A metabolic model based on a pangenome core reveals putative conserved biochemical features of the phytopathogen Xylella fastidiosa.

pAo: A Consensus Genome-Scale Metabolic Model for Aspergillus oryzae Capturing Intraspecies Diversity

Aspergillus oryzae (koji mold) is a key microorganism in traditional food fermentations including soy sauce, sake, and miso and is important in novel culinary applications and modern biotechnology, such as sustainable meat alternatives and enzyme production. Despite its industrial importance, until recently, the most recent genome-scale metabolic model (GEM) for A. oryzae dated back to 2008 and was limited to a single strain (RIB40). Here, we present pAo, a pan-GEM for A. oryzae, integrating genomic data from 187 strains to capture species-wide metabolic diversity. Our model comprises 2,025 reactions, representing a 52% increase in metabolic coverage over the RIB40-based model and includes previously overlooked pathways, such as cytochrome P450-mediated xenobiotic metabolism and extended amino acid metabolism. Using this pan-GEM, we derived strain-specific GEMs and validated 8 of them through high-throughput phenotypic screening on 285 substrates. Growth experiments on 4 industrially relevant carbon sources revealed substantial interstrain metabolic diversity, although flux balance analysis indicated that this variability originates at the regulatory rather than network-structural level. This resource provides a foundation for informed strain selection for biotechnological applications and future metabolic engineering in A. oryzae.

Complete genome sequence and mass spectrometry assist mining siderophore turnerbactin analogs in a non-model Pseudoduganella strain with biocontrol potential

Findings indicate that Pseudoduganella sp.

A pan-genome perspective uncovers the core genetic basis and evolutionary adaptation of lipid synthesis and vesicular transport in Nannochloropsis.

Nannochloropsis microalgae are widely recognized as sustainable cell factories for producing nutritional oils and biofuels due to their high-lipid content. However, a comprehensive understanding of the genetic basis of their oleaginous traits across diverse species has been limited. Here, we constructed a pan-genome of 17 Nannochloropsis species comprising 14,851 gene families. Our analysis defined a distinct genetic architecture for lipid metabolism: Gene families associated with vesicular transport formed a conserved core functional module, whereas the genetic collection for lipid metabolism showed greater plasticity and was primarily classified as part of the soft-core genome. This finding establishes a genetic blueprint for the coevolution between a stable cellular "logistics network" and an adaptable "biosynthetic factory." Evolutionary analysis further indicated that the DGAT and fatty acid desaturase families have species-specific expansions in Nannochloropsis, suggesting a potential role in enhancing lipid accumulation. By integrating 231 transcriptome datasets, we identified key genes (ACP2 and DGAT2) that were highly upregulated under nitrogen deprivation and pinpointed a set of core genes with high expression levels involved in vesicular transport. This "Infrastructure-Toolkit" model provides both genetic targets for strain improvement and a broader framework for understanding lipid accumulation in oleaginous microorganisms.

Predicting Strain-Specific Metabolic Capabilities in the Genus Pseudomonas with a Flux-to-AI Approach Reveals Hidden Cell Envelope Properties

Bacteria from the Pseudomonas genus are omnipresent in air, soil, and water. They have been widely studied for their broad metabolic versatility and their presence in epidemiological chains, bioproduction, bioremediation, and disease processes. Each year, more genomic sequences are reported in databases and repositories. However, the relationship between genomic variability in Pseudomonas strains and the diversity of their metabolic capabilities across environmentally relevant phenotypes remains unknown. Additionally, predictive tools for analyzing different strains within a systematic framework are limited. Here, we reconstructed genome-scale metabolic models (GEMs) of 44 Pseudomonas strains from various environments and investigated their capabilities to metabolize different carbon sources and metabolic intermediaries. By systematically testing the substrate utilization of the models, we demonstrate how GEM-predicted capabilities can differentiate between strains and that high metabolic versatility is associated with the predicted ability of the strains to remove toxic compounds while maintaining core functionalities. Hundreds of model simulations were used as input to a machine-learning classification schema, resulting in the identification of metabolic capabilities that better differentiate species. Interestingly, transcription and expression models supported these findings by showing that the metabolic capabilities identified by the Flux-to-AI framework are associated with differences in proteome allocation across strains, including pathways involved in amino acid and carbohydrate metabolism, ultimately revealing strain-specific resource allocation strategies linked to cell-envelope-associated functions.

Integrated Pangenomic and Systems Biology Analyses Reveal the Genomic Basis of Virulence and Adaptation in Bipolaris sorokiniana

Deciphering the genetic background of an industrial 2-ketogluconic acid-producing strain Pseudomonas plecoglossicida JUIM01 using whole-genome sequencing.

2-Ketogluconic acid (2KGA) is an important precursor for the food antioxidant erythorbic acid, currently produced via microbial fermentation using Pseudomonas species. To facilitate the genetic improvement of production strains, the complete genome of an industrial 2KGA producer P. plecoglossicida JUIM01 was sequenced and analyzed. The genome consists of a 5.13-Mb circular chromosome with a GC content of 63.58%, encoding 4,517 predicted proteins. Comprehensive functional annotation identified a putative global regulatory network comprising 75 core regulators, which were classified into six functionally cooperative modules, potentially governing the strain's metabolism and environmental adaptability. We further delineated the genetic determinants hypothetically linked to efficient 2KGA synthesis, including glucose metabolism, fatty acid metabolism, and the oxidative phosphorylation system. These outputs could provide the genomic resource for elucidating high productivity and robustness, and rationally engineering the high-performance chassis cells toward robust 2KGA production.