A metatranscriptomics strategy for efficient characterization of the microbiome in human tissues with low microbial biomass

ABSTRACT

The high background of host RNA poses a major challenge to metatranscriptome analysis of human samples. Hence, metatranscriptomics has been mainly applied to microbe-rich samples, while its application in human tissues with low ratio of microbial to host cells has yet to be explored. Since there is no computational workflow specifically designed for the taxonomic and functional analysis of this type of samples, we propose an effective metatranscriptomics strategy to accurately characterize the microbiome in human tissues with a low ratio of microbial to host content. We experimentally generated synthetic samples with well-characterized bacterial and host cell compositions, and mimicking human samples with high and low microbial loads. These synthetic samples were used for optimizing and establishing the workflow in a controlled setting. Our results show that the integration of the taxonomic analysis of optimized Kraken 2/Bracken with the functional analysis of HUMAnN 3 in samples with low microbial content, enables the accurate identification of a large number of microbial species with a low false-positive rate, while improving the detection of microbial functions. The effectiveness of our metatranscriptomics workflow was demonstrated in synthetic samples, simulated datasets, and most importantly, human gastric tissue specimens, thus providing a proof of concept for its applicability on mucosal tissues of the gastrointestinal tract. The use of an accurate and reliable metatranscriptomics approach for human tissues with low microbial content will expand our understanding of the functional activity of the mucosal microbiome, uncovering critical interactions between the microbiome and the host in health and disease.

KEYWORDS:

• Microbiome
• metatranscriptomics
• low microbial biomass
• mucosal tissues
• bacteria
• mock bacterial community
• computational biology

Acknowledgement

The authors greatly appreciate the helpful discussions with Bauke Ylstra (Amsterdam UMC, Vrije Universiteit Amsterdam).

Disclosure statement

RMF and CF own patent WO/2018/169423 on microbiome markers for gastric cancer. JP-M has no potential conflict of interest.

Author contributions

JP-M, RMF, and CF conceived and designed the study. JP-M performed experimental studies. JP-M and RMF performed bioinformatics and statistical analyses. RMF and CF coordinated the study, supervised the research work, and acquired funding. JP-M drafted the manuscript. All authors revised the manuscript for important intellectual content and approved the final version.

Data availability statement

The raw sequence data of the datasets generated in the current study are available at the NCBI BioProject under the accession number: PRJNA1003801.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19490976.2024.2323235

Additional information

Funding

RMF has a Fundação para a Ciência e a Tecnologia (FCT) researcher position under the Individual Call to Scientific Employment Stimulus (CEECIND/01854/2017). CF and RMF are also funded by national funds through FCT (PTDC/BTM-TEC/0367/2021 and 2022.02141.PTDC). This work was also funded by Programa Operacional Regional do Norte and co-funded by European Regional Development Fund (NORTE-01-0145-FEDER-072678 - Consórcio PORTO.CCC – Porto.Comprehensive Cancer Center).