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Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community

Abstract

Microbial communities often undergo intricate compositional changes yet also maintain stable coexistence of diverse species. The mechanisms underlying long-term coexistence remain unclear as system-wide studies have been largely limited to engineered communities, ex situ adapted cultures or synthetic assemblies. Here, we show how kefir, a natural milk-fermenting community of prokaryotes (predominantly lactic and acetic acid bacteria) and yeasts (family Saccharomycetaceae), realizes stable coexistence through spatiotemporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microorganisms) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a sequential manner in which early members open the niche for the followers by making available metabolites such as amino acids and lactate. Through metabolomics, transcriptomics and large-scale mapping of inter-species interactions, we show how microorganisms poorly suited for milk survive in—and even dominate—the community, through metabolic cooperation and uneven partitioning between grain and milk. Overall, our findings reveal how inter-species interactions partitioned in space and time lead to stable coexistence.

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Fig. 1: The kefir community undergoes extensive compositional change during milk fermentation.
Fig. 2: Metabolite changes during kefir fermentation depict niche dynamics.
Fig. 3: The growth performances of individual kefir community members highlight inter-species dependencies for milk colonization.
Fig. 4: Interactions between kefir community members are extensive and qualitatively differ between solid and liquid phases.
Fig. 5: Unravelling selected metabolic interactions in kefir.
Fig. 6: The kefir community exhibits a basecamp lifestyle.

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Data availability

All of the data generated or analysed during this study are included in this published article (and its Supplementary Information files). The genomes of isolated kefir species are available in the National Center for Biotechnology Information database under the accession no. PRJNA375758. The metatranscriptomic sequencing reads can be accessed from the European Nucleotide Archive under project ID PRJEB37001. The metabolomics data are available from the MetaboLights database (www.ebi.ac.uk/metabolights/) via accession nos. MTBLS1823, MTBLS1829 and MTBLS1830. Genome-scale metabolic models for kefir bacteria can be found at github.com/cdanielmachado/kefir_models. Source data are provided with this paper.

Code availability

The custom scripts, models and databases used for metatranscriptomics analysis and genome-scale metabolic modelling are available at github.com/cdanielmachado/kefir_paper/. All of the other computer code used in data analysis is available from the corresponding author upon reasonable request.

References

  1. Mueller, D. B., Vogel, C., Bai, Y. & Vorholt, J. A.The plant microbiota: systems-level insights and perspectives. Annu. Rev. Genet. 50, 211–234 (2016).

    Article  Google Scholar 

  2. Palleja, A. et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat. Microbiol. 3, 1255–1265 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Shade, A. et al. Lake microbial communities are resilient after a whole-ecosystem disturbance. ISME J. 6, 2153–2167 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Allesina, S. & Tang, S. Stability criteria for complex ecosystems. Nature 483, 205–208 (2012).

    Article  CAS  PubMed  Google Scholar 

  6. Fyodorov, Y. V. & Khoruzhenko, B. A. Nonlinear analogue of the May–Wigner instability transition. Proc. Natl Acad. Sci. USA 113, 6827–6832 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Pennekamp, F. et al. Biodiversity increases and decreases ecosystem stability. Nature 563, 109–112 (2018).

    Article  CAS  PubMed  Google Scholar 

  8. Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 0109 (2017).

    Article  Google Scholar 

  9. Blasche, S., Kim, Y., Oliveira, A. P. & Patil, K. R. Model microbial communities for ecosystems biology. Curr. Opin. Syst. Biol. 6, 51–57 (2017).

    Article  Google Scholar 

  10. Dubey, G. P. & Ben-Yehuda, S. Intercellular nanotubes mediate bacterial communication. Cell 144, 590–600 (2011).

    Article  CAS  PubMed  Google Scholar 

  11. Ponomarova, O. et al. Yeast creates a stable niche for symbiotic lactic acid bacteria through nitrogen overflow. Cell Syst. 5, 345–357 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wintermute, E. H. & Silver, P. A. Emergent cooperation in microbial metabolism. Mol. Syst. Biol. 6, 407 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Alessi, A. M. et al. Defining functional diversity for lignocellulose degradation in a microbial community using multi-omics studies. Biotech. Biofuels 11, 166 (2018).

    Article  Google Scholar 

  14. Rosenthal, A. Z., Matson, E. G., Eldar, A. & Leadbetter, J. R. RNA-seq reveals cooperative metabolic interactions between two termite-gut spirochete species in co-culture. ISME J. 5, 1133–1142 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Enke, T. N., Leventhal, G. E., Metzger, M., Saavedra, J. T. & Cordero, O. X. Microscale ecology regulates particulate organic matter turnover in model marine microbial communities. Nat. Commun. 9, 2743 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ratzke, C. & Gore, J. Modifying and reacting to the environmental pH can drive bacterial interactions. PLoS Biol. 16, e2004248 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Baran, R. et al. Exometabolite niche partitioning among sympatric soil bacteria. Nat. Commun. 6, 8289 (2015).

    Article  CAS  PubMed  Google Scholar 

  18. Yuan, C. & Chesson, P. The relative importance of relative nonlinearity and the storage effect in the lottery model. Theor. Popul. Biol. 105, 39–52 (2015).

    Article  PubMed  Google Scholar 

  19. Embree, M., Nagarajan, H., Movahedi, N., Chitsaz, H. & Zengler, K. Single-cell genome and metatranscriptome sequencing reveal metabolic interactions of an alkane-degrading methanogenic community. ISME J. 8, 757–767 (2014).

    Article  CAS  PubMed  Google Scholar 

  20. Venturelli, O. S. et al. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol. Syst. Biol. 14, e8157 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Wolfe, B. E., Button, J. E., Santarelli, M. & Dutton, R. J. Cheese rind communities provide tractable systems for in situ and in vitro studies of microbial diversity. Cell 158, 422–433 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Goyal, A., Dubinkina, V. & Maslov, S. Multiple stable states in microbial communities explained by the stable marriage problem. ISME J. 12, 2823–2834 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Konopka, A., Lindemann, S. & Fredrickson, J. Dynamics in microbial communities: unraveling mechanisms to identify principles. ISME J. 9, 1488–1495 (2015).

    Article  PubMed  Google Scholar 

  24. Bourrie, B. C. T., Willing, B. P. & Cotter, P. D. The microbiota and health promoting characteristics of the fermented beverage kefir. Front. Microbiol. 7, 647 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Motaghi, M. et al. Kefir production in Iran. World J. Microbiol. Biotechnol. 13, 579–581 (1997).

    Article  Google Scholar 

  26. Marsh, S. J., O'Sullivan, O., Hill, C., Ross, R. P. & Cotter, P. D. Sequencing-based analysis of the bacterial and fungal composition of kefir grains and milks from multiple sources. PLoS ONE 8, e69371 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Gao, J., Gu, F., Abdella, N. H., Ruan, H. & He, G. Investigation on culturable microflora in Tibetan kefir grains from different areas of China. J. Food Sci. 77, M425–M433 (2012).

    Article  CAS  PubMed  Google Scholar 

  28. Garofalo, C. et al. Bacteria and yeast microbiota in milk kefir grains from different Italian regions. Food Microbiol. 49, 123–133 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. Yang, Y. et al. Proteomics evidence for kefir dairy in Early Bronze Age China. J. Archaeol. Sci. 45, 178–186 (2014).

    Article  CAS  Google Scholar 

  30. Prado, M. R. et al. Milk kefir: composition, microbial cultures, biological activities, and related products. Front. Microbiol. 6, 1177 (2015).

  31. Farnworth, E. R. Handbook of Fermented Functional Foods (CRC Press, 2008).

  32. Link, H., Fuhrer, T., Gerosa, L., Zamboni, N. & Sauer, U. Real-time metabolome profiling of the metabolic switch between starvation and growth. Nat. Methods 12, 1091–1097 (2015).

    Article  CAS  PubMed  Google Scholar 

  33. Dols, M., Chraibi, W., Remaud-Simeon, M., Lindley, N. D. & Monsan, P. F. Growth and energetics of Leuconostoc mesenteroides NRRL B-1299 during metabolism of various sugars and their consequences for dextransucrase production. Appl. Environ. Microbiol. 63, 2159–2165 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Linares, D. M. et al. Factors influencing biogenic amines accumulation in dairy products. Front. Microbiol. 3, 180 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Schönfeldt, H. C., Hall, N. G. & Smit, L. E. The need for country specific composition data on milk. Food Res. Int. 47, 207–209 (2012).

    Article  Google Scholar 

  36. Park, Y. W. Impact of goat milk and milk products on human nutrition. Perspect. Agric. Vet. Sci. Nutr. Nat. Resour. 2, 81 (2007).

    Google Scholar 

  37. Apelblat, A. Citric Acid (Springer, 2014).

  38. Law, J. & Haandrikman, A. Proteolytic enzymes of lactic acid bacteria. Int. Dairy J. 7, 1–11 (1997).

    Article  CAS  Google Scholar 

  39. Marty-Teysset, C., Lolkema, J. S., Schmitt, P., Diviès, C. & Konings, W. N. The citrate metabolic pathway in Leuconostoc mesenteroides: expression, amino acid synthesis, and α-ketocarboxylate transport. J. Bacteriol. 178, 6209–6215 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Samaržija, D., Antunac, N. & Havranek, J. L. Taxonomy, physiology and growth of Lactococcus lactis: a review. Mljekarstvo 51, 35–48 (2001).

    Google Scholar 

  41. Blasche, S., Kim, Y. & Patil, K. R. Draft genome sequence of Corynebacterium kefirresidentii SB, isolated from kefir. Genome Announc. 5, e00877-17 (2017).

  42. Kim, Y., Blasche, S. & Patil, K. R. Draft genome sequences of three novel low-abundance species strains isolated from kefir grain. Genome Announc. 5, 57–58 (2017).

    Article  Google Scholar 

  43. Bellengier, P., Richard, J. & Foucaud, C. Nutritional requirements of Leuconostoc mesenteroides subsp. mesenteroides and subsp. dextranicum for growth in milk. J. Dairy Res. 64, 95–103 (1997).

    Article  CAS  Google Scholar 

  44. Davies, D. T. & White, J. C. D. The use of ultrafiltration and dialysis in isolating the aqueous phase of milk and in determining the partition of milk constituents between the aqueous and disperse phases. J. Dairy Res. 27, 171–190 (1960).

    Article  CAS  Google Scholar 

  45. Hache, C. et al. Influence of lactose–citrate co-metabolism on the differences of growth and energetics in Leuconostoc lactis, Leuconostoc mesenteroides ssp. mesenteroides and Leuconostoc mesenteroides ssp. cremoris. Syst. Appl. Microbiol. 22, 507–513 (1999).

    Article  CAS  PubMed  Google Scholar 

  46. Lacroix, N., St-Gelais, D., Champagne, C. P. & Vuillemard, J. C. Gamma-aminobutyric acid-producing abilities of lactococcal strains isolated from old-style cheese starters. Dairy Sci. Technol. 93, 315–327 (2013).

    Article  CAS  Google Scholar 

  47. Adler, P. et al. The key to acetate: metabolic fluxes of acetic acid bacteria under cocoa pulp fermentation-simulating conditions. Appl. Environ. Microbiol. 80, 4702–4716 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Hom, E. F. Y. & Murray, A. W. Niche engineering demonstrates a latent capacity for fungal–algal mutualism. Science 345, 94–98 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Thaiss, C. A. et al. Article transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Cell 159, 514–529 (2014).

    Article  CAS  PubMed  Google Scholar 

  51. Fuhrer, T., Heer, D., Begemann, B. & Zamboni, N. High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection–time-of-flight mass spectrometry. Anal. Chem. 83, 7074–7080 (2011).

    Article  CAS  PubMed  Google Scholar 

  52. Kowalczyk, M., Kolakowski, P., Radziwill-Bienkowska, J. M., Szmytkowska, A. & Bardowski, J. Cascade cell lyses and DNA extraction for identification of genes and microorganisms in kefir grains. J. Dairy Res. 79, 26–32 (2011).

    Article  PubMed  Google Scholar 

  53. Kim, D.-H. et al. Rapid detection of Lactobacillus kefiranofaciens in kefir grain and kefir milk using newly developed real-time PCR. J. Food Prot. 78, 855–858 (2015).

    Article  CAS  PubMed  Google Scholar 

  54. Zimmermann, M. et al. Dynamic exometabolome analysis reveals active metabolic pathways in non-replicating mycobacteria. Environ. Microbiol. 17, 4802–4815 (2015).

    Article  CAS  PubMed  Google Scholar 

  55. Kanani, H. H. & Klapa, M. I. Data correction strategy for metabolomics analysis using gas chromatography–mass spectrometry. Metab. Eng. 9, 39–51 (2007).

    Article  CAS  PubMed  Google Scholar 

  56. Corradini, C., Cavazza, A. & Bignardi, C. High-performance anion-exchange chromatography coupled with pulsed electrochemical detection as a powerful tool to evaluate carbohydrates of food interest: principles and applications. Int. J. Carbohydr. Chem. 2012, 487564 (2012).

    Article  Google Scholar 

  57. Mullin, W. J. & Emmons, D. B. Determination of organic acids and sugars in cheese, milk and whey by high performance liquid chromatography. Food Res. Int. 30, 147–151 (1997).

    Article  CAS  Google Scholar 

  58. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Article  Google Scholar 

  59. Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina paired-end read merger. Bioinformatics 30, 614–620 (2014).

    Article  CAS  PubMed  Google Scholar 

  60. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  63. McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).

    Article  CAS  PubMed  Google Scholar 

  64. Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).

    Article  PubMed  Google Scholar 

  65. Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).

    Article  CAS  PubMed  Google Scholar 

  66. Milanese, A. et al. Microbial abundance, activity and population genomic profiling with mOTUs2. Nat. Commun. 10, 1014 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).

    Article  CAS  PubMed  Google Scholar 

  68. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Machado, D., Herrg, M. J. & Rocha, I. Stoichiometric representation of gene–protein–reaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction. PLoS Comput. Biol. 12, e1005140 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Huerta-cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Machado, D., Andrejev, S., Tramontano, N. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the EMBL GeneCore facility for help with the (meta-)genomic sequencing, O. Ponomarova for help with collecting kefir grains, advice on cultivation and feedback on the manuscript, and K. Zirngibl for feedback on the manuscript. This work was sponsored by the German Ministry of Education and Research (BMBF; no. 031A601B) as part of the ERASysAPP project SysMilk, and by the Innovation Fund Denmark through the project Food Transcriptomics (grant no. 6150-00033A).

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S.B. and Y.K. conceived the project, designed and performed the experiments, analysed the data and wrote the paper. R.A.T.M. performed the untargeted metabolomics experiments and data analysis, as well as the quantitative analysis of aspartate and proline. E.K. and M.M. performed the GC–MS and ion chromatography analysis. V.B. contributed to the amplicon, metagenome and messenger RNA sequencing. J.N. and B.T. contributed to the experimental design. R.N. oversaw the targeted metabolomics analysis. D.M. contributed to the amino acid profile analysis and interpretation. A.M., D.M. and G.Z. analysed the RNA-seq data. U.S. oversaw the exo-metabolome analysis. K.R.P. conceived the project, designed the experimental approach, oversaw the project and wrote the paper.

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Correspondence to Kiran Raosaheb Patil.

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Extended data

Extended Data Fig. 1 Additional information related to Fig. 1d.

a, Temporal dynamics of bacterial composition during fermentation in the kefir fermented milk assessed by 16 S amplicon sequencing. The non-isolates represent the sum of all species, which are not in the kefir isolate collection. The fermentation is split into 6 different stages depending on the abundance changes of the major species. The shaded area marks the data range. N = 4, biologically independent samples. b, Fitting of DNA concentration dynamics to sigmoid curve. N = 4, biologically independent samples, error bars = mean values + /- SD. c, DNA extracted from fermentation samples. DNA concentration estimates, measured using Qubit (Supplementary Table 19), were used to determine absolute abundances shown in Fig. 1d. Raw gel images are depicted in Supplementary Fig. 3.

Extended Data Fig. 2 Amino acid dynamics in milk and kefir fermented milk.

a, Comparison of amino acid composition of milk total protein with that of free amino acids observed in kefir after 12 h, 40 h, and 90 h fermentation. Milk total protein amino acid composition used is average from two previous reports (Park, 2007; Schönfeldt et al., 2011)35,36. Lines depict the best linear fit and grey shading the 95% confidence interval of the linear fit. b, Comparison of expected accumulation (red dotted lines) and measured concentrations (blue lines) of amino acids in milk kefir. Green bars indicate the model-based estimation of uptake (negative values) and secretion (positive values) by kefir microbes.

Extended Data Fig. 3 Effect of EDTA and protein addition on grain wet-weight gain after 72 h fermentation.

Kefir grains grown in whey harvested after 36 h fermentation reveal decreased growth that is restored by casein supplementation. Addition of EDTA inhibits grain growth in both milk and casein-supplemented kefir whey. N = 4, error bars =SD.

Extended Data Fig. 4 Effect of acetate on growth of K. exigua, R. mucilaginosa, S. unisporus, and K. marxianus. S. unisporus and K. marxianus profit from low acetate concentrations.

K. exigua and R. mucilaginosa, are inhibited even by small acetate supplements. Changes in species growth are assessed relative to growth in non-supplemented milk whey. N = 4, biologically independent samples, error bars = mean values +/− SD.

Extended Data Fig. 5 Lactate concentration shapes consecutive time-windows of growth of Kazachstania exigua (yeast) and Acetobacter fabarum.

a, Evolution of lactate and acetate concentration during kefir fermentation. Different symbols mark data from replicates (N = 4). Colored block arrows indicate optimal lactate concentration ranges for the K. exigua and A. fabarum (Fig. 3d). Dotted lines connect the time-windows corresponding to these concentration ranges to the time-windows in panel B. b, Growth of kefir species over time with dotted lines marking the time-windows corresponding to the lactate concentration ranges from (a). c, Growth of K. exigua and A. fabarum in kefir spent whey harvested at different time points (N = 4 biologically independent samples; error bars = SD). Data are presented as mean values +/− SD.

Extended Data Fig. 6 Interaction network between kefir species based on milk acidification assay.

a, Schematic depiction of the method used to map metabolic interactions based on fermentation acidification kinetics. Species were grown in 96-well plates alone or in pairs; acidification of milk was assessed with a soluble pH-indicator. Positive interactions were identified as those that showed increased acidification in co-culture compared to mono-cultures, while negative interactions as those that showed decreased acidification in co-culture. b, Network of metabolic interactions between kefir species (Interaction calling based on N = 6; 3 biological and 2 technical replicates). Node sizes indicate number of interactions. Raw R-values extracted from scan images are provided in Supplementary Table 24.

Extended Data Fig. 7 Kefir grain growth profits from rare species and supplements.

Different kefir species were supplemented to the UHT-milk used for kefir propagation in this study (Methods). This suspension was then used as a medium to grow kefir grains in. The gain in wet-weight after 3 passages (circa 1 week) was then compared to kefir grains grown in milk and milk supplemented with proteinase K and yeast extract, respectively. Compared to the negative control, many rare species and few main kefir species supported the growth of the kefir grain. However, the effect of addition of proteinase K and yeast extract to the milk medium had the biggest effect on grain growth. Significance estimated by using two-sided t-test, p-values: * <0.05, ** <0.01, *** <0.001. N = 4 biologically independent samples, data are presented as mean values + /- SD. P-values: L. mesenteroides, 0,045; L. lactis (SB-150), 0,00034; S. haemolyticus, 0,0026; R. dentocariosa, 0,0012; Rhodotorula, 0,033; proteinase K, 0,00018; yeast extract, 8,83351E-07.

Extended Data Fig. 8 Prevalence of positive and negative interactions between kefir species in milk and on milk plates.

The outside layer shows distribution of interactions in milk (liquid), the inner layer for the milk plates (solid).

Extended Data Fig. 9 Integrated view of metabolite cross-feeding between, left: L. kefiranofaciens and L. mesenteroides, and right: L. lactis and A. fabarum, based on genome-scale metabolic modeling, gene expression data and metabolite measurements.

The colored metabolic maps connected to species mark reactions in the metabolic network that are assessed to be up- or down- regulated in co-cultures.

Extended Data Fig. 10 Kefir community shift when passaged using kefir fermented milk as an inoculum instead of the kefir grain.

Shown is the relative abundance of the bacterial members of the kefir community before and after five passages.

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Blasche, S., Kim, Y., Mars, R.A.T. et al. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nat Microbiol 6, 196–208 (2021). https://doi.org/10.1038/s41564-020-00816-5

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