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      Systems Analysis of the Liver Transcriptome in Adult Male Zebrafish Exposed to the Plasticizer (2-Ethylhexyl) Phthalate (DEHP)

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          Abstract

          The organic compound diethylhexyl phthalate (DEHP) represents a high production volume chemical found in cosmetics, personal care products, laundry detergents, and household items. DEHP, along with other phthalates causes endocrine disruption in males. Exposure to endocrine disrupting chemicals has been linked to the development of several adverse health outcomes with apical end points including Non-Alcoholic Fatty Liver Disease (NAFLD). This study examined the adult male zebrafish ( Danio rerio) transcriptome after exposure to environmental levels of DEHP and 17α-ethinylestradiol (EE2) using both DNA microarray and RNA-sequencing technologies. Our results show that exposure to DEHP is associated with differentially expressed (DE) transcripts associated with the disruption of metabolic processes in the liver, including perturbation of five biological pathways: ‘FOXA2 and FOXA3 transcription factor networks’, ‘Metabolic pathways’, ‘metabolism of amino acids and derivatives’, ‘metabolism of lipids and lipoproteins’, and ‘fatty acid, triacylglycerol, and ketone body metabolism’. DE transcripts unique to DEHP exposure, not observed with EE2 (i.e. non-estrogenic effects) exhibited a signature related to the regulation of transcription and translation, and ruffle assembly and organization. Collectively our results indicate that exposure to low DEHP levels modulates the expression of liver genes related to fatty acid metabolism and the development of NAFLD.

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          The zebrafish reference genome sequence and its relationship to the human genome.

          Zebrafish have become a popular organism for the study of vertebrate gene function. The virtually transparent embryos of this species, and the ability to accelerate genetic studies by gene knockdown or overexpression, have led to the widespread use of zebrafish in the detailed investigation of vertebrate gene function and increasingly, the study of human genetic disease. However, for effective modelling of human genetic disease it is important to understand the extent to which zebrafish genes and gene structures are related to orthologous human genes. To examine this, we generated a high-quality sequence assembly of the zebrafish genome, made up of an overlapping set of completely sequenced large-insert clones that were ordered and oriented using a high-resolution high-density meiotic map. Detailed automatic and manual annotation provides evidence of more than 26,000 protein-coding genes, the largest gene set of any vertebrate so far sequenced. Comparison to the human reference genome shows that approximately 70% of human genes have at least one obvious zebrafish orthologue. In addition, the high quality of this genome assembly provides a clearer understanding of key genomic features such as a unique repeat content, a scarcity of pseudogenes, an enrichment of zebrafish-specific genes on chromosome 4 and chromosomal regions that influence sex determination.
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            Combined analysis of oligonucleotide microarray data from transgenic and knockout mice identifies direct SREBP target genes.

            The synthesis of fatty acids and cholesterol, the building blocks of membranes, is regulated by three membrane-bound transcription factors: sterol regulatory element-binding proteins (SREBP)-1a, -1c, and -2. Their function in liver has been characterized in transgenic mice that overexpress each SREBP isoform and in mice that lack all three nuclear SREBPs as a result of gene knockout of SREBP cleavage-activating protein (SCAP), a protein required for nuclear localization of SREBPs. Here, we use oligonucleotide arrays hybridized with RNA from livers of three lines of mice (transgenic for SREBP-1a, transgenic for SREBP-2, and knockout for SCAP) to identify genes that are likely to be direct targets of SREBPs in liver. A total of 1,003 genes showed statistically significant increased expression in livers of transgenic SREBP-1a mice, 505 increased in livers of transgenic SREBP-2 mice, and 343 showed decreased expression in Scap-/- livers. A subset of 33 genes met the stringent combinatorial criteria of induction in both SREBP transgenics and decreased expression in SCAP-deficient mice. Of these 33 genes, 13 were previously identified as direct targets of SREBP action. Of the remaining 20 genes, 13 encode enzymes or carrier proteins involved in cholesterol metabolism, 3 participate in fatty acid metabolism, and 4 have no known connection to lipid metabolism. Through application of stringent combinatorial criteria, the transgenic/knockout approach allows identification of genes whose activities are likely to be controlled directly by one family of transcription factors, in this case the SREBPs.
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              Discovering Motifs in Ranked Lists of DNA Sequences

              Introduction Background This paper examines the problem of discovering “interesting” sequence motifs in biological sequence data. A widely accepted and more formal definition of this task is: given a target set and a background set of sequences (or a background model), identify sequence motifs that are enriched in the target set compared with the background set. The purpose of this paper is to extend this formulation and to make it more flexible so as to enable the determination of the target and background set in a data driven manner. Discovery of sequences or attributes that are enriched in a target set compared with a background set (or model) has become increasingly useful in a wide range of applications in molecular biology research. For example, discovery of DNA sequence motifs that are overabundant in a set of promoter regions of co-expressed genes (determined by clustering of expression data) can suggest an explanation for this co-expression. Another example is the discovery of DNA sequences that are enriched in a set of promoter regions to which a certain transcription factor (TF) binds strongly, inferred from chromatin immuno-precipitation on a microarray (ChIP–chip) [1] measurements. The same principle may be extended to many other applications such as discovery of genomic elements enriched in a set of highly methylated CpG island sequences [2]. Due to its importance, this task of discovering enriched DNA subsequences and capturing their corresponding motif profile has gained much attention in the literature. Any approach to motif discovery must address several fundamental issues. The first issue is the way by which motifs are represented. There are several strategies for motif representation: using a k-mer of IUPAC symbols where each symbol represents a fixed set of possible nucleotides at a single position (examples of methods that use this representation include REDUCE [3], YMF [4,5], ANN-SPEC [6], and a hypergeometric-based method [7]) or using a position weight matrix (PWM), which specifies the probability of observing each nucleotide at each motif position (for example MEME [8], BioProspector [9], MotifBooster [10], DME-X [11], and AlignACE [12]). Both representations assume base position independence. Alternatively, higher order representations that capture positional dependencies have been proposed (e.g., HMM and Bayesian networks motif representations [13]). While these representations circumvent the position independence assumption, they are more vulnerable to overfitting and lack of data for determining model parameters. The method described in this paper uses the k-mer model with symbols above IUPAC. The second issue is devising a motif scoring scheme. Many strategies for scoring motifs have been suggested in the literature. One simple yet powerful approach uses the hypergeometric distribution for identifying enriched motif kernels in a set of sequences and then expanding these motifs using an EM algorithm [7]. The framework described in this paper is a natural extension of the approach of [7]. YMF [4,5] is an exhaustive search algorithm which associates each motif with a z-score. AlignACE [12] uses a Gibbs sampling algorithm for finding global sequence alignments and produces a MAP score. This score is an internal metric used to determine the significance of an alignment. MEME [8] uses an expectation maximization strategy and outputs the log-likelihood and relative entropy associated with each motif. Once a scoring scheme is devised, a defined motif search space is scanned (either heuristically or exhaustively) and motifs with significantly high scores are identified. To determine the statistical significance of the obtained scores, many methods resort to simulations or ad hoc thresholds. Several excellent reviews narrate the different strategies for motif detection and use quantitative benchmarking to compare their performance [14–18]. A related aspect of motif discovery, which is outside the scope of this paper, focuses on properties of clusters and modules of TF binding sites (TFBS). Examples of approaches that search for combinatorial patterns and modules underlying TF binding and gene expression include [19–23]. Open Challenges in Motif Discovery One issue of motif discovery that is often overlooked concerns the partition of the input set of sequences into target and background sets. Many methods rely on the user to provide these two sets and search for motifs that are overabundant in the former set compared with the latter. The question of how to partition the data into target and background sets is left to the user. However, the boundary between the sets is often unclear and the exact choice of sequences in each set arbitrary. For example, suppose that one wishes to identify motifs within promoter sequences that constitute putative TFBS. An obvious strategy would be to partition the set of promoter sequences into target and background sets according to the TF binding signal (as measured by ChIP–chip experiments). The two sets would contain the sequences to which the TF binds “strongly” and “weakly,” respectively. A motif detection algorithm could then be applied to find motifs that are overabundant in the target set compared with the background set. In this scenario, the positioning of the cutoff between the strong and weak binding signal is somewhat arbitrary. Obviously, the final outcome of the motif identification process can be highly dependent on this choice of cutoff. A stringent cutoff will result in the exclusion of informative sequences from the target set while a promiscuous cutoff will cause inclusion of nonrelevant sequences—both extremes hinder the accuracy of motif prediction. This example demonstrates a fundamental difficulty in partitioning most types of data. Several methods attempt to circumvent this hurdle. For example, REDUCE [3] uses a regression model on the entire set of sequences. However, it is difficult to justify this model in the context of multiple motif occurrence (as explained below). In other work, a variant of the Kolmogorov-Smirnov test was used for motif discovery [24]. This approach successfully circumvents arbitrary data partition. However, it has other limitations such as the failure to address multiple motif occurrences in a single promoter, and the lack of an exact characterization of the null distribution. Overall, the following four major challenges in motif discovery still require consideration: (c1) the cutoff used to partition data into a target set and background set of sequences is often chosen arbitrarily; (c2) lack of an exact statistical score and p-value for motif enrichment. Current methods typically use arbitrarily set thresholds or simulations, which are inherently limited in precision and costly in terms of running time; (c3) a need for an appropriate framework that accounts for multiple motif occurrences in a single promoter. For example, how should one quantify the significance of a single motif occurrence in a promoter against two motif occurrences in a promoter? Linear models [3] assume that the weight of the latter is double that of the former. However, it is difficult to justify this approach since biological systems do not necessarily operate in such a linear fashion. Another issue related to motif multiplicity is low complexity or repetitive regions. These regions often contain multiple copies of degenerate motifs (e.g., CA repeats). Since the nucleotide frequency underlying these regions substantially deviates from the standard background frequency, they often cause false-motif discoveries. Consequently, most methods mask these regions in the preprocessing stage and thereby lose vital information that might reside therein; (c4) criticism has been made over the fact that motif discovery methods tend to report presumably significant motifs even when applied on randomly generated data [25]. These motifs are clear cases of false positives and should be avoided. Data Lends Itself to Ranking in a Natural Manner In this paper we describe a novel method that attempts to solve the above-mentioned four challenges in a principled manner. It exploits the following observation: data often lends itself to ranking in a natural manner, e.g., ranking sequences according to TF binding signal: ranking according to CpG methylation signal, ranking according to distance in expression space from a set of co-expressed genes, ranking according to differential expression, etc. We exploit this inherent ranking property of biological data in order to circumvent the need for an arbitrary and difficult-to-justify data partition. Consequently, we propose the following formulation of the motif finding task: given a list of ranked sequences, identify motifs that are overabundant at either end of the list. Our solution employs a statistical score termed mHG (minimal hypergeometric) [26]. It is related to the concept of rank-imbalanced motifs, which are sequence motifs that tend to appear at either end of a ranked sequence list. In previous work [26], the authors used mHG to identify sequence motifs in expression data. We use this simple yet powerful approach as the starting point for our study. Overview The rest of this paper is divided into two main parts, each of which is self-contained: in the Results we briefly outline our method and describe new biological findings that were obtained by applying this method to biological data. We address challenge (c4) by testing the algorithm on randomly ranked real genomic sequences. In the Methods, we describe the mHG probabilistic and algorithmic framework and explain how we deal with challenges (c1)–(c3). Results Statistics and Algorithms in a Nutshell Based on the mHG framework, we developed a software tool termed DRIM (discovery of rank imbalanced motifs) for motif identification in DNA sequences. A flow chart of DRIM is provided in Figure 1. The formal introduction and details of the mHG statistics are given in Methods. However, to facilitate the explanation and interpretation of our biological results, we begin with a brief description of the method. Figure 1 DRIM Flow Chart DRIM receives a list of DNA sequences as input and a criterion by which the sequences should be ranked, for example, TF binding signals as measured by ChIP ChIP–chip: (i) The sequences are ranked according to the criterion. (ii) A “blind search” is performed over all the motifs that reside in the restricted motif space (in this study the restricted motif space contains ∼100,000 motifs, see Methods, The DRIM software). For each motif an occurrence vector is generated. Each position in the vector is the number of motif occurrences in the corresponding sequence, (the figure shows the vector for the motif CACGTGW). (iii) The motif significance is computed using the mHG scheme, and the optimal partition into target and background sets in terms of motif enrichment is identified. The promising motif seeds are passed as input to the heuristic motif search model and the rest are filtered out. (iv,v) The motif seeds are expanded in an iterative manner (the mHG is computed in each lap), until a local optimum motif is found. (vi) The exact mHG p-value of the motif is computed. If it has a p-value p. The (0,0) → (N,B) path representing λ visits N distinct grid points (excluding the point (0,0)), representing the N different HGT scores that are considered when calculating its mHG score: mHG(λ) = min1≤n
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                Author and article information

                Contributors
                hardiman@musc.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                1 February 2018
                1 February 2018
                2018
                : 8
                : 2118
                Affiliations
                [1 ]ISNI 0000 0001 2189 3475, GRID grid.259828.c, MUSC Bioinformatics, , Center for Genomics Medicine, Medical University of South Carolina, ; Charleston, SC USA
                [2 ]ISNI 0000 0001 2189 3475, GRID grid.259828.c, MS in Biomedical Sciences Program, , Medical University of South Carolina, ; Charleston, SC USA
                [3 ]ISNI 0000 0001 2189 3475, GRID grid.259828.c, Department of Pathology and Laboratory Medicine, , Medical University of South Carolina, ; Charleston, SC USA
                [4 ]ISNI 0000 0001 1017 3210, GRID grid.7010.6, Dipartimento di Scienze della Vita e dell’Ambiente, , Università Politecnica delle Marche, ; 60131 Ancona, Italy
                [5 ]ISNI 0000 0001 2189 3475, GRID grid.259828.c, Department of Medicine, , Nephrology, Medical University of South Carolina, ; Charleston, SC USA
                [6 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, Department of Medicine, , University of California San Diego, ; La Jolla, CA USA
                [7 ]ISNI 0000 0001 2189 3475, GRID grid.259828.c, Department of Public Health Sciences, , Medical University of South Carolina, ; Charleston, SC USA
                [8 ]ISNI 0000 0000 9840 6850, GRID grid.417757.7, Laboratory for Marine Systems Biology, , Hollings Marine Laboratory, ; Charleston, SC USA
                Article
                20266
                10.1038/s41598-018-20266-8
                5794889
                29391432
                b1e78faf-6872-49bc-92d8-d667a5ac5220
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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                : 8 August 2017
                : 15 January 2018
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