How RNA-binding proteins recognize specific sets of target mRNAs remains poorly understood because current approaches depend primarily on sequence information. In this study, we demonstrate that specific recognition of messenger RNAs (mRNAs) by RNA-binding proteins requires the correct spatial positioning of these sequences. We characterized both the cis-acting sequence elements and the spatial restraints that define the mode of RNA binding of the zipcode-binding protein 1 (ZBP1/IMP1/IGF2BP1) to the β-actin zipcode. The third and fourth KH (hnRNP K homology) domains of ZBP1 specifically recognize a bipartite RNA element comprised of a 5' element (CGGAC) followed by a variable 3' element (C/A-CA-C/U) that must be appropriately spaced. Remarkably, the orientation of these elements is interchangeable within target transcripts bound by ZBP1. The spatial relationship of this consensus binding site identified conserved transcripts that were verified to associate with ZBP1 in vivo. The dendritic localization of one of these transcripts, spinophilin, was found to be dependent on both ZBP1 and the RNA elements recognized by ZBP1 KH34.

A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral plane and either high-throughput or high depth resolution but not both. Here, we exploit recent advances in unsupervised learning and signal processing to obtain high depth-resolution EM images computationally without sacrificing throughput. First, we show that the brain tissue can be represented as a sparse linear combination of localized basis functions that are learned using high-resolution datasets. We then develop compressive sensing-inspired techniques that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal processes and, hence, high throughput reconstruction of neural circuits on the level of individual synapses.

Pfam is a widely used database of protein families, currently containing more than 13,000 manually curated protein families as of release 26.0. Pfam is available via servers in the UK (http://pfam.sanger.ac.uk/), the USA (http://pfam.janelia.org/) and Sweden (http://pfam.sbc.su.se/). Here, we report on changes that have occurred since our 2010 NAR paper (release 24.0). Over the last 2 years, we have generated 1840 new families and increased coverage of the UniProt Knowledgebase (UniProtKB) to nearly 80%. Notably, we have taken the step of opening up the annotation of our families to the Wikipedia community, by linking Pfam families to relevant Wikipedia pages and encouraging the Pfam and Wikipedia communities to improve and expand those pages. We continue to improve the Pfam website and add new visualizations, such as the ’sunburst’ representation of taxonomic distribution of families. In this work we additionally address two topics that will be of particular interest to the Pfam community. First, we explain the definition and use of family-specific, manually curated gathering thresholds. Second, we discuss some of the features of domains of unknown function (also known as DUFs), which constitute a rapidly growing class of families within Pfam.

A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.

It is now widely recognized that as cells of developing tissues transition through successive states of decreasing pluripotency into a state of terminal differentiation, they undergo significant changes in their gene expression profiles. Interestingly, these successive states of increasing differentiation are marked by the spatially and temporally restricted expression of sets of transcription factors. Each wave of transcription factors not only signals the arrival of a given stage in cellular differentiation, but it is also necessary for the activation of the next set of transcription factors, creating the appearance of a smooth, directed, and deterministic genetic program of cellular differentiation. Until recently, however, it was largely unknown which genes, besides each other, these transcription factors were activating. Thus, the molecular definition of any given step of differentiation, and how it gave rise to the following step remained unclear. Recent advances in transcriptomics, bioinformatics, and molecular genetics resulted in the identification of numerous transcription factor target genes (TGs). These advances have opened the door to using similar approaches in developmental biology to understand what the transcriptional cascades of cellular differentiation might be. Using the development of the Drosophila eye as a model system, we discuss the role of transcription factors and their TGs in cell fate specification and terminal differentiation.

Phylogenetic footprinting has revealed that cis-regulatory enhancers consist of conserved DNA sequence clusters (CSCs). Currently, there is no systematic approach for enhancer discovery and analysis that takes full-advantage of the sequence information within enhancer CSCs.