Alpha-B-Crystallin overexpression is sufficient to promote tumorigenesis and metastasis in mice

Published: 3 August 2022| Version 1 | DOI: 10.17632/kp6dk64gn5.1
Behnam Rashidieh


Alpha-Crystallin B chain (αB-Crystallin) is a heat shock chaperone protein that is encoded in humans by CRYAB gene. It has been extensively studied as a chaperone that binds to misfolded proteins to prevent their aggregation and as a structural protein that contributes to intracellular architecture. In cancer, αB-crystallin enables tumor cells to survive stress-induced tumor microenvironment by promoting proliferation and angiogenesis and inhibiting apoptosis. While CRYAB overexpression is considered as a marker for poor prognosis in a wide variety of cancers, some studies – particularly in ovarian and testicular cancer - have reported its downregulation to be associated with tumor progression. This has led to controversy surrounding the role of CRYAB as either a tumor suppressor or an oncogene. To determine the causal relationship of the overexpression of CRYAB in cancer, we have generated a Cryab overexpression mouse model where Cryab was overexpressed from the Rosa26 locus. This model revealed that constitutive overexpression of Cryab results in the formation of a variety of lethal spontaneous primary and metastatic tumors in mice. In vivo, the overexpression of Cryab correlated with the upregulation of epithelial-to-mesenchymal (EMT) markers, angiogenesis and some oncogenic proteins including Basigin, an extracellular matrix metalloproteinase inducer. In vitro, using mouse embryonic fibroblasts (MEFs), we observed that the overexpression of Cryab led to the promotion of cell survival via upregulation of Akt signaling and downregulation of pro-apoptotic pathway mediator JNK, with subsequent attenuation of apoptosis as assessed by cleaved caspase 3. Overall, through the generation and characterization of Cryab overexpression model, we provide evidence supporting the role of αB-Crystallin as an oncogene, where its upregulation is sufficient to induce tumors, promote cell survival and inhibit apoptosis.


Steps to reproduce

CryabWt and CryabTg MEFs were treated for 1h at 43ºC to generate heat-shock stress before processing samples in triplicates for proteomic mass spectrometry acquisition. For mass-spectrometry Samples samples were loaded on to a Waters M-Class SYM100 trap column (180 um x 20 mm ID) for 6 min at a flow rate of 5 ul/min with 95% Solvent A (0.1% FA in water), and subsequently separated on a Waters BEH130 analytical column (75 um x 200 mm ID). Columns were equipped on a Waters nanoACQUITY UPLC coupled with a Thermo Orbitrap Fusion mass spectrometer. The solvent gradient ran at 300 nl/min and started at 92% Solvent A before ramping up to 27% Solvent B (0.1% FA in ACN) over 45 min. This was followed by column washing and reequilibration for a total run time of 60 min. Mass spectrometry data was collected in positive mode. Precursor spectra (350-1800 m/z) were detected in the orbitrap at a resolution of 60,000 on a 3 sec cycle time. The AGC target was set to 1e6 with a maximum injection time of 22 ms. Fragment spectra were detected in the orbitrap at a resolution of 15,000 with a collision energy 30. The AGC target was 5e4, with a maximum ion injection time of 40 ms. The isolation window was set to 1.2 m/z. Dynamic exclusion was set to 15 sec and precursors with charge states from 2-6 were accepted for fragmentation. Raw LCMS data was searched against the reviewed Uniprot mouse database (21,963 sequences, downloaded 24/04/2020) using Sequest HT on the Thermo Proteome Discoverer software (Version 2.2). Precursor and fragment mass tolerance were set to 20 ppm and 0.05 Da respectively. A maximum of two missed cleavages were allowed. A strict false discovery rate (FDR) of 1% was used to filter peptide spectrum matches (PSMs) and was calculated using a decoy search Carbamidomethylation of cysteines was set as a fixed modification, while oxidation of methionine and deamidation of glutamine and asparagine were set as dynamic modifications. Protein abundance was based on intensity of the parent ions and data was normalized based on total peptide amount.


QIMR Berghofer Department of Genetics and Computational Biology