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EGAS00001003772-sc-2022-04-14T12:46:36Z - samples

ABSTRACT: Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. This cohort comprises a subset of patients enrolled in the Genomic Advances in Sepsis (GAinS) study, an established biobank of adult sepsis patients. Patients with sepsis due to community acquired pneumonia or faecal peritonitis were recruited from 34 hospitals across the UK from 2005-2018, with samples for functional genomics and detailed clinical information collected on the first, third and/or fifth day following ICU admission. RNA was extracted from leukocytes isolated at the bedside using LeukoLOCK kits. We have previously identified sepsis response signatures (SRSs), transcriptomic endotypes that are associated with differential early mortality (Davenport et al, Lancet Respir Med, 2016; Burnham et al, AJRCCM, 2017) and response to treatment in a clinical trial (Antcliffe et al, AJRCCM, 2018). We generated RNA sequencing data on 903 samples, including 134 samples repeated from our previously released microarray data. Libraries were prepared using NEB Ultra II Library Prep kits (Illumina) and sequenced on a NovaSeq 6000. Reads were aligned to the reference genome (GRCh38) using STAR and gene counts quantified using featureCounts (annotation Ensembl v99). Counts were TMM-normalised and log-transformed. Following QC, processed data were available on 864 samples from 667 unique patients.

PROVIDER: EGAD00001008730 | EGA |

REPOSITORIES: EGA

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Project description:SARS-CoV-2 infections induce aberrant pulmonary and systemic inflammation, vascular leak, coagulation and fatal organ damage. To identify therapeutic strategies to suppress COVID-19-associated inflammation, we characterized lung tissue of COVID-19 patients using tissue transcriptomics. FFPE lung tissue from anonymized normal and postmortem COVID19 patients and brochoalveolar lavage specimens from normal and COVID19 patients were sequenced using Tempo-Seq from Biospyder, Calrbad, CA. Bioinformatics analysis revealed that lungs of deceased patients exhibited substantial infiltration by neutrophils and wound-healing macrophages, fibrosis, vascular leak and alveolar type II cell depletion, with a pervasive wound-healing transcriptional profile. Methods:Two five-micron FFPE sections from each of fourteen postmortem lung specimens from COVID patients, five normal lung specimens, nine BALF specimens from COVID patients and five BALF specimens from normal patients were used to perform TempoSeq FFPE human whole transcriptome RNA sequencing at BioSpyder Technologies, Inc, Carlsbad, CA, as described (Trejo et al., 2019; Turnbull et al., 2020). Prepared libraries were sequenced on NovaSeq6000; mapped reads were generated by TempO-SeqR alignment of demultiplexed FASTQ files using Bowtie, allowing for up to 2 mismatches in the 50-nucleotide target sequence.Unnormalized counts were obtained from BioSpyder TempO-seq. The R BioConductor packages edgeR (Robinson et al., 2010) and limma (Ritchie et al., 2015) were used to implement the limma-voom (Law et al., 2014) method for differential expression analysis. We analyzed normal lung, COVID-19 lung and COVID-19 BALF sample groups together and also lung samples alone due to low levels of alignment in the BALF samples. We also analyzed COVID-19 BALF and normal BALF together. One COVID-19 BALF sample was removed from downstream analysis because it had <300,000 quantified reads. Lowly expressed genes—those not having counts per million (cpm)  10 in at least 10 of the samples—were filtered out in the combined analysis and for lung samples only a less stringent filter of (cpm)  1 in at least 5 of the samples was applied. Trimmed mean of M-values (TMM) normalization (Robinson and Oshlack, 2010) was applied after filtering. The experimental design was modeled upon condition, called condition (~0 + condition). The voom method was employed to model the mean-variance relationship, after which lmFit was used to fit per-gene linear models and empirical Bayes moderation was applied with the eBayes function. Significance was defined by using an adjusted p-value cut-off of <0.05 after multiple testing correction (Benjamini and Hochberg, 1995) using a moderated t-statistic in limma. Functional enrichment of the differentially expressed genes was performed using SPIA (Tarca et al., 2009), gProfiler (Raudvere et al., 2019) and fGSEA (Korotkevich et al., 2021).

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EGAS00001003772-sc-2022-04-14T12:46:36Z - samples

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