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Record W4415299377 · doi:10.1016/j.crpvbd.2025.100329

Brugia malayi miRNAs and potential targets within the feline host (Felis catus)

2025· article· en· W4415299377 on OpenAlexaff
Erica Burkman, Lucienne Tritten, Christopher C. Evans, Guilherme G. Verocai, Caroline Sobotyk, Timothy G. Geary, Andrew R. Moorhead

Bibliographic record

VenueCurrent Research in Parasitology and Vector-Borne Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicParasitic Diseases Research and Treatment
Canadian institutionsMcGill UniversitySte. Anne's Hospital
Fundersnot available
KeywordsImmune systemHost (biology)Brugia malayiContext (archaeology)microRNAEvasion (ethics)GeneMechanism (biology)

Abstract

fetched live from OpenAlex

Host specificity is a critical feature of the survival and proliferation of parasites. In the context of interactions with the host, numerous mechanisms have been identified, particularly in parasitic helminths, that enable manipulation of the host immune system to enhance their own survival. The evolutionary history of these interactions often results in hosts becoming disease-tolerant or asymptomatic, even when burdened with a high number of worms, until a disruption in the host’s immune system can trigger a disease state. However, the molecular mechanisms underlying host specificity and the ways in which parasites alter the host’s immune system remain largely unexplored. Research conducted on parasite-derived microRNAs (miRNAs) suggests they may play a role in remodeling the host to improve parasite survival and growth, possibly through directed pathology. To further explore this host-parasite relationship, we analyzed plasma of four cats experimentally infected with the filarial nematode Brugia malayi , each with varying levels of microfilaremia, six months post-infection. Out of approximately 32 million sequencing reads, we detected 185 mature miRNA candidates potentially originating from B. malayi , with 26 miRNAs present in 10 or more copies. We also identified seven immunity-related host genes (Ptgs1, Irf4, Irf5, Numbl, Tnfsf15, Stat3, and Txlnb) that are predicted to be targets of parasite-derived miRNAs. Additional investigation is warranted to elucidate the role of these miRNAs in the host-parasite interaction. These data offer promising targets for further exploration, and potentially the discovery of novel therapeutics that disrupt parasite immune evasion and pathological alterations to the host. • The first analysis of Brugia malayi miRNA-host interactions was conducted in a feline infection model. • 185 B. malayi miRNAs were detected in infected feline host plasma samples. • 26 parasite-derived miRNAs were identified with ≥ 10 sequencing reads. • Seven immune genes (Ptgs1, Irf4, Irf5, Numbl, Tnfsf15, Stat3, and Txlnb) targeted by parasite miRNAs. • MicroRNAs linked to Rap1, AGE-RAGE, and mTOR signaling pathways in host cells.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.434
Teacher spread0.398 · 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 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
Published2025
Admission routes1
Has abstractyes

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