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Genome-wide analysis of the mouse lung transcriptome reveals novel molecular gene interaction networks and cell-specific expression signatures.

Alberts R, Lu L, Williams RW, Schughart K - Respir. Res. (2011)

Bottom Line: Our goal is to provide a key community resource on the genetics of the normative lung transcriptome that can serve as a foundation for experimental analysis and allow predicting genetic predisposition and response to pathogens, allergens, and xenobiotics.This interval contains the transcription factor Ahr that has a critical mis-sense allele in the DBA/2J haplotype and evidently modulates transcriptional activation by AhR.Large-scale gene expression analyses in genetic reference populations revealed lung-specific and immune-cell gene expression profiles and suggested specific gene regulatory interactions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Infection Genetics, University of Veterinary Medicine Hannover, Inhoffenstr, Braunschweig, Germany.

ABSTRACT

Background: The lung is critical in surveillance and initial defense against pathogens. In humans, as in mice, individual genetic differences strongly modulate pulmonary responses to infectious agents, severity of lung disease, and potential allergic reactions. In a first step towards understanding genetic predisposition and pulmonary molecular networks that underlie individual differences in disease vulnerability, we performed a global analysis of normative lung gene expression levels in inbred mouse strains and a large family of BXD strains that are widely used for systems genetics. Our goal is to provide a key community resource on the genetics of the normative lung transcriptome that can serve as a foundation for experimental analysis and allow predicting genetic predisposition and response to pathogens, allergens, and xenobiotics.

Methods: Steady-state polyA+ mRNA levels were assayed across a diverse and fully genotyped panel of 57 isogenic strains using the Affymetrix M430 2.0 array. Correlations of expression levels between genes were determined. Global expression QTL (eQTL) analysis and network covariance analysis was performed using tools and resources in GeneNetwork http://www.genenetwork.org.

Results: Expression values were highly variable across strains and in many cases exhibited a high heritability factor. Several genes which showed a restricted expression to lung tissue were identified. Using correlations between gene expression values across all strains, we defined and extended memberships of several important molecular networks in the lung. Furthermore, we were able to extract signatures of immune cell subpopulations and characterize co-variation and shared genetic modulation. Known QTL regions for respiratory infection susceptibility were investigated and several cis-eQTL genes were identified. Numerous cis- and trans-regulated transcripts and chromosomal intervals with strong regulatory activity were mapped. The Cyp1a1 P450 transcript had a strong trans-acting eQTL (LOD 11.8) on Chr 12 at 36 ± 1 Mb. This interval contains the transcription factor Ahr that has a critical mis-sense allele in the DBA/2J haplotype and evidently modulates transcriptional activation by AhR.

Conclusions: Large-scale gene expression analyses in genetic reference populations revealed lung-specific and immune-cell gene expression profiles and suggested specific gene regulatory interactions.

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Expression signals for strongly correlated genes in the lung. Numbers indicate BXD strains, and the parental C57BL/6J and DBA/2J strains as well as F1 individuals are presented. Expression signals of (A) Klra3 and Gzma (p < 10-16) and (B) Klra3 versus Il18rap (p < 10-11) were strongly correlated. (C) List of nine genes highly correlated with the first principal component of the expression of Gzma, Klrg1, Klra3 and Il18rap. (D) Strong correlation between the first principal component and Prf1 (p < 10-16). X- and Y-axis of the plots show the names of genes used for the analysis.
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Figure 2: Expression signals for strongly correlated genes in the lung. Numbers indicate BXD strains, and the parental C57BL/6J and DBA/2J strains as well as F1 individuals are presented. Expression signals of (A) Klra3 and Gzma (p < 10-16) and (B) Klra3 versus Il18rap (p < 10-11) were strongly correlated. (C) List of nine genes highly correlated with the first principal component of the expression of Gzma, Klrg1, Klra3 and Il18rap. (D) Strong correlation between the first principal component and Prf1 (p < 10-16). X- and Y-axis of the plots show the names of genes used for the analysis.

Mentions: The large data set for expression values for ~39,000 transcripts in 57 mouse strains allowed us to calculate correlations between any pair of genes. A Spearman rank correlation analysis identified 12,985 pairs of genes with a correlation value above 0.8, and 604 pairs showed a correlation value of 0.9 or higher. For example, the expression of Klra3 (killer cell lectin-like receptor subfamilily A, member 3) was strongly correlated with the expression of Gzma (granzyme A) (Figure 2A). Klra3 also appeared to be strongly correlated with Il18rap (interleukin 18 receptor accessory protein, Figure 2B). We then calculated the first principal component of the Klra3, Gzma, Il18rap and Klrg1 (killer cell lectin-like receptor subfamily G, member 1) genes and used it to determine the correlations with all other genes in the lung data set. In this way, we could identify a network of nine genes exhibiting a correlation of >= 0.8 with this principal component (Figure 2C). One of the newly identified genes was Prf1 (perforin 1) which was correlated with a p-value of < 10-16 with the principal component (Figure 2D). If genes exhibit a strong correlation of their expression values, one may hypothesize that they are involved in the same biological process or pathway, or they may be expressed in the same cell type.


Genome-wide analysis of the mouse lung transcriptome reveals novel molecular gene interaction networks and cell-specific expression signatures.

Alberts R, Lu L, Williams RW, Schughart K - Respir. Res. (2011)

Expression signals for strongly correlated genes in the lung. Numbers indicate BXD strains, and the parental C57BL/6J and DBA/2J strains as well as F1 individuals are presented. Expression signals of (A) Klra3 and Gzma (p < 10-16) and (B) Klra3 versus Il18rap (p < 10-11) were strongly correlated. (C) List of nine genes highly correlated with the first principal component of the expression of Gzma, Klrg1, Klra3 and Il18rap. (D) Strong correlation between the first principal component and Prf1 (p < 10-16). X- and Y-axis of the plots show the names of genes used for the analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3105947&req=5

Figure 2: Expression signals for strongly correlated genes in the lung. Numbers indicate BXD strains, and the parental C57BL/6J and DBA/2J strains as well as F1 individuals are presented. Expression signals of (A) Klra3 and Gzma (p < 10-16) and (B) Klra3 versus Il18rap (p < 10-11) were strongly correlated. (C) List of nine genes highly correlated with the first principal component of the expression of Gzma, Klrg1, Klra3 and Il18rap. (D) Strong correlation between the first principal component and Prf1 (p < 10-16). X- and Y-axis of the plots show the names of genes used for the analysis.
Mentions: The large data set for expression values for ~39,000 transcripts in 57 mouse strains allowed us to calculate correlations between any pair of genes. A Spearman rank correlation analysis identified 12,985 pairs of genes with a correlation value above 0.8, and 604 pairs showed a correlation value of 0.9 or higher. For example, the expression of Klra3 (killer cell lectin-like receptor subfamilily A, member 3) was strongly correlated with the expression of Gzma (granzyme A) (Figure 2A). Klra3 also appeared to be strongly correlated with Il18rap (interleukin 18 receptor accessory protein, Figure 2B). We then calculated the first principal component of the Klra3, Gzma, Il18rap and Klrg1 (killer cell lectin-like receptor subfamily G, member 1) genes and used it to determine the correlations with all other genes in the lung data set. In this way, we could identify a network of nine genes exhibiting a correlation of >= 0.8 with this principal component (Figure 2C). One of the newly identified genes was Prf1 (perforin 1) which was correlated with a p-value of < 10-16 with the principal component (Figure 2D). If genes exhibit a strong correlation of their expression values, one may hypothesize that they are involved in the same biological process or pathway, or they may be expressed in the same cell type.

Bottom Line: Our goal is to provide a key community resource on the genetics of the normative lung transcriptome that can serve as a foundation for experimental analysis and allow predicting genetic predisposition and response to pathogens, allergens, and xenobiotics.This interval contains the transcription factor Ahr that has a critical mis-sense allele in the DBA/2J haplotype and evidently modulates transcriptional activation by AhR.Large-scale gene expression analyses in genetic reference populations revealed lung-specific and immune-cell gene expression profiles and suggested specific gene regulatory interactions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Infection Genetics, University of Veterinary Medicine Hannover, Inhoffenstr, Braunschweig, Germany.

ABSTRACT

Background: The lung is critical in surveillance and initial defense against pathogens. In humans, as in mice, individual genetic differences strongly modulate pulmonary responses to infectious agents, severity of lung disease, and potential allergic reactions. In a first step towards understanding genetic predisposition and pulmonary molecular networks that underlie individual differences in disease vulnerability, we performed a global analysis of normative lung gene expression levels in inbred mouse strains and a large family of BXD strains that are widely used for systems genetics. Our goal is to provide a key community resource on the genetics of the normative lung transcriptome that can serve as a foundation for experimental analysis and allow predicting genetic predisposition and response to pathogens, allergens, and xenobiotics.

Methods: Steady-state polyA+ mRNA levels were assayed across a diverse and fully genotyped panel of 57 isogenic strains using the Affymetrix M430 2.0 array. Correlations of expression levels between genes were determined. Global expression QTL (eQTL) analysis and network covariance analysis was performed using tools and resources in GeneNetwork http://www.genenetwork.org.

Results: Expression values were highly variable across strains and in many cases exhibited a high heritability factor. Several genes which showed a restricted expression to lung tissue were identified. Using correlations between gene expression values across all strains, we defined and extended memberships of several important molecular networks in the lung. Furthermore, we were able to extract signatures of immune cell subpopulations and characterize co-variation and shared genetic modulation. Known QTL regions for respiratory infection susceptibility were investigated and several cis-eQTL genes were identified. Numerous cis- and trans-regulated transcripts and chromosomal intervals with strong regulatory activity were mapped. The Cyp1a1 P450 transcript had a strong trans-acting eQTL (LOD 11.8) on Chr 12 at 36 ± 1 Mb. This interval contains the transcription factor Ahr that has a critical mis-sense allele in the DBA/2J haplotype and evidently modulates transcriptional activation by AhR.

Conclusions: Large-scale gene expression analyses in genetic reference populations revealed lung-specific and immune-cell gene expression profiles and suggested specific gene regulatory interactions.

Show MeSH
Related in: MedlinePlus