scholarly journals Unraveling microRNA Mediated Gene Regulation in Centella asiatica (L.) Urb. by High-throughput Sequencing based Small-RNA Profiling.

2022 ◽  
Author(s):  
Gouri Priya Ranjith ◽  
Jisha Satheesan ◽  
K K Sabu
Keyword(s):  
Gene Regulation ◽  
High Throughput ◽  
Centella Asiatica ◽  
Plant Secondary Metabolite ◽  
Pcr Analysis ◽  
Shoot Cultures ◽  
Regulatory Pathways ◽  
Mrna Targets ◽  
Computational Screening ◽  
Wide Range

Abstract Centella asiatica is a widely spread herb mostly found in the tropics having extensive medicinal values. Here, we report for the first time, transcriptome-wide characterization of miRNA profile from the leaves of C. asiatica using high-throughput Illumina sequencing. We identified 227 conserved and 109 putative novel miRNAs. Computational screening revealed potential mRNA targets for both the conserved and novel miRNAs encoding diverse transcription factors and enzymes involved in plant development, disease resistance, metabolic and signaling pathways. Gene ontology annotation and KEGG analysis revealed the miRNA targets to be involved in a wide range of metabolomic and regulatory pathways. The differential expression of the miRNA encoding genes in diverse tissues was determined by real-time PCR analysis. We also found that gene expression levels of miR156, 159 and 1171 was reduced in salicylic acid treated axenic shoot cultures of C. asiatica compared to its control. Furthermore, RLM-RACE experiments mapped miRNA-mediated cleavage at two of the mRNA targets. The present study represents the large-scale identification of microRNAs from C. asiatica and contributes to the base for the up-coming studies on miRNA-mediated gene regulation of plant secondary metabolite pathways in particular.

scholarly journals Unraveling MicroRNA Mediated Gene Regulation in Centella Asiatica (L.) Urb. by High-Throughput Sequencing based Small-RNA Profiling.

2021 ◽  
Author(s):  
Gouri Priya Ranjith ◽  
Jisha Satheesan ◽  
K K Sabu
Keyword(s):  
Gene Regulation ◽  
High Throughput ◽  
Centella Asiatica ◽  
Plant Secondary Metabolite ◽  
Pcr Analysis ◽  
Shoot Cultures ◽  
Regulatory Pathways ◽  
Mrna Targets ◽  
Computational Screening ◽  
Wide Range

Abstract Centella asiatica is a widely spread herb mostly found in the tropics having extensive medicinal values. Here, we report for the first time, transcriptome-wide characterization of miRNA profile from the leaves of C. asiatica using high-throughput Illumina sequencing. We identified 227 conserved and 109 putative novel miRNAs. Computational screening revealed potential mRNA targets for both the conserved and novel miRNAs encoding diverse transcription factors and enzymes involved in plant development, disease resistance, metabolic and signaling pathways. Gene ontology annotation and KEGG analysis revealed the miRNA targets to be involved in a wide range of metabolomic and regulatory pathways. The differential expression of the miRNA encoding genes in diverse tissues was determined by real-time PCR analysis. We also found that gene expression levels of miR156, 159 and 1171 was reduced in salicylic acid treated axenic shoot cultures of C. asiatica compared to its control. Furthermore, RLM-RACE experiments mapped miRNA-mediated cleavage at two of the mRNA targets. The present study represents the large-scale identification of microRNAs from C. asiatica and contributes to the base for the up-coming studies on miRNA-mediated gene regulation of plant secondary metabolite pathways in particular.

SoftBV – a software tool for screening the materials genome of inorganic fast ion conductors

2019 ◽  
Vol 75 (1) ◽  
pp. 18-33 ◽  
Author(s):  
Haomin Chen ◽  
Lee Loong Wong ◽  
Stefan Adams
Keyword(s):  
Force Field ◽  
High Throughput ◽  
High Throughput Screening ◽  
Density Functional ◽  
Inorganic Compounds ◽  
Computational Cost ◽  
Lewis Base ◽  
Computational Screening ◽  
Wide Range ◽  
Dft Simulations

The identification of materials for advanced energy-storage systems is still mostly based on experimental trial and error. Increasingly, computational tools are sought to accelerate materials discovery by computational predictions. Here are introduced a set of computationally inexpensive software tools that exploit the bond-valence-based empirical force field previously developed by the authors to enable high-throughput computational screening of experimental or simulated crystal-structure models of battery materials predicting a variety of properties of technological relevance, including a structure plausibility check, surface energies, an inventory of equilibrium and interstitial sites, the topology of ion-migration paths in between those sites, the respective migration barriers and the site-specific attempt frequencies. All of these can be predicted from CIF files of structure models at a minute fraction of the computational cost of density functional theory (DFT) simulations, and with the added advantage that all the relevant pathway segments are analysed instead of arbitrarily predetermined paths. The capabilities and limitations of the approach are evaluated for a wide range of ion-conducting solids. An integrated simple kinetic Monte Carlo simulation provides rough (but less reliable) predictions of the absolute conductivity at a given temperature. The automated adaptation of the force field to the composition and charge distribution in the simulated material allows for a high transferability of the force field within a wide range of Lewis acid–Lewis base-type ionic inorganic compounds as necessary for high-throughput screening. While the transferability and precision will not reach the same levels as in DFT simulations, the fact that the computational cost is several orders of magnitude lower allows the application of the approach not only to pre-screen databases of simple structure prototypes but also to structure models of complex disordered or amorphous phases, and provides a path to expand the analysis to charge transfer across interfaces that would be difficult to cover by ab initio methods.

scholarly journals High-Throughput Screening of Metal–organic Frameworks for Macroscale Heteroepitaxial Alignment

2018 ◽  
Author(s):  
Andrew Tarzia ◽  
Masahide Takahashi ◽  
Paolo Falcaro ◽  
Aaron Thornton ◽  
Christian Doonan ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Defect Formation ◽  
Metal Organic Frameworks ◽  
Rectangular Lattice ◽  
Copper Hydroxide ◽  
Computational Screening ◽  
Screening Algorithm ◽  
Wide Range ◽  
Metal Organic

The ability to align porous metal–organic frameworks (MOFs) on substrate surfaces on a macroscopic scale is a vital step towards integrating MOFs into functional devices. But macroscale surface alignment of MOF crystals has only been demonstrated in a few cases. To accelerate the materials discovery process, we have developed a high-throughput computational screening algorithm to identify MOFs that are likely to undergo macroscale aligned heterepitaxial growth on a substrate. Screening of thousands of MOF structures by this process can be achieved in a few days on a desktop workstation. The algorithm filters MOFs based on surface chemical compatibility, lattice matching with the substrate, and interfacial bonding. Our method uses a simple new computationally efficient measure of the interfacial energy that considers both bond and defect formation at the interface. Furthermore, we show that this novel descriptor is a better predictor of aligned heteroepitaxial growth than other established interface descriptors, by testing our screening algorithm on a sample set of copper MOFs that have been grown heteroepitaxially on a copper hydroxide surface. Application of the screening process to several MOF databases reveals that the top candidates for aligned growth on copper hydroxide comprise mostly MOFs with rectangular lattice symmetry in the plane of the substrate. This result indicates a substrate-directing effect that could be exploited in targeted synthetic strategies. We also identify that MOFs likely to form aligned heterostructures have broad distributions of in-plane pore sizes and anisotropies. Accordingly, this suggests that aligned MOF thin films with a wide range of properties may be experimentally accessible.

scholarly journals High-Throughput Screening of Metal–organic Frameworks for Macroscale Heteroepitaxial Alignment

2018 ◽  
Author(s):  
Andrew Tarzia ◽  
Masahide Takahashi ◽  
Paolo Falcaro ◽  
Aaron Thornton ◽  
Christian Doonan ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Defect Formation ◽  
Metal Organic Frameworks ◽  
Rectangular Lattice ◽  
Copper Hydroxide ◽  
Computational Screening ◽  
Screening Algorithm ◽  
Wide Range ◽  
Metal Organic

The ability to align porous metal–organic frameworks (MOFs) on substrate surfaces on a macroscopic scale is a vital step towards integrating MOFs into functional devices. But macroscale surface alignment of MOF crystals has only been demonstrated in a few cases. To accelerate the materials discovery process, we have developed a high-throughput computational screening algorithm to identify MOFs that are likely to undergo macroscale aligned heterepitaxial growth on a substrate. Screening of thousands of MOF structures by this process can be achieved in a few days on a desktop workstation. The algorithm filters MOFs based on surface chemical compatibility, lattice matching with the substrate, and interfacial bonding. Our method uses a simple new computationally efficient measure of the interfacial energy that considers both bond and defect formation at the interface. Furthermore, we show that this novel descriptor is a better predictor of aligned heteroepitaxial growth than other established interface descriptors, by testing our screening algorithm on a sample set of copper MOFs that have been grown heteroepitaxially on a copper hydroxide surface. Application of the screening process to several MOF databases reveals that the top candidates for aligned growth on copper hydroxide comprise mostly MOFs with rectangular lattice symmetry in the plane of the substrate. This result indicates a substrate-directing effect that could be exploited in targeted synthetic strategies. We also identify that MOFs likely to form aligned heterostructures have broad distributions of in-plane pore sizes and anisotropies. Accordingly, this suggests that aligned MOF thin films with a wide range of properties may be experimentally accessible.

scholarly journals DNA Methylation Changes and Its Associated Genes in Mulberry (Morus alba L.) Yu-711 Response to Drought Stress Using MethylRAD Sequencing

Plants ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 190
Author(s):  
Michael Ackah ◽  
Liangliang Guo ◽  
Shaocong Li ◽  
Xin Jin ◽  
Charles Asakiya ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Gene Regulation ◽  
Drought Stress ◽  
Methylation Status ◽  
Enrichment Analysis ◽  
Plant Genome ◽  
Pcr Analysis ◽  
Kegg Pathways ◽  
Wide Range ◽  
And Control

Drought stress remains one of the most detrimental environmental cues affecting plant growth and survival. In this work, the DNA methylome changes in mulberry leaves under drought stress (EG) and control (CK) and their impact on gene regulation were investigated by MethylRAD sequencing. The results show 138,464 (37.37%) and 56,241 (28.81%) methylation at the CG and CWG sites (W = A or T), respectively, in the mulberry genome between drought stress and control. The distribution of the methylome was prevalent in the intergenic, exonic, intronic and downstream regions of the mulberry plant genome. In addition, we discovered 170 DMGs (129 in CG sites and 41 in CWG sites) and 581 DMS (413 in CG sites and 168 in CWG sites). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicates that phenylpropanoid biosynthesis, spliceosome, amino acid biosynthesis, carbon metabolism, RNA transport, plant hormone, signal transduction pathways, and quorum sensing play a crucial role in mulberry response to drought stress. Furthermore, the qRT-PCR analysis indicates that the selected 23 genes enriched in the KEGG pathways are differentially expressed, and 86.96% of the genes share downregulated methylation and 13.04% share upregulation methylation status, indicating the complex link between DNA methylation and gene regulation. This study serves as fundamentals in discovering the epigenomic status and the pathways that will significantly enhance mulberry breeding for adaptation to a wide range of environments.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Band Gap ◽  
Density Functional ◽  
2D Materials ◽  
Van Der Waals ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
Interlayer Distance ◽  
Computational Screening ◽  
Wide Range

Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.

scholarly journals Role of Transposable Elements in Gene Regulation in the Human Genome

Life ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 118
Author(s):  
Arsala Ali ◽  
Kyudong Han ◽  
Ping Liang
Keyword(s):  
Gene Regulation ◽  
Transposable Elements ◽  
Regulatory Sequences ◽  
Rna Sequences ◽  
Expression Of Genes ◽  
Wide Range ◽  
Lineage Specificity ◽  
Regulatory Rnas ◽  
Potential Factors ◽  
Interspersed Repeats

Transposable elements (TEs), also known as mobile elements (MEs), are interspersed repeats that constitute a major fraction of the genomes of higher organisms. As one of their important functional impacts on gene function and genome evolution, TEs participate in regulating the expression of genes nearby and even far away at transcriptional and post-transcriptional levels. There are two known principal ways by which TEs regulate the expression of genes. First, TEs provide cis-regulatory sequences in the genome with their intrinsic regulatory properties for their own expression, making them potential factors for regulating the expression of the host genes. TE-derived cis-regulatory sites are found in promoter and enhancer elements, providing binding sites for a wide range of trans-acting factors. Second, TEs encode for regulatory RNAs with their sequences showed to be present in a substantial fraction of miRNAs and long non-coding RNAs (lncRNAs), indicating the TE origin of these RNAs. Furthermore, TEs sequences were found to be critical for regulatory functions of these RNAs, including binding to the target mRNA. TEs thus provide crucial regulatory roles by being part of cis-regulatory and regulatory RNA sequences. Moreover, both TE-derived cis-regulatory sequences and TE-derived regulatory RNAs have been implicated in providing evolutionary novelty to gene regulation. These TE-derived regulatory mechanisms also tend to function in a tissue-specific fashion. In this review, we aim to comprehensively cover the studies regarding these two aspects of TE-mediated gene regulation, mainly focusing on the mechanisms, contribution of different types of TEs, differential roles among tissue types, and lineage-specificity, based on data mostly in humans.

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