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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-β, 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.

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.

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.

This paper theoretically studies the design and performance analysis of the reliable satellite-based quantum key distribution (QKD) over free-space optics channel. The proposed QKD system is based on the optical quadrature phase-shift keying (QPSK) modulation and the dual-threshold/heterodyne detection (DT/HD) receiver that helps to reduce quantum bit error rate (QBER) and improve the receiver sensitivity. In addition, a key retransmission scheme is also designed to enhance the reliability of the proposed QKD system. Performance of the key transmission is analyzed in terms of QBER and the probability of sifted key, taking into account the impacts of free-space path loss, atmospheric attenuation, beam spreading loss, atmospheric turbulence, and receiver noise. In addition, we newly develop an analytical framework by using the 3-D Markov chain model that allows us to investigate the key loss rate (KLR) performance at the link layer. Numerical results quantitatively show that our proposed satellite-based QKD system can offer significant performance improvement over the conventional ones.

The biological processes from genotype to phenotype are complex involving multi-scale mechanisms. Increasing multi-modal data enables deeper understanding of underlying complex mechanisms in various phenotypes. However, integrating and interpreting such large-scale multi-modal data remains challenging, especially given highly heterogeneous, nonlinear relationships across modalities. To address this, 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 align multi-modal features onto a common latent space. This space aims to preserve both global consistency and local smoothness across modalities and reveal higher-order nonlinear cross-modal relationships. Second, deepManReg uses cross-modal manifolds as a feature graph to regularize the classifiers for improving phenotype predictions and also prioritizing the multi-modal features and cross-modal interactions for the phenotypes. We applied deepManReg to recent single cell multi-modal data such as Patch-seq data including transcriptomics and electrophysiology for neuronal cells in the mouse brain. We show that deepManReg significantly improves predicting cellular phenotypes and also prioritizing genes and electrophysiological features for the phenotypes. Finally, deepManReg is open-source and general for phenotype prediction from multi-modal data. deepManReg is open-source available at https://github.com/daifengwanglab/deepManReg.

We propose to investigate the performance of a satellite-to-ground quantum key distribution (QKD) system that uses key retransmission as a method for improving the system reliability. We develop analytical frameworks based on two three-dimensional Markov chain models allowing us to comprehensively analyze the proposed system’s performance in terms of key loss rate, link utilization, and delay outage rate. Our performance analysis takes into account the physical layer impairments induced by free-space optical channel and receiver noise. Numerical results quantitatively demonstrate that the performance of satellite-to-ground QKD system is significantly improved due to key retransmission. In addition, the appropriate selection of system parameters corresponding to different turbulence conditions to achieve the best performance improvement is also provided.

This paper proposes to use high-altitude platform (HAP)-based relaying and link-layer automatic repeat request (ARQ) techniques to improve the performance of the satellite quantum key distribution (QKD) over free-space optics (FSO) systems. We also develop analytical frameworks based on two 3D Markov chain models allowing us to comprehensively analyze the performance of the proposed system in terms of key loss rate and delay outage rate. Our performance analysis takes into account the physical layer impairments induced by FSO channel and receiver noise. Numerical results quantitatively demonstrate that the satellite FSO/QKD system using HAP-based relaying and link-layer ARQ techniques can offer considerable performance improvement over the conventional ones.

Population studies such as GWAS 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, we develop 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 drop-connect without needing prior feature selections. Varmole is available as a Python package on GitHub at https://github.com/daifengwanglab/Varmole.

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 GSK3β also prevented MUC5AC overexpression. Collectively, our results suggest that activation of TRPA1 and EGFR, paired with alterations to p38 MAPK and GSK3β 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.

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 −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.

Prostate cancer (PCa) is one of the most common cancers in men worldwide and a leading cause of cancer-related mortality. Though PCa is known to be substantially heterogeneous at the molecular and histological level, there is still little understanding of the tumor subpopulations (“clones”) generating this intratumor heterogeneity (ITH) and their underlying clonal dynamics and interactions. The impact of ITH on disease outcome remains unknown and is thus a significant hurdle in treatment of PCa. This barrier is especially evident in progression to castration resistance: after initial androgen dependence, subpopulations that are castration resistant and less dependent on androgens emerge to populate the tumor, contributing to disease progression. The introduction of second generation androgen deprivation therapies has shown some success in treating castration resistant prostate cancer (CRPC), but eventually drug resistant clones emerge and the disease recurs. Understanding the fundamental differences–both phenotypic and genomic–between clones may hold the key to improving therapies, preventing disease progression, and prolonging patient survival. Using a multi-omics approach, we are exploring the dynamic changes in these subpopulations during progression to castration and drug resistance, focusing on the differences between clones at the transcriptomic and genomic level. Thus far, we have developed a mouse model of ITH in CRPC with the multicolor Confetti reporter, allowing for spatial distinction between different clones. Using single cell sequencing and RNAseq, we have mapped the evolution of early-stage clones post-castration and identified pathways and gene regulatory networks that potentially grant specific clones their castration resistant phenotype. We have extended our system to in vitro 3D organoid cultures to study the evolution of these clones longitudinally during treatment with the antiandrogens enzalutamide and abiraterone, developing a drug resistant in vitro model of CRPC. We are currently using our organoid system to identify novel drug resistance mechanisms in order to target advanced CRPC, which until now has been considered untreatable and lethal. By characterizing clonal dynamics during disease progression, our studies provide new mechanistic insights for identifying pre-existing high-risk clones with drug resistant phenotypes in PCa and uncovering novel therapeutic targets in CRPC. Our work has implications for designing new therapies aimed to control the emergence of advanced disease, preventing CRPC and drug resistance, with the ultimate goal of reducing mortality and improving prognosis. Citation Format: Lara F Tshering, Fu Luo, Nam D. Nguyen, James G. Rail, Ting Jin, Daifeng Wang, Flaminia Talos. Intratumor heterogeneity and clonal dynamics underlying treatment resistance in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3126.

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.

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.

The coordination of genome encoded function is a critical and complex process in biological systems, especially across phenotypes or states (e.g., time, disease, organism). Understanding how the complexity of genome-encoded function relates to these states remains a challenge. To address this, we have developed a novel computational method based on manifold learning and comparative analysis, ManiNetCluster, which simultaneously aligns and clusters multiple molecular networks to systematically reveal function links across multiple datasets. Specifically, ManiNetCluster employs manifold learning to match local and non-linear structures among the networks of different states, to identify cross-network linkages. By applying ManiNetCluster to the developmental gene expression datasets across model organisms (e.g., worm, fruit fly), we found that our tool significantly better aligns the orthologous genes than existing state-of-the-art methods, indicating the non-linear interactions between evolutionary functions in development. Moreover, we applied ManiNetCluster to a series of transcriptomes measured in the green algaChlamy-domonas reinhardtii, to determine the function links between various metabolic processes between the light and dark periods of a diurnally cycling culture. For example, we identify a number of genes putatively regulating processes across each lighting regime, and how comparative analyses between ManiNetCluster and other clustering tools can provide additional insights. ManiNetCluster is available as an R package together with a tutorial athttps://github.com/namtk/ManiNetCluster.

The coordination of functional genomics is a critical and complex process in biological systems, especially across different phenotypes or organism states (e.g., time, disease, organism). Understanding how the interactions of various genomic functions relate to these states remains a challenge. To address this, we have developed a machine learning method, ManiNetCluster, which integrates and simultaneously clusters multiple gene networks to identify cross-phenotype functional gene modules, revealing the genomic functional linkages. Particularly, this method extended the manifold learning to match local and nonlinear structures among networks for maximizing the functional connectivities. For example, we showed that ManiNetCluster significantly better aligns orthologous genes from cross-species gene expression datasets than the linear state-of-art methods. As a demonstration, we have applied our method to temporal gene co-expression networks of an algal day/night cycling transcriptome. This demonstration confirmed i) the validity of our clustering method, and ii) revealed the day-night linkages of photosynthetic functions, providing novel insights of temporal genomic functional coordination in bioproduction.