Additional file 3: Table S1. of Transcriptome-wide identification and expression profiles of the WRKY transcription factor family in Broomcorn millet (Panicum miliaceum L.)

The identified PmWRKY proteins in Broomcorn millet and their putative orthologous gene in rice. (DOC 47 kb

Additional file 1: Figure S1. of Transcriptome-wide identification and expression profiles of the WRKY transcription factor family in Broomcorn millet (Panicum miliaceum L.)

Multiple-sequence alignment of the WRKY protein domain from PmWRKYs and OsWRKYs. Conserved amino acids were indicated by blank background, conserved WRKY domains and zinc-finger motifs were indicated by red box. (DOC 1365 kb

Additional file 2: Figure S2. of Transcriptome-wide identification and expression profiles of the WRKY transcription factor family in Broomcorn millet (Panicum miliaceum L.)

Sequence logos of PmWRKY domain. The PmWRKY proteins domain submitted to MEME server. The total height of stack was used to shows ‘information content’ of that position in the motif. Height of letters in stack suggests probability of each amino acids at that position. (DOC 558 kb

Additional file 6: of Genome-wide identification, phylogeny and expressional profiles of mitogen activated protein kinase kinase kinase (MAPKKK) gene family in bread wheat (Triticum aestivum L.)

Detail information of the network of TaMAPKKK with other wheat genes. (XLSX 42 kb

Additional file 4: Figure S3. of Transcriptome-wide identification and expression profiles of the WRKY transcription factor family in Broomcorn millet (Panicum miliaceum L.)

Expression level of 32 PmWRKY genes in different tissues. Actin was an internal reference gene. (DOC 797 kb

Additional file 1 of Host genotype-specific rhizosphere fungus enhances drought resistance in wheat

Additional file 1: Table S1. Annual precipitation and average monthly precipitation at the Suqian (SQ) and Yangling (YL) planting sites. Table S2. Primers used for quantitative real-time PCR (qRT-PCR) in this study. Table S3. Effects of genotypes, planting sites, and niche compartments on the bacterial and fungal communities based on PERMANOVA analysis. Table S4. Relative abundances of dominant bacterial phyla for YH and CS wheats at the SQ and YL sites. Table S5. Relative abundances of dominant fungal phyla for YH and CS wheats at SQ and YL sites. Table S6. Relative abundances of drought-responsive bacterial OTUs at genus level. Table S7. Relative abundances of drought-responsive fungal OTUs at genus level. Table S8. The absolute abundance of plant pathogenic OTUs at phyla and genus level. Table S9. Topological characteristics of the microbial interkingdom association networks in the YH and CS sites. Table S10. Metagenomics sequencing data characteristics for rhizosphere microbiomes from different wheat cultivars at the SQ and YL sites. Table S11. Root morphology traits of wheat sample under different microbial inoculation treatments in the Experiment 1. Table S12. Spearman’s Correlation coefficients between rhizosphere microorganisms and root morphological traits. Table S13. Root morphology traits of China Spring (CS) in the Experiment 2. Table S14. Division by k-means clustering of differentially expressed genes (DEGs) into clusters 8. Table S15. Gene IDs of DEGs involved in responses to abiotic stress and mitogen-activated protein Kinase activity. Fig. S1. Rhizosphere soil microbiome diversity and distribution patterns. (a) Canonical analysis of principal coordinates (CAP) of bacteria and fungi was performed to determine whether there were differences in samples according to the Bray–Curtis dissimilarity matrix. (b) Distinct beta-diversity between bacterial and fungal communities was shown by principal coordinate analysis ordinations (PCoA). Abbreviations: Suqian site (SQ), Yangling site (YL), drought-susceptible wheat cultivar Chinese Spring (CS), drought-resistant wheat cultivar Yunhan 618 (YH), bulk soil (B), and rhizosphere soil (T). Fig. S2. Soil microbiome diversity in bulk and rhizosphere soils. (a) Results of CAP of bacterial and fungal microbiota in bulk and rhizosphere soils. (b) PCoA plots of the bacterial and fungal community structure. Fig. S3. Phylogenetic tree for bacterial (a) and fungal (b) communities at the phylum level showing the hierarchical relationships of operational taxonomic units (OTUs) with an average abundance of greater than 3000 in each sample population (inner circle). The outer rings indicate the abundance and distribution of OTUs in all samples. The values indicate the average abundance of each OTU (log-transformed). The colors represent the OTUs at the phylum level. Fig. S4. Differential abundance of bacteria and random forest classification revealing differences in rhizosphere microbial structure. (a) and (b) Volcano plots visualizing the enrichment and depletion patterns of bacteria in YH in comparison with CS at SQ (a) and YL (b). Black dots represent no significant differences in OTUs. Red and blue dots indicate an individual OTU significantly enriched in YH and CS, respectively. (c) and (d) The influence of YH and CS at SQ (c) and YL (d) on the 11 most important bacterial taxa in rhizosphere soil was determined by random forest classification. The relative abundance of rhizobacteria differed in accordance with the drought tolerance of the sample. OTUs are arranged along the y-axis in descending order of importance by calculating the Gini coefficient to validate the accuracy of the model. For the abbreviations see Fig. S1. Fig. S5. Differential abundance of fungi and random forest classification revealing differences in rhizosphere microbial structure. (a) and (b) Volcano plots visualizing the enrichment and depletion patterns of fungi in YH in comparison with CS at SQ (a) and YL (b). (c) and (d) The influence of YH and CS at SQ (c) and YL (d) on the five most important fungal taxa in rhizosphere soil was determined by random forest classification. Fig. S6. Heatmap showing the relative abundances of abundant genera present in drought-responsive bacteria and fungi, which varied between the drought-resistant cultivar and the drought-susceptible cultivar at the two sites. All taxa are indicated according to their significance (Wilcox test, P 2, p 1 and q-value < 0.05. Upregulated genes are shown in red, and downregulated genes are shown in blue. Fig. S16. Division of all DEGs into eight clusters by k-means clustering analysis. Fig. S17. Relative expression levels (2−ΔΔCT) of drought response-related genes obtained by qRT-PCR analysis. Fig. S18. Relative expression levels (2−ΔΔCT) of root development-related genes obtained by RNA-seq analysis. Fig. S19. Root-related parameters of China Spring (CS) in the Experiment 2. Fig. S20. Model of M. alpina-induced stress response signaling in wheat. M. alpina strongly induces expression of genes in the CIPK and PP2C families, CIPK-PP2C complexes activate MAPKKK17, and the activated MAPK cascade induces the expression of the transcription factors MYB36, MYB62, and NAC71 to regulate the expression of drought-responsive genes. However, suppression by E. nigrum of expression of the MAPK6 and NAC71 genes leads to repression of drought-responsive genes

Enantioselective Synthesis of the ABC-Tricyclic Core of Phomactin A by a γ‑Hydroxylation Strategy

An enantioselective synthesis of the ABC-tricyclic furanochroman core of phomactin A has been accomplished by a γ-hydroxylation approach. The C ring was established by γ-hydroxylation of an α-enone. The regioselectivity was optimized by using a strong base with an oxophilic cation (<i>t-</i>BuLi) and a bulky oxygen donor (Davis reagent), which afforded the γ-hydroxylation product selectively in 63% yield