Relationship between adiposity and admixture in African-American and Hispanic-American women.

ObjectiveThe objective of this study was to investigate whether differences in admixture in African-American (AFA) and Hispanic-American (HA) adult women are associated with adiposity and adipose distribution.DesignThe proportion of European, sub-Saharan African and Amerindian admixture was estimated for AFA and HA women in the Women's Heath Initiative using 92 ancestry informative markers. Analyses assessed the relationship between admixture and adiposity indices.SubjectsThe subjects included 11 712 AFA and 5088 HA self-identified post-menopausal women.ResultsThere was a significant positive association between body mass index (BMI) and African admixture when BMI was considered as a continuous variable, and age, education, physical activity, parity, family income and smoking were included covariates (P<10(-4)). A dichotomous model (upper and lower BMI quartiles) showed that African admixture was associated with a high odds ratio (OR=3.27 (for 100% admixture compared with 0% admixture), 95% confidence interval 2.08-5.15). For HA, there was no association between BMI and admixture. In contrast, when waist-to-hip ratio (WHR) was used as a measure of adipose distribution, there was no significant association between WHR and admixture in AFA but there was a strong association in HA (P<10(-4); OR Amerindian admixture=5.93, confidence interval=3.52-9.97).ConclusionThese studies show that: (1) African admixture is associated with BMI in AFA women; (2) Amerindian admixture is associated with WHR but not BMI in HA women; and (3) it may be important to consider different measurements of adiposity and adipose distribution in different ethnic population groups

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

A Normalized Tree Index for identification of correlated clinical parameters in microarray experiments

Martin C, Tauchen A, Becker A, Nattkemper TW. A Normalized Tree Index for identification of correlated clinical parameters in microarray data. BioData Mining. 2011;4(1): 2.BACKGROUND: Measurements on gene level are widely used to gain new insights in complex diseases e.g. cancer. A promising approach to understand basic biological mechanisms is to combine gene expression profiles and classical clinical parameters. However, the computation of a correlation coefficient between high-dimensional data and such parameters is not covered by traditional statistical methods. METHODS: We propose a novel index, the Normalized Tree Index (NTI), to compute a correlation coefficient between the clustering result of high-dimensional microarray data and nominal clinical parameters. The NTI detects correlations between hierarchically clustered microarray data and nominal clinical parameters (labels) and gives a measurement of significance in terms of an empiric p-value of the identified correlations. Therefore, the microarray data is clustered by hierarchical agglomerative clustering using standard settings. In a second step, the computed cluster tree is evaluated. For each label, a NTI is computed measuring the correlation between that label and the clustered microarray data. RESULTS: The NTI successfully identifies correlated clinical parameters at different levels of significance when applied on two real-world microarray breast cancer data sets. Some of the identified highly correlated labels confirm the actual state of knowledge whereas others help to identify new risk factors and provide a good basis to formulate new hypothesis. CONCLUSIONS: The NTI is a valuable tool in the domain of biomedical data analysis. It allows the identification of correlations between high-dimensional data and nominal labels, while at the same time a p-value measures the level of significance of the detected correlations

A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data

<p>Abstract</p> <p>Background</p> <p>A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk assessment and diagnostics. Here we focus on clinically relevant estimation of lipoprotein lipids by ¹H NMR spectroscopy of serum.</p> <p>Results</p> <p>A Bayesian methodology, with a biochemical motivation, is presented for a real ¹H NMR metabonomics data set of 75 serum samples. Lipoprotein lipid concentrations were independently obtained for these samples via ultracentrifugation and specific biochemical assays. The Bayesian models were constructed by Markov chain Monte Carlo (MCMC) and they showed remarkably good quantitative performance, the predictive R-values being 0.985 for the very low density lipoprotein triglycerides (VLDL-TG), 0.787 for the intermediate, 0.943 for the low, and 0.933 for the high density lipoprotein cholesterol (IDL-C, LDL-C and HDL-C, respectively). The modelling produced a kernel-based reformulation of the data, the parameters of which coincided with the well-known biochemical characteristics of the ¹H NMR spectra; particularly for VLDL-TG and HDL-C the Bayesian methodology was able to clearly identify the most characteristic resonances within the heavily overlapping information in the spectra. For IDL-C and LDL-C the resulting model kernels were more complex than those for VLDL-TG and HDL-C, probably reflecting the severe overlap of the IDL and LDL resonances in the ¹H NMR spectra.</p> <p>Conclusion</p> <p>The systematic use of Bayesian MCMC analysis is computationally demanding. Nevertheless, the combination of high-quality quantification and the biochemical rationale of the resulting models is expected to be useful in the field of metabonomics.</p

Cell Type–Specific Transcriptome Analysis Reveals a Major Role for Zeb1 and miR-200b in Mouse Inner Ear Morphogenesis

Cellular heterogeneity hinders the extraction of functionally significant results and inference of regulatory networks from wide-scale expression profiles of complex mammalian organs. The mammalian inner ear consists of the auditory and vestibular systems that are each composed of hair cells, supporting cells, neurons, mesenchymal cells, other epithelial cells, and blood vessels. We developed a novel protocol to sort auditory and vestibular tissues of newborn mouse inner ears into their major cellular components. Transcriptome profiling of the sorted cells identified cell type–specific expression clusters. Computational analysis detected transcription factors and microRNAs that play key roles in determining cell identity in the inner ear. Specifically, our analysis revealed the role of the Zeb1/miR-200b pathway in establishing epithelial and mesenchymal identity in the inner ear. Furthermore, we detected a misregulation of the ZEB1 pathway in the inner ear of Twirler mice, which manifest, among other phenotypes, malformations of the auditory and vestibular labyrinth. The association of misregulation of the ZEB1/miR-200b pathway with auditory and vestibular defects in the Twirler mutant mice uncovers a novel mechanism underlying deafness and balance disorders. Our approach can be employed to decipher additional complex regulatory networks underlying other hearing and balance mouse mutants

Promoter Methylation in Head and Neck Squamous Cell Carcinoma Cell Lines Is Significantly Different than Methylation in Primary Tumors and Xenografts

Studies designed to identify novel methylation events related to cancer often employ cancer cell lines in the discovery phase of the experiments and have a relatively low rate of discovery of cancer-related methylation events. An alternative algorithm for discovery of novel methylation in cancer uses primary tumor-derived xenografts instead of cell lines as the primary source of nucleic acid for evaluation. We evaluated DNA extracted from primary head and neck squamous cell carcinomas (HNSCC), xenografts grown from these primary tumors in nude mice, HNSCC-derived cell lines, normal oral mucosal samples, and minimally transformed oral keratinocyte-derived cell lines using Illumina Infinum Humanmethylation 27 genome-wide methylation microarrays. We found >2,200 statistically significant methylation differences between cancer cell lines and primary tumors and when comparing normal oral mucosa to keratinocyte cell lines. We found no statistically significant promoter methylation differences between primary tumor xenografts and primary tumors. This study demonstrates that tumor-derived xenografts are highly accurate representations of promoter methylation in primary tumors and that cancer derived cell lines have significant drawbacks for discovery of promoter methylation alterations in primary tumors. These findings also support use of primary tumor xenografts for the study of methylation in cancer, drug discovery, and the development of personalized cancer treatments

MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures

<p>Abstract</p> <p>Background</p> <p>One-dimensional ¹H-NMR spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, the accurate identification of individual compounds is still a challenging task, particularly in spectral regions with higher peak densities. The need for automatic tools to facilitate and further improve the accuracy of such tasks, while using increasingly larger reference spectral libraries becomes a priority of current metabolomics research.</p> <p>Results</p> <p>We introduce a web server application, called MetaboHunter, which can be used for automatic assignment of ¹H-NMR spectra of metabolites. MetaboHunter provides methods for automatic metabolite identification based on spectra or peak lists with three different search methods and with possibility for peak drift in a user defined spectral range. The assignment is performed using as reference libraries manually curated data from two major publicly available databases of NMR metabolite standard measurements (HMDB and MMCD). Tests using a variety of synthetic and experimental spectra of single and multi metabolite mixtures show that MetaboHunter is able to identify, in average, more than 80% of detectable metabolites from spectra of synthetic mixtures and more than 50% from spectra corresponding to experimental mixtures. This work also suggests that better scoring functions improve by more than 30% the performance of MetaboHunter's metabolite identification methods.</p> <p>Conclusions</p> <p>MetaboHunter is a freely accessible, easy to use and user friendly ¹H-NMR-based web server application that provides efficient data input and pre-processing, flexible parameter settings, fast and automatic metabolite fingerprinting and results visualization via intuitive plotting and compound peak hit maps. Compared to other published and freely accessible metabolomics tools, MetaboHunter implements three efficient methods to search for metabolites in manually curated data from two reference libraries.</p> <p>Availability</p> <p><url>http://www.nrcbioinformatics.ca/metabohunter/</url></p

53BP1 can limit sister-chromatid rupture and rearrangements driven by a distinct ultrafine DNA bridging-breakage process

Chromosome missegregation acts as one of the driving forces for chromosome instability and cancer development. Here, we find that in human cancer cells, HeLa and U2OS, depletion of 53BP1 (p53-binding protein 1) exacerbates chromosome non-disjunction resulting from a new type of sister-chromatid intertwinement, which is distinct from FANCD2-associated ultrafine DNA bridges (UFBs) induced by replication stress. Importantly, the sister DNA intertwinements trigger gross chromosomal rearrangements through a distinct process, named sister-chromatid rupture and bridging. In contrast to conventional anaphase bridge-breakage models, we demonstrate that chromatid axes of the intertwined sister-chromatids rupture prior to the breakage of the DNA bridges. Consequently, the ruptured sister arms remain tethered and cause signature chromosome rearrangements, including whole-arm (Robertsonian-like) translocation/deletion and isochromosome formation. Therefore, our study reveals a hitherto unreported chromatid damage phenomenon mediated by sister DNA intertwinements that may help to explain the development of complex karyotypes in tumour cells