DOI: 10.6084/m9.figshare.26226467
title: Raw LC-MS/MS and RNA-Seq Mitochondria data

description: <b>LC-MS/MS raw data</b>Spectrum matching and protein identification and validation were performed with MSFragger, and quantification of protein intensities with matching between runs was performed with IonQuant as components of the FragPipe analysis pipeline using the default settings of each module. The protein database used for the search was the Mus musculus reviewed sequence database downloaded from UniProt on June 1, 2023. The results were subsequently processed to filter out common contaminants, decoy hits from the reverse database, and protein groups identified by a single peptide. The data were filtered as follows: (a) binary expression of a protein (i.e., protein exclusively identified in either scLRP1+/+ or scLRP1-/-) was only considered relevant if all scLRP1+/+ samples or all scLRP1-/- samples expressed the protein. The missing values were imputed with the minimum intensity value for each specific data set; (b) for samples expressed in both scLRP1+/+ and scLRP1-/- tissue, the filtering process required 2 or more proteins to be detected in both scLRP1+/+ and scLRP1-/- samples. False discovery analysis was performed using the Benjamini, Krieger, and Yekutieli method using GraphPad Prism 10.0 software. Causal analysis of proteomic data was performed in IPA upstream analysis software (QIAGEN). For IPA, the binary values were imputed using local minimum intensities. Enrichment analyses for gene ontology (biological process) were performed using clusterProfiler 4.2.2 R package on R 4.1.0. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD038236. <i>Global quantification of protein expression</i>. Sciatic nerves from scLRP1+/+ and scLRP1-/- mice were rinsed in PBS to remove the blood and frozen with liquid nitrogen in cryogenic storage tubes (#5016-0001, Thermo Fisher Scientific). Fractured tissue was transferred to a 1 mL milliTUBE containing an AFA fiber (#520135, Covaris catalog) in 200 μL of 50 mM HEPES pH 8.5, 150 mM NaCl, and 2% Triton X-114 and sonicated with a M220 Focused-Ultrasonicator (#500295, Covaris catalog). Sonication parameters were temperature 15 °C, peak power 75 W, duty factor 26, cycles/burst = 1,000, and duration 600 seconds. Extracted proteins were clarified of insoluble material by centrifugation at 15,000 x g for 20 minutes at 4 °C. Protein concentrations were determined with the Micro BCA colorimetric assay (Pierce, Thermo Fisher Scientific) with the addition of SDS to a final concentration of 1% in the assay solvent to prevent detergent clouding. Aliquots containing approximately 5 μg of protein were processed using the SP3 protocol as described (89) with some modifications. Briefly, the sample aliquots were brought to 50 μL volume, and disulfide bonds were reduced and alkylated simultaneously with 10 mM TCEP, 40 mM 2-chloroacetamide in 50 mM HEPES pH 8.5, and 1% sodium deoxycholate at 70 °C for 10 minutes, then cooled on ice. Proteins were precipitated and captured following the addition of 10 μL of a washed 10 μg/μL suspension of SpeedBeads (Cytiva) and 400 μL of ethanol. After shaking for 10 minutes at room temperature, the beads were magnetically captured and washed 3 times with 200 μL of 80% ethanol in water. Proteins were digested on the beads in 50 μL of 50 mM HEPES pH 8.5, 1% sodium deoxycholate, and 10 ng/μL trypsin (Promega) overnight at room temperature with shaking sufficient to maintain the beads in suspension. The digest was diluted 10-fold with 80% acetonitrile and 1% formic acid, then separated from the beads magnetically, and the resulting peptides were captured on 2 mm discs of Empore Cation (CDS Analytical) fitted into 1,000 μL pipette tips (Sartorious catalog 791000). Detergents and other contaminants were removed by washing the tips serially with 1) ethyl acetate; 2) 80% acetonitrile, 1% formic acid; and 3) 10% acetonitrile, 0.2% formic acid. Peptides were eluted directly into injection vials with freshly prepared 80% acetonitrile and 5% ammonium hydroxide and immediately dried down in a centrifugal vacuum evaporator. One-fifth of the recovered peptides from each sample were subsequently analyzed by liquid chromatography-tandem mass spectrometry. In-house capillary columns were constructed from 360 μm OD and 100 μm internal diameter × 30 cm fused silica tubing (Molex) with laser-pulled tips (Sutter Instruments) and were packed with Reprosil-PUR 3 μm C18-AQ (Dr. Maisch GmBH). Solvents A and B consisted of 0.1% formic acid in water and 80% acetonitrile with 0.1% formic acid, respectively. A 180-minute linear gradient from 2% to 35% solvent B was used for chromatographic separation. Peptides were analyzed with an Orbitrap Elite (Thermo Fisher Scientific) mass spectrometer using nano-electrospray ionization with an applied voltage of 1,800 V. MS1 spectra were acquired at a resolution of 120,000, and the 15 most abundant precursor ions were selected for fragmentation by higher energy collision dissociation. MS2 spectra were acquired at a resolution of 15,000. Dynamic exclusion parameters were a list size of 500, a mass window of ±7 ppm, and a duration of 1 minute. Automatic gain control settings were MS1 target 1 × 106, maximum inject time 100 ms; MS2 target 4 × 104, maximum inject time 100 ms. Principal components of the 8 samples (2 groups: 4 scLRP1+/+, 4 scLRP1-/-) were analyzed. The centroid of each group, generated by the K-nearest neighbor (KNN) algorithm, was used to define each cluster. All samples from each group were restricted to the same cluster with no overlap.<br><br><b>RNA-Seq data</b>L4 and L5 DRGs from the left and right sides of each mouse were acutely isolated, pooled, and snap-frozen in liquid nitrogen (n=3 per genotype). RNA was extracted with the AllPrep DNA/RNA Micro Kit (Qiagen, Inc.). RNA quality was assessed on an Agilent 2100 Bioanalyzer. Samples with RNA integrity numbers ≥8 were used for RNA sequencing (RNA-seq). RNA-seq was conducted at the NYU Genomic Core. All samples were processed in the same time period, following the same protocol to limit batch effects and other confounders using the Illumina RNA-seq platform. The cDNA library was prepared via the standard Illumina protocol. Three samples were pooled into each lane and sequenced by 75-bp paired-end sequencing on an Illumina HiSeq 2500 using standard protocols. A Phi-X positive control provided by Illumina was spiked into all lanes at a concentration of 0.3% to monitor sequencing quality. The sample error rate was &lt; 2% and the distribution of reads per sample in a lane was within a reasonable tolerance. Data generated during sequencing runs were transferred to the high-performance computing (HPC) cluster, with individual base calls transferred for downstream analysis. The target for average reads per sample was approximately 25 million. The QC pipeline included: 1) quality check of the raw sequencing data using FastQC (v 0.11.9) and MultiQC (v 1.9); 2) mapping the sequencing reads to the human genome (build 102) using HISAT2 (v 2.2.1), followed by SAMtools (v 1.12) to convert BAM (Binary Alignment Map) into SAM (Sequence Alignment Map) files; 3) assembly of RNA-seq reads into transcripts using StringTie (v 2.1.4); and 4) calculation of expression levels from read counts, producing a gene count matrix. To improve the rigor of gene expression analysis, a minimum read depth of 10 gene counts in at least 90% of samples was required for retention in the matrix. The Ensembl identifiers (ID) of the gene counts were annotated to Entrez IDs using the EnrichmentBrowser (v.2.18.2) package in R. The Entrez IDs were annotated to gene symbols using Homo sapiens (v. 1.3.1). Gene expression was compared using DESeq2 (v. 1.32.0), which assumes a negative binomial distribution using the mean and variance estimated from gene counts and p-values calculated using the Wald test. GSEA was performed using GSEA 3.0 (http://www.broadinstitute.org/gsea/). Adjusted p &lt; 0.05 and a false discovery rate (FDR) q &lt; 0.25 were considered significant. DEGs were selected when presented with an adjusted p-value &lt; 0.05 and a log2FC (fold change) &gt; 1 for upregulated genes and &lt; -1 for downregulated genes. The Database for Annotation, Visualization, and Integrated Discovery (DAVID, http://david.abcc.ncifcrf.gov/, version 6.8) was used to perform GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Set enrichment analysis was used for the pathway by selecting non-significant differentially expressed genes specified as the “background universe” and accounting for multiple testing using a false discovery rate of q &lt; 0.1. Differentially altered pathways were evaluated by using the enrich plot package in R for visualization of functional enrichment (i.e., dot plot).