Isolation of Intermediate Filament Proteins from Multiple Mouse Tissues to Study Aging-associated Post-translational Modifications

Site-specific phosphorylation and caspase cleavage of GFAP are new markers of Alexander disease severity

Alexander disease (AxD) is a fatal neurodegenerative disorder caused by mutations in glial fibrillary acidic protein (GFAP), which supports the structural integrity of astrocytes. Over 70 GFAP missense mutations cause AxD, but the mechanism linking different mutations to disease-relevant phenotypes remains unknown. We used AxD patient brain tissue and induced pluripotent stem cell (iPSC)-derived astrocytes to investigate the hypothesis that AxD-causing mutations perturb key post-translational modifications (PTMs) on GFAP. Our findings reveal selective phosphorylation of GFAP-Ser13 in patients who died young, independently of the mutation they carried. AxD iPSC-astrocytes accumulated pSer13-GFAP in cytoplasmic aggregates within deep nuclear invaginations, resembling the hallmark Rosenthal fibers observed in vivo. Ser13 phosphorylation facilitated GFAP aggregation and was associated with increased GFAP proteolysis by caspase-6. Furthermore, caspase-6 was selectively expressed in young AxD patients, and correlated with the presence of cleaved GFAP. We reveal a novel PTM signature linking different GFAP mutations in infantile AxD.

Towards frailty biomarkers: Candidates from genes and pathways regulated in aging and age-related diseases

OBJECTIVE

Use of the frailty index to measure an accumulation of deficits has been proven a valuable method for identifying elderly people at risk for increased vulnerability, disease, injury, and mortality. However, complementary molecular frailty biomarkers or ideally biomarker panels have not yet been identified. We conducted a systematic search to identify biomarker candidates for a frailty biomarker panel.

METHODS

Gene expression databases were searched (http://genomics.senescence.info/genes including GenAge, AnAge, LongevityMap, CellAge, DrugAge, Digital Aging Atlas) to identify genes regulated in aging, longevity, and age-related diseases with a focus on secreted factors or molecules detectable in body fluids as potential frailty biomarkers. Factors broadly expressed, related to several "hallmark of aging" pathways as well as used or predicted as biomarkers in other disease settings, particularly age-related pathologies, were identified. This set of biomarkers was further expanded according to the expertise and experience of the authors. In the next step, biomarkers were assigned to six "hallmark of aging" pathways, namely (1) inflammation, (2) mitochondria and apoptosis, (3) calcium homeostasis, (4) fibrosis, (5) NMJ (neuromuscular junction) and neurons, (6) cytoskeleton and hormones, or (7) other principles and an extensive literature search was performed for each candidate to explore their potential and priority as frailty biomarkers.

RESULTS

A total of 44 markers were evaluated in the seven categories listed above, and 19 were awarded a high priority score, 22 identified as medium priority and three were low priority. In each category high and medium priority markers were identified.

CONCLUSION

Biomarker panels for frailty would be of high value and better than single markers. Based on our search we would propose a core panel of frailty biomarkers consisting of (1) CXCL10 (C-X-C motif chemokine ligand 10), IL-6 (interleukin 6), CX3CL1 (C-X3-C motif chemokine ligand 1), (2) GDF15 (growth differentiation factor 15), FNDC5 (fibronectin type III domain containing 5), vimentin (VIM), (3) regucalcin (RGN/SMP30), calreticulin, (4) PLAU (plasminogen activator, urokinase), AGT (angiotensinogen), (5) BDNF (brain derived neurotrophic factor), progranulin (PGRN), (6) α-klotho (KL), FGF23 (fibroblast growth factor 23), FGF21, leptin (LEP), (7) miRNA (micro Ribonucleic acid) panel (to be further defined), AHCY (adenosylhomocysteinase) and KRT18 (keratin 18). An expanded panel would also include (1) pentraxin (PTX3), sVCAM/ICAM (soluble vascular cell adhesion molecule 1/Intercellular adhesion molecule 1), defensin α, (2) APP (amyloid beta precursor protein), LDH (lactate dehydrogenase), (3) S100B (S100 calcium binding protein B), (4) TGFβ (transforming growth factor beta), PAI-1 (plasminogen activator inhibitor 1), TGM2 (transglutaminase 2), (5) sRAGE (soluble receptor for advanced glycosylation end products), HMGB1 (high mobility group box 1), C3/C1Q (complement factor 3/1Q), ST2 (Interleukin 1 receptor like 1), agrin (AGRN), (6) IGF-1 (insulin-like growth factor 1), resistin (RETN), adiponectin (ADIPOQ), ghrelin (GHRL), growth hormone (GH), (7) microparticle panel (to be further defined), GpnmB (glycoprotein nonmetastatic melanoma protein B) and lactoferrin (LTF). We believe that these predicted panels need to be experimentally explored in animal models and frail cohorts in order to ascertain their diagnostic, prognostic and therapeutic potential.

An image-based small-molecule screen identifies vimentin as a pharmacologically relevant target of simvastatin in cancer cells

Vimentin is a cytoskeletal intermediate filament protein that is expressed in mesenchymal cells and cancer cells during the epithelial-mesenchymal transition. The goal of this study was to identify vimentin-targeting small molecules by using the Tocriscreen library of 1120 biochemically active compounds. We monitored vimentin filament reorganization and bundling in adrenal carcinoma SW13 vimentin-positive (SW13-vim⁺) cells via indirect immunofluorescence. The screen identified 18 pharmacologically diverse hits that included 2 statins-simvastatin and mevastatin. Simvastatin induced vimentin reorganization within 15-30 min and significant perinuclear bundling within 60 min (IC₅₀ = 6.7 nM). Early filament reorganization coincided with increased vimentin solubility. Mevastatin produced similar effects at >1 µM, whereas the structurally related pravastatin and lovastatin did not affect vimentin. In vitro vimentin filament assembly assays revealed a direct targeting mechanism, as determined biochemically and by electron microscopy. In SW13-vim⁺ cells, simvastatin, but not pravastatin, reduced total cell numbers (IC₅₀ = 48.1 nM) and promoted apoptosis after 24 h. In contrast, SW13-vim^- cell viability was unaffected by simvastatin, unless vimentin was ectopically expressed. Simvastatin similarly targeted vimentin filaments and induced cell death in MDA-MB-231 (vim⁺), but lacked effect in MCF7 (vim^-) breast cancer cells. In conclusion, this study identified vimentin as a direct molecular target that mediates simvastatin-induced cell death in 2 different cancer cell lines.-Trogden, K. P., Battaglia, R. A., Kabiraj, P., Madden, V. J., Herrmann, H., Snider, N. T. An image-based small-molecule screen identifies vimentin as a pharmacologically relevant target of simvastatin in cancer cells.