Fine Particulate Matter Triggers α-Synuclein Fibrillization and Parkinson-like Neurodegeneration.

BACKGROUND: The deposition of α-synuclein (α-Syn) in the brain is the pathological hallmark of Parkinson's disease (PD). Epidemiological data indicate that exposure to fine particulate matter (≤2.5 µm in aerodynamic diameter [PM2.5]) is associated with an increased risk for PD. OBJECTIVE: The aim of this study is to investigate whether PM2.5 has a direct effect on α-Syn pathology and how it drives the risk for PD. METHODS: PM2.5 was added into α-Syn monomers and different cell models to test whether PM2.5 can promote the fibrillization and aggregation of α-Syn. α-Syn A53T transgenic mice and α-Syn knockout mice were used to investigate the effects of PM2.5 on PD-like pathology. RESULTS: PM2.5 triggers the fibrillization of α-Syn and promotes the formation of α-Syn fibrils with enhanced seeding activity and neurotoxicity. PM2.5 also induces mitochondrial dysfunction and oxidative stress. Intrastriatal injection or intranasal administration of PM2.5 exacerbates α-Syn pathology and dopaminergic neuronal degeneration in α-Syn A53T transgenic mice. The detrimental effect of PM2.5 was attenuated in α-Syn knockout mice. CONCLUSIONS: Our results identify that PM2.5 exposure could promote the α-Syn pathology, providing mechanistic insights into how PM2.5 increases the risk for PD. © 2022 International Parkinson and Movement Disorder Society.

Silica Nanoparticles Promote α-Synuclein Aggregation and Parkinson's Disease Pathology.

Silica nanoparticles (SiO2 NPs) are increasingly investigated for their potential in drug delivery systems. However, the neurotoxicity of SiO2 NPs remains to be fully clarified. Previously SiO2 NPs have been reported to be detected in the central nervous system, especially in the dopaminergic neurons which are deeply involved in Parkinson's disease (PD). In this article, we characterized the effects of SiO2 NPs on inducing PD-like pathology both in vitro and in vivo. Results showed that SiO2 NPs promote more severe hyperphosphorylation and aggregation of α-synuclein, mitochondria impairment, oxidative stress, autophagy dysfunction, and neuronal apoptosis in the α-Syn A53T transgenic mice intranasally administrated with SiO2 NPs compared with the control group. Our findings provide new evidence supporting that SiO2 NPs exposure might have a strong capability of promoting the initiation and development of PD.

Upregulation of long noncoding RNA MALAT1 in papillary thyroid cancer and its diagnostic value.

AIM: To investigate the expression level of lncRNA MALAT1 in papillary thyroid cancer (PTC) and evaluate its clinical diagnostic value as a biomarker in PTC. METHODS: MALAT1 lncRNA expression in tissues was detected by qRT-PCR. The diagnostic value of MALAT1 as a biomarker in PTC was evaluated with receiver operating characteristics. RESULTS: MALAT1 expression was upregulated in PTC tissues compared with paired corresponding noncancerous tissues. We also found that upregulated MALAT1 expression was correlated with tumor size, lymph node metastases (p = 0.011) and WHO disease stage. The area under the curve was 0.6320, 0.7192, 0.7089 and 0.7000 for PTC, lymph node metastasis, extrathyroidal extension and WHO disease stage prediction, respectively. CONCLUSION: Our finding suggests that MALAT1 may exert oncogenic function in PTC and may be a potential diagnostic marker for this cancer.

Effect of pyruvate kinase M2-regulating aerobic glycolysis on chemotherapy resistance of estrogen receptor-positive breast cancer.

This study aims to explore the effect and mechanism of pyruvate kinase M2 (PKM2) on chemotherapy resistance of estrogen receptor-positive breast cancer (ER BC) by regulating aerobic glycolysis. The expression of PKM2 in ER BC MCF-7 cells, T47D cells and MCF-7/ADR cells (which are subject to adriamycin/ADR induction) were determined by quantitative real-time PCR and western blot. MCF-7/ADR (M/A) cells were grouped into blank group (M/A), negative group (M/A+NC), low expression of PKM2 group (M/A+si-PKM2 group), overexpression of PKM2 group (M/A+PKM2 group) and glycolysis inhibition group (M/A+PKM2+2-DG group). Quantitative real-time PCR and western blot were applied to measure the expressions of PKM2, multidrug resistance, and glutathione-S-transferase π. Glucose and lactic acid kit was used to detect the amount of glucose uptake and lactic production. Cell variability, clone formation ability, and cell apoptosis were respectively measured by MTT, clone formation assay, and flow cytometry. Transwell assay and scratch assay were applied for cell invasion and migration ability. By overexpressing PKM2 in MCF-7 and T47D cells and using 2-DG, the effect on sensitivity of adriamycin amycin was explored. MCF-7/ADR cells have both elevated mRNA and protein expressions of PKM2 when compared with MCF-7 cells (both P<0.05). The cell activity of the M/A+si-PKM2+ADR group was notably lower than that in the M/A+ADR group and M/A+NC+ADR group (both P<0.05). In the M/A+si-PKM2 group, expressions of PKM2, multidrug resistance, and glutathione-S-transferase π, along with the amount of glucose uptake and lactic production, as well as cell variability, clone formation ability, and cell invasion and migration ability were inhibited, whereas cell apoptosis was increased in comparison with the M/A group and M/A+NC group (all P<0.05). On comparing with both the M/A group and the M/A+NC group, the M/A+si-PKM2 group displayed contrary tendency with the M/A+PKM2 group. The M/A+PKM2+2-DG group had elevated PKM2 expression compared with the M/A group and the M/A+NC group (all P<0.05). In MCF-7 and T47D cells with overexpression of PKM2, the sensitivity to adriamycin amycin, and cell apoptosis were suppressed, whereas the clone formation, invasion, and migration ability were enhanced. After 2-DG, the sensitivity on MCF-7 and T47D cells was enhanced while clone formation, invasion, migration and cell apoptosis rate were decreased (all P<0.05). PKM2 enhances chemotherapy resistance on ER BC by promoting aerobic glycolysis.

Identification of probabilistic cellular automata.

The identification of probabilistic cellular automata (PCA) is studied using a new two stage neighborhood detection algorithm. It is shown that a binary probabilistic cellular automaton (BPCA) can be described by an integer-parameterized polynomial corrupted by noise. Searching for the correct neighborhood of a BPCA is then equivalent to selecting the correct terms which constitute the polynomial model of the BPCA, from a large initial term set. It is proved that the contribution values for the correct terms can be calculated independently of the contribution values for the noise terms. This allows the neighborhood detection technique developed for deterministic rules in to be applied with a larger cutoff value to discard the majority of spurious terms and to produce an initial presearch for the BPCA neighborhood. A multiobjective genetic algorithm (GA) search with integer constraints is then evolved to refine the reduced neighborhood and to identify the polynomial rule which is equivalent to the probabilistic rule with the largest probability. A probability table representing the BPCA can then be determined based on the identified neighborhood and the deterministic rule. The new algorithm is tested over a large set of one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) BPCA rules. Simulation results demonstrate the efficiency of the new method.

Identification of the neighborhood and CA rules from spatio-temporal CA patterns.

Extracting the rules from spatio-temporal patterns generated by the evolution of cellular automata (CA) usually produces a CA rule table without providing a clear understanding of the structure of the neighborhood or the CA rule. In this paper, a new identification method based on using a modified orthogonal least squares or CA-OLS algorithm to detect the neighborhood structure and the underlying polynomial form of the CA rules is proposed. The Quine-McCluskey method is then applied to extract minimum Boolean expressions from the polynomials. Spatio-temporal patterns produced by the evolution of 1D, 2D, and higher dimensional binary CAs are used to illustrate the new algorithm, and simulation results show that the CA-OLS algorithm can quickly select both the correct neighborhood structure and the corresponding rule.