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.
METHODS: In this study, the metabolic responses of C. glabrata under acetate growth condition was explored using high-throughput transcriptomic and proteomic approaches.
RESULTS: Collectively, a total of 1482 transcripts (26.96%) and 242 proteins (24.69%) were significantly up- or down-regulated. Both transcriptome and proteome data revealed that the regulation of alternative carbon metabolism in C. glabrata resembled other fungal pathogens such as Candida albicans and Cryptococcus neoformans, with up-regulation of many proteins and transcripts from the glyoxylate cycle and gluconeogenesis, namely isocitrate lyase (ICL1), malate synthase (MLS1), phosphoenolpyruvate carboxykinase (PCK1) and fructose 1,6-biphosphatase (FBP1). In the absence of glucose, C. glabrata shifted its metabolism from glucose catabolism to anabolism of glucose intermediates from the available carbon source. This observation essentially suggests that the glyoxylate cycle and gluconeogenesis are potentially critical for the survival of phagocytosed C. glabrata within the glucose-deficient macrophages.
CONCLUSION: Here, we presented the first global metabolic responses of C. glabrata to alternative carbon source using transcriptomic and proteomic approaches. These findings implicated that reprogramming of the alternative carbon metabolism during glucose deprivation could enhance the survival and persistence of C. glabrata within the host.
MATERIALS AND METHODS: Using next-generation sequencing, the miRNAs profiles of CM (n=3) and PM (n=3) moles, including placenta of non-molar abortus (n=3) as control were determined. The differentially expressed miRNAs between each group were analysed. Subsequently, bioinformatics analysis using miRDB and Targetscan was utilised to predict target genes.
RESULTS: We found 10 differentially expressed miRNAs in CMs and PMs, compared to NMAs, namely miR- 518a-5p, miR-423-3p, miR-503-5p, miR-302a-3p, and miR-1323. The other 5 miRNAs were novel, not listed in the known database. The 3 differentially expressed miRNAs in CMs were predicted to commonly target ZTBT46 and FAM73B mRNAs.
DISCUSSION: miR-518 was consistently observed to be downregulated in CM versus PM, and CM versus NMA. Further bioinformatic analysis to provide insight into the possible role of these miRNAs in the pathogenesis of HMs, progression of disease and as potential diagnostic biomarkers as well as therapeutic targets for HMs is needed.
FINDINGS: We optimized the assembly of a Hevea bark transcriptome based on 16 Gb Illumina PE RNA-Seq reads using the Oases assembler across a range of k-mer sizes. We then assessed assembly quality based on transcript N50 length and transcript mapping statistics in relation to (a) known Hevea cDNAs with complete open reading frames, (b) a set of core eukaryotic genes and (c) Hevea genome scaffolds. This was followed by a systematic transcript mapping process where sub-assemblies from a series of incremental amounts of bark transcripts were aligned to transcripts from the entire bark transcriptome assembly. The exercise served to relate read amounts to the degree of transcript mapping level, the latter being an indicator of the coverage of gene transcripts expressed in the sample. As read amounts or datasize increased toward 16 Gb, the number of transcripts mapped to the entire bark assembly approached saturation. A colour matrix was subsequently generated to illustrate sequencing depth requirement in relation to the degree of coverage of total sample transcripts.
CONCLUSIONS: We devised a procedure, the "transcript mapping saturation test", to estimate the amount of RNA-Seq reads needed for deep coverage of transcriptomes. For Hevea de novo assembly, we propose generating between 5-8 Gb reads, whereby around 90% transcript coverage could be achieved with optimized k-mers and transcript N50 length. The principle behind this methodology may also be applied to other non-model plants, or with reads from other second generation sequencing platforms.