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Updated: Oct 18, 2025

MicroRNA-based Regulation of Picornavirus Tropism

MicroRNA-based Regulation of Picornavirus Tropism

Published on: February 6, 2017

Viral Inactivation Impacts Microbiome Estimates in a Tissue-Specific Manner.

Alba Boix-Amorós^1,2, Enrica Piras^1,2, Kevin Bu¹

¹Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinaigrid.59734.3c, New York, New York, USA.

|October 5, 2021

Summary

This summary is machine-generated.

Sample inactivation methods significantly alter microbiome composition and diversity, with effects varying by body site and method. Researchers must carefully choose inactivation techniques to avoid introducing bias in microbiome studies.

Keywords:

16S RNA DNA sequencing human microbiome viral inactivation

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Area of Science:

Microbiome research
Virology
Molecular biology

Background:

Emerging viral pathogens necessitate high biosafety levels for sample processing.
Inactivating viruses simplifies safety protocols but may impact other biological components.
The effect of inactivation methods on host-associated microbiomes is largely unknown.

Purpose of the Study:

To investigate the impact of five common inactivation methods on microbiome composition and diversity.
To compare these effects across four distinct human body sites (gut, nasal, oral, skin).
To guide the selection of appropriate inactivation methods for microbiome research.

Main Methods:

Samples from gut, nasal, oral, and skin sites were treated with heat, ethanol, formaldehyde, psoralen, or TRIzol.
Untreated samples served as controls.
16S rRNA gene sequencing was used to analyze bacterial taxa abundance and diversity.

Main Results:

Nasal and skin microbiomes were most sensitive to inactivation, with ethanol and TRIzol causing major compositional shifts.
Heat, formaldehyde, TRIzol, and psoralen significantly altered microbiome diversity across sample types.
Oral and gut microbiomes showed greater resilience, with minimal diversity changes and moderate compositional shifts.

Conclusions:

Inactivation methods can introduce significant, tissue-specific biases into microbiome data.
Bacterial taxa like Firmicutes were least affected, while Bacteroidetes, Actinobacteria, and Proteobacteria showed variable responses.
Method selection must be tailored to the specific sample type to ensure accurate microbiome analysis.