Sequence-specific DNA-binding activators, key regulators of gene expression, stimulate transcription in part by targeting the core promoter recognition TFIID complex and aiding in its recruitment to promoter DNA. Although it has been established that activators can interact with multiple components of TFIID, it is unknown whether common or distinct surfaces within TFIID are targeted by activators and what changes if any in the structure of TFIID may occur upon binding activators. As a first step toward structurally dissecting activator/TFIID interactions, we determined the three-dimensional structures of TFIID bound to three distinct activators (i.e., the tumor suppressor p53 protein, glutamine-rich Sp1 and the oncoprotein c-Jun) and compared their structures as determined by electron microscopy and single-particle reconstruction. By a combination of EM and biochemical mapping analysis, our results uncover distinct contact regions within TFIID bound by each activator. Unlike the coactivator CRSP/Mediator complex that undergoes drastic and global structural changes upon activator binding, instead, a rather confined set of local conserved structural changes were observed when each activator binds holo-TFIID. These results suggest that activator contact may induce unique structural features of TFIID, thus providing nanoscale information on activator-dependent TFIID assembly and transcription initiation.

Sensorimotor integration is a field rich in theory backed by a large body of psychophysical evidence. Relating the underlying neural circuitry to these theories has, however, been more challenging. With a wide array of complex behaviors coordinated by their small brains, insects provide powerful model systems to study key features of sensorimotor integration at a mechanistic level. Insect neural circuits perform both hard-wired and learned sensorimotor transformations. They modulate their neural processing based on both internal variables, such as the animal’s behavioral state, and external ones, such as the time of day. Here we present some studies using insect model systems that have produced insights, at the level of individual neurons, about sensorimotor integration and the various ways in which it can be modified by context.

The neural underpinnings of sensorimotor integration are best studied in the context of well-characterized behavior. A rich trove of Drosophila behavioral genetics research offers a variety of well-studied behaviors and candidate brain regions that can form the bases of such studies. The development of tools to perform in vivo physiology from the Drosophila brain has made it possible to monitor activity in defined neurons in response to sensory stimuli. More recently still, it has become possible to perform recordings from identified neurons in the brain of head-fixed flies during walking or flight behaviors. In this chapter, we discuss how experiments that simultaneously monitor behavior and physiology in Drosophila can be combined with other techniques to produce testable models of sensorimotor circuit function.

Cognition encompasses a range of higher-order mental processes, such as attention, working memory, and model-based decision-making. These processes are thought to involve the dynamic interaction of multiple central brain regions. A mechanistic understanding of such computations requires not only monitoring and manipulating specific neural populations during behavior, but also knowing the connectivity of the underlying circuitry. These goals are experimentally challenging in mammals, but are feasible in numerically simpler insect brains. In Drosophila melanogaster in particular, genetic tools enable precisely targeted physiology and optogenetics in actively behaving animals. In this article we discuss how these advantages are increasingly being leveraged to study abstract neural representations and sensorimotor computations that may be relevant for cognition in both insects and mammals.

The atomic structure of the infectious, protease-resistant, β-sheet-rich and fibrillar mammalian prion remains unknown. Through the cryo-EM method MicroED, we reveal the sub-ångström-resolution structure of a protofibril formed by a wild-type segment from the β2-α2 loop of the bank vole prion protein. The structure of this protofibril reveals a stabilizing network of hydrogen bonds that link polar zippers within a sheet, producing motifs we have named 'polar clasps'.

Protein kinase A (PKA) plays multiple roles in neurons. The localization and specificity of PKA are largely controlled by A-kinase anchoring proteins (AKAPs). However, the dynamics of PKA in neurons and the roles of specific AKAPs are poorly understood. We imaged the distribution of type II PKA in hippocampal and cortical layer 2/3 pyramidal neurons in vitro and in vivo. PKA was concentrated in dendritic shafts compared to the soma, axons, and dendritic spines. This spatial distribution was imposed by the microtubule-binding protein MAP2, indicating that MAP2 is the dominant AKAP in neurons. Following cAMP elevation, catalytic subunits dissociated from the MAP2-tethered regulatory subunits and rapidly became enriched in nearby spines. The spatial gradient of type II PKA between dendritic shafts and spines was critical for the regulation of synaptic strength and long-term potentiation. Therefore, the localization and activity-dependent translocation of type II PKA are important determinants of PKA function.

Fluorescence light microscopy allows multicolor visualization of cellular components with high specificity, but its utility has until recently been constrained by the intrinsic limit of spatial resolution. We applied three-dimensional structured illumination microscopy (3D-SIM) to circumvent this limit and to study the mammalian nucleus. By simultaneously imaging chromatin, nuclear lamina, and the nuclear pore complex (NPC), we observed several features that escape detection by conventional microscopy. We could resolve single NPCs that colocalized with channels in the lamin network and peripheral heterochromatin. We could differentially localize distinct NPC components and detect double-layered invaginations of the nuclear envelope in prophase as previously seen only by electron microscopy. Multicolor 3D-SIM opens new and facile possibilities to analyze subcellular structures beyond the diffraction limit of the emitted light.

Recent findings implicate alternate core promoter recognition complexes in regulating cellular differentiation. Here we report a spatial segregation of the alternative core factor TAF3, but not canonical TFIID subunits, away from the nuclear periphery, where the key myogenic gene MyoD is preferentially localized in myoblasts. This segregation is correlated with the differential occupancy of TAF3 versus TFIID at the MyoD promoter. Loss of this segregation by modulating either the intranuclear location of the MyoD gene or TAF3 protein leads to altered TAF3 occupancy at the MyoD promoter. Intriguingly, in differentiated myotubes, the MyoD gene is repositioned to the nuclear interior, where TAF3 resides. The specific high-affinity recognition of H3K4Me3 by the TAF3 PHD (plant homeodomain) finger appears to be required for the sequestration of TAF3 to the nuclear interior. We suggest that intranuclear sequestration of core transcription components and their target genes provides an additional mechanism for promoter selectivity during differentiation.

Commentary: Jie Yao in Bob Tijan’s lab used a combination of confocal microscopy and dual label PALM in thin sections cut from resin-embedded cells to show that certain core transcription components and their target genes are spatially segregated in myoblasts, but not in differentiated myotubes, suggesting that such spatial segregation may play a role in guiding cellular differentiation.

In the past few years, three-dimensional (3D) subtomogram alignment has become an important tool in cryo-electron tomography (CET). This technique allows one to produce higher resolution images of structures which can not be reconstructed using single-particle methods. Building on previous work, we present a new dissimilarity measure between subtomograms that works well for the noisy images that often occur in CET images. A technique that is more robust to noise provides the ability to analyze macromolecules in thicker samples such as whole cells or lower the defocus in thinner samples to push the first zero of the Contrast Transfer Function (CTF). Our method, Threshold Constrained Cross-Correlation (TCCC), uses statistics of the noise to automatically select only a small percentage of the Fourier coefficients to compute the cross-correlation, which has two main advantages: first, it reduces the influence of the noise by looking at only those peaks dominated by signal; and second, it avoids the missing wedge normalization problem since we consider the same number of coefficients for all possible pairs of subtomograms. We present results with synthetic and real data to compare our approach with other existing methods under different SNR and missing wedge conditions, and show that TCCC improves alignment results for datasets with SNR<0.1. We have made our source code freely available for the community.