Development and standardization of multiplexed antibody microarrays for use in quantitative proteomics

BACKGROUND: Quantitative proteomics is an emerging field that encompasses multiplexed measurement of many known proteins in groups of experimental samples in order to identify differences between groups. Antibody arrays are a novel technology that is increasingly being used for quantitative proteomics studies due to highly multiplexed content, scalability, matrix flexibility and economy of sample consumption. Key applications of antibody arrays in quantitative proteomics studies are identification of novel diagnostic assays, biomarker discovery in trials of new drugs, and validation of qualitative proteomics discoveries. These applications require performance benchmarking, standardization and specification. RESULTS: Six dual-antibody, sandwich immunoassay arrays that measure 170 serum or plasma proteins were developed and experimental procedures refined in more than thirty quantitative proteomics studies. This report provides detailed information and specification for manufacture, qualification, assay automation, performance, assay validation and data processing for antibody arrays in large scale quantitative proteomics studies. CONCLUSION: The present report describes development of first generation standards for antibody arrays in quantitative proteomics. Specifically, it describes the requirements of a comprehensive validation program to identify and minimize antibody cross reaction under highly multiplexed conditions; provides the rationale for the application of standardized statistical approaches to manage the data output of highly replicated assays; defines design requirements for controls to normalize sample replicate measurements; emphasizes the importance of stringent quality control testing of reagents and antibody microarrays; recommends the use of real-time monitors to evaluate sensitivity, dynamic range and platform precision; and presents survey procedures to reveal the significance of biomarker findings

A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2.

There is great potential for host-based gene expression analysis to impact the early diagnosis of infectious diseases. In particular, the influenza pandemic of 2009 highlighted the challenges and limitations of traditional pathogen-based testing for suspected upper respiratory viral infection. We inoculated human volunteers with either influenza A (A/Brisbane/59/2007 (H1N1) or A/Wisconsin/67/2005 (H3N2)), and assayed the peripheral blood transcriptome every 8 hours for 7 days. Of 41 inoculated volunteers, 18 (44%) developed symptomatic infection. Using unbiased sparse latent factor regression analysis, we generated a gene signature (or factor) for symptomatic influenza capable of detecting 94% of infected cases. This gene signature is detectable as early as 29 hours post-exposure and achieves maximal accuracy on average 43 hours (p = 0.003, H1N1) and 38 hours (p-value = 0.005, H3N2) before peak clinical symptoms. In order to test the relevance of these findings in naturally acquired disease, a composite influenza A signature built from these challenge studies was applied to Emergency Department patients where it discriminates between swine-origin influenza A/H1N1 (2009) infected and non-infected individuals with 92% accuracy. The host genomic response to Influenza infection is robust and may provide the means for detection before typical clinical symptoms are apparent

Development and standardization of multiplexed antibody microarrays for use in quantitative proteomics

Background Quantitative proteomics is an emerging field that encompasses multiplexed measurement of many known proteins in groups of experimental samples in order to identify differences between groups. Antibody arrays are a novel technology that is increasingly being used for quantitative proteomics studies due to highly multiplexed content, scalability, matrix flexibility and economy of sample consumption. Key applications of antibody arrays in quantitative proteomics studies are identification of novel diagnostic assays, biomarker discovery in trials of new drugs, and validation of qualitative proteomics discoveries. These applications require performance benchmarking, standardization and specification. Results Six dual-antibody, sandwich immunoassay arrays that measure 170 serum or plasma proteins were developed and experimental procedures refined in more than thirty quantitative proteomics studies. This report provides detailed information and specification for manufacture, qualification, assay automation, performance, assay validation and data processing for antibody arrays in large scale quantitative proteomics studies. Conclusion The present report describes development of first generation standards for antibody arrays in quantitative proteomics. Specifically, it describes the requirements of a comprehensive validation program to identify and minimize antibody cross reaction under highly multiplexed conditions; provides the rationale for the application of standardized statistical approaches to manage the data output of highly replicated assays; defines design requirements for controls to normalize sample replicate measurements; emphasizes the importance of stringent quality control testing of reagents and antibody microarrays; recommends the use of real-time monitors to evaluate sensitivity, dynamic range and platform precision; and presents survey procedures to reveal the significance of biomarker findings

In silico genotyping of the maize nested association mapping population

Nested Association Mapping (NAM) has been proposed as a means to combine the power of linkage mapping with the resolution of association mapping. It is enabled through sequencing or array genotyping of parental inbred lines while using low-cost, low-density genotyping technologies for their segregating progenies. For purposes of data analyses of NAM populations, parental genotypes at a large number of Single Nucleotide Polymorphic (SNP) loci need to be projected to their segregating progeny. Herein we demonstrate how approximately 0.5 million SNPs that have been genotyped in 26 parental lines of the publicly available maize NAM population can be projected onto their segregating progeny using only 1,106 SNP loci that have been genotyped in both the parents and their 5,000 progeny. The challenge is to estimate both the genotype and genetic location of the parental SNP genotypes in segregating progeny. Both challenges were met by estimating their expected genotypic values conditional on observed flanking markers through the use of both physical and linkage maps. About 90%, of 500,000 genotyped SNPs from the maize HapMap project, were assigned linkage map positions using linear interpolation between the maize Accessioned Gold Path (AGP) and NAM linkage maps. Of these, almost 70% provided high probability estimates of genotypes in almost 5,000 recombinant inbred lines

Quantitative Multicolor Compositional Imaging Resolves Molecular Domains in Cell-Matrix Adhesions

Background: Cellular processes occur within dynamic and multi-molecular compartments whose characterization requires analysis at high spatio-temporal resolution. Notable examples for such complexes are cell-matrix adhesion sites, consisting of numerous cytoskeletal and signaling proteins. These adhesions are highly variable in their morphology, dynamics, and apparent function, yet their molecular diversity is poorly defined. Methodology/Principal Findings: We present here a compositional imaging approach for the analysis and display of multicomponent compositions. This methodology is based on microscopy-acquired multicolor data, multi-dimensional clustering of pixels according to their composition similarity and display of the cellular distribution of these composition clusters. We apply this approach for resolving the molecular complexes associated with focal-adhesions, and the time-dependent effects of Rho-kinase inhibition. We show here compositional variations between adhesion sites, as well as ordered variations along the axis of individual focal-adhesions. The multicolor clustering approach also reveals distinct sensitivities of different focaladhesion-associated complexes to Rho-kinase inhibition. Conclusions/Significance: Multicolor compositional imaging resolves ‘‘molecular signatures’ ’ characteristic to focaladhesions and related structures, as well as sub-domains within these adhesion sites. This analysis enhances the spatial information with additional ‘‘contents-resolved’ ’ dimensions. We propose that compositional imaging can serve as

The Impact of Phenocopy on the Genetic Analysis of Complex Traits

A consistent debate is ongoing on genome-wide association studies (GWAs). A key point is the capability to identify low-penetrance variations across the human genome. Among the phenomena reducing the power of these analyses, phenocopy level (PE) hampers very seriously the investigation of complex diseases, as well known in neurological disorders, cancer, and likely of primary importance in human ageing. PE seems to be the norm, rather than the exception, especially when considering the role of epigenetics and environmental factors towards phenotype. Despite some attempts, no recognized solution has been proposed, particularly to estimate the effects of phenocopies on the study planning or its analysis design. We present a simulation, where we attempt to define more precisely how phenocopy impacts on different analytical methods under different scenarios. With our approach the critical role of phenocopy emerges, and the more the PE level increases the more the initial difficulty in detecting gene-gene interactions is amplified. In particular, our results show that strong main effects are not hampered by the presence of an increasing amount of phenocopy in the study sample, despite progressively reducing the significance of the association, if the study is sufficiently powered. On the opposite, when purely epistatic effects are simulated, the capability of identifying the association depends on several parameters, such as the strength of the interaction between the polymorphic variants, the penetrance of the polymorphism and the alleles (minor or major) which produce the combined effect and their frequency in the population. We conclude that the neglect of the possible presence of phenocopies in complex traits heavily affects the analysis of their genetic data

Bioinformatic analyses identifies novel protein-coding pharmacogenomic markers associated with paclitaxel sensitivity in NCI60 cancer cell lines

<p>Abstract</p> <p>Background</p> <p>Paclitaxel is a microtubule-stabilizing drug that has been commonly used in treating cancer. Due to genetic heterogeneity within patient populations, therapeutic response rates often vary. Here we used the NCI60 panel to identify SNPs associated with paclitaxel sensitivity. Using the panel's GI50 response data available from Developmental Therapeutics Program, cell lines were categorized as either sensitive or resistant. PLINK software was used to perform a genome-wide association analysis of the cellular response to paclitaxel with the panel's SNP-genotype data on the Affymetrix 125 k SNP array. FastSNP software helped predict each SNP's potential impact on their gene product. mRNA expression differences between sensitive and resistant cell lines was examined using data from BioGPS. Using Haploview software, we investigated for haplotypes that were more strongly associated with the cellular response to paclitaxel. Ingenuity Pathway Analysis software helped us understand how our identified genes may alter the cellular response to paclitaxel.</p> <p>Results</p> <p>43 SNPs were found significantly associated (FDR < 0.005) with paclitaxel response, with 10 belonging to protein-coding genes (<it>CFTR</it>, <it>ROBO1</it>, <it>PTPRD</it>, <it>BTBD12</it>, <it>DCT</it>, <it>SNTG1</it>, <it>SGCD</it>, <it>LPHN2</it>, <it>GRIK1</it>, <it>ZNF607</it>). SNPs in <it>GRIK1</it>, <it>DCT</it>, <it>SGCD </it>and <it>CFTR </it>were predicted to be intronic enhancers, altering gene expression, while SNPs in <it>ZNF607 </it>and <it>BTBD12 </it>cause conservative missense mutations. mRNA expression analysis supported these findings as <it>GRIK1</it>, <it>DCT</it>, <it>SNTG1</it>, <it>SGCD </it>and <it>CFTR </it>showed significantly (p < 0.05) increased expression among sensitive cell lines. Haplotypes found in <it>GRIK1, SGCD, ROBO1, LPHN2</it>, and <it>PTPRD </it>were more strongly associated with response than their individual SNPs.</p> <p>Conclusions</p> <p>Our study has taken advantage of available genotypic data and its integration with drug response data obtained from the NCI60 panel. We identified 10 SNPs located within protein-coding genes that were not previously shown to be associated with paclitaxel response. As only five genes showed differential mRNA expression, the remainder would not have been detected solely based on expression data. The identified haplotypes highlight the role of utilizing SNP combinations within genomic loci of interest to improve the risk determination associated with drug response. These genetic variants represent promising biomarkers for predicting paclitaxel response and may play a significant role in the cellular response to paclitaxel.</p

Genetic Evidence Supporting the Association of Protease and Protease Inhibitor Genes with Inflammatory Bowel Disease: A Systematic Review

As part of the European research consortium IBDase, we addressed the role of proteases and protease inhibitors (P/PIs) in inflammatory bowel disease (IBD), characterized by chronic mucosal inflammation of the gastrointestinal tract, which affects 2.2 million people in Europe and 1.4 million people in North America. We systematically reviewed all published genetic studies on populations of European ancestry (67 studies on Crohn's disease [CD] and 37 studies on ulcerative colitis [UC]) to identify critical genomic regions associated with IBD. We developed a computer algorithm to map the 807 P/PI genes with exact genomic locations listed in the MEROPS database of peptidases onto these critical regions and to rank P/PI genes according to the accumulated evidence for their association with CD and UC. 82 P/PI genes (75 coding for proteases and 7 coding for protease inhibitors) were retained for CD based on the accumulated evidence. The cylindromatosis/turban tumor syndrome gene (CYLD) on chromosome 16 ranked highest, followed by acylaminoacyl-peptidase (APEH), dystroglycan (DAG1), macrophage-stimulating protein (MST1) and ubiquitin-specific peptidase 4 (USP4), all located on chromosome 3. For UC, 18 P/PI genes were retained (14 proteases and 4protease inhibitors), with a considerably lower amount of accumulated evidence. The ranking of P/PI genes as established in this systematic review is currently used to guide validation studies of candidate P/PI genes, and their functional characterization in interdisciplinary mechanistic studies in vitro and in vivo as part of IBDase. The approach used here overcomes some of the problems encountered when subjectively selecting genes for further evaluation and could be applied to any complex disease and gene family

Analytical methods for inferring functional effects of single base pair substitutions in human cancers

Cancer is a genetic disease that results from a variety of genomic alterations. Identification of some of these causal genetic events has enabled the development of targeted therapeutics and spurred efforts to discover the key genes that drive cancer formation. Rapidly improving sequencing and genotyping technology continues to generate increasingly large datasets that require analytical methods to identify functional alterations that deserve additional investigation. This review examines statistical and computational approaches for the identification of functional changes among sets of single-nucleotide substitutions. Frequency-based methods identify the most highly mutated genes in large-scale cancer sequencing efforts while bioinformatics approaches are effective for independent evaluation of both non-synonymous mutations and polymorphisms. We also review current knowledge and tools that can be utilized for analysis of alterations in non-protein-coding genomic sequence

Meeting the challenges of implementing rapid genomic testing in acute pediatric care

Authors: Stark, Z. Lunke, S. Brett, G. Tan, N. Stapleton, R. Kumble, S. Yeung, A. Phelan, D. Chong, B. Fernandez, M.F. Marum, J.E. Hunter, M. Jarmolowicz, A. Yael Prawer, Y. Riseley, J.R. Regan, M. Elliott, J. Melissa Martyn, M. Best, S. Tan, T. Clara L Gaff, C.L. and White, S.M