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Results for “respiratory tract microbiome”

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PubMed2025

Transitions in lung microbiota landscape associate with distinct patterns of pneumonia progression.

The precise microbial determinants driving clinical outcomes in severe pneumonia are unknown. Competing ecological forces produce dynamic microbiota states in health and disease, and a more thorough understanding of these states has the potential to improve pneumonia therapy. Here, we leverage a large collection of bronchoscopic samples from patients with suspected pneumonia to determine lung microbial ecosystem dynamics throughout the course of pneumonia. We combine 16S rRNA gene, metagenomic, and metatranscriptomic sequencing with bacterial-load quantification to reveal clinically relevant drivers of pneumonia progression. Microbiota states are predictive of pneumonia subtypes and exhibit differential stability and pneumonia therapy response. Disruptive forces, such as aspiration, are associated with cohesive changes in gene expression and microbial community structure. In summary, we show that host and microbiota landscapes change in unison with clinical phenotypes and that microbiota state dynamics reflect pneumonia progression. We suggest that distinct pathways of lung microbial community succession mediate pneumonia progression.

Humans↗

PubMed2024

Effectiveness of stabilization methods for the immediate and short-term preservation of bovine fecal and upper respiratory tract genomic DNA.

Previous research on stabilization methods for microbiome investigations has largely focused on human fecal samples. There are a few studies using feces from other species, but no published studies investigating preservation of samples collected from cattle. Given that microbial taxa are differentially impacted during storage it is warranted to study impacts of preservation methods on microbial communities found in samples outside of human fecal samples. Here we tested methods of preserving bovine fecal respiratory specimens for up to 2 weeks at four temperatures (room temperature, 4°C, -20°C, and -80°C) by comparing microbial diversity and community composition to samples extracted immediately after collection. Importantly, fecal specimens preserved and analyzed were technical replicates, providing a look at the effects of preservation method in the absence of biological variation. We found that preservation with the OMNIgene®•GUT kit resulted in community structure most like that of fresh samples extracted immediately, even when stored at room temperature (~20°C). Samples that were flash-frozen without added preservation solution were the next most representative of original communities, while samples preserved with ethanol were the least representative. These results contradict previous reports that ethanol is effective in preserving fecal communities and suggest for studies investigating cattle either flash-freezing of samples without preservative or preservation with OMNIgene®•GUT will yield more representative microbial communities.

Cattle↗

PubMed2025

Investigation of associations between the neonatal gut microbiota and severe viral lower respiratory tract infections in the first 2 years of life: a birth cohort study with metagenomics.

BACKGROUND: Early-life gut microbiota affects immune system development, including the lung immune response (gut-lung axis). We aimed to investigate whether gut microbiota composition in neonates in the first week of life is associated with hospital admissions for viral lower respiratory tract infections (vLRTIs). METHODS: The Baby Biome Study (BBS) is a prospective birth cohort, which enrolled mother-baby pairs between Jan 1, 2016, and Dec 31, 2017, at three UK hospitals. In the present study, we only included BBS babies with a sequenced first-week stool sample and successful data linkage. Stool was collected in the first week of life for shotgun-metagenomic sequencing. We examined the following microbiota features: alpha diversity (Chao1, Shannon, and Simpson indices) and community structures (cluster-partitioning against medoids method). The participants were followed up through linkage to the Hospital Episode Statistics-Admitted Patient Care (HES-APC) database to determine vLRTI hospital admission incidence in the first 2 years of life. We used Poisson mixed-effects models for univariable and multivariable analyses to evaluate the association between microbiota features and vLRTI hospital admission incidence, adjusting for confounders identified through direct acyclic graphs. FINDINGS: 3305 (95%) of the 3476 BBS-enrolled babies for whom consent to data linkage was obtained were included in the present study. 1111 (34%) babies had a first-week sequenced stool sample, of whom 1082 (97%; 564 born vaginally and 518 born by caesarean section) were successfully linked to HES-APC, and had median follow-up of 2·0 years (IQR 1·4-2·9). Most babies were born at term (996 [92%] ≥37 weeks gestational age and 1070 [99%] >35 weeks gestational age) and healthy (1050 [97%] had no comorbidities), and 520 (48%) were female and 562 (52%) were male. Higher first-week gut microbiota alpha diversity was associated with reduced rates of vLRTI hospital admission (Chao1 Index adjusted hazard ratio [HR] 0·92 [95% CI 0·85-0·99]; Shannon Index adjusted HR 0·57 [0·33-0·98]; and Simpson Index adjusted HR 0·36 [0·11-1·20]). Three microbiota clusters were identified. Cluster 1 had a mixed composition and cluster 2 was dominated by Bifidobacterium breve, with both clusters observed in babies born vaginally and by caesarean section. Cluster 3 was found only in vaginally born babies and was dominated by Bifidobacterium longum. Having cluster 1 (mixed) or cluster 2 (B breve dominated) was independently associated with increased rates of vLRTI hospital admission compared with cluster 3 (B longum dominated; cluster 1 [mixed] 3·05 [1·25-7·41] and cluster 2 [B breve dominated] 2·80 [1·06-7·44]). INTERPRETATION: We report observational evidence that first-week gut microbiota differences are associated with clinically severe vLRTI in young children. This study identified bacterial species that could be of interest for vLRTI prevention. This finding has important implications for the design of future research and intervention strategies. FUNDING: The Wellcome Trust and Wellcome Sanger Institute core funding.

Humans↗

PubMed2026

Dual-transcriptomic analysis of human nasal transcriptome and microbiome reveals host-bacteria associations in symptomatic respiratory infection.

BACKGROUND: The human nasopharynx is colonized by a diverse community of commensal microbiota linked to many respiratory diseases, yet their associations with the host remain unclear. RESULTS: In this study, we introduced a dual-transcriptomics analysis strategy, which can characterize the host transcriptome and microbiome from nasal samples simultaneously. We applied this workflow to a local SARS-CoV-2 cohort with 76 asymptomatic infected patients, among whom 52 (68.42%) developed symptomatic infection during a 1-week follow-up period. Nasal swabs were collected from all 76 patients at enrollment and from 73 patients at one-week later follow-up. We detected a median of 8.94% reads that did not map to the human genome across all 149 samples, among which around half (median 49.68%) were successfully mapped to microbiome genome. Meta-transcriptomic analysis detected significantly higher SARS-related coronavirus loads in samples from the symptomatic group at enrollment (P = 0.004), and both groups showed decreased loads one week later (symptomatic, P = 0.001; asymptomatic, P = 0.035). Compared with benchmarking 16 S rRNA sequencing on 53 samples, our computational strategy showed high correlation of relative abundance in all top 20 genera (median Rho = 0.90, Pmax < 0.001). A total of 670 bacteria species were identified to show a relative abundance ≥ 0.01% in at least 10% samples. Differential abundance analysis identified 76 species (DASs) from six phyla with significantly decreased abundance in samples from the symptomatic group (log2(fold change or FC) < -1 and adjusted P < 0.05) compared to the asymptomatic group at enrollment. Integrating these symptom-associated DASs with host's gene expression using an expression quantitative trait bacteria (eQTB) model, we found 45 symptom-associated DASs identified at enrollment were significantly associated with one to 14 genes (adjusted P < 0.05). GSEA showed a series of symptom-associated DASs were significantly correlated with pathways related to olfactory function, keratinocyte differentiation, and DNA methylation. CONCLUSIONS: In summary, our dual-transcriptomic analysis strategy effectively characterized host-microbiome associations, offering insights into microbial contributions to respiratory diseases.

Humans↗