Genome scans and gene expression microarrays converge to identify gene regulatory loci relevant in schizophrenia

Multiple linkage regions have been reported in schizophrenia, and some appear to harbor susceptibility genes that are differentially expressed in postmortem brain tissue derived from unrelated individuals. We combined traditional genome-wide linkage analysis in a multiplex family with lymphocytic genome-wide expression analysis. A genome scan suggested linkage to a chromosome 4q marker (D4S1530, LOD 2.17, θ=0) using a dominant model. Haplotype analysis using flanking microsatellite markers delineated a 14 Mb region that cosegregated with all those affected. Subsequent genome-wide scan with SNP genotypes supported the evidence of linkage to 4q33–35.1 (LOD=2.39) using a dominant model. Genome-wide microarray analysis of five affected and five unaffected family members identified two differentially expressed genes within the haplotype AGA and GALNT7 (aspartylglucosaminidase and UDP-N-acetyl-alpha-D-galactosamine: polypeptide N-acetylgalactosaminyltransferase 7) with nominal significance; however, these genes did not remain significant following analysis of covariance. We carried out genome-wide linkage analyses between the quantitative expression phenotype and genetic markers. AGA expression levels showed suggestive linkage to multiple markers in the haplotype (maximum LOD=2.37) but to no other genomic region. GALNT7 expression levels showed linkage to regulatory loci at 4q28.1 (maximum LOD=3.15) and in the haplotype region at 4q33–35.1 (maximum LOD=2.37). ADH1B (alcohol dehydrogenase IB) was linked to loci at 4q21–q23 (maximum LOD=3.08) and haplotype region at 4q33–35.1 (maximum LOD=2.27). Seven differentially expressed genes were validated with RT-PCR. Three genes in the 4q33–35.1 haplotype region were also differentially expressed in schizophrenia in postmortem dorsolateral prefrontal cortex: AGA, HMGB2, and SCRG1. These results indicate that combining differential gene expression with linkage analysis may help in identifying candidate genes and potential regulatory sites. Moreover, they also replicate recent findings of complex trans- and cis- regulation of genes.     

A novel in silico approach to identify potential therapeutic targets in human bacterial pathogens

In recent years, genome-sequencing projects of pathogens and humans have revolutionized microbial drug target identification. Of the several known genomic strategies, subtractive genomics has been successfully utilized for identifying microbial drug targets. The present work demonstrates a novel genomics approach in which codon adaptation index (CAI), a measure used to predict the translational efficiency of a gene based on synonymous codon usage, is coupled with subtractive genomics approach for mining potential drug targets. The strategy adopted is demonstrated using respiratory pathogens, namely, Streptococcus pneumoniae and Haemophilus influenzae as examples. Our approach identified 8 potent target genes (Streptococcus pneumoniae?C2, H. influenzae?C6), which are functionally significant and also play key role in host-pathogen interactions. This approach facilitates swift identification of potential drug targets, thereby enabling the search for new inhibitors. These results underscore the utility of CAI for enhanced in silico drug target identification.     

Kidney disease models:tools to identify mechanisms and potential therapeutic targets

Acute kidney injury(AKI) and chronic kidney disease(CKD) are worldwide public health problems affecting millions of people and have rapidly increased in prevalence in recent years. Due to the multiple causes of renal failure, many animal models have been developed to advance our understanding of human nephropathy. Among these experimental models, rodents have been extensively used to enable mechanistic understanding of kidney disease induction and progression, as well as to identify potential targets for therapy. In this review, we discuss AKI models induced by surgical operation and drugs or toxins, as well as a variety of CKD models(mainly genetically modified mouse models).Results from recent and ongoing clinical trials and conceptual advances derived from animal models are also explored.     

GenePicker: replicate analysis of Affymetrix gene expression microarrays

SUMMARY: GenePicker allows efficient analysis of Affymetrix gene expression data performed in replicate, through definition of analysis schemes, data normalization, t-test/ANOVA, Change-Fold Change-analysis and yields lists of differentially expressed genes with high confidence. Comparison of noise and signal analysis schemes allows determining a signal-to-noise ratio in a given experiment. Change Call, Fold Change and Signal mean ratios are used in the analysis. While each parameter alone yields gene lists that contain up to 30% false positives, the combination of these parameters nearly eliminates the false positives as verified by northern blotting, quantitative PCR in numerous independent experiments as well as by the analysis of spike-in data. AVAILABILITY: http://www.ifom-firc.it/RESEARCH/Appl_Bioinfo/tools.html. SUPPLEMENTARY INFORMATION: http://www.ifom-firc.it/RESEARCH/Appl_Bioinfo/tools.html.     

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

MOTIVATION: An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. RESULTS: By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.     

TargetScreen: An unbiased approach to identify functionally important microRNA targets

We recently identified miR-19 as the critical activity for leukemogenesis within the oncogenic 17~92 cluster of microRNAs.¹ This finding prompted us to test an unbiased method for pinpointing those miR-19 targets may be key to its oncogenic action. Specifically, we used a large-scale short hairpin RNA screen to identify those miR-19 target genes, whose knockdown could reproduce miR-19's effects on lymphocyte transformation. In this way, we found that miR-19 produces a coordinate clampdown on multiple negative regulators of PI3K-related survival signals. These findings have implications for the therapy of miR-19 expressing tumors. They also validate a new strategy for the unbiased identification of functionally important microRNA target genes. Using the example of miR-19 in leukemia, we will discuss some possibilities and limitations of this new approach.     

The application of DNA microarrays in gene expression analysis

DNA microarray technology is a new and powerful technology that will substantially increase the speed of molecular biological research. This paper gives a survey of DNA microarray technology and its use in gene expression studies. The technical aspects and their potential improvements are discussed. These comprise array manufacturing and design, array hybridisation, scanning, and data handling. Furthermore, it is discussed how DNA microarrays can be applied in the working fields of: safety, functionality and health of food and gene discovery and pathway engineering in plants.     

Differential allelic expression in the human genome: a robust approach to identify genetic and epigenetic cis-acting mechanisms regulating gene expression

The recent development of whole genome association studies has lead to the robust identification of several loci involved in different common human diseases. Interestingly, some of the strongest signals of association observed in these studies arise from non-coding regions located in very large introns or far away from any annotated genes, raising the possibility that these regions are involved in the etiology of the disease through some unidentified regulatory mechanisms. These findings highlight the importance of better understanding the mechanisms leading to inter-individual differences in gene expression in humans. Most of the existing approaches developed to identify common regulatory polymorphisms are based on linkage/association mapping of gene expression to genotypes. However, these methods have some limitations, notably their cost and the requirement of extensive genotyping information from all the individuals studied which limits their applications to a specific cohort or tissue. Here we describe a robust and high-throughput method to directly measure differences in allelic expression for a large number of genes using the Illumina Allele-Specific Expression BeadArray platform and quantitative sequencing of RT-PCR products. We show that this approach allows reliable identification of differences in the relative expression of the two alleles larger than 1.5-fold (i.e., deviations of the allelic ratio larger than 6040) and offers several advantages over the mapping of total gene expression, particularly for studying humans or outbred populations. Our analysis of more than 80 individuals for 2,968 SNPs located in 1,380 genes confirms that differential allelic expression is a widespread phenomenon affecting the expression of 20% of human genes and shows that our method successfully captures expression differences resulting from both genetic and epigenetic cis-acting mechanisms.     

From protein microarrays to diagnostic antigen discovery: a study of the pathogen Francisella tularensis

MOTIVATION: An important application of protein microarray data analysis is identifying a serodiagnostic antigen set that can reliably detect patterns and classify antigen expression profiles. This work addresses this problem using antibody responses to protein markers measured by a novel high-throughput microarray technology. The findings from this study have direct relevance to rapid, broad-based diagnostic and vaccine development. RESULTS: Protein microarray chips are probed with sera from individuals infected with the bacteria Francisella tularensis, a category A biodefense pathogen. A two-step approach to the diagnostic process is presented (1) feature (antigen) selection and (2) classification using antigen response measurements obtained from F.tularensis microarrays (244 antigens, 46 infected and 54 healthy human sera measurements). To select antigens, a ranking scheme based on the identification of significant immune responses and differential expression analysis is described. Classification methods including k-nearest neighbors, support vector machines (SVM) and k-Means clustering are applied to training data using selected antigen sets of various sizes. SVM based models yield prediction accuracy rates in the range of approximately 90% on validation data, when antigen set sizes are between 25 and 50. These results strongly indicate that the top-ranked antigens can be considered high-priority candidates for diagnostic development. AVAILABILITY: All software programs are written in R and available at http://www.igb.uci.edu/index.php?page=tools and at http://www.r-project.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Comparative proteomics approach to screening of potential diagnostic and therapeutic targets for oral squamous cell carcinoma

This work demonstrates that a comprehensive strategy of proteomics identification combined with further validation and detailed functional analysis should be adopted in the field of cancer biomarker discovery. A comparative proteomics approach was utilized to identify differentially expressed proteins in 10 oral squamous carcinoma samples paired with their corresponding normal tissues. A total of 52 significantly and consistently altered proteins were identified with eight of these being reported for the first time in oral squamous carcinoma. Of the eight newly implicated proteins, RACK1 was chosen for detailed analysis. RACK1 was demonstrated to be up-regulated in cancer at both the mRNA and protein levels. Immunohistochemical examination showed that the enhanced expression of RACK1 was correlated with the severity of the epithelial dysplasia as well as clinical stage, lymph node involvement, and recurrence, which are known indicators of a relatively poor prognosis in oral squamous carcinoma patients. RNA interference specifically targeted to silence RACK1 could initiate apoptosis of oral squamous carcinoma cells. Taken together, the results indicate that RACK1 is up-regulated in oral squamous carcinoma, not only being closely related to cell proliferation and apoptosis but also linked to clinical invasiveness and metastasis in carcinogenesis. The observations suggest that RACK1 may be a potential biomarker for early diagnosis, prognosis, and monitoring in the therapy of oral squamous carcinoma. Further this comprehensive strategy could be used for identifying other differentially expressed proteins that have potential to be candidate biomarkers of oral squamous carcinoma.     

ProCAT: a data analysis approach for protein microarrays

Protein microarrays provide a versatile method for the analysis of many protein biochemical activities. Existing DNA microarray analytical methods do not translate to protein microarrays due to differences between the technologies. Here we report a new approach, ProCAT, which corrects for background bias and spatial artifacts, identifies significant signals, filters nonspecific spots, and normalizes the resulting signal to protein abundance. ProCAT provides a powerful and flexible new approach for analyzing many types of protein microarrays.     

Morphoproteomics: exposing protein circuitries in tumors to identify potential therapeutic targets in cancer patients

Morphoproteomics combines the disciplines of histopathology, molecular biology and protein chemistry to paint a portrait of the protein circuitry in diseased cells for the purpose of uncovering molecular targets amenable to specific intervention, thereby customizing therapy for individual patients. This review considers the clinical application of morphoproteomics in malignant cells in the context of currently available pharmaceutical agents and discusses opportunities for combinatorial approaches that involve one or more small molecule inhibitors and single-agent chemotherapy with relatively low toxicity profiles. Future directions that involve focusing on points of convergence in signal transduction pathways and which integrate morphoproteomic with genomic and pharmacoproteomic and protein-function microarray data are offered.     

Morphoproteomics: exposing protein circuitries in tumors to identify potential therapeutic targets in cancer patients

Morphoproteomics combines the disciplines of histopathology, molecular biology and protein chemistry to paint a portrait of the protein circuitry in diseased cells for the purpose of uncovering molecular targets amenable to specific intervention, thereby customizing therapy for individual patients. This review considers the clinical application of morphoproteomics in malignant cells in the context of currently available pharmaceutical agents and discusses opportunities for combinatorial approaches that involve one or more small molecule inhibitors and single-agent chemotherapy with relatively low toxicity profiles. Future directions that involve focusing on points of convergence in signal transduction pathways and which integrate morphoproteomic with genomic and pharmacoproteomic and protein-function microarray data are offered.     

Quantitative proteomic analysis to discover potential diagnostic markers and therapeutic targets in human renal cell carcinoma

Renal cell carcinoma (RCC) is relatively resistant to chemotherapy and radiotherapy. Recent advances in drug development are providing novel agents for the treatment of RCC, but the effects are still minimal. In addition, there is an urgent need to identify diagnostic markers for RCC. In this report, to discover potential diagnostic markers and therapeutic targets, we subjected RCC samples to a quantitative proteomic analysis utilizing 2-nitrobenzenesulfenyl (NBS) reagent. Proteins were extracted from RCC and adjacent normal tissue, obtained surgically from patients, and labeled with NBS reagent containing six (12)C or (13)C. This was followed by trypsin digestion and the enrichment of labeled peptides. Samples were then subjected to analysis by MALDI-TOF MS. NBS-labeled peptides with a 6 Da difference were identified by MS/MS. Thirty-four proteins were upregulated in more than 60% of the patients of which some were previously known, and some were novel. The identity of a few proteins was confirmed by Western blotting and quantitative real time RT-PCR. The results suggest that NBS-based quantitative proteomic analysis is useful for discovering diagnostic markers and therapeutic targets for RCC.