Biology subjects

Madhi, S. A.

Publications and source records attributed to Madhi, S. A..

4 recordsLinked to original sources

bioRxiv2026

Identifying candidate genetic determinants associated with invasion in pneumococcal lineages

Serotype invasiveness estimates help identify high-risk serotypes and interpret disease replacement following pneumococcal conjugate vaccination. Because serotype and lineage are strongly associated, their independent contributions must be separated to identify unexpectedly invasive lineages and investigate underlying mechanisms. Using temporally matched carriage and invasive-disease isolates from children aged <7 years in South Africa and the USA, we fitted a logistic mixed-effects model with Global Pneumococcal Sequence Cluster (GPSC) as a random effect and serotype and country as fixed effects. We conducted a species-wide genome-wide association study (GWAS) of invasion and compared the prevalence of significant unitigs in GPSC1 and GPSC21 to identify invasion-associated variants differentiating them. We additionally examined capsule-locus variation that could contribute to their contrasting invasiveness. After serotype and country adjustment, GPSC1 and GPSC21, which both express 19F and share an immediate common ancestor, ranked fifth and 149th of 150 lineages; odds multipliers were 1.51 (95% conditional interval 1.06-2.15) and 0.61 (0.41-0.93), respectively. Of 1,569 invasion-associated unitigs from the species-wide GWAS, 272 differentiated GPSC1 from GPSC21. Among these, the strongest signal mapped to hk11 (desK) and was associated with GPSC1; bgaA was the highest-ranked known antibody-binding target. Within GPSC1 CC271 had additional variants in the ATP-dependent Clp protease ATP-binding subunit ClpX compared to the CC420 subclade. Capsule-protein folds were conserved with local effects indistinguishable from prediction uncertainty. Separating serotype and lineage effects can identify unexpectedly invasive lineages and prioritize candidate serotype-independent determinants, vaccine antigens and capsule variation for experimental investigation.

microbiology↗

bioRxiv2023

Geographic migration and vaccine-induced fitness changes of Streptococcus pneumoniae

Streptococcus pneumoniae is a leading cause of pneumonia and meningitis worldwide. Many different serotypes co-circulate endemically in any one location. The extent and mechanisms of spread, and vaccine-driven changes in fitness and antimicrobial resistance (AMR), remain largely unquantified. Using geolocated genome sequences from South Africa (N=6910, 2000-2014) we developed models to reconstruct spread, pairing detailed human mobility data and genomic data. Separately we estimated the population level changes in fitness of strains that are (vaccine type, VT) and are not (non-vaccine type, NVT) included in the vaccine, first implemented in 2009, as well as differences in strain fitness between those that are and are not resistant to penicillin. We estimated that pneumococci only become homogenously mixed across South Africa after about 50 years of transmission, with the slow spread driven by the focal nature of human mobility. Further, in the years following vaccine implementation the relative fitness of NVT compared to VT strains increased (RR: 1.29 [95% CI 1.20-1.37]) - with an increasing proportion of these NVT strains becoming penicillin resistant. Our findings point to highly entrenched, slow transmission and indicate that initial vaccine-linked decreases in AMR may be transient. One-Sentence SummaryWe describe geographic migration, and fitness dynamics conferred by NVT strains and AMR, for the globally endemic pathogen Streptococcus pneumoniae.

evolutionary biology↗

bioRxiv2019

A novel mosaic tetracycline resistance gene tet(S/M) detected in a multidrug-resistant pneumococcal CC230 lineage that underwent capsular switching in South Africa

SynopsisO_ST_ABSObjectiveC_ST_ABSWe reported a novel tetracycline-resistant gene in Streptococcus pneumoniae and investigated its temporal spread in relation to nationwide clinical interventions.\n\nMethodsWe whole genome sequenced 12,254 pneumococcal isolates from twenty-nine countries on an Illumina HiSeq Sequencer. Serotypes, sequence types and antibiotic resistance were inferred from genomes. Phylogeny was built based on single-nucleotide variants. Temporal changes of spread were reconstructed using a birth-death model.\n\nResultsWe identified tet(S/M) in 131 pneumococcal isolates, 97 (74%) caused invasive pneumococcal diseases among young children (59% HIV-positive, where HIV status was available) in South Africa. A majority of tet(S/M)-positive isolates (129/131) belong to clonal complex (CC)230. A global phylogeny of CC230 (n=389) revealed that tet(S/M)-positive isolates formed a sub-lineage that exhibited multidrug-resistance. Using the genomic data and a birth-death model, we detected an unrecognised outbreak of this sub-lineage in South Africa between 2000 and 2004 with an expected secondary infections (R) of ~2.5. R declined to ~1.0 in 2005 and <1.0 in 2012. The declining epidemic coincided and could be related to the nationwide implementation of anti-retroviral treatment (ART) for HIV-infected individuals in 2004 and PCVs in late 2000s. Capsular switching from vaccine serotype 14 to non-vaccine serotype 23A was observed within the sub-lineage.\n\nConclusionsThe prevalence of tet(S/M) in pneumococci was low and its dissemination was due to an unrecognised outbreak of CC230 in South Africa prior to ART and PCVs. However, capsular switching in this multidrug-resistant sub-lineage highlighted its potential to continue to cause disease in the post-PCV13 era.

microbiology↗

bioRxiv2018

HLA*PRG:LA - HLA typing from linearly projected graph alignments

SummaryHLA*PRG:LA implements a new graph alignment model for HLA type inference, based on the projection of linear alignments onto a variation graph. It enables accurate HLA type inference from whole-genome (99% accuracy) and whole-exome (93% accuracy) Illumina data; from long-read Oxford Nanopore and Pacific Biosciences data (98% accuracy for whole-genome and targeted data); and from genome assemblies. Computational requirements for a typical sample vary between 0.7 and 14 CPU hours per sample.\n\nAvailability and ImplementationHLA*PRG:LA is implemented in C++ and Perl and freely available from https://github.com/DiltheyLab/HLA-PRG-LA (GPL v3).\n\nContactalexander.dilthey@med.uni-duesseldorf.de\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗