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The nitramine explosive, hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) is associated with acute and chronic toxicity in mammals and targets both the central nervous system and liver. After a single oral dose of RDX in male rats, the systemic distribution of RDX and the toxicodynamic response was measured using clinical chemistry and Affymetrix Rat Genome® 230 2.0 gene expression arrays, respectively. Nominal doses of 0, 9 and 36 mg/kg pure RDX were administered to animals followed by liver, cerebral cortex, and hippocampus gene expression analysis at 0, 3.5, 24, and 48 hours. RDX quickly entered the liver and brain, increasing up to 24 hours. For the 36 mg/kg dose, RDX was still measurable in liver and brain at 48 hours, but was non-detectible for the 9 mg/kg dose. At 3.5 hours, the time within which most convulsions reportedly occur after RDX ingestion, the hippocampus displayed the highest response for both gene expression and pathways, while the cortex was relatively non-responsive. The top 2 impacted pathways, primarily involved in neurotransmission, were the GABAergic and glutamatergic pathways. High numbers of genes also responded to RDX in the liver with P450 metabolism pathways significantly involved. Compared to the liver, the hippocampus displayed more consistent biological effects across dose and time with neurotransmission pathways predominating. Overall, based on gene expression data, RDX responses were high in both the hippocampus and liver, but were minimal in the cerebral cortex. These results identify the hippocampus as an important target for RDX based on gene expression.

The use of standardized data formats has increasingly facilitated both the submission of clinical trial data by pharmaceutical companies and its review by regulatory agencies. The Clinical Data Integration Standards Consortium (CDISC) format has become the de facto worldwide standard for clinical trial data submissions and review. CDISC data are captured and organized in defined datasets, or domains, with predefined variable names and attributes. As a result, researchers and reviewers have a consistent, repeatable, and rapid way to load, review, and analyze data. The benefits provided by this standardization have culminated in the development of software that intuitively utilizes CDISC structures to quickly import and analyze the data as well as summarize the results for scientists and clinicians in a collection of readable and easy-to-understand reports. In this paper, we will show how CDISC datasets with their required domains and respective variables can be utilized by JMP® Clinicalto create clinical trial analysis reports easily and quickly with interactive graphs and tables for use in FDA New Drug Applications and Clinical Reviews. These reports are also ready for inclusion in regulatory submissions such as regulatory documents and clinical study reports to improve reviewability, interpretability, and efficiency of regulatory submission and review.

Background: Continuous, safe, and reliable power supply, i.e., efficient transmission, is an indispensable prerequisite for the increasing demand for electric energy. Objective: Transmission line parameters are important parameters in power system. It is of great significance to accurately measure transmission line parameters to improve the operation of power system. Transmission line parameters mainly refer to zero-sequence parameters and positive-sequence parameters of transmission lines, including zero-sequence self-impedance, zero-sequence self-admittance, positive-sequence self-impedance and positive-sequence self-admittance of lines. Method: This paper focuses on how to use three broadband methods to realize the online anti-interference impedance measurement for the grid-connected inverter. This paper gives the design considerations of the single pulse response analysis and describes the online implementation of to obtain the grid impedance. Result: The experimental results are presented to demonstrate the feasibility and effectiveness of online measurement of grid impedance based on the three broadband excitation. Conclusion: The experimental results are verified through related experiments.

Starch digestibility can be decreased by phenolic acids. The physicochemical and digestive properties of high-amylose maize starch, normal maize starch (NMS) and waxy maize starch (WMS) complexed with caffeic acid (CA) were investigated to determine the effects of CA and amylose on starch digestibility. CA inhibited the activity of α-glucosidase and α-amylase in a dose-dependent manner. The inhibitory effect of CA on α-glucosidase was reversible and anti-competitive, while that on α-amylase was irreversible. With the increase of the amylose content in starches, the complexing index of starch-CA decreased, while the relative crystallinity increased. The crystallinity of all three starches decreased after incorporating CA, and the hydroxyl peak in the 1H-NMR spectrum of the WMS-CA complex increased significantly. The resistant starch content of the WMS-CA and NMS-CA complexes increased significantly compared with the control. The inhibitory effect of CA on amylolytic enzymes played a major role and the change of starch structure exhibited a minor role in the digestibility of the maize starch-CA complexes.

The rat is an important model organism in biomedical research for studying human disease mechanisms and treatments, but its annotated transcriptome is far from complete. We constructed a Rat Transcriptome Re-annotation named RTR using RNA-seq data from 320 samples in 11 different organs generated by the SEQC consortium. Totally, there are 52 807 genes and 114 152 transcripts in RTR. Transcribed regions and exons in RTR account for ∼42% and ∼6.5% of the genome, respectively. Of all 73 074 newly annotated transcripts in RTR, 34 213 were annotated as high confident coding transcripts and 24 728 as high confident long noncoding transcripts. Different tissues rather than different stages have a significant influence on the expression patterns of transcripts. We also found that 11 715 genes and 15 852 transcripts were expressed in all 11 tissues and that 849 house-keeping genes expressed different isoforms among tissues. This comprehensive transcriptome is freely available at http://www.unimd.org/rtr/. Our new rat transcriptome provides essential reference for genetics and gene expression studies in rat disease and toxicity models.

The transition metal nickel is used in a wide variety of alloys and medical devices. Nickel can cause a range of toxicities from allergy in humans to tumors when implanted in animals. Several microarray studies have examined nickel toxicity, but so far none have comprehensively profiled expression over an extended period. In this work, male mice were implanted with a single nickel pellet in the muscle of the right leg with the left leg used as a control. At 3 week intervals up to 12 months, nickel concentrations in bioflulids and microarrays of surrounding tissue were used to track gene expression patterns. Pellet biocorrosion resulted in varying levels of systemic nickel over time, with peaks of 600 μg L−1 in serum, while global gene expression was cyclical in nature with immune related genes topping the list of overexpressed genes. IPA and KEGG pathway analyses was used to attribute overall biological function to changes in gene expression levels, supported by GO enrichment analysis. IPA pathways identified sirtuin, mitochondria, and oxidative phosphorylation as top pathways, based predominantly on downregulated genes, whereas immune processes were associated with upregulated genes. Top KEGG pathways identified were lysosome, osteoclast differentiation, and phasgosome. Both pathway approaches identified common immune responses, as well as hypoxia, toll like receptor, and matrix metalloproteinases. Overall, pathway analysis identified a negative impact on energy metabolism, and a positive impact on immune function, in particular the acute phase response. Inside the cell the impacts were on mitochondria and lysosome. New pathways and genes responsive to nickel were identified from the large dataset in this study which represents the first long-term analysis of the effects of chronic nickel exposure on global gene expression.

BackgroundReproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS.ResultsTo dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×.ConclusionsOur findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.

Background To investigate the long-term outcome of trabeculotomy and to compare it with that of trabeculectomy. Methods We retrospectively reviewed the medical records of patients who had undergone standalone trabeculotomy. Inclusion criteria included a follow-up period of at least 6 years, availability of reliable static visual field results, etc. Age- and preoperative intraocular pressure -matched trabeculectomy cases served as controls. A Kaplan-Meier analysis was employed as a measure of surgical success. Additional clinical factors were also analyzed. Results Twenty-five eyes of 25 trabeculotomy patients and 20 eyes of 20 trabeculectomy patients with a mean postoperative follow-up period of 8.0 years were selected. The Kaplan-Meier analysis estimated that the success probability defined as intraocular pressure

BACKGROUND Real-world data (RWD) and real-world evidence (RWE) are playing increasingly important roles in clinical research and health care decision-making. To leverage RWD and generate reliable RWE, data should be well defined and structured in a way that is semantically interoperable and consistent across stakeholders. The adoption of data standards is one of the cornerstones supporting high-quality evidence for the development of clinical medicine and therapeutics. Clinical Data Interchange Standards Consortium (CDISC) data standards are mature, globally recognized, and heavily used by the pharmaceutical industry for regulatory submissions. The CDISC RWD Connect Initiative aims to better understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance needed to more easily implement them. OBJECTIVE The aim of this study is to understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance that may be needed to implement CDISC standards more easily for this purpose. METHODS We conducted a qualitative Delphi survey involving an expert advisory board with multiple key stakeholders, with 3 rounds of input and review. RESULTS Overall, 66 experts participated in round 1, 56 in round 2, and 49 in round 3 of the Delphi survey. Their inputs were collected and analyzed, culminating in group statements. It was widely agreed that the standardization of RWD is highly necessary, and the primary focus should be on its ability to improve data sharing and the quality of RWE. The priorities for RWD standardization included electronic health records, such as data shared using Health Level 7 Fast Health care Interoperability Resources (FHIR), and the data stemming from observational studies. With different standardization efforts already underway in these areas, a gap analysis should be performed to identify the areas where synergies and efficiencies are possible and then collaborate with stakeholders to create or extend existing mappings between CDISC and other standards, controlled terminologies, and models to represent data originating across different sources. CONCLUSIONS There are many ongoing data standardization efforts around human health data–related activities, each with different definitions, levels of granularity, and purpose. Among these, CDISC has been successful in standardizing clinical trial-based data for regulation worldwide. However, the complexity of the CDISC standards and the fact that they were developed for different purposes, combined with the lack of awareness and incentives to use a new standard and insufficient training and implementation support, are significant barriers to setting up the use of CDISC standards for RWD. The collection and dissemination of use cases, development of tools and support systems for the RWD community, and collaboration with other standards development organizations are potential steps forward. Using CDISC will help link clinical trial data and RWD and promote innovation in health data science.

Background Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called ‘Strengthening The Organization and Reporting of Microbiome Studies’ (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.

Previous studies showed that adding zero valent iron (ZVI) can increase the methane production and degradation rate of organic waste by improving the performance of anaerobic digester. However, our study firstly found that ZVI (37 μm, 10 g/L) inhibited the anaerobic digestion (AD) of cow manure and lignocellulose. ZVI significantly increased the methanogenic rate of cow manure in the first 6 days, but decreased the accumulative methane yield and volatile fatty acids yield by 10.3 and 12%, respectively. The effect of ZVI on AD of liquid biomass separated from cow manure was positive, but the effect on solid biomass was negative. These results indicated that ZVI enhanced the AD of easily biodegradable organics but inhibited the biodegradation of refractory organics (lignocellulose). By analyzing the varying effects of ZVI in diverse anaerobic systems, it was found that the effects were influenced by the characteristics of substrate and inoculum-substrate ratio. This study suggested that only proper ZVI addition can improve the AD process depending on the feeding materials.

Background Volatile fatty acids (VFAs) can be effective and promising alternate carbon sources for microbial lipid production by a few oleaginous yeasts. However, the severe inhibitory effect of high-content (> 10 g/L) VFAs on these yeasts has impeded the production of high lipid yields and their large-scale application. Slightly acidic conditions have been commonly adopted because they have been considered favorable to oleaginous yeast cultivation. However, the acidic pH environment further aggravates this inhibition because VFAs appear largely in an undissociated form under this condition. Alkaline conditions likely alleviate the severe inhibition of high-content VFAs by significantly increasing the dissociation degree of VFAs. This hypothesis should be verified through a systematic research. Results The combined effects of high acetic acid concentrations and alkaline conditions on VFA utilization, cell growth, and lipid accumulation of Yarrowia lipolytica were systematically investigated through batch cultures of Y. lipolytica by using high concentrations (30–110 g/L) of acetic acid as a carbon source at an initial pH ranging from 6 to 10. An initial pH of 8 was determined as optimal. The highest biomass and lipid production (37.14 and 10.11 g/L) were obtained with 70 g/L acetic acid, whereas cultures with > 70 g/L acetic acid had decreased biomass and lipid yield due to excessive anion accumulation. Feasibilities on high-content propionic acid, butyric acid, and mixed VFAs were compared and evaluated. Results indicated that YX/S and YL/S of cultures on butyric acid (0.570, 0.144) were comparable with those on acetic acid (0.578, 0.160) under alkaline conditions. The performance on propionic acid was much inferior to that on other acids. Mixed VFAs were more beneficial to fast adaptation and lipid production than single types of VFA. Furthermore, cultures on food waste (FW) and fruit and vegetable waste (FVW) fermentate were carried out and lipid production was effectively improved under this alkaline condition. The highest biomass and lipid production on FW fermentate reached 14.65 g/L (YX/S: 0.414) and 3.20 g/L (YL/S: 0.091) with a lipid content of 21.86%, respectively. By comparison, the highest biomass and lipid production on FVW fermentate were 11.84 g/L (YX/S: 0.534) and 3.08 g/L (YL/S: 0.139), respectively, with a lipid content of 26.02%. Conclusions This study assumed and verified that alkaline conditions (optimal pH 8) could effectively alleviate the lethal effect of high-content VFA on Y. lipolytica and significantly improve biomass and lipid production. These results could provide a new cultivation strategy to achieve simple utilizations of high-content VFAs and increase lipid production. Feasibilities on FW and FVW-derived VFAs were evaluated, and meaningful information was provided for practical applications.

The crystallinity is of importance for in vitro digestibility of starch. Six chestnut cultivars were selected to study the relationship between the crystallinity and in vitro digestibility of chestnut starch during thermal processing. After heat treatment, the total starch and amylose content remained unchanged or decreased, but the amount of damaged starch increased significantly (P 0.05). Thermal processing enhanced the short-range ordered structure of starch and reduced its relative crystallinity. Thermal processing also decreased the resistant starch (RS) content, but a high RS content (57.2-67.9% of total starch) still remained in cooked chestnut starch. The relative crystallinity was negatively correlated with the estimated glycemic index (eGI) (r = -0.6416), and positively correlated with RS content (r = 0.6189). Accordingly, by altering the relative crystallinity and damage degree of chestnut starch, thermal processing changed the overall starch fractions and eGI, which can affect the overall in vitro digestibility.

The present study investigated the differences in starch and amylose content among six chestnut cultivars widely distributed in China, as well as the relationship between their physicochemical characteristics and in vitro digestibility. The cultivar and geographical distribution significantly influenced the characteristics of chestnut starch. The starch granule from all six cultivars was round or irregular polygonal in shape, with a particle size that varied from 1 to 20 μm. The amylose content ranged from 22.27 to 29.80% of starch, while the damaged starch was 3.44–11.94%. The volume average particle size of the chestnut starches from the six cultivars varied from 8.33 to 10.24 μm, and the median diameter ranged from 6.82 to 8.69 μm. The short-range order of the six chestnut starches exhibited no significant difference, but their X-ray patterns were different and their relative crystallinity ranged between 19.3 and 27.9%. The resistant starch accounts for 70.1–85.6% of chestnut starch, and was found to be positively correlated with the amylose content. The glycemic index was negatively correlated with the relative crystallinity, indicating that the crystalline structure of starch had a retardant effect on its digestibility.

There are tremendous unmet needs in drug development for rare diseases. Computational drug repositioning is a promising approach and has been successfully applied to the development of treatments for diseases. However, how to utilize this knowledge and effectively conduct and implement computational drug repositioning approaches for rare disease therapies is still an open issue. Here, we focus on the means of utilizing accumulated genomic data for accelerating and facilitating drug repositioning for rare diseases. First, we summarize the current genome landscape of rare diseases. Second, we propose several promising bioinformatics approaches and pipelines for computational drug repositioning for rare diseases. Finally, we discuss recent regulatory incentives and other enablers in rare disease drug development and outline the remaining challenges.

Microbial oils have become a research hotspot in alleviating energy challenges and environmental problems because of their potential to be green alternatives of traditional fossil fuels. Yarrowia lipolytica is a promising oleaginous yeast that can utilize various, especially low-cost carbon sources to synthesize considerable lipids more than 30% of dry cell weight, which is attracting researchers’ attention. Based on well understanding of its lipid synthesis and metabolism mechanism, various optimization approaches have been studied dispersedly to improve the lipid synthesis and realize the industrial-scale application of Y. lipolytica. Some approaches are focused on improving and optimizing culture conditions, such as temperature, pH value, and rotating speed, etc. Other approaches are dedicated to the optimization of nutrient elements, such as carbon source, nitrogen source type and/or concentration, and C/N ratio, etc. Some adjust the cultivation mode to facilitate nutrient assimilation and transformation, and others use genetic engineering to modify this yeast. This review focuses on the comprehensive and detailed analyses of feasible enhancement approaches for lipid synthesis. Some prospects will also be introduced as references for further study.

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific-filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

Proteomics technology is an attractive biomarker candidate discovery tool that can be applied to study large sets of biological molecules. To identify novel biomarkers and molecular targets in arsenic-induced skin lesions, we have determined the protein profile of arsenic-affected human epidermal stratum corneum by shotgun proteomics. Samples of palm and foot sole from healthy subjects were analyzed, demonstrating similar protein patterns in palm and sole. Samples were collected from the palms of subjects with arsenic keratosis (lesional and adjacent non-lesional samples) and arsenic-exposed subjects without lesions (normal). Samples from non-exposed healthy individuals served as controls. We found that three proteins in arsenic-exposed lesional epidermis were consistently distinguishably expressed from the unaffected epidermis. One of these proteins, the cadherin-like transmembrane glycoprotein, desmoglein 1 (DSG1) was suppressed. Down-regulation of DSG1 may lead to reduced cell-cell adhesion, resulting in abnormal epidermal differentiation. The expression of keratin 6c (KRT6C) and fatty acid binding protein 5 (FABP5) were significantly increased. FABP5 is an intracellular lipid chaperone that plays an essential role in fatty acid metabolism in human skin. This raises a possibility that overexpression of FABP5 may affect the proliferation or differentiation of keratinocytes by altering lipid metabolism. KRT6C is a constituent of the cytoskeleton that maintains epidermal integrity and cohesion. Abnormal expression of KRT6C may affect its structural role in the epidermis. Our findings suggest an important approach for future studies of arsenic-mediated toxicity and skin cancer, where certain proteins may represent useful biomarkers of early diagnoses in high-risk populations and hopefully new treatment targets. Further studies are required to understand the biological role of these markers in skin pathogenesis from arsenic exposure.

Tungsten alloys are composed of tungsten microparticles embedded in a solid matrix of transition metals such as nickel, cobalt, or iron. To understand the toxicology of these alloys, male F344 rats were intramuscularly implanted with pellets of tungsten/nickel/cobalt, tungsten/nickel/iron, or pure tungsten, with tantalum pellets as a negative control. Between 6 and 12 months, aggressive rhabdomyosarcomas formed around tungsten/nickel/cobalt pellets, while those of tungsten/nickel/iron or pure tungsten did not cause cancers. Electron microscopy showed a progressive corrosion of the matrix phase of tungsten/nickel/cobalt pellets over 6 months, accompanied by high urinary concentrations of nickel and cobalt. In contrast, non-carcinogenic tungsten/nickel/iron pellets were minimally corroded and urinary metals were low; these pellets having developed a surface oxide layer in vivo that may have restricted the mobilization of carcinogenic nickel. Microarray analysis of tumors revealed large changes in gene expression compared with normal muscle, with biological processes involving the cell cycle significantly up-regulated and those involved with muscle development and differentiation significantly down-regulated. Top KEGG pathways disrupted were adherens junction, p53 signaling, and the cell cycle. Chromosomal enrichment analysis of genes showed a highly significant impact at cytoband 7q22 (chromosome 7) which included mouse double minute (MDM2) and cyclin-dependant kinase (CDK4) as well as other genes associated with human sarcomas. In conclusion, the tumorigenic potential of implanted tungsten alloys is related to mobilization of carcinogenic metals nickel and cobalt from corroding pellets, while gene expression changes in the consequent tumors are similar to radiation induced animal sarcomas as well as sporadic human sarcomas.

Over the past 5 years, increased attention has been focused on using high-throughput in vitro screening for identifying chemical hazards and prioritizing chemicals for additional in vivo testing. The U.S. Environmental Protection Agency's ToxCast program has generated a significant amount of high-throughput screening data allowing a broad-based assessment of the utility of these assays for predicting in vivo responses. In this study, a comprehensive cross-validation model comparison was performed to evaluate the predictive performance of the more than 600 in vitro assays from the ToxCast phase I screening effort across 60 in vivo endpoints using 84 different statistical classification methods. The predictive performance of the in vitro assays was compared and combined with that from chemical structure descriptors. With the exception of chronic in vivo cholinesterase inhibition, the overall predictive power of both the in vitro assays and the chemical descriptors was relatively low. The predictive power of the in vitro assays was not significantly different from that of the chemical descriptors and aggregating the assays based on genes reduced predictive performance. Prefiltering the in vitro assay data outside the cross-validation loop, as done in some previous studies, significantly biased estimates of model performance. The results suggest that the current ToxCast phase I assays and chemicals have limited applicability for predicting in vivo chemical hazards using standard statistical classification methods. However, if viewed as a survey of potential molecular initiating events and interpreted as risk factors for toxicity, the assays may still be useful for chemical prioritization.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80–90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.

The process for evaluating chemical safety is inefficient, costly, and animal intensive. There is growing consensus that the current process of safety testing needs to be significantly altered to improve efficiency and reduce the number of untested chemicals. In this study, the use of short-term gene expression profiles was evaluated for predicting the increased incidence of mouse lung tumors. Animals were exposed to a total of 26 diverse chemicals with matched vehicle controls over a period of 3 years. Upon completion, significant batch-related effects were observed. Adjustment for batch effects significantly improved the ability to predict increased lung tumor incidence. For the best statistical model, the estimated predictive accuracy under honest fivefold cross-validation was 79.3% with a sensitivity and specificity of 71.4 and 86.3%, respectively. A learning curve analysis demonstrated that gains in model performance reached a plateau at 25 chemicals, indicating that the size of current data set was sufficient to provide a robust classifier. The classification results showed that a small subset of chemicals contributed disproportionately to the misclassification rate. For these chemicals, the misclassification was more closely associated with genotoxicity status than with efficacy in the original bioassay. Statistical models were also used to predict dose-response increases in tumor incidence for methylene chloride and naphthalene. The average posterior probabilities for the top models matched the results from the bioassay for methylene chloride. For naphthalene, the average posterior probabilities for the top models overpredicted the tumor response, but the variability in predictions was significantly higher. The study provides both a set of gene expression biomarkers for predicting chemically induced mouse lung tumors and a broad assessment of important experimental and analysis criteria for developing microarray-based predictors of safety-related end points.

Background Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. Results We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. Conclusions We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0694-1) contains supplementary material, which is available to authorized users.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

Four triazole fungicides were studied using toxicogenomic techniques to identify potential mechanisms of action. Adult male Sprague-Dawley rats were dosed for 14 days by gavage with fluconazole, myclobutanil, propiconazole, or triadimefon. Following exposure, serum was collected for hormone measurements, and liver and testes were collected for histology, enzyme biochemistry, or gene expression profiling. Body and testis weights were unaffected, but liver weights were significantly increased by all four triazoles, and hepatocytes exhibited centrilobular hypertrophy. Myclobutanil exposure increased serum testosterone and decreased sperm motility, but no treatment-related testis histopathology was observed. We hypothesized that gene expression profiles would identify potential mechanisms of toxicity and used DNA microarrays and quantitative real-time PCR (qPCR) to generate profiles. Triazole fungicides are designed to inhibit fungal cytochrome P450 (CYP) 51 enzyme but can also modulate the expression and function of mammalian CYP genes and enzymes. Triazoles affected the expression of numerous CYP genes in rat liver and testis, including multiple Cyp2c and Cyp3a isoforms as well as other xenobiotic metabolizing enzyme (XME) and transporter genes. For some genes, such as Ces2 and Udpgtr2, all four triazoles had similar effects on expression, suggesting possible common mechanisms of action. Many of these CYP, XME and transporter genes are regulated by xeno-sensing nuclear receptors, and hierarchical clustering of CAR/PXR-regulated genes demonstrated the similarities of toxicogenomic responses in liver between all four triazoles and in testis between myclobutanil and triadimefon. Triazoles also affected expression of multiple genes involved in steroid hormone metabolism in the two tissues. Thus, gene expression profiles helped identify possible toxicological mechanisms of the triazole fungicides.

External RNA controls (ERCs), although important for microarray assay performance assessment, have yet to be fully implemented in the research community. As part of the MicroArray Quality Control (MAQC) study, two types of ERCs were implemented and evaluated; one was added to the total RNA in the samples before amplification and labeling; the other was added to the copyRNAs (cRNAs) before hybridization. ERC concentration-response curves were used across multiple commercial microarray platforms to identify problematic assays and potential sources of variation in the analytical process. In addition, the behavior of different ERC types was investigated, resulting in several important observations, such as the sample-dependent attributes of performance and the potential of using these control RNAs in a combinatorial fashion. This multiplatform investigation of the behavior and utility of ERCs provides a basis for articulating specific recommendations for their future use in evaluating assay performance across multiple platforms.

A critical component in the design of the Chemical Effects in Biological Systems (CEBS) Knowledgebase is a strategy to capture toxicogenomics study protocols and the toxicity endpoint data (clinical pathology and histopathology). A Study is generally an experiment carried out during a period of time for the purpose of obtaining data, and the Study Design Description captures the methods, timing, and organization of the Study. The CEBS Data Dictionary (CEBS-DD) has been designed to define and organize terms in an attempt to standardize nomenclature needed to describe a toxicogenomics Study in a structured yet intuitive format and provide a flexible means to describe a Study as conceptualized by the investigator. The CEBS-DD will organize and annotate information from a variety of sources, thereby facilitating the capture and display of toxicogenomics data in biological context in CEBS, i.e., associating molecular events detected in highly-parallel data with the toxicology/pathology phenotype as observed in the individual Study Subjects and linked to the experimental treatments. The CEBS-DD has been developed with a focus on acute toxicity studies, but with a design that will permit it to be extended to other areas of toxicology and biology with the addition of domain-specific terms. To illustrate the utility of the CEBS-DD, we present an example of integrating data from two proteomics and transcriptomics studies of the response to acute acetaminophen toxicity (A. N. Heinloth et al., 2004, Toxicol. Sci. 80, 193-202).