Introduction

Expanded access to the genomic data of microbial organisms has been transforming the way we approach microbiology research. Genome sequences are subsequently enhanced with knowledge from experimental, modeling, and field studies (e.g.1,2,3,4) with the goal of yielding insights into microbial physiology, ecology, and biogeochemistry. Yet because different research teams independently consolidate and curate genome-related information via ad hoc solutions, these diverse data streams have created challenges for interoperable analyses, especially in collaborative work. More generally, the lack of a framework for establishing consensus versions of genome-linked reference data hinders community coalescence around shared datasets. To extend the impact of curated and collated microbial data beyond a single research group, requirements are: (1) an established reference dataset5, which provides existing and updated knowledge in a standardized format; and (2) open access to these data, which allows multiple groups to collaboratively analyze and update the same genome and genome-linked information. The power of establishing a strategy for the open exchange of consensus microbial data linked to reference genomes for emerging model organisms, whether they are laboratory cultures or those reconstructed from metagenomes, is increasing as team science takes on growing roles in environmental and life sciences research.

Contemporary software solutions for the analysis and exchange of microbial genomes and associated ‘omics survey data can be broadly characterized into three groups: (1) online portals that provide a centralized location for uploading or downloading genomes and/or ‘omics datasets; (2) online portals with embedded applications that allow the user to choose from pre-selected genomes and/or ‘omics datasets or, in some cases, upload their own data for analysis; and (3) downloadable tools that enable local analysis of genomes and/or ‘omics data (Table 1). While they provide important services for individual research groups, these solutions do not necessarily maximize the efficiency of collaborative team science efforts. Typically, datasets are provided either as raw data or as highly-polished summaries, and intermediate data products for coordination of downstream analyses are not maintained. Moreover, most existing solutions are centralized, in which case data curation and platform maintenance falls on a single entity vulnerable to loss of funding, while data format, updates, and accessibility are not fully under the control of researchers. An alternative solution that partially solves the data sharing needs of collaborative team science efforts is anvi’o6 (https://anvio.org), an open-source software platform that can integrate a variety of data streams into interoperable, standalone SQL databases that can serve as collaborative data products6; however, anvi’o data products are not version-controlled. Inspired by the state-of-the-art technical opportunities offered by anvi’o, here we propose a general framework for the distribution and collaborative analysis of ‘omics datasets that is conducive to team science efforts. The ‘Digital Microbe’ (DM) concept describes features of a data product (#1–3) and a data implementation framework (#4–5) that:

  1. 1.

    Stores a genome sequence with sequence-linked information (e.g., curated gene calls, user-defined functional annotations, etc).

  2. 2.

    Supports additional layers of genome-associated data (e.g., genomic regions of particular interest, mutant strain availability, protein structures, etc).

  3. 3.

    Supports additional layers of experimental or environmental survey data, including intermediate analysis results of value to the research team (e.g., transcriptomic or proteomic activity across different experimental conditions, environmental distribution patterns through metagenomic or metatranscriptomic read recruitment analyses, etc).

  4. 4.

    Enables version-controlled addition of new data layers or curation of existing ones iteratively by any researcher.

  5. 5.

    Stores and enables the export of information in a universal format that is accessible to other programs and centralized or decentralized analysis platforms.

Table 1 Limitations of existing solutions for the sharing of ‘omics information.
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We developed the Digital Microbe concept and its implementation in the National Science Foundation (NSF) Science and Technology Center for Chemical Currencies of a Microbial Planet (C-CoMP; https://ccomp-stc.org) consisting of a research team geographically distributed across 12 institutions. Our construction of Digital Microbes enabled Center members to simultaneously access, analyze and update experimental and environmental datasets for the Center’s two model marine bacterial species, Ruegeria pomeroyi DSS-3 and Alteromonas macleodii MIT1002, including diverse data types ranging from ‘omics surveys and environmental parameters to metabolic models and metabolomes. Here we demonstrate the feasibility of the Digital Microbe concept as a solution addressing widespread needs in the microbiology community for reproducible, integrated data products and we describe Digital Microbe data packages for each of C-CoMP’s model bacteria. The first Digital Microbe compiles knowledge of transcriptional response by Ruegeria pomeroyi DSS-3 gathered from 8 independent studies carried out between 2014 and 2023 (https://doi.org/10.5281/zenodo.7304959); the second describes an Alteromonas pangenome created by merging data from 339 isolate and metagenome-assembled genomes (https://doi.org/10.5281/zenodo.7430118).

Results and Discussion

Digital microbe: concept and implementation

At its core, a Digital Microbe is a curated and versioned public data package that is (1) ‘self-contained’ (i.e., it can explain itself and its contents) and (2) ‘extensible’ (i.e., others can extend a Digital Microbe data package with additional layers of information coming from new experiments). The package consists of multiple datasets organized and linked through reference to the genome of a single microbe or the pangenome of a group of microbes (Fig. 1). Data collection consolidates information such as gene annotations, coverage and other read-mapping statistics, and sample metadata. These data types can be flexible in scope and the extensibility of Digital Microbes via the programmatic addition of new ‘omics data types make them future-proof.

Fig. 1
figure 1

Architecture of a Digital Microbe. The genome of a model bacterium is (a) sequenced and (b) assembled and serves as the foundation of a Digital Microbe, a self-contained data package for a collaborative research team or a science community. (c) Alternatively, a pangenomic data package is assembled. (d) Intermediate datasets useful for downstream analyses are stored and reused, and (e) various data files and tables can be exported. (f) The Digital Microbe is iteratively populated with data layers referenced to individual genes, including mapped proteomes, transcriptomes, or gene-specific metadata types such as inventories of mutants or new annotations. Each Digital Microbe can be assigned a DOI (digital object identifier) and be versioned as new gene- or genome-referenced data are added.

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The Digital Microbe framework utilizes a model organism’s genome or a clade’s pangenome as the foundation of a database file describing the DNA sequences (Fig. 2). This database file is hosted in a central data repository where it can be accessed by collaborators and community members. A software platform was needed for collaborative analyses, and we chose the open-source software platform anvi’o6, which implements many of the Digital Microbe features described above (Fig. 2). The concept behind the Digital Microbe framework, however, is independent of any one software platform. Similarly, C-CoMP hosts its Digital Microbe files on the data-sharing platform Zenodo (https://zenodo.org/), but other version-controlled storage solutions are available. As team science progresses, other genome- or gene-linked datasets (including both raw data and analysis results) can be added to the database by various groups, who update the publicly-hosted file to a new version that disseminates their data and findings to the team or community.

Fig. 2
figure 2

Situating the Digital Microbe concept in the existing computational environment. The Digital Microbe approach facilitates collaborative science by: establishing a version-controlled (pan)genomic reference; consolidating and cross-referencing collections of experimental and environmental data associated with a genome or pangenome; facilitating access to reusable intermediate analyses; and providing data export capabilities for transitioning to other programs or analysis software. While each of these features could be established by generating new software, we chose to use the existing open-source software platform anvi’o6, which implements several aspects of a Digital Microbe via (pan)genomic data storage in programmatically-queryable SQLite databases. The concept behind the Digital Microbe framework, however, is independent of any one software platform.

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Here, we present two examples of Digital Microbes – one for the model organism Ruegeria pomeroyi and another for the pangenome of Alteromonas spp. – as well as case studies that exemplify how they can be used.

The Ruegeria pomeroyi digital microbe

Ruegeria pomeroyi DSS-3 is a representative of the Roseobacteraceae family, an important bacterial group in marine microbial communities7 with its members among the most metabolically active bacterial cells in algal blooms and coastal environments8. R. pomeroyi has been well studied in the laboratory and field9,10,11; it grows well in both defined and rich media; and it is amenable to genetic alteration12,13.

The R. pomeroyi Digital Microbe (Fig. 3) is built on a well-curated genome assembly (DM feature 1) first annotated in 200414, reannotated in 201415, and enhanced with information from NCBI Clusters of Orthologous Groups (COG)16, Pfam17, and KEGG Kofam18. The Digital Microbe annotation is also continually updated (DM feature 4) with new experimental verifications of R. pomeroyi genes (e.g.15,19,20,21,22) that have not been captured in standardized genome annotation repositories (e.g., RefSeq GCF_000011965.2). The R. pomeroyi DSS-3 Digital Microbe is available on Zenodo23.

Fig. 3
figure 3

Contents of the R. pomeroyi Digital Microbe. As visualized in anvi’o ‘gene mode’, each item on the inner tree corresponds to one gene call in the R. pomeroyi genome, and the blue concentric circles display the coverage of each gene in a given transcriptome sample. The outermost red concentric circles correspond to normalized protein abundances from proteome samples (raw files available in the Proteomics Identifications Database (PRIDE) via Project PXD045824). Samples are grouped by their study of origin, with the data source indicated in text of the same color as the samples. The brown bar plot indicates the total number of reads that mapped from each transcriptome to the R. pomeroyi genome. This figure was generated from version 5.0 of the R. pomeroyi Digital Microbe databases on Zenodo.

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A use case: exploring the substrate landscape of R. pomeroyi

Our team is leveraging R. pomeroyi as a whole-cell biosensor of labile components of the marine dissolved organic carbon (DOC) pool. A recent study using R. pomeroyi knockout mutants definitively identified the cognate substrate of 18 organic compound transporters24 which were added to the Digital Microbe (DM feature 2). Previous homology-based annotations of most of these transporter systems were either incorrect or vague, and therefore of minimal ecological value. Although representing only a subset of the ~126 organic carbon influx transporter systems in the R. pomeroyi genome, the presence or expression of these 18 is unequivocally linked to a known metabolite. With the new annotations in hand, we undertook a meta-analysis of transporter expression across 133 previously sequenced R. pomeroyi transcriptomes from laboratory and field studies between 2014 and 2023 to gain insights into the availability of these 18 metabolites in marine environments.

We added transcriptomes of R. pomeroyi to the Digital Microbe by mapping them onto the genome as individual data layers (Fig. 3, Table S1) (DM feature 3). Using the anvi’o interactive interface, we established a custom dataset that consisted of the 62 protein components of the 18 experimentally annotated transporters (DM feature 2). We normalized the read counts for each protein to transcripts per million (TPM) and clustered the resulting data (Euclidean distance and Ward clustering). To generate a heatmap of transporter expression, we extracted the data from anvi’o and visualized it using python (DM feature 5).

This meta-analysis captured responses by R. pomeroyi to available substrates under 43 different ecological conditions (Fig. 4), including during co-culture growth with phytoplankton25,26,27,28, on defined single or mixed substrates20, and after introduction into a natural phytoplankton bloom10. At the broadest scale, the transporters enabling organic acid uptake (acetate, citrate, fumarate, and 3-hydroxybutyrate) had the highest relative expression across conditions, together accounting for an average of 48% (range: 9.7–86%) of the transcripts for transporters with confirmed substrates. Recent studies have indeed discovered that Roseobacteraceae members are organic acid catabolic specialists29,30. Transporter transcription patterns also revealed the differences in substrate availability across environments. Introduced into a natural dinoflagellate bloom10, the citrate transporter had the highest relative expression; in a diatom co-culture, the acetate transporter was the most highly expressed; co-cultured with a green alga, transporter genes indicated that taurine, glycerol, carnitine, and dimethylsulfoniopropionate (DMSP) were on the menu. The organic acid transporter that enables R. pomeroyi uptake of 3-hydroxybutyrate24 was expressed across most growth conditions, yet this metabolite, also a precursor to the bacterium’s storage polymer polyhydroxybutyrate (PHB), has not previously been identified as a relevant currency in bacterially-mediated carbon flux. The meta-analysis also showed a pattern in expression for transporters that contain a substrate binding protein gene (i.e., the ABC and TRAP transporter classes): the gene is expressed at consistently higher levels than other genes in the same transporter (i.e., higher than permeases and ATP-binding proteins) despite all having membership in the same operon. Additional layers of gene regulation are therefore occurring either as within-operon differential expression or as post-transcriptional selective degradation. Regardless, this regulatory strategy would benefit a bacterium in an environment where substrate acquisition is the growth-limiting step.

Fig. 4
figure 4

Clustered heatmap of relative gene expression for 18 experimentally annotated R. pomeroyi transporters compiled in a Digital Microbe. Each row represents a single transcriptome from the Digital Microbe dataset, and each column represents all transporter proteins with experimentally confirmed cognate substrates. Row labels indicate study and sample name (Table S1). Brighter colors indicate higher proportional expression (the scale maximum is ≥5% of the sum of the 18 transporter transcriptomes) while darker colors indicate lower. Arrows point out transcriptomes collected when substrates were derived from dinoflagellate-rich natural communities (red) or diatom co-cultures (brown); significant differences in transporter protein expression between these two substrate sources are indicated with asterisks colored red (enriched with dinoflagellates) or brown (with diatoms) (T-test, p ≤ 0.05).

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Three additional genome-linked data types recently added to the R. pomeroyi Digital Microbe include the locations of insertion sites of knockout mutants (covering 3,570 genes of 4,288 genes)13,24; proteomic data collected concurrently with one of the transcriptomic studies31,32, and TnSeq mutant fitness measurements in synthetic microbial communities11 (DM feature 2); these are enhancing collaborations among team members.

The Alteromonas digital microbe

Alteromonas is a genus of marine Gammaproteobacteria whose members associate with particles and can contribute significantly to heterotrophic activity of phytoplankton blooms, sometimes in the role of helper bacteria that provide benefits to the phytoplankton33,34,35. Bacteria in this genus are distinguished by genomes encoding an average of 4,000 genes that enable use of a broad spectrum of substrates36, provide protection from reactive oxygen species to community members33, and mediate polysaccharide degradation37. The type species of the genus is Alteromonas macleodii34,38, with other notable species including A. mediterranea39, A. australica40, and A. stellipolaris41. While no single species has emerged as the primary model organism for this genus, the many genomes available for study provide an opportunity for pangenomic analysis to improve understanding of the evolution and diversity of this ubiquitous marine clade42.

The assembled pangenome consists of 336 Alteromonas genomes with genes called and annotated using one standardized pipeline (Fig. 5) (DM feature 1). Of these, 78 are isolate genomes43,44,45 and 258 are metagenome-assembled genomes (MAGs) obtained from a variety of marine environments in the global ocean46. Genomes represent members of the closely related ‘surface’ species A. macleodii (n = 139) and ‘deep’ species A. mediterranea (n = 25)39, and the widely distributed A. australica (n = 63)47. The 34,390 gene clusters of the pangenome are linked to an imported phylogenetic tree assembled from single-copy core genes (see Methods), annotated using NCBI COGs16, KEGG KOfams18, CAZyme HMMs48 and orthology predictions from EggNOG-mapper49,50,51 (DM feature 2), and assigned as core or accessory genes for the genus (DM feature 3) based on a Bayesian approach available in anvi’o52. The Alteromonas Digital Microbe with relevant pangenome and phylogeny files is accessible on Zenodo53. Examples of future versioned enhancements of this Digital Microbe might include additions of new Alteromonas genomes and improved annotations from culture studies and novel annotation programs.

Fig. 5
figure 5

The Alteromonas Digital Microbe. Each concentric ring represents one Alteromonas genome, with colored rings identifying genomes from five clades of interest (A. macleodii, A. mediterranea, A. austalica, A. stellioolaris, and A. naphthalenivorans). The outermost green rings depict annotation sources applied to all genomes. Each spoke in the figure represents one gene cluster in the pangenome, with presence/absence denoted by darker/lighter colors, respectively. Genome metadata are shown next to each ring and include total genome length, GC content, completion, number of genes per kbp, and number of gene clusters per genome. The red heatmap above the metadata shows the average nucleotide identity (ANI) percentage scores between genomes. The tree above the ANI heatmap shows the imported phylogenomic tree, with clades of interest color-referenced in the circular portion of the figure. This figure was generated using the anvi’o ‘anvi-display-pan’ from a version of the Alteromonas digital microbe without singleton genes, which is available on Zenodo under https://doi.org/10.5281/zenodo.10421034.

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A use case: evolutionary patterns of Alteromonas carbohydrate use

We leveraged the information contained within the Digital Microbe to examine diversity in the ability of this opportunistic marine genus to use poly-/oligosaccharides36. Sugars and sugar polymers are an abundant and diverse component of the ocean’s dissolved organic carbon inventory54, and differences in how microbes use them provide important clues on the evolutionary diversification of their roles in the oceanic carbon cycle. Moreover, the ability to annotate genes with the Carbohydrate-Active enZYme (CAZyme) Database48 was recently added to anvi’o, allowing augmentation of the Digital Microbe with CAZyme annotations. The results indicate distinct CAZyme distributions across Alteromonas clades (Fig. 6). For example, the A. australica and A. stellipolaris clades have more polysaccharide lyases than neighboring clades, while the A. stellipolaris clade is enriched in several other CAZyme categories as well. As patterns of diversity in CAZyme inventories are most distinct at the clade level compared to the within-clade level, carbohydrate utilization emerges as a potentially key driver of the large-scale niche partitioning of Alteromonas species.

Fig. 6
figure 6

Distribution of CAZYme annotations across a phylogeny of 336 isolate and MAG genomes from the genus Alteromonas. The phylogeny of the genus is displayed on the left side of the figure, with genomes represented by points on the tree and five of the clades (A. macleodii, A. mediterranea, A. australica, A. napthalenivorans, and A. stellipolaris) highlighted. Each row on the right side of the figure represents one genome. Completeness and type of genome are shown in the two heatmaps to the right of the phylogeny. The horizontal bar plots of different colors show the proportion of CAZymes in each genome relative to the maximum number of all categories of CAZymes as indicated in the legend in the inset at the upper left. The maximum number for each CAZyme category is represented by the vertical bar plot at the top of the figure.

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We also gained insights into how carbohydrate usage has shaped Alteromonas evolution and ecology from gene phylogenies of selected CAZymes (Supplementary Figure 1). The topology of several CAZyme phylogenies broadly recapitulates the topology of the genome phylogeny built from single-copy core genes (Supplementary Figure 2), suggesting that vertical descent has dominated the evolution of these genes. However, the topologies of other CAZyme phylogenies have significant discordance with the genome phylogeny (Supplementary Figure 2), suggesting that horizontal transfer has also had an important role in the evolution of carbohydrate utilization strategies in Alteromonas. The divergent evolutionary trajectories of different CAZymes highlight selective pressures acting on the metabolic diversification of Alteromonas, and may offer clues on how this diversification has in turn impacted the evolution of carbon cycling in the ocean.

Future directions

Digital Microbe data packages furnish an architecture for reproducible, open, and extensible collaborative work in microbiology and its many derivative fields. While we present here a specific architecture tailored to our research focus, it is only one manifestation of the broader digital microbe concept: that decentralized taxon-specific databases are key mechanisms for capturing knowledge accumulating from genome-informed data that are now so vast and distributed as to be intractable to synthesize55. Digital Microbe packages allow one-stop shopping for data spread across multiple public archives, allow coordinated selection and documentation of genome structure and annotations within and between research teams, and are extensible to new data types. While the framework presented here is designed for bacterial and archaeal data, the development of digital microbes for eukaryotic model organisms is an important future application56. One enhancement under development by C-CoMP is an integrated toolkit for metabolic modeling, but the nature and scope of future applications can be defined by any research group that uses a digital microbe framework for their research. Organized and open access to taxon-explicit data is an essential foundation for modern microbiology.

Methods

Both Digital Microbes were generated and analyzed using v7.1-dev or later versions of anvi’o6.

Creation of the Ruegeria pomeroyi digital microbe

We created the Ruegeria pomeroyi Digital Microbe from the R. pomeroyi DSS-3 complete genome and megaplasmid sequences14 and (meta)transcriptome samples from10,27,28,32,57,58,59. We generated a contigs database from the genome and megaplasmid sequences with ‘anvi-gen-contigs-database’ and annotated the resulting Prodigal60 gene calls with de novo annotations from NCBI Clusters of Orthologous Genes (COGs)16, KEGG KOfams18, and Pfam17 via the associated anvi’o program for each annotation source. We also identified single-copy core genes using ‘anvi-run-hmms’ and associated these genes with taxonomy data from the GTDB61 using ‘anvi-run-scg-taxonomy’. We imported manually curated gene annotations, including annotations indicating which genes have available mutants13, using the program ‘anvi-import-functions’.

To process the (meta)transcriptomes, we quality-filtered the samples using FASTX-toolkit62 with the parameters described in25. We mapped the reads to the DSS-3 genome using Bowtie 263 and samtools64. Each sample’s read mapping data were converted into an anvi’o profile database using ‘anvi-profile’, and all samples were merged into a single database with ‘anvi-merge’. To add proteomic data31, we normalized spectral abundance counts with a normalized spectral abundance factor to make data comparable across all proteomes. We generated a ‘genes database’ to store gene-level information by running ‘anvi-interactive’ on the established contigs and profile databases with the ‘–gene-mode’ flag, and we imported the normalized abundances for each gene into the genes database using the program ‘anvi-import-misc-data’. We also used this program to import fitness data associated with gene mutants from11 into the same genes database.

Transporter expression analysis for Ruegeria pomeroyi

The genes database in the R. pomeroyi Digital Microbe contains gene-level transcript coverage information from > 100 samples. To assess the proportional expression of substrate-confirmed transporter genes, we used the anvi’o interactive interface to create a bin containing the transporter genes, and generated a static summary page with the “init-gene-coverages” box checked to export annotation and coverage data for each contig region where our genes of interest were located. After reading the exported data into dataframes using python v3.7.8 and pandas65,66, we extracted the coverage data for our specific genes of interest, normalized the coverages to TPM using the total number of reads per sample, and relativized these data to represent the proportional expression of each gene. We visualized these data as a clustermap using the seaborn package67 and assessed statistical differences in the mean gene expression using the a t-test implemented in the scipy stats package68.

Creation of the Alteromonas digital microbe

To create the Alteromonas Digital Microbe, we collected 78 isolate genomes and 258 MAGs from the Joint Genome Institute’s Integrated Microbial Genomes (IMG) project43, NCBI69, and46. We converted each genome into an anvi’o contigs database using ‘anvi-gen-contigs-database’. For the genomes from IMG and NCBI, we determined completion and contamination statistics using CheckM v1.0.1870; for the MAGs that were taken from46, we used the mean completeness and mean contamination statistics reported in that publication. We annotated the genes in each contigs database with the NCBI Clusters of Orthologous Genes (COGs)16, KEGG KOfams18, and Carbohydrate-Active enZYme (CAZyme) HMMs48 via the associated anvi’o program for each annotation source, and imported externally-run annotations from EggNOG-mapper49,50,51 and KEGG GhostKOALA71 into the databases using ‘anvi-import-functions’.

We ran ‘anvi-pan-genome’ to create the pangenome and computed the average nucleotide identity (ANI) between all pairs of genomes using ‘anvi-compute-genome-similarity’. To extract the core genome from the pangenome (i.e., genes found in all genomes), we used a Bayesian statistical method52 implemented in ‘anvi-script-compute-bayesian-pan-core’. This method employs mOTUpan.py to determine the gene clusters likely to be core based on individual genome completeness scores.

Phylogenomic analysis of the Alteromonas genomes

To build the phylogeny of Alteromonas, we aligned and concatenated the sequences from 110 single-copy core gene clusters using ‘anvi-get-sequences-for-gene-clusters’. We imported these sequences into the tree building software RAxML, version 8.2.1272, and built the tree under the “PROTGAMMAAUTO” model setting. We used FigTree v1.4.473 to midpoint root the tree and save it in newick file format. To incorporate the tree into the pangenome, we imported the newick tree with the program ‘anvi-import-misc-data’. For the phylogenomic CAZyme analysis, we used ‘anvi-split’ to subset gene clusters with known CAZyme functions into a new pangenome database and ran ‘anvi-summarize’ on this smaller pangenome to count the number of CAZymes per category, per genome. We visualized these data as a function of the previously-determined phylogeny in R v4.1.174 using the packages aplot v0.1.975, BiocManager v1.30.2076, dplyr v1.1.077, ggnewscale v0.4.878, ggplot2 v3.4.179, ggstance v0.3.6.900080, ggtree v3.7.1.00381,82,83,84,85, ggtreeExtra v1.9.1.99281,86, nationalparkcolors v0.1.087, plyr v1.8.888, RColorBrewer v1.1-389, scales v1.2.190, and tidyr v1.3.091.

We then repeated the initial steps above to generate a phylogeny for the subset of isolate genomes (n = 78), which resulted in a tree built from 111 single-copy core gene clusters. After subsetting the gene clusters with known CAZyme annotations into a smaller pangenome, we identified eight CAZyme-related gene clusters that were part of the single-copy core genome. We then generated an individual phylogeny from each of these CAZymes. We used R to compare the CAZyme phylogenies with the overall core genome phylogeny for these isolate genomes, with the packages listed above in addition to colorBlindness v0.1.992, easyalluvial v0.3.193, and gridExtra v2.394.