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von Hardenberg S, Klefenz I, Steinemann D, Di Donato N, Baumann U, Auber B, Klemann C. Current genetic diagnostics in inborn errors of immunity. Front Pediatr 2024; 12:1279112. [PMID: 38659694 PMCID: PMC11039790 DOI: 10.3389/fped.2024.1279112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 03/28/2024] [Indexed: 04/26/2024] Open
Abstract
New technologies in genetic diagnostics have revolutionized the understanding and management of rare diseases. This review highlights the significant advances and latest developments in genetic diagnostics in inborn errors of immunity (IEI), which encompass a diverse group of disorders characterized by defects in the immune system, leading to increased susceptibility to infections, autoimmunity, autoinflammatory diseases, allergies, and malignancies. Various diagnostic approaches, including targeted gene sequencing panels, whole exome sequencing, whole genome sequencing, RNA sequencing, or proteomics, have enabled the identification of causative genetic variants of rare diseases. These technologies not only facilitated the accurate diagnosis of IEI but also provided valuable insights into the underlying molecular mechanisms. Emerging technologies, currently mainly used in research, such as optical genome mapping, single cell sequencing or the application of artificial intelligence will allow even more insights in the aetiology of hereditary immune defects in the near future. The integration of genetic diagnostics into clinical practice significantly impacts patient care. Genetic testing enables early diagnosis, facilitating timely interventions and personalized treatment strategies. Additionally, establishing a genetic diagnosis is necessary for genetic counselling and prognostic assessments. Identifying specific genetic variants associated with inborn errors of immunity also paved the way for the development of targeted therapies and novel therapeutic approaches. This review emphasizes the challenges related with genetic diagnosis of rare diseases and provides future directions, specifically focusing on IEI. Despite the tremendous progress achieved over the last years, several obstacles remain or have become even more important due to the increasing amount of genetic data produced for each patient. This includes, first and foremost, the interpretation of variants of unknown significance (VUS) in known IEI genes and of variants in genes of unknown significance (GUS). Although genetic diagnostics have significantly contributed to the understanding and management of IEI and other rare diseases, further research, exchange between experts from different clinical disciplines, data integration and the establishment of comprehensive guidelines are crucial to tackle the remaining challenges and maximize the potential of genetic diagnostics in the field of rare diseases, such as IEI.
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Affiliation(s)
| | - Isabel Klefenz
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Doris Steinemann
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Nataliya Di Donato
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Ulrich Baumann
- Department of Pediatric Pneumology, Allergology and Neonatology, Hannover Medical School, Hannover, Germany
| | - Bernd Auber
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Christian Klemann
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
- Department of Pediatric Immunology, Rheumatology and Infectiology, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
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Sun C, Cheng X, Xu J, Chen H, Tao J, Dong Y, Wei S, Chen R, Meng X, Ma Y, Tian H, Guo X, Bi S, Zhang C, Kang J, Zhang M, Lv H, Shang Z, Lv W, Zhang R, Jiang Y. A review of disease risk prediction methods and applications in the omics era. Proteomics 2024:e2300359. [PMID: 38522029 DOI: 10.1002/pmic.202300359] [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: 09/15/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024]
Abstract
Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.
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Affiliation(s)
- Chen Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Xiangshu Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Jing Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Haiyan Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junxian Tao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Yu Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Rui Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yingnan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Hongsheng Tian
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xuying Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuo Bi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jingxuan Kang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mingming Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhenwei Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenhua Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
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Costa RL, Gadelha L, D'arc M, Ribeiro-Alves M, Robertson DL, Schwartz JM, Soares MA, Porto F. HIHISIV: a database of gene expression in HIV and SIV host immune response. BMC Bioinformatics 2024; 25:125. [PMID: 38519883 PMCID: PMC10958971 DOI: 10.1186/s12859-024-05740-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 03/11/2024] [Indexed: 03/25/2024] Open
Abstract
In the battle of the host against lentiviral pathogenesis, the immune response is crucial. However, several questions remain unanswered about the interaction with different viruses and their influence on disease progression. The simian immunodeficiency virus (SIV) infecting nonhuman primates (NHP) is widely used as a model for the study of the human immunodeficiency virus (HIV) both because they are evolutionarily linked and because they share physiological and anatomical similarities that are largely explored to understand the disease progression. The HIHISIV database was developed to support researchers to integrate and evaluate the large number of transcriptional data associated with the presence/absence of the pathogen (SIV or HIV) and the host response (NHP and human). The datasets are composed of microarray and RNA-Seq gene expression data that were selected, curated, analyzed, enriched, and stored in a relational database. Six query templates comprise the main data analysis functions and the resulting information can be downloaded. The HIHISIV database, available at https://hihisiv.github.io , provides accurate resources for browsing and visualizing results and for more robust analyses of pre-existing data in transcriptome repositories.
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Affiliation(s)
- Raquel L Costa
- DEXL Lab, National Laboratory for Scientific Computing, Petrópolis, Brazil.
| | - Luiz Gadelha
- German Human Genome-Phenome Archive (GHGA, W620), German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Mirela D'arc
- Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Marcelo Ribeiro-Alves
- Instituto Nacional de Infectologia Evandro Chagas, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
| | - David L Robertson
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Jean-Marc Schwartz
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT, UK
| | - Marcelo A Soares
- Departamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21941-902, Brazil
- Programa de Oncovirologia, Divisão de Pesquisa Translacional, Instituto Nacional do Câncer, Rio de Janeiro, 20230-130, Brazil
| | - Fábio Porto
- DEXL Lab, National Laboratory for Scientific Computing, Petrópolis, Brazil
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Wang M, Yang N, Laterrière M, Gagné D, Omonijo F, Ibeagha-Awemu EM. Multi-omics integration identifies regulatory factors underlying bovine subclinical mastitis. J Anim Sci Biotechnol 2024; 15:46. [PMID: 38481273 PMCID: PMC10938844 DOI: 10.1186/s40104-024-00996-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/14/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Mastitis caused by multiple factors remains one of the most common and costly disease of the dairy industry. Multi-omics approaches enable the comprehensive investigation of the complex interactions between multiple layers of information to provide a more holistic view of disease pathogenesis. Therefore, this study investigated the genomic and epigenomic signatures and the possible regulatory mechanisms underlying subclinical mastitis by integrating RNA sequencing data (mRNA and lncRNA), small RNA sequencing data (miRNA) and DNA methylation sequencing data of milk somatic cells from 10 healthy cows and 20 cows with naturally occurring subclinical mastitis caused by Staphylococcus aureus or Staphylococcus chromogenes. RESULTS Functional investigation of the data sets through gene set analysis uncovered 3458 biological process GO terms and 170 KEGG pathways with altered activities during subclinical mastitis, provided further insights into subclinical mastitis and revealed the involvement of multi-omics signatures in the altered immune responses and impaired mammary gland productivity during subclinical mastitis. The abundant genomic and epigenomic signatures with significant alterations related to subclinical mastitis were observed, including 30,846, 2552, 1276 and 57 differential methylation haplotype blocks (dMHBs), differentially expressed genes (DEGs), lncRNAs (DELs) and miRNAs (DEMs), respectively. Next, 5 factors presenting the principal variation of differential multi-omics signatures were identified. The important roles of Factor 1 (DEG, DEM and DEL) and Factor 2 (dMHB and DEM), in the regulation of immune defense and impaired mammary gland functions during subclinical mastitis were revealed. Each of the omics within Factors 1 and 2 explained about 20% of the source of variation in subclinical mastitis. Also, networks of important functional gene sets with the involvement of multi-omics signatures were demonstrated, which contributed to a comprehensive view of the possible regulatory mechanisms underlying subclinical mastitis. Furthermore, multi-omics integration enabled the association of the epigenomic regulatory factors (dMHBs, DELs and DEMs) of altered genes in important pathways, such as 'Staphylococcus aureus infection pathway' and 'natural killer cell mediated cytotoxicity pathway', etc., which provides further insights into mastitis regulatory mechanisms. Moreover, few multi-omics signatures (14 dMHBs, 25 DEGs, 18 DELs and 5 DEMs) were identified as candidate discriminant signatures with capacity of distinguishing subclinical mastitis cows from healthy cows. CONCLUSION The integration of genomic and epigenomic data by multi-omics approaches in this study provided a better understanding of the molecular mechanisms underlying subclinical mastitis and identified multi-omics candidate discriminant signatures for subclinical mastitis, which may ultimately lead to the development of more effective mastitis control and management strategies.
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Affiliation(s)
- Mengqi Wang
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada
- Department of Animal Science, Université Laval, Quebec City, QC, Canada
| | - Naisu Yang
- Department of Animal Science, Université Laval, Quebec City, QC, Canada
| | - Mario Laterrière
- Quebec Research and Development Centre, Agriculture and Agri-Food Canada, Quebec City, QC, Canada
| | - David Gagné
- Quebec Research and Development Centre, Agriculture and Agri-Food Canada, Quebec City, QC, Canada
| | - Faith Omonijo
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada
| | - Eveline M Ibeagha-Awemu
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada.
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Nasti A, Okumura M, Takeshita Y, Ho TTB, Sakai Y, Sato TA, Nomura C, Goto H, Nakano Y, Urabe T, Nakamura S, Tamura T, Matsubara K, Takamura T, Kaneko S. The declining insulinogenic index correlates with inflammation and metabolic dysregulation in non-obese individuals assessed by blood gene expression. Diabetes Res Clin Pract 2024; 208:111090. [PMID: 38216088 DOI: 10.1016/j.diabres.2024.111090] [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: 09/29/2023] [Revised: 12/19/2023] [Accepted: 01/04/2024] [Indexed: 01/14/2024]
Abstract
AIMS Diabetes onset is difficult to predict. Since decreased insulinogenic index (IGI) is observed in prediabetes, and blood gene expression correlates with insulin secretion, candidate biomarkers can be identified. METHODS We collected blood from 96 participants (54 males, 42 females) in 2008 (age: 52.5 years) and 2016 for clinical and gene expression analyses. IGI was derived from values of insulin and glucose at fasting and at 30 min post-OGTT. Two subgroups were identified based on IGI variation: "Minor change in IGI" group with absolute value variation between -0.05 and +0.05, and "Decrease in IGI" group with a variation between -20 and -0.05. RESULTS Following the comparison of "Minor change in IGI" and "Decrease in IGI" groups at time 0 (2008), we identified 77 genes correlating with declining IGI, related to response to lipid, carbohydrate, and hormone metabolism, response to stress and DNA metabolic processes. Over the eight years, genes correlating to declining IGI were related to inflammation, metabolic and hormonal dysregulation. Individuals with minor change in IGI, instead, featured homeostatic and regenerative responses. CONCLUSIONS By blood gene expression analysis of non-obese individuals, we identified potential gene biomarkers correlating to declining IGI, associated to a pathophysiology of inflammation and metabolic dysregulation.
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Affiliation(s)
- Alessandro Nasti
- Information-Based Medicine Development, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8641, Japan.
| | - Miki Okumura
- Department of Endocrinology and Metabolism, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8640, Japan
| | - Yumie Takeshita
- Department of Endocrinology and Metabolism, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8640, Japan
| | - Tuyen Thuy Bich Ho
- Information-Based Medicine Development, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8641, Japan
| | - Yoshio Sakai
- Department of Gastroenterology, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8641, Japan; Sakai Internal Medicine Clinic, Nonoichi, Ishikawa 921-8825, Japan
| | | | - Chiaki Nomura
- Department of Endocrinology and Metabolism, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8640, Japan
| | - Hisanori Goto
- Department of Endocrinology and Metabolism, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8640, Japan
| | - Yujiro Nakano
- Department of Endocrinology and Metabolism, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8640, Japan
| | - Takeshi Urabe
- Department of Gastroenterology, Public Central Hospital of Matto Ishikawa, 3-8 Kuramitsu, Hakusan, Ishikawa 924-8588, Japan
| | | | - Takuro Tamura
- Research and Development Center for Precision Medicine, University of Tsukuba, Tsukuba 305-8550, Japan
| | | | - Toshinari Takamura
- Department of Endocrinology and Metabolism, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8640, Japan
| | - Shuichi Kaneko
- Information-Based Medicine Development, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8641, Japan; Department of Gastroenterology, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8641, Japan.
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Arehart CH, Sterrett JD, Garris RL, Quispe-Pilco RE, Gignoux CR, Evans LM, Stanislawski MA. Poly-omic risk scores predict inflammatory bowel disease diagnosis. mSystems 2024; 9:e0067723. [PMID: 38095449 PMCID: PMC10805030 DOI: 10.1128/msystems.00677-23] [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: 07/26/2023] [Accepted: 11/02/2023] [Indexed: 01/11/2024] Open
Abstract
Inflammatory bowel disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data, which we apply to study the gut ecosystem in IBD. Specifically, we train and validate models using data on the metagenome, metatranscriptome, virome, and metabolome from the Human Microbiome Project 2 IBD multi-omic database, with 1,785 repeated samples from 130 individuals (103 cases and 27 controls). After splitting the participants into training and testing groups, we used mixed-effects least absolute shrinkage and selection operator regression to select features for each omic. These features, with demographic covariates, were used to generate separate single-omic prediction scores. All four single-omic scores were then combined into a final regression to assess the relative importance of the individual omics and the predictive benefits when considered together. We identified several species, pathways, and metabolites known to be associated with IBD risk, and we explored the connections between data sets. Individually, metabolomic and viromic scores were more predictive than metagenomics or metatranscriptomics, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke's R2 of 0.46 and an area under the curve of 0.80 (95% confidence interval: 0.63, 0.98). Our work supports that some single-omic models for complex traits are more predictive than others, that incorporating multiple omic data sets may improve prediction, and that each omic data type provides a combination of unique and redundant information. This modeling framework can be extended to other complex traits and multi-omic data sets.IMPORTANCEComplex traits are characterized by many biological and environmental factors, such that multi-omic data sets are well-positioned to help us understand their underlying etiologies. We applied a prediction framework across multiple omics (metagenomics, metatranscriptomics, metabolomics, and viromics) from the gut ecosystem to predict inflammatory bowel disease (IBD) diagnosis. The predicted scores from our models highlighted key features and allowed us to compare the relative utility of each omic data set in single-omic versus multi-omic models. Our results emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics for predicting IBD status. The greater predictive capability of metabolomics and viromics is likely because these omics serve as markers of lifestyle factors such as diet. This study provides a modeling framework for multi-omic data, and our results show the utility of combining multiple omic data types to disentangle complex disease etiologies and biological signatures.
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Affiliation(s)
- Christopher H. Arehart
- Interdisciplinary Quantitative Biology PhD Program, University of Colorado, Boulder, Colorado, USA
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA
- Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado, USA
| | - John D. Sterrett
- Interdisciplinary Quantitative Biology PhD Program, University of Colorado, Boulder, Colorado, USA
- Department of Integrative Physiology, University of Colorado, Boulder, Colorado, USA
| | - Rosanna L. Garris
- Interdisciplinary Quantitative Biology PhD Program, University of Colorado, Boulder, Colorado, USA
- Department of Biochemistry, University of Colorado, Boulder, Colorado, USA
| | - Ruth E. Quispe-Pilco
- Interdisciplinary Quantitative Biology PhD Program, University of Colorado, Boulder, Colorado, USA
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA
| | - Christopher R. Gignoux
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Luke M. Evans
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA
- Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado, USA
| | - Maggie A. Stanislawski
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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Vaskimo LM, Gomon G, Naamane N, Cordell HJ, Pratt A, Knevel R. The Application of Genetic Risk Scores in Rheumatic Diseases: A Perspective. Genes (Basel) 2023; 14:2167. [PMID: 38136989 PMCID: PMC10743278 DOI: 10.3390/genes14122167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Modest effect sizes have limited the clinical applicability of genetic associations with rheumatic diseases. Genetic risk scores (GRSs) have emerged as a promising solution to translate genetics into useful tools. In this review, we provide an overview of the recent literature on GRSs in rheumatic diseases. We describe six categories for which GRSs are used: (a) disease (outcome) prediction, (b) genetic commonalities between diseases, (c) disease differentiation, (d) interplay between genetics and environmental factors, (e) heritability and transferability, and (f) detecting causal relationships between traits. In our review of the literature, we identified current lacunas and opportunities for future work. First, the shortage of non-European genetic data restricts the application of many GRSs to European populations. Next, many GRSs are tested in settings enriched for cases that limit the transferability to real life. If intended for clinical application, GRSs are ideally tested in the relevant setting. Finally, there is much to elucidate regarding the co-occurrence of clinical traits to identify shared causal paths and elucidate relationships between the diseases. GRSs are useful instruments for this. Overall, the ever-continuing research on GRSs gives a hopeful outlook into the future of GRSs and indicates significant progress in their potential applications.
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Affiliation(s)
- Lotta M. Vaskimo
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Georgy Gomon
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Najib Naamane
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne NE2 4AX, UK
| | - Heather J. Cordell
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne NE2 4AX, UK
| | - Arthur Pratt
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Department of Rheumatology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE7 7DN, UK
| | - Rachel Knevel
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
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Cai Y, Zhang S, Chen L, Fu Y. Integrated multi-omics and machine learning approach reveals lipid metabolic biomarkers and signaling in age-related meibomian gland dysfunction. Comput Struct Biotechnol J 2023; 21:4215-4227. [PMID: 37675286 PMCID: PMC10480060 DOI: 10.1016/j.csbj.2023.08.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 08/26/2023] [Accepted: 08/26/2023] [Indexed: 09/08/2023] Open
Abstract
Meibomian gland dysfunction (MGD) is a prevalent inflammatory disorder of the ocular surface that significantly impacts patients' vision and quality of life. The underlying mechanism of aging and MGD remains largely uncharacterized. The aim of this work is to investigate lipid metabolic alterations in age-related MGD (ARMGD) through integrated proteomics, lipidomics and machine learning (ML) approach. For this purpose, we collected samples of female mouse meibomian glands (MGs) dissected from eyelids at age two months (n = 9) and two years (n = 9) for proteomic and lipidomic profilings using the liquid chromatography with tandem mass spectrometry (LC-MS/MS) method. To further identify ARMGD-related lipid biomarkers, ML model was established using the least absolute shrinkage and selection operator (LASSO) algorithm. For proteomic profiling, 375 differentially expressed proteins were detected. Functional analyses indicated the leading role of cholesterol biosynthesis in the aging process of MGs. Several proteins were proposed as potential biomarkers, including lanosterol synthase (Lss), 24-dehydrocholesterol reductase (Dhcr24), and farnesyl diphosphate farnesyl transferase 1 (Fdft1). Concomitantly, lipidomic analysis unveiled 47 lipid species that were differentially expressed and clustered into four classes. The most notable age-related alterations involved a decline in cholesteryl esters (ChE) levels and an increase in triradylglycerols (TG) levels, accompanied by significant differences in their lipid unsaturation patterns. Through ML construction, it was confirmed that ChE(26:0), ChE(26:1), and ChE(30:1) represent the most promising diagnostic molecules. The present study identified essential proteins, lipids, and signaling pathways in age-related MGD (ARMGD), providing a reference landscape to facilitate novel strategies for the disease transformation.
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Affiliation(s)
- Yuchen Cai
- Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Siyi Zhang
- Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Liangbo Chen
- Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Yao Fu
- Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
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9
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Ren J, Lin Z, Pan W. Integrating GWAS summary statistics, individual-level genotypic and omic data to enhance the performance for large-scale trait imputation. Hum Mol Genet 2023; 32:2693-2703. [PMID: 37369060 PMCID: PMC10460491 DOI: 10.1093/hmg/ddad097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/23/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
Recently, a non-parametric method has been proposed to impute the genetic component of a trait for a large set of genotyped individuals based on a separate genome-wide association study (GWAS) summary dataset of the same trait (from the same population). The imputed trait may contain linear, non-linear and epistatic effects of genetic variants, thus can be used for downstream linear or non-linear association analyses and machine learning tasks. Here, we propose an extension of the method to impute both genetic and environmental components of a trait using both single nucleotide polymorphism (SNP)-trait and omics-trait association summary data. We illustrate an application to a UK Biobank subset of individuals (n ≈ 80K) with both body mass index (BMI) GWAS data and metabolomic data. We divided the whole dataset into two equally sized and non-overlapping training and test datasets; we used the training data to build SNP- and metabolite-BMI association summary data and impute BMI on the test data. We compared the performance of the original and new imputation methods. As by the original method, the imputed BMI values by the new method largely retained SNP-BMI association information; however, the latter retained more information about BMI-environment associations and were more highly correlated with the original observed BMI values.
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Affiliation(s)
- Jingchen Ren
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Zhaotong Lin
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
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Zhong Y, Peng Y, Lin Y, Chen D, Zhang H, Zheng W, Chen Y, Wu C. MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model. BMC Med Inform Decis Mak 2023; 23:82. [PMID: 37147619 PMCID: PMC10161645 DOI: 10.1186/s12911-023-02173-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 04/11/2023] [Indexed: 05/07/2023] Open
Abstract
BACKGROUND Accurately classifying complex diseases is crucial for diagnosis and personalized treatment. Integrating multi-omics data has been demonstrated to enhance the accuracy of analyzing and classifying complex diseases. This can be attributed to the highly correlated nature of the data with various diseases, as well as the comprehensive and complementary information it provides. However, integrating multi-omics data for complex diseases is challenged by data characteristics such as high imbalance, scale variation, heterogeneity, and noise interference. These challenges further emphasize the importance of developing effective methods for multi-omics data integration. RESULTS We proposed a novel multi-omics data learning model called MODILM, which integrates multiple omics data to improve the classification accuracy of complex diseases by obtaining more significant and complementary information from different single-omics data. Our approach includes four key steps: 1) constructing a similarity network for each omics data using the cosine similarity measure, 2) leveraging Graph Attention Networks to learn sample-specific and intra-association features from similarity networks for single-omics data, 3) using Multilayer Perceptron networks to map learned features to a new feature space, thereby strengthening and extracting high-level omics-specific features, and 4) fusing these high-level features using a View Correlation Discovery Network to learn cross-omics features in the label space, which results in unique class-level distinctiveness for complex diseases. To demonstrate the effectiveness of MODILM, we conducted experiments on six benchmark datasets consisting of miRNA expression, mRNA, and DNA methylation data. Our results show that MODILM outperforms state-of-the-art methods, effectively improving the accuracy of complex disease classification. CONCLUSIONS Our MODILM provides a more competitive way to extract and integrate important and complementary information from multiple omics data, providing a very promising tool for supporting decision-making for clinical diagnosis.
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Affiliation(s)
- Yating Zhong
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China
| | - Yuzhong Peng
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China.
| | - Yanmei Lin
- School of Environment and Life Science, Nanning Normal University, Nanning, 530001, China.
| | - Dingjia Chen
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China
| | - Hao Zhang
- School of Computer Science, Fudan University, Shanghai, 200433, China
- School of Computer, Guangdong University of Petrochemical Technology, Maoming, 525000, China
| | - Wen Zheng
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China
| | - Yuanyuan Chen
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China
| | - Changliang Wu
- Department of Spleen, Stomach and Liver Diseases, Guangxi International Zhuang Medical Hospital, Nanning, 530201, China
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11
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Thu VTA, Dat LD, Jayanti RP, Trinh HKT, Hung TM, Cho YS, Long NP, Shin JG. Advancing personalized medicine for tuberculosis through the application of immune profiling. Front Cell Infect Microbiol 2023; 13:1108155. [PMID: 36844400 PMCID: PMC9950414 DOI: 10.3389/fcimb.2023.1108155] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 01/17/2023] [Indexed: 02/12/2023] Open
Abstract
While early and precise diagnosis is the key to eliminating tuberculosis (TB), conventional methods using culture conversion or sputum smear microscopy have failed to meet demand. This is especially true in high-epidemic developing countries and during pandemic-associated social restrictions. Suboptimal biomarkers have restricted the improvement of TB management and eradication strategies. Therefore, the research and development of new affordable and accessible methods are required. Following the emergence of many high-throughput quantification TB studies, immunomics has the advantages of directly targeting responsive immune molecules and significantly simplifying workloads. In particular, immune profiling has been demonstrated to be a versatile tool that potentially unlocks many options for application in TB management. Herein, we review the current approaches for TB control with regard to the potentials and limitations of immunomics. Multiple directions are also proposed to hopefully unleash immunomics' potential in TB research, not least in revealing representative immune biomarkers to correctly diagnose TB. The immune profiles of patients can be valuable covariates for model-informed precision dosing-based treatment monitoring, prediction of outcome, and the optimal dose prediction of anti-TB drugs.
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Affiliation(s)
- Vo Thuy Anh Thu
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Ly Da Dat
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Rannissa Puspita Jayanti
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Hoang Kim Tu Trinh
- Center for Molecular Biomedicine, University of Medicine and Pharmacy at Ho Chi Minh, Ho Chi Minh City, Vietnam
| | - Tran Minh Hung
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Yong-Soon Cho
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Nguyen Phuoc Long
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea,*Correspondence: Jae-Gook Shin, ; Nguyen Phuoc Long,
| | - Jae-Gook Shin
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea,Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan, Republic of Korea,*Correspondence: Jae-Gook Shin, ; Nguyen Phuoc Long,
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12
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Essential Role of Multi-Omics Approaches in the Study of Retinal Vascular Diseases. Cells 2022; 12:cells12010103. [PMID: 36611897 PMCID: PMC9818611 DOI: 10.3390/cells12010103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
Retinal vascular disease is a highly prevalent vision-threatening ocular disease in the global population; however, its exact mechanism remains unclear. The expansion of omics technologies has revolutionized a new medical research methodology that combines multiple omics data derived from the same patients to generate multi-dimensional and multi-evidence-supported holistic inferences, providing unprecedented opportunities to elucidate the information flow of complex multi-factorial diseases. In this review, we summarize the applications of multi-omics technology to further elucidate the pathogenesis and complex molecular mechanisms underlying retinal vascular diseases. Moreover, we proposed multi-omics-based biomarker and therapeutic strategy discovery methodologies to optimize clinical and basic medicinal research approaches to retinal vascular diseases. Finally, the opportunities, current challenges, and future prospects of multi-omics analyses in retinal vascular disease studies are discussed in detail.
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13
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-Omic Approaches and Treatment Response in Rheumatoid Arthritis. Pharmaceutics 2022; 14:pharmaceutics14081648. [PMID: 36015273 PMCID: PMC9412998 DOI: 10.3390/pharmaceutics14081648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/22/2022] [Accepted: 08/03/2022] [Indexed: 11/17/2022] Open
Abstract
Rheumatoid arthritis (RA) is an inflammatory disorder characterized by an aberrant activation of innate and adaptive immune cells. There are different drugs used for the management of RA, including disease-modifying antirheumatic drugs (DMARDs). However, a significant percentage of RA patients do not initially respond to DMARDs. This interindividual variation in drug response is caused by a combination of environmental, genetic and epigenetic factors. In this sense, recent -omic studies have evidenced different molecular signatures involved in this lack of response. The aim of this review is to provide an updated overview of the potential role of -omic approaches, specifically genomics, epigenomics, transcriptomics, and proteomics, to identify molecular biomarkers to predict the clinical efficacy of therapies currently used in this disorder. Despite the great effort carried out in recent years, to date, there are still no validated biomarkers of response to the drugs currently used in RA. -Omic studies have evidenced significant differences in the molecular profiles associated with treatment response for the different drugs used in RA as well as for different cell types. Therefore, global and cell type-specific -omic studies analyzing response to the complete therapeutical arsenal used in RA, including less studied therapies, such as sarilumab and JAK inhibitors, are greatly needed.
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Bar-Or A, Li R. Multiple sclerosis meets systems immunology - Authors' reply. Lancet Neurol 2021; 20:888. [PMID: 34687629 DOI: 10.1016/s1474-4422(21)00327-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 09/21/2021] [Indexed: 11/19/2022]
Affiliation(s)
- Amit Bar-Or
- Center for Neuroinflammation and Experimental Therapeutics and Department of Neurology, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Rui Li
- Center for Neuroinflammation and Experimental Therapeutics and Department of Neurology, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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INOMATA TAKENORI, SUNG JAEMYOUNG, NAKAMURA MASAHIRO, IWAGAMI MASAO, OKUMURA YUICHI, FUJIO KENTA, AKASAKI YASUTSUGU, FUJIMOTO KEIICHI, YANAGAWA AI, MIDORIKAWA-INOMATA AKIE, NAGINO KEN, EGUCHI ATSUKO, SHOKIROVA HURRRAMHON, ZHU JUN, MIURA MARIA, KUWAHARA MIZU, HIROSAWA KUNIHIKO, HUANG TIANXING, MOROOKA YUKI, MURAKAMI AKIRA. Cross-hierarchical Integrative Research Network for Heterogenetic Eye Disease Toward P4 Medicine: A Narrative Review. JUNTENDO MEDICAL JOURNAL 2021. [DOI: 10.14789/jmj.jmj21-0023-r] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- TAKENORI INOMATA
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - JAEMYOUNG SUNG
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - MASAHIRO NAKAMURA
- Department of Digital Medicine, Juntendo University Graduate School of Medicine
| | - MASAO IWAGAMI
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba
| | - YUICHI OKUMURA
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - KENTA FUJIO
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - YASUTSUGU AKASAKI
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - KEIICHI FUJIMOTO
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - AI YANAGAWA
- Department of Digital Medicine, Juntendo University Graduate School of Medicine
| | | | - KEN NAGINO
- Department of Hospital Administration, Juntendo University Graduate School of Medicine
| | - ATSUKO EGUCHI
- Department of Hospital Administration, Juntendo University Graduate School of Medicine
| | | | - JUN ZHU
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - MARIA MIURA
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - MIZU KUWAHARA
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - KUNIHIKO HIROSAWA
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - TIANXING HUANG
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - YUKI MOROOKA
- Department of Digital Medicine, Juntendo University Graduate School of Medicine
| | - AKIRA MURAKAMI
- Department of Digital Medicine, Juntendo University Graduate School of Medicine
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