Fang He

Fragile X-associated tremor/ataxia syndrome (FXTAS) is an inherited neurodegenerative disorder caused by the expansion of 55-200 CGG repeats in the 5' UTR of FMR1. These expanded CGG repeats are transcribed and accumulate in nuclear RNA aggregates that sequester one or more RNA-binding proteins, thus impairing their functions. Here, we have identified that the double-stranded RNA-binding protein DGCR8 binds to expanded CGG repeats, resulting in the partial sequestration of DGCR8 and its partner, DROSHA, within CGG RNA aggregates. Consequently, the processing of microRNAs (miRNAs) is reduced, resulting in decreased levels of mature miRNAs in neuronal cells expressing expanded CGG repeats and in brain tissue from patients with FXTAS. Finally, overexpression of DGCR8 rescues the neuronal cell death induced by expression of expanded CGG repeats. These results support a model in which a human neurodegenerative disease originates from the alteration, in trans, of the miRNA-processing machinery.

The tripartite DENN module, comprised of a N-terminal longin domain, followed by DENN, and d-DENN domains, is a GDP-GTP exchange factor (GEFs) for Rab GTPases, which are regulators of practically all membrane trafficking events in eukaryotes. Using sequence and structure analysis we identify multiple novel homologs of the DENN module, many of which can be traced back to the ancestral eukaryote. These findings provide unexpected leads regarding key cellular processes such as autophagy, vesicle-vacuole interactions, chromosome segregation, and human disease. Of these, SMCR8, the folliculin interacting protein-1 and 2 (FNIP1 and FNIP2), nitrogen permease regulator 2 (NPR2), and NPR3 are proposed to function in recruiting Rab GTPases during different steps of autophagy, fusion of autophagosomes with the vacuole and regulation of cellular metabolism. Another novel DENN protein identified in this study is C9ORF72; expansions of the hexanucleotide GGGGCC in its first intron have been recently implicated in amyotrophic lateral sclerosis (ALS) and fronto-temporal dementia (FTD). While this mutation is proposed to cause a RNA-level defect, the identification of C9ORF72 as a potential DENN-type GEF raises the possibility that at least part of the pathology might relate to a specific Rab-dependent vesicular trafficking process, as has been observed in the case of some other neurological conditions with similar phenotypes. We present evidence that the longin domain, such as those found in the DENN module, are likely to have been ultimately derived from the related domains found in prokaryotic GTPase-activating proteins of MglA-like GTPases. Thus, the origin of the longin domains from this ancient GTPase-interacting domain, concomitant with the radiation of GTPases, especially of the Rab clade, played an important role in the dynamics of eukaryotic intracellular membrane systems.

Community detection aims to partition network nodes into a set of clusters, such that nodes are more densely connected to each other within the same cluster than other clusters. For attributed networks, apart from the denseness requirement of topology structure, the attributes of nodes in the same community should also be homogeneous. Network embedding has been proved extremely useful in a variety of tasks, such as node classification, link prediction, and graph visualization, but few works dedicated to unsupervised embedding of node features specified for clustering task, which is vital for community detection and graph clustering. By post-processing with clustering algorithms like k -means, most existing network embedding methods can be applied to clustering tasks. However, the learned embeddings are not designed for clustering task, they only learn topological and attributed information of networks, and no clustering-oriented information is explored. In this article, we propose an algorithm named Network Embedding for node Clustering (NEC) to learn network embedding for node clustering in attributed graphs. Specifically, the presented work introduces a framework that simultaneously learns graph structure-based representations and clustering-oriented representations together. The framework consists of the following three modules: graph convolutional autoencoder module, soft modularity maximization module, and self-clustering module. Graph convolutional autoencoder module learns node embeddings based on topological structure and node attributes. We introduce soft modularity, which can be easily optimized using gradient descent algorithms, to exploit the community structure of networks. By integrating clustering loss and embedding loss, NEC can jointly optimize node cluster labels assignment and learn representations that keep local structure of network. This model can be effectively optimized using stochastic gradient algorithm. Empirical experiments on real-world networks and synthetic networks validate the feasibility and effectiveness of our algorithm on community detection task compared with network embedding based methods and traditional community detection methods.