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Fisher JL, Wilk EJ, Oza VH, Gary SE, Howton TC, Flanary VL, Clark AD, Hjelmeland AB, Lasseigne BN. Signature reversion of three disease-associated gene signatures prioritizes cancer drug repurposing candidates. FEBS Open Bio 2024; 14:803-830. [PMID: 38531616 PMCID: PMC11073506 DOI: 10.1002/2211-5463.13796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 03/28/2024] Open
Abstract
Drug repurposing is promising because approving a drug for a new indication requires fewer resources than approving a new drug. Signature reversion detects drug perturbations most inversely related to the disease-associated gene signature to identify drugs that may reverse that signature. We assessed the performance and biological relevance of three approaches for constructing disease-associated gene signatures (i.e., limma, DESeq2, and MultiPLIER) and prioritized the resulting drug repurposing candidates for four low-survival human cancers. Our results were enriched for candidates that had been used in clinical trials or performed well in the PRISM drug screen. Additionally, we found that pamidronate and nimodipine, drugs predicted to be efficacious against the brain tumor glioblastoma (GBM), inhibited the growth of a GBM cell line and cells isolated from a patient-derived xenograft (PDX). Our results demonstrate that by applying multiple disease-associated gene signature methods, we prioritized several drug repurposing candidates for low-survival cancers.
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Affiliation(s)
- Jennifer L. Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Elizabeth J. Wilk
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Vishal H. Oza
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Sam E. Gary
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Timothy C. Howton
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Victoria L. Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Amanda D. Clark
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Anita B. Hjelmeland
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
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2
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Nair VD, Pincas H, Smith GR, Zaslavsky E, Ge Y, Amper MAS, Vasoya M, Chikina M, Sun Y, Raja AN, Mao W, Gay NR, Esser KA, Smith KS, Zhao B, Wiel L, Singh A, Lindholm ME, Amar D, Montgomery S, Snyder MP, Walsh MJ, Sealfon SC. Molecular adaptations in response to exercise training are associated with tissue-specific transcriptomic and epigenomic signatures. CELL GENOMICS 2024:100421. [PMID: 38697122 DOI: 10.1016/j.xgen.2023.100421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/07/2023] [Accepted: 09/12/2023] [Indexed: 05/04/2024]
Abstract
Regular exercise has many physical and brain health benefits, yet the molecular mechanisms mediating exercise effects across tissues remain poorly understood. Here we analyzed 400 high-quality DNA methylation, ATAC-seq, and RNA-seq datasets from eight tissues from control and endurance exercise-trained (EET) rats. Integration of baseline datasets mapped the gene location dependence of epigenetic control features and identified differing regulatory landscapes in each tissue. The transcriptional responses to 8 weeks of EET showed little overlap across tissues and predominantly comprised tissue-type enriched genes. We identified sex differences in the transcriptomic and epigenomic changes induced by EET. However, the sex-biased gene responses were linked to shared signaling pathways. We found that many G protein-coupled receptor-encoding genes are regulated by EET, suggesting a role for these receptors in mediating the molecular adaptations to training across tissues. Our findings provide new insights into the mechanisms underlying EET-induced health benefits across organs.
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Affiliation(s)
- Venugopalan D Nair
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Hanna Pincas
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Gregory R Smith
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yongchao Ge
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Mary Anne S Amper
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Mital Vasoya
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Maria Chikina
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yifei Sun
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Weiguang Mao
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nicole R Gay
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Karyn A Esser
- Department of Physiology and Aging, University of Florida, Gainesville, FL 32610, USA
| | - Kevin S Smith
- Departments of Pathology and Genetics, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Bingqing Zhao
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Laurens Wiel
- Department of Medicine, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Aditya Singh
- Department of Medicine, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Malene E Lindholm
- Department of Medicine, Stanford School of Medicine, Stanford, CA 94305, USA
| | - David Amar
- Department of Medicine, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Stephen Montgomery
- Departments of Pathology and Genetics, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Michael P Snyder
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Martin J Walsh
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Stuart C Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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3
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Li X, Hao J, Li J, Zhao Z, Shang X, Li M. Pathway Activation Analysis for Pan-Cancer Personalized Characterization Based on Riemannian Manifold. Int J Mol Sci 2024; 25:4411. [PMID: 38673997 PMCID: PMC11050713 DOI: 10.3390/ijms25084411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/08/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
The pathogenesis of carcinoma is believed to come from the combined effect of polygenic variation, and the initiation and progression of malignant tumors are closely related to the dysregulation of biological pathways. Quantifying the alteration in pathway activation and identifying coordinated patterns of pathway dysfunction are the imperative part of understanding the malignancy process and distinguishing different tumor stages or clinical outcomes of individual patients. In this study, we have conducted in silico pathway activation analysis using Riemannian manifold (RiePath) toward pan-cancer personalized characterization, which is the first attempt to apply the Riemannian manifold theory to measure the extent of pathway dysregulation in individual patient on the tangent space of the Riemannian manifold. RiePath effectively integrates pathway and gene expression information, not only generating a relatively low-dimensional and biologically relevant representation, but also identifying a robust panel of biologically meaningful pathway signatures as biomarkers. The pan-cancer analysis across 16 cancer types reveals the capability of RiePath to evaluate pathway activation accurately and identify clinical outcome-related pathways. We believe that RiePath has the potential to provide new prospects in understanding the molecular mechanisms of complex diseases and may find broader applications in predicting biomarkers for other intricate diseases.
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Affiliation(s)
- Xingyi Li
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China; (X.L.); (J.H.); (X.S.)
| | - Jun Hao
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China; (X.L.); (J.H.); (X.S.)
| | - Junming Li
- School of Software, Northwestern Polytechnical University, Xi’an 710072, China; (J.L.); (Z.Z.)
| | - Zhelin Zhao
- School of Software, Northwestern Polytechnical University, Xi’an 710072, China; (J.L.); (Z.Z.)
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China; (X.L.); (J.H.); (X.S.)
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
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4
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Wong CJ, Friedman SD, Snider L, Bennett SR, Jones TI, Jones PL, Shaw DWW, Blemker SS, Riem L, DuCharme O, Lemmers RJFL, van der Maarel SM, Wang LH, Tawil R, Statland JM, Tapscott SJ. Regional and bilateral MRI and gene signatures in facioscapulohumeral dystrophy: implications for clinical trial design and mechanisms of disease progression. Hum Mol Genet 2024; 33:698-708. [PMID: 38268317 PMCID: PMC11000661 DOI: 10.1093/hmg/ddae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 11/11/2023] [Accepted: 01/02/2024] [Indexed: 01/26/2024] Open
Abstract
Identifying the aberrant expression of DUX4 in skeletal muscle as the cause of facioscapulohumeral dystrophy (FSHD) has led to rational therapeutic development and clinical trials. Several studies support the use of MRI characteristics and the expression of DUX4-regulated genes in muscle biopsies as biomarkers of FSHD disease activity and progression. We performed lower-extremity MRI and muscle biopsies in the mid-portion of the tibialis anterior (TA) muscles bilaterally in FSHD subjects and validated our prior reports of the strong association between MRI characteristics and expression of genes regulated by DUX4 and other gene categories associated with FSHD disease activity. We further show that measurements of normalized fat content in the entire TA muscle strongly predict molecular signatures in the mid-portion of the TA, indicating that regional biopsies can accurately measure progression in the whole muscle and providing a strong basis for inclusion of MRI and molecular biomarkers in clinical trial design. An unanticipated finding was the strong correlations of molecular signatures in the bilateral comparisons, including markers of B-cells and other immune cell populations, suggesting that a systemic immune cell infiltration of skeletal muscle might have a role in disease progression.
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Affiliation(s)
- Chao-Jen Wong
- Division of Human Biology, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States
| | - Seth D Friedman
- Department of Radiology, Seattle Children’s Hospital, 4540 Sandpoint Way, Seattle, WA 98105, United States
| | - Lauren Snider
- Division of Human Biology, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States
| | - Sean R Bennett
- Division of Human Biology, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States
| | - Takako I Jones
- Department of Pharmacology, University of Nevada, Reno School of Medicine, 1664 North Virginia Street, Reno, NV 89557, United States
| | - Peter L Jones
- Department of Pharmacology, University of Nevada, Reno School of Medicine, 1664 North Virginia Street, Reno, NV 89557, United States
| | - Dennis W W Shaw
- Department of Radiology, Seattle Children’s Hospital, 4540 Sandpoint Way, Seattle, WA 98105, United States
| | - Silvia S Blemker
- Springbok Analytics, 100 W South St, Charlottesville, VA 22902, United States
| | - Lara Riem
- Springbok Analytics, 100 W South St, Charlottesville, VA 22902, United States
| | - Olivia DuCharme
- Springbok Analytics, 100 W South St, Charlottesville, VA 22902, United States
| | - Richard J F L Lemmers
- Department of Human Genetics, Leiden University Medical Center, Einthovenweg 20, 2333 ZC Leiden, The Netherlands
| | - Silvère M van der Maarel
- Department of Human Genetics, Leiden University Medical Center, Einthovenweg 20, 2333 ZC Leiden, The Netherlands
| | - Leo H Wang
- Department of Neurology, University of Washington, 1959 NE Pacific St, Seattle, WA 98105, United States
| | - Rabi Tawil
- Department of Neurology, University of Rochester Medical Center, 601 Elm St, Rochester, NY 14642, United States
| | - Jeffrey M Statland
- Department of Neurology, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KA 66160, United States
| | - Stephen J Tapscott
- Division of Human Biology, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States
- Department of Neurology, University of Washington, 1959 NE Pacific St, Seattle, WA 98105, United States
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5
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Davidson NR, Zhang F, Greene CS. BuDDI: BulkDeconvolution withDomainInvariance to predict cell-type-specific perturbations from bulk. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.20.549951. [PMID: 37503097 PMCID: PMC10370205 DOI: 10.1101/2023.07.20.549951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance). BuDDI utilizes domain adaptation techniques to effectively integrate available corpora of case-control bulk and reference scRNA-seq observations to infer cell-type-specific perturbation effects. BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. Since each latent space is encouraged to be independent, we simulate perturbation responses by independently composing each latent space to simulate cell-type-specific perturbation responses. We evaluated BuDDI's performance on simulated and real data with experimental designs of increasing complexity. We first validated that BuDDI could learn domain invariant latent spaces on data with matched samples across each source of variability. Then we validated that BuDDI could accurately predict cell-type-specific perturbation response when no single-cell perturbed profiles were used during training; instead, only bulk samples had both perturbed and non-perturbed observations. Finally, we validated BuDDI on predicting sex-specific differences, an experimental design where it is not possible to have matched samples. In each experiment, BuDDI outperformed all other comparative methods and baselines. As more reference atlases are completed, BuDDI provides a path to combine these resources with bulk-profiled treatment or disease signatures to study perturbations, sex differences, or other factors at single-cell resolution.
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Affiliation(s)
- Natalie R Davidson
- Department of Biomedical Informatics, University of Colorado Anschutz School of Medicine, Aurora, Colorado, United States of America · Funded by the Gordon and Betty Moore Foundation (GBMF 4552), NHGRI of the National Institutes of Health (K99HG012945), NCI of the National Institutes of Health (R01CA237170, R01CA243188, R01CA200854)
| | - Fan Zhang
- Department of Medicine Rheumatology, University of Colorado Anschutz School of Medicine, Aurora, Colorado, United States of America; Department of Biomedical Informatics, University of Colorado Anschutz School of Medicine, Aurora, Colorado, United States of America · Funded by the Arthritis National Research Foundation Award, the PhRMA foundation, and the University of Colorado Translational Research Scholars Program Award
| | - Casey S Greene
- Department of Biomedical Informatics, University of Colorado Anschutz School of Medicine, Aurora, Colorado, United States of America · Funded by the Gordon and Betty Moore Foundation (GBMF 4552), NCI of the National Institutes of Health (R01CA237170, R01CA243188, R01CA200854)
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6
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Legouis D, Rinaldi A, Malpetti D, Arnoux G, Verissimo T, Faivre A, Mangili F, Rinaldi A, Ruinelli L, Pugin J, Moll S, Clivio L, Bolis M, de Seigneux S, Azzimonti L, Cippà PE. A transfer learning framework to elucidate the clinical relevance of altered proximal tubule cell states in kidney disease. iScience 2024; 27:109271. [PMID: 38487013 PMCID: PMC10937833 DOI: 10.1016/j.isci.2024.109271] [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: 06/16/2023] [Revised: 10/26/2023] [Accepted: 02/15/2024] [Indexed: 03/17/2024] Open
Abstract
The application of single-cell technologies in clinical nephrology remains elusive. We generated an atlas of transcriptionally defined cell types and cell states of human kidney disease by integrating single-cell signatures reported in the literature with newly generated signatures obtained from 5 patients with acute kidney injury. We used this information to develop kidney-specific cell-level information ExtractoR (K-CLIER), a transfer learning approach specifically tailored to evaluate the role of cell types/states on bulk RNAseq data. We validated the K-CLIER as a reliable computational framework to obtain a dimensionality reduction and to link clinical data with single-cell signatures. By applying K-CLIER on cohorts of patients with different kidney diseases, we identified the most relevant cell types associated with fibrosis and disease progression. This analysis highlighted the central role of altered proximal tubule cells in chronic kidney disease. Our study introduces a new strategy to exploit the power of single-cell technologies toward clinical applications.
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Affiliation(s)
- David Legouis
- Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, 1205 Geneva, Switzerland
- Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital and University of Geneva, 1205 Geneva, Switzerland
| | - Anna Rinaldi
- Laboratories for Translational Research, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
- Division of Nephrology, Department of Medicine, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland
| | - Daniele Malpetti
- Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), USI/SUPSI, Lugano, Switzerland
| | - Gregoire Arnoux
- Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital and University of Geneva, 1205 Geneva, Switzerland
| | - Thomas Verissimo
- Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital and University of Geneva, 1205 Geneva, Switzerland
| | - Anna Faivre
- Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital and University of Geneva, 1205 Geneva, Switzerland
- Division of Nephrology, Department of Medicine, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Francesca Mangili
- Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), USI/SUPSI, Lugano, Switzerland
| | - Andrea Rinaldi
- Institute of Oncological Research, 6500 Bellinzona, Switzerland
| | | | - Jerome Pugin
- Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Solange Moll
- Division of Pathology, Department of Diagnostic, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Luca Clivio
- Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland
| | - Marco Bolis
- Institute of Oncology Research, Università della Svizzera Italiana, Bellinzona, Switzerland
- Laboratory of Computational Oncology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
| | - Sophie de Seigneux
- Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital and University of Geneva, 1205 Geneva, Switzerland
- Division of Nephrology, Department of Medicine, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Laura Azzimonti
- Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), USI/SUPSI, Lugano, Switzerland
| | - Pietro E. Cippà
- Laboratories for Translational Research, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
- Division of Nephrology, Department of Medicine, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900 Lugano, Switzerland
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7
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Gore S, Meche B, Shao D, Ginnett B, Zhou K, Azad RK. DiseaseNet: a transfer learning approach to noncommunicable disease classification. BMC Bioinformatics 2024; 25:107. [PMID: 38468193 PMCID: PMC10926612 DOI: 10.1186/s12859-024-05734-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 03/06/2024] [Indexed: 03/13/2024] Open
Abstract
As noncommunicable diseases (NCDs) pose a significant global health burden, identifying effective diagnostic and predictive markers for these diseases is of paramount importance. Epigenetic modifications, such as DNA methylation, have emerged as potential indicators for NCDs. These have previously been exploited in other contexts within the framework of neural network models that capture complex relationships within the data. Applications of neural networks have led to significant breakthroughs in various biological or biomedical fields but these have not yet been effectively applied to NCD modeling. This is, in part, due to limited datasets that are not amenable to building of robust neural network models. In this work, we leveraged a neural network trained on one class of NCDs, cancer, as the basis for a transfer learning approach to non-cancer NCD modeling. Our results demonstrate promising performance of the model in predicting three NCDs, namely, arthritis, asthma, and schizophrenia, for the respective blood samples, with an overall accuracy (f-measure) of 94.5%. Furthermore, a concept based explanation method called Testing with Concept Activation Vectors (TCAV) was used to investigate the importance of the sample sources and understand how future training datasets for multiple NCD models may be improved. Our findings highlight the effectiveness of transfer learning in developing accurate diagnostic and predictive models for NCDs.
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Affiliation(s)
- Steven Gore
- Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, USA
| | - Bailey Meche
- Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA, USA
| | - Danyang Shao
- Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, USA
| | - Benjamin Ginnett
- Department of Engineering, Eastern Arizona College, Thatcher, AZ, USA
| | - Kelly Zhou
- Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA
| | - Rajeev K Azad
- Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, USA.
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8
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Takasawa K, Asada K, Kaneko S, Shiraishi K, Machino H, Takahashi S, Shinkai N, Kouno N, Kobayashi K, Komatsu M, Mizuno T, Okubo Y, Mukai M, Yoshida T, Yoshida Y, Horinouchi H, Watanabe SI, Ohe Y, Yatabe Y, Kohno T, Hamamoto R. Advances in cancer DNA methylation analysis with methPLIER: use of non-negative matrix factorization and knowledge-based constraints to enhance biological interpretability. Exp Mol Med 2024; 56:646-655. [PMID: 38433247 PMCID: PMC10985003 DOI: 10.1038/s12276-024-01173-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 11/27/2023] [Accepted: 12/13/2023] [Indexed: 03/05/2024] Open
Abstract
DNA methylation is an epigenetic modification that results in dynamic changes during ontogenesis and cell differentiation. DNA methylation patterns regulate gene expression and have been widely researched. While tools for DNA methylation analysis have been developed, most of them have focused on intergroup comparative analysis within a dataset; therefore, it is difficult to conduct cross-dataset studies, such as rare disease studies or cross-institutional studies. This study describes a novel method for DNA methylation analysis, namely, methPLIER, which enables interdataset comparative analyses. methPLIER combines Pathway Level Information Extractor (PLIER), which is a non-negative matrix factorization (NMF) method, with regularization by a knowledge matrix and transfer learning. methPLIER can be used to perform intersample and interdataset comparative analysis based on latent feature matrices, which are obtained via matrix factorization of large-scale data, and factor-loading matrices, which are obtained through matrix factorization of the data to be analyzed. We used methPLIER to analyze a lung cancer dataset and confirmed that the data decomposition reflected sample characteristics for recurrence-free survival. Moreover, methPLIER can analyze data obtained via different preprocessing methods, thereby reducing distributional bias among datasets due to preprocessing. Furthermore, methPLIER can be employed for comparative analyses of methylation data obtained from different platforms, thereby reducing bias in data distribution due to platform differences. methPLIER is expected to facilitate cross-sectional DNA methylation data analysis and enhance DNA methylation data resources.
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Affiliation(s)
- Ken Takasawa
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan.
| | - Ken Asada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Kouya Shiraishi
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Hidenori Machino
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Satoshi Takahashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Norio Shinkai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Nobuji Kouno
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Masaaki Komatsu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Takaaki Mizuno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
- Department of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yu Okubo
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Masami Mukai
- Division of Medical Informatics, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Tatsuya Yoshida
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yukihiro Yoshida
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Hidehito Horinouchi
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Shun-Ichi Watanabe
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yuichiro Ohe
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yasushi Yatabe
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Takashi Kohno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Ryuji Hamamoto
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan.
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9
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Raue U, Begue G, Minchev K, Jemiolo B, Gries KJ, Chambers T, Rubenstein A, Zaslavsky E, Sealfon SC, Trappe T, Trappe S. Fast and slow muscle fiber transcriptome dynamics with lifelong endurance exercise. J Appl Physiol (1985) 2024; 136:244-261. [PMID: 38095016 DOI: 10.1152/japplphysiol.00442.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 10/24/2023] [Accepted: 12/05/2023] [Indexed: 01/26/2024] Open
Abstract
We investigated fast and slow muscle fiber transcriptome exercise dynamics among three groups of men: lifelong exercisers (LLE, n = 8, 74 ± 1 yr), old healthy nonexercisers (OH, n = 9, 75 ± 1 yr), and young exercisers (YE, n = 8, 25 ± 1 yr). On average, LLE had exercised ∼4 day·wk-1 for ∼8 h·wk-1 over 53 ± 2 years. Muscle biopsies were obtained pre- and 4 h postresistance exercise (3 × 10 knee extensions at 70% 1-RM). Fast and slow fiber size and function were assessed preexercise with fast and slow RNA-seq profiles examined pre- and postexercise. LLE fast fiber size was similar to OH, which was ∼30% smaller than YE (P < 0.05) with contractile function variables among groups, resulting in lower power in LLE (P < 0.05). LLE slow fibers were ∼30% larger and more powerful compared with YE and OH (P < 0.05). At the transcriptome level, fast fibers were more responsive to resistance exercise compared with slow fibers among all three cohorts (P < 0.05). Exercise induced a comprehensive biological response in fast fibers (P < 0.05) including transcription, signaling, skeletal muscle cell differentiation, and metabolism with vast differences among the groups. Fast fibers from YE exhibited a growth and metabolic signature, with LLE being primarily metabolic, and OH showing a strong stress-related response. In slow fibers, only LLE exhibited a biological response to exercise (P < 0.05), which was related to ketone and lipid metabolism. The divergent exercise transcriptome signatures provide novel insight into the molecular regulation in fast and slow fibers with age and exercise and suggest that the ∼5% weekly exercise time commitment of the lifelong exercisers provided a powerful investment for fast and slow muscle fiber metabolic health at the molecular level.NEW & NOTEWORTHY This study provides the first insights into fast and slow muscle fiber transcriptome dynamics with lifelong endurance exercise. The fast fibers were more responsive to exercise with divergent transcriptome signatures among young exercisers (growth and metabolic), lifelong exercisers (metabolic), and old healthy nonexercisers (stress). Only lifelong exercisers had a biological response in slow fibers (metabolic). These data provide novel insights into fast and slow muscle fiber health at the molecular level with age and exercise.
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Affiliation(s)
- Ulrika Raue
- Human Performance Laboratory, Ball State University, Muncie, Indiana, United States
| | - Gwenaelle Begue
- Human Performance Laboratory, Ball State University, Muncie, Indiana, United States
| | - Kiril Minchev
- Human Performance Laboratory, Ball State University, Muncie, Indiana, United States
| | - Bozena Jemiolo
- Human Performance Laboratory, Ball State University, Muncie, Indiana, United States
| | - Kevin J Gries
- Human Performance Laboratory, Ball State University, Muncie, Indiana, United States
| | - Toby Chambers
- Human Performance Laboratory, Ball State University, Muncie, Indiana, United States
| | - Aliza Rubenstein
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - Stuart C Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - Todd Trappe
- Human Performance Laboratory, Ball State University, Muncie, Indiana, United States
| | - Scott Trappe
- Human Performance Laboratory, Ball State University, Muncie, Indiana, United States
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10
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Engquist EN, Greco A, Joosten LAB, van Engelen BGM, Zammit PS, Banerji CRS. FSHD muscle shows perturbation in fibroadipogenic progenitor cells, mitochondrial function and alternative splicing independently of inflammation. Hum Mol Genet 2024; 33:182-197. [PMID: 37856562 PMCID: PMC10772042 DOI: 10.1093/hmg/ddad175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/25/2023] [Accepted: 10/10/2023] [Indexed: 10/21/2023] Open
Abstract
Facioscapulohumeral muscular dystrophy (FSHD) is a prevalent, incurable myopathy. FSHD is highly heterogeneous, with patients following a variety of clinical trajectories, complicating clinical trials. Skeletal muscle in FSHD undergoes fibrosis and fatty replacement that can be accelerated by inflammation, adding to heterogeneity. Well controlled molecular studies are thus essential to both categorize FSHD patients into distinct subtypes and understand pathomechanisms. Here, we further analyzed RNA-sequencing data from 24 FSHD patients, each of whom donated a biopsy from both a non-inflamed (TIRM-) and inflamed (TIRM+) muscle, and 15 FSHD patients who donated peripheral blood mononucleated cells (PBMCs), alongside non-affected control individuals. Differential gene expression analysis identified suppression of mitochondrial biogenesis and up-regulation of fibroadipogenic progenitor (FAP) gene expression in FSHD muscle, which was particularly marked on inflamed samples. PBMCs demonstrated suppression of antigen presentation in FSHD. Gene expression deconvolution revealed FAP expansion as a consistent feature of FSHD muscle, via meta-analysis of 7 independent transcriptomic datasets. Clustering of muscle biopsies separated patients in an unbiased manner into clinically mild and severe subtypes, independently of known disease modifiers (age, sex, D4Z4 repeat length). Lastly, the first genome-wide analysis of alternative splicing in FSHD muscle revealed perturbation of autophagy, BMP2 and HMGB1 signalling. Overall, our findings reveal molecular subtypes of FSHD with clinical relevance and identify novel pathomechanisms for this highly heterogeneous condition.
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Affiliation(s)
- Elise N Engquist
- Randall Centre for Cell and Molecular Biophysics, King's College London, New Hunt's House, Guy's Campus, London SE1 1UL, United Kingdom
| | - Anna Greco
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands
- Department of Internal Medicine, Radboud Institute of Molecular Life Sciences (RIMLS) and Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Geert Grooteplein Zuid 10, Nijmegen 6525 GA, The Netherlands
| | - Leo A B Joosten
- Department of Internal Medicine, Radboud Institute of Molecular Life Sciences (RIMLS) and Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Geert Grooteplein Zuid 10, Nijmegen 6525 GA, The Netherlands
- Department of Medical Genetics, Iuliu Hatieganu University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania
| | - Baziel G M van Engelen
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands
| | - Peter S Zammit
- Randall Centre for Cell and Molecular Biophysics, King's College London, New Hunt's House, Guy's Campus, London SE1 1UL, United Kingdom
| | - Christopher R S Banerji
- Randall Centre for Cell and Molecular Biophysics, King's College London, New Hunt's House, Guy's Campus, London SE1 1UL, United Kingdom
- The Alan Turing Institute, The British Library, 96 Euston Road, London NW1 2DB, United Kingdom
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11
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Harrill JA, Everett LJ, Haggard DE, Bundy JL, Willis CM, Shah I, Friedman KP, Basili D, Middleton A, Judson RS. Exploring the effects of experimental parameters and data modeling approaches on in vitro transcriptomic point-of-departure estimates. Toxicology 2024; 501:153694. [PMID: 38043774 DOI: 10.1016/j.tox.2023.153694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/24/2023] [Accepted: 11/29/2023] [Indexed: 12/05/2023]
Abstract
Multiple new approach methods (NAMs) are being developed to rapidly screen large numbers of chemicals to aid in hazard evaluation and risk assessments. High-throughput transcriptomics (HTTr) in human cell lines has been proposed as a first-tier screening approach for determining the types of bioactivity a chemical can cause (activation of specific targets vs. generalized cell stress) and for calculating transcriptional points of departure (tPODs) based on changes in gene expression. In the present study, we examine a range of computational methods to calculate tPODs from HTTr data, using six data sets in which MCF7 cells cultured in two different media formulations were treated with a panel of 44 chemicals for 3 different exposure durations (6, 12, 24 hr). The tPOD calculation methods use data at the level of individual genes and gene set signatures, and compare data processed using the ToxCast Pipeline 2 (tcplfit2), BMDExpress and PLIER (Pathway Level Information ExtractoR). Methods were evaluated by comparing to in vitro PODs from a validated set of high-throughput screening (HTS) assays for a set of estrogenic compounds. Key findings include: (1) for a given chemical and set of experimental conditions, tPODs calculated by different methods can vary by several orders of magnitude; (2) tPODs are at least as sensitive to computational methods as to experimental conditions; (3) in comparison to an external reference set of PODs, some methods give generally higher values, principally PLIER and BMDExpress; and (4) the tPODs from HTTr in this one cell type are mostly higher than the overall PODs from a broad battery of targeted in vitro ToxCast assays, reflecting the need to test chemicals in multiple cell types and readout technologies for in vitro hazard screening.
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Affiliation(s)
- Joshua A Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Logan J Everett
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Derik E Haggard
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA; Oak Ridge Institute for Science and Education (ORISE), USA
| | - Joseph L Bundy
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Clinton M Willis
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA; Oak Ridge Associated Universities (ORAU), USA
| | - Imran Shah
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Danilo Basili
- Unilever Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Alistair Middleton
- Unilever Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Richard S Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA.
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12
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Zhou Y, Luo K, Liang L, Chen M, He X. A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening. Nat Methods 2023; 20:1693-1703. [PMID: 37770710 PMCID: PMC10630124 DOI: 10.1038/s41592-023-02017-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 08/18/2023] [Indexed: 09/30/2023]
Abstract
Clustered regularly interspaced short palindromic repeats (CRISPR) screening coupled with single-cell RNA sequencing has emerged as a powerful tool to characterize the effects of genetic perturbations on the whole transcriptome at a single-cell level. However, due to its sparsity and complex structure, analysis of single-cell CRISPR screening data is challenging. In particular, standard differential expression analysis methods are often underpowered to detect genes affected by CRISPR perturbations. We developed a statistical method for such data, called guided sparse factor analysis (GSFA). GSFA infers latent factors that represent coregulated genes or gene modules; by borrowing information from these factors, it infers the effects of genetic perturbations on individual genes. We demonstrated through extensive simulation studies that GSFA detects perturbation effects with much higher power than state-of-the-art methods. Using single-cell CRISPR data from human CD8+ T cells and neural progenitor cells, we showed that GSFA identified biologically relevant gene modules and specific genes affected by CRISPR perturbations, many of which were missed by existing methods, providing new insights into the functions of genes involved in T cell activation and neurodevelopment.
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Affiliation(s)
- Yifan Zhou
- Graduate Program of Biophysical Sciences, University of Chicago, Chicago, IL, USA
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Kaixuan Luo
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Lifan Liang
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Mengjie Chen
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Department of Medicine, University of Chicago, Chicago, IL, USA.
| | - Xin He
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
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13
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Rubenstein AB, Smith GR, Zhang Z, Chen X, Chambers TL, Ruf-Zamojski F, Mendelev N, Cheng WS, Zamojski M, Amper MAS, Nair VD, Marderstein AR, Montgomery SB, Troyanskaya OG, Zaslavsky E, Trappe T, Trappe S, Sealfon SC. Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.26.558914. [PMID: 37808658 PMCID: PMC10557702 DOI: 10.1101/2023.09.26.558914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Endurance exercise is an important health modifier. We studied cell-type specific adaptations of human skeletal muscle to acute endurance exercise using single-nucleus (sn) multiome sequencing in human vastus lateralis samples collected before and 3.5 hours after 40 min exercise at 70% VO2max in four subjects, as well as in matched time of day samples from two supine resting circadian controls. High quality same-cell RNA-seq and ATAC-seq data were obtained from 37,154 nuclei comprising 14 cell types. Among muscle fiber types, both shared and fiber-type specific regulatory programs were identified. Single-cell circuit analysis identified distinct adaptations in fast, slow and intermediate fibers as well as LUM-expressing FAP cells, involving a total of 328 transcription factors (TFs) acting at altered accessibility sites regulating 2,025 genes. These data and circuit mapping provide single-cell insight into the processes underlying tissue and metabolic remodeling responses to exercise.
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Affiliation(s)
- Aliza B. Rubenstein
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
| | - Gregory R. Smith
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
| | - Zidong Zhang
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Xi Chen
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA
| | - Toby L. Chambers
- Human Performance Laboratory, Ball State University, Muncie, IN 47306, USA
| | - Frederique Ruf-Zamojski
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Natalia Mendelev
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
| | - Wan Sze Cheng
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
| | - Michel Zamojski
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Mary Anne S. Amper
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
| | - Venugopalan D. Nair
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
| | - Andrew R. Marderstein
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Stephen B. Montgomery
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Olga G. Troyanskaya
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
| | - Todd Trappe
- Human Performance Laboratory, Ball State University, Muncie, IN 47306, USA
| | - Scott Trappe
- Human Performance Laboratory, Ball State University, Muncie, IN 47306, USA
- Senior author
| | - Stuart C. Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
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14
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O'Neill NK, Stein TD, Hu J, Rehman H, Campbell JD, Yajima M, Zhang X, Farrer LA. Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM. BMC Bioinformatics 2023; 24:349. [PMID: 37726653 PMCID: PMC10507917 DOI: 10.1186/s12859-023-05476-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 09/12/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND Quantifying cell-type abundance in bulk tissue RNA-sequencing enables researchers to better understand complex systems. Newer deconvolution methodologies, such as MuSiC, use cell-type signatures derived from single-cell RNA-sequencing (scRNA-seq) data to make these calculations. Single-nuclei RNA-sequencing (snRNA-seq) reference data can be used instead of scRNA-seq data for tissues such as human brain where single-cell data are difficult to obtain, but accuracy suffers due to sequencing differences between the technologies. RESULTS We propose a modification to MuSiC entitled 'DeTREM' which compensates for sequencing differences between the cell-type signature and bulk RNA-seq datasets in order to better predict cell-type fractions. We show DeTREM to be more accurate than MuSiC in simulated and real human brain bulk RNA-sequencing datasets with various cell-type abundance estimates. We also compare DeTREM to SCDC and CIBERSORTx, two recent deconvolution methods that use scRNA-seq cell-type signatures. We find that they perform well in simulated data but produce less accurate results than DeTREM when used to deconvolute human brain data. CONCLUSION DeTREM improves the deconvolution accuracy of MuSiC and outperforms other deconvolution methods when applied to snRNA-seq data. DeTREM enables accurate cell-type deconvolution in situations where scRNA-seq data are not available. This modification improves characterization cell-type specific effects in brain tissue and identification of cell-type abundance differences under various conditions.
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Affiliation(s)
- Nicholas K O'Neill
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Medicine (Biomedical Genetics), Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Thor D Stein
- Department of Pathology and Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Veterans Administration Medical Center, Bedford, MA, USA
| | - Junming Hu
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Medicine (Biomedical Genetics), Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Habbiburr Rehman
- Department of Medicine (Biomedical Genetics), Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Joshua D Campbell
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Medicine (Computational Biomedicine), Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Masanao Yajima
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Xiaoling Zhang
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Medicine (Biomedical Genetics), Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, USA.
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
| | - Lindsay A Farrer
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Medicine (Biomedical Genetics), Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, USA.
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
- Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
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15
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Pividori M, Lu S, Li B, Su C, Johnson ME, Wei WQ, Feng Q, Namjou B, Kiryluk K, Kullo IJ, Luo Y, Sullivan BD, Voight BF, Skarke C, Ritchie MD, Grant SFA, Greene CS. Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms. Nat Commun 2023; 14:5562. [PMID: 37689782 PMCID: PMC10492839 DOI: 10.1038/s41467-023-41057-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 08/18/2023] [Indexed: 09/11/2023] Open
Abstract
Genes act in concert with each other in specific contexts to perform their functions. Determining how these genes influence complex traits requires a mechanistic understanding of expression regulation across different conditions. It has been shown that this insight is critical for developing new therapies. Transcriptome-wide association studies have helped uncover the role of individual genes in disease-relevant mechanisms. However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. Here we introduce PhenoPLIER, a computational approach that maps gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. This representation is based on modules of genes with similar expression patterns across the same conditions. We observe that diseases are significantly associated with gene modules expressed in relevant cell types, and our approach is accurate in predicting known drug-disease pairs and inferring mechanisms of action. Furthermore, using a CRISPR screen to analyze lipid regulation, we find that functionally important players lack associations but are prioritized in trait-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets missed by single-gene strategies.
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Affiliation(s)
- Milton Pividori
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Sumei Lu
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Binglan Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Chun Su
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Matthew E Johnson
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Wei-Qi Wei
- Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Qiping Feng
- Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Bahram Namjou
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Krzysztof Kiryluk
- Department of Medicine, Division of Nephrology, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, 10032, USA
| | | | - Yuan Luo
- Northwestern University, Chicago, IL, 60611, USA
| | - Blair D Sullivan
- Kahlert School of Computing, University of Utah, Salt Lake City, UT, 84112, USA
| | - Benjamin F Voight
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Carsten Skarke
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Struan F A Grant
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Casey S Greene
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
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16
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Zhang S, Heil BJ, Mao W, Chikina M, Greene CS, Heller EA. MousiPLIER: A Mouse Pathway-Level Information Extractor Model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.31.551386. [PMID: 37577575 PMCID: PMC10418102 DOI: 10.1101/2023.07.31.551386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
High throughput gene expression profiling is a powerful approach to generate hypotheses on the underlying causes of biological function and disease. Yet this approach is limited by its ability to infer underlying biological pathways and burden of testing tens of thousands of individual genes. Machine learning models that incorporate prior biological knowledge are necessary to extract meaningful pathways and generate rational hypothesis from the vast amount of gene expression data generated to date. We adopted an unsupervised machine learning method, Pathway-level information extractor (PLIER), to train the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. mousiPLER converted gene expression data into a latent variables that align to known pathway or cell maker gene sets, substantially reducing data dimensionality and improving interpretability. To determine the utility of mousiPLIER, we applied it to a mouse brain aging study of microglia and astrocyte transcriptomic profiling. We found a specific set of latent variables that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. We next performed k-means clustering on the training data to identify studies that respond strongly to LV41, finding that the variable is relevant to striatum and aging across the scientific literature. Finally, we built a web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together this study provides proof of concept that mousiPLIER can uncover meaningful biological processes in mouse transcriptomic studies.
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Affiliation(s)
- Shuo Zhang
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Benjamin J. Heil
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Weiguang Mao
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Maria Chikina
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Casey S. Greene
- Department of Pharmacology, University of Colorado School of Medicine, Denver, CO 80045, USA
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Denver, CO 80045, USA
| | - Elizabeth A. Heller
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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17
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Kim Y, Lee H. PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease. Front Aging Neurosci 2023; 15:1126156. [PMID: 37520124 PMCID: PMC10380929 DOI: 10.3389/fnagi.2023.1126156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 06/20/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction Identification of Alzheimer's Disease (AD)-related transcriptomic signatures from blood is important for early diagnosis of the disease. Deep learning techniques are potent classifiers for AD diagnosis, but most have been unable to identify biomarkers because of their lack of interpretability. Methods To address these challenges, we propose a pathway information-based neural network (PINNet) to predict AD patients and analyze blood and brain transcriptomic signatures using an interpretable deep learning model. PINNet is a deep neural network (DNN) model with pathway prior knowledge from either the Gene Ontology or Kyoto Encyclopedia of Genes and Genomes databases. Then, a backpropagation-based model interpretation method was applied to reveal essential pathways and genes for predicting AD. Results The performance of PINNet was compared with a DNN model without a pathway. Performances of PINNet outperformed or were similar to those of DNN without a pathway using blood and brain gene expressions, respectively. Moreover, PINNet considers more AD-related genes as essential features than DNN without a pathway in the learning process. Pathway analysis of protein-protein interaction modules of highly contributed genes showed that AD-related genes in blood were enriched with cell migration, PI3K-Akt, MAPK signaling, and apoptosis in blood. The pathways enriched in the brain module included cell migration, PI3K-Akt, MAPK signaling, apoptosis, protein ubiquitination, and t-cell activation. Discussion By integrating prior knowledge about pathways, PINNet can reveal essential pathways related to AD. The source codes are available at https://github.com/DMCB-GIST/PINNet.
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Affiliation(s)
- Yeojin Kim
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Hyunju Lee
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
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18
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Toseef M, Olayemi Petinrin O, Wang F, Rahaman S, Liu Z, Li X, Wong KC. Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results. Brief Bioinform 2023:bbad254. [PMID: 37455245 DOI: 10.1093/bib/bbad254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/04/2023] [Accepted: 06/20/2023] [Indexed: 07/18/2023] Open
Abstract
The rapid growth of omics-based data has revolutionized biomedical research and precision medicine, allowing machine learning models to be developed for cutting-edge performance. However, despite the wealth of high-throughput data available, the performance of these models is hindered by the lack of sufficient training data, particularly in clinical research (in vivo experiments). As a result, translating this knowledge into clinical practice, such as predicting drug responses, remains a challenging task. Transfer learning is a promising tool that bridges the gap between data domains by transferring knowledge from the source to the target domain. Researchers have proposed transfer learning to predict clinical outcomes by leveraging pre-clinical data (mouse, zebrafish), highlighting its vast potential. In this work, we present a comprehensive literature review of deep transfer learning methods for health informatics and clinical decision-making, focusing on high-throughput molecular data. Previous reviews mostly covered image-based transfer learning works, while we present a more detailed analysis of transfer learning papers. Furthermore, we evaluated original studies based on different evaluation settings across cross-validations, data splits and model architectures. The result shows that those transfer learning methods have great potential; high-throughput sequencing data and state-of-the-art deep learning models lead to significant insights and conclusions. Additionally, we explored various datasets in transfer learning papers with statistics and visualization.
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Affiliation(s)
- Muhammad Toseef
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
| | | | - Fuzhou Wang
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
| | - Saifur Rahaman
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
| | - Zhe Liu
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University, Jilin, China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
- Hong Kong Institute for Data Science, City University of Hong Kong, Hong Kong SAR
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19
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Banerjee J, Taroni JN, Allaway RJ, Prasad DV, Guinney J, Greene C. Machine learning in rare disease. Nat Methods 2023:10.1038/s41592-023-01886-z. [PMID: 37248386 DOI: 10.1038/s41592-023-01886-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 04/22/2023] [Indexed: 05/31/2023]
Abstract
High-throughput profiling methods (such as genomics or imaging) have accelerated basic research and made deep molecular characterization of patient samples routine. These approaches provide a rich portrait of genes, molecular pathways and cell types involved in disease phenotypes. Machine learning (ML) can be a useful tool for extracting disease-relevant patterns from high-dimensional datasets. However, depending upon the complexity of the biological question, machine learning often requires many samples to identify recurrent and biologically meaningful patterns. Rare diseases are inherently limited in clinical cases, leading to few samples to study. In this Perspective, we outline the challenges and emerging solutions for using ML for small sample sets, specifically in rare diseases. Advances in ML methods for rare diseases are likely to be informative for applications beyond rare diseases for which few samples exist with high-dimensional data. We propose that the method community prioritize the development of ML techniques for rare disease research.
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Affiliation(s)
| | - Jaclyn N Taroni
- Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Philadelphia, PA, USA
| | | | | | | | - Casey Greene
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
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20
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Kim JY, Jeon K, Hong JJ, Park SI, Cho H, Park HJ, Kwak HW, Park HJ, Bang YJ, Lee YS, Bae SH, Kim SH, Hwang KA, Jung DI, Cho SH, Seo SH, Kim G, Oh H, Lee HY, Kim KH, Lim HY, Jeon P, Lee JY, Chung J, Lee SM, Ko HL, Song M, Cho NH, Lee YS, Hong SH, Nam JH. Heterologous vaccination utilizing viral vector and protein platforms confers complete protection against SFTSV. Sci Rep 2023; 13:8189. [PMID: 37210393 DOI: 10.1038/s41598-023-35328-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 05/16/2023] [Indexed: 05/22/2023] Open
Abstract
Severe fever with thrombocytopenia syndrome virus was first discovered in 2009 as the causative agent of severe fever with thrombocytopenia syndrome. Despite its potential threat to public health, no prophylactic vaccine is yet available. This study developed a heterologous prime-boost strategy comprising priming with recombinant replication-deficient human adenovirus type 5 (rAd5) expressing the surface glycoprotein, Gn, and boosting with Gn protein. This vaccination regimen induced balanced Th1/Th2 immune responses and resulted in potent humoral and T cell-mediated responses in mice. It elicited high neutralizing antibody titers in both mice and non-human primates. Transcriptome analysis revealed that rAd5 and Gn proteins induced adaptive and innate immune pathways, respectively. This study provides immunological and mechanistic insight into this heterologous regimen and paves the way for future strategies against emerging infectious diseases.
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Affiliation(s)
- Jae-Yong Kim
- Department of Medical and Biological Sciences, The Catholic University of Korea, 43-1 Yeokgok-dong, Wonmi-gu, Bucheon, 14662, Republic of Korea
- BK Plus Department of Biotechnology, The Catholic University of Korea, Bucheon, Gyeonggi-do, Republic of Korea
- SML Biopharm, Gwangmyeong, Gyeonggi-do, Republic of Korea
| | - Kyeongseok Jeon
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Jung Joo Hong
- Immunology and Infectious Disease Lab, National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB)/University of Science and Technology, 30 Yeongudanji-ro, Ochang-eup, Cheongwon-gu, Cheongju-si, Chungcheongbuk-do, 28116, Republic of Korea
| | - Sang-In Park
- SML Biopharm, Gwangmyeong, Gyeonggi-do, Republic of Korea
| | - Hyeonggon Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hyo-Jung Park
- Department of Medical and Biological Sciences, The Catholic University of Korea, 43-1 Yeokgok-dong, Wonmi-gu, Bucheon, 14662, Republic of Korea
- BK Plus Department of Biotechnology, The Catholic University of Korea, Bucheon, Gyeonggi-do, Republic of Korea
| | - Hye Won Kwak
- SML Biopharm, Gwangmyeong, Gyeonggi-do, Republic of Korea
| | - Hyeong-Jun Park
- Department of Medical and Biological Sciences, The Catholic University of Korea, 43-1 Yeokgok-dong, Wonmi-gu, Bucheon, 14662, Republic of Korea
- BK Plus Department of Biotechnology, The Catholic University of Korea, Bucheon, Gyeonggi-do, Republic of Korea
- SML Biopharm, Gwangmyeong, Gyeonggi-do, Republic of Korea
| | - Yoo-Jin Bang
- Department of Medical and Biological Sciences, The Catholic University of Korea, 43-1 Yeokgok-dong, Wonmi-gu, Bucheon, 14662, Republic of Korea
- BK Plus Department of Biotechnology, The Catholic University of Korea, Bucheon, Gyeonggi-do, Republic of Korea
- SML Biopharm, Gwangmyeong, Gyeonggi-do, Republic of Korea
| | - Yu-Sun Lee
- Department of Medical and Biological Sciences, The Catholic University of Korea, 43-1 Yeokgok-dong, Wonmi-gu, Bucheon, 14662, Republic of Korea
- BK Plus Department of Biotechnology, The Catholic University of Korea, Bucheon, Gyeonggi-do, Republic of Korea
| | - Seo-Hyeon Bae
- Department of Medical and Biological Sciences, The Catholic University of Korea, 43-1 Yeokgok-dong, Wonmi-gu, Bucheon, 14662, Republic of Korea
- BK Plus Department of Biotechnology, The Catholic University of Korea, Bucheon, Gyeonggi-do, Republic of Korea
| | - So-Hee Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Kyung-Ah Hwang
- Department of Research and Development, Genetree Research, Seoul, Republic of Korea
| | - Dae-Im Jung
- Science Unit, International Vaccine Institute, Seoul, Republic of Korea
| | - Seong Hoo Cho
- Science Unit, International Vaccine Institute, Seoul, Republic of Korea
| | - Sang Hwan Seo
- Science Unit, International Vaccine Institute, Seoul, Republic of Korea
| | - Green Kim
- Immunology and Infectious Disease Lab, National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB)/University of Science and Technology, 30 Yeongudanji-ro, Ochang-eup, Cheongwon-gu, Cheongju-si, Chungcheongbuk-do, 28116, Republic of Korea
| | - Hanseul Oh
- Immunology and Infectious Disease Lab, National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB)/University of Science and Technology, 30 Yeongudanji-ro, Ochang-eup, Cheongwon-gu, Cheongju-si, Chungcheongbuk-do, 28116, Republic of Korea
| | - Hwal-Yong Lee
- Immunology and Infectious Disease Lab, National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB)/University of Science and Technology, 30 Yeongudanji-ro, Ochang-eup, Cheongwon-gu, Cheongju-si, Chungcheongbuk-do, 28116, Republic of Korea
| | - Ki Hyun Kim
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Hee-Young Lim
- Center for Emerging Virus Research, National Institutes of Health, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Pyeonghwa Jeon
- Center for Emerging Virus Research, National Institutes of Health, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Joo-Yeon Lee
- Center for Emerging Virus Research, National Institutes of Health, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Junho Chung
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Sang-Myeong Lee
- College of Veterinary Medicine, Chungbuk National University, Cheongju, Republic of Korea
| | - Hae Li Ko
- Scripps Korea Antibody Institute, Chuncheon, 24341, Republic of Korea
| | - Manki Song
- Science Unit, International Vaccine Institute, Seoul, Republic of Korea
| | - Nam-Hyuk Cho
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
| | - Young-Suk Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
| | - So-Hee Hong
- Department of Microbiology, College of Medicine, Ewha Womans University, Seoul, 07804, Republic of Korea.
| | - Jae-Hwan Nam
- Department of Medical and Biological Sciences, The Catholic University of Korea, 43-1 Yeokgok-dong, Wonmi-gu, Bucheon, 14662, Republic of Korea.
- BK Plus Department of Biotechnology, The Catholic University of Korea, Bucheon, Gyeonggi-do, Republic of Korea.
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21
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Lavin KM, Graham ZA, McAdam JS, O'Bryan SM, Drummer D, Bell MB, Kelley CJ, Lixandrão ME, Peoples B, Tuggle SC, Seay RS, Van Keuren-Jensen K, Huentelman MJ, Pirrotte P, Reiman R, Alsop E, Hutchins E, Antone J, Bonfitto A, Meechoovet B, Palade J, Talboom JS, Sullivan A, Aban I, Peri K, Broderick TJ, Bamman MM. Dynamic transcriptomic responses to divergent acute exercise stimuli in young adults. Physiol Genomics 2023; 55:194-212. [PMID: 36939205 PMCID: PMC10110731 DOI: 10.1152/physiolgenomics.00144.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 02/08/2023] [Accepted: 03/06/2023] [Indexed: 03/21/2023] Open
Abstract
Acute exercise elicits dynamic transcriptional changes that, when repeated, form the fundamental basis of health, resilience, and performance adaptations. While moderate-intensity endurance training combined with conventional resistance training (traditional, TRAD) is often prescribed and recommended by public health guidance, high-intensity training combining maximal-effort intervals with intensive, limited-rest resistance training is a time-efficient alternative that may be used tactically (HITT) to confer similar benefits. Mechanisms of action of these distinct stimuli are incompletely characterized and have not been directly compared. We assessed transcriptome-wide responses in skeletal muscle and circulating extracellular vesicles (EVs) to a single exercise bout in young adults randomized to TRAD (n = 21, 12 M/9 F, 22 ± 3 yr) or HITT (n = 19, 11 M/8 F, 22 ± 2 yr). Next-generation sequencing captured small, long, and circular RNA in muscle and EVs. Analysis identified differentially expressed transcripts (|log2FC|>1, FDR ≤ 0.05) immediately (h0, EVs only), h3, and h24 postexercise within and between exercise protocols. In aaddition, all apparently responsive transcripts (FDR < 0.2) underwent singular value decomposition to summarize data structures into latent variables (LVs) to deconvolve molecular expression circuits and interregulatory relationships. LVs were compared across time and exercise protocol. TRAD, a longer but less intense stimulus, generally elicited a stronger transcriptional response than HITT, but considerable overlap and key differences existed. Findings reveal shared and unique molecular responses to the exercise stimuli and lay groundwork toward establishing relationships between protein-coding genes and lesser-understood transcripts that serve regulatory roles following exercise. Future work should advance the understanding of these circuits and whether they repeat in other populations or following other types of exercise/stress.NEW & NOTEWORTHY We examined small and long transcriptomics in skeletal muscle and serum-derived extracellular vesicles before and after a single exposure to traditional combined exercise (TRAD) and high-intensity tactical training (HITT). Across 40 young adults, we found more consistent protein-coding gene responses to TRAD, whereas HITT elicited differential expression of microRNA enriched in brain regions. Follow-up analysis revealed relationships and temporal dynamics across transcript networks, highlighting potential avenues for research into mechanisms of exercise response and adaptation.
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Affiliation(s)
- Kaleen M Lavin
- Healthspan, Resilience, and Performance, Florida Institute for Human and Machine Cognition, Pensacola, Florida, United States
- UAB Center for Exercise Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States
- Department of Cell, Developmental, and Integrative Biology, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Zachary A Graham
- Healthspan, Resilience, and Performance, Florida Institute for Human and Machine Cognition, Pensacola, Florida, United States
- UAB Center for Exercise Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States
- Department of Cell, Developmental, and Integrative Biology, The University of Alabama at Birmingham, Birmingham, Alabama, United States
- Birmingham Veterans Affairs Medical Center, Birmingham, Alabama, United States
| | - Jeremy S McAdam
- Healthspan, Resilience, and Performance, Florida Institute for Human and Machine Cognition, Pensacola, Florida, United States
- UAB Center for Exercise Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States
- Department of Cell, Developmental, and Integrative Biology, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Samia M O'Bryan
- UAB Center for Exercise Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States
- Department of Cell, Developmental, and Integrative Biology, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Devin Drummer
- UAB Center for Exercise Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States
- Department of Cell, Developmental, and Integrative Biology, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Margaret B Bell
- UAB Center for Exercise Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States
- Department of Cell, Developmental, and Integrative Biology, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Christian J Kelley
- UAB Center for Exercise Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States
- Department of Cell, Developmental, and Integrative Biology, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Manoel E Lixandrão
- UAB Center for Exercise Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States
- Department of Cell, Developmental, and Integrative Biology, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Brandon Peoples
- UAB Center for Exercise Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - S Craig Tuggle
- Healthspan, Resilience, and Performance, Florida Institute for Human and Machine Cognition, Pensacola, Florida, United States
- UAB Center for Exercise Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Regina S Seay
- UAB Center for Exercise Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States
- Department of Cell, Developmental, and Integrative Biology, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | | | - Matthew J Huentelman
- Cancer & Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona, United States
| | - Patrick Pirrotte
- Cancer & Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona, United States
- Integrated Mass Spectrometry Shared Resource, City of Hope Comprehensive Cancer Center, Duarte, California, United States
| | - Rebecca Reiman
- Cancer & Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona, United States
| | - Eric Alsop
- Cancer & Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona, United States
| | - Elizabeth Hutchins
- Cancer & Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona, United States
| | - Jerry Antone
- Cancer & Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona, United States
| | - Anna Bonfitto
- Cancer & Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona, United States
| | - Bessie Meechoovet
- Cancer & Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona, United States
| | - Joanna Palade
- Cancer & Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona, United States
| | - Joshua S Talboom
- Cancer & Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona, United States
| | - Amber Sullivan
- UAB Center for Exercise Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Inmaculada Aban
- Department of Biostatistics, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Kalyani Peri
- Department of Biostatistics, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Timothy J Broderick
- Healthspan, Resilience, and Performance, Florida Institute for Human and Machine Cognition, Pensacola, Florida, United States
| | - Marcas M Bamman
- Healthspan, Resilience, and Performance, Florida Institute for Human and Machine Cognition, Pensacola, Florida, United States
- UAB Center for Exercise Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States
- Department of Cell, Developmental, and Integrative Biology, The University of Alabama at Birmingham, Birmingham, Alabama, United States
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22
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Foltz SM, Greene CS, Taroni JN. Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously. Commun Biol 2023; 6:222. [PMID: 36841852 PMCID: PMC9968332 DOI: 10.1038/s42003-023-04588-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Accepted: 02/13/2023] [Indexed: 02/27/2023] Open
Abstract
Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, most available RNA assays were run on microarray, while RNA-seq is now the platform of choice for many new experiments. The data structure and distributions between the platforms differ, making it challenging to combine them directly. Here we perform supervised and unsupervised machine learning evaluations to assess which existing normalization methods are best suited for combining microarray and RNA-seq data. We find that quantile and Training Distribution Matching normalization allow for supervised and unsupervised model training on microarray and RNA-seq data simultaneously. Nonparanormal normalization and z-scores are also appropriate for some applications, including pathway analysis with Pathway-Level Information Extractor (PLIER). We demonstrate that it is possible to perform effective cross-platform normalization using existing methods to combine microarray and RNA-seq data for machine learning applications.
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Affiliation(s)
- Steven M Foltz
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Wynnewood, PA, USA
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA.
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
| | - Jaclyn N Taroni
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Wynnewood, PA, USA.
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23
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A patient advocating for transparent science in rare disease research. Orphanet J Rare Dis 2023; 18:14. [PMID: 36658594 PMCID: PMC9854194 DOI: 10.1186/s13023-022-02557-6] [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: 01/12/2022] [Revised: 06/14/2022] [Accepted: 10/12/2022] [Indexed: 01/20/2023] Open
Abstract
300 million people live with at least one of 6,000 rare diseases worldwide. However, rare disease research is not always reviewed with scrutiny, making it susceptible to what the author refers to as nontransparent science. Nontransparent science can obscure animal model flaws, misguide medicine regulators and drug developers, delay or frustrate orphan drug development, or waste limited resources for rare disease research. Flawed animal models not only lack pharmacologic relevance, but also give rise to issue of clinical translatability. Sadly, these consequences and risks are grossly overlooked. Nontransparency in science can take many forms, such as premature publication of animal models without clinically significant data, not providing corrections when flaws to the model are discovered, lack of warning of critical study limitations, missing critical control data, questionable data quality, surprising results without a sound explanation, failure to rule out potential factors which may affect study conclusions, lack of sufficient detail for others to replicate the study, dubious authorship and study accountability. Science has no boarders, neither does nontransparent science. Nontransparent science can happen irrespective of the researcher's senority, institutional affiliation or country. As a patient-turned researcher suffering from Bietti crystalline dystrophy (BCD), I use BCD as an example to analyze various forms of nontransparent science in rare disease research. This article analyzes three papers published by different research groups on Cyp4v3-/-, high-fat diet (HFD)-Cyp4v3-/-, and Exon1-Cyp4v3-/- mouse models of BCD. As the discussion probes various forms of nontransparent science, the flaws of these knockout mouse models are uncovered. These mouse models do not mimic BCD in humans nor do they address the lack of Cyp4v3 (murine ortholog of human CYP4V2) expression in wild type (WT) mouse retina which is markedly different from CYP4V2 expression in human retina. Further, this article discusses the impact of nontransparent science on drug development which can lead to significant delays ultimately affecting the patients. Lessons from BCD research can be helpful to all those suffering from rare diseases. As a patient, I call for transparent science in rare disease research.
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24
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Drummer DJ, Lavin KM, Graham ZA, O'Bryan SM, McAdam JS, Lixandrão ME, Seay R, Aban I, Siegel HJ, Ghanem E, Singh JA, Bonfitto A, Antone J, Reiman R, Hutchins E, Van Keuren-Jensen K, Schutzler SE, Barnes CL, Ferrando AA, Bridges SL, Bamman MM. Muscle transcriptomic circuits linked to periarticular physiology in end-stage osteoarthritis. Physiol Genomics 2022; 54:501-513. [PMID: 36278270 PMCID: PMC9762959 DOI: 10.1152/physiolgenomics.00092.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 09/08/2022] [Accepted: 10/20/2022] [Indexed: 02/01/2023] Open
Abstract
The ability of individuals with end-stage osteoarthritis (OA) to functionally recover from total joint arthroplasty is highly inconsistent. The molecular mechanisms driving this heterogeneity have yet to be elucidated. Furthermore, OA disproportionately impacts females, suggesting a need for identifying female-specific therapeutic targets. We profiled the skeletal muscle transcriptome in females with end-stage OA (n = 20) undergoing total knee or hip arthroplasty using RNA-Seq. Single-gene differential expression (DE) analyses tested for DE genes between skeletal muscle overlaying the surgical (SX) joint and muscle from the contralateral (CTRL) leg. Network analyses were performed using Pathway-Level Information ExtractoR (PLIER) to summarize genes into latent variables (LVs), i.e., gene circuits, and link them to biological pathways. LV differences in SX versus CTRL muscle and across sources of muscle tissue (vastus medialis, vastus lateralis, or tensor fascia latae) were determined with ANOVA. Linear models tested for associations between LVs and muscle phenotype on the SX side (inflammation, function, and integrity). DE analysis revealed 360 DE genes (|Log2 fold-difference| ≥ 1, FDR ≤ 0.05) between the SX and CTRL limbs, many associated with inflammation and lipid metabolism. PLIER analyses revealed circuits associated with protein degradation and fibro-adipogenic cell gene expression. Muscle inflammation and function were linked to an LV associated with endothelial cell gene expression highlighting a potential regulatory role of endothelial cells within skeletal muscle. These findings may provide insight into potential therapeutic targets to improve OA rehabilitation before and/or following total joint replacement.
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Affiliation(s)
- Devin J Drummer
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Kaleen M Lavin
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama
- Florida Institute for Human and Machine Cognition, Pensacola, Florida
| | - Zachary A Graham
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama
- Florida Institute for Human and Machine Cognition, Pensacola, Florida
- Birmingham VA Medical Center, Birmingham, Alabama
| | - Samia M O'Bryan
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Jeremy S McAdam
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama
- Florida Institute for Human and Machine Cognition, Pensacola, Florida
| | - Manoel E Lixandrão
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado
| | - Regina Seay
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Inmaculada Aban
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama
| | - Herrick J Siegel
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Orthopaedic Surgery, University of Alabama at Birmingham, Birmingham, Alabama
| | - Elie Ghanem
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Orthopaedic Surgery, University of Alabama at Birmingham, Birmingham, Alabama
| | - Jasvinder A Singh
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Birmingham VA Medical Center, Birmingham, Alabama
- Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Comprehensive Arthritis, Musculoskeletal, Bone, and Autoimmunity Center, University of Alabama at Birmingham, Birmingham, Alabama
| | - Anna Bonfitto
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, Arizona
| | - Jerry Antone
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, Arizona
| | - Rebecca Reiman
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, Arizona
| | - Elizabeth Hutchins
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, Arizona
| | | | - Scott E Schutzler
- Department of Geriatrics and Center for Translational Research in Aging and Longevity, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - C Lowry Barnes
- Department of Orthopaedic Surgery, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Arny A Ferrando
- Department of Geriatrics and Center for Translational Research in Aging and Longevity, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - S Louis Bridges
- Department of Medicine, Hospital for Special Surgery, New York, New York
- Division of Rheumatology, Weill Cornell Medical Center, New York, New York
| | - Marcas M Bamman
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama
- Florida Institute for Human and Machine Cognition, Pensacola, Florida
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25
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Sauerwald N, Zhang Z, Ramos I, Nair VD, Soares-Schanoski A, Ge Y, Mao W, Alshammary H, Gonzalez-Reiche AS, van de Guchte A, Goforth CW, Lizewski RA, Lizewski SE, Amper MAS, Vasoya M, Seenarine N, Guevara K, Marjanovic N, Miller CM, Nudelman G, Schilling MA, Sealfon RSG, Termini MS, Vangeti S, Weir DL, Zaslavsky E, Chikina M, Wu YN, Van Bakel H, Letizia AG, Sealfon SC, Troyanskaya OG. Pre-infection antiviral innate immunity contributes to sex differences in SARS-CoV-2 infection. Cell Syst 2022; 13:924-931.e4. [PMID: 36323307 PMCID: PMC9623453 DOI: 10.1016/j.cels.2022.10.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/21/2022] [Accepted: 10/18/2022] [Indexed: 11/05/2022]
Abstract
Male sex is a major risk factor for SARS-CoV-2 infection severity. To understand the basis for this sex difference, we studied SARS-CoV-2 infection in a young adult cohort of United States Marine recruits. Among 2,641 male and 244 female unvaccinated and seronegative recruits studied longitudinally, SARS-CoV-2 infections occurred in 1,033 males and 137 females. We identified sex differences in symptoms, viral load, blood transcriptome, RNA splicing, and proteomic signatures. Females had higher pre-infection expression of antiviral interferon-stimulated gene (ISG) programs. Causal mediation analysis implicated ISG differences in number of symptoms, levels of ISGs, and differential splicing of CD45 lymphocyte phosphatase during infection. Our results indicate that the antiviral innate immunity set point causally contributes to sex differences in response to SARS-CoV-2 infection. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Natalie Sauerwald
- Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA
| | - Zijun Zhang
- Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA
| | - Irene Ramos
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Venugopalan D Nair
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Yongchao Ge
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Weiguang Mao
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Hala Alshammary
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ana S Gonzalez-Reiche
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Adriana van de Guchte
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Carl W Goforth
- Naval Medical Research Center, Silver Spring, MD 20910, USA
| | | | | | - Mary Anne S Amper
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Mital Vasoya
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nitish Seenarine
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kristy Guevara
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nada Marjanovic
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Clare M Miller
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - German Nudelman
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Rachel S G Sealfon
- Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA
| | - Michael S Termini
- Navy Medicine Readiness and Training Command Beaufort, Beaufort, SC 29902, USA
| | - Sindhu Vangeti
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Dawn L Weir
- Naval Medical Research Center, Silver Spring, MD 20910, USA
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Maria Chikina
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Ying Nian Wu
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Harm Van Bakel
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Stuart C Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Olga G Troyanskaya
- Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA; Department of Computer Science, Princeton University, Princeton, NJ 08540, USA.
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26
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Han Y, Wennersten SA, Wright JM, Ludwig RW, Lau E, Lam MPY. Proteogenomics reveals sex-biased aging genes and coordinated splicing in cardiac aging. Am J Physiol Heart Circ Physiol 2022; 323:H538-H558. [PMID: 35930447 PMCID: PMC9448281 DOI: 10.1152/ajpheart.00244.2022] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/20/2022] [Accepted: 07/31/2022] [Indexed: 01/24/2023]
Abstract
The risks of heart diseases are significantly modulated by age and sex, but how these factors influence baseline cardiac gene expression remains incompletely understood. Here, we used RNA sequencing and mass spectrometry to compare gene expression in female and male young adult (4 mo) and early aging (20 mo) mouse hearts, identifying thousands of age- and sex-dependent gene expression signatures. Sexually dimorphic cardiac genes are broadly distributed, functioning in mitochondrial metabolism, translation, and other processes. In parallel, we found over 800 genes with differential aging response between male and female, including genes in cAMP and PKA signaling. Analysis of the sex-adjusted aging cardiac transcriptome revealed a widespread remodeling of exon usage patterns that is largely independent from differential gene expression, concomitant with upstream changes in RNA-binding protein and splice factor transcripts. To evaluate the impact of the splicing events on cardiac proteoform composition, we applied an RNA-guided proteomics computational pipeline to analyze the mass spectrometry data and detected hundreds of putative splice variant proteins that have the potential to rewire the cardiac proteome. Taken together, the results here suggest that cardiac aging is associated with 1) widespread sex-biased aging genes and 2) a rewiring of RNA splicing programs, including sex- and age-dependent changes in exon usages and splice patterns that have the potential to influence cardiac protein structure and function. These changes contribute to the emerging evidence for considerable sexual dimorphism in the cardiac aging process that should be considered in the search for disease mechanisms.NEW & NOTEWORTHY Han et al. used proteogenomics to compare male and female mouse hearts at 4 and 20 mo. Sex-biased cardiac genes function in mitochondrial metabolism, translation, autophagy, and other processes. Hundreds of cardiac genes show sex-by-age interactions, that is, sex-biased aging genes. Cardiac aging is accompanied with a remodeling of exon usage in functionally coordinated genes, concomitant with differential expression of RNA-binding proteins and splice factors. These features represent an underinvestigated aspect of cardiac aging that may be relevant to the search for disease mechanisms.
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Grants
- R21-HL150456 HHS | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R00-HL144829 HHS | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R00 HL127302 NHLBI NIH HHS
- R03-OD032666 HHS | NIH | NIH Office of the Director (OD)
- R01 HL141278 NHLBI NIH HHS
- F32 HL149191 NHLBI NIH HHS
- F32-HL149191 HHS | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R00-HL127302 HHS | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R21 HL150456 NHLBI NIH HHS
- R03 OD032666 NIH HHS
- R00 HL144829 NHLBI NIH HHS
- R01-HL141278 HHS | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- University of Colorado
- University of Colorado School of Medicine, Anschutz Medical Campus
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Affiliation(s)
- Yu Han
- Department of Medicine, Anschutz Medical Campus, University of Colorado School of Medicine, Aurora, Colorado
| | - Sara A Wennersten
- Department of Medicine, Anschutz Medical Campus, University of Colorado School of Medicine, Aurora, Colorado
| | - Julianna M Wright
- Department of Medicine, Anschutz Medical Campus, University of Colorado School of Medicine, Aurora, Colorado
| | - R W Ludwig
- Department of Medicine, Anschutz Medical Campus, University of Colorado School of Medicine, Aurora, Colorado
| | | | - Maggie P Y Lam
- Department of Medicine, Anschutz Medical Campus, University of Colorado School of Medicine, Aurora, Colorado
- Department of Biochemistry and Molecular Genetics, Anschutz Medical Campus, University of Colorado School of Medicine, Aurora, Colorado
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27
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Leng D, Zheng L, Wen Y, Zhang Y, Wu L, Wang J, Wang M, Zhang Z, He S, Bo X. A benchmark study of deep learning-based multi-omics data fusion methods for cancer. Genome Biol 2022; 23:171. [PMID: 35945544 PMCID: PMC9361561 DOI: 10.1186/s13059-022-02739-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/26/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A fused method using a combination of multi-omics data enables a comprehensive study of complex biological processes and highlights the interrelationship of relevant biomolecules and their functions. Driven by high-throughput sequencing technologies, several promising deep learning methods have been proposed for fusing multi-omics data generated from a large number of samples. RESULTS In this study, 16 representative deep learning methods are comprehensively evaluated on simulated, single-cell, and cancer multi-omics datasets. For each of the datasets, two tasks are designed: classification and clustering. The classification performance is evaluated by using three benchmarking metrics including accuracy, F1 macro, and F1 weighted. Meanwhile, the clustering performance is evaluated by using four benchmarking metrics including the Jaccard index (JI), C-index, silhouette score, and Davies Bouldin score. For the cancer multi-omics datasets, the methods' strength in capturing the association of multi-omics dimensionality reduction results with survival and clinical annotations is further evaluated. The benchmarking results indicate that moGAT achieves the best classification performance. Meanwhile, efmmdVAE, efVAE, and lfmmdVAE show the most promising performance across all complementary contexts in clustering tasks. CONCLUSIONS Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate deep learning-based multi-omics data fusion methods, but also suggest the future directions for the development of more effective multi-omics data fusion methods. The deep learning frameworks are available at https://github.com/zhenglinyi/DL-mo .
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Affiliation(s)
- Dongjin Leng
- Institute of Health Service and Transfusion Medicine, Beijing, People's Republic of China
| | - Linyi Zheng
- School of Informatics, Xiamen University, Xiamen, People's Republic of China
| | - Yuqi Wen
- Institute of Health Service and Transfusion Medicine, Beijing, People's Republic of China
| | - Yunhao Zhang
- School of Informatics, Xiamen University, Xiamen, People's Republic of China
| | - Lianlian Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
| | - Jing Wang
- School of Medicine, Tsinghua University, Beijing, People's Republic of China
| | - Meihong Wang
- School of Informatics, Xiamen University, Xiamen, People's Republic of China
| | - Zhongnan Zhang
- School of Informatics, Xiamen University, Xiamen, People's Republic of China.
| | - Song He
- Institute of Health Service and Transfusion Medicine, Beijing, People's Republic of China.
| | - Xiaochen Bo
- Institute of Health Service and Transfusion Medicine, Beijing, People's Republic of China.
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28
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Stogsdill JA, Kim K, Binan L, Farhi SL, Levin JZ, Arlotta P. Pyramidal neuron subtype diversity governs microglia states in the neocortex. Nature 2022; 608:750-756. [PMID: 35948630 PMCID: PMC10502800 DOI: 10.1038/s41586-022-05056-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 06/30/2022] [Indexed: 12/14/2022]
Abstract
Microglia are specialized macrophages in the brain parenchyma that exist in multiple transcriptional states and reside within a wide range of neuronal environments1-4. However, how and where these states are generated remains poorly understood. Here, using the mouse somatosensory cortex, we demonstrate that microglia density and molecular state acquisition are determined by the local composition of pyramidal neuron classes. Using single-cell and spatial transcriptomic profiling, we unveil the molecular signatures and spatial distributions of diverse microglia populations and show that certain states are enriched in specific cortical layers, whereas others are broadly distributed throughout the cortex. Notably, conversion of deep-layer pyramidal neurons to an alternate class identity reconfigures the distribution of local, layer-enriched homeostatic microglia to match the new neuronal niche. Leveraging the transcriptional diversity of pyramidal neurons in the neocortex, we construct a ligand-receptor atlas describing interactions between individual pyramidal neuron subtypes and microglia states, revealing rules of neuron-microglia communication. Our findings uncover a fundamental role for neuronal diversity in instructing the acquisition of microglia states as a potential mechanism for fine-tuning neuroimmune interactions within the cortical local circuitry.
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Affiliation(s)
- Jeffrey A Stogsdill
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kwanho Kim
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Loïc Binan
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Optical Profiling Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samouil L Farhi
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Optical Profiling Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joshua Z Levin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Paola Arlotta
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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29
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Oh S, Geistlinger L, Ramos M, Blankenberg D, van den Beek M, Taroni JN, Carey VJ, Greene CS, Waldron L, Davis S. GenomicSuperSignature facilitates interpretation of RNA-seq experiments through robust, efficient comparison to public databases. Nat Commun 2022; 13:3695. [PMID: 35760813 PMCID: PMC9237024 DOI: 10.1038/s41467-022-31411-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 06/14/2022] [Indexed: 02/04/2023] Open
Abstract
Millions of transcriptomic profiles have been deposited in public archives, yet remain underused for the interpretation of new experiments. We present a method for interpreting new transcriptomic datasets through instant comparison to public datasets without high-performance computing requirements. We apply Principal Component Analysis on 536 studies comprising 44,890 human RNA sequencing profiles and aggregate sufficiently similar loading vectors to form Replicable Axes of Variation (RAV). RAVs are annotated with metadata of originating studies and by gene set enrichment analysis. Functionality to associate new datasets with RAVs, extract interpretable annotations, and provide intuitive visualization are implemented as the GenomicSuperSignature R/Bioconductor package. We demonstrate the efficient and coherent database search, robustness to batch effects and heterogeneous training data, and transfer learning capacity of our method using TCGA and rare diseases datasets. GenomicSuperSignature aids in analyzing new gene expression data in the context of existing databases using minimal computing resources.
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Affiliation(s)
- Sehyun Oh
- grid.212340.60000000122985718Graduate School of Public Health and Health Policy and Institute for Implementation Sciences in Public Health, City University of New York, New York, NY USA
| | - Ludwig Geistlinger
- grid.38142.3c000000041936754XCenter for Computational Biomedicine, Harvard Medical School, Boston, MA USA
| | - Marcel Ramos
- grid.212340.60000000122985718Graduate School of Public Health and Health Policy and Institute for Implementation Sciences in Public Health, City University of New York, New York, NY USA
| | - Daniel Blankenberg
- grid.239578.20000 0001 0675 4725Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH USA ,grid.67105.350000 0001 2164 3847Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH USA
| | - Marius van den Beek
- grid.29857.310000 0001 2097 4281The Pennsylvania State University, State College, PA USA
| | - Jaclyn N. Taroni
- grid.430722.0Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Bala Cynwyd, PA USA
| | - Vincent J. Carey
- grid.38142.3c000000041936754XChanning Division of Network Medicine, Mass General Brigham, Harvard Medical School, Boston, MA USA
| | - Casey S. Greene
- grid.241116.10000000107903411Center for Health AI, University of Colorado Anschutz School of Medicine, Denver, CO USA
| | - Levi Waldron
- grid.212340.60000000122985718Graduate School of Public Health and Health Policy and Institute for Implementation Sciences in Public Health, City University of New York, New York, NY USA
| | - Sean Davis
- grid.241116.10000000107903411Center for Health AI, University of Colorado Anschutz School of Medicine, Denver, CO USA
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30
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Karagiannaki I, Gourlia K, Lagani V, Pantazis Y, Tsamardinos I. Learning biologically-interpretable latent representations for gene expression data: Pathway Activity Score Learning Algorithm. Mach Learn 2022; 112:4257-4287. [PMID: 37900054 PMCID: PMC10600308 DOI: 10.1007/s10994-022-06158-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 11/12/2021] [Accepted: 02/19/2022] [Indexed: 11/24/2022]
Abstract
Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (i.e., high dimensional data). However, lower-dimensional representations that retain the useful biological information do exist. We present a novel algorithm for such dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features directly correspond to known molecular pathways (genesets in general) and can be interpreted as pathway activity scores. Hence, unlike PCA and similar methods, PASL's latent space has a fairly straightforward biological interpretation. PASL is shown to outperform in predictive performance the state-of-the-art method (PLIER) on two collections of breast cancer and leukemia gene expression datasets. PASL is also trained on a large corpus of 50000 gene expression samples to construct a universal dictionary of features across different tissues and pathologies. The dictionary validated on 35643 held-out samples for reconstruction error. It is then applied on 165 held-out datasets spanning a diverse range of diseases. The AutoML tool JADBio is employed to show that the predictive information in the PASL-created feature space is retained after the transformation. The code is available at https://github.com/mensxmachina/PASL.
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Affiliation(s)
- Ioulia Karagiannaki
- Institute of Electronic Structure and Laser, Foundation for Research and Technology-Hellas (IESL-FORTH), Heraklion, Greece
| | | | - Vincenzo Lagani
- Institute of Chemical Biology, Ilia State University, Tbilisi, 0162 Georgia
- JADBio, Gnosis Data Analysis PC, Heraklion, Crete Greece
| | - Yannis Pantazis
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology - Hellas, Heraklion, Greece
| | - Ioannis Tsamardinos
- Department of Computer Science, University of Crete, Heraklion, Greece
- JADBio, Gnosis Data Analysis PC, Heraklion, Crete Greece
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology - Hellas, Heraklion, Greece
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31
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A single intranasal dose of human parainfluenza virus type 3-vectored vaccine induces effective antibody and memory T cell response in the lungs and protects hamsters against SARS-CoV-2. NPJ Vaccines 2022; 7:47. [PMID: 35468973 PMCID: PMC9038905 DOI: 10.1038/s41541-022-00471-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 03/21/2022] [Indexed: 12/31/2022] Open
Abstract
Respiratory tract vaccination has an advantage of needle-free delivery and induction of mucosal immune response in the portal of SARS-CoV-2 entry. We utilized human parainfluenza virus type 3 vector to generate constructs expressing the full spike (S) protein of SARS-CoV-2, its S1 subunit, or the receptor-binding domain, and tested them in hamsters as single-dose intranasal vaccines. The construct bearing full-length S induced high titers of neutralizing antibodies specific to S protein domains critical to the protein functions. Robust memory T cell responses in the lungs were also induced, which represent an additional barrier to infection and should be less sensitive than the antibody responses to mutations present in SARS-CoV-2 variants. Following SARS-CoV-2 challenge, animals were protected from the disease and detectable viral replication. Vaccination prevented induction of gene pathways associated with inflammation. These results indicate advantages of respiratory vaccination against COVID-19 and inform the design of mucosal SARS-CoV-2 vaccines.
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32
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Kowald A, Barrantes I, Möller S, Palmer D, Murua Escobar H, Schwerk A, Fuellen G. Transfer learning of clinical outcomes from preclinical molecular data, principles and perspectives. Brief Bioinform 2022; 23:6572661. [PMID: 35453145 PMCID: PMC9116218 DOI: 10.1093/bib/bbac133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 02/16/2022] [Accepted: 03/21/2022] [Indexed: 01/14/2023] Open
Abstract
Accurate transfer learning of clinical outcomes from one cellular context to another, between cell types, developmental stages, omics modalities or species, is considered tremendously useful. When transferring a prediction task from a source domain to a target domain, what counts is the high quality of the predictions in the target domain, requiring states or processes common to both the source and the target that can be learned by the predictor reflected by shared denominators. These may form a compendium of knowledge that is learned in the source to enable predictions in the target, usually with few, if any, labeled target training samples to learn from. Transductive transfer learning refers to the learning of the predictor in the source domain, transferring its outcome label calculations to the target domain, considering the same task. Inductive transfer learning considers cases where the target predictor is performing a different yet related task as compared with the source predictor. Often, there is also a need to first map the variables in the input/feature spaces and/or the variables in the output/outcome spaces. We here discuss and juxtapose various recently published transfer learning approaches, specifically designed (or at least adaptable) to predict clinical (human in vivo) outcomes based on preclinical (mostly animal-based) molecular data, towards finding the right tool for a given task, and paving the way for a comprehensive and systematic comparison of the suitability and accuracy of transfer learning of clinical outcomes.
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Affiliation(s)
- Axel Kowald
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Israel Barrantes
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Steffen Möller
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Daniel Palmer
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Hugo Murua Escobar
- Department of Medicine, Clinic III, Hematology, Oncology, Palliative Medicine, Rostock University Medical Center, Rostock, Germany
| | | | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany.,Centre for Transdisciplinary Neurosciences Rostock, Research Focus Oncology and Ageing of Individuals and Society, Interdisciplinary Faculty, Rostock, Germany
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33
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Basili D, Reynolds J, Houghton J, Malcomber S, Chambers B, Liddell M, Muller I, White A, Shah I, Everett LJ, Middleton A, Bender A. Latent Variables Capture Pathway-Level Points of Departure in High-Throughput Toxicogenomic Data. Chem Res Toxicol 2022; 35:670-683. [PMID: 35333521 PMCID: PMC9019810 DOI: 10.1021/acs.chemrestox.1c00444] [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] [Indexed: 11/28/2022]
Abstract
![]()
Estimation
of points of departure (PoDs) from high-throughput transcriptomic
data (HTTr) represents a key step in the development of next-generation
risk assessment (NGRA). Current approaches mainly rely on single key
gene targets, which are constrained by the information currently available
in the knowledge base and make interpretation challenging as scientists
need to interpret PoDs for thousands of genes or hundreds of pathways.
In this work, we aimed to address these issues by developing a computational
workflow to investigate the pathway concentration–response
relationships in a way that is not fully constrained by known biology
and also facilitates interpretation. We employed the Pathway-Level
Information ExtractoR (PLIER) to identify latent variables (LVs) describing
biological activity and then investigated in vitro LVs’ concentration–response
relationships using the ToxCast pipeline. We applied this methodology
to a published transcriptomic concentration–response data set
for 44 chemicals in MCF-7 cells and showed that our workflow can capture
known biological activity and discriminate between estrogenic and
antiestrogenic compounds as well as activity not aligning with the
existing knowledge base, which may be relevant in a risk assessment
scenario. Moreover, we were able to identify the known estrogen activity
in compounds that are not well-established ER agonists/antagonists
supporting the use of the workflow in read-across. Next, we transferred
its application to chemical compounds tested in HepG2, HepaRG, and
MCF-7 cells and showed that PoD estimates are in strong agreement
with those estimated using a recently developed Bayesian approach
(cor = 0.89) and in weak agreement with those estimated using a well-established
approach such as BMDExpress2 (cor = 0.57). These results demonstrate
the effectiveness of using PLIER in a concentration–response
scenario to investigate pathway activity in a way that is not fully
constrained by the knowledge base and to ease the biological interpretation
and support the development of an NGRA framework with the ability
to improve current risk assessment strategies for chemicals using
new approach methodologies.
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Affiliation(s)
- Danilo Basili
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K.,Unilever, Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K
| | - Joe Reynolds
- Unilever, Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K
| | - Jade Houghton
- Unilever, Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K
| | - Sophie Malcomber
- Unilever, Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K
| | - Bryant Chambers
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States
| | - Mark Liddell
- Unilever, Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K
| | - Iris Muller
- Unilever, Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K
| | - Andrew White
- Unilever, Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K
| | - Imran Shah
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States
| | - Logan J Everett
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States
| | - Alistair Middleton
- Unilever, Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K
| | - Andreas Bender
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
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34
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Zhang Z, Zamojski M, Smith GR, Willis TL, Yianni V, Mendelev N, Pincas H, Seenarine N, Amper MAS, Vasoya M, Cheng WS, Zaslavsky E, Nair VD, Turgeon JL, Bernard DJ, Troyanskaya OG, Andoniadou CL, Sealfon SC, Ruf-Zamojski F. Single nucleus transcriptome and chromatin accessibility of postmortem human pituitaries reveal diverse stem cell regulatory mechanisms. Cell Rep 2022; 38:110467. [PMID: 35263594 PMCID: PMC8957708 DOI: 10.1016/j.celrep.2022.110467] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 11/23/2021] [Accepted: 02/09/2022] [Indexed: 01/07/2023] Open
Abstract
Despite their importance in tissue homeostasis and renewal, human pituitary stem cells (PSCs) are incompletely characterized. We describe a human single nucleus RNA-seq and ATAC-seq resource from pediatric, adult, and aged postmortem pituitaries (snpituitaryatlas.princeton.edu) and characterize cell-type-specific gene expression and chromatin accessibility programs for all major pituitary cell lineages. We identify uncommitted PSCs, committing progenitor cells, and sex differences. Pseudotime trajectory analysis indicates that early-life PSCs are distinct from the other age groups. Linear modeling of same-cell multiome data identifies regulatory domain accessibility sites and transcription factors that are significantly associated with gene expression in PSCs compared with other cell types and within PSCs. We identify distinct deterministic mechanisms that contribute to heterogeneous marker expression within PSCs. These findings characterize human stem cell lineages and reveal diverse mechanisms regulating key PSC genes and cell type identity. This study profiles the gene expression and chromatin accessibility landscapes in postmortem male and female pituitaries of different ages using single nucleus multiomics technologies. Zhang et al. characterize the pituitary stem cell population and develop computational methods, which allow us to elucidate regulatory mechanisms underlying pituitary stem cell identity.
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Affiliation(s)
- Zidong Zhang
- Lewis-Sigler Institute for Integrative Genomics and Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, NJ, USA
| | - Michel Zamojski
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Gregory R Smith
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Thea L Willis
- Center for Craniofacial and Regenerative Biology, King's College London, London, UK
| | - Val Yianni
- Center for Craniofacial and Regenerative Biology, King's College London, London, UK
| | - Natalia Mendelev
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Hanna Pincas
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Nitish Seenarine
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Mary Anne S Amper
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Mital Vasoya
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Wan Sze Cheng
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Elena Zaslavsky
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Venugopalan D Nair
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Judith L Turgeon
- Department of Internal Medicine, University of California, Davis, Davis, CA, USA
| | - Daniel J Bernard
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Olga G Troyanskaya
- Lewis-Sigler Institute for Integrative Genomics and Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, NJ, USA; Department of Computer Science, Princeton University, Princeton, NJ, USA; Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Cynthia L Andoniadou
- Center for Craniofacial and Regenerative Biology, King's College London, London, UK; Department of Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
| | - Stuart C Sealfon
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA.
| | - Frederique Ruf-Zamojski
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA.
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35
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Rubenstein AB, Hinkley JM, Nair VD, Nudelman G, Standley RA, Yi F, Yu G, Trappe TA, Bamman MM, Trappe SW, Sparks LM, Goodpaster BH, Vega RB, Sealfon SC, Zaslavsky E, Coen PM. Skeletal muscle transcriptome response to a bout of endurance exercise in physically active and sedentary older adults. Am J Physiol Endocrinol Metab 2022; 322:E260-E277. [PMID: 35068187 PMCID: PMC8897039 DOI: 10.1152/ajpendo.00378.2021] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Age-related declines in cardiorespiratory fitness and physical function are mitigated by regular endurance exercise in older adults. This may be due, in part, to changes in the transcriptional program of skeletal muscle following repeated bouts of exercise. However, the impact of chronic exercise training on the transcriptional response to an acute bout of endurance exercise has not been clearly determined. Here, we characterized baseline differences in muscle transcriptome and exercise-induced response in older adults who were active/endurance trained or sedentary. RNA-sequencing was performed on vastus lateralis biopsy specimens obtained before, immediately after, and 3 h following a bout of endurance exercise (40 min of cycling at 60%-70% of heart rate reserve). Using a recently developed bioinformatics approach, we found that transcript signatures related to type I myofibers, mitochondria, and endothelial cells were higher in active/endurance-trained adults and were associated with key phenotypic features including V̇o2peak, ATPmax, and muscle fiber proportion. Immune cell signatures were elevated in the sedentary group and linked to visceral and intermuscular adipose tissue mass. Following acute exercise, we observed distinct temporal transcriptional signatures that were largely similar among groups. Enrichment analysis revealed catabolic processes were uniquely enriched in the sedentary group at the 3-h postexercise timepoint. In summary, this study revealed key transcriptional signatures that distinguished active and sedentary adults, which were associated with difference in oxidative capacity and depot-specific adiposity. The acute response signatures were consistent with beneficial effects of endurance exercise to improve muscle health in older adults irrespective of exercise history and adiposity.NEW & NOTEWORTHY Muscle transcript signatures associated with oxidative capacity and immune cells underlie important phenotypic and clinical characteristics of older adults who are endurance trained or sedentary. Despite divergent phenotypes, the temporal transcriptional signatures in response to an acute bout of endurance exercise were largely similar among groups. These data provide new insight into the transcriptional programs of aging muscle and the beneficial effects of endurance exercise to promote healthy aging in older adults.
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Affiliation(s)
- Aliza B Rubenstein
- Department of Neurology, Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Venugopalan D Nair
- Department of Neurology, Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, New York
| | - German Nudelman
- Department of Neurology, Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Fanchao Yi
- AdventHealth Translational Research Institute, Orlando, Florida
| | - GongXin Yu
- AdventHealth Translational Research Institute, Orlando, Florida
| | - Todd A Trappe
- Human Performance Laboratory, Ball State University, Indianapolis, Indiana
| | - Marcas M Bamman
- Department of Cell, Developmental, and Integrative Biology, UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Scott W Trappe
- Human Performance Laboratory, Ball State University, Indianapolis, Indiana
| | - Lauren M Sparks
- AdventHealth Translational Research Institute, Orlando, Florida
| | | | - Rick B Vega
- AdventHealth Translational Research Institute, Orlando, Florida
| | - Stuart C Sealfon
- Department of Neurology, Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, New York
| | - Elena Zaslavsky
- Department of Neurology, Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, New York
| | - Paul M Coen
- AdventHealth Translational Research Institute, Orlando, Florida
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36
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van den Heuvel A, Lassche S, Mul K, Greco A, San León Granado D, Heerschap A, Küsters B, Tapscott SJ, Voermans NC, van Engelen BGM, van der Maarel SM. Facioscapulohumeral dystrophy transcriptome signatures correlate with different stages of disease and are marked by different MRI biomarkers. Sci Rep 2022; 12:1426. [PMID: 35082321 PMCID: PMC8791933 DOI: 10.1038/s41598-022-04817-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 12/22/2021] [Indexed: 02/06/2023] Open
Abstract
With several therapeutic strategies for facioscapulohumeral muscular dystrophy (FSHD) entering clinical testing, outcome measures are becoming increasingly important. Considering the spatiotemporal nature of FSHD disease activity, clinical trials would benefit from non-invasive imaging-based biomarkers that can predict FSHD-associated transcriptome changes. This study investigated two FSHD-associated transcriptome signatures (DUX4 and PAX7 signatures) in FSHD skeletal muscle biopsies, and tested their correlation with a variety of disease-associated factors, including Ricci clinical severity score, disease duration, D4Z4 repeat size, muscle pathology scorings and functional outcome measures. It establishes that DUX4 and PAX7 signatures both show a sporadic expression pattern in FSHD-affected biopsies, possibly marking different stages of disease. This study analyzed two imaging-based biomarkers—Turbo Inversion Recovery Magnitude (TIRM) hyperintensity and fat fraction—and provides insights into their predictive power as non-invasive biomarkers for FSHD signature detection in clinical trials. Further insights in the heterogeneity of—and correlation between—imaging biomarkers and molecular biomarkers, as provided in this study, will provide important guidance to clinical trial design in FSHD. Finally, this study investigated the role of infiltrating non-muscle cell types in FSHD signature expression and detected potential distinct roles for two fibro-adipogenic progenitor subtypes in FSHD.
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37
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Ng B, Casazza W, Kim NH, Wang C, Farhadi F, Tasaki S, Bennett DA, De Jager PL, Gaiteri C, Mostafavi S. Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants. PLoS Genet 2021; 17:e1009918. [PMID: 34807913 PMCID: PMC8648125 DOI: 10.1371/journal.pgen.1009918] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 12/06/2021] [Accepted: 10/30/2021] [Indexed: 12/14/2022] Open
Abstract
The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms. The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, combines the effect of genetic variants on DNA methylation as well as gene expression. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes.
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Affiliation(s)
- Bernard Ng
- Department of Statistics and Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
| | - William Casazza
- Department of Statistics and Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
| | - Nam Hee Kim
- Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Chendi Wang
- Department of Statistics and Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
| | - Farnush Farhadi
- Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
| | - Shinya Tasaki
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
| | - Philip L. De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, New York, United States of America
| | - Christopher Gaiteri
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
| | - Sara Mostafavi
- Department of Statistics and Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Paul G. Allen School for Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America
- * E-mail:
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38
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Stein-O'Brien GL, Ainsile MC, Fertig EJ. Forecasting cellular states: from descriptive to predictive biology via single-cell multiomics. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 26:24-32. [PMID: 34660940 PMCID: PMC8516130 DOI: 10.1016/j.coisb.2021.03.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
As the single cell field races to characterize each cell type, state, and behavior, the complexity of the computational analysis approaches the complexity of the biological systems. Single cell and imaging technologies now enable unprecedented measurements of state transitions in biological systems, providing high-throughput data that capture tens-of-thousands of measurements on hundreds-of-thousands of samples. Thus, the definition of cell type and state is evolving to encompass the broad range of biological questions now attainable. To answer these questions requires the development of computational tools for integrated multi-omics analysis. Merged with mathematical models, these algorithms will be able to forecast future states of biological systems, going from statistical inferences of phenotypes to time course predictions of the biological systems with dynamic maps analogous to weather systems. Thus, systems biology for forecasting biological system dynamics from multi-omic data represents the future of cell biology empowering a new generation of technology-driven predictive medicine.
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Affiliation(s)
- Genevieve L Stein-O'Brien
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD
- Convergence Institute, Johns Hopkins University, Baltimore, MD
| | - Michaela C Ainsile
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD
| | - Elana J Fertig
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD
- Convergence Institute, Johns Hopkins University, Baltimore, MD
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
- Department of Applied Mathematics & Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD
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39
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Ruf-Zamojski F, Zhang Z, Zamojski M, Smith GR, Mendelev N, Liu H, Nudelman G, Moriwaki M, Pincas H, Castanon RG, Nair VD, Seenarine N, Amper MAS, Zhou X, Ongaro L, Toufaily C, Schang G, Nery JR, Bartlett A, Aldridge A, Jain N, Childs GV, Troyanskaya OG, Ecker JR, Turgeon JL, Welt CK, Bernard DJ, Sealfon SC. Single nucleus multi-omics regulatory landscape of the murine pituitary. Nat Commun 2021; 12:2677. [PMID: 33976139 PMCID: PMC8113460 DOI: 10.1038/s41467-021-22859-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 03/16/2021] [Indexed: 11/12/2022] Open
Abstract
To provide a multi-omics resource and investigate transcriptional regulatory mechanisms, we profile the transcriptome, chromatin accessibility, and methylation status of over 70,000 single nuclei (sn) from adult mouse pituitaries. Paired snRNAseq and snATACseq datasets from individual animals highlight a continuum between developmental epigenetically-encoded cell types and transcriptionally-determined transient cell states. Co-accessibility analysis-based identification of a putative Fshb cis-regulatory domain that overlaps the fertility-linked rs11031006 human polymorphism, followed by experimental validation illustrate the use of this resource for hypothesis generation. We also identify transcriptional and chromatin accessibility programs distinguishing each major cell type. Regulons, which are co-regulated gene sets sharing binding sites for a common transcription factor driver, recapitulate cell type clustering. We identify both cell type-specific and sex-specific regulons that are highly correlated with promoter accessibility, but not with methylation state, supporting the centrality of chromatin accessibility in shaping cell-defining transcriptional programs. The sn multi-omics atlas is accessible at snpituitaryatlas.princeton.edu.
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Affiliation(s)
- Frederique Ruf-Zamojski
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA.
| | - Zidong Zhang
- Lewis-Sigler Institute for Integrative Genomics, and Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, NJ, USA
| | - Michel Zamojski
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Gregory R Smith
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Natalia Mendelev
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - German Nudelman
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Mika Moriwaki
- Division of Endocrinology and Metabolism, University of Utah, Salt Lake City, UT, USA
| | - Hanna Pincas
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Rosa Gomez Castanon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Venugopalan D Nair
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Nitish Seenarine
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Mary Anne S Amper
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Xiang Zhou
- Dept. of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Luisina Ongaro
- Dept. of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Chirine Toufaily
- Dept. of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Gauthier Schang
- Dept. of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Andrew Aldridge
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Nimisha Jain
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Gwen V Childs
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Olga G Troyanskaya
- Lewis-Sigler Institute for Integrative Genomics, and Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Joseph R Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Judith L Turgeon
- Department of Internal Medicine, University of California, Davis, CA, USA
| | - Corrine K Welt
- Division of Endocrinology and Metabolism, University of Utah, Salt Lake City, UT, USA
| | - Daniel J Bernard
- Dept. of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Stuart C Sealfon
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA.
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40
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Lavin KM, Bell MB, McAdam JS, Peck BD, Walton RG, Windham ST, Tuggle SC, Long DE, Kern PA, Peterson CA, Bamman MM. Muscle transcriptional networks linked to resistance exercise training hypertrophic response heterogeneity. Physiol Genomics 2021; 53:206-221. [PMID: 33870722 DOI: 10.1152/physiolgenomics.00154.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
The skeletal muscle hypertrophic response to resistance exercise training (RT) is highly variable across individuals. The molecular underpinnings of this heterogeneity are unclear. This study investigated transcriptional networks linked to RT-induced muscle hypertrophy, classified as 1) predictive of hypertrophy, 2) responsive to RT independent of muscle hypertrophy, or 3) plastic with hypertrophy. Older adults (n = 31, 18 F/13 M, 70 ± 4 yr) underwent 14-wk RT (3 days/wk, alternating high-low-high intensity). Muscle hypertrophy was assessed by pre- to post-RT change in mid-thigh muscle cross-sectional area (CSA) [computed tomography (CT), primary outcome] and thigh lean mass [dual-energy X-ray absorptiometry (DXA), secondary outcome]. Transcriptome-wide poly-A RNA-seq was performed on vastus lateralis tissue collected pre- (n = 31) and post-RT (n = 22). Prediction networks (using only baseline RNA-seq) were identified by weighted gene correlation network analysis (WGCNA). To identify Plasticity networks, WGCNA change indices for paired samples were calculated and correlated to changes in muscle size outcomes. Pathway-level information extractor (PLIER) was applied to identify Response networks and link genes to biological annotation. Prediction networks (n = 6) confirmed transcripts previously connected to resistance/aerobic training adaptations in the MetaMEx database while revealing novel member genes that should fuel future research to understand the influence of baseline muscle gene expression on hypertrophy. Response networks (n = 6) indicated RT-induced increase in aerobic metabolism and reduced expression of genes associated with spliceosome biology and type-I myofibers. A single exploratory Plasticity network was identified. Findings support that interindividual differences in baseline gene expression may contribute more than RT-induced changes in gene networks to muscle hypertrophic response heterogeneity. Code/Data: https://github.com/kallavin/MASTERS_manuscript/tree/master.
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Affiliation(s)
- Kaleen M Lavin
- Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama.,Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama.,Florida Institute for Human and Machine Cognition, Pensacola, Florida
| | - Margaret B Bell
- Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama.,Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Jeremy S McAdam
- Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama.,Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Bailey D Peck
- Department of Physical Therapy, College of Health Sciences, University of Kentucky, Lexington, Kentucky.,Center for Muscle Biology, University of Kentucky, Lexington, Kentucky
| | - R Grace Walton
- Department of Physical Therapy, College of Health Sciences, University of Kentucky, Lexington, Kentucky.,Center for Muscle Biology, University of Kentucky, Lexington, Kentucky
| | - Samuel T Windham
- Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama.,Department of Surgery, The University of Alabama at Birmingham, Birmingham, Alabama
| | - S Craig Tuggle
- Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Douglas E Long
- Department of Physical Therapy, College of Health Sciences, University of Kentucky, Lexington, Kentucky.,Center for Muscle Biology, University of Kentucky, Lexington, Kentucky
| | - Philip A Kern
- Division of Endocrinology, Department of Internal Medicine, and Barnstable Brown Diabetes and Obesity Center, University of Kentucky, Lexington, Kentucky
| | - Charlotte A Peterson
- Department of Physical Therapy, College of Health Sciences, University of Kentucky, Lexington, Kentucky.,Center for Muscle Biology, University of Kentucky, Lexington, Kentucky
| | - Marcas M Bamman
- Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama.,Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama.,Florida Institute for Human and Machine Cognition, Pensacola, Florida
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41
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Cooley M, Greene CS, Issac D, Pividori M, Sullivan BD. Parameterized algorithms for identifying gene co-expression modules via weighted clique decomposition. PROCEEDINGS OF THE 2021 SIAM CONFERENCE ON APPLIED AND COMPUTATIONAL DISCRETE ALGORITHMS. SIAM CONFERENCE ON APPLIED AND COMPUTATIONAL DISCRETE ALGORITHMS (2021 : ONLINE) 2021; 2021:111-122. [PMID: 35391741 PMCID: PMC8985502 DOI: 10.1137/1.9781611976830.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We present a new combinatorial model for identifying regulatory modules in gene co-expression data using a decomposition into weighted cliques. To capture complex interaction effects, we generalize the previously-studied weighted edge clique partition problem. As a first step, we restrict ourselves to the noise-free setting, and show that the problem is fixed parameter tractable when parameterized by the number of modules (cliques). We present two new algorithms for finding these decompositions, using linear programming and integer partitioning to determine the clique weights. Further, we implement these algorithms in Python and test them on a biologically-inspired synthetic corpus generated using real-world data from transcription factors and a latent variable analysis of co-expression in varying cell types.
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42
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Eddy S, Mariani LH, Kretzler M. Integrated multi-omics approaches to improve classification of chronic kidney disease. Nat Rev Nephrol 2020; 16:657-668. [PMID: 32424281 DOI: 10.1038/s41581-020-0286-5] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/07/2020] [Indexed: 12/11/2022]
Abstract
Chronic kidney diseases (CKDs) are currently classified according to their clinical features, associated comorbidities and pattern of injury on biopsy. Even within a given classification, considerable variation exists in disease presentation, progression and response to therapy, highlighting heterogeneity in the underlying biological mechanisms. As a result, patients and clinicians experience uncertainty when considering optimal treatment approaches and risk projection. Technological advances now enable large-scale datasets, including DNA and RNA sequence data, proteomics and metabolomics data, to be captured from individuals and groups of patients along the genotype-phenotype continuum of CKD. The ability to combine these high-dimensional datasets, in which the number of variables exceeds the number of clinical outcome observations, using computational approaches such as machine learning, provides an opportunity to re-classify patients into molecularly defined subgroups that better reflect underlying disease mechanisms. Patients with CKD are uniquely poised to benefit from these integrative, multi-omics approaches since the kidney biopsy, blood and urine samples used to generate these different types of molecular data are frequently obtained during routine clinical care. The ultimate goal of developing an integrated molecular classification is to improve diagnostic classification, risk stratification and assignment of molecular, disease-specific therapies to improve the care of patients with CKD.
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Affiliation(s)
- Sean Eddy
- Division of Nephrology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, MI, USA
| | - Laura H Mariani
- Division of Nephrology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, MI, USA
| | - Matthias Kretzler
- Division of Nephrology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, Ann Arbor, MI, USA.
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43
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Kwon MS, Lee BT, Lee SY, Kim HU. Modeling regulatory networks using machine learning for systems metabolic engineering. Curr Opin Biotechnol 2020; 65:163-170. [DOI: 10.1016/j.copbio.2020.02.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 02/23/2020] [Accepted: 02/26/2020] [Indexed: 12/18/2022]
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44
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Kolberg L, Kerimov N, Peterson H, Alasoo K. Co-expression analysis reveals interpretable gene modules controlled by trans-acting genetic variants. eLife 2020; 9:e58705. [PMID: 32880574 PMCID: PMC7470823 DOI: 10.7554/elife.58705] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 08/20/2020] [Indexed: 12/16/2022] Open
Abstract
Understanding the causal processes that contribute to disease onset and progression is essential for developing novel therapies. Although trans-acting expression quantitative trait loci (trans-eQTLs) can directly reveal cellular processes modulated by disease variants, detecting trans-eQTLs remains challenging due to their small effect sizes. Here, we analysed gene expression and genotype data from six blood cell types from 226 to 710 individuals. We used co-expression modules inferred from gene expression data with five methods as traits in trans-eQTL analysis to limit multiple testing and improve interpretability. In addition to replicating three established associations, we discovered a novel trans-eQTL near SLC39A8 regulating a module of metallothionein genes in LPS-stimulated monocytes. Interestingly, this effect was mediated by a transient cis-eQTL present only in early LPS response and lost before the trans effect appeared. Our analyses highlight how co-expression combined with functional enrichment analysis improves the identification and prioritisation of trans-eQTLs when applied to emerging cell-type-specific datasets.
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Affiliation(s)
- Liis Kolberg
- Institute of Computer Science, University of TartuTartuEstonia
| | - Nurlan Kerimov
- Institute of Computer Science, University of TartuTartuEstonia
| | - Hedi Peterson
- Institute of Computer Science, University of TartuTartuEstonia
| | - Kaur Alasoo
- Institute of Computer Science, University of TartuTartuEstonia
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45
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Patrick E, Taga M, Ergun A, Ng B, Casazza W, Cimpean M, Yung C, Schneider JA, Bennett DA, Gaiteri C, De Jager PL, Bradshaw EM, Mostafavi S. Deconvolving the contributions of cell-type heterogeneity on cortical gene expression. PLoS Comput Biol 2020; 16:e1008120. [PMID: 32804935 PMCID: PMC7451979 DOI: 10.1371/journal.pcbi.1008120] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 08/27/2020] [Accepted: 07/02/2020] [Indexed: 12/26/2022] Open
Abstract
Complexity of cell-type composition has created much skepticism surrounding the interpretation of bulk tissue transcriptomic studies. Recent studies have shown that deconvolution algorithms can be applied to computationally estimate cell-type proportions from gene expression data of bulk blood samples, but their performance when applied to brain tissue is unclear. Here, we have generated an immunohistochemistry (IHC) dataset for five major cell-types from brain tissue of 70 individuals, who also have bulk cortical gene expression data. With the IHC data as the benchmark, this resource enables quantitative assessment of deconvolution algorithms for brain tissue. We apply existing deconvolution algorithms to brain tissue by using marker sets derived from human brain single cell and cell-sorted RNA-seq data. We show that these algorithms can indeed produce informative estimates of constituent cell-type proportions. In fact, neuronal subpopulations can also be estimated from bulk brain tissue samples. Further, we show that including the cell-type proportion estimates as confounding factors is important for reducing false associations between Alzheimer's disease phenotypes and gene expression. Lastly, we demonstrate that using more accurate marker sets can substantially improve statistical power in detecting cell-type specific expression quantitative trait loci (eQTLs).
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Affiliation(s)
- Ellis Patrick
- School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia
- The Westmead Institute for Medical Research, The University of Sydney, Sydney, New South Wales, Australia
| | - Mariko Taga
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York City, New York, United States of America
| | - Ayla Ergun
- Research and Development, Biogen, Cambridge, Massachusetts, United States of America
| | - Bernard Ng
- Departments of Statistics and Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
| | - William Casazza
- Departments of Statistics and Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
- The Bioinformatics Training Program, University of British Columbia, Vancouver, Canada
| | - Maria Cimpean
- Department of Pediatrics, Division of Rheumatology, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Christina Yung
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York City, New York, United States of America
| | - Julie A. Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
| | - Chris Gaiteri
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
| | - Philip L. De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York City, New York, United States of America
| | - Elizabeth M. Bradshaw
- Department of Neurology, Columbia University Medical Center, New York City, New York, United States of America
| | - Sara Mostafavi
- Departments of Statistics and Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
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46
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Lavin KM, Ge Y, Sealfon SC, Nair VD, Wilk K, McAdam JS, Windham ST, Kumar PL, McDonald MLN, Bamman MM. Rehabilitative Impact of Exercise Training on Human Skeletal Muscle Transcriptional Programs in Parkinson's Disease. Front Physiol 2020; 11:653. [PMID: 32625117 PMCID: PMC7311784 DOI: 10.3389/fphys.2020.00653] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 05/22/2020] [Indexed: 12/18/2022] Open
Abstract
Parkinson's disease (PD) is the most common motor neurodegenerative disease, and neuromuscular function deficits associated with PD contribute to disability. Targeting these symptoms, our laboratory has previously evaluated 16-week high-intensity resistance exercise as rehabilitative training (RT) in individuals with PD. We reported significant improvements in muscle mass, neuromuscular function (strength, power, and motor unit activation), indices of neuromuscular junction integrity, total and motor scores on the unified Parkinson's disease rating scale (UPDRS), and total and sub-scores on the 39-item PD Quality of Life Questionnaire (PDQ-39), supporting the use of RT to reverse symptoms. Our objective was to identify transcriptional networks that may contribute to RT-induced neuromuscular remodeling in PD. We generated transcriptome-wide skeletal muscle RNA-sequencing in 5 participants with PD [4M/1F, 67 ± 2 years, Hoehn and Yahr stages 2 (n = 3) and 3 (n = 2)] before and after 16-week high intensity RT to identify transcriptional networks that may in part underpin RT-induced neuromuscular remodeling in PD. Following RT, 304 genes were significantly upregulated, notably related to remodeling and nervous system/muscle development. Additionally, 402 genes, primarily negative regulators of muscle adaptation, were downregulated. We applied the recently developed Pathway-Level Information ExtractoR (PLIER) method to reveal coordinated gene programs (as latent variables, LVs) that differed in skeletal muscle among young (YA) and old (OA) healthy adults and PD (n = 12 per cohort) at baseline and in PD pre- vs. post-RT. Notably, one LV associated with angiogenesis, axon guidance, and muscle remodeling was significantly lower in PD than YA at baseline and was significantly increased by exercise. A different LV annotated to denervation, autophagy, and apoptosis was increased in both PD and OA relative to YA and was also reduced by 16-week RT in PD. Thus, this analysis identified two novel skeletal muscle transcriptional programs that are dysregulated by PD and aging, respectively. Notably, RT has a normalizing effect on both programs in individuals with PD. These results identify potential molecular transducers of the RT-induced improvements in neuromuscular remodeling and motor function that may aid in optimizing exercise rehabilitation strategies for individuals with PD.
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Affiliation(s)
- Kaleen M. Lavin
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
- UAB Center for Exercise Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Yongchao Ge
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Stuart C. Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Venugopalan D. Nair
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Katarzyna Wilk
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Jeremy S. McAdam
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
- UAB Center for Exercise Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Samuel T. Windham
- UAB Center for Exercise Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Surgery, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Preeti Lakshman Kumar
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Merry-Lynn N. McDonald
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Marcas M. Bamman
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
- UAB Center for Exercise Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
- Birmingham/Atlanta VA Geriatric Research, Education, and Clinical Center, Birmingham, AL, United States
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
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47
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Banerjee J, Allaway RJ, Taroni JN, Baker A, Zhang X, Moon CI, Pratilas CA, Blakeley JO, Guinney J, Hirbe A, Greene CS, Gosline SJC. Integrative Analysis Identifies Candidate Tumor Microenvironment and Intracellular Signaling Pathways that Define Tumor Heterogeneity in NF1. Genes (Basel) 2020; 11:E226. [PMID: 32098059 PMCID: PMC7073563 DOI: 10.3390/genes11020226] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 02/15/2020] [Accepted: 02/19/2020] [Indexed: 12/12/2022] Open
Abstract
Neurofibromatosis type 1 (NF1) is a monogenic syndrome that gives rise to numerous symptoms including cognitive impairment, skeletal abnormalities, and growth of benign nerve sheath tumors. Nearly all NF1 patients develop cutaneous neurofibromas (cNFs), which occur on the skin surface, whereas 40-60% of patients develop plexiform neurofibromas (pNFs), which are deeply embedded in the peripheral nerves. Patients with pNFs have a ~10% lifetime chance of these tumors becoming malignant peripheral nerve sheath tumors (MPNSTs). These tumors have a severe prognosis and few treatment options other than surgery. Given the lack of therapeutic options available to patients with these tumors, identification of druggable pathways or other key molecular features could aid ongoing therapeutic discovery studies. In this work, we used statistical and machine learning methods to analyze 77 NF1 tumors with genomic data to characterize key signaling pathways that distinguish these tumors and identify candidates for drug development. We identified subsets of latent gene expression variables that may be important in the identification and etiology of cNFs, pNFs, other neurofibromas, and MPNSTs. Furthermore, we characterized the association between these latent variables and genetic variants, immune deconvolution predictions, and protein activity predictions.
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Affiliation(s)
- Jineta Banerjee
- Computational Oncology, Sage Bionetworks, Seattle, WA 98121, USA
| | - Robert J Allaway
- Computational Oncology, Sage Bionetworks, Seattle, WA 98121, USA
| | - Jaclyn N Taroni
- Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Philadelphia, PA 19102, USA
| | - Aaron Baker
- Computational Oncology, Sage Bionetworks, Seattle, WA 98121, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53715, USA
- Morgridge Institute for Research, Madison, WI 53715, USA
| | - Xiaochun Zhang
- Division of Oncology, Washington University Medical School, St. Louis, MO 63110, USA
| | - Chang In Moon
- Division of Oncology, Washington University Medical School, St. Louis, MO 63110, USA
| | - Christine A Pratilas
- Sidney Kimmel Comprehensive Cancer Center and Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jaishri O Blakeley
- Sidney Kimmel Comprehensive Cancer Center and Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Neurology, Neurosurgery and Oncology, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Justin Guinney
- Computational Oncology, Sage Bionetworks, Seattle, WA 98121, USA
| | - Angela Hirbe
- Division of Oncology, Washington University Medical School, St. Louis, MO 63110, USA
| | - Casey S Greene
- Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Philadelphia, PA 19102, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sara JC Gosline
- Computational Oncology, Sage Bionetworks, Seattle, WA 98121, USA
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48
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Crawford J, Greene CS. Incorporating biological structure into machine learning models in biomedicine. Curr Opin Biotechnol 2020; 63:126-134. [PMID: 31962244 PMCID: PMC7308204 DOI: 10.1016/j.copbio.2019.12.021] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 12/17/2019] [Accepted: 12/19/2019] [Indexed: 12/19/2022]
Abstract
In biomedical applications of machine learning, relevant information
often has a rich structure that is not easily encoded as real-valued predictors.
Examples of such data include DNA or RNA sequences, gene sets or pathways, gene
interaction or coexpression networks, ontologies, and phylogenetic trees. We
highlight recent examples of machine learning models that use structure to
constrain model architecture or incorporate structured data into model training.
For machine learning in biomedicine, where sample size is limited and model
interpretability is crucial, incorporating prior knowledge in the form of
structured data can be particularly useful. The area of research would benefit
from performant open source implementations and independent benchmarking
efforts.
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Affiliation(s)
- Jake Crawford
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Philadelphia, PA, United States.
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49
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Rubenstein AB, Smith GR, Raue U, Begue G, Minchev K, Ruf-Zamojski F, Nair VD, Wang X, Zhou L, Zaslavsky E, Trappe TA, Trappe S, Sealfon SC. Single-cell transcriptional profiles in human skeletal muscle. Sci Rep 2020; 10:229. [PMID: 31937892 PMCID: PMC6959232 DOI: 10.1038/s41598-019-57110-6] [Citation(s) in RCA: 133] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 12/18/2019] [Indexed: 12/22/2022] Open
Abstract
Skeletal muscle is a heterogeneous tissue comprised of muscle fiber and mononuclear cell types that, in addition to movement, influences immunity, metabolism and cognition. We investigated the gene expression patterns of skeletal muscle cells using RNA-seq of subtype-pooled single human muscle fibers and single cell RNA-seq of mononuclear cells from human vastus lateralis, mouse quadriceps, and mouse diaphragm. We identified 11 human skeletal muscle mononuclear cell types, including two fibro-adipogenic progenitor (FAP) cell subtypes. The human FBN1+ FAP cell subtype is novel and a corresponding FBN1+ FAP cell type was also found in single cell RNA-seq analysis in mouse. Transcriptome exercise studies using bulk tissue analysis do not resolve changes in individual cell-type proportion or gene expression. The cell-type gene signatures provide the means to use computational methods to identify cell-type level changes in bulk studies. As an example, we analyzed public transcriptome data from an exercise training study and revealed significant changes in specific mononuclear cell-type proportions related to age, sex, acute exercise and training. Our single-cell expression map of skeletal muscle cell types will further the understanding of the diverse effects of exercise and the pathophysiology of muscle disease.
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Affiliation(s)
- Aliza B Rubenstein
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Gregory R Smith
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Ulrika Raue
- Human Performance Laboratory, Ball State University, Muncie, Indiana, 47306, USA
| | - Gwénaëlle Begue
- Human Performance Laboratory, Ball State University, Muncie, Indiana, 47306, USA
| | - Kiril Minchev
- Human Performance Laboratory, Ball State University, Muncie, Indiana, 47306, USA
| | - Frederique Ruf-Zamojski
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Venugopalan D Nair
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Xingyu Wang
- Department of Neurology, Boston University Medical Center, Boston, MA, 02118, USA
| | - Lan Zhou
- Department of Neurology, Boston University Medical Center, Boston, MA, 02118, USA
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Todd A Trappe
- Human Performance Laboratory, Ball State University, Muncie, Indiana, 47306, USA
| | - Scott Trappe
- Human Performance Laboratory, Ball State University, Muncie, Indiana, 47306, USA
| | - Stuart C Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA. .,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.
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50
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Appice A, Tsoumakas G, Manolopoulos Y, Matwin S. Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression Data. DISCOVERY SCIENCE 2020. [PMCID: PMC7556388 DOI: 10.1007/978-3-030-61527-7_17] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Abstract
Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (e.g., high dimensional data). However, there exist lower-dimensional representations that retain the useful information. We present a novel algorithm for such dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features directly correspond to known molecular pathways and can be interpreted as pathway activity scores. Hence, unlike PCA and similar methods, PASL’s latent space has a relatively straight-forward biological interpretation. As a use-case, PASL is applied on two collections of breast cancer and leukemia gene expression datasets. We show that PASL does retain the predictive information for disease classification on new, unseen datasets, as well as outperforming PLIER, a recently proposed competitive method. We also show that differential activation pathway analysis provides complementary information to standard gene set enrichment analysis. The code is available at https://github.com/mensxmachina/PASL.
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