Browsing by Author "Johnson, Travis"
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Item AIscEA: unsupervised integration of single-cell gene expression and chromatin accessibility via their biological consistency(Oxford University Press, 2022) Jafari, Elham; Johnson, Travis; Wang, Yue; Liu, Yunlong; Huang, Kun; Wang, Yijie; Biostatistics and Health Data Science, School of MedicineMotivation: The integrative analysis of single-cell gene expression and chromatin accessibility measurements is essential for revealing gene regulation, but it is one of the key challenges in computational biology. Gene expression and chromatin accessibility are measurements from different modalities, and no common features can be directly used to guide integration. Current state-of-the-art methods lack practical solutions for finding heterogeneous clusters. However, previous methods might not generate reliable results when cluster heterogeneity exists. More importantly, current methods lack an effective way to select hyper-parameters under an unsupervised setting. Therefore, applying computational methods to integrate single-cell gene expression and chromatin accessibility measurements remains difficult. Results: We introduce AIscEA-Alignment-based Integration of single-cell gene Expression and chromatin Accessibility-a computational method that integrates single-cell gene expression and chromatin accessibility measurements using their biological consistency. AIscEA first defines a ranked similarity score to quantify the biological consistency between cell clusters across measurements. AIscEA then uses the ranked similarity score and a novel permutation test to identify cluster alignment across measurements. AIscEA further utilizes graph alignment for the aligned cell clusters to align the cells across measurements. We compared AIscEA with the competing methods on several benchmark datasets and demonstrated that AIscEA is highly robust to the choice of hyper-parameters and can better handle the cluster heterogeneity problem. Furthermore, AIscEA significantly outperforms the state-of-the-art methods when integrating real-world SNARE-seq and scMultiome-seq datasets in terms of integration accuracy. Availability and implementation: AIscEA is available at https://figshare.com/articles/software/AIscEA_zip/21291135 on FigShare as well as {https://github.com/elhaam/AIscEA} onGitHub.Item AIscEA: unsupervised integration of single-cell gene expression and chromatin accessibility via their biological consistency(Oxford, 2022-12-01) Jafari, Elham; Johnson, Travis; Wang, Yue; Liu, Yunlong; Huang, Kun; Wang, Yijie; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthMotivation The integrative analysis of single-cell gene expression and chromatin accessibility measurements is essential for revealing gene regulation, but it is one of the key challenges in computational biology. Gene expression and chromatin accessibility are measurements from different modalities, and no common features can be directly used to guide integration. Current state-of-the-art methods lack practical solutions for finding heterogeneous clusters. However, previous methods might not generate reliable results when cluster heterogeneity exists. More importantly, current methods lack an effective way to select hyper-parameters under an unsupervised setting. Therefore, applying computational methods to integrate single-cell gene expression and chromatin accessibility measurements remains difficult. Results We introduce AIscEA—Alignment-based Integration of single-cell gene Expression and chromatin Accessibility—a computational method that integrates single-cell gene expression and chromatin accessibility measurements using their biological consistency. AIscEA first defines a ranked similarity score to quantify the biological consistency between cell clusters across measurements. AIscEA then uses the ranked similarity score and a novel permutation test to identify cluster alignment across measurements. AIscEA further utilizes graph alignment for the aligned cell clusters to align the cells across measurements. We compared AIscEA with the competing methods on several benchmark datasets and demonstrated that AIscEA is highly robust to the choice of hyper-parameters and can better handle the cluster heterogeneity problem. Furthermore, AIscEA significantly outperforms the state-of-the-art methods when integrating real-world SNARE-seq and scMultiome-seq datasets in terms of integration accuracy. Availability and implementation AIscEA is available at https://figshare.com/articles/software/AIscEA_zip/21291135 on FigShare as well as {https://github.com/elhaam/AIscEA} onGitHub.Item Association Between Tobacco Related Diagnoses and Alzheimer Disease: A Population Study(2022-05) Almalki, Amwaj Ghazi; Zhang, Pengyue; Johnson, Travis; Fadel, WilliamBackground: Tobacco use is associated with an increased risk of developing Alzheimer's disease (AD). 14% of the incidence of AD is associated with various types of tobacco exposure. Additional real-world evidence is warranted to reveal the association between tobacco use and AD in age/gender-specific subpopulations. Method: In this thesis, the relationships between diagnoses related to tobacco use and diagnoses of AD in gender- and age-specific subgroups were investigated, using health information exchange data. The non-parametric Kaplan-Meier method was used to estimate the incidence of AD. Furthermore, the log-rank test was used to compare incidence between individuals with and without tobacco related diagnoses. In addition, we used semi-parametric Cox models to examine the association between tobacco related diagnoses and diagnoses of AD, while adjusting covariates. Results: Tobacco related diagnosis was associated with increased risk of developing AD comparing to no tobacco related diagnosis among individuals aged 60-74 years (female hazard ratio [HR] =1.26, 95% confidence interval [CI]: 1.07 – 1.48, p-value = 0.005; and male HR =1.33, 95% CI: 1.10 - 1.62, p-value =0.004). Tobacco related diagnosis was associated with decreased risk of developing AD comparing to no tobacco related diagnosis among individuals aged 75-100 years (female HR =0.79, 95% CI: 0.70 - 0.89, p-value =0.001; and male HR =0.90, 95% CI: 0.82 - 0.99, p-value =0.023). Conclusion: Individuals with tobacco related diagnoses were associated with an increased risk of developing AD in older adults aged 60-75 years. Among older adults aged 75-100 years, individuals with tobacco related diagnoses were associated with a decreased risk of developing AD.Item Corrigendum: Regulatory T cells targeting a pathogenic MHC class II: insulin peptide epitope postpone spontaneous autoimmune diabetes(Frontiers Media, 2024-03-07) Obarorakpor, Nyerhovwo; Patel, Deep; Boyarov, Reni; Amarsaikhan, Nansalmaa; Cepeda, Joseph Ray; Eastes, Doreen; Robertson, Sylvia; Johnson, Travis; Yang, Kai; Tang, Qizhi; Zhang, Li; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthIn the published article, there was an error in the Funding statement. Grant “JDRF 2-SRA-2018-648-S-B” grant was missing in the statement. The correct Funding statement appears below. Funding This study was supported by grants from NIH R03AI139811-01A1, DoD W81XWH2210087, JDRF 2-SRA-2018-648-S-B, and a Pilot and Feasibility Award from the CDMD NIH/NIDDK Grant Number P30 DK097512 (to LZ). The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.Item The glutathione peroxidase Gpx4 prevents lipid peroxidation and ferroptosis to sustain Treg cell activation and suppression of antitumor immunity(Elsevier, 2021-06) Xu, Chengxian; Sun, Shaogang; Johnson, Travis; Qi, Rong; Zhang, Siyuan; Zhang, Jie; Yang, Kai; Pediatrics, School of MedicineT regulatory (Treg) cells are crucial to maintain immune tolerance and repress antitumor immunity, but the mechanisms governing their cellular redox homeostasis remain elusive. We report that glutathione peroxidase 4 (Gpx4) prevents Treg cells from lipid peroxidation and ferroptosis in regulating immune homeostasis and antitumor immunity. Treg-specific deletion of Gpx4 impairs immune homeostasis without substantially affecting survival of Treg cells at steady state. Loss of Gpx4 results in excessive accumulation of lipid peroxides and ferroptosis of Treg cells upon T cell receptor (TCR)/CD28 co-stimulation. Neutralization of lipid peroxides and blockade of iron availability rescue ferroptosis of Gpx4-deficient Treg cells. Moreover, Gpx4-deficient Treg cells elevate generation of mitochondrial superoxide and production of interleukin-1β (IL-1β) that facilitates T helper 17 (TH17) responses. Furthermore, Treg-specific ablation of Gpx4 represses tumor growth and concomitantly potentiates antitumor immunity. Our studies establish a crucial role for Gpx4 in protecting activated Treg cells from lipid peroxidation and ferroptosis and offer a potential therapeutic strategy to improve cancer treatment.Item Identify Signature Genes/Pathways to Characterize Alzheimer's Disease Subtypes Based on Uncoupled Tauopathies and Cognitive Decline(2024-06) Huang, Xiaoqing; Huang, Kun; Zhang, Jie; Johnson, Travis; Zhang, JianjunAlzheimer's disease (AD) is a slow-progressing dementia usually found in elderlies, with heterogeneous clinical phenotypes and possible underlying mechanisms. Widely spread tauopathy is one of the pathological change hallmarks in AD brains, in which microtube protein tau forms scar-like neurofibrillary tangles that kill neurons. However, subgroups of patients present unmatched tauopathy progression with their cognitive decline. A detailed study on these so-called atypical AD patients allows for a deeper understanding of possible various disease mechanisms and the factors contributing to disease vulnerability or resilience, which can help guide the drug development and treatment strategy tailored to different subgroups, as well as establish foundations for disease prevention. By identifying specific molecular biomarkers associated with each subtype, I hope to help clinicians diagnose various AD subtypes at an earlier stage. In this work, I have performed transcriptomic and proteomic characterization of two atypical AD subtypes on two large AD/normal brain cohorts to further understand the role of tauopathy in the AD etiology, identified several pathways that are associated with the two phenotypes’ AD-resilient and AD-vulnerable characteristics, and tried to identify the potential drug targets for the precision treatment of AD using extensive bioinformatic approaches. In the meanwhile, two methodologies were developed and applied. One is a new type of interpretable deep learning model (ParsVNN) coupled with the neural network architecture with the hierarchical structure of the gene/protein pathways is introduced and leveraged to address the complexity and improve the interpretability by making its biological hierarchy simple and specific to the predicted subgroup. The other is a label transferring approach using optimal transport from brain samples to blood samples in the hope of finding serum biomarkers for atypical AD groups in live patients and predicting their disease progression in a non-invasive fashion. Conclusively, the study improves our understanding of AD etiology and leads to more personalized care and disease prevention. It acknowledges the complexity of the disease and aims to uncover mechanistic distinctions within the broad Alzheimer’s disease spectrum.Item Mirikizumab-Induced Transcriptome Changes in Ulcerative Colitis Patient Biopsies at Week 12 Are Maintained Through Week 52(Wolters Kluwer, 2023-11-01) Johnson, Travis; Steere, Boyd; Zhang, Pengyue; Zang, Yong; Higgs, Richard; Milch, Catherine; Reinisch, Walter; Panés, Julian; Huang, Kun; D’Haens, Geert; Krishnan, Venkatesh; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthIntroduction: Mirikizumab, an anti-interleukin-23p19 monoclonal antibody, demonstrated efficacy in phase 2 and 3 randomized clinical trials of patients with moderate-to-severe ulcerative colitis (UC). Previous results have shown that 12 weeks of mirikizumab treatment downregulated transcripts associated with UC disease activity and tumor necrosis factor inhibitor resistance. We assessed week-52 gene expression from week-12 responders receiving mirikizumab or placebo. Methods: In the phase 2 AMAC study (NCT02589665), mirikizumab-treated patients achieving week-12 clinical response were rerandomized to mirikizumab 200 mg subcutaneous every 4 or 12 weeks through week 52 (N = 31). Week-12 placebo responders continued placebo through week 52 (N = 7). The limma R package clustered transcript changes in colonic mucosa biopsies from baseline to week 12 into differentially expressed genes (DEGs). Among DEGs, similarly expressed genes (DEGSEGs) maintaining week-12 expression through week 52 were identified. Results: Of 89 DEGSEGs, 63 (70.8%) were present only in mirikizumab induction responders, 5 (5.6%) in placebo responders, and 21 (23.6%) in both. Week-12 magnitudes and week-52 consistency of transcript changes were greater in mirikizumab than in placebo responders (log2FC > 1). DEGSEG clusters (from 84 DEGSEGs identified in mirikizumab and mirikizumab/placebo responders) correlated to modified Mayo score (26/84 with Pearson correlation coefficient [PCC] >0.5) and Robarts Histopathology Index (55/84 with PCC >0.5), sustained through week 52. Discussion: Mirikizumab responders had broader, more sustained transcriptional changes of greater magnitudes at week 52 vs placebo. Mirikizumab responder DEGSEGs suggest a distinct molecular healing pathway associated with mirikizumab interleukin-23 inhibition. The cluster's correlation with disease activity illustrates relationships between clinical, endoscopic, and molecular healing in UC.Item ParsVNN: parsimony visible neural networks for uncovering cancer-specific and drug-sensitive genes and pathways(Oxford University Press, 2021-10-27) Huang, Xiaoqing; Huang, Kun; Johnson, Travis; Radovich, Milan; Zhang, Jie; Ma, Jianzhu; Wang, Yijie; Biostatistics and Health Data Science, School of MedicinePrediction of cancer-specific drug responses as well as identification of the corresponding drug-sensitive genes and pathways remains a major biological and clinical challenge. Deep learning models hold immense promise for better drug response predictions, but most of them cannot provide biological and clinical interpretability. Visible neural network (VNN) models have emerged to solve the problem by giving neurons biological meanings and directly casting biological networks into the models. However, the biological networks used in VNNs are often redundant and contain components that are irrelevant to the downstream predictions. Therefore, the VNNs using these redundant biological networks are overparameterized, which significantly limits VNNs' predictive and explanatory power. To overcome the problem, we treat the edges and nodes in biological networks used in VNNs as features and develop a sparse learning framework ParsVNN to learn parsimony VNNs with only edges and nodes that contribute the most to the prediction task. We applied ParsVNN to build cancer-specific VNN models to predict drug response for five different cancer types. We demonstrated that the parsimony VNNs built by ParsVNN are superior to other state-of-the-art methods in terms of prediction performance and identification of cancer driver genes. Furthermore, we found that the pathways selected by ParsVNN have great potential to predict clinical outcomes as well as recommend synergistic drug combinations.Item Regulatory T cells targeting a pathogenic MHC class II: Insulin peptide epitope postpone spontaneous autoimmune diabetes(Frontiers Media, 2023-08-01) Obarorakpor, Nyerhovwo; Patel, Deep; Boyarov, Reni; Amarsaikhan, Nansalmaa; Cepeda, Joseph Ray; Eastes, Doreen; Robertson, Sylvia; Johnson, Travis; Yang, Kai; Tang, Qizhi; Zhang, Li; Biostatistics and Health Data Science, School of MedicineIntroduction: In spontaneous type 1 diabetes (T1D) non-obese diabetic (NOD) mice, the insulin B chain peptide 9-23 (B:9-23) can bind to the MHC class II molecule (IAg7) in register 3 (R3), creating a bimolecular IAg7/InsulinB:9-23 register 3 conformational epitope (InsB:R3). Previously, we showed that the InsB:R3-specific chimeric antigen receptor (CAR), constructed using an InsB:R3-monoclonal antibody, could guide CAR-expressing CD8 T cells to migrate to the islets and pancreatic lymph nodes. Regulatory T cells (Tregs) specific for an islet antigen can broadly suppress various pathogenic immune cells in the islets and effectively halt the progression of islet destruction. Therefore, we hypothesized that InsB:R3 specific Tregs would suppress autoimmune reactivity in islets and efficiently protect against T1D. Methods: To test our hypothesis, we produced InsB:R3-Tregs and tested their disease-protective effects in spontaneous T1D NOD.CD28-/- mice. Results: InsB:R3-CAR expressing Tregs secrete IL-10 dominated cytokines upon engagement with InsB:R3 antigens. A single infusion of InsB:R3 Tregs delayed the onset of T1D in 95% of treated mice, with 35% maintaining euglycemia for two healthy lifespans, readily home to the relevant target whereas control Tregs did not. Our data demonstrate that Tregs specific for MHC class II: Insulin peptide epitope (MHCII/Insulin) protect mice against T1D more efficiently than polyclonal Tregs lacking islet antigen specificity, suggesting that the MHC II/insulin-specific Treg approach is a promising immune therapy for safely preventing T1D.Item Topological Methods for Visualization and Analysis of High Dimensional Single-Cell RNA Sequencing Data(World Scientific Publishing Company, 2019) Wang, Tongxin; Johnson, Travis; Zhang, Jie; Huang, Kun; Department of Medical and Molecular Genetics, Indiana University School of MedicineSingle-cell RNA sequencing (scRNA-seq) techniques have been very powerful in analyzing heterogeneous cell population and identifying cell types. Visualizing scRNA-seq data can help researchers effectively extract meaningful biological information and make new discoveries. While commonly used scRNA-seq visualization methods, such as t-SNE, are useful in detecting cell clusters, they often tear apart the intrinsic continuous structure in gene expression profiles. Topological Data Analysis (TDA) approaches like Mapper capture the shape of data by representing data as topological networks. TDA approaches are robust to noise and different platforms, while preserving the locality and data continuity. Moreover, instead of analyzing the whole dataset, Mapper allows researchers to explore biological meanings of specific pathways and genes by using different filter functions. In this paper, we applied Mapper to visualize scRNA-seq data. Our method can not only capture the clustering structure of cells, but also preserve the continuous gene expression topologies of cells. We demonstrated that by combining with gene co-expression network analysis, our method can reveal differential expression patterns of gene co-expression modules along the Mapper visualization.