Protein-protein interaction networks as miners of biological discovery

Identifying an Abnormal Phosphorylated Adaptor by Viral Kinase Using Mass Spectrometry

Mass spectrometers are widely used to identify protein phosphorylation sites. The process usually involves selective isolation of phosphoproteins and subsequent fragmentation to identify both the peptide sequence and phosphorylation site. Immunoprecipitation could capture and purify the protein of interest, greatly reducing sample complexity before submitting it for mass spectrometry analysis. This chapter describes a method to identify an abnormal phosphorylated site of the adaptor protein by a viral kinase through immunoprecipitation followed by LC-MS/MS.

Deep-HPI-pred: An R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions

Host-pathogen interactions (HPIs) are vital in numerous biological activities and are intrinsically linked to the onset and progression of infectious diseases. HPIs are pivotal in the entire lifecycle of diseases: from the onset of pathogen introduction, navigating through the mechanisms that bypass host cellular defenses, to its subsequent proliferation inside the host. At the heart of these stages lies the synergy of proteins from both the host and the pathogen. By understanding these interlinking protein dynamics, we can gain crucial insights into how diseases progress and pave the way for stronger plant defenses and the swift formulation of countermeasures. In the framework of current study, we developed a web-based R/Shiny app, Deep-HPI-pred, that uses network-driven feature learning method to predict the yet unmapped interactions between pathogen and host proteins. Leveraging citrus and CLas bacteria training datasets as case study, we spotlight the effectiveness of Deep-HPI-pred in discerning Protein-protein interaction (PPIs) between them. Deep-HPI-pred use Multilayer Perceptron (MLP) models for HPI prediction, which is based on a comprehensive evaluation of topological features and neural network architectures. When subjected to independent validation datasets, the predicted models consistently surpassed a Matthews correlation coefficient (MCC) of 0.80 in host-pathogen interactions. Remarkably, the use of Eigenvector Centrality as the leading topological feature further enhanced this performance. Further, Deep-HPI-pred also offers relevant gene ontology (GO) term information for each pathogen and host protein within the system. This protein annotation data contributes an additional layer to our understanding of the intricate dynamics within host-pathogen interactions. In the additional benchmarking studies, the Deep-HPI-pred model has proven its robustness by consistently delivering reliable results across different host-pathogen systems, including plant-pathogens (accuracy of 98.4% and 97.9%), human-virus (accuracy of 94.3%), and animal-bacteria (accuracy of 96.6%) interactomes. These results not only demonstrate the model's versatility but also pave the way for gaining comprehensive insights into the molecular underpinnings of complex host-pathogen interactions. Taken together, the Deep-HPI-pred applet offers a unified web service for both identifying and illustrating interaction networks. Deep-HPI-pred applet is freely accessible at its homepage: https://cbi.gxu.edu.cn/shiny-apps/Deep-HPI-pred/ and at github: https://github.com/tahirulqamar/Deep-HPI-pred.

A protein blueprint of the diatom CO2-fixing organelle

Diatoms are central to the global carbon cycle. At the heart of diatom carbon fixation is an overlooked organelle called the pyrenoid, where concentrated CO2 is delivered to densely packed Rubisco. Diatom pyrenoids fix approximately one-fifth of global CO2, but the protein composition of this organelle is largely unknown. Using fluorescence protein tagging and affinity purification-mass spectrometry, we generate a high-confidence spatially defined protein-protein interaction network for the diatom pyrenoid. Within our pyrenoid interaction network are 10 proteins with previously unknown functions. We show that six of these form a shell that encapsulates the Rubisco matrix and is critical for pyrenoid structural integrity, shape, and function. Although not conserved at a sequence or structural level, the diatom pyrenoid shares some architectural similarities to prokaryotic carboxysomes. Collectively, our results support the convergent evolution of pyrenoids across the two main plastid lineages and uncover a major structural and functional component of global CO2 fixation.

Prediction of Oral Cancer Biomarkers by Salivary Proteomics Data

Oral cancer, representing 2-4% of all cancer cases, predominantly consists of Oral Squamous Cell Carcinoma (OSCC), which makes up 90% of oral malignancies. Early detection of OSCC is crucial, and identifying specific proteins in saliva as biomarkers could greatly improve early diagnosis. Here, we proposed a strategy to pinpoint candidate biomarkers. Starting from a list of salivary proteins detected in 10 OSCC patients and 20 healthy controls, we combined a univariate approach and a multivariate approach to select candidates. To reduce the number of proteins selected, a Protein-Protein Interaction network was built to consider only connected proteins. Then, an over-representation analysis (ORA) determined the enriched pathways. The network from 172 differentially abundant proteins highlighted 50 physically connected proteins, selecting relevant candidates for targeted experimental validations. Notably, proteins like Heat shock 70 kDa protein 1A/1B, Pyruvate kinase PKM, and Phosphoglycerate kinase 1 were suggested to be differentially regulated in OSCC patients, with implications for oral carcinogenesis and tumor growth. Additionally, the ORA revealed enrichment in immune system, complement, and coagulation pathways, all known to play roles in tumorigenesis and cancer progression. The employed method has successfully identified potential biomarkers for early diagnosis of OSCC using an accessible body fluid.

Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways

The mitogen-activated protein kinase (MAPK) pathway is an important intracellular signaling cascade that plays a key role in various cellular processes. Understanding the regulatory mechanisms of this pathway is essential for developing effective interventions and targeted therapies for related diseases. Recent advances in single-cell proteomic technologies have provided unprecedented opportunities to investigate the heterogeneity and noise within complex, multi-signaling networks across diverse cells and cell types. Mathematical modeling has become a powerful interdisciplinary tool that bridges mathematics and experimental biology, providing valuable insights into these intricate cellular processes. In addition, statistical methods have been developed to infer pathway topologies and estimate unknown parameters within dynamic models. This review presents a comprehensive analysis of how mathematical modeling of the MAPK pathway deepens our understanding of its regulatory mechanisms, enhances the prediction of system behavior, and informs experimental research, with a particular focus on recent advances in modeling and inference using single-cell proteomic data.

Exploring Protein S-Palmitoylation: Mechanisms, Detection, and Strategies for Inhibitor Discovery

S-palmitoylation is a reversible and dynamic process that involves the addition of long-chain fatty acids to proteins. This protein modification regulates various aspects of protein function, including subcellular localization, stability, conformation, and biomolecular interactions. The zinc finger DHHC (ZDHHC) domain-containing protein family is the main group of enzymes responsible for catalyzing protein S-palmitoylation, and 23 members have been identified in mammalian cells. Many proteins that undergo S-palmitoylation have been linked to disease pathogenesis and progression, suggesting that the development of effective inhibitors is a promising therapeutic strategy. Reducing the protein S-palmitoylation level can target either the PATs directly or their substrates. However, there are rare clinically effective S-palmitoylation inhibitors. This review aims to provide an overview of the S-palmitoylation field, including the catalytic mechanism of ZDHHC, S-palmitoylation detection methods, and the functional impact of protein S-palmitoylation. Additionally, this review focuses on current strategies for expanding the chemical toolbox to develop novel and effective inhibitors that can reduce the level of S-palmitoylation of the target protein.

Prediction of Multiple Degenerative Diseases Based on DNA Methylation in a Co-Physiology Mechanisms Perspective

Degenerative diseases oftentimes occur within the continuous process of aging, and the corresponding clinical manifestations may be neurodegeneration, neoplastic diseases, or various human complex diseases. DNA methylation provides the opportunity to explore aging and degenerative diseases as epigenetic traits. It has already been applied to age prediction and disease diagnosis. It has been shown that various degenerative diseases share co-physiology mechanisms with each other, clues of which may be gained from studying the aging process. Here, we endeavor to predict the risk of degenerative diseases in an aging-relevant comorbid mechanism perspective. Firstly, an epigenetic clock method was implemented based on a multi-scale convolutional neural network, and a Shapley feature attribution analysis was applied to discover the aging-related CpG sites. Then, these sites were further screened to a smaller subset composed of 196 sites by using biomics analysis according to their biological functions and mechanisms. Finally, we constructed a multilayer perceptron (MLP)-based degenerative disease risk prediction model, Mlp-DDR, which was well trained and tested to accurately classify nine degenerative diseases. Recent studies also suggest that DNA methylation plays a significant role in conditions like osteoporosis and osteoarthritis, broadening the potential applications of our model. This approach significantly advances the ability to understand degenerative diseases and represents a substantial shift from traditional diagnostic methods. Despite the promising results, limitations regarding model complexity and dataset diversity suggest directions for future research, including the development of tissue-specific epigenetic clocks and the inclusion of a wider range of diseases.

TOX: a potential new immune checkpoint in cancers by pancancer analysis

BACKGROUND

Thymocyte selection-associated HMG-BOX (TOX) belongs to a family of transcription factors containing a highly conserved region of the high mobility group box (HMG-Box). A growing body of research has shown that TOX is involved in the occurrence and development of tumors and promotes T-cell exhaustion. We assessed the role of TOX with The Cancer Genome Atlas (TCGA) Pancancer Data.

METHODS

TOX expression was examined with RNA-seq data from the TCGA and Genotype-Tissue Expression (GTEx) databases. The genetic alteration status and protein level of TOX were analyzed using databases, including the Human Protein Atlas (HPA), GeneCards, and STRING. The prognostic significance was estimated with survival data from the TCGA. Moreover, R software was used for enrichment analysis of TOX. The relationship between TOX and immune cell infiltration was assessed with the Tumor Immune Estimation Resource (TIMER) 2.0 database and the "CIBERSORT" method. The correlation between TOX and immune checkpoints was further explored. Immunohistochemical analysis was used to further verify the difference in TOX expression between cancerous and paracancerous tissues, and cell viability was evaluated using a CCK-8 assay.

RESULTS

In most cancer types in the TCGA cohort, differential TOX expression was observed. The genetic alteration status and protein level of TOX were examined, and the prognosis of cancers was associated with TOX expression. Moreover, TOX levels were closely related to different immune-related pathways, immune cell infiltration and immune checkpoints. Additionally, significant differences in TOX expression between several cancerous and paracancerous tissues were validated. Furthermore, TOX clearly impacted the viability of cancer cells.

CONCLUSIONS

TOX, a potential biomarker for cancer, may be involved in the regulation of the immune microenvironment and can be used for new targeted drugs.

Biomedical literature mining: graph kernel-based learning for gene-gene interaction extraction

The supervised machine learning method is often used for biomedical relationship extraction. The disadvantage is that it requires much time and money to manually establish an annotated dataset. Based on distant supervision, the knowledge base is combined with the corpus, thus, the training corpus can be automatically annotated. As many biomedical databases provide knowledge bases for study with a limited number of annotated corpora, this method is practical in biomedicine. The clinical significance of each patient's genetic makeup can be understood based on the healthcare provider's genetic database. Unfortunately, the lack of previous biomedical relationship extraction studies focuses on gene-gene interaction. The main purpose of this study is to develop extraction methods for gene-gene interactions that can help explain the heritability of human complex diseases. This study referred to the information on gene-gene interactions in the KEGG PATHWAY database, the abstracts in PubMed were adopted to generate the training sample set, and the graph kernel method was adopted to extract gene-gene interactions. The best assessment result was an F1-score of 0.79. Our developed distant supervision method automatically finds sentences through the corpus without manual labeling for extracting gene-gene interactions, which can effectively reduce the time cost for manual annotation data; moreover, the relationship extraction method based on a graph kernel can be successfully applied to extract gene-gene interactions. In this way, the results of this study are expected to help achieve precision medicine.

DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning

BACKGROUND

Proteins play a pivotal role in the diverse array of biological processes, making the precise prediction of protein-protein interaction (PPI) sites critical to numerous disciplines including biology, medicine and pharmacy. While deep learning methods have progressively been implemented for the prediction of PPI sites within proteins, the task of enhancing their predictive performance remains an arduous challenge.

RESULTS

In this paper, we propose a novel PPI site prediction model (DGCPPISP) based on a dynamic graph convolutional neural network and a two-stage transfer learning strategy. Initially, we implement the transfer learning from dual perspectives, namely feature input and model training that serve to supply efficacious prior knowledge for our model. Subsequently, we construct a network designed for the second stage of training, which is built on the foundation of dynamic graph convolution.

CONCLUSIONS

To evaluate its effectiveness, the performance of the DGCPPISP model is scrutinized using two benchmark datasets. The ensuing results demonstrate that DGCPPISP outshines competing methods in terms of performance. Specifically, DGCPPISP surpasses the second-best method, EGRET, by margins of 5.9%, 10.1%, and 13.3% for F1-measure, AUPRC, and MCC metrics respectively on Dset_186_72_PDB164. Similarly, on Dset_331, it eclipses the performance of the runner-up method, HN-PPISP, by 14.5%, 19.8%, and 29.9% respectively.

Network pharmacology and molecular docking: combined computational approaches to explore the antihypertensive potential of Fabaceae species

Hypertension is a major global public health issue, affecting quarter of adults worldwide. Numerous synthetic drugs are available for treating hypertension; however, they often come with a higher risk of side effects and long-term therapy. Modern formulations with active phytoconstituents are gaining popularity, addressing some of these issues. This study aims to discover novel antihypertensive compounds in Cassia fistula, Senna alexandrina, and Cassia occidentalis from family Fabaceae and understand their interaction mechanism with hypertension targeted genes, using network pharmacology and molecular docking. Total 414 compounds were identified; initial screening was conducted based on their pharmacokinetic and ADMET properties, with a particular emphasis on adherence to Lipinski's rules. 6 compounds, namely Germichrysone, Benzeneacetic acid, Flavan-3-ol, 5,7,3',4'-Tetrahydroxy-6, 8-dimethoxyflavon, Dihydrokaempferol, and Epiafzelechin, were identified as effective agents. Most of the compounds found non-toxic against various indicators with greater bioactivity score. 161 common targets were obtained against these compounds and hypertension followed by compound-target network construction and protein-protein interaction, which showed their role in diverse biological system. Top hub genes identified were TLR4, MMP9, MAPK14, AKT1, VEGFA and HSP90AA1 with their respective associates. Higher binding affinities was found with three compounds Dihydrokaempferol, Flavan-3-ol and Germichrysone, -7.1, -9.0 and -8.0 kcal/mol, respectively. The MD simulation results validate the structural flexibility of two complexes Flavan-MMP9 and Germich-TLR4 based on no. of hydrogen bonds, root mean square deviations and interaction energies. This study concluded that C. fistula (Dihydrokaempferol, Flavan-3-ol) and C. occidentalis (Germichrysone) have potential therapeutic active constituents to treat hypertension and in future novel drug formulation.

Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits

The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.

Prioritization of genes for translation: a computational approach

INTRODUCTION

Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.

AREAS COVERED

In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.

EXPERT OPINION

Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

NADP+ or CO2 reduction by frhAGB-encoded hydrogenase through interaction with formate dehydrogenase 3 in the hyperthermophilic archaeon Thermococcus onnurineus NA1

IMPORTANCE

The strategy using structural homology with the help of structure prediction by AlphaFold was very successful in finding potential targets for the frhAGB-encoded hydrogenase of Thermococcus onnurineus NA1. The finding that the hydrogenase can interact with FdhB to reduce the cofactor NAD(P)+ is significant in that the enzyme can function to supply reducing equivalents, just as F420-reducing hydrogenases in methanogens use coenzyme F420 as an electron carrier. Additionally, it was identified that T. onnurineus NA1 could produce formate from H2 and CO2 by the concerted action of frhAGB-encoded hydrogenase and formate dehydrogenase Fdh3.

Comparative proteomics reveals the mechanism of cyclosporine production and mycelial growth in Tolypocladium inflatum affected by different carbon sources

Cyclosporine A (CsA) is a secondary cyclopeptide metabolite produced by Tolypocladium inflatum that is widely used clinically as an immunosuppressant. CsA production and mycelial growth differed when T. inflatum was cultured in different carbon source media. During early fermentation, CsA was preferred to be produced in fructose medium, while the mycelium preferred to accumulate in sucrose medium. On the sixth day, the difference was most pronounced. In this study, high-throughput comparative proteomics methods were applied to analyze differences in protein expression of mycelial samples on day 6, revealing the proteins and mechanisms that positively regulate CsA production related to carbon metabolism. The differences included small molecule acid metabolism, lipid metabolism, organic catabolism, exocrine secretion, CsA substrate Bmt synthesis, and transcriptional regulation processes. The proteins involved in the regulation of mycelial growth related to carbon metabolism were also revealed and were associated with waste reoxidation processes or coenzyme metabolism, small molecule synthesis or metabolism, the stress response, genetic information or epigenetic changes, cell component assembly, cell wall integrity, membrane metabolism, vesicle transport, intramembrane localization, and the regulation of filamentous growth. This study provides a reliable reference for CsA production from high-efficiency fermentation. This study provides key information for obtaining more CsA high-yielding strains through metabolic engineering strategies.

DKK3 expression is correlated with poorer prognosis in head and neck squamous cell carcinoma: A bioinformatics study based on the TCGA database

OBJECTIVE

We previously reported that dickkopf WNT signaling pathway inhibitor 3 (DKK3) expression is correlated with poorer prognosis in head and neck squamous cell carcinoma (HNSCC). Here we investigated DKK3 expression by using The Cancer Genome Atlas (TCGA) public database and bioinformatic analyses.

METHODS

We used the RNA sequence data and divided the tumor samples into "DKK3-high" and "DKK3-low" groups according to median DKK3 expression. The correlations between DKK3 expression and the clinical data were investigated. Differentially expressed genes (DEGs) were detected using DESEq2 and analyzed by ShinyGO 0.77. A gene set enrichment analysis (GSEA) was also performed using GSEA software. The DEGs were also analyzed with TargetMine to establish the protein-protein interaction (PPI) network.

RESULTS

DKK3 expression was significantly increased in cancer samples, and a high DKK3 expression was significantly associated with shorter overall survival. We identified 854 DEGs, including 284 up-regulated and 570 down-regulated. Functional enrichment analyses revealed several Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with extracellular matrix remodeling. The PPI network identified COL8A1, AGTR1, FN1, P4HA3, PDGFRB, and CEP126 as the key genes.

CONCLUSIONS

These results suggested the cancer-promoting ability of DKK3, the expression of which is a promising prognostic marker and therapeutic target for HNSCC.

Medication-Related Osteonecrosis of the Jaw: A Systematic Review and a Bioinformatic Analysis

The objective was to evaluate the current evidence regarding the etiology of medication-related osteonecrosis of the jaw (MRONJ). This study systematically reviewed the literature by searching PubMed, Web of Science, and ProQuest databases for genes, proteins, and microRNAs associated with MRONJ from the earliest records through April 2023. Conference abstracts, letters, review articles, non-human studies, and non-English publications were excluded. Twelve studies meeting the inclusion criteria involving exposure of human oral mucosa, blood, serum, saliva, or adjacent bone or periodontium to anti-resorptive or anti-angiogenic agents were analyzed. The Cochrane Collaboration risk assessment tool was used to assess the quality of the studies. A total of 824 differentially expressed genes/proteins (DEGs) and 22 microRNAs were extracted for further bioinformatic analysis using Cytoscape, STRING, BiNGO, cytoHubba, MCODE, and ReactomeFI software packages and web-based platforms: DIANA mirPath, OmicsNet, and miRNet tools. The analysis yielded an interactome consisting of 17 hub genes and hsa-mir-16-1, hsa-mir-21, hsa-mir-23a, hsa-mir-145, hsa-mir-186, hsa-mir-221, and hsa-mir-424. A dominance of cytokine pathways was observed in both the cluster of hub DEGs and the interactome of hub genes with dysregulated miRNAs. In conclusion, a panel of genes, miRNAs, and related pathways were found, which is a step toward understanding the complexity of the disease.

Proteomic applications in identifying protein-protein interactions

There are many things that can be used to characterize a protein. Size, isoelectric point, hydrophobicity, structure (primary to quaternary), and subcellular location are just a few parameters that are used. The most important feature of a protein, however, is its function. While there are many experiments that can indicate a protein's role, identifying the molecules it interacts with is probably the most definitive way of determining its function. Owing to technology limitations, protein interactions have historically been identified on a one molecule per experiment basis. The advent of high throughput multiplexed proteomic technologies in the 1990s, however, made identifying hundreds and thousands of proteins interactions within single experiments feasible. These proteomic technologies have dramatically increased the rate at which protein-protein interactions (PPIs) are discovered. While the improvement in mass spectrometry technology was an early driving force in the rapid pace of identifying PPIs, advances in sample preparation and chromatography have recently been propelling the field. In this chapter, we will discuss the importance of identifying PPIs and describe current state-of-the-art technologies that demonstrate what is currently possible in this important area of biological research.

Molecular characterization, structural and expression analysis of twelve CXC chemokines and eight CXC chemokine receptors in largemouth bass (Micropterus salmoides)

The chemokine-receptor system plays important roles in the leukocyte trafficking, inflammation, immune cell differentiation, cancer and other biological processes. In the present study, the sequence features, structures and expression patterns of twelve CXC chemokine ligands (CXCL8a.1, CXCL8a.2, CXCL8b.1, CXCL8b.2, CXCL12a, CXCL12b, CXCL13.1, CXCL13.2, CXCL14, CXCL18a, CXCL18b and CXCL19) and eight CXC chemokine receptors (CXCR1, CXCR2, CXCR3.1, CXCR3.2, CXCR3.3, CXCR4a, CXCR4b and CXCR5) of largemouth bass (Micropterus salmoides) were analyzed. All the CXCLs and CXCRs of largemouth bass shared high sequence identities with their teleost counterparts and possessed conserved motifs and structures of CXCLs and CXCRs family. Realtime qPCR revealed that these CXCLs and CXCRs were ubiquitously expressed in all examined tissues, with high expression levels in the immune-related tissues (spleen, head kidney, and gill). Following lipopolysaccharide (LPS) and polyinosinic-polycytidylic acid (polyI:C) stimulations, most of these CXCLs and CXCRs were significantly up-regulated in spleen. In addition, the potential interacted molecules of these CXCLs and CXCRs were analyzed by protein-protein interaction network analysis. To the best of our knowledge, this is the first study that in detail analyzes the CXCLs and CXCRs of largemouth bass. Our results provide valuable basis for study the function and mechanism of chemokine-receptor system in largemouth bass.

On the identification of potential novel therapeutic targets for spinocerebellar ataxia type 1 (SCA1) neurodegenerative disease using EvoPPI3

EvoPPI (http://evoppi.i3s.up.pt), a meta-database for protein-protein interactions (PPI), has been upgraded (EvoPPI3) to accept new types of data, namely, PPI from patients, cell lines, and animal models, as well as data from gene modifier experiments, for nine neurodegenerative polyglutamine (polyQ) diseases caused by an abnormal expansion of the polyQ tract. The integration of the different types of data allows users to easily compare them, as here shown for Ataxin-1, the polyQ protein involved in spinocerebellar ataxia type 1 (SCA1) disease. Using all available datasets and the data here obtained for Drosophila melanogaster wt and exp Ataxin-1 mutants (also available at EvoPPI3), we show that, in humans, the Ataxin-1 network is much larger than previously thought (380 interactors), with at least 909 interactors. The functional profiling of the newly identified interactors is similar to the ones already reported in the main PPI databases. 16 out of 909 interactors are putative novel SCA1 therapeutic targets, and all but one are already being studied in the context of this disease. The 16 proteins are mainly involved in binding and catalytic activity (mainly kinase activity), functional features already thought to be important in the SCA1 disease.

Protein network analysis and functional enrichment via computational biotechnology unravel molecular and pathogenic mechanisms of kidney stone disease

Mass spectrometry-based proteomics has been extensively applied to current biomedical research. From such large-scale identification of proteins, several computational tools have been developed for determining protein-protein interactions (PPI) network and functional significance of the identified proteins and their complex. Analyses of PPI network and functional enrichment have been widely applied to various fields of biomedical research. Herein, we summarize commonly used tools for PPI network analysis and functional enrichment in kidney stone research and discuss their applications to kidney stone disease (KSD). Such computational approach has been used mainly to investigate PPI networks and functional significance of the proteins derived from urine of patients with kidney stone (stone formers), stone matrix, Randall's plaque, renal papilla, renal tubular cells, mitochondria and immune cells. The data obtained from computational biotechnology leads to experimental validation and investigations that offer new knowledge on kidney stone formation processes. Moreover, the computational approach may also lead to defining new therapeutic targets and preventive strategies for better outcome in KSD management.

Protein-Protein Interaction Network Exploration Using Cytoscape

As the protein-protein interaction (PPI) data increase exponentially, the development and usage of computational methods to analyze these datasets have become a new research horizon in systems biology. The PPI network analysis and visualization can help identify functional modules of the network, pathway genes involved in common cellular functions, and functional annotations of novel genes. Currently, a variety of tools are available for network graph visualization and analysis. Cytoscape, an open-source software tool, is one of them. It provides an interactive visualization interface along with other core features to import, navigate, filter, cluster, search, and export networks. It comes with hundreds of in-built Apps in App Manager to resolve research questions related to network visualization and integration. This chapter aims to illustrate the Cytoscape application to visualize and analyze the PPI network using Arabidopsis interactome-1 main (AI-1MAIN) PPI network dataset from Plant Interactome Database.

Building Protein-Protein Interaction Graph Database Using Neo4j

A cell's various components interact with each other in a coordinated manner to respond to environmental cues and intracellular signals. Compared to the other biological networks, the protein-protein interaction (PPI) is mostly responsible for maintaining signaling pathways. Increasing numbers of experimentally verified and predicted PPIs in plants demand a scalable platform to deal with large and complex datasets. Network/graph data can be organized and analyzed using different tools. This chapter uses Neo4j, a graph database management system, to store and analyze plant PPI networks. To make the graph database and analyze network centrality, we used Arabidopsis interactome-1 main (AI-1MAIN) PPI network.

GPI-anchored ligand-BioID2-tagging system identifies Galectin-1 mediating Zika virus entry

Identification of host factors facilitating pathogen entry is critical for preventing infectious diseases. Here, we report a tagging system consisting of a viral receptor-binding protein (RBP) linked to BioID2, which is expressed on the cell surface via a GPI anchor. Using VSV or Zika virus (ZIKV) RBP, the system (BioID2- RBP(V)-GPI; BioID2-RBP(Z)-GPI) faithfully identifies LDLR and AXL, the receptors of VSV and ZIKV, respectively. Being GPI-anchored is essential for the probe to function properly. Furthermore, BioID2-RBP(Z)-GPI expressed in human neuronal progenitor cells identifies galectin-1 on cell surface pivotal for ZIKV entry. This conclusion is further supported by antibody blocking and galectin-1 silencing in A549 and mouse neural cells. Importantly, Lgals1^−/− mice are significantly more resistant to ZIKV infection than Lgals1^+/+ littermates are, having significantly lower virus titers and fewer pathologies in various organs. This tagging system may have broad applications for identifying protein-protein interactions on the cell surface.

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A tagging system for identifying ligand-receptor interactions is developed

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Receptor binding domain determines the specificity of the system

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Being GPI-anchored is pivotal for the tagging system to function properly

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Galectin-1 is identified as an entry factor essential for ZIKV infection

Two-Dimensional Blue Native/SDS Polyacrylamide Gel Electrophoresis for Analysis of Brazilian Bothrops Snake Venoms

Viperidae snakes are the most important agents of snakebites in Brazil. The protein composition of snake venoms has been frequently analyzed by means of electrophoretic techniques, but the interaction of proteins in venoms has barely been addressed. An electrophoretic technique that has gained prominence to study this type of interaction is blue native polyacrylamide gel electrophoresis (BN-PAGE), which allows for the high-resolution separation of proteins in their native form. These protein complexes can be further discriminated by a second-dimension gel electrophoresis (SDS-PAGE) from lanes cut from BN-PAGE. Once there is no study on the use of bidimensional BN/SDS-PAGE with snake venoms, this study initially standardized the BN/SDS-PAGE technique in order to evaluate protein interactions in Bothrops atrox, Bothrops erythromelas, and Bothrops jararaca snake venoms. Results of BN/SDS-PAGE showed that native protein complexes were present, and that snake venom metalloproteinases and venom serine proteinases maintained their enzymatic activity after BN/SDS-PAGE. C-type lectin-like proteins were identified by Western blotting. Therefore, bidimensional BN/SDS-PAGE proved to be an easy, practical, and efficient method for separating functional venom proteins according to their assemblage in complexes, as well as to analyze their biological activities in further details.