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MicroRNA pathways: an emerging role in identification of therapeutic strategies

Vijayarathna S, Oon CE, Jothy SL, Chen Y, Kanwar JR, Sasidharan S

Curr Gene Ther, 2014;14(2):112-20.
PMID: 24588707

For years researchers have exerted every effort to improve the influential roles of microRNA (miRNA) in regulating genes that direct mammalian cell development and function. In spite of numerous advancements, many facets of miRNA generation remain unresolved due to the perplexing regulatory networks. The biogenesis of miRNA, eminently endures as a mystery as no universal pathway defines or explicates the variegation in the rise of miRNAs. Early evidence in biogenesis ignited specific steps of being omitted or replaced that eventuate in the individual miRNAs of different mechanisms. Understanding the basic foundation concerning how miRNAs are generated and function will help with diagnostic tools and therapeutic strategies. This review encompasses the canonical and the non-canonical pathways involved in miRNA biogenesis, while elucidating how miRNAs regulate genes at the nuclear level and also the mechanism that lies behind circulating miRNAs.

Matched MeSH terms: Gene Regulatory Networks/genetics
MicroRNAs and colorectal cancer: clinical potential and regulatory networks

Osei GY, Adu-Amankwaah J, Koomson S, Beletaa S, Asiamah EA, Smith-Togobo C, et al.

Mol Biol Rep, 2023 Nov;50(11):9575-9585.
PMID: 37776413 DOI: 10.1007/s11033-023-08810-w

Colorectal cancer (CRC) is a serious global health concern, with a high incidence and mortality rate. Although there have been advancements in the early detection and treatment of CRC, therapy resistance is common. MicroRNAs (miRNAs), a type of small non-coding RNA that regulates gene expression, are key players in the initiation and progression of CRC. Recently, there has been growing attention to the complex interplay of miRNAs in cancer development. miRNAs are powerful RNA molecules that regulate gene expression and have been implicated in various physiological and pathological processes, including carcinogenesis. By identifying current challenges and limitations of treatment strategies and suggesting future research directions, this review aims to contribute to ongoing efforts to enhance CRC diagnosis and treatment. It also provides a comprehensive overview of the role miRNAs play in CRC carcinogenesis and explores the potential of miRNA-based therapies as a treatment option. Importantly, this review highlights the exciting potential of targeted modulation of miRNA function as a therapeutic approach for CRC.

Matched MeSH terms: Gene Regulatory Networks/genetics
Fulltext Molecular Regulation of Lipogenesis, Adipogenesis and Fat Deposition in Chicken

Nematbakhsh S, Pei Pei C, Selamat J, Nordin N, Idris LH, Abdull Razis AF

Genes (Basel), 2021 03 13;12(3).
PMID: 33805667 DOI: 10.3390/genes12030414

In the poultry industry, excessive fat deposition is considered an undesirable factor, affecting feed efficiency, meat production cost, meat quality, and consumer's health. Efforts to reduce fat deposition in economically important animals, such as chicken, can be made through different strategies; including genetic selection, feeding strategies, housing, and environmental strategies, as well as hormone supplementation. Recent investigations at the molecular level have revealed the significant role of the transcriptional and post-transcriptional regulatory networks and their interaction on modulating fat metabolism in chickens. At the transcriptional level, different transcription factors are known to regulate the expression of lipogenic and adipogenic genes through various signaling pathways, affecting chicken fat metabolism. Alternatively, at the post-transcriptional level, the regulatory mechanism of microRNAs (miRNAs) on lipid metabolism and deposition has added a promising dimension to understand the structural and functional regulatory mechanism of lipid metabolism in chicken. Therefore, this review focuses on the progress made in unraveling the molecular function of genes, transcription factors, and more notably significant miRNAs responsible for regulating adipogenesis, lipogenesis, and fat deposition in chicken. Moreover, a better understanding of the molecular regulation of lipid metabolism will give researchers novel insights to use functional molecular markers, such as miRNAs, for selection against excessive fat deposition to improve chicken production efficiency and meat quality.

Matched MeSH terms: Gene Regulatory Networks/genetics*
Construction of a microRNA-mRNA Regulatory Network in De Novo Cytogenetically Normal Acute Myeloid Leukemia Patients

Esa E, Hashim AK, Mohamed EHM, Zakaria Z, Abu Hassan AN, Mat Yusoff Y, et al.

Genet Test Mol Biomarkers, 2021 Mar;25(3):199-210.
PMID: 33734890 DOI: 10.1089/gtmb.2020.0182

Background: The association between dysregulated microRNAs (miRNAs) and acute myeloid leukemia (AML) is well known. However, our understanding of the regulatory role of miRNAs in the cytogenetically normal AML (CN-AML) subtype pathway is still poor. The current study integrated miRNA and mRNA profiles to explore novel miRNA-mRNA interactions that affect the regulatory patterns of de novo CN-AML. Methods: We utilized a multiplexed nanoString nCounter platform to profile both miRNAs and mRNAs using similar sets of patient samples (n = 24). Correlations were assessed, and an miRNA-mRNA network was constructed. The underlying biological functions of the mRNAs were predicted by gene enrichment. Finally, the interacting pairs were assessed using TargetScan and microT-CDS. We identified 637 significant negative correlations (false discovery rate <0.05). Results: Network analysis revealed a cluster of 12 miRNAs representing the majority of mRNA targets. Within the cluster, five miRNAs (miR-495-3p, miR-185-5p, let-7i-5p, miR-409-3p, and miR-127-3p) were posited to play a pivotal role in the regulation of CN-AML, as they are associated with the negative regulation of myeloid leukocyte differentiation, negative regulation of myeloid cell differentiation, and positive regulation of hematopoiesis. Conclusion: Three novel interactions in CN-AML were predicted as let-7i-5p:HOXA9, miR-495-3p:PIK3R1, and miR-495-3p:CDK6 may be responsible for regulating myeloid cell differentiation in CN-AML.

Matched MeSH terms: Gene Regulatory Networks/genetics
Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme

Chan WH, Mohamad MS, Deris S, Zaki N, Kasim S, Omatu S, et al.

Comput Biol Med, 2016 10 01;77:102-15.
PMID: 27522238 DOI: 10.1016/j.compbiomed.2016.08.004

Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data. Therefore, efficient identification of informative genes is inevitable. Embedded methods like penalized classifiers have been used for microarray analysis due to their embedded gene selection. This paper proposes an improved penalized support vector machine with absolute t-test weighting scheme to identify informative genes and pathways. Experiments are done on four microarray data sets. The results are compared with previous methods using 10-fold cross validation in terms of accuracy, sensitivity, specificity and F-score. Our method shows consistent improvement over the previous methods and biological validation has been done to elucidate the relation of the selected genes and pathway with the phenotype under study.

Matched MeSH terms: Gene Regulatory Networks/genetics*
Fulltext Investigating meta-approaches for reconstructing gene networks in a mammalian cellular context

Nazri A, Lio P

PLoS One, 2012;7(1):e28713.
PMID: 22253694 DOI: 10.1371/journal.pone.0028713

The output of state-of-the-art reverse-engineering methods for biological networks is often based on the fitting of a mathematical model to the data. Typically, different datasets do not give single consistent network predictions but rather an ensemble of inconsistent networks inferred under the same reverse-engineering method that are only consistent with the specific experimentally measured data. Here, we focus on an alternative approach for combining the information contained within such an ensemble of inconsistent gene networks called meta-analysis, to make more accurate predictions and to estimate the reliability of these predictions. We review two existing meta-analysis approaches; the Fisher transformation combined coefficient test (FTCCT) and Fisher's inverse combined probability test (FICPT); and compare their performance with five well-known methods, ARACNe, Context Likelihood or Relatedness network (CLR), Maximum Relevance Minimum Redundancy (MRNET), Relevance Network (RN) and Bayesian Network (BN). We conducted in-depth numerical ensemble simulations and demonstrated for biological expression data that the meta-analysis approaches consistently outperformed the best gene regulatory network inference (GRNI) methods in the literature. Furthermore, the meta-analysis approaches have a low computational complexity. We conclude that the meta-analysis approaches are a powerful tool for integrating different datasets to give more accurate and reliable predictions for biological networks.

Matched MeSH terms: Gene Regulatory Networks/genetics*
Fulltext Genetic Differences in the Immediate Transcriptome Response to Stress Predict Risk-Related Brain Function and Psychiatric Disorders

Arloth J, Bogdan R, Weber P, Frishman G, Menke A, Wagner KV, et al.

Neuron, 2015 Jun 03;86(5):1189-202.
PMID: 26050039 DOI: 10.1016/j.neuron.2015.05.034

Depression risk is exacerbated by genetic factors and stress exposure; however, the biological mechanisms through which these factors interact to confer depression risk are poorly understood. One putative biological mechanism implicates variability in the ability of cortisol, released in response to stress, to trigger a cascade of adaptive genomic and non-genomic processes through glucocorticoid receptor (GR) activation. Here, we demonstrate that common genetic variants in long-range enhancer elements modulate the immediate transcriptional response to GR activation in human blood cells. These functional genetic variants increase risk for depression and co-heritable psychiatric disorders. Moreover, these risk variants are associated with inappropriate amygdala reactivity, a transdiagnostic psychiatric endophenotype and an important stress hormone response trigger. Network modeling and animal experiments suggest that these genetic differences in GR-induced transcriptional activation may mediate the risk for depression and other psychiatric disorders by altering a network of functionally related stress-sensitive genes in blood and brain.

Matched MeSH terms: Gene Regulatory Networks/genetics
Fulltext Endogenous HIF2A reporter systems for high-throughput functional screening

Zaini MN, Patel SA, Syafruddin SE, Rodrigues P, Vanharanta S

Sci Rep, 2018 08 13;8(1):12063.
PMID: 30104738 DOI: 10.1038/s41598-018-30499-2

Tissue-specific transcriptional programs control most biological phenotypes, including disease states such as cancer. However, the molecular details underlying transcriptional specificity is largely unknown, hindering the development of therapeutic approaches. Here, we describe novel experimental reporter systems that allow interrogation of the endogenous expression of HIF2A, a critical driver of renal oncogenesis. Using a focused CRISPR-Cas9 library targeting chromatin regulators, we provide evidence that these reporter systems are compatible with high-throughput screening. Our data also suggests redundancy in the control of cancer type-specific transcriptional traits. Reporter systems such as those described here could facilitate large-scale mechanistic dissection of transcriptional programmes underlying cancer phenotypes, thus paving the way for novel therapeutic approaches.

Matched MeSH terms: Gene Regulatory Networks/genetics
Fulltext Reconstructing directed gene regulatory network by only gene expression data

Zhang L, Feng XK, Ng YK, Li SC

BMC Genomics, 2016 Aug 18;17 Suppl 4:430.
PMID: 27556418 DOI: 10.1186/s12864-016-2791-2

BACKGROUND: Accurately identifying gene regulatory network is an important task in understanding in vivo biological activities. The inference of such networks is often accomplished through the use of gene expression data. Many methods have been developed to evaluate gene expression dependencies between transcription factor and its target genes, and some methods also eliminate transitive interactions. The regulatory (or edge) direction is undetermined if the target gene is also a transcription factor. Some methods predict the regulatory directions in the gene regulatory networks by locating the eQTL single nucleotide polymorphism, or by observing the gene expression changes when knocking out/down the candidate transcript factors; regrettably, these additional data are usually unavailable, especially for the samples deriving from human tissues.
RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.

Matched MeSH terms: Gene Regulatory Networks/genetics
Fulltext RNA sequencing analysis of human podocytes reveals glucocorticoid regulated gene networks targeting non-immune pathways

Jiang L, Hindmarch CC, Rogers M, Campbell C, Waterfall C, Coghill J, et al.

Sci Rep, 2016 10 24;6:35671.
PMID: 27774996 DOI: 10.1038/srep35671

Glucocorticoids are steroids that reduce inflammation and are used as immunosuppressive drugs for many diseases. They are also the mainstay for the treatment of minimal change nephropathy (MCN), which is characterised by an absence of inflammation. Their mechanisms of action remain elusive. Evidence suggests that immunomodulatory drugs can directly act on glomerular epithelial cells or 'podocytes', the cell type which is the main target of injury in MCN. To understand the nature of glucocorticoid effects on non-immune cell functions, we generated RNA sequencing data from human podocyte cell lines and identified the genes that are significantly regulated in dexamethasone-treated podocytes compared to vehicle-treated cells. The upregulated genes are of functional relevance to cytoskeleton-related processes, whereas the downregulated genes mostly encode pro-inflammatory cytokines and growth factors. We observed a tendency for dexamethasone-upregulated genes to be downregulated in MCN patients. Integrative analysis revealed gene networks composed of critical signaling pathways that are likely targeted by dexamethasone in podocytes.

Matched MeSH terms: Gene Regulatory Networks/genetics*