86 research outputs found
Soluble and insoluble PAs measured after acid-catalyzed hydrolysis in seed coats transgenic and control lines.
<p>The <i>P</i>-value is for a <i>t</i>-test for means of paired samples. W-10, W-12, W-13: transgenic lines with inhibited <i>BnTT10</i> expression; W-22: transgenic lines with no inhibition in <i>BnTT10</i> expression; W-24: control lines with normal <i>BnTT10</i> expression. T<sub>2</sub>-P: positive T<sub>2</sub> progenies; T<sub>2</sub>-N: negative T<sub>2</sub> progenies after separation. Soluble (A) and insoluble PA (B) content in seed coats of T<sub>2</sub> transgenic and control <i>B. napus</i> cv. Westar plants. Each value represents the means of three independent experiments +/− SD.</p
Screening of Candidate Leaf Morphology Genes by Integration of QTL Mapping and RNA Sequencing Technologies in Oilseed Rape (<i>Brassica napus</i> L.)
<div><p>Leaf size and shape play important roles in agronomic traits, such as yield, quality and stress responses. Wide variations in leaf morphological traits exist in cultivated varieties of many plant species. By now, the genetics of leaf shape and size have not been characterized in <i>Brassica napus</i>. In this study, a population of 172 recombinant inbred lines (RILs) was used for quantitative trait locus (QTL) analysis of leaf morphology traits. Furthermore, fresh young leaves of extreme lines with more leaf lobes (referred to as ‘A’) and extreme lines with fewer lobes (referred to as ‘B’) selected from the RIL population and leaves of dissected lines (referred to as ‘P’) were used for transcriptional analysis. A total of 31 QTLs for the leaf morphological traits tested in this study were identified on 12 chromosomes, explaining 5.32–39.34% of the phenotypic variation. There were 8, 6, 2, 5, 8, and 2 QTLs for PL (petiole length), PN (lobe number), LW (lamina width), LL (Lamina length), LL/LTL (the lamina size ratio) and LTL (leaf total length), respectively. In addition, 74, 1,166 and 1,272 differentially expressed genes (DEGs) were identified in ‘A vs B’, ‘A vs P’ and ‘B vs P’ comparisons, respectively. The Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to predict the functions of these DEGs. Gene regulators of leaf shape and size, such as <i>ASYMMETRIC LEAVES 2</i>, <i>gibberellin 20-oxidase 3</i>, genes encoding gibberellin-regulated family protein, genes encoding growth-regulating factor and KNOTTED1-like homeobox were also detected in DEGs. After integrating the QTL mapping and RNA sequencing data, 33 genes, including a gene encoding auxin-responsive GH3 family protein and a gene encoding sphere organelles protein-related gene, were selected as candidates that may control leaf shape. Our findings should be valuable for studies of the genetic control of leaf morphological trait regulation in <i>B</i>. <i>napus</i>.</p></div
Cloning and Phylogenetic Analysis of <i>Brassica napus</i> L. <i>Caffeic Acid O-Methyltransferase 1</i> Gene Family and Its Expression Pattern under Drought Stress
<div><p>For many plants, regulating lignin content and composition to improve lodging resistance is a crucial issue. Caffeic acid O-methyltransferase (COMT) is a lignin monomer-specific enzyme that controls S subunit synthesis in plant vascular cell walls. Here, we identified 12 <i>BnCOMT1</i> gene homologues, namely <i>BnCOMT1-1</i> to <i>BnCOMT1-12</i>. Ten of 12 genes were composed of four highly conserved exons and three weakly conserved introns. The length of intron I, in particular, showed enormous diversification. Intron I of homologous <i>BnCOMT1</i> genes showed high identity with counterpart genes in <i>Brassica rapa</i> and <i>Brassica oleracea</i>, and intron I from positional close genes in the same chromosome were relatively highly conserved. A phylogenetic analysis suggested that <i>COMT</i> genes experience considerable diversification and conservation in <i>Brassicaceae</i> species, and some <i>COMT1</i> genes are unique in the <i>Brassica</i> genus. Our expression studies indicated that <i>BnCOMT1</i> genes were differentially expressed in different tissues, with <i>BnCOMT1-4</i>, <i>BnCOMT1-5</i>, <i>BnCOMT1-8</i>, and <i>BnCOMT1-10</i> exhibiting stem specificity. These four <i>BnCOMT1</i> genes were expressed at all developmental periods (the bud, early flowering, late flowering and mature stages) and their expression level peaked in the early flowering stage in the stem. Drought stress augmented and accelerated lignin accumulation in high-lignin plants but delayed it in low-lignin plants. The expression levels of <i>BnCOMT1s</i> were generally reduced in water deficit condition. The desynchrony of the accumulation processes of total lignin and <i>BnCOMT1</i>s transcripts in most growth stages indicated that <i>BnCOMT1s</i> could be responsible for the synthesis of a specific subunit of lignin or that they participate in other pathways such as the melatonin biosynthesis pathway.</p></div
Microplastic Pollution in Table Salts from China
Microplastics have been found in
seas all over the world. We hypothesize
that sea salts might contain microplastics, because they are directly
supplied by seawater. To test our hypothesis, we collected 15 brands
of sea salts, lake salts, and rock/well salts from supermarkets throughout
China. The microplastics content was 550–681 particles/kg in
sea salts, 43–364 particles/kg in lake salts, and 7–204
particles/kg in rock/well salts. In sea salts, fragments and fibers
were the prevalent types of particles compared with pellets and sheets.
Microplastics measuring less than 200 μm represented the majority
of the particles, accounting for 55% of the total microplastics, and
the most common microplastics were polyethylene terephthalate, followed
by polyethylene and cellophane in sea salts. The abundance of microplastics
in sea salts was significantly higher than that in lake salts and
rock/well salts. This result indicates that sea products, such as
sea salts, are contaminated by microplastics. To the best of our knowledge,
this is the first report on microplastic pollution in abiotic sea
products
Expression analysis of the orthologous genes that influence leaf morphology in <i>Arabidopsis</i>.
<p>A: Extreme lines with more lobes; B: extreme lines with fewer lobes; P: dissected lines.</p
Expression analysis of candidate genes for the regulation of leaf development identified using QTL and RNA-Seq analyses.
<p>A: Extreme lines with more lobes; B: extreme lines with fewer lobes; P: dissected lines.</p
Detection of flavonoid composition in seed coats from transgenic and control <i>B. napus</i> plants.
<p>Analyses were performed by LC-UV-MS on seed coats of T<sub>2</sub> antisense <i>BnTT10</i> transgenic and control lines of <i>B. napus</i> cv. Zhongyou821. Q-3-G, Quercetin-3-glucoside; PC dimer B2, [DP2]-B2, epicatechin-(4β-8)-epicatechin; EC, epicatechin; K-3-O-G-7-O-G, kaempferol-3-O-glucoside-7-O-glucoside; I-di-H, isorhamnetin-dihexoside. Each value represents the means of three independent experiments +/− SD.</p
DataSheet_1_Identification of candidate genes regulating seed oil content by QTL mapping and transcriptome sequencing in Brassica napus.zip
Increasing oil production is a major goal in rapeseed (Brassica napus) molecular breeding programs. Identifying seed oil content (SOC)-related candidate genes is an important step towards achieving this goal. We performed quantitative trait locus (QTL) mapping of SOC in B. napus using a high-density SNP genetic map constructed from recombinant inbred lines and the Illumina InfiniumTM 60K SNP array. A total of 26 QTLs were detected in three years on A01, A03, A05, A06, A09, C01, C03 and C05, which accounted for 3.69%~18.47% of the phenotypic variation in SOC. Of these, 13 QTLs are reported here for the first time. 1713 candidate genes in the 26 QTLs confidence interval were obtained. We then identified differentially expressed genes (DEGs) between the high- and low-SOC accessions, to narrow down our focus to 21 candidate genes (Y1-Y21) related to SOC, and we will focus on 11 (Y1-Y11) candidate genes that contribute to the formation of high-SOC. In addition to providing insight into the genetic basis of SOC in B. napus, the loci identified and candidate genes in this study can be used in molecular breeding strategies to increase SOC in this important seed crop.</p
Solanaceae Plant Malformation in Chongqing City, China, Reveals a Pollution Threat to the Yangtze River
Water quality is
under increasing threat from industrial and natural
sources of pollutants. Here, we present our findings about a pollution
incident involving the tap water of Chongqing City in China. In recent
years, Solanaceae plants grown in greenhouses in this city have displayed
symptoms of cupped, strappy leaves. These symptoms resembled those
caused by chlorinated auxinic herbicides. We have determined that
these symptoms were caused by the tap water used for irrigation. Using
a bioactivity-guided fractionation method, we isolated a substance
with corresponding auxinic activity from the tap water. The substance
was named “solanicide” because of its strong bioactivity
against Solanaceae plants. Further investigation revealed that the
solanicide in the water system of Chongqing City is derived from the
Jialing River, a major tributary of the Yangtze River. Therefore,
it is also present in the Yangtze River downstream of Chongqing after
the inflow of the Jialing River. Biological analyses indicated that
solanicide is functionally similar to, but distinct from, other known
chlorinated auxinic herbicides. Chemical assays further showed that
solanicide structurally differs from those compounds. This study has
highlighted a water pollution threat to the Yangtze River and its
floodplain ecosystem
Significant QTLs associated with leaf morphological traits in the RIL population.
<p>LTL: leaf total length (cm); LW: lamina width (cm); LL: lamina length (cm); PL: petiole length (cm); PN: lobe number; LL/LTL: the ratio of lamina width: leaf total length. a, peak SNP location of the QTL; b, an additive value >0 indicates that additive effects came from GH06, or came from P174 otherwise; c, thresholds values; d, QTL size (cM); e, phenotypic variation.</p
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