ArticleMicrobiome2022
Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome.
Article in Microbiome, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Approaches for Microorganism Identification, Virulence Assessment, and Antimicrobial Susceptibility Evaluation Using DNA Sequencing Methods: A Systematic Review.Molecular biotechnology · 2025Pooled it
- Operationalising Genomic Surveillance for Antimicrobial Resistance in Low- and Middle-Income Countries: A One Health Perspective from Bangladesh.Microorganisms · 2026Review
- Development and validation of a machine learning-based early warning model for carbapenem-resistantFrontiers in microbiology · 2026Article
- Antibiotic resistance inFrontiers in microbiology · 2026Review
- Article
- On-orbit microbial succession patterns of the China Space Station during the construction period.Microbiome · 2025Article
- A cost and community perspective on the barriers to microbiome data reuse.Frontiers in bioinformatics · 2025Article
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- Giant viruses as reservoirs of antibiotic resistance genes.Nature communications · 2024Article
- Landscape of global urban environmental resistome and its association with local socioeconomic and medical status.Science China. Life sciences · 2024Article
- Microbial adaptation to spaceflight is correlated with bacteriophage-encoded functions.Nature communications · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Article
- Deep learning and single-cell phenotyping for rapid antimicrobial susceptibility detection in Escherichia coli.Communications biology · 2023Article
- Phylogenomics, phenotypic, and functional traits of five novel (Earth-derived) bacterial species isolated from the International Space Station and their prevalence in metagenomes.Scientific reports · 2023Article
- Article
- Phylogenetic affiliations and genomic characterization of novel bacterial species and their abundance in the International Space Station.Research square · 2023Article
- Profiling toxin genes and antibiotic resistance inFrontiers in microbiology · 2023Article
- Database of space life investigations and bioinformatics of microbiology in extreme environments.Frontiers in microbiology · 2022Article
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8 authors.
Funding
Abstract
backgroundAntimicrobial resistance (AMR) has a detrimental impact on human health on Earth and it is equally concerning in other environments such as space habitat due to microgravity, radiation and confinement, especially for long-distance space travel. The International Space Station (ISS) is ideal for investigating microbial diversity and virulence associated with spaceflight. The shotgun metagenomics data of the ISS generated during the Microbial Tracking-1 (MT-1) project and resulting metagenome-assembled genomes (MAGs) across three flights in eight different locations during 12 months were used in this study. The objective of this study was to identify the AMR genes associated with whole genomes of 226 cultivable strains, 21 shotgun metagenome sequences, and 24 MAGs retrieved from the ISS environmental samples that were treated with propidium monoazide (PMA; viable microbes).
resultsWe have analyzed the data using a deep learning model, allowing us to go beyond traditional cut-offs based only on high DNA sequence similarity and extending the catalog of AMR genes. Our results in PMA treated samples revealed AMR dominance in the last flight for Kalamiella piersonii, a bacteria related to urinary tract infection in humans. The analysis of 226 pure strains isolated from the MT-1 project revealed hundreds of antibiotic resistance genes from many isolates, including two top-ranking species that corresponded to strains of Enterobacter bugandensis and Bacillus cereus. Computational predictions were experimentally validated by antibiotic resistance profiles in these two species, showing a high degree of concordance. Specifically, disc assay data confirmed the high resistance of these two pathogens to various beta-lactam antibiotics.
conclusionOverall, our computational predictions and validation analyses demonstrate the advantages of machine learning to uncover concealed AMR determinants in metagenomics datasets, expanding the understanding of the ISS environmental microbiomes and their pathogenic potential in humans. Video Abstract.
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