Table_1_The Application and Limitation of Universal Chloroplast Markers in Discriminating East Asian Evergreen Oaks.xls

<p>The East Asian subtropics mostly occupied by evergreen broad-leaved forests (EBLFs), is one of the global diversity centers for evergreen oaks. Evergreen oaks are keystone canopy trees in EBLFs with important ecosystem function and crucial significance for regional biodiversity conservation. However, the species composition and diversity of Asian evergreen oaks are poorly understood. Here, we test whether the four chloroplast markers atpI-atpH, matK, psbA-trnH, and ycf1, can discriminate the two evergreen oak sections in Asia – Cyclobalanopsis and Ilex. Two hundred and seventy-two individuals representing 57 species were scanned and 17 species from other oaks sections were included for phylogenetic reconstruction. The genetic diversity of the Quercus sections was also compared. Overall, we found that universal chloroplast DNA (cpDNA) barcoding markers could resolve two clades in Quercus, i.e., subgenus Cerris (Old World Clade) and subgenus Quercus (New World Clade). The chloroplast markers distinguished the main sections, with few exceptions. Each cpDNA region showed no barcoding gap and none of them provided good resolution at the species level. The best species resolution (27.78%) was obtained when three or four markers were combined and analyzed using BLAST. The high conservation of the cpDNA and complicated evolutionary patterns, due to incomplete lineage sorting, interspecific hybridization and introgressions may hinder the ability of cpDNA markers to discriminate different species. When comparing diversification pattern across Quercus sections (Cyclobalanopsis, Ilex, Cerris, Quercus, and Protobalanus), we found that section Ilex was the most genetically diverse, and section Cyclobalanopsis was lower genetically diverse. This diversification pattern may have resulted from the interplay of the Eurasia Cenozoic tectonic movements, climate changes and different niches of their ancestral lineages.</p

MOESM2 of Ancient events and climate adaptive capacity shaped distinct chloroplast genetic structure in the oak lineages

Additional file 2. Distribution of shared haplotypes of section Ilex (a) and section Quercus (b)

MOESM1 of Ancient events and climate adaptive capacity shaped distinct chloroplast genetic structure in the oak lineages

Additional file 1. Detailed information and data

Additional file 1 of Characterization of UDP-glycosyltransferase family members reveals how major flavonoid glycoside accumulates in the roots of Scutellaria baicalensis

Additional file 1: Table S1. Flavonoid glycosides detected in the root metabolome. Table S2. Sequences of UGT genes identified from S. baicalensis genome. Table S3. The list of enzyme names, gene locus, and their subfamilies of predicted 7-O glycosyltransferases in S. baicalensis. Table S4. Primers used for the cloning of SbUGT and SbUGAT genes

Additional file 2 of Characterization of UDP-glycosyltransferase family members reveals how major flavonoid glycoside accumulates in the roots of Scutellaria baicalensis

Additional file 2: Figure S1. Representative 7-O flavonoid glycosides detected from roots of S. baicalensis. Red boxes indicated the different groups between sugar moieties. Figure S2. Alignment of SbUGTs and SbUGATs protein sequences. The consensus sequences were highlighted by red color. The arrows indicated the different amino acid residues between SbUGTs and SbUGATs, which were responsible for the functional divergent between these two types of glycosyltransferases. Figure S3. MS and MS2 patterns of oroxin A (A) and baicalin standard (B). Figure S4. SDS PAGE analysis of purification of SbUGT and SbUGAT proteins. A. Tracks from left to right showed protein markers (M), empty vector control (1), SbUGT1 (2), SbUGT2 (3), SbUGT3 (4), SbUGT7 (5), SbUGT8 (6) and SbUGT9 (7). B. Tracks from left to right showed protein markers (M), empty vector control (1), SbUGTA3 (2), SbUGAT4 (3), SbUGAT5 (4) and SbUGAT6 (5). Figure S5. Nonlinear regressions of the Michaelis−Menten equation for SbUGTs and SbUGATs

Additional file 1 of Comparative physiological and transcriptome analysis between potassium-deficiency tolerant and sensitive sweetpotato genotypes in response to potassium-deficiency stress

Additional file 1: Figure S1. Total soluble sugars contents in two different K+-sensitive sweetpotato cultivars Xu32 and NZ1 under normal (Control) and K+-deficient conditions (-K) for two weeks. Date are means ± SE (n=3) and there is no significant difference between mean values of - K and control. Figure S2. Transcript levels of 12 randomly selected common DEGs in both cv. Xu32 and cv. NZ1 by qRT-PCR analysis. The columns represent relative expression obtained by qRT-PCR, and solid lines represent relative expression obtained by RNA-seq. Date are means ± SE (n=3). Primers used for qRT-PCR are listed in Table S5. AFigure S3. Gene ontology (GO) classification of DEGs in sweetpotato plants under K+-deficiency conditions. The enriched biological process, cellular component and molecular function GO terms of DEGs in cv. Xu32 (A) and in cv. NZ1(B). Figure S4. KEGG enrichment of DEGs in sweetpotato plants under K+-deficiency conditions. The top 20 enrichment KEGG pathway of DEGs in cv. Xu32 (A) and in cv. NZ1 (B)