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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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2
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Gregory DJ, Han F, Li P, Gritsenko M, Kyle J, Riley FE, Chavez D, Yotova V, Sindeaux RH, Hawash MBF, Xu F, Hung LY, Hayden DL, Tompkins RG, Lanford RE, Kobzik L, Hellman J, Jacobs JM, Barreiro LB, Xiao W, Warren HS. Multi-Omic blood analysis reveals differences in innate inflammatory sensitivity between species. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.30.23299243. [PMID: 38076828 PMCID: PMC10705660 DOI: 10.1101/2023.11.30.23299243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
Vertebrates differ greatly in responses to pro-inflammatory agonists such as bacterial lipopolysaccharide (LPS), complicating use of animal models to study human sepsis or inflammatory disorders. We compared transcriptomes of resting and LPS-exposed blood from six LPS-sensitive species (rabbit, pig, sheep, cow, chimpanzee, human) and four LPS-resilient species (mice, rats, baboon, rhesus), as well as plasma proteomes and lipidomes. Unexpectedly, at baseline, sensitive species already had enhanced expression of LPS-responsive genes relative to resilient species. After LPS stimulation, maximally different genes in resilient species included genes that detoxify LPS, diminish bacterial growth, discriminate sepsis from SIRS, and play roles in autophagy and apoptosis. The findings reveal the molecular landscape of species differences in inflammation, and may inform better selection of species for pre-clinical models.
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Affiliation(s)
- David J. Gregory
- Department of Pediatrics, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Feifei Han
- Harvard Medical School, Boston, MA, USA
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Peng Li
- Harvard Medical School, Boston, MA, USA
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Marina Gritsenko
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, USA
| | - Jennifer Kyle
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, USA
| | - Frank E. Riley
- Department of Pediatrics, Massachusetts General Hospital, Boston, MA, USA
| | - Deborah Chavez
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio TX, USA
| | - Vania Yotova
- Centre Hospitalier Universitaire Sainte-Justine, Montréal, Québec, Canada
| | | | - Mohamed B. F. Hawash
- Centre Hospitalier Universitaire Sainte-Justine, Montréal, Québec, Canada
- Department of Biochemistry, University of Montréal, Montréal, Québec, Canada
| | - Fengyun Xu
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA, USA
| | - Li-Yuan Hung
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Douglas L. Hayden
- Harvard Medical School, Boston, MA, USA
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ron G. Tompkins
- Harvard Medical School, Boston, MA, USA
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Robert E. Lanford
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio TX, USA
| | - Lester Kobzik
- Program in Molecular and Integrative Physiological Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Judith Hellman
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan M. Jacobs
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, USA
| | - Luis B. Barreiro
- Centre Hospitalier Universitaire Sainte-Justine, Montréal, Québec, Canada
- Department of Biochemistry, University of Montréal, Montréal, Québec, Canada
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA
- Committee on Immunology, University of Chicago, Chicago, IL, USA
| | - Wenzhong Xiao
- Harvard Medical School, Boston, MA, USA
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - H. Shaw Warren
- Department of Pediatrics, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
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3
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Smirnov D, Konstantinovskiy N, Prokisch H. Integrative omics approaches to advance rare disease diagnostics. J Inherit Metab Dis 2023; 46:824-838. [PMID: 37553850 DOI: 10.1002/jimd.12663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/10/2023]
Abstract
Over the past decade high-throughput DNA sequencing approaches, namely whole exome and whole genome sequencing became a standard procedure in Mendelian disease diagnostics. Implementation of these technologies greatly facilitated diagnostics and shifted the analysis paradigm from variant identification to prioritisation and evaluation. The diagnostic rates vary widely depending on the cohort size, heterogeneity and disease and range from around 30% to 50% leaving the majority of patients undiagnosed. Advances in omics technologies and computational analysis provide an opportunity to increase these unfavourable rates by providing evidence for disease-causing variant validation and prioritisation. This review aims to provide an overview of the current application of several omics technologies including RNA-sequencing, proteomics, metabolomics and DNA-methylation profiling for diagnostics of rare genetic diseases in general and inborn errors of metabolism in particular.
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Affiliation(s)
- Dmitrii Smirnov
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
| | - Nikita Konstantinovskiy
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
| | - Holger Prokisch
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
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Degnan DJ, Bramer LM, Flores JE, Paurus VL, Corilo YE, Clendinen CS. Evaluating Retention Index Score Assumptions to Refine GC-MS Metabolite Identification. Anal Chem 2023; 95:7536-7544. [PMID: 37129113 DOI: 10.1021/acs.analchem.2c05783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
As metabolomics grows into a high-throughput and high demand research field, current metrics for the identification of small molecules in gas chromatography-mass spectrometry (GC-MS) still require manual verification. Though steps have been taken to improve scoring metrics by combining spectral similarity (SS) and retention index (RI), the problem persists. A large body of literature has analyzed and refined SS scores, but few studies have explicitly studied improvements to RI scores. Here, we examined whether uninvestigated assumptions of the RI score are valid and propose ways to improve them. Query RIs were matched to library RI with a generous window of ±35 to avoid unintentional removal of valid compound identifications. Each match was manually verified as a true positive (TP), true negative, or unknown. Metabolites with at least 30 TP identifications were included in downstream analyses, resulting in a total of 87 metabolites from samples of varying complexity and type (e.g., amino acid mixtures, human urine, fungal species, and so on.). Our results showed that the RI score assumptions of normality, consistent variance across metabolites, and a mean error centered at 0 are often violated. We demonstrated through a cross-validation analysis that modifying these underlying assumptions according to empirical metabolite-specific distributions improved the TP and negative rankings. Further, we statistically determined the minimum number of samples required to estimate distributional parameters for scoring metrics. Overall, this work proposes a robust statistical pipeline to reduce the time bottleneck of metabolite identification by improving RI scores and thus minimize the effort to complete manual verification.
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Affiliation(s)
- David J Degnan
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, 99354, United States
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, 99354, United States
| | - Javier E Flores
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, 99354, United States
| | - Vanessa L Paurus
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, 99354, United States
| | - Yuri E Corilo
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, 99354, United States
| | - Chaevien S Clendinen
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, 99354, United States
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Flores JE, Bramer LM, Degnan DJ, Paurus VL, Corilo YE, Clendinen CS. Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023. [PMID: 37084380 DOI: 10.1021/jasms.3c00039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The ability to reliably identify small molecules (e.g., metabolites) is key toward driving scientific advancement in metabolomics. Gas chromatography-mass spectrometry (GC-MS) is an analytic method that may be applied to facilitate this process. The typical GC-MS identification workflow involves quantifying the similarity of an observed sample spectrum and other features (e.g., retention index) to that of several references, noting the compound of the best-matching reference spectrum as the identified metabolite. While a deluge of similarity metrics exist, none quantify the error rate of generated identifications, thereby presenting an unknown risk of false identification or discovery. To quantify this unknown risk, we propose a model-based framework for estimating the false discovery rate (FDR) among a set of identifications. Extending a traditional mixture modeling framework, our method incorporates both similarity score and experimental information in estimating the FDR. We apply these models to identification lists derived from across 548 samples of varying complexity and sample type (e.g., fungal species, standard mixtures, etc.), comparing their performance to that of the traditional Gaussian mixture model (GMM). Through simulation, we additionally assess the impact of reference library size on the accuracy of FDR estimates. In comparing the best performing model extensions to the GMM, our results indicate relative decreases in median absolute estimation error (MAE) ranging from 12% to 70%, based on comparisons of the median MAEs across all hit-lists. Results indicate that these relative performance improvements generally hold despite library size; however FDR estimation error typically worsens as the set of reference compounds diminishes.
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Affiliation(s)
- Javier E Flores
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - David J Degnan
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Vanessa L Paurus
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Yuri E Corilo
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Chaevien S Clendinen
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
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Zandl-Lang M, Plecko B, Köfeler H. Lipidomics-Paving the Road towards Better Insight and Precision Medicine in Rare Metabolic Diseases. Int J Mol Sci 2023; 24:ijms24021709. [PMID: 36675224 PMCID: PMC9866746 DOI: 10.3390/ijms24021709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 01/18/2023] Open
Abstract
Even though the application of Next-Generation Sequencing (NGS) has significantly facilitated the identification of disease-associated mutations, the diagnostic rate of rare diseases is still below 50%. This causes a diagnostic odyssey and prevents specific treatment, as well as genetic counseling for further family planning. Increasing the diagnostic rate and reducing the time to diagnosis in children with unclear disease are crucial for a better patient outcome and improvement of quality of life. In many cases, NGS reveals variants of unknown significance (VUS) that need further investigations. The delineation of novel (lipid) biomarkers is not only crucial to prove the pathogenicity of VUS, but provides surrogate parameters for the monitoring of disease progression and therapeutic interventions. Lipids are essential organic compounds in living organisms, serving as building blocks for cellular membranes, energy storage and signaling molecules. Among other disorders, an imbalance in lipid homeostasis can lead to chronic inflammation, vascular dysfunction and neurodegenerative diseases. Therefore, analyzing lipids in biological samples provides great insight into the underlying functional role of lipids in healthy and disease statuses. The method of choice for lipid analysis and/or huge assemblies of lipids (=lipidome) is mass spectrometry due to its high sensitivity and specificity. Due to the inherent chemical complexity of the lipidome and the consequent challenges associated with analyzing it, progress in the field of lipidomics has lagged behind other omics disciplines. However, compared to the previous decade, the output of publications on lipidomics has increased more than 17-fold within the last decade and has, therefore, become one of the fastest-growing research fields. Combining multiple omics approaches will provide a unique and efficient tool for determining pathogenicity of VUS at the functional level, and thereby identifying rare, as well as novel, genetic disorders by molecular techniques and biochemical analyses.
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Affiliation(s)
- Martina Zandl-Lang
- Division of General Pediatrics, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, 8036 Graz, Austria
| | - Barbara Plecko
- Division of General Pediatrics, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, 8036 Graz, Austria
| | - Harald Köfeler
- Core Facility Mass Spectrometry, ZMF, Medical University of Graz, 8036 Graz, Austria
- Correspondence:
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Ardizzone A, Capra AP, Campolo M, Filippone A, Esposito E, Briuglia S. Neurofibromatosis: New Clinical Challenges in the Era of COVID-19. Biomedicines 2022; 10:biomedicines10050940. [PMID: 35625677 PMCID: PMC9138859 DOI: 10.3390/biomedicines10050940] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/15/2022] [Accepted: 04/18/2022] [Indexed: 02/01/2023] Open
Abstract
Rare diseases constitute a wide range of disorders thus defined for their low prevalence. However, taken together, rare diseases impact a considerable percentage of the world population, thus representing a public healthcare problem. In particular, neurofibromatoses are autosomal-dominant genetic disorders that include type 1 neurofibromatosis (NF1), type 2 neurofibromatosis (NF2) and schwannomatosis. Each of the three types is a genetically distinct disease with an unpredictable clinical course and for which there is still no resolutive cure. Therefore, a personalized therapeutic approach directed at improving the symptomatology as well as the search for new pharmacological strategies for the management of neurofibromatosis represents a priority for positive outcomes for affected patients. The coronavirus disease 2019 (COVID-19) pandemic has severely affected health systems around the world, impacting the provision of medical care and modifying clinical surveillance along with scientific research procedures. COVID-19 significantly worsened exchanges between healthcare personnel and neurofibromatosis patients, precluding continuous clinical monitoring in specialized clinic centers. In this new scenario, our article presents, for the first time, a comprehensive literature review on the clinical challenges for neurofibromatosis clinical care and research during the COVID-19 pandemic health emergency. The review was performed through PubMed (Medline) and Google Scholar databases until December 2021.
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Affiliation(s)
- Alessio Ardizzone
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Viale Ferdinando Stagno D’Alcontres 31, 98166 Messina, Italy; (A.A.); (A.P.C.); (M.C.); (A.F.)
| | - Anna Paola Capra
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Viale Ferdinando Stagno D’Alcontres 31, 98166 Messina, Italy; (A.A.); (A.P.C.); (M.C.); (A.F.)
| | - Michela Campolo
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Viale Ferdinando Stagno D’Alcontres 31, 98166 Messina, Italy; (A.A.); (A.P.C.); (M.C.); (A.F.)
| | - Alessia Filippone
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Viale Ferdinando Stagno D’Alcontres 31, 98166 Messina, Italy; (A.A.); (A.P.C.); (M.C.); (A.F.)
| | - Emanuela Esposito
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Viale Ferdinando Stagno D’Alcontres 31, 98166 Messina, Italy; (A.A.); (A.P.C.); (M.C.); (A.F.)
- Correspondence: ; Tel.: +39-090-676-5208
| | - Silvana Briuglia
- Department of Biomedical, Dental, Morphological and Functional Imaging Sciences, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy;
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Köhler N, Rose TD, Falk L, Pauling JK. Investigating Global Lipidome Alterations with the Lipid Network Explorer. Metabolites 2021; 11:metabo11080488. [PMID: 34436429 PMCID: PMC8398636 DOI: 10.3390/metabo11080488] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 11/18/2022] Open
Abstract
Lipids play an important role in biological systems and have the potential to serve as biomarkers in medical applications. Advances in lipidomics allow identification of hundreds of lipid species from biological samples. However, a systems biological analysis of the lipidome, by incorporating pathway information remains challenging, leaving lipidomics behind compared to other omics disciplines. An especially uncharted territory is the integration of statistical and network-based approaches for studying global lipidome changes. Here we developed the Lipid Network Explorer (LINEX), a web-tool addressing this gap by providing a way to visualize and analyze functional lipid metabolic networks. It utilizes metabolic rules to match biochemically connected lipids on a species level and combine it with a statistical correlation and testing analysis. Researchers can customize the biochemical rules considered, to their tissue or organism specific analysis and easily share them. We demonstrate the benefits of combining network-based analyses with statistics using publicly available lipidomics data sets. LINEX facilitates a biochemical knowledge-based data analysis for lipidomics. It is availableas a web-application and as a publicly available docker container.
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