The Cytochrome P450 2C8*3 Variant (rs11572080) Is Associated with Improved Asthma Symptom Control in Children and Altered Lipid Mediator Production and Inflammatory Response in Human Bronchial Epithelial Cells.

This study investigated an association between the cytochrome P450 (CYP) 2C8*3 polymorphism with asthma symptom control in children and changes in lipid metabolism and pro-inflammatory signaling by human bronchial epithelial cells (HBECs) treated with cigarette smoke condensate (CSC). CYP genes are inherently variable in sequence, and while such variations are known to produce clinically relevant effects on drug pharmacokinetics and pharmacodynamics, the effects on endogenous substrate metabolism and associated physiologic processes are less understood. In this study, CYP2C8*3 was associated with improved asthma symptom control among children: Mean asthma control scores were 3.68 (n = 207) for patients with one or more copies of the CYP2C8*3 allele versus 4.42 (n = 965) for CYP2C8*1/*1 (P = 0.0133). In vitro, CYP2C8*3 was associated with an increase in montelukast 36-hydroxylation and a decrease in linoleic acid metabolism despite lower mRNA and protein expression. Additionally, CYP2C8*3 was associated with reduced mRNA expression of interleukin-6 (IL-6) and C-X-C motif chemokine ligand 8 (CXCL-8) by HBECs in response to CSC, which was replicated using the soluble epoxide hydrolase inhibitor, 12-[[(tricyclo[3.3.1.13,7]dec-1-ylamino)carbonyl]amino]-dodecanoic acid. Interestingly, 9(10)- and 12(13)- dihydroxyoctadecenoic acid, the hydrolyzed metabolites of 9(10)- and 12(13)- epoxyoctadecenoic acid, increased the expression of IL-6 and CXCL-8 mRNA by HBECs. This study reveals previously undocumented effects of the CYP2C8*3 variant on the response of HBECs to exogenous stimuli. SIGNIFICANCE STATEMENT: These findings suggest a role for CYP2C8 in regulating the epoxyoctadecenoic acid:dihydroxyoctadecenoic acid ratio leading to a change in cellular inflammatory responses elicited by environmental stimuli that exacerbate asthma.

Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes.

SUMMARY: Population studies such as genome-wide association study have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g. gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics, is still challenging. To address this problem, we developed an interpretable deep learning method, Varmole, to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via biological drop-connect without needing prior feature selections. AVAILABILITY AND IMPLEMENTATION: Varmole is available as a Python tool on GitHub at https://github.com/daifengwanglab/Varmole. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ECMarker: interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of human disease in early stages.

MOTIVATION: Gene expression and regulation, a key molecular mechanism driving human disease development, remains elusive, especially at early stages. Integrating the increasing amount of population-level genomic data and understanding gene regulatory mechanisms in disease development are still challenging. Machine learning has emerged to solve this, but many machine learning methods were typically limited to building an accurate prediction model as a 'black box', barely providing biological and clinical interpretability from the box. RESULTS: To address these challenges, we developed an interpretable and scalable machine learning model, ECMarker, to predict gene expression biomarkers for disease phenotypes and simultaneously reveal underlying regulatory mechanisms. Particularly, ECMarker is built on the integration of semi- and discriminative-restricted Boltzmann machines, a neural network model for classification allowing lateral connections at the input gene layer. This interpretable model is scalable without needing any prior feature selection and enables directly modeling and prioritizing genes and revealing potential gene networks (from lateral connections) for the phenotypes. With application to the gene expression data of non-small-cell lung cancer patients, we found that ECMarker not only achieved a relatively high accuracy for predicting cancer stages but also identified the biomarker genes and gene networks implying the regulatory mechanisms in the lung cancer development. In addition, ECMarker demonstrates clinical interpretability as its prioritized biomarker genes can predict survival rates of early lung cancer patients (P-value < 0.005). Finally, we identified a number of drugs currently in clinical use for late stages or other cancers with effects on these early lung cancer biomarkers, suggesting potential novel candidates on early cancer medicine. AVAILABILITYAND IMPLEMENTATION: ECMarker is open source as a general-purpose tool at https://github.com/daifengwanglab/ECMarker. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dynamic Expression of Transient Receptor Potential Vanilloid-3 and Integrated Signaling with Growth Factor Pathways during Lung Epithelial Wound Repair following Wood Smoke Particle and Other Forms of Lung Cell Injury.

Prior studies revealed increased expression of the transient receptor potential vanilloid-3 (TRPV3) ion channel after wood smoke particulate matter (WSPM) treatment of human bronchial epithelial cells (HBECs). TRPV3 attenuated pathologic endoplasmic reticulum stress and cytotoxicity mediated by transient receptor potential ankyrin-1. Here, the basis for how TRPV3 expression is regulated by cell injury and the effects this has on HBEC physiology and WSPM-induced airway remodeling in mice was investigated. TRPV3 mRNA was rapidly increased in HBECs treated with WSPM and after monolayer damage caused by tryptic disruption, scratch wounding, and cell passaging. TRPV3 mRNA abundance varied with time, and stimulated expression occurred independent of new protein synthesis. Overexpression of TRPV3 in HBECs reduced cell migration and wound repair while enhancing cell adhesion. This phenotype correlated with disrupted mRNA expression of ligands of the epidermal growth factor, tumor growth factor-ß, and frizzled receptors. Accordingly, delayed wound repair by TRPV3 overexpressing cells was reversed by growth factor supplementation. In normal HBECs, TRPV3 upregulation was triggered by exogenous growth factor supplementation and was attenuated by inhibitors of growth factor receptor signaling. In mice, subacute oropharyngeal instillation with WSPM also promoted TRPV3 mRNA expression and epithelial remodeling, which was attenuated by TRPV3 antagonist pre- and cotreatment. This latter effect may be the consequence of antagonist-induced TRPV3 expression. These findings provide insights into the roles of TRPV3 in lung epithelial cells under basal and dynamic states, as well as highlight potential roles for TRPV3 ligands in modulating epithelial damage/repair. SIGNIFICANCE STATEMENT: Coordinated epithelial repair is essential for the maintenance of the airways, with deficiencies and exaggerated repair associated with adverse consequences to respiratory health. This study shows that TRPV3, an ion channel, is involved in coordinating repair through integrated repair signaling pathways, wherein TRPV3 expression is upregulated immediately after injury and returns to basal levels as cells complete the repair process. TRPV3 may be a novel target for understanding and/or treating conditions in which airway/lung epithelial repair is not properly orchestrated.

Multiview learning for understanding functional multiomics.

The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification of biological functions and mechanisms via multiple facets. However, integrating these large-scale multiomics data and discovering functional insights are, nevertheless, challenging tasks. To address these challenges, machine learning has been broadly applied to analyze multiomics. This review introduces multiview learning-an emerging machine learning field-and envisions its potentially powerful applications to multiomics. In particular, multiview learning is more effective than previous integrative methods for learning data's heterogeneity and revealing cross-talk patterns. Although it has been applied to various contexts, such as computer vision and speech recognition, multiview learning has not yet been widely applied to biological data-specifically, multiomics data. Therefore, this paper firstly reviews recent multiview learning methods and unifies them in a framework called multiview empirical risk minimization (MV-ERM). We further discuss the potential applications of each method to multiomics, including genomics, transcriptomics, and epigenomics, in an aim to discover the functional and mechanistic interpretations across omics. Secondly, we explore possible applications to different biological systems, including human diseases (e.g., brain disorders and cancers), plants, and single-cell analysis, and discuss both the benefits and caveats of using multiview learning to discover the molecular mechanisms and functions of these systems.

Transient Receptor Potential Ankyrin-1 and Vanilloid-3 Differentially Regulate Endoplasmic Reticulum Stress and Cytotoxicity in Human Lung Epithelial Cells After Pneumotoxic Wood Smoke Particle Exposure.

This study investigated the roles of transient receptor potential (TRP) ankyrin-1 (TRPA1) and TRP vanilloid-3 (TRPV3) in regulating endoplasmic reticulum stress (ERS) and cytotoxicity in human bronchial epithelial cells (HBECs) treated with pneumotoxic wood smoke particulate matter (WSPM) and chemical agonists of each channel. Functions of TRPA1 and TRPV3 in pulmonary epithelial cells remain largely undefined. This study shows that TRPA1 activity localizes to the plasma membrane and endoplasmic reticulum (ER) of cells, whereas TRPV3 resides primarily in the ER. Additionally, treatment of cells using moderately cytotoxic concentrations of pine WSPM, carvacrol, and other TRPA1 agonists caused ERS as a function of both TRPA1 and TRPV3 activities. Specifically, ERS and cytotoxicity were attenuated by TRPA1 inhibition, whereas inhibiting TRPV3 exacerbated ERS and cytotoxicity. Interestingly, after treatment with pine WSPM, TRPA1 transcription was suppressed, whereas TRPV3 was increased. TRPV3 overexpression in HBECs conferred resistance to ERS and an attenuation of ERS-associated cell cycle arrest caused by WSPM and multiple prototypical ERS-inducing agents. Alternatively, short hairpin RNA-mediated knockdown of TRPV3, like the TRPV3 antagonist, exacerbated ERS. This study reveals previously undocumented roles for TRPA1 in promoting pathologic ERS and cytotoxicity elicited by pneumotoxic WSPM and TRPA1 agonists, and a unique role for TRPV3 in fettering pathologic facets of the integrated ERS response. SIGNIFICANCE STATEMENT: These findings provide new insights into how wood smoke particulate matter and other transient receptor potential ankyrin-1 (TRPA1) and transient receptor potential vanilloid-3 (TRPV3) agonists can affect human bronchial epithelial cells and highlight novel physiological and pathophysiological roles for TRPA1 and TRPV3 in these cells.

ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks.

BACKGROUND: The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS: We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS: ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster.

Negative reinforcement impairs overnight memory consolidation.

Post-learning sleep is beneficial for human memory. However, it may be that not all memories benefit equally from sleep. Here, we manipulated a spatial learning task using monetary reward and performance feedback, asking whether enhancing the salience of the task would augment overnight memory consolidation and alter its incorporation into dreaming. Contrary to our hypothesis, we found that the addition of reward impaired overnight consolidation of spatial memory. Our findings seemingly contradict prior reports that enhancing the reward value of learned information augments sleep-dependent memory processing. Given that the reward followed a negative reinforcement paradigm, consolidation may have been impaired via a stress-related mechanism.

Mechanisms and Consequences of Variable TRPA1 Expression by Airway Epithelial Cells: Effects of TRPV1 Genotype and Environmental Agonists on Cellular Responses to Pollutants in Vitro and Asthma.

BACKGROUND: Transient receptor potential ankyrin-1 [transient receptor potential cation channel subfamily A member 1 (TRPA1)] and vanilloid-1 [transient receptor potential cation channel subfamily V member 1 (TRPV1)] detect inhaled irritants, including air pollutants and have roles in the development and exacerbation of asthma. OBJECTIVES: This study tested the hypothesis that increased expression of TRPA1, stemming from expression of the loss-of-function TRPV1 (I585V; rs8065080) polymorphic variant by airway epithelial cells may explain prior observations of worse asthma symptom control among children with the TRPV1 I585I/V genotype, by virtue of sensitizing epithelial cells to particulate materials and other TRPA1 agonists. METHODS: TRP agonists, antagonists, small interfering RNA (siRNA), a nuclear factor kappa light chain enhancer of activated B cells (NF-κB) pathway inhibitor, and kinase activators and inhibitors were used to modulate TRPA1 and TRPV1 expression and function. Treatment of genotyped airway epithelial cells with particulate materials and analysis of asthma control data were used to assess consequences of TRPV1 genotype and variable TRPA1 expression on cellular responses in vitro and asthma symptom control among children as a function of voluntarily reported tobacco smoke exposure. RESULTS: A relationship between higher TRPA1 expression and function and lower TRPV1 expression and function was revealed. Findings of this study pointed to a mechanism whereby NF-κB promoted TRPA1 expression, whereas NF-κB-regulated nucleotide-binding oligomerization domain, leucine rich repeat and pyrin domain containing 2 (NLRP2) limited expression. Roles for protein kinase C and p38 mitogen activated protein kinase were also demonstrated. Finally, the TRPV1 I585I/V genotype was associated with increased TRPA1 expression by primary airway epithelial cells and amplified responses to selected air pollution particles in vitro. However, the TRPV1 I585I/V genotype was not associated with worse asthma symptom control among children exposed to tobacco smoke, whereas other TRPA1 and TRPV1 variants were. DISCUSSION: This study provides insights on how airway epithelial cells regulate TRPA1 expression, how TRPV1 genetics can affect TRPA1 expression, and that TRPA1 and TRPV1 polymorphisms differentially affect asthma symptom control. https://doi.org/10.1289/EHP11076.

A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data.

The phenotypes of complex biological systems are fundamentally driven by various multi-scale mechanisms. Multi-modal data, such as single cell multi-omics data, enables a deeper understanding of underlying complex mechanisms across scales for phenotypes. We developed an interpretable regularized learning model, deepManReg, to predict phenotypes from multi-modal data. First, deepManReg employs deep neural networks to learn cross-modal manifolds and then to align multi-modal features onto a common latent space. Second, deepManReg uses cross-modal manifolds as a feature graph to regularize the classifiers for improving phenotype predictions and also for prioritizing the multi-modal features and cross-modal interactions for the phenotypes. We applied deepManReg to (1) an image dataset of handwritten digits with multi-features and (2) single cell multi-modal data (Patch-seq data) including transcriptomics and electrophysiology for neuronal cells in the mouse brain. We show that deepManReg improved phenotype prediction in both datasets, and also prioritized genes and electrophysiological features for the phenotypes of neuronal cells.

Wood Smoke Particles Stimulate MUC5AC Overproduction by Human Bronchial Epithelial Cells Through TRPA1 and EGFR Signaling.

Mucus hypersecretion is a pathological feature of acute inflammatory and chronic obstructive pulmonary diseases. Exposure to air pollutants can be a cause of pathological mucus overproduction, but mechanisms by which different forms of air pollutants elicit this response are not fully understood. In this study, particulate matter (PM) generated from burning pine wood and other types of biomass was used to determine mechanisms by which these forms of PM stimulate mucin gene expression and secretion by primary human bronchial epithelial cells (HBECs). Biomass PM < 2.5 µm generated from pine wood and several other fuels stimulated the expression and secretion of the gel-forming glycoprotein MUC5AC by HBECs. Muc5ac gene induction was also observed in mouse airways following subacute oropharyngeal delivery of pine wood smoke PM. In HBECs, MUC5AC was also induced by the transient receptor potential ankyrin-1 (TRPA1) agonists' coniferaldehyde, a component of pine smoke PM, and allyl isothiocyanate, and was attenuated by a TRPA1 antagonist. Additionally, inhibition of epidermal growth factor receptor (EGFR/ErbB1) and the EGFR signaling partners p38 MAPK and GSK3ß also prevented MUC5AC overexpression. Collectively, our results suggest that activation of TRPA1 and EGFR, paired with alterations to p38 MAPK and GSK3ß activity, plays a major role in MUC5AC overproduction by bronchial epithelial cells exposed to biomass smoke PM. These results reveal specific processes for how biomass smoke PM may impact the human respiratory system and highlight potential avenues for therapeutic manipulation of lung diseases that are affected by air pollutants.

Lambert-Eaton myasthenic syndrome and merkel cell carcinoma.

Lambert-Eaton myasthenic syndrome (LEMS) is an antibody-mediated disorder of the neuromuscular junction that is most commonly diagnosed in association with small cell lung carcinoma (SCLC). Small cell lung carcinoma is histologically similar to the aggressive cutaneous neuroendocrine malignancy Merkel cell carcinoma (MCC). We provide a full report and longitudinal clinical follow-up of a case of LEMS occurring with MCC. We also review the literature on paraneoplastic syndromes associated with MCC and other nonpulmonary small cell carcinomas.

Overnight Sleep Enhances Hippocampus-Dependent Aspects of Spatial Memory.

STUDY OBJECTIVES: Several studies have now demonstrated that spatial information is processed during sleep, and that posttraining sleep is beneficial for human navigation. However, it remains unclear whether the effects of sleep are primarily due to consolidation of cognitive maps, or alternatively, whether sleep might also affect nonhippocampal aspects of navigation (e.g., speed of motion) involved in moving through a virtual environment. DESIGN: Participants were trained on a virtual maze navigation task (VMT) and then given a memory test following either a day of wakefulness or a night of sleep. Subjects reported to the laboratory for training at either 10:00am or 10:00pm, depending on randomly assigned condition, and were tested 11 h later. Overnight subjects slept in the laboratory with polysomnography. SETTING: A hospital-based academic sleep laboratory. PATIENTS OR PARTICIPANTS: Thirty healthy college student volunteers. INTERVENTIONS: N/A. MEASUREMENTS AND RESULTS: Point-by-point position data were collected from the VMT. Analysis of the movement data revealed a sleep-dependent improvement in maze completion time (P < 0.001) due to improved spatial understanding of the maze layout, which led to a shortening of path from start to finish (P = 0.01) rather than faster exploration speed through the maze (P = 0.7). CONCLUSIONS: We found that overnight sleep benefitted performance, not because subjects moved faster through the maze, but because they were more accurate in navigating to the goal. These findings suggest that sleep enhances participants' knowledge of the spatial layout of the maze, contributing to the consolidation of hippocampus-dependent spatial information. CITATION: Nguyen ND; Tucker MA; Stickgold R; Wamsley EJ. Overnight sleep enhances hippocampus-dependent aspects of spatial memory. SLEEP 2013;36(7):1051-1057.