Nathan O. Siemers

A high-resolution association mapping panel for the dissection of complex traits in mice.

Systems genetics relies on common genetic variants to elucidate biologic networks contributing to complex disease-related phenotypes. Mice are ideal model organisms for such approaches, but linkage analysis has been only modestly successful due to low mapping resolution. Association analysis in mice has the potential of much better resolution, but it is confounded by population structure and inadequate power to map traits that explain less than 10% of the variance, typical of mouse quantitative trait loci (QTL). We report a novel strategy for association mapping that combines classic inbred strains for mapping resolution and recombinant inbred strains for mapping power. Using a mixed model algorithm to correct for population structure, we validate the approach by mapping over 2500 cis-expression QTL with a resolution an order of magnitude narrower than traditional QTL analysis. We also report the fine mapping of metabolic traits such as plasma lipids. This resource, termed the Hybrid Mouse Diversity Panel, makes possible the integration of multiple data sets and should prove useful for systems-based approaches to complex traits and studies of gene-by-environment interactions.

HBEGF, SRA1, and IK: Three cosegregating genes as determinants of cardiomyopathy

Human dilated cardiomyopathy (DCM), a disorder of the cardiac muscle, causes considerable morbidity and mortality and is one of the major causes of sudden cardiac death. Genetic factors play a role in the etiology and pathogenesis of DCM. Disease-associated genetic variations identified to date have been identified in single families or single sporadic patients and explain a minority of the etiology of DCM. We show that a 600-kb region of linkage disequilibrium (LD) on 5q31.2-3, harboring multiple genes, is associated with cardiomyopathy in three independent Caucasian populations (combined P-value = 0.00087). Functional assessment in zebrafish demonstrates that at least three genes, orthologous to loci in this LD block, HBEGF, IK, and SRA1, result independently in a phenotype of myocardial contractile dysfunction when their expression is reduced with morpholino antisense reagents. Evolutionary analysis across multiple vertebrate genomes suggests that this heart failure-associated LD block emerged by a series of genomic rearrangements across amphibian, avian, and mammalian genomes and is maintained as a cluster in mammals. Taken together, these observations challenge the simple notion that disease phenotypes can be traced to altered function of a single locus within a haplotype and suggest that a more detailed assessment of causality can be necessary.

Gene Set Enrichment Analysis

Set enrichment analytical methods have become commonplace tools applied to the analysis and interpretation of biological data. The statistical techniques are used to identify categorical biases within lists of genes, proteins, or metabolites. The goal is to discover the shared functions or properties of the biological items represented within the lists. Application of these methods can provide great biological insight, including the discovery of participation in the same biological activity or pathway, shared interacting genes or regulators, common cellular compartmentalization, or association with disease. The methods require ordered or unordered lists of biological items as input, understanding of the reference set from which the lists were selected, categorical classifiers describing the items, and a statistical algorithm to assess bias of each classifier. Due to the complexity of most algorithms and the number of calculations performed, computer software is almost always used for execution of the algorithm, as well as for presentation of the results. This chapter will provide an overview of the statistical methods used to perform an enrichment analysis. Guidelines for assembly of the requisite information will be presented, with a focus on careful definition of the sets used by the statistical algorithms. The need for multiple test correction when working with large libraries of classifiers is emphasized, and we outline several options for performing the corrections. Finally, interpreting the results of such analysis will be discussed along with examples of recent research utilizing the techniques.

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

BackgroundThe availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes).ResultsWe developed Nearest Neighbor Networks (NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods.ConclusionThe Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large datasets, and its ability to span a wide range of biological functions with high precision.

Exome Sequencing Reveals Comprehensive Genomic Alterations across Eight Cancer Cell Lines

It is well established that genomic alterations play an essential role in oncogenesis, disease progression, and response of tumors to therapeutic intervention. The advances of next-generation sequencing technologies (NGS) provide unprecedented capabilities to scan genomes for changes such as mutations, deletions, and alterations of chromosomal copy number. However, the cost of full-genome sequencing still prevents the routine application of NGS in many areas. Capturing and sequencing the coding exons of genes (the “exome”) can be a cost-effective approach for identifying changes that result in alteration of protein sequences. We applied an exome-sequencing technology (Roche Nimblegen capture paired with 454 sequencing) to identify sequence variation and mutations in eight commonly used cancer cell lines from a variety of tissue origins (A2780, A549, Colo205, GTL16, NCI-H661, MDA-MB468, PC3, and RD). We showed that this technology can accurately identify sequence variation, providing ∼95% concordance with Affymetrix SNP Array 6.0 performed on the same cell lines. Furthermore, we detected 19 of the 21 mutations reported in Sanger COSMIC database for these cell lines. We identified an average of 2,779 potential novel sequence variations/mutations per cell line, of which 1,904 were non-synonymous. Many non-synonymous changes were identified in kinases and known cancer-related genes. In addition we confirmed that the read-depth of exome sequence data can be used to estimate high-level gene amplifications and identify homologous deletions. In summary, we demonstrate that exome sequencing can be a reliable and cost-effective way for identifying alterations in cancer genomes, and we have generated a comprehensive catalogue of genomic alterations in coding regions of eight cancer cell lines. These findings could provide important insights into cancer pathways and mechanisms of resistance to anti-cancer therapies.

Cephalosporin prodrugs of paclitaxel for immunologically specific activation by L-49-sFv-β-Lactamase fusion protein

Paclitaxel conjugates of 7-phenylacetamidocephalosporanic acid were prepared as prodrugs for site specific activation by targeted beta-lactamase. Immunologically specific activation of the prodrug 5 containing 3,3-dimethyl-4-amino-butyric acid as linker in combination with the fusion protein L-49-sFv-beta-lactamase was demonstrated in vitro on 3677 melanoma cells.

Gene Expression Analyses of Mouse Aortic Endothelium in Response to Atherogenic Stimuli

Objective—Endothelial cells are central to the initiation of atherosclerosis, yet there has been limited success in studying their gene expression in the mouse aorta. To address this, we developed a method for determining the global transcriptional changes that occur in the mouse endothelium in response to atherogenic conditions and applied it to investigate inflammatory stimuli. Approach and Results—We characterized a method for the isolation of endothelial cell RNA with high purity directly from mouse aortas and adapted this method to allow for the treatment of aortas ex vivo before RNA collection. Expression array analysis was performed on endothelial cell RNA isolated from control and hyperlipidemic prelesion mouse aortas, and 797 differentially expressed genes were identified. We also examined the effect of additional atherogenic conditions on endothelial gene expression, including ex vivo treatment with inflammatory stimuli, acute hyperlipidemia, and age. Of the 14 most highly differentially expressed genes in endothelium from prelesion aortas, 8 were also perturbed significantly by ≥1 atherogenic conditions: 2610019E17Rik, Abca1, H2-Ab1, H2-D1, Pf4, Ppbp, Pvrl2, and Tnnt2. Conclusions—We demonstrated that RNA can be isolated from mouse aortic endothelial cells after in vivo and ex vivo treatments of the murine vessel wall. We applied these methods to identify a group of genes, many of which have not been described previously as having a direct role in atherosclerosis, that were highly regulated by atherogenic stimuli and may play a role in early atherogenesis.

Transcriptional Profiling of the Dose Response: A More Powerful Approach for Characterizing Drug Activities

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data.

SDRS – an algorithm for analyzing large scale dose response data

Summary: Dose–response information is critical to understanding drug effects, yet analytical methods for dose–response assays cannot cope with the dimensionality of large-scale screening data such as the microarray profiling data. To overcome this limitation, we developed and implemented the Sigmoidal Dose Response Search (SDRS) algorithm, a grid search-based method designed to handle large-scale dose–response data. This method not only calculates the pharmacological parameters for every assay, but also provides built-in statistic that enables downstream systematic analyses, such as characterizing dose response at the transcriptome level. Availability: Bio::SDRS is freely available from CPAN (www.cpan.org). Contacts: ruiruji@gmail.com; bruc@acm.org Supplementary Information: Supplementary data is available at Bioinformatics online.

Structural and functional characterization of CFE88: Evidence that a conserved and essential bacterial protein is a methyltransferase

CFE88 is a conserved essential gene product from Streptococcus pneumoniae. This 227‐residue protein has minimal sequence similarity to proteins of known 3Dstructure. Sequence alignment models and computational protein threading studies suggest that CFE88 is a methyltransferase. Characterization of the conformation and function of CFE88 has been performed by using several techniques. Backbone atom and limited side‐chain atom NMR resonance assignments have been obtained. The data indicate that CFE88 has two domains: an N‐terminal domain with 163 residues and a C‐terminal domain with 64 residues. The C‐terminal domain is primarily helical, while the N‐terminal domain has a mixed helical/extended (Rossmann) fold. By aligning the experimentally observed elements of secondary structure, an initial unrefined model of CFE88 has been constructed based on the X‐ray structure of ErmC′ methyltransferase (Protein Data Bank entry 1QAN). NMR and biophysical studies demonstrate binding of S‐adenosyl‐L‐homocysteine (SAH) to CFE88; these interactions have been localized by NMR to the predicted active site in the N‐terminal domain. Mutants that target this predicted active site (H26W, E46R, and E46W) have been constructed and characterized. Overall, our results both indicate that CFE88 is a methyltransferase and further suggest that the methyltransferase activity is essential for bacterial survival.