Browsing by Author "Wang, Jia"

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    A high-resolution view of the immune and stromal cell response to Haemophilus ducreyi infection in human volunteers
    (American Society for Microbiology, 2025) Brothwell, Julie A.; Wei, Yuhui; Wang, Jia; Guo, Tingbo; Zhang, Chi; Fortney, Kate R.; Duplantier, Rory; Chen, Li; Batteiger, Teresa A.; Kaplan, Mark H.; Spinola, Stanley M.; Cao, Sha; Microbiology and Immunology, School of Medicine
    Haemophilus ducreyi causes the genital ulcer disease chancroid and cutaneous ulcers in children. To study its pathogenesis, we developed a human challenge model in which we infect the skin on the upper arm of human volunteers with H. ducreyi to the pustular stage of disease. The model has been used to define lesional architecture, describe the immune infiltrate into the infected sites using flow cytometry, and explore the molecular basis of the immune response using bulk RNA-seq. Here, we used single cell RNA-seq (scRNA-seq) and spatial transcriptomics to simultaneously characterize multiple cell types within infected human skin and determine the cellular origin of differentially expressed transcripts that we had previously identified by bulk RNA-seq. We obtained paired biopsies of pustules and wounded (mock infected) sites from five volunteers for scRNA-seq. We identified 13 major cell types, including T- and NK-like cells, macrophages, dendritic cells, as well as other cell types typically found in the skin. Immune cell types were enriched in pustules, and some subtypes within the major cell types were exclusive to pustules. Sufficient tissue specimens for spatial transcriptomics were available from four of the volunteers. T- and NK-like cells were highly associated with multiple antigen presentation cell types. In pustules, type I interferon stimulation was high in areas that were high in antigen presentation-especially in macrophages near the abscess-compared to wounds. Together, our data provide a high-resolution view of the cellular immune response to the infection of the skin with a human pathogen. IMPORTANCE: A high-resolution view of the immune infiltrate due to infection with an extracellular bacterial pathogen in human skin has not yet been defined. Here, we used the human skin pathogen Haemophilus ducreyi in a human challenge model to identify on a single cell level the types of cells that are present in volunteers who fail to spontaneously clear infection and form pustules. We identified 13 major cell types. Immune cells and immune-activated stromal cells were enriched in pustules compared to wounded (mock infected) sites. Pustules formed despite the expression of multiple pro-inflammatory cytokines, such as IL-1β and type I interferon. Interferon stimulation was most evident in macrophages, which were proximal to the abscess. The pro-inflammatory response within the pustule may be tempered by regulatory T cells and cells that express indoleamine 2,3-dioxygenase, leading to failure of the immune system to clear H. ducreyi.
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    FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data
    (Oxford University Press, 2023) Zhang, Zixuan; Zhu, Haiqi; Dang, Pengtao; Wang, Jia; Chang, Wennan; Wang, Xiao; Alghamdi, Norah; Lu, Alex; Zang, Yong; Wu, Wenzhuo; Wang, Yijie; Zhang, Yu; Cao, Sha; Zhang, Chi; Medical and Molecular Genetics, School of Medicine
    Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies.
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    Inhibition of Glutamate-to-Glutathione Flux Promotes Tumor Antigen Presentation in Colorectal Cancer Cells
    (Wiley, 2025) Yu, Tao; Van der Jeught, Kevin; Zhu, Haiqi; Zhou, Zhuolong; Sharma, Samantha; Liu, Sheng; Eyvani, Haniyeh; So, Ka Man; Singh, Naresh; Wang, Jia; Sandusky, George E.; Liu, Yunlong; Opyrchal, Mateusz; Cao, Sha; Wan, Jun; Zhang, Chi; Zhang, Xinna; Medical and Molecular Genetics, School of Medicine
    Colorectal cancer (CRC) cells display remarkable adaptability, orchestrating metabolic changes that confer growth advantages, pro-tumor microenvironment, and therapeutic resistance. One such metabolic change occurs in glutamine metabolism. Colorectal tumors with high glutaminase (GLS) expression exhibited reduced T cell infiltration and cytotoxicity, leading to poor clinical outcomes. However, depletion of GLS in CRC cells has minimal effect on tumor growth in immunocompromised mice. By contrast, remarkable inhibition of tumor growth is observed in immunocompetent mice when GLS is knocked down. It is found that GLS knockdown in CRC cells enhanced the cytotoxicity of tumor-specific T cells. Furthermore, the single-cell flux estimation analysis (scFEA) of glutamine metabolism revealed that glutamate-to-glutathione (Glu-GSH) flux, downstream of GLS, rather than Glu-to-2-oxoglutarate flux plays a key role in regulating the immune response of CRC cells in the tumor. Mechanistically, inhibition of the Glu-GSH flux activated reactive oxygen species (ROS)-related signaling pathways in tumor cells, thereby increasing the tumor immunogenicity by promoting the activity of the immunoproteasome. The combinatorial therapy of Glu-GSH flux inhibitor and anti-PD-1 antibody exhibited a superior tumor growth inhibitory effect compared to either monotherapy. Taken together, the study provides the first evidence pointing to Glu-GSH flux as a potential therapeutic target for CRC immunotherapy.
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    A New Statistic to Evaluate Imputation Reliability
    (Public Library of Science, 2010-03-15) Lin, Peng; Hartz, Sarah M.; Zhang, Zhehao; Saccone, Scott F.; Wang, Jia; Tischfield, Jay A.; Edenberg, Howard J.; Kramer, John R.; Goate, Alison M.; Bierut, Laura J.; Rice, John P.; COGA Collaborators COGEND Collaborators, GENEVA; Biochemistry and Molecular Biology, School of Medicine
    Background As the amount of data from genome wide association studies grows dramatically, many interesting scientific questions require imputation to combine or expand datasets. However, there are two situations for which imputation has been problematic: (1) polymorphisms with low minor allele frequency (MAF), and (2) datasets where subjects are genotyped on different platforms. Traditional measures of imputation cannot effectively address these problems. Methodology/Principal Findings We introduce a new statistic, the imputation quality score (IQS). In order to differentiate between well-imputed and poorly-imputed single nucleotide polymorphisms (SNPs), IQS adjusts the concordance between imputed and genotyped SNPs for chance. We first evaluated IQS in relation to minor allele frequency. Using a sample of subjects genotyped on the Illumina 1 M array, we extracted those SNPs that were also on the Illumina 550 K array and imputed them to the full set of the 1 M SNPs. As expected, the average IQS value drops dramatically with a decrease in minor allele frequency, indicating that IQS appropriately adjusts for minor allele frequency. We then evaluated whether IQS can filter poorly-imputed SNPs in situations where cases and controls are genotyped on different platforms. Randomly dividing the data into “cases” and “controls”, we extracted the Illumina 550 K SNPs from the cases and imputed the remaining Illumina 1 M SNPs. The initial Q-Q plot for the test of association between cases and controls was grossly distorted (λ = 1.15) and had 4016 false positives, reflecting imputation error. After filtering out SNPs with IQS<0.9, the Q-Q plot was acceptable and there were no longer false positives. We then evaluated the robustness of IQS computed independently on the two halves of the data. In both European Americans and African Americans the correlation was >0.99 demonstrating that a database of IQS values from common imputations could be used as an effective filter to combine data genotyped on different platforms. Conclusions/Significance IQS effectively differentiates well-imputed and poorly-imputed SNPs. It is particularly useful for SNPs with low minor allele frequency and when datasets are genotyped on different platforms.