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

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 Leptospira appears present in some infants presenting within 48 h 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 often live in close proximity to the rural peoples of western Uganda. 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.

Changes in Ugandan Climate Rainfall at the Village and Forest Level

“…How such changes in rainfall patterns impact infectious disease prevalence and risks will be determined by individual disease characteristics, and will be important for specific locations. By fusing the satellite rainfall grid with the locations of all of the villages in Uganda, we have a finely granular way to track epidemic diseases given village case data for individuals who become ill. By helping to identify vulnerable locations, such fusion functions as a platform for seeking optimization of treatment and prevention of many infectious diseases, of which we are particularly focused on neonatal sepsis 28 , 29 and a critical sequela in survivors in Africa, postinfectious hydrocephalus 10 – 12 .…”

The Problem of Microbial Dark Matter in Neonatal Sepsis

“…Blood culturing may fail to identify NS pathogens, in part because very low levels of bacteremia can cause symptoms in neonates (9,16), and in part because it is difficult to obtain sufficient blood for sensitive culture recovery from small neonates. Furthermore, even if the blood obtained does contain bacteria known to cause NS, these bacteria may not grow well in culture (17). In addition, nonbacterial pathogens that do not grow in common culture media can cause NS symptoms; these include viruses such as enteroviruses, rhinoviruses, and coronaviruses (18,19), fungi such as Candida sp.…”

Prevalence of Anti-leptospiral IgM and Detection of Pathogenic Leptospira Species DNA in Neonates Presenting With Clinical Sepsis in Southwestern Uganda

“…Infections are reported to be caused mostly by the P1 group, explaining why commercial kits and diagnostic qPCR assays widely used detect only the P1 group. However, there are increasing reports of detection of P2 in clinical samples including in the current study site [10][11][12]. The reservoirs for the leptospires include rodents particularly rats, domestic animals mainly pigs but also dogs and cattle [5], all of which are common in the current study region.…”