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Related Experiment Video

Updated: Jun 1, 2026

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Multikingdom microbiome-based machine learning enables multiple sclerosis diagnosis.

Gaochen Zhu1, Guan Yang2,3

  • 1Department of Infectious Diseases and Public Health, City University of Hong Kong, Hong Kong SAR, China.

NPJ Biofilms and Microbiomes
|May 30, 2026
PubMed
Summary
This summary is machine-generated.

The gut microbiome, including bacteria, fungi, and viruses, shows distinct patterns in multiple sclerosis (MS) patients. These multikingdom microbiome markers can potentially serve as non-invasive diagnostic tools for MS.

Related Experiment Videos

Last Updated: Jun 1, 2026

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Area of Science:

  • Microbiome research
  • Immunology
  • Neurology

Background:

  • Gut bacteria are implicated in multiple sclerosis (MS) pathogenesis.
  • The role of other gut microorganisms and their diagnostic potential for MS are less understood.

Purpose of the Study:

  • To analyze multikingdom gut microbiome data to identify diagnostic markers for MS.
  • To develop machine learning models for distinguishing MS patients from healthy controls.

Main Methods:

  • Analysis of large-scale metagenomic fecal data from three diverse cohorts (total n=1306).
  • Development of machine learning models integrating multikingdom taxonomic and functional microbiome features.
  • Validation of models on external datasets.

Main Results:

  • Identified significant alterations in 90 bacterial, 3 fungal, and 2 viral species, 119 KEGG orthology genes, and 17 metabolic pathways associated with MS.
  • Machine learning models achieved high AUCs (0.977 for males, 0.978 for females) in discovery cohorts.
  • External validation showed AUCs ranging from 0.745 to 0.849, with accuracy linked to Faecalibacterium spp. and L-methionine biosynthesis pathways.

Conclusions:

  • Multikingdom gut microbiome alterations are significantly associated with MS.
  • Machine learning models utilizing these markers show strong potential for MS diagnosis.
  • Gut microbiome analysis offers a promising avenue for developing non-invasive MS biomarkers.