MétaCan
Menu
Back to cohort
Record W3094882130 · doi:10.3389/fimmu.2020.567838

Transcriptomic Profiling of the Adaptive and Innate Immune Responses of Atlantic Salmon to Renibacterium salmoninarum Infection

2020· article· en· W3094882130 on OpenAlexafffund
Khalil Eslamloo, Albert Caballero‐Solares, Sabrina M. Inkpen, Mohamed Emam, Surendra Kumar, Camila Bouniot, Rubén Avendaño‐Herrera, Eva Jakob, Matthew L. Rise

Bibliographic record

VenueFrontiers in Immunology · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsMemorial University of Newfoundland
FundersFondo de Financiamiento de Centros de Investigación en Áreas PrioritariasNatural Sciences and Engineering Research Council of CanadaAgencia Nacional de Investigación y DesarrolloCanada First Research Excellence FundOcean Frontier InstituteGovernment of CanadaGenome AtlanticCompute CanadaGenome Canada
KeywordsBiologyMicrobiologyTranscriptomeImmune systemInnate immune systemSalmoMicroarrayImmunologyGene expressionGeneFish <Actinopterygii>GeneticsFishery

Abstract

fetched live from OpenAlex

Bacterial Kidney Disease (BKD), which is caused by a Gram-positive, intracellular bacterial pathogen (Renibacterium salmoninarum), affects salmonids including Atlantic salmon (Salmo salar). However, the transcriptome response of Atlantic salmon to BKD remained unknown before the current study. We used a 44K salmonid microarray platform to characterise the global gene expression response of Atlantic salmon to BKD. Fish (~54 g) were injected with a dose of R. salmoninarum (H-2 strain, 2 × 108 CFU per fish) or sterile medium (control), and then head kidney samples were collected at 13 days post-infection/injection (dpi). Firstly, infection levels of individuals were determined through quantifying the R. salmoninarum level by RNA-based TaqMan qPCR assays. Thereafter, based on the qPCR results for infection level, fish (n=5) that showed no (control), higher (H-BKD) or lower (L-BKD) infection level at 13 dpi were subjected to microarray analyses. We identified 6,766 and 7,729 differentially expressed probes in the H-BKD and L-BKD groups, respectively. There were 357 probes responsive to the infection level (H-BKD vs. L-BKD). Several adaptive and innate immune processes were dysregulated in R. salmoninarum-infected Atlantic salmon. Adaptive immune pathways associated with lymphocyte differentiation and activation (e.g. lymphocyte chemotaxis, T-cell activation and immunoglobulin secretion), as well as antigen-presenting cell functions, were shown to be differentially regulated in response to BKD. The infection level-responsive transcripts were related to several mechanisms such as the JAK-STAT signalling pathway, B-cell differentiation and interleukin-1 responses. Sixty-five microarray-identified transcripts were subjected to qPCR validation, and they showed the same fold-change direction as microarray results. The qPCR-validated transcripts studied herein play putative roles in various immune processes including pathogen recognition (e.g. tlr5 and clec12b), antibacterial activity (e.g. hamp and camp), regulation of immune responses (e.g. tnfrsf11b and socs1), T-/B-cell differentiation (e.g. ccl4, irf1-1 and ccr5), T-cell functions (e.g. rnf144a, il13ra1b and tnfrsf6b) and antigen-presenting cell functions (e.g. mmp13). The present study revealed diverse immune mechanisms dysregulated by R. salmoninarum in Atlantic salmon, and enhanced the current understanding of Atlantic salmon response to BKD. The identified biomarker genes can be used for future studies on improving the resistance of Atlantic salmon to BKD.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.010
GPT teacher head0.210
Teacher spread0.200 · 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

Citations30
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in ImmunologySame topicAquaculture disease management and microbiotaFrench-language works237,207