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Research Article | Ecological and Evolutionary Science

Untangling Species-Level Composition of Complex Bacterial Communities through a Novel Metagenomic Approach

Christian Milani, Giulia Alessandri, Marta Mangifesta, Leonardo Mancabelli, Gabriele Andrea Lugli, Federico Fontana, Giulia Longhi, Rosaria Anzalone, Alice Viappiani, Sabrina Duranti, Francesca Turroni, Renato Costi, Alfredo Annicchiarico, Andrea Morini, Leopoldo Sarli, Maria Cristina Ossiprandi, Douwe van Sinderen, Marco Ventura
J. Gregory Caporaso, Editor
Christian Milani
aLaboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
bMicrobiome Research Hub, University of Parma, Parma, Italy
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Giulia Alessandri
cDepartment of Veterinary Medical Science, University of Parma, Parma, Italy
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Marta Mangifesta
aLaboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
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Leonardo Mancabelli
aLaboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
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Gabriele Andrea Lugli
aLaboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
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Federico Fontana
aLaboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
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Giulia Longhi
aLaboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
dGenProbio srl, Parma, Italy
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Rosaria Anzalone
dGenProbio srl, Parma, Italy
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Alice Viappiani
dGenProbio srl, Parma, Italy
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Sabrina Duranti
aLaboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
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Francesca Turroni
aLaboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
bMicrobiome Research Hub, University of Parma, Parma, Italy
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Renato Costi
eDepartment of Medicine and Surgery, University of Parma, Parma, Italy
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Alfredo Annicchiarico
eDepartment of Medicine and Surgery, University of Parma, Parma, Italy
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Andrea Morini
eDepartment of Medicine and Surgery, University of Parma, Parma, Italy
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Leopoldo Sarli
eDepartment of Medicine and Surgery, University of Parma, Parma, Italy
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Maria Cristina Ossiprandi
bMicrobiome Research Hub, University of Parma, Parma, Italy
cDepartment of Veterinary Medical Science, University of Parma, Parma, Italy
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Douwe van Sinderen
fAPC Microbiome Institute and School of Microbiology, Bioscience Institute, National University of Ireland, Cork, Ireland
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  • ORCID record for Douwe van Sinderen
Marco Ventura
aLaboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
bMicrobiome Research Hub, University of Parma, Parma, Italy
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J. Gregory Caporaso
Northern Arizona University
Roles: Editor
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DOI: 10.1128/mSystems.00404-20
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Figures

  • Supplemental Material
  • FIG 1
    • Open in new tab
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    FIG 1

    Positions of primers UNI_ITS_fw and UNI_ITS_rv within the ribosomal locus of Escherichia coli K-12 strain MG1655.

  • FIG 2
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    FIG 2

    In silico evaluation of microbial ITS profiling performance and comparison respect to different hypervariable 16S rRNA gene regions. (a) Percentage of bacterial taxa correctly classified by ITS and 16S rRNA gene profiling methods with respect to expected taxonomic profiles of artificial data sets. (b) Taxonomic profiles observed using ITS and 16S rRNA gene profiling methods and their comparison with respect to expected taxonomic profiles of artificial data sets.

  • FIG 3
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    FIG 3

    Performance of microbial ITS profiling compared to 16S rRNA gene cataloguing and read-based classification of shotgun metagenomic data sets. (a) Average percentage of reads classified to the species level using ITS and 16S rRNA gene profiling for each matrix analyzed. Data obtained for each sample are reported in Data Set S4. (b) Average percentage of reads classified by 16S rRNA gene profiling as a genus also detected by microbial ITS profiling of the same read, for each matrix analyzed. Data obtained for each sample are reported in Data Set S4. (c) Percentage of reads classified at the species level based on read length.

Supplemental Material

  • Figures
  • DATA SET S1

    UNI_ITS_fw/UNI_ITS_rv primers and UMID statistics. Download Data Set S1, XLSX file, 0.6 MB.

    Copyright © 2020 Milani et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S1

    Percentages of reads mapped to the SILVA SSU and LSU databases. Download Table S1, DOCX file, 0.02 MB.

    Copyright © 2020 Milani et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TEXT S1

    User manual for the analysis script available at http://probiogenomics.unipr.it/pbi/, discussion of the comparison between ITS profiling and shotgun metagenomics results, and detailed discussion of ITS profiling data obtained from the clinical test case encompassing colorectal cancer biopsy specimens. Download Text S1, DOCX file, 0.1 MB.

    Copyright © 2020 Milani et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • DATA SET S2

    Results obtained from the analysis of artificial datasets. Download Data Set S2, XLSX file, 0.3 MB.

    Copyright © 2020 Milani et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S2

    Quality filtering table of samples analyzed in this study. Download Table S2, DOCX file, 0.03 MB.

    Copyright © 2020 Milani et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • DATA SET S3

    Results obtained from analysis of the artificial community. Download Data Set S3, XLSX file, 0.01 MB.

    Copyright © 2020 Milani et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • DATA SET S4

    Results obtained from the analysis of real biological samples. Download Data Set S4, XLSX file, 1.3 MB.

    Copyright © 2020 Milani et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S1

    Identification of microbial biomarkers of colorectal cancer based on species-level data. The graphical representation reports the bacterial species that, when detected in the mucosal samples analyzed, showed higher abundance in adenocarcinoma than in healthy mucosa of the same individual. Download FIG S1, PDF file, 0.2 MB.

    Copyright © 2020 Milani et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • DATA SET S5

    Alignment file in Clustal format showing alignment of 100 16S rRNA gene sequences used to identify the optimal conserved region for primer design. Download Data Set S5, TXT file, 0.8 MB.

    Copyright © 2020 Milani et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • DATA SET S6

    Alignment file in Clustal format showing alignment of 100 23S rRNA gene sequences used to identify the optimal conserved region for primer design. Download Data Set S6, TXT file, 1.1 MB.

    Copyright © 2020 Milani et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

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Untangling Species-Level Composition of Complex Bacterial Communities through a Novel Metagenomic Approach
Christian Milani, Giulia Alessandri, Marta Mangifesta, Leonardo Mancabelli, Gabriele Andrea Lugli, Federico Fontana, Giulia Longhi, Rosaria Anzalone, Alice Viappiani, Sabrina Duranti, Francesca Turroni, Renato Costi, Alfredo Annicchiarico, Andrea Morini, Leopoldo Sarli, Maria Cristina Ossiprandi, Douwe van Sinderen, Marco Ventura
mSystems Jul 2020, 5 (4) e00404-20; DOI: 10.1128/mSystems.00404-20

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Untangling Species-Level Composition of Complex Bacterial Communities through a Novel Metagenomic Approach
Christian Milani, Giulia Alessandri, Marta Mangifesta, Leonardo Mancabelli, Gabriele Andrea Lugli, Federico Fontana, Giulia Longhi, Rosaria Anzalone, Alice Viappiani, Sabrina Duranti, Francesca Turroni, Renato Costi, Alfredo Annicchiarico, Andrea Morini, Leopoldo Sarli, Maria Cristina Ossiprandi, Douwe van Sinderen, Marco Ventura
mSystems Jul 2020, 5 (4) e00404-20; DOI: 10.1128/mSystems.00404-20
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    • ABSTRACT
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KEYWORDS

metagenomics
ITS
profiling

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