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Jia W, Wu X. Potential biomarkers analysis and protein internal mechanisms by cold plasma treatment: Is proteomics effective to elucidate protein-protein interaction network and biochemical pathway? Food Chem 2023; 426:136664. [PMID: 37352708 DOI: 10.1016/j.foodchem.2023.136664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/19/2023] [Accepted: 06/16/2023] [Indexed: 06/25/2023]
Abstract
New market trends of meat flavor, tenderness, and color quality indicators have prompted the research on meat preservation as a crucial topic to received attention. Present research about the effects of irradiation, cold plasma technology on meat is incomplete. There are strongly recommended that proteomics techniques be jointly to enhance the coverage of internal meat molecules for meat research. By identifying meat proteins, detecting biological functions, and quantifying the protein segments of specific meat biomarkers, which can be provided for the information of diagnostic components in preservative technologies. The current review provides scientific findings on various control strategies: (i) combine the data-independent acquisition to provide a reference for the meat molecular mechanism and rapid identification; (ii) design molecular networks biological functions assessment model; (iii) molecular investigations of cold plasma techniques and underlying mechanisms; (iv) explore the X-rays and γ-rays treatment in meat preservation and myoglobin change mechanism more comprehensively.
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Affiliation(s)
- Wei Jia
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China; Shaanxi Research Institute of Agricultural Products Processing Technology, Xi'an 710021, China.
| | - Xinyu Wu
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China
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Dotolo S, Esposito Abate R, Roma C, Guido D, Preziosi A, Tropea B, Palluzzi F, Giacò L, Normanno N. Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice. Biomedicines 2022; 10:biomedicines10092074. [PMID: 36140175 PMCID: PMC9495893 DOI: 10.3390/biomedicines10092074] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/12/2022] [Accepted: 08/22/2022] [Indexed: 11/22/2022] Open
Abstract
The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).
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Affiliation(s)
- Serena Dotolo
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, 80131 Naples, Italy
| | - Riziero Esposito Abate
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, 80131 Naples, Italy
| | - Cristin Roma
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, 80131 Naples, Italy
| | - Davide Guido
- Bioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy
| | - Alessia Preziosi
- Bioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy
| | - Beatrice Tropea
- Bioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy
| | - Fernando Palluzzi
- Bioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy
| | - Luciano Giacò
- Bioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy
| | - Nicola Normanno
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, 80131 Naples, Italy
- Correspondence:
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Huang CH, Zaenudin E, Tsai JJ, Kurubanjerdjit N, Ng KL. Network subgraph-based approach for analyzing and comparing molecular networks. PeerJ 2022; 10:e13137. [PMID: 35529499 PMCID: PMC9074881 DOI: 10.7717/peerj.13137] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 02/28/2022] [Indexed: 01/12/2023] Open
Abstract
Molecular networks are built up from genetic elements that exhibit feedback interactions. Here, we studied the problem of measuring the similarity of directed networks by proposing a novel alignment-free approach: the network subgraph-based approach. Our approach does not make use of randomized networks to determine modular patterns embedded in a network, and this method differs from the network motif and graphlet methods. Network similarity was quantified by gauging the difference between the subgraph frequency distributions of two networks using Jensen-Shannon entropy. We applied the subgraph approach to study three types of molecular networks, i.e., cancer networks, signal transduction networks, and cellular process networks, which exhibit diverse molecular functions. We compared the performance of our subgraph detection algorithm with other algorithms, and the results were consistent, but other algorithms could not address the issue of subgraphs/motifs embedded within a subgraph/motif. To evaluate the effectiveness of the subgraph-based method, we applied the method along with the Jensen-Shannon entropy to classify six network models, and it achieves a 100% accuracy of classification. The proposed information-theoretic approach allows us to determine the structural similarity of two networks regardless of node identity and network size. We demonstrated the effectiveness of the subgraph approach to cluster molecular networks that exhibit similar regulatory interaction topologies. As an illustration, our method can identify (i) common subgraph-mediated signal transduction and/or cellular processes in AML and pancreatic cancer, and (ii) scaffold proteins in gastric cancer and hepatocellular carcinoma; thus, the results suggested that there are common regulation modules for cancer formation. We also found that the underlying substructures of the molecular networks are dominated by irreducible subgraphs; this feature is valid for the three classes of molecular networks we studied. The subgraph-based approach provides a systematic scenario for analyzing, compare and classifying molecular networks with diverse functionalities.
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Affiliation(s)
- Chien-Hung Huang
- Department of Computer Science and Information Engineering, National Formosa University, Yun-Lin, Taiwan
| | - Efendi Zaenudin
- National Research and Innovation Agency, Bandung, Jawa Barat, Republic of Indonesia,Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
| | - Jeffrey J.P. Tsai
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
| | | | - Ka-Lok Ng
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan,Center for Artificial Intelligence and Precision Medicine Research, Asia University, Taichung, Taiwan,Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
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