{"doi":"10.1111/all.70009","title":"Altered Phage‐Related Gene Profiles in Asthmatic Airways","abstract":"This study provides a functional perspective on the airway microbiome in asthma by analyzing gene-level features through metagenomic sequencing. While prior studies have largely focused on taxonomic composition, our findings reveal function-level disruptions—particularly in phage-related and anti-phage gene profiles—that distinguish asthmatic airways from healthy controls. Induced sputum samples from patients with non-severe and severe asthma and healthy controls (17 per group) were analyzed (Table 1). Overall bacterial composition was similar across groups (Figure S1A). No genera differed significantly by disease severity, though Actinomyces showed a marginal difference between control and non-severe groups (Table S1A). Actinomyces, Selenomonas [1] and GGB4533 were relatively enriched in controls (Figure 1A), but their associations with clinical parameters were not statistically significant (Figure S1B). Viral composition analysis showed an overall abundance of bacteriophages across all groups (Figure S2A). Notably, the combined presence of both viruses (Cytomegalovirus and Epstein–Barr virus) showed a significant difference between controls and non-severe patients (Q = 0.083) (Figure S2B; Table S1B), consistent with our prior findings [2]. To evaluate microbial functional variability across asthma severity, we analyzed beta-diversity of functional gene profiles. PCoA based on Bray-Curtis distances showed greater dispersion in asthma samples compared to controls (p = 0.049) (Figure S3A). Linear regression analysis further demonstrated that within-group functional dissimilarity increased with severity (p = 0.001) (Figure S3B). These findings suggest that airway microbial function becomes increasingly heterogeneous as asthma progresses. Elevated within-group variability may indicate disrupted microbial homeostasis and potentially reflect functional dysbiosis [3] associated with disease severity. Among the functional genes examined, two phage-related genes showed significant reduction in asthma, while certain CRISPR-related genes were relatively increased (Figure 1B; Table S1C). A broader analysis showed that prophage-related genes were more abundant in controls, whereas CRISPR-associated genes were enriched in severe asthma (Figure 1C). Bacteria possess anti-phage defense mechanisms, such as CRISPR-Cas systems, which regulate interactions with bacteriophages and shape both the bacteriome and virome [4]. The concurrent rise in CRISPR elements and decline in prophage genes may suggest an adaptive bacterial response under viral stress, possibly reflecting a destabilized antiviral defense in the asthmatic airway environment (Figure 1D). Of the metabolic pathways analyzed, only alanine-aspartate–glutamate metabolism (KO00250) was significantly enriched in controls versus severe asthma (p = 0.002; Table S1D), consistent with prior gut microbiome studies linking asthma to impaired amino acid metabolism [5, 6]. To examine microbial interaction changes in asthma, we constructed co-occurrence networks of bacterial taxa, viral taxa, and functional genes. The control group showed a highly interconnected network with Actinomyces and Selenomonas as keystone taxa, suggesting ecological stability (Figure S4A). In contrast, the asthma group exhibited a simplified network with fewer correlations and reduced keystone nodes (Figure S4B). These findings suggest that asthma is linked to decreased microbial interaction complexity and potential disruption of host-microbiome functional balance. Due to the DNA-based nature of our metagenomic approach, RNA viruses were not captured in this study. As many eukaryotic viruses possess RNA genomes, this limitation should be considered when interpreting the results. Additionally, fungal sequences were not detected, likely due to limitations in sample processing. In conclusion, the airway microbiome in asthma shows altered abundance in selected CRISPR-associated and prophage-related genes. While limited in scop","journal":"Allergy","year":2025,"id":571978,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9535,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":409842,"name":"Sang‐Cheol Park","orcid":"0000-0002-9781-6203","position":1,"is_corresponding":false},{"id":79404,"name":"Young Chan Kim","orcid":"0000-0002-6726-5957","position":2,"is_corresponding":false},{"id":1477935,"name":"Kyoung‐Hee Sohn","orcid":"0000-0001-8407-8080","position":3,"is_corresponding":false},{"id":1477936,"name":"Jung Wook Kim","orcid":"0000-0002-4230-5991","position":4,"is_corresponding":false},{"id":1231428,"name":"Kwang Jun Lee","orcid":"0000-0002-7831-5905","position":5,"is_corresponding":false},{"id":1477937,"name":"Sang‐Heon Cho","orcid":"0000-0002-7644-6469","position":6,"is_corresponding":false},{"id":1477938,"name":"Hye‐Ryun Kang","orcid":"0000-0002-2317-4201","position":7,"is_corresponding":false},{"id":1477939,"name":"Hana Yi","orcid":"0000-0002-4910-122X","position":8,"is_corresponding":false},{"id":1477934,"name":"Min-gyung Baek","orcid":"0000-0001-6319-5405","position":0,"is_corresponding":true}],"reference_count":6,"raw_metadata":null,"created_at":"2026-07-19T02:57:19.669553Z","pmid":"40772646","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}