scholarly journals Proteomics (SWATH-MS) informed by transcriptomics approach of tropical herb Persicaria minor leaves upon methyl jasmonate elicitation

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5525 ◽  
Author(s):  
Wan Mohd Aizat ◽  
Sarah Ibrahim ◽  
Reyhaneh Rahnamaie-Tajadod ◽  
Kok-Keong Loke ◽  
Hoe-Han Goh ◽  
...  
Keyword(s):  
Protein Profiling ◽  
Data Sets ◽  
Proteomics Analysis ◽  
Meja Treatment ◽  
Transcriptome Database ◽  
Proteome Database ◽  
Post Transcriptional Regulation ◽  
Persicaria Minor ◽  
Herbal Species ◽  
Proteomics Informed By Transcriptomics

Background Jasmonic acid (JA) and its derivative, methyl JA (MeJA) are hormonal cues released by plants that signal defense response to curb damages from biotic and abiotic stresses. To study such response, a tropical herbal plant, Persicaria minor, which possesses pungent smell and various bioactivities including antimicrobial and anticancer, was treated with MeJA. Such elicitation has been performed in hairy root cultures and plants such as Arabidopsis and rice, yet how MeJA influenced the proteome of an herbal species like P. minor is unknown. Method In this study, P. minor plants were exogenously elicited with MeJA and leaf samples were subjected to SWATH-MS proteomics analysis. A previously published translated transcriptome database was used as a reference proteome database for a comprehensive protein sequence catalogue and to compare their differential expression. Results From this proteomics informed by transcriptomics approach, we have successfully profiled 751 proteins of which 40 proteins were significantly different between control and MeJA-treated samples. Furthermore, a correlation analysis between both proteome and the transcriptome data sets suggests that significantly upregulated proteins were positively correlated with their cognate transcripts (Pearson’s r = 0.677) while a weak correlation was observed for downregulated proteins (r = 0.147). Discussion MeJA treatment induced the upregulation of proteins involved in various biochemical pathways including stress response mechanism, lipid metabolism, secondary metabolite production, DNA degradation and cell wall degradation. Conversely, proteins involved in energy expensive reactions such as photosynthesis, protein synthesis and structure were significantly downregulated upon MeJA elicitation. Overall protein-transcript correlation was also weak (r = 0.341) suggesting the existence of post-transcriptional regulation during such stress. In conclusion, proteomics analysis using SWATH-MS analysis supplemented by the transcriptome database allows comprehensive protein profiling of this non-model herbal species upon MeJA treatment.

scholarly journals Serum Protein Profiling Reveals a Landscape of Inflammation and Immune Signaling in Early-stage COVID-19 Infection

2020 ◽  
Vol 19 (11) ◽  
pp. 1749-1759 ◽  
Author(s):  
Xin Hou ◽  
Xiaomei Zhang ◽  
Xian Wu ◽  
Minya Lu ◽  
Dan Wang ◽  
...  
Keyword(s):  
Serum Proteins ◽  
Early Stage ◽  
Protein Profiling ◽  
Cytokine Signaling ◽  
Large Set ◽  
Serum Samples ◽  
Antibody Microarray ◽  
Proteomics Analysis ◽  
Immune Signaling ◽  
Anti Inflammation

Coronavirus disease 2019 (COVID-19) is a highly contagious infection and threating the human lives in the world. The elevation of cytokines in blood is crucial to induce cytokine storm and immunosuppression in the transition of severity in COVID-19 patients. However, the comprehensive changes of serum proteins in COVID-19 patients throughout the SARS-CoV-2 infection is unknown. In this work, we developed a high-density antibody microarray and performed an in-depth proteomics analysis of serum samples collected from early COVID-19 (n = 15) and influenza (n = 13) patients. We identified a large set of differentially expressed proteins (n = 132) that participate in a landscape of inflammation and immune signaling related to the SARS-CoV-2 infection. Furthermore, the significant correlations of neutrophil and lymphocyte with the CCL2 and CXCL10 mediated cytokine signaling pathways was identified. These information are valuable for the understanding of COVID-19 pathogenesis, identification of biomarkers and development of the optimal anti-inflammation therapy.

scholarly journals iTRAQ-Based Proteomics Analysis of Autophagy-Mediated Responses against MeJA in Laticifers of Euphorbia kansui L.

2019 ◽  
Vol 20 (15) ◽  
pp. 3770
Author(s):  
Fang ◽  
Yao ◽  
Zhang ◽  
Tian ◽  
Wang ◽  
...  
Keyword(s):  
Developmental Process ◽  
Cysteine Proteinase ◽  
Defense Responses ◽  
Absolute Quantification ◽  
Pcr Analysis ◽  
Control Group ◽  
Proteomics Analysis ◽  
Meja Treatment ◽  
Euphorbia Kansui ◽  
Isobaric Tags

Autophagy is a well-defined catabolic mechanism whereby cytoplasmic materials are engulfed into a structure termed the autophagosome. Methyl jasmonate (MeJA), a plant hormone, mediates diverse developmental process and defense responses which induce a variety of metabolites. In plants, little is known about autophagy-mediated responses against MeJA. In this study, we used high-throughput comparative proteomics to identify proteins of latex in the laticifers. The isobaric tags for relative and absolute quantification (iTRAQ) MS/MS proteomics were performed, and 298 proteins among MeJA treated groups and the control group of Euphorbia kansui were identified. It is interesting to note that 29 significant differentially expressed proteins were identified and their associations with autophagy and ROS pathway were verified for several selected proteins as follows: α-L-fucosidase, β-galactosidase, cysteine proteinase, and Cu/Zn superoxide dismutase. Quantitative real-time PCR analysis of the selected genes confirmed the fact that MeJA might enhance the expression of some genes related to autophagy. The western blotting and immunofluorescence results of ATG8 and ATG18a which are two important proteins for the formation of autophagosomes also demonstrated that MeJA could promote autophagy at the protein level. Using the electron microscope, we observed an increase in autophagosomes after MeJA treatment. These results indicated that MeJA might promote autophagy in E. kansui laticifers; and it was speculated that MeJA mediated autophagy through two possible ways: the increase of ROS induces ATG8 accumulation and then aotophagosome formation, and MeJA promotes ATG18 accumulation and then autophagosome formation. Taken together, our results provide several novel insights for understanding the mechanism between autophagy and MeJA treatment. However, the specific mechanism remains to be further studied in the future.

scholarly journals De novo prediction of RNA-protein interactions with Graph Neural Networks

2021 ◽  
Author(s):  
Viplove Arora ◽  
Guido Sanguinetti
Keyword(s):  
Neural Networks ◽  
Protein Interactions ◽  
Large Scale ◽  
Rna Binding ◽  
De Novo ◽  
Rna Binding Proteins ◽  
Large Data ◽  
Data Sets ◽  
Graph Neural Networks ◽  
Post Transcriptional Regulation

RNA-binding proteins (RBPs) are key co- and post-transcriptional regulators of gene expression, playing a crucial role in many biological processes. Experimental methods like CLIP-seq have enabled the identification of transcriptome-wide RNA-protein interactions for select proteins, however the time and resource intensive nature of these technologies call for the development of computational methods to complement their predictions. Here we leverage recent, large-scale CLIP-seq experiments to construct a de novo predictor of RNA-protein interactions based on graph neural networks (GNN). We show that the GNN method allows not only to predict missing links in a RNA-protein network, but to predict the entire complement of targets of previously unassayed proteins, and even to reconstruct the entire network of RNA-protein interactions in different conditions based on minimal information. Our results demonstrate the potential of machine learning methods to extract useful information on post-transcriptional regulation from large data sets.

scholarly journals Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions

2020 ◽  
Author(s):  
Zichao Yan ◽  
William L. Hamilton ◽  
Mathieu Blanchette
Keyword(s):  
Protein Interactions ◽  
Graph Representation ◽  
Data Sets ◽  
Neural Network Approach ◽  
Rna Molecules ◽  
Significant Performance ◽  
Protein Interaction Prediction ◽  
Performance Gains ◽  
Post Transcriptional Regulation ◽  
Sequence Bias

AbstractMotivationRNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting the sequence and structural traits of RNA that impact the binding specificity for different proteins. Yet our ability to predict these interactions in silico remains relatively poor.ResultsIn this study, we introduce RPI-Net, a graph neural network approach for RNA-protein interaction prediction. RPI-Net learns and exploits a graph representation of RNA molecules, yielding significant performance gains over existing state-of-the-art approaches. We also introduce an approach to rectify particular type of sequence bias present in many CLIP-Seq data sets, and we show that correcting this bias is essential in order to learn meaningful predictors and properly evaluate their accuracy. Finally, we provide new approaches to interpret the trained models and extract simple, biologically-interpretable representations of the learned sequence and structural motifs.AvailabilitySource code can be accessed at https://www.github.com/HarveyYan/[email protected], [email protected]

scholarly journals Serum protein profiling reveals a landscape of inflammation and immune signaling in early-stage COVID-19 infection

2020 ◽  
Author(s):  
Xin Hou ◽  
Xiaomei Zhang ◽  
Xian Wu ◽  
Minya Lu ◽  
Dan Wang ◽  
...  
Keyword(s):  
Serum Proteins ◽  
Early Stage ◽  
Protein Profiling ◽  
Cytokine Signaling ◽  
Large Set ◽  
Serum Samples ◽  
Antibody Microarray ◽  
Proteomics Analysis ◽  
Immune Signaling ◽  
Anti Inflammation

Coronavirus disease 2019 (COVID-19) is a highly contagious infection and threating the human lives in the world. The elevation of cytokines in blood is crucial to induce cytokine storm and immunosuppression in the transition of severity in COVID-19 patients. However, the comprehensive changes of serum proteins in COVID-19 patients throughout the SARS-CoV-2 infection is unknown. In this work, we developed a high-density antibody microarray and performed an in-depth proteomics analysis of serum samples collected from early COVID-19 (n=15) and influenza (n=13) patients. We identified a large set of differentially expressed proteins (n=125) that participate in a landscape of inflammation and immune signaling related to the SARS-CoV-2 infection. Furthermore, the significant correlations of neutrophil and lymphocyte with the CCL2 and CXCL10 mediated cytokine signaling pathways was identified. These information are valuable for the understanding of COVID-19 pathogenesis, identification of biomarkers and development of the optimal anti-inflammation therapy.

scholarly journals A bioinformatics approach to defining the AU-Rich Element (AURE) Identification of novel motifs flanking AU-Rich Elements

SURG Journal ◽  
2009 ◽  
Vol 3 (1) ◽  
pp. 12-18
Author(s):  
Josh Blake ◽  
Timothy J. Gingerich ◽  
Jonathan LaMarre
Keyword(s):  
Transcriptional Regulation ◽  
Untranslated Region ◽  
Biological Significance ◽  
Regular Expressions ◽  
Gene Products ◽  
Transcriptome Database ◽  
Mrna Transcripts ◽  
Flanking Sequences ◽  
Temporal And Spatial ◽  
Post Transcriptional Regulation

Post-transcriptional regulation of genes is critical for the proper temporal and spatial expression of gene products. mRNA transcripts contain cis AU-rich elements (AUREs) in their 3’ untranslated region (UTR) that facilitate the binding of trans acting stability factors. AUREs however remain poorly characterized. Using regular expressions inherent in the PERL programming language, coupled to the Ensemble Human Transcriptome database, we confirm trends observed in already characterized 3’ UTRs as well as identify several significant and novel 3’ UTR flanking sequences that may have biological significance.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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