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Arif B, Yasir S, Saeed M, Fatmi MQ. Natural products can be potential inhibitors of metalloproteinase II from Bacteroides fragilis to intervene colorectal cancer. Heliyon 2024; 10:e32838. [PMID: 39005891 PMCID: PMC11239599 DOI: 10.1016/j.heliyon.2024.e32838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 05/03/2024] [Accepted: 06/10/2024] [Indexed: 07/16/2024] Open
Abstract
Bacteroides fragilis, a gram negative and obligate anaerobe bacterium, is a member of normal gut microbiota and facilitates many essential roles being performed in human body in normal circumstances specifically in Gastrointestinal or GI tract. Sometimes, due to genetics, epigenetics, and environmental factors, Bacteroides fragilis and their protein(s) start interacting with intestinal epithelium thus damaging the lining leading to colorectal cancers (CRC). To identify these protein(s), we incorporated a novel subtractive proteomics approach in the study. Metalloproteinase II (MPII), a Bacteroides fragilis toxin (bft), was investigated for its virulence and unique pathways to demonstrate its specificity and uniqueness in pathogenicity followed by molecular docking against a set of small drug-like natural molecules to discover potential inhibitors against the toxin. All these identified inhibitor-like molecules were analyzed for their ADMET calculations and detailed physiochemical properties to predict their druggability, GI absorption, blood brain barrier and skin permeation, and others. Resultantly, a total of ten compounds with the least binding energies were obtained and were subjected to protein-compound interaction analysis. Interaction analysis revealed the most common ligand-interacting residues in MPII are His 345, Glu 346, His 339, Gly 310, Tyr 341, Pro 340, Asp 187, Phe 309, Lys 307, Ile 185, Thr 308, and Pro 184. Therefore, top three compounds complexed with MPII having best binding energies were selected in order to analyze their trajectories. RMSD, RMSF, Rg and MMPBSA analysis revealed that all compounds showed good binding and keeping the complex stable and compact throughout the simulation time in addition to all properties and qualities of being a potential inhibitor against MPII.
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Affiliation(s)
- Bushra Arif
- Department of Biosciences, COMSATS University Islamabad, Islamabad Campus, Pakistan
| | - Saba Yasir
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Muhammad Saeed
- Department of Biosciences, COMSATS University Islamabad, Islamabad Campus, Pakistan
| | - M. Qaiser Fatmi
- Department of Biosciences, COMSATS University Islamabad, Islamabad Campus, Pakistan
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2
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Pedenla Bomzan D, Sharma A, Lemos Cruz P, Carqueijeiro I, Bellenger L, Rai A, Thippesh AK, Chinnegowda VS, Parihar D, Ducos E, Courdavault V, Nagegowda DA. ROP GTPases with a geranylgeranylation motif modulate alkaloid biosynthesis in Catharanthus roseus. PLANT PHYSIOLOGY 2024; 195:2213-2233. [PMID: 38466200 DOI: 10.1093/plphys/kiae142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 03/12/2024]
Abstract
Rho of Plant (ROP) GTPases function as molecular switches that control signaling processes essential for growth, development, and defense. However, their role in specialized metabolism is poorly understood. Previously, we demonstrated that inhibition of protein geranylgeranyl transferase (PGGT-I) negatively impacts the biosynthesis of monoterpene indole alkaloids (MIA) in Madagascar periwinkle (Catharanthus roseus), indicating the involvement of prenylated proteins in signaling. Here, we show through biochemical, molecular, and in planta approaches that specific geranylgeranylated ROPs modulate C. roseus MIA biosynthesis. Among the six C. roseus ROP GTPases (CrROPs), only CrROP3 and CrROP5, having a C-terminal CSIL motif, were specifically prenylated by PGGT-I. Additionally, their transcripts showed higher expression in most parts than other CrROPs. Protein-protein interaction studies revealed that CrROP3 and CrROP5, but not ΔCrROP3, ΔCrROP5, and CrROP2 lacking the CSIL motif, interacted with CrPGGT-I. Further, CrROP3 and CrROP5 exhibited nuclear localization, whereas CrROP2 was localized to the plasma membrane. In planta functional studies revealed that silencing of CrROP3 and CrROP5 negatively affected MIA biosynthesis, while their overexpression upregulated MIA formation. In contrast, silencing and overexpression of CrROP2 had no effect on MIA biosynthesis. Moreover, overexpression of ΔCrROP3 and ΔCrROP5 mutants devoid of sequence coding for the CSIL motif failed to enhance MIA biosynthesis. These results implicate that CrROP3 and CrROP5 have a positive regulatory role on MIA biosynthesis and thus shed light on how geranylgeranylated ROP GTPases mediate the modulation of specialized metabolism in C. roseus.
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Affiliation(s)
- Dikki Pedenla Bomzan
- Molecular Plant Biology and Biotechnology Lab, CSIR-Central Institute of Medicinal and Aromatic Plants, Research Centre, Bengaluru 560065, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Anuj Sharma
- Molecular Plant Biology and Biotechnology Lab, CSIR-Central Institute of Medicinal and Aromatic Plants, Research Centre, Bengaluru 560065, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Pamela Lemos Cruz
- Biomolécules et Biotechnologies Végétales, BBV, EA2106, Universitéde de Tours, 37200 Tours, France
| | - Ines Carqueijeiro
- Biomolécules et Biotechnologies Végétales, BBV, EA2106, Universitéde de Tours, 37200 Tours, France
| | - Léo Bellenger
- Biomolécules et Biotechnologies Végétales, BBV, EA2106, Universitéde de Tours, 37200 Tours, France
| | - Avanish Rai
- Molecular Plant Biology and Biotechnology Lab, CSIR-Central Institute of Medicinal and Aromatic Plants, Research Centre, Bengaluru 560065, India
| | - Akshay Kumar Thippesh
- Department of Biotechnology and Crop Improvement, College of Horticulture, UHS Bagalkot, Mysuru 571130, India
| | - Venkatesha S Chinnegowda
- Department of Biotechnology and Crop Improvement, College of Horticulture, UHS Bagalkot, Mysuru 571130, India
| | - Durgesh Parihar
- Molecular Plant Biology and Biotechnology Lab, CSIR-Central Institute of Medicinal and Aromatic Plants, Research Centre, Bengaluru 560065, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Eric Ducos
- Biomolécules et Biotechnologies Végétales, BBV, EA2106, Universitéde de Tours, 37200 Tours, France
| | - Vincent Courdavault
- Biomolécules et Biotechnologies Végétales, BBV, EA2106, Universitéde de Tours, 37200 Tours, France
| | - Dinesh A Nagegowda
- Molecular Plant Biology and Biotechnology Lab, CSIR-Central Institute of Medicinal and Aromatic Plants, Research Centre, Bengaluru 560065, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
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3
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Bhushan V, Nita-Lazar A. Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology. J Proteome Res 2024. [PMID: 38451675 DOI: 10.1021/acs.jproteome.3c00839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.
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Affiliation(s)
- Vanya Bhushan
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Aleksandra Nita-Lazar
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
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Munna MMR, Islam MA, Shanta SS, Monty MA. Structural, functional, molecular docking analysis of a hypothetical protein from Talaromyces marneffei and its molecular dynamic simulation: an in-silico approach. J Biomol Struct Dyn 2024:1-20. [PMID: 38345137 DOI: 10.1080/07391102.2024.2314264] [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: 07/31/2023] [Accepted: 01/29/2024] [Indexed: 03/01/2024]
Abstract
Telaromyces marneffei (formerly Penicillium marneffei) is an endemic pathogenic fungus in Southern China and Southeast Asia. It can cause disease in patients with travel-related exposure to this organism and high morbidity and mortality in acquired immune deficiency syndrome (AIDS). In this study, we analyzed the structure and function of a hypothetical protein from T. marneffei using several bioinformatics tools and servers to unveil novel pharmacological targets and design a peptide vaccine against specific epitopes. A total of seven functional epitopes were screened on the protein, and 'STGVDMWSV' was the most antigenic, non-allergenic and non-toxic. Molecular docking showed stronger affinity between the CTL epitope 'STGVDMWSV' and the MHC I allele HLA-A*02:01, a higher docking score -234.98 kcal/mol, revealed stable interactions during a 100 ns molecular dynamic simulation. Overall, the results of this study revealed that this hypothetical protein is crucial for comprehending biochemical, physiological pathways and identifying novel therapeutic targets for human health. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Md Masudur Rahman Munna
- Department of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Md Ariful Islam
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, PR China
| | - Saima Sajnin Shanta
- Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Masuma Akter Monty
- Institute of Biomedical Engineering and Technology, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, PR China
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Mackon E, Guo Y, Jeazet Dongho Epse Mackon GC, Ma Y, Yao Y, Luo D, Dai X, Zhao N, Lu Y, Jandan TH, Liu P. OsGSTU34, a Bz2-like anthocyanin-related glutathione transferase transporter, is essential for rice (Oryza sativa L.) organs coloration. PHYTOCHEMISTRY 2024; 217:113896. [PMID: 37866445 DOI: 10.1016/j.phytochem.2023.113896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 10/12/2023] [Accepted: 10/12/2023] [Indexed: 10/24/2023]
Abstract
Anthocyanins are a flavonoid compound known as one of the most important chromogenic substances. They play several functions, including health promotion and sustaining plants during adverse conditions. They are synthesized at the endoplasmic reticulum and sequestered in the vacuole. In this work, we generated knock-out lines of OsGSTU34, a glutathione transporter's tau gene family, with no transgene line and off-target through CRISPR/Cas9 mutagenesis and highlighted the loss of pigmentation in rice flowers, leaves, stems, shoots, and caryopsis. The anthocyanin quantification in the wild-type BLWT and mutant line BLG34-8 caryopsis showed that cyanidin-3-O-glucoside (C3G) and peonidin-3-O-glucoside (P3G) were almost undetectable in the mutant line. A tandem mass tag (TMT) labeling proteomic analysis was conducted to elucidate the proteomic changes in the BLWT and BLG34-8. The result revealed that 1175 proteins were altered, including 408 that were down-regulated and 767 that were upregulated. The accumulation of the OsGSTU34-related protein (Q8L576), along with several anthocyanin-related proteins, was down-regulated. The enrichment analysis showed that the down-regulated proteins were enriched in different pathways, among which the phenylpropanoid biosynthesis pathway, flavonoid biosynthesis metabolites, and anthocyanin biosynthesis pathway. Protein interaction network prediction revealed that glutathione-S-transferase (Q8L576) was connected to the proteins involved in the flavonoid and anthocyanin biosynthesis pathways, such as flavanone 3-dioxygenase 1 (Q7XM21), leucoanthocyanidin dioxygenase 1 (Q93VC3), 4-coumarate-CoA ligase 2 (Q42982), phenylalanine ammonia-lyase (P14717), chalcone synthase 1 (Q2R3A1), and 4-coumarate-CoA ligase 5 (Q6ZAC1). However, the expression of the most important anthocyanin biosynthesis gene was not altered, suggesting that only the transport mechanism was affected. Our findings highlight new insight into the anthocyanin pigmentation in black rice and provide new perspectives for future research.
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Affiliation(s)
- Enerand Mackon
- State Key Laboratory of Conservation and Utilization of Subtropical Agro-Bioresources, College of Life Science and Technology, Guangxi University PR China.
| | - Yongqiang Guo
- State Key Laboratory of Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning 530005, PR China.
| | | | - Yafei Ma
- State Key Laboratory of Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning 530005, PR China.
| | - Yuhang Yao
- State Key Laboratory of Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning 530005, PR China.
| | - Dengjie Luo
- State Key Laboratory of Conservation and Utilization of Subtropical Agro-Bioresources, College of Life Science and Technology, Guangxi University PR China.
| | - Xianggui Dai
- State Key Laboratory of Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning 530005, PR China.
| | - Neng Zhao
- State Key Laboratory of Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning 530005, PR China.
| | - Ying Lu
- State Key Laboratory of Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning 530005, PR China.
| | - Tahir Hussain Jandan
- State Key Laboratory of Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning 530005, PR China.
| | - Piqing Liu
- State Key Laboratory of Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning 530005, PR China.
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6
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Chen L, Qu R, Liu X. Improved multi-label classifiers for predicting protein subcellular localization. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:214-236. [PMID: 38303420 DOI: 10.3934/mbe.2024010] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Protein functions are closely related to their subcellular locations. At present, the prediction of protein subcellular locations is one of the most important problems in protein science. The evident defects of traditional methods make it urgent to design methods with high efficiency and low costs. To date, lots of computational methods have been proposed. However, this problem is far from being completely solved. Recently, some multi-label classifiers have been proposed to identify subcellular locations of human, animal, Gram-negative bacterial and eukaryotic proteins. These classifiers adopted the protein features derived from gene ontology information. Although they provided good performance, they can be further improved by adopting more powerful machine learning algorithms. In this study, four improved multi-label classifiers were set up for identification of subcellular locations of the above four protein types. The random k-labelsets (RAKEL) algorithm was used to tackle proteins with multiple locations, and random forest was used as the basic prediction engine. All classifiers were tested by jackknife test, indicating their high performance. Comparisons with previous classifiers further confirmed the superiority of the proposed classifiers.
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Affiliation(s)
- Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Ruyun Qu
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Xintong Liu
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
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7
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Yu H, Deng H, He J, Keasling JD, Luo X. UniKP: a unified framework for the prediction of enzyme kinetic parameters. Nat Commun 2023; 14:8211. [PMID: 38081905 PMCID: PMC10713628 DOI: 10.1038/s41467-023-44113-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 11/30/2023] [Indexed: 12/18/2023] Open
Abstract
Prediction of enzyme kinetic parameters is essential for designing and optimizing enzymes for various biotechnological and industrial applications, but the limited performance of current prediction tools on diverse tasks hinders their practical applications. Here, we introduce UniKP, a unified framework based on pretrained language models for the prediction of enzyme kinetic parameters, including enzyme turnover number (kcat), Michaelis constant (Km), and catalytic efficiency (kcat / Km), from protein sequences and substrate structures. A two-layer framework derived from UniKP (EF-UniKP) has also been proposed to allow robust kcat prediction in considering environmental factors, including pH and temperature. In addition, four representative re-weighting methods are systematically explored to successfully reduce the prediction error in high-value prediction tasks. We have demonstrated the application of UniKP and EF-UniKP in several enzyme discovery and directed evolution tasks, leading to the identification of new enzymes and enzyme mutants with higher activity. UniKP is a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzyme engineering and their industrial applications.
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Affiliation(s)
- Han Yu
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Huaxiang Deng
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jiahui He
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jay D Keasling
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Joint BioEnergy Institute, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Department of Chemical and Biomolecular Engineering & Department of Bioengineering, University of California, Berkeley, CA, 94720, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs, Lyngby, Denmark
| | - Xiaozhou Luo
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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8
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Chang S, Joyson M, Kelly A, Tang L, Iannotta J, Rich A, Castilho Coelho N, Carvunis AR. Unannotated Open Reading Frame in Saccharomyces cerevisiae Encodes Protein Localizing to the Endoplasmic Reticulum. MICROPUBLICATION BIOLOGY 2023; 2023:10.17912/micropub.biology.000992. [PMID: 37927910 PMCID: PMC10625025 DOI: 10.17912/micropub.biology.000992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/14/2023] [Accepted: 10/17/2023] [Indexed: 11/07/2023]
Abstract
There are thousands of unannotated translated open reading frames (ORFs) in the Saccharomyces cerevisiae genome. Previous investigation into one such unannotated ORF, which was systemically labeled YGR016C-A based on its genomic coordinates, showed that replacing the ORF's ATG start codon with AAG led to a change in cellular fitness under different stress conditions (Wacholder et al., 2023). This suggested translation of YGR016C-A plays a role in cellular fitness. Here, we investigate Ygr016c-a's subcellular localization to gain insight into its cellular function. Computational prediction tools, co-expression analysis and fluorescence microscopy suggest that the Ygr016c-a protein localizes to the endoplasmic reticulum.
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Affiliation(s)
- Scott Chang
- Computational and Systems Biology, University of Pittsburgh School of Medicine
| | - Matthew Joyson
- Computational and Systems Biology, University of Pittsburgh School of Medicine
| | - Anna Kelly
- Computational and Systems Biology, University of Pittsburgh School of Medicine
| | - Lucas Tang
- Computational and Systems Biology, University of Pittsburgh School of Medicine
| | - John Iannotta
- Computational and Systems Biology, University of Pittsburgh School of Medicine
- Pittsburgh Center for Evolutionary Biology and Medicine
| | - April Rich
- Computational and Systems Biology, University of Pittsburgh School of Medicine
- Pittsburgh Center for Evolutionary Biology and Medicine
- Joint CMU-Pitt PhD Program in Computational Biology
| | - Nelson Castilho Coelho
- Computational and Systems Biology, University of Pittsburgh School of Medicine
- Pittsburgh Center for Evolutionary Biology and Medicine
| | - Anne-Ruxandra Carvunis
- Computational and Systems Biology, University of Pittsburgh School of Medicine
- Pittsburgh Center for Evolutionary Biology and Medicine
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9
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Li J, Zou Q, Yuan L. A review from biological mapping to computation-based subcellular localization. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 32:507-521. [PMID: 37215152 PMCID: PMC10192651 DOI: 10.1016/j.omtn.2023.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Subcellular localization is crucial to the study of virus and diseases. Specifically, research on protein subcellular localization can help identify clues between virus and host cells that can aid in the design of targeted drugs. Research on RNA subcellular localization is significant for human diseases (such as Alzheimer's disease, colon cancer, etc.). To date, only reviews addressing subcellular localization of proteins have been published, which are outdated for reference, and reviews of RNA subcellular localization are not comprehensive. Therefore, we collated (the most up-to-date) literature on protein and RNA subcellular localization to help researchers understand changes in the field of protein and RNA subcellular localization. Extensive and complete methods for constructing subcellular localization models have also been summarized, which can help readers understand the changes in application of biotechnology and computer science in subcellular localization research and explore how to use biological data to construct improved subcellular localization models. This paper is the first review to cover both protein subcellular localization and RNA subcellular localization. We urge researchers from biology and computational biology to jointly pay attention to transformation patterns, interrelationships, differences, and causality of protein subcellular localization and RNA subcellular localization.
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Affiliation(s)
- Jing Li
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, 1 Chengdian Road, Quzhou, Zhejiang 324000, China
- School of Biomedical Sciences, University of Hong Kong, Hong Kong, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, 1 Chengdian Road, Quzhou, Zhejiang 324000, China
| | - Lei Yuan
- Department of Hepatobiliary Surgery, Quzhou People's Hospital, 100 Minjiang Main Road, Quzhou, Zhejiang 324000, China
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10
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Medvedeva A, Teimouri H, Kolomeisky AB. Predicting Antimicrobial Activity for Untested Peptide-Based Drugs Using Collaborative Filtering and Link Prediction. J Chem Inf Model 2023. [PMID: 37307501 DOI: 10.1021/acs.jcim.3c00137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The increase of bacterial resistance to currently available antibiotics has underlined the urgent need to develop new antibiotic drugs. Antimicrobial peptides (AMPs), alone or in combination with other peptides and/or existing antibiotics, have emerged as promising candidates for this task. However, given that there are thousands of known AMPs and an even larger number can be synthesized, it is impossible to comprehensively test all of them using standard wet lab experimental methods. These observations stimulated an application of machine-learning methods to identify promising AMPs. Currently, machine learning studies combine very different bacteria without considering bacteria-specific features or interactions with AMPs. In addition, the sparsity of current AMP data sets disqualifies the application of traditional machine-learning methods or makes the results unreliable. Here, we present a new approach, featuring neighborhood-based collaborative filtering, to predict with high accuracy a given bacteria's response to untested AMPs based on similarities between bacterial responses. Furthermore, we also developed a complementary bacteria-specific link prediction approach that can be used to visualize networks of AMP-antibiotic combinations, enabling us to propose new combinations that are likely to be effective.
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Affiliation(s)
- Angela Medvedeva
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
| | - Hamid Teimouri
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
| | - Anatoly B Kolomeisky
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
- Department of Physics and Astronomy, Rice University, Houston, Texas 77005, United States
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11
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Tran C, Khadkikar S, Porollo A. Survey of Protein Sequence Embedding Models. Int J Mol Sci 2023; 24:3775. [PMID: 36835188 PMCID: PMC9963412 DOI: 10.3390/ijms24043775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 01/23/2023] [Accepted: 02/09/2023] [Indexed: 02/16/2023] Open
Abstract
Derived from the natural language processing (NLP) algorithms, protein language models enable the encoding of protein sequences, which are widely diverse in length and amino acid composition, in fixed-size numerical vectors (embeddings). We surveyed representative embedding models such as Esm, Esm1b, ProtT5, and SeqVec, along with their derivatives (GoPredSim and PLAST), to conduct the following tasks in computational biology: embedding the Saccharomyces cerevisiae proteome, gene ontology (GO) annotation of the uncharacterized proteins of this organism, relating variants of human proteins to disease status, correlating mutants of beta-lactamase TEM-1 from Escherichia coli with experimentally measured antimicrobial resistance, and analyzing diverse fungal mating factors. We discuss the advances and shortcomings, differences, and concordance of the models. Of note, all of the models revealed that the uncharacterized proteins in yeast tend to be less than 200 amino acids long, contain fewer aspartates and glutamates, and are enriched for cysteine. Less than half of these proteins can be annotated with GO terms with high confidence. The distribution of the cosine similarity scores of benign and pathogenic mutations to the reference human proteins shows a statistically significant difference. The differences in embeddings of the reference TEM-1 and mutants have low to no correlation with minimal inhibitory concentrations (MIC).
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Affiliation(s)
- Chau Tran
- Department of Computer Science, University of Cincinnati, Cincinnati, OH 45219, USA
| | - Siddharth Khadkikar
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Aleksey Porollo
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH 45267, USA
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12
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Vu NT, Nguyen NBT, Ha HH, Nguyen LN, Luu LH, Dao HQ, Vu TT, Huynh HTT, Le HTT. Evolutionary analysis and expression profiling of the HSP70 gene family in response to abiotic stresses in tomato ( Solanum lycopersicum). Sci Prog 2023; 106:368504221148843. [PMID: 36650980 PMCID: PMC10358566 DOI: 10.1177/00368504221148843] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Heat shock protein 70 (HSP70) genes play essential roles in guarding plants against abiotic stresses, including heat, drought, and salt. In this study, the SlHSP70 gene family in tomatoes has been characterized using bioinformatic tools. 25 putative SlHSP70 genes in the tomato genome were found and classified into five subfamilies, with multi-subcellular localizations. Twelve pairs of gene duplications were identified, and segmental events were determined as the main factor for the gene family expansion. Based on public RNA-seq data, gene expression analysis identified the majority of genes expressed in the examined organelles. Further RNA-seq analysis and then quantitative RT-PCR validation showed that many SlHSP70 members are responsible for cellular feedback to heat, drought, and salt treatments, in which, at least five genes might be potential key players in the stress response. Our results provided a thorough overview of the SlHSP70 gene family in the tomato, which may be useful for the evolutionary and functional analysis of SlHSP70 under abiotic stress conditions.
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Affiliation(s)
- Nam Tuan Vu
- Department of Biotechnology, Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
- Laboratory of Genome Biodiversity, Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Ngoc Bich Thi Nguyen
- Laboratory of Genome Biodiversity, Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Hanh Hong Ha
- Laboratory of Genome Biodiversity, Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Linh Nhat Nguyen
- Laboratory of Genome Biodiversity, Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Ly Han Luu
- Laboratory of Genome Biodiversity, Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Ha Quang Dao
- Laboratory of Genome Biodiversity, Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Trinh Thi Vu
- Laboratory of Genome Biodiversity, Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Hue Thu Thi Huynh
- Department of Biotechnology, Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
- Laboratory of Genome Biodiversity, Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Hien Thu Thi Le
- Department of Biotechnology, Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
- Laboratory of Genome Biodiversity, Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam
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13
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Li Z, Wang D, Guo W, Zhang S, Chen L, Zhang YH, Lu L, Pan X, Huang T, Cai YD. Identification of cortical interneuron cell markers in mouse embryos based on machine learning analysis of single-cell transcriptomics. Front Neurosci 2022; 16:841145. [PMID: 35911980 PMCID: PMC9337837 DOI: 10.3389/fnins.2022.841145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Mammalian cortical interneurons (CINs) could be classified into more than two dozen cell types that possess diverse electrophysiological and molecular characteristics, and participate in various essential biological processes in the human neural system. However, the mechanism to generate diversity in CINs remains controversial. This study aims to predict CIN diversity in mouse embryo by using single-cell transcriptomics and the machine learning methods. Data of 2,669 single-cell transcriptome sequencing results are employed. The 2,669 cells are classified into three categories, caudal ganglionic eminence (CGE) cells, dorsal medial ganglionic eminence (dMGE) cells, and ventral medial ganglionic eminence (vMGE) cells, corresponding to the three regions in the mouse subpallium where the cells are collected. Such transcriptomic profiles were first analyzed by the minimum redundancy and maximum relevance method. A feature list was obtained, which was further fed into the incremental feature selection, incorporating two classification algorithms (random forest and repeated incremental pruning to produce error reduction), to extract key genes and construct powerful classifiers and classification rules. The optimal classifier could achieve an MCC of 0.725, and category-specified prediction accuracies of 0.958, 0.760, and 0.737 for the CGE, dMGE, and vMGE cells, respectively. The related genes and rules may provide helpful information for deepening the understanding of CIN diversity.
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Affiliation(s)
- Zhandong Li
- College of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Deling Wang
- State Key Laboratory of Oncology in South China, Department of Radiology, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Shiqi Zhang
- Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Yu-Hang Zhang
- Channing Division of Network Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, United States
| | - Lin Lu
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States
| | - XiaoYong Pan
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
| | - Tao Huang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- *Correspondence: Tao Huang,
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
- Yu-Dong Cai,
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14
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Zhang YH, Li ZD, Zeng T, Chen L, Huang T, Cai YD. Screening gene signatures for clinical response subtypes of lung transplantation. Mol Genet Genomics 2022; 297:1301-1313. [PMID: 35780439 DOI: 10.1007/s00438-022-01918-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 06/12/2022] [Indexed: 11/30/2022]
Abstract
Lung is the most important organ in the human respiratory system, whose normal functions are quite essential for human beings. Under certain pathological conditions, the normal lung functions could no longer be maintained in patients, and lung transplantation is generally applied to ease patients' breathing and prolong their lives. However, several risk factors exist during and after lung transplantation, including bleeding, infection, and transplant rejections. In particular, transplant rejections are difficult to predict or prevent, leading to the most dangerous complications and severe status in patients undergoing lung transplantation. Given that most common monitoring and validation methods for lung transplantation rejections may take quite a long time and have low reproducibility, new technologies and methods are required to improve the efficacy and accuracy of rejection monitoring after lung transplantation. Recently, one previous study set up the gene expression profiles of patients who underwent lung transplantation. However, it did not provide a tool to predict lung transplantation responses. Here, a further deep investigation was conducted on such profiling data. A computational framework, incorporating several machine learning algorithms, such as feature selection methods and classification algorithms, was built to establish an effective prediction model distinguishing patient into different clinical subgroups, corresponding to different rejection responses after lung transplantation. Furthermore, the framework also screened essential genes with functional enrichments and create quantitative rules for the distinction of patients with different rejection responses to lung transplantation. The outcome of this contribution could provide guidelines for clinical treatment of each rejection subtype and contribute to the revealing of complicated rejection mechanisms of lung transplantation.
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Affiliation(s)
- Yu-Hang Zhang
- School of Life Sciences, Shanghai University, Shanghai, 200444, China
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zhan Dong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, 130052, China
| | - Tao Zeng
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, 200444, China.
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15
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Li H, Zhang S, Chen L, Pan X, Li Z, Huang T, Cai YD. Identifying Functions of Proteins in Mice With Functional Embedding Features. Front Genet 2022; 13:909040. [PMID: 35651937 PMCID: PMC9149260 DOI: 10.3389/fgene.2022.909040] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 04/28/2022] [Indexed: 12/02/2022] Open
Abstract
In current biology, exploring the biological functions of proteins is important. Given the large number of proteins in some organisms, exploring their functions one by one through traditional experiments is impossible. Therefore, developing quick and reliable methods for identifying protein functions is necessary. Considerable accumulation of protein knowledge and recent developments on computer science provide an alternative way to complete this task, that is, designing computational methods. Several efforts have been made in this field. Most previous methods have adopted the protein sequence features or directly used the linkage from a protein–protein interaction (PPI) network. In this study, we proposed some novel multi-label classifiers, which adopted new embedding features to represent proteins. These features were derived from functional domains and a PPI network via word embedding and network embedding, respectively. The minimum redundancy maximum relevance method was used to assess the features, generating a feature list. Incremental feature selection, incorporating RAndom k-labELsets to construct multi-label classifiers, used such list to construct two optimum classifiers, corresponding to two key measurements: accuracy and exact match. These two classifiers had good performance, and they were superior to classifiers that used features extracted by traditional methods.
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Affiliation(s)
- Hao Li
- College of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - ShiQi Zhang
- Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
| | - ZhanDong Li
- College of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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16
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Li Z, Pan X, Cai YD. Identification of Type 2 Diabetes Biomarkers From Mixed Single-Cell Sequencing Data With Feature Selection Methods. Front Bioeng Biotechnol 2022; 10:890901. [PMID: 35721855 PMCID: PMC9201257 DOI: 10.3389/fbioe.2022.890901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/04/2022] [Indexed: 11/18/2022] Open
Abstract
Diabetes is the most common disease and a major threat to human health. Type 2 diabetes (T2D) makes up about 90% of all cases. With the development of high-throughput sequencing technologies, more and more fundamental pathogenesis of T2D at genetic and transcriptomic levels has been revealed. The recent single-cell sequencing can further reveal the cellular heterogenicity of complex diseases in an unprecedented way. With the expectation on the molecular essence of T2D across multiple cell types, we investigated the expression profiling of more than 1,600 single cells (949 cells from T2D patients and 651 cells from normal controls) and identified the differential expression profiling and characteristics at the transcriptomics level that can distinguish such two groups of cells at the single-cell level. The expression profile was analyzed by several machine learning algorithms, including Monte Carlo feature selection, support vector machine, and repeated incremental pruning to produce error reduction (RIPPER). On one hand, some T2D-associated genes (MTND4P24, MTND2P28, and LOC100128906) were discovered. On the other hand, we revealed novel potential pathogenic mechanisms in a rule manner. They are induced by newly recognized genes and neglected by traditional bulk sequencing techniques. Particularly, the newly identified T2D genes were shown to follow specific quantitative rules with diabetes prediction potentials, and such rules further indicated several potential functional crosstalks involved in T2D.
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Affiliation(s)
- Zhandong Li
- College of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Xiaoyong Pan
- Key Laboratory of System Control and Information Processing, Institute of Image Processing and Pattern Recognition, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
- *Correspondence: Yu-Dong Cai,
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17
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Wang S, Wu R, Lu J, Jiang Y, Huang T, Cai YD. Protein-protein interaction networks as miners of biological discovery. Proteomics 2022; 22:e2100190. [PMID: 35567424 DOI: 10.1002/pmic.202100190] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 03/28/2022] [Accepted: 04/29/2022] [Indexed: 11/12/2022]
Abstract
Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Steven Wang
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Runxin Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jiaqi Lu
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, USA
| | - Yijia Jiang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tao Huang
- Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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18
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Li ZD, Yu X, Mei Z, Zeng T, Chen L, Xu XL, Li H, Huang T, Cai YD. Identifying luminal and basal mammary cell specific genes and their expression patterns during pregnancy. PLoS One 2022; 17:e0267211. [PMID: 35486595 PMCID: PMC9053804 DOI: 10.1371/journal.pone.0267211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/05/2022] [Indexed: 11/25/2022] Open
Abstract
Mammary gland is present in all mammals and usually functions in producing milk to feed the young offspring. Mammogenesis refers to the growth and development of mammary gland, which begins at puberty and ends after lactation. Pregnancy is regulated by various cytokines, which further contributes to mammary gland development. Epithelial cells, including basal and luminal cells, are one of the major components of mammary gland cells. The development of basal and luminal cells has been observed to significantly differ at different stages. However, the underlying mechanisms for differences between basal and luminal cells have not been fully studied. To explore the mechanisms underlying the differentiation of mammary progenitors or their offspring into luminal and myoepithelial cells, the single-cell sequencing data on mammary epithelia cells of virgin and pregnant mouse was deeply investigated in this work. We evaluated features by using Monte Carlo feature selection and plotted the incremental feature selection curve with support vector machine or RIPPER to find the optimal gene features and rules that can divide epithelial cells into four clusters with different cell subtypes like basal and luminal cells and different phases like pregnancy and virginity. As representations, the feature genes Cldn7, Gjb6, Sparc, Cldn3, Cited1, Krt17, Spp1, Cldn4, Gjb2 and Cldn19 might play an important role in classifying the epithelial mammary cells. Notably, seven most important rules based on the combination of cell-specific and tissue-specific expressions of feature genes effectively classify the epithelial mammary cells in a quantitative and interpretable manner.
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Affiliation(s)
- Zhan Dong Li
- College of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Xiangtian Yu
- Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Zi Mei
- Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Tao Zeng
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Xian Ling Xu
- Guangdong AIB Polytechnic College, Guangzhou, China
| | - Hao Li
- College of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- * E-mail: (TH); (YDC)
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
- * E-mail: (TH); (YDC)
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19
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Chen L, Mei Z, Guo W, Ding S, Huang T, Cai YD. Recognition of Immune Cell Markers of COVID-19 Severity with Machine Learning Methods. BIOMED RESEARCH INTERNATIONAL 2022; 2022:6089242. [PMID: 35528178 PMCID: PMC9073549 DOI: 10.1155/2022/6089242] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 04/11/2022] [Indexed: 01/08/2023]
Abstract
COVID-19 is hypothesized to be linked to the host's excessive inflammatory immunological response to SARS-CoV-2 infection, which is regarded to be a major factor in disease severity and mortality. Numerous immune cells play a key role in immune response regulation, and gene expression analysis in these cells could be a useful method for studying disease states, assessing immunological responses, and detecting biomarkers. Here, we developed a machine learning procedure to find biomarkers that discriminate disease severity in individual immune cells (B cell, CD4+ cell, CD8+ cell, monocyte, and NK cell) using single-cell gene expression profiles of COVID-19. The gene features of each profile were first filtered and ranked using the Boruta feature selection method and mRMR, and the resulting ranked feature lists were then fed into the incremental feature selection method to determine the optimal number of features with decision tree and random forest algorithms. Meanwhile, we extracted the classification rules in each cell type from the optimal decision tree classifiers. The best gene sets discovered in this study were analyzed by GO and KEGG pathway enrichment, and some important biomarkers like TLR2, ITK, CX3CR1, IL1B, and PRDM1 were validated by recent literature. The findings reveal that the optimal gene sets for each cell type can accurately classify COVID-19 disease severity and provide insight into the molecular mechanisms involved in disease progression.
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Affiliation(s)
- Lei Chen
- School of Life Sciences, Shanghai University, Shanghai 200444, China
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Zi Mei
- Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
| | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai 200031, China
| | - ShiJian Ding
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai 200444, China
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20
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Li Z, Guo W, Ding S, Feng K, Lu L, Huang T, Cai Y. Detecting Blood Methylation Signatures in Response to Childhood Cancer Radiotherapy via Machine Learning Methods. BIOLOGY 2022; 11:biology11040607. [PMID: 35453806 PMCID: PMC9030135 DOI: 10.3390/biology11040607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 11/16/2022]
Abstract
Radiotherapy is a helpful treatment for cancer, but it can also potentially cause changes in many molecules, resulting in adverse effects. Among these changes, the occurrence of abnormal DNA methylation patterns has alarmed scientists. To explore the influence of region-specific radiotherapy on blood DNA methylation, we designed a computational workflow by using machine learning methods that can identify crucial methylation alterations related to treatment exposure. Irrelevant methylation features from the DNA methylation profiles of 2052 childhood cancer survivors were excluded via the Boruta method, and the remaining features were ranked using the minimum redundancy maximum relevance method to generate feature lists. These feature lists were then fed into the incremental feature selection method, which uses a combination of deep forest, k-nearest neighbor, random forest, and decision tree to find the most important methylation signatures and build the best classifiers and classification rules. Several methylation signatures and rules have been discovered and confirmed, allowing for a better understanding of methylation patterns in response to different treatment exposures.
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Affiliation(s)
- Zhandong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun 130052, China;
| | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai 200025, China;
| | - Shijian Ding
- School of Life Sciences, Shanghai University, Shanghai 200444, China;
| | - Kaiyan Feng
- Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou 510507, China;
| | - Lin Lu
- Department of Radiology, Columbia University Medical Center, New York, NY 10032, USA
- Correspondence: (L.L.); (T.H.); or (Y.C.); Tel.: +86-21-54923269 (T.H.); +86-21-66136132 (Y.C.)
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- Correspondence: (L.L.); (T.H.); or (Y.C.); Tel.: +86-21-54923269 (T.H.); +86-21-66136132 (Y.C.)
| | - Yudong Cai
- School of Life Sciences, Shanghai University, Shanghai 200444, China;
- Correspondence: (L.L.); (T.H.); or (Y.C.); Tel.: +86-21-54923269 (T.H.); +86-21-66136132 (Y.C.)
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21
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Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9547317. [PMID: 35401786 PMCID: PMC8993545 DOI: 10.1155/2022/9547317] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/18/2021] [Accepted: 03/15/2022] [Indexed: 12/23/2022]
Abstract
Drugs can treat different diseases but also bring side effects. Undetected and unaccepted side effects for approved drugs can greatly harm the human body and bring huge risks for pharmaceutical companies. Traditional experimental methods used to determine the side effects have several drawbacks, such as low efficiency and high cost. One alternative to achieve this purpose is to design computational methods. Previous studies modeled a binary classification problem by pairing drugs and side effects; however, their classifiers can only extract one feature from each type of drug association. The present work proposed a novel multiple-feature sampling scheme that can extract several features from one type of drug association. Thirteen classification algorithms were employed to construct classifiers with features yielded by such scheme. Their performance was greatly improved compared with that of the classifiers that use the features yielded by the original scheme. Best performance was observed for the classifier based on random forest with MCC of 0.8661, AUROC of 0.969, and AUPR of 0.977. Finally, one key parameter in the multiple-feature sampling scheme was analyzed.
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22
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Li Z, Wang D, Liao H, Zhang S, Guo W, Chen L, Lu L, Huang T, Cai YD. Exploring the Genomic Patterns in Human and Mouse Cerebellums Via Single-Cell Sequencing and Machine Learning Method. Front Genet 2022; 13:857851. [PMID: 35309141 PMCID: PMC8930846 DOI: 10.3389/fgene.2022.857851] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 02/09/2022] [Indexed: 12/29/2022] Open
Abstract
In mammals, the cerebellum plays an important role in movement control. Cellular research reveals that the cerebellum involves a variety of sub-cell types, including Golgi, granule, interneuron, and unipolar brush cells. The functional characteristics of cerebellar cells exhibit considerable differences among diverse mammalian species, reflecting a potential development and evolution of nervous system. In this study, we aimed to recognize the transcriptional differences between human and mouse cerebellum in four cerebellar sub-cell types by using single-cell sequencing data and machine learning methods. A total of 321,387 single-cell sequencing data were used. The 321,387 cells included 4 cell types, i.e., Golgi (5,048, 1.57%), granule (250,307, 77.88%), interneuron (60,526, 18.83%), and unipolar brush (5,506, 1.72%) cells. Our results showed that by using gene expression profiles as features, the optimal classification model could achieve very high even perfect performance for Golgi, granule, interneuron, and unipolar brush cells, respectively, suggesting a remarkable difference between the genomic profiles of human and mouse. Furthermore, a group of related genes and rules contributing to the classification was identified, which might provide helpful information for deepening the understanding of cerebellar cell heterogeneity and evolution.
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Affiliation(s)
- ZhanDong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Deling Wang
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - HuiPing Liao
- Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - ShiQi Zhang
- Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Lin Lu
- Department of Radiology, Columbia University Medical Center, New York, NY, United States
- *Correspondence: Lin Lu, ; Tao Huang, ; Yu-Dong Cai,
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- *Correspondence: Lin Lu, ; Tao Huang, ; Yu-Dong Cai,
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
- *Correspondence: Lin Lu, ; Tao Huang, ; Yu-Dong Cai,
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23
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Meng C, Ju Y, Shi H. TMPpred: A support vector machine-based thermophilic protein identifier. Anal Biochem 2022; 645:114625. [PMID: 35218736 DOI: 10.1016/j.ab.2022.114625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 11/13/2022]
Abstract
MOTIVATION The thermostability of proteins will cause them to break the temperature binding and play more functions. Using machine learning, we explored the mechanism of and reasons for protein thermostability characteristics. RESULTS Different from other methods that only pursue the performance of models, we aim to find important features so as to provide a powerful reference for in vitro experiments. We transformed this problem into a binary classification problem, that is, the distinction between thermophilic proteins and nonthermophilic proteins. Using support vector machine-based model construction and analysis, we inferred that Gly, Ala, Ser and Thr may be the most important components at the residue level that determine the thermal stability of proteins. It is also noteworthy that our proposed model obtains an Sn of 0.892, an Sp of 0.857, an ACC of 0.87566 and an AUC of 0.874. To facilitate other researchers, we wrapped our model and deployed it as a web server, which is accessible at http://112.124.26.17:7000/TMPpred/index.html.
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Affiliation(s)
- Chaolu Meng
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China; Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry, Hohhot, China
| | - Ying Ju
- School of Informatics, Xiamen University, Xiamen, China.
| | - Hua Shi
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China.
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24
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Li X, Lu L, Chen L. Identification of protein functions in mouse with a label space partition method. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:3820-3842. [PMID: 35341276 DOI: 10.3934/mbe.2022176] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Protein is very important for almost all living creatures because it participates in most complicated and essential biological processes. Determining the functions of given proteins is one of the most essential problems in protein science. Such determination can be conducted through traditional experiments. However, the experimental methods are always time-consuming and of high costs. In recent years, computational methods give useful aids for identification of protein functions. This study presented a new multi-label classifier for identifying functions of mouse proteins. Due to the number of functional types, which were termed as labels in the classification procedure, a label space partition method was employed to divide labels into some partitions. On each partition, a multi-label classifier was constructed. The classifiers based on all partitions were integrated in the proposed classifier. The cross-validation results proved that the proposed classifier was of good performance. Classifiers with label partition were superior to those without label partition or with random label partition.
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Affiliation(s)
- Xuan Li
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Lin Lu
- Department of Radiology, Columbia University Medical Center, New York 10032, USA
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
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25
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Yuan F, Cao X, Zhang YH, Chen L, Huang T, Li Z, Cai YD. Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm. Front Cell Dev Biol 2022; 10:825272. [PMID: 35155435 PMCID: PMC8826452 DOI: 10.3389/fcell.2022.825272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/06/2022] [Indexed: 12/21/2022] Open
Abstract
Cancer driver gene is a type of gene with abnormal alterations that initiate or promote tumorigenesis. Driver genes can be used to reveal the fundamental pathological mechanisms of tumorigenesis. These genes may have pathological changes at different omics levels. Thus, identifying cancer driver genes involving two or more omics levels is essential. In this study, a computational investigation was conducted on lung cancer driver genes. Four omics levels, namely, epigenomics, genomics, transcriptomics, and post-transcriptomics, were involved. From the driver genes at each level, the Laplacian heat diffusion algorithm was executed on a protein–protein interaction network for discovering latent driver genes at this level. A following screen procedure was performed to extract essential driver genes, which contained three tests: permutation, association, and function tests, which can exclude false-positive genes and screen essential ones. Finally, the intersection operation was performed to obtain novel driver genes involving two omic levels. The analyses on obtained genes indicated that they were associated with fundamental pathological mechanisms of lung cancer at two corresponding omics levels.
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Affiliation(s)
- Fei Yuan
- Department of Science and Technology, Binzhou Medical University Hospital, Binzhou, China
| | - Xiaoyu Cao
- Department of Neurology, Binzhou Medical University Hospital, Binzhou, China
| | - Yu-Hang Zhang
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- *Correspondence: Tao Huang, ; ZhanDong Li, ; Yu-Dong Cai,
| | - ZhanDong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
- *Correspondence: Tao Huang, ; ZhanDong Li, ; Yu-Dong Cai,
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
- *Correspondence: Tao Huang, ; ZhanDong Li, ; Yu-Dong Cai,
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26
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Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4035462. [PMID: 35071593 PMCID: PMC8776474 DOI: 10.1155/2022/4035462] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/07/2021] [Accepted: 12/22/2021] [Indexed: 12/15/2022]
Abstract
Methylation is one of the most common and considerable modifications in biological systems mediated by multiple enzymes. Recent studies have shown that methylation has been widely identified in different RNA molecules. RNA methylation modifications have various kinds, such as 5-methylcytosine (m5C). However, for individual methylation sites, their functions still remain to be elucidated. Testing of all methylation sites relies heavily on high-throughput sequencing technology, which is expensive and labor consuming. Thus, computational prediction approaches could serve as a substitute. In this study, multiple machine learning models were used to predict possible RNA m5C sites on the basis of mRNA sequences in human and mouse. Each site was represented by several features derived from
-mers of an RNA subsequence containing such site as center. The powerful max-relevance and min-redundancy (mRMR) feature selection method was employed to analyse these features. The outcome feature list was fed into incremental feature selection method, incorporating four classification algorithms, to build efficient models. Furthermore, the sites related to features used in the models were also investigated.
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27
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Yang Y, Chen L. Identification of Drug-Disease Associations by Using Multiple Drug and
Disease Networks. Curr Bioinform 2022. [DOI: 10.2174/1574893616666210825115406] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Drug repositioning is a new research area in drug development. It aims to discover
novel therapeutic uses of existing drugs. It could accelerate the process of designing novel drugs
for some diseases and considerably decrease the cost. The traditional method to determine novel therapeutic
uses of an existing drug is quite laborious. It is alternative to design computational methods to
overcome such defect.
Objective:
This study aims to propose a novel model for the identification of drug–disease associations.
Method:
Twelve drug networks and three disease networks were built, which were fed into a powerful
network-embedding algorithm called Mashup to produce informative drug and disease features. These
features were combined to represent each drug–disease association. Classic classification algorithm,
random forest, was used to build the model.
Results:
Tenfold cross-validation results indicated that the MCC, AUROC, and AUPR were 0.7156,
0.9280, and 0.9191, respectively.
Conclusion:
The proposed model showed good performance. Some tests indicated that a small dimension
of drug features and a large dimension of disease features were beneficial for constructing the
model. Moreover, the model was quite robust even if some drug or disease properties were not available.
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Affiliation(s)
- Ying Yang
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
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28
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Ding S, Li H, Zhang YH, Zhou X, Feng K, Li Z, Chen L, Huang T, Cai YD. Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines. Front Cell Dev Biol 2021; 9:781285. [PMID: 34917619 PMCID: PMC8669964 DOI: 10.3389/fcell.2021.781285] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/16/2021] [Indexed: 12/12/2022] Open
Abstract
There are many types of cancers. Although they share some hallmarks, such as proliferation and metastasis, they are still very different from many perspectives. They grow on different organ or tissues. Does each cancer have a unique gene expression pattern that makes it different from other cancer types? After the Cancer Genome Atlas (TCGA) project, there are more and more pan-cancer studies. Researchers want to get robust gene expression signature from pan-cancer patients. But there is large variance in cancer patients due to heterogeneity. To get robust results, the sample size will be too large to recruit. In this study, we tried another approach to get robust pan-cancer biomarkers by using the cell line data to reduce the variance. We applied several advanced computational methods to analyze the Cancer Cell Line Encyclopedia (CCLE) gene expression profiles which included 988 cell lines from 20 cancer types. Two feature selection methods, including Boruta, and max-relevance and min-redundancy methods, were applied to the cell line gene expression data one by one, generating a feature list. Such list was fed into incremental feature selection method, incorporating one classification algorithm, to extract biomarkers, construct optimal classifiers and decision rules. The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers (e.g. NCKAP1, TNFRSF12A, LAMB2, FKBP9, PFN2, TOM1L1) and rules identified in this work may provide a meaningful and precise reference for differentiating multiple types of cancer and contribute to the personalized treatment of tumors.
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Affiliation(s)
- ShiJian Ding
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Hao Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Yu-Hang Zhang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - XianChao Zhou
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - KaiYan Feng
- Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, China
| | - ZhanDong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Tao Huang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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29
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Chen L, Li Z, Zeng T, Zhang YH, Zhang S, Huang T, Cai YD. Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features. Front Genet 2021; 12:783128. [PMID: 34804131 PMCID: PMC8603309 DOI: 10.3389/fgene.2021.783128] [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: 09/25/2021] [Accepted: 10/22/2021] [Indexed: 12/12/2022] Open
Abstract
Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein-protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein-protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.
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Affiliation(s)
- Lei Chen
- School of Life Sciences, Shanghai University, Shanghai, China
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - ZhanDong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Tao Zeng
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Hang Zhang
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - ShiQi Zhang
- Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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30
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iMPT-FDNPL: Identification of Membrane Protein Types with Functional Domains and a Natural Language Processing Approach. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:7681497. [PMID: 34671418 PMCID: PMC8523280 DOI: 10.1155/2021/7681497] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 09/15/2021] [Accepted: 09/27/2021] [Indexed: 12/20/2022]
Abstract
Membrane protein is an important kind of proteins. It plays essential roles in several cellular processes. Based on the intramolecular arrangements and positions in a cell, membrane proteins can be divided into several types. It is reported that the types of a membrane protein are highly related to its functions. Determination of membrane protein types is a hot topic in recent years. A plenty of computational methods have been proposed so far. Some of them used functional domain information to encode proteins. However, this procedure was still crude. In this study, we designed a novel feature extraction scheme to obtain informative features of proteins from their functional domain information. Such scheme termed domains as words and proteins, represented by its domains, as sentences. The natural language processing approach, word2vector, was applied to access the features of domains, which were further refined to protein features. Based on these features, RAndom k-labELsets with random forest as the base classifier was employed to build the multilabel classifier, namely, iMPT-FDNPL. The tenfold cross-validation results indicated the good performance of such classifier. Furthermore, such classifier was superior to other classifiers based on features derived from functional domains via one-hot scheme or derived from other properties of proteins, suggesting the effectiveness of protein features generated by the proposed scheme.
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31
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Identification of Novel Choroidal Neovascularization-Related Genes Using Laplacian Heat Diffusion Algorithm. BIOMED RESEARCH INTERNATIONAL 2021; 2021:2295412. [PMID: 34532497 PMCID: PMC8440095 DOI: 10.1155/2021/2295412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 08/20/2021] [Indexed: 11/20/2022]
Abstract
Choroidal neovascularization (CNV) is a type of eye disease that can cause vision loss. In recent years, many studies have attempted to investigate the major pathological processes and molecular pathogenic mechanisms of CNV. Because many diseases are related to genes, the genes associated with CNV need to be identified. In this study, we proposed a network-based approach for identifying novel CNV-associated genes. To execute such method, we first employed a protein-protein interaction network reported in STRING. Then, we applied a network diffusion algorithm, Laplacian heat diffusion, on this network by selecting validated CNV-related genes as the seed nodes. As a result, some novel genes that had unknown but strong relationships with validated genes were identified. Furthermore, we used a screening procedure to extract the most essential genes. Eleven latent CNV-related genes were finally obtained. Extensive analyses were performed to confirm that these genes are novel CNV-related genes.
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32
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Chen L, Zhou X, Zeng T, Pan X, Zhang YH, Huang T, Fang Z, Cai YD. Recognizing Pattern and Rule of Mutation Signatures Corresponding to Cancer Types. Front Cell Dev Biol 2021; 9:712931. [PMID: 34513841 PMCID: PMC8427289 DOI: 10.3389/fcell.2021.712931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/02/2021] [Indexed: 11/20/2022] Open
Abstract
Cancer has been generally defined as a cluster of systematic malignant pathogenesis involving abnormal cell growth. Genetic mutations derived from environmental factors and inherited genetics trigger the initiation and progression of cancers. Although several well-known factors affect cancer, mutation features and rules that affect cancers are relatively unknown due to limited related studies. In this study, a computational investigation on mutation profiles of cancer samples in 27 types was given. These profiles were first analyzed by the Monte Carlo Feature Selection (MCFS) method. A feature list was thus obtained. Then, the incremental feature selection (IFS) method adopted such list to extract essential mutation features related to 27 cancer types, find out 207 mutation rules and construct efficient classifiers. The top 37 mutation features corresponding to different cancer types were discussed. All the qualitatively analyzed gene mutation features contribute to the distinction of different types of cancers, and most of such mutation rules are supported by recent literature. Therefore, our computational investigation could identify potential biomarkers and prediction rules for cancers in the mutation signature level.
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Affiliation(s)
- Lei Chen
- School of Life Sciences, Shanghai University, Shanghai, China.,College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Xianchao Zhou
- School of Life Sciences and Technology, ShanghaiTech University, Shanghai, China.,Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Zeng
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xiaoyong Pan
- Key Laboratory of System Control and Information Processing, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai, China
| | - Yu-Hang Zhang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Tao Huang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Zhaoyuan Fang
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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33
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Zhang YH, Guo W, Zeng T, Zhang S, Chen L, Gamarra M, Mansour RF, Escorcia-Gutierrez J, Huang T, Cai YD. Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes. Front Microbiol 2021; 12:711244. [PMID: 34305880 PMCID: PMC8299781 DOI: 10.3389/fmicb.2021.711244] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 06/21/2021] [Indexed: 01/03/2023] Open
Abstract
Type 2 diabetes (T2D) is a systematic chronic metabolic condition with abnormal sugar metabolism dysfunction, and its complications are the most harmful to human beings and may be life-threatening after long-term durations. Considering the high incidence and severity at late stage, researchers have been focusing on the identification of specific biomarkers and potential drug targets for T2D at the genomic, epigenomic, and transcriptomic levels. Microbes participate in the pathogenesis of multiple metabolic diseases including diabetes. However, the related studies are still non-systematic and lack the functional exploration on identified microbes. To fill this gap between gut microbiome and diabetes study, we first introduced eggNOG database and KEGG ORTHOLOGY (KO) database for orthologous (protein/gene) annotation of microbiota. Two datasets with these annotations were employed, which were analyzed by multiple machine-learning models for identifying significant microbiota biomarkers of T2D. The powerful feature selection method, Max-Relevance and Min-Redundancy (mRMR), was first applied to the datasets, resulting in a feature list for each dataset. Then, the list was fed into the incremental feature selection (IFS), incorporating support vector machine (SVM) as the classification algorithm, to extract essential annotations and build efficient classifiers. This study not only revealed potential pathological factors for diabetes at the microbiome level but also provided us new candidates for drug development against diabetes.
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Affiliation(s)
- Yu-Hang Zhang
- School of Life Sciences, Shanghai University, Shanghai, China.,Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences (CAS) and Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Zeng
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - ShiQi Zhang
- Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Margarita Gamarra
- Department of Computational Science and Electronic, Universidad de la Costa, CUC, Barranquilla, Colombia
| | - Romany F Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, Egypt
| | - José Escorcia-Gutierrez
- Electronic and Telecommunications Engineering Program, Universidad Autónoma del Caribe, Barranquilla, Colombia
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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34
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Chen L, Li Z, Zeng T, Zhang YH, Feng K, Huang T, Cai YD. Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9939134. [PMID: 34307679 PMCID: PMC8272456 DOI: 10.1155/2021/9939134] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/03/2021] [Accepted: 06/24/2021] [Indexed: 12/11/2022]
Abstract
COVID-19, a severe respiratory disease caused by a new type of coronavirus SARS-CoV-2, has been spreading all over the world. Patients infected with SARS-CoV-2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients. Both patients could further spread the virus to other susceptible people, thereby making the control of COVID-19 difficult. The two major challenges for COVID-19 diagnosis at present are as follows: (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus. Therefore, new biomarkers at different omics levels are required for the large-scale screening and diagnosis of COVID-19. Although some initial analyses could identify a group of candidate gene biomarkers for COVID-19, the previous work still could not identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases. As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with COVID-19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, COVID-19, and other kinds of coronavirus. This dataset was first analysed by Boruta, Max-Relevance and Min-Redundancy feature selection methods one by one, resulting in a feature list. This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules. The capacity of these findings to distinguish COVID-19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of COVID-19 diagnosis.
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Affiliation(s)
- Lei Chen
- School of Life Sciences, Shanghai University, shanghai 200444, China
- College of Information Engineering, Shanghai Maritime University, shanghai 201306, China
| | - Zhandong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun 130052, China
| | - Tao Zeng
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, shanghai 200031, China
| | - Yu-Hang Zhang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - KaiYan Feng
- Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou 510507, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, shanghai 200031, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, shanghai 200444, China
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35
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Zhu W, Guo Y, Zou Q. Prediction of presynaptic and postsynaptic neurotoxins based on feature extraction. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:5943-5958. [PMID: 34517517 DOI: 10.3934/mbe.2021297] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
A neurotoxin is essentially a protein that mainly acts on the nervous system; it has a selective toxic effect on the central nervous system and neuromuscular nodes, can cause muscle paralysis and respiratory paralysis, and has strong lethality. According to their principle of action, neurotoxins are divided into presynaptic neurotoxins and postsynaptic neurotoxins. Correctly identifying presynaptic and postsynaptic nerve toxins provides important clues for future drug development and the discovery of drug targets. Therefore, a predictive model, Neu_LR, was constructed in this paper. The monoMonokGap method was used to extract the frequency characteristics of presynaptic and postsynaptic neurotoxin sequences and carry out feature selection, then, based on the important features obtained after dimensionality reduction, the prediction model Neu_LR was constructed using a logistic regression algorithm, and ten-fold cross-validation and independent test set validation were used. The final accuracy rates were 99.6078 and 94.1176%, respectively, which proved that the Neu_LR model had good predictive performance and robustness, and could meet the prediction requirements of presynaptic and postsynaptic neurotoxins. The data and source code of the model can be freely download from https://github.com/gyx123681/.
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Affiliation(s)
- Wen Zhu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Yuxin Guo
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
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36
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CWLy-RF: A novel approach for identifying cell wall lyases based on random forest classifier. Genomics 2021; 113:2919-2924. [PMID: 34186189 DOI: 10.1016/j.ygeno.2021.06.038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 06/20/2021] [Accepted: 06/25/2021] [Indexed: 02/05/2023]
Abstract
Drug resistance of pathogenic bacteria has become increasingly serious due to the abuse of antibiotics in recent years. Researchers have found that cell wall lyases are effective antibacterial agents that can specifically recognize target bacteria and degrade bacterial peptidoglycan. Traditional wet experiments are usually expensive, time-consuming and laborious for the identification of lyases. Therefore, there is an urgent need to develop prediction tools based on computer methods to identify lyases quickly and accurately. In this paper, a new predictor, CWLy-RF, is proposed based on the random forest (RF) algorithm to identify cell wall lyases. In this method, we combined three features, namely, 400D, 188D and the composition of k-spaced amino acid group pairs, using mixed-feature representation methods. Afterward, we improved the feature representation ability with the selected top 100 features by using the information gain method and trained a predictive model using RF. The constructed prediction model is evaluated by using 10-fold cross-validation. The accuracy obtained was 96.09%, the AUC was 0.993, the MCC was 0.922, the sensitivity was 94.92%, and the specificity was 97.32%. We have proved that the proposed predictor CWLy-RF is superior to other latest models, and it will hopefully become an effective and useful tool for identifying lyases.
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37
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Analysis of the Sequence Characteristics of Antifreeze Protein. Life (Basel) 2021; 11:life11060520. [PMID: 34204983 PMCID: PMC8226703 DOI: 10.3390/life11060520] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 05/27/2021] [Accepted: 05/31/2021] [Indexed: 12/31/2022] Open
Abstract
Antifreeze protein (AFP) is a proteinaceous compound with improved antifreeze ability and binding ability to ice to prevent its growth. As a surface-active material, a small number of AFPs have a tremendous influence on the growth of ice. Therefore, identifying novel AFPs is important to understand protein–ice interactions and create novel ice-binding domains. To date, predicting AFPs is difficult due to their low sequence similarity for the ice-binding domain and the lack of common features among different AFPs. Here, a computational engine was developed to predict the features of AFPs and reveal the most important 39 features for AFP identification, such as antifreeze-like/N-acetylneuraminic acid synthase C-terminal, insect AFP motif, C-type lectin-like, and EGF-like domain. With this newly presented computational method, a group of previously confirmed functional AFP motifs was screened out. This study has identified some potential new AFP motifs and contributes to understanding biological antifreeze mechanisms.
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38
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Ao C, Zou Q, Yu L. RFhy-m2G: Identification of RNA N2-methylguanosine modification sites based on random forest and hybrid features. Methods 2021; 203:32-39. [PMID: 34033879 DOI: 10.1016/j.ymeth.2021.05.016] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 05/04/2021] [Accepted: 05/20/2021] [Indexed: 12/31/2022] Open
Abstract
N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors.
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Affiliation(s)
- Chunyan Ao
- School of Computer Science and Technology, Xidian University, Xi'an, China; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China.
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39
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Chen L, Li Z, Zeng T, Zhang YH, Li H, Huang T, Cai YD. Predicting gene phenotype by multi-label multi-class model based on essential functional features. Mol Genet Genomics 2021; 296:905-918. [PMID: 33914130 DOI: 10.1007/s00438-021-01789-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/13/2021] [Indexed: 12/19/2022]
Abstract
Phenotype is one of the most significant concepts in genetics, which is used to describe all the characteristics of a research object that can be observed. Considering that phenotype reflects the integrated features of genotype and environment factors, it is hard to define phenotype characteristics, even difficult to predict unknown phenotypes. Restricted by current biological techniques, it is still quite expensive and time-consuming to obtain sufficient structural information of large-scale phenotype-associated genes/proteins. Various bioinformatics methods have been presented to solve such problem, and researchers have confirmed the efficacy and prediction accuracy of functional network-based prediction. But general functional descriptions have highly complicated inner structures for phenotype prediction. To further address this issue and improve the efficacy of phenotype prediction on more than ten kinds of phenotypes, we first extract functional enrichment features from GO and KEGG, and then use node2vec to learn functional embedding features of genes from a gene-gene network. All these features are analyzed by some feature selection methods (Boruta, minimum redundancy maximum relevance) to generate a feature list. Such list is fed into the incremental feature selection, incorporating some multi-label classifiers built by RAkEL and some classic base classifiers, to build an optimum multi-label multi-class classification model for phenotype prediction. According to recent researches, our method has indeed identified many literature-supported genes/proteins and their associated phenotypes, and even some candidate genes with re-assigned new phenotypes, which provide a new computational tool for the accurate and effective phenotypic prediction.
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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
| | - Zhandong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, 130052, People's Republic of China
| | - Tao Zeng
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, People's Republic of China
| | - Yu-Hang Zhang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hao Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, 130052, People's Republic of China
| | - Tao Huang
- Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, 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.
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40
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Zhang YH, Li Z, Zeng T, Chen L, Li H, Gamarra M, Mansour RF, Escorcia-Gutierrez J, Huang T, Cai YD. Investigating gene methylation signatures for fetal intolerance prediction. PLoS One 2021; 16:e0250032. [PMID: 33886611 PMCID: PMC8062050 DOI: 10.1371/journal.pone.0250032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 03/29/2021] [Indexed: 11/29/2022] Open
Abstract
Pregnancy is a complicated and long procedure during one or more offspring development inside a woman. A short period of oxygen shortage after birth is quite normal for most babies and does not threaten their health. However, if babies have to suffer from a long period of oxygen shortage, then this condition is an indication of pathological fetal intolerance, which probably causes their death. The identification of the pathological fetal intolerance from the physical oxygen shortage is one of the important clinical problems in obstetrics for a long time. The clinical syndromes typically manifest five symptoms that indicate that the baby may suffer from fetal intolerance. At present, liquid biopsy combined with high-throughput sequencing or mass spectrum techniques provides a quick approach to detect real-time alteration in the peripheral blood at multiple levels with the rapid development of molecule sequencing technologies. Gene methylation is functionally correlated with gene expression; thus, the combination of gene methylation and expression information would help in screening out the key regulators for the pathogenesis of fetal intolerance. We combined gene methylation and expression features together and screened out the optimal features, including gene expression or methylation signatures, for fetal intolerance prediction for the first time. In addition, we applied various computational methods to construct a comprehensive computational pipeline to identify the potential biomarkers for fetal intolerance dependent on the liquid biopsy samples. We set up qualitative and quantitative computational models for the prediction for fetal intolerance during pregnancy. Moreover, we provided a new prospective for the detailed pathological mechanism of fetal intolerance. This work can provide a solid foundation for further experimental research and contribute to the application of liquid biopsy in antenatal care.
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Affiliation(s)
- Yu-Hang Zhang
- School of Life Sciences, Shanghai University, Shanghai, China
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Zhandong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Tao Zeng
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Hao Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Margarita Gamarra
- Department of Computational Science and Electronic, Universidad de la Costa, CUC, Barranquilla, Colombia
| | - Romany F. Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, Egypt
| | - José Escorcia-Gutierrez
- Electronic and Telecommunicacions Program, Universidad Autónoma del Caribe, Barranquilla, Colombia
- * E-mail: (JEG); (TH); (YDC)
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- * E-mail: (JEG); (TH); (YDC)
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
- * E-mail: (JEG); (TH); (YDC)
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41
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Identifying Infliximab- (IFX-) Responsive Blood Signatures for the Treatment of Rheumatoid Arthritis. BIOMED RESEARCH INTERNATIONAL 2021. [DOI: 10.1155/2021/5556784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Rheumatoid arthritis (RA) is a severe chronic pathogenic inflammatory abnormality that damages small joints. Comprehensive diagnosis and treatment procedures for RA have been established because of its severe symptoms and relatively high morbidity. Medication and surgery are the two major therapeutic approaches. Infliximab (IFX) is a novel biological agent applied for the treatment of RA. IFX improves physical functions and benefits the achievement of clinical remission even under discontinuous medication. However, not all patients react to IFX, and distinguishing IFX-sensitive and IFX-resistant patients is quite difficult. Thus, how to predict the therapeutic effects of IFX on patients with RA is one of the urgent translational medicine problems in the clinical treatment of RA. In this study, we present a novel computational method for the identification of the applicable and substantial blood gene signatures of IFX sensitivity by liquid biopsy, which may assist in the establishment of a clinical drug sensitivity test standard for RA and contribute to the revelation of unique IFX-associated pharmacological mechanisms.
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42
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Yuan F, Li Z, Chen L, Zeng T, Zhang YH, Ding S, Huang T, Cai YD. Identifying the Signatures and Rules of Circulating Extracellular MicroRNA for Distinguishing Cancer Subtypes. Front Genet 2021; 12:651610. [PMID: 33767734 PMCID: PMC7985347 DOI: 10.3389/fgene.2021.651610] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 02/10/2021] [Indexed: 12/24/2022] Open
Abstract
Cancer is one of the most threatening diseases to humans. It can invade multiple significant organs, including lung, liver, stomach, pancreas, and even brain. The identification of cancer biomarkers is one of the most significant components of cancer studies as the foundation of clinical cancer diagnosis and related drug development. During the large-scale screening for cancer prevention and early diagnosis, obtaining cancer-related tissues is impossible. Thus, the identification of cancer-associated circulating biomarkers from liquid biopsy targeting has been proposed and has become the most important direction for research on clinical cancer diagnosis. Here, we analyzed pan-cancer extracellular microRNA profiles by using multiple machine-learning models. The extracellular microRNA profiles on 11 cancer types and non-cancer were first analyzed by Boruta to extract important microRNAs. Selected microRNAs were then evaluated by the Max-Relevance and Min-Redundancy feature selection method, resulting in a feature list, which were fed into the incremental feature selection method to identify candidate circulating extracellular microRNA for cancer recognition and classification. A series of quantitative classification rules was also established for such cancer classification, thereby providing a solid research foundation for further biomarker exploration and functional analyses of tumorigenesis at the level of circulating extracellular microRNA.
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Affiliation(s)
- Fei Yuan
- School of Life Sciences, Shanghai University, Shanghai, China
- Department of Science and Technology, Binzhou Medical University Hospital, Binzhou, China
| | - Zhandong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Tao Zeng
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Hang Zhang
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Shijian Ding
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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43
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Identifying the Immunological Gene Signatures of Immune Cell Subtypes. BIOMED RESEARCH INTERNATIONAL 2021. [DOI: 10.1155/2021/6639698] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The immune system is a complicated defensive system that comprises multiple functional cells and molecules acting against endogenous and exogenous pathogenic factors. Identifying immune cell subtypes and recognizing their unique immunological functions are difficult because of the complicated cellular components and immunological functions of the immune system. With the development of transcriptomics and high-throughput sequencing, the gene expression profiling of immune cells can provide a new strategy to explore the immune cell subtyping. On the basis of the new profiling data of mouse immune cell gene expression from the Immunological Genome Project (ImmGen), a novel computational pipeline was applied to identify different immune cell subtypes, including αβ T cells, B cells, γδ T cells, and innate lymphocytes. First, the profiling data was analyzed by a powerful feature selection method, Monte-Carlo Feature Selection, resulting in a feature list and some informative features. For the list, the two-stage incremental feature selection method, incorporating random forest as the classification algorithm, was applied to extract essential gene signatures and build an efficient classifier. On the other hand, a rule learning scheme was applied on the informative features to construct quantitative expression rules. A group of gene signatures was found as qualitatively related to the biological processes of four immune cell subtypes. The quantitative expression rules can efficiently cluster immune cells. This work provides a novel computational tool for immune cell quantitative subtyping and biomarker recognition.
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