PubMed HealthSearch

PubMed · 41843749

Pseudomonas aeruginosa adaptation and persistence in the aspergilloma microbiome revealed by integrated multi-omics.

Abstract

Chronic pulmonary aspergillosis involves the formation of a fungal ball (aspergilloma) in lung cavities. Pseudomonas aeruginosa commonly co-colonizes these lesions; however, the in vivo mechanisms underlying its persistence are unknown. Using a multi-omics approach on resected aspergillomas, we defined the genomic, transcriptional, and metabolic adaptations of P. aeruginosa within this polymicrobial niche. We reconstructed high-quality P. aeruginosa genomes and identified a conserved core genome, along with accessory genes for secondary metabolism, virulence, and antimicrobial resistance. Phylogenomics revealed heterogeneous evolutionary paths among co-colonizing strains. Metatranscriptomics showed stark physiological heterogeneity, from metabolically aggressive to stress-adapted states. High expression of phenazine, quorum-sensing (PQS), siderophore, and secretion-system operons was corroborated by metabolomic detection of phenazine-1-carboxylic acid and 2-heptylquinolin-4(1H)-one, confirming active bacterial antagonism in vivo. Concurrent Aspergillus fumigatus transcriptomics revealed the activation of oxidative stress responses, secondary metabolism (eg fumagillin), and iron scavenging, demonstrating reciprocal competition. Host transcriptomics revealed patient-specific immune signatures that correlated with the metabolic activity of the co-colonizers. This work provides an integrated systems-level analysis of the tri-kingdom aspergilloma ecosystem. P. aeruginosa persistence is driven by genomic plasticity and context-dependent expression of competitive pathways, shaped within a chronic inflammatory environment. These findings redefine aspergillomas as active polymicrobial consortia, establishing a framework for targeting resilient microbial communities in chronic lung disease.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Matheus Mertz Ribeiro, Chan Liu, Jin-Fu Xu, Shuo Liang, Gustavo H Goldman. 2026-05-06. Pseudomonas aeruginosa adaptation and persistence in the aspergilloma microbiome revealed by integrated multi-omics.. https://doi.org/10.1093/g3journal%2Fjkag063

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Advancing the Deciphering of Host-Microbe Crosstalk with Spatial Omics: A Mini-Review.

Host-microbe crosstalk refers to the reciprocal influences between a host and its resident or invading microorganisms. This crosstalk plays important roles in maintaining host health, regulating physiological functions, and coordinating responses to infection. The rapid rise of spatial omics is transforming how this crosstalk is studied in both animals and plants. Unlike traditional bulk omics, which homogenize tissues and erase spatial context, spatial methods preserve in situ organization and can simultaneously capture molecular information from hosts and microbes. As a result, researchers can characterize the spatial organization of colonization and infection, identify spatial associations between microbial niches and host cell states, and visualize local host response gradients across intact tissues. Current spatial omics technologies encompass sequencing-based, imaging-based, and hybrid platforms. Spatial multi-omics approaches enable the joint measurement or integration of gene expression, protein abundance, and metabolite distributions. Although spatial association alone does not establish causality, spatial omics provides a high-resolution framework for characterizing host-microbe relationships within intact tissues and generating spatially constrained, testable hypotheses. When combined with perturbation experiments and complementary experimental evidence, these hypotheses can contribute to mechanistic interpretation of host-microbe crosstalk. Here, we review spatial omics technologies, compare their suitability and major trade-offs for host-microbe studies, and discuss computational strategies, analytical challenges, and future prospects.

Multiomics

Integrating multi-omics technologies to decipher microbiome functions.

Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.

Multiomics

Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.

Multiomics