Sputum microbiome profiles identify severe asthma phenotypes of relative stability at 12 to 18 months

Abstract

Sputum microbiome profiles identify severe asthma phenotypes of relative stability at 12-18 months Unsupervised microbiome-driven clustering Sputum microbial signature assessment Neutrophils Commensal bacteria Pathogenic bacteria Airway obstruction Macrophages Cluster 1 Severe adult asthma patients Baseline 12-18 month follow-up 2 robust microbiome clusters with relative stability after 12-18 months Cluster 2 More severe asthma Less severe asthma

Mathematical-based microbiome analytics for clinical translation

“…Mathematically, the persistence of homology generators encode the geometric information of structures. TDA may derive new insights and deepen our current understanding of microbiome data although the only TDA algorithm applied thus far to microbiome research is Mapper, an approach that aims to uncover and visualize the topological properties of microbiomes [67] , [68] , [69] , [70] .…”

The intersect of genetics, environment, and microbiota in asthma—perspectives and challenges

“…109 Interestingly, in adult patients with asthma, Prevotella and Veilonella, as well as other commensal species, were deficient in the sputum of a microbiome-derived cluster of patients with worse asthma outcomes. 112 This suggests that interactions and mechanisms may differ by age groups. In addition, there may be possible interaction between bacterial and viral respiratory pathogens-superinfection by pathogenic bacteria such as Haemophilus, Streptococcus, and Moraxella increases the symptomatic severity of viral illnesses, 113,114 and there is also some evidence of events in the opposite direction: that increasing M catarrhalis colonization can actually precede viral respiratory infections in infants.…”

“…To deal with this, methods to reduce dimensionality are useful: for instance, ''polygenic risk scores'' for genomic data, 186 ''microbial association indices'' for microbiome data, 187 or cluster analysis for other types of data. 96,105,112,188 These methods condense large data sets into derived variables that can then be carried over into other, better-powered analyses. But, regardless of method, researchers should consider environmental and other host factors as best they can when constructing these models.…”

Inhaled Corticosteroids and the Lung Microbiome in COPD

“…In inflammatory lung diseases such as bronchiectasis and cystic fibrosis, diversity has been shown to decrease, and studies indicate that changes in the composition of the lung microbiome might contribute to, and result from, chronic inflammation [49][50][51]. Further, studies have shown relationships between asthma severity and low diversity; for example, severe asthma has been associated with a clear loss of diversity [52], and lower diversity has been associated with neutrophilic asthma, which is often more severe than eosinophilic [53]. In addition, interactions between members of the microbiome can be analysed (whether members co-occur or co-exclude, and the strength of these relationships).…”