ABSTRACT: 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).
