MicroRNA Expression Array Identifies Novel Diagnostic Markers for Conventional and Oncocytic Follicular Thyroid Carcinomas

Objective:The most difficult thyroid tumors to be diagnosed by cytology and histology are conventional follicular carcinomas (cFTCs) and oncocytic follicular carcinomas (oFTCs). Several microRNAs (miRNAs) have been previously found to be consistently deregulated in papillary thyroid carcinomas; however, very limited information is available for cFTC and oFTC. The aim of this study was to explore miRNA deregulation and find candidate miRNA markers for follicular carcinomas that can be used diagnostically.Design:Thirty-eight follicular thyroid carcinomas (21 cFTCs, 17 oFTCs) and 10 normal thyroid tissue samples were studied for expression of 381 miRNAs using human microarray assays. Expression of deregulated miRNAs was confirmed by individual RT-PCR assays in all samples. In addition, 11 follicular adenomas, two hyperplastic nodules (HNs), and 19 fine-needle aspiration samples were studied for expression of novel miRNA markers detected in this study.Results:The unsupervised hierarchical clustering analysis demonstrated individual clusters for cFTC and oFTC, indicating the difference in miRNA expression between these tumor types. Both cFTCs and oFTCs showed an up-regulation of miR-182/-183/-221/-222/-125a-3p and a down-regulation of miR-542-5p/-574-3p/-455/-199a. Novel miRNA (miR-885-5p) was found to be strongly up-regulated (>40-fold) in oFTCs but not in cFTCs, follicular adenomas, and HNs. The classification and regression tree algorithm applied to fine-needle aspiration samples demonstrated that three dysregulated miRNAs (miR-885-5p/-221/-574-3p) allowed distinguishing follicular thyroid carcinomas from benign HNs with high accuracy.Conclusions:In this study we demonstrate that different histopathological types of follicular thyroid carcinomas have distinct miRNA expression profiles. MiR-885-5p is highly up-regulated in oncocytic follicular carcinomas and may serve as a diagnostic marker for these tumors. A small set of deregulated miRNAs allows for an accurate discrimination between follicular carcinomas and hyperplastic nodules and can be used diagnostically in fine-needle aspiration biopsies.

Geographical partitioning of goat diversity in Europe and the Middle East

Thirty microsatellite markers were analysed in 1426 goats from 45 traditional or rare breeds in 15 European and Middle Eastern countries. In all populations inbreeding was indicated by heterozygosity deficiency (mean FIS = 0.10). Genetic differentiation between breeds was moderate with a mean FST value of 0.07, but for most (c. 71%) northern and central European breeds, individuals could be assigned to their breeds with a success rate of more than 80%. Bayesian-based clustering analysis of allele frequencies and multivariate analysis revealed at least four discrete clusters: eastern Mediterranean (Middle East), central Mediterranean, western Mediterranean and central/northern Europe. About 41% of the genetic variability among the breeds could be explained by their geographical origin. A decrease in genetic diversity from the south-east to the north-west was accompanied by an increase in the level of differentiation at the breed level. These observations support the hypothesis that domestic livestock migrated from the Middle East towards western and northern Europe and indicate that breed formation was more systematic in north-central Europe than in the Middle East. We propose that breed differentiation and molecular diversity are independent criteria for conservation.

Gene expression profiling reveals consistent differences between clinical samples of human leukaemias and their model cell lines

Microarray gene expression profiles of fresh clinical samples of chronic myeloid leukaemia in chronic phase, acute promyelocytic leukaemia and acute monocytic leukaemia were compared with profiles from cell lines representing the corresponding types of leukaemia (K562, NB4, HL60). In a hierarchical clustering analysis, all clinical samples clustered separately from the cell lines, regardless of leukaemic subtype. Gene ontology analysis showed that cell lines chiefly overexpressed genes related to macromolecular metabolism, whereas in clinical samples genes related to the immune response were abundantly expressed. These findings must be taken into consideration when conclusions from cell line-based studies are extrapolated to patients.

Comprehensive MicroRNA expression profiling identifies novel markers in follicular variant of papillary thyroid carcinoma

BACKGROUND Follicular variant of papillary thyroid carcinoma (FVPTC) shares features of papillary (PTC) and follicular (FTC) thyroid carcinomas on a clinical, morphological, and genetic level. MicroRNA (miRNA) deregulation was extensively studied in PTCs and FTCs. However, very limited information is available for FVPTC. The aim of this study was to assess miRNA expression in FVPTC with the most comprehensive miRNA array panel and to correlate it with the clinicopathological data. METHODS Forty-four papillary thyroid carcinomas (17 FVPTC, 27 classic PTC) and eight normal thyroid tissue samples were analyzed for expression of 748 miRNAs using Human Microarray Assays on the ABI 7900 platform (Life Technologies, Carlsbad, CA). In addition, an independent set of 61 tumor and normal samples was studied for expression of novel miRNA markers detected in this study. RESULTS Overall, the miRNA expression profile demonstrated similar trends between FVPTC and classic PTC. Fourteen miRNAs were deregulated in FVPTC with a fold change of more than five (up/down), including miRNAs known to be upregulated in PTC (miR-146b-3p, -146-5p, -221, -222 and miR-222-5p) and novel miRNAs (miR-375, -551b, 181-2-3p, 99b-3p). However, the levels of miRNA expression were different between these tumor types and some miRNAs were uniquely dysregulated in FVPTC allowing separation of these tumors on the unsupervised hierarchical clustering analysis. Upregulation of novel miR-375 was confirmed in a large independent set of follicular cell derived neoplasms and benign nodules and demonstrated specific upregulation for PTC. Two miRNAs (miR-181a-2-3p, miR-99b-3p) were associated with an adverse outcome in FVPTC patients by a Kaplan-Meier (p < 0.05) and multivariate Cox regression analysis (p < 0.05). CONCLUSIONS Despite high similarity in miRNA expression between FVPTC and classic PTC, several miRNAs were uniquely expressed in each tumor type, supporting their histopathologic differences. Highly upregulated miRNA identified in this study (miR-375) can serve as a novel marker of papillary thyroid carcinoma, and miR-181a-2-3p and miR-99b-3p can predict relapse-free survival in patients with FVPTC thus potentially providing important diagnostic and predictive value.

Stable-Isotope Time Series and Precipitation Origin from Firn-Core and Snow Samples, Altai Glaciers, Siberia

In the summers of 2001 and 2002, glacio-climatological research was performed at 4110-4120 m a.s.l. on the Belukha snow/firn plateau, Siberian Altai. Hundreds of samples from snow pits and a 21 m snow/firn core were collected to establish the annual/seasonal/monthly depth-accumulation scale, based on stable-isotope records, stratigraphic analyses and meteorological and synoptic data. The fluctuations of water stable-isotope records show well-preserved seasonal variations. The delta(18)O and delta D relationships in precipitation, snow pits and the snow/firn core have the same slope to the covariance as that of the global meteoric water line. The origins of precipitation nourishing the Belukha plateau were determined based on clustering analysis of delta(18)O and d-excess records and examination of synoptic atmospheric patterns. Calibration and validation of the developed clusters occurred at event and monthly timescales with about 15% uncertainty. Two distinct moisture sources were shown: oceanic sources with d-excess < 12 parts per thousand, and the Aral-Caspian closed drainage basin sources with d-excess > 12 parts per thousand. Two-thirds of the annual accumulation was from oceanic precipitation, of which more than half had isotopic ratios corresponding to moisture evaporated over the Atlantic Ocean. Precipitation from the Arctic/Pacific Ocean had the lowest deuterium excess, contributing one-tenth to annual accumulation.

Crevicular fluid biomarkers and periodontal disease progression.

AIM Assess the ability of a panel of gingival crevicular fluid (GCF) biomarkers as predictors of periodontal disease progression (PDP). MATERIALS AND METHODS In this study, 100 individuals participated in a 12-month longitudinal investigation and were categorized into four groups according to their periodontal status. GCF, clinical parameters and saliva were collected bi-monthly. Subgingival plaque and serum were collected bi-annually. For 6 months, no periodontal treatment was provided. At 6 months, patients received periodontal therapy and continued participation from 6 to 12 months. GCF samples were analysed by ELISA for MMP-8, MMP-9, Osteoprotegerin, C-reactive Protein and IL-1β. Differences in median levels of GCF biomarkers were compared between stable and progressing participants using Wilcoxon Rank Sum test (p = 0.05). Clustering algorithm was used to evaluate the ability of oral biomarkers to classify patients as either stable or progressing. RESULTS Eighty-three individuals completed the 6-month monitoring phase. With the exception of GCF C-reactive protein, all biomarkers were significantly higher in the PDP group compared to stable patients. Clustering analysis showed highest sensitivity levels when biofilm pathogens and GCF biomarkers were combined with clinical measures, 74% (95% CI = 61, 86). CONCLUSIONS Signature of GCF fluid-derived biomarkers combined with pathogens and clinical measures provides a sensitive measure for discrimination of PDP (ClinicalTrials.gov NCT00277745).

MicroRNA profile of poorly differentiated thyroid carcinomas: new diagnostic and prognostic insights.

The diagnosis of conventional and oncocytic poorly differentiated (oPD) thyroid carcinomas is difficult. The aim of this study is to characterise their largely unknown miRNA expression profile and to compare it with well-differentiated thyroid tumours, as well as to identify miRNAs which could potentially serve as diagnostic and prognostic markers. A total of 14 poorly differentiated (PD), 13 oPD, 72 well-differentiated thyroid carcinomas and eight normal thyroid specimens were studied for the expression of 768 miRNAs using PCR-Microarrays. MiRNA expression was different between PD and oPD thyroid carcinomas, demonstrating individual clusters on the clustering analysis. Both tumour types showed upregulation of miR-125a-5p, -15a-3p, -182, -183-3p, -222, -222-5p, and downregulation of miR-130b, -139-5p, -150, -193a-5p, -219-5p, -23b, -451, -455-3p and of miR-886-3p as compared with normal thyroid tissue. In addition, the oPD thyroid carcinomas demonstrated upregulation of miR-221 and miR-885-5p. The difference in expression was also observed between miRNA expression in PD and well-differentiated tumours. The CHAID algorithm allowed the separation of PD from well-differentiated thyroid carcinomas with 73-79% accuracy using miR-23b and miR-150 as a separator. Kaplan-Meier and multivariate analysis showed a significant association with tumour relapses (for miR-23b) and with tumour-specific death (for miR-150) in PD and oPD thyroid carcinomas. MiRNA expression is different in conventional and oPD thyroid carcinomas in comparison with well-differentiated thyroid cancers and can be used for discrimination between these tumour types. The newly identified deregulated miRNAs (miR-150, miR-23b) bear the potential to be used in a clinical setting, delivering prognostic and diagnostic informations.

Variegation of comet 67P/Churyumov-Gerasimenko in regions showing activity

Aims. We carried out an investigation of the surface variegation of comet 67P/Churyumov-Gerasimenko, the detection of regions showing activity, the determination of active and inactive surface regions of the comet with spectral methods, and the detection of fallback material. Methods. We analyzed multispectral data generated with Optical, Spectroscopic, and Infrared Remote Imaging System (OSIRIS) narrow angle camera (NAC) observations via spectral techniques, reflectance ratios, and spectral slopes in order to study active regions. We applied clustering analysis to the results of the reflectance ratios, and introduced the new technique of activity thresholds to detect areas potentially enriched in volatiles. Results. Local color inhomogeneities are detected over the investigated surface regions. Active regions, such as Hapi, the active pits of Seth and Ma'at, the clustered and isolated bright features in Imhotep, the alcoves in Seth and Ma'at, and the large alcove in Anuket, have bluer spectra than the overall surface. The spectra generated with OSIRIS NAC observations are dominated by cometary emissions of around 700 nm to 750 nm as a result of the coma between the comet's surface and the camera. One of the two isolated bright features in the Imhotep region displays an absorption band of around 700 nm, which probably indicates the existence of hydrated silicates. An absorption band with a center between 800-900 nm is tentatively observed in some regions of the nucleus surface. This absorption band can be explained by the crystal field absorption of Fe2+, which is a common spectral feature seen in silicates.

Knowledge discovery using multiple sources of biological data

The primary aim of this dissertation is to develop data mining tools for knowledge discovery in biomedical data when multiple (homogeneous or heterogeneous) sources of data are available. The central hypothesis is that, when information from multiple sources of data are used appropriately and effectively, knowledge discovery can be better achieved than what is possible from only a single source. ^ Recent advances in high-throughput technology have enabled biomedical researchers to generate large volumes of diverse types of data on a genome-wide scale. These data include DNA sequences, gene expression measurements, and much more; they provide the motivation for building analysis tools to elucidate the modular organization of the cell. The challenges include efficiently and accurately extracting information from the multiple data sources; representing the information effectively, developing analytical tools, and interpreting the results in the context of the domain. ^ The first part considers the application of feature-level integration to design classifiers that discriminate between soil types. The machine learning tools, SVM and KNN, were used to successfully distinguish between several soil samples. ^ The second part considers clustering using multiple heterogeneous data sources. The resulting Multi-Source Clustering (MSC) algorithm was shown to have a better performance than clustering methods that use only a single data source or a simple feature-level integration of heterogeneous data sources. ^ The third part proposes a new approach to effectively incorporate incomplete data into clustering analysis. Adapted from K-means algorithm, the Generalized Constrained Clustering (GCC) algorithm makes use of incomplete data in the form of constraints to perform exploratory analysis. Novel approaches for extracting constraints were proposed. For sufficiently large constraint sets, the GCC algorithm outperformed the MSC algorithm. ^ The last part considers the problem of providing a theme-specific environment for mining multi-source biomedical data. The database called PlasmoTFBM, focusing on gene regulation of Plasmodium falciparum, contains diverse information and has a simple interface to allow biologists to explore the data. It provided a framework for comparing different analytical tools for predicting regulatory elements and for designing useful data mining tools. ^ The conclusion is that the experiments reported in this dissertation strongly support the central hypothesis.^

Hybrid approaches to estimating freeway travel times using point traffic detector data

The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.

Understanding the role of transforming growth factor beta signalling and epigenomics in osteoarthritis

Osteoarthritis (OA) is the most common form of arthritis with a high socioeconomic burden, with an incompletely understood etiology. Evidence suggests a role for the transforming growth factor beta (TGF-ß) signalling pathway and epigenomics in OA. The aim of this thesis was to understand the involvement of the TGF-ß pathway in OA and to determine the DNA methylation patterns of OA-affected cartilage as compared to the OA-free cartilage. First, I found that a common SNP in the BMP2 gene, a ligand in the Bone morphogenetic protein (BMP) subunit of TGF-ß pathway, was associated with OA in the Newfoundland population. I also showed a genetic association between SMAD3 - a signal transducer in the TGF-ß subunit of the TGF-ß signalling pathway - and the total radiographic burden of OA. I further demonstrated that SMAD3 is over-expressed in OA cartilage, suggesting an over activation of the TGF-ß signalling in OA. Next, I examined the connection of these genes in the regulation of matrix metallopeptidase 13 (MMP13) - an enzyme known to destroy extracellular matrix in OA cartilage - in the context of the TGF-ß signalling. The analyses showed that TGF-ß, MMP13, and SMAD3 were overexpressed in OA cartilage, whereas the expression of BMP2 was significantly reduced. The expression of TGF-ß was positively correlated with that of SMAD3 and MMP13, suggesting that TGF-ß signalling is involved in up-regulation of MMP13. This regulation, however, appears not to be controlled by SMAD3 signals, possibly due to the involvement of collateral signalling, and to be suppressed by BMP regulation in healthy cartilage, whose levels were reduced in end-stage OA. In a genome-wide DNA methylation analysis, I reported CpG sites differentially methylated in OA and showed that the cartilage methylome has a potential to distinguish between OA-affected and non-OA cartilage. Functional clustering analysis of the genes harbouring differentially methylated loci revealed that they are enriched in the skeletal system morphogenesis pathway, which could be a potential candidate for further OA studies. Overall, the findings from the present thesis provide evidence that the TGF-ß signalling pathway is associated with the development of OA, and epigenomics might be involved as a potential mechanism in OA.

Sources and mixing state of size-resolved elemental carbon particles in a European megacity: Paris

An Aerosol Time-Of-Flight Mass Spectrometer (ATOFMS) was deployed to investigate the size-resolved chemical composition of single particles at an urban background site in Paris, France, as part of the MEGAPOLI winter campaign in January/February 2010. ATOFMS particle counts were scaled to match coincident Twin Differential Mobility Particle Sizer (TDMPS) data in order to generate hourly size-resolved mass concentrations for the single particle classes observed. The total scaled ATOFMS particle mass concentration in the size range 150–1067 nm was found to agree very well with the sum of concurrent High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) and Multi-Angle Absorption Photometer (MAAP) mass concentration measurements of organic carbon (OC), inorganic ions and black carbon (BC) (R2 = 0.91). Clustering analysis of the ATOFMS single particle mass spectra allowed the separation of elemental carbon (EC) particles into four classes: (i) EC attributed to biomass burning (ECbiomass), (ii) EC attributed to traffic (ECtraffic), (iii) EC internally mixed with OC and ammonium sulfate (ECOCSOx), and (iv) EC internally mixed with OC and ammonium nitrate (ECOCNOx). Average hourly mass concentrations for EC-containing particles detected by the ATOFMS were found to agree reasonably well with semi-continuous quantitative thermal/optical EC and optical BC measurements (r2 = 0.61 and 0.65–0.68 respectively, n = 552). The EC particle mass assigned to fossil fuel and biomass burning sources also agreed reasonably well with BC mass fractions assigned to the same sources using seven-wavelength aethalometer data (r2 = 0.60 and 0.48, respectively, n = 568). Agreement between the ATOFMS and other instrumentation improved noticeably when a period influenced by significantly aged, internally mixed EC particles was removed from the intercomparison. 88% and 12% of EC particle mass was apportioned to fossil fuel and biomass burning respectively using the ATOFMS data compared with 85% and 15% respectively for BC estimated from the aethalometer model. On average, the mass size distribution for EC particles is bimodal; the smaller mode is attributed to locally emitted, mostly externally mixed EC particles, while the larger mode is dominated by aged, internally mixed ECOCNOx particles associated with continental transport events. Periods of continental influence were identified using the Lagrangian Particle Dispersion Model (LPDM) "FLEXPART". A consistent minimum between the two EC mass size modes was observed at approximately 400 nm for the measurement period. EC particles below this size are attributed to local emissions using chemical mixing state information and contribute 79% of the scaled ATOFMS EC particle mass, while particles above this size are attributed to continental transport events and contribute 21% of the EC particle mass. These results clearly demonstrate the potential benefit of monitoring size-resolved mass concentrations for the separation of local and continental EC emissions. Knowledge of the relative input of these emissions is essential for assessing the effectiveness of local abatement strategies.

Single particle characterization using the soot particle aerosol mass spectrometer (SP-AMS)

Understanding the impact of atmospheric black carbon (BC) containing particles on human health and radiative forcing requires knowledge of the mixing state of BC, including the characteristics of the materials with which it is internally mixed. In this study, we demonstrate for the first time the capabilities of the Aerodyne Soot-Particle Aerosol Mass Spectrometer equipped with a light scattering module (LS-SP-AMS) to examine the mixing state of refractory BC (rBC) and other aerosol components in an urban environment (downtown Toronto). K-means clustering analysis was used to classify single particle mass spectra into chemically distinct groups. One resultant cluster is dominated by rBC mass spectral signals (C+1 to C+5) while the organic signals fall into a few major clusters, identified as hydrocarbon-like organic aerosol (HOA), oxygenated organic aerosol (OOA), and cooking emission organic aerosol (COA). A nearly external mixing is observed with small BC particles only thinly coated by HOA ( 28% by mass on average), while over 90% of the HOA-rich particles did not contain detectable amounts of rBC. Most of the particles classified into other inorganic and organic clusters were not significantly associated with BC. The single particle results also suggest that HOA and COA emitted from anthropogenic sources were likely major contributors to organic-rich particles with low to mid-range aerodynamic diameter (dva). The similar temporal profiles and mass spectral features of the organic clusters and the factors from a positive matrix factorization (PMF) analysis of the ensemble aerosol dataset validate the conventional interpretation of the PMF results.

Human Ezrin: Prediction of the 3D structure and interactions with mTOR

The protein Ezrin, is a member of the ERM family (Ezrin, Radixin and Moesin) that links the F-actin to the plasma membrane. The protein is made of three domains namely the FERM domain, a central α-helical domain and the CERMAD domain. The residues in Ezrin such as Ser66, Tyr145, Tyr353 and Tyr477 regulate the function of the protein through phosphorylation. The protein is found in two distinct conformations of active and dormant (inactive) state. The initial step during the conformation change is the breakage of intramolecular interaction in dormant Ezrin by phosphorylation of residue Thr567. The dormant structure of human Ezrin was predicted computationally since only partial active form structure was available. The validation analysis showed that 99.7% residues were positioned in favored, allowed and generously allowed regions of the Ramachandran plot. The Z-score of Ezrin was −7.36, G-factor was 0.1, and the QMEAN score of the model was 0.61 indicating a good model for human Ezrin. The comparison of the conformations of the activated and dormant Ezrin showed a major shift in the F2 lobe (residues 142-149 and 161-177) while changes in the conformation induced mobility shifts in lobe F3 (residues 261 to 267). The 3D positions of the phosphorylation sites Tyr145, Tyr353, Tyr477, Tyr482 and Thr567 were also located. Using targeted molecular dynamic simulation, the molecular movements during conformational change from active to dormant were visualized. The dormant Ezrin auto-inhibits itself by a head-to-tail interaction of the N-terminal and C-terminal residues. The trajectory shows the breakage of the interactions and mobility of the CERMAD domain away from the FERM domain. Protein docking and clustering analysis were used to predict the residues involved in the interaction between dormant Ezrin and mTOR. Residues Tyr477 and Tyr482 were found to be involved in interaction with mTOR.

MLCut: exploring multi-level cuts in dendrograms for biological data.

Choosing a single similarity threshold for cutting dendrograms is not sufficient for performing hierarchical clustering analysis of heterogeneous data sets. In addition, alternative automated or semi-automated methods that cut dendrograms in multiple levels make assumptions about the data in hand. In an attempt to help the user to find patterns in the data and resolve ambiguities in cluster assignments, we developed MLCut: a tool that provides visual support for exploring dendrograms of heterogeneous data sets in different levels of detail. The interactive exploration of the dendrogram is coordinated with a representation of the original data, shown as parallel coordinates. The tool supports three analysis steps. Firstly, a single-height similarity threshold can be applied using a dynamic slider to identify the main clusters. Secondly, a distinctiveness threshold can be applied using a second dynamic slider to identify “weak-edges” that indicate heterogeneity within clusters. Thirdly, the user can drill-down to further explore the dendrogram structure - always in relation to the original data - and cut the branches of the tree at multiple levels. Interactive drill-down is supported using mouse events such as hovering, pointing and clicking on elements of the dendrogram. Two prototypes of this tool have been developed in collaboration with a group of biologists for analysing their own data sets. We found that enabling the users to cut the tree at multiple levels, while viewing the effect in the original data, is a promising method for clustering which could lead to scientific discoveries.