biostudies-arrayexpressGiulia SoldàHomo sapienshttps://www.ebi.ac.uk/biostudies/studies/E-MTAB-15584To identify possible deregulated pathways involved in COVID-19 severity, we analyzed the transcriptomic profiles of 59 patients affected by severe COVID-19 symptoms, comparing those who were only hospitalized and required supplemental oxygen support (N=24) with those who were also admitted to the ICU and received more intensive respiratory support, such as non-invasive ventilation or ventilator support (N=35, critically severe).biostudies-arrayexpressSample Collection - Blood samples were collected in 4 mL ethylene-diaminetetraacetic acid-K2 vacuum blood tubes, mixed immediately after the collection by inverting 10 times, and processed less than 8 hours after the blood withdrawal. Peripheral blood mononuclear cells (PBMCs) isolation was performed by using Ficoll (Sigma-Aldrich, St. Louis, Missouri, USA) density gradient centrifugation.Library Construction - Libraries were prepared from 35-57 ng of total RNA, using the SMARTer Stranded RNA-Seq Kit (Clontech-Takara, Kusatsu, Shiga, Japan), following the manufacturer's instructions.Sequencing - Libraries from 59 samples were multiplexed in two different equimolar pools and sequenced in two separate runs on a NextSeq 2000 Platform (Illumina; Illumina, San Diego, California, USA) generating paired-end 150-nucleotide reads.Nucleic Acid Extraction - Total RNA was isolated from PBMCs using the TriFast reagent (Euroclone, Pero, Milan, Italy). For assessing RNA quantity and quality we used a highly sensitive fluorescence-based quantitation system (Qubit HSRNA; Thermo Fisher Scientific, Waltham, Massachusetts, USA) and the High Sensitivity RNA ScreenTape Assay with a 4200 Tapestation System (Agilent Technologies, Santa Clara, California, USA).OrganizationMINSEQE ScoreAssays and DataProcessed DataMAGE-TAB FilesData Transformation - Quantification of reads mapping on each protein coding gene was obtained with STAR v2.7.9a (Spliced Transcripts Alignment to a Reference) using the command “--quantMode GeneCounts” and the UCSC transcript annotation.Sequence Alignment - Mapping and quantification of RNA-seq reads were performed on the hg38 reference human genome (UCSC) using STAR (v2.7.9a).MetabolomicsUnknownTranscriptomicsGenomicsProteomicsNextSeq 2000<h4>Background</h4>Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected over 26 million individuals in Italy, resulting in ∼200,000 COVID-19-related deaths. Unravelling host genetic factors underlying disease severity is key to understanding progression mechanisms.<h4>Methods</h4>We applied multi-omics approaches to investigate genetic susceptibility to COVID-19 severity in the Italian population. We combined an exome-wide case-control study of rare germline variants (215 severe/critically ill patients vs 1755 controls) with transcriptomic (differential gene expression and alternative splicing) analyses of 59 hospitalised patients to identify signatures associated with severe respiratory outcomes (ICU admission).<h4>Findings</h4>Rare variant analysis revealed significant aRNA-seq of total RNAHomo sapiensMulti-omics identifies oxidative stress, prothrombotic pathways, and lactoperoxidase variants as key factors in COVID-19 severityRosanna AsseltaClaudio Cappadona, Valeria Rimoldi, Francesca Tettamanzi, Giulia Cardamone, Alberto Mantovani, Giulia Soldà, Elvezia Maria Paraboschi, Rosanna AsseltaGiulia SoldàfalseRNA-seq of human Peripheral blood mononuclear cells (PBMCs) from COVID-19 patients requiring different degree of respiratory supportTo identify possible deregulated pathways involved in COVID-19 severity, we analyzed the transcriptomic profiles of 59 patients affected by severe COVID-19 symptoms, comparing those who were only hospitalized and required supplemental oxygen support (N=24) with those who were also admitted to the ICU and received more intensive respiratory support, such as non-invasive ventilation or ventilator support (N=35, critically severe).2026-07-20T00:00:00Z2026-09-07T12:10:14.139Z2025-09-12T12:01:17.284ZE-MTAB-1558441500120ERP180013EFO_0002944EFO_0004170EFO_0009653EFO_0004917EFO_0005518EFO_0003816EFO_000418410.1016/j.ebiom.2025.106111