1
|
Chang CC, Liu TC, Lu CJ, Chiu HC, Lin WN. Explainable machine learning model for identifying key gut microbes and metabolites biomarkers associated with myasthenia gravis. Comput Struct Biotechnol J 2024; 23:1572-1583. [PMID: 38650589 PMCID: PMC11035017 DOI: 10.1016/j.csbj.2024.04.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 03/14/2024] [Accepted: 04/07/2024] [Indexed: 04/25/2024] Open
Abstract
Diagnostic markers for myasthenia gravis (MG) are limited; thus, innovative approaches are required for supportive diagnosis and personalized care. Gut microbes are associated with MG pathogenesis; however, few studies have adopted machine learning (ML) to identify the associations among MG, gut microbiota, and metabolites. In this study, we developed an explainable ML model to predict biomarkers for MG diagnosis. We enrolled 19 MG patients and 10 non-MG individuals. Stool samples were collected and microbiome assessment was performed using 16S rRNA sequencing. Untargeted metabolic profiling was conducted to identify fecal amplicon significant variants (ASVs) and metabolites. We developed an explainable ML model in which the top ASVs and metabolites are combined to identify the best predictive performance. This model uses the SHapley Additive exPlanations method to generate both global and personalized explanations. Fecal microbe-metabolite composition differed significantly between groups. The key bacterial families were Lachnospiraceae and Ruminococcaceae, and the top three features were Lachnospiraceae, inosine, and methylhistidine. An ML model trained with the top 1 % ASVs and top 15 % metabolites combined outperformed all other models. Personalized explanations revealed different patterns of microbe-metabolite contributions in patients with MG. The integration of the microbiota-metabolite features and the development of an explainable ML framework can accurately identify MG and provide personalized explanations, revealing the associations between gut microbiota, metabolites, and MG. An online calculator employing this algorithm was developed that provides a streamlined interface for MG diagnosis screening and conducting personalized evaluations.
Collapse
Affiliation(s)
- Che-Cheng Chang
- PhD Program in Nutrition and Food Science, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City, Taiwan
- Graduate Institute of Biomedical and Pharmaceutical Science, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Tzu-Chi Liu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Hou-Chang Chiu
- School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Neurology, Taipei Medical University, Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Wei-Ning Lin
- PhD Program in Nutrition and Food Science, Fu Jen Catholic University, New Taipei City, Taiwan
- Graduate Institute of Biomedical and Pharmaceutical Science, Fu Jen Catholic University, New Taipei City, Taiwan
| |
Collapse
|
2
|
Hou A, Luo H, Liu H, Luo L, Ding P. Multi-scale DNA language model improves 6 mA binding sites prediction. Comput Biol Chem 2024; 112:108129. [PMID: 39067351 DOI: 10.1016/j.compbiolchem.2024.108129] [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: 03/04/2024] [Revised: 06/05/2024] [Accepted: 06/10/2024] [Indexed: 07/30/2024]
Abstract
DNA methylation at the N6 position of adenine (N6-methyladenine, 6 mA), which refers to the attachment of a methyl group to the N6 site of the adenine (A) of DNA, is an important epigenetic modification in prokaryotic and eukaryotic genomes. Accurately predicting the 6 mA binding sites can provide crucial insights into gene regulation, DNA repair, disease development and so on. Wet experiments are commonly used for analyzing 6 mA binding sites. However, they suffer from high cost and expensive time. Therefore, various deep learning methods have been widely used to predict 6 mA binding sites recently. In this study, we develop a framework based on multi-scale DNA language model named "iDNA6mA-MDL". "iDNA6mA-MDL" integrates multiple kmers and the nucleotide property and frequency method for feature embedding, which can capture a full range of DNA sequence context information. At the prediction stage, it also leverages DNABERT to compensate for the incomplete capture of global DNA information. Experiments show that our framework obtains average AUC of 0.981 on a classic 6 mA rice gene dataset, going beyond all existing advanced models under fivefold cross-validations. Moreover, "iDNA6mA-MDL" outperforms most of the popular state-of-the-art methods on another 11 6 mA datasets, demonstrating its effectiveness in 6 mA binding sites prediction.
Collapse
Affiliation(s)
- Anlin Hou
- School of Computer Science, University of South China, Hengyang 421001, China
| | - Hanyu Luo
- School of Computer Science, University of South China, Hengyang 421001, China
| | - Huan Liu
- School of Computer Science, University of South China, Hengyang 421001, China
| | - Lingyun Luo
- School of Computer Science, University of South China, Hengyang 421001, China.
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang 421001, China
| |
Collapse
|
3
|
Li F, Mou M, Li X, Xu W, Yin J, Zhang Y, Zhu F. DrugMAP 2.0: molecular atlas and pharma-information of all drugs. Nucleic Acids Res 2024:gkae791. [PMID: 39271119 DOI: 10.1093/nar/gkae791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2024] [Revised: 08/23/2024] [Accepted: 08/31/2024] [Indexed: 09/15/2024] Open
Abstract
The escalating costs and high failure rates have decelerated the pace of drug development, which amplifies the research interests in developing combinatorial/repurposed drugs and understanding off-target adverse drug reaction (ADR). In other words, it is demanded to delineate the molecular atlas and pharma-information for the combinatorial/repurposed drugs and off-target interactions. However, such invaluable data were inadequately covered by existing databases. In this study, a major update was thus conducted to the DrugMAP, which accumulated (a) 20831 combinatorial drugs and their interacting atlas involving 1583 pharmacologically important molecules; (b) 842 repurposed drugs and their interacting atlas with 795 molecules; (c) 3260 off-targets relevant to the ADRs of 2731 drugs and (d) various types of pharmaceutical information, including diverse ADMET properties, versatile diseases, and various ADRs/off-targets. With the growing demands for discovering combinatorial/repurposed therapies and the rapidly emerging interest in AI-based drug discovery, DrugMAP was highly expected to act as an indispensable supplement to existing databases facilitating drug discovery, which was accessible at: https://idrblab.org/drugmap/.
Collapse
Affiliation(s)
- Fengcheng Li
- College of Pharmaceutical Sciences, Children's Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Children's Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang University, Hangzhou 310058, China
- State Key Lab of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Xiaoyi Li
- College of Pharmaceutical Sciences, Children's Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang University, Hangzhou 310058, China
| | - Weize Xu
- College of Pharmaceutical Sciences, Children's Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang University, Hangzhou 310058, China
| | - Jiayi Yin
- Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yang Zhang
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Children's Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang University, Hangzhou 310058, China
- State Key Lab of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| |
Collapse
|
4
|
Singh S, Kaur N, Gehlot A. Application of artificial intelligence in drug design: A review. Comput Biol Med 2024; 179:108810. [PMID: 38991316 DOI: 10.1016/j.compbiomed.2024.108810] [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: 03/18/2024] [Revised: 05/31/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024]
Abstract
Artificial intelligence (AI) is a field of computer science that involves acquiring information, developing rule bases, and mimicking human behaviour. The fundamental concept behind AI is to create intelligent computer systems that can operate with minimal human intervention or without any intervention at all. These rule-based systems are developed using various machine learning and deep learning models, enabling them to solve complex problems. AI is integrated with these models to learn, understand, and analyse provided data. The rapid advancement of Artificial Intelligence (AI) is reshaping numerous industries, with the pharmaceutical sector experiencing a notable transformation. AI is increasingly being employed to automate, optimize, and personalize various facets of the pharmaceutical industry, particularly in pharmacological research. Traditional drug development methods areknown for being time-consuming, expensive, and less efficient, often taking around a decade and costing billions of dollars. The integration of artificial intelligence (AI) techniques addresses these challenges by enabling the examination of compounds with desired properties from a vast pool of input drugs. Furthermore, it plays a crucial role in drug screening by predicting toxicity, bioactivity, ADME properties (absorption, distribution, metabolism, and excretion), physicochemical properties, and more. AI enhances the drug design process by improving the efficiency and accuracy of predicting drug behaviour, interactions, and properties. These approaches further significantly improve the precision of drug discovery processes and decrease clinical trial costs leading to the development of more effective drugs.
Collapse
Affiliation(s)
- Simrandeep Singh
- Department of Electronics & Communication Engineering, UCRD, Chandigarh University, Gharuan, Punjab, India.
| | - Navjot Kaur
- Department of Pharmacognosy, Amar Shaheed Baba Ajit Singh Jujhar Singh Memorial College of Pharmacy, Bela, Ropar, India
| | - Anita Gehlot
- Uttaranchal Institute of technology, Uttaranchal University, Dehradun, India
| |
Collapse
|
5
|
Zhao C, Su KJ, Wu C, Cao X, Sha Q, Li W, Luo Z, Tian Q, Qiu C, Zhao LJ, Liu A, Jiang L, Zhang X, Shen H, Zhou W, Deng HW. Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data. Comput Biol Med 2024; 179:108813. [PMID: 38955127 PMCID: PMC11324385 DOI: 10.1016/j.compbiomed.2024.108813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. METHOD In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.
Collapse
Affiliation(s)
- Chen Zhao
- Department of Computer Science, Kennesaw State University, 680 Arntson Dr, Marietta, GA 30060
| | - Kuan-Jui Su
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Chong Wu
- Department of Biostatistics, University of Texas MD Anderson, Pickens Academic Tower, 1400 Pressler St., Houston, TX 77030
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931
| | - Wu Li
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Zhe Luo
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Qing Tian
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Chuan Qiu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Lan Juan Zhao
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Anqi Liu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Lindong Jiang
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Xiao Zhang
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Hui Shen
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931
- Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI 49931
| | - Hong-Wen Deng
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| |
Collapse
|
6
|
Raheel A. Emotion analysis and recognition in 3D space using classifier-dependent feature selection in response to tactile enhanced audio-visual content using EEG. Comput Biol Med 2024; 179:108807. [PMID: 38970831 DOI: 10.1016/j.compbiomed.2024.108807] [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: 04/01/2024] [Revised: 06/13/2024] [Accepted: 06/24/2024] [Indexed: 07/08/2024]
Abstract
Traditional media such as text, images, audio, and video primarily target specific senses like vision and hearing. In contrast, multiple sensorial media aims to create immersive experiences by integrating additional sensory modalities such as touch, smell, and taste where applicable. Tactile enhanced audio-visual content leverages the sense of touch in addition to visual and auditory stimuli, aiming to create a more immersive and engaging interaction for users. Previously, tactile enhanced content has been explored in 2D emotional space (valence and arousal). In this paper, EEG data against tactile enhanced audio-visual content is labeled based on a self-assessment manikin scale in 3 dimensions i.e., valence, arousal, and dominance. Statistical significance (with a 95% confidence interval) is also established based on gathered scores, highlighting a significant difference in the arousal and dominance dimension of traditional media and tactile enhanced media. A new methodology is proposed using classifier-dependent feature selection approach to classify valence, arousal, and dominance states using three different classifiers. A highest accuracy of 75%, 73.8%, and 75% is achieved for classifying valence, arousal, and dominance states, respectively. The proposed scheme outperforms previous emotion recognition based studies in response to enhanced multimedia content in terms of accuracy, F-score, and other error parameters.
Collapse
Affiliation(s)
- Aasim Raheel
- Department of Computer Engineering, University of Engineering and Technology Taxila, Pakistan.
| |
Collapse
|
7
|
Periwal N, Arora P, Thakur A, Agrawal L, Goyal Y, Rathore AS, Anand HS, Kaur B, Sood V. Antiprotozoal peptide prediction using machine learning with effective feature selection techniques. Heliyon 2024; 10:e36163. [PMID: 39247292 PMCID: PMC11380031 DOI: 10.1016/j.heliyon.2024.e36163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/09/2024] [Accepted: 08/11/2024] [Indexed: 09/10/2024] Open
Abstract
Background Protozoal pathogens pose a considerable threat, leading to notable mortality rates and the ongoing challenge of developing resistance to drugs. This situation underscores the urgent need for alternative therapeutic approaches. Antimicrobial peptides stand out as promising candidates for drug development. However, there is a lack of published research focusing on predicting antimicrobial peptides specifically targeting protozoal pathogens. In this study, we introduce a successful machine learning-based framework designed to predict potential antiprotozoal peptides effective against protozoal pathogens. Objective The primary objective of this study is to classify and predict antiprotozoal peptides using diverse negative datasets. Methods A comprehensive literature review was conducted to gather experimentally validated antiprotozoal peptides, forming the positive dataset for our study. To construct a robust machine learning classifier, multiple negative datasets were incorporated, including (i) non-antimicrobial, (ii) antiviral, (iii) antibacterial, (iv) antifungal, and (v) antimicrobial peptides excluding those targeting protozoal pathogens. Various compositional features of the peptides were extracted using the pfeature algorithm. Two feature selection methods, SVC-L1 and mRMR, were employed to identify highly relevant features crucial for distinguishing between the positive and negative datasets. Additionally, five popular classifiers i.e. Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and XGBoost were used to build efficient decision models. Results XGBoost was the most effective in classifying antiprotozoal peptides from each negative dataset based on the features selected by the mRMR feature selection method. The proposed machine learning framework efficiently differentiate the antiprotozoal peptides from (i) non-antimicrobial (ii) antiviral (iii) antibacterial (iv) antifungal and (v) antimicrobial with accuracy of 97.27 %, 93.64 %, 86.36 %, 90.91 %, and 89.09 % respectively on the validation dataset. Conclusion The models are incorporated in a user-friendly web server (www.soodlab.com/appred) to predict the antiprotozoal activity of given peptides.
Collapse
Affiliation(s)
- Neha Periwal
- Department of Biochemistry, Jamia Hamdard, India
| | - Pooja Arora
- Department of Zoology, Hansraj College, University of Delhi, India
| | | | | | - Yash Goyal
- Department of Computer Science, Hansraj College, University of Delhi, India
| | - Anand S Rathore
- Department of Zoology, Hansraj College, University of Delhi, India
| | | | - Baljeet Kaur
- Department of Computer Science, Hansraj College, University of Delhi, India
| | - Vikas Sood
- Department of Biochemistry, Jamia Hamdard, India
| |
Collapse
|
8
|
Rajaiah R, Pandey K, Acharya A, Ambikan A, Kumar N, Guda R, Avedissian SN, Montaner LJ, Cohen SM, Neogi U, Byrareddy SN. Differential immunometabolic responses to Delta and Omicron SARS-CoV-2 variants in golden syrian hamsters. iScience 2024; 27:110501. [PMID: 39171289 PMCID: PMC11338146 DOI: 10.1016/j.isci.2024.110501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 02/07/2024] [Accepted: 07/10/2024] [Indexed: 08/23/2024] Open
Abstract
Delta (B.1.617.2) and Omicron (B.1.1.529) variants of SARS-CoV-2 represents unique clinical characteristics. However, their role in altering immunometabolic regulations during acute infection remains convoluted. Here, we evaluated the differential immunopathogenesis of Delta vs. Omicron variants in Golden Syrian hamsters (GSH). The Delta variant resulted in higher virus titers in throat swabs and the lungs and exhibited higher lung damage with immune cell infiltration than the Omicron variant. The gene expression levels of immune mediators and metabolic enzymes, Arg-1 and IDO1 in the Delta-infected lungs were significantly higher compared to Omicron. Further, Delta/Omicron infection perturbed carbohydrates, amino acids, nucleotides, and TCA cycle metabolites and was differentially regulated compared to uninfected lungs. Collectively, our data provide a novel insight into immunometabolic/pathogenic outcomes for Delta vs. Omicron infection in the GSH displaying concordance with COVID-19 patients associated with inflammation and tissue injury during acute infection that offered possible new targets to develop potential therapeutics.
Collapse
Affiliation(s)
- Rajesh Rajaiah
- Department of Pharmacology and Experimental Neuroscience, University of Nebraska Medical Center, Omaha, NE, USA
| | - Kabita Pandey
- Department of Pharmacology and Experimental Neuroscience, University of Nebraska Medical Center, Omaha, NE, USA
| | - Arpan Acharya
- Department of Pharmacology and Experimental Neuroscience, University of Nebraska Medical Center, Omaha, NE, USA
| | - Anoop Ambikan
- The Systems Virology Lab, Department of Laboratory Medicine, Division of Clinical Microbiology, ANA Futura, Karolinska Institutet, 141 52 Stockholm, Sweden
| | - Narendra Kumar
- Department of Pharmacology and Experimental Neuroscience, University of Nebraska Medical Center, Omaha, NE, USA
| | - Reema Guda
- Department of Pharmacology and Experimental Neuroscience, University of Nebraska Medical Center, Omaha, NE, USA
| | - Sean N. Avedissian
- Antiviral Pharmacology Laboratory, College of Pharmacy, University of Nebraska Medical Center, Omaha, NE, USA
| | - Luis J. Montaner
- Vaccine and Immunotherapy Center, The Wistar Institute, Philadelphia, PA 19104, USA
| | - Samuel M. Cohen
- Havlik Wall Professor of Oncology, Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Ujjwal Neogi
- The Systems Virology Lab, Department of Laboratory Medicine, Division of Clinical Microbiology, ANA Futura, Karolinska Institutet, 141 52 Stockholm, Sweden
| | - Siddappa N. Byrareddy
- Department of Pharmacology and Experimental Neuroscience, University of Nebraska Medical Center, Omaha, NE, USA
- Havlik Wall Professor of Oncology, Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, USA
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| |
Collapse
|
9
|
Piacenza Florezi G, Pereira Barone F, Izidoro MA, Soares-Jr JM, Coutinho-Camillo CM, Lourenço SV. Targeted saliva metabolomics in Sjögren's syndrome. Clinics (Sao Paulo) 2024; 79:100459. [PMID: 39098147 PMCID: PMC11334732 DOI: 10.1016/j.clinsp.2024.100459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 06/12/2024] [Accepted: 07/12/2024] [Indexed: 08/06/2024] Open
Abstract
OBJECTIVE Sjögren's Syndrome (SS) is a chronic inflammatory autoimmune exocrinopathy, and although, the role of metabolism in the autoimmune responses has been discussed in diseases such as lupus erythematosus, rheumatoid arthritis, psoriasis and scleroderma. There is a lack of information regarding the metabolic implications of SS. Considering that the disease affects primarily salivary glands; the aim of this study is to evaluate the metabolic changes in the salivary glands' microenvironment using a targeted metabolomics approach. METHODS The saliva from 10 patients diagnosed with SS by the American-European consensus and 10 healthy volunteers was analyzed in an Ultra-high Performance Liquid Chromatograph Coupled Mass Spectrometry (UPLC-MS). RESULTS The results showed an increased concentration in SS of metabolites involved in oxidative stress such as lactate, alanine and malate, and amino acids involved in the growth and proliferation of T-cells, such as arginine, leucine valine and isoleucine. CONCLUSIONS These results revealed that is possible to differentiate the metabolic profile of SS and healthy individuals using a small amount of saliva, which in its turn may reflect the cellular changes observed in the microenvironments of damaged salivary glands from these patients.
Collapse
Affiliation(s)
- Giovanna Piacenza Florezi
- Stomatology Department, Faculdade de Odontologia, Universidade de São Paulo, São Paulo, SP, Brazil; Tropical Medicine Institute, LIM-06, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil.
| | - Felippe Pereira Barone
- Stomatology Department, Faculdade de Odontologia, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Mario Augusto Izidoro
- Laboratório de Espectrometria de Massas do Hospital São Paulo, São Paulo, SP, Brazil
| | - José Maria Soares-Jr
- Laboratório de Ginecologia Estrutural e Molecular (LIM-58), Disciplina de Ginecologia, Departamento de Obstetrícia e Ginecologia, Hospital das Clnicas HCFMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP, Brazil
| | | | - Silvia Vanessa Lourenço
- Stomatology Department, Faculdade de Odontologia, Universidade de São Paulo, São Paulo, SP, Brazil; Tropical Medicine Institute, LIM-06, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| |
Collapse
|
10
|
Shao B, Killion M, Oliver A, Vang C, Zeleke F, Neikirk K, Vue Z, Garza-Lopez E, Shao JQ, Mungai M, Lam J, Williams Q, Altamura CT, Whiteside A, Kabugi K, McKenzie J, Ezedimma M, Le H, Koh A, Scudese E, Vang L, Marshall AG, Crabtree A, Tanghal JI, Stephens D, Koh HJ, Jenkins BC, Murray SA, Cooper AT, Williams C, Damo SM, McReynolds MR, Gaddy JA, Wanjalla CN, Beasley HK, Hinton A. Ablation of Sam50 is associated with fragmentation and alterations in metabolism in murine and human myotubes. J Cell Physiol 2024; 239:e31293. [PMID: 38770789 PMCID: PMC11324413 DOI: 10.1002/jcp.31293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 03/30/2024] [Accepted: 04/26/2024] [Indexed: 05/22/2024]
Abstract
The sorting and assembly machinery (SAM) Complex is responsible for assembling β-barrel proteins in the mitochondrial membrane. Comprising three subunits, Sam35, Sam37, and Sam50, the SAM complex connects the inner and outer mitochondrial membranes by interacting with the mitochondrial contact site and cristae organizing system complex. Sam50, in particular, stabilizes the mitochondrial intermembrane space bridging (MIB) complex, which is crucial for protein transport, respiratory chain complex assembly, and regulation of cristae integrity. While the role of Sam50 in mitochondrial structure and metabolism in skeletal muscle remains unclear, this study aims to investigate its impact. Serial block-face-scanning electron microscopy and computer-assisted 3D renderings were employed to compare mitochondrial structure and networking in Sam50-deficient myotubes from mice and humans with wild-type (WT) myotubes. Furthermore, autophagosome 3D structure was assessed in human myotubes. Mitochondrial metabolic phenotypes were assessed using Gas Chromatography-Mass Spectrometry-based metabolomics to explore differential changes in WT and Sam50-deficient myotubes. The results revealed increased mitochondrial fragmentation and autophagosome formation in Sam50-deficient myotubes compared to controls. Metabolomic analysis indicated elevated metabolism of propanoate and several amino acids, including ß-Alanine, phenylalanine, and tyrosine, along with increased amino acid and fatty acid metabolism in Sam50-deficient myotubes. Furthermore, impairment of oxidative capacity was observed upon Sam50 ablation in both murine and human myotubes, as measured with the XF24 Seahorse Analyzer. Collectively, these findings support the critical role of Sam50 in establishing and maintaining mitochondrial integrity, cristae structure, and mitochondrial metabolism. By elucidating the impact of Sam50-deficiency, this study enhances our understanding of mitochondrial function in skeletal muscle.
Collapse
Affiliation(s)
- Bryanna Shao
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Mason Killion
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Ashton Oliver
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Chia Vang
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Faben Zeleke
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Kit Neikirk
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Zer Vue
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Edgar Garza-Lopez
- Department of Internal Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Jian-Qiang Shao
- Central Microscopy Research Facility, University of Iowa, Iowa City, Iowa, USA
| | - Margaret Mungai
- Department of Internal Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Jacob Lam
- Department of Internal Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Qiana Williams
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Christopher T Altamura
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Aaron Whiteside
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
- Department of Neuroscience, Cell Biology and Physiology, Wright State University, Dayton, Ohio, USA
| | - Kinuthia Kabugi
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Jessica McKenzie
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Maria Ezedimma
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Han Le
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Alice Koh
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Estevão Scudese
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Larry Vang
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Andrea G Marshall
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Amber Crabtree
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | | | - Dominique Stephens
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Ho-Jin Koh
- Department of Biological Sciences, Tennessee State University, Nashville, Tennessee, USA
| | - Brenita C Jenkins
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Sandra A Murray
- Department of Cell Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Anthonya T Cooper
- Department of Cell Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Clintoria Williams
- Department of Neuroscience, Cell Biology and Physiology, Wright State University, Dayton, Ohio, USA
| | - Steven M Damo
- Department of Life and Physical Sciences, Fisk University, Nashville, Tennessee, USA
| | - Melanie R McReynolds
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Jennifer A Gaddy
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- US Department of Veterans Affairs, Tennessee Valley Healthcare Systems, Nashville, Tennessee, USA
| | - Celestine N Wanjalla
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Heather K Beasley
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Antentor Hinton
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| |
Collapse
|
11
|
Asad M, Hassan A, Wang W, Alonazi WB, Khan MS, Ogunyemi SO, Ibrahim M, Bin L. An integrated in silico approach for the identification of novel potential drug target and chimeric vaccine against Neisseria meningitides strain 331401 serogroup X by subtractive genomics and reverse vaccinology. Comput Biol Med 2024; 178:108738. [PMID: 38870724 DOI: 10.1016/j.compbiomed.2024.108738] [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: 01/25/2024] [Revised: 05/15/2024] [Accepted: 06/08/2024] [Indexed: 06/15/2024]
Abstract
Neisseria meningitidis, commonly known as the meningococcus, leads to substantial illness and death among children and young adults globally, revealing as either epidemic or sporadic meningitis and/or septicemia. In this study, we have designed a novel peptide-based chimeric vaccine candidate against the N. meningitidis strain 331,401 serogroup X. Through rigorous analysis of subtractive genomics, two essential cytoplasmic proteins, namely UPI000012E8E0(UDP-3-O-acyl-GlcNAc deacetylase) and UPI0000ECF4A9(UDP-N-acetylglucosamine acyltransferase) emerged as potential drug targets. Additionally, using reverse vaccinology, the outer membrane protein UPI0001F4D537 (Membrane fusion protein MtrC) identified by subcellular localization and recognized for its known indispensable role in bacterial survival was identified as a novel chimeric vaccine target. Following a careful comparison of MHC-I, MHC-II, T-cell, and B-cell epitopes, three epitopes derived from UPI0001F4D537 were linked with three types of linkers-GGGS, EAAAK, and the essential PADRE-for vaccine construction. This resulted in eight distinct vaccine models (V1-V8). Among them V1 model was selected as the final vaccine construct. It exhibits exceptional immunogenicity, safety, and enhanced antigenicity, with 97.7 % of its residues in the Ramachandran plot's most favored region. Subsequently, the vaccine structure was docked with the TLR4/MD2 complex and six different HLA allele receptors using the HADDOCK server. The docking resulted in the lowest HADDOCK score of 39.3 ± 9.0 for TLR/MD2. Immune stimulation showed a strong immune response, including antibodies creation and the activation of B-cells, T Cytotoxic cells, T Helper cells, Natural Killer cells, and interleukins. Furthermore, the vaccine construct was successfully expressed in the Escherichia coli system by reverse transcription, optimization, and ligation in the pET-28a (+) vector for the expression study. The current study proposes V1 construct has the potential to elicit both cellular and humoral responses, crucial for the developing an epitope-based vaccine against N. meningitidis strain 331,401 serogroup X.
Collapse
Affiliation(s)
- Muhammad Asad
- Department of Biosciences, COMSATS University Islamabad, Sahiwal Campus, Pakistan
| | - Ahmad Hassan
- Department of Biosciences, COMSATS University Islamabad, Sahiwal Campus, Pakistan
| | - Weiyu Wang
- State Key Laboratory of Rice Biology and Breeding, Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Wadi B Alonazi
- Health Administration Department, College of Business Administration, King Saud University, Riyadh, Saudi Arabia
| | | | - Solabomi Olaitan Ogunyemi
- State Key Laboratory of Rice Biology and Breeding, Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Muhammad Ibrahim
- Department of Biosciences, COMSATS University Islamabad, Sahiwal Campus, Pakistan.
| | - Li Bin
- State Key Laboratory of Rice Biology and Breeding, Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Biotechnology, Zhejiang University, Hangzhou, 310058, China
| |
Collapse
|
12
|
Ma S, Li R, Li G, Wei M, Li B, Li Y, Ha C. Identification of a G-protein coupled receptor-related gene signature through bioinformatics analysis to construct a risk model for ovarian cancer prognosis. Comput Biol Med 2024; 178:108747. [PMID: 38897150 DOI: 10.1016/j.compbiomed.2024.108747] [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: 12/19/2023] [Revised: 05/31/2024] [Accepted: 06/08/2024] [Indexed: 06/21/2024]
Abstract
BACKGROUND Ovarian cancer (OV) is a common malignant tumor of the female reproductive system with a 5-year survival rate of ∼30 %. Inefficient early diagnosis and prognosis leads to poor survival in most patients. G protein-coupled receptors (GPCRs, the largest family of human cell surface receptors) are associated with OV. We aimed to identify GPCR-related gene (GPCRRG) signatures and develop a novel model to predict OV prognosis. METHOD We downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and a prognostic model was constructed. The predictive ability of the model was evaluated by Kaplan-Meier (K-M) survival analysis. The levels of GPCRRGs were examined in normal and OV cell lines using quantitative reverse-Etranscription polymerase chain reaction. The immunological characteristics of the high- and low-risk groups were analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT. RESULTS Based on the risks scores, 17 GPCRRGs were associated with OV prognosis. CXCR4, GPR34, LGR6, LPAR3, and RGS2 were significantly expressed in three OV datasets and enabled accurate OV diagnosis. K-M analysis of the prognostic model showed that it could differentiate high- and low-risk patients, which correspond to poorer and better prognoses, respectively. GPCRRG expression was correlated with immune infiltration rates. CONCLUSIONS Our prognostic model elaborates on the roles of GPCRRGs in OV and provides a new tool for prognosis and immune response prediction in patients with OV.
Collapse
Affiliation(s)
- Shaohan Ma
- Clinical Medical College, Ningxia Medical University, Yinchuan, Ningxia, China
| | - Ruyue Li
- Gynecology Department, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China
| | - Guangqi Li
- Medical Laboratory Center, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Meng Wei
- Gynecology Department, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China
| | - Bowei Li
- Clinical Medical College, Ningxia Medical University, Yinchuan, Ningxia, China
| | - Yongmei Li
- Gynecology Department, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China
| | - Chunfang Ha
- Gynecology Department, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China; Key Laboratory of Fertility Preservation & Maintenance of Ministry of Education, Ningxia Medical University, Yinchuan, Ningxia, 750000, China.
| |
Collapse
|
13
|
Biswas B, Kumar N, Sugimoto M, Hoque MA. scHD4E: Novel ensemble learning-based differential expression analysis method for single-cell RNA-sequencing data. Comput Biol Med 2024; 178:108769. [PMID: 38897145 DOI: 10.1016/j.compbiomed.2024.108769] [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: 04/01/2024] [Revised: 05/14/2024] [Accepted: 06/15/2024] [Indexed: 06/21/2024]
Abstract
Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been developed for targeting the scRNA-seq data analysis based on different modeling frameworks, assumptions, strategies and test statistic in considering various data features. The scDEA is an ensemble learning-based DE analysis method developed recently, yielding p-values using Lancaster's combination, generated by 12 individual DE analysis methods, and producing more accurate and stable results than individual methods. The objective of our study is to propose a new ensemble learning-based DE analysis method, scHD4E, using top performers in only 4 separate methods. The top performer 4 methods have been selected through an evaluation process using six real scRNA-seq data sets. We conducted comprehensive experiments for five experimental data sets to evaluate our proposed method based on the sample size effects, batch effects, type I error control, gene ontology enrichment analysis, runtime, identified matched DE genes, and semantic similarity measurement between methods. We also perform similar analyses (except the last 3 terms) and compute performance measures like accuracy, F1 score, Mathew's correlation coefficient etc. for a simulated data set. The results show that scHD4E is performs better than all the individual and scDEA methods in all the above perspectives. We expect that scHD4E will serve the modern data scientists for detecting the DEGs in scRNA-seq data analysis. To implement our proposed method, a Github R package scHD4E and its shiny application has been developed, and available in the following links: https://github.com/bbiswas1989/scHD4E and https://github.com/bbiswas1989/scHD4E-Shiny.
Collapse
Affiliation(s)
- Biplab Biswas
- Department of Statistics, Faculty of Science, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj, 8100, Bangladesh; Department of Statistics, Faculty of Science, University of Rajshahi, Rajshahi, 6205, Bangladesh.
| | - Nishith Kumar
- Department of Statistics, Faculty of Science, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj, 8100, Bangladesh.
| | - Masahiro Sugimoto
- Institute for Advanced Biosciences, Keio University 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan.
| | - Md Aminul Hoque
- Department of Statistics, Faculty of Science, University of Rajshahi, Rajshahi, 6205, Bangladesh.
| |
Collapse
|
14
|
House RRJ, Soper-Hopper MT, Vincent MP, Ellis AE, Capan CD, Madaj ZB, Wolfrum E, Isaguirre CN, Castello CD, Johnson AB, Escobar Galvis ML, Williams KS, Lee H, Sheldon RD. A diverse proteome is present and enzymatically active in metabolite extracts. Nat Commun 2024; 15:5796. [PMID: 38987243 PMCID: PMC11237058 DOI: 10.1038/s41467-024-50128-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 07/02/2024] [Indexed: 07/12/2024] Open
Abstract
Metabolite extraction is the critical first-step in metabolomics experiments, where it is generally regarded to inactivate and remove proteins. Here, arising from efforts to improve extraction conditions for polar metabolomics, we discover a proteomic landscape of over 1000 proteins within metabolite extracts. This is a ubiquitous feature across several common extraction and sample types. By combining post-resuspension stable isotope addition and enzyme inhibitors, we demonstrate in-extract metabolite interconversions due to residual transaminase activity. We extend these findings with untargeted metabolomics where we observe extensive protein-mediated metabolite changes, including in-extract formation of glutamate dipeptide and depletion of total glutathione. Finally, we present a simple extraction workflow that integrates 3 kDa filtration for protein removal as a superior method for polar metabolomics. In this work, we uncover a previously unrecognized, protein-mediated source of observer effects in metabolomics experiments with broad-reaching implications across all research fields using metabolomics and molecular metabolism.
Collapse
Affiliation(s)
- Rachel Rae J House
- Department of Cell Biology, Van Andel Institute, Grand Rapids, MI, USA
- Department of Metabolism and Nutritional Programming, Van Andel Institute, Grand Rapids, MI, USA
- Mass Spectrometry Core, Van Andel Institute, Grand Rapids, MI, USA
| | | | | | - Abigail E Ellis
- Mass Spectrometry Core, Van Andel Institute, Grand Rapids, MI, USA
| | - Colt D Capan
- Mass Spectrometry Core, Van Andel Institute, Grand Rapids, MI, USA
| | - Zachary B Madaj
- Bioinformatics and Biostatistics Core, Van Andel Institute, Grand Rapids, MI, USA
| | - Emily Wolfrum
- Bioinformatics and Biostatistics Core, Van Andel Institute, Grand Rapids, MI, USA
| | | | | | - Amy B Johnson
- Mass Spectrometry Core, Van Andel Institute, Grand Rapids, MI, USA
| | - Martha L Escobar Galvis
- Office of the Cores, Core Technologies and Services, Van Andel Institute, Grand Rapids, MI, USA
| | - Kelsey S Williams
- Department of Metabolism and Nutritional Programming, Van Andel Institute, Grand Rapids, MI, USA
| | - Hyoungjoo Lee
- Mass Spectrometry Core, Van Andel Institute, Grand Rapids, MI, USA
| | - Ryan D Sheldon
- Mass Spectrometry Core, Van Andel Institute, Grand Rapids, MI, USA.
| |
Collapse
|
15
|
Wang R, He Z, Chen H, Guo S, Zhang S, Wang K, Wang M, Ho SH. Enhancing biomass conversion to bioenergy with machine learning: Gains and problems. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172310. [PMID: 38599406 DOI: 10.1016/j.scitotenv.2024.172310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/20/2024] [Accepted: 04/06/2024] [Indexed: 04/12/2024]
Abstract
The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.
Collapse
Affiliation(s)
- Rupeng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Zixiang He
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Honglin Chen
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Silin Guo
- School of Medicine and Health, Harbin Institute of Technology, Harbin 150040, PR China
| | - Shiyu Zhang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Ke Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Meng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Shih-Hsin Ho
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.
| |
Collapse
|
16
|
Islam MA, Majumder MZH, Miah MS, Jannaty S. Precision healthcare: A deep dive into machine learning algorithms and feature selection strategies for accurate heart disease prediction. Comput Biol Med 2024; 176:108432. [PMID: 38744014 DOI: 10.1016/j.compbiomed.2024.108432] [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: 02/08/2024] [Revised: 04/06/2024] [Accepted: 04/07/2024] [Indexed: 05/16/2024]
Abstract
This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, the research sheds light on optimal strategies for early detection. MLAs such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and others were studied, with precision and recall metrics emphasized for robust predictions. Our study addresses challenges in real-world data through data cleaning and one-hot encoding, enhancing the integrity of our predictive models. Feature extraction techniques-Recursive Feature Extraction (RFE), Principal Component Analysis (PCA), and univariate feature selection-play a crucial role in identifying relevant features and reducing data dimensionality. Our findings showcase the impact of these techniques on improving prediction accuracy. Optimized models for each dataset have been achieved through grid search hyperparameter tuning, with configurations meticulously outlined. Notably, a remarkable 99.12 % accuracy was achieved on the first Kaggle dataset, showcasing the potential for accurate HDP. Model robustness across diverse datasets was highlighted, with caution against overfitting. The study emphasizes the need for validation of unseen data and encourages ongoing research for generalizability. Serving as a practical guide, this research aids researchers and practitioners in HDP model development, influencing clinical decisions and healthcare resource allocation. By providing insights into effective algorithms and techniques, the paper contributes to reducing heart disease-related morbidity and mortality, supporting the healthcare community's ongoing efforts.
Collapse
Affiliation(s)
- Md Ariful Islam
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh.
| | | | - Md Sohel Miah
- Department of Computer Science and Technology, Moulvibazar Polytechnic Institute, Bangladesh
| | - Sumaia Jannaty
- Gonoshasthaya Samaj Vittik Medical College, Savar, Dhaka, Bangladesh
| |
Collapse
|
17
|
Rudolph TE, Roths M, Freestone AD, Yap SQ, Michael A, Rhoads RP, White-Springer SH, Baumgard LH, Selsby JT. Biological sex impacts oxidative stress in skeletal muscle in a porcine heat stress model. Am J Physiol Regul Integr Comp Physiol 2024; 326:R578-R587. [PMID: 38708546 PMCID: PMC11381024 DOI: 10.1152/ajpregu.00268.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/03/2024] [Accepted: 04/25/2024] [Indexed: 05/07/2024]
Abstract
Oxidative stress contributes to heat stress (HS)-mediated alterations in skeletal muscle; however, the extent to which biological sex mediates oxidative stress during HS remains unknown. We hypothesized muscle from males would be more resistant to oxidative stress caused by HS than muscle from females. To address this, male and female pigs were housed in thermoneutral conditions (TN; 20.8 ± 1.6°C; 62.0 ± 4.7% relative humidity; n = 8/sex) or subjected to HS (39.4 ± 0.6°C; 33.7 ± 6.3% relative humidity) for 1 (HS1; n = 8/sex) or 7 days (HS7; n = 8/sex) followed by collection of the oxidative portion of the semitendinosus. Although HS increased muscle temperature, by 7 days, muscle from heat-stressed females was cooler than muscle from heat-stressed males (0.3°C; P < 0.05). Relative protein abundance of 4-hydroxynonenal (4-HNE)-modified proteins increased in HS1 females compared with TN (P = 0.05). Furthermore, malondialdehyde (MDA)-modified proteins and 8-hydroxy-2'-deoxyguanosine (8-OHdG) concentration, a DNA damage marker, was increased in HS7 females compared with TN females (P = 0.05). Enzymatic activities of catalase and superoxide dismutase (SOD) remained similar between groups; however, glutathione peroxidase (GPX) activity decreased in HS7 females compared with TN and HS1 females (P ≤ 0.03) and HS7 males (P = 0.02). Notably, HS increased skeletal muscle Ca2+ deposition (P = 0.05) and was greater in HS1 females compared with TN females (P < 0.05). Heat stress increased sarco(endo)plasmic reticulum Ca2+ ATPase (SERCA)2a protein abundance (P < 0.01); however, Ca2+ ATPase activity remained similar between groups. Overall, despite having lower muscle temperature, muscle from heat-stressed females had increased markers of oxidative stress and calcium deposition than muscle from males following identical environmental exposure.NEW & NOTEWORTHY Heat stress is a global threat to human health and agricultural production. We demonstrated that following 7 days of heat stress, skeletal muscle from females was more susceptible to oxidative stress than muscle from males in a porcine model, despite cooler muscle temperatures. The vulnerability to heat stress-induced oxidative stress in females may be driven, at least in part, by decreased antioxidant capacity and calcium dysregulation.
Collapse
Affiliation(s)
- Tori E Rudolph
- Department of Animal Science, Iowa State University, Ames, Iowa, United States
| | - Melissa Roths
- Department of Animal Science, Iowa State University, Ames, Iowa, United States
| | - Alyssa D Freestone
- Department of Animal Science, Iowa State University, Ames, Iowa, United States
| | - Sau Qwan Yap
- Department of Animal Science, Iowa State University, Ames, Iowa, United States
| | - Alyona Michael
- Department of Vet Diagnostic & Production Animal Med, Iowa State University, Ames, Iowa, United States
| | - Robert P Rhoads
- School of Animal Sciences, Virginia Tech, Blacksburg, Virginia, United States
| | - Sarah H White-Springer
- Department of Animal Science, Texas A&M University and Texas A&M AgriLife Research, College Station, Texas, United States
- Department of Kinesiology and Sport Management, Texas A&M University, College Station, Texas, United States
| | - Lance H Baumgard
- Department of Animal Science, Iowa State University, Ames, Iowa, United States
| | - Joshua T Selsby
- Department of Animal Science, Iowa State University, Ames, Iowa, United States
| |
Collapse
|
18
|
Fu Y, Wang C, Wu Z, Zhang X, Liu Y, Wang X, Liu F, Chen Y, Zhang Y, Zhao H, Wang Q. Discovery of the potential biomarkers for early diagnosis of endometrial cancer via integrating metabolomics and transcriptomics. Comput Biol Med 2024; 173:108327. [PMID: 38552279 DOI: 10.1016/j.compbiomed.2024.108327] [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: 02/06/2024] [Revised: 03/07/2024] [Accepted: 03/17/2024] [Indexed: 04/17/2024]
Abstract
Endometrial cancer (EC) is one of the most common malignant tumors in women, and the increasing incidence and mortality pose a serious threat to the public health. Early diagnosis of EC could prolong the survival period and optimize the survivorship, greatly alleviating patients' suffering and social medical pressure. In this study, we collected urine and serum samples from the recruited patients, analyzed the samples using LC-MS approach, and identified the differential metabolites through metabolomic analysis. Then, the differentially expressed genes were identified through the systematic transcriptomic analysis of EC-related dataset from Gene Expression Omnibus (GEO), followed by network profiling of metabolic-reaction-enzyme-gene. In this experiment, a total of 83 differential metabolites and 19 hub genes were discovered, of which 10 different metabolites and 3 hub genes were further evaluated as more potential biomarkers based on network analysis. According to the KEGG enrichment analysis, the potential biomarkers and gene-encoded proteins were found to be involved in the arginine and proline metabolism, histidine metabolism, and pyrimidine metabolism, which was of significance for the early diagnosis of EC. In particular, the combination of metabolites (histamine, 1-methylhistamine, and methylimidazole acetaldehyde) as well as the combination of RRM2, TYMS and TK1 exerted more accurate discrimination abilities between EC and healthy groups, providing more criteria for the early diagnosis of EC.
Collapse
Affiliation(s)
- Yan Fu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China; Core Facilities and Centers, Hebei Medical University, Shijiazhuang, 050017, China
| | - Chengzhao Wang
- College of Basic Medicine, Hebei Medical University, Shijiazhuang, 050017, China
| | - Zhimin Wu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Xiaoguang Zhang
- Core Facilities and Centers, Hebei Medical University, Shijiazhuang, 050017, China; College of Basic Medicine, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yan Liu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Xu Wang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Fangfang Liu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yujuan Chen
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yang Zhang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China.
| | - Huanhuan Zhao
- Department of Obstetrics and Gynecology, The Fourth Hospital of Hebei Medical University, 050011, China.
| | - Qiao Wang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China.
| |
Collapse
|
19
|
Ganesan R, Gupta H, Jeong JJ, Sharma SP, Won SM, Oh KK, Yoon SJ, Han SH, Yang YJ, Baik GH, Bang CS, Kim DJ, Suk KT. Characteristics of microbiome-derived metabolomics according to the progression of alcoholic liver disease. Hepatol Int 2024; 18:486-499. [PMID: 37000389 DOI: 10.1007/s12072-023-10518-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 03/07/2023] [Indexed: 04/01/2023]
Abstract
BACKGROUND AND AIM The prevalence and severity of alcoholic liver disease (ALD) are increasing. The incidence of alcohol-related cirrhosis has risen up to 2.5%. This study aimed to identify novel metabolite mechanisms involved in the development of ALD in patients. The use of gut microbiome-derived metabolites is increasing in targeted therapies. Identifying metabolic compounds is challenging due to the complex patterns that have long-term effects on ALD. We investigated the specific metabolite signatures in ALD patients. METHODS This study included 247 patients (heathy control, HC: n = 62, alcoholic fatty liver, AFL; n = 25, alcoholic hepatitis, AH; n = 80, and alcoholic cirrhosis, AC, n = 80) identified, and stool samples were collected. 16S rRNA sequencing and metabolomics were performed with MiSeq sequencer and liquid chromatography coupled to time-of-flight-mass spectrometry (LC-TOF-MS), respectively. The untargeted metabolites in AFL, AH, and AC samples were evaluated by multivariate statistical analysis and metabolic pathotypic expression. Metabolic network classifiers were used to predict the pathway expression of the AFL, AH, and AC stages. RESULTS The relative abundance of Proteobacteria was increased and the abundance of Bacteroides was decreased in ALD samples (p = 0.001) compared with that in HC samples. Fusobacteria levels were higher in AH samples (p = 0.0001) than in HC samples. Untargeted metabolomics was applied to quantitatively screen 103 metabolites from each stool sample. Indole-3-propionic acid levels are significantly lower in AH and AC (vs. HC, p = 0.001). Indole-3-lactic acid (ILA: p = 0.04) levels were increased in AC samples. AC group showed an increase in indole-3-lactic acid (vs. HC, p = 0.040) level. Compared with that in HC samples, the levels of short-chain fatty acids (SCFAs: acetic acid, butyric acid, propionic acid, iso-butyric acid, and iso-valeric acid) and bile acids (lithocholic acids) were significantly decreased in AC. The pathways of linoleic acid metabolism, indole compounds, histidine metabolism, fatty acid degradation, and glutamate metabolism were closely associated with ALD metabolism. CONCLUSIONS This study identified that microbial metabolic dysbiosis is associated with ALD-related metabolic dysfunction. The SCFAs, bile acids, and indole compounds were depleted during ALD progression. CLINICAL TRIAL Clinicaltrials.gov, number NCT04339725.
Collapse
Affiliation(s)
- Raja Ganesan
- Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, 24253, Republic of Korea
| | - Haripriya Gupta
- Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, 24253, Republic of Korea
| | - Jin-Ju Jeong
- Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, 24253, Republic of Korea
| | - Satya Priya Sharma
- Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, 24253, Republic of Korea
| | - Sung-Min Won
- Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, 24253, Republic of Korea
| | - Ki-Kwang Oh
- Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, 24253, Republic of Korea
| | - Sang Jun Yoon
- Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, 24253, Republic of Korea
| | - Sang Hak Han
- Department of Pathology, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Young Joo Yang
- Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, 24253, Republic of Korea
| | - Gwang Ho Baik
- Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, 24253, Republic of Korea
| | - Chang Seok Bang
- Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, 24253, Republic of Korea
| | - Dong Joon Kim
- Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, 24253, Republic of Korea
| | - Ki Tae Suk
- Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, 24253, Republic of Korea.
| |
Collapse
|
20
|
Zhou Y, Chen Z, Yang M, Chen F, Yin J, Zhang Y, Zhou X, Sun X, Ni Z, Chen L, Lv Q, Zhu F, Liu S. FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response. Brief Bioinform 2024; 25:bbae223. [PMID: 38742521 PMCID: PMC11091744 DOI: 10.1093/bib/bbae223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 03/25/2024] [Accepted: 04/21/2024] [Indexed: 05/16/2024] Open
Abstract
Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia-reperfusion injury and other pathological cell death. The regulation of ferroptosis mainly focuses on three pathways: system Xc-/GPX4 axis, lipid peroxidation and iron metabolism. The genes involved in these processes were divided into driver, suppressor and marker. Importantly, small molecules or drugs that mediate the expression of these genes are often good treatments in the clinic. Herein, a newly developed database, named 'FERREG', is documented to (i) providing the data of ferroptosis-related regulation of diseases occurrence, progression and drug response; (ii) explicitly describing the molecular mechanisms underlying each regulation; and (iii) fully referencing the collected data by cross-linking them to available databases. Collectively, FERREG contains 51 targets, 718 regulators, 445 ferroptosis-related drugs and 158 ferroptosis-related disease responses. FERREG can be accessed at https://idrblab.org/ferreg/.
Collapse
Affiliation(s)
- Yuan Zhou
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Mengjie Yang
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Fengyun Chen
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Jiayi Yin
- Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine
| | - Yintao Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Xuheng Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Ziheng Ni
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Lu Chen
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Qun Lv
- Department of Respiratory, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, 311121, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Shuiping Liu
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| |
Collapse
|
21
|
Nishiumi S, Yokoyama T, Ojima N. User-friendly relative quantification procedure for gas chromatography/mass spectrometry-based plasma metabolome analysis. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2024; 38:e9683. [PMID: 38212648 DOI: 10.1002/rcm.9683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 01/13/2024]
Abstract
RATIONALE Recently, metabolome analysis has been applied to a variety of research fields, but differences between batches or facilities can cause discrepancies in the results of such analyses. To resolve these issues using comprehensive metabolome analysis, in which it is difficult to perform quantitative analyses of all detected metabolites, internal standard compounds are used to obtain relative metabolite levels. This study investigated gas chromatography/mass spectrometry-based plasma metabolome analysis methods that are superior to relative quantification using internal standard compounds. METHODS In experiment I, four analyses were performed under different analytical conditions at one facility, and then the data from the four analyses were compared. In experiment II, the same samples were analyzed at three facilities, and then the data from the three facilities were compared. RESULTS Regarding the relative values obtained through comparisons with the internal standard compound, differences in the analytical results were observed among the four analytical conditions in experiment I and among the three facilities in experiment II, and the differences observed among the three facilities (experiment II) were larger. When correction was performed using plasma as a quality control, which is the procedure suggested in this study, these differences were markedly ameliorated. CONCLUSION The suggested procedure involves the analysis of a plasma standard as a quality control for each batch and the calculation of relative target plasma to quality-control plasma values for each metabolite. This is an easy and low-cost method and could be readily employed by researchers during comprehensive plasma metabolome analysis.
Collapse
Affiliation(s)
- Shin Nishiumi
- Department of Omics Medicine, Hyogo Medical University, Nishinomiya, Japan
- Department of Biosphere Sciences, School of Human Sciences, Kobe College, Nishinomiya, Japan
| | - Tomonori Yokoyama
- Department of Omics Medicine, Hyogo Medical University, Nishinomiya, Japan
- Analytical and Measuring Instruments Division, Shimadzu Corporation, Kyoto, Japan
| | - Noriyuki Ojima
- Department of Omics Medicine, Hyogo Medical University, Nishinomiya, Japan
- Analytical and Measuring Instruments Division, Shimadzu Corporation, Kyoto, Japan
| |
Collapse
|
22
|
Wang CL, Skeie JM, Allamargot C, Goldstein AS, Nishimura DY, Huffman JM, Aldrich BT, Schmidt GA, Teixeira LBC, Kuehn MH, Yorek M, Greiner MA. Rat Model of Type 2 Diabetes Mellitus Recapitulates Human Disease in the Anterior Segment of the Eye. THE AMERICAN JOURNAL OF PATHOLOGY 2024:S0002-9440(24)00073-7. [PMID: 38403162 DOI: 10.1016/j.ajpath.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 02/01/2024] [Accepted: 02/09/2024] [Indexed: 02/27/2024]
Abstract
Changes in the anterior segment of the eye due to type 2 diabetes mellitus (T2DM) are not well-characterized, in part due to the lack of a reliable animal model. This study evaluates changes in the anterior segment, including crystalline lens health, corneal endothelial cell density, aqueous humor metabolites, and ciliary body vasculature, in a rat model of T2DM compared with human eyes. Male Sprague-Dawley rats were fed a high-fat diet (45% fat) or normal diet, and rats fed the high-fat diet were injected with streptozotocin i.p. to generate a model of T2DM. Cataract formation and corneal endothelial cell density were assessed using microscopic analysis. Diabetes-related rat aqueous humor alterations were assessed using metabolomics screening. Transmission electron microscopy was used to assess qualitative ultrastructural changes ciliary process microvessels at the site of aqueous formation in the eyes of diabetic rats and humans. Eyes from the diabetic rats demonstrated cataracts, lower corneal endothelial cell densities, altered aqueous metabolites, and ciliary body ultrastructural changes, including vascular endothelial cell activation, pericyte degeneration, perivascular edema, and basement membrane reduplication. These findings recapitulated diabetic changes in human eyes. These results support the use of this model for studying ocular manifestations of T2DM and support a hypothesis postulating blood-aqueous barrier breakdown and vascular leakage at the ciliary body as a mechanism for diabetic anterior segment pathology.
Collapse
Affiliation(s)
- Cheryl L Wang
- Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, Iowa
| | - Jessica M Skeie
- Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, Iowa; Iowa Lions Eye Bank, Coralville, Iowa
| | - Chantal Allamargot
- Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, Iowa; Office of the Vice President for Research, Central Microscopy Research Facility, University of Iowa, Iowa City, Iowa
| | - Andrew S Goldstein
- Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, Iowa; Iowa Lions Eye Bank, Coralville, Iowa
| | - Darryl Y Nishimura
- Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, Iowa; Iowa Lions Eye Bank, Coralville, Iowa
| | - James M Huffman
- Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, Iowa
| | - Benjamin T Aldrich
- Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, Iowa; Iowa Lions Eye Bank, Coralville, Iowa
| | - Gregory A Schmidt
- Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, Iowa; Iowa Lions Eye Bank, Coralville, Iowa
| | - Leandro B C Teixeira
- Department of Pathobiological Sciences, University of Wisconsin-Madison School of Veterinary Medicine, Madison, Wisconsin
| | - Markus H Kuehn
- Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, Iowa; Center for the Prevention and Treatment of Visual Loss, Iowa City Veterans Affairs Health Care System, Iowa City, Iowa
| | - Mark Yorek
- Center for the Prevention and Treatment of Visual Loss, Iowa City Veterans Affairs Health Care System, Iowa City, Iowa
| | - Mark A Greiner
- Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, Iowa; Iowa Lions Eye Bank, Coralville, Iowa.
| |
Collapse
|
23
|
Chen B, Pan Z, Mou M, Zhou Y, Fu W. Is fragment-based graph a better graph-based molecular representation for drug design? A comparison study of graph-based models. Comput Biol Med 2024; 169:107811. [PMID: 38168647 DOI: 10.1016/j.compbiomed.2023.107811] [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: 09/27/2023] [Revised: 11/23/2023] [Accepted: 12/03/2023] [Indexed: 01/05/2024]
Abstract
Graph Neural Networks (GNNs) have gained significant traction in various sectors of AI-driven drug design. Over recent years, the integration of fragmentation concepts into GNNs has emerged as a potent strategy to augment the efficacy of molecular generative models. Nonetheless, challenges such as symmetry breaking and potential misrepresentation of intricate cycles and undefined functional groups raise questions about the superiority of fragment-based graph representation over traditional methods. In our research, we undertook a rigorous evaluation, contrasting the predictive prowess of eight models-developed using deep learning algorithms-across 12 benchmark datasets that span a range of properties. These models encompass established methods like GCN, AttentiveFP, and D-MPNN, as well as innovative fragment-based representation techniques. Our results indicate that fragment-based methodologies, notably PharmHGT, significantly improve model performance and interpretability, particularly in scenarios characterized by limited data availability. However, in situations with extensive training, fragment-based molecular graph representations may not necessarily eclipse traditional methods. In summation, we posit that the integration of fragmentation, as an avant-garde technique in drug design, harbors considerable promise for the future of AI-enhanced drug design.
Collapse
Affiliation(s)
- Baiyu Chen
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 202103, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Wei Fu
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 202103, China.
| |
Collapse
|
24
|
Yang Q, Chen S, Jiang W, Mi L, Liu J, Hu Y, Ji X, Wang J, Zhu F. MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics. Anal Chem 2024; 96:1410-1418. [PMID: 38221713 DOI: 10.1021/acs.analchem.3c03212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/multiclassmetabo/.
Collapse
Affiliation(s)
- Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Shuman Chen
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Wenyu Jiang
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Lan Mi
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Jiarui Liu
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Yu Hu
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Xinglai Ji
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Jun Wang
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| |
Collapse
|
25
|
Hlas A, Ganesh V, Marks J, He R, Salem AK, Buckwalter JA, Duchman KR, Shin K, Martin JA, Seol D. Buffering Mitigates Chondrocyte Oxidative Stress, Metabolic Dysfunction, and Death Induced by Normal Saline: Formulation of a Novel Arthroscopic Irrigant. Int J Mol Sci 2024; 25:1286. [PMID: 38279286 PMCID: PMC10816598 DOI: 10.3390/ijms25021286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 01/12/2024] [Accepted: 01/17/2024] [Indexed: 01/28/2024] Open
Abstract
For decades, surgeons have utilized 0.9% normal saline (NS) for joint irrigation to improve visualization during arthroscopic procedures. This continues despite mounting evidence that NS exposure impairs chondrocyte metabolism and compromises articular cartilage function. We hypothesized that chondrocyte oxidative stress induced by low pH is the dominant factor driving NS toxicity, and that buffering NS to increase its pH would mitigate these effects. Effects on chondrocyte viability, reactive oxygen species (ROS) production, and overall metabolic function were assessed. Even brief exposure to NS caused cell death, ROS overproduction, and disruption of glycolysis, pentose phosphate, and tricarboxylic acid (TCA) cycle pathways. NS also stimulated ROS overproduction in synovial cells that could adversely alter the synovial function and subsequently the entire joint health. Buffering NS with 25 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) significantly increased chondrocyte viability, reduced ROS production, and returned metabolite levels to near control levels while also reducing ROS production in synovial cells. These results confirm that chondrocytes and synoviocytes are vulnerable to insult from the acidic pH of NS and demonstrate that adding a buffering agent to NS averts many of its most harmful effects.
Collapse
Affiliation(s)
- Arman Hlas
- Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA;
| | - Venkateswaran Ganesh
- Department of Orthopedics and Rehabilitation, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA; (V.G.); (J.M.); (J.A.B.); (K.R.D.)
- Roy J. Carver Department of Biomedical Engineering, College of Engineering, University of Iowa, Iowa City, IA 52242, USA
| | - Jaison Marks
- Department of Orthopedics and Rehabilitation, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA; (V.G.); (J.M.); (J.A.B.); (K.R.D.)
- Roy J. Carver Department of Biomedical Engineering, College of Engineering, University of Iowa, Iowa City, IA 52242, USA
| | - Rui He
- Pharmaceutical Sciences and Experimental Therapeutics, College of Pharmacy, University of Iowa, Iowa City, IA 52242, USA; (R.H.); (A.K.S.)
| | - Aliasger K. Salem
- Pharmaceutical Sciences and Experimental Therapeutics, College of Pharmacy, University of Iowa, Iowa City, IA 52242, USA; (R.H.); (A.K.S.)
| | - Joseph A. Buckwalter
- Department of Orthopedics and Rehabilitation, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA; (V.G.); (J.M.); (J.A.B.); (K.R.D.)
| | - Kyle R. Duchman
- Department of Orthopedics and Rehabilitation, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA; (V.G.); (J.M.); (J.A.B.); (K.R.D.)
| | - Kyungsup Shin
- Department of Orthodontics, College of Dentistry and Dental Clinics, University of Iowa, Iowa City, IA 52242, USA;
| | - James A. Martin
- Department of Orthopedics and Rehabilitation, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA; (V.G.); (J.M.); (J.A.B.); (K.R.D.)
- Roy J. Carver Department of Biomedical Engineering, College of Engineering, University of Iowa, Iowa City, IA 52242, USA
| | - Dongrim Seol
- Department of Orthopedics and Rehabilitation, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA; (V.G.); (J.M.); (J.A.B.); (K.R.D.)
- Department of Orthodontics, College of Dentistry and Dental Clinics, University of Iowa, Iowa City, IA 52242, USA;
| |
Collapse
|
26
|
Yin J, Chen Z, You N, Li F, Zhang H, Xue J, Ma H, Zhao Q, Yu L, Zeng S, Zhu F. VARIDT 3.0: the phenotypic and regulatory variability of drug transporter. Nucleic Acids Res 2024; 52:D1490-D1502. [PMID: 37819041 PMCID: PMC10767864 DOI: 10.1093/nar/gkad818] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/01/2023] [Accepted: 09/27/2023] [Indexed: 10/13/2023] Open
Abstract
The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.
Collapse
Affiliation(s)
- Jiayi Yin
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Nanxin You
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- The Children's Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310052, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Jia Xue
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Hui Ma
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Qingwei Zhao
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Lushan Yu
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Su Zeng
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| |
Collapse
|
27
|
Zhang Y, Zhou Y, Zhou Y, Yu X, Shen X, Hong Y, Zhang Y, Wang S, Mou M, Zhang J, Tao L, Gao J, Qiu Y, Chen Y, Zhu F. TheMarker: a comprehensive database of therapeutic biomarkers. Nucleic Acids Res 2024; 52:D1450-D1464. [PMID: 37850638 PMCID: PMC10767989 DOI: 10.1093/nar/gkad862] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/21/2023] [Accepted: 09/29/2023] [Indexed: 10/19/2023] Open
Abstract
Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.
Collapse
Affiliation(s)
- Yintao Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyuan Yu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyi Shen
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven 06510, USA
| | - Yanfeng Hong
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuxin Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jinsong Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunqing Qiu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| |
Collapse
|
28
|
Wanichthanarak K, In-on A, Fan S, Fiehn O, Wangwiwatsin A, Khoomrung S. Data processing solutions to render metabolomics more quantitative: case studies in food and clinical metabolomics using Metabox 2.0. Gigascience 2024; 13:giae005. [PMID: 38488666 PMCID: PMC10941642 DOI: 10.1093/gigascience/giae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/22/2023] [Accepted: 02/02/2024] [Indexed: 03/18/2024] Open
Abstract
In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.
Collapse
Affiliation(s)
- Kwanjeera Wanichthanarak
- Siriraj Center of Research Excellence in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Ammarin In-on
- Siriraj Center of Research Excellence in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Sili Fan
- Department of Biostatistics, University of California Davis, Davis, CA 95616, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California Davis Genome Center, Davis, CA 95616, USA
| | - Arporn Wangwiwatsin
- Department of Systems Biosciences and Computational Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Sakda Khoomrung
- Siriraj Center of Research Excellence in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Center of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Bangkok 10700, Thailand
| |
Collapse
|
29
|
Pade LR, Stepler KE, Portero EP, DeLaney K, Nemes P. Biological mass spectrometry enables spatiotemporal 'omics: From tissues to cells to organelles. MASS SPECTROMETRY REVIEWS 2024; 43:106-138. [PMID: 36647247 PMCID: PMC10668589 DOI: 10.1002/mas.21824] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/14/2022] [Accepted: 09/17/2022] [Indexed: 06/17/2023]
Abstract
Biological processes unfold across broad spatial and temporal dimensions, and measurement of the underlying molecular world is essential to their understanding. Interdisciplinary efforts advanced mass spectrometry (MS) into a tour de force for assessing virtually all levels of the molecular architecture, some in exquisite detection sensitivity and scalability in space-time. In this review, we offer vignettes of milestones in technology innovations that ushered sample collection and processing, chemical separation, ionization, and 'omics analyses to progressively finer resolutions in the realms of tissue biopsies and limited cell populations, single cells, and subcellular organelles. Also highlighted are methodologies that empowered the acquisition and analysis of multidimensional MS data sets to reveal proteomes, peptidomes, and metabolomes in ever-deepening coverage in these limited and dynamic specimens. In pursuit of richer knowledge of biological processes, we discuss efforts pioneering the integration of orthogonal approaches from molecular and functional studies, both within and beyond MS. With established and emerging community-wide efforts ensuring scientific rigor and reproducibility, spatiotemporal MS emerged as an exciting and powerful resource to study biological systems in space-time.
Collapse
Affiliation(s)
- Leena R. Pade
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
| | - Kaitlyn E. Stepler
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
| | - Erika P. Portero
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
| | - Kellen DeLaney
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
| | - Peter Nemes
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
| |
Collapse
|
30
|
Gong Y, Ding W, Wang P, Wu Q, Yao X, Yang Q. Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics. J Chem Inf Model 2023; 63:7628-7641. [PMID: 38079572 DOI: 10.1021/acs.jcim.3c01525] [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] [Indexed: 12/26/2023]
Abstract
Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.
Collapse
Affiliation(s)
- Yaguo Gong
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Wei Ding
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Qibiao Wu
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Xiaojun Yao
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
| | - Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| |
Collapse
|
31
|
Chua AE, Pfeifer L, Sekera ER, Hummon AB, Desaire H. Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2775-2784. [PMID: 37897440 PMCID: PMC10919320 DOI: 10.1021/jasms.3c00295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/30/2023]
Abstract
To achieve high quality omics results, systematic variability in mass spectrometry (MS) data must be adequately addressed. Effective data normalization is essential for minimizing this variability. The abundance of approaches and the data-dependent nature of normalization have led some researchers to develop open-source academic software for choosing the best approach. While these tools are certainly beneficial to the community, none of them meet all of the needs of all users, particularly users who want to test new strategies that are not available in these products. Herein, we present a simple and straightforward workflow that facilitates the identification of optimal normalization strategies using straightforward evaluation metrics, employing both supervised and unsupervised machine learning. The workflow offers a "DIY" aspect, where the performance of any normalization strategy can be evaluated for any type of MS data. As a demonstration of its utility, we apply this workflow on two distinct datasets, an ESI-MS dataset of extracted lipids from latent fingerprints and a cancer spheroid dataset of metabolites ionized by MALDI-MSI, for which we identified the best-performing normalization strategies.
Collapse
Affiliation(s)
- Aleesa E. Chua
- Department of Chemistry, University of Kansas, Lawrence, Kansas, United States, 66045
| | - Leah Pfeifer
- Department of Chemistry, University of Kansas, Lawrence, Kansas, United States, 66045
| | - Emily R. Sekera
- Department of Chemistry and Biochemistry and the Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Amanda B. Hummon
- Department of Chemistry and Biochemistry and the Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Heather Desaire
- Department of Chemistry, University of Kansas, Lawrence, Kansas, United States, 66045
| |
Collapse
|
32
|
Vue Z, Garza‐Lopez E, Neikirk K, Katti P, Vang L, Beasley H, Shao J, Marshall AG, Crabtree A, Murphy AC, Jenkins BC, Prasad P, Evans C, Taylor B, Mungai M, Killion M, Stephens D, Christensen TA, Lam J, Rodriguez B, Phillips MA, Daneshgar N, Koh H, Koh A, Davis J, Devine N, Saleem M, Scudese E, Arnold KR, Vanessa Chavarin V, Daniel Robinson R, Chakraborty M, Gaddy JA, Sweetwyne MT, Wilson G, Zaganjor E, Kezos J, Dondi C, Reddy AK, Glancy B, Kirabo A, Quintana AM, Dai D, Ocorr K, Murray SA, Damo SM, Exil V, Riggs B, Mobley BC, Gomez JA, McReynolds MR, Hinton A. 3D reconstruction of murine mitochondria reveals changes in structure during aging linked to the MICOS complex. Aging Cell 2023; 22:e14009. [PMID: 37960952 PMCID: PMC10726809 DOI: 10.1111/acel.14009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 09/01/2023] [Accepted: 09/19/2023] [Indexed: 11/15/2023] Open
Abstract
During aging, muscle gradually undergoes sarcopenia, the loss of function associated with loss of mass, strength, endurance, and oxidative capacity. However, the 3D structural alterations of mitochondria associated with aging in skeletal muscle and cardiac tissues are not well described. Although mitochondrial aging is associated with decreased mitochondrial capacity, the genes responsible for the morphological changes in mitochondria during aging are poorly characterized. We measured changes in mitochondrial morphology in aged murine gastrocnemius, soleus, and cardiac tissues using serial block-face scanning electron microscopy and 3D reconstructions. We also used reverse transcriptase-quantitative PCR, transmission electron microscopy quantification, Seahorse analysis, and metabolomics and lipidomics to measure changes in mitochondrial morphology and function after loss of mitochondria contact site and cristae organizing system (MICOS) complex genes, Chchd3, Chchd6, and Mitofilin. We identified significant changes in mitochondrial size in aged murine gastrocnemius, soleus, and cardiac tissues. We found that both age-related loss of the MICOS complex and knockouts of MICOS genes in mice altered mitochondrial morphology. Given the critical role of mitochondria in maintaining cellular metabolism, we characterized the metabolomes and lipidomes of young and aged mouse tissues, which showed profound alterations consistent with changes in membrane integrity, supporting our observations of age-related changes in muscle tissues. We found a relationship between changes in the MICOS complex and aging. Thus, it is important to understand the mechanisms that underlie the tissue-dependent 3D mitochondrial phenotypic changes that occur in aging and the evolutionary conservation of these mechanisms between Drosophila and mammals.
Collapse
Affiliation(s)
- Zer Vue
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
| | | | - Kit Neikirk
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
| | - Prasanna Katti
- National Heart, Lung and Blood Institute, National Institutes of HealthMarylandBethesdaUSA
| | - Larry Vang
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
| | - Heather Beasley
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
| | - Jianqiang Shao
- Central Microscopy Research FacilityUniversity of IowaIowaIowa CityUSA
| | - Andrea G. Marshall
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
| | - Amber Crabtree
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
| | - Alexandria C. Murphy
- Department of Biochemistry and Molecular Biology, The Huck Institute of the Life SciencesPennsylvania State UniversityPennsylvaniaState CollegeUSA
| | - Brenita C. Jenkins
- Department of Biochemistry and Molecular Biology, The Huck Institute of the Life SciencesPennsylvania State UniversityPennsylvaniaState CollegeUSA
| | - Praveena Prasad
- Department of Biochemistry and Molecular Biology, The Huck Institute of the Life SciencesPennsylvania State UniversityPennsylvaniaState CollegeUSA
| | - Chantell Evans
- Department of Cell BiologyDuke University School of MedicineNorth CarolinaDurhamUSA
| | - Brittany Taylor
- J. Crayton Pruitt Family Department of Biomedical EngineeringUniversity of FloridaFloridaGainesvilleUSA
| | - Margaret Mungai
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
| | - Mason Killion
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
| | - Dominique Stephens
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
| | | | - Jacob Lam
- Department of Internal MedicineUniversity of IowaIowaIowa CityUSA
| | | | - Mark A. Phillips
- Department of Integrative BiologyOregon State UniversityOregonCorvallisUSA
| | - Nastaran Daneshgar
- Department of Integrative BiologyOregon State UniversityOregonCorvallisUSA
| | - Ho‐Jin Koh
- Department of Biological SciencesTennessee State UniversityTennesseeNashvilleUSA
| | - Alice Koh
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
- Department of MedicineVanderbilt University Medical CenterTennesseeNashvilleUSA
| | - Jamaine Davis
- Department of Biochemistry, Cancer Biology, Neuroscience, and PharmacologyMeharry Medical CollegeTennesseeNashvilleUSA
| | - Nina Devine
- Department of Integrative BiologyOregon State UniversityOregonCorvallisUSA
| | - Mohammad Saleem
- Department of MedicineVanderbilt University Medical CenterTennesseeNashvilleUSA
| | - Estevão Scudese
- Laboratory of Biosciences of Human Motricity (LABIMH) of the Federal University of State of Rio de Janeiro (UNIRIO)Rio de JaneiroBrazil
- Sport Sciences and Exercise Laboratory (LaCEE)Catholic University of Petrópolis (UCP)PetrópolisState of Rio de JaneiroBrazil
| | - Kenneth Ryan Arnold
- Department of Ecology and Evolutionary BiologyUniversity of California at IrvineCaliforniaIrvineUSA
| | - Valeria Vanessa Chavarin
- Department of Ecology and Evolutionary BiologyUniversity of California at IrvineCaliforniaIrvineUSA
| | - Ryan Daniel Robinson
- Department of Ecology and Evolutionary BiologyUniversity of California at IrvineCaliforniaIrvineUSA
| | | | - Jennifer A. Gaddy
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
- Department of MedicineVanderbilt University Medical CenterTennesseeNashvilleUSA
- Department of Medicine Health and SocietyVanderbilt UniversityTennesseeNashvilleUSA
- Department of Pathology, Microbiology and ImmunologyVanderbilt University Medical CenterTennesseeNashvilleUSA
- Department of Veterans AffairsTennessee Valley Healthcare SystemsTennesseeNashvilleUSA
| | - Mariya T. Sweetwyne
- Department of Laboratory Medicine and PathologyUniversity of WashingtonWashingtonSeattleUSA
| | - Genesis Wilson
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
| | - Elma Zaganjor
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
| | - James Kezos
- Sanford Burnham Prebys Medical Discovery InstituteCaliforniaLa JollaUSA
| | - Cristiana Dondi
- Sanford Burnham Prebys Medical Discovery InstituteCaliforniaLa JollaUSA
| | | | - Brian Glancy
- National Heart, Lung and Blood Institute, National Institutes of HealthMarylandBethesdaUSA
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of HealthMarylandBethesdaUSA
| | - Annet Kirabo
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
- Department of MedicineVanderbilt University Medical CenterTennesseeNashvilleUSA
| | - Anita M. Quintana
- Department of Biological Sciences, Border Biomedical Research CenterUniversity of Texas at El PasoTexasEl PasoUSA
| | - Dao‐Fu Dai
- Department of PathologyUniversity of Johns Hopkins School of MedicineMarylandBaltimoreUSA
| | - Karen Ocorr
- Sanford Burnham Prebys Medical Discovery InstituteCaliforniaLa JollaUSA
| | - Sandra A. Murray
- Department of Cell Biology, School of MedicineUniversity of PittsburghPennsylvaniaPittsburghUSA
| | - Steven M. Damo
- Department of Life and Physical SciencesFisk UniversityTennesseeNashvilleUSA
- Center for Structural BiologyVanderbilt UniversityTennesseeNashvilleUSA
| | - Vernat Exil
- Department of Pediatrics, Carver College of MedicineUniversity of IowaIowaIowa CityUSA
- Department of Pediatrics, Division of CardiologySt. Louis University School of MedicineMissouriSt. LouisUSA
| | - Blake Riggs
- Department of BiologySan Francisco State UniversityCaliforniaSan FranciscoUSA
| | - Bret C. Mobley
- Department of PathologyVanderbilt University Medical CenterTennesseeNashvilleUSA
| | - Jose A. Gomez
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
- Department of MedicineVanderbilt University Medical CenterTennesseeNashvilleUSA
| | - Melanie R. McReynolds
- Department of Biochemistry and Molecular Biology, The Huck Institute of the Life SciencesPennsylvania State UniversityPennsylvaniaState CollegeUSA
| | - Antentor Hinton
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityTennesseeNashvilleUSA
| |
Collapse
|
33
|
Roquencourt C, Lamy E, Bardin E, Devillier P, Grassin-Delyle S. A benchmark study of data normalisation methods for PTR-TOF-MS exhaled breath metabolomics. J Breath Res 2023; 18:016006. [PMID: 37917990 DOI: 10.1088/1752-7163/ad08ce] [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: 06/23/2023] [Accepted: 11/02/2023] [Indexed: 11/04/2023]
Abstract
Volatilomics is the branch of metabolomics dedicated to the analysis of volatile organic compounds in exhaled breath for medical diagnostic or therapeutic monitoring purposes. Real-time mass spectrometry (MS) technologies such as proton transfer reaction (PTR) MS are commonly used, and data normalisation is an important step to discard unwanted variation from non-biological sources, as batch effects and loss of sensitivity over time may be observed. As normalisation methods for real-time breath analysis have been poorly investigated, we aimed to benchmark known metabolomic data normalisation methods and apply them to PTR-MS data analysis. We compared seven normalisation methods, five statistically based and two using multiple standard metabolites, on two datasets from clinical trials for COVID-19 diagnosis in patients from the emergency department or intensive care unit. We evaluated different means of feature selection to select the standard metabolites, as well as the use of multiple repeat measurements of ambient air to train the normalisation methods. We show that the normalisation tools can correct for time-dependent drift. The methods that provided the best corrections for both cohorts were probabilistic quotient normalisation and normalisation using optimal selection of multiple internal standards. Normalisation also improved the diagnostic performance of the machine learning models, significantly increasing sensitivity, specificity and area under the receiver operating characteristic (ROC) curve for the diagnosis of COVID-19. Our results highlight the importance of adding an appropriate normalisation step during the processing of PTR-MS data, which allows significant improvements in the predictive performance of statistical models.Clinical trials: VOC-COVID-Diag (EudraCT 2020-A02682-37); RECORDS trial (EudraCT 2020-000296-21).
Collapse
Affiliation(s)
| | - Elodie Lamy
- Département de Biotechnologie de la Santé, Université Paris-Saclay, UVSQ, INSERM U1173, Infection et inflammation, Montigny le Bretonneux, France
| | - Emmanuelle Bardin
- Hôpital Foch, Exhalomics®, Suresnes, France
- Département de Biotechnologie de la Santé, Université Paris-Saclay, UVSQ, INSERM U1173, Infection et inflammation, Montigny le Bretonneux, France
- Institut Necker-Enfants Malades, Paris, France
| | - Philippe Devillier
- Hôpital Foch, Exhalomics®, Suresnes, France
- Laboratoire de recherche en Pharmacologie Respiratoire-VIM Suresnes, UMR 0892, Université Paris-Saclay, Suresnes, France
| | - Stanislas Grassin-Delyle
- Hôpital Foch, Exhalomics®, Suresnes, France
- Département de Biotechnologie de la Santé, Université Paris-Saclay, UVSQ, INSERM U1173, Infection et inflammation, Montigny le Bretonneux, France
| |
Collapse
|
34
|
Shao B, Killion M, Oliver A, Vang C, Zeleke F, Neikirk K, Vue Z, Garza-Lopez E, Shao JQ, Mungai M, Lam J, Williams Q, Altamura CT, Whiteside A, Kabugi K, McKenzie J, Koh A, Scudese E, Vang L, Marshall AG, Crabtree A, Tanghal JI, Stephens D, Koh HJ, Jenkins BC, Murray SA, Cooper AT, Williams C, Damo SM, McReynolds MR, Gaddy JA, Wanjalla CN, Beasley HK, Hinton A. Ablation of Sam50 is associated with fragmentation and alterations in metabolism in murine and human myotubes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.20.541602. [PMID: 37292887 PMCID: PMC10245823 DOI: 10.1101/2023.05.20.541602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The Sorting and Assembly Machinery (SAM) Complex is responsible for assembling β-barrel proteins in the mitochondrial membrane. Comprising three subunits, Sam35, Sam37, and Sam50, the SAM complex connects the inner and outer mitochondrial membranes by interacting with the mitochondrial contact site and cristae organizing system (MICOS) complex. Sam50, in particular, stabilizes the mitochondrial intermembrane space bridging (MIB) complex, which is crucial for protein transport, respiratory chain complex assembly, and regulation of cristae integrity. While the role of Sam50 in mitochondrial structure and metabolism in skeletal muscle remains unclear, this study aims to investigate its impact. Serial block-face-scanning electron microscopy (SBF-SEM) and computer-assisted 3D renderings were employed to compare mitochondrial structure and networking in Sam50-deficient myotubes from mice and humans with wild-type (WT) myotubes. Furthermore, autophagosome 3D structure was assessed in human myotubes. Mitochondrial metabolic phenotypes were assessed using Gas Chromatography-Mass Spectrometry-based metabolomics to explore differential changes in WT and Sam50-deficient myotubes. The results revealed increased mitochondrial fragmentation and autophagosome formation in Sam50-deficient myotubes compared to controls. Metabolomic analysis indicated elevated metabolism of propanoate and several amino acids, including ß-Alanine, phenylalanine, and tyrosine, along with increased amino acid and fatty acid metabolism in Sam50-deficient myotubes. Furthermore, impairment of oxidative capacity was observed upon Sam50 ablation in both murine and human myotubes, as measured with the XF24 Seahorse Analyzer. Collectively, these findings support the critical role of Sam50 in establishing and maintaining mitochondrial integrity, cristae structure, and mitochondrial metabolism. By elucidating the impact of Sam50-deficiency, this study enhances our understanding of mitochondrial function in skeletal muscle.
Collapse
Affiliation(s)
- Bryanna Shao
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Mason Killion
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Ashton Oliver
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Chia Vang
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Faben Zeleke
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Kit Neikirk
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Zer Vue
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Edgar Garza-Lopez
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Jian-Qiang Shao
- Central Microscopy Research Facility, University of Iowa, Iowa City, IA, 52242, USA
| | - Margaret Mungai
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Jacob Lam
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Qiana Williams
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Christopher T Altamura
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Aaron Whiteside
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
- Department of Neuroscience, Cell Biology and Physiology, Wright State University, Dayton, OH 45435 USA
| | - Kinuthia Kabugi
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Jessica McKenzie
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Alice Koh
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Estevão Scudese
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Larry Vang
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Andrea G Marshall
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Amber Crabtree
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | | | - Dominique Stephens
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Ho-Jin Koh
- Department of Biological Sciences, Tennessee State University, Nashville, TN 37209
| | - Brenita C Jenkins
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA
| | - Sandra A Murray
- Department of Cell Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Anthonya T Cooper
- Department of Cell Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Clintoria Williams
- Department of Neuroscience, Cell Biology and Physiology, Wright State University, Dayton, OH 45435 USA
| | - Steven M Damo
- Department of Life and Physical Sciences, Fisk University, Nashville, TN, 37208, USA
| | - Melanie R McReynolds
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA
| | - Jennifer A Gaddy
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Tennessee Valley Healthcare Systems, U.S. Department of Veterans Affairs, Nashville, TN, 37212, USA
| | - Celestine N Wanjalla
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Heather K Beasley
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Antentor Hinton
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| |
Collapse
|
35
|
Manochkumar J, Cherukuri AK, Kumar RS, Almansour AI, Ramamoorthy S, Efferth T. A critical review of machine-learning for "multi-omics" marine metabolite datasets. Comput Biol Med 2023; 165:107425. [PMID: 37696182 DOI: 10.1016/j.compbiomed.2023.107425] [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: 05/30/2023] [Revised: 07/12/2023] [Accepted: 08/28/2023] [Indexed: 09/13/2023]
Abstract
During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available "-omics" and "multi-omics" approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for "multi-omics" approaches is briefly discussed. In addition, the challenges involved in the analysis of "multi-omics" data and recommendations for conducting "multi-omics" study were discussed.
Collapse
Affiliation(s)
- Janani Manochkumar
- School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, India
| | - Aswani Kumar Cherukuri
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India
| | - Raju Suresh Kumar
- Department of Chemistry, College of Science, King Saud University, P. O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Abdulrahman I Almansour
- Department of Chemistry, College of Science, King Saud University, P. O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Siva Ramamoorthy
- School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, India.
| | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Mainz, Germany.
| |
Collapse
|
36
|
De Graeve M, Van de Walle E, Van Hecke T, De Smet S, Vanhaecke L, Hemeryck LY. Exploration and optimization of extraction, analysis and data normalization strategies for mass spectrometry-based DNA adductome mapping and modeling. Anal Chim Acta 2023; 1274:341578. [PMID: 37455087 DOI: 10.1016/j.aca.2023.341578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 06/23/2023] [Accepted: 06/29/2023] [Indexed: 07/18/2023]
Abstract
Although interest in characterizing DNA damage by means of DNA adductomics has substantially grown, the field of DNA adductomics is still in its infancy, with room for optimization of methods for sample analysis, data processing and DNA adduct identification. In this context, the first objective of this study was to evaluate the use of hydrophilic interaction (HILIC) vs. reversed phase liquid chromatography (RPLC) coupled to high resolution mass spectrometry (HRMS) and thermal acidic vs. enzymatic hydrolysis of DNA followed by DNA adduct purification and enrichment using solid-phase extraction (SPE) or fraction collection for DNA adductome mapping. The second objective was to assess the use of total ion count (TIC) and median intensity (MedI) normalization compared to QC (quality control), iQC (internal QC) and quality control-based robust locally estimated scatterplot smoothing (LOESS) signal correction (QC-RLSC) normalization for processing of the acquired data. The results demonstrate that HILIC compared to RPLC allowed better modeling of the tentative DNA adductome, particularly in combination with thermal acidic hydrolysis and SPE (more valid models, with an average Q2(Y) and R2(Y) of 0.930 and 0.998, respectively). Regarding the need for data normalization and the management of (limited) system instability and signal drift, QC normalization outperformed TIC, MedI, iQC and LOESS normalization. As such, QC normalization can be put forward as the default data normalization strategy. In case of momentous signal drift and/or batch effects however, comparison to other normalization strategies (like e.g. LOESS) is recommended. In future work, further optimization of DNA adductomics may be achieved by merging of HILIC and RPLC datasets and/or application of 2D-LC, as well as the inclusion of Schiff base stabilization and/or fraction collection in the thermal acidic hydrolysis-SPE sample preparation workflow.
Collapse
Affiliation(s)
- Marilyn De Graeve
- Laboratory of Integrative Metabolomics, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, B-9820, Merelbeke, Belgium.
| | - Emma Van de Walle
- Laboratory of Integrative Metabolomics, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, B-9820, Merelbeke, Belgium.
| | - Thomas Van Hecke
- Laboratory for Animal Nutrition and Animal Product Quality, Faculty of Bioscience Engineering, Ghent University, Coupure Links, 653, B-9000, Ghent, Belgium.
| | - Stefaan De Smet
- Laboratory for Animal Nutrition and Animal Product Quality, Faculty of Bioscience Engineering, Ghent University, Coupure Links, 653, B-9000, Ghent, Belgium.
| | - Lynn Vanhaecke
- Laboratory of Integrative Metabolomics, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, B-9820, Merelbeke, Belgium; Institute for Global Food Security, School of Biological Sciences, Queen's University, University Road, Belfast, United Kingdom.
| | - Lieselot Y Hemeryck
- Laboratory of Integrative Metabolomics, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, B-9820, Merelbeke, Belgium.
| |
Collapse
|
37
|
Li Q, Zhang Q, Kim YR, Gaddam RR, Jacobs JS, Bachschmid MM, Younis T, Zhu Z, Zingman L, London B, Rauckhorst AJ, Taylor EB, Norris AW, Vikram A, Irani K. Deficiency of endothelial sirtuin1 in mice stimulates skeletal muscle insulin sensitivity by modifying the secretome. Nat Commun 2023; 14:5595. [PMID: 37696839 PMCID: PMC10495425 DOI: 10.1038/s41467-023-41351-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 08/31/2023] [Indexed: 09/13/2023] Open
Abstract
Downregulation of endothelial Sirtuin1 (Sirt1) in insulin resistant states contributes to vascular dysfunction. Furthermore, Sirt1 deficiency in skeletal myocytes promotes insulin resistance. Here, we show that deletion of endothelial Sirt1, while impairing endothelial function, paradoxically improves skeletal muscle insulin sensitivity. Compared to wild-type mice, male mice lacking endothelial Sirt1 (E-Sirt1-KO) preferentially utilize glucose over fat, and have higher insulin sensitivity, glucose uptake, and Akt signaling in fast-twitch skeletal muscle. Enhanced insulin sensitivity of E-Sirt1-KO mice is transferrable to wild-type mice via the systemic circulation. Endothelial Sirt1 deficiency, by inhibiting autophagy and activating nuclear factor-kappa B signaling, augments expression and secretion of thymosin beta-4 (Tβ4) that promotes insulin signaling in skeletal myotubes. Thus, unlike in skeletal myocytes, Sirt1 deficiency in the endothelium promotes glucose homeostasis by stimulating skeletal muscle insulin sensitivity through a blood-borne mechanism, and augmented secretion of Tβ4 by Sirt1-deficient endothelial cells boosts insulin signaling in skeletal muscle cells.
Collapse
Affiliation(s)
- Qiuxia Li
- Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA.
- Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA.
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA.
- Division of Endocrinology, Diabetes and Hypertension, Department of Medicine, David Geffen School of Medicine and UCLA Health, University of California-Los Angeles, Los Angeles, CA, 90095, USA.
| | - Quanjiang Zhang
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Division of Endocrinology, Diabetes and Hypertension, Department of Medicine, David Geffen School of Medicine and UCLA Health, University of California-Los Angeles, Los Angeles, CA, 90095, USA
| | - Young-Rae Kim
- Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
| | - Ravinder Reddy Gaddam
- Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
| | - Julia S Jacobs
- Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
| | | | - Tsneem Younis
- Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
| | - Zhiyong Zhu
- Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Veterans Affairs Medical Center, Iowa City, IA, 52242, USA
| | - Leonid Zingman
- Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Veterans Affairs Medical Center, Iowa City, IA, 52242, USA
| | - Barry London
- Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
| | - Adam J Rauckhorst
- Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Fraternal Order of Eagles Diabetes Research Center (FOEDRC), University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Department of Physiology and Biophysics, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Pappajohn Biomedical Institute, University of Iowa Carver College of Medicine, Iowa City, IA, USA
- FOEDRC Metabolomics Core Facility, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
| | - Eric B Taylor
- Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Fraternal Order of Eagles Diabetes Research Center (FOEDRC), University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Department of Physiology and Biophysics, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Pappajohn Biomedical Institute, University of Iowa Carver College of Medicine, Iowa City, IA, USA
- FOEDRC Metabolomics Core Facility, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
| | - Andrew W Norris
- Fraternal Order of Eagles Diabetes Research Center (FOEDRC), University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- FOEDRC Metabolic Phenotyping Core Facility, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Department of Pediatrics, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
| | - Ajit Vikram
- Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Fraternal Order of Eagles Diabetes Research Center (FOEDRC), University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
- Pappajohn Biomedical Institute, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Kaikobad Irani
- Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA.
- Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA.
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA.
- Veterans Affairs Medical Center, Iowa City, IA, 52242, USA.
- Fraternal Order of Eagles Diabetes Research Center (FOEDRC), University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA.
- Pappajohn Biomedical Institute, University of Iowa Carver College of Medicine, Iowa City, IA, USA.
| |
Collapse
|
38
|
Liang S, Zhao Y, Jin J, Qiao J, Wang D, Wang Y, Wei L. Rm-LR: A long-range-based deep learning model for predicting multiple types of RNA modifications. Comput Biol Med 2023; 164:107238. [PMID: 37515874 DOI: 10.1016/j.compbiomed.2023.107238] [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: 05/24/2023] [Revised: 06/16/2023] [Accepted: 07/07/2023] [Indexed: 07/31/2023]
Abstract
Recent research has highlighted the pivotal role of RNA post-transcriptional modifications in the regulation of RNA expression and function. Accurate identification of RNA modification sites is important for understanding RNA function. In this study, we propose a novel RNA modification prediction method, namely Rm-LR, which leverages a long-range-based deep learning approach to accurately predict multiple types of RNA modifications using RNA sequences only. Rm-LR incorporates two large-scale RNA language pre-trained models to capture discriminative sequential information and learn local important features, which are subsequently integrated through a bilinear attention network. Rm-LR supports a total of ten RNA modification types (m6A, m1A, m5C, m5U, m6Am, Ψ, Am, Cm, Gm, and Um) and significantly outperforms the state-of-the-art methods in terms of predictive capability on benchmark datasets. Experimental results show the effectiveness and superiority of Rm-LR in prediction of various RNA modifications, demonstrating the strong adaptability and robustness of our proposed model. We demonstrate that RNA language pretrained models enable to learn dense biological sequential representations from large-scale long-range RNA corpus, and meanwhile enhance the interpretability of the models. This work contributes to the development of accurate and reliable computational models for RNA modification prediction, providing insights into the complex landscape of RNA modifications.
Collapse
Affiliation(s)
- Sirui Liang
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Yanxi Zhao
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Junru Jin
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Jianbo Qiao
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Ding Wang
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Yu Wang
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China.
| |
Collapse
|
39
|
Leach DT, Stratton KG, Irvahn J, Richardson R, Webb-Robertson BJM, Bramer LM. malbacR: A Package for Standardized Implementation of Batch Correction Methods for Omics Data. Anal Chem 2023; 95:12195-12199. [PMID: 37551970 DOI: 10.1021/acs.analchem.3c01289] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Mass spectrometry is a powerful tool for identifying and analyzing biomolecules such as metabolites and lipids in complex biological samples. Liquid chromatography and gas chromatography mass spectrometry studies quite commonly involve large numbers of samples, which can require significant time for sample preparation and analyses. To accommodate such studies, the samples are commonly split into batches. Inevitably, variations in sample handling, temperature fluctuation, imprecise timing, column degradation, and other factors result in systematic errors or biases of the measured abundances between the batches. Numerous methods are available via R packages to assist with batch correction for omics data; however, since these methods were developed by different research teams, the algorithms are available in separate R packages, each with different data input and output formats. We introduce the malbacR package, which consolidates 11 common batch effect correction methods for omics data into one place so users can easily implement and compare the following: pareto scaling, power scaling, range scaling, ComBat, EigenMS, NOMIS, RUV-random, QC-RLSC, WaveICA2.0, TIGER, and SERRF. The malbacR package standardizes data input and output formats across these batch correction methods. The package works in conjunction with the pmartR package, allowing users to seamlessly include the batch effect correction in a pmartR workflow without needing any additional data manipulation.
Collapse
Affiliation(s)
- Damon T Leach
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Kelly G Stratton
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Jan Irvahn
- Artificial Intelligence and Data Analytics Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Rachel Richardson
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Bobbie-Jo M Webb-Robertson
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| |
Collapse
|
40
|
Zhang Y, Fan S, Wohlgemuth G, Fiehn O. Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography-Mass Spectrometry. Metabolites 2023; 13:944. [PMID: 37623887 PMCID: PMC10456436 DOI: 10.3390/metabo13080944] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 07/31/2023] [Accepted: 08/08/2023] [Indexed: 08/26/2023] Open
Abstract
Large-scale metabolomics assays are widely used in epidemiology for biomarker discovery and risk assessments. However, systematic errors introduced by instrumental signal drifting pose a big challenge in large-scale assays, especially for derivatization-based gas chromatography-mass spectrometry (GC-MS). Here, we compare the results of different normalization methods for a study with more than 4000 human plasma samples involved in a type 2 diabetes cohort study, in addition to 413 pooled quality control (QC) samples, 413 commercial pooled plasma samples, and a set of 25 stable isotope-labeled internal standards used for every sample. Data acquisition was conducted across 1.2 years, including seven column changes. In total, 413 pooled QC (training) and 413 BioIVT samples (validation) were used for normalization comparisons. Surprisingly, neither internal standards nor sum-based normalizations yielded median precision of less than 30% across all 563 metabolite annotations. While the machine-learning-based SERRF algorithm gave 19% median precision based on the pooled quality control samples, external cross-validation with BioIVT plasma pools yielded a median 34% relative standard deviation (RSD). We developed a new method: systematic error reduction by denoising autoencoder (SERDA). SERDA lowered the median standard deviations of the training QC samples down to 16% RSD, yielding an overall error of 19% RSD when applied to the independent BioIVT validation QC samples. This is the largest study on GC-MS metabolomics ever reported, demonstrating that technical errors can be normalized and handled effectively for this assay. SERDA was further validated on two additional large-scale GC-MS-based human plasma metabolomics studies, confirming the superior performance of SERDA over SERRF or sum normalizations.
Collapse
Affiliation(s)
| | | | | | - Oliver Fiehn
- West Coast Metabolomics Center, UC Davis, 451 Health Sciences Drive, Davis, CA 95616, USA; (Y.Z.); (S.F.); (G.W.)
| |
Collapse
|
41
|
Findlay S, Nair R, Merrill RA, Kaiser Z, Cajelot A, Aryanpour Z, Heath J, St-Louis C, Papadopoli D, Topisirovic I, St-Pierre J, Sebag M, Kesarwala AH, Hulea L, Taylor EB, Shanmugam M, Orthwein A. The mitochondrial pyruvate carrier complex potentiates the efficacy of proteasome inhibitors in multiple myeloma. Blood Adv 2023; 7:3485-3500. [PMID: 36920785 PMCID: PMC10362273 DOI: 10.1182/bloodadvances.2022008345] [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: 06/28/2022] [Revised: 02/27/2023] [Accepted: 03/02/2023] [Indexed: 03/16/2023] Open
Abstract
Multiple myeloma (MM) is a hematological malignancy that emerges from antibody-producing plasma B cells. Proteasome inhibitors, including the US Food and Drug Administration-approved bortezomib (BTZ) and carfilzomib (CFZ), are frequently used for the treatment of patients with MM. Nevertheless, a significant proportion of patients with MM are refractory or develop resistance to this class of inhibitors, which represents a significant challenge in the clinic. Thus, identifying factors that determine the potency of proteasome inhibitors in MM is of paramount importance to bolster their efficacy in the clinic. Using genome-wide CRISPR-based screening, we identified a subunit of the mitochondrial pyruvate carrier (MPC) complex, MPC1, as a common modulator of BTZ response in 2 distinct human MM cell lines in vitro. We noticed that CRISPR-mediated deletion or pharmacological inhibition of the MPC complex enhanced BTZ/CFZ-induced MM cell death with minimal impact on cell cycle progression. In fact, targeting the MPC complex compromised the bioenergetic capacity of MM cells, which is accompanied by reduced proteasomal activity, thereby exacerbating BTZ-induced cytotoxicity in vitro. Importantly, we observed that the RNA expression levels of several regulators of pyruvate metabolism were altered in advanced stages of MM for which they correlated with poor patient prognosis. Collectively, this study highlights the importance of the MPC complex for the survival of MM cells and their responses to proteasome inhibitors. These findings establish mitochondrial pyruvate metabolism as a potential target for the treatment of MM and an unappreciated strategy to increase the efficacy of proteasome inhibitors in the clinic.
Collapse
Affiliation(s)
- Steven Findlay
- Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, Montreal, Canada
- Division of Experimental Medicine, McGill University, Montreal, Canada
| | - Remya Nair
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA
| | - Ronald A. Merrill
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, IA
| | - Zafir Kaiser
- Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, Montreal, Canada
- Department of Biochemistry, McGill University, Montreal, Canada
| | - Alexandre Cajelot
- Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, Montreal, Canada
- Polytech Nice-Sophia, Université Côte d’Azur, Sophia Antipolis, Nice, France
| | - Zahra Aryanpour
- Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, Montreal, Canada
| | - John Heath
- Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, Montreal, Canada
- Division of Experimental Medicine, McGill University, Montreal, Canada
| | - Catherine St-Louis
- Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, Canada
- Ottawa Institute of Systems Biology, Ottawa, Canada
| | - David Papadopoli
- Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, Montreal, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada
| | - Ivan Topisirovic
- Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, Montreal, Canada
- Division of Experimental Medicine, McGill University, Montreal, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada
- Department of Biochemistry, McGill University, Montreal, Canada
| | - Julie St-Pierre
- Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, Canada
- Ottawa Institute of Systems Biology, Ottawa, Canada
| | - Michael Sebag
- The Research Institute of the McGill University Health Center, Montreal, Canada
| | - Aparna H. Kesarwala
- Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, GA
| | - Laura Hulea
- Maisonneuve-Rosemont Hospital Research Center, Montreal, Canada
- Département de Biochimie et médecine moléculaire, Université de Montréal, Montreal, Canada
- Département de Médecine, Université de Montréal, Montreal, Canada
| | - Eric B. Taylor
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, IA
| | - Mala Shanmugam
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA
| | - Alexandre Orthwein
- Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, Montreal, Canada
- Division of Experimental Medicine, McGill University, Montreal, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada
- Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, GA
| |
Collapse
|
42
|
Buchanan JL, Rauckhorst AJ, Taylor EB. 3-hydroxykynurenine is a ROS-inducing cytotoxic tryptophan metabolite that disrupts the TCA cycle. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.10.548411. [PMID: 37502990 PMCID: PMC10369892 DOI: 10.1101/2023.07.10.548411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Tryptophan is an essential amino acid that is extensively characterized as a regulator of cellular function through its metabolism by indoleamine 2,3-deoxygenase (IDO) into the kynurenine pathway. However, despite decades of research on tryptophan metabolism, the metabolic regulatory roles of it and its metabolites are not well understood. To address this, we performed an activity metabolomics screen of tryptophan and most of its known metabolites in cell culture. We discovered that treatment of human colon cancer cells (HCT116) with 3-hydroxykynurenine (3-HK), a metabolite of kynurenine, potently disrupted TCA cycle function. Citrate and aconitate levels were increased, while isocitrate and all downstream TCA metabolites were decreased, suggesting decreased aconitase function. We hypothesized that 3HK or one of its metabolites increased reactive oxygen species (ROS) and inhibited aconitase activity. Accordingly, we observed almost complete depletion of reduced glutathione and a decrease in total glutathione levels. We observed a dose-dependent decrease in cell viability after 48 hours of 3HK treatment. These data suggest that raising the intracellular levels of 3HK could be sufficient to induce ROS-mediated apoptosis. We modulated the intracellular levels of 3HK by combined induction of IDO and knockdown of kynureninase (KYNU) in HCT116 cells. Cell viability decreased significantly after 48 hours of KYNU knockdown compared to controls, which was accompanied by increased ROS production and Annexin V staining revealing apoptosis. Finally, we identify xanthommatin production from 3-HK as a candidate radical-producing, cytotoxic mechanism. Our work indicates that KYNU may be a target for disrupting tryptophan metabolism. Interestingly, many cancers exhibit overexpression of IDO, providing a cancer-specific metabolic vulnerability that could be exploited by KYNU inhibition.
Collapse
Affiliation(s)
- Jane L. Buchanan
- Department of Molecular Physiology and Biophysics, University of Iowa Carver College of Medicine, Iowa City, IA 52240, USA
| | - Adam J. Rauckhorst
- Department of Molecular Physiology and Biophysics, University of Iowa Carver College of Medicine, Iowa City, IA 52240, USA
- FOEDRC Metabolomics Core Research Facility, University of Iowa Carver College of Medicine, Iowa City, IA 52240, USA
| | - Eric B. Taylor
- Department of Molecular Physiology and Biophysics, University of Iowa Carver College of Medicine, Iowa City, IA 52240, USA
- Fraternal Order of Eagles Diabetes Research Center (FOEDRC), University of Iowa Carver College of Medicine, Iowa City, IA 52240, USA
- FOEDRC Metabolomics Core Research Facility, University of Iowa Carver College of Medicine, Iowa City, IA 52240, USA
- Holden Comprehensive Cancer Center, University of Iowa Carver College of Medicine, Iowa City, IA 52240, USA
- Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa City, IA 52240, USA
- Pappajohn Biomedical Institute, University of Iowa Carver College of Medicine, Iowa City, IA 52240, USA
| |
Collapse
|
43
|
An S, Wang R, Lu M, Zhang C, Liu H, Wang J, Xie C, Yu C. MetaPro: a web-based metabolomics application for LC-MS data batch inspection and library curation. Metabolomics 2023; 19:57. [PMID: 37289291 DOI: 10.1007/s11306-023-02018-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 05/10/2023] [Indexed: 06/09/2023]
Abstract
INTRODUCTION Metabolomics analysis based on liquid chromatography-mass spectrometry (LC-MS) has been a prevalent method in the metabolic field. However, accurately quantifying all the metabolites in large metabolomics sample cohorts is challenging. The analysis efficiency is restricted by the abilities of software in many labs, and the lack of spectra for some metabolites also hinders metabolite identification. OBJECTIVES Develop software that performs semi-targeted metabolomics analysis with an optimized workflow to improve quantification accuracy. The software also supports web-based technologies and increases laboratory analysis efficiency. A spectral curation function is provided to promote the prosperity of homemade MS/MS spectral libraries in the metabolomics community. METHODS MetaPro is developed based on an industrial-grade web framework and a computation-oriented MS data format to improve analysis efficiency. Algorithms from mainstream metabolomics software are integrated and optimized for more accurate quantification results. A semi-targeted analysis workflow is designed based on the concept of combining artificial judgment and algorithm inference. RESULTS MetaPro supports semi-targeted analysis workflow and functions for fast QC inspection and self-made spectral library curation with easy-to-use interfaces. With curated authentic or high-quality spectra, it can improve identification accuracy using different peak identification strategies. It demonstrates practical value in analyzing large amounts of metabolomics samples. CONCLUSION We offer MetaPro as a web-based application characterized by fast batch QC inspection and credible spectral curation towards high-throughput metabolomics data. It aims to resolve the analysis difficulty in semi-targeted metabolomics.
Collapse
Affiliation(s)
- Shaowei An
- Fudan University, 220 Handan Road, Shanghai, 200433, China
- Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
- Shandong First Medical University, 6699 Qingdao Road, Jinan, Shandong Province, 250117, China
- Carbon Silicon (Hangzhou) Biotechnology Co., Ltd, 368 Jinpeng Street, Hangzhou, Zhejiang Province, 310030, China
| | - Ruimin Wang
- Fudan University, 220 Handan Road, Shanghai, 200433, China
- Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
- Shandong First Medical University, 6699 Qingdao Road, Jinan, Shandong Province, 250117, China
- Carbon Silicon (Hangzhou) Biotechnology Co., Ltd, 368 Jinpeng Street, Hangzhou, Zhejiang Province, 310030, China
| | - Miaoshan Lu
- Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
- Shandong First Medical University, 6699 Qingdao Road, Jinan, Shandong Province, 250117, China
- Carbon Silicon (Hangzhou) Biotechnology Co., Ltd, 368 Jinpeng Street, Hangzhou, Zhejiang Province, 310030, China
- Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang Province, 310009, China
| | - Chao Zhang
- Calibra Diagnostics Co., Ltd, 329 Jinpeng Street, Hangzhou, Zhejiang Province, 310030, China
| | - Huafen Liu
- Calibra Diagnostics Co., Ltd, 329 Jinpeng Street, Hangzhou, Zhejiang Province, 310030, China
| | - Jinyin Wang
- Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China
- Shandong First Medical University, 6699 Qingdao Road, Jinan, Shandong Province, 250117, China
- Carbon Silicon (Hangzhou) Biotechnology Co., Ltd, 368 Jinpeng Street, Hangzhou, Zhejiang Province, 310030, China
- Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang Province, 310009, China
| | - Cong Xie
- Shandong First Medical University, 6699 Qingdao Road, Jinan, Shandong Province, 250117, China
| | - Changbin Yu
- Shandong First Medical University, 6699 Qingdao Road, Jinan, Shandong Province, 250117, China.
| |
Collapse
|
44
|
Märtens A, Holle J, Mollenhauer B, Wegner A, Kirwan J, Hiller K. Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples. Metabolites 2023; 13:metabo13050665. [PMID: 37233706 DOI: 10.3390/metabo13050665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/08/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023] Open
Abstract
Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers (Logistic regression, Random Forest, and Support Vector Machine). In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes.
Collapse
Affiliation(s)
- Andre Märtens
- Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology, Technische Universität Braunschweig, 38118 Braunschweig, Germany
- Physikalisch-Technische Bundesanstalt, 38116 Braunschweig, Germany
| | - Johannes Holle
- Department of Pediatric Gastroenterology, Nephrology and Metabolic Diseases, Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Brit Mollenhauer
- Department of Neurology, University Medical Center Göttingen, 37073 Göttingen, Germany
- Paracelsus-Elena-Klinik, 34128 Kassel, Germany
| | - Andre Wegner
- Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology, Technische Universität Braunschweig, 38118 Braunschweig, Germany
| | - Jennifer Kirwan
- Berlin Institute of Health at Charité, Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Karsten Hiller
- Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology, Technische Universität Braunschweig, 38118 Braunschweig, Germany
| |
Collapse
|
45
|
Walakira A, Skubic C, Nadižar N, Rozman D, Režen T, Mraz M, Moškon M. Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma. Comput Biol Med 2023; 159:106957. [PMID: 37116239 DOI: 10.1016/j.compbiomed.2023.106957] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/17/2023] [Accepted: 04/16/2023] [Indexed: 04/30/2023]
Abstract
Hepatocellular carcinoma (HCC) is a major health problem around the world. The management of this disease is complicated by the lack of noninvasive diagnostic tools and the few treatment options available. Better clinical outcomes can be achieved if HCC is detected early, but unfortunately, clinical signs appear when the disease is in its late stages. We aim to identify novel genes that can be targeted for the diagnosis and therapy of HCC. We performed a meta-analysis of transcriptomics data to identify differentially expressed genes and applied network analysis to identify hub genes. Fatty acid metabolism, complement and coagulation cascade, chemical carcinogenesis and retinol metabolism were identified as key pathways in HCC. Furthermore, we integrated transcriptomics data into a reference human genome-scale metabolic model to identify key reactions and subsystems relevant in HCC. We conclude that fatty acid activation, purine metabolism, vitamin D, and E metabolism are key processes in the development of HCC and therefore need to be further explored for the development of new therapies. We provide the first evidence that GABRP, HBG1 and DAK (TKFC) genes are important in HCC in humans and warrant further studies.
Collapse
Affiliation(s)
- Andrew Walakira
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
| | - Cene Skubic
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Nejc Nadižar
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Tadeja Režen
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
| |
Collapse
|
46
|
Nie X, Qin D, Zhou X, Duo H, Hao Y, Li B, Liang G. Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges. Comput Biol Med 2023; 159:106939. [PMID: 37075602 DOI: 10.1016/j.compbiomed.2023.106939] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/31/2023] [Accepted: 04/14/2023] [Indexed: 04/21/2023]
Abstract
With the rapid development of single-cell RNA-sequencing techniques, various computational methods and tools were proposed to analyze these high-throughput data, which led to an accelerated reveal of potential biological information. As one of the core steps of single-cell transcriptome data analysis, clustering plays a crucial role in identifying cell types and interpreting cellular heterogeneity. However, the results generated by different clustering methods showed distinguishing, and those unstable partitions can affect the accuracy of the analysis to a certain extent. To overcome this challenge and obtain more accurate results, currently clustering ensemble is frequently applied to cluster analysis of single-cell transcriptome datasets, and the results generated by all clustering ensembles are nearly more reliable than those from most of the single clustering partitions. In this review, we summarize applications and challenges of the clustering ensemble method in single-cell transcriptome data analysis, and provide constructive thoughts and references for researchers in this field.
Collapse
Affiliation(s)
- Xiner Nie
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China; College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Dan Qin
- Department of Biology, College of Science, Northeastern University, Boston, MA, 02115, USA
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China.
| |
Collapse
|
47
|
Fajarda O, Almeida JR, Duarte-Pereira S, Silva RM, Oliveira JL. Methodology to identify a gene expression signature by merging microarray datasets. Comput Biol Med 2023; 159:106867. [PMID: 37060770 DOI: 10.1016/j.compbiomed.2023.106867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 03/01/2023] [Accepted: 03/30/2023] [Indexed: 04/17/2023]
Abstract
A vast number of microarray datasets have been produced as a way to identify differentially expressed genes and gene expression signatures. A better understanding of these biological processes can help in the diagnosis and prognosis of diseases, as well as in the therapeutic response to drugs. However, most of the available datasets are composed of a reduced number of samples, leading to low statistical, predictive and generalization power. One way to overcome this problem is by merging several microarray datasets into a single dataset, which is typically a challenging task. Statistical methods or supervised machine learning algorithms are usually used to determine gene expression signatures. Nevertheless, statistical methods require an arbitrary threshold to be defined, and supervised machine learning methods can be ineffective when applied to high-dimensional datasets like microarrays. We propose a methodology to identify gene expression signatures by merging microarray datasets. This methodology uses statistical methods to obtain several sets of differentially expressed genes and uses supervised machine learning algorithms to select the gene expression signature. This methodology was validated using two distinct research applications: one using heart failure and the other using autism spectrum disorder microarray datasets. For the first, we obtained a gene expression signature composed of 117 genes, with a classification accuracy of approximately 98%. For the second use case, we obtained a gene expression signature composed of 79 genes, with a classification accuracy of approximately 82%. This methodology was implemented in R language and is available, under the MIT licence, at https://github.com/bioinformatics-ua/MicroGES.
Collapse
Affiliation(s)
- Olga Fajarda
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal.
| | - João Rafael Almeida
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal; Department of Computation, University of A Coruña, A Coruña, Spain.
| | - Sara Duarte-Pereira
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal; Department of Medical Sciences and iBiMED-Institute of Biomedicine, University of Aveiro, Aveiro, Portugal.
| | - Raquel M Silva
- Universidade Católica Portuguesa, Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Viseu, Portugal.
| | | |
Collapse
|
48
|
Gallagher EM, Rizzo GM, Dorsey R, Dhummakupt ES, Moran TS, Mach PM, Jenkins CC. Normalization of organ-on-a-Chip samples for mass spectrometry based proteomics and metabolomics via Dansylation-based assay. Toxicol In Vitro 2023; 88:105540. [PMID: 36563973 DOI: 10.1016/j.tiv.2022.105540] [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: 05/25/2022] [Revised: 10/29/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022]
Abstract
Mass spectrometry based 'omics pairs well with organ-on-a-chip-based investigations, which often have limited cellular material for sampling. However, a common issue with these chip-based platforms is well-to-well or chip-to-chip variability in the proteome and metabolome due to factors such as plate edge effects, cellular asynchronization, effluent flow, and limited cell count. This causes high variability in the quantitative multi-omics analysis of samples, potentially masking true biological changes within the system. Solutions to this have been approached via data processing tools and post-acquisition normalization strategies such as constant median, constant sum, and overall signal normalization. Unfortunately, these methods do not adequately correct for the large variations, resulting in a need for increased biological replicates. The methods in this work utilize a dansylation based assay with a subset of labeled metabolites that allow for pre-acquisition normalization to better correlate the biological perturbations that truly occur in chip-based platforms. BCA protein assays were performed in tandem with a proteomics pipeline to achieve pre-acquisition normalization. The CN Bio PhysioMimix was seeded with primary hepatocytes and challenged with VX after six days of culture, and the metabolome and proteome were analyzed using the described normalization methods. A decreased coefficient of variation percentage is achieved, significant changes are observed through the proteome and metabolome, and better classification of biological replicates acquired because of these strategies.
Collapse
Affiliation(s)
- Erin M Gallagher
- U.S. Army, Threat Agent Sciences Division, Combat Capabilities Development Command (DEVCOM) Chemical Biological Center (CBC), 5183 Blackhawk Rd., Aberdeen Proving Ground, Gunpowder, MD 21010, USA; National Academies of Sciences, Engineering, and Medicine, NRC Research Associateship Program, 500 Fifth Street, NW, Washington, DC, 20001, USA.
| | - Gabrielle M Rizzo
- U.S. Army, BioSciences Division, Combat Capabilities Development Command (DEVCOM) Chemical Biological Center (CBC), 5183 Blackhawk Rd., Aberdeen Proving Ground, Gunpowder, MD 21010, USA
| | - Russell Dorsey
- U.S. Army, Threat Agent Sciences Division, Combat Capabilities Development Command (DEVCOM) Chemical Biological Center (CBC), 5183 Blackhawk Rd., Aberdeen Proving Ground, Gunpowder, MD 21010, USA
| | - Elizabeth S Dhummakupt
- U.S. Army, BioSciences Division, Combat Capabilities Development Command (DEVCOM) Chemical Biological Center (CBC), 5183 Blackhawk Rd., Aberdeen Proving Ground, Gunpowder, MD 21010, USA
| | - Theodore S Moran
- U.S. Army, Threat Agent Sciences Division, Combat Capabilities Development Command (DEVCOM) Chemical Biological Center (CBC), 5183 Blackhawk Rd., Aberdeen Proving Ground, Gunpowder, MD 21010, USA
| | - Phillip M Mach
- U.S. Army, BioSciences Division, Combat Capabilities Development Command (DEVCOM) Chemical Biological Center (CBC), 5183 Blackhawk Rd., Aberdeen Proving Ground, Gunpowder, MD 21010, USA
| | - Conor C Jenkins
- U.S. Army, BioSciences Division, Combat Capabilities Development Command (DEVCOM) Chemical Biological Center (CBC), 5183 Blackhawk Rd., Aberdeen Proving Ground, Gunpowder, MD 21010, USA
| |
Collapse
|
49
|
Nalepa J, Kotowski K, Machura B, Adamski S, Bozek O, Eksner B, Kokoszka B, Pekala T, Radom M, Strzelczak M, Zarudzki L, Krason A, Arcadu F, Tessier J. Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients. Comput Biol Med 2023; 154:106603. [PMID: 36738710 DOI: 10.1016/j.compbiomed.2023.106603] [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: 10/19/2022] [Revised: 01/11/2023] [Accepted: 01/22/2023] [Indexed: 02/05/2023]
Abstract
Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is, however, complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of glioblastoma patients. Our approach simultaneously identifies tumor sub-regions, including the enhancing tumor, peritumoral edema and surgical cavity in the first step, and then calculates the volumetric and bidimensional measurements that follow the current Response Assessment in Neuro-Oncology (RANO) criteria. Also, we introduce a rigorous manual annotation process which was followed to delineate the tumor sub-regions by the human experts, and to capture their segmentation confidences that are later used while training deep learning models. The results of our extensive experimental study performed over 760 pre-operative and 504 post-operative adult patients with glioma obtained from the public database (acquired at 19 sites in years 2021-2020) and from a clinical treatment trial (47 and 69 sites for pre-/post-operative patients, 2009-2011) and backed up with thorough quantitative, qualitative and statistical analysis revealed that our pipeline performs accurate segmentation of pre- and post-operative MRIs in a fraction of the manual delineation time (up to 20 times faster than humans). Volumetric measurements were in strong agreement with experts with the Intraclass Correlation Coefficient (ICC): 0.959, 0.703, 0.960 for ET, ED, and cavity. Similarly, automated RANO compared favorably with experienced readers (ICC: 0.681 and 0.866) producing consistent and accurate results. Additionally, we showed that RANO measurements are not always sufficient to quantify tumor burden. The high performance of the automated tumor burden measurement highlights the potential of the tool for considerably improving and simplifying radiological evaluation of glioblastoma in clinical trials and clinical practice.
Collapse
Affiliation(s)
- Jakub Nalepa
- Graylight Imaging, Gliwice, Poland; Department of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland.
| | | | | | | | - Oskar Bozek
- Department of Radiodiagnostics and Invasive Radiology, School of Medicine in Katowice, Medical University of Silesia in Katowice, Katowice, Poland
| | - Bartosz Eksner
- Department of Radiology and Nuclear Medicine, ZSM Chorzów, Chorzów, Poland
| | - Bartosz Kokoszka
- Department of Radiodiagnostics, Interventional Radiology and Nuclear Medicine, University Clinical Centre, Katowice, Poland
| | - Tomasz Pekala
- Department of Radiodiagnostics, Interventional Radiology and Nuclear Medicine, University Clinical Centre, Katowice, Poland
| | - Mateusz Radom
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Marek Strzelczak
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Lukasz Zarudzki
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Agata Krason
- Roche Pharmaceutical Research & Early Development, Early Clinical Development Oncology, Roche Innovation Center Basel, Basel, Switzerland
| | - Filippo Arcadu
- Roche Pharmaceutical Research & Early Development, Early Clinical Development Informatics, Roche Innovation Center Basel, Basel, Switzerland
| | - Jean Tessier
- Roche Pharmaceutical Research & Early Development, Early Clinical Development Oncology, Roche Innovation Center Basel, Basel, Switzerland
| |
Collapse
|
50
|
Baran Y, Doğan B. scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies. Comput Biol Med 2023; 155:106634. [PMID: 36774895 DOI: 10.1016/j.compbiomed.2023.106634] [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: 11/05/2022] [Revised: 01/28/2023] [Accepted: 02/04/2023] [Indexed: 02/11/2023]
Abstract
Single-Cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities for exploring gene expression and thus uncovering regulatory relationships between genes at the single-cell level. However, scRNA-seq relies on isolating cells from tissues. Therefore, the spatial context of the regulatory processes is lost. A recent technological innovation, spatial transcriptomics, allows for the measurement of gene expression while preserving spatial information. An initial step in the spatial transcriptomic analysis is to identify the cell type, which requires a careful selection of cell-specific marker genes. For this purpose, currently, scRNA-seq data is used to select a limited number of marker genes from among all genes that distinguish cell types from each other. This study proposes scMAGS (single-cell MArker Gene Selection), a novel method for marker gene selection from scRNA-seq data for spatial transcriptomics studies. scMAGS uses a filtering step in which the candidate genes are identified before the marker gene selection step. For the selection of marker genes, cluster validity indices, the Silhouette index, or the Calinski-Harabasz index (for large datasets) are utilized. Experimental results showed that, in comparison to the existing methods, scMAGS is scalable, fast, and accurate. Even for large datasets with millions of cells, scMAGS could find the required number of marker genes in a reasonable amount of time with fewer memory requirements. scMAGS is made freely available at https://github.com/doganlab/scmags and can be downloaded from the Python Package Directory (PyPI) software repository with the command pip install scmags.
Collapse
Affiliation(s)
- Yusuf Baran
- Department of Biomedical Engineering, Inonu University, Malatya, Turkey
| | - Berat Doğan
- Department of Biomedical Engineering, Inonu University, Malatya, Turkey.
| |
Collapse
|