Deep learning distinguishes connectomes from focal epilepsy patients and controls: feasibility and clinical implications.

The application of deep learning models to evaluate connectome data is gaining interest in epilepsy research. Deep learning may be a useful initial tool to partition connectome data into network subsets for further analysis. Few prior works have used deep learning to examine structural connectomes from patients with focal epilepsy. We evaluated whether a deep learning model applied to whole-brain connectomes could classify 28 participants with focal epilepsy from 20 controls and identify nodal importance for each group. Participants with epilepsy were further grouped based on whether they had focal seizures that evolved into bilateral tonic-clonic seizures (17 with, 11 without). The trained neural network classified patients from controls with an accuracy of 72.92%, while the seizure subtype groups achieved a classification accuracy of 67.86%. In the patient subgroups, the nodes and edges deemed important for accurate classification were also clinically relevant, indicating the model's interpretability. The current work expands the evidence for the potential of deep learning to extract relevant markers from clinical datasets. Our findings offer a rationale for further research interrogating structural connectomes to obtain features that can be biomarkers and aid the diagnosis of seizure subtypes.

Seizure detection with reduced electroencephalogram channels: research trends and outlook.

Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.

White matter alterations in focal to bilateral tonic-clonic seizures.

We examined the white matter of patients with and without focal to bilateral tonic-clonic seizures (FBTCS), and control participants. A neural network based tract segmentation model (Tractseg) was used to isolate tract-specific, track-weighted tensor-based measurements from the tracts of interest. We compared the group differences in the track-weighted tensor-based measurements derived from whole and hemispheric tracts. We identified several regions that displayed significantly altered white matter in patients with focal epilepsy compared to controls. Furthermore, patients without FBTCS showed significantly increased white matter disruption in the inferior fronto-occipital fascicle and the striato-occipital tract. In contrast, the track-weighted tensor-based measurements from the FBTCS cohort exhibited a stronger resemblance to the healthy controls (compared to the non-FBTCS group). Our findings revealed marked alterations in a range of subcortical tracts considered critical in the genesis of seizures in focal epilepsy. Our novel application of tract-specific, track-weighted tensor-based measurements to a new clinical dataset aided the elucidation of specific tracts that may act as a predictive biomarker to distinguish patients likely to develop FBTCS.

A Multimodal AI System for Out-of-Distribution Generalization of Seizure Identification.

Artificial intelligence (AI) and health sensory data-fusion hold the potential to automate many laborious and time-consuming processes in hospitals or ambulatory settings, e.g. home monitoring and telehealth. One such unmet challenge is rapid and accurate epileptic seizure annotation. An accurate and automatic approach can provide an alternative way to label seizures in epilepsy or deliver a substitute for inaccurate patient self-reports. Multimodal sensory fusion is believed to provide an avenue to improve the performance of AI systems in seizure identification. We propose a state-of-the-art performing AI system that combines electroencephalogram (EEG) and electrocardiogram (ECG) for seizure identification, tested on clinical data with early evidence demonstrating generalization across hospitals. The model was trained and validated on the publicly available Temple University Hospital (TUH) dataset. To evaluate performance in a clinical setting, we conducted non-patient-specific pseudo-prospective inference tests on three out-of-distribution datasets, including EPILEPSIAE (30 patients) and the Royal Prince Alfred Hospital (RPAH) in Sydney, Australia (31 neurologists-shortlisted patients and 30 randomly selected). Our multimodal approach improves the area under the receiver operating characteristic curve (AUC-ROC) by an average margin of 6.71% and 14.42% for deep learning techniques using EEG-only and ECG-only, respectively. Our model's state-of-the-art performance and robustness to out-of-distribution datasets show the accuracy and efficiency necessary to improve epilepsy diagnoses. To the best of our knowledge, this is the first pseudo-prospective study of an AI system combining EEG and ECG modalities for automatic seizure annotation achieved with fusion of two deep learning networks.

A comparative study of AI systems for epileptic seizure recognition based on EEG or ECG.

The majority of studies for automatic epileptic seizure (ictal) detection are based on electroencephalogram (EEG) data, but electrocardiogram (ECG) presents a simpler and more wearable alternative for long-term ambulatory monitoring. To assess the performance of EEG and ECG signals, AI systems offer a promising way forward for developing high performing models in securing both a reasonable sensitivity and specificity. There are crucial needs for these AI systems to be developed with more clinical relevance and inference generalization. In this work, we implement an ECG-specific convolutional neural network (CNN) model with residual layers and an EEG-specific convolutional long short-term memory (ConvLSTM) model. We trained, validated, and tested these models on a publicly accessible Temple University Hospital (TUH) dataset for reproducibility and performed a non-patient-specific inference-only test on patient EEG and ECG data of The Royal Prince Alfred Hospital (RPAH) in Sydney, Australia. We selected 31 adult patients to balance groups with the following seizure types: generalized, frontal, frontotemporal, temporal, parietal, and unspecific focal epilepsy. Our tests on both EEG and ECG of these patients achieve an AUC score of 0.75. Our results show ECG outperforms EEG with an average improvement of 0.21 and 0.11 AUC score in patients with frontal and parietal focal seizures, respectively.Clinical relevance-Prior research has demonstrated the value of using ECG for seizure documentation. It is believed that specific epileptic foci (seizure origin) may involve network inputs to the autonomic nervous system. Our result indicates that ECG could outperform EEG for individuals with specific seizure origin, particularly in the frontal and parietal lobes.

Seizure Susceptibility Prediction in Uncontrolled Epilepsy.

Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds the potential to greatly improve the quality of life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury and death in addition to several potential clinical benefits it may provide for patient care in hospitals. The challenge of seizure forecasting lies within the seemingly unpredictable transitions of brain dynamics into the ictal state. The main body of computational research on determining seizure risk has been focused solely on prediction algorithms, which involves a challenging issue of balancing sensitivity and false alarms. There have been some studies on identifying potential biomarkers for seizure forecasting; however, the questions of "What are the true biomarkers for seizure prediction" or even "Is there a valid biomarker for seizure prediction?" are yet to be fully answered. In this paper, we introduce a tool to facilitate the exploration of the potential biomarkers. We confirm using our tool that interictal slowing activities are a promising biomarker for epileptic seizure susceptibility prediction.

CD3ε Expression Defines Functionally Distinct Subsets of Vδ1 T Cells in Patients With Human Immunodeficiency Virus Infection.

Human γδ T cells expressing the Vδ1 T cell receptor (TCR) recognize self and microbial antigens and stress-inducible molecules in a major histocompatibility complex-unrestricted manner and are an important source of innate interleukin (IL)-17. Vδ1 T cells are expanded in the circulation and intestines of patients with human immunodeficiency virus (HIV) infection. In this study, we show that patients with HIV have elevated frequencies, but not absolute numbers, of circulating Vδ1 T cells compared to control subjects. This increase was most striking in the patients with Candida albicans co-infection. Using flow cytometry and confocal microscopy, we identify two populations of Vδ1 T cells, based on low and high expression of the ε chain of the CD3 protein complex responsible for transducing TCR-mediated signals (denoted CD3εlo and CD3εhi Vδ1 T cells). Both Vδ1 T cell populations expressed the CD3 ζ-chain, also used for TCR signaling. Using lines of Vδ1 T cells generated from healthy donors, we show that CD3ε can be transiently downregulated by activation but that its expression is restored over time in culture in the presence of exogenous IL-2. Compared to CD3εhi Vδ1 T cells, CD3εlo Vδ1 T cells more frequently expressed terminally differentiated phenotypes and the negative regulator of T cell activation, programmed death-1 (PD-1), but not lymphocyte-activation gene 3, and upon stimulation in vitro, only the CD3εhi subset of Vδ1 T cells, produced IL-17. Thus, while HIV can infect and kill IL-17-producing CD4+ T cells, Vδ1 T cells are another source of IL-17, but many of them exist in a state of exhaustion, mediated either by the induction of PD-1 or by downregulation of CD3ε expression.

Small-Molecule Sigma1 Modulator Induces Autophagic Degradation of PD-L1.

Emerging evidence suggests that Sigma1 (SIGMAR1, also known as sigma-1 receptor) is a unique ligand-regulated integral membrane scaffolding protein that contributes to cellular protein and lipid homeostasis. Previously, we demonstrated that some small-molecule modulators of Sigma1 alter endoplasmic reticulum (ER)-associated protein homeostasis pathways in cancer cells, including the unfolded protein response and autophagy. Programmed death-ligand 1 (PD-L1) is a type I integral membrane glycoprotein that is cotranslationally inserted into the ER and is processed and transported through the secretory pathway. Once at the surface of cancer cells, PD-L1 acts as a T-cell inhibitory checkpoint molecule and suppresses antitumor immunity. Here, we demonstrate that in Sigma1-expressing triple-negative breast and androgen-independent prostate cancer cells, PD-L1 protein levels were suppressed by RNAi knockdown of Sigma1 and by small-molecule inhibition of Sigma1. Sigma1-mediated action was confirmed by pharmacologic competition between Sigma1-selective inhibitor and activator ligands. When administered alone, the Sigma1 inhibitor decreased cell surface PD-L1 expression and suppressed functional interaction of PD-1 and PD-L1 in a coculture of T cells and cancer cells. Conversely, the Sigma1 activator increased PD-L1 cell surface expression, demonstrating the ability to positively and negatively modulate Sigma1 associated PD-L1 processing. We discovered that the Sigma1 inhibitor induced degradation of PD-L1 via autophagy, by a mechanism distinct from bulk macroautophagy or general ER stress-associated autophagy. Finally, the Sigma1 inhibitor suppressed IFNγ-induced PD-L1. Our data demonstrate that small-molecule Sigma1 modulators can be used to regulate PD-L1 in cancer cells and trigger its degradation by selective autophagy.Implications: Sigma1 modulators sequester and eliminate PD-L1 by autophagy, thus preventing functional PD-L1 expression at the cell surface. This posits Sigma1 modulators as novel therapeutic agents in PD-L1/PD-1 blockade strategies that regulate the tumor immune microenvironment.Visual Overview: http://mcr.aacrjournals.org/content/molcanres/16/2/243/F1.large.jpg Mol Cancer Res; 16(2); 243-55. ©2017 AACR.

Sigma1 Pharmacology in the Context of Cancer.

Sigma1 (also known as sigma-1 receptor, Sig1R, σ1 receptor) is a unique pharmacologically regulated integral membrane chaperone or scaffolding protein. The majority of publications on the subject have focused on the neuropharmacology of Sigma1. However, a number of publications have also suggested a role for Sigma1 in cancer. Although there is currently no clinically used anti-cancer drug that targets Sigma1, a growing body of evidence supports the potential of Sigma1 ligands as therapeutic agents to treat cancer. In preclinical models, compounds with affinity for Sigma1 have been reported to inhibit cancer cell proliferation and survival, cell adhesion and migration, tumor growth, to alleviate cancer-associated pain, and to have immunomodulatory properties. This review will highlight that although the literature supports a role for Sigma1 in cancer, several fundamental questions regarding drug mechanism of action and the physiological relevance of aberrant SIGMAR1 transcript and Sigma1 protein expression in certain cancers remain unanswered or only partially answered. However, emerging lines of evidence suggest that Sigma1 is a component of the cancer cell support machinery, that it facilitates protein interaction networks, that it allosterically modulates the activity of its associated proteins, and that Sigma1 is a selectively multifunctional drug target.

Sigma1 Targeting to Suppress Aberrant Androgen Receptor Signaling in Prostate Cancer.

Suppression of androgen receptor (AR) activity in prostate cancer by androgen depletion or direct AR antagonist treatment, although initially effective, leads to incurable castration-resistant prostate cancer (CRPC) via compensatory mechanisms including resurgence of AR and AR splice variant (ARV) signaling. Emerging evidence suggests that Sigma1 (also known as sigma-1 receptor) is a unique chaperone or scaffolding protein that contributes to cellular protein homeostasis. We reported previously that some Sigma1-selective small molecules can be used to pharmacologically modulate protein homeostasis pathways. We hypothesized that these Sigma1-mediated responses could be exploited to suppress AR protein levels and activity. Here we demonstrate that treatment with a small-molecule Sigma1 inhibitor prevented 5α- dihydrotestosterone-mediated nuclear translocation of AR and induced proteasomal degradation of AR and ARV, suppressing the transcriptional activity and protein levels of both full-length and splice-variant AR. Consistent with these data, RNAi knockdown of Sigma1 resulted in decreased AR levels and transcriptional activity. Furthermore, Sigma1 physically associated with ARV7 and ARv567es as well as full-length AR. Treatment of mice xenografted with ARV-driven CRPC tumors with a drug-like small-molecule Sigma1 inhibitor significantly inhibited tumor growth associated with elimination of AR and ARV7 in responsive tumors. Together, our data show that Sigma1 modulators can be used to suppress AR/ARV-driven prostate cancer cells via regulation of pharmacologically responsive Sigma1-AR/ARV interactions, both in vitro and in vivoCancer Res; 77(9); 2439-52. ©2017 AACR.

Meaningful conversations in living with and treating chronic conditions: development of the ICAN discussion aid.

BACKGROUND: The needs of the growing population of complex patients with multiple chronic conditions calls for a different approach to care. Clinical teams need to acknowledge, respect, and support the work that patients do and the capacity they mobilize to enact this work, and to adapt and self-manage. Tools that enable this approach to care are needed. METHODS: Using user-centered design principles, we set out to create a discussion aid for use by patients, clinicians, and other health professionals during clinical encounters. We observed clinical encounters, visited patient homes, and dialogued with patient support groups. We then developed and tested prototypes in routine clinical practice. Then we refined a final prototype with extensive stakeholder feedback. RESULTS: From this process resulted the ICAN Discussion Aid, a tool completed by the patient and reviewed during the consultation in which patients classified domains that contribute to capacity as sources of burden or satisfaction; clinical demands were also classified as sources of help or burden. The clinical review facilitated by ICAN generates hypotheses regarding why some treatment plans may be problematic and may not be enacted in the patient's situation. CONCLUSION: We successfully created a discussion aid to elucidate and share insights about the capacity patients have to enact the treatment plan and hypotheses as to why this plan may or may not be enacted. Next steps involve the evaluation of the impact of the ICAN Discussion Aid on clinical encounters with a variety of health professionals and the impact of ICAN-informed treatment plans on patient-important outcomes.

Aptamer-hybrid nanoparticle bioconjugate efficiently delivers miRNA-29b to non-small-cell lung cancer cells and inhibits growth by downregulating essential oncoproteins.

MicroRNAs (miRNAs) are potentially attractive candidates for cancer therapy. However, their therapeutic application is limited by lack of availability of an efficient delivery system to stably deliver these potent molecules intracellularly to cancer cells while avoiding healthy cells. We developed a novel aptamer-hybrid nanoparticle bioconjugate delivery system to selectively deliver miRNA-29b to MUC1-expressing cancer cells. Significant downregulation of oncoproteins DNMT3b and MCL1 was demonstrated by these MUC1 aptamer-functionalized hybrid nanoparticles in A549 cells. Furthermore, downregulation of these oncoproteins led to antiproliferative effect and induction of apoptosis in a superior version when compared with Lipofectamine 2000. This novel aptamer-hybrid nanoparticle bioconjugate delivery system could potentially serve as a platform for intracellular delivery of miRNAs to cancer cells, hence improving the therapeutic outcome of lung cancer.

Impact of prenatal polycyclic aromatic hydrocarbon exposure on behavior, cortical gene expression and DNA methylation of the Bdnf gene.

Prenatal exposure to polycyclic aromatic hydrocarbons (PAH) has been associated with sustained effects on the brain and behavior in offspring. However, the mechanisms have yet to be determined. We hypothesized that prenatal exposure to ambient PAH in mice would be associated with impaired neurocognition, increased anxiety, altered cortical expression of Bdnf and Grin2b, and greater DNA methylation of Bdnf. Our results indicated that during open-field testing, prenatal PAH exposed offspring spent more time immobile and less time exploring. Females produced more fecal boli. Offspring prenatally exposed to PAH displayed modest reductions in overall exploration of objects. Further, prenatal PAH exposure was associated with lower cortical expression of Grin2b and Bdnf in males, and greater Bdnf IV promoter methylation. Epigenetic differences within the Bdnf IV promoter correlated with Bdnf gene expression, but not with the observed behavioral outcomes, suggesting that additional targets may account for these PAH-associated effects.

Repeatedly high polycyclic aromatic hydrocarbon exposure and cockroach sensitization among inner-city children.

BACKGROUND: Exposures to traffic-related air pollutants including polycyclic aromatic hydrocarbons (PAH) have been associated with the development and exacerbation of asthma. However, there is limited evidence on whether these pollutants are associated with the development of cockroach sensitization, a strong risk factor for urban asthma. We hypothesized that repeatedly high PAH exposure during childhood would be associated with increased risk of new cockroach sensitization. METHODS: As part of the research being conducted by the Columbia Center for Children's Environmental Health (CCCEH) birth cohort study in New York, a spot urine sample was collected from children at age 5 years (2003-2008) and again at age 9-10 years (2008-2012; n=248) and analyzed for 10 PAH metabolites. Repeatedly high PAH (High-High) exposure was defined as measures above median for age 5 PAH metabolites at both time points. Child blood samples at age 5 and 9 years were analyzed for total, anti-cockroach, mouse, dust mite, cat and dog IgE. Relative risks (RR) were estimated with multivariable modified Poisson regression. RESULTS: Individual PAH metabolite levels, except for 1-naphthol (1-OH-NAP), increased by 10-60% from age 5 to age 9-10. The prevalence of cockroach sensitization increased from 17.6% (33/188) at age 5 to 33.0% (62/188) at 9 years (p=0.001). After controlling for potential covariates including cockroach sensitization at age 5 in regression analyses, positive associations were found between repeatedly high exposure (High-High) to 1-OH-NAP, 3-hydroxyphenanthrene (3-OH-PHEN), or 1-hydroxypyrene (1-OH-PYR) and cockroach sensitization at age 9 (p-values<0.05). Compared to Low-Low exposure, the relative risk (RR) [95% CI] with repeatedly high exposure was 1.83 [1.06-3.17] for 1-OH-NAP, 1.54 [1.06-2.23] for 3-OH-PHEN, and 1.59 [1.04-2.43] for 1-OH-PYR. CONCLUSIONS: Repeatedly high levels of urinary PAH metabolites during childhood may increase likelihood of sensitization to cockroach allergen in urban inner-city children at age 9 years.

Candida albicans stimulates IL-23 release by human dendritic cells and downstream IL-17 secretion by Vδ1 T cells.

γδ T cells expressing the Vδ1 TCR are expanded in patients with HIV infection. We show in this article that circulating Vδ1 T cell numbers are particularly high in patients with HIV and candidiasis, and that these cells expand and produce IL-17 in response to Candida albicans in vitro. Although C. albicans could directly stimulate IL-17 production by a subset of Vδ1 T cells, fungus-treated dendritic cells (DCs) were required to expand C. albicans-responsive Vδ1 T cells to generate sufficient numbers of cells to release IL-17 at levels detectable by ELISA. C. albicans induced the release of IL-1ß, IL-6, and IL-23 by DCs, but addition of these cytokines or supernatants of C. albicans-treated DCs to Vδ1 T cells was not sufficient to induce proliferation. We found that direct contact with DCs was required for Vδ1 T cell proliferation, whereas IL-23R-blocking studies showed that IL-23 was required for optimal C. albicans-induced IL-17 production. Because IL-17 affords protection against both HIV and C. albicans, and because Vδ1 T cells are not depleted by HIV, these cells are likely to be an important source of IL-17 in HIV-infected patients with candidiasis, in whom CD4(+) Th17 responses are impaired. These data show that C. albicans stimulates proliferation and IL-17 production by Vδ1 T cells by a mechanism that involves IL-23 release by DCs.

Prenatal polycyclic aromatic hydrocarbon, adiposity, peroxisome proliferator-activated receptor (PPAR) γ methylation in offspring, grand-offspring mice.

RATIONALE: Greater levels of prenatal exposure to polycyclic aromatic hydrocarbon (PAH) have been associated with childhood obesity in epidemiological studies. However, the underlying mechanisms are unclear. OBJECTIVES: We hypothesized that prenatal PAH over-exposure during gestation would lead to weight gain and increased fat mass in offspring and grand-offspring mice. Further, we hypothesized that altered adipose gene expression and DNA methylation in genes important to adipocyte differentiation would be affected. MATERIALS AND METHODS: Pregnant dams were exposed to a nebulized PAH mixture versus negative control aerosol 5 days a week, for 3 weeks. Body weight was recorded from postnatal day (PND) 21 through PND60. Body composition, adipose cell size, gene expression of peroxisome proliferator-activated receptor (PPAR) γ, CCAAT/enhancer-binding proteins (C/EBP) α, cyclooxygenase (Cox)-2, fatty acid synthase (FAS) and adiponectin, and DNA methylation of PPAR γ, were assayed in both the offspring and grand-offspring adipose tissue. FINDINGS: Offspring of dams exposed to greater PAH during gestation had increased weight, fat mass, as well as higher gene expression of PPAR γ, C/EBP α, Cox2, FAS and adiponectin and lower DNA methylation of PPAR γ. Similar differences in phenotype and DNA methylation extended through the grand-offspring mice. CONCLUSIONS: Greater prenatal PAH exposure was associated with increased weight, fat mass, adipose gene expression and epigenetic changes in progeny.

DNA methylation of the allergy regulatory gene interferon gamma varies by age, sex, and tissue type in asthmatics.

BACKGROUND: Asthma is associated with allergic sensitization in about half of all cases, and asthma phenotypes can vary by age and sex. DNA methylation in the promoter of the allergy regulatory gene interferon gamma (IFNγ) has been linked to the maintenance of allergic immune function in human cell and mouse models. We hypothesized that IFNγ promoter methylation at two well-studied, key cytosine phosphate guanine (CpG) sites (-186 and -54), may differ by age, sex, and airway versus systemic tissue in a cohort of 74 allergic asthmatics. RESULTS: After sampling buccal cells, a surrogate for airway epithelial cells, and CD4+ lymphocytes, we found that CD4+ lymphocyte methylation was significantly higher in children compared to adults at both CpG sites (P <0.01). Buccal cell methylation was significantly higher in children at CpG -186 (P = 0.03) but not CpG -54 (P = 0.66). Methylation was higher in males compared to females at both CpG sites in CD4+ lymphocytes (-186: P <0.01, -54: P = 0.02) but not buccal cells (-186: P = 0.14, -54: P = 0.60). In addition, methylation was lower in CD4+ lymphocytes compared to buccal cells (P <0.01) and neighboring CpG sites were strongly correlated in CD4+ lymphocytes (r = 0.84, P <0.01) and weakly correlated in buccal cells (r = 0.24, P = 0.04). At CpG -186, there was significant correlation between CD4+ lymphocytes and buccal cells (r = 0.24, P = 0.04) but not at CpG -54 (r = -0.03, P = 0.78). CONCLUSIONS: These findings highlight significant age, sex, and tissue-related differences in IFNγ promoter methylation that further our understanding of methylation in the allergic asthma pathway and in the application of biomarkers in clinical research.

Prenatal and postnatal polycyclic aromatic hydrocarbon exposure, airway hyperreactivity, and Beta-2 adrenergic receptor function in sensitized mouse offspring.

Despite data associating exposure to traffic-related polycyclic aromatic hydrocarbons (PAH) in asthma, mechanistic support has been limited. We hypothesized that both prenatal and early postnatal exposure to PAH would increase airway hyperreactivity (AHR) and that the resulting AHR may be insensitive to treatment with a ß 2AR agonist drug, procaterol. Further, we hypothesized that these exposures would be associated with altered ß 2AR gene expression and DNA methylation in mouse lungs. Mice were exposed prenatally or postnatally to a nebulized PAH mixture versus negative control aerosol 5 days a week. Double knockout ß 2AR mice were exposed postnatally only. Prenatal exposure to PAH was associated with reduced ß 2AR gene expression among nonsensitized mice offspring, but not increases in DNA methylation or AHR. Postnatal exposure to PAH was borderline associated with increased AHR among sensitized wildtype, but not knockout mice. In the first study that delivers PAH aerosols to mice in a relatively physiological manner, small effects on AHR and ß 2AR gene expression, but not ß 2AR agonist drug activity, were observed. If confirmed, the results may suggest that exposure to PAH, common ambient urban pollutants, affects ß 2AR function, although the impact on the efficacy of ß 2AR agonist drugs used in treating asthma remains uncertain.

Secondhand smoke in combination with ambient air pollution exposure is associated with increasedx CpG methylation and decreased expression of IFN-γ in T effector cells and Foxp3 in T regulatory cells in children.

BACKGROUND: Secondhand smoke (SHS) and ambient air pollution (AAP) exposures have been associated with increased prevalence and severity of asthma and DNA modifications of immune cells. In the current study, we examined the association between SHS and AAP with DNA methylation and expression of interferon-gamma (IFN-γ) and forkhead box protein 3 (Foxp3) in T cell populations. METHODS: Subjects 7-18 years old were recruited from Fresno (high AAP; n = 62) and Stanford, CA (low AAP; n = 40) and divided into SHS-exposed (Fresno: n = 31, Stanford: n = 6) and non-SHS-exposed (nSHS; Fresno: n = 31, Stanford: n = 34) groups. T cells purified from peripheral blood were assessed for levels of DNA methylation and expression of IFN-γ (in effector T cells) or Foxp3 (in regulatory T cells). RESULTS: Analysis showed a significant increase in mean % CpG methylation of IFN-γ and Foxp3 associated with SHS exposure (IFN-γ: FSHS 62.10%, FnSHS 41.29%, p < 0.05; SSHS 46.67%, SnSHS 24.85%, p < 0.05; Foxp3: FSHS 74.60%, FnSHS 54.44%, p < 0.05; SSHS 62.40%, SnSHS 18.41%, p < 0.05) and a significant decrease in mean transcription levels of both genes (IFN-γ: FSHS 0.75, FnSHS 1.52, p < 0.05; SHS 2.25, nSHS 3.53, p < 0.05; Foxp3: FSHS 0.75, FnSHS 3.29, p < 0.05; SSHS 4.8, SnSHS 7.2, p < 0.05). AAP was also associated with hypermethylation (IFN-γ: FSHS vs. SSHS, p < 0.05; FnSHS vs. SnSHS, p < 0.05; Foxp3: FSHS vs. SSHS, p < 0.05; FnSHS vs. SnSHS, p < 0.05) and decreased transcription of both genes (IFN-γ: FSHS vs. SSHS, p < 0.05; FnSHS vs. SnSHS, p < 0.05; Foxp3: FSHS vs. SSHS, p < 0.05; FnSHS vs. SnSHS, p < 0.05). Average methylation between AAP- and SHS-only exposures was not significantly different (IFN-γ: p = 0.15; Foxp3: p = 0.27), nor was Foxp3 expression (p = 0.08); IFN-γ expression was significantly decreased in AAP-only subjects (p < 0.05). CONCLUSIONS: Exposures to SHS and AAP are associated with significant hypermethylation and decreased expression of IFN-γ in Teffs and Foxp3 in Tregs. Relative contributions of each exposure to DNA modification and asthma pathogenesis warrant further investigation.