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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

a Heatmap of Moraxella catarrhalis signature genes distinguishes the asthma samples from the controls. The color scale goes from blue (low expression) to red (high expression). b, c The Moraxella catarrhalis signature strengths are highly concordant with the PathoScope read proportions in control and asthma samples with the exception of sample P003
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Fig4: a Heatmap of Moraxella catarrhalis signature genes distinguishes the asthma samples from the controls. The color scale goes from blue (low expression) to red (high expression). b, c The Moraxella catarrhalis signature strengths are highly concordant with the PathoScope read proportions in control and asthma samples with the exception of sample P003

Mentions: Regarding among-sample relatedness, we observe that the five samples where M. catarrhalis is relatively more abundant tend to differentiate from controls (PCoA; 95 % of variance), while asthma samples with low levels of M. catarrhalis tend to cluster with controls (Fig. 1b). Interestingly, the latter three samples also exhibit the lowest proportion of M. catarrhalis, and two of them exhibit no host response to M. catarrhalis-associated genes (below; Fig. 4; P001 and P005). Recently, Goleva et al. found no differences either in diversity or composition in patients with corticoid-sensitive or resistant phenotypes compared to controls in samples without M. catarrhalis [45]. This suggests that asthma microbiomes where M. catarrhalis is not detected resemble those of control individuals, however we do not discard the possibility that another unidentified microbe is driving this apparent similarity.


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)

a Heatmap of Moraxella catarrhalis signature genes distinguishes the asthma samples from the controls. The color scale goes from blue (low expression) to red (high expression). b, c The Moraxella catarrhalis signature strengths are highly concordant with the PathoScope read proportions in control and asthma samples with the exception of sample P003
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: a Heatmap of Moraxella catarrhalis signature genes distinguishes the asthma samples from the controls. The color scale goes from blue (low expression) to red (high expression). b, c The Moraxella catarrhalis signature strengths are highly concordant with the PathoScope read proportions in control and asthma samples with the exception of sample P003
Mentions: Regarding among-sample relatedness, we observe that the five samples where M. catarrhalis is relatively more abundant tend to differentiate from controls (PCoA; 95 % of variance), while asthma samples with low levels of M. catarrhalis tend to cluster with controls (Fig. 1b). Interestingly, the latter three samples also exhibit the lowest proportion of M. catarrhalis, and two of them exhibit no host response to M. catarrhalis-associated genes (below; Fig. 4; P001 and P005). Recently, Goleva et al. found no differences either in diversity or composition in patients with corticoid-sensitive or resistant phenotypes compared to controls in samples without M. catarrhalis [45]. This suggests that asthma microbiomes where M. catarrhalis is not detected resemble those of control individuals, however we do not discard the possibility that another unidentified microbe is driving this apparent similarity.

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