scholarly journals Tianlongkechuanling Inhibits Pulmonary Fibrosis Through Down-Regulation of Arginase-Ornithine Pathway

2021 ◽  
Vol 12 ◽  
Author(s):  
Peng Wang ◽  
Yuanyuan Shi ◽  
Yadong Li ◽  
Lili Zhang ◽  
Sihao Qu ◽  
...  
Keyword(s):  
Pulmonary Fibrosis ◽  
Protein Interactions ◽  
Smooth Muscle Actin ◽  
Collagen Deposition ◽  
Protein Protein Interactions ◽  
Down Regulation ◽  
Kegg Pathways ◽  
Ppi Networks ◽  
Tandem Mass Tag ◽  
Pathway Expression

Background: Pulmonary Fibrosis (PF) is an interstitial lung disease characterized by excessive accumulation of extracellular matrix in the lungs, which disrupts the structure and gas exchange of the alveoli. There are only two approved therapies for PF, nintedanib (Nib) and pirfenidone. Therefore, the use of Chinese medicine for PF is attracting attention. Tianlongkechuanling (TL) is an effective Chinese formula that has been applied clinically to alleviate PF, which can enhance lung function and quality of life.Purpose: The potential effects and specific mechanisms of TL have not been fully explored, yet. In the present study, proteomics was performed to explore the therapeutic protein targets of TL on Bleomycin (BLM)-induced Pulmonary Fibrosis.Method: BLM-induced PF mice models were established. Hematoxylineosin staining and Masson staining were used to analyze histopathological changes and collagen deposition. To screen the differential proteins expression between the Control, BLM, BLM + TL and BLM + Nib (BLM + nintedanib) groups, quantitative proteomics was performed using tandem mass tag (TMT) labeling with nanoLC-MS/MS [nano liquid chromatographymass spectrometry]). Changes in the profiles of the expressed proteins were analyzed using the bioinformatics tools Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein–protein interactions (PPI) were established by STRING. Expressions of α-smooth muscle actin (α-SMA), Collagen I (Col1a1), Fibronectin (Fn1) and enzymes in arginase-ornithine pathway were detected by Western blot or RT-PCR.Result: TL treatments significantly ameliorated BLM-induced collagen deposition in lung tissues. Moreover, TL can inhibit the protein expressions of α-SMA and the mRNA expressions of Col1a1 and Fn1. Using TMT technology, we observed 253 differentially expressed proteins related to PPI networks and involved different KEGG pathways. Arginase-ornithine pathway is highly significant. The expression of arginase1 (Arg1), carbamoyltransferase (OTC), carbamoy-phosphate synthase (CPS1), argininosuccinate synthase (ASS1), ornithine aminotransferase (OAT) argininosuccinate lyase (ASL) and inducible nitric oxide synthase (iNOS) was significantly decreased after TL treatments.Conclusion: Administration of TL in BLM-induced mice resulted in decreasing pulmonary fibrosis. Our findings propose that the down regulation of arginase-ornithine pathway expression with the reduction of arginase biosynthesis is a central mechanism and potential treatment for pulmonary fibrosis with the prevention of TL.

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

scholarly journals Protein–protein interactions in bacteria: a promising and challenging avenue towards the discovery of new antibiotics

2018 ◽  
Vol 14 ◽  
pp. 2881-2896 ◽  
Author(s):  
Laura Carro
Keyword(s):  
Protein Interactions ◽  
Bacterial Infections ◽  
Multidrug Resistant ◽  
Resistant Bacteria ◽  
Protein Protein Interactions ◽  
Lead Discovery ◽  
Antibiotic Development ◽  
Cellular Processes ◽  
Ppi Networks ◽  
New Antibiotics

Antibiotics are potent pharmacological weapons against bacterial infections; however, the growing antibiotic resistance of microorganisms is compromising the efficacy of the currently available pharmacotherapies. Even though antimicrobial resistance is not a new problem, antibiotic development has failed to match the growth of resistant pathogens and hence, it is highly critical to discover new anti-infective drugs with novel mechanisms of action which will help reducing the burden of multidrug-resistant microorganisms. Protein–protein interactions (PPIs) are involved in a myriad of vital cellular processes and have become an attractive target to treat diseases. Therefore, targeting PPI networks in bacteria may offer a new and unconventional point of intervention to develop novel anti-infective drugs which can combat the ever-increasing rate of multidrug-resistant bacteria. This review describes the progress achieved towards the discovery of molecules that disrupt PPI systems in bacteria for which inhibitors have been identified and whose targets could represent an alternative lead discovery strategy to obtain new anti-infective molecules.

scholarly journals Multimodal deep representation learning for protein interaction identification and protein family classification

2019 ◽  
Vol 20 (S16) ◽  
Author(s):  
Da Zhang ◽  
Mansur Kabuka
Keyword(s):  
Protein Interactions ◽  
Protein Sequence ◽  
Representation Learning ◽  
Superior Performance ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Learning Framework ◽  
Topological Features ◽  
Ppi Networks ◽  
Ppi Prediction

Abstract Background Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve the optimal drug-target therapeutic effect and describe the protein complex structures. However, compared to the protein sequences obtainable from various species and organisms, the number of revealed protein-protein interactions is relatively limited. To address this dilemma, lots of research endeavor have investigated in it to facilitate the discovery of novel PPIs. Among these methods, PPI prediction techniques that merely rely on protein sequence data are more widespread than other methods which require extensive biological domain knowledge. Results In this paper, we propose a multi-modal deep representation learning structure by incorporating protein physicochemical features with the graph topological features from the PPI networks. Specifically, our method not only bears in mind the protein sequence information but also discerns the topological representations for each protein node in the PPI networks. In our paper, we construct a stacked auto-encoder architecture together with a continuous bag-of-words (CBOW) model based on generated metapaths to study the PPI predictions. Following by that, we utilize the supervised deep neural networks to identify the PPIs and classify the protein families. The PPI prediction accuracy for eight species ranged from 96.76% to 99.77%, which signifies that our multi-modal deep representation learning framework achieves superior performance compared to other computational methods. Conclusion To the best of our knowledge, this is the first multi-modal deep representation learning framework for examining the PPI networks.

scholarly journals Sequential Transcriptome Changes in the Penumbra after Ischemic Stroke

2019 ◽  
Vol 20 (24) ◽  
pp. 6349 ◽  
Author(s):  
In-Ae Choi ◽  
Ji Hee Yun ◽  
Ji-Hye Kim ◽  
Hahn Young Kim ◽  
Dong-Hee Choi ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Antigen Processing ◽  
Marked Decrease ◽  
Biological Process ◽  
Inflammatory Responses ◽  
Protein Protein Interactions ◽  
Post Stroke ◽  
Kegg Pathways ◽  
Network Analyses ◽  
Gene Profiles

To investigate the changes in the expression of specific genes that occur during the acute-to-chronic post-stroke phase, we identified differentially expressed genes (DEGs) between naive cortical tissues and peri-infarct tissues at 1, 4, and 8 weeks after photothrombotic stroke. The profiles of DEGs were subjected to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology analyses, followed by string analysis of the protein–protein interactions (PPI) of the products of these genes. We found 3771, 536, and 533 DEGs at 1, 4, and 8 weeks after stroke, respectively. A marked decrease in biological–process categories, such as brain development and memory, and a decrease in neurotransmitter synaptic and signaling pathways were observed 1 week after stroke. The PPI analysis showed the downregulation of Dlg4, Bdnf, Gria1, Rhoa, Mapk8, and glutamatergic receptors. An increase in biological–process categories, including cell population proliferation, cell adhesion, and inflammatory responses, was detected at 4 and 8 weeks post-stroke. The KEGG pathways of complement and coagulation cascades, phagosomes, antigen processing, and antigen presentation were also altered. CD44, C1, Fcgr2b, Spp1, and Cd74 occupied a prominent position in network analyses. These time-dependent changes in gene profiles reveal the unique pathophysiological characteristics of stroke and suggest new therapeutic targets for this disease.

Molecular Biology of Protein-Protein Interactions for Computer Scientists

2009 ◽  
pp. 1-13 ◽  
Author(s):  
Christian Schönbach
Keyword(s):  
Molecular Biology ◽  
Protein Interactions ◽  
Wnt Signaling Pathway ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Detection Technology ◽  
Challenges And Opportunities ◽  
Computer Scientists ◽  
Assay Methods

Advances in protein-protein interaction (PPI) detection technology and computational analysis methods have produced numerous PPI networks, whose completeness appears to depend on the extent of data derived from different PPI assay methods and the complexity of the studied organism. Despite the partial nature of human PPI networks, computational data integration and analyses helped to elucidate new interactions and disease pathways. The success of computational analyses considerably depends on PPI data understanding. Exploration of the data and verification of their quality requires basic knowledge of the molecular biology of PPIs and familiarity with the assay methods used to detect PPIs. Both topics are reviewed in this chapter. After introducing various types of PPIs the principles of selected PPI assays are explained and their limitations discussed. Case studies of the Wnt signaling pathway and splice regulation demonstrate some of the challenges and opportunities that arise from assaying and analyzing PPIs. The chapter is concluded with an extrapolation to human systems biology that offers a glimpse into the future of PPI networks.

Bioinformatics studies on structures, functions and diversifications of rolling leaf related genes in rice (Oryza sativa L.)

2020 ◽  
pp. 1-14
Author(s):  
Md. Jahangir Alam ◽  
Md. Alamin ◽  
Most. Humaira Sultana ◽  
Md. Asif Ahsan ◽  
Md. Ripter Hossain ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Oryza Sativa L ◽  
Vital Role ◽  
High Yield ◽  
Leaf Rolling ◽  
Protein Protein Interactions ◽  
Conserved Domains ◽  
Kegg Pathways ◽  
Conserved Domain

Abstract Leaf morphology of crop plants has significant value in agronomy. Leaf rolling in rice plays a vital role to increase grain yield. However, collective information on the rolling leaf (RL) genes reported to date and different comparative bioinformatics studies of their sequences are still incomplete. This bioinformatics study was designed to investigate structures, functions and diversifications of the RL related genes reported till now through several studies. We performed different comparative and functional analyses of the selected 42 RL genes among 103 RL genes using different bioinformatics techniques including gene structure, conserved domain, phylogenetic, gene ontology (GO), transcription factor (TF), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein–protein network. Exon-intron organization and conserved domain analysis showed diversity in structures and conserved domains of RL genes. Phylogenetic analysis classified the proteins into five major groups. GO and TF analyses revealed that regulation-related genes were remarkably enriched in biological process and 10 different TF families were involved in rice leaf rolling. KEGG analysis demonstrated that 14 RL genes were involved in the KEGG pathways, among which 50% were involved in the metabolism pathways. Of the selected RL proteins, 55% proteins were non-interacting with other RL proteins and OsRL9 was the most interacting RL protein. These results provide important information regarding structures, conserved domains, phylogenetic revolution, protein–protein interactions and other genetic bases of RL genes which might be helpful to the researchers for functional analysis of new candidate RL genes to explore their characteristics and molecular mechanisms for high yield rice breeding.

New advances in extracting and learning from protein–protein interactions within unstructured biomedical text data

2019 ◽  
Vol 3 (4) ◽  
pp. 357-369
Author(s):  
J. Harry Caufield ◽  
Peipei Ping
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Historical Context ◽  
Specific Protein ◽  
Basic Unit ◽  
Biomedical Text ◽  
Protein Protein Interactions ◽  
Text Data ◽  
Ppi Networks ◽  
Protein Functions

Abstract Protein–protein interactions, or PPIs, constitute a basic unit of our understanding of protein function. Though substantial effort has been made to organize PPI knowledge into structured databases, maintenance of these resources requires careful manual curation. Even then, many PPIs remain uncurated within unstructured text data. Extracting PPIs from experimental research supports assembly of PPI networks and highlights relationships crucial to elucidating protein functions. Isolating specific protein–protein relationships from numerous documents is technically demanding by both manual and automated means. Recent advances in the design of these methods have leveraged emerging computational developments and have demonstrated impressive results on test datasets. In this review, we discuss recent developments in PPI extraction from unstructured biomedical text. We explore the historical context of these developments, recent strategies for integrating and comparing PPI data, and their application to advancing the understanding of protein function. Finally, we describe the challenges facing the application of PPI mining to the text concerning protein families, using the multifunctional 14-3-3 protein family as an example.

scholarly journals A Novel Network-Based Algorithm for Predicting Protein-Protein Interactions Using Gene Ontology

2021 ◽  
Vol 12 ◽  
Author(s):  
Lun Hu ◽  
Xiaojuan Wang ◽  
Yu-An Huang ◽  
Pengwei Hu ◽  
Zhu-Hong You
Keyword(s):  
Gene Ontology ◽  
Protein Interactions ◽  
Structural Information ◽  
Computational Prediction ◽  
Biological Information ◽  
Main Role ◽  
Protein Protein Interactions ◽  
Chronic Infections ◽  
Prediction Algorithms ◽  
Ppi Networks

Proteins are one of most significant components in living organism, and their main role in cells is to undertake various physiological functions by interacting with each other. Thus, the prediction of protein-protein interactions (PPIs) is crucial for understanding the molecular basis of biological processes, such as chronic infections. Given the fact that laboratory-based experiments are normally time-consuming and labor-intensive, computational prediction algorithms have become popular at present. However, few of them could simultaneously consider both the structural information of PPI networks and the biological information of proteins for an improved accuracy. To do so, we assume that the prior information of functional modules is known in advance and then simulate the generative process of a PPI network associated with the biological information of proteins, i.e., Gene Ontology, by using an established Bayesian model. In order to indicate to what extent two proteins are likely to interact with each other, we propose a novel scoring function by combining the membership distributions of proteins with network paths. Experimental results show that our algorithm has a promising performance in terms of several independent metrics when compared with state-of-the-art prediction algorithms, and also reveal that the consideration of modularity in PPI networks provides us an alternative, yet much more flexible, way to accurately predict PPIs.

scholarly journals Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1271
Author(s):  
Hoyeon Jeong ◽  
Yoonbee Kim ◽  
Yi-Sue Jung ◽  
Dae Ryong Kang ◽  
Young-Rae Cho
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Clustering Algorithms ◽  
Graph Clustering ◽  
Functional Modules ◽  
Protein Protein Interactions ◽  
Overlapping Clusters ◽  
Novel Proteins ◽  
Ppi Networks ◽  
Function Modules

Functional modules can be predicted using genome-wide protein–protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE algorithm is evaluated to prove the validity of predicting function modules. To measure clustering accuracy, the clustering results are compared to protein complexes and Gene Ontology (GO) annotations as references. We demonstrate that the GE algorithm is more accurate in overlapping clusters than the other competitive methods. Moreover, we confirm the biological feasibility of the proteins that occur most frequently in the set of identified clusters. Finally, novel proteins for the additional annotation of GO terms are revealed.

scholarly journals MIPPIE: the mouse integrated protein–protein interaction reference

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Gregorio Alanis-Lobato ◽  
Jannik S Möllmann ◽  
Martin H Schaefer ◽  
Miguel A Andrade-Navarro
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Model Organism ◽  
Web Interface ◽  
Protein Protein Interactions ◽  
Environmental Signals ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
User Friendly

Abstract Cells operate and react to environmental signals thanks to a complex network of protein–protein interactions (PPIs), the malfunction of which can severely disrupt cellular homeostasis. As a result, mapping and analyzing protein networks are key to advancing our understanding of biological processes and diseases. An invaluable part of these endeavors has been the house mouse (Mus musculus), the mammalian model organism par excellence, which has provided insights into human biology and disorders. The importance of investigating PPI networks in the context of mouse prompted us to develop the Mouse Integrated Protein–Protein Interaction rEference (MIPPIE). MIPPIE inherits a robust infrastructure from HIPPIE, its sister database of human PPIs, allowing for the assembly of reliable networks supported by different evidence sources and high-quality experimental techniques. MIPPIE networks can be further refined with tissue, directionality and effect information through a user-friendly web interface. Moreover, all MIPPIE data and meta-data can be accessed via a REST web service or downloaded as text files, thus facilitating the integration of mouse PPIs into follow-up bioinformatics pipelines.

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