6-h advances alter circadian activity patterns,fasting glucose,and insulin levels in C57BL6/J mice

Chronobiological disruptions, including shift work, have been linked to a number of disorders such as fatigue and diabetes. Additionally, there is evidence to support that exercise cannot only counteract fatigue and the onset of diabetes, but also alleviate the other negative symptoms associated with shift work. Therefore, the present study investigated the effects of wheel running and monthly 6-h phase advances on the circadian locomotor activity patterns and glucose and insulin levels in C57BL6/J mice. 6-h phase advances produced decreases in fasting glucose and increases in insulin, and wheel-running was able to alleviate the spike in insulin secretion. Additionally, mice experiencing the shift increased their food intake, despite having no change in body mass. Circadian wheel-running activity was also altered in phase-advanced mice. These results provide further evidence that chronobiological disruptions can lead to alterations in physiology and behavior, and that exercise can alleviate some of those symptoms.     

Molecular characterization and targeted quantitative profiling of the sphingolipidome in rice

Recent advances in comprehensive metabolite profiling techniques, the foundation of metabolomics, is facilitating our understanding of the functions, regulation and complex networks of various metabolites in organisms. Here, we report a quantitative metabolomics technique for complex plant sphingolipids, composed of various polar head groups as well as structural isomers of hydrophobic ceramide moieties. Rice (Oryza sativa L.) was used as an experimental model of monocotyledonous plants and has been demonstrated to possess a highly complex sphingolipidome including hundreds of molecular species with a wide range of abundance. We established a high‐throughput scheme for lipid preparation and mass spectrometry‐based characterization of complex sphingolipid structures, which provided basic information to create a comprehensive theoretical library for targeted quantitative profiling of complex sphingolipids in rice. The established sphingolipidomic approach combined with multivariate analyses of the large dataset obtained clearly showed that different classes of rice sphingolipids, particularly including subclasses of glycosylinositol phosphoceramide with various sugar‐chain head groups, are distributed with distinct quantitative profiles in various rice tissues, indicating tissue‐dependent metabolism and biological functions of the lipid classes and subclasses. The sphingolipidomic analysis also highlighted that disruption of a lipid‐associated gene causes a typical sphingolipidomic change in a gene‐dependent manner. These results clearly support the utility of the sphingolipidomic approach in application to wide screening of sphingolipid‐metabolic phenotypes as well as deeper investigation of metabolism and biological functions of complex sphingolipid species in plants.     

Sensitive whole mount in situ localization of small RNAs in plants

Small RNAs, such as microRNAs (miRNAs), regulate gene expression and play important roles in many plant processes. Although our knowledge of their biogenesis and mode of action has significantly progressed, we still have comparatively little information about their biological functions. In particular, knowledge about their spatio‐temporal expression patterns rely on either indirect detection by use of reporter constructs or labor‐intensive direct detection by in situ hybridization on sectioned material. None of the current approaches allows a systematic investigation of small RNA expression patterns. Here, we present a sensitive method for in situ detection of miRNAs and siRNAs in intact plant tissues that utilizes both double‐labeled probes and a specific cross‐linker. We determined the expression patterns of several small RNAs in diverse plant tissues.     

Using the knowns to discover the unknowns: MS‐based dereplication uncovers structural diversity in 17‐hydroxygeranyllinalool diterpene glycoside production in the Solanaceae

Exploring the diversity of plant secondary metabolism requires efficient methods to obtain sufficient structural insights to discriminate previously known from unknown metabolites. De novo structure elucidation and confirmation of known metabolites (dereplication) remain a major bottleneck for mass spectrometry‐based metabolomic workflows, and few systematic dereplication strategies have been developed for the analysis of entire compound classes across plant families, partly due to the complexity of plant metabolic profiles that complicates cross‐species comparisons. 17‐hydroxygeranyllinalool diterpene glycosides (HGL‐DTGs) are abundant defensive secondary metabolites whose malonyl and glycosyl decorations are induced by jasmonate signaling in the ecological model plant Nicotiana attenuata. The multiple labile glycosidic bonds of HGL‐DTGs result in extensive in‐source fragmentation (IS‐CID) during ionization. To reconstruct these IS‐CID clusters from profiling data and identify precursor ions, we applied a deconvolution algorithm and created an MS/MS library from positive‐ion spectra of purified HGL‐DTGs. From this library, 251 non‐redundant fragments were annotated, and a workflow to characterize leaf, flower and fruit extracts of 35 solanaceous species was established. These analyses predicted 105 novel HGL‐DTGs that were restricted to Nicotiana, Capsicum and Lycium species. Interestingly, malonylation is a highly conserved step in HGL‐DTG metabolism, but is differentially affected by jasmonate signaling among Nicotiana species. This MS‐based workflow is readily applicable for cross‐species re‐identification/annotation of other compound classes with sufficient fragmentation knowledge, and therefore has the potential to support hypotheses regarding secondary metabolism diversification.     

Highly efficient de novo mutant identification in a Sorghum bicolor TILLING population using the ComSeq approach

Screening large populations for carriers of known or de novo rare single nucleotide polymorphisms (SNPs) is required both in Targeting induced local lesions in genomes (TILLING) experiments in plants and in screening of human populations. We previously suggested an approach that combines the mathematical field of compressed sensing with next‐generation sequencing to allow such large‐scale screening. Based on pooled measurements, this method identifies multiple carriers of heterozygous or homozygous rare alleles while using only a small fraction of resources. Its rigorous mathematical foundations allow scalable and robust detection, and provide error correction and resilience to experimental noise. Here we present a large‐scale experimental demonstration of our computational approach, in which we targeted a TILLING population of 1024 Sorghum bicolor lines to detect carriers of de novo SNPs whose frequency was less than 0.1%, using only 48 pools. Subsequent validation confirmed that all detected lines were indeed carriers of the predicted mutations. This novel approach provides a highly cost‐effective and robust tool for biologists and breeders to allow identification of novel alleles and subsequent functional analysis.     

The relation between input-output transformation and gastrointestinal nematode infections on dairy farms

Efficiency analysis is used for assessing links between technical efficiency (TE) of livestock farms and animal diseases. However, previous studies often do not make the link with the allocation of inputs and mainly present average effects that ignore the often huge differences among farms. In this paper, we studied the relationship between exposure to gastrointestinal (GI) nematode infections, the TE and the input allocation on dairy farms. Although the traditional cost allocative efficiency (CAE) indicator adequately measures how a given input allocation differs from the cost-minimising input allocation, they do not represent the unique input allocation of farms. Similar CAE scores may be obtained for farms with different input allocations. Therefore, we propose an adjusted allocative efficiency index (AAEI) to measure the unique input allocation of farms. Combining this AAEI with the TE score allows determining the unique input-output position of each farm. The method is illustrated by estimating efficiency scores using data envelopment analysis (DEA) on a sample of 152 dairy farms in Flanders for which both accountancy and parasitic monitoring data were available. Three groups of farms with a different input-output position can be distinguished based on cluster analysis: (1) technically inefficient farms, with a relatively low use of concentrates per 100 l milk and a high exposure to infection, (2) farms with an intermediate TE, relatively high use of concentrates per 100 l milk and a low exposure to infection, (3) farms with the highest TE, relatively low roughage use per 100 l milk and a relatively high exposure to infection. Correlation analysis indicates for each group how the level of exposure to GI nematodes is associated or not with improved economic performance. The results suggest that improving both the economic performance and exposure to infection seems only of interest for highly TE farms. The findings indicate that current farm recommendations regarding GI nematode infections could be improved by also accounting for the allocation of inputs on the farm.     

山水林田湖草生态保护修复试点工程布局及技术策略

习近平总书记提出的山水林田湖草生命共同体理念对我国新时代生态保护修复工作具有重要的指导意义,开展山水林田湖草整体保护、系统修复、综合治理是解决我国生态环境问题的重要途径,也是推进生态文明建设的必然要求。2016以来,财政部、原国土资源部、原环境保护部在全国24个省（自治区、直辖市）共安排25个山水林田湖草生态保护修复工程试点,对现有试点工程布局和技术策略进行全面剖析,能够为全面推进生态保护修复工作提供依据。在介绍山水林田湖草生态保护修复工程概念和主要任务的基础上,揭示试点工程布局特征,总结生态保护修复技术策略,同时探讨试点目前已经取得的效益、工程实施所存在的问题,并提出相关的建议。研究表明,25个试点大多分布于我国生态安全战略格局骨架区域,并且基本都属于国家重点生态功能区,对维护国家生态安全、提升区域生态功能具有重要意义;总结25个试点实施措施可以发现,工程技术策略主要包含了重要生态系统保护修复、生物多样性保护、流域水环境保护治理、污染与退化土地修复治理、矿山生态修复、土地综合整治等内容。试点工程的实施,开始取得生态、社会和经济等多重效益,但也存在工程宏观布局不尽完善、生态系统理念没能贯彻、自然恢复策略有待实施、监测评价管理缺乏科学、区域全面发展尚需提升等问题。未来应从完善国家宏观生态战略格局保障体系、加强区域生态系统状况和恢复力调查评价、优化工程项目实施规模和时序、研究和实践自然恢复标准与技术、开展长期跟踪监测与进行适应性管理等方面构建山水林田湖草生态保护修复工程技术和制度体系。     

Reporter‐based forward genetic screen to identify bundle sheath anatomy mutants in A. thaliana

The evolution of C₄ photosynthesis proceeded stepwise with each small step increasing the fitness of the plant. An important pre‐condition for the introduction of a functional C₄ cycle is the photosynthetic activation of the C₃ bundle sheath by increasing its volume and organelle number. Therefore, to engineer C₄ photosynthesis into existing C₃ crops, information about genes that control the bundle sheath cell size and organelle content is needed. However, very little information is known about the genes that could be manipulated to create a more C₄–like bundle sheath. To this end, an ethylmethanesulfonate (EMS)‐based forward genetic screen was established in the Brassicaceae C₃ species Arabidopsis thaliana. To ensure a high‐throughput primary screen, the bundle sheath cells of A. thaliana were labeled using a luciferase (LUC68) or by a chloroplast‐targeted green fluorescent protein (sGFP) reporter using a bundle sheath specific promoter. The signal strengths of the reporter genes were used as a proxy to search for mutants with altered bundle sheath anatomy. Here, we show that our genetic screen predominantly identified mutants that were primarily affected in the architecture of the vascular bundle, and led to an increase in bundle sheath volume. By using a mapping‐by‐sequencing approach the genomic segments that contained mutated candidate genes were identified.     

A dissection model for mapping complex traits

Many quantitative traits are composites of other traits that contribute differentially to genetic variation. Quantitative trait locus (QTL) mapping of these composite traits can benefit by incorporating the mechanistic process of how their formation is mediated by the underlying components. We propose a dissection model by which to map these interconnected components traits under a joint likelihood setting. The model can test how a composite trait is determined by pleiotropic QTLs for its component traits or jointly by different sets of QTLs each responsible for a different component. The model can visualize the pattern of time‐varying genetic effects for individual components and their impacts on composite traits. The dissection model was used to map two composite traits, stemwood volume growth decomposed into its stem height, stem diameter and stem form components for Euramerican poplar adult trees, and total lateral root length constituted by its average lateral root length and lateral root number components for Euphrates poplar seedlings. We found the pattern of how QTLs for different components contribute to phenotypic variation in composite traits. The detailed understanding of the genetic machineries of composite traits will not only help in the design of molecular breeding in plants and animals, but also shed light on the evolutionary processes of quantitative traits under natural selection.