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Comparative transcriptomics of the chilling stress response in two Asian mangrove species, Bruguiera gymnorhiza and Rhizophora apiculata

Short AW, Sebastian JSV, Huang J, Wang G, Dassanayake M, Finnegan PM, et al.

Tree Physiol, 2024 Feb 11;44(3).
PMID: 38366388 DOI: 10.1093/treephys/tpae019

Low temperatures largely determine the geographic limits of plant species by reducing survival and growth. Inter-specific differences in the geographic distribution of mangrove species have been associated with cold tolerance, with exclusively tropical species being highly cold-sensitive and subtropical species being relatively cold-tolerant. To identify species-specific adaptations to low temperatures, we compared the chilling stress response of two widespread Indo-West Pacific mangrove species from Rhizophoraceae with differing latitudinal range limits-Bruguiera gymnorhiza (L.) Lam. ex Savigny (subtropical range limit) and Rhizophora apiculata Blume (tropical range limit). For both species, we measured the maximum photochemical efficiency of photosystem II (Fv/Fm) as a proxy for the physiological condition of the plants and examined gene expression profiles during chilling at 15 and 5 °C. At 15 °C, B. gymnorhiza maintained a significantly higher Fv/Fm than R. apiculata. However, at 5 °C, both species displayed equivalent Fv/Fm values. Thus, species-specific differences in chilling tolerance were only found at 15 °C, and both species were sensitive to chilling at 5 °C. At 15 °C, B. gymnorhiza downregulated genes related to the light reactions of photosynthesis and upregulated a gene involved in cyclic electron flow regulation, whereas R. apiculata downregulated more RuBisCo-related genes. At 5 °C, both species repressed genes related to CO2 assimilation. The downregulation of genes related to light absorption and upregulation of genes related to cyclic electron flow regulation are photoprotective mechanisms that likely contributed to the greater photosystem II photochemical efficiency of B. gymnorhiza at 15 °C. The results of this study provide evidence that the distributional range limits and potentially the expansion rates of plant species are associated with differences in the regulation of photosynthesis and photoprotective mechanisms under low temperatures.

Matched MeSH terms: Gene Expression Profiling
Unearth of small open reading frames (sORFs) in drought stress transcriptome of Oryza sativa subsp. indica

Ong SN, Tan BC, Hanada K, Teo CH

Gene, 2023 Aug 20;878:147579.
PMID: 37336274 DOI: 10.1016/j.gene.2023.147579

Drought is a major abiotic stress that influences rice production. Although the transcriptomic data of rice against drought is widely available, the regulation of small open reading frames (sORFs) in response to drought stress in rice is yet to be investigated. Different levels of drought stress have different regulatory mechanisms in plants. In this study, drought stress was imposed on four-leaf stage rice, divided into two treatments, 40% and 30% soil moisture content (SMC). The RNAs of the samples were extracted, followed by the RNA sequencing analysis on their sORF expression changes under 40%_SMC and 30%_SMC, and lastly, the expression was validated through NanoString. A total of 122 and 143 sORFs were differentially expressed (DE) in 40%_SMC and 30%_SMC, respectively. In 40%_SMC, 69 sORFs out of 696 (9%) DEGs were found to be upregulated. On the other hand, 69 sORFs out of 449 DEGs (11%) were significantly downregulated. The trend seemed to be higher in 30%_SMC, where 112 (12%) sORFs were found to be upregulated from 928 significantly upregulated DEGs. However, only 8% (31 sORFs out of 385 DEGs) sORFs were downregulated in 30%_SMC. Among the identified sORFs, 110 sORFs with high similarity to rice proteome in the PsORF database were detected in 40%_SMC, while 126 were detected in 30%_SMC. The Gene Ontology (GO) enrichment analysis of DE sORFs revealed their involvement in defense-related biological processes, such as defense response, response to biotic stimulus, and cellular homeostasis, whereas enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways indicated that DE sORFs were associated with tryptophan and phenylalanine metabolisms. Several DE sORFs were identified, including the top five sORFs (OsisORF_3394, OsisORF_0050, OsisORF_3007, OsisORF_6407, and OsisORF_7805), which have yet to be characterised. Since these sORFs were responsive to drought stress, they might hold significant potential as targets for future climate-resilient rice development.

Matched MeSH terms: Gene Expression Profiling
Gene expression profiling of host lipid metabolism in SARS-CoV-2 infected patients: a systematic review and integrated bioinformatics analysis

Munawar WASWA, Elias MH, Addnan FH, Hassandarvish P, AbuBakar S, Roslan N

BMC Infect Dis, 2024 Jan 23;24(1):124.
PMID: 38263024 DOI: 10.1186/s12879-024-08983-0

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic occurred due to the dispersion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Severe symptoms can be observed in COVID-19 patients with lipid-related comorbidities such as obesity and diabetes. Yet, the extensive molecular mechanisms of how SARS-CoV-2 causes dysregulation of lipid metabolism remain unknown.
METHODS: Here, an advanced search of articles was conducted using PubMed, Scopus, EBSCOhost, and Web of Science databases using terms from Medical Subject Heading (MeSH) like SARS-CoV-2, lipid metabolism and transcriptomic as the keywords. From 428 retrieved studies, only clinical studies using next-generation sequencing as a gene expression method in COVID-19 patients were accepted. Study design, study population, sample type, the method for gene expression and differentially expressed genes (DEGs) were extracted from the five included studies. The DEGs obtained from the studies were pooled and analyzed using the bioinformatics software package, DAVID, to determine the enriched pathways. The DEGs involved in lipid metabolic pathways were selected and further analyzed using STRING and Cytoscape through visualization by protein-protein interaction (PPI) network complex.
RESULTS: The analysis identified nine remarkable clusters from the PPI complex, where cluster 1 showed the highest molecular interaction score. Three potential candidate genes (PPARG, IFITM3 and APOBEC3G) were pointed out from the integrated bioinformatics analysis in this systematic review and were chosen due to their significant role in regulating lipid metabolism. These candidate genes were significantly involved in enriched lipid metabolic pathways, mainly in regulating lipid homeostasis affecting the pathogenicity of SARS-CoV-2, specifically in mechanisms of viral entry and viral replication in COVID-19 patients.
CONCLUSIONS: Taken together, our findings in this systematic review highlight the affected lipid-metabolic pathways along with the affected genes upon SARS-CoV-2 invasion, which could be a potential target for new therapeutic strategies study in the future.

Matched MeSH terms: Gene Expression Profiling
De Novo Transcriptomic and Life-History Responses of Moina Micrura Under Stress Environment Conditions

Razak MR, Aris AZ, Yusoff FM, Yusof ZNB, Kim SD, Kim KW

Mar Biotechnol (NY), 2023 Jun;25(3):473-487.
PMID: 37310522 DOI: 10.1007/s10126-023-10220-9

Moina micrura represents a promising model species for ecological and ecotoxicological investigations in tropical freshwater ecosystems. Illumina NovaSeq™ 6000 sequencing was employed in this study to analyze M. micrura across three distinct developmental stages: juvenile, adult, and male. Current study successfully annotated 51,547 unigenes (73.11%) derived from seven (7) different databases. A total of 554 genes were found to be significantly upregulated, while 452 genes showed significant downregulation between juvenile and male. Moreover, 1001 genes were upregulated, whereas 830 genes exhibited downregulation between the adult and male. Analysis of differentially expressed genes revealed upregulation of chitin, cuticle, myosin (MYO), mitogen-activated protein kinases (MAPK), fibrillin (FBN), cytochrome (CYP), glutathione s-transferase (GST), vitellogenin (VTG), acetylcholinesterase (AChE), and transforming growth factor beta (TGFB) under unfavorable environmental conditions (male), as compared to favorable environmental conditions (juveniles and adults). These alterations in gene expression significantly impact the phenological and life-history traits of M. micrura. Furthermore, the upregulation of hemoglobin (HMB), doublesex (DSX), juvenile hormone analogs (JHA), heat shock protein (HSP), and methyltransferase (METT) genes in males initiates the sex-switching effects observed in M. micrura. These findings hold substantial value for researchers interested in determining M. micrura sequences for future investigations of gene expression and comparative reproductive genome analysis within the Moina genus and cladoceran families.

Matched MeSH terms: Gene Expression Profiling
Fulltext Integrated Analysis of Transcriptome and Metabolome in the Brain After Cold Stress of Red Tilapia During Overwintering

Zhu C, Yang H, Zhu W, Jiang Q, Dong Z, Wang L

Int J Mol Sci, 2024 Dec 13;25(24).
PMID: 39769137 DOI: 10.3390/ijms252413372

Cold stress during overwintering is considered a bottleneck problem limiting the development of the red tilapia (Oreochromis spp.) industry, and the regulation mechanism is currently not well understood. In this study, the fish (initial weight: 72.71 ± 1.32 g) were divided into the cold stress group (cold) and the control (normal) group. In the control group, the water temperature was maintained at 20 °C, which is basically consistent with the overwintering water temperature in greenhouses of local areas. In the cold group, the water temperature decreased from 20 °C to 8 °C by 2 °C per day during the experiment. At the end of the experiment, the levels of fish serum urea nitrogen, glucose, norepinephrine, alkaline phosphatase, total bilirubin, and total cholesterol in the cold group changed significantly compared with that in the control group (P < 0.05). Then transcriptome sequencing and LC-MS metabolome of brain tissue were further employed to obtain the mRNA and metabolite datasets. We found that the FoxO signaling pathway and ABC transporters played an important role by transcriptome-metabolome association analysis. In the FoxO signaling pathway, the differentially expressed genes were related to cell cycle regulation, apoptosis and immune-regulation, and oxidative stress resistance and DNA repair. In the ABC transporters pathway, the ATP-binding cassette (ABC) subfamily abca, abcb, and abcc gene expression levels, and the deoxycytidine, L-lysine, L-glutamic acid, L-threonine, ornithine, and uridine metabolite contents changed. Our results suggested that the cold stress may promote apoptosis through regulation of the FoxO signaling pathway. The ABC transporters may respond to cold stress by regulating amino acid metabolism. The results provided a comprehensive understanding of fish cold stress during overwintering, which will facilitate the breeding of new cold-resistant varieties of red tilapia in the future.

Matched MeSH terms: Gene Expression Profiling
Transcriptome analysis of feline infectious peritonitis virus infection

Mehrbod P, Harun MS, Shuid AN, Omar AR

Methods Mol Biol, 2015;1282:241-50.
PMID: 25720485 DOI: 10.1007/978-1-4939-2438-7_20

Feline infectious peritonitis (FIP) is a lethal systemic disease caused by FIP virus (FIPV). There are no effective vaccines or treatment available, and the virus virulence determinants and pathogenesis are not fully understood. Here, we describe the sequencing of RNA extracted from Crandell Rees Feline Kidney (CRFK) cells infected with FIPV using the Illumina next-generation sequencing approach. Bioinformatics analysis, based on Felis catus 2X annotated shotgun reference genome, using CLC bio Genome Workbench is used to map both control and infected cells. Kal's Z test statistical analysis is used to analyze the differentially expressed genes from the infected CRFK cells. In addition, RT-qPCR analysis is used for further transcriptional profiling of selected genes in infected CRFK cells and Peripheral Blood Mononuclear Cells (PBMCs) from healthy and FIP-diagnosed cats.

Matched MeSH terms: Gene Expression Profiling*
Expression profiling of choline and ethanolamine kinases in MCF7, HCT116 and HepG2 cells, and the transcriptional regulation by epigenetic modification

Ling CS, Yin KB, Cun ST, Ling FL

Mol Med Rep, 2015 Jan;11(1):611-8.
PMID: 25333818 DOI: 10.3892/mmr.2014.2707

The function of choline kinase (CK) and ethanolamine kinase (EK) is to catalyse the phosphorylation of choline and ethanolamine, respectively, in order to yield phosphocholine (PCho) and phosphoethanolamine (PEtn). A high expression level of PCho, due to elevated CK activity, has previously been associated with malignant transformation. In the present study, a quantitative polymerase chain reaction was performed to determine the mRNA expression profiles of ck and ek mRNA variants in MCF7 breast, HCT116 colon and HepG2 liver cancer cells. The ck and ek mRNA expression profiles showed that total ckα was expressed most abundantly in the HepG2 cells. The HCT116 cells exhibited the highest ckβ and ek1 mRNA expression levels, whereas the highest ek2α mRNA expression levels were detected in the MCF7 cells. The ckβ variant had higher mRNA expression levels, as compared with total ckα, in both the MCF7 and HCT116 cells. Relatively low ek1 mRNA expression levels were detected, as compared with ek2α in the MCF7 cells; however, this was not observed in the HCT116 and HepG2 cells. Notably, the mRNA expression levels of ckα2 were markedly low, as compared with ckα1, in all three cancer cell lines. The effects of epigenetic modification on ck and ek mRNA expression, by treatment of the cells with the histone deacetylase inhibitor trichostatin A (TSA), were also investigated. The results of the present study showed that the mRNA expression levels of ckα, ckβ and ek2α were affected by TSA. An increase >8-fold was observed in ek2α mRNA expression upon treatment with TSA, in a concentration- and time-dependent manner. In conclusion, the levels of ck and ek transcript variants in the three cancer cell lines were varied. The effects of TSA treatment on the mRNA expression levels of ck and ek imply that ck and ek mRNA expression may be regulated by epigenetic modification.

Matched MeSH terms: Gene Expression Profiling*
Gene expression analysis in MCF-7 breast cancer cells treated with recombinant bromelain

Fouz N, Amid A, Hashim YZ

Appl Biochem Biotechnol, 2014 Aug;173(7):1618-39.
PMID: 24928548 DOI: 10.1007/s12010-014-0947-6

The contributing molecular pathways underlying the pathogenesis of breast cancer need to be better characterized. The principle of our study was to better understand the genetic mechanism of oncogenesis for human breast cancer and to discover new possible tumor markers for use in clinical practice. We used complimentary DNA (cDNA) microarrays to compare gene expression profiles of treated Michigan Cancer Foundation-7 (MCF-7) with recombinant bromelain and untreated MCF-7. SpringGene analysis was carried out of differential expression followed by Ingenuity Pathway Analysis (IPA), to understand the underlying consequence in developing disease and disorders. We identified 1,102 known genes differentially expressed to a significant degree (p<0.001) changed between the treatment. Within this gene set, 20 genes were significantly changed between treated cells and the control cells with cutoff fold change of more than 1.5. These genes are RNA-binding motif, single-stranded interacting protein 1 (RBMS1), ribosomal protein L29 (RPL29), glutathione S-transferase mu 2 (GSTM2), C15orf32, Akt3, B cell translocation gene 1 (BTG1), C6orf62, C7orf60, kinesin-associated protein 3 (KIFAP3), FBXO11, AT-rich interactive domain 4A (ARID4A), COPS2, TBPL1|SLC2A12, TMEM59, SNORD46, glioma tumor suppressor candidate region gene 2 (GLTSCR2), and LRRFIP. Our observation on gene expression indicated that recombinant bromelain produces a unique signature affecting different pathways, specific for each congener. The microarray results give a molecular mechanistic insight and functional effects, following recombinant bromelain treatment. The extent of changes in genes is related to and involved significantly in gap junction signaling, amyloid processing, cell cycle regulation by BTG family proteins, and breast cancer regulation by stathmin1 that play major roles.

Matched MeSH terms: Gene Expression Profiling*
Fulltext A review of feature extraction software for microarray gene expression data

Tan CS, Ting WS, Mohamad MS, Chan WH, Deris S, Shah ZA

Biomed Res Int, 2014;2014:213656.
PMID: 25250315 DOI: 10.1155/2014/213656

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method.

Matched MeSH terms: Gene Expression Profiling/methods*
Expression analysis for genes involved in arachidonic acid biosynthesis in Mortierella alpina CBS 754.68

Samadlouie HR, Hamidi-Esfahani Z, Alavi SM, Varastegani B

Braz J Microbiol, 2014;45(2):439-45.
PMID: 25242926

The time courses for production of fungal biomass, lipid, phenolic and arachidonic acid (ARA) as well as expression of the genes involved in biosynthesis of ARA and lipid were examined in Mortierella alpina CBS 754.68. A significant increase in the arachidonic acid content in lipids that coincided with reduced levels of lipid was obtained. Reduced gene expression occurred presumably due to the steady reduction of carbon and nitrogen resources. However, these energy resources were inefficiently compensated by the breakdown of the accumulated lipids that in turn, induced up-regulated expression of the candidate genes. The results further indicated that the expression of the GLELO encoding gene is a rate-limiting step in the biosynthesis of ARA in the early growth phase.

Matched MeSH terms: Gene Expression Profiling*
Fulltext Gene expression profiling of mitochondrial oxidative phosphorylation (OXPHOS) complex I in Friedreich ataxia (FRDA) patients

Salehi MH, Kamalidehghan B, Houshmand M, Yong Meng G, Sadeghizadeh M, Aryani O, et al.

PLoS One, 2014;9(4):e94069.
PMID: 24705504 DOI: 10.1371/journal.pone.0094069

Friedreich ataxia (FRDA) is the most frequent progressive autosomal recessive disorder associated with unstable expansion of GAA trinucleotide repeats in the first intron of the FXN gene, which encodes for the mitochondrial frataxin protein. The number of repeats correlates with disease severity, where impaired transcription of the FXN gene results in reduced expression of the frataxin protein. Gene expression studies provide insights into disease pathogenicity and identify potential biomarkers, an important goal of translational research in neurodegenerative diseases. Here, using real-time PCR (RT-PCR), the expression profiles of mitochondrial (mtDNA) and nuclear DNA (nDNA) genes that encode for the mitochondrial subunits of respiratory oxidative phosphorylation (OXPHOS) complex I in the blood panels of 21 FRDA patients and 24 healthy controls were investigated. Here, the expression pattern of mtDNA-encoded complex I subunits was distinctly different from the expression pattern of nDNA-encoded complex I subunits, where significant (p<0.05) down-regulation of the mitochondrial ND2, ND4L, and ND6 complex I genes, compared to controls, were observed. In addition, the expression pattern of one nDNA-encoded gene, NDUFA1, was significantly (p<0.05) down-regulated compared to control. These findings suggest, for the first time, that the regulation of complex I subunit expression in FRDA is complex, rather than merely being a reflection of global co-regulation, and may provide important clues toward novel therapeutic strategies for FRDA and mitochondrial complex I deficiency.

Matched MeSH terms: Gene Expression Profiling*
Fulltext Biases in small RNA deep sequencing data

Raabe CA, Tang TH, Brosius J, Rozhdestvensky TS

Nucleic Acids Res, 2014 Feb;42(3):1414-26.
PMID: 24198247 DOI: 10.1093/nar/gkt1021

High-throughput RNA sequencing (RNA-seq) is considered a powerful tool for novel gene discovery and fine-tuned transcriptional profiling. The digital nature of RNA-seq is also believed to simplify meta-analysis and to reduce background noise associated with hybridization-based approaches. The development of multiplex sequencing enables efficient and economic parallel analysis of gene expression. In addition, RNA-seq is of particular value when low RNA expression or modest changes between samples are monitored. However, recent data uncovered severe bias in the sequencing of small non-protein coding RNA (small RNA-seq or sRNA-seq), such that the expression levels of some RNAs appeared to be artificially enhanced and others diminished or even undetectable. The use of different adapters and barcodes during ligation as well as complex RNA structures and modifications drastically influence cDNA synthesis efficacies and exemplify sources of bias in deep sequencing. In addition, variable specific RNA G/C-content is associated with unequal polymerase chain reaction amplification efficiencies. Given the central importance of RNA-seq to molecular biology and personalized medicine, we review recent findings that challenge small non-protein coding RNA-seq data and suggest approaches and precautions to overcome or minimize bias.

Matched MeSH terms: Gene Expression Profiling/methods*
Multi-stage filtering for improving confidence level and determining dominant clusters in clustering algorithms of gene expression data

Kasim S, Deris S, Othman RM

Comput Biol Med, 2013 Sep;43(9):1120-33.
PMID: 23930805 DOI: 10.1016/j.compbiomed.2013.05.011

A drastic improvement in the analysis of gene expression has lead to new discoveries in bioinformatics research. In order to analyse the gene expression data, fuzzy clustering algorithms are widely used. However, the resulting analyses from these specific types of algorithms may lead to confusion in hypotheses with regard to the suggestion of dominant function for genes of interest. Besides that, the current fuzzy clustering algorithms do not conduct a thorough analysis of genes with low membership values. Therefore, we present a novel computational framework called the "multi-stage filtering-Clustering Functional Annotation" (msf-CluFA) for clustering gene expression data. The framework consists of four components: fuzzy c-means clustering (msf-CluFA-0), achieving dominant cluster (msf-CluFA-1), improving confidence level (msf-CluFA-2) and combination of msf-CluFA-0, msf-CluFA-1 and msf-CluFA-2 (msf-CluFA-3). By employing double filtering in msf-CluFA-1 and apriori algorithms in msf-CluFA-2, our new framework is capable of determining the dominant clusters and improving the confidence level of genes with lower membership values by means of which the unknown genes can be predicted.

Matched MeSH terms: Gene Expression Profiling/methods*
Fulltext GnRH neuron type-specific transcriptome analysis by laser captured single-cell microarray in the medaka

Moriya S, Ogawa S, Parhar IS

Biochem Biophys Res Commun, 2013 Jun 14;435(4):562-6.
PMID: 23669040 DOI: 10.1016/j.bbrc.2013.05.004

Most vertebrates possess at least two gonadotropin-releasing hormone (GnRH) neuron types. To understand the physiological significance of the multiple GnRH systems in the brain, we examined three GnRH neuron type-specific transcriptomes using single-cell microarray analyses in the medaka (Oryzias latipes). A microarray profile of the three GnRH neuron types revealed five genes that are uniquely expressed in specific GnRH neuron types. GnRH1 neurons expressed three genes that are homologous to functionally characterised genes, GnRH2 neurons uniquely expressed one unnamed gene, and GnRH3 neurons uniquely expressed one known gene. These genes may be involved in the modulation or maintenance of each GnRH neuron type.

Matched MeSH terms: Gene Expression Profiling/methods*
Fulltext Cellular transcripts of chicken brain tissues in response to H5N1 and Newcastle disease virus infection

Balasubramaniam VR, Wai TH, Omar AR, Othman I, Hassan SS

Virol J, 2012;9:53.
PMID: 22361110 DOI: 10.1186/1743-422X-9-53

Highly-pathogenic avian influenza (HPAI) H5N1 and Newcastle disease (ND) viruses are the two most important poultry viruses in the world, with the ability to cause classic central nervous system dysfunction in poultry and migratory birds. To elucidate the mechanisms of neurovirulence caused by these viruses, a preliminary study was design to analyze host's cellular responses during infections of these viruses.

Matched MeSH terms: Gene Expression Profiling/methods
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 Expression Profiling/methods
A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data

Mohamad MS, Omatu S, Deris S, Yoshioka M

IEEE Trans Inf Technol Biomed, 2011 Nov;15(6):813-22.
PMID: 21914573 DOI: 10.1109/TITB.2011.2167756

Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an improved (modified) binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce particles' speed for giving the rate at which a particle changes its position, and we propose a rule for updating particle's positions. By performing experiments on ten different gene expression datasets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.

Matched MeSH terms: Gene Expression Profiling/methods*
Using fuzzy association rule mining in cancer classification

Mahmoodian H, Hamiruce Marhaban M, Abdulrahim R, Rosli R, Saripan I

Australas Phys Eng Sci Med, 2011 Apr;34(1):41-54.
PMID: 21327594 DOI: 10.1007/s13246-011-0054-8

The classification of the cancer tumors based on gene expression profiles has been extensively studied in numbers of studies. A wide variety of cancer datasets have been implemented by the various methods of gene selection and classification to identify the behavior of the genes in tumors and find the relationships between them and outcome of diseases. Interpretability of the model, which is developed by fuzzy rules and linguistic variables in this study, has been rarely considered. In addition, creating a fuzzy classifier with high performance in classification that uses a subset of significant genes which have been selected by different types of gene selection methods is another goal of this study. A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. At first, different subset of genes which have been selected by different methods, were used to generate primary fuzzy classifiers separately and then proposed algorithm was implemented to mix the genes which have been associated in the primary classifiers and generate a new classifier. The results show that fuzzy classifier can classify the tumors with high performance while presenting the relationships between the genes by linguistic variables.

Matched MeSH terms: Gene Expression Profiling/methods*
Granulometric analysis of spots in DNA microarray images

Latha B, Venkatesh B

Genomics Proteomics Bioinformatics, 2004 Nov;2(4):222-36.
PMID: 15901251

As the topological properties of each spot in DNA microarray images may vary from one another, we employed granulometries to understand the shape-size content contributed due to a significant intensity value within a spot. Analysis was performed on the microarray image that consisted of 240 spots by using concepts from mathematical morphology. In order to find out indices for each spot and to further classify them, we adopted morphological multiscale openings, which provided microarrays at multiple scales. Successive opened microarrays were subtracted to identify the protrusions that were smaller than the size of structuring element. Spot-wise details, in terms of probability of these observed protrusions, were computed by placing a regularly spaced grid on microarray such that each spot was centered in each grid. Based on the probability of size distribution functions of these protrusions isolated at each level, we estimated the mean size and texture index for each spot. With these characteristics, we classified the spots in a microarray image into bright and dull categories through pattern spectrum and shape-size complexity measures. These segregated spots can be compared with those of hybridization levels.

Matched MeSH terms: Gene Expression Profiling/statistics & numerical data
Fulltext A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns

Mollah MM, Jamal R, Mokhtar NM, Harun R, Mollah MN

PLoS One, 2015;10(9):e0138810.
PMID: 26413858 DOI: 10.1371/journal.pone.0138810

Identifying genes that are differentially expressed (DE) between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA), are used to identify DE genes. However, most of these methods provide misleading results for two or more conditions with multiple patterns of expression in the presence of outlying genes. In this paper, an attempt is made to develop a hybrid one-way ANOVA approach that unifies the robustness and efficiency of estimation using the minimum β-divergence method to overcome some problems that arise in the existing robust methods for both small- and large-sample cases with multiple patterns of expression.

Matched MeSH terms: Gene Expression Profiling/methods*

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