Bovine ncRNAs Are Abundant, Primarily Intergenic, Conserved and Associated with Regulatory Genes

School of Molecular and Biomedical Science, The University of Adelaide, Adelaide, South Australia, Australia.
PLoS ONE (Impact Factor: 3.23). 08/2012; 7(8). DOI: 10.1371/journal.pone.0042638
Source: PubMed


It is apparent that non-coding transcripts are a common feature of higher organisms and encode uncharacterized layers of genetic regulation and information. We used public bovine EST data from many developmental stages and tissues, and developed a pipeline for the genome wide identification and annotation of non-coding RNAs (ncRNAs). We have predicted 23,060 bovine ncRNAs, 99% of which are un-annotated, based on known ncRNA databases. Intergenic transcripts accounted for the majority (57%) of the predicted ncRNAs and the occurrence of ncRNAs and genes were only moderately correlated (r = 0.55, p-value,2.2e-16). Many of these intergenic non-coding RNAs mapped close to the 39 or 59 end of thousands of genes and many of these were transcribed from the opposite strand with respect to the closest gene, particularly regulatory-related genes. Conservation analyses showed that these ncRNAs were evolutionarily conserved, and many intergenic ncRNAs proximate to genes contained sequence-specific motifs. Correlation analysis of expression between these intergenic ncRNAs and protein-coding genes using RNA-seq data from a variety of tissues showed significant correlations with many transcripts. These results support the hypothesis that ncRNAs are common, transcribed in a regulated fashion and have regulatory functions. Copyright: ß 2012 Qu, Adelson. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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    • "Moreover, there are a high number of intergenic ncRNAs which act through cis-regulation of mostly regulatory genes and locate in the 1 kb proximity of UTRs (Qu and Adelson, 2012). As the 3′-UTR associated/proximate ncRNAs are in highly conserved regions (Qu and Adelson, 2012), targeting the 3′-UTR of animal mRNA has been one of the most important pathways for regulation of gene expression (Glazov et al., 2009). "
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    ABSTRACT: AMPK is the key switch for providing the energy balance between cellular anabolic and catabolic processes. In this study, we aimed to screen the PRKAG1 (AMPKγ1) gene in high, moderate, and low producing Holstein dairy cows. A sample of 100 pregnant dairy cows, comprising 41 high, 33 moderate, and 26 low milk yields were selected from three large dairy herds in Isfahan province of Iran. Body condition score (BCS) was estimated before parturition while beta hydroxyl butyric acid (BHBA) as a measure of ketone bodies was measured at the fifth day postpartum. In addition, using three primer pairs covering exons 2-11 and 3′-UTR of the PRKAG1 gene, a random sample of 10 high milk yield dairy cows were amplified and sequenced. The sequencing results showed the presence of a T12571C mutation in intron 6 and a T14280C mutation in the 3′-untranslated region (UTR) of the PRKAG1 gene. Following a PCR reaction for amplification of the 3′-UTR amplicons, single strand conformation polymorphism (SSCP) assay was implemented for discrimination of the mutation in the studied population. Then, we evaluated if the mutation associates with the BCS, serum BHBA level, and production traits. The experimental analysis showed that the mutated allele significantly increased the BHBA level, BCS, as well as milk and protein yield. Bioinformatic study revealed that this 3′-UTR mutation distorts the target site of mir-423-5p microRNA which is one of the most highly expressed microRNA in bovine mammary gland, liver, and kidney. Given the role of AMPK in energy metabolism, the newly identified 3′-UTR mutation highlights the importance of AMPK and suggests a role of miRNAs for regulation of cellular metabolism, metabolism disorders, and production traits in Holstein dairy cows. Keywords: AMPK; BHBA; mir-423-5p; microRNA; PRKAG; Ketosis
    Gene 07/2015; 574(1). DOI:10.1016/j.gene.2015.07.077 · 2.14 Impact Factor
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    • "We then introduced a statistic, the maximal information coefficient (MIC), that behaves more equitably on functional relationships than the state of the art and also has the desired behavior on superpositions of functional relationships, given sufficient sample size. Although MIC has enjoyed widespread use in a variety of disciplines [9] [10] [11] [12] [13] [14] [15] [16] [17] [18], the original paper on equitability and MIC has generated much discussion, both published and otherwise, including some concerns and confusions. Perhaps the most frequent concern that we have heard is the desire for a richer and more formal theoretical framework for equitability, and this is the main issue we address in this paper. "
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    ABSTRACT: The maximal information coefficient (MIC) is a tool for finding the strongest pairwise relationships in a data set with many variables (Reshef et al., 2011). MIC is useful because it gives similar scores to equally noisy relationships of different types. This property, called {\em equitability}, is important for analyzing high-dimensional data sets. Here we formalize the theory behind both equitability and MIC in the language of estimation theory. This formalization has a number of advantages. First, it allows us to show that equitability is a generalization of power against statistical independence. Second, it allows us to compute and discuss the population value of MIC, which we call MIC_*. In doing so we generalize and strengthen the mathematical results proven in Reshef et al. (2011) and clarify the relationship between MIC and mutual information. Introducing MIC_* also enables us to reason about the properties of MIC more abstractly: for instance, we show that MIC_* is continuous and that there is a sense in which it is a canonical "smoothing" of mutual information. We also prove an alternate, equivalent characterization of MIC_* that we use to state new estimators of it as well as an algorithm for explicitly computing it when the joint probability density function of a pair of random variables is known. Our hope is that this paper provides a richer theoretical foundation for MIC and equitability going forward. This paper will be accompanied by a forthcoming companion paper that performs extensive empirical analysis and comparison to other methods and discusses the practical aspects of both equitability and the use of MIC and its related statistics.
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    • "Because of the key role of lncRNAs in regulation of gene expression and therefore possible impact on phenotypes, it is important to identify most lncRNAs. Catalogues of lncRNA have been established for many species, including cattle [27–29]. For example, Huang et al. (2012) have identified a total of 449 putative lncRNAs located in 405 intergenic regions using public bovine-specific expressed sequence tags sequences [28]. "
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    ABSTRACT: BACKGROUND: The advent of large-scale gene expression technologies has helped to reveal in eukaryotic cells, the existence of thousands of non-coding transcripts, whose function and significance remain mostly poorly understood. Among these non-coding transcripts, long non-coding RNAs (lncRNAs) are the least well-studied but are emerging as key regulators of diverse cellular processes. In the present study, we performed a survey in bovine Longissimus thoraci of lincRNAs (long intergenic non-coding RNAs not overlapping protein-coding transcripts). To our knowledge, this represents the first such study in bovine muscle. RESULTS: To identify lincRNAs, we used paired-end RNA sequencing (RNA-Seq) to explore the transcriptomes of Longissimus thoraci from nine Limousin bull calves. Approximately 14–45 million paired-end reads were obtained per library. A total of 30,548 different transcripts were identified. Using a computational pipeline, we defined a stringent set of 584 different lincRNAs with 418 lincRNAs found in all nine muscle samples. Bovine lincRNAs share characteristics seen in their mammalian counterparts: relatively short transcript and gene lengths, low exon number and significantly lower expression, compared to protein-encoding genes. As for the first time, our study identified lincRNAs from nine different samples from the same tissue, it is possible to analyse the inter-individual variability of the gene expression level of the identified lincRNAs. Interestingly, there was a significant difference when we compared the expression variation of the 418 lincRNAs with the 10,775 known selected protein-encoding genes found in all muscle samples. In addition, we found 2,083 pairs of lincRNA/proteinencoding genes showing a highly significant correlated expression. Fourteen lincRNAs were selected and 13 were validated by RT-PCR. Some of the lincRNAs expressed in muscle are located within quantitative trait loci for meat quality traits. CONCLUSIONS: Our study provides a glimpse into the lincRNA content of bovine muscle and will facilitate future experimental studies to unravel the function of these molecules. It may prove useful to elucidate their effect on mechanisms underlying the genetic variability of meat quality traits. This catalog will complement the list of lincRNAs already discovered in cattle and therefore will help to better annotate the bovine genome.
    BMC Genomics 06/2014; 15(1):499. DOI:10.1186/1471-2164-15-499 · 3.99 Impact Factor
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