Personalized genome sequencing coupled with iPSC technology identifies GTDC1 as a gene involved in neurodevelopmental disorders.

The cellular and molecular mechanisms underlying neurodevelopmental conditions such as autism spectrum disorders have been studied intensively for decades. The ability to generate patient-specific induced pluripotent stem cells (iPSCs) now offers a novel strategy for modelling human diseases. Recent studies have reported the derivation of iPSCs from patients with neurological disorders. The key challenge remains the demonstration of disease-related phenotypes and the ability to model the disease. Here we report a case study with signs of neurodevelopmental disorders (NDDs) harbouring chromosomal rearrangements that were sequenced using long-insert DNA paired-end tag (DNA-PET) sequencing approach. We identified the disruption of a specific gene, GTDC1. By deriving iPSCs from this patient and differentiating them into neural progenitor cells (NPCs) and neurons we dissected the disease process at the cellular level and observed defects in both NPCs and neuronal cells. We also showed that disruption of GTDC1 expression in wild type human NPCs and neurons showed a similar phenotype as patient's iPSCs. Finally, we utilized a zebrafish model to demonstrate a role for GTDC1 in the development of the central nervous system. Our findings highlight the importance of combining sequencing technologies with the iPSC technology for NDDs modelling that could be applied for personalized medicine.

Sensitive detection of chromatin-altering polymorphisms reveals autoimmune disease mechanisms.

Most disease associations detected by genome-wide association studies (GWAS) lie outside coding genes, but very few have been mapped to causal regulatory variants. Here, we present a method for detecting regulatory quantitative trait loci (QTLs) that does not require genotyping or whole-genome sequencing. The method combines deep, long-read chromatin immunoprecipitation-sequencing (ChIP-seq) with a statistical test that simultaneously scores peak height correlation and allelic imbalance: the genotype-independent signal correlation and imbalance (G-SCI) test. We performed histone acetylation ChIP-seq on 57 human lymphoblastoid cell lines and used the resulting reads to call 500,066 single-nucleotide polymorphisms de novo within regulatory elements. The G-SCI test annotated 8,764 of these as histone acetylation QTLs (haQTLs)­an order of magnitude larger than the set of candidates detected by expression QTL analysis. Lymphoblastoid haQTLs were highly predictive of autoimmune disease mechanisms. Thus, our method facilitates large-scale regulatory variant detection in any moderately sized cohort for which functional profiling data can be generated, thereby simplifying identification of causal variants within GWAS loci.

Systematic study of Drosophila microRNA functions using a collection of targeted knockout mutations.

MicroRNAs are abundant in animal genomes, yet little is known about their functions in vivo. Here, we report the production of 80 new Drosophila miRNA mutants by targeted homologous recombination. These mutants remove 104 miRNAs. Together with 15 previously reported mutants, this collection includes 95 mutants deleting 130 miRNAs. Collectively, these genes produce over 99% of all Drosophila miRNAs, measured by miRNA sequence reads. We present a survey of developmental and adult miRNA phenotypes. Over 80% of the mutants showed at least one phenotype using a p < 0.01 significance threshold. We observed a significant correlation between miRNA abundance and phenotypes related to survival and lifespan, but not to most other phenotypes. miRNA cluster mutants were no more likely than single miRNA mutants to produce significant phenotypes. This mutant collection will provide a resource for future analysis of the biological roles of Drosophila miRNAs.

Detection of chromosomal breakpoints in patients with developmental delay and speech disorders.

Delineating candidate genes at the chromosomal breakpoint regions in the apparently balanced chromosome rearrangements (ABCR) has been shown to be more effective with the emergence of next-generation sequencing (NGS) technologies. We employed a large-insert (7-11 kb) paired-end tag sequencing technology (DNA-PET) to systematically analyze genome of four patients harbouring cytogenetically defined ABCR with neurodevelopmental symptoms, including developmental delay (DD) and speech disorders. We characterized structural variants (SVs) specific to each individual, including those matching the chromosomal breakpoints. Refinement of these regions by Sanger sequencing resulted in the identification of five disrupted genes in three individuals: guanine nucleotide binding protein, q polypeptide (GNAQ), RNA-binding protein, fox-1 homolog (RBFOX3), unc-5 homolog D (C.elegans) (UNC5D), transmembrane protein 47 (TMEM47), and X-linked inhibitor of apoptosis (XIAP). Among them, XIAP is the causative gene for the immunodeficiency phenotype seen in the patient. The remaining genes displayed specific expression in the fetal brain and have known biologically relevant functions in brain development, suggesting putative candidate genes for neurodevelopmental phenotypes. This study demonstrates the application of NGS technologies in mapping individual gene disruptions in ABCR as a resource for deciphering candidate genes in human neurodevelopmental disorders (NDDs).

Identifying driver genes in cancer by triangulating gene expression, gene location, and survival data.

Driver genes are directly responsible for oncogenesis and identifying them is essential in order to fully understand the mechanisms of cancer. However, it is difficult to delineate them from the larger pool of genes that are deregulated in cancer (ie, passenger genes). In order to address this problem, we developed an approach called TRIAngulating Gene Expression (TRIAGE through clinico-genomic intersects). Here, we present a refinement of this approach incorporating a new scoring methodology to identify putative driver genes that are deregulated in cancer. TRIAGE triangulates - or integrates - three levels of information: gene expression, gene location, and patient survival. First, TRIAGE identifies regions of deregulated expression (ie, expression footprints) by deriving a newly established measure called the Local Singular Value Decomposition (LSVD) score for each locus. Driver genes are then distinguished from passenger genes using dual survival analyses. Incorporating measurements of gene expression and weighting them according to the LSVD weight of each tumor, these analyses are performed using the genes located in significant expression footprints. Here, we first use simulated data to characterize the newly established LSVD score. We then present the results of our application of this refined version of TRIAGE to gene expression data from five cancer types. This refined version of TRIAGE not only allowed us to identify known prominent driver genes, such as MMP1, IL8, and COL1A2, but it also led us to identify several novel ones. These results illustrate that TRIAGE complements existing tools, allows for the identification of genes that drive cancer and could perhaps elucidate potential future targets of novel anticancer therapeutics.

A discrimination index for selecting markers of tumor growth dynamic across multiple cancer studies with a cure fraction.

To identify genomic markers with consistent effect on tumor dynamics across multiple cancer series, discrimination indices based on proportional hazards models can be used since they do not depend heavily on the sample size. However, the underlying assumption of proportionality of the hazards does not always hold, especially when the studied population is a mixture of cured and uncured patients, like in early-stage cancers. We propose a novel index that quantifies the capability of a genomic marker to separate uncured patients, according to their time-to-event outcomes. It allows to identify genomic markers characterizing tumor growth dynamic across multiple studies. Simulation results show that our index performs better than classical indices based on the Cox model. It is neither affected by the sample size nor the cure rate fraction. In a cross-study of early-stage breast cancers, the index allows to select genomic markers with a potential consistent effect on tumor growth dynamics.

A pseudo-R2 measure for selecting genomic markers with crossing hazards functions.

BACKGROUND: In genomic medical studies, one of the major objectives is to identify genomic factors with a prognostic impact on time-to-event outcomes so as to provide new insights into the disease process. Selection usually relies on statistical univariate indices based on the Cox model. Such model assumes proportional hazards (PH) which is unlikely to hold for each genomic marker. METHODS: In this paper, we introduce a novel pseudo-R2 measure derived from a crossing hazards model and designed for the selection of markers with crossing effects. The proposed index is related to the score statistic and quantifies the extent of a genomic factor to separate patients according to their survival times and marker measurements. We also show the importance of considering genomic markers with crossing effects as they potentially reflect the complex interplay between markers belonging to the same pathway. RESULTS: Simulations show that our index is not affected by the censoring and the sample size of the study. It also performs better than classical indices under the crossing hazards assumption. The practical use of our index is illustrated in a lung cancer study. The use of the proposed pseudo-R2 allows the identification of cell-cycle dependent genes not identified when relying on the PH assumption. CONCLUSIONS: The proposed index is a novel and promising tool for selecting markers with crossing hazards effects.

Efficiency of Phytoseiulus longipes Evans as a control agent of Tetranychus evansi Baker & Pritchard (Acari: Phytoseiidae: Tetranychidae) on screenhouse tomatoes

The spider mite Tetranychus evansi Baker & Pritchard can cause severe damage to tomato crops. The predatory mite Phytoseiulus longipes Evans was recently reported in association with T. evansi in Uruguaiana, Rio Grande do Sul State, Brazil. The objective of the present study was to evaluate the effects of P. longipes on the population of T. evansi on tomatoes under screenhouse condition. The study consisted on four experiments, in each of which 80 potted plantlets were distributed in two plots of 40 plantlets each. Two weeks later, each plantlet of both plots was infested with eight adult females of T. evansi; one week after, four adult females of P. longipes were released onto each plant of one plot. The population levels of T. evansi and the damage caused by these mites were significantly lower (P < 0.05; linear mixed-effect model) in the plots where P. longipes had been released. The results indicate the potential of this predator as a candidate for classical biological control of T. evansi by inoculative releases on tomato plants.

Identifying common prognostic factors in genomic cancer studies: a novel index for censored outcomes.

BACKGROUND: With the growing number of public repositories for high-throughput genomic data, it is of great interest to combine the results produced by independent research groups. Such a combination allows the identification of common genomic factors across multiple cancer types and provides new insights into the disease process. In the framework of the proportional hazards model, classical procedures, which consist of ranking genes according to the estimated hazard ratio or the p-value obtained from a test statistic of no association between survival and gene expression level, are not suitable for gene selection across multiple genomic datasets with different sample sizes. We propose a novel index for identifying genes with a common effect across heterogeneous genomic studies designed to remain stable whatever the sample size and which has a straightforward interpretation in terms of the percentage of separability between patients according to their survival times and gene expression measurements. RESULTS: The simulations results show that the proposed index is not substantially affected by the sample size of the study and the censoring. They also show that its separability performance is higher than indices of predictive accuracy relying on the likelihood function. A simulated example illustrates the good operating characteristics of our index. In addition, we demonstrate that it is linked to the score statistic and possesses a biologically relevant interpretation.The practical use of the index is illustrated for identifying genes with common effects across eight independent genomic cancer studies of different sample sizes. The meta-selection allows the identification of four genes (ESPL1, KIF4A, HJURP, LRIG1) that are biologically relevant to the carcinogenesis process and have a prognostic impact on survival outcome across various solid tumors. CONCLUSION: The proposed index is a promising tool for identifying factors having a prognostic impact across a collection of heterogeneous genomic datasets of various sizes.

Efficiency of Phytoseiulus longipes Evans as a control agent of Tetranychus evansi Baker & Pritchard (Acari: Phytoseiidae: Tetranychidae) on screenhouse tomatoes.

The spider mite Tetranychus evansi Baker & Pritchard can cause severe damage to tomato crops. The predatory mite Phytoseiulus longipes Evans was recently reported in association with T. evansi in Uruguaiana, Rio Grande do Sul State, Brazil. The objective of the present study was to evaluate the effects of P. longipes on the population of T. evansi on tomatoes under screenhouse condition. The study consisted on four experiments, in each of which 80 potted plantlets were distributed in two plots of 40 plantlets each. Two weeks later, each plantlet of both plots was infested with eight adult females of T. evansi; one week after, four adult females of P. longipes were released onto each plant of one plot. The population levels of T. evansi and the damage caused by these mites were significantly lower (P < 0.05; linear mixed-effect model) in the plots where P. longipes had been released. The results indicate the potential of this predator as a candidate for classical biological control of T. evansi by inoculative releases on tomato plants.