An RNA-seq protocol to identify mRNA expression changes in mouse diaphyseal bone: applications in mice with bone property altering Lrp5 mutations

Abstract

Loss-of-function and certain missense mutations in the Wnt coreceptor low-density lipoprotein receptor-related protein 5 (LRP5) significantly decrease or increase bone mass, respectively. These human skeletal phenotypes have been recapitulated in mice harboring Lrp5 knockout and knock-in mutations. We hypothesized that measuring mRNA expression in diaphyseal bone from mice with Lrp5 wild-type (Lrp5(+/+) ), knockout (Lrp5(-/-) ), and high bone mass (HBM)-causing (Lrp5(p.A214V/+) ) knock-in alleles could identify genes and pathways that regulate or are regulated by LRP5 activity. We performed RNA-seq on pairs of tibial diaphyseal bones from four 16-week-old mice with each of the aforementioned genotypes. We then evaluated different methods for controlling for contaminating nonskeletal tissue (ie, blood, bone marrow, and skeletal muscle) in our data. These methods included predigestion of diaphyseal bone with collagenase and separate transcriptional profiling of blood, skeletal muscle, and bone marrow. We found that collagenase digestion reduced contamination, but also altered gene expression in the remaining cells. In contrast, in silico filtering of the diaphyseal bone RNA-seq data for highly expressed blood, skeletal muscle, and bone marrow transcripts significantly increased the correlation between RNA-seq data from an animal's right and left tibias and from animals with the same Lrp5 genotype. We conclude that reliable and reproducible RNA-seq data can be obtained from mouse diaphyseal bone and that lack of LRP5 has a more pronounced effect on gene expression than the HBM-causing LRP5 missense mutation. We identified 84 differentially expressed protein-coding transcripts between LRP5 "sufficient" (ie, Lrp5(+/+) and Lrp5(p.A214V/+) ) and "insufficient" (Lrp5(-/-) ) diaphyseal bone, and far fewer differentially expressed genes between Lrp5(p.A214V/+) and Lrp5(+/+) diaphyseal bone.

Keywords: CELLS OF BONE; DISEASES AND DISORDERS OF/RELATED TO BONE; GENETIC ANIMAL MODELS; MOLECULAR PATHWAY DEVELOPMENT; STATISTICAL METHODS; WNT/β-CATENIN/LRPS.

Figure 1. Strategy for generating tibia diaphyseal bone cDNA libraries for RNA-seq

Both tibiae were prepared within 10 minutes of euthanasia. Skeletal muscle and the metaphyseal/epiphyseal regions were removed with a scalpel and the marrow was removed by centrifugation. Bone shafts were flash frozen in liquid nitrogen and subjected to pulverization in Trizol for RNA extraction. mRNA was enriched using oligo-dT and then chemically fragmented into 120 to 200 nucleotide long segments prior to random hexamer primed reverse transcription. Bar-coded sequencing adapters were ligated to the cDNA fragments and the indexed cDNA was subjected to 15 cycles of PCR amplification. Each library was analyzed by gel electrophoresis to confirm that the correct fragment size range had been achieved (note the > 200 bp fragments include the sequencing adaptors). Libraries constructed from 10 different mouse tibiae are depicted here. Eight to 10 separately bar-coded libraries were pooled and loaded onto a single lane of an Illumina HiSeq2000 machine and 50 bp paired-end sequencing was performed.

Figure 2. RNA-seq metrics and reproducibility

(a) Table containing average values for tibia diaphyseal bone RNA-seq data from mice with the 3 different Lrp5 genotypes (n = 8 libraries/genotype). Columns indicate the mouse genotype from which the RNA-seq data were obtained, the number of paired-end reads/library, the percentage of reads that mapped to the mouse genome, the percentage of reads that mapped to unique regions within the mouse genome, and the percentage of reads that likely represent PCR duplicates created during library preparation. (b) Graphs depicting the distribution of RNA-seq data across two representative genes (Mepe and Dmp1). The read depth relative to the maximum read depth is graphed in alignment to each gene’s genomic DNA sequence. Thick bars below the plots indicate the positions of the exons, and the thin lines indicate the positions of the introns. Peaks overlap the exon-containing regions of each gene, consistent with this being RNA-seq data. Note the high degree of similarity with respect to RNA-seq coverage across the genes independent of the animals’ Lrp5 genotypes. (c) Scatterplot comparing the total number of uniquely mapped reads for RNA-seq data obtained from the right and left tibiae of a mouse. Each circle represents an individual gene. High abundance transcripts are at the upper right and low abundance transcripts are at the lower left of this plot. Examples of bone-expressed genes are color coded and identified. The Pearson correlation coefficient (R²) between the right and left tibia RNA-seq data is 0.99 for this sample pair. (d) Scatterplot comparing total number of uniquely mapped reads for RNA-seq data obtained from the right and left tibiae of another mouse. Note that the Pearson correlation coefficient (R²) between the right and left tibia RNA-seq data is 0.83 for this sample pair, although the bone-expressed genes appear to follow the y = x line. (e) Graph depicting the effect of sequentially filtering transcripts whose removal cause the greatest increases in correlation between the right and left tibia. The inset indicates the top 10 genes whose removal from the RNA-seq data depicted in panel d had the greatest positive effect on R². Note these genes are highly expressed in muscle and blood, suggesting the reduced correlation in the tibia pair depicted in panel d resulted from non-bone tissue contamination.

Figure 3. Identification of contaminating transcripts in diaphyseal bone RNA-seq data

(a) Venn diagrams indicating the intersection of genes that have 2-fold or greater expression (p < 0.05) in skeletal muscle, bone marrow, or blood compared to tibia diaphyseal bone and that have demonstrated a significant reduction in transcript abundance in collagenase-digested diaphyseal bone. The percentages of tissue-specific RNA-seq data accounted for by these genes are also indicated. Color-coding represents in heat map format (see scales on right) the average number of mapped reads/gene in each sector. This is determined by dividing the total number of reads that mapped to genes in this sector by the number of genes in this sector. For example, 2914 (2139 + 775) genes are expressed at least twice as abundantly in muscle compared to bone and 1000 (775 + 225) genes had significant reductions in transcript abundance following collagenase digestion. The intersection of these two data sets comprises 775 genes. These 775 genes account for 53% of all mapped reads in the skeletal muscle RNA-seq data. The average gene in this group of 775 has ~17,000 reads (~13,000,000/775) mapping to it in the muscle RNA-seq libraries. (b) Venn diagram indicating the distribution of ~6000 genes that were twice as abundantly expressed in skeletal muscle, bone marrow, and/or blood compared to tibia diaphyseal bone. (c) Scatterplot in log₁₀ scale indicating the fold changes in transcript abundance when RNA-seq data from collagenase-digested bone is compared to RNA-seq data from fresh-frozen bone. Circles represent individual genes and genes having statistically significant changes in abundance (p < 0.05) are colored blue. Note genes that are highly expressed in skeletal muscle (red symbols) decrease significantly (p < 0.001), while genes that are highly expressed in bone (green symbols) have less pronounced increases. The large increases in transcript abundance for other genes (orange symbols) likely represent collagenase-induced changes in gene expression. (d) Boxplots indicating the ranges of trimmed-mean-normalized Pearson’s correlation coefficients (R²) between paired tibiae from individual animals (contralateral pairs), and between all pairs of tibiae representing mice with the same Lrp5 genotype. Correlations before and after in silico transcript filtering of ~900 genes are shown; note that filtering increases R² for all data comparisons (p < 0.01).

Figure 4. Overview of data filtering and analysis pipeline

A list of contaminating transcripts (n = ~900) was compiled based upon tissue-specific expression profiling and transcript abundances in collagenase-digested versus fresh-frozen bone samples. Transcripts representing the genes on this list were in silico filtered from the diaphyseal bone RNA-seq data prior to downstream analyses. In each statistical comparison, p-values were computed with Fisher’s Exact test (with significance set at p < 0.05) and corrected for multiple hypothesis testing (false discovery rate < 0.05), grouped samples were subjected to leave-one-out cross validation, and genes expressed below the detectability threshold (RPKM<5) were eliminated. The numbers of differentially expressed (DE) genes remaining after each step are noted. Lastly, predicted genes, non-coding RNAs and microRNAs that overlapped with the introns of highly expressed protein coding genes were eliminated in order to ensure that the reads mapped to these regions due to incomplete transcription events were not registered in differential expression calculations. A comparison between RNA-seq data from the tibia diaphyseal bones of LRP5 sufficient (Lrp5^p.A214V/+ and Lrp5^+/+) and insufficient (Lrp5^−/−) mice yielded 84 differentially expressed protein-coding genes.