Showing papers by "Matthew Patrick published in 2019"

TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits

Abstract: The transcriptome-wide association studies (TWASs) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWASs facilitate gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, to improve on this, we employ a nonparametric Bayesian method that was originally proposed for genetic prediction of complex traits, which assumes a data-driven nonparametric prior for cis-eQTL effect sizes. The nonparametric Bayesian method is flexible and general because it includes both of the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R2 for transcriptomic data and the TWAS power over PrediXcan when ≥1% cis-SNPs co-regulate gene expression and gene expression heritability ≤0.2. In real applications, the nonparametric Bayesian method fitted transcriptomic imputation models for 57.8% more genes over PrediXcan, thus improving the power of follow-up TWASs. We implement both parametric PrediXcan and nonparametric Bayesian methods in a convenient software tool "TIGAR" (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWASs using individual-level or summary-level GWAS data.

IFN-γ enhances cell-mediated cytotoxicity against keratinocytes via JAK2/STAT1 in lichen planus

Abstract: Lichen planus (LP) is a chronic debilitating inflammatory disease of unknown etiology affecting the skin, nails, and mucosa with no current FDA-approved treatments. It is histologically characterized by dense infiltration of T cells and epidermal keratinocyte apoptosis. Using global transcriptomic profiling of patient skin samples, we demonstrate that LP is characterized by a type II interferon (IFN) inflammatory response. The type II IFN, IFN-γ, is demonstrated to prime keratinocytes and increase their susceptibility to CD8+ T cell-mediated cytotoxic responses through MHC class I induction in a coculture model. We show that this process is dependent on Janus kinase 2 (JAK2) and signal transducer and activator of transcription 1 (STAT1), but not JAK1 or STAT2 signaling. Last, using drug prediction algorithms, we identify JAK inhibitors as promising therapeutic agents in LP and demonstrate that the JAK1/2 inhibitor baricitinib fully protects keratinocytes against cell-mediated cytotoxic responses in vitro. In summary, this work elucidates the role and mechanisms of IFN-γ in LP pathogenesis and provides evidence for the therapeutic use of JAK inhibitors to limit cell-mediated cytotoxicity in patients with LP.

Hypersensitive IFN Responses in Lupus Keratinocytes Reveal Key Mechanistic Determinants in Cutaneous Lupus.

Abstract: Systemic lupus erythematosus (SLE) is a complex autoimmune disease in which 70% of patients experience disfiguring skin inflammation (grouped under the rubric of cutaneous lupus erythematosus [CLE]). There are limited treatment options for SLE and no Food and Drug Administration-approved therapies for CLE. Studies have revealed that IFNs are important mediators for SLE and CLE, but the mechanisms by which IFNs lead to disease are still poorly understood. We aimed to investigate how IFN responses in SLE keratinocytes contribute to development of CLE. A cohort of 72 RNA sequencing samples from 14 individuals (seven SLE and seven healthy controls) were analyzed to study the transcriptomic effects of type I and type II IFNs on SLE versus control keratinocytes. In-depth analysis of the IFN responses was conducted. Bioinformatics and functional assays were conducted to provide implications for the change of IFN response. A significant hypersensitive response to IFNs was identified in lupus keratinocytes, including genes (IFIH1, STAT1, and IRF7) encompassed in SLE susceptibility loci. Binding sites for the transcription factor PITX1 were enriched in genes that exhibit IFN-sensitive responses. PITX1 expression was increased in CLE lesions based on immunohistochemistry, and by using small interfering RNA knockdown, we illustrated that PITX1 was required for upregulation of IFN-regulated genes in vitro. SLE patients exhibit increased IFN signatures in their skin secondary to increased production and a robust, skewed IFN response that is regulated by PITX1. Targeting these exaggerated pathways may prove to be beneficial to prevent and treat hyperinflammatory responses in SLE skin.

Securing Android App Markets via Modeling and Predicting Malware Spread Between Markets

Abstract: The Android ecosystem has recently dominated mobile devices. Android app markets, including official Google Play and other third party markets, are becoming hotbeds, where malware originates and spreads. Android malware has been observed to both propagate within markets and spread between markets. If the spread of Android malware between markets can be predicted, market administrators can take appropriate measures to prevent the outbreak of malware and minimize the damages caused by malware. In this paper, we make the first attempt to protect the Android ecosystem by modeling and predicting the spread of Android malware between markets. To this end, we study the social behaviors that affect the spread of malware, model these spread behaviors with multiple epidemic models, and predict the infection time and order among markets for well-known malware families. To achieve an accurate prediction of malware spread, we model spread behaviors in the following fashion: 1) for a single market, we model the within-market malware growth by considering both the creation and removal of malware; 2) for multiple markets, we determine market relevance by calculating the mutual information among them; and 3) based on the previous two steps, we simulate a susceptible infected model stochastically for spread among markets. The model inference is performed using a publicly available well-labeled dataset AndRadar. To conduct extensive experiments to evaluate our approach, we collected a large number (334,782) of malware samples from 25 Android markets around the world. The experimental results show our approach can depict and simulate the growth of Android malware on a large scale, and predict the infection time and order among markets with 0.89 and 0.66 precision, respectively.

Integrative Approach to Reveal Cell Type Specificity and Gene Candidates for Psoriatic Arthritis Outside the MHC.

Abstract: We recently conducted a large association analysis to compare the genetic profiles between patients with psoriatic arthritis (PsA) and cutaneous-only psoriasis (PsC). Despite including over 7,000 genotyped patients, only the MHC achieved genome-wide significance. In this study, we hypothesized that appropriate epigenomic elements (H3K27ac marks for active enhancers) can guide us to reveal valuable information about the loci with suggestive evidence of association. Our aim is to investigate these loci and explore how they may lead to the development of PsA. We evaluated this potential by investigating the genes connected with these loci from the perspective of pharmacogenomics and gene expression. We illustrated that markers with suggestive evidence of association outside the MHC region are enriched in H3K27ac marks for osteoblast and chondrogenic differentiated cells; using pharmacogenomics resources, we showed that genes near these markers are targeted by existing drugs used to treat psoriatic arthritis. Significantly, six of the ten suggestive significant loci overlapping the regulatory elements encompass genes differentially expressed (FDR < 5%) in differentiated osteoblasts, including genes participating in the Wnt signaling such as RUNX1, FUT8, and CTNNAL1. Our approach shows that epigenomic information can be used as cost-effective approach to enhance the inferences for GWAS results, especially in situations when few genome-wide significant loci are available. Our results also point the way to more directed investigations comparing the genetics of PsA and PsC.