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Next Generation Sequencing Facilitates Quantitative Analysis of Transcriptomes in HeLa cells upon Hippo Components Overexpression or Knockout

ABSTRACT: Purpose: The goals of this study are to compare and integrate NGS-derived transcriptome when Hippo components are manipulated in HeLa cells. Methods: Total RNAs from three replicates were extracted using TRIzol (Thermo Fisher Scientific) from HeLa cells. RNA quality was assessed using a bioanalyzer (Agilent Technologies). RNA-seq libraries were prepared according to the Illumina TruSeq protocol. Then were pooled for deep sequencing by using Illumina Hiseq Xten (2 × 150) platforms at the Omics Core of Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai, China. Raw read qualities were evaluated with FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/, v0.11.5). Adaptor sequences and read sequences on both ends with Phred quality scores below 30 were trimmed. Trimmed reads were then mapped with the Hisat2 algorithm (version 2.1.0) to target sequences. Gene expression levels were quantified by the software package HTSeq (v0.6.1p1). The list of differentially expressed genes were generated by DESeq2 with FC：1.5，and adjust p-value<0.05.The significant genes were then compared with each other. Results: We finally get 558 genes that have dramatical change in different groups. Conclusions: Our study shows many common genes that can be loyally regulated by Hippo signaling.

ORGANISM(S): Homo sapiens

PROVIDER: GSE157717 | GEO | 2020/09/10

REPOSITORIES: GEO

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Project description:RNA-seq analysis was performed to understand the role of type I IFN response during SARS CoV-2 infection using transgenic mice. Each sample was collected from an individual C57BL/6J mouse. The total RNA was extracted from uninfected and SARS-CoV-2 infected mice lung tissue using RNeasy mini kit (QIAGEN #74104). The quantity of RNA was determined using Qubit RNA assay kit with Qubit 4.0 and the quality of RNA was tested using agarose gel electrophoresis and High Sensitivity Tape station Kit (Agilent 2200, #5067-5576, #5067-5577 and #5067-5578). After assessing the quality of RNA, ~900 ng of total RNA was taken for library preparation using NEBNext®Ultra™ II Directional RNA Library kit for Illumina (# E7760L) and NEBNext Poly (A) mRNA Magnetic Isolation Module (# E7490L) as per manufacturer's protocol. The prepared library was quantified using Qubit dsDNA assay kit (Invitrogen, Q32851) followed by quality check (QC) and fragment size distribution using a High Sensitivity Tape station Kit (Agilent 2200, #5067-5584 and #5067-5585). The library was sequenced using the HiSeq 4000 Illumina platform. The paired-end (PE) reads quality checks for each sample were carried out using FastQC v.0.11.5 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The adapter sequence was trimmed using the BBDuk version 37.58 version 37.58 and the alignment was performed using STAR v.2.5.3a with default parameters with human hg38 genome build, gencode v21 gtf 9GRCh38) from the gencode. The duplicates were discarded using Picard-2.9.4 (https://broadinstitute.github.io/picard/) from the aligned bam files and read counts were generated using featureCount v.1.5.3 from subread-1.5.3 package (https://bioinf.wehi.edu.au/) with Q = 10 for mapping quality. The count files were used as input for downstream differential gene expression analysis with DESeq2 version 1.14.1 9. The genes with read counts of ≤ 10 in any comparison were discarded followed by count transformation and statistical analysis using DESeq “R”. The “P” value were adjusted using the Benjamini and Hochberg multiple testing correction and the differentially expressed genes were identified (fold change of ≥1.5, P-value < 0.05). A unified non-redundant gene list was made for different comparisons and subjected to gene ontology (GO) analysis using the reactome database (https://reactome.org/). The top pathways (p < 0.05) were used for generating heat maps using Complexheatmap (Version 2.0.0) through unsupervised hierarchical clustering. The expression clusters were annotated based on enriched GO terms. Normalized gene expression was used to generate the boxplots with a median depicting the trends in the expression across the different conditions using ggplot2 [version 3.3.5]. The pathways analysis was performed using Metascape database (https://metascape.org/gp/index.html#/main/step1). The top pathways (p < 0.05) were taken for constructing bubble plots using ggplot2 [version 3.3.5].

Project description:Purpose: The goal of this study is to identify the miRNA clusters that are regulated by EGFR under normoxia or hypoxia. Method: Total RNAs were extracted from HeLa cells expressing scrambled control or EGFR shRNA-E1 that cultured under normoxia or hypoxia (1% O2) for 24h. Customized Next-Generation RNA deep sequencing, including both small RNA application and whole transcriptome analysis, was performed according to the standard procedure instructed by Applied Biosystems. For small RNA analysis, library inserts were size selected between 18 and 40nts and analyzed using CLC Genomics Workbench 4.7.1. 35nt colorspace reads were trimmed of adaptor sequence and mapped against human pre-miR sequences (miRBase version 16.0). Values of reads per million mapped reads (RPM) were based on mapped reads with no more than 2 mismatches total. A read was considered to come from a mature miRNA if it mapped to pre-miRNA sequences with no more than three upstream or downstream bases, and missing no more than two upstream or downstream bases from predicted mature or mature* sequences as defined in miRBase version 16.0. All the other pre-miRNA mapped reads were assigned as pre-miRNA signal. qRT–PCR validation was performed using TaqMan and SYBR Green assays. Results: Deep sequencing analysis identified specific miRNA clusters that their maturation (miRNA processing efficacy was reflected by the relative expression of precursor miRNAs affected by EGFR compared with the relative expression of mature miRNAs affected by EGFR) were regulated by EGFR under normixa or specifically in response to hypoxia. Top miRNA candidates that regulated by EGFR in response to hypoxia were further verified by TaqMan and SYBR Green qRT-PCR assays. Conclusion: Next-Generation Deep Sequencing for small RNA analysis revealed a novel function of EGFR involved in miRNA maturation in response to hypoxic stress. RNA profiles of HeLa cells expressing scrambled control (S) or EGFR shRNA-E1 (A1) that cultured under normoxia or hypoxia (1% O2) for 24h were generated by AB SOLiD next-generation sequencing. S: HeLa expressing scrambled control cultured under normoxia; A1: HeLa expressing EGFR shRNA-E1 cultured under normoxia; HS: HeLa expressing scrambled control cultured under hypoxia for 24h; HA1: HeLa expressing EGFR shRNA-E1 cultured under hypoxia for 24h. In total, 4 biological samples with no replicates resulted in 4 small RNA profiles.

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Next Generation Sequencing Facilitates Quantitative Analysis of Transcriptomes in HeLa cells upon Hippo Components Overexpression or Knockout

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