Blood transcriptional biomarkers of acute viral infection for detection of pre-symptomatic SARS-CoV-2 infection: a nested, case-control diagnostic accuracy study

Abstract

Background We hypothesised that host-response biomarkers of viral infections might contribute to early identification of individuals infected with SARS-CoV-2, which is critical to breaking the chains of transmission. We aimed to evaluate the diagnostic accuracy of existing candidate whole-blood transcriptomic signatures for viral infection to predict positivity of nasopharyngeal SARS-CoV-2 PCR testing. Methods We did a nested case-control diagnostic accuracy study among a prospective cohort of health-care workers (aged ≥18 years) at St Bartholomew's Hospital (London, UK) undergoing weekly blood and nasopharyngeal swab sampling for whole-blood RNA sequencing and SARS-CoV-2 PCR testing, when fit to attend work. We identified candidate blood transcriptomic signatures for viral infection through a systematic literature search. We searched MEDLINE for articles published between database inception and Oct 12, 2020, using comprehensive MeSH and keyword terms for “viral infection”, “transcriptome”, “biomarker”, and “blood”. We reconstructed signature scores in blood RNA sequencing data and evaluated their diagnostic accuracy for contemporaneous SARS-CoV-2 infection, compared with the gold standard of SARS-CoV-2 PCR testing, by quantifying the area under the receiver operating characteristic curve (AUROC), sensitivities, and specificities at a standardised Z score of at least 2 based on the distribution of signature scores in test-negative controls. We used pairwise DeLong tests compared with the most discriminating signature to identify the subset of best performing biomarkers. We evaluated associations between signature expression, viral load (using PCR cycle thresholds), and symptom status visually and using Spearman rank correlation. The primary outcome was the AUROC for discriminating between samples from participants who tested negative throughout the study (test-negative controls) and samples from participants with PCR-confirmed SARS-CoV-2 infection (test-positive participants) during their first week of PCR positivity. Findings We identified 20 candidate blood transcriptomic signatures of viral infection from 18 studies and evaluated their accuracy among 169 blood RNA samples from 96 participants over 24 weeks. Participants were recruited between March 23 and March 31, 2020. 114 samples were from 41 participants with SARS-CoV-2 infection, and 55 samples were from 55 test-negative controls. The median age of participants was 36 years (IQR 27–47) and 69 (72%) of 96 were women. Signatures had little overlap of component genes, but were mostly correlated as components of type I interferon responses. A single blood transcript for IFI27 provided the highest accuracy for discriminating between test-negative controls and test-positive individuals at the time of their first positive SARS-CoV-2 PCR result, with AUROC of 0·95 (95% CI 0·91–0·99), sensitivity 0·84 (0·70–0·93), and specificity 0·95 (0·85–0·98) at a predefined...

Rapid synchronous type 1 IFN and virus-specific T cell responses characterize first wave non-severe SARS-CoV-2 infections

“…As we have previously reported, an additional translational application of this finding is the detection of IFN-inducible transcripts in blood as a diagnostic biomarker of early viral infection that may precede PCR detection of the virus and symptoms. 14 Alongside type 1 IFN responses, we detected an early cell proliferation response in the blood transcriptome, which we primarily attribute to CD8, and to a lesser extent CD4, T cell proliferation by correlation with cell-type-specific transcriptional modules, corroborated by flow cytometry to show a significant increase in Ki67-positive CD8 T cells and TCR sequencing to show expansion of T cell clones. While type 1 IFN responses were evident in a range of other acute respiratory virus infections modeled in human challenge experiments, 11 the early T cell response to SARS-CoV-2 in our study was significantly greater than in other viral infections.…”

Targeted Down Regulation Of Core Mitochondrial Genes During SARS-CoV-2 Infection

“…PNPT1 (polyribonucleotide nucleotidyltransferase 1), also highly induced, is thought to be involved in processing mitochondrial double-stranded RNA released into the cytosol to engage MDA5-driven anti-viral signaling and trigger the type I interferon response ( 37 ). Also, up regulated was IFI27 (Interferon Alpha Inducible Protein 27), which in blood is the transcript that provides the greatest discrimination between SARS-CoV-2 positive and negative patients ( 38 ), and ACOD1 (Aconitate Decarboxylase 1 or IRG1 ), which regulates itaconate production and the metabolic adaptation of inflammatory myeloid cells ( 39 ). Additional up regulated genes included central energy metabolism genes including SCO2 (cytochrome c oxidase, COX assembly), SLC25A3 (phosphate carrier) critical for ATP synthesis, and genes for metabolism and for regulating apoptosis ( Table S3 ).…”

“…mtDNA transcripts and mitochondrial RNA polymerase ( POLRMT ) have been reported to be down regulated in blood cells while interferon response genes were induced ( 17 ) with the mitochondrial IFI27 transcript being the most discriminatory factor in SARS-CoV-2 blood ( 38, 43 ). We observed in COVID19 positive cases, the blood mRNA levels for FOXRED1 (complex I assembly) and SCO2 (complex IV assembly) were up regulated ( Fig.…”

“…Mitochondrial specific genes determined from our curated mitochondrial genes (top graph) and MitoCarta genes (bottom graph) comparing protein and RNA transcript changes in the blood from COVID patients (determined from COVIDome)( 31, 36 ).…”

New Insights into the Limitations of Host Transcriptional Biomarkers of Tuberculosis

“…Several signatures did show promising accuracy for prevalent TB in a systematic head-to-head analysis among an observational cohort of symptomatic patients attending a community-based TB clinic in South Africa ( 13 ). It may be possible to improve on their specificity for TB, for example, by combining with biomarkers of respiratory viral infection ( 14 ) for multiclass classification, or by deriving new signatures to discriminate TB from viral infections directly in larger, more generalizable training data sets, though this may be challenging in view of the common IFN-driven responses in both infections. Until then, the application of the current biomarkers for TB will need to overcome the limitations of specificity, for example, by targeted application in high-risk groups such as recent household contacts (in whom the prior probability of TB may be expected to be higher than that of incidental viral infection) and by integration into multivariable risk prediction tools ( 4 ).…”