In pursuit of food, hungry animals mobilize significant energy resources and overcome exhaustion and fear. How need and motivation control the decision to continue or change behavior is not understood. Using a single fly treadmill, we show that hungry flies persistently track a food odor and increase their effort over repeated trials in the absence of reward suggesting that need dominates negative experience. We further show that odor tracking is regulated by two mushroom body output neurons (MBONs) connecting the MB to the lateral horn. These MBONs, together with dopaminergic neurons and Dop1R2 signaling, control behavioral persistence. Conversely, an octopaminergic neuron, VPM4, which directly innervates one of the MBONs, acts as a brake on odor tracking by connecting feeding and olfaction. Together, our data suggest a function for the MB in internal state-dependent expression of behavior that can be suppressed by external inputs conveying a competing behavioral drive.

In the Drosophila model of aggression, males and females fight in same-sex pairings, but a wide disparity exists in the levels of aggression displayed by the 2 sexes. A screen of Drosophila Flylight Gal4 lines by driving expression of the gene coding for the temperature sensitive dTRPA1 channel, yielded a single line (GMR26E01-Gal4) displaying greatly enhanced aggression when thermoactivated. Targeted neurons were widely distributed throughout male and female nervous systems, but the enhanced aggression was seen only in females. No effects were seen on female mating behavior, general arousal, or male aggression. We quantified the enhancement by measuring fight patterns characteristic of female and male aggression and confirmed that the effect was female-specific. To reduce the numbers of neurons involved, we used an intersectional approach with our library of enhancer trap flp-recombinase lines. Several crosses reduced the populations of labeled neurons, but only 1 cross yielded a large reduction while maintaining the phenotype. Of particular interest was a small group (2 to 4 pairs) of neurons in the approximate position of the pC1 cluster important in governing male and female social behavior. Female brains have approximately 20 doublesex (dsx)-expressing neurons within pC1 clusters. Using dsxFLP instead of 357FLP for the intersectional studies, we found that the same 2 to 4 pairs of neurons likely were identified with both. These neurons were cholinergic and showed no immunostaining for other transmitter compounds. Blocking the activation of these neurons blocked the enhancement of aggression.

Macrophage fusion resulting in the formation of multinucleated giant cells occurs in a variety of chronic inflammatory diseases, yet the mechanism responsible for initiating macrophage fusion is unknown. Here, we used live cell imaging to show that actin-based protrusions at the leading edge initiate macrophage fusion. Phase contrast video microscopy demonstrated that in the majority of events, short protrusions (3 ± 1 μm) between two closely apposed cells initiated fusion, but occasionally we observed long protrusions (16 ± 7 μm). Using macrophages isolated from LifeAct mice and imaging with lattice light sheet microscopy, we further found that fusion-competent actin-based protrusions formed at sites enriched in podosomes. Inducing fusion in mixed populations of GFP- and mRFP-LifeAct macrophages showed rapid spatial overlap between GFP and RFP signal at the site of fusion. Cytochalasin B strongly reduced fusion and when rare fusion events occurred, protrusions were not observed. Fusion of macrophages deficient in Wiskott-Aldrich syndrome protein and Cdc42, key molecules involved in the formation of actin-based protrusions and podosomes, was also impaired both in vitro and in vivo. Finally, inhibiting the activity of the Arp2/3 complex decreased fusion and podosome formation. Together these data indicate that an actin-based protrusion formed at the leading edge macrophage fusion.

Animals can perform complex and purposeful behaviors by executing simpler movements in flexible sequences. It is particularly challenging to analyze behavior sequences when they are highly variable, as is the case in language production, certain types of birdsong and, as in our experiments, flies grooming. High sequence variability necessitates rigorous quantification of large amounts of data to identify organizational principles and temporal structure of such behavior. To cope with large amounts of data, and minimize human effort and subjective bias, researchers often use automatic behavior recognition software. Our standard grooming assay involves coating flies in dust and videotaping them as they groom to remove it. The flies move freely and so perform the same movements in various orientations. As the dust is removed, their appearance changes. These conditions make it difficult to rely on precise body alignment and anatomical landmarks such as eyes or legs and thus present challenges to existing behavior classification software. Human observers use speed, location, and shape of the movements as the diagnostic features of particular grooming actions. We applied this intuition to design a new automatic behavior recognition system (ABRS) based on spatiotemporal features in the video data, heavily weighted for temporal dynamics and invariant to the animal’s position and orientation in the scene. We use these spatiotemporal features in two steps of supervised classification that reflect two time-scales at which the behavior is structured. As a proof of principle, we show results from quantification and analysis of a large data set of stimulus-induced fly grooming behaviors that would have been difficult to assess in a smaller dataset of human-annotated ethograms. While we developed and validated this approach to analyze fly grooming behavior, we propose that the strategy of combining alignment-invariant features and multi-timescale analysis may be generally useful for movement-based classification of behavior from video data.

Imaging changes in membrane potential using genetically encoded fluorescent voltage indicators (GEVIs) has great potential for monitoring neuronal activity with high spatial and temporal resolution. Brightness and photostability of fluorescent proteins and rhodopsins have limited the utility of existing GEVIs. We engineered a novel GEVI, "Voltron", that utilizes bright and photostable synthetic dyes instead of protein-based fluorophores, extending the combined duration of imaging and number of neurons imaged simultaneously by more than tenfold relative to existing GEVIs. We used Voltron for in vivo voltage imaging in mice, zebrafish, and fruit flies. In mouse cortex, Voltron allowed single-trial recording of spikes and subthreshold voltage signals from dozens of neurons simultaneously, over 15 min of continuous imaging. In larval zebrafish, Voltron enabled the precise correlation of spike timing with behavior.

Serial-section electron microscopy such as FIB-SEM (focused ion beam scanning electron microscopy) has become an important tool for neuroscientists to trace the trajectories and global architecture of neural circuits in the brain, as well as to visualize the 3D ultrastructure of cellular organelles in neurons. In this study, we examined 3D features of mitochondria in electron microscope images generated from serial sections of four regions of mouse brains: nucleus accumbens (NA), hippocampal CA1, somatosensory cortex and dorsal cochlear nucleus (DCN). We compared mitochondria in the presynaptic terminals to those in the postsynaptic/dendritic compartments, and we focused on the shape and size of mitochondria. A common feature of mitochondria among the four brain regions is that presynaptic mitochondria generally are small and short, and most of them do not extend beyond presynaptic terminals. In contrast, the majority of postsynaptic/dendritic mitochondria are large and many of them spread through significant portions of the dendrites. Comparing among the brain areas, the cerebral cortex and DCN have even larger postsynaptic/dendritic mitochondria than the NA and CA1. Our analysis reveals that mitochondria in neurons are differentially sized and arranged according to their subcellular locations, suggesting a spatial organizing principle of mitochondria at the synapse.

Numerous efforts to generate "connectomes," or synaptic wiring diagrams, of large neural circuits or entire nervous systems are currently underway. These efforts promise an abundance of data to guide theoretical models of neural computation and test their predictions. However, there is not yet a standard set of tools for incorporating the connectivity constraints that these datasets provide into the models typically studied in theoretical neuroscience. This article surveys recent approaches to building models with constrained wiring diagrams and the insights they have provided. It also describes challenges and the need for new techniques to scale these approaches to ever more complex datasets.

Although most proteins conform to the classical one-structure/one-function paradigm, an increasing number of proteins with dual structures and functions have been discovered. In response to cellular stimuli, such proteins undergo structural changes sufficiently dramatic to remodel even their secondary structures and domain organization. This "fold-switching" capability fosters protein multi-functionality, enabling cells to establish tight control over various biochemical processes. Accurate predictions of fold-switching proteins could both suggest underlying mechanisms for uncharacterized biological processes and reveal potential drug targets. Recently, we developed a prediction method for fold-switching proteins using structure-based thermodynamic calculations and discrepancies between predicted and experimentally determined protein secondary structure. Here we seek to leverage the negative information found in these secondary structure prediction discrepancies. To do this, we quantified secondary structure prediction accuracies of 192 known fold-switching regions (FSRs) within solved protein structures found in the Protein Data Bank (PDB). We find that the secondary structure prediction accuracies for these FSRs vary widely. Inaccurate secondary structure predictions are strongly associated with fold-switching proteins compared to equally long segments of non-fold-switching proteins selected at random. These inaccurate predictions are enriched in helix-to-strand and strand-to-coil discrepancies. Finally, we find that most proteins with inaccurate secondary structure predictions are underrepresented in the PDB compared with their alternatively folded cognates, suggesting that unequal representation of fold-switching conformers within the PDB could be an important cause of inaccurate secondary structure predictions. These results demonstrate that inconsistent secondary structure predictions can serve as a useful preliminary marker of fold switching. This article is protected by copyright. All rights reserved.

The palette of tools for stimulation and regulation of neural activity is continually expanding. One of the new methods being introduced is magnetogenetics, where mechano-sensitive and thermo-sensitive ion channels are genetically engineered to be closely coupled to the iron-storage protein ferritin. Such genetic constructs could provide a powerful new way of non-invasively activating ion channels in-vivo using external magnetic fields that easily penetrate biological tissue. Initial reports that introduced this new technology have sparked a vigorous debate on the plausibility of physical mechanisms of ion channel activation by means of external magnetic fields. I argue that the initial criticisms leveled against magnetogenetics as being physically implausible were possibly based on the overly simplistic and unnecessarily pessimistic assumptions about the magnetic spin configurations of iron in ferritin protein. Additionally, all the possible magnetic-field-based mechanisms of ion channel activation in magnetogenetics might not have been fully considered. I present and propose several new magneto-mechanical and magneto-thermal mechanisms of ion channel activation by iron-loaded ferritin protein that may elucidate and clarify some of the mysteries that presently challenge our understanding of the reported biological experiments. Finally, I present some additional puzzles that will require further theoretical and experimental investigation.