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Record W3116935562 · doi:10.21203/rs.3.rs-23603/v1

Immune Transcripts of the Pacific Oyster, Crassostrea gigas Larvae that Change in Expression as a Result of Bacteria Challenge

2020· preprint· en· W3116935562 on OpenAlexaff
Jing Fang, Xiaohui Cai, Fengyi Mao, Hongyu Li, Honglin Chen, Minhua Qian, Jacob M Hambrook, Patrick C. Hanington, Xinzhong Wu

Bibliographic record

VenueResearch Square (Research Square) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsBiologyCrassostreaOysterBroodstockTranscriptomeKEGGVibrio alginolyticusImmune systemOstreidaePacific oysterGeneInnate immune systemGalectinMicrobiologyGene expressionVibrioShellfishGeneticsAquacultureFisheryAquatic animalImmunologyBacteria

Abstract

fetched live from OpenAlex

Abstract Background: Marine molluscs living in intertidal and estuarine areas, such as oysters, face numerous pathogen challenges during their development. Infection from bacteria such as Vibrio alginolyticus, represents a major factor affecting larval development and frequently leads to high mortality of the pacific oyster, Crassostrea gigas. The oyster immune response is known to play an important role in protecting the animal during development by mitigating the consequences of infection. Results: In this study, we undertook a comprehensive analysis of the immune response of C. gigas to V. alginolyticus challenge. We sequenced the transcriptome of C. gigas at 0, 6, 12, 24, 48 and 72 hours post infection. After RNA-seq, the raw reads are available through the NCBI Sequence Read Archive under accession number PRJNA623063. After filtering, a total of 58.24 Gb clean reads were produced and assembled using the reference genome of C. gigas. The distribution of quality Q30 was higher than 90.88% for each sample and the GC content ranged from 41.27% to 42.91%. When compared with sequences in the COG, GO, KEGG, Swiss-Prot, and NR databases, there were 1267, 1112, 2187, 682, 1133 differentially expressed genes annotated at 6, 12, 24, 48, 72 hours post infection respectively. Numerous immune-related genes displayed differential expression that varied over time: toll-like receptors, tripartite motif proteins, Lectin-like factors, scavenger receptors, signaling pathway components such as Myeloid differentiation factor 88, and stress proteins such as Heat shock 70 kDa protein were all found to be higher in abundance following V. alginolyticus challenge compared to control. For analysis, these genes were divided into several categories such as pattern recognition receptors, fibrinogen-like proteins, damage associated molecular patterns, complement factors, etc. These general categories allowed us to generate an immune response profile for C. gigas over the first 72 hours of infection. These results indicate that bacterial infection induce a complex pattern of immune gene expression in C. gigas larvae. Conclusion: Our study will facilitate targeted investigation into the function of specific immune factors that may explain the diversity and evolution of invertebrate immune molecules and lead to the development of effective measures to improve the performance of oyster culture.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

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 head0.147
GPT teacher head0.394
Teacher spread0.247 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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