bioRxivMicrobiologyPreprint
Sensitive detection of highly fragmented cytomegalovirus nucleic acid in human cfDNA
V. Peddu, B. Bradley, S. Shree, B. Colbert, H. Xie, T. Santo, M. Huang, E. Cheng, E. Konnick, S. Salipante, K. Jerome, C. Lockwood, A. L. Greninger
Abstract
Congenital human cytomegalovirus (CMV) infections are the leading cause of newborn hearing and central nervous system impairments worldwide. Currently, routine prenatal screening for congenital CMV is not performed in the United States and confirmation in suspected perinatal cases requires invasive sampling by amniocentesis. We hypothesized that detection of CMV from maternal cell-free DNA (cfDNA) plasma could provide a non-invasive indicator of congenital CMV infection. We analyzed sequence data from 2,208 individuals undergoing routine non-invasive prenatal aneuploidy screening at the University of Washington. CMV reads were identified in 117 (5.3%) samples. Positive samples were stratified based on CMV reads per million sample reads (RPM), resulting in ten samples being classified as strong positive (RPM > 0.3) and 107 as intermediate positive (0.01<RPM<0.3). Subsequent qPCR testing identified CMV in 9/10 strong positive samples and 2/32 intermediate positive samples. Median cfDNA insert size derived from CMV was significantly shorter than cfDNA derived from human chromosomes (103 vs 172 bp, p<0.0001), corresponding to the 3rd percentile of human cfDNA insert size. In addition, CMV cfDNA fragment lengths were distributed over a wider range than human cfDNA reads. These studies reveal the highly fragmented nature of CMV cfDNA and offer precise measurements of its length: these features likely explain discrepancies in serum CMV viral loads measurements determined by different qPCR assays, despite widespread efforts to standardize results. More work is required to determine how detection of CMV from maternal cfDNA can be best used as tool for congenital CMV screening or diagnosis.
- organism: human
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: variant calling
- approach: statistical model
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