scholarly journals α-Glucosidase I is required for cellulose biosynthesis and morphogenesis in Arabidopsis

2002 ◽  
Vol 156 (6) ◽  
pp. 1003-1013 ◽  
Author(s):  
C. Stewart Gillmor ◽  
Patricia Poindexter ◽  
Justin Lorieau ◽  
Monica M. Palcic ◽  
Chris Somerville
Keyword(s):  
Large Scale ◽  
Cellulose Synthase ◽  
Crystalline Cellulose ◽  
Small Subset ◽  
Enzyme Assays ◽  
Cellulose Content ◽  
Cellulose Biosynthesis ◽  
Catalytic Subunits ◽  
Cellular Processes ◽  
Protein Storage Vacuoles

Novel mutations in the RSW1 and KNOPF genes were identified in a large-scale screen for mutations that affect cell expansion in early Arabidopsis embryos. Embryos from both types of mutants were radially swollen with greatly reduced levels of crystalline cellulose, the principal structural component of the cell wall. Because RSW1 was previously shown to encode a catalytic subunit of cellulose synthase, the similar morphology of knf and rsw1-2 embryos suggests that the radially swollen phenotype of knf mutants is largely due to their cellulose deficiency. Map-based cloning of the KNF gene and enzyme assays of knf embryos demonstrated that KNF encodes α-glucosidase I, the enzyme that catalyzes the first step in N-linked glycan processing. The strongly reduced cellulose content of knf mutants indicates that N-linked glycans are required for cellulose biosynthesis. Because cellulose synthase catalytic subunits do not appear to be N glycosylated, the N-glycan requirement apparently resides in other component(s) of the cellulose synthase machinery. Remarkably, cellular processes other than extracellular matrix biosynthesis and the formation of protein storage vacuoles appear unaffected in knf embryos. Thus in Arabidopsis cells, like yeast, N-glycan trimming is apparently required for the function of only a small subset of N-glycoproteins.

scholarly journals Biochemical and physiological flexibility accompanies reduced cellulose biosynthesis in Brachypodium cesa1S830N

AoB Plants ◽  
2019 ◽  
Vol 11 (5) ◽  
Author(s):  
Chad Brabham ◽  
Abhishek Singh ◽  
Jozsef Stork ◽  
Ying Rong ◽  
Indrajit Kumar ◽  
...  
Keyword(s):  
Cell Wall ◽  
Molecular Dynamic ◽  
Missense Mutation ◽  
Primary Cell ◽  
Primary Cell Wall ◽  
Crystalline Cellulose ◽  
Molecular Dynamic Simulations ◽  
Cellulose Content ◽  
Cellulose Biosynthesis ◽  
Dynamic Simulations

Abstract Here, we present a study into the mechanisms of primary cell wall cellulose formation in grasses, using the model cereal grass Brachypodium distachyon. The exon found adjacent to the BdCESA1 glycosyltransferase QXXRW motif was targeted using Targeting Induced Local Lesions in Genomes (TILLING) and sequencing candidate amplicons in multiple parallel reactions (SCAMPRing) leading to the identification of the Bdcesa1S830N allele. Plants carrying this missense mutation exhibited a significant reduction in crystalline cellulose content in tissues that rely on the primary cell wall for biomechanical support. However, Bdcesa1S830N plants failed to exhibit the predicted reduction in plant height. In a mechanism unavailable to eudicotyledons, B. distachyon plants homozygous for the Bdcesa1S830N allele appear to overcome the loss of internode expansion anatomically by increasing the number of nodes along the stem. Stem biomechanics were resultantly compromised in Bdcesa1S830N. The Bdcesa1S830N missense mutation did not interfere with BdCESA1 gene expression. However, molecular dynamic simulations of the CELLULOSE SYNTHASE A (CESA) structure with modelled membrane interactions illustrated that Bdcesa1S830N exhibited structural changes in the translated gene product responsible for reduced cellulose biosynthesis. Molecular dynamic simulations showed that substituting S830N resulted in a stabilizing shift in the flexibility of the class specific region arm of the core catalytic domain of CESA, revealing the importance of this motion to protein function.

scholarly journals The TWD40-2 protein and the AP2 complex cooperate in the clathrin-mediated endocytosis of cellulose synthase to regulate cellulose biosynthesis

2015 ◽  
Vol 112 (41) ◽  
pp. 12870-12875 ◽  
Author(s):  
Logan Bashline ◽  
Shundai Li ◽  
Xiaoyu Zhu ◽  
Ying Gu
Keyword(s):  
Plasma Membrane ◽  
Genetic Analysis ◽  
Protein A ◽  
Adaptor Protein ◽  
Cellulose Synthase ◽  
Cellulose Content ◽  
Cellulose Biosynthesis ◽  
Cargo Proteins ◽  
Cellulose Synthases ◽  
Putative Member

Cellulose biosynthesis is performed exclusively by plasma membrane-localized cellulose synthases (CESAs). Therefore, the trafficking of CESAs to and from the plasma membrane is an important mechanism for regulating cellulose biosynthesis. CESAs were recently identified as cargo proteins of the classic adaptor protein 2 (AP2) complex of the clathrin-mediated endocytosis (CME) pathway. The AP2 complex of the CME pathway is conserved in yeast, animals, and plants, and has been well-characterized in many systems. In contrast, the recently discovered TPLATE complex (TPC), which is proposed to function as a CME adaptor complex, is only conserved in plants and a few other eukaryotes. In this study, we discovered that the TWD40-2 protein, a putative member of the TPC, is also important for the endocytosis of CESAs. Genetic analysis between TWD40-2 and AP2M of the AP2 complex revealed that the roles of TWD40-2 in CME are both distinct from and cooperative with the AP2 complex. Loss of efficient CME in twd40-2-3 resulted in the unregulated overaccumulation of CESAs at the plasma membrane. In seedlings of twd40-2-3 and other CME-deficient mutants, a direct correlation was revealed between endocytic deficiency and cellulose content deficiency, highlighting the importance of controlled CESA endocytosis in regulating cellulose biosynthesis.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

scholarly journals Rapid, large-scale species discovery in hyperdiverse taxa using 1D MinION sequencing

BMC Biology ◽  
2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Amrita Srivathsan ◽  
Emily Hartop ◽  
Jayanthi Puniamoorthy ◽  
Wan Ting Lee ◽  
Sujatha Narayanan Kutty ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Large Scale ◽  
Low Cost ◽  
Small Body ◽  
Small Subset ◽  
Similar Species ◽  
Large Species ◽  
Morphological Examination ◽  
Species Discovery ◽  
Short Period

Abstract Background More than 80% of all animal species remain unknown to science. Most of these species live in the tropics and belong to animal taxa that combine small body size with high specimen abundance and large species richness. For such clades, using morphology for species discovery is slow because large numbers of specimens must be sorted based on detailed microscopic investigations. Fortunately, species discovery could be greatly accelerated if DNA sequences could be used for sorting specimens to species. Morphological verification of such “molecular operational taxonomic units” (mOTUs) could then be based on dissection of a small subset of specimens. However, this approach requires cost-effective and low-tech DNA barcoding techniques because well-equipped, well-funded molecular laboratories are not readily available in many biodiverse countries. Results We here document how MinION sequencing can be used for large-scale species discovery in a specimen- and species-rich taxon like the hyperdiverse fly family Phoridae (Diptera). We sequenced 7059 specimens collected in a single Malaise trap in Kibale National Park, Uganda, over the short period of 8 weeks. We discovered > 650 species which exceeds the number of phorid species currently described for the entire Afrotropical region. The barcodes were obtained using an improved low-cost MinION pipeline that increased the barcoding capacity sevenfold from 500 to 3500 barcodes per flowcell. This was achieved by adopting 1D sequencing, resequencing weak amplicons on a used flowcell, and improving demultiplexing. Comparison with Illumina data revealed that the MinION barcodes were very accurate (99.99% accuracy, 0.46% Ns) and thus yielded very similar species units (match ratio 0.991). Morphological examination of 100 mOTUs also confirmed good congruence with morphology (93% of mOTUs; > 99% of specimens) and revealed that 90% of the putative species belong to the neglected, megadiverse genus Megaselia. We demonstrate for one Megaselia species how the molecular data can guide the description of a new species (Megaselia sepsioides sp. nov.). Conclusions We document that one field site in Africa can be home to an estimated 1000 species of phorids and speculate that the Afrotropical diversity could exceed 200,000 species. We furthermore conclude that low-cost MinION sequencers are very suitable for reliable, rapid, and large-scale species discovery in hyperdiverse taxa. MinION sequencing could quickly reveal the extent of the unknown diversity and is especially suitable for biodiverse countries with limited access to capital-intensive sequencing facilities.

scholarly journals Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

2021 ◽  
Author(s):  
Jin Zhou ◽  
Qing Zhang ◽  
Jian-Hao Fan ◽  
Wei Sun ◽  
Wei-Shi Zheng
Keyword(s):  
Large Scale ◽  
Assessment Model ◽  
Generic Model ◽  
Small Subset ◽  
Deep Convolutional Neural Networks ◽  
Personal Taste ◽  
Hinge Loss ◽  
Novel Approach ◽  
Large Scale Dataset ◽  
Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

scholarly journals Inhibition of cell expansion enhances cortical microtubule stability in the root apex of Arabidopsis thaliana

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Veronica Giourieva ◽  
Emmanuel Panteris
Keyword(s):  
Arabidopsis Thaliana ◽  
Cell Wall ◽  
Cell Expansion ◽  
Cortical Microtubule ◽  
Root Apex ◽  
Cellulose Synthase ◽  
Cellulose Biosynthesis ◽  
Cortical Microtubules ◽  
Wild Type ◽  
Microtubule Stability

Abstract Background Cortical microtubules regulate cell expansion by determining cellulose microfibril orientation in the root apex of Arabidopsis thaliana. While the regulation of cell wall properties by cortical microtubules is well studied, the data on the influence of cell wall to cortical microtubule organization and stability remain scarce. Studies on cellulose biosynthesis mutants revealed that cortical microtubules depend on Cellulose Synthase A (CESA) function and/or cell expansion. Furthermore, it has been reported that cortical microtubules in cellulose-deficient mutants are hypersensitive to oryzalin. In this work, the persistence of cortical microtubules against anti-microtubule treatment was thoroughly studied in the roots of several cesa mutants, namely thanatos, mre1, any1, prc1-1 and rsw1, and the Cellulose Synthase Interacting 1 protein (csi1) mutant pom2-4. In addition, various treatments with drugs affecting cell expansion were performed on wild-type roots. Whole mount tubulin immunolabeling was applied in the above roots and observations were performed by confocal microscopy. Results Cortical microtubules in all mutants showed statistically significant increased persistence against anti-microtubule drugs, compared to those of the wild-type. Furthermore, to examine if the enhanced stability of cortical microtubules was due to reduced cellulose biosynthesis or to suppression of cell expansion, treatments of wild-type roots with 2,6-dichlorobenzonitrile (DCB) and Congo red were performed. After these treatments, cortical microtubules appeared more resistant to oryzalin, than in the control. Conclusions According to these findings, it may be concluded that inhibition of cell expansion, irrespective of the cause, results in increased microtubule stability in A. thaliana root. In addition, cell expansion does not only rely on cortical microtubule orientation but also plays a regulatory role in microtubule dynamics, as well. Various hypotheses may explain the increased cortical microtubule stability under decreased cell expansion such as the role of cell wall sensors and the presence of less dynamic cortical microtubules.

scholarly journals Two β-glucuronosyltransferases involved in the biosynthesis of type II arabinogalactans function in mucilage polysaccharide matrix organization in Arabidopsis thaliana

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Oyeyemi O. Ajayi ◽  
Michael A. Held ◽  
Allan M. Showalter
Keyword(s):  
Arabidopsis Thaliana ◽  
Seed Size ◽  
Seed Coat ◽  
Glucuronic Acid ◽  
Industrial Applications ◽  
Crystalline Cellulose ◽  
Sugar Residue ◽  
Composition Analysis ◽  
Cellulose Content ◽  
Type Ii

Abstract Background Arabinogalactan-proteins (AGPs) are heavily glycosylated with type II arabinogalactan (AG) polysaccharides attached to hydroxyproline residues in their protein backbone. Type II AGs are necessary for plant growth and critically important for the establishment of normal cellular functions. Despite the importance of type II AGs in plant development, our understanding of the underlying role of these glycans/sugar residues in mucilage formation and seed coat epidermal cell development is poorly understood and far from complete. One such sugar residue is the glucuronic acid residues of AGPs that are transferred onto AGP glycans by the action of β-glucuronosyltransferase genes/enzymes. Results Here, we have characterized two β-glucuronosyltransferase genes, GLCAT14A and GLCAT14C, that are involved in the transfer of β-glucuronic acid (GlcA) to type II AGs. Using a reverse genetics approach, we observed that glcat14a-1 mutants displayed subtle alterations in mucilage pectin homogalacturonan (HG) compared to wild type (WT), while glcat14a-1glcat14c-1 mutants displayed much more severe mucilage phenotypes, including loss of adherent mucilage and significant alterations in cellulose ray formation and seed coat morphology. Monosaccharide composition analysis showed significant alterations in the sugar amounts of glcat14a-1glcat14c-1 mutants relative to WT in the adherent and non-adherent seed mucilage. Also, a reduction in total mucilage content was observed in glcat14a-1glcat14c-1 mutants relative to WT. In addition, glcat14a-1glcat14c-1 mutants showed defects in pectin formation, calcium content and the degree of pectin methyl-esterification (DM) as well as reductions in crystalline cellulose content and seed size. Conclusions These results raise important questions regarding cell wall polymer interactions and organization during mucilage formation. We propose that the enzymatic activities of GLCAT14A and GLCAT14C play partially redundant roles and are required for the organization of the mucilage matrix and seed size in Arabidopsis thaliana. This work brings us a step closer towards identifying potential gene targets for engineering plant cell walls for industrial applications.

scholarly journals A Potential of microRNAs for High-Content Screening

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Andrius Serva ◽  
Christoph Claas ◽  
Vytaute Starkuviene
Keyword(s):  
Large Scale ◽  
Cellular Processes ◽  
Comprehensive Knowledge ◽  
Functional Models ◽  
Rapid Accumulation ◽  
Health And Disease ◽  
Temporal And Spatial ◽  
And Function ◽  
Functional Analyses ◽  
Immense Potential

In the last years miRNAs have increasingly been recognised as potent posttranscriptional regulators of gene expression. Possibly, miRNAs exert their action on virtually any biological process by simultaneous regulation of numerous genes. The importance of miRNA-based regulation in health and disease has inspired research to investigate diverse aspects of miRNA origin, biogenesis, and function. Despite the recent rapid accumulation of experimental data, and the emergence of functional models, the complexity of miRNA-based regulation is still far from being well understood. In particular, we lack comprehensive knowledge as to which cellular processes are regulated by which miRNAs, and, furthermore, how temporal and spatial interactions of miRNAs to their targets occur. Results from large-scale functional analyses have immense potential to address these questions. In this review, we discuss the latest progress in application of high-content and high-throughput functional analysis for the systematic elucidation of the biological roles of miRNAs.

A Re-Examination of Research on the Language of Black Children: The Need for a New Framework

1982 ◽  
Vol 164 (2) ◽  
pp. 157-172 ◽  
Author(s):  
Ida J. Stockman ◽  
Fay Boyd Vaughn-Cooke
Keyword(s):  
Working Class ◽  
Large Scale ◽  
Black Children ◽  
Cross Sectional Study ◽  
Linguistic Knowledge ◽  
Small Subset ◽  
Review Of The Literature ◽  
Sectional Study ◽  
Cross Sectional ◽  
New Framework

A review of the literature on the language of working-class Black children revealed that only a small subset of this research has focused on the acquisition and development of linguistic knowledge. Consequently major gaps exist in what is known about the linguistic abilities of these children. In an attempt to narrow these gaps, a team of researchers has initiated a large scale longitudinal and cross-sectional study of the acquisition of language by working-class Black children at the Center for Applied Linguistics in Washington, D. C. This paper will present a detailed description of the Center study. It will also critically evaluate existing research on the linguistic abilities of working-class Black children and its impact on language acquisition studies focusing on this population. The evaluation reveals that a new framework for analyzing the language of working-class Black children should be selected. The theory and methodology of the new framework are described.

scholarly journals Exemplar Guided Neural Dialogue Generation

2020 ◽  
Author(s):  
Hengyi Cai ◽  
Hongshen Chen ◽  
Yonghao Song ◽  
Xiaofang Zhao ◽  
Dawei Yin
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Training Data ◽  
Small Subset ◽  
Generation Model ◽  
Retrieval Model ◽  
Training Set ◽  
Dialogue Model ◽  
Quantitative Metrics ◽  
The Given

Humans benefit from previous experiences when taking actions. Similarly, related examples from the training data also provide exemplary information for neural dialogue models when responding to a given input message. However, effectively fusing such exemplary information into dialogue generation is non-trivial: useful exemplars are required to be not only literally-similar, but also topic-related with the given context. Noisy exemplars impair the neural dialogue models understanding the conversation topics and even corrupt the response generation. To address the issues, we propose an exemplar guided neural dialogue generation model where exemplar responses are retrieved in terms of both the text similarity and the topic proximity through a two-stage exemplar retrieval model. In the first stage, a small subset of conversations is retrieved from a training set given a dialogue context. These candidate exemplars are then finely ranked regarding the topical proximity to choose the best-matched exemplar response. To further induce the neural dialogue generation model consulting the exemplar response and the conversation topics more faithfully, we introduce a multi-source sampling mechanism to provide the dialogue model with both local exemplary semantics and global topical guidance during decoding. Empirical evaluations on a large-scale conversation dataset show that the proposed approach significantly outperforms the state-of-the-art in terms of both the quantitative metrics and human evaluations.

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