James Hensman

The Circadian Clock in Murine Chondrocytes Regulates Genes Controlling Key Aspects of Cartilage Homeostasis

ObjectiveTo characterize the circadian clock in murine cartilage tissue and identify tissue-specific clock target genes, and to investigate whether the circadian clock changes during aging or during cartilage degeneration using an experimental mouse model of osteoarthritis (OA). MethodsCartilage explants were obtained from aged and young adult mice after transduction with the circadian clock fusion protein reporter PER2::luc, and real-time bioluminescence recordings were used to characterize the properties of the clock. Time-series microarrays were performed on mouse cartilage tissue to identify genes expressed in a circadian manner. Rhythmic genes were confirmed by quantitative reverse transcription–polymerase chain reaction using mouse tissue, primary chondrocytes, and a human chondrocyte cell line. Experimental OA was induced in mice by destabilization of the medial meniscus (DMM), and articular cartilage samples were microdissected and subjected to microarray analysis. ResultsMouse cartilage tissue and a human chondrocyte cell line were found to contain intrinsic molecular circadian clocks. The cartilage clock could be reset by temperature signals, while the circadian period was temperature compensated. PER2::luc bioluminescence demonstrated that circadian oscillations were significantly lower in amplitude in cartilage from aged mice. Time-series microarray analyses of the mouse tissue identified the first circadian transcriptome in cartilage, revealing that 615 genes (∼3.9% of the expressed genes) displayed a circadian pattern of expression. This included genes involved in cartilage homeostasis and survival, as well as genes with potential importance in the pathogenesis of OA. Several clock genes were disrupted in the early stages of cartilage degeneration in the DMM mouse model of OA. ConclusionThese results reveal an autonomous circadian clock in chondrocytes that can be implicated in key aspects of cartilage biology and pathology. Consequently, circadian disruption (e.g., during aging) may compromise tissue homeostasis and increase susceptibility to joint damage or disease.

Genome-wide occupancy links Hoxa2 to Wnt–β-catenin signaling in mouse embryonic development

The regulation of gene expression is central to developmental programs and largely depends on the binding of sequence-specific transcription factors with cis-regulatory elements in the genome. Hox transcription factors specify the spatial coordinates of the body axis in all animals with bilateral symmetry, but a detailed knowledge of their molecular function in instructing cell fates is lacking. Here, we used chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) to identify Hoxa2 genomic locations in a time and space when it is actively instructing embryonic development in mouse. Our data reveals that Hoxa2 has large genome coverage and potentially regulates thousands of genes. Sequence analysis of Hoxa2-bound regions identifies high occurrence of two main classes of motifs, corresponding to Hox and Pbx–Hox recognition sequences. Examination of the binding targets of Hoxa2 faithfully captures the processes regulated by Hoxa2 during embryonic development; in addition, it uncovers a large cluster of potential targets involved in the Wnt-signaling pathway. In vivo examination of canonical Wnt–β-catenin signaling reveals activity specifically in Hoxa2 domain of expression, and this is undetectable in Hoxa2 mutant embryos. The comprehensive mapping of Hoxa2-binding sites provides a framework to study Hox regulatory networks in vertebrate developmental processes.

Hoxa2 Selectively Enhances Meis Binding to Change a Branchial Arch Ground State

Summary Hox transcription factors (TFs) are essential for vertebrate development, but how these evolutionary conserved proteins function in vivo remains unclear. Because Hox proteins have notoriously low binding specificity, they are believed to bind with cofactors, mainly homeodomain TFs Pbx and Meis, to select their specific targets. We mapped binding of Meis, Pbx, and Hoxa2 in the branchial arches, a series of segments in the developing vertebrate head. Meis occupancy is largely similar in Hox-positive and -negative arches. Hoxa2, which specifies second arch (IIBA) identity, recognizes a subset of Meis prebound sites that contain Hox motifs. Importantly, at these sites Meis binding is strongly increased. This enhanced Meis binding coincides with active enhancers, which are linked to genes highly expressed in the IIBA and regulated by Hoxa2. These findings show that Hoxa2 operates as a tissue-specific cofactor, enhancing Meis binding to specific sites that provide the IIBA with its anatomical identity.

Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters

BackgroundTime course data from microarrays and high-throughput sequencing experiments require simple, computationally efficient and powerful statistical models to extract meaningful biological signal, and for tasks such as data fusion and clustering. Existing methodologies fail to capture either the temporal or replicated nature of the experiments, and often impose constraints on the data collection process, such as regularly spaced samples, or similar sampling schema across replications.ResultsWe propose hierarchical Gaussian processes as a general model of gene expression time-series, with application to a variety of problems. In particular, we illustrate the method’s capacity for missing data imputation, data fusion and clustering.The method can impute data which is missing both systematically and at random: in a hold-out test on real data, performance is significantly better than commonly used imputation methods. The method’s ability to model inter- and intra-cluster variance leads to more biologically meaningful clusters. The approach removes the necessity for evenly spaced samples, an advantage illustrated on a developmental Drosophila dataset with irregular replications.ConclusionThe hierarchical Gaussian process model provides an excellent statistical basis for several gene-expression time-series tasks. It has only a few additional parameters over a regular GP, has negligible additional complexity, is easily implemented and can be integrated into several existing algorithms. Our experiments were implemented in python, and are available from the authors’ website: http://staffwww.dcs.shef.ac.uk/people/J.Hensman/.

Gremlin-2 is a BMP antagonist that is regulated by the circadian clock

Tendons are prominent members of the family of fibrous connective tissues (FCTs), which collectively are the most abundant tissues in vertebrates and have crucial roles in transmitting mechanical force and linking organs. Tendon diseases are among the most common arthropathy disorders; thus knowledge of tendon gene regulation is essential for a complete understanding of FCT biology. Here we show autonomous circadian rhythms in mouse tendon and primary human tenocytes, controlled by an intrinsic molecular circadian clock. Time-series microarrays identified the first circadian transcriptome of murine tendon, revealing that 4.6% of the transcripts (745 genes) are expressed in a circadian manner. One of these genes was Grem2, which oscillated in antiphase to BMP signaling. Moreover, recombinant human Gremlin-2 blocked BMP2-induced phosphorylation of Smad1/5 and osteogenic differentiation of human tenocytes in vitro. We observed dampened Grem2 expression, deregulated BMP signaling, and spontaneously calcifying tendons in young CLOCKΔ19 arrhythmic mice and aged wild-type mice. Thus, disruption of circadian control, through mutations or aging, of Grem2/BMP signaling becomes a new focus for the study of calcific tendinopathy, which affects 1-in-5 people over the age of 50 years.

Acoustic emission for monitoring aircraft structures

Abstract Structural health monitoring (SHM) is of paramount importance in the aircraft industry: not only to ensure the safety and reliability of aircraft in flight and to ensure timely maintenance of critical components, but also increasingly to monitor structures under test for airworthiness certification of new designs. This article highlights some of the recent advances in the acoustic emission (AE) technique as applied to SHM, and the new approaches that are crucial for the successful use of AE data for diagnostic purposes. These include modal analysis, enhanced location techniques, and novel signal processing approaches. A case study is presented on a landing gear component undergoing fatigue loading in which a linear location analysis using conventional techniques identified the position of fracture and final rupture of the specimen. A principal component analysis approach was used to separate noise signals from signals arising from fatigue cracks, which identified and located further fatigue crack positions, subsequently confirmed by magnetic particle inspection. Kernel probability density functions are used to aid visualization of the damage location.

On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes

The variational framework for learning inducing variables (Titsias, 2009a) has had a large impact on the Gaussian process literature. The framework may be interpreted as minimizing a rigorously defined Kullback-Leibler divergence between the approximating and posterior processes. To our knowledge this connection has thus far gone unremarked in the literature. In this paper we give a substantial generalization of the literature on this topic. We give a new proof of the result for infinite index sets which allows inducing points that are not data points and likelihoods that depend on all function values. We then discuss augmented index sets and show that, contrary to previous works, marginal consistency of augmentation is not enough to guarantee consistency of variational inference with the original model. We then characterize an extra condition where such a guarantee is obtainable. Finally we show how our framework sheds light on interdomain sparse approximations and sparse approximations for Cox processes.

Fast and accurate approximate inference of transcript expression from RNA-seq data

Motivation: Assigning RNA-seq reads to their transcript of origin is a fundamental task in transcript expression estimation. Where ambiguities in assignments exist due to transcripts sharing sequence, e.g. alternative isoforms or alleles, the problem can be solved through probabilistic inference. Bayesian methods have been shown to provide accurate transcript abundance estimates compared with competing methods. However, exact Bayesian inference is intractable and approximate methods such as Markov chain Monte Carlo and Variational Bayes (VB) are typically used. While providing a high degree of accuracy and modelling flexibility, standard implementations can be prohibitively slow for large datasets and complex transcriptome annotations. Results: We propose a novel approximate inference scheme based on VB and apply it to an existing model of transcript expression inference from RNA-seq data. Recent advances in VB algorithmics are used to improve the convergence of the algorithm beyond the standard Variational Bayes Expectation Maximization algorithm. We apply our algorithm to simulated and biological datasets, demonstrating a significant increase in speed with only very small loss in accuracy of expression level estimation. We carry out a comparative study against seven popular alternative methods and demonstrate that our new algorithm provides excellent accuracy and inter-replicate consistency while remaining competitive in computation time. Availability and implementation: The methods were implemented in R and C++, and are available as part of the BitSeq project at github.com/BitSeq. The method is also available through the BitSeq Bioconductor package. The source code to reproduce all simulation results can be accessed via github.com/BitSeq/BitSeqVB_benchmarking. Contact: james.hensman@sheffield.ac.uk or panagiotis.papastamoulis@manchester.ac.uk or Magnus.Rattray@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Fast Nonparametric Clustering of Structured Time-Series

In this publication, we combine two Bayesian nonparametric models: the Gaussian Process (GP) and the Dirichlet Process (DP). Our innovation in the GP model is to introduce a variation on the GP prior which enables us to model structured time-series data, i.e., data containing groups where we wish to model inter- and intra-group variability. Our innovation in the DP model is an implementation of a new fast collapsed variational inference procedure which enables us to optimize our variational approximation significantly faster than standard VB approaches. In a biological time series application we show how our model better captures salient features of the data, leading to better consistency with existing biological classifications, while the associated inference algorithm provides a significant speed-up over EM-based variational inference.

Albumin level and patient age predict outcomes in patients referred for gastrostomy insertion: internal and external validation of a gastrostomy score and comparison with artificial neural networks

BACKGROUND Significant mortality after gastrostomy insertion remains and some risk factors have been identified, but no predictive scoring system exists. OBJECTIVE To identify risk factors for mortality, formulate a predictive scoring system, and validate the score. Comparison to an artificial neural network (ANN). DESIGN Endoscopic database analysis. SETTING Six hospitals (2 teaching hospitals) in the South Yorkshire region, United Kingdom. PATIENTS This study involved all patients referred for gastrostomy insertion. INTERVENTION Generation of clinical scores to predict 30-day mortality in patients undergoing gastrostomy insertion. MAIN OUTCOME MEASUREMENTS Risk factors for 30-day mortality. Internal and external validation of the score. Comparison with an ANN. RESULTS Univariate analysis showed that 30-day mortality was associated with age, albumin levels, and cardiac and neurological comorbidities. Multivariate analysis showed that only age and albumin levels were independent. Modeling provided scores of 0, 1, 2, and 3 corresponding to 30-day mortalities of 0% (0-2.1), 7% (2.9-13.9), 21.3% (13.5-30.9), and 37.3% (24.1-51.9), respectively. Application of the scoring system at the other teaching hospital and the 4 district general hospitals gave 30-day mortality rates that were not significantly different from those predicted. Receiver operating characteristic curves for the score and the ANN were comparable. LIMITATIONS Nonrandomized study. Score not used as a decision-making tool. CONCLUSION The gastrostomy score provides an estimate of 30-day mortality for patients (and their relatives) when gastrostomy insertion is being discussed. This score requires evaluation as a decision-making tool in clinical practice. ANN analysis results were similar to the outcomes from the clinical score.