Intrinsically disordered protein regions (IDRs) are peptide segments that fail to form stable 3-dimensional structures in the absence of partner proteins. They are abundant in eukaryotic proteomes and are often associated with human diseases, but their biological functions have been elusive to study. Here we report the identification of a tin(IV) oxochloride-derived cluster that binds an evolutionarily conserved IDR within the metazoan TFIID transcription complex. Binding arrests an isomerization of promoter-bound TFIID that is required for the engagement of Pol II during the first (de novo) round of transcription initiation. However, the specific chemical probe does not affect reinitiation, which requires the re-entry of Pol II, thus, mechanistically distinguishing these two modes of transcription initiation. This work also suggests a new avenue for targeting the elusive IDRs by harnessing certain features of metal-based complexes for mechanistic studies, and for the development of novel pharmaceutical interventions.

Preclinical animal models have provided strong evidence that estrogen (E) therapy (ET) enhances cognition and induces spinogenesis in neuronal circuits. However, clinical studies have been inconsistent, with some studies revealing adverse effects of ET, including an increased risk of dementia. In an effort to bridge this disconnect between the preclinical and clinical data, we have developed a nonhuman primate (NHP) model of ET combined with high-resolution dendritic spine analysis of dorsolateral prefrontal cortical (dlPFC) neurons. Previously, we reported cyclic ET in aged, ovariectomized NHPs increased spine density on dlPFC neurons. Here, we report that monkeys treated with cyclic E treatment paired with cyclic progesterone (P), continuous E combined with P (either cyclic or continuous), or unopposed continuous E failed to increase spines on dlPFC neurons. Given that the most prevalent form of ET prescribed to women is a combined and continuous E and P, these data bring into convergence the human neuropsychological findings and preclinical neurobiological evidence that standard hormone therapy in women is unlikely to yield the synaptic benefit presumed to underlie the cognitive enhancement reported in animal models.

Detecting meaningful structure in neural activity and connectivity data is challenging in the presence of hidden nonlinearities, where traditional eigenvalue-based methods may be misleading. We introduce a novel approach to matrix analysis, called clique topology, that extracts features of the data invariant under nonlinear monotone transformations. These features can be used to detect both random and geometric structure, and depend only on the relative ordering of matrix entries. We then analyzed the activity of pyramidal neurons in rat hippocampus, recorded while the animal was exploring a 2D environment, and confirmed that our method is able to detect geometric organization using only the intrinsic pattern of neural correlations. Remarkably, we found similar results during nonspatial behaviors such as wheel running and rapid eye movement (REM) sleep. This suggests that the geometric structure of correlations is shaped by the underlying hippocampal circuits and is not merely a consequence of position coding. We propose that clique topology is a powerful new tool for matrix analysis in biological settings, where the relationship of observed quantities to more meaningful variables is often nonlinear and unknown.

Programmed genome rearrangements in the unicellular eukaryote Oxytricha trifallax produce a transcriptionally active somatic nucleus from a copy of its germline nucleus during development. This process eliminates noncoding sequences that interrupt coding regions in the germline genome, and joins over 225,000 remaining DNA segments, some of which require inversion or complex permutation to build functional genes. This dynamic genomic organization permits some single DNA segments in the germline to contribute to multiple, distinct somatic genes via alternative processing. Like alternative mRNA splicing, the combinatorial assembly of DNA segments contributes to genetic variation and facilitates the evolution of new genes. In this study, we use comparative genomic analysis to demonstrate that the emergence of alternative DNA splicing is associated with the origin of new genes. Short duplications give rise to alternative gene segments that are spliced to the shared gene segments. Alternative gene segments evolve faster than shared, constitutive segments. Genes with shared segments frequently have different expression profiles, permitting functional divergence. This study reports alternative DNA splicing as a mechanism of new gene origination, illustrating how the process of programmed genome rearrangement gives rise to evolutionary innovation.

Neural computations are implemented by activity in spatially distributed neural circuits. Cellular imaging fills a unique niche in linking activity of specific types of neurons to behavior, over spatial scales spanning single neurons to entire brain regions, and temporal scales from milliseconds to months. Imaging may soon make it possible to track activity of all neurons in a brain region, such as a cortical column. We review recent methodological advances that facilitate optical imaging of neuronal populations in vivo, with an emphasis on calcium imaging using protein indicators in mice. We point out areas that are particularly ripe for future developments.

Evaluation of confidence about one's knowledge is key to the brain's ability to monitor cognition. To investigate the neural mechanism of confidence assessment, we examined a biologically realistic spiking network model and found that it reproduced salient behavioral observations and single-neuron activity data from a monkey experiment designed to study confidence about a decision under uncertainty. Interestingly, the model predicts that changes of mind can occur in a mnemonic delay when confidence is low; the probability of changes of mind increases (decreases) with task difficulty in correct (error) trials. Furthermore, a so-called "hard-easy effect" observed in humans naturally emerges, i.e., behavior shows underconfidence (underestimation of correct rate) for easy or moderately difficult tasks and overconfidence (overestimation of correct rate) for very difficult tasks. Importantly, in the model, confidence is computed using a simple neural signal in individual trials, without explicit representation of probability functions. Therefore, even a concept of metacognition can be explained by sampling a stochastic neural activity pattern.

Feature-selective firing allows networks to produce representations of the external and internal environments. Despite its importance, the mechanisms generating neuronal feature selectivity are incompletely understood. In many cortical microcircuits the integration of two functionally distinct inputs occurs nonlinearly through generation of active dendritic signals that drive burst firing and robust plasticity. To examine the role of this processing in feature selectivity, we recorded CA1 pyramidal neuron membrane potential and local field potential in mice running on a linear treadmill. We found that dendritic plateau potentials were produced by an interaction between properly timed input from entorhinal cortex and hippocampal CA3. These conjunctive signals positively modulated the firing of previously established place fields and rapidly induced new place field formation to produce feature selectivity in CA1 that is a function of both entorhinal cortex and CA3 input. Such selectivity could allow mixed network level representations that support context-dependent spatial maps.

Brains are optimized for processing ethologically relevant sensory signals. However, few studies have characterized the neural coding mechanisms that underlie the transformation from natural sensory information to behavior. Here, we focus on acoustic communication in Drosophila melanogaster and use computational modeling to link natural courtship song, neuronal codes, and female behavioral responses to song. We show that melanogaster females are sensitive to long timescale song structure (on the order of tens of seconds). From intracellular recordings, we generate models that recapitulate neural responses to acoustic stimuli. We link these neural codes with female behavior by generating model neural responses to natural courtship song. Using a simple decoder, we predict female behavioral responses to the same song stimuli with high accuracy. Our modeling approach reveals how long timescale song features are represented by the Drosophila brain and how neural representations can be decoded to generate behavioral selectivity for acoustic communication signals.

Recent powerful tools for reconstructing connectomes using electron microscopy (EM) have made outstanding contributions to the field of neuroscience. As a prime example, the detection of visual motion is a classic problem of neural computation, yet our understanding of the exact mechanism has been frustrated by our incomplete knowledge of the relevant neurons and synapses. Recent connectomic studies have successfully identified the concrete neuronal circuit in the fly's visual system that computes the motion signals. This identification was greatly aided by the comprehensiveness of the EM reconstruction. Compared with light microscopy, which gives estimated connections from arbor overlap, EM gives unequivocal connections with precise synaptic counts. This paper reviews the recent study of connectomics in a brain of the fruit fly Drosophila and highlights how connectomes can provide a foundation for understanding the mechanism of neuronal functions by identifying the underlying neural circuits.

"Regulatory evolution," that is, changes in a gene's expression pattern through changes at its regulatory sequence, rather than changes at the coding sequence of the gene or changes of the upstream transcription factors, has been increasingly recognized as a pervasive evolution mechanism. Many somatic sexually dimorphic features of Drosophila melanogaster are the results of gene expression regulated by the doublesex (dsx) gene, which encodes sex-specific transcription factors (DSX(F) in females and DSX(M) in males). Rapid changes in such sexually dimorphic features are likely a result of changes at the regulatory sequence of the target genes. We focused on the Flavin-containing monooxygenase-2 (Fmo-2) gene, a likely direct dsx target, to elucidate how sexually dimorphic expression and its evolution are brought about. We found that dsx is deployed to regulate the Fmo-2 transcription both in the midgut and in fat body cells of the spermatheca (a female-specific tissue), through a canonical DSX-binding site in the Fmo-2 regulatory sequence. In the melanogaster group, Fmo-2 transcription in the midgut has evolved rapidly, in contrast to the conserved spermathecal transcription. We identified two cis-regulatory modules (CRM-p and CRM-d) that direct sexually monomorphic or dimorphic Fmo-2 transcription, respectively, in the midguts of these species. Changes of Fmo-2 transcription in the midgut from sexually dimorphic to sexually monomorphic in some species are caused by the loss of CRM-d function, but not the loss of the canonical DSX-binding site. Thus, conferring transcriptional regulation on a CRM level allows the regulation to evolve rapidly in one tissue while evading evolutionary constraints posed by other tissues.