scholarly journals Noncoding-RNA-Mediated Regulation in Response to Macronutrient Stress in Plants

2021 ◽  
Vol 22 (20) ◽  
pp. 11205
Author(s):  
Ziwei Li ◽  
Peng Tian ◽  
Tengbo Huang ◽  
Jianzi Huang

Macronutrient elements including nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S) are required in relatively large and steady amounts for plant growth and development. Deficient or excessive supply of macronutrients from external environments may trigger a series of plant responses at phenotypic and molecular levels during the entire life cycle. Among the intertwined molecular networks underlying plant responses to macronutrient stress, noncoding RNAs (ncRNAs), mainly microRNAs (miRNAs) and long ncRNAs (lncRNAs), may serve as pivotal regulators for the coordination between nutrient supply and plant demand, while the responsive ncRNA-target module and the interactive mechanism vary among elements and species. Towards a comprehensive identification and functional characterization of nutrient-responsive ncRNAs and their downstream molecules, high-throughput sequencing has produced massive omics data for comparative expression profiling as a first step. In this review, we highlight the recent findings of ncRNA-mediated regulation in response to macronutrient stress, with special emphasis on the large-scale sequencing efforts for screening out candidate nutrient-responsive ncRNAs in plants, and discuss potential improvements in theoretical study to provide better guidance for crop breeding practices.

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i516-i524
Author(s):  
Midori Iida ◽  
Michio Iwata ◽  
Yoshihiro Yamanishi

Abstract Motivation Disease states are distinguished from each other in terms of differing clinical phenotypes, but characteristic molecular features are often common to various diseases. Similarities between diseases can be explained by characteristic gene expression patterns. However, most disease–disease relationships remain uncharacterized. Results In this study, we proposed a novel approach for network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets. We performed large-scale analyses of omics data and molecular interaction networks for 79 diseases, including adrenoleukodystrophy, leukaemia, Alzheimer's disease, asthma, atopic dermatitis, breast cancer, cystic fibrosis and inflammatory bowel disease. We quantified disease–disease similarities based on proximities of abnormally expressed genes in various molecular networks, and showed that similarities between diseases could be explained by characteristic molecular network topologies. Furthermore, we developed a kernel matrix regression algorithm to predict the commonalities of drugs and therapeutic targets among diseases. Our comprehensive prediction strategy indicated many new associations among phenotypically diverse diseases. Supplementary information Supplementary data are available at Bioinformatics online.


2019 ◽  
Vol 116 (18) ◽  
pp. 8960-8965 ◽  
Author(s):  
Michael Hicks ◽  
Istvan Bartha ◽  
Julia di Iulio ◽  
J. Craig Venter ◽  
Amalio Telenti

Sequence variation data of the human proteome can be used to analyze 3D protein structures to derive functional insights. We used genetic variant data from nearly 140,000 individuals to analyze 3D positional conservation in 4,715 proteins and 3,951 homology models using 860,292 missense and 465,886 synonymous variants. Sixty percent of protein structures harbor at least one intolerant 3D site as defined by significant depletion of observed over expected missense variation. Structural intolerance data correlated with deep mutational scanning functional readouts for PPARG, MAPK1/ERK2, UBE2I, SUMO1, PTEN, CALM1, CALM2, and TPK1 and with shallow mutagenesis data for 1,026 proteins. The 3D structural intolerance analysis revealed different features for ligand binding pockets and orthosteric and allosteric sites. Large-scale data on human genetic variation support a definition of functional 3D sites proteome-wide.


Author(s):  
Johan O. L. Andreasson ◽  
Michael R. Gotrik ◽  
Michelle J. Wu ◽  
Hannah K. Wayment-Steele ◽  
Wipapat Kladwang ◽  
...  

AbstractInternet-based scientific communities promise a means to apply distributed, diverse human intelligence towards previously intractable scientific problems. However, current implementations have not allowed communities to propose experiments to test all emerging hypotheses at scale or to modify hypotheses in response to experiments. We report high-throughput methods for molecular characterization of nucleic acids that enable the large-scale videogame-based crowdsourcing of functional RNA sensor design, followed by high-throughput functional characterization. Iterative design testing of thousands of crowdsourced RNA sensor designs produced near-thermodynamically optimal and reversible RNA switches that act as self-contained molecular sensors and couple five distinct small molecule inputs to three distinct protein binding and fluorogenic outputs—results that surpass computational and expert-based design. This work represents a new paradigm for widely distributed experimental bioscience.One Sentence SummaryOnline community discovers standalone RNA sensors.


Plants ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2239
Author(s):  
Zhen Liu ◽  
Zhenfei Dong ◽  
Binhui Zhan ◽  
Shifang Li

Apple (Malus domestica) fruits exhibiting bright stripe symptoms were identified in Weihai City, Shandong Province, China. To investigate the virome in the apple samples, the method of high throughput sequencing (HTS) was used to identify the viruses. It was found that the sequence of citrus concave gum-associated virus (CCGaV) was involved in the apple transcriptome dataset. The full-length genome of the CCGaV-Weihai isolate contained two segments, the RNA1 was 6674 nt in size containing a conserved RNA-dependent RNA polymerase (RdRp), and the RNA2 was ambisense, 2706 nt in length, encoding a movement protein (MP) and a coat protein (CP). Sequence alignment and phylogenetic analyses indicated that CCGaV-Weihai was more closely related to CCGaV-H2799 isolated from the apple host in the United States and distantly related to CCGaV-CGW2 from Citrus sinensis in Italy, indicating a possibly geographical and host differentiation of CCGaV isolates. This was the first identification and characterization of CCGaV infecting apples in China. Additionally, a rapid and sensitive reverse transcription recombinase polymerase amplification (RT-RPA) assay technique was established for CCGaV detection in apple plants. The RT-RPA of CCGaV was not affected by other common viruses in apple plants and is about 10-fold more sensitive than the conventional reverse transcription polymerase chain reaction method, which can be used in large-scale testing.


2019 ◽  
Author(s):  
Doreen Schultz ◽  
Daniela Zühlke ◽  
Jörg Bernhardt ◽  
Thomas Ben Francis ◽  
Dirk Albrecht ◽  
...  

SummaryThis study aimed to establish a robust, reproducible and reliable metaproteomic pipeline for an in-depth characterization of marine particle-associated (PA) bacteria. To this end, we compared six well-established protein extraction protocols together with different MS-sample preparation techniques using particles sampled during a North Sea spring algae bloom in 2009. In this optimized workflow, proteins are extracted using a combination of SDS-containing lysis buffer and cell disruption by bead-beating, separated by SDS-PAGE, in-gel digested and analysed by LC-MS/MS, before MASCOT search against a metagenome-based database and data processing/visualization with the in-house-developed bioinformatics tools Prophane and Paver.As proof of principle, free-living (FL) and particulate communities sampled in April 2009 were analysed, resulting in an as yet unprecedented number of 9,354 and 5,034 identified protein groups for FL and PA bacteria, respectively. Our data revealed that FL and PA communities appeared similar in their taxonomic distribution, with notable exceptions: eukaryotic proteins and proteins assigned to Flavobacteriia, Cyanobacteria, and some proteobacterial genera were found more abundant on particles, whilst overall proteins belonging to Proteobacteria were more dominant in the FL fraction. In contrast, significant functional differences including proteins involved in polysaccharide degradation, sugar- and phosphorus uptake, adhesion, motility, and stress response were detected.Originality-Significance StatementMarine particles consist of organic particulate matter (e.g. phyto- or zooplankton) and particle-associated (PA) microbial communities, which are often embedded in a sugary matrix. A significant fraction of the decaying algal biomass in marine ecosystems is expected to be mineralized by PA heterotrophic communities, which are thus greatly contributing to large-scale carbon fluxes. Whilst numerous studies have investigated the succession of planktonic marine bacteria along phytoplankton blooms, the community structure and functionality of PA bacterial communities remained largely unexplored and knowledge on specific contributions of these microorganisms to carbon cycling is still surprisingly limited. This has been mostly been due to technical problems, i.e. to the difficulty to retrieve genomic DNA and proteins from these polysaccharide-rich entities, their enormous complexity and the high abundance of eukaryotic microorganisms.Our study presents an innovative, robust, reproducible, and reliable metaproteomics pipeline for marine particles, which will help to address and fill the above-described knowledge gap. Employing the here established workflow enabled us to identify more than 5,000 PA proteins, which is, at least to our knowledge, the largest number of protein groups ever assigned to marine particles. Notably, the novel pipeline has been validated by a first, comparative metaproteome analysis of free-living and PA bacterial communities indicating a significant functional shift enabling surface-associated bacteria to adapt to particle-specific living conditions. In conclusion, our novel metaproteomics pipeline presents a solid and promising methodological groundwork for future culture-independent analyses of seasonal taxonomic and functional successions of PA microbial communities in aquatic habitats.


Biochemistry ◽  
2015 ◽  
Vol 54 (18) ◽  
pp. 2895-2902 ◽  
Author(s):  
Huaxia Luo ◽  
Yu Sun ◽  
Guifeng Wei ◽  
Jianjun Luo ◽  
Xinling Yang ◽  
...  

2006 ◽  
Vol 3 (11) ◽  
pp. 843-850 ◽  
Author(s):  
Thomas Manke ◽  
Lloyd Demetrius ◽  
Martin Vingron

The structure of molecular networks is believed to determine important aspects of their cellular function, such as the organismal resilience against random perturbations. Ultimately, however, cellular behaviour is determined by the dynamical processes, which are constrained by network topology. The present work is based on a fundamental relation from dynamical systems theory, which states that the macroscopic resilience of a steady state is correlated with the uncertainty in the underlying microscopic processes, a property that can be measured by entropy. Here, we use recent network data from large-scale protein interaction screens to characterize the diversity of possible pathways in terms of network entropy. This measure has its origin in statistical mechanics and amounts to a global characterization of both structural and dynamical resilience in terms of microscopic elements. We demonstrate how this approach can be used to rank network elements according to their contribution to network entropy and also investigate how this suggested ranking reflects on the functional data provided by gene knockouts and RNAi experiments in yeast and Caenorhabditis elegans . Our analysis shows that knockouts of proteins with large contribution to network entropy are preferentially lethal. This observation is robust with respect to several possible errors and biases in the experimental data. It underscores the significance of entropy as a fundamental invariant of the dynamical system, and as a measure of structural and dynamical properties of networks. Our analytical approach goes beyond the phenomenological studies of cellular robustness based on local network observables, such as connectivity. One of its principal achievements is to provide a rationale to study proxies of cellular resilience and rank proteins according to their importance within the global network context.


2014 ◽  
Vol 34 (6) ◽  
pp. 1249-1259 ◽  
Author(s):  
Robert D. Bell ◽  
Xiaochun Long ◽  
Mingyan Lin ◽  
Jan H. Bergmann ◽  
Vivek Nanda ◽  
...  

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