  Neonatal sepsis (NS) is responsible for over a 1 million yearly deaths
worldwide. In the developing world NS is often treated without an identified
microbial pathogen. Amplicon sequencing of the bacterial 16S rRNA gene can be
used to identify organisms that are difficult to detect by routine
microbiological methods. However, contaminating bacteria are ubiquitous in both
hospital settings and research reagents, and must be accounted for to make
effective use of these data. In the present study, we sequenced the bacterial
16S rRNA gene obtained from blood and cerebrospinal fluid (CSF) of 80 neonates
presenting with NS to the Mbarara Regional Hospital in Uganda. Assuming that
patterns of background contamination would be independent of pathogenic
microorganism DNA, we applied a novel quantitative approach using principal
orthogonal decomposition to separate background contamination from potential
pathogens in sequencing data. We designed our quantitative approach contrasting
blood, CSF, and control specimens, and employed a variety of statistical random
matrix bootstrap hypotheses to estimate statistical significance. These
analyses demonstrate that Leptospira appears present in some infants presenting
within 48 hr of birth, indicative of infection in utero, and up to 28 days of
age, suggesting environmental exposure. This organism cannot be cultured in
routine bacteriological settings, and is enzootic in the cattle that the rural
peoples of western Uganda often live in close proximity. Our findings
demonstrate that statistical approaches to remove background organisms common
in 16S sequence data can reveal putative pathogens in small volume biological
samples from newborns. This computational analysis thus reveals an important
medical finding that has the potential to alter therapy and prevention efforts
in a critically ill population.
