Identified Hub Proteins Research Articles

Decoding the duration of fertility of laying chicken through phenotypic and proteomic evaluation

ABSTRACT 1. This study determined the effective indicators and proteins involved in long-duration fertility (DF) in chickens. 2. Three lines of Chinese Xinhua chickens (900) were compared using seven phenotypic trait indicators, and the best was determined based on repeatability value. Subsequently, differential expression analysis, functional annotation and protein-protein interaction (PPI) network analyses were performed to investigate the pathways and hub proteins. Finally, qPCR analysis was conducted to validate the expression of identified hub proteins, and functional annotation with previously published genes was performed to explain how hub proteins work to maintain the trait. 3. The study found that the number of fertilised eggs (FN) and maximum fertilised eggs (MCF) were the most repeatable among the seven indicators. It identified 231 differentially expressed proteins, with 144 being down-regulated and 87 being up-regulated. The differentially expressed proteins exhibited high clustering within various cellular compartments, including the cytosol and cytoplasm and GTP binding. Multiple pathways were identified, including tight and adherens junctions, TGF-beta signalling, autophagy-animal, regulation of actin cytoskeleton and the ribosome that may regulate the trait. Three hub proteins, KRAS, RPL5 (p < 0.001), and HSPA4 (p < 0.01), were significantly differentially expressed between high and low DF groups. 4. This study identified FN and MCF as effective indicators for addressing DF. As it is a quantitative trait, KRAS, HSPA4, and RPL5 are potential hub proteins that work with other genes to maintain the trait.

Genome-Wide Investigation Reveals Potential Therapeutic Targets in Shigella spp.

Shigella stands as a major contributor to bacterial dysentery worldwide scale, particularly in developing countries with inadequate sanitation and hygiene. The emergence of multidrug-resistant strains exacerbates the challenge of treating Shigella infections, particularly in regions where access to healthcare and alternative antibiotics is limited. Therefore, investigations on how bacteria evade antibiotics and eventually develop resistance could open new avenues for research to develop novel therapeutics. The aim of this study was to analyze whole genome sequence (WGS) of human pathogenic Shigella spp. to elucidate the antibiotic resistance genes (ARGs) and their mechanism of resistance, gene-drug interactions, protein-protein interactions, and functional pathways to screen potential therapeutic candidate(s). We comprehensively analyzed 45 WGS of Shigella, including S. flexneri (n = 17), S. dysenteriae (n = 14), S. boydii (n = 11), and S. sonnei (n = 13), through different bioinformatics tools. Evolutionary phylogenetic analysis showed three distinct clades among the circulating strains of Shigella worldwide, with less genomic diversity. In this study, 2,146 ARGs were predicted in 45 genomes (average 47.69 ARGs/genome), of which only 91 ARGs were found to be shared across the genomes. Majority of these ARGs conferred their resistance through antibiotic efflux pump (51.0%) followed by antibiotic target alteration (23%) and antibiotic target replacement (18%). We identified 13 hub proteins, of which four proteins (e.g., tolC, acrR, mdtA, and gyrA) were detected as potential hub proteins to be associated with antibiotic efflux pump and target alteration mechanisms. These hub proteins were significantly (p < 0.05) enriched in biological process, molecular function, and cellular components. Therefore, the finding of this study suggests that human pathogenic Shigella strains harbored a wide range of ARGs that confer resistance through antibiotic efflux pumps and antibiotic target modification mechanisms, which must be taken into account to devise and formulate treatment strategy against this pathogen. Moreover, the identified hub proteins could be exploited to design and develop novel therapeutics against MDR pathogens like Shigella.

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

PurposeGraph 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.MethodsTo 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.ResultsWe 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.ConclusionOur 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.

Structural modelling and preventive strategy targeting of WSSV hub proteins to combat viral infection in shrimp Penaeus monodon.

White spot syndrome virus (WSSV) presents a considerable peril to the aquaculture sector, leading to notable financial consequences on a global scale. Previous studies have identified hub proteins, including WSSV051 and WSSV517, as essential binding elements in the protein interaction network of WSSV. This work further investigates the functional structures and potential applications of WSSV hub complexes in managing WSSV infection. Using computational methodologies, we have successfully generated comprehensive three-dimensional (3D) representations of hub proteins along with their three mutual binding counterparts, elucidating crucial interaction locations. The results of our study indicate that the WSSV051 hub protein demonstrates higher binding energy than WSSV517. Moreover, a unique motif, denoted as "S-S-x(5)-S-x(2)-P," was discovered among the binding proteins. This pattern perhaps contributes to the detection of partners by the hub proteins of WSSV. An antiviral strategy targeting WSSV hub proteins was demonstrated through the oral administration of dual hub double-stranded RNAs to the black tiger shrimp, Penaeus monodon, followed by a challenge assay. The findings demonstrate a decrease in shrimp mortality and a cessation of WSSV multiplication. In conclusion, our research unveils the structural features and dynamic interactions of hub complexes, shedding light on their significance in the WSSV protein network. This highlights the potential of hub protein-based interventions to mitigate the impact of WSSV infection in aquaculture.

Exploring potential pathways and biomarkers of pancreatic cancer associated with lynch syndrome and type 2 diabetes: An integrated bioinformatics analysis

Pancreatic cancer (PC) is a devastating malignancy with intricate genetic underpinnings and a complex etiology. Emerging evidence suggests the presence of lynch syndrome (LS) and type 2 diabetes (T2D) associated susceptibility to PC. This study presents integrated computational and systems biology approaches to identify the genetic risk factors underlying the association between PC, LS, and T2D. Patient data for these three diseases have been collected from NCBI and differentially expressed genes (DEGs) identified by the GREIN web platform. Furthermore, protein-protein interaction (PPI), gene ontology (GO), and signaling pathway networks were analyzed through STRING and DAVID databases, respectively. Autodock Vina has been used for prospective analysis of ligand-protein interaction. About 60 unique common DEGs were identified by statistical analysis. In addition to the utilization of five distinct algorithms within the Cytoscape framework, we have reported three potential target candidates: TNF, CXCL1, and TNFSF10. In particular, the immune and inflammatory response, the chemokine-mediated signaling pathway, rheumatoid arthritis, and IL-17 signaling pathways emerged as prominently enriched pathways. Furthermore, the interaction of 162 phytochemicals from Nigella sativa was assessed with the identified hub proteins. Among these, thujopsene emerged as a notable ligand candidate, demonstrating the most favorable binding energy against the TNF (−9.6 kca/mol TNFSF10 (−8.5 kcal/mol), and CXCL1 (−9.1 kcal/mol) proteins. Besides, pharmacokinetics, toxicity, and drug-likeness properties of the thujopsene ligand showed an acceptable range for selection of a drug candidate. Collectively, these findings shed light on the intricate interplay of genes, pathways, and potential therapeutic compounds, providing a basis for further exploration and validation in the context of relevant diseases.

Functional network analysis identifies multiple virulence and antibiotic resistance targets in Stenotrophomonas maltophilia

Stenotrophomonas maltophilia, an emerging multidrug-resistant opportunistic bacterium in humans is of major concern for immunocompromised individuals for causing pneumonia and bloodborne infections. This bacterial pathogen is associated with a considerable fatality/case ratio, with up to 100%, when presented as hemorrhagic fever. It is resistant to commonly used drugs as well as to antibiotic combinations. In-silico based functional network analysis is a key approach to get novel insights into virulence and resistance in pathogenic organisms. This study included the protein-protein interaction (PPI) network analysis of 150 specific genes identified for antibiotic resistance mechanism and virulence pathways. Eight proteins, namely, PilL, FliA, Smlt2260, Smlt2267, CheW, Smlt2318, CheZ, and FliM were identified as hub proteins. Further docking studies of 58 selected phytochemicals were performed against the identified hub proteins. Deoxytubulosine and corosolic acid were found to be potent inhibitors of hub proteins of pathogenic S. maltophilia based on protein-ligand interactive study. Further pharmacophore studies are warranted with these molecules to develop them as novel antibiotics against S. maltophilia.

A proteomics study to explore differential proteins associated with the pathogenesis of ameloblastoma.

Reports on the proteomic studies of ameloblastoma and other common odontogenic lesions are limited. We thus explored the differential proteins among ameloblastoma, odontogenic keratocyst, dentigerous cyst, and normal gingival tissue using proteomics and identified hub proteins involved in the local aggressiveness and recurrence of ameloblastoma. Samples were obtained from 14 patients with ameloblastoma, 6 with odontogenic keratocyst, 9 with a dentigerous cyst, and 5 with normal gingival tissue. Proteins were then extracted, purified, quantified, and analysed using Easy-nLC chromatography and mass spectrometry. Further functional annotation and enrichment analyses were performed using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes on the target protein collection. Protein clustering and protein-protein interaction network analyses were used to screen the hub proteins. Proteins with significant interactions were screened according to their degree index. These results were verified by immunohistochemical staining. Proteins meeting the screening criteria of expression difference ploidy >1.2-fold (upregulation and downregulation) and p < 0.05 were considered differential proteins. In ameloblastoma, 808 differential proteins were upregulated and 505 were downregulated compared with those in odontogenic keratocyst; 309 were upregulated and 453 were downregulated compared with those in dentigerous cyst; and 2210 were upregulated and 829 were downregulated compared with those in normal gingival tissue. The three groups of differential proteins were associated with cellular exosomes, antigen binding, complement activation, human papillomavirus infection, focal adhesion, cell adhesion molecules, and metabolic pathways. CDH3 is associated with the local aggressiveness and recurrence of ameloblastoma and is a potential therapeutic target.

Deciphering the role of Andrographis paniculata micro-RNAs in regulation of cancer

Background and objectiveAndrographis paniculata belonging to Acanthaceae family is well-known potential hepatoprotective herb. The role of diet on human health and diseases including Hepatocellular Carcinoma has been demonstrated by innumerable studies which have also testified that plant-derived micro-RNAs, transferred to animals via oral consumption wherein it alters host gene expression and this provides the evidence for inter-species gene regulation. Herein, we aim to explore the role of predicted miRNAs of A. paniculata on human health. Methods and resultsIn the present study, A. paniculata miRNAs have been predicted and their impact on human transcriptome is uncovered by employing computational approach. By implementing miRNA prediction on A. paniculata genomic data and plant miRNA sequences present in miRBase repository, we have identified 20 miRNAs which are confined to 16 miR families. 197 human target genes were predicted for the identified miRNAs, which were further subjected to Gene Ontology analysis and network analysis revealing their significant role in various biological processes and signaling pathways. The identified hub proteins HGF, NRP2 and CCND1 show their significant involvement in cancerous pathways including STAT3, MAPK, TGF-β, mTOR and VEGFR pathways. Deregulation of these pathways has been reported as oncogenic response involved in Hepatocellular Carcinoma, cell proliferation and metastasis. ConclusionThe findings of our study suggest potentially beneficiary role of A. paniculata miRNAs in regulating cancer via cross-species genetic regulation. Conclusively, our study sustains the promising theory of inter-species gene regulation.

Proteomics and weighted gene correlated network analysis reveal glutamatergic synapse signaling in diazepam treatment of alcohol withdrawal.

Background: Alcohol use disorder (AUD) is characterized by chronic excessive alcohol consumption, often alternating with periods of abstinence known as alcohol withdrawal syndrome (AWS). Diazepam is the preferred benzodiazepine for treatment of alcohol withdrawal syndrome under most circumstances, but the specific mechanism underlying the treatment needs further research. Methods: We constructed an animal model of two-bottle choices and chronic intermittent ethanol exposure. LC-MS/MS proteomic analysis based on the label-free and intensity-based quantification approach was used to detect the protein profile of the whole brain. Weighted gene correlated network analysis was applied for scale-free network topology analysis. We established a protein-protein interaction network based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software and identified hub proteins by CytoHubba and MCODE plugins of Cytoscape. The online tool Targetscan identified miRNA-mRNA pair interactions. Results: Seven hub proteins (Dlg3, Dlg4, Shank3, Grin2b, Camk2b, Camk2a and Syngap1) were implicated in alcohol withdrawal syndrome or diazepam treatment. In enrichment analysis, glutamatergic synapses were considered the most important pathway related to alcohol use disorder. Decreased glutamatergic synapses were observed in the late stage of withdrawal, as a protective mechanism that attenuated withdrawal-induced excitotoxicity. Diazepam treatment during withdrawal increased glutamatergic synapses, alleviating withdrawal-induced synapse inhibition. Conclusion: Glutamatergic synapses are considered the most important pathway related to alcohol use disorder that may be a potential molecular target for new interventional strategies.

Identification of hub proteins in cerebrospinal fluid as potential biomarkers of Alzheimer's disease by integrated bioinformatics.

Alzheimer's disease (AD) is a heterogeneous neurodegenerative disease with complex pathophysiology. Therefore, the identification of novel effective fluid biomarkers is essential for Alzheimer's disease diagnosis and drug development. This study aimed to identify potential candidate hub proteins in cerebrospinal fluid for precise Alzheimer's disease diagnosis using bioinformatics methods. A total of 29 co-significant differentially expressed proteins were identified by differential protein expression analysis in four different cohorts. Functional enrichment analysis revealed that most of these proteins were enriched in pathways related to glycometabolism. Using the Least Absolute Shrinkage and Selection Operator (LASSO) and random forest feature selection methods, six hub proteins [14-3-3 protein zeta/delta (YWHAZ), SPARC-related modular calcium-binding protein 1 (SMOC1), aldolase A (ALDOA), pyruvate kinase isoenzyme type M2 (PKM), chitinase-3-like protein 1 (CHI3L1), and secreted phosphoprotein 1 (SPP1)] were identified. These six hub proteins were upregulated in the cerebrospinal fluid of patients with Alzheimer's disease compared with cognitively unimpaired control individuals. Meanwhile, SMOC1, ALDOA, and PKM were specifically upregulated in the cerebrospinal fluid of patients with Alzheimer's disease but not in other neurodegenerative diseases. Build AD diagnostic models showed that a single hub protein or six hub proteins combination had an excellent ability to discriminate Alzheimer's disease. In conclusion, our study suggests that these identified hub proteins, which are related to glycometabolism, may be potential biomarkers for further basic and clinical research in Alzheimer's disease.

Functional analysis of Escherichia coli K12 toxin-antitoxin systems as novel drug targets using a network biology approach

Bacterial resistance to various drugs and antibiotics has become a significant issue in the fight against infectious diseases. Due to the presence of diverse toxin-antitoxin (TA) systems, bacteria undergo adaptive metabolic alterations and can tolerate the effects of drugs and antibiotics. Bacterial TA systems are unique and can be therapeutic targets for developing new antimicrobial agents, owing to their ability to influence bacterial fate. With this background, our study aims to identify novel drug targets against Escherichia coli K12 MG1655 antitoxin using homology modelling approach. In this study, the protein-protein interaction network of 87 E. coli K12 MG1655 TA systems identified through literature mining was screened for the identification of hub proteins. The model evaluation, assessment, and homology modelling of the hub proteins were evaluated. Furthermore, computer-aided mathematical models of selected phytochemicals have been tested against the identified hub proteins. The TA system was functionally enriched in regulation of cell growth, negative regulation of cell growth, regulation of mRNA stability, mRNA catabolic process and RNA phosphodiester bond hydrolysis. RelE, RelB, MazE, MazF, MqsR, MqsA, and YoeB were identified as hub proteins. The robustness and superior quality of the RelB and MazE modelled structure were discovered by model evaluation, quality assessment criteria, and homology modelling of hub proteins. Clorobiocin was found to be a strong inhibitor by docking these modelled structures. Clorobiocin could be utilized as an antibacterial agent against multidrug resistant E. coli which may inactivate antitoxins and cause programmed cell death.

In-depth proteomic profiling captures subtype-specific features of craniopharyngiomas

Craniopharyngiomas are rare epithelial tumors derived from pituitary gland embryonic tissue. This epithelial tumor can be categorized as an adamantinomatous craniopharyngioma (ACP) or papillary craniopharyngioma (PCP) subtype with histopathological and genetic differences. Genomic and transcriptomic profiles of craniopharyngiomas have been investigated; however, the proteomic profile has yet to be elucidated and added to these profiles. Recent improvements in high-throughput quantitative proteomic approaches have introduced new opportunities for a better understanding of these diseases and the efficient discovery of biomarkers. We aimed to confirm subtype-associated proteomic changes between ACP and PCP specimens. We performed a system-level proteomic study using an integrated approach that combines mass spectrometry-based quantitative proteomic, statistical, and bioinformatics analyses. The bioinformatics analysis showed that differentially expressed proteins between ACP and PCP were significantly involved in mitochondrial organization, fatty acid metabolic processes, exocytosis, the inflammatory response, the cell cycle, RNA splicing, cell migration, and neuron development. Furthermore, using network analysis, we identified hub proteins that were positively correlated with ACP and PCP phenotypes. Our findings improve our understanding of the pathogenesis of craniopharyngiomas and provide novel insights that may ultimately translate to the development of craniopharyngioma subtype-specific therapeutics.

Transcriptomic Changes in Hot Spring Frog Tadpoles (Buergeria otai) in Response to Heat Stress

Buergeria frog tadpoles exhibit high thermal tolerance and are occasionally found in water pools that temporarily exceed 40°C. With the aim of understanding how they can cope with the severe heat stress, we performed RNA-seq of three heat-treated (38°C) and three control (25°C) tadpoles and compared their transcriptomic profiles. We identified 382 differentially expressed transcripts. A protein-protein interaction (PPI) network analysis of these transcripts further identified hub proteins involved in protein degradation, stress granule assembly, and global suppression of DNA transcription and mRNA translation. Along with the avoidance behavior against high water temperature, these endurance mechanisms potentially support tadpoles to survive in high temperatures for short periods of time. Similar mechanisms may exist in many other amphibian species whose habitats are prone to high temperatures.

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.

Tandem Mass Tag-based quantitative proteomics analysis of metabolic associated fatty liver disease induced by high fat diet in mice

BackgroundAlthough metabolic associated fatty liver disease (MAFLD) is the most common chronic liver disease worldwide, the exact molecular mechanism of MAFLD progression remains unknown. In the present study, Tandem Mass Tag-labeled quantitative proteomic technology was used to elucidate the protein expression patterns of liver tissues in the progression of MAFLD, providing new potential therapeutic targets of it.MethodsFive 6-week-old male C57BL/6 mice were fed with high fat diet (HFD) for 22 weeks to establish the MAFLD mouse models. Five C57BL/6 mice of the same age were fed with normal diet (ND) and taken as controls. Mice serum were sampled for biochemical tests, and livers were isolated for histopathological examinations. Six mouse liver samples (three from each group) were performed for proteomic analysis. Differentially expressed proteins were defined using fold change of > 1.5 or < 0.67 and p value < 0.05 as thresholds. Bioinformatic analysis was used to identify the hub proteins. Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR), Gene Expression Omnibus dataset, western blotting and immunohistochemistry were used to validate the expression of identified hub proteins.ResultsAfter 22 weeks on HFD diet, all mice developed MAFLD demonstrated by histopathological examination. Mouse body weights, liver weights, serum alanine transaminase and aspartate transaminase levels were significantly higher in the HFD group than ND group. Proteomics technology identified 4915 proteins in the mouse livers, among which 71 proteins were differentially expressed. Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that majority of the differentially expressed proteins were involved in the peroxisome and peroxisome proliferator-activated receptor signaling pathway, as well as biosynthesis of unsaturated fatty acids. Protein–protein interaction analysis showed that these differentially expressed proteins interacted with each other and formed a complex network. Ten hub proteins were identified and validated using RT-qPCR. Five of these proteins were validated in the Gene Expression Omnibus dataset. Finally, Enoyl-CoA hydratase and 3-hydroxyacyl CoA dehydrogenase protein was validated in mouse liver tissue samples using western blotting and immunohistochemistry.ConclusionOur data showed that lipid metabolism-related pathways are closely associated with the development of MAFLD. The identified hub proteins might be novel targets for treating MAFLD.

Morpho-physiological and proteomic responses to water stress in two contrasting tobacco varieties

To gain insight into the molecular mechanisms underpinning tobacco (Nicotiana tabacum) tolerance to drought stress, we integrated anatomical, physiological, and proteomic analyses of drought-tolerant (Yuyan6, [Y6]) and -sensitive (Yunyan87 [Y87]) varieties. In comparison to Y87, Y6 exhibited higher water retention capability, improved photosynthetic performance, delayed leaf-senescence, stable leaf ultrastructure, a stronger antioxidant defense, and lesser ROS accumulation when subjected to water stress. Using an iTRAQ-based proteomics approach, 405 and 1,560 differentially accumulated proteins (DAPs) were identified from Y6 and Y87 plants, respectively, of which 114 were found to be present in both cultivars. A subsequent functional characterization analysis revealed that these DAPs were significantly enriched in eight biological processes, six molecular functions, and six cellular components and displayed differential expression patterns in Y6 and Y87 plants, suggesting that the response to water stress between both varieties differed at the proteomic level. Furthermore, we constructed protein coexpression networks and identified hub proteins regulating tobacco defenses to water stress. Additionally, qPCR analysis indicated that the majority of genes encoding selected proteins showed consistency between mRNA levels and their corresponding protein expression levels. Our results provide new insights into the genetic regulatory mechanisms associated with drought response in tobacco plants.

Identification of differentially expressed proteins in the injured lung from zinc chloride smoke inhalation based on proteomics analysis

BackgroundLung injury due to zinc chloride smoke inhalation is very common in military personnel and leads to a high incidence of pulmonary complications and mortality. The aim of this study was to uncover the underlying mechanisms of lung injury due to zinc chloride smoke inhalation using a rat model.Methods: Histopathology analysis of rat lungs after zinc chloride smoke inhalation was performed by using haematoxylin and eosin (H&E) and Mallory staining. A lung injury rat model of zinc chloride smoke inhalation (smoke inhalation for 1, 2, 7 and 14 days) was developed. First, isobaric tags for relative and absolute quantization (iTRAQ) and weighted gene co-expression network analysis (WGCNA) were used to identify important differentially expressed proteins. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to study the biological functions of differentially expressed proteins. Then, analysis of lung injury repair-related differentially expressed proteins in the early (day 1 and day 2) and middle-late stages (day 7 and day 14) of lung injury after smoke inhalation was performed, followed by the protein-protein interaction (PPI) analysis of these differentially expressed proteins. Finally, the injury repair-related proteins PARK7 and FABP5 were validated by immunohistochemistry and western blot analysis.ResultsMorphological changes were observed in the lung tissues after zinc chloride smoke inhalation. A total of 27 common differentially expressed proteins were obtained on days 1, 2, 7 and 14 after smoke inhalation. WGCNA showed that the turquoise module (which involved 909 proteins) was most associated with smoke inhalation time. Myl3, Ckm, Adrm1 and Igfbp7 were identified in the early stages of lung injury repair. Gapdh, Acly, Tnni2, Acta1, Actn3, Pygm, Eno3 and Tpi1 (hub proteins in the PPI network) were identified in the middle-late stages of lung injury repair. Eno3 and Tpi1 were both involved in the glycolysis/gluconeogenesis signalling pathway. The expression of PARK7 and FABP5 was validated and was consistent with the proteomics analysis.ConclusionThe identified hub proteins and their related signalling pathways may play crucial roles in lung injury repair due to zinc chloride smoke inhalation.

Drug repositioning and biomarkers in low-grade glioma via bioinformatics approach

Diffuse low-grade glioma (LGG) patients have a tendency to develop glioblastoma multiforme within 5–10 years, with only 15 months life expectancy. The present study aimed to decode a diagnostic and prognostic gene signature in LGG and identify repositioned potential candidate biomarker therapeutics. We have utilized a publicly available transcriptomics dataset to identify differentially expressed genes (DEGs) using Limma. The functional annotation of the DEGs was performed to identify Gene Ontology and pathways. The protein-protein interaction (PPI) network of the (DEGs) encoding proteins was analyzed utilizing STRING via NetworkAnalyst. The ROC analysis and survival analysis of the hub gene was also performed in R and SurvExpress-a biomarker validation tool. We have utilized the L1000CDS2 to identify candidate small molecules or drugs to target the diagnostic and prognostic biomarkers. We have identified 238 genes as DEGs in LGG compared to non-LGG samples involved in the biological process of signaling and metabolic systems. We then identified molecular pathways enriched by the DEGs including the VEGF signaling pathway, Fc epsilon RI signaling pathway, and p38 signaling mediated by MAPKAP kinases. We identified hub proteins (MAPK11, MRPL1, RAF1, CDC20, NOP2, TUBB4B, CTNND1, PWP2, FGR, BMP2) from PPI analysis. The ROC and survival analysis of the hub genes revealed that these biomarkers can be utilized as diagnostic and prognostic biomarker signatures in LGG. Several novel candidate drugs were repositioned; among them penfluridol, perheiline maleate is used for purposes other than cancer. Other repositioned drugs: AG14361, NCGC00185094-01, BRD-K61717269, C8273, BRD-A28970875, KIN001-127, BYL719, JW-7-24-1, HG-6-64-01, radicicol, MK-2206, and WZ-4-14 have not been described in the literature for treatment purposes. Our analysis of LGG datasets identified 10 hub genes as diagnostic and prognostic signatures, and the candidate repositioned drugs linked to these biomarkers. These biomolecules and repositioned drugs may be utilized as biomarkers for diagnosis and prognosis, and for development of a treatment strategy for LGG.

Hub Protein Controversy: Taking a Closer Look at Plant Stress Response Hubs.

Plant stress responses involve numerous changes at the molecular and cellular level and are regulated by highly complex signaling pathways. Studying protein-protein interactions (PPIs) and the resulting networks is therefore becoming increasingly important in understanding these responses. Crucial in PPI networks are the so-called hubs or hub proteins, commonly defined as the most highly connected central proteins in scale-free PPI networks. However, despite their importance, a growing amount of confusion and controversy seems to exist regarding hub protein identification, characterization and classification. In order to highlight these inconsistencies and stimulate further clarification, this review critically analyses the current knowledge on hub proteins in the plant interactome field. We focus on current hub protein definitions, including the properties generally seen as hub-defining, and the challenges and approaches associated with hub protein identification. Furthermore, we give an overview of the most important large-scale plant PPI studies of the last decade that identified hub proteins, pointing out the lack of overlap between different studies. As such, it appears that although major advances are being made in the plant interactome field, defining hub proteins is still heavily dependent on the quality, origin and interpretation of the acquired PPI data. Nevertheless, many hub proteins seem to have a reported role in the plant stress response, including transcription factors, protein kinases and phosphatases, ubiquitin proteasome system related proteins, (co-)chaperones and redox signaling proteins. A significant number of identified plant stress hubs are however still functionally uncharacterized, making them interesting targets for future research. This review clearly shows the ongoing improvements in the plant interactome field but also calls attention to the need for a more comprehensive and precise identification of hub proteins, allowing a more efficient systems biology driven unraveling of complex processes, including those involved in stress responses.

Comparative transcriptomics of hepatic differentiation of human pluripotent stem cells and adult human liver tissue.

Hepatocytes derived from human pluripotent stem cells (hPSC-HEP) have the potential to replace presently used hepatocyte sources applied in liver disease treatment and models of drug discovery and development. Established hepatocyte differentiation protocols are effective and generate hepatocytes, which recapitulate some key features of their in vivo counterparts. However, generating mature hPSC-HEP remains a challenge. In this study, we applied transcriptomics to investigate the progress of in vitro hepatic differentiation of hPSCs at the developmental stages, definitive endoderm, hepatoblasts, early hPSC-HEP, and mature hPSC-HEP, to identify functional targets that enhance efficient hepatocyte differentiation. Using functional annotation, pathway and protein interaction network analyses, we observed the grouping of differentially expressed genes in specific clusters representing typical developmental stages of hepatic differentiation. In addition, we identified hub proteins and modules that were involved in the cell cycle process at early differentiation stages. We also identified hub proteins that differed in expression levels between hPSC-HEP and the liver tissue controls. Moreover, we identified a module of genes that were expressed at higher levels in the liver tissue samples than in the hPSC-HEP. Considering that hub proteins and modules generally are essential and have important roles in the protein-protein interactions, further investigation of these genes and their regulators may contribute to a better understanding of the differentiation process. This may suggest novel target pathways and molecules for improvement of hPSC-HEP functionality, having the potential to finally bring this technology to a wider use.