Adamtsl3 mediates DCC signaling to selectively promote GABAergic synapse function

The molecular code that controls synapse formation and maintenance in vivo has remained quite sparse. Here, we identify that the secreted protein Adamtsl3 functions as critical hippocampal synapse organizer acting through the transmembrane receptor DCC (deleted in colorectal cancer). Traditionally, DCC function has been associated with glutamatergic synaptogenesis and plasticity in response to Netrin-1 signaling. We demonstrate that early post-natal deletion of Adamtsl3 in neurons impairs DCC protein expression, causing reduced density of both glutamatergic and GABAergic synapses. Adult deletion of Adamtsl3 in either GABAergic or glutamatergic neurons does not interfere with DCC-Netrin-1 function at glutamatergic synapses but controls DCC signaling at GABAergic synapses. The Adamtsl3-DCC signaling unit is further essential for activity-dependent adaptations at GABAergic synapses, involving DCC phosphorylation and Src kinase activation. These findings might be particularly relevant for schizophrenia because genetic variants in Adamtsl3 and DCC have been independently linked with schizophrenia in patients

Neuroprotective tissue adaptation induced by IL-12 attenuates CNS inflammation

IL-12 is a well-established driver of type 1 immune responses. Paradoxically, in several autoimmune conditions including neuroinflammation, IL-12 reduces pathology and exhibits regulatory properties. Yet, the mechanism and the involved cellular players behind this immune regulation remain elusive. To identify the IL-12-responsive elements which prevent immunopathology, we generated mouse models lacking a functional IL-12 receptor either in all cells or in specific populations within the immune or central nervous system (CNS) compartments, and induced experimental autoimmune encephalomyelitis (EAE), which models human Multiple Sclerosis (MS). This revealed that the CNS tissue-protective features of IL-12 are mediated by cells of the neuroectoderm, and not immune cells. Importantly, sections of brain from patients with MS show comparable patterns of expression, indicating parallel mechanisms in humans. By combining spectral flow cytometry, bulk and single-nucleus RNA sequencing, we uncovered an IL-12-induced neuroprotective adaption of the neuroectoderm critically involved in maintaining CNS tissue integrity during inflammation

IL-12 sensing in neurons induces neuroprotective CNS tissue adaptation and attenuates neuroinflammation in mice

Interleukin-12 (IL-12) is a potent driver of type 1 immunity. Paradoxically, in autoimmune conditions, including of the CNS, IL-12 reduces inflammation. The underlying mechanism behind these opposing properties and the involved cellular players remain elusive. Here we map IL-12 receptor (IL-12R) expression to NK and T cells as well as neurons and oligodendrocytes. Conditionally ablating the IL-12R across these cell types in adult mice and assessing their susceptibility to experimental autoimmune encephalomyelitis revealed that the neuroprotective role of IL-12 is mediated by neuroectoderm-derived cells, specifically neurons, and not immune cells. In human brain tissue from donors with multiple sclerosis, we observe an IL-12R distribution comparable to mice, suggesting similar mechanisms in mice and humans. Combining flow cytometry, bulk and single-nucleus RNA sequencing, we reveal an IL-12-induced neuroprotective tissue adaption preventing early neurodegeneration and sustaining trophic factor release during neuroinflammation, thereby maintaining CNS integrity in mice

Sniff-synchronized, gradient-guided olfactory search by freely moving mice.

For many organisms, searching for relevant targets such as food or mates entails active, strategic sampling of the environment. Finding odorous targets may be the most ancient search problem that motile organisms evolved to solve. While chemosensory navigation has been well characterized in microorganisms and invertebrates, spatial olfaction in vertebrates is poorly understood. We have established an olfactory search assay in which freely moving mice navigate noisy concentration gradients of airborne odor. Mice solve this task using concentration gradient cues and do not require stereo olfaction for performance. During task performance, respiration and nose movement are synchronized with tens of milliseconds precision. This synchrony is present during trials and largely absent during inter-trial intervals, suggesting that sniff-synchronized nose movement is a strategic behavioral state rather than simply a constant accompaniment to fast breathing. To reveal the spatiotemporal structure of these active sensing movements, we used machine learning methods to parse motion trajectories into elementary movement motifs. Motifs fall into two clusters, which correspond to investigation and approach states. Investigation motifs lock precisely to sniffing, such that the individual motifs preferentially occur at specific phases of the sniff cycle. The allocentric structure of investigation and approach indicates an advantage to sampling both sides of the sharpest part of the odor gradient, consistent with a serial-sniff strategy for gradient sensing. This work clarifies sensorimotor strategies for mouse olfactory search and guides ongoing work into the underlying neural mechanisms

Unraveling the forcings controlling the vegetation and climate of the best orbital analogues for the present interglacial in SW Europe

The suitability of MIS 11c and MIS 19c as analogues of our present interglacial and its natural evolution is still debated. Here we examine the regional expression of the Holocene and its orbital analogues over SW Iberia using a model-data comparison approach. Regional tree fraction and climate based on snapshot and transient experiments using the LOVECLIM model are evaluated against the terrestrial-marine profiles from Site U1385 documenting the regional vegetation and climatic changes. The pollen-based reconstructions show a larger forest optimum during the Holocene compared to MIS 11c and MIS 19c, putting into question their analogy in SW Europe. Pollen-based and model results indicate reduced MIS 11c forest cover compared to the Holocene primarily driven by lower winter precipitation, which is critical for Mediterranean forest development. Decreased precipitation was possibly induced by the amplified MIS 11c latitudinal insolation and temperature gradient that shifted the westerlies northwards. In contrast, the reconstructed lower forest optimum at MIS 19c is not reproduced by the simulations probably due to the lack of Eurasian ice sheets and its related feedbacks in the model. Transient experiments with time-varying insolation and CO2 reveal that the SW Iberian forest dynamics over the interglacials are mostly coupled to changes in winter precipitation mainly controlled by precession, CO2 playing a negligible role. Model simulations reproduce the observed persistent vegetation changes at millennial time scales in SW Iberia and the strong forest reductions marking the end of the interglacial "optimum".SFRH/BD/9079/2012, SFRH/BPD/108712/2015, SFRH/BPD/108600/2015info:eu-repo/semantics/publishedVersio

Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension

High blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to ~192,000 individuals, and used ~155,063 samples for independent replication. We identified 31 novel blood pressure or hypertension associated genetic regions in the general population, including three rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5mmHg/allele) than common variants. Multiple rare, nonsense and missense variant associations were found in A2ML1 and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

Software for the frontiers of quantum chemistry:An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages