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Title: Pan-genome-scale metabolic modeling of Bacillus subtilis reveals functionally distinct groups
ABSTRACT Bacillus subtilisis an important industrial and environmental microorganism known to occupy many niches and produce many compounds of interest. Although it is one of the best-studied organisms, much of this focus including the reconstruction of genome-scale metabolic models has been placed on a few key laboratory strains. Here, we substantially expand these prior models to pan-genome-scale, representing 481 genomes ofB. subtiliswith 2,315 orthologous gene clusters, 1,874 metabolites, and 2,239 reactions. Furthermore, we incorporate data from carbon utilization experiments for eight strains to refine and validate its metabolic predictions. This comprehensive pan-genome model enables the assessment of strain-to-strain differences related to nutrient utilization, fermentation outputs, robustness, and other metabolic aspects. Using the model and phenotypic predictions, we divideB. subtilisstrains into five groups with distinct patterns of behavior that correlate across these features. The pan-genome model offers deep insights intoB. subtilis’metabolism as it varies across environments and provides an understanding as to how different strains have adapted to dynamic habitats. IMPORTANCEAs the volume of genomic data and computational power have increased, so has the number of genome-scale metabolic models. These models encapsulate the totality of metabolic functions for a given organism.Bacillus subtilisstrain 168 is one of the first bacteria for which a metabolic network was reconstructed. Since then, several updated reconstructions have been generated for this model microorganism. Here, we expand the metabolic model for a single strain into a pan-genome-scale model, which consists of individual models for 481B. subtilisstrains. By evaluating differences between these strains, we identified five distinct groups of strains, allowing for the rapid classification of any particular strain. Furthermore, this classification into five groups aids the rapid identification of suitable strains for any application. more »« less
Perez, Ryan King; Chavez_Rios, Jocelyn Sofia; Grifaldo, Jessica; Regner, Kurt; Pedraza-Reyes, Mario; Robleto, Eduardo A
(, Microbiology Resource Announcements)
Roux, Simon
(Ed.)
ABSTRACT We report the genome sequence ofBacillus subtilisstrain YB955, a prophage-cured strain used as a model in DNA repair, bacterial physiology, and mutagenesis studies. The assembled and annotated draft genome contains 4,031 coding genes, 5 rRNAs, and 73 tRNAs. Compared to 168, YB955 has a 134,402 bp deletion.
Abstract The bacteriumBacillus subtilisundergoes asymmetric cell division during sporulation, producing a mother cell and a smaller forespore connected by the SpoIIQ-SpoIIIA (or Q-A) channel. The two cells differentiate metabolically, and the forespore becomes dependent on the mother cell for essential building blocks. Here, we investigate the metabolic interactions between mother cell and forespore using genome-scale metabolic and expression models as well as experiments. Our results indicate that nucleotides are synthesized in the mother cell and transported in the form of nucleoside di- or tri-phosphates to the forespore via the Q-A channel. However, if the Q-A channel is inactivated later in sporulation, then glycolytic enzymes can form an ATP and NADH shuttle, providing the forespore with energy and reducing power. Our integrated in silico and in vivo approach sheds light into the intricate metabolic interactions underlying cell differentiation inB. subtilis, and provides a foundation for future studies of metabolic differentiation.
Tran, Peter; Lander, Stephen M; Prindle, Arthur
(, mBio)
Kline, Kimberly A
(Ed.)
ABSTRACT Biofilms provide individual bacteria with many advantages, yet dense cellular proliferation can also create intrinsic metabolic challenges including excessive acidification. Because such pH stress can be masked in buffered laboratory media—such as MSgg commonly used to studyBacillus subtilisbiofilms—it is not always clear how such biofilms cope with minimally buffered natural environments. Here, we report howB. subtilisbiofilms overcome this intrinsic metabolic challenge through an active pH regulation mechanism. Specifically, we find that these biofilms can modulate their extracellular pH to the preferred neutrophile range, even when starting from acidic and alkaline initial conditions, while planktonic cells cannot. We associate this behavior with dynamic interplay between acetate and acetoin biosynthesis and show that this mechanism is required to buffer against biofilm acidification. Furthermore, we find that buffering-deficient biofilms exhibit dysregulated biofilm development when grown in minimally buffered conditions. Our findings reveal an active pH regulation mechanism inB. subtilisbiofilms that could lead to new targets to control unwanted biofilm growth.IMPORTANCEpH is known to influence microbial growth and community dynamics in multiple bacterial species and environmental contexts. Furthermore, in many bacterial species, rapid cellular proliferation demands the use of overflow metabolism, which can often result in excessive acidification. However, in the case of bacterial communities known as biofilms, these acidification challenges can be masked when buffered laboratory media are employed to stabilize the pH environment for optimal growth. Our study reveals thatB. subtilisbiofilms use an active pH regulation mechanism to mitigate both growth-associated acidification and external pH challenges. This discovery provides new opportunities for understanding microbial communities and could lead to new methods for controlling biofilm growth outside of buffered laboratory conditions.
Steenwyk, Jacob L; Balamurugan, Charu; Raja, Huzefa A; Gonçalves, Carla; Li, Ningxiao; Martin, Frank; Berman, Judith; Oberlies, Nicholas H; Gibbons, John G; Goldman, Gustavo H; et al
(, Microbiology Spectrum)
Alanio, Alexandre
(Ed.)
ABSTRACT Modern taxonomic classification is often based on phylogenetic analyses of a few molecular markers, although single-gene studies are still common. Here, we leverage genome-scale molecular phylogenetics (phylogenomics) of species and populations to reconstruct evolutionary relationships in a dense data set of 710 fungal genomes from the biomedically and technologically important genusAspergillus. To do so, we generated a novel set of 1,362 high-quality molecular markers specific forAspergillusand provided profile Hidden Markov Models for each, facilitating their use by others. Examining the resulting phylogeny helped resolve ongoing taxonomic controversies, identified new ones, and revealed extensive strain misidentification (7.59% of strains were previously misidentified), underscoring the importance of population-level sampling in species classification. These findings were corroborated using the current standard, taxonomically informative loci. These findings suggest that phylogenomics of species and populations can facilitate accurate taxonomic classifications and reconstructions of the Tree of Life.IMPORTANCEIdentification of fungal species relies on the use of molecular markers. Advances in genomic technologies have made it possible to sequence the genome of any fungal strain, making it possible to use genomic data for the accurate assignment of strains to fungal species (and for the discovery of new ones). We examined the usefulness and current limitations of genomic data using a large data set of 710 publicly available genomes from multiple strains and species of the biomedically, agriculturally, and industrially important genusAspergillus. Our evolutionary genomic analyses revealed that nearly 8% of publicly availableAspergillusgenomes are misidentified. Our work highlights the usefulness of genomic data for fungal systematic biology and suggests that systematic genome sequencing of multiple strains, including reference strains (e.g., type strains), of fungal species will be required to reduce misidentification errors in public databases.
ABSTRACT Environmental strains of the soil bacterium Bacillus subtilis have valuable applications in agriculture, industry, and biotechnology; however, environmental strains are genetically less accessible. This reduced accessibility is in sharp contrast to laboratory strains, which are well known for their natural competence, and a limitation in their applications. In this study, we observed that robust biofilm formation by environmental strains of B. subtilis greatly reduced the frequency of competent cells in the biofilm. By using model strain 3610, we revealed a cross-pathway regulation that allows biofilm matrix producers and competence-developing cells to undergo mutually exclusive cell differentiation. We further demonstrated that the competence activator ComK represses the key biofilm regulatory gene sinI by directly binding to the sinI promoter, thus blocking competent cells from simultaneously becoming matrix producers. In parallel, the biofilm activator SlrR represses competence through three distinct mechanisms involving both genetic regulation and cell morphological changes. Finally, we discuss the potential implications of limiting competence in a bacterial biofilm. IMPORTANCE The soil bacterium Bacillus subtilis can form robust biofilms, which are important for its survival in the environment. B. subtilis also exhibits natural competence. By investigating competence development in B. subtilis in situ during biofilm formation, we reveal that robust biofilm formation often greatly reduces the frequency of competent cells within the biofilm. We then characterize a cross-pathway regulation that allows cells in these two developmental events to undergo mutually exclusive cell differentiation during biofilm formation. Finally, we discuss potential biological implications of limiting competence in a bacterial biofilm.
@article{osti_10621304,
place = {Country unknown/Code not available},
title = {Pan-genome-scale metabolic modeling of Bacillus subtilis reveals functionally distinct groups},
url = {https://par.nsf.gov/biblio/10621304},
DOI = {10.1128/msystems.00923-24},
abstractNote = {ABSTRACT Bacillus subtilisis an important industrial and environmental microorganism known to occupy many niches and produce many compounds of interest. Although it is one of the best-studied organisms, much of this focus including the reconstruction of genome-scale metabolic models has been placed on a few key laboratory strains. Here, we substantially expand these prior models to pan-genome-scale, representing 481 genomes ofB. subtiliswith 2,315 orthologous gene clusters, 1,874 metabolites, and 2,239 reactions. Furthermore, we incorporate data from carbon utilization experiments for eight strains to refine and validate its metabolic predictions. This comprehensive pan-genome model enables the assessment of strain-to-strain differences related to nutrient utilization, fermentation outputs, robustness, and other metabolic aspects. Using the model and phenotypic predictions, we divideB. subtilisstrains into five groups with distinct patterns of behavior that correlate across these features. The pan-genome model offers deep insights intoB. subtilis’metabolism as it varies across environments and provides an understanding as to how different strains have adapted to dynamic habitats. IMPORTANCEAs the volume of genomic data and computational power have increased, so has the number of genome-scale metabolic models. These models encapsulate the totality of metabolic functions for a given organism.Bacillus subtilisstrain 168 is one of the first bacteria for which a metabolic network was reconstructed. Since then, several updated reconstructions have been generated for this model microorganism. Here, we expand the metabolic model for a single strain into a pan-genome-scale model, which consists of individual models for 481B. subtilisstrains. By evaluating differences between these strains, we identified five distinct groups of strains, allowing for the rapid classification of any particular strain. Furthermore, this classification into five groups aids the rapid identification of suitable strains for any application.},
journal = {mSystems},
volume = {9},
number = {11},
publisher = {mSystems},
author = {Neal, Maxwell and Brakewood, William and Betenbaugh, Michael and Zengler, Karsten},
editor = {Faust, Karoline}
}
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