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Xu Q, del Mundo IMA, Zewail-Foote M, Luke BT, Vasquez KM, Kowalski J. MoCoLo: a testing framework for motif co-localization. Brief Bioinform 2024; 25:bbae019. [PMID: 38521050 PMCID: PMC10960634 DOI: 10.1093/bib/bbae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/08/2024] [Accepted: 01/09/2024] [Indexed: 03/25/2024] Open
Abstract
Sequence-level data offers insights into biological processes through the interaction of two or more genomic features from the same or different molecular data types. Within motifs, this interaction is often explored via the co-occurrence of feature genomic tracks using fixed-segments or analytical tests that respectively require window size determination and risk of false positives from over-simplified models. Moreover, methods for robustly examining the co-localization of genomic features, and thereby understanding their spatial interaction, have been elusive. We present a new analytical method for examining feature interaction by introducing the notion of reciprocal co-occurrence, define statistics to estimate it and hypotheses to test for it. Our approach leverages conditional motif co-occurrence events between features to infer their co-localization. Using reverse conditional probabilities and introducing a novel simulation approach that retains motif properties (e.g. length, guanine-content), our method further accounts for potential confounders in testing. As a proof-of-concept, motif co-localization (MoCoLo) confirmed the co-occurrence of histone markers in a breast cancer cell line. As a novel analysis, MoCoLo identified significant co-localization of oxidative DNA damage within non-B DNA-forming regions that significantly differed between non-B DNA structures. Altogether, these findings demonstrate the potential utility of MoCoLo for testing spatial interactions between genomic features via their co-localization.
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Affiliation(s)
- Qi Xu
- Department of Molecular Biosciences, College of Natural Sciences, The University of Texas at Austin, Austin, TX, 78712, USA
- Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Imee M A del Mundo
- Dell Pediatric Research Institute, Division of Pharmacology and Toxicology, College of Pharmacy, The University of Texas at Austin, Austin, Texas, 78723, USA
| | - Maha Zewail-Foote
- Department of Chemistry and Biochemistry, Southwestern University, Georgetown, TX, 78626, USA
| | - Brian T Luke
- Bioinformatics and Computational Science, Frederick National Laboratory for Cancer Research, Frederick, Maryland, 21701, USA
| | - Karen M Vasquez
- Dell Pediatric Research Institute, Division of Pharmacology and Toxicology, College of Pharmacy, The University of Texas at Austin, Austin, Texas, 78723, USA
| | - Jeanne Kowalski
- Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX, 78712, USA
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Deo R, Dubin RF, Ren Y, Murthy AC, Wang J, Zheng H, Zheng Z, Feldman H, Shou H, Coresh J, Grams M, Surapaneni AL, Bhat Z, Cohen JB, Rahman M, He J, Saraf SL, Go AS, Kimmel PL, Vasan RS, Segal MR, Li H, Ganz P. Proteomic cardiovascular risk assessment in chronic kidney disease. Eur Heart J 2023; 44:2095-2110. [PMID: 37014015 PMCID: PMC10281556 DOI: 10.1093/eurheartj/ehad115] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 01/21/2023] [Accepted: 02/16/2023] [Indexed: 04/05/2023] Open
Abstract
AIMS Chronic kidney disease (CKD) is widely prevalent and independently increases cardiovascular risk. Cardiovascular risk prediction tools derived in the general population perform poorly in CKD. Through large-scale proteomics discovery, this study aimed to create more accurate cardiovascular risk models. METHODS AND RESULTS Elastic net regression was used to derive a proteomic risk model for incident cardiovascular risk in 2182 participants from the Chronic Renal Insufficiency Cohort. The model was then validated in 485 participants from the Atherosclerosis Risk in Communities cohort. All participants had CKD and no history of cardiovascular disease at study baseline when ∼5000 proteins were measured. The proteomic risk model, which consisted of 32 proteins, was superior to both the 2013 ACC/AHA Pooled Cohort Equation and a modified Pooled Cohort Equation that included estimated glomerular filtrate rate. The Chronic Renal Insufficiency Cohort internal validation set demonstrated annualized receiver operating characteristic area under the curve values from 1 to 10 years ranging between 0.84 and 0.89 for the protein and 0.70 and 0.73 for the clinical models. Similar findings were observed in the Atherosclerosis Risk in Communities validation cohort. For nearly half of the individual proteins independently associated with cardiovascular risk, Mendelian randomization suggested a causal link to cardiovascular events or risk factors. Pathway analyses revealed enrichment of proteins involved in immunologic function, vascular and neuronal development, and hepatic fibrosis. CONCLUSION In two sizeable populations with CKD, a proteomic risk model for incident cardiovascular disease surpassed clinical risk models recommended in clinical practice, even after including estimated glomerular filtration rate. New biological insights may prioritize the development of therapeutic strategies for cardiovascular risk reduction in the CKD population.
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Affiliation(s)
- Rajat Deo
- Division of Cardiovascular Medicine, Electrophysiology Section, Perelman School of Medicine at the University of Pennsylvania, One Convention Avenue, Level 2 / City Side, Philadelphia, PA 19104, USA
| | - Ruth F Dubin
- Division of Nephrology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA
| | - Yue Ren
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Ashwin C Murthy
- Division of Cardiovascular Medicine, Electrophysiology Section, Perelman School of Medicine at the University of Pennsylvania, One Convention Avenue, Level 2 / City Side, Philadelphia, PA 19104, USA
| | - Jianqiao Wang
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Haotian Zheng
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Zihe Zheng
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Harold Feldman
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Josef Coresh
- Department of Epidemiology; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
- Department of Medicine, Johns Hopkins University, 2024 E. Monument Street, Room 2-635, Suite 2-600, Baltimore, MD 21287, USA
| | - Morgan Grams
- Department of Epidemiology; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
- Department of Medicine, Johns Hopkins University, 2024 E. Monument Street, Room 2-635, Suite 2-600, Baltimore, MD 21287, USA
| | - Aditya L Surapaneni
- Department of Epidemiology; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Zeenat Bhat
- Division of Nephrology, University of Michigan, 5100 Brehm Tower, 1000 Wall Street, Ann Arbor, MI 48105, USA
| | - Jordana B Cohen
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
- Renal, Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, 831 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Mahboob Rahman
- Department of Medicine, Case Western Reserve University School of Medicine, 11100 Euclid Avenue, Wearn Bldg. 3 Floor. Rm 352, Cleveland, OH 44106, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, SL 18, New Orleans, LA 70112, USA
| | - Santosh L Saraf
- Division of Hematology and Oncology, University of Illinois at Chicago, 1740 West Taylor Street, Chicago, IL 60612, USA
| | - Alan S Go
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612, USA
- Departments of Epidemiology, Biostatistics and Medicine, University of California at San Francisco, San Francisco, CA, USA
| | - Paul L Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
| | - Ramachandran S Vasan
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Section of Cardiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Mark R Segal
- Department of Epidemiology and Biostatistics, University of California, 550 16th Street, 2nd Floor, Box #0560, San Francisco, CA 94143, USA
| | - Hongzhe Li
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Peter Ganz
- Division of Cardiology, Zuckerberg San Francisco General Hospital and Department of Medicine, University of California, San Francisco, 1001 Potrero Avenue, 5G1, San Francisco, CA 94110, USA
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