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                  <mods:dateIssued encoding="iso8601">2015-11</mods:dateIssued>
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               <mods:identifier type="citation">J Mol Med (Berl)  . 2015 Nov;93(11):1247-55</mods:identifier>
               <mods:identifier type="journal">Journal of Molecular Medicine</mods:identifier>
               <mods:identifier type="pubmedID">26070438</mods:identifier>
               <mods:identifier type="uri">https://hdl.handle.net/20.500.12105/26181</mods:identifier>
               <mods:abstract>The presence of germline mutations affecting the MYC-associated protein X (MAX) gene has recently been identified as one of the now 11 major genetic predisposition factors for the development of hereditary pheochromocytoma and/or paraganglioma. Little is known regarding how missense variants of unknown significance (VUS) in MAX affect its pivotal role in the regulation of the MYC/MAX/MXD axis. In the present study, we propose a consensus computational prediction based on five "state-of-the-art" algorithms. We also describe a PC12-based functional assay to assess the effects that 12 MAX VUS may have on MYC's E-box transcriptional activation. For all but two of these 12 VUS, the functional assay and the consensus computational prediction gave consistent results; we classified seven variants as pathogenic and three as nonpathogenic. The introduction of wild-type MAX cDNA into PC12 cells significantly decreased MYC's ability to bind to canonical E-boxes, while pathogenic MAX proteins were not able to fully repress MYC activity. Further clinical and molecular evaluation of variant carriers corroborated the results obtained with our functional assessment. In the absence of clear heritability, clinical information, and molecular data, consensus computational predictions and functional models are able to correctly classify VUS affecting MAX.A functional assay assesses the effects of MAX VUS over MYC transcriptional activity. A consensus computational prediction and the functional assay show high concordance. Variant carriers' clinical and molecular data support the functional assessment.</mods:abstract>
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               <mods:titleInfo>
                  <mods:title>Functional and in silico assessment of MAX variants of unknown significance.</mods:title>
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