SIGMAR1 is a causative gene for familial FTLD-MND with a unique neuropathology that differs from other FTLD and MND cases. Our findings also suggest Sigma-1 drugs as potential treatments for the TDP-43/FUS proteinopathies.

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Tau inclusions are a shared feature of many neurodegenerative diseases, among them frontotemporal dementia caused by tau mutations. Treatment approaches for these conditions include targeting posttranslational modifications of tau proteins, maintaining a steady-state amount of tau, and preventing its tendency to aggregate. We discovered a new regulatory pathway for tau degradation that operates through the farnesylated protein, Rhes, a GTPase in the Ras family. Here, we show that treatment with the farnesyltransferase inhibitor lonafarnib reduced Rhes and decreased brain atrophy, tau inclusions, tau sumoylation, and tau ubiquitination in the rTg4510 mouse model of tauopathy. In addition, lonafarnib treatment attenuated behavioral abnormalities in rTg4510 mice and reduced microgliosis in mouse brain. Direct reduction of Rhes in the rTg4510 mouse by siRNA reproduced the results observed with lonafarnib treatment. The mechanism of lonafarnib action mediated by Rhes to reduce tau pathology was shown to operate through activation of lysosomes. We finally showed in mouse brain and in human induced pluripotent stem cell–derived neurons a normal developmental increase in Rhes that was initially suppressed by tau mutations. The known safety of lonafarnib revealed in human clinical trials for cancer suggests that this drug could be repurposed for treating tauopathies.

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Prion protein gene polymorphism M129V represents a known risk factor for Creutzfeldt-Jakob disease. Recently, the meta-analysis revealed that homozygosity at codon 129 is connected with increased risk of Alzheimer's disease (AD). To determine whether M129V polymorphism is a risk factor for AD we analyzed a group of early-onset, and late-onset Polish AD patients. We observed that in LOAD patients there is a statistically significant increase of MM (p=0.0028) and decrease of MV (p=0.0006) genotype frequency, as compared to controls. When both groups were stratified according to APOE4 status, increase of MM and decrease of MV genotype frequency were significant in the LOAD subgroup with no APOE4 (p=0.017, and p=0.018, respectively). In the subgroup with APOE4 allele, only MV genotype frequency was significantly lower, as compared to controls (p=0.035). However, no interaction was found between APOE4 status and M129V polymorphism. We conclude that MM genotype increases LOAD risk in Polish population independently from the APOE4 status.

Improving accuracy of the available predictive DNA methods is important for their wider use in routine forensic work. Information on age in the process of identification of an unknown individual may provide important hints that can speed up the process of investigation. DNA methylation markers have been demonstrated to provide accurate age estimation in forensics, but there is growing evidence that DNA methylation can be modified by various factors including diseases. We analyzed DNA methylation profile in five markers from five different genes (ELOVL2, C1orf132, KLF14, FHL2, and TRIM59) used for forensic age prediction in three groups of individuals with diagnosed medical conditions. The obtained results showed that the selected age-related CpG sites have unchanged age prediction capacity in the group of late onset Alzheimer’s disease patients. Aberrant hypermethylation and decreased prediction accuracy were found for TRIM59 and KLF14 markers in the group of early onset Alzheimer’s disease suggesting accelerated aging of patients. In the Graves’ disease patients, altered DNA methylation profile and modified age prediction accuracy were noted for TRIM59 and FHL2 with aberrant hypermethylation observed for the former and aberrant hypomethylation for the latter. Our work emphasizes high utility of the ELOVL2 and C1orf132 markers for prediction of chronological age in forensics by showing unchanged prediction accuracy in individuals affected by three diseases. The study also demonstrates that artificial neural networks could be a convenient alternative for the forensic predictive DNA analyses.

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