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Record W4206188617 · doi:10.3389/fimmu.2021.789465

Transcriptome Profiling of Atlantic Salmon (Salmo salar) Parr With Higher and Lower Pathogen Loads Following Piscirickettsia salmonis Infection

2021· article· en· W4206188617 on OpenAlexafffund
Xi Xue, Albert Caballero‐Solares, Jennifer R. Hall, Navaneethaiyer Umasuthan, Surendra Kumar, Eva Jakob, Stanko Škugor, Christopher Hawes, Javier Santander, Richard G. Taylor, Matthew L. Rise

Bibliographic record

VenueFrontiers in Immunology · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of NewfoundlandGovernment of CanadaGenome AtlanticCompute CanadaGenome CanadaCargill
KeywordsBiologyTranscriptomeSalmoPathogenGene expression profilingMicroarrayInnate immune systemImmune systemMicrobiologyMicroarray analysis techniquesGeneGene expressionImmunologyGeneticsFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Salmonid rickettsial septicemia (SRS), caused by Piscirickettsia salmonis , is one of the most devastating diseases of salmonids. However, the transcriptomic responses of Atlantic salmon ( Salmon salar ) in freshwater to an EM-90-like isolate have not been explored. Here, we infected Atlantic salmon parr with an EM-90-like isolate and conducted time-course qPCR analyses of pathogen load and four biomarkers ( campb , hampa , il8a , tlr5a ) of innate immunity on the head kidney samples. Transcript expression of three of these genes (except hampa ), as well as pathogen level, peaked at 21 days post-injection (DPI). Multivariate analyses of infected individuals at 21 DPI revealed two infection phenotypes [lower (L-SRS) and higher (H-SRS) infection level]. Five fish from each group (Control, L-SRS, and H-SRS) were selected for transcriptome profiling using a 44K salmonid microarray platform. We identified 1,636 and 3,076 differentially expressed probes (DEPs) in the L-SRS and H-SRS groups compared with the control group, respectively (FDR = 1%). Gene ontology term enrichment analyses of SRS-responsive genes revealed the activation of a large number of innate (e.g. “phagocytosis”, “defense response to bacterium”, “inflammatory response”) and adaptive (e.g. “regulation of T cell activation”, “antigen processing and presentation of exogenous antigen”) immune processes, while a small number of general physiological processes (e.g. “apoptotic process”, development and metabolism relevant) was enriched. Transcriptome results were confirmed by qPCR analyses of 42 microarray-identified transcripts. Furthermore, the comparison of individuals with differing levels of infection (H-SRS vs. L-SRS) generated insights into the biological processes possibly involved in disease resistance or susceptibility. This study demonstrated a low mortality (~30%) EM-90-like infection model and broadened the current understanding of molecular pathways underlying P. salmonis -triggered responses of Atlantic salmon, identifying biomarkers that may assist to diagnose and combat this pathogen.

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 score0.002
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
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations29
Published2021
Admission routes2
Has abstractyes

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