Pan-genomics and the structural diversity of plant genomes

A central task of genetics research is to uncover genotypes linked to important phenotypes. However, many genomic loci are incompletely or inaccurately represented in genetics studies, thus obscuring their function and evolution. New technology can accurately and continuously sequence large segments of genomic DNA at affordable cost and unprecedented scale, raising the possibility of complete and accurate representations of genomes across the tree of life. However, new computational methods are required to automatically finish, validate, and curate the forthcoming wave of genome assemblies enabled by these technologies. Researchers must also devise analytical approaches to comparing previously unresolved and usually repetitive genomic loci within and between species. Here, we introduce RaGOO and RagTag, new methods that leverage genome maps to automatically scaffold and improve draft genome assemblies into chromosome-scale representations. By applying these new methods to a bread wheat genome, we show how the established reference falsely collapsed functional paralogs genome-wide. In Arabidopsis thaliana, we present a new reference assembly that completely resolves all five centromeres for the first time, revealing centromere architecture, genetics, epigenetics, and evolution. Finally, we present a catalog of natural structural variants (SVs) across 100 diverse tomato accessions revealing exceptional genetic diversity via artificial introgression as well as broad and specific examples of how SVs influence molecular, domestication, and improvement phenotypes. This work underscores the potential to accelerate genetics research with complete and diverse genotype data and apply these findings to plant breeding and engineering

Transcriptomic analysis links diverse hypothalamic cell types to fibroblast growth factor 1-induced sustained diabetes remission

n rodent models of type 2 diabetes (T2D), sustained remission of hyperglycemia can be induced by a single intracerebroventricular (icv) injection of fibroblast growth factor 1 (FGF1), and the mediobasal hypothalamus (MBH) was recently implicated as the brain area responsible for this effect. To better understand the cellular response to FGF1 in the MBH, we sequenced >79,000 single-cell transcriptomes from the hypothalamus of diabetic Lepob/ob mice obtained on Days 1 and 5 after icv injection of either FGF1 or vehicle. A wide range of transcriptional responses to FGF1 was observed across diverse hypothalamic cell types, with glial cell types responding much more robustly than neurons at both time points. Tanycytes and ependymal cells were the most FGF1-responsive cell type at Day 1, but astrocytes and oligodendrocyte lineage cells subsequently became more responsive. Based on histochemical and ultrastructural evidence of enhanced cell-cell interactions between astrocytes and Agrp neurons (key components of the melanocortin system), we performed a series of studies showing that intact melanocortin signaling is required for the sustained antidiabetic action of FGF1. These data collectively suggest that hypothalamic glial cells are leading targets for the effects of FGF1 and that sustained diabetes remission is dependent on intact melanocortin signaling

Continental-scale water and energy flux analysis and validation for North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow

This is the second part of a study on continental-scale water and energy flux analysis and validation conducted in phase 2 of the North American Land Data Assimilation System project (NLDAS-2). The first part concentrates on a model-by-model comparison of mean annual and monthly water fluxes, energy fluxes and state variables. In this second part, the focus is on the validation of simulated streamflow from four land surface models (Noah, Mosaic, Sacramento Soil Moisture Accounting (SAC-SMA), and Variable Infiltration Capacity (VIC) models) and their ensemble mean. Comparisons are made against 28-years (1 October 1979-30 September 2007) of United States Geological Survey observed streamflow for 961 small basins and 8 major basins over the conterminous United States (CONUS). Relative bias, anomaly correlation and Nash-Sutcliffe Efficiency (NSE) statistics at daily to annual time scales are used to assess model-simulated streamflow. The Noah (the Mosaic) model overestimates (underestimates) mean annual runoff and underestimates (overestimates) mean annual evapotranspiration. The SAC-SMA and VIC models simulate the mean annual runoff and evapotranspiration well when compared with the observations. The ensemble mean is closer to the mean annual observed streamflow for both the 961 small basins and the 8 major basins than is the mean from any individual model. All of the models, as well as the ensemble mean, have large daily, weekly, monthly, and annual streamflow anomaly correlations for most basins over the CONUS, implying strong simulation skill. However, the daily, weekly, and monthly NSE analysis results are not necessarily encouraging, in particular for daily streamflow. The Noah and Mosaic models are useful (NSE &gt; 0.4) only for about 10% of the 961 small basins, the SAC-SMA and VIC models are useful for about 30% of the 961 small basins, and the ensemble mean is useful for about 42% of the 961 small basins. As the time scale increases, the NSE increases as expected. However, even for monthly streamflow, the ensemble mean is useful for only 75% of the 961 small basins.</p

Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products

Results are presented from the second phase of the multiinstitution North American Land Data Assimilation System (NLDAS-2) research partnership. In NLDAS, the Noah, Variable Infiltration Capacity, Sacramento Soil Moisture Accounting, and Mosaic land surface models (LSMs) are executed over the conterminous U.S. (CONUS) in realtime and retrospective modes. These runs support the drought analysis, monitoring and forecasting activities of the National Integrated Drought Information System, as well as efforts to monitor large-scale floods. NLDAS-2 builds upon the framework of the first phase of NLDAS (NLDAS-1) by increasing the accuracy and consistency of the surface forcing data, upgrading the land surface model code and parameters, and extending the study from a 3-year (1997-1999) to a 30-year (1979-2008) time window. As the first of two parts, this paper details the configuration of NLDAS-2, describes the upgrades to the forcing, parameters, and code of the four LSMs, and explores overall model-to-model comparisons of land surface water and energy flux and state variables over the CONUS. Focusing on model output rather than on observations, this study seeks to highlight the similarities and differences between models, and to assess changes in output from that seen in NLDAS-1. The second part of the two-part article focuses on the validation of model-simulated streamflow and evaporation against observations. The results depict a higher level of agreement among the four models over much of the CONUS than was found in the first phase of NLDAS. This is due, in part, to recent improvements in the parameters, code, and forcing of the NLDAS-2 LSMs that were initiated following NLDAS-1. However, large inter-model differences still exist in the northeast, Lake Superior, and western mountainous regions of the CONUS, which are associated with cold season processes. In addition, variations in the representation of sub-surface hydrology in the four LSMs lead to large differences in modeled evaporation and subsurface runoff. These issues are important targets for future research by the land surface modeling community. Finally, improvement from NLDAS-1 to NLDAS-2 is summarized by comparing the streamflow measured from U.S. Geological Survey stream gauges with that simulated by four NLDAS models over 961 small basins.</p

Overview of the north American land data assimilation system (NLDAS)

The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC), together with its NOAA Climate Program Office (CPO) Climate Prediction Program of the Americas (CPPA) partners, have established a North American Land Data Assimilation System (NLDAS). The system runs multiple land surface models (LSMs) over the Continental United States (CONUS) to generate long-term hourly, 1/8th degree hydrological and meteorological products. NLDAS was initiated in 1998 as a collaborative project between NOAA, NASA, and several universities to improve the generation of initial land surface conditions for numerical weather prediction models. The first phase of NLDAS (NLDAS-1, 1998-2005) centered on the construction of the overall NLDAS system and on the assessment of the ability of the four NLDAS LSMs to accurately simulate water fluxes, energy fluxes, and state variables. These LSMs included the Noah, Mosaic, Sacramento Soil Moisture Accounting (SAC-SMA), and Variable Infiltration Capacity (VIC) models. Building on the results of NLDAS-1, the project entered into a second phase (NLDAS-2, 2006-present) which has included upgraded forcing data and LSMs, model intercomparison studies, real-time monitoring of extreme weather events, and seasonal hydrologic forecasts. NLDAS-1 and NLDAS-2 have also spurred and supported other modeling activities, including high-resolution 1 km land surface modeling and the establishment of regional and global land data assimilation systems. NLDAS-2 operates on both a real-time monitoring mode and an ensemble seasonal hydrologic forecast mode. In the monitoring mode, land states (soil moisture and snow water equivalent) and water fluxes (evaporation, total runoff, and streamflow) from real-time LSM executions are depicted as anomalies and percentiles with respect to their own modelbased climatology. One key application of the real-time updates is for drought monitoring over the CONUS, and NLDAS supports both NOAA Climate Prediction Center (CPC) and US National Integrated Drought Information System (NIDIS) drought monitoring activities. The uncoupled ensemble seasonal forecast mode generates downscaled ensemble seasonal forecasts of surface forcing based on a climatological Ensemble Stream flow Prediction (ESP) type approach, a method utilizing CPC Official Seasonal Climate Outlooks, and a third approach using NCEP Climate Forecast System (CFS) ensemble dynamical model predictions. The three sets of forcing ensembles are then used to drive a chosen LSM (currently VIC) in seasonal forecast mode over 14 large river basins that together span the CONUS domain. One-to six-month ensemble seasonal forecast products such as air temperature, precipitation, soil moisture, snowpack, total runoff, evaporation, and stream flow are derived using each forecasting approach. The anomalies and percentiles of the predicted products and the drought probability forecast based on the predicted total column soil moisture for each forcing approach can be used for the purpose of drought prediction over the CONUS, and provide key support for NIDIS and CPC drought forecast efforts.</p

Transcriptomic analysis links diverse hypothalamic cell types to fibroblast growth factor 1-induced sustained diabetes remission

In rodent models of type 2 diabetes (T2D), sustained remission of hyperglycemia can be induced by a single intracerebroventricular (icv) injection of fibroblast growth factor 1 (FGF1), and the mediobasal hypothalamus (MBH) was recently implicated as the brain area responsible for this effect. To better understand the cellular response to FGF1 in the MBH, we sequenced \u3e79,000 single-cell transcriptomes from the hypothalamus of diabetic Lep mice obtained on Days 1 and 5 after icv injection of either FGF1 or vehicle. A wide range of transcriptional responses to FGF1 was observed across diverse hypothalamic cell types, with glial cell types responding much more robustly than neurons at both time points. Tanycytes and ependymal cells were the most FGF1-responsive cell type at Day 1, but astrocytes and oligodendrocyte lineage cells subsequently became more responsive. Based on histochemical and ultrastructural evidence of enhanced cell-cell interactions between astrocytes and Agrp neurons (key components of the melanocortin system), we performed a series of studies showing that intact melanocortin signaling is required for the sustained antidiabetic action of FGF1. These data collectively suggest that hypothalamic glial cells are leading targets for the effects of FGF1 and that sustained diabetes remission is dependent on intact melanocortin signaling

Transcriptomic analysis links diverse hypothalamic cell types to fibroblast growth factor 1-induced sustained diabetes remission

In rodent models of type 2 diabetes (T2D), sustained remission of hyperglycemia can be induced by a single intracerebroventricular (icv) injection of fibroblast growth factor 1 (FGF1), and the mediobasal hypothalamus (MBH) was recently implicated as the brain area responsible for this effect. To better understand the cellular response to FGF1 in the MBH, we sequenced \u3e79,000 single-cell transcriptomes from the hypothalamus of diabetic Lep mice obtained on Days 1 and 5 after icv injection of either FGF1 or vehicle. A wide range of transcriptional responses to FGF1 was observed across diverse hypothalamic cell types, with glial cell types responding much more robustly than neurons at both time points. Tanycytes and ependymal cells were the most FGF1-responsive cell type at Day 1, but astrocytes and oligodendrocyte lineage cells subsequently became more responsive. Based on histochemical and ultrastructural evidence of enhanced cell-cell interactions between astrocytes and Agrp neurons (key components of the melanocortin system), we performed a series of studies showing that intact melanocortin signaling is required for the sustained antidiabetic action of FGF1. These data collectively suggest that hypothalamic glial cells are leading targets for the effects of FGF1 and that sustained diabetes remission is dependent on intact melanocortin signaling