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Biomolecules
Special Issues
Proteomics and Metabolomics Biomarkers in Different Diseases
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Proteomics and Metabolomics Biomarkers in Different Diseases

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Molecular Biomarkers".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 11160

Special Issue Editors

Prof. Dr. Haiteng Deng

E-Mail Website
Guest Editor
School of Life Sciences, Tsinghua University, Beijing 100084, China
Interests: development of new techniques in proteomics, metabolomics, and chemical biology; investigation of redox regulation in aging and associated diseases; development of novel therapeutic and diagnostic approaches for aging associated diseases
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wei Ge

E-Mail Website
Guest Editor
Department of Immunology, Chinese Academy of Medical Sciences, Beijing, China
Interests: protein expression; RNA; protein purification; PCR; cloning; DNA; gene expression; molecular genetics; western blot analysis; cell culture
Dr. Xiaohui Liu

E-Mail Website
Guest Editor
National Protein Science Facility, Tsinghua University, Beijing, China
Interests: lipidomics and metabolomics based on mass spectrometry

Special Issue Information

Dear Colleagues,

Molecular biomarkers, which are usually proteins and metabolites in human blood or urine samples, provide precise evidence to explore mechanistic insights for various kinds of diseases. The application of clinical biomarkers advances diagnosis, prognosis, and prediction of curative effect in personalized medical treatment and drug therapy, where biomarker discovery serves as a fundamental research topic for the applications of biomarkers.

In biomarker discovery, proteomics and metabolomics have been widely applied by leveraging the broad molecular coverage and high sensitivity. Either proteomics or metabolomics focuses on the high-throughput profiling of biomolecules, which turn out to be proteins and small-molecule metabolites, respectively. Furthermore, regulated proteins or metabolites in body fluid are closely correlated with the occurrence and progression of various diseases and in turn not only unravel the underlying comprehensive mechanism, but also possess the potential to be valid biomarkers clinically. The recent technical improvement, including mass spectrometry and nuclear magnetic resonance, further facilitates the discovery of new biomarkers.

This Special Issue aims to attract original research papers and review articles that address foundational and translational studies on potential clinical biomarkers using proteomics or metabolomics, particularly with novel technical improvement. Work evaluating drug therapy with biomarkers is also welcome.

Prof. Dr. Haiteng Deng
Prof. Dr. Wei Ge
Dr. Xiaohui Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • proteomics
  • metabolomics
  • molecular biomarkers
  • proteins
  • metabolites

Published Papers (6 papers)

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Research

17 pages, 10293 KiB  
Article
Multidimensional Landscape of SA-AKI Revealed by Integrated Proteomics and Metabolomics Analysis
by Jiatong Xu, Jiaying Li, Yan Li, Xiaoxiao Shi, Huadong Zhu and Limeng Chen
Biomolecules 2023, 13(9), 1329; https://0-doi-org.brum.beds.ac.uk/10.3390/biom13091329 - 30 Aug 2023
Cited by 1 | Viewed by 1227
Abstract
Sepsis-associated acute kidney injury (SA-AKI) is a severe and life-threatening condition with high morbidity and mortality among emergency patients, and it poses a significant risk of chronic renal failure. Clinical treatments for SA-AKI remain reactive and non-specific, lacking effective diagnostic biomarkers or treatment [...] Read more.
Sepsis-associated acute kidney injury (SA-AKI) is a severe and life-threatening condition with high morbidity and mortality among emergency patients, and it poses a significant risk of chronic renal failure. Clinical treatments for SA-AKI remain reactive and non-specific, lacking effective diagnostic biomarkers or treatment targets. In this study, we established an SA-AKI mouse model using lipopolysaccharide (LPS) and performed proteomics and metabolomics analyses. A variety of bioinformatic analyses, including gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), protein and protein interactions (PPI), and MetaboAnalyst analysis, were conducted to investigate the key molecules of SA-AKI. Integrated proteomics and metabolomics analysis revealed that sepsis led to impaired renal mitochondrial function and metabolic disorders. Immune-related pathways were found to be activated in kidneys upon septic infection. The catabolic products of polyamines accumulated in septic kidneys. Overall, our integrated analysis provides a multidimensional understanding of SA-AKI and identifies potential pathways for this condition. Full article
(This article belongs to the Special Issue Proteomics and Metabolomics Biomarkers in Different Diseases)
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12 pages, 2007 KiB  
Article
Lipidomics Profiling and Risk of Coronary Artery Disease in the BioHEART-CT Discovery Cohort
by Dantong Zhu, Stephen T. Vernon, Zac D’Agostino, Jingqin Wu, Corey Giles, Adam S. Chan, Katharine A. Kott, Michael P. Gray, Alireza Gholipour, Owen Tang, Habtamu B. Beyene, Ellis Patrick, Stuart M. Grieve, Peter J. Meikle, Gemma A. Figtree and Jean Y. H. Yang
Biomolecules 2023, 13(6), 917; https://0-doi-org.brum.beds.ac.uk/10.3390/biom13060917 - 31 May 2023
Cited by 1 | Viewed by 1726
Abstract
The current coronary artery disease (CAD) risk scores for predicting future cardiovascular events rely on well-recognized traditional cardiovascular risk factors derived from a population level but often fail individuals, with up to 25% of first-time heart attack patients having no risk factors. Non-invasive [...] Read more.
The current coronary artery disease (CAD) risk scores for predicting future cardiovascular events rely on well-recognized traditional cardiovascular risk factors derived from a population level but often fail individuals, with up to 25% of first-time heart attack patients having no risk factors. Non-invasive imaging technology can directly measure coronary artery plaque burden. With an advanced lipidomic measurement methodology, for the first time, we aim to identify lipidomic biomarkers to enable intervention before cardiovascular events. With 994 participants from BioHEART-CT Discovery Cohort, we collected clinical data and performed high-performance liquid chromatography with mass spectrometry to determine concentrations of 683 plasma lipid species. Statin-naive participants were selected based on subclinical CAD (sCAD) categories as the analytical cohort (n = 580), with sCAD+ (n = 243) compared to sCAD− (n = 337). Through a machine learning approach, we built a lipid risk score (LRS) and compared the performance of the existing Framingham Risk Score (FRS) in predicting sCAD+. We obtained individual classifiability scores and determined Body Mass Index (BMI) as the modifying variable. FRS and LRS models achieved similar areas under the receiver operating characteristic curve (AUC) in predicting the validation cohort. LRS enhanced the prediction of sCAD+ in the healthy-weight group (BMI < 25 kg/m2), where FRS performed poorly and identified individuals at risk that FRS missed. Lipid features have strong potential as biomarkers to predict CAD plaque burden and can identify residual risk not captured by traditional risk factors/scores. LRS compliments FRS in prediction and has the most significant benefit in healthy-weight individuals. Full article
(This article belongs to the Special Issue Proteomics and Metabolomics Biomarkers in Different Diseases)
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21 pages, 2544 KiB  
Article
Systemic Metabolomic Profiles in Adult Patients with Bacterial Sepsis: Characterization of Patient Heterogeneity at the Time of Diagnosis
by Knut Anders Mosevoll, Bent Are Hansen, Ingunn Margareetta Gundersen, Håkon Reikvam, Øyvind Bruserud, Øystein Bruserud and Øystein Wendelbo
Biomolecules 2023, 13(2), 223; https://0-doi-org.brum.beds.ac.uk/10.3390/biom13020223 - 24 Jan 2023
Cited by 3 | Viewed by 1505
Abstract
Sepsis is a dysregulated host response to infection that causes potentially life-threatening organ dysfunction. We investigated the serum metabolomic profile at hospital admission for patients with bacterial sepsis. The study included 60 patients; 35 patients fulfilled the most recent 2016 Sepsis-3 criteria whereas [...] Read more.
Sepsis is a dysregulated host response to infection that causes potentially life-threatening organ dysfunction. We investigated the serum metabolomic profile at hospital admission for patients with bacterial sepsis. The study included 60 patients; 35 patients fulfilled the most recent 2016 Sepsis-3 criteria whereas the remaining 25 patients only fulfilled the previous Sepsis-2 criteria and could therefore be classified as having systemic inflammatory response syndrome (SIRS). A total of 1011 identified metabolites were detected in our serum samples. Ninety-seven metabolites differed significantly when comparing Sepsis-3 and Sepsis-2/SIRS patients; 40 of these metabolites constituted a heterogeneous group of amino acid metabolites/peptides. When comparing patients with and without bacteremia, we identified 51 metabolites that differed significantly, including 16 lipid metabolites and 11 amino acid metabolites. Furthermore, 42 metabolites showed a highly significant association with the maximal total Sequential Organ Failure Assessment (SOFA )score during the course of the disease (i.e., Pearson’s correlation test, p-value < 0.005, and correlation factor > 0.6); these top-ranked metabolites included 23 amino acid metabolites and a subset of pregnenolone/progestin metabolites. Unsupervised hierarchical clustering analyses based on all 42 top-ranked SOFA correlated metabolites or the subset of 23 top-ranked amino acid metabolites showed that most Sepsis-3 patients differed from Sepsis-2/SIRS patients in their systemic metabolic profile at the time of hospital admission. However, a minority of Sepsis-3 patients showed similarities with the Sepsis-2/SIRS metabolic profile even though several of them showed a high total SOFA score. To conclude, Sepsis-3 patients are heterogeneous with regard to their metabolic profile at the time of hospitalization. Full article
(This article belongs to the Special Issue Proteomics and Metabolomics Biomarkers in Different Diseases)
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12 pages, 1732 KiB  
Article
Heritability of Protein and Metabolite Biomarkers Associated with COVID-19 Severity: A Metabolomics and Proteomics Analysis
by Amelia K. Haj, Haytham Hasan and Thomas J. Raife
Biomolecules 2023, 13(1), 46; https://0-doi-org.brum.beds.ac.uk/10.3390/biom13010046 - 27 Dec 2022
Cited by 3 | Viewed by 2105
Abstract
Objectives: Prior studies have characterized protein and metabolite changes associated with SARS-CoV-2 infection; we hypothesized that these biomarkers may be part of heritable metabolic pathways in erythrocytes. Methods: Using a twin study of erythrocyte protein and metabolite levels, we describe the heritability of, [...] Read more.
Objectives: Prior studies have characterized protein and metabolite changes associated with SARS-CoV-2 infection; we hypothesized that these biomarkers may be part of heritable metabolic pathways in erythrocytes. Methods: Using a twin study of erythrocyte protein and metabolite levels, we describe the heritability of, and correlations among, previously identified biomarkers that correlate with COVID-19 severity. We used gene ontology and pathway enrichment analysis tools to identify pathways and biological processes enriched among these biomarkers. Results: Many COVID-19 biomarkers are highly heritable in erythrocytes. Among heritable metabolites downregulated in COVID-19, metabolites involved in amino acid metabolism and biosynthesis are enriched. Specific amino acid metabolism pathways (valine, leucine, and isoleucine biosynthesis; glycine, serine, and threonine metabolism; and arginine biosynthesis) are heritable in erythrocytes. Conclusions: Metabolic pathways downregulated in COVID-19, particularly amino acid biosynthesis and metabolism pathways, are heritable in erythrocytes. This finding suggests that a component of the variation in COVID-19 severity may be the result of phenotypic variation in heritable metabolic pathways; future studies will be necessary to determine whether individual variation in amino acid metabolism pathways correlates with heritable outcomes of COVID-19. Full article
(This article belongs to the Special Issue Proteomics and Metabolomics Biomarkers in Different Diseases)
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22 pages, 2096 KiB  
Article
Integrated Quantitative Targeted Lipidomics and Proteomics Reveal Unique Fingerprints of Multiple Metabolic Conditions
by Anna A. Ivanova, Jon C. Rees, Bryan A. Parks, Michael Andrews, Michael Gardner, Eunice Grigorutsa, Zsuzsanna Kuklenyik, James L. Pirkle and John R. Barr
Biomolecules 2022, 12(10), 1439; https://0-doi-org.brum.beds.ac.uk/10.3390/biom12101439 - 08 Oct 2022
Cited by 2 | Viewed by 2037
Abstract
Aberrations in lipid and lipoprotein metabolic pathways can lead to numerous diseases, including cardiovascular disease, diabetes, neurological disorders, and cancer. The integration of quantitative lipid and lipoprotein profiling of human plasma may provide a powerful approach to inform early disease diagnosis and prevention. [...] Read more.
Aberrations in lipid and lipoprotein metabolic pathways can lead to numerous diseases, including cardiovascular disease, diabetes, neurological disorders, and cancer. The integration of quantitative lipid and lipoprotein profiling of human plasma may provide a powerful approach to inform early disease diagnosis and prevention. In this study, we leveraged data-driven quantitative targeted lipidomics and proteomics to identify specific molecular changes associated with different metabolic risk categories, including hyperlipidemic, hypercholesterolemic, hypertriglyceridemic, hyperglycemic, and normolipidemic conditions. Based on the quantitative characterization of serum samples from 146 individuals, we have determined individual lipid species and proteins that were significantly up- or down-regulated relative to the normolipidemic group. Then, we established protein–lipid topological networks for each metabolic category and linked dysregulated proteins and lipids with defined metabolic pathways. To evaluate the differentiating power of integrated lipidomics and proteomics data, we have built an artificial neural network model that simultaneously and accurately categorized the samples from each metabolic risk category based on the determined lipidomics and proteomics profiles. Together, our findings provide new insights into molecular changes associated with metabolic risk conditions, suggest new condition-specific associations between apolipoproteins and lipids, and may inform new biomarker discovery in lipid metabolism-associated disorders. Full article
(This article belongs to the Special Issue Proteomics and Metabolomics Biomarkers in Different Diseases)
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14 pages, 3645 KiB  
Article
Identification of the Thyrotropin-Releasing Hormone (TRH) as a Novel Biomarker in the Prognosis for Acute Myeloid Leukemia
by Yan Gao, Jia-Fan Zhou, Jia-Ying Mao, Lu Jiang and Xue-Ping Li
Biomolecules 2022, 12(10), 1359; https://0-doi-org.brum.beds.ac.uk/10.3390/biom12101359 - 23 Sep 2022
Cited by 2 | Viewed by 1631
Abstract
Acute myeloid leukemia (AML) is a biologically and genetically heterogeneous hematological malignance with an unsatisfactory risk stratification system. Recently, through the novel single-cell RNA sequencing technology, we revealed heterogeneous leukemia myeloblasts in RUNX1-RUNX1T1 AML. Thyrotropin-releasing hormone (TRH), as biomarkers of CD34 [...] Read more.
Acute myeloid leukemia (AML) is a biologically and genetically heterogeneous hematological malignance with an unsatisfactory risk stratification system. Recently, through the novel single-cell RNA sequencing technology, we revealed heterogeneous leukemia myeloblasts in RUNX1-RUNX1T1 AML. Thyrotropin-releasing hormone (TRH), as biomarkers of CD34+CD117bri myeloblasts, were found to be prognostic in RUNX1-RUNX1T1 AML. However, the clinical and genetic features of TRH in AML patients are poorly understood. Here, with data from TCGA AML, TRH was found to be downregulated in patients older than 60 years old, with DNMT3A and NPM1 mutations, while overexpressed in patients with KIT mutations. This was further validated in three other cohorts of primary AML including Beat AML (n = 223), GSE6891 (n = 461), and GSE17855 (n = 237). Furthermore, we demonstrated that the expression of TRH in AML could be used to improve the ELN 2017 risk stratification system. In conclusion, our preliminary analysis revealed that TRH, a novel biomarker for AML patients, could be used to evaluate the survival of AML. Full article
(This article belongs to the Special Issue Proteomics and Metabolomics Biomarkers in Different Diseases)
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