Secreted Human Amyloid Precursor Protein Binds Semaphorin 3a and Prevents Semaphorin-Induced Growth Cone Collapse

The amyloid precursor protein (APP) is well known for giving rise to the amyloid-β peptide and for its role in Alzheimer's disease. Much less is known, however, on the physiological roles of APP in the development and plasticity of the central nervous system. We have used phage display of a peptide library to identify high-affinity ligands of purified recombinant human sAPPα695 (the soluble, secreted ectodomain from the main neuronal APP isoform). Two peptides thus selected exhibited significant homologies with the conserved extracellular domain of several members of the semaphorin (Sema) family of axon guidance proteins. We show that sAPPα695 binds both purified recombinant Sema3A and Sema3A secreted by transfected HEK293 cells. Interestingly, sAPPα695 inhibited the collapse of embryonic chicken (Gallus gallus domesticus) dorsal root ganglia growth cones promoted by Sema3A (Kd≤8·10−9 M). Two Sema3A-derived peptides homologous to the peptides isolated by phage display blocked sAPPα binding and its inhibitory action on Sema3A function. These two peptides are comprised within a domain previously shown to be involved in binding of Sema3A to its cellular receptor, suggesting a competitive mechanism by which sAPPα modulates the biological action of semaphorins

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