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Record W4377137620 · doi:10.1186/s12864-023-09317-1

Combined transcriptome and metabolome analysis reveal key regulatory genes and pathways of feed conversion efficiency of oriental river prawn Macrobrachium nipponense

2023· article· en· W4377137620 on OpenAlexaff
Feiyue Ling, Yaoran Fan, Zefei Wang, Nan Xie, Jiale Li, Guiling Wang, Jianbin Feng

Bibliographic record

VenueBMC Genomics · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsMinistry of Agriculture
FundersShanghai Ocean University
KeywordsResidual feed intakeHepatopancreasBiologyTranscriptomeFeed conversion ratioMetabolomeKEGGPrawnMetabolic pathwayBiochemistryMetabolomicsMetabolismGeneGene expressionEcologyBioinformaticsEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Oriental river prawn Macrobrachium nipponense is an economically important aquaculture species in China, Japan, and Vietnam. In commercial prawn farming, feed cost constitutes about 50 to 65% of the actual variable cost. Improving feed conversion efficiency in prawn culture will not only increase economic benefit, but also save food and protect the environment. The common indicators used for feed conversion efficiency include feed conversion ratio (FCR), feed efficiency ratio (FER), and residual feed intake (RFI). Among these, RFI is much more suitable than FCR and FER during the genetic improvement of feed conversion efficiency for aquaculture species. RESULTS: In this study, the transcriptome and metabolome of hepatopancreas and muscle of M. nipponense from high RFI low RFI groups, which identified after culture for 75 days, were characterized using combined transcriptomic and metabolomic analysis. A total of 4540 differentially expressed genes (DEGs) in hepatopancreas, and 3894 DEGs in muscle were identified, respectively. The DEGs in hepatopancreas were mainly enriched in KEGG pathways including the metabolism of xenobiotics by cytochrome P450 (down-regulated), fat digestion and absorption (down-regulated) and aminoacyl-tRNA biosynthesis (up-regulated), etc. The DEGs in muscle were mainly enriched in KEGG pathways including the protein digestion and absorption (down-regulated), glycolysis/gluconeogenesis (down-regulated), and glutathione metabolism (up-regulated), etc. At the transcriptome level, the RFI of M. nipponense was mainly controlled in biological pathways such as the high immune expression and the reduction of nutrients absorption capacity. A total of 445 and 247 differently expressed metabolites (DEMs) were identified in the hepatopancreas and muscle, respectively. At the metabolome level, the RFI of M. nipponense was affected considerably by amino acid and lipid metabolism. CONCLUSIONS: M. nipponense from higher and lower RFI groups have various physiological and metabolic capability processes. The down-regulated genes, such as carboxypeptidase A1, 6-phosphofructokinase, long-chain-acyl-CoA dehydrogenase, et. al., in digestion and absorption of nutrients, and the up-regulated metabolites, such as aspirin, lysine, et. al., in response to immunity could be potential candidate factors contributed to RFI variation for M. nipponense. Overall, these results would provide new insights into the molecular mechanism of feed conversion efficiency and assist in selective breeding to improve feed conversion efficiency in M. nipponense.

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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.200
Teacher spread0.182 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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