- Open Access
Genome-wide analysis of the mouse lung transcriptome reveals novel molecular gene interaction networks and cell-specific expression signatures
© Alberts et al; licensee BioMed Central Ltd. 2011
- Received: 14 January 2011
- Accepted: 2 May 2011
- Published: 1 December 2011
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.
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.
Expression values were highly variable across strains and in many cases exhibited a high heri-tability 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.
Large-scale gene expression analyses in genetic reference populations revealed lung-specific and immune-cell gene expression profiles and suggested specific gene regulatory interactions.
- Cystic Fibrosis Transmembrane Conductance Regulator
- Surfactant Associate Protein
- Cell Signature Gene
- Genetic Reference Population
- White Sponge Nevus
The lung is the first line of defense against many pathogens and inhaled xenobiotics and is therefore a key part of the immune system. Host defense is strongly influenced by genetic differences and several studies have shown that the genetic background and sequence difference among humans and other host species modulate susceptibility and resistance to infectious diseases, allergens, and xenobiotics. Systems genetics is a modern extension of complex trait analysis that jointly analyzes and integrates large sets of genotypes and phenotypes to explain and predict variation in outcome measures and disease severity (for review see [1, 2]). A typical systems genetics study relies on extensive single nucleotide polymorphism (SNP) data sets, matched data on RNA expression in key cells, tissues, or organs and a core set of key dependent measures such as disease susceptibility . These data are collected across a panel or population of genetically diverse individuals or strains. This group of individuals represents a natural genetic perturbation, with well defined genotype and haplotype differences comprising the "treatment." The independent measurements in this case can consist either of the genotype or of crucial intervening variables such as the expression of genes and proteins.
In this study, we exploited a very well characterized panel of inbred strains of mice (a mouse genetic reference panel) that consists of two parts--a small set of standard inbred strains and a larger family of BXD type recombinant inbred strains. The genome of each BXD strain represents a mixture of the C57BL/6J and DBA/2J parental background and is homozygous at almost every genomic location. The genomic make-up of each BXD line has been determined by extensive mapping with molecular markers. After performing microarray expression analysis for each of the BXD mice, the expression level of each gene can be used as a quantitative trait (e.g. [4–6]). By comparing these expression values for all BXD mice with their molecular markers data along the genome, genomic expression quantitative trait loci (eQTL) can be identified that are likely to regulate the expression of one or several genes [2, 5, 7–12]. When an eQTL is located at the same genomic position as the gene itself (within a 10Mb interval of the gene) it is considered as a cis-eQTL. In this case, variations in the promoter sequence or in elements that determine the stability of the mRNA of the gene are the most likely causes for the observed differences in expression levels. If the eQTL is at a distant location from the regulated gene, the eQTL is referred to as a trans-eQTL.
Here, we performed a global gene expression analysis from the lungs of 47 BXD and eight widely used inbred strains. The aim of our study was to reveal genes and gene networks in mouse lung in steady state condition. We found that many genes had high variation in expression and that often this variation was highly heritable. This allowed us to identify many cis- and trans-eQTLs. In addition, we used the correlation structure in the data to obtain expression signatures for specific cell types within the lung.
Mouse strains and sample preparation
C57BL/6J, BALB/cByJ, FVB/NJ, and WSB/EiJ, as well as B6D2F1 and D2B6F1 lines were obtained from the University of Tennessee Health Science Center (UTHSC). DBA/2J, 129X1/SvJ, LP/J and SJL/J were obtained from The Jackson Laboratory. Mice from 38 BXD recombinant inbred strains were obtained from UTHSC and mice from nine BXD strains were obtained from The Jackson Laboratory. All animals were housed at UTHSC before sacrifice. Mice were killed by cervical dislocation and whole lungs including blood were removed and placed in RNAlater. Total RNA was extracted from the lungs using RNA STAT-60 (Tel-Test Inc.). RNA from two to five animals per strain were pooled and used for gene expression analysis. Animals used in this study were between 49 and 93 days of age. All inbred strains were profiled for both sexes, and for a given BXD strain either males or females were used. Mice were maintained under specific pathogen free conditions. All protocols involving mice were approved by the UTHSC Animal Care and Use Committee.
Gene expression profiling was performed using Affymetrix GeneChip Mouse Genome 430 2.0 Arrays at UTHSC. Samples were amplified according to the recommended protocols by the manufacturer (Affymetrix, Santa Clara, CA, USA). In all cases, 4-5 μg of each biotinylated cRNA preparation was fragmented and included in a hybridization cocktail containing four biotinylated hybridization controls (BioB, BioC, BioD, and Cre), as recommended by the manufacturer. Samples were hybridized for 16 hours. After hybridization, GeneChips were washed, stained with SAPE, and read using an Affymetrix GeneChip fluidic station and scanner.
Data preprocessing and analysis
Data analysis was performed using the GeneNetwork web service , a large resource with phenotypes and mRNA expression data for several genetic reference populations and multiple organisms. The expression data were preprocessed like all other datasets in GeneNetwork: adding an offset of 1 unit to each signal intensity value to ensure that the logarithm of all values were positive, computing the log2 value, performing a quantile normalization of the log2 values for the total set of arrays using the same initial steps used by the RMA transform , computing the Z scores for each cell value, multiplying all Z scores by 2 and adding 8 to the value of all Z scores. The advantage of this variant of a Z transformation is that all values are positive and that 1 unit represents approximately a 2-fold difference in expression as determined using the spike-in control probe sets (see  for details). For correlation analyses we used Pearson's correlation unless otherwise stated. Heritability was determined using ANOVA with one factor mouse strain, and by dividing the mean between-mouse-strain variance by the sum of the mean between-mouse strain variance plus the mean within-strain variance.
QTL Mapping and expression analyses
All probe sets were mapped using standard interval mapping methods at 1 cM intervals (~2 Mb) along all autosomes and the X chromosome. This procedure generates estimates of linkage between variation in transcript expression levels and chromosomal location. The entire set of values can be used to construct a set of QTL maps for all chromosomes (except Chr Y and the mitochondrial genome) in which position is plotted on the x-axis and the strength of linkage--the likelihood ratio statistic (LRS) or log of the odds ratio (LOD)-is plotted on the y-axis. An LRS of 18 or higher is significant at a genome-wide p value of < 0.5. To compute LRS values we exploited the computationally efficient Haley-Knott regression equations  and a set of 3796 SNPs and microsatellite markers that we and others have genotyped over the past decade [16, 17]. In order to rapidly map all 45,101 probe sets we used our customized QTL Reaper code http://qtlreaper.sourceforge.net/. QTL Reaper performs up to a million permutations of an expression trait to calculate the genome-wide empirical p value and the LRS scores associated with each interval or marker. The peak linkage value and position was databased in GeneNetwork and users can rapidly retrieve and view these mapping results for any probe set. Any of the QTL maps can also be rapidly regenerated using the same Haley-Knott methods, again using functions imbedded in GeneNetwork. GeneNetwork also enable a search for epistatic interactions (pair scanning function) and composite interval mapping with control for a single marker.
Data quality control
We used two simple but effective methods to confirm correct sample identification of all data entered into GeneNetwork. Expression of the Xist transcript (probe set 1427262_at) was used to validate the sex of the sample. Xist is involved in the inactivation of one X chromosome in females  and is only expressed at high levels in females. Other genes that show strong sex-specific expression are Eif2s3y, Jarid1d and Ddx3y. In addition, we investigated several genes that exhibit a strongly bimodal Mendelian expression pattern, meaning that one parental allele exhibits a high expression level whereas the other allele exhibits a low expression and only the F1 hybrids are intermediate. The expression level of such transcripts is directly correlated with the genotype at this locus and they can collectively be used to confirm sample genotype and hence strain. For example, expression of the Rpgrip1 transcript (probe set 1421144_at) has a distinctly bimodal distribution, intermediate values for F1 animals, and is associated with a LOD score peak of 50 that corresponds precisely to the location of the cognate gene on Chr 14 at 52.5 Mb.
Variation in gene expression
Variation in gene expression for 45,101 probe sets.
Fold change range
No. of genes
Heritability of variation in gene expression
To investigate to which extent the variation in expression was due to genetic effects, we calculated the heritability for each of the genes, which is the fraction of variation in expression caused by genetics. The heritability values ranged from as high as 0.96 (most of the variance was associated with between-strain differences) until as low as 0.01. Genes with the largest heritability were Cdk17/Pctk2 (cyclin-dependent kinase 17, probe set 1446130_at), Gm1337 (predicted gene 1337, 1443287_at) and Pdxdc1/KIAA0251 (pyridoxal-dependent decarboxylase domain containing 1, 1452705_at), all having a value above 0.99. High heritability values indicate that it is likely to successfully map QTLs that influence gene expression values.
List of 15 probe sets with highest expression signals in the lung.
Location (Chr, Mb)
hemoglobin alpha, adult chain 1
surfactant associated protein C
secretoglobin, family 1A, member 1 (uteroglobin)
hemoglobin, beta adult minor chain
advanced glycosylation end product-specific receptor
actin beta, cytoplasmic
hemoglobin alpha, adult chain 1
tumor protein, translationally-controlled 1
carbonyl reductase 2
P lysozyme structural and lysozyme
ubiquitin A-52 residue ribosomal protein fusion product 1
ferritin heavy chain 1
AFFX-MURINE_B2_at short interspersed nuclear element (SINE) class of repeat (probes target Chr 1 and Chr 2 most heavily)
thymosin, beta 4, X chromosome
List of genes with lung-restricted expression found by tissue correlation analysis with Sftpc.
Location (Chr, Mb)
surfactant associated protein C
high in lung, low in nucleus accumbens
surfactant associated protein B (nonciliated bronchiolar and alveolar type 2 cell signature)
high in lung only
surfactant associated protein A1
high in lung only
thyroid transcription factor 1
lysosomal-associated membrane protein 3
high in lung, low in ES cells and some cell lines
chemokine (C-X-C motif) ligand 15
high in lung only
advanced glycosylation end product-specific receptor
high in lung only
SEC14-like protein 3
only data for human available - not lung specific
secretoglobin, family 3A, member 2
high in lung only
highest in lung, lower in stomach
flavin containing monooxygenase 3
high in lung, maybe weak in some other tissues
high in lung, weak in macrophages
cytochrome c oxidase subunit IV isoform 2
not specific for lung
secretoglobin, family 3A, member 1
high in lung only
activin A receptor, type II-like 1
high in lung only
palate, lung, and nasal epithelium carcinoma associated
high in lung, low in heart
Identification of gene networks using correlations
Correlation analysis identified gene expression signatures for T and B cells
Identification of candidate genes regulating phenotypic traits in the lung
Cis-eQTLs identified in QTL inteval on chromosome 2 for influenza susceptibility.
Location (Chr, Mb)
Tnf receptor-associated factor 1
nuclear receptor subfamily 6, group A, member 1
zinc finger homeobox 1b
Rap1 interacting factor 1
Cis-eQTLs identified in QTL on chromosome 8 for Mycoplasmosis susceptibility trait.
Location (Chr, Mb)
expressed sequence AW413431
syntrophin, basic 2
RIKEN cDNA 5730419I09 gene
component of oligomeric golgi complex 8
RIKEN cDNA 1810044O22 gene
AT motif binding factor 1
carbohydrate (chondroitin 6/keratan) sulfotransferase 4
nudix (nucleoside diphosphate linked moiety X)-type motif 7
WW domain-containing oxidoreductase
avian musculoaponeurotic fibrosarcoma (v-maf) AS42 oncogene homolog
malonyl-CoA decarboxylase (test Mendelian in BXDs with high DBA/2J allele)
Fanconi anemia, complementation group A
DNA segment, Chr 8, ERATO Doi 325, expressed
dysbindin (dystrobrevin binding protein 1)
par-3 (partitioning defective 3) homolog (C. elegans)
Cis- and trans-eQTLs
Amount of cis- and trans-regulated transcripts for different significance thresholds
No. of ciseQTLs
No. of transeQTLs
Here, we performed global gene expression profiling in eight inbred mouse strains and a cohort of BXD recombinant inbred strains from whole lung tissues. Our studies identified several lung-specific genes, large variations in gene expression levels, and a strong heritability in many gene expression traits. Correlation analysis of gene expression and genotypes identified potential gene interaction networks, pairs of trans- and cis-eQTLs, and genes with cis-eQTLs that may represent candidate genes involved in susceptibility to respiratory infections. In addition, one specific gene interaction pathway was identified in which Ahr regulates the Cyp1a1 gene.
Using tissue correlations of gene expression patterns across the BXD strains, we identified 16 genes with a highly restricted expression in the lung of which 14 could be validated by comparison to the BioGPS database . The second most strongly expressed gene in the lung tissues was Sftpc which has been shown to play a role in lung development and the prevention of pneumonitis and emphysema [32, 33]. Also, Sftpc deficiency increases the severity of respiratory syncytial virus-induced pulmonary inflammation . Furthermore, Scgb1a1 and Ager were amongst the five most strongly expressed genes. Scgb1a1 is expressed in lung clara cells and its deficiency results in enhanced susceptibility to environmental agents . Scgb3a1 (secretoglobin, family 3A, member 1) and Scgb3a2 (secretoglobin, family 3A, member 2) were shown by others to be highly expressed in the lung and lower levels in other organs . Scgb3a2 is down-regulated in inflamed airways  and plays an important role in lung development . Sftpb (surfactant associated protein B (non-ciliated bronchiolar and alveolar type 2 cell signature) is a hydrophobic peptide which enhances the surface properties of pulmonary surfactant and is expressed in non-ciliated bronchiolar and aleveolar type 2 cells . Maintenance of Sftpb expression is critical for survival during acute lung injury  and reduction of alveolar expression causes surfactant dysfunction and respiratory failure . Plunc (palate, lung, and nasal epithelium carcinoma associated) is expressed in the oral, lingual, pharyngeal and respiratory epithelia  and members of the Plunc gene family are thought to pay a role in the innate immune response . The presence of Plunc protein in the lung decreases the levels of Mycoplasma pneumoniae and its levels are reduced in allergic inflammatory conditions . Thus, the lung data set allowed us to find important genes that are expressed primarily in the lung and are important for lung homeostasis and prevention of disease.
It should be noted that our analysis of genes with "restricted expression to the lung" is not ex-clusive; it only refers to the tissues that are represented in GeneNetwork and BioGPS. Also, the analysis performed here should not be considered to be comprehensive. More sophisticated approaches may be employed to identify additional genes which also fulfill the criterion of "lung-restricted" expression.
Furthermore, genes may not be apparent in the lung transcriptome because they are expressed only in a small fraction of cells within the lung. This issue of dilution of expression signals is an important one and we have studied it in several tissues with considerable care (eye, retina, and numerous brain regions) using the same genetic methods and the same array platform. We were consistently able to detect expression of genes that are only expressed in very small cell subpopulations (<0.1%) such as rare amacrine cell subclasses in the retina  or very rare oxytocin-expressing neurons (<2000) in whole brain samples. The reason for the increased sensitivity is that with such large sample sizes (~70 lung arrays) the signal-to-noise ratios are much better than standard studies using Affymetrix arrays. These stuides typically use far fewer arrays and do not use genetic methods to "validate" the source of signal.
The strong signal for hemoglobin and lymphocyte-specific genes clearly showed that gene ex-pression patterns of circulating blood cells are readily detectable in the lung transcriptome. This raises the question if an organ should be studied with or without containing blood. The correct answer to this question depends of course on the particular circumstances. However, we feel strongly that a global systems and genetic approach requires the analysis of the entire organ. The expression of genes is not cell-autonomous and depends on cellular micro envi-ronment, physical factors (gas pressure and gradients, etc), pathogen exposure, and many types of interactions. These factors also influence the expression of genes in blood cells. Therefore, we think that it is imperative to look simultaneously at all cells in a function unit: in this case the whole lung plus its containing blood.
In conclusion, the combined analysis of expression levels and correlations in a variety of tissues tissue allowed us to determine genes with restricted or preferential expression in the lung. For several of these genes, an important function in the lung has been described and the same may be assumed for the others. This information will also contribute to a better understanding of the biological function of these genes.
Many phenotypic traits have been studied for the BXD mouse populations and several QTLs were identified which influence diseases or vulnerability in the lung. The detection of cis eQTLs in the very same tissue is one method to identify potential candidate genes under the QTL which may causally influence the trait. Here, we investigated two traits in more detail, susceptibility to influenza virus and susceptibility to mycoplasmosis. Several cis-eQTLs were found in the corresponding QTL regions and in each case, genes could be identified with a presumed role in the host immune defense (discussed already in the results section). Thus, the study of cis-eQTLs in our data set may provide valuable candidates for other quantitative trait genes that influence important lung phenotypes. Furthermore, we found 13 BXD lines with low expression signals for Krt4, Krt13 and Krtdap. Krt4 and Krt13 have been shown to be responsible for White sponge nevus (WSN), also known as Cannon's disease, which is an autosomal dominant skin condition in humans [45–47]. We propose that the 13 mouse strains have genetic alterations which result in low transcript levels of these genes and they may represent a good model for Cannon's disease. It should be noted, however, that no cis-eQTLs found were found for any of the Krt genes.
We also identified a set of genes for which the expression levels correlated highly with members of the Klr gene family. Klra3 and Klrg1 are killer cell lectin-like receptors that are exclusively expressed on natural killer cells (NK cells). NK cells form a major component of the innate immune system and kill cells by releasing small cytoplasmic granules of proteins called perforins and granzymes . Both Gzma and Prf1 were in the gene network that we identified. In addition, correlations can also be used to expand already known gene networks in specific cell populations. When starting with the Cd3 T cell marker and calculating correlations with all other transcripts measured, we identified a strongly correlated network of genes, in which most of the genes were known as T cell markers or to be involved in T cell activation or homeostasis. In a similar way, when starting with the Cd19 B cell marker, we could identify a strongly correlated network of B cell signature genes. The analysis of these T cell and B cell expression signatures in the Bi-oGPS data base with expression profiles in mouse tissues revealed that indeed >90% of the T and B cell markers were specifically expressed in either T or B cells. Furthermore, most of the T and B cell signature genes represented genes with known function in B and T cell differentiation, activation and homeostasis. For example, the T cell signature included genes encoding subunits of the T cell receptor: Cd3d (CD3 antigen, delta po-lypeptide), Cd3g (CD3 antigen, gamma polypeptide), Tcra (T-cell receptor alpha chain) and Tcrb-V13 (T-cell receptor beta, variable 13) and Lat (linker for activation of T cells) which are involved in T cell activation. The B cell signature contained components of the B cell antigen receptor complex, Cd19 (CD19 antigen) and Cd79a (CD79A antigen (immunoglobulin-associated alpha)), as well as Blk (B lymphoid kinase) tyrosine kinase which is associated with the receptors. Also, the correlations for both signatures in the spleen expression data set in GeneNetwork could indeed confirm that the signatures were strongly correlated (data not shown). In summary, these studies demonstrate that correlation analyses are able to identify genes which very likely interact in a common network or biological process. The approach used here may thus have a great potential to identify new networks and biological processes in the lung. In addition, starting with a known bona-fide cell-specific gene and then analyzing gene expression values across strains, it is possible to identify a set of highly correlated genes. These gene sets genes can now be used as cell-specific signature genes in complex transcriptome studies, e.g. to detect infiltrating immune cells in the lungs after infection.
The genetic mapping of lung expression profiles revealed many cis- and trans-eQTLs, indicating that many gene expression patterns in lung have a strong genetic component. Trans-eQTLs allow the identification of gene-gene regulatory networks. As an example, we found that the transcription factor Ahr was present in a trans-eQTL region detected for the Cyp1a1 gene. Ahr is a transcription factor known to induce Cyp1a1 transcription levels after ligand binding [49–51]. Six binding sites for the Ahr receptor ligand have been revealed in the 700-basepair DNA domain upstream of Cyp1a1 . However, a critical leucine-to-proline substitution in Ahr results in a 15 to 20-fold reduction in the binding affinity of the proline variant found in DBA/2J compared to the leucine variant found in C57BL/6J . Indeed, in our data set, expression values for Cyp1a1were low for BXD strains carrying the DBA/2J allele at the Ahr locus and high for the strains carrying the C57BL/6J allele. Since Ahr is not cis-regulated in lung, the downstream effects appear to be only caused by changes in Ahr protein binding affinity. Although the interaction between Cyp1a1 and Ahr as such is not a new finding, it is quite remarkable that the interaction becomes apparent in lungs which were not exposed to an inducing xenobiotic. Furthermore, we do not see this relationship in several other tissues, such as liver. Therefore, our observation suggests that in the lung, which is potentially exposed to many xenobiotics, the Ahr receptor may always be activated at a low level. Alternatively, Ahr expression may be stimulated by yet unknown ligands that are also present under normal environmental conditions.
Here, we showed that whole genome expression analysis of the lungs from a large set of strains of the BXD mouse population can be exploited to identify important gene regulatory networks. We found a large number of expression correlations and QTLs which can be further investigated to better understand molecular interaction networks in the lung. The search for cis-eQTLs in genomic intervals that were identified previously as QTLs for infectious diseases revealed several quantitative trait candidate genes. In addition, we demonstrated that the analysis of gene expression correlations, starting with a few cell-specific genes, could identify a larger set of genes which allows detecting the presence of B and T cells within the transcriptome of the whole lung. Such expression signatures will be very important to follow normal and abnormal host responses during infections and other diseases of the lung.
This work was supported by intra-mural grants from the Helmholtz-Association (Program "Infection and Immunity") and a research grant "FluResearchNet" (No. 01KI07137) from the German Ministry of Education and Research to KS. RWW acknowledges the support of the UTHSC Center for Integrative and Translational Genomics and NIH grant P20DA21131 and U01AA13499, and LL was supported by the NIH grant U01AA014425. We thank Dr. Yan Jiao and Weikuan Gu at UTHSC and the VA Medical Center, Memphis, for running the Affymetrix arrays in their core facility. We also thank the GeneNetwork development team, including Arthur Centeno, Xiaodong Zhou, Ning Liu, Zachary Sloan, and Lei Yan, for their help in integrating and error-checking the lung transcriptome data. We also thank Samuel C. Cartner for making the data on the Mycoplasmosis susceptibility phenotype available to us prior to publication.
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