NDLI: De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation

Content Provider	Springer Nature : BioMed Central
Author	Rauer, Luise De Tomassi, Amedeo Müller, Christian L. Hülpüsch, Claudia Traidl-Hoffmann, Claudia Reiger, Matthias Neumann, Avidan U.
Abstract	Introduction Microbiome amplicon sequencing data are distorted by multiple protocol-dependent biases from bacterial DNA extraction, contamination, sequence errors, and chimeras, hindering clinical microbiome applications. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to computationally correct extraction bias based on bacterial morphological properties. Methods We compared dilution series of 3 cell mock communities with an even or staggered composition. DNA of these mock, and additional skin microbiome samples, was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1–V3 16S rRNA gene) together with corresponding DNA mocks. Results Microbiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Independent of the extraction protocol, chimera formation increased with higher input cell numbers. Contaminants originated mostly from buffers, and considerable cross-contamination was observed in low-input samples. Comparing the microbiome composition of the cell mocks to corresponding DNA mocks revealed taxon-specific protocol-dependent extraction bias. Strikingly, this extraction bias per species was predictable by bacterial cell morphology. Morphology-based computational correction of extraction bias significantly improved resulting microbial compositions when applied to different mock samples, even with different taxa. Equivalent correction of the skin samples showed a substantial impact on microbiome compositions. Conclusions Our results indicate that higher DNA density increases chimera formation during PCR amplification. Furthermore, we show that computational correction of extraction bias based on bacterial cell morphology would be feasible using appropriate positive controls, thus constituting an important step toward overcoming protocol biases in microbiome analysis. Video Abstract
Related Links	https://microbiomejournal.biomedcentral.com/counter/pdf/10.1186/s40168-024-01998-4.pdf
Ending Page	17
Page Count	17
Starting Page	1
File Format	HTM / HTML
ISSN	20492618
DOI	10.1186/s40168-024-01998-4
Journal	Microbiome
Issue Number	1
Volume Number	13
Language	English
Publisher	BioMed Central
Publisher Date	2025-02-04
Access Restriction	Open
Subject Keyword	Medical Microbiology Bioinformatics Microbial Ecology Microbiology Microbial Genetics and Genomics Virology Bacterial mock community Positive control Extraction bias Cell lysis Kitome Contamination Chimera formation Bias correction 16S rRNA gene sequencing
Content Type	Text
Resource Type	Article
Subject	Microbiology Microbiology (medical)
Journal Impact Factor	13.8/2023
5-Year Journal Impact Factor	17.9/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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