IMMU-35. TRANSCRIPTIONALLY DEFINED IMMUNE CONTEXTURE IN HUMAN GLIOMAS AT SINGLE-CELL RESOLUTION

Abstract The brain tumor immune microenvironment (TIME) continuously evolves during glioma progression and a comprehensive understanding of the glioma-centric immune cell repertoire beyond a priori cell types and/or states is uncharted. Consequently, we performed single-cell RNA-sequencing on ~123,000 tumor-derived immune cells from 17-pathologically stratified, IDH (isocitrate dehydrogenase)-differential primary, recurrent human gliomas, and non-glioma brains. Our analysis delineated predominant 34-myeloid cell clusters (~75%) over 28-lymphoid cell clusters (~25%) reflecting enormous heterogeneity within and across gliomas. The glioma immune diversity spanned functionally imprinted phagocytic, antigen-presenting, hypoxia, angiogenesis and, tumoricidal myeloid to classical cytotoxic lymphoid subpopulations. Specifically, IDH-mutant gliomas were enriched for brain-resident microglial subpopulations in contrast to enhanced bone barrow-derived infiltrates in IDH-wild type, especially in a recurrent setting. Microglia attrition in IDH-wild type -primary and -recurrent gliomas were concomitant with invading monocyte-derived cells with semblance to dendritic cell and macrophage/microglia like transcriptomic features. Additionally, microglial functional diversification was noted with disease severity and mostly converged to inflammatory states in IDH-wild type recurrent gliomas. Beyond dendritic cells, multiple antigen-presenting cellular states expanded with glioma severity especially in IDH-wild type primary and recurrent- gliomas. Furthermore, we noted differential microglia and dendritic cell inherent antigen presentation axis viz, osteopontin, and classical HLAs in IDH subtypes and, glioma-wide non-PD1 checkpoints associations in T cells like Galectin9 and Tim-3. As a general utility, our immune cell deconvolution approach with single-cell-matched bulk RNA sequencing data faithfully resolved 58-cell states which provides glioma specific immune reference for digital cytometry application to genomics datasets. Resultantly, we identified prognosticator immune cell-signatures from TCGA cohorts as one of many potential immune responsiveness applications of the curated signatures for basic and translational immune-genomics efforts. Thus, we not only provide an unprecedented insight of glioma TIME but also present an immune data resource that can be exploited to guide pragmatic glioma immunotherapy designs.

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540 Transcriptionally defined immune landscape in human gliomas

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0540 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A576-A576

Author(s):

Pravesh Gupta ◽

Minghao Dang ◽

Krishna Bojja ◽

Huma Shehwana ◽

Tuan Tran ◽

...

Keyword(s):

T Cells ◽

Dendritic Cell ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Myeloid Cell ◽

Wild Type ◽

Cell Clusters ◽

Md Anderson ◽

Antigen Presenting

BackgroundBrain immunity is largely myeloid cell dominated rather than lymphoid cells in healthy and diseased state including malignancies of glial origins called as gliomas. Despite this skewed myeloid centric immune contexture, immune checkpoint and T cell based therapeutic modalities are generalizably pursued in gliomas ignoring the following facts i) T cells are sparse in tumor brain ii) glioma patients are lymphopenic iii) gliomas harbor abundant and highly complex myeloid cell repertoire. We recognized these paradoxes pertaining to fundamental understanding of constituent immune cells and their functional states in the tumor immune microenvironment (TIME) of gliomas, which remains elusive beyond a priori cell types and/or states.MethodsTo dissect the TIME in gliomas, we performed single-cell RNA-sequencing on ~123,000 tumor-derived sorted CD45+ leukocytes from fifteen genomically classified patients comprising IDH-mutant primary (IMP; n=4), IDH-mutant recurrent (IMR; n=4), IDH-wild type primary (IWP; n=3), or IDH-wild type recurrent (IWR; n=4) gliomas (hereafter referred as glioma subtypes) and two non-glioma brains (NGBs) as controls.ResultsUnsupervised clustering analyses delineated predominant 34-myeloid cell clusters (~75%) over 28-lymphoid cell clusters (~25%) reflecting enormous heterogeneity within and across glioma subtypes. The glioma immune diversity spanned functionally imprinted phagocytic, antigen-presenting, hypoxia, angiogenesis and, tumoricidal myeloid to classical cytotoxic lymphoid subpopulations. Specifically, IDH-mutant gliomas were predominantly enriched for brain-resident microglial subpopulations in contrast to enriched bone barrow-derived infiltrates in IDH-wild type especially in a recurrent setting. Microglia attrition in IWP and IWR gliomas were concomitant with invading monocyte-derived cells with semblance to dendritic cell and macrophage like transcriptomic features. Additionally, microglial functional diversification was noted with disease severity and mostly converged to inflammatory states in IWR gliomas. Beyond dendritic cells, multiple antigen-presenting cellular states expanded with glioma severity especially in IWP and IWR gliomas. Furthermore, we noted differential microglia and dendritic cell inherent antigen presentation axis viz, osteopontin, and classical HLAs in IDH subtypes and, glioma-wide non-PD1 checkpoints associations in T cells like Galectin9 and Tim-3. As a general utility, our immune cell deconvolution approach with single-cell-matched bulk RNA sequencing data faithfully resolved 58-cell states which provides glioma specific immune reference for digital cytometry application to genomics datasets.ConclusionsAltogether, we identified prognosticator immune cell-signatures from TCGA cohorts as one of many potential immune responsiveness applications of the curated signatures for basic and translational immune-genomics efforts. Thus, we not only provide an unprecedented insight of glioma TIME but also present an immune data resource that can be exploited for immunotherapy applications.Ethics ApprovalThe brain tumor/tissue samples were collected as per MD Anderson internal review board (IRB)-approved protocol numbers LAB03-0687 and, LAB04-0001. One non-tumor brain tissue sample was collected from patient undergoing neurosurgery for epilepsy as per Baylor College of Medicine IRB-approved protocol number H-13798. All experiments were compliant with the review board of MD Anderson Cancer Center, USA.ConsentWritten informed consent was obtained from the patient for publication of this abstract and any accompanying images. A copy of the written consent is available for review by the Editor of this journal

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Characterization of dendritic cell subtypes in human cord blood by single-cell sequencing

Biophysics Reports ◽

10.1007/s41048-019-00096-5 ◽

2019 ◽

Vol 5 (4) ◽

pp. 199-208

Author(s):

Xiaoyang Jin ◽

Lingyuan Meng ◽

Zhao Yin ◽

Haisheng Yu ◽

Linnan Zhang ◽

...

Keyword(s):

T Cells ◽

Dendritic Cell ◽

Cord Blood ◽

Single Cell ◽

Rna Sequencing ◽

Differential Expression Analysis ◽

Sequencing Data ◽

Human Cord Blood ◽

Single Cell Rna Sequencing ◽

Antigen Presenting

Abstract Dendritic cells (DCs) are professional antigen-presenting cells (APCs). The key functions of DCs include engulfing, processing and presenting antigens to T cells and regulating the activation of T cells. There are two major DC subtypes in human blood: plasmacytoid DCs (pDCs) and conventional DCs. To define the differences between the adult and infant immune systems, especially in terms of DC constitution, we enriched DCs from human cord blood and generated single-cell RNA sequencing data from about 7000 cells using the 10x Genomics Single Cell 3′ Solution. After incorporating the differential expression analysis method in our clustering process, we identified all the known dendritic cell subsets. Interestingly, we also found a group of DCs with gene expression that was a mix of megakaryocytes and pDCs. Further, we verified the expression of selected genes at both the RNA level by PCR and the protein level by flow cytometry. This study further demonstrates the power of single-cell RNA sequencing in dendritic cell research.

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Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

F1000Research ◽

10.12688/f1000research.18490.2 ◽

2019 ◽

Vol 8 ◽

pp. 296 ◽

Cited By ~ 1

Author(s):

J. Javier Diaz-Mejia ◽

Elaine C. Meng ◽

Alexander R. Pico ◽

Sonya A. MacParland ◽

Troy Ketela ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Human Peripheral Blood ◽

Characteristic Curve ◽

Cell Type ◽

Sequencing Data ◽

Cell Clusters ◽

Reference Cell ◽

Mouse Tissues ◽

Single Cell Rna Sequencing

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. The datasets span Drop-seq, 10X Chromium and Seq-Well technologies and range in size from ~3,700 to ~68,000 cells. Results: Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). We observed an influence of the number of genes in cell type signatures on performance, with smaller signatures leading more frequently to incorrect results. Conclusions: GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods. CIBERSORT and METANEIGHBOR were more influenced than the other methods by analyses including only expected cell types. We provide an extensible framework that can be used to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

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A Comparative Analysis of Single Cell Small RNA Sequencing Data Reveals Heterogenous Isomir Expression and Regulation.

10.21203/rs.3.rs-1012676/v1 ◽

2021 ◽

Author(s):

Christopher Michael Smith ◽

Gyorgy Hutvagner

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Small Rna ◽

Critical Role ◽

Regulation Of Gene Expression ◽

Small Rna Sequencing ◽

Wild Type ◽

Sequencing Data ◽

Cell Level ◽

Regulatory Functions

Abstract MicroRNAs (miRNAs) are non-coding small RNAs which play a critical role in the regulation of gene expression in cells. It is known that miRNAs are often expressed as multiple isoforms, called isomiRs, which may have alternative regulatory functions. Despite the recent development of several single cell small RNA sequencing protocols, these methods have not been leveraged to investigate isomiR expression and regulation to better understand their role on a single cell level. Here we integrate sequencing data from three independent studies and find substantial differences in isomiR composition that suggest that cell autonomous mechanisms may drive isomiR processing. We also find evidence of altered regulatory functions of different classes of isomiRs, when compared to their respective wild-type miRNA, which supports a biological role for many of the isomiRs that are expressed.

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Comprehensive Single-Cell RNA-Sequencing Mapping of Primary Acute Myeloid Leukemias and Profiling of NPM1-Mutated Cells

Blood ◽

10.1182/blood-2018-99-111390 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 995-995

Author(s):

Vincent-Philippe Lavallee ◽

Elham Azizi ◽

Vaidotas Kiseliovas ◽

Ignas Masilionis ◽

Linas Mazutis ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Research Funding ◽

Single Cells ◽

Cell Types ◽

Wild Type ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Equity Ownership ◽

Acute Myeloid

Abstract Introduction: Acute myeloid leukemia (AML) evolution is a multistep process in which cells evolve from hematopoietic stem and progenitor cells (HSPCs) that acquire genetic anomalies, such as chromosomal rearrangements and mutations, which define distinct subgroups. Mutations in Nucleophosmin 1 (NPM1), which occur in ~30% patients, are the most frequent subgroup-defining mutations in AML and appear to be a late driver event in this disease. Bulk RNA-sequencing studies have identified differentially expressed genes between AML subgroups, but they are uninformative of the composition of cell types populating each sample. Large scale Single-cell RNA sequencing (scRNA-seq) technologies now enable a detailed characterization of intra tumoral heterogeneity, and could help to better understand the stepwise evolution from normal to malignant cells. Methods: Twelve primary human AML specimens from MSKCC and Quebec Leukemia Cell Bank, including 8 with NPM1 mutations, were included in this cohort. Cells were subjected to scRNA-seq using 10X Genomics Chromium Single Cell 3' protocols and libraries were sequenced on Illumina HiSeq or NovaSeq platforms. FASTQ files were processed using SEQC pipeline (Azizi E et al, Cell 2018), resulting in a carefully filtered count matrix of > 100,000 single cells (4877 to 11532 cells per sample). Results: Using euclidean distance metrics and t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization, we explored the phenotypic overlap between samples and showed that leukemia cells from different patients were mostly dissimilar, suggesting inter-sample heterogeneity. However, samples with similar morphology and similar NPM1 mutational status were phenotypically closer (Fig A), as anticipated from bulk RNA-sequencing data (TCGA, NEJM 2013). We partitioned cells into distinct clusters using Phenograph (Levine J et al, Cell 2015) (Fig B) and measured the diversity of samples per cluster using Shannon's entropy metric, revealing that mature cell types (B/plasma cells, T/NK and erythroid cells, Fig C), presumably excluded from the tumor bulk, are transcriptionally similar across samples. Most notably, the next most diverse cluster (C36), comprising 438 cells from 11/12 samples, contains cells with a HSPC-like phenotype, as suggested by i) highest correlation of the centroid of this cluster with HSC1 (lin-/CD133+/CD34dim) population from sorted bulk RNA-sequencing data (Novershtern N et al, Cell 2011), and ii) marked GSEA enrichment for stem cell signatures (top enrichment: Jaatinen_hematopoeitic_stem_cell_up, NES = 9.04, FDR q-val = 0). To study the extent to which NPM1 or other mutations drive heterogeneity in leukemia populations, we interrogated 3'-derived single-cell sequences for all recurrent mutations in AML and found that NPM1 gene has unique features (e.g. relatively high single-cell expression and 3' localization) that allow specific identification of mutations in 5 to 34% of cells per mutated sample. To control for the high frequency of false negatives caused by dropouts in scRNA-seq data, we normalized the abundance of mutated vs wild-type cells to provide an estimation of mutation frequency in different cell types (Fig D). As expected, NPM1 mutations were rare in B and T/NK lymphoid cells (also observed using RT-qPCR in sorted populations by Dvorakova D et al, Leuk Lymphoma 2013) and were found in the majority of leukemia and myeloid cells. Interestingly, these mutations were detected at various frequencies in erythroid cells, suggesting that NPM1 mutations are acquired in cells with different lineage commitment in different patients. Most notably, the HSPC-like cluster C36 also contained a subpopulation of cells that have acquired NPM1 mutations and are transcriptionally different from wild-type cells. Conclusion: This study presents a first comprehensive single-cell map of primary AML, and the first 3'-based interrogation of mutations in single cells. It led to the identification phenotypically distinct cells presenting a HSPC-like expression profile which were sub-clonally harboring NPM1 mutations, providing the means to identify deregulated genes in these important leukemia subpopulations. Figure Figure. Disclosures Levine: Epizyme: Patents & Royalties; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Honoraria; Isoplexis: Equity Ownership; C4 Therapeutics: Equity Ownership; Prelude: Research Funding; Gilead: Honoraria; Imago: Equity Ownership; Novartis: Consultancy; Roche: Consultancy, Research Funding; Loxo: Consultancy, Equity Ownership; Qiagen: Equity Ownership, Membership on an entity's Board of Directors or advisory committees.

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Discovery of potential imaging and therapeutic targets for severe inflammation in COVID-19 patients

Scientific Reports ◽

10.1038/s41598-021-93743-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hyunjong Lee ◽

Jeongbin Park ◽

Hyung-Jun Im ◽

Kwon Joong Na ◽

Hongyoon Choi

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Lavage Fluid ◽

Severe Illness ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Key Factor ◽

Target Binding ◽

Druggable Targets

AbstractThe Coronavirus disease 2019 (COVID-19) has been spreading worldwide with rapidly increased number of deaths. Hyperinflammation mediated by dysregulated monocyte/macrophage function is considered to be the key factor that triggers severe illness in COVID-19. However, no specific targeting molecule has been identified for detecting or treating hyperinflammation related to dysregulated macrophages in severe COVID-19. In this study, previously published single-cell RNA-sequencing data of bronchoalveolar lavage fluid cells from thirteen COVID-19 patients were analyzed with publicly available databases for surface and imageable targets. Immune cell composition according to the severity was estimated with the clustering of gene expression data. Expression levels of imaging target molecules for inflammation were evaluated in macrophage clusters from single-cell RNA-sequencing data. In addition, candidate targetable molecules enriched in severe COVID-19 associated with hyperinflammation were filtered. We found that expression of SLC2A3, which can be imaged by [18F]fluorodeoxyglucose, was higher in macrophages from severe COVID-19 patients. Furthermore, by integrating the surface target and drug-target binding databases with RNA-sequencing data of severe COVID-19, we identified candidate surface and druggable targets including CCR1 and FPR1 for drug delivery as well as molecular imaging. Our results provide a resource in the development of specific imaging and therapy for COVID-19-related hyperinflammation.

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Development of a Prognostic Signature Based on Single-Cell RNA Sequencing Data of Immune Cells in Intrahepatic Cholangiocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2020.615680 ◽

2021 ◽

Vol 11 ◽

Author(s):

Miao Su ◽

Kuang-Yuan Qiao ◽

Xiao-Li Xie ◽

Xin-Ying Zhu ◽

Fu-Lai Gao ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Intrahepatic Cholangiocarcinoma ◽

Immune Cells ◽

Immune Cell ◽

Functional Enrichment ◽

Marker Genes ◽

Tissue Cell ◽

Sequencing Data ◽

Single Cell Rna Sequencing

Analysis of single-cell RNA sequencing (scRNA-seq) data of immune cells from the tumor microenvironment (TME) may identify tumor progression biomarkers. This study was designed to investigate the prognostic value of differentially expressed genes (DEGs) in intrahepatic cholangiocarcinoma (ICC) using scRNA-seq. We downloaded the scRNA-seq data of 33,991 cell samples, including 17,090 ICC cell samples and 16,901 ICC adjacent tissue cell samples regarded as normal cells. scRNA-seq data were processed and classified into 20 clusters. The immune cell clusters were extracted and processed again in the same way, and each type of immune cells was divided into several subclusters. In total, 337 marker genes of macrophages and 427 marker genes of B cells were identified by comparing ICC subclusters with normal subclusters. Finally, 659 DEGs were obtained by merging B cell and macrophage marker genes. ICC sample clinical information and gene expression data were downloaded. A nine-prognosis-related-gene (PRG) signature was established by analyzing the correlation between DEGs and overall survival in ICC. The robustness and validity of the signature were verified. Functional enrichment analysis revealed that the nine PRGs were mainly involved in tumor immune mechanisms. In conclusion, we established a PRG signature based on scRNA-seq data from immune cells of patients with ICC. This PRG signature not only reflects the TME immune status but also provides new biomarkers for ICC prognosis.

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Clinical and molecular characteristics of COVID-19 patients with persistent SARS-CoV-2 infection

10.21203/rs.3.rs-86940/v1 ◽

2020 ◽

Author(s):

Chaoyang Sun ◽

Junpeng Fan ◽

Jia Huang ◽

Ensong Guo ◽

Yu Fu ◽

...

Keyword(s):

Nk Cells ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Cell Phenotype ◽

Molecular Characteristics ◽

Clinical Parameters ◽

Sequencing Data ◽

Viral Shedding ◽

Single Cell Rna Sequencing

Abstract The clinical features, molecular characteristics, and immune responses of COVID-19 patients with persistent SARS-CoV-2 infection are not yet well described. In this study, we investigated the differences in clinical parameters, laboratory indexes, plasma cytokines, and peripheral blood mononuclear cell responses, which were assessed using single-cell RNA-sequencing in patients with non-critical COVID-19 with long durations (LDs) and short durations (SDs) of viral shedding. Our results revealed that clinical parameters and laboratory indexes, such as c-reactive protein (CRP) and D-dimer, were comparable between SDs and LDs. Most inflammatory cytokines/chemokines, such as IL-2, IL2R, TNFα/β, IL1β, and CCL5 were present at low levels in LDs. Our single-cell RNA-sequencing revealed a reconfiguration of the peripheral immune cell phenotype in LDs, including decreases in natural killer (NK) cells and CD14+ monocytes and an increase in regulatory T cells (Tregs). Furthermore, most cell subsets in LDs consistently exhibited reduced expression of ribosomal protein (RP) genes, indicating dysfunctions in cytokine/chemokine synthesis, folding, modification, and assembly. Accordingly, the negative correlation between the RP levels and viral shedding duration was validated in an independent cohort of bulk-RNA-sequencing data from 103 non-critical patients, which may help guide clinical management and resource allocation. Moreover, peripheral T and NK cells and memory B cells in LDs likely failed to activate, which contributed to the persistence of viral shedding.

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Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

F1000Research ◽

10.12688/f1000research.18490.3 ◽

2019 ◽

Vol 8 ◽

pp. 296 ◽

Cited By ~ 2

Author(s):

J. Javier Diaz-Mejia ◽

Elaine C. Meng ◽

Alexander R. Pico ◽

Sonya A. MacParland ◽

Troy Ketela ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Human Peripheral Blood ◽

Characteristic Curve ◽

Cell Type ◽

Sequencing Data ◽

Cell Clusters ◽

Reference Cell ◽

Mouse Tissues ◽

Single Cell Rna Sequencing

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Multimodal single-cell/nucleus RNA-sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer’s disease

10.1101/2020.09.23.310466 ◽

2020 ◽

Author(s):

Jielin Xu ◽

Pengyue Zhang ◽

Yin Huang ◽

Lynn Bekris ◽

Justin Lathia ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Association Studies ◽

Molecular Networks ◽

Genome Wide Association Studies ◽

Sequencing Data ◽

Network Analyses

AbstractSystematic identification of molecular networks in disease relevant immune cells of the nervous system is critical for elucidating the underlying pathophysiology of Alzheimer’s disease (AD). Two key immune cell types, disease-associated microglia (DAM) and disease-associated astrocytes (DAA), are biologically involved in AD pathobiology. Therefore, uncovering molecular determinants of DAM and DAA will enhance our understanding of AD biology, potentially identifying novel therapeutic targets for AD treatment. Here, we present an integrative, network-based methodology to uncover conserved molecular networks between DAM and DAA. Specifically, we leverage single-cell and single-nucleus RNA sequencing data from both AD transgenic mouse models and AD patient brains, drug-target networks, metabolite-enzyme associations, and the human protein-protein interactome, along with large-scale patient data validation from the MarketScan Medicare Supplemental Database. We find that common and unique molecular network regulators between DAM (i.e, PAK1, MAPK14, and SYK) and DAA (i.e., NFKB1, FOS, and JUN) are significantly enriched by multiple neuro-inflammatory pathways and well-known genetic variants (i.e., BIN1) from genome-wide association studies. Further network analysis reveal shared immune pathways between DAM and DAA, including Fc gamma R-mediated phagocytosis, Th17 cell differentiation, and chemokine signaling. Furthermore, integrative metabolite-enzyme network analyses imply that fatty acids (i.e., elaidic acid) and amino acids (i.e., glutamate, serine, and phenylalanine) may trigger molecular alterations between DAM and DAA. Finally, we prioritize repurposed drug candidates for potential treatment of AD by agents that specifically reverse dysregulated gene expression of DAM or DAA, including an antithrombotic anticoagulant triflusal, a beta2-adrenergic receptor agonist salbutamol, and the steroid medications (fluticasone and mometasone). Individuals taking fluticasone (an approved anti-inflammatory and inhaled corticosteroid) displayed a significantly decreased incidence of AD (hazard ratio (HR) = 0.858, 95% confidence interval [CI] 0.829-0.888, P < 0.0001) in retrospective case-control validation. Furthermore, propensity score matching cohort studies also confirmed an association of mometasone with reduced incidence of AD in comparison to fluticasone (HR =0.921, 95% CI 0.862-0.984, P < 0.0001).

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