Computational protocol: Kinome and Transcriptome Profiling Reveal Broad and Distinct Activities of Erlotinib, Sunitinib, and Sorafenib in the Mouse Heart and Suggest Cardiotoxicity From Combined Signal Transducer and Activator of Transcription and Epidermal Growth Factor Receptor Inhibition

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Protocol publication

[…] Five percent of each sample was first run on a 60‐minute LC gradient and then equalized on total peptide content before combining. Peptides were resuspended in 2% ACN and 0.1% formic acid. Thirty percent of the final peptide suspension was injected onto an Easy nLC‐1000 through a Thermo Easy‐Spray 75 μm×25 cm C‐18 column and separated on a 300‐minute gradient (5%–40% ACN). ESI parameters: 3e6 AGC MS1, 80 ms MS1 max inject time, 1e5 AGC MS2, 100 ms MS2 max inject time, 20 loop count, 1.8 m/z isolation window, 45‐s dynamic exclusion. Spectra were searched against the Uniprot/Swiss‐Prot database with Sequest HT on Proteome Discoverer software. Only peptides with medium or greater confidence (5% FDR) were considered for quantitation, and peptides with >75% co‐isolation interference were omitted. Data for each KI‐treated sample were processed as fold change relative to a pool of 4 vehicle‐treated control samples. After log2, average and SD were calculated to determine consistent changes in kinase MIB‐binding. [...] mRNA‐Seq libraries were constructed using 4 μg total RNA with the Stranded mRNA‐Seq Kit (KAPA Biosystems). Three hearts each were used from each condition (control, erlotinib, sunitinib, sorafenib), multiplexed with Illumina TruSeq adapters, and run on a single 75‐cycle single‐end sequencing run with an Illumina NextSeq‐500. QC‐passed reads were aligned to the mouse reference genome (mm9) using MapSplice. The alignment profile was determined by Picard Tools v1.64. Aligned reads were sorted and indexed using SAMtools and translated to transcriptome coordinates and filtered for indels, large inserts, and zero mapping quality using UBU v1.0. Transcript abundance estimates for each sample were performed using an Expectation‐Maximization algorithm. Raw RNAseq by Expectation Maximization read counts for all RNAseq samples and raw FASTQ files of RNAseq runs have been uploaded to National Center for Biotechnology Information Gene Expression Omnibus under accession number GSE98973. Reviewers may access these private data at the following link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=qtsduckchxghladamp;acc=GSE98973.The DEseq2 algorithm was used to determine differential expression analysis of each set of KI‐treated samples versus controls using the expected counts column for each data set. Gene Set Enrichment Analysis (GSEA) was performed on each set of treated versus control data sets using normalized RNAseq by Expectation Maximization read counts. Data were 50‐read filtered such that at least 1 sample for each comparison (3 control versus 3 treated) for each gene must have had a value of at least 50 normalized RNAseq by Expectation Maximization reads. Mouse gene names were converted to their human homolog and GSEA was performed against MSigDB gene sets for Hallmarks, Gene Ontology, KEGG, Reactome, and Oncogenic Signatures. Default parameters were used and only gene sets with nominal P<0.05 and FDR <25% were considered. […]

Pipeline specifications

Software tools MapSplice, Picard, SAMtools, DESeq2
Databases GEO
Application RNA-seq analysis
Organisms Mus musculus
Diseases Neoplasms