David Allaway

Biological actions of curcumin on articular chondrocytes

OBJECTIVES Curcumin (diferuloylmethane) is the principal biochemical component of the spice turmeric and has been shown to possess potent anti-catabolic, anti-inflammatory and antioxidant, properties. This article aims to provide a summary of the actions of curcumin on articular chondrocytes from the available literature with the use of a text-mining tool. We highlight both the potential benefits and drawbacks of using this chemopreventive agent for treating osteoarthritis (OA). We also explore the recent literature on the molecular mechanisms of curcumin mediated alterations in gene expression mediated via activator protein 1 (AP-1)/nuclear factor-kappa B (NF-kappaB) signalling in chondrocytes, osteoblasts and synovial fibroblasts. METHODS A computer-aided search of the PubMed/Medline database aided by a text-mining tool to interrogate the ResNet Mammalian database 6.0. RESULTS Recent work has shown that curcumin protects human chondrocytes from the catabolic actions of interleukin-1 beta (IL-1beta) including matrix metalloproteinase (MMP)-3 up-regulation, inhibition of collagen type II and down-regulation of beta1-integrin expression. Curcumin blocks IL-1beta-induced proteoglycan degradation, AP-1/NF-kappaB signalling, chondrocyte apoptosis and activation of caspase-3. CONCLUSIONS The available data from published in vitro and in vivo studies suggest that curcumin may be a beneficial complementary treatment for OA in humans and companion animals. Nevertheless, before initiating extensive clinical trials, more basic research is required to improve its solubility, absorption and bioavailability and gain additional information about its safety and efficacy in different species. Once these obstacles have been overcome, curcumin and structurally related biochemicals may become safer and more suitable nutraceutical alternatives to the non-steroidal anti-inflammatory drugs that are currently used for the treatment of OA.

Targeting Matrix Metalloproteinases in Inflammatory Conditions

The matrix metalloproteinases (MMPs) and their endogenous regulators, the tissue inhibitors of MMPs (TIMPs) are responsible for the physiological remodelling of the extracellular matrix (ECM) in healthy connective tissues. MMPs are also involved in the regulation of cell behaviour via the release of growth factors and cytokines from the substrates they cleave, increasing the magnitude of their effects. Excess MMP activity is associated with ECM destruction in various inflammatory conditions, such as osteoarthritis (OA), while MMP under-activity potentially impairs healing by promoting fibrosis and preventing the effective removal of scar tissue. Both direct (TIMPs, small molecule MMP inhibitor drugs, blocking antibodies and anti-sense technologies) and indirect (glucocorticoids and non-steroidal anti-inflammatory drugs, statins, anti-sense technologies and various phytochemicals) strategies for MMP inhibition have been proposed and investigated. The strategy of MMP inhibition for degenerative and neoplastic diseases has been relatively unsuccessful due to undesired sequelae, often caused by non-selectivity of the MMP inhibition method. Therapeutic strategies for MMP-related conditions ideally should regulate MMP activity in order to maintain the optimum balance between MMPs and TIMPs. By avoiding complete inhibition it may be possible to prevent the complications of MMP over- and under-activity. Furthermore, MMP sub-type specificity is critical for minimising detrimental off-target effects that have been observed with broad-spectrum MMP inhibitors. Any potential MMP inhibitor or modulator must be subjected to rigorous pharmacokinetic, toxicity and safety studies and data obtained using in vitro models must be verified in clinically relevant animal models before therapeutic use is considered.

Matrix metalloproteinases in inflammatory pathologies of the horse.

The extracellular matrix (ECM) of connective tissue is constantly being remodelled to allow for growth and regeneration. Normal tissue maintenance requires the ECM components to be degraded and re-synthesised in relatively equal proportions. This degradation is facilitated by matrix metalloproteinases (MMPs) and their proteolytic action is controlled primarily by the tissue inhibitors of metalloproteinases (TIMPs). Both MMPs and TIMPs exist in a state of dynamic equilibrium, with a slight excess of one or the other depending on the need for either ECM breakdown or synthesis. Long-term disruption to this balance between MMPs and TIMPs will have pathological consequences. Matrix metalloproteinases are involved in a number of diseases in mammals, including the horse. Excess MMP activity can cause ECM destruction, as seen in the lamellar basement membrane in laminitis and the articular cartilage in osteoarthritis. Matrix metalloproteinase under-activity can potentially impede healing by preventing fibrinolysis in fibrotic conditions and the removal of scar tissue in wounds. Matrix metalloproteinases also degrade non-ECM proteins and regulate cell behaviour via the release of growth factors from the substrates they cleave, increasing the scope of their effects. This review looks at the involvement of MMPs in equine health and pathologies, whilst exploring the potential consequences of therapeutic intervention.

Application of Machine Learning to Proteomics Data: Classification and Biomarker Identification in Postgenomics Biology

Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes.

Interleukin‐1β–Induced Extracellular Matrix Degradation and Glycosaminoglycan Release Is Inhibited by Curcumin in an Explant Model of Cartilage Inflammation

Osteoarthritis (OA) is a degenerative and inflammatory disease of synovial joints that is characterized by the loss of articular cartilage, for which there is increasing interest in natural remedies. Curcumin (diferuloylmethane) is the main polyphenol in the spice turmeric, derived from rhizomes of the plant Curcuma longa. Curcumin has potent chemopreventive properties and has been shown to inhibit nuclear factor κB‐mediated inflammatory signaling in many cell types, including chondrocytes. In this study, normal articular cartilage was harvested from metacarpophalangeal and metatarsophalangeal joints of eight horses, euthanized for reasons other than research purposes, to establish an explant model mimicking the inflammatory events that occur in OA. Initially, cartilage explants (N= 8) were stimulated with increasing concentrations of the proinflammatory cytokine IL‐1β to select effective doses for inducing cartilage degeneration in the explant model. Separate cartilage explants were then cotreated with IL‐1β at either 10 ng/mL (n= 3) or 25 ng/mL (n= 3) and curcumin (0.1 μmol/L, 0.5 μmol/L, 1 μmol/L, 10 μmol/L, and 100 μmol/L). After 5 days, the percentage of glycosaminoglycan (GAG) release from the explants was assessed using a dimethylmethylene blue colorimetric assay. Curcumin (100 μmol/L) significantly reduced IL‐1β‐stimulated GAG release in the explants by an average of 20% at 10 ng/mL and 27% at 25 ng/mL back to unstimulated control levels (P < 0.001). Our results suggest that this explant model effectively simulates the proinflammatory cytokine‐mediated release of articular cartilage components seen in OA. Furthermore, the evidence suggests that the inflammatory cartilage explant model is useful for studying the effects of curcumin on inflammatory pathways and gene expression in IL‐1β‐stimulated chondrocytes.

High throughput proteomic analysis of the secretome in an explant model of articular cartilage inflammation

This study employed a targeted high-throughput proteomic approach to identify the major proteins present in the secretome of articular cartilage. Explants from equine metacarpophalangeal joints were incubated alone or with interleukin-1beta (IL-1β, 10 ng/ml), with or without carprofen, a non-steroidal anti-inflammatory drug, for six days. After tryptic digestion of culture medium supernatants, resulting peptides were separated by HPLC and detected in a Bruker amaZon ion trap instrument. The five most abundant peptides in each MS scan were fragmented and the fragmentation patterns compared to mammalian entries in the Swiss-Prot database, using the Mascot search engine. Tryptic peptides originating from aggrecan core protein, cartilage oligomeric matrix protein (COMP), fibronectin, fibromodulin, thrombospondin-1 (TSP-1), clusterin (CLU), cartilage intermediate layer protein-1 (CILP-1), chondroadherin (CHAD) and matrix metalloproteinases MMP-1 and MMP-3 were detected. Quantitative western blotting confirmed the presence of CILP-1, CLU, MMP-1, MMP-3 and TSP-1. Treatment with IL-1β increased MMP-1, MMP-3 and TSP-1 and decreased the CLU precursor but did not affect CILP-1 and CLU levels. Many of the proteins identified have well-established extracellular matrix functions and are involved in early repair/stress responses in cartilage. This high throughput approach may be used to study the changes that occur in the early stages of osteoarthritis.

Validation of a urine metabolome fingerprint in dog for phenotypic classification

Selective breeding of dogs over hundreds of years has inadvertently resulted in breed-specific propensities to particular diseases. Furthermore, it has likely induced more subtle affects on the physiology of certain breeds and moved them from their evolutionary optima. In the absence of obvious disease phenotypes such subtle changes could have yet unrecognised breed-specific implications for health and well-being. Here we have applied NMR metabolomics as a discovery-driven approach to identify the impact of breed on the urinary profile of dog and to determine if non-disease-related breed differences can be identified. Multiple urines were collected non-invasively over a two-week period from seven neutered male Labrador retrievers and miniature Schnauzers. Following NMR analyses by 1-dimensional 1H and 2-dimensional 1H J-resolved (JRES) spectroscopy, principal component analysis revealed that the metabolic variability within each individual is relatively small compared to inter-individual variability, and that some separation between breeds was evident. A supervised model, using partial least squares discriminant analysis (PLS-DA) with class based upon breed, was trained using the JRES data. The model predicted correctly the breed of seven additional urines, yielding a model sensitivity and specificity of 100%. Several significant metabolic differences between the breeds were identified. A second model was developed using PLS-DA with class based upon individual dogs, which again achieved high classification accuracy for the test set. Overall, this confirms that canine urine is information-rich and that breed is a major determinant of urinary metabolic fingerprints. In the future this may enable a more accurate development of specific nutritional care for an individual or breed.

Deep Illumina-Based Shotgun Sequencing Reveals Dietary Effects on the Structure and Function of the Fecal Microbiome of Growing Kittens

Background Previously, we demonstrated that dietary protein:carbohydrate ratio dramatically affects the fecal microbial taxonomic structure of kittens using targeted 16S gene sequencing. The present study, using the same fecal samples, applied deep Illumina shotgun sequencing to identify the diet-associated functional potential and analyze taxonomic changes of the feline fecal microbiome. Methodology & Principal Findings Fecal samples from kittens fed one of two diets differing in protein and carbohydrate content (high–protein, low–carbohydrate, HPLC; and moderate-protein, moderate-carbohydrate, MPMC) were collected at 8, 12 and 16 weeks of age (n = 6 per group). A total of 345.3 gigabases of sequence were generated from 36 samples, with 99.75% of annotated sequences identified as bacterial. At the genus level, 26% and 39% of reads were annotated for HPLC- and MPMC-fed kittens, with HPLC-fed cats showing greater species richness and microbial diversity. Two phyla, ten families and fifteen genera were responsible for more than 80% of the sequences at each taxonomic level for both diet groups, consistent with the previous taxonomic study. Significantly different abundances between diet groups were observed for 324 genera (56% of all genera identified) demonstrating widespread diet-induced changes in microbial taxonomic structure. Diversity was not affected over time. Functional analysis identified 2,013 putative enzyme function groups were different (p<0.000007) between the two dietary groups and were associated to 194 pathways, which formed five discrete clusters based on average relative abundance. Of those, ten contained more (p<0.022) enzyme functions with significant diet effects than expected by chance. Six pathways were related to amino acid biosynthesis and metabolism linking changes in dietary protein with functional differences of the gut microbiome. Conclusions These data indicate that feline feces-derived microbiomes have large structural and functional differences relating to the dietary protein:carbohydrate ratio and highlight the impact of diet early in life.

A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data

BackgroundInvestigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machine learning pipelines for omics data is feature selection, which is used to reduce the raw high-dimensional data into a tractable number of features. Feature selection needs to balance the objective of using as few features as possible, while maintaining high predictive power. This balance is crucial when the goal of data analysis is the identification of highly accurate but small panels of biomarkers with potential clinical utility. In this paper we propose a heuristic for the selection of very small feature subsets, via an iterative feature elimination process that is guided by rule-based machine learning, called RGIFE (Rule-guided Iterative Feature Elimination). We use this heuristic to identify putative biomarkers of osteoarthritis (OA), articular cartilage degradation and synovial inflammation, using both proteomic and transcriptomic datasets.Results and discussionOur RGIFE heuristic increased the classification accuracies achieved for all datasets when no feature selection is used, and performed well in a comparison with other feature selection methods. Using this method the datasets were reduced to a smaller number of genes or proteins, including those known to be relevant to OA, cartilage degradation and joint inflammation. The results have shown the RGIFE feature reduction method to be suitable for analysing both proteomic and transcriptomics data. Methods that generate large ‘omics’ datasets are increasingly being used in the area of rheumatology.ConclusionsFeature reduction methods are advantageous for the analysis of omics data in the field of rheumatology, as the applications of such techniques are likely to result in improvements in diagnosis, treatment and drug discovery.

A Longitudinal Study of the Feline Faecal Microbiome Identifies Changes into Early Adulthood Irrespective of Sexual Development

Companion animals provide an excellent model for studies of the gut microbiome because potential confounders such as diet and environment can be more readily controlled for than in humans. Additionally, domestic cats and dogs are typically neutered early in life, enabling an investigation into the potential effect of sex hormones on the microbiome. In a longitudinal study to investigate the potential effects of neutering, neutering age and gender on the gut microbiome during growth, the faeces of kittens (16 male, 14 female) were sampled at 18, 30 and 42 weeks of age. DNA was shotgun sequenced on the Illumina platform and sequence reads were annotated for taxonomy and function by comparison to a database of protein coding genes. In a statistical analysis of diversity, taxonomy and functional potential of the microbiomes, age was identified as the only factor with significant associations. No significant effects were detected for gender, neutering, or age when neutered (19 or 31 weeks). At 18 weeks of age the microbiome was dominated by the genera Lactobacillus and Bifidobacterium (35% and 20% average abundance). Structural and functional diversity was significantly increased by week 30 but there was no further significant increase. At 42 weeks of age the most abundant genera were Bacteroides (16%), Prevotella (14%) and Megasphaera (8%). Significant differences in functional potential included an enrichment for genes in energy metabolism (carbon metabolism and oxidative phosphorylation) and depletion in cell motility (flagella and chemotaxis). We conclude that the feline faecal microbiome is predominantly determined by age when diet and environment are controlled for. We suggest this finding may also be informative for studies of the human microbiome, where control over such factors is usually limited.