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Li Z, Zhang Y, Zhou P. Temporal Protein Complex Identification Based on Dynamic Heterogeneous Protein Information Network Representation Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:1154-1164. [PMID: 38190662 DOI: 10.1109/tcbb.2024.3351078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Protein complexes, as the fundamental units of cellular function and regulation, play a crucial role in understanding the normal physiological functions of cells. Existing methods for protein complex identification attempt to introduce other biological information on top of the protein-protein interaction (PPI) network to assist in evaluating the degree of association between proteins. However, these methods usually treat protein interaction networks as flat homogeneous static networks. They cannot distinguish the roles and importance of different types of biological information, nor can they reflect the dynamic changes of protein complexes. In recent years, heterogeneous network representation learning has achieved great success in processing complex heterogeneous information and mining deep semantics. We thus propose a temporal protein complex identification method based on Dynamic Heterogeneous Protein information network Representation Learning, DHPRL. DHPRL naturally integrates multiple types of heterogeneous biological information in the cellular temporal dimension. It simultaneously models the temporal dynamic properties of proteins and the heterogeneity of biological information to improve the understanding of protein interactions and the accuracy of complex prediction. Firstly, we construct Dynamic Heterogeneous Protein Information Network (DHPIN) by integrating temporal gene expression information and GO attribute information. Then we design a dual-view collaborative contrast mechanism. Specifically, proposing to learn protein representations from two views of DHPIN (1-hop relation view and meta-path view) to model the consistency and specificity between nearest-neighbour bio information and deeper biological semantics. The dynamic PPI network is thereafter re-weighted based on the learned protein representations. Finally, we perform protein identification on the re-weighted dynamic PPI network. Extensive experimental results demonstrate that DHPRL can effectively model complicated biological information and achieve state-of-the-art performance in most cases.
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Shevtsov M, Bobkov D, Yudintceva N, Likhomanova R, Kim A, Fedorov E, Fedorov V, Mikhailova N, Oganesyan E, Shabelnikov S, Rozanov O, Garaev T, Aksenov N, Shatrova A, Ten A, Nechaeva A, Goncharova D, Ziganshin R, Lukacheva A, Sitovskaya D, Ulitin A, Pitkin E, Samochernykh K, Shlyakhto E, Combs SE. Membrane-bound Heat Shock Protein mHsp70 Is Required for Migration and Invasion of Brain Tumors. CANCER RESEARCH COMMUNICATIONS 2024; 4:2025-2044. [PMID: 39015084 PMCID: PMC11317918 DOI: 10.1158/2767-9764.crc-24-0094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 05/13/2024] [Accepted: 07/12/2024] [Indexed: 07/18/2024]
Abstract
Molecular chaperones, especially 70 kDa heat shock protein, in addition to their intracellular localization in cancer cells, can be exposed on the surface of the plasma membrane. We report that the membrane-associated chaperone mHsp70 of malignant brain tumors is required for high migratory and invasive activity of cancer cells. Live-cell inverted confocal microscopy of tumor samples from adult (n = 23) and pediatric (n = 9) neurooncologic patients showed pronounced protein expression on the membrane, especially in the perifocal zone. Mass spectrometry analysis of lipid rafts isolated from tumor cells confirmed the presence of the protein in the chaperone cluster (including representatives of other families, such as Hsp70, Hsc70, Hsp105, and Hsp90), which in turn, during interactome analysis, was associated with proteins involved in cell migration (e.g., Rac1, RhoC, and myosin-9). The use of small-molecule inhibitors of HSP70 (PES and JG98) led to a substantial decrease in the invasive potential of cells isolated from a tumor sample of patients, which indicates the role of the chaperone in invasion. Moreover, the use of HSP70 inhibitors in animal models of orthotopic brain tumors significantly delayed tumor progression, which was accompanied by an increase in overall survival. Data demonstrate that chaperone inhibitors, particularly JG98, disrupt the function of mHsp70, thereby providing an opportunity to better understand the diverse functions of this protein and offer aid in the development of novel cancer therapies. SIGNIFICANCE Membrane-bound mHsp70 is required for brain tumor cell migration and invasion and therefore could be employed as a target for anticancer therapies.
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Affiliation(s)
- Maxim Shevtsov
- Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
- Institute of Cytology of the Russian Academy of Sciences (RAS), St. Petersburg, Russia.
- School of Medicine and Life Sciences, Far Eastern Federal University, Vladivostok, Russia.
| | - Danila Bobkov
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
- Institute of Cytology of the Russian Academy of Sciences (RAS), St. Petersburg, Russia.
- Smorodintsev Research Institute of Influenza, St. Petersburg, Russia.
| | - Natalia Yudintceva
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
- Institute of Cytology of the Russian Academy of Sciences (RAS), St. Petersburg, Russia.
| | - Ruslana Likhomanova
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
- Institute of Cytology of the Russian Academy of Sciences (RAS), St. Petersburg, Russia.
| | - Alexander Kim
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
| | - Evegeniy Fedorov
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
| | - Viacheslav Fedorov
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
| | - Natalia Mikhailova
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
| | - Elena Oganesyan
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
| | - Sergey Shabelnikov
- Institute of Cytology of the Russian Academy of Sciences (RAS), St. Petersburg, Russia.
| | - Oleg Rozanov
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
| | - Timur Garaev
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
| | - Nikolay Aksenov
- Institute of Cytology of the Russian Academy of Sciences (RAS), St. Petersburg, Russia.
| | - Alla Shatrova
- Institute of Cytology of the Russian Academy of Sciences (RAS), St. Petersburg, Russia.
| | - Artem Ten
- School of Medicine and Life Sciences, Far Eastern Federal University, Vladivostok, Russia.
| | - Anastasiya Nechaeva
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
| | - Daria Goncharova
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
| | - Rustam Ziganshin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry Russian Academy of Sciences (RAS), Moscow, Russia.
| | - Anastasiya Lukacheva
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
- Institute of Cytology of the Russian Academy of Sciences (RAS), St. Petersburg, Russia.
| | - Daria Sitovskaya
- Polenov Neurosurgical Institute, Almazov National Medical Research Centre, St. Petersburg, Russia.
| | - Alexey Ulitin
- Polenov Neurosurgical Institute, Almazov National Medical Research Centre, St. Petersburg, Russia.
| | - Emil Pitkin
- Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Konstantin Samochernykh
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
- Polenov Neurosurgical Institute, Almazov National Medical Research Centre, St. Petersburg, Russia.
| | - Evgeny Shlyakhto
- Personalized Medicine Centre, Almazov National Medical Research Centre, St. Petersburg, Russia.
| | - Stephanie E. Combs
- Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
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Maya-Aguirre CA, Torres A, Gutiérrez-Castañeda LD, Salazar LM, Abreu-Villaça Y, Manhães AC, Arenas NE. Changes in the proteome of Apis mellifera acutely exposed to sublethal dosage of glyphosate and imidacloprid. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:45954-45969. [PMID: 38980489 PMCID: PMC11269427 DOI: 10.1007/s11356-024-34185-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 06/26/2024] [Indexed: 07/10/2024]
Abstract
Uncontrolled use of pesticides has caused a dramatic reduction in the number of pollinators, including bees. Studies on the effects of pesticides on bees have reported effects on both metabolic and neurological levels under chronic exposure. In this study, variations in the differential expression of head and thorax-abdomen proteins in Africanized A. mellifera bees treated acutely with sublethal doses of glyphosate and imidacloprid were studied using a proteomic approach. A total of 92 proteins were detected, 49 of which were differentially expressed compared to those in the control group (47 downregulated and 2 upregulated). Protein interaction networks with differential protein expression ratios suggested that acute exposure of A. mellifera to sublethal doses of glyphosate could cause head damage, which is mainly associated with behavior and metabolism. Simultaneously, imidacloprid can cause damage associated with metabolism as well as, neuronal damage, cellular stress, and impairment of the detoxification system. Regarding the thorax-abdomen fractions, glyphosate could lead to cytoskeleton reorganization and a reduction in defense mechanisms, whereas imidacloprid could affect the coordination and impairment of the oxidative stress response.
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Affiliation(s)
- Carlos Andrés Maya-Aguirre
- Instituto de Biotecnología, Facultad de Ciencias, Universidad Nacional de Colombia, Ciudad Universitaria, Avenida Carrera 30 N° 45-03, Bogota, D.C, Colombia
- Grupo Ciencias Básicas en Salud-CBS-FUCS, Fundación Universitaria de Ciencias de La Salud, Hospital Infanti L Universitario de San José, Carrera 54 No.67A-80, Bogota, D.C., Colombia
| | - Angela Torres
- Departmento de Química, Facultad de Ciencias, Universidad Nacional de Colombia, Ciudad Universitaria, Avenida Carrera 30 N° 45-03, Bogota, D.C., Colombia
| | - Luz Dary Gutiérrez-Castañeda
- Grupo Ciencias Básicas en Salud-CBS-FUCS, Fundación Universitaria de Ciencias de La Salud, Hospital Infanti L Universitario de San José, Carrera 54 No.67A-80, Bogota, D.C., Colombia
| | - Luz Mary Salazar
- Departmento de Química, Facultad de Ciencias, Universidad Nacional de Colombia, Ciudad Universitaria, Avenida Carrera 30 N° 45-03, Bogota, D.C., Colombia
| | - Yael Abreu-Villaça
- Laboratório de Neurofisiologia, Departamento de Ciências Fisiológicas, Instituto de Biologia Roberto Alcantara Gomes, Universidade Do Estado Do Rio de Janeiro (UERJ), Rio de Janeiro, RJ, 20550-170, Brazil
| | - Alex Christian Manhães
- Laboratório de Neurofisiologia, Departamento de Ciências Fisiológicas, Instituto de Biologia Roberto Alcantara Gomes, Universidade Do Estado Do Rio de Janeiro (UERJ), Rio de Janeiro, RJ, 20550-170, Brazil
| | - Nelson Enrique Arenas
- Facultad de Medicina, Universidad de Cartagena, Campus Zaragocilla, Barrio Zaragocilla, Carrera 50a #24-63, Cartagena de Indias, Bolivar, Colombia.
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Yudintceva N, Bobkov D, Sulatsky M, Mikhailova N, Oganesyan E, Vinogradova T, Muraviov A, Remezova A, Bogdanova E, Garapach I, Maslak O, Esmedlyaeva D, Dyakova M, Yablonskiy P, Ziganshin R, Kovalchuk S, Blum N, Sonawane SH, Sonawane A, Behl A, Shailja Singh, Shevtsov M. Mesenchymal stem cells-derived extracellular vesicles for therapeutics of renal tuberculosis. Sci Rep 2024; 14:4495. [PMID: 38402260 PMCID: PMC10894196 DOI: 10.1038/s41598-024-54992-z] [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: 11/11/2023] [Accepted: 02/19/2024] [Indexed: 02/26/2024] Open
Abstract
Extrapulmonary tuberculosis with a renal involvement can be a manifestation of a disseminated infection that requires therapeutic intervention, particularly with a decrease in efficacy of conventional regimens. In the present study, we investigated the therapeutic potency of mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) in the complex anti-tuberculosis treatment (ATT). A rabbit model of renal tuberculosis (rTB) was constructed by injecting of the standard strain Mycobacterium tuberculosis H37Rv into the cortical layer of the kidney parenchyma. Isolated rabbit MSC-EVs were intravenously administered once as an addition to standard ATT (isoniazid, pyrazinamide, and ethambutol). The therapeutic efficacy was assessed by analyzing changes of blood biochemical biomarkers and levels of anti- and pro-inflammatory cytokines as well as by renal computed tomography with subsequent histological and morphometric examination. The therapeutic effect of therapy with MSC-EVs was shown by ELISA method that confirmed a statistically significant increase of the anti-inflammatory and decrease of pro-inflammatory cytokines as compared to conventional treatment. In addition, there is a positive trend in increase of ALP level, animal weigh, and normalization of ADA activity that can indicate an improvement of kidney state. A significant reduction of the area of specific and interstitial inflammation indicated positive affect of MSC-EVs that suggests a shorter duration of ATT. The number of MSC-EVs proteins (as identified by mass-spectometry analysis) with anti-microbial, anti-inflammatory and immunoregulatory functions reduced the level of the inflammatory response and the severity of kidney damage (further proved by morphometric analysis). In conclusion, MSC-EVs can be a promising tool for the complex treatment of various infectious diseases, in particularly rTB.
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Affiliation(s)
- Natalia Yudintceva
- Institute of Cytology of the Russian Academy of Sciences (RAS), Tikhoretsky Ave., 4, Saint Petersburg, Russia, 194064.
| | - Danila Bobkov
- Institute of Cytology of the Russian Academy of Sciences (RAS), Tikhoretsky Ave., 4, Saint Petersburg, Russia, 194064
| | - Maksim Sulatsky
- Institute of Cytology of the Russian Academy of Sciences (RAS), Tikhoretsky Ave., 4, Saint Petersburg, Russia, 194064
| | - Natalia Mikhailova
- Institute of Cytology of the Russian Academy of Sciences (RAS), Tikhoretsky Ave., 4, Saint Petersburg, Russia, 194064
| | - Elena Oganesyan
- Personalized Medicine Centre, Almazov National Medical Research Centre, Akkuratova Str. 2, Saint Petersburg, Russia, 197341
| | - Tatiana Vinogradova
- Saint-Petersburg State Research Institute of Phthisiopulmonology of the Ministry of Healthcare of the Russian Federation, Ligovsky Ave., 2-4, Saint Petersburg, Russia, 191036
| | - Alexandr Muraviov
- Saint-Petersburg State Research Institute of Phthisiopulmonology of the Ministry of Healthcare of the Russian Federation, Ligovsky Ave., 2-4, Saint Petersburg, Russia, 191036
- Private University St. Petersburg Medico-Social Institute, Kondratievskiy Ave., 72A, Saint Petersburg, Russia, 195271
| | - Anna Remezova
- Saint-Petersburg State Research Institute of Phthisiopulmonology of the Ministry of Healthcare of the Russian Federation, Ligovsky Ave., 2-4, Saint Petersburg, Russia, 191036
| | - Evdokia Bogdanova
- Saint-Petersburg State Research Institute of Phthisiopulmonology of the Ministry of Healthcare of the Russian Federation, Ligovsky Ave., 2-4, Saint Petersburg, Russia, 191036
| | - Irina Garapach
- Saint-Petersburg State Research Institute of Phthisiopulmonology of the Ministry of Healthcare of the Russian Federation, Ligovsky Ave., 2-4, Saint Petersburg, Russia, 191036
| | - Olga Maslak
- Saint-Petersburg State Research Institute of Phthisiopulmonology of the Ministry of Healthcare of the Russian Federation, Ligovsky Ave., 2-4, Saint Petersburg, Russia, 191036
| | - Dilyara Esmedlyaeva
- Saint-Petersburg State Research Institute of Phthisiopulmonology of the Ministry of Healthcare of the Russian Federation, Ligovsky Ave., 2-4, Saint Petersburg, Russia, 191036
| | - Marina Dyakova
- Saint-Petersburg State Research Institute of Phthisiopulmonology of the Ministry of Healthcare of the Russian Federation, Ligovsky Ave., 2-4, Saint Petersburg, Russia, 191036
| | - Petr Yablonskiy
- Saint-Petersburg State Research Institute of Phthisiopulmonology of the Ministry of Healthcare of the Russian Federation, Ligovsky Ave., 2-4, Saint Petersburg, Russia, 191036
| | - Rustam Ziganshin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry Russian Academy of Sciences, Miklukho-Maklaya Str., 16/10, Moscow, Russia, 117997
| | - Sergey Kovalchuk
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry Russian Academy of Sciences, Miklukho-Maklaya Str., 16/10, Moscow, Russia, 117997
| | - Natalya Blum
- Kirov Military Medical Academy, Akademika Lebedeva Str., 6, Saint Petersburg, Russia, 194044
| | | | | | - Ankita Behl
- Special Centre for Molecular Medicine, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Shailja Singh
- Special Centre for Molecular Medicine, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Maxim Shevtsov
- Institute of Cytology of the Russian Academy of Sciences (RAS), Tikhoretsky Ave., 4, Saint Petersburg, Russia, 194064.
- Department of Radiation Oncology, Central Institute for Translational Cancer Research (TranslaTUM), Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
- School of Medicine and Life Sciences, Far Eastern Federal University, Campus 10 Ajax Bay, Russky Island, Vladivostok, Russia, 690922.
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Prithvisagar KS, Gollapalli P, D’Souza C, Rai P, Karunasagar I, Karunasagar I, Ballamoole KK. Genome analysis of clinical genotype Vibrio vulnificus isolated from seafood in Mangaluru Coast, India provides insights into its pathogenicity. Vet Q 2023; 43:1-17. [PMID: 37478018 PMCID: PMC10438861 DOI: 10.1080/01652176.2023.2240389] [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: 11/29/2022] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 07/23/2023] Open
Abstract
Vibrio vulnificus an opportunistic human pathogen native to marine/estuarine environment, is one of the leading causes of death due to seafood consumption and exposure of wounds to seawater worldwide. The present study involves the whole genome sequence analysis of an environmental strain of V. vulnificus (clinical genotype) isolated from seafood along the Mangaluru coast of India. The sequenced genome data was subjected to in-silico analysis of phylogeny, virulence genes, antimicrobial resistance determinants, and secretary proteins using suitable bioinformatics tools. The sequenced isolate had an overall genome length of 4.8 Mb and GC content of 46% with 4400 coding DNA sequences. The sequenced strain belongs to a new sequence type (Multilocus sequence typing) and was also found to branch with a phylogenetic lineage that groups the most infectious strains of V. vulnificus. The seafood isolate had complete genes involved in conferring serum resistance yet showed limited serum resistance. The study identified several genes against the antibiotics that are commonly used in their treatment, highlighting the need for alternative treatments. Also, the secretory protein analysis revealed genes associated with major pathways like ABC transporters, two-component systems, quorum sensing, biofilm formation, cationic antimicrobial peptide (CAMP) resistance, and others that play a critical role in the pathogenesis of the V. vulnificus. To the best of our knowledge, this is the first report of a detailed analysis of the genomic information of a V. vulnificus isolated from the Indian subcontinent and provides evidence that raises public health concerns about the safety of seafood.
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Affiliation(s)
- Kattapuni Suresh Prithvisagar
- Department of Infectious Diseases and Microbial Genomics, Nitte University Centre for Science Education and Research, Nitte (Deemed to be University), Mangaluru, India
| | - Pavan Gollapalli
- Center for Bioinformatics and Biostatistics, Nitte (Deemed to be University), Mangaluru, India
| | - Caroline D’Souza
- Department of Infectious Diseases and Microbial Genomics, Nitte University Centre for Science Education and Research, Nitte (Deemed to be University), Mangaluru, India
| | - Praveen Rai
- Department of Infectious Diseases and Microbial Genomics, Nitte University Centre for Science Education and Research, Nitte (Deemed to be University), Mangaluru, India
| | - Iddya Karunasagar
- Department of Infectious Diseases and Microbial Genomics, Nitte University Centre for Science Education and Research, Nitte (Deemed to be University), Mangaluru, India
| | - Indrani Karunasagar
- Department of Infectious Diseases and Microbial Genomics, Nitte University Centre for Science Education and Research, Nitte (Deemed to be University), Mangaluru, India
| | - Krishna Kumar Ballamoole
- Department of Infectious Diseases and Microbial Genomics, Nitte University Centre for Science Education and Research, Nitte (Deemed to be University), Mangaluru, India
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Monterde B, Rojano E, Córdoba-Caballero J, Seoane P, Perkins JR, Medina MÁ, Ranea JAG. Integrating differential expression, co-expression and gene network analysis for the identification of common genes associated with tumor angiogenesis deregulation. J Biomed Inform 2023; 144:104421. [PMID: 37315831 DOI: 10.1016/j.jbi.2023.104421] [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: 02/27/2023] [Revised: 06/05/2023] [Accepted: 06/09/2023] [Indexed: 06/16/2023]
Abstract
Angiogenesis is essential for tumor growth and cancer metastasis. Identifying the molecular pathways involved in this process is the first step in the rational design of new therapeutic strategies to improve cancer treatment. In recent years, RNA-seq data analysis has helped to determine the genetic and molecular factors associated with different types of cancer. In this work we performed integrative analysis using RNA-seq data from human umbilical vein endothelial cells (HUVEC) and patients with angiogenesis-dependent diseases to find genes that serve as potential candidates to improve the prognosis of tumor angiogenesis deregulation and understand how this process is orchestrated at the genetic and molecular level. We downloaded four RNA-seq datasets (including cellular models of tumor angiogenesis and ischaemic heart disease) from the Sequence Read Archive. Our integrative analysis includes a first step to determine differentially and co-expressed genes. For this, we used the ExpHunter Suite, an R package that performs differential expression, co-expression and functional analysis of RNA-seq data. We used both differentially and co-expressed genes to explore the human gene interaction network and determine which genes were found in the different datasets that may be key for the angiogenesis deregulation. Finally, we performed drug repositioning analysis to find potential targets related to angiogenesis inhibition. We found that that among the transcriptional alterations identified, SEMA3D and IL33 genes are deregulated in all datasets. Microenvironment remodeling, cell cycle, lipid metabolism and vesicular transport are the main molecular pathways affected. In addition to this, interacting genes are involved in intracellular signaling pathways, especially in immune system and semaphorins, respiratory electron transport and fatty acid metabolism. The methodology presented here can be used for finding common transcriptional alterations in other genetically-based diseases.
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Affiliation(s)
- Beatriz Monterde
- Departamento de Señalización Celular y Molecular, Instituto de Biomedicina y Biotecnología de Cantabria, Universidad de Cantabria-CSIC., C/Albert Einstein, 22, Santander, 39011, Spain
| | - Elena Rojano
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Bulevar Louis Pasteur, 31, Málaga, 29010, Spain; Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA-Plataforma BIONAND), C/ Severo Ochoa, 35, Parque Tecnológico de Andalucía (PTA), Campanillas, Málaga, 29590, Spain
| | - José Córdoba-Caballero
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Bulevar Louis Pasteur, 31, Málaga, 29010, Spain; Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), Avda. Ana de Viya, 21, Cádiz, 11009, Spain
| | - Pedro Seoane
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Bulevar Louis Pasteur, 31, Málaga, 29010, Spain; Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA-Plataforma BIONAND), C/ Severo Ochoa, 35, Parque Tecnológico de Andalucía (PTA), Campanillas, Málaga, 29590, Spain; CIBER de Enfermedades Raras (CIBERER), Avda. Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid, 28029, Spain.
| | - James R Perkins
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Bulevar Louis Pasteur, 31, Málaga, 29010, Spain; Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA-Plataforma BIONAND), C/ Severo Ochoa, 35, Parque Tecnológico de Andalucía (PTA), Campanillas, Málaga, 29590, Spain; CIBER de Enfermedades Raras (CIBERER), Avda. Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid, 28029, Spain
| | - Miguel Ángel Medina
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Bulevar Louis Pasteur, 31, Málaga, 29010, Spain; Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA-Plataforma BIONAND), C/ Severo Ochoa, 35, Parque Tecnológico de Andalucía (PTA), Campanillas, Málaga, 29590, Spain; CIBER de Enfermedades Raras (CIBERER), Avda. Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid, 28029, Spain
| | - Juan A G Ranea
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Bulevar Louis Pasteur, 31, Málaga, 29010, Spain; Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA-Plataforma BIONAND), C/ Severo Ochoa, 35, Parque Tecnológico de Andalucía (PTA), Campanillas, Málaga, 29590, Spain; CIBER de Enfermedades Raras (CIBERER), Avda. Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid, 28029, Spain; Spanish National Bioinformatics Institute (INB/ELIXIR-ES), Instituto de Salud Carlos III (ISCIII), C/ Sinesio Delgado, 4, Madrid, 28029, Spain
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Huang BS, Chen CT, Yeh CC, Fan TY, Chen FY, Liou JM, Shun CT, Wu MS, Chow LP. miR-21 Targets ASPP2 to Inhibit Apoptosis via CHOP-Mediated Signaling in Helicobacter pylori-Infected Gastric Cancer Cells. JOURNAL OF ONCOLOGY 2023; 2023:6675265. [PMID: 37547633 PMCID: PMC10403333 DOI: 10.1155/2023/6675265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 06/05/2023] [Accepted: 06/21/2023] [Indexed: 08/08/2023]
Abstract
Helicobacter pylori (H. pylori) infection affects cell survival pathways, including apoptosis and proliferation in host cells, and disruption of this balance is the key event in the development of H. pylori-induced gastric cancer (HPGC). H. pylori infection induces alterations in microRNAs expression that may be involved in GC development. Bioinformatic analysis showed that microRNA-21 (miR-21) is significantly upregulated in HPGC. Furthermore, quantitative proteomics and in silico prediction were employed to identify potential targets of miR-21. Following functional enrichment and clustered interaction network analyses, five candidates of miR-21 targets, PDCD4, ASPP2, DAXX, PIK3R1, and MAP3K1, were found across three functional clusters in association with cell death and survival, cellular movement, and cellular growth and proliferation. ASPP2 is inhibited by H. pylori-induced miR-21 overexpression. Moreover, ASPP2 levels are inversely correlated with miR-21 levels in HPGC tumor tissues. Thus, ASPP2 was identified as a miR-21 target in HPGC. Here, we observed that H. pylori-induced ASPP2 suppression enhances resistance to apoptosis in GC cells using apoptosis assays. Using protein interaction network and coimmunoprecipitation assay, we identified CHOP as a direct mediator of the ASPP2 proapoptotic activity in H. pylori-infected GC cells. Mechanistically, ASPP2 suppression promotes p300-mediated CHOP degradation, in turn inhibiting CHOP-mediated transcription of Noxa, Bak, and suppression of Bcl-2 to enact antiapoptosis in the GC cells after H. pylori infection. Clinicopathological analysis revealed correlations between decreased ASPP2 expression and higher HPGC risk and poor prognosis. In summary, the discovery of H. pylori-induced antiapoptosis via miR-21-mediated suppression of ASPP2/CHOP-mediated signaling provides a novel perspective for developing HPGC management and treatment.
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Affiliation(s)
- Bo-Shih Huang
- Graduate Institute of Biochemistry and Molecular Biology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chih-Ta Chen
- Graduate Institute of Biochemistry and Molecular Biology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chao-Chi Yeh
- Graduate Institute of Biochemistry and Molecular Biology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Ting-Yu Fan
- Graduate Institute of Biochemistry and Molecular Biology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Fang-Yun Chen
- Graduate Institute of Biochemistry and Molecular Biology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Jyh-Ming Liou
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chia-Tung Shun
- Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan
| | - Ming-Shiang Wu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Lu-Ping Chow
- Graduate Institute of Biochemistry and Molecular Biology, College of Medicine, National Taiwan University, Taipei, Taiwan
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8
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Manipur I, Giordano M, Piccirillo M, Parashuraman S, Maddalena L. Community Detection in Protein-Protein Interaction Networks and Applications. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:217-237. [PMID: 34951849 DOI: 10.1109/tcbb.2021.3138142] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.
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Lyu J, Yao Z, Liang B, Liu Y, Zhang Y. Small protein complex prediction algorithm based on protein-protein interaction network segmentation. BMC Bioinformatics 2022; 23:405. [PMID: 36180820 PMCID: PMC9524060 DOI: 10.1186/s12859-022-04960-z] [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: 05/05/2022] [Accepted: 09/19/2022] [Indexed: 11/23/2022] Open
Abstract
Background Identifying protein complexes from protein-protein interaction network is one of significant tasks in the postgenome era. Protein complexes, none of which exceeds 10 in size play an irreplaceable role in life activities and are also a hotspot of scientific research, such as PSD-95, CD44, PKM2 and BRD4. And in MIPS, CYC2008, SGD, Aloy and TAP06 datasets, the proportion of small protein complexes is over 75%. But up to now, protein complex identification methods do not perform well in the field of small protein complexes. Results In this paper, we propose a novel method, called BOPS. It is a three-step procedure. Firstly, it calculates the balanced weights to replace the original weights. Secondly, it divides the graphs larger than MAXP until the original PPIN is divided into small PPINs. Thirdly, it enumerates the connected subset of each small PPINs, identifies potential protein complexes based on cohesion and removes those that are similar. Conclusions In four yeast PPINs, experimental results have shown that BOPS has an improvement of about 5% compared with the SOTA model. In addition, we constructed a weighted Homo sapiens PPIN based on STRINGdb and BioGRID, and BOPS gets the best result in it. These results give new insights into the identification of small protein complexes, and the weighted Homo sapiens PPIN provides more data for related research.
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Affiliation(s)
- Jiaqing Lyu
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Zhen Yao
- School of Chemical Engineering, Dalian University of Technology, Dalian, China
| | - Bing Liang
- School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, China.
| | - Yiwei Liu
- School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, China
| | - Yijia Zhang
- School of Information Science and Technology, Dalian Maritime University, Dalian, China.
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10
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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [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: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein-protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
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11
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Wang R, Ma H, Wang C. An Ensemble Learning Framework for Detecting Protein Complexes From PPI Networks. Front Genet 2022; 13:839949. [PMID: 35281831 PMCID: PMC8908451 DOI: 10.3389/fgene.2022.839949] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 01/31/2022] [Indexed: 11/14/2022] Open
Abstract
Detecting protein complexes is one of the keys to understanding cellular organization and processes principles. With high-throughput experiments and computing science development, it has become possible to detect protein complexes by computational methods. However, most computational methods are based on either unsupervised learning or supervised learning. Unsupervised learning-based methods do not need training datasets, but they can only detect one or several topological protein complexes. Supervised learning-based methods can detect protein complexes with different topological structures. However, they are usually based on a type of training model, and the generalization of a single model is poor. Therefore, we propose an Ensemble Learning Framework for Detecting Protein Complexes (ELF-DPC) within protein-protein interaction (PPI) networks to address these challenges. The ELF-DPC first constructs the weighted PPI network by combining topological and biological information. Second, it mines protein complex cores using the protein complex core mining strategy we designed. Third, it obtains an ensemble learning model by integrating structural modularity and a trained voting regressor model. Finally, it extends the protein complex cores and forms protein complexes by a graph heuristic search strategy. The experimental results demonstrate that ELF-DPC performs better than the twelve state-of-the-art approaches. Moreover, functional enrichment analysis illustrated that ELF-DPC could detect biologically meaningful protein complexes. The code/dataset is available for free download from https://github.com/RongquanWang/ELF-DPC.
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Affiliation(s)
- Rongquan Wang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
| | - Huimin Ma
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
- *Correspondence: Huimin Ma,
| | - Caixia Wang
- School of International Economics, China Foreign Affairs University, Beijing, China
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12
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He Z, Zhao C, Liang H, Xu B, Zou Q. Protein Complexes Identification with Family-Wise Error Rate Control. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:2062-2073. [PMID: 31027047 DOI: 10.1109/tcbb.2019.2912602] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The detection of protein complexes from protein-protein interaction network is a fundamental issue in bioinformatics and systems biology. To solve this problem, numerous methods have been proposed from different angles in the past decades. However, the study on detecting statistically significant protein complexes still has not received much attention. Although there are a few methods available in the literature for identifying statistically significant protein complexes, none of these methods can provide a more strict control on the error rate of a protein complex in terms of family-wise error rate (FWER). In this paper, we propose a new detection method SSF that is capable of controlling the FWER of each reported protein complex. More precisely, we first present a p-value calculation method based on Fisher's exact test to quantify the association between each protein and a given candidate protein complex. Consequently, we describe the key modules of the SSF algorithm: a seed expansion procedure for significant protein complexes search and a set cover strategy for redundancy elimination. The experimental results on five benchmark data sets show that: (1) our method can achieve the highest precision; (2) it outperforms three competing methods in terms of normalized mutual information (NMI) and F1 score in most cases.
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13
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Ying KC, Lin SW. Maximizing cohesion and separation for detecting protein functional modules in protein-protein interaction networks. PLoS One 2020; 15:e0240628. [PMID: 33048996 PMCID: PMC7553341 DOI: 10.1371/journal.pone.0240628] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 09/29/2020] [Indexed: 12/26/2022] Open
Abstract
Protein Function Module (PFM) identification in Protein-Protein Interaction Networks (PPINs) is one of the most important and challenging tasks in computational biology. The quick and accurate detection of PFMs in PPINs can contribute greatly to the understanding of the functions, properties, and biological mechanisms in research on various diseases and the development of new medicines. Despite the performance of existing detection approaches being improved to some extent, there are still opportunities for further enhancements in the efficiency, accuracy, and robustness of such detection methods. Based on the uniqueness of the network-clustering problem in the context of PPINs, this study proposed a very effective and efficient model based on the Lin-Kernighan-Helsgaun algorithm for detecting PFMs in PPINs. To demonstrate the effectiveness and efficiency of the proposed model, computational experiments are performed using three different categories of species datasets. The computational results reveal that the proposed model outperforms existing detection techniques in terms of two key performance indices, i.e., the degree of polymerization inside PFMs (cohesion) and the deviation degree between PFMs (separation), while being very fast and robust. The proposed model can be used to help researchers decide whether to conduct further expensive and time-consuming biological experiments and to select target proteins from large-scale PPI data for further detailed research.
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Affiliation(s)
- Kuo-Ching Ying
- Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan
| | - Shih-Wei Lin
- Department of Information Management, Chang Gung University, Taoyuan, Taiwan
- Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Ming Chi University of Technology, Taipei, Taiwan
- * E-mail:
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Li M, Meng X, Zheng R, Wu FX, Li Y, Pan Y, Wang J. Identification of Protein Complexes by Using a Spatial and Temporal Active Protein Interaction Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:817-827. [PMID: 28885159 DOI: 10.1109/tcbb.2017.2749571] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The rapid development of proteomics and high-throughput technologies has produced a large amount of Protein-Protein Interaction (PPI) data, which makes it possible for considering dynamic properties of protein interaction networks (PINs) instead of static properties. Identification of protein complexes from dynamic PINs becomes a vital scientific problem for understanding cellular life in the post genome era. Up to now, plenty of models or methods have been proposed for the construction of dynamic PINs to identify protein complexes. However, most of the constructed dynamic PINs just focus on the temporal dynamic information and thus overlook the spatial dynamic information of the complex biological systems. To address the limitation of the existing dynamic PIN analysis approaches, in this paper, we propose a new model-based scheme for the construction of the Spatial and Temporal Active Protein Interaction Network (ST-APIN) by integrating time-course gene expression data and subcellular location information. To evaluate the efficiency of ST-APIN, the commonly used classical clustering algorithm MCL is adopted to identify protein complexes from ST-APIN and the other three dynamic PINs, NF-APIN, DPIN, and TC-PIN. The experimental results show that, the performance of MCL on ST-APIN outperforms those on the other three dynamic PINs in terms of matching with known complexes, sensitivity, specificity, and f-measure. Furthermore, we evaluate the identified protein complexes by Gene Ontology (GO) function enrichment analysis. The validation shows that the identified protein complexes from ST-APIN are more biologically significant. This study provides a general paradigm for constructing the ST-APINs, which is essential for further understanding of molecular systems and the biomedical mechanism of complex diseases.
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15
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Cheng L, Liu P, Wang D, Leung KS. Exploiting locational and topological overlap model to identify modules in protein interaction networks. BMC Bioinformatics 2019; 20:23. [PMID: 30642247 PMCID: PMC6332531 DOI: 10.1186/s12859-019-2598-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 01/03/2019] [Indexed: 12/27/2022] Open
Abstract
Background Clustering molecular network is a typical method in system biology, which is effective in predicting protein complexes or functional modules. However, few studies have realized that biological molecules are spatial-temporally regulated to form a dynamic cellular network and only a subset of interactions take place at the same location in cells. Results In this study, considering the subcellular localization of proteins, we first construct a co-localization human protein interaction network (PIN) and systematically investigate the relationship between subcellular localization and biological functions. After that, we propose a Locational and Topological Overlap Model (LTOM) to preprocess the co-localization PIN to identify functional modules. LTOM requires the topological overlaps, the common partners shared by two proteins, to be annotated in the same localization as the two proteins. We observed the model has better correspondence with the reference protein complexes and shows more relevance to cancers based on both human and yeast datasets and two clustering algorithms, ClusterONE and MCL. Conclusion Taking into consideration of protein localization and topological overlap can improve the performance of module detection from protein interaction networks. Electronic supplementary material The online version of this article (10.1186/s12859-019-2598-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lixin Cheng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong. .,Institute of translation medicine, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, China.
| | - Pengfei Liu
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Dong Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong.
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16
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Xu B, Li K, Zheng W, Liu X, Zhang Y, Zhao Z, He Z. Protein complexes identification based on go attributed network embedding. BMC Bioinformatics 2018; 19:535. [PMID: 30572820 PMCID: PMC6302388 DOI: 10.1186/s12859-018-2555-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 11/30/2018] [Indexed: 01/19/2023] Open
Abstract
Background Identifying protein complexes from protein-protein interaction (PPI) network is one of the most important tasks in proteomics. Existing computational methods try to incorporate a variety of biological evidences to enhance the quality of predicted complexes. However, it is still a challenge to integrate different types of biological information into the complexes discovery process under a unified framework. Recently, attributed network embedding methods have be proved to be remarkably effective in generating vector representations for nodes in the network. In the transformed vector space, both the topological proximity and node attributed affinity between different nodes are preserved. Therefore, such attributed network embedding methods provide us a unified framework to integrate various biological evidences into the protein complexes identification process. Results In this article, we propose a new method called GANE to predict protein complexes based on Gene Ontology (GO) attributed network embedding. Firstly, it learns the vector representation for each protein from a GO attributed PPI network. Based on the pair-wise vector representation similarity, a weighted adjacency matrix is constructed. Secondly, it uses the clique mining method to generate candidate cores. Consequently, seed cores are obtained by ranking candidate cores based on their densities on the weighted adjacency matrix and removing redundant cores. For each seed core, its attachments are the proteins with correlation score that is larger than a given threshold. The combination of a seed core and its attachment proteins is reported as a predicted protein complex by the GANE algorithm. For performance evaluation, we compared GANE with six protein complex identification methods on five yeast PPI networks. Experimental results showes that GANE performs better than the competing algorithms in terms of different evaluation metrics. Conclusions GANE provides a framework that integrate many valuable and different biological information into the task of protein complex identification. The protein vector representation learned from our attributed PPI network can also be used in other tasks, such as PPI prediction and disease gene prediction. Electronic supplementary material The online version of this article (10.1186/s12859-018-2555-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bo Xu
- School of Software Technology, Dalian University of Technology, No.321 Tuqiang Road, Economic Development Zone, Dalian, 116024, China. .,Key Laboratory for Ubiquitous Network and Service Software of Liaoning, Dalian, 116000, China.
| | - Kun Li
- School of Software Technology, Dalian University of Technology, No.321 Tuqiang Road, Economic Development Zone, Dalian, 116024, China
| | - Wei Zheng
- College of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, China.,College of software, Dalian JiaoTong University, Dalian, 116000, China
| | - Xiaoxia Liu
- College of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, China
| | - Yijia Zhang
- College of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, China
| | - Zhehuan Zhao
- School of Software Technology, Dalian University of Technology, No.321 Tuqiang Road, Economic Development Zone, Dalian, 116024, China.,Key Laboratory for Ubiquitous Network and Service Software of Liaoning, Dalian, 116000, China
| | - Zengyou He
- School of Software Technology, Dalian University of Technology, No.321 Tuqiang Road, Economic Development Zone, Dalian, 116024, China.,Key Laboratory for Ubiquitous Network and Service Software of Liaoning, Dalian, 116000, China
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17
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Abstract
We provide computational protocols to identify chaperone interacting proteins using a combination of both physical (protein-protein) and genetic (gene-gene or epistatic) interaction data derived from the published large-scale proteomic and genomic studies for the budding yeast Saccharomyces cerevisiae. Using these datasets, we discuss bioinformatic analyses that can be employed to build comprehensive high-fidelity chaperone interaction networks. Given that many proteins typically function as complexes in the cell, we highlight various step-wise approaches for combining both the genetic and physical interaction datasets to decipher intra- and inter-connections for distinct chaperone- and non-chaperone-containing complexes in the network. Together, these informatics procedures will aid in identifying protein complexes with distinctive functional specializations in the cell that yield a very broad and diverse set of interactions. The described procedures can also be leveraged to datasets from other eukaryotes, including humans.
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18
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Ou-Yang L, Yan H, Zhang XF. A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks. BMC Bioinformatics 2017; 18:463. [PMID: 29219066 PMCID: PMC5773919 DOI: 10.1186/s12859-017-1877-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023] Open
Abstract
Background The accurate identification of protein complexes is important for the understanding of cellular organization. Up to now, computational methods for protein complex detection are mostly focus on mining clusters from protein-protein interaction (PPI) networks. However, PPI data collected by high-throughput experimental techniques are known to be quite noisy. It is hard to achieve reliable prediction results by simply applying computational methods on PPI data. Behind protein interactions, there are protein domains that interact with each other. Therefore, based on domain-protein associations, the joint analysis of PPIs and domain-domain interactions (DDI) has the potential to obtain better performance in protein complex detection. As traditional computational methods are designed to detect protein complexes from a single PPI network, it is necessary to design a new algorithm that could effectively utilize the information inherent in multiple heterogeneous networks. Results In this paper, we introduce a novel multi-network clustering algorithm to detect protein complexes from multiple heterogeneous networks. Unlike existing protein complex identification algorithms that focus on the analysis of a single PPI network, our model can jointly exploit the information inherent in PPI and DDI data to achieve more reliable prediction results. Extensive experiment results on real-world data sets demonstrate that our method can predict protein complexes more accurately than other state-of-the-art protein complex identification algorithms. Conclusions In this work, we demonstrate that the joint analysis of PPI network and DDI network can help to improve the accuracy of protein complex detection.
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Affiliation(s)
- Le Ou-Yang
- College of Information Engineering & Shenzhen Key Laboratory of Media Security, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China
| | - Hong Yan
- College of Information Engineering & Shenzhen Key Laboratory of Media Security, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China.,Department of Electronic and Engineering, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China
| | - Xiao-Fei Zhang
- School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, 430079, China.
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Liu C, Liu L, Zhou C, Zhuang J, Wang L, Sun Y, Sun C. Protein-protein interaction networks and different clustering analysis in Burkitt's lymphoma. ACTA ACUST UNITED AC 2017; 23:391-398. [PMID: 29189103 DOI: 10.1080/10245332.2017.1409947] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
OBJECTIVE Burkitt's lymphoma (BL) is a highly aggressive malignant lymphoma, its molecular biological mechanism has not been fully investigated. The construction of protein-protein interaction (PPI) networks and the identification of complexes through a cluster analysis are important research directions in the post-genome era. However, different cluster analysis algorithms have their own characteristics, and a single analysis has some limitations. In this study, we obtained the target and pathway information of BL using different clustering analyses. MATERIAL AND METHODS First, we obtained 50 BL genes by screening the Online Mendelian Inheritance in Man (OMIM) database; their related genes were further extracted from the literature. The PPI network was constructed with the Search Tool for Retrieval of Interacting Genes/Proteins (STRING). Afterward, the interaction data were input in Cytoscape3.4.0 software and related plug-ins were used to implement topology analysis and clustering analysis. Functional analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were used to characterize the biological importance of the clusters. RESULTS We constructed a PPI network consisting of 459 nodes (proteins) and 1399 sides (interactions), 12 genes and 8 signaling pathways were found to be closely related to BL. CONCLUSION In this study, the use of combined algorithms to analyse gene interactions provides a new perspective for network-based analysis. The results of this study reveal new insights into the molecular mechanisms underlying BL, which may be novel therapeutic targets for disease management and may provide a bioinformatic basis for the further understanding of BL.
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Affiliation(s)
- Cun Liu
- a College of Traditional Chinese Medicine , Shandong University of Traditional Chinese Medicine , Jinan , Shandong Province , People's Republic of China
| | - Lijuan Liu
- b Department of oncology , Weifang Traditional Chinese Hospital , Weifang , Shandong Province , People's Republic of China
| | - Chao Zhou
- b Department of oncology , Weifang Traditional Chinese Hospital , Weifang , Shandong Province , People's Republic of China
| | - Jing Zhuang
- b Department of oncology , Weifang Traditional Chinese Hospital , Weifang , Shandong Province , People's Republic of China
| | - Lu Wang
- a College of Traditional Chinese Medicine , Shandong University of Traditional Chinese Medicine , Jinan , Shandong Province , People's Republic of China
| | - Yue Sun
- c Weifang Medical University , Weifang , Shandong Province , People's Republic of China
| | - Changgang Sun
- b Department of oncology , Weifang Traditional Chinese Hospital , Weifang , Shandong Province , People's Republic of China
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20
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Glatigny A, Gambette P, Bourand-Plantefol A, Dujardin G, Mucchielli-Giorgi MH. Development of an in silico method for the identification of subcomplexes involved in the biogenesis of multiprotein complexes in Saccharomyces cerevisiae. BMC SYSTEMS BIOLOGY 2017; 11:67. [PMID: 28693620 PMCID: PMC5504824 DOI: 10.1186/s12918-017-0442-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 06/28/2017] [Indexed: 11/23/2022]
Abstract
Background Large sets of protein-protein interaction data coming either from biological experiments or predictive methods are available and can be combined to construct networks from which information about various cell processes can be extracted. We have developed an in silico approach based on these information to model the biogenesis of multiprotein complexes in the yeast Saccharomyces cerevisiae. Results Firstly, we have built three protein interaction networks by collecting the protein-protein interactions, which involved the subunits of three complexes, from different databases. The protein-protein interactions come from different kinds of biological experiments or are predicted. We have chosen the elongator and the mediator head complexes that are soluble and exhibit an architecture with subcomplexes that could be functional modules, and the mitochondrial bc1 complex, which is an integral membrane complex and for which a late assembly subcomplex has been described. Secondly, by applying a clustering strategy to these networks, we were able to identify subcomplexes involved in the biogenesis of the complexes as well as the proteins interacting with each subcomplex. Thirdly, in order to validate our in silico results for the cytochrome bc1 complex we have analysed the physical interactions existing between three subunits by performing immunoprecipitation experiments in several genetic context. Conclusions For the two soluble complexes (the elongator and mediator head), our model shows a strong clustering of subunits that belong to a known subcomplex or module. For the membrane bc1 complex, our approach has suggested new interactions between subunits in the early steps of the assembly pathway that were experimentally confirmed. Scripts can be downloaded from the site: http://bim.igmors.u-psud.fr/isips. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0442-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Annie Glatigny
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Sud, Université Paris-Saclay, Avenue de la Terrasse, 91198, Gif-sur-Yvette, France
| | - Philippe Gambette
- Université Paris-Est, LIGM (UMR 8049), CNRS, ENPC, ESIEE, UPEM, 77454, Champs-sur-Marne, France
| | - Alexa Bourand-Plantefol
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Sud, Université Paris-Saclay, Avenue de la Terrasse, 91198, Gif-sur-Yvette, France
| | - Geneviève Dujardin
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Sud, Université Paris-Saclay, Avenue de la Terrasse, 91198, Gif-sur-Yvette, France
| | - Marie-Hélène Mucchielli-Giorgi
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Sud, Université Paris-Saclay, Avenue de la Terrasse, 91198, Gif-sur-Yvette, France. .,Sorbonne Universités, UPMC Univ Paris 06, UFR927, F-75005, Paris, France.
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Dutra de Souza J, de Andrade Silva EM, Coelho Filho MA, Morillon R, Bonatto D, Micheli F, da Silva Gesteira A. Different adaptation strategies of two citrus scion/rootstock combinations in response to drought stress. PLoS One 2017; 12:e0177993. [PMID: 28545114 PMCID: PMC5435350 DOI: 10.1371/journal.pone.0177993] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2017] [Accepted: 05/05/2017] [Indexed: 01/31/2023] Open
Abstract
Scion/rootstock interaction is important for plant development and for breeding programs. In this context, polyploid rootstocks presented several advantages, mainly in relation to biotic and abiotic stresses. Here we analyzed the response to drought of two different scion/rootstock combinations presenting different polyploidy: the diploid (2x) and autotetraploid (4x) Rangpur lime (Citrus limonia, Osbeck) rootstocks grafted with 2x Valencia Delta sweet orange (Citrus sinensis) scions, named V/2xRL and V/4xRL, respectively. Based on previous gene expression data, we developed an interactomic approach to identify proteins involved in V/2xRL and V/4xRL response to drought. A main interactomic network containing 3,830 nodes and 97,652 edges was built from V/2xRL and V/4xRL data. Exclusive proteins of the V/2xRL and V/4xRL networks (2,056 and 1,001, respectively), as well as common to both networks (773) were identified. Functional clusters were obtained and two models of drought stress response for the V/2xRL and V/4xRL genotypes were designed. Even if the V/2xRL plant implement some tolerance mechanisms, the global plant response to drought was rapid and quickly exhaustive resulting in a general tendency to dehydration avoidance, which presented some advantage in short and strong drought stress conditions, but which, in long terms, does not allow the plant survival. At the contrary, the V/4xRL plants presented a response which strong impacts on development but that present some advantages in case of prolonged drought. Finally, some specific proteins, which presented high centrality on interactomic analysis were identified as good candidates for subsequent functional analysis of citrus genes related to drought response, as well as be good markers of one or another physiological mechanism implemented by the plants.
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Affiliation(s)
- Joadson Dutra de Souza
- Universidade Estadual de Santa Cruz (UESC), Departamento de Ciências Biológicas (DCB), Centro de Biotecnologia e Genética (CBG), Rodovia Ilhéus-Itabuna, Ilhéus-BA, Brazil
| | - Edson Mario de Andrade Silva
- Universidade Estadual de Santa Cruz (UESC), Departamento de Ciências Biológicas (DCB), Centro de Biotecnologia e Genética (CBG), Rodovia Ilhéus-Itabuna, Ilhéus-BA, Brazil
| | - Mauricio Antônio Coelho Filho
- Embrapa Mandioca e Fruticultura, Departamento de Biologia Molecular, Rua Embrapa, s/n°, Cruz das Almas, Bahia, Brazil
| | | | - Diego Bonatto
- Universidade Federal do Rio Grande do Sul (UFRGS), Departamento de Biologia Molecular e Biotecnologia, Centro de Biotecnologia, Avenida Bento Goncalves 9500–Predio 43421, Porto Alegre-RS, Brazil
| | - Fabienne Micheli
- Universidade Estadual de Santa Cruz (UESC), Departamento de Ciências Biológicas (DCB), Centro de Biotecnologia e Genética (CBG), Rodovia Ilhéus-Itabuna, Ilhéus-BA, Brazil
- CIRAD, UMR AGAP, Montpellier, France
- * E-mail:
| | - Abelmon da Silva Gesteira
- Embrapa Mandioca e Fruticultura, Departamento de Biologia Molecular, Rua Embrapa, s/n°, Cruz das Almas, Bahia, Brazil
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Zhu L, Deng SP, You ZH, Huang DS. Identifying Spurious Interactions in the Protein-Protein Interaction Networks Using Local Similarity Preserving Embedding. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:345-352. [PMID: 28368812 DOI: 10.1109/tcbb.2015.2407393] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In recent years, a remarkable amount of protein-protein interaction (PPI) data are being available owing to the advance made in experimental high-throughput technologies. However, the experimentally detected PPI data usually contain a large amount of spurious links, which could contaminate the analysis of the biological significance of protein links and lead to incorrect biological discoveries, thereby posing new challenges to both computational and biological scientists. In this paper, we develop a new embedding algorithm called local similarity preserving embedding (LSPE) to rank the interaction possibility of protein links. By going beyond limitations of current geometric embedding methods for network denoising and emphasizing the local information of PPI networks, LSPE can avoid the unstableness of previous methods. We demonstrate experimental results on benchmark PPI networks and show that LSPE was the overall leader, outperforming the state-of-the-art methods in topological false links elimination problems.
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23
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Li M, Lu Y, Niu Z, Wu FX. United Complex Centrality for Identification of Essential Proteins from PPI Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:370-380. [PMID: 28368815 DOI: 10.1109/tcbb.2015.2394487] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Essential proteins are indispensable for the survival or reproduction of an organism. Identification of essential proteins is not only necessary for the understanding of the minimal requirements for cellular life, but also important for the disease study and drug design. With the development of high-throughput techniques, a large number of protein-protein interaction data are available, which promotes the studies of essential proteins from the network level. Up to now, though a series of computational methods have been proposed, the prediction precision still needs to be improved. In this paper, we propose a new method, United complex Centrality (UC), to identify essential proteins by integrating the protein complexes with the topological features of protein-protein interaction (PPI) networks. By analyzing the relationship between the essential proteins and the known protein complexes of S. cerevisiae and human, we find that the proteins in complexes are more likely to be essential compared with the proteins not included in any complexes and the proteins appeared in multiple complexes are more inclined to be essential compared to those only appeared in a single complex. Considering that some protein complexes generated by computational methods are inaccurate, we also provide a modified version of UC with parameter alpha, named UC-P. The experimental results show that protein complex information can help identify the essential proteins more accurate both for the PPI network of S. cerevisiae and that of human. The proposed method UC performs obviously better than the eight previously proposed methods (DC, IC, EC, SC, BC, CC, NC, and LAC) for identifying essential proteins.
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24
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Functional Genomics, Genetics, and Bioinformatics 2016. BIOMED RESEARCH INTERNATIONAL 2016; 2016:2625831. [PMID: 27995138 PMCID: PMC5138440 DOI: 10.1155/2016/2625831] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 10/17/2016] [Indexed: 12/05/2022]
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Pellegrini M, Baglioni M, Geraci F. Protein complex prediction for large protein protein interaction networks with the Core&Peel method. BMC Bioinformatics 2016; 17:372. [PMID: 28185552 PMCID: PMC5123419 DOI: 10.1186/s12859-016-1191-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Background Biological networks play an increasingly important role in the exploration of functional modularity and cellular organization at a systemic level. Quite often the first tools used to analyze these networks are clustering algorithms. We concentrate here on the specific task of predicting protein complexes (PC) in large protein-protein interaction networks (PPIN). Currently, many state-of-the-art algorithms work well for networks of small or moderate size. However, their performance on much larger networks, which are becoming increasingly common in modern proteome-wise studies, needs to be re-assessed. Results and discussion We present a new fast algorithm for clustering large sparse networks: Core&Peel, which runs essentially in time and storage O(a(G)m+n) for a network G of n nodes and m arcs, where a(G) is the arboricity of G (which is roughly proportional to the maximum average degree of any induced subgraph in G). We evaluated Core&Peel on five PPI networks of large size and one of medium size from both yeast and homo sapiens, comparing its performance against those of ten state-of-the-art methods. We demonstrate that Core&Peel consistently outperforms the ten competitors in its ability to identify known protein complexes and in the functional coherence of its predictions. Our method is remarkably robust, being quite insensible to the injection of random interactions. Core&Peel is also empirically efficient attaining the second best running time over large networks among the tested algorithms. Conclusions Our algorithm Core&Peel pushes forward the state-of the-art in PPIN clustering providing an algorithmic solution with polynomial running time that attains experimentally demonstrable good output quality and speed on challenging large real networks. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1191-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marco Pellegrini
- Laboratory for Integrative Systems Medicine - Istituto di Informatica e Telematica and Istituto di Fisiologia Clinica del CNR, via Moruzzi 1, Pisa, 56124, Italy.
| | - Miriam Baglioni
- Laboratory for Integrative Systems Medicine - Istituto di Informatica e Telematica and Istituto di Fisiologia Clinica del CNR, via Moruzzi 1, Pisa, 56124, Italy
| | - Filippo Geraci
- Laboratory for Integrative Systems Medicine - Istituto di Informatica e Telematica and Istituto di Fisiologia Clinica del CNR, via Moruzzi 1, Pisa, 56124, Italy
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26
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Folador EL, de Carvalho PVSD, Silva WM, Ferreira RS, Silva A, Gromiha M, Ghosh P, Barh D, Azevedo V, Röttger R. In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks. BMC SYSTEMS BIOLOGY 2016; 10:103. [PMID: 27814699 PMCID: PMC5097352 DOI: 10.1186/s12918-016-0346-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Accepted: 10/18/2016] [Indexed: 12/27/2022]
Abstract
Background Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis. Results Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous. Conclusions The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0346-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Edson Luiz Folador
- Department of General Biology, Instituto de Ciências Biológicas (ICB), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.,Institute of Biological Sciences, Federal University of Para, Belém, PA, Brazil.,Biotechnology Center (CBiotec), Federal University of Paraiba (UFPB), João Pessoa, Brazil
| | - Paulo Vinícius Sanches Daltro de Carvalho
- Department of General Biology, Instituto de Ciências Biológicas (ICB), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Wanderson Marques Silva
- Department of General Biology, Instituto de Ciências Biológicas (ICB), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Rafaela Salgado Ferreira
- Department of Biochemistry and Immunology, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Artur Silva
- Institute of Biological Sciences, Federal University of Para, Belém, PA, Brazil
| | - Michael Gromiha
- Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Tamilnadu, India
| | - Preetam Ghosh
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Debmalya Barh
- Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal, India
| | - Vasco Azevedo
- Department of General Biology, Instituto de Ciências Biológicas (ICB), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Richard Röttger
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
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27
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Jaeger PA, Lucin KM, Britschgi M, Vardarajan B, Huang RP, Kirby ED, Abbey R, Boeve BF, Boxer AL, Farrer LA, Finch N, Graff-Radford NR, Head E, Hofree M, Huang R, Johns H, Karydas A, Knopman DS, Loboda A, Masliah E, Narasimhan R, Petersen RC, Podtelezhnikov A, Pradhan S, Rademakers R, Sun CH, Younkin SG, Miller BL, Ideker T, Wyss-Coray T. Network-driven plasma proteomics expose molecular changes in the Alzheimer's brain. Mol Neurodegener 2016; 11:31. [PMID: 27112350 PMCID: PMC4845325 DOI: 10.1186/s13024-016-0095-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 04/08/2016] [Indexed: 12/17/2022] Open
Abstract
Background Biological pathways that significantly contribute to sporadic Alzheimer’s disease are largely unknown and cannot be observed directly. Cognitive symptoms appear only decades after the molecular disease onset, further complicating analyses. As a consequence, molecular research is often restricted to late-stage post-mortem studies of brain tissue. However, the disease process is expected to trigger numerous cellular signaling pathways and modulate the local and systemic environment, and resulting changes in secreted signaling molecules carry information about otherwise inaccessible pathological processes. Results To access this information we probed relative levels of close to 600 secreted signaling proteins from patients’ blood samples using antibody microarrays and mapped disease-specific molecular networks. Using these networks as seeds we then employed independent genome and transcriptome data sets to corroborate potential pathogenic pathways. Conclusions We identified Growth-Differentiation Factor (GDF) signaling as a novel Alzheimer’s disease-relevant pathway supported by in vivo and in vitro follow-up experiments, demonstrating the existence of a highly informative link between cellular pathology and changes in circulatory signaling proteins. Electronic supplementary material The online version of this article (doi:10.1186/s13024-016-0095-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Philipp A Jaeger
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA. .,Institute of Chemistry and Biochemistry, Free University Berlin, Berlin, Germany. .,Departments of Bioengineering and Medicine, University of California San Diego, La Jolla, CA, USA.
| | - Kurt M Lucin
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.,Present address: Biology Department, Eastern Connecticut State University, Willimantic, CT, USA
| | - Markus Britschgi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.,Present address: Roche Pharma Research and Early Development, NORD DTA, Roche Innovation, Center Basel, Basel, Switzerland
| | - Badri Vardarajan
- Department of Medicine (Biomedical Genetics), Boston University Schools of Medicine, Boston, MA, USA
| | - Ruo-Pan Huang
- RayBiotech, Guangzhou, China.,RayBiotech, Norcrosse, GA, USA
| | - Elizabeth D Kirby
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Rachelle Abbey
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Adam L Boxer
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Lindsay A Farrer
- Department of Medicine (Biomedical Genetics), Boston University Schools of Medicine, Boston, MA, USA.,Departments of Neurology, Ophthalmology, Genetics and Genomics, Epidemiology, and Biostatistics, Boston University Schools of Medicine and Public Health, Boston, MA, USA
| | - NiCole Finch
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | | | - Elizabeth Head
- Departments of Pharmacology and Nutritional Sciences and Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Matan Hofree
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Ruochun Huang
- RayBiotech, Guangzhou, China.,RayBiotech, Norcrosse, GA, USA
| | - Hudson Johns
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Anna Karydas
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | | | - Andrey Loboda
- Genetics and Pharmacogenomics, Merck Research Laboratories, West Point, PA, USA
| | - Eliezer Masliah
- Department of Pathology, University of California San Diego, La Jolla, CA, USA
| | - Ramya Narasimhan
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | - Suraj Pradhan
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Chung-Huan Sun
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Bruce L Miller
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Trey Ideker
- Departments of Bioengineering and Medicine, University of California San Diego, La Jolla, CA, USA
| | - Tony Wyss-Coray
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA. .,Center for Tissue Regeneration, Repair and Restoration, VA Palo Alto Health Care System, Palo Alto, CA, USA.
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28
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Ou-Yang L, Wu M, Zhang XF, Dai DQ, Li XL, Yan H. A two-layer integration framework for protein complex detection. BMC Bioinformatics 2016; 17:100. [PMID: 26911324 PMCID: PMC4765032 DOI: 10.1186/s12859-016-0939-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Accepted: 01/27/2016] [Indexed: 01/05/2023] Open
Abstract
Background Protein complexes carry out nearly all signaling and functional processes within cells. The study of protein complexes is an effective strategy to analyze cellular functions and biological processes. With the increasing availability of proteomics data, various computational methods have recently been developed to predict protein complexes. However, different computational methods are based on their own assumptions and designed to work on different data sources, and various biological screening methods have their unique experiment conditions, and are often different in scale and noise level. Therefore, a single computational method on a specific data source is generally not able to generate comprehensive and reliable prediction results. Results In this paper, we develop a novel Two-layer INtegrative Complex Detection (TINCD) model to detect protein complexes, leveraging the information from both clustering results and raw data sources. In particular, we first integrate various clustering results to construct consensus matrices for proteins to measure their overall co-complex propensity. Second, we combine these consensus matrices with the co-complex score matrix derived from Tandem Affinity Purification/Mass Spectrometry (TAP) data and obtain an integrated co-complex similarity network via an unsupervised metric fusion method. Finally, a novel graph regularized doubly stochastic matrix decomposition model is proposed to detect overlapping protein complexes from the integrated similarity network. Conclusions Extensive experimental results demonstrate that TINCD performs much better than 21 state-of-the-art complex detection techniques, including ensemble clustering and data integration techniques. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-0939-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Le Ou-Yang
- College of Information Engineering, Shenzhen University, Shenzhen, 518060, China. .,Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, China. .,Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.
| | - Min Wu
- Institute for Infocomm Research (I2R), A*STAR, 1 Fusionopolis Way, Singapore, Singapore.
| | - Xiao-Fei Zhang
- School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, 430079, China.
| | - Dao-Qing Dai
- Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, China.
| | - Xiao-Li Li
- Institute for Infocomm Research (I2R), A*STAR, 1 Fusionopolis Way, Singapore, Singapore.
| | - Hong Yan
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.
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Didier G, Brun C, Baudot A. Identifying communities from multiplex biological networks. PeerJ 2015; 3:e1525. [PMID: 26713261 PMCID: PMC4690346 DOI: 10.7717/peerj.1525] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Accepted: 12/01/2015] [Indexed: 02/04/2023] Open
Abstract
Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression). However, this diversity is classically not considered and the different interaction categories are usually aggregated in a single network. The multiplex framework, where biological relationships are represented by different network layers reflecting the various nature of interactions, is expected to retain more information. Here we assessed aggregation, consensus and multiplex-modularity approaches to detect communities from multiple network sources. By simulating random networks, we demonstrated that the multiplex-modularity method outperforms the aggregation and consensus approaches when network layers are incomplete or heterogeneous in density. Application to a multiplex biological network containing 4 layers of physical or functional interactions allowed recovering communities more accurately annotated than their aggregated counterparts. Overall, taking into account the multiplexity of biological networks leads to better-defined functional modules. A user-friendly graphical software to detect communities from multiplex networks, and corresponding C source codes, are available at GitHub (https://github.com/gilles-didier/MolTi).
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Affiliation(s)
- Gilles Didier
- Aix Marseille Université, CNRS, Centrale Marseille, I2M UMR 7373 , Marseille , France
| | - Christine Brun
- Aix Marseille Université, Inserm, TAGC UMR_S1090 , Marseille , France ; CNRS , Marseille , France
| | - Anaïs Baudot
- Aix Marseille Université, CNRS, Centrale Marseille, I2M UMR 7373 , Marseille , France
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30
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Stoney RA, Ames RM, Nenadic G, Robertson DL, Schwartz JM. Disentangling the multigenic and pleiotropic nature of molecular function. BMC SYSTEMS BIOLOGY 2015; 9 Suppl 6:S3. [PMID: 26678917 PMCID: PMC4674882 DOI: 10.1186/1752-0509-9-s6-s3] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Biological processes at the molecular level are usually represented by molecular interaction networks. Function is organised and modularity identified based on network topology, however, this approach often fails to account for the dynamic and multifunctional nature of molecular components. For example, a molecule engaging in spatially or temporally independent functions may be inappropriately clustered into a single functional module. To capture biologically meaningful sets of interacting molecules, we use experimentally defined pathways as spatial/temporal units of molecular activity. RESULTS We defined functional profiles of Saccharomyces cerevisiae based on a minimal set of Gene Ontology terms sufficient to represent each pathway's genes. The Gene Ontology terms were used to annotate 271 pathways, accounting for pathway multi-functionality and gene pleiotropy. Pathways were then arranged into a network, linked by shared functionality. Of the genes in our data set, 44% appeared in multiple pathways performing a diverse set of functions. Linking pathways by overlapping functionality revealed a modular network with energy metabolism forming a sparse centre, surrounded by several denser clusters comprised of regulatory and metabolic pathways. Signalling pathways formed a relatively discrete cluster connected to the centre of the network. Genetic interactions were enriched within the clusters of pathways by a factor of 5.5, confirming the organisation of our pathway network is biologically significant. CONCLUSIONS Our representation of molecular function according to pathway relationships enables analysis of gene/protein activity in the context of specific functional roles, as an alternative to typical molecule-centric graph-based methods. The pathway network demonstrates the cooperation of multiple pathways to perform biological processes and organises pathways into functionally related clusters with interdependent outcomes.
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31
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Quantitative assessment of gene expression network module-validation methods. Sci Rep 2015; 5:15258. [PMID: 26470848 PMCID: PMC4607977 DOI: 10.1038/srep15258] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2015] [Accepted: 09/21/2015] [Indexed: 02/01/2023] Open
Abstract
Validation of pluripotent modules in diverse networks holds enormous potential for systems biology and network pharmacology. An arising challenge is how to assess the accuracy of discovering all potential modules from multi-omic networks and validating their architectural characteristics based on innovative computational methods beyond function enrichment and biological validation. To display the framework progress in this domain, we systematically divided the existing Computational Validation Approaches based on Modular Architecture (CVAMA) into topology-based approaches (TBA) and statistics-based approaches (SBA). We compared the available module validation methods based on 11 gene expression datasets, and partially consistent results in the form of homogeneous models were obtained with each individual approach, whereas discrepant contradictory results were found between TBA and SBA. The TBA of the Zsummary value had a higher Validation Success Ratio (VSR) (51%) and a higher Fluctuation Ratio (FR) (80.92%), whereas the SBA of the approximately unbiased (AU) p-value had a lower VSR (12.3%) and a lower FR (45.84%). The Gray area simulated study revealed a consistent result for these two models and indicated a lower Variation Ratio (VR) (8.10%) of TBA at 6 simulated levels. Despite facing many novel challenges and evidence limitations, CVAMA may offer novel insights into modular networks.
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32
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Wang J, Zhong J, Chen G, Li M, Wu FX, Pan Y. ClusterViz: A Cytoscape APP for Cluster Analysis of Biological Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:815-822. [PMID: 26357321 DOI: 10.1109/tcbb.2014.2361348] [Citation(s) in RCA: 74] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed. In order to reduce complexity and enable extendibility for ClusterViz, we designed the architecture of ClusterViz based on the framework of Open Services Gateway Initiative. According to the architecture, the implementation of ClusterViz is partitioned into three modules including interface of ClusterViz, clustering algorithms and visualization and export. ClusterViz fascinates the comparison of the results of different algorithms to do further related analysis. Three commonly used clustering algorithms, FAG-EC, EAGLE and MCODE, are included in the current version. Due to adopting the abstract interface of algorithms in module of the clustering algorithms, more clustering algorithms can be included for the future use. To illustrate usability of ClusterViz, we provided three examples with detailed steps from the important scientific articles, which show that our tool has helped several research teams do their research work on the mechanism of the biological networks.
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Jiang Y, Qin H, Yang L. Using network clustering to predict copy number variations associated with health disparities. PeerJ 2015; 3:e677. [PMID: 25780754 PMCID: PMC4358638 DOI: 10.7717/peerj.677] [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: 05/30/2014] [Accepted: 01/08/2015] [Indexed: 11/20/2022] Open
Abstract
Substantial health disparities exist between African Americans and Caucasians in the United States. Copy number variations (CNVs) are one form of human genetic variations that have been linked with complex diseases and often occur at different frequencies among African Americans and Caucasian populations. Here, we aimed to investigate whether CNVs with differential frequencies can contribute to health disparities from the perspective of gene networks. We inferred network clusters from human gene/protein networks based on two different data sources. We then evaluated each network cluster for the occurrences of known pathogenic genes and genes located in CNVs with different population frequencies, and used false discovery rates to rank network clusters. This approach let us identify five clusters enriched with known pathogenic genes and with genes located in CNVs with different frequencies between African Americans and Caucasians. These clustering patterns predict two candidate causal genes located in four population-specific CNVs that play potential roles in health disparities
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Affiliation(s)
- Yi Jiang
- Department of Computer Science and Engineering, University of Tennessee at Chattanooga , TN , USA
| | - Hong Qin
- Departement of Biology, Spelman College , Atlanta, GA , United States
| | - Li Yang
- Department of Computer Science and Engineering, University of Tennessee at Chattanooga , TN , USA
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34
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Abstract
Clustering is one of main methods to identify functional modules from protein-protein interaction (PPI) data. Nevertheless traditional clustering methods may not be effective for clustering PPI data. In this paper, we proposed a novel method for clustering PPI data by combining firefly algorithm (FA) and synchronization-based hierarchical clustering (SHC) algorithm. Firstly, the PPI data are preprocessed via spectral clustering (SC) which transforms the high-dimensional similarity matrix into a low dimension matrix. Then the SHC algorithm is used to perform clustering. In SHC algorithm, hierarchical clustering is achieved by enlarging the neighborhood radius of synchronized objects continuously, while the hierarchical search is very difficult to find the optimal neighborhood radius of synchronization and the efficiency is not high. So we adopt the firefly algorithm to determine the optimal threshold of the neighborhood radius of synchronization automatically. The proposed algorithm is tested on the MIPS PPI dataset. The results show that our proposed algorithm is better than the traditional algorithms in precision, recall and f-measure value.
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35
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Pizzuti C, Rombo SE. An evolutionary restricted neighborhood search clustering approach for PPI networks. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2014.06.061] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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A least square method based model for identifying protein complexes in protein-protein interaction network. BIOMED RESEARCH INTERNATIONAL 2014; 2014:720960. [PMID: 25405206 PMCID: PMC4227386 DOI: 10.1155/2014/720960] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Accepted: 08/27/2014] [Indexed: 12/02/2022]
Abstract
Protein complex formed by a group of physical interacting proteins plays a crucial role in cell activities. Great effort has been made to computationally identify protein complexes from protein-protein interaction (PPI) network. However, the accuracy of the prediction is still far from being satisfactory, because the topological structures of protein complexes in the PPI network are too complicated. This paper proposes a novel optimization framework to detect complexes from PPI network, named PLSMC. The method is on the basis of the fact that if two proteins are in a common complex, they are likely to be interacting. PLSMC employs this relation to determine complexes by a penalized least squares method. PLSMC is applied to several public yeast PPI networks, and compared with several state-of-the-art methods. The results indicate that PLSMC outperforms other methods. In particular, complexes predicted by PLSMC can match known complexes with a higher accuracy than other methods. Furthermore, the predicted complexes have high functional homogeneity.
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Li M, Zhang J, Liu Q, Wang J, Wu FX. Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation. BMC Med Genomics 2014; 7 Suppl 2:S4. [PMID: 25350763 PMCID: PMC4243158 DOI: 10.1186/1755-8794-7-s2-s4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Predicting disease-related genes is one of the most important tasks in bioinformatics and systems biology. With the advances in high-throughput techniques, a large number of protein-protein interactions are available, which make it possible to identify disease-related genes at the network level. However, network-based identification of disease-related genes is still a challenge as the considerable false-positives are still existed in the current available protein interaction networks (PIN). RESULTS Considering the fact that the majority of genetic disorders tend to manifest only in a single or a few tissues, we constructed tissue-specific networks (TSN) by integrating PIN and tissue-specific data. We further weighed the constructed tissue-specific network (WTSN) by using DNA methylation as it plays an irreplaceable role in the development of complex diseases. A PageRank-based method was developed to identify disease-related genes from the constructed networks. To validate the effectiveness of the proposed method, we constructed PIN, weighted PIN (WPIN), TSN, WTSN for colon cancer and leukemia, respectively. The experimental results on colon cancer and leukemia show that the combination of tissue-specific data and DNA methylation can help to identify disease-related genes more accurately. Moreover, the PageRank-based method was effective to predict disease-related genes on the case studies of colon cancer and leukemia. CONCLUSIONS Tissue-specific data and DNA methylation are two important factors to the study of human diseases. The same method implemented on the WTSN can achieve better results compared to those being implemented on original PIN, WPIN, or TSN. The PageRank-based method outperforms degree centrality-based method for identifying disease-related genes from WTSN.
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Affiliation(s)
- Min Li
- School of Information Science and Engineering, Central South University, Changsha 410083, Hunan, P. R. China
| | - Jiayi Zhang
- School of Information Science and Engineering, Central South University, Changsha 410083, Hunan, P. R. China
| | - Qing Liu
- School of Information Science and Engineering, Central South University, Changsha 410083, Hunan, P. R. China
| | - Jianxin Wang
- School of Information Science and Engineering, Central South University, Changsha 410083, Hunan, P. R. China
| | - Fang-Xiang Wu
- School of Information Science and Engineering, Central South University, Changsha 410083, Hunan, P. R. China
- College of Engineering, University of Saskatchewan, 57 Campus Dr., Saskatoon, SK Canada
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Ganegoda GU, Wang J, Wu FX, Li M. Prediction of disease genes using tissue-specified gene-gene network. BMC SYSTEMS BIOLOGY 2014; 8 Suppl 3:S3. [PMID: 25350876 PMCID: PMC4243117 DOI: 10.1186/1752-0509-8-s3-s3] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Tissue specificity is an important aspect of many genetic diseases in the context of genetic disorders as the disorder affects only few tissues. Therefore tissue specificity is important in identifying disease-gene associations. Hence this paper seeks to discuss the impact of using tissue specificity in predicting new disease-gene associations and how to use tissue specificity along with phenotype information for a particular disease. METHODS In order to find out the impact of using tissue specificity for predicting new disease-gene associations, this study proposes a novel method called tissue-specified genes to construct tissues-specific gene-gene networks for different tissue samples. Subsequently, these networks are used with phenotype details to predict disease genes by using Katz method. The proposed method was compared with three other tissue-specific network construction methods in order to check its effectiveness. Furthermore, to check the possibility of using tissue-specific gene-gene network instead of generic protein-protein network at all time, the results are compared with three other methods. RESULTS In terms of leave-one-out cross validation, calculation of the mean enrichment and ROC curves indicate that the proposed approach outperforms existing network construction methods. Furthermore tissues-specific gene-gene networks make a more positive impact on predicting disease-gene associations than generic protein-protein interaction networks. CONCLUSIONS In conclusion by integrating tissue-specific data it enabled prediction of known and unknown disease-gene associations for a particular disease more effectively. Hence it is better to use tissue-specific gene-gene network whenever possible. In addition the proposed method is a better way of constructing tissue-specific gene-gene networks.
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Affiliation(s)
| | - JianXin Wang
- School of Information Science and Engineering, Central South University, Changsha, China
| | - Fang-Xiang Wu
- School of Information Science and Engineering, Central South University, Changsha, China
- College of Engineering, University of Saskatchewan, 57 Campus Dr., Saskatoon, SK Canada
| | - Min Li
- School of Information Science and Engineering, Central South University, Changsha, China
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Clancy T, Hovig E. From proteomes to complexomes in the era of systems biology. Proteomics 2014; 14:24-41. [PMID: 24243660 DOI: 10.1002/pmic.201300230] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2013] [Revised: 10/22/2013] [Accepted: 11/06/2013] [Indexed: 01/16/2023]
Abstract
Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex-complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery-potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.
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Affiliation(s)
- Trevor Clancy
- Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
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40
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Wong DCJ, Sweetman C, Ford CM. Annotation of gene function in citrus using gene expression information and co-expression networks. BMC PLANT BIOLOGY 2014; 14:186. [PMID: 25023870 PMCID: PMC4108274 DOI: 10.1186/1471-2229-14-186] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Accepted: 06/30/2014] [Indexed: 05/20/2023]
Abstract
BACKGROUND The genus Citrus encompasses major cultivated plants such as sweet orange, mandarin, lemon and grapefruit, among the world's most economically important fruit crops. With increasing volumes of transcriptomics data available for these species, Gene Co-expression Network (GCN) analysis is a viable option for predicting gene function at a genome-wide scale. GCN analysis is based on a "guilt-by-association" principle whereby genes encoding proteins involved in similar and/or related biological processes may exhibit similar expression patterns across diverse sets of experimental conditions. While bioinformatics resources such as GCN analysis are widely available for efficient gene function prediction in model plant species including Arabidopsis, soybean and rice, in citrus these tools are not yet developed. RESULTS We have constructed a comprehensive GCN for citrus inferred from 297 publicly available Affymetrix Genechip Citrus Genome microarray datasets, providing gene co-expression relationships at a genome-wide scale (33,000 transcripts). The comprehensive citrus GCN consists of a global GCN (condition-independent) and four condition-dependent GCNs that survey the sweet orange species only, all citrus fruit tissues, all citrus leaf tissues, or stress-exposed plants. All of these GCNs are clustered using genome-wide, gene-centric (guide) and graph clustering algorithms for flexibility of gene function prediction. For each putative cluster, gene ontology (GO) enrichment and gene expression specificity analyses were performed to enhance gene function, expression and regulation pattern prediction. The guide-gene approach was used to infer novel roles of genes involved in disease susceptibility and vitamin C metabolism, and graph-clustering approaches were used to investigate isoprenoid/phenylpropanoid metabolism in citrus peel, and citric acid catabolism via the GABA shunt in citrus fruit. CONCLUSIONS Integration of citrus gene co-expression networks, functional enrichment analysis and gene expression information provide opportunities to infer gene function in citrus. We present a publicly accessible tool, Network Inference for Citrus Co-Expression (NICCE, http://citrus.adelaide.edu.au/nicce/home.aspx), for the gene co-expression analysis in citrus.
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Affiliation(s)
- Darren CJ Wong
- School of Agriculture, Food and Wine, University of Adelaide, Adelaide 5064, South Australia, Australia
| | - Crystal Sweetman
- School of Agriculture, Food and Wine, University of Adelaide, Adelaide 5064, South Australia, Australia
| | - Christopher M Ford
- School of Agriculture, Food and Wine, University of Adelaide, Adelaide 5064, South Australia, Australia
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41
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Zhang XF, Dai DQ, Ou-Yang L, Yan H. Detecting overlapping protein complexes based on a generative model with functional and topological properties. BMC Bioinformatics 2014; 15:186. [PMID: 24928559 PMCID: PMC4073817 DOI: 10.1186/1471-2105-15-186] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Accepted: 06/09/2014] [Indexed: 11/20/2022] Open
Abstract
Background Identification of protein complexes can help us get a better understanding of cellular mechanism. With the increasing availability of large-scale protein-protein interaction (PPI) data, numerous computational approaches have been proposed to detect complexes from the PPI networks. However, most of the current approaches do not consider overlaps among complexes or functional annotation information of individual proteins. Therefore, they might not be able to reflect the biological reality faithfully or make full use of the available domain-specific knowledge. Results In this paper, we develop a Generative Model with Functional and Topological Properties (GMFTP) to describe the generative processes of the PPI network and the functional profile. The model provides a working mechanism for capturing the interaction structures and the functional patterns of proteins. By combining the functional and topological properties, we formulate the problem of identifying protein complexes as that of detecting a group of proteins which frequently interact with each other in the PPI network and have similar annotation patterns in the functional profile. Using the idea of link communities, our method naturally deals with overlaps among complexes. The benefits brought by the functional properties are demonstrated by real data analysis. The results evaluated using four criteria with respect to two gold standards show that GMFTP has a competitive performance over the state-of-the-art approaches. The effectiveness of detecting overlapping complexes is also demonstrated by analyzing the topological and functional features of multi- and mono-group proteins. Conclusions Based on the results obtained in this study, GMFTP presents to be a powerful approach for the identification of overlapping protein complexes using both the PPI network and the functional profile. The software can be downloaded from
http://mail.sysu.edu.cn/home/stsddq@mail.sysu.edu.cn/dai/others/GMFTP.zip.
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Affiliation(s)
| | - Dao-Qing Dai
- Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang Road West, 510275 Guangzhou, China.
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42
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Tully JP, Hill AE, Ahmed HMR, Whitley R, Skjellum A, Mukhtar MS. Expression-based network biology identifies immune-related functional modules involved in plant defense. BMC Genomics 2014; 15:421. [PMID: 24888606 PMCID: PMC4070563 DOI: 10.1186/1471-2164-15-421] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2013] [Accepted: 05/27/2014] [Indexed: 01/12/2023] Open
Abstract
Background Plants respond to diverse environmental cues including microbial perturbations by coordinated regulation of thousands of genes. These intricate transcriptional regulatory interactions depend on the recognition of specific promoter sequences by regulatory transcription factors. The combinatorial and cooperative action of multiple transcription factors defines a regulatory network that enables plant cells to respond to distinct biological signals. The identification of immune-related modules in large-scale transcriptional regulatory networks can reveal the mechanisms by which exposure to a pathogen elicits a precise phenotypic immune response. Results We have generated a large-scale immune co-expression network using a comprehensive set of Arabidopsis thaliana (hereafter Arabidopsis) transcriptomic data, which consists of a wide spectrum of immune responses to pathogens or pathogen-mimicking stimuli treatments. We employed both linear and non-linear models to generate Arabidopsis immune co-expression regulatory (AICR) network. We computed network topological properties and ascertained that this newly constructed immune network is densely connected, possesses hubs, exhibits high modularity, and displays hallmarks of a “real” biological network. We partitioned the network and identified 156 novel modules related to immune functions. Gene Ontology (GO) enrichment analyses provided insight into the key biological processes involved in determining finely tuned immune responses. We also developed novel software called OCCEAN (One Click Cis-regulatory Elements ANalysis) to discover statistically enriched promoter elements in the upstream regulatory regions of Arabidopsis at a whole genome level. We demonstrated that OCCEAN exhibits higher precision than the existing promoter element discovery tools. In light of known and newly discovered cis-regulatory elements, we evaluated biological significance of two key immune-related functional modules and proposed mechanism(s) to explain how large sets of diverse GO genes coherently function to mount effective immune responses. Conclusions We used a network-based, top-down approach to discover immune-related modules from transcriptomic data in Arabidopsis. Detailed analyses of these functional modules reveal new insight into the topological properties of immune co-expression networks and a comprehensive understanding of multifaceted plant defense responses. We present evidence that our newly developed software, OCCEAN, could become a popular tool for the Arabidopsis research community as well as potentially expand to analyze other eukaryotic genomes. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-421) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | | | | | | | - M Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, 35294-1170, USA.
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43
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ABC and IFC: modules detection method for PPI network. BIOMED RESEARCH INTERNATIONAL 2014; 2014:968173. [PMID: 24991575 PMCID: PMC4060787 DOI: 10.1155/2014/968173] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2014] [Accepted: 05/09/2014] [Indexed: 11/17/2022]
Abstract
Many clustering algorithms are unable to solve the clustering problem of protein-protein interaction (PPI) networks effectively. A novel clustering model which combines the optimization mechanism of artificial bee colony (ABC) with the fuzzy membership matrix is proposed in this paper. The proposed ABC-IFC clustering model contains two parts: searching for the optimum cluster centers using ABC mechanism and forming clusters using intuitionistic fuzzy clustering (IFC) method. Firstly, the cluster centers are set randomly and the initial clustering results are obtained by using fuzzy membership matrix. Then the cluster centers are updated through different functions of bees in ABC algorithm; then the clustering result is obtained through IFC method based on the new optimized cluster center. To illustrate its performance, the ABC-IFC method is compared with the traditional fuzzy C-means clustering and IFC method. The experimental results on MIPS dataset show that the proposed ABC-IFC method not only gets improved in terms of several commonly used evaluation criteria such as precision, recall, and P value, but also obtains a better clustering result.
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44
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Identifying dynamic protein complexes based on gene expression profiles and PPI networks. BIOMED RESEARCH INTERNATIONAL 2014; 2014:375262. [PMID: 24963481 PMCID: PMC4052612 DOI: 10.1155/2014/375262] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2014] [Accepted: 03/06/2014] [Indexed: 11/22/2022]
Abstract
Identification of protein complexes from protein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of “closeness” and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. The proposed algorithm DPC was applied on the data of Saccharomyces cerevisiae and the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures.
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45
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Heinzel A, Mühlberger I, Fechete R, Mayer B, Perco P. Functional molecular units for guiding biomarker panel design. Methods Mol Biol 2014; 1159:109-133. [PMID: 24788264 DOI: 10.1007/978-1-4939-0709-0_7] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The field of biomarker research has experienced a major boost in recent years, and the number of publications on biomarker studies evaluating given, but also proposing novel biomarker candidates is increasing rapidly for numerous clinically relevant disease areas. However, individual markers often lack sensitivity and specificity in the clinical context, resting essentially on the intra-individual phenotype variability hampering sensitivity, or on assessing more general processes downstream of the causative molecular events characterizing a disease term, in consequence impairing disease specificity. The trend to circumvent these shortcomings goes towards utilizing multimarker panels, thus combining the strength of individual markers to further enhance performance regarding both sensitivity and specificity. A way of identifying the optimal composition of individual markers in a panel approach is to pick each marker as representative for a specific pathophysiological (mechanistic) process relevant for the disease under investigation, hence resulting in a multimarker panel for covering the set of pathophysiological processes underlying the frequently multifactorial composition of a clinical phenotype.Here we outline a procedure of identifying such sets of disease-specific pathophysiological processes (units) delineated on the basis of disease-associated molecular feature lists derived from literature mining as well as aggregated, publicly available Omics profiling experiments. With such molecular units in hand, providing an improved reflection of a specific clinical phenotype, biomarker candidates can then be assigned to or novel candidates are to be selected from these units, subsequently resulting in a multimarker panel promising improved accuracy in disease diagnosis as well as prognosis.
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Affiliation(s)
- Andreas Heinzel
- emergentec biodevelopment GmbH, Gersthofer Strasse 29-31, 1180, Vienna, Austria
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46
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Wong DCJ, Sweetman C, Drew DP, Ford CM. VTCdb: a gene co-expression database for the crop species Vitis vinifera (grapevine). BMC Genomics 2013; 14:882. [PMID: 24341535 PMCID: PMC3904201 DOI: 10.1186/1471-2164-14-882] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Accepted: 11/29/2013] [Indexed: 11/16/2022] Open
Abstract
Background Gene expression datasets in model plants such as Arabidopsis have contributed to our understanding of gene function and how a single underlying biological process can be governed by a diverse network of genes. The accumulation of publicly available microarray data encompassing a wide range of biological and environmental conditions has enabled the development of additional capabilities including gene co-expression analysis (GCA). GCA is based on the understanding that genes encoding proteins involved in similar and/or related biological processes may exhibit comparable expression patterns over a range of experimental conditions, developmental stages and tissues. We present an open access database for the investigation of gene co-expression networks within the cultivated grapevine, Vitis vinifera. Description The new gene co-expression database, VTCdb (http://vtcdb.adelaide.edu.au/Home.aspx), offers an online platform for transcriptional regulatory inference in the cultivated grapevine. Using condition-independent and condition-dependent approaches, grapevine co-expression networks were constructed using the latest publicly available microarray datasets from diverse experimental series, utilising the Affymetrix Vitis vinifera GeneChip (16 K) and the NimbleGen Grape Whole-genome microarray chip (29 K), thus making it possible to profile approximately 29,000 genes (95% of the predicted grapevine transcriptome). Applications available with the online platform include the use of gene names, probesets, modules or biological processes to query the co-expression networks, with the option to choose between Affymetrix or Nimblegen datasets and between multiple co-expression measures. Alternatively, the user can browse existing network modules using interactive network visualisation and analysis via CytoscapeWeb. To demonstrate the utility of the database, we present examples from three fundamental biological processes (berry development, photosynthesis and flavonoid biosynthesis) whereby the recovered sub-networks reconfirm established plant gene functions and also identify novel associations. Conclusions Together, we present valuable insights into grapevine transcriptional regulation by developing network models applicable to researchers in their prioritisation of gene candidates, for on-going study of biological processes related to grapevine development, metabolism and stress responses.
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Affiliation(s)
| | | | | | - Christopher M Ford
- School of Agriculture, Food and Wine, University of Adelaide, Adelaide 5064, South Australia, Australia.
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Tong M, Li X, Wegener Parfrey L, Roth B, Ippoliti A, Wei B, Borneman J, McGovern DPB, Frank DN, Li E, Horvath S, Knight R, Braun J. A modular organization of the human intestinal mucosal microbiota and its association with inflammatory bowel disease. PLoS One 2013; 8:e80702. [PMID: 24260458 PMCID: PMC3834335 DOI: 10.1371/journal.pone.0080702] [Citation(s) in RCA: 127] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2013] [Accepted: 10/07/2013] [Indexed: 02/08/2023] Open
Abstract
Abnormalities of the intestinal microbiota are implicated in the pathogenesis of Crohn's disease (CD) and ulcerative colitis (UC), two spectra of inflammatory bowel disease (IBD). However, the high complexity and low inter-individual overlap of intestinal microbial composition are formidable barriers to identifying microbial taxa representing this dysbiosis. These difficulties might be overcome by an ecologic analytic strategy to identify modules of interacting bacteria (rather than individual bacteria) as quantitative reproducible features of microbial composition in normal and IBD mucosa. We sequenced 16S ribosomal RNA genes from 179 endoscopic lavage samples from different intestinal regions in 64 subjects (32 controls, 16 CD and 16 UC patients in clinical remission). CD and UC patients showed a reduction in phylogenetic diversity and shifts in microbial composition, comparable to previous studies using conventional mucosal biopsies. Analysis of weighted co-occurrence network revealed 5 microbial modules. These modules were unprecedented, as they were detectable in all individuals, and their composition and abundance was recapitulated in an independent, biopsy-based mucosal dataset 2 modules were associated with healthy, CD, or UC disease states. Imputed metagenome analysis indicated that these modules displayed distinct metabolic functionality, specifically the enrichment of oxidative response and glycan metabolism pathways relevant to host-pathogen interaction in the disease-associated modules. The highly preserved microbial modules accurately classified IBD status of individual patients during disease quiescence, suggesting that microbial dysbiosis in IBD may be an underlying disorder independent of disease activity. Microbial modules thus provide an integrative view of microbial ecology relevant to IBD.
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Affiliation(s)
- Maomeng Tong
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
| | - Xiaoxiao Li
- Cedars-Sinai F. Widjaja Inflammatory Bowel and Immunobiology Research Institute, Los Angeles, California, United States of America
| | - Laura Wegener Parfrey
- Department of Chemistry & Biochemistry, University of Colorado, Boulder, Colorado, United States of America
| | - Bennett Roth
- Department of Medicine, Division of Digestive Disease, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
| | - Andrew Ippoliti
- Cedars-Sinai F. Widjaja Inflammatory Bowel and Immunobiology Research Institute, Los Angeles, California, United States of America
| | - Bo Wei
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
| | - James Borneman
- Department of Plant Pathology and Microbiology, University of California Riverside, Riverside, California, United States of America
| | - Dermot P. B. McGovern
- Cedars-Sinai F. Widjaja Inflammatory Bowel and Immunobiology Research Institute, Los Angeles, California, United States of America
| | - Daniel N. Frank
- Division of Infectious Diseases, University of Colorado, School of Medicine, Aurora, Colorado, United States of America
- Union Council, Denver Microbiome Research Consortium (MiRC), University of Colorado, School of Medicine, Aurora, Colorado, United States of America
| | - Ellen Li
- Department of Medicine, Stony Brook University, Stony Brook, New York, United States of America
| | - Steve Horvath
- Department of Human Genetics and Biostatistics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
| | - Rob Knight
- Department of Chemistry & Biochemistry, University of Colorado, Boulder, Colorado, United States of America
- Howard Hughes Medical Institute, University of Colorado, Boulder, Colorado, United States of America;
| | - Jonathan Braun
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
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48
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Lei C, Tamim S, Bishop AJR, Ruan J. Fully automated protein complex prediction based on topological similarity and community structure. Proteome Sci 2013; 11:S9. [PMID: 24564887 PMCID: PMC3908383 DOI: 10.1186/1477-5956-11-s1-s9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
To understand the function of protein complexes and their association with biological processes, a lot of studies have been done towards analyzing the protein-protein interaction (PPI) networks. However, the advancement in high-throughput technology has resulted in a humongous amount of data for analysis. Moreover, high level of noise, sparseness, and skewness in degree distribution of PPI networks limits the performance of many clustering algorithms and further analysis of their interactions.
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Affiliation(s)
- Chengwei Lei
- Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Saleh Tamim
- Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Alexander JR Bishop
- Greehey Children's Cancer Research Institute, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Department of Cellular and Structural Biology, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Jianhua Ruan
- Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA
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Xiao Q, Wang J, Peng X, Wu FX. Detecting protein complexes from active protein interaction networks constructed with dynamic gene expression profiles. Proteome Sci 2013; 11:S20. [PMID: 24565281 PMCID: PMC3908890 DOI: 10.1186/1477-5956-11-s1-s20] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Background Protein interaction networks (PINs) are known to be useful to detect protein complexes. However, most available PINs are static, which cannot reflect the dynamic changes in real networks. At present, some researchers have tried to construct dynamic networks by incorporating time-course (dynamic) gene expression data with PINs. However, the inevitable background noise exists in the gene expression array, which could degrade the quality of dynamic networkds. Therefore, it is needed to filter out contaminated gene expression data before further data integration and analysis. Results Firstly, we adopt a dynamic model-based method to filter noisy data from dynamic expression profiles. Then a new method is proposed for identifying active proteins from dynamic gene expression profiles. An active protein at a time point is defined as the protein the expression level of whose corresponding gene at that time point is higher than a threshold determined by a standard variance involved threshold function. Furthermore, a noise-filtered active protein interaction network (NF-APIN) is constructed. To demonstrate the efficiency of our method, we detect protein complexes from the NF-APIN, compared with those from other dynamic PINs. Conclusion A dynamic model based method can effectively filter out noises in dynamic gene expression data. Our method to compute a threshold for determining the active time points of noise-filtered genes can make the dynamic construction more accuracy and provide a high quality framework for network analysis, such as protein complex prediction.
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McHardy IH, Goudarzi M, Tong M, Ruegger PM, Schwager E, Weger JR, Graeber TG, Sonnenburg JL, Horvath S, Huttenhower C, McGovern DPB, Fornace AJ, Borneman J, Braun J. Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships. MICROBIOME 2013; 1:17. [PMID: 24450808 PMCID: PMC3971612 DOI: 10.1186/2049-2618-1-17] [Citation(s) in RCA: 204] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2013] [Accepted: 05/12/2013] [Indexed: 05/10/2023]
Abstract
BACKGROUND Consistent compositional shifts in the gut microbiota are observed in IBD and other chronic intestinal disorders and may contribute to pathogenesis. The identities of microbial biomolecular mechanisms and metabolic products responsible for disease phenotypes remain to be determined, as do the means by which such microbial functions may be therapeutically modified. RESULTS The composition of the microbiota and metabolites in gut microbiome samples in 47 subjects were determined. Samples were obtained by endoscopic mucosal lavage from the cecum and sigmoid colon regions, and each sample was sequenced using the 16S rRNA gene V4 region (Illumina-HiSeq 2000 platform) and assessed by UPLC mass spectroscopy. Spearman correlations were used to identify widespread, statistically significant microbial-metabolite relationships. Metagenomes for identified microbial OTUs were imputed using PICRUSt, and KEGG metabolic pathway modules for imputed genes were assigned using HUMAnN. The resulting metabolic pathway abundances were mostly concordant with metabolite data. Analysis of the metabolome-driven distribution of OTU phylogeny and function revealed clusters of clades that were both metabolically and metagenomically similar. CONCLUSIONS The results suggest that microbes are syntropic with mucosal metabolome composition and therefore may be the source of and/or dependent upon gut epithelial metabolites. The consistent relationship between inferred metagenomic function and assayed metabolites suggests that metagenomic composition is predictive to a reasonable degree of microbial community metabolite pools. The finding that certain metabolites strongly correlate with microbial community structure raises the possibility of targeting metabolites for monitoring and/or therapeutically manipulating microbial community function in IBD and other chronic diseases.
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Affiliation(s)
- Ian H McHardy
- Pathology and Laboratory Medicine UCLA, Los Angeles, CA, USA
| | - Maryam Goudarzi
- Biochemistry and Molecular and Cellular Biology, Georgetown University, Washington, DC, USA
| | - Maomeng Tong
- Molecular and Medical Pharmacology, UCLA, Los Angeles, CA, USA
| | | | | | - John R Weger
- Plant Pathology, UC Riverside, Riverside, CA, USA
| | | | | | | | | | - Dermot PB McGovern
- The F. Widjaja Family Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedar's Sinai Medical Center, Los Angeles, CA, USA
| | - Albert J Fornace
- Biochemistry and Molecular and Cellular Biology, Georgetown University, Washington, DC, USA
| | | | - Jonathan Braun
- Pathology and Laboratory Medicine UCLA, Los Angeles, CA, USA
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