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Halama A, Zaghlool S, Thareja G, Kader S, Al Muftah W, Mook-Kanamori M, Sarwath H, Mohamoud YA, Stephan N, Ameling S, Pucic Baković M, Krumsiek J, Prehn C, Adamski J, Schwenk JM, Friedrich N, Völker U, Wuhrer M, Lauc G, Najafi-Shoushtari SH, Malek JA, Graumann J, Mook-Kanamori D, Schmidt F, Suhre K. A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes. Nat Commun 2024; 15:7111. [PMID: 39160153 PMCID: PMC11333501 DOI: 10.1038/s41467-024-51134-x] [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: 08/01/2023] [Accepted: 07/26/2024] [Indexed: 08/21/2024] Open
Abstract
In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.
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Affiliation(s)
- Anna Halama
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar.
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
| | - Shaza Zaghlool
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Gaurav Thareja
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Sara Kader
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Wadha Al Muftah
- Qatar Genome Program, Qatar Foundation, Qatar Science and Technology Park, Innovation Center, Doha, Qatar
- Department of Genetic Medicine, Weill Cornell Medicine, Doha, Qatar
| | | | - Hina Sarwath
- Proteomics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | | | - Nisha Stephan
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Sabine Ameling
- German Centre for Cardiovascular Research, Partner Site Greifswald, University Medicine Greifswald, Greifswald, Germany
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | | | - Jan Krumsiek
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Nele Friedrich
- German Centre for Cardiovascular Research, Partner Site Greifswald, University Medicine Greifswald, Greifswald, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- German Centre for Cardiovascular Research, Partner Site Greifswald, University Medicine Greifswald, Greifswald, Germany
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Manfred Wuhrer
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - S Hani Najafi-Shoushtari
- MicroRNA Core Laboratory, Division of Research, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Cell and Developmental Biology, Weill Cornell Medicine, New York, NY, USA
| | - Joel A Malek
- Department of Genetic Medicine, Weill Cornell Medicine, Doha, Qatar
- Genomics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Johannes Graumann
- Institute of Translational Proteomics, Department of Medicine, Philipps-Universität Marburg, Marburg, Germany
| | - Dennis Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Biochemistry, Weill Cornell Medicine, New York, NY, USA
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar.
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
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Yun T, Cosentino J, Behsaz B, McCaw ZR, Hill D, Luben R, Lai D, Bates J, Yang H, Schwantes-An TH, Zhou Y, Khawaja AP, Carroll A, Hobbs BD, Cho MH, McLean CY, Hormozdiari F. Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction. Nat Genet 2024; 56:1604-1613. [PMID: 38977853 PMCID: PMC11319202 DOI: 10.1038/s41588-024-01831-6] [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: 05/18/2023] [Accepted: 06/13/2024] [Indexed: 07/10/2024]
Abstract
Although high-dimensional clinical data (HDCD) are increasingly available in biobank-scale datasets, their use for genetic discovery remains challenging. Here we introduce an unsupervised deep learning model, Representation Learning for Genetic Discovery on Low-Dimensional Embeddings (REGLE), for discovering associations between genetic variants and HDCD. REGLE leverages variational autoencoders to compute nonlinear disentangled embeddings of HDCD, which become the inputs to genome-wide association studies (GWAS). REGLE can uncover features not captured by existing expert-defined features and enables the creation of accurate disease-specific polygenic risk scores (PRSs) in datasets with very few labeled data. We apply REGLE to perform GWAS on respiratory and circulatory HDCD-spirograms measuring lung function and photoplethysmograms measuring blood volume changes. REGLE replicates known loci while identifying others not previously detected. REGLE are predictive of overall survival, and PRSs constructed from REGLE loci improve disease prediction across multiple biobanks. Overall, REGLE contain clinically relevant information beyond that captured by existing expert-defined features, leading to improved genetic discovery and disease prediction.
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Affiliation(s)
| | | | | | - Zachary R McCaw
- Google Research, Mountain View, CA, USA
- Insitro, South San Francisco, CA, USA
| | - Davin Hill
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Robert Luben
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and University College London (UCL) Institute of Ophthalmology, London, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - John Bates
- Verily Life Sciences, South San Francisco, CA, USA
| | | | - Tae-Hwi Schwantes-An
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Division of Cardiology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Anthony P Khawaja
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and University College London (UCL) Institute of Ophthalmology, London, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Brian D Hobbs
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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3
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Schlosser P, Surapaneni AL, Borisov O, Schmidt IM, Zhou L, Anderson A, Deo R, Dubin R, Ganz P, He J, Kimmel PL, Li H, Nelson RG, Porter AC, Rahman M, Rincon-Choles H, Shah V, Unruh ML, Vasan RS, Zheng Z, Feldman HI, Waikar SS, Köttgen A, Rhee EP, Coresh J, Grams ME. Association of Integrated Proteomic and Metabolomic Modules with Risk of Kidney Disease Progression. J Am Soc Nephrol 2024; 35:923-935. [PMID: 38640019 PMCID: PMC11230725 DOI: 10.1681/asn.0000000000000343] [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: 12/06/2023] [Accepted: 04/01/2024] [Indexed: 04/21/2024] Open
Abstract
Key Points Integrated analysis of proteome and metabolome identifies modules associated with CKD progression and kidney failure. Ephrin transmembrane proteins and podocyte-expressed CRIM1 and NPNT emerged as central components and warrant experimental and clinical investigation. Background Proteins and metabolites play crucial roles in various biological functions and are frequently interconnected through enzymatic or transport processes. Methods We present an integrated analysis of 4091 proteins and 630 metabolites in the Chronic Renal Insufficiency Cohort study (N =1708; average follow-up for kidney failure, 9.5 years, with 537 events). Proteins and metabolites were integrated using an unsupervised clustering method, and we assessed associations between clusters and CKD progression and kidney failure using Cox proportional hazards models. Analyses were adjusted for demographics and risk factors, including the eGFR and urine protein–creatinine ratio. Associations were identified in a discovery sample (random two thirds, n =1139) and then evaluated in a replication sample (one third, n =569). Results We identified 139 modules of correlated proteins and metabolites, which were represented by their principal components. Modules and principal component loadings were projected onto the replication sample, which demonstrated a consistent network structure. Two modules, representing a total of 236 proteins and 82 metabolites, were robustly associated with both CKD progression and kidney failure in both discovery and validation samples. Using gene set enrichment, several transmembrane-related terms were identified as overrepresented in these modules. Transmembrane–ephrin receptor activity displayed the largest odds (odds ratio=13.2, P value = 5.5×10−5). A module containing CRIM1 and NPNT expressed in podocytes demonstrated particularly strong associations with kidney failure (P value = 2.6×10−5). Conclusions This study demonstrates that integration of the proteome and metabolome can identify functions of pathophysiologic importance in kidney disease.
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Affiliation(s)
- Pascal Schlosser
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Institute of Genetic Epidemiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Centre for Integrative Biological Signalling Studies (CIBSS), University of Freiburg, Freiburg, Germany
| | - Aditya L. Surapaneni
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Division of Precision Medicine, Department of Medicine, NYU Langone Health, New York, New York
| | - Oleg Borisov
- Institute of Genetic Epidemiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Insa M. Schmidt
- Section of Nephrology, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Linda Zhou
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Amanda Anderson
- Department of Epidemiology, Tulane University, New Orleans, Louisiana
| | - Rajat Deo
- Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ruth Dubin
- Division of Nephrology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Peter Ganz
- Division of Cardiology, University of California, San Francisco, San Francisco, California
| | - Jiang He
- Department of Epidemiology, Tulane University, New Orleans, Louisiana
| | - Paul L. Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Hongzhe Li
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Robert G. Nelson
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona
- Research Division, Joslin Diabetes Center, Boston, Massachusetts
| | - Anna C. Porter
- Renal Service, Wellington Regional Hospital, Wellington, New Zealand
| | - Mahboob Rahman
- Department of Kidney Medicine, Cleveland Clinic Foundation, Cleveland, Ohio
| | | | - Vallabh Shah
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico
| | - Mark L. Unruh
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico
| | - Ramachandran S. Vasan
- University of Texas Health Sciences Center, San Antonio, Texas
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Zihe Zheng
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Harold I. Feldman
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sushrut S. Waikar
- Section of Nephrology, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Institute of Genetic Epidemiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Eugene P. Rhee
- Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Optimal Aging Institute, Departments of Population Health and Medicine, NYU Grossman School of Medicine, New York, New York
| | - Morgan E. Grams
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Division of Precision Medicine, Department of Medicine, NYU Langone Health, New York, New York
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4
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Breeur M, Stepaniants G, Keski-Rahkonen P, Rigollet P, Viallon V. Optimal transport for automatic alignment of untargeted metabolomic data. eLife 2024; 12:RP91597. [PMID: 38896449 PMCID: PMC11186628 DOI: 10.7554/elife.91597] [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] [Indexed: 06/21/2024] Open
Abstract
Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.
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Affiliation(s)
- Marie Breeur
- Nutrition and Metabolism Branch, International Agency for Research on CancerLyonFrance
| | - George Stepaniants
- Massachusetts Institute of Technology, Department of MathematicsBostonUnited States
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on CancerLyonFrance
| | - Philippe Rigollet
- Massachusetts Institute of Technology, Department of MathematicsBostonUnited States
| | - Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on CancerLyonFrance
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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [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: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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Qin S, Sun S, Wang Y, Li C, Fu L, Wu M, Yan J, Li W, Lv J, Chen L. Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework. Sci Rep 2024; 14:527. [PMID: 38177198 PMCID: PMC10767103 DOI: 10.1038/s41598-023-51108-x] [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] [Accepted: 12/30/2023] [Indexed: 01/06/2024] Open
Abstract
Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using the TCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational autoencoder (VAE) and multilayer perceptron (MLP) was proposed. And the Shapley Additive Explanations (SHAP) method was introduced to evaluate the contribution of feature genes to the classification decision, which helped us to develop a biologically meaningful biomarker potential scoring algorithm. Nineteen potential biomarkers for LUAD were identified, which were involved in the regulation of immune and metabolic functions in LUAD. A prognostic risk model for LUAD was constructed by the biomarkers HLA-DRB1, SCGB1A1, and HLA-DRB5 screened by Cox regression analysis, dividing the patients into high-risk and low-risk groups. The prognostic risk model was validated with external datasets. The low-risk group was characterized by enrichment of immune pathways and higher immune infiltration compared to the high-risk group. While, the high-risk group was accompanied by an increase in metabolic pathway activity. There were significant differences between the high- and low-risk groups in metabolic reprogramming of aerobic glycolysis, amino acids, and lipids, as well as in angiogenic activity, epithelial-mesenchymal transition, tumorigenic cytokines, and inflammatory response. Furthermore, high-risk patients were more sensitive to Afatinib, Gefitinib, and Gemcitabine as predicted by the pRRophetic algorithm. This study provides prognostic signatures capable of revealing the immune and metabolic landscapes for LUAD, and may shed light on the identification of other cancer biomarkers.
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Affiliation(s)
- Shimei Qin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Shibin Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Yahui Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Chao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Lei Fu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Ming Wu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Jinxing Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China.
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China.
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Anwardeen NR, Diboun I, Mokrab Y, Althani AA, Elrayess MA. Statistical methods and resources for biomarker discovery using metabolomics. BMC Bioinformatics 2023; 24:250. [PMID: 37322419 DOI: 10.1186/s12859-023-05383-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 06/09/2023] [Indexed: 06/17/2023] Open
Abstract
Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.
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Affiliation(s)
- Najeha R Anwardeen
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Ilhame Diboun
- Department of Human Genetics, Sidra Medicine, Doha, Qatar
| | - Younes Mokrab
- Department of Human Genetics, Sidra Medicine, Doha, Qatar
| | - Asma A Althani
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar
- QU Health, Qatar University, Doha, Qatar
| | - Mohamed A Elrayess
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar.
- QU Health, Qatar University, Doha, Qatar.
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8
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Csore J, Karmonik C, Wilhoit K, Buckner L, Roy TL. Automatic Classification of Magnetic Resonance Histology of Peripheral Arterial Chronic Total Occlusions Using a Variational Autoencoder: A Feasibility Study. Diagnostics (Basel) 2023; 13:diagnostics13111925. [PMID: 37296778 DOI: 10.3390/diagnostics13111925] [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: 04/10/2023] [Revised: 05/18/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
The novel approach of our study consists in adapting and in evaluating a custom-made variational autoencoder (VAE) using two-dimensional (2D) convolutional neural networks (CNNs) on magnetic resonance imaging (MRI) images for differentiate soft vs. hard plaque components in peripheral arterial disease (PAD). Five amputated lower extremities were imaged at a clinical ultra-high field 7 Tesla MRI. Ultrashort echo time (UTE), T1-weighted (T1w) and T2-weighted (T2w) datasets were acquired. Multiplanar reconstruction (MPR) images were obtained from one lesion per limb. Images were aligned to each other and pseudo-color red-green-blue images were created. Four areas in latent space were defined corresponding to the sorted images reconstructed by the VAE. Images were classified from their position in latent space and scored using tissue score (TS) as following: (1) lumen patent, TS:0; (2) partially patent, TS:1; (3) mostly occluded with soft tissue, TS:3; (4) mostly occluded with hard tissue, TS:5. Average and relative percentage of TS was calculated per lesion defined as the sum of the tissue score for each image divided by the total number of images. In total, 2390 MPR reconstructed images were included in the analysis. Relative percentage of average tissue score varied from only patent (lesion #1) to presence of all four classes. Lesions #2, #3 and #5 were classified to contain tissues except mostly occluded with hard tissue while lesion #4 contained all (ranges (I): 0.2-100%, (II): 46.3-75.9%, (III): 18-33.5%, (IV): 20%). Training the VAE was successful as images with soft/hard tissues in PAD lesions were satisfactory separated in latent space. Using VAE may assist in rapid classification of MRI histology images acquired in a clinical setup for facilitating endovascular procedures.
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Affiliation(s)
- Judit Csore
- DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin Street, Houston, TX 77030, USA
- Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, 1122 Budapest, Hungary
| | - Christof Karmonik
- MRI Core, Translational Imaging Center, Houston Methodist Research Institute, 6670 Bertner Avenue, Houston, 77030 TX, USA
| | - Kayla Wilhoit
- MRI Core, Translational Imaging Center, Houston Methodist Research Institute, 6670 Bertner Avenue, Houston, 77030 TX, USA
| | - Lily Buckner
- MRI Core, Translational Imaging Center, Houston Methodist Research Institute, 6670 Bertner Avenue, Houston, 77030 TX, USA
| | - Trisha L Roy
- DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin Street, Houston, TX 77030, USA
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