Evidence map›Paper›PMID 37872465›Full record

ArticleMarine biotechnology (New York, N.Y.)2023

Comparative Transcriptome and WGCNA Analysis Reveal Molecular Responses to Salinity Change in Larvae of the Iwagaki Oyster Crassostrea Nippona.

Jianwen Gong, Qi Li

Abstract read
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In one paragraph

Article in Marine biotechnology (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 27% of its field
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

4 citing papers in PubMed, 10 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Jianwen GongKey Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao, 266003, China.
Qi LiKey Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao, 266003, China. qili66@ouc.edu.cn.
Ocean University of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Iwagaki oyster Crassostrea nippona is an important aquaculture species with significant potential for large-scale oyster farming. It is susceptible to the fluctuated salinity in the coastal area. In this study, we compared the transcriptome of Crassostrea nippona larvae under variant conditions with low-salinity stress (28, 20, 15, 10, and 5 practical salinity units (psu)) for 24 h. KEGG enrichment analysis of differentially expressed genes (DEGs) from pairwise comparisons identified several free amino acid metabolism pathway (taurine and hypotaurine, arginine and proline, glycine, and beta-alanine) contributing to the salinity change adaptation and activated "lysosome" and "apoptosis" pathway in response to the low-salinity stress (10 and 5 psu). Trend analysis revealed sustained upregulation of transmembrane transport-related genes (such as SLC family) and downregulation of ribosomal protein synthesis genes faced with decreasing salinities. In addition, 9 biomarkers in response to low-salinity stress were identified through weighted gene co-expression network analysis (WGCNA) and validated by qRT-PCR. Our transcriptome analysis provides a comprehensive view of the molecular mechanisms and regulatory networks underlying the adaptive responses of oyster larvae to hypo-salinity conditions. These findings contribute to our understanding of the complex biological processes involved in oyster resilience and adaptation to changing environmental conditions.

Indexed as

CrassostreaTranscriptomeAnimalsGene Expression ProfilingLarvaSalinityComparative transcriptomeCrassostrea nipponaLarvaeSalinity

Identifiers

PMID37872465
OpenAlexW4387880975

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.