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Deep RNA Sequencing of Intensive Care Unit Patients with COVID-19

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Deep RNA Sequencing of Intensive Care Unit Patients with COVID-19 medRxiv preprint doi: https://doi.org/10.1101/2021.01.11.21249276; this version posted January 13, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Deep RNA Sequencing of Intensive Care Unit Patients with COVID-19 Sean F. Monaghan, MD1, Alger M. Fredericks, PhD1, Maximilian S. Jentzsch, MS1, William G. Cioffi, MD1, Maya Cohen, MD2, William G. Fairbrother, PhD3, Shivam J. Gandhi3, Elizabeth O. Harrington, PhD3, Gerard J. Nau, MD, PhD4, Jonathan S. Reichner, PhD1, Corey E. Ventetuolo, MD2, MS, Mitchell M. Levy, MD2, Alfred Ayala, PhD1 1. Alpert Medical School of Brown University /Rhode Island Hospital, Department of Surgery, Division of Surgical Research 2. Alpert Medical School of Brown University /Rhode Island Hospital, Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine 3. Brown University (W.G.F., S.J.G., E.O.H.) 4. Alpert Medical School of Brown University /Rhode Island Hospital, Department of Medicine, Division of Infectious Disease (G.J.N.) Address reprint requests to Dr. Sean F. Monaghan, Rhode Island Hospital, 593 Eddy Street, Middle House 211, Providence, RI 02903 or [email protected] Take Home Message: Deep RNA sequencing provides a novel diagnostic tool for critically ill patients. Among ICU patients with COVID-19, RNA sequencings can identify gene expression, pathogens (including SARS-CoV-2), and can predict mortality. Tweet: Deep RNA sequencing is a novel technology that can assist in the care of critically ill COVID-19 patients & can be applied to other disease NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2021.01.11.21249276; this version posted January 13, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Purpose COVID-19 has impacted millions of patients across the world. Molecular testing occurring now identifies the presence of the virus at the sampling site: nasopharynx, nares, or oral cavity. RNA sequencing has the potential to establish both the presence of the virus and define the host’s response in COVID-19. Methods Single center, prospective study of patients with COVID-19 admitted to the intensive care unit where deep RNA sequencing (>100 million reads) of peripheral blood with computational biology analysis was done. All patients had positive SARS-CoV-2 PCR. Clinical data was prospectively collected. Results We enrolled fifteen patients at a single hospital. Patients were critically ill with a mortality of 47% and 67% were on a ventilator. All the patients had the SARS-CoV-2 RNA identified in the blood in addition to RNA from other viruses, bacteria, and archaea. The expression of many immune modulating genes, including PD-L1 and PD-L2, were significantly different in patients who died from COVID-19. Some proteins were influenced by alternative transcription and splicing events, as seen in HLA-C, HLA-E, NRP1 and NRP2. Entropy calculated from alternative RNA splicing and transcription start/end predicted mortality in these patients. Conclusions Current upper respiratory tract testing for COVID-19 only determines if the virus is present. Deep RNA sequencing with appropriate computational biology may provide important prognostic information and point to therapeutic foci to be precisely targeted in future studies. Keywords COVID-19, RNA sequencing, RNA processing, pathogen identification, gene expression medRxiv preprint doi: https://doi.org/10.1101/2021.01.11.21249276; this version posted January 13, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) has led to millions of cases worldwide.[1] Current testing is by polymerase chain reaction to detect viral RNA in the nares[2], but provides no insight into the host response. Patients with COVID-19 that require intensive care unit (ICU) care are sick and difficult to manage, thus, there is a need for other diagnostic tests during the hospital stay to assist the clinicians. Deep RNA sequencing refers to a process of sequencing where (at least) 100 million reads of sequence are generated per sample. Deep sequencing allows for the study of low abundance RNA and biologic processes beyond gene expression. Typically, RNA sequencing data is aligned to the genome of interest, such as aligning to human genes when the sample comes from a human. Reads that do not align to the genome of interest are usually discarded. When the RNA sequencing is performed with this large number of reads, it could be used to identify the presence of specific pathogens in the blood by aligning the reads that would have been discarded to other genomes of interest. In COVID-19, sequencing reads of SARS-CoV-2 may provide insight into the biology of the virus during active illness. In addition, secondary infections could be identified, potentially allowing for better, pathogen-directed antibiotic treatment. The host response to the virus is responsible for some of the morbidity and mortality observed.[3] Acute respiratory distress syndrome (ARDS) is the most common complication encountered with COVID-19.[3] Our laboratory has shown that there are significant changes in alternative RNA splicing and transcription start and end in ARDS as assessed by deep RNA sequencing.[4] These changes are thought to be due to the physiology of ARDS, e.g., hypoxia and acidosis, which are known to influence splicing. Whether this occurs in patients infected by COVID-19 is not known. While RNA sequencing can be used to measure immune modulating gene expression, an alternative approach is the evaluation of global entropy, or disorder in the processing of RNA.[5] In this study, we propose that this entropy metric combined with Principal Component Analysis (PCA) can be leveraged to distinguish COVID-19 patients that develop life-threatening illness from those likely to recover. Here we examine deep RNA sequencing data from patients in the ICU with COVID-19 to characterize both pathogens and host responses. We evaluate the sequences for the presence of the SARS-CoV-2 virus and other potential infectious agents. The host response to COVID-19 is also characterized. The long-term goal is to combine these measurements to better assist clinical decision-making. Methods Study design, Population and Setting The study enrolled ICU participants at a single tertiary care hospital evidence of SARS-CoV-2 infection based on positive PCR from the nasopharynx documented during admission. All participants, or their appropriate surrogate, provided informed consent as approved by the Institutional Review Board (Approval #: 411616). Blood samples were collected on day 0 of ICU admission. Clinical data including COVID specific therapies was collected prospectively from the electronic medical record and participants were followed until hospital discharge or death. Ordinal scale was collected as previously described;[6] along with sepsis and associated SOFA score[7] and the diagnosis of ARDS.[8] RNA extraction and sequencing Whole blood was collected in PAXgene tubes (Qiagen, Germantown, MD) and sent to Genewiz (South Plainfield, NJ) for RNA extraction, ribosomal RNA depletion and sequencing. Sequencing was done on Illumina HiSeq machines to provide 150 base pair, paired-end reads. Libraries were medRxiv preprint doi: https://doi.org/10.1101/2021.01.11.21249276; this version posted January 13, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. prepared to have three samples per lane. Each lane provided 350 million reads ensuring each sample had >100 million reads. Raw data was returned on password protected external hard drives to ensure the security of the genomic data. Computational Biology and Statistical Analysis All computational analysis was done blinded to the clinical data. The data was assessed for quality control using FastQC.[9] RNA sequencing data was aligned to the human genome utilizing the STAR aligner.[10] Reads that aligned to the human genome were separated and are now referred to as ‘mapped’ reads. Reads that did not align to the human genome, which are typically discarded during standard RNA sequencing analysis, were kept and identified as ‘unmapped’ reads. The unmapped reads then aligned to the SARS-CoV-2 genome (NC_045512) and counted per sample using Magic-BLAST.[11] In addition, a coverage map of the SARS- CoV-2 genome was generated using all the subjects to identify the gene expression patterns of the virus in critically ill COVID-19 patients. The unmapped reads were further analyzed with Kraken2[12] using the PlusPFP index[13] to identify other bacterial, fungal, archaeal and viral pathogens. Reads that aligned to the human genome, the mapped reads, also underwent analysis for gene expression, alternative RNA splicing, and alternative transcription start/end via Whippet.[5] When comparisons were made between groups (died vs. survived) differential gene expression was set with thresholds of both p<0.05 and +/- 1.5 log2 fold change. Alternative splicing was defined as core exon, alternative acceptor splice site, alternative donor splice site, retained intron, alternative first exon and alternative last exon. Alternative transcription start/end events were defined as tandem transcription start site and tandem alternative polyadenylation site.
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