Insecticide Resistance and Control Failure Likelihood Analysis in Plutella xylostella (Lepidoptera: Plutellidae) Populations From Taiwan.

The status of insecticide resistance levels is important for applying suitable pest management approaches. The present study investigated the insecticide resistance of Plutella xylostella (Linnaeus) (Lepidoptera: Plutellidae) populations from five major cruciferous growing areas in Taiwan. The surveyed locations were distributed from central to southern Taiwan and included Taichung, Changhua, Yunlin, Chiayi, and Kaohsiung. High levels of resistance to spinosad, indoxacarb, metaflumizone, and chlorantraniliprole were recorded among the surveyed populations except in Taichung. The resistance ratios ranged from 2.376 to 1,236-fold for spinosad and 24.63-1,511-fold for indoxacarb. Similarly, those for metaflumizone and chlorantraniliprole, were 2.563-76.21- and 4.457-683.0-fold, respectively. However, Bacillus thuringiensis subsp. aizawai, Bacillus thuringiensis subsp. kurstaki, emamectin benzoate, and diafenthiuron were still relatively effective against most field populations of P. xylostella. After approximately 10 generations of being maintained in the laboratory without exposure to insecticides, the resistance ratios of the Kaohsiung and Changhua populations declined to approximately 1.4-10-fold, and insecticides with control failure likelihood also began to show a negligible risk of control failure. Although spinosad, indoxacarb, metaflumizone, and chlorantraniliprole have lost their effectiveness in most field populations of P. xylostella in Taiwan, their effectiveness may be recovered in the absence of insecticide-selection pressure for approximately 10 generations. Therefore, we suggest that a constant survey of insecticide resistance and well-designed insecticide rotation based on the survey results are necessary for the effective control and insecticide resistance management of P. xylostella.

Implementation of preemptive DNA sequence-based pharmacogenomics testing across a large academic medical center: The Mayo-Baylor RIGHT 10K Study.

PURPOSE: The Mayo-Baylor RIGHT 10K Study enabled preemptive, sequence-based pharmacogenomics (PGx)-driven drug prescribing practices in routine clinical care within a large cohort. We also generated the tools and resources necessary for clinical PGx implementation and identified challenges that need to be overcome. Furthermore, we measured the frequency of both common genetic variation for which clinical guidelines already exist and rare variation that could be detected by DNA sequencing, rather than genotyping. METHODS: Targeted oligonucleotide-capture sequencing of 77 pharmacogenes was performed using DNA from 10,077 consented Mayo Clinic Biobank volunteers. The resulting predicted drug response-related phenotypes for 13 genes, including CYP2D6 and HLA, affecting 21 drug-gene pairs, were deposited preemptively in the Mayo electronic health record. RESULTS: For the 13 pharmacogenes of interest, the genomes of 79% of participants carried clinically actionable variants in 3 or more genes, and DNA sequencing identified an average of 3.3 additional conservatively predicted deleterious variants that would not have been evident using genotyping. CONCLUSION: Implementation of preemptive rather than reactive and sequence-based rather than genotype-based PGx prescribing revealed nearly universal patient applicability and required integrated institution-wide resources to fully realize individualized drug therapy and to show more efficient use of health care resources.

Neptune: an environment for the delivery of genomic medicine.

PURPOSE: Genomic medicine holds great promise for improving health care, but integrating searchable and actionable genetic data into electronic health records (EHRs) remains a challenge. Here we describe Neptune, a system for managing the interaction between a clinical laboratory and an EHR system during the clinical reporting process. METHODS: We developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis, and EHR integration. RESULTS: Neptune has been applied for the generation and delivery of over 15,000 clinical genomic reports. This work spans two clinical tests based on targeted gene panels that contain 68 and 153 genes respectively. These projects demanded customizable clinical reports that contained a variety of genetic data types including single-nucleotide variants (SNVs), copy-number variants (CNVs), pharmacogenomics, and polygenic risk scores. Two variant reanalysis activities were also supported, highlighting this important workflow. CONCLUSION: Methods are needed for delivering structured genetic data to EHRs. This need extends beyond developing data formats to providing infrastructure that manages the reporting process itself. Neptune was successfully applied on two high-throughput clinical sequencing projects to build and deliver clinical reports to EHR systems. The software is open source and available at https://gitlab.com/bcm-hgsc/neptune .

Atlas-CNV: a validated approach to call single-exon CNVs in the eMERGESeq gene panel.

PURPOSE: To provide a validated method to confidently identify exon-containing copy-number variants (CNVs), with a low false discovery rate (FDR), in targeted sequencing data from a clinical laboratory with particular focus on single-exon CNVs. METHODS: DNA sequence coverage data are normalized within each sample and subsequently exonic CNVs are identified in a batch of samples, when the target log2 ratio of the sample to the batch median exceeds defined thresholds. The quality of exonic CNV calls is assessed by C-scores (Z-like scores) using thresholds derived from gold standard samples and simulation studies. We integrate an ExonQC threshold to lower FDR and compare performance with alternate software (VisCap). RESULTS: Thirteen CNVs were used as a truth set to validate Atlas-CNV and compared with VisCap. We demonstrated FDR reduction in validation, simulation, and 10,926 eMERGESeq samples without sensitivity loss. Sixty-four multiexon and 29 single-exon CNVs with high C-scores were assessed by Multiplex Ligation-dependent Probe Amplification (MLPA). CONCLUSION: Atlas-CNV is validated as a method to identify exonic CNVs in targeted sequencing data generated in the clinical laboratory. The ExonQC and C-score assignment can reduce FDR (identification of targets with high variance) and improve calling accuracy of single-exon CNVs respectively. We propose guidelines and criteria to identify high confidence single-exon CNVs.

The role of Lutheran/basal cell adhesion molecule in human bladder carcinogenesis.

BACKGROUND: Lutheran/basal cell adhesion molecule (Lu/BCAM) is a membrane bound glycoprotein. This study was performed to investigate the role and downstream signaling pathway of Lu/BCAM in human bladder tumorigenesis. METHODS: Five human bladder cancer (E6, RT4, TSGH8301, TCCSUP and J82), one stable mouse fibroblast cell line (NIH-Lu) expressing Lu/BCAM transgene and sixty human uroepithelial carcinoma specimens were analyzed by real-time PCR, immunohistochemistry (IHC), immunofluorescence (IFA) staining, Western blotting and promoter luciferase assay for Lu/BCAM, respectively. The tumorigenicity of Lu/BCAM was demonstrated by focus formation, colony-forming ability, tumour formation, cell adhesion and migration. RESULTS: H-ras V12 was revealed to up-regulate Lu/BCAM at both transcriptional and translation levels. Lu/BCAM expression was detected on the membrane of primary human bladder cancer cells. Over-expression of Lu/BCAM in NIH-Lu stable cells increased focus number, colony formation and cell adhesion accompanied with F-actin rearrangement and decreased cell migration compared with parental NIH3T3 fibroblasts. In the presence of laminin ligand, Lu/BCAM overexpression further suppressed cell migration accompanied with increased cell adhesion. We further revealed that laminin-Lu/BCAM-induced cell adhesion and F-actin rearrangement were through increased Erk phosphorylation with an increase of RhoA and a decrease of Rac1 activity. Similarly, high Lu/BCAM expression was detected in the tumors of human renal pelvis, ureter and bladder, and was significantly associated with advanced tumor stage (p = 0.02). Patients with high Lu/BCAM expression showed a trend toward larger tumor size (p = 0.07) and lower disease-specific survival (p = 0.08), although not reaching statistical significance. CONCLUSION: This is the first report showing that Lu/BCAM, in the presence of its ligand laminin, is oncogenic in human urothelial cancers and may have potential as a novel therapeutic target.

DiMeX: A Text Mining System for Mutation-Disease Association Extraction.

The number of published articles describing associations between mutations and diseases is increasing at a fast pace. There is a pressing need to gather such mutation-disease associations into public knowledge bases, but manual curation slows down the growth of such databases. We have addressed this problem by developing a text-mining system (DiMeX) to extract mutation to disease associations from publication abstracts. DiMeX consists of a series of natural language processing modules that preprocess input text and apply syntactic and semantic patterns to extract mutation-disease associations. DiMeX achieves high precision and recall with F-scores of 0.88, 0.91 and 0.89 when evaluated on three different datasets for mutation-disease associations. DiMeX includes a separate component that extracts mutation mentions in text and associates them with genes. This component has been also evaluated on different datasets and shown to achieve state-of-the-art performance. The results indicate that our system outperforms the existing mutation-disease association tools, addressing the low precision problems suffered by most approaches. DiMeX was applied on a large set of abstracts from Medline to extract mutation-disease associations, as well as other relevant information including patient/cohort size and population data. The results are stored in a database that can be queried and downloaded at http://biotm.cis.udel.edu/dimex/. We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases.

High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis.

The High-performance Integrated Virtual Environment (HIVE) is a distributed storage and compute environment designed primarily to handle next-generation sequencing (NGS) data. This multicomponent cloud infrastructure provides secure web access for authorized users to deposit, retrieve, annotate and compute on NGS data, and to analyse the outcomes using web interface visual environments appropriately built in collaboration with research and regulatory scientists and other end users. Unlike many massively parallel computing environments, HIVE uses a cloud control server which virtualizes services, not processes. It is both very robust and flexible due to the abstraction layer introduced between computational requests and operating system processes. The novel paradigm of moving computations to the data, instead of moving data to computational nodes, has proven to be significantly less taxing for both hardware and network infrastructure.The honeycomb data model developed for HIVE integrates metadata into an object-oriented model. Its distinction from other object-oriented databases is in the additional implementation of a unified application program interface to search, view and manipulate data of all types. This model simplifies the introduction of new data types, thereby minimizing the need for database restructuring and streamlining the development of new integrated information systems. The honeycomb model employs a highly secure hierarchical access control and permission system, allowing determination of data access privileges in a finely granular manner without flooding the security subsystem with a multiplicity of rules. HIVE infrastructure will allow engineers and scientists to perform NGS analysis in a manner that is both efficient and secure. HIVE is actively supported in public and private domains, and project collaborations are welcomed. Database URL: https://hive.biochemistry.gwu.edu.

Generating a focused view of disease ontology cancer terms for pan-cancer data integration and analysis.

Bio-ontologies provide terminologies for the scientific community to describe biomedical entities in a standardized manner. There are multiple initiatives that are developing biomedical terminologies for the purpose of providing better annotation, data integration and mining capabilities. Terminology resources devised for multiple purposes inherently diverge in content and structure. A major issue of biomedical data integration is the development of overlapping terms, ambiguous classifications and inconsistencies represented across databases and publications. The disease ontology (DO) was developed over the past decade to address data integration, standardization and annotation issues for human disease data. We have established a DO cancer project to be a focused view of cancer terms within the DO. The DO cancer project mapped 386 cancer terms from the Catalogue of Somatic Mutations in Cancer (COSMIC), The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium, Therapeutically Applicable Research to Generate Effective Treatments, Integrative Oncogenomics and the Early Detection Research Network into a cohesive set of 187 DO terms represented by 63 top-level DO cancer terms. For example, the COSMIC term 'kidney, NS, carcinoma, clear_cell_renal_cell_carcinoma' and TCGA term 'Kidney renal clear cell carcinoma' were both grouped to the term 'Disease Ontology Identification (DOID):4467 / renal clear cell carcinoma' which was mapped to the TopNodes_DOcancerslim term 'DOID:263 / kidney cancer'. Mapping of diverse cancer terms to DO and the use of top level terms (DO slims) will enable pan-cancer analysis across datasets generated from any of the cancer term sources where pan-cancer means including or relating to all or multiple types of cancer. The terms can be browsed from the DO web site (http://www.disease-ontology.org) and downloaded from the DO's Apache Subversion or GitHub repositories. Database URL: http://www.disease-ontology.org

BioXpress: an integrated RNA-seq-derived gene expression database for pan-cancer analysis.

BioXpress is a gene expression and cancer association database in which the expression levels are mapped to genes using RNA-seq data obtained from The Cancer Genome Atlas, International Cancer Genome Consortium, Expression Atlas and publications. The BioXpress database includes expression data from 64 cancer types, 6361 patients and 17 469 genes with 9513 of the genes displaying differential expression between tumor and normal samples. In addition to data directly retrieved from RNA-seq data repositories, manual biocuration of publications supplements the available cancer association annotations in the database. All cancer types are mapped to Disease Ontology terms to facilitate a uniform pan-cancer analysis. The BioXpress database is easily searched using HUGO Gene Nomenclature Committee gene symbol, UniProtKB/RefSeq accession or, alternatively, can be queried by cancer type with specified significance filters. This interface along with availability of pre-computed downloadable files containing differentially expressed genes in multiple cancers enables straightforward retrieval and display of a broad set of cancer-related genes.

Single-nucleotide variations in cardiac arrhythmias: prospects for genomics and proteomics based biomarker discovery and diagnostics.

Cardiovascular diseases are a large contributor to causes of early death in developed countries. Some of these conditions, such as sudden cardiac death and atrial fibrillation, stem from arrhythmias-a spectrum of conditions with abnormal electrical activity in the heart. Genome-wide association studies can identify single nucleotide variations (SNVs) that may predispose individuals to developing acquired forms of arrhythmias. Through manual curation of published genome-wide association studies, we have collected a comprehensive list of 75 SNVs associated with cardiac arrhythmias. Ten of the SNVs result in amino acid changes and can be used in proteomic-based detection methods. In an effort to identify additional non-synonymous mutations that affect the proteome, we analyzed the post-translational modification S-nitrosylation, which is known to affect cardiac arrhythmias. We identified loss of seven known S-nitrosylation sites due to non-synonymous single nucleotide variations (nsSNVs). For predicted nitrosylation sites we found 1429 proteins where the sites are modified due to nsSNV. Analysis of the predicted S-nitrosylation dataset for over- or under-representation (compared to the complete human proteome) of pathways and functional elements shows significant statistical over-representation of the blood coagulation pathway. Gene Ontology (GO) analysis displays statistically over-represented terms related to muscle contraction, receptor activity, motor activity, cystoskeleton components, and microtubule activity. Through the genomic and proteomic context of SNVs and S-nitrosylation sites presented in this study, researchers can look for variation that can predispose individuals to cardiac arrhythmias. Such attempts to elucidate mechanisms of arrhythmia thereby add yet another useful parameter in predicting susceptibility for cardiac diseases.

A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE).

Years of sequence feature curation by UniProtKB/Swiss-Prot, PIR-PSD, NCBI-CDD, RefSeq and other database biocurators has led to a rich repository of information on functional sites of genes and proteins. This information along with variation-related annotation can be used to scan human short sequence reads from next-generation sequencing (NGS) pipelines for presence of non-synonymous single-nucleotide variations (nsSNVs) that affect functional sites. This and similar workflows are becoming more important because thousands of NGS data sets are being made available through projects such as The Cancer Genome Atlas (TCGA), and researchers want to evaluate their biomarkers in genomic data. BioMuta, an integrated sequence feature database, provides a framework for automated and manual curation and integration of cancer-related sequence features so that they can be used in NGS analysis pipelines. Sequence feature information in BioMuta is collected from the Catalogue of Somatic Mutations in Cancer (COSMIC), ClinVar, UniProtKB and through biocuration of information available from publications. Additionally, nsSNVs identified through automated analysis of NGS data from TCGA are also included in the database. Because of the petabytes of data and information present in NGS primary repositories, a platform HIVE (High-performance Integrated Virtual Environment) for storing, analyzing, computing and curating NGS data and associated metadata has been developed. Using HIVE, 31 979 nsSNVs were identified in TCGA-derived NGS data from breast cancer patients. All variations identified through this process are stored in a Curated Short Read archive, and the nsSNVs from the tumor samples are included in BioMuta. Currently, BioMuta has 26 cancer types with 13 896 small-scale and 308 986 large-scale study-derived variations. Integration of variation data allows identifications of novel or common nsSNVs that can be prioritized in validation studies. Database URL: BioMuta: http://hive.biochemistry.gwu.edu/tools/biomuta/index.php; CSR: http://hive.biochemistry.gwu.edu/dna.cgi?cmd=csr; HIVE: http://hive.biochemistry.gwu.edu.

Transcriptional activation of the Axl and PDGFR-α by c-Met through a ras- and Src-independent mechanism in human bladder cancer.

BACKGROUND: A cross-talk between different receptor tyrosine kinases (RTKs) plays an important role in the pathogenesis of human cancers. METHODS: Both NIH-Met5 and T24-Met3 cell lines harboring an inducible human c-Met gene were established. C-Met-related RTKs were screened by RTK microarray analysis. The cross-talk of RTKs was demonstrated by Western blotting and confirmed by small interfering RNA (siRNA) silencing, followed by elucidation of the underlying mechanism. The impact of this cross-talk on biological function was demonstrated by Trans-well migration assay. Finally, the potential clinical importance was examined in a cohort of 65 cases of locally advanced and metastatic bladder cancer patients. RESULTS: A positive association of Axl or platelet-derived growth factor receptor-alpha (PDGFR-α) with c-Met expression was demonstrated at translational level, and confirmed by specific siRNA knock-down. The transactivation of c-Met on Axl or PDGFR-α in vitro was through a ras- and Src-independent activation of mitogen-activated protein kinase/extracellular signal-regulated kinase (MEK/ERK) pathway. In human bladder cancer, co-expression of these RTKs was associated with poor patient survival (p < 0.05), and overexpression of c-Met/Axl/PDGFR-α or c-Met alone showed the most significant correlation with poor survival (p < 0.01). CONCLUSIONS: In addition to c-Met, the cross-talk with Axl and/or PDGFR-α also contributes to the progression of human bladder cancer. Evaluation of Axl and PDGFR-α expression status may identify a subset of c-Met-positive bladder cancer patients who may require co-targeting therapy.

Effects of combinatorial treatment with pituitary adenylate cyclase activating peptide and human mesenchymal stem cells on spinal cord tissue repair.

The aim of this study is to understand if human mesenchymal stem cells (hMSCs) and neuropeptide pituitary adenylate cyclase-activating polypeptide (PACAP) have synergistic protective effect that promotes functional recovery in rats with severe spinal cord injury (SCI). To evaluate the effect of delayed combinatorial therapy of PACAP and hMSCs on spinal cord tissue repair, we used the immortalized hMSCs that retain their potential of neuronal differentiation under the stimulation of neurogenic factors and possess the properties for the production of several growth factors beneficial for neural cell survival. The results indicated that delayed treatment with PACAP and hMSCs at day 7 post SCI increased the remaining neuronal fibers in the injured spinal cord, leading to better locomotor functional recovery in SCI rats when compared to treatment only with PACAP or hMSCs. Western blotting also showed that the levels of antioxidant enzymes, Mn-superoxide dismutase (MnSOD) and peroxiredoxin-1/6 (Prx-1 and Prx-6), were increased at the lesion center 1 week after the delayed treatment with the combinatorial therapy when compared to that observed in the vehicle-treated control. Furthermore, in vitro studies showed that co-culture with hMSCs in the presence of PACAP not only increased a subpopulation of microglia expressing galectin-3, but also enhanced the ability of astrocytes to uptake extracellular glutamate. In summary, our in vivo and in vitro studies reveal that delayed transplantation of hMSCs combined with PACAP provides trophic molecules to promote neuronal cell survival, which also foster beneficial microenvironment for endogenous glia to increase their neuroprotective effect on the repair of injured spinal cord tissue.

An unusual function of RON receptor tyrosine kinase as a transcriptional regulator in cooperation with EGFR in human cancer cells.

Homodimerization of RON (MST1R), a receptor tyrosine kinase, usually occurs in cells stimulated by a ligand and leads to the downstream activation of signaling pathways. Here we report that bladder cancer cells, in response to physiological stress, use an alternative mechanism for signaling activation. Time-course studies indicated that RON migrated directly from the membrane to the nucleus of bladder cancer cells in response to serum starvation. Biochemical and genetic studies implied that this nuclear internalization was complexed with epidermal growth factor receptor (EGFR) and required the docking of importins. In vivo analysis confirmed that nuclear RON was present in 38.4% (28/73) of primary bladder tumors. Chromatin immunoprecipitation (ChIP) on microarray analysis further revealed that this internalized complex bound to at least 134 target genes known to participate in three stress-responsive networks: p53, stress-activated protein kinase/c-jun N-terminal kinase and phosphatidylinositol 3-kinase/Akt. These findings suggest that RON, in a complex with EGFR, acts as a transcriptional regulator in response to acute disturbances (e.g. serum starvation) imposed on cancer cells. In an attempt to re-establish homeostasis, these cells bypass regular mechanisms required by ligand stimulation and trigger the RON-directed transcriptional response, which confers a survival advantage.