Munc18c: a controversial regulator of peripheral insulin action

Insulin resistance, a hallmark of impaired glucose tolerance and type 2 diabetes (T2D), arises from dysfunction of insulin action and subsequent glucose uptake by peripheral tissues, predominantly skeletal muscle and fat. Exocytosis of glucose transporter (GLUT4)-containing vesicles facilitated by soluble NSF (N-ethylmaleimide-sensitive factor) attachment receptor (SNARE) protein isoforms, and Munc18c (mammalian homolog of Unc-18c) mediates this glucose uptake. Emerging evidences, including recent human clinical studies, point to pivotal roles for Munc18c in peripheral insulin action in adipose and skeletal muscle. Intriguing new advances are also initiating debates regarding the molecular mechanism(s) controlling Munc18c action. The objective of this review is therefore to present a balanced perspective of new continuities and controversies surrounding the regulation and requirement for Munc18c in the regulation of peripheral insulin action

Linear Self-Motion Cues Support the Spatial Distribution and Stability of Hippocampal Place Cells

The vestibular system provides a crucial component of place-cell and head-direction cell activity [1-7]. Otolith signals are necessary for head-direction signal stability and associated behavior [8, 9], and the head-direction signal's contribution to parahippocampal spatial representations [10-14] suggests that place cells may also require otolithic information. Here, we demonstrate that self-movement information from the otolith organs is necessary for the development of stable place fields within and across sessions. Place cells in otoconia-deficient tilted mice showed reduced spatial coherence and formed place fields that were located closer to environmental boundaries, relative to those of control mice. These differences reveal an important otolithic contribution to place-cell functioning and provide insight into the cognitive deficits associated with otolith dysfunction

The p21-activated kinase (PAK1) is involved in diet-induced beta cell mass expansion and survival in mice and human islets

AIMS/HYPOTHESIS: Human islets from type 2 diabetic donors are reportedly 80% deficient in the p21 (Cdc42/Rac)-activated kinase, PAK1. PAK1 is implicated in beta cell function and maintenance of beta cell mass. We questioned the mechanism(s) by which PAK1 deficiency potentially contributes to increased susceptibility to type 2 diabetes. METHODS: Non-diabetic human islets and INS 832/13 beta cells cultured under diabetogenic conditions (i.e. with specific cytokines or under glucolipotoxic [GLT] conditions) were evaluated for changes to PAK1 signalling. Combined effects of PAK1 deficiency with GLT stress were assessed using classic knockout (Pak1 (-/-) ) mice fed a 45% energy from fat/palmitate-based, 'western' diet (WD). INS 832/13 cells overexpressing or depleted of PAK1 were also assessed for apoptosis and signalling changes. RESULTS: Exposure of non-diabetic human islets to diabetic stressors attenuated PAK1 protein levels, concurrent with increased caspase 3 cleavage. WD-fed Pak1 knockout mice exhibited fasting hyperglycaemia and severe glucose intolerance. These mice also failed to mount an insulin secretory response following acute glucose challenge, coinciding with a 43% loss of beta cell mass when compared with WD-fed wild-type mice. Pak1 knockout mice had fewer total beta cells per islet, coincident with decreased beta cell proliferation. In INS 832/13 beta cells, PAK1 deficiency combined with GLT exposure heightened beta cell death relative to either condition alone; PAK1 deficiency resulted in decreased extracellular signal-related kinase (ERK) and B cell lymphoma 2 (Bcl2) phosphorylation levels. Conversely, PAK1 overexpression prevented GLT-induced cell death. CONCLUSIONS/INTERPRETATION: These findings suggest that PAK1 deficiency may underlie an increased diabetic susceptibility. Discovery of ways to remediate glycaemic dysregulation via altering PAK1 or its downstream effectors offers promising opportunities for disease intervention

Regional to Global Assessments of Phytoplankton Dynamics From The SeaWiFS Mission

Photosynthetic production of organic matter by microscopic oceanic phytoplankton fuels ocean ecosystems and contributes roughly half of the Earth's net primary production. For 13 years, the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission provided the first consistent, synoptic observations of global ocean ecosystems. Changes in the surface chlorophyll concentration, the primary biological property retrieved from SeaWiFS, have traditionally been used as a metric for phytoplankton abundance and its distribution largely reflects patterns in vertical nutrient transport. On regional to global scales, chlorophyll concentrations covary with sea surface temperature (SST) because SST changes reflect light and nutrient conditions. However, the oceanmay be too complex to be well characterized using a single index such as the chlorophyll concentration. A semi-analytical bio-optical algorithm is used to help interpret regional to global SeaWiFS chlorophyll observations from using three independent, well-validated ocean color data products; the chlorophyll a concentration, absorption by CDM and particulate backscattering. First, we show that observed long-term, global-scale trends in standard chlorophyll retrievals are likely compromised by coincident changes in CDM. Second, we partition the chlorophyll signal into a component due to phytoplankton biomass changes and a component caused by physiological adjustments in intracellular chlorophyll concentrations to changes in mixed layer light levels. We show that biomass changes dominate chlorophyll signals for the high latitude seas and where persistent vertical upwelling is known to occur, while physiological processes dominate chlorophyll variability over much of the tropical and subtropical oceans. The SeaWiFS data set demonstrates complexity in the interpretation of changes in regional to global phytoplankton distributions and illustrates limitations for the assessment of phytoplankton dynamics using chlorophyll retrievals alone

Evidence of adaptive evolutionary divergence during biological invasion

Rapid phenotypic diversification during biological invasions can either arise by adaptation to alternative environments or by adaptive phenotypic plasticity. Where experimental evidence for adaptive plasticity is common, support for evolutionary diversification is rare. Here, we performed a controlled laboratory experiment using full-sib crosses between ecologically divergent threespine stickleback populations to test for a genetic basis of adaptation. Our populations are from two very different habitats, lake and stream, of a recently invaded range in Switzerland and differ in ecologically relevant morphological traits. We found that in a lake-like food treatment lake fish grow faster than stream fish, resembling the difference among wild type individuals. In contrast, in a stream-like food treatment individuals from both populations grow similarly. Our experimental data suggest that genetically determined diversification has occurred within less than 140 years after the arrival of stickleback in our studied region

Early-Onset Neonatal Sepsis 2015 to 2017, the Rise of Escherichia coli, and the Need for Novel Prevention Strategies

Importance: Early-onset sepsis (EOS) remains a potentially fatal newborn condition. Ongoing surveillance is critical to optimize prevention and treatment strategies. Objective: To describe the current incidence, microbiology, morbidity, and mortality of EOS among a cohort of term and preterm infants. Design, setting, and participants: This prospective surveillance study included a cohort of infants born at a gestational age (GA) of at least 22 weeks and birth weight of greater than 400 g from 18 centers of the Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network from April 1, 2015, to March 31, 2017. Data were analyzed from June 14, 2019, to January 28, 2020. Main outcomes and measures: Early-onset sepsis defined by isolation of pathogenic species from blood or cerebrospinal fluid culture within 72 hours of birth and antibiotic treatment for at least 5 days or until death. Results: A total of 235 EOS cases (127 male [54.0%]) were identified among 217 480 newborns (1.08 [95% CI, 0.95-1.23] cases per 1000 live births). Incidence varied significantly by GA and was highest among infants with a GA of 22 to 28 weeks (18.47 [95% CI, 14.57-23.38] cases per 1000). No significant differences in EOS incidence were observed by sex, race, or ethnicity. The most frequent pathogens were Escherichia coli (86 [36.6%]) and group B streptococcus (GBS; 71 [30.2%]). E coli disease primarily occurred among preterm infants (68 of 131 [51.9%]); GBS disease primarily occurred among term infants (54 of 104 [51.9%]), with 24 of 45 GBS cases (53.3%) seen in infants born to mothers with negative GBS screening test results. Intrapartum antibiotics were administered to 162 mothers (68.9%; 110 of 131 [84.0%] preterm and 52 of 104 [50.0%] term), most commonly for suspected chorioamnionitis. Neonatal empirical antibiotic treatment most frequently included ampicillin and gentamicin. All GBS isolates were tested, but only 18 of 81 (22.2%) E coli isolates tested were susceptible to ampicillin; 6 of 77 E coli isolates (7.8%) were resistant to both ampicillin and gentamicin. Nearly all newborns with EOS (220 of 235 [93.6%]) displayed signs of illness within 72 hours of birth. Death occurred in 38 of 131 infected infants with GA of less than 37 weeks (29.0%); no term infants died. Compared with earlier surveillance (2006-2009), the rate of E coli infection increased among very low-birth-weight (401-1500 g) infants (8.68 [95% CI, 6.50-11.60] vs 5.07 [95% CI, 3.93-6.53] per 1000 live births; P = .008). Conclusions and relevance: In this study, EOS incidence and associated mortality disproportionately occurred in preterm infants. Contemporary cases have demonstrated the limitations of current GBS prevention strategies. The increase in E coli infections among very low-birth-weight infants warrants continued study. Ampicillin and gentamicin remained effective antibiotics in most cases, but ongoing surveillance should monitor antibiotic susceptibilities of EOS pathogens

Phytoplankton spring bloom initiation: The impact of atmospheric forcing and light in the temperate North Atlantic Ocean

The spring bloom dominates the annual cycle of phytoplankton abundance in large regions of the world oceans. The mechanisms that trigger blooms have been studied for decades, but are still keenly debated, due in part to a lack of data on phytoplankton stocks in winter and early spring. Now however autonomous underwater gliders can provide high-resolution sampling of the upper ocean over inter-seasonal timescales and advance our understanding of spring blooms. In this study, we analyze bio-optical and physical observations collected by gliders at the Porcupine Abyssal Plain observatory site to investigate the impact of atmospheric forcing and light conditions on phytoplankton blooms in the temperate North Atlantic. We contrast three hypotheses for the mechanism of bloom initiation: the critical depth, critical turbulence, and dilution-recoupling hypotheses. Bloom initiation at our study site corresponded to an improvement in growth conditions for phytoplankton (increasing light, decreasing mixing layer depth) and was most consistent with the critical depth hypothesis, with the proviso that mixing depth (rather than mixed layer depth) was considered. After initiation, the observed bloom developed slowly: over several months both depth-integrated inventories and surface concentrations of chlorophyll a increased only by a factor of ~2 and ~3 respectively. We find that periods of convective mixing and high winds in winter and spring can substantially decrease (up to an order of magnitude) light-dependent mean specific growth rate for phytoplankton and prevent the development of rapid, high-magnitude blooms

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