Biomedical subjects

Paul Pavlidis

Publications and source records attributed to Paul Pavlidis.

28 records · Page 2Linked to original sources

PubMed2003

Inducible enhancement of memory storage and synaptic plasticity in transgenic mice expressing an inhibitor of ATF4 (CREB-2) and C/EBP proteins.

To examine the role of C/EBP-related transcription factors in long-term synaptic plasticity and memory storage, we have used the tetracycline-regulated system and expressed in the forebrain of mice a broad dominant-negative inhibitor of C/EBP (EGFP-AZIP), which preferentially interacts with several inhibiting isoforms of C/EBP. EGFP-AZIP also reduces the expression of ATF4, a distant member of the C/EBP family of transcription factors that is homologous to the Aplysia memory suppressor gene ApCREB-2. Consistent with the removal of inhibitory constraints on transcription, we find an increase in the pattern of gene transcripts in the hippocampus of EGFP-AZIP transgenic mice and both a reversibly enhanced hippocampal-based spatial memory and LTP. These results suggest that several proteins within the C/EBP family including ATF4 (CREB-2) act to constrain long-term synaptic changes and memory formation. Relief of this inhibition lowers the threshold for hippocampal-dependent long-term synaptic potentiation and memory storage in mice.

Activating Transcription Factor 4↗

PubMed2003

Classification of clear-cell sarcoma as a subtype of melanoma by genomic profiling.

PURPOSE: To develop a genome-based classification scheme for clear-cell sarcoma (CCS), also known as melanoma of soft parts (MSP), which would have implications for diagnosis and treatment. This tumor displays characteristic features of soft tissue sarcoma (STS), including deep soft tissue primary location and a characteristic translocation, t(12;22)(q13;q12), involving EWS and ATF1 genes. CCS/MSP also has typical melanoma features, including immunoreactivity for S100 and HMB45, pigmentation, MITF-M expression, and a propensity for regional lymph node metastases. MATERIALS AND METHODS: RNA samples from 21 cell lines and 60 pathologically confirmed cases of STS, melanoma, and CCS/MSP were examined using the U95A GeneChip (Affymetrix, Santa Clara, CA). Hierarchical cluster analysis, principal component analysis, and support vector machine (SVM) analysis exploited genomic correlations within the data to classify CCS/MSP. RESULTS: Unsupervised analyses demonstrated a clear distinction between STS and melanoma and, furthermore, showed that CCS/MSP cluster with the melanomas as a distinct group. A supervised SVM learning approach further validated this finding and provided a user-independent approach to diagnosis. Genes of interest that discriminate CCS/MSP included those encoding melanocyte differentiation antigens, MITF, SOX10, ERBB3, and FGFR1. CONCLUSION: Gene expression profiles support the classification of CCS/MSP as a distinct genomic subtype of melanoma. Analysis of these gene profiles using the SVM may be an important diagnostic tool. Genomic analysis identified potential targets for the development of therapeutic strategies in the treatment of this disease.

Algorithms↗

PubMed2003

Matrix2png: a utility for visualizing matrix data.

UNLABELLED: We describe a simple software tool, 'matrix2png', for creating color images of matrix data. Originally designed with the display of microarray data sets in mind, it is a general tool that can be used to make simple visualizations of matrices for use in figures, web pages, slide presentations and the like. It can also be used to generate images 'on the fly' in web applications. Both continuous-valued and discrete-valued (categorical) data sets can be displayed. Many options are available to the user, including the colors used, the display of row and column labels, and scale bars. In this note we describe some of matrix2png's features and describe some places it has been useful in the authors' work. AVAILABILITY: A simple web interface is available, and Unix binaries are available from http://microarray.cpmc.columbia.edu/matrix2png. Source code is available on request.

Color↗

PubMed2003

Classification and subtype prediction of adult soft tissue sarcoma by functional genomics.

Adult soft tissue sarcomas are a heterogeneous group of tumors, including well-described subtypes by histological and genotypic criteria, and pleomorphic tumors typically characterized by non-recurrent genetic aberrations and karyotypic heterogeneity. The latter pose a diagnostic challenge, even to experienced pathologists. We proposed that gene expression profiling in soft tissue sarcoma would identify a genomic-based classification scheme that is useful in diagnosis. RNA samples from 51 pathologically confirmed cases, representing nine different histological subtypes of adult soft tissue sarcoma, were examined using the Affymetrix U95A GeneChip. Statistical tests were performed on experimental groups identified by cluster analysis, to find discriminating genes that could subsequently be applied in a support vector machine algorithm. Synovial sarcomas, round-cell/myxoid liposarcomas, clear-cell sarcomas and gastrointestinal stromal tumors displayed remarkably distinct and homogenous gene expression profiles. Pleomorphic tumors were heterogeneous. Notably, a subset of malignant fibrous histiocytomas, a controversialhistological subtype, was identified as a distinct genomic group. The support vector machine algorithm supported a genomic basis for diagnosis, with both high sensitivity and specificity. In conclusion, we showed gene expression profiling to be useful in classification and diagnosis, providing insights into pathogenesis and pointing to potential new therapeutic targets of soft tissue sarcoma.

Adult↗

PubMed2003

Using ANOVA for gene selection from microarray studies of the nervous system.

Methods are presented for detecting differential expression using statistical hypothesis testing methods including analysis of variance (ANOVA). Practicalities of experimental design, power, and sample size are discussed. Methods for multiple testing correction and their application are described. Instructions for running typical analyses are given in the R programming environment. R code and the sample data set used to generate the examples are available at http://microarray.cpmc.columbia.edu/pavlidis/pub/aovmethods/.

Analysis of Variance↗

PubMed2002

Cutting edge: STAT6 serves as a positive and negative regulator of gene expression in IL-4-stimulated B lymphocytes.

STAT6 plays an important role in IL-4-mediated B cell activation and differentiation. To identify primary and secondary target genes of STAT6, gene expression profiles of IL-4-stimulated B cells from STAT6+/+ vs STAT6-/- mice were compared. Statistical analysis revealed that 106 distinct probe sets including 70 known genes were differentially expressed between the 2 genotypes. These genes include transcription factors, kinases, and other enzymes, cell surface receptors, and Ig H chains. Surprisingly, although 31 genes were expressed at higher levels in STAT6+/+ B cells, 39 genes were expressed at higher abundance in STAT6-/- B cells. This result implies both positive and negative regulatory functions of STAT6 in IL-4-mediated gene expression. Furthermore, IL-4 induces expression of the transcription factor Krox20, which is required for maximal IL-4-induced transcription.

Animals↗

PubMed2002

Learning gene functional classifications from multiple data types.

In our attempts to understand cellular function at the molecular level, we must be able to synthesize information from disparate types of genomic data. We consider the problem of inferring gene functional classifications from a heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence comparisons. We demonstrate the application of the support vector machine (SVM) learning algorithm to this functional inference task. Our results suggest the importance of exploiting prior information about the heterogeneity of the data. In particular, we propose an SVM kernel function that is explicitly heterogeneous. In addition, we describe feature scaling methods for further exploiting prior knowledge of heterogeneity by giving each data type different weights.

Algorithms↗

PubMed2002

Exploring gene expression data with class scores.

We address a commonly asked question about gene expression data sets: "What functional classes of genes are most interesting in the data?" In the methods we present, expression data is partitioned into classes based on existing annotation schemes. Each class is then given three separately derived "interest" scores. The first score is based on an assessment of the statistical significance of gene expression changes experienced by members of the class, in the context of the experimental design. The second is based on the co-expression of genes in the class. The third score is based on the learnability of the classification. We show that all three methods reveal significant classes in each of three different gene expression data sets. Many classes are identified by one method but not the others, indicating that the methods are complementary. The classes identified are in many cases of clear relevance to the experiment. Our results suggest that these class scoring methods are useful tools for exploring gene expression data.

Analysis of Variance↗

PubMed2002

Differential amplification of gene expression in lens cell lines conditioned to survive peroxide stress.

PURPOSE: The response of lens systems to oxidative stress is confusing. Antioxidative defense systems are not mobilized as expected, and unanticipated defenses appear important. Therefore, mouse lens cell lines conditioned to survive different peroxide stresses have been analyzed to determine their global changes in gene expression. METHODS: The immortal mouse lens epithelial cell line alphaTN4-1 was conditioned to survive 125 microM H2O2 (H cells) or a combination of both 100 microM tertiary butyl hydroperoxide (TBHP) and 125 microM H2O2 (HT cells), by a methodology previously described. The total RNA was isolated from the different cell lines and analyzed with oligonucleotide mouse expression microarrays. Four microarrays were used for each cell line. Microarray results were confirmed by real-time RT-PCR. RESULTS: A new cell line resistant to both 125 microM H2O2 and 100 micro M TBHP was developed, because cells resistant to H2O2 were killed by TBHP. Analysis of classic antioxidative enzyme activities showed little change between cells that survive H2O2 (H) and those that survive H2O2 and TBHP (HT). Therefore, the global change in gene expression in these cell lines was determined with gene expression microarrays. The fluorescent signal changes of the genes within the three cell lines, H, HT, and control (C), were analyzed by statistical methods including Tukey analysis. It was found that from the 12,422 gene fragments and expressed sequence tags (ESTs) analyzed--based on a one-way ANOVA with a stringent cutoff of one false positive per 1000 genes and correcting for microarray background and noise--approximately 950 (7.6%) genes had a significant change in expression in comparing the C, H, and HT groups. A small group of antioxidative defense genes were found in this population, including catalase, members of the glutathione (GSH)-S-transferase family, NAD(P)H menadione oxidoreductase 1, and the ferritin light chain. The remaining genes are involved in a broad spectrum of other biological systems. In the HT versus H comparison, only a few genes were found that had increased expression in the HT line compared with expression in the H line, including GSH-S-transferase alpha 3 and hephaestin. Many genes that are frequently considered antioxidative defense genes, including most of the GSH peroxidases, unexpectedly showed little change. CONCLUSIONS: An unusual and generally unexpected small group of antioxidative defense genes appear to have increased expression in response to H2O2 stress. Cell lines resistant to H2O2 do not appear to survive challenge with another type of peroxide, TBHP, a lipid peroxide prototype. However, acquisition of TBHP resistance by H cells was found to be accompanied by significantly amplified expression of only a few additional antioxidative defense genes. Many of the amplified genes do not appear to be involved with antioxidative systems, reflecting the complexity of the cells' response to oxidative stress.

Animals↗

PubMedDate not supplied

Altered hippocampal transcript profile accompanies an age-related spatial memory deficit in mice.

We have carried out a global survey of age-related changes in mRNA levels in the C57BL/6NIA mouse hippocampus and found a difference in the hippocampal gene expression profile between 2-month-old young mice and 15-month-old middle-aged mice correlated with an age-related cognitive deficit in hippocampal-based explicit memory formation. Middle-aged mice displayed a mild but specific deficit in spatial memory in the Morris water maze. By using Affymetrix GeneChip microarrays, we found a distinct pattern of age-related change, consisting mostly of gene overexpression in the middle-aged mice, suggesting that the induction of negative regulators in the middle-aged hippocampus could be involved in impairment of learning. Interestingly, we report changes in transcript levels for genes that could affect synaptic plasticity. Those changes could be involved in the memory deficits we observed in the 15-month-old mice. In agreement with previous reports, we also found altered expression in genes related to inflammation, protein processing, and oxidative stress.

Aging↗