Bioinformatics-assisted, integrated omics studies on medicinal plants

2019 ◽  
Vol 21 (6) ◽  
pp. 1857-1874
Author(s):  
Xiaoxia Ma ◽  
Yijun Meng ◽  
Pu Wang ◽  
Zhonghai Tang ◽  
Huizhong Wang ◽  
...  
Keyword(s):  
Medicinal Plants ◽  
Medicinal Plant ◽  
Protein Interactions ◽  
Metabolic Pathways ◽  
Regulatory Networks ◽  
Omics Data ◽  
Protein Protein Interactions ◽  
Transcriptional Regulatory Networks ◽  
Plant Materials ◽  
Integrated Omics

Abstract The immense therapeutic and economic values of medicinal plants have attracted increasing attention from the worldwide researchers. It has been recognized that production of the authentic and high-quality herbal drugs became the prerequisite for maintaining the healthy development of the traditional medicine industry. To this end, intensive research efforts have been devoted to the basic studies, in order to pave a way for standardized authentication of the plant materials, and bioengineering of the metabolic pathways in the medicinal plants. In this paper, the recent advances of omics studies on the medicinal plants were summarized from several aspects, including phenomics and taxonomics, genomics, transcriptomics, proteomics and metabolomics. We proposed a multi-omics data-based workflow for medicinal plant research. It was emphasized that integration of the omics data was important for plant authentication and mechanistic studies on plant metabolism. Additionally, the computational tools for proper storage, efficient processing and high-throughput analyses of the omics data have been introduced into the workflow. According to the workflow, authentication of the medicinal plant materials should not only be performed at the phenomics level but also be implemented by genomic and metabolomic marker-based examination. On the other hand, functional genomics studies, transcriptional regulatory networks and protein–protein interactions will contribute greatly for deciphering the secondary metabolic pathways. Finally, we hope that our work could inspire further efforts on the bioinformatics-assisted, integrated omics studies on the medicinal plants.

Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

2019 ◽  
Vol 19 (6) ◽  
pp. 413-425 ◽  
Author(s):  
Athanasios Alexiou ◽  
Stylianos Chatzichronis ◽  
Asma Perveen ◽  
Abdul Hafeez ◽  
Ghulam Md. Ashraf
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Review Paper ◽  
Boolean Networks ◽  
Diagnostic Tools ◽  
Complex Nature ◽  
Cellular Interactions ◽  
Protein Protein Interactions ◽  
Deterministic Models

Background:Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.Objective:Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.Methods:Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.Results:GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.Conclusion:In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

scholarly journals RNA-Centric Approaches to Profile the RNA–Protein Interaction Landscape on Selected RNAs

2021 ◽  
Vol 7 (1) ◽  
pp. 11 ◽  
Author(s):  
André P. Gerber
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Regulatory Networks ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Protein Complexes ◽  
Cell Protein ◽  
Transcriptional Regulatory Networks ◽  
Technological Advances

RNA–protein interactions frame post-transcriptional regulatory networks and modulate transcription and epigenetics. While the technological advances in RNA sequencing have significantly expanded the repertoire of RNAs, recently developed biochemical approaches combined with sensitive mass-spectrometry have revealed hundreds of previously unrecognized and potentially novel RNA-binding proteins. Nevertheless, a major challenge remains to understand how the thousands of RNA molecules and their interacting proteins assemble and control the fate of each individual RNA in a cell. Here, I review recent methodological advances to approach this problem through systematic identification of proteins that interact with particular RNAs in living cells. Thereby, a specific focus is given to in vivo approaches that involve crosslinking of RNA–protein interactions through ultraviolet irradiation or treatment of cells with chemicals, followed by capture of the RNA under study with antisense-oligonucleotides and identification of bound proteins with mass-spectrometry. Several recent studies defining interactomes of long non-coding RNAs, viral RNAs, as well as mRNAs are highlighted, and short reference is given to recent in-cell protein labeling techniques. These recent experimental improvements could open the door for broader applications and to study the remodeling of RNA–protein complexes upon different environmental cues and in disease.

In Silico Recognition of Protein-Protein Interaction

2011 ◽  
pp. 248-268
Author(s):  
Byung-Hoon Park ◽  
Phuongan Dam ◽  
Chongle Pan ◽  
Ying Xu ◽  
Al Geist ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Regulatory Networks ◽  
Machine Learning Techniques ◽  
Translation Regulation ◽  
Future Research ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Protein Levels ◽  
Protein Functions ◽  
Model Protein

Protein-protein interactions are fundamental to cellular processes. They are responsible for phenomena like DNA replication, gene transcription, protein translation, regulation of metabolic pathways, immunologic recognition, signal transduction, etc. The identification of interacting proteins is therefore an important prerequisite step in understanding their physiological functions. Due to the invaluable importance to various biophysical activities, reliable computational methods to infer protein-protein interactions from either structural or genome sequences are in heavy demand lately. Successful predictions, for instance, will facilitate a drug design process and the reconstruction of metabolic or regulatory networks. In this chapter, we review: (a) high-throughput experimental methods for identification of protein-protein interactions, (b) existing databases of protein-protein interactions, (c) computational approaches to predicting protein-protein interactions at both residue and protein levels, (d) various statistical and machine learning techniques to model protein-protein interactions, and (e) applications of protein-protein interactions in predicting protein functions. We also discuss intrinsic drawbacks of the existing approaches and future research directions.

scholarly journals Signaling Protein Networks as Targets of New Antineoplastic Drugs

2003 ◽  
Vol 18 (1) ◽  
pp. 57-61 ◽  
Author(s):  
S. Alberti ◽  
S. Parodi
Keyword(s):  
Protein Interactions ◽  
High Throughput Screening ◽  
Regulatory Networks ◽  
Signaling Proteins ◽  
Protein Protein Interactions ◽  
Therapeutic Approaches ◽  
Chemical Libraries ◽  
Drug Candidates ◽  
Microbial Extracts ◽  
Depth Analysis

In-depth analysis of molecular regulatory networks in cancer holds the promise of improved knowledge of the pathophysiology of tumor cells so that it will become possible to design a detailed molecular tumor taxonomy. This knowledge will also offer new opportunities for the identification and validation of key molecular tumor targets to be exploited for novel therapeutic approaches. Some signaling proteins have already been identified as such, e.g. c-Myc, Cyclin D1, Bcl-XL, kinases and some nuclear receptors. This has led to the successful development of a few function-modulatory drugs (Glivec, SERM, Iressa), providing proof-of-principle of the validity of this approach. Further developments are likely to derive from “-omic” approaches, aimed at the understanding of signaling networks and of the mechanism of action of newfound lead molecules. High-throughput screening of small drug-like molecules from combinatorial chemical libraries or from microbial extracts will identify novel, “intelligent” drug candidates. An additional medicinal chemistry strategy (via 40–50 unit rosary-bead chains) has the potential to be much more effective than small molecules in interfering with protein-protein interactions. This may lead to considerably higher selectivity and effectiveness compared with historical approaches in drug discovery.

scholarly journals Integrative analysis identified common and unique molecular signatures in hepatobiliary cancers

2021 ◽  
Author(s):  
Nabanita Roy ◽  
Ria Lodh ◽  
Anupam Sarma ◽  
Dhruba Kumar Bhattacharyya ◽  
Pankaj Barah
Keyword(s):  
Protein Interactions ◽  
Metabolic Pathways ◽  
Expression Patterns ◽  
Molecular Signature ◽  
Cancer Type ◽  
Molecular Signatures ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Cellular Processes ◽  
Differential Gene

Hepatobiliary cancers (HBCs) are the most aggressive and sixth most diagnosed cancers globally. Biomarkers for timely diagnosis and targeted therapy in HBCs are still limited. Considering the gap, our objective is to identify unique and overlapping molecular signatures associated with HBCs. We analyzed publicly available transcriptomic datasets on Gallbladder cancer (GBC), Hepatocellular carcinoma (HCC), and Intrahepatic cholangiocarcinoma (ICC) to identify potential biomarkers using integrative systems approaches. An effective Common and Unique Molecular Signature Identification (CUMSI) approach has been employed, which contains analysis of differential gene expression (DEG), gene co-expression networks (GCN), and protein-protein interactions (PPIs) networks. Functional analysis of the DEGs unique for GBC, HCC, and ICC indicated that GBC is associated with cellular processes, HCC is associated with immune signaling pathways, and ICC is associated with lipid metabolic pathways. Our findings shows that the hub genes and pathways identified for each individual cancer type of the HBS are related with the primary function of each organ and each cancer exhibit unique expression patterns despite being part of the same organ system.

Phytochrome Signaling Networks

2021 ◽  
Vol 72 (1) ◽  
Author(s):  
Mei-Chun Cheng ◽  
Praveen Kumar Kathare ◽  
Inyup Paik ◽  
Enamul Huq
Keyword(s):  
Protein Interactions ◽  
Translational Regulation ◽  
Regulatory Networks ◽  
Annual Review ◽  
Publication Date ◽  
Protein Protein Interactions ◽  
Conformational Switching ◽  
E3 Ligases ◽  
Almost All

The perception of light signals by the phytochrome family of photoreceptors has a crucial influence on almost all aspects of growth and development throughout a plant's life cycle. The holistic regulatory networks orchestrated by phytochromes, including conformational switching, subcellular localization, direct protein-protein interactions, transcriptional and posttranscriptional regulations, and translational and posttranslational controls to promote photomorphogenesis, are highly coordinated and regulated at multiple levels. During the past decade, advances using innovative approaches have substantially broadened our understanding of the sophisticated mechanisms underlying the phytochrome-mediated light signaling pathways. This review discusses and summarizes these discoveries of the role of the modular structure of phytochromes, phytochrome-interacting proteins, and their functions; the reciprocal modulation of both positive and negative regulators in phytochrome signaling; the regulatory roles of phytochromes in transcriptional activities, alternative splicing, and translational regulation; and the kinases and E3 ligases that modulate PHYTOCHROME INTERACTING FACTORs to optimize photomorphogenesis. Expected final online publication date for the Annual Review of Plant Biology, Volume 72 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

In vitro culture and cultivation of Chinese medicinal plants for industrial utilization and genetic resource conservation

2005 ◽  
Vol 3 (2) ◽  
pp. 116-126 ◽  
Author(s):  
Wenyuan Gao ◽  
Wei Jia ◽  
Xianfu Gao ◽  
Renfeng Wang ◽  
Peigen Xiao
Keyword(s):  
Cell Culture ◽  
Tissue Culture ◽  
Medicinal Plants ◽  
Medicinal Plant ◽  
Chinese Government ◽  
Taxus Chinensis ◽  
Agricultural Practice ◽  
Plant Materials ◽  
Good Agricultural Practice ◽  
Culture Technology

In China, medicinal plants enjoy an inherent and prominent role in the general health service. Due to excessive collection in the wild of rare and endangered plants, the natural resources of medicinal plants are depleting fast. In order to protect the medicinal plant resources, the Chinese government has implemented Good Agricultural Practice (GAP) programmes to cultivate the main popular medicinal plants in China. Thus far, around 800 GAP cultivation bases have been established nationwide and the total cultivation area of medicinal plants has reached 5000 km2. Besides GAP cultivation of medicinal plants, tissue cultural biotechnology has been applied to serve as an alternative for the supply of medicinal plant materials in China. Thus far, shoot production by tissue culture technology has been successful in medicinal plants such as Anoectochilus formosanus, Dalbergia odorifera, Dendrobium, Momordica grosvenorii, Pseudostellaria heterophylla and Taxus chinensis. In addition, the cell culture of Lithospermum erythrorhizon and Saussurea involucrata has been industrialized in 300–20,000-litre bioreactors. Besides the production of shoot and cell culture in bioreactors, tissue culture technology is also being practised for the conservation of rare medicinal plants.

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