Browsing by Author "Zhang, Yu"

Now showing 1 - 8 of 8
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    Divergent actions of Myofibroblast and Myocyte β2-Adrenoceptor in Heart Failure and Fibrotic Remodeling
    (American Heart Association, 2023) Deng, Bingqing; Zhang, Yu; Zhu, Chaoqun; Wang, Ying; Weatherford, Eric; Xu, Bing; Liu, Xuanhui; Conway, Simon J.; Abel, E. Dale; Xiang, Yang K.; Pediatrics, School of Medicine
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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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    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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    M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data
    (BMC, 2019-12-20) Zhang, Yu; Wan, Changlin; Wang, Pengcheng; Chang, Wennan; Huo, Yan; Chen, Jian; Ma, Qin; Cao, Sha; Zhang, Chi; Medical and Molecular Genetics, School of Medicine
    Background Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. Results We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. Conclusion A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.
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    QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data
    (Oxford, 2020) Xie, Juan; Ma, Anjun; Zhang, Yu; Liu, Bingqiang; Cao, Sha; Wang, Cankun; Xu, Jennifer; Zhang, Chi; Ma, Qin; Medical and Molecular Genetics, School of Medicine
    Motivation The biclustering of large-scale gene expression data holds promising potential for detecting condition-specific functional gene modules (i.e. biclusters). However, existing methods do not adequately address a comprehensive detection of all significant bicluster structures and have limited power when applied to expression data generated by RNA-Sequencing (RNA-Seq), especially single-cell RNA-Seq (scRNA-Seq) data, where massive zero and low expression values are observed. Results We present a new biclustering algorithm, QUalitative BIClustering algorithm Version 2 (QUBIC2), which is empowered by: (i) a novel left-truncated mixture of Gaussian model for an accurate assessment of multimodality in zero-enriched expression data, (ii) a fast and efficient dropouts-saving expansion strategy for functional gene modules optimization using information divergency and (iii) a rigorous statistical test for the significance of all the identified biclusters in any organism, including those without substantial functional annotations. QUBIC2 demonstrated considerably improved performance in detecting biclusters compared to other five widely used algorithms on various benchmark datasets from E.coli, Human and simulated data. QUBIC2 also showcased robust and superior performance on gene expression data generated by microarray, bulk RNA-Seq and scRNA-Seq.
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    Response to correspondence on “Reproducibility of CRISPR-Cas9 methods for generation of conditional mouse alleles: a multi-center evaluation”
    (BMC, 2021-04-07) Gurumurthy, Channabasavaiah B.; O’Brien, Aidan R.; Quadros, Rolen M.; Adams, John, Jr.; Alcaide, Pilar; Ayabe, Shinya; Ballard, Johnathan; Batra, Surinder K.; Beauchamp, Marie-Claude; Becker, Kathleen A.; Bernas, Guillaume; Brough, David; Carrillo-Salinas, Francisco; Chan, Wesley; Chen, Hanying; Dawson, Ruby; DeMambro, Victoria; D’Hont, Jinke; Dibb, Katharine; Eudy, James D.; Gan, Lin; Gao, Jing; Gonzales, Amy; Guntur, Anyonya; Guo, Huiping; Harms, Donald W.; Harrington, Anne; Hentges, Kathryn E.; Humphreys, Neil; Imai, Shiho; Ishii, Hideshi; Iwama, Mizuho; Jonasch, Eric; Karolak, Michelle; Keavney, Bernard; Khin, Nay-Chi; Konno, Masamitsu; Kotani, Yuko; Kunihiro, Yayoi; Lakshmanan, Imayavaramban; Larochelle, Catherine; Lawrence, Catherine B.; Li, Lin; Lindner, Volkhard; Liu, Xian-De; Lopez-Castejon, Gloria; Loudon, Andrew; Lowe, Jenna; Jerome-Majeweska, Loydie; Matsusaka, Taiji; Miura, Hiromi; Miyasaka, Yoshiki; Morpurgo, Benjamin; Moty, Katherine; Nabeshima, Yo-ichi; Nakade, Koji; Nakashiba, Toshiaki; Nakashima, Kenichi; Obata, Yuichi; Ogiwara, Sanae; Ouellet, Mariette; Oxburgh, Leif; Piltz, Sandra; Pinz, Ilka; Ponnusamy, Moorthy P.; Ray, David; Redder, Ronald J.; Rosen, Clifford J.; Ross, Nikki; Ruhe, Mark T.; Ryzhova, Larisa; Salvador, Ane M.; Shameen Alam, Sabrina; Sedlacek, Radislav; Sharma, Karan; Smith, Chad; Staes, Katrien; Starrs, Lora; Sugiyama, Fumihiro; Takahashi, Satoru; Tanaka, Tomohiro; Trafford, Andrew; Uno, Yoshihiro; Vanhoutte, Leen; Vanrockeghem, Frederique; Willis, Brandon J.; Wright, Christian S.; Yamauchi, Yuko; Yi, Xin; Yoshimi, Kazuto; Zhang, Xuesong; Zhang, Yu; Ohtsuka, Masato; Das, Satyabrata; Garry, Daniel J.; Hochepied, Tino; Thomas, Paul; Parker-Thornburg, Jan; Adamson, Antony D.; Yoshiki, Atsushi; Schmouth, Jean-Francois; Golovko, Andrei; Thompson, William R.; Lloyd, K.C. Kent; Wood, Joshua A.; Cowan, Mitra; Mashimo, Tomoji; Mizuno, Seiya; Zhu, Hao; Kasparek, Petr; Liaw, Lucy; Miano, Joseph M.; Burgio, Gaetan; Medicine, School of Medicine
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    Silica infiltration on translucent zirconia restorations: effects on the antagonist wear and survivability
    (Elsevier, 2022) Martins Alves, Larissa Marcia; da Silva Rodrigues, Camila; de Carvalho Ramos, Nathalia; Buizastrow, Jeff; Bastos Campos, Tiago Moreira; Bottino, Marco Antonio; Zhang, Yu; de Melo, Renata Marques; Biomedical and Applied Sciences, School of Dentistry
    Objective: To assess potential antagonist wear and survival probability of silica-infiltrated zirconia compared to glass-graded, glazed, and polished zirconia. Methods: Table top restorations made of 3Y-TZP (3Y), 5Y-PSZ (5Y), and lithium disilicate (LD) were bonded onto epoxy resin preparations. Each zirconia was divided into five groups according to the surface treatment: polishing; glaze; polishing-glaze; glass infiltration; and silica infiltration. The LD restorations received a glaze layer. Specimens were subjected to sliding fatigue wear using a steatite antagonist (1.25 ×106 cycles, 200 N). The presence of cracks, fractures, and/or debonding was checked every one/third of the total number of cycles was completed. Roughness, microstructural, Scanning electron microscopy, wear and residual stress analyses were conducted. Kaplan-Meier, Mantel-Cox (log-rank) and ANOVA tests were performed for statistical analyses. Results: The survival probability was different among the groups. Silica infiltration and polishing-glaze led to lower volume loss than glaze and glass-infiltration. Difference was observed for roughness among the zirconia and surface treatment, while lithium disilicate presented similar roughness compared to both glazed zirconia. Scanning electron microscopy revealed the removal of the surface treatment after sliding fatigue wear in all groups. Compressive stress was detected on 3Y surfaces, while tensile stress was observed on 5Y. Significance: 3Y and 5Y zirconia behaved similarly regarding antagonist wear, presenting higher antagonist wear than the glass ceramic. Silica-infiltrated and polished-glazed zirconia produced lower antagonist volume loss than glazed and glass-infiltrated zirconia. Silica-infiltrated 3Y and lithium disilicate restorations were the only groups to show survival probabilities lower than 85%.
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    SrxTi1–xCoO3±δ Perovskite-like Catalysts with Enhanced Activity for Hydrogen Production
    (Wiley, 2021-08) Liu, Yan; Li, Huigu; Huang, Jia; Hu, Xiaomin; Zhang, Yu; Huang, Lihong; Biomedical Engineering, School of Engineering and Technology
    Autothermal reforming (ATR) of acetic acid (HOAc) is a promising and alternative route for hydrogen production from renewable resources, but catalyst deactivation caused by cobalt metal sintering and coking is a major concern in ATR. In this work, perovskite-like catalysts of the formula SrxTi1–xCoO3±δ (x = 0, 0.2, 0.5, and 0.8) were prepared by evaporation-induced self-assembly and then evaluated in ATR for hydrogen production. The SrxTi1–xCoO3±δ catalysts exhibited high activity and stability as well as 100 % conversion of HOAc. Additionally, the Co particles with strong metal-support interaction of SrTi(Co)O3 inhibited coking and agglomeration, and showed potential for hydrogen production by the ATR process.