Impact of trp2 allele mutation of α2 chain in collagen IX on the structural integrity of human annulus fibrosus

Conference Theme: Spinal Motion Segment: From Basic Science to Clinical Applicationpublished_or_final_versio

Unsupervised Bayesian linear unmixing of gene expression microarrays

Background: This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Results: Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. Conclusions: The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor

FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data

BACKGROUND: Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. To this aim, existing clustering approaches, mainly developed in computer science, have been adapted to microarray data analysis. However, previous studies revealed that microarray datasets have very diverse structures, some of which may not be correctly captured by current clustering methods. We therefore approached the problem from a new starting point, and developed a clustering algorithm designed to capture dataset-specific structures at the beginning of the process. RESULTS: The clustering algorithm is named Fuzzy clustering by Local Approximation of MEmbership (FLAME). Distinctive elements of FLAME are: (i) definition of the neighborhood of each object (gene or sample) and identification of objects with "archetypal" features named Cluster Supporting Objects, around which to construct the clusters; (ii) assignment to each object of a fuzzy membership vector approximated from the memberships of its neighboring objects, by an iterative converging process in which membership spreads from the Cluster Supporting Objects through their neighbors. Comparative analysis with K-means, hierarchical, fuzzy C-means and fuzzy self-organizing maps (SOM) showed that data partitions generated by FLAME are not superimposable to those of other methods and, although different types of datasets are better partitioned by different algorithms, FLAME displays the best overall performance. FLAME is implemented, together with all the above-mentioned algorithms, in a C++ software with graphical interface for Linux and Windows, capable of handling very large datasets, named Gene Expression Data Analysis Studio (GEDAS), freely available under GNU General Public License. CONCLUSION: The FLAME algorithm has intrinsic advantages, such as the ability to capture non-linear relationships and non-globular clusters, the automated definition of the number of clusters, and the identification of cluster outliers, i.e. genes that are not assigned to any cluster. As a result, clusters are more internally homogeneous and more diverse from each other, and provide better partitioning of biological functions. The clustering algorithm can be easily extended to applications different from gene expression analysis

SMART: Unique splitting-while-merging framework for gene clustering

Copyright @ 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named “splitting merging awareness tactics” (SMART), which does not require any a priori knowledge of either the number of clusters or even the possible range of this number. Unlike existing self-splitting algorithms, which over-cluster the dataset to a large number of clusters and then merge some similar clusters, our framework has the ability to split and merge clusters automatically during the process and produces the the most reliable clustering results, by intrinsically integrating many clustering techniques and tasks. The SMART framework is implemented with two distinct clustering paradigms in two algorithms: competitive learning and finite mixture model. Nevertheless, within the proposed SMART framework, many other algorithms can be derived for different clustering paradigms. The minimum message length algorithm is integrated into the framework as the clustering selection criterion. The usefulness of the SMART framework and its algorithms is tested in demonstration datasets and simulated gene expression datasets. Moreover, two real microarray gene expression datasets are studied using this approach. Based on the performance of many metrics, all numerical results show that SMART is superior to compared existing self-splitting algorithms and traditional algorithms. Three main properties of the proposed SMART framework are summarized as: (1) needing no parameters dependent on the respective dataset or a priori knowledge about the datasets, (2) extendible to many different applications, (3) offering superior performance compared with counterpart algorithms.National Institute for Health Researc

Altered thymic differentiation and modulation of arthritis by invariant NKT cells expressing mutant ZAP70

Various subsets of invariant natural killer T (iNKT) cells with different cytokine productions develop in the mouse thymus, but the factors driving their differentiation remain unclear. Here we show that hypomorphic alleles of Zap70 or chemical inhibition of Zap70 catalysis leads to an increase of IFN-gamma-producing iNKT cells (NKT1 cells), suggesting that NKT1 cells may require a lower TCR signal threshold. Zap70 mutant mice develop IL-17-dependent arthritis. In a mouse experimental arthritis model, NKT17 cells are increased as the disease progresses, while NKT1 numbers negatively correlates with disease severity, with this protective effect of NKT1 linked to their IFN-gamma expression. NKT1 cells are also present in the synovial fluid of arthritis patients. Our data therefore suggest that TCR signal strength during thymic differentiation may influence not only IFN-gamma production, but also the protective function of iNKT cells in arthritis

Multi-Class Clustering of Cancer Subtypes through SVM Based Ensemble of Pareto-Optimal Solutions for Gene Marker Identification

With the advancement of microarray technology, it is now possible to study the expression profiles of thousands of genes across different experimental conditions or tissue samples simultaneously. Microarray cancer datasets, organized as samples versus genes fashion, are being used for classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer types. In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic clustering of the tissue samples. In this regard, a real-coded encoding of the cluster centers is used and cluster compactness and separation are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of non-dominated solutions. A novel approach to combine the clustering information possessed by the non-dominated solutions through Support Vector Machine (SVM) classifier has been proposed. Final clustering is obtained by consensus among the clusterings yielded by different kernel functions. The performance of the proposed multiobjective clustering method has been compared with that of several other microarray clustering algorithms for three publicly available benchmark cancer datasets. Moreover, statistical significance tests have been conducted to establish the statistical superiority of the proposed clustering method. Furthermore, relevant gene markers have been identified using the clustering result produced by the proposed clustering method and demonstrated visually. Biological relationships among the gene markers are also studied based on gene ontology. The results obtained are found to be promising and can possibly have important impact in the area of unsupervised cancer classification as well as gene marker identification for multiple cancer subtypes

Metric for Measuring the Effectiveness of Clustering of DNA Microarray Expression

BACKGROUND: The recent advancement of microarray technology with lower noise and better affordability makes it possible to determine expression of several thousand genes simultaneously. The differentially expressed genes are filtered first and then clustered based on the expression profiles of the genes. A large number of clustering algorithms and distance measuring matrices are proposed in the literature. The popular ones among them include hierarchal clustering and k-means clustering. These algorithms have often used the Euclidian distance or Pearson correlation distance. The biologists or the practitioners are often confused as to which algorithm to use since there is no clear winner among algorithms or among distance measuring metrics. Several validation indices have been proposed in the literature and these are based directly or indirectly on distances; hence a method that uses any of these indices does not relate to any biological features such as biological processes or molecular functions. RESULTS: In this paper we have proposed a metric to measure the effectiveness of clustering algorithms of genes by computing inter-cluster cohesiveness and as well as the intra-cluster separation with respect to biological features such as biological processes or molecular functions. We have applied this metric to the clusters on the data set that we have created as part of a larger study to determine the cancer suppressive mechanism of a class of chemicals called retinoids. We have considered hierarchal and k-means clustering with Euclidian and Pearson correlation distances. Our results show that genes of similar expression profiles are more likely to be closely related to biological processes than they are to molecular functions. The findings have been supported by many works in the area of gene clustering. CONCLUSION: The best clustering algorithm of genes must achieve cohesiveness within a cluster with respect to some biological features, and as well as maximum separation between clusters in terms of the distribution of genes of a behavioral group across clusters. We claim that our proposed metric is novel in this respect and that it provides a measure of both inter and intra cluster cohesiveness. Best of all, computation of the proposed metric is easy and it provides a single quantitative value, which makes comparison of different algorithms easier. The maximum cluster cohesiveness and the maximum intra-cluster separation are indicated by the metric when its value is 0. We have demonstrated the metric by applying it to a data set with gene behavioral groupings such as biological process and molecular functions. The metric can be easily extended to other features of a gene such as DNA binding sites and protein-protein interactions of the gene product, special features of the intron-exon structure, promoter characteristics, etc. The metric can also be used in other domains that use two different parametric spaces; one for clustering and the other one for measuring the effectiveness

Annexin-A5 assembled into two-dimensional arrays promotes cell membrane repair

Eukaryotic cells possess a universal repair machinery that ensures rapid resealing of plasma membrane disruptions. Before resealing, the torn membrane is submitted to considerable tension, which functions to expand the disruption. Here we show that annexin-A5 (AnxA5), a protein that self-assembles into two-dimensional (2D) arrays on membranes upon Ca2+ activation, promotes membrane repair. Compared with wild-type mouse perivascular cells, AnxA5-null cells exhibit a severe membrane repair defect. Membrane repair in AnxA5-null cells is rescued by addition of AnxA5, which binds exclusively to disrupted membrane areas. In contrast, an AnxA5 mutant that lacks the ability of forming 2D arrays is unable to promote membrane repair. We propose that AnxA5 participates in a previously unrecognized step of the membrane repair process: triggered by the local influx of Ca2+, AnxA5 proteins bind to torn membrane edges and form a 2D array, which prevents wound expansion and promotes membrane resealing

The malaria testing and treatment landscape in mainland Tanzania, 2016

Abstract Background Understanding the key characteristics of malaria testing and treatment is essential to the control of a disease that continues to pose a major risk of morbidity and mortality in mainland Tanzania, with evidence of a resurgence of the disease in recent years. The introduction of artemisinin combination therapy (ACT) as the first-line treatment for malaria, alongside policies to promote rational case management following testing, highlights the need for evidence of anti-malarial and testing markets in the country. The results of the most recent mainland Tanzania ACTwatch outlet survey are presented here, including data on the availability, market share and price of anti-malarials and malaria diagnosis in 2016. Methods A nationally-representative malaria outlet survey was conducted between 18th May and 2nd July, 2016. A census of public and private outlets with potential to distribute malaria testing and/or treatment was conducted among a representative sample of administrative units. An audit was completed for all anti-malarials, malaria rapid (RDT) diagnostic tests and microscopy. Results A total of 5867 outlets were included in the nationally representative survey, across both public and private sectors. In the public sector, availability of malaria testing was 92.3% and quality-assured (QA) ACT was 89.1% among all screened outlets. Sulfadoxine–pyrimethamine (SP) was stocked by 51.8% of the public sector and injectable artesunate was found in 71.4% of all screened public health facilities. Among anti-malarial private-sector stockists, availability of testing was 15.7, and 65.1% had QA ACT available. The public sector accounted for 83.4% of the total market share for malaria diagnostics. The private sector accounted for 63.9% of the total anti-malarial market, and anti-malarials were most commonly distributed through accredited drug dispensing outlets (ADDOs) (39.0%), duka la dawa baridi (DLDBs) (13.3%) and pharmacies (6.7%). QA ACT comprised 33.1% of the national market share (12.2% public sector and 20.9% private sector). SP accounted for 53.3% of the total market for anti-malarials across both private and public sectors (31.3 and 22.0% of the total market, respectively). The median price per adult equivalent treatment dose (AETD) of QA ACT in the private sector was $1.40, almost 1.5 times more expensive than the median price per AETD of SP ($1.05). In the private sector, 79.3% of providers perceived ACT to be the most effective treatment for uncomplicated malaria for adults and 88.4% perceived this for children. Conclusions While public sector preparedness for appropriate malaria testing and case management is showing encouraging signs, QA ACT availability and market share in the private sector continues to be sub-optimal for most outlet types. Furthermore, it is concerning that SP continues to predominate in the anti-malarial market. The reasons for this remain unclear, but are likely to be in part related to price, availability and provider knowledge or preferences. Continued efforts to implement government policy around malaria diagnosis and case management should be encouraged