Kinetics of gene expression and bone remodelling in the clinical phase of collagen-induced arthritis

INTRODUCTION: Pathological bone changes differ considerably between inflammatory arthritic diseases and most studies have focused on bone erosion. Collagen-induced arthritis (CIA) is a model for rheumatoid arthritis, which, in addition to bone erosion, demonstrates bone formation at the time of clinical manifestations. The objective of this study was to use this model to characterise the histological and molecular changes in bone remodelling, and relate these to the clinical disease development. METHODS: A histological and gene expression profiling time-course study on bone remodelling in CIA was linked to onset of clinical symptoms. Global gene expression was studied with a gene chip array system. RESULTS: The main histopathological changes in bone structure and inflammation occurred during the first two weeks following the onset of clinical symptoms in the joint. Hereafter, the inflammation declined and remodelling of formed bone dominated. Global gene expression profiling showed simultaneous upregulation of genes related to bone changes and inflammation in week 0 to 2 after onset of clinical disease. Furthermore, we observed time-dependent expression of genes involved in early and late osteoblast differentiation and function, which mirrored the histopathological bone changes. The differentially expressed genes belong to the bone morphogenetic pathway (BMP) and, in addition, include the osteoblast markers integrin-binding sialoprotein (Ibsp), bone gamma-carboxyglutamate protein (Bglap1), and secreted phosphoprotein 1 (Spp1). Pregnancy-associated protein A (Pappa) and periostin (Postn), differentially expressed in the early disease phase, are proposed to participate in bone formation, and we suggest that they play a role in early bone formation in the CIA model. Comparison to human genome-wide association studies (GWAS) revealed differential expression of several genes associated with human arthritis. CONCLUSIONS: In the CIA model, bone formation in the joint starts shortly after onset of clinical symptoms, which results in bony fusion within one to two weeks. This makes it a candidate model for investigating the relationship between inflammation and bone formation in inflammatory arthritis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13075-015-0531-7) contains supplementary material, which is available to authorized users

JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update

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

Persistent Organic Pollutant Exposure Leads to Insulin Resistance Syndrome

International audienceBackground: 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, epide-miological 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. Objective: We investigated whether exposure to POPs contributes to insulin resistance and meta-bolic disorders. Methods: Sprague-Dawley rats were exposed for 28 days to lipophilic POPs through the con-sumption 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 expres-sion and we performed micro array analysis. Results: Adult male rats exposed to crude, but not refined, salmon oil developed insulin resis-tance, 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. Conclusion: Our findings provide evidence that exposure to POPs commonly present in food chains leads to insulin resistance and associated metabolic disorder

Identifying and mapping cell-type-specific chromatin programming of gene expression

A problem of substantial interest is to systematically map variation in chromatin structure to gene-expression regulation across conditions, environments, or differentiated cell types. We developed and applied a quantitative framework for determining the existence, strength, and type of relationship between high-resolution chromatin structure in terms of DNaseI hypersensitivity and genome-wide gene-expression levels in 20 diverse human cell types. We show that ∼25% of genes show cell-type-specific expression explained by alterations in chromatin structure. We find that distal regions of chromatin structure (e.g., ±200 kb) capture more genes with this relationship than local regions (e.g., ±2.5 kb), yet the local regions show a more pronounced effect. By exploiting variation across cell types, we were capable of pinpointing the most likely hypersensitive sites related to cell-type-specific expression, which we show have a range of contextual uses. This quantitative framework is likely applicable to other settings aimed at relating continuous genomic measurements to gene-expression variation

Identifying and mapping cell-type-specific chromatin programming of gene expression

A problem of substantial interest is to systematically map variation in chromatin structure to gene expression regulation across conditions, environments, or differentiated cell types. We developed and applied a quantitative framework for determining the existence, strength, and type of relationship between high-resolution chromatin structure in terms of DNaseI hypersensitivity (DHS) and genome-wide gene expression levels in 20 diverse human cell lines. We show that ∼25% of genes show cell-type specific expression explained by alterations in chromatin structure. We find that distal regions of chromatin structure (e.g., ±200kb) capture more genes with this relationship than local regions (e.g., ±2.5kb), yet the local regions show a more pronounced effect. By exploiting variation across cell-types, we were capable of pinpointing the most likely hypersensitive sites related to cell-type specific expression, which we show have a range of contextual usages. This quantitative framework is likely applicable to other settings aimed at relating continuous genomic measurements to gene expression variation

Asap: A Framework for Over-Representation Statistics for Transcription Factor Binding Sites

Background: 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. Methodology: 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 overrepresented 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. Conclusions: 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 onlin

The genome landscape of ER{alpha}- and ER{beta}-binding DNA regions.

In this article, we have applied the ChIP-on-chip approach to pursue a large scale identification of ERα- and ERβ-binding DNA regions in intact chromatin. We show that there is a high degree of overlap between the regions identified as bound by ERα and ERβ, respectively, but there are also regions that are bound by ERα only in the presence of ERβ, as well as regions that are selectively bound by either receptor. Analysis of bound regions shows that regions bound by ERα have distinct properties in terms of genome landscape, sequence features, and conservation compared with regions that are bound by ERβ. ERβ-bound regions are, as a group, located more closely to transcription start sites. ERα- and ERβ-bound regions differ in sequence properties, with ERα-bound regions having an overrepresentation of TA-rich motifs including forkhead binding sites and ERβ-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