Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics.

Optimizing and benchmarking data reduction methods for dynamic or spatial visualization and interpretation (DSVI) face challenges due to many factors, including data complexity, lack of ground truth, time-dependent metrics, dimensionality bias and different visual mappings of the same data. Current studies often focus on independent static visualization or interpretability metrics that require ground truth. To overcome this limitation, we propose the MIBCOVIS framework, a comprehensive and interpretable benchmarking and computational approach. MIBCOVIS enhances the visualization and interpretability of high-dimensional data without relying on ground truth by integrating five robust metrics, including a novel time-ordered Markov-based structural metric, into a semi-supervised hierarchical Bayesian model. The framework assesses method accuracy and considers interaction effects among metric features. We apply MIBCOVIS using linear and nonlinear dimensionality reduction methods to evaluate optimal DSVI for four distinct dynamic and spatial biological processes captured by three single-cell data modalities: CyTOF, scRNA-seq and CODEX. These data vary in complexity based on feature dimensionality, unknown cell types and dynamic or spatial differences. Unlike traditional single-summary score approaches, MIBCOVIS compares accuracy distributions across methods. Our findings underscore the joint evaluation of visualization and interpretability, rather than relying on separate metrics. We reveal that prioritizing average performance can obscure method feature performance. Additionally, we explore the impact of data complexity on visualization and interpretability. Specifically, we provide optimal parameters and features and recommend methods, like the optimized variational contractive autoencoder, for targeted DSVI for various data complexities. MIBCOVIS shows promise for evaluating dynamic single-cell atlases and spatiotemporal data reduction models.

Polygenic subtype identified in ACCORD trial displays a favorable type 2 diabetes phenotype in the UKBiobank population.

INTRODUCTION: We previously identified a genetic subtype (C4) of type 2 diabetes (T2D), benefitting from intensive glycemia treatment in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Here, we characterized the population of patients that met the C4 criteria in the UKBiobank cohort. RESEARCH DESIGN AND METHODS: Using our polygenic score (PS), we identified C4 individuals in the UKBiobank and tested C4 status with risk of developing T2D, cardiovascular disease (CVD) outcomes, and differences in T2D medications. RESULTS: C4 individuals were less likely to develop T2D, were slightly older at T2D diagnosis, had lower HbA1c values, and were less likely to be prescribed T2D medications (P < .05). Genetic variants in MAS1 and IGF2R, major components of the C4 PS, were associated with fewer overall T2D prescriptions. CONCLUSION: We have confirmed C4 individuals are a lower risk subpopulation of patients with T2D.

Visualization, benchmarking and characterization of nested single-cell heterogeneity as dynamic forest mixtures.

A major topic of debate in developmental biology centers on whether development is continuous, discontinuous, or a mixture of both. Pseudo-time trajectory models, optimal for visualizing cellular progression, model cell transitions as continuous state manifolds and do not explicitly model real-time, complex, heterogeneous systems and are challenging for benchmarking with temporal models. We present a data-driven framework that addresses these limitations with temporal single-cell data collected at discrete time points as inputs and a mixture of dependent minimum spanning trees (MSTs) as outputs, denoted as dynamic spanning forest mixtures (DSFMix). DSFMix uses decision-tree models to select genes that account for variations in multimodality, skewness and time. The genes are subsequently used to build the forest using tree agglomerative hierarchical clustering and dynamic branch cutting. We first motivate the use of forest-based algorithms compared to single-tree approaches for visualizing and characterizing developmental processes. We next benchmark DSFMix to pseudo-time and temporal approaches in terms of feature selection, time correlation, and network similarity. Finally, we demonstrate how DSFMix can be used to visualize, compare and characterize complex relationships during biological processes such as epithelial-mesenchymal transition, spermatogenesis, stem cell pluripotency, early transcriptional response from hormones and immune response to coronavirus disease. Our results indicate that the expression of genes during normal development exhibits a high proportion of non-uniformly distributed profiles that are mostly right-skewed and multimodal; the latter being a characteristic of major steady states during development. Our study also identifies and validates gene signatures driving complex dynamic processes during somatic or germline differentiation.

Plasma protein signatures of adult asthma.

BACKGROUND: Adult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma. METHODS: Protein abundance in plasma was measured in individuals from the Agricultural Lung Health Study (ALHS) (761 asthma, 1095 non-case) and the Atherosclerosis Risk in Communities study (470 asthma, 10,669 non-case) using the SOMAScan 5K array. Associations with asthma were estimated using covariate adjusted logistic regression and meta-analyzed using inverse-variance weighting. Additionally, in ALHS, we examined phenotypes based on both asthma and seroatopy (asthma with atopy (n = 207), asthma without atopy (n = 554), atopy without asthma (n = 147), compared to neither (n = 948)). RESULTS: Meta-analysis of 4860 proteins identified 115 significantly (FDR<0.05) associated with asthma. Multiple signaling pathways related to airway inflammation and pulmonary injury were enriched (FDR<0.05) among these proteins. A proteomic score generated using machine learning provided predictive value for asthma (AUC = 0.77, 95% CI = 0.75-0.79 in training set; AUC = 0.72, 95% CI = 0.69-0.75 in validation set). Twenty proteins are targeted by approved or investigational drugs for asthma or other conditions, suggesting potential drug repurposing. The combined asthma-atopy phenotype showed significant associations with 20 proteins, including five not identified in the overall asthma analysis. CONCLUSION: This first large-scale proteomics study identified over 100 plasma proteins associated with current asthma in adults. In addition to validating previous associations, we identified many novel proteins that could inform development of diagnostic biomarkers and therapeutic targets in asthma management.

Occupational farm work activities influence workers' indoor home microbiome.

BACKGROUND: Farm work entails a heterogeneous mixture of exposures that vary considerably across farms and farmers. Farm work is associated with various health outcomes, both adverse and beneficial. One mechanism by which farming exposures can impact health is through the microbiome, including the indoor home environment microbiome. It is unknown how individual occupational exposures shape the microbial composition in workers' homes. OBJECTIVES: We investigated associations between farm work activities, including specific tasks and pesticide use, and the indoor microbiome in the homes of 468 male farmers. METHODS: Participants were licensed pesticide applicators, mostly farmers, enrolled in the Agricultural Lung Health Study from 2008 to 2011. Vacuumed dust from participants' bedrooms underwent whole-genome shotgun sequencing for indoor microbiome assessment. Using questionnaire data, we evaluated 6 farm work tasks (processing of either hay, silage, animal feed, fertilizer, or soy/grains, and cleaning grain bins) and 19 pesticide ingredients currently used in the past year, plus 7 banned persistent pesticide ingredients ever used. RESULTS: All 6 work tasks were associated with increased microbial diversity levels, with a positive dose-response for the total number of tasks performed (P = 0.001). All tasks were associated with altered microbial compositions (weighted UniFrac P = 0.001) and with higher abundance of specific microbes, including soil-based commensal microbes such as Haloterrigena. Among the 19 pesticides, current use of glyphosate and past use of lindane were associated with increased microbial diversity (P = 0.02-0.04). Ten currently used pesticides and all 7 banned pesticides were associated with altered microbial composition (P = 0.001-0.04). Six pesticides were associated with differential abundance of certain microbes. DISCUSSION: Different farm activities and exposures can uniquely impact the dust microbiome inside homes. Our work suggests that changes to the home microbiome could serve as one pathway for how occupational exposures impact the health of workers and their cohabitating family members, offering possible future intervention targets.

High-throughput screening and genome-wide analyses of 44 anticancer drugs in the 1000 Genomes cell lines reveals an association of the NQO1 gene with the response of multiple anticancer drugs.

Cancer patients exhibit a broad range of inter-individual variability in response and toxicity to widely used anticancer drugs, and genetic variation is a major contributor to this variability. To identify new genes that influence the response of 44 FDA-approved anticancer drug treatments widely used to treat various types of cancer, we conducted high-throughput screening and genome-wide association mapping using 680 lymphoblastoid cell lines from the 1000 Genomes Project. The drug treatments considered in this study represent nine drug classes widely used in the treatment of cancer in addition to the paclitaxel + epirubicin combination therapy commonly used for breast cancer patients. Our genome-wide association study (GWAS) found several significant and suggestive associations. We prioritized consistent associations for functional follow-up using gene-expression analyses. The NAD(P)H quinone dehydrogenase 1 (NQO1) gene was found to be associated with the dose-response of arsenic trioxide, erlotinib, trametinib, and a combination treatment of paclitaxel + epirubicin. NQO1 has previously been shown as a biomarker of epirubicin response, but our results reveal novel associations with these additional treatments. Baseline gene expression of NQO1 was positively correlated with response for 43 of the 44 treatments surveyed. By interrogating the functional mechanisms of this association, the results demonstrate differences in both baseline and drug-exposed induction.

Pulmonary Function and Blood DNA Methylation: A Multiancestry Epigenome-Wide Association Meta-analysis.

Rationale: Methylation integrates factors present at birth and modifiable across the lifespan that can influence pulmonary function. Studies are limited in scope and replication. Objectives: To conduct large-scale epigenome-wide meta-analyses of blood DNA methylation and pulmonary function. Methods: Twelve cohorts analyzed associations of methylation at cytosine-phosphate-guanine probes (CpGs), using Illumina 450K or EPIC/850K arrays, with FEV1, FVC, and FEV1/FVC. We performed multiancestry epigenome-wide meta-analyses (total of 17,503 individuals; 14,761 European, 2,549 African, and 193 Hispanic/Latino ancestries) and interpreted results using integrative epigenomics. Measurements and Main Results: We identified 1,267 CpGs (1,042 genes) differentially methylated (false discovery rate, <0.025) in relation to FEV1, FVC, or FEV1/FVC, including 1,240 novel and 73 also related to chronic obstructive pulmonary disease (1,787 cases). We found 294 CpGs unique to European or African ancestry and 395 CpGs unique to never or ever smokers. The majority of significant CpGs correlated with nearby gene expression in blood. Findings were enriched in key regulatory elements for gene function, including accessible chromatin elements, in both blood and lung. Sixty-nine implicated genes are targets of investigational or approved drugs. One example novel gene highlighted by integrative epigenomic and druggable target analysis is TNFRSF4. Mendelian randomization and colocalization analyses suggest that epigenome-wide association study signals capture causal regulatory genomic loci. Conclusions: We identified numerous novel loci differentially methylated in relation to pulmonary function; few were detected in large genome-wide association studies. Integrative analyses highlight functional relevance and potential therapeutic targets. This comprehensive discovery of potentially modifiable, novel lung function loci expands knowledge gained from genetic studies, providing insights into lung pathogenesis.

Knockoff boosted tree for model-free variable selection.

MOTIVATION: The recently proposed knockoff filter is a general framework for controlling the false discovery rate (FDR) when performing variable selection. This powerful new approach generates a 'knockoff' of each variable tested for exact FDR control. Imitation variables that mimic the correlation structure found within the original variables serve as negative controls for statistical inference. Current applications of knockoff methods use linear regression models and conduct variable selection only for variables existing in model functions. Here, we extend the use of knockoffs for machine learning with boosted trees, which are successful and widely used in problems where no prior knowledge of model function is required. However, currently available importance scores in tree models are insufficient for variable selection with FDR control. RESULTS: We propose a novel strategy for conducting variable selection without prior model topology knowledge using the knockoff method with boosted tree models. We extend the current knockoff method to model-free variable selection through the use of tree-based models. Additionally, we propose and evaluate two new sampling methods for generating knockoffs, namely the sparse covariance and principal component knockoff methods. We test and compare these methods with the original knockoff method regarding their ability to control type I errors and power. In simulation tests, we compare the properties and performance of importance test statistics of tree models. The results include different combinations of knockoffs and importance test statistics. We consider scenarios that include main-effect, interaction, exponential and second-order models while assuming the true model structures are unknown. We apply our algorithm for tumor purity estimation and tumor classification using Cancer Genome Atlas (TCGA) gene expression data. Our results show improved discrimination between difficult-to-discriminate cancer types. AVAILABILITY AND IMPLEMENTATION: The proposed algorithm is included in the KOBT package, which is available at https://cran.r-project.org/web/packages/KOBT/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the Personalized Environment and Genes Study.

While multiple factors are associated with cardiovascular disease (CVD), many environmental exposures that may contribute to CVD have not been examined. To understand environmental effects on cardiovascular health, we performed an exposome-wide association study (ExWAS), a hypothesis-free approach, using survey data on endogenous and exogenous exposures at home and work and data from health and medical histories from the North Carolina-based Personalized Environment and Genes Study (PEGS) (n = 5015). We performed ExWAS analyses separately on six cardiovascular outcomes (cardiac arrhythmia, congestive heart failure, coronary artery disease, heart attack, stroke, and a combined atherogenic-related outcome comprising angina, angioplasty, atherosclerosis, coronary artery disease, heart attack, and stroke) using logistic regression and a false discovery rate of 5%. For each CVD outcome, we tested 502 single exposures and built multi-exposure models using the deletion-substitution-addition (DSA) algorithm. To evaluate complex nonlinear relationships, we employed the knockoff boosted tree (KOBT) algorithm. We adjusted all analyses for age, sex, race, BMI, and annual household income. ExWAS analyses revealed novel associations that include blood type A (Rh-) with heart attack (OR[95%CI] = 8.2[2.2:29.7]); paint exposures with stroke (paint related chemicals: 6.1[2.2:16.0], acrylic paint: 8.1[2.6:22.9], primer: 6.7[2.2:18.6]); biohazardous materials exposure with arrhythmia (1.8[1.5:2.3]); and higher paternal education level with reduced risk of multiple CVD outcomes (stroke, heart attack, coronary artery disease, and combined atherogenic outcome). In multi-exposure models, trouble sleeping and smoking remained important risk factors. KOBT identified significant nonlinear effects of sleep disorder, regular intake of grapefruit, and a family history of blood clotting problems for multiple CVD outcomes (combined atherogenic outcome, congestive heart failure, and coronary artery disease). In conclusion, using statistics and machine learning, these findings identify novel potential risk factors for CVD, enable hypothesis generation, provide insights into the complex relationships between risk factors and CVD, and highlight the importance of considering multiple exposures when examining CVD outcomes.

Clomifene and Assisted Reproductive Technology in Humans Are Associated with Sex-Specific Offspring Epigenetic Alterations in Imprinted Control Regions.

Children conceived with assisted reproductive technology (ART) have an increased risk of adverse outcomes, including congenital malformations and imprinted gene disorders. In a retrospective North Carolina-based-birth-cohort, we examined the effect of ovulation drugs and ART on CpG methylation in differentially methylated CpGs in known imprint control regions (ICRs). Nine ICRs containing 48 CpGs were assessed for methylation status by pyrosequencing in mixed leukocytes from cord blood. After restricting to non-smoking, college-educated participants who agreed to follow-up, ART-exposed (n = 27), clomifene-only-exposed (n = 22), and non-exposed (n = 516) groups were defined. Associations of clomifene and ART with ICR CpG methylation were assessed with linear regression and stratifying by offspring sex. In males, ART was associated with hypomethylation of the PEG3 ICR [ß(95% CI) = -1.46 (-2.81, -0.12)] and hypermethylation of the MEG3 ICR [3.71 (0.01, 7.40)]; clomifene-only was associated with hypomethylation of the NNAT ICR [-5.25 (-10.12, -0.38)]. In female offspring, ART was associated with hypomethylation of the IGF2 ICR [-3.67 (-6.79, -0.55)]. Aberrant methylation of these ICRs has been associated with cardiovascular disease and metabolic and behavioral outcomes in children. The results suggest that the increased risk of adverse outcomes in offspring conceived through ART may be due in part to altered methylation of ICRs. Larger studies utilizing epigenome-wide interrogation are warranted.

Race and smoking status associated with paclitaxel drug response in patient-derived lymphoblastoid cell lines.

The use of ex-vivo model systems to provide a level of forecasting for in-vivo characteristics remains an important need for cancer therapeutics. The use of lymphoblastoid cell lines (LCLs) is an attractive approach for pharmacogenomics and toxicogenomics, due to their scalability, efficiency, and cost-effectiveness. There is little data on the impact of demographic or clinical covariates on LCL response to chemotherapy. Paclitaxel sensitivity was determined in LCLs from 93 breast cancer patients from the University of North Carolina Lineberger Comprehensive Cancer Center Breast Cancer Database to test for potential associations and/or confounders in paclitaxel dose-response assays. Measures of paclitaxel cell viability were associated with patient data included treatment regimens, cancer status, demographic and environmental variables, and clinical outcomes. We used multivariate analysis of variance to identify the in-vivo variables associated with ex-vivo dose-response. In this unique dataset that includes both in-vivo and ex-vivo data from breast cancer patients, race (P = 0.0049) and smoking status (P = 0.0050) were found to be significantly associated with ex-vivo dose-response in LCLs. Racial differences in clinical dose-response have been previously described, but the smoking association has not been reported. Our results indicate that in-vivo smoking status can influence ex-vivo dose-response in LCLs, and more precise measures of covariates may allow for more precise forecasting of clinical effect. In addition, understanding the mechanism by which exposure to smoking in-vivo effects ex-vivo dose-response in LCLs may open up new avenues in the quest for better therapeutic prediction.

Interaction between Genetic Risk Scores for reduced pulmonary function and smoking, asthma and endotoxin.

RATIONALE: Genome-wide association studies (GWASs) have identified numerous loci associated with lower pulmonary function. Pulmonary function is strongly related to smoking and has also been associated with asthma and dust endotoxin. At the individual SNP level, genome-wide analyses of pulmonary function have not identified appreciable evidence for gene by environment interactions. Genetic Risk Scores (GRSs) may enhance power to identify gene-environment interactions, but studies are few. METHODS: We analysed 2844 individuals of European ancestry with 1000 Genomes imputed GWAS data from a case-control study of adult asthma nested within a US agricultural cohort. Pulmonary function traits were FEV1, FVC and FEV1/FVC. Using data from a recent large meta-analysis of GWAS, we constructed a weighted GRS for each trait by combining the top (p value<5×10-9) genetic variants, after clumping based on distance (±250 kb) and linkage disequilibrium (r2=0.5). We used linear regression, adjusting for relevant covariates, to estimate associations of each trait with its GRS and to assess interactions. RESULTS: Each trait was highly significantly associated with its GRS (all three p values<8.9×10-8). The inverse association of the GRS with FEV1/FVC was stronger for current smokers (pinteraction=0.017) or former smokers (pinteraction=0.064) when compared with never smokers and among asthmatics compared with non-asthmatics (pinteraction=0.053). No significant interactions were observed between any GRS and house dust endotoxin. CONCLUSIONS: Evaluation of interactions using GRSs supports a greater impact of increased genetic susceptibility on reduced pulmonary function in the presence of smoking or asthma.

Community-Based Testing Sites for SARS-CoV-2 - United States, March 2020-November 2021.

Immediately following the March 13, 2020 declaration of COVID-19 as a national emergency (1), the U.S. government began implementing national testing programs for epidemiologic surveillance, monitoring of frontline workers and populations at higher risk for acquiring COVID-19, and identifying and allocating limited testing resources. Effective testing supports identification of COVID-19 cases; facilitates isolation, quarantine, and timely treatment measures that limit the spread of SARS-CoV-2 (the virus that causes COVID-19); and guides public health officials about the incidence of COVID-19 in a community. A White House Joint Task Force, co-led by the Department of Health and Human Services (HHS) and the Federal Emergency Management Agency (FEMA), created the Community-Based Testing Sites (CBTS) program working with state and local partners (2). This report describes the timeline, services delivered, and scope of the CBTS program. During March 19, 2020-April 11, 2021, the CBTS program conducted 11,661,923 SARS-CoV-2 tests at 8,319 locations across the United States and its territories, including 402,223 (3.5%) administered through Drive-Through Testing, 10,129,142 (86.9%) through Pharmacies+ Testing, and 1,130,558 (9.7%) through Surge Testing programs. Tests administered through the CBTS program yielded 1,176,959 (10.1%) positive results for SARS-CoV-2. Among tested persons with available race data,* positive test results were highest among American Indian or Alaska Native (14.1%) and Black persons (10.4%) and lowest among White persons (9.9%), Asian persons (7.3%), and Native Hawaiian or Other Pacific Islanders (6.4%). Among persons with reported ethnicity, 25.3% were Hispanic, 15.9% of whom received a positive test result. Overall, 82.0% of test results were returned within 2 days, but the percentage of test results returned within 2 days was as low as 40.7% in July 2020 and 59.3% in December 2020 during peak testing periods. Strong partnerships enabled a rapid coordinated response to establish the federally supported CBTS program to improve access to no-charge diagnostic testing, including for frontline workers, symptomatic persons and close contacts, and persons living in high-prevalence areas. In April 2021, the CBTS Pharmacies+ Testing and Surge Testing programs were expanded into the Increasing Community Access to Testing (ICATT) program. As of November 12, 2021, the CBTS and ICATT programs conducted approximately 26.6 million tests with approximately 10,000 active testing sites. Although the CBTS program represented a relatively small portion of overall U.S. SARS-CoV-2 testing, with its successful partnerships and adaptability, the CBTS program serves as a model to guide current community-based screening, surveillance, and disease control programs, and responses to future public health emergencies.

Untargeted metabolomic profiling identifies disease-specific signatures in food allergy and asthma.

BACKGROUND: Food allergy (FA) affects an increasing proportion of children for reasons that remain obscure. Novel disease biomarkers and curative treatment options are strongly needed. OBJECTIVE: We sought to apply untargeted metabolomic profiling to identify pathogenic mechanisms and candidate disease biomarkers in patients with FA. METHODS: Mass spectrometry-based untargeted metabolomic profiling was performed on serum samples of children with either FA alone, asthma alone, or both FA and asthma, as well as healthy pediatric control subjects. RESULTS: In this pilot study patients with FA exhibited a disease-specific metabolomic signature compared with both control subjects and asthmatic patients. In particular, FA was uniquely associated with a marked decrease in sphingolipid levels, as well as levels of a number of other lipid metabolites, in the face of normal frequencies of circulating natural killer T cells. Specific comparison of patients with FA and asthmatic patients revealed differences in the microbiota-sensitive aromatic amino acid and secondary bile acid metabolism. Children with both FA and asthma exhibited a metabolomic profile that aligned with that of FA alone but not asthma. Among children with FA, the history of severe systemic reactions and the presence of multiple FAs were associated with changes in levels of tryptophan metabolites, eicosanoids, plasmalogens, and fatty acids. CONCLUSIONS: Children with FA have a disease-specific metabolomic profile that is informative of disease mechanisms and severity and that dominates in the presence of asthma. Lower levels of sphingolipids and ceramides and other metabolomic alterations observed in children with FA might reflect the interplay between an altered microbiota and immune cell subsets in the gut.

Epigenome-wide association study of DNA methylation and adult asthma in the Agricultural Lung Health Study.

Epigenome-wide studies of methylation in children support a role for epigenetic mechanisms in asthma; however, studies in adults are rare and few have examined non-atopic asthma. We conducted the largest epigenome-wide association study (EWAS) of blood DNA methylation in adults in relation to non-atopic and atopic asthma.We measured DNA methylation in blood using the Illumina MethylationEPIC array among 2286 participants in a case-control study of current adult asthma nested within a United States agricultural cohort. Atopy was defined by serum specific immunoglobulin E (IgE). Participants were categorised as atopy without asthma (n=185), non-atopic asthma (n=673), atopic asthma (n=271), or a reference group of neither atopy nor asthma (n=1157). Analyses were conducted using logistic regression.No associations were observed with atopy without asthma. Numerous cytosine-phosphate-guanine (CpG) sites were differentially methylated in non-atopic asthma (eight at family-wise error rate (FWER) p<9×10-8, 524 at false discovery rate (FDR) less than 0.05) and implicated 382 novel genes. More CpG sites were identified in atopic asthma (181 at FWER, 1086 at FDR) and implicated 569 novel genes. 104 FDR CpG sites overlapped. 35% of CpG sites in non-atopic asthma and 91% in atopic asthma replicated in studies of whole blood, eosinophils, airway epithelium, or nasal epithelium. Implicated genes were enriched in pathways related to the nervous system or inflammation.We identified numerous, distinct differentially methylated CpG sites in non-atopic and atopic asthma. Many CpG sites from blood replicated in asthma-relevant tissues. These circulating biomarkers reflect risk and sequelae of disease, as well as implicate novel genes associated with non-atopic and atopic asthma.

Low-Dose Silver Nanoparticle Surface Chemistry and Temporal Effects on Gene Expression in Human Liver Cells.

Silver nanoparticles (AgNPs) are widely incorporated into consumer and biomedical products for their antimicrobial and plasmonic properties with limited risk assessment of low-dose cumulative exposure in humans. To evaluate cellular responses to low-dose AgNP exposures across time, human liver cells (HepG2) are exposed to AgNPs with three different surface charges (1.2 µg mL-1 ) and complete gene expression is monitored across a 24 h period. Time and AgNP surface chemistry mediate gene expression. In addition, since cells are fed, time has marked effects on gene expression that should be considered. Surface chemistry of AgNPs alters gene transcription in a time-dependent manner, with the most dramatic effects in cationic AgNPs. Universal to all surface coatings, AgNP-treated cells responded by inactivating proliferation and enabling cell cycle checkpoints. Further analysis of these universal features of AgNP cellular response, as well as more detailed analysis of specific AgNP treatments, time points, or specific genes, is facilitated with an accompanying application. Taken together, these results provide a foundation for understanding hepatic response to low-dose AgNPs for future risk assessment.

Identifying individual risk rare variants using protein structure guided local tests (POINT).

Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set. To perform inference on a localized level, additional information, e.g., biological annotation, is often needed to boost the information content of a rare variant. Following the observation that important variants are likely to cluster together on functional domains, we propose a protein structure guided local test (POINT) to provide variant-specific association information using structure-guided aggregation of signal. Constructed under a kernel machine framework, POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion. Besides merely providing a list of promising variants, POINT assigns each variant a p-value to permit variant ranking and prioritization. We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9, ANGPTL4 and CETP in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data.

Comparative Exposure Assessment Using Silicone Passive Samplers Indicates That Domestic Dogs Are Sentinels To Support Human Health Research.

Silicone wristbands are promising passive samplers to support epidemiological studies in characterizing exposure to organic contaminants; however, investigating associated health risks remains challenging because of the latency period for many chronic diseases that take years to manifest. Dogs provide valuable insights as sentinels for exposure-related human disease because they share similar exposures in the home, have shorter life spans, share many clinical/biological features, and have closely related genomes. Here, we evaluated exposures among pet dogs and their owners using silicone dog tags and wristbands to determine if contaminant levels were correlated with validated exposure biomarkers. Significant correlations between measures on dog tags and wristbands were observed (rs = 0.38-0.90; p < 0.05). Correlations with their respective urinary biomarkers were stronger in dog tags compared to that in human wristbands (rs = 0.50-0.71; p < 0.01) for several organophosphate esters. This supports the value of using silicone bands with dogs to investigate health impacts on humans from shared exposures.

Genome-wide DNA copy number analysis and targeted transcriptional analysis of canine histiocytic malignancies identifies diagnostic signatures and highlights disruption of spindle assembly complex.

Canine histiocytic malignancies (HM) are rare across the general dog population, but overrepresented in certain breeds, such as Bernese mountain dog and flat-coated retriever. Accurate diagnosis relies on immunohistochemical staining to rule out histologically similar cancers with different prognoses and treatment strategies (e.g., lymphoma and hemangiosarcoma). HM are generally treatment refractory with overall survival of less than 6 months. A lack of understanding regarding the mechanisms of disease development and progression hinders development of novel therapeutics. While the study of human tumors can benefit veterinary medicine, the rarity of the suggested orthologous disease (dendritic cell sarcoma) precludes this. This study aims to improve the understanding of underlying disease mechanisms using genome-wide DNA copy number and gene expression analysis of spontaneous HM across several dog breeds. Extensive DNA copy number disruption was evident, with losses of segments of chromosomes 16 and 31 detected in 93% and 72% of tumors, respectively. Droplet digital PCR (ddPCR) evaluation of these regions in numerous cancer specimens effectively discriminated HM from other common round cell tumors, including lymphoma and hemangiosarcoma, resulting in a novel, rapid diagnostic aid for veterinary medicine. Transcriptional analysis demonstrated disruption of the spindle assembly complex, which is linked to genomic instability and reduced therapeutic impact in humans. A key signature detected was up-regulation of Matrix Metalloproteinase 9 (MMP9), supported by an immunohistochemistry-based assessment of MMP9 protein levels. Since MMP9 has been linked with rapid metastasis and tumor aggression in humans, the data in this study offer a possible mechanism of aggression in HM.

Altered bile acid profile associates with cognitive impairment in Alzheimer's disease-An emerging role for gut microbiome.

INTRODUCTION: Increasing evidence suggests a role for the gut microbiome in central nervous system disorders and a specific role for the gut-brain axis in neurodegeneration. Bile acids (BAs), products of cholesterol metabolism and clearance, are produced in the liver and are further metabolized by gut bacteria. They have major regulatory and signaling functions and seem dysregulated in Alzheimer's disease (AD). METHODS: Serum levels of 15 primary and secondary BAs and their conjugated forms were measured in 1464 subjects including 370 cognitively normal older adults, 284 with early mild cognitive impairment, 505 with late mild cognitive impairment, and 305 AD cases enrolled in the AD Neuroimaging Initiative. We assessed associations of BA profiles including selected ratios with diagnosis, cognition, and AD-related genetic variants, adjusting for confounders and multiple testing. RESULTS: In AD compared to cognitively normal older adults, we observed significantly lower serum concentrations of a primary BA (cholic acid [CA]) and increased levels of the bacterially produced, secondary BA, deoxycholic acid, and its glycine and taurine conjugated forms. An increased ratio of deoxycholic acid:CA, which reflects 7α-dehydroxylation of CA by gut bacteria, strongly associated with cognitive decline, a finding replicated in serum and brain samples in the Rush Religious Orders and Memory and Aging Project. Several genetic variants in immune response-related genes implicated in AD showed associations with BA profiles. DISCUSSION: We report for the first time an association between altered BA profile, genetic variants implicated in AD, and cognitive changes in disease using a large multicenter study. These findings warrant further investigation of gut dysbiosis and possible role of gut-liver-brain axis in the pathogenesis of AD.