Autophagy Activation by Transcription Factor EB (TFEB) in Striatum of HDQ175/Q7 Mice

Background: Mutant huntingtin (mHTT) is encoded by the Huntington’s disease (HD) gene and its accumulation in the brain contributes to HD pathogenesis. Reducing mHTT levels through activation of the autophagosome-lysosomal pathway may have therapeutic benefit. Transcription factor EB (TFEB) regulates lysosome biogenesis and autophagy. Objective: To examine if increasing TFEB protein levels in HD mouse striatum induces autophagy and influences mHTT levels. Methods: We introduced cDNA encoding TFEB with an HA tag (TFEB-HA) under the control of neuron specific synapsin 1 promoter into the striatum of 3 month old HDQ175/Q7 mice using adeno-associated virus AAV2/9. The levels of exogenous TFEB were analyzed using qPCR and Western blot. Proteins involved in autophagy, levels of huntingtin, and striatal-enriched proteins were examined using biochemical and/or immunohistochemical methods. Results: In HD mice expressing TFEB-HA, HA immunoreactivity distributed throughout the striatum in neuronal cell bodies and processes and preferentially in neuronal nuclei and overlapped with a loss of DARPP32 immunoreactivity. TFEB-HA mRNA and protein were detected in striatal lysates. There were increased levels of proteins involved with autophagosome/lysosome activity including LAMP-2A, LC3II, and cathepsin D and reduced levels of mutant HTT and the striatal enriched proteins DARPP32 and PDE10A. Compared to WT mice, HDQ175/Q7 mice had elevated levels of the ER stress protein GRP78/BiP and with TFEB-HA expression, increased levels of the astrocyte marker GFAP and pro-caspase 3. Conclusion: These results suggest that TFEB expression in the striatum of HDQ175/Q7 mice stimulates autophagy and lysosome activity, and lowers mHTT, but may also increase a neuronal stress response

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe