Displaying publications 301 - 320 of 340 in total

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  1. Tay CY, Mitchell H, Dong Q, Goh KL, Dawes IW, Lan R
    BMC Microbiol, 2009;9:126.
    PMID: 19538757 DOI: 10.1186/1471-2180-9-126
    Helicobacter pylori is a major gastric bacterial pathogen. This pathogen has been shown to follow the routes of human migration by their geographical origin and currently the global H. pylori population has been divided into six ancestral populations, three from Africa, two from Asia and one from Europe. Malaysia is made up of three major ethnic populations, Malay, Chinese and Indian, providing a good population for studying recent H. pylori migration and admixture.
    Matched MeSH terms: Computational Biology
  2. Seal A, Reddy PPN, Chaithanya P, Meghana A, Jahnavi K, Krejcar O, et al.
    Comput Math Methods Med, 2020;2020:8303465.
    PMID: 32831902 DOI: 10.1155/2020/8303465
    Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications. However, most of the state-of-the-art methods identified emotions after analyzing facial images. Emotion recognition using electroencephalogram (EEG) signals has got less attention. However, the advantage of using EEG signals is that it can capture real emotion. However, very few EEG signals databases are publicly available for affective computing. In this work, we present a database consisting of EEG signals of 44 volunteers. Twenty-three out of forty-four are females. A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos. So, 3 video files are devoted to each emotion. Participants are mapped with the emotion that they had felt after watching each video. The recorded EEG signals are considered further to classify four types of emotions based on discrete wavelet transform and extreme learning machine (ELM) for reporting the initial benchmark classification performance. The ELM algorithm is used for channel selection followed by subband selection. The proposed method performs the best when features are captured from the gamma subband of the FP1-F7 channel with 94.72% accuracy. The presented database would be available to the researchers for affective recognition applications.
    Matched MeSH terms: Computational Biology
  3. Abidin SZ, Leong JW, Mahmoudi M, Nordin N, Abdullah S, Cheah PS, et al.
    Neurosci Bull, 2017 Aug;33(4):373-382.
    PMID: 28597341 DOI: 10.1007/s12264-017-0143-0
    MicroRNAs are small non-coding RNAs that play crucial roles in the regulation of gene expression and protein synthesis during brain development. MiR-3099 is highly expressed throughout embryogenesis, especially in the developing central nervous system. Moreover, miR-3099 is also expressed at a higher level in differentiating neurons in vitro, suggesting that it is a potential regulator during neuronal cell development. This study aimed to predict the target genes of miR-3099 via in-silico analysis using four independent prediction algorithms (miRDB, miRanda, TargetScan, and DIANA-micro-T-CDS) with emphasis on target genes related to brain development and function. Based on the analysis, a total of 3,174 miR-3099 target genes were predicted. Those predicted by at least three algorithms (324 genes) were subjected to DAVID bioinformatics analysis to understand their overall functional themes and representation. The analysis revealed that nearly 70% of the target genes were expressed in the nervous system and a significant proportion were associated with transcriptional regulation and protein ubiquitination mechanisms. Comparison of in situ hybridization (ISH) expression patterns of miR-3099 in both published and in-house-generated ISH sections with the ISH sections of target genes from the Allen Brain Atlas identified 7 target genes (Dnmt3a, Gabpa, Gfap, Itga4, Lxn, Smad7, and Tbx18) having expression patterns complementary to miR-3099 in the developing and adult mouse brain samples. Of these, we validated Gfap as a direct downstream target of miR-3099 using the luciferase reporter gene system. In conclusion, we report the successful prediction and validation of Gfap as an miR-3099 target gene using a combination of bioinformatics resources with enrichment of annotations based on functional ontologies and a spatio-temporal expression dataset.
    Matched MeSH terms: Computational Biology
  4. Kim SY, Ko KS
    Microb Drug Resist, 2019 Mar;25(2):227-232.
    PMID: 30212274 DOI: 10.1089/mdr.2018.0020
    To reveal whether an increase of CTX-M-15-producing Klebsiella pneumoniae ST11 isolates is due to clonal dissemination across the countries, plasmids (pHK02-026, pM16-13, pIN03-01, and pTH02-34) were extracted from four K. pneumoniae isolates collected in Hong Kong, Malaysia, Thailand, and Indonesia, respectively. Complete sequencing of blaCTX-M-15-carrying plasmids was performed. In addition to the four plasmids, a previously sequenced plasmid (pKP12226) of a K. pneumoniae ST11 isolate from Korea was included in the analysis. While pIN03-01 and pTH02-34, which belonged to the incompatibility group IncX3, showed nearly the same structure, the others of IncF1A or IncFII exhibited very different structures. The number and kinds of antibiotic genes found in the plasmids were also different from each other. Cryptic prophage genes were identified in all five blaCTX-M-15-harboring plasmids from the ST11 isolates; P1-like region in pKP12226, CPZ-55 prophage region in pHK02-026, phage shock operon pspFABCD in pM16-13, and SPBc2 prophage yokD in pIN03-01 and pTH02-34. The plasmids with blaCTX-M-15 in the prevailing K. pneumoniae ST11 isolates in Asian countries might emerge from diverse origins by recombination. The prevalence of CTX-M-15-producing K. pneumoniae ST11 clone in Asian countries is not mainly due to the dissemination of a single strain.
    Matched MeSH terms: Computational Biology
  5. Awasthi R, Singh AK, Mishra G, Maurya A, Chellappan DK, Gupta G, et al.
    Adv Exp Med Biol, 2018 9 28;1087:3-14.
    PMID: 30259353 DOI: 10.1007/978-981-13-1426-1_1
    Circular RNAs (cirRNAs) are long, noncoding endogenous RNA molecules and covalently closed continuous loop without 5'-3' polarity and polyadenylated tail which are largely concentrated in the nucleus. CirRNA regulates gene expression by modulating microRNAs and functions as potential biomarker. CirRNAs can translate in vivo to link between their expression and disease. They are resistant to RNA exonuclease and can convert to the linear RNA by microRNA which can then act as competitor to endogenous RNA. This chapter summarizes the evolutionary conservation and expression of cirRNAs, their identification, highlighting various computational approaches on cirRNA, and translation with a focus on the breakthroughs and the challenges in this new field.
    Matched MeSH terms: Computational Biology
  6. Shanmugapriya, Huda HA, Vijayarathna S, Oon CE, Chen Y, Kanwar JR, et al.
    Adv Exp Med Biol, 2018 9 28;1087:95-105.
    PMID: 30259360 DOI: 10.1007/978-981-13-1426-1_8
    Circular RNAs characterize a class of widespread and diverse endogenous RNAs which are non-coding RNAs that are made by back-splicing events and have covalently closed loops with no polyadenylated tails. Various indications specify that circular RNAs (circRNAs) are plentiful in the human transcriptome. However, their participation in biological processes remains mostly undescribed. To date thousands of circRNAs have been revealed in organisms ranging from Drosophila melanogaster to Homo sapiens. Functional studies specify that these transcripts control expression of protein-coding linear transcripts and thus encompass a key component of gene expression regulation. This chapter provide a comprehensive overview on functional validation of circRNAs. Furthermore, we discuss the recent modern methodologies for the functional validation of circRNAs such as RNA interference (RNAi) gene silencing assay, luciferase reporter assays, circRNA gain-of-function investigation via overexpression of circular transcript assay, RT-q-PCR quantification, and other latest applicable assays. The methods described in this chapter are demonstrated on the cellular model.
    Matched MeSH terms: Computational Biology
  7. Li XP, Lin D, Zhang Y, Chen SQ, Bai HQ, Zhang SN, et al.
    Trop Biomed, 2020 Mar 01;37(1):116-126.
    PMID: 33612723
    Several bioactive molecules isolated from the saliva of blood-sucking arthropods, such as mosquitoes, have been shown to exhibit potential anticoagulant function. We have previously identified a 30kDa allergen named Aegyptin-like protein (alALP), which is highly homologous to Aegyptin, from the salivary glands of female Aedes albopictus (Asian tiger mosquito). In this study, we identified the conserved functional domain of alALP by using bioinformatic tools, and expressed the His-tagged alALP recombinant protein in sf9 insect cells by generation and transfection of a baculoviral expression plasmid carrying the fulllength cDNA of alALP. We purified this recombinant protein and examined its function on the inhibition of blood coagulation. The results showed that the purified His-alALP prolonged the Activated Partial Thromboplastin Time (APTT), Prothrombin Time (PT) and Thrombin Time (TT) in vitro as well as the Bleeding Time (BT) in vivo, which suggest that alALP could be a novel anticoagulant.
    Matched MeSH terms: Computational Biology
  8. Junejo AR, Kaabar MKA, Li X
    Comput Math Methods Med, 2021;2021:9949328.
    PMID: 34938362 DOI: 10.1155/2021/9949328
    Developing new treatments for emerging infectious diseases in infectious and noninfectious diseases has attracted a particular attention. The emergence of viral diseases is expected to accelerate; these data indicate the need for a proactive approach to develop widely active family specific and cross family therapies for future disease outbreaks. Viral disease such as pneumonia, severe acute respiratory syndrome type 2, HIV infection, and Hepatitis-C virus can cause directly and indirectly cardiovascular disease (CVD). Emphasis should be placed not only on the development of broad-spectrum molecules and antibodies but also on host factor therapy, including the reutilization of previously approved or developing drugs. Another new class of therapeutics with great antiviral therapeutic potential is molecular communication networks using deep learning autoencoder (DL-AEs). The use of DL-AEs for diagnosis and prognosis prediction of infectious and noninfectious diseases has attracted a particular attention. MCN is map to molecular signaling and communication that are found inside and outside the human body where the goal is to develop a new black box mechanism that can serve the future robust healthcare industry (HCI). MCN has the ability to characterize the signaling process between cells and infectious disease locations at various levels of the human body called point-to-point MCN through DL-AE and provide targeted drug delivery (TDD) environment. Through MCN, and DL-AE healthcare provider can remotely measure biological signals and control certain processes in the required organism for the maintenance of the patient's health state. We use biomicrodevices to promote the real-time monitoring of human health and storage of the gathered data in the cloud. In this paper, we use the DL-based AE approach to design and implement a new drug source and target for the MCN under white Gaussian noise. Simulation results show that transceiver executions for a given medium model that reduces the bit error rate which can be learned. Then, next development of molecular diagnosis such as heart sounds is classified. Furthermore, biohealth interface for the inside and outside human body mechanism is presented, comparative perspective with up-to-date current situation about MCN.
    Matched MeSH terms: Computational Biology
  9. Algamal ZY, Qasim MK, Lee MH, Ali HTM
    SAR QSAR Environ Res, 2020 Nov;31(11):803-814.
    PMID: 32938208 DOI: 10.1080/1062936X.2020.1818616
    High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSEtrain, Y-randomization test, MSEtest, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSEtrain. For the test dataset, proposed QSAR model shows lower value of MSEtest compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC50 values of neuraminidase inhibitors that have not been experimentally tested.
    Matched MeSH terms: Computational Biology
  10. Evaristus NA, Wan Abdullah WN, Gan CY
    Peptides, 2018 04;102:61-67.
    PMID: 29510154 DOI: 10.1016/j.peptides.2018.03.001
    The potential of N. lappacheum and N. mutabile seed as a source of α-amylase inhibitor peptides was explored based on the local traditional practice of using the seed. Different gastro-digestive enzymes (i.e. pepsin or chymotrypsin) or a sequential digestion were used to extract the peptides. The effects of digestion time and enzyme to substrate (E:S) ratio on the α-amylase inhibitory activity were investigated. Results showed that chymotrypsin was effective in producing the inhibitor peptides from rambutan seed protein at E:S ratio 1:20 for 1 h, whereas pepsin was more effective for pulasan seed protein under the same condition. A total of 20 and 31 novel inhibitor peptides were identified, respectively. These peptides could bind with the subsites of α-amylase (i.e. Trp58, Trp59, Tyr62, Asp96, Arg195, Asp197, Glu233, His299, Asp300, and His305) and formed a sliding barrier that preventing the formation of enzyme/substrate intermediate leading to lower α-amylase activity.
    Matched MeSH terms: Computational Biology
  11. Khoo YL, Cheah SH, Chong H
    Immunotherapy, 2017 06;9(7):567-577.
    PMID: 28595518 DOI: 10.2217/imt-2017-0016
    AIM: To develop a fully bioactive humanized antibody from the chimeric rituximab for potential clinical applications using a relatively simpler and faster logical and bioinformatics approach.

    METHODS: From bioinformatics data, mismatched mouse amino acids in variable light and heavy chain amphipathic regions were identified and substituted with those common to human antibody framework. Appropriate synthetic DNA sequences inserted into vectors were transfected into HEK293 cells to produce the humanized antibody.

    RESULTS: Humanized antibodies showed specific binding to CD20 and greater cytotoxicity to cancer WIL2-NS cell proliferation than rituximab in vitro.

    CONCLUSION: A humanized version of rituximab with potential to be developed into a biobetter for treatment of B-cell disorders has been successfully generated using a logical and bioinformatics approach.

    Matched MeSH terms: Computational Biology
  12. Jagadeesan B, Gerner-Smidt P, Allard MW, Leuillet S, Winkler A, Xiao Y, et al.
    Food Microbiol, 2019 Jun;79:96-115.
    PMID: 30621881 DOI: 10.1016/j.fm.2018.11.005
    Next Generation Sequencing (NGS) combined with powerful bioinformatic approaches are revolutionising food microbiology. Whole genome sequencing (WGS) of single isolates allows the most detailed comparison possible hitherto of individual strains. The two principle approaches for strain discrimination, single nucleotide polymorphism (SNP) analysis and genomic multi-locus sequence typing (MLST) are showing concordant results for phylogenetic clustering and are complementary to each other. Metabarcoding and metagenomics, applied to total DNA isolated from either food materials or the production environment, allows the identification of complete microbial populations. Metagenomics identifies the entire gene content and when coupled to transcriptomics or proteomics, allows the identification of functional capacity and biochemical activity of microbial populations. The focus of this review is on the recent use and future potential of NGS in food microbiology and on current challenges. Guidance is provided for new users, such as public health departments and the food industry, on the implementation of NGS and how to critically interpret results and place them in a broader context. The review aims to promote the broader application of NGS technologies within the food industry as well as highlight knowledge gaps and novel applications of NGS with the aim of driving future research and increasing food safety outputs from its wider use.
    Matched MeSH terms: Computational Biology
  13. Salleh MZ, Teh LK, Lee LS, Ismet RI, Patowary A, Joshi K, et al.
    PLoS One, 2013;8(8):e71554.
    PMID: 24009664 DOI: 10.1371/journal.pone.0071554
    BACKGROUND: With a higher throughput and lower cost in sequencing, second generation sequencing technology has immense potential for translation into clinical practice and in the realization of pharmacogenomics based patient care. The systematic analysis of whole genome sequences to assess patient to patient variability in pharmacokinetics and pharmacodynamics responses towards drugs would be the next step in future medicine in line with the vision of personalizing medicine.

    METHODS: Genomic DNA obtained from a 55 years old, self-declared healthy, anonymous male of Malay descent was sequenced. The subject's mother died of lung cancer and the father had a history of schizophrenia and deceased at the age of 65 years old. A systematic, intuitive computational workflow/pipeline integrating custom algorithm in tandem with large datasets of variant annotations and gene functions for genetic variations with pharmacogenomics impact was developed. A comprehensive pathway map of drug transport, metabolism and action was used as a template to map non-synonymous variations with potential functional consequences.

    PRINCIPAL FINDINGS: Over 3 million known variations and 100,898 novel variations in the Malay genome were identified. Further in-depth pharmacogenetics analysis revealed a total of 607 unique variants in 563 proteins, with the eventual identification of 4 drug transport genes, 2 drug metabolizing enzyme genes and 33 target genes harboring deleterious SNVs involved in pharmacological pathways, which could have a potential role in clinical settings.

    CONCLUSIONS: The current study successfully unravels the potential of personal genome sequencing in understanding the functionally relevant variations with potential influence on drug transport, metabolism and differential therapeutic outcomes. These will be essential for realizing personalized medicine through the use of comprehensive computational pipeline for systematic data mining and analysis.

    Matched MeSH terms: Computational Biology
  14. Alghamdi A, A Awadh Ali N, Alafnan A, Zainal Abidin SA, Alamri A, Hussein W, et al.
    Food Chem Toxicol, 2024 Nov;193:115028.
    PMID: 39368542 DOI: 10.1016/j.fct.2024.115028
    This study explores the phytochemical composition and biological activities of Verbascum yemenense, a plant known for its medicinal properties. The plant extract revealed a rich presence of bioactive compounds that exhibited significant antioxidant properties against free radicals. The enzyme inhibition potential was particularly notable against cholinesterases (AChE: 2.56 mg GALAE/g; BChE: 1.98 mg GALAE/g), and tyrosinase (87.94 mg KAE/g), α-glucosidase suggesting potential therapeutic applications in neurodegenerative diseases, skin disorders and diabetes. Molecular docking studies and Molecular Dynamics simulations, providing insights into the interaction mechanisms of the identified compounds with the target proteins. Molecular docking studies revealed high binding affinities of the phytoconstituents, with compounds like VY4 and phyllanthusol-A (VY15) showing substantial docking scores against AChE (-9.840 kcal/mol) and BChE (-9.853 kcal/mol), respectively. For instance, the RMSD values during the MD simulations for compound VY17 in the AML complex showed a stable conformation, fluctuating within a range of 0.75 Å to 1.75 Å, indicating a strong and consistent interaction with the enzyme. MESP studies highlighted VY17's distinctive electrostatic features, notably a pronounced electronegative region, which might contribute to its binding efficiency. These findings suggest that V. yemenense is a promising candidate for developing novel therapeutic agents.
    Matched MeSH terms: Computational Biology
  15. Alameri M, Hasikin K, Kadri NA, Nasir NFM, Mohandas P, Anni JS, et al.
    Comput Math Methods Med, 2021;2021:6953593.
    PMID: 34497665 DOI: 10.1155/2021/6953593
    Infertility is a condition whereby pregnancy does not occur despite having unprotected sexual intercourse for at least one year. The main reason could originate from either the male or the female, and sometimes, both contribute to the fertility disorder. For the male, sperm disorder was found to be the most common reason for infertility. In this paper, we proposed male infertility analysis based on automated sperm motility tracking. The proposed method worked in multistages, where the first stage focused on the sperm detection process using an improved Gaussian Mixture Model. A new optimization protocol was proposed to accurately detect the motile sperms prior to the sperm tracking process. Since the optimization protocol was imposed in the proposed system, the sperm tracking and velocity estimation processes are improved. The proposed method attained the highest average accuracy, sensitivity, and specificity of 92.3%, 96.3%, and 72.4%, respectively, when tested on 10 different samples. Our proposed method depicted better sperm detection quality when qualitatively observed as compared to other state-of-the-art techniques.
    Matched MeSH terms: Computational Biology
  16. Forde BM, Ben Zakour NL, Stanton-Cook M, Phan MD, Totsika M, Peters KM, et al.
    PLoS One, 2014;9(8):e104400.
    PMID: 25126841 DOI: 10.1371/journal.pone.0104400
    Escherichia coli ST131 is now recognised as a leading contributor to urinary tract and bloodstream infections in both community and clinical settings. Here we present the complete, annotated genome of E. coli EC958, which was isolated from the urine of a patient presenting with a urinary tract infection in the Northwest region of England and represents the most well characterised ST131 strain. Sequencing was carried out using the Pacific Biosciences platform, which provided sufficient depth and read-length to produce a complete genome without the need for other technologies. The discovery of spurious contigs within the assembly that correspond to site-specific inversions in the tail fibre regions of prophages demonstrates the potential for this technology to reveal dynamic evolutionary mechanisms. E. coli EC958 belongs to the major subgroup of ST131 strains that produce the CTX-M-15 extended spectrum β-lactamase, are fluoroquinolone resistant and encode the fimH30 type 1 fimbrial adhesin. This subgroup includes the Indian strain NA114 and the North American strain JJ1886. A comparison of the genomes of EC958, JJ1886 and NA114 revealed that differences in the arrangement of genomic islands, prophages and other repetitive elements in the NA114 genome are not biologically relevant and are due to misassembly. The availability of a high quality uropathogenic E. coli ST131 genome provides a reference for understanding this multidrug resistant pathogen and will facilitate novel functional, comparative and clinical studies of the E. coli ST131 clonal lineage.
    Matched MeSH terms: Computational Biology
  17. Hariharan M, Polat K, Sindhu R
    Comput Methods Programs Biomed, 2014 Mar;113(3):904-13.
    PMID: 24485390 DOI: 10.1016/j.cmpb.2014.01.004
    Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.
    Matched MeSH terms: Computational Biology
  18. Kadir FA, Kassim NM, Abdulla MA, Yehye WA
    PMID: 24305067 DOI: 10.1186/1472-6882-13-343
    Hepatocellular carcinoma is a common type of tumour worldwide with a high mortality rate and with low response to current cytotoxic and chemotherapeutic drugs. The prediction of activity spectra for the substances (PASS) software, which predicted that more than 300 pharmacological effects, biological and biochemical mechanisms based on the structural formula of the substance was efficiently used in this study to reveal new multitalented actions for Vitex negundo (VN) constituents.
    Matched MeSH terms: Computational Biology
  19. Lee CF, Abdullah MZ, Ahmad KA, Lutfi Shuaib I
    Comput Math Methods Med, 2013;2013:519071.
    PMID: 23840279 DOI: 10.1155/2013/519071
    This research focuses on creating a standardized nasal cavity model of adult Malaysian females. The methodology implemented in this research is a new approach compared to other methods used by previous researchers. This study involves 26 females who represent the test subjects for this preliminary study. Computational fluid dynamic (CFD) analysis was carried out to better understand the characteristics of the standardized model and to compare it to the available standardized Caucasian model. This comparison includes cross-sectional areas for both half-models as well as velocity contours along the nasal cavities. The Malaysian female standardized model is larger in cross-sectional area compared to the standardized Caucasian model thus leading to lower average velocity magnitudes. The standardized model was further evaluated with four more Malaysian female test subjects based on its cross-sectional areas and average velocity magnitudes along the nasal cavities. This evaluation shows that the generated model represents an averaged and standardized model of adult Malaysian females.
    Matched MeSH terms: Computational Biology
  20. Othman R, Omar MH, Shan LP, Shafiee MN, Jamal R, Mokhtar NM
    Reprod Biol, 2012 Jul;12(2):183-99.
    PMID: 22850470
    The aim of the present study was to identify differentially expressed genes and their related biological pathways in the secretory phase endometrium from patients with recurrent miscarriage (RM) and fertile subjects. Endometrial samples from RM and fertile patients were analyzed using the Affymetrix GeneChip® ST Array. The bioinformatic analysis using the Partek Genomic Suite revealed 346 genes (175 up-regulated and 171 down-regulated) that were differentially expressed in the endometrium of RM patients compared to the fertile subjects (fold change ≥1.5, p<0.005). Validation step using quantitative real-time polymerase chain reaction (qPCR) confirmed a similar expression pattern of four exemplary genes: one up-regulated gene (fibroblast growth factor 9, FGF9) and three down-regulated genes: integrin β3 (ITGB3), colony stimulating factor 1 (CSF1) and matrix-metalloproteinases 19 (MMP19). The Gene Set Enrichment Analysis (GSEA) and the Pathway Studio software have found 101 signaling pathways (p<0.05) associated with the affected genes including the FGFR3 /signal transducer and activator of transcription (STAT) pathway and the CSF1R/STAT pathway. Cell adhesion, cell differentiation and angiogenesis were among biological processes indicated by this system. In conclusion, microarray technique is a useful tool to study gene expression in the secretory phase-endometrium of RM patients. The differences in endometrial gene expressions between healthy and RM subjects contribute to an increase in our knowledge on molecular mechanisms of RM development and may improve the outcome of pregnancies in high-risk women with RM.
    Matched MeSH terms: Computational Biology
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