Displaying publications 1 - 20 of 62 in total

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  1. Molecular cloning and sequence analysis of a cDNA encoding ornithine decarboxylase cDNA from chilli (Capsicum annuum)
    Zainal Z, Sajari R, Ismail I
    J. Biochem. Mol. Biol. Biophys., 2002 Dec;6(6):415-9.
    PMID: 14972797
    Ornithine decarboxylase (ODC) is an enzyme of one of the two pathways of putrescine biosynthesis in plants. The genes encoding ODC have previously been cloned from Datura stramonium and human. Using differential screening, we isolated ODC cDNA clone from a cDNA library of ripening Capsicum annuum fruit. The cDNA clone designated CUKM10 contains an insert of 1523 bp. The longest open reading frame potentially encodes a peptide of 345 amino acids with an estimated molecular mass of 47 kDa and exhibit striking similarity to other ODCs. Expression analysis showed that the capODC hybridised to a single transcript with a size of 1.7 kb. The capODC transcript was first observed in early ripening and increased steadily until it reached fully ripening stage. From the observation it is suggested that capODC is developmentally regulated especially during later stage of ripening.
    Matched MeSH terms: Gene Expression Profiling/methods*
  2. Metabolomics and its role in understanding cellular responses in plants
    Bhalla R, Narasimhan K, Swarup S
    Plant Cell Rep, 2005 Dec;24(10):562-71.
    PMID: 16220342
    A natural shift is taking place in the approaches being adopted by plant scientists in response to the accessibility of systems-based technology platforms. Metabolomics is one such field, which involves a comprehensive non-biased analysis of metabolites in a given cell at a specific time. This review briefly introduces the emerging field and a range of analytical techniques that are most useful in metabolomics when combined with computational approaches in data analyses. Using cases from Arabidopsis and other selected plant systems, this review highlights how information can be integrated from metabolomics and other functional genomics platforms to obtain a global picture of plant cellular responses. We discuss how metabolomics is enabling large-scale and parallel interrogation of cell states under different stages of development and defined environmental conditions to uncover novel interactions among various pathways. Finally, we discuss selected applications of metabolomics.
    Matched MeSH terms: Gene Expression Profiling/methods
  3. Transcriptional profiling of oral squamous cell carcinoma using formalin-fixed paraffin-embedded samples
    Saleh A, Zain RB, Hussaini H, Ng F, Tanavde V, Hamid S, et al.
    Oral Oncol, 2010 May;46(5):379-86.
    PMID: 20371203 DOI: 10.1016/j.oraloncology.2010.02.022
    Despite the advances in cancer treatment, the 5-year survival rate for oral cancer has not changed significantly for the past 40 years and still remains among the worst of all anatomic sites. Gene expression microarrays have been used successfully in the identification of genetic alterations in cancer development, however, these have hitherto been limited by the need for specimens with good quality intact RNA. Here, we demonstrated the use of formalin-fixed paraffin-embedded tissues in microarray experiments to identify genes differentially expressed between cancerous and normal oral tissues. Forty-three tissue samples were macrodissected and gene expression analyses were conducted using the Illumina DASL assay. We report RNA yield of 2.4 and 0.8 microg/mm(3) from tumour and normal tissues, respectively and this correlated directly with the tissue volume used for RNA extraction. Using unsupervised hierarchical clustering, distinct gene expression profiles for tumour and normal samples could be generated, and differentially expressed genes could be identified. The majority of these genes were involved in regulation of apoptosis and cell cycle, metastasis and cell adhesion including BCL2A1, BIRC5, MMP1, MMP9 and ITGB4. Representative genes were further validated in independent samples suggesting that these genes may be directly associated with oral cancer development. The ability to conduct microarrays on formalin-fixed paraffin-embedded specimens represents a significant advancement that could open up avenues for finding genes that could be used as prognostication and predictive tools for cancer.
    Matched MeSH terms: Gene Expression Profiling/methods*
  4. 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*
  5. 1'S-1'-acetoxyeugenol acetate: a novel phenylpropanoid from Alpinia conchigera enhances the apoptotic effects of paclitaxel in MCF-7 cells through NF-κB inactivation
    In LL, Azmi MN, Ibrahim H, Awang K, Nagoor NH
    Anticancer Drugs, 2011 Jun;22(5):424-34.
    PMID: 21346553 DOI: 10.1097/CAD.0b013e328343cbe6
    In this study, the apoptotic mechanism and combinatorial chemotherapeutic effects of the cytotoxic phenylpropanoid compound 1'S-1'-acetoxyeugenol acetate (AEA), extracted from rhizomes of the Malaysian ethnomedicinal plant Alpinia conchigera Griff. (Zingiberaceae), on MCF-7 human breast cancer cells were investigated for the first time. Data from cytotoxic and apoptotic assays such as live and dead and poly-(ADP-ribose) polymerase cleavage assays indicated that AEA was able to induce apoptosis in MCF-7 cells, but not in normal human mammary epithelial cells. A microarray global gene expression analysis of MCF-7 cells, treated with AEA, suggested that the induction of tumor cell death through apoptosis was modulated through dysregulation of the nuclear factor-kappaB (NF-κB) pathway, as shown by the reduced expression of various κB-regulated gene targets. Consequent to this, western blot analysis of proteins corresponding to the NF-κB pathway indicated that AEA inhibited phosphorylation levels of the inhibitor of κB-kinase complex, resulting in the elimination of apoptotic resistance originating from NF-κB activation. This AEA-based apoptotic modulation was elucidated for the first time in this study, and gave rise to the proposal of an NF-κB model termed the 'Switching/Alternating Model.' In addition to this, AEA was also found to synergistically enhance the proapoptotic effects of paclitaxel, when used in combination with MCF-7 cells, presumably by a chemosensitizing role. Therefore, it was concluded that AEA isolated from the Malaysian tropical ginger (A. conchigera) served as a very promising candidate for further in-vivo development in animal models and in subsequent clinical trials involving patients with breast-related malignancies.
    Matched MeSH terms: Gene Expression Profiling/methods
  6. 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*
  7. 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
  8. 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
  9. Fulltext A hybrid distance measure for clustering expressed sequence tags originating from the same gene family
    Ng KH, Ho CK, Phon-Amnuaisuk S
    PLoS One, 2012;7(10):e47216.
    PMID: 23071763 DOI: 10.1371/journal.pone.0047216
    Clustering is a key step in the processing of Expressed Sequence Tags (ESTs). The primary goal of clustering is to put ESTs from the same transcript of a single gene into a unique cluster. Recent EST clustering algorithms mostly adopt the alignment-free distance measures, where they tend to yield acceptable clustering accuracies with reasonable computational time. Despite the fact that these clustering methods work satisfactorily on a majority of the EST datasets, they have a common weakness. They are prone to deliver unsatisfactory clustering results when dealing with ESTs from the genes derived from the same family. The root cause is the distance measures applied on them are not sensitive enough to separate these closely related genes.
    Matched MeSH terms: Gene Expression Profiling/methods*
  10. Microarray and quantitative PCR analysis of gene expression profiles in response to treatment with tomato leaf extract in mcf-7 breast cancer cells
    Amid A, Wan Chik WD, Jamal P, Hashim YZ
    Asian Pac J Cancer Prev, 2012;13(12):6319-25.
    PMID: 23464452
    We previously found cytotoxic effects of tomato leaf extract (TLE) on the MCF-7 breast cancer cell line. The aim of this study was to ascertain the molecular mechanisms associated with the usage of TLE as an anticancer agent by microarray analysis using mRNA from MCF-7 breast cancer cells after treatment with TLE for 1 hr and 48 hrs. Approximately 991 genes out of the 30,000 genes in the human genome were significantly (p<0.05) changed after the treatment. Within this gene set, 88 were significantly changed between the TLE treated cells and the untreated MCF-7 cells (control cells) with a cut-off fold change >2.00. In order to focus on genes that were involved in cancer cell growth, only twenty-nine genes were selected, either down-regulated or up-regulated after treatment with TLE. Microarray assay results were confirmed by analyzing 10 of the most up and down regulated genes related to cancer cells progression using real-time PCR. Treatment with TLE induced significant up-regulation in the expression of the CRYAB, PIM1, BTG1, CYR61, HIF1-α and CEBP-β genes after 1 hr and 48 hrs, whereas the TXNIP and THBS1 genes were up-regulated after 1 hr of treatment but down-regulated after 48 hrs. In addition both the HMG1L1 and HIST2H3D genes were down-regulated after 1 hr and 48 hrs of treatment. These results demonstrate the potent activity of TLE as an anticancer agent.
    Matched MeSH terms: Gene Expression Profiling/methods
  11. 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*
  12. 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*
  13. 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*
  14. Fulltext RNA sequencing read depth requirement for optimal transcriptome coverage in Hevea brasiliensis
    Chow KS, Ghazali AK, Hoh CC, Mohd-Zainuddin Z
    BMC Res Notes, 2014 Feb 01;7:69.
    PMID: 24484543 DOI: 10.1186/1756-0500-7-69
    BACKGROUND: One of the concerns of assembling de novo transcriptomes is determining the amount of read sequences required to ensure a comprehensive coverage of genes expressed in a particular sample. In this report, we describe the use of Illumina paired-end RNA-Seq (PE RNA-Seq) reads from Hevea brasiliensis (rubber tree) bark to devise a transcript mapping approach for the estimation of the read amount needed for deep transcriptome coverage.

    FINDINGS: We optimized the assembly of a Hevea bark transcriptome based on 16 Gb Illumina PE RNA-Seq reads using the Oases assembler across a range of k-mer sizes. We then assessed assembly quality based on transcript N50 length and transcript mapping statistics in relation to (a) known Hevea cDNAs with complete open reading frames, (b) a set of core eukaryotic genes and (c) Hevea genome scaffolds. This was followed by a systematic transcript mapping process where sub-assemblies from a series of incremental amounts of bark transcripts were aligned to transcripts from the entire bark transcriptome assembly. The exercise served to relate read amounts to the degree of transcript mapping level, the latter being an indicator of the coverage of gene transcripts expressed in the sample. As read amounts or datasize increased toward 16 Gb, the number of transcripts mapped to the entire bark assembly approached saturation. A colour matrix was subsequently generated to illustrate sequencing depth requirement in relation to the degree of coverage of total sample transcripts.

    CONCLUSIONS: We devised a procedure, the "transcript mapping saturation test", to estimate the amount of RNA-Seq reads needed for deep coverage of transcriptomes. For Hevea de novo assembly, we propose generating between 5-8 Gb reads, whereby around 90% transcript coverage could be achieved with optimized k-mers and transcript N50 length. The principle behind this methodology may also be applied to other non-model plants, or with reads from other second generation sequencing platforms.

    Matched MeSH terms: Gene Expression Profiling/methods*
  15. 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*
  16. Fulltext Methods and matrices: approaches to identifying miRNAs for nasopharyngeal carcinoma
    Plieskatt JL, Rinaldi G, Feng Y, Levine PH, Easley S, Martinez E, et al.
    J Transl Med, 2014;12:3.
    PMID: 24393330 DOI: 10.1186/1479-5876-12-3
    Nasopharyngeal carcinoma (NPC) is a solid tumor of the head and neck. Multimodal therapy is highly effective when NPC is detected early. However, due to the location of the tumor and the absence of clinical signs, early detection is difficult, making a biomarker for the early detection of NPC a priority. The dysregulation of small non-coding RNAs (miRNAs) during carcinogenesis is the focus of much current biomarker research. Herein, we examine several miRNA discovery methods using two sample matrices to identify circulating miRNAs (c-miRNAs) associated with NPC.
    Matched MeSH terms: Gene Expression Profiling/methods*
  17. Fulltext Comprehensive analysis of the venom gland transcriptome of the spider Dolomedes fimbriatus
    Kozlov SA, Lazarev VN, Kostryukova ES, Selezneva OV, Ospanova EA, Alexeev DG, et al.
    Sci Data, 2014;1:140023.
    PMID: 25977780 DOI: 10.1038/sdata.2014.23
    A comprehensive transcriptome analysis of an expressed sequence tag (EST) database of the spider Dolomedes fimbriatus venom glands using single-residue distribution analysis (SRDA) identified 7,169 unique sequences. Mature chains of 163 different toxin-like polypeptides were predicted on the basis of well-established methodology. The number of protein precursors of these polypeptides was appreciably numerous than the number of mature polypeptides. A total of 451 different polypeptide precursors, translated from 795 unique nucleotide sequences, were deduced. A homology search divided the 163 mature polypeptide sequences into 16 superfamilies and 19 singletons. The number of mature toxins in a superfamily ranged from 2 to 49, whereas the diversity of the original nucleotide sequences was greater (2-261 variants). We observed a predominance of inhibitor cysteine knot toxin-like polypeptides among the cysteine-containing structures in the analyzed transcriptome bank. Uncommon spatial folds were also found.
    Matched MeSH terms: Gene Expression Profiling/methods
  18. Fulltext A comparison of physiological and transcriptome responses to water deprivation and salt loading in the rat supraoptic nucleus
    Greenwood MP, Mecawi AS, Hoe SZ, Mustafa MR, Johnson KR, Al-Mahmoud GA, et al.
    Am J Physiol Regul Integr Comp Physiol, 2015 Apr 01;308(7):R559-68.
    PMID: 25632023 DOI: 10.1152/ajpregu.00444.2014
    Salt loading (SL) and water deprivation (WD) are experimental challenges that are often used to study the osmotic circuitry of the brain. Central to this circuit is the supraoptic nucleus (SON) of the hypothalamus, which is responsible for the biosynthesis of the hormones, arginine vasopressin (AVP) and oxytocin (OXT), and their transport to terminals that reside in the posterior lobe of the pituitary. On osmotic challenge evoked by a change in blood volume or osmolality, the SON undergoes a function-related plasticity that creates an environment that allows for an appropriate hormone response. Here, we have described the impact of SL and WD compared with euhydrated (EU) controls in terms of drinking and eating behavior, body weight, and recorded physiological data including circulating hormone data and plasma and urine osmolality. We have also used microarrays to profile the transcriptome of the SON following SL and remined data from the SON that describes the transcriptome response to WD. From a list of 2,783 commonly regulated transcripts, we selected 20 genes for validation by qPCR. All of the 9 genes that have already been described as expressed or regulated in the SON by osmotic stimuli were confirmed in our models. Of the 11 novel genes, 5 were successfully validated while 6 were false discoveries.
    Matched MeSH terms: Gene Expression Profiling/methods
  19. Suppression Subtractive Hybridization Versus Next-Generation Sequencing in Plant Genetic Engineering: Challenges and Perspectives
    Sahebi M, Hanafi MM, Azizi P, Hakim A, Ashkani S, Abiri R
    Mol Biotechnol, 2015 Oct;57(10):880-903.
    PMID: 26271955 DOI: 10.1007/s12033-015-9884-z
    Suppression subtractive hybridization (SSH) is an effective method to identify different genes with different expression levels involved in a variety of biological processes. This method has often been used to study molecular mechanisms of plants in complex relationships with different pathogens and a variety of biotic stresses. Compared to other techniques used in gene expression profiling, SSH needs relatively smaller amounts of the initial materials, with lower costs, and fewer false positives present within the results. Extraction of total RNA from plant species rich in phenolic compounds, carbohydrates, and polysaccharides that easily bind to nucleic acids through cellular mechanisms is difficult and needs to be considered. Remarkable advancement has been achieved in the next-generation sequencing (NGS) field. As a result of progress within fields related to molecular chemistry and biology as well as specialized engineering, parallelization in the sequencing reaction has exceptionally enhanced the overall read number of generated sequences per run. Currently available sequencing platforms support an earlier unparalleled view directly into complex mixes associated with RNA in addition to DNA samples. NGS technology has demonstrated the ability to sequence DNA with remarkable swiftness, therefore allowing previously unthinkable scientific accomplishments along with novel biological purposes. However, the massive amounts of data generated by NGS impose a substantial challenge with regard to data safe-keeping and analysis. This review examines some simple but vital points involved in preparing the initial material for SSH and introduces this method as well as its associated applications to detect different novel genes from different plant species. This review evaluates general concepts, basic applications, plus the probable results of NGS technology in genomics, with unique mention of feasible potential tools as well as bioinformatics.
    Matched MeSH terms: Gene Expression Profiling/methods
  20. Integrated genomic and transcriptomic analysis of human brain metastases identifies alterations of potential clinical significance
    Saunus JM, Quinn MC, Patch AM, Pearson JV, Bailey PJ, Nones K, et al.
    J Pathol, 2015 Nov;237(3):363-78.
    PMID: 26172396 DOI: 10.1002/path.4583
    Treatment options for patients with brain metastases (BMs) have limited efficacy and the mortality rate is virtually 100%. Targeted therapy is critically under-utilized, and our understanding of mechanisms underpinning metastatic outgrowth in the brain is limited. To address these deficiencies, we investigated the genomic and transcriptomic landscapes of 36 BMs from breast, lung, melanoma and oesophageal cancers, using DNA copy-number analysis and exome- and RNA-sequencing. The key findings were as follows. (a) Identification of novel candidates with possible roles in BM development, including the significantly mutated genes DSC2, ST7, PIK3R1 and SMC5, and the DNA repair, ERBB-HER signalling, axon guidance and protein kinase-A signalling pathways. (b) Mutational signature analysis was applied to successfully identify the primary cancer type for two BMs with unknown origins. (c) Actionable genomic alterations were identified in 31/36 BMs (86%); in one case we retrospectively identified ERBB2 amplification representing apparent HER2 status conversion, then confirmed progressive enrichment for HER2-positivity across four consecutive metastatic deposits by IHC and SISH, resulting in the deployment of HER2-targeted therapy for the patient. (d) In the ERBB/HER pathway, ERBB2 expression correlated with ERBB3 (r(2)  = 0.496; p < 0.0001) and HER3 and HER4 were frequently activated in an independent cohort of 167 archival BM from seven primary cancer types: 57.6% and 52.6% of cases were phospho-HER3(Y1222) or phospho-HER4(Y1162) membrane-positive, respectively. The HER3 ligands NRG1/2 were barely detectable by RNAseq, with NRG1 (8p12) genomic loss in 63.6% breast cancer-BMs, suggesting a microenvironmental source of ligand. In summary, this is the first study to characterize the genomic landscapes of BM. The data revealed novel candidates, potential clinical applications for genomic profiling of resectable BMs, and highlighted the possibility of therapeutically targeting HER3, which is broadly over-expressed and activated in BMs, independent of primary site and systemic therapy.
    Matched MeSH terms: Gene Expression Profiling/methods*
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