A Fire Severity Mapping System for Real-Time Fire Management Applications and Long-Term Planning: The FIRESEV project

Accurate, consistent, and timely fire severity maps are needed in all phases of fire management including planning, managing, and rehabilitating wildfires. The problem is that fire severity maps are commonly developed from satellite imagery that is difficult to use for planning wildfire responses before a fire has actually happened and can’t be used for real-time wildfire management because of the timing of the imagery delivery. Moreover, imagery is difficult to use for controlled fires such as prescribed burning. This study, called FIRESEV (FIRE SEVerity Mapping Tools) created a comprehensive set of tools and protocols to deliver, create, and evaluate fire severity maps for all phases of fire management. The first tool is a Severe Fire Potential Map (SFPM) that quantifies the potential for fires to burn with high severity, should they occur, for any 30m x 30m piece of ground across the western United States. This map was developed using empirical models that related topographic, vegetation, and fire weather variables to burn severity as mapped using the Monitoring Trends in Burn Severity (MTBS) digital products. This SFPM map is currently available on the Fire Research and Management Exchange System (FRAMES, http://www.frames.gov/firesev) web site and can be used to plan for future wildfires or for managing wildfires in real time, e.g. by including it as a layer in Wildland Fire Decision Support System or other GIS analysis. The next tool was the inclusion of a fire severity mapping algorithm in the Wildland Fire Assessment Tool (WFAT) developed by the National Interagency Fuels Technology Transfer (NIFTT) team. WFAT is used for fuel treatment planning to predict potential fire effects under prescribed fire weather conditions (http://www.frames.gov/partner-sites/niftt/tools/niftt-current-resources/). Now, fire severity can be mapped explicitly from fire effects simulation models (FOFEM, Consume) for real-time and planning wildfire applications. Next, the FIRESEV project showed how results from the WFAT simulated fire severity can be integrated with satellite imagery to improve fire severity mapping. And last, the FIRESEV project produced a suite of research studies, synthesis papers, and popular articles designed to improve the description, interpretation, and mapping of fire severity for wildland fire management: (1) a research study created a completely objective method of quantifying fire severity from fire effects to obtain nine unique classes of fire severity, (2) a research study comprehensively contrasted all current classifications of fire severity using Composite Burn Index (CBI) as measured on over 300 plots across the western United States to determine commonalities and differences, and (3) a synthesis paper was written discussing the problems involved in measuring, describing, and quantifying fire severity. This FIRESEV project yielded over 15 deliverables that we feel provides a comprehensive suite of products to create useful fire severity maps, along with current satellite imagery products, and also FIRESEV provides a thorough background on how to measure, interpret, and apply fire severity in fire management

Tousled-like Kinases Modulate Reactivation of Gammaherpesviruses from Latency

Kaposi’s sarcoma-associated herpesvirus (KSHV) is linked to human malignancies. The majority of tumor cells harbor latent virus and a small percentage undergo spontaneous lytic replication. Both latency and lytic replication are important for viral pathogenesis and spread but the cellular players involved in the switch between the two viral lifecycle phases are not clearly understood. We conducted a siRNA screen targeting the cellular kinome and identified Tousled-like kinases (TLKs) as cellular kinases that control KSHV reactivation from latency. Upon treatment of latent KSHV-infected cells with siRNAs targeting TLKs, we saw robust viral reactivation. Knockdown of TLKs in latent KSHV-infected cells induced expression of viral lytic proteins and production of infectious virus. TLKs were also found to play a role in regulating reactivation from latency of another related oncogenic gammaherpesvirus, Epstein-Barr virus (EBV). Our results establish the TLKs as cellular repressors of gammaherpesviral reactivation

CLASP2 links Reelin to the cytoskeleton during neocortical development

Published in final edited form as: Neuron. 2017 March 22; 93(6): 1344–1358.e5. doi:10.1016/j.neuron.2017.02.039.INTRODUCTION The complex architecture of the brain requires precise control over the timing of neurogenesis, neuron migration, and differentiation. These three developmental processes are exquisitely controlled during the expansion of the mammalian neocortex. The six morphologically distinct layers of the neocortex form in an “inside-out” pattern with early-born neurons forming deeper layers and later-born neurons migrating past them to form superficial layers of the cortical plate (Rakic, 1974). The Reelin signaling pathway plays a crucial role in cortical lamination. Reelin is a secreted glycoprotein that exerts its function by binding to the lipoprotein receptors ApoER2 and VLDLR and inducing tyrosine phosphorylation of the intracellular adaptor protein Disabled (Dab1) (Howell et al., 2000, Bock and Herz, 2003). Phosphorylated Dab1 then recruits downstream signaling molecules to promote cytoskeletal changes necessary for neuronal migration, final positioning, and morphology (D’Arcangelo, 2005). Mutations of Reelin, the dual ApoER2/VLDLR receptor, or Dab1 lead to an inversion of the normal inside-out pattern of cortex development (D’Arcangelo et al., 1995, Howell et al., 1997, Trommsdorff et al., 1999). In addition, a number of mutations in cytoskeletal-encoded genes produce deficits in neuron migration and cortical lamination phenotypically similar to Reelin mutants, firmly establishing a mechanistic and developmentally critical connection between Reelin and the cytoskeleton. For example, human mutations in lissencephaly-1, doublecortin, and tubulin, integral components of the microtubule cytoskeleton, cause severe cortical lamination defects with later-born neurons failing to migrate past previously born neurons (Reiner et al., 1993, Gleeson et al., 1998, Romaniello et al., 2015). The culmination of these genetic studies indicates that several signaling pathways, including the Reelin pathway, converge on downstream cytoskeletal proteins to affect proper neuronal migration and brain development. However, the molecular effectors of these pathways have not been fully characterized. CLASPs (cytoplasmic linker associated proteins) belong to a heterogeneous family of plus-end tracking proteins (+TIPs) that specifically accumulate at the growth cone. This localization strategically places them in a position to control neurite growth, directionality, and the crosstalk between microtubules and the actin cytoskeleton (Akhmanova and Hoogenraad, 2005, Basu and Chang, 2007, Akhmanova and Steinmetz, 2008). Previous evidence showed that CLASPs accumulate asymmetrically toward the leading edge of migrating fibroblasts, indicating a role for CLASPs in cell polarity and movement (Akhmanova et al., 2001, Wittmann and Waterman-Storer, 2005). We found that CLASP2 protein levels steadily increase throughout neuronal development and are specifically enriched at the growth cones of extending neurites. In particular, short-hairpin RNA (shRNA)-mediated knockdown of CLASP2 in primary mouse neurons decreases neurite length, whereas overexpression of human CLASP2 causes the formation of multiple axons, enhanced dendritic branching, and Golgi condensation (Beffert et al., 2012). These results implicate a role for CLASP2 in neuronal morphogenesis and polarization; however, the function of CLASP2 during brain development is unknown. Here we demonstrate that CLASP2 is a modifier of the Reelin signaling pathway during cortical development. In vivo knockdown experiments demonstrate that CLASP2 plays significant roles in neural precursor proliferation, neuronal migration, and morphogenesis. In addition, we show that GSK3β-mediated phosphorylation of CLASP2 controls its binding to the Reelin adaptor protein Dab1, a required molecular step governing CLASP2’s regulatory effects on neuron morphology and movement. RESULTS CLASP2 Expression Is Functionally Associated with the Reelin Signaling Pathway To identify novel genes downstream of Reelin signaling, we examined the expression of mRNA transcripts by microarray between adult brain cortices from mice deficient in either Reelin, the double ApoER2/VLDLR receptor mutant, or Dab1 and compared Affymetrix gene expression profiles against age-matched, wild-type mice. Importantly, each of these mutant mouse models present a similar phenotype that includes severe neuronal migration defects (D’Arcangelo et al., 1995, Howell et al., 1997, Trommsdorff et al., 1999). We defined a large network of genes perturbed above a threshold of 1.5-fold in response to deficient Reelin signaling, identifying 832 upregulated and 628 downregulated genes that were common to all three mouse models (Figure 1A). Ingenuity Pathway Analysis revealed a network of genes that is functionally related to cytoskeleton organization, microtubule dynamics, neurogenesis, and migration of cells (Figure 1B). Of the few cytoskeletal candidate genes identified, CLASP2 was the only microtubule +TIP. Specifically, CLASP2 mRNA expression was increased in all three Reelin mutant phenotypes, while CLASP1 mRNA expression remained unchanged (Figure 1B). Consistent with the microarray data, CLASP2 protein levels were ∼2.8-fold higher in Dab1 knockout mice (Figure 1C). These findings suggest that Reelin signaling controls CLASP2 expression and establishes the first molecular link between a plus-end, microtubule binding protein downstream of extracellular Reelin signaling.We thank Drs. Thomas C. Sudhof, Joachim Herz, Santos Franco, and Torsten Wittmann for plasmids and antibodies. We thank Alicia Dupre, Elias Fong, and Christine Learned for technical support. This work was supported by grants from the National Institutes of Health (R21 MH100581 to T.F.H., U.B., and A.H.). (R21 MH100581 - National Institutes of Health)Accepted manuscrip

Whole genome comparisons reveal panmixia among fall armyworm (Spodoptera frugiperda) from diverse locations

Background: The fall armyworm (Spodoptera frugiperda (J.E. Smith)) is a highly polyphagous agricultural pest with long-distance migratory behavior threatening food security worldwide. This pest has a host range of > 80 plant species, but two host strains are recognized based on their association with corn (C-strain) or rice and smaller grasses (R-strain). The population genomics of the United States (USA) fall armyworm remains poorly characterized to date despite its agricultural threat. Results: In this study, the population structure and genetic diversity in 55 S. frugiperda samples from Argentina, Brazil, Kenya, Puerto Rico and USA were surveyed to further our understanding of whole genome nuclear diversity. Comparisons at the genomic level suggest a panmictic S. frugiperda population, with only a minor reduction in gene flow between the two overwintering populations in the continental USA, also corresponding to distinct host strains at the mitochondrial level. Two maternal lines were detected from analysis of mitochondrial genomes. We found members from the Eastern Hemisphere interspersed within both continental USA overwintering subpopulations, suggesting multiple individuals were likely introduced to Africa. Conclusions: Our research is the largest diverse collection of United States S. frugiperda whole genome sequences characterized to date, covering eight continental states and a USA territory (Puerto Rico). The genomic resources presented provide foundational information to understand gene flow at the whole genome level among S. frugiperda populations. Based on the genomic similarities found between host strains and laboratory vs. field samples, our findings validate the experimental use of laboratory strains and the host strain differentiation based on mitochondria and sex-linked genetic markers extends to minor genome wide differences with some exceptions showing mixture between host strains is likely occurring in field populations.Fil: Schlum, Katrina A.. University of Tennessee; Estados UnidosFil: Lamour, Kurt. University of Tennessee; Estados UnidosFil: Placidi de Bortoli, Caroline. University of Tennessee; Estados UnidosFil: Banerjee, Rahul. University of Tennessee; Estados UnidosFil: Meagher, Robert. United States Department Of Agriculture. Center For Medical Agric And Vet Entomology; Estados UnidosFil: Pereira, Eliseu. Universidade Federal de Viçosa; BrasilFil: Murúa, María Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Tecnología Agroindustrial del Noroeste Argentino. Provincia de Tucumán. Ministerio de Desarrollo Productivo. Estación Experimental Agroindustrial "Obispo Colombres" (p). Instituto de Tecnología Agroindustrial del Noroeste Argentino; ArgentinaFil: Sword, Gregory A.. Texas A&M University; Estados UnidosFil: Tessnow, Ashley E.. Texas A&M University; Estados UnidosFil: Viteri Dillon, Diego. Universidad de Puerto Rico; Puerto RicoFil: Linares Ramirez, Angela M.. Universidad de Puerto Rico; Puerto RicoFil: Akutse, Komivi S.. International Centre Of Insect Physiology And Ecology; KeniaFil: Schmidt Jeffris, Rebecca. United States Department Of Agriculture. Center For Medical Agric And Vet Entomology; Estados UnidosFil: Huang, Fangneng. State University of Louisiana; Estados UnidosFil: Reisig, Dominic. North Carolina State University; Estados UnidosFil: Emrich, Scott J.. University of Tennessee; Estados UnidosFil: Jurat Fuentes, Juan Luis. University of Tennessee; Estados Unido

Canvass: a crowd-sourced, natural-product screening library for exploring biological space

NCATS thanks Dingyin Tao for assistance with compound characterization. This research was supported by the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH). R.B.A. acknowledges support from NSF (CHE-1665145) and NIH (GM126221). M.K.B. acknowledges support from NIH (5R01GM110131). N.Z.B. thanks support from NIGMS, NIH (R01GM114061). J.K.C. acknowledges support from NSF (CHE-1665331). J.C. acknowledges support from the Fogarty International Center, NIH (TW009872). P.A.C. acknowledges support from the National Cancer Institute (NCI), NIH (R01 CA158275), and the NIH/National Institute of Aging (P01 AG012411). N.K.G. acknowledges support from NSF (CHE-1464898). B.C.G. thanks the support of NSF (RUI: 213569), the Camille and Henry Dreyfus Foundation, and the Arnold and Mabel Beckman Foundation. C.C.H. thanks the start-up funds from the Scripps Institution of Oceanography for support. J.N.J. acknowledges support from NIH (GM 063557, GM 084333). A.D.K. thanks the support from NCI, NIH (P01CA125066). D.G.I.K. acknowledges support from the National Center for Complementary and Integrative Health (1 R01 AT008088) and the Fogarty International Center, NIH (U01 TW00313), and gratefully acknowledges courtesies extended by the Government of Madagascar (Ministere des Eaux et Forets). O.K. thanks NIH (R01GM071779) for financial support. T.J.M. acknowledges support from NIH (GM116952). S.M. acknowledges support from NIH (DA045884-01, DA046487-01, AA026949-01), the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program (W81XWH-17-1-0256), and NCI, NIH, through a Cancer Center Support Grant (P30 CA008748). K.N.M. thanks the California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board for support. B.T.M. thanks Michael Mullowney for his contribution in the isolation, elucidation, and submission of the compounds in this work. P.N. acknowledges support from NIH (R01 GM111476). L.E.O. acknowledges support from NIH (R01-HL25854, R01-GM30859, R0-1-NS-12389). L.E.B., J.K.S., and J.A.P. thank the NIH (R35 GM-118173, R24 GM-111625) for research support. F.R. thanks the American Lebanese Syrian Associated Charities (ALSAC) for financial support. I.S. thanks the University of Oklahoma Startup funds for support. J.T.S. acknowledges support from ACS PRF (53767-ND1) and NSF (CHE-1414298), and thanks Drs. Kellan N. Lamb and Michael J. Di Maso for their synthetic contribution. B.S. acknowledges support from NIH (CA78747, CA106150, GM114353, GM115575). W.S. acknowledges support from NIGMS, NIH (R15GM116032, P30 GM103450), and thanks the University of Arkansas for startup funds and the Arkansas Biosciences Institute (ABI) for seed money. C.R.J.S. acknowledges support from NIH (R01GM121656). D.S.T. thanks the support of NIH (T32 CA062948-Gudas) and PhRMA Foundation to A.L.V., NIH (P41 GM076267) to D.S.T., and CCSG NIH (P30 CA008748) to C.B. Thompson. R.E.T. acknowledges support from NIGMS, NIH (GM129465). R.J.T. thanks the American Cancer Society (RSG-12-253-01-CDD) and NSF (CHE1361173) for support. D.A.V. thanks the Camille and Henry Dreyfus Foundation, the National Science Foundation (CHE-0353662, CHE-1005253, and CHE-1725142), the Beckman Foundation, the Sherman Fairchild Foundation, the John Stauffer Charitable Trust, and the Christian Scholars Foundation for support. J.W. acknowledges support from the American Cancer Society through the Research Scholar Grant (RSG-13-011-01-CDD). W.M.W.acknowledges support from NIGMS, NIH (GM119426), and NSF (CHE1755698). A.Z. acknowledges support from NSF (CHE-1463819). (Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH); CHE-1665145 - NSF; CHE-1665331 - NSF; CHE-1464898 - NSF; RUI: 213569 - NSF; CHE-1414298 - NSF; CHE1361173 - NSF; CHE1755698 - NSF; CHE-1463819 - NSF; GM126221 - NIH; 5R01GM110131 - NIH; GM 063557 - NIH; GM 084333 - NIH; R01GM071779 - NIH; GM116952 - NIH; DA045884-01 - NIH; DA046487-01 - NIH; AA026949-01 - NIH; R01 GM111476 - NIH; R01-HL25854 - NIH; R01-GM30859 - NIH; R0-1-NS-12389 - NIH; R35 GM-118173 - NIH; R24 GM-111625 - NIH; CA78747 - NIH; CA106150 - NIH; GM114353 - NIH; GM115575 - NIH; R01GM121656 - NIH; T32 CA062948-Gudas - NIH; P41 GM076267 - NIH; R01GM114061 - NIGMS, NIH; R15GM116032 - NIGMS, NIH; P30 GM103450 - NIGMS, NIH; GM129465 - NIGMS, NIH; GM119426 - NIGMS, NIH; TW009872 - Fogarty International Center, NIH; U01 TW00313 - Fogarty International Center, NIH; R01 CA158275 - National Cancer Institute (NCI), NIH; P01 AG012411 - NIH/National Institute of Aging; Camille and Henry Dreyfus Foundation; Arnold and Mabel Beckman Foundation; Scripps Institution of Oceanography; P01CA125066 - NCI, NIH; 1 R01 AT008088 - National Center for Complementary and Integrative Health; W81XWH-17-1-0256 - Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program; P30 CA008748 - NCI, NIH, through a Cancer Center Support Grant; California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board; American Lebanese Syrian Associated Charities (ALSAC); University of Oklahoma Startup funds; 53767-ND1 - ACS PRF; PhRMA Foundation; P30 CA008748 - CCSG NIH; RSG-12-253-01-CDD - American Cancer Society; RSG-13-011-01-CDD - American Cancer Society; CHE-0353662 - National Science Foundation; CHE-1005253 - National Science Foundation; CHE-1725142 - National Science Foundation; Beckman Foundation; Sherman Fairchild Foundation; John Stauffer Charitable Trust; Christian Scholars Foundation)Published versionSupporting documentatio

Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway

This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−(13)C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown

Impacts of climate change on plant diseases – opinions and trends

There has been a remarkable scientific output on the topic of how climate change is likely to affect plant diseases in the coming decades. This review addresses the need for review of this burgeoning literature by summarizing opinions of previous reviews and trends in recent studies on the impacts of climate change on plant health. Sudden Oak Death is used as an introductory case study: Californian forests could become even more susceptible to this emerging plant disease, if spring precipitations will be accompanied by warmer temperatures, although climate shifts may also affect the current synchronicity between host cambium activity and pathogen colonization rate. A summary of observed and predicted climate changes, as well as of direct effects of climate change on pathosystems, is provided. Prediction and management of climate change effects on plant health are complicated by indirect effects and the interactions with global change drivers. Uncertainty in models of plant disease development under climate change calls for a diversity of management strategies, from more participatory approaches to interdisciplinary science. Involvement of stakeholders and scientists from outside plant pathology shows the importance of trade-offs, for example in the land-sharing vs. sparing debate. Further research is needed on climate change and plant health in mountain, boreal, Mediterranean and tropical regions, with multiple climate change factors and scenarios (including our responses to it, e.g. the assisted migration of plants), in relation to endophytes, viruses and mycorrhiza, using long-term and large-scale datasets and considering various plant disease control methods