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Abstract Heart failure (HF) and cardiac arrhythmias share overlapping pathological mechanisms that act cooperatively to accelerate disease pathogenesis. Cardiac fibrosis is associated with both pathological conditions. Our previous work identified a link between phytosterol accumulation and cardiac injury in a mouse model of phytosterolemia, a rare disorder characterized by elevated circulating phytosterols and increased cardiovascular disease risk. Here, we uncover a previously unknown pathological link between phytosterols and cardiac arrhythmias in the same animal model. Phytosterolemia resulted in inflammatory pathway induction, premature ventricular contractions (PVC) and ventricular tachycardia (VT). Blockade of phytosterol absorption either by therapeutic inhibition or by genetic inactivation of NPC1L1 prevented the induction of inflammation and arrhythmogenesis. Inhibition of phytosterol absorption reduced inflammation and cardiac fibrosis, improved cardiac function, reduced the incidence of arrhythmias and increased survival in a mouse model of phytosterolemia. Collectively, this work identified a pathological mechanism whereby elevated phytosterols result in inflammation and cardiac fibrosis leading to impaired cardiac function, arrhythmias and sudden death. These comorbidities provide insight into the underlying pathophysiological mechanism for phytosterolemia-associated risk of sudden cardiac death.

Abstract There are millions of river barriers worldwide, ranging from wooden locks to concrete dams, many of which form associated impoundments to store water in small ponds or large reservoirs. Besides their benefits, there is growing recognition of important environmental and social trade-offs related to these artificial structures. However, global datasets describing their characteristics and geographical distribution are often biased towards particular regions or specific applications, such as hydropower dams affecting fish migration, and are thus not globally consistent. Here, we present a new river barrier and reservoir database developed by the Global Dam Watch (GDW) consortium that integrates, harmonizes, and augments existing global datasets to support large-scale analyses. Data curation involved extensive quality control processes to create a single, globally consistent data repository of instream barriers and reservoirs that are co-registered to a digital river network. Version 1.0 of the GDW database contains 41,145 barrier locations and 35,295 associated reservoir polygons representing a cumulative storage capacity of 7,420 km3 and an artificial terrestrial surface water area of 304,600 km2.

IntroductionEnvironmental DNA (eDNA) metabarcoding of water is increasingly being used to monitor coastal biodiversity shifts. However, we have limited knowledge of whether samples collected during discreet temporal periods depict holistic ecosystem changes over longer time spans.MethodsHere, we show how eDNA community structure varies across repeated sampling events at different temporal scales ranging from years to months to days at an Arctic coastal site, Churchill (Canada), using metabarcoding analyses of water eDNA samples with four universal primer pairs (two primers in COI and two in the 18S rRNA).ResultsDaily variations were highly dynamic and less structured, likely due to the stochastic nature of estuarine ecosystems, but there was a clear annual consistency in eDNA communities with a high proportion of shared taxa between years. However, monthly sampling was the most efficient for capturing holistic biodiversity.DiscussionWe provide recommendations for optimal eDNA metabarcoding sampling design based on our observations. The study underscores the importance of understanding biological and physical factors altering eDNA detection to improve the efficiency of detecting and interpreting long-term eDNA changes.

CD200-CD200R pathway regulates immune responses and has been implicated in the pathogenesis of a number of cancer types. CD200 blockade is considered a strategy for immunotherapy of CD200-positive cancers such as melanoma. Thus, it is critical to understand the potential impacts of CD200 blockade in a more human relevant tumor model. In this study, we evaluated these issues using the CD200+ Yumm1.7 mouse melanoma model. Yumm1.7 cells bear Braf/Pten mutations resembling human melanoma. We found that Yumm1.7 tumors grow significantly faster in CD200R–/– mice compared to wild type mice. Analysis of tumor immune microenvironment (TIME) revealed that tumors from CD200R–/– or anti-CD200 treated mice had downregulated immune cell contents and reduced TCR clonality compared to tumors from untreated wild type mice. T cells also showed impaired effector functions, as reflected by reduced numbers of IFN-γ+ and TNF-α+ T cells. Mechanistically, we found upregulation of the CCL8 gene in CD200R–/– tumors. In vitro co-culture experiments using Yumm1.7 tumor cells with bone marrow derived macrophages (BMDM) from WT and CD200R–/– mice confirmed upregulation of macrophage CCL8 in the absence of CD200-CD200R interaction. Finally, we found that anti-CD200 therapy failed to show efficacy either alone or in combination with checkpoint inhibitors such as anti-PD-1 or anti-CTLA4 in inhibiting Yumm1.7 tumor growth. Given that CD200R-deficiency or anti-CD200 treatment leads to reduced T cell responses in TME, using blockade of CD200 as an immunotherapy for cancers such as melanoma should be practiced with caution.

In the event of a foot-and-mouth disease (FMD) incursion, response strategies are required to control, contain and eradicate the pathogen as efficiently as possible. Infectious disease simulation models are widely used tools that mimic disease dispersion in a population and that can be useful in the design and support of prevention and mitigation activities. However, there are often gaps in evidence-based research to supply models with quantities that are necessary to accurately reflect the system of interest. The objective of this study was to quantify values associated with the duration of the stages of FMD infection (latent period, subclinical period, incubation period, and duration of infection), probability of transmission (within-herd and between-herd via spatial spread), and diagnosis of a vesicular disease within a herd using a meta-analysis of the peer-reviewed literature and expert opinion. The latent period ranged from 1 to 7 days and incubation period ranged from 1 to 9 day; both were influenced by strain. In contrast, the subclinical period ranged from 0 to 6 days and was influenced by sampling method only. The duration of infection ranged from 1 to 10 days. The probability of spatial spread between an infected and fully susceptible swine farm was estimated as greatest within 5 km of the infected farm, highlighting the importance of possible long-range transmission through the movement of infected animals. Lastly, while most swine practitioners are confident in their ability to detect a vesicular disease in an average sized swine herd, a small proportion expect that up to half of the herd would need to show clinical signs before detection via passive surveillance would occur. The results of this study will be useful in within- and between-herd simulation models to develop efficient response strategies in the event an FMD in swine populations of disease-free countries or regions.

Lumpy skin disease virus (LSDV) is an infectious disease of cattle that can have severe economic implications. New LSD outbreaks are currently circulating in the Middle East (ME). Since 2012, severe outbreaks were reported in cattle across the region. Characterizing the spatial and temporal dynamics of LSDV in cattle populations is prerequisite for guiding successful surveillance and control efforts at a regional level in the ME. Here, we aim to model the ecological niche of LSDV, and identify epidemic progression patterns over the course of the epidemic. We analyzed publically available outbreak data from the ME for the period 2012-2015 using presence-only maximum entropy ecological niche modeling, and the time-dependent method for the estimation of the effective reproductive number (R-TD). High-risk areas (Probability > 0.60) for LSDV identified by ecological niche modeling included parts of many northeastern ME countries, though Israel and Turkey were estimated to be the most suitable locations for occurrence of LSDV outbreaks. The most important environmental predictors that contributed to the ecological niche of LSDV included annual precipitation, land cover, mean diurnal range, type of livestock production system and global livestock densities. Average monthly effective R-TD was equal to 2.2 (95% CI: 1.2, 3.5), whereas the largest R-TD was estimated in Israel (R-TD = 22.2, 95 CI: 15.2, 31.5) in September 2013, which indicated that the demographic and environmental conditions during this period were suitable to LSDV super-spreading events. The sharp drop of Isreal’s inferred R-TD in the following month reflected the success of their 2013 vaccination campaign in controlling the disease. Our results identified areas in which under-reporting of LSDV outbreaks may have occurred. More epidemiological information related to cattle populations are needed to further improve the inferred spatial and temporal characteristics of currently circulating LSDV. However, the methodology presented here may be useful in guiding the design of risk-based surveillance and control programs in the region as well as aid in the formulation of epidemic preparedness plans in neighboring LSDV-free countries.