Human Protein-protein Interaction Research Articles

Identification of a Two-Gene (PML-EPB41) Signature With Independent Prognostic Value in Osteosarcoma.

Background: Osteosarcoma (OSA) is the most prevalent form of malignant bone cancer and it occurs predominantly in children and adolescents. OSA is associated with a poor prognosis and highest cause of cancer-related death. However, there are a few biomarkers that can serve as reasonable assessments of prognosis.Methods: Gene expression profiling data were downloaded from dataset GSE39058 and GSE21257 from the Gene Expression Omnibus database as well as TARGET database. Bioinformatic analysis with data integration was conducted to discover the significant biomarkers for predicting prognosis. Verification was conducted by qPCR and western blot to measure the expression of genes.Results: 733 seed genes were selected by combining the results of the expression profiling data with hub nodes in a human protein-protein interaction network with their gene functional enrichment categories identified. Following by Cox proportional risk regression modeling, a 2-gene (PML-EPB41) signature was developed for prognostic prediction of patients with OSA. Patients in the high-risk group had significantly poorer survival outcomes than in the low-risk group. Finally, the signature was validated and analyzed by the external dataset along with Kaplan–Meier survival analysis as well as biological experiment. A molecular gene model was built to serve as an innovative predictor of prognosis for patients with OSA.Conclusion: Our findings define novel biomarkers for OSA prognosis, which will possibly aid in the discovery of novel therapeutic targets with clinical applications.

Investigating the human protein-host protein interactome of SARS-CoV-2 infection in the small intestine.

The present study aimed to identify human protein-host protein interactions of SARS-CoV-2 infection in the small intestine to discern the potential mechanisms and gain insights into the associated biomarkers and treatment strategies. Deciphering the tissue and organ interactions of the SARS-CoV-2 infection can be important to discern the potential underlying mechanisms. In the present study, we investigated the human protein-host protein interactions in the small intestine. Public databases and published works were used to collect data related to small intestine tissue and SARS-CoV-2 infection. We constructed a human protein-protein interaction (PPI) network and showed interactions of host proteins in the small intestine. Associated modules, biological processes, functional pathways, regulatory transcription factors, disease ontology categories, and possible drug candidates for therapeutic targets were identified. Thirteen primary protein neighbors were found for the SARS-CoV-2 receptor ACE2. ACE2 and its four partners were observed in a highly clustered module; moreover, 8 host proteins belonged to this module. The protein digestion and absorption as a significant pathway was highlighted with enriched genes of ACE2, MEP1A, MEP1B, DPP4, and XPNPEP2. The HNF4A, HNF1A, and HNF1B transcription factors were found to be regulating the expression of ACE2. A significant association with 12 diseases was deciphered and 116 drug-target interactions were identified. The protein-host protein interactome revealed the important elements and interactions for SARS-CoV-2 infection in the small intestine, which can be useful in clarifying the mechanisms of gastrointestinal symptoms and inflammation. The results suggest that antiviral targeting of these interactions may improve the condition of COVID-19 patients.

Tracking the functional meaning of the human oral-microbiome protein-protein interactions.

The interactome - the network of protein-protein interactions (PPIs) within a cell or organism - is technically difficult to assess. Bioinformatic tools can, not only, identify potential PPIs that can be later experimentally validated, but also be used to assign functional meaning to PPIs. Saliva's potential as a non-invasive diagnostic fluid is currently being explored by several research groups. But, in order to fully attain its potential, it is necessary to achieve the full characterization of the mechanisms that take place within this ecosystem. The onset of omics technologies, and specifically of proteomics, delivered a huge set of data that is largely underexplored. Quantitative information relative to proteins within a given context (for example a given disease) can be used by computational algorithms to generate information regarding PPIs. These PPIs can be further analyzed concerning their functional meaning and used to identify potential biomarkers, therapeutic targets, defense and pathogenicity mechanisms. We describe a computational pipeline that can be used to identify and analyze PPIs between human and microbial proteins. The pipeline was tested within the scenario of human PPIs of systemic (Zika Virus infection) and of oral conditions (Periodontal disease) and also in the context of microbial interactions (Candida-Streptococcus) and showed to successfully predict functionally relevant PPIs. The pipeline can be applied to different scientific areas, such as pharmacological research, since a functional meaningful PPI network can provide insights on potential drug targets, and even new uses for existing drugs on the market.

In-Silico Analysis of Interaction of Human Tetherin Protein with SARS-COV-2ORF7A Proteins and its Mutants

Objective: One of the best ways to counter any pathogen including SARS-CoV-2 is through enhancement of self-immune response Tetherin or BST2 is one such interferon-induced antiviral protein or immune regulatory protein of the human host that can restrict the release of virions of SARS-CoV-2 The ORF7a encoded protein of SARS-CoV-2 in-turn has been found to act as an antagonist to the human tetherin protein by interacting with key amino acid residues, thereby weakening the immune response Methods: In this research after consensus analysis of results from various servers mutations have been induced in selected residues of ORF7a coronavirus protein Most conserved residues have been obtained through Consurf server, ligand binding pockets, and active sites have been identified after combining results from multiple servers like CastP and 3DLigand Site Results: The most deleterious mutation has been identified through DUET and DynaMut servers and mutations induced thereafter have been tested for a decrease in binding affinity through the HawkDock server The protein-protein interaction studies have been performed before and after the induction of mutation through the HawkDock server Conclusions: The decrease in binding affinity between human tetherin and ORF7a protein is supportive of the need to study the induction of mutation and its detrimental effect on viruses, so that effective combat study can be designed with the interplay of our native immune system and drugs from external sources Moreover, other deterrents like anti-viral peptides can be designed against selected amino acids © IJCRR

Multilayer Flows in Molecular Networks Identify Biological Modules in the Human Proteome

A variety of complex systems exhibits different types of relationships simultaneously that can be modeled by multiplex networks. A typical problem is to determine the community structure of such systems that, in general, depend on one or more parameters to be tuned. In this study we propose one measure, grounded on information theory, to find the optimal value of the relax rate characterizing Multiplex Infomap, the generalization of the Infomap algorithm to the realm of multilayer networks. We evaluate our methodology on synthetic networks, to show that the most representative community structure can be reliably identified when the most appropriate relax rate is used. Capitalizing on these results, we use this measure to identify the most reliable meso-scale functional organization in the human protein-protein interaction multiplex network and compare the observed clusters against a collection of independently annotated gene sets from the Molecular Signatures Database (MSigDB). Our analysis reveals that modules obtained with the optimal value of the relax rate are biologically significant and, remarkably, with higher functional content than the ones obtained from the aggregate representation of the human proteome. Our framework allows us to characterize the meso-scale structure of those multilayer systems whose layers are not explicitly interconnected each other-as in the case of edge-colored models-the ones describing most biological networks, from proteomes to connectomes.

Alpha influenza virus infiltration prediction using virus-human protein-protein interaction network.

More than ten million deaths make influenza virus one of the deadliest of history. About half a million sever illnesses are annually reported consequent of influenza. Influenza is a parasite which needs the host cellular machinery to replicate its genome. To reach the host, viral proteins need to interact with the host proteins. Therefore, identification of host-virus protein interaction network (HVIN) is one of the crucial steps in treating viral diseases. Being expensive, time-consuming and laborious of HVIN experimental identification, force the researches to use computational methods instead of experimental ones to obtain a better understanding of HVIN. In this study, several features are extracted from physicochemical properties of amino acids, combined with different centralities of human protein-protein interaction network (HPPIN) to predict protein-protein interactions between human proteins and Alphainfluenzavirus proteins (HI-PPIs). Ensemble learning methods were used to predict such PPIs. Our model reached 0.93 accuracy, 0.91 sensitivity and 0.95 specificity. Moreover, a database including 694522 new PPIs was constructed by prediction results of the model. Further analysis showed that HPPIN centralities, gene ontology semantic similarity and conjoint triad of virus proteins are the most important features to predict HI-PPIs.

SARS-CoV-2-human protein-protein interaction network.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the novel coronavirus which caused the coronavirus disease 2019 pandemic and infected more than 12 million victims and resulted in over 560,000 deaths in 213 countries around the world. Having no symptoms in the first week of infection increases the rate of spreading the virus. The increasing rate of the number of infected individuals and its high mortality necessitates an immediate development of proper diagnostic methods and effective treatments. SARS-CoV-2, similar to other viruses, needs to interact with the host proteins to reach the host cells and replicate its genome. Consequently, virus-host protein-protein interaction (PPI) identification could be useful in predicting the behavior of the virus and the design of antiviral drugs. Identification of virus-host PPIs using experimental approaches are very time consuming and expensive. Computational approaches could be acceptable alternatives for many preliminary investigations. In this study, we developed a new method to predict SARS-CoV-2-human PPIs. Our model is a three-layer network in which the first layer contains the most similar Alphainfluenzavirus proteins to SARS-CoV-2 proteins. The second layer contains protein-protein interactions between Alphainfluenzavirus proteins and human proteins. The last layer reveals protein-protein interactions between SARS-CoV-2 proteins and human proteins by using the clustering coefficient network property on the first two layers. To further analyze the results of our prediction network, we investigated human proteins targeted by SARS-CoV-2 proteins and reported the most central human proteins in human PPI network. Moreover, differentially expressed genes of previous researches were investigated and PPIs of SARS-CoV-2-human network, the human proteins of which were related to upregulated genes, were reported.

MIPPIE: the mouse integrated protein-protein interaction reference.

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.

Protein-protein Interaction Analysis of Contributing Molecules in Dura mater Healing Process

Background: Dura mater is a special tissue that fulfills a critical function in brain anatomy and physiology. This tissue contains numerous cells, stem cells, and growth factors. This research investigates the protein interaction contributing to dura mater healing process. Methods: We use the available analysis software to perform the protein-protein interaction (PPI) analysis (http://gpsprot.org/index.php). GPS Protein is an interactive platform for visualizing human protein interaction by integrating HIPPIE and CORUM databases. We excluded HIV-1 proteomic and RNAi databases, instead focused on human PPI (Confidence level 0.75). Two proteins were inputted as query to identify the potential protein network in Dura mater healing according to previous studies, i.e. fibroblast growth factor-2 (FGF2) and transforming growth factor beta-1 (TGFβ1). Results: PPI results shows a high level (confidence level > 0.75) of protein-protein interaction of TGFβ1 to 197 other proteins (Confidence level ranges: 0.49 - 0.87), and PPI of FGF2 to 26 other proteins (Confidence level ranges: 0.0-0.97). TGFβ1 interactions showed the important interactions to some remodeling proteins. TGFβ1 encoded regulates cell proliferation, differentiation, growth, expression modulation and the activation of other growth factors. It also induces epithelial-to-mesenchymal transition (EMT) and cell migration. Conclusion: This bioinformatics approach is the more efficient and cheaper method for analyzing the molecular aspect of protein that has a special contribution in Dura mater healing process. These results could beneficial in focusing further researches for more complex laboratory examinations.

In silico unravelling pathogen-host signaling cross-talks via pathogen mimicry and human protein-protein interaction networks

Pathogen-host protein interactions are fundamental for pathogens to manipulate host signaling pathways and subvert host immune defense. For most pathogens, very few or no experimental studies have been conducted to investigate their signaling cross-talks with host. In this study, we propose a computational framework to validate the biological assumption that human protein–protein interaction (PPI) networks alone are sufficient to infer pathogen-host PPIs via pathogen functional mimicry. Pathogen functional mimicry assumes that a pathogen functionally mimics and substitutes host counterpart proteins in order for the pathogen to get involved in or hijack the host cellular processes. Through pathogen functional mimicry defined via gene ontology (GO) semantic similarity, we first use the known human PPIs as templates to infer pathogen-host PPIs, and the PPIs are further used as training data to build an l2-regularized logistic regression model for novel pathogen-host PPI prediction. Independent tests on the experimental data from human immunodeficiency virus and Francisella tularensis validate the effectiveness of the proposed pathogen functional mimicry technique. Performance comparisons also show that the proposed technique y excels the existing pathogen sequence mimicry approaches and transfer learning methods. The proposed framework provides a new avenue to study the experimentally less-studied pathogens in the worst scenarios that very few or no experimental pathogen-host PPIs are available. As two case studies, we apply the proposed framework to Salmonella typhimurium and Human respiratory syncytial virus to reconstruct the pathogen-host PPI networks and further investigate the interference of these two pathogens with human immune signaling and transcription regulatory system.

Identifying potential entry inhibitors for emerging Nipah virus by molecular docking and chemical-protein interaction network

Nipah Virus (NiV) is a newly emergent paramyxovirus that has caused various outbreaks in Asian countries. Despite its acute pathogenicity and lack of approved therapeutics for human use, there is an urgent need to determine inhibitors against NiV. Hence, this work includes prospection of potential entry inhibitors by implementing an integrative structure- and network-based drug discovery approach. FDA-approved drugs were screened against attachment glycoprotein (NiV-G, PDB: 2VSM), one of the prime targets to inhibit viral entry, using a molecular docking approach that was benchmarked both on CCDC/ASTEX and known NIV-G inhibitor set. The predicted small molecules were prioritized on the basis of topological analysis of the chemical-protein interaction network, which was inferred by integrating the drug-target network, NiV-human interaction network, and human protein-protein interaction network. A total of 17 drugs were predicted to be NiV-G inhibitors using molecular docking studies that were further prioritized to 3 novel leads − Nilotinib, Deslanoside and Acetyldigitoxin − on the basis of topological analysis of inferred chemical-protein interaction network. While Deslanoside and Acetyldigitoxin belong to an already known class of anti-NiV inhibitors, Nilotinib belongs to Benzenoids chemical class that has not been reported hitherto for developing anti-NiV inhibitors. These identified drugs are expected to be successful in further experimental evaluation and therefore could be used for anti-Nipah drug discovery. Apart, we also obtained various insights into the underlying chemical-protein interaction network, based on which several important network nodes were predicted. The applicability of our proposed approach was also demonstrated by prospecting for anti-NiV phytochemicals on an independent dataset.Communicated by Ramaswamy H. Sarma

Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution.

The human interactome is instrumental in the systems-level study of the cell and the contextualization of disease-associated gene perturbations. However, reference organismal interactomes do not capture the cell-type-specific context in which proteins and modules preferentially act. Here, we introduce SCINET, a computational framework that reconstructs an ensemble of cell-type-specific interactomes by integrating a global, context-independent reference interactome with a single-cell gene-expression profile. SCINET addresses technical challenges of single-cell data by robustly imputing, transforming, and normalizing the initially noisy and sparse expression of data. Inferred cell-level gene interaction probabilities and group-level interaction strengths define cell-type-specific interactomes. We use SCINET to reconstruct and analyze interactomes of the major human brain and immune cell types, revealing specificity and modularity of perturbations associated with neurodegenerative, neuropsychiatric, and autoimmune disorders. We report cell-type interactomes for brain and immune cell types, together with the SCINET package.

Zinc Binding to Tau Influences Aggregation Kinetics and Oligomer Distribution.

Metal ions are well known modulators of protein aggregation and are key players in Alzheimer’s Disease, being found to be associated to pathologic protein deposits in diseased brains. Therefore, understanding how metals influence amyloid aggregation is critical in establishing molecular mechanisms that underlie disease onset and progression. Here, we report data on the interaction of full-length human Tau protein with calcium and zinc ions, evidencing that Tau self-assembly is differently regulated, depending on the type of bound metal ion. We established that Tau binds 4 Zn2+ and 1 Ca2+ per monomer while using native mass spectrometry analysis, without inducing order or substantial conformational changes in the intrinsically disordered Tau, as determined by structural analysis using circular dichroism and Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopies. However, Tau aggregation is found to proceed differently in the calcium- and -zinc bound forms. While the rate of aggregation, as determined from thioflavin-T (ThT) fluorescence kinetics, is highly increased in both cases, the reaction proceeds via different mechanisms, as evidenced by the absence of the lag phase in the reaction of zinc-bound Tau. Monitoring Tau aggregation using native mass spectrometry indeed evidenced a distinct distribution of Tau conformers along the reaction, as confirmed by dynamic light scattering analysis. We propose that such differences arise from zinc binding at distinct locations within the Tau sequence that prompt both the rapid formation of seeding oligomers through interactions at high affinity sites within the repeat domains, as well as amorphous aggregation, through low affinity interactions with residues elsewhere in the sequence, including at the fuzzy coat domain.

Hydrogen-Deuterium Exchange and Hydroxyl Radical Footprinting for Mapping Hydrophobic Interactions of Human Bromodomain with a Small Molecule Inhibitor.

Mass spectrometry (MS)-based protein footprinting, a valuable structural tool in mapping protein-ligand interaction, has been extensively applied to protein-protein complexes, showing success in mapping large interfaces. Here, we utilized an integrated footprinting strategy incorporating both hydrogen-deuterium exchange (HDX) and hydroxyl radical footprinting (i.e., fast photochemical oxidation of proteins (FPOP)) for molecular-level characterization of the interaction of human bromodomain-containing protein 4 (BRD4) with a hydrophobic benzodiazepine inhibitor. HDX does not provide strong evidence for the location of the binding interface, possibly because the shielding of solvent by the small molecule is not large. Instead, HDX suggests that BRD4 appears to be stabilized by showing a modest decrease in dynamics caused by binding. In contrast, FPOP points to a critical binding region in the hydrophobic cavity, also identified by crystallography, and, therefore, exhibits higher sensitivity than HDX in mapping the interaction of BRD4 with compound 1. In the absence or under low concentrations of the radical scavenger, FPOP modifications on Met residues show significant differences that reflect the minor change in protein conformation. This problem can be avoided by using a sufficient amount of proper scavenger, as suggested by the FPOP kinetics directed by a dosimeter of the hydroxyl radical.

Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine.

Traditional approaches to cancer therapy seek common molecular targets in tumors from different patients. However, molecular profiles differ between patients, and most tumors exhibit inherent heterogeneity. Hence, imprecise targeting commonly results in side effects, reduced efficacy, and drug resistance. By contrast, personalized medicine aims to establish a molecular diagnosis specific to each patient, which is currently feasible due to the progress achieved with high-throughput technologies. In this report, we explored data from human RNA-seq and protein–protein interaction (PPI) networks using bioinformatics to investigate the relationship between tumor entropy and aggressiveness. To compare PPI subnetworks of different sizes, we calculated the Shannon entropy associated with vertex connections of differentially expressed genes comparing tumor samples with their paired control tissues. We found that the inhibition of up-regulated connectivity hubs led to a higher reduction of subnetwork entropy compared to that obtained with the inhibition of targets selected at random. Furthermore, these hubs were described to be participating in tumor processes. We also found a significant negative correlation between subnetwork entropies of tumors and the respective 5-year survival rates of the corresponding cancer types. This correlation was also observed considering patients with lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) based on the clinical data from The Cancer Genome Atlas database (TCGA). Thus, network entropy increases in parallel with tumor aggressiveness but does not correlate with PPI subnetwork size. This correlation is consistent with previous reports and allowed us to assess the number of hubs to be inhibited for therapy to be effective, in the context of precision medicine, by reference to the 100% patient survival rate 5 years after diagnosis. Large standard deviations of subnetwork entropies and variations in target numbers per patient among tumor types characterize tumor heterogeneity.

Integrative Analysis of Whole-genome Expression Profiling and Regulatory Network Identifies Novel Biomarkers for Insulin Resistance in Leptin Receptor-deficient Mice.

Molecular characterization of insulin resistance, a growing health issue worldwide, will help to develop novel strategies and accurate biomarkers for disease diagnosis and treatment. Integrative analysis of gene expression profiling and gene regulatory network was exploited to identify potential biomarkers early in the development of insulin resistance. RNA was isolated from livers of animals at three weeks of age, and whole-genome expression profiling was performed and analyzed with Agilent mouse 4×44K microarrays. Differentially expressed genes were subsequently validated by qRT-PCR. Functional characterizations of genes and their interactions were performed by Gene Ontology (GO) analysis and gene regulatory network (GRN) analysis. A total of 197 genes were found to be differentially expressed by fold change ≥2 and P < 0.05 in BKS-db +/+ mice relative to sex and age-matched controls. Functional analysis suggested that these differentially expressed genes were enriched in the regulation of phosphorylation and generation of precursor metabolites which are closely associated with insulin resistance. Then a gene regulatory network associated with insulin resistance (IRGRN) was constructed by integration of these differentially expressed genes and known human protein-protein interaction network. The principal component analysis demonstrated that 67 genes in IRGRN could clearly distinguish insulin resistance from the non-disease state. Some of these candidate genes were further experimentally validated by qRT-PCR, highlighting the predictive role as biomarkers in insulin resistance. Our study provides new insight into the pathogenesis and treatment of insulin resistance and also reveals potential novel molecular targets and diagnostic biomarkers for insulin resistance.

Generation and Interpretation of Context-Specific Human Protein-Protein Interaction Networks with HIPPIE.

High-throughput techniques for the detection of protein-protein interactions (PPIs) have enabled a systems approach for the study of the living cell. However, the increasing amount of protein interaction data, the varying quality of these measurements, and the lack of context information make it difficult to construct meaningful and reliable protein networks.The Human Integrated Protein-Protein Interaction rEference (HIPPIE) is a web tool that integrates and annotates experimentally supported human PPIs from a heterogeneous set of data sources. In HIPPIE, one can query for the interactors of one or more proteins and generate high-quality and context-specific networks. This chapter highlights HIPPIE's most important features and exemplifies its functionality through a proposed use case.

A Graph-Based Approach for Finding the Dengue Infection Pathways in Humans Using Protein-Protein Interactions.

Dengue virus (DENV) is one of the deadly arboviruses, which is primarily transmitted by Aedes aegypti, and causes dengue infection to the humans. According to WHO, every year around 390 million humans are affected by DENV, of which around 50 million deaths are reported. Knowledge of the various diseases caused by the DENV would greatly encourage to understand the infection mechanism and help to design new antiviral drug discovery. We propose a quasi-clique and quasi-biclique algorithm to classify infection gateway proteins of the human body and possible pathways of DENV leading to various diseases. For this, we have examined three networks, dengue-human protein-protein interaction network, human protein interaction network, and human proteins-disease association network. The prediction result states that DENV may lead to various diseases in the human body, including cancer, asthma, ulcerative colitis, multiple sclerosis, premature birth, and so on. Some of the results have recently been validated experimentally. This study may endow with potential targets for more effective anti-dengue remedial contribution.

Identification of Thyroid Carcinoma Related Molecular Targets and Signatures Using Human Protein Interaction Network

Tiroid kanseri görülme sıklığı yüksek olan ve ölümcül bir kanser türüdür. Dolayısıyla tiroid kanserinde etkin rol alan moleküllerin belirlenmesi hastalığın erken tanı ve tedavi stratejilerinin oluşturulması için çok önemlidir. Bu çalışmada yüksek boyutlu işlevsel genomiks verilerinin sistem biyolojisi araçları ile bütünleştirilerek analizi sonucu tiroid kanserine özgü moleküler hedefler ve biyoişaretçi adaylar belirlenmiştir. Zenginleştirme analizi sonucunda önemli kanser yolaklarının, metabolik yolakların ve immun sistem ilgili yolların aktifleştiği belirlenmiştir. İleri istatistiksel analizler ile belirlenen gen anlatımı farklılık gösteren genlerin protein etkileşim ağı oluşturulmuş ve tiroid kanserine özgü moleküler hedefler ve biyoişaretçi adaylar JUN, LRRK2, BCL2, CCND1, TLE1, MET, ICAM1, DDB2 ve RXRG olarak belirlenmiştir. Bağımsız bir veri setinin analizi ile, bu genlerin tümör ve normal dokuları ayırt edebileceği belirlenmiştir. Bu proteinler arasından JUN, TLE1 ve DBB2’nin yeni moleküler hedef ve biyoişaretçi aday olabileceği bulunmuştur. Belirlenen hedeflerin papiller tiroid kanserinin teşhis ve tedavi stratejilerinin oluşturulmasında kullanılabileceği öngörülmektedir. Ancak söz konusu adayların eş zamanlı PCR ile deneysel çalışmalarının yapılması gerekmektedir.

Link clustering explains non-central and contextually essential genes in protein interaction networks

Recent studies have shown that many essential genes (EGs) change their essentiality across various contexts. Finding contextual EGs in pathogenic conditions may facilitate the identification of therapeutic targets. We propose link clustering as an indicator of contextual EGs that are non-central in protein-protein interaction (PPI) networks. In various human and yeast PPI networks, we found that 29–47% of EGs were better characterized by link clustering than by centrality. Importantly, non-central EGs were prone to change their essentiality across different human cell lines and between species. Compared with central EGs and non-EGs, non-central EGs had intermediate levels of expression and evolutionary conservation. In addition, non-central EGs exhibited a significant impact on communities at lower hierarchical levels, suggesting that link clustering is associated with contextual essentiality, as it depicts locally important nodes in network structures.