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Record W4404067958 · doi:10.1128/spectrum.03643-23

Evaluating methods for identifying and quantifying <i>Streptococcus pneumoniae</i> co-colonization using next-generation sequencing data

2024· article· en· W4404067958 on OpenAlexaff
Jada Hackman, Martin L. Hibberd, Todd D. Swarthout, Jason Hinds, James Ashall, Carmen Sheppard, Gerry Tonkin‐Hill, Kate Gould, Comfort Brown, Jacquline Msefula, Andrew A. Mataya, Michiko Toizumi, Lay‐Myint Yoshida, Neil French, Robert S. Heyderman, Stefan Flasche, Brenda Kwambana-Adams, Stéphane Hué

Bibliographic record

VenueMicrobiology Spectrum · 2024
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsInstitute of Infection and Immunity
FundersEuropean and Developing Countries Clinical Trials PartnershipMedical Research CouncilSanofi PasteurNorges ForskningsrådMinistry of Education, Culture, Sports, Science and TechnologyNational Institute for Health and Care ResearchPfizerDepartment for International DevelopmentUK Research and InnovationGovernment of the United KingdomWellcome TrustGlaxoSmithKlineSanofiEuropean CommissionBill and Melinda Gates Foundation
KeywordsSerotypeBiologyStreptococcus pneumoniaeSingle-nucleotide polymorphismDNA sequencingCarriageGeneticsMicrobiologyGeneGenotypeMedicineBacteria

Abstract

fetched live from OpenAlex

Detection of multiple pneumococcal serotype carriage can enhance monitoring of pneumococcal vaccine impact, particularly among high-burden childhood populations. We assessed methods for identifying co-carriage of pneumococcal serotypes from whole-genome sequences. Twenty-four nasopharyngeal samples were collected during community carriage surveillance from healthy children in Blantyre, Malawi, which were then serotyped by microarray. Pneumococcal DNA from culture plate sweeps were sequenced using Illumina MiSeq, and genomic serotyping was carried out using SeroCall and PneumoKITy. Their sensitivity was calculated in reference to the microarray data. Local maxima in the single-nucleotide polymorphism (SNP) density distributions were assessed for their correspondence to the relative abundance of serotypes. Across the 24 individuals, the microarray detected 77 non-unique serotypes, of which 42 occurred at high relative abundance (>10%) (per individual, median, 3; range, 1-6 serotypes). The average sequencing depth was 57X (range: 21X-88X). The sensitivity of SeroCall for identifying high-abundance serotypes was 98% (95% CI, 0.87-1.00), 20% (0.08-0.36) for low abundance (<10%), and 62% (0.50-0.72) overall. PneumoKITy's sensitivity was 86% (0.72-0.95), 20% (0.06-0.32), and 56% (0.42-0.65), respectively. Local maxima in the SNP frequency distribution were highly correlated with the relative abundance of high-abundance serotypes. Six samples were resequenced, and the pooled runs had an average fourfold increase in sequencing depth. This allowed genomic serotyping of two of the previously undetectable seven low-abundance serotypes. Genomic serotyping is highly sensitive for the detection of high-abundance serotypes in samples with co-carriage. Serotype-associated reads may be identified through SNP frequency, and increased read depth can increase sensitivity for low-abundance serotype detection.IMPORTANCEPneumococcal carriage is a prerequisite for invasive pneumococcal disease, which is a leading cause of childhood pneumonia. Multiple carriage of unique pneumococcal serotypes at a single time point is prevalent among high-burden childhood populations. This study assessed the sensitivity of different genomic serotyping methods for identifying pneumococcal serotypes during co-carriage. These methods were evaluated against the current gold standard for co-carriage detection. The results showed that genomic serotyping methods have high sensitivity for detecting high-abundance serotypes in samples with co-carriage, and increasing sequencing depth can increase sensitivity for low-abundance serotypes. These results are important for monitoring vaccine impact, which aims to reduce the prevalence of specific pneumococcal serotypes. By accurately detecting and identifying multiple pneumococcal serotypes in carrier populations, we can better evaluate the effectiveness of vaccination programs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.495
GPT teacher head0.516
Teacher spread0.021 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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