GEO Accession viewer

NCBI > GEO > Accession Display

Not logged in | Login

GEO help: Mouse over screen elements for information.

Sample GSM7113857

Query DataSets for GSM7113857

Status

Public on May 30, 2023

Title

Pig 10, control/sham_BZ

Sample type

SRA

Source name

cardiac tissue

Organism

Sus scrofa domesticus

Characteristics

tissue: cardiac tissue
tissue region: border of infarcted cardiac LV tissue to non injured tissue
genotype: wild type
disease state: healthy
treatment: N/A

Extracted molecule

total RNA

Extraction protocol

Following excision of LV core from IZ, BZ, and RZ, samples were rinsed in 1X PBS (10x PBS Solution, 1.37M NaCl, 0.027M KCl and 0.119M phosphates, pH 7.4, Fisher BioReagents, Ref: BP3994) to remove any residual blood. Tissue was submerged in RNAlater® (Sigma-Aldrich, St. Louis, MO, Ref: R0901-500ML) and placed in 4ºC for 48 hours prior to storing in -80ºC. The epicardial surface of the LV core biopsy (1/3 of the tissue) was utilized for RNA extraction. Tissue samples were submerged in 400μL of Trizol reagent (ThermoFisher, Carlsbad, CA, Ref:15596026) and ground for 30 seconds in a 2mL flat centrifuge tube using an OMNI THQ digital rotor-stator (OMNI, Kennesaw, GA, Ref:12-500) at 15,000RPM and hard tissue OMNI TipTM (OMNI, Kennesaw, GA, Ref: 30750H). Each sample was ground using a fresh tip to prevent cross contamination. Tubes were gently rocked back and forth to mix contents and were left undisturbed at room temperature for 10 minutes. Thereafter, 100μL of chloroform (Millipore-Sigma, Milwaukee, WI, Ref:288306) was added, mixed, and centrifuged (Eppendorf 5424R, Hamburg, Germany) at 15,000RPM for 5 minutes. The supernatant was transferred to a new tube, to which lysis buffer from the Qiagen RNEasy Plus Mini kit (Qiagen, Hilden, Germany, Ref: 74134) containing BME was added to bring samples up to a volume of 650μL. The total volume of sample was then transferred to a gDNA eliminator column from the Qiagen RNEasy Plus Mini kit and centrifuged in the Eppendorf at 8,000RPM for 30 seconds. The flow-through from the centrifugation step was then mixed in a 1:1 volume ratio of 70% EtOH, resulting in a final concentration of 35% EtOH by volume of the total sample volume. The subsequent steps were followed according to kit manufacturer’s protocol. RNA samples were assessed for quality with an Advanced Analytics Fragment Analyzer (High Sensitivity RNA Analysis Kit – Ref: DNF-471-0500) and quantity with a Qubit RNA quantification kit (Qubit® RNA HS Assay KitAssay Kit – Ref: Q32852). RNA Quality Number (RQN) of samples ranged from 5.2 to 9.1.
Given satisfactory quality and quantity, samples were used for library builds. A total amount of 1μg RNA per sample was used as input material for the RNA sample preparations. Libraries were constructed using NEBNext®UltraTMRNA Library Prep Kit for Illumina® (NEB, USA) according to manufacturer’s recommendations. Index codes were added to attribute sequences to each sample. Poly-T oligo-attached magnetic beads were used to purify mRNA from the total RNA sample, and fragmentation was achieved using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer (5X). Subsequently, second strand cDNA synthesis was performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. To prepare for hybridization, NEBNext Adaptor with hairpin loop structure were ligated following 3’ end adenylation. The AMPure XP system (Beckman Coulter, Beverly, USA) was used to isolate cDNA fragments of approximately 150-200 base pairs long. PCR was performed using Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer following addition of 3μl USER Enzyme (NEB, USA) with size-selected, adaptor-ligated cDNA at 37°C for 15 minutes followed by 5 minutes at 95°C. Lastly, PCR products were purified (AMPure XP system), and library quality was assessed on the Agilent Bioanalyzer 2100 system.
A total amount of 1μg RNA per sample was used as input material for the RNA sample preparations. Libraries were constructed using NEBNext®UltraTMRNA Library Prep Kit for Illumina® (NEB, USA) according to manufacturer’s recommendations

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina NovaSeq 6000

Data processing

The clustering of the index-coded samples was performed on a cBot Cluster Generation Systemusing PECluster KitcBot-HS (Illumina) according to the manufacturer’s instructions. After cluster generation, the library preparationsweresequenced on an Illumina platform and 125 bp/150 bp paired-end reads were generated
Raw data (raw reads) of fastq format were firstly processed. In this step, clean data (clean reads) were obtainedbyremoving reads containing adapter, reads containing ploy-N and low quality reads from raw data. At the sametime, Q20,Q30 and GC content the clean data were calculated. All the downstream analyses were based on the cleandatawithhighquality
Reference genome and gene model annotation files were downloaded from genome website directly. Indexof thereference genome was built using hisat2 2.1.0 and paired-end clean reads were aligned to the reference genomeusingHISAT2. We selected HISAT2 as the mapping tool for that Hisat2 can generate a database of splice junctions basedon the gene model annotation file and thus a better mapping result than other non-splice mapping tools, whichis afast andsensitive alignment program for mapping next-generation sequencing reads against the general human population.
FeatureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene(Liao Y et al., 2013). AndthenFPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. FPKM, expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced, considers the effect of sequencing depth and gene length for the reads count at the same time, and is currentlythemostcommonly used method for estimating gene expression levels (Trapnell, Cole, et al., 2010)
(For DESeq2 with biological replicates) Differential expression analysis between two conditions/groups(two biological replicates per condition) was performed using the DESeq2 R package (1.14.1). DESeq2provide statistical routines for determining differential expression in digital gene expressiondatausingamodel based on the negative binomial distribution. The resulting P-values were adjusted usingtheBenjamini and Hochberg’s approach for controlling the False Discovery Rate(FDR). Genes withanadjustedP-value
Gene Ontology (GO) enrichment analysis of differentially expressed genes was implementedbytheclusterProfiler R package, in which gene length bias was corrected. GO terms with correctedPvaluelessthan 0.05 were considered significantly enriched by differential expressed genes. KEGG is a database resource for understanding high-level functions and utilities of the biological system,such as the cell, the organism and the ecosystem, from molecular level information, especiallylarge-scalemolecular datasets generated by genome sequencing and other high-through put experimental technologies(http://www.genome.jp/kegg/). We used clusterProfiler R package to test the statistical enrichmentofdifferential expression genes in KEGG pathways.
PPI analysis of differentially expressed genes was based on the STRING database, which known and predicted Protein-Protein Interactions. For the species existing in the database, we construct the networks by extract the target gene list from the database; Otherwise, Blastx (v2.2.28) was used to align the target gene sequences to the selected reference protein sequences, and then the networks was built according to the known interaction of selected reference species. Novel transcripts prediction and alternative splicing analysis The Cufflinks v2.1.1 Reference Annotation Based Transcript (RABT) assembly method was used to construct and identify both known and novel transcripts from TopHat alignment results. Alternative splicing events were classified to 12 basic types by the software Asprofile v1.0. The number of AS events in each sample was estimated, separately
Picard tools(v1.111) and samtools v0.1.18 were used to sort, mark duplicated reads and reorder the bam alignment results of each sample. GATK4.1 software was used to perform SNP calling
References Liao Y, Smyth GK and Shi W. featureCounts: an efficient general-purpose program for assigning sequence readstogenomic features. Bioinformatics, 2013. doi: 10.1093/bioinformatics/btt656(featureCounts) Anders S, Huber W. (2010).Differential expression analysis for sequence count data.Genome Biology,doi:10.1186/gb-2010-11-10-r106. (DESeq2) Robinson M D, McCarthy D J, Smyth G K. edgeR: a Bioconductor package for differential expressionanalysisof digital gene expression data[J]. Bioinformatics, 2010, 26(1): 139-140. (edgeR) Kanehisa, M., M. Araki, et al. (2008). KEGG for linking genomes to life and the environment. Nucleic acids research.(KEGG) Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. (2009). Ultrafast and memory‐efficient alignment of short DNA sequences to the human genome. Genome Biol.(Bowtie) Langmead, B. and S. L. Salzberg (2012). Fast gapped‐read alignment with Bowtie 2. Nature methods.(Bowtie 2) Mao, X., Cai, T., Olyarchuk, J.G., Wei, L. (2005). Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics.(KOBAS) Marioni, J. C., C. E. Mason, et al. (2008). RNA‐seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome research. Van der Auwera et al. (2013). From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Current protocols in bioinformatics. (GATK) Mortazavi, A., B. A. Williams, et al. (2008). Mapping and quantifying mammalian transcriptomes by RNA0Seq. Naturemethods. Shannon et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interactionnetworks.(Cytoscape) Trapnell, C. et al. (2010). Transcript assembly and quantification by RNA0 seq reveals unannotated transcripts andisoform switching during cell differentiation. Nat. Biotechnol.(Cufflinks) Daehwan K.,Ben L., et al. (2008).HISAT: Hierarchical Indexing for Spliced Alignment of Transcripts.(HISAT2) Trapnell, C., A. Roberts, et al. (2012). Differential gene and transcriptexpression analysis of RNA0 seq experimentswith TopHat and Cufflinks. nature protocols.(Tophat & Cufflinks) Wang, L.Feng, Z.Wang, X.Zhang, X. (2010). DEGseq:an R package for identifying differentially expressed genes from RNA0 seq data. Bioinformatics.(DEGseq) Wang, Z.,M.Gerstein, et al. (2009). RNA0 Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics. Young, M. D., Wakefield, M. J., Smyth, G. K., and Oshlack, A. (2010).Gene ontology analysis for RNA0seq:accounting for selection bias. Genome
Assembly: Sus scrofa version 10.2
Supplementary files format and content: tab-delimited text files include RPKM values for each sample

Submission date

Mar 23, 2023

Last update date

May 30, 2023

Contact name

Marissa Anne Lopez-Pier

E-mail(s)

mal1@arizona.edu

Phone

5202718292

Organization name

The University of Arizona