells. Results: The results revealed the heterogeneity and compositions of circulating immune cells among 12 different species. Additionally, we explored the conserved and divergent cellular cross-talks and genetic regulatory networks among vertebrate immune cells. Notably, the ligand and receptor pair VIM-CD44 was highly conserved among the immune cells. Conclusions: This study is the first to provide a comprehensive analysis of the cross-species single-cell atlas for peripheral blood mononuclear cells (PBMCs). This research should advance our understanding of the cellular taxonomy and fundamental functions of PBMCs, with important implications in evolutionary biology, developmental biology, and immune system disorders

Adapting CRISPR/Cas9 Lentivirus Technology for Functional Studies of GATA6 & GATA4 during Bovine Preimplantation Embryogenesis

<p>In recent times, cattle embryology has been under the spotlight of investigation due to its apparent economic values. This is especially relevant in the case of New Zealand, owing to its high percentage of livestock export. Specifically, the period of peri-implantation development has been of particular relevance. During this stage, the developing zygote will establish 3 key lineages – epiblast, hypoblast and trophoblast. Previous studies have elucidated that a significant number of embryos die prior to implantation, therefore highlighting the importance of correctly establishing these 3 lineages to overall embryonic survival. However, while embryological stages of the preimplantation embryo have been extensively studied in their eutherian cousin, mice, the molecular regulation of that of cattle remains much less addressed. Whereas the regulation of bovine embryo development is orchestrated by many transcriptional regulators, or genetic regulatory networks (GNP), we aimed to focus our studies on 2 key transcriptional regulators, GATA4 and GATA6. During early embryogenesis, both these transcriptional factors are known molecular regulators that drive the establishment of the hypoblast lineage in mice. By and large, while their respective expression has been documented in cattle embryos, functional studies towards these markers have not yet been performed. Latest advances in molecular biology have given us novel methods to study the mechanism of bovine embryogenesis. To this end, the continuing perfection of CRISPR technologies in the last decade - in particular its delivery through lentiviral vectors, has established an ability to generate stable, targeted knock-out mutants. Therefore, it is aimed in this thesis to design and test lentiviral particles that induce knock-out mutants of GATA4 and GATAT6, to test their efficacy in primary cell cultures (bovine cumulus cells) and to functionally analyse the effect of GATA4 and GATA6 knockdowns in early bovine embryos.</p>

Adapting CRISPR/Cas9 Lentivirus Technology for Functional Studies of GATA6 & GATA4 during Bovine Preimplantation Embryogenesis

<p>In recent times, cattle embryology has been under the spotlight of investigation due to its apparent economic values. This is especially relevant in the case of New Zealand, owing to its high percentage of livestock export. Specifically, the period of peri-implantation development has been of particular relevance. During this stage, the developing zygote will establish 3 key lineages – epiblast, hypoblast and trophoblast. Previous studies have elucidated that a significant number of embryos die prior to implantation, therefore highlighting the importance of correctly establishing these 3 lineages to overall embryonic survival. However, while embryological stages of the preimplantation embryo have been extensively studied in their eutherian cousin, mice, the molecular regulation of that of cattle remains much less addressed. Whereas the regulation of bovine embryo development is orchestrated by many transcriptional regulators, or genetic regulatory networks (GNP), we aimed to focus our studies on 2 key transcriptional regulators, GATA4 and GATA6. During early embryogenesis, both these transcriptional factors are known molecular regulators that drive the establishment of the hypoblast lineage in mice. By and large, while their respective expression has been documented in cattle embryos, functional studies towards these markers have not yet been performed. Latest advances in molecular biology have given us novel methods to study the mechanism of bovine embryogenesis. To this end, the continuing perfection of CRISPR technologies in the last decade - in particular its delivery through lentiviral vectors, has established an ability to generate stable, targeted knock-out mutants. Therefore, it is aimed in this thesis to design and test lentiviral particles that induce knock-out mutants of GATA4 and GATAT6, to test their efficacy in primary cell cultures (bovine cumulus cells) and to functionally analyse the effect of GATA4 and GATA6 knockdowns in early bovine embryos.</p>

Mathematical Modelling of Gene Regulatory Networks

This research includes the use of an artificial intelligence algorithm, which is one of the algorithms of biological systems which is the algorithm of genetic regulatory networks (GRNs), which is a dynamic system for a group of variables representing space within time. To construct this biological system, we use (ODEs) and to analyze the stationarity of the model we use Euler's method. And through the factors that affect the process of gene expression in terms of inhibition and activation of the transcription process on DNA, we will use TF transcription factors. The current research aims to use the latest methods of the artificial intelligence algorithm. To apply Gene Regulation Networks (GRNs), we used a program (MATLAB2020), which provides facilitation to the most important biological concepts for building this biological interaction