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Liang C, Murray S, Li Y, Lee R, Low A, Sasaki S, Chiang AWT, Lin WJ, Mathews J, Barnes W, Lewis NE. LipidSIM: Inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, Markov modeling framework. Metab Eng 2024; 82:110-122. [PMID: 38311182 DOI: 10.1016/j.ymben.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 01/03/2024] [Accepted: 01/21/2024] [Indexed: 02/10/2024]
Abstract
Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. Here, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following Keap1 knockdown and found that changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl4 to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a Dgat2-knockdown study dataset. Thus, we show that as it demands no a priori knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data.
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Affiliation(s)
- Chenguang Liang
- Department of Bioengineering, University of California, La Jolla, CA, 92093, USA
| | - Sue Murray
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Yang Li
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Richard Lee
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Audrey Low
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Shruti Sasaki
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, La Jolla, CA, 92093, USA
| | - Wen-Jen Lin
- Graduate Institute of Biomedical Science, China Medical University, Taichung 404333, Taiwan
| | - Joel Mathews
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Will Barnes
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Nathan E Lewis
- Department of Bioengineering, University of California, La Jolla, CA, 92093, USA; Department of Pediatrics, University of California, La Jolla, CA, 92093, USA.
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2
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Rawson R, Duong L, Tkachenko E, Chiang AWT, Okamoto K, Dohil R, Lewis NE, Kurten R, Abud EM, Aceves SS. Mechanotransduction-induced interplay between phospholamban and yes-activated protein induces smooth muscle cell hypertrophy. Mucosal Immunol 2024:S1933-0219(24)00016-3. [PMID: 38423390 DOI: 10.1016/j.mucimm.2024.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/12/2024] [Accepted: 02/21/2024] [Indexed: 03/02/2024]
Abstract
The gastrointestinal system is a hollow organ affected by fibrostenotic diseases that cause volumetric compromise of the lumen via smooth muscle hypertrophy and fibrosis. Many of the driving mechanisms remain unclear. Yes-associated protein-1 (YAP) is a critical mechanosensory transcriptional regulator that mediates cell hypertrophy in response to elevated extracellular rigidity. In the type 2 inflammatory disorder, eosinophilic esophagitis (EoE), phospholamban (PLN) can induce smooth muscle cell hypertrophy. We used EoE as a disease model for understanding a mechanistic pathway in which PLN and YAP interact in response to rigid extracellular substrate to induce smooth muscle cell hypertrophy. PLN-induced YAP nuclear sequestration in a feed-forward loop caused increased cell size in response to a rigid substrate. This mechanism of rigidity sensing may have previously unappreciated clinical implications for PLN-expressing hollow systems such as the esophagus and heart.
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Affiliation(s)
- Renee Rawson
- Department of Pediatrics, University of California, San Diego, California; Division of Allergy Immunology, University of California, San Diego, California
| | - Loan Duong
- Department of Pediatrics, University of California, San Diego, California; Division of Allergy Immunology, University of California, San Diego, California
| | - Eugene Tkachenko
- Department of Pediatrics, University of California, San Diego, California; Division of Allergy Immunology, University of California, San Diego, California; Department of Medicine, University of California, San Diego, California
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, California; Department of Bioengineering, University of California, San Diego, California
| | - Kevin Okamoto
- Department of Pediatrics, University of California, San Diego, California; Division of Allergy Immunology, University of California, San Diego, California
| | - Ranjan Dohil
- Department of Pediatrics, University of California, San Diego, California; Division of Gastroenterology, University of California, San Diego, California; XXX, Rady Children's Hospital, San Diego, California
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, California; Department of Bioengineering, University of California, San Diego, California
| | - Richard Kurten
- Department of Physiology and Biophysics, University of Arkansas for Medical Sciences, Arkansas Children's Hospital Research Institute, Little Rock, Arkansas; Department of Pediatrics, University of California, San Diego, California; Division of Allergy Immunology, University of California, San Diego, California
| | - Edsel M Abud
- Department of Pediatrics, University of California, San Diego, California; Division of Allergy Immunology, University of California, San Diego, California; XXX, Scripps Research Translational Institute, Scripps Research, San Diego, California
| | - Seema S Aceves
- Department of Pediatrics, University of California, San Diego, California; Division of Allergy Immunology, University of California, San Diego, California; XXX, Rady Children's Hospital, San Diego, California; Department of Medicine, University of California, San Diego, California.
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Liang C, Chiang AWT, Lewis NE. GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data. STAR Protoc 2023; 4:102244. [PMID: 37086409 PMCID: PMC10160804 DOI: 10.1016/j.xpro.2023.102244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/07/2023] [Accepted: 03/24/2023] [Indexed: 04/23/2023] Open
Abstract
Variations in N-glycosylation, which is crucial to glycoprotein functions, impact many diseases and the safety and efficacy of biotherapeutic drugs. Here, we present a protocol for using GlycoMME (Glycosylation Markov Model Evaluator) to study N-glycosylation biosynthesis from glycomics data. We describe steps for annotating glycomics data and quantifying perturbations to N-glycan biosynthesis with interpretable models. We then detail procedures to predict the impact of mutations in disease or potential glycoengineering strategies in drug development. For complete details on the use and execution of this protocol, please refer to Liang et al. (2020).1.
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Affiliation(s)
- Chenguang Liang
- Department of Pediatrics, University of California, San Diego, La Jolla, San Diego, CA 92130, USA; Department of Bioengineering, University of California, San Diego, La Jolla, San Diego, CA 92130, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, La Jolla, San Diego, CA 92130, USA.
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, San Diego, CA 92130, USA; Department of Bioengineering, University of California, San Diego, La Jolla, San Diego, CA 92130, USA.
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4
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Zhang Y, Krishnan S, Bao B, Chiang AWT, Sorrentino JT, Schinn SM, Kellman BP, Lewis NE. Preparing glycomics data for robust statistical analysis with GlyCompareCT. STAR Protoc 2023; 4:102162. [PMID: 36920914 PMCID: PMC10025275 DOI: 10.1016/j.xpro.2023.102162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/27/2022] [Accepted: 02/13/2023] [Indexed: 03/16/2023] Open
Abstract
GlyCompareCT is a portable command-line tool to facilitate downstream glycomic data analyses, by addressing data inherent sparsity and non-independence. Inputting glycan abundances, users can run GlyCompareCT with one line of code to obtain the abundances of a minimal substructure set, named glycomotif, thereby quantifying hidden biosynthetic relationships between measured glycans. Optional parameters tuning and annotation are supported for personal preference. For complete details on the use and execution of this protocol, please refer to Bao et al. (2021).1.
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Affiliation(s)
- Yujie Zhang
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA; Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA
| | - Sridevi Krishnan
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA
| | - Bokan Bao
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA
| | - James T Sorrentino
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA
| | - Song-Min Schinn
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA
| | - Benjamin P Kellman
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA; Augment Biologics, 9450 SW Gemini Dr. #46664, Beaverton, OR 97008, USA.
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA.
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5
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Kotidis P, Donini R, Arnsdorf J, Hansen AH, Voldborg BGR, Chiang AWT, Haslam SM, Betenbaugh M, Jimenez Del Val I, Lewis NE, Krambeck F, Kontoravdi C. CHOGlycoNET: Comprehensive glycosylation reaction network for CHO cells. Metab Eng 2023; 76:87-96. [PMID: 36610518 DOI: 10.1016/j.ymben.2022.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 09/22/2022] [Accepted: 12/27/2022] [Indexed: 01/06/2023]
Abstract
Chinese hamster ovary (CHO) cells are extensively used for the production of glycoprotein therapeutics proteins, for which N-linked glycans are a critical quality attribute due to their influence on activity and immunogenicity. Manipulation of protein glycosylation is commonly achieved through cell or process engineering, which are often guided by mathematical models. However, each study considers a unique glycosylation reaction network that is tailored around the cell line and product at hand. Herein, we use 200 glycan datasets for both recombinantly produced and native proteins from different CHO cell lines to reconstruct a comprehensive reaction network, CHOGlycoNET, based on the individual minimal reaction networks describing each dataset. CHOGlycoNET is used to investigate the distribution of mannosidase and glycosyltransferase enzymes in the Golgi apparatus and identify key network reactions using machine learning and dimensionality reduction techniques. CHOGlycoNET can be used for accelerating glycomodel development and predicting the effect of glycoengineering strategies. Finally, CHOGlycoNET is wrapped in a SBML file to be used as a standalone model or in combination with CHO cell genome scale models.
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Affiliation(s)
- Pavlos Kotidis
- Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, SW7 2AZ, UK.
| | - Roberto Donini
- Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Johnny Arnsdorf
- National Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Anders Holmgaard Hansen
- National Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Bjørn Gunnar Rude Voldborg
- National Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, CA, 92093, USA
| | - Stuart M Haslam
- Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Michael Betenbaugh
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | | | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, CA, 92093, USA; Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | | | - Cleo Kontoravdi
- Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, SW7 2AZ, UK.
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6
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Bao B, Zahiri J, Gazestani VH, Lopez L, Xiao Y, Kim R, Wen TH, Chiang AWT, Nalabolu S, Pierce K, Robasky K, Wang T, Hoekzema K, Eichler EE, Lewis NE, Courchesne E. A predictive ensemble classifier for the gene expression diagnosis of ASD at ages 1 to 4 years. Mol Psychiatry 2023; 28:822-833. [PMID: 36266569 PMCID: PMC9908553 DOI: 10.1038/s41380-022-01826-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 09/13/2022] [Accepted: 09/27/2022] [Indexed: 11/09/2022]
Abstract
Autism Spectrum Disorder (ASD) diagnosis remains behavior-based and the median age of diagnosis is ~52 months, nearly 5 years after its first-trimester origin. Accurate and clinically-translatable early-age diagnostics do not exist due to ASD genetic and clinical heterogeneity. Here we collected clinical, diagnostic, and leukocyte RNA data from 240 ASD and typically developing (TD) toddlers (175 toddlers for training and 65 for test). To identify gene expression ASD diagnostic classifiers, we developed 42,840 models composed of 3570 gene expression feature selection sets and 12 classification methods. We found that 742 models had AUC-ROC ≥ 0.8 on both Training and Test sets. Weighted Bayesian model averaging of these 742 models yielded an ensemble classifier model with accurate performance in Training and Test gene expression datasets with ASD diagnostic classification AUC-ROC scores of 85-89% and AUC-PR scores of 84-92%. ASD toddlers with ensemble scores above and below the overall ASD ensemble mean of 0.723 (on a scale of 0 to 1) had similar diagnostic and psychometric scores, but those below this ASD ensemble mean had more prenatal risk events than TD toddlers. Ensemble model feature genes were involved in cell cycle, inflammation/immune response, transcriptional gene regulation, cytokine response, and PI3K-AKT, RAS and Wnt signaling pathways. We additionally collected targeted DNA sequencing smMIPs data on a subset of ASD risk genes from 217 of the 240 ASD and TD toddlers. This DNA sequencing found about the same percentage of SFARI Level 1 and 2 ASD risk gene mutations in TD (12 of 105) as in ASD (13 of 112) toddlers, and classification based only on the presence of mutation in these risk genes performed at a chance level of 49%. By contrast, the leukocyte ensemble gene expression classifier correctly diagnostically classified 88% of TD and ASD toddlers with ASD risk gene mutations. Our ensemble ASD gene expression classifier is diagnostically predictive and replicable across different toddler ages, races, and ethnicities; out-performs a risk gene mutation classifier; and has potential for clinical translation.
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Affiliation(s)
- Bokan Bao
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Javad Zahiri
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Vahid H Gazestani
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Linda Lopez
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Yaqiong Xiao
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Raphael Kim
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Teresa H Wen
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Austin W T Chiang
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Srinivasa Nalabolu
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Karen Pierce
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Kimberly Robasky
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, US
- School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Carolina Health and Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Tianyun Wang
- Department of Medical Genetics, Center for Medical Genetics, Peking University Health Science Center, 100191, Beijing, China
- Neuroscience Research Institute, Peking University; Key Laboratory for Neuroscience, Ministry of Education of China & National Health Commission of China, 100191, Beijing, China
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, 98195, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
| | - Eric Courchesne
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA.
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Tsai CM, Caldera JR, Hajam IA, Chiang AWT, Tsai CH, Li H, Díez ML, Gonzalez C, Trieu D, Martins GA, Underhill DM, Arditi M, Lewis NE, Liu GY. Non-protective immune imprint underlies failure of Staphylococcus aureus IsdB vaccine. Cell Host Microbe 2022; 30:1163-1172.e6. [PMID: 35803276 PMCID: PMC9378590 DOI: 10.1016/j.chom.2022.06.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 05/10/2022] [Accepted: 06/09/2022] [Indexed: 12/25/2022]
Abstract
Humans frequently encounter Staphylococcus aureus (SA) throughout life. Animal studies have yielded SA candidate vaccines, yet all human SA vaccine trials have failed. One notable vaccine "failure" targeted IsdB, critical for host iron acquisition. We explored a fundamental difference between humans and laboratory animals-natural SA exposure. Recapitulating the failed phase III IsdB vaccine trial, mice previously infected with SA do not mount protective antibody responses to vaccination, unlike naive animals. Non-protective antibodies exhibit increased α2,3 sialylation that blunts opsonophagocytosis and preferentially targets a non-protective IsdB domain. IsdB vaccination of SA-infected mice recalls non-neutralizing humoral responses, further reducing vaccine efficacy through direct antibody competition. IsdB vaccine interference was overcome by immunization against the IsdB heme-binding domain. Purified human IsdB-specific antibodies also blunt IsdB passive immunization, and additional SA vaccines are susceptible to SA pre-exposure. Thus, failed anti-SA immunization trials could be explained by non-protective imprint from prior host-SA interaction.
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Affiliation(s)
- Chih-Ming Tsai
- Department of Pediatrics, University of California, San Diego, San Diego, CA 92093, USA
| | - J R Caldera
- Department of Pediatrics, University of California, San Diego, San Diego, CA 92093, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Irshad A Hajam
- Department of Pediatrics, University of California, San Diego, San Diego, CA 92093, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, San Diego, CA 92093, USA
| | - Chih-Hsiung Tsai
- Department of Life Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Haining Li
- Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA
| | - María Lázaro Díez
- Department of Pediatrics, University of California, San Diego, San Diego, CA 92093, USA
| | - Cesia Gonzalez
- Department of Pediatrics, University of California, San Diego, San Diego, CA 92093, USA
| | - Desmond Trieu
- Department of Pediatrics, University of California, San Diego, San Diego, CA 92093, USA
| | - Gislâine A Martins
- Research Division of Immunology, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - David M Underhill
- Research Division of Immunology, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Moshe Arditi
- Division of Pediatric Infectious Diseases, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, San Diego, CA 92093, USA; Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA
| | - George Y Liu
- Department of Pediatrics, University of California, San Diego, San Diego, CA 92093, USA; Division of Infectious Diseases, Rady Children's Hospital, San Diego, CA 92123, USA.
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8
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Li H, Chiang AWT, Lewis NE. Artificial intelligence in the analysis of glycosylation data. Biotechnol Adv 2022; 60:108008. [PMID: 35738510 DOI: 10.1016/j.biotechadv.2022.108008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 06/15/2022] [Accepted: 06/16/2022] [Indexed: 11/18/2022]
Abstract
Glycans are complex, yet ubiquitous across biological systems. They are involved in diverse essential organismal functions. Aberrant glycosylation may lead to disease development, such as cancer, autoimmune diseases, and inflammatory diseases. Glycans, both normal and aberrant, are synthesized using extensive glycosylation machinery, and understanding this machinery can provide invaluable insights for diagnosis, prognosis, and treatment of various diseases. Increasing amounts of glycomics data are being generated thanks to advances in glycoanalytics technologies, but to maximize the value of such data, innovations are needed for analyzing and interpreting large-scale glycomics data. Artificial intelligence (AI) provides a powerful analysis toolbox in many scientific fields, and here we review state-of-the-art AI approaches on glycosylation analysis. We further discuss how models can be analyzed to gain mechanistic insights into glycosylation machinery and how the machinery shapes glycans under different scenarios. Finally, we propose how to leverage the gained knowledge for developing predictive AI-based models of glycosylation. Thus, guiding future research of AI-based glycosylation model development will provide valuable insights into glycosylation and glycan machinery.
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Affiliation(s)
- Haining Li
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
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9
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Kellman BP, Richelle A, Yang JY, Chapla D, Chiang AWT, Najera JA, Liang C, Fürst A, Bao B, Koga N, Mohammad MA, Bruntse AB, Haymond MW, Moremen KW, Bode L, Lewis NE. Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration. Nat Commun 2022; 13:2455. [PMID: 35508452 PMCID: PMC9068700 DOI: 10.1038/s41467-022-29867-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 04/04/2022] [Indexed: 12/18/2022] Open
Abstract
Human Milk Oligosaccharides (HMOs) are abundant carbohydrates fundamental to infant health and development. Although these oligosaccharides were discovered more than half a century ago, their biosynthesis in the mammary gland remains largely uncharacterized. Here, we use a systems biology framework that integrates glycan and RNA expression data to construct an HMO biosynthetic network and predict glycosyltransferases involved. To accomplish this, we construct models describing the most likely pathways for the synthesis of the oligosaccharides accounting for >95% of the HMO content in human milk. Through our models, we propose candidate genes for elongation, branching, fucosylation, and sialylation of HMOs. Our model aggregation approach recovers 2 of 2 previously known gene-enzyme relations and 2 of 3 empirically confirmed gene-enzyme relations. The top genes we propose for the remaining 5 linkage reactions are consistent with previously published literature. These results provide the molecular basis of HMO biosynthesis necessary to guide progress in HMO research and application with the goal of understanding and improving infant health and development. Human milk oligosaccharides are fundamental to infant health. Here the authors deploy a multi-omics systems biology approach to elucidate their biosynthetic network, including the associated enzymes and likely structures of ambiguous oligosaccharides.
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Affiliation(s)
- Benjamin P Kellman
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Anne Richelle
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Jeong-Yeh Yang
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Digantkumar Chapla
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Julia A Najera
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Chenguang Liang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Annalee Fürst
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Bokan Bao
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Natalia Koga
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Mahmoud A Mohammad
- Department of Pediatrics, Children's Nutrition Research Center, US Department of Agriculture/Agricultural Research Service, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Anders Bech Bruntse
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Morey W Haymond
- Department of Pediatrics, Children's Nutrition Research Center, US Department of Agriculture/Agricultural Research Service, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kelley W Moremen
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Lars Bode
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.,Larsson-Rosenquist Foundation Mother-Milk-Infant Center of Research Excellence (MOMI CORE), University of California, San Diego, La Jolla, CA, 92093, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA. .,Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA.
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10
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Manresa MC, Wu A, Nhu QM, Chiang AWT, Okamoto K, Miki H, Kurten R, Pham E, Duong LD, Lewis NE, Akuthota P, Croft M, Aceves SS. LIGHT controls distinct homeostatic and inflammatory gene expression profiles in esophageal fibroblasts via differential HVEM and LTβR-mediated mechanisms. Mucosal Immunol 2022; 15:327-337. [PMID: 34903876 PMCID: PMC8866113 DOI: 10.1038/s41385-021-00472-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/08/2021] [Indexed: 02/04/2023]
Abstract
Fibroblasts mediate tissue remodeling in eosinophilic esophagitis (EoE), a chronic allergen-driven inflammatory pathology. Diverse fibroblast subtypes with homeostasis-regulating or inflammatory profiles have been recognized in various tissues, but which mediators induce these alternate differentiation states remain largely unknown. We recently identified that TNFSF14/LIGHT promotes an inflammatory esophageal fibroblast in vitro. Herein we used esophageal biopsies and primary fibroblasts to investigate the role of the LIGHT receptors, herpes virus entry mediator (HVEM) and lymphotoxin-beta receptor (LTβR), and their downstream activated pathways, in EoE. In addition to promoting inflammatory gene expression, LIGHT down-regulated homeostatic factors including WNTs, BMPs and type 3 semaphorins. In vivo, WNT2B+ fibroblasts were decreased while ICAM-1+ and IL-34+ fibroblasts were expanded in EoE, suggesting that a LIGHT-driven gene signature was imprinted in EoE versus normal esophageal fibroblasts. HVEM and LTβR overexpression and deficiency experiments demonstrated that HVEM regulates a limited subset of LIGHT targets, whereas LTβR controls all transcriptional effects. Pharmacologic blockade of the non-canonical NIK/p100/p52-mediated NF-κB pathway potently silenced LIGHT's transcriptional effects, with a lesser role found for p65 canonical NF-κB. Collectively, our results show that LIGHT promotes differentiation of esophageal fibroblasts toward an inflammatory phenotype and represses homeostatic gene expression via a LTβR-NIK-p52 NF-κB dominant pathway.
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Affiliation(s)
- Mario C. Manresa
- grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California, San Diego, CA USA ,Division of Allergy Immunology, San Diego, CA USA ,grid.185006.a0000 0004 0461 3162La Jolla Institute for Immunology, La Jolla, CA USA
| | - Amanda Wu
- grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California, San Diego, CA USA ,Division of Allergy Immunology, San Diego, CA USA
| | - Quan M. Nhu
- grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California, San Diego, CA USA ,Division of Allergy Immunology, San Diego, CA USA ,grid.419794.60000 0001 2111 8997Division of Gastroenterology and Hepatology, Scripps Clinic, San Diego, CA USA
| | - Austin W. T. Chiang
- grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California, San Diego, CA USA
| | - Kevin Okamoto
- grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California, San Diego, CA USA
| | - Haruka Miki
- grid.185006.a0000 0004 0461 3162La Jolla Institute for Immunology, La Jolla, CA USA
| | - Richard Kurten
- grid.239305.e0000 0001 2157 2081Department of Physiology and Biophysics, University of Arkansas for Medical Sciences, Arkansas Children’s Hospital Research Institute, Little Rock, AR USA
| | - Elaine Pham
- grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California, San Diego, CA USA ,Division of Allergy Immunology, San Diego, CA USA
| | - Loan D. Duong
- grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California, San Diego, CA USA ,Division of Allergy Immunology, San Diego, CA USA
| | - Nathan E. Lewis
- grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California, San Diego, CA USA
| | - Praveen Akuthota
- grid.266100.30000 0001 2107 4242Division of Pulmonary, Critical Care, and Sleep Medicine, University of California San Diego, La Jolla, CA USA
| | - Michael Croft
- grid.185006.a0000 0004 0461 3162La Jolla Institute for Immunology, La Jolla, CA USA ,grid.266100.30000 0001 2107 4242Department of Medicine, University of California, San Diego, CA USA
| | - Seema S. Aceves
- grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California, San Diego, CA USA ,Division of Allergy Immunology, San Diego, CA USA ,grid.266100.30000 0001 2107 4242Department of Medicine, University of California, San Diego, CA USA ,grid.286440.c0000 0004 0383 2910Rady Children’s Hospital San Diego, San Diego, CA USA
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11
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Kuo CC, Chiang AWT, Baghdassarian HM, Lewis NE. Dysregulation of the secretory pathway connects Alzheimer's disease genetics to aggregate formation. Cell Syst 2021; 12:873-884.e4. [PMID: 34171228 PMCID: PMC8505362 DOI: 10.1016/j.cels.2021.06.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 02/24/2021] [Accepted: 06/02/2021] [Indexed: 12/14/2022]
Abstract
Amyloid disorders such as Alzheimer's disease (AD) involve the aggregation of secreted proteins. However, it is largely unclear how secretory-pathway proteins contribute to amyloid formation. We developed a systems biology framework integrating expression data with protein-protein interaction networks to estimate a tissue's fitness for producing specific secreted proteins and analyzed the fitness of the secretory pathway of various brain regions and cell types for synthesizing the AD-associated amyloid precursor protein (APP). While key amyloidogenic pathway components were not differentially expressed in AD brains, we found Aβ deposition correlates with systemic down- and upregulation of the secretory-pathway components proximal to APP and amyloidogenic secretases, respectively, in AD. Our analyses suggest that perturbations from three AD risk loci cascade through the APP secretory-support network and into the endocytosis pathway, connecting amyloidogenesis to dysregulation of secretory-pathway components supporting APP and suggesting novel therapeutic targets for AD. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Chih-Chung Kuo
- Department of Bioengineering, University of California, San Diego, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability at UC San Diego, San Diego, La Jolla, CA 92093, USA
| | - Austin W T Chiang
- Novo Nordisk Foundation Center for Biosustainability at UC San Diego, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, San Diego, La Jolla, CA 92093, USA
| | - Hratch M Baghdassarian
- Department of Pediatrics, University of California, San Diego, San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, San Diego, La Jolla, CA 92093, USA
| | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability at UC San Diego, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, San Diego, La Jolla, CA 92093, USA.
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12
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Bao B, Kellman BP, Chiang AWT, Zhang Y, Sorrentino JT, York AK, Mohammad MA, Haymond MW, Bode L, Lewis NE. Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis. Nat Commun 2021; 12:4988. [PMID: 34404781 PMCID: PMC8371009 DOI: 10.1038/s41467-021-25183-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 07/27/2021] [Indexed: 11/20/2022] Open
Abstract
Glycans are fundamental cellular building blocks, involved in many organismal functions. Advances in glycomics are elucidating the essential roles of glycans. Still, it remains challenging to properly analyze large glycomics datasets, since the abundance of each glycan is dependent on many other glycans that share many intermediate biosynthetic steps. Furthermore, the overlap of measured glycans can be low across samples. We address these challenges with GlyCompare, a glycomic data analysis approach that accounts for shared biosynthetic steps for all measured glycans to correct for sparsity and non-independence in glycomics, which enables direct comparison of different glycoprofiles and increases statistical power. Using GlyCompare, we study diverse N-glycan profiles from glycoengineered erythropoietin. We obtain biologically meaningful clustering of mutant cell glycoprofiles and identify knockout-specific effects of fucosyltransferase mutants on tetra-antennary structures. We further analyze human milk oligosaccharide profiles and find mother’s fucosyltransferase-dependent secretor-status indirectly impact the sialylation. Finally, we apply our method on mucin-type O-glycans, gangliosides, and site-specific compositional glycosylation data to reveal tissues and disease-specific glycan presentations. Our substructure-oriented approach will enable researchers to take full advantage of the growing power and size of glycomics data. Glycomics can uncover important molecular changes but measured glycans are highly interconnected and incompatible with common statistical methods, introducing pitfalls during analysis. Here, the authors develop an approach to identify glycan dependencies across samples to facilitate comparative glycomics.
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Affiliation(s)
- Bokan Bao
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Benjamin P Kellman
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.,The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA
| | - Yujie Zhang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - James T Sorrentino
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Austin K York
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Mahmoud A Mohammad
- Department of Pediatrics, Children's Nutrition Research Center, US Department of Agriculture/Agricultural Research Service, Baylor College of Medicine, Houston, TX, USA
| | - Morey W Haymond
- Department of Pediatrics, Children's Nutrition Research Center, US Department of Agriculture/Agricultural Research Service, Baylor College of Medicine, Houston, TX, USA
| | - Lars Bode
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA. .,Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA. .,The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA.
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13
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Hsieh LY, Chiang AWT, Duong LD, Kuo CC, Dong SX, Dohil R, Kurten R, Lewis NE, Aceves SS. A unique esophageal extracellular matrix proteome alters normal fibroblast function in severe eosinophilic esophagitis. J Allergy Clin Immunol 2021; 148:486-494. [PMID: 33556465 PMCID: PMC8342625 DOI: 10.1016/j.jaci.2021.01.023] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 12/16/2020] [Accepted: 01/12/2021] [Indexed: 01/23/2023]
Abstract
BACKGROUND Eosinophilic esophagitis (EoE) is a chronic TH2 disorder complicated by tissue fibrosis and loss of esophageal luminal patency. The fibrostenotic esophagus does not respond well to therapy, but profibrotic therapeutic targets are largely unclear. OBJECTIVE Our aim was to utilize proteomics and primary cells as a novel approach to determine relevant profibrotic factors. METHODS We utilized primary esophageal EoE and normal fibroblasts, their derivative extracellular matrixes (ECMs), an approach of fibroblast culture on autologous versus nonautologous ECM, and proteomics to elucidate EoE ECM proteins that dysregulate cellular function. RESULTS We cultured esophageal fibroblasts from normal esophagi and esophagi from patients with severe EoE on autologous versus nonautologous ECM. The EoE ECM proteome shifted normal esophageal fibroblast protein expression. Proteomic analysis demonstrated that thrombospondin-1 is detected only in the EoE ECM, is central in the EoE ECM protein-protein interactome, is found at significantly elevated levels in biopsy specimens from patients with active EoE, and induces fibroblast collagen I production. CONCLUSION Fibroblasts from patients with EoE secrete a unique ECM proteome that reflects their in vivo state and induces collagen I and α-smooth muscle actin protein expression from normal fibroblasts. Thrombospondin-1 is a previously unappreciated profibrotic molecule in EoE.
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Affiliation(s)
- Lance Y Hsieh
- Department of Pediatrics, University of California, San Diego, La Jolla, Calif; Division of Allergy Immunology, University of California, San Diego, La Jolla, Calif
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, La Jolla, Calif; Department of Bioengineering, University of California, San Diego, La Jolla, Calif
| | - Loan D Duong
- Department of Pediatrics, University of California, San Diego, La Jolla, Calif; Division of Allergy Immunology, University of California, San Diego, La Jolla, Calif
| | - Chih-Chung Kuo
- Department of Bioengineering, University of California, San Diego, La Jolla, Calif
| | - Stephanie X Dong
- Department of Pediatrics, University of California, San Diego, La Jolla, Calif; Division of Allergy Immunology, University of California, San Diego, La Jolla, Calif
| | - Ranjan Dohil
- Department of Pediatrics, University of California, San Diego, La Jolla, Calif; Division of Gastroenterology, University of California, San Diego, La Jolla, Calif; Rady Children's Hospital San Diego, Calif, San Diego, Calif
| | - Richard Kurten
- Department of Physiology and Biophysics, University of Arkansas for Medical Sciences, Arkansas Children's Hospital Research Institute, Little Rock, Ark
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, Calif; Department of Bioengineering, University of California, San Diego, La Jolla, Calif
| | - Seema S Aceves
- Department of Pediatrics, University of California, San Diego, La Jolla, Calif; Division of Allergy Immunology, University of California, San Diego, La Jolla, Calif; Rady Children's Hospital San Diego, Calif, San Diego, Calif; Department of Medicine, University of California, San Diego, La Jolla, Calif.
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14
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Chiang AWT, Baghdassarian HM, Kellman BP, Bao B, Sorrentino JT, Liang C, Kuo CC, Masson HO, Lewis NE. Systems glycobiology for discovering drug targets, biomarkers, and rational designs for glyco-immunotherapy. J Biomed Sci 2021; 28:50. [PMID: 34158025 PMCID: PMC8218521 DOI: 10.1186/s12929-021-00746-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 06/16/2021] [Indexed: 02/06/2023] Open
Abstract
Cancer immunotherapy has revolutionized treatment and led to an unprecedented wave of immuno-oncology research during the past two decades. In 2018, two pioneer immunotherapy innovators, Tasuku Honjo and James P. Allison, were awarded the Nobel Prize for their landmark cancer immunotherapy work regarding “cancer therapy by inhibition of negative immune regulation” –CTLA4 and PD-1 immune checkpoints. However, the challenge in the coming decade is to develop cancer immunotherapies that can more consistently treat various patients and cancer types. Overcoming this challenge requires a systemic understanding of the underlying interactions between immune cells, tumor cells, and immunotherapeutics. The role of aberrant glycosylation in this process, and how it influences tumor immunity and immunotherapy is beginning to emerge. Herein, we review current knowledge of miRNA-mediated regulatory mechanisms of glycosylation machinery, and how these carbohydrate moieties impact immune cell and tumor cell interactions. We discuss these insights in the context of clinical findings and provide an outlook on modulating the regulation of glycosylation to offer new therapeutic opportunities. Finally, in the coming age of systems glycobiology, we highlight how emerging technologies in systems glycobiology are enabling deeper insights into cancer immuno-oncology, helping identify novel drug targets and key biomarkers of cancer, and facilitating the rational design of glyco-immunotherapies. These hold great promise clinically in the immuno-oncology field.
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Affiliation(s)
- Austin W T Chiang
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA. .,The Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, San Diego, CA, 92093, USA.
| | - Hratch M Baghdassarian
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,The Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, San Diego, CA, 92093, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Benjamin P Kellman
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,The Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, San Diego, CA, 92093, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Bokan Bao
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,The Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, San Diego, CA, 92093, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, La Jolla, San Diego, CA, 92093, USA
| | - James T Sorrentino
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,The Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, San Diego, CA, 92093, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Chenguang Liang
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,Department of Bioengineering, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Chih-Chung Kuo
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,The Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, San Diego, CA, 92093, USA.,Department of Bioengineering, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Helen O Masson
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,Department of Bioengineering, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,The Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, San Diego, CA, 92093, USA.,Department of Bioengineering, University of California, La Jolla, San Diego, CA, 92093, USA.,The National Biologics Facility, Technical University of Denmark, Kongens Lyngby, Denmark
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15
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Manresa MC, Chiang AWT, Kurten RC, Dohil R, Brickner H, Dohil L, Herro R, Akuthota P, Lewis NE, Croft M, Aceves SS. Increased Production of LIGHT by T Cells in Eosinophilic Esophagitis Promotes Differentiation of Esophageal Fibroblasts Toward an Inflammatory Phenotype. Gastroenterology 2020; 159:1778-1792.e13. [PMID: 32712105 PMCID: PMC7726704 DOI: 10.1053/j.gastro.2020.07.035] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 06/07/2020] [Accepted: 07/18/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND & AIMS Eosinophilic esophagitis (EoE) is an antigen-mediated eosinophilic disease of the esophagus that involves fibroblast activation and progression to fibrostenosis. Cytokines produced by T-helper type 2 cells and transforming growth factor beta 1 (TGFβ1) contribute to the development of EoE, but other cytokines involved in pathogenesis are unknown. We investigate the effects of tumor necrosis factor superfamily member 14 (TNFSF14, also called LIGHT) on fibroblasts in EoE. METHODS We analyzed publicly available esophageal CD3+ T-cell single-cell sequencing data for expression of LIGHT. Esophageal tissues were obtained from pediatric patients with EoE or control individuals and analyzed by immunostaining. Human primary esophageal fibroblasts were isolated from esophageal biopsy samples of healthy donors or patients with active EoE. Fibroblasts were cultured; incubated with TGFβ1 and/or LIGHT; and analyzed by RNA sequencing, flow cytometry, immunoblots, immunofluorescence, or reverse transcription polymerase chain reaction. Eosinophils were purified from peripheral blood of healthy donors, incubated with interleukin 5, cocultured with fibroblasts, and analyzed by immunohistochemistry. RESULTS LIGHT was up-regulated in the esophageal tissues from patients with EoE, compared with control individuals, and expressed by several T-cell populations, including T-helper type 2 cells. TNF receptor superfamily member 14 (TNFRSF14, also called HVEM) and lymphotoxin beta receptor are receptors for LIGHT that were expressed by fibroblasts from healthy donors or patients with active EoE. Stimulation of esophageal fibroblasts with LIGHT induced inflammatory gene transcription, whereas stimulation with TGFβ1 induced transcription of genes associated with a myofibroblast phenotype. Stimulation of fibroblasts with TGFβ1 increased expression of HVEM; subsequent stimulation with LIGHT resulted in their differentiation into cells that express markers of myofibroblasts and inflammatory chemokines and cytokines. Eosinophils tethered to esophageal fibroblasts after LIGHT stimulation via intercellular adhesion molecule-1. CONCLUSIONS T cells in esophageal tissues from patients with EoE express increased levels of LIGHT compared with control individuals, which induces differentiation of fibroblasts into cells with inflammatory characteristics. TGFβ1 increases fibroblast expression of HVEM, a receptor for LIGHT. LIGHT mediates interactions between esophageal fibroblasts and eosinophils via ICAM1. This pathway might be targeted for the treatment of EoE.
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Affiliation(s)
- Mario C Manresa
- Department of Pediatrics, University of California, San Diego, San Diego; Division of Allergy Immunology; La Jolla Institute for Immunology, La Jolla, California
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, San Diego; Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, San Diego, California
| | - Richard C Kurten
- Department of Physiology and Biophysics, University of Arkansas for Medical Sciences, Arkansas Children's Hospital Research Institute, Little Rock, Arkansas
| | | | - Howard Brickner
- Department of Medicine, University of California, San Diego, San Diego, California
| | - Lucas Dohil
- Department of Pediatrics, University of California, San Diego, San Diego
| | - Rana Herro
- Cincinnati Children's Hospital Medical Center, Immunobiology Division, Cincinnati, Ohio
| | - Praveen Akuthota
- Division of Gastroenterology, Department of Pediatrics, University of California, San Diego; Division of Pulmonary, Critical Care, and Sleep Medicine, University of California San Diego, La Jolla, California
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, San Diego; Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, San Diego, California; Department of Bioengineering, University of California, San Diego, San Diego, California
| | - Michael Croft
- La Jolla Institute for Immunology, La Jolla, California; Division of Gastroenterology, Department of Pediatrics, University of California, San Diego
| | - Seema S Aceves
- Department of Pediatrics, University of California, San Diego, San Diego; Division of Allergy Immunology; Rady Children's Hospital, San Diego; Division of Gastroenterology, Department of Pediatrics, University of California, San Diego.
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16
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Kellman BP, Zhang Y, Logomasini E, Meinhardt E, Godinez-Macias KP, Chiang AWT, Sorrentino JT, Liang C, Bao B, Zhou Y, Akase S, Sogabe I, Kouka T, Winzeler EA, Wilson IBH, Campbell MP, Neelamegham S, Krambeck FJ, Aoki-Kinoshita KF, Lewis NE. A consensus-based and readable extension of Linear Code for Reaction Rules (LiCoRR). Beilstein J Org Chem 2020; 16:2645-2662. [PMID: 33178355 PMCID: PMC7607430 DOI: 10.3762/bjoc.16.215] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 09/17/2020] [Indexed: 12/18/2022] Open
Abstract
Systems glycobiology aims to provide models and analysis tools that account for the biosynthesis, regulation, and interactions with glycoconjugates. To facilitate these methods, there is a need for a clear glycan representation accessible to both computers and humans. Linear Code, a linearized and readily parsable glycan structure representation, is such a language. For this reason, Linear Code was adapted to represent reaction rules, but the syntax has drifted from its original description to accommodate new and originally unforeseen challenges. Here, we delineate the consensuses and inconsistencies that have arisen through this adaptation. We recommend options for a consensus-based extension of Linear Code that can be used for reaction rule specification going forward. Through this extension and specification of Linear Code to reaction rules, we aim to minimize inconsistent symbology thereby making glycan database queries easier. With a clear guide for generating reaction rule descriptions, glycan synthesis models will be more interoperable and reproducible thereby moving glycoinformatics closer to compliance with FAIR standards. Here, we present Linear Code for Reaction Rules (LiCoRR), version 1.0, an unambiguous representation for describing glycosylation reactions in both literature and code.
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Weiss RJ, Spahn PN, Toledo AG, Chiang AWT, Kellman BP, Li J, Benner C, Glass CK, Gordts PLSM, Lewis NE, Esko JD. ZNF263 is a transcriptional regulator of heparin and heparan sulfate biosynthesis. Proc Natl Acad Sci U S A 2020; 117:9311-9317. [PMID: 32277030 PMCID: PMC7196839 DOI: 10.1073/pnas.1920880117] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Heparin is the most widely prescribed biopharmaceutical in production globally. Its potent anticoagulant activity and safety makes it the drug of choice for preventing deep vein thrombosis and pulmonary embolism. In 2008, adulterated material was introduced into the heparin supply chain, resulting in several hundred deaths and demonstrating the need for alternate sources of heparin. Heparin is a fractionated form of heparan sulfate derived from animal sources, predominantly from connective tissue mast cells in pig mucosa. While the enzymes involved in heparin biosynthesis are identical to those for heparan sulfate, the factors regulating these enzymes are not understood. Examination of the promoter regions of all genes involved in heparin/heparan sulfate assembly uncovered a transcription factor-binding motif for ZNF263, a C2H2 zinc finger protein. CRISPR-mediated targeting and siRNA knockdown of ZNF263 in mammalian cell lines and human primary cells led to dramatically increased expression levels of HS3ST1, a key enzyme involved in imparting anticoagulant activity to heparin, and HS3ST3A1, another glucosaminyl 3-O-sulfotransferase expressed in cells. Enhanced 3-O-sulfation increased binding to antithrombin, which enhanced Factor Xa inhibition, and binding of neuropilin-1. Analysis of transcriptomics data showed distinctively low expression of ZNF263 in mast cells compared with other (non-heparin-producing) immune cells. These findings demonstrate a novel regulatory factor in heparan sulfate modification that could further advance the possibility of bioengineering anticoagulant heparin in cultured cells.
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Affiliation(s)
- Ryan J Weiss
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093-0687
| | - Philipp N Spahn
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093-0760
| | - Alejandro Gómez Toledo
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093-0687
| | - Austin W T Chiang
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093-0760
| | - Benjamin P Kellman
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093-0760
| | - Jing Li
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093-0687
| | - Christopher Benner
- Department of Medicine, University of California San Diego, La Jolla, CA 92093-0687
| | - Christopher K Glass
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093-0687
- Department of Medicine, University of California San Diego, La Jolla, CA 92093-0687
| | - Philip L S M Gordts
- Department of Medicine, University of California San Diego, La Jolla, CA 92093-0687
- Glycobiology Research and Training Center, University of California San Diego, La Jolla, CA 92093-0687
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093-0760
- Glycobiology Research and Training Center, University of California San Diego, La Jolla, CA 92093-0687
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093-0687
| | - Jeffrey D Esko
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093-0687;
- Glycobiology Research and Training Center, University of California San Diego, La Jolla, CA 92093-0687
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Richelle A, Chiang AWT, Kuo CC, Lewis NE. Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions. PLoS Comput Biol 2019; 15:e1006867. [PMID: 30986217 PMCID: PMC6483243 DOI: 10.1371/journal.pcbi.1006867] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 04/25/2019] [Accepted: 02/13/2019] [Indexed: 12/26/2022] Open
Abstract
Genome-scale metabolic models provide a valuable context for analyzing data from diverse high-throughput experimental techniques. Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. However, these methods make differing assumptions that influence the content and associated predictive capacity of resulting models, such that model content varies more due to methods used than cell types. Here we overcome this problem with a novel framework for inferring the metabolic functions of a cell before model construction. For this, we curated a list of metabolic tasks and developed a framework to infer the activity of these functionalities from transcriptomic data. We protected the data-inferred tasks during the implementation of diverse context-specific model extraction algorithms for 44 cancer cell lines. We show that the protection of data-inferred metabolic tasks decreases the variability of models across extraction methods. Furthermore, resulting models better capture the actual biological variability across cell lines. This study highlights the potential of using biological knowledge, inferred from omics data, to obtain a better consensus between existing extraction algorithms. It further provides guidelines for the development of the next-generation of data contextualization methods.
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Affiliation(s)
- Anne Richelle
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
| | - Austin W. T. Chiang
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
| | - Chih-Chung Kuo
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Nathan E. Lewis
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
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Chiang AWT, Wu WYL, Wang T, Hwang MJ. Identification of Entry Factors Involved in Hepatitis C Virus Infection Based on Host-Mimicking Short Linear Motifs. PLoS Comput Biol 2017; 13:e1005368. [PMID: 28129350 PMCID: PMC5302801 DOI: 10.1371/journal.pcbi.1005368] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 02/10/2017] [Accepted: 01/17/2017] [Indexed: 12/15/2022] Open
Abstract
Host factors that facilitate viral entry into cells can, in principle, be identified from a virus-host protein interaction network, but for most viruses information for such a network is limited. To help fill this void, we developed a bioinformatics approach and applied it to hepatitis C virus (HCV) infection, which is a current concern for global health. Using this approach, we identified short linear sequence motifs, conserved in the envelope proteins of HCV (E1/E2), that potentially can bind human proteins present on the surface of hepatocytes so as to construct an HCV (envelope)-host protein interaction network. Gene Ontology functional and KEGG pathway analyses showed that the identified host proteins are enriched in cell entry and carcinogenesis functionalities. The validity of our results is supported by much published experimental data. Our general approach should be useful when developing antiviral agents, particularly those that target virus-host interactions. Viruses recruit host proteins, called entry factors, to help gain entry to host cells. Identification of entry factors can provide targets for developing antiviral drugs. By exploring the concept that short linear peptide motifs involved in human protein-protein interactions may be mimicked by viruses to hijack certain host cellular processes and thereby assist viral infection/survival, we developed a bioinformatics strategy to computationally identify entry factors of hepatitis C virus (HCV) infection, which is a worldwide health problem. Analysis of cellular functions and biochemical pathways indicated that the human proteins we identified usually play a role in cell entry and/or carcinogenesis, and results of the analysis are generally supported by experimental studies on HCV infection, including the ~80% (15 of 19) prediction rate of known HCV hepatocyte entry factors. Because molecular mimicry is a general concept, our bioinformatics strategy is a timely approach to identify new targets for antiviral research, not only for HCV but also for other viruses.
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Affiliation(s)
| | - Walt Y. L. Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Ting Wang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Ming-Jing Hwang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- * E-mail:
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Hsu CH, Chiang AWT, Hwang MJ, Liao BY. Proteins with Highly Evolvable Domain Architectures Are Nonessential but Highly Retained. Mol Biol Evol 2016; 33:1219-30. [DOI: 10.1093/molbev/msw006] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
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Chiang AWT, Liu WC, Charusanti P, Hwang MJ. Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters. BMC Syst Biol 2014; 8:4. [PMID: 24428922 PMCID: PMC3896785 DOI: 10.1186/1752-0509-8-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2013] [Accepted: 01/06/2014] [Indexed: 12/15/2022]
Abstract
Background A major challenge in mathematical modeling of biological systems is to determine how model parameters contribute to systems dynamics. As biological processes are often complex in nature, it is desirable to address this issue using a systematic approach. Here, we propose a simple methodology that first performs an enrichment test to find patterns in the values of globally profiled kinetic parameters with which a model can produce the required system dynamics; this is then followed by a statistical test to elucidate the association between individual parameters and different parts of the system’s dynamics. Results We demonstrate our methodology on a prototype biological system of perfect adaptation dynamics, namely the chemotaxis model for Escherichia coli. Our results agreed well with those derived from experimental data and theoretical studies in the literature. Using this model system, we showed that there are motifs in kinetic parameters and that these motifs are governed by constraints of the specified system dynamics. Conclusions A systematic approach based on enrichment statistical tests has been developed to elucidate the relationships between model parameters and the roles they play in affecting system dynamics of a prototype biological network. The proposed approach is generally applicable and therefore can find wide use in systems biology modeling research.
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Affiliation(s)
| | | | | | - Ming-Jing Hwang
- Institute of BioMedical Informatics, National Yang-Ming University, Taipei, Taiwan.
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Chiang AWT, Shaw GTW, Hwang MJ. Partitioning the human transcriptome using HKera, a novel classifier of housekeeping and tissue-specific genes. PLoS One 2013; 8:e83040. [PMID: 24376628 PMCID: PMC3869736 DOI: 10.1371/journal.pone.0083040] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2013] [Accepted: 10/30/2013] [Indexed: 01/12/2023] Open
Abstract
High-throughput transcriptomic experiments have made it possible to classify genes that are ubiquitously expressed as housekeeping (HK) genes and those expressed only in selective tissues as tissue-specific (TS) genes. Although partitioning a transcriptome into HK and TS genes is conceptually problematic owing to the lack of precise definitions and gene expression profile criteria for the two, information whether a gene is an HK or a TS gene can provide an initial clue to its cellular and/or functional role. Consequently, the development of new and novel HK (TS) classification methods has been a topic of considerable interest in post-genomics research. Here, we report such a development. Our method, called HKera, differs from the others by utilizing a novel property of HK genes that we have previously uncovered, namely that the ranking order of their expression levels, as opposed to the expression levels themselves, tends to be preserved from one tissue to another. Evaluated against multiple benchmark sets of human HK genes, including one recently derived from second generation sequencing data, HKera was shown to perform significantly better than five other classifiers that use different methodologies. An enrichment analysis of pathway and gene ontology annotations showed that HKera-predicted HK and TS genes have distinct functional roles and, together, cover most of the ontology categories. These results show that HKera is a good transcriptome partitioner that can be used to search for, and obtain useful expression and functional information for, novel HK (TS) genes.
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Affiliation(s)
- Austin W. T. Chiang
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
- Institute of BioMedical Informatics, NationalYang-MingUniversity, Taipei, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Grace T. W. Shaw
- Institute of BioMedical Informatics, NationalYang-MingUniversity, Taipei, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Ming-Jing Hwang
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
- Institute of BioMedical Informatics, NationalYang-MingUniversity, Taipei, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- * E-mail:
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Chiang AWT, Hwang MJ. A computational pipeline for identifying kinetic motifs to aid in the design and improvement of synthetic gene circuits. BMC Bioinformatics 2013; 14 Suppl 16:S5. [PMID: 24564638 PMCID: PMC3853143 DOI: 10.1186/1471-2105-14-s16-s5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND An increasing number of genetic components are available in several depositories of such components to facilitate synthetic biology research, but picking out those that will allow a designed circuit to achieve the specified function still requires multiple cycles of testing. Here, we addressed this problem by developing a computational pipeline to mathematically simulate a gene circuit for a comprehensive range and combination of the kinetic parameters of the biological components that constitute the gene circuit. RESULTS We showed that, using a well-studied transcriptional repression cascade as an example, the sets of kinetic parameters that could produce the specified system dynamics of the gene circuit formed clusters of recurrent combinations, referred to as kinetic motifs, which appear to be associated with both the specific topology and specified dynamics of the circuit. Furthermore, the use of the resulting "handbook" of performance-ranked kinetic motifs in finding suitable circuit components was illustrated in two application scenarios. CONCLUSIONS These results show that the computational pipeline developed here can provide a rational-based guide to aid in the design and improvement of synthetic gene circuits.
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