Evidence map›Paper›PMID 36631881›Full record

ArticleEnvironmental microbiome2023

In silico prediction of potential indigenous microbial biomarkers in Penaeus vannamei identified through meta-analysis and genome-scale metabolic modelling.

Neelakantan Thulasi Devika, Vinaya Kumar Katneni, Ashok Kumar Jangam, Panjan Nathamuni Suganya, Mudagandur Shashi Shekhar, Karingalakkandy Poochirian Jithendran

Open access · goldAbstract read
In one paragraph

Article in Environmental microbiome, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 26% 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
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

6 authors at 2 institutions in 1 country.

Neelakantan Thulasi DevikaNutrition Genetics and Biotechnology Division, Indian Council of Agricultural Research - Central Institute of Brackishwater Aquaculture, Chennai, India.
Vinaya Kumar KatneniNutrition Genetics and Biotechnology Division, Indian Council of Agricultural Research - Central Institute of Brackishwater Aquaculture, Chennai, India. Vinaya.Katneni@icar.gov.in.
Ashok Kumar JangamNutrition Genetics and Biotechnology Division, Indian Council of Agricultural Research - Central Institute of Brackishwater Aquaculture, Chennai, India.
Panjan Nathamuni SuganyaNutrition Genetics and Biotechnology Division, Indian Council of Agricultural Research - Central Institute of Brackishwater Aquaculture, Chennai, India.
Mudagandur Shashi ShekharNutrition Genetics and Biotechnology Division, Indian Council of Agricultural Research - Central Institute of Brackishwater Aquaculture, Chennai, India.
Karingalakkandy Poochirian JithendranAquatic Animal Health and Environment Division, Indian Council of Agricultural Research - Central Institute of Brackishwater Aquaculture, Chennai, India.
Central Institute of Brackishwater Aquaculture · INIndian Council of Agricultural Research · IN

Funding

Department of Science and Technology, India PDF 2020/000267Indian Council of Agriculture Research Network project on Agricultural Bioinformatics and Computational Biology
6 · The paper itself

Abstract

backgroundUnderstanding the microbiome is crucial as it contributes to the metabolic health of the host and, upon dysbiosis, may influence disease development. With the recent surge in high-throughput sequencing technology, the availability of microbial genomic data has increased dramatically. Amplicon sequence-based analyses majorly profile microbial abundance and determine taxonomic markers. Furthermore, the availability of genome sequences for various microbial organisms has prompted the integration of genome-scale metabolic modelling that provides insights into the metabolic interactions influencing host health. However, the analysis from a single study may not be consistent, necessitating a meta-analysis.

resultsWe conducted a meta-analysis and integrated with constraint-based metabolic modelling approach, focusing on the microbiome of pacific white shrimp Penaeus vannamei, an extensively cultured marine candidate species. Meta-analysis revealed that Acinetobacter and Alteromonas are significant indicators of "health" and "disease" specific taxonomic biomarkers, respectively. Further, we enumerated metabolic interactions among the taxonomic biomarkers by applying a constraint-based approach to the community metabolic models (4416 pairs). Under different nutrient environments, a constraint-based flux simulation identified five beneficial species: Acinetobacter spWCHA55, Acinetobacter tandoii SE63, Bifidobacterium pseudolongum 49 D6, Brevundimonas pondensis LVF1, and Lutibacter profundi LP1 mediating parasitic interactions majorly under sucrose environment in the pairwise community. The study also reports the healthy biomarkers that can co-exist and have functionally dependent relationships to maintain a healthy state in the host.

conclusionsToward this, we collected and re-analysed the amplicon sequence data of P. vannamei (encompassing 117 healthy and 142 disease datasets). By capturing the taxonomic biomarkers and modelling the metabolic interaction between them, our study provides a valuable resource, a first-of-its-kind analysis in aquaculture scenario toward a sustainable shrimp farming.

Indexed as

16S amplicon sequence analysisFlux balance analysisFlux variability analysisGenome-scale metabolic modellingMeta-analysisMetagenomicsMicrobial biomarkerPenaeus vannamei

Identifiers

PMID36631881
PMCPMC9835370
OpenAlexW4315644936

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.