Citizen-science data provides new insight into annual and seasonal variation in migration patterns

Current rates of global environmental and climate change pose potential challenges for migratory species that must cope with or adapt to new conditions and different rates of change across broad spatial scales throughout their annual life cycle. North American migratory hummingbirds may be especially sensitive to changes in environment and climate due to their extremely small body size, high metabolic rates, and dependence on nectar as a main resource. We used occurrence information from the eBird citizen-science database to track migratory movements of five North American hummingbird species (Archilochus alexandri, A. colubris, Selasphorus calliope, S. platycercus, and S. rufus) across 6 years (2008–2013) at a daily temporal resolution to describe annual and seasonal variation in migration patterns. Our findings suggest that the timing of the onset of spring migration generally varies less than the arrival on the wintering grounds. Species follow similar routes across years, but exhibit more variation in daily longitude than latitude. Long distance migrants generally had less annual variation in geographic location and timing than shorter distance migrants. Our study is among the first to examine variation in migration routes and timing for hummingbirds, but more work is needed to understand the capacity of these species to respond to different rates of environmental change along their migratory routes

The evolution of antimicrobial peptides in Chiroptera

High viral tolerance coupled with an extraordinary regulation of the immune response makes bats a great model to study host-pathogen evolution. Although many immune-related gene gains and losses have been previously reported in bats, important gene families such as antimicrobial peptides (AMPs) remain understudied. We built an exhaustive bioinformatic pipeline targeting the major gene families of defensins and cathelicidins to explore AMP diversity and analyze their evolution and distribution across six bat families. A combination of manual and automated procedures identified 29 AMP families across queried species, with α-, β-defensins, and cathelicidins representing around 10% of AMP diversity. Gene duplications were inferred in both α-defensins, which were absent in five species, and three β-defensin gene subfamilies, but cathelicidins did not show significant shifts in gene family size and were absent in Anoura caudifer and the pteropodids. Based on lineage-specific gains and losses, we propose diet and diet-related microbiome evolution may determine the evolution of α- and β-defensins gene families and subfamilies. These results highlight the importance of building species-specific libraries for genome annotation in non-model organisms and shed light on possible drivers responsible for the rapid evolution of AMPs. By focusing on these understudied defenses, we provide a robust framework for explaining bat responses to pathogens

DIS3 isoforms vary in their endoribonuclease activity and are differentially expressed within haematological cancers

DIS3 is the catalytic subunit of the exosome, a protein complex involved in the 3’ to 5’ degradation of RNAs. DIS3 is a highly conserved exoribonuclease, also known as Rrp44. Global sequencing studies have identified DIS3 as being mutated in a range of cancers, with a considerable incidence in multiple myeloma. In this work, we have identified two proteincoding isoforms of DIS3. Both isoforms are functionally relevant and result from alternative splicing. They differ from each other in the size of their N-terminal PIN domain, which has been shown to have endoribonuclease activity and tether DIS3 to the exosome. Isoform 1 encodes a full-length PIN domain, whereas the PIN domain of isoform 2 is shorter and is missing a segment with conserved amino-acids. We have carried out biochemical activity assays on both isoforms of full-length DIS3 as well as the isolated PIN domains. We find that isoform 2, despite missing part of the PIN domain, has greater endonuclease activity compared to isoform 1. Examination of the available structural information allows us to provide a hypothesis to explain this altered behaviour. Our results also show that multiple myeloma patient cells and all cancer cell lines tested have higher levels of isoform 1 compared to isoform 2 whereas Acute Myeloid Leukemia (AML) and chronic myelomonocytic leukaemia (CMML) patient cells and samples from healthy donors have similar levels of isoforms 1 and 2. Together, our data indicate that significant changes in the ratios of the two isoforms could be symptomatic of haematological cancers

Loss-of-function mutations in UDP-Glucose 6-Dehydrogenase cause recessive developmental epileptic encephalopathy

Developmental epileptic encephalopathies are devastating disorders characterized by intractable epileptic seizures and developmental delay. Here, we report an allelic series of germline recessive mutations in UGDH in 36 cases from 25 families presenting with epileptic encephalopathy with developmental delay and hypotonia. UGDH encodes an oxidoreductase that converts UDP-glucose to UDP-glucuronic acid, a key component of specific proteoglycans and glycolipids. Consistent with being loss-of-function alleles, we show using patients’ primary fibroblasts and biochemical assays, that these mutations either impair UGDH stability, oligomerization, or enzymatic activity. In vitro, patient-derived cerebral organoids are smaller with a reduced number of proliferating neuronal progenitors while mutant ugdh zebrafish do not phenocopy the human disease. Our study defines UGDH as a key player for the production of extracellular matrix components that are essential for human brain development. Based on the incidence of variants observed, UGDH mutations are likely to be a frequent cause of recessive epileptic encephalopathy

Loss-of-function mutations in UDP-Glucose 6-Dehydrogenase cause recessive developmental epileptic encephalopathy

AbstractDevelopmental epileptic encephalopathies are devastating disorders characterized by intractable epileptic seizures and developmental delay. Here, we report an allelic series of germline recessive mutations in UGDH in 36 cases from 25 families presenting with epileptic encephalopathy with developmental delay and hypotonia. UGDH encodes an oxidoreductase that converts UDP-glucose to UDP-glucuronic acid, a key component of specific proteoglycans and glycolipids. Consistent with being loss-of-function alleles, we show using patients’ primary fibroblasts and biochemical assays, that these mutations either impair UGDH stability, oligomerization, or enzymatic activity. In vitro, patient-derived cerebral organoids are smaller with a reduced number of proliferating neuronal progenitors while mutant ugdh zebrafish do not phenocopy the human disease. Our study defines UGDH as a key player for the production of extracellular matrix components that are essential for human brain development. Based on the incidence of variants observed, UGDH mutations are likely to be a frequent cause of recessive epileptic encephalopathy.</jats:p

Screening ethnically diverse human embryonic stem cells identifies a chromosome 20 minimal amplicon conferring growth advantage

The International Stem Cell Initiative analyzed 125 human embryonic stem (ES) cell lines and 11 induced pluripotent stem (iPS) cell lines, from 38 laboratories worldwide, for genetic changes occurring during culture. Most lines were analyzed at an early and late passage. Single-nucleotide polymorphism (SNP) analysis revealed that they included representatives of most major ethnic groups. Most lines remained karyotypically normal, but there was a progressive tendency to acquire changes on prolonged culture, commonly affecting chromosomes 1, 12, 17 and 20. DNA methylation patterns changed haphazardly with no link to time in culture. Structural variants, determined from the SNP arrays, also appeared sporadically. No common variants related to culture were observed on chromosomes 1, 12 and 17, but a minimal amplicon in chromosome 20q11.21, including three genes expressed in human ES cells, ID1, BCL2L1 and HM13, occurred in >20% of the lines. Of these genes, BCL2L1 is a strong candidate for driving culture adaptation of ES cells

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

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM2.5 air pollution, 1990-2019 : an analysis of data from the Global Burden of Disease Study 2019

Background Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM2.5 originating from ambient and household air pollution.Methods We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM2.5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure-response curve from the extracted relative risk estimates using the MR-BRT (meta-regression-Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM2.5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM2.5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals.Findings In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM2.5 exposure, with an estimated 3.78 (95% uncertainty interval 2.68-4.83) deaths per 100 000 population and 167 (117-223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13.4% (9.49-17.5) of deaths and 13.6% (9.73-17.9) of DALYs due to type 2 diabetes were contributed by ambient PM2.5, and 6.50% (4.22-9.53) of deaths and 5.92% (3.81-8.64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South America. Since 1990, the attributable burden has increased by 50%, driven largely by population growth and ageing. Globally, the impact of reductions in household air pollution was largely offset by increased ambient PM2.5.Interpretation Air pollution is a major risk factor for diabetes. We estimated that about a fifth of the global burden of type 2 diabetes is attributable PM2.5 pollution. Air pollution mitigation therefore might have an essential role in reducing the global disease burden resulting from type 2 diabetes. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.Peer reviewe

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM2.5 air pollution, 1990-2019 : An analysis of data from the Global Burden of Disease Study 2019

Background Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM2·5 originating from ambient and household air pollution. Methods We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South America. Since 1990, the attributable burden has increased by 50%, driven largely by population growth and ageing. Globally, the impact of reductions in household air pollution was largely offset by increased ambient PM2·5. Interpretation Air pollution is a major risk factor for diabetes. We estimated that about a fifth of the global burden of type 2 diabetes is attributable PM2·5 pollution. Air pollution mitigation therefore might have an essential role in reducing the global disease burden resulting from type 2 diabetes