Multi-level transcriptome sequencing identifies COL1A1 as a candidate marker in human heart failure progression

Hua, Xiumeng; Wang, Yin-Ying; Jia, Peilin; Xiong, Qing; Hu, Yiqing; Chang, Yuan; Lai, Songqing; Xu, Yong; Zhao, Zhongming; Song, Jiangping

doi:10.1186/s12916-019-1469-4

Cited by 80 publications

(74 citation statements)

References 58 publications

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“…PCR was then amplified using Phusion High-Fidelity DNA polymerase with Universal PCR primers and Index (X) Primers. Finally, the PCR fragments were purified with AMPure XP system again, and each library quality was assessed with the Agilent Bioanalyzer 2100 system (Agilent Technologies, Inc. USA) 46 , 47 .…”

Section: Methodsmentioning

confidence: 99%

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Global tissue transcriptomic analysis to improve genome annotation and unravel skin pigmentation in goldfish

Gan

Chung-Davidson

Chen

et al. 2021

Sci Rep

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Goldfish is an ornamental fish with diverse phenotypes. However, the limited genomic resources of goldfish hamper our understanding of the genetic basis for its phenotypic diversity. To provide enriched genomic resources and infer possible mechanisms underlying skin pigmentation, we performed a large-scale transcriptomic sequencing on 13 adult goldfish tissues, larvae at one- and three-days post hatch, and skin tissues with four different color pigmentation. A total of 25.52 Gb and 149.80 Gb clean data were obtained using the PacBio and Illumina platforms, respectively. Onto the goldfish reference genome, we mapped 137,674 non-redundant transcripts, of which 5.54% was known isoforms and 78.53% was novel isoforms of the reference genes, and the remaining 21,926 isoforms are novel isoforms of additional new genes. Both skin-specific and color-specific transcriptomic analyses showed that several significantly enriched genes were known to be involved in melanogenesis, tyrosine metabolism, PPAR signaling pathway, folate biosynthesis metabolism and so on. Thirteen differentially expressed genes across different color skins were associated with melanogenesis and pteridine synthesis including mitf, ednrb, mc1r, tyr, mlph and gch1, and xanthophore differentiation such as pax7, slc2a11 and slc2a15. These transcriptomic data revealed pathways involved in goldfish pigmentation and improved the gene annotation of the reference genome.

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Section: Methodsmentioning

confidence: 99%

“…The clustering for the index-coded samples was carried on a cBot Cluster Generation System by using the TruSeq PE Cluster Kit v3-cBot-HS (Illumina) according to the manufacturer’s specifications. After cluster generation, the libraries were sequenced with the paired-end 150 bp reads model on an Illumina HiSeq X Ten platform 46 , 47 .…”

Section: Methodsmentioning

confidence: 99%

Global tissue transcriptomic analysis to improve genome annotation and unravel skin pigmentation in goldfish

Gan

Chung-Davidson

Chen

et al. 2021

Sci Rep

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“…Furthermore, we used Seurat ( Stuart et al, 2019 ) to identify the marker genes of cell groups by calling differentially expressed genes (DEGs) between the cells of each sub-group and the remaining cells. DEGs were selected if the fold change of log2-transformed expression level was > 1 and p < 0.05 ( Xu et al, 2012 ; Hua et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Differential Expression of Viral Transcripts From Single-Cell RNA Sequencing of Moderate and Severe COVID-19 Patients and Its Implications for Case Severity

et al. 2020

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With steady increase of new COVID-19 cases around the world, especially in the United States, health care resources in areas with the disease outbreak are quickly exhausted by overwhelming numbers of COVID-19 patients. Therefore, strategies that can effectively and quickly predict the disease progression and stratify patients for appropriate health care arrangements are urgently needed. We explored the features and evolutionary difference of viral gene expression in the SARS-CoV-2 infected cells from the bronchoalveolar lavage fluids of patients with moderate and severe COVID-19 using both single cell and bulk tissue transcriptome data. We found SARS-CoV-2 sequences were detectable in 8 types of immune related cells, including macrophages, T cells, and NK cells. We first reported that the SARS-CoV-2 ORF10 gene was differentially expressed in the severe vs. moderate samples. Specifically, ORF10 was abundantly expressed in infected cells of severe cases, while it was barely detectable in the infected cells of moderate cases. Consequently, the expression ratio of ORF10 to nucleocapsid (N) was significantly higher in severe than moderate cases (p = 0.0062). Moreover, we found transcription regulatory sequences (TRSs) of the viral leader sequence-independent fusions with a 5' joint point at position 1073 of SARS-CoV-2 genome were detected mainly in the patients with death outcome, suggesting its potential indication of clinical outcome. Finally, we identified the motifs in TRS of the viral leader sequence-dependent fusion events of SARS-CoV-2 and compared with that in SARS-CoV, suggesting its evolutionary trajectory. These results implicated potential roles and predictive features of viral transcripts in the pathogenesis of COVID-19 moderate and severe patients. Such features and evolutionary patterns require more data to validate in future.

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“…Experiments show that expression of FAP (fibroblast activation protein alpha) [66], THBS4 [67], CD27 [68], LEF1 [69], CTHRC1 [70], ESR1 [71], CXCL9 [72], SERPINA3 [73], TRPC4 [74], F13A1 [75], PIK3C2A [76], KCNIP2 [77] and GPR4 [78] contributed to myocardial infarction. MFAP4 [79], ALOX15 [80], COL1A1 [81], APOA1 [82], PDE5A [83], CX3CR1 [84], THY1 [85], GREM1 [86], FMOD (fibromodulin) [87], NPPA (natriuretic peptide A) [88], LTBP2 [89], LUM (lumican) [90], IL34 [91], NRG1 [92], CXCL14 [93], CXCL10 [94], ACE (angiotensin I converting enzyme) [95], CFTR (ystic fibrosis transmembrane conductance regulator) [96], S100A8 [97], S100A9 [97], HP (haptoglobin) [98] [162], Zhang et al [163] and Chen et al [164] study indicated that the expression of CCL22, CCR1, FPR1, KNG1, CRISPLD2, CD38 and GPRC5A were linked with progression of ischemic heart disease. Li et al [165] showed that STEAP3 expression can be associated with cardiac hypertrophy progression.…”

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Vastrad¹,

Tengli

Vastrad³

2021

Preprint

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Heart 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. The 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. A 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. The 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.

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Multi-level transcriptome sequencing identifies COL1A1 as a candidate marker in human heart failure progression

Cited by 80 publications

References 58 publications

Global tissue transcriptomic analysis to improve genome annotation and unravel skin pigmentation in goldfish

Global tissue transcriptomic analysis to improve genome annotation and unravel skin pigmentation in goldfish

Differential Expression of Viral Transcripts From Single-Cell RNA Sequencing of Moderate and Severe COVID-19 Patients and Its Implications for Case Severity

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

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