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Fulltext Meta-analysis of gene expression in relapsed childhood B-acute lymphoblastic leukemia

Chow YP, Alias H, Jamal R

BMC Cancer, 2017 02 10;17(1):120.
PMID: 28183295 DOI: 10.1186/s12885-017-3103-1

BACKGROUND: Relapsed pediatric B-acute lymphoblastic leukemia (B-ALL) remains as the leading cause of cancer death among children. Other than stem cell transplantation and intensified chemotherapy, no other improved treatment strategies have been approved clinically. Gene expression profiling represents a powerful approach to identify potential biomarkers and new therapeutic targets for various diseases including leukemias. However, inadequate sample size in many individual experiments has failed to provide adequate study power to yield translatable findings. With the hope of getting new insights into the biological mechanisms underpinning relapsed ALL and identifying more promising biomarkers or therapeutic targets, we conducted a meta-analysis of gene expression studies involving ALL from 3 separate studies.
METHOD: By using the keywords "acute lymphoblastic leukemia", and "microarray", a total of 280 and 275 microarray datasets were found listed in Gene Expression Omnibus database GEO and ArrayExpress database respectively. Further manual inspection found that only three studies (GSE18497, GSE28460, GSE3910) were focused on gene expression profiling of paired diagnosis-relapsed pediatric B-ALL. These three datasets which comprised of a total of 108 matched diagnosis-relapsed pediatric B-ALL samples were then included for this meta-analysis using RankProd approach.
RESULTS: Our analysis identified a total of 1795 upregulated probes which corresponded to 1527 genes (pfp 1), and 1493 downregulated probes which corresponded to 1214 genes (pfp gene (pfp gene ontology biological process annotation, the upregulated genes were most enriched in cell cycle processes (enrichment score = 15.3), whilst the downregulated genes were clustered in transcription regulation (enrichment score = 12.6). Elevated expression of cell cycle regulators (e.g kinesins, AURKA, CDKs) was the key genetic defect implicated in relapsed ALL, and serve as attractive targets for therapeutic intervention.
CONCLUSION: We identified S100A8 as the most overexpressed gene, and the cell cycle pathway as the most promising biomarker and therapeutic target for relapsed childhood B-ALL. The validity of the results warrants further investigation.

Matched MeSH terms: Gene Regulatory Networks
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
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*
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 Rare Copy Number Variants Identified Suggest the Regulating Pathways in Hypertension-Related Left Ventricular Hypertrophy

Boon-Peng H, Mat Jusoh JA, Marshall CR, Majid F, Danuri N, Basir F, et al.

PLoS One, 2016;11(3):e0148755.
PMID: 26930585 DOI: 10.1371/journal.pone.0148755

Left ventricular hypertrophy (LVH) is an independent risk factor for cardiovascular morbidity and mortality, and a powerful predictor of adverse cardiovascular outcomes in the hypertensive patients. It has complex multifactorial and polygenic basis for its pathogenesis. We hypothesized that rare copy number variants (CNVs) contribute to the LVH pathogenesis in hypertensive patients. Copy number variants (CNV) were identified in 258 hypertensive patients, 95 of whom had LVH, after genotyping with a high resolution SNP array. Following stringent filtering criteria, we identified 208 rare, or private CNVs that were only present in our patients with hypertension related LVH. Preliminary findings from Gene Ontology and pathway analysis of this study confirmed the involvement of the genes known to be functionally involved in cardiac development and phenotypes, in line with previously reported transcriptomic studies. Network enrichment analyses suggested that the gene-set was, directly or indirectly, involved in the transcription factors regulating the "foetal cardiac gene programme" which triggered the hypertrophic cascade, confirming previous reports. These findings suggest that multiple, individually rare copy number variants altering genes may contribute to the pathogenesis of hypertension-related LVH. In summary, we have provided further supporting evidence that rare CNV could potentially impact this common and complex disease susceptibility with lower heritability.

Matched MeSH terms: Gene Regulatory Networks
Fulltext Identification of Gene Modules Associated with Low Temperatures Response in Bambara Groundnut by Network-Based Analysis

Bonthala VS, Mayes K, Moreton J, Blythe M, Wright V, May ST, et al.

PLoS One, 2016;11(2):e0148771.
PMID: 26859686 DOI: 10.1371/journal.pone.0148771

Bambara groundnut (Vigna subterranea (L.) Verdc.) is an African legume and is a promising underutilized crop with good seed nutritional values. Low temperature stress in a number of African countries at night, such as Botswana, can effect the growth and development of bambara groundnut, leading to losses in potential crop yield. Therefore, in this study we developed a computational pipeline to identify and analyze the genes and gene modules associated with low temperature stress responses in bambara groundnut using the cross-species microarray technique (as bambara groundnut has no microarray chip) coupled with network-based analysis. Analyses of the bambara groundnut transcriptome using cross-species gene expression data resulted in the identification of 375 and 659 differentially expressed genes (p<0.01) under the sub-optimal (23°C) and very sub-optimal (18°C) temperatures, respectively, of which 110 genes are commonly shared between the two stress conditions. The construction of a Highest Reciprocal Rank-based gene co-expression network, followed by its partition using a Heuristic Cluster Chiseling Algorithm resulted in 6 and 7 gene modules in sub-optimal and very sub-optimal temperature stresses being identified, respectively. Modules of sub-optimal temperature stress are principally enriched with carbohydrate and lipid metabolic processes, while most of the modules of very sub-optimal temperature stress are significantly enriched with responses to stimuli and various metabolic processes. Several transcription factors (from MYB, NAC, WRKY, WHIRLY & GATA classes) that may regulate the downstream genes involved in response to stimulus in order for the plant to withstand very sub-optimal temperature stress were highlighted. The identified gene modules could be useful in breeding for low-temperature stress tolerant bambara groundnut varieties.

Matched MeSH terms: Gene Regulatory Networks
Genome-wide identification and characterization of long intergenic noncoding RNAs in the regenerative flatworm Macrostomum lignano

Azlan A, Halim MA, Azzam G

Genomics, 2020 03;112(2):1273-1281.
PMID: 31381967 DOI: 10.1016/j.ygeno.2019.07.016

The free-living flatworm Macrostoma lignano (M. lignano) is an emerging model organism for aging and regeneration research. Long intergenic non-coding RNAs (lincRNAs) have important roles in many biological processes such as aging, stem cell maintenance and differentiation. However, to date, there is no systematic identification of lincRNAs in M. lignano. By using public RNA-seq data, we identified a total of 2547 lincRNA transcripts in M. lignano genome. We discovered that M. lignano lincRNAs shared many characteristics with other species such as shorter in length, lower GC content, and lower in expression compared to protein-coding genes. Unlike protein-coding genes, M. lignano lincRNAs showed higher tendency to be expressed in temporal and region-specific fashion. Additionally, co-expression network analysis and functional enrichment suggest that M. lignano lincRNAs have potential roles in regeneration. This study will provide important resources and pave the way for investigations on non-coding genes involved in aging and regeneration.

Matched MeSH terms: Gene Regulatory Networks
Fulltext A side-effect free method for identifying cancer drug targets

Ashraf MI, Ong SK, Mujawar S, Pawar S, More P, Paul S, et al.

Sci Rep, 2018 04 27;8(1):6669.
PMID: 29703908 DOI: 10.1038/s41598-018-25042-2

Identifying effective drug targets, with little or no side effects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side effect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identification of effective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying effective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as effective candidates for drug development.

Matched MeSH terms: Gene Regulatory Networks/drug effects
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 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
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 Comparative genome-wide analysis of WRKY, MADS-box and MYB transcription factor families in Arabidopsis and rice

Abdullah-Zawawi MR, Ahmad-Nizammuddin NF, Govender N, Harun S, Mohd-Assaad N, Mohamed-Hussein ZA

Sci Rep, 2021 10 04;11(1):19678.
PMID: 34608238 DOI: 10.1038/s41598-021-99206-y

Transcription factors (TFs) form the major class of regulatory genes and play key roles in multiple plant stress responses. In most eukaryotic plants, transcription factor (TF) families (WRKY, MADS-box and MYB) activate unique cellular-level abiotic and biotic stress-responsive strategies, which are considered as key determinants for defense and developmental processes. Arabidopsis and rice are two important representative model systems for dicot and monocot plants, respectively. A comprehensive comparative study on 101 OsWRKY, 34 OsMADS box and 122 OsMYB genes (rice genome) and, 71 AtWRKY, 66 AtMADS box and 144 AtMYB genes (Arabidopsis genome) showed various relationships among TFs across species. The phylogenetic analysis clustered WRKY, MADS-box and MYB TF family members into 10, 7 and 14 clades, respectively. All clades in WRKY and MYB TF families and almost half of the total number of clades in the MADS-box TF family are shared between both species. Chromosomal and gene structure analysis showed that the Arabidopsis-rice orthologous TF gene pairs were unevenly localized within their chromosomes whilst the distribution of exon-intron gene structure and motif conservation indicated plausible functional similarity in both species. The abiotic and biotic stress-responsive cis-regulatory element type and distribution patterns in the promoter regions of Arabidopsis and rice WRKY, MADS-box and MYB orthologous gene pairs provide better knowledge on their role as conserved regulators in both species. Co-expression network analysis showed the correlation between WRKY, MADs-box and MYB genes in each independent rice and Arabidopsis network indicating their role in stress responsiveness and developmental processes.

Matched MeSH terms: Gene Regulatory Networks

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