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Wang G, Hua R, Chen X, He X, Dingming Y, Chen H, Zhang B, Dong Y, Liu M, Liu J, Liu T, Zhao J, Zhao YQ, Qiao L. MX1 and UBE2L6 are potential metaflammation gene targets in both diabetes and atherosclerosis. PeerJ 2024; 12:e16975. [PMID: 38406276 PMCID: PMC10893863 DOI: 10.7717/peerj.16975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 01/29/2024] [Indexed: 02/27/2024] Open
Abstract
Background The coexistence of diabetes mellitus (DM) and atherosclerosis (AS) is widespread, although the explicit metabolism and metabolism-associated molecular patterns (MAMPs) responsible for the correlation are still unclear. Methods Twenty-four genetically wild-type male Ba-Ma mini pigs were randomly divided into five groups distinguished by different combinations of 90 mg/kg streptozotocin (STZ) intravenous injection and high-cholesterol/lipid (HC) or high-lipid (HL) diet feeding for 9 months in total. Pigs in the STZ+HC and STZ+HL groups were injected with STZ first and then fed the HC or HL diet for 9 months. In contrast, pigs in the HC+STZ and HL+STZ groups were fed the HC or HL diet for 9 months and injected with STZ at 3 months. The controls were only fed a regular diet for 9 months. The blood glucose and abdominal aortic plaque observed through oil red O staining were used as evaluation indicators for successful modelling of DM and AS. A microarray gene expression analysis of all subjects was performed. Results Atherosclerotic lesions were observed only in the HC+STZ and STZ+HC groups. A total of 103 differentially expressed genes (DEGs) were identified as common between them. The most significantly enriched pathways of 103 common DEGs were influenza A, hepatitis C, and measles. The global and internal protein-protein interaction (PPI) networks of the 103 common DEGs consisted of 648 and 14 nodes, respectively. The top 10 hub proteins, namely, ISG15, IRG6, IRF7, IFIT3, MX1, UBE2L6, DDX58, IFIT2, USP18, and IFI44L, drive aspects of DM and AS. MX1 and UBE2L6 were the intersection of internal and global PPI networks. The expression of MX1 and UBE2L6 was 507.22 ± 342.56 and 96.99 ± 49.92 in the HC+STZ group, respectively, which was significantly higher than others and may be linked to the severity of hyperglycaemia-related atherosclerosis. Further PPI network analysis of calcium/micronutrients, including MX1 and UBE2L6, consisted of 58 and 18 nodes, respectively. The most significantly enriched KEGG pathways were glutathione metabolism, pyrimidine metabolism, purine metabolism, and metabolic pathways. Conclusions The global and internal PPI network of the 103 common DEGs consisted of 648 and 14 nodes, respectively. The intersection of the nodes of internal and global PPI networks was MX1 and UBE2L6, suggesting their key role in the comorbidity mechanism of DM and AS. This inference was partly verified by the overexpression of MX1 and UBE2L6 in the HC+STZ group but not others. Further calcium- and micronutrient-related enriched KEGG pathway analysis supported that MX1 and UBE2L6 may affect the inflammatory response through micronutrient metabolic pathways, conceptually named metaflammation. Collectively, MX1 and UBE2L6 may be potential common biomarkers for DM and AS that may reveal metaflammatory aspects of the pathological process, although proper validation is still needed to determine their contribution to the detailed mechanism.
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Affiliation(s)
- Guisheng Wang
- Department of Radiology, Third Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Rongrong Hua
- Department of Radiology, Third Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiaoxia Chen
- Department of Radiology, Third Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xucheng He
- Department of Radiology, Third Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yao Dingming
- Department of Radiology, Third Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Hua Chen
- Laboratory Animal Center, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Buhuan Zhang
- Department of Radiology, Third Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yuru Dong
- Department of Radiology, Third Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Muqing Liu
- Department of Radiology, Third Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jiaxiong Liu
- Department of Radiology, Third Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ting Liu
- Department of Radiology, Beijing YouAn Hospital, Capital Medical University, Beijing, China
| | - Jingwei Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Qiong Zhao
- Laboratory Animal Center, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Li Qiao
- Department of International Business, Business College of Beijing Union University, Beijing, China
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Beer LA, Yin X, Ding J, Senapati S, Sammel MD, Barnhart KT, Liu Q, Speicher DW, Goldman AR. Identification and verification of plasma protein biomarkers that accurately identify an ectopic pregnancy. Clin Proteomics 2023; 20:37. [PMID: 37715129 PMCID: PMC10503165 DOI: 10.1186/s12014-023-09425-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 08/21/2023] [Indexed: 09/17/2023] Open
Abstract
BACKGROUND Differentiating between a normal intrauterine pregnancy (IUP) and abnormal conditions including early pregnancy loss (EPL) or ectopic pregnancy (EP) is a major clinical challenge in early pregnancy. Currently, serial β-human chorionic gonadotropin (β-hCG) and progesterone are the most commonly used plasma biomarkers for evaluating pregnancy prognosis when ultrasound is inconclusive. However, neither biomarker can predict an EP with sufficient and reproducible accuracy. Hence, identification of new plasma biomarkers that can accurately diagnose EP would have great clinical value. METHODS Plasma was collected from a discovery cohort of 48 consenting women having an IUP, EPL, or EP. Samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by a label-free proteomics analysis to identify significant changes between pregnancy outcomes. A panel of 14 candidate biomarkers were then verified in an independent cohort of 74 women using absolute quantitation by targeted parallel reaction monitoring mass spectrometry (PRM-MS) which provided the capacity to distinguish between closely related protein isoforms. Logistic regression and Lasso feature selection were used to evaluate the performance of individual biomarkers and panels of multiple biomarkers to predict EP. RESULTS A total of 1391 proteins were identified in an unbiased plasma proteome discovery. A number of significant changes (FDR ≤ 5%) were identified when comparing EP vs. non-EP (IUP + EPL). Next, 14 candidate biomarkers (ADAM12, CGA, CGB, ISM2, NOTUM, PAEP, PAPPA, PSG1, PSG2, PSG3, PSG9, PSG11, PSG6/9, and PSG8/1) were verified as being significantly different between EP and non-EP in an independent cohort (FDR ≤ 5%). Using logistic regression models, a risk score for EP was calculated for each subject, and four multiple biomarker logistic models were identified that performed similarly and had higher AUCs than models with single predictors. CONCLUSIONS Overall, four multivariable logistic models were identified that had significantly better prediction of having EP than those logistic models with single biomarkers. Model 4 (NOTUM, PAEP, PAPPA, ADAM12) had the highest AUC (0.987) and accuracy (96%). However, because the models are statistically similar, all markers in the four models and other highly correlated markers should be considered in further validation studies.
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Affiliation(s)
- Lynn A Beer
- Molecular and Cellular Oncogenesis Program, The Wistar Institute, 3601 Spruce Street, Philadelphia, PA, 19104, USA
| | - Xiangfan Yin
- Molecular and Cellular Oncogenesis Program, The Wistar Institute, 3601 Spruce Street, Philadelphia, PA, 19104, USA
| | - Jianyi Ding
- Molecular and Cellular Oncogenesis Program, The Wistar Institute, 3601 Spruce Street, Philadelphia, PA, 19104, USA
| | - Suneeta Senapati
- Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, PA, USA
| | - Mary D Sammel
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA
| | - Kurt T Barnhart
- Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Qin Liu
- Molecular and Cellular Oncogenesis Program, The Wistar Institute, 3601 Spruce Street, Philadelphia, PA, 19104, USA.
| | - David W Speicher
- Molecular and Cellular Oncogenesis Program, The Wistar Institute, 3601 Spruce Street, Philadelphia, PA, 19104, USA.
| | - Aaron R Goldman
- Molecular and Cellular Oncogenesis Program, The Wistar Institute, 3601 Spruce Street, Philadelphia, PA, 19104, USA.
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Hasan MI, Rahman MH, Islam MB, Islam MZ, Hossain MA, Moni MA. Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19. Inform Med Unlocked 2022; 28:100840. [PMID: 34981034 DOI: 10.1016/j.imu.2021.100840] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 12/27/2021] [Indexed: 01/08/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease’s initiation and progression as well as developing drug therapeutics.
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Hampel H, Nisticò R, Seyfried NT, Levey AI, Modeste E, Lemercier P, Baldacci F, Toschi N, Garaci F, Perry G, Emanuele E, Valenzuela PL, Lucia A, Urbani A, Sancesario GM, Mapstone M, Corbo M, Vergallo A, Lista S. Omics sciences for systems biology in Alzheimer's disease: State-of-the-art of the evidence. Ageing Res Rev 2021; 69:101346. [PMID: 33915266 DOI: 10.1016/j.arr.2021.101346] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 04/06/2021] [Accepted: 04/22/2021] [Indexed: 12/12/2022]
Abstract
Alzheimer's disease (AD) is characterized by non-linear, genetic-driven pathophysiological dynamics with high heterogeneity in biological alterations and disease spatial-temporal progression. Human in-vivo and post-mortem studies point out a failure of multi-level biological networks underlying AD pathophysiology, including proteostasis (amyloid-β and tau), synaptic homeostasis, inflammatory and immune responses, lipid and energy metabolism, oxidative stress. Therefore, a holistic, systems-level approach is needed to fully capture AD multi-faceted pathophysiology. Omics sciences - genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics - embedded in the systems biology (SB) theoretical and computational framework can generate explainable readouts describing the entire biological continuum of a disease. Such path in Neurology is encouraged by the promising results of omics sciences and SB approaches in Oncology, where stage-driven pathway-based therapies have been developed in line with the precision medicine paradigm. Multi-omics data integrated in SB network approaches will help detect and chart AD upstream pathomechanistic alterations and downstream molecular effects occurring in preclinical stages. Finally, integrating omics and neuroimaging data - i.e., neuroimaging-omics - will identify multi-dimensional biological signatures essential to track the clinical-biological trajectories, at the subpopulation or even individual level.
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Gerdtsson AS, Wingren C, Persson H, Delfani P, Nordström M, Ren H, Wen X, Ringdahl U, Borrebaeck CAK, Hao J. Plasma protein profiling in a stage defined pancreatic cancer cohort - Implications for early diagnosis. Mol Oncol 2016; 10:1305-16. [PMID: 27522951 DOI: 10.1016/j.molonc.2016.07.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Revised: 07/01/2016] [Accepted: 07/04/2016] [Indexed: 12/30/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a disease where detection preceding clinical symptoms significantly increases the life expectancy of patients. In this study, a recombinant antibody microarray platform was used to analyze 213 Chinese plasma samples from PDAC patients and normal control (NC) individuals. The cohort was stratified according to disease stage, i.e. resectable disease (stage I/II), locally advanced (stage III) and metastatic disease (stage IV). Support vector machine analysis showed that all PDAC stages could be discriminated from controls and that the accuracy increased with disease progression, from stage I to IV. Patients with stage I/II PDAC could be discriminated from NC with high accuracy based on a plasma protein signature, indicating a possibility for early diagnosis and increased detection rate of surgically resectable tumors.
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Affiliation(s)
- Anna Sandström Gerdtsson
- Department of Immunotechnology, CREATE Health Translational Cancer Center, Medicon Village bldg. 406, Lund University, SE 223 81 Lund, Sweden.
| | - Christer Wingren
- Department of Immunotechnology, CREATE Health Translational Cancer Center, Medicon Village bldg. 406, Lund University, SE 223 81 Lund, Sweden.
| | - Helena Persson
- Department of Immunotechnology, CREATE Health Translational Cancer Center, Medicon Village bldg. 406, Lund University, SE 223 81 Lund, Sweden.
| | - Payam Delfani
- Department of Immunotechnology, CREATE Health Translational Cancer Center, Medicon Village bldg. 406, Lund University, SE 223 81 Lund, Sweden.
| | | | - He Ren
- Tianjin Medical University Cancer Institute & Hospital, Huan-Hu-Xi Road, Ti-Huan-Bei, He Xi District, Tianjin 300060, PR China.
| | - Xin Wen
- Tianjin Medical University Cancer Institute & Hospital, Huan-Hu-Xi Road, Ti-Huan-Bei, He Xi District, Tianjin 300060, PR China.
| | - Ulrika Ringdahl
- Department of Immunotechnology, CREATE Health Translational Cancer Center, Medicon Village bldg. 406, Lund University, SE 223 81 Lund, Sweden.
| | - Carl A K Borrebaeck
- Department of Immunotechnology, CREATE Health Translational Cancer Center, Medicon Village bldg. 406, Lund University, SE 223 81 Lund, Sweden.
| | - Jihui Hao
- Tianjin Medical University Cancer Institute & Hospital, Huan-Hu-Xi Road, Ti-Huan-Bei, He Xi District, Tianjin 300060, PR China.
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Melanie S, Benedikt K, Pfaffl MW, Irmgard R. The potential of circulating extracellular small RNAs (smexRNA) in veterinary diagnostics-Identifying biomarker signatures by multivariate data analysis. Biomol Detect Quantif 2015; 5:15-22. [PMID: 27077039 DOI: 10.1016/j.bdq.2015.08.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Revised: 08/03/2015] [Accepted: 08/07/2015] [Indexed: 02/05/2023]
Abstract
Worldwide growth and performance-enhancing substances are used in cattle husbandry to increase productivity. In certain countries however e.g., in the EU, these practices are forbidden to prevent the consumers from potential health risks of substance residues in food. To maximize economic profit, ‘black sheep‘ among farmers might circumvent the detection methods used in routine controls, which highlights the need for an innovative and reliable detection method. Transcriptomics is a promising new approach in the discovery of veterinary medicine biomarkers and also a missing puzzle piece, as up to date, metabolomics and proteomics are paramount. Due to increased stability and easy sampling, circulating extracellular small RNAs (smexRNAs) in bovine plasma were small RNA-sequenced and their potential to serve as biomarker candidates was evaluated using multivariate data analysis tools. After running the data evaluation pipeline, the proportion of miRNAs (microRNAs) and piRNAs (PIWI-interacting small non-coding RNAs) on the total sequenced reads was calculated. Additionally, top 10 signatures were compared which revealed that the readcount data sets were highly affected by the most abundant miRNA and piRNA profiles. To evaluate the discriminative power of multivariate data analyses to identify animals after veterinary drug application on the basis of smexRNAs, OPLS-DA was performed. In summary, the quality of miRNA models using all mapped reads for both treatment groups (animals treated with steroid hormones or the β-agonist clenbuterol) is predominant to those generated with combined data sets or piRNAs alone. Using multivariate projection methodologies like OPLS-DA have proven the best potential to generate discriminative miRNA models, supported by small RNA-Seq data. Based on the presented comparative OPLS-DA, miRNAs are the favorable smexRNA biomarker candidates in the research field of veterinary drug abuse.
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Key Words
- Biomarker signatures
- CLEN, treated group with clenbuterol-hydrochloride
- CON, control group
- Circulating small RNAs
- DA, discriminant analysis
- EU, European Union
- Multivariate data analysis
- OPLS, orthogonal partial least-squares
- PCA, principal component analysis
- PLS, partial least-squares projection
- P + EB, treated group with steroid hormone implant: progesterone plus estradiol benzoate
- Small RNA-Sequencing
- Transcriptomics
- Veterinary diagnostics
- exRNA, extracellular RNA
- miRNA, microRNA
- piRNA, PIWI-interacting small non-coding RNA
- rpm, reads per million
- small RNA-Seq, small RNA-Sequencing
- smexRNA, circulating extracellular small RNA
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