Text-based over-representation analysis of microarray gene lists with annotation bias

A major challenge in microarray data analysis is the functional interpretation of gene lists. A common approach to address this is over-representation analysis (ORA), which uses the hypergeometric test (or its variants) to evaluate whether a particular functionally defined group of genes is represented more than expected by chance within a gene list. Existing applications of ORA have been largely limited to pre-defined terminologies such as GO and KEGG. We report our explorations of whether ORA can be applied to a wider mining of free-text. We found that a hitherto underappreciated feature of experimentally derived gene lists is that the constituents have substantially more annotation associated with them, as they have been researched upon for a longer period of time. This bias, a result of patterns of research activity within the biomedical community, is a major problem for classical hypergeometric test-based ORA approaches, which cannot account for such bias. We have therefore developed three approaches to overcome this bias, and demonstrate their usability in a wide range of published datasets covering different species. A comparison with existing tools that use GO terms suggests that mining PubMed abstracts can reveal additional biological insight that may not be possible by mining pre-defined ontologies alone

Application of improved automated text mining to transcriptome datasets

A major challenge in microarray data analysis is the functional interpretation of gene lists. A common approach to address this is over-representation analysis (ORA), which uses the hypergeometric test (or its variants) to evaluate whether a particular functionally-defined group of genes is represented more than expected by chance within a gene list. Existing applications of ORA have been largely limited to controlled vocabularies such as Gene Ontology (GO) terms and KEGG pathways. Therefore, this work aims at determining whether ORA can be applied to a wider mining of free-text. Initial explorations using the classical hypergeometric distribution to analyse tokens from PubMed abstracts revealed a hitherto unexpected feature: gene lists derived from typical microarray experiment tend to have more annotation (PubMed abstracts) associated with them than would be expected by chance. This bias, a result of patterns of research activity within the biomedical community, is a major problem for the classical hypergeometric test-based ORA approach, as it cannot account for such bias. The negative effect of annotation bias is a marked over-representation of many common (and likely uninformative) terms, interspersed with terms that appear to convey real biological insight. Several solutions have been developed to address this issue. The first is based on the use of a permutation test, but this nonparametric approach is hampered by being computationally intensive. Two computationally tractable approaches were subsequently developed, which are based on the detection of outliers and the extended hypergeometric distribution. The performances of the proposed text-based ORA approaches were demonstrated on a wide range of published datasets covering different species. A comparison with existing tools that use GO terms suggests that mining PubMed abstracts can reveal additional biological insight that may not be possible by mining pre-defined ontologies alone.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Application of improved automated text mining to transcriptome datasets

A major challenge in microarray data analysis is the functional interpretation of gene lists. A common approach to address this is over-representation analysis (ORA), which uses the hypergeometric test (or its variants) to evaluate whether a particular functionally-defined group of genes is represented more than expected by chance within a gene list. Existing applications of ORA have been largely limited to controlled vocabularies such as Gene Ontology (GO) terms and KEGG pathways. Therefore, this work aims at determining whether ORA can be applied to a wider mining of free-text. Initial explorations using the classical hypergeometric distribution to analyse tokens from PubMed abstracts revealed a hitherto unexpected feature: gene lists derived from typical microarray experiment tend to have more annotation (PubMed abstracts) associated with them than would be expected by chance. This bias, a result of patterns of research activity within the biomedical community, is a major problem for the classical hypergeometric test-based ORA approach, as it cannot account for such bias. The negative effect of annotation bias is a marked over-representation of many common (and likely uninformative) terms, interspersed with terms that appear to convey real biological insight. Several solutions have been developed to address this issue. The first is based on the use of a permutation test, but this nonparametric approach is hampered by being computationally intensive. Two computationally tractable approaches were subsequently developed, which are based on the detection of outliers and the extended hypergeometric distribution. The performances of the proposed text-based ORA approaches were demonstrated on a wide range of published datasets covering different species. A comparison with existing tools that use GO terms suggests that mining PubMed abstracts can reveal additional biological insight that may not be possible by mining pre-defined ontologies alone

Probing resistive switching in HfO2/Al2O3 bilayer oxides using in-situ transmission electron microscopy

In this work, we investigate the resistive switching in hafnium dioxide (HfO2) and aluminum oxide (Al2O3) bilayered stacks using in-situ transmission electron microscopy and X-ray energy dispersive spectroscopy. Conductance of the HfO2/Al2O3 stack changes gradually upon electrical stressing which is related to the formation of extended nanoscale physical defects at the HfO2/Al2O3 interface and the migration and re-crystallization of Al into the oxide bulk. The results suggest two competing physical mechanisms including the redistribution of oxygen ions and the migration of Al species from the Al electrode during the switching process. While the HfO2/Al2O3 bilayered stack appears to be a good candidate for RRAM technology, the low diffusion barrier of the active Al electrode causes severe Al migration in the bi-layered oxides leading to the device to fail in resetting, and thereby, largely limiting the overall switching performance and material reliability

The Role of Scleraxis in Fate Determination of Mesenchymal Stem Cells for Tenocyte Differentiation

Mesenchymal stem cells (MSCs) are pluripotent cells that primarily differentiate into osteocytes, chondrocytes, and adipocytes. Recent studies indicate that MSCs can also be induced to generate tenocyte-like cells; moreover, MSCs have been suggested to have great therapeutic potential for tendon pathologies. Yet the precise molecular cascades governing tenogenic differentiation of MSCs remain unclear. We demonstrate scleraxis, a transcription factor critically involved in embryonic tendon development and formation, plays a pivotal role in the fate determination of MSC towards tenocyte differentiation. Using murine C3H10T1/2 pluripotent stem cells as a model system, we show scleraxis is extensively expressed in the early phase of bone morphogenetic protein (BMP)-12-triggered tenocytic differentiation. Once induced, scleraxis directly transactivates tendon lineage-related genes such as tenomodulin and suppresses osteogenic, chondrogenic, and adipogenic capabilities, thus committing C3H10T1/2 cells to differentiate into the specific tenocyte-like lineage, while eliminating plasticity for other lineages. We also reveal that mechanical loading-mediated tenocytic differentiation follows a similar pathway and that BMP-12 and cyclic uniaxial strain act in an additive fashion to augment the maximal response by activating signal transducer Smad8. These results provide critical insights into the determination of multipotent stem cells to the tenocyte lineage induced by both chemical and physical signals

Green tea polyphenol treatment is chondroprotective, anti-inflammatory and palliative in a mouse posttraumatic osteoarthritis model

Introduction Epigallocatechin 3-gallate (EGCG), a polyphenol present in green tea, was shown to exert chondroprotective effects in vitro. In this study, we used a posttraumatic osteoarthritis (OA) mouse model to test whether EGCG could slow the progression of OA and relieve OA-associated pain. Methods C57BL/6 mice were subjected to surgical destabilization of the medial meniscus (DMM) or sham surgery. EGCG (25 mg/kg) or vehicle control was administered daily for 4 or 8 weeks by intraperitoneal injection starting on the day of surgery. OA severity was evaluated using Safranin O staining and Osteoarthritis Research Society International (OARSI) scores, as well as by immunohistochemical analysis to detect cleaved aggrecan and type II collagen and expression of proteolytic enzymes matrix metalloproteinase 13 (MMP-13) and A disintegrin and metalloproteinase with thrombospondin motifs 5 (ADAMTS5). Real-time PCR was performed to characterize the expression of genes critical for articular cartilage homeostasis. During the course of the experiments, tactile sensitivity testing (von Frey test) and open-field assays were used to evaluate pain behaviors associated with OA, and expression of pain expression markers and inflammatory cytokines in the dorsal root ganglion (DRG) was determined by real-time PCR. Results Four and eight weeks after DMM surgery, the cartilage in EGCG-treated mice exhibited less Safranin O loss and cartilage erosion, as well as lower OARSI scores compared to vehicle-treated controls, which was associated with reduced staining for aggrecan and type II collagen cleavage epitopes, and reduced staining for MMP-13 and ADAMTS5 in the articular cartilage. Articular cartilage in the EGCG-treated mice also exhibited reduced levels of Mmp1, Mmp3, Mmp8, Mmp13,Adamts5, interleukin 1 beta (Il1b) and tumor necrosis factor alpha (Tnfa) mRNA and elevated gene expression of the MMP regulator Cbp/p300 interacting transactivator 2 (Cited2). Compared to vehicle controls, mice treated with EGCG exhibited reduced OA-associated pain, as indicated by higher locomotor behavior (that is, distance traveled). Moreover, expression of the chemokine receptor Ccr2 and proinflammatory cytokines Il1b and Tnfa in the DRG were significantly reduced to levels similar to those of sham-operated animals. Conclusions This study provides the first evidence in an OA animal model that EGCG significantly slows OA disease progression and exerts a palliative effect. Electronic supplementary material The online version of this article (doi:10.1186/s13075-014-0508-y) contains supplementary material, which is available to authorized users

BMP-12 Treatment of Adult Mesenchymal Stem Cells In Vitro Augments Tendon-Like Tissue Formation and Defect Repair In Vivo

We characterized the differentiation of rat bone marrow-derived mesenchymal stem cells (BM-MSCs) into tenocyte-like cells in response to bone morphogenetic protein-12 (BMP-12). BM-MSCs were prepared from Sprague-Dawley rats and cultured as monolayers. Recombinant BMP-12 treatment (10 ng/ml) of BM-MSCs for 12 hours in vitro markedly increased expression of the tenocyte lineage markers scleraxis (Scx) and tenomodulin (Tnmd) over 14 days. Treatment with BMP-12 for a further 12-hour period had no additional effect. Colony formation assays revealed that ∼80% of treated cells and their progeny were Scx- and Tnmd-positive. BM-MSCs seeded in collagen scaffolds and similarly treated with a single dose of BMP-12 also expressed high levels of Scx and Tnmd, as well as type I collagen and tenascin-c. Furthermore, when the treated BM-MSC-seeded scaffolds were implanted into surgically created tendon defects in vivo, robust formation of tendon-like tissue was observed after 21 days as evidenced by increased cell number, elongation and alignment along the tensile axis, greater matrix deposition and the elevated expression of tendon markers. These results indicate that brief stimulation with BMP-12 in vitro is sufficient to induce BM-MSC differentiation into tenocytes, and that this phenotype is sustained in vivo. This strategy of pretreating BM-MSCs with BMP-12 prior to in vivo transplantation may be useful in MSC-based tendon reconstruction or tissue engineering

Therapeutic Ultrasound: Osteoarthritis Symptom-Modification and Potential for Disease Modification

Osteoarthritis (OA) is a degenerative joint disease and a leading cause of adult disability. While joint replacement surgery is a common treatment option for end-stage disease, non-surgical management is critical for preventing disability and maintaining quality of life. Although therapeutic ultrasound, which applies mechanical and may also apply thermal energy in the form of sound waves, is widely used to treat various musculoskeletal disorders such as bone fractures, tendinopathy, and muscle contusions, its symptom- and disease-modifying effects on osteoarthritis have not been clearly demonstrated. Recent clinical evidence indicates therapeutic ultrasound is capable of relieving OAassociated pain and improving function of diseased joints. Furthermore, in vitro and in vivo studies are beginning to emerge which suggest ultrasound may exert chondroprotection, such as enhancing anabolic activity, suppressing proteolytic enzyme-mediated degradation of the cartilage matrix, preventing chondrocyte apoptosis and modifying the endocrinology of adipose tissue that may potentially contribute to OA disease initiation and progression. Therefore, ultrasound may have great potential to serve as an effective and non-invasive therapeutic treatment for osteoarthritis