| Literature DB >> 32072252 |
Karin Hufnagl1, Isabella Pali-Schöll1, Franziska Roth-Walter1, Erika Jensen-Jarolim2,3.
Abstract
Worldwide 300 million children and adults are affected by asthma. The development of asthma is influenced by environmental and other exogenous factors synergizing with genetic predisposition, and shaping the lung microbiome especially during birth and in very early life. The healthy lung microbial composition is characterized by a prevalence of bacteria belonging to the phyla Bacteroidetes, Actinobacteria, and Firmicutes. However, viral respiratory infections are associated with an abundance of Proteobacteria with genera Haemophilus and Moraxella in young children and adult asthmatics. This dysbiosis supports the activation of inflammatory pathways and contributes to bronchoconstriction and bronchial hyperresponsiveness. Exogenous factors can affect the natural lung microbiota composition positively (farming environment) or negatively (allergens, air pollutants). It is evident that also gut microbiota dysbiosis has a high influence on asthma pathogenesis. Antibiotics, antiulcer medications, and other drugs severely impair gut as well as lung microbiota. Resulting dysbiosis and reduced microbial diversity dysregulate the bidirectional crosstalk across the gut-lung axis, resulting in hypersensitivity and hyperreactivity to respiratory and food allergens. Efforts are undertaken to reconstitute the microbiota and immune balance by probiotics and engineered bacteria, but results from human studies do not yet support their efficacy in asthma prevention or treatment. Overall, dysbiosis of gut and lung seem to be critical causes of the increased emergence of asthma.Entities:
Keywords: Allergy; Antibiotics; Asthma; Microbiome; Probiotics; Th2 inflammation
Year: 2020 PMID: 32072252 PMCID: PMC7066092 DOI: 10.1007/s00281-019-00775-y
Source DB: PubMed Journal: Semin Immunopathol ISSN: 1863-2297 Impact factor: 9.623
Definitions of terms used in this work
| Term | Definition |
|---|---|
| Microbiome | Collective genomes of all microorganisms associated with the human body |
| Microbiota | Microorganisms (bacteria, viruses, fungi, protozoa) populating the inner and outer surfaces of the human body |
| Dysbiosis | Imbalance of the microbial community and loss of microbial diversity |
| 16S rRNA gene | Component of the 30S subunit of prokaryotic ribosomes used for bacterial classification |
| Next-generation sequencing (NGS) | High-throughput technologies allowing rapid parallel DNA sequencing |
| Alpha diversity | A measure of the richness (how many?) and the evenness (how different?) of bacterial species in a sample |
| Beta diversity | A measure of the similarity of the bacterial composition between samples/individuals |
| Gut-lung axis | Bidirectional communication pathway between gut and lung |
| Atopy | Genetic predisposition to develop allergic hypersensitivity reactions |
| Probiotics | Live microorganisms that provide health benefits |
Fig. 1A Bacterial taxonomy: classification of the organisms in a rank-based classification (left) and exemplary taxonomical classification of Moraxella ssp according to bacterial taxonomy (right). B Distribution of common phyla and genera in the airways of healthy and asthmatic subjects: The graph depicts the relative abundance (in %) of the five most common phyla of bacteria colonizing the human airways and lung in healthy (white bars) and in asthmatic (black bars) subjects. Phyla Actinobacteria, Firmicutes and Bacteroidetes are less abundant in airways of asthmatics, while Proteobacteria are enriched. The table includes bacterial genera that seem to have a growth advantage in asthmatic airways, such as Moraxella and Haemophilus from Proteobacteria. In contrast, some genera are less abundant in asthmatics such as Prevotella and Corynebacterium, leading to a dysbiosis of the airway microbiome
Bacterial genera associated with microbial dysbiosis and asthma
| Subjects | Microbiota linked to asthma | Reference |
|---|---|---|
| Asthmatic children, nasal microbiome | Increased abundance of | [ |
| Children with respiratory disease, nasopharyngeal microbiome | Increased abundance of | [ |
| Preschool children with severe wheeze, lower airway microbiome | Genus | [ |
| Asthmatic and healthy adults, bronchial brushings | Asthmatic status associated with increased abundance of | [ |
| Adults with severe asthma | Increased abundance of | [ |
| Asthmatic and healthy adults, BAL samples | Increased abundance of | [ |
| Asthmatic and healthy adults, bronchial epithelial brushings | Increased abundance of | [ |
| Asthmatic and healthy children, gut microbiome | [ | |
| Infants at risk for asthma, gut microbiome | Decreased relative abundance of genera | [ |
| Preschool age asthmatic and healthy children, gut microbiome | Decreased relative abundance of genus | [ |
| Preschool age asthmatic and healthy children, gut microbiome | Lower abundance of genera | [ |
| Infants at high risk for asthma, gut microbiome | [ | |
Fig. 2Environmental factors associated with asthma and their influence on the gut-lung axis. Environmental factors can have a positive/protective effect (green circles) or a negative/enhancing effect (red circles) on asthma development. For some of these factors (e.g., antibiotics, pollution), it was demonstrated that they are able to interfere with the gut and/or lung microbiome, leading to dysbiosis and disturbances in the bidirectional exchange via the gut-lung axis, thereby enhancing asthma prevalence. Protective factors such as farming environment or intake of probiotics account for lower asthma incidences, but the direct impact on the gut or lung microbiome still needs to be analyzed in more detail
The effects of drugs on microbiota composition
| Category | Drug | Strains affected | Alpha diversity1 | Beta diversity2 | Abundance | Organism | Notes | Study reference |
|---|---|---|---|---|---|---|---|---|
| Antibiotic | Vancomycin | ↓ | ↓ | Mouse, male, female | Colonic, 14 days after treatment | [ | ||
| Ciprofloxacin-metronidazole | ↓ | ↓ | Mouse, male | [ | ||||
| ↓ | Human | [ | ||||||
| Vancomycin | Mouse, perinatal treatment | Hypersensitivity pneumonitis unaffected | [ | |||||
| Streptomycin | Mouse | Hypersensitivity pneumonitis | [ | |||||
| Amoxicillin | n.s. | Human | 5-day treatment course | [ | ||||
| Azithromycin | ↓ | Human | [ | |||||
| Cotrimoxazole | n.s. | Human | [ | |||||
| Antibiotics mix | ↓ | Mouse | Intermittent exposure, 3 cycles. Reduction of pulmonary Tregs | [ | ||||
| ↓ | Altered | Human | [ | |||||
| Altered | Human | [ | ||||||
| Altered | Human | [ | ||||||
| Altered | Human | Gastric, intestinal, esophageal and oral microbiome; after short-term treatments | [ | |||||
While on PPI: | On PPI: no change after PPI: ↑ | On PPI: no change after PPI: ↑ | After PPI: changed | Human, children | 12 GERD infants treated 18 weeks with PPi. After discontinuation correlation to normal age/nutrition | [ | ||
| GERD patients, 4, 8 weeks treatment | [ | |||||||
| n.s. | Human | Long-term use over at least 1 year | [ | |||||
| [ | ||||||||
| n.s. | Moderate shift | Long-term use over more than 5 years | [ | |||||
| Altered | Human | [ | ||||||
| Human | [ | |||||||
| Human | ||||||||
| Human | ||||||||
| Gut commensals | ↓ | Human | Paired analysis: 70 monozygotic twin pairs | [ | ||||
| ↓ | Mouse | Dominant in responders (vs non-responders) to food allergen | [ | |||||
| ↓ | Mouse | [ | ||||||
| ↓ | Mouse | [ | ||||||
| Mouse | [ | |||||||
| Other | Olanzapine | ↓ | Altered | Mouse | Acts bactericidic | [ | ||
| metformin | No change | Altered | Human | [ | ||||
| [ | ||||||||
| NSAID** | No change | Not altered | Human | [ | ||||
| Opioids | ↑ | Altered | Human | Due to obstipation | [ |
1 Alpha diversity: microbiota diversity in individual site or sample (one value per sample)
2 Diversity between separate samples
*PPI, proton pump inhibitor
**NSAID, non-steroidal anti-inflammatory drug