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

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

Published on: October 15, 2019

Harnessing human microbiomes for disease prediction.

Yang Liu¹, Muhamad Fachrul², Michael Inouye³

¹Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.

Trends in Microbiology

|January 21, 2024

Summary

This summary is machine-generated.

The human microbiome shows promise for predicting disease risk. Research is exploring its use in prospective studies and as a therapeutic target for disease management.

Keywords:

disease prediction gut microbiota machine learning metagenomics microbiome

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Area of Science:

Microbiology
Genomics
Preventive Medicine

Background:

The human microbiome's role in health and disease is gaining recognition.
Predictive models for diseases like cardiovascular disease and breast cancer traditionally integrate genomic data.
Few studies have incorporated the human microbiome into prospective disease risk prediction models.

Purpose of the Study:

To review current research on the human microbiome for disease prediction.
To highlight the importance of prospective studies in this field.
To discuss microbiome modulation and engineering as therapeutic strategies.

Main Methods:

Literature review of prospective studies on human microbiome and disease prediction.
Analysis of research on microbiome modulation and engineering for therapeutic applications.

Main Results:

The human microbiome is increasingly recognized for its potential in disease prediction.
Integrating microbiome data into predictive models is an emerging area of research.
Microbiome modulation and engineering represent promising therapeutic avenues.

Conclusions:

The human microbiome holds significant potential for transforming disease prediction and patient care.
Further research, particularly prospective studies, is needed to fully harness its predictive capabilities.
Microbiome-based therapies offer a novel strategy for managing and preventing diseases.