Dataset Information

Sub-pathway based approach to systematically track candidate sub-pathway biomarkers for heart failure.

ABSTRACT: Identification of potential novel biomarkers for heart failure was undertaken using a sub-pathway based method. To realize this goal, heart failure-relevant dataset, reference pathways, and lncRNA-miRNA-mRNA interactions were firstly recruited. Secondly, the informative pathways were extracted relying on KEGG pathways and the mRNAs in the PCC-weighted lncRNA-mRNA interactions. Thirdly, lncRNA-regulated sub-pathways were dissected after construction of condition-specific lncRNA competitively regulated pathways (LCRP). To detect crucial heart failure-relevant lncRNAs, degree analysis was conducted for all nodes within the LCRP. Ultimately, the significance of candidate sub-pathways were assessed to further identify the significant sub-pathways. There were 44 lncRNAs, 165 mRNAs and 224 co-expressed interactions. After putting the 165 mRNAs into the reference pathways, 56 informative pathways were obtained which were then embedded into undirected graphs, and 44 lncRNAs were inserted into the pathway graphs to further construct the condition-specific LCRP. According to degree distribution, 4 hub lncRNAs were selected, including ERVK13-1, YLPM1, PDXDC2P, and LINC00482. Based on the LCRP information, a total of 36 sub-pathways mediated by lncRNAs participated in 40 complete pathways. Among these 40 pathways, we mainly concentrated on the top three sub-pathways, including a sub-part of MAPK signaling pathway, an important sub-part in ErbB signaling pathway, and a part of chemokine signaling pathway. In the top 3 significant sub-pathways, gene AKT3 was simultaneously regulated by ERVK13-1, YLPM1, and PDXDC2P. Sub-pathways including MAPK signaling pathway and hub lncRNAs (ERVK13-1, YLPM1, and PDXDC2P) may play an important role in heart failure.

SUBMITTER: Han DM

PROVIDER: S-EPMC6434253 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Sub-pathway based approach to systematically track candidate sub-pathway biomarkers for heart failure.

Han Dong-Mei DM

Experimental and therapeutic medicine 20190226 4

Identification of potential novel biomarkers for heart failure was undertaken using a sub-pathway based method. To realize this goal, heart failure-relevant dataset, reference pathways, and lncRNA-miRNA-mRNA interactions were firstly recruited. Secondly, the informative pathways were extracted relying on KEGG pathways and the mRNAs in the PCC-weighted lncRNA-mRNA interactions. Thirdly, lncRNA-regulated sub-pathways were dissected after construction of condition-specific lncRNA competitively regu ...[more]

PMID: 30936989

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Project description:IntroductionHeart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules.MethodsThe expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed.ResultsA total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene-miRNA regulatory network and target gene-TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed.ConclusionThe current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

Project description:BackgroundReduction of 30-day all-cause readmissions for heart failure (HF) has become an important quality-of-care metric for health care systems. Many hospitals have implemented quality improvement programs designed to reduce 30-day all-cause readmissions for HF. Electronic medical record (EMR)-based measures have been employed to aid in these efforts, but their use has been largely adjunctive to, rather than integrated with, the overall effort.ObjectivesWe hypothesized that a comprehensive EMR-based approach utilizing an HF dashboard in addition to an established HF readmission reduction program would further reduce 30-day all-cause index hospital readmission rates for HF.MethodsAfter establishing a quality improvement program to reduce 30-day HF readmission rates, we instituted EMR-based measures designed to improve cohort identification, intervention tracking, and readmission analysis, the latter 2 supported by an electronic HF dashboard. Our primary outcome measure was the 30-day index hospital readmission rate for HF, with secondary measures including the accuracy of identification of patients with HF and the percentage of patients receiving interventions designed to reduce all-cause readmissions for HF.ResultsThe HF dashboard facilitated improved penetration of our interventions and reduced readmission rates by allowing the clinical team to easily identify cohorts with high readmission rates and/or low intervention rates. We significantly reduced 30-day index hospital all-cause HF readmission rates from 18.2% at baseline to 14% after implementation of our quality improvement program ( P = .045). Implementation of our EMR-based approach further significantly reduced 30-day index hospital readmission rates for HF to 10.1% ( P for trend = .0001). Daily time to screen patients decreased from 1 hour to 15 minutes, accuracy of cohort identification improved from 83% to 94.6% ( P = .0001), and the percentage of patients receiving our interventions, such as patient education, also improved significantly from 22% to 100% over time ( P < .0001).ConclusionsIn an institution with a quality improvement program already in place to reduce 30-day readmission rates for HF, an EMR-based approach further significantly reduced 30-day index hospital readmission rates.

Project description:Spermathecal fluid proteins were cleaned up via acetone precipitation overnight at 20 C. The precipitated protein was pelleted and washed twice with 500 ul of 80% acetone, then the pellet was allowed to air dry (ca. 5 min) prior to solubilization in 50 ul of digestion buffer (6 M urea, 2 M thiourea). Approximately 25 ug of protein were reduced (0.5 ug dithiothreitol, 20 min), alkylated (2.5 ug iodoacetamide, 30 min, dark), and digested (0.5 ug Lys-C for 3 h, then 0.5 ug trypsin overnight). Digested peptides were acidified with one volume of 1% trifluoroacetic acid and desalted with high-capacity STAGE tips. Eluted samples were dried (SpeedVac, Eppendorf, 45 min) and resuspended in Buffer A (0.1% formic acid). Peptide concentrations were determined using a NanoDrop (Thermo, 280 nm) and sample orders were randomized for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Peptides (0.5 ug for each sample) were injected on an EASY-nLC 1000 liquid chromatography system (Thermo) coupled to an Impact II Q-TOF mass spectrometer (Bruker). The LC system included a fused-silica (5 um Aqua C18 particles (Phenomenex)) fritted 2 cm trap column connected to a 50 cm analytical column packed with ReproSil C18 (3 um C18 particles (Dr. Maisch)). The separation gradient ran from 5% to 35% Buffer B (80% acetonitrile, 0.1% formic acid) over 120 min, followed by a 15 min wash at 95% Buffer B (flow rate: 250 uL/min). The instrument parameters were: scan from 150 to 2200 m/z, 100 us transient time, 10 us prepulse storage, 7 eV collision energy, 1500 Vpp Collision RF, a +2 default charge state, 18 Hz spectral acquisition rate, 3.0 s cycle time, and the intensity threshold was 250 counts. Mass spectrometry data were searched using MaxQuant (v1.6.1.0) using default parameters, except match between runs was enabled. Peptide spectral matches, peptide identifications and protein identifications were controlled at 1% false discovery rates (FDRs). Data were analyzed in Perseus (v1.6.1.1). Proteins differentially expressed between heat-shocked and non-heat-shocked samples, as well as among tissues (semen, virgin spermathecae, and mated spermathecae), were identified using t-tests and significant results were corrected to either 5% (heat-shock and cold-shock) or 10% FDR (pesticide stress) (Banjamini-Hochberg method).

Project description:BackgroundThe ShockOmics study (ClinicalTrials.gov identifier NCT02141607) is a multicenter prospective observational trial aimed at identifying new biomarkers of acute heart failure in circulatory shock, by means of a multiscale analysis of blood samples and hemodynamic data from subjects with circulatory shock.Methods and designNinety septic shock and cardiogenic shock patients will be recruited in three intensive care units (ICU) (Hôpital Erasme, Université Libre de Bruxelles, Belgium; Hospital Universitari Mutua Terrassa, Spain; Hôpitaux Universitaires de Genève, Switzerland). Hemodynamic signals will be recorded every day for up to seven days from shock diagnosis (time T0). Clinical data and blood samples will be collected for analysis at: i) T1 < 16 h from T0; ii) T2 = 48 h after T0; iii) T3 = day 7 or before discharge or before discontinuation of therapy in case of fatal outcome; iv) T4 = day 100. The inclusion criteria are: shock, Sequential Organ Failure Assessment (SOFA) score > 5 and lactate levels ≥ 2 mmol/L. The exclusion criteria are: expected death within 24 h since ICU admission; > 4 units of red blood cells or >1 fresh frozen plasma transfused; active hematological malignancy; metastatic cancer; chronic immunodepression; pre-existing end stage renal disease requiring renal replacement therapy; recent cardiac surgery; Child-Pugh C cirrhosis; terminal illness. Enrollment will be preceded by the signature of the Informed Consent by the patient or his/her relatives and by the physician in charge. Three non-shock control groups will be included in the study: a) healthy blood donors (n = 5); b) septic patients (n = 10); c) acute myocardial infarction or patients with prolonged acute arrhythmia (n = 10). The hemodynamic data will be downloaded from the ICU monitors by means of dedicated software. The blood samples will be utilized for transcriptomics, proteomics and metabolomics ("-omics") analyses.DiscussionShockOmics will provide new insights into the pathophysiological mechanisms underlying shock as well as new biomarkers for the timely diagnosis of cardiac dysfunction in shock and quantitative indices for assisting the therapeutic management of shock patients.

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Sub-pathway based approach to systematically track candidate sub-pathway biomarkers for heart failure.

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Sub-pathway based approach to systematically track candidate sub-pathway biomarkers for heart failure.

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