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Shukla H, John D, Banerjee S, Tiwari AK. Drug repurposing for neurodegenerative diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 207:249-319. [PMID: 38942541 DOI: 10.1016/bs.pmbts.2024.03.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Neurodegenerative diseases (NDDs) are neuronal problems that include the brain and spinal cord and result in loss of sensory and motor dysfunction. Common NDDs include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS) etc. The occurrence of these diseases increases with age and is one of the challenging problems among elderly people. Though, several scientific research has demonstrated the key pathologies associated with NDDs still the underlying mechanisms and molecular details are not well understood and need to be explored and this poses a lack of effective treatments for NDDs. Several lines of evidence have shown that NDDs have a high prevalence and affect more than a billion individuals globally but still, researchers need to work forward in identifying the best therapeutic target for NDDs. Thus, several researchers are working in the directions to find potential therapeutic targets to alter the disease pathology and treat the diseases. Several steps have been taken to identify the early detection of the disease and drug repurposing for effective treatment of NDDs. Moreover, it is logical that current medications are being evaluated for their efficacy in treating such disorders; therefore, drug repurposing would be an efficient, safe, and cost-effective way in finding out better medication. In the current manuscript we discussed the utilization of drugs that have been repurposed for the treatment of AD, PD, HD, MS, and ALS.
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Affiliation(s)
- Halak Shukla
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Diana John
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Shuvomoy Banerjee
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Anand Krishna Tiwari
- Genetics and Developmental Biology Laboratory, Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India.
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Edwards CV, Ferri GM, Villegas-Galaviz J, Ghosh S, Bawa PS, Wang F, Klimtchuk E, Ajayi TB, Morgan GJ, Prokaeva T, Staron A, Ruberg FL, Sanchorawala V, Giadone RM, Murphy GJ. Abnormal global longitudinal strain and reduced serum inflammatory markers in cardiac AL amyloidosis patients without significant amyloid fibril deposition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.14.584987. [PMID: 38558967 PMCID: PMC10980073 DOI: 10.1101/2024.03.14.584987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Background Cardiac dysfunction in AL amyloidosis is thought to be partly related to the direct impact of AL LCs on cardiomyocyte function, with the degree of dysfunction at diagnosis as a major determinant of clinical outcomes. Nonetheless, mechanisms underlying LC-induced myocardial toxicity are not well understood. Methods We identified gene expression changes correlating with human cardiac cells exposed to a cardiomyopathy-associated κAL LC. We then sought to confirm these findings in a clinical dataset by focusing on clinical parameters associated with the pathways dysregulated at the gene expression level. Results Upon exposure to a cardiomyopathy-associated κAL LC, cardiac cells exhibited gene expression changes related to myocardial contractile function and inflammation, leading us to hypothesize that there could be clinically detectable changes in GLS on echocardiogram and serum inflammatory markers in patients. Thus, we identified 29 patients with normal IVSd but abnormal cardiac biomarkers suggestive of LC-induced cardiac dysfunction. These patients display early cardiac biomarker staging, abnormal GLS, and significantly reduced serum inflammatory markers compared to patients with clinically evident amyloid fibril deposition. Conclusion Collectively, our findings highlight early molecular and functional signatures of cardiac AL amyloidosis, with potential impact for developing improved patient biomarkers and novel therapeutics.
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Behl T, Kumar A, Vishakha, Sehgal A, Singh S, Sharma N, Yadav S, Rashid S, Ali N, Ahmed AS, Vargas-De-La-Cruz C, Bungau SG, Khan H. Understanding the mechanistic pathways and clinical aspects associated with protein and gene based biomarkers in breast cancer. Int J Biol Macromol 2023; 253:126595. [PMID: 37648139 DOI: 10.1016/j.ijbiomac.2023.126595] [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: 05/02/2023] [Revised: 08/22/2023] [Accepted: 08/27/2023] [Indexed: 09/01/2023]
Abstract
Cancer is one of the most widespread and severe diseases with a huge mortality rate. In recent years, the second-leading mortality rate of any cancer globally has been breast cancer, which is one of the most common and deadly cancers found in women. Detecting breast cancer in its initial stages simplifies treatment, decreases death risk, and recovers survival rates for patients. The death rate for breast cancer has risen to 0.024 % in some regions. Sensitive and accurate technologies are required for the preclinical detection of BC at an initial stage. Biomarkers play a very crucial role in the early identification as well as diagnosis of women with breast cancer. Currently, a wide variety of cancer biomarkers have been discovered for the diagnosis of cancer. For the identification of these biomarkers from serum or other body fluids at physiological amounts, many detection methods have been developed. In the case of breast cancer, biomarkers are especially helpful in discovering those who are more likely to develop the disease, determining prognosis at the time of initial diagnosis and choosing the best systemic therapy. In this study we have compiled various clinical aspects and signaling pathways associated with protein-based biomarkers and gene-based biomarkers.
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Affiliation(s)
- Tapan Behl
- School of Health Sciences and Technology, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India
| | - Ankush Kumar
- Institute of Pharmaceutical Sciences, IET Bhaddal Technical Campus, Ropar 140108, Punjab, India
| | - Vishakha
- Institute of Pharmaceutical Sciences, IET Bhaddal Technical Campus, Ropar 140108, Punjab, India
| | - Aayush Sehgal
- GHG Khalsa College of Pharmacy, Gurusar Sadhar, 141104 Ludhiana, Punjab, India
| | - Sukhbir Singh
- Department of Pharmaceutics, MM College of Pharmacy, Maharishi Markandeshwar (Deemed to be University), Mullana Ambala 133203, Haryana, India
| | - Neelam Sharma
- Department of Pharmaceutics, MM College of Pharmacy, Maharishi Markandeshwar (Deemed to be University), Mullana Ambala 133203, Haryana, India
| | - Shivam Yadav
- School of Pharmacy, Babu Banarasi Das University, Lucknow 226028, Uttar Pradesh, India
| | - Summya Rashid
- Department of Pharmacology and Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj 11942, Saudi Arabia.
| | - Nemat Ali
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadah 11451, Saudi Arabia
| | - Amira Saber Ahmed
- Hormones Department, Medical Research and Clinical Studies Institute, National Research Centre, Giza 12622, Egypt
| | - Celia Vargas-De-La-Cruz
- Department of Pharmacology, Bromatology and Toxicology, Faculty of Pharmacy and Biochemistry, Universidad Nacional Mayor de San Marcos, Lima 150001, Peru; E-Health Research Center, Universidad de Ciencias y Humanidades, Lima 15001, Peru
| | - Simona Gabriela Bungau
- Department of Pharmacy, Faculty of Medicine and Pharmacy, University of Oradea, Oradea 410087, Romania; Doctoral School of Biomedical Sciences, University of Oradea, Oradea 410087, Romania
| | - Haroon Khan
- Department of Pharmacy, Abdul Wali Khan University, Mardan 23200, Pakistan.
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Rosilan NF, Waiho K, Fazhan H, Sung YY, Zakaria NH, Afiqah-Aleng N, Mohamed-Hussein ZA. Current trends of host-pathogen relationship in shrimp infectious disease via computational protein-protein interaction: A bibliometric analysis. FISH & SHELLFISH IMMUNOLOGY 2023; 142:109171. [PMID: 37858788 DOI: 10.1016/j.fsi.2023.109171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 10/12/2023] [Accepted: 10/16/2023] [Indexed: 10/21/2023]
Abstract
Protein-protein interactions (PPIs) are essential for understanding cell physiology in normal and pathological conditions, as they might involve in all cellular processes. PPIs have been widely used to elucidate the pathobiology of human and plant diseases. Therefore, they can also be used to unveil the pathobiology of infectious diseases in shrimp, which is one of the high-risk factors influencing the success or failure of shrimp production. PPI network analysis, specifically host-pathogen PPI (HP-PPI), provides insights into the molecular interactions between the shrimp and pathogens. This review quantitatively analyzed the research trends within this field through bibliometric analysis using specific keywords, countries, authors, organizations, journals, and documents. This analysis has screened 206 records from the Scopus database for determining eligibility, resulting in 179 papers that were retrieved for bibliometric analysis. The analysis revealed that China and Thailand were the driving forces behind this specific field of research and frequently collaborated with the United States. Aquaculture and Diseases of Aquatic Organisms were the prominent sources for publications in this field. The main keywords identified included "white spot syndrome virus," "WSSV," and "shrimp." We discovered that studies on HP-PPI are currently quite scarce. As a result, we further discussed the significance of HP-PPI by highlighting various approaches that have been previously adopted. These findings not only emphasize the importance of HP-PPI but also pave the way for future researchers to explore the pathogenesis of infectious diseases in shrimp. By doing so, preventative measures and enhanced treatment strategies can be identified.
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Affiliation(s)
- Nur Fathiah Rosilan
- Institute of Climate Adaptation and Marine Biotechnology (ICAMB), Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia
| | - Khor Waiho
- Higher Institution Centre of Excellence (HICoE), Institute of Tropical Aquaculture and Fisheries, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia; Centre for Chemical Biology, Universiti Sains Malaysia, Minden, 11900, Penang, Malaysia; Department of Aquaculture, Faculty of Fisheries, Kasetsart University, 10900, Bangkok, Thailand
| | - Hanafiah Fazhan
- Higher Institution Centre of Excellence (HICoE), Institute of Tropical Aquaculture and Fisheries, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia; Centre for Chemical Biology, Universiti Sains Malaysia, Minden, 11900, Penang, Malaysia; Department of Aquaculture, Faculty of Fisheries, Kasetsart University, 10900, Bangkok, Thailand
| | - Yeong Yik Sung
- Institute of Climate Adaptation and Marine Biotechnology (ICAMB), Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia
| | - Nor Hafizah Zakaria
- Institute of Climate Adaptation and Marine Biotechnology (ICAMB), Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia.
| | - Nor Afiqah-Aleng
- Institute of Climate Adaptation and Marine Biotechnology (ICAMB), Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia.
| | - Zeti-Azura Mohamed-Hussein
- UKM Medical Molecular Biology Institute, UKM Medical Centre, Jalan Yaacob Latiff, 56000, Cheras, Kuala Lumpur, Malaysia; Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia
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Identification of potential microRNA diagnostic panels and uncovering regulatory mechanisms in breast cancer pathogenesis. Sci Rep 2022; 12:20135. [PMID: 36418345 PMCID: PMC9684445 DOI: 10.1038/s41598-022-24347-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 11/14/2022] [Indexed: 11/24/2022] Open
Abstract
Early diagnosis of breast cancer (BC), as the most common cancer among women, increases the survival rate and effectiveness of treatment. MicroRNAs (miRNAs) control various cell behaviors, and their dysregulation is widely involved in pathophysiological processes such as BC development and progress. In this study, we aimed to identify potential miRNA biomarkers for early diagnosis of BC. We also proposed a consensus-based strategy to analyze the miRNA expression data to gain a deeper insight into the regulatory roles of miRNAs in BC initiation. Two microarray datasets (GSE106817 and GSE113486) were analyzed to explore the differentially expressed miRNAs (DEMs) in serum of BC patients and healthy controls. Utilizing multiple bioinformatics tools, six serum-based miRNA biomarkers (miR-92a-3p, miR-23b-3p, miR-191-5p, miR-141-3p, miR-590-5p and miR-190a-5p) were identified for BC diagnosis. We applied our consensus and integration approach to construct a comprehensive BC-specific miRNA-TF co-regulatory network. Using different combination of these miRNA biomarkers, two novel diagnostic models, consisting of miR-92a-3p, miR-23b-3p, miR-191-5p (model 1) and miR-92a-3p, miR-23b-3p, miR-141-3p, and miR-590-5p (model 2), were obtained from bioinformatics analysis. Validation analysis was carried out for the considered models on two microarray datasets (GSE73002 and GSE41922). The model based on similar network topology features, comprising miR-92a-3p, miR-23b-3p and miR-191-5p was the most promising model in the diagnosis of BC patients from healthy controls with 0.89 sensitivity, 0.96 specificity and area under the curve (AUC) of 0.98. These findings elucidate the regulatory mechanisms underlying BC and represent novel biomarkers for early BC diagnosis.
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Long J, Tian L, Baranova A, Cao H, Yao Y, Rao S, Zhang F. Convergent lines of evidence supporting involvement of NFKB1 in schizophrenia. Psychiatry Res 2022; 312:114588. [PMID: 35524996 DOI: 10.1016/j.psychres.2022.114588] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 04/22/2022] [Accepted: 04/30/2022] [Indexed: 01/02/2023]
Abstract
OBJECTIVES NFKB1 was associated with treatment-refractory schizophrenia (SZ) and response to antipsychotics; however, the underlying mechanisms through which NFKB1 confers its risk for SZ are largely unknown. We aimed to investigate the potential role of NFKB1 in SZ. METHODS In the present study, we investigated the association of the risk SNP rs230529 of NFKB1 with gray matter density and with NFKB1 mRNA levels in various human brain regions. The spatiotemporal expression pattern of NFKB1 in human brains was explored. We constructed a miRNA-NFKB1-target gene regulatory network and analyzed its druggability through targeting NFKB1 for SZ treatment. RESULTS NFKB1 showed the highest expression levels in the cerebellum, in which these levels were stratified by genotypes of rs230529. Interestingly, the allelic state of rs230529 was significantly associated with regional gray matter density in multiple brain regions (including the cerebellum), which also differed between patients with schizophrenia and controls. Furthermore, regulatory targets of NFKB1 were enriched among SZ susceptibility genes. A substantial proportion of NFKB1 target genes were subject to combinatorial regulation by NFKB1 and miRNAs, constituting a hybrid NFKB1-miRNA-gene regulatory network. Some components of this network showed expression changes relevant to both the disease and the treatment. Finally, we detected the dynamic changes of NFKB1-miR-155-5p-GSK3B and NFKB1-miR-155-5p/let-7a-5p-IL6 networks in course of the treatment of SZ. CONCLUSION Taken together, our findings support the involvement of NFKB1-mediated dysregulation in the development of SZ.
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Affiliation(s)
- Jing Long
- Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, China; Haihe Laboratory of Cell Ecosystem, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Lin Tian
- Wuxi Mental Health Center of Nanjing Medical University, Wuxi, 214151, China
| | - Ancha Baranova
- School of Systems Biology, George Mason University, Fairfax, 22030, USA; Research Centre for Medical Genetics, Moscow, 115478, Russia
| | - Hongbao Cao
- School of Systems Biology, George Mason University, Fairfax, 22030, USA
| | - Yao Yao
- Haihe Laboratory of Cell Ecosystem, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Shuquan Rao
- Haihe Laboratory of Cell Ecosystem, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Fuquan Zhang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China; Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China.
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Gollapalli P, Selvan G T, H M, Shetty P, Kumari N S. Genome-scale protein interaction network construction and topology analysis of functional hypothetical proteins in Helicobacter pylori divulges novel therapeutic targets. Microb Pathog 2021; 161:105293. [PMID: 34800634 DOI: 10.1016/j.micpath.2021.105293] [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: 07/05/2021] [Revised: 08/25/2021] [Accepted: 11/12/2021] [Indexed: 02/07/2023]
Abstract
The emergence and spread of multi-drug resistance among Helicobacter pylori (H. pylori) strain raise more stakes for genetic research for discovering new drugs. The quantity of uncharacterized hypothetical proteins in the genome may provide an opportunity to explore their property and promulgation could act as a platform for designing the drugs, making them an intriguing genetic target. In this context, the present study aims to identify the key hypothetical proteins (HPs) and their biological regulatory processes in H. pylori. This investigation could provide a foundation to establish the molecular connectivity among the pathways using topological analysis of the protein interaction networks (PINs). The giant network derived from the extended network has 374 nodes connected via 925 edges. A total of 43 proteins with high betweenness centrality (BC), 54 proteins with a large degree, and 23 proteins with high BC and large degrees have been identified. HP 1479, HP 0056, HP 1481, HP 1021, HP 0043, HP 1019, gmd, flgA, HP 0472, HP 1486, HP 1478, and HP 1473 are categorized as hub nodes because they have a higher number of direct connections and are potentially more important in understanding HP's molecular interactions. The pathway enrichment analysis of the network clusters revealed significant involvement of HPs in pathways such as flagellar assembly, bacterial chemotaxis and lipopolysaccharide biosynthesis. This comprehensive computational study revealed HP's functional role and its druggability characteristics, which could be useful in the development of drugs to combat H. pylori infections.
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Affiliation(s)
- Pavan Gollapalli
- Central Research Laboratory, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, 575018, Karnataka, India.
| | - Tamizh Selvan G
- Central Research Laboratory, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, 575018, Karnataka, India
| | - Manjunatha H
- Department of Biochemistry, Jnana Bharathi Campus, Bangalore University, Bangalore, Karnataka, 560056, India
| | - Praveenkumar Shetty
- Central Research Laboratory, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, 575018, Karnataka, India
| | - Suchetha Kumari N
- Central Research Laboratory, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, 575018, Karnataka, India
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Kumar A, Nemeroff CB, Cooper JJ, Widge A, Rodriguez C, Carpenter L, McDonald WM. Amyloid and Tau in Alzheimer's Disease: Biomarkers or Molecular Targets for Therapy? Are We Shooting the Messenger? Am J Psychiatry 2021; 178:1014-1025. [PMID: 34734743 DOI: 10.1176/appi.ajp.2021.19080873] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Alzheimer's disease is a neuropsychiatric disorder with devastating clinical and socioeconomic consequences. Since the original description of the neuropathological correlates of the disorder, neuritic plaques and neurofibrillary tangles have been presumed to be critical to the underlying pathophysiology of the illness. The authors review the clinical and neuropathological origins of Alzheimer's disease and trace the evolution of modern biomarkers from their historical roots. They describe how technological innovations such as neuroimaging and biochemical assays have been used to measure and quantify key proteins and lipids in the brain, cerebrospinal fluid, and blood and advance their role as biomarkers of Alzheimer's disease. Together with genomics, these approaches have led to the development of a thematic and focused science in the area of degenerative disorders. The authors conclude by drawing distinctions between legitimate biomarkers of disease and molecular targets for therapeutic intervention and discuss future approaches to this complex neurobehavioral illness.
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Affiliation(s)
- Anand Kumar
- Department of Psychiatry, University of Illinois at Chicago (Kumar, Cooper); Department of Psychiatry and Behavioral Sciences, University of Texas Dell Medical School in Austin, and Mulva Clinic for the Neurosciences, UT Health Austin (Nemeroff); Department of Psychiatry, University of Minnesota, Minneapolis (Widge); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif. (Rodriguez); Department of Psychiatry and Human Behavior, Warren Alpert Medical School at Brown University, Providence, R.I. (Carpenter); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald)
| | - Charles B Nemeroff
- Department of Psychiatry, University of Illinois at Chicago (Kumar, Cooper); Department of Psychiatry and Behavioral Sciences, University of Texas Dell Medical School in Austin, and Mulva Clinic for the Neurosciences, UT Health Austin (Nemeroff); Department of Psychiatry, University of Minnesota, Minneapolis (Widge); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif. (Rodriguez); Department of Psychiatry and Human Behavior, Warren Alpert Medical School at Brown University, Providence, R.I. (Carpenter); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald)
| | - Joseph J Cooper
- Department of Psychiatry, University of Illinois at Chicago (Kumar, Cooper); Department of Psychiatry and Behavioral Sciences, University of Texas Dell Medical School in Austin, and Mulva Clinic for the Neurosciences, UT Health Austin (Nemeroff); Department of Psychiatry, University of Minnesota, Minneapolis (Widge); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif. (Rodriguez); Department of Psychiatry and Human Behavior, Warren Alpert Medical School at Brown University, Providence, R.I. (Carpenter); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald)
| | - Alik Widge
- Department of Psychiatry, University of Illinois at Chicago (Kumar, Cooper); Department of Psychiatry and Behavioral Sciences, University of Texas Dell Medical School in Austin, and Mulva Clinic for the Neurosciences, UT Health Austin (Nemeroff); Department of Psychiatry, University of Minnesota, Minneapolis (Widge); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif. (Rodriguez); Department of Psychiatry and Human Behavior, Warren Alpert Medical School at Brown University, Providence, R.I. (Carpenter); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald)
| | - Carolyn Rodriguez
- Department of Psychiatry, University of Illinois at Chicago (Kumar, Cooper); Department of Psychiatry and Behavioral Sciences, University of Texas Dell Medical School in Austin, and Mulva Clinic for the Neurosciences, UT Health Austin (Nemeroff); Department of Psychiatry, University of Minnesota, Minneapolis (Widge); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif. (Rodriguez); Department of Psychiatry and Human Behavior, Warren Alpert Medical School at Brown University, Providence, R.I. (Carpenter); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald)
| | - Linda Carpenter
- Department of Psychiatry, University of Illinois at Chicago (Kumar, Cooper); Department of Psychiatry and Behavioral Sciences, University of Texas Dell Medical School in Austin, and Mulva Clinic for the Neurosciences, UT Health Austin (Nemeroff); Department of Psychiatry, University of Minnesota, Minneapolis (Widge); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif. (Rodriguez); Department of Psychiatry and Human Behavior, Warren Alpert Medical School at Brown University, Providence, R.I. (Carpenter); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald)
| | - William M McDonald
- Department of Psychiatry, University of Illinois at Chicago (Kumar, Cooper); Department of Psychiatry and Behavioral Sciences, University of Texas Dell Medical School in Austin, and Mulva Clinic for the Neurosciences, UT Health Austin (Nemeroff); Department of Psychiatry, University of Minnesota, Minneapolis (Widge); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif. (Rodriguez); Department of Psychiatry and Human Behavior, Warren Alpert Medical School at Brown University, Providence, R.I. (Carpenter); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald)
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Network topology analysis of essential genes interactome of Helicobacter pylori to explore novel therapeutic targets. Microb Pathog 2021; 158:105059. [PMID: 34157412 DOI: 10.1016/j.micpath.2021.105059] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 06/09/2021] [Accepted: 06/14/2021] [Indexed: 11/24/2022]
Abstract
The Helicobacter pylori chronic colonization produces a wide range of gastric diseases in the gastric mucosa by abetting inflammation. Amidst coevolution and reorganization of its metabolism with humans, it has become difficult still imperative to understand and prevent its growth. This study focus to explore functional insights into identification of hub proteins/genes by aggregating the behavior of genes connected in a protein-protein interaction (PPI) network. We have constructed a PPI network of 123 essential genes along with 1213 interactions in H. pylori 26695. The degree and other centrality measures analysis assist in identifying the important hub nodes, which are top-ranked proteins. A total of nine proteins (recA, guaA, dnaK, rpsB, rplQ, rpmA, rpmC, rpmF, and rpsE) were obtained with high degree (k), betweenness centrality (BC) value. Gene ontology analysis reveals 8, 5 and 3 GO terms correspond to biological processes, cellular components and molecular function respectively. Gene complexes of hypothetical proteins (HPs) were related to aminoacyl-tRNA biosynthesis, biosynthesis of secondary metabolites, bacterial secretion system and protein export. The MCODE analysis revealed that protein from module M1, M3 and M6 include the proteins which have highest degree and BC values. It is noteworthy to mention that the bifunctional GMP synthase/glutamine amidotransferase protein (guaA), molecular chaperon (dnaK), recombinase A (recA) constitute as hub proteins. As a result, these genes are considered as network hub nodes that might be used as therapeutic targets. Our analysis affords a detailed understanding of the molecular process and pathways regulated by the essential genes in H. pylori 26695.
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10
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Tarazona A, Forment J, Elena SF. Identifying Early Warning Signals for the Sudden Transition from Mild to Severe Tobacco Etch Disease by Dynamical Network Biomarkers. Viruses 2019; 12:E16. [PMID: 31861938 PMCID: PMC7019593 DOI: 10.3390/v12010016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 12/17/2019] [Accepted: 12/19/2019] [Indexed: 12/16/2022] Open
Abstract
Complex systems exhibit critical thresholds at which they transition among alternative phases. Complex systems theory has been applied to analyze disease progression, distinguishing three stages along progression: (i) a normal noninfected state; (ii) a predisease state, in which the host is infected and responds and therapeutic interventions could still be effective; and (iii) an irreversible state, where the system is seriously threatened. The dynamical network biomarker (DNB) theory sought for early warnings of the transition from health to disease. Such DNBs might range from individual genes to complex structures in transcriptional regulatory or protein-protein interaction networks. Here, we revisit transcriptomic data obtained during infection of tobacco plants with tobacco etch potyvirus to identify DNBs signaling the transition from mild/reversible to severe/irreversible disease. We identified genes showing a sudden transition in expression along disease categories. Some of these genes cluster in modules that show the properties of DNBs. These modules contain both genes known to be involved in response to pathogens (e.g., ADH2, CYP19, ERF1, KAB1, LAP1, MBF1C, MYB58, PR1, or TPS5) and other genes not previously related to biotic stress responses (e.g., ABCI6, BBX21, NAP1, OSM34, or ZPN1).
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Affiliation(s)
- Adrián Tarazona
- Instituto de Biología Integrativa de Sistemas (I2SysBio), CSIC-Universitat de València, Paterna, 46980 València, Spain;
| | - Javier Forment
- Instituto de Biología Molecular y Celular de Plantas (IBMCP), CSIC-Universitat Politècnica de València, 46022 València, Spain;
| | - Santiago F. Elena
- Instituto de Biología Integrativa de Sistemas (I2SysBio), CSIC-Universitat de València, Paterna, 46980 València, Spain;
- The Santa Fe Institute, Santa Fe, NM 87501, USA
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11
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Chen SJ, Liao DL, Chen CH, Wang TY, Chen KC. Construction and Analysis of Protein-Protein Interaction Network of Heroin Use Disorder. Sci Rep 2019; 9:4980. [PMID: 30899073 PMCID: PMC6428805 DOI: 10.1038/s41598-019-41552-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 03/11/2019] [Indexed: 12/17/2022] Open
Abstract
Heroin use disorder (HUD) is a complex disease resulting from interactions among genetic and other factors (e.g., environmental factors). The mechanism of HUD development remains unknown. Newly developed network medicine tools provide a platform for exploring complex diseases at the system level. This study proposes that protein–protein interactions (PPIs), particularly those among proteins encoded by casual or susceptibility genes, are extremely crucial for HUD development. The giant component of our constructed PPI network comprised 111 nodes with 553 edges, including 16 proteins with large degree (k) or high betweenness centrality (BC), which were further identified as the backbone of the network. JUN with the largest degree was suggested to be central to the PPI network associated with HUD. Moreover, PCK1 with the highest BC and MAPK14 with the secondary largest degree and 9th highest BC might be involved in the development HUD and other substance diseases.
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Affiliation(s)
- Shaw-Ji Chen
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan.,Department of Psychiatry, Mackay Memorial Hospital, Taitung Branch, Taiwan
| | - Ding-Lieh Liao
- Bali Psychiatric Center, Department of Health, Executive Yuan, New Taipei, Taiwan
| | - Chia-Hsiang Chen
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou and Chang Gung University School of Medicine, Taoyuan, Taiwan
| | - Tse-Yi Wang
- Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan
| | - Kuang-Chi Chen
- Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan.
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12
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Gomez-Varela D, Barry AM, Schmidt M. Proteome-based systems biology in chronic pain. J Proteomics 2019; 190:1-11. [DOI: 10.1016/j.jprot.2018.04.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 03/15/2018] [Accepted: 04/05/2018] [Indexed: 02/07/2023]
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13
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Ozturk K, Dow M, Carlin DE, Bejar R, Carter H. The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine. J Mol Biol 2018; 430:2875-2899. [PMID: 29908887 PMCID: PMC6097914 DOI: 10.1016/j.jmb.2018.06.016] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 05/30/2018] [Accepted: 06/06/2018] [Indexed: 12/19/2022]
Abstract
Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However, biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but also by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis, and the path for such tools to the clinic.
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Affiliation(s)
- Kivilcim Ozturk
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Michelle Dow
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Daniel E Carlin
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Rafael Bejar
- Moores Cancer Center, Division of Hematology and Oncology, University of California San Diego, La Jolla, CA 92093, USA
| | - Hannah Carter
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA; Moores Cancer Center and Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA; CIFAR, MaRS Centre, West Tower, 661 University Ave., Suite 505, Toronto, ON M5G 1M1, Canada.
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14
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Barry AM, Sondermann JR, Sondermann JH, Gomez-Varela D, Schmidt M. Region-Resolved Quantitative Proteome Profiling Reveals Molecular Dynamics Associated With Chronic Pain in the PNS and Spinal Cord. Front Mol Neurosci 2018; 11:259. [PMID: 30154697 PMCID: PMC6103001 DOI: 10.3389/fnmol.2018.00259] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 07/10/2018] [Indexed: 12/27/2022] Open
Abstract
To obtain a thorough understanding of chronic pain, large-scale molecular mapping of the pain axis at the protein level is necessary, but has not yet been achieved. We applied quantitative proteome profiling to build a comprehensive protein compendium of three regions of the pain neuraxis in mice: the sciatic nerve (SN), the dorsal root ganglia (DRG), and the spinal cord (SC). Furthermore, extensive bioinformatics analysis enabled us to reveal unique protein subsets which are specifically enriched in the peripheral nervous system (PNS) and SC. The immense value of these datasets for the scientific community is highlighted by validation experiments, where we monitored protein network dynamics during neuropathic pain. Here, we resolved profound region-specific differences and distinct changes of PNS-enriched proteins under pathological conditions. Overall, we provide a unique and validated systems biology proteome resource (summarized in our online database painproteome.em.mpg.de), which facilitates mechanistic insights into somatosensory biology and chronic pain—a prerequisite for the identification of novel therapeutic targets.
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Affiliation(s)
- Allison M Barry
- Max-Planck Institute of Experimental Medicine, Somatosensory Signaling and Systems Biology Group, Goettingen, Germany
| | - Julia R Sondermann
- Max-Planck Institute of Experimental Medicine, Somatosensory Signaling and Systems Biology Group, Goettingen, Germany
| | - Jan-Hendrik Sondermann
- Max-Planck Institute of Experimental Medicine, Somatosensory Signaling and Systems Biology Group, Goettingen, Germany
| | - David Gomez-Varela
- Max-Planck Institute of Experimental Medicine, Somatosensory Signaling and Systems Biology Group, Goettingen, Germany
| | - Manuela Schmidt
- Max-Planck Institute of Experimental Medicine, Somatosensory Signaling and Systems Biology Group, Goettingen, Germany
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15
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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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16
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Yan X, Liang A, Gomez J, Cohn L, Zhao H, Chupp GL. A novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression. BMC Bioinformatics 2017. [PMID: 28637421 PMCID: PMC5480187 DOI: 10.1186/s12859-017-1727-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Distance based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples. An alternative method to examine disease phenotypes is to use pre-defined biological pathways. These pathways have been shown to be perturbed in different ways in different subjects who have similar clinical features. We hypothesize that differences in the expressions of genes in a given pathway are more predictive of differences in biological differences compared to standard approaches and if integrated into clustering analysis will enhance the robustness and accuracy of the clustering method. To examine this hypothesis, we developed a novel computational method to assess the biological differences between samples using gene expression data by assuming that ontologically defined biological pathways in biologically similar samples have similar behavior. RESULTS Pre-defined biological pathways were downloaded and genes in each pathway were used to cluster samples using the Gaussian mixture model. The clustering results across different pathways were then summarized to calculate the pathway-based distance score between samples. This method was applied to both simulated and real data sets and compared to the traditional Euclidean distance and another pathway-based clustering method, Pathifier. The results show that the pathway-based distance score performs significantly better than the Euclidean distance, especially when the heterogeneity is low and genes in the same pathways are correlated. Compared to Pathifier, we demonstrated that our approach achieves higher accuracy and robustness for small pathways. When the pathway size is large, by downsampling the pathways into smaller pathways, our approach was able to achieve comparable performance. CONCLUSIONS We have developed a novel distance score that represents the biological differences between samples using gene expression data and pre-defined biological pathway information. Application of this distance score results in more accurate, robust, and biologically meaningful clustering results in both simulated data and real data when compared to traditional methods. It also has comparable or better performance compared to Pathifier.
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Affiliation(s)
- Xiting Yan
- Center for Pulmonary Personalized Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06520, USA. .,Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06520, USA.
| | - Anqi Liang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06520, USA
| | - Jose Gomez
- Center for Pulmonary Personalized Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Lauren Cohn
- Center for Pulmonary Personalized Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Hongyu Zhao
- Center for Pulmonary Personalized Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06520, USA.,Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06520, USA.,Department of Genetics, Yale School of Medicine, New Haven, CT, 06520, USA.,Computational Biology and Bioinformatics Program, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Geoffrey L Chupp
- Center for Pulmonary Personalized Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06520, USA
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17
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Lin Y, Chen J, Shen B. Interactions Between Genetics, Lifestyle, and Environmental Factors for Healthcare. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 1005:167-191. [PMID: 28916933 DOI: 10.1007/978-981-10-5717-5_8] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The occurrence and progression of diseases are strongly associated with a combination of genetic, lifestyle, and environmental factors. Understanding the interplay between genetic and nongenetic components provides deep insights into disease pathogenesis and promotes personalized strategies for people healthcare. Recently, the paradigm of systems medicine, which integrates biomedical data and knowledge at multidimensional levels, is considered to be an optimal way for disease management and clinical decision-making in the era of precision medicine. In this chapter, epigenetic-mediated genetics-lifestyle-environment interactions within specific diseases and different ethnic groups are systematically discussed, and data sources, computational models, and translational platforms for systems medicine research are sequentially presented. Moreover, feasible suggestions on precision healthcare and healthy longevity are kindly proposed based on the comprehensive review of current studies.
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Affiliation(s)
- Yuxin Lin
- Center for Systems Biology, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China
| | - Jiajia Chen
- School of Chemistry, Biology and Materials Engineering, Suzhou University of Science and Technology, No.1 Kerui road, Suzhou, Jiangsu, 215011, China
| | - Bairong Shen
- Center for Systems Biology, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China. .,Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, China. .,Medical College of Guizhou University, Guiyang, 550025, China.
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18
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A new strategy for exploring the hierarchical structure of cancers by adaptively partitioning functional modules from gene expression network. Sci Rep 2016; 6:28720. [PMID: 27349736 PMCID: PMC4923884 DOI: 10.1038/srep28720] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 06/08/2016] [Indexed: 12/23/2022] Open
Abstract
The interactions among the genes within a disease are helpful for better understanding the hierarchical structure of the complex biological system of it. Most of the current methodologies need the information of known interactions between genes or proteins to create the network connections. However, these methods meet the limitations in clinical cancer researches because different cancers not only share the common interactions among the genes but also own their specific interactions distinguished from each other. Moreover, it is still difficult to decide the boundaries of the sub-networks. Therefore, we proposed a strategy to construct a gene network by using the sparse inverse covariance matrix of gene expression data, and divide it into a series of functional modules by an adaptive partition algorithm. The strategy was validated by using the microarray data of three cancers and the RNA-sequencing data of glioblastoma. The different modules in the network exhibited specific functions in cancers progression. Moreover, based on the gene expression profiles in the modules, the risk of death was well predicted in the clustering analysis and the binary classification, indicating that our strategy can be benefit for investigating the cancer mechanisms and promoting the clinical applications of network-based methodologies in cancer researches.
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19
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Gao SG, Liu RM, Zhao YG, Wang P, Ward DG, Wang GC, Guo XQ, Gu J, Niu WB, Zhang T, Martin A, Guo ZP, Feng XS, Qi YJ, Ma YF. Integrative topological analysis of mass spectrometry data reveals molecular features with clinical relevance in esophageal squamous cell carcinoma. Sci Rep 2016; 6:21586. [PMID: 26898710 PMCID: PMC4761933 DOI: 10.1038/srep21586] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2015] [Accepted: 01/26/2016] [Indexed: 02/06/2023] Open
Abstract
Combining MS-based proteomic data with network and topological features of such network would identify more clinically relevant molecules and meaningfully expand the repertoire of proteins derived from MS analysis. The integrative topological indexes representing 95.96% information of seven individual topological measures of node proteins were calculated within a protein-protein interaction (PPI) network, built using 244 differentially expressed proteins (DEPs) identified by iTRAQ 2D-LC-MS/MS. Compared with DEPs, differentially expressed genes (DEGs) and comprehensive features (CFs), structurally dominant nodes (SDNs) based on integrative topological index distribution produced comparable classification performance in three different clinical settings using five independent gene expression data sets. The signature molecules of SDN-based classifier for distinction of early from late clinical TNM stages were enriched in biological traits of protein synthesis, intracellular localization and ribosome biogenesis, which suggests that ribosome biogenesis represents a promising therapeutic target for treating ESCC. In addition, ITGB1 expression selected exclusively by integrative topological measures correlated with clinical stages and prognosis, which was further validated with two independent cohorts of ESCC samples. Thus the integrative topological analysis of PPI networks proposed in this study provides an alternative approach to identify potential biomarkers and therapeutic targets from MS/MS data with functional insights in ESCC.
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Affiliation(s)
- She-Gan Gao
- Henan Key Laboratory of Cancer Epigenetics, Cancer Institute, The First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang, P. R. China, 471003
| | - Rui-Min Liu
- Henan Key Laboratory of Engineering Antibody Medicine, Henan International United Laboratory of Antibody Medicine, Key Laboratory of Cellular and Molecular Immunology, Henan University School of Medicine, Kaifeng 475004, P.R. China
| | - Yun-Gang Zhao
- Henan Key Laboratory of Engineering Antibody Medicine, Henan International United Laboratory of Antibody Medicine, Key Laboratory of Cellular and Molecular Immunology, Henan University School of Medicine, Kaifeng 475004, P.R. China
| | - Pei Wang
- School of Mathematics and Statistics, Henan University, Kaifeng, China, Henan 475004, P. R. China
| | - Douglas G. Ward
- School of Cancer Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Guang-Chao Wang
- Henan Key Laboratory of Engineering Antibody Medicine, Henan International United Laboratory of Antibody Medicine, Key Laboratory of Cellular and Molecular Immunology, Henan University School of Medicine, Kaifeng 475004, P.R. China
| | - Xiang-Qian Guo
- Henan Key Laboratory of Engineering Antibody Medicine, Henan International United Laboratory of Antibody Medicine, Key Laboratory of Cellular and Molecular Immunology, Henan University School of Medicine, Kaifeng 475004, P.R. China
| | - Juan Gu
- Henan Key Laboratory of Engineering Antibody Medicine, Henan International United Laboratory of Antibody Medicine, Key Laboratory of Cellular and Molecular Immunology, Henan University School of Medicine, Kaifeng 475004, P.R. China
| | - Wan-Bin Niu
- Henan Key Laboratory of Engineering Antibody Medicine, Henan International United Laboratory of Antibody Medicine, Key Laboratory of Cellular and Molecular Immunology, Henan University School of Medicine, Kaifeng 475004, P.R. China
| | - Tian Zhang
- Henan Key Laboratory of Engineering Antibody Medicine, Henan International United Laboratory of Antibody Medicine, Key Laboratory of Cellular and Molecular Immunology, Henan University School of Medicine, Kaifeng 475004, P.R. China
| | - Ashley Martin
- School of Cancer Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Zhi-Peng Guo
- Henan Key Laboratory of Engineering Antibody Medicine, Henan International United Laboratory of Antibody Medicine, Key Laboratory of Cellular and Molecular Immunology, Henan University School of Medicine, Kaifeng 475004, P.R. China
| | - Xiao-Shan Feng
- Henan Key Laboratory of Cancer Epigenetics, Cancer Institute, The First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang, P. R. China, 471003
| | - Yi-Jun Qi
- Henan Key Laboratory of Engineering Antibody Medicine, Henan International United Laboratory of Antibody Medicine, Key Laboratory of Cellular and Molecular Immunology, Henan University School of Medicine, Kaifeng 475004, P.R. China
| | - Yuan-Fang Ma
- Henan Key Laboratory of Engineering Antibody Medicine, Henan International United Laboratory of Antibody Medicine, Key Laboratory of Cellular and Molecular Immunology, Henan University School of Medicine, Kaifeng 475004, P.R. China
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20
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Lin Y, Yuan X, Shen B. Network-Based Biomedical Data Analysis. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 939:309-332. [DOI: 10.1007/978-981-10-1503-8_13] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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21
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Phenotype-Driven Plasma Biobanking Strategies and Methods. J Pers Med 2015; 5:140-52. [PMID: 26110578 PMCID: PMC4493492 DOI: 10.3390/jpm5020140] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2015] [Accepted: 05/05/2015] [Indexed: 12/22/2022] Open
Abstract
Biobank development and integration with clinical data from electronic medical record (EMR) databases have enabled recent strides in genomic research and personalized medicine. BioVU, Vanderbilt's DNA biorepository linked to de-identified clinical EMRs, has proven fruitful in its capacity to extensively appeal to numerous areas of biomedical and clinical research, supporting the discovery of genotype-phenotype interactions. Expanding on experiences in BioVU creation and development, we have recently embarked on a parallel effort to collect plasma in addition to DNA from blood specimens leftover after routine clinical testing at Vanderbilt. This initiative offers expanded utility of BioVU by combining proteomic and metabolomic approaches with genomics and/or clinical outcomes, widening the breadth for potential research and subsequent future impact on clinical care. Here, we describe the considerations and components involved in implementing a plasma biobank program from a feasibility assessment through pilot sample collection.
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