Network-based methods for identifying critical pathways of complex diseases: a survey

Proteome Analysis of Alpine Merino Sheep Skin Reveals New Insights into the Mechanisms Involved in Regulating Wool Fiber Diameter

Wool fiber is a textile material that is highly valued based on its diameter, which is crucial in determining its economic value. To analyze the molecular mechanisms regulating wool fiber diameter, we used a Data-independent acquisition-based quantitative proteomics approach to analyze the skin proteome of Alpine Merino sheep with four fiber diameter ranges. From three contrasts of defined groups, we identified 275, 229, and 190 differentially expressed proteins (DEPs). Further analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways revealed that pathways associated with cyclic adenosine monophosphate and peroxisome proliferator-activated receptor signaling are relevant to wool fiber diameter. Using the K-means method, we investigated the DEP expression patterns across wool diameter ranges. Using weighted gene co-expression network analysis, we identified seven key proteins (CIDEA, CRYM, MLX, TPST2, GPD1, GOPC, and CAMK2G) that may be involved in regulating wool fiber diameter. Our findings provide a theoretical foundation for identifying DEPs and pathways associated with wool fiber diameter in Alpine Merino sheep to enable a better understanding of the molecular mechanisms underlying the genetic regulation of wool fiber quality.

Transcriptional Signatures and Network-Based Approaches Identified Master Regulators Transcription Factors Involved in Experimental Periodontitis Pathogenesis

Periodontitis is a chronic inflammatory disease characterized by the progressive and irreversible destruction of the periodontium. Its aetiopathogenesis lies in the constant challenge of the dysbiotic biofilm, which triggers a deregulated immune response responsible for the disease phenotype. Although the molecular mechanisms underlying periodontitis have been extensively studied, the regulatory mechanisms at the transcriptional level remain unclear. To generate transcriptomic data, we performed RNA shotgun sequencing of the oral mucosa of periodontitis-affected mice. Since genes are not expressed in isolation during pathological processes, we disclose here the complete repertoire of differentially expressed genes (DEG) and co-expressed modules to build Gene Regulatory Networks (GRNs) and identify the Master Transcriptional Regulators of periodontitis. The transcriptional changes revealed 366 protein-coding genes and 42 non-coding genes differentially expressed and enriched in the immune response. Furthermore, we found 13 co-expression modules with different representation degrees and gene expression levels. Our GRN comprises genes from 12 gene clusters, 166 nodes, of which 33 encode Transcription Factors, and 201 connections. Finally, using these strategies, 26 master regulators of periodontitis were identified. In conclusion, combining the transcriptomic analyses with the regulatory network construction represents a powerful and efficient strategy for identifying potential periodontitis-therapeutic targets.

Progress in application of terahertz time-domain spectroscopy for pharmaceutical analyses

Terahertz time-domain spectroscopy is an analytical method using terahertz time-domain pulses to study the physical and chemical properties of substances. It has strong potential for application in pharmaceutical analyses as an original non-destructive, efficient and convenient technology for spectral detection. This review briefly introduces the working principle of terahertz time-domain spectroscopy technology, focuses on the research achievements of this technology in analyses of chemical drugs, traditional Chinese medicine and biological drugs in the past decade. We also reveal the scientific feasibility of practical application of terahertz time-domain spectroscopy for pharmaceutical detection. Finally, we discuss the problems in practical application of terahertz time-domain spectroscopy technology, and the prospect of further development of this technology in pharmaceutical analyses. We hope that this review can provide a reference for application of terahertz time-domain spectroscopy technology in pharmaceutical analyses in the future.

Network Analysis of Biomarkers Associated with Occupational Exposure to Benzene and Malathion

Complex diseases are associated with the effects of multiple genes, proteins, and biological pathways. In this context, the tools of Network Medicine are compatible as a platform to systematically explore not only the molecular complexity of a specific disease but may also lead to the identification of disease modules and pathways. Such an approach enables us to gain a better understanding of how environmental chemical exposures affect the function of human cells, providing better perceptions about the mechanisms involved and helping to monitor/prevent exposure and disease to chemicals such as benzene and malathion. We selected differentially expressed genes for exposure to benzene and malathion. The construction of interaction networks was carried out using GeneMANIA and STRING. Topological properties were calculated using MCODE, BiNGO, and CentiScaPe, and a Benzene network composed of 114 genes and 2415 interactions was obtained. After topological analysis, five networks were identified. In these subnets, the most interconnected nodes were identified as: IL-8, KLF6, KLF4, JUN, SERTAD1, and MT1H. In the Malathion network, composed of 67 proteins and 134 interactions, HRAS and STAT3 were the most interconnected nodes. Path analysis, combined with various types of high-throughput data, reflects biological processes more clearly and comprehensively than analyses involving the evaluation of individual genes. We emphasize the central roles played by several important hub genes obtained by exposure to benzene and malathion.

Bioinformatic analysis of underlying mechanisms of Kawasaki disease via Weighted Gene Correlation Network Analysis (WGCNA) and the Least Absolute Shrinkage and Selection Operator method (LASSO) regression model

BACKGROUND

Kawasaki disease (KD) is a febrile systemic vasculitis involvingchildren younger than five years old. However, the specific biomarkers and precise mechanisms of this disease are not fully understood, which can delay the best treatment time, hence, this study aimed to detect the potential biomarkers and pathophysiological process of KD through bioinformatic analysis.

METHODS

The Gene Expression Omnibus database (GEO) was the source of the RNA sequencing data from KD patients. Differential expressed genes (DEGs) were screened between KD patients and healthy controls (HCs) with the "limma" R package. Weighted gene correlation network analysis (WGCNA) was performed to discover the most corresponding module and hub genes of KD. The node genes were obtained by the combination of the least absolute shrinkage and selection operator (LASSO) regression model with the top 5 genes from five algorithms in CytoHubba, which were further validated with the receiver operating characteristic curve (ROC curve). CIBERSORTx was employed to discover the constitution of immune cells in KDs and HCs. Functional enrichment analysis was performed to understand the biological implications of the modular genes. Finally, competing endogenous RNAs (ceRNA) networks of node genes were predicted using online databases.

RESULTS

A total of 267 DEGs were analyzed between 153 KD patients and 92 HCs in the training set, spanning two modules according to WGCNA. The turquoise module was identified as the hub module, which was mainly enriched in cell activation involved in immune response, myeloid leukocyte activation, myeloid leukocyte mediated immunity, secretion and leukocyte mediated immunity biological processes; included type II diabetes mellitus, nicotinate and nicotinamide metabolism, O-glycan biosynthesis, glycerolipid and glutathione metabolism pathways. The node genes included ADM, ALPL, HK3, MMP9 and S100A12, and there was good performance in the validation studies. Immune cell infiltration analysis revealed that gamma delta T cells, monocytes, M0 macrophage, activated dendritic cells, activated mast cells and neutrophils were elevated in KD patients. Regarding the ceRNA networks, three intact networks were constructed: NEAT1/NORAD/XIST-hsa-miR-524-5p-ADM, NEAT1/NORAD/XIST-hsa-miR-204-5p-ALPL, NEAT1/NORAD/XIST-hsa-miR-524-5p/hsa-miR-204-5p-MMP9.

CONCLUSION

To conclude, the five-gene signature and three ceRNA networks constructed in our study are of great value in the early diagnosis of KD and might help to elucidate our understanding of KD at the RNA regulatory level.

Nucleoporins in cardiovascular disease

Cardiovascular disease is a pressing health problem with significant global health, societal, and financial burdens. Understanding the molecular basis of polygenic cardiac pathology is thus essential to devising novel approaches for management and treatment. Recent identification of uncharacterized regulatory functions for a class of nuclear envelope proteins called nucleoporins offers the opportunity to understand novel putative mechanisms of cardiac disease development and progression. Consistent reports of nucleoporin deregulation associated with ischemic and dilated cardiomyopathies, arrhythmias and valvular disorders suggests that nucleoporin impairment may be a significant but understudied variable in cardiopathologic disorders. This review discusses and converges existing literature regarding nuclear pore complex proteins and their association with cardiac pathologies, and proposes a role for nucleoporins as facilitators of cardiac disease.

LCN2-interacting proteins and their expression patterns in brain tumors

Lipocalin 2 (LCN2) is a member of the lipocalin family. Elevated expression of LCN2 has been observed in many human tumors, suggesting it might be a potential biomarker and/or therapeutic target in malignancies. In this study, we aimed to explore LCN2 interacting proteins through bioinformatics, as well as their biological functions. Protein-protein interaction networks (PPIN) were constructed using LCN2 and its interacting proteins as the core node. These PPINs were scale free biological networks in which LCN2 and its interacting proteins could connect or cross-talk with at least one partner protein. Both functional and KEGG pathway enrichment analyses identified the known and potential biological functions of the PPIN, such as cell migration and cancer-related pathways. Expression levels of the PPIN proteins, as well as their expression correlations, in five types of brain tumor, were analyzed and integrated into the PPIN to illustrate a dynamic change. A significant correlation was found between the survival time of glioblastoma patients and the expression level of 10 genes (LCN2, MMP9, MMP2, PDE4DIP, L2HGDH, HNRNPA1, DDX31, LOXL2, FAM60A and RNF25). Taken together, our results suggest that LCN2 and its interacting proteins are mostly differentially expressed and have a distinguishing co-expression pattern. They might promote proliferation and migration via cell migration signaling and cancer-related pathways. LCN2 and its interacting proteins might be potential biomarkers in glioblastoma.

Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks

Biological pathways play important roles in the development of complex diseases, such as cancers, which are multifactorial complex diseases that are usually caused by multiple disorders gene mutations or pathway. It has become one of the most important issues to analyze pathways combining multiple types of high-throughput data, such as genomics and proteomics, to understand the mechanisms of complex diseases. In this paper, we propose a method for constructing the pathway network of gene phenotype and find out disease pathogenesis pathways through the analysis of the constructed network. The specific process of constructing the network includes, firstly, similarity calculation between genes expressing data combined with phenotypic mutual information and GO ontology information, secondly, calculating the correlation between pathways based on the similarity between differential genes and constructing the pathway network, and, finally, mining critical pathways to identify diseases. Experimental results on Breast Cancer Dataset using this method show that our method is better. In addition, testing on an alternative dataset proved that the key pathways we found were more accurate and reliable as biological markers of disease. These results show that our proposed method is effective.

Chalcogen-containing phenolics as antiproliferative agents

Aim: The increasing number of cancer cases has stimulated researchers to seek for novel approaches. We have combined two bioactive moieties: a polyphenolic scaffold and an organoselenium motif. Four different families (isothiocyanates/thioureas, and their selenium isosters) derived from dopamine, (±)-norepinephrine and R-epinephrine were accessed. Results: Heterocumulenes derived from dopamine and β-O-methylnoradrenaline were strong antiproliferative agents (GI50<10 μM). Selenoureas derived from β-O-methylnoradrenaline bearing electron-withdrawing groups (halogen, -NO2, -Ph) on the phenyl ring, were also strong antiproliferative agents, besides exhibiting good antiradical and glutathione peroxidase-like activities. Up to a 14-fold increased activity was achieved compared with classical chemotherapeutic agents, exhibiting also different mechanisms of action (cell cycle assays). Redox analysis on HeLa cells suggested an increase of ROS levels after the incubation period. Conclusion: the combination of organoselenium and phenolic moieties might provide valuable lead compounds with relevant antiproliferative properties.

The integration of weighted gene association networks based on information entropy

Constructing genome scale weighted gene association networks (WGAN) from multiple data sources is one of research hot spots in systems biology. In this paper, we employ information entropy to describe the uncertain degree of gene-gene links and propose a strategy for data integration of weighted networks. We use this method to integrate four existing human weighted gene association networks and construct a much larger WGAN, which includes richer biology information while still keeps high functional relevance between linked gene pairs. The new WGAN shows satisfactory performance in disease gene prediction, which suggests the reliability of our integration strategy. Compared with existing integration methods, our method takes the advantage of the inherent characteristics of the component networks and pays less attention to the biology background of the data. It can make full use of existing biological networks with low computational effort.

Finding disagreement pathway signatures and constructing an ensemble model for cancer classification

Cancer classification based on molecular level is a relatively routine research procedure with advances in high-throughput molecular profiling techniques. However, the number of genes typically far exceeds the number of the sample size in gene expression studies. The existing gene selection methods are almost based on statistics and machine learning, overlooking relevant biological principles or knowledge while working with biological data. Here, we propose a robust ensemble learning paradigm, which incorporates multiple pathways information, to predict cancer classification. We compare the proposed method with other methods, such as Elastic SCAD and PPDMF, and estimate the classification performance. The results show that the proposed method has the higher performances on most metrics and robust performance. We further investigate the biological mechanism of the ensemble feature genes. The results demonstrate that the ensemble feature genes are associated with drug targets/clinically-relevant cancer. In addition, some core biological pathways and biological process underlying clinically-relevant phenotypes are identified by function annotation. Overall, our research can provide a new perspective for the further study of molecular activities and manifestations of cancer.

A network-based pathway-expanding approach for pathway analysis

Background

Pathway analysis combining multiple types of high-throughput data, such as genomics and proteomics, has become the first choice to gain insights into the pathogenesis of complex diseases. Currently, several pathway analysis methods have been developed to study complex diseases. However, these methods did not take into account the interaction between internal and external genes of the pathway and between pathways. Hence, these approaches still face some challenges. Here, we propose a network-based pathway-expanding approach that takes the topological structures of biological networks into account.

Results

First, two weighted gene-gene interaction networks (tumor and normal) are constructed integrating protein-protein interaction(PPI) information, gene expression data and pathway databases. Then, they are used to identify significant pathways through testing the difference of topological structures of expanded pathways in the two weighted networks. The proposed method is employed to analyze two breast cancer data. As a result, the top 15 pathways identified using the proposed method are supported by biological knowledge from the published literatures and other methods. In addition, the proposed method is also compared with other methods, such as GSEA and SPIA, and estimated using the classification performance of the top 15 expanded pathways.

Conclusions

A novel network-based pathway-expanding approach is proposed to avoid the limitations of existing pathway analysis approaches. Experimental results indicate that the proposed method can accurately and reliably identify significant pathways which are related to the corresponding disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1333-x) contains supplementary material, which is available to authorized users.

WGCNA Application to Proteomic and Metabolomic Data Analysis

Progresses in mass spectrometric instrumentation and bioinformatics identification algorithms made over the past decades allow quantitative measurements of relative or absolute protein/metabolite amounts in cells in a high-throughput manner, which has significantly expedited the exploration into functions and dynamics of complex biological systems. However, interpretation of high-throughput data is often restricted by the limited availability of suitable computational methods and enough statistical power. While many computational methodologies have been developed in the past decades to address the issue, it becomes clear that network-focused rather than individual gene/protein-focused strategies would be more appropriate to obtain a complete picture of cellular responses. Recently, an R analytical package named as weighted gene coexpression network analysis (WGCNA) was developed and applied to high-throughput microarray or RNA-seq datasets since it provides a systems-level insights, high sensitivity to low abundance, or small fold changes genes without any information loss. The approach was also recently applied to proteomic and metabolomic data analysis. However, due to the fact that low coverage of the current proteomic and metabolomic analytical technologies, causing the format of datasets are often incomplete, the method needs to be modified so that it can be properly utilized for meaningful biologically interpretation. In this chapter, we provide a detailed introduction of the modified protocol and its tutorials for applying the WGCNA approach in analyzing proteomic and metabolomic datasets.