Integrating in silico resources to map a signaling network

Novel Impact of Colchicine on Interleukin-10 Expression in Acute Myocardial Infarction: An Integrative Approach

Background: Inflammation plays a critical role in myocardial infarction as a critical process in the development of heart failure, involving the development of cardiac fibrosis. Colchicine is a well-established anti-inflammatory drug, but its scientific application in controlling post-acute myocardial infarction (AMI) inflammatory processes has not been established. IL-10 is a key cytokine in modulating inflammatory responses, underscoring its potential as a crucial therapeutic target of colchicine. The objective was to explore the protective role of IL-10 modulated by colchicine in myocardial healing and repair following AMI, particularly cardiac fibrosis. Methods: The predicted protein of colchicine was assessed using WAY2DRUG PASS as probability active value. Proteins associated with colchicine, cardiac fibrosis, and acute myocardial infarction were analyzed with DisGeNET and Open Target databases. Analysis and visualization of protein-protein interactions were conducted using STRING and Cytoscape. A 3T3 cell line treated with CoCl2 was used to mimic hypoxic. HIF-1α and IL-10 expression were measured by flow cytometry and analyzed using a one-way ANOVA test. This observational clinical trial examined acute myocardial infarction patients undergoing immediate and delayed primary percutaneous coronary interventions. Subjects were randomized into control groups receiving placebo and intervention groups treated with colchicine. Assessments occurred at 24 h and five days after the intervention. IL-10 expression in the clinical trial was measured by ELISA and analyzed using a T-test. Results: Colchicine demonstrates promising bioactivity in treating acute myocardial infarction, with notably activity values highlighting its probable role as a tubulin antagonist (0.744), beta-tubulin antagonist (0.673), and NOS2 inhibitor (0.529). Its primary action targets IL-10, with the protein-protein interactions analysis indicating interactions between IL-10 and key inflammatory mediators-IL-1β, IFN-γ, CCL2, TNF, and TGF-β1-during acute myocardial infarction and cardiac fibrosis. Hypoxic conditions in the CoCl2-induced 3T3 cell model show significantly elevated HIF-1α compared to controls (p < 0.0001). Colchicine use significantly increased IL-10 expression in CoCl2-treated cells (p < 0.0001) and in AMI patients within five days (p < 0.05). Conclusions: Colchicine may bolster the anti-inflammatory response post-myocardial infarction by activating IL-10 pathways in fibroblasts and in clinical settings, potentially reducing inflammation after AMI. Further investigation into broader aspects of this pathway, particularly in cardiac fibroblasts, is required.

Oral Mucosa in Cancer Patients-Putting the Pieces Together: A Narrative Review and New Perspectives

The oral mucosa is a key player in cancer patients and during cancer treatment. The increasing prevalence of cancer and cancer-therapy-associated side effects are behind the major role that oral mucosa plays in oncological patients. Oral mucositis is a debilitating severe complication caused by the early toxicity of chemo and/or radiotherapy that can restrict treatment outcome possibilities, even challenging a patient's survival. It has been referred to as the most feared cancer treatment complication. Predictive variables as to who will be affected, and to what extent, are still unclear. Additionally, oral mucositis is one of the sources of the increasing economic burden of cancer, not only for patients and their families but also for institutions and governments. All efforts should be implemented in the search for new approaches to minimize the apparently ineluctable outburst of oral mucositis during cancer treatment. New perspectives derived from different approaches to explaining the interrelation between oral mucositis and the oral microbiome or the similarities with genitourinary mucosa may help elucidate the biomolecular pathways and mechanisms behind oral mucosa cancer-therapy-related toxicity, and what is more important is its management in order to minimize treatment side effects and provide enhanced cancer support.

Computational Drug Discovery in Patients with Endometriosis-Induced Infertility via Text Mining and Biomedical Databases

Background: Endometriosis (EMT) is the most common benign gynecological disease among women of reproductive age, causing infertility and seriously affects women’s physical and mental health. However, the current treatment was not always effective. This study was designed to use publicly available data to identify drugs targeting the relevant gene with EMT-induced-infertility using computational tools. Methods: EMT and infertility genes were determined by text mining, and the GeneCodis program was used to analyzed gene ontology of the intersection of the two gene sets. A string database was used to analyze the protein-protein interaction network. The Drug-Gene Interaction database is queried for the rich gene set belonging to the identified pathways to find drug candidates that can be used in EMT-induced infertility. Results: Our analysis identified 550 genes common to both the EMT and infertility by text mining. Gene enrichment analysis and protein-protein interaction analysis found 39 genes potentially targetable by a total of 49 drugs that could be formulated for application, which have not been used in EMT-induced infertility. Conclusions: The findings from the present analysis can facilitate the Identification of existing drugs that have the potential of topical administration to improve EMT-induced infertility and present tremendous opportunities to study novel targets pharmacology using in silico text mining and pathway analysis tools. However, all the results were based on online bioinformatics databases, and as such require validation experiments. And some of the drugs highlighted as possibly relevant may be toxic and as such safely data is required before any experiments are undertaken in humans.

In silico and gene expression analysis of the acute inflammatory response of gilthead seabream (Sparus aurata) after subcutaneous administration of carrageenin

Inflammation is one of the main causes of loss of homeostasis at both the systemic and molecular levels. The aim of this study was to investigate in silico the conservation of inflammation-related proteins in the gilthead seabream (Sparus aurata L.). Open reading frames of the selected genes were used as input in the STRING database for protein-protein interaction network analysis, comparing them with other teleost protein sequences. Proteins of the large yellow croaker (Larimichthys crocea L.) presented the highest percentages of identity with the gilthead seabream protein sequence. The gene expression profile of these proteins was then studied in gilthead seabream specimens subcutaneously injected with carrageenin (1%) or phosphate-buffered saline (control) by analyzing skin samples from the injected zone 12 and 24 h after injection. Gene expression analysis indicated that the mechanisms necessary to terminate the inflammatory response to carrageenin and recover skin homeostasis were activated between 12 and 24 h after injection (at the tested dose). The gene analysis performed in this study could contribute to the identification of the main mechanisms of acute inflammatory response and validate the use of carrageenin as an inflammation model to elucidate these mechanisms in fish.

Fat regulatory mechanisms of pine nut oil based on protein interaction network analysis

BACKGROUND

Pine nut oil (PNO), a standardized and well-defined extract of Pinus koraiensis (Korean pine), has beneficial effects on wound healing, inflammatory diseases, and cancer. However, the explanation for the mechanism by which PNO reduces body fat remains uncertain. We performed a protein-protein interaction network (PPIN) analysis to explore the genes associated with pinolenic acid using the MEDILINE database from PubChem and PubMed. It was concluded through the PPIN analysis that PNO was involved in a neutral lipid biosynthetic process.

PURPOSE

This study evaluated the effects of PNO predicted by the network analysis of fat accumulation in chronic obesity mouse models established by feeding a high fat diet (HFD) to C57BL/6J mice and explored potential mechanisms.

METHODS

HFD mice were fed only HFD or HFD with PNO at 822 and 1644 mg/kg. After an oral administration of 7 weeks, several body weight and body fat-related parameters were examined, including the following: adipose weight, adipocyte size, serum lipid profiles, adipocyte expression of PPAR-γ, sterol regulatory element binding protein (SREBP)-1c, lipoprotein lipase (LPL) and leptin.

RESULTS

We showed that oral administration of PNO to HFD mice reduces body fat weight, fat in tissue, white adipose tissue weight, and adipocyte size. The serum cholesterol was improved in the HFD mice treated with PNO. Additionally, PNO has significantly attenuated the HFD-induced changes in the adipose tissue expression of PPAR-γ, SREBP-1c, LPL, and leptin.

CONCLUSIONS

The findings from this study based on the PPIN analysis suggest that PNO has potential as drug to reduce body fat through fat regulatory mechanisms by PPAR-γ and SREBP-1c.

Potential genes and pathways along with immune cells infiltration in the progression of atherosclerosis identified via microarray gene expression dataset re-analysis

OBJECTIVE

Atherosclerosis is a chronic inflammatory process characterized by the accumulation and formation of lipid-rich plaques within the layers of the arterial wall. Although numerous studies have reported the underlying pathogenesis, no data-based studies have been conducted to analyze the potential genes and immune cells infiltration in the different stages of atherosclerosis via bioinformatics analysis.

METHODS

In this study, we downloaded GSE100927 and GSE28829 from NCBI-GEO database. Gene ontology and pathway enrichment were performed via the DAVID database. The protein interaction network was constructed via STRING. Enriched hub genes were analyzed by the Cytoscape software. The evaluation of the infiltrating immune cells in the dataset samples was performed by the CIBERSORT algorithm.

RESULTS

We identified 114 common upregulated differentially expressed genes and 22 common downregulated differentially expressed genes. (adjust p value < 0.01 and log FC ≥ 1). A cluster of 10 genes including CYBA, SLC11A1, FCER1G, ITGAM, ITGB2, CD53, ITGAX, VAMP8, CLEC5A, and CD300A were found to be significant. Through the deconvolution algorithm CIBERSORT, we analyzed the significant alteration of immune cells infiltration in the progression of atherosclerosis with the threshold of the Wilcoxon test at p value <0.05.

CONCLUSIONS

These results may reveal the underlying correlations between genes and immune cells in atherosclerosis, which enable us to investigate the novel insights for the development of treatments and drugs.

Identification of key candidate genes and molecular pathways in white fat browning: an anti-obesity drug discovery based on computational biology

Background

Obesity—with its increased risk of obesity-associated metabolic diseases—has become one of the greatest public health epidemics of the twenty-first century in affluent countries. To date, there are no ideal drugs for treating obesity. Studies have shown that activation of brown adipose tissue (BAT) can promote energy consumption and inhibit obesity, which makes browning of white adipose tissue (WAT) a potential therapeutic target for obesity. Our objective was to identify genes and molecular pathways associated with WAT and the activation of BAT to WAT browning, by using publicly available data and computational tools; this knowledge might help in targeting relevant signaling pathways for treating obesity and other related metabolic diseases.

Results

In this study, we used text mining to find out genes related to brown fat and white fat browning. Combined with biological process and pathway analysis in GeneCodis and protein-protein interaction analysis by using STRING and Cytoscape, a list of high priority target genes was developed. The Human Protein Atlas was used to analyze protein expression. Candidate drugs were derived on the basis of the drug-gene interaction analysis of the final genes. Our study identified 18 genes representing 6 different pathways, targetable by a total of 33 drugs as possible drug treatments. The final list included 18 peroxisome proliferator-activated receptor gamma (PPAR-γ) agonists, 4 beta 3 adrenoceptor (β3-AR) agonists, 1 insulin sensitizer, 3 insulins, 6 lipase clearing factor stimulants and other drugs.

Conclusions

Drug discovery using in silico text mining, pathway, and protein-protein interaction analysis tools may be a method of exploring drugs targeting the activation of brown fat or white fat browning, which provides a basis for the development of novel targeted therapies as potential treatments for obesity and related metabolic diseases.

Circulating tumor cell and cell-free RNA capture and expression analysis identify platelet-associated genes in metastatic lung cancer

Background

Circulating tumor cells (CTC) and plasma cell-free RNA (cfRNA) can serve as biomarkers for prognosis and treatment response in lung cancer. One barrier to the selected or routine use of CTCs and plasma cfRNA in precision oncology is the limited quantity of both, and CTCs are only seen in metastatic disease. As capture of CTCs and plasma cfRNA presents an opportunity to monitor and assess malignancies without invasive procedures, we compared two methods for CTC capture and identification, and profiled mRNA from CTCs and plasma cfRNA to identify potential tumor-associated biomarkers.

Methods

Peripheral blood was collected from ten patients with small cell lung cancer (SCLC), ten patients with non-small cell lung cancer (NSCLC) and four healthy volunteers. Two methods were used for CTC capture: the standard epithelial cell adhesion molecule (EpCam) CellSearch kit (unicapture) and EpCAM plus HER2, EGFR and MUC-1 specific combined ferrofluid capture (quadcapture). For the quadcapture, anti-cytokeratin 7 (CK7) was additionally used to assist in CTC identification. NanoString analysis was performed on plasma cfRNA and on mRNA from combined ferrofluid isolated CTCs. Expression data was analyzed using STRING and Reactome.

Results

Unicapture detected CTCs in 40% of NSCLC and 60% of SCLC; whereas, quadcapture/CK7 identified CTCs in 20% of NSCLC and 80% of SCLC. Bioinformatic analysis of NanoString data identified high expression of a platelet factor 4 (PF4)-related group of transcripts.

Conclusions

Quadcapture ferrofluid reagent did not significantly improve CTC capture efficacy. NanoString analysis based on CTC and plasma cfRNA data highlighted an intriguing PF-4-centric network in patients with metastatic lung cancer.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5795-x) contains supplementary material, which is available to authorized users.

Assessment of the key regulatory genes and their Interologs for Turner Syndrome employing network approach

Turner Syndrome (TS) is a condition where several genes are affected but the molecular mechanism remains unknown. Identifying the genes that regulate the TS network is one of the main challenges in understanding its aetiology. Here, we studied the regulatory network from manually curated genes reported in the literature and identified essential proteins involved in TS. The power-law distribution analysis showed that TS network carries scale-free hierarchical fractal attributes. This organization of the network maintained the self-ruled constitution of nodes at various levels without having centrality-lethality control systems. Out of twenty-seven genes culminating into leading hubs in the network, we identified two key regulators (KRs) i.e. KDM6A and BDNF. These KRs serve as the backbone for all the network activities. Removal of KRs does not cause its breakdown, rather a change in the topological properties was observed. Since essential proteins are evolutionarily conserved, the orthologs of selected interacting proteins in C. elegans, cat and macaque monkey (lower to higher level organisms) were identified. We deciphered three important interologs i.e. KDM6A-WDR5, KDM6A-ASH2L and WDR5-ASH2L that form a triangular motif. In conclusion, these KRs and identified interologs are expected to regulate the TS network signifying their biological importance.

Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy

INTRODUCTION

Oral mucositis (OM) is a major dose-limiting side effect of chemotherapy and radiation used in cancer treatment. Due to the complex nature of OM, currently available drug-based treatments are of limited efficacy.

OBJECTIVES

Our objectives were (i) to determine genes and molecular pathways associated with OM and wound healing using computational tools and publicly available data and (ii) to identify drugs formulated for topical use targeting the relevant OM molecular pathways.

METHODS

OM and wound healing-associated genes were determined by text mining, and the intersection of the two gene sets was selected for gene ontology analysis using the GeneCodis program. Protein interaction network analysis was performed using STRING-db. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in OM.

RESULTS

Our analysis identified 447 genes common to both the "OM" and "wound healing" text mining concepts. Gene enrichment analysis yielded 20 genes representing six pathways and targetable by a total of 32 drugs which could possibly be formulated for topical application. A manual search on ClinicalTrials.gov confirmed no relevant pathway/drug candidate had been overlooked. Twenty-five of the 32 drugs can directly affect the PTGS2 (COX-2) pathway, the pathway that has been targeted in previous clinical trials with limited success.

CONCLUSIONS

Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.

Evaluation of the cytotoxic properties, gene expression profiles and secondary signalling responses of cultured cells exposed to fumonisin B1, deoxynivalenol and zearalenone mycotoxins

Mycotoxins are toxic secondary metabolites produced by a range of fungi and are common contaminants of agricultural crops. These toxins are chemically diverse and structurally stable, enabling them to enter the food chain which can lead to numerous adverse health effects in animals and humans. Although mycotoxin exposure is associated with the development of several cancers, it has proved challenging to show a direct connection between exposure and oncogenic change. This study investigates the in vitro cytotoxicity, molecular mechanisms and secondary signalling responses associated with the exposure to three major mycotoxins, fumonisin B1 (FB1), deoxynivalenol (Don) and zearalenone (Zea). The cytotoxicity of FB1, Don and Zea were investigated in cultured HepG2 and Caco-2 cells using cell viability assays as well as flow cytometry. FB1 proved to be less cytotoxic than its counterparts, while Don and Zea demonstrated high cytotoxicity through an apoptotic mechanism. Expression profiles of 84 genes involved in mediating communication between tumour cells and the cellular mediators of inflammation as well as the innate immune system were also studied. The expression profiles associated with the different mycotoxins were further explored for functional networks, biological functions, canonical pathways, toxicological association as well as to predict network associations between the differentially expressed genes. RT-qPCR revealed the significant differential expression of 46 genes, including the expression of several genes strongly associated with cancer and aberrant inflammatory signalling, after mycotoxin exposure. Aberrant inflammatory signalling seems to be a credible contributing factor that initiates the malignant change observed in cells exposed to mycotoxins.

Pancreatic Beta Cell Survival and Signaling Pathways: Effects of Type 1 Diabetes-Associated Genetic Variants

Type 1 diabetes (T1D) is a complex autoimmune disease in which pancreatic beta cells are specifically destroyed by the immune system. The disease has an important genetic component and more than 50 loci across the genome have been associated with risk of developing T1D. The molecular mechanisms by which these putative T1D candidate genes modulate disease risk, however, remain poorly characterized and little is known about their effects in pancreatic beta cells. Functional studies in in vitro models of pancreatic beta cells, based on techniques to inhibit or overexpress T1D candidate genes, allow the functional characterization of several T1D candidate genes. This requires a multistage procedure comprising two major steps, namely accurate selection of genes of potential interest and then in vitro and/or in vivo mechanistic approaches to characterize their role in pancreatic beta cell dysfunction and death in T1D. This chapter details the methods and settings used by our groups to characterize the role of T1D candidate genes on pancreatic beta cell survival and signaling pathways, with particular focus on potentially relevant pathways in the pathogenesis of T1D, i.e., inflammation and innate immune responses, apoptosis, beta cell metabolism and function.

DNA methylation modifies urine biomarker levels in 1,6-hexamethylene diisocyanate exposed workers: a pilot study

DNA methylation may mediate inter-individual responses to chemical exposure and, thus, modify biomarker levels of exposure and effects. We analyzed inter-individual differences in inhalation and skin exposure to 1,6-hexamethylene diisocyanate (HDI) and urine biomarker 1,6-hexamethylene diamine (HDA) levels in 20 automotive spray-painters. Genome-wide 5-methyl cytosine (CpG) DNA methylation was assessed in each individual's peripheral blood mononuclear cells (PBMC) DNA using the Illumina 450K CpG array. Mediation analysis using linear regression models adjusted for age, ethnicity, and smoking was conducted to identify and assess the association between HDI exposure, CpG methylation, and urine HDA biomarker levels. We did not identify any CpGs common to HDI exposure and biomarker level suggesting that CpG methylation is a mediator that only partially explains the phenotype. Functional significance of genic- and intergenic-CpG methylation status was tested using protein-protein or protein-DNA interactions and gene-ontology enrichment to infer networks. Combined, the results suggest that methylation has the potential to affect HDI mass transport, permeation, and HDI metabolism. We demonstrate the potential use of PBMC methylation along with quantitative exposure and biomarker data to guide further investigation into the mediators of occupational exposure and biomarkers and its role in risk assessment.

Bioinformatic approaches to augment study of epithelial-to-mesenchymal transition in lung cancer

Bioinformatic approaches are intended to provide systems level insight into the complex biological processes that underlie serious diseases such as cancer. In this review we describe current bioinformatic resources, and illustrate how they have been used to study a clinically important example: epithelial-to-mesenchymal transition (EMT) in lung cancer. Lung cancer is the leading cause of cancer-related deaths and is often diagnosed at advanced stages, leading to limited therapeutic success. While EMT is essential during development and wound healing, pathological reactivation of this program by cancer cells contributes to metastasis and drug resistance, both major causes of death from lung cancer. Challenges of studying EMT include its transient nature, its molecular and phenotypic heterogeneity, and the complicated networks of rewired signaling cascades. Given the biology of lung cancer and the role of EMT, it is critical to better align the two in order to advance the impact of precision oncology. This task relies heavily on the application of bioinformatic resources. Besides summarizing recent work in this area, we use four EMT-associated genes, TGF-β (TGFB1), NEDD9/HEF1, β-catenin (CTNNB1) and E-cadherin (CDH1), as exemplars to demonstrate the current capacities and limitations of probing bioinformatic resources to inform hypothesis-driven studies with therapeutic goals.