Rec reconstructed genome-scale metabolic models of 44 Pseudomonas strains from various environments and investigated their capabilities to metabolize different carbon sources and metabolic intermediaries, demonstrating 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.
Abstract
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.
The genus Pseudomonas consists of diverse and ecologically significant species that form close associations with both plants and animals. This genus is widely studied due to the clinically relevant Pseudomonas aeruginosa, model plant pathogen Pseudomonas syringae, and non-pathogenic, industrially relevant Pseudomonas putida. The different metabolic and physiological capabilities of these species are enabled by their unique genetic makeup as well as varying regulatory mechanisms. To study the transcriptional basis for the diversity of the three species, we applied independent component analysis to strain-specific RNA-seq datasets to identify independently modulated gene sets (iModulons) and their condition-specific activity levels. We then mapped iModulons across strains based on their similarity in orthologous gene membership. Through comparison of iModulon gene membership and activities, we find that: (i) iModulons reveal shared and unique regulatory modalities across strains; (ii) unique adaptations in common functions, such as translation and pyoverdine production/uptake, manifest through both differential iModulon gene membership and condition-specific activation states in each strain; (iii) iModulons facilitate comparison of stress responses at the systems level; and (iv) iModulons highlight unique virulence factor enrichment and host-specific adaptations in human and plant pathogens. Altogether, comparing the modularized transcriptomes of the three strains provides unique and comprehensive insights into their differential evolution. Importance Closely related bacterial species often have vastly different metabolic and physiological capabilities, yet the regulatory mechanisms underlying these adaptations remain poorly understood. Here, we compare the transcriptional regulatory networks of three representative Pseudomonas strains through cross-strain iModulon analysis. By comparing both iModulon gene composition and activity across strains, we identify conserved regulatory modules alongside lineage-specific adaptations in functions associated with virulence, translation, iron acquisition, motility, and stress responses. Our results demonstrate that iModulons provide a genome-scale framework for comparing transcriptional regulation across closely related organisms, revealing regulatory innovations that are not apparent from genome comparisons alone. This work establishes a scalable approach for studying the evolution of bacterial transcriptional regulatory networks and the regulatory basis of niche specialization.
Heera Bajpe, Ying Hefner, R. Szubin et al.· bioRxiv· 0 citations
ABSTRACT Genomes reveal vast functional potential, but harbor genomic noise that obscures prediction of metabolic and environmental preferences. Genomic databases are skewed towards clinically relevant and easily cultivated bacteria, limiting predictions for diverse and underrepresented environmental taxa. Psychrotrophic bacteria, which can survive and grow in cold, nutrient-limited, dry, and saline environments, are especially underrepresented despite their relevance for understanding microbial responses to changing cold environments and potential biotechnological value given growth at low temperatures. Assembling complete genomes of 48 isolates from Alaskan permafrost, seasonally frozen active layer soils, and terrestrial ice, we used Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog annotations to evaluate the predictability of metabolic resource-use traits observed using phenotypic tests. Genome-predicted values for glycolytic versus gluconeogenic catabolic preference index, or sugar-acid preference (SAP), explained over 50% of the variance in empirically observed SAP. SAP was inversely correlated to genomic GC content, which follows phylum-level trends, indicating that coarse metabolic preference covaries with phylogeny. Regularized elastic net models offered a more granular view, linking KEGG genes to specific substrate utilization and sensitivity phenotypes and yielding moderate but reproducible accuracy (AUC 0.70–0.79) for 11 substrates, demonstrating that specific substrate responses may be predictable from relatively small subsets of KO genes. These results extend recent advances, such as the SAP metric, and highlight associations among genomic GC content, phylum, and broad metabolic strategy. Linking genomic content to phenotype using isolates is a necessary step toward predictive models of microbial function in environmental communities, and this work can be used for hypothesis generation, with applications towards more expansive data sets. IMPORTANCE Cold region soils and ice host psychrotrophic bacteria with metabolic traits and adaptations that enable persistence in harsh, resource-limited environments. However, these taxa are underrepresented in genomic reference databases dominated by well-studied, mesophilic organisms. This gap limits inference of ecological strategies and our ability to predict how these microbes may influence the large, thaw-vulnerable carbon reservoirs in permafrost. Here, we show that genomic GC content is associated with the sugar-versus-acid catabolic preference (SAP) of isolates across major phyla, suggesting that broad genomic features may provide a coarse signal of metabolic strategy. We demonstrate that a modified SAP metric, using binary (positive/negative) substrate utilization rather than detailed growth rate measurements, is moderately predictive, thus extending its application to slow-growing or difficult-to-culture taxa. Together, these advances broaden the toolkit for linking genome content to resource-use traits (phenotype) in poorly characterized, cold-adapted bacteria and offer a tractable entry point to broad prediction and hypothesis generation. Cold region soils and ice host psychrotrophic bacteria with metabolic traits and adaptations that enable persistence in harsh, resource-limited environments. However, these taxa are underrepresented in genomic reference databases dominated by well-studied, mesophilic organisms. This gap limits inference of ecological strategies and our ability to predict how these microbes may influence the large, thaw-vulnerable carbon reservoirs in permafrost. Here, we show that genomic GC content is associated with the sugar-versus-acid catabolic preference (SAP) of isolates across major phyla, suggesting that broad genomic features may provide a coarse signal of metabolic strategy. We demonstrate that a modified SAP metric, using binary (positive/negative) substrate utilization rather than detailed growth rate measurements, is moderately predictive, thus extending its application to slow-growing or difficult-to-culture taxa. Together, these advances broaden the toolkit for linking genome content to resource-use traits (phenotype) in poorly characterized, cold-adapted bacteria and offer a tractable entry point to broad prediction and hypothesis generation.
Jaimie R. West, Q. Faber, Abigail Shepherd et al.· Microbiology spectrum· 0 citations
Bacterial genome sequencing has been used to identify a myriad of bioactive compounds that have yet to be characterised. Pseudomonas fluorescens is a widely distributed Gram-negative bacterium known for its plant growth-promoting traits and production of diverse secondary metabolites. P. fluorescens genome harbours several biosynthetic gene clusters (BGCs), and it is challenging to link many of these BGCs with their respective product under laboratory conditions. The current study employed a bioinformatic approach to gain in-depth genomic insight into the distribution, evolution, and diversity of these BGCs in P. fluorescens, facilitating the exploration of cryptic biosynthetic capabilities. In addition, to understand the genomic landscape and evolutionary dynamics, core pan-genome analysis was conducted for P. fluorescens species. We identified a total of 2098 BGCs across the high-quality targeted genomes (n = 174), including non-ribosomal peptide synthetases (NRPSs), ribosomal-synthesised and post-translationally modified peptides (RiPPs), polyketides (PKSs), Terpenes, siderophores, and arylpolyenes, which were the most prevalent. Several low similarity clusters (n = 14) were identified that encode putative novel metabolites. Core Pan-genome analysis revealed an “open pan-genome” and large accessory genome enriched in secondary metabolism genes, supporting the biosynthetic versatility of P. fluorescens. This work enhances our understanding of the metabolic capabilities and genomic landscape of the P. fluorescens species, providing a foundation for natural product discovery using bioinformatic approaches.
Sajid Iqbal, Farida Begum· Discover Genetics and Evolut...· 0 citations
ABSTRACT Pseudomonas aeruginosa is a gram-negative bacterial pathogen capable of forming antimicrobial-tolerant subpopulations known as persister cells. These cells are transient phenotypic variants that can tolerate antimicrobial treatment and have been associated with chronic infections and the development of antibiotic resistance. While persister cells are classically associated with reduced metabolic activity, the characteristics of their metabolism are not well understood, especially in the context of biocides. In this work, we performed an experimental and computational system-level analysis to characterize the metabolic state of biocide persister cells. To accomplish this, we conducted in-depth profiling of both wild-type and persister samples of P. aeruginosa with transcriptomic sequencing and metabolomic analyses. These analyses revealed a distinct metabolic repertoire in biocide persister cells, marked by an upregulation in genes associated with activity in central metabolism. Integration of both the transcriptomic data set with a P. aeruginosa genome-scale metabolic network reconstruction (GENRE) provided condition-specific models, which were used to identify metabolic reactions and genes that differentiated the persister phenotype from the untreated. Experimental testing of model predictions revealed metabolic functions, such as pyrimidine synthesis and methionine recycling, which could serve as potential targets for inhibiting persister cell formation. IMPORTANCE Bacterial persister cells represent a transient subpopulation that can survive lethal antimicrobial treatments and are a major barrier to eliminating chronic infections, yet their formation and maintenance of this state remain poorly understood. By examining Pseudomonas aeruginosa treated with the industrial biocide benzisothiazolinone, this study reveals that persister cells retain active metabolism and exhibit a metabolic program distinct from that of untreated cells. We integrated transcriptomics, metabolomics, and genome-scale metabolic modeling to identify specific metabolic pathways, notably pyrimidine biosynthesis and methionine recycling, that are critical for persister survival. These findings provide insight into persister cell biology and highlight metabolism as a promising target for strategies aimed at preventing or eliminating these tolerant bacterial subpopulations. Bacterial persister cells represent a transient subpopulation that can survive lethal antimicrobial treatments and are a major barrier to eliminating chronic infections, yet their formation and maintenance of this state remain poorly understood. By examining Pseudomonas aeruginosa treated with the industrial biocide benzisothiazolinone, this study reveals that persister cells retain active metabolism and exhibit a metabolic program distinct from that of untreated cells. We integrated transcriptomics, metabolomics, and genome-scale metabolic modeling to identify specific metabolic pathways, notably pyrimidine biosynthesis and methionine recycling, that are critical for persister survival. These findings provide insight into persister cell biology and highlight metabolism as a promising target for strategies aimed at preventing or eliminating these tolerant bacterial subpopulations.
Joseph M. Ficarrotta, Anna S. Blazier, Aline Métris et al.· Journal of Bacteriology· 0 citations
Favoured by global changes, freshwater cyanobacterial harmful blooms generate major ecological, economical and public health challenges. Microcystis, one of the most widespread cyanobacterial genera, grows within a phycosphere where specialised interactions with its microbiome occur, and are suspected to influence bloom appearance and its potential toxicity. Using a combination of metagenomic, metabolomic and metabolic modelling, we characterised the culture-associated phycospheres of twelve Microcystis strains isolated from a French pond. The distribution of metabolic reactions within Microcystis was consistent with their genospecies, whereas the metabolic landscape at the community level diverged from cyanobacterial phylogeny indicating partial functional decoupling between cyanobacteria and their associated microbiomes. Bacteria associated with the simplified phycospheres substantially expand the metabolic repertoire of the system, while maintaining functional redundancy within and across communities. On the other hand, endometabolomic profiles were largely driven by cyanobacterial metabolic outputs whereas exometabolomic analysis did not reveal metabolites involved in exchange processes. Metabolic modelling, together with the identification of toxic specialised metabolites produced by specific biosynthetic gene clusters, further highlighted differences in metabolic potential among phycospheres. Together, these findings deepen the understanding of Microcystis’ phycosphere functioning and demonstrate the value of multi-omics systems biology approaches, while suggesting that metabolic complementarity between species and across phycospheres could play a role in bloom-associated microbiome structure.
Juliette Audemard, Nicolas Creusot, Julie Leloup et al.· ISME Communications· 0 citations
Aspergillus welwitschiae is a widespread fungus with diverse roles as a plant mutualist, opportunistic human pathogen and industrial enzyme producer. The endophytic strain AwOcstreb1, isolated from halophytic rice (Oryza coarctata), promotes growth in commercial rice under normal and saline conditions. Despite its significance, genomic and metabolic resources for A. welwitschiae remain limited, with no complete genome information available for endophytic strains within the species. Moreover, the close relationship of this species to Aspergillus niger complicates its taxonomic resolution. We performed whole-genome and transcriptomic sequencing of AwOcstreb1 cultured on potato dextrose agar, along with -MS-based volatile metabolite profiling. Comparative analyses included simple sequence repeat (SSR), transposable element (TE; including starships) and carbohydrate-active enzyme (CAZyme) profiling across A. welwitschiae strains. Evolutionary relationships with A. niger were examined using average nucleotide identity (ANI) and orthologous gene clustering, supported by phylogenomic reconstruction. Genes for mycotoxin production and plant growth-promoting traits were also searched in this strain. The AwOcstreb1 genome is 37.7 Mb with 13,242 predicted genes, of which 66.6% were actively expressed under potato dextrose agar growth. The genome harbours 5,126 SSRs, 19,434 TEs and a CAZyme composition similar to other A. welwitschiae strains. Although established marker genes such as CaM and β-tubulin identify AwOcstreb1 as A. welwitschiae, whole-genome ANI and orthologous gene-based analyses place A. welwitschiae strains within the broader A. niger species complex, suggesting that it represents a population-level group rather than a clearly separated species, a view that is still not widely adopted. Synteny analysis showed that the AwOcstreb1 genes are highly collinear with those of A. niger. Genes involved in phosphate and zinc solubilization and siderophore biosynthesis were detected, whereas ochratoxin A biosynthetic genes were absent. Although the presence of fumonisin genes was detected, only a trace amount of the toxin was detected both in culture as well as rice grains. Among 172 strain-specific orthogroups, several encode intrinsically disordered, secreted or membrane-associated proteins that are potentially linked to endophytic lifestyle adaptations. Volatile metabolite profiling identified compounds such as 17-pentatriacontene, eicosane and octanal, each linked to known biological sources and potential functions, such as antifungal, antibacterial and anti-inflammatory activities. Several additional metabolites were also identified, whose biological roles need further investigation. This integrated omics study provides foundational insights into the endophytic potential and genomic distinctiveness of AwOcstreb1. This work opens new avenues for exploring A. welwitschiae for sustainable agriculture and fungal biology.
Nishat Tamanna, Md Nafis Ul Alam, Arifa Akhter Airin et al.· Microbial Genomics· 0 citations