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Snanoudj S, Derambure C, Zhang C, Hai Yen NT, Lesueur C, Coutant S, Abily-Donval L, Marret S, Yang H, Mardinoglu A, Bekri S, Tebani A. Genome-wide expression analysis in a Fabry disease human podocyte cell line. Heliyon 2024; 10:e34357. [PMID: 39100494 PMCID: PMC11295972 DOI: 10.1016/j.heliyon.2024.e34357] [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: 03/14/2024] [Revised: 07/08/2024] [Accepted: 07/08/2024] [Indexed: 08/06/2024] Open
Abstract
Fabry disease (FD) is an X-linked lysosomal disease caused by an enzyme deficiency of alpha-galactosidase A (α-gal A). This deficiency leads to the accumulation of glycosphingolipids in lysosomes, resulting in a range of clinical symptoms. The complex pathogenesis of FD involves lysosomal dysfunction, altered autophagy, and mitochondrial abnormalities. Omics sciences, particularly transcriptomic analysis, comprehensively understand molecular mechanisms underlying diseases. This study focuses on genome-wide expression analysis in an FD human podocyte model to gain insights into the underlying mechanisms of podocyte dysfunction. Human control and GLA-edited podocytes were used. Gene expression data was generated using RNA-seq analysis, and differentially expressed genes were identified using DESeq2. Principal component analysis and Spearman correlation have explored gene expression trends. Functional enrichment and Reporter metabolite analyses were conducted to identify significantly affected metabolites and metabolic pathways. Differential expression analysis revealed 247 genes with altered expression levels in GLA-edited podocytes compared to control podocytes. Among these genes, 136 were underexpressed, and 111 were overexpressed in GLA-edited cells. Functional analysis of differentially expressed genes showed their involvement in various pathways related to oxidative stress, inflammation, fatty acid metabolism, collagen and extracellular matrix homeostasis, kidney injury, apoptosis, autophagy, and cellular stress response. The study provides insights into molecular mechanisms underlying Fabry podocyte dysfunction. Integrating transcriptomics data with genome-scale metabolic modeling further unveiled metabolic alterations in GLA-edited podocytes. This comprehensive approach contributes to a better understanding of Fabry disease and may lead to identifying new biomarkers and therapeutic targets for this rare lysosomal disorder.
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Affiliation(s)
- Sarah Snanoudj
- Normandie Univ, UNIROUEN, INSERM, U1245, CHU Rouen, Department of Metabolic Biochemistry, Referral Center for Lysosomal Diseases, Filière G2M, 76000, Rouen, France
| | - Céline Derambure
- Normandie Univ, UNIROUEN, INSERM U1245 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, FHU-G4 Génomique, F-76000, Rouen, France
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Nguyen Thi Hai Yen
- Normandie Univ, UNIROUEN, INSERM, U1245, CHU Rouen, Department of Metabolic Biochemistry, Referral Center for Lysosomal Diseases, Filière G2M, 76000, Rouen, France
| | - Céline Lesueur
- Normandie Univ, UNIROUEN, INSERM, U1245, CHU Rouen, Department of Metabolic Biochemistry, Referral Center for Lysosomal Diseases, Filière G2M, 76000, Rouen, France
| | - Sophie Coutant
- Normandie Univ, UNIROUEN, INSERM U1245 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, FHU-G4 Génomique, F-76000, Rouen, France
| | - Lénaïg Abily-Donval
- Normandie Univ, UNIROUEN, INSERM, U1245, CHU Rouen, Department of Neonatal Pediatrics, Intensive Care, and Neuropediatrics, 76000, Rouen, France
| | - Stéphane Marret
- Normandie Univ, UNIROUEN, INSERM, U1245, CHU Rouen, Department of Neonatal Pediatrics, Intensive Care, and Neuropediatrics, 76000, Rouen, France
| | - Hong Yang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom
| | - Soumeya Bekri
- Normandie Univ, UNIROUEN, INSERM, U1245, CHU Rouen, Department of Metabolic Biochemistry, Referral Center for Lysosomal Diseases, Filière G2M, 76000, Rouen, France
| | - Abdellah Tebani
- Normandie Univ, UNIROUEN, INSERM, U1245, CHU Rouen, Department of Metabolic Biochemistry, Referral Center for Lysosomal Diseases, Filière G2M, 76000, Rouen, France
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2
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Glinton KE, Gijavanekar C, Rajagopal A, Mackay LP, Martin KA, Pearl PL, Gibson KM, Wilson TA, Sutton VR, Elsea SH. Succinic semialdehyde dehydrogenase deficiency: a metabolic and genomic approach to diagnosis. Front Genet 2024; 15:1405468. [PMID: 39011401 PMCID: PMC11247174 DOI: 10.3389/fgene.2024.1405468] [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: 03/22/2024] [Accepted: 05/02/2024] [Indexed: 07/17/2024] Open
Abstract
Genomic sequencing offers an untargeted, data-driven approach to genetic diagnosis; however, variants of uncertain significance often hinder the diagnostic process. The discovery of rare genomic variants without previously known functional evidence of pathogenicity often results in variants being overlooked as potentially causative, particularly in individuals with undifferentiated phenotypes. Consequently, many neurometabolic conditions, including those in the GABA (gamma-aminobutyric acid) catabolism pathway, are underdiagnosed. Succinic semialdehyde dehydrogenase deficiency (SSADHD, OMIM #271980) is a neurometabolic disorder in the GABA catabolism pathway. The disorder is due to bi-allelic pathogenic variants in ALDH5A1 and is usually characterized by moderate-to-severe developmental delays, hypotonia, intellectual disability, ataxia, seizures, hyperkinetic behavior, aggression, psychiatric disorders, and sleep disturbances. In this study, we utilized an integrated approach to diagnosis of SSADHD by examining molecular, clinical, and metabolomic data from a single large commercial laboratory. Our analysis led to the identification of 16 patients with likely SSADHD along with three novel variants. We also showed that patients with this disorder have a clear metabolomic signature that, along with molecular and clinical findings, may allow for more rapid and efficient diagnosis. We further surveyed all available pathogenic/likely pathogenic variants and used this information to estimate the global prevalence of this disease. Taken together, our comprehensive analysis allows for a global approach to the diagnosis of SSADHD and provides a pathway to improved diagnosis and potential incorporation into newborn screening programs. Furthermore, early diagnosis facilitates referral to genetic counseling, family support, and access to targeted treatments-taken together, these provide the best outcomes for individuals living with either GABA-TD or SSADHD, as well as other rare conditions.
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Affiliation(s)
- Kevin E. Glinton
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | - Charul Gijavanekar
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | - Abbhirami Rajagopal
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | - Laura P. Mackay
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | - Kirt A. Martin
- NeoGenomics Laboratories, Aliso Viejo, CA, United States
| | - Phillip L. Pearl
- Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - K. Michael Gibson
- Department of Pharmacotherapy, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, WA, United States
| | - Theresa A. Wilson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | - V. Reid Sutton
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
- Baylor Genetics Laboratories, Houston, TX, United States
| | - Sarah H. Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
- Baylor Genetics Laboratories, Houston, TX, United States
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3
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Kernohan KD, Boycott KM. The expanding diagnostic toolbox for rare genetic diseases. Nat Rev Genet 2024; 25:401-415. [PMID: 38238519 DOI: 10.1038/s41576-023-00683-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/22/2023] [Indexed: 05/23/2024]
Abstract
Genomic technologies, such as targeted, exome and short-read genome sequencing approaches, have revolutionized the care of patients with rare genetic diseases. However, more than half of patients remain without a diagnosis. Emerging approaches from research-based settings such as long-read genome sequencing and optical genome mapping hold promise for improving the identification of disease-causal genetic variants. In addition, new omic technologies that measure the transcriptome, epigenome, proteome or metabolome are showing great potential for variant interpretation. As genetic testing options rapidly expand, the clinical community needs to be mindful of their individual strengths and limitations, as well as remaining challenges, to select the appropriate diagnostic test, correctly interpret results and drive innovation to address insufficiencies. If used effectively - through truly integrative multi-omics approaches and data sharing - the resulting large quantities of data from these established and emerging technologies will greatly improve the interpretative power of genetic and genomic diagnostics for rare diseases.
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Affiliation(s)
- Kristin D Kernohan
- CHEO Research Institute, University of Ottawa, Ottawa, ON, Canada
- Newborn Screening Ontario, CHEO, Ottawa, ON, Canada
| | - Kym M Boycott
- CHEO Research Institute, University of Ottawa, Ottawa, ON, Canada.
- Department of Genetics, CHEO, Ottawa, ON, Canada.
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4
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Yurkovich JT, Evans SJ, Rappaport N, Boore JL, Lovejoy JC, Price ND, Hood LE. The transition from genomics to phenomics in personalized population health. Nat Rev Genet 2024; 25:286-302. [PMID: 38093095 DOI: 10.1038/s41576-023-00674-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2023] [Indexed: 03/21/2024]
Abstract
Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.
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Affiliation(s)
- James T Yurkovich
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Simon J Evans
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Noa Rappaport
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Jeffrey L Boore
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Jennifer C Lovejoy
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA
- Thorne HealthTech, New York, NY, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Leroy E Hood
- Phenome Health, Seattle, WA, USA.
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA.
- Institute for Systems Biology, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
- Department of Immunology, University of Washington, Seattle, WA, USA.
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5
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Napolitano G, Has C, Schwerk A, Yuan JH, Ullrich C. Potential of Artificial Intelligence to Accelerate Drug Development for Rare Diseases. Pharmaceut Med 2024; 38:79-86. [PMID: 38315404 DOI: 10.1007/s40290-023-00504-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2023] [Indexed: 02/07/2024]
Abstract
The growth in breadth and depth of artificial intelligence (AI) applications has been fast, running hand in hand with the increasing amount of digital data available. Here, we comment on the application of AI in the field of drug development, with a strong focus on the specific achievements and challenges posed by rare diseases. Data paucity and high costs make drug development for rare diseases especially hard. AI can enable otherwise inaccessible approaches based on the large-scale integration of heterogeneous datasets and knowledge bases, guided by expert biological understanding. Obstacles still exist for the routine use of AI in the usually conservative pharmaceutical domain, which can easily become disillusioned. It is crucial to acknowledge that AI is a powerful, supportive tool that can assist but not replace human expertise in the various phases and aspects of drug discovery and development.
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Affiliation(s)
| | - Canan Has
- Centogene GmbH, Alboinstraße 36-42, 12103, Berlin, Germany
| | - Anne Schwerk
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jui-Hung Yuan
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Carsten Ullrich
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
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6
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Licata L, Via A, Turina P, Babbi G, Benevenuta S, Carta C, Casadio R, Cicconardi A, Facchiano A, Fariselli P, Giordano D, Isidori F, Marabotti A, Martelli PL, Pascarella S, Pinelli M, Pippucci T, Russo R, Savojardo C, Scafuri B, Valeriani L, Capriotti E. Resources and tools for rare disease variant interpretation. Front Mol Biosci 2023; 10:1169109. [PMID: 37234922 PMCID: PMC10206239 DOI: 10.3389/fmolb.2023.1169109] [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: 02/18/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.
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Affiliation(s)
- Luana Licata
- Department of Biology, University of Rome Tor Vergata, Roma, Italy
| | - Allegra Via
- Department of Biochemical Sciences “A. Rossi Fanelli”, University of Rome “La Sapienza”, Roma, Italy
| | - Paola Turina
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Giulia Babbi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | | | - Claudio Carta
- National Centre for Rare Diseases, Istituto Superiore di Sanità, Roma, Italy
| | - Rita Casadio
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Andrea Cicconardi
- Department of Physics, University of Genova, Genova, Italy
- Italiano di Tecnologia—IIT, Genova, Italy
| | - Angelo Facchiano
- National Research Council, Institute of Food Science, Avellino, Italy
| | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Torino, Italy
| | - Deborah Giordano
- National Research Council, Institute of Food Science, Avellino, Italy
| | - Federica Isidori
- Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Anna Marabotti
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, Fisciano, SA, Italy
| | - Pier Luigi Martelli
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Stefano Pascarella
- Department of Biochemical Sciences “A. Rossi Fanelli”, University of Rome “La Sapienza”, Roma, Italy
| | - Michele Pinelli
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Napoli, Italy
| | - Tommaso Pippucci
- Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Roberta Russo
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Napoli, Italy
- CEINGE Biotecnologie Avanzate Franco Salvatore, Napoli, Italy
| | - Castrense Savojardo
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Bernardina Scafuri
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, Fisciano, SA, Italy
| | | | - Emidio Capriotti
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
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Yang J, Shu L, Duan H, Li H. A Robust Phenotype-driven Likelihood Ratio Analysis Approach Assisting Interpretable Clinical Diagnosis of Rare Diseases. J Biomed Inform 2023; 142:104372. [PMID: 37105510 DOI: 10.1016/j.jbi.2023.104372] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/20/2023] [Accepted: 04/20/2023] [Indexed: 04/29/2023]
Abstract
Phenotype-based prioritization of candidate genes and diseases has become a well-established approach for multi-omics diagnostics of rare diseases. Most current algorithms exploit semantic analysis and probabilistic statistics based on Human Phenotype Ontology and are commonly superior to naive search methods. However, these algorithms are mostly less interpretable and do not perform well in real clinical scenarios due to noise and imprecision of query terms, and the fact that individuals may not display all phenotypes of the disease they belong to. We present a Phenotype-driven Likelihood Ratio analysis approach (PheLR) assisting interpretable clinical diagnosis of rare diseases. With a likelihood ratio paradigm, PheLR estimates the posterior probability of candidate diseases and how much a phenotypic feature contributes to the prioritization result. Benchmarked using simulated and realistic patients, PheLR shows significant advantages over current approaches and is robust to noise and inaccuracy. To facilitate clinical practice and visualized differential diagnosis, PheLR is implemented as an online web tool (http://phelr.nbscn.org).
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Affiliation(s)
- Jian Yang
- Clinical Data Center, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang, China; The College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhejiang, China
| | - Liqi Shu
- Rhode Island Hospital, Warren Alpert Medical School of Brown University, Rhode Island, USA
| | - Huilong Duan
- The College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhejiang, China
| | - Haomin Li
- Clinical Data Center, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang, China.
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8
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WGS Data Collections: How Do Genomic Databases Transform Medicine? Int J Mol Sci 2023; 24:ijms24033031. [PMID: 36769353 PMCID: PMC9917848 DOI: 10.3390/ijms24033031] [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: 12/30/2022] [Revised: 01/23/2023] [Accepted: 01/26/2023] [Indexed: 02/09/2023] Open
Abstract
As a scientific community we assumed that exome sequencing will elucidate the basis of most heritable diseases. However, it turned out it was not the case; therefore, attention has been increasingly focused on the non-coding sequences that encompass 98% of the genome and may play an important regulatory function. The first WGS-based datasets have already been released including underrepresented populations. Although many databases contain pooled data from several cohorts, recently the importance of local databases has been highlighted. Genomic databases are not only collecting data but may also contribute to better diagnostics and therapies. They may find applications in population studies, rare diseases, oncology, pharmacogenetics, and infectious and inflammatory diseases. Further data may be analysed with Al technologies and in the context of other omics data. To exemplify their utility, we put a highlight on the Polish genome database and its practical application.
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Benito-Lozano J, López-Villalba B, Arias-Merino G, Posada de la Paz M, Alonso-Ferreira V. Diagnostic delay in rare diseases: data from the Spanish rare diseases patient registry. Orphanet J Rare Dis 2022; 17:418. [PMID: 36397119 PMCID: PMC9670379 DOI: 10.1186/s13023-022-02530-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 09/30/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND According to the International Rare Diseases Research Consortium (IRDiRC), a known rare disease (RD) should be diagnosable within a year. This study sought: firstly, to ascertain how long it takes to obtain the diagnosis of a RD in Spain, along with its associated time trend; and secondly, to identify and measure diagnostic delay (defined by the IRDiRC as any period exceeding a year) by reference to the characteristics of RDs and the persons affected by them. METHODS Using data sourced from the Spanish Rare Diseases Patient Registry, we performed a descriptive analysis of the time elapsed between symptom onset and diagnosis of each RD, by sex, age and date of symptom onset, and type of RD. We analysed the time trend across the period 1960-2021 and possible change points, using a Joinpoint regression model and assuming a Poisson distribution. The multivariate analysis was completed with backward stepwise logistic regression. RESULTS Detailed information was obtained on 3304 persons with RDs: 56.4% had experienced delay in diagnosis of their RDs, with the mean time taken being 6.18 years (median = 2; IQR 0.2-7.5). Both the percentage of patients with diagnostic delay and the average time to diagnosis underwent a significant reduction across the study period (p < 0.001). There was a higher percentage of diagnostic delays: in women (OR 1.25; 95% CI 1.07-1.45); in cases with symptom onset at age 30-44 years (OR 1.48; 95% CI 1.19-1.84): and when analysed by type of RD, in mental and behavioural disorders (OR 4.21; 95% CI 2.26-7.85), followed by RDs of the nervous system (OR 1.39; 95% CI 1.02-1.88). CONCLUSIONS This is the first study to quantify time to diagnosis of RDs in Spain, based on data from a national registry open to any RD. Since over half of all persons affected by RDs experience delay in diagnosis, new studies are needed to ascertain the factors associated with this delay and the implications this has on the lives of patients and their families.
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Affiliation(s)
- Juan Benito-Lozano
- grid.413448.e0000 0000 9314 1427Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain ,grid.10702.340000 0001 2308 8920Universidad Nacional de Educación a Distancia (UNED), 28015 Madrid, Spain
| | - Blanca López-Villalba
- grid.411057.60000 0000 9274 367XPreventive Medicine and Public Health, Hospital Clínico Universitario de Valladolid (HCUV), 47003 Valladolid, Spain
| | - Greta Arias-Merino
- grid.413448.e0000 0000 9314 1427Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Manuel Posada de la Paz
- grid.413448.e0000 0000 9314 1427Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Verónica Alonso-Ferreira
- grid.413448.e0000 0000 9314 1427Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain ,grid.413448.e0000 0000 9314 1427Centre for Biomedical Network Research On Rare Diseases (CIBERER), Instituto de Salud Carlos III, 28029 Madrid, Spain
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10
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Denton N, Mulberg AE, Molloy M, Charleston S, Fajgenbaum DC, Marsh ED, Howard P. Sharing is caring: a call for a new era of rare disease research and development. Orphanet J Rare Dis 2022; 17:389. [PMID: 36303170 PMCID: PMC9612604 DOI: 10.1186/s13023-022-02529-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 08/05/2022] [Accepted: 10/02/2022] [Indexed: 01/25/2023] Open
Abstract
Scientific advances in the understanding of the genetics and mechanisms of many rare diseases with previously unknown etiologies are inspiring optimism in the patient, clinical, and research communities and there is hope that disease-specific treatments are on the way. However, the rare disease community has reached a critical point in which its increasingly fragmented structure and operating models are threatening its ability to harness the full potential of advancing genomic and computational technologies. Changes are therefore needed to overcome these issues plaguing many rare diseases while also supporting economically viable therapy development. In "Data silos are undermining drug development and failing rare disease patients (Orphanet Journal of Rare Disease, Apr 2021)," we outlined many of the broad issues underpinning the increasingly fragmented and siloed nature of the rare disease space, as well as how the issues encountered by this community are representative of biomedical research more generally. Here, we propose several initiatives for key stakeholders - including regulators, private and public foundations, and research institutions - to reorient the rare disease ecosystem and its incentives in a way that we believe would cultivate and accelerate innovation. Specifically, we propose supporting non-proprietary patient registries, greater data standardization, global regulatory harmonization, and new business models that encourage data sharing and research collaboration as the default mode. Leadership needs to be integrated across sectors to drive meaningful change between patients, industry, sponsors, and academic medical centers. To transform the research and development landscape and unlock its vast healthcare, economic, and scientific potential for rare disease patients, a new model is ultimately the goal for all.
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Affiliation(s)
- Nathan Denton
- grid.25879.310000 0004 1936 8972Gene Therapy Program, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA ,grid.427771.00000 0004 0619 7027Amicus Therapeutics, Philadelphia, PA 19104 USA
| | | | - Monique Molloy
- grid.25879.310000 0004 1936 8972Department of Medicine, Orphan Disease Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Samantha Charleston
- grid.25879.310000 0004 1936 8972Department of Medicine, Orphan Disease Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - David C. Fajgenbaum
- grid.25879.310000 0004 1936 8972Department of Medicine, Orphan Disease Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA ,grid.25879.310000 0004 1936 8972Translational Medicine & Human Genetics, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA 19104 USA
| | - Eric D. Marsh
- grid.25879.310000 0004 1936 8972Department of Medicine, Orphan Disease Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA ,grid.25879.310000 0004 1936 8972Departments of Neurology and Pediatrics, Perelman School of Medicine, Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA 19104 USA ,grid.239552.a0000 0001 0680 8770Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA ,grid.427771.00000 0004 0619 7027Amicus Therapeutics, Philadelphia, PA 19104 USA
| | - Paul Howard
- grid.427771.00000 0004 0619 7027Amicus Therapeutics, Philadelphia, PA 19104 USA
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11
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Hill C, Avila-Palencia I, Maxwell AP, Hunter RF, McKnight AJ. Harnessing the Full Potential of Multi-Omic Analyses to Advance the Study and Treatment of Chronic Kidney Disease. FRONTIERS IN NEPHROLOGY 2022; 2:923068. [PMID: 37674991 PMCID: PMC10479694 DOI: 10.3389/fneph.2022.923068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 05/30/2022] [Indexed: 09/08/2023]
Abstract
Chronic kidney disease (CKD) was the 12th leading cause of death globally in 2017 with the prevalence of CKD estimated at ~9%. Early detection and intervention for CKD may improve patient outcomes, but standard testing approaches even in developed countries do not facilitate identification of patients at high risk of developing CKD, nor those progressing to end-stage kidney disease (ESKD). Recent advances in CKD research are moving towards a more personalised approach for CKD. Heritability for CKD ranges from 30% to 75%, yet identified genetic risk factors account for only a small proportion of the inherited contribution to CKD. More in depth analysis of genomic sequencing data in large cohorts is revealing new genetic risk factors for common diagnoses of CKD and providing novel diagnoses for rare forms of CKD. Multi-omic approaches are now being harnessed to improve our understanding of CKD and explain some of the so-called 'missing heritability'. The most common omic analyses employed for CKD are genomics, epigenomics, transcriptomics, metabolomics, proteomics and phenomics. While each of these omics have been reviewed individually, considering integrated multi-omic analysis offers considerable scope to improve our understanding and treatment of CKD. This narrative review summarises current understanding of multi-omic research alongside recent experimental and analytical approaches, discusses current challenges and future perspectives, and offers new insights for CKD.
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Affiliation(s)
| | | | | | | | - Amy Jayne McKnight
- Centre for Public Health, Queen’s University Belfast, Belfast, United Kingdom
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12
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A Formative Study of the Implementation of Whole Genome Sequencing in Northern Ireland. Genes (Basel) 2022; 13:genes13071104. [PMID: 35885887 PMCID: PMC9316942 DOI: 10.3390/genes13071104] [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: 05/16/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 02/05/2023] Open
Abstract
Background: The UK 100,000 Genomes Project was a transformational research project which facilitated whole genome sequencing (WGS) diagnostics for rare diseases. We evaluated experiences of introducing WGS in Northern Ireland, providing recommendations for future projects. Methods: This formative evaluation included (1) an appraisal of the logistics of implementing and delivering WGS, (2) a survey of participant self-reported views and experiences, (3) semi-structured interviews with healthcare staff as key informants who were involved in the delivery of WGS and (4) a workshop discussion about interprofessional collaboration with respect to molecular diagnostics. Results: We engaged with >400 participants, with detailed reflections obtained from 74 participants including patients, caregivers, key National Health Service (NHS) informants, and researchers (patient survey n = 42; semi-structured interviews n = 19; attendees of the discussion workshop n = 13). Overarching themes included the need to improve rare disease awareness, education, and support services, as well as interprofessional collaboration being central to an effective, mainstreamed molecular diagnostic service. Conclusions: Recommendations for streamlining precision medicine for patients with rare diseases include administrative improvements (e.g., streamlining of the consent process), educational improvements (e.g., rare disease training provided from undergraduate to postgraduate education alongside genomics training for non-genetic specialists) and analytical improvements (e.g., multidisciplinary collaboration and improved computational infrastructure).
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13
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New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches. Int J Mol Sci 2022; 23:ijms23126792. [PMID: 35743235 PMCID: PMC9224427 DOI: 10.3390/ijms23126792] [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: 04/30/2022] [Revised: 06/13/2022] [Accepted: 06/15/2022] [Indexed: 11/21/2022] Open
Abstract
Rare diseases impact the lives of 300 million people in the world. Rapid advances in bioinformatics and genomic technologies have enabled the discovery of causes of 20–30% of rare diseases. However, most rare diseases have remained as unsolved enigmas to date. Newer tools and availability of high throughput sequencing data have enabled the reanalysis of previously undiagnosed patients. In this review, we have systematically compiled the latest developments in the discovery of the genetic causes of rare diseases using machine learning methods. Importantly, we have detailed methods available to reanalyze existing whole exome sequencing data of unsolved rare diseases. We have identified different reanalysis methodologies to solve problems associated with sequence alterations/mutations, variation re-annotation, protein stability, splice isoform malfunctions and oligogenic analysis. In addition, we give an overview of new developments in the field of rare disease research using whole genome sequencing data and other omics.
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14
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Groft SC, Posada M, Taruscio D. Progress, challenges and global approaches to rare diseases. Acta Paediatr 2021; 110:2711-2716. [PMID: 34105798 DOI: 10.1111/apa.15974] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 06/01/2021] [Accepted: 06/07/2021] [Indexed: 12/21/2022]
Abstract
Rare diseases occur globally at every stage of life. Patients, families and caregivers have many unmet medical and social needs leading to extraordinary psychosocial and economic burdens. Efforts to improve diagnostic capabilities and to develop therapies for an estimated 7000 rare diseases have met with considerable success. In the United States, a rare disease or condition is one affecting fewer than 200,000 people. In the European Union (EU), a rare disease is any disease affecting fewer than 5 people in 10,000 (less than 1 in 2000 people). However, there are no effective treatments for 90 per cent of rare diseases. There is a need to expand awareness, advocacy and outreach to everyone including those with low incomes, poor literacy, minority ethnic status and living in underserved and marginalised populations in urban and rural areas as well as in developing nations throughout the world. The acceptance of patients as research partners complements the increased research emphasis and major regulatory initiatives leading to expedited review and approval programmes for products for serious or life-threatening conditions. The pipeline of new therapies provides hope to untreated patients. Advances in medical bioinformatics, artificial intelligence and machine learning with access to big data continue to identify novel therapeutics for screening and evaluation. Advanced analytics can identify the patterns of disease occurrence, predict disease progression, identify patient response to treatments, establish optimal care guidelines and generate research hypotheses with the narrowly identified research patient populations.
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Affiliation(s)
- Stephen C. Groft
- National Center for Advancing Translational Sciences National Institutes of Health Bethesda MD USA
| | - Manuel Posada
- Institute of Rare Diseases Research (IIER) Instituto de Salud Carlos III Madrid Spain
| | - Domenica Taruscio
- National Centre for Rare Diseases Istituto Superiore di Sanità Rome Italy
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15
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Abstract
[Figure: see text].
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Affiliation(s)
- Tuuli Lappalainen
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.,New York Genome Center, New York, NY, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, and UNSW Sydney, Sydney, New South Wales, Australia.,Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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16
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de Castro VF, Mattos D, de Carvalho FM, Cavalcanti DP, Duenas-Roque MM, Llerena J, Cosentino VR, Honjo RS, Leite JCL, Sanseverino MT, de Souza MPA, Bernardi P, Bolognese AM, Santana da Silva LC, Barbero P, Correia PS, Bueno LSM, Savastano CP, Orioli IM. New SHH and Known SIX3 Variants in a Series of Latin American Patients with Holoprosencephaly. Mol Syndromol 2021; 12:219-233. [PMID: 34421500 DOI: 10.1159/000515044] [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: 07/03/2020] [Accepted: 02/04/2021] [Indexed: 11/19/2022] Open
Abstract
Holoprosencephaly (HPE) is the failure of the embryonic forebrain to develop into 2 hemispheres promoting midline cerebral and facial defects. The wide phenotypic variability and causal heterogeneity make genetic counseling difficult. Heterozygous variants with incomplete penetrance and variable expressivity in the SHH, SIX3, ZIC2, and TGIF1 genes explain ∼25% of the known causes of nonchromosomal HPE. We studied these 4 genes and clinically described 27 Latin American families presenting with nonchromosomal HPE. Three new SHH variants and a third known SIX3 likely pathogenic variant found by Sanger sequencing explained 15% of our cases. Genotype-phenotype correlation in these 4 families and published families with identical or similar driver gene, mutated domain, conservation of residue in other species, and the type of variant explain the pathogenicity but not the phenotypic variability. Nine patients, including 2 with SHH pathogenic variants, presented benign variants of the SHH, SIX3, ZIC2, and TGIF1 genes with potential alteration of splicing, a causal proposition in need of further studies. Finding more families with the same SIX3 variant may allow further identification of genetic or environmental modifiers explaining its variable phenotypic expression.
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Affiliation(s)
- Viviane Freitas de Castro
- ECLAMC at Departamento de Genética, UFRJ, Rio de Janeiro, Brazil.,Instituto Nacional de Genética Médica Populacional INAGEMP, Porto Alegre, Brazil
| | - Daniel Mattos
- ECLAMC at Departamento de Genética, UFRJ, Rio de Janeiro, Brazil.,Instituto Nacional de Genética Médica Populacional INAGEMP, Porto Alegre, Brazil
| | - Flavia Martinez de Carvalho
- Instituto Nacional de Genética Médica Populacional INAGEMP, Porto Alegre, Brazil.,ECLAMC at Laboratorio Epidemiol. Malformações Congênitas, IOC/FIOCRUZ, Rio de Janeiro, Brazil
| | | | - Milagros M Duenas-Roque
- ECLAMC at Servicio de Genética, Hospital Nacional Edgardo Rebagliati Martins/EsSalud, Lima, Peru
| | - Juan Llerena
- Instituto Nacional de Genética Médica Populacional INAGEMP, Porto Alegre, Brazil.,ECLAMC at Centro de Genética Médica, IFF/FIOCRUZ, Rio de Janeiro, Brazil
| | | | | | | | | | | | - Pricila Bernardi
- Núcleo de Genética Clínica, Departamento de Clínica Médica/UFSC, Florianópolis, Brazil
| | - Ana Maria Bolognese
- Departamento de Ortodontia, Faculdade de Odontologia/UFRJ, Rio de Janeiro, Brazil
| | - Luiz Carlos Santana da Silva
- Instituto Nacional de Genética Médica Populacional INAGEMP, Porto Alegre, Brazil.,Laboratório de Erros Inatos de Metabolismo, Instituto de Ciências Biológicas/UFP, Belém, Brazil
| | - Pablo Barbero
- RENAC, Centro Nacional de Genética Médica Dr. Eduardo E. Castilla/MS, Buenos Aires, Argentina
| | | | | | | | - Iêda Maria Orioli
- ECLAMC at Departamento de Genética, UFRJ, Rio de Janeiro, Brazil.,Instituto Nacional de Genética Médica Populacional INAGEMP, Porto Alegre, Brazil
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17
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Brasil S, Neves CJ, Rijoff T, Falcão M, Valadão G, Videira PA, Dos Reis Ferreira V. Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases. Front Mol Biosci 2021; 8:648012. [PMID: 34026829 PMCID: PMC8131862 DOI: 10.3389/fmolb.2021.648012] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 04/09/2021] [Indexed: 12/29/2022] Open
Abstract
More than 7,000 rare diseases (RDs) exist worldwide, affecting approximately 350 million people, out of which only 5% have treatment. The development of novel genome sequencing techniques has accelerated the discovery and diagnosis in RDs. However, most patients remain undiagnosed. Epigenetics has emerged as a promise for diagnosis and therapies in common disorders (e.g., cancer) with several epimarkers and epidrugs already approved and used in clinical practice. Hence, it may also become an opportunity to uncover new disease mechanisms and therapeutic targets in RDs. In this “big data” age, the amount of information generated, collected, and managed in (bio)medicine is increasing, leading to the need for its rapid and efficient collection, analysis, and characterization. Artificial intelligence (AI), particularly deep learning, is already being successfully applied to analyze genomic information in basic research, diagnosis, and drug discovery and is gaining momentum in the epigenetic field. The application of deep learning to epigenomic studies in RDs could significantly boost discovery and therapy development. This review aims to collect and summarize the application of AI tools in the epigenomic field of RDs. The lower number of studies found, specific for RDs, indicate that this is a field open to expansion, following the results obtained for other more common disorders.
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Affiliation(s)
- Sandra Brasil
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
| | - Cátia José Neves
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
| | - Tatiana Rijoff
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
| | - Marta Falcão
- UCIBIO, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Gonçalo Valadão
- Instituto de Telecomunicações, Lisbon, Portugal.,Departamento de Ciências e Tecnologias, Autónoma Techlab - Universidade Autónoma de Lisboa, Lisbon, Portugal.,Electronics, Telecommunications and Computers Engineering Department, Instituto Superior de Engenharia de Lisboa, Lisbon, Portugal
| | - Paula A Videira
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal.,UCIBIO, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Vanessa Dos Reis Ferreira
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
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18
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Yahia A, Stevanin G. The History of Gene Hunting in Hereditary Spinocerebellar Degeneration: Lessons From the Past and Future Perspectives. Front Genet 2021; 12:638730. [PMID: 33833777 PMCID: PMC8021710 DOI: 10.3389/fgene.2021.638730] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 03/02/2021] [Indexed: 01/02/2023] Open
Abstract
Hereditary spinocerebellar degeneration (SCD) encompasses an expanding list of rare diseases with a broad clinical and genetic heterogeneity, complicating their diagnosis and management in daily clinical practice. Correct diagnosis is a pillar for precision medicine, a branch of medicine that promises to flourish with the progressive improvements in studying the human genome. Discovering the genes causing novel Mendelian phenotypes contributes to precision medicine by diagnosing subsets of patients with previously undiagnosed conditions, guiding the management of these patients and their families, and enabling the discovery of more causes of Mendelian diseases. This new knowledge provides insight into the biological processes involved in health and disease, including the more common complex disorders. This review discusses the evolution of the clinical and genetic approaches used to diagnose hereditary SCD and the potential of new tools for future discoveries.
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Affiliation(s)
- Ashraf Yahia
- Department of Biochemistry, Faculty of Medicine, University of Khartoum, Khartoum, Sudan
- Department of Biochemistry, Faculty of Medicine, National University, Khartoum, Sudan
- Institut du Cerveau, INSERM U1127, CNRS UMR7225, Sorbonne Université, Paris, France
- Ecole Pratique des Hautes Etudes, EPHE, PSL Research University, Paris, France
| | - Giovanni Stevanin
- Institut du Cerveau, INSERM U1127, CNRS UMR7225, Sorbonne Université, Paris, France
- Ecole Pratique des Hautes Etudes, EPHE, PSL Research University, Paris, France
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19
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He Y, Rashan EH, Linke V, Shishkova E, Hebert AS, Jochem A, Westphall MS, Pagliarini DJ, Overmyer KA, Coon JJ. Multi-Omic Single-Shot Technology for Integrated Proteome and Lipidome Analysis. Anal Chem 2021; 93:4217-4222. [PMID: 33617230 PMCID: PMC8028036 DOI: 10.1021/acs.analchem.0c04764] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Mass spectrometry (MS) serves as the centerpiece technology for proteome, lipidome, and metabolome analysis. To gain a better understanding of the multifaceted networks of myriad regulatory layers in complex organisms, integration of different multiomic layers is increasingly performed, including joint extraction methods of diverse biomolecular classes and comprehensive data analyses of different omics. Despite the versatility of MS systems, fractured methodology drives nearly all MS laboratories to specialize in analysis of a single ome at the exclusion of the others. Although liquid chromatography-mass spectrometry (LC-MS) analysis is similar for different biomolecular classes, the integration on the instrument level is lagging behind. The recent advancements in high flow proteomics enable us to take a first step towards integration of protein and lipid analysis. Here, we describe a technology to achieve broad and deep coverage of multiple molecular classes simultaneously through multi-omic single-shot technology (MOST), requiring only one column, one LC-MS instrument, and a simplified workflow. MOST achieved great robustness and reproducibility. Its application to a Saccharomyces cerevisiae study consisting of 20 conditions revealed 2842 protein groups and 325 lipids and potential molecular relationships.
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Affiliation(s)
- Yuchen He
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison WI 53706, USA
| | - Edrees H. Rashan
- Department of Biochemistry, University of Wisconsin-Madison, Madison WI 53706, USA
| | - Vanessa Linke
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison WI 53706, USA
| | - Evgenia Shishkova
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison WI 53706, USA
| | - Alexander S. Hebert
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison WI 53706, USA
| | - Adam Jochem
- Morgridge Institute for Research, Madison, WI 53715, USA
| | - Michael S. Westphall
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison WI 53706, USA
| | - David J. Pagliarini
- Departments of Cell Biology and Physiology; Biochemistry and Molecular Biophysics; and Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Katherine A. Overmyer
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison WI 53706, USA
- Morgridge Institute for Research, Madison, WI 53715, USA
| | - Joshua J. Coon
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison WI 53706, USA
- Department of Chemistry, University of Wisconsin-Madison, Madison WI 53706, USA
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20
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Stenzinger A, Alber M, Allgäuer M, Jurmeister P, Bockmayr M, Budczies J, Lennerz J, Eschrich J, Kazdal D, Schirmacher P, Wagner AH, Tacke F, Capper D, Müller KR, Klauschen F. Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling. Semin Cancer Biol 2021; 84:129-143. [PMID: 33631297 DOI: 10.1016/j.semcancer.2021.02.011] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 01/29/2021] [Accepted: 02/16/2021] [Indexed: 02/07/2023]
Abstract
The complexity of diagnostic (surgical) pathology has increased substantially over the last decades with respect to histomorphological and molecular profiling. Pathology has steadily expanded its role in tumor diagnostics and beyond from disease entity identification via prognosis estimation to precision therapy prediction. It is therefore not surprising that pathology is among the disciplines in medicine with high expectations in the application of artificial intelligence (AI) or machine learning approaches given their capabilities to analyze complex data in a quantitative and standardized manner to further enhance scope and precision of diagnostics. While an obvious application is the analysis of histological images, recent applications for the analysis of molecular profiling data from different sources and clinical data support the notion that AI will enhance both histopathology and molecular pathology in the future. At the same time, current literature should not be misunderstood in a way that pathologists will likely be replaced by AI applications in the foreseeable future. Although AI will transform pathology in the coming years, recent studies reporting AI algorithms to diagnose cancer or predict certain molecular properties deal with relatively simple diagnostic problems that fall short of the diagnostic complexity pathologists face in clinical routine. Here, we review the pertinent literature of AI methods and their applications to pathology, and put the current achievements and what can be expected in the future in the context of the requirements for research and routine diagnostics.
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Affiliation(s)
- Albrecht Stenzinger
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, Heidelberg, 69120, Germany; German Cancer Consortium (DKTK), Partner Site Heidelberg, and German Cancer Research Center (DKFZ), Heidelberg, Germany; German Center for Lung Research (DZL), Partner Site Heidelberg, Heidelberg, Germany.
| | - Maximilian Alber
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; Aignostics GmbH, Schumannstr. 17, Berlin, 10117, Germany
| | - Michael Allgäuer
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, Heidelberg, 69120, Germany
| | - Philipp Jurmeister
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Bockmayr
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Research Institute, Children's Cancer Center Hamburg, Hamburg, Germany
| | - Jan Budczies
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, Heidelberg, 69120, Germany; German Cancer Consortium (DKTK), Partner Site Heidelberg, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jochen Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Johannes Eschrich
- Department of Hepatology & Gastroenterology, Charité University Medical Center, Berlin, Germany
| | - Daniel Kazdal
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, Heidelberg, 69120, Germany; German Center for Lung Research (DZL), Partner Site Heidelberg, Heidelberg, Germany
| | - Peter Schirmacher
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, Heidelberg, 69120, Germany; German Cancer Consortium (DKTK), Partner Site Heidelberg, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alex H Wagner
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, 43205, USA; Department of Pediatrics, The Ohio State University, Columbus, OH, 43210, USA
| | - Frank Tacke
- Department of Hepatology & Gastroenterology, Charité University Medical Center, Berlin, Germany
| | - David Capper
- German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Neuropathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Klaus-Robert Müller
- Machine Learning Group, Technische Universität Berlin, Berlin, 10587, Germany; Department of Artificial Intelligence, Korea University, Seoul, 136-713, South Korea; Max-Planck-Institute for Informatics, Saarland Informatics Campus E1 4, Saarbrücken, 66123, Germany; Google Research, Brain Team, Berlin, Germany.
| | - Frederick Klauschen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Institute of Pathology, Ludwig-Maximilians-Universität München, Thalkirchner Strasse 36, München, 80337, Germany.
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21
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Smyth LJ, Patterson CC, Swan EJ, Maxwell AP, McKnight AJ. DNA Methylation Associated With Diabetic Kidney Disease in Blood-Derived DNA. Front Cell Dev Biol 2020; 8:561907. [PMID: 33178681 PMCID: PMC7593403 DOI: 10.3389/fcell.2020.561907] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 09/15/2020] [Indexed: 12/23/2022] Open
Abstract
A subset of individuals with type 1 diabetes will develop diabetic kidney disease (DKD). DKD is heritable and large-scale genome-wide association studies have begun to identify genetic factors that influence DKD. Complementary to genetic factors, we know that a person’s epigenetic profile is also altered with DKD. This study reports analysis of DNA methylation, a major epigenetic feature, evaluating methylome-wide loci for association with DKD. Unique features (n = 485,577; 482,421 CpG probes) were evaluated in blood-derived DNA from carefully phenotyped White European individuals diagnosed with type 1 diabetes with (cases) or without (controls) DKD (n = 677 samples). Explicitly, 150 cases were compared to 100 controls using the 450K array, with subsequent analysis using data previously generated for a further 96 cases and 96 controls on the 27K array, and de novo methylation data generated for replication in 139 cases and 96 controls. Following stringent quality control, raw data were quantile normalized and beta values calculated to reflect the methylation status at each site. The difference in methylation status was evaluated between cases and controls; resultant P-values for array-based data were adjusted for multiple testing. Genes with significantly increased (hypermethylated) and/or decreased (hypomethylated) levels of DNA methylation were considered for biological relevance by functional enrichment analysis using KEGG pathways. Twenty-two loci demonstrated statistically significant fold changes associated with DKD and additional support for these associated loci was sought using independent samples derived from patients recruited with similar inclusion criteria. Markers associated with CCNL1 and ZNF187 genes are supported as differentially regulated loci (P < 10–8), with evidence also presented for AFF3, which has been identified from a meta-analysis and subsequent replication of genome-wide association studies. Further supporting evidence for differential gene expression in CCNL1 and ZNF187 is presented from kidney biopsy and blood-derived RNA in people with and without kidney disease from NephroSeq. Evidence confirming that methylation sites influence the development of DKD may aid risk prediction tools and stimulate research to identify epigenomic therapies which might be clinically useful for this disease.
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Affiliation(s)
- Laura J Smyth
- Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom
| | | | - Elizabeth J Swan
- Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom
| | - Alexander P Maxwell
- Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom.,Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom
| | - Amy Jayne McKnight
- Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom
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22
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Fjermestad KW, Kanavin Ø, Nyhus L, Hoxmark LB. Health service experiences among adults with hereditary spastic paraparesis or neurofibromatosis type 1. Mol Genet Genomic Med 2020; 8:e1399. [PMID: 32924306 PMCID: PMC7549577 DOI: 10.1002/mgg3.1399] [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: 01/29/2020] [Revised: 05/22/2020] [Accepted: 06/01/2020] [Indexed: 11/23/2022] Open
Abstract
Background Persons with rare disorders may experience poorer health services due to limited knowledge about rare disorders among health professionals. Knowledge about how persons with rare disorders perceive health services can help inform service providers to enhance their practices. Methods We conducted a self‐report survey among adults with the rare disorders hereditary spastic paraparesis (HSP; n = 108; mean age 57.7 years; 54.2% females) and neurofibromatosis type 1 (NF1, n = 142; mean age = 50.3 years; 62.0% females). Their responses concerning perceived health experiences were compared to healthy controls from the population study HUNT‐3 (n = 7,312). Results Both rare disorder groups reported lower satisfaction, trust, and participation in meetings with their general practitioner and specialist health services. More reported health problems were overall associated with poorer health service experiences. Conclusion There is a need to identify predictors of health service experiences at the patient and health service provider levels with the aim to tighten the gap between the health experiences of patients with and without rare disorders.
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Affiliation(s)
- Krister W. Fjermestad
- Department of Psychology University of Oslo Oslo Norway
- Frambu resource centre for rare disorders Siggerud Norway
| | - Øivind Kanavin
- Frambu resource centre for rare disorders Siggerud Norway
| | - Livø Nyhus
- Frambu resource centre for rare disorders Siggerud Norway
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Yamaguchi A, Queralt-Rosinach N. A proof-of-concept study of extracting patient histories for rare/intractable diseases from social media. Genomics Inform 2020; 18:e17. [PMID: 32634871 PMCID: PMC7362943 DOI: 10.5808/gi.2020.18.2.e17] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 06/18/2020] [Indexed: 12/22/2022] Open
Abstract
The amount of content on social media platforms such as Twitter is expanding rapidly. Simultaneously, the lack of patient information seriously hinders the diagnosis and treatment of rare/intractable diseases. However, these patient communities are especially active on social media. Data from social media could serve as a source of patient-centric knowledge for these diseases complementary to the information collected in clinical settings and patient registries, and may also have potential for research use. To explore this question, we attempted to extract patient-centric knowledge from social media as a task for the 3-day Biomedical Linked Annotation Hackathon 6 (BLAH6). We selected amyotrophic lateral sclerosis and multiple sclerosis as use cases of rare and intractable diseases, respectively, and we extracted patient histories related to these health conditions from Twitter. Four diagnosed patients for each disease were selected. From the user timelines of these eight patients, we extracted tweets that might be related to health conditions. Based on our experiment, we show that our approach has considerable potential, although we identified problems that should be addressed in future attempts to mine information about rare/intractable diseases from Twitter.
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