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Screening <i>Leishmania donovani</i>‐specific genes required for visceral infection

2010· article· en· W2102667591 on OpenAlexafffund
Wen‐Wei Zhang, Greg Matlashewski

Bibliographic record

VenueMolecular Microbiology · 2010
Typearticle
Languageen
FieldMedicine
TopicResearch on Leishmaniasis Studies
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsBiologyLeishmania infantumVisceral leishmaniasisLeishmania donovaniAmastigoteLeishmaniaVirulenceGenePseudogeneLeishmania braziliensisComparative genomicsGeneticsGenomeLeishmaniasisMicrobiologyGenomicsCutaneous leishmaniasisParasite hosting

Abstract

fetched live from OpenAlex

Comparison of the Leishmania infantum genome with Leishmania braziliensis and Leishmania major genomes has identified 25 L. infantum species-specific genes that are absent or pseudogenes in L. major and L. braziliensis. To determine whether these L. infantum species-specific genes are involved in visceral Leishmania infection, we cloned the orthologues of 14 L. infantum species-specific genes from the genetically closely related Leishmania donovani and introduced them into L. major. Two of these L. donovani species-specific genes were found to significantly increase L. major survival in visceral organs in BALB/c mice. One (orthologue of LinJ28_V3.0340; Ld2834) of these two genes was further investigated. The L. donovani Ld2834 null mutants displayed dramatically reduced virulence in BALB/c mice and were unable to survive in axenic amastigote culture conditions arguing that Ld2834 plays a crucial role in enabling L. donovani survive at the increased temperature typically associated with visceral organs. Ld2834 encodes a 50 kDa protein that is localized in the cytoplasma and has no significant sequence similarity with other known genes. This study validates the importance of comparative genomics for understanding Leishmania species pathology and argues that Leishmania species-specific genes play important roles in tissue tropism and virulence.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.301
Teacher spread0.275 · 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 teacher head, not a consensus.

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

Citations46
Published2010
Admission routes2
Has abstractyes

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