A similarity-based method for genome-wide prediction of disease-relevant human genes

MOTIVATION: A method for prediction of disease relevant human genes from the phenotypic appearance of a query disease is presented. Diseases of known genetic origin are clustered according to their phenotypic similarity. Each cluster entry consists of a disease and its underlying disease gene. Potential disease genes from the human genome are scored by their functional similarity to known disease genes in these clusters, which are phenotypically similar to the query disease. RESULTS: For assessment of the approach, a leave-one-out cross-validation of 878 diseases from the OMIM database, using 10672 candidate genes from the human genome, is performed. Depending on the applied parameters, in roughly one-third of cases the true solution is contained within the top scoring 3% of predictions and in two-third of cases the true solution is contained within the top scoring 15% of predictions. The prediction results can either be used to identify target genes, when searching for a mutation in monogenic diseases or for selection of loci in genotyping experiments in genetically complex diseases.     

A genome-wide association study on obesity and obesity-related traits

Large-scale genome-wide association studies (GWAS) have identified many loci associated with body mass index (BMI), but few studies focused on obesity as a binary trait. Here we report the results of a GWAS and candidate SNP genotyping study of obesity, including extremely obese cases and never overweight controls as well as families segregating extreme obesity and thinness. We first performed a GWAS on 520 cases (BMI>35 kg/m(2)) and 540 control subjects (BMI<25 kg/m(2)), on measures of obesity and obesity-related traits. We subsequently followed up obesity-associated signals by genotyping the top ～500 SNPs from GWAS in the combined sample of cases, controls and family members totaling 2,256 individuals. For the binary trait of obesity, we found 16 genome-wide significant signals within the FTO gene (strongest signal at rs17817449, P = 2.5 × 10(-12)). We next examined obesity-related quantitative traits (such as total body weight, waist circumference and waist to hip ratio), and detected genome-wide significant signals between waist to hip ratio and NRXN3 (rs11624704, P = 2.67 × 10(-9)), previously associated with body weight and fat distribution. Our study demonstrated how a relatively small sample ascertained through extreme phenotypes can detect genuine associations in a GWAS.     

RNIE: genome-wide prediction of bacterial intrinsic terminators

Bacterial Rho-independent terminators (RITs) are important genomic landmarks involved in gene regulation and terminating gene expression. In this investigation we present RNIE, a probabilistic approach for predicting RITs. The method is based upon covariance models which have been known for many years to be the most accurate computational tools for predicting homology in structural non-coding RNAs. We show that RNIE has superior performance in model species from a spectrum of bacterial phyla. Further analysis of species where a low number of RITs were predicted revealed a highly conserved structural sequence motif enriched near the genic termini of the pathogenic Actinobacteria, Mycobacterium tuberculosis. This motif, together with classical RITs, account for up to 90% of all the significantly structured regions from the termini of M. tuberculosis genic elements. The software, predictions and alignments described below are available from http://github.com/ppgardne/RNIE.     

Reducing dimensionality for prediction of genome-wide breeding values

Partial least square regression (PLSR) and principal component regression (PCR) are methods designed for situations where the number of predictors is larger than the number of records. The aim was to compare the accuracy of genome-wide breeding values (EBV) produced using PLSR and PCR with a Bayesian method, ''BayesB''. Marker densities of 1, 2, 4 and 8 N_e markers/Morgan were evaluated when the effective population size (N_e) was 100. The correlation between true breeding value and estimated breeding value increased with density from 0.611 to 0.681 and 0.604 to 0.658 using PLSR and PCR respectively, with an overall advantage to PLSR of 0.016 (s.e = 0.008). Both methods gave a lower accuracy compared to the ''BayesB'', for which accuracy increased from 0.690 to 0.860. PLSR and PCR appeared less responsive to increased marker density with the advantage of ''BayesB'' increasing by 17% from a marker density of 1 to 8N_e/M. PCR and PLSR showed greater bias than ''BayesB'' in predicting breeding values at all densities. Although, the PLSR and PCR were computationally faster and simpler, these advantages do not outweigh the reduction in accuracy, and there is a benefit in obtaining relevant prior information from the distribution of gene effects.     

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

Background  

The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes.     

Sexual dimorphism in obesity-related genes in the epicardial fat during aging

Aging increases the risk of cardiovascular disease and metabolic syndrome. Alterations in epicardial fat play an important pathophysiological role in coronary artery disease and hypertension. We investigated the impact of normal aging on obesity-related genes in epicardial fat. Sex-specific changes in obesity-related genes with aging in epicardial fat (EF) were determined in young (6 months) and old (30/36 months) female and male, Fischer 344 × Brown Norway hybrid (FBN) rats, using a rat obesity RT² PCR Array. Circulating sex hormone levels, body and heart weights were determined. Statistical significance was determined using two-tailed Student’s t test and Pearson’s correlation. Our results revealed sex-specific differences in obesity-related genes with aging. Dramatic changes in the expression profile of obesity-related genes in EF with aging in female, but not in male, FBN rats were observed. The older (30 months) female rats had more significant variations in the abundance of obesity-related genes in the EF compared to that seen in younger female rats or both age groups in male rats. A correlation of changes in obesity-related genes in EF to heart weights was observed in female rats, but not in male rats with aging. No correlation was observed to circulating sex hormone levels. Our findings indicate a dysfunctional EF in female rats with aging compared to male rats. These findings, with further functional validation, might help explain the sex differences in cardiovascular risk and mortality associated with aging observed in humans.     

Efficient genome-wide genotyping strategies and data integration in crop plants

Key message

Next-generation sequencing (NGS) has revolutionized plant and animal research by providing powerful genotyping methods. This review describes and discusses the advantages, challenges and, most importantly, solutions to facilitate data processing, the handling of missing data, and cross-platform data integration.

Abstract

Next-generation sequencing technologies provide powerful and flexible genotyping methods to plant breeders and researchers. These methods offer a wide range of applications from genome-wide analysis to routine screening with a high level of accuracy and reproducibility. Furthermore, they provide a straightforward workflow to identify, validate, and screen genetic variants in a short time with a low cost. NGS-based genotyping methods include whole-genome re-sequencing, SNP arrays, and reduced representation sequencing, which are widely applied in crops. The main challenges facing breeders and geneticists today is how to choose an appropriate genotyping method and how to integrate genotyping data sets obtained from various sources. Here, we review and discuss the advantages and challenges of several NGS methods for genome-wide genetic marker development and genotyping in crop plants. We also discuss how imputation methods can be used to both fill in missing data in genotypic data sets and to integrate data sets obtained using different genotyping tools. It is our hope that this synthetic view of genotyping methods will help geneticists and breeders to integrate these NGS-based methods in crop plant breeding and research.

     

New and previously unknown Eurytomidae (Hymenoptera) species from Yemen

Ten new species of the genera Eurytoma (8 species) and Tetramesa (2 species) from Yemen are described (Eurytoma lahji Zerova, E. thoraxica Zerova, E. cyrtophorae Zerova, E. longipes Zerova, E. yemeni Zerova, E. mabari Zerova, E. tibiaspinae Zerova, E. longitarsis Zerova, Tetramesa sanai Zerova, and T. rujumi Zerova). The type specimens of the new species are deposited in the collection of the Institute of Zoology, National Academy of Sciences of Ukraine, Kiev.     

Probabilistic prediction of unknown metabolic and signal-transduction networks.

Regulatory networks provide control over complex cell behavior in all kingdoms of life. Here we describe a statistical model, based on representing proteins as collections of domains or motifs, which predicts unknown molecular interactions within these biological networks. Using known protein-protein interactions of Saccharomyces cerevisiae as training data, we were able to predict the links within this network with only 7% false-negative and 10% false-positive error rates. We also use Markov chain Monte Carlo simulation for the prediction of networks with maximum probability under our model. This model can be applied across species, where interaction data from one (or several) species can be used to infer interactions in another. In addition, the model is extensible and can be analogously applied to other molecular data (e.g., DNA sequences).     

Evaluation of five ab initio gene prediction programs for the discovery of maize genes

Five ab initio programs (FGENESH, GeneMark.hmm, GENSCAN, GlimmerR and Grail) were evaluated for their accuracy in predicting maize genes. Two of these programs, GeneMark.hmm and GENSCAN had been trained for maize; FGENESH had been trained for monocots (including maize), and the others had been trained for rice or Arabidopsis. Initial evaluations were conducted using eight maize genes (gl8a, pdc2, pdc3, rf2c, rf2d, rf2e1, rth1, and rth3) of which the sequences were not released to the public prior to conducting this evaluation. The significant advantage of this data set for this evaluation is that these genes could not have been included in the training sets of the prediction programs. FGENESH yielded the most accurate and GeneMark.hmm the second most accurate predictions. The five programs were used in conjunction with RT-PCR to identify and establish the structures of two new genes in the a1-sh2 interval of the maize genome. FGENESH, GeneMark.hmm and GENSCAN were tested on a larger data set consisting of maize assembled genomic islands (MAGIs) that had been aligned to ESTs. FGENESH, GeneMark.hmm and GENSCAN correctly predicted gene models in 773, 625, and 371 MAGIs, respectively, out of the 1353 MAGIs that comprise data set 2.these authors contributed equally to this work     

A high throughput method for genome-wide analysis of retroviral integration

Retroviral and lentiviral vectors integrate their DNA into the host cell genome leading to stable transgene expression. Integration preferentially occurs in the proximity of active genes, and may in some case disturb their activity, with adverse toxic consequences. To efficiently analyze high numbers of lentiviral insertion sites in the DNA of transduced cells, we developed an improved high-throughput method called vector integration tag analysis (VITA). VITA is based on the identification of Genomic Tags associated to the insertion sites, which are used as signatures of the integration events. We use the capacity of MmeI to cleave DNA at a defined distance of its recognition site, in order to generate 21 bp long tags from libraries of junction fragments between vector and cellular DNA. The length of the tags is sufficient in most cases, to identify without ambiguity an unique position in the human genome. Concatenation, cloning and sequencing of the tags allow to obtain information about 20–25 insertion sites in a single sequencing reaction. As a validation of this method, we have characterized 1349 different lentiviral vector insertion sites in transduced HeLa cells, from only 487 sequencing reactions, with a background of <2% false positive tags.     

Identification of neural outgrowth genes using genome-wide RNAi

While genetic screens have identified many genes essential for neurite outgrowth, they have been limited in their ability to identify neural genes that also have earlier critical roles in the gastrula, or neural genes for which maternally contributed RNA compensates for gene mutations in the zygote. To address this, we developed methods to screen the Drosophila genome using RNA-interference (RNAi) on primary neural cells and present the results of the first full-genome RNAi screen in neurons. We used live-cell imaging and quantitative image analysis to characterize the morphological phenotypes of fluorescently labelled primary neurons and glia in response to RNAi-mediated gene knockdown. From the full genome screen, we focused our analysis on 104 evolutionarily conserved genes that when downregulated by RNAi, have morphological defects such as reduced axon extension, excessive branching, loss of fasciculation, and blebbing. To assist in the phenotypic analysis of the large data sets, we generated image analysis algorithms that could assess the statistical significance of the mutant phenotypes. The algorithms were essential for the analysis of the thousands of images generated by the screening process and will become a valuable tool for future genome-wide screens in primary neurons. Our analysis revealed unexpected, essential roles in neurite outgrowth for genes representing a wide range of functional categories including signalling molecules, enzymes, channels, receptors, and cytoskeletal proteins. We also found that genes known to be involved in protein and vesicle trafficking showed similar RNAi phenotypes. We confirmed phenotypes of the protein trafficking genes Sec61alpha and Ran GTPase using Drosophila embryo and mouse embryonic cerebral cortical neurons, respectively. Collectively, our results showed that RNAi phenotypes in primary neural culture can parallel in vivo phenotypes, and the screening technique can be used to identify many new genes that have important functions in the nervous system.     

Context-sensitive data integration and prediction of biological networks

MOTIVATION: Several recent methods have addressed the problem of heterogeneous data integration and network prediction by modeling the noise inherent in high-throughput genomic datasets, which can dramatically improve specificity and sensitivity and allow the robust integration of datasets with heterogeneous properties. However, experimental technologies capture different biological processes with varying degrees of success, and thus, each source of genomic data can vary in relevance depending on the biological process one is interested in predicting. Accounting for this variation can significantly improve network prediction, but to our knowledge, no previous approaches have explicitly leveraged this critical information about biological context. RESULTS: We confirm the presence of context-dependent variation in functional genomic data and propose a Bayesian approach for context-sensitive integration and query-based recovery of biological process-specific networks. By applying this method to Saccharomyces cerevisiae, we demonstrate that leveraging contextual information can significantly improve the precision of network predictions, including assignment for uncharacterized genes. We expect that this general context-sensitive approach can be applied to other organisms and prediction scenarios. AVAILABILITY: A software implementation of our approach is available on request from the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at http://avis.princeton.edu/contextPIXIE/     

Exploiting tumor-specific defects in the interferon pathway with a previously unknown oncolytic virus

Interferons are circulating factors that bind to cell surface receptors, activating a signaling cascade, ultimately leading to both an antiviral response and an induction of growth inhibitory and/or apoptotic signals in normal and tumor cells. Attempts to exploit the ability of interferons to limit the growth of tumors in patients has met with limited results because of cancer-specific mutations of gene products in the interferon pathway. Although interferon-non-responsive cancer cells may have acquired a growth/survival advantage over their normal counterparts, they may have simultaneously compromised their antiviral response. To test this, we used vesicular stomatitis virus (VSV), an enveloped, negative-sense RNA virus exquisitely sensitive to treatment with interferon. VSV rapidly replicated in and selectively killed a variety of human tumor cell lines even in the presence of doses of interferon that completely protected normal human primary cell cultures. A single intratumoral injection of VSV was effective in reducing the tumor burden of nude mice bearing subcutaneous human melanoma xenografts. Our results support the use of VSV as a replication-competent oncolytic virus and demonstrate a new strategy for the treatment of interferon non-responsive tumors.