diff --git a/kb/communities/Kombucha_KMC_IMBG1_Fermentation_Community.yaml b/kb/communities/Kombucha_KMC_IMBG1_Fermentation_Community.yaml index 59518c82..d2b223b4 100644 --- a/kb/communities/Kombucha_KMC_IMBG1_Fermentation_Community.yaml +++ b/kb/communities/Kombucha_KMC_IMBG1_Fermentation_Community.yaml @@ -234,4 +234,56 @@ related_ingredients: evidence_source: IN_VITRO snippet: cellulose-based pellicles created by the cellulose-producing bacteria explanation: Names cellulose as the pellicle material produced by community bacteria. +- preferred_term: glucose + chebi_term: + id: CHEBI:17234 + label: glucose + relevance: > + Glucose is the monosaccharide that acetobacteria polymerize into the cellulose pellicle, linking + the sugar substrate to the structural biofilm of the kombucha community. + evidence: + - reference: PMID:26061774 + supports: SUPPORT + evidence_source: IN_VITRO + snippet: polymerizing glucose to cellulose + explanation: Names glucose as the substrate polymerized into cellulose by the acetobacteria. +- preferred_term: acetic acid + chebi_term: + id: CHEBI:15366 + label: acetic acid + relevance: > + Acetic acid is a fermentation product formed from carbohydrate metabolism in the kombucha and + associated lactobacilli, contributing to the acidic character of the beverage. + evidence: + - reference: PMID:26061774 + supports: SUPPORT + evidence_source: IN_VITRO + snippet: cabbage carbohydrates to the lactic acid or acetic acid + explanation: Names acetic acid as a fermentation product of carbohydrate metabolism in the culture. +- preferred_term: lactic acid + chebi_term: + id: CHEBI:28358 + label: rac-lactic acid + relevance: > + Lactic acid is produced when recruited lactobacilli ferment carbohydrates in the kombucha culture, + contributing to the fermentative capacity of the probiotic drink. + evidence: + - reference: PMID:26061774 + supports: SUPPORT + evidence_source: IN_VITRO + snippet: cabbage carbohydrates to the lactic acid or acetic acid + explanation: Names lactic acid as a fermentation product of carbohydrate metabolism in the culture. +- preferred_term: lactose + chebi_term: + id: CHEBI:17716 + label: lactose + relevance: > + Lactose is the dairy sugar that kombucha lacks, making the non-dairy beverage suitable for people + with lactose intolerance. + evidence: + - reference: PMID:26061774 + supports: SUPPORT + evidence_source: IN_VITRO + snippet: for people with lactose intolerance + explanation: Names lactose in the context of kombucha as a non-dairy substitute for the intolerant. metal_relevance: NOT_APPLICABLE diff --git a/kb/communities/Lotus_LjSC3.yaml b/kb/communities/Lotus_LjSC3.yaml index 6d9051c7..aee1e4fa 100644 --- a/kb/communities/Lotus_LjSC3.yaml +++ b/kb/communities/Lotus_LjSC3.yaml @@ -467,6 +467,125 @@ ecological_interactions: snippet: japonicus and Arabidopsis thaliana in a multi-species gnotobiotic system and detected signatures of host preference among commensal bacteria in a community context, but not in mono-associations +related_ingredients: +- preferred_term: glucose + chebi_term: + id: CHEBI:17234 + label: glucose + relevance: > + Glucose is a primary sugar in the artificial root exudate used to grow the + Lj-SC3 community in vitro, mimicking the carbon sources Lotus roots secrete + to shape rhizosphere bacterial colonization. + evidence: + - reference: PMID:34312531 + supports: SUPPORT + evidence_source: IN_VITRO + snippet: 'Artificial root exudates (modified from ref. 45 ) were composed of 0.9 mM glucose' + explanation: Glucose listed as a defined component of the artificial root exudate medium. +- preferred_term: fructose + chebi_term: + id: CHEBI:28757 + label: fructose + relevance: > + Fructose is a root-exudate sugar supplied in the artificial exudate that supports + growth of the synthetic community during host-preference and colonization assays. + evidence: + - reference: PMID:34312531 + supports: SUPPORT + evidence_source: IN_VITRO + snippet: '0.9 mM glucose, 0.9 mM fructose' + explanation: Fructose listed as a defined component of the artificial root exudate. +- preferred_term: sucrose + chebi_term: + id: CHEBI:17992 + label: sucrose + relevance: > + Sucrose is a plant-derived disaccharide included in the artificial root exudate + representing the photosynthate-derived carbon legume roots release to microbes. + evidence: + - reference: PMID:34312531 + supports: SUPPORT + evidence_source: IN_VITRO + snippet: '0.9 mM fructose, 0.2 mM sucrose' + explanation: Sucrose listed as a defined component of the artificial root exudate. +- preferred_term: succinic acid + chebi_term: + id: CHEBI:15741 + label: succinic acid + relevance: > + Succinic acid is an organic acid in the artificial root exudate; such dicarboxylic + acids are common rhizosphere carbon sources favored by root-associated bacteria. + evidence: + - reference: PMID:34312531 + supports: SUPPORT + evidence_source: IN_VITRO + snippet: '0.2 mM sucrose, 0.8 mM succinic acid' + explanation: Succinic acid listed as a defined component of the artificial root exudate. +- preferred_term: sodium lactate + chebi_term: + id: CHEBI:75228 + label: sodium lactate + relevance: > + Sodium lactate provides a lactate carbon source in the artificial root exudate + used to culture the Lj-SC3 community under defined rhizosphere-mimicking conditions. + evidence: + - reference: PMID:34312531 + supports: SUPPORT + evidence_source: IN_VITRO + snippet: '0.8 mM succinic acid, 0.6 mM sodium lactate' + explanation: Sodium lactate listed as a defined component of the artificial root exudate. +- preferred_term: citric acid + chebi_term: + id: CHEBI:30769 + label: citric acid + relevance: > + Citric acid is a tricarboxylic acid in the artificial root exudate, mimicking + the organic acids legume roots exude to recruit and feed rhizosphere bacteria. + evidence: + - reference: PMID:34312531 + supports: SUPPORT + evidence_source: IN_VITRO + snippet: '0.6 mM sodium lactate, 0.3 mM citric acid' + explanation: Citric acid listed as a defined component of the artificial root exudate. +- preferred_term: serine + chebi_term: + id: CHEBI:17822 + label: serine + relevance: > + Serine is an amino acid component of the artificial root exudate, representing + the nitrogen-containing exudate compounds that influence community assembly. + evidence: + - reference: PMID:34312531 + supports: SUPPORT + evidence_source: IN_VITRO + snippet: '0.3 mM citric acid, 0.9 mM serine' + explanation: Serine listed as a defined amino-acid component of the artificial root exudate. +- preferred_term: alanine + chebi_term: + id: CHEBI:16449 + label: alanine + relevance: > + Alanine is an amino acid supplied in the artificial root exudate, contributing + to the nitrogen and carbon resources available to the synthetic community. + evidence: + - reference: PMID:34312531 + supports: SUPPORT + evidence_source: IN_VITRO + snippet: '0.9 mM serine, 0.9 mM alanine' + explanation: Alanine listed as a defined amino-acid component of the artificial root exudate. +- preferred_term: glutamic acid + chebi_term: + id: CHEBI:18237 + label: glutamic acid + relevance: > + Glutamic acid is an amino acid in the artificial root exudate, among the exuded + compounds that root-associated bacteria use during colonization assays. + evidence: + - reference: PMID:34312531 + supports: SUPPORT + evidence_source: IN_VITRO + snippet: 0.9 mM alanine and 0.5 mM glutamic acid + explanation: Glutamic acid listed as a defined amino-acid component of the artificial root exudate. growth_media: - name: Tryptic Soy Agar (TSA) for bacterial isolation ph: '7.0' diff --git a/kb/communities/OMM12_Gnotobiotic_Mouse_Gut_Community.yaml b/kb/communities/OMM12_Gnotobiotic_Mouse_Gut_Community.yaml index 1e7a7aa4..28ac199d 100644 --- a/kb/communities/OMM12_Gnotobiotic_Mouse_Gut_Community.yaml +++ b/kb/communities/OMM12_Gnotobiotic_Mouse_Gut_Community.yaml @@ -122,6 +122,18 @@ environmental_factors: evidence_source: IN_VIVO snippet: established in several germ-free mouse facilities world-wide explanation: Supports facility-to-facility use of OMM12. +related_ingredients: +- preferred_term: complex carbohydrates + relevance: > + The OMM12 member Muribaculum intestinale YL27 is predicted from its genome to degrade complex + carbohydrates, identifying dietary/host-derived polysaccharides as substrates utilized within the + community. + evidence: + - reference: PMID:31998276 + supports: SUPPORT + evidence_source: COMPUTATIONAL + snippet: genome-based prediction indicates the potential to degrade complex carbohydrates + explanation: Names complex carbohydrates as substrates degraded by OMM12 member M. intestinale YL27. associated_datasets: - name: Oligo-MM12 whole-genome sequence resource dataset_type: GENOME diff --git a/references_cache/PMID_26061774.md b/references_cache/PMID_26061774.md index 5d174ed2..d078d78c 100644 --- a/references_cache/PMID_26061774.md +++ b/references_cache/PMID_26061774.md @@ -23,3 +23,8 @@ Key snippets used in curated records: - "maintained in a filter sterilized black tea" - "A matured KMC-IMBG1 was obtained after cultivation for 14 days at 28°C without shaking" - "submitted to the GenBank database under an accession numbers KF908872-KF90879" + + +Full text (re-fetched 2026-06-15 via Europe PMC fullTextXML): + + 1591 ambex AMB Express AMB Express Springer PMC4467805 4467805 4467805 26061774 10.1186/s13568-015-0124-5 Metabarcoding of the kombucha microbial community grown in different microenvironments Reva Oleg N 1 ✉ Zaets Iryna E 2 Ovcharenko Leonid P 2 Kukharenko Olga E 2 Shpylova Switlana P 2 Podolich Olga V 2 de Vera Jean-Pierre 3 Kozyrovska Natalia O 2 1 Bioinformatics and Computational Biology Unit, Department of Biochemistry, University of Pretoria, Lynnwood road, Hillcrest, Pretoria, 0002 South Africa 2 Institute of Molecular Biology and Genetics of National Academy of Sciences of Ukraine, Acad. Zabolotnoho str., 150, Kiev, 03680 Ukraine 3 Institute of Planetary Science, DLR, Rutherfordstr. 2, 12489 Berlin, Germany ✉ Corresponding author. 11 6 2015 5 35 35 18 6 2015 © Reva et al. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Abstract Introducing of the DNA metabarcoding analysis of probiotic microbial communities allowed getting insight into their functioning and establishing a better control on safety and efficacy of the probiotic communities. In this work the kombucha poly-microbial probiotic community was analysed to study its flexibility under different growth conditions. Environmental DNA sequencing revealed a complex and flexible composition of the kombucha microbial culture (KMC) constituting more bacterial and fungal organisms in addition to those found by cultural method. The community comprised bacterial and yeast components including cultured and uncultivable microorganisms. Culturing the KMC under different conditions revealed the core part of the community which included acetobacteria of two genera Komagataeibacter (former Gluconacetobacter ) and Gluconobacter , and representatives of several yeast genera among which Brettanomyces/Dekkera and Pichia (including former Issatchenkia ) were dominant. Herbaspirillum spp. and Halomonas spp., which previously had not been described in KMC, were found to be minor but permanent members of the community. The community composition was dependent on the growth conditions. The bacterial component of KMC was relatively stable, but may include additional member—lactobacilli. The yeast species composition was significantly variable. High-throughput sequencing showed complexity and variability of KMC that may affect the quality of the probiotic drink. It was hypothesized that the kombucha core community might recruit some environmental bacteria, particularly lactobacilli, which potentially may contribute to the fermentative capacity of the probiotic drink. As many KMC-associated microorganisms cannot be cultured out of the community, a robust control for community composition should be provided by using DNA metabarcoding. Electronic supplementary material The online version of this article (doi:10.1186/s13568-015-0124-5) contains supplementary material, which is available to authorized users. Keywords: Kombucha microbial community, Metabarcoding, Pyrosequencing status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no Received 2015 May 26; Accepted 2015 May 28; Collection date 2015. Introduction Culture-dependent methods have revealed an enormous microbial diversity in various fermented products. However, there is still much to be discovered about development and functioning of microbial communities. The high-throughput sequencing technologies known also as next generation sequencing (NGS) are in use to examine the phylogenetic diversity, composition, and dynamic structural changes in microbial communities of fermented foods, giving an opportunity to describe and predict relationships between species in these complex ecosystems (Kim et al. 2011 ; Oguntoyinbo and Narbad 2012 ; Park et al. 2012 ; Nam et al. 2012a , b ; Illeghems et al. 2012 ; Marsh et al. 2014 ). Applicability of NGS for metabarcoding and metagenomic analysis of environmental DNA samples allows identifying uncultured microbial species constituting the communities. Moreover, these approaches allow coupling of structural changes in the communities with environmental factors—e.g., temperature, salinity, pH, etc.,—to perform a meta-analysis of dynamic changes of microbiota (Shade et al. 2013 ). DNA metabarcoding of complex bacterial and fungal communities by profiling of 16S rDNA sequences and internal transcribed regions (ITS) had opened new prospects in studying and designing of new efficient probiotics based on fermentation process. Nowadays, when people became more concerned about obesity and prophylaxis chronic diseases, the probiotics and synbiotics have occupied an important sector within the functional food market. Most probiotic drinks are from dairy products. The tendency to veganism implied consuming of non-dairy nutraceuticals that called for design of new safe non-dairy probiotics, which became an essential health-keeping food category (Prado et al. 2008 ; Vasudha and Mishra 2013 ). Thus kombucha, in range with other fermented functional foods like kvass, fermented herb drinks, etc., may substitute dairy products for people with lactose intolerance (Gupta and Abu-Ghannam 2012 ). Fermented probiotic products are produced by complex microbial communities, which remain to be open environments characterized by rather unstable species composition dependent on nutritional sources and growth conditions. Health improving effects of the kombucha probiotic beverage have been reported in a number of publications (Yapar et al. 2010 ; Bhattacharya et al. 2011 , 2013 ; Aloulou et al. 2012 ; Kallel et al. 2012 ; Srihari et al. 2013 ). Kombucha microbial community (KMC) is an example of mutualistic metabolic cooperation of pro- and eukaryotic microorganisms (bacteria and yeasts). Several types of KMC are cultivated on different continents, which differed in the community structure and diversity (Teoh et al. 2004 ; Ovcharenko 2013 ; Marsh et al. 2014 ), but all of them always possessed cellulose-forming acetobacteria and yeasts. Close biochemical interplay between yeasts and bacteria was facilitated by enclosing the mixed community within cellulose-based pellicles created by the cellulose-producing bacteria on the surface of the liquid medium. The kombucha drink contains organic acids, amino acids, antibiotic substances, vitamins and also many other unidentified bioactive compounds beneficial for human health (Jayabalan et al. 2010 ). Kombucha was proved to exert an antimicrobial activity against pathogens (Battikh et al. 2012 ). Because of a relative stability of the community and the beneficial effect to human health, KCM was domesticated and widely spread around the world. It is usually cultivated in sweetened tea. Recently the kombucha and kombucha-like products with different supplements have been commercialized in many countries. It might be assumed that the KMC community is quite complex and many associated micro-organisms cannot be cultured out of the community. A robust control on the community composition might be provided by using NGS. In this study, the microbial diversity of the kombucha variant from Ukraine (KMC-IMBG1) grown in different conditions was examined using both culture-dependent and culture-independent approaches. Study of a hybrid KMC-IMBG1 was performed to elucidate flexibility of KMC and its ability to recruit organisms from other communities in a similar way as it was reported for kimchi where additives had influenced the microbial community (Jung et al. 2011 ). Roche 454 pyrosequencing of amplified barcode sequences followed by a computer-based profiling of microbial species have uncovered multiple uncultivable members of KMC-IMBG1 in the pellicles and cultural liquid. KMC composition was depending on the growth conditions and showed ability to recruit accessory members such as lactobacilli. Materials and methods Microbial cultures and culturing conditions The kombucha microbial culture was obtained from the collection of microorganisms of the Institute of Molecular Biology and Genetics of National Academy of Sciences (Kyiv, Ukraine). It was maintained in a filter sterilized black tea (Lipton, 1.2%, w/v) extract with sucrose (3.0%, w/v) (sBTS) or non-sterile BTS (nsBTS). KMC-IMBG1 also has been maintained in filter (0.22 µm, Millipore) sterilized black tea supplemented with honey (2.0%) (BTH). A matured KMC-IMBG1 was obtained after cultivation for 14 days at 28°C without shaking. A hybrid KMC was obtained by growing KMC-IMBG1 in fermented cabbage brine. More specifically, the KMC cultural liquid (10%), which previously was pre-cultured in BTH, was added to the minced cabbage supplemented with honey (2.0%). The cultivation conditions were the same as described above. Newly formed pellicles were used for inoculation of fresh BTH in a weekly basis for 5 weeks. For isolation and cultivation of acetobacteria, HS agar medium (Hestrin and Schramm 1954 ) was used. The isolates were incubated for 3–7 days at 30°C under stationary conditions in HS until formation of pellicles. Yeast cultures were isolated on the Glucose Yeast Peptone agar medium (HiMedia Laboratories, India). Concomitant bacteria were screened on the minimal agar medium with sucrose (Miller 1972 ). For medium selectivity, the antibiotics cycloheximide (100 µg/ml, Sigma-Aldrich) against yeast and ceftriaxone (50 µg/ml, Roshe) against bacteria were added to corresponding media. Confocal scanning laser microscopy After cultivation for 14 days in sBTS, the bacterial cellulose-based pellicle samples were fixed in formaldehyde vapor during 1 h and stained with calcofluor (excitation 405 nm, filter BP 420–480) and thiazine red dyes (excitation 514 nm, filter BP 530–600 nm). A microscopic examination of sample fluorescence was performed, using CSLM AXIOSKOP-2 ZEISS equipped with the LSM 510 PASCAL (CarlZeiss, FRG) software. DNA extraction Total DNA samples from the kombucha liquid culture and pellicle were isolated for further barcode amplification and pyrosequencing. Microbial DNA isolation from the 14 day-old KMC-IMBG1 liquid hybrid culture was performed with innuSPEED bacteria/fungi DNA isolation kit (Analytik Jena AG). In parallel, total DNA samples from cellulose-based hybrid kombucha pellicle (as well as the 14 day-old pellicles produced by KMC-IMBG1 grown in sBTS, nsBTS, and BTH) were isolated from three specimens, using modified soft lyses method after blending of the pellicle (Gabor et al. 2003 ). The nucleic acids were quantified and qualified by a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE). PCR amplification, DNA sequencing and analysis Bacterial and yeast isolates from KMC-IMBG1 were identified by PCR amplification using standard primers 27F/1494R (AGAGTTTGATCCTGGCTCAG/TGACTGACTGAGGYTACCTTGTTACGACTT) for bacterial 16S rDNA and NL1/NL4 (GCATATCAATAAGCGGAGGAAAAG/GGTCCGTGTTTCAAGACGG) for fungal 26S rDNA amplification as it was described previously (Ogino et al. 2001 ; Kurtzman and Robnett 1997 ). More specifically, the PCR reactions for both primers were run for 35 cycles with annealing temperature 54°C for 27F/1494R and 52°C for NL1/NL4. PCR products were cleaned with UltraClean™ PCR Clean-up DNA purification kit (MoBio Laboratories). The PCR products were sequenced by the Sanger method (Sanger et al. 1977 ) using Big Dye Terminator Sequencing Standard Kit v3.1 (Applied Biosystems, USA) and apparatus 3130 Genetic Analyser (Applied Biosystems). The 16S rDNA sequences were binned by BLASTN search through the National Center for Biotechnology Information (NCBI) GenBank (US National Library of Medicine, Bethesda, Maryland, USA). These sequence data have been submitted to the GenBank database under an accession numbers KF908872 -KF90879. DNA pyrosequencing DNA sequencing has been performed by using Roche GS FLX in Inqaba Biotec ( http://www.inqababiotec.co.za ). Pairs of standard primers 27F 5′-AGAGTTTGATCCTGGCTCAG-3′ (Lane 1991 ) and 518R 5′ATTACCGCGGCTGCTGG-3′ (Muyzer et al. 1993 ) for 16S; and ITS1 5′-TCCGTAGGTGAACCTGCGG-3′ and ITS4 5′-TCCTCCGCTTATTGATATGC-3′ (White et al. 1990 ) for ITS amplification were used. Generated 16S rRNA reads were checked for chimers by using DECIPHER algorithm (Wright et al. 2012 ) set for analysis of short-length sequences. In total 30 putative chimeras were identified and removed from the read datasets. Quality control was performed by locally installed Fast QC program ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc ). Poor quality reads with Phred quality score below 20 (that corresponded to p value ≥0.05) and reads shorter than 100 bp were filtered out. Metabarcoding dataset statistics DNA reads obtained from the sequencer were aligned by the local BLASTN against combined NCBI 16S Microbial and GreenGenes16S databases for identification of 16S rDNA reads and against the NCBI nt-database for identification of ITS reads. The latest versions of GreenGene and NCBI databases available at the time of running of this analysis, i.e., the mid of 2014, were used in this study. The BLASTN results were merged and visualized by MEGAN 5.2.3 (Huson et al. 2011 ). Additionally, the BLASTN output files were searched by an in-house BioPython based script to retrieve the statistics of the top scored hits over all reads. A taxon presence in a sample was accepted, if there were at least five reads binned to this taxonomic unit. The minimum BLASTN score for taxon identification was 300. Statistics of pyrosequencing is shown in Table 1 . Table 1 DNA reads obtained by Roche 454 sequencing of different samples Sample Total number of reads before and after filtering and chimera removal Total length, bp Average Min. read length a Max. read length S obs /S exp † sBTS Pellicle: 16S 2,384/2,356 1,123,074 471 77 607 14/46 Pellicle: ITS 532/530 277,232 521 61 568 5/10 BTH Pellicle: 16S 2,632/2,626 1,244,214 472 47 828 14/46 Pellicle: ITS 7,888/7,783 3,303,150 418 43 561 23/87 nsBTS Pellicle: 16S 1,880/1,828 870,798 463 65 563 24/33 Pellicle: ITS 3,741/2,310 1,138,925 304 41 536 7/23 Hybrid KMC Pellicle: 16S 8,716/8,250 2,975,027 341 40 513 9/10 Pellicle: ITS 7,943/7,113 2,500,278 314 40 541 18/34 Liquid phase: 16S 6,494/6,325 2,294,949 353 40 513 16/17 Liquid phase: ITS 9,541/8,281 3,165,774 331 40 521 26/38 a All reads shorter than 100 bp were filtered out. † S obs observed number of species including those identified by a single read, S exp expected number of species according to Chao estimation (Eq. 1 ). Not filtered metabarcoding data sets were deposited in the Metagenomics RAST database server (4543580.3-4543590.3). Expected species richness of a sample was estimated according to Chao 1 equation (Chao 1984 ): 1 S exp = S obs + F 1 2 2 F 2 where S exp —expected species richness; S obs —observed number of species; F1 is the number of singletons (i.e., the number of species with only a single occurrence in the sample) and F2 is the number of doubletons (the number of species with exactly two occurrences in the sample). Rarefication curves were estimated by counting of number of identified species after successful binning of every 200 reads. An exception was the dataset ITS_sBTS when species number increment was measured every 100 successfully binned reads because of the small size of this dataset. The binning was considered as successful if the BLASTN score was ≥300. Distance between two metabarcode datasets was measured by the Eq. 2 : 2 D = ∑ N comb m 1 N 1 - m 2 N 2 2 N comb where N comb —total number of identified species in both datasets; m 1 and m 2 —numbers of reads binned to the species m in the datasets 1 and 2, respectively; N 1 and N 2 —total numbers of binned reads in the datasets 1 and 2, respectively. Distances were used to infer dendrograms of dataset diversity by using the Neighbor–joining algorithm implemented in MEGA6 (Tamura et al. 2013 ). Results Isolation of cultivable forms of microorganisms associated with KMC DNA fragments amplified by PCR from DNA samples extracted from cultivable isolates of KMC-IMBG1 were binned to taxonomic units by BLASTN alignment. Members of four yeast genera Pichia , Brettanomyces/Dekkera , Candida and Zygosaccharomyces ; and two bacterial genera Gluconacetobacter (now Komagataeibacter gen. nov., Yamada et al. 2012 ) and Gluconobacter were identified. On the species level there were Komagataeibacter sp. (99% homology to K. xylinus and K. saccharivorans ), K. intermedius, K. kombuchae , and Gluconobacter oxydans . As it was revealed by culture methods, the simplest structure of KMC cultivated in sterile black tea with sugar (sBTS) composed of two yeast species of Pichia and Brettanomyces/Dekkera ; and two acetobacteria: Komagataeibacter sp. and K. intermedius . In classic non-sterile sweetened black tea medium (nsBTS), KMC-IMBG1 comprised Pichia sp., Dekkera anomala , Candida sp., Komagataeibacter sp., K. intermedius and Gluconobacter oxydans. Additional yeast species Zygosaccharomyces bailii and acetobacterium K. kombuchae were isolated from the culture maintained in the sterile black tea medium with honey (BTH). In the hybrid kombucha culture grown in BTH mixed with cabbage brine, several atypical bacterial species have been identified including Bacillus subtilis , B. pumilis (Firmicutes) and Microbacterium sp. ( Actinobacteria ). At the same time, the confocal scanning laser microscopy revealed a higher level of diversity of KMC-IMBG1 especially those associated with the cellulose 3D web (Additional file 1 : Figure S1). It was hypothesized that uncultivable microbial organisms might be abundant in this network. Particularly, there were peculiar long cells observed during the dormancy state (Additional file 1 : Figure S1b), which were dissimilar to any cultivable bacteria (Puspita et al. 2012 ). To overcome the problem of identification of uncultivable representatives of KMC-IMBG1, a metabarcoding approach has been used. Metabarcoding analysis of KMC-IMBG1 grown in different conditions According to the results of analysis of metabarcodes, the acetobacteria of Komagataeibacter and Gluconobacter genera (α- Proteobacteria ) dominated in KMC-IMBG1 grown in sBTS, nsBTS and BTH with a few other bacterial species of Komagataeibacter . K. xylinus prevailed in all analysed bacteriomes (77.7–96.8%); K. intermedius reached up to 4.9%, and Gluconobacter spp., most of which belonged to G. oxydans , composed up to 10% of bacterial community in BTH. However, when grown in nsBTS, the proportion of Gluconobacter decreased 50-folds (Figure 1 ). This can be explained by the preferred consumption of different sugars present in honey (Mandal and Mandal 2011 ). Estimated richness of bacterial and fungal species of KMC is shown in Table 1 . Interestingly, Gluconoacetobacter diazotrophicus known as an obligate sugarcane endophyte (Baldani et al. 1997 ) was constantly present in all variants of KMC studied in this work; however, this species was not isolated by culture-dependent method. Herbaspirillum spp. and Halomonas spp. were a minor, but permanent component in KMC-IMBG1 grown in all the different conditions. Presence of Halomonas sp. was also reported in kombucha microbiota revealed in the metabarcoding study by Shade ( 2011 ). The minor fractions of the KMC-IMBG1 were represented by several occasional Firmicutes , β-and γ- Proteobacteria (see Figure 1 ). Figure 1 Profiles of bacterial species of KMC-IMBG1 grown in sterile black tea with sugar (sBTS), non-sterile black tea with honey (BTH) and non-sterile black tea with sugar (nsBTS) identified by binning of 16S rDNA reads. The metabarcoding showed that KMC-IMBG1 comprised yeast species belonging to Pichia , Brettanomyces/Dekkera , Candida and Saccharomyces genera, as well as unknown OTUs similar to ‘compost fungus’ and ‘unknown yeast’. The yeast composition widely varied in different cultures. The major yeast species of KMC-IMBG1 grown in sBTS was Dekkera anomala . Pichia fermentas was abundant in BTH, and Pichia occidentalis (former Issatchenkia occidentalis ) was the most frequent in nsBTS (Figure 2 ). This observation suggested that the domination of that or another yeast species significantly depended on the cultivation conditions at much higher extend than it was observed for the core bacterial community (see Figures 1 , 2 ). It was remarkable that an uncultured unknown fungal species identified as ‘compost fungus’ was the most abundant in BTH and to some extend in nsBTS. Figure 2 Profiles of yeast species in KMC-IMBG1 grown in sterile black tea with sugar (sBTS), sterile black tea with honey (BTH) and non-sterile black tea with sugar (nsBTS) identified by binning of ITS reads. Metabarcoding analysis of the hybrid kombucha culture Bacterial and yeast communities of the hybrid KMC-IMBG1 grown in a mixture of filter-sterilized BTH with added sweetened fermented cabbage brine were expectedly much more diverse (Figure 3 ). K. xylinus was a dominant bacterial species. Lactobacilli, which probably originated from the cabbage brine and remained here in series of passages, were abundant in the hybrid KMC-IMBG1 pellicles. Lactobacillus spp. isolates were reported before as indispensable kombucha community members (Marsh et al. 2014 ). Lactobacilli are known also as the indigenous inhabitants of fermented cabbage (Jung et al. 2011 ). L. plantarum causes fermentation of cabbage carbohydrates to the lactic acid or acetic acid. Figure 3 Normalized abundance of the most frequent OTUs of KMC identified by BLASTN in a 16S rDNA and b ITS reads. A1—liquid phase of the culture; A2—cellulose based biofilm. Numbers of identified reads were normalized by the total numbers of reads in the samples. Several bacterial OTUs failed with taxonomic affiliation because of lack of appropriate reference sequences in the searched databases (Figure 3 a). Yeast DNA barcoding discovered a much higher number of OTUs in pellicles and cultural liquid in the hybrid kombucha culture as compared to the parental KMC-IMBG1. P. occidentalis / P. cecembensis were the dominant yeast species the same as in nsBTS (Figure 3 b). Many OTUs were not affiliated to any taxonomic units because of a weak sequence similarity, or they showed similarity to unknown microorganisms. Anyway, even the weak similarity was consistently against the same reference sequences that suggested that the total number of species in KMC-IMBG1 was limited but many of them still remained unknown. Comparative analysis of microbiomes produced by KMC under different microenvironments Rarefication curves for studied metabarcode datasets and dendrograms representing species diversity of KMC pellicle grown at different conditions are shown in Figure 4 . Remarkably, fungal biomes of KMC varied to a much higher extend depending on the growth conditions than the bacterial component (see the scaling bars in dendrograms in Figure 4 ). The biggest number of bacterial OUTs, which were singletons or represented only by a few reads, was observed in KMC grown in non-sterile conditions (nsBTS). It is reflected in the steepness of the corresponding rarefication curve in Figure 4 . Interestingly, the richness of fungal species of the sample was depleted, that may be explained by presence of Bacillus and Pseudomonas , which might synthesize antifungal antibiotics. Nevertheless, the bacterial core component in nsBTS remained the same. The biggest alteration in the KMC bacteriome structure was observed in BTH (Figure 4 a) caused probably by honey addition. Microbial composition of the hybrid KMC including both the bacterial and fungal components showed a higher level of stability as the rarefication curves calculated for this community has got faster the saturation level (Figure 4 c, d). Figure 4 Statistical analysis of metabarcode datasets based on 16S rDNA (parts a and c ) and ITS (pats b and d ) amplicons. Dendrograms in a and b of diversity of datasets were built by neighbor–joining algorithm based on distance tables calculated by Eq. 2 . Parts c and d represent rarefication curves. Discussion This study showed that KMC-IMBG1 is quite flexible and variable community. The main highlight of this study was that KMC-IMBG1 grown on different sterile and non-sterile media produced a stable core microbiome comprising acetobacteria and few associated strains of yeast species, and a number of accessory species, which may or may not occur at different conditions. The core part of KMC-IMBG1 probably is critical for functioning of the whole community and might be responsible for recovery of the community after disturbance. In addition to prevalent community members, several minor but permanently occurring bacterial species of KMC-IMBG1 were also discovered. Among them there were Herbaspirillum spp. and Halomonas spp. These organisms were identified by binning the DNA reads originated from KMC grown in sterile and non-sterile media. In other studies on species composition of kombucha from North American and Ireland ecotypes these species were not reported (Marsh et al. 2014 ). Another disagreement with the report by Marsh et al. ( 2014 ) was that according to these authors the highest diversity of micro-flora was associated with the cellulose pellicle. In Figure 4 c, d it is seen in rarefication curves of the hybrid KMC-IMBG1 that both fungal and bacterial micro-flora of pellicle was more stable and less rich in different species than that from the liquid phase. Further research is needed to uncover the role of the core and accessory members of KMC, including the uncultivable bacteria, and how they contribute to stabilizing the community and gaining its biologically active. The ability to modify KMC is of practical importance as a possible approach to improve the medicinal and biotechnological properties of the kombucha products (Kozyrovska et al. 2012 ). This work is the promising first step to design efficient and safe probiotics and synbiotics based on synthetic KMC communities of beneficial and harmless microbial species. It was hypothesized that the positive activity of kombucha probiotic on human health may be improved and extended by ‘domestication’ in the kombucha of other probiotic bacteria, e.g., lactobacilli, which in the current study most likely were recruited by KMC from the cabbage brine. There is still much to be discovered about bacteria-yeast communal interrelationships and their impact on the human microbiota. It also may be concluded that the DNA metabarcoding based on NGS is the best choice for profiling of complex microbial communities of fermented products. Authors’ contributions RO performed the bioinformatics analysis and helped to draft the manuscript; ZI coordinated the study, helped to draft the manuscript and designed figures; OL carried out the molecular genetic studies; KO isolated bacterial and yeast strains from the kombucha culture; SS carried out the CSLM observations; PO performed the sequence alignment; VJ-P participated in the design of the study; KN conceived the study, participated in its design, drafted the manuscript. All authors read and approved the final manuscript. Acknowledgements We thank Dr. Olha Yaneva (Institute of Microbiology and Virology of National Academy of Sciences, Kyiv) for consultations in yeast isolation and culturing. This study has been partially supported by the grant of National Academy of Sciences of Ukraine (N47/2013). Compliance with ethical guidelines Competing interests The authors declare that they have no competing interests. Abbreviations KMC kombucha microbial community KMC-IMBG1 kombucha microbial community variant from Ukrainian collection NGS next generation sequencing ITS internal transcribed spacer sBTS kombucha culture grown on sterile black tea with sucrose nsBTS kombucha culture grown on non-sterile black tea with sucrose BTH kombucha culture grown on sterile black tea with honey Additional files Additional file 1: Figure S1. Confocal scanning laser microscopy images of a cross section of cellulose-based pellicle produced by KMC in a sugared black tea, showing a variety of both bacteria and yeast cell morphotypes ( a ); cells of unusual morphology (a long shape), which may indicate the existence of dormant uncultivable microbial (sub)populations ( b ). Cellulose and yeast cells stained with calcofluor ( a blue signal ), bacterial cells and proteins stained with thiazine red ( a yellow signal ). Scale bar is 10 μm. Contributor Information Oleg N Reva, Email: oleg.reva@up.ac.za. Iryna E Zaets, Email: zkora@ukr.net. Leonid P Ovcharenko, Email: leonst@ukr.net. Olga E Kukharenko, Email: olinku@meta.ua. Switlana P Shpylova, Email: shpylova@ukr.net. Olga V Podolich, Email: podololga@ukr.net. Jean-Pierre de Vera, Email: jean-pierre.devera@dlr.de. Natalia O Kozyrovska, Email: kozyrna@ukr.net. References Aloulou A, Hamden K, Elloumi D, Ali MB, Hargafi K, Jaouadi B, et al. Hypoglycemic and antilipidemic properties of kombucha tea in alloxan-induced diabetic rats. BMC Complement Alternat Med. 2012;16:12–63. doi: 10.1186/1472-6882-12-63. Baldani JI, Caruso L, Baldani VLD, Goi SR, Döbereiner J. Recent advances in BNF with nonlegume plants. Soil Biol Biochem. 1997;29:911–922. doi: 10.1016/S0038-0717(96)00218-0. Battikh H, Bakhrouf A, Ammar E. Antimicrobial effect of kombucha analogues. LWT Food Sci Technol. 2012;47:71–77. doi: 10.1016/j.lwt.2011.12.033. Bhattacharya S, Manna P, Gachhui R, Sil PC. Protective effect of kombucha tea against tertiary butyl hydroperoxide induced cytotoxicity and cell death in murine hepatocytes. Indian J Exp Biol. 2011;49:511–524. Bhattacharya S, Gachhui R, Sil PC. Effect of kombucha, a fermented black tea in attenuating oxidative stress mediated tissue damage in alloxan induced diabetic rats. Food Chemical Toxicol. 2013;60:328–340. doi: 10.1016/j.fct.2013.07.051. Chao A. Nonparametric estimation of the number of classes in a population. Scand J Stat. 1984;11:265–270. Gabor EM, de Vries EJ, Janssen DB. Efficient recovery of environmental DNA for expression cloning by indirect extraction methods. FEMS Microbiol Ecol. 2003;44:153–163. doi: 10.1016/S0168-6496(02)00462-2. Gupta S, Abu-Ghannam N. Probiotic fermentation of plant based products: possibilities and opportunities. Crit Rev Food Sci Nutr. 2012;52:183–199. doi: 10.1080/10408398.2010.499779. Hestrin S, Schramm M. Synthesis of cellulose by Acetobacter xylinum 2 preparation of freeze-dried cells capable of polymerizing glucose to cellulose. Biochem J. 1954;58:345–352. doi: 10.1042/bj0580345. Huson DH, Mitra S, Ruscheweyh H-J, Weber N, Schuster SC. Integrative analysis of environmental sequences using MEGAN4. Genome Res. 2011;21:1552–1560. doi: 10.1101/gr.120618.111. Illeghems K, De Vuyst L, Papalexandratou Z, Weckx S. Phylogenetic analysis of a spontaneous cocoa bean fermentation metagenome reveals new insights into its bacterial and fungal community diversity. PLoS One. 2012;7:e38040. doi: 10.1371/journal.pone.0038040. Jayabalan R, Malini K, Sathishkumar M, Swaminathan K, Yun S-E. Biochemical characteristics of tea fungus produced during kombucha fermentation. Food Sci Biotechnol. 2010;19:843–847. doi: 10.1007/s10068-010-0119-6. Jung J, Lee SH, Kim JM, Park MS, Bae J-W, Hahn Y, et al. Metagenomic analysis of kimchi, a traditional Korean fermented food. Appl Environ Microbiol. 2011;77:2264–2274. doi: 10.1128/AEM.02157-10. Kallel L, Desseaux V, Hamdi M, Stocker P, Ajandouz EH. Insights into the fermentation biochemistry of kombucha teas and potential impacts of kombucha drinking on starch digestion. Food Res Internat. 2012;49:226–232. doi: 10.1016/j.foodres.2012.08.018. Kim YS, Kim MC, Kwon SW, Kim SJ, Park IC, Ka JO, et al. Analyses of bacterial communities in meju, a Korean traditional fermented soybean bricks, by cultivation-based and pyrosequencing methods. J Microbiol. 2011;49:340–348. doi: 10.1007/s12275-011-0302-3. Kozyrovska N, Reva O, Goginyan V, de Vera J-P. Kombucha microbiome as a probiotic: a view from the perspective of post-genomics and synthetic ecology. Biopolym Cell. 2012;28:103–110. doi: 10.7124/bc.000034. Kurtzman CP, Robnett CJ. Identification of clinically important ascomycetous yeasts based on nucleotide divergence in the 5′ end of the large-subunit (26S) ribosomal DNA gene. J Clin Microbiol. 1997;35:1216–1223. doi: 10.1128/jcm.35.5.1216-1223.1997. Lane DJ. 16S/23S rRNA sequencing. In: Stackebrandt E, Goodfellow M, editors. Nucleic acid techniques in bacterial systematics. New York: Wiley; 1991. pp. 115–175. Mandal MD, Mandal S. Honey: its medicinal property and antibacterial activity. Asian Pac J Trop Biomed. 2011;1:154–160. doi: 10.1016/S2221-1691(11)60016-6. Marsh AJ, O’Sullivan O, Hill C, Ross RP, Cotter PD. Sequence-based analysis of the bacterial and fungal compositions of multiple kombucha (tea fungus) samples. Food Microbiol. 2014;38:171–178. doi: 10.1016/j.fm.2013.09.003. Miller JH (1972) Experiments in molecular genetics. Cold Spring Harbor Laboratory, Cold Spring Harbor, p 436 Muyzer G, De Waal EC, Uitterlinden AG. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol. 1993;59:695–700. doi: 10.1128/aem.59.3.695-700.1993. Nam YD, Lee SY, Lim SI. Microbial community analysis of Korean soybean pastes by next-generation sequencing. Int J Food Microbiol. 2012;155:36–42. doi: 10.1016/j.ijfoodmicro.2012.01.013. Nam YD, Park SL, Lim SI. Microbial composition of the Korean traditional food “kochujang” analyzed by a massive sequencing technique. J Food Sci. 2012;77:250–256. doi: 10.1111/j.1750-3841.2012.02656.x. Ogino A, Koshikawa H, Nakahara T, Uchiyama H. Succession of microbial communities during a biostimulation process as evaluated by DGGE and clone library analyses. J Appl Microbiol. 2001;91:625–635. doi: 10.1046/j.1365-2672.2001.01424.x. Oguntoyinbo FA, Narbad A. Molecular characterization of lactic acid bacteria and in situ amylase expression during traditional fermentation of cereal foods. Food Microbiol. 2012;31:254–262. doi: 10.1016/j.fm.2012.03.004. Ovcharenko LP (2013) Metagenomic analysis of domesticated kombucha multi-microbial culture. Biopolym Cell 29 (special issue: p 16) Park E-J, Chun J, Cha C, Park W-C, Jeon C, Bae J-W. Bacterial community analysis during fermentation of ten representative kinds of kimchi with barcoded pyrosequencing. Food Microbiol. 2012;30:197–204. doi: 10.1016/j.fm.2011.10.011. Prado FC, Parada J, Pandey A, Soccol CR. Trends in non-dairy probiotic beverages. Food Res Int. 2008;41:111–123. doi: 10.1016/j.foodres.2007.10.010. Puspita ID, Kamagata Y, Tanaka M, Asano K, Nakatsu CH. Are uncultivated bacteria really uncultivable? Microb Environ. 2012;27:356–366. doi: 10.1264/jsme2.ME12092. Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci USA. 1977;74:5463–5467. doi: 10.1073/pnas.74.12.5463. Shade A (2011) The kombucha biofilm: a model system for microbial ecology. In: Final report on research conducted during the Microbial Diversity course. Marine Biological Laboratories, Woods Hole, MA Shade A, Caporaso G, Handelsman J, Knight R, Fierer N. A meta-analysis of changes in bacterial and archaeal communities with time. ISME J. 2013;7:1493–1506. doi: 10.1038/ismej.2013.54. Srihari T, Karthikesan K, Ashokkumar N, Satyanarayana U. Antihyperglycaemic efficacy of kombucha in streptozotocin-induced rats. J Funct Foods. 2013;5:1794–1802. doi: 10.1016/j.jff.2013.08.008. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;30:2725–2729. doi: 10.1093/molbev/mst197. Teoh AL, Heard G, Cox J. Yeast ecology of kombucha fermentation. Int J Food Microbiol. 2004;95(2):119–126. doi: 10.1016/j.ijfoodmicro.2003.12.020. Vasudha S, Mishra HN. Non-dairy probiotic beverages. Int Food Res J. 2013;20:7–15. White TJ, Bruns T, Lee S, Taylor J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis MA, Gelfand DH, Sninsky JJ, White TJ, editors. PCR protocols: a guide to methods and applications. New York: Academic Press; 1990. pp. 315–322. Wright ES, Yilmaz LS, Noguera DR. DECIPHER, a search-based approach to chimera identification for 16S rRNA sequences. Appl Environ Microbiol. 2012;78:717–725. doi: 10.1128/AEM.06516-11. Yamada Y, Yukphan P, Lan Vu HT, Ochaikul D, Muramatsu Y, Tanasupawat S. Description of Komagataeibacter gen nov with proposals of new combinations (Acetobacteraceae) J Gen Appl Microbiol. 2012;58:397–404. doi: 10.2323/jgam.58.397. Yapar K, Cavusoglu K, Oruc E, Yalcin E. Protective effect of kombucha mushroom (KM) tea on phenol-induced cytotoxicity in albino mice. J Environ Biol. 2010;31:615–621. \ No newline at end of file diff --git a/references_cache/PMID_29051233.md b/references_cache/PMID_29051233.md index d382fd6e..c59a6eff 100644 --- a/references_cache/PMID_29051233.md +++ b/references_cache/PMID_29051233.md @@ -12,3 +12,8 @@ Quoted snippets used in curated records: URL: https://pubmed.ncbi.nlm.nih.gov/29051233/ + + +Full text (re-fetched 2026-06-15 via Europe PMC fullTextXML): + + Genome Announc Genome Announc 1979 genann GA Genome Announcements 2169-8287 American Society for Microbiology (ASM) PMC5646386 PMC5646386.1 5646386 5646386 29051233 10.1128/genomeA.00758-17 genomeA00758-17 1 Prokaryotes High-Quality Whole-Genome Sequences of the Oligo-Mouse-Microbiota Bacterial Community Genome Announcement Garzetti et al. Garzetti Debora a b Brugiroux Sandrine a Bunk Boyke c Pukall Rüdiger c McCoy Kathy D. d Macpherson Andrew J. d Stecher Bärbel a b a Max von Pettenkofer Institute of Hygiene and Medical Microbiology, Ludwig-Maximilians-University of Munich, Munich, Germany b German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany c Leibniz Institute DSMZ–German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany d Maurice Müller Laboratories, Department of Clinical Research (DKF), UVCM, University Hospital, Bern, Switzerland Address correspondence to Debora Garzetti, garzetti@mvp.uni-muenchen.de . 19 10 2017 10 2017 5 42 300104 e00758-17 20 6 2017 11 8 2017 19 10 2017 24 10 2017 24 10 2017 Copyright © 2017 Garzetti et al. 2017 Garzetti et al. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license . ABSTRACT The Oligo-Mouse-Microbiota (Oligo-MM 12 ) is a community of 12 mouse intestinal bacteria to be used for microbiome research in gnotobiotic mice. We present here the high-quality whole genome sequences of the Oligo-MM 12 strains, which were obtained by combining the accuracy of the Illumina platforms with the long reads of the PacBio technology. Deutsches Zentrum für Infektionsforschung (DZIF) https://doi.org/10.13039/100009139 Debora Garzetti Sandrine Brugiroux Bärbel Stecher pmc-status-qastatus 0 pmc-status-live yes pmc-status-embargo no pmc-status-released yes pmc-prop-open-access yes pmc-prop-olf no pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement no pmc-prop-pdf-only no pmc-prop-suppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY cover-date October 2017 GENOME ANNOUNCEMENT In a recent study, we described a defined intestinal community of 12 murine strains, termed Oligo-Mouse-Microbiota (Oligo-MM 12 ), which permanently colonize gnotobiotic mice over several generations and provide colonization resistance against Salmonella enterica serovar Typhimurium ( 1 ). This bacterial consortium has been thoroughly characterized by biochemical and molecular methods, and the individual strains have been deposited at the German Culture Collection of Microorganisms and Cell Cultures (DSMZ) ( Table 1 ). The genomes of the 12 bacteria were previously sequenced and assembled via different techniques and algorithms ( 1 – 3 ). Since the Oligo-MM 12 strains are being used by an increasing number of research groups ( 1 , 3 – 5 ), the multitude of genome sequences precludes the possibility of a meaningful exchange of data within the scientific community. Thus, there is a strong need for availability and constant update of the Oligo-MM 12 reference genomes. TABLE 1 Assembly information and accession numbers of the Oligo-MM 12 genomes Oligo-MM strain Total length (bp) No. of contigs No. of genes DSM no. Accession no. [ Clostridium ] innocuum I46 4,468,984 1 4,629 26113 CP022722 Bacteroides caecimuris I48 4,800,416 19 4,225 26085 NHMU00000000 Lactobacillus reuteri I49 2,063,604 3 2,006 32035 NHMT00000000 Enterococcus faecalis KB1 3,025,555 1 2,942 32036 CP022712 Acutalibacter muris KB18 3,802,813 1 3,990 26090 CP021422 Bifidobacterium animalis subsp. animalis YL2 2,021,926 2 1,732 26074 NHMR00000000 Muribaculum intestinale YL27 3,306,969 1 2,786 28989 CP021421 Flavonifractor plautii YL31 3,813,655 5 3,924 26117 NHMQ00000000 [ Clostridium ] clostridioforme YL32 7,157,460 16 7,735 26114 NHTR00000000 Akkermansia muciniphila YL44 2,737,167 1 2,731 26127 CP021420 Turicimonas muris YL45 2,887,709 20 2,754 26109 NHMP00000000 Blautia coccoides YL58 5,128,482 1 5,230 26115 CP022713 It is well recognized that sequences from the Illumina platforms have low error rates, with systematic errors being mainly situated at the end of the reads, but are too short for an efficient complete genome assembly ( 6 ). On the contrary, the long reads generated by PacBio sequencing are less accurate and contain random errors ( 6 ). Aiming to create a set of reference genomes, in this study we present the high-quality genome sequences of the Oligo-MM 12 bacteria, which were assembled by a hybrid approach combining Illumina and PacBio sequences ( Table 1 ). As previously described ( 1 ), the complete genome sequence of Acutalibacter muris KB18 was obtained on the PacBio RSII platform and assembled using the RS_HGAP_Assembly.3 protocol (default parameters). Error correction was then performed by mapping Illumina reads onto the finished genome with the Burrows–Wheeler Alignment tool ( 7 ), with subsequent variant calling using CLC Genomics Workbench version 7.0.4. Here, Illumina MiSeq reads ( 1 ) of the remaining 11 bacterial genomes were assembled onto their respective PacBio complete genomes ( 2 ) by applying a reference-guided approach using SPAdes ( 8 ), with a minimum contig length of 500 bp. Assemblies were evaluated with QUAST (Quality Assessment Tool for genome assemblies) ( 9 ), and the final genomes were automatically annotated using RAST (Rapid Annotations using Subsystems Technology) ( 10 ). In future studies, genetic variation, genome evolution, and functional genomics, among other research applications, of the Oligo-MM 12 community can be assessed by high-quality analyses. Accession number(s). The assembled whole-genome sequences of the Oligo-MM 12 strains have been deposited in DDBJ/ENA/GenBank under the accession numbers given in Table 1 . Citation Garzetti D, Brugiroux S, Bunk B, Pukall R, McCoy KD, Macpherson AJ, Stecher B. 2017. High-quality whole-genome sequences of the Oligo-Mouse-Microbiota bacterial community. Genome Announc 5:e00758-17. https://doi.org/10.1128/genomeA.00758-17 . ACKNOWLEDGMENTS We thank Cathrin Spröer, Nicole Heyer, and Simone Severitt for sequencing of the KB18 PacBio genome. This work was supported by the German Center for Infection Research (DZIF), the Center for Gastrointestinal Microbiome Research (CEGIMIR), and the German Research Foundation (DFG). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. REFERENCES 1. Brugiroux S , Beutler M , Pfann C , Garzetti D , Ruscheweyh HJ , Ring D , Diehl M , Herp S , Lötscher Y , Hussain S , Bunk B , Pukall R , Huson DH , Münch PC , McHardy AC , McCoy KD , Macpherson AJ , Loy A , Clavel T , Berry D , Stecher B 2016 Genome-guided design of a defined mouse microbiota that confers colonization resistance against Salmonella enterica serovar Typhimurium . Nat Microbiol 2 : 16215 . doi: 10.1038/nmicrobiol.2016.215 . 27869789 2. Uchimura Y , Wyss M , Brugiroux S , Limenitakis JP , Stecher B , McCoy KD , Macpherson AJ 2016 Complete genome sequences of 12 species of stable defined moderately diverse mouse microbiota 2 . Genome Announc 4 ( 5 ): e00951-16 . doi: 10.1128/genomeA.00951-16 . 27634994 PMC5026434 3. Lagkouvardos I , Pukall R , Abt B , Foesel BU , Meier-Kolthoff JP , Kumar N , Bresciani A , Martínez I , Just S , Ziegler C , Brugiroux S , Garzetti D , Wenning M , Bui TP , Wang J , Hugenholtz F , Plugge CM , Peterson DA , Hornef MW , Baines JF , Smidt H , Walter J , Kristiansen K , Nielsen HB , Haller D , Overmann J , Stecher B , Clavel T 2016 The Mouse intestinal Bacterial Collection (miBC) provides host-specific insight into cultured diversity and functional potential of the gut microbiota . Nat Microbiol 1 : 16131 . doi: 10.1038/nmicrobiol.2016.131 . 27670113 4. Studer N , Desharnais L , Beutler M , Brugiroux S , Terrazos MA , Menin L , Schürch CM , McCoy KD , Kuehne SA , Minton NP , Stecher B , Bernier-Latmani R , Hapfelmeier S 2016 Functional intestinal bile acid 7α-dehydroxylation by Clostridium scindens associated with protection from Clostridium difficile infection in a gnotobiotic mouse model . Front Cell Infect Microbiol 6 : 191 . doi: 10.3389/fcimb.2016.00191 . 28066726 PMC5168579 5. Li H , Limenitakis JP , Fuhrer T , Geuking MB , Lawson MA , Wyss M , Brugiroux S , Keller I , Macpherson JA , Rupp S , Stolp B , Stein JV , Stecher B , Sauer U , McCoy KD , Macpherson AJ 2015 The outer mucus layer hosts a distinct intestinal microbial niche . Nat Commun 6 : 8292 . doi: 10.1038/ncomms9292 . 26392213 PMC4595636 6. Loman NJ , Pallen MJ 2015 Twenty years of bacterial genome sequencing . Nat Rev Microbiol 13 : 787 – 794 . doi: 10.1038/nrmicro3565 . 26548914 7. Li H , Durbin R 2009 Fast and accurate short read alignment with Burrows-Wheeler transform . Bioinformatics 25 : 1754 – 1760 . doi: 10.1093/bioinformatics/btp324 . 19451168 PMC2705234 8. Bankevich A , Nurk S , Antipov D , Gurevich AA , Dvorkin M , Kulikov AS , Lesin VM , Nikolenko SI , Pham S , Prjibelski AD , Pyshkin AV , Sirotkin AV , Vyahhi N , Tesler G , Alekseyev MA , Pevzner PA 2012 SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing . J Comput Biol 19 : 455 – 477 . doi: 10.1089/cmb.2012.0021 . 22506599 PMC3342519 9. Gurevich A , Saveliev V , Vyahhi N , Tesler G 2013 QUAST: quality assessment tool for genome assemblies . Bioinformatics 29 : 1072 – 1075 . doi: 10.1093/bioinformatics/btt086 . 23422339 PMC3624806 10. Aziz RK , Bartels D , Best AA , DeJongh M , Disz T , Edwards RA , Formsma K , Gerdes S , Glass EM , Kubal M , Meyer F , Olsen GJ , Olson R , Osterman AL , Overbeek RA , McNeil LK , Paarmann D , Paczian T , Parrello B , Pusch GD , Reich C , Stevens R , Vassieva O , Vonstein V , Wilke A , Zagnitko O 2008 The RAST server: rapid annotations using subsystems technology . BMC Genomics 9 : 75 . doi: 10.1186/1471-2164-9-75 . 18261238 PMC2265698 \ No newline at end of file diff --git a/references_cache/PMID_31998276.md b/references_cache/PMID_31998276.md index 055e5f85..5679bd97 100644 --- a/references_cache/PMID_31998276.md +++ b/references_cache/PMID_31998276.md @@ -13,3 +13,8 @@ Quoted snippets used in curated records: URL: https://pubmed.ncbi.nlm.nih.gov/31998276/ + + +Full text (re-fetched 2026-06-15 via Europe PMC fullTextXML): + + 1526 frontmicrobio Frontiers in Microbiology Front Microbiol Frontiers Media SA PMC6965490 6965490 6965490 31998276 10.3389/fmicb.2019.02999 Reproducible Colonization of Germ-Free Mice With the Oligo-Mouse-Microbiota in Different Animal Facilities Eberl Claudia 1 Ring Diana 1 2 Münch Philipp C 1 3 Beutler Markus 1 Basic Marijana 4 Slack Emma Caroline 5 Schwarzer Martin 6 Srutkova Dagmar 6 Lange Anna 7 8 Frick Julia S 7 8 Bleich André 4 Stecher Bärbel 1 2 * 1 Max von Pettenkofer-Institute, LMU Munich, Munich, Germany 2 German Center for Infection Research (DZIF), LMU Munich, Munich, Germany 3 Department for Computational Biology of Infection Research, Helmholtz Center for Infection Research, Brunswick, Germany 4 Institute for Laboratory Animal Science and Central Animal Facility, Hannover Medical School, Hanover, Germany 5 Institute of Food, Nutrition and Health, ETH Zürich, Zurich, Switzerland 6 Institute of Microbiology of the Czech Academy of Sciences, Nový Hrádek, Czechia 7 Institute of Medical Microbiology and Hygiene, University of Tübingen, Tübingen, Germany 8 German Center for Infection Research (DZIF), Tübingen, Germany Edited by: Markus M. Heimesaat, Charité – Universitätsmedizin Berlin, Germany Reviewed by: Andrew James Macpherson, University of Bern, Switzerland; Gary B. Huffnagle, University of Michigan, United States ✉ *Correspondence: Bärbel Stecher, stecher@mvp.lmu.de This article was submitted to Microbial Immunology, a section of the journal Frontiers in Microbiology 10 1 2020 10 2999 2999 29 1 2020 Copyright © 2020 Eberl, Ring, Münch, Beutler, Basic, Slack, Schwarzer, Srutkova, Lange, Frick, Bleich and Stecher. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Abstract The Oligo-Mouse-Microbiota (OMM 12 ) is a recently developed synthetic bacterial community for functional microbiome research in mouse models ( Brugiroux et al., 2016 ). To date, the OMM 12 model has been established in several germ-free mouse facilities world-wide and is employed to address a growing variety of research questions related to infection biology, mucosal immunology, microbial ecology and host-microbiome metabolic cross-talk. The OMM 12 consists of 12 sequenced and publically available strains isolated from mice, representing five bacterial phyla that are naturally abundant in the murine gastrointestinal tract ( Lagkouvardos et al., 2016 ). Under germ-free conditions, the OMM 12 colonizes mice stably over multiple generations. Here, we investigated whether stably colonized OMM 12 mouse lines could be reproducibly established in different animal facilities. Germ-free C57Bl/6J mice were inoculated with a frozen mixture of the OMM 12 strains. Within 2 weeks after application, the OMM 12 community reached the same stable composition in all facilities, as determined by fecal microbiome analysis. We show that a second application of the OMM 12 strains after 72 h leads to a more stable community composition than a single application. The availability of such protocols for reliable de novo generation of gnotobiotic rodents will certainly contribute to increasing experimental reproducibility in biomedical research. Keywords: syncom, Oligo-MM12, sDMDMm2, minimal microbiome, 3R, gnotobiology, defined bacterial consortia, isobiotic mice status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no Received 2019 Jul 30; Accepted 2019 Dec 11; Collection date 2019. Introduction The mammalian gut is a complex ecosystem, hosting a diverse microbial community that influences normal physiology and disease susceptibility (mainly) through its metabolic activities. The gut microbiome is highly dynamic throughout life and can vary substantially between individuals due to diet, lifestyle and genetic factors ( Ursell et al., 2012 ; Falony et al., 2016 ). Microbiome community profiling and metagenome-based approaches have recently elucidated how inter-individual microbiome differences correlate with disease states and health ( Knight et al., 2017 ; Hadrich, 2018 ). However, causal relationships between the microbiota and disease conditions can rarely be deciphered with these methods. Ethical and regulatory issues further limit the possibilities of human intervention studies and robust experimental animal models are urgently needed to research the causality between the gut microbiota and various human diseases. The laboratory mouse is currently the primary experimental model organism in biomedical research ( Taylor et al., 2008 ). The availability of numerous genetically engineered and mutant mouse strains greatly facilitates functional studies ( Eppig et al., 2015 ). Mice raised in different research institutions, obtained from different vendors or the wild can exhibit profound differences in microbiota composition ( Stecher et al., 2010 ; Wang et al., 2014 ; Rausch et al., 2016 ; Sadler et al., 2017 ; Thiemann et al., 2017 ), mimicking the inter-personal microbiota variation in human populations. Apart from vendor-specific microbiomes, genotype and environmental conditions such as diet, cage-type, temperature and bedding can profoundly influence microbiota composition and function ( Hildebrand et al., 2013 ). These microbiome differences between genetically identical mouse models have led to the serendipitous discovery of numerous microbiota-dependent disease phenotypes in the past, including immune-cell priming, colitis susceptibility and resistance to infections ( Ivanov et al., 2009 ; Surana and Kasper, 2017 ; Velazquez et al., 2019 ). Overall, there is growing evidence that the microbiota is a major confounding factor, which complicates cross-study comparisons and eventually jeopardizes experimental reproducibility ( Franklin and Ericsson, 2017 ). To identify the mechanisms of host genetics-imposed control of the microbiome, adequate experimental design is instrumental ( Laukens et al., 2015 ; McCoy et al., 2017 ; Mamantopoulos et al., 2018 ). In this respect, gnotobiology (greek: gnosis: knowledge; bios: life; logos: study) has gained importance as it allows for optimal control of microbiota within and in between animal facilities. Besides, gnotobiology has become an essential method to mechanistically investigate microbiota functioning and to assess causality in disease-associated alterations of gut microbiota composition ( Trexler and Reynolds, 1957 ; Orcutt et al., 1987 ). Gnotobiotic mice can be generated by microbial reconstitution of germ-free mice with fecal transplants, single organisms and defined mouse- or human-derived microbial consortia ( Clavel et al., 2016 ; McCoy et al., 2017 ). Several recent studies have focused on the isolation of bacteria from humans and mice and the establishment of culture collections ( Hugon et al., 2015 ; Lagkouvardos et al., 2016 ; Forster et al., 2019 ). The most important requirement for the assembly of defined bacterial consortia is the availability of well-characterized and genome-sequenced strains – preferably in public culture collections. Further, state of the art methods to trace and quantify each member of a microbial consortium are needed for quality control and functional studies. Finally, experimental protocols should be optimized to ensure reproducible colonization of germ-free mice with a given microbial consortium. A majority of studies uses human-derived bacteria to colonize gnotobiotic mice even though phylogenetic differences between human and mouse microbiota ( Xiao et al., 2015 ) may affect microbe-host interaction and long-term stability of colonization. To circumvent this problem, we have developed a model based on twelve bacteria from the murine gut microbiota ( Brugiroux et al., 2016 ; Garzetti et al., 2017 ). This Oligo-Mouse-Microbiota (OMM 12 ) provides several improvements to other defined consortia, including broad phylogenetic diversity, public availability of the strains from the German Type Culture Collection 1 ( Brugiroux et al., 2016 ). Most importantly, the OMM 12 exhibits long-term stability in gnotobiotic mice ( Brugiroux et al., 2016 ). Correspondingly, C57BL/6 mice stably colonized with OMM 12 have recently also been designated stable defined moderately diverse microbiota mice (sDMDMm2) ( Li et al., 2015 ). The Altered Schaedler Flora (ASF), another frequently used model for gnotobiotic research for more than 40 years is also based on murine bacteria ( Wymore Brand et al., 2015 ). Compared to ASF-colonized mice, OMM 12 mice show increased resistance to pathogen colonization ( Brugiroux et al., 2016 ). These characteristics indicate that OMM 12 mice mimic the normal physiology better than ASF mice and are therefore preferable for microbiome intervention studies that investigate microbiome-correlated diseases. Accordingly, several studies have already implemented the OMM 12 as model to systematically probe for a causal role of individual microbes in protection against different pathogens along the lines of Koch’s postulates ( Brugiroux et al., 2016 ; Studer et al., 2016 ; Herp et al., 2019 ). To date, the OMM 12 model is used by over 30 research groups world-wide to address fundamental research questions related to microbial ecology, metabolism, mucosal immunology and infection biology ( Li et al., 2015 ; Studer et al., 2016 ; Uchimura et al., 2018 ). Here, we present a feasibility trial and scrutinize colonization of germ-free C57BL/6 mice with the OMM 12 community. We established a protocol using a standardized OMM 12 inoculum and compared community composition when inoculations were performed repeatedly in the same facility or at different facilities. We show that double inoculation leads to efficient introduction of consortium members in different facilities. Notably, we observed subtle differences in the absolute abundance of some of the strains between different experiments. This also translates to differences in community profiles. Whether this induces functional changes to the community and phenotypic differences in the mice should be a matter of future investigation. Still, care should be taken when comparing results obtained with the model at different facilities. Overall, our study shows that generating mice colonized with the OMM 12 synthetic bacterial consortium shows excellent reproducibility between different animal facilities. Materials and Methods Animal Facilities Five European germ-free rodent facilities participated in this study. All mouse experiments were approved by the local authorities and performed according to the legal requirements. Detailed questionnaires were distributed to record the mouse husbandry conditions in the different breeding facilities ( Table 1 ). Germ-free status was routinely confirmed by aerobic and anaerobic culture as well as Sytox green (Invitrogen) and Gram staining (Harleco) of caecal contents to detect unculturable contaminants. C57BL/6J Agr2 –/– mice were provided by David Erle ( Park et al., 2009 ) and re-derived germ-free from Agr2 ± and Agr2 –/– conventional mice as described and colonized with OMM 12 to generate an isobiotic mouse line ( Herp et al., 2019 ). Agr2 encodes a disulfide isomerase that is required for folding and export of the mucin Muc2. For the experiment shown in Figure 1 , germ-free Agr2 ± mice were used. Agr2 ± mice behave like wild-type mice in terms of Muc2 secretion and mucus structure ( Bergstrom et al., 2014 ). For all other experiments, wild-type C57BL/6J mice were used. TABLE 1 Description of housing conditions at Germ-free animal facilities. 1 2 3 4 5 Housing type Isolators (TSE Systems, PLEXX) Isolator (metal + plastic) Isolator Gnotocage (Thermo Fischer Scientific, metal + plastic) Trexler-type plastic isolators Bedding material Wood chip (SAFE select) AsBe-wood GmBH, bedding poplar Wood shavings Abedd espe classic; 2,5 mm [LtE E-001] JELUXYL-SAWI HW 300/500 (JELU-WERK J. Ehrler GmbH & Co., Germany) Enrichment Kleenex tissue paper, res plastic nest-houses Nestlets (Enviro-dri from Bedding Natur), pieces of kitchen paper towels from time to time Nestlets Nestlets (Zoonlab, 3097055) Lignocel nesting small (Velaz, CZ) Chow provider Kilba Nafag Ssniff Spezialdiäten GmbH, Soest, Germany Ssniff Spezialdiäten GmbH, Soest, Germany Ssniff Spezialdiäten GmbH, Soest, Germany Ssniff Spezialdiäten GmbH, Soest, Germany Chow type Breeding chow (3807) Mouse breeding, 10 mm (V1124-027) Grain-based (19% Protein, 3.3% Fett) (V1534-927) V1124-300 V1126-000 Mouse breeding, extrudate Chow treatment Autoclaved Gamma-irradiated, 50 kGy Gamma-irradiated, 50 kGy Autoclaved Gamma-irradiated, 25 kGy Water type 0.22 um filtered tap water then autoclaved Autoclaved (reverse osmosis) Tap water Autoclaved ampuwa; fresenius) Autoclaved non-chlorinated tap water Mouse genotype C57BL/6JZTm C57BL/6JZtm C57BL/6J C57BL/6JZtm C57BL/6J Mouse supplier Clean mouse facility, University of Bern, then bred independently for >10 generations Mice originally from Jackson Laboratory, delivered as GF colony from Ulm Own breeding, originally from facility 2 Mice from facility 2 Mice bred in germ-free conditions for >10 generations Sterility control Selective culture, Sytox-green quantitative flow cytometry, H2 production Selective culture, Gram-staining Selective culture, Gram-staining Day/night cycle 12:12-h light-dark cycles 12:12-h light-dark cycles 12:12-h light-dark cycles 12:12-h light-dark cycles 12:12-h light-dark cycles Temperature (°C) 22°C 20–22°C 22°C 20–24°C 22 ± 2°C Humidity (%) 50–56% 50–55% 56% 45–55% 50–60% FIGURE 1 Dynamics of fecal community composition after inoculation of OMM 12 colonization in germ-free mice. (A) Experimental scheme. Germ-free mice were inoculated with the OMM 12 mixture and kept in a germ-free isolator in facility 4. Total number of mice and collection time points of fecal samples are indicated. (B) Relative abundance of fecal microbiota composition at the indicated time points. Abundance of individual strains is shown as relative abundance and expressed as% of cumulative 16S rRNA gene copy numbers of all OMM 12 strains. One bar corresponds to one mouse. (C) PCoA based on the distance matrix of Bray–Curtis dissimilarity of relative OMM 12 abundance profiles shows the effect of time after inoculation. Points are colored by time (days) after inoculation. Samples taken from two mice are connected to visualize their trajectory during time. Bacterial Cultivation All glassware and media used for cultivation were kept under anoxic conditions (3% H 2 , rest N 2 ) in an anaerobic chamber for at least 2 days before the start of the experiment. Glycerol cryostocks (for preparation see Brugiroux et al., 2016 ) of individual OMM 12 strains ( Table 2 ) were thawed in a 1% Virkon S (V.P. Products) solution (37°C) and the entire content of the vial was transferred into 100 ml Wheaton glass serum bottles (Sigma) sealed with a butyl rubber stoppers (Geo-Microbial Technologies) containing 10 ml of Anaerobic Akkermansia Medium (AAM; 18.5 g l –1 brain heart infusion (BHI), 5 g l –1 yeast extract, 15 g l –1 trypticase soy broth, 2.5 g l –1 K 2 HPO 4 , 1 mg l –1 haemin, 0.5 g l –1 glucose, 0.4 g l –1 Na 2 CO 3 , 0.5 g l –1 cysteine hydrochloride, 5 mg l –1 menadione, 3% complement-inactivated fetal calf serum). These subcultures were gassed (7% H 2 , 10% CO 2 , 83% N 2 ) and incubated at 37°C for 24 h. TABLE 2 Growth conditions for OMM 12 strains. Strain Cultivation time (days) Clostridium innocuum I46 DSM 26113 1 Bacteroides caecimuris I48 DSM 26085 1 Lactobacillus reuteri I49 DSM 32035 1 Bifidobacterium longum subsp. animalis YL2 DSM 26074 1 Muribaculum intestinale YL27 DSM 28989 1 Flavonifractor plautii YL31 DSM 26117 1 Clostridium clostridioforme YL32 DSM 26114 1 Akkermansia muciniphila YL44 DSM 26109 2 Turicimonas muris YL45 DSM 26109 2 Blautia coccoides YL58 DSM 26115 1 Acutalibacter muris KB18 DSM 26090 2 Enterococcus faecalis KB1 DSM 32036 1 Subsequently, 100 μl of each subculture was transferred into a 100 ml Wheaton glass serum bottle containing 10 ml of AAM. These cultures were gassed (7% H 2 , 10% CO 2 , 83% N 2 ) and incubated at 37°C for 1 or 2 days depending on the growth rate ( Table 2 ). Afterward, culture purity of each strain was confirmed by Gram staining and 16S rRNA gene sequencing. The OD 600 of individual cultures was determined and all cultures were adjusted to the lowest OD 600 value by dilution. The respective culture volumes of all strains were transferred into a 50 ml Falcon tube under anoxic conditions. For cryopreservation, glycerol supplemented with palladium black crystals (Sigma-Aldrich) was added to these bacterial mixtures [final concentration of 10% (v/v)]. 1 ml aliquots were prepared in 1.5 ml glass vials (Sigma-Aldrich), sealed with butyl-rubber stoppers (Wheaton) and aluminum crimp seals (Sigma-Aldrich). Mixtures were frozen at −80°C within 1 h of preparation. Frozen aliquots were shipped to the different facilities on dry ice and stored at −80°C. Inoculation of Mice and Fecal Sampling The frozen OMM 12 mixtures were thawn in a 1% Virkon S (V.P. Produkte) disinfectant solution (37°C) and used for inoculation of germ-free mice in gnotocages or germ-free isolators. In any case, the mixture was used within 30 min after thawing. Mice were inoculated by gavage (50 μl orally, 100 μl rectally). Exposure of the mixture to oxygen was restricted to a short time (up to 5 min). For the double inoculation protocol, inoculation was repeated 72 h after the initial inoculation using the same protocol. To confirm the colonization of the 12 strains, fresh fecal pellets were obtained and frozen at −80°C within 30 min. gDNA Extraction From Fecal Samples Fecal samples were shipped on dry ice. DNA extraction and qPCR were performed centralized at the same laboratory to minimize experimental bias, which is known to be introduced by laboratory-specific experimental procedures. Fecal gDNA was either extracted using the QIAamp DNA Stool Mini Kit (Qiagen; time course shown in Figure 1 ) or a phenol-chloroform based protocol (all other experiments). The QIAamp DNA Stool Mini Kit protocol was performed following the manufacturer’s instructions with the following modifications. An initial bead-beating step using differentially sized beads [Zirkonia beads: 0.5–0.75 mm (BioSpec products) and acid-washed glass beads: <100 μm (Sigma-Aldrich)] was included and 20 mg/ml lysozyme was added to the lysis buffer. gDNA extraction using the phenol-chloroform based protocol was performed as described previously ( Herp et al., 2019 ). The resulting gDNA was purified using the NucleoSpin gDNA clean-up kit (Macherey-Nagel). Quantitative PCR of Bacterial 16S rRNA Genes Quantitative PCR (qPCR) was performed as described previously ( Brugiroux et al., 2016 ). All samples were analyzed by the same person in a centralized way. Briefly, OMM strain-specific 16S rRNA primers and hydrolysis probes were used for amplification. Standard curves using linearized plasmids containing the 16S rRNA gene sequence of the individual OMM 12 strains were used for absolute quantification of 16S rRNA gene copy numbers of individual strains. Since the fecal weight was not always available, 16S rRNA gene copy numbers were normalized to equal volumes of extracted DNA, assuming that DNA extraction is equally efficient between different samples. We confirmed that there is a linear relationship between stool weight and extracted DNA concentration ( Supplementary Figure S1 ). Statistical Analysis For comparison of absolute abundance levels of OMM 12 strains between experiments, Kruskal–Wallis test with Dunn’s multiple comparison test was performed using GraphPad Prism version 5.01 for Windows (GraphPad Software). p -values below 0.05 were considered as statistically significant ( ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001). The vegist function of the R library vegan version 2.5–4 was employed to obtain Bray–Curtis (BC) dissimilarities between the samples based on relative abundance estimates. Principal coordinate analysis was performed in R using ade4 package and figures were generated using the ggplot2 library. Permutational multivariate analyses of variance (PERMANOVA) were performed in R using the function adonis. We used the function capscale with the variable “facility” as constraint to estimate the effect of the facility on the overall BC variance. Statistical significance of the ordinations as well as confidence intervals for the variance were determined by an ANOVA-like permutation test (functions permutest and anova.cca) with 5,000 permutations. The heatmap ( Supplementary Figure S1 ) was generated using the ComplexHeatmap library on the BC values. To estimate the within facility stability we calculated the mean of all pairwise BC dissimilarities of community profiles of mice from the same facility. Between-facility estimates were calculated using the mean value of all pairwise distances between the community profile of each mouse of one facility to the community profile of all mice of different facilities. R scripts are available under https://github.com/philippmuench/OligoMM-facilities . Results OMM 12 Colonization Dynamics After Oral Inoculation In order to determine the time necessary for stable community formation, we tested temporal dynamics of the OMM 12 community after oral inoculation to germ-free mice in one facility. Changes in OMM 12 microbial community composition were closely monitored over time in fecal samples for 99 days ( Figure 1A ). In this experiment, we used two germ-free breeders from a heterozygous C57BL/6J Agr2 ± breeding, which were reconstituted with the OMM 12 consortium to generate an isobiotic mouse line ( Herp et al., 2019 ). Fecal community composition, represented as relative abundance profiles quantified by strain-specific qPCR, rapidly changed within the first week after OMM 12 inoculation. Beyond day 7 post inoculation, OMM 12 community stabilized and profiles remained highly similar afterward ( Figure 1B ). Principal Coordinates Analysis (PCoA) of Bray–Curtis dissimilarities showed a gradual shift with time toward a more similar community ( Figure 1C ). At early time points, high abundance of Bifidobacterium longum subsp. animalis YL2 and Enterococcus faecalis KB1 was observed, which gradually declined within the first week ( Figures 2B,J ). Inverse colonization dynamics were seen for several other strains ( Figure 2 ). Notably, Muribaculum intestinale YL27 took 4 days to reach detectable levels in all mice ( Figure 2I ). Colonization levels for Clostridium innocuum I46 and Akkermansia muciniphila YL44 were constant throughout ( Figure 2K ). The number of 16S rRNA gene copies of Acutalibacter muris KB18 remained below the detection limit in the majority of fecal samples, which was observed previously in stably colonized OMM 12 mice ( Brugiroux et al., 2016 ). We conclude that the OMM 12 consortium adopts a stable composition between 10 and 20 days post-inoculation. Therefore, we reasoned, that a 3-week colonization phase after inoculation is sufficient to verify successful colonization of OMM 12 in the feasibility trial outlined below. FIGURE 2 Absolute abundance of individual OMM 12 strains in time course analysis of OMM 12 colonization in germ-free mice. Germ-free mice were inoculated with the OMM 12 mixture and kept in a germ-free isolator. Fecal samples were collected at different time points for microbiota analysis. Absolute abundance of each strain was determined by a strain-specific qPCR assay and is plotted as 16S rRNA gene copy numbers of the individual strains per μl of extracted gDNA: (A) Lactobacillus reuteri I49, (B) Enterococcus faecalis KB1, (C) Blautia coccoides YL58, (D) Clostridium innocuum I46, (E) Flavonifractor plautii YL31, (F) Clostridium clostridioforme YL32, (G) Acutalibacter muris KB18, (H) Bacteroides caecimuris I48, (I) Muribaculum intestinale YL27, (J) Bifidobacterium longum subsp. animalis YL2, (K) Akkermansia muciniphila YL44, (L) Turicimonas muris YL45. Statistical analysis was performed using Kruskal-Wallis test with Dunn’s multiple comparison test ( ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001). Green symbols indicate samples collected <20 days post inoculation. Dotted lines indicate detection limits. A Single Inoculation Does Not Lead to Reproducible Introduction of Muribaculum intestinale in Different Germ-Free Mouse Facilities Next, we investigated, whether germ-free mice can be reproducibly associated with OMM 12 in different germ-free mouse facilities across four participating institutions in Germany and Switzerland. Germ-free C57Bl/6J mice ( n = 2–5) at different facilities were orally inoculated once with the same batch of inoculum, to avoid variations introduced by differences in the inoculum. We obtained feces from the animals and different time points (day 10–72) post-inoculation ( Figure 3A ). Facility-specific characteristics are outlined in Table 1 . FIGURE 3 Colonization dynamics of OMM 12 mice in four different germ-free facilities after single-dose inoculation. (A) Experimental scheme. Germ-free C57BL/6J mice were inoculated with the OMM 12 mixtures and kept in germ-free isolators or gnotocages at four different animal facilities (1-1, 2, 3, 4-1); the number of mice and collection time points of fecal samples are indicated. (B) Fecal microbiota composition at the different time points, displayed as relative abundance and expressed as the fraction of cumulated 16S rRNA gene copy numbers. One bar corresponds to one mouse. (C) PCoA based on Bray–Curtis dissimilarity (relative abundances) between samples obtained from mice in different facilities. Points are colored by facility. Relative OMM 12 abundance profiles of mice from different facilities showed, at large, high similarity ( Figure 3B ). PCoA based on Bray–Curtis dissimilarity (relative abundances) between samples showed that community composition was overall similar with the exception of facility 2, which clustered separately ( Figure 3C ). We used canonical analysis of principal coordinates (CAP; Anderson and Willis, 2003 ) to estimate the influence of the facility on the beta diversity. CAP analysis constrained by the facility revealed that the facility explains 35% of the overall variance of Bray–Curtis dissimilarity between samples from different facilities ( p < 0.001). Based on PERMANOVA analysis of Bray–Curtis dissimilarities, facility “2” was clearly distinguishable from the other three facilities ( Figure 3C and Supplementary Table S1 ). Absolute abundance, as determined by qPCR revealed that 7 of the 12 species were detected in all mice at the different facilities at comparable levels ( Figure 4 ). The absolute abundance of Lactobacillus reuteri I49, E. faecalis KB1, B. longum subsp. animalis YL2 and A. muris KB18 varied substantially between the facilities and was below the detection limit in most samples ( Figures 4A,B,G,J ). Additionally, Muribaculum intestinale YL27 was only detectable in facility “2” ( Figure 4I ). This may explain the notable different community profile of mice from this facility ( Figure 3C and Supplementary Table S1 ). We reasoned that single inoculation of mice might not be sufficient to ensure reliable colonization of M. intestinale YL27. FIGURE 4 Colonization dynamics of OMM 12 mice in four different germ-free facilities after single-dose inoculation: absolute abundance of individual OMM 12 strains. Absolute abundance of each strain was determined using a strain-specific qPCR assay for the experiment described in Figure 3 , Data are plotted as 16S rRNA gene copy numbers of the individual strains per μl of extracted gDNA: (A) Lactobacillus reuteri I49, (B) Enterococcus faecalis KB1, (C) Blautia coccoides YL58, (D) Clostridium innocuum I46, (E) Flavonifractor plautii YL31, (F) Clostridium clostridioforme YL32, (G) Acutalibacter muris KB18, (H) Bacteroides caecimuris I48, (I) Muribaculum intestinale YL27, (J) Bifidobacterium longum subsp. animalis YL2, (K) Akkermansia muciniphila YL44, (L) Turicimonas muris YL45. Statistical analysis was performed using Kruskal-Wallis test with Dunn’s multiple comparison test ( ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001). Green points indicate samples collected <20 days post inoculation. Double-Dose Application Increases Reproducibility of OMM 12 Colonization in Different Germ-Free Mouse Facilities Since single application of the OMM 12 mixture did not lead to reproducible colonization of OMM 12 strains across different germ-free mouse facilities, we aimed to amend the protocol. Previous work indicated that consecutive inoculations might increase the chance of successful introduction of oxygen-sensitive members of a consortium ( Becker et al., 2011 ) (Taconic protocols). Therefore, we modified the initial inoculation protocol and applied the mixture twice with 72 h in-between inoculations. This time, three facilities participated and two independent trials were performed at facility “4.” We found that relative OMM 12 abundance profiles of mice from different facilities and trials were rather uniform ( Figure 5B ). PCoA based on Bray–Curtis dissimilarity (relative abundances) between samples showed that community composition was overall similar between facilities ( Figure 5C ). CAP analysis constrained by the facility revealed that in this trial, the facility explains 20% of the overall variance in Bray–Curtis dissimilarity of the data ( p < 0.001). In this trial, no obvious clustering was apparent between samples from different facilities ( Figure 5C ), yet based on PERMANOVA analysis of Bray–Curtis dissimilarities, some differences between facilities were observed ( Supplementary Table S2 ). Muribaculum intestinale YL27 was reliably detected in all mice colonized in the different facilities ( Figure 6I ), which is a substantial improvement compared to the single-dose experiment. However, levels of Lactobacillus reuteri I49, E. faecalis KB1, B. longum subsp. animalis YL2 and A. muris KB18 still varied between the tested facilities and were below detection limit in some samples ( Figure 6 ). FIGURE 5 Colonization dynamics of OMM 12 mice in four different germ-free facilities after double-dose inoculation reveals high reproducibility. (A) Experimental scheme. Germ-free C57BL/6 mice were inoculated twice with the OMM 12 mixtures and kept in germ-free isolators or gnotocages at different facilities (1-2, 5, 4-2, 4-3). In case of facility “1” and “4,” inoculations were done on several independent occasions. The number of mice and collection time points of fecal samples are indicated. (B) Fecal microbiota composition at the different time points. Microbiota composition is shown as relative abundance and expressed as the fraction of cumulated 16S rRNA gene copy numbers. One bar corresponds to one mouse. (C) PCoA based on the distance matrix of Bray–Curtis dissimilarity of relative OMM 12 abundance profiles shows samples obtained from mice in different facilities. Points are colored by facility. FIGURE 6 Absolute abundance of OMM 12 strains in different germ-free facilities after double-dose inoculation. Absolute abundance of each strain was determined using a strain-specific qPCR assay for the experiment described in Figure 5 , Data are plotted as 16S rRNA gene copy numbers of the individual strains per μl of extracted gDNA: (A) Lactobacillus reuteri I49, (B) Enterococcus faecalis KB1, (C) Blautia coccoides YL58, (D) Clostridium innocuum I46, (E) Flavonifractor plautii YL31, (F) Clostridium clostridioforme YL32, (G) Acutalibacter muris KB18, (H) Bacteroides caecimuris I48, (I) Muribaculum intestinale YL27, (J) Bifidobacterium longum subsp. animalis YL2, (K) Akkermansia muciniphila YL44, (L) Turicimonas muris YL45. Statistical analysis was performed using Kruskal–Wallis test with Dunn’s multiple comparison test ( ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001). Green color indicate samples collected <20 days post inoculation. Finally, we assessed variations in overall community profiles within and between the facilities for the two different trials. To this end, we compared Bray–Curtis dissimilarities between samples of the same facility to dissimilarities between samples of different facilities. An overview of the pair-wise compositional Bray–Curtis dissimilarity of relative community profiles of mice between and across facilities and studies (single-dose and double-dose) is shown in Supplementary Figure S2 . For the first trial (single-dose inoculation), the mean of pairwise Bray–Curtis dissimilarity values of the community profile of mice housed in the same facility is lower than the mean pairwise Bray–Curtis dissimilarity between samples from mice located in different facilities. The mean Bray–Curtis dissimilarity for within-and between facility are 0.12 ± 0.04 (mean ± SD) and 0.18 ± 0.04, respectively; p -value = 0.07; paired two-sided t -test ( Figure 7A ). For the double-dose trial, the mean Bray–Curtis dissimilarity was lower (0.16 ± 0.06 and 0.19 ± 0.03 for within and between facilities, respectively; p -value = 0.25; paired two-sided t -test, Figure 7B ). FIGURE 7 OMM 12 community profiles within and between different facilities for the two trials. The mean of pairwise BC dissimilarity values of the community profiles of mice housed in the same facility (within facility analysis) was plotted against the mean pairwise BC dissimilarity values of the community profile of mice located in different facilities for single-dose inoculation trial for (A) Single-dose inoculation and (B) double-dose inoculation trial. For single-dose inoculation, the mean BC dissimilarity is 0.12 ± 0.04 (mean ± SD) and 0.18 ± 0.04, within and between the facilities, respectively ( p -value = 0.07, paired two-sided t -test). For double-dose inoculation, the mean BC dissimilarity is 0.16 ± 0.06 and 0.19 ± 0.03 for within and between the facilities, respectively ( p -value = 0.25; paired two-sided t -test). Discussion Variations in the gut microbiota within and between animal facilities can be a major factor accounting for the lack of reproducibility of animal models of human biology and disease ( Franklin and Ericsson, 2017 ). Several guidelines were established to optimally control for microbiota differences when using different genetic strains of experimental mice within an animal unit ( McCoy et al., 2017 ; Wullaert et al., 2018 ). Gnotobiotic mouse models based synthetic microbial consortia are becoming increasingly popular. In particular, these models offer the opportunity to generate “isobiotic” mice, which may significantly enhance experimental reproducibility across different institutions ( Macpherson and McCoy, 2015 ). This study reports on the first comparative inter-facility trial conducted to evaluate and optimize the effectiveness of a protocol for colonization of germ-free mice with a synthetic bacterial community. The majority of current protocols for introducing synthetic bacterial communities to germ-free mice use mixtures generated from fresh pure bacterial cultures for inoculations ( Faith et al., 2011 ; Desai et al., 2016 ; Gomes-Neto et al., 2017 ). These protocols require a sophisticated cultivation setup, including devices for anaerobic bacterial cultivation. Our protocol overcomes these limitations by generating frozen aliquots of a mixture of the strains, which can be distributed, thawed and directly applied. We note that in our trial, frozen aliquots of bacterial mixtures remain viable for at least 18 months at −80°C. Another common method for generating gnotobiotic mice is by co-housing with a colonized donor animal ( Geuking et al., 2011 ), a setup which requires no expertise for bacterial cultivation. When using this approach, it should be considered that serial passage of a bacterial community within the same facility or in between facilities, e.g., through breeding, might promote genomic diversification of the individual community members by consecutive rounds of within-host selection ( Robinson et al., 2018 ). The evolved bacterial community would differentiate genetically and functionally from the parental strains over time. To date, the degree and temporal course of within-host evolution of microbial communities is not known and experimental data are only available for evolution of individual bacterial populations in the gut ( Leatham et al., 2005 ; De Paepe et al., 2011 ; Barroso-Batista et al., 2014 ). Based on the analysis of individual bacterial populations, it is expected that mutants emerge rapidly and are selected based on improved competitiveness within days. In order to retain genetic identity of a minimal bacterial consortium, it is advisable to regenerate gnotobiotic mouse lines every 18–24 months using original cultures. This period is the result of cost-benefit considerations, keeping the degree of genomic diversification within acceptable boundaries but at the same time, minimizing costs and experimental efforts. In this trial, we used aliquots of the same batch of frozen OMM 12 mixed cultures for the inoculations. This allowed us to compare efficiency of inoculation between different facilities using the same starting material. Thereby the inoculum could be eliminated as confounding factor in microbial community establishment in the gut. The gut microbiome of mice can be significantly influenced by husbandry-related factors, such as type of laboratory animal diet ( Hildebrandt et al., 2009 ; Ooi et al., 2014 ) water ( Sofi et al., 2014 ), housing effects, genetic background ( Deloris Alexander et al., 2006 ; Hildebrand et al., 2013 ) and a wide range of other environmental and stress-related factors ( Bangsgaard Bendtsen et al., 2012 ). Many of these variables are likely to differ across germ-free animal facilities and account for the small but measurable differences in OMM 12 community composition observed in our study. Further, due to facility-specific differences in procedures and experimental protocols, it was not possible to match sex, age and number of inoculated mice and obtain fecal samples at matched time points post inoculation. This may account for part of the facility-dependent differences and should be optimized in future trials. Our results suggest that a double-dose application with a 72 h interval improves the engraftment of Muribaculum intestinale YL27. Representatives of the Muribaculaceae family (former S24-7) are highly diverse and dominant members of the murine gut microbiota ( Lagkouvardos et al., 2019 ). M. intestinale YL27, the first cultured representative, is strictly anaerobic and genome-based prediction indicates the potential to degrade complex carbohydrates ( Lagkouvardos et al., 2016 ; Lee et al., 2019 ). In our study, M. intestinale YL27 showed slow colonization dynamics after oral inoculation compared to all other OMM 12 strains disclose that expansion time can take up to 20 days. The oxidation/reduction (O/R) potential is increased in germ-free mice (+200 mV) compared to conventional mice but becomes reduced in response to colonization with a complex microbiota (−200 mV) ( Celesk et al., 1976 ). As M. intestinale YL27 is oxygen-sensitive, we reason that high O/R potential in the germ-free mouse gut may inhibit its expansion early after inoculation. Although a recent study showed that the luminal contents of germ-free mice can chemically consume oxygen (e.g., via lipid oxidation reactions), the gut lumen of germ-free and antibiotic-treated mice may also exhibit increased luminal oxygen concentration compared to mice colonized with a complex microbiota ( Friedman et al., 2018 ). Oxygen-tolerant members of the microbiota are among the first colonizers in a germ-free environment, after antibiotic treatment or in the course of intestinal colonization of the neonatal gut. They are thought to consume oxygen and anaerobic electron acceptors and to reduce the O/R potential sufficiently for oxygen-sensitive strains to colonize ( Reese et al., 2018 ). Presumably, colonization dynamics of the OMM 12 consortium are subject to similar principles: Some members of the consortium ( Enterococcus faecalis, Bifidobacterium longum ) that are also among the early colonizers of the human neonatal gut predominate at early colonization stages. Obligate anaerobic bacteria ( Bacteroides acidifaciens , Clostridiales) follow with a delay of 3 days. We assume that a second dose of OMM 12 administered when the O/R potential has already been lowered by the initial colonizers increases the chance of successful engraftment of obligate anaerobes such as Muribaculum intestinale . In summary, our study demonstrates that germ-free mice in different facilities can be reproducibly associated with the OMM 12 synthetic bacterial community, employing a protocol using previously frozen aliquots of a mixture of the strains. Furthermore, application of the consortium at two consecutive time points increased the chance of community engraftment. We envision that guidelines and validated protocols for generation of gnotobiotic models based on synthetic microbial communities will contribute to optimizing experimental reproducibility in this intense area of research. Data Availability Statement All datasets generated for this study are included in the article/ Supplementary Material . Ethics Statement The animal study was reviewed and approved by the Regierung von Oberbayern, Kantonales Veterinäramt Zürich, and the Lower Saxony State Office for Consumer Protection and Food Safety (LAVES). Author Contributions BS and CE conceived and designed the experiments. CE, DR, PM, MBe, MBa, ES, MS, DS, and AL performed the experiments. CE and PM analyzed the data. JF, AB, and BS contributed the materials and analysis tools. BS coordinated the project and wrote the original draft. All authors reviewed and edited the draft of the manuscript. Correspondence and requests for materials should be addressed to BS. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Acknowledgments We thank Eric Franzosa for advice on the data analysis. Funding. This research was supported by the German Research Foundation (DFG) Priority Program SPP1656 (Grant Numbers STE 1971/4-2 and BL 953/52) and the CRC1371 to BS, the German Center of Infection Research (DZIF), and the Center for Gastrointestinal Microbiome Research (CEGIMIR). 1 https://www.dsmz.de/ Supplementary Material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2019.02999/full#supplementary-material Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. References Anderson M. J., Willis T. J. (2003). Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology 84 511–525. 10.1890/0012-9658(2003)084[0511:CAOPCA]2.0.CO;2 Bangsgaard Bendtsen K. M., Krych L., Sorensen D. B., Pang W., Nielsen D. S., Josefsen K., et al. (2012). Gut microbiota composition is correlated to grid floor induced stress and behavior in the BALB/c mouse. PLoS One 7:e46231. 10.1371/journal.pone.0046231 Barroso-Batista J., Sousa A., Lourenco M., Bergman M. L., Sobral D., Demengeot J., et al. (2014). The first steps of adaptation of Escherichia coli to the gut are dominated by soft sweeps. PLoS Genet. 10:e1004182. 10.1371/journal.pgen.1004182 Becker N., Kunath J., Loh G., Blaut M. (2011). Human intestinal microbiota: characterization of a simplified and stable gnotobiotic rat model. Gut Microbes 2 25–33. 10.4161/gmic.2.1.14651 Bergstrom J. H., Berg K. A., Rodriguez-Pineiro A. M., Stecher B., Johansson M. E., Hansson G. C. (2014). AGR2, an endoplasmic reticulum protein, is secreted into the gastrointestinal mucus. PLoS One 9:e104186. 10.1371/journal.pone.0104186 Brugiroux S., Beutler M., Pfann C., Garzetti D., Ruscheweyh H. J., Ring D., et al. (2016). Genome-guided design of a defined mouse microbiota that confers colonization resistance against Salmonella enterica serovar Typhimurium. Nat. Microbiol. 2:16215. 10.1038/nmicrobiol.2016.215 Celesk R. A., Asano T., Wagner M. (1976). The size pH, and redox potential of the cecum in mice associated with various microbial floras. Proc Soc. Exp. Biol. Med. 151 260–263. 10.3181/00379727-151-39187 Clavel T., Lagkouvardos I., Blaut M., Stecher B. (2016). The mouse gut microbiome revisited: from complex diversity to model ecosystems. Int. J. Med. Microbiol. 306 316–327. 10.1016/j.ijmm.2016.03.002 De Paepe M., Gaboriau-Routhiau V., Rainteau D., Rakotobe S., Taddei F., Cerf-Bensussan N. (2011). Trade-off between bile resistance and nutritional competence drives Escherichia coli diversification in the mouse gut. PLoS Genet. 7:e1002107. 10.1371/journal.pgen.1002107 Deloris Alexander A., Orcutt R. P., Henry J. C., Baker J., Jr., Bissahoyo A. C., Threadgill D. W. (2006). Quantitative PCR assays for mouse enteric flora reveal strain-dependent differences in composition that are influenced by the microenvironment. Mamm. Genome 17 1093–1104. 10.1007/s00335-006-0063-1 Desai M. S., Seekatz A. M., Koropatkin N. M., Kamada N., Hickey C. A., Wolter M., et al. (2016). A dietary fiber-deprived gut microbiota degrades the colonic mucus barrier and enhances pathogen susceptibility. Cell 167:1339-1353.e21. 10.1016/j.cell.2016.10.043 Eppig J. T., Blake J. A., Bult C. J., Kadin J. A., Richardson J. E. Mouse Genome Database, (2015). The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease. Nucleic Acids Res. 43 D726–D736. Faith J. J., McNulty N. P., Rey F. E., Gordon J. I. (2011). Predicting a human gut microbiota’s response to diet in gnotobiotic mice. Science 333 101–104. 10.1126/science.1206025 Falony G., Joossens M., Vieira-Silva S., Wang J., Darzi Y., Faust K., et al. (2016). Population-level analysis of gut microbiome variation. Science 352 560–564. 10.1126/science.aad3503 Forster S. C., Kumar N., Anonye B. O., Almeida A., Viciani E., Stares M. D. M., et al. (2019). A human gut bacterial genome and culture collection for improved metagenomic analyses. Nat. Biotechnol. 37 186–192. 10.1038/s41587-018-0009-7 Franklin C. L., Ericsson A. C. (2017). Microbiota and reproducibility of rodent models. Lab Anim. 46 114–122. 10.1038/laban.1222 Friedman E. S., Bittinger K., Esipova T. V., Hou L., Chau L., Jiang J., et al. (2018). Microbes vs. chemistry in the origin of the anaerobic gut lumen. Proc. Natl. Acad. Sci. U.S.A. 115 4170–4175. 10.1073/pnas.1718635115 Garzetti D., Brugiroux S., Bunk B., Pukall R., McCoy K. D., Macpherson A. J., et al. (2017). High-quality whole-genome sequences of the oligo-mouse-microbiota bacterial community. Genome Announc. 5:e758-17. 10.1128/genomeA.00758-17 Geuking M. B., Cahenzli J., Lawson M. A., Ng D. C., Slack E., Hapfelmeier S., et al. (2011). Intestinal bacterial colonization induces mutualistic regulatory T cell responses. Immunity 34 794–806. 10.1016/j.immuni.2011.03.021 Gomes-Neto J. C., Kittana H., Mantz S., Segura Munoz R. R., Schmaltz R. J., Bindels L. B., et al. (2017). A gut pathobiont synergizes with the microbiota to instigate inflammatory disease marked by immunoreactivity against other symbionts but not itself. Sci. Rep. 7:17707. 10.1038/s41598-017-18014-5 Hadrich D. (2018). Microbiome research is becoming the key to better understanding health and nutrition. Front. Genet. 9:212. 10.3389/fgene.2018.00212 Herp S., Brugiroux S., Garzetti D., Ring D., Jochum L. M., Beutler M., et al. (2019). Mucispirillum schaedleri antagonizes salmonella virulence to protect mice against colitis. Cell Host Microbe 25:681-694.e8. 10.1016/j.chom.2019.03.004 Hildebrand F., Nguyen T. L., Brinkman B., Yunta R. G., Cauwe B., Vandenabeele P., et al. (2013). Inflammation-associated enterotypes, host genotype, cage and inter-individual effects drive gut microbiota variation in common laboratory mice. Genome Biol. 14:R4. 10.1186/gb-2013-14-1-r4 Hildebrandt M. A., Hoffmann C., Sherrill-Mix S. A., Keilbaugh S. A., Hamady M., Chen Y. Y., et al. (2009). High-fat diet determines the composition of the murine gut microbiome independently of obesity. Gastroenterology 137 e1–e2. 10.1053/j.gastro.2009.08.042 Hugon P., Dufour J. C., Colson P., Fournier P. E., Sallah K., Raoult D. (2015). A comprehensive repertoire of prokaryotic species identified in human beings. Lancet Infect. Dis. 15 1211–1219. 10.1016/S1473-3099(15)00293-5 Ivanov I. I., Atarashi K., Manel N., Brodie E. L., Shima T., Karaoz U., et al. (2009). Induction of intestinal Th17 cells by segmented filamentous bacteria. Cell 139 485–498. 10.1016/j.cell.2009.09.033 Knight R., Callewaert C., Marotz C., Hyde E. R., Debelius J. W., McDonald D., et al. (2017). The microbiome and human biology. Annu. Rev. Genomics Hum. Genet. 18 65–86. Lagkouvardos I., Lesker T. R., Hitch T. C. A., Galvez E. J. C., Smit N., Neuhaus K., et al. (2019). Sequence and cultivation study of Muribaculaceae reveals novel species, host preference, and functional potential of this yet undescribed family. Microbiome 7:28. 10.1186/s40168-019-0637-2 Lagkouvardos I., Pukall R., Abt B., Foesel B. U., Meier-Kolthoff J. P., Kumar N., et al. (2016). The mouse intestinal bacterial collection (miBC) provides host-specific insight into cultured diversity and functional potential of the gut microbiota. Nat. Microbiol. 1:16131. Laukens D., Brinkman B. M., Raes J., De Vos M., Vandenabeele P. (2015). Heterogeneity of the gut microbiome in mice: guidelines for optimizing experimental design. FEMS Microbiol. Rev. 40 117–132. 10.1093/femsre/fuv036 Leatham M. P., Stevenson Gauger E. J., Krogfelt K. A., Lins J. J., Haddock T. L., et al. (2005). Mouse intestine selects nonmotile flhDC mutants of Escherichia coli MG1655 with increased colonizing ability and better utilization of carbon sources. Infect. Immun. 73 8039–8049. 10.1128/iai.73.12.8039-8049.2005 Lee K. S., Palatinszky M., Pereira F. C., Nguyen J., Fernandez V. I., Mueller A. J., et al. (2019). An automated Raman-based platform for the sorting of live cells by functional properties. Nat. Microbiol. 4 1035–1048. 10.1038/s41564-019-0394-9 Li H., Limenitakis J. P., Fuhrer T., Geuking M. B., Lawson M. A., Wyss M., et al. (2015). The outer mucus layer hosts a distinct intestinal microbial niche. Nat. Commun. 6:8292. 10.1038/ncomms9292 Macpherson A. J., McCoy K. D. (2015). Standardised animal models of host microbial mutualism. Mucosal Immunol. 8 476–486. 10.1038/mi.2014.113 Mamantopoulos M., Ronchi F., McCoy K. D., Wullaert A. (2018). Inflammasomes make the case for littermate-controlled experimental design in studying host-microbiota interactions. Gut Microbes 9 374–381. 10.1080/19490976.2017.1421888 McCoy K. D., Geuking M. B., Ronchi F. (2017). Gut microbiome standardization in control and experimental mice. Curr. Protoc. Immunol. 117 2311–23113. 10.1002/cpim.25 Ooi J. H., Waddell A., Lin Y. D., Albert I., Rust L. T., Holden V., et al. (2014). Dominant effects of the diet on the microbiome and the local and systemic immune response in mice. PLoS One 9:e86366. 10.1371/journal.pone.0086366 Orcutt R. P., Gianni F. J., Judge R. J. (1987). Development of an “altered Schaedler flora” for NCI gnotobiotic rodents. Microecol. Ther. 17:59. Park S. W., Zhen G., Verhaeghe C., Nakagami Y., Nguyenvu L. T., Barczak A. J., et al. (2009). The protein disulfide isomerase AGR2 is essential for production of intestinal mucus. Proc. Natl. Acad. Sci. U.S.A. 106 6950–6955. 10.1073/pnas.0808722106 Rausch P., Basic M., Batra A., Bischoff S. C., Blaut M., Clavel T., et al. (2016). Analysis of factors contributing to variation in the C57BL/6J fecal microbiota across German animal facilities. Int. J. Med. Microbiol. 306 343–355. 10.1016/j.ijmm.2016.03.004 Reese A. T., Cho E. H., Klitzman B., Nichols S. P., Wisniewski N. A., Villa M. M., et al. (2018). Antibiotic-induced changes in the microbiota disrupt redox dynamics in the gut. eLife 7:e35987. 10.7554/eLife.35987 Robinson C. D., Klein H. S., Murphy K. D., Parthasarathy R., Guillemin K., Bohannan B. J. M. (2018). Experimental bacterial adaptation to the zebrafish gut reveals a primary role for immigration. PLoS Biol. 16:e2006893. 10.1371/journal.pbio.2006893 Sadler R., Singh V., Benakis C., Garzetti D., Brea D., Stecher B., et al. (2017). Microbiota differences between commercial breeders impacts the post-stroke immune response. Brain Behav. Immun. 66 23–30. 10.1016/j.bbi.2017.03.011 Sofi M. H., Gudi R., Karumuthil-Melethil S., Perez N., Johnson B. M., Vasu C. (2014). pH of drinking water influences the composition of gut microbiome and type 1 diabetes incidence. Diabetes Metab. Res. Rev. 63 632–644. 10.2337/db13-0981 Stecher B., Chaffron S., Kappeli R., Hapfelmeier S., Freedrich S., Weber T. C., et al. (2010). Like will to like: abundances of closely related species can predict susceptibility to intestinal colonization by pathogenic and commensal bacteria. PLoS Pathog. 6:e1000711. 10.1371/journal.ppat.1000711 Studer N., Desharnais L., Beutler M., Brugiroux S., Terrazos M. A., Menin L., et al. (2016). Functional intestinal bile acid 7alpha-Dehydroxylation by clostridium scindens associated with protection from clostridium difficile infection in a gnotobiotic mouse model. Front. Cell Infect. Microbiol. 6:191. 10.3389/fcimb.2016.00191 Surana N. K., Kasper D. L. (2017). Moving beyond microbiome-wide associations to causal microbe identification. Nature 552 244–247. 10.1038/nature25019 Taylor K., Gordon N., Langley G., Higgins W. (2008). Estimates for worldwide laboratory animal use in 2005. Altern. Lab. Anim. 36 327–342. 10.1177/026119290803600310 Thiemann S., Smit N., Roy U., Lesker T. R., Galvez E. J. C., Helmecke J., et al. (2017). Enhancement of IFNgamma production by distinct commensals ameliorates Salmonella -Induced Disease. Cell Host Microbe 21:682-694.e5. 10.1016/j.chom.2017.05.005 Trexler P. C., Reynolds L. I. (1957). Flexible film apparatus for the rearing and use of germfree animals. Appl. Microbiol. 5 406–412. Uchimura Y., Fuhrer T., Li H., Lawson M. A., Zimmermann M., Yilmaz B., et al. (2018). Antibodies set boundaries limiting microbial metabolite penetration and the resultant mammalian host response. Immunity 49:545-559.e5. 10.1016/j.immuni.2018.08.004 Ursell L. K., Clemente J. C., Rideout J. R., Gevers D., Caporaso J. G., Knight R. (2012). The interpersonal and intrapersonal diversity of human-associated microbiota in key body sites. J. Allergy Clin. Immunol. 129 1204–1208. 10.1016/j.jaci.2012.03.010 Velazquez E. M., Nguyen H., Heasley K. T., Saechao C. H., Gil L. M., Rogers A. W. L., et al. (2019). Endogenous Enterobacteriaceae underlie variation in susceptibility to Salmonella infection. Nat. Microbiol. 4 1057–1064. 10.1038/s41564-019-0407-8 Wang J., Linnenbrink M., Kunzel S., Fernandes R., Nadeau M. J., Rosenstiel P., et al. (2014). Dietary history contributes to enterotype-like clustering and functional metagenomic content in the intestinal microbiome of wild mice. Proc. Natl. Acad. Sci. U.S.A. 111 E2703–E2710. 10.1073/pnas.1402342111 Wullaert A., Lamkanfi M., McCoy K. D. (2018). Defining the impact of host genotypes on microbiota composition requires meticulous control of experimental variables. Immunity 48 605–607. 10.1016/j.immuni.2018.04.001 Wymore Brand M., Wannemuehler M. J., Phillips G. J., Proctor A., Overstreet A. M., Jergens A. E., et al. (2015). The altered schaedler flora: continued applications of a defined murine microbial community. ILAR J. 56 169–178. 10.1093/ilar/ilv012 Xiao L., Feng Q., Liang S., Sonne S. B., Xia Z., Qiu X., et al. (2015). A catalog of the mouse gut metagenome. Nat. Biotechnol. 33 1103–1108. 10.1038/nbt.3353 Associated Data Supplementary Materials Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Data Availability Statement All datasets generated for this study are included in the article/ Supplementary Material . \ No newline at end of file diff --git a/references_cache/PMID_34312531.md b/references_cache/PMID_34312531.md new file mode 100644 index 00000000..20932531 --- /dev/null +++ b/references_cache/PMID_34312531.md @@ -0,0 +1,7 @@ +# PMID:34312531 + + + +Full text (re-fetched 2026-06-15 via Europe PMC fullTextXML): + + pmc Nat Microbiol Nat Microbiol 981 npgopen Nature Microbiology 2058-5276 pmc-is-collection-domain yes pmc-collection-title Nature Portfolio PMC8387241 PMC8387241.1 8387241 8387241 34312531 10.1038/s41564-021-00941-9 941 1 Article Host preference and invasiveness of commensal bacteria in the Lotus and Arabidopsis root microbiota http://orcid.org/0000-0001-5901-3381 Wippel Kathrin 1 Tao Ke 2 Niu Yulong 1 Zgadzaj Rafal 1 Kiel Niklas 3 http://orcid.org/0000-0003-0862-2368 Guan Rui 1 http://orcid.org/0000-0002-7452-1146 Dahms Eik 1 Zhang Pengfan 1 Jensen Dorthe B. 2 Logemann Elke 1 http://orcid.org/0000-0002-8841-1415 Radutoiu Simona radutoiu@mbg.au.dk 2 http://orcid.org/0000-0002-8978-1717 Schulze-Lefert Paul schlef@mpipz.mpg.de 1 3 http://orcid.org/0000-0003-1769-892X Garrido-Oter Ruben garridoo@mpipz.mpg.de 1 3 1 grid.419498.9 0000 0001 0660 6765 Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany 2 grid.7048.b 0000 0001 1956 2722 Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, Aarhus, Denmark 3 grid.503026.2 Cluster of Excellence on Plant Sciences, Düsseldorf, Germany 26 7 2021 2021 6 9 388674 1150 1162 12 1 2021 25 6 2021 26 07 2021 15 09 2021 10 01 2025 © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ . Roots of different plant species are colonized by bacterial communities, that are distinct even when hosts share the same habitat. It remains unclear to what extent the host actively selects these communities and whether commensals are adapted to a specific plant species. To address this question, we assembled a sequence-indexed bacterial culture collection from roots and nodules of Lotus japonicus that contains representatives of most species previously identified using metagenomics. We analysed taxonomically paired synthetic communities from L. japonicus and Arabidopsis thaliana in a multi-species gnotobiotic system and detected signatures of host preference among commensal bacteria in a community context, but not in mono-associations. Sequential inoculation experiments revealed priority effects during root microbiota assembly, where established communities are resilient to invasion by latecomers, and that host preference of commensal bacteria confers a competitive advantage in their cognate host. Our findings show that host preference in commensal bacteria from diverse taxonomic groups is associated with their invasiveness into standing root-associated communities. Host preferences of commensal bacteria in the root microbiota are revealed using systematic analyses of synthetic bacterial communities in a gnotobiotic system. Subject terms Microbiome Plant sciences https://doi.org/10.13039/501100001659 Deutsche Forschungsgemeinschaft (German Research Foundation) ‘2125 DECRyPT’ Priority Programme EXC-Nummer 2048/1– project 390686111 ‘2125 DECRyPT’ Priority Programme Schulze-Lefert Paul Garrido-Oter Ruben https://doi.org/10.13039/501100004189 Max-Planck-Gesellschaft (Max Planck Society) https://doi.org/10.13039/501100009708 Novo Nordisk Fonden (Novo Nordisk Foundation) NNF19SA0059362 Radutoiu Simona pmc-status-qastatus 0 pmc-status-live yes pmc-status-embargo no pmc-status-released yes pmc-prop-open-access yes pmc-prop-olf no pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement yes pmc-prop-pdf-only no pmc-prop-suppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY issue-copyright-statement © The Author(s), under exclusive licence to Springer Nature Limited 2021 Main Plant roots associate with diverse microorganisms that are recruited from the surrounding soil biome and that assemble into structured communities known as the root microbiota. These communities provide the host with beneficial functions, such as indirect pathogen protection or mineral nutrient mobilization 1 – 3 . Despite conservation at higher taxonomic ranks 4 – 7 , comparison of community profiles across diverse land plants shows a clear separation according to host species 5 , 7 . These patterns could be explained by a process in which the root microbiota assemble according to niches defined by plant traits that in turn diversify as a result of plant adaptation to their environment. Alternatively, variation of microbiota profiles along the host phylogeny may be at least partially caused by coadaptation between the plant and its associated microbial communities. Culture-independent amplicon sequencing allows characterization of community structures and taxonomic composition but does not allow the study of phenotypes of individual community members. To overcome this fundamental limitation in microbiota studies, comprehensive culture collections of sequenced strains isolated from root and leaf tissue have been established 2 , 3 , 8 , 9 . Synthetic communities (SynComs) built from these collections can be used in gnotobiotic reconstitution systems of reduced complexity to explore the role of immune signalling 10 , nutritional status 3 , 11 , biotic and abiotic stress 2 and priority effects 12 in the establishment of the root and leaf microbiota. To investigate plant host preference of commensal bacteria, we assembled a collection of cultured bacterial species from the roots and nodules of the model legume Lotus japonicus (hereafter Lj ) that is comparable to the collection previously established from Arabidopsis thaliana (hereafter At ) roots 8 in terms of taxonomic and genomic composition, despite 125 Myr of divergence between Lj and At 13 whose crown groups evolved 65 and 32 Mya, respectively 14 . These two collections originate from plants grown in the same soil, enabling us to design SynComs for microbiota reconstitution experiments. Using this setup, we investigated host preference of commensal communities and the role of nitrogen-fixing nodule symbiosis, immunity and root exudation in microbiota establishment. Results Host-species-specific bacterial culture collections We compared the bacterial communities associated with roots of Lj and At plants grown in the same soil (experiment (exp.) A, Extended Data Fig. 1a and Supplementary Table 2 ) 2 , 3 , 15 and confirmed that both hosts associate with communities that are clearly distinct from those of the surrounding soil (Fig. 1a,b ). This shift is characterized by a decrease in alpha diversity (within-sample diversity; Fig. 1a ) as well as by a separation between root, rhizosphere and soil samples (beta diversity; Fig. 1b , principal coordinates analysis (PCoA) 1). In addition, Lj and At root samples formed two distinct clusters, indicating host-species-specific recruitment of commensals from identical pools of soil-dwelling bacteria (Fig. 1b , PCoA 2), which is in line with previous studies 15 , 16 . This separation (28% of variance, P = 0.001) was mainly explained by the different relative abundance of Proteobacteria, Actinobacteria, Bacteroidetes (Flavobacteria and Sphingobacteria) and Firmicutes (Bacilli) in Lj compared to At (Extended Data Fig. 2 ). Fig. 1 Lotus and Arabidopsis root-associated bacterial communities. a , Alpha diversity analysis of soil- ( n = 8), rhizosphere- ( n = 13 for Gifu, n = 15 for Col-0) and root-associated bacterial communities ( n = 13 for Gifu, n = 15 for Col-0) from Lj and At plants grown in natural soil (exp. A), assessed using the Shannon index. b , PCoA of Bray–Curtis dissimilarities of the same communities ( n = 64). c , e , Rank abundance plots of OTUs found in the Lotus ( c ) and Arabidopsis ( e ) natural root communities. Community members captured in the corresponding culture collection are depicted as black while non-recovered OTUs are shown in white. The vertical axis on the right shows the accumulated relative abundance in natural communities of all recovered OTUs. d , f , Percentage of abundant OTUs (≥0.1% RA) associated with Lotus ( d ) or Arabidopsis ( f ) roots in nature (natural communities, NatComs) that are captured in the Lotus or the Arabidopsis IRLs ( At- and Lj -IRL). Source data To explore the mechanisms by which different plant species associate with distinct microbial communities, we established a taxonomically and functionally diverse culture collection of the Lj root and nodule microbiota (Extended Data Fig. 1b ). A total of 3,960 colony-forming units were obtained and taxonomically characterized by sequencing the bacterial 16S rRNA (Supplementary Data 1 ), resulting in a comprehensive sequence-indexed rhizobacterial library (IRL) from Lj ( Lj -IRL). In parallel, a subset of the root samples was also subjected to amplicon sequencing to obtain culture-independent community profiles for cross-referencing with the Lj -IRL data. In the Lj collection, we were able to recover up to 53% of the most abundant bacterial operational taxonomic units (OTUs, defined by 97% 16S rRNA sequence identity) found in the corresponding natural community profiles, compared with 57% for the At collection (Fig. 1c,e and Supplementary Note ). The recovered bacterial taxa in the respective collection accounted for 82% of all sequencing reads from Lj root samples and 59% from At . Approximately 45% of the abundant OTUs found in the natural communities of one host were captured in the culture collection of the other species (Fig. 1d,f ), indicating a substantial overlap of the recovered bacterial taxa. Both culture collections include members of the Actinobacteria, Proteobacteria, Bacteroidetes and Firmicutes, the four phyla robustly found in the root microbiota of diverse plant species 5 , 7 . To establish a core Lj culture collection of whole-genome sequenced strains ( Lj -SPHERE), we selected from the Lj -IRL a taxonomically representative subset of bacterial isolates maximizing the number of covered taxa, as previously done for At ( Methods ) 8 . A total of 294 isolates belonging to 20 families and 124 species, including both commensal and mutualistic bacteria, were subjected to whole-genome sequencing (WGS) (Supplementary Data 2 ). Comparative analyses of all sequenced isolates from both collections revealed an extensive taxonomic and genomic overlap between exemplars derived from Lj and At (Extended Data Fig. 3 and Supplementary Note ). This indicates that the observed differences in natural community structures (Fig. 1b ) are probably not driven by the presence of host-specific bacterial taxonomic groups. Instead, the distinct root community profiles of the two hosts are possibly due to differences in the relative abundance of shared taxonomic groups (Extended Data Fig. 2 ). Host preference of commensal synthetic communities Given the overlap between the Lj - and At -SPHERE culture collections at a high taxonomic and whole-genome level, we speculated that strain-specific phenotypic variation in planta could allow commensal bacteria to preferentially colonize their cognate host. To test this hypothesis, we designed taxonomically paired SynComs for each host, representing 16 bacterial families present in both collections (Fig. 2 ). We then combined these SynComs into a mixed community composed of 32 strains (Supplementary Table 1 ). We allowed commensal bacteria to compete for colonization of the host from which they were derived (hereafter referred to as native strains) with strains isolated from the other plant species (non-native strains, Supplementary Fig. 1a ). We used a gnotobiotic system 2 , 17 to grow wild-type At (Col-0), Lj (Gifu) and a Lj mutant deficient in root nodule symbiosis ( Ljnfr5 ) 18 in the presence of the mixed community (Fig. 3a ). After 5 weeks, we performed community profiling via 16S rRNA gene amplicon sequencing of the root, rhizosphere and unplanted soil compartments. Analysis of community diversity revealed a significant separation ( P = 0.001) of communities of root samples from those of rhizosphere and soil, which in turn clustered together (exp. B, Fig. 3b ). In addition, we observed that the two hosts are colonized by distinct root microbial communities starting from the same input, and that samples from wild-type Lj are differentiable from those of Ljnfr5 (Fig. 3b ). These results were confirmed by two independent, full factorial experiments using different mixed communities (exp. C and M, Extended Data Fig. 4a,c ). An additional experiment, where strains belonging to families found exclusively in the Lj or At culture collections (two and five families, respectively) were added to the mixed community, resulted in similar patterns of beta diversity (exp. D, Extended Data Fig. 4b ). These results recapitulate the community shifts between compartment, host species and plant genotype, which were previously observed in culture-independent community profiles obtained from plants grown in natural soils (Fig. 1b ) 4 , 6 , 15 , 16 , thus validating our comparative reconstitution system to study host-species-specific microbiota establishment. Fig. 2 Whole-genome phylogeny of the Lotus and Arabidopsis core culture collections. Maximum likelihood phylogeny, constructed from a concatenated alignment of 31 conserved, single-copy genes (AMPHORA) showing the taxonomic overlap of the Lj -SPHERE ( n = 294, blue track) and At -SPHERE ( n = 194, red track) core culture collections. Arrows in the outer rings indicate the strains selected for five mixed communities used in reconstitution experiments. Source data Fig. 3 Reconstitution experiments recapitulate culture-independent patterns and show signatures of host preference by commensal communities. a , Setup of the competition experiments. b – d , Constrained PCoA (CPCoA) of Bray–Curtis dissimilarities (constrained by all biological factors and conditioned by all technical variables) of soil, rhizosphere and root samples. b , Lj wild-type Gifu, nfr5 mutant and At wild-type Col-0 plants cocultivated with the mixed community LjAt -SC2 (exp. B, n = 155, variance explained 53.8%, P = 0.001). c , Gifu, Col-0, A. lyrata MN47 ( Al ) and L. corniculatus cocultivated with LjAt -SC3 (exp. F, n = 173, variance explained 65.1%, P = 0.001). d , Dead roots of Gifu and Col-0, and toothpick cocultivated with LjAt -SC3 (exp. J, n = 250, variance explained 43.9%, P = 0.001). e – g , Aggregated RA of the 16 Lj -derived and the 16 At -derived strains in the live ( e , f ) or dead roots ( g ) of Lotus and Arabidopsis plants inoculated with LjAt -SC2 ( n = 66, e ) or LjAt -SC3 ( n = 72, f and n = 89, g ). Source data Next, we tested whether communities of commensal bacteria would preferentially colonize roots of their cognate host species (that is, from which they were originally isolated) compared to those of the other host. We found that the aggregated relative abundance of strains from the Lj -SPHERE collection was higher in wild-type Lj root samples than in those of At (Fig. 3e and Extended Data Figs. 4d–f ). Likewise, strains from the At -SPHERE collection were more abundant on their cognate host than on Lj . Commensal host preference and host species community separation was reduced but still present in the Ljnfr5 mutant (Fig. 3e ), suggesting that nodule symbiosis only partially contributes to commensal host preference. Further, sequential in silico removal of individual bacterial families did not alter the observed patterns of host preference at the community level (Extended Data Fig. 5 ), indicating that host preference was not driven by a single taxonomic group. Mono-association experiments with Lj and At wild-type plants grown on agar plates revealed that most community members maintained their root colonization capacity, but did not show a significant host preference in isolation (exp. E, Extended Data Fig. 6 ), suggesting that this commensal phenotype requires a community context. Moreover, we found that shoot biomass of both host species was not affected by these strains, confirming their commensal lifestyle in mono-associations (Extended Data Fig. 7 ). We then investigated if the phenotype of commensal host preference was conserved in a plant phylogenetic framework. We selected two additional plant species, Lotus corniculatus and A rabidopsis lyrata , which diverged from Lj and At approximately 12.5 and 13 Mya, respectively 19 , 20 , and are indigenous to the region from which the soil used to isolate these bacterial strains was collected 21 , 22 . We inoculated these four species with a mixed community of Lj and At commensals and obtained amplicon profiles of root, rhizosphere and unplanted soil samples (exp. F). We observed a significant separation between Lotus and Arabidopsis root communities (Fig. 3c , P = 0.001), and to a lesser extent between samples from the sister species within the same genus (Extended Data Fig. 8 ), which is in line with similar results obtained from At relatives grown in natural sites 23 . We found that the patterns of host preference observed in Lj and At were retained in their relative species (Fig. 3f ), suggesting that this community phenotype might be the result of commensal adaptation to root features conserved in a given host lineage. Host factors driving preferred associations in the root microbiota Previous studies have reported shifts in At leaf or root microbiota structure in mutants impaired in different host immunity pathways 10 , 24 . We speculated that the plant immune system might also play a role in selecting commensal bacteria in a host-specific manner. We thus tested whether host mutants impaired in perception of ubiquitous microbe-associated molecular patterns (MAMPs) were also preferentially colonized by native commensal strains (exp. G). Community profiles of roots of At and Lj mutants lacking the receptor FLS2, which detects the bacterial flagellin epitope flg22 ( Ljfls2 and Atfls2 ) 25 , 26 , were indistinguishable from those of their respective wild types (Extended Data Fig. 9a ). Similar results were obtained with an At mutant lacking MAMP coreceptors BAK1 and BKK1 as well as CERK1 receptor kinase, known to play a role in the perception of the bacterial MAMP peptidoglycan ( Atbbc triple mutant) 27 . In addition, bacterial host preference was retained in those mutants (Extended Data Fig. 9b ). A separate experiment using the dde2 ein2 pad4 sid2 ( deps ) mutant in At , which is simultaneously defective in all three major defence phytohormone signalling pathways (salicylic acid, jasmonate and ethylene) 28 , showed comparable results (exp. H, Extended Data Fig. 9c,d ). Together, these data indicate that the tested MAMP receptors and immune signalling pathways do not play a crucial role in preferential colonization by native commensal bacteria. Plant root exudates contain molecular cues that can be differentially metabolized or perceived as signals by root microbiota members 29 , 30 . In particular, glucosinolates, a group of nitrogen- and sulfur-containing metabolites found in root exudates throughout the family Brassicaceae, including At , are known to play a role in plant defence and serve as precursor of compounds that inhibit microbial growth 31 – 33 . Since legumes such as Lj lack genes required for glucosinolate biosynthesis, we speculated that secretion of these compounds by At might contribute to the observed differences in community structure. We therefore tested whether the At cyp79b2 cyp79b3 double mutant 34 , which is defective in the production of microbe-inducible and tryptophan-derived metabolites, including indole glucosinolates, was also preferentially colonized by native commensal strains (exp. H). Comparison of bacterial community profiling data indicates that indole glucosinolate had no effect on overall community structure or bacterial host preference in planta (Extended Data Fig. 10 ). Notably, incubation of bacterial SynComs in root exudates from Lj and At plants in an in vitro millifluidics system (exp. I) resulted in small but significant community separation according to the plant genotype (Supplementary Fig. 1a , 5% of variance, P = 0.002). However, in this system, we observed a loss of the host preference phenotype (Supplementary Fig. 1b ), indicating that root exudates from axenic plants are not sufficient to recapitulate this phenomenon. This observation prompted the question of whether live root tissue was required for preferential colonization by native commensals. We profiled the bacterial communities associated with dead root material from flowering Lj and At wild-type plants and with inert lignocellulose matrices (softwood birch toothpicks) at 5, 12 and 19 d after inoculation with a mixed community (exp. J). Diversity analyses showed that dead roots and toothpicks harboured distinct microbial communities that were separated from those of soil or detritusphere (soil surrounding dead roots), independently of the timepoint (Fig. 3d ). This separation was probably driven by an increase in the relative abundance of Flavobacteria, a taxon associated with the capacity to decompose complex polysaccharides 35 , and which dominates the dead root communities (53% RA on average). Unlike the large separation between living Lj and At roots (36% of the variance), we observed only a small but significant differentiation between Lj and At dead root communities (6.4% of variance, P = 0.001). Additionally, commensal host preference was undetectable in dead roots, where Lj - and At -derived strains reached similar aggregated relative abundances in root material harvested from either host (Fig. 3g ). Taken together, these results suggest that a living root and other factors besides root exudates, such as a physical contact with the plant (that is, host-commensal feedbacks) are required for host preference in the root microbiota. SynCom-specific transcriptional responses of Lj and At roots Next, we sought to assess whether native, non-native or mixed commensal communities elicited a differential response in either host species. We grew wild-type Lj and At plants in our soil-based gnotobiotic system inoculated with Lj -, At - or mixed SynComs for 5 weeks (exp. K). Assessment of plant performance revealed that treatment with commensal communities led to increased plant biomass and bacterial load compared to axenic controls, but not to differences according to SynCom treatment (Supplementary Fig. 2 ). Given the observation that a living root is required for commensal host preference, we conducted RNA-sequencing (RNA-Seq) of cross-inoculated Lj and At roots to explore host transcriptional responses that might mediate this process (exp. K). Analysis of these data showed that transcriptional outputs separated according to SynCom treatment in both hosts (Fig. 4 ). Analysis of k -means clustering of whole transcriptomes revealed gene clusters associated with general response to bacterial colonization, as well as clusters specific to treatment with native or non-native SynComs. Among genes specifically induced by the native SynComs in both plant hosts we found several transcriptional regulators of immunity (for example, WRKY20, WRKY32 and MYB15), well-characterized MAMP receptor kinases (LYK4) and ethylene response factors (for example, ERF34). This conserved pattern of differential response in the two plant species suggests a specific transcriptional response to native commensal communities that involves components of the host immune system. The differentially expressed transcription factors identified here constitute prime candidates for future exploration of the underlying mechanisms of differential microbiota assembly. Fig. 4 SynCom-specific transcriptional outputs in Lotus and Arabidopsis roots. a , b , Whole transcriptome-level principal component analysis of Arabidopsis ( n = 12 biologically independent samples, a ) and Lotus ( n = 12, b ) roots after coinoculation with host-specific SynComs (SC) ( Lj - and At -SC3, exp. K). In the case of Lotus plants, a nodule isolate from the Lj -SPHERE collection was added to all treatments to prevent transcriptional outputs from being dominated by symbiosis or nitrogen starvation responses. c , d , Heatmaps showing scaled counts of genes arranged according to k -means clustering results (only differentially expressed genes shown) for Arabidopsis ( c ) and Lotus ( d ). e , Distribution of expression patterns for clusters of genes upregulated after coinoculation with native SynComs. f , Overlap in terms of homologues identified in the same clusters between the two host and a list of relevant transcription factors identified as potential key regulators of differential transcriptional responses. Source data Invasiveness and persistence in the root microbiota The results obtained from four independent experiments using five different mixed communities (Fig. 3 and Extended Data Figs. 4 , 9 and 10 ) show that native strains have a competitive advantage when colonizing roots of their cognate host. Ecological theory suggests that in the presence of a competitive hierarchy, the order of species arrival does not matter, as better adapted species tend to dominate irrespective of the history of the community 36 . To investigate the role that priority effects play in root community assembly we designed a series of sequential inoculation experiments using host-specific SynComs (exp. L, Fig. 5a ). At and Lj wild-type plants were inoculated with taxonomically paired SynComs derived from Lj ( Lj -SC3), At ( At -SC3) or a mixed community ( LjAt -SC3) for 4 weeks. Subsequently, we challenged the established root communities by adding the complementary SynCom ( At -SC3 or Lj -SC3, respectively) to the soil matrix or, in the case of plants initially treated with the mixed community ( LjAt -SC3), a mock solution (Fig. 5a ). We then allowed all plants to grow for an additional 2 weeks before harvesting. Amplicon sequencing showed a significant separation of communities by compartment, and, within root samples, according to host species (Fig. 5b , P = 0.001), mirroring the patterns observed in culture-independent community profiles (Fig. 1a ). Analysis of beta diversity of root samples at strain-level resolution revealed an effect of the treatment on community structure (Fig. 5c ), demonstrating that the order of arrival of strains affects community assembly. Examination of aggregated relative abundances showed that, in a competition context (that is, initial inoculation with the mixed community LjAt -SC3), commensal SynComs preferentially colonized roots of their cognate host (Fig. 5d ), in line with results from the previous competition experiment shown in Fig. 3 . However, in an invasion context, early-arriving SynComs invariably reached higher proportions in the output communities compared to the late-arriving SynComs (Fig. 5d, e ). Notably, estimation of absolute bacterial abundances showed that a secondary inoculation with an invading SynCom did not result in a significant increase in total bacterial load (Supplementary Fig. 3 ). Together, the results from our sequential inoculation experiments (Fig. 5c,d ) are indicative of the existence of priority effects in the root and rhizosphere microbiota, a well-known phenomenon in microbial community assembly 36 . These effects could be explained by niche preemption, where early-arriving community members reduce the number of resources available (for example, nutrients, space) for latecomers 37 ; alternatively, they could be the result of a feedback process between the host and the early-arriving commensals. Fig. 5 Invasion and persistence of commensal bacteria. a , Setup of the sequential inoculation experiment. Lj Gifu and At Col-0 plants were cocultivated with the mixed community LjAt -SC3, or individual SynComs Lj -SC3 and At -SC3, followed by inoculation with the contrasting SynCom (exp. L). b , c , Constrained PCoA (CPCoA) of Bray–Curtis dissimilarities (constrained by all biological factors and conditioned by all technical variables; n = 267; variance explained 14.7%, P = 0.001) of soil, rhizosphere and root samples ( b ), and PCoA of root samples only ( n = 137, c ). d , e , Aggregated RA of the 16 Lj -derived and the 16 At -derived strains in Lotus and Arabidopsis root ( d ) ( n = 120) and rhizosphere ( e ) ( n = 120) samples in the indicated treatments. Different letters above boxes indicate different significance groups according to a Kruscal–Wallis test, followed by a Dunn’s post hoc. Source data We proposed that commensal bacteria would be less affected by priority effects when colonizing their cognate host, given their competitive advantage with respect to non-native strains. To test this, we examined aggregated relative abundances of Lj - and At -derived SynComs in the root and rhizosphere communities. We found that host-specific SynComs were better able to invade a resident community in the roots of their cognate host compared to those of the other plant species (Fig. 5d ), thus reducing the strength of the priority effects. However, in the rhizosphere compartment of either plant species, host-specific SynComs showed neither host preference in a competition context nor differences in their ability to invade standing communities (Fig. 5e ). However, it is also possible that root communities did not reach equilibrium 2 weeks after invasion, and that the observed patterns could change over time. We then tested if host preference was directly linked to invasiveness and to what extent these traits were found in individual community members. First, we quantified the strength of host preference by calculating the ratio between the relative abundance of each strain in their cognate host compared to the other plant species (host preference index, Methods ). Notably, although Lj root samples did not include nodules, but possibly contained incipient symbiotic events, the strains with the highest host preference index were the nitrogen-fixing Lj strains belonging to the Phyllobacteriaceae family (Fig. 6a ), indicating that host preference of mutualistic rhizobia is not limited to nodule tissue. In addition, multiple other commensal strains showed significant host preference, with members of the families Pseudomonadaceae, Oxalobacteriaceae, Rhizobiaceae and Microbacteriaceae robustly displaying a high host preference index. Members of these last two bacterial families also had an impact on community structure during invasion in a recent study with phyllosphere bacteria 12 . Next, we calculated an invasiveness index by comparing the ability of each strain to invade a standing community on their cognate host compared to the other plant species ( Methods ). We found a strong correlation between host preference and invasiveness of commensal bacteria, which is independent of their relative abundance ( r = 0.89, P = 4.3 × 10 −10 ; Fig. 6b ). In contrast, this correlation was absent in the rhizosphere samples (Fig. 6c ), indicating that the link between these two bacterial traits is mediated by host attributes that do not extend to the rhizosphere. Together, our data show that host preference is prevalent in commensal bacteria from diverse taxonomic groups and that this trait is tightly linked to invasiveness and together play a role during root microbiota assembly. Fig. 6 Host preference is linked to invasiveness. a , Analysis of host preference of individual commensal strains across gnotobiotic experiments ( n = 366). Each strain is represented by a dot, whose colour corresponds to its host preference index and whose size to its average relative abundance. A significant host preference (Mann–Whitney test, false-discovery-rate corrected) is depicted by a black circle around a dot. NS, not significant. b , c , Correlation between host preference and invasiveness index for each strain in root ( n = 115) ( b ) and rhizosphere samples ( n = 119) ( c ), respectively, obtained from the sequential inoculation experiment (exp. L). The colour of each point designates the host of origin of each strain and the size denotes its mean relative abundance (log 2 transformed). Each point is labelled with a numeric identifier that corresponds to the strains in a ( LjAt -SC3). At , A. thaliana ; Lj , L. japonicus ; Al , A. lyrata ; Lc , L. corniculatus . Source data Discussion The current concept of host specificity in plant–microbe interactions was originally developed based on studies using microorganisms with either pathogenic or mutualistic lifestyles. Recently, it has been shown that soilborne, nitrogen-fixing Ensifer meliloti mutualists can adapt to local host genotypes in only five plant generations and proliferate to greater abundances in hosts with shared evolutionary histories 38 . We show here that in the Lj and At root microbiota, there is a gradient of host preference among commensals belonging to diverse taxonomic lineages. Maintenance of host preference in the sympatric relative species L. corniculatus and A. lyrata raises the possibility that these commensals might have adapted to host features conserved in the respective plant genera. Alternatively, the observed host preference patterns might be the consequence of other ecological processes, such as ecological fitting, whereby organisms are able to colonize and persist in a new environment using traits that they already possess 39 . Diversification of plant traits as a result of adaptation to edaphic or other environmental factors is expected to result in new host features that constitute new root niches for microbial colonization. It is also possible that host diversification is partly driven by the adaptation of plants to commensal microbiota in soils with contrasting properties. However, the observation that in our experimental conditions colonization by native or non-native bacterial SynComs had no impact on plant growth suggests that host preference is the result of microbial adaptation to host features instead of coevolution. However, it is possible that a significant impact on host fitness might be observed in long-term experimentation, or in the presence of biotic or abiotic stresses, which were absent in the tested conditions, or in direct competition with other plant species. This latter hypothesis is supported by the observation that similarity between the root microbiota of different species affects competitive plant–plant interactions and has an impact on host performance through plant–soil feedback 5 . Future experimentation using multi-species gnotobiotic systems and varying environmental conditions will serve to test these hypotheses. In aquatic and terrestrial ecosystems, microbial traits such as growth rate, antagonistic activity or resource use efficiency are known determinants of invasiveness 40 , 41 . In microbial communities associated with eukaryotic organisms, the ability to interact with the host might also be required for successful invasion. Our results indicate that native commensals have a competitive advantage when invading standing communities in the root but not in soil or rhizosphere. One possibility is that increased invasiveness by native bacteria is enabled by the existence of unfilled host-species-specific root niches that can be occupied by latecomers. Alternatively, direct interaction of commensals with their host may be required to trigger the formation of host-species-specific root niches, which could be linked to the specific transcriptional reprogramming in roots observed during colonization by native SynComs. This latter hypothesis is further supported by the observation that bacterial SynComs colonizing dead roots or incubated in root exudates in vitro showed no significant host preference. Our study provides a framework to test these hypotheses and to investigate the molecular basis of host preference in multiple taxa of the bacterial root microbiota in comparison with host adaptation mechanisms in plant pathogens and mutualists. Methods Bacterial and plant material Bacterial strains were grown in tryptic soy broth (15 g l −1 , TSB, Sigma-Aldrich) liquid medium or on agar plates containing 15 g l −1 of Bacto Agar (Difco) at 25 °C. Mesorhizobium strains LjNodule210, LjNodule215 and LjNodule218, isolated from Lj root nodules, were cultured in TY medium (5 g l −1 tryptone, 3 g l −1 yeast extract) supplemented with 10 mM CaCl 2 or in YMB medium (5 g l −1 mannitol, 0.5 g l −1 yeast extract, 0.5 g l −1 K 2 HPO 4 ·3H 2 O, 0.2 g l −1 MgSO 4 ·7H 2 O, 0.1 gl l −1 NaCl). The composition of synthetic bacterial communities (SynComs) is listed in Supplementary Table 1 . Lj ecotype Gifu B-129 was used as wild-type. Symbiosis-deficient mutant nfr5-2 (ref. 18 ) and flagellin receptor-deficient mutant fls2 (LORE1-30003492) 25 were derived from the Gifu B-129 genotype. For At , ecotype Columbia-0 was used as wild-type. Mutant genotypes fls2 (ref. 26 ), bbc 27 , deps 28 and cyp79b2 cyb79b3 (ref. 34 ) were available in our seed stock. L. corniculatus seeds, cultivated in the North-Western German lowland, were retrieved from Rieger-Hofmann GmbH, Blaufelden-Raboldshausen, Germany. A. lyrata MN47 seeds were a gift from J. de Meaux, University of Cologne. Establishment of the Lj bacterial culture collection The Lj culture collection combines strains isolated during three independent isolation events. Bacterial isolation, DNA isolation and identification using Illumina sequencing were performed as previously described 8 . Wild-type Lj (ecotype Gifu B-129) plants were grown in natural soil (Cologne agriculture soil (CAS), batch 10 from spring 2014 and batch 11 from spring 2015) in the greenhouse and harvested after 4 or 8 weeks to cover different developmental stages. Root systems of 20 plants were subjected to DNA isolation and culture-independent community profiling via amplicon sequencing. From 45 plants, a 4-cm section of the roots was collected and rigorously washed three times with phosphate-buffered saline (130 mM NaCl (7.6 g l −1 ), 7 mM Na 2 HPO 4 (1.246 g l −1 ), 3 mM NaH 2 PO 4 (0.414 g l −1 ), pH 7.0) and three times with sterile water. Nodule and root parts were separated and homogenized independently. Homogenized roots from each individual plant were allowed to sediment for 15 min and the supernatant was diluted (1:20,000, 1:40,000 and 1:60,000) with four different media: 3 g l −1 TSB, 50% TY, Casitone yeast for enrichment of Myxococcales (3 g l −1 Casitone, 1.36 g l −1 CaCl 2 ·2H 2 O and 1 g l −1 yeast extract; pH adjusted to 7.2) and yeast agar van Niel’s, for enriching of Burkholderiales (10 g l −1 yeast extract, 1 g l −1 K 2 HPO 4 and 0.5 g l −1 MgSO 4 ·7H 2 O). Bacterial dilutions cultivated in 96-well microtitre plates. Homogenized nodules from each individual plant were directly diluted (1:20,000, 1:40,000 and 1:60,000) and cultivated in 96-well microtitre plates. This procedure was carried out for individual plants to obtain bacterial isolates from different plant roots. After 10–20 d at room temperature, plates that showed visible bacterial growth in around 30 wells were chosen for high-throughput sequencing. Bacterial isolates were identified with a two-step barcoded PCR protocol described previously 8 , with the difference that at the first step of the PCR, the v5-v7 fragments of the 16S rRNA gene were amplified by the degenerate primers 799F (AACMGGATTAGATACCCKG) and 1192R (ACGTCATCCCCACCTTCC), and indexing was done using Illumina-barcoded primers. The indexed 16S rRNA amplicons were pooled, purified and sequenced on the Illumina MiSeq platform. Strains isolated from nodules were tested for their ability to form functional nodules in Lj Gifu plants grown on agar plates. Cross-referencing of IRL sequences with culture-independent profiles was used to identify candidate strains for further characterization, purification and WGS. Two main selection criteria were used: maximum taxonomic coverage, selecting candidates from as many taxa as possible and priority to strains whose 16S sequences were highly abundant in the natural communities. Whenever multiple candidates from the same phylogroup were identified, we aimed to obtain multiple independent strains, if possible, coming from separate biological replicates to ensure they represented independent isolation events. After validation of selected strains, 294 (including nine isolated from nodules) were successfully subjected to WGS. For WGS, DNA was isolated from strains using the QiAmp Micro DNA kit (Qiagen), treated with RNase and purified. Quality control, library preparation and sequencing (on the Illumina HiSeq3000 platform) were performed by the Max Planck Genome Centre, Cologne, Germany ( https://mpgc.mpipz.mpg.de/home/ ). Sequencing depth was 5 million reads per sample. Culture-independent community profiling Bacterial communities were profiled by amplicon sequencing of the variable v5-v7 regions of the bacterial 16S rRNA gene. Library preparation for Illumina MiSeq sequencing was performed as described previously 2 . In all experiments, multiplexing of samples was performed by double-indexing (barcoded forward and reverse oligonucleotides for 16S rRNA gene amplification). Greenhouse experiment Lj Gifu and At Col-0 were grown for 5 weeks in CAS soil (batch 15 from January 2020) in 7 × 7 cm pots alongside unplanted control pots under short-day conditions. Pots were watered with sterile water from the bottom as needed. Root, rhizosphere and soil samples were harvested and processed as described previously 42 . In total, 15, 13 and eight replicates were sampled for Col-0, Gifu and unplanted controls, respectively. DNA was isolated from those samples using the MP Biomedicals FastDNA Spin Kit for Soil. Multi-species microbiota reconstitution experiments We used the gnotobiotic FlowPot system 2 , 17 to grow At and Lj plants with and without bacterial SynComs. In brief, the system allows for even inoculation of each growth pot with microbes by the flushing of pots with the help of a syringe attached to the bottom opening. Sterilized seeds are placed on the matrix (peat and vermiculite, 2:1 ratio), and pots are incubated under short-day conditions (10 h light, 21 °C; 14 h dark, 19 °C), standing in customized metal racks in sterile plastic boxes with filter lids (SacO2 microboxes, www.saco2.com ). For SynCom preparation, bacterial commensals were grown separately in liquid culture for 2–5 d to reach high density, harvested and washed in 10 mM MgSO 4 . Equivalent amounts of each strain were combined to yield the desired SynComs with an optical density (OD 600 ) of 1. An aliquot of 200 µl of the SynCom as reference sample for the experiment start, and aliquots of 50 µl of the individual strains were taken and stored at −80 °C for sequencing. The SynCom was added to the desired medium to reach a final OD 600 of 0.02. FlowPots were each flushed with 50 ml of inoculum (medium/SynCom mix). Generally, the medium used for inoculation was 0.25× B&D 43 supplemented with 1 mM KNO 3 for both plant species. In experiments D, F, K and M (Supplementary Table 2 ), 0.5× MS (2.22 g l −1 Murashige+Skoog basal salts, Duchefa; 0.5 g l −1 MES anhydrous, BioChemica; adjusted to pH 5.7 with KOH) was used for Arabidopsis . The two plant species were grown in separate FlowPots side-by-side, with ten pots in total per plastic box. After 5 weeks of growth, roots were harvested and cleaned thoroughly from attached soil using sterile water and forceps. Lotus root segments containing nodules were omitted. Soil samples from planted and unplanted pots were collected as rhizosphere and soil samples, respectively. All root (epiphytic and endophytic compartments), rhizosphere and soil samples were transferred to Lysing Matrix E tubes (FastDNA Spin Kit for Soil, MP Biomedicals), frozen in liquid nitrogen and stored at −80 °C for further processing. DNA was isolated from those samples using the FastDNA Spin Kit for Soil, and from individual strains of the SynCom via quick alkaline lysis 8 , then subjected to bacterial community profiling or absolute quantification of bacteria. For RNA isolation, samples were harvested the same way and processed using the RNeasy Plant Mini kit (Qiagen). Dead root experiment Mature root systems from Gifu and Col-0 plants grown in potting soil in the greenhouse were harvested from flowering plants (13-week old Lotus , 7-week old Arabidopsis ), washed several times in water, padded on kitchen paper to remove moisture and dried in big glass petri dishes at 120 °C for 1 h. Note that Gifu plants had a few small, most probably ineffective root nodules. Pieces of the dried, dead roots were planted into FlowPots under sterile conditions, and SynCom ( LjAt -SC3) inoculation was performed as described above. Dead roots were recovered from the FlowPots after 5, 12 and 19 d of incubation, and washed and stored as described above for live roots. SynCom invasion experiments FlowPots were sequentially inoculated with native and non-native strains. FlowPots were prepared as usual, with the addition of a round nylon filter (pore size 200 µm) at the bottom of the pot to avoid clogging of the bottom opening by matrix material. FlowPots were first inoculated with the mixed SynCom (16 Lj - and 16 At -strains), the At SynCom (16 At -strains), the Lj SynCom (16 Lj strains) or the mock solution (medium only). The medium used for inoculation was 0.25× B&D 43 supplemented with 1 mM KNO 3 for both plant species. For sterilization, At seeds were incubated for 5 min in 70% ethanol, then twice for 1 min in 100% ethanol, washed five times with sterile water and stored at 4 °C in the dark for stratification. Lj seeds were scarified by abrading the surface using sand paper, incubated for 20 min in diluted bleach and washed five times with sterile water. Sterilized seeds were placed on sterile Whatman paper wetted with sterile water in a squared petri dish and allowed to germinate under short-day conditions. Sterilized Col-0 seeds and germinated sterile Gifu seeds were placed on the soil surface. Note that a few drops of Mesorhizobium culture ( Lotus root nodule symbiont, strain LjNodule218, OD 600 0.02) were applied to Gifu seedlings in the At SynCom treatment to allow for normal root nodule symbiosis to occur and ensure healthy plant growth. After growth for 4 weeks, a second inoculation was performed, where a mock inoculum (medium) was added to the mixed SynCom-treated pots, the Lj SynCom was added to the At SynCom-treated pots, the At SynCom was added to the Lj SynCom-treated pots and mock inoculum was added to the mock-treated pots. The pots were flushed in reverse by adding the inoculum from the top and applying vacuum from the bottom. On a sterile bench, FlowPots (cut 60-ml syringes with a male Luer Lok connector) were placed onto female Luer Lok connectors of a vacuum manifold (QIAvac 24 Plus, Qiagen), keeping the valves of the manifold closed. Vacuum was applied to the manifold with an attached vacuum pump. Next, 20 ml of inoculum were carefully added to a pot with a 20-ml syringe and needle, avoiding damage of the plant shoots. Each pot was inoculated by opening and closing the corresponding valve. Pots were put back into the plastic containers and plants grown for another 2 weeks. Root, rhizosphere and soil samples were harvested as described above. Collection of root exudates Arabidopsis and Lotus plants were grown in a customized hydroponic system (original design by M. Peukert, University of Cologne, unpublished). This sterile growth setup consists of glass jars filled with glass beads and a stainless-steel mesh on top. Nutrient solution (modified 0.25× B&D medium, Fe-EDTA instead of Fe-citrate) was poured into the jars until the beads were covered in liquid and the liquid touched the metal mesh. We used the same medium for both plant species to allow for direct comparison of exudate composition, and to minimize differential effects on the bacterial community originating from different media types. We chose the Lotus B&D medium since Arabidopsis grew reasonably well in it. Sterilized and pregerminated seeds were placed onto the mesh, jars were put into sterile plastic boxes with filter lids (SacO2 microboxes) and plants were grown for 5 weeks. The medium containing root exudates was removed from the jars in the clean bench using a sterile metal needle and plastic syringe. After transfer to 50-ml Falcon tubes, exudates were frozen at −80 °C, freeze-dried until a volume of 2–3 ml was left, thawed and adjusted with sterile water to 5 ml. Exudates were kept at −80 °C until further usage. Millifluidics experiment Bacterial incubation in root exudates was performed in a millifluidics system (MilliDrop Analyzer, MilliDrop, www.millidrop.com ). This drop-based system allows incubation of bacteria in very small volumes of root exudates or growth medium. In brief, bacteria and exudates or growth medium are combined in wells of a 96-well plate using a pipetting robot Freedom Evo 100 (Tecan). Droplets of approximately 100–200 nl are then sucked in from the wells of the loading plate by a tip on the robotic arm of the MilliDrop Analyzer, generating hundreds of droplets within an oil-filled tube, separated by air spacers. During incubation, the droplet ‘train’ moves back and forth, so that during each round, each droplet passes a detector that counts the droplets. Culture droplets are collected after the experiment and subjected to community profiling. The mixed community LjAt -SC1 was used and was essentially prepared as described above for the in planta experiments, adjusted to OD 600 of 0.1 and used as input for preparation of the loading plate. Pure exudates (pH between 7.0 and 8.0) or a defined M9 + carbon growth medium (1× M9 salts including phosphate buffer, 1 mM magnesium sulfate, 0.3 mM calcium chloride, 1× vitamin B solution and artificial root exudates, pH 7.0) was used for incubation. Vitamin B solution contained 0.4 mg l −1 4-aminobenzoic acid, 1 mg l −1 nicotinic acid, 0.5 mg l −1 calcium- d -pantothenate, 1.5 mg l −1 pyridoxine hydrochloride, 1 mg l −1 thiamine hydrochloride, 0.1 mg l −1 biotin and 0.1 mg l −1 folic acid (modified from ref. 44 ). Artificial root exudates (modified from ref. 45 ) were composed of 0.9 mM glucose, 0.9 mM fructose, 0.2 mM sucrose, 0.8 mM succinic acid, 0.6 mM sodium lactate, 0.3 mM citric acid, 0.9 mM serine, 0.9 mM alanine and 0.5 mM glutamic acid. Bacteria were incubated for 3 d, during which the pH of the cultures stayed stable. Droplets were collected in 6-µl amounts, and DNA isolated via quick alkaline lysis 8 , which consisted of addition of 10 µl of buffer 1 (25 mM NaOH, 0.2 mM EDTA, pH 12), incubation at 95 °C for 30 min, addition of 10 µl of buffer 2 (40 mM Tris-HCl at pH 7.5) and storage at −20 °C. Mono-associations of SynCom members with host plants Lotus seeds were sterilized and placed on sterile wet Whatman paper for germination. Seedlings were transferred to squared petri dishes containing 0.25× B&D medium (with Fe-EDTA instead of Fe-citrate) supplemented with 3 mM KNO 3 and 1% Difco Bacteriological agar, and sterile filter paper was put on top of the sloped solidified medium before placing the seedlings to prevent root growth inside the agar. Arabidopsis seeds were sterilized and germinated on 0.5× MS medium plus 1% Difco Bacteriological agar. Seedlings were transferred to squared petri dishes containing 0.5× MS medium (neutral pH, buffered with 2 mM HEPES) plus 1% agar. The 32 strains of the mixed community LjAt -SC3 were grown individually in liquid medium, harvested and adjusted to an OD 600 of 0.02. Seedlings were inoculated by adding 500 µl of bacterial culture to the roots. Plants were grown for 14 d under long-day conditions (16/8 day–night cycles) at 21 °C. Three biological replicates were prepared for each genotype–bacteria combination. Absolute quantification of bacteria Genomic DNA was isolated from roots of plants grown in FlowPots (experiments K and L). DNA concentration was determined with the Quant-iT PicoGreen double-stranded DNA Assay Kit (Thermo Fisher Scientific). To quantify bacterial load on plant roots, the amount of bacterial DNA relative to the amount of plant DNA was determined via quantitative PCR (qPCR). For bacteria, the v5-v7 region of the 16S rRNA gene was amplified using the AACMGGATTAGATACCCKG (799F) and ACGTCATCCCCACCTTCC (1192R) primers. For Col-0, a fragment of At1g12360 was amplified using the TCCGGTCAATATTTTTGTTCG and TATAGCAGCGAAAGCCTCGT primers, and for Gifu, a fragment of the NFR5 gene was amplified using the TCATATGATGGAGGAGTTGTCTGTT and ATATGAGCTTCGGAGCATGG primers. qPCR was performed as described previously 46 . The amount of 16S rRNA was normalized to plant gene within each individual sample using the following equation: 16S rRNA gene over plant gene = 2 - C t(16S) /2 - C t(plant) . For colony counts (exp. E), roots were harvested, washed, weighed and crushed in 500 µl (Col-0) or 750 µl (Gifu) of sterile water. Serial dilutions of 10 −1 , 10 −2 , 10 −3 , 10 −4 and 10 −5 of the crushed roots were prepared in sterile water. Then 10 µl of each were spotted onto 10% TSB agar square plates. Single colonies were counted after 1–3 d. Processing of 16S rRNA gene amplicon data Amplicon sequencing data from Lj 15 and At 2 roots of plants grown in CAS soil in the greenhouse, along with unplanted controls, were demultiplexed according to their barcode sequence using the QIIME 47 pipeline. DADA2 (ref, 48 ) was used to process the raw sequencing reads of each sample. Unique amplicon variants (ASVs) were inferred from error-corrected reads, followed by chimera filtering, also using the DADA2 pipeline. ASVs were aligned to the SILVA database 49 for the taxonomic assignment using the naïve Bayesian classifier implemented by DADA2. Raw reads were mapped to the inferred ASVs to generate a relative abundance table, which was subsequently used for analyses of diversity and differential abundance using the R package vegan 50 . Amplicon sequencing reads from the Lotus and Arabidopsis 8 IRLs and from their corresponding culture-independent root community profiling were quality-filtered and demultiplexed according to their two-barcode (well and plate) identifiers using custom scripts and a combination of tools included in the QIIME 47 and USEARCH 51 pipelines. Sequences were clustered into OTUs with a 97% sequence identity similarity using the UPARSE algorithm, followed by identification of chimeras using UCHIME 52 . Samples (wells) with fewer than 100 good quality reads were removed from the data set as well as OTUs not found in a well with at least ten reads. A purity threshold of 90% was chosen for identification of recoverable OTUs. We identified Lj -IRL samples matching OTUs found in the culture-independent root samples and selected a set of 294 representative strains maximizing taxonomic coverage for subsequent validation and WGS, forming the basis of the core Lj -SPHERE collection. Sequencing data from SynCom experiments (including FlowPot and millifluidics experiments) were preprocessed similarly as natural community 16S rRNA data. Quality-filtered, merged paired-end reads were then aligned to a reference set of sequences extracted from the whole-genome assemblies of every strain included in a given gnotobiotic experiment, using USEARCH (uparse_ref command) 53 . Only sequences with a perfect match to the reference database were retained. We checked that the fraction of unmapped reads did not significantly differ between compartment, experiment or host species. We generated a count table that was used for downstream analyses of diversity with the R package vegan 50 . We visualized amplicon data from all experimental systems using the ggplot2 R package 54 . Host preference and invasiveness indices To quantify the strength of the host preference of each bacterial strain individually, we calculated the ratio between the mean relative abundance of a given SynCom member in root samples of their cognate host and its mean relative abundance in root samples of the other plant species. The host preference indices depicted in Fig. 5a were calculated independently for each experiment. To avoid obtaining very high ratios due to small denominator values, strains with mean relative abundances below 0.1% in either of the two hosts were removed from the analysis. Similarly, an invasiveness index was calculated by obtaining the ratio between mean relative abundance of a strain when invading resident communities on roots of their cognate host, compared to the other plant species. The invasiveness index was calculated using samples from the sequential inoculation experiment (experiment L, Fig. 4 ). The direct comparison between the two indices shown in Fig. 5b,c were calculated using samples from experiment L only, where invasion and competition treatments were performed in parallel. To test whether a SynCom member was significantly more abundant in the roots of their cognate host (that is, significant host preference), we used the non-parametric Wilcoxon test controlling for false-discovery rate with α = 0.05. Bacterial genome assembly and annotation Paired-end Illumina reads were first subjected to length-trimming and quality-filtering using Trimmomatic 55 . Reads were assembled using the A5 assembly pipeline 56 , which uses the IDBA algorithm 57 to assemble error-corrected reads. Detailed assembly statistics and corresponding metadata can be found in Supplementary Data 2 . Genomes with multi-modal k -mer and GC content distributions or multiple instances of marker genes from diverse taxonomic groups were flagged as not originating from clonal cultures. These samples were processed using a metagenome binning approach 58 . Briefly, contigs from each metagenome sample were clustered using METABAT2 (ref. 59 ), followed by an assessment of completeness and contamination of each metagenome-assembled genome using CheckM 60 . Only bins with completeness scores larger than 75% and contamination rates lower than 5% were retained and added to the collection (Supplementary Data 2 , designated metagenome-assembled genome (MAG) in the column ‘type’). Functional annotation of genes was conducted using Prokka and using a custom database based on Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologue groups 61 downloaded from the KEGG FTP server in November 2019. Hits to sequences in the database were filtered using an E value threshold of 10 × 10 −9 and a minimum coverage of 80% of the length of the query sequence. Phylogenomic analysis of the Lj - and At -SPHERE culture collections Genomes from the Lj - and At -SPHERE culture collections 8 were searched for the presence of a set of 31 conserved, single-copy marker genes, known as AMPHORA 62 genes. Sequences of each gene were aligned using Clustal Omega 63 with default parameters. Using a concatenated alignment of each gene, we inferred a maximum likelihood phylogeny using FastTree 64 . We visualized this tree using the Interactive Tree of Life web tool 65 . Genomes from both collections ( Lj -SPHERE and At -SPHERE) were clustered into phylogroups, roughly corresponding to a species designation 66 using FastANI 67 and a threshold of average nucleotide identity at the whole-genome level of at least 97%. RNA-Seq and data analysis RNA isolated from FlowPot samples was subjected to quality control, library preparation and sequencing (on the Illumina HiSeq3000 platform) at the Max Planck Genome centre, Cologne, Germany ( https://mpgc.mpipz.mpg.de/home/ ). Sequencing depth was 6 million reads per sample. Raw Illumina RNA-Seq reads were preprocessed using fastp (v.0.19.10) 68 with default settings for pair-end reads. High-quality reads were pseudo-aligned to the Lj Gifu or At Col-0 transcriptome reference using kallisto (v.0.46.1) 69 . After removal of low abundant transcripts that were not present in at least two replicates under each condition, count data were imported using the tximport package 70 . Differential expression analyses were performed using the DESeq2 package 71 . First, raw counts were normalized with respect to the library size (rlog function) and transformed into log 2 scale. We tested for sample effects by surrogate variable analysis using the sva package 72 . Significant surrogate variables were automatically detected and integrated into the model for differential analyses. Principal component analysis based on whole transcripts were then conducted and plotted to visualize the cluster and variance of biological replicates under each condition. Transcripts with fold-changes >1.5 and adjusted P value for multiple comparisons (Benjamini–Hochberg method) equal to or below 0.05 were considered significant. The log 2 scaled counts were normalized by the identified surrogate variables using the limma package 73 (‘removeBatchEffect’ function), and transformed as median-centred z -score (by transcripts, ‘scale’ function). Then z -scores was used to conduct k -means clustering for all transcripts. The cluster number ( k = 10) was determined by sum of squared error and Akaike information criterion. Differential expressed transcripts and cluster results were visualized using heatmaps generated by ComplexHeatmap package 74 . Gene ontology enrichment for each cluster using the whole Lotus and Arabidopsis transcriptomes as backgrounds were performed with the goseq package 75 , which considers the transcripts length bias in RNA-Seq data. Gene ontology annotations were retrieved from the Gene Ontology Consortium (September 2019) 76 , 77 . Significantly changed biological process Gene ontology terms (adjusted P < 0.05) were visualized in dot plots using the clusterProfiler package 78 . Statistics and reproducibility All experiments were performed with full factorial (biological and technical) replication. Competition experiments using SynComs were in addition repeated multiple times (Extended Data Fig. 1 ) using independent bacterial communities. Whenever bacterial abundances or plant growth parameters were compared, we used a two-sided, non-parametric Mann–Whitney test or, in the case of multiple comparisons, a Kruskal–Wallis test, followed by a Dunn’s post hoc. Whenever appropriate, P values were adjusted for multiple testing using the Benjamini–Hochberg method ( α = 0.005). Statistical tests on beta-diversity analyses were performed using a PERMANOVA test with 5,000 random permutations. Whenever boxplots were used in figures, data were represented as median values (horizontal line), Q1 − 1.5× interquartile range (boxes) and Q3 + 1.5× interquartile range (whiskers). Reporting Summary Further information on research design is available in the Nature Research Reporting Summary linked to this article. Supplementary information Supplementary Information Supplementary Note, Figs. 1–3 and Tables 1 and 2. Reporting Summary Supplementary Data 1 Data and metadata of Lj - and At -IRLs. Supplementary Data 2 Metadata of the Lj - and At -SPHERE core culture collection. Supplementary Data 3 Data and metadata of LjAt SynCom experiments. Extended data Extended Data Fig. 1 Flow chart of experimental procedures. a , Overview of experiments and type of analyses performed on root, rhizosphere, and soil samples, or millifluidic droplets. b , Culture collection establishment of Lotus japonicus root and nodule bacterial isolates. Image created with BioRender.com. Extended Data Fig. 2 Culture-independent diversity analysis of root-associated bacterial communities from Lotus and Arabidopsis . Rank abundance plot of bacterial communities from Lotus or Arabidopsis roots, aggregated to the class level, including all families with a mean accumulated relative abundance of > 0.1% on either host. Statistical differences were assessed using a two-sided, non-parametric Mann–Whitney test. Asterisks represent significant values after multiple testing correction using the Benjamini–Hochberg method ( P < 0.05). Extended Data Fig. 3 Functional overlap of Lotus and Arabidopsis culture collection genomes. PCoA of functional distances of genomes from bacterial isolates of the Lotus ( Lj -SPHERE; n = 294) and the Arabidopsis ( At -SPHERE; n = 194) culture collections. Extended Data Fig. 4 Host-species specific bacterial root communities and commensal host preference is confirmed using independent mixed communities. a and c , Constrained PCoA of Bray-Curtis dissimilarity (constrained by all biological factors and conditioned by all technical variables) of soil, rhizosphere, and root samples from L. japonicus wild-type Gifu, nfr5 mutant, and A. thaliana wild type Col-0 plants co-cultivated with the mixed community LjAt -SC1 ( a , experiment C, n = 155, variance explained 53.8%, P = 0.001), from Gifu and Col-0 co-cultivated with LjAt -SC4 ( b , experiment D, n = 87, variance explained 60%, P = 0.001), or from Gifu and Col-0 co-cultivated with LjAt -SC5 ( b , experiment M, n = 100, variance explained 67%, P = 0.001). d , e and f , Aggregated RA of the 16 Lj -derived and the 16 At -derived strains in the roots of Lotus and Arabidopsis plants inoculated with LjAt -SC1 ( d; n = 68), LjAt -SC4 ( e ; n = 34), or LjAt -SC5 ( f ; n = 40). n refers to biologically independent samples. Extended Data Fig. 5 Host preference is retained after in silico removal of individual bacterial families. Aggregated relative abundance of Lotus - ( a , n = 20) and Arabidopsis -derived ( b , n = 16) strains in roots from plants inoculated with the mixed community LjAt -SC3 (experiment L). Host preference was assessed using a Mann-Whitney non-parametric test after in silico removal of each family. The x-axis labels indicate each depleted family. n refers to biologically independent samples. Extended Data Fig. 6 Bacterial abundance in mono-association with host plants. Bacterial abundances of commensal bacteria (strain IDs indicated at x -axis) colonizing Col-0, Gifu, and nfr5 roots, assessed by counting of colony forming units (CFUs) after extraction from root tissue. Plants were grown for two weeks on agar plates in mono-association with the indicated Lj -SPHERE ( a ) and At -SPHERE ( b ) strains (exp. E). n = 6 (3 biologically independent samples × 2 technical replicates). Statistical differences were assessed using a two-sided, non-parametric Mann-Whitney test. Asterisks represent significant values after multiple testing correction using the Benjamini–Hochberg method ( P < 0.05). Extended Data Fig. 7 Plant performance in mono-associations. Shoot fresh weight of Gifu ( a ) and Col-0 ( b ) plants grown for two weeks on agar plates in mono-association with the indicated Lj -SPHERE and At -SPHERE strains (exp. E). n = 30 (3 biologically independent samples × 10 technical replicates). Extended Data Fig. 8 Sister species of L. japonicus and A. thaliana establish distinct bacterial root communities. Constrained PCoA of Bray-Curtis dissimilarity (constrained by all biological factors and conditioned by all technical variables) of root samples from L. japonicus wild type Gifu and L. corniculatus ( a ; n = 87; variance explained 58.7%, P = 0.001), and or root samples from A. thaliana wild type Col-0 and A. lyrata MN47 ( b ; n = 86; variance explained 65%, P = 0.001), inoculated and grown with the mixed community LjAt -SC3, and of the corresponding rhizosphere and bulk soil communities (exp. F). Extended Data Fig. 9 Tested plant immune receptors and signaling pathways do not affect host preference of commensals. a , Constrained PCoA of Bray-Curtis dissimilarity (constrained by all biological factors and conditioned by all technical variables; n = 98; variance explained 24.6%, P = 0.001) of root samples from L. japonicus wild type Gifu, Ljfls2 mutant, A. thaliana wild type Col-0, Atfls2 mutant, and Atbbc mutant inoculated and grown with the mixed SynCom LjAt -SC1 (exp. G), and of the corresponding bacterial input communities. b , Aggregated relative abundance of the 16 Lj -derived and the 16 At -derived strains in the roots of Lotus and Arabidopsis plants. n = 21 for Gifu, n = 16 for Col-0, n = 20 for Ljfls2 and Atfls2 , n = 18 for Atbbc . c , Constrained PCoA of Bray-Curtis dissimilarity (constrained by all biological factors and conditioned by all technical variables; n = 64; variance explained 42.2%, P = 0.001) of soil and root samples from Gifu, Col-0, and Atdeps mutant inoculated and grown with the mixed SynCom LjAt -SC3 (exp. H). d , Aggregated relative abundance of the 16 Lj -derived and the 16 At -derived strains in the roots of Lotus and Arabidopsis plants. n = 14 for Gifu, n = 20 for Col-0 and Atdeps . n refers to biologically independent samples. Extended Data Fig. 10 Effect of secreted indole glucosinolates on host preference of commensals. a , Constrained PCoA of Bray-Curtis dissimilarity (constrained by all biological factors and conditioned by all technical variables; n = 50; variance explained 47.2%, P = 0.001) of soil and root samples from L. japonicus wild type Gifu, A. thaliana wild type Col-0, and Arabidopsis cyp79b2 cyb79b3 mutant inoculated and grown with the mixed SynCom LjAt -SC3 (exp. H). b , Aggregated relative abundance of the 16 Lj -derived and the 16 At -derived strains in the roots of Lotus and Arabidopsis plants. n = 20 for Col-0, n = 14 for Gifu, n = 6 for Atcyp79b2b3 . n refers to biologically independent samples. Source data Source Data Fig. 1 Statistical source data. Source Data Fig. 2 Statistical source data. Source Data Fig. 3 Statistical source data. Source Data Fig. 4 Statistical source data. Source Data Fig. 5 Statistical source data. Source Data Fig. 6 Statistical source data. Peer review information Nature Microbiology thanks Rebecca Batstone, Sarah Lebeis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. These authors contributed equally: Kathrin Wippel, Ke Tao. Change history 10/26/2021 In the version of this article initially published online, the following metadata was omitted and has now been included: Open access funding provided by Max Planck Institute for Plant Breeding Research. Extended data is available for this paper at 10.1038/s41564-021-00941-9. Supplementary information The online version contains supplementary material available at 10.1038/s41564-021-00941-9. Acknowledgements We acknowledge P. Duran and S. Zhang for their assistance while performing the SynCom experiments, A.L. Roth and Z. Blahovska for their help in maintaining the culture collection, J. Garnica and E. Brambilla for their help in optimizing millifluidics protocols, M. Peukert and S. Kopriva for their advice on root exudate collection, J. de Meaux for providing A. lyrata seeds, and N. Donnelly for scientific English editing. This research was funded by the Max Planck Society and Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy, EXC-Nummer 2048/1, project no. 390686111 and the ‘2125 DECRyPT’ Priority Programme through P.S.-L. and R.G.-O. K.T. was funded by the Chinese Scholarship Council. The Novo Nordisk programme InRoot, grant no. NNF19SA0059362, funded K.T. and S.R. Author contributions K.W., K.T., S.R., P.S.-L. and R.G.-O. conceived the research and designed the experiments. K.W, K.T., R.Z. and D.B.J. established the Lj -SPHERE culture collection. K.W., K.T. and N.K. performed the gnotobiotic competition experiments. K.W. and E.L. conducted the in planta invasion and millifluidics SynCom experiments. R.G. and R.G.-O. analysed culture-independent amplicon data. E.D. and R.G.-O. analysed the Lj -IRL data. P.Z. and R.G.-O. processed bacterial whole-genome data from the Lj -SPHERE collection. Y.N. and R.G.-O. analysed the transcriptome data. K.W., R.G.-O. and N.K. analysed sequencing data from the SynCom experiments. K.W., K.T., S.R., P.S.-L. and R.G.-O. interpreted data and wrote the paper. Funding Open access funding provided by Max Planck Institute for Plant Breeding Research. Data availability The strains of the Lj -SPHERE collection will be deposited at and will be available on request from the Leibniz Institute DSMZ in Braunschweig, Germany. Raw 16S rRNA amplicon reads have been deposited in the European Nucleotide Archive under the accession number PRJEB37695 . Similarly, sequencing reads and genome assemblies of the Lj -SPHERE core collection have been uploaded to the same database with the accession number PRJEB37696 . Source data are provided with this paper. Code availability The scripts used for the computational analyses described in this study are available at http://www.github.com/garridoo/ljsphere , to ensure replicability and reproducibility of these results. Competing interests The authors declare no competing interests. References 1. Carrion VJ Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome Science 2019 366 606 612 10.1126/science.aaw9285 31672892 2. Duran P Microbial interkingdom interactions in roots promote Arabidopsis survival Cell 2018 175 973 983 10.1016/j.cell.2018.10.020 30388454 PMC6218654 3. Zhang J NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice Nat. Biotechnol. 2019 37 676 684 10.1038/s41587-019-0104-4 31036930 4. Bulgarelli D Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota Nature 2012 488 91 95 10.1038/nature11336 22859207 5. Fitzpatrick CR Assembly and ecological function of the root microbiome across angiosperm plant species Proc. Natl Acad. Sci. USA 2018 115 E1157 E1165 10.1073/pnas.1717617115 29358405 PMC5819437 6. Lundberg DS Defining the core Arabidopsis thaliana root microbiome Nature 2012 488 86 90 10.1038/nature11237 22859206 PMC4074413 7. Yeoh YK Evolutionary conservation of a core root microbiome across plant phyla along a tropical soil chronosequence Nat. Commun. 2017 8 215 10.1038/s41467-017-00262-8 28790312 PMC5548757 8. Bai Y Functional overlap of the Arabidopsis leaf and root microbiota Nature 2015 528 364 369 10.1038/nature16192 26633631 9. Levy A Genomic features of bacterial adaptation to plants Nat. Genet. 2017 50 138 150 10.1038/s41588-017-0012-9 29255260 PMC5957079 10. Lebeis SL Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa Science 2015 349 860 864 10.1126/science.aaa8764 26184915 11. Castrillo G Root microbiota drive direct integration of phosphate stress and immunity Nature 2017 543 513 518 10.1038/nature21417 28297714 PMC5364063 12. Carlström CI Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere Nat. Ecol. Evol. 2019 3 1445 1454 10.1038/s41559-019-0994-z 31558832 PMC6774761 13. Van de Peer Y Mizrachi E Marchal K The evolutionary significance of polyploidy Nat. Rev. Genet. 2017 18 411 424 10.1038/nrg.2017.26 28502977 14. Koenen EJM The origin of the legumes is a complex paleopolyploid phylogenomic tangle closely associated with the Cretaceous–Paleogene (K–Pg) mass extinction event Syst. Biol. 2021 70 508 526 10.1093/sysbio/syaa041 32483631 PMC8048389 15. Thiergart T Lotus japonicus symbiosis genes impact microbial interactions between symbionts and multikingdom commensal communities mBio 2019 10 e01833-19 10.1128/mBio.01833-19 31594815 PMC6786870 16. Zgadzaj R Root nodule symbiosis in Lotus japonicus drives the establishment of distinctive rhizosphere, root, and nodule bacterial communities Proc. Natl Acad. Sci. USA 2016 113 E7996 E8005 10.1073/pnas.1616564113 27864511 PMC5150415 17. Kremer, J. M. et al. FlowPot axenic plant growth system for microbiota research. Preprint at bioRxiv 10.1101/254953 (2018). 18. Madsen EB A receptor kinase gene of the LysM type is involved in legume perception of rhizobial signals Nature 2003 425 637 640 10.1038/nature02045 14534591 19. Beilstein MA Nagalingum NS Clements MD Manchester SR Mathews S Dated molecular phylogenies indicate a Miocene origin for Arabidopsis thaliana Proc. Natl Acad. Sci. USA. 2010 107 18724 18728 10.1073/pnas.0909766107 20921408 PMC2973009 20. Ojeda, D. I. et al. DNA barcodes successfully identified Macaronesian Lotus (Leguminosae) species within early diverged lineages of Cape Verde and mainland Africa. AoB Plants 10.1093/aobpla/plu050 (2014). 10.1093/aobpla/plu050 PMC4168286 25147310 21. Clauss MJ Mitchell-Olds T Population genetic structure of Arabidopsis lyrata in Europe Mol. Ecol. 2006 15 2753 2766 10.1111/j.1365-294X.2006.02973.x 16911198 22. Steiner JJ Garcia de los Santos G Adaptive ecology of Lotus corniculatus L. genotypes: I. Plant morphology and RAPD marker characterizations Crop Sci. 2001 41 552 563 10.2135/cropsci2001.412552x 23. Schlaeppi K Dombrowski N Garrido-Oter R Ver Loren van Themaat E Schulze-Lefert P Quantitative divergence of the bacterial root microbiota in Arabidopsis thaliana relatives Proc. Natl Acad. Sci. USA 2013 111 585 592 10.1073/pnas.1321597111 24379374 PMC3896156 24. Chen T A plant genetic network for preventing dysbiosis in the phyllosphere Nature 2020 580 653 657 10.1038/s41586-020-2185-0 32350464 PMC7197412 25. Mun T Bachmann A Gupta V Stougaard J Andersen SU Lotus base: an integrated information portal for the model legume Lotus japonicus Sci. Rep. 2016 6 39447 10.1038/srep39447 28008948 PMC5180183 26. Zipfel C Bacterial disease resistance in Arabidopsis through flagellin perception Nature 2004 428 764 767 10.1038/nature02485 15085136 27. Xin XF Bacteria establish an aqueous living space in plants crucial for virulence Nature 2016 539 524 529 10.1038/nature20166 27882964 PMC5135018 28. Tsuda K Sato M Stoddard T Glazebrook J Katagiri F Network properties of robust immunity in plants PLoS Genet. 2009 5 e1000772 10.1371/journal.pgen.1000772 20011122 PMC2782137 29. Bressan M Exogenous glucosinolate produced by Arabidopsis thaliana has an impact on microbes in the rhizosphere and plant roots ISME J. 2009 3 1243 1257 10.1038/ismej.2009.68 19554039 30. Zhalnina K Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly Nat. Microbiol. 2018 3 470 480 10.1038/s41564-018-0129-3 29556109 31. Bednarek P Chemical warfare or modulators of defence responses—the function of secondary metabolites in plant immunity Curr. Opin. Plant Biol. 2012 15 407 414 10.1016/j.pbi.2012.03.002 22445190 32. Klein AP Sattely ES Biosynthesis of cabbage phytoalexins from indole glucosinolate Proc. Natl Acad. Sci. USA 2017 114 1910 1915 10.1073/pnas.1615625114 28154137 PMC5338394 33. Pastorczyk, M. & Bednarek, P. in Advances in Botanical Research Vol. 80 (ed. Kopriva, S.) 171–198 (Elsevier, 2016). 34. Zhao Y Trp-dependent auxin biosynthesis in Arabidopsis : involvement of cytochrome P450s CYP79B2 and CYP79B3 Genes Dev. 2002 16 3100 3112 10.1101/gad.1035402 12464638 PMC187496 35. Lapébie, P., Lombard, V., Drula, E., Terrapon, N. & Henrissat, B. Bacteroidetes use thousands of enzyme combinations to break down glycans. Nat. Commun. 10.1038/s41467-019-10068-5 (2019). 10.1038/s41467-019-10068-5 PMC6499787 31053724 36. Fukami T Historical contingency in community assembly: integrating niches, species pools, and priority effects Annu. Rev. Ecol., Evolution, Syst. 2015 46 1 23 10.1146/annurev-ecolsys-110411-160340 37. Chase JM Community assembly: when should history matter? Oecologia 2003 136 489 498 10.1007/s00442-003-1311-7 12836009 38. Batstone RT O’Brien AM Harrison TL Frederickson ME Experimental evolution makes microbes more cooperative with their local host genotype Science 2020 370 476 478 10.1126/science.abb7222 33093112 39. Agosta SJ Klemens JA Ecological fitting by phenotypically flexible genotypes: implications for species associations, community assembly and evolution Ecol. Lett. 2008 11 1123 1134 10.1111/j.1461-0248.2008.01237.x 18778274 40. Kinnunen M A conceptual framework for invasion in microbial communities ISME J. 2016 10 2773 2775 10.1038/ismej.2016.75 27137125 PMC5148196 41. Litchman E Invisible invaders: non-pathogenic invasive microbes in aquatic and terrestrial ecosystems Ecol. Lett. 2010 13 1560 1572 10.1111/j.1461-0248.2010.01544.x 21054733 42. Thiergart T Root microbiota assembly and adaptive differentiation among European Arabidopsis populations Nat. Ecol. Evol. 2020 4 122 131 10.1038/s41559-019-1063-3 31900452 43. Broughton WJ Dilworth MJ Control of leghaemoglobin synthesis in snake beans Biochem. J. 1971 125 1075 1080 10.1042/bj1251075 5144223 PMC1178271 44. Pfennig N Rhodocyclus purpureus gen. nov. and sp. nov., a ring-shaped, vitamin B12-requiring member of the family Rhodospirillaceae Int. J. Syst. Bacteriol. 1978 28 283 288 10.1099/00207713-28-2-283 45. Baudoin E Benizri E Guckert A Impact of artificial root exudates on the bacterial community structure in bulk soil and maize rhizosphere Soil Biol. Biochem. 2003 35 1183 1192 10.1016/S0038-0717(03)00179-2 46. Lohmann GV Evolution and regulation of the Lotus japonicus LysM receptor gene family Mol. Plant Microbe Interact. 2010 23 510 521 10.1094/MPMI-23-4-0510 20192837 47. Caporaso JG QIIME allows analysis of high-throughput community sequencing data Nat. Methods 2010 7 335 336 10.1038/nmeth.f.303 20383131 PMC3156573 48. Callahan BJ DADA2: High-resolution sample inference from Illumina amplicon data Nat. Methods 2016 13 581 583 10.1038/nmeth.3869 27214047 PMC4927377 49. Quast C The SILVA ribosomal RNA gene database project: improved data processing and web-based tools Nucleic Acids Res. 2013 41 D590 D596 10.1093/nar/gks1219 23193283 PMC3531112 50. Oksanen, J., Kindt, R., Legendre, P., O’Hara, B. & Stevens, M. H. H. vegan: community ecology package (R Project, 2007). 51. Edgar RC Search and clustering orders of magnitude faster than BLAST Bioinformatics 2010 26 2460 2461 20709691 10.1093/bioinformatics/btq461 52. Edgar RC Haas BJ Clemente JC Quince C Knight R UCHIME improves sensitivity and speed of chimera detection Bioinformatics 2011 27 2194 2200 10.1093/bioinformatics/btr381 21700674 PMC3150044 53. Edgar RC UPARSE: highly accurate OTU sequences from microbial amplicon reads Nat. Methods 2013 10 996 998 10.1038/nmeth.2604 23955772 54. Wickham, H. ggplot2 - Elegant Graphics for Data Analysis Vol. 2 (Springer International Publishing, 2016). 55. Bolger AM Lohse M Usadel B Trimmomatic: a flexible trimmer for Illumina sequence data Bioinformatics 2014 30 2114 2120 10.1093/bioinformatics/btu170 24695404 PMC4103590 56. Tritt A Eisen JA Facciotti MT Darling AE An integrated pipeline for de novo assembly of microbial genomes PLoS ONE 2012 7 e42304 10.1371/journal.pone.0042304 23028432 PMC3441570 57. Peng Y Leung HC Yiu SM Chin FY IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth Bioinformatics 2012 28 1420 1428 10.1093/bioinformatics/bts174 22495754 58. Pasolli E Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle Cell 2019 176 649 662 10.1016/j.cell.2019.01.001 30661755 PMC6349461 59. Kang DD MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies PeerJ 2019 7 e7359 10.7717/peerj.7359 31388474 PMC6662567 60. Parks DH Imelfort M Skennerton CT Hugenholtz P Tyson GW CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes Genome Res. 2015 25 1043 1055 10.1101/gr.186072.114 25977477 PMC4484387 61. Kanehisa M Data, information, knowledge and principle: back to metabolism in KEGG Nucleic Acids Res. 2014 42 D199 D205 10.1093/nar/gkt1076 24214961 PMC3965122 62. Wu M Eisen JA A simple, fast, and accurate method of phylogenomic inference Genome Biol. 2008 9 R151 10.1186/gb-2008-9-10-r151 18851752 PMC2760878 63. Sievers F Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega Mol. Syst. Biol. 2011 7 539 10.1038/msb.2011.75 21988835 PMC3261699 64. Price MN Dehal PS Arkin AP FastTree 2 - approximately maximum-likelihood trees of large alignments PLoS ONE 2010 5 e9490 10.1371/journal.pone.0009490 20224823 PMC2835736 65. Letunic I Bork P Interactive Tree Of Life (iTOL) v4: recent updates and new developments Nucleic Acids Res. 2019 47 W256 W259 10.1093/nar/gkz239 30931475 PMC6602468 66. Olm, M. R. et al. Consistent metagenome-derived metrics verify and delineate bacterial species boundaries. mSystems 10.1128/mSystems.00731-19 (2020). 10.1128/mSystems.00731-19 PMC6967389 31937678 67. Jain C Rodriguez-R LM Phillippy AM Konstantinidis KT Aluru S High throughput ANI analysis of 90 K prokaryotic genomes reveals clear species boundaries Nat. Commun. 2018 9 5114 10.1038/s41467-018-07641-9 30504855 PMC6269478 68. Chen S Zhou Y Chen Y Gu J fastp: an ultra-fast all-in-one FASTQ preprocessor Bioinformatics 2018 34 i884 i890 10.1093/bioinformatics/bty560 30423086 PMC6129281 69. Bray NL Pimentel H Melsted P Pachter L Near-optimal probabilistic RNA-seq quantification Nat. Biotechnol. 2016 34 525 527 10.1038/nbt.3519 27043002 70. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 10.12688/f1000research.7563.1 (2015). 10.12688/f1000research.7563.1 PMC4712774 26925227 71. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 10.1186/s13059-014-0550-8 (2014). 10.1186/s13059-014-0550-8 PMC4302049 25516281 72. Leek JT Johnson WE Parker HS Jaffe AE Storey JD The sva package for removing batch effects and other unwanted variation in high-throughput experiments Bioinformatics 2012 28 882 883 10.1093/bioinformatics/bts034 22257669 PMC3307112 73. Ritchie ME limma powers differential expression analyses for RNA-sequencing and microarray studies Nucleic Acids Res. 2015 43 e47 10.1093/nar/gkv007 25605792 PMC4402510 74. Gu Z Eils R Schlesner M Complex heatmaps reveal patterns and correlations in multidimensional genomic data Bioinformatics 2016 32 2847 2849 10.1093/bioinformatics/btw313 27207943 75. Young MD Wakefield MJ Smyth GK Oshlack A Gene ontology analysis for RNA-seq: accounting for selection bias Genome Biol. 2010 11 R14 10.1186/gb-2010-11-2-r14 20132535 PMC2872874 76. Ashburner M Gene Ontology: tool for the unification of biology Nat. Genet. 2000 25 25 29 10.1038/75556 10802651 PMC3037419 77. The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong Nucleic Acids Res. 2019 47 D330 D338 10.1093/nar/gky1055 30395331 PMC6323945 78. Yu G Wang L-G Han Y He Q-Y clusterProfiler: an R package for comparing biological themes among gene clusters OMICS 2012 16 284 287 10.1089/omi.2011.0118 22455463 PMC3339379 \ No newline at end of file