ArticleiMeta2025
Metagenomics and digital cell modeling facilitate targeted high-throughput sorting of anaerobic hydrogen-producing microorganisms.
Article in iMeta, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Bridging the gap:Gut microbes · 2026Review
- Enzyme-constrained genome-scale modeling resolves growth-production trade-offs in fermentative biohydrogen production.Environmental science and ecotechnology · 2026Article
- Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study proposes a novel strategy that prioritizes functional recognition, followed by targeted high-throughput sorting, to enable the comprehensive, rapid, and efficient acquisition of target microorganisms. Using metagenomic sequencing and binning analysis, we identified 215 potential anaerobic hydrogen-producing strains from 12 large-scale biogas samples. Digital cell models were subsequently constructed from metagenome-assembled genomes, which guided the design of 14 selective culture media for enriching these hydrogen-producing bacteria. Flow cytometry-based high-throughput sorting successfully isolated 81 potential anaerobic hydrogen-producing strains, achieving a target acquisition rate above 37% and a survival rate exceeding 70%. This method holds broad potential for the discovery and sorting of functional microorganisms across diverse environments and may ultimately facilitate the development of synthetic microbiomes for industrial applications.
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Registered trials
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.