RT-PCR verification of selected TDFs.

<p>RT-PCR verification of selected TDFs.</p

Molecular cloning and sequencing analysis differentially expressed TDFs.

<p>Molecular cloning and sequencing analysis differentially expressed TDFs.</p

Dendrogram of effects of <i>Curvularia eragrostidis</i> on crabgrass based on UPGMA analysis of gene expression using cDNA-AFLP by NYSYS2.0.

<p>CK, 1, 2, 3, 4 same to those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075430#pone-0075430-t002" target="_blank">Table 2</a></p

Effect of application of <i>Curvularia eragrostidis on Digitaria sanguinalis</i> grown in pots.

<p>CK: Control, sprayed with water, S: Sprayed with water containing 1×10⁶ conidia mL⁻¹. The photograph was taken seven days after plants were sprayed.</p

Gel electrophoresis analysis of cDNA-AFLP.

<p>M: 100 bp ladder, 0: CK, 1∼4: Treatment 1∼4 as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075430#pone-0075430-t002" target="_blank">Table 2</a>, every 5 lanes are derived from selective PCR of one pair of primers.</p

Differential Gene Expression for <i>Curvularia eragrostidis</i> Pathogenic Incidence in Crabgrass (<i>Digitaria sanguinalis</i>) Revealed by cDNA-AFLP Analysis

<div><p>Gene expression profiles of <i>Digitaria sanguinalis</i> infected by <i>Curvularia eragrostidis</i> strain QZ-2000 at two concentrations of conidia and two dew durations were analyzed by cDNA amplified fragment length polymorphisms (cDNA-AFLP). Inoculum strength was more determinant of gene expression than dew duration. A total of 256 primer combinations were used for selective amplification and 1214 transcript-derived fragments (TDFs) were selected for their differential expression. Of these, 518 up-regulated differentially expressed TDFs were identified. Forty-six differential cDNA fragments were chosen to be cloned and 35 of them were successfully cloned and sequenced, of which 25 were homologous to genes of known function according to the GenBank database. Only 6 genes were up-regulated in <i>Curvularia eragrostidis</i>-inoculated <i>D. sanguinalis</i>, with functions involved in signal transduction, energy metabolism, cell growth and development, stress responses, abscisic acid biosynthesis and response. It appears that a few pathways may be important parts of the pathogenic strategy of <i>C.</i><i>eragrostidis</i> strain QZ-2000 on <i>D. sanguinalis</i>. Our study provides the fundamentals to further study the pathogenic mechanism, screen for optimal <i>C. eragrostidis</i> strains as potential mycoherbicide and apply this product to control <i>D.</i><i>sanguinalis</i>.</p></div

Primers for rt-PCR analysis.

<p>Primers for rt-PCR analysis.</p

Effects of inoculation concentrations and dew durations of <i>Curvularia eragrostidis</i> on crabgrass growth.

a<p>Values denoted by different letters are significantly different according to Tukey’s Test at <i>p</i><0.05.</p

DataSheet_1_Genome-wide analysis and expression of the aquaporin gene family in Avena sativa L..zip

BackgroundOat (Avena sativa L.) belongs to the early maturity grass subfamily of the Gramineae subfamily oats (Avena) and has excellent characteristics, such as tolerance to barrenness, salt, cold, and drought. Aquaporin (AQP) proteins belong to the major intrinsic protein (MIP) superfamily, are widely involved in plant growth and development, and play an important role in abiotic stress responses. To date, previous studies have not identified or analyzed the AsAQP gene family system, and functional studies of oat AQP genes in response to drought, cold, and salt stress have not been performed.MethodsIn this study, AQP genes (AsAQP) were identified from the oat genome, and various bioinformatics data on the AQP gene family, gene structure, gene replication, promoters and regulatory networks were analyzed. Quantitative real-time PCR technology was used to verify the expression patterns of the AQP gene family in different oat tissues under different abiotic stresses.ResultsIn this study, a total of 45 AQP genes (AsAQP) were identified from the oat reference genome. According to a phylogenetic analysis, 45 AsAQP were divided into 4 subfamilies (PIP, SIP, NIP, and TIP). Among the 45 AsAQP, 23 proteins had interactions, and among these, 5AG0000633.1 had the largest number of interacting proteins. The 20 AsAQP genes were expressed in all tissues, and their expression varied greatly among different tissues and organs. All 20 AsAQP genes responded to salt, drought and cold stress. The NIP subfamily 6Ag0000836.1 gene was significantly upregulated under different abiotic stresses and could be further verified as a key candidate gene.ConclusionThe findings of this study provide a comprehensive list of members and their sequence characteristics of the AsAQP protein family, laying a solid theoretical foundation for further functional analysis of AsAQP in oats. This research also offers valuable reference for the creation of stress-tolerant oat varieties through genetic engineering techniques.</p

Additional file 1: Table S1. of Population genetic structure is shaped by historical, geographic, and environmental factors in the leguminous shrub Caragana microphylla on the Inner Mongolia Plateau of China

Sampled populations, geographical variables and individual numbers used in the study for different molecular marker datasets. Table S2. The 22 SSR loci analyzed in our 10 studied C. microphylla populations. Table S3. 19 bioclimatic variables of 10 C. micraphylla populations. Table S4. Pairwise FST detected by SSRs among our 10 studied C. microphylla populations. Table S5. Pairwise FST detected by cpDNA among our 10 studied C. microphylla populations. Table S6. Pairwise FST detected by GBS among 9 of our studied C. microphylla populations. Figure S1. Plots of our 10 sample localities in climate space. Each point represents the environmental values of a population on PC axes 1 and 2 (left) and PC axes 2 and 3 (right). The distribution of points across the climate space graphs shows how our sampling scheme captures a wide range of environmental variation. Figure S2. Population genetic structure analysis for SSR (A) and GBS (B) based markers of C. microphylla, showing the ΔK statistics calculated according to Evanno et al. (2005). Figure S3. Jackknife analyses of individual predictor importance for C. microphylla applied to the Maxent model, presented in relation to overall model quality or “total gain”. Dark blue bars indicate the gain achieved when including that predictor only and excluding remaining predictors; light blue bars show how the total gain is diminished without the given predictor. (DOCX 219 kb