Displaying publications 1 - 20 of 356 in total

  1. Toh C, Mohd-Hairul AR, Ain NM, Namasivayam P, Go R, Abdullah NAP, et al.
    BMC Res Notes, 2017 Nov 02;10(1):554.
    PMID: 29096695 DOI: 10.1186/s13104-017-2872-6
    BACKGROUND: Vanda Mimi Palmer (VMP) is commercially valuable for its strong fragrance but little is known regarding the fragrance production and emission sites on the flowers.

    RESULTS: Olfactory perception detected fragrance only from the petals and sepals. Light and Environmental Scanning Electron microscopy analyses on fresh tissues showed distributions of stomata and trichomes concentrated mostly around the edges. These results paralleled the rich starch deposits and intense neutral red stain, indicating strong fragrance and trichomes as potential main fragrance release sites. Next Generation Sequencing (NGS) transcriptomic data of adaxial and abaxial layers of the tissues showed monoterpene synthase transcripts specifically linalool and ocimene synthases distributed throughout the tissues. qPCR analyses taken at different time points revealed high levels of linalool and ocimene synthases transcripts in the early morning with maximal level at 4.00 am but remained low throughout daylight hours.

    CONCLUSIONS: Knowledge of the VMP floral anatomy and its fragrance production characteristics, which complemented our previous molecular and biochemical data on VMP, provided additional knowledge on how fragrance and flower morphology are closely intertwined. Further investigation on the mechanisms of fragrance biosynthesis and interaction of potential pollinators would elucidate the evolution of the flower morphology to maximize the reproduction success of this plant.

    Matched MeSH terms: Gene Expression Profiling/methods*
  2. Aizat WM, Ismail I, Noor NM
    Adv Exp Med Biol, 2018 11 2;1102:1-9.
    PMID: 30382565 DOI: 10.1007/978-3-319-98758-3_1
    The central dogma of molecular biology (DNA, RNA, protein and metabolite) has engraved our understanding of genetics in all living organisms. While the concept has been embraced for many decades, the development of high-throughput technologies particularly omics (genomics, transcriptomics, proteomics and metabolomics) has revolutionised the field to incorporate big data analysis including bioinformatics and systems biology as well as synthetic biology area. These omics approaches as well as systems and synthetic biology areas are now increasingly popular as seen by the growing numbers of publication throughout the years. Several journals which have published most of these related fields are also listed in this chapter to overview their impact and target journals.
    Matched MeSH terms: Gene Expression Profiling/trends*
  3. Tan MS, Tan JW, Chang SW, Yap HJ, Abdul Kareem S, Zain RB
    PeerJ, 2016;4:e2482.
    PMID: 27688975 DOI: 10.7717/peerj.2482
    The potential of genetic programming (GP) on various fields has been attained in recent years. In bio-medical field, many researches in GP are focused on the recognition of cancerous cells and also on gene expression profiling data. In this research, the aim is to study the performance of GP on the survival prediction of a small sample size of oral cancer prognosis dataset, which is the first study in the field of oral cancer prognosis.
    Matched MeSH terms: Gene Expression Profiling
  4. Mohandas S, Shete A, Sarkale P, Kumar A, Mote C, Yadav P
    Virulence, 2023 Dec;14(1):2224642.
    PMID: 37312405 DOI: 10.1080/21505594.2023.2224642
    Nipah virus (NiV) is a high-risk pathogen which can cause fatal infections in humans. The Indian isolate from the 2018 outbreak in the Kerala state of India showed ~ 4% nucleotide and amino acid difference in comparison to the Bangladesh strains of NiV and the substitutions observed were mostly not present in the region of any functional significance except for the phosphoprotein gene. The differential expression of viral genes was observed following infection in Vero (ATCC® CCL-81™) and BHK-21 cells. Intraperitoneal infection in the 10-12-week-old, Syrian hamster model induced dose dependant multisystemic disease characterized by prominent vascular lesions in lungs, brain, kidney and extra vascular lesions in brain and lungs. Congestion, haemorrhages, inflammatory cell infiltration, thrombosis and rarely endothelial syncitial cell formation were seen in the blood vessels. Intranasal infection resulted in respiratory tract infection characterised by pneumonia. The model showed disease characteristics resembling the human NiV infection except that of myocarditis similar to that reported by NiV-Malaysia and NiV-Bangladesh isolates in hamster model. The variation observed in the genome of the Indian isolate at the amino acid levels should be explored further for any functional significance.
    Matched MeSH terms: Gene Expression Profiling
  5. Lau NS, Foong CP, Kurihara Y, Sudesh K, Matsui M
    PLoS One, 2014;9(1):e86368.
    PMID: 24466058 DOI: 10.1371/journal.pone.0086368
    The photosynthetic cyanobacterium, Synechocystis sp. strain 6803, is a potential platform for the production of various chemicals and biofuels. In this study, direct photosynthetic production of a biopolymer, polyhydroxyalkanoate (PHA), in genetically engineered Synechocystis sp. achieved as high as 14 wt%. This is the highest production reported in Synechocystis sp. under photoautotrophic cultivation conditions without the addition of a carbon source. The addition of acetate increased PHA accumulation to 41 wt%, and this value is comparable to the highest production obtained with cyanobacteria. Transcriptome analysis by RNA-seq coupled with real-time PCR was performed to understand the global changes in transcript levels of cells subjected to conditions suitable for photoautotrophic PHA biosynthesis. There was lower expression of most PHA synthesis-related genes in recombinant Synechocystis sp. with higher PHA accumulation suggesting that the concentration of these enzymes is not the limiting factor to achieving high PHA accumulation. In order to cope with the higher PHA production, cells may utilize enhanced photosynthesis to drive the product formation. Results from this study suggest that the total flux of carbon is the possible driving force for the biosynthesis of PHA and the polymerizing enzyme, PHA synthase, is not the only critical factor affecting PHA-synthesis. Knowledge of the regulation or control points of the biopolymer production pathways will facilitate the further use of cyanobacteria for biotechnological applications.
    Matched MeSH terms: Gene Expression Profiling*
  6. 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*
  7. Saghir FS, Rose IM, Dali AZ, Shamsuddin Z, Jamal AR, Mokhtar NM
    Int. J. Gynecol. Cancer, 2010 Jul;20(5):724-31.
    PMID: 20973258
    INTRODUCTION: Malignant transformation of type I endometrium involves alteration in gene expression with subsequent uncontrolled proliferation of altered cells.

    OBJECTIVE: The main objective of the present study was to identify the cancer-related genes and gene pathways in the endometrium of healthy and cancer patients.

    MATERIALS AND METHODS: Thirty endometrial tissues from healthy and type I EC patients were subjected to total RNA isolation. The RNA samples with good integrity number were hybridized to a new version of Affymetrix Human Genome GeneChip 1.0 ST array. We analyzed the results using the GeneSpring 9.0 GX and the Pathway Studio 6.1 software. For validation assay, quantitative real-time polymerase chain reaction was used to analyze 4 selected genes in normal and EC tissue.

    RESULTS: Of the 28,869 genes profiled, we identified 621 differentially expressed genes (2-fold) in the normal tissue and the tumor. Among these genes, 146 were up-regulated and 476 were down-regulated in the tumor as compared with the normal tissue (P < 0.001). Up-regulated genes included the v-erb-a erythroblastic leukemia viral oncogene homolog 3 (ErbB3), ErbB4, E74-like factor 3 (ELF3), and chemokine ligand 17 (CXCL17). The down-regulated genes included signal transducer and activator transcription 5B (STAT5b), transforming growth factor A receptor III (TGFA3), caveolin 1 (CAV1), and protein kinase C alpha (PKCA). The gene set enrichment analysis showed 10 significant gene sets with related genes (P < 0.05). The quantitative polymerase chain reaction of 4 selected genes using similar RNA confirmed the microarray results (P < 0.05).

    CONCLUSIONS: Identification of molecular pathways with their genes related to type I EC contribute to the understanding of pathophysiology of this cancer, probably leading to identifying potential biomarkers of the cancer.

    Matched MeSH terms: Gene Expression Profiling*
  8. Azlina A, Samsudin AR
    Med J Malaysia, 2004 May;59 Suppl B:166-7.
    PMID: 15468870
    In Malaysia, the field of genomics in toxicology is still in infancy. The purpose of this study is to focus on the use of toxicogenomics for determination of gene expressions changes in cultured human fibroblast cells treated with genotoxicology free biomaterial (using Ames test), a locally produced hyroxyapatite. Dose and time response is similar to Ames test with time interval up to 21 days. mRNA is extracted, followed with RT-PCR and polyacrilamide gel electrophoresis. Changes of the gene expressions compared to the non-treated fibroblast mRNA would suggest some gene interactions in the molecule level associated with the exposure of the fibroblast cell line to the biomaterials. Further analysis (cloning & sequencing) shall be carried out to investigate the genes involved as simple changes might not signified toxicity.
    Matched MeSH terms: Gene Expression Profiling*
  9. Moorthy K, Jaber AN, Ismail MA, Ernawan F, Mohamad MS, Deris S
    Methods Mol Biol, 2019;1986:255-266.
    PMID: 31115893 DOI: 10.1007/978-1-4939-9442-7_12
    In gene expression studies, missing values are a common problem with important consequences for the interpretation of the final data (Satija et al., Nat Biotechnol 33(5):495, 2015). Numerous bioinformatics examination tools are used for cancer prediction, including the data set matrix (Bailey et al., Cell 173(2):371-385, 2018); thus, it is necessary to resolve the problem of missing-values imputation. This chapter presents a review of the research on missing-values imputation approaches for gene expression data. By using local and global correlation of the data, we were able to focus mostly on the differences between the algorithms. We classified the algorithms as global, hybrid, local, or knowledge-based techniques. Additionally, this chapter presents suitable assessments of the different approaches. The purpose of this review is to focus on developments in the current techniques for scientists rather than applying different or newly developed algorithms with identical functional goals. The aim was to adapt the algorithms to the characteristics of the data.
    Matched MeSH terms: Gene Expression Profiling*
  10. Reza Etemadi M, Ling KH, Zainal Abidin S, Chee HY, Sekawi Z
    PLoS One, 2017;12(5):e0176947.
    PMID: 28558071 DOI: 10.1371/journal.pone.0176947
    Human rhinovirus (HRV) is the common virus that causes acute respiratory infection (ARI) and is frequently associated with lower respiratory tract infections (LRTIs). We aimed to investigate whether HRV infection induces a specific gene expression pattern in airway epithelial cells. Alveolar epithelial cell monolayers were infected with HRV species B (HRV-B). RNA was extracted from both supernatants and infected monolayer cells at 6, 12, 24 and 48 hours post infection (hpi) and transcriptional profile was analyzed using Affymetrix GeneChip and the results were subsequently validated using quantitative Real-time PCR method. HRV-B infects alveolar epithelial cells which supports implication of the virus with LRTIs. In total 991 genes were found differentially expressed during the course of infection. Of these, 459 genes were up-regulated whereas 532 genes were down-regulated. Differential gene expression at 6 hpi (187 genes up-regulated vs. 156 down-regulated) were significantly represented by gene ontologies related to the chemokines and inflammatory molecules indicating characteristic of viral infection. The 75 up-regulated genes surpassed the down-regulated genes (35) at 12 hpi and their enriched ontologies fell into discrete functional entities such as regulation of apoptosis, anti-apoptosis, and wound healing. At later time points of 24 and 48 hpi, predominated down-regulated genes were enriched for extracellular matrix proteins and airway remodeling events. Our data provides a comprehensive image of host response to HRV infection. The study suggests the underlying molecular regulatory networks genes which might be involved in pathogenicity of the HRV-B and potential targets for further validations and development of effective treatment.
    Matched MeSH terms: Gene Expression Profiling*
  11. Ealam Selvan M, Lim KS, Teo CH, Lim YY
    J Vis Exp, 2022 Oct 21.
    PMID: 36342167 DOI: 10.3791/64565
    Circular RNAs (circRNAs) are a class of non-coding RNAs that are formed via back-splicing. These circRNAs are predominantly studied for their roles as regulators of various biological processes. Notably, emerging evidence demonstrates that host circRNAs can be differentially expressed (DE) upon infection with pathogens (e.g., influenza and coronaviruses), suggesting a role for circRNAs in regulating host innate immune responses. However, investigations on the role of circRNAs during pathogenic infections are limited by the knowledge and skills required to carry out the necessary bioinformatic analysis to identify DE circRNAs from RNA sequencing (RNA-seq) data. Bioinformatics prediction and identification of circRNAs is crucial before any verification, and functional studies using costly and time-consuming wet-lab techniques. To solve this issue, a step-by-step protocol of in silico prediction and characterization of circRNAs using RNA-seq data is provided in this manuscript. The protocol can be divided into four steps: 1) Prediction and quantification of DE circRNAs via the CIRIquant pipeline; 2) Annotation via circBase and characterization of DE circRNAs; 3) CircRNA-miRNA interaction prediction through Circr pipeline; 4) functional enrichment analysis of circRNA parental genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). This pipeline will be useful in driving future in vitro and in vivo research to further unravel the role of circRNAs in host-pathogen interactions.
    Matched MeSH terms: Gene Expression Profiling/methods
  12. Das S, Kumar S
    J Med Virol, 2023 Sep;95(9):e29077.
    PMID: 37675861 DOI: 10.1002/jmv.29077
    Long coronavirus disease (COVID) has emerged as a global health issue, affecting a substantial number of people worldwide. However, the underlying mechanisms that contribute to the persistence of symptoms in long COVID remain obscure, impeding the development of effective diagnostic and therapeutic interventions. In this study, we utilized computational methods to examine the gene expression profiles of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and their associations with the wide range of symptoms observed in long COVID patients. Using a comprehensive data set comprising over 255 symptoms affecting multiple organ systems, we identified differentially expressed genes and investigated their functional similarity, leading to the identification of key genes with the potential to serve as biomarkers for long COVID. We identified the participation of hub genes associated with G-protein-coupled receptors (GPCRs), which are essential regulators of T-cell immunity and viral infection responses. Among the identified common genes were CTLA4, PTPN22, KIT, KRAS, NF1, RET, and CTNNB1, which play a crucial role in modulating T-cell immunity via GPCR and contribute to a variety of symptoms, including autoimmunity, cardiovascular disorders, dermatological manifestations, gastrointestinal complications, pulmonary impairments, reproductive and genitourinary dysfunctions, and endocrine abnormalities. GPCRs and associated genes are pivotal in immune regulation and cellular functions, and their dysregulation may contribute to the persistent immune responses, chronic inflammation, and tissue abnormalities observed in long COVID. Targeting GPCRs and their associated pathways could offer promising therapeutic strategies to manage symptoms and improve outcomes for those experiencing long COVID. However, the complex mechanisms underlying the condition require continued study to develop effective treatments. Our study has significant implications for understanding the molecular mechanisms underlying long COVID and for identifying potential therapeutic targets. In addition, we have developed a comprehensive website (https://longcovid.omicstutorials.com/) that provides a curated list of biomarker-identified genes and treatment recommendations for each specific disease, thereby facilitating informed clinical decision-making and improved patient management. Our study contributes to the understanding of this debilitating disease, paving the way for improved diagnostic precision, and individualized therapeutic interventions.
    Matched MeSH terms: Gene Expression Profiling*
  13. 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/economics; Gene Expression Profiling/methods
  14. Wong MY, Govender NT, Ong CS
    BMC Res Notes, 2019 Sep 24;12(1):631.
    PMID: 31551084 DOI: 10.1186/s13104-019-4652-y
    OBJECTIVE: Basal stem rot disease causes severe economic losses to oil palm production in South-east Asia and little is known on the pathogenicity of the pathogen, the basidiomyceteous Ganoderma boninense. Our data presented here aims to identify both the house-keeping and pathogenicity genes of G. boninense using Illumina sequencing reads.

    DESCRIPTION: The hemibiotroph G. boninense establishes via root contact during early stage of colonization and subsequently kills the host tissue as the disease progresses. Information on the pathogenicity factors/genes that causes BSR remain poorly understood. In addition, the molecular expressions corresponding to G. boninense growth and pathogenicity are not reported. Here, six transcriptome datasets of G. boninense from two contrasting conditions (three biological replicates per condition) are presented. The first datasets, collected from a 7-day-old axenic condition provide an insight onto genes responsible for sustenance, growth and development of G. boninense while datasets of the infecting G. boninense collected from oil palm-G. boninense pathosystem (in planta condition) at 1 month post-inoculation offer a comprehensive avenue to understand G. boninense pathogenesis and infection especially in regard to molecular mechanisms and pathways. Raw sequences deposited in Sequence Read Archive (SRA) are available at NCBI SRA portal with PRJNA514399, bioproject ID.

    Matched MeSH terms: Gene Expression Profiling/methods*; Gene Expression Profiling/statistics & numerical data
  15. 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; Gene Expression Profiling/trends*
  16. Selvarajah GT, Bonestroo FAS, Timmermans Sprang EPM, Kirpensteijn J, Mol JA
    BMC Vet Res, 2017 Nov 25;13(1):354.
    PMID: 29178874 DOI: 10.1186/s12917-017-1281-3
    BACKGROUND: Quantitative PCR (qPCR) is a common method for quantifying mRNA expression. Given the heterogeneity present in tumor tissues, it is crucial to normalize target mRNA expression data using appropriate reference genes that are stably expressed under a variety of pathological and experimental conditions. No studies have validated specific reference genes in canine osteosarcoma (OS). Previous gene expression studies involving canine OS have used one or two reference genes to normalize gene expression. This study aimed to validate a panel of reference genes commonly used for normalization of canine OS gene expression data using the geNorm algorithm. qPCR analysis of nine canine reference genes was performed on 40 snap-frozen primary OS tumors and seven cell lines.

    RESULTS: Tumors with a variety of clinical and pathological characteristics were selected. Gene expression stability and the optimal number of reference genes for gene expression normalization were calculated. RPS5 and HNRNPH were highly stable among OS cell lines, while RPS5 and RPS19 were the best combination for primary tumors. Pairwise variation analysis recommended four and two reference genes for optimal normalization of the expression data of canine OS tumors and cell lines, respectively.

    CONCLUSIONS: Appropriate combinations of reference genes are recommended to normalize mRNA levels in canine OS tumors and cell lines to facilitate standardized and reliable quantification of target gene expression, which is essential for investigating key genes involved in canine OS metastasis and for comparative biomarker discovery.

    Matched MeSH terms: Gene Expression Profiling/methods; Gene Expression Profiling/veterinary*
  17. Wasito I, Hashim SZ, Sukmaningrum S
    Bioinformation, 2007 Dec 30;2(5):175-81.
    PMID: 18305825
    Gene expression profiling plays an important role in the identification of biological and clinical properties of human solid tumors such as colorectal carcinoma. Profiling is required to reveal underlying molecular features for diagnostic and therapeutic purposes. A non-parametric density-estimation-based approach called iterative local Gaussian clustering (ILGC), was used to identify clusters of expressed genes. We used experimental data from a previous study by Muro and others consisting of 1,536 genes in 100 colorectal cancer and 11 normal tissues. In this dataset, the ILGC finds three clusters, two large and one small gene clusters, similar to their results which used Gaussian mixture clustering. The correlation of each cluster of genes and clinical properties of malignancy of human colorectal cancer was analysed for the existence of tumor or normal, the existence of distant metastasis and the existence of lymph node metastasis.
    Matched MeSH terms: Gene Expression Profiling
  18. Maisarah Y, Hashida HN, Yusmin MY
    Vet Res Forum, 2020;11(2):179-184.
    PMID: 32782748 DOI: 10.30466/vrf.2018.92133.2230
    The extraction of intact RNA from oocyte is quite challenging and time-consuming. A standard protocol using commercial RNA extraction kit, yields a low quantity of RNA in oocytes. In the past, several attempts in getting RNA for gene expression study ended up with a few different modified methods. Extraction of high-quality RNA from oocyte is important before further downstream analyses such as reverse transcription-polymerase chain reaction, quantitative polymerase chain reaction, or northern blot analysis. In this review, the efficiency of RNA extraction methods from all species oocytes was compared between published articles and our research to gather all possible methods of RNA extraction. Two different methods of RNA extraction that were proposed from various experiments were reviewed to determine the best method of RNA extraction from the oocyte. Modified TRIzol method can be concluded as an efficient RNA extraction method especially for good RNA from oocytes. Meanwhile, comparing RNA extraction kits to extract the RNA from oocytes or pre-implantation embryos, the micro RNA extraction kit type is the best. Therefore, an appropriate RNA extraction method is important to obtain high quality of total RNA for gene expression profiling analysis.
    Matched MeSH terms: Gene Expression Profiling
  19. Zhang Y, Wu Q, Fang S, Li S, Zheng H, Zhang Y, et al.
    BMC Genomics, 2020 Aug 14;21(1):559.
    PMID: 32795331 DOI: 10.1186/s12864-020-06965-5
    BACKGROUND: Mud crab, Scylla paramamosain, a euryhaline crustacean species, mainly inhabits the Indo-Western Pacific region. Wild mud crab spawn in high-salt condition and the salinity reduced with the growth of the hatching larvae. When the larvae grow up to megalopa, they migrate back to estuaries and coasts in virtue of the flood tide, settle and recruit adult habitats and metamorphose into the crablet stage. Adult crab can even survive in a wide salinity of 0-35 ppt. To investigate the mRNA profile after salinity stress, S. paramamosain megalopa were exposed to different salinity seawater (low, 14 ppt; control, 25 ppt; high, 39 ppt).

    RESULTS: Firstly, from the expression profiles of Na+/K+/2Cl- cotransporter, chloride channel protein 2, and ABC transporter, it turned out that the 24 h might be the most influenced duration in the short-term stress. We collected megalopa under different salinity for 24 h and then submitted to mRNA profiling. Totally, 57.87 Gb Clean Data were obtained. The comparative genomic analysis detected 342 differentially expressed genes (DEGs). The most significantly DEGs include gamma-butyrobetaine dioxygenase-like, facilitated trehalose transporter Tret1, sodium/potassium-transporting ATPase subunit alpha, rhodanese 1-like protein, etc. And the significantly enriched pathways were lysine degradation, choline metabolism in cancer, phospholipase D signaling pathway, Fc gamma R-mediated phagocytosis, and sphingolipid signaling pathway. The results indicate that in the short-term salinity stress, the megalopa might regulate some mechanism such as metabolism, immunity responses, osmoregulation to adapt to the alteration of the environment.

    CONCLUSIONS: This study represents the first genome-wide transcriptome analysis of S. paramamosain megalopa for studying its stress adaption mechanisms under different salinity. The results reveal numbers of genes modified by salinity stress and some important pathways, which will provide valuable resources for discovering the molecular basis of salinity stress adaptation of S. paramamosain larvae and further boost the understanding of the potential molecular mechanisms of salinity stress adaptation for crustacean species.

    Matched MeSH terms: Gene Expression Profiling
  20. Hameed SS, Hassan R, Hassan WH, Muhammadsharif FF, Latiff LA
    PLoS One, 2021;16(1):e0246039.
    PMID: 33507983 DOI: 10.1371/journal.pone.0246039
    The selection and classification of genes is essential for the identification of related genes to a specific disease. Developing a user-friendly application with combined statistical rigor and machine learning functionality to help the biomedical researchers and end users is of great importance. In this work, a novel stand-alone application, which is based on graphical user interface (GUI), is developed to perform the full functionality of gene selection and classification in high dimensional datasets. The so-called HDG-select application is validated on eleven high dimensional datasets of the format CSV and GEO soft. The proposed tool uses the efficient algorithm of combined filter-GBPSO-SVM and it was made freely available to users. It was found that the proposed HDG-select outperformed other tools reported in literature and presented a competitive performance, accessibility, and functionality.
    Matched MeSH terms: Gene Expression Profiling
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