Insights into gut microbiome and its functional pathways in asthma patients through high-throughput sequencing

Association Between Adult Antibiotic Use, Microbial Dysbiosis and Atopic Conditions - A Systematic Review

Background

Strong associations between early antibiotic exposure and increased risk of childhood allergies have been established. Antibiotics have the potential to induce microbial dysbiosis that may be linked to allergic conditions. This review examines the limited available evidence on the associations between adult antibiotic use, microbial dysbiosis and atopic conditions.

Methods

A systematic literature search was conducted using PubMed and Embase for relevant studies, published between 01-01-2000 and 08-17-2022. We searched for associations between antibiotic use, microbial dysbiosis, and allergic conditions in adults, defined as over 13 years of age for the purposes of this review.

Results

Twenty-one studies were analyzed, with the inclusion of four narrative reviews as scarce relevant literature was found when stricter selection criteria were employed. Relevant studies predominantly focused on asthma. Significant microbial differences were observed in most measures between healthy subjects and subjects with allergic conditions. However, no system-wise and strain-wise associations were evident. Notably, at the phyla level, the Bacillota and Pseudomonadota phyla were associated with asthmatics, while the Actinobacteria phylum was linked to healthy controls. Asthmatics tends to reflect upregulation in the Bacillota and Pseudomonadota phyla in both airway and gut microbiomes.

Conclusion

No compelling evidence could be found between adult antibiotic exposure, consequent microbial dysbiosis, and allergic conditions in adults. Our review is limited by scarce literature and therefore remains inconclusive. However, potential implications of antibiotic use impacting on allergic conditions justify additional research and heightened pharmacovigilance in this area.

Study on the Mechanism of Qing-Fei-Shen-Shi Decoction on Asthma Based on Integrated 16S rRNA Sequencing and Untargeted Metabolomics

Qing-Fei-Shen-Shi decoction (QFSS) consists of Prunus armeniaca L., Gypsum Fibrosum, Smilax glabra Roxb., Coix lacryma-jobi L., Benincasa hispida (Thunb.) Cogn., Plantago asiatica L., Pyrrosia lingua (Thunb.) Farw., Houttuynia cordata Thunb., Fritillaria thunbergii Miq., Cicadae Periostracum, and Glycyrrhizae Radix Et Rhizoma Praeparata Cum Melle. QFSS shows significant clinical efficacy in the treatment of asthma. However, the specific mechanism of QFSS on asthma remains unclear. Recently, multiomics techniques are widely used in elucidating the mechanisms of Chinese herbal formulas. The use of multiomics techniques can better illuminate the multicomponents and multitargets of Chinese herbal formulas. In this study, ovalbumin (OVA) was first employed to induce an asthmatic mouse model, followed by a gavage of QFSS. First, we evaluated the therapeutic effects of QFSS on the asthmatic model mice. Second, we investigated the mechanism of QFSS in treating asthma by using an integrated 16S rRNA sequencing technology and untargeted metabolomics. Our results showed that QFSS treatment ameliorated asthma in mice. In addition, QFSS treatment affected the relative abundances of gut microbiota including Lactobacillus, Dubosiella, Lachnospiraceae_NK4A136_group, and Helicobacter. Untargeted metabolomics results showed that QFSS treatment regulated the metabolites such as 2-(acetylamino)-3-[4-(acetylamino) phenyl] acrylic acid, D-raffinose, LysoPC (15 : 1), methyl 10-undecenoate, PE (18 : 1/20 : 4), and D-glucose6-phosphate. These metabolites are associated with arginine and proline metabolism, arginine biosynthesis, pyrimidine metabolism, and glycerophospholipid metabolism. Correlation analysis indicated that arginine and proline metabolism and pyrimidine metabolism metabolic pathways were identified as the common metabolic pathways of 16s rRNA sequencing and untargeted metabolomics. In conclusion, our results showed that QFSS could ameliorate asthma in mice. The possible mechanism of QFSS on asthma may be associated with regulating the gut microbiota and arginine and proline metabolism and pyrimidine metabolism. Our study may be useful for researchers to study the integrative mechanisms of Chinese herbal formulas based on modulating gut microbiota and metabolism.

Integrated Network Pharmacology and Gut Microbiota Analysis to Explore the Mechanism of Sijunzi Decoction Involved in Alleviating Airway Inflammation in a Mouse Model of Asthma

Background

Asthma is a chronic inflammatory disease of the airways with recurrent attacks, which seriously affects the patients' quality of life and even threatens their lives. The disease can even threaten the lives of patients. Sijunzi decoction (SJZD), a classical Chinese medicine formula with a long history of administration, is a basic formula used for the treatment of asthma and demonstrates remarkable efficacy. However, the underlying mechanism has not been elucidated.

Materials and Methods

We aimed to integrate network pharmacology and intestinal flora sequencing analysis to study the mechanism of SJZD in the treatment of allergic asthmatic mice. The active compounds of SJZD and their asthma-related targets were predicted by various databases. We performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to identify potentially relevant pathways for target genes. Furthermore, the active compound-target and target-signaling pathway network maps were constructed by using Cytoscape 3.8.2. These results were combined with those of the intestinal flora sequencing analysis to study the influence of SJZD on airway inflammation in allergic asthmatic mice.

Result

We obtained 137 active compounds from SJZD and associated them with 1445 asthma-related targets acquired from the databases. A total of 109 common targets were identified. We visualized active compound-target and target-signaling pathway network maps. The pathological analysis and inflammation score results suggested that SJZD could alleviate airway inflammation in asthmatic mice. Sequencing analysis of intestinal flora showed that SJZD could increase the relevant abundance of beneficial bacterial genus and maintain the balance of the intestinal flora. The core toll-like receptor (TLR) signaling pathway was identified based on network pharmacology analysis, and the important role TLRs play in intestinal flora and organismal immunity was also recognized. The analysis of the correlation between environmental factors and intestinal flora revealed that beneficial bacterial genera were negatively correlated with TLR2 and positively correlated with the TLR7 expression. Furthermore, they were positively correlated with IFN-γ and IL-10 levels and negatively correlated with IL-4 and IL-17 levels.

Conclusion

SJZD alleviated the airway inflammation state in asthmatic mice. The findings suggest that increasing the relevant abundance of beneficial intestinal bacteria in mice with asthma, regulating intestinal flora, interfering with the level of TLR2 and TLR7 expression to adjust the secretion of inflammatory factors, and alleviating asthmatic airway inflammation may be the possible mechanism involved in the treatment of asthma by SJZD, providing a basis for further studies on SJZD.

A pilot study exploring the association of bronchial bacterial microbiota and recurrent wheezing in infants with atopy

Background

Differences in bronchial microbiota composition have been found to be associated with asthma; however, it is still unclear whether these findings can be applied to recurrent wheezing in infants especially with aeroallergen sensitization.

Objectives

To determine the pathogenesis of atopic wheezing in infants and to identify diagnostic biomarkers, we analyzed the bronchial bacterial microbiota of infants with recurrent wheezing and with or without atopic diseases using a systems biology approach.

Methods

Bacterial communities in bronchoalveolar lavage samples from 15 atopic wheezing infants, 15 non-atopic wheezing infants, and 18 foreign body aspiration control infants were characterized using 16S rRNA gene sequencing. The bacterial composition and community-level functions inferred from between-group differences from sequence profiles were analyzed.

Results

Both α- and β-diversity differed significantly between the groups. Compared to non-atopic wheezing infants, atopic wheezing infants showed a significantly higher abundance in two phyla (Deinococcota and unidentified bacteria) and one genus (Haemophilus) and a significantly lower abundance in one phylum (Actinobacteria). The random forest predictive model of 10 genera based on OTU-based features suggested that airway microbiota has diagnostic value for distinguishing atopic wheezing infants from non-atopic wheezing infants. PICRUSt2 based on KEGG hierarchy (level 3) revealed that atopic wheezing-associated differences in predicted bacterial functions included cytoskeleton proteins, glutamatergic synapses, and porphyrin and chlorophyll metabolism pathways.

Conclusion

The differential candidate biomarkers identified by microbiome analysis in our work may have reference value for the diagnosis of wheezing in infants with atopy. To confirm that, airway microbiome combined with metabolomics analysis should be further investigated in the future.

AgingBank: a manually curated knowledgebase and high-throughput analysis platform that provides experimentally supported multi-omics data relevant to aging in multiple species

Discovering the biological basis of aging is one of the greatest remaining challenges for biomedical field. Work on the biology of aging has discovered a range of interventions and pathways that control aging rate. Thus, we developed AgingBank (http://bio-bigdata.hrbmu.edu.cn/AgingBank) which was a manually curated comprehensive database and high-throughput analysis platform that provided experimentally supported multi-omics data relevant to aging in multiple species. AgingBank contained 3771 experimentally verified aging-related multi-omics entries from studies across more than 50 model organisms, including human, mice, worms, flies and yeast. The records included genome (single nucleotide polymorphism, copy number variation and somatic mutation), transcriptome [mRNA, long non-coding RNA (lncRNA), microRNA (miRNA) and circular RNA (circRNA)], epigenome (DNA methylation and histone modification), other modification and regulation elements (transcription factor, enhancer, promoter, gene silence, alternative splicing and RNA editing). In addition, AgingBank was also an online computational analysis platform containing five useful tools (Aging Landscape, Differential Expression Analyzer, Data Heat Mapper, Co-Expression Network and Functional Annotation Analyzer), nearly 112 high-throughput experiments of genes, miRNAs, lncRNAs, circRNAs and methylation sites related with aging. Cancer & Aging module was developed to explore the relationships between aging and cancer. Submit & Analysis module allows users upload and analyze their experiments data. AginBank is a valuable resource for elucidating aging-related biomarkers and relationships with other diseases.