Amy L. Olex

Disease progression and phasic changes in gene expression in a mouse model of osteoarthritis.

Osteoarthritis (OA) is the most common form of arthritis and has multiple risk factors including joint injury. The purpose of this study was to characterize the histologic development of OA in a mouse model where OA is induced by destabilization of the medial meniscus (DMM model) and to identify genes regulated during different stages of the disease, using RNA isolated from the joint “organ” and analyzed using microarrays. Histologic changes seen in OA, including articular cartilage lesions and osteophytes, were present in the medial tibial plateaus of the DMM knees beginning at the earliest (2 week) time point and became progressively more severe by 16 weeks. 427 probe sets (371 genes) from the microarrays passed consistency and significance filters. There was an initial up-regulation at 2 and 4 weeks of genes involved in morphogenesis, differentiation, and development, including growth factor and matrix genes, as well as transcription factors including Atf2, Creb3l1, and Erg. Most genes were off or down-regulated at 8 weeks with the most highly down-regulated genes involved in cell division and the cytoskeleton. Gene expression increased at 16 weeks, in particular extracellular matrix genes including Prelp, Col3a1 and fibromodulin. Immunostaining revealed the presence of these three proteins in cartilage and soft tissues including ligaments as well as in the fibrocartilage covering osteophytes. The results support a phasic development of OA with early matrix remodeling and transcriptional activity followed by a more quiescent period that is not maintained. This implies that the response to an OA intervention will depend on the timing of the intervention. The quiescent period at 8 weeks may be due to the maturation of the osteophytes which are thought to temporarily stabilize the joint.

A Kinetic Analysis of the Auxin Transcriptome Reveals Cell Wall Remodeling Proteins That Modulate Lateral Root Development in Arabidopsis

We identified genes involved in auxin-dependent lateral root formation using high temporal resolution and genome-wide transcript abundance analysis of auxin-treated Arabidopsis roots. Cell wall modification mutants, revealed by a reverse-genetic screen, had root phenotypes, supporting the hypothesis that auxin-mediated cell wall remodeling is an essential feature of lateral root development. To identify gene products that participate in auxin-dependent lateral root formation, a high temporal resolution, genome-wide transcript abundance analysis was performed with auxin-treated Arabidopsis thaliana roots. Data analysis identified 1246 transcripts that were consistently regulated by indole-3-acetic acid (IAA), partitioning into 60 clusters with distinct response kinetics. We identified rapidly induced clusters containing auxin-response functional annotations and clusters exhibiting delayed induction linked to cell division temporally correlated with lateral root induction. Several clusters were enriched with genes encoding proteins involved in cell wall modification, opening the possibility for understanding mechanistic details of cell structural changes that result in root formation following auxin treatment. Mutants with insertions in 72 genes annotated with a cell wall remodeling function were examined for alterations in IAA-regulated root growth and development. This reverse-genetic screen yielded eight mutants with root phenotypes. Detailed characterization of seedlings with mutations in CELLULASE3/GLYCOSYLHYDROLASE9B3 and LEUCINE RICH EXTENSIN2, genes not normally linked to auxin response, revealed defects in the early and late stages of lateral root development, respectively. The genes identified here using kinetic insight into expression changes lay the foundation for mechanistic understanding of auxin-mediated cell wall remodeling as an essential feature of lateral root development.

Integration of gene expression data with network-based analysis to identify signaling and metabolic pathways regulated during the development of osteoarthritis

Osteoarthritis (OA) is characterized by remodeling and degradation of joint tissues. Microarray studies have led to a better understanding of the molecular changes that occur in tissues affected by conditions such as OA; however, such analyses are limited to the identification of a list of genes with altered transcript expression, usually at a single time point during disease progression. While these lists have identified many novel genes that are altered during the disease process, they are unable to identify perturbed relationships between genes and gene products. In this work, we have integrated a time course gene expression dataset with network analysis to gain a better systems level understanding of the early events that occur during the development of OA in a mouse model. The subnetworks that were enriched at one or more of the time points examined (2, 4, 8, and 16 weeks after induction of OA) contained genes from several pathways proposed to be important to the OA process, including the extracellular matrix-receptor interaction and the focal adhesion pathways and the Wnt, Hedgehog and TGF-β signaling pathways. The genes within the subnetworks were most active at the 2 and 4 week time points and included genes not previously studied in the OA process. A unique pathway, riboflavin metabolism, was active at the 4 week time point. These results suggest that the incorporation of network-type analyses along with time series microarray data will lead to advancements in our understanding of complex diseases such as OA at a systems level, and may provide novel insights into the pathways and processes involved in disease pathogenesis.

Analysis of The Cancer Genome Atlas sequencing data reveals novel properties of the human papillomavirus 16 genome in head and neck squamous cell carcinoma

Human papillomavirus (HPV) DNA is detected in up to 80% of oropharyngeal carcinomas (OPC) and this HPV positive disease has reached epidemic proportions. To increase our understanding of the disease, we investigated the status of the HPV16 genome in HPV-positive head and neck cancers (HNC). Raw RNA-Seq and Whole Genome Sequence data from The Cancer Genome Atlas HNC samples were analyzed to gain a full understanding of the HPV genome status for these tumors. Several remarkable and novel observations were made following this analysis. Firstly, there are three main HPV genome states in these tumors that are split relatively evenly: An episomal only state, an integrated state, and a state in which the viral genome exists as a hybrid episome with human DNA. Secondly, none of the tumors expressed high levels of E6; E6*I is the dominant variant expressed in all tumors. The most striking conclusion from this study is that around three quarters of HPV16 positive HNC contain episomal versions of the viral genome that are likely replicating in an E1-E2 dependent manner. The clinical and therapeutic implications of these observations are discussed.

Dynamics of dendritic cell maturation are identified through a novel filtering strategy applied to biological time-course microarray replicates

BackgroundDendritic cells (DC) play a central role in primary immune responses and become potent stimulators of the adaptive immune response after undergoing the critical process of maturation. Understanding the dynamics of DC maturation would provide key insights into this important process. Time course microarray experiments can provide unique insights into DC maturation dynamics. Replicate experiments are necessary to address the issues of experimental and biological variability. Statistical methods and averaging are often used to identify significant signals. Here a novel strategy for filtering of replicate time course microarray data, which identifies consistent signals between the replicates, is presented and applied to a DC time course microarray experiment.ResultsThe temporal dynamics of DC maturation were studied by stimulating DC with poly(I:C) and following gene expression at 5 time points from 1 to 24 hours. The novel filtering strategy uses standard statistical and fold change techniques, along with the consistency of replicate temporal profiles, to identify those differentially expressed genes that were consistent in two biological replicate experiments. To address the issue of cluster reproducibility a consensus clustering method, which identifies clusters of genes whose expression varies consistently between replicates, was also developed and applied. Analysis of the resulting clusters revealed many known and novel characteristics of DC maturation, such as the up-regulation of specific immune response pathways. Intriguingly, more genes were down-regulated than up-regulated. Results identify a more comprehensive program of down-regulation, including many genes involved in protein synthesis, metabolism, and housekeeping needed for maintenance of cellular integrity and metabolism.ConclusionsThe new filtering strategy emphasizes the importance of consistent and reproducible results when analyzing microarray data and utilizes consistency between replicate experiments as a criterion in both feature selection and clustering, without averaging or otherwise combining replicate data. Observation of a significant down-regulation program during DC maturation indicates that DC are preparing for cell death and provides a path to better understand the process. This new filtering strategy can be adapted for use in analyzing other large-scale time course data sets with replicates.

Broad Phenotypic Changes Associated with Gain of Radiation Resistance in Head and Neck Squamous Cell Cancer

AIMS The central issue of resistance to radiation remains a significant challenge in the treatment of cancer despite improvements in treatment modality and emergence of new therapies. To facilitate the identification of molecular factors that elicit protection against ionizing radiation, we developed a matched model of radiation resistance for head and neck squamous cell cancer (HNSCC) and characterized its properties using quantitative mass spectrometry and complementary assays. RESULTS Functional network analysis of proteomics data identified DNA replication and base excision repair, extracellular matrix-receptor interaction, cell cycle, focal adhesion, and regulation of actin cytoskeleton as significantly up- or downregulated networks in resistant (rSCC-61) HNSCC cells. Upregulated proteins in rSCC-61 included a number of cytokeratins, fatty acid synthase, and antioxidant proteins. In addition, the rSCC-61 cells displayed two unexpected features compared with parental radiation-sensitive SCC-61 cells: (i) rSCC-61 had increased sensitivity to Erlotinib, a small-molecule inhibitor of epidermal growth factor receptor; and (ii) there was evidence of mesenchymal-to-epithelial transition in rSCC-61, confirmed by the expression of protein markers and functional assays (e.g., Vimentin, migration). INNOVATION The matched model of radiation resistance presented here shows that multiple signaling and metabolic pathways converge to produce the rSCC-61 phenotype, and this points to the function of the antioxidant system as a major regulator of resistance to ionizing radiation in rSCC-61, a phenomenon further confirmed by analysis of HNSCC tumor samples. CONCLUSION The rSCC-61/SCC-61 model provides the opportunity for future investigations of the redox-regulated mechanisms of response to combined radiation and Erlotinib in a preclinical setting.

SC2ATmd: a tool for integration of the figure of merit with cluster analysis for gene expression data

UNLABELLED Standard and Consensus Clustering Analysis Tool for Microarray Data (SC²ATmd) is a MATLAB-implemented application specifically designed for the exploration of microarray gene expression data via clustering. Implementation of two versions of the clustering validation method figure of merit allows for performance comparisons between different clustering algorithms, and tailors the cluster analysis process to the varying characteristics of each dataset. Along with standard clustering algorithms this application also offers a consensus clustering method that can generate reproducible clusters across replicate experiments or different clustering algorithms. This application was designed specifically for the analysis of gene expression data, but may be used with any numerical data as long as it is in the right format. AVAILABILITY SC²ATmd may be freely downloaded from http://www.compbiosci.wfu.edu/tools.htm.

Microarray analysis reveals age-related differences in gene expression during the development of osteoarthritis in mice

Background Osteoarthritis (OA) is the most common form of arthritis and is the type most closely associated with aging. Joint injury is also a key risk factor in OA development. The mechanisms by which aging contributes to the development of OA are not completely understood. This study uses microarray analysis to compare the differences in gene expression in the joint tissues of young and old mice in a model of post-traumatic OA.

Additional limitations of the clustering validation method figure of merit

Clustering analysis is an important exploratory tool that aids in the analysis and organization of genomic data. Each biological data set has different characteris, and the decision of which clustering method is appropriate and how many clusters are optimal on a dataset-by-dataset basis can be problematic. The Figure of Merit (FOM) is a quantitative clustering validation method designed to aid in these decisions. While FOM is useful, it does have limitations which must be considered when using it. This research shows that the FOM is biased toward Euclidean distance. Performing FOM analysis on clusters created by using Pearsons correlation coefficient as a similarity measure is shown to be non-optimal, and mathematically inadvisable. A new, correlation coefficient-biased version of the FOM has been developed, and preliminary results indicate that this new FOM is effectively biased toward clusters generated using the correlation coefficient.

Characterizing the efficacy of cancer therapeutics in patient-derived xenograft models of metastatic breast cancer

PurposeBasal-like breast cancers are aggressive and often metastasize to vital organs. Treatment is largely limited to chemotherapy. This study aims to characterize the efficacy of cancer therapeutics in vitro and in vivo within the primary tumor and metastatic setting, using patient-derived xenograft (PDX) models.MethodsWe employed two basal-like, triple-negative PDX models, WHIM2 and WHIM30. PDX cells, obtained from mammary tumors grown in mice, were treated with twelve cancer therapeutics to evaluate their cytotoxicity in vitro. Four of the effective drugs—carboplatin, cyclophosphamide, bortezomib, and dacarbazine—were tested in vivo for their efficacy in treating mammary tumors, and metastases generated by intracardiac injection of tumor cells.ResultsRNA sequencing showed that global gene expression of PDX cells grown in the mammary gland was similar to those tested in culture. In vitro, carboplatin was cytotoxic to WHIM30 but not WHIM2, whereas bortezomib, dacarbazine, and cyclophosphamide were cytotoxic to both lines. Yet, these drugs were ineffective in treating both primary and metastatic WHIM2 tumors in vivo. Carboplatin and cyclophosphamide were effective in treating WHIM30 mammary tumors and reducing metastatic burden in the brain, liver, and lungs. WHIM2 and WHIM30 metastases showed distinct patterns of cytokeratin and vimentin expression, regardless of treatment, suggesting that different tumor cell subpopulations may preferentially seed in different organs.ConclusionsThis study highlights the utility of PDX models for studying the efficacy of therapeutics in reducing metastatic burden in specific organs. The differential treatment responses between two PDX models of the same intrinsic subtype, in both the primary and metastatic setting, recapitulates the challenges faced in treating cancer patients and highlights the need for combination therapies and predictive biomarkers.