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OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes

View ORCID ProfileJoão Pedro Saraiva, View ORCID ProfileAlexandre Bartholomäus, View ORCID ProfileRené Kallies, Marta Gomes, Marcos Bicalho, Jonas Coelho Kasmanas, View ORCID ProfileCarsten Vogt, Antonis Chatzinotas, View ORCID ProfilePeter Stadler, Oscar Dias, View ORCID ProfileUlisses Nunes da Rocha  Correspondence email
João Pedro Saraiva
1Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
Roles: Data curation, Software, Formal analysis, Validation, Visualization, Writing—original draft, review, and editing
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  • ORCID record for João Pedro Saraiva
Alexandre Bartholomäus
2GFZ German Research Centre for Geosciences, Section Geomicrobiology, Potsdam, Germany
Roles: Data curation, Software, Validation, Visualization, Methodology, Writing—review and editing
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  • ORCID record for Alexandre Bartholomäus
René Kallies
1Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
Roles: Resources, Methodology, Writing—review and editing
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Marta Gomes
3Centre of Biological Engineering, University of Minho, Braga, Portugal
Roles: Formal analysis, Validation, Methodology
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Marcos Bicalho
1Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
Roles: Software, Validation
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Jonas Coelho Kasmanas
1Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
4Institute of Mathematics and Computer Sciences, University of Sao Paulo, Sao Carlos, Brazil
7Department of Computer Science, Bioinformatics Group, Interdisciplinary Center for Bioinformatics, and Competence Center for Scalable Data Services and Solutions Dresden/Leipzig, University of Leipzig, Leipzig, Germany
Roles: Software, Formal analysis, Writing—review and editing
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Carsten Vogt
1Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
Roles: Data curation, Writing—original draft, review, and editing
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Antonis Chatzinotas
1Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
5Institute of Biology, Leipzig University, Leipzig, Germany
6German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig, Leipzig, Germany
Roles: Resources, Writing—review and editing
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Peter Stadler
7Department of Computer Science, Bioinformatics Group, Interdisciplinary Center for Bioinformatics, and Competence Center for Scalable Data Services and Solutions Dresden/Leipzig, University of Leipzig, Leipzig, Germany
8Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
9Institute for Theoretical Chemistry, University of Vienna, Wien, Austria
10Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá, Colombia
11Santa Fe Institute, Santa Fe, NM, USA
Roles: Methodology, Writing—review and editing
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Oscar Dias
3Centre of Biological Engineering, University of Minho, Braga, Portugal
Roles: Conceptualization, Writing—review and editing
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Ulisses Nunes da Rocha
1Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
Roles: Conceptualization, Resources, Software, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing—original draft, review, and editing
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  • ORCID record for Ulisses Nunes da Rocha
  • For correspondence: ulisses.rocha@ufz.de
Published 27 September 2021. DOI: 10.26508/lsa.202101167
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  • Figure 1.
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    Figure 1. OrtSuite workflow.

    OrtSuite takes a text file containing a list of identifiers for each reaction in the pathway of interest supplied by the user to retrieve all protein sequences from KEGG Orthology and are stored in ORAdb. Subsequently, the same list of identifiers is used to obtain the gene-protein-reaction (GPR) rules from KEGG modules (Task 1). Protein sequences from samples supplied by the user are clustered using OrthoFinder (Task 2). In Task 3, the functional annotation, identification of putative synergistic species interactions and graphical visualization of the network are performed. The functional annotation consists of a two-stage process (relaxed and restrictive search). Relaxed search performs sequence alignments between 50% of randomly selected sequences from each generated cluster. Clusters whose representative sequences share a minimum E-value of 0.001 to sequences in the reaction set(s) in ORAdb continue to the restrictive search. Here, all sequences from the cluster are aligned to all sequences in the corresponding reaction set(s) to which they had a hit (default E-value = 1 × 10−9). Next, the annotated sequences are further filtered to those with a bit score greater than 50 and are used to identify putative microbial interactions based on their functional potential. Constraints can also be added to reduce the search space of microbial interactions (e.g., subsets of reactions required to be performed by single species, transport-related reactions). In addition, an interactive network visualization of the results is produced and accessed via a HTML file.

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    Figure 2. Mapping of the Fetzer genome set to benzoate pathways.

    Mapping of the genomic potential of each species from the Fetzer_genome_set dataset to each reaction in aerobic (yellow) and anaerobic (blue) benzoate-to-acetyl-CoA conversion pathways. Circles highlighted in green represent species that showed biomass growth in medium containing benzoate in the Fetzer study.

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    Figure 3. Example of the interactive network visualization included on OrtSuite results.

    (A) The complete network with species is colored by reaction. (B) Species can be highlighted for simple identification. (C) Tooltips on reaction link out the KEGG if the reaction identifier is given.

Tables

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    Table 1.

    Species names, strain and abbreviation codes of the genomes used to validate OrtSuite (Supplementary data - Test_genome_set).

    Name and strainAbbreviation codeKEGG idBTA pathwayAccession numberReference
    Acinetobacter defluvii WCHA30advT05474P3CP029389-CP029397Hu et al (2017)
    Arabidopsis thalianaathT00041—GCF_000001735*
    Azoarcus sp. KH32CazaT02502P2AP012304, AP012305Junghare et al (2015)
    Azoarcus sp. DN11azdT05691P2CP021731Devanadera et al (2019)
    Azoarcus sp. CIBaziT04019P2CP011072Valderrama et al (2012)
    Burkholderia cepacia DDS 7H-2bcedT03302P3CP007785-CP007787Jenul et al (2018)
    Burkholderia vietnamiensis G4bviT00493P3CP000614-CP000621O’Sullivan et al (2007)
    Cycloclasticus sp. P1cyqT02265P3CP003230Wang et al (2008)
    Cycloclasticus zancles 78-MEczaT02780P3CP005996Messina et al (2016)
    Desulfosporosinus orientis DSM 765dorT01675—CP003108Robertson et al (2000)
    Aromatoleum aromaticum EbN1ebaT00222P2CR555306-CR5553068Rabus et al (2016)
    Latimeria chalumnae (coelacanth)lcmT02913—GCF_000225785*
    Magnetospirillum sp. XM-1magxT04231P2LN997848-LN997849Meyer-Cifuentes et al (2017)
    Paraburkholderia aromaticivorans BN5parbT05169P3CP022989-CP022996Lee et al (2019)
    Rhodococcus ruber P14rrzT05142P3CP024315Peng et al (2018)
    Sulfuritalea hydrogenivorans sk43HshdT03591P2AP012547Sperfeld et al (2019)
    Staphylococcus sciuri FDAARGOS 285sscuT05176—CP022046-CP022047Mrozik and Labuzek (2002)
    Thauera sp. MZ1TtmzT00804P2, P3CP001281-CP001282Suvorova and Gelfand (2019)
    • The genomic potential, based on the KEGG database, to completely encode all proteins involved in a BTA pathway is identified in the column “BTA pathway” (P1: anaerobic conversion of benzoate to acetyl-CoA 1; P2: anaerobic conversion of benzoate to acetyl-CoA 2; P3: aerobic conversion of benzoate to acetyl-CoA). * indicates no literature was found connecting benzoate degradation and the respective species.

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    Table 2.

    OrtSuite workflow runtime and clustering performance.

    OrtSuite stepRuntime
    ORAdb construction and Generation of GPR_rules2 h 47 min
    Generation of protein ortholog clusters54 min
    Functional annotation of sequences in ortholog clusters6 min
    Defining putative microbial interactions3 min
    Total3 h 50 min
    Precision (BLAST)0.63
    Recall (BLAST)0.77
    Precision (DIAMOND)0.64
    Recall (DIAMOND)0.85
    • The total runtime of each OrtSuite step when analyzing the genomic potential of species in Test_genome_set dataset in three pathways (P1, P2, and P3) for the conversion of benzoate to acetyl-CoA (BTA). Steps were performed with default parameters on a laptop with four cores and 16 GB of RAM. Pair-wise precision and recall results of OrthoFinder using BLAST and DIAMOND as an alignment search tool. Clustering was performed on the Test_genome_set dataset plus the mutated genomes.

Supplementary Materials

  • Figures
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  • Table S1 Example of user-defined constraints.

  • Table S2 Sequence alignments of original (query acc.ver) and mutated sequences (subject acc.ver) using BLAST (Altschul et al, 1990).

  • Table S3 Statistics obtained during the clustering of protein orthologs using the Test_genome_set.

  • Table S4 Overview of OrtSuite results using the Test_genome set using an E-value of 0.001 during the relaxed search and four different e-values during the restrictive search (1 × 10−4, 1 × 10−6, 1 × 10−9 and 1 × 10−16): number of orthogroups, number of KEGG orthologs (KO) in ORAdb, and the number of ortholog clusters that transition from each annotation phase, number of consistent (ConOG), and divergent (DivOG) orthogroups.

  • Table S5 Performance of OrtSuite in functional annotation of the Test_genome_set using four different e-value cutoffs (1 × 10−4,1 × 10−6,1 × 10−9,1 × 10−16).

  • Table S6 Mapping of species annotated with KEGG Ortholog (KO) identifiers from ORAdb using a restrictive search e-value cutoff of 1 × 10−4.

  • Table S7 Mapping of species annotated with KEGG Ortholog (KO) identifiers from ORAdb using a restrictive search e-value cutoff of 1 × 10−6.

  • Table S8 Mapping of species annotated with KEGG Ortholog (KO) identifiers from ORAdb using a restrictive search e-value cutoff of 1 × 10−9.

  • Table S9 Mapping of species annotated with KEGG Ortholog (KO) identifiers from ORAdb using a restrictive search e-value cutoff of 1 × 10−16.

  • Table S10 Potential of species in the Test_genome_set to perform reactions associated with benzoate degradation based on ORAdb using a restrictive search e-value cutoff of 1 × 10−4.

  • Table S11 Potential of species in the Test_genome_set to perform reactions associated with benzoate degradation based on ORAdb using a restrictive search e-value cutoff of 1 × 10−6.

  • Table S12 Potential of species in the Test_genome_set to perform reactions associated with benzoate degradation based on ORAdb using a restrictive search e-value cutoff of 1 × 10−9.

  • Table S13 Potential of species in the Test_genome_set to perform reactions associated with benzoate degradation based on ORAdb using a restrictive search e-value cutoff of 1 × 10−16.

  • Table S14 Benzoate degradation pathways used for evaluation of OrtSuite.

  • Table S15 OrtSuite identification of species from the Test_genome_set with the genomic potential to perform all reactions in each of the alternative benzoate to acetyl-CoA conversion pathways using different restrictive e-value cutoffs.

  • Table S16 Statistics obtained during the clustering of protein orthologs using the Fetzer_genome_set.

  • Table S17 Overview of the number of clusters, sequences, and KOs during the annotation of the Fetzer_genome_set.

  • Table S18 Mapping of species from the Fetzer_genome_set annotated with KEGG Ortholog (KO) identifiers from ORAdb.

  • Table S19 Potential of species in the Fetzer_genome_set to perform reactions associated with benzoate degradation based on ORAdb.

  • Table S20 Gene-protein-reaction (GPR) rules for selected reactions present in aerobic (P3) and anaerobic (P1 and P2) conversion of benzoate to acetyl-CoA pathways.

  • Table S21 Number of species that contain all genes required to perform each reaction (KEGG RID) involved in the aerobic degradation of benzoate to acetyl-CoA (P3).

  • Table S22 All predicted species interactions using the Fetzer_genome_set.

  • Table S23 Number of species that contain all genes required to perform each reaction involved in the anaerobic degradation of benzoate to acetyl-CoA (P1 and P2).

  • Table S24 Growth of single species and in combination with others measured by Fetzer and collaborators in three different media (low_env: 1 g/l benzoate, high_env: 6 g/l benzoate, and mixed: 6 g/l benzoate supplemented with NaCl). Species identifiers: (A) Bacillus subtilis ATCC, (B) Paenibacillus polymyxa ATCC 842, (C) Brevibacillus brevis ATCC 8246, (D) Comamonas testosterone ATCC 11996, (E) Cupriavidus necator JMP 134, (F) Pseudomonas putida ATCC 17514, (G) Pseudomonas fluorescens DSM 6290, (H) Variovorax paradoxus ATCC 17713, (I) Rhodococcus sp. (isolate UFZ), (J) Acidovorax facilis (isolate UFZ), (K) Rhodococcus ruber BU3, (L) Sphingobium yanoikuyae.

  • Table S25 Number of reactions, enzymes, KO groups, and KO-associated sequences represented in each alternative benzoate to acetyl-CoA conversion pathway used (P1, P2, and P3).

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Mining interactions with OrtSuite
João Pedro Saraiva, Alexandre Bartholomäus, René Kallies, Marta Gomes, Marcos Bicalho, Jonas Coelho Kasmanas, Carsten Vogt, Antonis Chatzinotas, Peter Stadler, Oscar Dias, Ulisses Nunes da Rocha
Life Science Alliance Sep 2021, 4 (12) e202101167; DOI: 10.26508/lsa.202101167

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Mining interactions with OrtSuite
João Pedro Saraiva, Alexandre Bartholomäus, René Kallies, Marta Gomes, Marcos Bicalho, Jonas Coelho Kasmanas, Carsten Vogt, Antonis Chatzinotas, Peter Stadler, Oscar Dias, Ulisses Nunes da Rocha
Life Science Alliance Sep 2021, 4 (12) e202101167; DOI: 10.26508/lsa.202101167
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