Hydro-tRNAseq of human cell lines HACAT and HepG2
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB41890 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR4976130_1.fastq.gz | Fastqsanger.gz | |||
| ERR4976130_2.fastq.gz | Fastqsanger.gz | |||
| ERR4976131_1.fastq.gz | Fastqsanger.gz | |||
| ERR4976131_2.fastq.gz | Fastqsanger.gz | |||
| ERR4976132_1.fastq.gz | Fastqsanger.gz |
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Project description:To quantify tRNA expression, we perform hydro-tRNA sequencing (Gogakos et al. 2017) in HACAT (keratinoytes) and HepG2 (liver) human cell lines. This study contains 6 samples. Three replicates for each of the five cell lines. This data complements our previously published data (GEO: GSE137834), which contained five additional cell lines from different tissues: HEK293 (kidney), HCT116 (colon), HeLa (cervix), MDA-MB-231 (breast), and BJ fibroblasts. Therefore, with these extra two cell lines, this constitutes a tissue-wide dataset of tRNA sequencing covering a total of seven human cell lines.
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Project description:We analyze whether tRNA quantifications based on small RNA-seq data are informative enough to distinguish between different human cell lines covering multiple tissue types. We therefore apply both small RNA-seq and Hydro-tRNAseq to HEK293 (kidney), HCT116 (colon), HeLa (cervix), MDA-MB-231 (breast), and BJ fibroblasts. First, the correlations between the two methods of identical samples and computational mapping pipeline range between 0.93 and 0.96 for all cell lines. tRNA quantifications from both protocols are compared and significantly higher Spearman correlations are obtained within matching samples versus mismatching cell lines. In consequence, we demonstrate that small RNA-seq quantifications of sample-specific tRNA profiles show a good agreement with conventional tRNA-seq.
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Project description:tRNA quantification of five human cell lines by hydro-tRNAseq and small RNA-seq
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Project description:The participation of transfer RNAs (tRNAs) in fundamental aspects of biology and disease necessitates an accurate, experimentally confirmed annotation of tRNA genes, and curation of precursor and mature tRNA sequences. This has been challenging, mainly because RNA secondary structure and nucleotide modifications, together with tRNA gene multiplicity, complicate sequencing and sequencing read mapping efforts. To address these issues, we developed hydro-tRNAseq, a method based on partial alkaline RNA hydrolysis that generates fragments amenable for sequencing. To identify transcribed tRNA genes, we further complemented this approach with Photoactivatable Crosslinking and Immunoprecipitation (PAR-CLIP) of SSB/La, a conserved protein involved in pre-tRNA processing. Our results show that approximately half of all predicted tRNA genes are transcribed in human cells. We also report predominant nucleotide modification sites, their order of introduction, and identify tRNA leader, trailer and intron sequences. By using complementary sequencing-based methodologies, we present a human tRNA atlas, and determine expression levels of mature and processing intermediates of tRNAs in human cells.
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Project description:Specific regulation of target genes by transforming growth factor-β (TGF-β) in a given cellular context is determined in part by transcription factors and cofactors that interact with the Smad complex. In the present study, we determined Smad2 and Smad3 (Smad2/3) binding regions in the promoters of known genes in HepG2 hepatoblastoma cells, and compared them to those in HaCaT epidermal keratinocytes to elucidate the mechanisms of cell-type- and context-dependent regulation of transcription induced by TGF-β. Our results show that 81% of the Smad2/3 binding regions in HepG2 cells were not shared with those found in HaCaT cells. Hepatocyte nuclear factor 4α (HNF4α) is expressed in HepG2 cells, but not in HaCaT cells, and the HNF4α binding motif was identified as an enriched motif in the HepG2-specific Smad2/3 binding regions. ChIP-sequencing analysis of HNF4α binding regions under TGFα stimulation revealed that 32.5% of the Smad2/3 binding regions overlapped HNF4α bindings. MIXL1 was identified as a new combinatorial target of HNF4α and Smad2/3, and both the HNF4α protein and its binding motif were required for the induction of MIXL1 by TGF-β in HepG2 cells. These findings generalize the importance of binding of HNF4α on Smad2/3 binding genomic regions for HepG2-specific regulation of transcription by TGF-β, and suggest that certain transcription factors expressed in a cell-type-specific manner play important roles in the transcription regulated by the TGF-β-Smad signaling pathway.
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Project description:Specific regulation of target genes by transforming growth factor-β (TGF-β) in a given cellular context is determined in part by transcription factors and cofactors that interact with the Smad complex. In the present study, we determined Smad2 and Smad3 (Smad2/3) binding regions in the promoters of known genes in HepG2 hepatoblastoma cells, and compared them to those in HaCaT epidermal keratinocytes to elucidate the mechanisms of cell type- and context-dependent regulation of transcription induced by TGF-β. Our results show that 81% of the Smad2/3 binding regions in HepG2 cells were not shared with those found in HaCaT cells. Hepatocyte nuclear factor 4α (HNF4α) is expressed in HepG2 cells, but not in HaCaT cells, and the HNF4α binding motif was identified as an enriched motif in the HepG2-specific Smad2/3 binding regions. ChIP-sequencing analysis of HNF4A binding regions under TGF-β stimulation revealed that 32.5% of the Smad2/3 binding regions overlapped HNF4A bindings. MIXL1 was identified as a new combinatorial target of HNF4A and Smad2/3, and both the HNF4A protein and its binding motif were required for the induction of MIXL1 by TGF-β in HepG2 cells. These findings generalize the importance of binding of HNF4A on Smad2/3 binding genomic regions for HepG2-specific regulation of transcription by TGF-β, and suggest that certain transcription factors expressed in a cell-type-specific manner play important roles in the transcription regulated by the TGF-β-Smad signaling pathway. HepG2 cells were treated with TGF-beta for 1.5 h or left untreated. anti-HNF4A ChIP-seq was performed. One lane was used for each sample.
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Project description:The p53 protein is encoded by TP53 gene and plays the key role in significant number of cellular processes including proliferation, apoptosis and regulation of many stress response pathways. P53 acts like a direct transcription activator of numerous genes regulating cell cycle arrest, DNA repair, growth inhibition and many others (Mollereau and Ma, 2014). The canonical biological function of p53 is maintaining genome integrity via elimination of damaged or exposed to genotoxic stress cells. Immortalized HaCaT cells are widely used for keratinocyte research, since they maintain stable keratinocyte phenotype, have nearly unlimited proliferative potential, do not require specific growth and differentiation factors (Colombo et al., 2017). Also, HaCaT cells produce typical differentiation markers such as cytokeratins K14 and K10, involucrin (Colombo et al., 2017) and respond to keratinocyte differentiation stimuli. Taking together, HaCaT cells have similar to normal human keratinocytes (NHK) properties, however, as many of spontaneously immortalized cell lines HaCaT cells bear two mutant p53 alleles - R282Q and H179Y (Lehman et al., 1993). Mutp53 in HaCaT has an increased affinity to other p53 family members (p63, p73), which significantly expands p53 properties. Moreover, mutp53 indirectly affects specific target genes via protein-protein interactions with other transcription factors (NF-Y, E2F1, NF-KB) or by tethering p63 to new promotor locations. For more detailed investigation of mutp53 impact on various processes in HaCaT cells we performed a shRNA mediated knockdown of mutp53. For generation of stable TP53 knockdown we employed plasmid vector pLKO-p53-shRNA-941 (Addgene # #25637) followed by puromycin selection of transduced cells. Here we present proteomic dataset obtained from wild type HaCaT cells and p53 knock down HaCaT keratinocytes.
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Project description:High-resolution quantitative profiling of tRNA abundance and modification status in hematopoietic cells upon Trmt6 deletion by mim-tRNAseq
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