Abstract: ABSTRACT : BACKGROUND : Cancer cachexia is a multi-organ tissue wasting syndrome that contributes to morbidity and mortality in many cancer patients. Skeletal muscle loss represents an established key feature yet there is no molecular understanding of the disease process. In fact, the postulated molecular regulators of cancer cachexia originate largely from pre-clinical models and it is unclear how these translate to the clinical environment. METHODS : Rectus abdominis muscle biopsies were obtained from 65 upper gastrointestinal (UGI) cancer patients during open surgery and RNA profiling was performed on a subset of this cohort (n = 21) using the Affymetrix U133+2 platform. Quantitative analysis revealed a gene signature, which underwent technical validation and independent confirmation in a separate clinical cohort. RESULTS : Quantitative significance analysis of microarrays produced an 83-gene signature that was able to identify patients with greater than 5% weight loss, while this molecular profile was unrelated to markers of systemic inflammation. Selected genes correlating with weight loss were validated using quantitative real-time PCR and independently studied as general cachexia biomarkers in diaphragm and vastus lateralis from a second cohort (n = 13; UGI cancer patients). CaMKIIbeta correlated positively with weight loss in all muscle groups and CaMKII protein levels were elevated in rectus abdominis. TIE1 was also positively associated with weight loss in both rectus abdominis and vastus lateralis muscle groups while other biomarkers demonstrated tissue-specific expression patterns. Candidates selected from the pre-clinical literature, including FOXO protein and ubiquitin E3 ligases, were not related to weight loss in this human clinical study. Furthermore, promoter analysis identified that the 83 weight loss-associated genes had fewer FOXO binding sites than expected by chance. CONCLUSION : We were able to discover and validate new molecular biomarkers of human cancer cachexia. The exercise activated genes CaMKIIbeta and TIE1 related positively to weight-loss across muscle groups, indicating that this cachexia signature is not simply due to patient inactivity. Indeed, excessive CaMKIIbeta activation is a potential mechanism for reduced muscle protein synthesis. Our genomics analysis also supports the view that the available preclinical models do not accurately reflect the molecular characteristics of human muscle from cancer cachexia patients.
Abstract: The incidence of the insulin resistance syndrome has increased at an alarming rate worldwide, creating a serious challenge to public health care in the 21st century. Recently, epidemiological studies have associated the prevalence of type 2 diabetes with elevated body burdens of persistent organic pollutants (POPs). However, experimental evidence demonstrating a causal link between POPs and the development of insulin resistance is lacking.We investigated whether exposure to POPs contributes to insulin resistance and metabolic disorders.Sprague-Dawley rats were exposed for 28 days to lipophilic POPs through the consumption of a high-fat diet containing either refined or crude fish oil obtained from farmed Atlantic salmon. In addition, differentiated adipocytes were exposed to several POP mixtures that mimicked the relative abundance of organic pollutants present in crude salmon oil. We measured body weight, whole-body insulin sensitivity, POP accumulation, lipid and glucose homeostasis, and gene expression and we performed microarray analysis.Adult male rats exposed to crude, but not refined, salmon oil developed insulin resistance, abdominal obesity, and hepatosteatosis. The contribution of POPs to insulin resistance was confirmed in cultured adipocytes where POPs, especially organochlorine pesticides, led to robust inhibition of insulin action. Moreover, POPs induced down-regulation of insulin-induced gene-1 (Insig-1) and Lpin1, two master regulators of lipid homeostasis.Our findings provide evidence that exposure to POPs commonly present in food chains leads to insulin resistance and associated metabolic disorders.
Abstract: We report gene profiling data on genomic processes underlying the progression towards recurrent seizures after injection of kainic acid (KA) into the mouse hippocampus. Focal injection enabled us to separate the effects of proepileptic stimuli initiated by KA injection. Both the injected and contralateral hippocampus participated in the status epilepticus. However, neuronal death induced by KA treatment was restricted to the injected hippocampus, although there was some contralateral axonal degeneration. We profiled gene expression changes in dorsal and ventral regions of both the injected and contralateral hippocampus. Changes were detected in the expression of 1526 transcripts in samples from three time-points: (i) during the KA-induced status epilepticus, (ii) at 2 weeks, before recurrent seizures emerged, and (iii) at 6 months after seizures emerged. Grouping genes with similar spatio-temporal changes revealed an early transcriptional response, strong immune, cell death and growth responses at 2 weeks and an activation of immune and extracellular matrix genes persisting at 6 months. Immunostaining for proteins coded by genes identified from array studies provided evidence for gliogenesis and suggested that the proteoglycan biglycan is synthesized by astrocytes and contributes to a glial scar. Gene changes at 6 months after KA injection were largely restricted to tissue from the injection site. This suggests that either recurrent seizures might depend on maintained processes including immune responses and changes in extracellular matrix proteins near the injection site or alternatively might result from processes, such as growth, distant from the injection site and terminated while seizures are maintained.
Abstract: Chromosomal translocations of transcription factors generating fusion proteins with aberrant transcriptional activity are common in acute leukemia. In acute promyelocytic leukemia (APL), the promyelocytic leukemia-retinoic-acid receptor alpha (PML-RARA) fusion protein, which emerges as a consequence of the t(15;17) translocation, acts as a transcriptional repressor that blocks neutrophil differentiation at the promyelocyte (PM) stage. In this study, we used publicly available microarray data sets and identified signatures of genes dysregulated in APL by comparison of gene expression profiles of APL cells and normal PMs representing the same stage of differentiation. We next subjected our identified APL signatures of dysregulated genes to a series of computational analyses leading to (i) the finding that APL cells show stem cell properties with respect to gene expression and transcriptional regulation, and (ii) the identification of candidate drugs and drug targets for therapeutic interventions. Significantly, our study provides a conceptual framework that can be applied to any subtype of AML and cancer in general to uncover novel information from published microarray data sets at low cost. In a broader perspective, our study provides strong evidence that genomic strategies might be used in a clinical setting to prospectively identify candidate drugs that subsequently are validated in vitro to define the most effective drug combination for individual cancer patients on a rational basis.
Abstract: Finding and characterizing mRNAs, their transcription start sites (TSS), and their associated promoters is a major focus in post-genome biology. Mammalian cells have at least 5-10 magnitudes more TSS than previously believed, and deeper sequencing is necessary to detect all active promoters in a given tissue. Here, we present a new method for high-throughput sequencing of 5’ cDNA tags-DeepCAGE: merging the Cap Analysis of Gene Expression method with ultra-high-throughput sequence technology. We apply DeepCAGE to characterize 1.4 million sequenced TSS from mouse hippocampus and reveal a wealth of novel core promoters that are preferentially used in hippocampus: This is the most comprehensive promoter data set for any tissue to date. Using these data, we present evidence indicating a key role for the Arnt2 transcription factor in hippocampus gene regulation. DeepCAGE can also detect promoters used only in a small subset of cells within the complex tissue.
Abstract: Describing and modeling biological features of eukaryotic promoters remains an important and challenging problem within computational biology. The promoters of higher eukaryotes in particular display a wide variation in regulatory features, which are difficult to model. Often several factors are involved in the regulation of a set of co-regulated genes. If so, promoters can be modeled with connected regulatory features, where the network of connections is characteristic for a particular mode of regulation.With the goal of automatically deciphering such regulatory structures, we present a method that iteratively evolves an ensemble of regulatory grammars using a hidden Markov Model (HMM) architecture composed of interconnected blocks representing transcription factor binding sites (TFBSs) and background regions of promoter sequences. The ensemble approach reduces the risk of overfitting and generally improves performance. We apply this method to identify TFBSs and to classify promoters preferentially expressed in macrophages, where it outperforms other methods due to the increased predictive power given by the grammar.The software and the datasets are available from http://modem.ucsd.edu/won/eHMM.tar.gz
Abstract: In this article, we have applied the ChIP-on-chip approach to pursue a large scale identification of ERalpha- and ERbeta-binding DNA regions in intact chromatin. We show that there is a high degree of overlap between the regions identified as bound by ERalpha and ERbeta, respectively, but there are also regions that are bound by ERalpha only in the presence of ERbeta, as well as regions that are selectively bound by either receptor. Analysis of bound regions shows that regions bound by ERalpha have distinct properties in terms of genome landscape, sequence features, and conservation compared with regions that are bound by ERbeta. ERbeta-bound regions are, as a group, located more closely to transcription start sites. ERalpha- and ERbeta-bound regions differ in sequence properties, with ERalpha-bound regions having an overrepresentation of TA-rich motifs including forkhead binding sites and ERbeta-bound regions having a predominance of classical estrogen response elements (EREs) and GC-rich motifs. Differences in the properties of ER bound regions might explain some of the differences in gene expression programs and physiological effects shown by the respective estrogen receptors.
Abstract: In studies of gene regulation the efficient computational detection of over-represented transcription factor binding sites is an increasingly important aspect. Several published methods can be used for testing whether a set of hypothesised co-regulated genes share a common regulatory regime based on the occurrence of the modelled transcription factor binding sites. However there is little or no information available for guiding the end users choice of method. Furthermore it would be necessary to obtain several different software programs from various sources to make a well-founded choice.We introduce a software package, Asap, for fast searching with position weight matrices that include several standard methods for assessing over-representation. We have compared the ability of these methods to detect over-represented transcription factor binding sites in artificial promoter sequences. Controlling all aspects of our input data we are able to identify the optimal statistics across multiple threshold values and for sequence sets containing different distributions of transcription factor binding sites.We show that our implementation is significantly faster than more naïve scanning algorithms when searching with many weight matrices in large sequence sets. When comparing the various statistics, we show that those based on binomial over-representation and Fisher’s exact test performs almost equally good and better than the others. An online server is available at http://servers.binf.ku.dk/asap/.
Abstract: Cholecystokinin (CCK) is abundantly expressed in the CNS, in which it regulates feeding behavior and long-term memory. Moreover, CCK has been implicated in mental disorders, such as anxiety and schizophrenia. Despite its manifest physiological and pathophysiological role, the molecular targets of neuronal CCK are incompletely understood. To identify genes regulated by neuronal CCK, we generated neuronal PC12 cells stably expressing the CCK-2 receptor (CCK-2R) and treated the cells with sulphated CCK-8 for 2-16 h, before the global expression profile was examined. The changes in gene expression peaked after 2 h, with 67 differentially expressed transcripts identified. A pathway analysis indicated that CCK was implicated in the regulation of the circadian clock system, the plasminogen system and cholesterol metabolism. But transcripts encoding proteins involved in dopamine signaling, ornithine decarboxylase (ODC) regulation, memory and epidermal growth factor receptor (EGFR) signaling were also found. Several target genes contained cAMP response elements (CREs), serum response elements (SREs), activator protein 1 (AP1) elements and GC-rich regions, but otherwise no common regulatory promoter element could be identified. Comparison with forskolin- and nerve growth factor (NGF)-treated PC12 cells showed that CCK induced a separate set of target genes. Taken together, we propose that neuronal CCK may have a role in the regulation of the circadian rhythm, the metabolism of cerebral cholesterol and in the regulation of the plasminogen system.
Abstract: JASPAR is a popular open-access database for matrix models describing DNA-binding preferences for transcription factors and other DNA patterns. With its third major release, JASPAR has been expanded and equipped with additional functions aimed at both casual and power users. The heart of the JASPAR database-the JASPAR CORE sub-database-has increased by 12% in size, and three new specialized sub-databases have been added. New functions include clustering of matrix models by similarity, generation of random matrices by sampling from selected sets of existing models and a language-independent Web Service applications programming interface for matrix retrieval. JASPAR is available at http://jaspar.genereg.net.
