Infectious Pancreatic Necrosis Virus in Scottish Atlantic Salmon Farms, 1996–2001

The rapid growth of aquaculture has provided opportunities for the emergence of diseases. Programs designed to monitor these pathogens are useful for analysis of regional variation and trends, provided methods are standardized. Data from an official monitoring program were used to analyze the emergence of infectious pancreatic necrosis virus in Scottish salmon farms from 1996 to 2001. An annual increase in the prevalence of this virus was found in saltwater (10%) and freshwater sites (2% to 3%), with a much faster increase (6.5%) in Shetland’s freshwater sites. No significant increase in the virus was detected in the marine farms of southern mainland Scotland. However, the virus had become very prevalent at marine sites and was almost ubiquitous in Shetland by 2001, and thus the prevalence of this virus at marine sites may be underestimated. Because several diseases have emerged or are emerging in fish farming, aquaculture surveillance programs represent a rich potential source of data on emerging diseases

An AgMIP Framework for Improved Agricultural Representation in Integrated Assessment Models

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications

Uncertainty in Simulating Wheat Yields Under Climate Change

Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1,3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking

Linear domain interactome and biological function of anterior gradient 2

The Anterior Gradient 2 (AGR2) protein has been implicated in a variety of biological systems linked to cancer and metastasis, tamoxifen-induced drug resistance, pro-inflammatory diseases like IBD and asthma, and limb regeneration. The molecular mechanisms by which AGR2 mediates these various phenotypes in disease progression in both cancer and IBD are poorly understood, as is the biological function(s) of AGR2 under non-disease conditions. Here, we use a combination of biochemical techniques, organ culture, cell biology and mouse genetics to investigate the biological significance of AGR2 both in cell lines and in vivo. We present data based on phage-peptide inter-actomics screens suggesting a role for AGR2 in mediating the maturation and trafficking of a class of membrane and secretory proteins, and investigate a putative interaction between AGR2 and one member of this class of proteins. We also describe the construction of a universal vector for use in making a variety of transgenic animals, and then present data showing its use as a promoter reporter, and attempt to investigate the temporal and spatial expression of AGR2 in the developing and adult mouse. Further, we present data describing the localisation pattern of AGR2 in the developing murine kidney using a combination of organ culture and antibody staining, and suggest a role for AGR2 in the developing kidney based on this data that is in agreement with a chaperone function for membrane and secretory proteins. Together, these data suggest that AGR2 has an intrinsic consensus docking site for a subset of its client proteins, that AGR2 plays a role in protein maturation in ciliated cell types, and provides a novel biological model to dissect the role of AGR2 in ER-trafficking

Visceral and subcutaneous fat have different origins and evidence supports a mesothelial source

International audience: Fuelled by the obesity epidemic, there is considerable interest in the developmental origins of white adipose tissue (WAT) and the stem and progenitor cells from which it arises. Whereas increased visceral fat mass is associated with metabolic dysfunction, increased subcutaneous WAT is protective. There are six visceral fat depots: perirenal, gonadal, epicardial, retroperitoneal, omental and mesenteric, and it is a subject of much debate whether these have a common developmental origin and whether this differs from that for subcutaneous WAT. Here we show that all six visceral WAT depots receive a significant contribution from cells expressing Wt1 late in gestation. Conversely, no subcutaneous WAT or brown adipose tissue arises from Wt1-expressing cells. Postnatally, a subset of visceral WAT continues to arise from Wt1-expressing cells, consistent with the finding that Wt1 marks a proportion of cell populations enriched in WAT progenitors. We show that all visceral fat depots have a mesothelial layer like the visceral organs with which they are associated, and provide several lines of evidence that Wt1-expressing mesothelium can produce adipocytes. These results reveal a major ontogenetic difference between visceral and subcutaneous WAT, and pinpoint the lateral plate mesoderm as a major source of visceral WAT. They also support the notion that visceral WAT progenitors are heterogeneous, and suggest that mesothelium is a source of adipocytes