1
|
Bešlo D, Golubić N, Rastija V, Agić D, Karnaš M, Šubarić D, Lučić B. Antioxidant Activity, Metabolism, and Bioavailability of Polyphenols in the Diet of Animals. Antioxidants (Basel) 2023; 12:1141. [PMCID: PMC10294820 DOI: 10.3390/antiox12061141] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/04/2023] [Accepted: 05/15/2023] [Indexed: 06/29/2023] Open
Abstract
As the world’s population grows, so does the need for more and more animal feed. In 2006, the EU banned the use of antibiotics and other chemicals in order to reduce chemical residues in food consumed by humans. It is well known that oxidative stress and inflammatory processes must be combated to achieve higher productivity. The adverse effects of the use of pharmaceuticals and other synthetic compounds on animal health and product quality and safety have increased interest in phytocompounds. With the use of plant polyphenols in animal nutrition, they are gaining more attention as a supplement to animal feed. Livestock feeding based on a sustainable, environmentally friendly approach (clean, safe, and green agriculture) would also be a win–win for farmers and society. There is an increasing interest in producing healthier products of animal origin with a higher ratio of polyunsaturated fatty acids (PUFAs) to saturated fatty acids by modulating animal nutrition. Secondary plant metabolites (polyphenols) are essential chemical compounds for plant physiology as they are involved in various functions such as growth, pigmentation, and resistance to pathogenic organisms. Polyphenols are exogenous antioxidants that act as one of the first lines of cell defense. Therefore, the discoveries on the intracellular antioxidant activity of polyphenols as a plant supplement have contributed significantly to the improvement of antioxidant activity, as polyphenols prevent oxidative stress damage and eliminate excessively produced free radicals. To achieve animal welfare, reduce stress and the need for medicines, and increase the quality of food of animal origin, the addition of polyphenols to research and breeding can be practised in part with a free-choice approach to animal nutrition.
Collapse
Affiliation(s)
- Drago Bešlo
- Faculty of Agrobiotechnical Sciences Osijek, J. J. Strossmayer University Osijek, Vladimira Preloga 1, HR-31000 Osijek, Croatia; (N.G.); (V.R.); (D.A.); (M.K.); (D.Š.)
| | - Nataša Golubić
- Faculty of Agrobiotechnical Sciences Osijek, J. J. Strossmayer University Osijek, Vladimira Preloga 1, HR-31000 Osijek, Croatia; (N.G.); (V.R.); (D.A.); (M.K.); (D.Š.)
| | - Vesna Rastija
- Faculty of Agrobiotechnical Sciences Osijek, J. J. Strossmayer University Osijek, Vladimira Preloga 1, HR-31000 Osijek, Croatia; (N.G.); (V.R.); (D.A.); (M.K.); (D.Š.)
| | - Dejan Agić
- Faculty of Agrobiotechnical Sciences Osijek, J. J. Strossmayer University Osijek, Vladimira Preloga 1, HR-31000 Osijek, Croatia; (N.G.); (V.R.); (D.A.); (M.K.); (D.Š.)
| | - Maja Karnaš
- Faculty of Agrobiotechnical Sciences Osijek, J. J. Strossmayer University Osijek, Vladimira Preloga 1, HR-31000 Osijek, Croatia; (N.G.); (V.R.); (D.A.); (M.K.); (D.Š.)
| | - Domagoj Šubarić
- Faculty of Agrobiotechnical Sciences Osijek, J. J. Strossmayer University Osijek, Vladimira Preloga 1, HR-31000 Osijek, Croatia; (N.G.); (V.R.); (D.A.); (M.K.); (D.Š.)
| | - Bono Lučić
- NMR Center, Ruđer Bošković Institute, Bijenička cesta 54, HR-10000 Zagreb, Croatia;
| |
Collapse
|
2
|
Marie V, Gordon M. Understanding the co-evolutionary molecular mechanisms of resistance in the HIV-1 Gag and protease. J Biomol Struct Dyn 2022; 40:10852-10861. [PMID: 34253143 DOI: 10.1080/07391102.2021.1950569] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Due to high human immunodeficiency virus type 1 (HIV-1) subtype C infections coupled with increasing antiretroviral treatment failure, the elucidation of complex drug resistance mutational patterns arising through protein co-evolution is required. Despite the inclusion of potent protease inhibitors Lopinavir (LPV) and Darunavir (DRV) in second- and third-line therapies, many patients still fail treatment due to the accumulation of mutations in protease (PR) and recently, Gag. To understand the co-evolutionary molecular mechanisms of resistance in the HIV-1 PR and Gag, we performed 100 ns molecular dynamic simulations on multidrug resistant PR's when bound to LPV, DRV or a mutated A431V NC|p1 Gag cleavage site (CS). Here we showed that distinct changes in PR's active site, flap and elbow regions due to several PR resistance mutations (L10F, M46I, I54V, L76V, V82A) were found to alter LPV and DRV drug binding. However, binding was significantly exacerbated when the mutant PRs were bound to the NC|p1 Gag CS. Although A431V was shown to coordinate several residues in PR, the L76V PR mutation was found to have a significant role in substrate recognition. Consequently, a greater binding affinity was observed when the mutated substrate was bound to an L76V-inclusive PR mutant (Gbind: -62.46 ± 5.75 kcal/mol) than without (Gbind: -50.34 ± 6.28 kcal/mol). These data showed that the co-selection of resistance mutations in the enzyme and substrate can simultaneously constrict regions in PR's active site whilst flexing the flaps to allow flexible movement of the substrate and multiple, complex mechanisms of resistance to occur. Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Veronna Marie
- KwaZulu-Natal Research Innovation & Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, DurbanSouth Africa
| | - Michelle Gordon
- KwaZulu-Natal Research Innovation & Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, DurbanSouth Africa
| |
Collapse
|
3
|
Adu GB, Badu-Apraku B, Akromah R, Awuku FJ. Combining Abilities and Heterotic Patterns among Early Maturing Maize Inbred Lines under Optimal and Striga-Infested Environments. Genes (Basel) 2022; 13:genes13122289. [PMID: 36553556 PMCID: PMC9778638 DOI: 10.3390/genes13122289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 11/23/2022] [Accepted: 11/28/2022] [Indexed: 12/09/2022] Open
Abstract
Information on the general combining ability of inbred lines and the specific combining ability of hybrid combinations is crucial for successful hybrid development. The objectives of this study were to (i) determine the combining ability of thirty selected early maturing maize inbred lines under Striga-infested and optimal environments, (ii) classify the inbred lines into heterotic groups using the general combining ability effects of multiple traits (HGCAMT) and the single nucleotide polymorphism genetic distance (SNP- GD) methods, and (iii) assess the effectiveness of the heterotic grouping methods. One hundred and fifty single-cross hybrids were generated from the thirty inbred lines using the North Carolina Design II mating method. The hybrids and six local check varieties were tested across optimal and Striga-infested environments in Ghana and Nigeria in 2016 and 2017. The inheritance of grain yield was controlled by the non-additive gene action under both environments and the additive gene action across the two research environments. The non-additive gene action modulated the inheritance of measured traits under Striga-infested environments, except for the Striga damage syndrome rating at 8 weeks after planting. Maternal effects were observed for most traits in each environment and across environments. The inbred lines TZEI 127 and TZEI 40 exhibited significant and positive GCA male and female effects for grain yield under each environment and across the two research environments, indicating the presence of favorable alleles for yield improvements. The SNP-GD heterotic grouping method was identified as the most adequate in grouping the thirty inbred lines.
Collapse
Affiliation(s)
- Gloria Boakyewaa Adu
- Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute (SARI), Tamale, Ghana
- Correspondence: (G.B.A.); (B.B.-A.)
| | - Baffour Badu-Apraku
- International Institute of Tropical Agriculture (UK) Limited, Carolyn House, 26 Dingwall Road, Croydon CR0 9XP, UK
- Correspondence: (G.B.A.); (B.B.-A.)
| | - Richard Akromah
- Department of Crop and Soil Sciences, Faculty of Agriculture, Kwame Nkrumah University of Science and Technology, Private Mail Bag, University Post Office, Kumasi, Ghana
| | - Frederick Justice Awuku
- Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute (SARI), Tamale, Ghana
| |
Collapse
|
4
|
Wamonje FO, Zhou N, Bamrah R, Wist T, Prager SM. Detection and Identification of a ' Candidatus Liberibacter solanacearum' Species from Ash Tree Infesting Psyllids. PHYTOPATHOLOGY 2022; 112:76-80. [PMID: 34346758 DOI: 10.1094/phyto-02-21-0060-sc] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
'Candidatus Liberibacter' species are associated with severe, economically important diseases. Nearly all known species are putatively insect transmitted, specifically by psyllids. Detection of 'Ca. Liberibacter' in plants is complicated by their uneven distribution in host plants and largely fastidius nature. The death of black (Fraxinus nigra) and mancana (Fraxinus mandshurica) ash trees in Saskatchewan, Canada has been associated with infestation by the cottony ash psyllid (Psyllopsis discrepans). A combination of conventional PCR amplification and Sanger sequencing of the 16S recombinant DNA was used to detect and identify 'Ca. Liberibacter' in psyllids collected from ash trees in Saskatchewan. BLAST analysis of two 16S sequences that were 1,058 and 1,085 bp long (NTHA 5, GenBank accession number MK942379 and NTHA 6, GenBank accession number MK937570, respectively) revealed they were 99 to 100% similar to a 'Ca. Liberibacter solanacearum' sequence (GenBank accession number KX197200) isolated from the Nearctic psyllid (Bactericera maculipennis) of U.S. provenance. Sequencing the psyllid genes CO1 and Cyt-b confirmed that the psyllids from which the bacterial DNA was isolated were P. discrepans, based on comparisons with sequences in GenBank and BOLD and a reference sample from the United Kingdom. These results provide the first evidence that 'Ca. Liberibacter solanacearum' species are associated with psyllids collected from ash trees and specifically P. discrepans. The recent episodes of dieback of ash in Saskatchewan associated with psyllid feeding are consistent with disease symptoms caused by 'Ca. Liberibacter' pathogens, and this possibility warrants further study.
Collapse
Affiliation(s)
- Francis O Wamonje
- Department of Plant Sciences, University of Saskatchewan, Saskatoon, Canada
| | - Ningxing Zhou
- Department of Plant Sciences, University of Saskatchewan, Saskatoon, Canada
| | - Ramandeep Bamrah
- Department of Plant Sciences, University of Saskatchewan, Saskatoon, Canada
| | - Tyler Wist
- Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, Saskatoon, Canada
| | - Sean M Prager
- Department of Plant Sciences, University of Saskatchewan, Saskatoon, Canada
| |
Collapse
|
5
|
Sheng M, Cai H, Yang Q, Li J, Zhang J, Liu L. A Random Walk-Based Method to Identify Candidate Genes Associated With Lymphoma. Front Genet 2021; 12:792754. [PMID: 34899868 PMCID: PMC8655984 DOI: 10.3389/fgene.2021.792754] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 11/02/2021] [Indexed: 11/16/2022] Open
Abstract
Lymphoma is a serious type of cancer, especially for adolescents and elder adults, although this malignancy is quite rare compared with other types of cancer. The cause of this malignancy remains ambiguous. Genetic factor is deemed to be highly associated with the initiation and progression of lymphoma, and several genes have been related to this disease. Determining the pathogeny of lymphoma by identifying the related genes is important. In this study, we presented a random walk-based method to infer the novel lymphoma-associated genes. From the reported 1,458 lymphoma-associated genes and protein–protein interaction network, raw candidate genes were mined by using the random walk with restart algorithm. The determined raw genes were further filtered by using three screening tests (i.e., permutation, linkage, and enrichment tests). These tests could control false-positive genes and screen out essential candidate genes with strong linkages to validate the lymphoma-associated genes. A total of 108 inferred genes were obtained. Analytical results indicated that some inferred genes, such as RAC3, TEC, IRAK2/3/4, PRKCE, SMAD3, BLK, TXK, PRKCQ, were associated with the initiation and progression of lymphoma.
Collapse
Affiliation(s)
- Minjie Sheng
- Department of Ophthalmology, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Haiying Cai
- Department of Ophthalmology, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Qin Yang
- Department of Ophthalmology, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jing Li
- Department of Ophthalmology, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jian Zhang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China.,Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China.,National Clinical Research Center for Eye Diseases, Shanghai, China.,Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Lihua Liu
- Department of Ophthalmology, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, China
| |
Collapse
|
6
|
Benkoova B, Pospisilova M, Kramna L, Kissova R, Berakova K, Klement C, Cinek O, Bopegamage S. Coxsackievirus B4 sewage-isolate induces pancreatitis after oral infection of mice. FEMS Microbiol Lett 2021; 368:6326620. [PMID: 34297106 PMCID: PMC8346287 DOI: 10.1093/femsle/fnab092] [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: 02/24/2021] [Accepted: 07/21/2021] [Indexed: 11/21/2022] Open
Abstract
Numerous serotypes which belong to the genus Enterovirus (EV) show variability in their virulence and clinical manifestations. They are also known to undergo changes caused by mutations and recombination during their circulation in the environment and the population. Various EV serotypes are prevalent in groundwater, wastewater and surface waters. Our previous studies showed that oral infection induces pancreatitis depending on specific conditions, such as gravidity, in an outbred murine model. Our aim in the present study was to further explore the pancreatic histopathology in an outbred mouse model following oral infection with clinical isolates from a patient who had aseptic meningitis and an isolate from a treated-sewage sample recovered from the residential area of the patient. The isolates were identified as coxsackievirus B4 (CVB4) in tissue culture. The CVB4 sewage-isolate induced pancreatitis after oral infection. In contrast, pancreatitis was absent following infection with the clinical isolates. Comparison of polyprotein sequences showed that the treated-sewage strains differed from the patient's isolates by 9 and 11 amino acids. We conclude that the isolates of clinical and environmental origin differed in their pathogenic properties and showed genetic variation.
Collapse
Affiliation(s)
- Brigita Benkoova
- Faculty of Medicine, Enterovirus Laboratory, Institute of Microbiology, Slovak Medical University, Limbova 12, 83303 Bratislava, Slovak Republic
| | - Michaela Pospisilova
- Faculty of Medicine, Enterovirus Laboratory, Institute of Microbiology, Slovak Medical University, Limbova 12, 83303 Bratislava, Slovak Republic
| | - Lenka Kramna
- 2nd Faculty of Medicine, Department of Pediatrics, Charles University in Prague and University Hospital Motol, Prague, Czech Republic
| | - Renata Kissova
- Department of Medical Microbiology, Regional Authority of Public Health Banska Bystrica, Cesta k nemocnici 25, Banska Bystrica, Slovak Republic
| | - Katarina Berakova
- Martinske biopticke centrum s.r.o., V. Spanyola 47A street, 010 01 Zilina, Slovak Republic
| | - Cyril Klement
- Department of Medical Microbiology, Regional Authority of Public Health Banska Bystrica, Cesta k nemocnici 25, Banska Bystrica, Slovak Republic.,Faculty of Public Health, Slovak Medical University, Limbova 12, 83303 Bratislava, Slovak Republic
| | - Ondrej Cinek
- 2nd Faculty of Medicine, Department of Pediatrics, Charles University in Prague and University Hospital Motol, Prague, Czech Republic
| | - Shubhada Bopegamage
- Faculty of Medicine, Enterovirus Laboratory, Institute of Microbiology, Slovak Medical University, Limbova 12, 83303 Bratislava, Slovak Republic
| |
Collapse
|
7
|
Halakou F, Gursoy A, Keskin O. Embedding Alternative Conformations of Proteins in Protein-Protein Interaction Networks. Methods Mol Biol 2020; 2074:113-124. [PMID: 31583634 DOI: 10.1007/978-1-4939-9873-9_9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
While many proteins act alone, the majority of them interact with others and form molecular complexes to undertake biological functions at both cellular and systems levels. Two proteins should have complementary shapes to physically connect to each other. As proteins are dynamic and changing their conformations, it is vital to track in which conformation a specific interaction can happen. Here, we present a step-by-step guide to embedding the protein alternative conformations in each protein-protein interaction in a systems level. All external tools/websites used in each step are explained, and some notes and suggestions are provided to clear any ambiguous point.
Collapse
Affiliation(s)
- Farideh Halakou
- Computer Science and Engineering Department, Koc University, Istanbul, Turkey
| | - Attila Gursoy
- Computer Science and Engineering Department, Koc University, Istanbul, Turkey.
| | - Ozlem Keskin
- Chemical and Biological Engineering Department, Koc University, Istanbul, Turkey
| |
Collapse
|
8
|
Horta MAC, Thieme N, Gao Y, Burnum-Johnson KE, Nicora CD, Gritsenko MA, Lipton MS, Mohanraj K, de Assis LJ, Lin L, Tian C, Braus GH, Borkovich KA, Schmoll M, Larrondo LF, Samal A, Goldman GH, Benz JP. Broad Substrate-Specific Phosphorylation Events Are Associated With the Initial Stage of Plant Cell Wall Recognition in Neurospora crassa. Front Microbiol 2019; 10:2317. [PMID: 31736884 PMCID: PMC6838226 DOI: 10.3389/fmicb.2019.02317] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 09/23/2019] [Indexed: 12/26/2022] Open
Abstract
Fungal plant cell wall degradation processes are governed by complex regulatory mechanisms, allowing the organisms to adapt their metabolic program with high specificity to the available substrates. While the uptake of representative plant cell wall mono- and disaccharides is known to induce specific transcriptional and translational responses, the processes related to early signal reception and transduction remain largely unknown. A fast and reversible way of signal transmission are post-translational protein modifications, such as phosphorylations, which could initiate rapid adaptations of the fungal metabolism to a new condition. To elucidate how changes in the initial substrate recognition phase of Neurospora crassa affect the global phosphorylation pattern, phospho-proteomics was performed after a short (2 min) induction period with several plant cell wall-related mono- and disaccharides. The MS/MS-based peptide analysis revealed large-scale substrate-specific protein phosphorylation and de-phosphorylations. Using the proteins identified by MS/MS, a protein-protein-interaction (PPI) network was constructed. The variance in phosphorylation of a large number of kinases, phosphatases and transcription factors indicate the participation of many known signaling pathways, including circadian responses, two-component regulatory systems, MAP kinases as well as the cAMP-dependent and heterotrimeric G-protein pathways. Adenylate cyclase, a key component of the cAMP pathway, was identified as a potential hub for carbon source-specific differential protein interactions. In addition, four phosphorylated F-Box proteins were identified, two of which, Fbx-19 and Fbx-22, were found to be involved in carbon catabolite repression responses. Overall, these results provide unprecedented and detailed insights into a so far less well known stage of the fungal response to environmental cues and allow to better elucidate the molecular mechanisms of sensory perception and signal transduction during plant cell wall degradation.
Collapse
Affiliation(s)
- Maria Augusta C. Horta
- Holzforschung München, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Nils Thieme
- Holzforschung München, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Yuqian Gao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | | | - Carrie D. Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Marina A. Gritsenko
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Mary S. Lipton
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Karthikeyan Mohanraj
- The Institute of Mathematical Sciences (IMSc), Homi Bhabha National Institute (HBNI), Chennai, India
| | - Leandro José de Assis
- Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brazil
| | - Liangcai Lin
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Chaoguang Tian
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Gerhard H. Braus
- Department of Molecular Microbiology and Genetics, Institute of Microbiology and Genetics, Göttingen Center for Molecular Biosciences, University of Göttingen, Göttingen, Germany
| | - Katherine A. Borkovich
- Department of Microbiology & Plant Pathology, Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, United States
| | - Monika Schmoll
- AIT - Austrian Institute of Technology GmbH, Center for Health & Bioresources, Tulln, Austria
| | - Luis F. Larrondo
- Millennium Institute for Integrative Biology (iBio), Departamento Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Homi Bhabha National Institute (HBNI), Chennai, India
| | - Gustavo H. Goldman
- Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brazil
- Institute for Advanced Study, Technical University of Munich, Garching, Germany
| | - J. Philipp Benz
- Holzforschung München, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
- Institute for Advanced Study, Technical University of Munich, Garching, Germany
| |
Collapse
|
9
|
Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms. Gene Ther 2019; 26:465-478. [PMID: 31455874 DOI: 10.1038/s41434-019-0099-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 06/18/2019] [Accepted: 07/15/2019] [Indexed: 12/14/2022]
Abstract
Oral cancer (OC) is one of the most common cancers threatening human lives. However, OC pathogenesis has yet to be fully uncovered, and thus designing effective treatments remains difficult. Identifying genes related to OC is an important way for achieving this purpose. In this study, we proposed three computational models for inferring novel OC-related genes. In contrast to previously proposed computational methods, which lacked the learning procedures, each proposed model adopted a one-class learning algorithm, which can provide a deep insight into features of validated OC-related genes. A network embedding algorithm (i.e., node2vec) was applied to the protein-protein interaction network to produce the representation of genes. The features of the OC-related genes were used in the training of the one-class algorithm, and the performance of the final inferring model was improved through a feature selection procedure. Then, candidate genes were produced by applying the trained inferring model to other genes. Three tests were performed to screen out the important candidate genes. Accordingly, we obtained three inferred gene sets, any two of which were different. The inferred genes were also different from previous reported genes and some of them have been included in the public Oral Cancer Gene Database. Finally, we analyzed several inferred genes to confirm whether they are novel OC-related genes.
Collapse
|
10
|
Lu S, Zhao K, Wang X, Liu H, Ainiwaer X, Xu Y, Ye M. Use of Laplacian Heat Diffusion Algorithm to Infer Novel Genes With Functions Related to Uveitis. Front Genet 2018; 9:425. [PMID: 30349554 PMCID: PMC6186792 DOI: 10.3389/fgene.2018.00425] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 09/10/2018] [Indexed: 12/17/2022] Open
Abstract
Uveitis is the inflammation of the uvea and is a serious eye disease that can cause blindness for middle-aged and young people. However, the pathogenesis of this disease has not been fully uncovered and thus renders difficulties in designing effective treatments. Completely identifying the genes related to this disease can help improve and accelerate the comprehension of uveitis. In this study, a new computational method was developed to infer potential related genes based on validated ones. We employed a large protein–protein interaction network reported in STRING, in which Laplacian heat diffusion algorithm was applied using validated genes as seed nodes. Except for the validated ones, all genes in the network were filtered by three tests, namely, permutation, association, and function tests, which evaluated the genes based on their specialties and associations to uveitis. Results indicated that 59 inferred genes were accessed, several of which were confirmed to be highly related to uveitis by literature review. In addition, the inferred genes were compared with those reported in a previous study, indicating that our reported genes are necessary supplements.
Collapse
Affiliation(s)
- Shiheng Lu
- Department of Ophthalmology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Pudong, China
| | - Ke Zhao
- Department of Ophthalmology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Pudong, China
| | - Xuefei Wang
- Department of Ophthalmology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Pudong, China
| | - Hui Liu
- Department of Ophthalmology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Pudong, China
| | - Xiamuxiya Ainiwaer
- Department of Ophthalmology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Pudong, China
| | - Yan Xu
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Min Ye
- Department of Ophthalmology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Pudong, China
| |
Collapse
|
11
|
Kulmanov M, Schofield PN, Gkoutos GV, Hoehndorf R. Ontology-based validation and identification of regulatory phenotypes. Bioinformatics 2018; 34:i857-i865. [PMID: 30423068 PMCID: PMC6129279 DOI: 10.1093/bioinformatics/bty605] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Motivation Function annotations of gene products, and phenotype annotations of genotypes, provide valuable information about molecular mechanisms that can be utilized by computational methods to identify functional and phenotypic relatedness, improve our understanding of disease and pathobiology, and lead to discovery of drug targets. Identifying functions and phenotypes commonly requires experiments which are time-consuming and expensive to carry out; creating the annotations additionally requires a curator to make an assertion based on reported evidence. Support to validate the mutual consistency of functional and phenotype annotations as well as a computational method to predict phenotypes from function annotations, would greatly improve the utility of function annotations. Results We developed a novel ontology-based method to validate the mutual consistency of function and phenotype annotations. We apply our method to mouse and human annotations, and identify several inconsistencies that can be resolved to improve overall annotation quality. We also apply our method to the rule-based prediction of regulatory phenotypes from functions and demonstrate that we can predict these phenotypes with Fmax of up to 0.647. Availability and implementation https://github.com/bio-ontology-research-group/phenogocon.
Collapse
Affiliation(s)
- Maxat Kulmanov
- Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Centre, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Paul N Schofield
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Georgios V Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, UK
- Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, Birmingham, UK
- NIHR Experimental Cancer Medicine Centre, Birmingham, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, Birmingham, UK
- NIHR Biomedical Research Centre, Birmingham, UK
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Centre, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| |
Collapse
|
12
|
Li L, Wang Y, An L, Kong X, Huang T. A network-based method using a random walk with restart algorithm and screening tests to identify novel genes associated with Menière's disease. PLoS One 2017; 12:e0182592. [PMID: 28787010 PMCID: PMC5546581 DOI: 10.1371/journal.pone.0182592] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 07/20/2017] [Indexed: 12/28/2022] Open
Abstract
As a chronic illness derived from hair cells of the inner ear, Menière’s disease (MD) negatively influences the quality of life of individuals and leads to a number of symptoms, such as dizziness, temporary hearing loss, and tinnitus. The complete identification of novel genes related to MD would help elucidate its underlying pathological mechanisms and improve its diagnosis and treatment. In this study, a network-based method was developed to identify novel MD-related genes based on known MD-related genes. A human protein-protein interaction (PPI) network was constructed using the PPI information reported in the STRING database. A classic ranking algorithm, the random walk with restart (RWR) algorithm, was employed to search for novel genes using known genes as seed nodes. To make the identified genes more reliable, a series of screening tests, including a permutation test, an interaction test and an enrichment test, were designed to select essential genes from those obtained by the RWR algorithm. As a result, several inferred genes, such as CD4, NOTCH2 and IL6, were discovered. Finally, a detailed biological analysis was performed on fifteen of the important inferred genes, which indicated their strong associations with MD.
Collapse
Affiliation(s)
- Lin Li
- Department of Otorhinolaryngology and Head & Neck, China-Japan Union Hospital of Jilin University, Changchun, China
| | - YanShu Wang
- Department of Anesthesia, The First Hospital of Jilin University, Changchun, China
| | - Lifeng An
- Department of Otorhinolaryngology and Head & Neck, China-Japan Union Hospital of Jilin University, Changchun, China
- * E-mail:
| | - XiangYin Kong
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| |
Collapse
|
13
|
Investigating genetic characteristics of hepatitis B virus-associated and -non-associated hepatocellular carcinoma. Genet Res (Camb) 2016; 98:e14. [PMID: 27834158 DOI: 10.1017/s0016672316000124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is a primary liver malignancy that mainly occurs in patients with chronic liver disease and cirrhosis. Risk factors for HCC include hepatitis B virus (HBV) infection. However, the specific role of HBV infection in HCC development is not yet completely understood. In order to reveal the effects of HBV on HCC, we compare the genes of HCC patients infected with HBV with those who are not infected. METHODS We encoded the genes of these two types of HCC in databases using enrichment scores of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway terms. A random forest algorithm was employed in order to distinguish these two types in the classifier, and a series of feature selection approaches was used in order to select their optimal features. Novel HBV-associated and -non-associated HCC genes were predicted, respectively, based on their optimal features in the classifier. A shortest-path algorithm was also employed in order to find all of the shortest-paths genes connecting the known related genes. RESULTS A total of 54 different features between HBV-associated and -non-associated HCC genes were identified. In total, 1236 and 881 novel related genes were predicted for HBV-associated and -non-associated HCC, respectively. By integrating the predicted genes and shortest path genes in their gene interaction network, we identified 679 common genes involved in the two types of HCC. CONCLUSION We identified the significantly different genetic features between two types of HCC. We also predicted related genes for the two types based on their specific features. Finally, we determined the common genes and features that were involved in both of these two types of HCC.
Collapse
|
14
|
Zhu L, Chen X, Kong X, Cai YD. Investigation of the roles of trace elements during hepatitis C virus infection using protein-protein interactions and a shortest path algorithm. Biochim Biophys Acta Gen Subj 2016; 1860:2756-68. [PMID: 27208424 DOI: 10.1016/j.bbagen.2016.05.018] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 05/05/2016] [Accepted: 05/13/2016] [Indexed: 12/12/2022]
Abstract
BACKGROUND Hepatitis is a type of infectious disease that induces inflammation of the liver without pinpointing a particular pathogen or pathogenesis. Type C hepatitis, as a type of hepatitis, has been reported to induce cirrhosis and hepatocellular carcinoma within a very short amount of time. It is a great threat to human health. Some studies have revealed that trace elements are associated with infection with and immune rejection against hepatitis C virus (HCV). However, the mechanism underlying this phenomenon is still unclear. METHODS In this study, we aimed to expand our knowledge of this phenomenon by designing a computational method to identify genes that may be related to both HCV and trace element metabolic processes. The searching procedure included three stages. First, a shortest path algorithm was applied to a large network, constructed by protein-protein interactions, to identify potential genes of interest. Second, a permutation test was executed to exclude false discoveries. Finally, some rules based on the betweenness and associations between candidate genes and HCV and trace elements were built to select core genes among the remaining genes. RESULTS 12 lists of genes, corresponding to 12 types of trace elements, were obtained. These genes are deemed to be associated with HCV infection and trace elements metabolism. CONCLUSIONS The analyses indicate that some genes may be related to both HCV and trace element metabolic processes, further confirming the associations between HCV and trace elements. The method was further tested on another set of HCV genes, the results indicate that this method is quite robustness. GENERAL SIGNIFICANCE The newly found genes may partially reveal unknown mechanisms between HCV infection and trace element metabolism. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
Collapse
Affiliation(s)
- LiuCun Zhu
- School of Life Sciences, Shanghai University, Shanghai 200444, People's Republic of China
| | - XiJia Chen
- School of Life Sciences, Shanghai University, Shanghai 200444, People's Republic of China
| | - Xiangyin Kong
- The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai 200025, People's Republic of China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai 200444, People's Republic of China.
| |
Collapse
|
15
|
The Use of Protein-Protein Interactions for the Analysis of the Associations between PM2.5 and Some Diseases. BIOMED RESEARCH INTERNATIONAL 2016; 2016:4895476. [PMID: 27243032 PMCID: PMC4875974 DOI: 10.1155/2016/4895476] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2016] [Revised: 04/08/2016] [Accepted: 04/18/2016] [Indexed: 12/28/2022]
Abstract
Nowadays, pollution levels are rapidly increasing all over the world. One of the most important pollutants is PM2.5. It is known that the pollution environment may cause several problems, such as greenhouse effect and acid rain. Among them, the most important problem is that pollutants can induce a number of serious diseases. Some studies have reported that PM2.5 is an important etiologic factor for lung cancer. In this study, we extensively investigate the associations between PM2.5 and 22 disease classes recommended by Goh et al., such as respiratory diseases, cardiovascular diseases, and gastrointestinal diseases. The protein-protein interactions were used to measure the linkage between disease genes and genes that have been reported to be modulated by PM2.5. The results suggest that some diseases, such as diseases related to ear, nose, and throat and gastrointestinal, nutritional, renal, and cardiovascular diseases, are influenced by PM2.5 and some evidences were provided to confirm our results. For example, a total of 18 genes related to cardiovascular diseases are identified to be closely related to PM2.5, and cardiovascular disease relevant gene DSP is significantly related to PM2.5 gene JUP.
Collapse
|
16
|
Chen L, Zhang YH, Huang T, Cai YD. Identifying novel protein phenotype annotations by hybridizing protein-protein interactions and protein sequence similarities. Mol Genet Genomics 2016; 291:913-34. [PMID: 26728152 DOI: 10.1007/s00438-015-1157-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Accepted: 12/08/2015] [Indexed: 01/18/2023]
Abstract
Studies of protein phenotypes represent a central challenge of modern genetics in the post-genome era because effective and accurate investigation of protein phenotypes is one of the most critical procedures to identify functional biological processes in microscale, which involves the analysis of multifactorial traits and has greatly contributed to the development of modern biology in the post genome era. Therefore, we have developed a novel computational method that identifies novel proteins associated with certain phenotypes in yeast based on the protein-protein interaction network. Unlike some existing network-based computational methods that identify the phenotype of a query protein based on its direct neighbors in the local network, the proposed method identifies novel candidate proteins for a certain phenotype by considering all annotated proteins with this phenotype on the global network using a shortest path (SP) algorithm. The identified proteins are further filtered using both a permutation test and their interactions and sequence similarities to annotated proteins. We compared our method with another widely used method called random walk with restart (RWR). The biological functions of proteins for each phenotype identified by our SP method and the RWR method were analyzed and compared. The results confirmed a large proportion of our novel protein phenotype annotation, and the RWR method showed a higher false positive rate than the SP method. Our method is equally effective for the prediction of proteins involving in all the eleven clustered yeast phenotypes with a quite low false positive rate. Considering the universality and generalizability of our supporting materials and computing strategies, our method can further be applied to study other organisms and the new functions we predicted can provide pertinent instructions for the further experimental verifications.
Collapse
Affiliation(s)
- Lei Chen
- School of Life Sciences, Shanghai University, Shanghai, 200444, People's Republic of China. .,College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People's Republic of China.
| | - Yu-Hang Zhang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, People's Republic of China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, People's Republic of China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, 200444, People's Republic of China.
| |
Collapse
|
17
|
Chen L, Yang J, Huang T, Kong X, Lu L, Cai YD. Mining for novel tumor suppressor genes using a shortest path approach. J Biomol Struct Dyn 2015. [PMID: 26209080 DOI: 10.1080/07391102.2015.1042915] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Cancer, being among the most serious diseases, causes many deaths every year. Many investigators have devoted themselves to designing effective treatments for this disease. Cancer always involves abnormal cell growth with the potential to invade or spread to other parts of the body. In contrast, tumor suppressor genes (TSGs) act as guardians to prevent a disordered cell cycle and genomic instability in normal cells. Studies on TSGs can assist in the design of effective treatments against cancer. In this study, we propose a computational method to discover potential TSGs. Based on the known TSGs, a number of candidate genes were selected by applying the shortest path approach in a weighted graph that was constructed using protein-protein interaction network. The analysis of selected genes shows that some of them are new TSGs recently reported in the literature, while others may be novel TSGs.
Collapse
Affiliation(s)
- Lei Chen
- a College of Life Science , Shanghai University , Shanghai 200444 , P.R. China.,b College of Information Engineering , Shanghai Maritime University , Shanghai 201306 , P.R. China
| | - Jing Yang
- c The Key Laboratory of Stem Cell Biology , Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) , Shanghai 200025 , P.R. China
| | - Tao Huang
- c The Key Laboratory of Stem Cell Biology , Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) , Shanghai 200025 , P.R. China
| | - Xiangyin Kong
- c The Key Laboratory of Stem Cell Biology , Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) , Shanghai 200025 , P.R. China
| | - Lin Lu
- d Department of Radiology , Columbia University Medical Center , New York , NY 10032 , USA
| | - Yu-Dong Cai
- a College of Life Science , Shanghai University , Shanghai 200444 , P.R. China
| |
Collapse
|
18
|
Chen J, Huang C, Zhu Y, Dong L, Cao W, Sun L, Sun H, Wan D, Liu Y, Zhang Z, Wang C. Identification of similarities and differences between myeloid and lymphoid acute leukemias using a gene-gene interaction network. Pathol Res Pract 2015; 211:789-96. [DOI: 10.1016/j.prp.2015.07.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Revised: 06/16/2015] [Accepted: 07/13/2015] [Indexed: 10/23/2022]
|
19
|
Gao YF, Yuan F, Liu J, Li LP, He YC, Gao RJ, Cai YD, Jiang Y. Identification of New Candidate Genes and Chemicals Related to Esophageal Cancer Using a Hybrid Interaction Network of Chemicals and Proteins. PLoS One 2015; 10:e0129474. [PMID: 26058041 PMCID: PMC4461353 DOI: 10.1371/journal.pone.0129474] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Accepted: 05/10/2015] [Indexed: 01/04/2023] Open
Abstract
Cancer is a serious disease responsible for many deaths every year in both developed and developing countries. One reason is that the mechanisms underlying most types of cancer are still mysterious, creating a great block for the design of effective treatments. In this study, we attempted to clarify the mechanism underlying esophageal cancer by searching for novel genes and chemicals. To this end, we constructed a hybrid network containing both proteins and chemicals, and generalized an existing computational method previously used to identify disease genes to identify new candidate genes and chemicals simultaneously. Based on jackknife test, our generalized method outperforms or at least performs at the same level as those obtained by a widely used method - the Random Walk with Restart (RWR). The analysis results of the final obtained genes and chemicals demonstrated that they highly shared gene ontology (GO) terms and KEGG pathways with direct and indirect associations with esophageal cancer. In addition, we also discussed the likelihood of selected candidate genes and chemicals being novel genes and chemicals related to esophageal cancer.
Collapse
Affiliation(s)
- Yu-Fei Gao
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, People’s Republic of China
| | - Fei Yuan
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People’s Republic of China
| | - Junbao Liu
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, People’s Republic of China
| | - Li-Peng Li
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, People’s Republic of China
| | - Yi-Chun He
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, People’s Republic of China
| | - Ru-Jian Gao
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, People’s Republic of China
| | - Yu-Dong Cai
- College of Life Science, Shanghai University, Shanghai 200444, People’s Republic of China
| | - Yang Jiang
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, People’s Republic of China
- * E-mail:
| |
Collapse
|
20
|
Jiang Y, Zhang P, Li LP, He YC, Gao RJ, Gao YF. Identification of novel thyroid cancer-related genes and chemicals using shortest path algorithm. BIOMED RESEARCH INTERNATIONAL 2015; 2015:964795. [PMID: 25874234 PMCID: PMC4385622 DOI: 10.1155/2015/964795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2014] [Accepted: 12/05/2014] [Indexed: 02/07/2023]
Abstract
Thyroid cancer is a typical endocrine malignancy. In the past three decades, the continued growth of its incidence has made it urgent to design effective treatments to treat this disease. To this end, it is necessary to uncover the mechanism underlying this disease. Identification of thyroid cancer-related genes and chemicals is helpful to understand the mechanism of thyroid cancer. In this study, we generalized some previous methods to discover both disease genes and chemicals. The method was based on shortest path algorithm and applied to discover novel thyroid cancer-related genes and chemicals. The analysis of the final obtained genes and chemicals suggests that some of them are crucial to the formation and development of thyroid cancer. It is indicated that the proposed method is effective for the discovery of novel disease genes and chemicals.
Collapse
Affiliation(s)
- Yang Jiang
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Peiwei Zhang
- The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Li-Peng Li
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Yi-Chun He
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Ru-jian Gao
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Yu-Fei Gao
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| |
Collapse
|
21
|
Lu J, Huang G, Li HP, Feng KY, Chen L, Zheng MY, Cai YD. Prediction of cancer drugs by chemical-chemical interactions. PLoS One 2014; 9:e87791. [PMID: 24498372 PMCID: PMC3912061 DOI: 10.1371/journal.pone.0087791] [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] [Received: 10/04/2013] [Accepted: 12/31/2013] [Indexed: 11/19/2022] Open
Abstract
Cancer, which is a leading cause of death worldwide, places a big burden on health-care system. In this study, an order-prediction model was built to predict a series of cancer drug indications based on chemical-chemical interactions. According to the confidence scores of their interactions, the order from the most likely cancer to the least one was obtained for each query drug. The 1(st) order prediction accuracy of the training dataset was 55.93%, evaluated by Jackknife test, while it was 55.56% and 59.09% on a validation test dataset and an independent test dataset, respectively. The proposed method outperformed a popular method based on molecular descriptors. Moreover, it was verified that some drugs were effective to the 'wrong' predicted indications, indicating that some 'wrong' drug indications were actually correct indications. Encouraged by the promising results, the method may become a useful tool to the prediction of drugs indications.
Collapse
Affiliation(s)
- Jing Lu
- Department of Medicinal Chemistry, School of Pharmacy, Yantai University, Yantai, Shandong, People’s Republic of China
| | - Guohua Huang
- Institute of Systems Biology, Shanghai University, Shanghai, People’s Republic of China
- Department of Mathematics, Shaoyang University, Shaoyang, Hunan, People’s Republic of China
| | - Hai-Peng Li
- CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Kai-Yan Feng
- Beijing Genomics Institute, Shenzhen Beishan Industrial zone, Shenzhen, People’s Republic of China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, People’s Republic of China
- * E-mail: (LC); (MYZ); (YDC)
| | - Ming-Yue Zheng
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Shanghai, People’s Republic of China
- * E-mail: (LC); (MYZ); (YDC)
| | - Yu-Dong Cai
- Institute of Systems Biology, Shanghai University, Shanghai, People’s Republic of China
- * E-mail: (LC); (MYZ); (YDC)
| |
Collapse
|
22
|
Identification of age-related macular degeneration related genes by applying shortest path algorithm in protein-protein interaction network. BIOMED RESEARCH INTERNATIONAL 2013; 2013:523415. [PMID: 24455700 PMCID: PMC3878555 DOI: 10.1155/2013/523415] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2013] [Accepted: 11/27/2013] [Indexed: 12/31/2022]
Abstract
This study attempted to find novel age-related macular degeneration (AMD) related genes based on 36 known AMD genes. The well-known shortest path algorithm, Dijkstra's algorithm, was applied to find the shortest path connecting each pair of known AMD related genes in protein-protein interaction (PPI) network. The genes occurring in any shortest path were considered as candidate AMD related genes. As a result, 125 novel AMD genes were predicted. The further analysis based on betweenness and permutation test indicates that there are 10 genes involved in the formation or development of AMD and may be the actual AMD related genes with high probability. We hope that this contribution would promote the study of age-related macular degeneration and discovery of novel effective treatments.
Collapse
|
23
|
Chen L, Lu J, Zhang J, Feng KR, Zheng MY, Cai YD. Predicting chemical toxicity effects based on chemical-chemical interactions. PLoS One 2013; 8:e56517. [PMID: 23457578 PMCID: PMC3574107 DOI: 10.1371/journal.pone.0056517] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2012] [Accepted: 01/10/2013] [Indexed: 12/02/2022] Open
Abstract
Toxicity is a major contributor to high attrition rates of new chemical entities in drug discoveries. In this study, an order-classifier was built to predict a series of toxic effects based on data concerning chemical-chemical interactions under the assumption that interactive compounds are more likely to share similar toxicity profiles. According to their interaction confidence scores, the order from the most likely toxicity to the least was obtained for each compound. Ten test groups, each of them containing one training dataset and one test dataset, were constructed from a benchmark dataset consisting of 17,233 compounds. By a Jackknife test on each of these test groups, the 1st order prediction accuracies of the training dataset and the test dataset were all approximately 79.50%, substantially higher than the rate of 25.43% achieved by random guesses. Encouraged by the promising results, we expect that our method will become a useful tool in screening out drugs with high toxicity.
Collapse
Affiliation(s)
- Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Jing Lu
- Drug Discovery and Design Center (DDDC), Shanghai Institute of Materia Medica, Shanghai, China
| | - Jian Zhang
- Department of Ophthalmology, Shanghai First People’s Hospital Affiliated to Shanghai Jiaotong University, Shanghai, China
| | - Kai-Rui Feng
- Simcyp Limited, Blades Enterprise Centre, Sheffield, United Kingdom
| | - Ming-Yue Zheng
- Drug Discovery and Design Center (DDDC), Shanghai Institute of Materia Medica, Shanghai, China
- * E-mail: (MYZ); (YDC)
| | - Yu-Dong Cai
- Institute of Systems Biology, Shanghai University, Shanghai, China
- * E-mail: (MYZ); (YDC)
| |
Collapse
|
24
|
Wang YC, Huang SH, Lan CY, Chen BS. Prediction of phenotype-associated genes via a cellular network approach: a Candida albicans infection case study. PLoS One 2012; 7:e35339. [PMID: 22509408 PMCID: PMC3324557 DOI: 10.1371/journal.pone.0035339] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2011] [Accepted: 03/15/2012] [Indexed: 02/04/2023] Open
Abstract
Candida albicans is the most prevalent opportunistic fungal pathogen in humans causing superficial and serious systemic infections. The infection process can be divided into three stages: adhesion, invasion, and host cell damage. To enhance our understanding of these C. albicans infection stages, this study aimed to predict phenotype-associated genes involved during these three infection stages and their roles in C. albicans-host interactions. In light of the principles that proteins that lie closer to one another in a protein interaction network are more likely to have similar functions, and that genes regulated by the same transcription factors tend to have similar functions, a cellular network approach was proposed to predict the phenotype-associated genes in this study. A total of 4, 12, and 3 genes were predicted as adhesion-, invasion-, and damage-associated genes during C. albicans infection, respectively. These predicted genes highlight the facts that cell surface components are critical for cell adhesion, and that morphogenesis is crucial for cell invasion. In addition, they provide targets for further investigations into the mechanisms of the three C. albicans infection stages. These results give insights into the responses elicited in C. albicans during interaction with the host, possibly instrumental in identifying novel therapies to treat C. albicans infection.
Collapse
Affiliation(s)
- Yu-Chao Wang
- Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - Shin-Hao Huang
- Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - Chung-Yu Lan
- Department of Life Science, National Tsing Hua University, Hsinchu, Taiwan
- Institute of Molecular and Cellular Biology, National Tsing Hua University, Hsinchu, Taiwan
| | - Bor-Sen Chen
- Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
- * E-mail:
| |
Collapse
|
25
|
Park B, Cui G, Lee H, Huang DS, Han K. PPISEARCHENGINE: gene ontology-based search for protein-protein interactions. Comput Methods Biomech Biomed Engin 2012; 16:691-8. [PMID: 22316075 DOI: 10.1080/10255842.2011.631528] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
This paper presents a new search engine called PPISearchEngine which finds protein-protein interactions (PPIs) using the gene ontology (GO) and the biological relations of proteins. For efficient retrieval of PPIs, each GO term is assigned a prime number and the relation between the terms is represented by the product of prime numbers. This representation is hidden from users but facilitates the search for the interactions of a query protein by unique prime factorisation of the number that represents the query protein. For a query protein, PPISearchEngine considers not only the GO term associated with the query protein but also the GO terms at the lower level than the GO term in the GO hierarchy, and finds all the interactions of the query protein which satisfy the search condition. In contrast, the standard keyword-matching or ID-matching search method cannot find the interactions of a protein unless the interactions involve a protein with explicit annotations. To the best of our knowledge, this search engine is the first method that can process queries like 'for protein p with GO [Formula: see text], find p's interaction partners with GO [Formula: see text]'. PPISearchEngine is freely available to academics at http://search.hpid.org/.
Collapse
Affiliation(s)
- Byungkyu Park
- Institute for Information and Electronics Research, Inha University, Incheon, 402-751, South Korea
| | | | | | | | | |
Collapse
|
26
|
Zhang S, Chang Z, Li Z, DuanMu H, Li Z, Li K, Liu Y, Qiu F, Xu Y. Calculating phenotypic similarity between genes using hierarchical structure data based on semantic similarity. Gene 2012; 497:58-65. [PMID: 22305981 DOI: 10.1016/j.gene.2012.01.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2011] [Revised: 01/16/2012] [Accepted: 01/18/2012] [Indexed: 01/25/2023]
Abstract
Phenotypic similarity is correlated with a number of measures of gene function, such as relatedness at the level of direct protein-protein interaction. The phenotypic effect of a deleted or mutated gene, which is one part of gene annotation, has caught broad attention. However, there have been few measures to study phenotypic similarity with the data from Human Phenotype Ontology (HPO) database, therefore more analogous measures should be developed and investigated. We used five semantic similarity-based measures (Jiang and Conrath, Lin, Schlicker, Yu and Wu) to calculate the human phenotypic similarity between genes (PSG) with data from HPO database, and evaluated their accuracy with information of protein-protein interaction, protein complex, protein family, gene function or DNA sequence. Compared with the gene pairs that were random selected, the results of these methods were statistically significant (all P<0.001). Furthermore, we assessed the performance of these five measures by receiver operating characteristic (ROC) curve analysis, and found that most of them performed better than the previous methods. This work had proved that these measures based on semantic similarity for calculation of PSG were effective for hierarchical structure data. Our study contributes to the development and optimization of novel algorithms of PSG calculation and provides more alternative methods to researchers as well as tools and directions for PSG study.
Collapse
Affiliation(s)
- Shanzhen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China
| | | | | | | | | | | | | | | | | |
Collapse
|
27
|
Hu LL, Chen C, Huang T, Cai YD, Chou KC. Predicting biological functions of compounds based on chemical-chemical interactions. PLoS One 2011; 6:e29491. [PMID: 22220213 PMCID: PMC3248422 DOI: 10.1371/journal.pone.0029491] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2011] [Accepted: 11/29/2011] [Indexed: 11/18/2022] Open
Abstract
Given a compound, how can we effectively predict its biological function? It is a fundamentally important problem because the information thus obtained may benefit the understanding of many basic biological processes and provide useful clues for drug design. In this study, based on the information of chemical-chemical interactions, a novel method was developed that can be used to identify which of the following eleven metabolic pathway classes a query compound may be involved with: (1) Carbohydrate Metabolism, (2) Energy Metabolism, (3) Lipid Metabolism, (4) Nucleotide Metabolism, (5) Amino Acid Metabolism, (6) Metabolism of Other Amino Acids, (7) Glycan Biosynthesis and Metabolism, (8) Metabolism of Cofactors and Vitamins, (9) Metabolism of Terpenoids and Polyketides, (10) Biosynthesis of Other Secondary Metabolites, (11) Xenobiotics Biodegradation and Metabolism. It was observed that the overall success rate obtained by the method via the 5-fold cross-validation test on a benchmark dataset consisting of 3,137 compounds was 77.97%, which is much higher than 10.45%, the corresponding success rate obtained by the random guesses. Besides, to deal with the situation that some compounds may be involved with more than one metabolic pathway class, the method presented here is featured by the capacity able to provide a series of potential metabolic pathway classes ranked according to the descending order of their likelihood for each of the query compounds concerned. Furthermore, our method was also applied to predict 5,549 compounds whose metabolic pathway classes are unknown. Interestingly, the results thus obtained are quite consistent with the deductions from the reports by other investigators. It is anticipated that, with the continuous increase of the chemical-chemical interaction data, the current method will be further enhanced in its power and accuracy, so as to become a useful complementary vehicle in annotating uncharacterized compounds for their biological functions.
Collapse
Affiliation(s)
- Le-Le Hu
- Institute of Systems Biology, Shanghai University, Shanghai, China
- Department of Chemistry, College of Sciences, Shanghai University, Shanghai, China
| | - Chen Chen
- Department of Chemistry, College of Sciences, Shanghai University, Shanghai, China
| | - Tao Huang
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
- Shanghai Center for Bioinformation Technology, Shanghai, China
| | - Yu-Dong Cai
- Institute of Systems Biology, Shanghai University, Shanghai, China
- Gordon Life Science Institute, San Diego, California, United States of America
- * E-mail:
| | - Kuo-Chen Chou
- Gordon Life Science Institute, San Diego, California, United States of America
| |
Collapse
|