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Zang JCS, May C, Marcus K, Kumsta R. Molecular correlates of childhood adversity - a multi-omics perspective on stress regulation. Stress 2025; 28:2495918. [PMID: 40305005 DOI: 10.1080/10253890.2025.2495918] [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: 07/18/2023] [Accepted: 03/31/2025] [Indexed: 05/02/2025] Open
Abstract
The experience of adversity in childhood can have life-long consequences on health outcomes. In search of mediators of this relationship, alterations of bio-behavioral and cellular regulatory systems came into focus, including those dealing with basic gene regulatory processes. System biology oriented approaches have been proposed to gain a more comprehensive understanding of the complex multiple interrelations between and within layers of analysis. Here, we used co-expression based, supervised and unsupervised single and multi-omics systems approaches to investigate the association between childhood adversity and gene expression, protein expression and DNA methylation in CD14+ monocytes in the context of psychosocial stress exposure, in a sample of healthy adults with (n = 29) or without (n = 27) a history of childhood adversity. Childhood adversity explained some variance at the single analyte level and within gene and protein co-expression structures. A single-omics, post-stress gene expression model differentiated best between participants with a history of childhood adversity and control participants in supervised analyses. In unsupervised analyses, a multi-omics based model showed best performance but separated participants based on sex only. Multi-omics analyses are a promising concept but might yield different results based on the specific approach taken and the omics-datasets supplied. We found that stress associated gene-expression pattern were most strongly associated with childhood adversity, and integrating multiple cellular layers did not results in better discriminatory performance in our rather small sample. The capacity and yield of different omics-profiling methods might currently limit the full potential of integrative approaches.
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Affiliation(s)
- Johannes C S Zang
- Faculty of Psychology, Institute for Health and Development, Ruhr University Bochum, Bochum, Germany
| | - Caroline May
- Medizinisches Proteom-Center, Medical Proteome Analysis Centre for Protein Diagnostics (PRODI), Ruhr University, Bochum, Germany
| | - Katrin Marcus
- Medizinisches Proteom-Center, Medical Proteome Analysis Centre for Protein Diagnostics (PRODI), Ruhr University, Bochum, Germany
| | - Robert Kumsta
- Faculty of Psychology, Institute for Health and Development, Ruhr University Bochum, Bochum, Germany
- Department of Behavioral and Cognitive Sciences, Laboratory for Stress and Gene-Environment Interplay, University of Luxemburg, Esch-sur-Alzette, Luxemburg
- DZPG (German Center for Mental Health), partner site Bochum/Marburg, Germany
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2
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Wang Z, Sofer T. Recent Progress in Omics Studies of Sleep and Circadian Phenotypes. CURRENT SLEEP MEDICINE REPORTS 2025; 11:17. [PMID: 40321983 PMCID: PMC12048028 DOI: 10.1007/s40675-025-00335-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/03/2025] [Indexed: 05/08/2025]
Abstract
Purpose of review Sleep and circadian biology is fundamental to human health. Following the advancement in sleep medicine and availability of multi-omics technology, this review outlines the current knowledge regarding genetic basis and multi-omics research on circadian rhythm and the two most prevalent sleep disorders, obstructive sleep apnea (OSA) and insomnia. Recent findings Genome wide association analyses identified variants across circadian genes and genes pertinent to inflammation, obesity and neuronal function associated with OSA and insomnia. Multi-omics integration has led to novel breakthroughs in identifying systemic biomarkers and elucidating cascades, and causal associations underpinning these complex traits. Summary Multi-omics studies in sleep and circadian rhythm possess great potential in unveiling molecular mechanisms behind circadian rhythm and sleep, thereby advancing personalized medicine in the long term. Nevertheless, researchers should remain mindful of existing challenges in genetic and multi-omics sleep research, including data harmonization and existing racial and ethnic disparities in data collection and availability, limiting research generalizability.
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Affiliation(s)
- Ziqing Wang
- Department of Medicine, Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, USA
| | - Tamar Sofer
- Department of Medicine, Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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3
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Shen K, Hu C, Zhang Y, Cheng X, Xu Z, Pan S. Advances and applications of multiomics technologies in precision diagnosis and treatment for gastric cancer. Biochim Biophys Acta Rev Cancer 2025; 1880:189336. [PMID: 40311712 DOI: 10.1016/j.bbcan.2025.189336] [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: 01/25/2025] [Revised: 04/24/2025] [Accepted: 04/25/2025] [Indexed: 05/03/2025]
Abstract
Gastric cancer (GC), one of the most prevalent malignancies worldwide, is distinguished by extensive genetic and phenotypic heterogeneity, posing persistent challenges to conventional diagnostic and therapeutic strategies. The significant global burden of GC highlights an urgent need to unravel its complex underlying mechanisms, discover novel diagnostic and prognostic biomarkers, and develop more effective therapeutic interventions. In this context, this review comprehensively examines the transformative roles of cutting-edge technologies, including radiomics, pathomics, genomics, transcriptomics, epigenomics, proteomics, and metabolomics, in advancing precision diagnosis and treatment for GC. Multiomics data analysis not only deepens our understanding of GC pathogenesis and molecular subtypes but also identifies promising biomarkers, facilitating the creation of tailored therapeutic approaches. Additionally, integrating multiomics approaches holds immense potential for elucidating drug resistance mechanisms, predicting patient outcomes, and uncovering novel therapeutic targets, thereby laying a robust foundation for precision medicine in the comprehensive management of GC.
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Affiliation(s)
- Ke Shen
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China; Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, China
| | - Can Hu
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China; Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, Zhejiang 310022, China; Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Yanqiang Zhang
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China; Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, Zhejiang 310022, China; Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Xiangdong Cheng
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China; Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, Zhejiang 310022, China; Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Zhiyuan Xu
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China; Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, Zhejiang 310022, China; Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China.
| | - Siwei Pan
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China; Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, Zhejiang 310022, China; Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China.
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4
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Li M, Bai Y, Zhang J, Wang H, Li J, Wang W. Sperm metabolomics identifies freezability markers in Duroc, Landrace, and Large White boars. Theriogenology 2025; 240:117395. [PMID: 40112454 DOI: 10.1016/j.theriogenology.2025.117395] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2024] [Revised: 02/14/2025] [Accepted: 03/15/2025] [Indexed: 03/22/2025]
Abstract
Cryopreservation of boar semen is widely applied in the conservation of genetic resources and animal breeding to enhance the utilization efficiency of superior boars. However, accurately identifying individuals with good freezing tolerance in boar sperm remains challenging. In this study, based on the differences in sperm motility before and after cryopreservation from 328 boars, we selected six boars each from the Duroc, Landrace, and Large White breeds, and categorized them into poor freezability ejaculates (PFE) and good freezability ejaculates (GFE) groups for sperm metabolomic analysis. A total of 1288 metabolites were identified using both positive and negative ion modes. There were 148 differentially expressed metabolites between the GFE and PFE groups, which were enriched in pathways such as alanine, aspartate and glutamate metabolism; arginine biosynthesis; D-amino acid metabolism; histidine metabolism; beta-alanine metabolism; citrate cycle (TCA cycle); pantothenate and CoA biosynthesis; and pyruvate metabolism. Further analysis, including ROC curve evaluation, identified seven potential biomarkers for sperm cryopreservation. Argininosuccinic acid, asparagine, L-aspartate, fumarate, D-ornithine, DL-serine and histidine were tightly interconnected in a series of amino acids metabolism. In conclusion, our findings imply that differences in certain amino acid biosynthetic pathways contribute to the variations in freezing tolerance of boar sperm.
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Affiliation(s)
- Meicheng Li
- College of Animal Science and Technology, Hebei Agricultural University, Baoding, 071000, China
| | - Yifan Bai
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China
| | - Jiajun Zhang
- College of Animal Science and Technology, Hebei Agricultural University, Baoding, 071000, China
| | - Hongyang Wang
- Institute of Animal Science and Veterinary Medicine, Shanghai Academy of Agricultural Sciences, Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Shanghai, China
| | - Junjie Li
- College of Animal Science and Technology, Hebei Agricultural University, Baoding, 071000, China
| | - Wenjun Wang
- College of Animal Science and Technology, Hebei Agricultural University, Baoding, 071000, China.
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Shovon MHJ, Biswas P, Imtiaz M, Mobin S, Hasan MN. Single-cell RNA seq data analysis reveals molecular markers and possible treatment targets for laryngeal squamous cell carcinoma (LSCC): an in-silico approach. In Silico Pharmacol 2025; 13:89. [PMID: 40539082 PMCID: PMC12174029 DOI: 10.1007/s40203-025-00382-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2025] [Accepted: 06/04/2025] [Indexed: 06/22/2025] Open
Abstract
Laryngeal squamous cell carcinoma (LSCC), a complex cancer driven by genetic mutations, poses significant challenges for detection and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as a promising tool to uncover the cellular heterogeneity in cancer and identify novel therapeutic targets. In this study, we used scRNA-seq data (GSE252490) to explore molecular biomarkers for LSCC diagnosis and treatment. After processing and standardizing the data, we performed principal component analysis to identify highly variable genes. Cell clustering revealed 12 distinct clusters with unique molecular features. Differential gene expression analysis identified 6434 differentially expressed genes (DEGs), which were further analyzed using gene ontology enrichment to explore biological processes involved in LSCC progression. Protein-protein interaction (PPI) network analysis revealed 20 central genes associated with key cancer pathways. Pathway enrichment analysis through KEGG highlighted the involvement of these genes in various cancer-related pathways. Notably, genes such as CCL3, EPCAM, and IL8, with elevated expression, were linked to survival outcomes in LSCC. This comprehensive analysis provides valuable insights into the molecular landscape of LSCC, identifying potential biomarkers and therapeutic targets for improved diagnosis and treatment.
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Affiliation(s)
- Md. Hasan Jafre Shovon
- Laboratory of Pharmaceutical Biotechnology and Bioinformatics, Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, 7408 Bangladesh
| | - Partha Biswas
- Laboratory of Pharmaceutical Biotechnology and Bioinformatics, Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, 7408 Bangladesh
| | - Md. Imtiaz
- Laboratory of Pharmaceutical Biotechnology and Bioinformatics, Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, 7408 Bangladesh
| | - Shirajut Mobin
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Science and Technology, Jashore University of Science and Technology, Jashore, 7408 Bangladesh
| | - Md. Nazmul Hasan
- Laboratory of Pharmaceutical Biotechnology and Bioinformatics, Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, 7408 Bangladesh
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6
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Liu H, Wu T, Zhang J, He P. Exploring the over-wintering and over-summering mechanisms of Ulva prolifera from physiological and transcriptome perspectives and their impacts on green tides. MARINE ENVIRONMENTAL RESEARCH 2025; 210:107310. [PMID: 40541110 DOI: 10.1016/j.marenvres.2025.107310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2025] [Revised: 06/08/2025] [Accepted: 06/16/2025] [Indexed: 06/22/2025]
Abstract
Since 2007, the South Yellow Sea region of China has endured the world's largest recurrent green tide disaster for 18 consecutive years, resulting in substantial economic losses and ecological risks. Ulva prolifera the dominant species in these green tides, demonstrates remarkable tolerance to both elevated and reduced temperatures, with its thermal adaptation mechanisms critically linked to seasonal outbreak dynamics. This study established three temperature regimes (5 °C, 20 °C, and 30 °C) based on recent extreme temperature records from the Yellow Sea region to systematically evaluate growth rates, photosynthetic performance, pigment profiles, antioxidant enzyme activities, and transcriptomic responses of U. prolifera. Results revealed optimal growth at 15-25 °C, with significant growth inhibition beyond this range. Elevated temperature (30 °C) induced modest increases in photosynthetic fluorescence parameters and pigment content, coupled with pronounced enhancement of peroxidase (POD) activity. Conversely, low-temperature exposure (5 °C) substantially suppressed both photosynthetic efficiency and pigment levels, while eliciting only marginal POD activation. Transcriptomic profiling demonstrated distinct survival strategies: Low temperatures triggered the activation of the protein synthesis pathway and basal metabolic maintenance to prolong viability, whereas high temperatures activated antioxidant defenses and metabolic reprogramming to sustain photosynthetic function and nutrient cycling. Machine learning analysis revealed that proteins related to protein modification and cell differentiation exhibited strong responses under temperature stress. These temperature-responsive regulatory networks underpin the seasonal proliferation patterns of U. prolifera green tides. The findings advance mechanistic understanding of its overwintering and summer endurance strategies, offering critical theoretical frameworks for ecological management and mitigation technologies.
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Affiliation(s)
- Hongtao Liu
- College of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai, 201306, China
| | - Tingting Wu
- College of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai, 201306, China
| | - Jianheng Zhang
- College of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai, 201306, China; Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, Ministry of Education, Shanghai Ocean University, Shanghai, 201306, China; Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Ocean University, Lianyungang, 222005, China.
| | - Peimin He
- College of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai, 201306, China; Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, Ministry of Education, Shanghai Ocean University, Shanghai, 201306, China; Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Ocean University, Lianyungang, 222005, China.
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7
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Isaac A, Klontzas ME, Dalili D, Akdogan AI, Fawzi M, Gugliemi G, Filippiadis D. Revolutionising osseous biopsy: the impact of artificial intelligence in the era of personalized medicine. Br J Radiol 2025; 98:795-809. [PMID: 39878877 PMCID: PMC12089761 DOI: 10.1093/bjr/tqaf018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2024] [Revised: 01/13/2025] [Accepted: 01/16/2025] [Indexed: 01/31/2025] Open
Abstract
In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalized medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted. We provide a brief review of AI in image- guided biopsy of bone tumours (primary and secondary) and specimen handling, in the era of personalized medicine. This article explores AI's role in enhancing diagnostic accuracy, improving safety in biopsies, and enabling more precise targeting in bone lesion biopsies, ultimately contributing to better patient outcomes in personalized medicine. We dive into various AI technologies applied to osseous biopsies, such as traditional machine learning, deep learning, radiomics, simulation, and generative models. We explore their roles in tumour-board meetings, communication between clinicians, radiologists, and pathologists. Additionally, we inspect ethical considerations associated with the integration of AI in bone biopsy procedures, technical limitations, and we delve into health equity, generalizability, deployment issues, and reimbursement challenges in AI-powered healthcare. Finally, we explore potential future developments and offer a list of open-source AI tools and algorithms relevant to bone biopsies, which we include to encourage further discussion and research.
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Affiliation(s)
- Amanda Isaac
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 100 Lambeth Palace Rd, London SE1 7AR, United Kingdom
| | - Michail E Klontzas
- Department of Radiology, School of Medicine, University of Crete, Heraklion, Crete, P.C. 71003, Greece
| | - Danoob Dalili
- Southwest London Elective Orthopaedic Centre, Epsom and St Helier Hospitals, Surrey, London SM5 1AA, United Kingdom
| | - Asli Irmak Akdogan
- Ataturk Training and Research Hospital, Izmir Katip Çelebi University, Izmir, Turkey
| | - Mohamed Fawzi
- Department of Radiology, National Hepatology and Tropical Research Institute, Cairo, Egypt
| | | | - Dimitrios Filippiadis
- 2nd Department of Radiology, University General Hospital “ATTIKON”, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece
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8
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Yang L, Xu Z, Liu J, Chang X, Ren Z, Xiao W. Multi-omics insights into bone tissue injury and healing: bridging orthopedic trauma and regenerative medicine. BURNS & TRAUMA 2025; 13:tkaf019. [PMID: 40438296 PMCID: PMC12118463 DOI: 10.1093/burnst/tkaf019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 06/18/2024] [Accepted: 02/27/2025] [Indexed: 06/01/2025]
Abstract
To preserve functionality, bone is an active tissue that can constantly reconstruct itself through modeling and remodeling. It plays critical roles in the body, including maintaining mineral homeostasis, serving as the adult human body's core site of hematopoiesis, and supporting the structures of the body's soft tissues. It possesses the natural regeneration capacity, but large and complex lesions often require surgical intervention. Multiple omics integrate proteomics, metabolomics, genomics, and transcriptomics to provide a comprehensive understanding of biological processes like bone tissue injury and healing in bone tissue regeneration and engineering. Recently, bone tissue engineering and regenerative medicines have offered promising tools for bone regeneration using a multi-omics approach. Thus, this article will highlight the role of multiple omics in understanding bone tissue injury and healing. It will discuss the role of bone tissue engineering in developing bone substitutes that can replace translational medicine. Lastly, new developments in bone tissue engineering and regenerative medicine, along with multi-omics approaches, offer promising tools for bone regeneration.
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Affiliation(s)
- Liyu Yang
- Department of Orthopedics, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang, Liaoning 110004, China
| | - Zhijie Xu
- Department of Orthopedics, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang, Liaoning 110004, China
| | - Jie Liu
- Department of Orthopedics, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang, Liaoning 110004, China
- Department of Epidemiology, School of Public Health, China Medical University, 77 Puhe Road, Shenbei New District, Shenyang, Liaoning 110013, China
| | - Xiyue Chang
- Department of Orthopedics, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang, Liaoning 110004, China
- Department of Epidemiology, School of Public Health, China Medical University, 77 Puhe Road, Shenbei New District, Shenyang, Liaoning 110013, China
| | - Zhaozhou Ren
- Department of Orthopedics, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang, Liaoning 110004, China
| | - Wan’an Xiao
- Department of Orthopedics, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang, Liaoning 110004, China
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Kumar R, Romano JD, Ritchie MD. Network-based analyses of multiomics data in biomedicine. BioData Min 2025; 18:37. [PMID: 40426270 PMCID: PMC12117783 DOI: 10.1186/s13040-025-00452-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2024] [Accepted: 05/10/2025] [Indexed: 05/29/2025] Open
Abstract
Network representations of data are designed to encode relationships between concepts as sets of edges between nodes. Human biology is inherently complex and is represented by data that often exists in a hierarchical nature. One canonical example is the relationship that exists within and between various -omics datasets, including genomics, transcriptomics, and proteomics, among others. Encoding such data in a network-based or graph-based representation allows the explicit incorporation of such relationships into various biomedical big data tasks, including (but not limited to) disease subtyping, interaction prediction, biomarker identification, and patient classification. This review will present various existing approaches in using network representations and analysis of data in multiomics in the framework of deep learning and machine learning approaches, subdivided into supervised and unsupervised approaches, to identify benefits and drawbacks of various approaches as well as the possible next steps for the field.
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Affiliation(s)
- Rachit Kumar
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joseph D Romano
- Division of Informatics, Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marylyn D Ritchie
- Division of Informatics, Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Anuarbekov A, Kléma J. Utilizing RNA-seq data in monotone iterative generalized linear model to elevate prior knowledge quality of the circRNA-miRNA-mRNA regulatory axis. BMC Bioinformatics 2025; 26:139. [PMID: 40426030 PMCID: PMC12117772 DOI: 10.1186/s12859-025-06161-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2025] [Accepted: 05/07/2025] [Indexed: 05/29/2025] Open
Abstract
BACKGROUND Current experimental data on RNA interactions remain limited, particularly for non-coding RNAs, many of which have only recently been discovered and operate within complex regulatory networks. Researchers often rely on in-silico interaction detection algorithms, such as TargetScan, which are based on biochemical sequence alignment. However, these algorithms have limited performance. RNA-seq expression data can provide valuable insights into regulatory networks, especially for understudied interactions such as circRNA-miRNA-mRNA. By integrating RNA-seq data with prior interaction networks obtained experimentally or through in-silico predictions, researchers can discover novel interactions, validate existing ones, and improve interaction prediction accuracy. RESULTS This paper introduces Pi-GMIFS, an extension of the generalized monotone incremental forward stagewise (GMIFS) regression algorithm that incorporates prior knowledge. The algorithm first estimates prior response values through a prior-only regression, interpolates between these prior values and the original data, and then applies the GMIFS method. Our experimental results on circRNA-miRNA-mRNA regulatory interaction networks demonstrate that Pi-GMIFS consistently enhances precision and recall in RNA interaction prediction by leveraging implicit information from bulk RNA-seq expression data, outperforming the initial prior knowledge. CONCLUSION Pi-GMIFS is a robust algorithm for inferring acyclic interaction networks when the variable ordering is known. Its effectiveness was confirmed through extensive experimental validation. We proved that RNA-seq data of a representative size help infer previously unknown interactions available in TarBase v9 and improve the quality of circRNA disease annotation.
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Affiliation(s)
- Alikhan Anuarbekov
- Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, 16627, Prague, Czech Republic
| | - Jiří Kléma
- Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, 16627, Prague, Czech Republic.
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11
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Shi L, Wang X, Li C, Bai Y, Zhang Y, Li H. Radiomics applications in the modern management of esophageal squamous cell carcinoma. Med Oncol 2025; 42:221. [PMID: 40425893 DOI: 10.1007/s12032-025-02775-5] [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: 02/19/2025] [Accepted: 05/11/2025] [Indexed: 05/29/2025]
Abstract
Esophageal cancer ranks among the most lethal malignancies globally, with China accounting for more than half of worldwide esophageal squamous cell carcinoma (ESCC) cases. Late-stage diagnosis frequently precludes surgical intervention, contributing to poor outcomes. While precise clinical assessment is essential for treatment planning, therapeutic responses and prognosis exhibit substantial inter-patient heterogeneity, underscoring the urgent need for reliable biomarkers to enhance prognostic accuracy and guide personalized therapeutic strategies. Radiomics, an emerging field that extracts high-dimensional features from medical images, provides non-invasive approaches to improve diagnostic accuracy, predict survival, monitor adverse events, detect recurrence, and optimize treatment strategies. Radiomics has shown promising potential in the modern management of ESCC. Here, we review the critical contributions of radiomics to ESCC research and clinical practice, examining its workflow, applications, strengths, and limitations. Radiomics represents a compelling frontier with substantial potential to advance precision medicine for ESCC patients.
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Affiliation(s)
- Liqiang Shi
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
| | - Xipeng Wang
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
| | - Chengqiang Li
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
| | - Yaya Bai
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yajie Zhang
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China.
| | - Hecheng Li
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China.
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12
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Liao L, Xie M, Zheng X, Zhou Z, Deng Z, Gao J. Molecular insights fast-tracked: AI in biosynthetic pathway research. Nat Prod Rep 2025; 42:911-936. [PMID: 40130306 DOI: 10.1039/d4np00003j] [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: 03/26/2025]
Abstract
Covering: 2000 to 2025This review explores the potential of artificial intelligence (AI) in addressing challenges and accelerating molecular insights in biosynthetic pathway research, which is crucial for developing bioactive natural products with applications in pharmacology, agriculture, and biotechnology. It provides an overview of various AI techniques relevant to this research field, including machine learning (ML), deep learning (DL), natural language processing, network analysis, and data mining. AI-powered applications across three main areas, namely, pathway discovery and mining, pathway design, and pathway optimization, are discussed, and the benefits and challenges of integrating omics data and AI for enhanced pathway research are also elucidated. This review also addresses the current limitations, future directions, and the importance of synergy between AI and experimental approaches in unlocking rapid advancements in biosynthetic pathway research. The review concludes with an evaluation of AI's current capabilities and future outlook, emphasizing the transformative impact of AI on biosynthetic pathway research and the potential for new opportunities in the discovery and optimization of bioactive natural products.
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Affiliation(s)
- Lijuan Liao
- Key BioAI Synthetica Lab for Natural Product Drug Discovery, College of Bee, Biomedical and Pharmaceutical Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, P. R. China
| | - Mengjun Xie
- Key BioAI Synthetica Lab for Natural Product Drug Discovery, College of Bee, Biomedical and Pharmaceutical Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
| | - Xiaoshan Zheng
- Key BioAI Synthetica Lab for Natural Product Drug Discovery, College of Bee, Biomedical and Pharmaceutical Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
| | - Zhao Zhou
- Key BioAI Synthetica Lab for Natural Product Drug Discovery, College of Bee, Biomedical and Pharmaceutical Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
| | - Zixin Deng
- State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Jiangtao Gao
- Key BioAI Synthetica Lab for Natural Product Drug Discovery, College of Bee, Biomedical and Pharmaceutical Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
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13
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Lee C, Park T. Deep learning health space model for ordered responses. BMC Med Inform Decis Mak 2025; 25:191. [PMID: 40380121 DOI: 10.1186/s12911-025-03026-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2024] [Accepted: 05/09/2025] [Indexed: 05/19/2025] Open
Abstract
BACKGROUND As personalized medicine becomes more prevalent, the objective measurement and visualization of an individual's health status are becoming increasingly crucial. However, as the dimensions of data collected from each individual increase, this task becomes more challenging. The Health Space (HS) model provides a statistical framework for visualizing an individual's health status on biologically meaningful axes. In our previous study, we developed HS models using statistical models such as logistic regression model (LRM) and the proportional odds model (POM). However, these statistical HS models are limited in their ability to accommodate complex non-linear biological relationships. METHODS In order to model complex non-linear biological relationship, we developed deep learning HS models. Specifically, we formulated five distinct deep learning HS models: four standard binary deep neural networks (DNNs) for binary outcomes and one deep ordinal neural network (DONN) that accounts for the ordinality of the dependent variable. We trained these models using 32,140 samples from the Korea National Health and Nutrition Examination Survey (KNHANES) and validated them with data from the Ewha-Boramae cohort (862 samples) and the Korea Association Resource (KARE) project (3,199 samples). RESULTS The proposed deep learning HS models were compared with the existing statistical HS model based on the POM. Deep learning HS model using DONN demonstrated the best performance in discriminating health status in both the training and external datasets. CONCLUSION We developed deep learning HS models to capture complex non-linear biological relationships in HS and compared their performance with our previously best-performing statistical HS model. The deep learning HS models show promise as effective tools for objectively and meaningfully visualizing an individual's health status. CLINICAL TRIAL NUMBER Not applicable.
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Affiliation(s)
- Chanhee Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Korea
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Korea.
- Department of Statistics, Seoul National University, Seoul, 08826, Korea.
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14
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Hamidi H, Boudhabhay I, Dragon-Durey MA. Harnessing complement biomarkers for precision cancer care. Semin Immunol 2025; 78:101963. [PMID: 40378538 DOI: 10.1016/j.smim.2025.101963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2025] [Revised: 05/02/2025] [Accepted: 05/02/2025] [Indexed: 05/19/2025]
Abstract
The tumor microenvironment (TME) consists of various immune and non-immune cells, along with proteins from different origins, and plays a crucial role in tumor development, treatment response, and patient prognosis. Complement system is a key player in the TME. It is a proteolytic cascade that generates cleavage fragments capable to activate cells through specific receptors or deposit on cells and tissues. This review summarizes current data on the complement system as a potential biomarker in cancer. Transcriptomic analyses have classified tumors based on the impact of complement gene expression on prognosis. Immunostaining provides insights into the expression and deposition of complement proteins and fragments in tumors and TME cells. In body fluids such as blood, measuring complement activation fragments and detecting anti-complement autoantibodies have identified non-invasive biomarkers relevant to certain cancer types. With the rise of complement-targeting therapies and new tools for analyzing the complement system in tumors and body fluids, it is time to define its role in cancer management. This includes its potential for cancer detection, staging, and potentially for treatment monitoring.
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Affiliation(s)
- Houcine Hamidi
- Centre de Recherche des Cordeliers, Sorbonne Université, Inserm, Université Paris Cité, Inflammation, Complement and Cancer team, Paris, France; Laboratoire d'Immunologie, Hôpital Européen Georges Pompidou, APHP, Paris, France; University Hospital Federation (FHU) COMET, Paris, France
| | - Idris Boudhabhay
- Centre de Recherche des Cordeliers, Sorbonne Université, Inserm, Université Paris Cité, Inflammation, Complement and Cancer team, Paris, France; University Hospital Federation (FHU) COMET, Paris, France
| | - Marie-Agnes Dragon-Durey
- Centre de Recherche des Cordeliers, Sorbonne Université, Inserm, Université Paris Cité, Inflammation, Complement and Cancer team, Paris, France; Laboratoire d'Immunologie, Hôpital Européen Georges Pompidou, APHP, Paris, France; University Hospital Federation (FHU) COMET, Paris, France.
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15
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Taha BA, Abdulrahm ZM, Addie AJ, Haider AJ, Alkawaz AN, Yaqoob IAM, Arsad N. Advancing optical nanosensors with artificial intelligence: A powerful tool to identify disease-specific biomarkers in multi-omics profiling. Talanta 2025; 287:127693. [PMID: 39919475 DOI: 10.1016/j.talanta.2025.127693] [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: 12/04/2024] [Revised: 01/30/2025] [Accepted: 02/03/2025] [Indexed: 02/09/2025]
Abstract
Multi-omics profiling integrates genomic, epigenomic, transcriptomic, and proteomic data, essential for understanding complex health and disease pathways. This review highlights the transformative potential of combining optical nanosensors with artificial intelligence (AI). It is possible to identify disease-specific biomarkers using real-time and sensitive molecular interactions. These technologies are precious for genetic, epigenetic, and proteomic changes critical to disease progression and treatment response. AI improves multi-omics profiling by analyzing large, diverse data sets and common patterns traditional methods overlook. Machine learning tools Biomarkers Discovery is revolutionizing, drug resistance is being understood, and medicine is being personalized as the combination of AI and nanosensors has advanced the detection of DNA methylation and proteomic signatures and improved our understanding of cancer, cardiovascular disease and vascular disease. Despite these advances, challenges still exist. Difficulties in integrating data sets, retaining sensors, and building scalable computing tools are the biggest obstacles. It also examines various solutions with advanced AI algorithms and innovations, including fabrication in nanosensor design. Moreover, it highlights the potential of nanosensor-assisted, AI-driven multi-omics profiling to revolutionize disease diagnosis and treatment. As technology advances, these tools pave the way for faster diagnosis, more accurate treatment and improved patient outcomes, offering new hope for personalized medicine.
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Affiliation(s)
- Bakr Ahmed Taha
- Photonics Technology Lab, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi, 43600, Malaysia; Alimam University College, Balad, Iraq.
| | | | - Ali J Addie
- Center of Industrial Applications and Materials Technology, Scientific Research Commission, Baghdad 10070, Iraq.
| | - Adawiya J Haider
- Applied Sciences Department/Laser Science and Technology Branch, University of Technology, Iraq.
| | - Ali Najem Alkawaz
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
| | - Isam Ahmed M Yaqoob
- Faculty of Computer Sciences, Universiti Putra Malaysia, 43400, Selangor, Malaysia.
| | - Norhana Arsad
- Photonics Technology Lab, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi, 43600, Malaysia.
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16
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Liou K, Wang JP. Integrating genetic and gene expression data in network-based stratification analysis of cancers. BMC Bioinformatics 2025; 26:126. [PMID: 40360993 PMCID: PMC12070578 DOI: 10.1186/s12859-025-06143-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2024] [Accepted: 04/15/2025] [Indexed: 05/15/2025] Open
Abstract
BACKGROUND Cancers are complex diseases that have heterogeneous genetic drivers and varying clinical outcomes. A critical area of cancer research is organizing patient cohorts into subtypes and associating subtypes with clinical and biological outcomes for more effective prognosis and treatment. Large-scale studies have collected a plethora of omics data across multiple tumor types, providing an extensive dataset for stratifying patient cohorts. Network-based stratification (NBS) approaches have been presented to classify cancer tumors using somatic mutation data. A challenge in cancer stratification is integrating omics data to yield clinically meaningful subtypes. In this study, we investigate a novel approach to the NBS framework by integrating somatic mutation data with RNA sequencing data and investigating the effectiveness of integrated NBS on three cancers: ovarian, bladder, and uterine cancer. RESULTS We show that integrated NBS subtypes are more significantly associated with overall survival or histology. Specifically, we observe that integrated NBS subtypes for ovarian and bladder cancer were more significantly associated with patient survival than single-data type NBS subtypes, even when accounting for covariates. In addition, we show that integrated NBS subtypes for bladder and uterine are more significantly associated with tumor histology than single-data type NBS subtypes. Integrated NBS networks also reveal highly influential genes that span across multiple integrated NBS subtypes and subtype-specific genes. Pathway enrichment analysis of integrated NBS subtypes reveal overarching biological differences between subtypes. These genes and pathways are involved in a heterogeneous set of cell functions, including ubiquitin homeostasis, p53 regulation, cytokine and chemokine signaling, and cell proliferation, emphasizing the importance of identifying not only cancer-specific gene drivers but also subtype-specific tumor drivers. CONCLUSIONS Our study highlights the significance of integrating multi-omics data within the NBS framework to enhance cancer subtyping, specifically its utility in offering profound implications for personalized prognosis and treatment strategies. These insights contribute to the ongoing advancement of computational subtyping methods to uncover more targeted and effective therapeutic treatments while facilitating the discovery of cancer driver genes.
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Affiliation(s)
- Kenny Liou
- Stevens Neuroimaging and Informatics Institute, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - Ji-Ping Wang
- Department of Statistics and Data Science, Northwestern University, 2006 Sheridan Road, Evanston, IL, 60208, USA.
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17
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Saxena A, Nixon B, Boyd A, Evans J, Faraone SV. A Systematic Review of the Application of Graph Neural Networks to Extract Candidate Genes and Biological Associations. Am J Med Genet B Neuropsychiatr Genet 2025:e33031. [PMID: 40317893 DOI: 10.1002/ajmg.b.33031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2024] [Revised: 02/27/2025] [Accepted: 04/15/2025] [Indexed: 05/07/2025]
Abstract
The development of high throughput technologies has resulted in the collection of large quantities of genomic and transcriptomic data. However, identifying disease-associated genes or networks from these data has remained an ongoing challenge. In recent years, graph neural networks (GNNs) have emerged as a promising analytical tool, but it is not well understood which characteristics of these models result in improved performance. We conducted a systematic search and review of publications that used GNNs to identify disease-associated biological interactions. Information was extracted about model characteristics and performance with the goal of examining the relationship between these factors and performance. Data leakage was found in 31% of these models. For node level tasks, univariate positive associations were identified between model accuracy and use of hyper parameter optimization, data leakage via hyperparameter optimization, test set size, and total dataset size. Among graph level tasks, an increase in AUC was identified in association with testing method and a decrease with optimization reporting. Data leakage may pose an issue for GNN-based approaches; the adoption of best practice guidelines and consistent reporting of model design would be beneficial for future studies.
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Affiliation(s)
- Ankita Saxena
- Department of Neuroscience and Physiology, State University of New York-Norton College of Medicine at Upstate Medical University, New York, USA
- Department of Psychiatry and Behavioral Sciences, State University of new York-Norton College of Medicine at Upstate Medical University, New York, USA
| | - Bridgette Nixon
- College of Medicine, MD Program, Norton College of Medicine at SUNY Upstate Medical University, New York, USA
| | - Amelia Boyd
- College of Medicine, MD Program, Norton College of Medicine at SUNY Upstate Medical University, New York, USA
| | - James Evans
- Health Sciences Library, State University of new York-Upstate Medical University, New York, USA
| | - Stephen V Faraone
- Department of Neuroscience and Physiology, State University of New York-Norton College of Medicine at Upstate Medical University, New York, USA
- Department of Psychiatry and Behavioral Sciences, State University of new York-Norton College of Medicine at Upstate Medical University, New York, USA
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18
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Agoro R, Churchill GA. Challenges and opportunities for conceiving genetically diverse sickle cell mice. Trends Mol Med 2025; 31:413-423. [PMID: 39643521 PMCID: PMC12084145 DOI: 10.1016/j.molmed.2024.11.004] [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: 07/29/2024] [Revised: 10/21/2024] [Accepted: 11/11/2024] [Indexed: 12/09/2024]
Abstract
A milestone in sickle cell disease (SCD) therapeutics was achieved in December 2023 with the FDA-approved gene therapy for patients aged 12 years and older. However, these therapies may only suit a fraction of patients because of cost or health risks. A better understanding of SCD outcome heterogeneity is needed to propose patient-specific pharmacological interventions. To achieve this, humanized and genetically diverse mouse models are essential for associating candidate genotypes with specific hematological traits, organ function, and disease resilience. Here, we discuss the challenges and opportunities in developing genetically diverse sickle cell mice (GDS mice). These models are expected to complement current approaches in SCD research and enhance our understanding of SCD heterogeneity and anemia.
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Affiliation(s)
- Rafiou Agoro
- The Jackson Laboratory, Bar Harbor, ME 04609, USA.
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19
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Ma Y, Zhu W. RETRACTED: Development of gene panel for predicting recurrence in early-stage cervical cancer patients. ENVIRONMENTAL TOXICOLOGY 2025; 40:E44-E58. [PMID: 38563455 DOI: 10.1002/tox.24270] [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: 02/15/2024] [Revised: 03/19/2024] [Accepted: 03/24/2024] [Indexed: 04/04/2024]
Abstract
Cervical cancer (CC) is a common malignancy affecting women worldwide. Our objective was to develop a consensus-based gene panel using multi-omics data that could effectively predict recurrence in early-stage cervical cancer patients. We utilized the "Multi-Omics Consensus Integration Analysis (MOVICS)" package for consensus clustering design to integrate multiple omics datasets and improve the molecular classification landscape of early-stage CC. We identified the "resting and naive" tumor microenvironment (TME) as cancer subtype (CS) 2. Leveraging the feature genes from the CS classifier, we employed machine learning algorithms to identify a gene panel, including ALDH1A1, CLDN10, MUC13, and C10orf99, which could generate a consensus machine learning-driven score (CMLS) for each patient. Stratifying patients into high and low CMLS groups resulted in Kaplan-Meier curves demonstrating a significant difference in recurrence rates between the two groups. This difference remained significant even after adjusting for clinical features in multivariate Cox regression analysis, with the risk ratio of CMLS surpassing that of clinical characteristics. Furthermore, the TME exhibited notable differences between the different CMLS groups, suggesting that patients with low CMLS may exhibit a better response to immunotherapy. This study highlights the potential of the CMLS approach in predicting recurrence in early-stage cervical cancer patients and provides a screening model for selecting patients suitable for immunotherapy.
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Affiliation(s)
- Yun Ma
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Weipei Zhu
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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20
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Srivastava R. Advancing precision oncology with AI-powered genomic analysis. Front Pharmacol 2025; 16:1591696. [PMID: 40371349 PMCID: PMC12075946 DOI: 10.3389/fphar.2025.1591696] [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: 03/11/2025] [Accepted: 04/21/2025] [Indexed: 05/16/2025] Open
Abstract
Multiomics data integration approaches offer a comprehensive functional understanding of biological systems, with significant applications in disease therapeutics. However, the quantitative integration of multiomics data presents a complex challenge, requiring highly specialized computational methods. By providing deep insights into disease-associated molecular mechanisms, multiomics facilitates precision medicine by accounting for individual omics profiles, enabling early disease detection and prevention, aiding biomarker discovery for diagnosis, prognosis, and treatment monitoring, and identifying molecular targets for innovative drug development or the repurposing of existing therapies. AI-driven bioinformatics plays a crucial role in multiomics by computing scores to prioritize available drugs, assisting clinicians in selecting optimal treatments. This review will explain the potential of AI and multiomics data integration for disease understanding and therapeutics. It highlight the challenges in quantitative integration of diverse omics data and clinical workflows involving AI in cancer genomics, addressing the ethical and privacy concerns related to AI-driven applications in oncology. The scope of this text is broad yet focused, providing readers with a comprehensive overview of how AI-powered bioinformatics and integrative multiomics approaches are transforming precision oncology. Understanding bioinformatics in Genomics, it explore the integrative multiomics strategies for drug selection, genome profiling and tumor clonality analysis with clinical application of drug prioritization tools, addressing the technical, ethical, and practical hurdles in deploying AI-driven genomics tools.
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21
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Šamec N, Krapež G, Skubic C, Jovčevska I, Videtič Paska A. From Biomarker Discovery to Clinical Applications of Metabolomics in Glioblastoma. Metabolites 2025; 15:295. [PMID: 40422872 DOI: 10.3390/metabo15050295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2025] [Revised: 04/18/2025] [Accepted: 04/26/2025] [Indexed: 05/28/2025] Open
Abstract
BACKGROUND/OBJECTIVES In recent years, interest in studying changes in cancer metabolites has resulted in significant advances in the metabolomics field. Glioblastoma remains the most aggressive and lethal brain malignancy, which presents with notable metabolic reprogramming. METHODS We performed literature research from the PubMed database and considered research articles focused on the key metabolic pathways altered in glioblastoma (e.g., glycolysis, lipid metabolism, TCA cycle), the role of oncometabolites and metabolic plasticity, and the differential expression of metabolites in glioblastoma. Currently used metabolomics approaches can be either targeted, focusing on specific metabolites and pathways, or untargeted, which involves data-driven exploration of the metabolome and also results in the identification of new metabolites. Data processing and analysis is of great importance and can be improved with the integration of machine learning approaches for metabolite identification. RESULTS Changes in α/β-glucose, lactate, choline, and 2-hydroxyglutarate were detected in glioblastoma compared with non-tumor tissues. Different metabolites such as fumarate, tyrosine, and leucine, as well as citric acid, isocitric acid, shikimate, and GABA were detected in blood and CSF, respectively. CONCLUSIONS Although promising new technological and bioinformatic approaches help us understand glioblastoma better, challenges associated with biomarker availability, tumor heterogeneity, interpatient variability, standardization, and reproducibility still remain. Metabolomics research, either alone or combined with genomics or proteomics (i.e., multiomics) in glioblastoma, can lead to biomarker identification, tracking of metabolic therapy response, discovery of novel metabolites and pathways, and identification of potential therapeutic targets.
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Affiliation(s)
- Neja Šamec
- Centre for Functional Genomics and Biochips, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Zaloška Cesta 4, 1000 Ljubljana, Slovenia
| | - Gloria Krapež
- Centre for Functional Genomics and Biochips, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Zaloška Cesta 4, 1000 Ljubljana, Slovenia
| | - Cene Skubic
- Centre for Functional Genomics and Biochips, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Zaloška Cesta 4, 1000 Ljubljana, Slovenia
| | - Ivana Jovčevska
- Centre for Functional Genomics and Biochips, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Zaloška Cesta 4, 1000 Ljubljana, Slovenia
| | - Alja Videtič Paska
- Centre for Functional Genomics and Biochips, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Zaloška Cesta 4, 1000 Ljubljana, Slovenia
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22
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Dai H, Ren J, Wang C, Huang J, Wang X. Prognostic molecular subtype reveals the heterogeneity of tumor immune microenvironment in gastric cancer. Sci Rep 2025; 15:14453. [PMID: 40281016 PMCID: PMC12032113 DOI: 10.1038/s41598-025-96686-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2024] [Accepted: 03/31/2025] [Indexed: 04/29/2025] Open
Abstract
Gastric cancer (GC) remains a leading cause of cancer-related deaths and exhibits considerable heterogeneity among patients. Thus, accurate classifications are essential for predicting prognosis and developing personalized therapeutic strategies. To address this, we retrospectively analyzed multi-omics data from 359 GC samples, incorporating transcriptomic RNA (mRNA), DNA methylation, mutation data, and clinical parameters. Using ten clustering algorithms, we integrated these datasets to classify GC into molecular subtypes. The robustness of our clustering approach was externally validated using an independent cohort generated from different sequencing technologies, and we characterized the heterogeneity of each subtype. Our analysis identified three distinct molecular subtypes of GC, designated CS1, CS2, and CS3. These subtypes exhibited significant differences in survival outcomes, activation of cancer-related pathways, immune microenvironment composition, genomic alterations, and responses to immunotherapy and chemotherapy. Notably, Cathepsin V (CTSV) was significantly downregulated in the immunologically active and highly responsive CS3 subtype, while it was upregulated in the immunologically exhausted CS2 subtype. These findings suggest that CTSV could serve as both a prognostic marker and a molecular classifier. Furthermore, this study provides the first evidence of CTSV's high expression in GC and its potential role in tumor progression. The novel clustering approach, based on ten clustering algorithms and comprehensive analysis of multi-omics data in gastric cancer, can guide prognosis, characterize different clinical and biological features, and elucidate the tumor immune microenvironment, providing insights into the intratumor heterogeneity of GC and potential novel therapeutic strategies. Additionally, CTSV emerges as a prognostic marker linked to tumor immunity and disease progression, which lays the foundation for improved stratification strategies and the development of targeted therapeutic approaches in GC.
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Affiliation(s)
- Hui Dai
- Medical School, Nantong University, Nantong, 226001, Jiangsu, China
| | - Jing Ren
- Medical School, Nantong University, Nantong, 226001, Jiangsu, China
| | - Chun Wang
- Medical School, Nantong University, Nantong, 226001, Jiangsu, China
| | - Jianfei Huang
- Department of Clinical Biobank, Affiliated Hospital of Nantong University, Nantong, 226001, Jiangsu, China
| | - Xudong Wang
- Department of Laboratory Medicine, Affiliated Hospital of Nantong University, No. 20, Xisi Road, Nantong, 226001, Jiangsu, China.
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23
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Srivastav AK, Mishra MK, Lillard JW, Singh R. Transforming Pharmacogenomics and CRISPR Gene Editing with the Power of Artificial Intelligence for Precision Medicine. Pharmaceutics 2025; 17:555. [PMID: 40430848 PMCID: PMC12114816 DOI: 10.3390/pharmaceutics17050555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2025] [Revised: 04/16/2025] [Accepted: 04/22/2025] [Indexed: 05/29/2025] Open
Abstract
Background: Advancements in pharmacogenomics, artificial intelligence (AI), and CRISPR gene-editing technology are revolutionizing precision medicine by enabling highly individualized therapeutic strategies. Artificial intelligence-driven computational techniques improve biomarker discovery and drug optimization while pharmacogenomics helps to identify genetic polymorphisms affecting medicine metabolism, efficacy, and toxicity. Genetically editing based on CRISPR presents a precise method for changing gene expression and repairing damaging mutations. This review explores the convergence of these three fields to enhance improved precision medicine. Method: A methodical study of the current literature was performed on the effects of pharmacogenomics on drug response variability, artificial intelligence, and CRISPR in predictive modeling and gene-editing applications. Results: Driven by artificial intelligence, pharmacogenomics allows clinicians to classify patients and select the appropriate medications depending on their DNA profiles. This reduces the side effect risk and increases the therapeutic efficacy. Precision genetic modifications made feasible by CRISPR technology improve therapy outcomes in oncology, metabolic illnesses, neurological diseases, and other fields. The integration of artificial intelligence streamlines genome-editing applications, lowers off-target effects, and increases CRISPR specificity. Notwithstanding these advances, issues including computational biases, moral dilemmas, and legal constraints still arise. Conclusions: The synergy of artificial intelligence, pharmacogenomics, and CRISPR alters precision medicine by letting customized therapeutic interventions. Clinically translating, however, hinges on resolving data privacy concerns, assuring equitable access, and strengthening legal systems. Future research should focus on refining CRISPR gene-editing technologies, enhancing AI-driven pharmacogenomics, and developing moral guidelines for applying these tools in individualized medicine going forward.
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Affiliation(s)
- Amit Kumar Srivastav
- Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, 720 Westview Drive SW, Atlanta, GA 30310, USA; (A.K.S.); (J.W.L.J.)
| | - Manoj Kumar Mishra
- Department of Biological Sciences, Alabama State University, Montgomery, AL 36104, USA;
| | - James W. Lillard
- Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, 720 Westview Drive SW, Atlanta, GA 30310, USA; (A.K.S.); (J.W.L.J.)
- Cancer Health Equity Institute, Morehouse School of Medicine, 720 Westview Drive SW, Atlanta, GA 30310-1495, USA
| | - Rajesh Singh
- Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, 720 Westview Drive SW, Atlanta, GA 30310, USA; (A.K.S.); (J.W.L.J.)
- Cancer Health Equity Institute, Morehouse School of Medicine, 720 Westview Drive SW, Atlanta, GA 30310-1495, USA
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24
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May SA, Rosenbaum SW, Pearse DE, Kardos M, Primmer CR, Baetscher DS, Waples RS. The Genomics Revolution in Nonmodel Species: Predictions vs. Reality for Salmonids. Mol Ecol 2025:e17758. [PMID: 40249276 DOI: 10.1111/mec.17758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2024] [Revised: 03/12/2025] [Accepted: 03/28/2025] [Indexed: 04/19/2025]
Abstract
The increasing feasibility of whole-genome sequencing has been highly anticipated, promising to transform our understanding of the biology of nonmodel species. Notably, dramatic cost reductions beginning around 2007 with the advent of high-throughput sequencing inspired publications heralding the 'genomics revolution', with predictions about its future impacts. Although such predictions served as useful guideposts, value is added when statements are evaluated with the benefit of hindsight. Here, we review 10 key predictions made early in the genomics revolution, highlighting those realised while identifying challenges limiting others. We focus on predictions concerning applied aspects of genomics and examples involving salmonid species which, due to their socioeconomic and ecological significance, have been frontrunners in applications of genomics in nonmodel species. Predicted outcomes included enhanced analytical power, deeper insights into the genetic basis of phenotype and fitness variation, disease management and breeding program advancements. Although many predictions have materialised, several expectations remain unmet due to technological, analytical and knowledge barriers. Additionally, largely unforeseen advancements, including the identification and management applicability of large-effect loci, close-kin mark-recapture, environmental DNA and gene editing have added under-anticipated value. Finally, emerging innovations in artificial intelligence and bioinformatics offer promising new directions. This retrospective evaluation of the impacts of the genomic revolution offers insights into the future of genomics for nonmodel species.
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Affiliation(s)
- Samuel A May
- National Cold Water Marine Aquaculture Center, Agricultural Research Service, United States Department of Agriculture, Orono, Maine, USA
| | - Samuel W Rosenbaum
- Wildlife Biology Program, Department of Ecosystem and Conservation Sciences, College of Forestry and Conservation, University of Montana, Missoula, Montana, USA
| | - Devon E Pearse
- Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, California, USA
| | - Marty Kardos
- Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, USA
| | - Craig R Primmer
- Organismal and Evolutionary Biology Research Program, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
- Institute of Biotechnology, Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Diana S Baetscher
- Auke Bay Laboratories, Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Juneau, Alaska, USA
| | - Robin S Waples
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, USA
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Jaffe IS, Aljabban I, Kim JI, Dundas N, Khalil K, Rosa S, Zayas Z, Nally M, Gallego E, Griesemer A, Montgomery RA, Stern JM. Unused Samples from Clinical Blood Draws as a Resource for Maximizing Research Samples while Mitigating Iatrogenic Anemia Risks: A Pilot Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.04.17.25326023. [PMID: 40321263 PMCID: PMC12047949 DOI: 10.1101/2025.04.17.25326023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/11/2025]
Abstract
Background Translational research driven by large-scale biological testing requires significant volumes of blood for research testing. However, blood is also required for clinical management of research subjects, which must take priority. Paradoxically, much of the blood drawn for clinical management goes unused. Here, we present our approach for retrieving unused blood samples collected for clinical management and recycling them for research purposes. Methods Clinical Blood samples were collected for 60 days during a 61-day xenotransplantation experiment in a brain-dead decedent. Twice weekly, research staff went to the chemistry and hematology laboratories and collected stored blood, serum, and plasma samples that were >12 hours old. Sample collection and storage before retrieval was per standard clinical protocols. Samples were de-identified and relabeled and brought to a central biorepository for processing and storage. The quantity of plasma, serum, red blood cells (RBCs), and peripheral blood mononuclear cells (PBMCs) collected from clinical labs and bespoke research blood draws were compared. Results Unused blood from clinical samples yielded a minimum of 6.0 ml per day of plasma, representing 62% of all plasma obtained. Serum was only recoverable on 13 days (22%), with a mean 2.3 ml collected on those days, representing 8% of all serum obtained. PBMCs were only recoverable on six days (10%). Conclusions Overdrawn clinical laboratory samples represent an untapped resource of blood samples for research and can help augment samples collected explicitly for research purposes. With careful planning, this represents an opportunity to minimize iatrogenic blood loss in clinical-translational research.
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Affiliation(s)
- Ian S. Jaffe
- Department of Surgery, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
| | - Imad Aljabban
- Department of Surgery, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
- Department of Surgery, Columbia University School of Medicine, 622 W 168th St, New York, NY USA 10032
| | - Jacqueline I. Kim
- Department of Surgery, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
| | - Nicolas Dundas
- Transplant Institute, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
| | - Karen Khalil
- Transplant Institute, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
| | - Stefany Rosa
- Department of Pathology, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
| | - Zasha Zayas
- Center for Biospecimen Research & Development, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
| | - McKenna Nally
- Center for Biospecimen Research & Development, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
| | - Estefania Gallego
- Center for Biospecimen Research & Development, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
| | - Adam Griesemer
- Department of Surgery, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
- Transplant Institute, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
| | - Robert A. Montgomery
- Department of Surgery, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
- Transplant Institute, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
| | - Jeffrey M. Stern
- Department of Surgery, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
- Transplant Institute, New York University Grossman School of Medicine, 317 East 34th Street, New York, NY, USA 10016
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Zhao B, Lu W, Chen Y, Cai X. Predictive model for prognosis, immune microenvironment and drug sensitivity of colon carcinoma based on cuproptosis-related genes. INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL PATHOLOGY 2025; 18:148-165. [PMID: 40371092 PMCID: PMC12070126 DOI: 10.62347/feef1483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2024] [Accepted: 03/08/2025] [Indexed: 05/16/2025]
Abstract
BACKGROUND Colon cancer is a major cause of morbidity and mortality worldwide. Copper-induced cell death, known as cuproptosis, is a form of apoptosis that has been extensively studied in human diseases and is widely associated with tumor progression, prognosis, and immune response. However, the role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of colon cancer remains unclear. OBJECTIVE This study aims to explore the role of cuproptosis-related long non-coding RNAs (lncRNAs) in predicting the prognosis of colon cancer and to establish a risk prediction model based on these lncRNAs to guide clinical decisions and improve patient outcomes. METHODS A total of 19 cuproptosis-related genes were collected, and 1330 lncRNAs associated with cuproptosis were identified. Seven cuproptosis-related lncRNAs with prognostic value were selected from The Cancer Genome Atlas (TCGA) database. Using R software (version 4.1.0), the expression levels of the 19 genes were extracted, and the subjects were divided into high- and low-risk subgroups. A risk score model was developed based on cuproptosis-related genes and the seven co-expressed lncRNAs. The dataset was randomly split into a training set and a validation set. Analysis of clinicopathologic features, TME infiltration, and mutations was conducted, and nomogram predictions were validated using calibration plots to assess the predictive accuracy of the model. RESULTS The high-risk group had significantly shorter overall survival compared to the low-risk group (P<0.001), and the risk score was an independent prognostic factor (P<0.001). In the training set, the AUC values at 1, 3, and 5 years were 0.666, 0.621, and 0.669, respectively. Furthermore, low-risk patients had a higher survival rate. The genetic markers also correlated with tumor immune cell infiltration, clinical features, and prognosis. CONCLUSION This study established a novel method based on cuproptosis-related lncRNAs to predict the prognosis of colon cancer. The model has potential clinical applications in identifying patients sensitive to immunotherapy and antitumor treatments, thereby enhancing precision treatment strategies for colon cancer.
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Affiliation(s)
| | | | | | - Xiaoyong Cai
- Department of General Surgery, The Second Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi Zhuang Autonomous Region, The People’s Republic of China
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Choi J, Shakeri M, Bowker B, Zhuang H, Kong B. Differentially abundant proteins, metabolites, and lipid molecules in spaghetti meat compared to normal chicken breast meat: Multiomics analysis. Poult Sci 2025; 104:105165. [PMID: 40286572 DOI: 10.1016/j.psj.2025.105165] [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: 02/10/2025] [Revised: 04/08/2025] [Accepted: 04/14/2025] [Indexed: 04/29/2025] Open
Abstract
Spaghetti meat (SM), a recently emerging muscle myopathy in chicken breast meat, is characterized by a loss of muscle fiber integrity, resulting in a spaghetti-like appearance. Understanding the differences in proteins, metabolites, and lipids through a multiomics approach in SM can identify its quality traits and elucidate its exact causes. The purpose of this study was to investigate differentially abundant proteins, metabolites, and lipid molecules in SM compared to normal chicken breast meat (Control). The supernatant from sample homogenates was subjected to ultra-high performance liquid chromatography (UHPLC) analysis for multiomic profiling. A total of 16 chicken breast fillets (Pectoralis major) representing Control (n = 8) and SM (n = 8) groups were collected from a commercial slaughterhouse. A total of 2593 molecules were identified and composed of 1903 proteins, 506 lipids, 181 compounds and 3 electrolytes. There were 632 differential molecules composed of 503 proteins, 76 lipids, 50 metabolites, and 3 electrolytes. In comparing SM and Control, the protein, metabolite, and lipid molecules with the greatest fold change were calponin, decanoylcarnitine, and ceramide [N‑hydroxy-sphingosine] (Cer[NS]) d18:1_26:1, respectively. Plasmenylphosphatidylcholine (Plasmenyl-PC) and triglycerides (TG) were significantly decreased and increased, respectively, in SM compared to Control. Acylcarnitines (AC) were significantly decreased in SM compared to Control. Decanoylcarnitine, lauroylcarnitine, linoleyl-carnitine, oleoyl-carnitine, hexanoylcarnitine were downregulated in SM compared to Control, and adenosine 5'-diphosphoribose and nicotinamide adenine dinucleotide (NAD) were downregulated in SM. Carbon metabolism, glycolysis/glucogenesis, ribosome, biosynthesis of amino acids, and aminoacyl-tRNA biosynthesis were selected in the top 10 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, identified by using differential proteins. Hence, SM had different proteins, metabolites, and lipid molecules related to β-oxidation, carbon and energy metabolism, lipid formation, and protein and amino acid metabolism compared to Control. Results from this study showed physiological alterations found in SM myopathy. Therefore, to mitigate SM in broilers, interventions should: 1) increase NAD and carnitines, 2) reduce triglycerides, and 3) modulate β-oxidation and energy metabolism via nutritional, genetic, or systemic approaches.
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Affiliation(s)
- Janghan Choi
- US National Poultry Research Center, USDA-ARS, Athens, GA 30605, USA
| | - Majid Shakeri
- US National Poultry Research Center, USDA-ARS, Athens, GA 30605, USA
| | - Brian Bowker
- US National Poultry Research Center, USDA-ARS, Athens, GA 30605, USA
| | - Hong Zhuang
- US National Poultry Research Center, USDA-ARS, Athens, GA 30605, USA
| | - Byungwhi Kong
- US National Poultry Research Center, USDA-ARS, Athens, GA 30605, USA.
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28
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England-Mason G, MacEachern SJ, Amador K, Soomro MH, Reardon AJF, MacDonald AM, Kinniburgh DW, Letourneau N, Giesbrecht GF, Martin JW, Forkert ND, Dewey D. Using machine learning to investigate the influence of the prenatal chemical exposome on neurodevelopment of young children. Neurotoxicology 2025; 108:218-230. [PMID: 40222479 DOI: 10.1016/j.neuro.2025.04.001] [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: 11/22/2024] [Revised: 03/31/2025] [Accepted: 04/01/2025] [Indexed: 04/15/2025]
Abstract
Research investigating the prenatal chemical exposome and child neurodevelopment has typically focused on a limited number of chemical exposures and controlled for sociodemographic factors and maternal mental health. Emerging machine learning approaches may facilitate more comprehensive examinations of the contributions of chemical exposures, sociodemographic factors, and maternal mental health to child neurodevelopment. A machine learning pipeline that utilized feature selection and ranking was applied to investigate which common prenatal chemical exposures and sociodemographic factors best predict neurodevelopmental outcomes in young children. Data from 406 maternal-child pairs enrolled in the APrON study were used. Maternal concentrations of 32 environmental chemical exposures (i.e., phthalates, bisphenols, per- and polyfluoroalkyl substances (PFAS), metals, trace elements) measured during pregnancy and 11 sociodemographic factors, as well as measures of maternal mental health and urinary creatinine were entered into the machine learning pipeline. The pipeline, which consisted of a RReliefF variable selection algorithm and support vector machine regression model, was used to identify and rank the best subset of variables predictive of cognitive, language, and motor development outcomes on the Bayley Scales of Infant Development-Third Edition (Bayley-III) at 2 years of age. Bayley-III cognitive scores were best predicted using 29 variables, resulting in a correlation coefficient of r = 0.27 (R2=0.07). For language outcomes, 45 variables led to the best result (r = 0.30; R2=0.09), whereas for motor outcomes 33 variables led to the best result (r = 0.28, R2=0.09). Environmental chemicals, sociodemographic factors, and maternal mental health were found to be highly ranked predictors of cognitive, language, and motor development in young children. Our findings demonstrate the potential of machine learning approaches to identify and determine the relative importance of different predictors of child neurodevelopmental outcomes. Future developmental neurotoxicology research should consider the prenatal chemical exposome as well as sample characteristics such as sociodemographic factors and maternal mental health as important predictors of child neurodevelopment.
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Affiliation(s)
- Gillian England-Mason
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Sarah J MacEachern
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Department of Psychology, Faculty of Arts, University of Calgary, Calgary, Alberta
| | - Kimberly Amador
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Biomedical Engineering Graduate Program, University of Calgary, Calgary, Alberta, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Munawar Hussain Soomro
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Anthony J F Reardon
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Amy M MacDonald
- Alberta Centre for Toxicology, University of Calgary, Calgary, Alberta, Canada
| | - David W Kinniburgh
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada; Alberta Centre for Toxicology, University of Calgary, Calgary, Alberta, Canada
| | - Nicole Letourneau
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Faculty of Nursing, University of Calgary, Calgary, Alberta, Canada; Department of Community Health Sciences, Cumming School of Medicine University of Calgary, Calgary, Alberta, Canada; Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Gerald F Giesbrecht
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Department of Psychology, Faculty of Arts, University of Calgary, Calgary, Alberta; Department of Community Health Sciences, Cumming School of Medicine University of Calgary, Calgary, Alberta, Canada
| | - Jonathan W Martin
- Science for Life Laboratory, Department of Environmental Science, Stockholm University, Stockholm, Sweden
| | - Nils D Forkert
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Deborah Dewey
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Department of Community Health Sciences, Cumming School of Medicine University of Calgary, Calgary, Alberta, Canada.
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Ding X, Liang S, Zhang T, Zhang M, Fang H, Tian J, Liu J, Peng Y, Zheng L, Wang B, Feng W. Surface Modification of Gold Nanoparticle Impacts Distinct Lipid Metabolism. Molecules 2025; 30:1727. [PMID: 40333646 PMCID: PMC12029855 DOI: 10.3390/molecules30081727] [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: 02/08/2025] [Revised: 04/01/2025] [Accepted: 04/09/2025] [Indexed: 05/09/2025] Open
Abstract
Gold nanomaterials have garnered significant attention in biomedicine owing to their tunable size and morphology, facile surface modification capabilities, and distinctive optical properties. The surface functionalization of these nanoparticles can enhance their safety and efficacy in nanomedical applications. In this study, we examined the biological effects of gold nanoparticles (GNPs) with three distinct surface modifications (polyethylene glycol, chitosan, and polyethylenimine) in murine models, elucidating their mechanisms of action on hepatic tissue at both the transcriptomic and metabolomic levels. Our findings revealed that PEG-modified GNPs did not significantly alter any major metabolic pathway. In contrast, CS-GNPs markedly affected the metabolic pathways of retinol, arachidonic acid, linoleic acid, and glycerophospholipids (FDR < 0.05). Similarly, PEI-GNPs significantly influenced the metabolic pathways of retinol, arachidonic acid, linoleic acid, and sphingolipids (FDR < 0.05). Through a comprehensive analysis of the regulatory information within these pathways, we identified phosphatidylcholine compounds as potential biomarkers that may underlie the differential biological effects of the three functionalized GNPs. These findings provide valuable experimental data for evaluating the biological safety of functionalized GNPs.
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Affiliation(s)
- Xinyu Ding
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; (X.D.); (S.L.); (T.Z.); (M.Z.); (H.F.); (J.T.); (J.L.); (Y.P.); (L.Z.)
| | - Shanshan Liang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; (X.D.); (S.L.); (T.Z.); (M.Z.); (H.F.); (J.T.); (J.L.); (Y.P.); (L.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingfeng Zhang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; (X.D.); (S.L.); (T.Z.); (M.Z.); (H.F.); (J.T.); (J.L.); (Y.P.); (L.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Minglu Zhang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; (X.D.); (S.L.); (T.Z.); (M.Z.); (H.F.); (J.T.); (J.L.); (Y.P.); (L.Z.)
- State Key Laboratory of Medicinal Chemical Biology, Key Laboratory of Molecular Drug Research and KLMDASR of Tianjin, College of Pharmacy, Nankai University, Tianjin 300350, China
| | - Hao Fang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; (X.D.); (S.L.); (T.Z.); (M.Z.); (H.F.); (J.T.); (J.L.); (Y.P.); (L.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiale Tian
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; (X.D.); (S.L.); (T.Z.); (M.Z.); (H.F.); (J.T.); (J.L.); (Y.P.); (L.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jinke Liu
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; (X.D.); (S.L.); (T.Z.); (M.Z.); (H.F.); (J.T.); (J.L.); (Y.P.); (L.Z.)
| | - Yuyuan Peng
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; (X.D.); (S.L.); (T.Z.); (M.Z.); (H.F.); (J.T.); (J.L.); (Y.P.); (L.Z.)
| | - Lingna Zheng
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; (X.D.); (S.L.); (T.Z.); (M.Z.); (H.F.); (J.T.); (J.L.); (Y.P.); (L.Z.)
| | - Bing Wang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; (X.D.); (S.L.); (T.Z.); (M.Z.); (H.F.); (J.T.); (J.L.); (Y.P.); (L.Z.)
| | - Weiyue Feng
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; (X.D.); (S.L.); (T.Z.); (M.Z.); (H.F.); (J.T.); (J.L.); (Y.P.); (L.Z.)
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Danaeifar M, Najafi A. Artificial Intelligence and Computational Biology in Gene Therapy: A Review. Biochem Genet 2025; 63:960-983. [PMID: 38635012 DOI: 10.1007/s10528-024-10799-1] [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: 08/16/2023] [Accepted: 04/02/2024] [Indexed: 04/19/2024]
Abstract
One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.
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Affiliation(s)
- Mohsen Danaeifar
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Science, P.O. Box 19395-5487, Tehran, Iran
| | - Ali Najafi
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Science, P.O. Box 19395-5487, Tehran, Iran.
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Ponce Alencastro JA, Salinas Lucero DA, Solis RP, Herrera Giron CG, Estrella López AS, Anda Suárez PX. Molecular Mechanisms and Emerging Precision Therapeutics in the Gut Microbiota-Cardiovascular Axis. Cureus 2025; 17:e83022. [PMID: 40421334 PMCID: PMC12104768 DOI: 10.7759/cureus.83022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/26/2025] [Indexed: 05/28/2025] Open
Abstract
A microbiome in the gut plays a significant role in cardiovascular health and disease. Dysbiosis is an imbalance in the gut microbiome, leading to multiple cardiovascular diseases (CVD) such as atherosclerosis, hypertension, and heart failure. Gut microbe-derived metabolites such as trimethylamine-N-oxide (TMAO) and short-chain fatty acids (SCFAs) are important mediators of the gut-heart axis. Evaluation of the relationship between the gut microbiome and host biomarkers with CVD requires the integration of metagenomics and metabolomics with meta-omics approaches. The literature review found that microbes and metabolic signatures are associated with the risk and progression of CVD. The development of precision therapeutic approaches for targeting gut microbiota includes preventing adverse microbial effects using probiotics, prebiotics, and the drug-as-bug approach to inhibit harmful metabolites of microbiomes, and fecal microbiota transplantation (FMT). However, the implication and practice of these findings in clinical settings face challenges due to the heterogeneity of study designs, difficulty in the determination of causality, and the impact of confounding factors such as diet, medication, and potential inter-individual gut microbiome variability. Future researchers are recommended to conduct longitudinal studies to further establish both gut microbiome associations with CVD and develop successful precision therapeutics approaches based on the microbiome for the treatment of CVD.
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Affiliation(s)
| | | | - Ricardo Perez Solis
- Material Sciences, Instituto Tecnológico Superior de Atlixco, Tecnológico Nacional de México (TecNM), Atlixco, MEX
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Sun H, Wang Y, Deng G, Gao R, Zhang M, Huang L, He W, Zhang Z, Yu D, Chen P, Lu F, Liu S. Cortex Dictamni-induced hepatotoxicity by enhanced oxidative phosphorylation: Insights from integrative transcriptomics, proteomics, and metabolomics analyses. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2025; 139:156511. [PMID: 39954621 DOI: 10.1016/j.phymed.2025.156511] [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: 11/03/2024] [Revised: 02/11/2025] [Accepted: 02/12/2025] [Indexed: 02/17/2025]
Abstract
BACKGROUND Cortex Dictamni (CD) is a traditional Chinese medicine that is commonly used to treat various skin diseases. Recently, clinical reports have highlighted its potential to induce severe hepatotoxicity. However, the underlying mechanisms of toxicity remain inadequately explored. PURPOSE The aim of this study was to elucidate the intrinsic mechanisms of CD-induced hepatotoxicity. STUDY DESIGN Hepatotoxicity was assessed in SD rats, and human primary hepatocytes (HPHs) and differentiated HepaRG (dHepaRG) cells were used for in vitro testing. METHODS The major components of CD were determined using ultra-performance liquid chromatography (UPLC). Rats were randomly divided into control, CD-high (CD-H), CD-middle (CD-M), CD-low (CD-L), and isoniazid (INH) groups and administered oral gavage for four weeks. Serum biochemical indices, histopathological changes, apoptotic markers, and liver function were evaluated to assess hepatotoxicity. A comprehensive analysis of rat liver samples was performed using transcriptomic, proteomic, and metabolomic approaches to identify key pathways involved in CD-induced hepatotoxicity. In vitro toxicity validation of CD was performed using HPHs and dHepaRG cells. The key pathway was validated in vivo and in vitro. RESULTS CD primarily contained obacunone, fraxinellone, and dictamine. Administration of CD-H (9 times the maximum daily clinical dose in adults) and CD-M (3 times the maximum daily clinical dose in adults) for 4 weeks induced varying degrees of hepatotoxicity in rats. The CD-H group presented increased absolute and relative liver weights, reduced alanine aminotransferase (ALT) and bile acid transporter levels, and increased albumin (ALB) and cytochrome P450 (CYP) 3A4 levels, indicating significant hepatotoxicity in rats. Integrated multiomics analysis revealed that NADH dehydrogenase (ubiquinone) Fe-S protein 2 (Ndufs2) is a critical regulator of CD-induced hepatotoxicity involving oxidative phosphorylation (OXPHOS). CD inhibited the viability of HPHs and dHepaRG cells, demonstrating its significant cytotoxicity. Mechanistic validation revealed that CD upregulated Ndufs2, reactive oxygen species (ROS) and mitochondrial respiratory chain complex (MRCC) I, leading to nuclear factor erythroid 2-related factor 2 (Nrf2) pathway activation, apoptosis, mitochondrial dysfunction, and hepatotoxicity. CONCLUSION In summary, our study presents a comprehensive picture of the toxicity of CD in terms of dose and sex and reveals, for the first time, the central role of Ndufs2-regulated OXPHOS in CD-induced hepatotoxicity.
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Affiliation(s)
- Huijuan Sun
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, PR China
| | - Yu Wang
- Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin, PR China
| | - Geyu Deng
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, PR China
| | - Rui Gao
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, PR China
| | - Mengmeng Zhang
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, PR China
| | - Lin Huang
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, PR China
| | - Wenjie He
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, PR China
| | - Zhendong Zhang
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, PR China
| | - Donghua Yu
- Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin, PR China
| | - Pingping Chen
- Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin, PR China
| | - Fang Lu
- Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin, PR China.
| | - Shumin Liu
- Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin, PR China.
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Coletti R, Carrilho JF, Martins EP, Gonçalves CS, Costa BM, Lopes MB. A novel tool for multi-omics network integration and visualization: A study of glioma heterogeneity. Comput Biol Med 2025; 188:109811. [PMID: 39965391 DOI: 10.1016/j.compbiomed.2025.109811] [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: 03/19/2024] [Revised: 01/29/2025] [Accepted: 02/04/2025] [Indexed: 02/20/2025]
Abstract
Gliomas are highly heterogeneous tumors with generally poor prognoses. Leveraging multi-omics data and network analysis holds great promise in uncovering crucial signatures and molecular relationships that elucidate glioma heterogeneity. However, the complexity of the problem and the high dimensionality of the data increase the challenges of integrating information across various biological levels. This study develops a comprehensive framework aimed at identifying potential glioma-type-specific biomarkers through innovative variable selection and integrated network visualization. We designed a two-step framework for variable selection using sparse network estimation across various omics datasets. This framework incorporates MINGLE (Multi-omics Integrated Network for GraphicaL Exploration), a novel methodology designed to merge distinct multi-omics information into a single network, enabling the identification of underlying relations through an innovative integrated visualization. The analysis was conducted using glioma omics datasets, with patients grouped based on the latest glioma classification guidelines. Our investigation of the glioma data led to the identification of variables potentially serving as glioma-type-specific biomarkers. The integration of multi-omics data into a single network through MINGLE facilitated the discovery of molecular relationships that reflect glioma heterogeneity, supporting the biological interpretation. Scripts and files for reproducing the analysis or adapting it to other applications, are available in R software.
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Affiliation(s)
- Roberta Coletti
- Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, 2829-516, Caparica, Portugal.
| | - João F Carrilho
- Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, 2829-516, Caparica, Portugal; NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516, Caparica, Portugal
| | - Eduarda P Martins
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, 4710-057, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Céline S Gonçalves
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, 4710-057, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Bruno M Costa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, 4710-057, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Marta B Lopes
- Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, 2829-516, Caparica, Portugal; NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516, Caparica, Portugal; UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, 2829-516, Caparica, Portugal
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Nguyen JH, Curtis MA, Imami AS, Ryan WG, Alganem K, Neifer KL, Saferin N, Nawor CN, Kistler BP, Miller GW, Shukla R, McCullumsmith RE, Burkett JP. Developmental pyrethroid exposure disrupts molecular pathways for MAP kinase and circadian rhythms in mouse brain. Physiol Genomics 2025; 57:240-253. [PMID: 39961078 DOI: 10.1152/physiolgenomics.00033.2024] [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: 03/27/2024] [Revised: 05/07/2024] [Accepted: 02/10/2025] [Indexed: 02/26/2025] Open
Abstract
Neurodevelopmental disorders (NDDs) are a category of pervasive disorders of the developing nervous system with few or no recognized biomarkers. A significant portion of the risk for NDDs, including attention deficit hyperactivity disorder (ADHD), is contributed by the environment, and exposure to pyrethroid pesticides during pregnancy has been identified as a potential risk factor for NDD in the unborn child. We recently showed that low-dose developmental exposure to the pyrethroid pesticide deltamethrin in mice causes male-biased changes to ADHD- and NDD-relevant behaviors as well as the striatal dopamine system. Here, we used an integrated multiomics approach to determine the broadest possible set of biological changes in the mouse brain caused by developmental pyrethroid exposure (DPE). Using a litter-based, split-sample design, we exposed mouse dams during pregnancy and lactation to deltamethrin (3 mg/kg or vehicle every 3 days) at a concentration well below the EPA-determined benchmark dose used for regulatory guidance. We raised male offspring to adulthood, euthanized them, and pulverized and divided whole brain samples for split-sample transcriptomics, kinomics, and multiomics integration. Transcriptome analysis revealed alterations to multiple canonical clock genes, and kinome analysis revealed changes in the activity of multiple kinases involved in synaptic plasticity, including the mitogen-activated protein (MAP) kinase ERK. Multiomics integration revealed a dysregulated protein-protein interaction network containing primary clusters for MAP kinase cascades, regulation of apoptosis, and synaptic function. These results demonstrate that DPE causes a multimodal biophenotype in the brain relevant to ADHD and identifies new potential mechanisms of action.NEW & NOTEWORTHY Here, we provide the first evidence that low-dose developmental exposure to a pyrethroid pesticide, deltamethrin, results in molecular disruptions in the adult mouse brain in pathways regulating circadian rhythms and neuronal growth (MAP kinase). This same exposure causes a neurodevelopmental disorder (NDD)-relevant behavioral change in adult mice, making these findings relevant to the prevention of NDDs.
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Affiliation(s)
- Jennifer H Nguyen
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, United States
| | - Melissa A Curtis
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, United States
| | - Ali S Imami
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, United States
| | - William G Ryan
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, United States
| | - Khaled Alganem
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, United States
| | - Kari L Neifer
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, United States
| | - Nilanjana Saferin
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, United States
| | - Charlotte N Nawor
- Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, United States
| | - Brian P Kistler
- Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, United States
| | - Gary W Miller
- Department of Environmental Health, Emory Rollins School of Public Health, Atlanta, Georgia, United States
| | - Rammohan Shukla
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, United States
| | - Robert E McCullumsmith
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, United States
- Neurosciences Institute, ProMedica, Toledo, Ohio, United States
| | - James P Burkett
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, United States
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Mukherjee A, Abraham S, Singh A, Balaji S, Mukunthan KS. From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies. Mol Biotechnol 2025; 67:1269-1289. [PMID: 38565775 PMCID: PMC11928429 DOI: 10.1007/s12033-024-01133-6] [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: 12/27/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
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Affiliation(s)
- Arnab Mukherjee
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Suzanna Abraham
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Akshita Singh
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - S Balaji
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - K S Mukunthan
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
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Reza M, Qiu C, Lin X, Su K, Liu A, Zhang X, Gong Y, Luo Z, Tian Q, Nwadiugwu M, Liang S, Shen H, Deng H. An Attention-Aware Multi-Task Learning Framework Identifies Candidate Targets for Drug Repurposing in Sarcopenia. J Cachexia Sarcopenia Muscle 2025; 16:e13661. [PMID: 40045692 PMCID: PMC11883102 DOI: 10.1002/jcsm.13661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 09/19/2024] [Accepted: 10/31/2024] [Indexed: 03/09/2025] Open
Abstract
BACKGROUND Sarcopenia presents a pressing public health concern due to its association with age-related muscle mass decline, strength loss and reduced physical performance, particularly in the growing older population. Given the absence of approved pharmacological therapies for sarcopenia, the need to discover effective pharmacological interventions has become critical. METHODS To address this challenge and discover new therapies, we developed a novel Multi-Task Attention-aware method for Multi-Omics data (MTA-MO) to extract complex biological insights from various biomedical data sources, including transcriptome, methylome and genome data to identify drug targets and discover new therapies. Additionally, MTA-MO integrates human protein-protein interaction (PPI) networks and drug-target networks to improve target identification. The novel method is applied to a multi-omics dataset that included 1055 participants aged 20-50 (mean (± SD) age 36.88 (± 8.64)), comprising 37.82% African-American and 62.18% Caucasian/White individuals. Physical activity levels were self-reported and categorized into three groups: ≥ 3 times/week, < 3 times/week and no regular exercise. Mean (± SD) measures for grip strength, appendicular lean mass (ALM), exercise frequency and smoking status (no/yes, n (%)) were 38.72 (± 8.93) kg, 28.65 (± 4.63) kg, 4.31 (± 1.79) and 30.81%/69.19%, respectively. Significant differences (p < 0.05) were found between groups in age, ALM, smoking, and consumption of milk, alcohol, beer and wine. RESULTS Using the MTA-MO method, we identified 639 gene targets, and by analysing PPIs and querying public databases, we narrowed this list down to seven potential hub genes associated with sarcopenia (ESR1, ATM, CDC42, EP300, PIK3CA, EGF and PTK2B). These findings were further validated through diverse levels of pathobiological evidence associated with sarcopenia. Gene Ontology and KEGG pathways analysis highlighted five key functions and signalling pathways relevant to skeletal muscle. The interaction network analysis identified three transcriptional factors (GATA2, JUN and FOXC1) as the key transcriptional regulators of the seven potential genes. In silico analysis of 1940 drug candidates identified canagliflozin as a promising candidate for repurposing in sarcopenia, demonstrating the strongest binding affinity to the PTK2B protein (inhibition constant 6.97 μM). This binding is stabilized by hydrophobic bonds, Van der Waals forces, pi-alkyl interactions and pi-anion interactions around PTK2B's active residues, suggesting its potential as a therapeutic option. CONCLUSIONS Our novel approach effectively integrates multi-omics data to identify potential treatments for sarcopenia. The findings suggest that canagliflozin could be a promising therapeutic candidate for sarcopenia.
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Affiliation(s)
- Md Selim Reza
- Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and GenomicsTulane UniversityNew OrleansLouisianaUSA
| | - Chuan Qiu
- Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and GenomicsTulane UniversityNew OrleansLouisianaUSA
| | - Xu Lin
- Shunde Hospital of Southern Medical UniversityFoshanChina
| | - Kuan‐Jui Su
- Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and GenomicsTulane UniversityNew OrleansLouisianaUSA
| | - Anqi Liu
- Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and GenomicsTulane UniversityNew OrleansLouisianaUSA
| | - Xiao Zhang
- Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and GenomicsTulane UniversityNew OrleansLouisianaUSA
| | - Yun Gong
- Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and GenomicsTulane UniversityNew OrleansLouisianaUSA
| | - Zhe Luo
- Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and GenomicsTulane UniversityNew OrleansLouisianaUSA
| | - Qing Tian
- Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and GenomicsTulane UniversityNew OrleansLouisianaUSA
| | - Martin Nwadiugwu
- Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and GenomicsTulane UniversityNew OrleansLouisianaUSA
| | | | - Hui Shen
- Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and GenomicsTulane UniversityNew OrleansLouisianaUSA
| | - Hong‐Wen Deng
- Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and GenomicsTulane UniversityNew OrleansLouisianaUSA
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Gandara L, Foreman AL, Crocker J. Using AI to prevent the insect apocalypse: toward new environmental risk assessment procedures. CURRENT OPINION IN INSECT SCIENCE 2025; 68:101324. [PMID: 39731925 DOI: 10.1016/j.cois.2024.101324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2024] [Revised: 10/25/2024] [Accepted: 12/06/2024] [Indexed: 12/30/2024]
Abstract
Insect populations are declining globally, with multiple potential drivers identified. However, experimental data are needed to understand their relative contributions. We highlight the sublethal effects of pesticides at field-relevant concentrations, often overlooked in standard environmental risk assessments (ERA), as significant contributors to these declines. Behavior, as an easily monitored high-level phenotype, reflects alterations at various phenotypic levels. We propose incorporating behavioral assays with AI-based analytical methods into ERA protocols to better assess the safety of molecules intended for large-scale field use. This approach aims to safeguard food supplies and protect vital ecosystems in the future.
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Affiliation(s)
- Lautaro Gandara
- European Molecular Biology Laboratory, Heidelberg, Trust Genome Campus, Hinxton CB10 1SD, UK.
| | - Amy L Foreman
- European Molecular Biology Laboratory & European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
| | - Justin Crocker
- European Molecular Biology Laboratory, Heidelberg, Trust Genome Campus, Hinxton CB10 1SD, UK.
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Liu W, Pratte KA, Castaldi PJ, Hersh C, Bowler RP, Banaei-Kashani F, Kechris KJ. A generalized higher-order correlation analysis framework for multi-omics network inference. PLoS Comput Biol 2025; 21:e1011842. [PMID: 40228208 PMCID: PMC11996223 DOI: 10.1371/journal.pcbi.1011842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 01/31/2025] [Indexed: 04/16/2025] Open
Abstract
Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.
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Affiliation(s)
- Weixuan Liu
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Katherine A. Pratte
- Department of Biostatistics, National Jewish Health, Denver, Colorado, United States of America
| | - Peter J. Castaldi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Craig Hersh
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Russell P. Bowler
- Division of Pulmonary Medicine, Department of Medicine, National Jewish Health, Denver, Colorado, United States of America
| | - Farnoush Banaei-Kashani
- Department of Computer Science and Engineering, College of Engineering, Design and Computing, University of Colorado Denver, Denver, Colorado, United States of America
| | - Katerina J. Kechris
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
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Tian KJ, Yang Y, Chen GS, Deng NH, Tian Z, Bai R, Zhang F, Jiang ZS. Omics research in atherosclerosis. Mol Cell Biochem 2025; 480:2077-2102. [PMID: 39446251 DOI: 10.1007/s11010-024-05139-1] [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: 03/18/2024] [Accepted: 10/12/2024] [Indexed: 10/25/2024]
Abstract
Atherosclerosis (AS) is a chronic inflammatory disease characterized by lipid deposition within the arterial intima, as well as fibrous tissue proliferation and calcification. AS has long been recognized as one of the primary pathological foundations of cardiovascular diseases in humans. Its pathogenesis is intricate and not yet fully elucidated. Studies have shown that AS is associated with oxidative stress, inflammatory response, lipid deposition, and changes in cell phenotype. Unfortunately, there is currently no effective prevention or targeted treatment for AS. The rapid advancement of omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, has opened up novel avenues to elucidate the fundamental pathophysiology and associated mechanisms of AS. Here, we review articles published over the past decade and focus on the current status, challenges, limitations, and prospects of omics in AS research and clinical practice. Emphasizing potential targets based on omics technologies will improve our understanding of this pathological condition and assist in the development of potential therapeutic approaches for AS-related diseases.
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Affiliation(s)
- Kai-Jiang Tian
- Pathology Department, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, China
- Key Lab for Arteriosclerology of Hunan Province, International Joint Laboratory for Arteriosclerotic Disease Research of Hunan Province, Institute of Cardiovascular Disease, University of South China, Hengyang, 421001, China
| | - Yu Yang
- Key Lab for Arteriosclerology of Hunan Province, International Joint Laboratory for Arteriosclerotic Disease Research of Hunan Province, Institute of Cardiovascular Disease, University of South China, Hengyang, 421001, China
| | - Guo-Shuai Chen
- Emergency Department, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, China
| | - Nian-Hua Deng
- Anesthesiology Department, Dongguan Songshanhu Central Hospital, Dongguan, 523000, China
| | - Zhen Tian
- Clinical Laboratory, Dongguan Songshanhu Central Hospital, Dongguan, 523000, China
| | - Rui Bai
- Pathology Department, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, China
| | - Fan Zhang
- Pathology Department, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, China
| | - Zhi-Sheng Jiang
- Key Lab for Arteriosclerology of Hunan Province, International Joint Laboratory for Arteriosclerotic Disease Research of Hunan Province, Institute of Cardiovascular Disease, University of South China, Hengyang, 421001, China.
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Guan H, Zhang P, Park RF, Ding Y. Genomics Research on the Road of Studying Biology and Virulence of Cereal Rust Fungi. MOLECULAR PLANT PATHOLOGY 2025; 26:e70082. [PMID: 40181494 PMCID: PMC11968332 DOI: 10.1111/mpp.70082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2024] [Revised: 03/06/2025] [Accepted: 03/23/2025] [Indexed: 04/05/2025]
Abstract
Rust fungi are highly destructive pathogens that pose a significant threat to crop production worldwide, especially cereals. Obligate biotrophy and, in many cases, complex life cycles make rust fungi particularly challenging to study. However, recent rapid advances in sequencing technologies and genomic analysis tools have revolutionised rust fungal research. It is anticipated that the increasing availability and ongoing substantial improvements in genome assemblies will propel the field of rust biology into the post-genomic era, instigating a cascade of research endeavours encompassing multi-omics and gene discoveries. This is especially the case for many cereal rust pathogens, for which continental-scale studies of virulence have been conducted over many years and historical collections of viable isolates have been sequenced and assembled. Genomic analysis plays a crucial role in uncovering the underlying causes of the high variability of virulence and the complexity of population dynamics in rust fungi. Here, we provide an overview of progress in rust genomics, discuss the strategies employed in genomic analysis, and elucidate the strides that will drive cereal rust biology into the post-genomic era.
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Affiliation(s)
- Haixia Guan
- School of Life and Environment SciencesPlant Breeding Institute, The University of SydneyCobbittyNew South WalesAustralia
| | - Peng Zhang
- School of Life and Environment SciencesPlant Breeding Institute, The University of SydneyCobbittyNew South WalesAustralia
| | - Robert F. Park
- School of Life and Environment SciencesPlant Breeding Institute, The University of SydneyCobbittyNew South WalesAustralia
| | - Yi Ding
- School of Life and Environment SciencesPlant Breeding Institute, The University of SydneyCobbittyNew South WalesAustralia
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Jiang W, Ye W, Tan X, Bao YJ. Network-based multi-omics integrative analysis methods in drug discovery: a systematic review. BioData Min 2025; 18:27. [PMID: 40155979 PMCID: PMC11954193 DOI: 10.1186/s13040-025-00442-z] [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: 09/25/2024] [Accepted: 03/17/2025] [Indexed: 04/01/2025] Open
Abstract
The integration of multi-omics data from diverse high-throughput technologies has revolutionized drug discovery. While various network-based methods have been developed to integrate multi-omics data, systematic evaluation and comparison of these methods remain challenging. This review aims to analyze network-based approaches for multi-omics integration and evaluate their applications in drug discovery. We conducted a comprehensive review of literature (2015-2024) on network-based multi-omics integration methods in drug discovery, and categorized methods into four primary types: network propagation/diffusion, similarity-based approaches, graph neural networks, and network inference models. We also discussed the applications of the methods in three scenario of drug discovery, including drug target identification, drug response prediction, and drug repurposing, and finally evaluated the performance of the methods by highlighting their advantages and limitations in specific applications. While network-based multi-omics integration has shown promise in drug discovery, challenges remain in computational scalability, data integration, and biological interpretation. Future developments should focus on incorporating temporal and spatial dynamics, improving model interpretability, and establishing standardized evaluation frameworks.
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Affiliation(s)
- Wei Jiang
- School of Life Sciences, Hubei University, Wuhan, China
| | - Weicai Ye
- School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, National Engineering Laboratory for Big Data Analysis and Application, Sun Yat-sen University, Guangzhou, China
| | - Xiaoming Tan
- School of Life Sciences, Hubei University, Wuhan, China
| | - Yun-Juan Bao
- School of Life Sciences, Hubei University, Wuhan, China.
- , No.368 Youyi Avenue, Wuhan, 430062, China.
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42
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Yang SY, Han SM, Lee JY, Kim KS, Lee JE, Lee DW. Advancing Gut Microbiome Research: The Shift from Metagenomics to Multi-Omics and Future Perspectives. J Microbiol Biotechnol 2025; 35:e2412001. [PMID: 40223273 PMCID: PMC12010094 DOI: 10.4014/jmb.2412.12001] [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: 12/02/2024] [Revised: 02/14/2025] [Accepted: 02/24/2025] [Indexed: 04/15/2025]
Abstract
The gut microbiome, a dynamic and integral component of human health, has co-evolved with its host, playing essential roles in metabolism, immunity, and disease prevention. Traditional microbiome studies, primarily focused on microbial composition, have provided limited insights into the functional and mechanistic interactions between microbiota and their host. The advent of multi-omics technologies has transformed microbiome research by integrating genomics, transcriptomics, proteomics, and metabolomics, offering a comprehensive, systems-level understanding of microbial ecology and host-microbiome interactions. These advances have propelled innovations in personalized medicine, enabling more precise diagnostics and targeted therapeutic strategies. This review highlights recent breakthroughs in microbiome research, demonstrating how these approaches have elucidated microbial functions and their implications for health and disease. Additionally, it underscores the necessity of standardizing multi-omics methodologies, conducting large-scale cohort studies, and developing novel platforms for mechanistic studies, which are critical steps toward translating microbiome research into clinical applications and advancing precision medicine.
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Affiliation(s)
- So-Yeon Yang
- Department of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Seung Min Han
- Department of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Ji-Young Lee
- Department of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Kyoung Su Kim
- Department of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Jae-Eun Lee
- Department of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Dong-Woo Lee
- Department of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
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43
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Zhang H, Goedegebuure SP, Ding L, DeNardo D, Fields RC, Province M, Chen Y, Payne P, Li F. M3NetFlow: A multi-scale multi-hop graph AI model for integrative multi-omic data analysis. iScience 2025; 28:111920. [PMID: 40034855 PMCID: PMC11872513 DOI: 10.1016/j.isci.2025.111920] [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: 03/21/2024] [Revised: 10/17/2024] [Accepted: 01/27/2025] [Indexed: 03/05/2025] Open
Abstract
Multi-omic data-driven studies are at the forefront of precision medicine by characterizing complex disease signaling systems across multiple views and levels. The integration and interpretation of multi-omic data are critical for identifying disease targets and deciphering disease signaling pathways. However, it remains an open problem due to the complex signaling interactions among many proteins. Herein, we propose a multi-scale multi-hop multi-omic network flow model, M3NetFlow, to facilitate both hypothesis-guided and generic multi-omic data analysis tasks. We evaluated M3NetFlow using two independent case studies: (1) uncovering mechanisms of synergy of drug combinations (hypothesis/anchor-target guided multi-omic analysis) and (2) identifying biomarkers of Alzheimer's disease (generic multi-omic analysis). The evaluation and comparison results showed that M3NetFlow achieved the best prediction accuracy and identified a set of drug combination synergy- and disease-associated targets. The model can be directly applied to other multi-omic data-driven studies.
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Affiliation(s)
- Heming Zhang
- Institute for Informatics, Data Science and Biostatistics (I2DB), Washington University in St. Louis, St. Louis, MO, USA
| | - S. Peter Goedegebuure
- Department of Surgery, Washington University in St. Louis, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Li Ding
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - David DeNardo
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Ryan C. Fields
- Department of Surgery, Washington University in St. Louis, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael Province
- Division of Statistical Genomics, Department of Genetics, Washington University in St. Louis, St. Louis, MO, USA
| | - Yixin Chen
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Philip Payne
- Institute for Informatics, Data Science and Biostatistics (I2DB), Washington University in St. Louis, St. Louis, MO, USA
| | - Fuhai Li
- Institute for Informatics, Data Science and Biostatistics (I2DB), Washington University in St. Louis, St. Louis, MO, USA
- Division of Statistical Genomics, Department of Genetics, Washington University in St. Louis, St. Louis, MO, USA
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA
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44
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Rozera T, Pasolli E, Segata N, Ianiro G. Machine Learning and Artificial Intelligence in the Multi-Omics Approach to Gut Microbiota. Gastroenterology 2025:S0016-5085(25)00526-8. [PMID: 40118220 DOI: 10.1053/j.gastro.2025.02.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2024] [Revised: 01/26/2025] [Accepted: 02/10/2025] [Indexed: 03/23/2025]
Abstract
The gut microbiome is involved in human health and disease, and its comprehensive understanding is necessary to exploit it as a diagnostic or therapeutic tool. Multi-omics approaches, including metagenomics, metatranscriptomics, metabolomics, and metaproteomics, enable depiction of the gut microbial ecosystem's complexity. However, these tools generate a large data stream in which integration is needed to produce clinically useful readouts, but, in turn, might be difficult to carry out with conventional statistical methods. Artificial intelligence and machine learning have been increasingly applied to multi-omics datasets in several conditions associated with microbiome disruption, from chronic disorders to cancer. Such tools have potential for clinical implementation, including discovery of microbial biomarkers for disease classification or prediction, prediction of response to specific treatments, and fine-tuning of microbiome-modulating therapies. The state of the art, potential, and limits, of artificial intelligence and machine learning in the multi-omics approach to gut microbiome are discussed.
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Affiliation(s)
- Tommaso Rozera
- Department of Translational Medicine and Surgery, Università Cattolica del Sacro Cuore, Rome, Italy; Department of Medical and Surgical Sciences, L'Unità Operativa Complessa Gastroenterologia, Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy; Department of Medical and Surgical Sciences, L'Unità Operativa Complessa Centro Malattie dell'Apparato Digerente, Medicina Interna e Gastroenterologia, Fondazione Policlinico Universitario Gemelli Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Edoardo Pasolli
- University of Naples Federico II, Department of Agricultural Sciences, Piazza Carlo di Borbone 1, Portici, Italy
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy; Department of Experimental Oncology, European Institute of Oncology Istituto di Ricovero e Cura a Carattere Scientifico, Milan, Italy
| | - Gianluca Ianiro
- Department of Translational Medicine and Surgery, Università Cattolica del Sacro Cuore, Rome, Italy; Department of Medical and Surgical Sciences, L'Unità Operativa Complessa Gastroenterologia, Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy; Department of Medical and Surgical Sciences, L'Unità Operativa Complessa Centro Malattie dell'Apparato Digerente, Medicina Interna e Gastroenterologia, Fondazione Policlinico Universitario Gemelli Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy.
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45
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Feng J, Liu Y, Li K, Wu Y. Challenges and opportunities in targeting epigenetic mechanisms for pulmonary arterial hypertension treatment. Int J Pharm 2025; 672:125332. [PMID: 39929327 DOI: 10.1016/j.ijpharm.2025.125332] [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: 11/21/2024] [Revised: 01/16/2025] [Accepted: 02/07/2025] [Indexed: 02/14/2025]
Abstract
Pulmonary arterial hypertension (PAH) is a devastating disorder characterized by elevated pulmonary vascular resistance and pulmonary artery pressure, resulting from a multitude of etiological factors. If left untreated, PAH progressively leads to right heart failure and is associated with high mortality. The etiology of PAH is multifactorial, encompassing both congenital genetic predispositions and acquired secondary influences. Epigenetics, which refers to the regulation of gene expression through chromosomal alterations that do not involve changes in the DNA sequence, has garnered significant attention in PAH research. This includes mechanisms such as DNA methylation, histone modification, and RNA modification. Aberrant epigenetic modifications have been closely linked to the dysregulated proliferation and apoptosis of pulmonary artery smooth muscle cells and endothelial cells, suggesting that these alterations may serve as pivotal drivers of the pathophysiological changes observed in PAH. This review examines the potential impact of epigenetic alterations on the pathogenesis of PAH, highlighting their promise as therapeutic targets. Furthermore, we explore emerging therapeutic strategies and compounds aimed at modulating these epigenetic markers, and discusses their potential applications in both preclinical models and clinical trials. As our understanding of epigenetics deepens, it holds the potential to unlock novel avenues for the precise, individualized treatment of PAH, offering a new frontier in the fight against this debilitating disease.
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Affiliation(s)
- Jie Feng
- Department of Cardiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
| | - Yunman Liu
- Department of Cardiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
| | - Kai Li
- Department of Cardiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
| | - Yanqing Wu
- Department of Cardiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China.
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46
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Lateef Junaid MA. Artificial intelligence driven innovations in biochemistry: A review of emerging research frontiers. BIOMOLECULES & BIOMEDICINE 2025; 25:739-750. [PMID: 39819459 PMCID: PMC11959397 DOI: 10.17305/bb.2024.11537] [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: 10/24/2024] [Revised: 12/15/2024] [Accepted: 12/15/2024] [Indexed: 01/19/2025]
Abstract
Artificial intelligence (AI) has become a powerful tool in biochemistry, greatly enhancing research capabilities by enabling the analysis of complex datasets, predicting molecular interactions, and accelerating drug discovery. As AI continues to evolve, its applications in biochemistry are poised to expand, revolutionizing both theoretical and applied research. This review explores current and potential AI applications in biochemistry, with a focus on data analysis, molecular modeling, enzyme engineering, and metabolic pathway studies. Key AI techniques-such as machine learning algorithms, natural language processing, and AI-based molecular modeling-are discussed. The review also highlights emerging research areas benefiting from AI, including personalized medicine and synthetic biology. The methodology involves an extensive analysis of existing literature, particularly peer-reviewed studies on AI applications in biochemistry. AI-driven tools like AlphaFold, which have significantly advanced protein structure prediction, are evaluated alongside AI's role in expediting drug discovery. The review also addresses challenges such as data quality, model interpretability, and ethical considerations. Results indicate that AI has expanded the scope of biochemical research by facilitating large-scale data analysis, enhancing molecular simulations, and opening new avenues of inquiry. However, challenges remain, particularly in data handling and ethical concerns. In conclusion, AI is transforming biochemistry by driving innovation and expanding research possibilities. Future advancements in AI algorithms, interdisciplinary collaboration, and integration with automated techniques will be crucial to fully unlocking AI's potential in advancing biochemical research.
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Affiliation(s)
- Mohammed Abdul Lateef Junaid
- Department of Basic Medical Sciences, College of Medicine, Majmaah University, Al Majmaah, Kingdom of Saudi Arabia
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47
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Zhou X, Parisi L, Huang W, Zhang Y, Huang X, Youseffi M, Javid F, Ma R. A novel integrative multimodal classifier to enhance the diagnosis of Parkinson's disease. Brief Bioinform 2025; 26:bbaf088. [PMID: 40062615 PMCID: PMC11891661 DOI: 10.1093/bib/bbaf088] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2024] [Revised: 12/30/2024] [Accepted: 02/18/2025] [Indexed: 05/13/2025] Open
Abstract
Parkinson's disease (PD) is a complex, progressive neurodegenerative disorder with high heterogeneity, making early diagnosis difficult. Early detection and intervention are crucial for slowing PD progression. Understanding PD's diverse pathways and mechanisms is key to advancing knowledge. Recent advances in noninvasive imaging and multi-omics technologies have provided valuable insights into PD's underlying causes and biological processes. However, integrating these diverse data sources remains challenging, especially when deriving meaningful low-level features that can serve as diagnostic indicators. This study developed and validated a novel integrative, multimodal predictive model for detecting PD based on features derived from multimodal data, including hematological information, proteomics, RNA sequencing, metabolomics, and dopamine transporter scan imaging, sourced from the Parkinson's Progression Markers Initiative. Several model architectures were investigated and evaluated, including support vector machine, eXtreme Gradient Boosting, fully connected neural networks with concatenation and joint modeling (FCNN_C and FCNN_JM), and a multimodal encoder-based model with multi-head cross-attention (MMT_CA). The MMT_CA model demonstrated superior predictive performance, achieving a balanced classification accuracy of 97.7%, thus highlighting its ability to capture and leverage cross-modality inter-dependencies to aid predictive analytics. Furthermore, feature importance analysis using SHapley Additive exPlanations not only identified crucial diagnostic biomarkers to inform the predictive models in this study but also holds potential for future research aimed at integrated functional analyses of PD from a multi-omics perspective, ultimately revealing targets required for precision medicine approaches to aid treatment of PD aimed at slowing down its progression.
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Affiliation(s)
- Xiaoyan Zhou
- Department of Biology, Shenzhen MSU-BIT University, Longcheng Street, Shenzhen 518115, Guangdong, China
| | - Luca Parisi
- Department of Computer Science, Tutorantis, 5 South Charlotte Street, Edinburgh EH2 4AN, United Kingdom
| | - Wentao Huang
- Department of Biology, Shenzhen MSU-BIT University, Longcheng Street, Shenzhen 518115, Guangdong, China
| | - Yihan Zhang
- Department of Biology, Shenzhen MSU-BIT University, Longcheng Street, Shenzhen 518115, Guangdong, China
| | - Xiaoqun Huang
- Department of Biology, Shenzhen MSU-BIT University, Longcheng Street, Shenzhen 518115, Guangdong, China
| | - Mansour Youseffi
- Department of Engineering and Informatics, University of Bradford, Richmond Road, Bradford BD7 1DP, United Kingdom
| | - Farideh Javid
- Department of Pharmacy, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, United Kingdom
| | - Renfei Ma
- Department of Biology, Shenzhen MSU-BIT University, Longcheng Street, Shenzhen 518115, Guangdong, China
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48
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Hu Y, Li X, Yi Y, Huang Y, Wang G, Wang D. Deep learning-driven survival prediction in pan-cancer studies by integrating multimodal histology-genomic data. Brief Bioinform 2025; 26:bbaf121. [PMID: 40116660 PMCID: PMC11926983 DOI: 10.1093/bib/bbaf121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2024] [Revised: 02/10/2025] [Accepted: 02/28/2025] [Indexed: 03/23/2025] Open
Abstract
Accurate cancer prognosis is essential for personalized clinical management, guiding treatment strategies and predicting patient survival. Conventional methods, which depend on the subjective evaluation of histopathological features, exhibit significant inter-observer variability and limited predictive power. To overcome these limitations, we developed cross-attention transformer-based multimodal fusion network (CATfusion), a deep learning framework that integrates multimodal histology-genomic data for comprehensive cancer survival prediction. By employing self-supervised learning strategy with TabAE for feature extraction and utilizing cross-attention mechanisms to fuse diverse data types, including mRNA-seq, miRNA-seq, copy number variation, DNA methylation variation, mutation data, and histopathological images. By successfully integrating this multi-tiered patient information, CATfusion has become an advanced survival prediction model to utilize the most diverse data types across various cancer types. CATfusion's architecture, which includes a bidirectional multimodal attention mechanism and self-attention block, is adept at synchronizing the learning and integration of representations from various modalities. CATfusion achieves superior predictive performance over traditional and unimodal models, as demonstrated by enhanced C-index and survival area under the curve scores. The model's high accuracy in stratifying patients into distinct risk groups is a boon for personalized medicine, enabling tailored treatment plans. Moreover, CATfusion's interpretability, enabled by attention-based visualization, offers insights into the biological underpinnings of cancer prognosis, underscoring its potential as a transformative tool in oncology.
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Affiliation(s)
- Yongfei Hu
- Dermatology Hospital, Southern Medical University, No. 2, Lujing Road, Yuexiu District, Guangzhou 510091, China
| | - Xinyu Li
- Department of Bioinformatics, School of Basic Medical Sciences, Guangdong Province Key Laboratory of Molecular Tumor Pathology, Southern Medical University, 1023 Shatai South Road, Baiyun District, Guangzhou 510515, China
| | - Ying Yi
- Dermatology Hospital, Southern Medical University, No. 2, Lujing Road, Yuexiu District, Guangzhou 510091, China
| | - Yan Huang
- Cancer Research Institute, School of Basic Medical Sciences, Southern Medical University, 1023 Shatai South Road, Baiyun District, Guangzhou 510515, China
| | - Guangyu Wang
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Nangang District, Harbin 150000, China
| | - Dong Wang
- Dermatology Hospital, Southern Medical University, No. 2, Lujing Road, Yuexiu District, Guangzhou 510091, China
- Department of Bioinformatics, School of Basic Medical Sciences, Guangdong Province Key Laboratory of Molecular Tumor Pathology, Southern Medical University, 1023 Shatai South Road, Baiyun District, Guangzhou 510515, China
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49
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Tran D, Nguyen H, Pham VD, Nguyen P, Nguyen Luu H, Minh Phan L, Blair DeStefano C, Jim Yeung SC, Nguyen T. A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables. Brief Bioinform 2025; 26:bbaf150. [PMID: 40221959 PMCID: PMC11994034 DOI: 10.1093/bib/bbaf150] [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: 10/27/2024] [Revised: 01/29/2025] [Accepted: 03/19/2025] [Indexed: 04/15/2025] Open
Abstract
Cancer is an umbrella term that includes a wide spectrum of disease severity, from those that are malignant, metastatic, and aggressive to benign lesions with very low potential for progression or death. The ability to prognosticate patient outcomes would facilitate management of various malignancies: patients whose cancer is likely to advance quickly would receive necessary treatment that is commensurate with the predicted biology of the disease. Former prognostic models based on clinical variables (age, gender, cancer stage, tumor grade, etc.), though helpful, cannot account for genetic differences, molecular etiology, tumor heterogeneity, and important host biological mechanisms. Therefore, recent prognostic models have shifted toward the integration of complementary information available in both molecular data and clinical variables to better predict patient outcomes: vital status (overall survival), metastasis (metastasis-free survival), and recurrence (progression-free survival). In this article, we review 20 survival prediction approaches that integrate multi-omics and clinical data to predict patient outcomes. We discuss their strategies for modeling survival time (continuous and discrete), the incorporation of molecular measurements and clinical variables into risk models (clinical and multi-omics data), how to cope with censored patient records, the effectiveness of data integration techniques, prediction methodologies, model validation, and assessment metrics. The goal is to inform life scientists of available resources, and to provide a complete review of important building blocks in survival prediction. At the same time, we thoroughly describe the pros and cons of each methodology, and discuss in depth the outstanding challenges that need to be addressed in future method development.
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Affiliation(s)
- Dao Tran
- Department of Computer Science and Software Engineering, Auburn University, 345 W Magnolia Avenue, Auburn, AL 36849, United States
| | - Ha Nguyen
- Department of Computer Science and Software Engineering, Auburn University, 345 W Magnolia Avenue, Auburn, AL 36849, United States
| | - Van-Dung Pham
- Department of Computer Science and Software Engineering, Auburn University, 345 W Magnolia Avenue, Auburn, AL 36849, United States
| | - Phuong Nguyen
- Department of Computer Science and Software Engineering, Auburn University, 345 W Magnolia Avenue, Auburn, AL 36849, United States
| | - Hung Nguyen Luu
- UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, 5150 Centre Avenue, Pittsburgh, PA 15232, United States
- Department of Epidemiology, School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, PA 15261, United States
| | - Liem Minh Phan
- David Grant USAF Medical Center—Clinical Investigation Facility, 60 Medical Group, Defense Health Agency, 101 Bodin Circle, Travis Air Force Base, CA 94535, United States
| | - Christin Blair DeStefano
- Walter Reed National Military Medical Center, Defense Health Agency, 8901 Rockville Pike, Bethesda, MD 20889, United States
| | - Sai-Ching Jim Yeung
- Department of Emergency Medicine, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, United States
| | - Tin Nguyen
- Department of Computer Science and Software Engineering, Auburn University, 345 W Magnolia Avenue, Auburn, AL 36849, United States
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50
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Jiang J, Xie H, Cao S, Xu X, Zhou J, Liu Q, Ding C, Liu M. Post-stroke depression: exploring gut microbiota-mediated barrier dysfunction through immune regulation. Front Immunol 2025; 16:1547365. [PMID: 40098959 PMCID: PMC11911333 DOI: 10.3389/fimmu.2025.1547365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2024] [Accepted: 02/17/2025] [Indexed: 03/19/2025] Open
Abstract
Post-stroke depression (PSD) is one of the most common and devastating neuropsychiatric complications in stroke patients, affecting more than one-third of survivors of ischemic stroke (IS). Despite its high incidence, PSD is often overlooked or undertreated in clinical practice, and effective preventive measures and therapeutic interventions remain limited. Although the exact mechanisms of PSD are not fully understood, emerging evidence suggests that the gut microbiota plays a key role in regulating gut-brain communication. This has sparked great interest in the relationship between the microbiota-gut-brain axis (MGBA) and PSD, especially in the context of cerebral ischemia. In addition to the gut microbiota, another important factor is the gut barrier, which acts as a frontline sensor distinguishing between beneficial and harmful microbes, regulating inflammatory responses and immunomodulation. Based on this, this paper proposes a new approach, the microbiota-immune-barrier axis, which is not only closely related to the pathophysiology of IS but may also play a critical role in the occurrence and progression of PSD. This review aims to systematically analyze how the gut microbiota affects the integrity and function of the barrier after IS through inflammatory responses and immunomodulation, leading to the production or exacerbation of depressive symptoms in the context of cerebral ischemia. In addition, we will explore existing technologies that can assess the MGBA and potential therapeutic strategies for PSD, with the hope of providing new insights for future research and clinical interventions.
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Affiliation(s)
- Jia Jiang
- The Second Affiliated Hospital, Hunan University of Chinese Medicine, Changsha, China
| | - Haihua Xie
- School of Acupuncture & Tuina and Rehabilitation, Hunan University of Chinese Medicine, Changsha, China
| | - Sihui Cao
- School of Acupuncture & Tuina and Rehabilitation, Hunan University of Chinese Medicine, Changsha, China
| | - Xuan Xu
- School of Acupuncture & Tuina and Rehabilitation, Hunan University of Chinese Medicine, Changsha, China
| | - Jingying Zhou
- School of Acupuncture & Tuina and Rehabilitation, Hunan University of Chinese Medicine, Changsha, China
| | - Qianyan Liu
- School of Acupuncture & Tuina and Rehabilitation, Hunan University of Chinese Medicine, Changsha, China
| | - Changsong Ding
- School of Information Science and Engineering, Hunan University of Chinese Medicine, Changsha, China
| | - Mi Liu
- School of Acupuncture & Tuina and Rehabilitation, Hunan University of Chinese Medicine, Changsha, China
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