title: scATAC-Seq reveals epigenetic heterogeneity associated with an EMT-like process in male germline stem cells and its regulation by G9a

abstract:
BackgroundEpithelial-mesenchymal transition (EMT) is a phenomenon in which epithelial cells acquire mesenchymal traits. It contributes to organogenesis and tissue homeostasis, as well as stem cell differentiation. Emerging evidence indicates that heterogeneous expression of EMT gene markers presents in sub-populations of germline stem cells (GSCs). However, the functional implications of such heterogeneity are largely elusive.

ResultsWe unravelled an EMT-like process in GSCs by in vitro extracellular matrix (ECM) model and single-cell genomics approaches. We found that histone methyltransferase G9a regulated an EMT-like program in GSC in vitro and contributed to neonatal germ cell migration in vivo. Through modulating ECM, we demonstrated that GSCs exist in interconvertible epithelial-like and mesenchymal-like cell states. GSCs gained higher migratory ability after transition to a mesenchymal-like cell state, which was largely mediated by the TGF-{beta} signaling pathway. Dynamics of epigenetic regulation at the single-cell level was also found to align with the EMT-like process. Chromatin accessibility profiles generated by single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) clustered GSCs into epithelial-like and mesenchymal-like states, which were associated with differentiation status. The high-resolution data revealed regulators in the EMT-like process, including transcription factors Zeb1. We further identified putative enhancer-promoter interactions and cis-co-accessibility networks at loci such as Tgfb1, Notch1 and Lin28a. Lastly, we identified HES1 as the putative target underlying G9as regulation.

ConclusionOur work provides the foundation for understanding the EMT-like process and a comprehensive resource for future investigation of epigenetic regulatory networks in GSCs.


title: Structural implications of agonist efficiency estimated from concentration-response curves

abstract:
Agonists are evaluated by a concentration-response curve (CRC), with a midpoint (EC50) that indicates potency, a high-concentration asymptote that indicates efficacy and a low-concentration asymptote that indicates constitutive activity. A third agonist attribute, efficiency ({eta}), is the fraction of binding energy that is applied to the conformational change that activates the receptor. We show that {eta} can be calculated from EC50 and the asymptotes of a CRC derived from either single-channel or whole-cell responses. For 20 agonists of skeletal muscle nicotinic receptors, the distribution of {eta} values is bimodal with population means at 51% (including ACh, nornicotine and DMPP) and 40% (including epibatidine, varenicline and cytisine). The value of {eta} is related inversely to the size of the agonists head-group, with high-versus low-efficiency ligands having an average volume of 70 [A]3 versus 102 [A]3. Most binding site mutations have only a small effect on ACh efficiency except for Y190A (35%), W149A (60%) and those at G153 (42%). If {eta} is known, the midpoint and high-concentration asymptote can be calculated from each other. Hence, an entire CRC can be estimated from the response to a single agonist concentration, and efficacy can be estimated from EC50 of a CRC that has been normalized to 1. Given {eta}, the level of constitutive activity can be estimated from a single CRC.

Statement of significanceReceptors are molecular machines that convert chemical energy from agonist binding into mechanical energy of a global conformational change that generates a cell response. Agonists are distinguished by their potency (proportional to affinity) and efficacy, but also by the efficiency at which their binding energy is applied to receptor activation. Here we show that agonist efficiency can be estimated from a single concentrationresponse curve, and estimate efficiencies of 20 nicotinic receptor agonists. These have a bimodal distribution with larger agonists belonging to the lower-efficiency population. We further show that mutations of some binding site residues alter efficiency, and that knowledge of agonist efficiency simplifies and extends dose-response curve analysis.

category: biophysics


title: Agonist efficiency from concentration-response curves: structural implications and applications


title: Anticipation of temporally structured events in the brain

abstract: 1Learning about temporal structure is adaptive because it enables the generation of expectations. We examined how the brain uses experience in structured environments to anticipate upcoming events. During fMRI, individuals watched a 90-second movie clip six times. Using a Hidden Markov Model applied to searchlights across the whole brain, we identified temporal shifts between activity patterns evoked by the first vs. repeated viewings of the movie clip. In many regions throughout the cortex, neural activity patterns for repeated viewings shifted to precede those of initial viewing by up to 15 seconds. This anticipation varied hierarchically in a posterior (less anticipation) to anterior (more anticipation) fashion. We also identified specific regions in which the timing of the brains event boundaries were related to those of human-labeled event boundaries, with the timing of this relationship shifting on repeated viewings. With repeated viewing, the brains event boundaries came to precede human-annotated boundaries by 1-4 seconds on average. Together, these results demonstrate a hierarchy of anticipatory signals in the human brain and link them to subjective experiences of events.

category: neuroscience


title: Isoform-specific roles of the Drosophila filamin-type protein Jitterbug (Jbug) during development

abstract: Filamins are highly conserved actin-crosslinking proteins that regulate organization of the actin cytoskeleton. As key components of versatile signaling scaffolds, filamins are implicated in developmental anomalies and cancer. Multiple isoforms of filamins exist, raising the possibility of distinct functions for each isoform during development and in disease. Here, we provide an initial characterization of jitterbug (jbug), which encodes one of the two filamin-type proteins in Drosophila. We generate Jbug antiserum that recognizes all of the spliced forms and reveals differential expression of different Jbug isoforms during development, and a significant maternal contribution of Jbug protein. To reveal the function of Jbug isoforms, we create new genetic tools, including a null allele that deletes all isoforms, hypomorphic alleles that affect only a subset, and UAS lines for Gal4-driven expression of the major isoforms. Using these tools, we demonstrate that Jbug is required for viability and that specific isoforms are required in the formation of actin-rich protrusions including thoracic bristles in adults and ventral denticles in the embryo. We also show that specific isoforms of Jbug show differential localization within epithelia and that maternal and zygotic loss of jbug disrupts Crumbs (Crb) localization in several epithelial cell types.

category: developmental biology


title: Novel epigenetic clock for fetal brain development predicts prenatal age for cellular stem cell models and derived neurons.

abstract: Induced pluripotent stem cells (iPSCs) and their differentiated neurons (iPSC-neurons) are a widely used cellular model in the research of the central nervous system. However, it is unknown how well they capture age-associated processes, particularly given that pluripotent cells are only present during the earliest stages of mammalian development. Epigenetic clocks utilize coordinated age-associated changes in DNA methylation to make predictions that correlate strongly with chronological age. It has been shown that the induction of pluripotency rejuvenates predicted epigenetic age. As existing clocks are not optimized for the study of brain development, we developed the fetal brain clock (FBC), a bespoke epigenetic clock trained in human prenatal brain samples in order to investigate more precisely the epigenetic age of iPSCs and iPSC-neurons. The FBC was tested in two independent validation cohorts across a total of 194 samples, confirming that the FBC outperforms other established epigenetic clocks in fetal brain cohorts. We applied the FBC to DNA methylation data from iPSCs and iPSC-derived neuronal precursor cells and neurons, finding that these cell types are epigenetically characterized as having an early fetal age. Furthermore, while differentiation from iPSCs to neurons significantly increases epigenetic age, iPSC-neurons are still predicted as being fetal. Together our findings reiterate the need to better understand the limitations of existing epigenetic clocks for answering biological research questions and highlight a limitation of iPSC-neurons as a cellular model of age-related diseases.


title: SWOTein: A structure-based approach to predict stability Strengths and Weaknesses of prOTEINs

abstract:
MotivationAlthough structured proteins adopt their lowest free energy conformation in physiological conditions, the individual residues are generally not in their lowest free energy conformation. Residues that are stability weaknesses are often involved in functional regions, whereas stability strengths ensure local structural stability. The detection of strengths and weaknesses provides key information to guide protein engineering experiments aiming to modulate folding and various functional processes.

ResultsWe developed the SWOTein predictor which identifies strong and weak residues in proteins on the basis of three types of statistical energy functions describing local interactions along the chain, hydrophobic forces and tertiary interactions. The large-scale comparison of the different types of strengths and weaknesses showed their complementarity and the enhancement of the information they provide. We applied SWOTein to apocytochrome b562 and found good agreement between predicted strengths and weaknesses and native hydrogen exchange data. Its application to an amino acid-binding protein identified the hinge at the basis of the conformational change. SWOTein is both fast and accurate and can be applied at small and large scale to analyze and modulate folding and molecular recognition processes.

AvailabilityThe SWOTein webserver provides the list of predicted strengths and weaknesses and a protein structure visualization tool that facilitates the interpretation of the predictions. It is freely available for academic use at http://babylone.ulb.ac.be/SWOTein.

category: bioinformatics


title: Bringing the Pig to Comparative Neuroimaging: A Common Space Approach Contextualizes the Pig and Human Structural Connectome

abstract: Neuroimagings capability to quickly and rapidly phenotype the cortical organization of the whole brain brings with it the possibility to extend our understanding of cortical organization across the mammalian lineage. However, neuroimaging has thus far generally limited itself to a small number of species, with most animal studies being performed in either rodents or Non-Human Primates. Here we perform a first pass characterization of an animal which has recently seen its stock rise in the neuroscience community with the development of new models of neurological disease; the domestic pig. Characterizing the structural connectome of the pig, we create a white matter atlas, and an anatomical template which we use to build a horizontal translation between the pig and human based on a connectivity blueprint approach. We find that conserved trends of structural connectivity across species enabled spatial prediction of regions of interest between the pig and human, showing the potential horizontal translations have as a tool to assess the translational validity of porcine models of neurological disease. Releasing the anatomical template, white matter atlas, and connectivity blueprints, we hope to ease and promote the acceptance of the pig as an alternative large-animal model by the neuroimaging community.


title: Framework for Rapid Comparison of Extracellular Vesicle Isolation Methods

abstract: Extracellular vesicles (EVs) are released by all cells into biofluids and hold great promise as reservoirs of disease biomarkers. One of the main challenges in studying EVs is a lack of methods to quantify EVs that are sensitive enough and can differentiate EVs from similarly sized lipoproteins and protein aggregates. We demonstrate the use of ultrasensitive, single molecule array (Simoa) assays for the quantification of EVs using three widely expressed transmembrane proteins: the tetraspanins CD9, CD63, and CD81. Using Simoa to measure these three EV markers, as well as albumin to measure protein contamination, we were able to compare the relative efficiency and purity of several commonly used EV isolation methods in plasma and cerebrospinal fluid (CSF): ultracentrifugation, precipitation, and size exclusion chromatography (SEC). We further used these assays, all on one platform, to improve SEC isolation from plasma and CSF. Our results highlight the utility of quantifying EV proteins using Simoa and provide a rapid framework for comparing and improving EV isolation methods from biofluids.

category: biochemistry


title: Autophagic signaling promotes systems-wide remodeling in skeletal muscle upon oncometabolic stress

abstract: About 20-30% of cancer-associated deaths are due to complications from cachexia which is characterized by skeletal muscle atrophy. Metabolic reprogramming in cancer cells causes body-wide metabolic and proteomic remodeling, which remain poorly understood. Here, we present evidence that the oncometabolite D-2-hydroxylgutarate (D2-HG) impairs NAD+ redox homeostasis in skeletal myotubes, causing atrophy via deacetylation of LC3-II by the nuclear deacetylase Sirt1. Overexpression of p300 or silencing of Sirt1 abrogate its interaction with LC3, and subsequently reduced levels of LC3 lipidation. Using RNA-sequencing and mass spectrometry-based metabolomics and proteomics, we demonstrate that prolonged treatment with the oncometabolite D2-HG in mice promotes cachexia in vivo and increases the abundance of proteins and metabolites, which are involved in energy substrate metabolism, chromatin acetylation and autophagy regulation. We further show that D2-HG promotes a sex-dependent adaptation in skeletal muscle using network modeling and machine learning algorithms. Our multi-omics approach exposes new metabolic vulnerabilities in response to D2-HG in skeletal muscle and provides a conceptual framework for identifying therapeutic targets in cachexia.

category: systems biology


title: Detecting changes in dynamical structures in synchronous neural oscillations using probabilistic inference

abstract: Recent neuroscience studies have suggested that cognitive functions and learning capacity are reflected in the time-evolving dynamics of brain networks. However, an efficient method to detect changes in dynamical brain structures using neural data has yet to be established. To address this issue, we developed a new model-based approach to detect change points in dynamical network structures by combining the model-based network estimation with a phase-coupled oscillator model and sequential Bayesian inference. By giving the model parameter as the prior distribution, applying Bayesian inference allows the extent of temporal changes in dynamic brain networks to be quantified by comparing the prior distribution with posterior distribution using information theoretical criteria. For this, we used the Kullback-Leibler divergence as an index of such changes. To validate our method, we applied it to numerical data and electroencephalography data. As a result, we confirmed that the Kullback-Leibler divergence only increased when changes in dynamical network structures occurred. Our proposed method successfully estimated both directed network couplings and change points of dynamical structures in the numerical and electroencephalography data. These results suggest that our proposed method can reveal the neural basis of dynamic brain networks.


title: Inference of deformation mechanisms and constitutive response of soft material surrogates of biological tissue by full-field characterization and data-driven variational system identification

abstract: We present a novel, fully three-dimensional approach to soft material characterization and constitutive modeling with relevance to soft biological tissue. Our approach leverages recent advances in experimental techniques and data-driven computation. The experimental component of this approach involves in situ mechanical loading in a magnetic field (using MRI), yielding the entire deformation tensor field throughout the specimen regardless of the possible irregularities in its three-dimensional shape. Characterization can therefore be accomplished with data at a reduced number of deformation states. We refer to this experimental technique as MR-u. Its combination with powerful approaches to inverse modelling, specifically methods of model inference, would open the door to insightful mechanical characterization for soft materials. In recent computational advances that answer this need, we have developed new, data-driven inverse techniques to infer the model that best explains the physics governing observed phenomena from a spectrum of admissible ones, while maintaining parsimony of representation. This approach is referred to as Variational System Identification (VSI). In this communication, we apply the MR-u approach to characterize soft polymers regarding them as surrogates of soft biological tissue, and using VSI, we infer the physically best-suited and parsimonious mathematical models of their mechanical response. We demonstrate the performance of our methods in the face of noisy data with physical constraints that challenge the identification of mathematical models, while attaining high accuracy in the predicted response of the inferred models.


title: The Oncogenic Function of PLAGL2 is Mediated via Specific Target Genes Through a Wnt-independent Mechanism in Colorectal Cancer

abstract: Colorectal cancer (CRC) tumorigenesis and progression are linked to common oncogenic mutations, especially in the tumor suppressor APC, whose loss triggers the deregulation of TCF4/{beta}-Catenin activity. CRC tumorigenesis is also driven by multiple epi-mutational modifiers, such as transcriptional regulators. We describe the common (and near-universal) activation of the zinc finger transcription factor and Let-7 target PLAGL2 in CRC and find that it is a key driver of intestinal epithelial transformation. PLAGL2 drives proliferation, cell cycle progression, and anchorage-independent growth in CRC cell lines and non-transformed intestinal cells. Investigating effects of PLAGL2 on downstream pathways revealed very modest effects on canonical Wnt signaling. Alternatively, we find pronounced effects on the direct PLAGL2 target genes IGF2, a fetal growth factor, and ASCL2, an intestinal stem cell-specific bHLH transcription factor. Inactivation of PLAGL2 in CRC cell lines has pronounced effects on ASCL2 reporter activity. Furthermore, ASCL2 expression can partially rescue deficits of proliferation and cell cycle progression caused by depletion of PLAGL2 in CRC cell lines. Thus, the oncogenic effects of PLAGL2 appear to be mediated via core stem cell and onco-fetal pathways, with minimal effects on downstream Wnt signaling.

category: cancer biology


title: Dynamic visualisation of million-tip trees: the OneZoom project

abstract:
O_LIThe complete tree of life is now available, but methods to visualise it are still needed to meet needs in research, teaching and science communication. Dynamic visualisation of million-tip trees requires many challenges in data synthesis, data handling and computer graphics to be overcome.
C_LIO_LIOur approach is to automate data processing, synthesise data from a wide range of available sources, then to feed these data to a client-side visualisation engine in parts. We develop a way to store the whole tree topology locally in a highly compressed form, then dynamically populate metadata such as text and images as the user explores.
C_LIO_LIThe result is a seamless and smooth way to explore the complete tree of life, including images and metadata, even on relatively old mobile devices.
C_LIO_LIThe underlying methods developed have applications that transcend tree of life visualisation. For the whole complete tree, we describe automated ID mappings between well known resources without resorting to taxonomic name resolution, automated methods to collate sets of public domain representative images for higher taxa, and an index to measure public interest of individual species.
C_LIO_LIThe visualisation layout and the client user interface are both abstracted components of the codebase enabling other zoomable tree layouts to be swapped in, and supporting multiple applications including exhibition kiosks and digital art.
C_LIO_LIAfter ten years of work, our tree of life explorer is now broadly complete, it has attracted nearly 1.5 million online users, and is backed by a novel long-term sustainability plan. We conclude our description of the OneZoom project by suggesting the next challenges that need to be solved in this field: extinct species and guided tours around the tree.
C_LI

category: evolutionary biology


title: HDAC3 regulates senescence and lineage-specific transcriptional programs in non-small cell lung cancer

abstract: Transcriptional deregulation is a common feature of many cancers, which is often accompanied by changes in epigenetic controls. These findings have led to the development of therapeutic agents aimed at broad modulation and reprogramming of transcription in a variety of cancers. Histone Deacetylase 3, HDAC3, is one of the main targets of HDAC inhibitors currently in clinical development as cancer therapies, yet the in vivo role of HDAC3 in solid tumors is unknown. Here, we define the role of HDAC3 in two genetic engineered models of the most common subtypes of Kras-driven Non-Small Cell Lung Cancer (NSCLC), KrasG12D, STK11-/- (KL) and KrasG12D, p53-/- (KP), where we found that HDAC3 is strongly required for tumor growth of both genotypes in vivo. Transcriptional profiling and mechanistic studies revealed that HDAC3 represses p65 NF-{kappa}B-mediated induction of the Senescence Associated Secretory Program (SASP) and HDAC3 binds directly at the promoters of SASP CXC chemokines. Additionally, HDAC3 was found to cooperate with the lung cancer lineage transcription factor NKX2-1 to mediate expression of a common set of target genes. Leveraging observations about one HDAC3/NKX2-1 common target, FGFR1, we identified that an HDAC3-dependent transcriptional cassette becomes hyperactivated as Kras mutant cancer cells develop resistance to the MEK inhibitor Trametinib, and this can be rescued by treatment with the Class I HDAC inhibitor Entinostat. These unexpected findings reveal new roles for HDAC3 in proliferation control in tumors in vivo and identify specific therapeutic contexts for the utilization of HDAC3 inhibitors, whose ability to mechanistically induce SASP may be harnessed therapeutically.


title: Vaginal Isolates of Candida glabrata are Uniquely Susceptible to Ionophoric Killer Toxins Produced by Saccharomyces cerevisiae.

abstract: Compared to other species of Candida yeasts, the growth of Candida glabrata was inhibited by many different strains of Saccharomyces killer yeasts. The ionophoric K1 and K2 killer toxins were broadly inhibitory to all clinical isolates of C. glabrata from patients with recurrent vulvovaginal candidiasis, despite high levels of resistance to clinically relevant antifungal therapeutics.

category: microbiology


title: Toxoplasma gondii GRA28 is required for placenta-specific induction of the regulatory chemokine CCL22 in human and mouse.

abstract:
Toxoplasma gondii is an intracellular protozoan pathogen of humans that can cross the placenta and result in adverse pregnancy outcomes and long-term birth defects. The mechanism used by T. gondii to cross the placenta are unknown but complex interactions with the host immune response are likely to play a role in dictating infection outcomes during pregnancy. Prior work showed that T. gondii infection dramatically and specifically increases the secretion of the immunomodulatory chemokine CCL22 in human placental cells during infection.. Given the important role of this chemokine during pregnancy, we hypothesized that CCL22 induction was driven by a specific T. gondii-secreted effector. Using a combination of bioinformatics and molecular genetics, we have now identified T. gondii GRA28 as the gene product required for CCL22 induction. GRA28 is secreted into the host cell where it localizes to the nucleus, and deletion of this gene results in reduced CCL22 placental cells as well as a human monocyte cell line. The impact of GRA28 on CCL22 production is also conserved in mouse immune and placental cells both in vitro and in vivo. Moreover, parasites lacking GRA28 are impaired in their ability to disseminate throughout the animal, suggesting a link between CCL22 induction and the ability of the parasite to cause disease. Overall these data demonstrate a clear function for GRA28 in altering the immunomodulatory landscape during infection of both placental and peripheral immune cells, and show a clear impact of this immunomodulation on infection outcome.

AUTHOR SUMMARYToxoplasma gondii is a globally ubiquitous pathogen that can cause severe disease in HIV/AIDS patients and can also cross the placenta and infect the developing fetus. We have found that placental and immune cells infected with T. gondii secrete significant amounts of a chemokine (called "CCL22") that is critical for immune tolerance during pregnancy. In order to better understand whether this is a response by the host or a process that is driven by the parasite, we have identified a T. gondii gene that is absolutely required to induce CCL22 production in human cells, indicating that CCL22 production is a process driven almost entirely by the parasite rather than the host. Consistent with its role in immune tolerance, we also found that T. gondii parasites lacking this gene are less able to proliferate and disseminate throughout the host. Taken together these data illustrate a direct relationship between CCL22 levels in the infected host and a key parasite effector, and provide an interesting example of how T. gondii can directly modulate host signaling pathways in order to facilitate its growth and dissemination.


title: Characterisation of growth plate dynamics in murine models of osteoarthritis

abstract:
BackgroundThe purpose of this study was to investigate growth plate dynamics in surgical and loading murine models of osteoarthritis, to understand whether abnormalities in these dynamics predict osteoarthritis vulnerability.

Methods8-week-old C57BL/6 male mice underwent destabilisation of medial meniscus (DMM) (n = 8) surgery in right knee joints. Contralateral left knee joints had no intervention (controls). In 16-week-old C57BL/6 male mice (n = 6), osteoarthritis was induced using non-invasive mechanical loading of right knee joints with peak force of 11N. Non-loaded left knee joints were internal controls. Chondrocyte transiency in tibial articular cartilage and growth plate was confirmed by histology and immunohistochemistry. Tibial subchondral bone parameters were measured using microCT and correlated to 3D GP bridging analysis.

ResultsHigher expression of chondrocyte hypertrophy markers; Col10a1 and MMP13 were observed in tibial articular cartilage chondrocytes of DMM and loaded mice. In tibial growth plate, Col10a1 and MMP13 expressions were widely dispersed in a significantly enlarged zones of proliferative and hypertrophic chondrocytes in DMM (p=0.002 and p<0.0001, respectively) and loaded (both p<0.0001) tibiae of mice compared to their controls. 3D quantification revealed enriched growth plate bridging and higher bridge densities in medial compared to lateral tibiae of DMM and loaded knee joints of the mice. Growth plate dynamics were associated with higher subchondral bone volume fraction (BV/TV; %) in medial tibiae of DMM and loaded knee joints and epiphyseal trabecular bone volume fraction in medial tibiae of loaded knee joints.

ConclusionsThe results confirm articular cartilage chondrocyte transiency in a surgical and loaded murine model of osteoarthritis. Herein, we reveal for the first time spatial variation of growth plate bridging in surgical and loaded osteoarthritis models and how these may contribute to anatomical variation in vulnerability of osteoarthritis development.

category: physiology


title: Hand-selective visual regions represent how to grasp 3D tools: brain decoding during real actions

abstract:
Most neuroimaging experiments that investigate how tools and their actions are represented in the brain use visual paradigms where tools or hands are displayed as 2D images and no real movements are performed. These studies discovered selective visual responses in occipito-temporal and parietal cortices for viewing pictures of hands or tools, which are assumed to reflect action processing, but this has rarely been directly investigated. Here, we examined the responses of independently visually defined category-selective brain areas when participants grasped 3D tools. Using real action fMRI and multi-voxel pattern analysis, we found that grasp typicality representations (i.e., whether a tool is being grasped appropriately for use) were decodable from hand-selective areas in occipito-temporal and parietal cortices, but not from tool-, object-, or body-selective areas, even if partially overlapping. Importantly, these effects were exclusive for actions with tools, but not for biomechanically matched actions with control nontools. In addition, decoding of grasp typicality was significantly higher in hand than tool-selective parietal regions. Notably, grasp typicality representations were automatically evoked even when there was no requirement for tool use and participants were naive to object category (tool vs non-tools). Finding a specificity for typical tool grasping in hand-, rather than tool-, selective regions challenges the long-standing assumption that brain activation for viewing tool images reflects sensorimotor processing linked to tool manipulation. Instead our results show that typicality representations for tool grasping are automatically evoked in visual regions specialised for representing the human hand, the brains primary tool for interacting with the world.

Significance StatementThe unique ability of humans to manufacture and use tools is unsurpassed across the animal kingdom, with tool use considered a defining feature of our species. Most neuroscientific studies that investigate the brain mechanisms that support tool use, record brain activity while people simply view images of tools or hands and not when people perform actual hand movements with tools. Here we show that specific areas of the human visual system that preferentially process hands automatically encode how to appropriately grasp 3D tools, even when no actual tool use is required. These findings suggest that visual areas optimized for processing hands represent fundamental aspects of tool grasping in humans, such as which side they should be grasped for correct manipulation.


title: Bayesian Encoding and Decoding as Distinct Perspectives on Neural Coding

abstract: One of the most influential, and controversial, ideas in neuroscience has been to understand the brain in terms of Bayesian computations. Unstated differences in how Bayesian ideas are operationalized across different models make it difficult to ascertain both which empirical data support which models, and how Bayesian computations might be implemented by neural circuits. In this paper, we make one such difference explicit by identifying two distinct philosophies that underlie existing neural models of Bayesian inference: one in which the brain recovers experimenter-defined structures in the world from sensory neural activity (Decoding), and another in which the brain represents latent quantities in an internal model (Encoding). These philosophies require profoundly different assumptions about the nature of inference in the brain, and lead to different interpretations of empirical data. Here, we characterize and contrast both philosophies in terms of motivations, empirical support, and relationship to neural responses. We also show that this implicit difference in philosophy underlies some of the debate on whether neural activity is better described as a sampling-based, or a parametric, distributional code. Using a simple model of primary visual cortex as an example, we show mathematically that probabilistic inference by neural sampling in the encoding framework is, in fact, indistinguishable from a linear probabilistic population code (PPC) in the decoding framework. In sum, Bayesian Encoding and Bayesian Decoding are distinct, non-exclusive philosophies, and appreciating their similarities and differences will help organize future work on the nature of inference in the brain.


title: Systematic benchmarking of tools for CpG methylation detection from Nanopore sequencing

abstract: DNA methylation plays a fundamental role in the control of gene expression and genome integrity. Although there are multiple tools that enable its detection from Nanopore sequencing, their accuracy remains largely unknown. Here, we present a systematic benchmarking of tools for the detection of CpG methylation from Nanopore sequencing using individual reads, control mixtures of methylated and unmethylated reads, and bisulfite sequencing. We found that tools have a tradeoff between false positives and false negatives and present a high dispersion with respect to the expected methylation frequency values. We described various strategies to improve the accuracy of these tools, including a new consensus approach, METEORE (https://github.com/comprna/METEORE), based on the combination of the predictions from two or more tools that shows improved accuracy over individual tools. Snakemake pipelines are also provided for reproducibility and to enable the systematic application of our analyses to other datasets.


title: Macroevolutionary Analyses Provide New Evidences of Phasmids Wings Evolution as a Reversible Process.

abstract: AO_SCPLOWBSTRACTC_SCPLOWThe concept that complex ancestral traits can never be re-acquired after their loss has grown popular since its initial formulation and its often referred to as Dollos law. Nonetheless, several macroevolutionary evidences - along with molecular ones - suggest instances where complex phenotypes could have been lost throughout a clade evolutionary history and subsequently reverted to their former state in derived lineages. One of the first and most notable rejection of Dollos law is represented by wing evolution in phasmids: this polyneopteran order of insects - which comprises stick and leaf insects - has played a central role in initiating a long-standing debate on the topic. In this study, a novel and comprehensive time-tree - including over 300 Phasmatodea species - is used as a framework for investigating wings evolutionary patterns in the clade. Despite accounting for several possible biases and sources of uncertainty, macroevolutionary analyses consistently support a dynamic and reversible evolution of wings, with multiple transitions to ancestral states taking place after their loss. Our findings suggest that wings and flight are decoupled from Phasmatodea diversification dynamics and that brachyptery is an unstable state, unless when co-opted for non-aerodynamic adaptations. We also explored how different assumptions of wings reversals probability could impact their inference: we found that until reversals are assumed to be over 30 times more unlikely than losses, they are consistently retrieved despite uncertainty in tree and model parameters. Our findings demonstrate that wings evolution can be a reversible and dynamic process in phasmids and contribute to shape our understanding of how complex phenotypes evolve.


title: Protein Structure Determination via Distance GeometryStructural Similarity based Initialization

abstract: The problem of finding the configuration of points given partial information on pairwise inter-point distances, the Euclidean distance geometry problem, appears in multiple applications. In this paper, we propose an approach that integrates structural similarity and a nonconvex distance geometry algorithm for the protein structure determination problem. When initialized with a homologous structure, reconstruction of ubiquitin structure with our non convex algorithm resulted in an RMSE of less than 2 [A] with 1.5% available inter proton distance and up to 20% relative error in the input distances. To test the robustness of this algorithm with regard to initialization, we also initialized with a nonhomologous structure on a larger protein with pdb coordinate 1W2E. Even though the initialization structure 1JYB is far different from 1W2E with an RMSE of 25 [A], reconstruction generated structures with RMSE of close to 2 [A], using 1.7% available proton distances and up to 10% relative error in input distances. These results suggest EDG-based approach may be applied to fast NMR structure determination in the future.


title: Simple muscle-lever systems are not so simple:The need for dynamic analyses to predict lever mechanics that maximize speed

abstract: Here we argue that quasi-static analyses are insufficient to predict the speed of an organism from its skeletal mechanics alone (i.e. lever arm mechanics). Using a musculoskeletal numerical model we specifically demonstrate that 1) a single lever morphology can produce a range of output velocities, and 2) a single output velocity can be produced by a drastically different set of lever morphologies. These two sets of simulations quantitatively demonstrate that it is incorrect to assume a one-to-one relationship between lever arm morphology and organism maximum velocity. We then use a statistical analysis to quantify what parameters are determining output velocity, and find that muscle physiology, geometry, and limb mass are all extremely important. Lastly we argue that the functional output of a simple lever is dependent on the dynamic interaction of two opposing factors: those decreasing velocity at low mechanical advantage (low torque and muscle work) and those decreasing velocity at high mechanical advantage (muscle force-velocity effects). These dynamic effects are not accounted for in static analyses and are inconsistent with a force-velocity tradeoff in lever systems. Therefore, we advocate for a dynamic, integrative approach that takes these factors into account when analyzing changes in skeletal levers.

category: zoology


title: Sonogenetic control of mammalian cells using exogenous Transient Receptor Potential A1 channels

abstract: Ultrasound has been used to non-invasively manipulate neuronal functions in humans and other animals1-4. However, this approach is limited as it has been challenging to target specific cells within the brain or body5-8. Here, we identify human Transient Receptor Potential A1 (hsTRPA1) as a candidate that confers ultrasound sensitivity to mammalian cells. Ultrasound-evoked gating of hsTRPA1 specifically requires its N-terminal tip region and cholesterol interactions; and target cells with an intact actin cytoskeleton, revealing elements of the sonogenetic mechanism. Next, we use calcium imaging and electrophysiology to show that hsTRPA1 potentiates ultrasound-evoked responses in primary neurons. Furthermore, unilateral expression of hsTRPA1 in mouse layer V motor cortical neurons leads to c-fos expression and contralateral limb responses in response to ultrasound delivered through an intact skull. Collectively, we demonstrate that hsTRPA1-based sonogenetics can effectively manipulate neurons within the intact mammalian brain, a method that could be used across species.


title: Polygamous breeding system identified in the distylous genus Psychotria: P. manillensis in the Ryukyu archipelago, Japan

abstract: Distyly is a genetic polymorphism composed of long- and short-styled flowers in a population. The evolutionary breakdown of distyly has been reported in many taxa, and mainly involves a shift toward monomorphism or dioecism. However, a shift toward monoecism has not been reported in distylous species. Psychotria (Rubiaceae), one of the world largest genera, consists of distylous species and their derivatives. In our preliminary study, however, we identified some monoecious individuals in a population of Psychotria manillensis. To understand the breeding system and reproductive biology of P. manillensis, we investigated floral traits, open fruit set, and flower visitors, and performed hand pollination and bagging experiments in five populations of Okinawa and Iriomote islands, Ryukyu Islands, Japan. The populations of P. manillensis were composed mainly of monoecious individuals (54%), followed by female (30%), male (14%), and hermaphroditic (2%) individuals at the time of flower collection. Of the collected flowers, 93% were functionally unisexual (male or female), whereas only 6.5% were perfect (hermaphroditic). However, some individuals changed sex mainly towards increasing femaleness during the flowering period. Moreover, 35% of the studied plants changed their sexual expression over the years. P. manillensis showed self-compatibility and no agamospermy. The fruit set under open pollination varied among populations and years (1.8-21.9%), but it was significantly higher than that of auto-selfing (0.68-1.56%). Wasps and flies were the main flower visitors and probably the main pollinators of the species. In conclusion, P. manillensis was revealed to be polygamous, involving monoecious, female, male, and hermaphroditic individuals. This is the first report of the polygamous breeding system not only in the genus Psychotria, but also in all heterostylous taxa.

category: ecology


title: TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields

abstract: Automated visual tracking of animals is rapidly becoming an indispensable tool for the study of behavior. It offers a quantitative methodology by which organisms sensing and decision-making can be studied in a wide range of ecological contexts. Despite this, existing solutions tend to be challenging to deploy in practice, especially when considering long and/or high-resolution video-streams. Here, we present TRex, a fast and easy-to-use solution for tracking a large number of individuals simultaneously using background-subtraction with real-time (60Hz) tracking performance for up to approximately 256 individuals and estimates 2D visual-fields, outlines, and head/rear of bilateral animals, both in open and closed-loop contexts. Additionally, TRex offers highly-accurate, deep-learning-based visual identification of up to approximately 100 unmarked individuals, where it is between 2.5-46.7 times faster, and requires 2-10 times less memory, than comparable software (with relative performance increasing for more organisms/longer videos) and provides interactive data-exploration within an intuitive, platform-independent graphical user-interface.

category: animal behavior and cognition


title: Ultrafast phasor-based hyperspectral snapshot microscopy for biomedical imaging

abstract: Hyperspectral imaging is highly sought after in many fields including mineralogy and geology, environment and agriculture, astronomy and, importantly, biomedical imaging and biological fluorescence. We developed ultrafast phasor-based hyperspectral snapshot microscopy based on sine/cosine interference filters for biomedical imaging not feasible with conventional hyperspectral detection methods. Current approaches rely on slow spatial or spectral scanning limiting their application in living biological tissues, while faster snapshot methods such as image mapping spectrometry and multispectral interferometry are limited in spatial and/or spectral resolution, are computationally demanding, and imaging devices are very expensive to manufacture. Leveraging light sheet microscopy, phasor-based hyperspectral snapshot microscopy improved imaging speed 10-100 fold which, combined with minimal light exposure and high detection efficiency, enabled hyperspectral metabolic imaging of live, three-dimensional mouse tissues not feasible with other methods. As a fit-free method that does not require any a priori information often unavailable in complex and evolving biological systems, the rule of linear combinations of the phasor could spectrally resolve subtle differences between cell types in the developing zebrafish retina and spectrally separate and track multiple organelles in 3D cultured cells over time. The sine/cosine snapshot method is adaptable to any microscope or imaging device thus making hyperspectral imaging and fit-free analysis based on linear combinations broadly available to researchers and the public.


title: Hebbian learning with elasticity explains how the spontaneous motor tempo affects music performance synchronization

abstract:
Music has a tempo (or frequency of the underlying beat) that musicians maintain throughout a performance. Musicians can maintain this tempo on their own or paced by a metronome. Behavioral studies have found that each musician shows a spontaneous rate of movement, called spontaneous motor tempo (SMT), which can be measured when a musician spontaneously plays a simple melody. Data shows that a musicians SMT systematically influences how actions affect tempo and synchronization. In this study we present a model that captures this phenomenon. To develop our model, we review the results from three musical performance settings that have been previously published: (1) solo musical performance with a pacing metronome tempo that is different from the SMT, (2) solo musical performance without a metronome at a tempo that is faster or slower than the SMT, and (3) duet musical performance between musician pairs with matching or mismatching SMTs. In the first setting, the asynchrony between the pacing metronome and the musicians tempo grew as a function of the difference between the metronome tempo and the musicians SMT. In the second setting, musicians drifted away from the initial spontaneous tempo toward the SMT. And in the third setting, the absolute asynchronies between performing musicians were smaller if their SMTs matched compared to when they did not. Based on these observations, we hypothesize that, while musicians can perform musical actions at a tempo different from their SMT, the SMT constantly acts as a pulling force. We developed a model to test our hypothesis. The model is an oscillatory dynamical system with Hebbian and elastic tempo learning that simulates music performance. We model SMT as the dynamical systems natural frequency. Hebbian learning lets the systems frequency adapt to match the stimulus frequency. The pulling force is modeled as an elasticity term that pulls the learned frequency toward the systems natural frequency. We used this model to simulate the three music performance settings, replicating behavioral results. Our model also lets us make predictions about performance settings not yet tested. The present study offers a dynamical explanation of how an individuals SMT affects adaptive synchronization in realistic musical performance.

Author summaryIndividuals can keep a musical tempo on their own or timed by another individual or a metronome. Experiments show that individuals show a specific spontaneous rate of periodic action, for example walking, blinking, or singing. Moreover, in a simple metronome synchronization task, an individuals spontaneous rate determines that the individual will tend to anticipate a metronome that is slower, and lag a metronome that is faster. Researchers have hypothesized the mechanisms explaining how spontaneous rates affect synchronization, but no hypothesis can account for all observations yet. Our hypothesis is that individuals rely on elastic Hebbian frequency learning during synchronization tasks to adapt the rate of their movements and match another individuals actions or metronome tempo. Elastic Hebbian frequency learning also explains why an individuals spontaneous rate persists after carrying out a musical synchronization task. We define a new model with elastic Hebbian frequency learning and use it to simulate existing empirical data. Not only can our model explain the empirical data, but it can also make testable predictions. Our results support the theory that the brains endogenous rhythms give rise to spontaneous rates of movement, and that learning dynamics interact with such brain rhythms to allow for flexible synchronization.


title: SiGMoiD: A super-statistical generative model for binary data

abstract: In modern computational biology, there is great interest in building probabilistic models to describe collections of a large number of co-varying binary variables. However, current approaches to build generative models rely on modelers identification of constraints and are computationally expensive to infer when the number of variables is large (N [~]100). Here, we address both these issues with Super-statistical Generative Model for binary Data (SiGMoiD). SiGMoiD is a maximum entropy-based framework where we imagine the data as arising from super-statistical system; individual binary variables in a given sample are coupled to the same  bath whose intensive variables vary from sample to sample. Importantly, unlike standard maximum entropy approaches where modeler specifies the constraints, the SiGMoiD algorithm infers them directly from the data. Notably, unlike current approaches, SiGMoiD allows to model collections of a very large number (N > 1000) of binary variables. Finally, SiGMoiD offers a reduced dimensional description of the data, allowing us to identify clusters of similar data points as well as binary variables. We illustrate the versatility of SiGMoiD using several datasets spanning several time- and length-scales.


title: Behavioral and genomic sensory adaptations underlying the pest activity of Drosophila suzukii

abstract: Studying how novel phenotypes originate and evolve is fundamental to the field of evolutionary biology as it allows us to understand how organismal diversity is generated and maintained. However, determining the basis of novel phenotypes is challenging as it involves orchestrated changes at multiple biological levels. Here, we aim to overcome this challenge by using a comparative species framework combining behavioral, gene expression, and genomic analyses to understand the evolutionary novel egg-laying substrate-choice behavior of the invasive pest species Drosophila suzukii. First, we used egg-laying behavioral assays to understand the evolution of ripe fruit oviposition preference in D. suzukii as compared to closely related species D. subpulchrella and D. biarmipes, as well as D. melanogaster. We show that D. subpulchrella and D. biarmipes lay eggs on both ripe and rotten fruits, suggesting that the transition to ripe fruit preference was gradual. Secondly, using two-choice oviposition assays, we studied how D. suzukii, D. subpulchrella, D. biarmipes and D. melanogaster differentially process key sensory cues distinguishing ripe from rotten fruit during egg-laying. We found that D. suzukiis preference for ripe fruit is in part mediated through a species-specific preference for stiff substrates. Lastly, we sequenced and annotated a high-quality genome for D. subpulchrella. Using comparative genomic approaches, we identified candidate genes involved in D. suzukiis ability to seek out and target ripe fruits. Our results provide detail to the stepwise evolution of pest activity in D. suzukii, indicating important cues used by this species when finding a host, and the molecular mechanisms potentially underlying their adaptation to a new ecological niche.


title: Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data

abstract:
BackgroundExisting computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation.

ResultsIn this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to use single-cell miRNA-mRNA co-sequencing data to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks for understanding miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. Finally, through exploring cell-cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells to help understand cell-cell crosstalk.

ConclusionsTo the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.


title: Empirical variance component regression for sequence-function relationships

abstract: Contemporary high-throughput mutagenesis experiments are providing an increasingly detailed view of the complex patterns of genetic interaction that occur between multiple mutations within a single protein or regulatory element. By simultaneously measuring the effects of thousands of combinations of mutations, these experiments have revealed that the genotype-phenotype relationship typically reflects genetic interactions not only between pairs of sites, but also higher-order interactions among larger numbers of sites. However, modeling and understanding these higher-order interactions remains challenging. Here, we present a method for reconstructing sequence-to-function mappings from partially observed data that can accommodate all orders of genetic interaction. The main idea is to make predictions for unobserved genotypes that match the type and extent of epistasis found in the observed data. This information on the type and extent of epistasis can be extracted by considering how phenotypic correlations change as a function of mutational distance, which is equivalent to estimating the fraction of phenotypic variance due to each order of genetic interaction (additive, pairwise, three-way, etc.). Using these estimated variance components, we then define an empirical Bayes prior that in expectation matches the observed pattern of epistasis, and reconstruct the genotype-phenotype mapping by conducting Gaussian process regression under this prior. To demonstrate the power of this approach, we present an application to the antibody-binding domain GB1 and also provide a detailed exploration of a dataset consisting of high-throughput measurements for the splicing efficiency of human pre-mRNA 5' splice sites, for which we also validate our model predictions via additional low-throughput experiments.


title: Higher-order epistasis and phenotypic prediction


title: Beta-cell specific Insr deletion promotes glucose-stimulated insulin hypersecretion and improves glucose tolerance prior to global insulin resistance

abstract:
Abstract

Insulin receptor (Insr) protein can be found at higher levels in pancreatic {beta}-cells than in most other tissues, but the consequences of {beta}-cell insulin resistance remain enigmatic. Ins1cre allele was used to delete Insr specifically in {beta}-cells of both female and male mice. Experimental mice were compared to Ins1cre-containing littermate controls at multiple ages and on multiple diets. RNA-seq of purified recombined {beta}-cells revealed transcriptomic consequences of Insr loss, which differed between female and male mice. Action potential and calcium oscillation frequencies were increased in Insr knockout {beta}- cells from female, but not male mice, whereas only male {beta}InsrKO mice had reduced ATP-coupled oxygen consumption rate and reduced expression of genes involved in ATP synthesis. Female {beta}InsrKO and {beta}InsrHET mice exhibited elevated insulin release in perifusion experiments, during hyperglycemic clamps, and following i.p. glucose challenge. Deletion of Insr did not alter {beta}-cell area up to 9 months of age, nor did it impair hyperglycemia-induced proliferation. Based on our data, we adapted a mathematical model to include {beta}-cell insulin resistance, which predicted that {beta}-cell Insr knockout would improve glucose tolerance depending on the degree of whole-body insulin resistance. Indeed, glucose tolerance was significantly improved in female {beta}InsrKO and {beta}InsrHET mice when compared to controls at 9, 21 and 39 weeks, and also in insulin-sensitive 4-week old males. We did not observe improved glucose tolerance in older male mice or in high fat diet-fed mice, corroborating the prediction that global insulin resistance obscures the effects of {beta}-cell specific insulin resistance. The propensity for hyperinsulinemia was associated with mildly reduced fasting glucose and increased body weight. We further validated our main in vivo findings using the Ins1-CreERT transgenic line and found that male mice had improved glucose tolerance 4 weeks after tamoxifen-mediated Insr deletion. Collectively, our data show that loss of {beta}-cell Insr contributes to glucose-induced hyperinsulinemia, thereby improving glucose homeostasis in otherwise insulin sensitive dietary and age contexts.


title: Beta-cell specific Insr deletion promotes insulin hypersecretion and improves glucose tolerance prior to global insulin resistance


title: K29-Linked Ubiquitin Signaling Regulates Proteotoxic Stress Response and Cell Cycle

abstract: Protein ubiquitination shows remarkable topological and functional diversity through the polymerization of ubiquitin via different linkages. Deciphering the cellular ubiquitin code is of central importance to understand the physiology of the cell. Among the eight possible linkages, K29-linked polyubiquitin is a relatively abundant type of polyubiquitin in both human and yeast cells. However, our understanding of its function is rather limited due to the lack of specific binders as tools to detect K29-linked polyubiquitin. In this study, we screened and characterized a synthetic antigen-binding fragment, termed sAB-K29, that can specifically recognize K29-linked polyubiquitin using chemically synthesized K29-linked diubiquitin. We further determined the crystal structure of this fragment bound to the K29-linked diubiquitin, which revealed the molecular basis of specificity. Using sAB-K29 as a tool, we uncovered that K29-linked ubiquitination is involved in different kinds of cellular proteotoxic stress response as well as cell cycle regulation. In particular, we showed that K29-linked ubiquitination is enriched in the midbody and downregulation of the K29-linked ubiquitination signal arrests cells in G1/S phase.

category: cell biology


title: High-throughput transcriptomic evaluation of per- and polyfluoroalkyl substances (PFAS) in primary human liver spheroids to inform read-across

abstract: Per- and poly-fluoroalkyl substances (PFAS) are widely found in the environment because of their extensive use and persistence. Although several PFAS are well studied, most lack toxicity data to inform human health hazard and risk assessment. This study focussed on four model PFAS: perfluorooctanoic acid (PFOA; 8 carbon), perfluorobutane sulfonate (PFBS; 4 carbon), perfluorooctane sulfonate (PFOS; 8 carbon), and perfluorodecane sulfonate (PFDS; 10 carbon). Human primary liver cell spheroids (pooled from 10 donors) were exposed to 10 concentrations of each PFAS and analyzed at four time-points. The approach aimed to: (1) identify gene expression changes mediated by the PFAS; (2) identify similarities in biological responses; (3) compare PFAS potency through benchmark concentration analysis; and (4) derive bioactivity exposure ratios (ratio of the concentration at which biological responses occur, relative to daily human exposure). All PFAS induced transcriptional changes in cholesterol biosynthesis and lipid metabolism pathways, and predicted PPAR activation. PFOS exhibited the most transcriptional activity and had a highly similar gene expression profile to PFDS. PFBS induced the least transcriptional changes and the highest benchmark concentration (i.e., was the least potent). The data indicate that these PFAS may have common molecular targets and toxicities, but that PFOS and PFDS are the most similar. The transcriptomic bioactivity exposure ratios derived here for PFOA and PFOS were comparable to those derived using rodent apical endpoints in risk assessments. These data provide a baseline level of toxicity for comparison with other known PFAS using this testing strategy.

category: genomics


title: Quantifying the subjective cost of self-control in humans

abstract: Since Odysseus committed to resisting the Sirens, mechanisms to limit self-control failure have been a central feature of human behavior. Psychologists have long argued that the use of self-control is an effortful process and, more recently, that its failure arises when the cognitive costs of self-control outweigh its perceived benefits. In a similar way, economists have argued that sophisticated choosers can adopt "pre-commitment strategies" that tie the hands of their future selves in order to reduce these costs. Yet, we still lack an empirical tool to quantify and demonstrate the cost of self-control. Here, we develop and validate a novel economic decision-making task to quantify the subjective cost of self-control by determining the monetary cost a person is willing to incur in order to eliminate the need for self-control. We find that humans will pay to avoid having to exert self-control in a way that scales with increasing levels of temptation and that these costs are modulated both by motivational incentives and stress exposure. Our psychophysical approach allows us to index moment-to-moment self-control costs at the within-subject level, validating important theoretical work across multiple disciplines and opening new avenues of self-control research in healthy and clinical populations.


title: Numerical Uncertainty in Analytical Pipelines Lead to Impactful Variability in Brain Networks

abstract: The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions1-7. We explored the instability of results by instrumenting a connectome estimation pipeline with Monte Carlo Arithmetic8,9 to introduce random noise throughout. We evaluated the reliability of the connectomes, their features10,11, and the impact on analysis12,13. The stability of results was found to range from perfectly stable to highly unstable. This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks alongside increasing the robustness of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational science. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also leads to an increase in the reliability of datasets.


title: Synaptic diversity naturally arises from neural decoding of heterogeneous populations

abstract: The synaptic inputs to single cortical neurons exhibit substantial diversity in their sensory-driven activity. What this diversity reflects is unclear, and appears counter-productive in generating selective somatic responses to specific stimuli. We propose that synaptic diversity arises because neurons decode information from upstream populations. Focusing on a single sensory variable, orientation, we construct a probabilistic decoder that estimates the stimulus orientation from the responses of a realistic, hypothetical input population of neurons. We provide a straightforward mapping from the decoder weights to real excitatory synapses, and find that optimal decoding requires diverse input weights. Analytically derived weights exhibit diversity whenever upstream input populations consist of noisy, correlated, and heterogeneous neurons, as is typically found in vivo. In fact, in silico weight diversity was necessary to accurately decode orientation and matched the functional heterogeneity of dendritic spines imaged in vivo. Our results indicate that synaptic diversity is a necessary component of information transmission and reframes studies of connectivity through the lens of probabilistic population codes. These results suggest that the mapping from synaptic inputs to somatic selectivity may not be directly interpretable without considering input covariance and highlights the importance of population codes in pursuit of the cortical connectome.


title: Noise correlations for faster and more robust learning.

abstract:
Distributed population codes are ubiquitous in the brain and pose a challenge to downstream neurons that must learn an appropriate readout. Here we explore the possibility that this learning problem is simplified through inductive biases implemented by stimulus-independent noise correlations that constrain learning to task-relevant dimensions. We test this idea in a set of neural networks that learn to perform a perceptual discrimination task. Correlations among similarly tuned units were manipulated independently of overall population signal-to-noise ratio in order to test how the format of stored information affects learning. Higher noise correlations among similarly tuned units led to faster and more robust learning, favoring homogenous weights assigned to neurons within a functionally similar pool, and could emerge through Hebbian learning. When multiple discriminations were learned simultaneously, noise correlations across relevant feature dimensions sped learning whereas those across irrelevant feature dimensions slowed it. Our results complement existing theory on noise correlations by demonstrating that when such correlations are produced without significant degradation of the signal-to-noise ratio, they can improve the speed of readout learning by constraining it to appropriate dimensions.

Significance statementPositive noise correlations between similarly tuned neurons theoretically reduce the representational capacity of the brain, yet they are commonly observed, emerge dynamically in complex tasks, and persist even in well-trained animals. Here we show that such correlations, when embedded in a neural population with a fixed signal to noise ratio, can improve the speed and robustness with which an appropriate readout is learned. In a simple discrimination task such correlations can emerge naturally through Hebbian learning. In more complex tasks that require multiple discriminations, correlations between neurons that similarly encode the task-relevant feature improve learning by constraining it to the appropriate task dimension.


title: A Structured Professional Development Curriculum for Postdoctoral Fellows Leads to Recognized Knowledge Growth

abstract: Postdoctoral training enables research independence and professional readiness. National reports have emphasized professional development as a critical component of this training period. In response, many institutions are establishing transferable skills training workshops for postdocs; however, the lack of structured programs and an absence of methods to assess outcomes beyond participant satisfaction surveys are critical gaps in postdoctoral training. To address these shortcomings, we took the approach of structured programming and developed a method for controlled assessment of outcomes. Our program You3 (You, Your Team, Your Project), co-designed by postdoctoral fellows, focused on a structured array of management and leadership skills agnostic of ultimate career path(s). We then measured outcomes in a controlled manner, by systematically comparing perceived knowledge and growth of participants with non-participants as the control group. You3 participants discern greater growth, independent of number of years in training, in competencies overall compared to the control group. This growth was shown by multiple criteria including self-reporting and associative analysis. Correspondingly, You3 participants reported greater knowledge in 75% of the modules when compared to controls. These data indicate that structured learning, where postdocs commit to a curriculum via a cohort-structure, leads to positive outcomes and provides a framework for programs to assess outcomes in a rigorous manner.

category: scientific communication and education


title: In vitro Antibacterial effect of Commiphora myrrha Oil against Dental Pathogens

abstract: Dental caries is a chronic disease caused by the interaction of oral microorganisms, diet and host factors over time. Streptococcus mutans is considered as the main bacteria involve in dental decay, while the level of Lactobacillus spp. is directly related to the presence or onset of caries. Commiphora myrrha is an ancient plant which extracts are used as antiseptic and anti-inflammatory for mouth and throat due to it is antimicrobial activity. This study assessed the impact of Commiphora myrrha on two bacteria Streptococcus mutans and Lactobacillus spp. involved in dental caries. Three samples of Streptococcus mutans bacteria were collected randomly from patients with dental caries in Khartoum dental teaching hospital, while Lactobacillus spp. were obtained from fermented milk. Disk and well diffusion methods were used to test the effect of four concentration (100,50,25 and 12.5 mg/ml) of Myrrha volatile oil, extracted by hydro-distillation technique. The biochemical analysis of Commiphora myrrha oil was carried out using Gas Chromatography/Mass Spectrophotometric technique. The finding revealed that the four concentrations of oil were effective on Streptococcus mutans with the largest inhibition zone (18.7{+/-} 0.6 mm) through the well diffusion method and inhibition zone of (14.00 mm) with disc diffusion method regardless the two methods these inhibition zones were recorded at 100 mg/ml, with Minimum Bactericidal Concentration (MBC) at 3.125 mg/ml. While Lactobacillus spp. bacteria sensitive to three concentrations (100,50 and 25 mg/ml) and resistance to concentration 12.5 mg/ml, it is MBC found to be 25 mg/ml. In conclusion, this research revealed that Myrrh oil is effective on both S.mutans and Lactobacillus spp. Hence, Myrrha oil is a potential antibacterial product of interest in dental caries.


title: Ongoing Global and Regional Adaptive Evolution of SARS-CoV-2

abstract:
Understanding the trends in SARS-CoV-2 evolution is paramount to control the COVID- 19 pandemic. We analyzed more than 300,000 high quality genome sequences of SARS-CoV-2 variants available as of January 2021. The results show that the ongoing evolution of SARS-CoV-2 during the pandemic is characterized primarily by purifying selection, but a small set of sites appear to evolve under positive selection. The receptor-binding domain of the spike protein and the nuclear localization signal (NLS) associated region of the nucleocapsid protein are enriched with positively selected amino acid replacements. These replacements form a strongly connected network of apparent epistatic interactions and are signatures of major partitions in the SARS-CoV-2 phylogeny. Virus diversity within each geographic region has been steadily growing for the entirety of the pandemic, but analysis of the phylogenetic distances between pairs of regions reveals four distinct periods based on global partitioning of the tree and the emergence of key mutations. The initial period of rapid diversification into region- specific phylogenies that ended in February 2020 was followed by a major extinction event and global homogenization concomitant with the spread of D614G in the spike protein, ending in March 2020. The NLS associated variants across multiple partitions rose to global prominence in March-July, during a period of stasis in terms of inter- regional diversity. Finally, beginning July 2020, multiple mutations, some of which have since been demonstrated to enable antibody evasion, began to emerge associated with ongoing regional diversification, which might be indicative of speciation.

SignificanceUnderstanding the ongoing evolution of SARS-CoV-2 is essential to control and ultimately end the pandemic. We analyzed more than 300,000 SARS-CoV-2 genomes available as of January 2021 and demonstrate adaptive evolution of the virus that affects, primarily, multiple sites in the spike and nucleocapsid protein. Selection appears to act on combinations of mutations in these and other SARS-CoV-2 genes. Evolution of the virus is accompanied by ongoing adaptive diversification within and between geographic regions. This diversification could substantially prolong the pandemic and the vaccination campaign, in which variant-specific vaccines are likely to be required.


title: Functional repertoire convergence of distantly related eukaryotic plankton lineages revealed by genome-resolved metagenomics

abstract:
Marine planktonic eukaryotes play a critical role in global biogeochemical cycles and climate. However, their poor representation in culture collections limits our understanding of the evolutionary history and genomic underpinnings of planktonic ecosystems. Here, we used 280 billion Tara Oceans metagenomic reads from polar, temperate, and tropical sunlit oceans to reconstruct and manually curate more than 700 abundant and widespread eukaryotic environmental genomes ranging from 10 Mbp to 1.3 Gbp. This genomic resource covers a wide range of poorly characterized eukaryotic lineages that complement long-standing contributions from culture collections while better representing plankton in the upper layer of the oceans. We performed the first comprehensive genome-wide functional classification of abundant unicellular eukaryotic plankton, revealing four major groups connecting distantly related lineages. Neither trophic modes of plankton nor its vertical evolutionary history could explain the functional repertoire convergence of major eukaryotic lineages that coexisted within oceanic currents for millions of years.

CoverNavigating on the map of plankton genomics with Tara Oceans and anvio: a comprehensive genome-resolved metagenomic survey dedicated to eukaryotic plankton.

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title: GIANI: open-source software for automated analysis of 3D microscopy images

abstract: The study of cellular and developmental processes in physiologically relevant three-dimensional (3D) systems facilitates an understanding of mechanisms underlying cell fate, disease and injury. While cutting-edge microscopy technologies permit the routine acquisition of 3D datasets, there is currently a limited number of open-source software packages to analyse such images. Here we describe GIANI (djpbarry.github.io/Giani), new software for the analysis of 3D images, implemented as a plugin for the popular FIJI platform. The design primarily facilitates segmentation of nuclei and cells, followed by quantification of morphology and protein expression. GIANI enables routine and reproducible batch-processing of large numbers of images and also comes with scripting and command line tools, allowing users to incorporate its functionality into their own scripts and also run GIANI on a high-performance computing cluster. We demonstrate the utility of GIANI by quantifying cell morphology and protein expression in confocal images of mouse early embryos and by segmenting nuclei from light sheet microscopy images of the flour beetle embryo. We also validate the performance of the software using simulated data. More generally, we anticipate that GIANI will be a useful tool for researchers in a variety of biomedical fields.


title: Avoiding tensional equilibrium in cells migrating on a matrix with cell-scale stiffness-heterogeneity

abstract: Intracellular stresses affect various cell functions, including proliferation, differentiation and movement, which are dynamically modulated in migrating cells through continuous cell-shaping and remodeling of the cytoskeletal architecture induced by spatiotemporal interactions with extracellular matrix stiffness. When cells migrate on a matrix with cell-scale stiffness-heterogeneity, which is a common situation in living tissues, what intracellular stress dynamics (ISD) emerge? In this study, to explore this issue, finite element method-based traction force microscopy was applied to cells migrating on microelastically patterned gels. Two model systems of microelastically patterned gels (stiff/soft stripe and stiff triangular patterns) were designed to characterize the effects of a spatial constraint on cell-shaping and of the presence of different types of cues to induce competing cellular taxis (usual and reverse durotaxis) on the ISD, respectively. As the main result, the prolonged fluctuation of traction stress on a whole-cell scale was markedly enhanced on single cell-size triangular stiff patterns compared with homogeneous gels. Such ISD enhancement was found to be derived from the interplay between the nomadic migration of cells to regions with different degrees of stiffness and domain shape-dependent traction force dynamics, which should be an essential factor for keeping cells far from tensional equilibrium.


title: The role of behavioural flexibility in primate diversification

abstract: Identifying the factors that influence species diversification is fundamental to our understanding of the evolutionary processes underlying extant biodiversity. Behavioural innovation, coupled with the social transmission of new behaviours, has been proposed to increase rates of evolutionary diversification, as novel behaviours expose populations to new selective regimes. Thus, it is believed that behavioural flexibility may play an important role in driving evolutionary diversification across animals. We test this hypothesis within the primates, a taxonomic group with considerable among-lineage variation in both species diversity and behavioural flexibility. We employ a time cut-off in our phylogeny to help account for biases associated with recent taxonomic reclassifications and compare three alternative measures of diversification rate that consider different phylogenetic depths. We find that the presence of behavioural innovation and social learning are positively correlated with diversification rates among primate genera, but not at shallower phylogenetic depths. Given that we find stronger associations when examining older rather than more recent diversification events, we suggest that extinction resistance, as opposed to speciation, may be an important mechanism linking behavioural flexibility and primate diversification. Our results contrast with work linking behavioural flexibility with diversification of birds at various phylogenetic depths. We offer a possible dispersal-mediated explanation for these conflicting patterns, such that the influence behavioural flexibility plays in dictating evolutionary trajectories differs across clades. Our results suggest that behavioural flexibility may act through several different pathways to shape the evolutionary trajectories of lineages.


title: Leveraging Sequential and Spatial Neighbors Information by Using CNNs Linked With GCNs for Paratope Prediction

abstract: Antibodies consisting of variable and constant regions, are a special type of proteins playing a vital role in immune system of the vertebrate. They have the remarkable ability to bind a large range of diverse antigens with extraordinary affinity and specificity. This malleability of binding makes antibodies an important class of biological drugs and biomarkers. In this article, we propose a method to identify which amino acid residues of an antibody directly interact with its associated antigen based on the features from sequence and structure. Our algorithm uses convolution neural networks (CNNs) linked with graph convolution networks (GCNs) to make use of information from both sequential and spatial neighbors to understand more about the local environment of target amino acid residue. Furthermore, we process the antigen partner of an antibody by employing an attention layer. Our method improves on the state-of-the-art methodology.


title: Spike-timing-dependent plasticity can account for aftereffects of dual-site transcranial alternating current stimulation

abstract:
Transcranial alternating current stimulation (tACS), applied to two brain sites with different phase lags, has been shown to modulate stimulation-outlasting functional EEG connectivity between the targeted regions. Given the lack of knowledge on mechanisms of tACS aftereffects, it is difficult to further enhance effect sizes and reduce variability in experiments. In this computational study, we tested if spike-timing-dependent plasticity (STDP) can explain stimulation-outlasting connectivity modulation by dual-site tACS and explored the effects of tACS parameter choices. Two populations of spiking neurons were coupled with synapses subject to STDP, and results were validated via a re-analysis of EEG data. Our simulations showed stimulation-outlasting connectivity changes between in- and anti-phase tACS, dependent on both tACS frequency and synaptic conduction delays. Importantly, both a simple network entraining to a wide range of tACS frequencies as well as a more realistic network that spontaneously oscillated at alpha frequency predicted that the largest effects would occur for short conduction delays between the stimulated regions. This finding agreed with experimental EEG connectivity modulation by 10 Hz tACS, showing a clear negative correlation of tACS effects with estimated conduction delays between regions. In conclusion, STDP can explain connectivity aftereffects of dual-site tACS. However, not all combinations of tACS frequency and application sites are expected to effectively modulate connectivity via STDP. We therefore suggest using appropriate computational models and/or EEG analysis for planning and interpretation of dual-site tACS studies relying on aftereffects.

HighlightsO_LINetwork model with STDP explains EEG connectivity change after dual-site tACS
C_LIO_LIEffects are predicted to depend on tACS frequency and conduction delays
C_LIO_LIEEG data confirm dependence on conduction delays between regions
C_LIO_LIModel can be used to estimate and maximize experimental effects
C_LI


title: Spike-timing-dependent plasticity can account for connectivity aftereffects of dual-site transcranial alternating current stimulation


title: Predicting COVID-19 Severity with a Specific Nucleocapsid Antibody plus Disease Risk Factor Score

abstract:
Effective methods for predicting COVID-19 disease trajectories are urgently needed. Here, ELISA and coronavirus antigen microarray (COVAM) analysis mapped antibody epitopes in the plasma of COVID-19 patients (n = 86) experiencing a wide-range of disease states. The experiments identified antibodies to a 21-residue epitope from nucleocapsid (termed Ep9) associated with severe disease, including admission to the ICU, requirement for ventilators, or death. Importantly, anti-Ep9 antibodies can be detected within six days post-symptom onset and sometimes within one day. Furthermore, anti-Ep9 antibodies correlate with various comorbidities and hallmarks of immune hyperactivity. We introduce a simple-to-calculate, disease risk factor score to quantitate each patients comorbidities and age. For patients with anti-Ep9 antibodies, scores above 3.0 predict more severe disease outcomes with a 13.42 Likelihood Ratio (96.7% specificity). The results lay the groundwork for a new type of COVID-19 prognostic to allow early identification and triage of high-risk patients. Such information could guide more effective therapeutic intervention.

Significance statementThe COVID-19 pandemic has resulted in over two million deaths worldwide. Despite efforts to fight the virus, the disease continues to overwhelm hospitals with severely ill patients. Diagnosis of COVID-19 is readily accomplished through a multitude of reliable testing platforms; however, prognostic prediction remains elusive. To this end, we identified a short epitope from the SARS-CoV-2 nucleocapsid protein and also a disease risk factor score based upon comorbidities and age. The presence of antibodies specifically binding to this epitope plus a score cutoff can predict severe COVID-19 outcomes with 96.7% specificity.

category: immunology


title: A comparison of Remdesivir versus Au22Glutathione18 in COVID-19 golden hamsters: a better therapeutic outcome of gold compound

abstract:
We firstly disclose single compound yields better therapeutic outcome than Remdesivir in COVID-19 hamster treatments as it is armed with direct inhibition viral replication and intrinsic suppression inflammatory cytokines expression. Crystal data reveals that Au (I), released from Au22Glutathione18 (GA), covalently binds thiolate of Cys145 of SARS-CoV-2 Mpro. GA directly decreases SARS-CoV-2 viral replication (EC50: ~0.24 M) and intrinsically down-regulates NF{kappa}B pathway therefore significantly inhibiting expression of inflammatory cytokines in cells. The lung viral load and inflammatory cytokines in GA-treated COVID-19 transgenic mice are found to be significantly lower than that of control mice. When COVID-19 golden hamsters are treated by GA, the lung inflammatory cytokines levels are significantly lower than that of Remdesivir while their lung viral load are decreased to same level. The pathological results show that GA treatment significantly reduce lung inflammatory injuries when compared to that of Remdesivir-treated COVID-19 golden hamsters.

One Sentence SummaryWe found that gold cluster molecule directly inhibits SARS-CoV-2 replication and intrinsically suppresses inflammatory cytokines expression in COVID-19 transgenic mouse and golden hamster model, gold cluster providing a better lung injury protection than Remdesivir in COVID-19 golden hamsters via intranasally dropping administration.

category: pharmacology and toxicology


title: Real-time luminescence assay for cytoplasmic cargo delivery of extracellular vesicles

abstract: Extracellular vesicles (EVs) have been considered to deliver biological cargos between cells and mediate intercellular communication. However, the mechanisms that underlie the biological process of EV uptake and cytoplasmic cargo release in recipient cells are largely unknown. Quantitative and real-time assays for assessment of the cargo delivery efficiency inside recipient cells have not been feasible. In this study, we developed an EV cargo delivery (EVCD) assay using a split luciferase called the NanoBiT system. Recipient cells expressing LgBiT, a large subunit of luciferase, emit luminescence when the EV cargo proteins fused with a small luminescence tag (HiBiT tag) that can complement LgBiT are delivered to the cytoplasm of recipient cells. Using the EVCD assay, the cargo delivery efficiency of EVs could be quantitatively measured in real time. This assay was highly sensitive in detecting a single event of cargo delivery per cell. We found that modification of EVs with a virus-derived fusogenic protein significantly enhanced the cytoplasmic cargo delivery; however, in the absence of a fusogenic protein, the cargo delivery efficiency of EVs was below the threshold of the assay. The EVCD assay could assess the effect of entry inhibitors on EV cargo delivery. Furthermore, using a luminescence microscope, the cytoplasmic cargo delivery of EVs was directly visualized in living cells. This assay could reveal the biological mechanism of the cargo delivery processes of EVs.


title: Visualization and Modeling of Inhibition of IL-1β and TNFα mRNA Transcription at the Single-Cell Level

abstract: IL-1{beta} and TNF- are canonical immune response mediators that play key regulatory roles in a wide range of inflammatory responses to both chronic and acute conditions. Here we employ an automated microscopy platform for the analysis of messenger RNA (mRNA) expression of IL-1{beta} and TNF- at the single-cell level. The amount of IL-1{beta} and TNF- mRNA expressed in a human monocytic leukemia cell line (THP-1) is visualized and counted using single-molecule fluorescent in-situ hybridization (smFISH) following exposure of the cells to lipopolysaccharide (LPS), an outer-membrane component of Gram-negative bacteria. We show that the small molecule inhibitors MG132 (a 26S proteasome inhibitor used to block NF-{kappa}B signaling) and U0126 (a MAPK Kinase inhibitor used to block CCAAT-enhancer-binding proteins C/EBP) successfully block IL-1{beta} and TNF- mRNA expression. Based upon this single-cell mRNA expression data, we screened 36 different mathematical models of gene expression, and found two similar models that capture the effects by which the drugs U0126 and MG132 affect the rates at which the genes transition into highly activated states. When their parameters were informed by the action of each drug independently, both models were able to predict the effects of the combined drug treatment. From our data and models, we postulate that IL-1{beta} is activated by both NF-{kappa}B and C/EBP, while TNF- is predominantly activated by NF-{kappa}B. Our combined single-cell experimental modeling efforts shows the interconnection between these two genes and demonstrates how the single-cell responses, including the distribution shapes, mean expression, and kinetics of gene expression, change with inhibition.


title: Beyond broadband: towards a spectral decomposition of EEG microstates

abstract: Originally applied to alpha oscillations in the 1970s, MS analysis has since been used to decompose mainly broadband EEG signals (e.g. 1-40 Hz). We hypothesized that MS decomposition within separate, narrow frequency bands could provide more fine-grained information for capturing the spatio-temporal complexity of multichannel EEG. In this study using a large open-access dataset (n=203), we decomposed EEG recordings into 4 classical frequency bands (delta, theta, alpha, beta) in order to compare their individual MS segmentations using mutual information as well as traditional MS measures (e.g. mean duration, time coverage). Firstly, we confirmed that MS topographies were spatially equivalent across all frequencies, matching the canonical broadband maps (A, B, C, D). Interestingly however, we observed strong informational independence of MS temporal sequences between spectral bands, together with significant divergence in traditional MS measures. For example, relative to broadband, alpha/beta band dynamics displayed greater time coverage of maps A & B, while map D was more prevalent in delta/theta bands. Moreover, by using a frequency-specificMS taxonomy (e.g. {theta}A, C), we were able to predict the eyes-open vs closed-behavioural state significantly better using alpha-band MS features compared with broadband ones (80% vs 73% accuracy). Overall, our findings demonstrate the value and validity of spectrally-specific MS analyses, which may prove useful for identifying new neural mechanisms in fundamental research and/or for biomarker discovery in clinical populations.

category: neuroscience


title: Targeting androgen regulation of TMPRSS2 and ACE2 as a therapeutic strategy to combat COVID-19

abstract: Epidemiological data showing increased severity and mortality of COVID-19 in men suggests a potential role for androgen in SARS-CoV-2 infection. Here, we present evidence for the transcriptional regulation of SARS-CoV-2 host cell receptor ACE2 and TMPRSS2 by androgen in mouse and human cells. Additionally, we demonstrate the endogenous interaction between TMPRSS2 and ACE2 in human cells and validate ACE2 as a TMPRSS2 substrate. Further, Camostat - a TMPRSS2 inhibitor, blocked the cleavage of pseudotype SARS-CoV-2 surface Spike without disrupting TMPRSS2-ACE2 interaction. Thus providing evidence for the first time a direct role of TMPRSS2 in priming the SARS-CoV-2 Spike, required for viral fusion to the host cell. Importantly, androgen-deprivation, anti-androgens, or Camostat attenuated the SARS-CoV-2 S-mediated cellular entry. Together, our data provide a strong rationale for clinical evaluations of TMPRSS2 inhibitors, androgen-deprivation therapy/androgen receptor antagonists alone or in combination with antiviral drugs as early as clinically possible to prevent COVID-19 progression.

category: molecular biology


title: Accelerometer-measured physical activity and cognitive functioning: A Mendelian Randomization study

abstract:
Physical activity and cognitive functioning are strongly intertwined. However, the causal relationships underlying this association are still unclear. Physical activity can enhance brain functions, but healthy cognition may also promote engagement in physical activity. Here, we assessed the bidirectional relationships between physical activity and general cognitive functioning using Latent Heritable Confounder Mendelian Randomization (LHC-MR). Association data were drawn from two large-scale genome-wide association studies (UK Biobank and COGENT) on accelerometer-measured moderate, vigorous, and average physical activity (N = 91,084) and cognitive functioning (N = 257,841). After Bonferroni correction, we observed significant LHC-MR associations suggesting that increased fraction of both moderate (b = 0.32, CI95% = [0.17,0.47], P = 2.89e-05) and vigorous physical activity (b = 0.22, CI95% = [0.06,0.37], P = 0.007) lead to increased cognitive functioning. In contrast, we found no evidence of a causal effect of average physical activity on cognitive functioning, and no evidence of a reverse causal effect (cognitive functioning on any physical activity measures). These findings provide new evidence supporting a beneficial role of moderate and vigorous physical activity (MVPA) on cognitive functioning.

Significance StatementWhether the observed correlation between physical activity and cognitive functioning reflects causal relationships in either direction is still unclear. To investigate the potential causal relationships underlying this association, we conducted recently-improved genetically-informed analyses with two large-scale datasets including accelerometer-measured physical activity. In line with theoretical models and previous experimental work explaining the mechanisms underlying the association between physical activity and cognitive functioning, our results revealed a one-way association: Higher levels of moderate and vigorous physical activity can potentially cause higher cognitive functioning. These findings suggest that physical activity plays a fundamental role in general cognitive functioning, and that health policies and interventions promoting moderate or vigorous physical activity are relevant to improve cognitive performance and to delay its decline.


title: The discovery, distribution and diversity of DNA viruses associated with Drosophila melanogaster in Europe

abstract: Drosophila melanogaster is an important model for antiviral immunity in arthropods, but very few DNA viruses have been described from the family Drosophilidae. This deficiency limits our opportunity to use natural host-pathogen combinations in experimental studies, and may bias our understanding of the Drosophila virome. Here we report fourteen DNA viruses detected in a metagenomic analysis of approximately 6500 pool-sequenced Drosophila, sampled from 47 European locations between 2014 and 2016. These include three new Nudiviruses, a new and divergent Entomopox virus, a virus related to Leptopilina boulardi filamentous virus, and a virus related to Musca domestica salivary gland hypertrophy virus. We also find an endogenous genomic copy of Galbut virus, a dsRNA Partitivirus, segregating at very low frequency. Remarkably, we find that Drosophila Vesanto virus, a small DNA virus previously described as a Bidnavirus, may be composed of up to 12 segments and represent a new lineage of segmented DNA viruses. Two of the DNA viruses, Drosophila Kallithea nudivirus and Drosophila Vesanto virus are relatively common, found in 2% or more of wild flies. The others are rare, with many likely to be represented by a single infected fly. We find that virus prevalence in Europe reflects the prevalence seen in publicly-available datasets, with Drosophila Kallithea nudivirus and Drosophila Vesanto virus the only ones commonly detectable in public data from wild-caught flies and large population cages, and the other viruses being rare or absent. These analyses suggest that DNA viruses are at lower prevalence than RNA viruses in D. melanogaster, and may be less likely to persist in laboratory cultures. Our findings go some way to redressing an earlier bias toward RNA virus studies in Drosophila, and lay the foundation needed to harness the power of Drosophila as a model system for the study of DNA viruses.