Network analysis to identify driver genes and combination drugs in brain cancer.

Brain cancer is one of the deadliest diseases, although many efforts have been made to treat it, there is no comprehensive and effective treatment approach yet. In recent years, the use of network-based analysis to identify important biological genes and pathways involved in various complex diseases, including brain cancer, has attracted the attention of researchers. The goal of this manuscript is to perform a comprehensive analysis of the various results presented related to brain cancer. For this purpose, firstly, based on the CORMINE medical database, collected all the genes related to brain cancer with a valid P-value. Then the structural and functional relationships between the above gene sets have been identified based on the STRING database. Next, in the PPI network, hub centrality analysis was performed to determine the proteins that have many connections with other proteins. After the modularization of the network, the module with the most hub vertices is considered as the most relevant module to the formation and progression of brain cancer. Since the driver vertices play an important role in biological systems, the edges of the selected module were oriented, and by analyzing the controllability of complex networks, a set of five proteins with the highest control power has been identified. Finally, based on the drug-gene interaction, a set of drugs effective on each of the driver genes has been obtained, which can potentially be used as new combination drugs. Validation of the hub and driver proteins shows that they are mainly essential proteins in the biological processes related to the various cancers and therefore the drugs that affect them can be considered as new combination therapy. The presented procedure can be used for any other complex disease.

Proteomics Analysis of Duck Lung Tissues in Response to Highly Pathogenic Avian Influenza Virus.

Domestic ducks (Anas platyrhynchos domesticus) are resistant to most of the highly pathogenic avian influenza virus (HPAIV) infections. In this study, we characterized the lung proteome and phosphoproteome of ducks infected with the HPAI H5N1 virus (A/duck/India/02CA10/2011/Agartala) at 12 h, 48 h, and 5 days post-infection. A total of 2082 proteins were differentially expressed and 320 phosphorylation sites mapping to 199 phosphopeptides, corresponding to 129 proteins were identified. The functional annotation of the proteome data analysis revealed the activation of the RIG-I-like receptor and Jak-STAT signaling pathways, which led to the induction of interferon-stimulated gene (ISG) expression. The pathway analysis of the phosphoproteome datasets also confirmed the activation of RIG-I, Jak-STAT signaling, NF-kappa B signaling, and MAPK signaling pathways in the lung tissues. The induction of ISG proteins (STAT1, STAT3, STAT5B, STAT6, IFIT5, and PKR) established a protective anti-viral immune response in duck lung tissue. Further, the protein-protein interaction network analysis identified proteins like AKT1, STAT3, JAK2, RAC1, STAT1, PTPN11, RPS27A, NFKB1, and MAPK1 as the main hub proteins that might play important roles in disease progression in ducks. Together, the functional annotation of the proteome and phosphoproteome datasets revealed the molecular basis of the disease progression and disease resistance mechanism in ducks infected with the HPAI H5N1 virus.

Distinct dynamics and proximity networks of hub proteins at the prey-invading cell pole in a predatory bacterium.

In bacteria, cell poles function as subcellular compartments where proteins localize during specific lifecycle stages, orchestrated by polar "hub" proteins. Whereas most described bacteria inherit an "old" pole from the mother cell and a "new" pole from cell division, generating cell asymmetry at birth, non-binary division poses challenges for establishing cell polarity, particularly for daughter cells inheriting only new poles. We investigated polarity dynamics in the obligate predatory bacterium Bdellovibrio bacteriovorus, proliferating through filamentous growth followed by non-binary division within prey bacteria. Monitoring the subcellular localization of two proteins known as polar hubs in other species, RomR and DivIVA, revealed RomR as an early polarity marker in B. bacteriovorus. RomR already marks the future anterior poles of the progeny during the predator's growth phase, during a precise period closely following the onset of divisome assembly and the end of chromosome segregation. In contrast to RomR's stable unipolar localization in the progeny, DivIVA exhibits a dynamic pole-to-pole localization. This behavior changes shortly before the division of the elongated predator cell, where DivIVA accumulates at all septa and both poles. In vivo protein interaction networks for DivIVA and RomR, mapped through endogenous miniTurbo-based proximity labeling, further underscore their distinct roles in cell polarization and reinforce the importance of the anterior "invasive" cell pole in prey-predator interactions. Our work also emphasizes the precise spatiotemporal order of cellular processes underlying B. bacteriovorus proliferation, offering insights into the subcellular organization of bacteria with filamentous growth and non-binary division.IMPORTANCEIn bacteria, cell poles are crucial areas where "hub" proteins orchestrate lifecycle events through interactions with multiple partners at specific times. While most bacteria exhibit one "old" and one "new" pole, inherited from the previous division event, setting polar identity poses challenges in bacteria with non-binary division. This study explores polar proteins in the predatory bacterium Bdellovibrio bacteriovorus, which undergoes filamentous growth followed by non-binary division inside another bacterium. Our research reveals distinct localization dynamics of the polar proteins RomR and DivIVA, highlighting RomR as an early "hub" marking polar identity in the filamentous mother cell. Using miniTurbo-based proximity labeling, we uncovered their unique protein networks. Overall, our work provides new insights into the cell polarity in non-binary dividing bacteria.

Screening out molecular pathways and prognostic biomarkers of ultraviolet-mediated melanoma through computational techniques.

PURPOSE: Ultraviolet radiation causes skin cancer, but the exact mechanism by which it occurs and the most effective methods of intervention to prevent it are yet unknown. For this purpose, our study will use bioinformatics and systems biology approaches to discover potential biomarkers of skin cancer for early diagnosis and prevention of disease with applicable clinical treatments. METHODS: This study compared gene expression and protein levels in ultraviolet-mediated cultured keratinocytes and adjacent normal skin tissue using RNA sequencing data from the National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) database. Then, pathway analysis was employed with a selection of hub genes from the protein-protein interaction (PPI) network and the survival and expression profiles. Finally, potential clinical biomarkers were validated by receiver operating characteristic (ROC) curve analysis. RESULTS: We identified 32 shared differentially expressed genes (DEGs) by analyzing three different subsets of the GSE85443 dataset. Skin cancer development is related to the control of several DEGs through cyclin-dependent protein serine/threonine kinase activity, cell cycle regulation, and activation of the NIMA kinase pathways. The cytoHubba plugin in Cytoscape identified 12 hub genes from PPI; among these 3 DEGs, namely, AURKA, CDK4, and PLK1 were significantly associated with survival (P < 0.05) and highly expressed in skin cancer tissues. For validation purposes, ROC curve analysis indicated two biomarkers: AURKA (area under the curve (AUC) value = 0.8) and PLK1 (AUC value = 0.7), which were in an acceptable range. CONCLUSIONS: Further translational research, including clinical experiments, teratogenicity tests, and in-vitro or in-vivo studies, will be performed to evaluate the expression of these identified biomarkers regarding the prognosis of skin cancer patients.

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.

Exploration of key drug target proteins highlighting their related regulatory molecules, functional pathways and drug candidates associated with delirium: evidence from meta-data analyses.

BACKGROUND: Delirium is a prevalent neuropsychiatric medical phenomenon that causes serious emergency outcomes, including mortality and morbidity. It also increases the suffering and the economic burden for families and carers. Unfortunately, the pathophysiology of delirium is still unknown, which is a major obstacle to therapeutic development. The modern network-based system biology and multi-omics analysis approach has been widely used to recover the key drug target biomolecules and signaling pathways associated with disease pathophysiology. This study aimed to identify the major drug target hub-proteins associated with delirium, their regulatory molecules with functional pathways, and repurposable drug candidates for delirium treatment. METHODS: We used a comprehensive proteomic seed dataset derived from a systematic literature review and the Comparative Toxicogenomics Database (CTD). An integrated multi-omics network-based bioinformatics approach was utilized in this study. The STRING database was used to construct the protein-protein interaction (PPI) network. The gene set enrichment and signaling pathways analysis, the regulatory transcription factors and microRNAs were conducted using delirium-associated genes. Finally, hub-proteins associated repurposable drugs were retrieved from CMap database. RESULTS: We have distinguished 11 drug targeted hub-proteins (MAPK1, MAPK3, TP53, JUN, STAT3, SRC, RELA, AKT1, MAPK14, HSP90AA1 and DLG4), 5 transcription factors (FOXC1, GATA2, YY1, TFAP2A and SREBF1) and 6 microRNA (miR-375, miR-17-5, miR-17-5p, miR-106a-5p, miR-125b-5p, and miR-125a-5p) associated with delirium. The functional enrichment and pathway analysis revealed the cytokines, inflammation, postoperative pain, oxidative stress-associated pathways, developmental biology, shigellosis and cellular senescence which are closely connected with delirium development and the hallmarks of aging. The hub-proteins associated computationally identified repurposable drugs were retrieved from database. The predicted drug molecules including aspirin, irbesartan, ephedrine-(racemic), nedocromil, and guanidine were characterized as anti-inflammatory, stimulating the central nervous system, neuroprotective medication based on the existing literatures. The drug molecules may play an important role for therapeutic development against delirium if they are investigated more extensively through clinical trials and various wet lab experiments. CONCLUSION: This study could possibly help future research on investigating the delirium-associated therapeutic target biomarker hub-proteins and repurposed drug compounds. These results will also aid understanding of the molecular mechanisms that underlie the pathophysiology of delirium onset and molecular function.

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.

Thigh muscle metabolic response is linked to feed efficiency and meat characteristics in slow-growing chicken.

The Korat chicken (KR) is a slow-growing Thai chicken breed with relatively poor feed efficiency (FE) but very tasty meat with high protein and low fat contents, and a unique texture. To enhance the competitiveness of KR, its FE should be improved. However, selecting for FE has an unknown effect on meat characteristics. Thus, understanding the genetic basis underlying FE traits and meat characteristics is needed. In this study, 75 male KR birds were raised up to 10 wk of age. For each bird, the feed conversion ratio (FCR), residual feed intake (RFI), and physicochemical properties, flavor precursors, and biological compounds in the thigh meat were evaluated. At 10 wk of age, thigh muscle samples from 6 birds (3 with high FCR and 3 with low FCR values) were selected, and their proteomes were investigated using a label-free proteomic method. Weighted gene coexpression network analysis (WGCNA) was used to screen the key protein modules and pathways. The WGCNA results revealed that FE and meat characteristics significantly correlated with the same protein module. However, the correlation was unfavorable; improving FE may result in a decrease in meat quality through the alteration in biological processes including glycolysis/gluconeogenesis, metabolic pathway, carbon metabolism, biosynthesis of amino acids, pyruvate metabolism, and protein processing in the endoplasmic reticulum. The hub proteins of the significant module (TNNT1, TNNT3, TNNI2, TNNC2, MYLPF, MYH10, GADPH, PGK1, LDHA, and GPI) were also identified to be associated with energy metabolism, and muscle growth and development. Given that the same proteins and pathways are present in FE and meat characteristics but in opposite directions, selection practices for KR should simultaneously consider both trait groups to maintain the high meat quality of KR while improving FE.

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.

Construction and analysis of protein-protein interaction networks based on nuclear proteomics data of the desiccation-tolerant Xerophyta schlechteri leaves subjected to dehydration stress.

In order to understand the mechanism of desiccation tolerance in Xerophyta schlechteri, we carried out an in silico study to identify hub proteins and functional modules in the nuclear proteome of the leaves. Protein-protein interaction networks were constructed and analyzed from proteome data obtained from Abdalla and Rafudeen. We constructed networks in Cytoscape using the GeneMania software and analyzed them using a Network Analyzer. Functional enrichment analysis of key proteins in the respective networks was done using GeneMania network enrichment analysis, and GO (Gene Ontology) terms were summarized using REViGO. Also, community analysis of differentially expressed proteins was conducted using the Cytoscape Apps, GeneMania and ClusterMaker. Functional modules associated with the communities were identified using an online tool, ShinyGO. We identified HSP 70-2 as the super-hub protein among the up-regulated proteins. On the other hand, 40S ribosomal protein S2-3 (a protein added by GeneMANIA) was identified as a super-hub protein associated with the down-regulated proteins. For up-regulated proteins, the enriched biological process terms were those associated with chromatin organization and negative regulation of transcription. In the down-regulated protein-set, terms associated with protein synthesis were significantly enriched. Community analysis identified three functional modules that can be categorized as chromatin organization, anti-oxidant activity and metabolic processes.

Linker Length Drives Heterogeneity of Multivalent Complexes of Hub Protein LC8 and Transcription Factor ASCIZ.

LC8, a ubiquitous and highly conserved hub protein, binds over 100 proteins involved in numerous cellular functions, including cell death, signaling, tumor suppression, and viral infection. LC8 binds intrinsically disordered proteins (IDPs), and although several of these contain multiple LC8 binding motifs, the effects of multivalency on complex formation are unclear. Drosophila ASCIZ has seven motifs that vary in sequence and inter-motif linker lengths, especially within subdomain QT2-4 containing the second, third, and fourth LC8 motifs. Using isothermal-titration calorimetry, analytical-ultracentrifugation, and native mass-spectrometry of QT2-4 variants, with methodically deactivated motifs, we show that inter-motif spacing and specific motif sequences combine to control binding affinity and compositional heterogeneity of multivalent duplexes. A short linker separating strong and weak motifs results in stable duplexes but forms off-register structures at high LC8 concentrations. Contrastingly, long linkers engender lower cooperativity and heterogeneous complexation at low LC8 concentrations. Accordingly, two-mers, rather than the expected three-mers, dominate negative-stain electron-microscopy images of QT2-4. Comparing variants containing weak-strong and strong-strong motif combinations demonstrates sequence also regulates IDP/LC8 assembly. The observed trends persist for trivalent ASCIZ subdomains: QT2-4, with long and short linkers, forms heterogeneous complexes, whereas QT4-6, with similar mid-length linkers, forms homogeneous complexes. Implications of linker length variations for function are discussed.

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.

The protein-protein interaction network of intestinal gastric cancer patients reveals hub proteins with potential prognostic value.

BACKGROUND: Gastric cancer (GC) is the third leading cause of cancer worldwide. According to the Lauren classification, gastric adenocarcinoma is divided into two subtypes: diffuse and intestinal. The development of intestinal gastric cancer (IGC) can take years and involves multiple factors. OBJECTIVE: To investigate the protein profile of tumor samples from patients with IGC in comparison with adjacent nontumor tissue samples. METHODS: We used label-free nano-LC-MS/MS to identify proteins from the tissues samples. The results were analyzed using MetaCore™ software to access functional enrichment information. Protein-protein interactions (PPI) were predicted using STRING analysis. Hub proteins were determined using the Cytoscape plugin, CytoHubba. Survival analysis was performed using KM plotter. We identified 429 differentially expressed proteins whose pathways and processes were related to protein folding, apoptosis, and immune response. RESULTS: The PPI network of these proteins showed enrichment modules related to the regulation of cell death, immune system, neutrophil degranulation, metabolism of RNA and chromatin DNA binding. From the PPI network, we identified 20 differentially expressed hub proteins, and assessed the prognostic value of the expression of genes that encode them. Among them, the expression of four hub genes was significantly associated with the overall survival of IGC patients. CONCLUSIONS: This study reveals important findings that affect IGC development based on specific biological alterations in IGC patients. Bioinformatics analysis showed that the pathogenesis of IGC patients is complex and involves different interconnected biological processes. These findings may be useful in research on new targets to develop novel therapies to improve the overall survival of patients with IGC.

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.

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.

A network-based systems biology approach for identification of shared Gene signatures between male and female in COVID-19 datasets.

The novel coronavirus (SARS-CoV-2) has expanded rapidly worldwide. Now it has covered more than 150 countries worldwide. It is referred to as COVID-19. SARS-CoV-2 mainly affects the respiratory systems of humans that can lead up to serious illness or even death in the presence of different comorbidities. However, most COVID-19 infected people show mild to moderate symptoms, and no medication is suggested. Still, drugs of other diseases have been used to treat COVID-19. Nevertheless, the absence of vaccines and proper drugs against the COVID-19 virus has increased the mortality rate. Albeit sex is a risk factor for COVID-19, none of the studies considered this risk factor for identifying biomarkers from the RNASeq count dataset. Men are more likely to undertake severe symptoms with different comorbidities and show greater mortality compared with women. From this standpoint, we aim to identify shared gene signatures between males and females from the human COVID-19 RNAseq count dataset of peripheral blood cells using a robust voom approach. We identified 1341 overlapping DEGs between male and female datasets. The gene ontology (GO) annotation and pathway enrichment analysis revealed that DEGs are involved in various BP categories such as nucleosome assembly, DNA conformation change, DNA packaging, and different KEGG pathways such as cell cycle, ECM-receptor interaction, progesterone-mediated oocyte maturation, etc. Ten hub-proteins (UBC, KIAA0101, APP, CDK1, SUMO2, SP1, FN1, CDK2, E2F1, and TP53) were unveiled using PPI network analysis. The top three miRNAs (mir-17-5p, mir-20a-5p, mir-93-5p) and TFs (PPARG, E2F1 and KLF5) were uncovered. In conclusion, the top ten significant drugs (roscovitine, curcumin, simvastatin, fulvestrant, troglitazone, alvocidib, L-alanine, tamoxifen, serine, and doxorubicin) were retrieved using drug repurposing analysis of overlapping DEGs, which might be therapeutic agents of COVID-19.

Identification of Differential Expression Cytokines in Hemolysis, Elevated Liver Enzymes, and Low Platelet Syndrome by Proteome Microarray Analysis and Further Verification.

To screen the differential expression cytokines (DECs) in hemolysis, elevated liver enzymes, and low platelet (HELLP) syndrome, establish its differential cytokines spectra, and provide the clues for its diagnosis and pathogenic mechanism researches. Sera from four HELLP syndrome patients and four healthy controls were detected by proteome microarray. Then the analysis of Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) network were performed and possible hub proteins were selected out, further verified by Enzyme Linked Immunosorbent Assay (ELISA) in sera from 21 HELLP syndrome patients and 21 healthy controls. Thirty DECs were defined according to P-value and fold change between HELLP group and control group. GO enrichment analysis showed that DECs were mainly involved in the regulation of inflammatory response and have relationship to growth factor binding, transmembrane receptor protein kinase, and cytokine receptor activity. Seven possible hub proteins were defined by PPI analysis, including IGFBP-3/Follistatin-like 1/FLRG/Fetuin A and MMP-13/Thrombospondin-5/Aggrecan. ELISA showed higher serum levels of Fetuin A/IGFBP-3/FLGR/MMP-13/Thrombospondin-5 in HELLP group than those in controls, while the levels of Follistatin-like 1 and Aggrecan were lower in HELLP patients (all P < 0.05 or <0.01).The serological DECs spectra of HELLP syndrome was established and seven possible hub proteins that may be more closely related to the disease have been verified, providing new clues for its pathogenesis, diagnosis, and clinical treatment.

Large-Scale Proteomic Analysis of Follicular Lymphoma Reveals Extensive Remodeling of Cell Adhesion Pathway and Identifies Hub Proteins Related to the Lymphomagenesis.

Follicular lymphoma (FL) represents the major subtype of indolent B-cell non-Hodgkin lymphomas (B-NHLs) and results from the malignant transformation of mature B-cells in lymphoid organs. Although gene expression and genomic studies have identified multiple disease driving gene aberrations, only a few proteomic studies focused on the protein level. The present work aimed to examine the proteomic profiles of follicular lymphoma vs. normal B-cells obtained by fine-needle aspiration biopsy (FNAB) to gain deep insight into the most perturbed pathway of FL. The cells of interest were purified by magnetic-activated cell sorting (MACS). High-throughput proteomic profiling was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and allowed to identify of 6724 proteins in at least 75% of each group of samples. The 'Total Protein Approach' (TPA) was applied to the absolute quantification of proteins in this study. We identified 1186 differentially abundant proteins (DAPs) between FL and control samples, causing an extensive remodeling of several molecular pathways, including the B-cell receptor signaling pathway, cellular adhesion molecules, and PPAR pathway. Additionally, the construction of protein-protein interactions networks (PPINs) and identification of hub proteins allowed us to indicate the key player proteins for FL pathology. Finally, ICAM1, CD9, and CD79B protein expression was validated in an independent cohort by flow cytometry (FCM), and the results were consistent with the mass spectrometry (MS) data.

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. Video Abstract.