Network and Pathway-Based Analyses of Genes Associated with Parkinson's Disease

Benzo[b]fluoranthene induces male reproductive toxicity and apoptosis via Akt-Mdm2-p53 signaling axis in mouse Leydig cells: Integrating computational toxicology and experimental approaches

This study aims to explore the male reproductive toxicity of Benzo[b]fluoranthene (BbF) and related mechanisms. The results of computational toxicology analysis indicated male reproductive toxicity of BbF was related to apoptosis of Leydig cells and that Akt/p53 pathway might play a key role. In experiments, BbF induced testosterone decline, decreased concentration and motility of sperm and aggravated testicular pathological injury in mice. Besides, BbF led to apoptosis in Leydig cells, and decreased expressions of p-Akt and Bcl2, while improving the expressions of p53, Bax and Cleaved Caspase-3 in vivo and in vitro. Further, compared with BbF group, Akt activator SC79 significantly reduced cell apoptosis rate, improved cell viability, promoted the expressions of p-Akt and p-Mdm2, and reversed the above molecular expressions. Similarly, p53 inhibitor Pifithrin-α also significantly enhanced the cell vitality, alleviated the apoptosis of TM3 cells induced by BbF, and decreased the expressions of Bax and Cleaved Caspase-3, with the up-regulation of Bcl2. To sum up, by inhibiting Akt-Mdm2 signaling, BbF activated the p53-mediated mitochondrial apoptosis pathway, further inducing the apoptosis of Leydig cells, therefore resulting in testosterone decline and male reproductive damage. Besides, this study provided a valid mode integrating computational toxicology and experimental approaches in toxicity testing.

IL-17A and TNF-α inhibitors induce multiple molecular changes in psoriasis

Adalimumab and secukinumab are commonly used for moderate to severe psoriasis vulgaris (PV). Although distinct individual responses to and impaired effectiveness of these biological agents occur occasionally, little is known about the underlying reasons. Here, we report a proteomic analysis of psoriatic lesions from patients treated with these drugs using data-independent acquisition mass spectrometry (DIA-MS). Thousands of differentially expressed proteins (DEPs) changed over 12 weeks of treatment. Network analysis showed that DEPs could interact and induce transformation in matrix components, metabolic regulation, and immune response. The results of parallel reaction monitoring (PRM) analysis suggested that S100s, STAT1, KRT2, TYMP, SOD2, HSP90AB1, TFRC, and COL5A1 were the most significantly changed proteins in both groups. There was a positive association between the Psoriasis Area and Severity Index (PASI) score and three proteins (TFRC, IMPDH2, KRT2). Our study findings suggest that inhibition of IL-17A and TNF-α can induce changes in multiple molecules in psoriatic lesions and have an overlapping influence on the immune response and process through direct or indirect effects.

Identification of molecular initiating events and key events leading to endocrine disrupting effects of PFOA: Integrated molecular dynamic, transcriptomic, and proteomic analyses

Perfluorooctanoic acid (PFOA) can rapidly activate signaling pathways independent of nuclear hormone receptors through membrane receptor regulation, which leads to endocrine disrupting effects. In the present work, the molecular initiating event (MIE) and the key events (KEs) which cause the endocrine disrupting effects of PFOA have been explored and determined based on molecular dynamics simulation (MD), fluorescence analysis, transcriptomics, and proteomics. MD modeling and fluorescence analysis proved that, on binding to the G protein-coupled estrogen receptor-1 (GPER), PFOA could induce a conformational change in the receptor, turning it into an active state. The results also indicated that the binding to GPER was the MIE that led to the adverse outcome (AO) of PFOA. In addition, the downstream signal transduction pathways of GPER, as regulated by PFOA, were further investigated through genomics and proteomics to identify the KEs leading to thr endocrine disrupting effects. Two pathways (Endocrine resistance, ERP and Estrogen signaling pathway, ESP) containing GPER were regulated by different concentration of PFOA and identified as the KEs. The knowledge of MIE, KEs, and AO of PFOA is necessary to understand the links between PFOA and the possible pathways that lead to its negative effects.

Using Computational Drug-Gene Analysis to Identify Novel Therapeutic Candidates for Retinal Neuroprotection

Retinal cell death is responsible for irreversible vision loss in many retinal disorders. No commercially approved treatments are currently available to attenuate retinal cell loss and preserve vision. We seek to identify chemicals/drugs with thoroughly-studied biological functions that possess neuroprotective effects in the retina using a computational bioinformatics approach. We queried the National Center for Biotechnology Information (NCBI) to identify genes associated with retinal neuroprotection. Enrichment analysis was performed using ToppGene to identify compounds related to the identified genes. This analysis constructs a Pharmacome from multiple drug-gene interaction databases to predict compounds with statistically significant associations to genes involved in retinal neuroprotection. Compounds with known deleterious effects (e.g., asbestos, ethanol) or with no clinical indications (e.g., paraquat, ozone) were manually filtered. We identified numerous drug/chemical classes associated to multiple genes implicated in retinal neuroprotection using a systematic computational approach. Anti-diabetics, lipid-lowering medicines, and antioxidants are among the treatments anticipated by this analysis, and many of these drugs could be readily repurposed for retinal neuroprotection. Our technique serves as an unbiased tool that can be utilized in the future to lead focused preclinical and clinical investigations for complex processes such as neuroprotection, as well as a wide range of other ocular pathologies.

Comparative Transcriptome Analysis Provides Novel Molecular Events for the Differentiation and Maturation of Hepatocytes during the Liver Development of Zebrafish

The liver plays an essential role in multiple biological functions including metabolism, detoxification, digestion, coagulation, and homeostasis in vertebrates. The specification and differentiation of embryonic hepatoblasts, the proliferation of hepatocytes, and the hepatic tissue architecture are well documented, but molecular events governing the maturation of hepatocytes during liver development remain largely unclear. In this study, we performed a comparative transcriptome analysis of hepatocytes that were sorted by flow cytometry from developing zebrafish embryos at 60, 72, and 96 hpf. We identified 667 up-regulated and 3640 down-regulated genes in hepatocytes between 60 and 72 hpf, 606 up-regulated and 3924 down-regulated genes between 60 and 96 hpf, and 1693 up-regulated genes and 1508 down-regulated genes between 72 and 96 hpf. GO enrichment analysis revealed that key biological processes, cellular components, and molecular functions in hepatocytes between 60 to 72 hpf, such as cell cycle, DNA replication, DNA repair, RNA processing, and transcription regulation, are mainly associated with the proliferation of hepatocytes. In addition to biological processes, cellular components, and molecular functions for cell proliferation, molecular functions for carbohydrate metabolism were enriched in hepatocytes during 72 to 96 hpf. KEGG enrichment analysis identified key signaling pathways, such as cell cycle, RNA degradation, ubiquitin-mediated proteolysis, ErbB and Hedgehog signaling, basal transcription factors, Wnt signaling, and glycan degradation, which are closely associated with cell proliferation or carbohydrate metabolism in hepatocytes between 60 to 72 hpf. Newly enriched signaling pathways in hepatocytes during 72 to 96 hpf include metabolisms of pyrimidine, purine, nicotinate and nicotinamide, caffeine, glycine, serine and threonine, ABC transporters, and p53 signaling that function in metabolisms of lipid, protein and energy, cellular secretion, or detoxification, indicating the functional maturation of hepatocytes between 72 to 96 hpf. These findings provide novel clues for further understanding the functional differentiation and maturation of hepatocytes during liver development.

Investigation of CRS-associated cytokines in CAR-T therapy with meta-GNN and pathway crosstalk

Background

Chimeric antigen receptor T-cell (CAR-T) therapy is a new and efficient cellular immunotherapy. The therapy shows significant efficacy, but also has serious side effects, collectively known as cytokine release syndrome (CRS). At present, some CRS-related cytokines and their roles in CAR-T therapy have been confirmed by experimental studies. However, the mechanism of CRS remains to be fully understood.

Methods

Based on big data for human protein interactions and meta-learning graph neural network, we employed known CRS-related cytokines to comprehensively investigate the CRS associated cytokines in CAR-T therapy through protein interactions. Subsequently, the clinical data for 119 patients who received CAR-T therapy were examined to validate our prediction results. Finally, we systematically explored the roles of the predicted cytokines in CRS occurrence by protein interaction network analysis, functional enrichment analysis, and pathway crosstalk analysis.

Results

We identified some novel cytokines that would play important roles in biological process of CRS, and investigated the biological mechanism of CRS from the perspective of functional analysis.

Conclusions

128 cytokines and related molecules had been found to be closely related to CRS in CAR-T therapy, where several important ones such as IL6, IFN-γ, TNF-α, ICAM-1, VCAM-1 and VEGFA were highlighted, which can be the key factors to predict CRS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04917-2.

Integrated genomic, transcriptomic and metabolomic analysis reveals MDH2 mutation-induced metabolic disorder in recurrent focal segmental glomerulosclerosis

Focal segmental glomerulosclerosis (FSGS) has an over 30% risk of recurrence after kidney transplantation (Ktx) and is associated with an extremely high risk of graft loss. However, mechanisms remain largely unclear. Thus, this study identifies novel genes related to the recurrence of FSGS (rFSGS). Whole genome-wide sequencing and next-generation RNA sequencing were used to identify the candidate mutant genes associated with rFSGS in peripheral blood mononuclear cells (PBMCs) from patients with biopsy-confirmed rFSGS after KTx. To confirm the functional role of the identified gene with the MDH2 c.26C >T mutation, a homozygous MDH2 c.26C >T mutation in HMy2.CIR cell line was induced by CRISPR/Cas9 and co-cultured with podocytes, mesangial cells, or HK2 cells, respectively, to detect the potential pathogenicity of the c.26C >T variant in MDH2. A total of 32 nonsynonymous single nucleotide polymorphisms (SNPs) and 610 differentially expressed genes (DEGs) related to rFSGS were identified. DEGs are mainly enriched in the immune and metabolomic-related pathways. A variant in MDH2, c.26C >T, was found in all patients with rFSGS, which was also accompanied by lower levels of mRNA expression in PBMCs from relapsed patients compared with patients with remission after KTx. Functionally, co-cultures of HMy2.CIR cells overexpressing the mutant MDH2 significantly inhibited the expression of synaptopodin, podocin, and F-actin by podocytes compared with those co-cultured with WT HMy2.CIR cells or podocytes alone. We identified that MDH2 is a novel rFSGS susceptibility gene in patients with recurrence of FSGS after KTx. Mutation of the MDH2 c.26C >T variant may contribute to progressive podocyte injury in rFSGS patients.

Potential gene identification and pathway crosstalk analysis of age-related macular degeneration

Age-related macular degeneration (AMD), the most prevalent visual disorder among the elderly, is confirmed as a multifactorial disease. Studies demonstrated that genetic factors play an essential role in its pathogenesis. Our study aimed to make a relatively comprehensive study about biological functions of AMD related genes and crosstalk of their enriched pathways. 1691 AMD genetic studies were reviewed, GO enrichment and pathway crosstalk analyses were conducted to elucidate the biological features of these genes and to demonstrate the pathways that these genes participate. Moreover, we identified novel AMD-specific genes using shortest path algorithm in the context of human interactome. We retrieved 176 significantly AMD-related genes. GO results showed that the most significant term in each of these three GO categories was: signaling receptor binding (P_BH = 4.835 × 10⁻⁷), response to oxygen-containing compound (P_BH = 2.764 × 10⁻²¹), and extracellular space (P_BH = 2.081 × 10⁻¹⁹). The pathway enrichment analysis showed that complement pathway is the most enriched. The pathway crosstalk study showed that the pathways could be divided into two main modules. These two modules were connected by cytokine-cytokine receptor interaction pathway. 42 unique genes potentially participating AMD development were obtained. The aberrant expression of the mRNA of FASN and LRP1 were validated in AMD cell and mouse models. Collectively, our study carried out a comprehensive analysis based on genetic association study of AMD and put forward several evidence-based genes for future study of AMD.

Using Advanced Bioinformatics Tools to Identify Novel Therapeutic Candidates for Age-Related Macular Degeneration

Purpose

Age-related macular degeneration (AMD) is the most common cause of aging-related blindness in the developing world. Although medications can slow progressive wet AMD, currently, no drugs to treat dry-AMD are available. We use a systems or in silico biology analysis to identify chemicals and drugs approved by the Food and Drug Administration for other indications that can be used to treat and prevent AMD.

Methods

We queried National Center for Biotechnology Information to identify genes associated with AMD, wet AMD, dry AMD, intermediate AMD, and geographic atrophy to date. We combined genes from various AMD subtypes to reflect distinct stages of disease. Enrichment analysis using the ToppGene platform predicted molecules that can influence AMD genes. Compounds without clinical indications or with deleterious effects were manually filtered.

Results

We identified several drug/chemical classes that can affect multiple genes involved in AMD. The drugs predicted from this analysis include antidiabetics, lipid-lowering agents, and antioxidants, which could theoretically be repurposed for AMD. Metformin was identified as the drug with the strongest association with wet AMD genes and is among the top candidates in all dry AMD subtypes. Curcumin, statins, and antioxidants are also among the top drugs correlating with AMD-risk genes.

Conclusions

We use a systematic computational process to discover potential therapeutic targets for AMD. Our systematic and unbiased approach can be used to guide targeted preclinical/clinical studies for AMD and other ocular diseases.

Translational Relevance

Advanced bioinformatics models identify novel chemicals and approved drug candidates that can be efficacious for different subtypes of AMD.

Huangqin Decoction Exerts Beneficial Effects on Rotenone-Induced Rat Model of Parkinson's Disease by Improving Mitochondrial Dysfunction and Alleviating Metabolic Abnormality of Mitochondria

Parkinson's disease (PD) is a common neurodegenerative disease, and the pathogenesis of PD is closely related to mitochondrial dysfunction. Previous studies have indicated that traditional Chinese medicine composition of Huangqin Decoction (HQD), including Scutellariae Radix, licorice, and Paeoniae Radix Alba, has therapeutic effects on PD, but whether HQD has a therapeutic effect on PD has not been reported. In this study, the protective effects of HQD on rotenone-induced PD rats were evaluated by behavioral assays (open field, rotating rod, suspension, gait, inclined plate, and grid) and immunohistochemistry. The mechanisms of HQD on attenuation of mitochondrial dysfunction were detected by biochemical assays and mitochondrial metabolomics. The results showed that HQD (20 g/kg) can protect rats with PD by improving motor coordination and muscle strength, increasing the number of tyrosine hydroxylase (TH)-positive neurons in rats with PD. Besides, HQD can improve mitochondrial dysfunction by increasing the content of adenosine triphosphate (ATP) and mitochondrial complex I. Mitochondrial metabolomics analysis revealed that the ketone body of acetoacetic acid (AcAc) in the rotenone group was significantly higher than that of the control group. Ketone bodies have been known to be used as an alternative energy source to provide energy to the brain when glucose was deficient. Further studies demonstrated that HQD could increase the expression of glucose transporter GLUT1, the content of tricarboxylic acid cycle rate-limiting enzyme citrate synthase (CS), and the level of hexokinase (HK) in rats with PD but could decrease the content of ketone bodies [AcAc and β-hydroxybutyric acid (β-HB)] and the expression of their transporters (MCT1). Our study revealed that the decrease of glucose metabolism in the rotenone group was parallel to the increase of substitute substrates (ketone bodies) and related transporters, and HQD could improve PD symptoms by activating the aerobic glycolysis pathway.

The adverse outcome pathway (AOP) of estrogen interference effect induced by triphenyl phosphate (TPP): Integrated multi-omics and molecular dynamics approaches

Triphenyl phosphate (TPP) has been detected with increasing frequency in various biota and environmental media, and it has been confirmed that G protein-coupled estrogen receptor (GPER) was involved in the estrogenic activity of TPP. Therefore, it is necessary to link the estrogen-interfering effects and possible mechanisms of action of TPP with the molecular initiation event (MIE) to improve its adverse outcome pathway framework. In this study, transcriptomic and proteomic methods were used to analyze the estrogen interference effect of TPP mediated by GPER, and the causal relationship was supplemented by molecular dynamics simulation and fluorescence analysis. The omics results showed that TPP could regulate the response of key GPER signaling factors and the activation of downstream pathways including PI3K-Akt signaling pathway, MAPK signaling pathway, and estrogen signaling pathway. The similar activation effect of TPP and agonist G1 change of GPER was proved by molecular dynamics simulation. After TPP binding, the conformation of GPER will change from the inactive to active state. Therefore, TPP may affect cell proliferation, metastasis, and apoptosis and regulate gene transcription and kinase activity, leading to abnormal immune function and other estrogen-dependent cell processes and cancer through GPER, ultimately causing the estrogen interference effect.

PFOA regulate adenosine receptors and downstream concentration-response cAMP-PKA pathway revealed by integrated omics and molecular dynamics analyses

As an important pollutant, perfluorooctane acid (PFOA) has been widely concerned and reported by thousands of times, while less is known about the concentration-response pathway of PFOA. The aim of the present work was to reveal the concentration-response mechanism of PFOA in human cells. Omics results showed that calcium-related pathways play key roles in PFOA injury mechanisms. The results of GO and KEGG analyses showed that the cAMP signaling pathway was presented as the top one in all of the regulatory patterns and concentrations groups of PFOA. In the cAMP signaling pathway, the adenosine A₁ receptor (ADORA1) recognized the low concentration of PFOA and induced pathway "G_i-cAMP-PKA" to decrease the concentration of cAMP. This indicated that the low concentration of PFOA may promote breast hyperplasia and inhibit lactation. While adenosine A_2A receptor (ADORA2A) recognized the high concentration of PFOA and induced pathway "G_S-AC-cAMP-RKA" to increase the concentration of cAMP, induce cell damage and may lead to the deterioration of breast cancer. The results of molecular dynamics simulation showed that PFOA could bind to ADORA1 and ADORA2A, thus cause subsequent signal transduction. Furthermore, considering the strong binding ability of PFOA with ADORA1, PFOA tends to bind to ADORA1 at a low concentration. On the other side, PFOA at high concentration will continue to bind to another receptor protein, ADORA2A, and activate subsequent signaling pathways. Combined analyses of transcriptomic and proteomic revealed that different concentrations of PFOA regulate cellular calcium-related pathways. The cAMP pathway showed a concentration-response effect of PFOA. After treatment with different concentrations of PFOA, ADORA1 and ADORA2A were activated respectively, showing opposite cellular effects, leading to kinds of breast lesions. In the nervous system, PFOA might induce a variety of nervous system diseases. The present work was an exploration on the toxicological mechanism of PFOA, providing important information on the health impacts of PFOA in humans.

Identifying the biomarkers and pathways associated with hepatocellular carcinoma based on an integrated analysis approach

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related death worldwide. The molecular mechanism underlying HCC is still unclear. In this study, we conducted a comprehensive analysis to explore the genes, pathways and their interactions involved in HCC.

METHODS

We analysed the gene expression datasets corresponding to 488 samples from 10 studies on HCC and identified the genes differentially expressed in HCC samples. Then, the genes were compared against Phenolyzer and GeneCards to screen those potentially associated with HCC. The features of the selected genes were explored by mapping them onto the human protein-protein interaction network, and a subnetwork related to HCC was constructed. Hub genes in this HCC specific subnetwork were identified, and their relevance with HCC was investigated by survival analysis.

RESULTS

We identified 444 differentially expressed genes (177 upregulated and 267 downregulated) related to HCC. Functional enrichment analysis revealed that pathways like p53 signalling and chemical carcinogenesis were eriched in HCC genes. In the subnetwork related to HCC, five disease modules were detected. Further analysis identified six hub genes from the HCC specific subnetwork. Survival analysis showed that the expression levels of these genes were negatively correlated with survival rate of HCC patients.

CONCLUSIONS

Based on a systems biology framework, we identified the genes, pathways, as well as the disease specific network related to HCC. We also found novel biomarkers whose expression patterns were correlated with progression of HCC, and they could be candidates for further investigation.

Characterizing the Expression Patterns of Parkinson's Disease Associated Genes

Background

The expression pattern represents a quantitative phenotype that provides an in-depth view of the molecular mechanism in Parkinson’s disease (PD); however, the expression patterns of PD-associated genes (PAGs) and their relation to age at onset (AAO) remain unclear.

Methods

The known PD-causing genes and PD-risk genes, which were collected from latest published authoritative meta-analysis, were integrated as PAGs. The expression data from Genotype-Tissue Expression database, Allen Brian Map database, and BrainSpan database, were extracted to characterize the tissue specificity, inhibitory-excitatory neuron expression profile, and spatio-temporal expression pattern of PAGs, respectively. The AAO information of PD-causing gene was download from Gene4PD and MDSgene database.

Results

We prioritized 107 PAGs and found that the PAGs were more likely to be expressed in brain-related tissues than non-brain tissues and that more PAGs had higher expression levels in excitatory neurons than inhibitory neurons. In addition, we identified two spatio-temporal expression modules of PAGs in human brain: the first module showed a higher expression level in the adult period than in the prenatal period, and the second module showed the opposite features. It showed that more PAGs belong to the first module that the second module. Furthermore, we found that the median AAO of patients with mutations in PD-causing genes of the first module was lower than that of the second module.

Conclusion

In conclusion, this study provided comprehensive landscape of expression patterns, AAO features and their relationship for the first time, improving the understanding of pathogenesis, and precision medicine in PD.

Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression

The complexity of biological systems suggests that current definitions of molecular dysfunctions are essential distinctions of a complex phenotype. This is well seen in neurodegenerative diseases (ND), such as Alzheimer's disease (AD) and Parkinson's disease (PD), multi-factorial pathologies characterized by high heterogeneity. These challenges make it necessary to understand the effectiveness of candidate biomarkers for early diagnosis, as well as to obtain a comprehensive mapping of how selective treatment alters the progression of the disorder. A large number of computational methods have been developed to explain network-based approaches by integrating individual components for modeling a complex system. In this review, high-throughput omics methodologies are presented for the identification of potent biomarkers associated with AD and PD pathogenesis as well as for monitoring the response of dysfunctional molecular pathways incorporating multilevel clinical information. In addition, principles for efficient data analysis pipelines are being discussed that can help address current limitations during the experimental process by increasing the reproducibility of benchmarking studies.

Metabolomic analysis of the hippocampus in a rat model of chronic mild unpredictable stress-induced depression based on a pathway crosstalk and network module approach

BACKGROUND

The molecular alterations underlying the pathogenesis of depression have not been systematically defined. Increasing evidence suggests that hippocampus metabolism is strongly involved in the pathogenesis of chronic mild unpredictable stress (CUMS)-induced depression. The principal objective of this study was to reveal important information concerning the pathogenesis of depression through a comprehensive analysis of metabolites in the hippocampus in a CUMS rat model.

METHODS

Metabolites related to metabolic changes in the hippocampus in the CUMS model were collected from a depression-specific database and published literature. Potential metabolite pathways were identified by the Omicsolution tool. Then, crosstalk analysis was carried out to investigate the relationship between different important pathways. In addition, MetaboAnalyst was used to analyze potential metabolites for drug-related metabolite enrichment analysis, which was used to study hippocampus metabolite-related drug pathways in a CUMS model. Then, a metabolite-protein interaction (MPI) network was constructed and analyzed to identify important metabolites and proteins. The functional modules were extracted using the CNM network decomposition algorithm. Finally, neurotransmitters in the hippocampus of rats with CUMS depression were detected to verify the important pathways.

RESULTS

In the current study, 53 significantly enriched pathways related to the 107 identified metabolites were selected, and the top ranked enriched pathways included arginine and proline metabolism, neuroactive ligand-receptor interaction, phenylalanine metabolism, bile secretion, and glutathione metabolism. Pathway crosstalk analysis showed that the significantly enriched pathways were divided into two interrelated modules, which were mainly involved in metabolism, signal transduction, neurotransmitters, and the endocrine system. Enrichment analysis of drug-related metabolic KEGG pathways identified the antibiotic pathways as the most important pathways. In the MPI network, the hub metabolites were phosphate, arachidonic acid, oxoglutaric acid, l-glutamic acid, and glutathione, and the hub proteins were Got1, Got2, Tat, Ccbl1, Ccbl2, Il4i1. A total of 16 functional modules were extracted from the MPI network by using the CNM algorithm. Finally, metabolites related to serotonergic synapses, dopaminergic synapses, and glutamatergic synapses were found to be involved in the pathology of depression.

CONCLUSION

We found that neurotransmitter pathways (serotonergic synapses, dopaminergic synapses and glutamatergic synapses) in the hippocampus play a crucial role in the underlying molecular mechanism of depression, which provides useful clues for identifying the detailed depression-associated metabolic profiles.

Identification of Candidate Genes Associated with Charcot-Marie-Tooth Disease by Network and Pathway Analysis

Charcot-Marie-Tooth Disease (CMT) is the most common clinical genetic disease of the peripheral nervous system. Although many studies have focused on elucidating the pathogenesis of CMT, few focuses on achieving a systematic analysis of biology to decode the underlying pathological molecular mechanisms and the mechanism of its disease remains to be elucidated. So our study may provide further useful insights into the molecular mechanisms of CMT based on a systematic bioinformatics analysis. In the current study, by reviewing the literatures deposited in PUBMED, we identified 100 genes genetically related to CMT. Then, the functional features of the CMT-related genes were examined by R software and KOBAS, and the selected biological process crosstalk was visualized with the software Cytoscape. Moreover, CMT specific molecular network analysis was conducted by the Molecular Complex Detection (MCODE) Algorithm. The biological function enrichment analysis suggested that myelin sheath, axon, peripheral nervous system, mitochondrial function, various metabolic processes, and autophagy played important roles in CMT development. Aminoacyl-tRNA biosynthesis, metabolic pathways, and vasopressin-regulated water reabsorption were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway network, suggesting that these pathways may play key roles in CMT occurrence and development. According to the crosstalk, the biological processes could be roughly divided into a correlative module and two separate modules. MCODE clusters showed that in top 3 clusters, 13 of CMT-related genes were included in the network and 30 candidate genes were discovered which might be potentially related to CMT. The study may help to update the new understanding of the pathogenesis of CMT and expand the potential genes of CMT for further exploration.

Comprehensive gene and pathway analysis of cervical cancer progression

Cervical Cancer is one of the leading causes of cancer-associated mortality in women. The present study aimed to identify key genes and pathways involved in cervical cancer (CC) progression, via a comprehensive bioinformatics analysis. The GSE63514 dataset from the Gene Expression Omnibus database was analyzed for hub genes and cancer progression was divided into four phases (phases I-IV). Pathway enrichment, protein-protein interaction (PPI) and pathway crosstalk analyses were performed, to identify key genes and pathways using a criterion nodal degree ≥5. Gene pathway analysis was determined by mapping the key genes into the key pathways. Co-expression between key genes and their effect on overall survival (OS) time was assessed using The Cancer Genome Atlas database. A total of 3,446 differentially expressed genes with 107 hub genes were identified within the four phases. A total of 14 key genes with 11 key pathways were obtained, following extraction of ≥5 degree nodes from the PPI and pathway crosstalk networks. Gene pathway analysis revealed that CDK1 and CCNB1 regulated the cell cycle and were activated in phase I. Notably, the following terms, 'pathways in cancer', 'focal adhesion' and the 'PI3K-Akt signaling pathway' ranked the highest in phases II-IV. Furthermore, FN1, ITGB1 and MMP9 may be associated with metastasis of tumor cells. STAT1 was indicated to predominantly function at the phase IV via cancer-associated signaling pathways, including 'pathways in cancer' and 'Toll-like receptor signaling pathway'. Survival analysis revealed that high ITGB1 and FN1 expression levels resulted in significantly worse OS. CDK1 and CCNB1 were revealed to regulate proliferation and differentiation through the cell cycle and viral tumorigenesis, while FN1 and ITGB1, which may be developed as novel prognostic factors, were co-expressed to induce metastasis via cancer-associated signaling pathways, including PI3K-Art signaling pathway, and focal adhesion in CC; however, the underlying molecular mechanisms require further research.

Transcriptomic Analysis Reveals Cu/Zn SODs Acting as Hub Genes of SODs in Hylocereus undatus Induced by Trypsin during Storage

It has been revealed by us that superoxide scavenging is a new activity of trypsin. In this study, the synergistic mechanisms of trypsin and superoxide dismutases (SODs) were evaluated in Hylocereus undatus (pitaya). Trypsin significantly improved the storage quality of H. undatus, including weight loss impediment and decrease of cellular injury. The regulatory mechanisms of 16 SOD genes by trypsin were revealed using transcriptomic analysis on H. undatus. Results revealed that important physiological metabolisms, such as antioxidant activities or metal ion transport were induced, and defense responses were inhibited by trypsin. Furthermore, the results of protein–protein interaction (PPI) networks showed that besides the entire ROS network, the tiny SODs sub-network was also a scale-free network. Cu/Zn SODs acted as the hub that SODs synergized with trypsin during the storage of H. undatus.

Network and pathway-based analyses of genes associated with osteoporosis

Osteoporosis (OP) is a disease characterized by bone mass loss, bone microstructure damage, increased bone fragility, and easy fracture. The molecular mechanism underlying OP remains unclear.In this study, we identified 217 genes associated with OP, and formed a gene set [OP-related genes gene set (OPgset)].The highly enriched GOs and pathways showed OPgset genes were significantly involved in multiple biological processes (skeletal system development, ossification, and osteoblast differentiation), and several OP-related pathways (Wnt signaling pathway, osteoclast differentiation, steroid hormone biosynthesis, and adipocytokine signaling pathway). Besides, pathway crosstalk analysis indicated three major modules, with first module consisted of pathways mainly involved in bone development-related signaling pathways, second module in Wnt-related signaling pathway and third module in metabolic pathways. Further, we calculated degree centrality of a node and selected ten key genes/proteins, including TGFB1, IL6, WNT3A, TNF, PTH, TP53, WNT1, IGF1, IL10, and SERPINE1. We analyze the K-core and construct three k-core sub-networks of OPgset genes.In summary, we for the first time explored the molecular mechanism underlying OP via network- and pathway-based methods, results from our study will improve our understanding of the pathogenesis of OP. In addition, these methods performed in this study can be used to explore pathogenesis and genes related to a specific disease.

Identification of biomarkers associated with Parkinson's disease by gene expression profiling studies and bioinformatics analysis

Parkinson's disease (PD) is associated with a selective loss of the neurons in the midbrain area called the substantia nigra pars compacta and the loss of projecting nerve fibers in the striatum. Predominant pathological hallmarks of PD are the degeneration of discrete neuronal populations and progressive accumulation of α-synuclein-containing intracytoplasmic inclusions called Lewy bodies and dystrophic Lewy neuritis. There is currently no therapy to terminate or delay the neurodegenerative process as the exact mechanisms underlying the pathogenesis of PD require further investigation. The identification and validation of novel biomarkers for the diagnosis of PD is a great challenge using contemporary approaches and optimizing sampling handling as well as interpretation using bioinformatics analysis. In this review, recent evidences associated with multi-omic data-sets and molecular mechanisms underlying PD are examined. A combined mapping of several transcriptional evidences could establish a patient-specific signature for early diagnose of PD though eligible systems biology tools, which can also help develop effective drug-based therapeutic approaches.

The core genes involved in the promotion of depression in patients with ovarian cancer

The present study aimed to identify the core genes and pathways involved in depression in patients with ovarian cancer (OC) who suffer from high or low-grade depression. The dataset GSE9116 from Gene Expression Omnibus database was analyzed to identify differentially expressed genes (DEGs) in these patients. To elucidate how certain genes could promote depression in patients with OC, pathway crosstalk, protein-protein interaction (PPI) and comprehensive gene-pathway analyses were determined using WebGestalt, ToppGene and Search Tool for the Retrieval of Interacting Genes and gene ontology analysis. Key genes and pathways were extracted from the gene-pathway network, and gene expression and survival analysis were evaluated. A total of 93 DEGs were identified from GSE9116 dataset, including 84 upregulated genes and nine downregulated genes. The PPI, pathway crosstalk and comprehensive gene-pathway analyses highlighted C-C motif chemokine ligand 2 (CCL2), Fos proto-oncogene, AP-1 transcription factor subunit (FOS), serpin family E member 1 (SERPINE1) and serpin family G member 1 (SERPING1) as core genes involved in the promotion of depression in patients with OC. These core genes were involved in the following four pathways 'Ensemble of genes encoding ECM-associated proteins including ECM-affiliated proteins', 'ECM regulators and secreted factors', 'Ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins' and 'MAPK signaling pathway and IL-17 signaling pathway'. The results from gene expression and survival analysis demonstrated that these four key genes were upregulated in patients with OC and high-grade depression and could worsen patients' survival. These results suggested that CCL2, FOS, SERPINE1 and SERPING1 may serve a crucial role in the promotion of depression in patients with OC. This finding may provide novel markers for predicting and treating depression in patients with OC; however, the underlying mechanisms remain unknown and require further investigation.

Bioinformatics prediction and analysis of hub genes and pathways of three types of gynecological cancer

Cervical, endometrial and vulvar cancer are three common types of gynecological tumor that threaten the health of females worldwide. Since their underlying mechanisms and associations remain unclear, a comprehensive and systematic bioinformatics analysis is required. The present study downloaded GSE63678 from the GEO database and then performed functional enrichment analyses, including gene ontology and pathway analysis. To further investigate the molecular mechanisms underlying the three types of gynecological cancer, protein-protein interaction (PPI) analysis was performed. A biological network was generated with the guidance of the Kyoto Encyclopedia of Genes and Genomes database and was presented in Cytoscape. A total of 1,219 DEGs were identified for the three types of cancer, and 25 hub genes were revealed. Pathway analysis and the PPI network indicated that four main types of pathway participate in the mechanism of gynecological cancer, including viral infections and cancer formation, tumorigenesis and development, signal transduction, and endocrinology and metabolism. A preliminary gynecological cancer biological network was constructed. Notably, following all analysis, the phosphoinositide 3-kinase (PI3K)/Akt pathway was identified as a potential biomarker pathway. Seven pivotal hub genes (CCNA2, CDK1, CCND1, FGF2, IGF1, BCL2 and VEGFA) of the three gynecological cancer types were proposed. The seven hub genes may serve as targets in gynecological cancer for prevention and early intervention. The PI3K/Akt pathway was identified as a critical biomarker of the three types of gynecological cancer, which may serve a role in the pathogenesis. In summary, the present study provided evidence that could support the treatment of gynecologic tumors in the future.

Pathway and network analysis of genes related to osteoporosis

As a common degenerative disease, osteoporosis (OS) is characterized by reduced bone mass and microarchitectural deterioration of bone tissue. Both genetic and environmental factors are involved in OS development. To date, ~300 genes have been confirmed to be involved in the pathogenesis of OS, a large majority of which have been independently investigated. As OS is a polygenetic disease, a comprehensive analysis focusing on the biological functions and interactions of OS‑related genes would provide valuable information. In this study, OS related research deposited in PubMed was retrieved and genes related to OS were catalogued. Pathways with an enriched biological function for these genes were extracted, and the crosstalk between the enriched pathways was analyzed. A comprehensive network was constructed, and a minimal network was extracted using the Steiner minimal network algorithm. In this study, a total of 294 genes in were retrieved from PubMed. Biological processes found to be enriched included those related to bone metabolism and the immune system. In total, 58 pathways were enriched. Furthermore, the comprehensive network consisting of 3,943 nodes and 7,976 edges was constructed, among which 631 nodes and 2,581 edges contributed to the OS‑specific molecular network. In this network, in excess of 300 potential genes associated with OS and two modules were identified. Thus, this study provides a mechanistic insight into OS and suggests more than 300 potential OS‑related genes for future research.

MENDA: a comprehensive curated resource of metabolic characterization in depression

Depression is a seriously disabling psychiatric disorder with a significant burden of disease. Metabolic abnormalities have been widely reported in depressed patients and animal models. However, there are few systematic efforts that integrate meaningful biological insights from these studies. Herein, available metabolic knowledge in the context of depression was integrated to provide a systematic and panoramic view of metabolic characterization. After screening more than 10 000 citations from five electronic literature databases and five metabolomics databases, we manually curated 5675 metabolite entries from 464 studies, including human, rat, mouse and non-human primate, to develop a new metabolite-disease association database, called MENDA (http://menda.cqmu.edu.cn:8080/index.php). The standardized data extraction process was used for data collection, a multi-faceted annotation scheme was developed, and a user-friendly search engine and web interface were integrated for database access. To facilitate data analysis and interpretation based on MENDA, we also proposed a systematic analytical framework, including data integration and biological function analysis. Case studies were provided that identified the consistently altered metabolites using the vote-counting method, and that captured the underlying molecular mechanism using pathway and network analyses. Collectively, we provided a comprehensive curation of metabolic characterization in depression. Our model of a specific psychiatry disorder may be replicated to study other complex diseases.

Pathway-based network analyses and candidate genes associated with Kashin-Beck disease

To perform a comprehensive analysis focusing on the biological functions and interactions of Kashin-Beck disease (KBD)-related genes to provide information towards understanding the pathogenesis of KBD.A retrospective, integrated bioinformatics analysis was designed and conducted. First, by reviewing the literature deposited in PubMed, we identified 922 genes genetically associated with KBD. Then, biological function and network analyses were conducted with Cytoscape software. Moreover, KBD specific molecular network analysis was conducted by Cytocluster using the Molecular Complex Detection Algorithm (MCODE).The biological function enrichment analysis suggested that collagen catabolic process, protein activation cascade, cellular response to growth factor stimulus, skeletal system development, and extrinsic apoptosis played important roles in KBD development. The apoptosis pathway, NF-kappa B signaling pathway, and the glutathione metabolism pathway were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway network, suggesting that these pathways may play key roles in KBD occurrence and development. MCODE clusters showed that in top 3 clusters, 54 of KBD-related genes were included in the network and 110 candidate genes were discovered might be potentially related to KBD.The 110 candidate genes discovered in the current study may be related to the development of KBD. The expression changes of apoptosis and oxidative stress-related genes might serve as biomarkers for early diagnosis and treatment of KBD.

Transcriptomic analysis reveals key genes related to antioxidant mechanisms of Hylocereus undatus quality improvement by trypsin during storage

The synergistic effect of trypsin with antioxidant enzymes can improve the storage quality of H. undatus. Transcriptomic analysis and PPI network indicated that CAT is the key one among the enzymes of the complicated antioxidant system.

The role of miR-106p-5p in cervical cancer: from expression to molecular mechanism

This study aims to investigate the role of miR-106b-5p in cervical cancer by performing a comprehensive analysis on its expression and identifying its putative molecular targets and pathways based on The Cancer Genome Atlas (TCGA) dataset, Gene Expression Omnibus (GEO) dataset, and literature review. Significant upregulation of miR-106b-5p in cervical cancer is confirmed by meta-analysis with the data from TCGA, GEO, and literature. Moreover, the expression of miR-106b-5p is significantly correlated with the number of metastatic lymph nodes. Our bioinformatics analyses show that miR-106b could promote cervical cancer progression by modulating the expression of GSK3B, VEGFA, and PTK2 genes. Importantly, these three genes play a crucial role in PI3K-Akt signaling, focal adhesion, and cancer. Both the expression of miR-106b-5p and key genes are upregulated in cervical cancer. Several explanations could be implemented for this upregulation. However, the specific mechanism needs to be investigated further.

Modulatory Effects of Nicotine on neuroHIV/neuroAIDS

Nicotine, one of the key active ingredients in tobacco smoke, exerts its effects via binding to nicotinic acetylcholine receptors (nAChRs). Although both negative and positive pharmacological effects of nicotine have been shown in numerous animals and human studies, its interaction with human immunodeficiency virus-1 (HIV-1) have not been fully elucidated. Even though combined anti-retroviral therapy (cART) limits the progression of HIV-1 to acquired immune deficiency syndrome (AIDS), HIV-associated neurocognitive disorders (HAND) remain prevalent. There is thus a compelling need to enhance our understanding of HAND-related neurologic dysfunction. Some biochemical pathways and physiological dysfunctions have been found to be shared by HAND and Alzheimer's (AD) or Parkinson's (PD) diseases, and nicotine may exert the same neuroprotection in HAND that has been observed in both AD and PD. In the past dozen years, various potential therapeutic effects of nicotine such as neuroprotection have been revealed in both in vivo and in vitro studies, including using HIV-1 transgenic (HIV-1Tg) rat model, which mimics HIV-infected patients receiving cART. In the current review, we describe recent progress in the prevalence of HIV/AIDS with and without cigarette smoking, some animal models for studying neural dysfunction associated with HIV-1 infection, elucidating the modulatory effects of cigarette smoking/nicotine on HIV/AIDS, the anti-inflammatory effects of nicotine, and the neuroprotective effects observed in HIV-1Tg rat model. Taken together, these findings suggest the following: although tobacco smoking does cause deleterious effects in both health and disease conditions such as HIV infection, nicotine, the significant component of tobacco smoke, has been shown to possess some neuroprotective effects in HIV patients, possible via its anti-inflammatory activities. It is therefore necessary to study nicotine's dual effects on neuroHIV/neuroAIDS in hope of better defining the potential medical uses of nicotine or its analogues, and to make them available in a purer and less dangerous form.

Identification of pathway-based recurrence-associated signatures in optimally debulked patients with serous ovarian cancer

Serous ovarian cancer (SOC) is the most common form of the histological subtype of epithelial ovarian cancer, with the worst clinical outcome. Despite improvements in surgery and chemotherapy, most patients with SOC experience recurrence within 12-18 months of first-line treatment. Current studies are unable to robustly predict the recurrence of SOC, and more accurate predictive models are urgently required. We have, therefore, developed a novel pathway-structured model to predict the recurrence of SOC. We trained the model on a set of 333 patients and validated it in 3 diversified validation datasets of 403 patients. Genes significantly associated with recurrence within each pathway were identified using a Cox proportional hazards model based on LASSO estimation in the training dataset. Next, a pathway-structured scoring matrix was obtained after computation of the prognostic score for each pathway by fitting to the Cox proportional hazards model. With the pathway-structure scoring matrix as an input, the pathway-based recurrent signatures were identified using the Cox proportional hazards model based on LASSO estimation and the significant pathway-based signatures were externally validated in 3 independent datasets. Meanwhile, our pathway-structured model was compared with a commonly used gene-based model. Our results revealed that our 12 pathway-based signatures successfully predicted the recurrence of SOC with high accuracy in the training dataset and in the 3 validation datasets. Moreover, our pathway-structured model was superior to the gene-based model in 4 datasets. The pathways selected in our study will provide new insights into the pathogenesis and clinical treatments of SOC.

Analyzing the disease module associated with osteosarcoma via a network- and pathway-based approach

Osteosarcoma is the most common type of primary malignant bone tumor observed in children and adolescents. The aim of the present study was to identify an osteosarcoma-related gene module (OSM) by looking for a dense module following the integration of signals from genome-wide association studies (GWAS) into the human protein-protein interaction (PPI) network. A dataset of somatic mutations in osteosarcoma was obtained from the dbGaP database and their testing P-values were incorporated into the PPI network from a recent study using the dmGWAS bioconductor package. An OSM containing 201 genes (OS genes) and 268 interactions, which were closely associated with immune response, intracellular signal transduction and cell activity was identified. Topological analysis of the OSM identified 11 genes, including APP, APPBP2, ATXN1, HSP90B1, IKZF1, KRTAP10-1, PAK1, PDPK1, SMAD4, SUZ12 and TP53 as potential diagnostic biomarkers for osteosarcoma. The overall survival analysis of osteosarcoma for those 11 genes based on a dataset from the Cancer Genome Atlas, identified APP, HSP90B1, SUZ12 and IKZF1 as osteosarcoma survival-related genes. The results of the present study should be helpful in understanding the diagnosis and treatment of osteosarcoma and its underlying mechanisms. In addition, the methodology used in the present study may be suitable for the analysis of other types of disease.

Computational systems biology approaches for Parkinson's disease

Parkinson's disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions. This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches.

Network Medicine for Alzheimer's Disease and Traditional Chinese Medicine

Alzheimer’s Disease (AD) is a neurodegenerative condition that currently has no known cure. The principles of the expanding field of network medicine (NM) have recently been applied to AD research. The main principle of NM proposes that diseases are much more complicated than one mutation in one gene, and incorporate different genes, connections between genes, and pathways that may include multiple diseases to create full scale disease networks. AD research findings as a result of the application of NM principles have suggested that functional network connectivity, myelination, myeloid cells, and genes and pathways may play an integral role in AD progression, and may be integral to the search for a cure. Different aspects of the AD pathology could be potential targets for drug therapy to slow down or stop the disease from advancing, but more research is needed to reach definitive conclusions. Additionally, the holistic approaches of network pharmacology in traditional Chinese medicine (TCM) research may be viable options for the AD treatment, and may lead to an effective cure for AD in the future.

Combined analysis of DNA methylation and gene expression profiles of osteosarcoma identified several prognosis signatures

Osteosarcoma (OS) is a common primary malignancy in children and adolescents with relative high survival rate after chemotherapy. While, the toxicity of chemotherapy and personalized different response to chemotherapy makes it difficult for the selection of therapeutics and improvement of diagnosis. In this study, we conducted a combined analysis of two types of microarray datasets (gene expression and DNA methylation) from the Gene Expression Omnibus (GEO). Differential methylation sites (DMS) were identified by the IMA package and differential expression genes (DEGs) were screened out via the limma package. A total of 11,242 DMS (corresponding to 3080 genes (DMGs)) and 337 DEGs, with 40 overlaps (OS genes) between DEGs and DMGs, were identified. Enriched functions of OS genes were obtained through the Database for Annotation, Visualization and Integrated Discovery (DAVID). The OS genes were mainly enriched in the biological processes related to inflammatory/immune response and Pertussis pathways and Hematopoietic cell lineage pathways. Besides, OS-specific disease network was obtained, and found that UBS and NRF1 were regulated by multiple OS genes. Kaplan Meier analysis of OS genes identified BHMT2, DOCK2, DNALI1 and RIPK3 as significant OS survival-related genes. SEMA3A and PRAME are included in the 40 OS genes and within the top 10 most up-regulated DEGs. Their expression changes were further validated in U2OS osteosarcoma cell lines and hOB normal cell lines through quantitative PCR (qPCR) and consistent result with microarray analysis was obtained. Based on this study, some novel targets were identified for OS, which would be helpful in its early diagnosis and treatment.