Browsing by Author "Lu, Xiaoyu"

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    A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data
    (Cold Spring Harbor Laboratory, 2021) Alghamdi, Norah; Chang, Wennan; Dang, Pengtao; Lu, Xiaoyu; Wan, Changlin; Gampala, Silpa; Huang, Zhi; Wang, Jiashi; Ma, Qin; Zang, Yong; Fishel, Melissa; Cao, Sha; Zhang, Chi; Medical and Molecular Genetics, School of Medicine
    The metabolic heterogeneity and metabolic interplay between cells are known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single-cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely, single-cell flux estimation analysis (scFEA), to infer the cell-wise fluxome from single-cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network-based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq data set with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this data set showed the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group-specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analyses including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.
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    Acid–base Homeostasis and Implications to the Phenotypic Behaviors of Cancer
    (Elsevier, 2023) Zhou, Yi; Chang, Wennan; Lu, Xiaoyu; Wang, Jin; Zhang, Chi; Xu, Ying; Medical and Molecular Genetics, School of Medicine
    Acid-base homeostasis is a fundamental property of living cells, and its persistent disruption in human cells can lead to a wide range of diseases. In this study, we conducted a computational modeling analysis of transcriptomic data of 4750 human tissue samples of 9 cancer types in The Cancer Genome Atlas (TCGA) database. Built on our previous study, we quantitatively estimated the average production rate of OH- by cytosolic Fenton reactions, which continuously disrupt the intracellular pH (pHi) homeostasis. Our predictions indicate that all or at least a subset of 43 reprogrammed metabolisms (RMs) are induced to produce net protons (H+) at comparable rates of Fenton reactions to keep the pHi stable. We then discovered that a number of well-known phenotypes of cancers, including increased growth rate, metastasis rate, and local immune cell composition, can be naturally explained in terms of the Fenton reaction level and the induced RMs. This study strongly suggests the possibility to have a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors. In addition, strong evidence is provided to demonstrate that a popular view that Na+/H+ exchangers along with lactic acid exporters and carbonic anhydrases are responsible for the intracellular alkalization and extracellular acidification in cancer may not be justified.
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    Astrocytes modulate neurodegenerative phenotypes associated with glaucoma in OPTN(E50K) human stem cell-derived retinal ganglion cells
    (Elsevier, 2022) Gomes, Cátia; VanderWall, Kirstin B.; Pan, Yanling; Lu, Xiaoyu; Lavekar, Sailee S.; Huang, Kang-Chieh; Fligor, Clarisse M.; Harkin, Jade; Zhang, Chi; Cummins, Theodore R.; Meyer, Jason S.; Medical and Molecular Genetics, School of Medicine
    Although the degeneration of retinal ganglion cells (RGCs) is a primary characteristic of glaucoma, astrocytes also contribute to their neurodegeneration in disease states. Although studies often explore cell-autonomous aspects of RGC neurodegeneration, a more comprehensive model of glaucoma should take into consideration interactions between astrocytes and RGCs. To explore this concept, RGCs and astrocytes were differentiated from human pluripotent stem cells (hPSCs) with a glaucoma-associated OPTN(E50K) mutation along with corresponding isogenic controls. Initial results indicated significant changes in OPTN(E50K) astrocytes, including evidence of autophagy dysfunction. Subsequently, co-culture experiments demonstrated that OPTN(E50K) astrocytes led to neurodegenerative properties in otherwise healthy RGCs, while healthy astrocytes rescued some neurodegenerative features in OPTN(E50K) RGCs. These results are the first to identify disease phenotypes in OPTN(E50K) astrocytes, including how their modulation of RGCs is affected. Moreover, these results support the concept that astrocytes could offer a promising target for therapeutic intervention in glaucoma.
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    BATF Regulates T Regulatory Cell Functional Specification and Fitness of Triglyceride Metabolism in Restraining Allergic Responses
    (American Association of Immunologists, 2021) Xu, Chengxian; Fu, Yongyao; Liu, Sheng; Trittipo, Jack; Lu, Xiaoyu; Qi, Rong; Du, Hong; Yan, Cong; Zhang, Chi; Wan, Jun; Kaplan, Mark H.; Yang, Kai; Pediatrics, School of Medicine
    Preserving appropriate function and metabolism in regulatory T (Treg) cells is crucial for controlling immune tolerance and inflammatory responses. Yet how Treg cells coordinate cellular metabolic programs to support their functional specification remains elusive. In this study, we report that BATF couples the TH2-suppressive function and triglyceride (TG) metabolism in Treg cells for controlling allergic airway inflammation and IgE responses. Mice with Treg-specific ablation of BATF developed an inflammatory disorder characterized by TH2-type dominant responses and were predisposed to house dust mite-induced airway inflammation. Loss of BATF enabled Treg cells to acquire TH2 cell-like characteristics. Moreover, BATF-deficient Treg cells displayed elevated levels of cellular TGs, and repressing or elevating TGs, respectively, restored or exacerbated their defects. Mechanistically, TCR/CD28 costimulation enhanced expression and function of BATF, which sustained IRF4 activity to preserve Treg cell functionality. Thus, our studies reveal that BATF links Treg cell functional specification and fitness of cellular TGs to control allergic responses, and suggest that therapeutic targeting of TG metabolism could be used for the treatment of allergic disease.
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    Discovery and Interpretation of Subspace Structures in Omics Data by Low-Rank Representation
    (2022-10) Lu, Xiaoyu; Cao, Sha; Zhang, Chi; Yan, Jingwen; Zang, Yong
    Biological functions in cells are highly complicated and heterogenous, and can be reflected by omics data, such as gene expression levels. Detecting subspace structures in omics data and understanding the diversity of the biological processes is essential to the full comprehension of biological mechanisms and complicated biological systems. In this thesis, we are developing novel statistical learning approaches to reveal the subspace structures in omics data. Specifically, we focus on three types of subspace structures: low-rank subspace, sparse subspace and covariates explainable subspace. For low-rank subspace, we developed a semi-supervised model SSMD to detect cell type specific low-rank structures and predict their relative proportions across different tissue samples. SSMD is the first computational tool that utilizes semi-supervised identification of cell types and their marker genes specific to each mouse tissue transcriptomics data, for better understanding of the disease microenvironment and downstream disease mechanism. For sparsity-driven sparse subspace, we proposed a novel positive and unlabeled learning model, namely PLUS, that could identify cancer metastasis related genes, predict cancer metastasis status and specifically address the under-diagnosis issue in studying metastasis potential. We found PLUS predicted metastasis potential at diagnosis have significantly strong association with patient’s progression-free survival in their follow-up data. Lastly, to discover the covariates explainable subspace, we proposed an analytical pipeline based on covariance regression, namely, scCovReg. We utilized scCovReg to detect the pathway level second-order variations using scRNA-Seq data in a statistically powerful manner, and to associate the second-order variations with important subject-level characteristics, such as disease status. In conclusion, we presented a set of state-of-the-art computational solutions for identifying sparse subspaces in omics data, which promise to provide insights into the mechanism in complex diseases.
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    Hyperglycemia cooperates with Tet2 heterozygosity to induce leukemia driven by proinflammatory cytokine–induced lncRNA Morrbid
    (American Society for Clinical Investigation, 2021-01-04) Cai, Zhigang; Lu, Xiaoyu; Zhang, Chi; Nelanuthala, Sai; Aguilera, Fabiola; Hadley, Abigail; Ramdas, Baskar; Fang, Fang; Nephew, Kenneth; Kotzin, Jonathan J.; Williams, Adam; Henao-Mejia, Jorge; Haneline, Laura; Kapur, Reuben; Microbiology and Immunology, School of Medicine
    Diabetes mellitus (DM) is a risk factor for cancer. The role of DM-induced hyperglycemic (HG) stress in blood cancer is poorly understood. Epidemiologic studies show that individuals with DM are more likely to have a higher rate of mutations in genes found in pre-leukemic hematopoietic stem and progenitor cells (pre-LHSPCs) including TET2. TET2-mutant pre-LHSPCs require additional hits to evolve into full-blown leukemia and/or an aggressive myeloproliferative neoplasm (MPN). Intrinsic mutations have been shown to cooperate with Tet2 to promote leukemic transformation. However, the extrinsic factors are poorly understood. Using a mouse model carrying Tet2 haploinsufficiency to mimic the human pre-LHSPC condition and HG stress, in the form of an Ins2Akita/+ mutation, which induces hyperglycemia and type 1 DM, we show that the compound mutant mice developed a lethal form of MPN and/or acute myeloid leukemia (AML). RNA-Seq revealed that this was due in part to upregulation of proinflammatory pathways, thereby generating a feed-forward loop, including expression of the antiapoptotic, long noncoding RNA (lncRNA) Morrbid. Loss of Morrbid in the compound mutants rescued the lethality and mitigated MPN/AML. We describe a mouse model for age-dependent MPN/AML and suggest that hyperglycemia acts as an environmental driver for myeloid neoplasms, which could be prevented by reducing expression levels of the inflammation-related lncRNA Morrbid.
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    ICTD: A semi-supervised cell type identification and deconvolution method for multi-omics data
    (BioRxiv, 2019) Chang, Wennan; Wan, Changlin; Lu, Xiaoyu; Tu, Szu-wei; Sun, Yifan; Zhang, Xinna; Zang, Yong; Zhang, Anru; Huang, Kun; Liu, Yunlong; Lu, Xiongbin; Cao, Sha; Zhang, Chi; Medical and Molecular Genetics, School of Medicine
    We developed a novel deconvolution method, namely Inference of Cell Types and Deconvolution (ICTD) that addresses the fundamental issue of identifiability and robustness in current tissue data deconvolution problem. ICTD provides substantially new capabilities for omics data based characterization of a tissue microenvironment, including (1) maximizing the resolution in identifying resident cell and sub types that truly exists in a tissue, (2) identifying the most reliable marker genes for each cell type, which are tissue and data set specific, (3) handling the stability problem with co-linear cell types, (4) co-deconvoluting with available matched multi-omics data, and (5) inferring functional variations specific to one or several cell types. ICTD is empowered by (i) rigorously derived mathematical conditions of identifiable cell type and cell type specific functions in tissue transcriptomics data and (ii) a semi supervised approach to maximize the knowledge transfer of cell type and functional marker genes identified in single cell or bulk cell data in the analysis of tissue data, and (iii) a novel unsupervised approach to minimize the bias brought by training data. Application of ICTD on real and single cell simulated tissue data validated that the method has consistently good performance for tissue data coming from different species, tissue microenvironments, and experimental platforms. Other than the new capabilities, ICTD outperformed other state-of-the-art devolution methods on prediction accuracy, the resolution of identifiable cell, detection of unknown sub cell types, and assessment of cell type specific functions. The premise of ICTD also lies in characterizing cell-cell interactions and discovering cell types and prognostic markers that are predictive of clinical outcomes.
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    LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data
    (Oxford University Press, 2019-10-10) Wan, Changlin; Chang, Wennan; Zhang, Yu; Shah, Fenil; Lu, Xiaoyu; Zang, Yong; Zhang, Anru; Cao, Sha; Fishel, Melissa L.; Ma, Qin; Zhang, Chi; Medical and Molecular Genetics, School of Medicine
    A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional regulatory inputs across individual cells, which is further complicated by largely observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model, from the kinetic relationships of the transcriptional regulatory inputs, mRNA metabolism and abundance in single cells. LTMG infers the expression multi-modalities across single cells, meanwhile, the dropouts and low expressions are treated as left truncated. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of scRNA-seq data, comparing to three other state-of-the-art models. Our biological assumption of the low non-zero expressions, rationality of the multimodality setting, and the capability of LTMG in extracting expression states specific to cell types or functions, are validated on independent experimental data sets. A differential gene expression test and a co-regulation module identification method are further developed. We experimentally validated that our differential expression test has higher sensitivity and specificity, compared with other five popular methods. The co-regulation analysis is capable of retrieving gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.
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    Lysine-Specific Demethylase 1 Mediates AKT Activity and Promotes Epithelial-to-Mesenchymal Transition in PIK3CA-Mutant Colorectal Cancer
    (American Association for Cancer Research, 2020-02-01) Miller, Samuel A.; Policastro, Robert A.; Savant, Sudha S.; Sriramkumar, Shruthi; Ding, Ning; Lu, Xiaoyu; Mohammad, Helai P.; Cao, Sha; Kalin, Jay H.; Cole, Philip A.; Zentner, Gabriel E.; O'Hagan, Heather M.; Medical and Molecular Genetics, School of Medicine
    Activation of the epithelial-mesenchymal transition (EMT) program is a critical mechanism for initiating cancer progression and migration. Colorectal cancers (CRCs) contain many genetic and epigenetic alterations that can contribute to EMT. Mutations activating the PI3K/AKT signaling pathway are observed in >40% of patients with CRC contributing to increased invasion and metastasis. Little is known about how oncogenic signaling pathways such as PI3K/AKT synergize with chromatin modifiers to activate the EMT program. Lysine Specific Demethylase 1 (LSD1) is a chromatin-modifying enzyme that is overexpressed in colorectal cancer (CRC) and enhances cell migration. In this study we determine that LSD1 expression is significantly elevated in CRC patients with mutation of the catalytic subunit of PI3K, PIK3CA, compared to CRC patients with WT PIK3CA. LSD1 enhances activation of the AKT kinase in CRC cells through a non-catalytic mechanism, acting as a scaffolding protein for the transcription-repressing CoREST complex. Additionally, growth of PIK3CA mutant CRC cells is uniquely dependent on LSD1. Knockdown or CRISPR knockout of LSD1 blocks AKT-mediated stabilization of the EMT-promoting transcription factor Snail and effectively blocks AKT-mediated EMT and migration. Overall we uniquely demonstrate that LSD1 mediates AKT activation in response to growth factors and oxidative stress, and LSD1-regulated AKT activity promotes EMT-like characteristics in a subset of PIK3CA mutant cells. Implications Our data supports the hypothesis that inhibitors targeting the CoREST complex may be clinically effective in CRC patients harboring PIK3CA mutations.
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    Pipeline for characterizing alternative mechanisms (PCAM) based on bi-clustering to study colorectal cancer heterogeneity
    (Elsevier, 2023-03-17) Cao, Sha; Chang, Wennan; Wan, Changlin; Lu, Xiaoyu; Dang, Pengtao; Zhou, Xinyu; Zhu, Haiqi; Chen, Jian; Li, Bo; Zang, Yong; Wang, Yijie; Zhang, Chi; Biostatistics and Health Data Science, School of Medicine
    The cells of colorectal cancer (CRC) in their microenvironment experience constant stress, leading to dysregulated activity in the tumor niche. As a result, cancer cells acquire alternative pathways in response to the changing microenvironment, posing significant challenges for the design of effective cancer treatment strategies. While computational studies on high-throughput omics data have advanced our understanding of CRC subtypes, characterizing the heterogeneity of this disease remains remarkably complex. Here, we present a novel computational Pipeline for Characterizing Alternative Mechanisms (PCAM) based on biclustering to gain a more detailed understanding of cancer heterogeneity. Our application of PCAM to large-scale CRC transcriptomics datasets suggests that PCAM can generate a wealth of information leading to new biological understanding and predictive markers of alternative mechanisms. Our key findings include: 1) A comprehensive collection of alternative pathways in CRC, associated with biological and clinical factors. 2) Full annotation of detected alternative mechanisms, including their enrichment in known pathways and associations with various clinical outcomes. 3) A mechanistic relationship between known clinical subtypes and outcomes on a consensus map, visualized by the presence of alternative mechanisms. 4) Several potential novel alternative drug resistance mechanisms for Oxaliplatin, 5-Fluorouracil, and FOLFOX, some of which were validated on independent datasets. We believe that gaining a deeper understanding of alternative mechanisms is a critical step towards characterizing the heterogeneity of CRC. The hypotheses generated by PCAM, along with the comprehensive collection of biologically and clinically associated alternative pathways in CRC, could provide valuable insights into the underlying mechanisms driving cancer progression and drug resistance, which could aid in the development of more effective cancer therapies and guide experimental design towards more targeted and personalized treatment strategies. The computational pipeline of PCAM is available in GitHub (https://github.com/changwn/BC-CRC).