Control of false discoveries in grouped hypothesis testing for eQTL data.

BACKGROUND: Expression quantitative trait locus (eQTL) analysis aims to detect the genetic variants that influence the expression of one or more genes. Gene-level eQTL testing forms a natural grouped-hypothesis testing strategy with clear biological importance. Methods to control family-wise error rate or false discovery rate for group testing have been proposed earlier, but may not be powerful or easily apply to eQTL data, for which certain structured alternatives may be defensible and may enable the researcher to avoid overly conservative approaches. RESULTS: In an empirical Bayesian setting, we propose a new method to control the false discovery rate (FDR) for grouped hypotheses. Here, each gene forms a group, with SNPs annotated to the gene corresponding to individual hypotheses. The heterogeneity of effect sizes in different groups is considered by the introduction of a random effects component. Our method, entitled Random Effects model and testing procedure for Group-level FDR control (REG-FDR), assumes a model for alternative hypotheses for the eQTL data and controls the FDR by adaptive thresholding. As a convenient alternate approach, we also propose Z-REG-FDR, an approximate version of REG-FDR, that uses only Z-statistics of association between genotype and expression for each gene-SNP pair. The performance of Z-REG-FDR is evaluated using both simulated and real data. Simulations demonstrate that Z-REG-FDR performs similarly to REG-FDR, but with much improved computational speed. CONCLUSION: Our results demonstrate that the Z-REG-FDR method performs favorably compared to other methods in terms of statistical power and control of FDR. It can be of great practical use for grouped hypothesis testing for eQTL analysis or similar problems in statistical genomics due to its fast computation and ability to be fit using only summary data.

Effect of perfusate volume on amikacin concentrations after saphenous intravenous regional limb perfusion in standing, sedated horses.

OBJECTIVE: To determine the influence of perfusate volume on synovial fluid amikacin concentrations in the joints of the hind limb after standing saphenous intravenous regional limb perfusion (IVRLP). STUDY DESIGN: Randomized crossover design. ANIMALS: Six adult horses. METHODS: Saphenous IVRLP was performed in 6 standing horses with 1 g of amikacin diluted with 0.9% NaCl to volumes of 10 ml, 60 ml, and 120 ml. Samples of synovial fluid from the tarsocrural, metatarsophalangeal, and hind limb distal interphalangeal joints were collected at 15 and 30 min after perfusate administration. Concentrations of 40 µg/ml and 160 µg/ml were considered therapeutic for susceptible and resistant pathogens, respectively. RESULTS: No difference in synovial fluid amikacin concentrations was detected between volumes in any joint (P = .4). All synovial fluid amikacin concentrations were higher at 30 min compared to 15 min (P = .003). All median synovial fluid amikacin concentrations at 30 min were > 40 µg/ml using the 60 ml and 120 ml volumes. Synovial fluid amikacin concentrations >40 µg/ml were only reached in the hind limb distal interphalangeal joint when the 10 ml volume was used. All median synovial fluid amikacin concentrations observed were < 160 µg/ml. CONCLUSIONS: Target concentrations for pathogens that were considered susceptible were consistently reached with perfusate volumes of 60 ml and 120 ml. However, median synovial fluid amikacin concentrations did not reach target levels for resistant pathogens. CLINICAL SIGNIFICANCE: Perfusate volumes of 60 ml or 120 ml are recommended to treat infections due to susceptible pathogens in the joints of the distal hind limb. These results justify investigation of saphenous IVRLP with different perfusate volumes using higher doses of amikacin.

Unsupervised discovery of phenotype-specific multi-omics networks.

MOTIVATION: Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits. RESULTS: We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA-mRNA networks. Through simulations, we demonstrate that SmCCNet has better overall prediction performance compared to popular gene expression network construction and integration approaches under realistic settings. Applying SmCCNet to studies on chronic obstructive pulmonary disease (COPD) and breast cancer, we found enrichment of known relevant pathways (e.g. the Cadherin pathway for COPD and the interferon-gamma signaling pathway for breast cancer) as well as less known omics features that may be important to the diseases. Although those applications focus on miRNA-mRNA co-expression networks, SmCCNet is applicable to a variety of omics and other data types. It can also be easily generalized to incorporate multiple quantitative phenotype simultaneously. The versatility of SmCCNet suggests great potential of the approach in many areas. AVAILABILITY AND IMPLEMENTATION: The SmCCNet algorithm is written in R, and is freely available on the web at https://cran.r-project.org/web/packages/SmCCNet/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Testing cross-phenotype effects of rare variants in longitudinal studies of complex traits.

Many gene mapping studies of complex traits have identified genes or variants that influence multiple phenotypes. With the advent of next-generation sequencing technology, there has been substantial interest in identifying rare variants in genes that possess cross-phenotype effects. In the presence of such effects, modeling both the phenotypes and rare variants collectively using multivariate models can achieve higher statistical power compared to univariate methods that either model each phenotype separately or perform separate tests for each variant. Several studies collect phenotypic data over time and using such longitudinal data can further increase the power to detect genetic associations. Although rare-variant approaches exist for testing cross-phenotype effects at a single time point, there is no analogous method for performing such analyses using longitudinal outcomes. In order to fill this important gap, we propose an extension of Gene Association with Multiple Traits (GAMuT) test, a method for cross-phenotype analysis of rare variants using a framework based on the distance covariance. The approach allows for both binary and continuous phenotypes and can also adjust for covariates. Our simple adjustment to the GAMuT test allows it to handle longitudinal data and to gain power by exploiting temporal correlation. The approach is computationally efficient and applicable on a genome-wide scale due to the use of a closed-form test whose significance can be evaluated analytically. We use simulated data to demonstrate that our method has favorable power over competing approaches and also apply our approach to exome chip data from the Genetic Epidemiology Network of Arteriopathy.

Insight into genetic regulation of miRNA in mouse brain.

BACKGROUND: micro RNA (miRNA) are important regulators of gene expression and may influence phenotypes and disease traits. The connection between genetics and miRNA expression can be determined through expression quantitative loci (eQTL) analysis, which has been extensively used in a variety of tissues, and in both human and model organisms. miRNA play an important role in brain-related diseases, but eQTL studies of miRNA in brain tissue are limited. We aim to catalog miRNA eQTL in brain tissue using miRNA expression measured on a recombinant inbred mouse panel. Because samples were collected without any intervention or treatment (naïve), the panel allows characterization of genetic influences on miRNAs' expression levels. We used brain RNA expression levels of 881 miRNA and 1416 genomic locations to identify miRNA eQTL. To address multiple testing, we employed permutation p-values and subsequent zero permutation p-value correction. We also investigated the underlying biology of miRNA regulation using additional analyses, including hotspot analysis to search for regions controlling multiple miRNAs, and Bayesian network analysis to identify scenarios where a miRNA mediates the association between genotype and mRNA expression. We used addiction related phenotypes to illustrate the utility of our results. RESULTS: Thirty-eight miRNA eQTL were identified after appropriate multiple testing corrections. Ten of these miRNAs had target genes enriched for brain-related pathways and mapped to four miRNA eQTL hotspots. Bayesian network analysis revealed four biological networks relating genetic variation, miRNA expression and gene expression. CONCLUSIONS: Our extensive evaluation of miRNA eQTL provides valuable insight into the role of miRNA regulation in brain tissue. Our miRNA eQTL analysis and extended statistical exploration identifies miRNA candidates in brain for future study.

Association of genetic polymorphisms in the pore domains of mechano-gated TREK-1 channel with overactive lower urinary tract symptoms in humans.

AIMS: Mechanosensitivity of the urinary bladder is regulated by many factors including mechano-gated two-pore domain (K2 P, KCNK) potassium channels. TWIK-related K+ channel, TREK-1, is a predominantly expressed member of K2 P channel family in the human detrusor, and its expression and function are diminished in patients with overactive lower urinary tract symptoms (LUTS). The changes in channel activity may result from spontaneously occurring gene mutations. The aim of this study was to compare single nucleotide polymorphisms (SNPs) in TREK-1 channel between patients with LUTS and healthy donors. METHODS: Six SNPs (rs370266806, rs373919966, rs758937019, rs769301539, rs772497750, and rs775158737) in two pore domains of human TREK-1 gene were analyzed using TaqMan SNP genotyping assay with manufacturer-designed primers and allele-specific probes. The screening was done in control bladders and detrusor specimens from patients with overactive LUTS. Statistical analyses were performed using R, Fisher's exact test and Hardy-Weinberg Equilibrium. RESULTS: Six SNPs in two pore domains of the human TREK-1 gene were analyzed in human bladder specimens. The frequencies of rs758937019-CT genotype (P = 0.0016) and rs758937019-T allele (P = 0.0022) were significantly higher in the group with overactive LUTS. There was no significant association of rs775158737-GA genotype and rs775158737-A allele with the overactive LUTS, though they were present only in the overactive LUTS group. CONCLUSIONS: Our results provide evidence that altered expression and function of TREK-1 channel in patients with overactive LUTS could be due to genetic polymorphisms in the pore domains of TREK-1 channel (rs758937019).

Predictive modeling of miRNA-mediated predisposition to alcohol-related phenotypes in mouse.

BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNAs that bind messenger RNAs and promote their degradation or repress their translation. There is increasing evidence of miRNAs playing an important role in alcohol related disorders. However, the role of miRNAs as mediators of the genetic effect on alcohol phenotypes is not fully understood. We conducted a high-throughput sequencing study to measure miRNA expression levels in alcohol naïve animals in the LXS panel of recombinant inbred (RI) mouse strains. We then combined the sequencing data with genotype data, microarry gene expression data, and data on alcohol-related behavioral phenotypes such as 'Drinking in the dark', 'Sleep time', and 'Low dose activation' from the same RI panel. SNP-miRNA-gene triplets with strong association within the triplet that were also associated with one of the 4 alcohol phenotypes were selected and a Bayesian network analysis was used to aggregate results into a directed network model. RESULTS: We found several triplets with strong association within the triplet that were also associated with one of the alcohol phenotypes. The Bayesian network analysis found two networks where a miRNA mediates the genetic effect on the alcohol phenotype. The miRNAs were found to influence the expression of protein-coding genes, which in turn influences the quantitative phenotypes. The pathways in which these genes are enriched have been previously associated with alcohol-related traits. CONCLUSION: This work enhances association studies by identifying miRNAs that may be mediating the association between genetic markers (SNPs) and the alcohol phenotypes. It suggests a mechanism of how genetic variants are affecting traits of interest through the modification of miRNA expression.

Model based heritability scores for high-throughput sequencing data.

BACKGROUND: Heritability of a phenotypic or molecular trait measures the proportion of variance that is attributable to genotypic variance. It is an important concept in breeding and genetics. Few methods are available for calculating heritability for traits derived from high-throughput sequencing. RESULTS: We propose several statistical models and different methods to compute and test a heritability measure for such data based on linear and generalized linear mixed effects models. We also provide methodology for hypothesis testing and interval estimation. Our analyses show that, among the methods, the negative binomial mixed model (NB-fit), compound Poisson mixed model (CP-fit), and the variance stabilizing transformed linear mixed model (VST) outperform the voom-transformed linear mixed model (voom). NB-fit and VST appear to be more robust than CP-fit for estimating and testing the heritability scores, while NB-fit is the most computationally expensive. CP-fit performed best in terms of the coverage of the confidence intervals. In addition, we applied the methods to both microRNA (miRNA) and messenger RNA (mRNA) sequencing datasets from a recombinant inbred mouse panel. We show that miRNA and mRNA expression can be a highly heritable molecular trait in mouse, and that some top heritable features coincide with expression quantitative trait loci. CONCLUSIONS: The models and methods we investigated in this manuscript is applicable and extendable to sequencing experiments where some biological replicates are available and the environmental variation is properly controlled. The CP-fit approach for assessing heritability was implemented for the first time to our knowledge. All the methods presented, as well as the generation of simulated sequencing data under either negative binomial or compound Poisson mixed models, are provided in the R package HeritSeq.

Fecal microbiota composition, serum metabolomics, and markers of inflammation in dogs fed a raw meat-based diet compared to those on a kibble diet.

Introduction: Despite the potential health risks associated with feeding raw and non-traditional diets, the use of these diets in dogs is increasing, yet the health outcomes associated with these diets is not well understood. This study investigates the effect of feeding dogs a kibble or raw meat-based diets on fecal microbiota composition, serum metabolomics and inflammatory markers. Methods: Clinically healthy dogs with a history of consuming either kibble (KD, n = 27) or raw meat-based diets (RMBD, n = 28) for more than 1 year were enrolled. Dogs were fed a standardized diet of either a single brand of KD or RMBD for 28 days. Serum and fecal samples were collected for analysis of microbiota, metabolomics, and inflammatory markers. Multiple regression analysis was performed for each of the metabolites and inflammatory markers, with feed group, age and BCS included as independent variables. Results: The fecal microbiota composition differed between the KD and RMBD groups. Beta-diversity and some indices of alpha-diversity (i.e., Shannon and Simpson) were different between the two diet groups. Sixty- three serum metabolites differed between KD and RMBD-fed dogs with the majority reflecting the differences in macronutrient composition of the two diets.Fecal IAP, IgG and IgA were significantly higher in RMBD dogs compared to KD dogs, while systemic markers of inflammation, including serum c-reactive protein (CRP), galectin, secretory receptor of advanced glycation end-products (sRAGE), haptoglobin, and serum IgG were similar in dogs fed either diet. Discussion: Diet composition significantly affected fecal microbiota composition and metabolome. Although it had a potentially beneficial effect on local inflammatory markers, feeding RMBD had no impact on systemic inflammation. The influence of these changes on long term health outcomes provides an area for future study.

cytoKernel: Robust kernel embeddings for assessing differential expression of single cell data.

High-throughput sequencing of single-cell data can be used to rigorously evlauate cell specification and enable intricate variations between groups or conditions. Many popular existing methods for differential expression target differences in aggregate measurements (mean, median, sum) and limit their approaches to detect only global differential changes. We present a robust method for differential expression of single-cell data using a kernel-based score test, cytoKernel. cytoKernel is specifically designed to assess the differential expression of single cell RNA sequencing and high-dimensional flow or mass cytometry data using the full probability distribution pattern. cytoKernel is based on kernel embeddings which employs the probability distributions of the single cell data, by calculating the pairwise divergence/distance between distributions of subjects. It can detect both patterns involving aggregate changes, as well as more elusive variations that are often overlooked due to the multimodal characteristics of single cell data. We performed extensive benchmarks across both simulated and real data sets from mass cytometry data and single-cell RNA sequencing. The cytoKernel procedure effectively controls the False Discovery Rate (FDR) and shows favourable performance compared to existing methods. The method is able to identify more differential patterns than existing approaches. We apply cytoKernel to assess gene expression and protein marker expression differences from cell subpopulations in various publicly available single-cell RNAseq and mass cytometry data sets. The methods described in this paper are implemented in the open-source R package cytoKernel, which is freely available from Bioconductor at http://bioconductor.org/packages/cytoKernel.

Diagnostic utility of ultrasound-guided fine-needle aspiration and needle-core biopsy sampling of canine splenic masses.

This prospective clinical study sought to determine the accuracy of cytopathologic examination and needle-core biopsy (NCB) against diagnoses obtained by excisional histopathology (EH) for canine splenic masses. Twenty-five masses were evaluated ex vivo by ultrasound-guided fine-needle aspiration (FNA) and NCB tissue sampling. Each spleen was placed in a container and artificial skin placed over its surface. Ultrasound-guided FNA using a 22-gauge needle and 2 NCB samples [14-gauge (NCB-14), 16-gauge (NCB-16)] were obtained and submitted for analysis. Results were compared to results obtained by splenic excisional histopathology (EH). There was no difference noted between FNA, NCB-14, or NCB-16 analyses. In addition, there was no difference in accuracy between FNA and NCB-14 or between FNA and NCB-14 versus NCB-16. Reported accuracy of FNA was 0.72, NCB-14 was 0.72, and NCB-16 was 0.64, respectively. Both FNA and NCB-14 displayed a sensitivity of 71% and NCB-16 a sensitivity of 53%. Both FNA and NCB-14 displayed a specificity of 75% and NCB-16 a specificity of 88%. The results demonstrated that NCB had no advantage clinically over FNA at diagnosing splenic pathology. This study further demonstrates that preoperative diagnostic evaluation of the spleen is not highly accurate and cannot be recommended prior to splenectomy.

Cette étude clinique prospective visait à déterminer la précision de l'examen cytopathologique et de la biopsie au trocart (NCB) par rapport aux diagnostics obtenus par histopathologie excisionnelle (EH) pour les masses spléniques canines. Vingt-cinq masses ont été évaluées ex vivo par aspiration à l'aiguille fine guidée par ultrasons (FNA) et prélèvement de tissu par NCB. Chaque rate a été placée dans un récipient et une peau artificielle placée sur sa surface. Une FNA guidée par ultrasons à l'aide d'une aiguille de calibre 22 et de 2 échantillons de NCB (calibre 14 (NCB-14), calibre 16 (NCB-16)) ont été obtenues et soumises pour analyse. Les résultats ont été comparés aux résultats obtenus par histopathologie excisionnelle splénique (EH). Aucune différence n'a été notée entre les analyses FNA, NCB-14 ou NCB-16. De plus, il n'y avait aucune différence de précision entre FNA et NCB-14 ou entre FNA et NCB-14 par rapport à NCB-16. La précision rapportée de FNA était de 0,72, celle de NCB-14 de 0,72 et de NCB-16 était de 0,64, respectivement. FNA et NCB-14 ont affiché une sensibilité de 71 % et NCB-16 une sensibilité de 53 %. FNA et NCB-14 ont affiché une spécificité de 75 % et NCB-16 une spécificité de 88 %. Les résultats ont démontré que la NCB n'avait aucun avantage clinique sur la FNA pour diagnostiquer la pathologie splénique. Cette étude démontre en outre que l'évaluation diagnostique préopératoire de la rate n'est pas très précise et ne peut être recommandée avant la splénectomie.(Traduit par Docteur Serge Messier).

Expansion of extrafollicular B and T cell subsets in childhood-onset systemic lupus erythematosus.

Introduction: Most childhood-onset SLE patients (cSLE) develop lupus nephritis (cLN), but only a small proportion achieve complete response to current therapies. The prognosis of children with LN and end-stage renal disease is particularly dire. Mortality rates within the first five years of renal replacement therapy may reach 22%. Thus, there is urgent need to decipher and target immune mechanisms that drive cLN. Despite the clear role of autoantibody production in SLE, targeted B cell therapies such as rituximab (anti-CD20) and belimumab (anti-BAFF) have shown only modest efficacy in cLN. While many studies have linked dysregulation of germinal center formation to SLE pathogenesis, other work supports a role for extrafollicular B cell activation in generation of pathogenic antibody secreting cells. However, whether extrafollicular B cell subsets and their T cell collaborators play a role in specific organ involvement in cLN and/or track with disease activity remains unknown. Methods: We analyzed high-dimensional mass cytometry and gene expression data from 24 treatment naïve cSLE patients at the time of diagnosis and longitudinally, applying novel computational tools to identify abnormalities associated with clinical manifestations (cLN) and disease activity (SLEDAI). Results: cSLE patients have an extrafollicular B cell expansion signature, with increased frequency of i) DN2, ii) Bnd2, iii) plasmablasts, and iv) peripheral T helper cells. Most importantly, we discovered that this extrafollicular signature correlates with disease activity in cLN, supporting extrafollicular T/B interactions as a mechanism underlying pediatric renal pathogenesis. Discussion: This study integrates established and emerging themes of extrafollicular B cell involvement in SLE by providing evidence for extrafollicular B and peripheral T helper cell expansion, along with elevated type 1 IFN activation, in a homogeneous cohort of treatment-naïve cSLE patients, a point at which they should display the most extreme state of their immune dysregulation.

Healing time of experimentally induced distal limb wounds in horses is not reduced by local injection of equine-origin liquid amnion allograft.

OBJECTIVE: To assess the efficacy of an equine-origin liquid amnion allograft (ELAA) derived from both amniotic fluid and amniotic membrane on the healing time of experimentally induced distal limb wounds in horses. ANIMALS: 8 adult horses. PROCEDURES: On day 0, horses were anesthetized and a 2.5 X 2.5-cm, full-thickness skin wound was created on the dorsal aspect of each metacarpus and bandaged. On day 9, wound margins were injected with ELAA (treatment) or 0.9% NaCl (control). Bandages were changed at specific intervals through day 91 and, on each occasion, wounds were photographed to allow calculation of wound area. Exuberant granulation tissue was resected, if present. Wounds were deemed healed when completely epithelialized. Mean wound area was compared between groups throughout the study period. RESULTS: Only 1 wound (control) remained unhealed at day 91. No difference was found between the treatment and control groups in either wound area over time (P = 1.0) or time for wounds to reduce in size by 95% (P = .2) Exuberant granulation tissue required resection twice (1 control wound and 1 treatment wound). CLINICAL RELEVANCE: In this model, a single treatment with ELAA administered locally by SC injection did not accelerate distal limb wound healing in horses. However, it is possible that naturally occurring, chronic, or nonhealing wounds would respond differently.

Compositional Data Analysis using Kernels in mass cytometry data.

MOTIVATION: Cell-type abundance data arising from mass cytometry experiments are compositional in nature. Classical association tests do not apply to the compositional data due to their non-Euclidean nature. Existing methods for analysis of cell type abundance data suffer from several limitations for high-dimensional mass cytometry data, especially when the sample size is small. RESULTS: We proposed a new multivariate statistical learning methodology, Compositional Data Analysis using Kernels (CODAK), based on the kernel distance covariance (KDC) framework to test the association of the cell type compositions with important predictors (categorical or continuous) such as disease status. CODAK scales well for high-dimensional data and provides satisfactory performance for small sample sizes (n < 25). We conducted simulation studies to compare the performance of the method with existing methods of analyzing cell type abundance data from mass cytometry studies. The method is also applied to a high-dimensional dataset containing different subgroups of populations including Systemic Lupus Erythematosus (SLE) patients and healthy control subjects. AVAILABILITY AND IMPLEMENTATION: CODAK is implemented using R. The codes and the data used in this manuscript are available on the web at http://github.com/GhoshLab/CODAK/. CONTACT: prudra@okstate.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.

Clinical health markers in dogs fed raw meat-based or commercial extruded kibble diets.

The interest and demand for healthy and less processed foods for human consumption have been mirrored in the pet industry, with an explosion of alternative diets available. Several nontraditional feeding methodologies including raw meat-based diets (RMBDs) are believed by many dog owners to be superior to traditional extruded commercial dog foods. Despite the strong opinions, limited data are available comparing objective health measures among healthy dogs fed using different methods of diet preparation. Therefore, we compared health markers in client-owned dogs fed an RMBD to markers in dogs fed a high-quality extruded kibble. We hypothesized that healthy adult dogs fed RMBD would show differences in biochemical and hematological parameters and improved clinical health scores (e.g., dental, external ear canal, and integument scores) compared with dogs fed a kibble diet. A cross-sectional observational study was performed comparing hematology, serum biochemistry, urinalysis management history, and clinical health scores in healthy client-owned dogs reported as fed RMBD (n = 28) or kibble (n = 27) for >1 yr. Dental, external ear canal, and integument health scores were assigned by a single veterinary evaluator blinded to feed group, using a scale where 0 was normal and 3 was most severely affected. Spearman correlation coefficient (rs) was calculated to assess the strength and direction of the relationship of biochemical outcomes with age and body condition score (BCS), while analysis of variance was used to determine if biochemical analytes differed by breed or gender. Biochemical data were analyzed using multiple linear regression models, adjusting for the covariates gender, breed, age, and BCS. A composite clinical health score, (CCS) = 9 - (dental score + otitis score + integument score), was compared between feeding groups using Mann-Whitney test. Serum alkaline phosphatase activity (P < 0.001) and globulin concentration (P < 0.001) were lower, while lymphocyte count (P < 0.05) was higher in dogs fed RMBD. No differences were found in urinalysis between diet groups. Dogs fed RMBD showed a slight improvement in CCS compared with kibble-fed dogs (CCS: P = 0.03). Owner management significantly differed with a greater likelihood of management interventions including dietary supplements and sporting activities in the RMBD group. Further work is needed to specifically determine the impact of diet processing and nutrient content on canine health.

Systems genetics analysis of the LXS recombinant inbred mouse strains:Genetic and molecular insights into acute ethanol tolerance.

We have been using the Inbred Long- and Short-Sleep mouse strains (ILS, ISS) and a recombinant inbred panel derived from them, the LXS, to investigate the genetic underpinnings of acute ethanol tolerance which is considered to be a risk factor for alcohol use disorders (AUDs). Here, we have used RNA-seq to examine the transcriptome of whole brain in 40 of the LXS strains 8 hours after a saline or ethanol "pretreatment" as in previous behavioral studies. Approximately 1/3 of the 14,184 expressed genes were significantly heritable and many were unique to the pretreatment. Several thousand cis- and trans-eQTLs were mapped; a portion of these also were unique to pretreatment. Ethanol pretreatment caused differential expression (DE) of 1,230 genes. Gene Ontology (GO) enrichment analysis suggested involvement in numerous biological processes including astrocyte differentiation, histone acetylation, mRNA splicing, and neuron projection development. Genetic correlation analysis identified hundreds of genes that were correlated to the behaviors. GO analysis indicated that these genes are involved in gene expression, chromosome organization, and protein transport, among others. The expression profiles of the DE genes and genes correlated to AFT in the ethanol pretreatment group (AFT-Et) were found to be similar to profiles of HDAC inhibitors. Hdac1, a cis-regulated gene that is located at the peak of a previously mapped QTL for AFT-Et, was correlated to 437 genes, most of which were also correlated to AFT-Et. GO analysis of these genes identified several enriched biological process terms including neuron-neuron synaptic transmission and potassium transport. In summary, the results suggest widespread genetic effects on gene expression, including effects that are pretreatment-specific. A number of candidate genes and biological functions were identified that could be mediating the behavioral responses. The most prominent of these was Hdac1 which may be regulating genes associated with glutamatergic signaling and potassium conductance.

Minimizing Batch Effects in Mass Cytometry Data.

Cytometry by Time-Of-Flight (CyTOF) uses antibodies conjugated to isotopically pure metals to identify and quantify a large number of cellular features with single-cell resolution. A barcoding approach allows for 20 unique samples to be pooled and processed together in one tube, reducing the intra-barcode technical variability. However, with only 20 samples per barcode, multiple barcode sets (batches) are required to address questions in robustly powered study designs. A batch adjustment procedure is required to reduce variability across batches and to facilitate direct comparison of runs performed across multiple barcodes run over weeks, months, or years. We describe a method using technical replicates that are included in each run to determine and apply an appropriate adjustment per batch without manual intervention. The use of technical replicate samples (i.e., anchors or reference samples) avoids assumptions of sample homogeneity among batches, and allows direct estimation of batch effects and appropriate adjustment parameters applicable to all samples within a batch. Quantification of cell subpopulations and mean signal intensity pre- and post-adjustment using both manual gating and unsupervised clustering demonstrate substantial mitigation of batch effects in the anchor samples used for this adjustment calculation, and in a second validation set of technical replicates.

SIX2 Mediates Late-Stage Metastasis via Direct Regulation of SOX2 and Induction of a Cancer Stem Cell Program.

The capacity for tumor cells to metastasize efficiently is directly linked to their ability to colonize secondary sites. Here we identify Six2, a developmental transcription factor, as a critical regulator of a breast cancer stem cell program that enables metastatic colonization. In several triple-negative breast cancer (TNBC) models, Six2 enhanced the expression of genes associated with embryonic stem cell programs. Six2 directly bound the Sox2 Srr2 enhancer, promoting Sox2 expression and downstream expression of Nanog, which are both key pluripotency factors. Regulation of Sox2 by Six2 enhanced cancer stem cell properties and increased metastatic colonization. Six2 and Sox2 expression correlated highly in breast cancers including TNBC, where a Six2 expression signature was predictive of metastatic burden and poor clinical outcome. Our findings demonstrate that a SIX2/SOX2 axis is required for efficient metastatic colonization, underscoring a key role for stemness factors in outgrowth at secondary sites. SIGNIFICANCE: These findings provide novel mechanistic insight into stemness and the metastatic outgrowth of triple-negative breast cancer cells.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/4/720/F1.large.jpg.

Structural and Functional Analyses of an Allosteric EYA2 Phosphatase Inhibitor That Has On-Target Effects in Human Lung Cancer Cells.

EYA proteins (EYA1-4) are critical developmental transcriptional cofactors that contain an EYA domain (ED) harboring Tyr phosphatase activity. EYA proteins are largely downregulated after embryogenesis but are reexpressed in cancers, and their Tyr phosphatase activity plays an important role in the DNA damage response and tumor progression. We previously identified a class of small-molecule allosteric inhibitors that specifically inhibit the Tyr phosphatase activity of EYA2. Herein, we determined the crystal structure of the EYA2 ED in complex with NCGC00249987 (a representative compound in this class), revealing that it binds to an induced pocket distant from the active site. NCGC00249987 binding leads to a conformational change of the active site that is unfavorable for Mg2+ binding, thereby inhibiting EYA2's Tyr phosphatase activity. We demonstrate, using genetic mutations, that migration, invadopodia formation, and invasion of lung adenocarcinoma cells are dependent on EYA2 Tyr phosphatase activity, whereas growth and survival are not. Further, we demonstrate that NCGC00249987 specifically targets migration, invadopodia formation, and invasion of lung cancer cells, but that it does not inhibit cell growth or survival. The compound has no effect on lung cancer cells carrying an EYA2 F290Y mutant that abolishes compound binding, indicating that NCGC00249987 is on target in lung cancer cells. These data suggest that the NCGC00249987 allosteric inhibitor can be used as a chemical probe to study the function of the EYA2 Tyr phosphatase activity in cells and may have the potential to be developed into an antimetastatic agent for cancers reliant on EYA2's Tyr phosphatase activity.

miR-MaGiC improves quantification accuracy for small RNA-seq.

OBJECTIVE: Many tools have been developed to profile microRNA (miRNA) expression from small RNA-seq data. These tools must contend with several issues: the small size of miRNAs, the small number of unique miRNAs, the fact that similar miRNAs can be transcribed from multiple loci, and the presence of miRNA isoforms known as isomiRs. Methods failing to address these issues can return misleading information. We propose a novel quantification method designed to address these concerns. RESULTS: We present miR-MaGiC, a novel miRNA quantification method, implemented as a cross-platform tool in Java. miR-MaGiC performs stringent mapping to a core region of each miRNA and defines a meaningful set of target miRNA sequences by collapsing the miRNA space to "functional groups". We hypothesize that these two features, mapping stringency and collapsing, provide more optimal quantification to a more meaningful unit (i.e., miRNA family). We test miR-MaGiC and several published methods on 210 small RNA-seq libraries, evaluating each method's ability to accurately reflect global miRNA expression profiles. We define accuracy as total counts close to the total number of input reads originating from miRNAs. We find that miR-MaGiC, which incorporates both stringency and collapsing, provides the most accurate counts.