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Record W3036091675 · doi:10.1101/2020.06.22.164186

<i>Mycobacterium tuberculosis</i> complex lineage 5 exhibits high levels of within-lineage genomic diversity and differing gene content compared to the type strain H37Rv

2020· preprint· en· W3036091675 on OpenAlexaff
C. N’Dira Sanoussi, Mireia Coscollá, Boatema Ofori-Anyinam, Isaac Darko Otchere, Martín Antonio, Stefan Niemann, Julian Parkhill, Simon R. Harris, Dorothy Yeboah‐Manu, Sébastien Gagneux, Leen Rigouts, Dissou Affolabi, Bouke C. de Jong, Conor J. Meehan

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsGlobal Institute for Water Security
FundersEuropean Society of Clinical Microbiology and Infectious DiseasesGeneralitat ValencianaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsGenomeBiologyGeneticsMycobacterium tuberculosis complexGeneReference genomeLineage (genetic)Comparative genomicsMycobacterium tuberculosisGC-contentGenomicsTuberculosis

Abstract

fetched live from OpenAlex

Abstract Pathogens of the Mycobacterium tuberculosis complex (MTBC) are considered monomorphic, with little gene content variation between strains. Nevertheless, several genotypic and phenotypic factors separate the different MTBC lineages (L), especially L5 and L6 (traditionally termed Mycobacterium africanum ), from each other. However, genome variability and gene content especially of L5 and L6 strains have not been fully explored and may be potentially important for pathobiology and current approaches for genomic analysis of MTBC isolates, including transmission studies. We compared the genomes of 358 L5 clinical isolates (including 3 completed genomes and 355 Illumina WGS (whole genome sequenced) isolates) to the L5 complete genomes and H37Rv, and identified multiple genes differentially present or absent between H37Rv and L5 strains. Additionally, considerable gene content variability was found across L5 strains, including a split in the L5.3 sublineage into L5.3.1 and L5.3.2. These gene content differences had a small knock on effect on transmission cluster estimation, with clustering rates influenced by the selection of reference genome, and with potential over-estimation of recent transmission when using H37Rv as the reference genome. Our data show that the use of H37Rv as reference genome results in missing SNPs in genes unique for L5 strains. This potentially leads to an underestimation of the diversity present in the genome of L5 strains and in turn affects the transmission clustering rates. As such, a full capture of the gene diversity, especially for high resolution outbreak analysis, requires a variation of the single H37Rv-centric reference genome mapping approach currently used in most WGS data analysis pipelines. Moreover, the high within-lineage gene content variability suggests that the pan-genome of M. tuberculosis is at least several kilobases larger than previously thought, implying a concatenated or reference-free genome assembly ( de novo ) approach may be needed for particular questions. Data summary Sequence data for the Illumina dataset are available at European Genome-phenome Archive (EGA; https://www.ebi.ac.uk/ega/ ) under the study accession numbers PRJEB38317 and PRJEB38656. Individual runs accession numbers are indicated in Table S8. PacBio raw reads for the L5 Benin genome are available on the ENA accession SAME3170744. The assembled L5 Benin genome is available on NCBI with accession PRJNA641267. To ensure naming conventions of the genes in the three L5 genomes can be followed, we have uploaded these annotated GFF files to figshare at https://doi.org/10.6084/m9.figshare.12911849.v1 . Custom python scripts used in this analysis can be found at https://github.com/conmeehan/pathophy .

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.094
GPT teacher head0.277
Teacher spread0.182 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2020
Admission routes1
Has abstractyes

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