1
|
Wang J, Yang Y, Han P, Qin J, Huang D, Tang B, An M, Yao X, Zhang X. A chitosan-based hydrogel with ultrasound-driven immuno-sonodynamic therapeutic effect for accelerated bacterial infected wound healing. Int J Biol Macromol 2024; 279:135180. [PMID: 39214213 DOI: 10.1016/j.ijbiomac.2024.135180] [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: 04/29/2024] [Revised: 08/13/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024]
Abstract
Sonodynamic therapy has attracted much attention as a noninvasive treatment for deep infections. However, it is challenging to achieve high antibacterial activity for hydrogels under ultrasonic irradiation due to the relatively weak sono-catalysis capability of sonosensitizers. Herein, an ultrasound-responsive antibacterial hydrogel (Fe3O4/HA/Ber-LA) composed of Fe3O4-grafted-Berberine, chitosan molecules modified with L-arginine and poly (vinyl alcohol) is prepared for enhanced sonodynamic therapy and immunoregulation. The formation of heterojunction between berberine and Fe3O4 with different work function promotes the charge separation and electron flow and disrupts the conjugated structure of berberine, causing a significant decrease in the band gap, eventually enhancing the sonocatalytic activity. The combination of berberine with Fe3O4 also significantly improves the oxygen adsorption energy, enabling more O2 molecules to react with the electron-rich regions on the surface of Fe3O4 to generate more reactive oxygen species (ROS). L-arginine grafted in the hydrogel is catalyzed by the ROS to release nitric oxide, which not only possesses antibacterial activity, but also positively affects macrophage M1 polarization to display potent phagocytosis to Staphylococcus aureus, thus achieving immuno-sonodynamic therapy. Hence, Fe3O4/HA/Ber-LA hydrogel under ultrasound irradiation shows excellent antibacterial activity. Furthermore, the antioxidative activity and anti-inflammatory effect of berberine released from the hybrid hydrogel induces macrophages to polarize towards the anti-inflammatory M2 status as infection comes under control, thus accelerating the wound healing. The hybrid hydrogel based on the immuno-sonodynamic therapy may be an extraordinary candidate for the treatment of deep infections.
Collapse
Affiliation(s)
- Jiameng Wang
- Department of Biomedical Engineering, Research Center for Nano-biomaterials & Regenerative Medicine, College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan 030024, China; Shanxi Key Laboratory of Biomedical Metal Materials, Taiyuan University of Technology, Taiyuan 030024, China
| | - Yongqiang Yang
- National Graphene Products Quality Inspection and Testing Center (Jiangsu), Special Equipment Safety Supervision Inspection Institute of Jiangsu Province, Wuxi 214174, China.
| | - Peide Han
- College of Materials Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China
| | - Jien Qin
- Graphene Source Technology Wuxi Co., Ltd, Wuxi 214174, China
| | - Di Huang
- Department of Biomedical Engineering, Research Center for Nano-biomaterials & Regenerative Medicine, College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan 030024, China
| | - Bin Tang
- Shanxi Key Laboratory of Biomedical Metal Materials, Taiyuan University of Technology, Taiyuan 030024, China
| | - Meiwen An
- Department of Biomedical Engineering, Research Center for Nano-biomaterials & Regenerative Medicine, College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan 030024, China
| | - Xiaohong Yao
- Shanxi Key Laboratory of Biomedical Metal Materials, Taiyuan University of Technology, Taiyuan 030024, China.
| | - Xiangyu Zhang
- Department of Biomedical Engineering, Research Center for Nano-biomaterials & Regenerative Medicine, College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan 030024, China; Shanxi Key Laboratory of Biomedical Metal Materials, Taiyuan University of Technology, Taiyuan 030024, China.
| |
Collapse
|
2
|
Wen R, Song T, Gossen BD, Peng G. Comparative transcriptome analysis of canola carrying a single vs stacked resistance genes against clubroot. FRONTIERS IN PLANT SCIENCE 2024; 15:1358605. [PMID: 38835867 PMCID: PMC11148231 DOI: 10.3389/fpls.2024.1358605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 04/30/2024] [Indexed: 06/06/2024]
Abstract
Pyramiding resistance genes may expand the efficacy and scope of a canola variety against clubroot (Plasmodiophora brassicae), a serious threat to canola production in western Canada. However, the mechanism(s) of multigenic resistance, especially the potential interaction among clubroot resistance (CR) genes, are not well understood. In this study, transcriptome was compared over three canola (Brassica napus L.) inbred/hybrid lines carrying a single CR gene in chromosome A03 (CRaM, Line 16) or A08 (Crr1rutb, Line 20), and both genes (CRaM+Crr1rutb, Line 15) inoculated with a field population (L-G2) of P. brassicae pathotype X, a new variant found in western Canada recently. The line16 was susceptible, while lines 15 and 20 were partially resistant. Functional annotation identified differential expression of genes (DEGs) involved in biosynthetic processes responsive to stress and regulation of cellular process; The Venn diagram showed that the partially resistant lines 15 and 20 shared 1,896 differentially expressed genes relative to the susceptible line 16, and many of these DEGs are involved in defense responses, activation of innate immunity, hormone biosynthesis and programmed cell death. The transcription of genes involved in Pathogen-Associated Molecular Pattern (PAMP)-Triggered and Effector-Triggered Immunity (PTI and ETI) was particularly up-regulated, and the transcription level was higher in line 15 (CRaM + Crr1rutb) than in line 20 (Crr1rutb only) for most of the DEGs. These results indicated that the partial resistance to the pathotype X was likely conferred by the CR gene Crr1rutb for both lines 15 and 20 that functioned via the activation of both PTI and ETI signaling pathways. Additionally, these two CR genes might have synergistic effects against the pathotype X, based on the higher transcription levels of defense-related DEGs expressed by inoculated line 15, highlighting the benefit of gene stacking for improved canola resistance as opposed to a single CR gene alone.
Collapse
Affiliation(s)
- Rui Wen
- Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, Saskatoon SK, Canada
| | - Tao Song
- Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, Saskatoon SK, Canada
| | - Bruce D Gossen
- Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, Saskatoon SK, Canada
| | - Gary Peng
- Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, Saskatoon SK, Canada
| |
Collapse
|
3
|
Oliveira TT, Freitas JF, de Medeiros VPB, Xavier TJDS, Agnez-Lima LF. Integrated analysis of RNA-seq datasets reveals novel targets and regulators of COVID-19 severity. Life Sci Alliance 2024; 7:e202302358. [PMID: 38262689 PMCID: PMC10806258 DOI: 10.26508/lsa.202302358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/25/2024] Open
Abstract
During the COVID-19 pandemic, RNA-seq datasets were produced to investigate the virus-host relationship. However, much of these data remains underexplored. To improve the search for molecular targets and biomarkers, we performed an integrated analysis of multiple RNA-seq datasets, expanding the cohort and including patients from different countries, encompassing severe and mild COVID-19 patients. Our analysis revealed that severe COVID-19 patients exhibit overexpression of genes coding for proteins of extracellular exosomes, endomembrane system, and neutrophil granules (e.g., S100A9, LY96, and RAB1B), which may play an essential role in the cellular response to infection. Concurrently, these patients exhibit down-regulation of genes encoding components of the T cell receptor complex and nucleolus, including TP53, IL2RB, and NCL Finally, SPI1 may emerge as a central transcriptional factor associated with the up-regulated genes, whereas TP53, MYC, and MAX were associated with the down-regulated genes during COVID-19. This study identified targets and transcriptional factors, lighting on the molecular pathophysiology of syndrome coronavirus 2 infection.
Collapse
Affiliation(s)
- Thais Teixeira Oliveira
- https://ror.org/04wn09761 Departamento de Biologia Celular e Genética, Universidade Federal do Rio Grande do Norte, UFRN, Natal, Brazil
| | - Júlia Firme Freitas
- https://ror.org/04wn09761 Departamento de Biologia Celular e Genética, Universidade Federal do Rio Grande do Norte, UFRN, Natal, Brazil
| | | | - Thiago Jesus da Silva Xavier
- https://ror.org/04wn09761 Departamento de Biologia Celular e Genética, Universidade Federal do Rio Grande do Norte, UFRN, Natal, Brazil
| | - Lucymara Fassarella Agnez-Lima
- https://ror.org/04wn09761 Departamento de Biologia Celular e Genética, Universidade Federal do Rio Grande do Norte, UFRN, Natal, Brazil
| |
Collapse
|
4
|
Gong L, Cui X, Liu Y, Lin C, Gao Z. SinCWIm: An imputation method for single-cell RNA sequence dropouts using weighted alternating least squares. Comput Biol Med 2024; 171:108225. [PMID: 38442556 DOI: 10.1016/j.compbiomed.2024.108225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/28/2024] [Accepted: 02/25/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND AND OBJECTIVES Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for exploring cellular heterogeneity, discovering novel or rare cell types, distinguishing between tissue-specific cellular composition, and understanding cell differentiation during development. However, due to technological limitations, dropout events in scRNA-seq can mistakenly convert some entries in the real data to zero. This is equivalent to introducing noise into the data of cell gene expression entries. The data is contaminated, which affects the performance of downstream analyses, including clustering, cell annotation, differential gene expression analysis, and so on. Therefore, it is a crucial work to accurately determine which zeros are due to dropout events and perform imputation operations on them. METHODS Considering the different confidence levels of different zeros in the gene expression matrix, this paper proposes a SinCWIm method for dropout events in scRNA-seq based on weighted alternating least squares (WALS). The method utilizes Pearson correlation coefficient and hierarchical clustering to quantify the confidence of zero entries. It is then combined with WALS for matrix decomposition. And the imputation result is made close to the actual number by outlier removal and data correction operations. RESULTS A total of eight single-cell sequencing datasets were used for comparative experiments to demonstrate the overall superiority of SinCWIm over state-of-the-art models. SinCWIm was applied to cluster the data to obtain an adjusted RAND index evaluation, and the Usoskin, Pollen and Bladder datasets scored 94.46%, 96.48% and 76.74%, respectively. In addition, significant improvements were made in the retention of differential expression genes and visualization. CONCLUSIONS SinCWIm provides a valuable imputation method for handling dropout events in single-cell sequencing data. In comparison to advanced methods, SinCWIm demonstrates excellent performance in clustering, visualization and other aspects. It is applicable to various single-cell sequencing datasets.
Collapse
Affiliation(s)
- Lejun Gong
- Jiangsu Key Lab of Big Data Security & Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China.
| | - Xiong Cui
- Jiangsu Key Lab of Big Data Security & Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Yang Liu
- Jiangsu Key Lab of Big Data Security & Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Cai Lin
- Department of Burn, Wound Repair and Regenerative Medicine Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| | - Zhihong Gao
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
| |
Collapse
|
5
|
Zhu Y, Chen B, Zu Y. Identifying OGN as a Biomarker Covering Multiple Pathogenic Pathways for Diagnosing Heart Failure: From Machine Learning to Mechanism Interpretation. Biomolecules 2024; 14:179. [PMID: 38397416 PMCID: PMC10886937 DOI: 10.3390/biom14020179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/14/2024] [Accepted: 01/23/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND The pathophysiologic heterogeneity of heart failure (HF) necessitates a more detailed identification of diagnostic biomarkers that can reflect its diverse pathogenic pathways. METHODS We conducted weighted gene and multiscale embedded gene co-expression network analysis on differentially expressed genes obtained from HF and non-HF specimens. We employed a machine learning integration framework and protein-protein interaction network to identify diagnostic biomarkers. Additionally, we integrated gene set variation analysis, gene set enrichment analysis (GSEA), and transcription factor (TF)-target analysis to unravel the biomarker-dominant pathways. Leveraging single-sample GSEA and molecular docking, we predicted immune cells and therapeutic drugs related to biomarkers. Quantitative polymerase chain reaction validated the expressions of biomarkers in the plasma of HF patients. A two-sample Mendelian randomization analysis was implemented to investigate the causal impact of biomarkers on HF. RESULTS We first identified COL14A1, OGN, MFAP4, and SFRP4 as candidate biomarkers with robust diagnostic performance. We revealed that regulating biomarkers in HF pathogenesis involves TFs (BNC2, MEOX2) and pathways (cell adhesion molecules, chemokine signaling pathway, cytokine-cytokine receptor interaction, oxidative phosphorylation). Moreover, we observed the elevated infiltration of effector memory CD4+ T cells in HF, which was highly related to biomarkers and could impact immune pathways. Captopril, aldosterone antagonist, cyclopenthiazide, estradiol, tolazoline, and genistein were predicted as therapeutic drugs alleviating HF via interactions with biomarkers. In vitro study confirmed the up-regulation of OGN as a plasma biomarker of HF. Mendelian randomization analysis suggested that genetic predisposition toward higher plasma OGN promoted the risk of HF. CONCLUSIONS We propose OGN as a diagnostic biomarker for HF, which may advance our understanding of the diagnosis and pathogenesis of HF.
Collapse
Affiliation(s)
- Yihao Zhu
- International Research Center for Marine Biosciences, Ministry of Science and Technology, Shanghai Ocean University, Shanghai 201306, China
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China
| | - Bin Chen
- Department of Cardiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Lin-gang), Shanghai 201306, China
| | - Yao Zu
- International Research Center for Marine Biosciences, Ministry of Science and Technology, Shanghai Ocean University, Shanghai 201306, China
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China
- Marine Biomedical Science and Technology Innovation Platform of Lin-gang Special Area, Shanghai 201306, China
| |
Collapse
|
6
|
Li X, Lv X, Li H, Zhang G, Long Y, Li K, Fan Y, Jin D, Zhou F, Liu H. Undifferentially Expressed CXXC5 as a Transcriptionally Regulatory Biomarker of Breast Cancer. Adv Biol (Weinh) 2023; 7:e2300189. [PMID: 37423953 DOI: 10.1002/adbi.202300189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 06/17/2023] [Indexed: 07/11/2023]
Abstract
This work hypothesizes that some genes undergo radically changed transcription regulations (TRs) in breast cancer (BC), but don't show differential expressions for unknown reasons. The TR of a gene is quantitatively formulated by a regression model between the expression of this gene and multiple transcription factors (TFs). The difference between the predicted and real expression levels of a gene in a query sample is defined as the mqTrans value of this gene, which quantitatively reflects its regulatory changes. This work systematically screens the undifferentially expressed genes with differentially expressed mqTrans values in 1036 samples across five datasets and three ethnic groups. This study calls the 25 genes satisfying the above hypothesis in at least four datasets as dark biomarkers, and the strong dark biomarker gene CXXC5 (CXXC Finger Protein 5) is even supported by all the five independent BC datasets. Although CXXC5 does not show differential expressions in BC, its transcription regulations show quantitative associations with BCs in diversified cohorts. The overlapping long noncoding RNAs (lncRNAs) may have contributed their transcripts to the expression miscalculations of dark biomarkers. The mqTrans analysis serves as a complementary view of the transcriptome-based detections of biomarkers that are ignored by many existing studies.
Collapse
Affiliation(s)
- Xue Li
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Xiaoying Lv
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Haijun Li
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Gongyou Zhang
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Yaohang Long
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Kewei Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Yusi Fan
- College of Software, Jilin University, Changchun, 130012, China
| | - Dawei Jin
- Research Institute of Guizhou Huada Life Big Data, Guiyang, Guizhou, 550025, China
| | - Fengfeng Zhou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Hongmei Liu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| |
Collapse
|
7
|
Rabie AH, Saleh AI. Monkeypox diagnosis using ensemble classification. Artif Intell Med 2023; 143:102618. [PMID: 37673562 DOI: 10.1016/j.artmed.2023.102618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 05/23/2023] [Accepted: 06/24/2023] [Indexed: 09/08/2023]
Abstract
The world has recently been exposed to a fierce attack from many viral diseases, such as Covid-19, that exhausted medical systems around the world. Such attack had a negative impact not only on the health status of people or the high death rate, but also had a bad impact on the economic situation, which affected all countries of the world especially the poor and the developing ones. Monkeypox is one of the latest viral diseases that may cause a pandemic in the near future if not dealt and diagnosed with appropriately. This paper provides a new strategy for diagnosing monkeypox, which is called; Accurate Monkeypox Diagnosing Strategy (AMDS). The proposed AMDS consists of two phases, which are; (i) pre-processing and (ii) classification. During the pre-processing phase, the most effective feature are selected using Binary Tiki-Taka Algorithm (BTTA). On the other hand, in the classification phase, ensemble classification is used for diagnosing new cases, which combines evidence from three different new classifiers, namely; (a) Layered K-Nearest Neighbors (LKNN), (b) Statistical Naïve Bayes (SNB), and (c) Deep Learning Classifier (DLC). Moreover, the decisions of the proposed classifiers are merged in a new voting scheme called Fuzzified Voting Scheme (FVS). AMDS has been compared against recent diagnostic strategies. Experimental results have proven that AMDS outperforms other monkeypox diagnostic strategies as it introduces the most accurate diagnosis according to two different datasets.
Collapse
Affiliation(s)
- Asmaa H Rabie
- Computer Engineering and Systems Dept., Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Ahmed I Saleh
- Computer Engineering and Systems Dept., Faculty of Engineering, Mansoura University, Mansoura, Egypt.
| |
Collapse
|
8
|
Di Salvatore V, Crispino E, Maleki A, Nicotra G, Russo G, Pappalardo F. Computational identification of differentially-expressed genes as suggested novel COVID-19 biomarkers: A bioinformatics analysis of expression profiles. Comput Struct Biotechnol J 2023; 21:3339-3354. [PMID: 37347079 PMCID: PMC10259169 DOI: 10.1016/j.csbj.2023.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/07/2023] [Accepted: 06/07/2023] [Indexed: 06/23/2023] Open
Abstract
COVID-19 was declared a pandemic in March 2020, and since then, it has not stopped spreading like wildfire in almost every corner of the world, despite the many efforts made to stem its spread. SARS-CoV-2 has one of the biggest genomes among RNA viruses and presents unique characteristics that differentiate it from other coronaviruses, making it even more challenging to find a cure or vaccine that is efficient enough. This work aims, using RNA sequencing (RNA-Seq) data, to evaluate whether the expression of specific human genes in the host can vary in different grades of disease severity and to determine the molecular origins of the differences in response to SARS-CoV-2 infection in different patients. In addition to quantifying gene expression, data coming from RNA-Seq allow for the discovery of new transcripts, the identification of alternative splicing events, the detection of allele-specific expression, and the detection of post-transcriptional alterations. For this reason, we performed differential expression analysis on different expression profiles of COVID-19 patients, using RNA-Seq data coming from NCBI public repository, and we obtained the lists of all differentially expressed genes (DEGs) emerging from 7 experimental conditions. We performed a Gene Set Enrichment Analysis (GSEA) on these genes to find possible correlations between DEGs and known disease phenotypes. We mainly focused on DEGs coming out from the analysis of the contrasts involving severe conditions to infer any possible relation between a worsening of the clinical picture and an over-representation of specific genes. Based on the obtained results, this study indicates a small group of genes that result up-regulated in the severe form of the disease. EXOSC5, MESD, REXO2, and TRMT2A genes are not differentially expressed or not present in the other conditions, being for that reason, good biomarkers candidates for the severe form of COVID-19 disease. The use of specific over-expressed genes, whether up-regulated or down-regulated, which have an individual role in each different condition of COVID-19 as a biomarker, can assist in early diagnosis.
Collapse
Affiliation(s)
| | - Elena Crispino
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Avisa Maleki
- Department of Mathematics and Computer Science, University of Catania, Catania, Italy
| | - Giulia Nicotra
- Department of Drug and Health Sciences, University of Catania, Catania, Italy
| | - Giulia Russo
- Department of Drug and Health Sciences, University of Catania, Catania, Italy
- Mimesis SRL, Catania, Italy
| | | |
Collapse
|
9
|
A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 179:1-9. [PMID: 36809830 PMCID: PMC9938959 DOI: 10.1016/j.pbiomolbio.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 02/07/2023] [Accepted: 02/12/2023] [Indexed: 02/21/2023]
Abstract
This study systematically reviews the Artificial Intelligence (AI) methods developed to resolve the critical process of COVID-19 gene data analysis, including diagnosis, prognosis, biomarker discovery, drug responsiveness, and vaccine efficacy. This systematic review follows the guidelines of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA). We searched PubMed, Embase, Web of Science, and Scopus databases to identify the relevant articles from January 2020 to June 2022. It includes the published studies of AI-based COVID-19 gene modeling extracted through relevant keyword searches in academic databases. This study included 48 articles discussing AI-based genetic studies for several objectives. Ten articles confer about the COVID-19 gene modeling with computational tools, and five articles evaluated ML-based diagnosis with observed accuracy of 97% on SARS-CoV-2 classification. Gene-based prognosis study reviewed three articles and found host biomarkers detecting COVID-19 progression with 90% accuracy. Twelve manuscripts reviewed the prediction models with various genome analysis studies, nine articles examined the gene-based in silico drug discovery, and another nine investigated the AI-based vaccine development models. This study compiled the novel coronavirus gene biomarkers and targeted drugs identified through ML approaches from published clinical studies. This review provided sufficient evidence to delineate the potential of AI in analyzing complex gene information for COVID-19 modeling on multiple aspects like diagnosis, drug discovery, and disease dynamics. AI models entrenched a substantial positive impact by enhancing the efficiency of the healthcare system during the COVID-19 pandemic.
Collapse
|
10
|
Izonin I, Kutucu H, Singh KK. Smart systems and data-driven services in healthcare. Comput Biol Med 2022; 158:106074. [PMID: 36109250 DOI: 10.1016/j.compbiomed.2022.106074] [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: 07/21/2022] [Revised: 08/31/2022] [Accepted: 09/03/2022] [Indexed: 11/27/2022]
Abstract
The modern development of Medicine and Healthcare is primarily based on the automation of various processes to support the correct and timely medical decisions. A doctor or other medical staff's fast, accurate, reliable diagnosis, prevention, and treatment processes are the keys to quality patient care. This aim can be achieved by developing reliable, intelligent Healthcare and medicine service systems for various purposes. The Special Issue covers science-intensive real-time solutions that underlie smart systems and data-driven services in Healthcare and Medicine.
Collapse
Affiliation(s)
- Ivan Izonin
- Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, 79013, Ukraine.
| | - Hakan Kutucu
- Department of Software Engineering, Karabuk University, Karabuk, 78050, Turkey.
| | - Krishna Kant Singh
- Amity School of Engineering and Technology, Amity University, Uttar Pradesh, India.
| |
Collapse
|
11
|
Lai G, Liu H, Deng J, Li K, Xie B. A Novel 3-Gene Signature for Identifying COVID-19 Patients Based on Bioinformatics and Machine Learning. Genes (Basel) 2022; 13:genes13091602. [PMID: 36140771 PMCID: PMC9498787 DOI: 10.3390/genes13091602] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 08/29/2022] [Accepted: 09/06/2022] [Indexed: 12/15/2022] Open
Abstract
Although many biomarkers associated with coronavirus disease 2019 (COVID-19) were found, a novel signature relevant to immune cells has not been developed. In this work, the “CIBERSORT” algorithm was used to assess the fraction of immune infiltrating cells in GSE152641 and GSE171110. Key modules associated with important immune cells were selected by the “WGCNA” package. The “GO” enrichment analysis was used to reveal the biological function associated with COVID-19. The “Boruta” algorithm was used to screen candidate genes, and the “LASSO” algorithm was used for collinearity reduction. A novel gene signature was developed based on multivariate logistic regression analysis. Subsequently, M0 macrophages (PRAUC = 0.948 in GSE152641 and PRAUC = 0.981 in GSE171110) and neutrophils (PRAUC = 0.892 in GSE152641 and PRAUC = 0.960 in GSE171110) were considered as important immune cells. Forty-three intersected genes from two modules were selected, which mainly participated in some immune-related activities. Finally, a three-gene signature comprising CLEC4D, DUSP13, and UNC5A that can accurately distinguish COVID-19 patients and healthy controls in three datasets was constructed. The ROCAUC was 0.974 in the training set, 0.946 in the internal test set, and 0.709 in the external test set. In conclusion, we constructed a three-gene signature to identify COVID-19, and CLEC4D, DUSP13, and UNC5A may be potential biomarkers for COVID-19 patients.
Collapse
|