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A review on the computational approaches for gene regulatory network construction

Chai LE, Loh SK, Low ST, Mohamad MS, Deris S, Zakaria Z

Comput Biol Med, 2014 May;48:55-65.
PMID: 24637147 DOI: 10.1016/j.compbiomed.2014.02.011

Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.

Matched MeSH terms: Gene Regulatory Networks*
Fulltext A gene co-expression network model identifies yield-related vicinity networks in Jatropha curcas shoot system

Govender N, Senan S, Mohamed-Hussein ZA, Wickneswari R

Sci Rep, 2018 Jun 15;8(1):9211.
PMID: 29907786 DOI: 10.1038/s41598-018-27493-z

The plant shoot system consists of reproductive organs such as inflorescences, buds and fruits, and the vegetative leaves and stems. In this study, the reproductive part of the Jatropha curcas shoot system, which includes the aerial shoots, shoots bearing the inflorescence and inflorescence were investigated in regard to gene-to-gene interactions underpinning yield-related biological processes. An RNA-seq based sequencing of shoot tissues performed on an Illumina HiSeq. 2500 platform generated 18 transcriptomes. Using the reference genome-based mapping approach, a total of 64 361 genes was identified in all samples and the data was annotated against the non-redundant database by the BLAST2GO Pro. Suite. After removing the outlier genes and samples, a total of 12 734 genes across 17 samples were subjected to gene co-expression network construction using petal, an R library. A gene co-expression network model built with scale-free and small-world properties extracted four vicinity networks (VNs) with putative involvement in yield-related biological processes as follow; heat stress tolerance, floral and shoot meristem differentiation, biosynthesis of chlorophyll molecules and laticifers, cell wall metabolism and epigenetic regulations. Our VNs revealed putative key players that could be adapted in breeding strategies for J. curcas shoot system improvements.

Matched MeSH terms: Gene Regulatory Networks/physiology*
Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient

Mohamed Salleh FH, Arif SM, Zainudin S, Firdaus-Raih M

Comput Biol Chem, 2015 Dec;59 Pt B:3-14.
PMID: 26278974 DOI: 10.1016/j.compbiolchem.2015.04.012

A gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other's state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5.

Matched MeSH terms: Gene Regulatory Networks
CircRNA-miRNA interactions in atherogenesis

Tong KL, Tan KE, Lim YY, Tien XY, Wong PF

Mol Cell Biochem, 2022 Dec;477(12):2703-2733.
PMID: 35604519 DOI: 10.1007/s11010-022-04455-8

Atherosclerosis is the major cause of coronary artery disease (CAD) which includes unstable angina, myocardial infarction, and heart failure. The onset of atherogenesis, a process of atherosclerotic lesion formation in the intima of arteries, is driven by lipid accumulation, a vicious cycle of reactive oxygen species (ROS)-induced oxidative stress and inflammatory reactions leading to endothelial cell (EC) dysfunction, vascular smooth muscle cell (VSMC) activation, and foam cell formation which further fuel plaque formation and destabilization. In recent years, there is a surge in the number of publications reporting the involvement of circular RNAs (circRNAs) in the pathogenesis of cardiovascular diseases, cancers, and metabolic syndromes. These studies have advanced our understanding on the biological functions of circRNAs. One of the most common mechanism of action of circRNAs reported is the sponging of microRNAs (miRNAs) by binding to the miRNAs response element (MRE), thereby indirectly increases the transcription of their target messenger RNAs (mRNAs). Individual networks of circRNA-miRNA-mRNA associated with atherogenesis have been extensively reported, however, there is a need to connect these findings for a complete overview. This review aims to provide an update on atherogenesis-related circRNAs and analyze the circRNA-miRNA-mRNA interactions in atherogenesis. The atherogenic mechanisms and clinical relevance of each atherogenesis-related circRNA were systematically discussed for better understanding of the knowledge gap in this area.

Matched MeSH terms: Gene Regulatory Networks
Rice WRKY13 TF protein binds to motifs in the promoter region to regulate downstream disease resistance-related genes

Jimmy JL, Karn R, Kumari S, Sruthilaxmi CB, Pooja S, Emerson IA, et al.

Funct Integr Genomics, 2023 Jul 20;23(3):249.
PMID: 37474674 DOI: 10.1007/s10142-023-01167-0

In plants, pathogen resistance is brought about by the binding of certain transcription factor (TF) proteins to the cis-elements of certain target genes. These cis-elements are present upstream in the motif of the promoters of each gene. This ensures the binding of a specific TF to a specific promoter, therefore regulating the expression of that gene. Therefore, the study of each promoter sequence of all the rice genes would help identify the target genes of a specific TF. Rice 1 kb upstream promoter sequences of 55,986 annotated genes were analyzed using the Perl program algorithm to detect WRKY13 binding motifs (bm). The resulting genes were grouped using Gene Ontology and gene set enrichment analysis. A gene with more than 4 TF bm in their promoter was selected. Ten genes reported to have a role in rice disease resistance were selected for further analysis. Cis-acting regulatory element analysis was carried out to find the cis-elements and confirm the presence of the corresponding motifs in the promoter sequences of these genes. The 3D structure of WRKY13 TF and the corresponding ten genes were built, and the interacting residues were determined. The binding capacity of WRKY13 to the promoter of these selected genes was analyzed using docking studies. WRKY13 was considered for docking analysis based on the prior reports of autoregulation. Molecular dynamic simulations provided more details regarding the interactions. Expression data revealed the expression of the genes that helped provide the mechanism of interaction. Further co-expression network helped to characterize the interaction of these selected disease resistance-related genes with the WRKY13 TF protein. This study suggests downstream target genes that are regulated by the WRKY13 TF. The molecular mechanism involving the gene network regulated by WRKY13 TF in disease resistance against rice fungal pathogens is explored.

Matched MeSH terms: Gene Regulatory Networks
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
IRF5-mediated immune responses and its implications in immunological disorders

Kaur A, Lee LH, Chow SC, Fang CM

Int Rev Immunol, 2018;37(5):229-248.
PMID: 29985675 DOI: 10.1080/08830185.2018.1469629

Transcription factors are gene regulators that activate or repress target genes. One family of the transcription factors that have been extensively studied for their crucial role in regulating gene network in the immune system is the interferon regulatory factors (IRFs). IRFs possess a novel turn-helix turn motif that recognizes a specific DNA consensus found in the promoters of many genes that are involved in immune responses. IRF5, a member of IRFs has recently gained much attention for its role in regulating inflammatory responses and autoimmune diseases. Here, we discuss the role of IRF5 in regulating immune cells functions and how the dysregulation of IRF5 contributes to the pathogenesis of immune disorders. We also review the latest findings of potential IRF5 inhibitors that modulate IRF5 activity in the effort of developing therapeutic approaches for treating inflammatory disorders.

Matched MeSH terms: Gene Regulatory Networks
Fulltext Circular RNA- and microRNA-Mediated Post-Transcriptional Regulation of Preadipocyte Differentiation in Adipogenesis: From Expression Profiling to Signaling Pathway

Huang CJ, Choo KB

Int J Mol Sci, 2023 Feb 25;24(5).
PMID: 36901978 DOI: 10.3390/ijms24054549

Adipogenesis is an indispensable cellular process that involves preadipocyte differentiation into mature adipocyte. Dysregulated adipogenesis contributes to obesity, diabetes, vascular conditions and cancer-associated cachexia. This review aims to elucidate the mechanistic details on how circular RNA (circRNA) and microRNA (miRNA) modulate post-transcriptional expression of targeted mRNA and the impacted downstream signaling and biochemical pathways in adipogenesis. Twelve adipocyte circRNA profiling and comparative datasets from seven species are analyzed using bioinformatics tools and interrogations of public circRNA databases. Twenty-three circRNAs are identified in the literature that are common to two or more of the adipose tissue datasets in different species; these are novel circRNAs that have not been reported in the literature in relation to adipogenesis. Four complete circRNA-miRNA-mediated modulatory pathways are constructed via integration of experimentally validated circRNA-miRNA-mRNA interactions and the downstream signaling and biochemical pathways involved in preadipocyte differentiation via the PPARγ/C/EBPα gateway. Despite the diverse mode of modulation, bioinformatics analysis shows that the circRNA-miRNA-mRNA interacting seed sequences are conserved across species, supporting mandatory regulatory functions in adipogenesis. Understanding the diverse modes of post-transcriptional regulation of adipogenesis may contribute to the development of novel diagnostic and therapeutic strategies for adipogenesis-associated diseases and in improving meat quality in the livestock industries.

Matched MeSH terms: Gene Regulatory Networks
Identifying miRNA as biomarker for breast cancer subtyping using association rule

Md Zaki FA, Mohamad Hanif EA

Comput Biol Med, 2024 Aug;178:108696.
PMID: 38850957 DOI: 10.1016/j.compbiomed.2024.108696

- This paper presents a comprehensive study focused on breast cancer subtyping, utilizing a multifaceted approach that integrates feature selection, machine learning classifiers, and miRNA regulatory networks. The feature selection process begins with the CFS algorithm, followed by the Apriori algorithm for association rule generation, resulting in the identification of significant features tailored to Luminal A, Luminal B, HER-2 enriched, and Basal-like subtypes. The subsequent application of Random Forest (RF) and Support Vector Machine (SVM) classifiers yielded promising results, with the SVM model achieving an overall accuracy of 76.60 % and the RF model demonstrating robust performance at 80.85 %. Detailed accuracy metrics revealed strengths and areas for refinement, emphasizing the potential for optimizing subtype-specific recall. To explore the regulatory landscape in depth, an analysis of selected miRNAs was conducted using MIENTURNET, a tool for visualizing miRNA-target interactions. While FDR analysis raised concerns for HER-2 and Basal-like subtypes, Luminal A and Luminal B subtypes showcased significant miRNA-gene interactions. Functional enrichment analysis for Luminal A highlighted the role of Ovarian steroidogenesis, implicating specific miRNAs such as hsa-let-7c-5p and hsa-miR-125b-5p as potential diagnostic biomarkers and regulators of Luminal A breast cancer. Luminal B analysis uncovered associations with the MAPK signaling pathway, with miRNAs like hsa-miR-203a-3p and hsa-miR-19a-3p exhibiting potential diagnostic and therapeutic significance. In conclusion, this integrative approach combines machine learning techniques with miRNA analysis to provide a holistic understanding of breast cancer subtypes. The identified miRNAs and associated pathways offer insights into potential diagnostic biomarkers and therapeutic targets, contributing to the ongoing efforts to improve breast cancer diagnostics and personalized treatment strategies.

Matched MeSH terms: Gene Regulatory Networks
Fulltext The inference of breast cancer metastasis through gene regulatory networks

Ahmad FK, Deris S, Othman NH

J Biomed Inform, 2012 Apr;45(2):350-62.
PMID: 22179053 DOI: 10.1016/j.jbi.2011.11.015

Understanding the mechanisms of gene regulation during breast cancer is one of the most difficult problems among oncologists because this regulation is likely comprised of complex genetic interactions. Given this complexity, a computational study using the Bayesian network technique has been employed to construct a gene regulatory network from microarray data. Although the Bayesian network has been notified as a prominent method to infer gene regulatory processes, learning the Bayesian network structure is NP hard and computationally intricate. Therefore, we propose a novel inference method based on low-order conditional independence that extends to the case of the Bayesian network to deal with a large number of genes and an insufficient sample size. This method has been evaluated and compared with full-order conditional independence and different prognostic indices on a publicly available breast cancer data set. Our results suggest that the low-order conditional independence method will be able to handle a large number of genes in a small sample size with the least mean square error. In addition, this proposed method performs significantly better than other methods, including the full-order conditional independence and the St. Gallen consensus criteria. The proposed method achieved an area under the ROC curve of 0.79203, whereas the full-order conditional independence and the St. Gallen consensus criteria obtained 0.76438 and 0.73810, respectively. Furthermore, our empirical evaluation using the low-order conditional independence method has demonstrated a promising relationship between six gene regulators and two regulated genes and will be further investigated as potential breast cancer metastasis prognostic markers.

Matched MeSH terms: Gene Regulatory Networks*
Fulltext Network Analysis for the Identification of Differentially Expressed Hub Genes Using Myogenin Knock-down Muscle Satellite Cells

Malik A, Lee EJ, Jan AT, Ahmad S, Cho KH, Kim J, et al.

PLoS One, 2015;10(7):e0133597.
PMID: 26200109 DOI: 10.1371/journal.pone.0133597

Muscle, a multinucleate syncytium formed by the fusion of mononuclear myoblasts, arises from quiescent progenitors (satellite cells) via activation of muscle-specific transcription factors (MyoD, Myf5, myogenin: MYOG, and MRF4). Subsequent to a decline in Pax7, induction in the expression of MYOG is a hallmark of myoblasts that have entered the differentiation phase following cell cycle withdrawal. It is evident that MYOG function cannot be compensated by any other myogenic regulatory factors (MRFs). Despite a plethora of information available regarding MYOG, the mechanism by which MYOG regulates muscle cell differentiation has not yet been identified. Using an RNA-Seq approach, analysis of MYOG knock-down muscle satellite cells (MSCs) have shown that genes associated with cell cycle and division, DNA replication, and phosphate metabolism are differentially expressed. By constructing an interaction network of differentially expressed genes (DEGs) using GeneMANIA, cadherin-associated protein (CTNNA2) was identified as the main hub gene in the network with highest node degree. Four functional clusters (modules or communities) were identified in the network and the functional enrichment analysis revealed that genes included in these clusters significantly contribute to skeletal muscle development. To confirm this finding, in vitro studies revealed increased expression of CTNNA2 in MSCs on day 12 compared to day 10. Expression of CTNNA2 was decreased in MYOG knock-down cells. However, knocking down CTNNA2, which leads to increased expression of extracellular matrix (ECM) genes (type I collagen α1 and type I collagen α2) along with myostatin (MSTN), was not found significantly affecting the expression of MYOG in C2C12 cells. We therefore propose that MYOG exerts its regulatory effects by acting upstream of CTNNA2, which in turn regulates the differentiation of C2C12 cells via interaction with ECM genes. Taken together, these findings highlight a new mechanism by which MYOG interacts with CTNNA2 in order to promote myoblast differentiation.

Matched MeSH terms: Gene Regulatory Networks*
Fulltext Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

Walters K, Sarsenov R, Too WS, Hare RK, Paterson IC, Lambert DW, et al.

BMC Genomics, 2019 Jun 03;20(1):454.
PMID: 31159744 DOI: 10.1186/s12864-019-5850-7

BACKGROUND: Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of cellular processes in diseases such as cancer, although the functions of most remain poorly understood. To address this, here we apply a novel strategy to integrate gene expression profiles across 32 cancer types, and cluster human lncRNAs based on their pan-cancer protein-coding gene associations. By doing so, we derive 16 lncRNA modules whose unique properties allow simultaneous inference of function, disease specificity and regulation for over 800 lncRNAs.
RESULTS: Remarkably, modules could be grouped into just four functional themes: transcription regulation, immunological, extracellular, and neurological, with module generation frequently driven by lncRNA tissue specificity. Notably, three modules associated with the extracellular matrix represented potential networks of lncRNAs regulating key events in tumour progression. These included a tumour-specific signature of 33 lncRNAs that may play a role in inducing epithelial-mesenchymal transition through modulation of TGFβ signalling, and two stromal-specific modules comprising 26 lncRNAs linked to a tumour suppressive microenvironment and 12 lncRNAs related to cancer-associated fibroblasts. One member of the 12-lncRNA signature was experimentally supported by siRNA knockdown, which resulted in attenuated differentiation of quiescent fibroblasts to a cancer-associated phenotype.
CONCLUSIONS: Overall, the study provides a unique pan-cancer perspective on the lncRNA functional landscape, acting as a global source of novel hypotheses on lncRNA contribution to tumour progression.

Matched MeSH terms: Gene Regulatory Networks*
Fulltext Gene co-expression networks from RNA sequencing of dairy cattle identifies genes and pathways affecting feed efficiency

Salleh SM, Mazzoni G, Løvendahl P, Kadarmideen HN

BMC Bioinformatics, 2018 Dec 17;19(1):513.
PMID: 30558534 DOI: 10.1186/s12859-018-2553-z

BACKGROUND: Selection for feed efficiency is crucial for overall profitability and sustainability in dairy cattle production. Key regulator genes and genetic markers derived from co-expression networks underlying feed efficiency could be included in the genomic selection of the best cows. The present study identified co-expression networks associated with high and low feed efficiency and their regulator genes in Danish Holstein and Jersey cows. RNA-sequencing data from Holstein and Jersey cows with high and low residual feed intake (RFI) and treated with two diets (low and high concentrate) were used. Approximately 26 million and 25 million pair reads were mapped to bovine reference genome for Jersey and Holstein breed, respectively. Subsequently, the gene count expressions data were analysed using a Weighted Gene Co-expression Network Analysis (WGCNA) approach. Functional enrichment analysis from Ingenuity® Pathway Analysis (IPA®), ClueGO application and STRING of these modules was performed to identify relevant biological pathways and regulatory genes.
RESULTS: WGCNA identified two groups of co-expressed genes (modules) significantly associated with RFI and one module significantly associated with diet. In Holstein cows, the salmon module with module trait relationship (MTR) = 0.7 and the top upstream regulators ATP7B were involved in cholesterol biosynthesis, steroid biosynthesis, lipid biosynthesis and fatty acid metabolism. The magenta module has been significantly associated (MTR = 0.51) with the treatment diet involved in the triglyceride homeostasis. In Jersey cows, the lightsteelblue1 (MTR = - 0.57) module controlled by IFNG and IL10RA was involved in the positive regulation of interferon-gamma production, lymphocyte differentiation, natural killer cell-mediated cytotoxicity and primary immunodeficiency.
CONCLUSION: The present study provides new information on the biological functions in liver that are potentially involved in controlling feed efficiency. The hub genes and upstream regulators (ATP7b, IFNG and IL10RA) involved in these functions are potential candidate genes for the development of new biomarkers. However, the hub genes, upstream regulators and pathways involved in the co-expressed networks were different in both breeds. Hence, additional studies are required to investigate and confirm these findings prior to their use as candidate genes.

Matched MeSH terms: Gene Regulatory Networks*
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
Prosthetic gene networks as an alternative to standard pharmacotherapies for metabolic disorders

Heng BC, Aubel D, Fussenegger M

Curr Opin Biotechnol, 2015 Dec;35:37-45.
PMID: 25679308 DOI: 10.1016/j.copbio.2015.01.010

Synthetic biology makes inroads into clinical therapy with the debut of closed-loop prosthetic gene networks specifically designed to treat human diseases. Prosthetic networks are synthetic sensor/effector devices that could functionally integrate and interface with host metabolism to monitor disease states and coordinate appropriate therapeutic responses in a self-sufficient, timely and automatic manner. Prosthetic networks hold particular promise for the current global epidemic of closely interrelated metabolic disorders encompassing obesity, type 2 diabetes, hypertension and hyperlipidaemia, which arise from the unhealthy lifestyle and dietary factors in the modern urbanised world. This review will critically examine the various attempts at constructing prosthetic gene networks for the treatment of these metabolic disorders, as well as provide insight into future developments in the field.

Matched MeSH terms: Gene Regulatory Networks*
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 Neuroinflammation as a Key Driver of Secondary Neurodegeneration Following Stroke?

Stuckey SM, Ong LK, Collins-Praino LE, Turner RJ

Int J Mol Sci, 2021 Dec 03;22(23).
PMID: 34884906 DOI: 10.3390/ijms222313101

Ischaemic stroke involves the rapid onset of focal neurological dysfunction, most commonly due to an arterial blockage in a specific region of the brain. Stroke is a leading cause of death and common cause of disability, with over 17 million people worldwide suffering from a stroke each year. It is now well-documented that neuroinflammation and immune mediators play a key role in acute and long-term neuronal tissue damage and healing, not only in the infarct core but also in distal regions. Importantly, in these distal regions, termed sites of secondary neurodegeneration (SND), spikes in neuroinflammation may be seen sometime after the initial stroke onset, but prior to the presence of the neuronal tissue damage within these regions. However, it is key to acknowledge that, despite the mounting information describing neuroinflammation following ischaemic stroke, the exact mechanisms whereby inflammatory cells and their mediators drive stroke-induced neuroinflammation are still not fully understood. As a result, current anti-inflammatory treatments have failed to show efficacy in clinical trials. In this review we discuss the complexities of post-stroke neuroinflammation, specifically how it affects neuronal tissue and post-stroke outcome acutely, chronically, and in sites of SND. We then discuss current and previously assessed anti-inflammatory therapies, with a particular focus on how failed anti-inflammatories may be repurposed to target SND-associated neuroinflammation.

Matched MeSH terms: Gene Regulatory Networks/drug effects
Fulltext RNA-seq and ChIP-seq as Complementary Approaches for Comprehension of Plant Transcriptional Regulatory Mechanism

Muhammad II, Kong SL, Akmar Abdullah SN, Munusamy U

Int J Mol Sci, 2019 Dec 25;21(1).
PMID: 31881735 DOI: 10.3390/ijms21010167

The availability of data produced from various sequencing platforms offer the possibility to answer complex questions in plant research. However, drawbacks can arise when there are gaps in the information generated, and complementary platforms are essential to obtain more comprehensive data sets relating to specific biological process, such as responses to environmental perturbations in plant systems. The investigation of transcriptional regulation raises different challenges, particularly in associating differentially expressed transcription factors with their downstream responsive genes. In this paper, we discuss the integration of transcriptional factor studies through RNA sequencing (RNA-seq) and Chromatin Immunoprecipitation sequencing (ChIP-seq). We show how the data from ChIP-seq can strengthen information generated from RNA-seq in elucidating gene regulatory mechanisms. In particular, we discuss how integration of ChIP-seq and RNA-seq data can help to unravel transcriptional regulatory networks. This review discusses recent advances in methods for studying transcriptional regulation using these two methods. It also provides guidelines for making choices in selecting specific protocols in RNA-seq pipelines for genome-wide analysis to achieve more detailed characterization of specific transcription regulatory pathways via ChIP-seq.

Matched MeSH terms: Gene Regulatory Networks*

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