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Neonatal sepsis (NS) is responsible for over a 1 million yearly deathsworldwide. In the developing world NS is often treated without an identifiedmicrobial pathogen. Amplicon sequencing of the bacterial 16S rRNA gene can beused to identify organisms that are difficult to detect by routinemicrobiological methods. However, contaminating bacteria are ubiquitous in bothhospital settings and research reagents, and must be accounted for to makeeffective use of these data. In the present study, we sequenced the bacterial16S rRNA gene obtained from blood and cerebrospinal fluid (CSF) of 80 neonatespresenting with NS to the Mbarara Regional Hospital in Uganda. Assuming thatpatterns of background contamination would be independent of pathogenicmicroorganism DNA, we applied a novel quantitative approach using principalorthogonal decomposition to separate background contamination from potentialpathogens in sequencing data. We designed our quantitative approach contrastingblood, CSF, and control specimens, and employed a variety of statistical randommatrix bootstrap hypotheses to estimate statistical significance. Theseanalyses demonstrate that Leptospira appears present in some infants presentingwithin 48 hr of birth, indicative of infection in utero, and up to 28 days ofage, suggesting environmental exposure. This organism cannot be cultured inroutine bacteriological settings, and is enzootic in the cattle that the ruralpeoples of western Uganda often live in close proximity. Our findingsdemonstrate that statistical approaches to remove background organisms commonin 16S sequence data can reveal putative pathogens in small volume biologicalsamples from newborns. This computational analysis thus reveals an importantmedical finding that has the potential to alter therapy and prevention effortsin a critically ill population.

Separating Putative Pathogens from Background Contamination with  Principal Orthogonal Decomposition: Evidence for Leptospira in the Ugandan  Neonatal Septisome