Displaying publications 1 - 20 of 72 in total

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  1. 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*
  2. 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*
  3. Fulltext MicroRNA 299-3p modulates replicative senescence in endothelial cells
    Jong HL, Mustafa MR, Vanhoutte PM, AbuBakar S, Wong PF
    Physiol Genomics, 2013 Apr 1;45(7):256-67.
    PMID: 23362143 DOI: 10.1152/physiolgenomics.00071.2012
    MicroRNAs (miRNAs) regulate various cellular processes. While several genes associated with replicative senescence have been described in endothelial cells, miRNAs that regulate these genes remain largely unknown. The present study was designed to identify miRNAs associated with replicative senescence and their target genes in human umbilical vein endothelial cells (HUVECs). An integrated miRNA and gene profiling approach revealed that hsa-miR-299-3p is upregulated in senescent HUVECs compared with the young cells, and one of its target genes could be IGF1. IGF1 was upregulated in senescent compared with young HUVECs, and knockdown of hsa-miR-299-3p dose-dependently increased the mRNA expression of IGF1, more significantly observed in the presenescent cells (passage 19) compared with the senescent cells (passage 25). Knockdown of hsa-miR-299-3p also resulted in significant reduction in the percentage of cells positively stained for senescence-associated β-galactosidase and increases in cell viability measured by MTT assay but marginal increases in cell proliferation and cell migration capacity measured by real-time growth kinetics analysis. Moreover, knockdown of hsa-miR-299-3p also increased proliferation of cells treated with H2O2 to induce senescence. These findings suggest that hsa-miR-299-3p may delay or protect against replicative senescence by improving the metabolic activity of the senesced cells but does not stimulate growth of the remaining cells in senescent cultures. Hence, these findings provide an early insight into the role of hsa-miR-299-3p in the modulation of replicative senescence in HUVECs.
    Matched MeSH terms: Gene Regulatory Networks
  4. Fulltext Gamma-tocotrienol modulated gene expression in senescent human diploid fibroblasts as revealed by microarray analysis
    Makpol S, Zainuddin A, Chua KH, Mohd Yusof YA, Ngah WZ
    Oxid Med Cell Longev, 2013;2013:454328.
    PMID: 23634235 DOI: 10.1155/2013/454328
    The effect of γ -tocotrienol, a vitamin E isomer, in modulating gene expression in cellular aging of human diploid fibroblasts was studied. Senescent cells at passage 30 were incubated with 70  μ M of γ -tocotrienol for 24 h. Gene expression patterns were evaluated using Sentrix HumanRef-8 Expression BeadChip from Illumina, analysed using GeneSpring GX10 software, and validated using quantitative RT-PCR. A total of 100 genes were differentially expressed (P < 0.001) by at least 1.5 fold in response to γ -tocotrienol treatment. Amongst the genes were IRAK3, SelS, HSPA5, HERPUD1, DNAJB9, SEPR1, C18orf55, ARF4, RINT1, NXT1, CADPS2, COG6, and GLRX5. Significant gene list was further analysed by Gene Set Enrichment Analysis (GSEA), and the Normalized Enrichment Score (NES) showed that biological processes such as inflammation, protein transport, apoptosis, and cell redox homeostasis were modulated in senescent fibroblasts treated with γ -tocotrienol. These findings revealed that γ -tocotrienol may prevent cellular aging of human diploid fibroblasts by modulating gene expression.
    Matched MeSH terms: Gene Regulatory Networks
  5. 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*
  6. Fulltext The use of protein-DNA, chromatin immunoprecipitation, and transcriptome arrays to describe transcriptional circuits in the dehydrated male rat hypothalamus
    Qiu J, Kleineidam A, Gouraud S, Yao ST, Greenwood M, Hoe SZ, et al.
    Endocrinology, 2014 Nov;155(11):4380-90.
    PMID: 25144923 DOI: 10.1210/en.2014-1448
    The supraoptic nucleus (SON) of the hypothalamus is responsible for maintaining osmotic stability in mammals through its elaboration of the antidiuretic hormone arginine vasopressin. Upon dehydration, the SON undergoes a function-related plasticity, which includes remodeling of morphology, electrical properties, and biosynthetic activity. This process occurs alongside alterations in steady state transcript levels, which might be mediated by changes in the activity of transcription factors. In order to identify which transcription factors might be involved in changing patterns of gene expression, an Affymetrix protein-DNA array analysis was carried out. Nuclear extracts of SON from dehydrated and control male rats were analyzed for binding to the 345 consensus DNA transcription factor binding sequences of the array. Statistical analysis revealed significant changes in binding to 26 consensus elements, of which EMSA confirmed increased binding to signal transducer and activator of transcription (Stat) 1/Stat3, cellular Myelocytomatosis virus-like cellular proto-oncogene (c-Myc)-Myc-associated factor X (Max), and pre-B cell leukemia transcription factor 1 sequences after dehydration. Focusing on c-Myc and Max, we used quantitative PCR to confirm previous transcriptomic analysis that had suggested an increase in c-Myc, but not Max, mRNA levels in the SON after dehydration, and we demonstrated c-Myc- and Max-like immunoreactivities in SON arginine vasopressin-expressing cells. Finally, by comparing new data obtained from Roche-NimbleGen chromatin immunoprecipitation arrays with previously published transcriptomic data, we have identified putative c-Myc target genes whose expression changes in the SON after dehydration. These include known c-Myc targets, such as the Slc7a5 gene, which encodes the L-type amino acid transporter 1, ribosomal protein L24, histone deactylase 2, and the Rat sarcoma proto-oncogene (Ras)-related nuclear GTPase.
    Matched MeSH terms: Gene Regulatory Networks*
  7. Fulltext Comparative analysis of cardiovascular development related genes in stem cells isolated from deciduous pulp and adipose tissue
    Loo ZX, Kunasekaran W, Govindasamy V, Musa S, Abu Kasim NH
    ScientificWorldJournal, 2014;2014:186508.
    PMID: 25548778 DOI: 10.1155/2014/186508
    Human exfoliated deciduous teeth (SHED) and adipose stem cells (ASC) were suggested as alternative cell choice for cardiac regeneration. However, the true functionability of these cells toward cardiac regeneration is yet to be discovered. Hence, this study was carried out to investigate the innate biological properties of these cell sources toward cardiac regeneration. Both cells exhibited indistinguishable MSCs characteristics. Human stem cell transcription factor arrays were used to screen expression levels in SHED and ASC. Upregulated expression of transcription factor (TF) genes was detected in both sources. An almost equal percentage of >2-fold changes were observed. These TF genes fall under several cardiovascular categories with higher expressions which were observed in growth and development of blood vessel, angiogenesis, and vasculogenesis categories. Further induction into cardiomyocyte revealed ASC to express more significantly cardiomyocyte specific markers compared to SHED during the differentiation course evidenced by morphology and gene expression profile. Despite this, spontaneous cellular beating was not detected in both cell lines. Taken together, our data suggest that despite being defined as MSCs, both ASC and SHED behave differently when they were cultured in a same cardiomyocytes culture condition. Hence, vigorous characterization is needed before introducing any cell for treating targeted diseases.
    Matched MeSH terms: Gene Regulatory Networks
  8. 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
  9. Blood gene signature for early hepatocellular carcinoma detection in patients with chronic hepatitis B
    Omar H, Lim CR, Chao S, Lee MM, Bong CW, Ooi EJ, et al.
    J Clin Gastroenterol, 2015 Feb;49(2):150-7.
    PMID: 25569223 DOI: 10.1097/MCG.0000000000000112
    Up to 25% of chronic hepatitis B (CHB) patients eventually develop hepatocellular carcinoma (HCC), a disease with poor prognosis unless detected early. This study identifies a blood-based RNA biomarker panel for early HCC detection in CHB.
    Matched MeSH terms: Gene Regulatory Networks
  10. 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
  11. Discovery of precursor and mature microRNAs and their putative gene targets using high-throughput sequencing in pineapple (Ananas comosus var. comosus)
    Yusuf NH, Ong WD, Redwan RM, Latip MA, Kumar SV
    Gene, 2015 Oct 15;571(1):71-80.
    PMID: 26115767 DOI: 10.1016/j.gene.2015.06.050
    MicroRNAs (miRNAs) are a class of small, endogenous non-coding RNAs that negatively regulate gene expression, resulting in the silencing of target mRNA transcripts through mRNA cleavage or translational inhibition. MiRNAs play significant roles in various biological and physiological processes in plants. However, the miRNA-mediated gene regulatory network in pineapple, the model tropical non-climacteric fruit, remains largely unexplored. Here, we report a complete list of pineapple mature miRNAs obtained from high-throughput small RNA sequencing and precursor miRNAs (pre-miRNAs) obtained from ESTs. Two small RNA libraries were constructed from pineapple fruits and leaves, respectively, using Illumina's Solexa technology. Sequence similarity analysis using miRBase revealed 579,179 reads homologous to 153 miRNAs from 41 miRNA families. In addition, a pineapple fruit transcriptome library consisting of approximately 30,000 EST contigs constructed using Solexa sequencing was used for the discovery of pre-miRNAs. In all, four pre-miRNAs were identified (MIR156, MIR399, MIR444 and MIR2673). Furthermore, the same pineapple transcriptome was used to dissect the function of the miRNAs in pineapple by predicting their putative targets in conjunction with their regulatory networks. In total, 23 metabolic pathways were found to be regulated by miRNAs in pineapple. The use of high-throughput sequencing in pineapples to unveil the presence of miRNAs and their regulatory pathways provides insight into the repertoire of miRNA regulation used exclusively in this non-climacteric model plant.
    Matched MeSH terms: Gene Regulatory Networks
  12. 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
  13. 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*
  14. Identification of hub glycogenes and their nsSNP analysis from mouse RNA-Seq data
    Firoz A, Malik A, Singh SK, Jha V, Ali A
    Gene, 2015 Dec 15;574(2):235-46.
    PMID: 26260015 DOI: 10.1016/j.gene.2015.08.012
    Glycogenes regulate a large number of biological processes such as cancer and development. In this work, we created an interaction network of 923 glycogenes to detect potential hubs from different mouse tissues using RNA-Seq data. DAVID functional cluster analysis revealed enrichment of immune response, glycoprotein and cholesterol metabolic processes. We also explored nsSNPs that may modify the expression and function of identified hubs using computational methods. We observe that the number of nsSNPs predicted by any two methods to affect protein function is 4, 7 and 2 for FLT1, NID2 and TNFRSF1B. Residues in the native and mutant proteins were analyzed for solvent accessibility and secondary structure change. Analysis of hubs can help in determining their degree of conservation and understanding their functions in biological processes. The nsSNPs proposed in this work may be further targeted through experimental methods for understanding structural and functional relationships of hub mutants.
    Matched MeSH terms: Gene Regulatory Networks
  15. Fulltext Identification of four functionally important microRNA families with contrasting differential expression profiles between drought-tolerant and susceptible rice leaf at vegetative stage
    Cheah BH, Nadarajah K, Divate MD, Wickneswari R
    BMC Genomics, 2015;16:692.
    PMID: 26369665 DOI: 10.1186/s12864-015-1851-3
    Developing drought-tolerant rice varieties with higher yield under water stressed conditions provides a viable solution to serious yield-reduction impact of drought. Understanding the molecular regulation of this polygenic trait is crucial for the eventual success of rice molecular breeding programmes. microRNAs have received tremendous attention recently due to its importance in negative regulation. In plants, apart from regulating developmental and physiological processes, microRNAs have also been associated with different biotic and abiotic stresses. Hence here we chose to analyze the differential expression profiles of microRNAs in three drought treated rice varieties: Vandana (drought-tolerant), Aday Sel (drought-tolerant) and IR64 (drought-susceptible) in greenhouse conditions via high-throughput sequencing.
    Matched MeSH terms: Gene Regulatory Networks
  16. Fulltext Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data
    Aketarawong N, Isasawin S, Sojikul P, Thanaphum S
    Zookeys, 2015.
    PMID: 26798262 DOI: 10.3897/zookeys.540.10058
    The Carambola fruit fly, Bactrocera carambolae, is an invasive pest in Southeast Asia. It has been introduced into areas in South America such as Suriname and Brazil. Bactrocera carambolae belongs to the Bactrocera dorsalis species complex, and seems to be separated from Bactrocera dorsalis based on morphological and multilocus phylogenetic studies. Even though the Carambola fruit fly is an important quarantine species and has an impact on international trade, knowledge of the molecular ecology of Bactrocera carambolae, concerning species status and pest management aspects, is lacking. Seven populations sampled from the known geographical areas of Bactrocera carambolae including Southeast Asia (i.e., Indonesia, Malaysia, Thailand) and South America (i.e., Suriname), were genotyped using eight microsatellite DNA markers. Genetic variation, genetic structure, and genetic network among populations illustrated that the Suriname samples were genetically differentiated from Southeast Asian populations. The genetic network revealed that samples from West Sumatra (Pekanbaru, PK) and Java (Jakarta, JK) were presumably the source populations of Bactrocera carambolae in Suriname, which was congruent with human migration records between the two continents. Additionally, three populations of Bactrocera dorsalis were included to better understand the species boundary. The genetic structure between the two species was significantly separated and approximately 11% of total individuals were detected as admixed (0.100 ≤ Q ≤ 0.900). The genetic network showed connections between Bactrocera carambolae and Bactrocera dorsalis groups throughout Depok (DP), JK, and Nakhon Sri Thammarat (NT) populations. These data supported the hypothesis that the reproductive isolation between the two species may be leaky. Although the morphology and monophyly of nuclear and mitochondrial DNA sequences in previous studies showed discrete entities, the hypothesis of semipermeable boundaries may not be rejected. Alleles at microsatellite loci could be introgressed rather than other nuclear and mitochondrial DNA. Bactrocera carambolae may be an incipient rather than a distinct species of Bactrocera dorsalis. Regarding the pest management aspect, the genetic sexing Salaya5 strain (SY5) was included for comparison with wild populations. The SY5 strain was genetically assigned to the Bactrocera carambolae cluster. Likewise, the genetic network showed that the strain shared greatest genetic similarity to JK, suggesting that SY5 did not divert away from its original genetic makeup. Under laboratory conditions, at least 12 generations apart, selection did not strongly affect genetic compatibility between the strain and wild populations. This knowledge further confirms the potential utilization of the Salaya5 strain in regional programs of area-wide integrated pest management using SIT.
    Matched MeSH terms: Gene Regulatory Networks
  17. 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*
  18. Fulltext MicroRNA Expression Profile of Neural Progenitor-Like Cells Derived from Rat Bone Marrow Mesenchymal Stem Cells under the Influence of IGF-1, bFGF and EGF
    Huat TJ, Khan AA, Abdullah JM, Idris FM, Jaafar H
    Int J Mol Sci, 2015;16(5):9693-718.
    PMID: 25938966 DOI: 10.3390/ijms16059693
    Insulin-like growth factor 1 (IGF-1) enhances cellular proliferation and reduces apoptosis during the early differentiation of bone marrow derived mesenchymal stem cells (BMSCs) into neural progenitor-like cells (NPCs) in the presence of epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF). BMSCs were differentiated in three groups of growth factors: (A) EGF + bFGF, (B) EGF + bFGF + IGF-1, and (C) without growth factor. To unravel the molecular mechanisms of the NPCs derivation, microarray analysis using GeneChip miRNA arrays was performed. The profiles were compared among the groups. Annotated microRNA fingerprints (GSE60060) delineated 46 microRNAs temporally up-regulated or down-regulated compared to group C. The expressions of selected microRNAs were validated by real-time PCR. Among the 46 microRNAs, 30 were consistently expressed for minimum of two consecutive time intervals. In Group B, only miR-496 was up-regulated and 12 microRNAs, including the let-7 family, miR-1224, miR-125a-3p, miR-214, miR-22, miR-320, miR-708, and miR-93, were down-regulated. Bioinformatics analysis reveals that some of these microRNAs (miR-22, miR-214, miR-125a-3p, miR-320 and let-7 family) are associated with reduction of apoptosis. Here, we summarize the roles of key microRNAs associated with IGF-1 in the differentiation of BMSCs into NPCs. These findings may provide clues to further our understanding of the mechanisms and roles of microRNAs as key regulators of BMSC-derived NPC maintenance.
    Matched MeSH terms: Gene Regulatory Networks/drug effects
  19. Fulltext Research trends in microarray data analysis: modelling gene regulatory network by integrating transcription factors data
    Farzana Kabir Ahmad, Siti Sakira Kamaruddin
    Scientific Research Journal, 2015;12(1):1-10.
    MyJurnal
    The invention of microarray technology has enabled expression levels of thousands of genes to be monitored at once. This modernized approach has created large amount of data to be examined. Recently, gene regulatory network has been an interesting topic and generated impressive research goals in computational biology. Better understanding of the genetic regulatory processes would bring significant implications in the biomedical fields and many other pharmaceutical industries. As a result, various mathematical and computational methods have been used to model gene regulatory network from microarray data. Amongst those methods, the Bayesian network model attracts the most attention and has become the prominent technique since it can capture nonlinear and stochastic relationships between variables. However, structure learning of this model is NP-hard and computationally complex as the number of potential edges increase drastically with the number of genes. In addition, most of the studies only focused on the predicted results while neglecting the fact that microarray data is a fragmented information on the whole biological process. Hence this study proposed a network-based inference model that combined biological knowledge in order to verify the constructed gene regulatory relationships. The gene regulatory network is constructed using Bayesian network based on low-order conditional independence approach. This technique aims to identify from the data the dependencies to construct the network structure, while addressing the structure learning problem. In addition, three main toolkits such as Ensembl, TFSearch and TRANSFAC have been used to determine the false positive edges and verify reliability of regulatory relationships. The experimental results show that by integrating biological knowledge it could enhance the precision results and reduce the number of false positive edges in the trained gene regulatory network.
    Matched MeSH terms: Gene Regulatory Networks
  20. 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
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