AUTHOR=Schiff Steven J. , Kiwanuka Julius , Riggio Gina , Nguyen Lan , Mu Kevin , Sproul Emily , Bazira Joel , Mwanga-Amumpaire Juliet , Tumusiime Dickson , Nyesigire Eunice , Lwanga Nkangi , Bogale Kaleb T. , Kapur Vivek , Broach James R. , Morton Sarah U. , Warf Benjamin C. , Poss Mary TITLE=Separating Putative Pathogens from Background Contamination with Principal Orthogonal Decomposition: Evidence for Leptospira in the Ugandan Neonatal Septisome JOURNAL=Frontiers in Medicine VOLUME=3 YEAR=2016 URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2016.00022 DOI=10.3389/fmed.2016.00022 ISSN=2296-858X ABSTRACT=
Neonatal sepsis (NS) is responsible for over 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 this 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