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Sousa AM, Ferreira D, Rodrigues LR, Pereira MO. Aptamer-based therapy for fighting biofilm-associated infections. J Control Release 2024; 367:522-539. [PMID: 38295992 DOI: 10.1016/j.jconrel.2024.01.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/06/2024] [Accepted: 01/27/2024] [Indexed: 02/06/2024]
Abstract
Biofilms are key players in the pathogenesis of most of chronic infections associated with host tissue or fluids and indwelling medical devices. These chronic infections are hard to be treated due to the increased biofilms tolerance towards antibiotics in comparison to planktonic (or free living) cells. Despite the advanced understanding of their formation and physiology, biofilms continue to be a challenge and there is no standardized therapeutic approach in clinical practice to eradicate them. Aptamers offer distinctive properties, including excellent affinity, selectivity, stability, making them valuable tools for therapeutic purposes. This review explores the flexibility and designability of aptamers as antibiofilm drugs but, importantly, as targeting tools for diverse drug and delivery systems. It highlights specific examples of application of aptamers in biofilms of diverse species according to different modes of action including inhibition of motility and adhesion, blocking of quorum sensing molecules, and dispersal of biofilm-cells to planktonic state. Moreover, it discusses the limitations and challenges that impaired an increased success of the use of aptamers on biofilm management, as well as the opportunities related to aptamers modifications that can significantly expand their applicability on the biofilm field.
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Affiliation(s)
- Ana Margarida Sousa
- CEB - Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal; LABBELS - Associate Laboratory, Braga, Guimarães, Portugal.
| | - Débora Ferreira
- CEB - Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal; LABBELS - Associate Laboratory, Braga, Guimarães, Portugal
| | - Lígia Raquel Rodrigues
- CEB - Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal; LABBELS - Associate Laboratory, Braga, Guimarães, Portugal
| | - Maria Olívia Pereira
- CEB - Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal; LABBELS - Associate Laboratory, Braga, Guimarães, Portugal.
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2
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Song JS, Manjunath M. Predicting the molecular functions of regulatory genetic variants associated with cancer. Oncotarget 2023; 14:775-777. [PMID: 37646780 PMCID: PMC10467629 DOI: 10.18632/oncotarget.28451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Indexed: 09/01/2023] Open
Affiliation(s)
- Jun S. Song
- Correspondence to:Jun S. Song, Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA email
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3
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Novakovsky G, Fornes O, Saraswat M, Mostafavi S, Wasserman WW. ExplaiNN: interpretable and transparent neural networks for genomics. Genome Biol 2023; 24:154. [PMID: 37370113 DOI: 10.1186/s13059-023-02985-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
Deep learning models such as convolutional neural networks (CNNs) excel in genomic tasks but lack interpretability. We introduce ExplaiNN, which combines the expressiveness of CNNs with the interpretability of linear models. ExplaiNN can predict TF binding, chromatin accessibility, and de novo motifs, achieving performance comparable to state-of-the-art methods. Its predictions are transparent, providing global (cell state level) as well as local (individual sequence level) biological insights into the data. ExplaiNN can serve as a plug-and-play platform for pretrained models and annotated position weight matrices. ExplaiNN aims to accelerate the adoption of deep learning in genomic sequence analysis by domain experts.
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Affiliation(s)
- Gherman Novakovsky
- Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Oriol Fornes
- Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Manu Saraswat
- Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Sara Mostafavi
- Paul G. Allen School of Computer Science and Engineering, University of Washington (UW), Seattle, USA
| | - Wyeth W Wasserman
- Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada.
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4
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Cooper BH, Dantas Machado AC, Gan Y, Aparicio O, Rohs R. DNA binding specificity of all four Saccharomyces cerevisiae forkhead transcription factors. Nucleic Acids Res 2023; 51:5621-5633. [PMID: 37177995 PMCID: PMC10287902 DOI: 10.1093/nar/gkad372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 04/19/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
Quantifying the nucleotide preferences of DNA binding proteins is essential to understanding how transcription factors (TFs) interact with their targets in the genome. High-throughput in vitro binding assays have been used to identify the inherent DNA binding preferences of TFs in a controlled environment isolated from confounding factors such as genome accessibility, DNA methylation, and TF binding cooperativity. Unfortunately, many of the most common approaches for measuring binding preferences are not sensitive enough for the study of moderate-to-low affinity binding sites, and are unable to detect small-scale differences between closely related homologs. The Forkhead box (FOX) family of TFs is known to play a crucial role in regulating a variety of key processes from proliferation and development to tumor suppression and aging. By using the high-sequencing depth SELEX-seq approach to study all four FOX homologs in Saccharomyces cerevisiae, we have been able to precisely quantify the contribution and importance of nucleotide positions all along an extended binding site. Essential to this process was the alignment of our SELEX-seq reads to a set of candidate core sequences determined using a recently developed tool for the alignment of enriched k-mers and a newly developed approach for the reprioritization of candidate cores.
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Affiliation(s)
- Brendon H Cooper
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Ana Carolina Dantas Machado
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Yan Gan
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Oscar M Aparicio
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
| | - Remo Rohs
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
- Departments of Chemistry, Physics & Astronomy, and Computer Science, University of Southern California, Los Angeles, CA 90089, USA
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Chowdhury R, Taguchi AT, Kelbauskas L, Stafford P, Diehnelt C, Zhao ZG, Williamson PC, Green V, Woodbury NW. Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease. PLoS Comput Biol 2023; 19:e1010773. [PMID: 37339137 DOI: 10.1371/journal.pcbi.1010773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/15/2023] [Indexed: 06/22/2023] Open
Abstract
Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a variety of infectious diseases. This is true even though these arrays consist of peptides with near-random amino acid sequences that were not designed to mimic biological antigens. This "immunosignature" approach, is based on a statistical evaluation of the binding pattern for each sample but it ignores the information contained in the amino acid sequences that the antibodies are binding to. Here, similar array-based antibody profiles are instead used to train a neural network to model the sequence dependence of molecular recognition involved in the immune response of each sample. The binding profiles used resulted from incubating serum from 5 infectious disease cohorts (Hepatitis B and C, Dengue Fever, West Nile Virus and Chagas disease) and an uninfected cohort with 122,926 peptide sequences on an array. These sequences were selected quasi-randomly to represent an even but sparse sample of the entire possible combinatorial sequence space (~1012). This very sparse sampling of combinatorial sequence space was sufficient to capture a statistically accurate representation of the humoral immune response across the entire space. Processing array data using the neural network not only captures the disease-specific sequence-binding information but aggregates binding information with respect to sequence, removing sequence-independent noise and improving the accuracy of array-based classification of disease compared with the raw binding data. Because the neural network model is trained on all samples simultaneously, a highly condensed representation of the differential information between samples resides in the output layer of the model, and the column vectors from this layer can be used to represent each sample for classification or unsupervised clustering applications.
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Affiliation(s)
- Robayet Chowdhury
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, United States of America
| | | | - Laimonas Kelbauskas
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
| | - Phillip Stafford
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
| | - Chris Diehnelt
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
| | - Zhan-Gong Zhao
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
| | | | - Valerie Green
- Creative Testing Solutions, Tempe, Arizona, United States of America
| | - Neal W Woodbury
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, United States of America
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A multimorphic mutation in IRF4 causes human autosomal dominant combined immunodeficiency. Sci Immunol 2023; 8:eade7953. [PMID: 36662884 PMCID: PMC10825898 DOI: 10.1126/sciimmunol.ade7953] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 12/22/2022] [Indexed: 01/22/2023]
Abstract
Interferon regulatory factor 4 (IRF4) is a transcription factor (TF) and key regulator of immune cell development and function. We report a recurrent heterozygous mutation in IRF4, p.T95R, causing an autosomal dominant combined immunodeficiency (CID) in seven patients from six unrelated families. The patients exhibited profound susceptibility to opportunistic infections, notably Pneumocystis jirovecii, and presented with agammaglobulinemia. Patients' B cells showed impaired maturation, decreased immunoglobulin isotype switching, and defective plasma cell differentiation, whereas their T cells contained reduced TH17 and TFH populations and exhibited decreased cytokine production. A knock-in mouse model of heterozygous T95R showed a severe defect in antibody production both at the steady state and after immunization with different types of antigens, consistent with the CID observed in these patients. The IRF4T95R variant maps to the TF's DNA binding domain, alters its canonical DNA binding specificities, and results in a simultaneous multimorphic combination of loss, gain, and new functions for IRF4. IRF4T95R behaved as a gain-of-function hypermorph by binding to DNA with higher affinity than IRF4WT. Despite this increased affinity for DNA, the transcriptional activity on IRF4 canonical genes was reduced, showcasing a hypomorphic activity of IRF4T95R. Simultaneously, IRF4T95R functions as a neomorph by binding to noncanonical DNA sites to alter the gene expression profile, including the transcription of genes exclusively induced by IRF4T95R but not by IRF4WT. This previously undescribed multimorphic IRF4 pathophysiology disrupts normal lymphocyte biology, causing human disease.
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Rube HT, Rastogi C, Feng S, Kribelbauer JF, Li A, Becerra B, Melo LAN, Do BV, Li X, Adam HH, Shah NH, Mann RS, Bussemaker HJ. Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning. Nat Biotechnol 2022; 40:1520-1527. [PMID: 35606422 PMCID: PMC9546773 DOI: 10.1038/s41587-022-01307-0] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 04/04/2022] [Indexed: 01/02/2023]
Abstract
Protein-ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions. Here we describe a flexible machine learning method, called ProBound, that accurately defines sequence recognition in terms of equilibrium binding constants or kinetic rates. This is achieved using a multi-layered maximum-likelihood framework that models both the molecular interactions and the data generation process. We show that ProBound quantifies transcription factor (TF) behavior with models that predict binding affinity over a range exceeding that of previous resources; captures the impact of DNA modifications and conformational flexibility of multi-TF complexes; and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with an assay called KD-seq, it determines the absolute affinity of protein-ligand interactions. We also apply ProBound to profile the kinetics of kinase-substrate interactions. ProBound opens new avenues for decoding biological networks and rationally engineering protein-ligand interactions.
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Affiliation(s)
- H Tomas Rube
- Department of Bioengineering, University of California, Merced, Merced, CA, USA
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Chaitanya Rastogi
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Siqian Feng
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | | | - Allyson Li
- Department of Chemistry, Columbia University, New York, NY, USA
| | - Basheer Becerra
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Lucas A N Melo
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Bach Viet Do
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Xiaoting Li
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Hammaad H Adam
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Neel H Shah
- Department of Chemistry, Columbia University, New York, NY, USA
| | - Richard S Mann
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Harmen J Bussemaker
- Department of Biological Sciences, Columbia University, New York, NY, USA.
- Department of Systems Biology, Columbia University, New York, NY, USA.
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8
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Barissi S, Sala A, Wieczór M, Battistini F, Orozco M. DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors. Nucleic Acids Res 2022; 50:9105-9114. [PMID: 36018808 PMCID: PMC9458447 DOI: 10.1093/nar/gkac708] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 07/21/2022] [Accepted: 08/08/2022] [Indexed: 12/24/2022] Open
Abstract
We present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predict affinities obtained with techniques as different as uPBM, gcPBM and HT-SELEX with an excellent performance, much better than existing algorithms. Due to its nature, the method can be extended to epigenetic variants, mismatches, mutations, or any non-coding nucleobases. When complemented with chromatin structure information, our in vitro trained method provides also good estimates of in vivo binding sites in yeast.
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Affiliation(s)
| | | | - Miłosz Wieczór
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and Technology. Baldiri Reixac 10–12, 08028 Barcelona, Spain,Department of Physical Chemistry. Gdansk University of Technology, 80-233 Gdańsk, Poland
| | | | - Modesto Orozco
- Correspondence may also be addressed to Modesto Orozco. Tel: +34 934 037 156;
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Barshai M, Aubert A, Orenstein Y. G4detector: Convolutional Neural Network to Predict DNA G-Quadruplexes. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1946-1955. [PMID: 33872156 DOI: 10.1109/tcbb.2021.3073595] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
G-quadruplexes (G4s) are nucleic acid secondary structures that form within guanine-rich DNA or RNA sequences. G4 formation can affect chromatin architecture and gene regulation, and has been associated with genomic instability, genetic diseases, and cancer progression. The experimental data produced by the G4-seq experiment provides unprecedented details on G4 formation in the genome. Still, running the experimental protocol on a whole genome is an expensive and time-consuming process. Thus, it is highly desirable to have a computational method to predict G4 formation in new DNA sequences or whole genomes. Here, we present G4detector, a new method based on a convolutional neural network to predict G4s from DNA sequences. On top of the sequence information, we improved prediction accuracy by the addition of RNA secondary structure information. To train and test G4detector, we compiled novel high-throughput benchmarks over multiple species genomes measured by the G4-seq protocol. We show that G4detector outperforms extant methods for the same task on all benchmark datasets, can detect G4s genome-wide with high accuracy, and is able to extrapolate human-trained measurements to various non-human species. The code and benchmarks are publicly available on github.com/OrensteinLab/G4detector.
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Liu S, Xu Y, Jiang X, Tan H, Ying B. Translation of aptamers toward clinical diagnosis and commercialization. Biosens Bioelectron 2022; 208:114168. [PMID: 35364525 DOI: 10.1016/j.bios.2022.114168] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 02/28/2022] [Accepted: 03/07/2022] [Indexed: 02/08/2023]
Abstract
The dominance of antibodies in diagnostics has gradually changed following the discovery of aptamers in the early 1990s. Aptamers offer inherent advantages over traditional antibodies, including higher specificity, higher affinity, smaller size, greater stability, ease of manufacture, and low immunogenicity, rendering them the best candidates for point-of-care testing (POCT). In the past 20 years, the research community and pharmaceutical companies have made great efforts to promote the development of aptamer technology. Macugen® (pegaptanib) was the first aptamer drug approved by the US Food and Drug Administration (FDA), and various aptamer-based diagnostics show great promise in preclinical research and clinical trials. In this review, we introduce recent literature, ongoing clinical trials, commercial reagents of aptamer-based diagnostics, discuss the FDA regulatory mechanisms, and highlight the prospects and challenges in translating these studies into viable clinical diagnostic tools.
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Affiliation(s)
- Shan Liu
- Sichuan Provincial Key Laboratory for Human Disease Gene Study, Department of Medical Genetics, Department of Laboratory Medicine, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, University of Electronic Science and Technology, Chengdu, 610072, China
| | - Yixin Xu
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China; Med+ Molecular Diagnostics Institute of West China Hospital/West China School of Medicine, Chengdu, 610041, China
| | - Xin Jiang
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China; Med+ Molecular Diagnostics Institute of West China Hospital/West China School of Medicine, Chengdu, 610041, China
| | - Hong Tan
- Department of General Surgery, Chengdu Integrated TCM&Western Medicine Hospital (Chengdu First People's Hospital), Chengdu, 610041, China.
| | - Binwu Ying
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China; Med+ Molecular Diagnostics Institute of West China Hospital/West China School of Medicine, Chengdu, 610041, China.
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11
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Xu Y, Jiang X, Zhou Y, Ma M, Wang M, Ying B. Systematic Evolution of Ligands by Exponential Enrichment Technologies and Aptamer-Based Applications: Recent Progress and Challenges in Precision Medicine of Infectious Diseases. Front Bioeng Biotechnol 2021; 9:704077. [PMID: 34447741 PMCID: PMC8383106 DOI: 10.3389/fbioe.2021.704077] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 07/26/2021] [Indexed: 02/05/2023] Open
Abstract
Infectious diseases are considered as a pressing challenge to global public health. Accurate and rapid diagnostics tools for early recognition of the pathogen, as well as individualized precision therapy are essential for controlling the spread of infectious diseases. Aptamers, which were screened by systematic evolution of ligands by exponential enrichment (SELEX), can bind to targets with high affinity and specificity so that have exciting potential in both diagnosis and treatment of infectious diseases. In this review, we provide a comprehensive overview of the latest development of SELEX technology and focus on the applications of aptamer-based technologies in infectious diseases, such as targeted drug-delivery, treatments and biosensors for diagnosing. The challenges and the future development in this field of clinical application will also be discussed.
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Affiliation(s)
- Yixin Xu
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Xin Jiang
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Yanhong Zhou
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Ming Ma
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.,The First People's Hospital of Shuangliu District, Chengdu/West China (Airport)Hospital Sichuan University, Chengdu, China
| | - Minjin Wang
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Binwu Ying
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
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