Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology

PURPOSE

Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations.

METHODS

To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins.

RESULTS

We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences.

CONCLUSION

Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

In silico based analysis to explore genetic linkage between atherosclerosis and its potential risk factors

Atherosclerosis (ATH) is a chronic cardiovascular disease characterized by plaque formation in arteries, and it is a major cause of illness and death. Although therapeutic advances have significantly improved the prognosis of ATH, missing therapeutic targets pose a significant residual threat. This research used a systems biology approach to identify the molecular biomarkers involved in the onset and progression of ATH, analysing microarray gene expression datasets from ATH and tissues impacted by risk factors such as high cholesterol, adipose tissue, smoking, obesity, sedentary lifestyle, stress, alcohol consumption, hypertension, hyperlipidaemia, high fat, diabetes to find the differentially expressed genes (DEGs). Bioinformatic analyses of Protein-Protein Interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted on differentially expressed genes, revealing metabolic and signaling pathways (the chemokine signaling pathway, cytokine-cytokine receptor interaction, the cytosolic DNA-sensing pathway, the peroxisome proliferator-activated receptors signaling pathway, and the nuclear factor-kappa B signaling pathway), ten hubs proteins (CCL5, CCR1, TLR1, CCR2, FCGR2A, IL1B, CD163, AIF1, CXCL-1 and TNF), five transcription factors (YY1, FOXL1, FOXC1, SRF, and GATA2), and five miRNAs (mir-27a-3p, mir-124-3p, mir-16-5p, mir-129-2-3p, mir-1-3p). These findings identify potential biomarkers that may increase knowledge of the mechanisms underlying ATH and their connection to risk factors, aiding in the development of new therapies.

Protein-protein interaction (PPI) network analysis reveals important hub proteins and sub-network modules for root development in rice (Oryza sativa)

BACKGROUND

The root system is vital to plant growth and survival. Therefore, genetic improvement of the root system is beneficial for developing stress-tolerant and improved plant varieties. This requires the identification of proteins that significantly contribute to root development. Analyzing protein-protein interaction (PPI) networks is vastly beneficial in studying developmental phenotypes, such as root development, because a phenotype is an outcome of several interacting proteins. PPI networks can be analyzed to identify modules and get a global understanding of important proteins governing the phenotypes. PPI network analysis for root development in rice has not been performed before and has the potential to yield new findings to improve stress tolerance.

RESULTS

Here, the network module for root development was extracted from the global Oryza sativa PPI network retrieved from the STRING database. Novel protein candidates were predicted, and hub proteins and sub-modules were identified from the extracted module. The validation of the predictions yielded 75 novel candidate proteins, 6 sub-modules, 20 intramodular hubs, and 2 intermodular hubs.

CONCLUSIONS

These results show how the PPI network module is organized for root development and can be used for future wet-lab studies for producing improved rice varieties.

Drug Repurposing and Systems Biology approaches of Enzastaurin can target potential biomarkers and critical pathways in Colorectal Cancer

Colorectal cancer (CRC) is a severe health concern that results from a cocktail of genetic, epigenetic, and environmental abnormalities. Because it is the second most lethal malignancy in the world and the third-most common malignant tumor, but the treatment is unavailable. The goal of the current study was to use bioinformatics and systems biology techniques to determine the pharmacological mechanism underlying putative important genes and linked pathways in early-onset CRC. Computer-aided methods were used to uncover similar biological targets and signaling pathways associated with CRC, along with bioinformatics and network pharmacology techniques to assess the effects of enzastaurin on CRC. The KEGG and gene ontology (GO) pathway analysis revealed several significant pathways including in positive regulation of protein phosphorylation, negative regulation of the apoptotic process, nucleus, nucleoplasm, protein tyrosine kinase activity, PI3K-Akt signaling pathway, pathways in cancer, focal adhesion, HIF-1 signaling pathway, and Rap1 signaling pathway. Later, the hub protein module identified from the protein-protein interactions (PPIs) network, molecular docking and molecular dynamics simulation represented that enzastaurin showed strong binding interaction with two hub proteins including CASP3 (-8.6 kcal/mol), and MCL1 (-8.6 kcal/mol), which were strongly implicated in CRC management than other the five hub proteins. Moreover, the pharmacokinetic features of enzastaurin revealed that it is an effective therapeutic agent with minimal adverse effects. Enzastaurin may inhibit the potential biological targets that are thought to be responsible for the advancement of CRC and this study suggests a potential novel therapeutic target for CRC.

An integrated in-silico Pharmaco-BioInformatics approaches to identify synergistic effects of COVID-19 to HIV patients

BACKGROUND

With high inflammatory states from both COVID-19 and HIV conditions further result in complications. The ongoing confrontation between these two viral infections can be avoided by adopting suitable management measures.

PURPOSE

The aim of this study was to figure out the pharmacological mechanism behind apigenin's role in the synergetic effects of COVID-19 to the progression of HIV patients.

METHOD

We employed computer-aided methods to uncover similar biological targets and signaling pathways associated with COVID-19 and HIV, along with bioinformatics and network pharmacology techniques to assess the synergetic effects of apigenin on COVID-19 to the progression of HIV, as well as pharmacokinetics analysis to examine apigenin's safety in the human body.

RESULT

Stress-responsive, membrane receptor, and induction pathways were mostly involved in gene ontology (GO) pathways, whereas apoptosis and inflammatory pathways were significantly associated in the Kyoto encyclopedia of genes and genomes (KEGG). The top 20 hub genes were detected utilizing the shortest path ranked by degree method and protein-protein interaction (PPI), as well as molecular docking and molecular dynamics simulation were performed, revealing apigenin's strong interaction with hub proteins (MAPK3, RELA, MAPK1, EP300, and AKT1). Moreover, the pharmacokinetic features of apigenin revealed that it is an effective therapeutic agent with minimal adverse effects, for instance, hepatoxicity.

CONCLUSION

Synergetic effects of COVID-19 on the progression of HIV may still be a danger to global public health. Consequently, advanced solutions are required to give valid information regarding apigenin as a suitable therapeutic agent for the management of COVID-19 and HIV synergetic effects. However, the findings have yet to be confirmed in patients, suggesting more in vitro and in vivo studies.

Interactome of human and SARS-CoV-2 proteins to identify human hub proteins associated with comorbidities

SARS-CoV-2 has a higher chance of progression in adults of any age with certain underlying health conditions or comorbidities like cancer, neurological diseases and in certain cases may even lead to death. Like other viruses, SARS-CoV-2 also interacts with host proteins to pave its entry into host cells. Therefore, to understand the behaviour of SARS-CoV-2 and design of effective antiviral drugs, host-virus protein-protein interactions (PPIs) can be very useful. In this regard, we have initially created a human-SARS-CoV-2 PPI database from existing works in the literature which has resulted in 7085 unique PPIs. Subsequently, we have identified at most 10 proteins with highest degrees viz. hub proteins from interacting human proteins for individual virus protein. The identification of these hub proteins is important as they are connected to most of the other human proteins. Consequently, when they get affected, the potential diseases are triggered in the corresponding pathways, thereby leading to comorbidities. Furthermore, the biological significance of the identified hub proteins is shown using KEGG pathway and GO enrichment analysis. KEGG pathway analysis is also essential for identifying the pathways leading to comorbidities. Among others, SARS-CoV-2 proteins viz. NSP2, NSP5, Envelope and ORF10 interacting with human hub proteins like COX4I1, COX5A, COX5B, NDUFS1, CANX, HSP90AA1 and TP53 lead to comorbidities. Such comorbidities are Alzheimer, Parkinson, Huntington, HTLV-1 infection, prostate cancer and viral carcinogenesis. Subsequently, using Enrichr tool possible repurposable drugs which target the human hub proteins are reported in this paper as well. Therefore, this work provides a consolidated study for human-SARS-CoV-2 protein interactions to understand the relationship between comorbidity and hub proteins so that it may pave the way for the development of anti-viral drugs.

An Ebola, Neisseria and Trypanosoma human protein interaction census reveals a conserved human protein cluster targeted by various human pathogens

Filovirus ebolavirus (ZE; Zaire ebolavirus, Bundibugyo ebolavirus), Neisseria meningitidis (NM), and Trypanosoma brucei (Tb) are serious infectious pathogens, spanning viruses, bacteria and protists and all may target the blood and central nervous system during their life cycle. NM and Tb are extracellular pathogens while ZE is obligatory intracellular, targetting immune privileged sites. By using interactomics and comparative evolutionary analysis we studied whether conserved human proteins are targeted by these pathogens. We examined 2797 unique pathogen-targeted human proteins. The information derived from orthology searches of experimentally validated protein-protein interactions (PPIs) resulted both in unique and shared PPIs for each pathogen. Comparing and analyzing conserved and pathogen-specific infection pathways for NM, TB and ZE, we identified human proteins predicted to be targeted in at least two of the compared host-pathogen networks. However, four proteins were common to all three host-pathogen interactomes: the elongation factor 1-alpha 1 (EEF1A1), the SWI/SNF complex subunit SMARCC2 (matrix-associated actin-dependent regulator of chromatin subfamily C), the dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 1 (RPN1), and the tubulin beta-5 chain (TUBB). These four human proteins all are also involved in cytoskeleton and its regulation and are often addressed by various human pathogens. Specifically, we found (i) 56 human pathogenic bacteria and viruses that target these four proteins, (ii) the well researched new pandemic pathogen SARS-CoV-2 targets two of these four human proteins and (iii) nine human pathogenic fungi (yet another evolutionary distant organism group) target three of the conserved proteins by 130 high confidence interactions.

Connecting the αα-hubs: same fold, disordered ligands, new functions

Background

Signal fidelity depends on protein–protein interaction–‘hubs’ integrating cues from large interactomes. Recently, and based on a common secondary structure motif, the αα-hubs were defined, which are small α-helical domains of large, modular proteins binding intrinsically disordered transcriptional regulators.

Methods

Comparative structural biology.

Results

We assign the harmonin-homology-domain (HHD, also named the harmonin N-terminal domain, NTD) present in large proteins such as harmonin, whirlin, cerebral cavernous malformation 2, and regulator of telomere elongation 1 to the αα-hubs. The new member of the αα-hubs expands functionality to include scaffolding of supra-modular complexes mediating sensory perception, neurovascular integrity and telomere regulation, and reveal novel features of the αα-hubs. As a common trait, the αα-hubs bind intrinsically disordered ligands of similar properties integrating similar cellular cues, but without cross-talk.

Conclusion

The inclusion of the HHD in the αα-hubs has uncovered new features, exemplifying the utility of identifying groups of hub domains, whereby discoveries in one member may cross-fertilize discoveries in others. These features make the αα-hubs unique models for decomposing signal specificity and fidelity. Using these as models, together with other suitable hub domain, we may advance the functional understanding of hub proteins and their role in cellular communication and signaling, as well as the role of intrinsically disordered proteins in signaling networks.

In Silico Protein Interaction Network Analysis of Virulence Proteins Associated with Invasive Aspergillosis for Drug Discovery

BACKGROUND

Protein-Protein interaction (PPI) network analysis of virulence proteins of Aspergillus fumigatus is a prevailing strategy to understand the mechanism behind the virulence of A. fumigatus. The identification of major hub proteins and targeting the hub protein as a new antifungal drug target will help in treating the invasive aspergillosis.

MATERIALS & METHOD

In the present study, the PPI network of 96 virulence (drug target) proteins of A. fumigatus were investigated which resulted in 103 nodes and 430 edges. Topological enrichment analysis of the PPI network was also carried out by using STRING database and Network analyzer a cytoscape plugin app. The key enriched KEGG pathway and protein domains were analyzed by STRING.

CONCLUSION

Manual curation of PPI data identified three proteins (PyrABCN-43, AroM-34, and Glt1- 34) of A. fumigatus possessing the highest interacting partners. Top 10% hub proteins were also identified from the network using cytohubba on the basis of seven algorithms, i.e. betweenness, radiality, closeness, degree, bottleneck, MCC and EPC. Homology model and the active pocket of top three hub proteins were also predicted.

Phosphorylated proteomics analysis of human coronary artery endothelial cells stimulated by Kawasaki disease patients serum

BACKGROUND

Kawasaki disease (KD) is an acute febrile childhood systemic vasculitis that disturbs coronary arteries. The pathogenesis remains unknown. The study of phosphorylated proteins helps to elucidate the relevant pathophysiological mechanisms of cardiovascular disease. However, few researches explored phosphorylated proteins in KD patients.

METHODS

We compared phosphoprotein profiles of HCAECs stimulated by the serum of KD patients and normal children using iTRAQ technology, TiO₂ enrichment phosphorylated peptide and MS analysis. Then we conducted the functional analysis by ClueGO and the biological interaction networking analysis by ReactomeFIViz. Western blotting was performed to identify the hub proteins.

RESULTS

Our results revealed that phosphorylation of 148 proteins showed different intensities between the two HCAECs groups, which are enriched in MAPK, VEGFR, EGFR, Angiopoietin receptor, mTOR, FAK signaling pathway and so on. Through the Network Analyzer analysis, the hub proteins are CDKN1A, MAPK1 and POLR2A, which were experimentally validated.

CONCLUSION

In summary, we provided evidence addressing the valuable phosphorylation signaling that could be useful resource to understand the molecular mechanism and the potential targets for novel therapy of KD.

Protein Interaction Mapping related to Becker Muscular Dystrophy

OBJECTIVE

Becker Muscular Dystrophy (BMD) is a neuromuscular disorder which is incurable. In this research protein interaction network of most associated proteins with BMD to provide better clarification of disorder underlying mechanism was investigated.

MATERIALS & METHODS

The related genes to BMD were retrieved via string database and conducted by Cytoscape and the related algorithms. The network centrality analysis was performed based on degree, betweenness, closeness, and stress parameters. Gene ontology and clustering were performed via ClueGO analysis.

RESULTS

DMD as the super-hub as well as other central proteins including UTRN, TTN, DNM2, and RYR1 are important in BMD in terms of interactive features. The impairment of muscular contraction may be vital in BMD disease pathogenesis as it is the highlighted biological process term obtained by ClueGO analysis.

CONCLUSION

DMD targeting may be the main concern for dystrophy clinical approaches. However, the other suggested proteins should be evaluated. Targeting these key proteins are required for treatment goals following extensive validation studies.

Structural pliability adjacent to the kinase domain highlights contribution of FAK1 IDRs to cytoskeletal remodeling

Therapeutic protein kinase inhibitors are designed on the basis of kinase structures. Here, we define intrinsically disordered regions (IDRs) in structurally hybrid kinases. We reveal that 65% of kinases have an IDR adjacent to their kinase domain (KD). These IDRs are evolutionarily more conserved than IDRs distant to KDs. Strikingly, 36 kinases have adjacent IDRs extending into their KDs, defining a unique structural and functional subset of the kinome. Functional network analysis of this subset of the kinome uncovered FAK1 as topologically the most connected hub kinase. We identify that KD-flanking IDR of FAK1 is more conserved and undergoes more post-translational modifications than other IDRs. It preferentially interacts with proteins regulating scaffolding and kinase activity, which contribute to cytoskeletal remodeling. In summary, spatially and evolutionarily conserved IDRs in kinases may influence their functions, which can be exploited for targeted therapies in diseases including those that involve aberrant cytoskeletal remodeling.

A proteomic approach of pediatric astrocytomas: MiRNAs and network insight

Pediatric astrocytomas, a leading cause of death associated with cancer, are the most common primary central nervous system tumors found in children. Most studies of these tumors focus on adults, not on children. We examined the global protein and microRNA expression pattern by 2D SDS-PAGE, mass spectrometry (MALDI-TOF), and RT(2) miRNA PCR Array System. Proteomic studies revealed 49 proteins with changes on the expression. Interactome showed that vimentin, calreticulin, and 14-3-3 epsilon protein are hub proteins in these neoplasms. MicroRNA analyses demonstrated for the first time novel microRNAs involved in the astrocytoma biology. In conclusion, our results show that novel proteins and microRNAs with expression changes on pediatric astrocytoma could serve as biomarkers of tumor progression.

BIOLOGICAL SIGNIFICANCE

Astrocytomas are tumors that progress rapidly and that invade surrounding tissues. Although some drugs have been developed to treat these neoplasms, the mortality of patients is still very high. In this study, we describe for the first time, to our knowledge, some proteins and miRNAs associated with the biology of astrocytic tumors that could be postulated as possible diagnostic or prognostic biomarkers. Altogether, our results indicate that large-scale analyses allow making a fairly accurate prediction of different cellular processes altered in astrocytic tumors.