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Bacterial–fungal interactions revealed by genome-wide analysis of bacterial mutant fitness

Nature Microbiology volume 6, pages 87–102 (2021)Cite this article

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Abstract

Microbial interactions are expected to be major determinants of microbiome structure and function. Although fungi are found in diverse microbiomes, their interactions with bacteria remain largely uncharacterized. In this work, we characterize interactions in 16 different bacterial–fungal pairs, examining the impacts of 8 different fungi isolated from cheese rind microbiomes on 2 bacteria (Escherichia coli and a cheese-isolated Pseudomonas psychrophila). Using random barcode transposon-site sequencing with an analysis pipeline that allows statistical comparisons between different conditions, we observed that fungal partners caused widespread changes in the fitness of bacterial mutants compared to growth alone. We found that all fungal species modulated the availability of iron and biotin to bacterial species, which suggests that these may be conserved drivers of bacterial–fungal interactions. Species-specific interactions were also uncovered, a subset of which suggested fungal antibiotic production. Changes in both conserved and species-specific interactions resulted from the deletion of a global regulator of fungal specialized metabolite production. This work highlights the potential for broad impacts of fungi on bacterial species within microbiomes.

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Fig. 1: Fungal interaction partners span the phylogenetic and morphological diversity of the cheese ecosystem.
Fig. 2: Comparison of bacterial gene fitness with fungi against growth alone and identification of bacterial genes with significant interaction fitness across fungal partners.
Fig. 3: Cross-comparison and functional characterization of bacterial genes with interaction fitness in the presence of fungi.
Fig. 4: BCP of E. coli grown with Penicillium sp. str. SAM3, Penicillium sp. str. 12 or ΔlaeA Penicillium sp. str. 12 on CCA plates.
Fig. 5: Utilization of fungal siderophores by E. coli.
Fig. 6: Fungal metabolite production affects bacterial–fungal interactions.

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Data availability

Sequence data that support the findings of this study (RB-TnSeq and RNA-seq) have been deposited in the NCBI SRA database with SRA accession codes SRR11514793–SRR11514872 and BioProject code PRJNA624168. MS data are available in the MassIVE database under accession numbers MSV000085070 and MSV000085054. The GNPS molecular network is available at https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=464b331ef9d54de9957d23b4f9b9db14. The E. coli annotation database used for GO functional enrichment is available at http://bioconductor.org/packages/release/data/annotation/html/org.EcK12.eg.db.html. The Whole Genome Shotgun project for Penicillium sp. str. 12, including reads, genome assembly and annotation has been deposited at DDBJ/ENA/GenBank under the accession JAASRZ000000000 in BioProject PRJNA612335 (BioSample SAMN14369290 and SRA SRR11536435). In addition to these sources, the data used to create Figs. 2, 3, 5 and 6 are available in the Supplementary Tables provided with the paper. Uncropped Southern blots associated with Supplementary Fig. 3 are provided with the manuscript as Supplementary Data. Source data are provided with this paper.

Code availability

The R scripts developed for processing RB-TnSeq data described in this manuscript are available at https://github.com/DuttonLab/RB-TnSeq-Microbial-interactions along with usage instructions. The perl scripts needed for initial processing of RB-TnSeq data published in Wetmore et al.18 are available at https://bitbucket.org/berkeleylab/feba/src/master/.

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Acknowledgements

The authors would like to thank the following people and groups: the Arkin Lab and the Deutschbauer Lab at UC Berkeley for the E. coli Keio_ML9 RB-TnSeq library; K. Jepsen at the IGM Genomics Center at the University of California, San Diego for assistance with sequencing; S. Kryazhimskiy (UCSD) for his input on RB-TnSeq data processing; C. Dinh (UCSD) for assistance with fungal genome assembly; W. Bushnell (UCSD) for assistance with fitness validation experiments; S. Beyhan (JCVI) for advice on fungal genome annotation; L. Marotz (UCSD) for assistance with non-cheese yeasts; and members of the Dutton Lab, especially B. Anderson and C. Saak, for constructive comments on the manuscript. This work was supported by the National Institutes of Health grant nos. T32-AT007533 (to J.C.L.) and F31-AT010418 (to J.C.L.), the National Institutes of Health grant no. R01-AI117712 (to R.B.L), National Science Foundation grant no. MCB-1817955 (to L.M.S.), National Science Foundation grant no. MCB-1817887 (to R.J.D. and L.M.S.), National Science Foundation grant no. MCB-1715553 (to B.E.W.), the UCSD Center for Microbiome Innovation (to E.C.P.), the UCSD Ruth Stern Award (to E.C.P.), NIH Institutional Training grant no. 5 T32 GM 7240-40 (to E.C.P.) and the National Institutes of Health grant no. R01GM112739-01 (to N.P.K.).

Author information

Authors and Affiliations

  1. Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA

    Emily C. Pierce, Manon Morin, Roland B. Liu, Kit Pogliano & Rachel J. Dutton

  2. Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA

    Jessica C. Little & Laura M. Sanchez

  3. Department of Medical Microbiology and Immunology, University of Wisconsin–Madison, Madison, WI, USA

    Joanna Tannous & Nancy P. Keller

  4. Department of Bacteriology, University of Wisconsin–Madison, Madison, WI, USA

    Nancy P. Keller

  5. Food Research Institute, University of Wisconsin–Madison, Madison, WI, USA

    Nancy P. Keller

  6. Department of Biology, Tufts University, Medford, MA, USA

    Benjamin E. Wolfe

  7. Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, USA

    Rachel J. Dutton

Authors
  1. Emily C. Pierce
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  2. Manon Morin
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  3. Jessica C. Little
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  4. Roland B. Liu
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  5. Joanna Tannous
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  6. Nancy P. Keller
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  7. Kit Pogliano
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  8. Benjamin E. Wolfe
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  9. Laura M. Sanchez
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  10. Rachel J. Dutton
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Contributions

R.J.D., L.M.S. and B.E.W. conceptualized the study. E.C.P., M.M., J.T., R.B.L. and J.C.L. performed the experiments. The R data processing pipeline was written by M.M. Data analyses were performed by E.C.P., J.C.L. and M.M. The article was written by E.C.P., M.M., L.M.S., J.C.L. and R.J.D. and revised with input from all authors. The figures were made by E.C.P. with input from L.M.S., J.C.L., R.J.D. and M.M., except for Fig. 4a and Extended Data Fig. 7 (R.B.L.), Fig. 6d (J.C.L.), Extended Data Figs. 1 and 2 (M.M.) and Supplementary Fig. 3 (J.T.). The study was supervised by R.J.D. and L.M.S.

Corresponding author

Correspondence to Rachel J. Dutton.

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Extended data

Extended Data Fig. 1 RB-TnSeq R data processing pipeline for gene fitness comparison across multiple conditions.

The pipeline is divided into three main scripts. Script 1 calculates the normalized gene fitness for each replicate of an RB-TnSeq experiment. This script has to be run for each replicate independently. Then, the .Rdata files from Script 1 are loaded in Script 2. Script 2 calculates for each RB-TnSeq condition the average gene fitness across replicates (inverse-variance weighted average). Finally, Script 3 compares gene fitness values of each RB-TnSeq condition against a chosen reference condition.

Extended Data Fig. 2 RB-TnSeq assay for fungal impacts on bacterial gene fitness.

Characterized pooled bacterial mutant libraries were grown in a biofilm either alone or with a fungal partner. After seven days of growth, mutant abundances were compared to the starting library abundances for each condition. Changes in barcode abundances were used to calculate gene fitness values. Genes with fitness values that differed significantly between co-culture and alone conditions (significant interaction fitness) were identified as potentially relevant to fungal interaction.

Extended Data Fig. 3 Comparison of E. coli and P. psychrophila interaction fitness values for the 874 genes found in both bacteria.

BLAST comparison (e-value cutoff of 1e-2) of protein sequences from P. psychrophila to those from E. coli and comparison of eggNOG gene assignments were used to find the best cross-species gene match for all genes with significant interaction fitness for at least one of the two bacterial species. A match was found for 874 genes. For each fungal condition, the fitness value of these genes with E. coli is on the x-axis and with P. psychrophila on the y-axis. In each condition, the genes are colored according to whether they have significant interaction fitness in the condition for E. coli (red), P. psychrophila (blue), both (purple), or neither (white).

Extended Data Fig. 4 Network of E. coli (left) or P. psychrophila (right) genes with positive and negative RB-TnSeq interaction fitness.

Each purple node represents a fungal partner and is labeled as follows: fungal partner (number of genes with interaction fitness; number of genes with interaction fitness unique to this condition). Each blue or orange node represents a bacterial gene. Nodes are colored by whether the average interaction fitness is positive (blue) or negative (orange) as shown in the legend below and are sized by average strength of interaction fitness across partners.

Extended Data Fig. 5 Principal Component Analysis of RB-TnSeq data.

Analysis was done on the fitness values in each fungal condition for all E. coli (left) or P. psychrophila (right) genes with an interaction fitness in at least one fungal condition. Each colored circle represents a fungal condition.

Extended Data Fig. 6 Clusters of Orthologous Genes (COG) categories of genes with interaction fitness.

Charts display the number of genes with interaction fitness that fall into each COG category for E. coli (left) or P. psychrophila (right).

Extended Data Fig. 7 Bacterial Cytological Profiling of ΔtolC E.coli treated with known antibiotic compounds on cheese curd agar.

DAPI dye stains DNA and FM4-64 dye stains bacterial membranes. SYTOX green stains nucleic acids but cannot penetrate live cells. Scale bars represent 2 µm. Testing of each antibiotic at four concentrations was performed once, and cells from the edges of zones of clearing were imaged for at least 5 fields from each condition to ensure consistency in phenotype.

Extended Data Fig. 8 Siderophore production by filamentous fungi.

Liquid CAS assay was performed on filtered and concentrated fungal supernatants from three replicates grown in 2% liquid cheese pH 7 for 12 days. Row A) 1-3: Liquid cheese control 4-6: Penicillium SAM3. Row B) 1-3: Debaryomyces 135B 4-6: Penicillium #12. Row C) 1-3: Candida 135E. 4-6: Penicillium RS17. Row D) 1-3: Scopulariopsis 165-5 Row E) 1-3: Scopulariopsis JB370. Row F) 1-3: Fusarium 554A. % Siderophore units calculated as [(Ar - As)/(Ar)]*100, where Ar is the absorbance of the cheese curd agar supernatant blank and As is the absorbance of the sample. N=3 biologically independent samples, error bars show standard deviation and black point is the mean.

Extended Data Fig. 9 Fitness defect of Δfep mutants on iron-limiting CCA.

Visual assays of E. coli mutant growth spotted alone on CCA pH 7 supplemented with tetrazolium chloride, an indicator of respiration.

Extended Data Fig. 10 Comparison of Penicillium sp. str. 12 WT and laeA deletion mutant growth on CCA.

Radial growth assay, including quantification, of Penicillium sp. str. 12 WT and laeA deletion mutant grown alone or with E. coli on CCA pH 7 (N=3 biologically independent experiments, error bars show standard deviation and black point is the mean). Spore counts from Penicillium sp. str. 12 WT and laeA deletion mutant grown alone or with E. coli for 7 days on CCA are also shown (N=3 biologically independent samples, error bars show standard deviation and black point is the mean).

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Supplementary Method 1.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–19.

Supplementary Data

Uncropped Southern blots related to Supplementary Fig. 3.

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Pierce, E.C., Morin, M., Little, J.C. et al. Bacterial–fungal interactions revealed by genome-wide analysis of bacterial mutant fitness. Nat Microbiol 6, 87–102 (2021). https://doi.org/10.1038/s41564-020-00800-z

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