Cyclometalated Platinum(II) Complexes of 1,3-Bis(1‑<i>n</i>‑butylpyrazol-3-yl)benzenes: Synthesis, Characterization, Electrochemical, Photophysical, and Gelation Behavior Studies

A new series of cyclometalated platinum­(II) complexes of N^C^N ligands, where N^C^N = 1,3-bis­(1-<i>n</i>-alkylpyrazol-3-yl)­benzene (bpzb), namely, [Pt­(bpzb)­Cl] (<b>1</b> and <b>2</b>) and [Pt­(bpzb)­(CC–R)] (<b>3</b>–<b>10</b>) (R = C₆H₅, C₆H₄–OCH₃-<i>p</i>, C₆H₄–NO₂-<i>p</i>, C₆H₄–NH₂-<i>p</i>, 4-cholesteryl phenyl carbamate, and cholesteryl methylcarbamate) were synthesized and characterized. Their electrochemical and photophysical properties were investigated. Two of the platinum­(II) complexes were also structurally characterized by X-ray crystallography, and short intermolecular C–H···Pt contacts were observed. Vibronic-structured emission bands originating from triplet IL (³IL) excited states of the bpzb ligands with mixing of some ³MLCT [dπ­(Pt)→π*­(bpzb)] character were observed in solution state. Interestingly, complex <b>5</b> shows a low-energy emission that is derived from the involvement of the <i>p</i>-nitrophenylethynyl ligand. Complex <b>9</b> with hydrophobic cholesteryl 4-ethynylphenyl carbamate ligand was found to form stable metallogels in several organic solvents, which are responsive to mechanical sonication and thermal stimuli and show circular dichroism activity

Mapping Isoflavone QTL with Main, Epistatic and QTL × Environment Effects in Recombinant Inbred Lines of Soybean

<div><p>Soybean (<i>Glycine max</i> (L.) Merr.) isoflavone is important for human health and plant defense system. To identify novel quantitative trait loci (QTL) and epistatic QTL underlying isoflavone content in soybean, F_5:6, F_5:7 and F_5:8 populations of 130 recombinant inbred (RI) lines, derived from the cross of soybean cultivar ‘Zhong Dou 27′ (high isoflavone) and ‘Jiu Nong 20′ (low isoflavone), were analyzed with 95 new SSR markers. A new linkage map including 194 SSR markers and covering 2,312 cM with mean distance of about 12 cM between markers was constructed. Thirty four QTL for both individual and total seed isoflavone contents of soybean were identified. Six, seven, ten and eleven QTL were associated with daidzein (DZ), glycitein (GC), genistein (GT) and total isoflavone (TI), respectively. Of them 23 QTL were newly identified. The qTIF_1 between Satt423 and Satt569 shared the same marker Satt569 with qDZF_2, qGTF_1 and qTIF_2. The qGTD2_1 between Satt186 and Satt226 was detected in four environments and explained 3.41%-10.98% of the phenotypic variation. The qGTA2_1, overlapped with qGCA2_1 and detected in four environments, was close to the previously identified major QTL for GT, which were responsible for large <i>a</i> effects. QTL (qDZF_2, qGTF_1 and qTIF_2) between Satt144-Satt569 were either clustered or pleiotropic. The qGCM_1, qGTM_1 and qTIM_1 between Satt540-Sat_244 explained 2.02%–9.12% of the phenotypic variation over six environments. Moreover, the qGCE_1 overlapped with qGTE_1 and qTIE_1, the qTIH_2 overlapped with qGTH_1, qGCI_1 overlapped with qDZI_1, qTIL_1 overlapped with qGTL_1, and qTIO_1 overlapped with qGTO_1. In this study, some of unstable QTL were detected in different environments, which were due to weak expression of QTL, QTL by environment interaction in the opposite direction to <i>a</i> effects, and/or epistasis. The markers identified in multi-environments in this study could be applied in the selection of soybean cultivars for higher isoflavone content and in the map-based gene cloning.</p></div

GT biplot analysis for the relatedness of QTL and environment.

<p>PC1: first principle component, PC2: second principle component.</p

Summary of QTL locations detected in the soybean genome.

<p>QTL represented by bars were shown on the left of the linkage groups, close to their corresponding markers. The lengths of the bars were proportional to the confidence intervals of the corresponding QTL in which the inner line indicates the position of maximum LOD score.</p

QTLs for individual and total isoflavone content.

<p>^a DZ: Daidzein; GC:Glycitein; GT: Genistein; TI: Total isoflavone</p><p>^b The nomenclature of the QTL included four parts: QTL, trait, linkage group name and QTL order in the linkage group, respectively</p><p>^c Position from the left marker of the interval on each linkage group</p><p>^d Proportion of phenotypic variance (R²) explained by a QTL</p><p>^e Additional QTL for individual and total isoflavone content</p><p>^f QTL in accordance with Zeng et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118447#pone.0118447.ref030" target="_blank">30</a>]</p><p>^g E1: at Harbin in 2005, E2: at Harbin in 2006, E3: at Hulan in 2006, E4:at Suihua in 2006, E5: at Harbin in 2007, E6: at Hulan in 2007, E7: at Suihua in 2007</p><p>QTLs for individual and total isoflavone content.</p

Identification of MicroRNAs in Response to Different Day Lengths in Soybean Using High-Throughput Sequencing and qRT-PCR

<div><p>MicroRNAs (miRNAs) are short, non-coding single-strand RNA molecules that play important roles in plant growth, development and stress responses. Flowering time affects the seed yield and quality of soybean. However, the miRNAs involved in the regulation of flowering time in soybean have not been reported until recently. Here, high-throughput sequencing and qRT-PCR were used to identify miRNAs involved in soybean photoperiodic pathways. The first trifoliate leaves of soybean that receive the signal of light treatment were used to construct six libraries (0, 8, and 16 h under short-day (SD) treatment and 0, 8, and 16 h under long-day (LD) treatment). The libraries were sequenced using Illumina Solexa. A total of 318 known plant miRNAs belonging to 163 miRNA families and 81 novel predicted miRNAs were identified. Among these, 23 miRNAs at 0 h, 65 miRNAs at 8 h and 83 miRNAs at 16 h, including six novel predicted miRNAs at 8 h and six novel predicted miRNAs at 16 h, showed differences in abundance between LD and SD treatments. Furthermore, the results of GO and KEGG analyses indicated that most of the miRNA targets were transcription factors. Seven miRNAs at 0 h, 23 miRNAs (including four novel predicted miRNAs) at 8 h, 16 miRNAs (including one novel predicted miRNA) at 16 h and miRNA targets were selected for qRT-PCR analysis to assess the accuracy of the sequencing and target prediction. The results indicated that the expression patterns of the selected miRNAs and miRNA targets showed no differences between the qRT-PCR and sequencing results. In addition, 23 miRNAs at 0 h, 65 miRNAs at 8 h and 83 miRNAs at 16 h responded to day length changes in soybean, including six novel predicted miRNAs at 8 h and six novel predicted miRNAs at 16 h. These results provided an important molecular basis to understand the regulation of flowering time through photoperiodic pathways in soybean.</p></div

Size distribution of the clean tags after preliminary analysis of the sequencing mapped to the database named miRBase.

<p>(A) The size distribution of the clean tags. The clean tags could be mapped to the soybean genome, but were not observed among known noncoding RNAs through BLAST against the Rfame (a database of noncoding RNAs). These results were compared with the miRBase (a database of known miRNAs). (B) The size distribution of the tags of the six libraries mapped to the miRBase.</p

qRT-PCR validation of some miRNAs and their targets identified by Solexa sequencing.

<p>qRT-PCR reactions were used to verify the sequencing and the target predictions. (A) The result of the qRT-PCR validation of some miRNAs were identified through sequencing. The expression was represented as the ratios of the expression under SD treatment to that under LD treatment, and the 5SrRNA was used as a control. (B) The results of the qRT-PCR validation of the target predictions for miR159e-3p. The expression was presented as the ratio of expression under SD and LD treatment, and the 18SrRNA acted as a beta-actin (C) The result of the qRT-PCR validation of the target predictions for miR156a. (D) The result of qRT-PCR validation of the target predictions for miR160. (E) The results of qRT-PCR validation of the target predictions for miR395 and miR408. (F) The result of qRT-PCR validation of some miRNAs identified by sequencing.</p

The novel soybean miRNAs verified using qRT-PCR.

<p>The novel soybean miRNAs verified using qRT-PCR.</p

Differential expression analysis of soybean miRNAs identified using Solexa sequencing.

<p>The software IDE6 was used to analyze the known and novel predicted miRNAs obtained through high-throughput sequencing. Panels <b>a</b> to <b>f</b> show LD to SD abundance differences for time points A to C.</p