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Integrating microbial and host transcriptomics to characterize asthma-associated microbial communities.

Castro-Nallar E, Shen Y, Freishtat RJ, Pérez-Losada M, Manimaran S, Liu G, Johnson WE, Crandall KA - BMC Med Genomics (2015)

Bottom Line: A number of microbiome studies analyzing respiratory tract samples have found increased proportions of gamma-Proteobacteria including Haemophilus influenzae, Moraxella catarrhalis, and Firmicutes such as Streptococcus pneumoniae.The resulting data were analyzed by partitioning human and microbial reads.Differential host gene expression analysis confirms that the presence of Moraxella catarrhalis is associated to a specific M. catarrhalis core gene signature expressed by the host.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology Institute, George Washington University, Ashburn, VA, 20147, USA. eduardo.castro@unab.cl.

ABSTRACT

Background: The relationships between infections in early life and asthma are not completely understood. Likewise, the clinical relevance of microbial communities present in the respiratory tract is only partially known. A number of microbiome studies analyzing respiratory tract samples have found increased proportions of gamma-Proteobacteria including Haemophilus influenzae, Moraxella catarrhalis, and Firmicutes such as Streptococcus pneumoniae. The aim of this study was to present a new approach that combines RNA microbial identification with host gene expression to characterize and validate metagenomic taxonomic profiling in individuals with asthma.

Methods: Using whole metagenomic shotgun RNA sequencing, we characterized and compared the microbial communities of individuals, children and adolescents, with asthma and controls. The resulting data were analyzed by partitioning human and microbial reads. Microbial reads were then used to characterize the microbial diversity of each patient, and potential differences between asthmatic and healthy groups. Human reads were used to assess the expression of known genes involved in the host immune response to specific pathogens and detect potential differences between those with asthma and controls.

Results: Microbial communities in the nasal cavities of children differed significantly between asthmatics and controls. After read count normalization, some bacterial species were significantly overrepresented in asthma patients (Wald test, p-value < 0.05), including Escherichia coli and Psychrobacter. Among these, Moraxella catarrhalis exhibited ~14-fold over abundance in asthmatics versus controls. Differential host gene expression analysis confirms that the presence of Moraxella catarrhalis is associated to a specific M. catarrhalis core gene signature expressed by the host.

Conclusions: For the first time, we show the power of combining RNA taxonomic profiling and host gene expression signatures for microbial identification. Our approach not only identifies microbes from metagenomic data, but also adds support to these inferences by determining if the host is mounting a response against specific infectious agents. In particular, we show that M. catarrhalis is abundant in asthma patients but not in controls, and that its presence is associated with a specific host gene expression signature.

No MeSH data available.


Related in: MedlinePlus

Alpha and beta diversity for asthma and control samples as estimated by different distance metrics. a Alpha diversity measures show controls are more diverse than asthma individuals in metrics that account for evenness, however in asthma individuals we observed more species. Observed = observed diversity; Chao1 = Chao estimator; Shannon = Shannon diversity index; Simpson = Simpson diversity index. b Multidimensional scaling using principal coordinate analysis (PCoA). Coordinates 1 and 2 explain 95 % of the observed variance
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Fig1: Alpha and beta diversity for asthma and control samples as estimated by different distance metrics. a Alpha diversity measures show controls are more diverse than asthma individuals in metrics that account for evenness, however in asthma individuals we observed more species. Observed = observed diversity; Chao1 = Chao estimator; Shannon = Shannon diversity index; Simpson = Simpson diversity index. b Multidimensional scaling using principal coordinate analysis (PCoA). Coordinates 1 and 2 explain 95 % of the observed variance

Mentions: We performed analyses of alpha and beta diversity to assess species richness and evenness within and among samples (Fig. 1 and b). We obtained estimates of various indices to characterize the richness and heterogeneity of the samples partitioned by asthma and control samples (Observed, Chao1, Shannon, Simpson). Observed and Chao1 are measures of species richness (number of species); the latter including a correction for unobserved species [36, 37]. In turn, Shannon and Simpson incorporate relative species abundance and thus represent Evenness or Heterogeneity [38]. We observed that asthma samples have more species (richer) compared to control individuals (Fig. 1a; Observed and Chao1). However, measures that explicitly model Evenness (Shannon and Simpson indices) suggest that asthmatic samples are dominated by fewer species (5 of 8 cases dominated by Moraxella catarrhalis; Fig. 1a; Fig. 2) and are thus less diverse than controls.Fig. 1


Integrating microbial and host transcriptomics to characterize asthma-associated microbial communities.

Castro-Nallar E, Shen Y, Freishtat RJ, Pérez-Losada M, Manimaran S, Liu G, Johnson WE, Crandall KA - BMC Med Genomics (2015)

Alpha and beta diversity for asthma and control samples as estimated by different distance metrics. a Alpha diversity measures show controls are more diverse than asthma individuals in metrics that account for evenness, however in asthma individuals we observed more species. Observed = observed diversity; Chao1 = Chao estimator; Shannon = Shannon diversity index; Simpson = Simpson diversity index. b Multidimensional scaling using principal coordinate analysis (PCoA). Coordinates 1 and 2 explain 95 % of the observed variance
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4537781&req=5

Fig1: Alpha and beta diversity for asthma and control samples as estimated by different distance metrics. a Alpha diversity measures show controls are more diverse than asthma individuals in metrics that account for evenness, however in asthma individuals we observed more species. Observed = observed diversity; Chao1 = Chao estimator; Shannon = Shannon diversity index; Simpson = Simpson diversity index. b Multidimensional scaling using principal coordinate analysis (PCoA). Coordinates 1 and 2 explain 95 % of the observed variance
Mentions: We performed analyses of alpha and beta diversity to assess species richness and evenness within and among samples (Fig. 1 and b). We obtained estimates of various indices to characterize the richness and heterogeneity of the samples partitioned by asthma and control samples (Observed, Chao1, Shannon, Simpson). Observed and Chao1 are measures of species richness (number of species); the latter including a correction for unobserved species [36, 37]. In turn, Shannon and Simpson incorporate relative species abundance and thus represent Evenness or Heterogeneity [38]. We observed that asthma samples have more species (richer) compared to control individuals (Fig. 1a; Observed and Chao1). However, measures that explicitly model Evenness (Shannon and Simpson indices) suggest that asthmatic samples are dominated by fewer species (5 of 8 cases dominated by Moraxella catarrhalis; Fig. 1a; Fig. 2) and are thus less diverse than controls.Fig. 1

Bottom Line: A number of microbiome studies analyzing respiratory tract samples have found increased proportions of gamma-Proteobacteria including Haemophilus influenzae, Moraxella catarrhalis, and Firmicutes such as Streptococcus pneumoniae.The resulting data were analyzed by partitioning human and microbial reads.Differential host gene expression analysis confirms that the presence of Moraxella catarrhalis is associated to a specific M. catarrhalis core gene signature expressed by the host.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology Institute, George Washington University, Ashburn, VA, 20147, USA. eduardo.castro@unab.cl.

ABSTRACT

Background: The relationships between infections in early life and asthma are not completely understood. Likewise, the clinical relevance of microbial communities present in the respiratory tract is only partially known. A number of microbiome studies analyzing respiratory tract samples have found increased proportions of gamma-Proteobacteria including Haemophilus influenzae, Moraxella catarrhalis, and Firmicutes such as Streptococcus pneumoniae. The aim of this study was to present a new approach that combines RNA microbial identification with host gene expression to characterize and validate metagenomic taxonomic profiling in individuals with asthma.

Methods: Using whole metagenomic shotgun RNA sequencing, we characterized and compared the microbial communities of individuals, children and adolescents, with asthma and controls. The resulting data were analyzed by partitioning human and microbial reads. Microbial reads were then used to characterize the microbial diversity of each patient, and potential differences between asthmatic and healthy groups. Human reads were used to assess the expression of known genes involved in the host immune response to specific pathogens and detect potential differences between those with asthma and controls.

Results: Microbial communities in the nasal cavities of children differed significantly between asthmatics and controls. After read count normalization, some bacterial species were significantly overrepresented in asthma patients (Wald test, p-value < 0.05), including Escherichia coli and Psychrobacter. Among these, Moraxella catarrhalis exhibited ~14-fold over abundance in asthmatics versus controls. Differential host gene expression analysis confirms that the presence of Moraxella catarrhalis is associated to a specific M. catarrhalis core gene signature expressed by the host.

Conclusions: For the first time, we show the power of combining RNA taxonomic profiling and host gene expression signatures for microbial identification. Our approach not only identifies microbes from metagenomic data, but also adds support to these inferences by determining if the host is mounting a response against specific infectious agents. In particular, we show that M. catarrhalis is abundant in asthma patients but not in controls, and that its presence is associated with a specific host gene expression signature.

No MeSH data available.


Related in: MedlinePlus