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We present a new method to measure sub-microcurie activities of photon-emitting radionuclides in organs and lesions of small animals in vivo. Our technique, named the collimator-less likelihood fit, combines a very high sensitivity collimatorless detector with a Monte Carlo-based likelihood fit in order to estimate the activities in previously segmented regions of interest along with their uncertainties. This is done directly from the photon projections in our collimatorless detector and from the region of interest segmentation provided by an x-ray computed tomography scan. We have extensively validated our approach with 225Ac experimentally in spherical phantoms and mouse phantoms, and also numerically with simulations of a realistic mouse anatomy. Our method yields statistically unbiased results with uncertainties smaller than 20% for activities as low as ~111Bq (3nCi) and for exposures under 30 minutes. We demonstrate that our method yields more robust recovery coefficients when compared to SPECT imaging with a commercial pre-clinical scanner, specially at very low activities. Thus, our technique is complementary to traditional SPECT/CT imaging since it provides a more accurate and precise organ and tumor dosimetry, with a more limited spatial information. Finally, our technique is specially significant in extremely low-activity scenarios when SPECT/CT imaging is simply not viable.

Adaptation is a universal aspect of neural systems that changes circuit computations to match prevailing inputs. These changes facilitate efficient encoding of sensory inputs while avoiding saturation. Conventional artificial neural networks (ANNs) have limited adaptive capabilities, hindering their ability to reliably predict neural output under dynamic input conditions. Can embedding neural adaptive mechanisms in ANNs improve their performance? To answer this question, we develop a new deep learning model of the retina that incorporates the biophysics of photoreceptor adaptation at the front-end of conventional convolutional neural networks (CNNs). These conventional CNNs build on 'Deep Retina,' a previously developed model of retinal ganglion cell (RGC) activity. CNNs that include this new photoreceptor layer outperform conventional CNN models at predicting male and female primate and rat RGC responses to naturalistic stimuli that include dynamic local intensity changes and large changes in the ambient illumination. These improved predictions result directly from adaptation within the phototransduction cascade. This research underscores the potential of embedding models of neural adaptation in ANNs and using them to determine how neural circuits manage the complexities of encoding natural inputs that are dynamic and span a large range of light levels.

Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-efficient pipeline for single-cell RNA classification. We benchmark SIMS against datasets from different tissues and species. We demonstrate SIMS's efficacy in classifying cells in the brain, achieving high accuracy even with small training sets (

Within the islets of Langerhans, beta cells orchestrate synchronized insulin secretion, a pivotal aspect of metabolic homeostasis. Despite the inherent heterogeneity and multimodal activity of individual cells, intercellular coupling acts as a homogenizing force, enabling coordinated responses through the propagation of intercellular waves. Disruptions in this coordination are implicated in irregular insulin secretion, a hallmark of diabetes. Recently, innovative approaches, such as integrating multicellular calcium imaging with network analysis, have emerged for a quantitative assessment of the cellular activity in islets. However, different groups use distinct experimental preparations, microscopic techniques, apply different methods to process the measured signals and use various methods to derive functional connectivity patterns. This makes comparisons between findings and their integration into a bigger picture difficult and has led to disputes in functional connectivity interpretations. To address these issues, we present here a systematic analysis of how different approaches influence the network representation of islet activity. Our findings show that the choice of methods used to construct networks is not crucial, although care is needed when combining data from different islets. Conversely, the conclusions drawn from network analysis can be heavily affected by the pre-processing of the time series, the type of the oscillatory component in the signals, and by the experimental preparation. Our tutorial-like investigation aims to resolve interpretational issues, reconcile conflicting views, advance functional implications, and encourage researchers to adopt connectivity analysis. As we conclude, we outline challenges for future research, emphasizing the broader applicability of our conclusions to other tissues exhibiting complex multicellular dynamics.

In droplet-based single-cell and single-nucleus RNA-seq assays, systematic contamination of ambient RNA molecules biases the quantification of gene expression levels. Existing methods correct the contamination for all genes globally. However, there lacks specific evaluation of correction efficacy for varying contamination levels. Here, we show that DecontX and CellBender under-correct highly contaminating genes, while SoupX and scAR over-correct lowly/non-contaminating genes. Here, we develop scCDC as the first method to detect the contamination-causing genes and only correct expression levels of these genes, some of which are cell-type markers. Compared with existing decontamination methods, scCDC excels in decontaminating highly contaminating genes while avoiding over-correction of other genes.

Spermatogonial stem cells functionality reside in the slow-cycling and heterogeneous undifferentiated spermatogonia cell population. This pool of cells supports lifelong fertility in adult males by balancing self-renewal and differentiation to produce haploid gametes. However, the molecular mechanisms underpinning long-term stemness of undifferentiated spermatogonia during adulthood remain unclear. Here, we discover that an epigenetic regulator, Polycomb repressive complex 1 (PRC1), shields adult undifferentiated spermatogonia from differentiation, maintains slow cycling, and directs commitment to differentiation during steady-state spermatogenesis in adults. We show that PRC2-mediated H3K27me3 is an epigenetic hallmark of adult undifferentiated spermatogonia. Indeed, spermatogonial differentiation is accompanied by a global loss of H3K27me3. Disruption of PRC1 impairs global H3K27me3 deposition, leading to precocious spermatogonial differentiation. Therefore, PRC1 directs PRC2-H3K27me3 deposition to maintain the self-renewing state of undifferentiated spermatogonia. Importantly, in contrast to its role in other tissue stem cells, PRC1 negatively regulates the cell cycle to maintain slow cycling of undifferentiated spermatogonia. Our findings have implications for how epigenetic regulators can be tuned to regulate the stem cell potential, cell cycle and differentiation to ensure lifelong fertility in adult males.

Mammalian polynucleotide kinase 3'-phosphatase (PNKP), a DNA end-processing enzyme with 3'-phosphatase and 5'-kinase activities, is involved in multiple DNA repair pathways, including base excision (BER), single-strand break (SSBR), and double-strand break repair (DSBR). However, little is known as to how PNKP functions in such diverse repair processes. Here we report that PNKP is acetylated at K142 (AcK142) by p300 constitutively but at K226 (AcK226) by CBP, only after DSB induction. Co-immunoprecipitation analysis using AcK142 or AcK226 PNKP-specific antibodies showed that AcK142-PNKP associates only with BER/SSBR, and AcK226 PNKP with DSBR proteins. Despite the modest effect of acetylation on PNKP's enzymatic activity in vitro, cells expressing non-acetylable PNKP (K142R or K226R) accumulated DNA damage in transcribed genes. Intriguingly, in striatal neuronal cells of a Huntington's Disease (HD)-based mouse model, K142, but not K226, was acetylated. This is consistent with the reported degradation of CBP, but not p300, in HD cells. Moreover, transcribed genomes of HD cells progressively accumulated DSBs. Chromatin-immunoprecipitation analysis demonstrated the association of Ac-PNKP with the transcribed genes, consistent with PNKP's role in transcription-coupled repair. Thus, our findings demonstrate that acetylation at two lysine residues, located in different domains of PNKP, regulates its distinct role in BER/SSBR versus DSBR.

First-order thalamic nuclei receive feedforward signals from peripheral receptors and relay these signals to primary sensory cortex. Primary sensory cortex, in turn, provides reciprocal feedback to first-order thalamus. Because the vast majority of sensory thalamocortical inputs target primary sensory cortex, their complementary corticothalamic neurons are assumed to be similarly restricted to primary sensory cortex. We upend this assumption by characterizing morphologically diverse neurons in multiple mid-level visual cortical areas of the primate (Macaca mulatta) brain that provide direct feedback to the primary visual thalamus, the dorsal lateral geniculate nucleus (LGN). Although the majority of geniculocortical neurons project to primary visual cortex (V1), a minority, located mainly in the koniocellular LGN layers, provide direct input to extrastriate visual cortex. These "V1-bypassing" projections may be implicated in blindsight. We hypothesized that geniculocortical inputs directly targeting extrastriate cortex should be complemented by reciprocal corticogeniculate circuits. Using virus-mediated circuit tracing, we discovered corticogeniculate neurons throughout three mid-level extrastriate areas: MT, MST, and V4. Quantitative morphological analyses revealed nonuniform distributions of unique cell types across areas. Many extrastriate corticogeniculate neurons had spiny stellate morphology, suggesting possible targeting of koniocellular LGN layers. Importantly though, multiple morphological types were observed across areas. Such morphological diversity could suggest parallel streams of V1-bypassing corticogeniculate feedback at multiple stages of the visual processing hierarchy. Furthermore, the presence of corticogeniculate neurons across visual cortex necessitates a reevaluation of the LGN as a hub for visual information rather than a simple relay.

The UCSC Genome Browser (https://genome.ucsc.edu) is a web-based genomic visualization and analysis tool that serves data to over 7,000 distinct users per day worldwide. It provides annotation data on thousands of genome assemblies, ranging from human to SARS-CoV2. This year, we have introduced new data from the Human Pangenome Reference Consortium and on viral genomes including SARS-CoV2. We have added 1,200 new genomes to our GenArk genome system, increasing the overall diversity of our genomic representation. We have added support for nine new user-contributed track hubs to our public hub system. Additionally, we have released 29 new tracks on the human genome and 11 new tracks on the mouse genome. Collectively, these new features expand both the breadth and depth of the genomic knowledge that we share publicly with users worldwide.

BackgroundMénière's disease (MD) is a disorder of the inner ear that causes episodic bouts of severe dizziness, roaring tinnitus, and fluctuating hearing loss. To date, no targeted therapy exists. As such, we have undertaken a large whole genome sequencing study on carefully phenotyped unilateral MD patients with the goal of gene/pathway discovery and a move towards targeted intervention. This study was a retrospective review of patients with a history of Ménière's disease. Genomic DNA, acquired from saliva samples, was purified and subjected to whole genome sequencing.ResultsStringent variant calling, performed on 511 samples passing quality checks, followed by gene-based filtering by recurrence and proximity in molecular interaction networks, led to 481 high priority MD genes. These high priority genes, including MPHOSPH8, MYO18A, TRIOBP, OTOGL, TNC, and MYO6, were previously implicated in hearing loss, balance, and cochlear function, and were significantly enriched in common variant studies of hearing loss. Validation in an independent MD cohort confirmed 82 recurrent genes. Pathway analysis pointed to cell-cell adhesion, extracellular matrix, and cellular energy maintenance as key mediators of MD. Furthermore, the MD-prioritized genes were highly expressed in human inner ear hair cells and dark/vestibular cells, and were differentially expressed in a mouse model of hearing loss.ConclusionBy enabling the development of model systems that may lead to targeted therapies and MD screening panels, the genes and variants identified in this study will inform diagnosis and treatment of MD.

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

BackgroundStructural variations (SVs) have significant impacts on complex phenotypes by rearranging large amounts of DNA sequence.ResultsWe present a comprehensive SV catalog based on the whole-genome sequence of 1060 pigs (Sus scrofa) representing 101 breeds, covering 9.6% of the pig genome. This catalog includes 42,487 deletions, 37,913 mobile element insertions, 3308 duplications, 1664 inversions, and 45,184 break ends. Estimates of breed ancestry and hybridization using genotyped SVs align well with those from single nucleotide polymorphisms. Geographically stratified deletions are observed, along with known duplications of the KIT gene, responsible for white coat color in European pigs. Additionally, we identify a recent SINE element insertion in MYO5A transcripts of European pigs, potentially influencing alternative splicing patterns and coat color alterations. Furthermore, a Yorkshire-specific copy number gain within ABCG2 is found, impacting chromatin interactions and gene expression across multiple tissues over a stretch of genomic region of ~200 kb. Preliminary investigations into SV's impact on gene expression and traits using the Pig Genotype-Tissue Expression (PigGTEx) data reveal SV associations with regulatory variants and gene-trait pairs. For instance, a 51-bp deletion is linked to the lead eQTL of the lipid metabolism regulating gene FADS3, whose expression in embryo may affect loin muscle area, as revealed by our transcriptome-wide association studies.ConclusionsThis SV catalog serves as a valuable resource for studying diversity, evolutionary history, and functional shaping of the pig genome by processes like domestication, trait-based breeding, and adaptive evolution.

Cellular DNA is subject to damage from a multitude of sources and repair or bypass of sites of damage utilize an array of context or cell cycle dependent systems. The recognition and removal of oxidatively damaged bases is the task of DNA glycosylases from the base excision repair pathway utilizing two structural families that excise base lesions in a wide range of DNA contexts including duplex, single-stranded and bubble structures arising during transcription. The mammalian NEIL2 glycosylase of the Fpg/Nei family excises lesions from each of these DNA contexts favoring the latter two with a preference for oxidized cytosine products and abasic sites. We have determined the first liganded crystal structure of mammalian NEIL2 in complex with an abasic site analog containing DNA duplex at 2.08 Å resolution. Comparison to the unliganded structure revealed a large interdomain conformational shift upon binding the DNA substrate accompanied by local conformational changes in the C-terminal domain zinc finger and N-terminal domain void-filling loop necessary to position the enzyme on the DNA. The detailed biochemical analysis of NEIL2 with an array of oxidized base lesions indicates a significant preference for its lyase activity likely to be paramount when interpreting the biological consequences of variants.

The brain's resident macrophages have many roles beyond synaptic pruning.

Sperm chromatin retains small amounts of histones, and chromatin states of sperm mirror gene expression programs of the next generation. However, it remains largely unknown how paternal epigenetic information is transmitted through sperm chromatin. Here, we present a novel mouse model of paternal epigenetic inheritance, in which deposition of Polycomb repressive complex 2 (PRC2) mediated-repressive H3K27me3 is attenuated in the paternal germline. By applying modified methods of assisted reproductive technology using testicular sperm, we rescued infertility of mice missing Polycomb protein SCML2, which regulates germline gene expression by establishing H3K27me3 on bivalent promoters with other active marks H3K4me2/3. We profiled epigenomic states (H3K27me3 and H3K4me3) of testicular sperm and epididymal sperm, demonstrating that the epididymal pattern of the sperm epigenome is already established in testicular sperm and that SCML2 is required for this process. In F1 males of X-linked Scml2-knockout mice, which have a wild-type genotype, gene expression is dysregulated in the male germline during spermiogenesis. These dysregulated genes are targets of SCML2-mediated H3K27me3 in F0 sperm. Further, dysregulation of gene expression was observed in the mutant-derived wild-type F1 preimplantation embryos. Together, we present functional evidence that the classic epigenetic regulator Polycomb mediates paternal epigenetic inheritance through sperm chromatin.

Hybridization capture approaches allow targeted high-throughput sequencing analysis at reduced costs compared to shotgun sequencing. Hybridization capture is particularly useful in analyses of genomic data from ancient, environmental, and forensic samples, where target content is low, DNA is fragmented and multiplex PCR or other targeted approaches often fail. Here, we describe a DNA bait synthesis approach for hybridization capture that we call Circular Nucleic acid Enrichment Reagent, or CNER (pronounced 'snare'). The CNER method uses rolling-circle amplification followed by restriction digestion to discretize microgram quantities of hybridization probes. We demonstrate the utility of the CNER method by generating probes for a panel of 23 771 known sites of single nucleotide polymorphism in the horse genome. Using these probes, we capture and sequence from a panel of ten ancient horse DNA libraries, comparing CNER capture efficiency to a commercially available approach. With about one million read pairs per sample, CNERs captured more targets (90.5% versus 66.5%) at greater mean depth than an alternative commercial approach.

BackgroundRapid adaptation to new environments can facilitate species invasions and range expansions. Understanding the mechanisms of adaptation used by invasive disease vectors in new regions has key implications for mitigating the prevalence and spread of vector-borne disease, although they remain relatively unexplored.ResultsHere, we integrate whole-genome sequencing data from 96 Aedes aegypti mosquitoes collected from various sites in southern and central California with 25 annual topo-climate variables to investigate genome-wide signals of local adaptation among populations. Patterns of population structure, as inferred using principal components and admixture analysis, were consistent with three genetic clusters. Using various landscape genomics approaches, which all remove the confounding effects of shared ancestry on correlations between genetic and environmental variation, we identified 112 genes showing strong signals of local environmental adaptation associated with one or more topo-climate factors. Some of them have known effects in climate adaptation, such as heat-shock proteins, which shows selective sweep and recent positive selection acting on these genomic regions.ConclusionsOur results provide a genome wide perspective on the distribution of adaptive loci and lay the foundation for future work to understand how environmental adaptation in Ae. aegypti impacts the arboviral disease landscape and how such adaptation could help or hinder efforts at population control.

Changes in patterns of activity within the medial prefrontal cortex enable rodents, non-human primates and humans to update their behaviour to adapt to changes in the environment-for example, during cognitive tasks1-5. Parvalbumin-expressing inhibitory neurons in the medial prefrontal cortex are important for learning new strategies during a rule-shift task6-8, but the circuit interactions that switch prefrontal network dynamics from maintaining to updating task-related patterns of activity remain unknown. Here we describe a mechanism that links parvalbumin-expressing neurons, a new callosal inhibitory connection, and changes in task representations. Whereas nonspecifically inhibiting all callosal projections does not prevent mice from learning rule shifts or disrupt the evolution of activity patterns, selectively inhibiting only callosal projections of parvalbumin-expressing neurons impairs rule-shift learning, desynchronizes the gamma-frequency activity that is necessary for learning8 and suppresses the reorganization of prefrontal activity patterns that normally accompanies rule-shift learning. This dissociation reveals how callosal parvalbumin-expressing projections switch the operating mode of prefrontal circuits from maintenance to updating by transmitting gamma synchrony and gating the ability of other callosal inputs to maintain previously established neural representations. Thus, callosal projections originating from parvalbumin-expressing neurons represent a key circuit locus for understanding and correcting the deficits in behavioural flexibility and gamma synchrony that have been implicated in schizophrenia and related conditions9,10.

The maintenance of cellular function relies on the close regulation of adenosine triphosphate (ATP) synthesis and hydrolysis. ATP hydrolysis by mitochondrial ATP Synthase (CV) is induced by loss of proton motive force and inhibited by the mitochondrial protein ATPase inhibitor (ATPIF1). The extent of CV hydrolytic activity and its impact on cellular energetics remains unknown due to the lack of selective hydrolysis inhibitors of CV. We find that CV hydrolytic activity takes place in coupled intact mitochondria and is increased by respiratory chain defects. We identified (+)-Epicatechin as a selective inhibitor of ATP hydrolysis that binds CV while preventing the binding of ATPIF1. In cells with Complex-III deficiency, we show that inhibition of CV hydrolytic activity by (+)-Epichatechin is sufficient to restore ATP content without restoring respiratory function. Inhibition of CV-ATP hydrolysis in a mouse model of Duchenne Muscular Dystrophy is sufficient to improve muscle force without any increase in mitochondrial content. We conclude that the impact of compromised mitochondrial respiration can be lessened using hydrolysis-selective inhibitors of CV.

Predator-prey interactions are one of ecology's central research themes, but with many interdisciplinary implications across the social and natural sciences. Here we consider an often-overlooked species in these interactions, namely parasites. We first show that a simple predator-prey-parasite model, inspired by the classical Lotka-Volterra equations, fails to produce a stable coexistence of all three species, thus failing to provide a biologically realistic outcome. To improve this, we introduce free space as a relevant eco-evolutionary component in a new mathematical model that uses a game-theoretical payoff matrix to describe a more realistic setup. We then show that the consideration of free space stabilizes the dynamics by means of cyclic dominance that emerges between the three species. We determine the parameter regions of coexistence as well as the types of bifurcations leading to it by means of analytical derivations as well as by means of numerical simulations. We conclude that the consideration of free space as a finite resource reveals the limits of biodiversity in predator-prey-parasite interactions, and it may also help us in the determination of factors that promote a healthy biota.

BackgroundWound healing involves careful coordination among various cell types carrying out unique or even multifaceted functions. The abstraction of this complex dynamic process into four primary wound stages is essential to the study of wound care for timing treatment and tracking wound progression. For example, a treatment that may promote healing in the inflammatory stage may prove detrimental in the proliferative stage. Additionally, the time scale of individual responses varies widely across and within the same species. Therefore, a robust method to assess wound stages can help advance translational work from animals to humans.ResultsIn this work, we present a data-driven model that robustly identifies the dominant wound healing stage using transcriptomic data from biopsies gathered from mouse and human wounds, both burn and surgical. A training dataset composed of publicly available transcriptomic arrays is used to derive 58 shared genes that are commonly differentially expressed. They are divided into 5 clusters based on temporal gene expression dynamics. The clusters represent a 5-dimensional parametric space containing the wound healing trajectory. We then create a mathematical classification algorithm in the 5-dimensional space and demonstrate that it can distinguish between the four stages of wound healing: hemostasis, inflammation, proliferation, and remodeling.ConclusionsIn this work, we present an algorithm for wound stage detection based on gene expression. This work suggests that there are universal characteristics of gene expression in wound healing stages despite the seeming disparities across species and wounds. Our algorithm performs well for human and mouse wounds of both burn and surgical types. The algorithm has the potential to serve as a diagnostic tool that can advance precision wound care by providing a way of tracking wound healing progression with more accuracy and finer temporal resolution compared to visual indicators. This increases the potential for preventive action.

Motor skill learning induces changes in synaptic structure and function in the primary motor cortex (M1). In the fragile X syndrome (FXS) mouse model an impairment in motor skill learning and associated formation of new dendritic spines was previously reported. However, whether modulation of synaptic strength through trafficking of AMPA receptors (AMPARs) with motor skill training is impaired in FXS is not known. Here, we performed in vivo imaging of a tagged AMPA receptor subunit, GluA2, in layer (L)2/3 neurons in the primary motor cortex of wild-type (WT) and Fmr1 knock-out (KO) male mice at different stages of learning a single forelimb-reaching task. Surprisingly, in the Fmr1 KO mice, despite impairments in learning there was no deficit in motor skill training-induced spine formation. However, the gradual accumulation of GluA2 in WT stable spines, which persists after training is completed and past the phase of spine number normalization, is absent in the Fmr1 KO mouse. These results demonstrate that motor skill learning not only reorganizes circuits through formation of new synapses, but also strengthens existing synapses through accumulation of AMPA receptors and GluA2 changes are better associated with learning than new spine formation.

Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities - inherited from over 500 million years of evolution - that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI.

Inefficient endosomal escape remains the primary barrier to the broad application of oligonucleotide therapeutics. Liver uptake after systemic administration is sufficiently robust that a therapeutic effect can be achieved but targeting extrahepatic tissues remains challenging. Prior attempts to improve oligonucleotide activity using small molecules that increase the leakiness of endosomes have failed due to unacceptable toxicity. Here, we show that the well-tolerated and orally bioavailable synthetic sphingolipid analog, SH-BC-893, increases the activity of antisense oligonucleotides (ASOs) and small interfering RNAs (siRNAs) up to 200-fold in vitro without permeabilizing endosomes. SH-BC-893 treatment trapped endocytosed oligonucleotides within extra-lysosomal compartments thought to be more permeable due to frequent membrane fission and fusion events. Simultaneous disruption of ARF6-dependent endocytic recycling and PIKfyve-dependent lysosomal fusion was necessary and sufficient for SH-BC-893 to increase non-lysosomal oligonucleotide levels and enhance their activity. In mice, oral administration of SH-BC-893 increased ASO potency in the liver by 15-fold without toxicity. More importantly, SH-BC-893 enabled target RNA knockdown in the CNS and lungs of mice treated subcutaneously with cholesterol-functionalized duplexed oligonucleotides or unmodified ASOs, respectively. Together, these results establish the feasibility of using a small molecule that disrupts endolysosomal trafficking to improve the activity of oligonucleotides in extrahepatic tissues.

To survive, animals must convert sensory information into appropriate behaviours1,2. Vision is a common sense for locating ethologically relevant stimuli and guiding motor responses3-5. How circuitry converts object location in retinal coordinates to movement direction in body coordinates remains largely unknown. Here we show through behaviour, physiology, anatomy and connectomics in Drosophila that visuomotor transformation occurs by conversion of topographic maps formed by the dendrites of feature-detecting visual projection neurons (VPNs)6,7 into synaptic weight gradients of VPN outputs onto central brain neurons. We demonstrate how this gradient motif transforms the anteroposterior location of a visual looming stimulus into the fly's directional escape. Specifically, we discover that two neurons postsynaptic to a looming-responsive VPN type promote opposite takeoff directions. Opposite synaptic weight gradients onto these neurons from looming VPNs in different visual field regions convert localized looming threats into correctly oriented escapes. For a second looming-responsive VPN type, we demonstrate graded responses along the dorsoventral axis. We show that this synaptic gradient motif generalizes across all 20 primary VPN cell types and most often arises without VPN axon topography. Synaptic gradients may thus be a general mechanism for conveying spatial features of sensory information into directed motor outputs.

Alkaline exonucleases (AE) are present in several large DNA viruses including bacteriophage λ and herpesviruses, where they play roles in viral DNA processing during genome replication. Given the genetic conservation of AEs across viruses infecting different kingdoms of life, these enzymes likely assume central roles in the lifecycles of viruses where they have yet to be well characterized. Here, we applied a structure-guided functional analysis of the bifunctional AE in the oncogenic human gammaherpesvirus Kaposi's sarcoma-associated herpesvirus (KSHV), called SOX. In addition to identifying a preferred DNA substrate preference for SOX, we define key residues important for DNA binding and DNA processing, and how SOX activity on DNA partially overlaps with its functionally separable cleavage of mRNA. By engineering these SOX mutants into KSHV, we reveal roles for its DNase activity in viral gene expression and infectious virion production. Our results provide mechanistic insight into gammaherpesviral AE activity as well as areas of functional conservation between this mammalian virus AE and its distant relative in phage λ.

Systems consolidation-a process for long-term memory stabilization-has been hypothesized to occur in two stages1-4. Whereas new memories require the hippocampus5-9, they become integrated into cortical networks over time10-12, making them independent of the hippocampus. How hippocampal-cortical dialogue precisely evolves during this and how cortical representations change in concert is unknown. Here, we use a skill learning task13,14 to monitor the dynamics of cross-area coupling during non-rapid eye movement sleep along with changes in primary motor cortex (M1) representational stability. Our results indicate that precise cross-area coupling between hippocampus, prefrontal cortex and M1 can demarcate two distinct stages of processing. We specifically find that each animal demonstrates a sharp increase in prefrontal cortex and M1 sleep slow oscillation coupling with stabilization of performance. This sharp increase then predicts a drop in hippocampal sharp-wave ripple (SWR)-M1 slow oscillation coupling-suggesting feedback to inform hippocampal disengagement and transition to a second stage. Notably, the first stage shows significant increases in hippocampal SWR-M1 slow oscillation coupling in the post-training sleep and is closely associated with rapid learning and variability of the M1 low-dimensional manifold. Strikingly, even after consolidation, inducing new manifold exploration by changing task parameters re-engages hippocampal-M1 coupling. We thus find evidence for dynamic hippocampal-cortical dialogue associated with manifold exploration during learning and adaptation.

BackgroundEnhancers are essential in defining cell fates through the control of cell-type-specific gene expression. Enhancer activation is a multi-step process involving chromatin remodelers and histone modifiers including the monomethylation of H3K4 (H3K4me1) by MLL3 (KMT2C) and MLL4 (KMT2D). MLL3/4 are thought to be critical for enhancer activation and cognate gene expression including through the recruitment of acetyltransferases for H3K27.ResultsHere we test this model by evaluating the impact of MLL3/4 loss on chromatin and transcription during early differentiation of mouse embryonic stem cells. We find that MLL3/4 activity is required at most if not all sites that gain or lose H3K4me1 but is largely dispensable at sites that remain stably methylated during this transition. This requirement extends to H3K27 acetylation (H3K27ac) at most transitional sites. However, many sites gain H3K27ac independent of MLL3/4 or H3K4me1 including enhancers regulating key factors in early differentiation. Furthermore, despite the failure to gain active histone marks at thousands of enhancers, transcriptional activation of nearby genes is largely unaffected, thus uncoupling the regulation of these chromatin events from transcriptional changes during this transition. These data challenge current models of enhancer activation and imply distinct mechanisms between stable and dynamically changing enhancers.ConclusionsCollectively, our study highlights gaps in knowledge about the steps and epistatic relationships of enzymes necessary for enhancer activation and cognate gene transcription.

GCLiPP is a global RNA interactome capture method that detects RNA-binding protein (RBP) occupancy transcriptome-wide. GCLiPP maps RBP-occupied sites at a higher resolution than phase separation-based techniques. GCLiPP sequence tags correspond with known RBP binding sites and are enriched for sites detected by RBP-specific crosslinking immunoprecipitation (CLIP) for abundant cytosolic RBPs. Comparison of human Jurkat T cells and mouse primary T cells uncovers shared peaks of GCLiPP signal across homologous regions of human and mouse 3' UTRs, including a conserved mRNA-destabilizing cis-regulatory element. GCLiPP signal overlapping with immune-related SNPs uncovers stabilizing cis-regulatory regions in CD5, STAT6, and IKZF1.

Demixing signals in transcranial videos of neuronal calcium flux across the cerebral hemispheres is a key step before mapping features of cortical organization. Here we demonstrate that independent component analysis can optimally recover neural signal content in widefield recordings of neuronal cortical calcium dynamics captured at a minimum sampling rate of 1.5×106 pixels per one-hundred millisecond frame for seventeen minutes with a magnification ratio of 1:1. We show that a set of spatial and temporal metrics obtained from the components can be used to build a random forest classifier, which separates neural activity and artifact components automatically at human performance. Using this data, we establish functional segmentation of the mouse cortex to provide a map of ~115 domains per hemisphere, in which extracted time courses maximally represent the underlying signal in each recording. Domain maps revealed substantial regional motifs, with higher order cortical regions presenting large, eccentric domains compared with smaller, more circular ones in primary sensory areas. This workflow of data-driven video decomposition and machine classification of signal sources can greatly enhance high quality mapping of complex cerebral dynamics.

BackgroundHigh throughput sequencing has enabled the interrogation of the transcriptomic landscape of glucagon-secreting alpha cells, insulin-secreting beta cells, and somatostatin-secreting delta cells. These approaches have furthered our understanding of expression patterns that define healthy or diseased islet cell types and helped explicate some of the intricacies between major islet cell crosstalk and glucose regulation. All three endocrine cell types derive from a common pancreatic progenitor, yet alpha and beta cells have partially opposing functions, and delta cells modulate and control insulin and glucagon release. While gene expression signatures that define and maintain cellular identity have been widely explored, the underlying epigenetic components are incompletely characterized and understood. However, chromatin accessibility and remodeling is a dynamic attribute that plays a critical role to determine and maintain cellular identity.ResultsHere, we compare and contrast the chromatin landscape between mouse alpha, beta, and delta cells using ATAC-Seq to evaluate the significant differences in chromatin accessibility. The similarities and differences in chromatin accessibility between these related islet endocrine cells help define their fate in support of their distinct functional roles. We identify patterns that suggest that both alpha and delta cells are poised, but repressed, from becoming beta-like. We also identify patterns in differentially enriched chromatin that have transcription factor motifs preferentially associated with different regions of the genome. Finally, we not only confirm and visualize previously discovered common endocrine- and cell specific- enhancer regions across differentially enriched chromatin, but identify novel regions as well. We compiled our chromatin accessibility data in a freely accessible database of common endocrine- and cell specific-enhancer regions that can be navigated with minimal bioinformatics expertise.ConclusionsBoth alpha and delta cells appear poised, but repressed, from becoming beta cells in murine pancreatic islets. These data broadly support earlier findings on the plasticity in identity of non-beta cells under certain circumstances. Furthermore, differential chromatin accessibility shows preferentially enriched distal-intergenic regions in beta cells, when compared to either alpha or delta cells.

The mosquito Aedes aegypti is the vector of a number of medically-important viruses, including dengue virus, yellow fever virus, chikungunya virus, and Zika virus, and as such vector control is a key approach to managing the diseases they cause. Understanding the impact of vector control on these diseases is aided by first understanding its impact on Ae. aegypti population dynamics. A number of detail-rich models have been developed to couple the dynamics of the immature and adult stages of Ae. aegypti. The numerous assumptions of these models enable them to realistically characterize impacts of mosquito control, but they also constrain the ability of such models to reproduce empirical patterns that do not conform to the models' behavior. In contrast, statistical models afford sufficient flexibility to extract nuanced signals from noisy data, yet they have limited ability to make predictions about impacts of mosquito control on disease caused by pathogens that the mosquitoes transmit without extensive data on mosquitoes and disease. Here, we demonstrate how the differing strengths of mechanistic realism and statistical flexibility can be fused into a single model. Our analysis utilizes data from 176,352 household-level Ae. aegypti aspirator collections conducted during 1999-2011 in Iquitos, Peru. The key step in our approach is to calibrate a single parameter of the model to spatio-temporal abundance patterns predicted by a generalized additive model (GAM). In effect, this calibrated parameter absorbs residual variation in the abundance time-series not captured by other features of the mechanistic model. We then used this calibrated parameter and the literature-derived parameters in the agent-based model to explore Ae. aegypti population dynamics and the impact of insecticide spraying to kill adult mosquitoes. The baseline abundance predicted by the agent-based model closely matched that predicted by the GAM. Following spraying, the agent-based model predicted that mosquito abundance rebounds within about two months, commensurate with recent experimental data from Iquitos. Our approach was able to accurately reproduce abundance patterns in Iquitos and produce a realistic response to adulticide spraying, while retaining sufficient flexibility to be applied across a range of settings.

Enhancers are stretches of regulatory DNA that bind transcription factors (TFs) and regulate the expression of a target gene. Shadow enhancers are two or more enhancers that regulate the same target gene in space and time and are associated with most animal developmental genes. These multi-enhancer systems can drive more consistent transcription than single enhancer systems. Nevertheless, it remains unclear why shadow enhancer TF binding sites are distributed across multiple enhancers rather than within a single large enhancer. Here, we use a computational approach to study systems with varying numbers of TF binding sites and enhancers. We employ chemical reaction networks with stochastic dynamics to determine the trends in transcriptional noise and fidelity, two key performance objectives of enhancers. This reveals that while additive shadow enhancers do not differ in noise and fidelity from their single enhancer counterparts, sub- and superadditive shadow enhancers have noise and fidelity trade-offs not available to single enhancers. We also use our computational approach to compare the duplication and splitting of a single enhancer as mechanisms for the generation of shadow enhancers and find that the duplication of enhancers can decrease noise and increase fidelity, although at the metabolic cost of increased RNA production. A saturation mechanism for enhancer interactions similarly improves on both of these metrics. Taken together, this work highlights that shadow enhancer systems may exist for several reasons: genetic drift or the tuning of key functions of enhancers, including transcription fidelity, noise and output.

A large-scale application of the "stacked modeling" approach for chromatin state discovery previously provides a single "universal" chromatin state annotation of the human genome based jointly on data from many cell and tissue types. Here, we produce an analogous chromatin state annotation for mouse based on 901 datasets assaying 14 chromatin marks in 26 cell or tissue types. To characterize each chromatin state, we relate the states to external annotations and compare them to analogously defined human states. We expect the universal chromatin state annotation for mouse to be a useful resource for studying this key model organism's genome.

Neural communication networks form the fundamental basis for brain function. These communication networks are enabled by emitted ligands such as neurotransmitters, which activate receptor complexes to facilitate communication. Thus, neural communication is fundamentally dependent on the transcriptome. Here we develop NeuronChat, a method and package for the inference, visualization and analysis of neural-specific communication networks among pre-defined cell groups using single-cell expression data. We incorporate a manually curated molecular interaction database of neural signaling for both human and mouse, and benchmark NeuronChat on several published datasets to validate its ability in predicting neural connectivity. Then, we apply NeuronChat to three different neural tissue datasets to illustrate its functionalities in identifying interneural communication networks, revealing conserved or context-specific interactions across different biological contexts, and predicting communication pattern changes in diseased brains with autism spectrum disorder. Finally, we demonstrate NeuronChat can utilize spatial transcriptomics data to infer and visualize neural-specific cell-cell communication.

Spatial cognition depends on an accurate representation of orientation within an environment. Head direction cells in distributed brain regions receive a range of sensory inputs, but visual input is particularly important for aligning their responses to environmental landmarks. To investigate how population-level heading responses are aligned to visual input, we recorded from retrosplenial cortex (RSC) of head-fixed mice in a moving environment using two-photon calcium imaging. We show that RSC neurons are tuned to the animal's relative orientation in the environment, even in the absence of head movement. Next, we found that RSC receives functionally distinct projections from visual and thalamic areas and contains several functional classes of neurons. While some functional classes mirror RSC inputs, a newly discovered class coregisters visual and thalamic signals. Finally, decoding analyses reveal unique contributions to heading from each class. Our results suggest an RSC circuit for anchoring heading representations to environmental visual landmarks.

The cerebral cortex contains billions of neurons, and their disorganization or misspecification leads to neurodevelopmental disorders. Understanding how the plethora of projection neuron subtypes are generated by cortical neural stem cells (NSCs) is a major challenge. Here, we focused on elucidating the transcriptional landscape of murine embryonic NSCs, basal progenitors (BPs), and newborn neurons (NBNs) throughout cortical development. We uncover dynamic shifts in transcriptional space over time and heterogeneity within each progenitor population. We identified signature hallmarks of NSC, BP, and NBN clusters and predict active transcriptional nodes and networks that contribute to neural fate specification. We find that the expression of receptors, ligands, and downstream pathway components is highly dynamic over time and throughout the lineage implying differential responsiveness to signals. Thus, we provide an expansive compendium of gene expression during cortical development that will be an invaluable resource for studying neural developmental processes and neurodevelopmental disorders.

Hair follicles (HFs) are stem-cell-rich mammalian mini-organs that can undergo cyclic regenerations over the life span of the organism. The cycle of a HF consists of three consecutive phases: anagen-the active proliferation phase, catagen-the degeneration phase, and telogen-the resting phase. While HFs undergo irreversible degeneration during catagen, recent experimental research on mice shows that when anagen HFs are subject to ionizing radiation (IR), they undergo a transient degeneration, followed by a nearly full regeneration that makes the HFs return to homeostatic state. The mechanisms underlying these IR-induced HF regenerative dynamics and the catagen degenerative dynamics, remain unknown. In this work, we develop an ODE type cell differentiation population model to study the control mechanisms of HF regeneration. The model is built based on current theoretical knowledge in biology and mathematically formulated using feedback mechanisms. Model parameters are calibrated to IR experimental data, and we then provide modeling results with both deterministic ODE simulations and corresponding stochastic simulations. We perform stability and bifurcation analyses on the ODE model, which reveal that for anagen HFs, a low spontaneous apoptosis rate secures the stability of the HF homeostatic steady state, allowing the HF to regenerate even when subject to strong IR. On the other hand, the irreversible degeneration during catagen results from both strong spontaneous apoptosis rate and strong apoptosis feedback. Lastly, we perform sensitivity analysis to identify key parameters in the model to validate these hypotheses.

BackgroundZebrafish have practical features that make them a useful model for higher-throughput tests of gene function using CRISPR/Cas9 editing to create 'knockout' models. In particular, the use of G0 mosaic mutants has potential to increase throughput of functional studies significantly but may suffer from transient effects of introducing Cas9 via microinjection. Further, a large number of computational and empirical tools exist to design CRISPR assays but often produce varied predictions across methods leaving uncertainty in choosing an optimal approach for zebrafish studies.MethodsTo systematically assess accuracy of tool predictions of on- and off-target gene editing, we subjected zebrafish embryos to CRISPR/Cas9 with 50 different guide RNAs (gRNAs) targeting 14 genes. We also investigate potential confounders of G0-based CRISPR screens by assaying control embryos for spurious mutations and altered gene expression.ResultsWe compared our experimental in vivo editing efficiencies in mosaic G0 embryos with those predicted by eight commonly used gRNA design tools and found large discrepancies between methods. Assessing off-target mutations (predicted in silico and in vitro) found that the majority of tested loci had low in vivo frequencies (

Aging is a major risk factor to develop neurodegenerative diseases and is associated with decreased buffering capacity of the proteostasis network. We investigated the significance of the unfolded protein response (UPR), a major signaling pathway activated to cope with endoplasmic reticulum (ER) stress, in the functional deterioration of the mammalian brain during aging. We report that genetic disruption of the ER stress sensor IRE1 accelerated age-related cognitive decline. In mouse models, overexpressing an active form of the UPR transcription factor XBP1 restored synaptic and cognitive function, in addition to reducing cell senescence. Proteomic profiling of hippocampal tissue showed that XBP1 expression significantly restore changes associated with aging, including factors involved in synaptic function and pathways linked to neurodegenerative diseases. The genes modified by XBP1 in the aged hippocampus where also altered. Collectively, our results demonstrate that strategies to manipulate the UPR in mammals may help sustain healthy brain aging.

Nonsense-mediated RNA decay (NMD) is a highly conserved and selective RNA turnover pathway that depends on the endonuclease SMG6. Here, we show that SMG6 is essential for male germ cell differentiation in mice. Germ-cell conditional knockout (cKO) of Smg6 induces extensive transcriptome misregulation, including a failure to eliminate meiotically expressed transcripts in early haploid cells, and accumulation of NMD target mRNAs with long 3' untranslated regions (UTRs). Loss of SMG6 in the male germline results in complete arrest of spermatogenesis at the early haploid cell stage. We find that SMG6 is strikingly enriched in the chromatoid body (CB), a specialized cytoplasmic granule in male germ cells also harboring PIWI-interacting RNAs (piRNAs) and the piRNA-binding protein PIWIL1. This raises the possibility that SMG6 and the piRNA pathway function together, which is supported by several findings, including that Piwil1-KO mice phenocopy Smg6-cKO mice and that SMG6 and PIWIL1 co-regulate many genes in round spermatids. Together, our results demonstrate that SMG6 is an essential regulator of the male germline transcriptome, and highlight the CB as a molecular platform coordinating RNA regulatory pathways to control sperm production and fertility.

Heterogeneous Stock (HS) rats are a genetically diverse outbred rat population that is widely used for studying genetics of behavioral and physiological traits. Mapping Quantitative Trait Loci (QTL) associated with transcriptional changes would help to identify mechanisms underlying these traits. We generated genotype and transcriptome data for five brain regions from 88 HS rats. We identified 21 392 cis-QTLs associated with expression and splicing changes across all five brain regions and validated their effects using allele specific expression data. We identified 80 cases where eQTLs were colocalized with genome-wide association study (GWAS) results from nine physiological traits. Comparing our dataset to human data from the Genotype-Tissue Expression (GTEx) project, we found that the HS rat data yields twice as many significant eQTLs as a similarly sized human dataset. We also identified a modest but highly significant correlation between genetic regulatory variation among orthologous genes. Surprisingly, we found less genetic variation in gene regulation in HS rats relative to humans, though we still found eQTLs for the orthologs of many human genes for which eQTLs had not been found. These data are available from the RatGTEx data portal (RatGTEx.org) and will enable new discoveries of the genetic influences of complex traits.

There is considerable interest in screening ultralarge chemical libraries for ligand discovery, both empirically and computationally1-4. Efforts have focused on readily synthesizable molecules, inevitably leaving many chemotypes unexplored. Here we investigate structure-based docking of a bespoke virtual library of tetrahydropyridines-a scaffold that is poorly sampled by a general billion-molecule virtual library but is well suited to many aminergic G-protein-coupled receptors. Using three inputs, each with diverse available derivatives, a one pot C-H alkenylation, electrocyclization and reduction provides the tetrahydropyridine core with up to six sites of derivatization5-7. Docking a virtual library of 75 million tetrahydropyridines against a model of the serotonin 5-HT2A receptor (5-HT2AR) led to the synthesis and testing of 17 initial molecules. Four of these molecules had low-micromolar activities against either the 5-HT2A or the 5-HT2B receptors. Structure-based optimization led to the 5-HT2AR agonists (R)-69 and (R)-70, with half-maximal effective concentration values of 41 nM and 110 nM, respectively, and unusual signalling kinetics that differ from psychedelic 5-HT2AR agonists. Cryo-electron microscopy structural analysis confirmed the predicted binding mode to 5-HT2AR. The favourable physical properties of these new agonists conferred high brain permeability, enabling mouse behavioural assays. Notably, neither had psychedelic activity, in contrast to classic 5-HT2AR agonists, whereas both had potent antidepressant activity in mouse models and had the same efficacy as antidepressants such as fluoxetine at as low as 1/40th of the dose. Prospects for using bespoke virtual libraries to sample pharmacologically relevant chemical space will be considered.

BackgroundMany organizations face challenges in managing and analyzing data, especially when relevant datasets arise from multiple sources and methods. Analyzing heterogeneous datasets and additional derived data requires rigorous tracking of their interrelationships and provenance. This task has long been a Grand Challenge of data science and has more recently been formalized in the FAIR principles: that all data objects be Findable, Accessible, Interoperable, and Reusable, both for machines and for people. Adherence to these principles is necessary for proper stewardship of information, for testing regulatory compliance, for measuring the efficiency of processes, and for facilitating reuse of data-analytical frameworks.FindingsWe present the Contextual Ontology-based Repository Analysis Library (CORAL), a platform that greatly facilitates adherence to all 4 of the FAIR principles, including the especially difficult challenge of making heterogeneous datasets Interoperable and Reusable across all parts of a large, long-lasting organization. To achieve this, CORAL's data model requires that data generators extensively document the context for all data, and our tools maintain that context throughout the entire analysis pipeline. CORAL also features a web interface for data generators to upload and explore data, as well as a Jupyter notebook interface for data analysts, both backed by a common API.ConclusionsCORAL enables organizations to build FAIR data types on the fly as they are needed, avoiding the expense of bespoke data modeling. CORAL provides a uniquely powerful platform to enable integrative cross-dataset analyses, generating deeper insights than are possible using traditional analysis tools.

Brain-machine interfaces (BMIs) provide a framework for studying how cortical population dynamics evolve over learning in a task in which the mapping between neural activity and behavior is precisely defined. Learning to control a BMI is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-related changes in cortical population dynamics within these groups compare.To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this increase in coordination was primarily driven by changes in the direct subpopulation. These findings suggest that motor cortex refines cortical dynamics by increasing neural variance throughout the entire population during learning, with a more pronounced coordination of firing activity in subpopulations that are causally linked to behavior.

PLEs (phage-inducible chromosomal island-like elements) are phage parasites integrated into the chromosome of epidemic Vibrio cholerae. In response to infection by its viral host ICP1, PLE excises, replicates and hijacks ICP1 structural components for transduction. Through an unknown mechanism, PLE prevents ICP1 from transitioning to rolling circle replication (RCR), a prerequisite for efficient packaging of the viral genome. Here, we characterize a PLE-encoded nuclease, NixI, that blocks phage development likely by nicking ICP1's genome as it transitions to RCR. NixI-dependent cleavage sites appear in ICP1's genome during infection of PLE(+) V. cholerae. Purified NixI demonstrates in vitro nuclease activity specifically for sites in ICP1's genome and we identify a motif that is necessary for NixI-mediated cleavage. Importantly, NixI is sufficient to limit ICP1 genome replication and eliminate progeny production, representing the most inhibitory PLE-encoded mechanism revealed to date. We identify distant NixI homologs in an expanded family of putative phage parasites in vibrios that lack nucleotide homology to PLEs but nonetheless share genomic synteny with PLEs. More generally, our results reveal a previously unknown mechanism deployed by phage parasites to limit packaging of their viral hosts' genome and highlight the prominent role of nuclease effectors as weapons in the arms race between antagonizing genomes.

The nuanced mechanisms driving primordial germ cells (PGC) specification remain incompletely understood since genome-wide transcriptional regulation in developing PGCs has previously only been defined indirectly. Here, using SLAMseq analysis, we determined genome-wide transcription rates during the differentiation of embryonic stem cells (ESCs) to form epiblast-like (EpiLC) cells and ultimately PGC-like cells (PGCLCs). This revealed thousands of genes undergoing bursts of transcriptional induction and rapid shut-off not detectable by RNAseq analysis. Our SLAMseq datasets also allowed us to infer RNA turnover rates, which revealed thousands of mRNAs stabilized and destabilized during PGCLC specification. mRNAs tend to be unstable in ESCs and then are progressively stabilized as they differentiate. For some classes of genes, mRNA turnover regulation collaborates with transcriptional regulation, but these processes oppose each other in a surprisingly high frequency of genes. To test whether regulated mRNA turnover has a physiological role in PGC development, we examined three genes that we found were regulated by RNA turnover: Sox2, Klf2 and Ccne1. Circumvention of their regulated RNA turnover severely impaired the ESC-to-EpiLC and EpiLC-to-PGCLC transitions. Our study demonstrates the functional importance of regulated RNA stability in germline development and provides a roadmap of transcriptional and post-transcriptional regulation during germline specification.

Autophagy is a cellular degradative pathway that plays diverse roles in maintaining cellular homeostasis. Cellular stress caused by starvation, organelle damage, or proteotoxic aggregates can increase autophagy, which uses the degradative capacity of lysosomal enzymes to mitigate intracellular stresses. Early studies have shown a role for autophagy in the suppression of tumorigenesis. However, work in genetically engineered mouse models and in vitro cell studies have now shown that autophagy can be either cancer-promoting or inhibiting. Here, we summarize the effects of autophagy on cancer initiation, progression, immune infiltration, and metabolism. We also discuss the efforts to pharmacologically target autophagy in the clinic and highlight future areas for exploration.

Cellular metabolism must adapt to changing demands to enable homeostasis. During immune responses or cancer metastasis, cells leading migration into challenging environments require an energy boost, but what controls this capacity is unclear. Here, we study a previously uncharacterized nuclear protein, Atossa (encoded by CG9005), which supports macrophage invasion into the germband of Drosophila by controlling cellular metabolism. First, nuclear Atossa increases mRNA levels of Porthos, a DEAD-box protein, and of two metabolic enzymes, lysine-α-ketoglutarate reductase (LKR/SDH) and NADPH glyoxylate reductase (GR/HPR), thus enhancing mitochondrial bioenergetics. Then Porthos supports ribosome assembly and thereby raises the translational efficiency of a subset of mRNAs, including those affecting mitochondrial functions, the electron transport chain, and metabolism. Mitochondrial respiration measurements, metabolomics, and live imaging indicate that Atossa and Porthos power up OxPhos and energy production to promote the forging of a path into tissues by leading macrophages. Since many crucial physiological responses require increases in mitochondrial energy output, this previously undescribed genetic program may modulate a wide range of cellular behaviors.

Axonally synthesized proteins support nerve regeneration through retrograde signaling and local growth mechanisms. RNA binding proteins (RBP) are needed for this and other aspects of post-transcriptional regulation of neuronal mRNAs, but only a limited number of axonal RBPs are known. We used targeted proteomics to profile RBPs in peripheral nerve axons. We detected 76 proteins with reported RNA binding activity in axoplasm, and levels of several change with axon injury and regeneration. RBPs with altered levels include KHSRP that decreases neurite outgrowth in developing CNS neurons. Axonal KHSRP levels rapidly increase after injury remaining elevated up to 28 days post axotomy. Khsrp mRNA localizes into axons and the rapid increase in axonal KHSRP is through local translation of Khsrp mRNA in axons. KHSRP can bind to mRNAs with 3'UTR AU-rich elements and targets those transcripts to the cytoplasmic exosome for degradation. KHSRP knockout mice show increased axonal levels of KHSRP target mRNAs, Gap43, Snap25, and Fubp1, following sciatic nerve injury and these mice show accelerated nerve regeneration in vivo. Together, our data indicate that axonal translation of the RNA binding protein Khsrp mRNA following nerve injury serves to promote decay of other axonal mRNAs and slow axon regeneration.