Acetylated microtubules are required for fusion of autophagosomes with lysosomes

<p>Abstract</p> <p>Background</p> <p>Autophagy is a dynamic process during which isolation membranes package substrates to form autophagosomes that are fused with lysosomes to form autolysosomes for degradation. Although it is agreed that the LC3II-associated mature autophagosomes move along microtubular tracks, it is still in dispute if the conversion of LC3I to LC3II before autophagosomes are fully mature and subsequent fusion of mature autophagosomes with lysosomes require microtubules.</p> <p>Results</p> <p>We use biochemical markers of autophagy and a collection of microtubule interfering reagents to test the question. Results show that interruption of microtubules with either microtubule stabilizing paclitaxel or destabilizing nocodazole similarly impairs the conversion of LC3I to LC3II, but does not block the degradation of LC3II-associated autophagosomes. Acetylation of microtubules renders them resistant to nocodazole treatment. Treatment with vinblastine that causes depolymerization of both non-acetylated and acetylated microtubules results in impairment of both LC3I-LC3II conversion and LC3II-associated autophagosome fusion with lysosomes.</p> <p>Conclusions</p> <p>Acetylated microtubules are required for fusion of autophagosomes with lysosomes to form autolysosomes.</p

Gunrock: GPU Graph Analytics

For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We characterize the performance of various optimization strategies and evaluate Gunrock's overall performance on different GPU architectures on a wide range of graph primitives that span from traversal-based algorithms and ranking algorithms, to triangle counting and bipartite-graph-based algorithms. The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries such as Ligra and Galois, and better performance than any other GPU high-level graph library.Comment: 52 pages, invited paper to ACM Transactions on Parallel Computing (TOPC), an extended version of PPoPP'16 paper "Gunrock: A High-Performance Graph Processing Library on the GPU

Early Screening of Children With Autism Spectrum Disorder Based on Electroencephalogram Signal Feature Selection With L1-Norm Regularization

Early screening is vital and helpful for implementing intensive intervention and rehabilitation therapy for children with autism spectrum disorder (ASD). Research has shown that electroencephalogram (EEG) signals can reflect abnormal brain function of children with ASD, and screening with EEG signals has the characteristics of good real-time performance and high sensitivity. However, the existing EEG screening algorithms mostly focus on the data analysis in the resting state, and the extracted EEG features have some disadvantages such as weak representation capacity and information redundancy. In this study, we utilized the event-related potential (ERP) technique to acquire the EEG data of the subjects under positive and negative emotional stimulation and proposed an EEG Feature Selection Algorithm based on L1-norm regularization to perform screening of autism. The proposed EEG Feature Selection Algorithm includes the following steps: (1) extracting 20 EEG features from the raw data, (2) classification with support vector machine, (3) selecting appropriate EEG feature with L1-norm regularization according to the classification performance. The experimental results show that the accuracy for screening of children with ASD can reach 93.8% and 87.5% under positive and negative emotional stimulation and the proposed algorithm can effectively eliminate redundant features and improve screening accuracy

Autophagy Inhibitor LRPPRC Suppresses Mitophagy through Interaction with Mitophagy Initiator Parkin

Autophagy plays an important role in tumorigenesis. Mitochondrion-associated protein LRPPRC interacts with MAP1S that interacts with LC3 and bridges autophagy components with microtubules and mitochondria to affect autophagy flux. Dysfunction of LRPPRC and MAP1S is associated with poor survival of ovarian cancer patients. Furthermore, elevated levels of LRPPRC predict shorter overall survival in patients with prostate adenocarcinomas or gastric cancer. To understand the role of LRPPRC in tumor development, previously we reported that LRPPRC forms a ternary complex with Beclin 1 and Bcl-2 to inhibit autophagy. Here we further show that LRPPRC maintains the stability of Parkin that mono-ubiquitinates Bcl-2 to increase Bcl-2 stability to inhibit autophagy. Under mitophagy stress, Parkin translocates to mitochondria to cause rupture of outer mitochondrial membrane and bind with exposed LRPPRC. Consequently, LRPPRC and Parkin help mitochondria being engulfed in autophagosomes to be degraded. In cells under long-term mitophagy stress, both LRPPRC and Parkin become depleted coincident with disappearance of mitochondria and final autophagy inactivation due to depletion of ATG5-ATG12 conjugates. LRPPRC functions as a checkpoint protein that prevents mitochondria from autophagy degradation and impact tumorigenesis

Establishing reaction networks in the 16-electron sulfur reduction reaction

The sulfur reduction reaction (SRR) plays a central role in high-capacity lithium sulfur (Li-S) batteries. The SRR involves an intricate, 16-electron conversion process featuring multiple lithium polysulfide intermediates and reaction branches1-3. Establishing the complex reaction network is essential for rational tailoring of the SRR for improved Li-S batteries, but represents a daunting challenge4-6. Herein we systematically investigate the electrocatalytic SRR to decipher its network using the nitrogen, sulfur, dual-doped holey graphene framework as a model electrode to understand the role of electrocatalysts in acceleration of conversion kinetics. Combining cyclic voltammetry, in situ Raman spectroscopy and density functional theory calculations, we identify and directly profile the key intermediates (S8, Li2S8, Li2S6, Li2S4 and Li2S) at varying potentials and elucidate their conversion pathways. Li2S4 and Li2S6 were predominantly observed, in which Li2S4 represents the key electrochemical intermediate dictating the overall SRR kinetics. Li2S6, generated (consumed) through a comproportionation (disproportionation) reaction, does not directly participate in electrochemical reactions but significantly contributes to the polysulfide shuttling process. We found that the nitrogen, sulfur dual-doped holey graphene framework catalyst could help accelerate polysulfide conversion kinetics, leading to faster depletion of soluble lithium polysulfides at higher potential and hence mitigating the polysulfide shuttling effect and boosting output potential. These results highlight the electrocatalytic approach as a promising strategy for tackling the fundamental challenges regarding Li-S batteries

Efficacy and mechanism of Baicao Fuyanqing suppository on mixed vaginitis based on 16S rRNA and metabolomics

BackgroundMixed vaginitis is the infection of the vagina by at least two different pathogens at the same time, both of which contribute to an abnormal vaginal environment leading to signs and symptoms. Baicao Fuyanqing suppository (BCFYQ) is a Miao ethnomedicine, used to treat various vaginitis. The aim of this study was to investigate the efficacy and possible mechanism of BCFYQ in the treatment of mixed vaginitis based on 16S rRNA high-throughput sequencing and metabonomics.MethodsEscherichia coli and Candida albicans were used to establish mixed vaginitis model in SD rats. Three groups of low, medium and high doses (0.18/0.36/0.64 g.kg-1) were established, and administered vaginally once a day for 6 consecutive days. After the last administration, vaginal pH and IL-1β, IL-2, IL-13 and IgA levels were measured, and the vaginal tissue was examined pathologically. In addition, the vaginal flora was characterised by 16S rRNA, and endogenous metabolites in the vaginal tissue were detected by UHPLC-Q-Exactive MS.ResultsCompared with the model group, BCFYQ can reduce the vaginal pH of rats, make it close to the normal group and improve the damaged vaginal epithelial tissue. The results of ELISA showed that BCFYQ decreased the levels of IL-1 β and IL-2 and increased the levels of IL-13 and IgA (P&lt;0.05). In addition, BCFYQ may increase the abundance of vaginal flora, especially Lactobacillus. The differential metabolite enrichment pathway suggests that the therapeutic mechanism of BCFYQ is mainly related to lipid metabolism and amino acid metabolism.ConclusionOur research shows that BCFYQ has a good therapeutic effect on mixed vaginitis. It repairs the damaged vaginal mucosa by regulating the vaginal flora and lipid metabolism disorders to improve the local immune function of the vagina and inhibit the growth and reproduction of pathogens

New insights into the characteristic skin microorganisms in different grades of acne and different acne sites

BackgroundThe increasing maturity of sequencing technology provides a convenient approach to studying the role of skin microorganisms in acne pathogenesis. However, there are still too few studies about the skin microbiota of Asian acne patients, especially a lack of detailed analysis of the characteristics of the skin microbiota in the different acne sites.MethodsIn this study, a total of 34 college students were recruited and divided into the health, mild acne, and severe acne groups. The bacterial and fungal flora of samples were separately detected by 16S and 18S rRNA gene sequencing. The biomarkers of different acne grades and different acne sites [forehead, cheek, chin, torso (including chest and back)] were excavated.Results and DiscussionOur results indicated that there was no significant difference in species diversity between groups. The genera like Propionibacterium, Staphylococcus, Corynebacterium, and Malassezia, which have a relatively high abundance in the skin microbiota and were reported as the most acne-associated microbes, were no obvious differences between groups. On the contrary, the abundance of less reported Gram-negative bacteria (Pseudomonas, Ralstonia, and Pseudidiomarina) and Candida has a significant alteration. Compared with the health group and the mild group, in the severe group, the abundance of Pseudomonas and Ralstonia sharply reduced while that of Pseudidiomarina and Candida remarkably raised. Moreover, different acne sites have different numbers and types of biomarkers. Among the four acne sites, the cheek has the greatest number of biomarkers including Pseudomonas, Ralstonia, Pseudidiomarina, Malassezia, Saccharomyces, and Candida, while no biomarker was observed for the forehead. The network analysis indicated that there might be a competitive relationship between Pseudomonas and Propionibacterium. This study would provide a new insight and theoretical basis for precise and personalized acne microbial therapy