Identification of Differentially Expressed Genes and Protein-Protein Interaction in Patients With COVID-19 and Diabetes Peripheral Neuropathy: A Bioinformatics and System Biology Approach.

The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact globally, resulting in a higher death toll and persistent health issues for survivors, particularly those with pre-existing medical conditions. Numerous studies have demonstrated a strong correlation between catastrophic COVID-19 results and diabetes. To gain deeper insights, we analysed the transcriptome dataset from COVID-19 and diabetic peripheral neuropathic patients. Using the R programming language, differentially expressed genes (DEGs) were identified and classified based on up and down regulations. The overlaps of DEGs were then explored between these groups. Functional annotation of those common DEGs was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Bio-Planet, Reactome, and Wiki pathways. A protein-protein interaction (PPI) network was created with bioinformatics tools to understand molecular interactions. Through topological analysis of the PPI network, we determined hub gene modules and explored gene regulatory networks (GRN). Furthermore, the study extended to suggesting potential drug molecules for the identified mutual DEG based on the comprehensive analysis. These approaches may contribute to understanding the molecular intricacies of COVID-19 in diabetic peripheral neuropathy patients through insights into potential therapeutic interventions.

Identification and experimental validation of immune-related gene PPARG is involved in ulcerative colitis.

BACKGROUND: The pathophysiology of ulcerative colitis (UC) is believed to be heavily influenced by immunology, which presents challenges for both diagnosis and treatment. The main aims of this study are to deepen our understanding of the immunological characteristics associated with the disease and to identify valuable biomarkers for diagnosis and treatment. METHODS: The UC datasets were sourced from the GEO database and were analyzed using unsupervised clustering to identify different subtypes of UC. Twelve machine learning algorithms and Deep learning model DNN were developed to identify potential UC biomarkers, with the LIME and SHAP methods used to explain the models' findings. PPI network is used to verify the identified key biomarkers, and then a network connecting super enhancers, transcription factors and genes is constructed. Single-cell sequencing technology was utilized to investigate the role of Peroxisome Proliferator Activated Receptor Gamma (PPARG) in UC and its correlation with macrophage infiltration. Furthermore, alterations in PPARG expression were validated through Western blot (WB) and immunohistochemistry (IHC) in both in vitro and in vivo experiments. RESULT: By utilizing bioinformatics techniques, we were able to pinpoint PPARG as a key biomarker for UC. The expression of PPARG was significantly reduced in cell models, UC animal models, and colitis models induced by dextran sodium sulfate (DSS). Interestingly, overexpression of PPARG was able to restore intestinal barrier function in H2O2-induced IEC-6 cells. Additionally, immune-related differentially expressed genes (DEGs) allowed for efficient classification of UC samples into neutrophil and mitochondrial metabolic subtypes. A diagnostic model incorporating the three disease-specific genes PPARG, PLA2G2A, and IDO1 demonstrated high accuracy in distinguishing between the UC group and the control group. Furthermore, single-cell analysis revealed that decreased PPARG expression in colon tissue may contribute to the polarization of M1 macrophages through activation of inflammatory pathways. CONCLUSION: In conclusion, PPARG, a gene related to immunity, has been established as a reliable potential biomarker for the diagnosis and treatment of UC. The immune response it controls plays a key role in the progression and development of UC by enabling interaction between characteristic biomarkers and immune infiltrating cells.

A protein network refinement method based on module discovery and biological information.

BACKGROUND: The identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a common way to identify essential proteins, but the poor data quality of the underlying PIN has somewhat hindered the identification accuracy of essential proteins for these methods in the PIN. Therefore, researchers constructed refinement networks by considering certain biological properties of interacting protein pairs to improve the performance of node ranking methods in the PIN. Studies show that proteins in a complex are more likely to be essential than proteins not present in the complex. However, the modularity is usually ignored for the refinement methods of the PINs. METHODS: Based on this, we proposed a network refinement method based on module discovery and biological information. The idea is, first, to extract the maximal connected subgraph in the PIN, and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules according to the orthologous information, subcellular localization information and topology information within each module; finally, to construct a more refined network (CM-PIN) by using the identified critical modules. RESULTS: To evaluate the effectiveness of the proposed method, we used 12 typical node ranking methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR, PeC, WDC) to compare the overall performance of the CM-PIN with those on the S-PIN, D-PIN and RD-PIN. The experimental results showed that the CM-PIN was optimal in terms of the identification number of essential proteins, precision-recall curve, Jackknifing method and other criteria, and can help to identify essential proteins more accurately.

Deciphering the similarities and disparities of molecular mechanisms behind respiratory epithelium response to HCoV-229E and SARS-CoV-2 and drug repurposing, a systems biology approach.

PURPOSE: Identifying the molecular mechanisms behind SARS-CoV-2 disparities and similarities will help find new treatments. The present study determines networks' shared and non-shared (specific) crucial elements in response to HCoV-229E and SARS-CoV-2 viruses to recommend candidate medications. METHODS: We retrieved the omics data on respiratory cells infected with HCoV-229E and SARS-CoV-2, constructed PPIN and GRN, and detected clusters and motifs. Using a drug-gene interaction network, we determined the similarities and disparities of mechanisms behind their host response and drug-repurposed. RESULTS: CXCL1, KLHL21, SMAD3, HIF1A, and STAT1 were the shared DEGs between both viruses' protein-protein interaction network (PPIN) and gene regulatory network (GRN). The NPM1 was a specific critical node for HCoV-229E and was a Hub-Bottleneck shared between PPI and GRN in HCoV-229E. The HLA-F, ADCY5, TRIM14, RPF1, and FGA were the seed proteins in subnetworks of the SARS-CoV-2 PPI network, and HSPA1A and RPL26 proteins were the seed in subnetworks of the PPI network of HCOV-229E. TRIM14, STAT2, and HLA-F played the same role for SARS-CoV-2. Top enriched KEGG pathways included cell cycle and proteasome in HCoV-229E and RIG-I-like receptor, Chemokine, Cytokine-cytokine, NOD-like receptor, and TNF signaling pathways in SARS-CoV-2. We suggest some candidate medications for COVID-19 patient lungs, including Noscapine, Isoetharine mesylate, Cycloserine, Ethamsylate, Cetylpyridinium, Tretinoin, Ixazomib, Vorinostat, Venetoclax, Vorinostat, Ixazomib, Venetoclax, and epoetin alfa for further in-vitro and in-vivo investigations. CONCLUSION: We suggested CXCL1, KLHL21, SMAD3, HIF1A, and STAT1, ADCY5, TRIM14, RPF1, and FGA, STAT2, and HLA-F as critical genes and Cetylpyridinium, Cycloserine, Noscapine, Ethamsylate, Epoetin alfa, Isoetharine mesylate, Ribavirin, and Tretinoin drugs to study further their importance in treating COVID-19 lung complications.

Reconstruction of Metabolic-Protein Interaction Integrated Network of Eriocheir sinensis and Analysis of Ecdysone Synthesis.

Integrated networks have become a new interest in genome-scale network research due to their ability to comprehensively reflect and analyze the molecular processes in cells. Currently, none of the integrated networks have been reported for higher organisms. Eriocheir sinensis is a typical aquatic animal that grows through ecdysis. Ecdysone has been identified to be a crucial regulator of ecdysis, but the influence factors and regulatory mechanisms of ecdysone synthesis in E. sinensis are still unclear. In this work, the genome-scale metabolic network and protein-protein interaction network of E. sinensis were integrated to reconstruct a metabolic-protein interaction integrated network (MPIN). The MPIN was used to analyze the influence factors of ecdysone synthesis through flux variation analysis. In total, 236 integrated reactions (IRs) were found to influence the ecdysone synthesis of which 16 IRs had a significant impact. These IRs constitute three ecdysone synthesis routes. It is found that there might be alternative pathways to obtain cholesterol for ecdysone synthesis in E. sinensis instead of absorbing it directly from the feeds. The MPIN reconstructed in this work is the first integrated network for higher organisms. The analysis based on the MPIN supplies important information for the mechanism analysis of ecdysone synthesis in E. sinensis.

Exploration of the in vitro Antiviral Effects and the Active Components of Changyanning Tablets Against Enterovirus 71.

Purpose: This study aims to investigate the in vitro antiviral effects of the aqueous solution of Changyanning (CYN) tablets on Enterovirus 71 (EV71), and to analyze its active components. Methods: The in vitro anti-EV71 effects of CYN solution and its herbal ingredients were assessed by testing the relative viral RNA (vRNA) expression level and the cell viability rates. Material basis analysis was performed using HPLC-Q-TOF-MS/MS detection. Potential targets and active components were identified by network pharmacology and molecular docking. The screened components were verified by in vitro antiviral experiments. Results: CYN solution exerted anti-EV71 activities as the vRNA is markedly reduced after treatment, with a half maximal inhibitory concentration (IC50) of 996.85 µg/mL. Of its five herbal ingredients, aqueous extract of Mosla chinensis (AEMC) and leaves of Liquidambar formosana Hance (AELLF) significantly inhibited the intracellular replication of EV71, and the IC50 was tested as 202.57 µg/mL and 174.77 µg/mL, respectively. Based on HPLC-Q-TOF-MS/MS results, as well as the comparison with the material basis of CYN solution, a total of 44 components were identified from AEMC and AELLF. Through network pharmacology, AKT1, ALB, and SRC were identified as core targets. Molecular docking performed between core targets and the components indicated that 21 components may have anti-EV71 effects. Of these, nine were selected for in vitro pharmacodynamic verification, and only rosmarinic acid manifested in vitro anti-EV71 activity, with an IC50 of 11.90 µg/mL. Moreover, rosmarinic acid can stably bind with three core targets by forming hydrogen bonds. Conclusion: CYN solution has inhibitory effects on EV71 replication in vitro, and its active component was identified as rosmarinic acid. Our study provides a new approach for screening and confirmation of the effective components in Chinese herbal preparation.

The Interplay Between Epilepsy and Parkinson's Disease: Gene Expression Profiling and Functional Analysis.

The results of many epidemiological studies suggest a bidirectional causality may exist between epilepsy and Parkinson's disease (PD). However, the underlying molecular landscape linking these two diseases remains largely unknown. This study aimed to explore this possible bidirectional causality by identifying differentially expressed genes (DEGs) in each disease as well as their intersection based on two respective disease-related datasets. We performed enrichment analyses and explored immune cell infiltration based on an intersection of the DEGs. Identifying a protein-protein interaction (PPI) network between epilepsy and PD, and this network was visualised using Cytoscape software to screen key modules and hub genes. Finally, exploring the diagnostic values of the identified hub genes. NetworkAnalyst 3.0 and Cytoscape software were also used to construct and visualise the transcription factor-micro-RNA regulatory and co-regulatory networks, the gene-microRNA interaction network, as well as gene-disease association. Based on the enrichment results, the intersection of the DEGs mainly revealed enrichment in immunity-, phosphorylation-, metabolism-, and inflammation-related pathways. The boxplots revealed similar trends in infiltration of many immune cells in epilepsy and Parkinson's disease, with greater infiltration in patients than in controls. A complex PPI network comprising 186 nodes and 512 edges were constructed. According to node connection degree, top 15 hub genes were considered the kernel targets of epilepsy and PD. The area under curve values of hub gene expression profiles confirmed their excellent diagnostic values. This study is the first to analyse the molecular landscape underlying the epidemiological link between epilepsy and Parkinson's disease. The two diseases are closely linked through immunity-, inflammation-, and metabolism-related pathways. This information was of great help in understanding the pathogenesis, diagnosis, and treatment of the diseases. The present results may provide guidance for further in-depth analysis about molecular mechanisms of epilepsy and PD and novel potential targets.

EPI-SF: essential protein identification in protein interaction networks using sequence features.

Proteins are considered indispensable for facilitating an organism's viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins. Despite being a popular choice in machine learning (ML) applications, the deep learning (DL) method is not suggested for this specific research work based on sequence features due to the restricted availability of high-quality training sets of positive and negative samples. However, some DL works on limited availability of data are also executed at recent times which will be our future scope of work. Conventional ML techniques are thus utilized in this work due to their superior performance compared to DL methodologies. In consideration of the aforementioned, a technique called EPI-SF is proposed here, which employs ML to identify essential proteins within the protein-protein interaction network (PPIN). The protein sequence is the primary determinant of protein structure and function. So, initially, relevant protein sequence features are extracted from the proteins within the PPIN. These features are subsequently utilized as input for various machine learning models, including XGB Boost Classifier, AdaBoost Classifier, logistic regression (LR), support vector classification (SVM), Decision Tree model (DT), Random Forest model (RF), and Naïve Bayes model (NB). The objective is to detect the essential proteins within the PPIN. The primary investigation conducted on yeast examined the performance of various ML models for yeast PPIN. Among these models, the RF model technique had the highest level of effectiveness, as indicated by its precision, recall, F1-score, and AUC values of 0.703, 0.720, 0.711, and 0.745, respectively. It is also found to be better in performance when compared to the other state-of-arts based on traditional centrality like betweenness centrality (BC), closeness centrality (CC), etc. and deep learning methods as well like DeepEP, as emphasized in the result section. As a result of its favorable performance, EPI-SF is later employed for the prediction of novel essential proteins inside the human PPIN. Due to the tendency of viruses to selectively target essential proteins involved in the transmission of diseases within human PPIN, investigations are conducted to assess the probable involvement of these proteins in COVID-19 and other related severe diseases.

Utilizing systems genetics to enhance understanding into molecular targets of skin cancer.

Despite progress made with immune checkpoint inhibitors and targeted therapies, skin cancer remains a significant public health concern in the United States. The intricacies of the disease, encompassing genetics, immune responses, and external factors, call for a comprehensive approach. Techniques in systems genetics, including transcriptional correlation analysis, functional pathway enrichment analysis, and protein-protein interaction network analysis, prove valuable in deciphering intricate molecular mechanisms and identifying potential diagnostic and therapeutic targets for skin cancer. Recent studies demonstrate the efficacy of these techniques in uncovering molecular processes and pinpointing diagnostic markers for various skin cancer types, highlighting the potential of systems genetics in advancing innovative therapies. While certain limitations exist, such as generalizability and contextualization of external factors, the ongoing progress in AI technologies provides hope in overcoming these challenges. By providing protocols and a practical example involving Braf, we aim to inspire early-career experimental dermatologists to adopt these tools and seamlessly integrate these techniques into their skin cancer research, positioning them at the forefront of innovative approaches in combating this devastating disease.

TransNeT-CGP: A cluster-based comorbid gene prioritization by integrating transcriptomics and network-topological features.

The local disruptions caused by the genes of one disease can influence the pathways associated with the other diseases resulting in comorbidity. For gene therapies, it is necessary to prioritize the key genes that regulate common biological mechanisms to tackle the issues caused by overlapping diseases. This work proposes a clustering-based computational approach for prioritising the comorbid genes within the overlapping disease modules by analyzing Protein-Protein Interaction networks. For this, a sub-network with gene interactions of the disease pair was extracted from the interactome. The edge weights are assigned by combining the pairwise gene expression correlation and betweenness centrality scores. Further, a weighted graph clustering algorithm is applied and dominant nodes of high-density clusters are ranked based on clustering coefficients and neighborhood connectivity. Case studies based on neurodegenerative diseases such as Amyotrophic Lateral Sclerosis- Spinal Muscular Atrophy (ALS-SMA) pair and cancers such as Ovarian Carcinoma-Invasive Ductal Breast Carcinoma (OC-IDBC) pair were conducted to examine the efficacy of the proposed method. To identify the mechanistic role of top-ranked genes, we used Functional and Pathway enrichment analysis, connectivity analysis with leave-one-out (LOO) method, analysis of associated disease-related protein complexes, and prioritization tools such as TOPPGENE and Heml2.0. From pathway analysis, it was observed that the top 10 genes obtained using the proposed method were associated with 10 pathways in ALS-SMA comorbidity and 15 in the case of OC-IDBC, while that in similar methods like SAPDSB and S2B were 4, 6 respectively for ALS-SMA and 9, 10 respectively for OC-IDBC. In both case studies, 70 % of the disease-specific benchmark protein complexes were linked to top-ranked genes of the proposed method while that of SAPDSB and S2B were 55 % and 60 % respectively. Additionally, it was found that the removal of the top 10 genes disconnect the network into 14 distinct components in the case of ALS-SMA and 9 in the case of OC-IDBC. The experimental results shows that the proposed method can be effectively used for identifying key genes in comorbidity and can offer insights about the intricate molecular relationship driving comorbid diseases.

Identification of potential regulatory mechanisms and therapeutic targets for lung cancer.

Lung cancer poses a significant health threat globally, especially in regions like India, with 5-year survival rates remain alarmingly low. Our study aimed to uncover key markers for effective treatment and early detection. We identified specific genes related to lung cancer using the BioXpress database and delved into their roles through DAVID enrichment analysis. By employing network theory, we explored the intricate interactions within lung cancer networks, identifying ASPM and MKI67 as crucial regulator genes. Predictions of microRNA and transcription factor interactions provided additional insights. Examining gene expression patterns using GEPIA and KM Plotter revealed the clinical relevance of these key genes. In our pursuit of targeted therapies, Drug Bank pointed to methotrexate as a potential drug for the identified key regulator genes. Confirming this, molecular docking studies through Swiss Dock showed promising binding interactions. To ensure stability, we conducted molecular dynamics simulations using the AMBER 16 suite. In summary, our study pinpoints ASPM and MKI67 as vital regulators in lung cancer networks. The identification of hub genes and functional pathways enhances our understanding of molecular processes, offering potential therapeutic targets. Importantly, methotrexate emerged as a promising drug candidate, supported by robust docking and simulation studies. These findings lay a solid foundation for further experimental validations and hold promise for advancing personalized therapeutic strategies in lung cancer.Communicated by Ramaswamy H. Sarma.

Exploration of functional relations among differentially co-expressed genes identifies regulators in glioblastoma.

The conventional computational approaches to investigating a disease confront inherent constraints as they often need to improve in delving beyond protein functional associations and grasping their deeper contextual significance within the disease framework. Such context-specificity can be explored using clinical data by evaluating the change in interaction between the biological entities in different conditions by investigating the differential co-expression relationships. We believe that the integration and analysis of differential co-expression and the functional relationships, primarily focusing on the source nodes, will open novel insights about disease progression as the source proteins could trigger signaling cascades, mostly because they are transcription factors, cell surface receptors, or enzymes that respond instantly to a particular stimulus. A thorough contextual investigation of these nodes could lead to a helpful beginning point for identifying potential causal linkages and guiding subsequent scientific investigations to uncover mechanisms underlying observed associations. Our methodology includes functional protein-protein Interaction (PPI) data and co-expression information and filters functional linkages through a series of critical steps, culminating in the identification of a robust set of regulators. Our analysis identified eleven key regulators-AKT1, BRCA1, CAMK2G, CUL1, FGFR3, KIF3A, NUP210, PRKACB, RAB8A, RPS6KA2 and TGFB3-in glioblastoma. These regulators play a pivotal role in disease classification, cell growth control, and patient survivability and exhibit associations with immune infiltrations and disease hallmarks. This underscores the importance of assessing correlation towards causality in unraveling complex biological insights.

Exploring the Mechanisms of Sanguinarine in the Treatment of Osteoporosis by Integrating Network Pharmacology Analysis and Deep Learning Technology.

BACKGROUND: Sanguinarine (SAN) has been reported to have antioxidant, antiinflammatory, and antimicrobial activities with potential for the treatment of osteoporosis (OP). OBJECTIVE: This work purposed to unravel the molecular mechanisms of SAN in the treatment of OP. METHODS: OP-related genes and SAN-related targets were predicted from public databases. Differential expression analysis and VennDiagram were adopted to detect SAN-related targets against OP. Protein-protein interaction (PPI) network was served for core target identification. Molecular docking and DeepPurpose algorithm were further adopted to investigate the binding ability between core targets and SAN. Gene pathway scoring of these targets was calculated utilizing gene set variation analysis (GSVA). Finally, we explored the effect of SAN on the expressions of core targets in preosteoblastic MC3T3-E1 cells. RESULTS: A total of 21 candidate targets of SAN against OP were acquired. Furthermore, six core targets were identified, among which CASP3, CTNNB1, and ERBB2 were remarkably differentially expressed in OP and healthy individuals. The binding energies of SAN with CASP3, CTNNB1, and ERBB2 were -6, -6.731, and -7.162 kcal/mol, respectively. Moreover, the GSVA scores of the Wnt/calcium signaling pathway were significantly lower in OP cases than in healthy individuals. In addition, the expression of CASP3 was positively associated with Wnt/calcium signaling pathway. CASP3 and ERBB2 were significantly lower expressed in SAN group than in DMSO group, whereas the expression of CTNNB1 was in contrast. CONCLUSION: CASP3, CTNNB1, and ERBB2 emerge as potential targets of SAN in OP prevention and treatment.

Comparative transcriptomics analysis identifies crucial genes and pathways during goose spleen development.

As the largest peripheral lymphoid organ in poultry, the spleen plays an essential role in regulating the body's immune capacity. However, compared with chickens and ducks, information about the age- and breed-related changes in the goose spleen remains scarce. In this study, we systematically analyzed and compared the age-dependent changes in the morphological, histological, and transcriptomic characteristics between Landes goose (LG; Anser anser) and Sichuan White goose (SWG; Anser cygnoides). The results showed a gradual increase in the splenic weights for both LG and SWG until week 10, while their splenic organ indexes reached the peak at week 6. Meanwhile, the splenic histological indexes of both goose breeds continuously increased with age, reaching the highest levels at week 30. The red pulp (RP) area was significantly higher in SWG than in LG at week 0, while the splenic corpuscle (AL) diameter was significantly larger in LG than in SWG at week 30. At the transcriptomic level, a total of 1710 and 1266 differentially expressed genes (DEGs) between week 0 and week 30 were identified in spleens of LG and SWG, respectively. Meanwhile, a total of 911 and 808 DEGs in spleens between LG and SWG were identified at weeks 0 and 30, respectively. Both GO and KEGG enrichment analysis showed that the age-related DEGs of LG or SWG were dominantly enriched in the Cell cycle, TGF-beta signaling, and Wnt signaling pathways, while most of the breed-related DEGs were enriched in the Neuroactive ligand-receptor interaction, Cytokine-cytokine receptor interaction, ECM-receptor interaction, and metabolic pathways. Furthermore, through construction of protein-protein interaction networks using significant DEGs, it was inferred that three hub genes including BUB1, BUB1B, and TTK could play crucial roles in regulating age-dependent goose spleen development while GRIA2, GRIA4, and RYR2 could be crucial for the breed-specific goose spleen development. These data provide novel insights into the splenic developmental differences between Chinese and European domestic geese, and the identified crucial pathways and genes are helpful for a better understanding of the mechanisms regulating goose immune functions.

ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization.

BACKGROUND: In cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species. RESULTS: We introduce ECDEP, an essential protein identification model based on evolutionary community discovery. ECDEP integrates temporal gene expression data with a protein-protein interaction (PPI) network and employs the 3-Sigma rule to eliminate outliers at each time point, constructing a dynamic network. Next, we utilize edge birth and death information to establish an interaction streaming source to feed into the evolutionary community discovery algorithm and then identify overlapping communities during the evolution of the dynamic network. SVM recursive feature elimination (RFE) is applied to extract the most informative communities, which are combined with subcellular localization data for classification predictions. We assess the performance of ECDEP by comparing it against ten centrality methods, four shallow machine learning methods with RFE, and two deep learning methods that incorporate multiple biological data sources on Saccharomyces. Cerevisiae (S. cerevisiae), Homo sapiens (H. sapiens), Mus musculus, and Caenorhabditis elegans. ECDEP achieves an AP value of 0.86 on the H. sapiens dataset and the contribution ratio of community features in classification reaches 0.54 on the S. cerevisiae (Krogan) dataset. CONCLUSIONS: Our proposed method adeptly integrates network dynamics and yields outstanding results across various datasets. Furthermore, the incorporation of evolutionary community discovery algorithms amplifies the capacity of gene expression data in classification.

Computational drug repurposing for viral infectious diseases: a case study on monkeypox.

The traditional method of drug reuse or repurposing has significantly contributed to the identification of new antiviral compounds and therapeutic targets, enabling rapid response to developing infectious illnesses. This article presents an overview of how modern computational methods are used in drug repurposing for the treatment of viral infectious diseases. These methods utilize data sets that include reviewed information on the host's response to pathogens and drugs, as well as various connections such as gene expression patterns and protein-protein interaction networks. We assess the potential benefits and limitations of these methods by examining monkeypox as a specific example, but the knowledge acquired can be applied to other comparable disease scenarios.

Inhibitory effects of Acanthopanax sessiliflorus Harms extract on the etiology of rheumatoid arthritis in a collagen-induced arthritis mouse model.

BACKGROUND: The biological function of Acanthopanax sessiliflorus Harm (ASH) has been investigated on various diseases; however, the effects of ASH on arthritis have not been investigated so far. This study investigates the effects of ASH on rheumatoid arthritis (RA). METHODS: Supercritical carbon dioxide (CO2) was used for ASH extract preparation, and its primary components, pimaric and kaurenoic acids, were identified using gas chromatography-mass spectrometer (GC-MS). Collagenase-induced arthritis (CIA) was used as the RA model, and primary cultures of articular chondrocytes were used to examine the inhibitory effects of ASH extract on arthritis in three synovial joints: ankle, sole, and knee. RESULTS: Pimaric and kaurenoic acids attenuated pro-inflammatory cytokine-mediated increase in the catabolic factors and retrieved pro-inflammatory cytokine-mediated decrease in related anabolic factors in vitro; however, they did not affect pro-inflammatory cytokine (IL-1ß, TNF-α, and IL-6)-mediated cytotoxicity. ASH effectively inhibited cartilage degradation in the knee, ankle, and toe in the CIA model and decreased pannus development in the knee. Immunohistochemistry demonstrated that ASH mostly inhibited the IL-6-mediated matrix metalloproteinase. Gene Ontology and pathway studies bridge major gaps in the literature and provide insights into the pathophysiology and in-depth mechanisms of RA-like joint degeneration. CONCLUSIONS: To the best of our knowledge, this is the first study to conduct extensive research on the efficacy of ASH extract in inhibiting the pathogenesis of RA. However, additional animal models and clinical studies are required to validate this hypothesis.

Identifying diseases associated with Post-COVID syndrome through an integrated network biology approach.

A growing body of research shows that COVID-19 is now recognized as a multi-organ disease with a wide range of manifestations that can have long-lasting repercussions, referred to as post-COVID-19 syndrome. It is unknown why the vast majority of COVID-19 patients develop post-COVID-19 syndrome, or why patients with pre-existing disorders are more likely to experience severe COVID-19. This study used an integrated network biology approach to obtain a comprehensive understanding of the relationship between COVID-19 and other disorders. The approach involved building a PPI network with COVID-19 genes and identifying highly interconnected regions. The molecular information contained within these subnetworks, as well as the pathway annotations, were used to reveal the link between COVID-19 and other disorders. Using Fisher's exact test and disease-specific gene information, significant COVID-19-disease associations were discovered. The study discovered diseases that affect multiple organs and organ systems, thus proving the theory of multiple organ damage caused by COVID-19. Cancers, neurological disorders, hepatic diseases, cardiac disorders, pulmonary diseases, and hypertensive diseases are just a few of the conditions linked to COVID-19. Pathway enrichment analysis of shared proteins revealed the shared molecular mechanism of COVID-19 and these diseases. The findings of the study shed new light on the major COVID-19-associated disease conditions and how their molecular mechanisms interact with COVID-19. The novelty of studying disease associations in the context of COVID-19 provides new insights into the management of rapidly evolving long-COVID and post-COVID syndromes, which have significant global implications.Communicated by Ramaswamy H. Sarma.

Identification of common candidate genes and pathways for Spina Bifida and Wilm's Tumor using an integrative bioinformatics analysis.

Spina Bifida (SB) and Wilm's Tumor (WT) are conditions, both associated with children. Several studies have shown that WT later develops in SB patients, which led us to elucidate common key genes and linked pathways of both conditions, aimed at their concurrent therapeutic management. For this, integrated bioinformatics analysis was employed. A comprehensive manual curation of genes identified 133 and 139 genes associated with SB and WT, respectively, which were used to construct a single protein-protein interaction (PPI) network. Topological parameters analysis of the network showed its scale-free and hierarchical nature. Centrality-based analysis of the network identified 116 hubs, of which, 6 were called the key genes attributed to being common between SB and WT besides being the hubs. Gene enrichment analysis of the 5 most essential modules, identified important biological processes and pathways possibly linking SB to WT. Additionally, miRNA-key gene-transcription factor (TF) regulatory network elucidated a few important miRNAs and TFs that regulate our key genes. In closing, we put forward TP53, DICER1, NCAM1, PAX3, PTCH1, MTHFR; hsa-mir-107, hsa-mir-137, hsa-mir-122, hsa-let-7d; and YY1, SOX4, MYC, STAT3; key genes, miRNAs and TFs, respectively, as the key regulators. Further, MD simulation studies of wild and Glu429Ala forms of MTHFR proteins showed that there is a slight change in MTHFR protein structure due to Glu429Ala polymorphism. We anticipate that the interplay of these three entities will be an interesting area of research to explore the regulatory mechanism of SB and WT and may serve as candidate target molecules to diagnose, monitor, and treat SB and WT, parallelly.Communicated by Ramaswamy H. Sarma.