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Evolutionary Theories of Aging: A Systemic and Mechanistic Perspective
Published in Shamim I. Ahmad, Aging: Exploring a Complex Phenomenon, 2017
These trends have also been accompanied by a conceptual change within biology with the transition from a qualitative, structural, and most of the time static description of the cell to a more systemic description in terms of functional but also dynamical properties [35–37]. For example, in addition to the traditional approaches of biochemistry and biophysics, studies focused on the hierarchical organization of the cellular environment and, on the dynamics of its components, have identified dynamical motifs and cycles [38,39] as key elements involved in the regulation of the cellular behavior. Inside the cytoplasm, proteins interacting together are organized as structured modules such as the signaling pathways which are well known for being involved in the transmission of all signals received from the external environment to any concerned cellular components. Inside the nucleus, genes and transcription factors also form structured dynamical patterns called gene regulatory networks (GRN). GRN can be visualized in the form of a directed graph whose nodes represent the genes. An edge between two nodes represents an interaction which can be either an inhibition or activation. These structures are deeply involved in many regulatory processes including the regulation of genes expression. The regulation of gene expression is a complex process also involving mechanisms such as mRNA splicing [40,41], chromatin remodeling [42], and epigenetics modifications. Epigenetics refers to how transcription, DNA replication, and other aspects of genome function are regulated in a manner that is independent of DNA sequence.
Functional Omics and Big Data Analysis in Microalgae
Published in Gokare A. Ravishankar, Ranga Rao Ambati, Handbook of Algal Technologies and Phytochemicals, 2019
Chetan Paliwal, Tonmoy Ghosh, Asha A. Nesamma, Pavan P. Jutur
Gene regulatory networks (GRNs) are the graphical representation of biological systems data, serving as a network-based model for understanding underlying systems and mechanisms. Why some genes are more active than others during stress (Fan et al. 2012; Macneil and Walhout 2011) is not fully understood as there are undefined underlying regulatory mechanisms involved which are very much interconnected and interdependent. Recent studies have identified and characterized set of genes encoding transcription factors (TFs) and transcription regulators (TRs), which chiefly control lipid accumulation and metabolism (Sardar et al. 2016). Complex networks with underlying transcriptional regulatory hubs that control the lipid accumulation, a set of architecture guided by universal principles, like many networks, are linked to cells’ metabolic system, which are interconnected by a large number of nodes and internodes, called hubs. Accordingly, these hubs can become robust or fragile under certain conditions. Lipid accumulation in Chlamydomonas during N stress expresses 70 TF and TR genes bringing out metabolic regulation and response to cellular growth in a chronological order. Some novel genes directly involved in TAG metabolism included AP2-15, FHA10, and MYBL13 and two groups of specific and permanent hubs were identified (Gargouri et al. 2015). The GRN modules and the overall topology are analysed in many systems, as observed by the occurrence of TF and gene hubs; these GRNs are not random in nature and can be visualized and studied by computational and mathematical tools (Babu et al. 2004). Identification of various networks, their interconnectivity and dynamics might allow better understanding and characterization of the key nodes involved in various pathway and metabolic analyses (Macneil and Walhout 2011).
Personalized Nutrition in Cardiovascular Disease
Published in Nilanjana Maulik, Personalized Nutrition as Medical Therapy for High-Risk Diseases, 2020
Marcella O’Reilly, Sarina Kajani, Sean Curley, Sarah Mahayni, Helen M. Roche, Fiona C. McGillicuddy
The scientific community is only at the beginning of understanding the complexity of gene regulation. In turn, the nutrition field is in its infancy in terms of understanding how environmental factors, including diet, are overlaid on genotypic data to affect disease outcomes. GWAS is a method that searches for small variations in the genome called SNPs that occur at a higher frequency in people with a particular disease than those without the disease (Bush and Moore 2012). SNPs are single base-pair changes in the DNA sequence (Genomes Project, Abecasis et al. 2010) and occur at high frequency in the human genome, although not all have a biological impact (Genomes Project, Abecasis et al. 2010). In general, in rare disorders that are inherited, the affected gene tends to have a large effect size or biological effect; however, common disorders are likely influenced by genetic variation that is common in the population. Thus, the effect size of common genetic variants is small, relative to those found in rare disorders (Bush and Moore 2012). This common disease/common variant hypothesis led to the use of polygenetic risk scores (GRS) whereby a considerable proportion of phenotypic variation can be explained by grouping together SNPs (Dudbridge 2013). Modest effect sizes of SNPs have the potential to hinder prediction of risk using a single SNP, therefore grouping SNPs together give greater predictability of an individual’s risk of a disease (Lewis and Vassos 2017). Boyle et al., have further hypothesized that for complex traits, association signals are spread out across most of the genome including near many genes that have no known connection to the disease. They have established an ‘omnigenic’ model whereby it is hypothesized that gene regulatory networks are sufficiently interconnected that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes (Boyle, Li et al. 2017). Hence, the multitude of SNPs that have been identified as associating with CVD, but not with any known risk factor, may be affecting the function of disease-related genes.
Identification of a unique tumor cell subset employing myeloid transcriptional circuits to create an immunomodulatory microenvironment in glioblastoma
Published in OncoImmunology, 2022
Kaidi Yang, Yu Shi, Min Luo, Min Mao, Xiaoning Zhang, Cong Chen, Yuqi Liu, Zhicheng He, Qing Liu, Wenying Wang, Chunhua Luo, Wen Yin, Chao Wang, Qin Niu, Hui Zeng, Xiu-Wu Bian, Yi-Fang Ping
To identify transcriptional regulatory networks determining the state of each cell subset, we applied single-cell regulatory network inference and clustering (SCENIC) to evaluate the activity of the gene regulatory networks (GRNs, termed regulons). Eighty regulons were recognized across different cell subsets (Figure 2a). To evaluate the performance of SCENIC, annotations inferred from the expression profiling were mapped to the t-SNE clustering of regulon activity matrix (Supplementary Figure S3b). Cell entity clustering was similar to those defined by transcriptome profiling. Among the 88 regulons, IRF1, IRF2, IRF3, IRF7, STAT1, and STAT2 were identified as specific regulators for TC-6 based on the Regulon Specificity Score (RSS) (Figure 2b).33 TC-6 also showed high gene set activities in these regulons (Figure 2c) that may mediate unique immune transcriptional programs.
Heterogeneity of T cells and macrophages in chlorine-induced acute lung injury in mice using single-cell RNA sequencing
Published in Inhalation Toxicology, 2022
Chen-qian Zhao, Jiang-zheng Liu, Meng-meng Liu, Xiao-ting Ren, De-qin Kong, Jie Peng, Meng Cao, Rui Liu, Chun-xu Hai, Xiao-di Zhang
Transcription factors (TFs) and their downstream regulatory genes constitute a complex and intertwined gene regulatory network, which determines and maintains cell characteristics. We performed SCENIC analysis to infer the activity of T-cell regulatory factors (a TF and its target gene together constitute a regulatory factor) (Figure 4(D)). Genes regulated by Ets1, Elf1, and Elk3 were highly upregulated in Cl2 exposure group, while genes regulated by Maf, Creb3l2, and Rara were upregulated in control group. Ets1 is a sequence-specific TF whose timely expression plays an important role in the evolution of the T and natural killer (NK) cell lineages (Cauchy et al. 2016). In in vivo studies, we also found a significant upregulation in Elk3 in the Cl2 exposure group (Figure 4(E)). These results demonstrated that T cells play a key role in the development of Cl2-induced ALI.
Regulatory network analysis of hypertension and hypotension microarray data from mouse model
Published in Clinical and Experimental Hypertension, 2018
Yanli Zhu, Jingming Zhuo, Chunmei Li, Qian Wang, Xuefei Liu, Lin Ye
Candidate disease genes are most often selected for study by combining information on disease characteristics with functional information (34). Based on the complex gene regulatory networks in the study, we also suggest that genes with differential expression have interactive effects. Understanding a complex disease such as HTN will not only require a much deeper understanding of one gene but also require that of the interactive effects of genes, which together constitute the genetic architecture of the disease. On the other hand, there are many causes for blood pressure changes, such as age and other diseases (35). The mouse model used in the dataset of GSE19817 was developed in a selection program, resulting in mice inbred to homozygosity and that closely mimic human HTN and hypotension (13). However, further detailed experiments are required to determine the roles of the genes identified in this study. In addition, many other factors must be integrated into a final explanatory model.