02634nas a2200277   4500008004100000245014400041210006900185490001300254520174100267653004302008653001202051653001502063653001002078653001902088653002002107653001102127653002402138100001602162700001602178700001302194700001702207700001802224700001802242700001802260856007802278       2011                        eng d00aGenomic and resistance gene homolog diversity of the dominant tallgrass prairie species across the U.S. Great Plains precipitation gradient0 aGenomic and resistance gene homolog diversity of the dominant ta0 v6:e176413 a<p>Background Environmental variables such as moisture availability are often important in determining species prevalence and intraspecific diversity. The population genetic structure of dominant plant species in response to a cline of these variables has rarely been addressed. We evaluated the spatial genetic structure and diversity of Andropogon gerardii populations across the U.S. Great Plains precipitation gradient, ranging from approximately 48 cm/year to 105 cm/year. Methodology/Principal Findings Genomic diversity was evaluated with AFLP markers and diversity of a disease resistance gene homolog was evaluated by PCR-amplification and digestion with restriction enzymes. We determined the degree of spatial genetic structure using Mantel tests. Genomic and resistance gene homolog diversity were evaluated across prairies using Shannon&#39;s index and by averaging haplotype dissimilarity. Trends in diversity across prairies were determined using linear regression of diversity on average precipitation for each prairie. We identified significant spatial genetic structure, with genomic similarity decreasing as a function of distance between samples. However, our data indicated that genome-wide diversity did not vary consistently across the precipitation gradient. In contrast, we found that disease resistance gene homolog diversity was positively correlated with precipitation. Significance Prairie remnants differ in the genetic resources they maintain. Selection and evolution in this disease resistance homolog is environmentally dependent. Overall, we found that, though this environmental gradient may not predict genomic diversity, individual traits such as disease resistance genes may vary significantly.</p>10aAmplified fragment length polymorphism10aCloning10aHexaploidy10aMaize10aPlant genomics10aPlant pathology10aPloidy10aPopulation genetics1 aRouse, M.N.1 aSaleh, A.A.1 aSeck, A.1 aKeeler, K.H.1 aTravers, S.E.1 aHulbert, S.H.1 aGarrett, K.A.  uhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.001764102253nas a2200265   4500008004100000245013500041210006900176300001300245490000700258520141500265100001801680700001301698700001601711700001801727700001801745700001601763700001201779700001401791700002001805700001401825700002301839700001901862700001601881856009001897       2010                        eng d00aVariation in gene expression of Andropogon gerardii in response to altered environmental conditions associated with climate change0 aVariation in gene expression of Andropogon gerardii in response   a374 -3830 v983 a<p>1.&ensp;If we are to understand the mechanisms underlying species responses to climate change in natural systems, studies are needed that focus on responses of non-model species under field conditions. We measured transcriptional profiles of individuals of Andropogon gerardii, a C4 grass native to North American grasslands, in a field experiment in which both temperature and precipitation were manipulated to simulate key aspects of forecasted climate change. 2.&ensp;By using microarrays developed for a closely related model species, Zea mays, we were able to compare the relative influence of warming versus altered soil moisture availability on expression levels of over 7000 genes, identify responsive functional groups of genes and correlate changes in gene transcription with physiological responses. 3.&ensp;We observed more statistically significant shifts in transcription levels of genes in response to thermal stress than in response to water stress. We also identified candidate genes that demonstrated transcription levels closely associated with physiological variables, in particular chlorophyll fluorescence. 4.Synthesis. These results suggest that an ecologically important species responds differently to different environmental aspects of forecast climate change. These translational changes have the potential to influence phenotypic characters and ultimately adaptive responses.</p>1 aTravers, S.E.1 aTang, Z.1 aCaragea, D.1 aGarrett, K.A.1 aHulbert, S.H.1 aLeach, J.E.1 aBai, J.1 aSaleh, A.1 aKnapp, Alan, K.1 aFay, P.A.1 aNippert, Jesse, B.1 aSchnable, P.S.1 aSmith, M.D.  uhttps://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/j.1365-2745.2009.01618.x02177nas a2200241   4500008004100000245014800041210006900189300001100258490000600269520134800275100001801623700001601641700001201657700001801669700001601687700001901703700002001722700001901742700001401761700001401775700001801789856012801807       2007                        eng d00aEcological genomics: making the leap from model systems in the lab to native populations in the field. Frontiers in Ecology and the Environment0 aEcological genomics making the leap from model systems in the la  a19 -240 v53 aRecent reviews have emphasized the need to incorporate genomics into ecological field studies to further understand how species respond to changing environmental conditions. Genomic tools, such as cDNA (complementary DNA) microarrays, allow for the simultaneous analysis of gene expression of thousands of genes from all or part of an organism's genome (the transcription profile), thereby revealing the genetic mechanisms that underlie species' responses to environmental change. However, despite their potential, two major limitations have hindered the incorporation of microarrays and other genomic tools into field studies: (1) the limited availability of microarrays for ecologically relevant, non-model species and limited financial resources for developing new microarrays; and (2) concern that high sensitivity of gene expression to even subtle alterations in environmental conditions will hinder detection of relevant changes in field measures of transcription profiles. Here, we show that with cross-species hybridizations of microarrays developed for a closely related model organism, an appropriate experimental design, and sufficient replication, transcriptional profiling can successfully be incorporated into field studies. In this way, relevant changes in gene expression with changing environmental conditions can be detected.1 aTravers, S.E.1 aSmith, M.D.1 aBai, J.1 aHulbert, S.H.1 aLeach, J.E.1 aSchnable, P.S.1 aKnapp, Alan, K.1 aMilliken, G.A.1 aFay, P.A.1 aSaleh, A.1 aGarrett, K.A.  uhttp://knz.lternet.edu/content/ecological-genomics-making-leap-model-systems-lab-native-populations-field-frontiers-ecology02353nas a2200157   4500008004100000245004100041210004100082300001100123490000800134520191100142100001802053700001802071700001602089700001802105856007202123       2006                        eng d00aEcological genomics and epidemiology0 aEcological genomics and epidemiology  a35 -510 v1153 aThe huge amount of genomic data now becoming available offers both opportunities and challenges for epidemiologists. In this “preview” of likely developments as the field of ecological genomics evolves and merges with epidemiology, we discuss how epidemiology can use new information about genetic sequences and gene expression to form predictions about epidemic features and outcomes and for understanding host resistance and pathogen evolution. DNA sequencing is now complete for some hosts and several pathogens. Microarrays make it possible to measure gene expression simultaneously for thousands of genes. These tools will contribute to plant disease epidemiology by providing information about which resistance or pathogenicity genes are present in individuals and populations, what genes other than those directly involved in resistance and virulence are important in epidemics, the role of the phenotypic status of hosts and pathogens, and the role of the status of the environmental metagenome. Conversely, models of group dynamics supplied by population biology and ecology may be used to interpret gene expression within individual organisms and in populations of organisms. Genomic tools have great potential for improving understanding of resistance gene evolution and the durability of resistance. For example, DNA sequence analysis can be used to evaluate whether an arms race model of co-evolution is supported. Finally, new genomic tools will make it possible to consider the landscape ecology of epidemics in terms of host resistance both as determined by genotype and as expressed in host phenotypes in response to the biotic and abiotic environment. Host phenotype mixtures can be modeled and evaluated, with epidemiological predictions based on phenotypic characteristics such as physiological age and status in terms of induced systemic resistance or systemic acquired resistance.1 aGarrett, K.A.1 aHulbert, S.H.1 aLeach, J.E.1 aTravers, S.E.  uhttp://knz.lternet.edu/content/ecological-genomics-and-epidemiology