BACKGROUND: Drosophila melanogaster adult males perform an elaborate courtship ritual to entice females to mate. fruitless (fru), a gene that is one of the key regulators of male courtship behavior, encodes multiple male-specific isoforms (Fru(M)). These isoforms vary in their carboxy-terminal zinc finger domains, which are predicted to facilitate DNA binding. RESULTS: By over-expressing individual Fru(M) isoforms in fru-expressing neurons in either males or females and assaying the global transcriptional response by RNA-sequencing, we show that three Fru(M) isoforms have different regulatory activities that depend on the sex of the fly. We identified several sets of genes regulated downstream of Fru(M) isoforms, including many annotated with neuronal functions. By determining the binding sites of individual Fru(M) isoforms using SELEX we demonstrate that the distinct zinc finger domain of each Fru(M) isoforms confers different DNA binding specificities. A genome-wide search for these binding site sequences finds that the gene sets identified as induced by over-expression of Fru(M) isoforms in males are enriched for genes that contain the binding sites. An analysis of the chromosomal distribution of genes downstream of Fru(M) shows that those that are induced and repressed in males are highly enriched and depleted on the X chromosome, respectively. CONCLUSIONS: This study elucidates the different regulatory and DNA binding activities of three Fru(M) isoforms on a genome-wide scale and identifies genes regulated by these isoforms. These results add to our understanding of sex chromosome biology and further support the hypothesis that in some cell-types genes with male-biased expression are enriched on the X chromosome.

Temporal patterning is a seminal method of expanding neuronal diversity. Here we unravel a mechanism decoding neural stem cell temporal gene expression and transforming it into discrete neuronal fates. This mechanism is characterized by hierarchical gene expression. First, neuroblasts express opposing temporal gradients of RNA-binding proteins, Imp and Syp. These proteins promote or inhibit translation, yielding a descending neuronal gradient. Together, first and second-layer temporal factors define a temporal expression window of BTB-zinc finger nuclear protein, Mamo. The precise temporal induction of Mamo is achieved via both transcriptional and post-transcriptional regulation. Finally, Mamo is essential for the temporally defined, terminal identity of α'/β' mushroom body neurons and identity maintenance. We describe a straightforward paradigm of temporal fate specification where diverse neuronal fates are defined via integrating multiple layers of gene regulation. The neurodevelopmental roles of orthologous/related mammalian genes suggest a fundamental conservation of this mechanism in brain development.

The eukaryotic genome is highly organized in the nucleus. Genes can be localized to specific nuclear compartments in a manner reflecting their activity. A plethora of recent reports has described multiple levels of chromosomal folding that can be related to gene-specific expression states. Here we discuss studies designed to probe the causal impact of genome organization on gene expression. The picture that emerges is that of a reciprocal relationship in which nuclear organization is not only shaped by gene expression states but also directly influences them.

Hundreds of millions of structured proteins sustain life through chemical interactions and catalytic reactions1. Though dynamic, these proteins are assumed to be built upon fixed scaffolds of secondary structure, α-helices and β-sheets. Experimentally determined structures of over >58,000 non-redundant proteins support this assumption, though it has recently been challenged by ∼100 fold-switching proteins2. These “metamorphic3” proteins, though ostensibly rare, raise the question of how many uncharacterized proteins have shapeshifting–rather than fixed–secondary structures. To address this question, we developed a comparative sequence-based approach that predicts fold-switching proteins from differences in secondary structure propensity. We applied this approach to the universally conserved NusG transcription factor family of ∼15,000 proteins, one of which has a 50-residue regulatory subunit experimentally shown to switch between α-helical and β-sheet folds4. Our approach predicted that 25% of the sequences in this family undergo similar α-helix ⇌ β-sheet transitions, a frequency two orders of magnitude larger than previously observed. Our predictions evade state-of-the-art computational methods but were confirmed experimentally by circular dichroism and nuclear magnetic resonance spectroscopy for all 10 assiduously chosen dissimilar variants. These results suggest that fold switching is a pervasive mechanism of transcriptional regulation in all kingdoms of life and imply that numerous uncharacterized proteins may also switch folds.

The protein folding paradigm asserts that the three-dimensional structure of a protein is determined by its amino acid sequence. Here we show that a substantial population of proteins from the NusG superfamily of transcription factors do not adhere to this paradigm. Previous work demonstrated that one member of this superfamily has a regulatory domain that completely switches between α-helical and β-sheet folds, but the pervasiveness of this fold-switching mechanism is uncertain. To address this question, we developed a sequence-based predictor, which revealed that thousands of proteins from this superfamily switch folds. Circular dichroism and nuclear magnetic resonance spectroscopies of 10 sequence-diverse variants confirmed our predictions. By contrast, state-of-the-art methods based on the protein folding paradigm assume that related sequences adopt the same fold and thus predicted that the regulatory domains of all variants adopt only the β-sheet fold. Removal of this bias revealed that residue-residue contacts from both α-helical and β-sheet folds are conserved in a large subpopulation of fold-switching domains, poising them to assume disparate conformations. Our results suggest that fold switching is a pervasive mechanism of transcriptional regulation in all kingdoms of life and indicate that expanding the protein folding paradigm may reveal the involvement of fold-switching proteins in diverse biological processes.

Drosophila is a marvelous system to study the underlying principles that govern how neural circuits govern behaviors. The scale of the fly brain (approximately 100,000 neurons) and the complexity of the behaviors the fly can perform make it a tractable experimental model organism. In addition, 100 years and hundreds of labs have contributed to an extensive array of tools and techniques that can be used to dissect the function and organization of the fly nervous system. This review discusses both the conceptual challenges and the specific tools for a neurogenetic approach to circuit mapping in Drosophila.

Understanding brain function requires monitoring and interpreting the activity of large networks of neurons during behavior. Advances in recording technology are greatly increasing the size and complexity of neural data. Analyzing such data will pose a fundamental bottleneck for neuroscience. We present a library of analytical tools called Thunder built on the open-source Apache Spark platform for large-scale distributed computing. The library implements a variety of univariate and multivariate analyses with a modular, extendable structure well-suited to interactive exploration and analysis development. We demonstrate how these analyses find structure in large-scale neural data, including whole-brain light-sheet imaging data from fictively behaving larval zebrafish, and two-photon imaging data from behaving mouse. The analyses relate neuronal responses to sensory input and behavior, run in minutes or less and can be used on a private cluster or in the cloud. Our open-source framework thus holds promise for turning brain activity mapping efforts into biological insights.

Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed neural network models. These experimental and computational results suggest that mapping low-dimensional dynamics to high-dimensional neural space is a common feature of cortical computation. Despite the ubiquity of this observation, the constraints arising from such mapping are poorly understood. Here we consider a specific example of mapping low-dimensional dynamics to high-dimensional neural activity-the neural engineering framework. We analytically solve the framework for the classic ring model-a neural network encoding a static or dynamic angular variable. Our results provide a complete characterization of the success and failure modes for this model. Based on similarities between this and other frameworks, we speculate that these results could apply to more general scenarios.

Mapping mammalian synaptic connectivity has long been an important goal of neuroscientists since it is considered crucial for explaining human perception and behavior. Yet, despite enormous efforts, the overwhelming complexity of the neural circuitry and the lack of appropriate techniques to unravel it have limited the success of efforts to map connectivity. However, recent technological advances designed to overcome the limitations of conventional methods for connectivity mapping may bring about a turning point. Here, we address the promises and pitfalls of these new mapping technologies.

A tool to map changes in synaptic strength during a defined time window could provide powerful insights into the mechanisms governing learning and memory. We developed a technique, Extracellular Protein Surface Labeling in Neurons (EPSILON), to map α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) insertion in vivo by pulse-chase labeling of surface AMPARs with membrane-impermeable dyes. This approach allows for single-synapse resolution maps of plasticity in genetically targeted neurons during memory formation. We investigated the relationship between synapse-level and cell-level memory encodings by mapping synaptic plasticity and cFos expression in hippocampal CA1 pyramidal cells upon contextual fear conditioning (CFC). We observed a strong correlation between synaptic plasticity and cFos expression, suggesting a synaptic mechanism for the association of cFos expression with memory engrams. The EPSILON technique is a useful tool for mapping synaptic plasticity and may be extended to investigate trafficking of other transmembrane proteins.