De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation
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-ba...
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BMC
2025-02-01
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| Series: | Microbiome |
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| Online Access: | https://doi.org/10.1186/s40168-024-01998-4 |
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| author | Luise Rauer Amedeo De Tomassi Christian L. Müller Claudia Hülpüsch Claudia Traidl-Hoffmann Matthias Reiger Avidan U. Neumann |
| author_facet | Luise Rauer Amedeo De Tomassi Christian L. Müller Claudia Hülpüsch Claudia Traidl-Hoffmann Matthias Reiger Avidan U. Neumann |
| author_sort | Luise Rauer |
| collection | DOAJ |
| description | 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 |
| format | Article |
| id | doaj-art-fd3940659c064fe8918d05607edea6ac |
| institution | Kabale University |
| issn | 2049-2618 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Microbiome |
| spelling | doaj-art-fd3940659c064fe8918d05607edea6ac2025-02-09T12:46:43ZengBMCMicrobiome2049-26182025-02-0113111710.1186/s40168-024-01998-4De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formationLuise Rauer0Amedeo De Tomassi1Christian L. Müller2Claudia Hülpüsch3Claudia Traidl-Hoffmann4Matthias Reiger5Avidan U. Neumann6Institute of Environmental Medicine and Integrative Health, Faculty of Medicine, University of AugsburgInstitute of Environmental Medicine and Integrative Health, Faculty of Medicine, University of AugsburgInstitute of Computational Biology, Helmholtz MunichInstitute of Environmental Medicine and Integrative Health, Faculty of Medicine, University of AugsburgInstitute of Environmental Medicine and Integrative Health, Faculty of Medicine, University of AugsburgInstitute of Environmental Medicine and Integrative Health, Faculty of Medicine, University of AugsburgInstitute of Environmental Medicine and Integrative Health, Faculty of Medicine, University of AugsburgAbstract 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 Abstracthttps://doi.org/10.1186/s40168-024-01998-4Bacterial mock communityPositive controlExtraction biasCell lysisKitomeContamination |
| spellingShingle | Luise Rauer Amedeo De Tomassi Christian L. Müller Claudia Hülpüsch Claudia Traidl-Hoffmann Matthias Reiger Avidan U. Neumann De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation Microbiome Bacterial mock community Positive control Extraction bias Cell lysis Kitome Contamination |
| title | De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation |
| title_full | De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation |
| title_fullStr | De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation |
| title_full_unstemmed | De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation |
| title_short | De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation |
| title_sort | de biasing microbiome sequencing data bacterial morphology based correction of extraction bias and correlates of chimera formation |
| topic | Bacterial mock community Positive control Extraction bias Cell lysis Kitome Contamination |
| url | https://doi.org/10.1186/s40168-024-01998-4 |
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