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Sadeghi M, Bahrami A, Hasankhani A, Kioumarsi H, Nouralizadeh R, Abdulkareem SA, Ghafouri F, Barkema HW. lncRNA-miRNA-mRNA ceRNA Network Involved in Sheep Prolificacy: An Integrated Approach. Genes (Basel) 2022; 13:genes13081295. [PMID: 35893032 PMCID: PMC9332185 DOI: 10.3390/genes13081295] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/15/2022] [Accepted: 07/19/2022] [Indexed: 02/06/2023] Open
Abstract
Understanding the molecular pattern of fertility is considered as an important step in breeding of different species, and despite the high importance of the fertility, little success has been achieved in dissecting the interactome basis of sheep fertility. However, the complex mechanisms associated with prolificacy in sheep have not been fully understood. Therefore, this study aimed to use competitive endogenous RNA (ceRNA) networks to evaluate this trait to better understand the molecular mechanisms responsible for fertility. A competitive endogenous RNA (ceRNA) network of the corpus luteum was constructed between Romanov and Baluchi sheep breeds with either good or poor genetic merit for prolificacy using whole-transcriptome analysis. First, the main list of lncRNAs, miRNAs, and mRNA related to the corpus luteum that alter with the breed were extracted, then miRNA−mRNA and lncRNA−mRNA interactions were predicted, and the ceRNA network was constructed by integrating these interactions with the other gene regulatory networks and the protein−protein interaction (PPI). A total of 264 mRNAs, 14 lncRNAs, and 34 miRNAs were identified by combining the GO and KEGG enrichment analyses. In total, 44, 7, 7, and 6 mRNAs, lncRNAs, miRNAs, and crucial modules, respectively, were disclosed through clustering for the corpus luteum ceRNA network. All these RNAs involved in biological processes, namely proteolysis, actin cytoskeleton organization, immune system process, cell adhesion, cell differentiation, and lipid metabolic process, have an overexpression pattern (Padj < 0.01). This study increases our understanding of the contribution of different breed transcriptomes to phenotypic fertility differences and constructed a ceRNA network in sheep (Ovis aries) to provide insights into further research on the molecular mechanism and identify new biomarkers for genetic improvement.
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Affiliation(s)
- Masoumeh Sadeghi
- Environmental Health, Zahedan University of Medical Sciences, Zahedan 98, Iran;
| | - Abolfazl Bahrami
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 31, Iran; (A.H.); (F.G.)
- Biomedical Center for Systems Biology Science Munich, Ludwig-Maximilians-University, 80333 Munich, Germany
- Correspondence: (A.B.); (R.N.); Tel.: +98-9199300065 (A.B.)
| | - Aliakbar Hasankhani
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 31, Iran; (A.H.); (F.G.)
| | - Hamed Kioumarsi
- Department of Animal Science Research, Gilan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Rasht 43, Iran;
| | - Reza Nouralizadeh
- Department of Food and Drug Control, Faculty of Pharmacy, Jundishapour University of Medical Sciences, Ahvaz 63, Iran
- Correspondence: (A.B.); (R.N.); Tel.: +98-9199300065 (A.B.)
| | - Sarah Ali Abdulkareem
- Department of Computer Science, Al-Turath University College, Al Mansour, Baghdad 10011, Iraq;
| | - Farzad Ghafouri
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 31, Iran; (A.H.); (F.G.)
| | - Herman W. Barkema
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N4Z6, Canada;
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Chuang LY, Yang CS, Yang HS, Yang CH. Identification of High-Order Single-Nucleotide Polymorphism Barcodes in Breast Cancer Using a Hybrid Taguchi-Genetic Algorithm: Case-Control Study. JMIR Med Inform 2020; 8:e16886. [PMID: 32554381 PMCID: PMC7351259 DOI: 10.2196/16886] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 02/09/2020] [Accepted: 04/08/2020] [Indexed: 12/24/2022] Open
Abstract
Background Breast cancer has a major disease burden in the female population, and it is a highly genome-associated human disease. However, in genetic studies of complex diseases, modern geneticists face challenges in detecting interactions among loci. Objective This study aimed to investigate whether variations of single-nucleotide polymorphisms (SNPs) are associated with histopathological tumor characteristics in breast cancer patients. Methods A hybrid Taguchi-genetic algorithm (HTGA) was proposed to identify the high-order SNP barcodes in a breast cancer case-control study. A Taguchi method was used to enhance a genetic algorithm (GA) for identifying high-order SNP barcodes. The Taguchi method was integrated into the GA after the crossover operations in order to optimize the generated offspring systematically for enhancing the GA search ability. Results The proposed HTGA effectively converged to a promising region within the problem space and provided excellent SNP barcode identification. Regression analysis was used to validate the association between breast cancer and the identified high-order SNP barcodes. The maximum OR was less than 1 (range 0.870-0.755) for two- to seven-order SNP barcodes. Conclusions We systematically evaluated the interaction effects of 26 SNPs within growth factor–related genes for breast carcinogenesis pathways. The HTGA could successfully identify relevant high-order SNP barcodes by evaluating the differences between cases and controls. The validation results showed that the HTGA can provide better fitness values as compared with other methods for the identification of high-order SNP barcodes using breast cancer case-control data sets.
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Affiliation(s)
| | - Cheng-San Yang
- Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan
| | - Huai-Shuo Yang
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan
| | - Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan.,Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung, Taiwan.,College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
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Najafi A, Masoudi-Nejad A, Ghanei M, Nourani MR, Moeini A. Pathway reconstruction of airway remodeling in chronic lung diseases: a systems biology approach. PLoS One 2014; 9:e100094. [PMID: 24978043 PMCID: PMC4076832 DOI: 10.1371/journal.pone.0100094] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2013] [Accepted: 05/22/2014] [Indexed: 01/01/2023] Open
Abstract
Airway remodeling is a pathophysiologic process at the clinical, cellular, and molecular level relating to chronic obstructive airway diseases such as chronic obstructive pulmonary disease (COPD), asthma and mustard lung. These diseases are associated with the dysregulation of multiple molecular pathways in the airway cells. Little progress has so far been made in discovering the molecular causes of complex disease in a holistic systems manner. Therefore, pathway and network reconstruction is an essential part of a systems biology approach to solve this challenging problem. In this paper, multiple data sources were used to construct the molecular process of airway remodeling pathway in mustard lung as a model of airway disease. We first compiled a master list of genes that change with airway remodeling in the mustard lung disease and then reconstructed the pathway by generating and merging the protein-protein interaction and the gene regulatory networks. Experimental observations and literature mining were used to identify and validate the master list. The outcome of this paper can provide valuable information about closely related chronic obstructive airway diseases which are of great importance for biologists and their future research. Reconstructing the airway remodeling interactome provides a starting point and reference for the future experimental study of mustard lung, and further analysis and development of these maps will be critical to understanding airway diseases in patients.
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Affiliation(s)
- Ali Najafi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- * E-mail:
| | - Mostafa Ghanei
- Genomics Division, Chemical Injury Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Mohamad-Reza Nourani
- Genomics Division, Chemical Injury Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Ali Moeini
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- Department of Algorithms and Computation, College of Engineering, University of Tehran, Tehran, Iran
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Double-bottom chaotic map particle swarm optimization based on chi-square test to determine gene-gene interactions. BIOMED RESEARCH INTERNATIONAL 2014; 2014:172049. [PMID: 24895547 PMCID: PMC4033510 DOI: 10.1155/2014/172049] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2013] [Accepted: 04/16/2014] [Indexed: 11/19/2022]
Abstract
Gene-gene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of gene-gene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing high-order interaction, and finding an available high-order model of gene-gene interaction remains a challenge. In this study, an improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBM-PSO successfully determined two- to six-order models of gene-gene interaction for the risk association with breast cancer (odds ratio > 1.0; P value <0.05). Analysis results supported that the proposed DBM-PSO can identify good models and provide higher chi-square values than conventional PSO. This study indicates that DBM-PSO is a robust and precise algorithm for determination of gene-gene interaction models for breast cancer.
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Vangala RK, Ravindran V, Ghatge M, Shanker J, Arvind P, Bindu H, Shekar M, Rao VS. Integrative bioinformatics analysis of genomic and proteomic approaches to understand the transcriptional regulatory program in coronary artery disease pathways. PLoS One 2013; 8:e57193. [PMID: 23468932 PMCID: PMC3585295 DOI: 10.1371/journal.pone.0057193] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2012] [Accepted: 01/18/2013] [Indexed: 11/19/2022] Open
Abstract
Patients with cardiovascular disease show a panel of differentially regulated serum biomarkers indicative of modulation of several pathways from disease onset to progression. Few of these biomarkers have been proposed for multimarker risk prediction methods. However, the underlying mechanism of the expression changes and modulation of the pathways is not yet addressed in entirety. Our present work focuses on understanding the regulatory mechanisms at transcriptional level by identifying the core and specific transcription factors that regulate the coronary artery disease associated pathways. Using the principles of systems biology we integrated the genomics and proteomics data with computational tools. We selected biomarkers from 7 different pathways based on their association with the disease and assayed 24 biomarkers along with gene expression studies and built network modules which are highly regulated by 5 core regulators PPARG, EGR1, ETV1, KLF7 and ESRRA. These network modules in turn comprise of biomarkers from different pathways showing that the core regulatory transcription factors may work together in differential regulation of several pathways potentially leading to the disease. This kind of analysis can enhance the elucidation of mechanisms in the disease and give better strategies of developing multimarker module based risk predictions.
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Affiliation(s)
- Rajani Kanth Vangala
- Tata Proteomics and Coagulation Department, Thrombosis Research Institute, Bangalore, Karnataka, India.
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Chuang LY, Lin YD, Chang HW, Yang CH. An improved PSO algorithm for generating protective SNP barcodes in breast cancer. PLoS One 2012; 7:e37018. [PMID: 22623973 PMCID: PMC3356401 DOI: 10.1371/journal.pone.0037018] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2012] [Accepted: 04/11/2012] [Indexed: 11/18/2022] Open
Abstract
Background Possible single nucleotide polymorphism (SNP) interactions in breast cancer are usually not investigated in genome-wide association studies. Previously, we proposed a particle swarm optimization (PSO) method to compute these kinds of SNP interactions. However, this PSO does not guarantee to find the best result in every implement, especially when high-dimensional data is investigated for SNP–SNP interactions. Methodology/Principal Findings In this study, we propose IPSO algorithm to improve the reliability of PSO for the identification of the best protective SNP barcodes (SNP combinations and genotypes with maximum difference between cases and controls) associated with breast cancer. SNP barcodes containing different numbers of SNPs were computed. The top five SNP barcode results are retained for computing the next SNP barcode with a one-SNP-increase for each processing step. Based on the simulated data for 23 SNPs of six steroid hormone metabolisms and signalling-related genes, the performance of our proposed IPSO algorithm is evaluated. Among 23 SNPs, 13 SNPs displayed significant odds ratio (OR) values (1.268 to 0.848; p<0.05) for breast cancer. Based on IPSO algorithm, the jointed effect in terms of SNP barcodes with two to seven SNPs show significantly decreasing OR values (0.84 to 0.57; p<0.05 to 0.001). Using PSO algorithm, two to four SNPs show significantly decreasing OR values (0.84 to 0.77; p<0.05 to 0.001). Based on the results of 20 simulations, medians of the maximum differences for each SNP barcode generated by IPSO are higher than by PSO. The interquartile ranges of the boxplot, as well as the upper and lower hinges for each n-SNP barcode (n = 3∼10) are more narrow in IPSO than in PSO, suggesting that IPSO is highly reliable for SNP barcode identification. Conclusions/Significance Overall, the proposed IPSO algorithm is robust to provide exact identification of the best protective SNP barcodes for breast cancer.
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Affiliation(s)
- Li-Yeh Chuang
- Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
| | - Yu-Da Lin
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
| | - Hsueh-Wei Chang
- Department of Biomedical Science and Environmental Biology, Center of Excellence for Environmental Medicine, Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- * E-mail: (HWC); (CHY)
| | - Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
- * E-mail: (HWC); (CHY)
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