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Discovery and validation of a three-gene signature to distinguish COVID-19 and other viral infections in emergency infectious disease presentations: a case-control and observational cohort study

Summary Background Emergency admissions for infection often lack initial diagnostic certainty. COVID-19 has highlighted a need for novel diagnostic approaches to indicate likelihood of viral infection in a pandemic setting. We aimed to derive and validate a blood transcriptional signature to detect viral infections, including COVID-19, among adults with suspected infection who presented to the emergency department. Methods Individuals (aged ≥18 years) presenting with suspected infection to an emergency department at a major teaching hospital in the UK were prospectively recruited as part of the Bioresource in Adult Infectious Diseases (BioAID) discovery cohort. Whole-blood RNA sequencing was done on samples from participants with subsequently confirmed viral, bacterial, or no infection diagnoses. Differentially expressed host genes that met additional filtering criteria were subjected to feature selection to derive the most parsimonious discriminating signature. We validated the signature via RT-qPCR in a prospective validation cohort of participants who presented to an emergency department with undifferentiated fever, and a second case-control validation cohort of emergency department participants with PCR-positive COVID-19 or bacterial infection. We assessed signature performance by calculating the area under receiver operating characteristic curves (AUROCs), sensitivities, and specificities. Findings A three-gene transcript signature, comprising HERC6, IGF1R, and NAGK, was derived from the discovery cohort of 56 participants with bacterial infections and 27 with viral infections. In the validation cohort of 200 participants, the signature differentiated bacterial from viral infections with an AUROC of 0·976 (95% CI 0·919−1·000), sensitivity of 97·3% (85·8−99·9), and specificity of 100% (63·1−100). The AUROC for C-reactive protein (CRP) was 0·833 (0·694−0·944) and for leukocyte count was 0·938 (0·840−0·986). The signature achieved higher net benefit in decision curve analysis than either CRP or leukocyte count for discriminating viral infections from all other infections. In the second validation analysis, which included SARS-CoV-2-positive participants, the signature discriminated 35 bacterial infections from 34 SARS-CoV-2-positive COVID-19 infections with AUROC of 0·953 (0·893−0·992), sensitivity 88·6%, and specificity of 94·1%. Interpretation This novel three-gene signature discriminates viral infections, including COVID-19, from other emergency infection presentations in adults, outperforming both leukocyte count and CRP, thus potentially providing substantial clinical utility in managing acute presentations with infection. Funding National Institute for Health Research, Medical Research Council, Wellcome Trust, and EU-FP7.

stabilising transformation function from the DESeq2 package (9,10) in R. Genes with minimal variation (less than 10% non-zero values) and genes that had zero reads in more than 90% of the samples were removed. All analyses were undertaken using R version 4.0.0 (R Foundation for Statistical Computing).(11) RNA-Seq data has been uploaded to EMBL-EBI (https://www.ebi.ac.uk/arrayexpress/ -dataset identifier awaited).
Principal Component Analysis (PCA) was implemented as part of our quality control workflow to look for evidence of batch-effect and explore variation of gene expression. This demonstrated substantial batch effect due to sequencing plates used. To correct for this, we used samples from the not-infected group and ComBat (12), an empirical Bayes batch adjustment approach to batch correction, implemented from the Bioconductor package sva in R (13). Inspection of heatmaps (not shown) and principal components (Supplementary Figure   S11, p23) demonstrated resolution of the batch effect.

3-gene signature derivation
Raw RNA-Seq data underwent quality control, alignment, gene counting and normalisation.
To derive diagnostic gene signatures, we first identified genes that were differentially expressed between bacterial and viral groups using DESeq2. The differential expression model was conditional on age, gender and sequencing plate. Variables with only small expression differences between disease categories are likely to have poor distinguishing power when translated to more diagnostically useful technologies, such as qPCR. To ensure we deselected these variables the differential expression model incorporated a null hypothesis that nonsignificant genes had |log2 fold-change| < 0.1. Adjusted p-values were calculated to optimise test power using data-driven hypothesis weights independent of the p-values (14), implemented with Bioconductor package IHW in R. The genes that were significantly differentially expressed with an absolute log2 fold-change >1, adjusted p-value <0.05 and sufficient mean expression across the cohort (Supplementary Figure S4, p14) using a model conditional on age, gender and sequencing plate were subjected to further analysis. The log2 fold change cut-off was decided to ensure cross-platform reproducibility using PCR, while the adjusted p-value <0.05 cut-off was employed as a less stringent option to allow more genes to enter the feature selection step. To generate lists of differentially expressed genes between the 'no infection' and other groups, a less restrictive null hypothesis was used, with |log2 fold-change| = 0. Two feature selection methods with different qualities were used to determine small gene sets with the potential for a clinically useful diagnostic tool. The glmnet package (15) was used to implement penalised logistic regression via the elastic net (16). Model parameter alpha was set at 0.95, to mimic the sparse variable selection of lasso whilst allowing grouping of correlated variables. Ten-fold cross-validation was used to optimise remaining parameters. An additional algorithm, Forward Selection -Partial Least Squares (FS-PLS) (17,18) method was employed to identify a second minimal diagnostic signature, resulting in a logistic regression model. FS-PLS selects for minimally correlated genes and has previously been described in detail (17,18). The Forward Selection -Partial Least Squares (FS-PLS) method was used to discover gene expression signatures by following an iterative process. The first iteration of the algorithm considers the expression levels of all transcripts (N) and initially fits N univariate regression models. The variable with the highest Maximum Likelihood Estimation and smallest t-test pvalue to assess the goodness of fit is selected first (SV1). The algorithm then projects the variation explained by SV1 using Singular Value Decomposition and selects new variables based on the residual (unexplained) variation, until the MLE p-value exceeds a pre-defined threshold. The FS-PLS score was calculated into a single metric using weighted sums of log2 gene counts from the RNA-Seq analysis. For the validation of the pre-COVID-19 and COVID-19 cohorts, the FS-PLS score was calculated as the weighted sum of RT-qPCR cycle threshold (Ct) values for each gene. To ensure consistent interpretation of the score's direction, the weights were inverted when applied to the RT-qPCR Ct values (Supplementary Table S5, p25).
High-throughput qPCR assay for the 3-gene signature in the Pre-COVID-19 prospective validation cohort TaqMan ® Gene Expression assays were purchased from ThermoFisher Scientific targeting HERC6 (assay ID: Hs01111944_m1), NAGK (assay ID: Hs00895035_m1), and IFG1R (assay ID: Hs00609583_m1). Analytical sensitivity and specificity of the three assays were evaluated using transcribed synthetic RNA from gBlocks gene fragments (Integrated DNA Technologies) and human genomic DNA (Promega Corporation), respectively.
We assessed the performance of the signature using a Biomark HD high-throughput qPCR platform and 192.24 Dynamic Array TM integrated fluidic circuit (IFC) following manufacturer instructions. The experimental workflow consisted of 4 steps: reverse transcription, preamplification, on-chip gene expression, and data analysis.
Reverse transcription The first step consisted of performing reverse transcription (RT) using the Fluidigm Master Mix, which contains all components required for cDNA synthesis, including buffer dNTPs, ribonuclease inhibitor and an engineered RNA reverse transcriptase (Fluidigm product numbers: #100-5580 and #100-5581). Each RT reaction was performed in 5 µL of final reaction volume with 1 µL of Reverse Transcription Master Mix, 3 µL of RNAfree water and 1 µL of RNA (2.5 pg/µL -250 ng/µL). The reaction was incubated for 5 minutes at 25°C, 30 minutes at 42°C and 5 minutes at 85°C. This was performed in a conventional realtime qPCR instrument (Roche LightCycler96).
Pre-amplification In a microcentrifuge tube, we combined an equal volume of each of the three 20X TaqMan ® Gene Expression assays. The pool was diluted in a buffer, containing 10 mM Tris, pH 8.0, 0.1 mM EDTA, to reach a final concentration of 0.2X. The Pre-Amp reaction was performed in a total volume of 5 µL, which includes 1 µL of Pre-Amp master mix (Fluidigm), 1.25 µL of pooled TaqMan assay mix (0.2X), 1.5 µL of nuclease-free water and 1.25 µL of cDNA for each sample. Pre-Amp cDNA was subjected to a pre-incubation step of 95°C for 2 minutes, followed by 20 cycles of 95°C for 15 seconds and 60°C for 4 minutes. This was performed in a conventional real-time qPCR instrument (Roche LightCycler96). To avoid amplification of genomic DNA, assays were selected with forward and reverse primers separated by a large intron region. RT-qPCR data analysis The real-time data from RT-qPCR was pre-processed by performing baseline correction and CT values were extracted using a global threshold, as determined by

Decision Curve Analysis of 3-gene signature and other biomarkers in the Pre-COVID-19 prospective validation cohort
For each DCA, a logistic regression model was first fitted with the diagnostic test (WCC, CRP or RNA signature) as the only predictor to give the positive predictive value of a test across a range of test thresholds. Net benefit was then calculated as sensitivity × prevalence -(1 -specificity) × (1 -prevalence) × w where w is the odds at the threshold probability and prevalence is the proportion of patients who met the stated outcome. (21) The threshold probability represents the outcome probability above which a given intervention treatment would be recommended and reflects the risk:benefit ratio for any given intervention or 'treatment'. Each DCA was benchmarked against the net-benefit derived from a treat all or treat none approach. Figure S1. Categorisation algorithm for the Discovery cohort.

SUPPLEMENTARY FIGURES
Algorithm used to categorise cases for the discovery cohort into bacterial (comprising both Gram positive and Gram negative), viral and not infected groups. Non-acute or mixed phenotypes were excluded from RNA-Seq analysis.  Figure S2. Categorisation algorithm for the Pre-COVID-19 validation cohort.
Algorithm used to categorise cases for the pre-COVID-19 validation cohort into definite bacterial, probable bacterial, definite viral, probable viral and indeterminate (co-infection or lacking diagnostic certainty) groups.
Non-infected cases and other infections (mycobacterial, fungal or parasitic pathogen cases) were separately categorised.    Figure S4. Volcano plot of differentially expressed genes.
Volcano plot highlighting the significantly differentially expressed genes between patients with bacterial and viral infection. Genes that pass the adjusted p-value threshold of 0.05 and the absolute log2 fold-change threshold of 1, have been highlighted in red (n=1622).  Supplementary Table   S4.                 Table S11. Assessment of diagnostic test accuracy by two-way contingency tables. Youden's J statistic (20) was used to determine the most optimal thresholds for sensitivity and specificity.