Dissection of hubs and bottlenecks in a protein-protein interaction network

Revitalising Aging Oocytes: Echinacoside Restores Mitochondrial Function and Cellular Homeostasis Through Targeting GJA1/SIRT1 Pathway

As maternal age increases, the decline in oocyte quality emerges as a critical factor contributing to reduced reproductive capacity, highlighting the urgent need for effective strategies to combat oocyte aging. This study investigated the protective effects and underlying mechanisms of Echinacoside (ECH) on aging oocytes. ECH significantly improved cytoskeletal stability and chromosomal integrity, as demonstrated by restored spindle morphology and reinforced F-actin structures, essential for meiotic progression. It also preserved mitochondrial function by restoring membrane potential and dynamics, reducing ROS levels, and downregulating the DNA damage marker γ-H2AX, thereby alleviating oxidative stress and enhancing genomic stability. Furthermore, ECH promoted cellular homeostasis through modulation of lipid metabolism, autophagy and lysosomal function. Transcriptomic analyses identified GJA1 as a pivotal mediator of ECH's effects, validated through molecular docking and bio-layer interferometry. Functional studies showed that inhibiting GJA1 significantly reduced ECH's ability to enhance first polar body extrusion rates, mitochondrial function and antioxidant capacity, validating the critical role of the GJA1/SIRT1 pathway in combating oocyte aging. This study provides novel insights into the mechanisms of oocyte rejuvenation and highlights ECH as a promising therapeutic candidate for addressing age-related reproductive challenges.

Elucidating the Molecular Mechanisms of Hederagenin-Regulated Mitophagy in Cervical Cancer SiHa Cells through an Integrative Approach Combining Proteomics and Advanced Network Association Algorithm

Hederagenin (Hed), a natural triterpenoid, exhibits antitumor potential in cervical cancer. The present study was designed to explore Hed's regulatory mechanisms on mitophagy in SiHa cervical cancer cells, employing tandem mass tag (TMT) proteomics and an advanced network association algorithm (NAA). Our findings revealed that Hed decreased SiHa cell viability, induced apoptosis, and altered mitochondrial membrane potential. Notably, Hed inhibited mitophagic flux under both normoxic and hypoxic conditions. Through TMT proteomics analysis and innovative NAA, we identified a close association between the HIF-1 signaling pathway and mitophagy. Network analysis further suggested that Hed acts on a target network centered on SRC, STAT3, AKT1, and HIF1A. Western blot analysis confirmed the expression and phosphorylation status of these targets in response to Hed. This study elucidates the molecular mechanisms underlying Hed's regulation of mitophagy in SiHa cells, offering novel insights and potential therapeutic targets for cervical cancer treatment.

Comprehensive Studies on the Regulation of Type 2 Diabetes by Cucurbitane-Type Triterpenoids in Momordica charantia L.: Insights from Network Pharmacology and Molecular Docking and Dynamics

Background/Objectives:Momordica charantia L. (M. charantia), a widely cultivated and frequently consumed medicinal plant, is utilized in traditional medicine. Cucurbitane-type triterpenoids, significant saponin components of M. charantia, exhibit hypoglycemic effects; however, the underlying mechanisms remain unclear. Methods: This study utilized comprehensive network pharmacology to identify potential components of M. charantia cucurbitane-type triterpenoids that may influence type 2 diabetes mellitus (T2DM). Additionally, molecular docking and molecular dynamics studies were performed to assess the stability of the interactions between the selected components and key targets. Results: In total, 22 candidate active components of M. charantia cucurbitane-type triterpenoids and 1165 disease targets for T2DM were identified through database screening. Molecular docking and molecular dynamics simulations were conducted for five key components (Kuguacin J, 25-O-methylkaravilagenin D, Momordicine I, momordic acid, and Kuguacin S) and three key targets (AKT1, IL6, and SRC), and the results demonstrated stable binding. The experimental results indicate that the interactions between momordic acid-AKT1 and momordic acid-IL6 are stable. Conclusions: Momordic acid may play a crucial role in M. charantia's regulation of T2DM, and AKT1 and IL6 seem to be key targets for the therapeutic action of M. charantia in managing T2DM.

Accelerating crop improvement via integration of transcriptome-based network biology and genome editing

MAIN CONCLUSION

Big data and network biology infer functional coupling between genes. In combination with machine learning, network biology can dramatically accelerate the pace of gene discovery using modern transcriptomics approaches and be validated via genome editing technology for improving crops to stresses. Unlike other living things, plants are sessile and frequently face various environmental challenges due to climate change. The cumulative effects of combined stresses can significantly influence both plant growth and yields. In navigating the complexities of climate change, ensuring the nourishment of our growing population hinges on implementing precise agricultural systems. Conventional breeding methods have been commonly employed; however, their efficacy has been impeded by limitations in terms of time, cost, and infrastructure. Cutting-edge tools focussing on big data are being championed to usher in a new era in stress biology, aiming to cultivate crops that exhibit enhanced resilience to multifactorial stresses. Transcriptomics, combined with network biology and machine learning, is proving to be a powerful approach for identifying potential genes to target for gene editing, specifically to enhance stress tolerance. The integration of transcriptomic data with genome editing can yield significant benefits, such as gaining insights into gene function by modifying or manipulating of specific genes in the target plant. This review provides valuable insights into the use of transcriptomics platforms and the application of biological network analysis and machine learning in the discovery of novel genes, thereby enhancing the understanding of plant responses to combined or sequential stress. The transcriptomics as a forefront omics platform and how it is employed through biological networks and machine learning that lead to novel gene discoveries for producing multi-stress-tolerant crops, limitations, and future directions have also been discussed.

Leveraging pleiotropy for the improved treatment of psychiatric disorders

Over 90% of drug candidates fail in clinical trials, while it takes 10-15 years and one billion US dollars to develop a single successful drug. Drug development is more challenging for psychiatric disorders, where disease comorbidity and complex symptom profiles obscure the identification of causal mechanisms for therapeutic intervention. One promising approach for determining more suitable drug candidates in clinical trials is integrating human genetic data into the selection process. Genome-wide association studies have identified thousands of replicable risk loci for psychiatric disorders, and sophisticated statistical tools are increasingly effective at using these data to pinpoint likely causal genes. These studies have also uncovered shared or pleiotropic genetic risk factors underlying comorbid psychiatric disorders. In this article, we argue that leveraging pleiotropic effects will provide opportunities to discover novel drug targets and identify more effective treatments for psychiatric disorders by targeting a common mechanism rather than treating each disease separately.

Beyond nutrition: Exploring immune proteins, bioactive peptides, and allergens in cow and Arabian camel milk

Bovine milk has dominated the dairy segment, yet alternative milk sources are gaining attention due to perceived superior health benefits, with immune proteins and bioactive peptides (BPs) contributing to these benefits. Fractionation affects protein recovery and composition. Here, the cream fraction resulted in the highest yield of proteins, identifying 1143 camel and 851 cow proteins. The cream fraction contained a significantly higher concentration of immune system-related proteins. Straightforward filtration and protein precipitation methods achieved average BP detections of 170 and 177, compared to 31 by a solvent-solvent extraction method. Considering potentially allergenic proteins, 53 (camel) and 52 (cow) were identified. Of these, 62 % of the potential allergens in cow, had orthologous counterparts in camel milk. However, the major milk allergen β-lactoglobulin (β-Lg) was not detected in camel milk. Our results provide a comprehensive proteomic resource of camel and cow milk products, mapping potential allergens and BPs that affect health.

Combined High-Throughput Proteomics and Random Forest Machine-Learning Approach Differentiates and Classifies Metabolic, Immune, Signaling and ECM Intra-Tumor Heterogeneity of Colorectal Cancer

Colorectal cancer (CRC) is a frequent, worldwide tumor described for its huge complexity, including inter-/intra-heterogeneity and tumor microenvironment (TME) variability. Intra-tumor heterogeneity and its connections with metabolic reprogramming and epithelial-mesenchymal transition (EMT) were investigated with explorative shotgun proteomics complemented by a Random Forest (RF) machine-learning approach. Deep and superficial tumor regions and distant-site non-tumor samples from the same patients (n = 16) were analyzed. Among the 2009 proteins analyzed, 91 proteins, including 23 novel potential CRC hallmarks, showed significant quantitative changes. In addition, a 98.4% accurate classification of the three analyzed tissues was obtained by RF using a set of 21 proteins. Subunit E1 of 2-oxoglutarate dehydrogenase (OGDH-E1) was the best classifying factor for the superficial tumor region, while sorting nexin-18 and coatomer-beta protein (beta-COP), implicated in protein trafficking, classified the deep region. Down- and up-regulations of metabolic checkpoints involved different proteins in superficial and deep tumors. Analogously to immune checkpoints affecting the TME, cytoskeleton and extracellular matrix (ECM) dynamics were crucial for EMT. Galectin-3, basigin, S100A9, and fibronectin involved in TME-CRC-ECM crosstalk were found to be differently variated in both tumor regions. Different metabolic strategies appeared to be adopted by the two CRC regions to uncouple the Krebs cycle and cytosolic glucose metabolism, promote lipogenesis, promote amino acid synthesis, down-regulate bioenergetics in mitochondria, and up-regulate oxidative stress. Finally, correlations with the Dukes stage and budding supported the finding of novel potential CRC hallmarks and therapeutic targets.

Network proteomics of the Lewy body dementia brain reveals presynaptic signatures distinct from Alzheimer's disease

Lewy body dementia (LBD), a class of disorders comprising Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB), features substantial clinical and pathological overlap with Alzheimer's disease (AD). The identification of biomarkers unique to LBD pathophysiology could meaningfully advance its diagnosis, monitoring, and treatment. Using quantitative mass spectrometry (MS), we measured over 9,000 proteins across 138 dorsolateral prefrontal cortex (DLPFC) tissues from a University of Pennsylvania autopsy collection comprising control, Parkinson's disease (PD), PDD, and DLB diagnoses. We then analyzed co-expression network protein alterations in those with LBD, validated these disease signatures in two independent LBD datasets, and compared these findings to those observed in network analyses of AD cases. The LBD network revealed numerous groups or "modules" of co-expressed proteins significantly altered in PDD and DLB, representing synaptic, metabolic, and inflammatory pathophysiology. A comparison of validated LBD signatures to those of AD identified distinct differences between the two diseases. Notably, synuclein-associated presynaptic modules were elevated in LBD but decreased in AD relative to controls. We also found that glial-associated matrisome signatures consistently elevated in AD were more variably altered in LBD, ultimately stratifying those LBD cases with low versus high burdens of concurrent beta-amyloid deposition. In conclusion, unbiased network proteomic analysis revealed diverse pathophysiological changes in the LBD frontal cortex distinct from alterations in AD. These results highlight the LBD brain network proteome as a promising source of biomarkers that could enhance clinical recognition and management.

Efficacy Evaluation of Treatment of Psoriasis Via Narrow Band-Ultraviolet Radiation

Introduction: Psoriasis is a common autoimmune skin disease associated with genetically influenced chronic inflammation accompanied by remitting and deteriorating scaly skin. T-cell targeted biologics, IL-17 inhibitors, IL-12/IL-23 inhibitors, TNF-α inhibitors, PDE4 inhibitors, and ultraviolet (UV) radiation are applied to treat psoriasis. Efficacy evaluation of narrow band UVB (NB-UVB) radiation was the aim of this study. Methods: Data were extracted from Gene Expression Omnibus (GEO) and were pre-evaluated via the GEO2R program. The significant differentially expressed genes (DEGs) were included in the protein-protein interaction (PPI) network analysis. The hubs, bottlenecks, and hub-bottleneck DEGs were introduced as central genes. Activation, inhibition, and expression relationship between central genes were assessed to explore the critical individuals. Results: Among 513 analyzed significant DEGs, 22 hub-bottleneck genes were identified. Further analysis revealed that FN1, STAT3, HIF1A, IL1B, P4HB, SOD2, MMP2, and STAT1 were the crucial genes in psoriasis samples targeted by NB-UVB radiation. Conclusion: In conclusion, NB-UVB radiation as a treatment targets critical genes in peri-lesion skin tissue biopsy of psoriasis patients via a complicated mechanism. This therapeutic method downregulates STAT3, HIF1A, IL1B, and P4HB to treat psoriasis but downregulates STAT1 and SOD2 and upregulates MMP2 and FN1 to develop disease.

Apoplastomes of contrasting cacao genotypes to witches' broom disease reveals differential accumulation of PR proteins

Witches' broom disease (WBD) affects cocoa trees (Theobroma cacao L.) and is caused by the fungus Moniliophthora perniciosa that grows in the apoplast in its biotrophic phase and later progresses into the tissues, causing serious losses in the production of cocoa beans. Therefore, the apoplast of T. cacao can provide important defense responses during the interaction with M. perniciosa. In this work, the protein profile of the apoplast of the T. cacao genotypes Catongo, susceptible to WBD, and CCN-51, resistant one, was evaluated. The leaves of T. cacao were collected from asymptomatic plants grown in a greenhouse (GH) and from green witches' brooms grown under field (FD) conditions for extraction of apoplastic washing fluid (AWF). AWF was used in proteomic and enzymatic analysis. A total of 14 proteins were identified in Catongo GH and six in Catongo FD, with two proteins being common, one up-accumulated, and one down-accumulated. In CCN-51, 19 proteins were identified in the GH condition and 13 in FD, with seven proteins being common, one up-accumulated, and six down-accumulated. Most proteins are related to defense and stress in both genotypes, with emphasis on pathogenesis-related proteins (PR): PR-2 (β-1,3-glucanases), PR-3 and PR-4 (chitinases), PR-5 (thaumatine), PR-9 (peroxidases), and PR-14 (lipid transfer proteins). Furthermore, proteins from microorganisms were detected in the AWF. The enzymatic activities of PR-3 showed a significant increase (p < 0.05) in Catongo GH and PR-2 activity (p < 0.01) in CCN-51 FD. The protein profile of the T. cacao apoplastome offers insight into the defense dynamics that occur in the interaction with the fungus M. perniciosa and offers new insights in exploring future WBD control strategies.

A Tiny Viral Protein, SARS-CoV-2-ORF7b: Functional Molecular Mechanisms

This study presents the interaction with the human host metabolism of SARS-CoV-2 ORF7b protein (43 aa), using a protein-protein interaction network analysis. After pruning, we selected from BioGRID the 51 most significant proteins among 2753 proven interactions and 1708 interactors specific to ORF7b. We used these proteins as functional seeds, and we obtained a significant network of 551 nodes via STRING. We performed topological analysis and calculated topological distributions by Cytoscape. By following a hub-and-spoke network architectural model, we were able to identify seven proteins that ranked high as hubs and an additional seven as bottlenecks. Through this interaction model, we identified significant GO-processes (5057 terms in 15 categories) induced in human metabolism by ORF7b. We discovered high statistical significance processes of dysregulated molecular cell mechanisms caused by acting ORF7b. We detected disease-related human proteins and their involvement in metabolic roles, how they relate in a distorted way to signaling and/or functional systems, in particular intra- and inter-cellular signaling systems, and the molecular mechanisms that supervise programmed cell death, with mechanisms similar to that of cancer metastasis diffusion. A cluster analysis showed 10 compact and significant functional clusters, where two of them overlap in a Giant Connected Component core of 206 total nodes. These two clusters contain most of the high-rank nodes. ORF7b acts through these two clusters, inducing most of the metabolic dysregulation. We conducted a co-regulation and transcriptional analysis by hub and bottleneck proteins. This analysis allowed us to define the transcription factors and miRNAs that control the high-ranking proteins and the dysregulated processes within the limits of the poor knowledge that these sectors still impose.

Protein supersaturation powers innate immune signaling

Innate immunity protects us in youth but turns against us as we age. The reason for this tradeoff is unclear. Seeking a thermodynamic basis, we focused on death fold domains (DFDs), whose ordered polymerization has been stoichiometrically linked to innate immune signal amplification. We hypothesized that soluble ensembles of DFDs function as phase change batteries that store energy via supersaturation and subsequently release it through nucleated polymerization. Using imaging and FRET-based cytometry to characterize the phase behaviors of all 109 human DFDs, we found that the hubs of innate immune signaling networks encode large nucleation barriers that are intrinsically insulated from cross-pathway activation. We showed via optogenetics that supersaturation drives signal amplification and that the inflammasome is constitutively supersaturated in vivo. Our findings reveal that the soluble "inactive" states of adaptor DFDs function as essential, yet impermanent, kinetic barriers to inflammatory cell death, suggesting a thermodynamic driving force for aging.

Network Proteomics of the Lewy Body Dementia Brain Reveals Presynaptic Signatures Distinct from Alzheimer's Disease

Lewy body dementia (LBD), a class of disorders comprising Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB), features substantial clinical and pathological overlap with Alzheimer's disease (AD). The identification of biomarkers unique to LBD pathophysiology could meaningfully advance its diagnosis, monitoring, and treatment. Using quantitative mass spectrometry (MS), we measured over 9,000 proteins across 138 dorsolateral prefrontal cortex (DLPFC) tissues from a University of Pennsylvania autopsy collection comprising control, Parkinson's disease (PD), PDD, and DLB diagnoses. We then analyzed co-expression network protein alterations in those with LBD, validated these disease signatures in two independent LBD datasets, and compared these findings to those observed in network analyses of AD cases. The LBD network revealed numerous groups or "modules" of co-expressed proteins significantly altered in PDD and DLB, representing synaptic, metabolic, and inflammatory pathophysiology. A comparison of validated LBD signatures to those of AD identified distinct differences between the two diseases. Notably, synuclein-associated presynaptic modules were elevated in LBD but decreased in AD relative to controls. We also found that glial-associated matrisome signatures consistently elevated in AD were more variably altered in LBD, ultimately stratifying those LBD cases with low versus high burdens of concurrent beta-amyloid deposition. In conclusion, unbiased network proteomic analysis revealed diverse pathophysiological changes in the LBD frontal cortex distinct from alterations in AD. These results highlight the LBD brain network proteome as a promising source of biomarkers that could enhance clinical recognition and management.

Hubs and Bottlenecks in Protein-Protein Interaction Networks

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

Network analysis of H. pylori effect on AGS human gastric adenocarcinoma cells gene expression profile

Aim

To better understand the molecular mechanism of Helicobacter pylori (H. pylori) in adenocarcinoma, the gene expression profile of AGS cells was analyzed by complementary study.

Background

Gastric cancer, as one of the most lethal malignancies in the world, is important to be studied in terms of biomarkers. On the other hand, Helicobacter pylori is one of the key risk factors in this type of disease.

Methods

In this cross-sectional study, we evaluated the seroprevalence of total and IgM anti-HAV antibodies of 254 institutionalized people with intellectual disabilities. Total and IgM anti-HAV antibodies of the blood samples of these people were determined by ELISA method. Protein-protein interaction (PPI) network analysis is a bioinformatic study with validation values for biomarker identification and clarification of molecular mechanisms. Cytoscape V 3.10.2 and its application identified potential central elements of the PPI network and its corresponding roles.

Results

GAPDH and P53 are the most promising candidates in this study. In addition, the microRNA signatures assessment provided more information about these biomarkers and added more value.

Conclusion

Consequently, a new outlook for the relationship between gastric cancer and H. pylori was explored based on the new key biomarkers.

Molecular mechanism detection of stage I to stage II transition of esophageal squamous cell carcinoma: a system biology approach

Aim

Molecular mechanism detection of stage I to stage II transition of esophageal squamous cell carcinoma via protein-protein interaction (PPI) network analysis is the main aim of this study.

Background

Esophageal cancer (EC) is recognized as cancer with a very poor prognosis and malignancy. It is characterized by a high prevalence rate within the world and a very low survival rate, even with treatment.

Methods

To detect esophageal squamous cell carcinoma (ESCC) related genes, gene expression profiles (GEPs) of GSE161533 from the Gene Expression Omnibus (GEO) database were considered to be analyzed. Data was evaluated via the GEO2R program to explore the significant differential genes (DEGs) associated to stages I and II of esophageal squamous cell carcinoma. Each analysis's top 250 significant DEGs were evaluated, and the non-common genes were assessed via PPI network analysis. The hub-bottleneck DEGs were determined and enriched via gene ontology.

Results

Results indicate 373 significant DEGs discriminate stage I from stage II. PPI network analysis associated with gene expression assessment showed that COL1A1, SERPINE1, PDGFRB, AURKA, TGFBI, LGALS3, BRCA1, and TFRC are the critical DEGs which are related to ESCC transition state from stage I to II of disease. A total of 13 biological processes and molecular functions were related to the crucial genes.

Conclusion

In conclusion, the Upregulation of COL1A1, SERPINE1, PDGFRB, AURKA, TGFB1, and LGALS3 and downregulation of BRCA1 and TFRC in stage II of ESCC relative to stage I were pointed out as the key events which are associated with promotion of stage I to stage II transition.

Targeting the NCAPD3 gene activates EGFR and ASNS as two pivotal contributors to gastric cancer progression

Aim

This study was conducted to discover the effect of NCAPD3 knockdown on the gene expression profile of gastric cancer.

Background

Gastric cancer, a potentially fatal disease, requires thorough evaluation for targeted interventions. Through the post-analysis of microarray data, it is crucial to further examine the impact of NCAPD3 (Non-SMC condensin II complex subunit D3) inhibition in gastric cancer, emphasizing the need for a more comprehensive analysis of this knockdown.

Methods

The use of Cytoscape and its plug-ins for protein-protein interaction network analysis enables the identification of genes that significantly affect network stability. These hub-bottlenecks are regulated due to the NCAPD3 inhibition and some of them act as compensators in this condition. The hub-bottlenecks pathways identified by ClueGO indicate their relationships in underlying mechanisms of knockdown. These identified central differentially expressed genes could be considered eligible targets for therapeutic interventions. Some of them play compensative roles while others are regulated in NCAPD3 knockdown.

Results

It can be concluded that some of the hub-bottlenecks contribute to compensation mechanisms including NPM1, PTEN, EGFR, HSPA5, and ASNS, while the other ones including HSPA4, DHX9, CAV1, MAP1LC3B, and SRSF1 are among the regulated genes.

Conclusion

In particular, the up-regulation of EGFR and ASNS genes in the knockdown scenario could significantly impact and deteriorate cancer treatment outcomes after comprehensive validation studies.

Combined Salivary Proteome Profiling and Machine Learning Analysis Provides Insight into Molecular Signature for Autoimmune Liver Diseases Classification

Autoimmune hepatitis (AIH) and primary biliary cholangitis (PBC) are autoimmune liver diseases that target the liver and have a wide spectrum of presentation. A global overview of quantitative variations on the salivary proteome in presence of these two pathologies is investigated in this study. The acid-insoluble salivary fraction of AIH and PBC patients, and healthy controls (HCs), was analyzed using a gel-based bottom-up proteomic approach combined with a robust machine learning statistical analysis of the dataset. The abundance of Arginase, Junction plakoglobin, Desmoplakin, Hexokinase-3 and Desmocollin-1 decreased, while that of BPI fold-containing family A member 2 increased in AIHp compared to HCs; the abundance of Gelsolin, CD14, Tumor-associated calcium signal transducer 2, Clusterin, Heterogeneous nuclear ribonucleoproteins A2/B1, Cofilin-1 and BPI fold-containing family B member 2 increased in PBCp compared to HCs. The abundance of Hornerin decreased in both AIHp and PBCp with respect to HCs and provided an area under the ROC curve of 0.939. Machine learning analysis confirmed the feasibility of the salivary proteome to discriminate groups of subjects based on AIH or PBC occurrence as previously suggested by our group. The topology-based functional enrichment analysis performed on these potential salivary biomarkers highlights an enrichment of terms mostly related to the immune system, but also with a strong involvement in liver fibrosis process and with antimicrobial activity.