Generated April 15, 2025
Authors: Gracee K. Tothero, Pamela Weisenhorn, Filipe Liu, José P. Faria, Christopher S. Henry, Clara S. Chan
When applying this workflow in your own research, please cite the associated Microbiology Resource Announcement: Insert full citation with link here
In this tutorial narrative, we introduce a novel template developed to enable the creation of stoichiometric genome-scale metabolic models for iron-oxidizing bacteria. We demonstrate the development of this template by applying it to Sideroxydans lithotrophicus ES-1, and validate our model using transcriptomic data (Published in Zhou et al., 2022 AEM). Below, we further show that our template facilitates the modeling of mixotrophic iron-oxidizing bacteria and metagenome-assembled genomes (MAGs), by applying our template to the MAG of the mixotrophic iron oxidizer Leptothrix ochracea (Published in Tothero et al, 2024). This work represents the first instance of a generalized and adaptable template for modeling diverse iron-oxidizing microbial systems, expanding the accessibility and applicability of metabolic modeling in this field.
We recommend using this template with caution. These models should not be used to identify iron-oxidizing bacteria. Rather, they are useful for assessing whether iron oxidation can generate energy or biomass or for examining interconnected metabolic pathways in uncultured iron oxidizers. \ \ This template should ideally be used once the iron oxidation function has been verified. Using the default annotation by RASTtk is not sufficient given the current state of knowledge on iron-oxidizing genes. Some recommended methods of verification are:
This tutorial makes use of a genome nucleotide FASTA file and a Media formulation file. You may upload your own data or use the publicly available genomes and media accessible through the Public tab of the data browser. In the absence of a provided media formulation, MS2 will default to Auxotrophy Media. For more information on formatting and uploading data, see the Data Upload and Download Guide. For more information on adding data to your Narrative, please see the Narrative Interface User Guide.
We recommend checking the MS2 Tutorial - Build and gap-fill genome-scale metabolic models with ModelSEED2 and the Microbial Metabolic Model Reconstruction and Analysis Tutorial for additional in-depth tutorials that provide essential background on KBase's metabolic modeling functionality and the methods applied in this workflow.
Sideroxydans lithotrophicus ES-1 is a long-studied iron-oxidizing isolate capable of chemolithoautotrophic growth on varied substrates, including Fe(II), thiosulfate, magnetite, and smectite. Its metabolic flexibility and extensive physiological and transcriptomic data make this isolate ideal for model development and validation. This tutorial uses the genome of Sideroxydans lithotrophicus ES-1 that is available through RefSeq and transcriptome data published in Zhou et al., 2022 (AEM). We also use a media formulation for Modified Wolfe's Minimal Media (MWMM) with Fe(II) to correspond with the transcriptome data.
If you wish to use publicly-available data, both genome and media files can be accessed through the "Public" tab of the data browser.
If you wish to upload your own data, you may do so using the "Import" tab of the data browser:
Import Media from a tsv file
For more information on formatting your own media file, see How to format reaction media files
In the absence of a provided media formulation, MS2 will default to Auxotrophy Media. For more information on formatting and uploading data, see the Data Upload and Download Guide. For more information on adding data to your Narrative, please see the Narrative Interface User Guide.
from biokbase.narrative.jobs.appmanager import AppManager
AppManager().run_app_batch(
[{
"app_id": "kb_uploadmethods/import_fasta_as_assembly_from_staging",
"tag": "release",
"version": "5b9346463df88a422ff5d4f4cba421679f63c73f",
"params": [{
"staging_file_subdir_path": "NC_013959.1_Sideroxydans lithotrophicus ES-1_complete sequence.fasta",
"assembly_name": "Sideroxydans_lithotrophicus_ES-1"
}],
"shared_params": {
"type": "draft isolate",
"min_contig_length": 500
}
}],
cell_id="114de465-72dd-4fde-948c-c823ba12921a",
run_id="4468d3ad-3d7f-403f-809d-063e7871e27d"
)
| Created Object Name | Type | Description |
|---|---|---|
| Sideroxydans_lithotrophicus_ES-1_RASTtk | Genome | RAST re-annotated genome |
After browsing the model results (including the genome annotations, reactions that were included or gapfilled, objective value, and reaction fluxes), model curation may be useful to improve the accuracy of the modeling results. This may be done by using the Edit Media, Gapfill Metabolic Model, or Edit Metabolic Model functions in KBase.
Leptothrix ochracea is a conspicuous sheath-forming iron oxidizer which grows prolifically as iron microbial mats in freshwater environments. It has famously evaded isolation, and near-complete genomes have only recently been reconstructed. Its genomic potential and transcriptomes suggest that it thrives by growing mixotrophically, using Fe(II) as an energy source alongside organics (Tothero et al., 2024). However, the lack of culturing-based physiological data to confirm this activity makes L. ochracea an ideal candidate for investigation through metabolic models to explore its potential behavior in an iron-oxidizing context.This tutorial uses a metagenome-assembled genome of L. ochracea that is available through RefSeq and published in Tothero et al., 2024.
This use case produces metabolic models using multiple media formulations. We will use the Modified Wolfe's Mineral Media with Fe(II) as done above, but we will also demonstrate that this template can be used to model growth on minimal media containing organic carbon (Lactate) for mixotrophic iron-oxidizing bacteria. Therefore, the lactate minimal media will be uploaded in this section.
from biokbase.narrative.jobs.appmanager import AppManager
AppManager().run_app_batch(
[{
"app_id": "kb_uploadmethods/import_fasta_as_assembly_from_staging",
"tag": "release",
"version": "5b9346463df88a422ff5d4f4cba421679f63c73f",
"params": [{
"staging_file_subdir_path": "Leptothrix_ochracea_DE1.021_GCF_041083635.1.fna",
"assembly_name": "Leptothrix_ochracea_DE1.021_GCF_041083635.1"
}],
"shared_params": {
"type": "draft isolate",
"min_contig_length": 500
}
}],
cell_id="1afbddc8-dc23-4a3e-99da-b6153f0c29db",
run_id="c3b7a731-844d-4389-ba2d-1316e5852229"
)
| Created Object Name | Type | Description |
|---|---|---|
| Leptothrix_ochracea_RASTtk | Genome | RAST re-annotated genome |
This method was used to model L. ochracea growth on nine organic carbon substrates in minimal media, with and without iron oxidation (Tothero et al., 2024). The objective value of these models demonstrated growth via mixotrophic iron oxidation; that is, L. ochracea produced biomass while oxidizing both iron and organic carbon. This offered quantitative support for the proposed metabolism of L. ochracea, strengthening the metagenomic and metatranscriptomic evidence for mixotrophy. These results reinforce the 'omics-based findings, highlighting the value of metabolic modeling in predicting metabolic strategies.
This tutorial has demonstrated a complete workflow for constructing, curating, and validating a stoichiometric metabolic model of iron-oxidizing bacteria using KBase. By developing and applying the template to the reference organism isolate, we have shown how expression data can validate model predictions and highlight the reliability of the approach. The subsequent use case for an uncultured iron oxidizer illustrates the template's versatility in exploring metabolic pathways when physiological data are lacking. This workflow, from data import to final model presentation, provides a robust framework for investigating energy metabolism and biomass production in iron-oxidizing bacteria, supporting research on both cultivated and uncultivated organisms.