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Record W4402541121 · doi:10.1093/jas/skae234.041

200 Transcriptomics analyses of mink spleen infected by Aleutian Disease

2024· article· en· W4402541121 on OpenAlexaffabout
Gregory Bishop, Hossain Farid, Duy Ngoc, Younes Miar

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMinkSpleenBiologyDiseaseTranscriptomeZoologyVirologyImmunologyMedicineEcologyPathologyGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

Abstract Aleutian disease (AD) is one of the most challenging mink diseases that causes high mortality and affects several economically important traits. Aleutian mink disease virus (AMDV) targets multiple organs and among them, the spleen is one of the major targeted organs. Transcriptomics has been widely used to reveal genes and biological pathways and identify biomarkers for early detection or prevention of diseases. This study aimed to identify genes and pathways related to the host response to AD infection in the spleen via transcriptomic study. Twelve (3 sets of 4 full-sibs) AMDV-free black male mink were housed in the clean section of the animal housing facility at the AD Research Centre (Truro, NS, Canada) between 4 to 7 d before inoculation. Before inoculation, sampling, and euthanasia, animals were anesthetized. The viral inoculum was a 10% (W/V) passage 2 of AMDV prepared from the spleens of mink infected with a local strain and stored at − 80 °C. Animals were intranasally inoculated under sedation with 60 µL of the viral homogenate, corresponding to approximately 300 to 700 ID50. Infected mink were humanely euthanized and the spleen tissues were collected for RNA isolation at 24h (d 1), 48h (d 2), and d 7. Libraries were prepared from total RNA using the Illumina TruSeqTM RNA kit, and were sequenced using the HiScanSQ platform, producing 101 bp reads from each end. The raw RNA-Seq data were cleaned using Cutadapt V1.4.2 and after the cleaning process was completed, 942,803,540 paired-end reads remained to be used in downstream analysis. Differential gene expression analyses revealed most (168) significant differentially expressed (DE) genes between d 1 and d 0 (Control or ADMV-free mink) while fewer DE genes (23) were between d 7 and d 0 (Table 1; Figure 1). A total of 19 DE genes are identified between three pair comparisons and seven of them are directly involved in immune response (FGL2, TLF8, LRP1, SERPINB9, MSR1, C3, PLA2R1, and XCR1). Gene enrichment analyses revealed pathways related to innate immune and fat metabolism which are important for the host mechanism to fight against infections. One of the important pathways is neutrophil degranulation which functions as aiding in the elimination of pathogens and the initiation of the inflammatory process. In conclusion, the current study provides insight into the transcriptomic profiles of the spleen in mink infected with AD. Identified candidate genes might be used for functional studies or as prior information for markers or genomic selection against AD in mink. Further studies in other tissues or single-cell RNA sequencing might deliver more comprehensive pictures of host responses to AD infections.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.324
Teacher spread0.304 · 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
Published2024
Admission routes2
Has abstractyes

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