Leveraging human microbiomes for disease prediction and treatment

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• The human microbiome comprises diverse microorganisms crucial for health.
• Individual microbiome profiles offer potential for disease detection, prevention, and predicting drug efficacy.
• Integrating microbiome data into clinical practice is challenging due to data complexity and study inconsistencies.

Purpose of the Study:

• To review advances in microbiome research for precision diagnostics and personalized treatments.
• To highlight progress in microbiome sequencing technologies and bioinformatics tools.
• To address challenges in establishing causality for robust clinical applications.

Main Methods:

• Examination of recent advances in 16S rRNA and shotgun-metagenomics sequencing.
• Review of bioinformatics tools for microbiome data integration.
• Discussion of microbiome-based therapies and causality challenges.

Main Results:

• Technological and demographic biases impede reliable disease classifier development.
• Advances in sequencing and bioinformatics enhance microbiome data integration.
• Progress is being made in microbiome-based therapies.

Conclusions:

• Overcoming data complexity and biases is key to clinical microbiome integration.
• Enhanced sequencing and bioinformatics tools facilitate precision diagnostics.
• Establishing causality is critical for effective microbiome-based diagnostics and treatments.