Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests

OBJECTIVE

Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids).

MATERIALS AND METHODS

We harmonized survey data from the Personalized Environment and Genes Study (PEGS) on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison.

RESULTS

Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant or suggestive predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures.

DISCUSSION

Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal but can support hypothesis generation.

CONCLUSION

This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

Differences in sleep spindle wave density between patients with diabetes mellitus and matched controls: implications for sensing and regulation of peripheral blood glucose

Background

Brain waves during sleep are involved in sensing and regulating peripheral glucose level. Whether brain waves in patients with diabetes differ from those of healthy subjects is unknown. We examined the hypothesis that patients with diabetes have reduced sleep spindle waves, a form of brain wave implicated in periphery glucose regulation during sleep.

Methods

From a retrospective analysis of polysomnography (PSG) studies on patients who underwent sleep apnea evaluation, we identified 1,214 studies of patients with diabetes mellitus (>66% type 2) and included a sex- and age-matched control subject for each within the scope of our analysis. We similarly identified 376 patients with prediabetes and their matched controls. We extracted spindle characteristics from artifact-removed PSG electroencephalograms and other patient data from records. We used rank-based statistical methods to test hypotheses. We validated our finding on an external PSG dataset.

Results

Patients with diabetes mellitus exhibited on average about half the spindle density (median=0.38 spindles/min) during sleep as their matched control subjects (median=0.70 spindles/min) (P<2.2e-16). Compared to controls, spindle loss was more pronounced in female patients than in male patients in the frontal regions of the brain (P=0.04). Patients with prediabetes also exhibited signs of lower spindle density compared to matched controls (P=0.01-0.04).

Conclusions

Patients with diabetes have fewer spindle waves that are implicated in glucose regulation than matched controls during sleep. Besides offering a possible explanation for neurological complications from diabetes, our findings open the possibility that reversing/reducing spindle loss could improve the overall health of patients with diabetes mellitus.

Predictors of upstream inflammation and oxidative stress pathways during early pregnancy

BACKGROUND

Inflammation and oxidative stress are critical to pregnancy, but most human study has focused on downstream, non-causal indicators. Oxylipins are lipid mediators of inflammation and oxidative stress that act through many biological pathways. Our aim was to characterize predictors of circulating oxylipin concentrations based on maternal characteristics.

METHODS

Our study was conducted among 901 singleton pregnancies in the LIFECODES Fetal Growth Study, a nested case-cohort with recruitment from 2007 to 2018. We measured a targeted panel of oxylipins in early pregnancy plasma and urine samples from several biosynthetic pathways, defined by the polyunsaturated fatty acid (PUFA) precursor and enzyme group. We evaluated levels across predictors, including characteristics of participants' pregnancy, socioeconomic determinants, and obstetric and medical history.

RESULTS

Current pregnancy and sociodemographic characteristics were the most important predictors of circulating oxylipins concentrations. Plasma oxylipins were lower and urinary oxylipins higher for participants with a later gestational age at sampling (13-23 weeks), higher prepregnancy BMI (obesity class I, II, or III), Black or Hispanic race and ethnicity, and lower socioeconomic status (younger age, lower education, and uninsured). For example, compared to those with normal or underweight prepregnancy BMI, participants with class III prepregnancy obesity had 45-46% lower plasma epoxy-eicosatrienoic acids, the anti-inflammatory oxylipins produced from arachidonic acid (AA) by cytochrome P450, and had 81% higher urinary 15-series F2-isoprostanes, an indicator of oxidative stress produced from non-enzymatic AA oxidation. Similarly, in urine, Black participants had 92% higher prostaglandin E2 metabolite, a pro-inflammatory oxylipin, and 41% higher 5-series F2-isoprostane, an oxidative stress indicator.

CONCLUSIONS

In this large pregnancy study, we found that circulating levels of oxylipins were different for participants of lower socioeconomic status or of a systematically marginalized racial and ethnic groups. Given associations differed along biosynthetic pathways, results provide insight into etiologic links between maternal predictors and inflammation and oxidative stress.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Whole genome sequence analysis of apparent treatment resistant hypertension status in participants from the Trans-Omics for Precision Medicine program

Introduction: Apparent treatment-resistant hypertension (aTRH) is characterized by the use of four or more antihypertensive (AHT) classes to achieve blood pressure (BP) control. In the current study, we conducted single-variant and gene-based analyses of aTRH among individuals from 12 Trans-Omics for Precision Medicine cohorts with whole-genome sequencing data. Methods: Cases were defined as individuals treated for hypertension (HTN) taking three different AHT classes, with average systolic BP ≥ 140 or diastolic BP ≥ 90 mmHg, or four or more medications regardless of BP (n = 1,705). A normotensive control group was defined as individuals with BP < 140/90 mmHg (n = 22,079), not on AHT medication. A second control group comprised individuals who were treatment responsive on one AHT medication with BP < 140/ 90 mmHg (n = 5,424). Logistic regression with kinship adjustment using the Scalable and Accurate Implementation of Generalized mixed models (SAIGE) was performed, adjusting for age, sex, and genetic ancestry. We assessed variants using SKAT-O in rare-variant analyses. Single-variant and gene-based tests were conducted in a pooled multi-ethnicity stratum, as well as self-reported ethnic/racial strata (European and African American). Results: One variant in the known HTN locus, KCNK3, was a top finding in the multi-ethnic analysis (p = 8.23E-07) for the normotensive control group [rs12476527, odds ratio (95% confidence interval) = 0.80 (0.74-0.88)]. This variant was replicated in the Vanderbilt University Medical Center's DNA repository data. Aggregate gene-based signals included the genes AGTPBP, MYL4, PDCD4, BBS9, ERG, and IER3. Discussion: Additional work validating these loci in larger, more diverse populations, is warranted to determine whether these regions influence the pathobiology of aTRH.

Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests

Objective

Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (e.g., endometriosis, ovarian cyst, and uterine fibroids).

Materials and Methods

We harmonized survey data from the Personalized Environment and Genes Study on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison.

Results

Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures.

Discussion

Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal, but can support hypothesis generation.

Conclusion

This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

The skin is no barrier to mixtures: Air pollutant mixtures and reported psoriasis or eczema in the Personalized Environment and Genes Study (PEGS)

BACKGROUND

Autoimmune (AI) diseases appear to be a product of genetic predisposition and environmental triggers. Disruption of the skin barrier causes exacerbation of psoriasis/eczema. Oxidative stress is a mechanistic pathway for pathogenesis of the disease and is also a primary mechanism for the detrimental effects of air pollution.

METHODS

We evaluated the association between autoimmune skin diseases (psoriasis or eczema) and air pollutant mixtures in 9060 subjects from the Personalized Environment and Genes Study (PEGS) cohort. Pollutant exposure data on six criteria air pollutants are publicly available from the Center for Air, Climate, and Energy Solutions and the Atmospheric Composition Analysis Group. For increased spatial resolution, we included spatially cumulative exposure to volatile organic compounds from sites in the United States Environmental Protection Agency Toxic Release Inventory and the density of major roads within a 5 km radius of a participant's address from the United States Geological Survey. We applied logistic regression with quantile g-computation, adjusting for age, sex, diagnosis with an autoimmune disease in family or self, and smoking history to evaluate the relationship between self-reported diagnosis of an AI skin condition and air pollution mixtures.

RESULTS

Only one air pollution variable, sulfate, was significant individually (OR = 1.06, p = 3.99E-2); however, the conditional odds ratio for the combined mixture components of PM2.5 (black carbon, sulfate, sea salt, and soil), CO, SO2, benzene, toluene, and ethylbenzene is 1.10 (p-value = 5.4E-3).

SIGNIFICANCE

While the etiology of autoimmune skin disorders is not clear, this study provides evidence that air pollutants are associated with an increased prevalence of these disorders. The results provide further evidence of potential health impacts of air pollution exposures on life-altering diseases.

SIGNIFICANCE AND IMPACT STATEMENT

The impact of air pollution on non-pulmonary and cardiovascular diseases is understudied and under-reported. We find that air pollution significantly increased the odds of psoriasis or eczema in our cohort and the magnitude is comparable to the risk associated with smoking exposure. Autoimmune diseases like psoriasis and eczema are likely impacted by air pollution, particularly complex mixtures and our study underscores the importance of quantifying air pollution-associated risks in autoimmune disease.

LBMON114 Enrichment Of Rare Sequence Variants In Genes That Communicate Metabolic Signals To The GnRH System In Hypothalamic Amenorrhea

Introduction

Functional hypothalamic amenorrhea (HA) is commonly associated with increased exercise or decreased caloric intake and often with stress. We have previously demonstrated an increased burden of rare sequence variants (RSVs) in genes involved in GnRH ontogeny and upstream regulation in women with HA, but the role of metabolic and stress signaling to the GnRH neuronal system is poorly defined in this population.

Methods

The study included 100 women with a confirmed diagnosis of HA. The control cohort consisted of 468 women (aged 45­­-65 years) drawn from the NIH ClinSeq® Project. Exome sequencing was performed on peripheral blood genomic DNA. A subset of 72 genes was analyzed that have been shown to: 1) link metabolic or stress with reproductive phenotypes or 2) integrate metabolic and stress pathways with control of GnRH secretion. Joint genotyping of case and control samples was performed using the GATK GenotypeGVCFs function, locus-filtering using the VariantRecalibrator function, and genotype refinement using CalculateGenotypePosteriors with computation of median depths. Median depth positions <10, positions failing GATK VQSR or GATK genotype quality scores <20 were excluded. RSVs were identified by < 1% frequency in any subpopulation in gnomeAD for all-subjects (AS) and < 1% frequency in non-Finnish Europeans for Caucasians (CS). Data were analyzed for AS and for CS using a one-sided Fisher exact test for metabolism genes and stress genes. An additional regression analysis was conducted on the number of RSVs in a given gene as a predictor of HA vs. control. Comparisons with a p-value of < 0.1 are reported.

Results

HA patients exhibited an increased burden of RSVs in metabolism genes vs. controls (AS p=0. 043; CS p=0.105). The total number of RSVs per gene highlighted differences between HA and controls for the following genes: ADAMTSL1, GRINA, GRIN1, HCRTR1, TENM3, and NOS1 (AS p<0. 001, p=0. 032, p=0. 057, p=0. 082, p=0. 091, p=0. 095; CS p=0. 024, p=0. 044, p=0. 044, p NS, p=0. 086, p NS). Interestingly, RSVs in NOS1 and TENM3 appeared to be protective for HA (odds ratio <1 for both). In contrast, candidate stress genes were not significant in either the AS or CS (p=0.788, p=0.910).

Conclusions

These data suggest that RSVs in genes involved in phenotypes or signaling pathways that link metabolism to GnRH secretion may predispose to development of HA in the setting of decreased energy balance, but not for stress-related genes. GRINA and GRIN1 are important components of glutamate signaling that facilitate both appetite and GnRH secretion either directly or through kisspeptin. HCRTR1 plays a similar role in linking appetite and GnRH secretion while NOS1, which facilitates kisspeptin signaling, may be protective. This work highlights the need for further studies to understand the potential roles of ADAMTSL1 and TENM3 as risk factors for HA.

Presentation: Monday, June 13, 2022 12:30 p.m. - 2:30 p.m.

The AGMK1-9T7 cell model of neoplasia: Evolution of DNA copy-number aberrations and miRNA expression during transition from normal to metastatic cancer cells

To study neoplasia in tissue culture, cell lines representing the evolution of normal cells to tumor cells are needed. To produce such cells, we developed the AGMK1-9T7 cell line, established cell banks at 10-passage intervals, and characterized their biological properties. Here we examine the evolution of chromosomal DNA copy-number aberrations and miRNA expression in this cell line from passage 1 to the acquisition of a tumorigenic phenotype at passage 40. We demonstrated the use of a human microarray platform for DNA copy-number profiling of AGMK1-9T7 cells using knowledge of synteny to 'recode' data from human chromosome coordinates to those of the African green monkey. This approach revealed the accumulation of DNA copy-number gains and losses in AGMK1-9T7 cells from passage 3 to passage 40, which spans the period in which neoplastic transformation occurred. These alterations occurred in the sequences of genes regulating DNA copy-number imbalance of several genes that regulate endothelial cell angiogenesis, survival, migration, and proliferation. Regarding miRNA expression, 195 miRNAs were up- or down-regulated at passage 1 at levels that appear to be biologically relevant (i.e., log2 fold change >2.0 (q<0.05)). At passage 10, the number of up/down-regulated miRNAs fell to 63; this number increased to 93 at passage 40. Principal-component analysis grouped these miRNAs into 3 clusters; miRNAs in sub-clusters of these groups could be correlated with initiation, promotion, and progression, stages that have been described for neoplastic development. Thirty-four of the AGMK1-9T7 miRNAs have been associated with these stages in human cancer. Based on these data, we propose that the evolution of AGMK1-9T7 cells represents a detailed model of neoplasia in vitro.

Correlation Analysis of Variables From the Atherosclerosis Risk in Communities Study

The need to test chemicals in a timely and cost-effective manner has driven the development of new alternative methods (NAMs) that utilize in silico and in vitro approaches for toxicity prediction. There is a wealth of existing data from human studies that can aid in understanding the ability of NAMs to support chemical safety assessment. This study aims to streamline the integration of data from existing human cohorts by programmatically identifying related variables within each study. Study variables from the Atherosclerosis Risk in Communities (ARIC) study were clustered based on their correlation within the study. The quality of the clusters was evaluated via a combination of manual review and natural language processing (NLP). We identified 391 clusters including 3,285 variables. Manual review of the clusters containing more than one variable determined that human reviewers considered 95% of the clusters related to some degree. To evaluate potential bias in the human reviewers, clusters were also scored via NLP, which showed a high concordance with the human classification. Clusters were further consolidated into cluster groups using the Louvain community finding algorithm. Manual review of the cluster groups confirmed that clusters within a group were more related than clusters from different groups. Our data-driven approach can facilitate data harmonization and curation efforts by providing human annotators with groups of related variables reflecting the themes present in the data. Reviewing groups of related variables should increase efficiency of the human review, and the number of variables reviewed can be reduced by focusing curator attention on variable groups whose theme is relevant for the topic being studied.

Genomic map of candidate human imprint control regions: the imprintome

Imprinted genes – critical for growth, metabolism, and neuronal function – are expressed from one parental allele. Parent-of-origin-dependent CpG methylation regulates this expression at imprint control regions (ICRs). Since ICRs are established before tissue specification, these methylation marks are similar across cell types. Thus, they are attractive for investigating the developmental origins of adult diseases using accessible tissues, but remain unknown. We determined genome-wide candidate ICRs in humans by performing whole-genome bisulphite sequencing (WGBS) of DNA derived from the three germ layers and from gametes. We identified 1,488 hemi-methylated candidate ICRs, including 19 of 25 previously characterized ICRs (https://humanicr.org/). Gamete methylation approached 0% or 100% in 332 ICRs (178 paternally and 154 maternally methylated), supporting parent-of-origin-specific methylation, and 65% were in well-described CTCF-binding or DNaseI hypersensitive regions. This draft of the human imprintome will allow for the systematic determination of the role of early-acquired imprinting dysregulation in the pathogenesis of human diseases and developmental and behavioural disorders.

Comparison of National Vulnerability Indices Used by the Centers for Disease Control and Prevention for the COVID-19 Response

OBJECTIVE

Vulnerability indices use quantitative indicators and geospatial data to examine the level of vulnerability to morbidity in a community. The Centers for Disease Control and Prevention (CDC) uses 3 indices for the COVID-19 response: the CDC Social Vulnerability Index (CDC-SVI), the US COVID-19 Community Vulnerability Index (CCVI), and the Pandemic Vulnerability Index (PVI). The objective of this review was to describe these tools and explain the similarities and differences between them.

METHODS

We described the 3 indices, outlined the underlying data sources and metrics for each, and discussed their use by CDC for the COVID-19 response. We compared the percentile score for each county for each index by calculating Spearman correlation coefficients (Spearman ρ).

RESULTS

These indices have some, but not all, component metrics in common. The CDC-SVI is a validated metric that estimates social vulnerability, which comprises the underlying population-level characteristics that influence differences in health risk among communities. To address risk specific to the COVID-19 pandemic, the CCVI and PVI build on the CDC-SVI and include additional variables. The 3 indices were highly correlated. Spearman ρ for comparisons between the CDC-SVI score and the CCVI and between the CCVI and the PVI score was 0.83. Spearman ρ for the comparison between the CDC-SVI score and PVI score was 0.73.

CONCLUSION

The indices can empower local and state public health officials with additional information to focus resources and interventions on disproportionately affected populations to combat the ongoing pandemic and plan for future pandemics.

Prediction of synergistic drug combinations using PCA-initialized deep learning

Background

Cancer is one of the main causes of death worldwide. Combination drug therapy has been a mainstay of cancer treatment for decades and has been shown to reduce host toxicity and prevent the development of acquired drug resistance. However, the immense number of possible drug combinations and large synergistic space makes it infeasible to screen all effective drug pairs experimentally. Therefore, it is crucial to develop computational approaches to predict drug synergy and guide experimental design for the discovery of rational combinations for therapy.

Results

We present a new deep learning approach to predict synergistic drug combinations by integrating gene expression profiles from cell lines and chemical structure data. Specifically, we use principal component analysis (PCA) to reduce the dimensionality of the chemical descriptor data and gene expression data. We then propagate the low-dimensional data through a neural network to predict drug synergy values. We apply our method to O’Neil’s high-throughput drug combination screening data as well as a dataset from the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. We compare the neural network approach with and without dimension reduction. Additionally, we demonstrate the effectiveness of our deep learning approach and compare its performance with three state-of-the-art machine learning methods: Random Forests, XGBoost, and elastic net, with and without PCA-based dimensionality reduction.

Conclusions

Our developed approach outperforms other machine learning methods, and the use of dimension reduction dramatically decreases the computation time without sacrificing accuracy.

Extending the lymphoblastoid cell line model for drug combination pharmacogenomics

Combination drug therapies have become an integral part of precision oncology, and while evidence of clinical effectiveness continues to grow, the underlying mechanisms supporting synergy are poorly understood. Immortalized human lymphoblastoid cell lines (LCLs) have been proven as a particularly useful, scalable and low-cost model in pharmacogenetics research, and are suitable for elucidating the molecular mechanisms of synergistic combination therapies. In this review, we cover the advantages of LCLs in synergy pharmacogenomics and consider recent studies providing initial evidence of the utility of LCLs in synergy research. We also discuss several opportunities for LCL-based systems to address gaps in the research through the expansion of testing regimens, assessment of new drug classes and higher-order combinations, and utilization of integrated omics technologies.

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 (p_interaction=0.017) or former smokers (p_interaction=0.064) when compared with never smokers and among asthmatics compared with non-asthmatics (p_interaction=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.

The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring county-level vulnerability using visualization, statistical modeling, and machine learning

BACKGROUND

While the COVID-19 pandemic presents a global challenge, the U.S. response places substantial responsibility for both decision-making and communication on local health authorities, necessitating tools to support decision-making at the community level.

OBJECTIVES

We created a Pandemic Vulnerability Index (PVI) to support counties and municipalities by integrating baseline data on relevant community vulnerabilities with dynamic data on local infection rates and interventions. The PVI visually synthesizes county-level vulnerability indicators, enabling their comparison in regional, state, and nationwide contexts.

METHODS

We describe the data streams used and how these are combined to calculate the PVI, detail the supporting epidemiological modeling and machine-learning forecasts, and outline the deployment of an interactive web Dashboard. Finally, we describe the practical application of the PVI for real-world decision-making.

RESULTS

Considering an outlook horizon from 1 to 28 days, the overall PVI scores are significantly associated with key vulnerability-related outcome metrics of cumulative deaths, population adjusted cumulative deaths, and the proportion of deaths from cases. The modeling results indicate the most significant predictors of case counts are population size, proportion of black residents, and mean PM2.5. The machine learning forecast results were strongly predictive of observed cases and deaths up to 14 days ahead. The modeling reinforces an integrated concept of vulnerability that accounts for both dynamic and static data streams and highlights the drivers of inequities in COVID-19 cases and deaths. These results also indicate that local areas with a highly ranked PVI should take near-term action to mitigate vulnerability.

DISCUSSION

The COVID-19 PVI Dashboard monitors multiple data streams to communicate county-level trends and vulnerabilities and facilitates decision-making and communication among government officials, scientists, community leaders, and the public to enable effective and coordinated action to combat the pandemic.

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.

Distinct methylation signals are found in non-atopic and atopic asthma. Most are related to gene expression and are replicated in asthma-relevant tissues, confirming the value of blood DNA methylation for identifying novel genes linked in asthma pathogenesis.https://bit.ly/2VnbJg3

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 (r_s = 0.38-0.90; p < 0.05). Correlations with their respective urinary biomarkers were stronger in dog tags compared to that in human wristbands (r_s = 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.

Nonlinear Dose-Response Modeling of High-Throughput Screening Data Using an Evolutionary Algorithm

Nonlinear dose-response relationships exist extensively in the cellular, biochemical, and physiologic processes that are affected by varying levels of biological, chemical, or radiation stress. Modeling such responses is a crucial component of toxicity testing and chemical screening. Traditional model fitting methods such as nonlinear least squares (NLS) are very sensitive to initial parameter values and often had convergence failure. The use of evolutionary algorithms (EAs) has been proposed to address many of the limitations of traditional approaches, but previous methods have been limited in the types of models they can fit. Therefore, we propose the use of an EA for dose-response modeling for a range of potential response model functional forms. This new method can not only fit the most commonly used nonlinear dose-response models (eg, exponential models and 3-, 4-, and 5-parameter logistic models) but also select the best model if no model assumption is made, which is especially useful in the case of high-throughput curve fitting. Compared with NLS, the new method provides stable and robust solutions without sensitivity to initial values.

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.

Comparison of smoking-related DNA methylation between newborns from prenatal exposure and adults from personal smoking

Aim: Cigarette smoking influences DNA methylation genome wide, in newborns from pregnancy exposure and in adults from personal smoking. Whether a unique methylation signature exists for in utero exposure in newborns is unknown. Materials & methods: We separately meta-analyzed newborn blood DNA methylation (assessed using Illumina450k Beadchip), in relation to sustained maternal smoking during pregnancy (9 cohorts, 5648 newborns, 897 exposed) and adult blood methylation and personal smoking (16 cohorts, 15907 participants, 2433 current smokers). Results & conclusion: Comparing meta-analyses, we identified numerous signatures specific to newborns along with many shared between newborns and adults. Unique smoking-associated genes in newborns were enriched in xenobiotic metabolism pathways. Our findings may provide insights into specific health impacts of prenatal exposure on offspring.

Current Methods for Quantifying Drug Synergism

The effectiveness of drug combinations for treatment of a variety of complex diseases is well established. "Drug cocktail" treatments are often prescribed to improve the overall efficacy, decrease toxicity, alter pharmacodynamics, etc in an overall treatment strategy. Specifically, if when combined, drugs interact in some way that causes the total effect to be greater than that predicted by their individual potencies, then drugs are considered synergistic. While there are established ways to quantify the impact of drug combinations clinically, it is an open challenge to quantitatively summarize a synergistic interaction. In this paper, we discuss an overview of the current statistical and mathematical methods for the study of drug combination effects, especially drug synergy quantification (where the interaction effect is not just detected, but quantified according to its magnitude). We first introduce two popular reference models for testing to null hypothesis of non-interaction for a combination, including the Bliss independence model and the Loewe additivity model. Then we discuss several methods for quantifying drug synergism. The advantages and disadvantages with these methods are also provided, and finally, we discuss important next directions in this area.

Cheminformatics approach to exploring and modeling trait-associated metabolite profiles

Developing predictive and transparent approaches to the analysis of metabolite profiles across patient cohorts is of critical importance for understanding the events that trigger or modulate traits of interest (e.g., disease progression, drug metabolism, chemical risk assessment). However, metabolites’ chemical structures are still rarely used in the statistical modeling workflows that establish these trait-metabolite relationships. Herein, we present a novel cheminformatics-based approach capable of identifying predictive, interpretable, and reproducible trait-metabolite relationships. As a proof-of-concept, we utilize a previously published case study consisting of metabolite profiles from non-small-cell lung cancer (NSCLC) adenocarcinoma patients and healthy controls. By characterizing each structurally annotated metabolite using both computed molecular descriptors and patient metabolite concentration profiles, we show that these complementary features enhance the identification and understanding of key metabolites associated with cancer. Ultimately, we built multi-metabolite classification models for assessing patients’ cancer status using specific groups of metabolites identified based on high structural similarity through chemical clustering. We subsequently performed a metabolic pathway enrichment analysis to identify potential mechanistic relationships between metabolites and NSCLC adenocarcinoma. This cheminformatics-inspired approach relies on the metabolites’ structural features and chemical properties to provide critical information about metabolite-trait associations. This method could ultimately facilitate biological understanding and advance research based on metabolomics data, especially with respect to the identification of novel biomarkers.

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.

International collaborative study to assess cardiovascular risk and evaluate long-term health in cats with preclinical hypertrophic cardiomyopathy and apparently healthy cats: The REVEAL Study

Background

Hypertrophic cardiomyopathy is the most prevalent heart disorder in cats and principal cause of cardiovascular morbidity and mortality. Yet, the impact of preclinical disease is unresolved.

Hypothesis/Objectives

Observational study to characterize cardiovascular morbidity and survival in cats with preclinical nonobstructive (HCM) and obstructive (HOCM) hypertrophic cardiomyopathy and in apparently healthy cats (AH).

Animals

One thousand seven hundred and thirty client‐owned cats (430 preclinical HCM; 578 preclinical HOCM; 722 AH).

Methods

Retrospective multicenter, longitudinal, cohort study. Cats from 21 countries were followed through medical record review and owner or referring veterinarian interviews. Data were analyzed to compare long‐term outcomes, incidence, and risk for congestive heart failure (CHF), arterial thromboembolism (ATE), and cardiovascular death.

Results

During the study period, CHF, ATE, or both occurred in 30.5% and cardiovascular death in 27.9% of 1008 HCM/HOCM cats. Risk assessed at 1, 5, and 10 years after study entry was 7.0%/3.5%, 19.9%/9.7%, and 23.9%/11.3% for CHF/ATE, and 6.7%, 22.8%, and 28.3% for cardiovascular death, respectively. There were no statistically significant differences between HOCM compared with HCM for cardiovascular morbidity or mortality, time from diagnosis to development of morbidity, or cardiovascular survival. Cats that developed cardiovascular morbidity had short survival (mean ± standard deviation, 1.3 ± 1.7 years). Overall, prolonged longevity was recorded in a minority of preclinical HCM/HOCM cats with 10% reaching 9‐15 years.

Conclusions and Clinical Importance

Preclinical HCM/HOCM is a global health problem of cats that carries substantial risk for CHF, ATE, and cardiovascular death. This finding underscores the need to identify therapies and monitoring strategies that decrease morbidity and mortality.

Sphingolipid Metabolic Pathway Impacts Thiazide Diuretics Blood Pressure Response: Insights From Genomics, Metabolomics, and Lipidomics

Background

Although hydrochlorothiazide (HCTZ) is a well‐established first‐line antihypertensive in the United States, <50% of HCTZ treated patients achieve blood pressure (BP) control. Thus, identifying biomarkers that could predict the BP response to HCTZ is critically important. In this study, we utilized metabolomics, genomics, and lipidomics to identify novel pathways and biomarkers associated with HCTZ BP response.

Methods and Results

First, we conducted a pathway analysis for 13 metabolites we recently identified to be significantly associated with HCTZ BP response. From this analysis, we found the sphingolipid metabolic pathway as the most significant pathway (P=5.8E‐05). Testing 78 variants, within 14 genes involved in the sphingolipid metabolic canonical pathway, with the BP response to HCTZ identified variant rs6078905, within the SPTLC3 gene, as a novel biomarker significantly associated with the BP response to HCTZ in whites (n=228). We found that rs6078905 C‐allele carriers had a better BP response to HCTZ versus noncarriers (∆SBP/∆DBP: −11.4/−6.9 versus −6.8/−3.5 mm Hg; ∆SBP P=6.7E‐04; ∆DBP P=4.8E‐04). Additionally, in blacks (n=148), we found genetic signals in the SPTLC3 genomic region significantly associated with the BP response to HCTZ (P<0.05). Last, we observed that rs6078905 significantly affects the baseline level of 4 sphingomyelins (N24:2, N24:3, N16:1, and N22:1; false discovery rate <0.05), from which N24:2 sphingomyelin has a significant correlation with both HCTZ DBP‐response (r=−0.42; P=7E‐03) and SBP‐response (r=−0.36; P=2E‐02).

Conclusions

This study provides insight into potential pharmacometabolomic and genetic mechanisms underlying HCTZ BP response and suggests that SPTLC3 is a potential determinant of the BP response to HCTZ.

Clinical Trial Registration

URL: http://www.clinicaltrials.gov. Unique identifier: NCT00246519.

The steroid metabolome in women with premenstrual dysphoric disorder during GnRH agonist-induced ovarian suppression: effects of estradiol and progesterone addback

Clinical evidence suggests that symptoms in premenstrual dysphoric disorder (PMDD) reflect abnormal responsivity to ovarian steroids. This differential steroid sensitivity could be underpinned by abnormal processing of the steroid signal. We used a pharmacometabolomics approach in women with prospectively confirmed PMDD (n=15) and controls without menstrual cycle-related affective symptoms (n=15). All were medication-free with normal menstrual cycle lengths. Notably, women with PMDD were required to show hormone sensitivity in an ovarian suppression protocol. Ovarian suppression was induced for 6 months with gonadotropin-releasing hormone (GnRH)-agonist (Lupron); after 3 months all were randomized to 4 weeks of estradiol (E2) or progesterone (P4). After a 2-week washout, a crossover was performed. Liquid chromatography/tandem mass spectrometry measured 49 steroid metabolites in serum. Values were excluded if >40% were below the limit of detectability (n=21). Analyses were performed with Wilcoxon rank-sum tests using false-discovery rate (q<0.2) for multiple comparisons. PMDD and controls had similar basal levels of metabolites during Lupron and P4-derived neurosteroids during Lupron or E2/P4 conditions. Both groups had significant increases in several steroid metabolites compared with the Lupron alone condition after treatment with E2 (that is, estrone-SO4 (q=0.039 and q=0.002, respectively) and estradiol-3-SO4 (q=0.166 and q=0.001, respectively)) and after treatment with P4 (that is, allopregnanolone (q=0.001 for both PMDD and controls), pregnanediol (q=0.077 and q=0.030, respectively) and cortexone (q=0.118 and q=0.157, respectively). Only sulfated steroid metabolites showed significant diagnosis-related differences. During Lupron plus E2 treatment, women with PMDD had a significantly attenuated increase in E2-3-sulfate (q=0.035) compared with control women, and during Lupron plus P4 treatment a decrease in DHEA-sulfate (q=0.07) compared with an increase in controls. Significant effects of E2 addback compared with Lupron were observed in women with PMDD who had significant decreases in DHEA-sulfate (q=0.065) and pregnenolone sulfate (q=0.076), whereas controls had nonsignificant increases (however, these differences did not meet statistical significance for a between diagnosis effect). Alterations of sulfotransferase activity could contribute to the differential steroid sensitivity in PMDD. Importantly, no differences in the formation of P4-derived neurosteroids were observed in this otherwise highly selected sample of women studied under controlled hormone exposures.

Metabolic network failures in Alzheimer's disease: A biochemical road map

INTRODUCTION

The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.

METHODS

Fasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted.

RESULTS

Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ_1-42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease.

DISCUSSION

Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

Use of RNA-seq to identify cardiac genes and gene pathways differentially expressed between dogs with and without dilated cardiomyopathy

OBJECTIVE To identify cardiac tissue genes and gene pathways differentially expressed between dogs with and without dilated cardiomyopathy (DCM). ANIMALS 8 dogs with and 5 dogs without DCM. PROCEDURES Following euthanasia, samples of left ventricular myocardium were collected from each dog. Total RNA was extracted from tissue samples, and RNA sequencing was performed on each sample. Samples from dogs with and without DCM were grouped to identify genes that were differentially regulated between the 2 populations. Overrepresentation analysis was performed on upregulated and downregulated gene sets to identify altered molecular pathways in dogs with DCM. RESULTS Genes involved in cellular energy metabolism, especially metabolism of carbohydrates and fats, were significantly downregulated in dogs with DCM. Expression of cardiac structural proteins was also altered in affected dogs. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested that RNA sequencing may provide important insights into the pathogenesis of DCM in dogs and highlight pathways that should be explored to identify causative mutations and develop novel therapeutic interventions.

Maternal smoking impacts key biological pathways in newborns through epigenetic modification in Utero

Background

Children exposed to maternal smoking during pregnancy exhibit increased risk for many adverse health effects. Maternal smoking influences methylation in newborns at specific CpG sites (CpGs). Here, we extend evaluation of individual CpGs to gene-level and pathway-level analyses among 1062 participants in the Norwegian Mother and Child Cohort Study (MoBa) using the Illumina 450 K platform to measure methylation in newborn DNA and maternal smoking in pregnancy, assessed using the biomarker, plasma cotinine. We used novel implementations of bioinformatics tools to collapse epigenome-wide methylation data into gene- and pathway-level effects to test whether exposure to maternal smoking in utero differentially methylated CpGs in genes enriched in biologic pathways. Unlike most pathway analysis applications, our approach allows replication in an independent cohort.

Results

Data on 485,577 CpGs, mapping to a total of 20,199 genes, were used to create gene scores that were tested for association with maternal plasma cotinine levels using Sequence Kernel Association Test (SKAT), and 15 genes were found to be associated (q < 0.25). Six of these 15 genes (GFI1, MYO1G, CYP1A1, RUNX1, LCTL, and AHRR) contained individual CpGs that were differentially methylated with regards to cotinine levels (p < 1.06 × 10⁻⁷). Nine of the 15 genes (FCRLA, MIR641, SLC25A24, TRAK1, C1orf180, ITLN2, GLIS1, LRFN1, and MIR451) were associated with cotinine at the gene-level (q < 0.25) but had no genome-wide significant individual CpGs (p > 1.06 × 10⁻⁷). Pathway analyses using gene scores resulted in 51 significantly associated pathways, which we tested for replication in an independent cohort (q < 0.05). Of those 32 replicated in an independent cohort, which clustered into six groups. The largest cluster consisted of pathways related to cancer, cell cycle, ERα receptor signaling, and angiogenesis. The second cluster, organized into five smaller pathway groups, related to immune system function, such as T-cell regulation and other white blood cell related pathways.

Conclusions

Here we use novel implementations of bioinformatics tools to determine biological pathways impacted through epigenetic changes in utero by maternal smoking in 1062 participants in the MoBa, and successfully replicate these findings in an independent cohort. The results provide new insight into biological mechanisms that may contribute to adverse health effects from exposure to tobacco smoke in utero.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3310-1) contains supplementary material, which is available to authorized users.

Eigenvector metabolite analysis reveals dietary effects on the association among metabolite correlation patterns, gene expression, and phenotypes

INTRODUCTION

'Multi-omics' datasets obtained from an organism of interest reared under different environmental treatments are increasingly common. Identifying the links among metabolites and transcripts can help to elucidate our understanding of the impact of environment at different levels within the organism. However, many methods for characterizing physiological connections cannot address unidentified metabolites.

OBJECTIVES

Here, we use Eigenvector Metabolite Analysis (EvMA) to examine links between metabolomic, transcriptomic, and phenotypic variation data and to assess the impact of environmental factors on these associations. Unlike other methods, EvMA can be used to analyze datasets that include unidentified metabolites and unannotated transcripts.

METHODS

To demonstrate the utility of EvMA, we analyzed metabolomic, transcriptomic, and phenotypic datasets produced from 20 Drosophila melanogaster genotypes reared on four dietary treatments. We used a hierarchical distance-based method to cluster the metabolites. The links between metabolite clusters, gene expression, and overt phenotypes were characterized using the eigenmetabolite (first principal component) of each cluster.

RESULTS

EvMA recovered chemically related groups of metabolites within the clusters. Using the eigenmetabolite, we identified genes and phenotypes that significantly correlated with each cluster. EvMA identifies new connections between the phenotypes, metabolites, and gene transcripts. Conclusion EvMA provides a simple method to identify correlations between metabolites, gene expression, and phenotypes, which can allow us to partition multivariate datasets into meaningful biological modules and identify under-studied metabolites and unannotated gene transcripts that may be central to important biological processes. This can be used to inform our understanding of the effect of environmental mechanisms underlying physiological states of interest.

Thromboelastographic monitoring of the effect of unfractionated heparin in healthy dogs

OBJECTIVE

To characterize the correlation between thromboelastography (TEG) variables using strong activators and anti-Xa (AXa) activity in healthy dogs administered subcutaneous unfractionated heparin (UFH).

DESIGN

Prospective experimental study.

SETTING

University research facility.

ANIMALS

Eight adult random-source male dogs.

INTERVENTION

Dogs were randomized to receive subcutaneous UFH at 200, 250, or 300 IU/kg every 8 hours for a total of 10 injections. Blood samples were collected at time 0 (preheparin) and 3, 6, and 8 hours after the 1st (Day 1) and 10th (Day 4) UFH injection.  After the 8-hour blood sample was obtained on day 4, a 100 IU/kg IV bolus of UFH was administered and an additional blood sample was collected 1 hour later (hour 9). AXa activity, activated partial thromboplastin time (aPTT), and TEG (with up to 5 activators) were performed at each time point.  Modes of activation for TEG included  recalcified (Ca), Ca with heparinase (CaH), CaH and tissue factor 1:3600 (CTF3600H), Ca with tissue factor 1:100 (CTF100), and RapidTEG. Spearman rank correlations were calculated for each of the aforementioned parameters and the AXa activity. P-values were corrected for multiple comparisons with a Bonferroni correction.

MEASUREMENTS AND MAIN RESULTS

Significant correlations were found between AXa activity and the TEG R values generated with CTF100 (R = 0.83, P ≤ 0.0001) and RapidTEG (R = 0.90, P < 0.0001), as well as both forms of aPTT measurement (R = 0.86 and 0.84, P < 0.0001).

CONCLUSIONS

This study demonstrates that TEG variables derived using robust activation correlate with AXa activity as well as aPTT and have the potential to be used for monitoring UFH therapy in healthy dogs.  Future studies are warranted to evaluate its diagnostic utility in critically ill animals.

Metabolomic signatures of drug response phenotypes for ketamine and esketamine in subjects with refractory major depressive disorder: new mechanistic insights for rapid acting antidepressants

Ketamine, at sub-anesthetic doses, is reported to rapidly decrease depression symptoms in patients with treatment-resistant major depressive disorder (MDD). Many patients do not respond to currently available antidepressants, (for example, serotonin reuptake inhibitors), making ketamine and its enantiomer, esketamine, potentially attractive options for treatment-resistant MDD. Although mechanisms by which ketamine/esketamine may produce antidepressant effects have been hypothesized on the basis of preclinical data, the neurobiological correlates of the rapid therapeutic response observed in patients receiving treatment have not been established. Here we use a pharmacometabolomics approach to map global metabolic effects of these compounds in treatment-refractory MDD patients upon 2 h from infusion with ketamine (n=33) or its S-enantiomer, esketamine (n=20). The effects of esketamine on metabolism were retested in the same subjects following a second exposure administered 4 days later. Two complementary metabolomics platforms were used to provide broad biochemical coverage. In addition, we investigated whether changes in particular metabolites correlated with treatment outcome. Both drugs altered metabolites related to tryptophan metabolism (for example, indole-3-acetate and methionine) and/or the urea cycle (for example, citrulline, arginine and ornithine) at 2 h post infusion (q<0.25). In addition, we observed changes in glutamate and circulating phospholipids that were significantly associated with decreases in depression severity. These data provide new insights into the mechanism underlying the rapid antidepressant effects of ketamine and esketamine, and constitute some of the first detailed metabolomics mapping for these promising therapies.

Comprehensive genomic characterization of five canine lymphoid tumor cell lines

BACKGROUND

Leukemia/lymphoma cell lines have been critical in the investigation of the pathogenesis and therapy of hematological malignancies. While human LL cell lines have generally been found to recapitulate the primary tumors from which they were derived, appropriate characterization including cytogenetic and transcriptional assessment is crucial for assessing their clinical predictive value.

RESULTS

In the following study, five canine LL cell lines, CLBL-1, Ema, TL-1 (Nody-1), UL-1, and 3132, were characterized using extensive immunophenotyping, karyotypic analysis, oligonucleotide array comparative genomic hybridization (oaCGH), and gene expression profiling. Genome-wide DNA copy number data from the cell lines were also directly compared with 299 primary canine round cell tumors to determine whether the cell lines represent primary tumors, and, if so, what subtype each most closely resembled.

CONCLUSIONS

Based on integrated analyses, CLBL-1 was classified as B-cell lymphoma, Ema and TL-1 as T-cell lymphoma, and UL-1 as T-cell acute lymphoblastic leukemia. 3132, originally classified as a B-cell lymphoma, was reclassified as a histiocytic sarcoma based on characteristic cytogenomic properties. In combination, these data begin to elucidate the clinical predictive value of these cell lines which will enhance the appropriate selection of in vitro models for future studies of canine hematological malignancies.

A Genetic Response Score for Hydrochlorothiazide Use: Insights From Genomics and Metabolomics Integration

Hydrochlorothiazide is among the most commonly prescribed antihypertensives; yet, <50% of hydrochlorothiazide-treated patients achieve blood pressure (BP) control. Herein, we integrated metabolomic and genomic profiles of hydrochlorothiazide-treated patients to identify novel genetic markers associated with hydrochlorothiazide BP response. The primary analysis included 228 white hypertensives treated with hydrochlorothiazide from the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) study. Genome-wide analysis was conducted using Illumina Omni 1 mol/L-Quad Chip, and untargeted metabolomics was performed on baseline fasting plasma samples using a gas chromatography-time-of-flight mass spectrometry platform. We found 13 metabolites significantly associated with hydrochlorothiazide systolic BP (SBP) and diastolic BP (DBP) responses (false discovery rate, <0.05). In addition, integrating genomic and metabolomic data revealed 3 polymorphisms (rs2727563 PRKAG2, rs12604940 DCC, and rs13262930 EPHX2) along with arachidonic acid, converging in the netrin signaling pathway (P=1×10(-5)), as potential markers, significantly influencing hydrochlorothiazide BP response. We successfully replicated the 3 genetic signals in 212 white hypertensives treated with hydrochlorothiazide and created a response score by summing their BP-lowering alleles. We found patients carrying 1 response allele had a significantly lower response than carriers of 6 alleles (∆SBP/∆DBP: -1.5/1.2 versus -16.3/-10.4 mm Hg, respectively, SBP score, P=1×10(-8) and DBP score, P=3×10(-9)). This score explained 11.3% and 11.9% of the variability in hydrochlorothiazide SBP and DBP responses, respectively, and was further validated in another independent study of 196 whites treated with hydrochlorothiazide (DBP score, P=0.03; SBP score, P=0.07). This study suggests that PRKAG2, DCC, and EPHX2 might be important determinants of hydrochlorothiazide BP response.

Genome-Wide Association Study in Immunocompetent Patients with Delayed Hypersensitivity to Sulfonamide Antimicrobials

Background

Hypersensitivity (HS) reactions to sulfonamide antibiotics occur uncommonly, but with potentially severe clinical manifestations. A familial predisposition to sulfonamide HS is suspected, but robust predictive genetic risk factors have yet to be identified. Strongly linked genetic polymorphisms have been used clinically as screening tests for other HS reactions prior to administration of high-risk drugs.

Objective

The purpose of this study was to evaluate for genetic risk of sulfonamide HS in the immunocompetent population using genome-wide association.

Methods

Ninety-one patients with symptoms after trimethoprim-sulfamethoxazole (TMP-SMX) attributable to “probable” drug HS based on medical record review and the Naranjo Adverse Drug Reaction Probability Scale, and 184 age- and sex-matched patients who tolerated a therapeutic course of TMP-SMX, were included in a genome-wide association study using both common and rare variant techniques. Additionally, two subgroups of HS patients with a more refined clinical phenotype (fever and rash; or fever, rash and eosinophilia) were evaluated separately.

Results

For the full dataset, no single nucleotide polymorphisms were suggestive of or reached genome-wide significance in the common variant analysis, nor was any genetic locus significant in the rare variant analysis. A single, possible gene locus association (COL12A1) was identified in the rare variant analysis for patients with both fever and rash, but the sample size was very small in this subgroup (n = 16), and this may be a false positive finding. No other significant associations were found for the subgroups.

Conclusions

No convincing genetic risk factors for sulfonamide HS were identified in this population. These negative findings may be due to challenges in accurately confirming the phenotype in exanthematous drug eruptions, or to unidentified gene-environment interactions influencing sulfonamide HS.

Carboplatin/taxane-induced gastrointestinal toxicity: a pharmacogenomics study on the SCOTROC1 trial

Carboplatin/taxane combination is first-line therapy for ovarian cancer. However, patients can encounter treatment delays, impaired quality of life, even death because of chemotherapy-induced gastrointestinal (GI) toxicity. A candidate gene study was conducted to assess potential association of genetic variants with GI toxicity in 808 patients who received carboplatin/taxane in the Scottish Randomized Trial in Ovarian Cancer 1 (SCOTROC1). Patients were randomized into discovery and validation cohorts consisting of 404 patients each. Clinical covariates and genetic variants associated with grade III/IV GI toxicity in discovery cohort were evaluated in replication cohort. Chemotherapy-induced GI toxicity was significantly associated with seven single-nucleotide polymorphisms in the ATP7B, GSR, VEGFA and SCN10A genes. Patients with risk genotypes were at 1.53 to 18.01 higher odds to develop carboplatin/taxane-induced GI toxicity (P<0.01). Variants in the VEGF gene were marginally associated with survival time. Our data provide potential targets for modulation/inhibition of GI toxicity in ovarian cancer patients.

α-Hydroxybutyric Acid Is a Selective Metabolite Biomarker of Impaired Glucose Tolerance

OBJECTIVE

Plasma metabolites that distinguish isolated impaired glucose tolerance (iIGT) from isolated impaired fasting glucose (iIFG) may be useful biomarkers to predict IGT, a high-risk state for the development of type 2 diabetes.

RESEARCH DESIGN AND METHODS

Targeted metabolomics with 23 metabolites previously associated with dysglycemia was performed with fasting plasma samples from subjects without diabetes at time 0 of an oral glucose tolerance test (OGTT) in two observational cohorts: RISC (Relationship Between Insulin Sensitivity and Cardiovascular Disease) and DMVhi (Diabetes Mellitus and Vascular Health Initiative). Odds ratios (ORs) for a one-SD change in the metabolite level were calculated using multiple logistic regression models controlling for age, sex, and BMI to test for associations with iIGT or iIFG versus normal. Selective biomarkers of iIGT were further validated in the Botnia study.

RESULTS

α-Hydroxybutyric acid (α-HB) was most strongly associated with iIGT in RISC (OR 2.54 [95% CI 1.86-3.48], P value 5E-9) and DMVhi (2.75 [1.81-4.19], 4E-5) while having no significant association with iIFG. In Botnia, α-HB was selectively associated with iIGT (2.03 [1.65-2.49], 3E-11) and had no significant association with iIFG. Linoleoyl-glycerophosphocholine (L-GPC) and oleic acid were also found to be selective biomarkers of iIGT. In multivariate IGT prediction models, addition of α-HB, L-GPC, and oleic acid to age, sex, BMI, and fasting glucose significantly improved area under the curve in all three cohorts.

CONCLUSIONS

α-HB, L-GPC, and oleic acid were shown to be selective biomarkers of iIGT, independent of age, sex, BMI, and fasting glucose, in 4,053 subjects without diabetes from three European cohorts. These biomarkers can be used in predictive models to identify subjects with IGT without performing an OGTT.

Association of breed and histopathological grade in canine mast cell tumours

The aim of this study was to evaluate the relationship between breed and the histopathological grade of canine mast cell tumours (MCTs). A retrospective survey of pathology data of 9375 histopathologically confirmed diagnoses of cutaneous MCTs in the US was evaluated in the context of breed prevalence in over two million registered purebred dogs. Association of histopathological grade with breed, age, sex and spay/neuter status was assessed. The data indicate that the proportion of high-grade tumours increases with advancing age, and that male and intact dogs have increased odds of developing high-grade tumours. A significant difference in the proportion of high-grade tumours between breeds was detected. The Pug was at significantly increased risk of developing low/intermediate-grade tumours, but not high-grade tumours, resulting in preponderance of less aggressive MCTs in this breed. The results of this study suggest a genetic association for the development of high-grade MCTs.

Abstract LB-177: Novel approaches for improving interpretation and predictive models of comparative genomic hybridization data

As costs of genome wide analyses decline and become more accessible, their use in both human and animal cancer studies are generating increasing information regarding underlying cancer etiology. Comparative genomic hybridization (CGH) is providing valuable information relating copy number aberrations (CNAs) to cancer mechanisms and clinical outcomes. However, challenges exist to interpreting and fully utilizing these data. First, without matched tumor and healthy tissue samples from individuals, distinguishing naturally occurring copy number variations (CNVs) from CNAs is difficult. Second, the large search space of genome wide analyses makes finding combinations of CNAs with improved predictive potential compared to single CNAs challenging. Here we provide novel methods to address these challenges associated with CGH data. Many new resources (e.g. The Cancer Genome Atlas (TCGA)), are making large volumes of genomic data publically accessible. However, most datasets do not have matched normal and tumor tissue samples between subjects. We tested matched normal and tissue samples from 30 patients with colorectal, lung, and pancreatic cancer and compared CNVs and CNAs to findings in larger, non-matched samples in TCGA. Even with limited matched samples, this approach allows for the differentiation of CNVs from CNAs discovered in analyses of non-matched samples.

In some cases, combinations of CNAs can provide improved predictive capability compared to any single CNA. However, it is computationally intractable to exhaustively test combinations of CNAs in a genome-wide study. To address this limitation, we use a novel approach for CNA feature reduction that minimizes the variance within CNA segments across subjects, and Random Forest Ensemble Classification. This approach provides CNA combinations with balanced accuracies of 83.5% and 94.9% for distinguishing 52 cases of canine ALL/AML and 71 cases of B-CLL/T-CLL, respectively. These two approaches address frequent limitations in the interpretation CGH data. Better distinguishing CNVs and interrogating CNA combinations, can provide additional information about the role of CNAs in disease mechanisms and improve treatment decisions.

Citation Format: Daniel Rotroff, Matthew Breen, Alison Motsinger-Reif. Novel approaches for improving interpretation and predictive models of comparative genomic hybridization data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-177. doi:10.1158/1538-7445.AM2015-LB-177

Genome-wide assessment of recurrent genomic imbalances in canine leukemia identifies evolutionarily conserved regions for subtype differentiation

Leukemia in dogs is a heterogeneous disease with survival ranging from days to years, depending on the subtype. Strides have been made in both human and canine leukemia to improve classification and understanding of pathogenesis through immunophenotyping, yet classification and choosing appropriate therapy remains challenging. In this study, we assessed 123 cases of canine leukemia (28 ALLs, 24 AMLs, 25 B-CLLs, and 46 T-CLLs) using high-resolution oligonucleotide array comparative genomic hybridization (oaCGH) to detect DNA copy number alterations (CNAs). For the first time, such data were used to identify recurrent CNAs and inclusive genes that may be potential drivers of subtype-specific pathogenesis. We performed predictive modeling to identify CNAs that could reliably differentiate acute subtypes (ALL vs. AML) and chronic subtypes (B-CLL vs. T-CLL) and used this model to differentiate cases with up to 83.3 and 95.8 % precision, respectively, based on CNAs at only one to three genomic regions. In addition, CGH datasets for canine and human leukemia were compared to reveal evolutionarily conserved copy number changes between species, including the shared gain of HSA 21q in ALL and ∼25 Mb of shared gain of HSA 12 and loss of HSA 13q14 in CLL. These findings support the use of canine leukemia as a relevant in vivo model for human leukemia and justify the need to further explore the conserved genomic regions of interest for their clinical impact.

Comparative cytogenetic characterization of primary canine melanocytic lesions using array CGH and fluorescence in situ hybridization

Melanocytic lesions originating from the oral mucosa or cutaneous epithelium are common in the general dog population, with up to 100,000 diagnoses each year in the USA. Oral melanoma is the most frequent canine neoplasm of the oral cavity, exhibiting a highly aggressive course. Cutaneous melanocytomas occur frequently, but rarely develop into a malignant form. Despite the differential prognosis, it has been assumed that subtypes of melanocytic lesions represent the same disease. To address the relative paucity of information about their genomic status, molecular cytogenetic analysis was performed on the three recognized subtypes of canine melanocytic lesions. Using array comparative genomic hybridization (aCGH) analysis, highly aberrant distinct copy number status across the tumor genome for both of the malignant melanoma subtypes was revealed. The most frequent aberrations included gain of dog chromosome (CFA) 13 and 17 and loss of CFA 22. Melanocytomas possessed fewer genome wide aberrations, yet showed a recurrent gain of CFA 20q15.3-17. A distinctive copy number profile, evident only in oral melanomas, displayed a sigmoidal pattern of copy number loss followed immediately by a gain, around CFA 30q14. Moreover, when assessed by fluorescence in situ hybridization (FISH), copy number aberrations of targeted genes, such as gain of c-MYC (80 % of cases) and loss of CDKN2A (68 % of cases), were observed. This study suggests that in concordance with what is known for human melanomas, canine melanomas of the oral mucosa and cutaneous epithelium are discrete and initiated by different molecular pathways.

Initial evaluation of nighttime restlessness in a naturally occurring canine model of osteoarthritis pain

Chronic pain due to osteoarthritis (OA) can lead to significant disruption of sleep and increased restlessness. Our objective was to assess whether naturally occurring canine OA is associated with nighttime restlessness and so has potential as a model of OA-associated sleep disturbance. The study was designed as a two-part prospective masked, placebo-controlled study using client-owned dogs (Part A n = 60; Part B n = 19). Inclusion criteria consisted of OA-associated joint pain and mobility impairment. The primary outcome measure for both parts was nighttime accelerometry. In Part B, quality of sleep was assessed using a clinical metrology instrument (Sleep and Night Time Restlessness Evaluation Score, SNoRE). Part A included dogs receiving two weeks of non-steroidal anti-inflammatory drug (NSAID) preceded with two weeks of no treatment. Part B was a crossover study, with NSAID/placebo administered for two weeks followed by a washout period of one week and another two weeks of NSAID/placebo. Repeated measures analysis of variance was used to assess differences between baseline and treatment. There were no significant changes in accelerometry-measured nighttime activity as a result of NSAID administration. SNoRE measures indicated significant improvements in aspects of the quality of nighttime sleep that did not involve obvious movement. These results reflect the few similar studies in human OA patients. Although accelerometry does not appear to be useful, this model has potential to model the human pain-related nighttime sleep disturbance, and other outcome measures should be explored in this model.

Bayesian neural networks for detecting epistasis in genetic association studies

Background

Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions.

Results

A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships.

Conclusions

The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0368-0) contains supplementary material, which is available to authorized users.

A novel canine model of immune thrombocytopenia: has immune thrombocytopenia (ITP) gone to the dogs?

Canine immune thrombocytopenia (ITP) is analogous to human ITP, with similar platelet counts and heterogeneity in bleeding phenotype among affected individuals. With a goal of ultimately investigating this bleeding heterogeneity, a canine model of antibody-mediated ITP was developed. Infusion of healthy dogs with 2F9, a murine IgG2a monoclonal antibody to the canine platelet glycoprotein GPIIb (a common target of autoantibodies in ITP) resulted in profound, dose-dependent thrombocytopenia. Model dogs developed variable bleeding phenotypes, e.g. petechiae and haematuria, despite similar degrees of thrombocytopenia. 2F9 infusion was not associated with systemic inflammation, consumptive coagulopathy, or impairment of platelet function. Unexpectedly however, evaluation of cytokine profiles led to the identification of platelets as a potential source of serum interleukin-8 (IL8) in dogs. This finding was confirmed in humans with ITP, suggesting that platelet IL8 may be a previously unrecognized modulator of platelet-neutrophil crosstalk. The utility of this model will allow future study of bleeding phenotypic heterogeneity including the role of neutrophils and endothelial cells in ITP.