The MYC associated factor X (MAX) is the heterodimeric partner of the MYC paralogs (MYC, MYCN and MYCL). When deregulated, high level of the MYC paralogs contribute to all aspects of tumorigenesis and tumor growth. MAX can also heterodimerize with the MXD proteins, MNT and MGA. Heterodimerization and sequence specific DNA binding to the E-Box sequences at gene promoters is controlled by their heterodimerization with the MAX b-HLH-LZ. As a heterodimer with MAX, MYC proteins activate genes involved in cell metabolism, growth and proliferation whereas MXD proteins, MNT and MGA repress them. MAX can also bind to the E-Bos sequence as a homodimer. Being devoid of a transactivation domain it can act as an antagonist of the MYC/MAX heterodimers. Variants of MAX have been reported to be linked to cancer. These variants are either not expressed, inactivated or lead to missense mutations. This has led to the notion that MAX may have a tumor suppressor role. Here, we characterize three of those variants with missense mutations in the basic region, i.e. E32K, R35P and R35C. We analyzed their heterodimerization with the b-HLH-LZ of MYC and their DNA binding properties as homo- and heterodimers. The R35C variant b-HLH-LZ was found to have a markedly increased affinity for the b-HLH-LZ of MYC. We also observed that all three b-HLH-LZ variants have a lower affinity as homodimers for the E-Box than the WT. This was shown to lead to a preferential binding of all the heterodimeric b-LHLH-LZ to the E-Box. This effect is exacerbated in the case of the R35C variant. We argue that this preferential binding of MYC as heterodimers with these variants to E-Box sequences could contribute to tumorigenesis. Hence, our results suggest that, mechanistically, the MAX homodimer bound to the E-Box could act as a tumor suppressor.
Assessing nurses’ attitudes toward artificial intelligence in Kazakhstan: psychometric validation of a nine-item scale
BackgroundArtificial intelligence (AI) is increasingly integrated into healthcare, yet the attitudes and knowledge of nurses, who are the key mediators of AI implementation, remain underexplored.


