Exploring protein-protein interactions for the development of new analgesics.

The development of new analgesics has been challenging. Candidate drugs often have limited clinical utility due to side effects that arise because many drug targets are involved in signaling pathways other than pain transduction. Here, we explored the potential of targeting protein-protein interactions (PPIs) that mediate pain signaling as an approach to developing drugs to treat chronic pain. We reviewed the approaches used to identify small molecules and peptide modulators of PPIs and their ability to decrease pain-like behaviors in rodent animal models. We analyzed data from rodent and human sensory nerve tissues to build associated signaling networks and assessed both validated and potential interactions and the structures of the interacting domains that could inform the design of synthetic peptides and small molecules. This resource identifies PPIs that could be explored for the development of new analgesics, particularly between scaffolding proteins and receptors for various growth factors and neurotransmitters, as well as ion channels and other enzymes. Targeting the adaptor function of CBL by blocking interactions between its proline-rich carboxyl-terminal domain and its SH3-domain-containing protein partners, such as GRB2, could disrupt endosomal signaling induced by pain-associated growth factors. This approach would leave intact its E3-ligase functions, which are mediated by other domains and are critical for other cellular functions. This potential of PPI modulators to be more selective may mitigate side effects and improve the clinical management of pain.

Proteomic analysis of cerebrospinal fluid of amyotrophic lateral sclerosis patients in the presence of autologous bone marrow derived mesenchymal stem cells.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal and rapidly progressive motoneuron degenerative disorder. There are still no drugs capable of slowing disease evolution or improving life quality of ALS patients. Thus, autologous stem cell therapy has emerged as an alternative treatment regime to be investigated in clinical ALS. METHOD: Using Proteomics and Protein-Protein Interaction Network analyses combined with bioinformatics, the possible cellular mechanisms and molecular targets related to mesenchymal stem cells (MSCs, 1 × 106 cells/kg, intrathecally in the lumbar region of the spine) were investigated in cerebrospinal fluid (CSF) of ALS patients who received intrathecal infusions of autologous bone marrow-derived MSCs thirty days after cell therapy. Data are available via ProteomeXchange with identifier PXD053129. RESULTS: Proteomics revealed 220 deregulated proteins in CSF of ALS subjects treated with MSCs compared to CSF collected from the same patients prior to MSCs infusion. Bioinformatics enriched analyses highlighted events of Extracellular matrix and Cell adhesion molecules as well as related key targets APOA1, APOE, APP, C4A, C5, FGA, FGB, FGG and PLG in the CSF of cell treated ALS subjects. CONCLUSIONS: Extracellular matrix and cell adhesion molecules as well as their related highlighted components have emerged as key targets of autologous MSCs in CSF of ALS patients. TRIAL REGISTRATION: Clinicaltrial.gov identifier NCT0291768. Registered 28 September 2016.

Decoding potential targets and pharmacologic mechanisms of curcumin in treating non-small cell lung carcinoma via bioinformatics and molecular docking.

Emerging evidence demonstrates that curcumin has an inhibitory effect on non-small cell lung cancer (NSCLC), and its targets and mechanism of action need further exploration. The goal of this study was to explore the potential targets and mechanism of curcumin against NSCLC by network pharmacology, bioinformatics, and experimental validation, thereby providing more insight into combination treatment with curcumin for NSCLC in preclinical and clinical research. Curcumin targets against NSCLC were predicted based on HIT2.0, STD, CTD, and DisGeNET, and the core targets were analyzed via protein-protein interaction network construction (PPI), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and molecular docking. The gene expression levels of samples in A549 cells, NCI-H460, and curcumin treated groups were detected by real-time quantitative PCR. A total of 67 common targets between curcumin and NSCLC were collected by screening public databases. GO and KEGG analysis suggested that curcumin treatment of NSCLC mainly involves cancer-related pathways, such as PI3K-AKT signaling pathway, Foxo signaling pathway, microRNAs, MAPK signaling pathway, HIF-1 signaling pathway, etc. The targets with the highest degree were identified through the PPI network, namely CASP3, CTNNB1, JUN, IL6, MAPK3, HIF1A, STAT3, AKT1, TP53, CCND1, VEGFA, and EGFR. The results of the in vitro experiments showed that curcumin treatment of NSCLC down-regulated the gene expressions of CCND1, CASP3, HIF1A, IL-6, MAPK3, STAT3, AKT1, and TP53. Our findings revealed that curcumin functions as a potential therapeutic candidate for NSCLC by suppressing multiple signaling pathways and interacting with multiple gene targets.

The Molecular Comorbidity Network of Periodontal Disease.

Periodontal disease, a multifactorial inflammatory condition affecting the supporting structures of the teeth, has been increasingly recognized for its association with various systemic diseases. Understanding the molecular comorbidities of periodontal disease is crucial for elucidating shared pathogenic mechanisms and potential therapeutic targets. In this study, we conducted comprehensive literature and biological database mining by utilizing DisGeNET2R for extracting gene-disease associations, Romin for integrating and modeling molecular interaction networks, and Rentrez R libraries for accessing and retrieving relevant information from NCBI databases. This integrative bioinformatics approach enabled us to systematically identify diseases sharing associated genes, proteins, or molecular pathways with periodontitis. Our analysis revealed significant molecular overlaps between periodontal disease and several systemic conditions, including cardiovascular diseases, diabetes mellitus, rheumatoid arthritis, and inflammatory bowel diseases. Shared molecular mechanisms implicated in the pathogenesis of these diseases and periodontitis encompassed dysregulation of inflammatory mediators, immune response pathways, oxidative stress pathways, and alterations in the extracellular matrix. Furthermore, network analysis unveiled the key hub genes and proteins (such as TNF, IL6, PTGS2, IL10, NOS3, IL1B, VEGFA, BCL2, STAT3, LEP and TP53) that play pivotal roles in the crosstalk between periodontal disease and its comorbidities, offering potential targets for therapeutic intervention. Insights gained from this integrative approach shed light on the intricate interplay between periodontal health and systemic well-being, emphasizing the importance of interdisciplinary collaboration in developing personalized treatment strategies for patients with periodontal disease and associated comorbidities.

A Comprehensive Systematic Review Coupled with an Interacting Network Analysis Identified Candidate Genes and Biological Pathways Related to Bovine Temperament.

Comprehension of the genetic basis of temperament has been improved by recent advances in the identification of genes and genetic variants. However, due to the complexity of the temperament traits, the elucidation of the genetic architecture of temperament is incomplete. A systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to analyze candidate genes related to bovine temperament, using bovine as the population, SNPs and genes as the exposure, and temperament test as the outcome, as principal search terms for population, exposure, and outcome (PEO) categories to define the scope of the search. The search results allowed the selection of 36 articles after removing duplicates and filtering by relevance. One hundred-two candidate genes associated with temperament traits were identified. The genes were further analyzed to construct an interaction network using the STRING database, resulting in 113 nodes and 346 interactions and the identification of 31 new candidate genes for temperament. Notably, the main genes identified were SST and members of the Kelch family. The candidate genes displayed interactions with pathways associated with different functions such as AMPA receptors, hormones, neuronal maintenance, protein signaling, neuronal regulation, serotonin synthesis, splicing, and ubiquitination activities. These new findings demonstrate the complexity of interconnected biological processes that regulate behavior and stress response in mammals. This insight now enables our targeted analysis of these newly identified temperament candidate genes in bovines.

Identification of differentially expressed genes, pathways, and immune infiltration in diabetes.

This study aimed to perform exhaustive bioinformatic analysis by using GSE29221 micro-array maps obtained from healthy controls and Type 2 Diabetes (T2DM) patients. Raw data are downloaded from the Gene Expression Omnibus database and processed by the limma package in R software to identify Differentially Expressed Genes (DEGs). Gene ontology functional analysis and Kyoto Gene Encyclopedia and Genome Pathway analysis are performed to determine the biological functions and pathways of DEGs. A protein interaction network is constructed using the STRING database and Cytoscape software to identify key genes. Finally, immune infiltration analysis is performed using the Cibersort method. This study has implications for understanding the underlying molecular mechanism of T2DM and provides potential targets for further research.

Podoplanin immunoexpression in odontogenic lesions: a systematic review, meta-analysis, and integrated bioinformatic analysis.

BACKGROUND: Podoplanin (PDPN) is a transmembrane glycoprotein implicated in the pathogenesis of odontogenic lesions (OL). It is localized at the membrane and cytoplasmic level, and its interaction with other proteins could trigger cell proliferation, invasion and migration. The main objective of this systematic review is to explore the immunoexpression pattern of podoplanin in OL. In addition, as secondary objectives, we aimed to compare the immunostaining intensity of PDPN in OL, to analyze its interaction networks by bioinformatic analysis and to highlight its importance as a potential diagnostic marker useful in the pathogenesis of OL. METHODS: The protocol was developed following PRISMA and Cochrane guidelines. The digital search was performed in the databases: PubMed/MEDLINE, ScienceDirect, Scopus, Web of Science and Google Schoolar from August 15, 2010 to June 15, 2023. We included cross-sectional and cohort studies that will analyze the pattern of PDPN immunoexpression in OL. Two investigators independently searched for eligible articles, selected titles and abstracts, analyzed full text, conducted data collection, and performed assessment of study quality and risk of bias. In addition, part of the results were summarized through a random-effects meta-analysis. STRING database was used for protein-protein interaction analysis. RESULTS: Twenty-nine relevant studies were included. The ages of the subjects ranged from 2 to 89 years, with a mean age of 33.41 years. Twenty-two point two percent were female, 21.4% were male, and in 56.4% the gender of the participants was not specified. A total of 1,337 OL samples were analyzed for PDPN immunoexpression pattern. Ninety-four (7.03%) were dental follicles and germs, 715 (53.47%) were odontogenic cysts, and 528 (39.49%) were odontogenic tumors. Meta-analysis indicated that the immunostaining intensity was significantly stronger in odontogenic keratocysts compared to dentigerous cysts (SMD=3.3(CI=1.85-4.82, p=0.000*). Furthermore, bioinformatic analysis revealed that PECAM-1, TNFRF10B, MSN, EZR and RDX interact directly with PDPN and their expression in OL was demonstrated. CONCLUSIONS: The results of the present systematic review support the unique immunoexpression of PDPN as a potential useful diagnostic marker in the pathogenesis of OL.

The power of computational proteomics platforms to decipher protein-protein interactions.

Adopting computational tools for analyzing extensive biological datasets has profoundly transformed our understanding and interpretation of biological phenomena. Innovative platforms have emerged, providing automated analysis to unravel essential insights about proteins and the complexities of their interactions. These computational advancements align with traditional studies, which employ experimental techniques to discern and quantify physical and functional protein-protein interactions (PPIs). Among these techniques, tandem mass spectrometry is notably recognized for its precision and sensitivity in identifying PPIs. These approaches might serve as important information enabling the identification of PPIs with potential pharmacological significance. This review aims to convey our experience using computational tools for detecting PPI networks and offer an analysis of platforms that facilitate predictions derived from experimental data.

Proteomic Biomarkers Associated with Low Bone Mineral Density: A Systematic Review.

Osteoporosis is a globally relevant public health issue. Our study aimed to summarize the knowledge on the proteomic biomarkers for low bone mineral density over the last years. We conducted a systematic review following the PRISMA guidelines; the scoured databases were PubMed, Web of Sciences, Scopus, and EBSCO, from inception to 2 June 2023. A total of 610 relevant studies were identified and 33 were assessed for eligibility. Finally, 29 studies met the criteria for this systematic review. The risk of bias was evaluated using the Joanna Briggs Institute Critical Appraisal Checklist tool. From the studies selected, 154 proteins were associated with changes of bone mineral density, from which only 10 were reported in at least two articles. The protein-protein network analysis indicated potential biomarkers involved in the skeletal system, immune system process, regulation of protein metabolic process, regulation of signaling, transport, cellular component assembly, cell differentiation, hemostasis, and extracellular matrix organization. Mass spectrometry-based proteomic profiling has allowed the discovery of new biomarkers with diagnostic potential. However, it is necessary to compare and validate the potential biomarkers in different populations to determine their association with bone metabolism and evaluate their translation to the clinical management of osteoporosis.

Genome-Wide Admixture and Association Study of Serum Selenium Deficiency to Identify Genetic Variants Indirectly Linked to Selenium Regulation in Brazilian Adults.

Blood selenium (Se) concentrations differ substantially by population and could be influenced by genetic variants, increasing Se deficiency-related diseases. We conducted a genome-wide association study (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with serum Se deficiency in 382 adults with admixed ancestry. Genotyping arrays were combined to yield 90,937 SNPs. R packages were applied to quality control and imputation. We also performed the ancestral proportion analysis. The Search Tool for the Retrieval of Interacting Genes was used to interrogate known protein-protein interaction networks (PPIs). Our ancestral proportion analysis estimated 71% of the genome was from Caucasians, 22% was from Africans, and 8% was from East Asians. We identified the SNP rs1561573 in the TraB domain containing 2B (TRABD2B), rs425664 in MAF bZIP transcription factor (MAF), rs10444656 in spermatogenesis-associated 13 (SPATA13), and rs6592284 in heat shock protein nuclear import factor (HIKESHI) genes. The PPI analysis showed functional associations of Se deficiency, thyroid hormone metabolism, NRF2-ARE and the Wnt pathway, and heat stress. Our findings show evidence of a genetic association between Se deficiency and metabolic pathways indirectly linked to Se regulation, reinforcing the complex relationship between Se intake and the endogenous factors affecting the Se requirements for optimal health.

Kinesin binding as a shared pathway underlying the genetic basis of male factor infertility and insomnia.

OBJECTIVE: To study whether male factor infertility and insomnia share genetic risk variants and identify any molecular, cellular, and biologic interactions between these traits. DESIGN: The in silico study was performed. Two lists of genetic variants were manually curated through a literature review, one of those associated with male factor infertility and the other with insomnia. Genes were assigned to these variants to compose male factor infertility-associated (454 genes) and insomnia-associated (921 genes) gene lists. SETTING: Not applicable. PATIENT(S): Not applicable. INTERVENTION(S): Not applicable. MAIN OUTCOME MEASURE(S): Enrichment of biologic pathways and protein-protein interaction analysis. RESULT(S): Twenty-eight genes were common to both lists, representing a greater overlap than would be expected by chance. In the 28 genes contained in the intersection list, there was a significant enrichment of pathways related to kinesin binding. A protein-protein interaction analysis using the intersection list as input retrieved 25 nodes and indicated that two of them were kinesin-related proteins (PLEKHM2 and KCL1). CONCLUSION(S): The shared male factor infertility and insomnia genes, and the biologic pathways highlighted in this study, suggest that further functional investigations into the interplay between fertility and sleep are warranted.

Transcriptomic signatures of classical monocytes reveal pro-inflammatory modules and heterogeneity in polyarticular juvenile idiopathic arthritis.

Introduction: Polyarticular juvenile idiopathic arthritis (pJIA) is a childhood-onset autoimmune disease. Immune cells contribute to persistent inflammation observed in pJIA. Despite the crucial role of monocytes in arthritis, the precise involvement of classical monocytes in the pathogenesis of pJIA remains uncertain. Here, we aimed to uncover the transcriptomic patterns of classical monocytes in pJIA, focusing on their involvement in disease mechanism and heterogeneity. Methods: A total of 17 healthy subjects and 18 premenopausal women with pJIA according to ILAR criteria were included. Classical monocytes were isolated, and RNA sequencing was performed. Differential expression analysis was used to compare pJIA patients and healthy control group. Differentially expressed genes (DEGs) were identified, and gene set enrichment analysis (GSEA) was performed. Using unsupervised learning approach, patients were clustered in two groups based on their similarities at transcriptomic level. Subsequently, these clusters underwent a comparative analysis to reveal differences at the transcriptomic level. Results: We identified 440 DEGs in pJIA patients of which 360 were upregulated and 80 downregulated. GSEA highlighted TNF-α and IFN-γ response. Importantly, this analysis not only detected genes targeted by pJIA therapy but also identified new modulators of immuno-inflammation. PLAUR, IL1B, IL6, CDKN1A, PIM1, and ICAM1 were pointed as drivers of chronic hyperinflammation. Unsupervised learning approach revealed two clusters within pJIA, each exhibiting varying inflammation levels. Conclusion: These findings indicate the pivotal role of immuno-inflammation driven by classical monocytes in pJIA and reveals the existence of two subclusters within pJIA, regardless the positivity of rheumatoid factor and anti-CCP, paving the way to precision medicine.

Network-Based Transcriptome Analysis Reveals FAM3C as a Novel Potential Biomarker for Glioblastoma.

Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a high mortality rate. The aim of the present study was to investigate the clinical significance of Family with Sequence Similarity 3, Member C, FAM3C, in GBM using bioinformatic-integrated analysis. First, we performed the transcriptomic integration analysis to assess the expression profile of FAM3C in GBM using several data sets (RNA-sequencing and scRNA-sequencing), which were obtained from TCGA and GEO databases. By using the STRING platform, we investigated FAM3C-coregulated genes to construct the protein-protein interaction network. Next, Metascape, Enrichr, and CIBERSORT databases were used. We found FAM3C high expression in GBM with poor survival rates. Further, we observed, via FAM3C coexpression network analysis, that FAM3C plays key roles in several hallmarks of cancer. Surprisingly, we also highlighted five FAM3C­coregulated genes overexpressed in GBM. Specifically, we demonstrated the association between the high expression of FAM3C and the abundance of the different immune cells, which may markedly worsen GBM prognosis. For the first time, our findings suggest that FAM3C not only can be a new emerging biomarker with promising therapeutic values to GBM patients but also gave a new insight into a potential resource for future GBM studies.

Upregulation of NPC1 and its association with poor prognosis in gastric cancer.

BACKGROUND: The Niemann-Pick disease type C1 (NPC1) protein plays a pivotal role in lipid transport, particularly free cholesterol, within lysosomal/late endosomal membranes. Previous studies have highlighted NPC1 as a promising target for cholesterol trafficking and cancer therapy. Nevertheless, the expression of NPC1 in gastric cancer (GC) and its clinical implications remain unexplored. This study aims to investigate NPC1 expression in GC and its correlation with patient prognosis. METHODS: NPC1 expression levels in GC and normal tissues were assessed using the GEPIA database, and survival analysis was conducted via Kaplan‒Meier Plotter. Evaluation of potential biological effects of NPC1 in GC by protein-protein interaction network and GO, KEGG bioenrichment analysis. Immunohistochemistry was performed on surgical samples collected from 306 GC patients. Correlations between NPC1 expression, clinical characteristics, and patient prognosis were analyzed. RESULTS: NPC1 mRNA expression was elevated in GC tissues compared to normal tissues (P < 0.05) and significantly associated with poorer prognosis. In our cohort of 306 patients, NPC1 exhibited significant upregulation in GC versus adjacent normal tissues (P = 0.031). High NPC1 expression correlated with adverse clinical characteristics, including lymph node metastasis, distant metastasis, and advanced TNM stage (all P < 0.05). Patients with high NPC1 expression experienced notably shorter overall survival (P < 0.001), particularly in stages III and IV (P = 0.003). Multivariate Cox regression analysis identified high NPC1 expression as an independent prognostic factor for GC patients (HR 1.57, 95% CI 1.14-2.18, P = 0.006). Lastly, an optimized nomogram incorporating NPC1, tumor size, and TNM stage was constructed. CONCLUSIONS: NPC1 expression is upregulated in GC and serves as a pivotal prognostic factor for adverse outcomes in GC patients.

Identification of core gene in chronic rhinosinusitis with nasal polyps and correlations with inflammation-related genes.

OBJECTIVE: Our aim in this study is to identify the core genes of chronic rhinosinusitis with nasal polyps and analyze the correlations between it and inflammation-related genes. METHODS: GSE72713 dataset containing gene expression data of ECRSwNP, nonECRSwNP and healthy samples was obtained from Gene Expression Omnibus (GEO) and filtered by limma to identify DEGs among three groups, then the functions and correlated pathways of DEGs were analyzed using GO and KEGG. The core DEGs were selected by the intersection of DEGs and the PPI network was constructed via STRING. The correlations between the expression levels of CRSwNP core gene and inflammation-related genes were analyzed via the Mann-Whitney U test. RESULTS: The DEGs among ECRSwNP, nonECRSwNP, and CTRL were filtered respectively, and enrichment analysis showed they were associated with olfaction and/or immune responses. The PPI network was constructed by 7 core DEGs obtained via the intersection among three groups, and ALOX15 was confirmed as the core gene in the network. Subsequently, the correlations between the expression levels of ALOX15 and inflammation-related genes were illustrated. CONCLUSION: In this study, the core gene ALOX15 was selected from the DEGs among ECRSwNP, nonECRSwNP, and CTRL. IL5, IL1RL1, and IL1RAP were found to exhibit a significant positive correlation with ALOX15. LEVEL OF EVIDENCE: Level 3.

Potential of semen coicis in enhancing the anti-tumor effects of PD-1 inhibitor on A549 cell lines by blocking the PI3K-AKT-mTOR pathway.

BACKGROUND: The objective of this research was to investigate how the combination of semen coicis extract and PD-1 inhibitors can potentially work together to enhance the anti-tumor effects, with a focus on understanding the underlying mechanism. METHODS: We obtained the active components and specific targets of semen coicis in the treatment of NSCLC from various databases, namely TCMSP, GeneCard, and OMIM. By utilizing the STRING database and Cytoscape software, we established a protein interaction network (PPI) for the active ingredient of semen coicis and the target genes related to NSCLC. To explore the potential pathways involved, we conducted gene ontology (GO) and biological pathway (KEGG) enrichment analyses, which were further supported by molecular docking technology. Additionally, we conducted cyto-inhibition experiments to verify the inhibitory effects of semen coicis alone or in combination with a PD-1 inhibitor on A549 cells, along with examining the associated pathways. Furthermore, we investigated the synergistic mechanism of these two drugs through cytokine release experiments and the PD-L1 expression study on A549 cells. RESULTS: Semen coicis contains two main active components, Omaine and (S)-4-Nonanolide. Its primary targets include PIK3R1, PIK3CD, PIK3CA, AKT2, and mTOR. Molecular docking experiments confirmed that these ingredients and targets form stable bonds. In vitro experiments showed that semen coicis demonstrates inhibitory effects against A549 cells, and this effect was further enhanced when combined with PD-1 inhibitors. PCR and WB analysis confirmed that the inhibition of the PI3K-AKT-mTOR pathway may contribute to this effect. Additionally, semen coicis was observed to decrease the levels of IFN-γ, IL-6, and TNF-α, promoting the recovery of the human anti-tumor immune response. And semen coicis could inhibit the induced expression of PD­L1 of A549 cells stimulated by IFN­Î³ as well. CONCLUSION: Semen coicis not only has the ability to kill tumor cells directly but also alleviates the immunosuppression found in the tumor microenvironment. Additionally, it collaboratively enhances the effectiveness of PD-1 inhibitors against tumors by blocking the activation of PI3K-AKT-mTOR.

Identification of metabolic pathways and key genes associated with atypical parkinsonism using a systems biology approach.

Atypical parkinsonism (AP) is a group of complex neurodegenerative disorders with marked clinical and pathophysiological heterogeneity. The use of systems biology tools may contribute to the characterization of hub-bottleneck genes, and the identification of its biological pathways to broaden the understanding of the bases of these disorders. A systematic search was performed on the DisGeNET database, which integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. The tools STRING 11.0 and Cytoscape 3.8.2 were used for analysis of protein-protein interaction (PPI) network. The PPI network topography analyses were performed using the CytoHubba 0.1 plugin for Cytoscape. The hub and bottleneck genes were inserted into 4 different sets on the InteractiveVenn. Additional functional enrichment analyses were performed to identify Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene ontology for a described set of genes. The systematic search in the DisGeNET database identified 485 genes involved with Atypical Parkinsonism. Superimposing these genes, we detected a total of 31 hub-bottleneck genes. Moreover, our functional enrichment analyses demonstrated the involvement of these hub-bottleneck genes in 3 major KEGG pathways. We identified 31 highly interconnected hub-bottleneck genes through a systems biology approach, which may play a key role in the pathogenesis of atypical parkinsonism. The functional enrichment analyses showed that these genes are involved in several biological processes and pathways, such as the glial cell development, glial cell activation and cognition, pathways were related to Alzheimer disease and Parkinson disease. As a hypothesis, we highlight as possible key genes for AP the MAPT (microtubule associated protein tau), APOE (apolipoprotein E), SNCA (synuclein alpha) and APP (amyloid beta precursor protein) genes.

In Silico Analysis of Genes Associated with the Pathogenesis of Odontogenic Keratocyst.

Odontogenic keratocyst (OK) is a benign intraosseous cystic lesion characterized by a parakeratinized stratified squamous epithelial lining with palisade basal cells. It represents 10-12% of odontogenic cysts. The changes in its classification as a tumor or cyst have increased interest in its pathogenesis. OBJECTIVE: Identify key genes in the pathogenesis of sporadic OK through in silico analysis. MATERIALS AND METHODS: The GSE38494 technical sheet on OK was analyzed using GEOR2. Their functional and canonical signaling pathways were enriched in the NIH-DAVID bioinformatic platform. The protein-protein interaction network was constructed by STRING and analyzed with Cytoscape-MCODE software v 3.8.2 (score > 4). Post-enrichment analysis was performed by Cytoscape-ClueGO. RESULTS: A total of 768 differentially expressed genes (DEG) with a fold change (FC) greater than 2 and 469 DEG with an FC less than 2 were identified. In the post-enrichment analysis of upregulated genes, significance was observed in criteria related to the organization of the extracellular matrix, collagen fibers, and endodermal differentiation, while the downregulated genes were related to defensive response mechanisms against viruses and interferon-gamma activation. CONCLUSIONS: Our in silico analysis showed a significant relationship with mechanisms of extracellular matrix organization, interferon-gamma activation, and response to viral infections, which must be validated through molecular assays.

Graph embedding on mass spectrometry- and sequencing-based biomedical data.

Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for graph embedding techniques in biomedical data analysis have emerged. While these approaches remain computationally demanding, several developments over the last years facilitate their application to study biomedical data and thus may help advance biological discoveries. Therefore, in this review, we discuss the principles of graph embedding techniques and explore the usefulness for understanding biological network data derived from mass spectrometry and sequencing experiments, the current workhorses of systems biology studies. In particular, we focus on recent examples for characterizing protein-protein interaction networks and predicting novel drug functions.

Gene expression in multiple sclerosis during pregnancy based on integrated bioinformatics analysis.

BACKGROUND: The modulation of the activity disease in patients with Multiple Sclerosis (MS) that occurs during pregnancy is a helpful model which could provide insight into central disease mechanisms and facilitate treatment. Therefore, the aim of the study was to identify differentially expressed genes in-silico to perform biological function pathway enrichment analysis and protein-protein interaction from pregnant women with MS. METHODS: Transcriptome data were obtained from the Gene Expression Omnibus (GEO) database. We selected the microarray dataset GSE17449. The gene expression dataset contains the data of mononuclear cells from four different groups sought, including seven healthy women (H), four healthy pregnant women (HP), eight women with multiple sclerosis (WMS), and nine women nine months pregnant with multiple sclerosis (PMS). The GSEA software was employed for enrichment analysis, and the REACTOME database was used for biological pathways. The protein-protein interaction (PPI) network was plotted with STRING. The databases used to identify the connection of DEGs with different signaling pathways were KEGG and WIKIPATHWAYS. RESULTS: We identified 42 differentially expressed genes in pregnant women with MS. The significant pathways included IL-10 signaling pathway, ErbB2 activates, the hemoglobin complex (HBD, HBB, HBA1, AHSP, and HBA2), IL-17 signaling pathway (LCN2 and MMP9), antigen processing and presentation, and Th17 cell differentiation (HLA-DQA1), Rap1 signaling pathway (ID1), NOD-Like receptor signaling pathway (CAMP and DEFA4), PD-L1 Signaling, Interferon gamma signaling (MMP9 and ARG1), Neutrophil degranulation (CAMP, DEFA4, ELANE, CEACAM8, S100P, CHI3L1, AZU1, OLFM4, CRISP3, LTF, ARG1, PGLYRP1, and TCN1). In the WIKIPATHWAYS set, significance was found Vitamin B12 metabolism (TCN1, HBB, and HBA2), and IL-18 signaling pathway (S100P). CONCLUSION: This study can be used to understand several essential target genes and pathways identified in the present study, which may serve as feasible targets for MS therapies.