1
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Lyu C, Chen L, Liu X. Detecting tipping points of complex diseases by network information entropy. Brief Bioinform 2024; 25:bbae311. [PMID: 38960408 PMCID: PMC11221888 DOI: 10.1093/bib/bbae311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 05/30/2024] [Accepted: 06/14/2024] [Indexed: 07/05/2024] Open
Abstract
The progression of complex diseases often involves abrupt and non-linear changes characterized by sudden shifts that trigger critical transformations. Identifying these critical states or tipping points is crucial for understanding disease progression and developing effective interventions. To address this challenge, we have developed a model-free method named Network Information Entropy of Edges (NIEE). Leveraging dynamic network biomarkers, sample-specific networks, and information entropy theories, NIEE can detect critical states or tipping points in diverse data types, including bulk, single-sample expression data. By applying NIEE to real disease datasets, we successfully identified critical predisease stages and tipping points before disease onset. Our findings underscore NIEE's potential to enhance comprehension of complex disease development.
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Affiliation(s)
- Chengshang Lyu
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 1 Xiangshan Branch Alley, Xihu District, Hangzhou 310024, China
- Department of Biomedical Sciences, City University of Hong Kong, 31 To Yuen Street, Kowloon Tong, Kowloon, Hong Kong 999077, China
| | - Lingxi Chen
- Department of Biomedical Sciences, City University of Hong Kong, 31 To Yuen Street, Kowloon Tong, Kowloon, Hong Kong 999077, China
| | - Xiaoping Liu
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 1 Xiangshan Branch Alley, Xihu District, Hangzhou 310024, China
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2
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Ma T, Wang J. GraphPath: a graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network. Bioinformatics 2024; 40:btae165. [PMID: 38530778 PMCID: PMC11007237 DOI: 10.1093/bioinformatics/btae165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 02/22/2024] [Accepted: 03/22/2024] [Indexed: 03/28/2024] Open
Abstract
MOTIVATION Studying the molecular heterogeneity of cancer is essential for achieving personalized therapy. At the same time, understanding the biological processes that drive cancer development can lead to the identification of valuable therapeutic targets. Therefore, achieving accurate and interpretable clinical predictions requires paramount attention to thoroughly characterizing patients at both the molecular and biological pathway levels. RESULTS Here, we present GraphPath, a biological knowledge-driven graph neural network with multi-head self-attention mechanism that implements the pathway-pathway interaction network. We train GraphPath to classify the cancer status of patients with prostate cancer based on their multi-omics profiling. Experiment results show that our method outperforms P-NET and other baseline methods. Besides, two external cohorts are used to validate that the model can be generalized to unseen samples with adequate predictive performance. We reduce the dimensionality of latent pathway embeddings and visualize corresponding classes to further demonstrate the optimal performance of the model. Additionally, since GraphPath's predictions are interpretable, we identify target cancer-associated pathways that significantly contribute to the model's predictions. Such a robust and interpretable model has the potential to greatly enhance our understanding of cancer's biological mechanisms and accelerate the development of targeted therapies. AVAILABILITY AND IMPLEMENTATION https://github.com/amazingma/GraphPath.
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Affiliation(s)
- Teng Ma
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 41083, Hunan, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 41083, Hunan, China
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Milano M, Cinaglia P, Guzzi PH, Cannataro M. Aligning Cross-Species Interactomes for Studying Complex and Chronic Diseases. Life (Basel) 2023; 13:1520. [PMID: 37511895 PMCID: PMC10381714 DOI: 10.3390/life13071520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/03/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Neurodegenerative diseases (NDs) are a group of complex disorders characterized by the progressive degeneration and dysfunction of neurons in the central nervous system. NDs encompass many conditions, including Alzheimer's disease and Parkinson's disease. Alzheimer's disease (AD) is a complex disease affecting almost forty million people worldwide. AD is characterized by a progressive decline of cognitive functions related to the loss of connections between nerve cells caused by the prevalence of extracellular Aβ plaques and intracellular neurofibrillary tangles plaques. Parkinson's disease (PD) is a neurodegenerative disorder that primarily affects the movement of an individual. The exact cause of Parkinson's disease is not fully understood, but it is believed to involve a combination of genetic and environmental factors. Some cases of PD are linked to mutations in the LRRK2, PARKIN and other genes, which are associated with familial forms of the disease. Different research studies have applied the Protein Protein Interaction (PPI) networks to understand different aspects of disease progression. For instance, Caenorhabditis elegans is widely used as a model organism for the study of AD due to roughly 38% of its genes having a human ortholog. This study's goal consists of comparing PPI network of C. elegans and human by applying computational techniques, widely used for the analysis of PPI networks between species, such as Local Network Alignment (LNA). For this aim, we used L-HetNetAligner algorithm to build a local alignment among two PPI networks, i.e., C. elegans and human PPI networks associated with AD and PD built-in silicon. The results show that L-HetNetAligner can find local alignments representing functionally related subregions. In conclusion, since local alignment enables the extraction of functionally related modules, the method can be used to study complex disease progression.
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Affiliation(s)
- Marianna Milano
- Department of Experimental and Clinical Medicine, University Magna Græcia, 88100 Catanzaro, Italy
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
| | - Pietro Cinaglia
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
- Department of Health Sciences, University Magna Græcia, 88100 Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
- Department of Medical and Surgical Sciences, University Magna Græcia, 88100 Catanzaro, Italy
| | - Mario Cannataro
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
- Department of Medical and Surgical Sciences, University Magna Græcia, 88100 Catanzaro, Italy
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Liu H, Yuan M, Mitra R, Zhou X, Long M, Lei W, Zhou S, Huang YE, Hou F, Eischen CM, Jiang W. CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research. Genome Med 2022; 14:118. [PMID: 36229842 PMCID: PMC9563764 DOI: 10.1186/s13073-022-01119-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 09/26/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated. METHODS To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with >440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes. RESULTS Analysis of >8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy. CONCLUSIONS Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/ . The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway .
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Affiliation(s)
- Haizhou Liu
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Avenue, Nanjing, 211106, Jiangsu Province, China
| | - Mengqin Yuan
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Avenue, Nanjing, 211106, Jiangsu Province, China
| | - Ramkrishna Mitra
- Department of Pharmacology, Physiology, and Cancer Biology, Sidney Kimmel Cancer Center, Thomas Jefferson University, 233 South 10th St., Philadelphia, PA, 19107, USA
| | - Xu Zhou
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Avenue, Nanjing, 211106, Jiangsu Province, China
| | - Min Long
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Avenue, Nanjing, 211106, Jiangsu Province, China
| | - Wanyue Lei
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Avenue, Nanjing, 211106, Jiangsu Province, China
| | - Shunheng Zhou
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Avenue, Nanjing, 211106, Jiangsu Province, China
| | - Yu-E Huang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Avenue, Nanjing, 211106, Jiangsu Province, China
| | - Fei Hou
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Avenue, Nanjing, 211106, Jiangsu Province, China
| | - Christine M Eischen
- Department of Pharmacology, Physiology, and Cancer Biology, Sidney Kimmel Cancer Center, Thomas Jefferson University, 233 South 10th St., Philadelphia, PA, 19107, USA.
| | - Wei Jiang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Avenue, Nanjing, 211106, Jiangsu Province, China.
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Qiao X, Zhang X, Chen W, Xu X, Chen YW, Liu ZP. tensorGSEA: Detecting Differential Pathways in Type 2 Diabetes via Tensor-Based Data Reconstruction. Interdiscip Sci 2022; 14:520-531. [PMID: 35195883 DOI: 10.1007/s12539-022-00506-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 01/24/2022] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
Abstract
Detecting significant signaling pathways in disease progression highlights the dysfunctions and pathogenic mechanisms of complex disease development. Since tensor decomposition has been proven effective for multi-dimensional data representation and reconstruction, differences between original and tensor-processed data are expected to extract crucial information and differential indication. This paper provides a tensor-based gene set enrichment analysis, called tensorGSEA, based on a data reconstruction method to identify relevant significant pathways during disease development. As a proof-of-concept study, we identify the differential pathways of diabetes in rats. Specifically, we first arrange gene expression profiles of each documented pathway as tensors with three dimensions: genes, samples, and periods. Then we compress tensors into core tensors with lower ranks. The pathways with lower reconstruction rates are obtained after reconstructing gene expression profiles in another state via these cores. Thus, differences underlying pathways are extracted by cross-state data reconstruction between controls and diseases. The experiments reveal several critical pathways with diabetes-specific functions which otherwise cannot be identified by alternative methods. Our proposed tensorGSEA is efficient in evaluating pathways by achieving their empirical statistical significance, respectively. The classification experiments demonstrate that the selected pathways can be implemented as biomarkers to identify the diabetic state. The code of tensorGSEA is available at https://github.com/zhxr37/tensorGSEA .
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Affiliation(s)
- Xu Qiao
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, Shandong, China
| | - Xianru Zhang
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, Shandong, China
| | - Wei Chen
- Shandong Provincial Key Laboratory of Oral Tissue Regeneration, School of Stomatology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Xin Xu
- Shandong Provincial Key Laboratory of Oral Tissue Regeneration, School of Stomatology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Yen-Wei Chen
- Graduate School of Information Science and Engineering, Ritsumeikan University, Shiga, 525-8577, Japan
| | - Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, Shandong, China.
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6
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Onisiforou A, Spyrou GM. Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches. Brief Bioinform 2021; 22:bbab141. [PMID: 34237135 PMCID: PMC8574625 DOI: 10.1093/bib/bbab141] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 03/01/2021] [Accepted: 03/23/2021] [Indexed: 12/18/2022] Open
Abstract
During the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to replicate and survive within the host. These interspecies molecular interactions can lead to viral-mediated perturbations of the human interactome causing the generation of various complex diseases. Evidences suggest that viral-mediated perturbations are a possible pathogenic etiology in several neurodegenerative diseases (NDs). These diseases are characterized by chronic progressive degeneration of neurons, and current therapeutic approaches provide only mild symptomatic relief; therefore, there is unmet need for the discovery of novel therapeutic interventions. In this paper, we initially review databases and tools that can be utilized to investigate viral-mediated perturbations in complex NDs using network-based analysis by examining the interaction between the ND-related PPI disease networks and the virus-host PPI network. Afterwards, we present our theoretical-driven integrative network-based bioinformatics approach that accounts for pathogen-genes-disease-related PPIs with the aim to identify viral-mediated pathogenic mechanisms focusing in multiple sclerosis (MS) disease. We identified seven high centrality nodes that can act as disease communicator nodes and exert systemic effects in the MS-enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways network. In addition, we identified 12 KEGG pathways, 5 Reactome pathways and 52 Gene Ontology Immune System Processes by which 80 viral proteins from eight viral species might exert viral-mediated pathogenic mechanisms in MS. Finally, our analysis highlighted the Th17 differentiation pathway, a disease communicator node and part of the 12 underlined KEGG pathways, as a key viral-mediated pathogenic mechanism and a possible therapeutic target for MS disease.
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Affiliation(s)
- Anna Onisiforou
- Department of Bioinformatics, Cyprus Institute of Neurology & Genetics, and the Cyprus School of Molecular Medicine, Cyprus
| | - George M Spyrou
- Department of Bioinformatics, Cyprus Institute of Neurology & Genetics, and professor at the Cyprus School of Molecular Medicine, Cyprus
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7
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Hua J, Yang Z, Jiang T, Yu S. Pairwise interactions in gene expression determine a hierarchical transcriptional profile in the human brain. Sci Bull (Beijing) 2021; 66:1437-1447. [PMID: 36654370 DOI: 10.1016/j.scib.2021.01.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 08/25/2020] [Accepted: 10/13/2020] [Indexed: 01/20/2023]
Abstract
The orchestrated expression of thousands of genes gives rise to the complexity of the human brain. However, the structures governing these myriad gene-gene interactions remain unclear. By analyzing transcription data from more than 2000 sites in six human brains, we found that pairwise interactions between genes, without considering any higher-order interactions, are sufficient to predict the transcriptional pattern of the genome for individual brain regions and the transcriptional profile of the entire brain consisting of more than 200 areas. These findings suggest a quadratic complexity of transcriptional patterns in the human brain, which is much simpler than expected. In addition, using a pairwise interaction model, we revealed that the strength of gene-gene interactions in the human brain gives rise to the nearly maximal number of transcriptional clusters, which may account for the functional and structural richness of the brain.
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Affiliation(s)
- Jiaojiao Hua
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhengyi Yang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
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8
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Tian L, Pu J, Liu Y, Gui S, Zhong X, Song X, Xu S, Zhang H, Wang H, Zhou W, Xie P. Metabolomic analysis of animal models of depression. Metab Brain Dis 2020; 35:979-990. [PMID: 32440806 DOI: 10.1007/s11011-020-00574-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 04/14/2020] [Indexed: 11/26/2022]
Abstract
BACKGROUND Our understanding of the molecular mechanisms of depression remains largely unclear. Previous studies have shown that the prefrontal cortex (PFC) is among most important brain regions that exhibits metabolic changes in depression. A comprehensive analysis based on candidate metabolites in the PFC of animal models of depression will provide valuable information for understanding the pathogenic mechanism underlying depression. METHODS Candidate metabolites that are potentially involved in the metabolic changes of the PFC in animal models of depression were retrieved from the Metabolite Network of Depression Database. The significantly altered metabolic pathways were revealed by canonical pathway analysis, and the relationships among altered pathways were explored by pathway crosstalk analysis. Additionally, drug-associated pathways were investigated using drug-associated metabolite set enrichment analysis. The interrelationships among metabolites, proteins, and other molecules were analyzed by molecular network analysis. RESULTS Among 88 candidate metabolites, 87 altered canonical pathways were identified, and the top five ranked pathways were tRNA charging, the endocannabinoid neuronal synapse pathway, (S)-reticuline biosynthesis II, catecholamine biosynthesis, and GABA receptor signaling. Pathway crosstalk analysis revealed that these altered pathways were grouped into three interlinked modules involved in amino acid metabolism, nervous system signaling/neurotransmitters, and nucleotide metabolism. In the drug-associated metabolite set enrichment analysis, the main enriched drug pathways were opioid-related and antibiotic-related action pathways. Furthermore, the most significantly altered molecular network was involved in amino acid metabolism, molecular transport, and small molecule biochemistry. CONCLUSIONS This study provides important clues for the metabolic characteristics of the PFC in depression.
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Affiliation(s)
- Lu Tian
- School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
| | - Juncai Pu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, 400016, Chongqing, China
| | - Yiyun Liu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, 400016, Chongqing, China
| | - Siwen Gui
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
- College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China
| | - Xiaogang Zhong
- School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
| | - Xuemian Song
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
- College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China
| | - Shaohua Xu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, 400016, Chongqing, China
| | - Hanpin Zhang
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, 400016, Chongqing, China
| | - Haiyang Wang
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
- College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China
| | - Wei Zhou
- School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
| | - Peng Xie
- School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China.
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China.
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, 400016, Chongqing, China.
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9
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Mao Y, Fisher DW, Yang S, Keszycki RM, Dong H. Protein-protein interactions underlying the behavioral and psychological symptoms of dementia (BPSD) and Alzheimer's disease. PLoS One 2020; 15:e0226021. [PMID: 31951614 PMCID: PMC6968845 DOI: 10.1371/journal.pone.0226021] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Accepted: 11/19/2019] [Indexed: 12/25/2022] Open
Abstract
Alzheimer’s Disease (AD) is a devastating neurodegenerative disorder currently affecting 45 million people worldwide, ranking as the 6th highest cause of death. Throughout the development and progression of AD, over 90% of patients display behavioral and psychological symptoms of dementia (BPSD), with some of these symptoms occurring before memory deficits and therefore serving as potential early predictors of AD-related cognitive decline. However, the biochemical links between AD and BPSD are not known. In this study, we explored the molecular interactions between AD and BPSD using protein-protein interaction (PPI) networks built from OMIM (Online Mendelian Inheritance in Man) genes that were related to AD and two distinct BPSD domains, the Affective Domain and the Hyperactivity, Impulsivity, Disinhibition, and Aggression (HIDA) Domain. Our results yielded 8 unique proteins for the Affective Domain (RHOA, GRB2, PIK3R1, HSPA4, HSP90AA1, GSK3beta, PRKCZ, and FYN), 5 unique proteins for the HIDA Domain (LRP1, EGFR, YWHAB, SUMO1, and EGR1), and 6 shared proteins between both BPSD domains (APP, UBC, ELAV1, YWHAZ, YWHAE, and SRC) and AD. These proteins might suggest specific targets and pathways that are involved in the pathogenesis of these BPSD domains in AD.
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Affiliation(s)
- Yimin Mao
- School of Information and Technology, Jiangxi University of Science and Technology, Jiangxi, China
- Applied Science Institute, Jiangxi University of Science and Technology, Jiangxi, China
| | - Daniel W. Fisher
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Shuxing Yang
- School of Information and Technology, Jiangxi University of Science and Technology, Jiangxi, China
| | - Rachel M. Keszycki
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Hongxin Dong
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
- * E-mail:
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10
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Mrdjen D, Fox EJ, Bukhari SA, Montine KS, Bendall SC, Montine TJ. The basis of cellular and regional vulnerability in Alzheimer's disease. Acta Neuropathol 2019; 138:729-749. [PMID: 31392412 PMCID: PMC6802290 DOI: 10.1007/s00401-019-02054-4] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 07/24/2019] [Accepted: 07/31/2019] [Indexed: 12/11/2022]
Abstract
Alzheimer's disease (AD) differentially and specifically affects brain regions and neuronal cell types in a predictable pattern. Damage to the brain appears to spread and worsens with time, taking over more regions and activating multiple stressors that can converge to promote vulnerability of certain cell types. At the same time, other cell types and brain regions remain intact in the face of this onslaught of neuropathology. Although neuropathologic descriptions of AD have been extensively expanded and mapped over the last several decades, our understanding of the mechanisms underlying how certain regions and cell populations are specifically vulnerable or resistant has lagged behind. In this review, we detail what is known about the selectivity of local initiation of AD pathology in the hippocampus, its proposed spread via synaptic connections, and the diversity of clinical phenotypes and brain atrophy patterns that may arise from different fibrillar strains of pathologic proteins or genetic predispositions. We summarize accumulated and emerging knowledge of the cellular and molecular basis for neuroanatomic selectivity, consider potential disease-relevant differences between vulnerable and resistant neuronal cell types and isolate molecular markers to identify them.
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Affiliation(s)
- Dunja Mrdjen
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Edward J Fox
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Syed A Bukhari
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Kathleen S Montine
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Sean C Bendall
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Thomas J Montine
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA.
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11
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Wang B, Cai Y, Kong Y, Li X, Fu H, Zhang S, Zhang T. Analysis of the role of DAMTC in lung adenocarcinoma cells based on the DNA microarrays. Oncol Lett 2019; 17:4787-4794. [PMID: 31186684 PMCID: PMC6507315 DOI: 10.3892/ol.2019.10146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 01/21/2019] [Indexed: 11/06/2022] Open
Abstract
The present study aimed to investigate the effect of 7, 8-diacetoxy-4-methylcoumarin (DAMTC) on lung adenocarcinoma cells (A549) and analyze the molecular mechanism underlying DAMTC-treated lung adenocarcinoma. Gene expression profile GSE29698 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) in 3 DAMTC-treated A549 samples were analyzed and compared with 3 DAMTC-untreated samples using the limma package. Gene Ontology (GO) and pathway enrichment analyses of DEGs were performed, followed by the functional annotation and protein-protein interaction (PPI) network construction. Finally, pathway crosstalk analysis was conducted. A total of 500 upregulated and 389 downregulated DEGs were identified. The upregulated and downregulated DEGs were enriched in different GO terms and pathways, including metabolic process, p53 signaling pathway and metabolic pathways. A total of 9 DEGs were determined to have node degrees >16 in the PPI network, including interleukin 6 (IL6), MDM2 oncogene, E3 ubiquitin protein ligase (MDM2), cell division cycle 42 (CDC42) and MYC associated factor X (MAX). Furthermore, numerous DEGs were identified to function as transcription factors and tumor suppressor genes, including histone deacetylase 3 and MAX. Additionally, apoptosis, tight junction, and endocytosis pathway were determined to cross-talk with small cell and non-small cell lung cancer. The DEGs (IL6, MDM2, CDC42 and MAX) involved in different pathways, including the p53 signaling pathway and endocytosis, may be the potential targets for DAMTC in lung adenocarcinoma. The elucidation of the underlying mechanism of the DAMTC effect may make it a potential drug.
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Affiliation(s)
- Binliang Wang
- Respiratory Medicine Department, Taizhou First People's Hospital, Taizhou, Zhejiang 318020, P.R. China
| | - Yuanyuan Cai
- Respiratory Medicine Department, Taizhou First People's Hospital, Taizhou, Zhejiang 318020, P.R. China
| | - Yiming Kong
- Respiratory Medicine Department, Taizhou First People's Hospital, Taizhou, Zhejiang 318020, P.R. China
| | - Xiaobo Li
- Respiratory Medicine Department, Taizhou First People's Hospital, Taizhou, Zhejiang 318020, P.R. China
| | - Haiwei Fu
- Respiratory Medicine Department, Taizhou First People's Hospital, Taizhou, Zhejiang 318020, P.R. China
| | - Song Zhang
- Respiratory Medicine Department, Taizhou First People's Hospital, Taizhou, Zhejiang 318020, P.R. China
| | - Tianwei Zhang
- Respiratory Medicine Department, Taizhou First People's Hospital, Taizhou, Zhejiang 318020, P.R. China
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12
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Qi G, Kong W, Mou X, Wang S. A new method for excavating feature lncRNA in lung adenocarcinoma based on pathway crosstalk analysis. J Cell Biochem 2018; 120:9034-9046. [DOI: 10.1002/jcb.28177] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 11/08/2018] [Indexed: 12/21/2022]
Affiliation(s)
- Guoqiang Qi
- Department of Electronic Engineering College of Information Engineering, Shanghai Maritime University Shanghai China
| | - Wei Kong
- Department of Electronic Engineering College of Information Engineering, Shanghai Maritime University Shanghai China
| | - Xiaoyang Mou
- Department of Biochemistry Rowan University and Guava Medicine Glassboro New Jersey
| | - Shuaiqun Wang
- Department of Electronic Engineering College of Information Engineering, Shanghai Maritime University Shanghai China
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13
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Clustering, Pathway Enrichment, and Protein-Protein Interaction Analysis of Gene Expression in Neurodevelopmental Disorders. Adv Pharmacol Sci 2018; 2018:3632159. [PMID: 30598663 PMCID: PMC6288580 DOI: 10.1155/2018/3632159] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 10/30/2018] [Indexed: 12/21/2022] Open
Abstract
Neuronal developmental disorder is a class of diseases in which there is impairment of the central nervous system and brain function. The brain in its developmental phase undergoes tremendous changes depending upon the stage and environmental factors. Neurodevelopmental disorders include abnormalities associated with cognitive, speech, reading, writing, linguistic, communication, and growth disorders with lifetime effects. Computational methods provide great potential for betterment of research and insight into the molecular mechanism of diseases. In this study, we have used four samples of microarray neuronal developmental data: control, RV (resveratrol), NGF (nerve growth factor), and RV + NGF. By using computational methods, we have identified genes that are expressed in the early stage of neuronal development and also involved in neuronal diseases. We have used MeV application to cluster the raw data using distance metric Pearson correlation coefficient. Finally, 60 genes were selected on the basis of coexpression analysis. Further pathway analysis was done using the Metascape tool, and the biological process was studied using gene ontology database. A total of 13 genes AKT1, BAD, BAX, BCL2, BDNF, CASP3, CASP8, CASP9, MYC, PIK3CD, MAPK1, MAPK10, and CYCS were identified that are common in all clusters. These genes are involved in neuronal developmental disorders and cancers like colorectal cancer, apoptosis, tuberculosis, amyotrophic lateral sclerosis (ALS), neuron death, and prostate cancer pathway. A protein-protein interaction study was done to identify proteins that belong to the same pathway. These genes can be used to design potential inhibitors against neurological disorders at the early stage of neuronal development. The microarray samples discussed in this publication are part of the data deposited in NCBI's Gene Expression Omnibus (Yadav et al., 2018) and are accessible through GEO Series (accession number GSE121261).
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14
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Ray S, Hossain SMM, Khatun L, Mukhopadhyay A. A comprehensive analysis on preservation patterns of gene co-expression networks during Alzheimer's disease progression. BMC Bioinformatics 2017; 18:579. [PMID: 29262769 PMCID: PMC5738049 DOI: 10.1186/s12859-017-1946-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 11/21/2017] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a chronic neuro-degenerative disruption of the brain which involves in large scale transcriptomic variation. The disease does not impact every regions of the brain at the same time, instead it progresses slowly involving somewhat sequential interaction with different regions. Analysis of the expression patterns of the genes in different regions of the brain influenced in AD surely contribute for a enhanced comprehension of AD pathogenesis and shed light on the early characterization of the disease. RESULTS Here, we have proposed a framework to identify perturbation and preservation characteristics of gene expression patterns across six distinct regions of the brain ("EC", "HIP", "PC", "MTG", "SFG", and "VCX") affected in AD. Co-expression modules were discovered considering a couple of regions at once. These are then analyzed to know the preservation and perturbation characteristics. Different module preservation statistics and a rank aggregation mechanism have been adopted to detect the changes of expression patterns across brain regions. Gene ontology (GO) and pathway based analysis were also carried out to know the biological meaning of preserved and perturbed modules. CONCLUSIONS In this article, we have extensively studied the preservation patterns of co-expressed modules in six distinct brain regions affected in AD. Some modules are emerged as the most preserved while some others are detected as perturbed between a pair of brain regions. Further investigation on the topological properties of preserved and non-preserved modules reveals a substantial association amongst "betweenness centrality" and "degree" of the involved genes. Our findings may render a deeper realization of the preservation characteristics of gene expression patterns in discrete brain regions affected by AD.
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Affiliation(s)
- Sumanta Ray
- Department of Computer Science and Engineering, Aliah University, Kolkata, 700156, West Bengal, India
| | - Sk Md Mosaddek Hossain
- Department of Computer Science and Engineering, Aliah University, Kolkata, 700156, West Bengal, India.
| | - Lutfunnesa Khatun
- Department of Computer Science and Engineering, Aliah University, Kolkata, 700156, West Bengal, India
| | - Anirban Mukhopadhyay
- Department of Computer Science and Engineering, University of Kalyani, Kalyani, 741235, West Bengal, India
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15
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Zhang Y, Xu Y, Li F, Li X, Feng L, Shi X, Wang L, Li X. Dissecting dysfunctional crosstalk pathways regulated by miRNAs during glioma progression. Oncotarget 2017; 7:25769-82. [PMID: 27013589 PMCID: PMC5041942 DOI: 10.18632/oncotarget.8265] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Accepted: 03/08/2016] [Indexed: 01/14/2023] Open
Abstract
Glioma is a malignant nervous system tumor with a high fatality rate and poor prognosis. MicroRNAs (miRNAs) are important post-transcriptional modulators of glioma initiation and progression. Tumor progression often results from dysfunctional co-operation between pathways regulated by miRNAs. We therefore constructed a glioma progression-related miRNA-pathway crosstalk network that not only revealed some key miRNA-pathway patterns, but also helped characterize the functional roles of miRNAs during glioma progression. Our data indicate that crosstalk between cell cycle and p53 pathways is associated with grade II to grade III progression, while cell communications-related pathways involving regulation of actin cytoskeleton and adherens junctions are associated with grade IV glioblastoma progression. Furthermore, miRNAs and their crosstalk pathways may be useful for stratifying glioma and glioblastoma patients into groups with short or long survival times. Our data indicate that a combination of miRNA and pathway crosstalk information can be used for survival prediction.
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Affiliation(s)
- Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yanjun Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Feng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xiang Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Li Feng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xinrui Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Lihua Wang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
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16
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Han JH, Cai XJ, Sun HJ, Dong GH, He B, Zhang HX, Zhou X, Yan JQ. Identifying dysregulated pathways in postmenopausal osteoporosis through investigation of crosstalk between pathways. Mol Med Rep 2017; 16:9029-9034. [PMID: 28990094 DOI: 10.3892/mmr.2017.7703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 09/09/2017] [Indexed: 11/06/2022] Open
Abstract
The present study aimed to identify potential dysregulated pathways to further reveal the molecular mechanisms of postmenopausal osteoporosis (PMOP) based on pathway‑interaction network (PIN) analysis, which considers crosstalk between pathways. Protein‑protein interaction (PPI) data and pathway information were derived from STRING and Reactome Pathway databases, respectively. According to the gene expression profiles, pathway data and PPI information, a PIN was constructed with each node representing a biological pathway. Principal component analysis was used to compute the pathway activity for each pathway, and the seed pathway was selected. Subsequently, dysregulated pathways were extracted from the PIN based on the seed pathway and the increased classification accuracy, which was measured using the area under the curve (AUC) index according to 5‑fold cross validation. A PIN comprising 2,725 interactions was constructed, which was used to detect dysregulated pathways. Notably, the 'mitotic prometaphase' pathway was selected and defined as a seed pathway. Starting with the seed pathway, network‑based analysis successfully identified one pathway set for PMOP comprising eight dysregulated pathways (such as mitotic prometaphase, resolution of sister chromatid cohesion, mRNA splicing and mRNA splicing‑major) with an AUC score of 0.85, which may provide potential biomarkers for targeted therapy for PMOP.
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Affiliation(s)
- Jian-Hua Han
- Department of Orthopedics, Zunyi First People's Hospital, Zunyi, Guizhou 563002, P.R. China
| | - Xiao-Jun Cai
- Department of Orthopedics, Zunyi First People's Hospital, Zunyi, Guizhou 563002, P.R. China
| | - Hou-Jie Sun
- Department of Orthopedics, Zunyi First People's Hospital, Zunyi, Guizhou 563002, P.R. China
| | - Ge-Hui Dong
- Department of Orthopedics, Zunyi First People's Hospital, Zunyi, Guizhou 563002, P.R. China
| | - Bin He
- Department of Orthopedics, Zunyi First People's Hospital, Zunyi, Guizhou 563002, P.R. China
| | - Han-Xiang Zhang
- Department of Orthopedics, Zunyi First People's Hospital, Zunyi, Guizhou 563002, P.R. China
| | - Xin Zhou
- Department of Orthopedics, Zunyi First People's Hospital, Zunyi, Guizhou 563002, P.R. China
| | - Jia-Qiang Yan
- Department of Orthopedics, Zunyi First People's Hospital, Zunyi, Guizhou 563002, P.R. China
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17
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Padmanabhan K, Shpanskaya K, Bello G, Doraiswamy PM, Samatova NF. Toward Personalized Network Biomarkers in Alzheimer's Disease: Computing Individualized Genomic and Protein Crosstalk Maps. Front Aging Neurosci 2017; 9:315. [PMID: 29085293 PMCID: PMC5649142 DOI: 10.3389/fnagi.2017.00315] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2017] [Accepted: 09/15/2017] [Indexed: 01/12/2023] Open
Affiliation(s)
- Kanchana Padmanabhan
- Department of Computer Science, North Carolina State University, Raleigh, NC, United States.,Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Katie Shpanskaya
- Stanford University School of Medicine, Stanford, CA, United States
| | - Gonzalo Bello
- Department of Computer Science, North Carolina State University, Raleigh, NC, United States
| | - P Murali Doraiswamy
- Department of Psychiatry, Duke University, Durham, NC, United States.,Duke Institute for Brain Sciences, Duke University, Durham, NC, United States
| | - Nagiza F Samatova
- Department of Computer Science, North Carolina State University, Raleigh, NC, United States.,Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
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18
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Xia Y, Cai T, Cai TT. Multiple Testing of Submatrices of a Precision Matrix with Applications to Identification of Between Pathway Interactions. J Am Stat Assoc 2017; 113:328-339. [PMID: 29881130 PMCID: PMC5988269 DOI: 10.1080/01621459.2016.1251930] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2015] [Accepted: 10/01/2016] [Indexed: 10/20/2022]
Abstract
Making accurate inference for gene regulatory networks, including inferring about pathway by pathway interactions, is an important and difficult task. Motivated by such genomic applications, we consider multiple testing for conditional dependence between subgroups of variables. Under a Gaussian graphical model framework, the problem is translated into simultaneous testing for a collection of submatrices of a high-dimensional precision matrix with each submatrix summarizing the dependence structure between two subgroups of variables. A novel multiple testing procedure is proposed and both theoretical and numerical properties of the procedure are investigated. Asymptotic null distribution of the test statistic for an individual hypothesis is established and the proposed multiple testing procedure is shown to asymptotically control the false discovery rate (FDR) and false discovery proportion (FDP) at the pre-specified level under regularity conditions. Simulations show that the procedure works well in controlling the FDR and has good power in detecting the true interactions. The procedure is applied to a breast cancer gene expression study to identify between pathway interactions.
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Affiliation(s)
- Yin Xia
- Department of Statistics, Fudan University and Department of Statistics & Operations Research, University of North Carolina at Chapel Hill
| | - Tianxi Cai
- Department of Biostatistics, Harvard School of Public Health, Harvard University
| | - T Tony Cai
- Department of Statistics, The Wharton School, University of Pennsylvania
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19
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Khayer N, Marashi SA, Mirzaie M, Goshadrou F. Three-way interaction model to trace the mechanisms involved in Alzheimer's disease transgenic mice. PLoS One 2017; 12:e0184697. [PMID: 28934252 PMCID: PMC5608283 DOI: 10.1371/journal.pone.0184697] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Accepted: 08/29/2017] [Indexed: 11/19/2022] Open
Abstract
Alzheimer's disease (AD) is the most common cause for dementia in human. Currently, more than 46 million people in the world suffer from AD and it is estimated that by 2050 this number increases to more than 131 million. AD is considered as a complex disease. Therefore, understanding the mechanism of AD is a universal challenge. Nowadays, a huge number of disease-related high-throughput “omics” datasets are freely available. Such datasets contain valuable information about disease-related pathways and their corresponding gene interactions. In the present work, a three-way interaction model is used as a novel approach to understand AD-related mechanisms. This model can trace the dynamic nature of co-expression relationship between two genes by introducing their link to a third gene. Apparently, such relationships cannot be traced by the classical two-way interaction model. Liquid association method was applied to capture the statistically significant triplets which are involved in three-way interaction. Subsequently, gene set enrichment analysis (GSEA) and gene regulatory network (GRN) inference were applied to analyze the biological relevance of the statistically significant triplets. The results of this study suggest that the innate immunity processes are important in AD. Specifically, our results suggest that H2-Ob as the switching gene and the gene pair {Csf1r, Milr1} form a statistically significant and biologically relevant triplet, which may play an important role in AD. We propose that the homeostasis-related link between mast cells and microglia is presumably controlled with H2-Ob expression levels as a switching gene.
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Affiliation(s)
- Nasibeh Khayer
- Department of Basic Sciences, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- * E-mail:
| | - Mehdi Mirzaie
- Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Fatemeh Goshadrou
- Department of Basic Sciences, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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20
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Characterizing Gene and Protein Crosstalks in Subjects at Risk of Developing Alzheimer’s Disease: A New Computational Approach. Processes (Basel) 2017. [DOI: 10.3390/pr5030047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
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21
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Wang L, Li J, Zhao H, Hu J, Ping Y, Li F, Lan Y, Xu C, Xiao Y, Li X. Identifying the crosstalk of dysfunctional pathways mediated by lncRNAs in breast cancer subtypes. MOLECULAR BIOSYSTEMS 2016; 12:711-20. [PMID: 26725846 DOI: 10.1039/c5mb00700c] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Crosstalk among abnormal pathways widely occurs in human cancer and generally leads to insensitivity to cancer treatment. How long non-coding RNAs (lncRNAs) participate in the regulation of an abnormal pathway crosstalk in human cancer is largely unknown. Here, we proposed a strategy that integrates mRNA and lncRNA expression profiles for systematic identification of lncRNA-mediated crosstalk among risk pathways in different breast cancer subtypes. We identified 12 to 44 crosstalking pathway pairs mediated by 28 to 49 lncRNAs in four breast cancer subtypes. An LncRNA-mediated crosstalking pathway network in each breast cancer subtype was then constructed. We observed a number of breast cancer subtype-specific crosstalks of risk pathways. These subtype-specific lncRNA-mediated pathway crosstalks largely determined subtype-selective functions. Notably, we observed that lncRNAs mediated the crosstalk of pathways by cooperating with known important protein-coding genes, which play core roles in the deterioration of breast cancer. And we also identified key lncRNAs contributing to the crosstalk network in each subtype. As an example, the low expression of LIFR-AS1 was associated with poor survival in LumB subtype, and its cooperated genes IL1R and TGFBR located at the most upstream of the MAPK signaling pathway shared a common cascade path (p38 MAPKs-MEF2C) that can result in proliferation, differentiation and apoptosis. In summary, we offer an effective way to characterize complex crosstalks mediated by lncRNAs in breast cancer subtypes, which can be applied to other diseases and provide useful information for understanding the pathogenesis of human cancer.
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Affiliation(s)
- Li Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Jing Li
- Department of Ultrasonic medicine, The 1st Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, 150040, China
| | - Hongying Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Jing Hu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Yanyan Ping
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Feng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Yujia Lan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Chaohan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China. and Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, Harbin, 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
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22
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Liu ZP. Identifying network-based biomarkers of complex diseases from high-throughput data. Biomark Med 2016; 10:633-50. [DOI: 10.2217/bmm-2015-0035] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
In this work, we review the main available computational methods of identifying biomarkers of complex diseases from high-throughput data. The emerging omics techniques provide powerful alternatives to measure thousands of molecules in cells in parallel manners. The generated genomic, transcriptomic, proteomic, metabolomic and phenomic data provide comprehensive molecular and cellular information for detecting critical signals served as biomarkers by classifying disease phenotypic states. Networks are often employed to organize these profiles in the identification of biomarkers to deal with complex diseases in diagnosis, prognosis and therapy as well as mechanism deciphering from systematic perspectives. Here, we summarize some representative network-based bioinformatics methods in order to highlight the importance of computational strategies in biomarker discovery.
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Affiliation(s)
- Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science & Engineering, Shandong University, Jinan, Shandong 250061, China
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23
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Puthiyedth N, Riveros C, Berretta R, Moscato P. Identification of Differentially Expressed Genes through Integrated Study of Alzheimer's Disease Affected Brain Regions. PLoS One 2016; 11:e0152342. [PMID: 27050411 PMCID: PMC4822961 DOI: 10.1371/journal.pone.0152342] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 03/11/2016] [Indexed: 11/28/2022] Open
Abstract
Background Alzheimer’s disease (AD) is the most common form of dementia in older adults that damages the brain and results in impaired memory, thinking and behaviour. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. In the past decade, several studies have reported many genes that are associated with AD. This wealth of information has become difficult to follow and interpret as most of the results are conflicting. In that case, it is worth doing an integrated study of multiple datasets that helps to increase the total number of samples and the statistical power in detecting biomarkers. In this study, we present an integrated analysis of five different brain region datasets and introduce new genes that warrant further investigation. Methods The aim of our study is to apply a novel combinatorial optimisation based meta-analysis approach to identify differentially expressed genes that are associated to AD across brain regions. In this study, microarray gene expression data from 161 samples (74 non-demented controls, 87 AD) from the Entorhinal Cortex (EC), Hippocampus (HIP), Middle temporal gyrus (MTG), Posterior cingulate cortex (PC), Superior frontal gyrus (SFG) and visual cortex (VCX) brain regions were integrated and analysed using our method. The results are then compared to two popular meta-analysis methods, RankProd and GeneMeta, and to what can be obtained by analysing the individual datasets. Results We find genes related with AD that are consistent with existing studies, and new candidate genes not previously related with AD. Our study confirms the up-regualtion of INFAR2 and PTMA along with the down regulation of GPHN, RAB2A, PSMD14 and FGF. Novel genes PSMB2, WNK1, RPL15, SEMA4C, RWDD2A and LARGE are found to be differentially expressed across all brain regions. Further investigation on these genes may provide new insights into the development of AD. In addition, we identified the presence of 23 non-coding features, including four miRNA precursors (miR-7, miR570, miR-1229 and miR-6821), dysregulated across the brain regions. Furthermore, we compared our results with two popular meta-analysis methods RankProd and GeneMeta to validate our findings and performed a sensitivity analysis by removing one dataset at a time to assess the robustness of our results. These new findings may provide new insights into the disease mechanisms and thus make a significant contribution in the near future towards understanding, prevention and cure of AD.
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Affiliation(s)
- Nisha Puthiyedth
- Information Based Medicine Program, Hunter Medical Research Institute, New Lambton Heights NSW, Australia
- Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan NSW, Australia
| | - Carlos Riveros
- Clinical Research Design, Information Technology and Statistics Suport Unit, Hunter Medical Research Institute, New Lambton Heights NSW, Australia
| | - Regina Berretta
- Information Based Medicine Program, Hunter Medical Research Institute, New Lambton Heights NSW, Australia
- Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan NSW, Australia
| | - Pablo Moscato
- Information Based Medicine Program, Hunter Medical Research Institute, New Lambton Heights NSW, Australia
- Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan NSW, Australia
- * E-mail:
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24
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Amato SP, Pan F, Schwartz J, Ragan TM. Whole Brain Imaging with Serial Two-Photon Tomography. Front Neuroanat 2016; 10:31. [PMID: 27047350 PMCID: PMC4802409 DOI: 10.3389/fnana.2016.00031] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Accepted: 03/07/2016] [Indexed: 12/12/2022] Open
Abstract
Imaging entire mouse brains at submicron resolution has historically been a challenging undertaking and largely confined to the province of dedicated atlasing initiatives. This has limited systematic investigations into important areas of neuroscience, such as neural circuits, brain mapping and neurodegeneration. In this article, we describe in detail Serial Two-Photon (STP) tomography, a robust, reliable method for imaging entire brains with histological detail. We provide examples of how the basic methodology can be extended to other imaging modalities, such as Optical Coherence Tomography (OCT), in order to provide unique contrast mechanisms. Furthermore, we provide a survey of the research that STP tomography has enabled in the field of neuroscience, provide examples of how this technology enables quantitative whole brain studies, and discuss the current limitations of STP tomography-based approaches.
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25
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Wang D, Jin S, Zou X. Crosstalk between pathways enhances the controllability of signalling networks. IET Syst Biol 2016; 10:2-9. [PMID: 26816393 DOI: 10.1049/iet-syb.2014.0061] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
The control of complex networks is one of the most challenging problems in the fields of biology and engineering. In this study, the authors explored the controllability and control energy of several signalling networks, which consisted of many interconnected pathways, including networks with a bow-tie architecture. On the basis of the theory of structure controllability, they revealed that biological mechanisms, such as cross-pathway interactions, compartmentalisation and so on make the networks easier to fully control. Furthermore, using numerical simulations for two realistic examples, they demonstrated that the control energy of normal networks with crosstalk is lower than in networks without crosstalk. These results indicate that the biological networks are optimally designed to achieve their normal functions from the viewpoint of the control theory. The authors' work provides a comprehensive understanding of the impact of network structures and properties on controllability.
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Affiliation(s)
- Dingjie Wang
- School of Mathematics and Statistics, Wuhan University, Wuhan 430072, People's Republic of China
| | - Suoqin Jin
- School of Mathematics and Statistics, Wuhan University, Wuhan 430072, People's Republic of China
| | - Xiufen Zou
- School of Mathematics and Statistics, Wuhan University, Wuhan 430072, People's Republic of China.
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Rollo JL, Banihashemi N, Vafaee F, Crawford JW, Kuncic Z, Holsinger RMD. Unraveling the mechanistic complexity of Alzheimer's disease through systems biology. Alzheimers Dement 2015; 12:708-18. [PMID: 26703952 DOI: 10.1016/j.jalz.2015.10.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Revised: 08/18/2015] [Accepted: 10/21/2015] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is a complex, multifactorial disease that has reached global epidemic proportions. The challenge remains to fully identify its underlying molecular mechanisms that will enable development of accurate diagnostic tools and therapeutics. Conventional experimental approaches that target individual or small sets of genes or proteins may overlook important parts of the regulatory network, which limits the opportunity of identifying multitarget interventions. Our perspective is that a more complete insight into potential treatment options for AD will only be made possible through studying the disease as a system. We propose an integrative systems biology approach that we argue has been largely untapped in AD research. We present key publications to demonstrate the value of this approach and discuss the potential to intensify research efforts in AD through transdisciplinary collaboration. We highlight challenges and opportunities for significant breakthroughs that could be made if a systems biology approach is fully exploited.
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Affiliation(s)
- Jennifer L Rollo
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; Laboratory of Molecular Neuroscience, Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Department of Molecular Neuroscience, Institute of Neurology, University College of London, London, UK.
| | - Nahid Banihashemi
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - Fatemeh Vafaee
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia
| | | | - Zdenka Kuncic
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - R M Damian Holsinger
- Laboratory of Molecular Neuroscience, Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Discipline of Biomedical Science, School of Medical Sciences, Sydney Medical School, The University of Sydney, Lidcombe, NSW, Australia
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Suphavilai C, Zhu L, Chen JY. A method for developing regulatory gene set networks to characterize complex biological systems. BMC Genomics 2015; 16 Suppl 11:S4. [PMID: 26576648 PMCID: PMC4652563 DOI: 10.1186/1471-2164-16-s11-s4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Background Traditional approaches to studying molecular networks are based on linking genes or proteins. Higher-level networks linking gene sets or pathways have been proposed recently. Several types of gene set networks have been used to study complex molecular networks such as co-membership gene set networks (M-GSNs) and co-enrichment gene set networks (E-GSNs). Gene set networks are useful for studying biological mechanism of diseases and drug perturbations. Results In this study, we proposed a new approach for constructing directed, regulatory gene set networks (R-GSNs) to reveal novel relationships among gene sets or pathways. We collected several gene set collections and high-quality gene regulation data in order to construct R-GSNs in a comparative study with co-membership gene set networks (M-GSNs). We described a method for constructing both global and disease-specific R-GSNs and determining their significance. To demonstrate the potential applications to disease biology studies, we constructed and analysed an R-GSN specifically built for Alzheimer's disease. Conclusions R-GSNs can provide new biological insights complementary to those derived at the protein regulatory network level or M-GSNs. When integrated properly to functional genomics data, R-GSNs can help enable future research on systems biology and translational bioinformatics.
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Yu X, Zeng T, Li G. Integrative enrichment analysis: a new computational method to detect dysregulated pathways in heterogeneous samples. BMC Genomics 2015; 16:918. [PMID: 26556243 PMCID: PMC4641376 DOI: 10.1186/s12864-015-2188-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2015] [Accepted: 11/02/2015] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Pathway enrichment analysis is a useful tool to study biology and biomedicine, due to its functional screening on well-defined biological procedures rather than separate molecules. The measurement of malfunctions of pathways with a phenotype change, e.g., from normal to diseased, is the key issue when applying enrichment analysis on a pathway. The differentially expressed genes (DEGs) are widely focused in conventional analysis, which is based on the great purity of samples. However, the disease samples are usually heterogeneous, so that, the genes with great differential expression variance (DEVGs) are becoming attractive and important to indicate the specific state of a biological system. In the context of differential expression variance, it is still a challenge to measure the enrichment or status of a pathway. To address this issue, we proposed Integrative Enrichment Analysis (IEA) based on a novel enrichment measurement. RESULTS The main competitive ability of IEA is to identify dysregulated pathways containing DEGs and DEVGs simultaneously, which are usually under-scored by other methods. Next, IEA provides two additional assistant approaches to investigate such dysregulated pathways. One is to infer the association among identified dysregulated pathways and expected target pathways by estimating pathway crosstalks. The other one is to recognize subtype-factors as dysregulated pathways associated to particular clinical indices according to the DEVGs' relative expressions rather than conventional raw expressions. Based on a previously established evaluation scheme, we found that, in particular cohorts (i.e., a group of real gene expression datasets from human patients), a few target disease pathways can be significantly high-ranked by IEA, which is more effective than other state-of-the-art methods. Furthermore, we present a proof-of-concept study on Diabetes to indicate: IEA rather than conventional ORA or GSEA can capture the under-estimated dysregulated pathways full of DEVGs and DEGs; these newly identified pathways could be significantly linked to prior-known disease pathways by estimated crosstalks; and many candidate subtype-factors recognized by IEA also have significant relation with the risk of subtypes of genotype-phenotype associations. CONCLUSIONS Totally, IEA supplies a new tool to carry on enrichment analysis in the complicate context of clinical application (i.e., heterogeneity of disease), as a necessary complementary and cooperative approach to conventional ones.
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Affiliation(s)
- Xiangtian Yu
- School of Mathematics, Shandong University, Jinan, 250100, China.
| | - Tao Zeng
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Cell Building Level 3, YueYang Road 320, Shanghai, 200031, China.
| | - Guojun Li
- School of Mathematics, Shandong University, Jinan, 250100, China.
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Bai J, Li Y, Shao T, Zhao Z, Wang Y, Wu A, Chen H, Li S, Jiang C, Xu J, Li X. Integrating analysis reveals microRNA-mediated pathway crosstalk among Crohn's disease, ulcerative colitis and colorectal cancer. MOLECULAR BIOSYSTEMS 2015; 10:2317-28. [PMID: 24949825 DOI: 10.1039/c4mb00169a] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Inflammatory bowel disease (IBD), which can increase the risk of colorectal cancer (CRC), includes two primary subtypes, ulcerative colitis (UC) and Crohn's disease (CD). Although several individual genes involved in inflammation or cancer characterization have been identified, it is still difficult to elucidate functional relationship details between the molecules underlying pathogenesis at the system level. The global effect of miRNAs on genes or their involved functions is also poorly understood. We first integrated genome-wide gene expression profiles and biological pathway information to explore the underlying associations among UC, CD and CRC at the function and gene level. After identifying the pathways regulated by miRNAs, a global map of miRNA-mediated pathway crosstalk shared by the three diseases was further constructed to vertically explain the links of three level alterations. The three types of diseases have close associations with each other at the levels of function, gene and miRNA regulation. Several key biological pathways are involved in the three diseases, related to the immune system and inflammation, metabolism, or cell proliferation and apoptosis etc. Moreover, miRNAs exhibit dominant effects on multiple pathways. It is worth noting that UC shows relatively close associations with CD and CRC at the three levels. Finally, the miRNAs could mediate the crosstalk within or between pathways. For example, hsa-miR-125b, hsa-miR-335 and hsa-miR-155 mediated the crosstalk between three metabolic pathways. The crosstalk within the Toll-like receptor signaling pathway could be mediated by hsa-miR-124, hsa-miR-146a and hsa-mir-221/222. Our results make sense for the prevention and treatment of intestinal-related chronic inflammation or cancer.
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Affiliation(s)
- Jing Bai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
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Talwar P, Sinha J, Grover S, Rawat C, Kushwaha S, Agarwal R, Taneja V, Kukreti R. Dissecting Complex and Multifactorial Nature of Alzheimer's Disease Pathogenesis: a Clinical, Genomic, and Systems Biology Perspective. Mol Neurobiol 2015; 53:4833-64. [PMID: 26351077 DOI: 10.1007/s12035-015-9390-0] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Accepted: 08/11/2015] [Indexed: 01/14/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by loss of memory and other cognitive functions. AD can be classified into familial AD (FAD) and sporadic AD (SAD) based on heritability and into early onset AD (EOAD) and late onset AD (LOAD) based on age of onset. LOAD cases are more prevalent with genetically complex architecture. In spite of significant research focused on understanding the etiological mechanisms, search for diagnostic biomarker(s) and disease-modifying therapy is still on. In this article, we aim to comprehensively review AD literature on established etiological mechanisms including role of beta-amyloid and apolipoprotein E (APOE) along with promising newer etiological factors such as epigenetic modifications that have been associated with AD suggesting its multifactorial nature. As genomic studies have recently played a significant role in elucidating AD pathophysiology, a systematic review of findings from genome-wide linkage (GWL), genome-wide association (GWA), genome-wide expression (GWE), and epigenome-wide association studies (EWAS) was conducted. The availability of multi-dimensional genomic data has further coincided with the advent of computational and network biology approaches in recent years. Our review highlights the importance of integrative approaches involving genomics and systems biology perspective in elucidating AD pathophysiology. The promising newer approaches may provide reliable means of early and more specific diagnosis and help identify therapeutic interventions for LOAD.
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Affiliation(s)
- Puneet Talwar
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB) Campus, New Delhi, India.,Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi, 110 007, India
| | - Juhi Sinha
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi, 110 007, India
| | - Sandeep Grover
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi, 110 007, India.,Department of Paediatrics, Division of Pneumonology-Immunology, Charité University Medical Centre, Berlin, Germany
| | - Chitra Rawat
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB) Campus, New Delhi, India.,Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi, 110 007, India
| | - Suman Kushwaha
- Institute of Human Behaviour and Allied Sciences (IHBAS), Delhi, India
| | - Rachna Agarwal
- Institute of Human Behaviour and Allied Sciences (IHBAS), Delhi, India
| | - Vibha Taneja
- Department of Research, Sir Ganga Ram Hospital, New Delhi, India
| | - Ritushree Kukreti
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB) Campus, New Delhi, India. .,Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi, 110 007, India.
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Han J, Li C, Yang H, Xu Y, Zhang C, Ma J, Shi X, Liu W, Shang D, Yao Q, Zhang Y, Su F, Feng L, Li X. A novel dysregulated pathway-identification analysis based on global influence of within-pathway effects and crosstalk between pathways. J R Soc Interface 2015; 12:20140937. [PMID: 25551156 DOI: 10.1098/rsif.2014.0937] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Identifying dysregulated pathways from high-throughput experimental data in order to infer underlying biological insights is an important task. Current pathway-identification methods focus on single pathways in isolation; however, consideration of crosstalk between pathways could improve our understanding of alterations in biological states. We propose a novel method of pathway analysis based on global influence (PAGI) to identify dysregulated pathways, by considering both within-pathway effects and crosstalk between pathways. We constructed a global gene–gene network based on the relationships among genes extracted from a pathway database. We then evaluated the extent of differential expression for each gene, and mapped them to the global network. The random walk with restart algorithm was used to calculate the extent of genes affected by global influence. Finally, we used cumulative distribution functions to determine the significance values of the dysregulated pathways. We applied the PAGI method to five cancer microarray datasets, and compared our results with gene set enrichment analysis and five other methods. Based on these analyses, we demonstrated that PAGI can effectively identify dysregulated pathways associated with cancer, with strong reproducibility and robustness. We implemented PAGI using the freely available R-based and Web-based tools (http://bioinfo.hrbmu.edu.cn/PAGI).
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Xin J, Ren X, Chen L, Wang Y. Identifying network biomarkers based on protein-protein interactions and expression data. BMC Med Genomics 2015; 8 Suppl 2:S11. [PMID: 26044366 PMCID: PMC4460625 DOI: 10.1186/1755-8794-8-s2-s11] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Identifying effective biomarkers to battle complex diseases is an important but challenging task in biomedical research today. Molecular data of complex diseases is increasingly abundant due to the rapid advance of high throughput technologies. However, a great gap remains in identifying the massive molecular data to phenotypic changes, in particular, at a network level, i.e., a novel method for identifying network biomarkers is in pressing need to accurately classify and diagnose diseases from molecular data and shed light on the mechanisms of disease pathogenesis. Rather than seeking differential genes at an individual-molecule level, here we propose a novel method for identifying network biomarkers based on protein-protein interaction affinity (PPIA), which identify the differential interactions at a network level. Specifically, we firstly define PPIAs by estimating the concentrations of protein complexes based on the law of mass action upon gene expression data. Then we select a small and non-redundant group of protein-protein interactions and single proteins according to the PPIAs, that maximizes the discerning ability of cases from controls. This method is mathematically formulated as a linear programming, which can be efficiently solved and guarantees a globally optimal solution. Extensive results on experimental data in breast cancer demonstrate the effectiveness and efficiency of the proposed method for identifying network biomarkers, which not only can accurately distinguish the phenotypes but also provides significant biological insights at a network or pathway level. In addition, our method provides a new way to integrate static protein-protein interaction information with dynamical gene expression data.
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Bioinformatics approach reveals systematic mechanism underlying lung adenocarcinoma. TUMORI JOURNAL 2015; 101:281-6. [PMID: 26045113 DOI: 10.5301/tj.5000278] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/30/2015] [Indexed: 01/22/2023]
Abstract
BACKGROUND The purpose of this work was to explore the systematic molecular mechanism of lung adenocarcinoma and gain a deeper insight into it. METHODS Comprehensive bioinformatics methods were applied. Initially, significant differentially expressed genes (DEGs) were analyzed from the Affymetrix microarray data (GSE27262) deposited in the Gene Expression Omnibus (GEO). Subsequently, gene ontology (GO) analysis was performed using online Database for Annotation, Visualization and Integration Discovery (DAVID) software. Finally, significant pathway crosstalk was investigated based on the information derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. RESULTS According to our results, the N-terminal globular domain of the type X collagen (COL10A1) gene and transmembrane protein 100 (TMEM100) gene were identified to be the most significant DEGs in tumor tissue compared with the adjacent normal tissues. The main GO categories were biological process, cellular component and molecular function. In addition, the crosstalk was significantly different between non-small cell lung cancer pathways and inositol phosphate metabolism pathway, focal adhesion signal pathway, vascular smooth muscle contraction signal pathway, peroxisome proliferator-activated receptor (PPAR) signaling pathway and calcium signaling pathway in tumor. CONCLUSIONS Dysfunctional genes and pathways may play key roles in the progression and development of lung adenocarcinoma. Our data provide a systematic perspective for understanding this mechanism and may be helpful in discovering an effective treatment for lung adenocarcinoma.
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Pathway crosstalk analysis of high-metastasis lung cancer cells. TUMORI JOURNAL 2015; 0:0. [PMID: 25983092 DOI: 10.5301/tj.5000292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/02/2015] [Indexed: 11/20/2022]
Abstract
BACKGROUND The aim of this study was to investigate the important role of pathway crosstalk and pathway dysfunction in the high-metastasis process of lung cancer cells, by using the microarray expression profiles of lung cancer cells at different metastasis levels. METHODS The gene expression profile GSE10096 was downloaded from the Gene Expression Omnibus database, including 4 nonmetastasis samples, 3 low-metastasis samples (M1) and 3 high-metastasis samples (M5) of lung cancer cells. After the conversion from probe level to expression values using Jetset, the data were identified by limma package in R language to screen differentially expressed genes (DEGs). The pathways of DEGs were further enriched by the Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein-protein interaction (PPI) network of genes related to the core pathway (pathway in cancer) and its neighbor pathways was constructed. Based on the PPI network, significantly changed pathway crosstalk and pathways were analyzed. RESULTS Compared with those in the M1 lung cancer cells, the pathways hsa00564 (glycerophospholipid metabolism) and hsa0098 (metabolism of xenobiotics by cytochrome P450) of the M5 lung cancer cells showed significant functional changes. The dysfunction of pathway crosstalk mainly occurred between pathways hsa0098 and hsa04916 (melanogenesis pathway) and other pathways. CONCLUSIONS The results of our analysis indicate the significance of pathway crosstalk dysfunction and pathway dysfunction of M1 and M5 lung cancer cells as shown by bioinformatics methods. The present findings have the potential to lead to the study of the mechanisms of lung cancer in future.
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Liu ZP. Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data. Curr Genomics 2015; 16:3-22. [PMID: 25937810 PMCID: PMC4412962 DOI: 10.2174/1389202915666141110210634] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Revised: 09/05/2014] [Accepted: 09/05/2014] [Indexed: 12/17/2022] Open
Abstract
Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented.
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Affiliation(s)
- Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
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The construction of common and specific significance subnetworks of Alzheimer's disease from multiple brain regions. BIOMED RESEARCH INTERNATIONAL 2015; 2015:394260. [PMID: 25866779 PMCID: PMC4383160 DOI: 10.1155/2015/394260] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Accepted: 10/07/2014] [Indexed: 01/09/2023]
Abstract
Alzheimer's disease (AD) is a progressively and fatally neurodegenerative disorder and leads to irreversibly cognitive and memorial damage in different brain regions. The identification and analysis of the dysregulated pathways and subnetworks among affected brain regions will provide deep insights for the pathogenetic mechanism of AD. In this paper, commonly and specifically significant subnetworks were identified from six AD brain regions. Protein-protein interaction (PPI) data were integrated to add molecular biological information to construct the functional modules of six AD brain regions by Heinz algorithm. Then, the simulated annealing algorithm based on edge weight is applied to predicting and optimizing the maximal scoring networks for common and specific genes, respectively, which can remove the weak interactions and add the prediction of strong interactions to increase the accuracy of the networks. The identified common subnetworks showed that inflammation of the brain nerves is one of the critical factors of AD and calcium imbalance may be a link among several causative factors in AD pathogenesis. In addition, the extracted specific subnetworks for each brain region revealed many biologically functional mechanisms to understand AD pathogenesis.
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Pathway crosstalk analysis of non-small cell lung cancer based on microarray gene expression profiling. TUMORI JOURNAL 2015; 101:111-6. [PMID: 25702679 DOI: 10.5301/tj.5000225] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/11/2014] [Indexed: 02/03/2023]
Abstract
AIMS AND BACKGROUND Lung cancer is characterized by uncontrolled cell growth in the lung tissue. A major challenge in cancer research is the biological interpretation of the complexity of cancer somatic mutation profiles. This study examines the role of pathway crosstalk in the metastatic process of lung cancer cells based on DNA microarray analysis. METHODS We downloaded the gene expression profile GSE10096 from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified and the gene functions of selected DEGs were further analyzed. After KEGG pathway analysis, dysfunctional pathways and dysfunctional crosstalk between pathways in two types of lung cancer cells (low metastasis, M1, and high metastasis, M5) were examined. RESULTS A total of 13433 genes were filtered as DEGs, and after pathway analysis, 108 signaling pathways related to cancer signaling pathways were screened, including a host pathway hsa05223 and 79 neighbor pathways. Dysfunctional crosstalk analysis of pathways revealed that pathway crosstalk dysfunction of M1 and M5 cells mainly occurred between hsa05223 (non-small cell lung cancer) and hsa04310 (Wnt signaling pathway), and between non-small cell lung cancer and hsa04520 (adherens junction), respectively. Significant pathway crosstalk dysfunction also existed between adherens junction and other classical signaling pathways such as hsa04110 (cell cycle), hsa04310 (Wnt signaling pathway), hsa04350 (TGF-beta signaling pathway), and hsa04630 (Jak-STAT signaling pathway). CONCLUSIONS Our discovery will help to elucidate the molecular mechanisms of the high carcinogenic and metastatic potential of lung cancer cells. In addition, it will pave the way to developing effective therapies for lung cancer.
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Chen QY, Jiao DM, Wang LF, Wang L, Hu HZ, Song J, Yan J, Wu LJ, Shi JG. Curcumin inhibits proliferation–migration of NSCLC by steering crosstalk between a Wnt signaling pathway and an adherens junction via EGR-1. MOLECULAR BIOSYSTEMS 2015; 11:859-68. [PMID: 25578635 DOI: 10.1039/c4mb00336e] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Crosstalk between Wnt pathways and adherens junction is associated with NSCLC. Curcumin blocks cell proliferation and migration in non-small cell cancer by regulating EGR-1.
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Affiliation(s)
- Qing-yong Chen
- Department of Respiratory Disease
- The 117th hospital of PLA
- Hangzhou
- P. R. China
| | - De-min Jiao
- Department of Respiratory Disease
- The 117th hospital of PLA
- Hangzhou
- P. R. China
| | - Li-feng Wang
- Department of Information
- The 117th hospital of PLA
- Hangzhou
- P. R. China
| | - Lishan Wang
- FengHe (ShangHai) Information Technology Co., Ltd
- Bio-X Institutes
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education)
- Shanghai Jiao Tong University
- Shanghai 200030
| | - Hui-zhen Hu
- Department of Respiratory Disease
- The 117th hospital of PLA
- Hangzhou
- P. R. China
| | - Jia Song
- Department of Respiratory Disease
- The 117th hospital of PLA
- Hangzhou
- P. R. China
| | - Jie Yan
- Department of Respiratory Disease
- The 117th hospital of PLA
- Hangzhou
- P. R. China
| | - Li-jun Wu
- Department of Respiratory Disease
- The 117th hospital of PLA
- Hangzhou
- P. R. China
| | - Jian-guo Shi
- Department of Oncology Disease
- The 117th hospital of PLA
- Hangzhou
- P. R. China
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Pathway crosstalk analysis of microarray gene expression profile in human hepatocellular carcinoma. Pathol Oncol Res 2014; 21:563-9. [PMID: 25480734 DOI: 10.1007/s12253-014-9855-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2013] [Accepted: 10/14/2014] [Indexed: 01/03/2023]
Abstract
Liver cancer is the third most common cause of cancer death in the world. Hepatocellular carcinoma (HCC) is the main pathological types in liver cancer, which amounts to 70-85 % of primary liver cancer in the world and 90 % in China. The aim of this study was to establish a PPI network and a pathway crosstalk network to isolate important dysfunctional pathways which play an important role in the pathogenesis of HCC. System biology approach was used in this research. A PPI network was firstly built and then a dysfunctional crosstalk network of HCC related pathways was constructed. Several important significant dysfunctional crosstalk pathways were identified. Basal transcription factors (hsa03022), Glycerophospholipid metabolism (hsa00564) and Metabolism of xenobiotics by cytochrome P450 (hsa00980) were significantly interact with Pathway in cancer (hsa05200). Besides, pathway Axon guidance (hsa04360) was also dysfunctional crosstalk with Pathway in cancer (hsa05200). The crosstalks among these pathways reveal some evidence that the pathways closely cooperated and play important tasks in HCC progression. Besides, the pathway hsa04360 dysfunctional crosstalk with the hsa05200 indicates there would be a same mechanism for HCC invasion and migration.
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Wang H, Wang C, Zhang L, Lu Y, Duan Q, Gong Z, Liang A, Song H, Wang L. Analysis of the protein-protein interaction networks of differentially expressed genes in pulmonary embolism. Mol Med Rep 2014; 11:2527-33. [PMID: 25434468 PMCID: PMC4337743 DOI: 10.3892/mmr.2014.3006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Accepted: 10/24/2014] [Indexed: 12/27/2022] Open
Abstract
The aim of the present study was to explore the function and interaction of differentially expressed genes (DEGs) in pulmonary embolism (PE). The gene expression profile GSE13535, was downloaded from the Gene Expression Omnibus database. The DEGs 2 and 18 h post-PE initiation were identified using the affy package in R software. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DEGs were analyzed using Database for Annotation Visualization and Integrated Discovery (DAVID) online analytical tools. In addition, protein-protein interaction (PPI) networks of the DEGs were constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins. The PPI network at 18 h was modularized using ClusterONE, and a functional enrichment analysis of the DEGs in the top three modules was performed with DAVID. Overall, 80 and 346 DEGs were identified 2 and 18 h after PE initiation, respectively. The KEGG pathways, including chemokine signaling and toll-like receptor signaling, were shown to be significantly enriched. The five highest degree nodes in the PPI networks at 2 or 18 h were screened. The module analysis of the PPI network at 18 h revealed 11 hub nodes. A Gene Ontology terms analysis demonstrated that the DEGs in the top three modules were associated with the inflammatory, defense and immune responses. The results of the present study suggest that the DEGs identified, including chemokine-related genes TFPI2 and TNF, may be potential target genes for the treatment of PE. The chemokine signaling pathway, inflammatory response and immune response were explored, and it may be suggested that these pathways have important roles in PE.
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Affiliation(s)
- Hao Wang
- Department of Family Medicine, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, P.R. China
| | - Chen Wang
- Department of Family Medicine, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, P.R. China
| | - Lei Zhang
- Department of Family Medicine, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, P.R. China
| | - Yinghua Lu
- Department of Family Medicine, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, P.R. China
| | - Qianglin Duan
- Department of Cardiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, P.R. China
| | - Zhu Gong
- Department of Cardiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, P.R. China
| | - Aibin Liang
- Department of Hematology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, P.R. China
| | - Haoming Song
- Department of Cardiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, P.R. China
| | - Lemin Wang
- Department of Cardiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, P.R. China
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Wang W, Xu Z, Fu L, Liu W, Li X. Pathogenesis analysis of pituitary adenoma based on gene expression profiling. Oncol Lett 2014; 8:2423-2430. [PMID: 25360166 PMCID: PMC4214395 DOI: 10.3892/ol.2014.2613] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Accepted: 09/08/2014] [Indexed: 12/21/2022] Open
Abstract
The aim of the current study was to investigate the pathogenesis of pituitary adenoma through screening of the differentially-expressed genes (DEGs) and proteins in normal pituitary and pituitary adenoma tissues, and analyzing the interactions among them. Following the acquisition of gene expression profiling data from a public functional genomics data repository, Gene Expression Omnibus, DEGs were screened in normal pituitary and pituitary adenoma tissues. Upregulated and downregulated DEGs were further identified through gene ontology functional enrichment analysis. Subsequently, the DEGs were mapped to the Search Tool for the Retrieval of Interacting Genes database, and the protein-protein interaction (PPI) networks of the upregulated and downregulated DEGs were constructed. Finally, the functional modules of the PPI network of the downregulated DEGs were analyzed. In total, 211 upregulated and 413 downregulated DEGs were screened between the normal pituitary and pituitary adenoma samples. Downregulated DEGs were associated with certain functions, including the immune response, hormone regulation and cell proliferation. Upregulated genes were associated with cation transport functions. Five modules were acquired from the PPI network of the downregulated DEGs. Transcription factors, including signal transducer and activator of transcription 3 (STAT3), interleukin 6 (IL-6), B-cell lymphoma 6 protein, early growth response 1, POU1F1, jun B proto-oncogene and FOS were the core nodes in the functional modules. In summary, the DEGs and proteins were identified through screening gene expression profiling and PPI networks. The results of the present study indicated that low expression levels of hormone- and immune-related genes facilitated the occurrence of pituitary adenoma. Low expression levels of IL-6 and STAT3 were significant in the dysimmunity of pituitary adenoma. Furthermore, the low expression level of POU1F1 contributed to the reduction in pituitary hormone secretion.
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Affiliation(s)
- Weimin Wang
- School of Medicine, Shandong University, Jinan, Shandong 250012, P.R. China ; Department of Neurosurgery, Qingdao Municipal Hospital, Qingdao, Shandong 266071, P.R. China
| | - Zhiming Xu
- Department of Neurosurgery, Qingdao Municipal Hospital, Qingdao, Shandong 266071, P.R. China
| | - Li Fu
- Department of General Surgery, Qingdao Municipal Hospital, Qingdao, Shandong 266071, P.R. China
| | - Wei Liu
- Department of Neurosurgery, Qingdao Municipal Hospital, Qingdao, Shandong 266071, P.R. China
| | - Xingang Li
- School of Medicine, Shandong University, Jinan, Shandong 250012, P.R. China ; Department of Neurosurgery, Qilu Hospital of Shandong University, Jinan, Shandong 250012, P.R. China
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Chen WY, Wu F, You ZY, Zhang ZM, Guo YL, Zhong LX. Analyzing the differentially expressed genes and pathway cross-talk in aggressive breast cancer. J Obstet Gynaecol Res 2014; 41:132-40. [PMID: 25227254 DOI: 10.1111/jog.12495] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Accepted: 05/03/2014] [Indexed: 01/03/2023]
Abstract
AIM The aim of this study was to explore the genes and pathways involved in the aggressive breast cancer cells. METHODS The gene expression profiles of GSE40057, including four aggressive breast cell lines and six less aggressive cell lines, were downloaded from the Gene Expression Omnibus (GEO) database. The gene differential expression analysis was carried out with limma software with the method of Bayes for multiple tests. The gene ontology (GO) term enrichment and pathway cross-talk analysis were performed with the online tool of DAVID and Cytoscape software. RESULTS A total of 401 differentially expressed genes (DEG), such as pentraxin 3 (PTX3), snail family zinc finger 2 (SNAI2), interleukin-8/6 (IL-8/6), osteonectin (SPARC), matrix metallopeptidase-1 (MMP-1) and Ras-related protein Rab-25 (Rab 25), were identified between aggressive and less aggressive cell lines. They were mainly enriched in the GO terms of response to wounding, negative regulation of cell proliferation and calcium binding. Pathways in cancer dysfunctionally interacted with glyoxylate and dicarboxylate metabolism (P < 0.0001), basal transcription factors (P < 0.0001), tyrosine metabolism (P < 0.0001), calcium signaling pathway (P = 0.0021), FcγR-mediated phagocytosis (P = 0.0022), metabolism of xenobiotics by cytochrome P450 (P = 0.0097) and phagosome (P = 0.0102). CONCLUSION The screened aggressive cancer-associated DEG (PTX3, SNAI2, IL-8/6, SPARC, MMP-1 and Rab25) and significant pathways (calcium signaling pathway, tyrosine metabolism, alanine, aspartate and glutamate metabolism) give us new insights into the mechanism of aggressive breast cancer cells, and these DEG may become promising target genes in the treatment of metastatic breast cancer.
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Affiliation(s)
- Wen-Yan Chen
- Department of Oncology, The First Affiliated Hospital of Nanchang University, Nanchang, China
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43
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Identifying candidate genes for discrimination of ulcerative colitis and Crohn’s disease. Mol Biol Rep 2014; 41:6349-55. [DOI: 10.1007/s11033-014-3469-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2013] [Accepted: 06/14/2014] [Indexed: 01/01/2023]
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44
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Pathway and network analysis in proteomics. J Theor Biol 2014; 362:44-52. [PMID: 24911777 DOI: 10.1016/j.jtbi.2014.05.031] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2014] [Revised: 05/15/2014] [Accepted: 05/21/2014] [Indexed: 12/14/2022]
Abstract
Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics.
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Li GL, Xu XH, Wang BA, Yao YM, Qin Y, Bai SR, Rong J, Deng T, Hu YH. Analysis of protein-protein interaction network and functional modules on primary osteoporosis. Eur J Med Res 2014; 19:15. [PMID: 24656062 PMCID: PMC3994448 DOI: 10.1186/2047-783x-19-15] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2013] [Accepted: 02/19/2014] [Indexed: 11/22/2022] Open
Abstract
Background Primary osteoporosis is an age-related disease, and the main cause of this disease is the failure of bone homeostasis. Previous studies have shown that primary osteoporosis is associated with gene mutations. To explore the functional modules of the PPI (protein-protein interaction) network of differentially expressed genes (DEGs), and the related pathways participating in primary osteoporosis. Methods The gene expression profile of primary osteoporosis GSE35956 was downloaded from the GEO (Gene Expression Omnibus) database and included five MSC (mesenchymal stem cell) specimens of normal osseous tissue and five MSC specimens of osteoporosis. The DEGs between the two types of MSC specimens were identified by the samr package in R language. In addition, the functions and pathways of DEGs were enriched. Then the DEGs were mapped to String to acquire PPI pairs and the PPI network was constructed with by these PPI pairs. Topological properties of the network were calculated by Network Analyzer, and modules in the network were screened by Cluster ONE software. Subsequently, the fronting five modules whose P-value was less than 1.0e-05 were identified and function analysis was conducted. Results A total of 797 genes were filtered as DEGs from these ten specimens of GSE35956 with 660 up-regulated genes and 137 down-regulated genes. Meanwhile, up-regulated DEGs were mainly enriched in functions and pathways related to cell cycle and DNA replication. Furthermore, there were 4,135 PPI pairs and 377 nodes in the PPI network. Four modules were enriched in different pathways, including cell cycle and DNA replication pathway in module 2. Conclusions In this paper, we explored the genes and pathways involved in primary osteoporosis based on gene expression profiles, and the present findings have the potential to be used clinically for the future treatment of primary osteoporosis.
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Affiliation(s)
| | | | - Bing-Ang Wang
- Department of Geriatrics, Chengdu Military General Hospital, No, 270, Rongdu Avenue, Jinniu District, Chengdu 610083, China.
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Talwar P, Silla Y, Grover S, Gupta M, Agarwal R, Kushwaha S, Kukreti R. Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease. BMC Genomics 2014; 15:199. [PMID: 24628925 PMCID: PMC4028079 DOI: 10.1186/1471-2164-15-199] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2013] [Accepted: 02/21/2014] [Indexed: 01/28/2023] Open
Abstract
Background Alzheimer’s disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling. Results Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (SR) based method followed by prioritization using clusters derived from PPI network. SR for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes. Conclusions With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-199) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | | | | | | | | | - Ritushree Kukreti
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, Delhi 110 007, India.
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Liu ZP, Zhang W, Horimoto K, Chen L. Gaussian graphical model for identifying significantly responsive regulatory networks from time course high-throughput data. IET Syst Biol 2014; 7:143-52. [PMID: 24067414 DOI: 10.1049/iet-syb.2012.0062] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
With rapid accumulation of functional relationships between biological molecules, knowledge-based networks have been constructed and stocked in many databases. These networks provide curated and comprehensive information for functional linkages among genes and proteins, whereas their activities are highly related with specific phenotypes and conditions. To evaluate a knowledge-based network in a specific condition, the consistency between its structure and conditionally specific gene expression profiling data are an important criterion. In this study, the authors propose a Gaussian graphical model to evaluate the documented regulatory networks by the consistency between network architectures and time course gene expression profiles. They derive a dynamic Bayesian network model to evaluate gene regulatory networks in both simulated and true time course microarray data. The regulatory networks are evaluated by matching network structure with gene expression to achieve consistency measurement. To demonstrate the effectiveness of the authors method, they identify significant regulatory networks in response to the time course of circadian rhythm. The knowledge-based networks are screened and ranked by their structural consistencies with dynamic gene expression profiling.
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48
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Nguyen TP, Caberlotto L, Morine MJ, Priami C. Network analysis of neurodegenerative disease highlights a role of Toll-like receptor signaling. BIOMED RESEARCH INTERNATIONAL 2014; 2014:686505. [PMID: 24551850 PMCID: PMC3914352 DOI: 10.1155/2014/686505] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2013] [Revised: 11/20/2013] [Accepted: 11/30/2013] [Indexed: 01/23/2023]
Abstract
Despite significant advances in the study of the molecular mechanisms altered in the development and progression of neurodegenerative diseases (NDs), the etiology is still enigmatic and the distinctions between diseases are not always entirely clear. We present an efficient computational method based on protein-protein interaction network (PPI) to model the functional network of NDs. The aim of this work is fourfold: (i) reconstruction of a PPI network relating to the NDs, (ii) construction of an association network between diseases based on proximity in the disease PPI network, (iii) quantification of disease associations, and (iv) inference of potential molecular mechanism involved in the diseases. The functional links of diseases not only showed overlap with the traditional classification in clinical settings, but also offered new insight into connections between diseases with limited clinical overlap. To gain an expanded view of the molecular mechanisms involved in NDs, both direct and indirect connector proteins were investigated. The method uncovered molecular relationships that are in common apparently distinct diseases and provided important insight into the molecular networks implicated in disease pathogenesis. In particular, the current analysis highlighted the Toll-like receptor signaling pathway as a potential candidate pathway to be targeted by therapy in neurodegeneration.
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Affiliation(s)
- Thanh-Phuong Nguyen
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto, Italy
| | - Laura Caberlotto
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto, Italy
| | - Melissa J. Morine
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto, Italy
- Department of Mathematics, University of Trento, Via Sommarive, 14-38123 Povo, Italy
| | - Corrado Priami
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto, Italy
- Department of Mathematics, University of Trento, Via Sommarive, 14-38123 Povo, Italy
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Meng F, Dai E, Yu X, Zhang Y, Chen X, Liu X, Wang S, Wang L, Jiang W. Constructing and characterizing a bioactive small molecule and microRNA association network for Alzheimer's disease. J R Soc Interface 2013; 11:20131057. [PMID: 24352679 DOI: 10.1098/rsif.2013.1057] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's disease (AD) is an incurable neurodegenerative disorder. Much effort has been devoted to developing effective therapeutic agents. Recently, targeting microRNAs (miRNAs) with small molecules has become a novel therapy for human diseases. In this study, we present a systematic computational approach to construct a bioactive Small molecule and miRNA association Network in AD (SmiRN-AD), which is based on the gene expression signatures of bioactive small molecule perturbation and AD-related miRNA regulation. We also performed topological and functional analysis of the SmiRN-AD from multiple perspectives. At the significance level of p ≤ 0.01, 496 small molecule-miRNA associations, including 25 AD-related miRNAs and 275 small molecules, were recognized and used to construct the SmiRN-AD. The drugs that were connected with the same miRNA tended to share common drug targets (p = 1.72 × 10(-4)) and belong to the same therapeutic category (p = 4.22 × 10(-8)). The miRNAs that were linked to the same small molecule regulated more common miRNA targets (p = 6.07 × 10(-3)). Further analysis of the positive connections (quinostatin and miR-148b, amantadine and miR-15a) and the negative connections (melatonin and miR-30e-5p) indicated that our large-scale predictions afforded specific biological insights into AD pathogenesis and therapy. This study proposes a holistic strategy for deciphering the associations between small molecules and miRNAs in AD, which may be helpful for developing a novel effective miRNA-associated therapeutic strategy for AD. A comprehensive database for the SmiRN-AD and the differential expression patterns of the miRNA targets in AD is freely available at http://bioinfo.hrbmu.edu.cn/SmiRN-AD/.
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Affiliation(s)
- Fanlin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, , 194 Xuefu Road, Harbin 150081, People's Republic of China
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Kikuchi M, Ogishima S, Miyamoto T, Miyashita A, Kuwano R, Nakaya J, Tanaka H. Identification of unstable network modules reveals disease modules associated with the progression of Alzheimer's disease. PLoS One 2013; 8:e76162. [PMID: 24348898 PMCID: PMC3858171 DOI: 10.1371/journal.pone.0076162] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2013] [Accepted: 08/20/2013] [Indexed: 11/18/2022] Open
Abstract
Alzheimer's disease (AD), the most common cause of dementia, is associated with aging, and it leads to neuron death. Deposits of amyloid β and aberrantly phosphorylated tau protein are known as pathological hallmarks of AD, but the underlying mechanisms have not yet been revealed. A high-throughput gene expression analysis previously showed that differentially expressed genes accompanying the progression of AD were more down-regulated than up-regulated in the later stages of AD. This suggested that the molecular networks and their constituent modules collapsed along with AD progression. In this study, by using gene expression profiles and protein interaction networks (PINs), we identified the PINs expressed in three brain regions: the entorhinal cortex (EC), hippocampus (HIP) and superior frontal gyrus (SFG). Dividing the expressed PINs into modules, we examined the stability of the modules with AD progression and with normal aging. We found that in the AD modules, the constituent proteins, interactions and cellular functions were not maintained between consecutive stages through all brain regions. Interestingly, the modules were collapsed with AD progression, specifically in the EC region. By identifying the modules that were affected by AD pathology, we found the transcriptional regulation-associated modules that interact with the proteasome-associated module via UCHL5 hub protein, which is a deubiquitinating enzyme. Considering PINs as a system made of network modules, we found that the modules relevant to the transcriptional regulation are disrupted in the EC region, which affects the ubiquitin-proteasome system.
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Affiliation(s)
- Masataka Kikuchi
- Department of Bioinformatics, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
| | - Soichi Ogishima
- Department of Bioclinical Informatics, Tohoku Medical Megabank Organization, Tohoku University, Sendai-shi, Miyagi, Japan
- * E-mail:
| | - Tadashi Miyamoto
- Department of Bioinformatics, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
| | - Akinori Miyashita
- Bioresource Science Branch, Center for Bioresources, Brain Research Institute, Niigata University, Niigata-shi, Niigata, Japan
| | - Ryozo Kuwano
- Bioresource Science Branch, Center for Bioresources, Brain Research Institute, Niigata University, Niigata-shi, Niigata, Japan
| | - Jun Nakaya
- Department of Bioclinical Informatics, Tohoku Medical Megabank Organization, Tohoku University, Sendai-shi, Miyagi, Japan
| | - Hiroshi Tanaka
- Department of Bioinformatics, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
- Department of Bioclinical Informatics, Tohoku Medical Megabank Organization, Tohoku University, Sendai-shi, Miyagi, Japan
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