Paul Pavlidis

Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms

The growth in popularity of RNA expression microarrays has been accompanied by concerns about the reliability of the data especially when comparing between different platforms. Here, we present an evaluation of the reproducibility of microarray results using two platforms, Affymetrix GeneChips and Illumina BeadArrays. The study design is based on a dilution series of two human tissues (blood and placenta), tested in duplicate on each platform. The results of a comparison between the platforms indicate very high agreement, particularly for genes which are predicted to be differentially expressed between the two tissues. Agreement was strongly correlated with the level of expression of a gene. Concordance was also improved when probes on the two platforms could be identified as being likely to target the same set of transcripts of a given gene. These results shed light on the causes or failures of agreement across microarray platforms. The set of probes we found to be most highly reproducible can be used by others to help increase confidence in analyses of other data sets using these platforms.

Gene functional classification from heterogeneous data

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. We also show how to use knowledge about heterogeneity to aid in feature selection.

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.

Using the Gene Ontology for Microarray Data Mining: A Comparison of Methods and Application to Age Effects in Human Prefrontal Cortex

One of the challenges in the analysis of gene expression data is placing the results in the context of other data available about genes and their relationships to each other. Here, we approach this problem in the study of gene expression changes associated with age in two areas of the human prefrontal cortex, comparing two computational methods. The first method, “overrepresentation analysis” (ORA), is based on statistically evaluating the fraction of genes in a particular gene ontology class found among the set of genes showing age-related changes in expression. The second method, “functional class scoring” (FCS), examines the statistical distribution of individual gene scores among all genes in the gene ontology class and does not involve an initial gene selection step. We find that FCS yields more consistent results than ORA, and the results of ORA depended strongly on the gene selection threshold. Our findings highlight the utility of functional class scoring for the analysis of complex expression data sets and emphasize the advantage of considering all available genomic information rather than sets of genes that pass a predetermined “threshold of significance.”

Transcriptional and behavioral interaction between 22q11.2 orthologs modulates schizophrenia-related phenotypes in mice

Microdeletions of 22q11.2 represent one of the highest known genetic risk factors for schizophrenia. It is likely that more than one gene contributes to the marked risk associated with this locus. Two of the candidate risk genes encode the enzymes proline dehydrogenase (PRODH) and catechol-O-methyltransferase (COMT), which modulate the levels of a putative neuromodulator (L-proline) and the neurotransmitter dopamine, respectively. Mice that model the state of PRODH deficiency observed in humans with schizophrenia show increased neurotransmitter release at glutamatergic synapses as well as deficits in associative learning and response to psychomimetic drugs. Transcriptional profiling and pharmacological manipulations identified a transcriptional and behavioral interaction between the Prodh and Comt genes that is likely to represent a homeostatic response to enhanced dopaminergic signaling in the frontal cortex. This interaction modulates a number of schizophrenia-related phenotypes, providing a framework for understanding the high disease risk associated with this locus, the expression of the phenotype, or both.

Hierarchical model of gene regulation by transforming growth factor β

Transforming growth factor βs (TGF-βs) regulate key aspects of embryonic development and major human diseases. Although Smad2, Smad3, and extracellular signal-regulated kinase (ERK) mitogen-activated protein kinases (MAPKs) have been proposed as key mediators in TGF-β signaling, their functional specificities and interactivity in controlling transcriptional programs in different cell types and (patho)physiological contexts are not known. We investigated expression profiles of genes controlled by TGF-β in fibroblasts with ablations of Smad2, Smad3, and ERK MAPK. Our results suggest that Smad3 is the essential mediator of TGF-β signaling and directly activates genes encoding regulators of transcription and signal transducers through Smad3/Smad4 DNA-binding motif repeats that are characteristic for immediate-early target genes of TGF-β but absent in intermediate target genes. In contrast, Smad2 and ERK predominantly transmodulated regulation of both immediate-early and intermediate genes by TGF-β/Smad3. These results suggest a previously uncharacterized hierarchical model of gene regulation by TGF-β in which TGF-β causes direct activation by Smad3 of cascades of regulators of transcription and signaling that are transmodulated by Smad2 and/or ERK.

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.

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.

Activation of MAPK pathways links LMNA mutations to cardiomyopathy in Emery-Dreifuss muscular dystrophy.

Mutations in LMNA, which encodes nuclear Lamins A and C cause diseases affecting various organs, including the heart. We have determined the effects of an Lmna H222P mutation on signaling pathways involved in the development of cardiomyopathy in a knockin mouse model of autosomal dominant Emery-Dreifuss muscular dystrophy. Analysis of genome-wide expression profiles in hearts using Affymetrix GeneChips showed statistically significant differences in expression of genes in the MAPK pathways at the incipience of the development of clinical disease. Using real-time PCR, we showed that activation of MAPK pathways preceded clinical signs or detectable molecular markers of cardiomyopathy. In heart tissue and isolated cardiomyocytes, there was activation of MAPK cascades and downstream targets, implicated previously in the pathogenesis of cardiomyopathy. Expression of H222P Lamin A in cultured cells activated MAPKs and downstream target genes. Activation of MAPK signaling by mutant A-type lamins could be a cornerstone in the development of heart disease in autosomal dominant Emery-Dreifuss muscular dystrophy.

De novo Mutations in Schizophrenia Implicate Chromatin Remodeling and Support a Genetic Overlap with Autism and Intellectual Disability

Schizophrenia is a serious psychiatric disorder with a broadly undiscovered genetic etiology. Recent studies of de novo mutations (DNMs) in schizophrenia and autism have reinforced the hypothesis that rare genetic variation contributes to risk. We carried out exome sequencing on 57 trios with sporadic or familial schizophrenia. In sporadic trios, we observed a ~3.5-fold increase in the proportion of nonsense DNMs (0.101 vs 0.031, empirical P=0.01, Benjamini–Hochberg-corrected P=0.044). These mutations were significantly more likely to occur in genes with highly ranked probabilities of haploinsufficiency (P=0.0029, corrected P=0.006). DNMs of potential functional consequence were also found to occur in genes predicted to be less tolerant to rare variation (P=2.01 × 10−5, corrected P=2.1 × 10−3). Genes with DNMs overlapped with genes implicated in autism (for example, AUTS2, CHD8 and MECP2) and intellectual disability (for example, HUWE1 and TRAPPC9), supporting a shared genetic etiology between these disorders. Functionally CHD8, MECP2 and HUWE1 converge on epigenetic regulation of transcription suggesting that this may be an important risk mechanism. Our results were consistent in an analysis of additional exome-based sequencing studies of other neurodevelopmental disorders. These findings suggest that perturbations in genes, which function in the epigenetic regulation of brain development and cognition, could have a central role in the susceptibility to, pathogenesis and treatment of mental disorders.