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Background Multiple testing to understand global changes in gene expression based on genetic and epigenetic modifications is evolving. Chorionic villi, obtained for prenatal testing, is limited, but can be used to understand ongoing human pregnancies. However, optimal storage, processing and utilization of CVS for multiple platform testing has not been established. Results Leftover CVS samples were flash-frozen or preserved in RNAlater. Modifications to standard isolation kits were performed to isolate quality DNA and RNA from samples as small as 2-5 mg. RNAlater samples had significantly higher RNA yields and quality and were successfully used in microarray and RNA-sequencing (RNA-seq). RNA-seq libraries generated using 200 vs. 800 ng RNA showed similar biological coefficients of variation. RNAlater samples had lower DNA yields and quality, which improved by heating the elution buffer to 70 °C. Purification of DNA was not necessary for bisulfite-conversion and genome-wide methylation profiling. CVS cells were propagated and continue to express genes found in freshly-isolated chorionic villi. Conclusions CVS samples preserved in RNAlater are superior. Our optimized techniques provide specimens for genetic, epigenetic and gene expression studies from a single small sample which can be used to develop diagnostics and treatments using a systems biology approach in the prenatal period.

Purpose Clinical trial monitoring is an essential component of drug development aimed at safeguarding subject safety, data quality, and protocol compliance by focusing sponsor oversight on the most important aspects of study conduct. In recent years, regulatory agencies, industry consortia, and nonprofit collaborations between industry and regulators, such as TransCelerate and International Committee for Harmonization, have been advocating a new, risk-based approach to monitoring clinical trials that places increased emphasis on critical data and processes and encourages greater use of centralized monitoring. However, how best to implement risk-based monitoring (RBM) remains unclear and subject to wide variations in tools and methodologies. The nonprescriptive nature of the regulatory guidelines, coupled with limitations in software technology, challenges in operationalization, and lack of robust evidence of superior outcomes, have hindered its widespread adoption. Methods We describe a holistic solution that combines convenient access to data, advanced analytics, and seamless integration with established technology infrastructure to enable comprehensive assessment and mitigation of risk at the study, site, and subject level. Findings Using data from completed RBM studies carried out in the last 4 years, we demonstrate that our implementation of RBM improves the efficiency and effectiveness of the clinical oversight process as measured on various quality, timeline, and cost dimensions. Implications These results provide strong evidence that our RBM methodology can significantly improve the clinical oversight process and do so at a lower cost through more intelligent deployment of monitoring resources to the sites that need the most attention.

Despite finding more than 40 risk loci for type 1 diabetes (T1D), the causative variants and genes remain largely unknown. Here, we sought to identify rare deleterious variants of moderate-to-large effects contributing to T1D. We deeply sequenced 301 protein-coding genes located in 49 previously reported T1D risk loci in 70 T1D cases of European ancestry. These cases were selected from putatively high-risk families that had three or more siblings diagnosed with T1D at early ages. A cluster of rare deleterious variants in PTPN22 was identified, including two novel frameshift mutations (ss538819444 and rs371865329) and two missense variants (rs74163663 and rs56048322). Genotyping in 3,609 T1D families showed that rs56048322 was significantly associated with T1D and that this association was independent of the T1D-associated common variant rs2476601. The risk allele at rs56048322 affects splicing of PTPN22, resulting in the production of two alternative PTPN22 transcripts and a novel isoform of LYP (the protein encoded by PTPN22). This isoform competes with the wild-type LYP for binding to CSK and results in hyporesponsiveness of CD4+ T cells to antigen stimulation in T1D subjects. These findings demonstrate that in addition to common variants, rare deleterious variants in PTPN22 exist and can affect T1D risk.

// Devin G. Roller 1 , Brian Capaldo 2 , Stefan Bekiranov 2 , Aaron J. Mackey 3 , Mark R. Conaway 3 , Emanuel F. Petricoin 4 , Daniel Gioeli 1 , Michael J. Weber 1 1 Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, 22908 USA 2 Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, 22908 USA 3 Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908 USA 4 Center for Applied Proteomics and Molecular Medicine, School of Systems Biology, College of Science, George Mason University, Manassas, VA 20110, USA Correspondence to: Michael J. Weber, e-mail: mjw@virginia.edu Keywords: melanoma, therapeutic resistance, cell signaling, BRAF, MAP Kinase Received: August 20, 2015 Accepted: November 21, 2015 Published: December 10, 2015 ABSTRACT Over half of BRAFV600E melanomas display intrinsic resistance to BRAF inhibitors, in part due to adaptive signaling responses. In this communication we ask whether BRAFV600E melanomas share common adaptive responses to BRAF inhibition that can provide clinically relevant targets for drug combinations. We screened a panel of 12 treatment-naive BRAFV600E melanoma cell lines with MAP Kinase pathway inhibitors in pairwise combination with 58 signaling inhibitors, assaying for synergistic cytotoxicity. We found enormous diversity in the drug combinations that showed synergy, with no two cell lines having an identical profile. Although the 6 lines most resistant to BRAF inhibition showed synergistic benefit from combination with lapatinib, the signaling mechanisms by which this combination generated synergistic cytotoxicity differed between the cell lines. We conclude that adaptive responses to inhibition of the primary oncogenic driver (BRAFV600E) are determined not only by the primary oncogenic driver but also by diverse secondary genetic and epigenetic changes (“back-seat drivers”) and hence optimal drug combinations will be variable. Because upregulation of receptor tyrosine kinases is a major source of drug resistance arising from diverse adaptive responses, we propose that inhibitors of these receptors may have substantial clinical utility in combination with inhibitors of the MAP Kinase pathway.

Genome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk. Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of β-cell failure. In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D. Genes with a cytokine-induced change of >2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium. Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3), F13a1 (6p25.3), Klhl6 (3q27.1), Nid1 (1q42.3), Pamr1 (11p13), Ripk2 (8q21.3), and Steap4 (7q21.12). To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity. RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3. Thapsigargin-induced ER stress up-regulated both Pamr1 and Klhl6. Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5- to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation). Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population. This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D.

Relational databases can integrate diverse types of information and manage large sets of similarity search results, greatly simplifying genome-scale analyses. By focusing on taxonomic subsets of sequences, relational databases can reduce the size and redundancy of sequence libraries and improve the statistical significance of homologs. In addition, by loading similarity search results into a relational database, it becomes possible to explore and summarize the relationships between all of the proteins in an organism and those in other biological kingdoms. This unit describes how to use relational databases to improve the efficiency of sequence similarity searching and demonstrates various large-scale genomic analyses of homology-related data. It also describes the installation and use of a simple protein sequence database, seqdb_demo, which is used as a basis for the other protocols. The unit also introduces search_demo, a database that stores sequence similarity search results. The search_demo database is then used to explore the evolutionary relationships between E. coli proteins and proteins in other organisms in a large-scale comparative genomic analysis. © 2017 by John Wiley & Sons, Inc.

Nonalcoholic steatohepatitis (NASH) is an emerging health crisis with no approved therapies. Obeticholic acid (OCA), a farnesoid X receptor (FXR) agonist, shows promise in NASH trials. However, the precise mechanisms mediating OCA effects and impact on cholesterol metabolism are not fully understood. We explored the pharmaco-toxicological effects of OCA on patho-physiological pathways in hepatocytes using a previously described perfused organotypic liver system that allows culture in near-physiological insulin/glucose milieus, and exhibits drug responses at clinically-relevant concentrations. Primary hepatocytes experienced 48-hour exposure to OCA at concentrations approximating therapeutic (0.5μM) and supratherapeutic (10μM) levels. Global transcriptomics by RNAseq was complimented by cellular viability (MTT), CYP activity assays, and secreted FGF19 levels in the media. Dose-dependent, transcriptional effects suggested suppression of bile acid synthesis (↓CYP7A1, ↓CYP27A1) and increased bile efflux (↑ABCB4, ↑ABCB11, ↑OSTA, ↑OSTB). Pleiotropic effects included suppression of TGFβ and IL-6 signaling pathways, and signatures suggestive of HDL suppression (↑SCARB1, ↓ApoAI, ↓LCAT) and LDL elevation (↑ApoB, ↓CYP7A1). OCA exhibited direct FXR-mediated effects with increased FGF19 secretion. Transcriptomics revealed regulation of metabolic, anti-inflammatory, and anti-fibrotic pathways beneficial in NASH, and predicted cholesterol profiles consistent with clinical findings. Follow-up studies under lipotoxic/inflammatory conditions would corroborate these effects in a disease-relevant environment.

Recent data show that extracellular signals are transmitted through a network of proteins rather than hierarchical signaling pathways, suggesting that the inhibition of a single component of a canonical pathway is insufficient for the treatment of cancer. The biologic outcome of signaling through a network is inherently more robust and resistant to inhibition of a single network component. In this study, we conducted a functional chemical genetic screen to identify novel interactions between signaling inhibitors that would not be predicted on the basis of our current understanding of signaling networks. We screened over 300 drug combinations in nine melanoma cell lines and have identified pairs of compounds that show synergistic cytotoxicity. The synergistic cytotoxicities identified did not correlate with the known RAS and BRAF mutational status of the melanoma cell lines. Among the most robust results was synergy between sorafenib, a multikinase inhibitor with activity against RAF, and diclofenac, a nonsteroidal anti-inflammatory drug (NSAID). Drug substitution experiments using the NSAIDs celecoxib and ibuprofen or the MAP–ERK kinase inhibitor PD325901 and the RAF inhibitor RAF265 suggest that inhibition of COX and mitogen-activated protein kinase signaling are targets for the synergistic cytotoxicity of sorafenib and diclofenac. Cotreatment with sorafenib and diclofenac interrupts a positive feedback signaling loop involving extracellular signal–regulated kinase, cellular phospholipase A2, and COX. Genome-wide expression profiling shows synergy-specific downregulation of survival-related genes. This study has uncovered novel functional drug combinations and suggests that the underlying signaling networks that control responses to targeted agents can vary substantially, depending on unexplored components of the cell genotype. Mol Cancer Ther; 11(11); 2505–15. ©2012 AACR.

The adipocytokine leptin links nutritional status to immune function. Leptin signaling protects from amebiasis, but the molecular mechanism is not understood. We developed an in vitro model of ameba-host cell interaction to test the hypothesis that leptin prevents ameba-induced apoptosis in host epithelial cells. We demonstrated that activation of mammalian leptin signaling increased cellular resistance to amebic cytotoxicity, including caspase-3 activation. Exogenous expression of the leptin receptor conferred resistance in susceptible cells, and leptin stimulation enhanced protection. A series of leptin receptor signaling mutants showed that resistance to amebic cytotoxicity was dependent on activation of STAT3 but not the Src homology-2 domain-containing tyrosine phosphatase (SHP-2) or STAT5. A common polymorphism in the leptin receptor (Q223R) that increases susceptibility to amebiasis in humans and mice was found to increase susceptibility to amebic cytotoxicity in single cells. The Q223R polymorphism also decreased leptin-dependent STAT3 activation by 21% relative to that of the wild-type (WT) receptor ( P = 0.035), consistent with a central role of STAT3 signaling in protection. A subset of genes uniquely regulated by STAT3 in response to leptin was identified. Most notable were the TRIB1 and suppressor of cytokine signaling 3 (SOCS3) genes, which have opposing roles in the regulation of apoptosis. Overall apoptotic genes were highly enriched in this gene set ( P < 1E−05), supporting the hypothesis that leptin regulation of host apoptotic genes via STAT3 is responsible for protection. This is the first demonstration of a mammalian signaling pathway that restricts amebic pathogenesis and represents an important advance in our mechanistic understanding of how leptin links nutrition and susceptibility to infection.

The mushroom Coprinopsis cinerea is a classic experimental model for multicellular development in fungi because it grows on defined media, completes its life cycle in 2 weeks, produces some 10 8 synchronized meiocytes, and can be manipulated at all stages in development by mutation and transformation. The 37-megabase genome of C. cinerea was sequenced and assembled into 13 chromosomes. Meiotic recombination rates vary greatly along the chromosomes, and retrotransposons are absent in large regions of the genome with low levels of meiotic recombination. Single-copy genes with identifiable orthologs in other basidiomycetes are predominant in low-recombination regions of the chromosome. In contrast, paralogous multicopy genes are found in the highly recombining regions, including a large family of protein kinases (FunK1) unique to multicellular fungi. Analyses of P450 and hydrophobin gene families confirmed that local gene duplications drive the expansions of paralogous copies and the expansions occur in independent lineages of Agaricomycotina fungi. Gene-expression patterns from microarrays were used to dissect the transcriptional program of dikaryon formation (mating). Several members of the FunK1 kinase family are differentially regulated during sexual morphogenesis, and coordinate regulation of adjacent duplications is rare. The genomes of C. cinerea and Laccaria bicolor , a symbiotic basidiomycete, share extensive regions of synteny. The largest syntenic blocks occur in regions with low meiotic recombination rates, no transposable elements, and tight gene spacing, where orthologous single-copy genes are overrepresented. The chromosome assembly of C. cinerea is an essential resource in understanding the evolution of multicellularity in the fungi.

Acidithiobacillus ferrooxidans is a chemolithoautotrophic acidophile capable of obtaining energy by oxidizing ferrous iron or sulfur compounds such as metal sulfides. Some of the proteins involved in these oxidations have been described as forming part of the periplasm of this extremophile. The detailed study of the periplasmic components constitutes an important area to understand the physiology and environmental interactions of microorganisms. Proteomics analysis of the periplasmic fraction of A. ferrooxidans ATCC 23270 was performed by using high resolution linear ion trap-FT MS. We identified a total of 131 proteins in the periplasm of the microorganism grown in thiosulfate. When possible, functional categories were assigned to the proteins: 13.8% were transport and binding proteins, 14.6% were several kinds of cell envelope proteins, 10.8% were involved in energy metabolism, 10% were related to protein fate and folding, 10% were proteins with unknown functions, and 26.1% were proteins without homologues in databases. These last proteins are most likely characteristic of A. ferrooxidans and may have important roles yet to be assigned. The majority of the periplasmic proteins from A. ferrooxidans were very basic compared with those of neutrophilic microorganisms such as Escherichia coli, suggesting a special adaptation of the chemolithoautotrophic bacterium to its very acidic environment. The high throughput proteomics approach used here not only helps to understand the physiology of this extreme acidophile but also offers an important contribution to the functional annotation for the available genomes of biomining microorganisms such as A. ferrooxidans for which no efficient genetic systems are available to disrupt genes by procedures such as homologous recombination.

Drug induced liver injury (DILI), a major cause of pre- and post-approval failure, is challenging to predict pre-clinically. Predicting DILI is challenging due to varied underlying direct and indirect mechanisms. Nevirapine, a non-nucleoside reverse transcriptase inhibitor (NNRTI) and Ritonavir, a protease inhibitor, are antiviral drugs that cause clinical DILI with different phenotypes via different mechanisms. Assessing DILI in vitro in hepatocyte cultures typically requires drug exposures significantly higher than clinical plasma Cmax concentrations, making clinical interpretations of mechanistic pathway changes challenging. We previously described a system that uses liver-derived hemodynamic blood flow and transport parameters to restore primary human hepatocyte biology, and drug responses at concentrations relevant to in vivo or clinical exposure levels. Using this system, primary hepatocytes from 5 human donors were exposed to approximating clinical therapeutic and supra-therapeutic concentrations levels of Nevirapine (11.3 and 175.0 μM) and Ritonavir (3.5 and 62.4 μM) for 48 hours. Whole genome transcriptomics was performed by RNAseq along with functional assays for metabolic activity and function. We observed effects at both doses, but a greater number of genes were differentially expressed with higher probability at the toxic concentrations. At the toxic doses, both drugs showed direct cholestatic potential with Nevirapine increasing bile synthesis and Ritonavir inhibiting bile acid transport. Clear differences in antigen presentation were noted, with marked activation of MHC Class I by Nevirapine and suppression by Ritonavir. This suggests CD8+ T cell involvement for Nevirapine and possibly NK Killer cells for Ritonavir. Both compounds induced several drug metabolizing genes (including CYP2B6, CYP3A4 and UGT1A1), mediated by CAR activation in Nevirapine and PXR in Ritonavir. Unlike Ritonavir, Nevirapine did not increase fatty acid synthesis or activate the respiratory electron chain with simultaneous mitochondrial uncoupling supporting clinical reports of a lower propensity for steatosis. This in vitro study offers insights into the disparate direct and immune mediated toxicity mechanisms underlying Nevirapine and Ritonavir toxicity in the clinic.

Objectives— The predictive value of animal and in vitro systems for drug development is limited, particularly for nonhuman primate studies as it is difficult to deduce the drug mechanism of action. We describe the development of an in vitro cynomolgus macaque vascular system that reflects the in vivo biology of healthy, atheroprone, or advanced inflammatory cardiovascular disease conditions. Approach and Results— We compare the responses of the in vitro human and cynomolgus vascular systems to 4 statins. Although statins exert beneficial pleiotropic effects on the human vasculature, the mechanism of action is difficult to investigate at the tissue level. Using RNA sequencing, we quantified the response to statins and report that most statins significantly increased the expression of genes that promote vascular health while suppressing inflammatory cytokine gene expression. Applying computational pathway analytics, we identified statin-regulated biological themes, independent of cholesterol lowering, that provide mechanisms for off-target effects, including thrombosis, cell cycle regulation, glycogen metabolism, and ethanol degradation. Conclusions— The cynomolgus vascular system described herein mimics the baseline and inflammatory regional biology of the human vasculature, including statin responsiveness, and provides mechanistic insight not achievable in vivo.

The OrthoMCL database (http://orthomcl.cbil.upenn.edu) houses ortholog group predictions for 55 species, including 16 bacterial and 4 archaeal genomes representing phylogenetically diverse lineages, and most currently available complete eukaryotic genomes: 24 unikonts (12 animals, 9 fungi, microsporidium, Dictyostelium, Entamoeba), 4 plants/algae and 7 apicomplexan parasites. OrthoMCL software was used to cluster proteins based on sequence similarity, using an all-against-all BLAST search of each species' proteome, followed by normalization of inter-species differences, and Markov clustering. A total of 511,797 proteins (81.6% of the total dataset) were clustered into 70,388 ortholog groups. The ortholog database may be queried based on protein or group accession numbers, keyword descriptions or BLAST similarity. Ortholog groups exhibiting specific phyletic patterns may also be identified, using either a graphical interface or a text-based Phyletic Pattern Expression grammar. Information for ortholog groups includes the phyletic profile, the list of member proteins and a multiple sequence alignment, a statistical summary and graphical view of similarities, and a graphical representation of domain architecture. OrthoMCL software, the entire FASTA dataset employed and clustering results are available for download. OrthoMCL-DB provides a centralized warehouse for orthology prediction among multiple species, and will be updated and expanded as additional genome sequence data become available.

Cryptosporidium species cause acute gastroenteritis and diarrhoea worldwide. They are members of the Apicomplexa--protozoan pathogens that invade host cells by using a specialized apical complex and are usually transmitted by an invertebrate vector or intermediate host. In contrast to other Apicomplexans, Cryptosporidium is transmitted by ingestion of oocysts and completes its life cycle in a single host. No therapy is available, and control focuses on eliminating oocysts in water supplies. Two species, C. hominis and C. parvum, which differ in host range, genotype and pathogenicity, are most relevant to humans. C. hominis is restricted to humans, whereas C. parvum also infects other mammals. Here we describe the eight-chromosome approximately 9.2-million-base genome of C. hominis. The complement of C. hominis protein-coding genes shows a striking concordance with the requirements imposed by the environmental niches the parasite inhabits. Energy metabolism is largely from glycolysis. Both aerobic and anaerobic metabolisms are available, the former requiring an alternative electron transport system in a simplified mitochondrion. Biosynthesis capabilities are limited, explaining an extensive array of transporters. Evidence of an apicoplast is absent, but genes associated with apical complex organelles are present. C. hominis and C. parvum exhibit very similar gene complements, and phenotypic differences between these parasites must be due to subtle sequence divergence.

The Gal/GalNAc lectin gene of Entamoeba histolytica is a major amebic virulence protein responsible for interaction with host tissues. We investigated sequence differences in the Gal/GalNAc lectin heavy subunit in three isolates from Bangladesh and one isolate from Georgia, each of which was determined to be genetically distinct by SREHP AluI digestion. Interestingly, we observed only slight genetic diversity in the lectin gene as compared with the HM1:IMSS laboratory strain, originally a clinical isolate from Mexico. Genetic conservation of the Gal/GalNAc lectin between isolates may reflect that the lectin is under strong functional selection or possibly, that E. histolytica is a clonal population. Sequence conservation of the lectin indicates that immune responses against it should be cross-protective.

We describe two novel sequence similarity search algorithms, FASTS and FASTF, that use multiple short peptide sequences to identify homologous sequences in protein or DNA databases. FASTS searches with peptide sequences of unknown order, as obtained by mass spectrometry-based sequencing, evaluating all possible arrangements of the peptides. FASTF searches with mixed peptide sequences, as generated by Edman sequencing of unseparated mixtures of peptides. FASTF deconvolutes the mixture, using a greedy heuristic that allows rapid identification of high scoring alignments while reducing the total number of explored alternatives. Both algorithms use the heuristic FASTA comparison strategy to accelerate the search but use alignment probability, rather than similarity score, as the criterion for alignment optimality. Statistical estimates are calculated using an empirical correction to a theoretical probability. These calculated estimates were accurate within a factor of 10 for FASTS and 1000 for FASTF on our test dataset. FASTS requires only 15-20 total residues in three or four peptides to robustly identify homologues sharing 50% or greater protein sequence identity. FASTF requires about 25% more sequence data than FASTS for equivalent sensitivity, but additional sequence data are usually available from mixed Edman experiments. Thus, both algorithms can identify homologues that diverged 100 to 500 million years ago, allowing proteomic identification from organisms whose genomes have not been sequenced.

Rationale The rationale was to utilize a bioinformatics approach to identify miRNA binding sites in genes with single nucleotide mutations (SNPs) to discover pathways in heart failure (HF). Objective The objective was to focus on the genes containing miRNA binding sites with miRNAs that were significantly altered in end-stage HF and in response to a left ventricular assist device (LVAD). Methods and Results BEDTools v2.14.3 was used to discriminate SNPs within predicted 3′UTR miRNA binding sites. A member of the miR-15/107 family, miR-16, was decreased in the circulation of end-stage HF patients and increased in response to a LVAD (p

Background Accurate genomic variant detection is an essential step in gleaning medically useful information from genome data. However, low concordance among variant-calling methods reduces confidence in the clinical validity of whole genome and exome sequence data, and confounds downstream analysis for applications in genome medicine. Here we describe BAYSIC (BAYeSian Integrated Caller), which combines SNP variant calls produced by different methods (e.g. GATK, FreeBayes, Atlas, SamTools, etc.) into a more accurate set of variant calls. BAYSIC differs from majority voting, consensus or other ad hoc intersection-based schemes for combining sets of genome variant calls. Unlike other classification methods, the underlying BAYSIC model does not require training using a “gold standard” of true positives. Rather, with each new dataset, BAYSIC performs an unsupervised, fully Bayesian latent class analysis to estimate false positive and false negative error rates for each input method. The user specifies a posterior probability threshold according to the user’s tolerance for false positive and false negative errors; lowering the posterior probability threshold allows the user to trade specificity for sensitivity while raising the threshold increases specificity in exchange for sensitivity. Results We assessed the performance of BAYSIC in comparison to other variant detection methods using ten low coverage (~5X) samples from The 1000 Genomes Project, a tumor/normal exome pair (40X), and exome sequences (40X) from positive control samples previously identified to contain clinically relevant SNPs. We demonstrated BAYSIC’s superior variant-calling accuracy, both for somatic mutation detection and germline variant detection. Conclusions BAYSIC provides a method for combining sets of SNP variant calls produced by different variant calling programs. The integrated set of SNP variant calls produced by BAYSIC improves the sensitivity and specificity of the variant calls used as input. In addition to combining sets of germline variants, BAYSIC can also be used to combine sets of somatic mutations detected in the context of tumor/normal sequencing experiments.

Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or

Genetic background of the T cell can influence T helper (Th) phenotype development, with some murine strains (e.g., B10.D2) favoring Th1 development and others (e.g., BALB/c) favoring Th2 development. Recently we found that B10.D2 exhibit an intrinsically greater capacity to maintain interleukin 12 (IL-12) responsiveness under neutral conditions in vitro compared with BALB/c T cells, allowing for prolonged capacity to undergo IL-12-induced Th1 development. To begin identification of the loci controlling this genetic effect, we used a T-cell antigen receptor-transgenic system for in vitro analysis of intercrosses between BALB/c and B10.D2 mice and have identified a locus on murine chromosome 11 that controls the maintenance of IL-12 responsiveness, and therefore the subsequent Th1/Th2 response. This chromosomal region is syntenic with a locus on human chromosome 5q31.1 shown to be associated with elevated serum IgE levels, suggesting that genetic control of Th1/Th2 differentiation in mouse, and of atopy development in humans, may be expressed through similar mechanisms.

The CRP (Cleavage of Radiolabeled Phosphoproteins) program guides the design and interpretation of experiments to identify protein phosphorylation sites by Edman sequencing of unseparated peptides. Traditionally, phosphorylation sites are determined by cleaving the phosphoprotein and separating the peptides for Edman 32 P-phosphate release sequencing. CRP analysis of a phosphoprotein’s sequence accelerates this process by omitting the separation step: given a protein sequence of interest, the CRP program performs an in silico proteolytic cleavage of the sequence and reports the predicted Edman cycles in which radioactivity would be observed if a given serine, threonine or tyrosine were phosphorylated. Experimentally observed cycles containing 32 P can be compared with CRP predictions to confirm candidate sites and/or explore the ability of additional cleavage experiments to resolve remaining ambiguities. To reduce ambiguity, the phosphorylated residue (P-Tyr, P-Ser or P-Thr) can be determined experimentally, and CRP will ignore sites with alternative residues. CRP also provides simple predictions of likely phosphorylation sites using known kinase recognition motifs. The CRP interface is available at http://fasta.bioch.virginia.edu/crp.

Background Cowpea, Vigna unguiculata L. Walp., is one of the most important food and forage legumes in the semi-arid tropics. While most domesticated forms of cowpea are susceptible to the root parasitic weed Striga gesnerioides, several cultivars have been identified that show race-specific resistance. Cowpea cultivar B301 contains the RSG3-301 gene for resistance to S. gesnerioides race SG3, but is susceptible to race SG4z. When challenged by SG3, roots of cultivar B301 develop a strong resistance response characterized by a hypersensitive reaction and cell death at the site of parasite attachment. In contrast, no visible response occurs in B301 roots parasitized by SG4z. Results Gene expression in the roots of the cowpea cultivar B301 during compatible (susceptible) and incompatible (resistant) interactions with S. gesnerioides races SG4z and SG3, respectively, were investigated at the early (6 days post-inoculation (dpi)) and late (13 dpi) stages of the resistance response using a Nimblegen custom design cowpea microarray. A total of 111 genes were differentially expressed in B301 roots at 6 dpi; this number increased to 2102 genes at 13 dpi. At 13 dpi, a total of 1944 genes were differentially expressed during compatible (susceptible) interactions of B301 with SG4z. Genes and pathways involved in signal transduction, programmed cell death and apoptosis, and defense response to biotic and abiotic stress were differentially expressed in the early resistance response; at the later time point, enrichment was primarily for defense-related gene expression, and genes encoding components of lignifications and secondary wall formation. In compatible interactions (B301 – SG4z), multiple defense pathways were repressed, including those involved in lignin biosynthesis and secondary cell wall modifications, while cellular transport processes for nitrogen and sulfur were increased. Conclusion Distinct changes in global gene expression profiles occur in host roots following successful and unsuccessful attempted parasitism by Striga. Induction of specific defense related genes and pathways defines components of a unique resistance mechanism. Some genes and pathways up-regulated in the host resistance response to SG3 are repressed in the susceptible interactions, suggesting that the parasite is targeting specific components of the host’s defense. These results add to our understanding of plant-parasite interactions and the evolution of resistance to parasitic weeds.

MicroRNAs are a class of small non-coding RNAs that regulate gene expression by binding to discrete sites of genes and inhibiting translation. The objective of this study was to identify microRNAs and downstream enriched targets associated with symptomatic carotid plaques. Carotid plaque samples from patients were defined as symptomatic if they were associated with a prior ischemic stroke or transient ischemic attack, or as asymptomatic if there were no prior ischemic events. Digital microRNA profiling was performed on the NanoString nCounter analysis system (NanoString Technologies, Seattle, WA) on carotid plaques from symptomatic (n=6) and asymptomatic (n=6) patients. We used the BioConductor/R package "edgeR" to perform moderated-ANOVA testing for differences in gene abundance between groups, assuming a negative binomial distribution of gene counts, with Benjamini-Hochberg multiple hypothesis correction to control false discovery rate. Analysis revealed five microRNAs that were significantly up-regulated in the symptomatic cohort (see Table 1) including miR-16, which was confirmed by RTqPCR (p=0.02). Predicted downstream targets of miR-16 were enriched for proteoglycan related genes including hyaluronan synthase 2, perlecan and betaglycan. Additionally, RTqPCR demonstrated up-regulation of CathepsinB transcript in symptomatic patients (n=46-50 per cohort, p=0.02) while CathepsinL and heparanase transcripts were not significantly different. CathepsinL enzyme activity was also not significantly different (n=46-50 per cohort). Taken together, these findings may reveal novel proteoglycan mediated signaling pathways, mediated through miR-16, regulating the progression of carotid plaque formation.

A major challenge of biology is understanding the relationship between molecular genetic variation and variation in quantitative traits, including fitness. This relationship determines our ability to predict phenotypes from genotypes and to understand how evolutionary forces shape variation within and between species. Previous efforts to dissect the genotype-phenotype map were based on incomplete genotypic information. Here, we describe the Drosophila melanogaster Genetic Reference Panel (DGRP), a community resource for analysis of population genomics and quantitative traits. The DGRP consists of fully sequenced inbred lines derived from a natural population. Population genomic analyses reveal reduced polymorphism in centromeric autosomal regions and the X chromosome, evidence for positive and negative selection, and rapid evolution of the X chromosome. Many variants in novel genes, most at low frequency, are associated with quantitative traits and explain a large fraction of the phenotypic variance. The DGRP facilitates genotype-phenotype mapping using the power of Drosophila genetics.

Relational databases are designed to integrate diverse types of information and manage large sets of search results, greatly simplifying genome-scale analyses. Relational databases are essential for management and analysis of large-scale sequence analyses, and can also be used to improve the statistical significance of similarity searches by focusing on subsets of sequence libraries most likely to contain homologs. This unit describes using relational databases to improve the efficiency of sequence similarity searching and to demonstrate various large-scale genomic analyses of homology-related data. This unit describes the installation and use of a simple protein sequence database, seqdb_demo, which is used as a basis for the other protocols. These include basic use of the database to generate a novel sequence library subset, how to extend and use seqdb_demo for the storage of sequence similarity search results and making use of various kinds of stored search results to address aspects of comparative genomic analysis. Keywords: relational database; sequence similarity; comparative genomic analysis; homology

ToxoDB (http://ToxoDB.org) is a genome and functional genomic database for the protozoan parasite Toxoplasma gondii. It incorporates the sequence and annotation of the T. gondii ME49 strain, as well as genome sequences for the GT1, VEG and RH (Chr Ia, Chr Ib) strains. Sequence information is integrated with various other genomic-scale data, including community annotation, ESTs, gene expression and proteomics data. ToxoDB has matured significantly since its initial release. Here we outline the numerous updates with respect to the data and increased functionality available on the website.

Acidithiobacillus ferrooxidans is a chemolithoautotrophic acidophile capable of obtaining energy by oxidizing ferrous iron or sulfur compounds such as metal sulfides. Some of the proteins involved in these oxidations have been described as forming part of the periplasm of this extremophile. The detailed study of the periplasmic components constitutes an important area to understand the physiology and environmental interactions of microorganisms. Proteomics analysis of the periplasmic fraction of A. ferrooxidans ATCC 23270 was performed by using high resolution linear ion trap-FT MS. We identified a total of 131 proteins in the periplasm of the microorganism grown in thiosulfate. When possible, functional categories were assigned to the proteins: 13.8% were transport and binding proteins, 14.6% were several kinds of cell envelope proteins, 10.8% were involved in energy metabolism, 10% were related to protein fate and folding, 10% were proteins with unknown functions, and 26.1% were proteins without homologues in databases. These last proteins are most likely characteristic of A. ferrooxidans and may have important roles yet to be assigned. The majority of the periplasmic proteins from A. ferrooxidans were very basic compared with those of neutrophilic microorganisms such as Escherichia coli, suggesting a special adaptation of the chemolithoautotrophic bacterium to its very acidic environment. The high throughput proteomics approach used here not only helps to understand the physiology of this extreme acidophile but also offers an important contribution to the functional annotation for the available genomes of biomining microorganisms such as A. ferrooxidans for which no efficient genetic systems are available to disrupt genes by procedures such as homologous recombination.

Orthology detection is critically important for accurate functional annotation, and has been widely used to facilitate studies on comparative and evolutionary genomics. Although various methods are now available, there has been no comprehensive analysis of performance, due to the lack of a genomic-scale ‘gold standard’ orthology dataset. Even in the absence of such datasets, the comparison of results from alternative methodologies contains useful information, as agreement enhances confidence and disagreement indicates possible errors. Latent Class Analysis (LCA) is a statistical technique that can exploit this information to reasonably infer sensitivities and specificities, and is applied here to evaluate the performance of various orthology detection methods on a eukaryotic dataset. Overall, we observe a trade-off between sensitivity and specificity in orthology detection, with BLAST-based methods characterized by high sensitivity, and tree-based methods by high specificity. Two algorithms exhibit the best overall balance, with both sensitivity and specificity>80%: INPARANOID identifies orthologs across two species while OrthoMCL clusters orthologs from multiple species. Among methods that permit clustering of ortholog groups spanning multiple genomes, the (automated) OrthoMCL algorithm exhibits better within-group consistency with respect to protein function and domain architecture than the (manually curated) KOG database, and the homolog clustering algorithm TribeMCL as well. By way of using LCA, we are also able to comprehensively assess similarities and statistical dependence between various strategies, and evaluate the effects of parameter settings on performance. In summary, we present a comprehensive evaluation of orthology detection on a divergent set of eukaryotic genomes, thus providing insights and guides for method selection, tuning and development for different applications. Many biological questions have been addressed by multiple tests yielding binary (yes/no) outcomes but no clear definition of truth, making LCA an attractive approach for computational biology.

Fifty percent of cutaneous melanomas are driven by activated BRAF V600E, but tumors treated with RAF inhibitors, even when they respond dramatically, rapidly adapt and develop resistance. Thus, there is a pressing need to identify the major mechanisms of intrinsic and adaptive resistance and develop drug combinations that target these resistance mechanisms. In a combinatorial drug screen on a panel of 12 treatment-naïve BRAF V600E mutant melanoma cell lines of varying levels of resistance to mitogen-activated protein kinase (MAPK) pathway inhibition, we identified the combination of PLX4720, a targeted inhibitor of mutated BRaf, and lapatinib, an inhibitor of the ErbB family of receptor tyrosine kinases, as synergistically cytotoxic in the subset of cell lines that displayed the most resistance to PLX4720. To identify potential mechanisms of resistance to PLX4720 treatment and synergy with lapatinib treatment, we performed a multi-platform functional genomics analysis to profile the genome as well as the transcriptional and proteomic responses of these cell lines to treatment with PLX4720. We found modest levels of resistance correlated with the zygosity of the BRAF V600E allele and receptor tyrosine kinase (RTK) mutational status. Layered over base-line resistance was substantial upregulation of many ErbB pathway genes in response to BRaf inhibition, thus generating the vulnerability to combination with lapatinib. The transcriptional responses of ErbB pathway genes are associated with a number of transcription factors, including ETS2 and its associated cofactors that represent a convergent regulatory mechanism conferring synergistic drug susceptibility in the context of diverse mutational landscapes.

We report the sequence and analysis of the 814-megabase genome of the sea urchin Strongylocentrotus purpuratus , a model for developmental and systems biology. The sequencing strategy combined whole-genome shotgun and bacterial artificial chromosome (BAC) sequences. This use of BAC clones, aided by a pooling strategy, overcame difficulties associated with high heterozygosity of the genome. The genome encodes about 23,300 genes, including many previously thought to be vertebrate innovations or known only outside the deuterostomes. This echinoderm genome provides an evolutionary outgroup for the chordates and yields insights into the evolution of deuterostomes.

Ce travail résulte de la collaboration de très nombreux chercheurs. Seuls les auteurs de la rubrique Physical and Genetic Mapping sont cités explicitement.; Here we report the genome sequence of the honeybee Apis mellifera, a key model for social behaviour and essential to global ecology through pollination. Compared with other sequenced insect genomes, the A. mellifera genome has high A1T and CpG contents, lacks major transposon families, evolves more slowly, and is more similar to vertebrates for circadian rhythm, RNA interference and DNA methylation genes, among others. Furthermore, A.mellifera has fewer genes for innate immunity, detoxification enzymes, cuticle-forming proteins and gustatory receptors, more genes for odorant receptors, and novel genes for nectar and pollen utilization, consistent with its ecology and social organization. Compared to Drosophila, genes in early developmental pathways differ in Apis, whereas similarities exist for functions that differ markedly, such as sex determination, brain function and behaviour. Population genetics suggests a novel African origin for the species A.mellifera and insights into whether Africanized bees spread throughout the New World via hybridization or displacement

BackgroundToxoplasma gondiigives rise to toxoplasmosis, among the most prevalent parasitic diseases of animals and man. Transformation of the tachzyoite stage into the latent bradyzoite-cyst form underlies chronic disease and leads to a lifetime risk of recrudescence in individuals whose immune system becomes compromised. Given the importance of tissue cyst formation, there has been intensive focus on the development of methods to study bradyzoite differentiation, although the molecular basis for the developmental switch is still largely unknown.ResultsWe have used serial analysis of gene expression (SAGE) to define theToxoplasma gondiitranscriptome of the intermediate-host life cycle that leads to the formation of the bradyzoite/tissue cyst. A broad view of gene expression is provided by >4-fold coverage from nine distinct libraries (~300,000 SAGE tags) representing key developmental transitions in primary parasite populations and in laboratory strains representing the three canonical genotypes. SAGE tags, and their corresponding mRNAs, were analyzed with respect to abundance, uniqueness, and antisense/sense polarity and chromosome distribution and developmental specificity.ConclusionThis study demonstrates that phenotypic transitions during parasite development were marked by unique stage-specific mRNAs that accounted for 18% of the total SAGE tags and varied from 1–5% of the tags in each developmental stage. We have also found thatToxoplasmamRNA pools have a unique parasite-specific composition with 1 in 5 transcripts encoding Apicomplexa-specific genes functioning in parasite invasion and transmission. Developmentally co-regulated genes were dispersed across allToxoplasmachromosomes, as were tags representing each abundance class, and a variety of biochemical pathways indicating that trans-acting mechanisms likely control gene expression in this parasite. We observed distinct similarities in the specificity and expression levels of mRNAs in primary populations (Day-6 post-sporozoite infection) that occur prior to the onset of bradyzoite development that were uniquely shared with the virulent Type I-RH laboratory strain suggesting that development of RH may be arrested. By contrast, strains from Type II-Me49B7 and Type III-VEGmsj contain SAGE tags corresponding to bradyzoite genes, which suggests that priming of developmental expression likely plays a role in the greater capacity of these strains to complete bradyzoite development.

Toxoplasma gondii is a highly successful protozoan parasite in the phylum Apicomplexa, which contains numerous animal and human pathogens. T.gondii is amenable to cellular, biochemical, molecular and genetic studies, making it a model for the biology of this important group of parasites. To facilitate forward genetic analysis, we have developed a high-resolution genetic linkage map for T.gondii. The genetic map was used to assemble the scaffolds from a 10X shotgun whole genome sequence, thus defining 14 chromosomes with markers spaced at approximately 300 kb intervals across the genome. Fourteen chromosomes were identified comprising a total genetic size of approximately 592 cM and an average map unit of approximately 104 kb/cM. Analysis of the genetic parameters in T.gondii revealed a high frequency of closely adjacent, apparent double crossover events that may represent gene conversions. In addition, we detected large regions of genetic homogeneity among the archetypal clonal lineages, reflecting the relatively few genetic outbreeding events that have occurred since their recent origin. Despite these unusual features, linkage analysis proved to be effective in mapping the loci determining several drug resistances. The resulting genome map provides a framework for analysis of complex traits such as virulence and transmission, and for comparative population genetic studies.

Evolution of protein function can be driven by positive selection of advantageous nonsynonymous codon mutations that arise following gene duplication. By observing the presence and degree of site-specific positive selection for change between divergent paralogs, residue positions responsible for functional changes can be identified. We applied this analysis to genes encoding Mu class glutathione transferases, which differ widely in substrate specificities. Approximately 3% of the amino acid residue positions, both near to and distant from the active site, are under statistically significant positive selection for change. Relevant human glutathione transferase (GST) M1-1 and GST M2-2 codons were mutated. A chemically conservative threonine to serine mutation in GST M2-2 elicited a 1,000-fold increase in specific activity with the GST M1-1-specific substrate trans-stilbene oxide and a 30-fold increase with the alternative epoxide substrates styrene oxide and nitrophenyl glycidol. The reverse mutation in GST M1-1 resulted in reciprocal decreases in activity. Thus, identification of hypervariable codon positions can be a powerful aid in the redesign of protein function, lessening the requirement for extensive mutagenesis or structural knowledge and sometimes suggesting mutations that would otherwise be considered functionally conservative.

We describe two novel sequence similarity search algorithms, FASTS and FASTF, that use multiple short peptide sequences to identify homologous sequences in protein or DNA databases. FASTS searches with peptide sequences of unknown order, as obtained by mass spectrometry-based sequencing, evaluating all possible arrangements of the peptides. FASTF searches with mixed peptide sequences, as generated by Edman sequencing of unseparated mixtures of peptides. FASTF deconvolutes the mixture, using a greedy heuristic that allows rapid identification of high scoring alignments while reducing the total number of explored alternatives. Both algorithms use the heuristic FASTA comparison strategy to accelerate the search but use alignment probability, rather than similarity score, as the criterion for alignment optimality. Statistical estimates are calculated using an empirical correction to a theoretical probability. These calculated estimates were accurate within a factor of 10 for FASTS and 1000 for FASTF on our test dataset. FASTS requires only 15-20 total residues in three or four peptides to robustly identify homologues sharing 50% or greater protein sequence identity. FASTF requires about 25% more sequence data than FASTS for equivalent sensitivity, but additional sequence data are usually available from mixed Edman experiments. Thus, both algorithms can identify homologues that diverged 100 to 500 million years ago, allowing proteomic identification from organisms whose genomes have not been sequenced.

Edman phosphate ((32)P) release sequencing provides a high sensitivity means of identifying phosphorylation sites in proteins that complements mass spectrometry techniques. We have developed a bioinformatic assessment tool, the cleavage of radiolabeled protein (CRP) program, which enables experimental identification of phosphorylation sites via (32)P labeling and Edman degradation of cleaved proteins obtained at femtomole levels. By observing the Edman cycle(s) in which radioactivity is found, candidate phosphorylation sites are identified by determining which residues occur at the observed number of cycles downstream from a peptide cleavage site. In cases where more than one residue could be responsible for the observed radioactivity, additional experiments with cleavage reagents having alternative specificities may resolve the ambiguity. Given a protein sequence and a cleavage site, CRP performs these experiments in silico, identifying resolved sites based on user-supplied experimental data, as well as suggesting combinations of reagents for additional analyses. Analysis of the PhosphoBase protein sequence database suggests that CRP data from two cleavage experiments can be used to identify unambiguously 60% of known phosphorylation sites. Data from additional cleavage experiments may increase the overall coverage to 70% of known sites. By comparing theoretical data obtained from the CRP program with (32)P release data obtained from an Edman sequencer, a known phosphorylation site was identified unambiguously and correctly. In addition, our results show that in vivo phosphorylation sites can be determined routinely by differential proteolysis analysis and Edman cycling with less than 1 fmol of protein and 1000 cpm.

Integrated molecular profiling of melanoma cell lines reveals genotype-drug phenotype associations giving insight on development of combination therapies Adaptive survival responses to targeted therapy reduce therapeutic benefit, and thus there is a need to identify drug combinations that co-target both oncogenic drivers and critical adaptive responses. We screened a panel of twelve V600E BRAF melanoma cell lines with a library of targeted drugs, and identified the combination of lapatinib (a HER family inhibitor) plus PLX4720 (a RAF inhibitor) as synergistically cytotoxic in the six lines that displayed complete or partial resistance to PLX4720 alone. To understand the mechanism(s) of drug resistance and synergy, and to identify potential biomarkers for utilization of this combination, we have performed an extensive molecular analysis of the cell lines and their responses to treatment with the drugs, singly and in combination. Alamar blue was used to calculate the cytotoxic effects of the drugs, and the cells were profiled by gene expression microarrays and reverse phase protein microarrays, before and after drug treatment. DNA methylation microarrays, and exome sequencing were used to identify the epigenetic and genetic context of the lines. Unsupervised hierarchical clustering analysis of the cytotoxic effects of the single drug and combinatorial treatments revealed four well defined groups: synergistic to combination treatment, PLX4720 sensitive, lapatinib resistant, and insensitive to either drug. The cell lines were analyzed using principal component analysis on the basal gene expression profiles. This analysis revealed four major clusters whose membership was identical to the clusters observed in the cytotoxicity analysis, indicating a strong genotype-drug phenotype relationship (i.e. cell lines that showed similar basal transcriptional programs also demonstrated similar responses to treatment. Differential gene expression analysis was performed between the synergistic group and each of the other groups. The most prominent gene expression differences were between the synergistic and PLX4720 sensitive lines, which correlated with differences in MITF expression and transcriptional output. Changes in gene expression and phosphoproteome levels in response to PLX4720 and lapatinib treatment between the four groups were used to identify genes and pathways associated with drug resistance and synergy. Genes involved in apoptosis and cell cycle regulation were found that could play a role in conferring resistance and synergistic response to PLX4720 and lapatinib treatment. These differing patterns of adaptive responses provide insights into molecular mechanisms of drug resistance and synergy and a pathway to rational construction of drug combinations. Citation Format: Brian J. Capaldo, Devin G. Roller, Mark Axelrod, Alex Koeppel, Michael J. Weber, Aaron Mackey, Dan Gioeli, Stefan Bekiranov. Integrated molecular profiling of melanoma cell lines reveals genotype-drug phenotype associations giving insight on development of combination therapies. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4166. doi:10.1158/1538-7445.AM2014-4166