Evidence map›Paper›PMID 38858615›Full record

ArticleBMC genomics2024

Combinatorial metabolomic and transcriptomic analysis of muscle growth in hybrid striped bass (female white bass Morone chrysops x male striped bass M. saxatilis).

Sarah A S Rajab, Linnea K Andersen, Linas W Kenter, David L Berlinsky, Russell J Borski, Andrew S McGinty, Christopher M Ashwell, Peter R Ferket, Harry V Daniels, Benjamin J Reading

Abstract read
In one paragraph

Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Sarah A S RajabDepartment of Applied Ecology, North Carolina State University, 100 Eugene Brooks Avenue, Box 7617, Raleigh, NC, 27695, USA.
Linnea K AndersenDepartment of Applied Ecology, North Carolina State University, 100 Eugene Brooks Avenue, Box 7617, Raleigh, NC, 27695, USA.
Linas W KenterDepartment of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH, USA.
David L BerlinskyDepartment of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH, USA.
Russell J BorskiDepartment of Biological Sciences, North Carolina State University, Raleigh, NC, USA.
Andrew S McGintyNorth Carolina State University, Pamlico Aquaculture Field Laboratory, Aurora, NC, USA.
Christopher M AshwellPrestage Department of Poultry Science, North Carolina State University, Raleigh, NC, USA.
Peter R FerketPrestage Department of Poultry Science, North Carolina State University, Raleigh, NC, USA.
Harry V DanielsDepartment of Applied Ecology, North Carolina State University, 100 Eugene Brooks Avenue, Box 7617, Raleigh, NC, 27695, USA.
Benjamin J ReadingDepartment of Applied Ecology, North Carolina State University, 100 Eugene Brooks Avenue, Box 7617, Raleigh, NC, 27695, USA. bjreadin@ncsu.edu.

Funding

National Institute of Food and Agriculture National Research Support Project 8 (NRSP-8) National Animal Genome Project (USDA 0018889)National Marine Fisheries Service NOAA-OAR-SG-2022-2007054 Grant: NA22OAR4170155National Oceanic and Atmospheric Administration NOAA-OAR-SG-2019-2005963 Grant NA18OAR4170069NOAA Sea Grant NOAA-OAR-SG 2022-2007012 Grant NA22OAR4170632North Carolina Sea Grant, North Carolina State University R/12-SSS-3, R/MG-1411
6 · The paper itself

Abstract

backgroundUnderstanding growth regulatory pathways is important in aquaculture, fisheries, and vertebrate physiology generally. Machine learning pattern recognition and sensitivity analysis were employed to examine metabolomic small molecule profiles and transcriptomic gene expression data generated from liver and white skeletal muscle of hybrid striped bass (white bass Morone chrysops x striped bass M. saxatilis) representative of the top and bottom 10 % by body size of a production cohort.

resultsLarger fish (good-growth) had significantly greater weight, total length, hepatosomatic index, and specific growth rate compared to smaller fish (poor-growth) and also had significantly more muscle fibers of smaller diameter (≤ 20 µm diameter), indicating active hyperplasia. Differences in metabolomic pathways included enhanced energetics (glycolysis, citric acid cycle) and amino acid metabolism in good-growth fish, and enhanced stress, muscle inflammation (cortisol, eicosanoids) and dysfunctional liver cholesterol metabolism in poor-growth fish. The majority of gene transcripts identified as differentially expressed between groups were down-regulated in good-growth fish. Several molecules associated with important growth-regulatory pathways were up-regulated in muscle of fish that grew poorly: growth factors including agt and agtr2 (angiotensins), nicotinic acid (which stimulates growth hormone production), gadd45b, rgl1, zfp36, cebpb, and hmgb1; insulin-like growth factor signaling (igfbp1 and igf1); cytokine signaling (socs3, cxcr4); cell signaling (rgs13, rundc3a), and differentiation (rhou, mmp17, cd22, msi1); mitochondrial uncoupling proteins (ucp3, ucp2); and regulators of lipid metabolism (apoa1, ldlr). Growth factors pttg1, egfr, myc, notch1, and sirt1 were notably up-regulated in muscle of good-growing fish.

conclusionA combinatorial pathway analysis using metabolomic and transcriptomic data collectively suggested promotion of cell signaling, proliferation, and differentiation in muscle of good-growth fish, whereas muscle inflammation and apoptosis was observed in poor-growth fish, along with elevated cortisol (an anti-inflammatory hormone), perhaps related to muscle wasting, hypertrophy, and inferior growth. These findings provide important biomarkers and mechanisms by which growth is regulated in fishes and other vertebrates as well.

Indexed as

BassGene Expression ProfilingAnimalsFemaleLiverMaleMetabolomeMetabolomicsMuscle DevelopmentMuscle, SkeletalTranscriptomeFishGenomeGrowthMachine learningMetabolomicsStriped bassTranscriptomics

Identifiers

PMID38858615
PMCPMC11165755

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.