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IETS 2016
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Fertility and genomics: comparison of gene expression in contrasting
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reproductive tissues of female cattle
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PA McGettigan1, JA Browne1, SD Carrington2, MA Crowe2, T Fair1, N Forde1,6, BJ Loftus3, A
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Lohan3, P Lonergan1, K Pluta2, S Mamo1, A Murphy4, J Roche3, SW Walsh1,7, CJ Creevey5,8, B
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Earley5, S Keady5, DA Kenny5, D Matthews5, M McCabe5, D Morris5, A O’Loughlin5, S Waters5,
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MG Diskin5, ACO Evans1,4,*
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School of Agriculture and Food Science, 2School of Veterinary Medicine, 3School of Medicine
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and Medical Sciences, 4Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
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Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
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Waterford, Ireland
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University, Aberystwyth, Ceredigion, United Kingdom
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Animal and Grassland Research and Innovation Centre, Teagasc, Athenry, Co Galway, Ireland.
Present Address: Division of Reproduction and Early Development, Leeds Institute of
Present Address: Department of Chemical and Life Sciences, Waterford Institute of Technology,
Present Address: Institute of Biological, Environmental and Rural Sciences, Aberystwyth
Correspondence: A Evans, alex.evans@ucd.ie.
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Abstract
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To compare gene expression among bovine tissues we used large bovine RNAseq datasets
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comprising 280 samples from 10 different bovine tissues (uterine endometrium, granulosa cells,
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theca cells, cervix, embryos, leukocytes, liver, hypothalamus, pituitary, muscle) generating 260
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Gbases of data. We used twin approaches of an information-theoretic analysis of the existing
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annotated transcriptome to identify the most tissue-specific genes, as well as a de-novo
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transcriptome annotation to evaluate general features of the transcription landscape. We detected
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expression of 97% of the Ensembl transcriptome with at least one read in one sample and between
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28% and 66% at a level of 10 Tags Per Million (TPM)or greater in individual tissues. Over 95% of
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genes exhibited some level of tissue-specific gene expression. This was mostly due to different
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levels of expression in different tissues rather than exclusive expression in a single tissue. Less than
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1% of annotated genes exhibited a highly restricted tissue-specific expression profile and
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approximately 2% exhibited classic housekeeping profiles. We conclude that it is combined effects
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of the variable expression of large numbers of genes (73 to 93% of the genome) with the specific
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expression of a small number of genes (less than 1% of the transcriptome) that contributes to
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determining the outcome of the function of individual tissues.
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Keywords: RNAseq, transcription, bovine, reproduction, uterus, endometrium, embryo, cervix,
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ovary, follicle, theca, granulosa, hypothalamus, pituitary, leukocyte, muscle, liver
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Short title: Gene expression in bovine reproductive tissues
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1. Introduction
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Recently, there has been a flood of development of new ‘omic’ technologies such as proteomics,
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transcriptomics and metabolomics that are enabling the generation of vast amounts of novel data
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characterizing different aspects of cellular biology at a global level. These technologies have been
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used in an attempt to better understand the development of various tissues with transcriptomics
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studies producing the most data. A major challenge of this new era is to determine the biological
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importance of these data in the context of cell and tissue function. In this paper, we focus on the
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large volumes of data that we have produced in our studies of bovine tissues in recent years, with a
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focus on reproductive tissues.
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2. Development of reproductive tissues
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While most tissues in the body are continually engaged in turnover, many reproductive tissues are
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actively engaged in vigorous periods of tissue (cellular) proliferation that are often followed by
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periods of dramatic and whole-sale tissue degradation and regression (this is especially true in
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female reproductive tissues). Cumulatively, the coordination of the proliferation and regression of
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tissues determines the success of reproduction and fertility. It is for this reason that many
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investigators have chosen to focus their attentions on the development of specific tissues, usually
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during narrow timeframes during the reproductive process, to understand their contribution to the
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outcome of reproduction.
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In taking a step back from the mass of data available on individual tissues, it is interesting to
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consider some of the similarities and differences among tissues during their developmental
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processes. For example, some reproductive tissues are relatively static in mass (although not
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always function) with the hypothalamus and pituitary being clear examples. Other tissues develop
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over long periods of time; oocytes and follicles being the best examples, developing over months
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(or years depending on your point of view) with the most dramatic changes occurring in the final
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days before ovulation. Uterine tissues undergo changes during the reproductive cycle in preparation
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for pregnancy and then changes again over the months of gestation. Others tissues develop much
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more rapidly, with the early embryo and the corpus luteum undergoing changes in size and
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morphology that change them almost unrecognizably over a period of just a few days.
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To better understand these changes, and the factors controlling them, many studies have focused on
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measuring the expression of genes and significant amounts of data have been generated during the
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last few years. One of the key technologies enabling this has been RNA Sequencing (RNA-seq).
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Since its initial development in 2006 (Bainbridge et al. 2006), RNA-seq has rapidly displaced gene
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expression microarrays for large-scale transcriptional profiling and is now the technology of choice
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in many laboratories. Some of the advantages of RNA-seq over microarrays include global
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profiling of transcripts including currently unannotated transcripts, identification of novel transcript
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isoforms as well as more accurate quantification of transcript levels. In practice, many researchers,
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including ourselves, have used the technology as a direct replacement for microarrays and tended
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to restrict their initial analyses to the known annotated transcriptome of the organism of interest
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(Mamo et al. 2011; Foley et al. 2012; Forde et al. 2012; O'Loughlin et al. 2012; Pluta et al. 2012;
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Walsh et al. 2012; Keady In preparation; Matthews In preparation).
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The availability of a complete bovine genome sequence has enabled the application of the RNA-
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seq protocol to bovine samples (Elsik et al. 2009). We have previously published several RNA-
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seq-derived transcriptomic studies focusing on aspects of bovine reproduction, fertility and
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productivity traits under various experimental conditions (see Table 1). However, the question of
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the completeness of the current bovine transcriptome annotation, and characteristic gene expression
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differences between tissue types remain unaddressed. There are also other gaps in our knowledge
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of the transcriptome; for example, to what extent are genes universally or uniquely expressed in
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individual tissue types. There are also long-standing questions about the existence or otherwise of
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so-called “housekeeping genes” with consistent expression levels in all tissue types at all times
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(which could be used as reference genes for calibration of global expression studies).
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In order to address these questions the aim of this study was to conduct a de-novo
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annotation of the bovine transcriptome using data from 280 bovine samples taken from 10 distinct
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tissue types and to compare it to the Ensembl bovine annotation (Ensemble 65) to identify novel
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bovine transcripts. In addition, the patterns of transcription between different tissues types were
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compared to identify genes with highly tissue-specific expression patterns and housekeeping genes.
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3. Materials and Methods
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Animal Handling
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All animal procedures performed for the generation of tissue samples in this study were conducted
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under experimental license from the Irish Department of Health and Children in accordance with
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the Cruelty to Animals Act 1876 and the European Communities (Amendment of Cruelty to
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Animals Act 1876) regulation 2002 and 2005 with approval from individual institutional ethics
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committees.
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Tissue sources
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The sequencing data were generated from 10 different tissue types as part of 8 separate
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experiments, some of which have been published. The details of tissue type, cattle breed and
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number of samples are listed in Table 1. In brief, the samples consisted of 20 uterine endometrium
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samples from mixed breed beef heifers collected on Day 13 and Day 16 after estrus of which 5
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samples at each time point were confirmed pregnant and 5 were non-pregnant (Forde et al. 2012).
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The follicular granulosa and theca cell samples were paired samples taken from dominant pre-
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ovulatory follicles of Holstein-Friesian cows and heifers (37 animals in total) at 3 stages of
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follicular development: selection, differentiation and luteinization (Walsh et al. 2012). The cervical
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tissues were taken from 30 mixed breed beef heifers at 6 time points during the peri-estrus period
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(Pluta et al. 2012). The embryo samples consisted of 28 pooled samples from mixed breed beef
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cattle taken at 5 different days post-fertilization (Mamo et al. 2011). The leukocytes were taken
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from 16 Simmental male beef calves at 4 different time points post-weaning, resulting in 55
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samples in total (not all animal yielded 4 samples each) (O'Loughlin et al. 2012). The liver samples
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were taken from 12 early post-partum Holstein-Friesian dairy cows in either mild or severe
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negative energy balance (McCabe et al. 2012). The hypothalamus and pituitary samples were taken
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from 23 mixed breed beef animals (Matthews In preparation). The muscle samples were taken from
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the M. longissimus dorsi of 27 Aberdeen Angus steers undergoing nutritional restriction and
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compensatory growth (Keady In preparation). In some cases multiple samples were not collected
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from all individual animals giving rise to the actual number of samples contributing to the study as
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shown in Table 1.
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RNA-seq library preparation
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All samples were sequenced on a GAIIx sequencer (Illumina) by the Conway Institute
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Transcriptomics laboratory at University College Dublin, Ireland. All libraries were non-strand
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specific and were processed as single read libraries (with the exception of the muscle samples that
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were processed as paired-end libraries). The library type, read length, total numbers of reads
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generated for each library type and Gene Expression Omnibus (GEO) ID (Edgar et al. 2002)
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(where available) are listed in Table 1.
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Alignment and preprocessing
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FASTQ files (Cock et al. 2010) from each library were converted to the Sanger FASTQ format and
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were then aligned individually to the UMD3.1/BosTau6 bovine genome assembly using the
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software Tophat version 1.4.0 (Trapnell et al. 2009). Individual alignments, in bam format, from
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each library were merged together using the samtools merge command (Li et al. 2009). Finally, all
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combined tissue bam files were merged together into a single file. De novo transcriptome
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annotation was carried out on each individual tissue and on the combined dataset using cufflinks
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v1.1.0 (Trapnell et al. 2010). The Ensembl v65 annotation of the bovine genome was taken as the
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reference transcriptome (Flicek et al. 2012). Coordinates of repetitive regions were downloaded for
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the UMD3.1 assembly from UCSC genome browser (Kent et al. 2002). Introns were identified
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from the alignments with Python code utilizing the pysam library. Visualization of the alignments
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was carried out using the Integrated Genome Viewer (IGV) v2.0 (Robinson et al. 2011). Eval
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version 2.2.8 (Keibler and Brent 2003) was used to generate summary statistics for the de-novo
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transcriptomes. The BEDTools (Quinlan and Hall 2010) intersectbed program as well as the
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GenomicRanges (Aboyoun 2015) and rtracklayer (Lawrence et al. 2009) packages from
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R/Bioconductor were used to identify overlap of exons with genomic features such as repetitive
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regions or annotated Ensembl/refseq exons.
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Quality Control
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Density plots of the logged Reads Per Kilobase per Million (RPKM) (Mortazavi et al. 2008) levels
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for each gene in each sample were generated. Samples with read depth of less than 2 million reads
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in total, or with >80% non-unique mapping reads > 80% were excluded from further analysis.
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None of the 280 samples were excluded from further analysis using these QC criteria.
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Initial quality control checks identified a bias in the data, exhibited by the paired-end muscle data
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which had a much-reduced percentage of non-unique reads compared to the single read libraries. In
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order to ensure the libraries were comparable with each other for the purposes of identifying
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differential expression, all FASTQ files were trimmed to a common length of 36 bp following the
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approach of Anders et al. (Anders et al. 2012). In addition, only the first read in the paired-end
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muscle data was used for differential gene expression analysis.
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Dimensional reduction and clustering
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Hierarchical clustering of the individual samples was carried out using the ColorDendrogram
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function from the sparcl R package. The Eisen distance metric was used as implemented in the
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MADE4 bioconductor package (Culhane et al. 2005). Principal Component Analysis plots were
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generated using the function prcomp in R.
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Quantifying the diversity/dispersion of gene expression in a tissue
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The Gini coefficient, a measure of the unevenness of the distribution of reads, was calculated using
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the Gini function in the R library reldist (Handcock et al. 1999). It is most commonly used in the
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social sciences as a measure of income inequality across different segments of a population. It is
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defined as twice the area between the 45 degree line and the Lorenz curve where, in this case, the
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Lorenz curve is a graph describing the cumulative share of total reads assigned to the bottom x% of
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the gene universe. A tissue with an exactly equal distribution of reads among all genes would have
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a Gini coefficient of zero and a tissue where all reads come from a single gene would have a Gini
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of 1. The total count of reads for each gene from all samples of each tissue was obtained and the
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Gini index for each tissue was calculated separately. The Gini index has been used by others to
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measure skewness in other aspects of transcriptomics such as PolyA length (Morozov et al. 2012).
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Gene expression measures of Ensembl 65 annotated transcripts
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HTseq (Anders and Huber 2010) was used to generate raw read counts for each gene in each
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library using the Ensembl 65 bovine annotation as the reference. These raw counts were converted
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into TPM (Tags Per Million) (Li et al. 2010) and RPKM metrics.
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Categorical tissue specificity
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Following the method of Schug et al. (Schug et al. 2005), tissue specificity of each gene was
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measured using the categorical tissue specificity metric (Qgt). Qgt weights genes according to the
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degree to which the expression of a gene is skewed towards a single tissue; it is based on the
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Shannon entropy of the gene Hg (Shannon and Weaver 1949).
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The following calculation was used: given expression levels of a gene in N tissues, the relative
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expression of a gene g in a tissue t was defined as:
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pt |g 
wg,t
w
g,t
1t  N
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where wg,t is the expression level of the gene in the tissue. In this case, either TPM or RPKM can
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be used as the expression level as for the purposes of this calculation they are equivalent. To avoid
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division by zero in later calculations, a count of 1 is added to the raw counts for each gene in each
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sample before calculation of TPM and RPKM. The relative expression is then the RPKM of the
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gene in the tissue divided by the sum of RPKMs for that gene across all tissues.
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The Shannon entropy of a gene's expression was calculated as:
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Hg 
 p
t |g
log 2 ( pt |g )
1t  N
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Hg has units of bits and ranges from zero for genes expressed in a single tissue to a maximum of
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log2(N) for genes expressed uniformly in all tissues. In this case, the maximum entropy of a gene
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was log2(10)=3.32 bits, which would represent a ubiquitously and uniformly expressed gene (i.e. an
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ideal housekeeping gene). This relative expression derived entropy calculation does not
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discriminate between absolute expression levels, so in order to give higher weight to genes with
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greater absolute expression levels, the categorical tissue specificity was defined as
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Qg|t  H g  log 2 ( pt |g )
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The expression of a particular gene becomes more specific to a single tissue as the value of Qg|t
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approaches zero. By contrast, ideal housekeeping genes would have a Qg|t of 2log2(10)=6.64 bits in
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all tissues.
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Tissue-specific gene lists and pathway analysis
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Permutation testing of a balanced set of tissue samples was used to estimate the null distribution of
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the entropy values. 10 samples were taken from each tissue type (pituitary, which consisted of 3
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pooled samples was excluded from this step because of the low number of samples). The labels
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were randomly permuted 100 times and the minimum entropy score across all genes (0.404 bits)
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was used as the cut-off. Genes having an entropy value below this score in the original set were
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considered to be expressed in a highly tissue specific manner.
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The DAVID pathway annotation tool (Huang da et al. 2009) was used to determine over-
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represented KEGG pathways among the tissue-specific gene list generated for each tissue.
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Correlation of tissue specific expression ranking between species
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The probesets from the matching tissues from the Schug dataset were sorted according to the
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Qgt(rma) field. This ordering generated the ranks which were used to compare to the ranked list of
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genes from our RNAseq generated Qgt values. Human and mouse affymetrix IDs were matched to
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bovine Ensembl gene IDs using cross-reference tables downloaded from the Ensembl Biomart tool.
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The Spearman correlation (spearmans rho) was calculated using the cor.test function in R.
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ANOVA analysis
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Analysis of variance was carried out to determine the overall amount of tissue-specific expression
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in the dataset. The TPM matrix was used in conjunction with the limma (Smyth 2004) and puma
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(Pearson et al. 2009) packages in R/bioconductor to calculate the tissue effects. False discovery
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rate thresholds of 0.05 and 0.01 were used.
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4. Results
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Of the 24,616 annotated genes, 23,818 were expressed at a level of one read in at least one of the
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280 samples (22,963 genes with ≥ 5 reads). Of these 17,893 were expressed at a level of ≥ 10 tags
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per million (TPM). Embryos showed the greatest number of expressed genes and muscle had the
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fewest (Table 2). There were 1,838 genes in the ens65 annotation that were not detected in any of
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the samples. A full list of genes and their expression levels in tissues is shown in Supplementary
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Table S1.
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Clustering of samples
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Initial clustering of the samples both by Hierarchical Clustering (Figure 1) and Principal
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Components Analysis (Figure 2) showed almost perfect grouping of samples by tissues. The
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exceptions were some of the cervical samples that initially grouped with the theca samples. Further
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analysis revealed that this was due to very high expression of several collagen and basement
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membrane genes (see Supplementary Table S1) in a number of cervical and thecal tissue samples.
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We hypothesized that this could have been from inclusion of some connective tissue in these
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samples. These transcripts were removed and TPMs were recalculated based on the new sample
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totals (as the numerator of the TPM calculation is affected by these highly expressed genes).
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Following this filtering perfect grouping of samples by tissue was achieved. It is notable that the
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clustering by tissue was true also for the paired theca and granulosa samples (which came from the
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same animals).
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Overall tissue-specific expression
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The results of the Analysis of Variance (ANOVA)/Differentially Expressed Genes (DEG) analysis
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of gene expression among the 10 tissue types indicate that 95.8% of genes were differentially
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expressed between tissues at a False Discovery Rate (FDR) of 0.05 and 93.6% at an FDR of 0.01.
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The principal components analysis showed clustering together of samples from the same tissue and
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very specific separation of samples along consecutive principal components. This was reflected by
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the separation of these samples in PC1 and PC2. The first 2 principal components accounted for
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33% of total variance, the first 10 principal components accounted for 89%.
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Gini Index
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The Gini index for each tissue is shown in Table 3. The gene expression was skewed in all tissues.
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The most extreme skew was seen in the muscle tissue (Gini coefficient 0.96). The top 10 most
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highly expressed genes in muscle made up 34% of all reads (gene aligned reads); while the top
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gene (ENSBTAG00000046332 Actin, alpha skeletal muscle) on its own accounted for 11% of all
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gene-aligned reads. The tissue with the lowest Gini coefficient was the endometrium tissue where
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the top 10 genes constituted just 3% of total gene aligned reads and the most highly expressed gene
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(ENSBTAG00000021466, collagen alpha-1(III) chain) accounted for 0.5% of total reads.
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Tissue-specific gene expression
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The full table of Qgt for each gene in each tissue as well as the overall entropy (Hg) for each gene is
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available in supplementary Table S1.
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Using the permutation analysis, 452 transcripts exhibited highly significant tissue-specific
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expression (< 0.404 bits entropy score) (Table 4). The top tissue-specific gene for each tissue (as
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determined by Qgt score) is shown in Figure 3. In many cases there was a large variance associated
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with the top gene and it would not have been ranked top using other types of analysis such as
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ANOVA.
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Pathway analysis of tissue-specific genes
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The most tissue-specific genes for each tissue (after permutation testing) were analyzed to identify
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over-represented pathways (see Table 4). In some cases, the number of genes was insufficient to
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detect pathways; however, in each case, inspection of the individual list confirmed the presence of
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genes, most of which have a previously reported biological function in the tissue in question. The
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tissue with the most highly specific genes was the liver with 196 genes. Tissues with 10 or fewer
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highly specific genes were the pituitary, granulosa, endometrium and theca (Table 4).
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Pathway analysis of housekeeping genes
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411 genes were identified as candidate housekeeping genes (high and not differentially expressed
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among tissues) based on entropy scores of ≥ 3.25 (Table 4). The pathways over-represented
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included mitochondrial pathways (and diseases related to mitochondrial dysfunction such as
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Huntington’s, Parkinson’s and Alzheimer’s) and basic cellular processes such as DNA repair,
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protein translation, protein degradation and various protein modifications (ubiquitination,
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methylation, neddylation).
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Comparison of tissue specific genes between species
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Of the 10 bovine tissues, 5 of them had matching samples in the human and mouse dataset
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generated by Schug et al (Schug et al. 2005). The matched tissues for human were liver, uterus
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(endometrium) and pituitary, while for mouse the matches were hypothalamus, liver, muscle and
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uterus. A total of 8,737 bovine genes were matched to the human probes and 8,914 to the mouse
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microarray probes. The most significant expression correlations were with the pituitary profiles
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(0.61) followed by muscle (0.42) and then liver (0.23). Comparison of the expression correlations
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between the mouse endometrium vs the bovine endometrium was marginally significant.
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Comparison of the expression correlations between the bovine and human endometrium and
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hypothalamic tissues were non-significant.
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5. Discussion
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Comparison with other mammalian gene atlases
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The current study presents the first RNA-seq-derived multi-tissue comparison of gene expression
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in bovine tissues. Our comparison is based on more biological replicates, greater sequencing depth
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and higher gene coverage than other comparisons and includes a focus on reproductive tissues. A
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bovine gene atlas (BGA) based on Illumina Digital Gene Expression (DGE) tags generated using
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the DpnII restriction enzyme has also been published (Harhay et al. 2010) which used 300 million
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tags (20 bp sequences) from 92 tissues and 3 cell lines. Similar to the current study, the tissue
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samples were from different animals, breeds and sexes. The DGE approach has several limitations
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compared with RNA-seq in that only a 16-17 bp tag (+4 bp recognition sequence) is generated per
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transcript so information about the full transcript extent is missing. The shorter DGE tags are
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frequently non-unique and so information about some transcripts is lost. Five of the ten tissues
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included in this study were also profiled in the BGA (i.e. muscle, liver, pituitary, hypothalamus and
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leukocytes).
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The archetype for this project is the original mouse Gene Atlas (Su et al. 2004) that profiled 61
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tissues in mouse and 79 tissues in human using Affymetrix microarrays. It remains the most
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comprehensive profiling of mammalian tissues to date and is a popular resource for researchers
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attempting to determine the tissue expression profiles of specific genes. Other notable mammalian
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expression atlases include the Allen Brain Atlas (Lein et al. 2007) which profiles the expression of
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genes in the mouse brain via in situ hybridization and the mouse brain atlas generated by Siddiqui
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et al (Siddiqui et al. 2005) using the Long Sage protocol on 72 individual tissues from mice. More
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recently the Human BodyMap project has conducted RNA-seq on 16 different human tissue types
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(GEO ID: GSE30611, http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE30611)
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and a pig expression atlas (using microarrays) was generated based on 62 different tissues and cell
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types with a particular focus on the gastrointestinal tract (Freeman et al. 2012). Another group
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profiled 900 different regions of the human brain using expression microarrays (Hawrylycz et al.
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2012). An equine RNA-seq atlas has also been published based on 8 different tissues (Coleman et
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al. 2010).
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Our comparison sets this study apart from those listed above due to the higher overall number of
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samples per tissue and the range of experimental conditions under which these tissues were
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recovered. The perturbation of the tissue by developmental changes or experimental challenges
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provides a more realistic picture of the diversity of transcription in an individual tissue. Our use of
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information theory (entropy) to measure tissue specificity enables the identification of tissue-
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specific expression even in cases where the most tissue-specifically expressed genes are not
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expressed in all samples from the tissue in question. For example, aromatase (CYP19A1) is not
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expressed at all stages of granulosa tissue development but it is the gene most characteristic of this
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tissue (Stocco 2008; Walsh et al. 2012) and was confirmed in this study as such.
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The nature of gene expression in different tissues
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Transcription in different mammalian tissues is very variable and this is reflected in our bovine
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tissue data as evidenced by the Gini coefficient. In most tissues a small number of genes
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disproportionately contribute to the overall mRNA pool. The most extreme example of this in our
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dataset was the gene expression in the muscle tissue (Keady In preparation). This probably reflects
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the specialization of muscle tissue for a specific task (with few cell types), compared with more
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diverse and complex functions required by a tissue such as the endometrium (with many cell
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types). This may also explain why muscle was one of the earliest and most successful cases of
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identifying transcriptional regulatory control regions determining tissue-specific transcription
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(Wasserman and Fickett 1998).
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Differential gene analysis reveals that almost all genes have an element of tissue-specific
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expression; however, it is usually a matter of different levels of expression in different tissues (i.e.
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along a continuum of expression rather than a digital pattern of presence or absence of gene
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expression). This is reflected in the fact that almost 95% of genes have some measurable
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component of tissue type contribution to expression level as determine by the ANOVA analysis.
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However, a small number of genes (452, see Table 4) were identified as having highly specific
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expression restricted to either 1 or 2 tissues. These low entropy/high information genes have very
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specific functions characteristic of their tissue of expression such as prolactin in the pituitary (Egli
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et al. 2010). The liver had many more of these types of genes than the other tissue types. There was
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also a similarly small number (411) of potential housekeeping genes exhibiting high entropy and
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little evidence of tissue-specific expression patterns. This was similar to the number of genes
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proposed as housekeeping genes by other groups (Warrington et al. 2000; Hsiao et al. 2001;
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Eisenberg and Levanon 2003). However, there was relatively little overlap in the housekeeping
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lists that we generated compared with these earlier human lists (24 and 22 housekeeping genes
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overlap with the data from Warrington et al., 2000 and Hsiao et al., 2001, respectively).
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While it is tempting to consider these as potential reference genes for normalization processes,
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either for quantitative polymerase chain reaction (qPCR) or RNA-seq experiments, the addition of
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further tissues or similar tissues under differing conditions would reduce this number. The recent
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discovery of the impact of transcriptional amplification by C-myc on global transcription would
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suggest that external standards (spike-ins) calibrated to cell number may be the only viable
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universal approach (Loven et al. 2012).
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The Qgt values generated in this study indicate much higher tissue specificity than that indicated by
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Schug and colleagues (Schug et al. 2005). This can be explained by several factors. Schug et al.
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(2005) relied on the Gene Atlas dataset that profiled many more tissues using microarrays (79
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human tissues of which 43 were used) but with an n=2 per tissue. RNA-seq avoids the coverage
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issues present with microarrays and the digital nature of the data generated is well suited to entropy
370
calculations. It is likely that some of the genes that are considered highly tissue-specific in the
371
current study will become less so as additional tissue types are profiled. The tissue specificity
372
results for some of our bovine tissues were compared with equivalent tissues in human and mouse
373
where Qgt values were generated by Schug et al. (2005) with mixed results. The pituitary and
374
muscle tissues exhibited high correlation among the most specifically expressed genes, liver
375
showed an intermediate level of concordance and the hypothalamus and endometrium displayed
376
limited species similarities. Brawand et al. (2011) compared the expression of 6 tissues across 10
377
different mammalian species (Brawand et al. 2011). They detected differing rates of gene
378
expression changes in different tissues including the liver. This may reflect differing evolutionary
12
379
pressures on different tissues/organs. It suggests that the genes that are highly specific to pituitary
380
and muscle function are not changing as much as those involved in endometrium, liver and
381
hypothalamus.
382
Overall completeness of the bovine transcriptome
383
It is remarkable that so much of the bovine genome is transcribed. Transcription from 97% of the
384
assembled bovine genome in at least one sample was detected. However, such gross percentages
385
can be misleading. Most of the transcription is at a very low level and the read density in exonic
386
regions is orders of magnitude higher than intergenic and intronic regions.
387
Most of the novel transcription units that were identified were single exon transcripts. Of the
388
12,041 novel multi-exon transcripts 4,494 have some overlap with genes mapped to the Bovine
389
Genome from other species such as human and mouse (as determined by coordinates available
390
from the UCSC xenoRef table) (Kent et al. 2002). However, the contribution of these novel reads
391
to the overall RNA-seq dataset is relatively low (less than 1%). This is in stark contrast to the
392
amount of RNA sequence derived from the known exonic regions. 28% of the reads fall within
393
known Ensembl exons and 68% fall within the entire gene span (including exons and introns).
394
While 30% of the reads fall outside these categories, it is distributed over a much larger portion of
395
the genome and the level of transcription is several orders of magnitude lower. The fact of almost
396
ubiquitous transcription of the genome is not new. Pol2 (the key enzyme involved in transcription)
397
binding to DNA is relatively non-specific with as much as 90% of the binding and resultant
398
transcription suggested to be noisy and non-functional (Struhl 2007).
399
The Encyclopedia of DNA elements (ENCODE) results from both the prototype (Birney et al.
400
2007) and scale-up phase (Bernstein et al. 2012) have confirmed the ubiquity of transcription but
401
controversially they have suggested that this transcription is functional. These claims have been
402
challenged by others (van Bakel et al. 2010; Eddy 2012). Despite the ENCODE claims, we
403
recommend a cautious and conservative interpretation of the low abundance intergenic
404
transcription.
405
406
6. Conclusions
407
In this paper we have used profiled 280 different bovine samples from 10 different tissues using
408
RNA-seq. It is remarkable that so much of the bovine genome is transcribed with 23,818 out of
409
24,616 (97%) genes of the assembled bovine genome being detected in at least one sample.
410
However, most of the transcription is at a very low level giving rise to individual tissues with
411
highly characteristic patterns of gene expression, even among the majority of genes that are
13
412
expressed in all tissues (95.8% of genes were differentially expressed). We have shown that a
413
small number of genes disproportionately contribute to the majority of the mRNA pool in a given
414
tissue; that tissues have a limited number of uniquely expressed genes (ranging from 2 to 196 genes
415
in this study) and also that we detected 411 housekeeping genes there were expressed at high levels
416
in all of the 10 tissue types examined. We conclude that it is combined effects of the variable
417
expression of large numbers of genes (73 to 93% of the genome) with the specific expression of a
418
small number of genes (less than 1% of the genome) that contributes to determining the outcome of
419
the function of individual tissues.
420
421
7. Acknowledgements
422
The authors are grateful to Science Foundation Ireland (07/SRC/B1156) for funding a large portion
423
of this research.
424
425
Competing interests
426
None
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428
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Supplementary Table S1.
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21
Table 1: Sequencing metrics for the 10 bovine tissue samples under investigation
Tissue type
Breed
of
animals
Number
of
animals
Sex
Number
of
samples
Library
Type
Read
length
(base
pairs)
Endometrium
20
F
20
Single read
36
F
36
37
F
30
Embryo
(Day 5 to 19)
Mixed
breed beef
HolsteinFriesian
HolsteinFriesian
Mixed
breed beef
Mixed
breed beef
Leukocytes
Liver
Granulosa
Theca
Cervix
Hypothalamus
Pituitary
Muscle
Total
GEO ID
Study
Original
study
Pubmed ID
36
Total
tissue
library
size
(Gbases)
119,950,155
4.3
GSE56392
22759920
Single read
36
263,863,637
9.5
GSE34317
37
Single read
36
356,596,059
12.8
GSE34317
F
30
Single read
42
475,102,282
20.0
GSE38225
28
Mixed
28
Pooled
Single read
84
664,986,589
55.9
GSE56513
Forde et al
2012
Walsh et al
2012
Walsh et al
2012
Pluta et al
2012
Mamo et al
2011
Simmental
16
M
55
Single read
36
1,229,589,572
44.3
GSE37447
22708644
HolsteinFriesian
Mixed
breed beef
Mixed
breed beef
12
F
21
Single read
36
289,137,975
10.4
GSE37544
23
F
23
Single read
42
594,937,671
24.9
GSE49540
3 pools
F
3
Pooled
Single read
42
106,119,419
4.5
O’Loughlin
et al 2012
McCabe et
al 2012
Matthews et
al (In prep.)
Matthews et
al (In prep)
In
preparation
In
preparation
27
M
27
Paired End
2x40
936,006,782
74.9
Keady et al
(in prep)
In
preparation
5,034,250,870
261.4
Aberdeen
Angus
280
22
Number of
reads
GSE48481
22414914
22414914
23092952
21795669
22607119
Table 2: Numbers of Ensembl annotated genes detected in each tissue type with ≥1,
≥5 reads (transcripts) and ≥10 tags per million reads (TPM). Note there are 24,616
genes in total in the Ensembl bovine annotation.
Tissue type
Number of
transcripts
detected
(reads ≥ 1)
Number of
transcripts
detected
(reads ≥ 5)
Number of
transcripts
detected
(TPM ≥ 10)
Endometrium
19,360
17,078
12,104
Granulosa
19,377
16,973
11,205
Theca
19,840
17,571
11,133
Cervix
20,287
18,186
12,596
Embryo (Day 5 to 19)
22,874
21,444
16,141
Leukocytes
19,772
17,632
10,205
Liver
19,141
16,740
9,782
Hypothalamus
21,259
19,006
11,996
Pituitary
18,465
15,861
10,080
Muscle
18,031
15,913
7,020
All above tissues
23,818
22,963
17,893
23
Table 3: Gini coefficient for each tissue. First column shows Gini coefficient for all
24,616 genes in Ensemble. Second column shows Gini coefficient for all non-zero
genes. The Gini coefficient is a measure of the unevenness in the distribution of the
reads (transcripts) among the genes. Samples with a Gini coefficient of 0.00 would
have an equal distribution of reads among all genes. Samples with a Gini coefficient
of 1.00 would have all reads from a single gene.
Gini
Gini
(all genes)
(all non-zero genes)
0.00
0.00
Endometrium
0.79
0.79
Hypothalamus
0.81
0.80
Embryo
0.83
0.82
Cervix
0.84
0.84
Pituitary
0.85
0.84
Granulosa
0.85
0.84
Theca
0.85
0.85
Leukocytes
0.87
0.87
Liver
0.89
0.89
Muscle
0.96
0.96
1.00
1.00
24
Decreasing number of genes contributing to total read count
Tissue
Table 4: List of over-represented KEGG pathways and DAVID functional clusters for the significantly tissue expressed genes. For those tissues with no overrepresented pathways or clusters the complete list of genes is shown. Final row of table shows over-represented pathways among genes exhibiting a
housekeeping profile.
Tissue
Total
sig
genes
Cervix
35
Extracellular region
Secretory Granule
Embryo
32
Protease
Proteinase inhibitor 12, Kunitz metazoa
Signal peptide
Regulation of transcription, DNA-dependent
Endometrium
3
Proenkephalin (PENK); Protease serine 16
(thymus) (PRSS16); Similar to thioesterase
superfamily member 5 (THEM5/Bt.33457)
Granulosa
4
Inhibin beta A (INHBA); Inhibin alpha (INHBB);
Aromatase (Cytochrome P450 XIX) (CYP19A1);
Follicle stimulating hormone receptor (FSHR)
Hypothalamus
36
Taurine and hypotaurine metabolism
Beta-alanine metabolism
Alanine, aspartate and glutamate metabolism
Butanoate metabolism
Type I diabetes mellitus
Neuron projection
Immunoglobulin V-set
Integral to membrane
Cell surface receptor linked signal
transduction
Membrane fraction
Leukocytes
35
Chemokine signaling
Cytokine-cytokine receptor interaction
Cell adhesion molecules (CAMs)
Primary immunodeficiency
Chemokine receptor activity
Plasma membrane
Integral to membrane
KEGG top pathways
DAVID top clusters
25
Genes (symbol)
Liver
196
Complement and coagulation cascades
Retinol metabolism
Drug metabolism
Steroid hormone biosynthesis
Metabolism of xenobiotics by cytochrome P450
Secreted
Enzyme inhibitor activity
Transition metal ion binding
Ion binding
Complement and coagulation cascades
Muscle
99
Tight junction
Viral myocarditis
Focal adhesion
Arrhythmogenic right ventricular cardiomyopathy
Hypertrophic cardiomyopathy
Myofibiril
Muscle organ development
Muscle protein
Striated muscle development
Sarcoplasmic reticulum
Pituitary
10
Theca
2
Housekeeping
411
Neuroactive ligand-receptor interaction
Follicle stimulating hormone beta (FSHB);
Glycoprotein hormones alpha polypeptide (CGA);
Gonadotrophin releasing hormone receptor
(GNRHR); Growth hormone (GH1)
Prolactin (PRL); Thyroid stimulating hormone
beta (TSHB); Similar to peptidyl-pro cis trans
isomerase (LOC782178); Pituitary specific
transcription factor (POU class 1 homeobox 1)
(POU1F1); Growth hormone releasing hormone
receptor (GHRHR); Immunoglobulin superfamily
member 1 (IGSF1)
CYPXVII, cythochrome P450 17A1 (CYP17A1);
Insulin-like 3 (leydig cell) (INSL3)
Huntington’s disease
Parkinson’s disease
Oxidative phosphorylation
Alzherimer’s disease
Proteasome
Amioacyl t-RNA synthesis
Nucleotide excision repair
Purine metabolism
RNA polymerase
Valine, leucine and isoleucine biosynthesis
Ubiquitin mediated proteolysis
Mitochondrion
Proteasome
Proteolysis
Translation/ribosome
DNA repair
Ubiquitin
Protein neddylation
tRNA aminoacylation
Methyltransferase activity
26
Figure 1: Hierarchical clustering of individual samples in ten bovine tissues.
27
Figure 2: Principal components plot showing first 2 principal components of 10 bovine
tissues.
28
Figure 3: Boxplot of Transcripts per million (TPM) showing the gene in each tissue that has the strongest tissue specific signature i.e. lowest Qgt
value. The last 2 plots show the 2 genes with strongest housekeeping profile – i.e. ubiquitously expressed at the same high level in all tissues
(highest entropy score Hg).
29