Transcriptional enhancers are regions of DNA that drive precise patterns of gene expression. While many studies have elucidated how individual enhancers can evolve, most of this work has focused on what are called "minimal" enhancers, the smallest DNA regions that drive expression that approximates an aspect of native gene expression. Here we explore how the Drosophila erecta even-skipped (eve) locus has evolved by testing its activity in the divergent D. melanogaster genome. We found, as has been reported previously, that the D. erecta eve stripe 2 enhancer (eveS2) fails to drive appreciable expression in D. melanogaster (1). However, we found that a large transgene carrying the entire D. erecta eve locus drives normal eve expression, including in stripe 2. We performed a functional dissection of the region upstream of the D. erecta eveS2 region and found multiple Zelda motifs that are required for normal expression. Our results illustrate how sequences outside of minimal enhancer regions can evolve functionally through mechanisms other than changes in transcription factor binding sites that drive patterning.

Individual neurons in prefrontal cortex – a key brain area involved in cognitive functions – are selective for variables such as space or time, as well as more cognitive aspects of tasks, such as learned categories. Many neurons exhibit mixed selectivity, that is, they show selectivity for multiple variables. A fundamental question is whether neurons are functionally specialized for particular variables and how selectivity for different variables intersects across the population. Here, we analyzed neural correlates of space and time in rats performing a navigational task with two behaviorally important categories – starts and goals. Using simultaneous recordings of many medial prefrontal cortex (mPFC) neurons during behavior, we found that population codes for elapsed time were invariant to different locations within categories, and subsets of neurons had functional preferences for time or space across categories. Thus, mPFC exhibits structured selectivity, which may facilitate complex behaviors by efficiently generating informative representations of multiple variables.

Small cell lung cancer (SCLC) is a highly aggressive type of lung cancer, characterized by rapid proliferation, early metastatic spread, frequent early relapse and a high mortality rate. Recent evidence has suggested that innervation has an important role in the development and progression of several types of cancer. Cancer-to-neuron synapses have been reported in gliomas, but whether peripheral tumours can form such structures is unknown. Here we show that SCLC cells can form functional synapses and receive synaptic transmission. Using in vivo insertional mutagenesis screening in conjunction with cross-species genomic and transcriptomic validation, we identified neuronal, synaptic and glutamatergic signalling gene sets in mouse and human SCLC. Further experiments revealed the ability of SCLC cells to form synaptic structures with neurons in vitro and in vivo. Electrophysiology and optogenetic experiments confirmed that cancer cells can receive NMDA receptor- and GABA receptor-mediated synaptic inputs. Fitting with a potential oncogenic role of neuron-SCLC interactions, we showed that SCLC cells derive a proliferation advantage when co-cultured with vagal sensory or cortical neurons. Moreover, inhibition of glutamate signalling had therapeutic efficacy in an autochthonous mouse model of SCLC. Therefore, following malignant transformation, SCLC cells seem to hijack synaptic signalling to promote tumour growth, thereby exposing a new route for therapeutic intervention.

Small unilamellar vesicles (SUVs) are indispensable model membranes, organelle mimics, and drug and vaccine carriers. However, the lack of robust techniques to functionalize or organize preformed SUVs limits their applications. Here we use DNA nanostructures to coat, cluster, and pattern sub-100-nm liposomes, generating distance-controlled vesicle networks, strings and dimers, among other configurations. The DNA coating also enables attachment of proteins to liposomes, and temporal control of membrane fusion driven by SNARE protein complexes. Such a convenient and versatile method of engineering premade vesicles both structurally and functionally is highly relevant to bottom-up biology and targeted delivery.

Processing bodies (p-bodies) are a prototypical phase-separated RNA-containing granule. Their abundance is highly dynamic and has been linked to translation. Yet, the molecular mechanisms responsible for coordinate control of the two processes are unclear. Here, we uncover key roles for eEF2 kinase (eEF2K) in the control of ribosome availability and p-body abundance. eEF2K acts on a sole known substrate, eEF2, to inhibit translation. We find that the eEF2K agonist nelfinavir abolishes p-bodies in sensory neurons and impairs translation. To probe the latter, we used cryo-electron microscopy. Nelfinavir stabilizes vacant 80S ribosomes. They contain SERBP1 in place of mRNA and eEF2 in the acceptor site. Phosphorylated eEF2 associates with inactive ribosomes that resist splitting in vitro. Collectively, the data suggest that eEF2K defines a population of inactive ribosomes resistant to recycling and protected from degradation. Thus, eEF2K activity is central to both p-body abundance and ribosome availability in sensory neurons.

Human memory appears to be fragile and unpredictable. Free recall of random lists of words is a standard paradigm used to probe episodic memory. We proposed an associative search process that can be reduced to a deterministic walk on random graphs defined by the structure of memory representations. The corresponding graph model can be solved analytically, resulting in a novel parameter-free prediction for the average number of memory items recalled (R) out of M items in memory: R=sqrt[3πM/2]. This prediction was verified with a specially designed experimental protocol combining large-scale crowd-sourced free recall and recognition experiments with randomly assembled lists of words or common facts. Our results show that human memory can be described by universal laws derived from first principles.

Predictive remapping (PRE )—the ability of cells in retinotopic brain structures to transiently exhibit spatiotemporal shifts beyond the spatial extent of their classical anatomical receptive fields—has been proposed as a primary mechanism that stabilizes an organism’s percept of the visual world around the time of a saccadic eye movement. Despite the well-documented effects of PRE , a biologically plausible mathematical framework that specifies a fundamental law and the functional neural architecture that actively mediates this ubiquitous phenomenon does not exist. We introduce the Newtonian model of PRE , where each modular component of PRE manifests as three temporally overlapping forces: centripetal ( fC ), convergent ( fP ), and translational ( fT ), that perturb retinotopic cells from their equilibrium extent. The resultant and transient influences of these forces fC + fP + fT gives rise to a neuronal force field that governs the spatiotemporal dynamics of PRE . This neuronal force field fundamentally obeys an inverse-distance law PRE ∝ 1 r1.6 , akin to Newton’s law of universal gravitation [I. Newton, Newton’s Principia: The Mathematical Principles of Natural Philosophy (Geo. P. Putnam, New-York, 1850)] and activates retinotopic elastic fields elϕ’s. We posit that elϕ’s are transient functional neural structures that are self-generated by visual systems during active vision and approximate the sloppiness (or degrees of spatial freedom) within which receptive fields are allowed to shift, while ensuring that retinotopic organization does not collapse. The predictions of this general model are borne out by the spatiotemporal changes in visual sensitivity to probe stimuli in human subjects around the time of an eye movement and qualitatively match neural sensitivity signatures associated with predictive shifts in the receptive fields of cells in premotor and higher-order retinotopic brain structures. The introduction of this general model opens the search for possible biophysical implementations and provides experimentalists with a simple, elegant, yet powerful mathematical framework they can now use to generate experimentally testable predictions across a range of biological systems.

Tremor is a common movement disorder associated with several neurodegenerative diseases, yet its mechanisms are not well understood. Using a machine learning method, FLLIT, we previously characterised gait and tremor signatures in the Drosophila model for Spinocerebellar ataxia 3 (SCA3), and found them to be analogous to human SCA3. Here, we carried out a functional screen for neuronal populations that underlie tremor, and found that dysfunction of a specific population of neurons in the ventral nerve cord (VNC) is necessary and sufficient for tremor. Adult-onset expression of mutant ATXN3 in or genetic hypo-activation of these neurons leads to tremor, indicating their important role in adult motor control. RNAseq and functional experiments showed that dysfunction of GABAergic neurons, and not other neurotransmitter populations tested, causes tremor. Finally, we identified a small subset of approximately 30 predominantly GABAergic neurons within the adult VNC that are essential for smooth walking. This study demonstrates that tremor in SCA3 flies arises from GABAergic dysfunction, and that FLLIT can be used to dissect motor control mechanisms.

We demonstrate gas cluster ion beam scanning electron microscopy (SEM), in which wide-area ion milling is performed on a series of thick tissue sections. This three-dimensional electron microscopy technique acquires datasets with <10 nm isotropic resolution of each section, and these can then be stitched together to span the sectioned volume. Incorporating gas cluster ion beam SEM into existing single-beam and multibeam SEM workflows should be straightforward, increasing reliability while improving z resolution by a factor of three or more.