Displaying publications 41 - 60 of 368 in total

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  1. Fouz N, Amid A, Hashim YZ
    Appl Biochem Biotechnol, 2014 Aug;173(7):1618-39.
    PMID: 24928548 DOI: 10.1007/s12010-014-0947-6
    The contributing molecular pathways underlying the pathogenesis of breast cancer need to be better characterized. The principle of our study was to better understand the genetic mechanism of oncogenesis for human breast cancer and to discover new possible tumor markers for use in clinical practice. We used complimentary DNA (cDNA) microarrays to compare gene expression profiles of treated Michigan Cancer Foundation-7 (MCF-7) with recombinant bromelain and untreated MCF-7. SpringGene analysis was carried out of differential expression followed by Ingenuity Pathway Analysis (IPA), to understand the underlying consequence in developing disease and disorders. We identified 1,102 known genes differentially expressed to a significant degree (p<0.001) changed between the treatment. Within this gene set, 20 genes were significantly changed between treated cells and the control cells with cutoff fold change of more than 1.5. These genes are RNA-binding motif, single-stranded interacting protein 1 (RBMS1), ribosomal protein L29 (RPL29), glutathione S-transferase mu 2 (GSTM2), C15orf32, Akt3, B cell translocation gene 1 (BTG1), C6orf62, C7orf60, kinesin-associated protein 3 (KIFAP3), FBXO11, AT-rich interactive domain 4A (ARID4A), COPS2, TBPL1|SLC2A12, TMEM59, SNORD46, glioma tumor suppressor candidate region gene 2 (GLTSCR2), and LRRFIP. Our observation on gene expression indicated that recombinant bromelain produces a unique signature affecting different pathways, specific for each congener. The microarray results give a molecular mechanistic insight and functional effects, following recombinant bromelain treatment. The extent of changes in genes is related to and involved significantly in gap junction signaling, amyloid processing, cell cycle regulation by BTG family proteins, and breast cancer regulation by stathmin1 that play major roles.
    Matched MeSH terms: Gene Expression Profiling*
  2. 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*
  3. Samadlouie HR, Hamidi-Esfahani Z, Alavi SM, Varastegani B
    Braz J Microbiol, 2014;45(2):439-45.
    PMID: 25242926
    The time courses for production of fungal biomass, lipid, phenolic and arachidonic acid (ARA) as well as expression of the genes involved in biosynthesis of ARA and lipid were examined in Mortierella alpina CBS 754.68. A significant increase in the arachidonic acid content in lipids that coincided with reduced levels of lipid was obtained. Reduced gene expression occurred presumably due to the steady reduction of carbon and nitrogen resources. However, these energy resources were inefficiently compensated by the breakdown of the accumulated lipids that in turn, induced up-regulated expression of the candidate genes. The results further indicated that the expression of the GLELO encoding gene is a rate-limiting step in the biosynthesis of ARA in the early growth phase.
    Matched MeSH terms: Gene Expression Profiling*
  4. Salehi MH, Kamalidehghan B, Houshmand M, Yong Meng G, Sadeghizadeh M, Aryani O, et al.
    PLoS One, 2014;9(4):e94069.
    PMID: 24705504 DOI: 10.1371/journal.pone.0094069
    Friedreich ataxia (FRDA) is the most frequent progressive autosomal recessive disorder associated with unstable expansion of GAA trinucleotide repeats in the first intron of the FXN gene, which encodes for the mitochondrial frataxin protein. The number of repeats correlates with disease severity, where impaired transcription of the FXN gene results in reduced expression of the frataxin protein. Gene expression studies provide insights into disease pathogenicity and identify potential biomarkers, an important goal of translational research in neurodegenerative diseases. Here, using real-time PCR (RT-PCR), the expression profiles of mitochondrial (mtDNA) and nuclear DNA (nDNA) genes that encode for the mitochondrial subunits of respiratory oxidative phosphorylation (OXPHOS) complex I in the blood panels of 21 FRDA patients and 24 healthy controls were investigated. Here, the expression pattern of mtDNA-encoded complex I subunits was distinctly different from the expression pattern of nDNA-encoded complex I subunits, where significant (p<0.05) down-regulation of the mitochondrial ND2, ND4L, and ND6 complex I genes, compared to controls, were observed. In addition, the expression pattern of one nDNA-encoded gene, NDUFA1, was significantly (p<0.05) down-regulated compared to control. These findings suggest, for the first time, that the regulation of complex I subunit expression in FRDA is complex, rather than merely being a reflection of global co-regulation, and may provide important clues toward novel therapeutic strategies for FRDA and mitochondrial complex I deficiency.
    Matched MeSH terms: Gene Expression Profiling*
  5. 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*
  6. 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*
  7. 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*
  8. 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. 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
  10. 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*
  11. 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*
  12. Latha B, Venkatesh B
    Genomics Proteomics Bioinformatics, 2004 Nov;2(4):222-36.
    PMID: 15901251
    As the topological properties of each spot in DNA microarray images may vary from one another, we employed granulometries to understand the shape-size content contributed due to a significant intensity value within a spot. Analysis was performed on the microarray image that consisted of 240 spots by using concepts from mathematical morphology. In order to find out indices for each spot and to further classify them, we adopted morphological multiscale openings, which provided microarrays at multiple scales. Successive opened microarrays were subtracted to identify the protrusions that were smaller than the size of structuring element. Spot-wise details, in terms of probability of these observed protrusions, were computed by placing a regularly spaced grid on microarray such that each spot was centered in each grid. Based on the probability of size distribution functions of these protrusions isolated at each level, we estimated the mean size and texture index for each spot. With these characteristics, we classified the spots in a microarray image into bright and dull categories through pattern spectrum and shape-size complexity measures. These segregated spots can be compared with those of hybridization levels.
    Matched MeSH terms: Gene Expression Profiling/statistics & numerical data
  13. Mollah MM, Jamal R, Mokhtar NM, Harun R, Mollah MN
    PLoS One, 2015;10(9):e0138810.
    PMID: 26413858 DOI: 10.1371/journal.pone.0138810
    Identifying genes that are differentially expressed (DE) between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA), are used to identify DE genes. However, most of these methods provide misleading results for two or more conditions with multiple patterns of expression in the presence of outlying genes. In this paper, an attempt is made to develop a hybrid one-way ANOVA approach that unifies the robustness and efficiency of estimation using the minimum β-divergence method to overcome some problems that arise in the existing robust methods for both small- and large-sample cases with multiple patterns of expression.
    Matched MeSH terms: Gene Expression Profiling/methods*
  14. Wang R, Hu X, Lü A, Liu R, Sun J, Sung YY, et al.
    Fish Shellfish Immunol, 2019 Nov;94:510-516.
    PMID: 31541778 DOI: 10.1016/j.fsi.2019.09.039
    Skin plays an important role in the innate immune responses of fish, particularly towards bacterial infection. To understand the molecular mechanism of mucosal immunity of fish during bacterial challenge, a de novo transcriptome assembly of crucian carp Carassius auratus skin upon Aeromonas hydrophila infection was performed, the latter with Illumina Hiseq 2000 platform. A total of 118111 unigenes were generated and of these, 9693 and 8580 genes were differentially expressed at 6 and 12 h post-infection, respectively. The validity of the transcriptome results of eleven representative genes was verified by quantitative real-time PCR (qRT-PCR) analysis. A comparison with the transcriptome profiling of zebrafish skin to A. hydrophila with regards to the mucosal immune responses revealed similarities in the complement system, chemokines, heat shock proteins and the acute-phase response. GO and KEGG enrichment pathway analyses displayed the significant immune responses included TLR, MAPK, JAK-STAT, phagosome and three infection-related pathways (ie., Salmonella, Vibrio cholerae and pathogenic Escherichia coli) in skin. To our knowledge, this study is the first to describe the transcriptome analysis of C. auratus skin during A. hydrophila infection. The outcome of this study contributed to the understanding of the mucosal defense mechanisms in cyprinid species.
    Matched MeSH terms: Gene Expression Profiling/veterinary
  15. Lim JC, Thevarajoo S, Selvaratnam C, Goh KM, Shamsir MS, Ibrahim Z, et al.
    J Basic Microbiol, 2017 Feb;57(2):151-161.
    PMID: 27859397 DOI: 10.1002/jobm.201600494
    Anoxybacillus sp. SK 3-4 is a Gram-positive, rod-shaped bacterium and a member of family Bacillaceae. We had previously reported that the strain is an aluminum resistant thermophilic bacterium. This is the first report to provide a detailed analysis of the global transcriptional response of Anoxybacillus when the cells were exposed to 600 mg L(-1) of aluminum. The transcriptome was sequenced using Illumina MiSeq sequencer. Total of 708 genes were differentially expressed (fold change >2.00) with 316 genes were up-regulated while 347 genes were down-regulated, in comparing to control with no aluminum added in the culture. Based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, the majority of genes encoding for cell metabolism such as glycolysis, sulfur metabolism, cysteine and methionine metabolism were up-regulated; while most of the gene associated with tricarboxylic acid cycle (TCA cycle) and valine, leucine and isoleucine metabolism were down-regulated. In addition, a significant number of the genes encoding ABC transporters, metal ions transporters, and some stress response proteins were also differentially expressed following aluminum exposure. The findings provide further insight and help us to understand on the resistance of Anoxybacillus sp. SK 3-4 toward aluminium.
    Matched MeSH terms: Gene Expression Profiling*
  16. Tan YC, Wong MY, Ho CL
    Plant Physiol Biochem, 2015 Nov;96:296-300.
    PMID: 26322853 DOI: 10.1016/j.plaphy.2015.08.014
    Basal stem rot is one of the major diseases of oil palm (Elaies guineensis Jacq.) caused by pathogenic Ganoderma species. Trichoderma and mycorrhizae were proposed to be able to reduce the disease severity. However, their roles in improving oil palm defence system by possibly inducing defence-related genes in the host are not well characterized. To better understand that, transcript profiles of eleven putative defence-related cDNAs in the roots of oil palm inoculated with Trichoderma harzianum T32 and mycorrhizae at different time points were studied. Transcripts encoding putative Bowman-Birk protease inhibitor (EgBBI2) and defensin (EgDFS) increased more than 2 fold in mycorrhizae-treated roots at 6 weeks post inoculation (wpi) compared to those in controls. Transcripts encoding putative dehydrin (EgDHN), glycine-rich RNA binding protein (EgGRRBP), isoflavone reductase (EgIFR), type 2 ribosome inactivating protein (EgT2RIP), and EgDFS increased in the oil palm roots treated with T. harzianum at 6 and/or 12 wpi compared to those in the controls. Some of these genes were also expressed in oil palm roots treated with Ganoderma boninense. This study provides an insight of some defence-related genes induced by Trichoderma and mycorrhizae, and their roles as potential agents to boost the plant defence system.
    Matched MeSH terms: Gene Expression Profiling*
  17. Nawaratna SS, Gobert GN, Willis C, Chuah C, McManus DP, Jones MK
    Mol Biochem Parasitol, 2014 Sep;196(2):82-9.
    PMID: 25149559 DOI: 10.1016/j.molbiopara.2014.08.002
    The intestinal tract of schistosomes opens at the mouth and leads into the foregut or oesophageal region that is lined with syncytium continuous with the apical cytoplasm of the tegument. The oesophagus is surrounded by a specialised gland, the oesophageal gland. This gland releases materials into the lumen of the oesophagus and the region is thought to initiate the lysis of erythrocytes and neutralisation of immune effectors of the host. The oesophageal region is present in the early invasive schistosomulum, a stage potentially targetable by anti-schistosome vaccines. We used a 44k oligonucleotide microarray to identify highly up-regulated genes in microdissected frozen sections of the oesophageal gland of male worms of S. mansoni. We show that 122 genes were up-regulated 2-fold or higher in the oesophageal gland compared with a whole male worm tissue control. The enriched genes included several associated with lipid metabolism and transmembrane transport as well as some micro-exon genes. Since the oesophageal gland is important in the initiation of digestion and the fact that it develops early after invasion of the mammalian host, further study of selected highly up-regulated functionally important genes in this tissue may reveal new anti-schistosome intervention targets for schistosomiasis control.
    Matched MeSH terms: Gene Expression Profiling*
  18. Liu S, Punthambaker S, Iyer EPR, Ferrante T, Goodwin D, Fürth D, et al.
    Nucleic Acids Res, 2021 06 04;49(10):e58.
    PMID: 33693773 DOI: 10.1093/nar/gkab120
    We present barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel insitu analyses (BOLORAMIS), a reverse transcription-free method for spatially-resolved, targeted, in situ RNA identification of single or multiple targets. BOLORAMIS was demonstrated on a range of cell types and human cerebral organoids. Singleplex experiments to detect coding and non-coding RNAs in human iPSCs showed a stem-cell signature pattern. Specificity of BOLORAMIS was found to be 92% as illustrated by a clear distinction between human and mouse housekeeping genes in a co-culture system, as well as by recapitulation of subcellular localization of lncRNA MALAT1. Sensitivity of BOLORAMIS was quantified by comparing with single molecule FISH experiments and found to be 11%, 12% and 35% for GAPDH, TFRC and POLR2A, respectively. To demonstrate BOLORAMIS for multiplexed gene analysis, we targeted 96 mRNAs within a co-culture of iNGN neurons and HMC3 human microglial cells. We used fluorescence in situ sequencing to detect error-robust 8-base barcodes associated with each of these genes. We then used this data to uncover the spatial relationship among cells and transcripts by performing single-cell clustering and gene-gene proximity analyses. We anticipate the BOLORAMIS technology for in situ RNA detection to find applications in basic and translational research.
    Matched MeSH terms: Gene Expression Profiling/methods*
  19. Esa E, Hashim AK, Mohamed EHM, Zakaria Z, Abu Hassan AN, Mat Yusoff Y, et al.
    Genet Test Mol Biomarkers, 2021 Mar;25(3):199-210.
    PMID: 33734890 DOI: 10.1089/gtmb.2020.0182
    Background: The association between dysregulated microRNAs (miRNAs) and acute myeloid leukemia (AML) is well known. However, our understanding of the regulatory role of miRNAs in the cytogenetically normal AML (CN-AML) subtype pathway is still poor. The current study integrated miRNA and mRNA profiles to explore novel miRNA-mRNA interactions that affect the regulatory patterns of de novo CN-AML. Methods: We utilized a multiplexed nanoString nCounter platform to profile both miRNAs and mRNAs using similar sets of patient samples (n = 24). Correlations were assessed, and an miRNA-mRNA network was constructed. The underlying biological functions of the mRNAs were predicted by gene enrichment. Finally, the interacting pairs were assessed using TargetScan and microT-CDS. We identified 637 significant negative correlations (false discovery rate <0.05). Results: Network analysis revealed a cluster of 12 miRNAs representing the majority of mRNA targets. Within the cluster, five miRNAs (miR-495-3p, miR-185-5p, let-7i-5p, miR-409-3p, and miR-127-3p) were posited to play a pivotal role in the regulation of CN-AML, as they are associated with the negative regulation of myeloid leukocyte differentiation, negative regulation of myeloid cell differentiation, and positive regulation of hematopoiesis. Conclusion: Three novel interactions in CN-AML were predicted as let-7i-5p:HOXA9, miR-495-3p:PIK3R1, and miR-495-3p:CDK6 may be responsible for regulating myeloid cell differentiation in CN-AML.
    Matched MeSH terms: Gene Expression Profiling/methods
  20. Chan MY, Efthymios M, Tan SH, Pickering JW, Troughton R, Pemberton C, et al.
    Circulation, 2020 10 13;142(15):1408-1421.
    PMID: 32885678 DOI: 10.1161/CIRCULATIONAHA.119.045158
    BACKGROUND: Heart failure (HF) is the most common long-term complication of acute myocardial infarction (MI). Understanding plasma proteins associated with post-MI HF and their gene expression may identify new candidates for biomarker and drug target discovery.

    METHODS: We used aptamer-based affinity-capture plasma proteomics to measure 1305 plasma proteins at 1 month post-MI in a New Zealand cohort (CDCS [Coronary Disease Cohort Study]) including 181 patients post-MI who were subsequently hospitalized for HF in comparison with 250 patients post-MI who remained event free over a median follow-up of 4.9 years. We then correlated plasma proteins with left ventricular ejection fraction measured at 4 months post-MI and identified proteins potentially coregulated in post-MI HF using weighted gene co-expression network analysis. A Singapore cohort (IMMACULATE [Improving Outcomes in Myocardial Infarction through Reversal of Cardiac Remodelling]) of 223 patients post-MI, of which 33 patients were hospitalized for HF (median follow-up, 2.0 years), was used for further candidate enrichment of plasma proteins by using Fisher meta-analysis, resampling-based statistical testing, and machine learning. We then cross-referenced differentially expressed proteins with their differentially expressed genes from single-cell transcriptomes of nonmyocyte cardiac cells isolated from a murine MI model, and single-cell and single-nucleus transcriptomes of cardiac myocytes from murine HF models and human patients with HF.

    RESULTS: In the CDCS cohort, 212 differentially expressed plasma proteins were significantly associated with subsequent HF events. Of these, 96 correlated with left ventricular ejection fraction measured at 4 months post-MI. Weighted gene co-expression network analysis prioritized 63 of the 212 proteins that demonstrated significantly higher correlations among patients who developed post-MI HF in comparison with event-free controls (data set 1). Cross-cohort meta-analysis of the IMMACULATE cohort identified 36 plasma proteins associated with post-MI HF (data set 2), whereas single-cell transcriptomes identified 15 gene-protein candidates (data set 3). The majority of prioritized proteins were of matricellular origin. The 6 most highly enriched proteins that were common to all 3 data sets included well-established biomarkers of post-MI HF: N-terminal B-type natriuretic peptide and troponin T, and newly emergent biomarkers, angiopoietin-2, thrombospondin-2, latent transforming growth factor-β binding protein-4, and follistatin-related protein-3, as well.

    CONCLUSIONS: Large-scale human plasma proteomics, cross-referenced to unbiased cardiac transcriptomics at single-cell resolution, prioritized protein candidates associated with post-MI HF for further mechanistic and clinical validation.

    Matched MeSH terms: Gene Expression Profiling*
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