Faculty Bibliography

Isolating high-priority segments of genomes greatly enhances the efficiency of next-generation sequencing (NGS) by allowing researchers to focus on their regions of interest. For the 2010-11 DNA Sequencing Research Group (DSRG) study, we compared outcomes from two leading companies, Agilent Technologies (Santa Clara, CA, USA) and Roche NimbleGen (Madison, WI, USA), which offer custom-targeted genomic enrichment methods. Both companies were provided with the same genomic sample and challenged to capture identical genomic locations for DNA NGS. The target region totaled 3.5 Mb and included 31 individual genes and a 2-Mb contiguous interval. Each company was asked to design its best assay, perform the capture in replicates, and return the captured material to the DSRG-participating laboratories. Sequencing was performed in two different laboratories on Genome Analyzer IIx systems (Illumina, San Diego, CA, USA). Sequencing data were analyzed for sensitivity, specificity, and coverage of the desired regions. The success of the enrichment was highly dependent on the design of the capture probes. Overall, coverage variability was higher for the Agilent samples. As variant discovery is the ultimate goal for a typical targeted sequencing project, we compared samples for their ability to sequence single-nucleotide polymorphisms (SNPs) as a test of the ability to capture both chromosomes from the sample. In the targeted regions, we detected 2546 SNPs with the NimbleGen samples and 2071 with Agilent's. When limited to the regions that both companies included as baits, the number of SNPs was approximately 1000 for each, with Agilent and NimbleGen finding a small number of unique SNPs not found by the other.

Drosophila olfactory sensory neurons express either odorant receptors or ionotropic glutamate receptors (IRs). The sensory neurons that express IR64a, a member of the IR family, send axonal projections to either the DC4 or DP1m glomeruli in the antennal lobe. DC4 neurons respond specifically to acids/protons, whereas DP1m neurons respond to a broad spectrum of odorants. The molecular composition of IR64a-containing receptor complexes in either DC4 or DP1m neurons is not known, however. Here, we immunoprecipitated the IR64a protein from lysates of fly antennal tissue and identified IR8a as a receptor subunit physically associated with IR64a by mass spectrometry. IR8a mutants and flies in which IR8a was knocked down by RNAi in IR64a+ neurons exhibited defects in acid-evoked physiological and behavioral responses. Furthermore, we found that the loss of IR8a caused a significant reduction in IR64a protein levels. When expressed in Xenopus oocytes, IR64a and IR8a formed a functional ion channel that allowed ligand-evoked cation currents. These findings provide direct evidence that IR8a is a subunit that forms a functional olfactory receptor with IR64a in vivo to mediate odor detection.

Selected Reaction Monitoring (SRM) is a method of choice for accurate quantitation of low-abundance proteins in complex backgrounds. This strategy is, however, sensitive to interference from other components in the sample that have the same precursor and fragment masses as the monitored transitions. We present here an approach to detect interference by using the expected relative intensity of SRM transitions. We also designed an algorithm to automatically detect the linear range of calibration curves. These approaches were applied to the experimental data of Clinical Proteomic Tumor Analysis Consortium (CPTAC) Verification Work Group Study 7 and show that the corrected measurements provide more accurate quantitation than the uncorrected data.

The Proteome Informatics Research Group (iPRG) this year performed a study to evaluate the benefits of using databases derived from RNA-Seq data for peptide identifi-cation. The proteomic dataset provided consisted of high mass accuracy tandem mass spectra acquired when analyzing human peripheral blood mononuclear cells. A variety of different types of sequence databases were supplied. These included a standard protein sequence database; a database containing only sequences of proteins expressed in the sample based on RNA-Seq data; a database that included sequence and splice variants; a database of sequences that could not be reconciled to known expressed gene sequences. Participants were asked to report spectral identifications in the form of an Excel spreadsheet, highlighting those identifications that were only identified using one of the RNA-Seq derived specialized sequence databases. Participants were also required to complete a web-based questionnaire summarizing the tools and methods they used. Additional peptide identifications were achieved by the use of each of the different RNA-Seq derived databases, although the number of additional identifications was modest. Nevertheless, these new identifications could have potential biological significance, so this type of analysis may still be worthwhile

The Proteome Informatics Research Group (iPRG) this year performed a study to evaluate the benefits of using databases derived from RNA-Seq data for peptide identification. The proteomic dataset provided consisted of high mass accuracy tandem mass spectra acquired when analyzing human peripheral blood mononuclear cells. A variety of different types of sequence databases were supplied. These included a standard protein sequence database; a database containing only sequences of proteins expressed in the sam- ple based on RNA-Seq data; a database that included sequence and splice variants; a database of sequences that could not be reconciled to known expressed gene sequences. Participants were asked to report spectral identifications in the form of an Excel spreadsheet, highlighting those identifications that were only identified using one of the RNA-Seq derived specialized sequence databases. Participants were also required to complete a web-based questionnaire summarizing the tools and methods they used. Additional peptide identifications were achieved by the use of each of the different RNA-Seq derived databases, although the number of additional identifications was modest. Nevertheless, these new identifications could have potential biological significance, so this type of analysis may still be worthwhile

ATP-sensitive K(+) (K(ATP) ) channels are expressed ubiquitously, but have diverse roles in various organs and cells. Their diversity can partly be explained by distinct tissue-specific compositions of four copies of the pore-forming inward rectifier potassium channel subunits (Kir6.1 and/or Kir6.2) and four regulatory sulfonylurea receptor subunits (SUR1 and/or SUR2). Channel function and/or subcellular localization also can be modified by the proteins with which they transiently or permanently interact to generate even more diversity. We performed a quantitative proteomic analysis of K(ATP) channel complexes in the heart, endothelium, insulin-secreting min6 cells (pancreatic beta-cell like), and the hypothalamus to identify proteins with which they interact in different tissues. Glycolysis is an overrepresented pathway in identified proteins of the heart, min6 cells, and the endothelium. Proteins with other energy metabolic functions were identified in the hypothalamic samples. These data suggest that the metabolo-electrical coupling conferred by K(ATP) channels is conferred partly by proteins with which they interact. A large number of identified cytoskeletal and trafficking proteins suggests endocytic recycling may help control K(ATP) channel surface density and/or subcellular localization. Overall, our data demonstrate that K(ATP) channels in different tissues may assemble with proteins having common functions, but that tissue-specific complex organization also occurs.

Hyaluronan-linked protein 1 (HAPLN1) which has been shown to be highly expressed in malignant pleural mesotheliomas (MPM), was detected in serum using an electrochemical surface-imprinting method. First, the detection method was optimized using Bovine serum albumin (BSA) as a model protein to mimic the optimal conditions required to imprint the similar molecular weight protein HAPLN1. BSA was imprinted on the gold electrode with hydroxyl terminated alkane thiols, which formed a self-assembled monolayer (SAM) around BSA. The analyte (BSA) was then washed away and its imprint (empty cavity with shape-memory) was used for detection of BSA in a solution, using electrochemical open-circuit potential method, namely potentiometry. Factors considered to optimize the conditions include incubation time, protein concentration, limit of detection and size of electrode. Matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) was used to confirm selectivity of imprints. With the obtained imprinting control parameters, HAPLN1 was imprinted in duplicate and the detection of spiked HAPLN1 was successfully conducted in serum.

The field of label-free biophysical technologies used to quantitatively characterize macromolecular interactions with each other and with small molecules has grown enormously in the last 10 years. The most widely used analytical technologies for characterizing biomolecular interactions are surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), biolayer interferometry (BLI), and analytical ultracentrifugation (AUC). Measuring interaction parameters accurately and quantitatively is challenging, as it requires specialized expertise, training, and instrumentation. The Molecular Interaction Research Group (MIRG) conducted an online survey designed to capture the current profile of label-free technologies, including ITC, SPR, and other biosensors used in academia and the pharmaceutical industry sector. The main goal of the survey was to take a snapshot of laboratory, instrumentation, applications for measuring various biophysical parameters, confidence in data interpretation, data validation and acceptability, and limitations of using various technologies. Through this survey, we anticipate that the participating laboratories will be able to gauge their own capabilities and gain insights into the relative success of the different technologies that they use for characterizing molecular interactions.