Microarray gene expression profiling of osteoarthritic bone suggests altered bone remodelling, WNT and transforming growth factor-β/bone morphogenic protein signalling

Osteoarthritis (OA) is characterized by alterations to subchondral bone as well as articular cartilage. Changes to bone in OA have also been identified at sites distal to the affected joint, which include increased bone volume fraction and reduced bone mineralization. Altered bone remodelling has been proposed to underlie these bone changes in OA. To investigate the molecular basis for these changes, we performed microarray gene expression profiling of bone obtained at autopsy from individuals with no evidence of joint disease (control) and from individuals undergoing joint replacement surgery for either degenerative hip OA, or fractured neck of femur (osteoporosis [OP]). The OP sample set was included because an inverse association, with respect to bone density, has been observed between OA and the low bone density disease OP. Compugen human 19K-oligo microarray slides were used to compare the gene expression profiles of OA, control and OP bone samples. Four sets of samples were analyzed, comprising 10 OA-control female, 10 OA-control male, 10 OA-OP female and 9 OP-control female sample pairs. Print tip Lowess normalization and Bayesian statistical analyses were carried out using linear models for microarray analysis, which identified 150 differentially expressed genes in OA bone with t scores above 4. Twenty-five of these genes were then confirmed to be differentially expressed (P < 0.01) by real-time PCR analysis. A substantial number of the top-ranking differentially expressed genes identified in OA bone are known to play roles in osteoblasts, osteocytes and osteoclasts. Many of these genes are targets of either the WNT (wingless MMTV integration) signalling pathway (TWIST1, IBSP, S100A4, MMP25, RUNX2 and CD14) or the transforming growth factor (TGF)-β/bone morphogenic protein (BMP) signalling pathway (ADAMTS4, ADM, MEPE, GADD45B, COL4A1 and FST). Other differentially expressed genes included WNT (WNT5B, NHERF1, CTNNB1 and PTEN) and TGF-β/BMP (TGFB1, SMAD3, BMP5 and INHBA) signalling pathway component or modulating genes. In addition a subset of genes involved in osteoclast function (GSN, PTK9, VCAM1, ITGB2, ANXA2, GRN, PDE4A and FOXP1) was identified as being differentially expressed in OA bone between females and males. Altered expression of these sets of genes suggests altered bone remodelling and may in part explain the sex disparity observed in OA

Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy

© 2009 Liu et al; licensee BioMed Central Ltd.Background: microRNAs (miRNAs) regulate target gene expression by controlling their mRNAs post-transcriptionally. Increasing evidence demonstrates that miRNAs play important roles in various biological processes. However, the functions and precise regulatory mechanisms of most miRNAs remain elusive. Current research suggests that miRNA regulatory modules are complicated, including up-, down-, and mix-regulation for different physiological conditions. Previous computational approaches for discovering miRNA-mRNA interactions focus only on down-regulatory modules. In this work, we present a method to capture complex miRNA-mRNA interactions including all regulatory types between miRNAs and mRNAs. Results: We present a method to capture complex miRNA-mRNA interactions using Bayesian network structure learning with splitting-averaging strategy. It is designed to explore all possible miRNA-mRNA interactions by integrating miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. We also present an analysis of data sets for epithelial and mesenchymal transition (EMT). Our results show that the proposed method identified all possible types of miRNA-mRNA interactions from the data. Many interactions are of tremendous biological significance. Some discoveries have been validated by previous research, for example, the miR-200 family negatively regulates ZEB1 and ZEB2 for EMT. Some are consistent with the literature, such as LOX has wide interactions with the miR-200 family members for EMT. Furthermore, many novel interactions are statistically significant and worthy of validation in the near future. Conclusions: This paper presents a new method to explore the complex miRNA-mRNA interactions for different physiological conditions using Bayesian network structure learning with splitting-averaging strategy. The method makes use of heterogeneous data including miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. Results on EMT data sets show that the proposed method uncovers many known miRNA targets as well as new potentially promising miRNA-mRNA interactions. These interactions could not be achieved by the normal Bayesian network structure learning.Bing Liu, Jiuyong Li, Anna Tsykin, Lin Liu, Arti B. Gaur and Gregory J. Goodal

Evidence that Meningeal Mast Cells Can Worsen Stroke Pathology in Mice

Stroke is the leading cause of adult disability and the fourth most common cause of death in the United States. Inflammation is thought to play an important role in stroke pathology, but the factors that promote inflammation in this setting remain to be fully defined. An understudied but important factor is the role of meningeal-located immune cells in modulating brain pathology. Although different immune cells traffic through meningeal vessels en route to the brain, mature mast cells do not circulate but are resident in the meninges. With the use of genetic and cell transfer approaches in mice, we identified evidence that meningeal mast cells can importantly contribute to the key features of stroke pathology, including infiltration of granulocytes and activated macrophages, brain swelling, and infarct size. We also obtained evidence that two mast cell-derived products, interleukin-6 and, to a lesser extent, chemokine (C-C motif) ligand 7, can contribute to stroke pathology. These findings indicate a novel role for mast cells in the meninges, the membranes that envelop the brain, as potential gatekeepers for modulating brain inflammation and pathology after stroke

Pre-therapy mRNA expression of TNF is associated with regimen-related gastrointestinal toxicity in patients with esophageal cancer: a pilot study

Author version made available following 12 month embargo from date of publication (27 March 2015) in accordance with publisher copyright policy.Purpose Esophageal cancer has a high mortality rate, and its multimodality treatment is often associated with significant rates of severe toxicity. Effort is needed to uncover ways to maximize effectiveness of therapy through identification of predictive markers of response and toxicity. As such, the aim of this study was to identify genes predictive of chemoradiotherapy-induced gastrointestinal toxicity using an immune pathway-targeted approach. Methods Adults with esophageal cancer treated with chemotherapy consisting of 5-fluorouracil and cisplatin and 45–50 Gy radiation were recruited to the study. Pre-therapy-collected whole blood was analyzed for relative expression of immune genes using real-time polymerase chain reaction (RT-PCR). Gene expression was compared between patients who experienced severe regimen-related gastrointestinal toxicity vs. those experiencing mild to moderate toxicity. Results Blood from 31 patients were analyzed by RT-PCR. Out of 84 immune genes investigated, TNF was significantly elevated (2.05-fold, p = 0.025) in the toxic group (n = 12) compared to the non-toxic group (n = 19). Nausea and vomiting was the most commonly documented severe toxicity. No associations between toxicity and response, age, sex, histology, or treatment were evident. Conclusions This study supports evidence of TNF as a predictive biomarker in regimen-related gastrointestinal toxicity. Confirming these findings in a larger cohort is warranted

Gene expression microarray analysis of early oxygen-induced retinopathy in the rat

Different inbred strains of rat differ in their susceptibility to oxygen-induced retinopathy (OIR), an animal model of human retinopathy of prematurity. We examined gene expression in Sprague–Dawley (susceptible) and Fischer 344 (resistant) neonatal rats after 3 days exposure to cyclic hyperoxia or room air, using Affymetrix rat Genearrays. False discovery rate analysis was used to identify differentially regulated genes. Such genes were then ranked by fold change and submitted to the online database, DAVID. The Sprague–Dawley list returned the term “response to hypoxia,” absent from the Fischer 344 output. Manual analysis indicated that many genes known to be upregulated by hypoxia-inducible factor-1α were downregulated by cyclic hyperoxia. Quantitative real-time RT-PCR analysis of Egln3, Bnip3, Slc16a3, and Hk2 confirmed the microarray results. We conclude that combined methodologies are required for adequate dissection of the pathophysiology of strain susceptibility to OIR in the rat

Inferring microRNA and transcription factor regulatory networks in heterogeneous data

Background: Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA. Results: We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then, in order to produce more meaningful results for further biological experimentation and research, the method searches the learnt network to identify the interplay between miRNAs and TFs and applies a network motif finding algorithm to further infer the network. We apply the proposed framework to the data sets of epithelial-to-mesenchymal transition (EMT). The results elucidate the complex gene regulatory mechanism for EMT which involves both TFs and miRNAs. Several discovered interactions and molecular functions have been confirmed by literature. In addition, many other discovered interactions and bio-markers are of high statistical significance and thus can be good candidates for validation by experiments. Moreover, the results generated by our method are compact, involving a small number of interactions which have been proved highly relevant to EMT. Conclusions: We have designed a framework to infer gene regulatory networks involving both TFs and miRNAs from multiple sources of data, including gene expression data, target information, and sample categories. Results on the EMT data sets have shown that the proposed approach is able to produce compact and meaningful gene regulatory networks that are highly relevant to the biological conditions of the data sets. This framework has the potential for application to other heterogeneous datasets to reveal the complex gene regulatory relationships.Thuc D Le, Lin Liu, Bing Liu, Anna Tsykin, Gregory J Goodall, Kenji Satou and Jiuyong L

Discovery of functional miRNA-mRNA regulatory modules with computational methods

The identification of miRNAs and their target mRNAs and the construction of their regulatory networks may give new insights into biological procedures. This study proposes a computational method to discover the functional miRNA–mRNA regulatory modules (FMRMs), that is, groups of miRNAs and their target mRNAs that are believed to participate cooperatively in post-transcriptional gene regulation under specific conditions. The proposed method identifies negatively regulated patterns of miRNAs and mRNAs which associate with cancer and normal conditions, respectively, in a prostate cancer data set. GO and the literature also suggest that they may relate with prostate cancer. It can potentially identify the biologically relevant chains of ‘miRNA → target gene → condition’.

PCR analysis of , , and expression between females and males in OA bone

<p><b>Copyright information:</b></p><p>Taken from "Microarray gene expression profiling of osteoarthritic bone suggests altered bone remodelling, WNT and transforming growth factor-β/bone morphogenic protein signalling"</p><p>http://arthritis-research.com/content/9/5/R100</p><p>Arthritis Research & Therapy 2007;9(5):R100-R100.</p><p>Published online 27 Sep 2007</p><p>PMCID:PMC2212557.</p><p></p> For each gene, a graph depicts relative real-time PCR product/cycle threshold (C) ratios generated from osteoarthritis (OA) and control (CTL) female and male intertrochanteric bone samples analyzed. The mean of eight samples for each sample group analyzed is represented by black diamonds (mean values given alongside). Asterisks signify statistical significance (< 0.01) for differential gene expression between OA and CTL bone. F, female; M, male