Investigation of stratification of infected human neonates on the basis of transcriptional change in whole blood
Akomolafe, Olanrewaju Samuel
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Failure to clinically distinguish between viral and bacterial neonatal infections with lack of a reliable tool to rapidly differentiate between these pathogens often poses a diagnostic challenge for clinicians. Available knowledge on how neonates respond to infection on the microbiological level is insufficient. The paradigm shift towards host-directed diagnosis motivates our hypothesis that human host response may generate distinct transcriptional profiles that can distinguish viral from bacterial infections. I investigated a viral (cytomegalovirus) case in relation to bacterial infection with a goal to understand whole-blood-genome signatures that can discriminate between viral and bacterial infections in human neonates. Previously, whole blood samples were collected from neonates with confirmed infection (n=28) and controls (n=35). The infected samples were from 27 patients with culture-confirmed bacterial infections, and one CMV-infected case. mRNA was extracted from the samples and run on Illumina microarray platform. Normalised microarray gene expression data were examined to identify distinct patterns in systemic transcriptional response from human neonatal whole blood. Alongside, I analysed clinical variables of the neonates in which the samples were collected. In this analysis, I observed that 8 out of 35 control samples clustered distinctly with the single CMV case and separated clearly from the remaining 27 controls. These 8 patient samples with the CMV-infected case were termed as “virus-correlated group” Statistical testing of confounding factors and clinical variables between the virus-correlated (n=9) group and the remaining control (n=27) samples revealed no significant difference between the two groups except in terms of age and birth weight. However, virus-correlated group had significantly lower age and birth weight compared to the controls, which is associated with high-risk group to neonatal infection. I determined differentially regulated genes between the virus-correlated samples and controls, and similarly, transcripts with statistically significant regulation in bacterial infection in comparison to controls were identified. Excluding significantly differentially expressed transcripts (with adjusted p <0.05 and >2 fold change) that overlapped between the virus-correlated group and bacterial infection, 19 genes, including three novel (LOC644852, LOC440313 and LOC441763) transcripts, were exclusively up-regulated in virus-correlated group with respect to controls. These findings were next validated in other independent viral data. The 8 control samples were not expected to be transcriptionally similar to the viral sample. This work demonstrates that distinct transcriptional patterns generated from neonatal whole blood might distinguish viral from bacterial infections and could enable clinicians to rapidly and reliably diagnose neonatal infection. It is evident from the microarray data that blood-genome expression profiling in neonatal infection has promising diagnostic potential.