Combined linkage mapping and association analysis uncovers candidate genes for 25 leaf-related traits across three environments in maize.

KEY MESSAGE: Combined linkage and association analysis revealed five co-localized genetic loci across multiple environments. The key gene Zm00001d026491 was further verified to influence leaf length by candidate gene association analysis. Leaf morphology and number determine the canopy structure and thus affect crop yield. Herein, the genetic basis and key genes for 25 leaf-related traits, including leaf lengths (LL), leaf widths (LW), and leaf areas (LA) of eight continuous leaves under the tassel, and the number of leaves above the primary ear (LAE), were dissected by using an association panel and a biparental population. Using an intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, 290 quantitative trait loci (QTL) controlling these traits were detected across different locations, among which 115 QTL were individually repeatedly identified in at least two environments. Using the association panel, 165 unique significant single-nucleotide polymorphisms (SNPs) were associated with target traits (P < 2.15E-06), of which 35 were separately detected across multiple environments. In total, 42 pleiotropic QTL/SNPs (pQTL/SNPs) were responsible for at least two of the LL, LW, LA, and LAE traits across multiple environments. Combining the QTL mapping and association study, five unique SNPs were located within the confidence intervals of seven QTL, and 77 genes were identified based on the linkage disequilibrium regions of co-localized SNP loci. Gene-based association studies verified that the intragenic variants in the candidate gene Zm00001d026491 influenced LL of the third leaf counted from the top node. These findings will provide vital information to understanding the genetic basis of leaf-related traits and help to cultivate maize varieties with ideal plant architecture.

Association studies of genes in a Pb response-associated network in maize (Zea mays L.) reveal that ZmPIP2;5 is involved in Pb tolerance.

Lead (Pb) in the soil affects the growth and development of plants and causes damages to the human body through the food chain. Here, we identified and cloned a Pb-tolerance gene ZmPIP2;5 based on a weighted gene co-expression network analysis and gene-based association studies. We showed that ZmPIP2;5 encodes a plasma membrane aquaporin and positively regulated Pb tolerance and accumulation in Arabidopsis and yeast. Overexpression of ZmPIP2;5 increased root length and fresh weight of Arabidopsis seedlings under Pb stress. Heterologous expression of ZmPIP2;5 in yeast caused the enhanced growth speed under Pb treatment and Pb accumulation in yeast cells. A (T/A) SNP in the ZmPIP2;5 promoter affected the expression abundance of ZmPIP2;5 and thereby led to the difference in Pb tolerance among different maize lines. Our study helps to understand the mechanism underlying plant tolerance to Pb stress and provides new ideas for breeding Pb-tolerance maize varieties via molecular marker-assisted selection.

Quantification analysis of microplastics released from disposable polystyrene tableware with fluorescent polymer staining.

Ingesting microplastics (MPs) from plastic tableware is an important source of health risk to human bodies. However, the comprehensive information of MPs released from disposable tableware has not been explored. Herein, a new visual quantification method for polystyrene MPs is proposed with carbon nitride fluorescent polymers staining, which can overcome the disadvantages of high signal background and photobleaching derived from organic dyes staining. Combining with fluorescence microscope and ImageJ software, the quantity, shape, and size distribution of MPs carried by the brand-new disposable polystyrene tableware (DPT) samples before usage and released from the clean DPT samples in different simulated usage scenes were studied. The brand-new DPT samples were found to carry a large number of MPs particles and the clean DPT samples could release MPs during usage. Fiber and fragment are the main morphology of the detected MPs and fiber accounts for 45-52 %. The particles with size <50 µm are the majority of the detected MPs and the distribution fraction of MPs particles is gradually decreased with the raising of particle size within 50 µm. The released MPs particles are increased with the raising of contact time and temperature, and greatly boosted for the DPT samples with cracks. The DPT samples are more like to release MPs in weak acidic condition (pH 4.0) than in weak alkaline (pH 8.3) and neutral (pH 7.0) conditions. The obtained results help to assess the food safety of tack-out food and the health risk of MPs exposure to human.

Genome-Wide Association Study Reveals the Genetic Basis of Kernel and Cob Moisture Changes in Maize at Physiological Maturity Stage.

Low moisture content (MC) and high dehydration rate (DR) at physiological maturity affect grain mechanical harvest, transport, and storage. In this study, we used an association panel composed of 241 maize inbred lines to analyze ear moisture changes at physiological maturity stage. A genome-wide association study revealed nine significant SNPs and 91 candidate genes. One SNP (SYN38588) was repeatedly detected for two traits, and 15 candidate genes were scanned in the linkage disequilibrium regions of this SNP. Of these, genes Zm00001d020615 and Zm00001d020623 were individually annotated as a polygalacturonase (PG) and a copper transporter 5.1 (COPT5.1), respectively. Candidate gene association analysis showed that three SNPs located in the exons of Zm00001d020615 were significantly associated with the dehydration rate, and AATTAA was determined as the superior haplotype. All these findings suggested that Zm00001d020615 was a key gene affecting moisture changes of maize at the physiological maturity stage. These results have demonstrated the genetic basis of ear moisture changes in maize and indicated a superior haplotype for cultivating maize varieties with low moisture content and high dehydration rates.

Resonance-Based Time-Frequency Manifold for Feature Extraction of Ship-Radiated Noise.

In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method.