One-third of the mammalian proteome is comprised of transmembrane and secretory proteins that are synthesized on endoplasmic reticulum (ER). Here, we investigate the spatial distribution and regulation of mRNAs encoding these membrane and secretory proteins (termed "secretome" mRNAs) through live cell, single molecule tracking to directly monitor the position and translation states of secretome mRNAs on ER and their relationship to other organelles. Notably, translation of secretome mRNAs occurred preferentially near lysosomes on ER marked by the ER junction-associated protein, Lunapark. Knockdown of Lunapark reduced the extent of secretome mRNA translation without affecting translation of other mRNAs. Less secretome mRNA translation also occurred when lysosome function was perturbed by raising lysosomal pH or inhibiting lysosomal proteases. Secretome mRNA translation near lysosomes was enhanced during amino acid deprivation. Addition of the integrated stress response inhibitor, ISRIB, reversed the translation inhibition seen in Lunapark knockdown cells, implying an eIF2 dependency. Altogether, these findings uncover a novel coordination between ER and lysosomes, in which local release of amino acids and other factors from ER-associated lysosomes patterns and regulates translation of mRNAs encoding secretory and membrane proteins.

A DNA damage-inducible mutagenic gene cassette has been implicated in the emergence of drug resistance in during anti-tuberculosis (TB) chemotherapy. However, the molecular composition and operation of the encoded 'mycobacterial mutasome' - minimally comprising DnaE2 polymerase and ImuA' and ImuB accessory proteins - remain elusive. Following exposure of mycobacteria to DNA damaging agents, we observe that DnaE2 and ImuB co-localize with the DNA polymerase III β subunit (β clamp) in distinct intracellular foci. Notably, genetic inactivation of the mutasome in an mutant containing a disrupted β clamp-binding motif abolishes ImuB-β clamp focus formation, a phenotype recapitulated pharmacologically by treating bacilli with griselimycin and in biochemical assays in which this β clamp-binding antibiotic collapses pre-formed ImuB-β clamp complexes. These observations establish the essentiality of the ImuB-β clamp interaction for mutagenic DNA repair in mycobacteria, identifying the mutasome as target for adjunctive therapeutics designed to protect anti-TB drugs against emerging resistance.

Our ability to remember the past is essential for guiding our future behavior. Psychological and neurobiological features of declarative memories are known to transform over time in a process known as systems consolidation. While many theories have sought to explain the time-varying role of hippocampal and neocortical brain areas, the computational principles that govern these transformations remain unclear. Here we propose a theory of systems consolidation in which hippocampal-cortical interactions serve to optimize generalizations that guide future adaptive behavior. We use mathematical analysis of neural network models to characterize fundamental performance tradeoffs in systems consolidation, revealing that memory components should be organized according to their predictability. The theory shows that multiple interacting memory systems can outperform just one, normatively unifying diverse experimental observations and making novel experimental predictions. Our results suggest that the psychological taxonomy and neurobiological organization of declarative memories reflect a system optimized for behaving well in an uncertain future.

Animal brains are complex organs composed of thousands of interconnected neurons. Characterizing the network properties of these brains is a requisite step towards understanding mechanisms of computation and information flow. With the completion of the Flywire project, we now have access to the connectome of a complete adult Drosophila brain, containing 130,000 neurons and millions of connections. Here, we present a statistical summary and data products of the Flywire connectome, delving into its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We uncovered a population of highly connected neurons known as the “rich club” and identified subsets of neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions. The freely available data and neuron populations presented here will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Neurophysiology has long progressed through exploratory experiments and chance discoveries. Anecdotes abound of researchers setting up experiments while listening to spikes in real time and observing a pattern of consistent firing when certain stimuli or behaviors happened. With the advent of large-scale recordings, such close observation of data has become harder because high-dimensional spaces are impenetrable to our pattern-finding intuitions. To help ourselves find patterns in neural data, our lab has been openly developing a visualization framework known as “Rastermap” over the past five years. Rastermap takes advantage of a new global optimization algorithm for sorting neural responses along a one-dimensional manifold. Displayed as a raster plot, the sorted neurons show a variety of activity patterns, which can be more easily identified and interpreted. We first benchmark Rastermap on realistic simulations with multiplexed cognitive variables. Then we demonstrate it on recordings of tens of thousands of neurons from mouse visual and sensorimotor cortex during spontaneous, stimulus-evoked and task-evoked epochs, as well as on whole-brain zebrafish recordings, widefield calcium imaging data, population recordings from rat hippocampus and artificial neural networks. Finally, we illustrate high-dimensional scenarios where Rastermap and similar algorithms cannot be used effectively.

Genetically encoded pH sensors based on fluorescent proteins are valuable tools for the imaging of cellular events that are associated with pH changes, such as exocytosis and endocytosis. Superecliptic pHluorin (SEP) is a pH-sensitive green fluorescent protein (GFP) variant widely used for such applications. Here, we report the rational design, development, structure, and applications of Lime, an improved SEP variant with higher fluorescence brightness and greater pH sensitivity. The X-ray crystal structure of Lime supports the mechanistic rationale that guided the introduction of beneficial mutations. Lime provides substantial improvements relative to SEP for imaging of endocytosis and exocytosis. Furthermore, Lime and its variants are advantageous for a broader range of applications including the detection of synaptic release and neuronal voltage changes.

The density and distribution of regulatory information in non-coding DNA of eukaryotic genomes is largely unknown. Evolutionary analyses have estimated that ∼60% of nucleotides in intergenic regions of the D. melanogaster genome is functionally relevant. This estimate is difficult to reconcile with the commonly accepted idea that enhancers are compact regulatory elements that generally encompass less than 1 kilobase of DNA. Here, we approached this issue through a functional dissection of the regulatory region of the gene shavenbaby (svb). Most of the ∼90 kilobases of this large regulatory region is highly conserved in the genus Drosophila, though characterized enhancers occupy a small fraction of this region. By analyzing the regulation of svb in different contexts of Drosophila development, we found that the regulatory architecture that drives svb expression in the abdominal pupal epidermis is organized in a dramatically different way than the information that drives svb expression in the embryonic epidermis. While in the embryonic epidermis svb is activated by compact and dispersed enhancers, svb expression in the pupal epidermis is driven by large regions with enhancer activity, which occupy a great portion of the svb cis-regulatory DNA. We observed that other developmental genes also display a dense distribution of putative regulatory elements in their regulatory regions. Furthermore, we found that a large percentage of conserved non-coding DNA of the Drosophila genome is contained within putative regulatory DNA. These results suggest that part of the evolutionary constraint on non-coding DNA of Drosophila is explained by the density of regulatory information.

There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat "neural manifold". Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey motor cortex, mouse motor cortex, mouse striatum, and human motor cortex, we show that: 1) neural manifolds are intrinsically nonlinear; 2) the degree of their nonlinearity varies across architecturally distinct brain regions; and 3) manifold nonlinearity becomes more evident during complex tasks that require more varied activity patterns. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.