Background
Cancer is a complex genetic disease that stems from the mutations of various genes. These mutations lead to the hallmarks of cancer which favor survival, angiogenesis, invasion, and metastasis [
1]. However, the mechanisms that create these advantages also lead to challenges such as proteostasis stress, oxidative stress and hypoxia that can stimulate cell death [
2,
3]. Cancer cells must overcome these stress responses to develop from a benign tumor into invasive metastatic cancer through rewiring of proteostatic processes such as upregulation of protein folding chaperones including the αB-Crystallin protein encoded by the CRYAB gene to avoid proteotoxicity [
2]. Through oligomerizing with other heat-shock proteins, αB-Crystallin allows misfolded or unfolded proteins to be sequestered and prevents their detrimental aggregation which would otherwise create a harmful environment.
The expression of
CRYAB has been thoroughly investigated in the context of a wide range of cancers where it has been validated as a prognostic marker [
4]. In clear cell and papillary type renal cell carcinoma and colorectal cancer,
CRYAB is used as a marker for higher tumor stage and distant metastases, and in osteosarcoma, it is a marker for increased metastases and poor chemotherapy response [
4]. In breast cancer,
CRYAB is a marker for aggressive behavior in triple-negative basal-like breast cancer and mammary metaplastic carcinoma and its overexpression is associated with the presence of lymph nodal and brain metastasis and relapse [
5].
CRYAB is also categorized as a marker for lower overall survival in cancers such as renal cell carcinoma, osteosarcoma, colorectal, hepatocellular carcinoma, gastric, ovarian, and non-small cell lung cancer [
4].
At the molecular level,
CRYAB has been shown to disrupt apoptosis through both the intrinsic and extrinsic pathways [
2] via inhibition of pro-apoptotic Bcl-2 family proteins including Bax and Bcl-xs which contribute to caspase 3 activation. In human mammary epithelial cells, the overexpression of
CRYAB led to disruption of normal mammary acinar morphology and induction of neoplastic changes [
4]. These phenotypes resulted from the activation of survival pathways such as p38, AKT and ERK with
CRYAB overexpression which could in part be rescued through inhibition of the MEK/ERK pathway [
4]. Furthermore, breast cancer cells can induce VEGF expression in co-cultured endothelial cells via activation of the unfolded protein response and its downstream effector
CRYAB which protects VEGF from proteolytic degradation [
6].
Despite multiple studies demonstrating a role for CRYAB in promoting tumorigenesis in vitro, it remains undetermined whether there is a causal link between CRYAB overexpression and cancer formation. Here, we demonstrate for the first time, using a transgenic mouse model, that overexpression of Cryab is sufficient for de novo tumorigenesis. Cryab -overexpressing mice show high incidence (almost 50%) of spontaneous tumor formation with a wide-spectrum of highly proliferative primary and metastatic tumors. Notably, Cryab overexpression significantly increased tumor load in a carcinogen-induced tumor model. Using the mouse embryonic fibroblasts (MEFs) from this mouse model, we show that Cryab overexpression alters multiple signaling pathways, particularly those related to apoptosis, survival, and metastasis which have potential implications for tumor initiation and therapy development.
Materials and methods
Generation of the targeting construct
Rosa26-UbiC- Cryab floxed mice were generated by Ozgene (Perth, WA, Australia). To generate the Cryab knock-in model, we designed a targeting vector containing a Flag-tagged Cryab cDNA (fl-Tg) preceded by a human ubiquitin C (UbiC) promoter as well as lox-P flanked polyadenylation (pA +) stop region, with a downstream flippase recombinase target site-flanked neomycin resistance cassette (PGK-NEO) for embryonic stem cell (ESC) selection. Genomic targeting of the construct was attained in ESCs of wild-type BALB/C, by utilizing standard homologous recombination and blastocyst manipulation techniques. Gene manipulation was validated by Ozgene using Southern blot analysis, with probes against both the endogenous coding region and NEO selection cassette following restriction digest of genomic DNA with the EcoRV restriction enzyme.
Generation of the ubiquitous Cryab knock-in mouse model
Cryab knock-in mice were generated by crossing heterozygous (het) Rosa26Ubiq-polyA-flTg(Neo)/wt mice with FLPe mice to remove the PGK-Neo cassette, followed by backcrossing to C57BL/6 wild-type mice to excise the FLPe transgene. Rosa26UbiC-polyA-flTg(Neo)/Wt mice were then crossed with Rosa26EIIA-Cre mice (from Ozgene) to remove the (pA +) stop region, allowing overexpression of
Cryab cDNA from the Rosa26 locus (Rosa26Tg/Tg). These mice were then crossed to BALB/C Wt mice to separate the Rosa26EIIA-Cre and RosT26Tg/Tg alleles. The resulting
Cryab heterozygous (
CryabWt/Tg) mice were intercrossed to generate 3 genotypes: Wt (
CryabWt/Wt), het (
CryabWt/Tg), and homozygous (
CryabTg/Tg) mice. Wt control mice used for Kaplan–Meier survival analysis served as joint controls with [
7].
Animal husbandry
All experimental animals were maintained on a mixed background (Balb/c X C57BL/6) strain. Mice were housed at 25 ℃ in a 12 h light–dark cycle.
Cell culture
Mouse embryonic fibroblasts (MEFs) were generated as described previously [
8]. Briefly, primary MEFs were immortalized and transformed by E1A/Ras oncogene. All cell lines were annually authenticated using short tandem repeat (STR) profiling and routinely tested for Mycoplasma infection by scientific services at QIMR Berghofer Medical Research Institute.
The cells were maintained in culture in Dulbecco’s Modified Eagle’s Medium (DMEM) (Life Technologies TM, Carlsbad, CA, USA) containing 20% Fetal Bovine Serum (SAFC BiosciencesTM, Lenexa, USA) 1% penicillin–streptomycin (Life Technology TM) and 1% Amphotericin B.
siRNA transfection
MEFs were plated in 6 well plates at density of 200,000 cells/well followed by double reverse transfection using 20 nM of siRNAs with following sequence (siRNA against BSG):
UTR 5' CCUUCUGAAGUGUUGUCACUACAGC 3'
5' GCUGUAGUGACAACACUUCAGAAGGGA 3'
Exon2 5' GGUUUGAAGGGAAUGCUCCAAACGA 3'.
5' UCGUUUGGAGCAUUCCCUUCAAACCAC 3'
Exon6 5' GUCACAGCUGACCAUCAGCAACCTT 3'.
5' AAGGUUGCUGAUGGUCAGCUGUGACUU 3'
Scr 5’ CAAUGUUGAUUUGGUGUCUGCA 3’.
5’ UGAAU AGGAUUGUAAC 3’
Cell proliferation assay
Cell proliferation was performed as previously described [
9]. Cells were plated in a 24-well plate at density of 5000 cells per well in duplicate and cultured overnight. The following morning, plates were transferred to an incubator equipped with an IncuCyte
® S3 Live-Cell Analysis system (Essen BioSciences Inc, USA) for 6 days. Cell confluency was analyzed using the in-built IncuCyte
® S3 software.
Clonogenic assays
Cells were plated on a 6-well plate at a density of 500 cells per well and incubated for two weeks to determine colony viability. Then, colonies were fixed with 0.05% crystal violet for 30 min, washed and quantified for crystal violet colony counting and measurement by imaging on a GE InCell 2000 microscope and analysis by GE InCell 2000 3-D Deconvolution Software (GE Health care, Life Sciences, USA) and total surface area quantified by ImageJ v1.53q.
Transwell assay
To measure cell migration 104 cells were seeded 0.05% FBS onto the top of a transwell insert with 8 µM pores (Corning Inc. New York, USA) placed into a 24-well cell culture dish where 20% FBS was used as a chemoattractant in the base of the culture dish.Cells were incubated in the transwell plate at 37 ℃ for 20 h and the migrated cells on the lower surface fixed at − 20 ℃ in ice cold MeOH for 30 min. After fixing, cells were stained with Crystal violet (0.5% (w/v) in 25% MeOH (v/v)) for 30 min. To quantitate cell migration 6 fields of view per membrane were photographed with EVOS™ FL Auto 2 Imaging System (Thermo Scientific™ Invitrogen™), followed by quantification by ImageJ v1.53q (National Institutes of Health, USA).
Apoptosis detection assay
For detection of apoptotic and necrotic cells, we used Annexin V and propidium iodide (PI) staining. Cells were plated at a density of 1 × 105 in triplicate in 6-well cell culture plates and incubated for 16 h. Cells were harvested with trypsin–EDTA and washed twice in media containing FBS. Cell pellets were resuspended in 100 µL of 1X binding buffer (0.1 M HEPES/NaOH (pH 7.4), 1.4 M NaCl, 25 mM CaCl2) and 2 µL of Fluorochrome conjugated Annexin V-FITC (Invitrogen™, USA). The cells were incubated for 15–20 min on ice followed by addition of 5 µL of PI (1 mg/mL) and 400 µL of binding buffer. Annexin V-FITC binding was detected via flow cytometry (Ex = 488 nm; Em = 350 nm) using FITC signal detector. The analysis was performed using FlowJo v. 10.0.6 (Tree Star, Ashland, Oregon, USA).
Immunoblotting
Cells were prepared for lysis as described previously [
8] with indicated antibodies (Table
1). The Super Signal chemiluminescent ECL‐plus (Amersham) was used for signal detection.
Table 1
Primary and secondary antibodies used for Western blot
Alpha B Crystallin | Novus | NBP2-49,246 | 1:500 |
Basigin (CD147) | Abcam | ab230921 | 1:1000 |
GAPDH | R&D Systems | RDS2275PC100 | 1:2000 |
Vinculin | Cell signalling technology | 13,901 | 1:2000 |
β-Catenin | Cell signalling technology | 9582 | 1:1000 |
Cleaved Caspase-3 | Cell signalling technology | 9664 | 1:500 |
pAKTS473 | Cell signalling technology | 4060 | 1:1000 |
AKT | Cell signalling technology | 9271 | 1:1000 |
p-JNK | Cell signalling technology | 4671 | 1:1000 |
Rabbit secondary (peroxidase) | Sigma Aldrich | A0545 | 1:5000 |
Mouse secondary (peroxidase) | Sigma Aldrich | A9044 | 1:5000 |
Polymerase chain reaction
PCR was performed as described previously [
8] using Genomic DNA from animal tissue or cell pellet was extracted using QuickExtract DNA solution (Gene target solutions, QE09050).
Mass-spectrometry
CryabWt and
CryabTg MEFs were treated for 1 h at 43ºC to generate heat-shock stress before processing samples in triplicates for in-situ protein digestion as per previously described method [
10]. For mass-spectrometry samples were loaded on to a Waters M-Class SYM100 trap column (180 um × 20 mm ID) for 6 min at a flow rate of 5 µl/min with 95% Solvent A (0.1% FA in water), and subsequently separated on a Waters BEH130 analytical column (75 µm × 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 acetonitrile) over 45 min. This was followed by column washing and reequilibration for a total run time of 60 min. MS spectra were acquired in the mass range = 350–1800 m/z (orbitrap resolution = 60,000). Fragmentation for MS/MS spectra were acquired 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 s and precursors with charge states from 2 to 6 were accepted for fragmentation.
Raw LCMS data was searched for protein IDs 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.
Differentially expressed proteins were ranked based on upregulated ones over log
2(0.6) or downregulated under log
2(− 0.6) in
CryabTg MEFs against
CryabWt MEFs. The biological pathway enrichment analysis was performed using GO analysis, and MCL cluster annotations and visualized using Cytoscape [
11] v.3.8.1
DMBA tumorigenic treatments
DMBA (7,12-Dimethylbenz[a]anthracene) treatments consisted of a single administration of 50 μl of a solution 0.5% DMBA (Sigma) to the dorsal surface on postnatal day 5 of mice.
Histopathological analysis and immunohistochemistry staining
Immunohistochemistry (IHC) was performed as previously described [
8]. Briefly, for histopathologic investigation with hematoxylin and eosin (H&E), tissues were collected and fixed in formalin solution and embedded in paraffin blocks. Sections were prepared and stained following the standard procedure with indicated antibodies (Table
2) and imaged with Aperio AT turbo/FL scanner (Leica Biosystems, Buffalo Grove, IL, USA). Two independent pathologists then provided their comments, in a detailed report and highlighted the regions of interest for analysis. The H-Score value was calculated in a semi-quantitative fashion which incorporated both the intensity and the distribution of staining. This value was derived by summing the percent of positive staining cells at each intensity multiplied by the weighted intensity of staining for each tissue. The intensity evaluations were recorded as four intensity categories which were designated as 0 (no staining), 1 + (weak but detectable above control), 2 + (distinct), 3 + (strong). H-Scores were generated for the cohort of indicated number of regions of interest from tumor and adjacent tumor tissues. IHC image analysis and quantitation was performed using QuPath software version 0.2.3. Correlation studies were performed in a blinded fashion for each target following by Pearson correlation coefficient (R) or linear regression as noted in figure's legend.
Table 2
Primary Antibodies used for IHC/IF
Alpha B Crystallin | Novus | NBP2-49,246 |
Basigin (CD147) | Abcam | ab230921 |
Vinculin | Cell signalling technology | 13,901 |
β-Catenin | Cell signalling technology | 9582 |
pAKTS473 | Cell signalling technology | 4060 |
E-cadherin | Dako | M3612 |
Vimentin | Cell signalling technology | 5741 |
pan Cytokeratin | Dako | M351529 |
Smooth Muscle Actin | Biocare medical | CM001C |
p53 | Novocastra (Leica) | NCL-p53-CM5p |
p44/42 MAPK (Erk1/2) | Cell signalling technology | 4695 |
CD4 | eBioscience | 14–9766 |
CD8 | eBioscience | 14–0808 |
F4/80 | Abcam | ab16911 |
CRYAB expression analyses
In human tumors,
CRYAB gene expression and gene expression signature analyses were performed in samples from The Cancer Genome Atlas (TCGA) as described in the Additional file
4: Additional Methods. All other CRYAB expression analyses were performed using samples from various datasets from the Gene Expression Omnibus (GEO,
https://www.ncbi.nlm.nih.gov/geo) with the following accession numbers: normal cervix and early-stage cervical cancers (GSE7410), primary and metastatic renal cell carcinoma (GSE31232), primary tumors without and with metastasis in hepatocellular carcinoma (GSE45114), pancreatic cancer (GSE63124), breast cancer (GSE9893) and colorectal cancer (GSE87211), locally and distantly metastasized pancreatic cancer (GSE34153), prostate cancer (GSE74367) and lung cancer (GSE18549).
Statistical analyses
All statistical analyses were performed utilizing GraphPad Prism v 9.0 software, using a general linear statistical model, as defined in each section. The error bar represents the mean ± standard deviation (SD) unless indicated otherwise. The statistical significance of the p-value is designated with an asterisk (*); p-values: * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001.
Discussion
We find that transcriptional upregulation of αB-Crystallin is widespread in human cancers and correlates with poor patient prognosis. To understand the contribution of αB-Crystallin to de novo tumorigenesis, we generated and characterized a mouse model that constitutively overexpresses
Cryab in multiple tissues.
Cryab overexpressing mice (homozygous
CryabTg) develop a wide spectrum of solid and hematological tumors with an approximate 50% incidence rate and metastatic potential, although with late latency. Notably, in the time-frame of the study only one control mice died due to idiopathic cause. Long latencies are characteristics of many other mouse models which suggest that cells may need to acquire time-dependent genetic and/or epigenetic changes before malignant transformation occurs [
18,
19]. Strikingly, we found higher p53 protein levels, which are most likely an indication of
Trp53 mutation in representative
CryabTg tumor tissues, suggesting that Tp53 might be a critical secondary hit required for tumor initiation observed in the homozygous
CryabTg mice. The tumors were further characterized histopathologically which identified lung adenocarcinoma, lung metastasis, hepatocellular carcinoma, and lymphoma as the major types of malignancies. Furthermore,
CryabTg mice also showed the increased carcinogen-DMBA induced tumor load. Like
Cryab (also known as
HSPB5), other heat shock proteins have been functionally linked to spontaneous tumorigenesis, for example, transgenic mice expressing human HSP70 developed lung and lymph node tumours before 18 months of age [
20]. In addition, HSP27 increased the tumorigenicity of rat colon adenocarcinoma in a syngeneic model [
21]. Furthermore, HSP90 is similarly up-regulated in a wide variety of cancers and inhibitors of HSP90 are currently in clinical trials as chemotherapeutic drugs [
22].
In our transgenic mouse model, we found elevated levels of
Cryab expression in
CryabTg tumors compared to adjacent non-tumor tissues and age-matched normal tissues from
CryabWt [
2].
Cryab is a multi-functional protein with postulated roles in the regulation of cell architecture, apoptosis, and autophagy through interactions with a multitude of its substrate proteins [
23]. Therefore, there are several potential mechanisms by which
Cryab may promote tumorigenesis. We have found that this may be caused by elevated levels of AKT and ERK survival pathways in
CryabTg tissues (both tumors and adjacent tissues) compared to tissues from
CryabWt mice. Interestingly, focusing on solid tumors from mice,
Cryab overexpression was correlated with cytokeratin-screening (CK-WSS), which reflects tumor cell activity and is considered a tumor marker. Furthermore, we found a correlation between overexpressed
Cryab and both angiogenesis and EMT markers in
CryabTg tumors, which may both enhance tumor phenotypes. The negative consequence of
Cryab depletion on tumor angiogenesis has been established in in vitro studies using cancer cell lines although, increased
Cryab expression has not previously been functionally linked to increased angiogenesis in tumors [
24,
25]. In terms of tumor infiltration, we could only find a correlation of tumor associated macrophage infiltration in areas of high expression of
CryabTg in hepatocellular carcinoma tumors which again is in line with Cibersort analysis of TCGA data for human liver hepatocellular carcinoma. These results are consistent with our pan-cancer analysis of TCGA data which revealed strong correlation of high
CRYAB expression with angiogenesis, EMT and metastasis in a variety of human cancer types, suggesting that our observations are relevant to cancer development in patients.
To complement and extend prior knowledge of αB-Crystallin biology, we performed further phenotypic analysis of E1A/RAS-transformed MEFs derived from
CryabTg and
CryabWt mice. Notably, we show that overexpression of
Cryab in transformed MEFs is sufficient to impart tumorigenic properties in vitro including increased clonogenic capacity, and migratory and invasive potential. Functional consequences of
Cryab overexpression were examined by proteomics analysis of the transformed MEFs of both genotypes, which also pointed to pleiotropic roles of
Cryab in the regulation of many metastatic and oncogenic proteins. The most significantly upregulated protein was Basigin, which we found had a positive correlation with the expression of
Cryab, validated both in tumors compared to adjacent normal tissues and in vitro in MEFs. Basigin is also a transmembrane protein that mainly functions in metabolic pathways, such as glycolysis, but its overexpression is associated with several pathologies including cancer [
26‐
29]. In particular, its overexpression is correlated with worse overall survival in acute myeloid leukaemia, and non-small-cell lung cancer (NSCLC) and plays important role in regulation of cancer cell proliferation, invasion and metastasis [
27,
29,
30]. Its cancer connection is mostly linked to its capacity to regulate expression/activity of monocarboxylate transporters, matrix metalloproteinases and PI3K and MAPK pathways. It also functions as a key mediator of inflammatory/immune response. Interestingly, the knockdown of Basigin reduced the oncogenic colony formation ability and migratory potential of
CryabTg MEFs, suggesting that some of the phenotypic effects of
Cryab overexpression might-in part be mediated by regulation of Basigin expression.
We also found elevated levels of AKT and pAKT in
CryabTg MEFs, consistent with our observations in
CryabTg tumors, and this might be a potential cause of spontaneous tumorigenesis in our model. The PI3K/AKT pathway is involved in various cellular processes, including the promotion of cell survival, and cell cycle and is often altered in various cancers [
31]. AKT can inhibit apoptosis by phosphorylating and inhibiting pro-apoptotic proteins such as Bad, Bim and caspase 9 [
32]. It can also promote the cell cycle progression by inhibiting both Cyclin-Dependent Kinase (CDK) degradation and CDK Inhibitors expression, allowing cell cycle activation [
31]. The upregulation of pAKT and total AKT by
Cryab overexpression are consistent with another in vitro model in particular near-normal mammary epithelial cell line, MCF10A, in which
Cryab-overexpression promoted malignant transformation and growth of mouse mammary xenograft tumors through regulation of AKT [
6]. On the other hand, JNK was found to be downregulated in
CryabTg MEFs. JNK directly induces apoptosis by activating pro-apoptotic proteins such as the previously mentioned Bad and Bim and inhibiting anti-apoptotic proteins such as Bcl-xL. JNK has been reported to antagonize PI3K/AKT pathway, and the reduced apoptosis observed in our model may partly potentiate the tumor formation where JNK antagonism of Akt-mediated survival signals is suppressed. Evasion of apoptosis is a well-described hallmark of cancer resulting in cellular resistance to conventional chemotherapeutic agents [
1]. Indeed, resistance to apoptosis during myocardial ischemia was observed in mice overexpressing
Cryab in cardiomyocytes [
33]. Altogether, these results show that the overexpression of
CRYAB leads to the promotion of cell survival after induction of cellular stress, which is consistent with its oncogenic potential. This may occur via the activation of the AKT pathway and inhibition of the JNK pathway. The promotion of cell survival was validated by the decreased levels of cleaved caspase 3.
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