Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia
Data integration stands to improve interpretation of RNAi screens which, as a result of off-target effects, typically yield numerous gene hits of which only a few validate. These off-target effects can result from seed matches to unintended gene targets (reagent-based) or cellular pathways, which ca...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en_US |
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Royal Society of Chemistry
2017
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| Online Access: | http://hdl.handle.net/1721.1/107703 https://orcid.org/0000-0003-4188-0414 https://orcid.org/0000-0001-5024-9718 https://orcid.org/0000-0002-6534-4774 https://orcid.org/0000-0001-9249-8181 |
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| author | Wilson, Jennifer Lynn Dalin, Simona Hemann, Michael Fraenkel, Ernest Lauffenburger, Douglas A Gosline, Sara Jane Calafell |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Wilson, Jennifer Lynn Dalin, Simona Hemann, Michael Fraenkel, Ernest Lauffenburger, Douglas A Gosline, Sara Jane Calafell |
| author_sort | Wilson, Jennifer Lynn |
| collection | MIT |
| description | Data integration stands to improve interpretation of RNAi screens which, as a result of off-target effects, typically yield numerous gene hits of which only a few validate. These off-target effects can result from seed matches to unintended gene targets (reagent-based) or cellular pathways, which can compensate for gene perturbations (biology-based). We focus on the biology-based effects and use network modeling tools to discover pathways de novo around RNAi hits. By looking at hits in a functional context, we can uncover novel biology not identified from any individual ‘omics measurement. We leverage multiple ‘omic measurements using the Simultaneous Analysis of Multiple Networks (SAMNet) computational framework to model a genome scale shRNA screen investigating Acute Lymphoblastic Leukemia (ALL) progression in vivo. Our network model is enriched for cellular processes associated with hematopoietic differentiation and homeostasis even though none of the individual ‘omic sets showed this enrichment. The model identifies genes associated with the TGF-beta pathway and predicts a role in ALL progression for many genes without this functional annotation. We further experimentally validate the hidden genes – Wwp1, a ubiquitin ligase, and Hgs, a multi-vesicular body associated protein – for their role in ALL progression. Our ALL pathway model includes genes with roles in multiple types of leukemia and roles in hematological development. We identify a tumor suppressor role for Wwp1 in ALL progression. This work demonstrates that network integration approaches can compensate for off-target effects, and that these methods can uncover novel biology retroactively on existing screening data. We anticipate that this framework will be valuable to multiple functional genomic technologies – siRNA, shRNA, and CRISPR – generally, and will improve the utility of functional genomic studies. |
| first_indexed | 2024-09-23T11:04:35Z |
| format | Article |
| id | mit-1721.1/107703 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:04:35Z |
| publishDate | 2017 |
| publisher | Royal Society of Chemistry |
| record_format | dspace |
| spelling | mit-1721.1/1077032022-09-27T17:00:33Z Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia Wilson, Jennifer Lynn Dalin, Simona Hemann, Michael Fraenkel, Ernest Lauffenburger, Douglas A Gosline, Sara Jane Calafell Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Biology Wilson, Jennifer Lynn Dalin, Simona Gosline, Sara Calafell Hemann, Michael Fraenkel, Ernest Lauffenburger, Douglas A Data integration stands to improve interpretation of RNAi screens which, as a result of off-target effects, typically yield numerous gene hits of which only a few validate. These off-target effects can result from seed matches to unintended gene targets (reagent-based) or cellular pathways, which can compensate for gene perturbations (biology-based). We focus on the biology-based effects and use network modeling tools to discover pathways de novo around RNAi hits. By looking at hits in a functional context, we can uncover novel biology not identified from any individual ‘omics measurement. We leverage multiple ‘omic measurements using the Simultaneous Analysis of Multiple Networks (SAMNet) computational framework to model a genome scale shRNA screen investigating Acute Lymphoblastic Leukemia (ALL) progression in vivo. Our network model is enriched for cellular processes associated with hematopoietic differentiation and homeostasis even though none of the individual ‘omic sets showed this enrichment. The model identifies genes associated with the TGF-beta pathway and predicts a role in ALL progression for many genes without this functional annotation. We further experimentally validate the hidden genes – Wwp1, a ubiquitin ligase, and Hgs, a multi-vesicular body associated protein – for their role in ALL progression. Our ALL pathway model includes genes with roles in multiple types of leukemia and roles in hematological development. We identify a tumor suppressor role for Wwp1 in ALL progression. This work demonstrates that network integration approaches can compensate for off-target effects, and that these methods can uncover novel biology retroactively on existing screening data. We anticipate that this framework will be valuable to multiple functional genomic technologies – siRNA, shRNA, and CRISPR – generally, and will improve the utility of functional genomic studies. National Institutes of Health (U.S.) (Grants U01-CA155758, U54-CA112967, U01-CA184898, and U01-CA155758) National Science Foundation (U.S.). Graduate Research Fellowship Program David H. Koch Institute for Integrative Cancer Research at MIT (Graduate Fellowship) 2017-03-24T20:53:21Z 2017-03-24T20:53:21Z 2016-06 2016-03 Article http://purl.org/eprint/type/JournalArticle 1757-9694 1757-9708 http://hdl.handle.net/1721.1/107703 Wilson, Jennifer L. et al. “Pathway-Based Network Modeling Finds Hidden Genes in shRNA Screen for Regulators of Acute Lymphoblastic Leukemia.” Integr. Biol. 8.7 (2016): 761–774. © 2016 The Royal Society of Chemistry https://orcid.org/0000-0003-4188-0414 https://orcid.org/0000-0001-5024-9718 https://orcid.org/0000-0002-6534-4774 https://orcid.org/0000-0001-9249-8181 en_US http://dx.doi.org/10.1039/c6ib00040a Integrative Biology Creative Commons Attribution-NonCommercial 3.0 Unported https://creativecommons.org/licenses/by-nc/3.0/ application/pdf Royal Society of Chemistry Royal Society of Chemistry |
| spellingShingle | Wilson, Jennifer Lynn Dalin, Simona Hemann, Michael Fraenkel, Ernest Lauffenburger, Douglas A Gosline, Sara Jane Calafell Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia |
| title | Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia |
| title_full | Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia |
| title_fullStr | Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia |
| title_full_unstemmed | Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia |
| title_short | Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia |
| title_sort | pathway based network modeling finds hidden genes in shrna screen for regulators of acute lymphoblastic leukemia |
| url | http://hdl.handle.net/1721.1/107703 https://orcid.org/0000-0003-4188-0414 https://orcid.org/0000-0001-5024-9718 https://orcid.org/0000-0002-6534-4774 https://orcid.org/0000-0001-9249-8181 |
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