Biology subjects

MacLeod, W.

Publications and source records attributed to MacLeod, W..

2 recordsLinked to original sources

bioRxiv2026

Genomic analysis of Klebsiella pneumoniae causing community-acquired respiratory deaths among Zambian infants and children using targeted RNA-probe hybridization-capture metagenomics

Klebsiella pneumoniae (Kp) is a leading cause of neonatal and infant deaths in sub-Saharan Africa and frequently associated with antimicrobial resistance. Previously, we identified Kp as a major cause of fatal community-associated lower respiratory infections among infants and children under five years in Lusaka, Zambia, using postmortem tissue sampling and pathogen specific multiplex qPCR. In this follow-up study, we employed a novel culture-independent RNA-probe hybridization-capture metagenomic sequencing approach, targeting Kp pan-genome core and accessory genes, to perform in-depth genomic analysis of Kp from eleven post-mortem lung biopsy samples from seven of these children. Analysis detected Kp in all cases except one, which identified Klebsiella quasipneumoniae subspecies similipneumoniae. Core-genome multi-locus sequence typing (cgMLST) revealed six clonal groups (CG607, CG1123, CG10072, CG280, CG3648, and CG10344) belonging to five sublineages (SL607, SL17, SL280, SL37, and SL10072), with perfect concordance between paired samples from the same case. Two infants sampled the same month harbored SL607 lineages sharing 621 out of 629 cgMLST alleles, suggesting clonal spread. Kp capsule (K) loci were detected in all but one case and included potential vaccine targets KL25, KL23, and KL122. Antimicrobial resistance genes were widespread among samples, particularly encoding resistance toward aminoglycosides, {beta}-lactams, sulphonamides, tetracyclines, and trimethoprim. Extended spectrum {beta}-lactamases were identified in four cases, three of which were blaCTX-M-15. The acquired Kp sideophore yersiniabactin (lineage ybt14) was identified in both cases associated with SL607, and the acquired siderophore aerobactin (lineage iuc5) was identified in one of these, suggesting possible convergence of antimicrobial resistance and hypervirulence. The detection of Kp with extensive antimicrobial resistance causing fatal community acquired pneumonia signals a deeply concerning epidemiologic shift from a largely nosocomial pathogen. This calls for urgent epidemiological investigations to better understand the burden, transmission dynamics, antimicrobial resistances, and potential vaccine targets for Kp in other community settings across sub-Saharan Africa. Author SummaryKlebsiella pneumoniae is a major cause of infections and death among newborns and young children, particularly in low-income countries, where it is frequently resistant to antibiotics. While well-known as a hospital-associated pathogen, we previously showed K. pneumoniae is also a leading cause of fatal community lung infections among infants and children in Lusaka, Zambia. In this follow-on analysis, we performed deeper genetic analysis of K. pneumoniae detected from the cluster of community pneumonia deaths using lung tissue samples from seven of these children. Since traditional bacterial cultures were unavailable, we instead used a novel approach that enriched and sequenced specific regions of the K. pneumoniae genome directly from the biopsy samples without culturing bacterial isolates. We identified five different K. pneumoniae genetic subtypes, known as sublineages. Two sublineages, which came from children sampled the same month, were highly similar, suggesting clonal spread. Multiple acquired antimicrobial resistance genes were detected across all sublineages. Acquired virulence factors, which may cause more aggressive infections, were also detected in two cases. We also identified capsule types previously suggested as potential vaccine targets. This study underscores the urgent need to better understand and address the emerging burden of antibiotic-resistant K. pneumoniae pneumonia and other invasive infections among infants and children in community settings in sub-Saharan Africa.

microbiology↗

bioRxiv2018

Building a National HIV Cohort from Routine Laboratory Data: Probabilistic Record-Linkage with Graphs

BackgroundChronic disease management requires the ability to link patient records across multiple interactions with the health sector. South Africas National Health Laboratory Service (NHLS) conducts all routine laboratory monitoring for the countrys national public sector HIV program. However, the absence of a validated patient identifier has limited the potential of the NHLS database for epidemiological research, policy evaluation, and longitudinal patient care. We developed and validated a record linkage algorithm, creating a unique patient identifier and enabling analysis of the NHLS database as a national HIV cohort. To our knowledge, this is the first national HIV cohort in any low-or middle-income country.\n\nMethods.We linked data on all CD4 counts, HIV viral loads (VL), and ART workup laboratory tests from 2004-2016. Each NHLS laboratory test result is associated with a name, sex, date of birth (DOB), gender, and facility. However, due to typographical and other errors and patient mobility between facilities, different patient specimens may be associated with different sets of identifying information. We developed a graph-based probabilistic record linkage algorithm and used it to construct a unique identifier for all patients with laboratory results in the national HIV program. We used standard probabilistic linkage methods with Jaro-Winkler string comparisons and weights informed by response frequency. We also used graph concepts to guide the linkage in determining whether a cluster of patient specimens could plausibly reflect a single patient. This approach allows matching thresholds to vary with the density of the network and limits over-matching.\n\nTo train and validate our approach, we constructed a quasi-gold standard based on manual review of 59,000 candidate matches associated with 1000 randomly sampled specimens. These data were divided into training and validation sets. Domain weights and graph parameters were optimized using the manually matched training data.\n\nTo evaluate performance, we calculated the probability that a true match was correctly identified by our algorithm (sensitivity, Sen) and the probability that a match identified by our algorithm was truly a match (positive predictive value, PPV) in the manually-matched data. We also assessed validity in the full cohort using proxies for under-and over-matching and assessed sensitivity vis-a-vis national identification numbers and patient folder numbers, which were available for a sub-set of records. We compared the performance of our algorithm for exact matching and a prior identifier that had been developed by the NHLS Corporate Data Warehouse.\n\nResults.As of December 2016, the NHLS database contained 117 million patient specimens with a CD4, VL, or other laboratory test used in HIV care. These specimens had 63 million unique combinations of patient identifying information. From these data, our matching algorithm identified 11.6 million unique HIV patients who had at least one CD4 count or VL result. These patients 70.9 million total specimens, with a median of 3 specimens per patient (IQR 1 to 8). Sensitivity and PPV of the algorithm were estimated to be 93.7% and 98.6% in manually-matched data, compared to 64.1% and 100.0% for the existing NHLS identifier. We estimated that in 2016 there were 3.35 million patients on ART and virologically monitored, similar to the National Department of Health estimate of 3.50 million.\n\nConclusion.We constructed a South African National HIV Cohort by applying novel graph-based probabilistic record linkage techniques to routinely collected laboratory data, with high sensitivity and positive predictive value. Information on graph structure can guide record linkage in large populations when identifying data are limited.

epidemiology↗