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Paul S, Ruiz-Manriquez LM, Ambriz-Gonzalez H, Medina-Gomez D, Valenzuela-Coronado E, Moreno-Gomez P, Pathak S, Chakraborty S, Srivastava A. Impact of smoking-induced dysregulated human miRNAs in chronic disease development and their potential use in prognostic and therapeutic purposes. J Biochem Mol Toxicol 2022; 36:e23134. [PMID: 35695328 DOI: 10.1002/jbt.23134] [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] [Received: 07/17/2021] [Revised: 04/20/2022] [Accepted: 05/29/2022] [Indexed: 12/14/2022]
Abstract
MicroRNAs (miRNAs) are evolutionary conserved small noncoding RNA molecules with a significant ability to regulate gene expression at the posttranscriptional level either through translation repression or messenger RNA degradation. miRNAs are differentially expressed in various pathophysiological conditions, affecting the course of the disease by modulating several critical target genes. As the persistence of irreversible molecular changes caused by cigarette smoking is central to the pathogenesis of various chronic diseases, several studies have shown its direct correlation with the dysregulation of different miRNAs, affecting numerous essential biological processes. This review provides an insight into the current status of smoking-induced miRNAs dysregulation in chronic diseases such as COPD, atherosclerosis, pulmonary hypertension, and different cancers and explores the diagnostic/prognostic potential of miRNA-based biomarkers and their efficacy as therapeutic targets.
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Affiliation(s)
- Sujay Paul
- Tecnologico de Monterrey, School of Engineering and Sciences, Campus Queretaro, Av. Epigmenio Gonzalez, San Pablo, Queretaro, Mexico
| | - Luis M Ruiz-Manriquez
- Tecnologico de Monterrey, School of Engineering and Sciences, Campus Queretaro, Av. Epigmenio Gonzalez, San Pablo, Queretaro, Mexico
| | - Hector Ambriz-Gonzalez
- Tecnologico de Monterrey, School of Engineering and Sciences, Campus Queretaro, Av. Epigmenio Gonzalez, San Pablo, Queretaro, Mexico
| | - Daniel Medina-Gomez
- Tecnologico de Monterrey, School of Engineering and Sciences, Campus Queretaro, Av. Epigmenio Gonzalez, San Pablo, Queretaro, Mexico
| | - Estefania Valenzuela-Coronado
- Tecnologico de Monterrey, School of Engineering and Sciences, Campus Queretaro, Av. Epigmenio Gonzalez, San Pablo, Queretaro, Mexico
| | - Paloma Moreno-Gomez
- Tecnologico de Monterrey, School of Engineering and Sciences, Campus Queretaro, Av. Epigmenio Gonzalez, San Pablo, Queretaro, Mexico
| | - Surajit Pathak
- Department of Medical Biotechnology, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI), Chettinad Academy of Research and Education (CARE), Kelambakkam, Chennai, Tamil Nadu, India
| | - Samik Chakraborty
- Division of Nephrology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Aashish Srivastava
- Section of Bioinformatics, Clinical Laboratory, Haukeland University Hospital, Bergen, Norway.,Department of Clinical Science, University of Bergen, Bergen, Norway
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Huang YA, Huang ZA, Li JQ, You ZH, Wang L, Yi HC, Yu CQ. GBDR: a Bayesian model for precise prediction of pathogenic microorganisms using 16S rRNA gene sequences. BMC Genomics 2022; 22:916. [PMID: 35296232 PMCID: PMC8925046 DOI: 10.1186/s12864-022-08423-w] [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: 12/26/2021] [Accepted: 02/25/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Recent evidences have suggested that human microorganisms participate in important biological activities in the human body. The dysfunction of host-microbiota interactions could lead to complex human disorders. The knowledge on host-microbiota interactions can provide valuable insights into understanding the pathological mechanism of diseases. However, it is time-consuming and costly to identify the disorder-specific microbes from the biological "haystack" merely by routine wet-lab experiments. With the developments in next-generation sequencing and omics-based trials, it is imperative to develop computational prediction models for predicting microbe-disease associations on a large scale. RESULTS Based on the known microbe-disease associations derived from the Human Microbe-Disease Association Database (HMDAD), the proposed model shows reliable performance with high values of the area under ROC curve (AUC) of 0.9456 and 0.8866 in leave-one-out cross validations and five-fold cross validations, respectively. In case studies of colorectal carcinoma, 80% out of the top-20 predicted microbes have been experimentally confirmed via published literatures. CONCLUSION Based on the assumption that functionally similar microbes tend to share the similar interaction patterns with human diseases, we here propose a group based computational model of Bayesian disease-oriented ranking to prioritize the most potential microbes associating with various human diseases. Based on the sequence information of genes, two computational approaches (BLAST+ and MEGA 7) are leveraged to measure the microbe-microbe similarity from different perspectives. The disease-disease similarity is calculated by capturing the hierarchy information from the Medical Subject Headings (MeSH) data. The experimental results illustrate the accuracy and effectiveness of the proposed model. This work is expected to facilitate the characterization and identification of promising microbial biomarkers.
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Affiliation(s)
- Yu-An Huang
- Department of Information Engineering, Xijing University, Xi'an, 710123, China.
| | - Zhi-An Huang
- Center for Computer Science and Information Technology, City University of Hong Kong Dongguan Research Institute, Dongguan, China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China.
| | - Zhu-Hong You
- Department of Information Engineering, Xijing University, Xi'an, 710123, China
| | - Lei Wang
- Guangxi Academy of Science, Nanning, 530000, China
| | - Hai-Cheng Yi
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, 830000, China
| | - Chang-Qing Yu
- Department of Information Engineering, Xijing University, Xi'an, 710123, China
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Dai Q, Chu Y, Li Z, Zhao Y, Mao X, Wang Y, Xiong Y, Wei DQ. MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information. Comput Biol Med 2021; 136:104706. [PMID: 34371319 DOI: 10.1016/j.compbiomed.2021.104706] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 07/26/2021] [Accepted: 07/26/2021] [Indexed: 01/17/2023]
Abstract
MicroRNAs (miRNAs) are significant regulators in various biological processes. They may become promising biomarkers or therapeutic targets, which provide a new perspective in diagnosis and treatment of multiple diseases. Since the experimental methods are always costly and resource-consuming, prediction of disease-related miRNAs using computational methods is in great need. In this study, we developed MDA-CF to identify underlying miRNA-disease associations based on a cascade forest model. In this method, multi-source information was integrated to represent miRNAs and diseases comprehensively, and the autoencoder was utilized for dimension reduction to obtain the optimal feature space. The cascade forest model was then employed for miRNA-disease association prediction. As a result, the average AUC of MDA-CF was 0.9464 on HMDD v3.2 in five-fold cross-validation. Compared with previous computational methods, MDA-CF performed better on HMDD v2.0 with an average AUC of 0.9258. Moreover, MDA-CF was implemented to investigate colon neoplasm, breast neoplasm, and gastric neoplasm, and 100%, 86%, 88% of the top 50 potential miRNAs were validated by authoritative databases. In conclusion, MDA-CF appears to be a reliable method to uncover disease-associated miRNAs. The source code of MDA-CF is available at https://github.com/a1622108/MDA-CF.
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Affiliation(s)
- Qiuying Dai
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yanyi Chu
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Zhiqi Li
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yusong Zhao
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xueying Mao
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yanjing Wang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong, 518055, China.
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Jalali-Qomi S, Motovali-bashi M, Rezaei H, Khalilian S. Experimental validation of a predicted microRNA within human FVIII gene. MOLECULAR BIOLOGY RESEARCH COMMUNICATIONS 2021; 10:45-53. [PMID: 34316492 PMCID: PMC8310658 DOI: 10.22099/mbrc.2021.39067.1573] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Hemophilia A is an X-linked bleeding disorder that occurs due to the deficiency of Factor VIII (FVIII) protein clotting activity. The mutations in the F8 gene, which encodes FVIII coagulating protein have been widely reviewed. However, there is a wide range of criteria that in addition to F8 gene mutations, different molecular mechanisms may be associated with hemophilia A. Various functions of FVIII could be related to the hypothetical small non-coding RNAs, located within the F8 gene sequence. Therefore, miRNAs that can post-transcriptionally regulate gene expression might confer susceptibility to developing hemophilia A. Here, we have selected a bioinformatically predicted hairpin structure sequence in the first intron of the F8 gene that has the potential to produce a real miRNA (named put-miR1). We tried to experimentally detect the predicted miRNA via RT-PCR following its precursor overexpression in HEK 293 cell lines. Despite the accuracy of miRNA prediction, according to the reliable bioinformatics studies, we couldn't confirm the existence of considered mature miRNA in transfected cells. We hope that through changing experimental conditions, designing new primers, or altering cell lines and expression vectors, the exogenous and endogenous expression of the predicted miRNA will be confirmed.
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Affiliation(s)
| | - Majid Motovali-bashi
- Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Iran
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Chu Y, Wang X, Dai Q, Wang Y, Wang Q, Peng S, Wei X, Qiu J, Salahub DR, Xiong Y, Wei DQ. MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph. Brief Bioinform 2021; 22:6261915. [PMID: 34009265 DOI: 10.1093/bib/bbab165] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 04/02/2021] [Accepted: 04/08/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.
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Affiliation(s)
- Yanyi Chu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Xuhong Wang
- School of Electronic, Information and Electrical Engineering (SEIEE), Shanghai Jiao Tong University, China
| | - Qiuying Dai
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Yanjing Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Qiankun Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, China
| | | | | | - Dennis Russell Salahub
- Department of Chemistry, University of Calgary, Fellow Royal Society of Canada and Fellow of the American Association for the Advancement of Science, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
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Lassandro G, Ciaccia L, Amoruso A, Palladino V, Palmieri VV, Giordano P. Focus on MicroRNAs as Biomarker in Pediatric Diseases. Curr Pharm Des 2021; 27:826-832. [PMID: 33087027 DOI: 10.2174/1381612826666201021125512] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 09/25/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND MiRNAs are a class of small non-coding RNAs that are involved in the post-transcriptional regulation of gene expression. MiRNAs are considered a class of epigenetic biomarkers. These biomarkers can investigate disease at different stages: diagnosis, therapy or clinical follow-up. OBJECTIVE The aim of this paper is to highlight the innovative use of miRNAs in several childhood diseases. METHODS We conducted a literature review to search the usage of miRNAs in pediatric clinical routine or experimental trials. RESULTS We found a possible key role of miRNAs in different pediatric illnesses (metabolic alterations, coagulation defects, cancer). CONCLUSION The modest literature production denotes that further investigation is needed to assess and validate the promising role of miRNAs as non-invasive biomarkers in pediatric disorders.
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Affiliation(s)
- Giuseppe Lassandro
- Department of Biomedical Science and Human Oncology, University of Bari "Aldo Moro", Bari, Italy
| | - Loredana Ciaccia
- Department of Biomedical Science and Human Oncology, University of Bari "Aldo Moro", Bari, Italy
| | - Anna Amoruso
- Department of Biomedical Science and Human Oncology, University of Bari "Aldo Moro", Bari, Italy
| | - Valentina Palladino
- Department of Biomedical Science and Human Oncology, University of Bari "Aldo Moro", Bari, Italy
| | - Viviana V Palmieri
- Department of Biomedical Science and Human Oncology, University of Bari "Aldo Moro", Bari, Italy
| | - Paola Giordano
- Department of Biomedical Science and Human Oncology, University of Bari "Aldo Moro", Bari, Italy
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Li S, Xie M, Liu X. A Novel Approach Based on Bipartite Network Recommendation and KATZ Model to Predict Potential Micro-Disease Associations. Front Genet 2019; 10:1147. [PMID: 31803235 PMCID: PMC6873782 DOI: 10.3389/fgene.2019.01147] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 10/21/2019] [Indexed: 12/24/2022] Open
Abstract
Accumulating evidence indicates that the microbes colonizing human bodies have crucial effects on human health and the discovery of disease-related microbes will promote the discovery of biomarkers and drugs for the prevention, diagnosis, treatment, and prognosis of diseases. However clinical experiments of disease-microbe associations are time-consuming, laborious and expensive, and there are few methods for predicting potential microbe-disease association. Therefore, developing effective computational models utilizing the accumulated public data of clinically validated microbe-disease associations to identify novel disease-microbe associations is of practical importance. We propose a novel method based on the KATZ model and Bipartite Network Recommendation Algorithm (KATZBNRA) to discover potential associations between microbes and diseases. We calculate the Gaussian interaction profile kernel similarity of diseases and microbes based on validated disease-microbe associations. Then, we construct a bipartite graph and execute a bipartite network recommendation algorithm. Finally, we integrate the disease similarity, microbe similarity and bipartite network recommendation score to obtain the final score, which is used to infer whether there are some novel disease-microbe interactions. To evaluate the predictive power of KATZBNRA, we tested it with the walk length 2 using global leave-one-out cross validation (LOOV), two-fold and five-fold cross validations, with AUCs of 0.9098, 0.8463 and 0.8969, respectively. The test results also show that KATZBNRA is more accurate than two recent similar methods KATZHMDA and BNPMDA.
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Affiliation(s)
- Shiru Li
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Minzhu Xie
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Xinqiu Liu
- Hunan Vocational College of Engineering, Changsha, China
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Huang YA, Huang ZA, You ZH, Zhu Z, Huang WZ, Guo JX, Yu CQ. Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder. Front Genet 2019; 10:758. [PMID: 31555320 PMCID: PMC6727066 DOI: 10.3389/fgene.2019.00758] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 07/17/2019] [Indexed: 12/14/2022] Open
Abstract
The interaction of miRNA and lncRNA is known to be important for gene regulations. However, the number of known lncRNA-miRNA interactions is still very limited and there are limited computational tools available for predicting new ones. Considering that lncRNAs and miRNAs share internal patterns in the partnership between each other, the underlying lncRNA-miRNA interactions could be predicted by utilizing the known ones, which could be considered as a semi-supervised learning problem. It is shown that the attributes of lncRNA and miRNA have a close relationship with the interaction between each other. Effective use of side information could be helpful for improving the performance especially when the training samples are limited. In view of this, we proposed an end-to-end prediction model called GCLMI (Graph Convolution for novel lncRNA-miRNA Interactions) by combining the techniques of graph convolution and auto-encoder. Without any preprocessing process on the feature information, our method can incorporate raw data of node attributes with the topology of the interaction network. Based on a real dataset collected from a public database, the results of experiments conducted on k-fold cross validations illustrate the robustness and effectiveness of the prediction performance of the proposed prediction model. We prove the graph convolution layer as designed in the proposed model able to effectively integrate the input data by filtering the graph with node features. The proposed model is anticipated to yield highly potential lncRNA-miRNA interactions in the scenario that different types of numerical features describing lncRNA or miRNA are provided by users, serving as a useful computational tool.
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Affiliation(s)
- Yu-An Huang
- College of Electronics and Information Engineering, Xijing University, Xi'an, China
| | - Zhi-An Huang
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
| | - Zhu-Hong You
- College of Electronics and Information Engineering, Xijing University, Xi'an, China
| | - Zexuan Zhu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Wen-Zhun Huang
- College of Electronics and Information Engineering, Xijing University, Xi'an, China
| | - Jian-Xin Guo
- College of Electronics and Information Engineering, Xijing University, Xi'an, China
| | - Chang-Qing Yu
- College of Electronics and Information Engineering, Xijing University, Xi'an, China
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