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Biomedical subjects

Nicholas Knowlton

Publications and source records attributed to Nicholas Knowlton.

6 recordsLinked to original sources

Analysis of the interaction of extracellular matrix and phenotype of bladder cancer cells.

BACKGROUND: The extracellular matrix has a major effect upon the malignant properties of bladder cancer cells both in vitro in 3-dimensional culture and in vivo. Comparing gene expression of several bladder cancer cells lines grown under permissive and suppressive conditions in 3-dimensional growth on cancer-derived and normal-derived basement membrane gels respectively and on plastic in conventional tissue culture provides a model system for investigating the interaction of malignancy and extracellular matrix. Understanding how the extracellular matrix affects the phenotype of bladder cancer cells may provide important clues to identify new markers or targets for therapy. METHODS: Five bladder cancer cell lines and one immortalized, but non-tumorigenic, urothelial line were grown on Matrigel, a cancer-derived ECM, on SISgel, a normal-derived ECM, and on plastic, where the only ECM is derived from the cells themselves. The transcriptomes were analyzed on an array of 1186 well-annotated cancer derived cDNAs containing most of the major pathways for malignancy. Hypervariable genes expressing more variability across cell lines than a set expressing technical variability were analyzed further. Expression values were clustered, and to identify genes most likely to represent biological factors, statistically over-represented ontologies and transcriptional regulatory elements were identified. RESULTS: Approximately 400 of the 1186 total genes were expressed 2 SD above background. Approximately 100 genes were hypervariable in cells grown on each ECM, but the pattern was different in each case. A core of 20 were identified as hypervariable under all 3 growth conditions, and 33 were hypervariable on both SISgel and Matrigel, but not on plastic. Clustering of the hypervariable genes showed very different patterns for the same 6 cell types on the different ECM. Even when loss of cell cycle regulation was identified, different genes were involved, depending on the ECM. Under the most permissive conditions of growth where the malignant phenotype was fully expressed, activation of AKT was noted. TGFbeta1 signaling played a major role in the response of bladder cancer cells to ECM. Identification of TREs on genes that clustered together suggested some clustering was driven by specific transcription factors. CONCLUSION: The extracellular matrix on which cancer cells are grown has a major effect on gene expression. A core of 20 malignancy-related genes were not affected by matrix, and 33 were differentially expressed on 3-dimensional culture as opposed to plastic. Other than these genes, the patterns of expression were very different in cells grown on SISgel than on Matrigel or even plastic, supporting the hypothesis that growth of bladder cancer cells on normal matrix suppresses some malignant functions. Unique underlying regulatory networks were driving gene expression and could be identified by the approach outlined here.

Cell Line, Tumor↗

Distinct profiles of Sjögren's syndrome patients with ectopic salivary gland germinal centers revealed by serum cytokines and BAFF.

The formation of ectopic germinal centers (GC) has been described in Sjögren's syndrome (SS), although little is known about the molecular basis of this phenomenon. These structures are a focus of in situ autoantibody production and have been hypothesized to be involved in lymphomagenesis in SS patients. Serum cytokines also play an important role in SS pathogenesis in part via immune dysregulation and may therefore contribute to ectopic GC formation. Herein, highly multiplex cytokine screening of SS patients with (SSGC+) and without (SSGC-) GC formation was done to identify cytokine profiles that correlate with this phenomenon. Serum levels of B-cell activating factor (BAFF) were also screened as a potential biomarker of immune dysregulation in SS and SSGC formation. Univariate analysis demonstrated that serum levels of a broad spectrum of immune and inflammatory modulating cytokines are upregulated in SSGC+ and SSGC- patients relative to unaffected controls IL-1beta, IL-2, IL-6, IL-15, IFN-gamma and CCL4 (MIP-1beta). SSGC+ patients were distinguished from healthy individuals by higher levels of IL-4, IL-10, GM-CSF, IFN-alpha, CCL3 (MIP-1alpha), CCL11 (Eotaxin) and BAFF, while SSGC+ and SSGC- patients differed in CCL2 (MCP-1) expression. Discriminant function analysis (DFA), a multivariate discrimination method that uses observed differences to characterize groups when casual relationships are not well understood, was employed to identify a subset of these biomarkers that maximally discriminate among SSGC+, SSGC- and unaffected individuals. The biomarker having the strongest discriminatory power identified by DFA besides CCL11 (Eotaxin) and IFN-gamma was BAFF. The variables identified by DFA are interdependent and are often of mechanistic significance to the pathologic states they distinguish, suggesting that these factors modulate SS pathology and SSGC formation in a synergistic manner.

Adult↗

Hypervariable genes--experimental error or hidden dynamics.

In a homogeneous group of samples, not all genes of high variability stem from experimental errors in microarray experiments. These expression variations can be attributed to many factors including natural biological oscillations or metabolic processes. The behavior of these genes can tease out important clues about naturally occurring dynamic processes in the organism or experimental system under study. We developed a statistical procedure for the selection of genes with high variability denoted hypervariable (HV) genes. After the exclusion of low expressed genes and a stabilizing log-transformation, the majority of genes have comparable residual variability. Based on an F-test, HV genes are selected as having a statistically significant difference from the majority of variability stabilized genes measured by the 'reference group'. A novel F-test clustering technique, further noted as 'F-means clustering', groups HV genes with similar variability patterns, presumably from their participation in a common dynamic biological process. F-means clustering establishes, for the first time, groups of co-expressed HV genes and is illustrated with microarray data from patients with juvenile rheumatoid arthritis and healthy controls.

Adolescent↗

Microarray Data Analysis Toolbox (MDAT): for normalization, adjustment and analysis of gene expression data.

SUMMARY: We introduce a novel Matlab toolbox for microarray data analysis. This toolbox uses normalization based upon a normally distributed background and differential gene expression based on five statistical measures. The objects in this toolbox are open source and can be implemented to suit your application. AVAILABILITY: MDAT v1.0 is a Matlab toolbox and requires Matlab to run. MDAT is freely available at http://microarray.omrf.org/publications/2004/knowlton/MDAT.zip.

Algorithms↗

Statistical monitoring of weak spots for improvement of normalization and ratio estimates in microarrays.

BACKGROUND: Several aspects of microarray data analysis are dependent on identification of genes expressed at or near the limits of detection. For example, regression-based normalization methods rely on the premise that most genes in compared samples are expressed at similar levels and therefore require accurate identification of nonexpressed genes (additive noise) so that they can be excluded from the normalization procedure. Moreover, key regulatory genes can maintain stringent control of a given response at low expression levels. If arbitrary cutoffs are used for distinguishing expressed from nonexpressed genes, some of these key regulatory genes may be unnecessarily excluded from the analysis. Unfortunately, no accurate method for differentiating additive noise from genes expressed at low levels is currently available. RESULTS: We developed a multistep procedure for analysis of mRNA expression data that robustly identifies the additive noise in a microarray experiment. This analysis is predicated on the fact that additive noise signals can be accurately identified by both distribution and statistical analysis. CONCLUSIONS: Identification of additive noise in this manner allows exclusion of noncorrelated weak signals from regression-based normalization of compared profiles thus maximizing the accuracy of these methods. Moreover, genes expressed at very low levels can be clearly identified due to the fact that their expression distribution is stable and distinguishable from the random pattern of additive noise.

Animals↗

Connective molecular pathways of experimental bladder inflammation.

Inflammation is an inherent response of the organism that permits its survival despite constant environmental challenges. The process normally leads to recovery from injury and to healing. However, if targeted destruction and assisted repair are not properly phased, chronic inflammation can result in persistent tissue damage. To better understand the inflammatory process, we recently introduced a profiling methodology to identify common genes involved in bladder inflammation. The method represents a complementation to the classic quantification of inflammation and provides information regarding the early, intermediate, and late events in gene regulation. However, gene profiling fails to describe the molecular pathways and their interconnections involved in the particular inflammatory response. The present work introduces a new statistical technique for inferring functional interconnections between inflammatory pathways underlying classic models of bladder inflammation and permits the modeling of the inflammatory network. This new statistical method is based on variants of cluster analysis, Boolean networking, differential equations, Bayesian networking, and partial correlation. By applying partial correlation analysis, we developed mosaics of gene expression that permitted a global visualization of common and unique pathways elicited by different stimuli. The significance of these processes was tested from both biological and statistical viewpoints. We propose that connective mosaic may represent the necessary simplification step to visualize cDNA array results.

Animals↗