1
|
Liang A, Zhao W, Lv T, Zhu Z, Haotian R, Zhang J, Xie B, Yi Y, Hao Z, Sun L, Luo A. Advances in novel biosensors in biomedical applications. Talanta 2024; 280:126709. [PMID: 39151317 DOI: 10.1016/j.talanta.2024.126709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 07/09/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024]
Abstract
Biosensors, devices capable of detecting biomolecules or bioactive substances, have recently become one of the important tools in the fields of bioanalysis and medical diagnostics. A biosensor is an analytical system composed of biosensitive elements and signal-processing elements used to detect various biological and chemical substances. Biomimetic elements are key to biosensor technology and are the components in a sensor that are responsible for identifying the target analyte. The construction methods and working principles of biosensors based on synthetic biomimetic elements, such as DNAzyme, molecular imprinted polymers and aptamers, and their updated applications in biomedical analysis are summarised. Finally, the technical bottlenecks and future development prospects for biomedical analysis are summarised and discussed.
Collapse
Affiliation(s)
- Axin Liang
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Weidong Zhao
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Tianjian Lv
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Ziyu Zhu
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Ruilin Haotian
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Jiangjiang Zhang
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Bingteng Xie
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Yue Yi
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Zikai Hao
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Liquan Sun
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Aiqin Luo
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China.
| |
Collapse
|
2
|
Chakraborty N, Dimitrov G, Kanan S, Lawrence A, Moyler C, Gautam A, Fatanmi OO, Wise SY, Carpenter AD, Hammamieh R, Singh VK. Cross-species conserved miRNA as biomarker of radiation injury over a wide dose range using nonhuman primate model. PLoS One 2024; 19:e0311379. [PMID: 39570918 PMCID: PMC11581275 DOI: 10.1371/journal.pone.0311379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Accepted: 09/18/2024] [Indexed: 11/24/2024] Open
Abstract
Multiple accidents in nuclear power plants and the growing concerns about the misuse of radiation exposure in warfare have called for the rapid determination of absorbed radiation doses (RDs). The latest findings about circulating microRNA (miRNAs) using several animal models revealed considerable promises, although translating this knowledge to clinics remains a major challenge. To address this issue, we randomly divided 36 nonhuman primates (NHPs) into six groups and exposed these groups to six different radiation doses ranging from 6.0-8.5 Gy in increments of 0.5 Gy. Serum samples were collected pre-irradiation as well as three post-irradiation timepoints, namely 1, 2 and 6 days post-total body irradiation (TBI). Generated from a deep sequencing platform, the miRNA reads were multi-variate analyzed to find the differentially expressed putative biomarkers that were linked to RDs, time since irradiation (TSI) and sex. To increase these biomarkers' translational potential, we aligned the NHP-miRNAs' sequences and their functional responses to humans following an in-silico routine. Those miRNAs, which were sequentially and functionally conserved between NHPs and humans, were down selected for further analysis. A linear regression model identified miRNA markers that were consistently regulated with increasing RD but independent TSI. Likewise, a set of potential TSI-markers were identified that consistently shifted with increasing TSI, but independent of RD. Additional molecular analysis found a considerable gender bias in the low-ranges of doses when the risk to radiation-induced fatality was low. Bionetworks linked to cell quantity and cell invasion were significantly altered between the survivors and decedents. Using these biomarkers, an assay could be developed to retrospectively determine the RD and TSI with high translational potential. Ultimately, this knowledge can lead to precise and personalized medicine.
Collapse
Affiliation(s)
- Nabarun Chakraborty
- Medical Readiness Systems Biology, CMPN, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
| | - George Dimitrov
- Medical Readiness Systems Biology, CMPN, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
- Vysnova, Inc., Landover, MD, United States of America
| | - Swapna Kanan
- Medical Readiness Systems Biology, CMPN, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
- Vysnova, Inc., Landover, MD, United States of America
| | - Alexander Lawrence
- Medical Readiness Systems Biology, CMPN, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
- Oak Ridge Institute for Science and Education (ORISE), MD, United States of America
| | - Candance Moyler
- Medical Readiness Systems Biology, CMPN, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
- Vysnova, Inc., Landover, MD, United States of America
| | - Aarti Gautam
- Medical Readiness Systems Biology, CMPN, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
| | - Oluseyi O. Fatanmi
- Division of Radioprotectants, Department of Pharmacology and Molecular Therapeutics, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America
- Armed Forces Radiobiology Research Institute, Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America
| | - Stephen Y. Wise
- Division of Radioprotectants, Department of Pharmacology and Molecular Therapeutics, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America
- Armed Forces Radiobiology Research Institute, Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America
| | - Alana D. Carpenter
- Division of Radioprotectants, Department of Pharmacology and Molecular Therapeutics, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America
- Armed Forces Radiobiology Research Institute, Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America
| | - Rasha Hammamieh
- Medical Readiness Systems Biology, CMPN, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
| | - Vijay K. Singh
- Division of Radioprotectants, Department of Pharmacology and Molecular Therapeutics, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America
- Armed Forces Radiobiology Research Institute, Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America
| |
Collapse
|
3
|
Ekman M, Salminen T, Raiko K, Soukka T, Gidwani K, Martiskainen I. Spectrally separated dual-label upconversion luminescence lateral flow assay for cancer-specific STn-glycosylation in CA125 and CA15-3. Anal Bioanal Chem 2024; 416:3251-3260. [PMID: 38584178 PMCID: PMC11068694 DOI: 10.1007/s00216-024-05275-z] [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: 01/17/2024] [Revised: 03/06/2024] [Accepted: 03/26/2024] [Indexed: 04/09/2024]
Abstract
Multiplexed lateral flow assays (LFAs) offer efficient on-site testing by simultaneously detecting multiple biomarkers from a single sample, reducing costs. In cancer diagnostics, where biomarkers can lack specificity, multiparameter detection provides more information at the point-of-care. Our research focuses on epithelial ovarian cancer (EOC), where STn-glycosylated forms of CA125 and CA15-3 antigens can better discriminate cancer from benign conditions. We have developed a dual-label LFA that detects both CA125-STn and CA15-3-STn within a single anti-STn antibody test line. This utilizes spectral separation of green (540 nm) and blue (450 nm) emitting erbium (NaYF4:Yb3+, Er3+)- and thulium (NaYF4: Yb3+, Tm3+)-doped upconverting nanoparticle (UCNP) reporters conjugated with antibodies against the protein epitopes in CA125 or CA15-3. This technology allows the simultaneous detection of different antigen variants from a single test line. The developed proof-of-concept dual-label LFA was able to distinguish between the ascites fluid samples from diagnosed ovarian cancer patients (n = 10) and liver cirrhosis ascites fluid samples (n = 3) used as a negative control. The analytical sensitivity of CA125-STn for the dual-label LFA was 1.8 U/ml in buffer and 3.6 U/ml in ascites fluid matrix. Here we demonstrate a novel approach of spectrally separated measurement of STn-glycosylated forms of two different cancer-associated protein biomarkers by using UCNP reporter technology.
Collapse
Affiliation(s)
- Miikka Ekman
- Biotechnology Unit, Department of Life Technologies, Faculty of Technology, University of Turku, Turku, Finland
| | - Teppo Salminen
- Biotechnology Unit, Department of Life Technologies, Faculty of Technology, University of Turku, Turku, Finland
| | - Kirsti Raiko
- Biotechnology Unit, Department of Life Technologies, Faculty of Technology, University of Turku, Turku, Finland
| | - Tero Soukka
- Biotechnology Unit, Department of Life Technologies, Faculty of Technology, University of Turku, Turku, Finland
| | - Kamlesh Gidwani
- Biotechnology Unit, Department of Life Technologies, Faculty of Technology, University of Turku, Turku, Finland
| | - Iida Martiskainen
- Biotechnology Unit, Department of Life Technologies, Faculty of Technology, University of Turku, Turku, Finland.
| |
Collapse
|
4
|
Alikiaii B, Bagherniya M, Askari G, Rajendram R, Sahebkar A. MicroRNA Profiles in Critically Ill Patients. Curr Med Chem 2024; 31:6801-6825. [PMID: 37496239 DOI: 10.2174/0929867331666230726095222] [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: 01/07/2023] [Revised: 05/19/2023] [Accepted: 06/01/2023] [Indexed: 07/28/2023]
Abstract
The use of biomarkers to expedite diagnosis, prognostication, and treatment could significantly improve patient outcomes. The early diagnosis and treatment of critical illnesses can greatly reduce mortality and morbidity. Therefore, there is great interest in the discovery of biomarkers for critical illnesses. Micro-ribonucleic acids (miRNAs) are a highly conserved group of non-coding RNA molecules. They regulate the expression of genes involved in several developmental, physiological, and pathological processes. The characteristics of miRNAs suggest that they could be versatile biomarkers. Assay panels to measure the expression of several miRNAs could facilitate clinical decision-- making for a range of diseases. We have, in this paper, reviewed the current understanding of the role of miRNAs as biomarkers in critically ill patients.
Collapse
Affiliation(s)
- Babak Alikiaii
- Anesthesia and Critical Care Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammad Bagherniya
- Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
- Department of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Gholamreza Askari
- Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
- Department of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Rajkumar Rajendram
- Department of Medicine, King Abdulaziz Medical City, King Abdulaziz International Medical Research Center, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
- College of Medicine, King Saud bin Abdulaziz University of Health Sciences, Riyadh, Saudi Arabia
| | - Amirhossein Sahebkar
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
- Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Biotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
| |
Collapse
|
5
|
van Lier D, Beunders R, Kox M, Pickkers P. Associations of dipeptidyl-peptidase 3 with short-term outcome in a mixed admission ICU-cohort. J Crit Care 2023; 78:154383. [PMID: 37482013 DOI: 10.1016/j.jcrc.2023.154383] [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: 05/10/2023] [Revised: 07/14/2023] [Accepted: 07/16/2023] [Indexed: 07/25/2023]
Abstract
PURPOSE Biomarkers independently associated with outcome of intensive care unit (ICU) patients can improve risk assessment. The cytosolic protease dipeptidyl-peptidase 3 (DPP3) is released into the circulation upon cell necrosis. We aimed to investigate the prognostic properties of cDPP3 in a mixed-admission ICU cohort. MATERIALS AND METHODS Prospective observational study in 650 adult ICU patients. cDPP3 concentrations were measured at ICU admission (day 1), and on days 2 and 3. RESULTS cDPP3 concentrations on days 1 and 2, but not on day 3 were associated with 28-day mortality; HR 1.36 (95%CI 1.01-1.83, p = 0.043) and HR 1.49 (95%CI 1.16-1.93, p = 0.002) for days 1 and 2, respectively. cDPP3 was also associated with acute kidney injury (AKI), with OR's of 1.31 (95%CI 1.05-1.64, p = 0.016), 1.87 (95%CI 1.51-2.34, p < 0.001) and 1.49 (95%CI 1.16-1.92, p = 0.002) for measurements performed on days 1, 2, and 3, respectively. In multivariate analyses including SOFA or APACHE-II scores, cDPP3 assessed at day 2 of admission remained an independent predictor of mortality and all-stage AKI. CONCLUSIONS In a mixed-ICU cohort, cDPP3 concentrations after start of initial treatment were independently associated with both mortality and development of AKI. Therefore, measurement of cDPP3 can improve risk-stratification provided by established disease severity scores.
Collapse
Affiliation(s)
- Dirk van Lier
- Department of Intensive Care Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, the Netherlands
| | - Remi Beunders
- Department of Intensive Care Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, the Netherlands
| | - Matthijs Kox
- Department of Intensive Care Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, the Netherlands
| | - Peter Pickkers
- Department of Intensive Care Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, the Netherlands.
| |
Collapse
|
6
|
Lee MY, Son M, Lee HH, Kang MG, Yun SJ, Seo HG, Kim Y, Oh BM. Proteomic discovery of prognostic protein biomarkers for persisting problems after mild traumatic brain injury. Sci Rep 2023; 13:19786. [PMID: 37957236 PMCID: PMC10643618 DOI: 10.1038/s41598-023-45965-9] [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: 07/04/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023] Open
Abstract
Some individuals with mild traumatic brain injury (mTBI), also known as concussion, have neuropsychiatric and physical problems that last longer than a few months. Symptoms following mTBI are not only impacted by the kind and severity of the injury but also by the post-injury experience and the individual's responses to it, making the persistence of mTBI particularly difficult to predict. We aimed to identify prognostic blood-based protein biomarkers predicting 6-month outcomes, in light of the clinical course after the injury, in a longitudinal mTBI cohort (N = 42). Among 420 target proteins quantified by multiple-reaction monitoring-mass spectrometry assays of blood samples, 31, 43, and 15 proteins were significantly associated with the poor recovery of neuropsychological symptoms at < 72 h, 1 week, and 1 month after the injury, respectively. Sequential associations among clinical assessments (depressive symptoms and cognitive function) affecting the 6-month outcomes were evaluated. Then, candidate biomarker proteins indirectly affecting the outcome via neuropsychological symptoms were identified. Using the identified proteins, prognostic models that can predict the 6-month outcome of mTBI were developed. These protein biomarkers established in the context of the clinical course of mTBI may have potential for clinical application.
Collapse
Affiliation(s)
- Min-Yong Lee
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Rehabilitation Medicine, National Traffic Injury Rehabilitation Hospital, Yangpyeong, Korea
| | - Minsoo Son
- Interdisciplinary Program of Bioengineering, Seoul National University College of Engineering, Seoul, Korea
- Mass Spectrometry Technology Access Center, McDonnell Genome Institute, Washington University School of Medicine in Saint Louis, St. Louis, MO, USA
| | - Hyun Haeng Lee
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Rehabilitation Medicine, Konkuk University School of Medicine and Konkuk University Medical Center, Seoul, Korea
| | - Min-Gu Kang
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Korea
| | - Seo Jung Yun
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Korea
| | - Han Gil Seo
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Youngsoo Kim
- Interdisciplinary Program of Bioengineering, Seoul National University College of Engineering, Seoul, Korea.
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea.
- Department of Biomedical Science, School of Medicine, CHA University, Seongnam-si, Kyeonggi-do, Korea.
| | - Byung-Mo Oh
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Korea.
- Department of Rehabilitation Medicine, National Traffic Injury Rehabilitation Hospital, Yangpyeong, Korea.
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, Korea.
- Institute on Aging, Seoul National University, Seoul, Korea.
| |
Collapse
|
7
|
Huang HJ, Chou CL, Sandar TT, Liu WC, Yang HC, Lin YC, Zheng CM, Chiu HW. Currently Used Methods to Evaluate the Efficacy of Therapeutic Drugs and Kidney Safety. Biomolecules 2023; 13:1581. [PMID: 38002263 PMCID: PMC10669823 DOI: 10.3390/biom13111581] [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] [Received: 08/26/2023] [Revised: 10/22/2023] [Accepted: 10/24/2023] [Indexed: 11/26/2023] Open
Abstract
Kidney diseases with kidney failure or damage, such as chronic kidney disease (CKD) and acute kidney injury (AKI), are common clinical problems worldwide and have rapidly increased in prevalence, affecting millions of people in recent decades. A series of novel diagnostic or predictive biomarkers have been discovered over the past decade, enhancing the investigation of renal dysfunction in preclinical studies and clinical risk assessment for humans. Since multiple causes lead to renal failure, animal studies have been extensively used to identify specific disease biomarkers for understanding the potential targets and nephropathy events in therapeutic insights into disease progression. Mice are the most commonly used model to investigate the mechanism of human nephropathy, and the current alternative methods, including in vitro and in silico models, can offer quicker, cheaper, and more effective methods to avoid or reduce the unethical procedures of animal usage. This review provides modern approaches, including animal and nonanimal assays, that can be applied to study chronic nonclinical safety. These specific situations could be utilized in nonclinical or clinical drug development to provide information on kidney disease.
Collapse
Affiliation(s)
- Hung-Jin Huang
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan (C.-L.C.)
| | - Chu-Lin Chou
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan (C.-L.C.)
- Division of Nephrology, Department of Internal Medicine, Hsin Kuo Min Hospital, Taipei Medical University, Taoyuan City 320, Taiwan
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 110, Taiwan
| | - Tin Tin Sandar
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Wen-Chih Liu
- Department of Biology and Anatomy, National Defense Medical Center, Taipei 114, Taiwan
- Section of Nephrology, Department of Medicine, Antai Medical Care Corporation Antai Tian-Sheng Memorial Hospital, Pingtung 928, Taiwan
| | - Hsiu-Chien Yang
- Division of Nephrology, Department of Internal Medicine, Zuoying Branch of Kaohsiung Armed Forces General Hospital, Kaohsiung 813, Taiwan
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Yen-Chung Lin
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan (C.-L.C.)
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 110, Taiwan
- Division of Nephrology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan
| | - Cai-Mei Zheng
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan (C.-L.C.)
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 110, Taiwan
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235, Taiwan
| | - Hui-Wen Chiu
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 110, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Department of Medical Research, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235, Taiwan
- Ph.D. Program in Drug Discovery and Development Industry, College of Pharmacy, Taipei Medical University, Taipei 110, Taiwan
| |
Collapse
|
8
|
Peng Y, Wu Q, Liu H, Zhang J, Han Q, Yin F, Wang L, Chen Q, Zhang F, Feng C, Zhu H. An immune-related gene signature predicts the 28-day mortality in patients with sepsis. Front Immunol 2023; 14:1152117. [PMID: 37033939 PMCID: PMC10076848 DOI: 10.3389/fimmu.2023.1152117] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 03/13/2023] [Indexed: 04/11/2023] Open
Abstract
Introduction Sepsis is the leading cause of death in intensive care units and is characterized by multiple organ failure, including dysfunction of the immune system. In the present study, we performed an integrative analysis on publicly available datasets to identify immune-related genes (IRGs) that may play vital role in the pathological process of sepsis, based on which a prognostic IRG signature for 28-day mortality prediction in patients with sepsis was developed and validated. Methods Weighted gene co-expression network analysis (WGCNA), Cox regression analysis and least absolute shrinkage and selection operator (LASSO) estimation were used to identify functional IRGs and construct a model for predicting the 28-day mortality. The prognostic value of the model was validated in internal and external sepsis datasets. The correlations of the IRG signature with immunological characteristics, including immune cell infiltration and cytokine expression, were explored. We finally validated the expression of the three IRG signature genes in blood samples from 12 sepsis patients and 12 healthy controls using qPCR. Results We established a prognostic IRG signature comprising three gene members (LTB4R, HLA-DMB and IL4R). The IRG signature demonstrated good predictive performance for 28-day mortality on the internal and external validation datasets. The immune infiltration and cytokine analyses revealed that the IRG signature was significantly associated with multiple immune cells and cytokines. The molecular pathway analysis uncovered ontology enrichment in myeloid cell differentiation and iron ion homeostasis, providing clues regarding the underlying biological mechanisms of the IRG signature. Finally, qPCR detection verified the differential expression of the three IRG signature genes in blood samples from 12 sepsis patients and 12 healthy controls. Discussion This study presents an innovative IRG signature for 28-day mortality prediction in sepsis patients, which may be used to facilitate stratification of risky sepsis patients and evaluate patients' immune state.
Collapse
Affiliation(s)
- Yaojun Peng
- Department of Graduate Administration, Medical School of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
- Department of Emergency, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Qiyan Wu
- Institute of Oncology, The Fifth Medical Centre, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Hongyu Liu
- Department of Graduate Administration, Medical School of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
- Department of Neurosurgery, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
- Department of Neurosurgery, Hainan Hospital of Chinese People's Liberation Army (PLA) General Hospital, Sanya, Hainan, China
| | - Jinying Zhang
- Department of Basic Medicine, Medical School of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Qingru Han
- Department of Emergency, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Fan Yin
- Department of Oncology, The Second Medical Center & National Clinical Research Center of Geriatric Disease, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Lingxiong Wang
- Institute of Oncology, The Fifth Medical Centre, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Qi Chen
- Department of Traditional Chinese Medicine, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Fei Zhang
- Department of Emergency, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
- *Correspondence: Fei Zhang, ; Cong Feng, ; Haiyan Zhu,
| | - Cong Feng
- Department of Emergency, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
- *Correspondence: Fei Zhang, ; Cong Feng, ; Haiyan Zhu,
| | - Haiyan Zhu
- Department of Emergency, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
- *Correspondence: Fei Zhang, ; Cong Feng, ; Haiyan Zhu,
| |
Collapse
|
9
|
Li D, Liang J, Guo W, Zhang Y, Wu X, Zhang W. Integrative analysis of DNA methylation and gene expression data for the diagnosis and underlying mechanism of Parkinson’s disease. Front Aging Neurosci 2022; 14:971528. [PMID: 36062142 PMCID: PMC9434001 DOI: 10.3389/fnagi.2022.971528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundParkinson’s disease (PD) is the second most common progressive neurodegenerative disorder and the leading cause of disability in the daily activities. In the management of PD, accurate and specific biomarkers in blood for the early diagnosis of PD are urgently needed. DNA methylation is one of the main epigenetic mechanisms and associated with the gene expression and disease initiation of PD. We aimed to construct a methylation signature for the diagnosis of PD patients, and explore the potential value of DNA methylation in therapeutic options.Materials and methodsWhole blood DNA methylation and gene expression data of PD patients as well as healthy controls were extracted from Gene Expression Omnibus database. Next, differentially expressed genes (DEGs) and differentially methylated genes (DMGs) between PD patients and healthy controls were identified. Least absolute shrinkage and selection operator cox regression analysis was carried out to construct a diagnostic signature based on the overlapped genes. And, the receiver operating characteristic (ROC) curves were drawn and the area under the curve (AUC) was used to assess the diagnostic performance of the signature in both the training and testing datasets. Finally, gene ontology and gene set enrichment analysis were subsequently carried out to explore the underlying mechanisms.ResultsWe obtained a total of 9,596 DMGs, 1,058 DEGs, and 237 overlapped genes in the whole blood between PD patients and healthy controls. Eight methylation-driven genes (HIST1H4L, CDC42EP3, KIT, GNLY, SLC22A1, GCM1, INO80B, and ARHGAP26) were identified to construct the gene expression signature. The AUCs in predicting PD patients were 0.84 and 0.76 in training dataset and testing dataset, respectively. Additionally, eight methylation-altered CpGs were also identified to construct the CpGs signature which showed a similarly robust diagnostic capability, with AUCs of 0.8 and 0.73 in training dataset and testing dataset, respectively.ConclusionWe conducted an integrated analysis of the gene expression and DNA methylation data, and constructed a methylation-driven genes signature and a methylation-altered CpGs signature to distinguish the patients with PD from healthy controls. Both of them had a robust prediction power and provide a new insight into personalized diagnostic and therapeutic strategies for PD.
Collapse
Affiliation(s)
- Ding Li
- Department of Pharmacy, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
- Henan Engineering Research Center for Tumor Precision Medicine and Comprehensive Evaluation, Henan Cancer Hospital, Zhengzhou, China
- Henan Provincial Key Laboratory of Anticancer Drug Research, Henan Cancer Hospital, Zhengzhou, China
| | - Jiaming Liang
- Department of Internal Medicine, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wenbin Guo
- Department of Pathology, Pingtan Comprehensive Experimental Area Hospital, Fuzhou, China
| | - Yongna Zhang
- Department of Pharmacy, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
- Henan Engineering Research Center for Tumor Precision Medicine and Comprehensive Evaluation, Henan Cancer Hospital, Zhengzhou, China
- Henan Provincial Key Laboratory of Anticancer Drug Research, Henan Cancer Hospital, Zhengzhou, China
| | - Xuan Wu
- Academy of Medical Science, Zhengzhou University, Zhengzhou, China
- *Correspondence: Wenzhou Zhang,
| | - Wenzhou Zhang
- Department of Pharmacy, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
- Henan Engineering Research Center for Tumor Precision Medicine and Comprehensive Evaluation, Henan Cancer Hospital, Zhengzhou, China
- Henan Provincial Key Laboratory of Anticancer Drug Research, Henan Cancer Hospital, Zhengzhou, China
- Xuan Wu,
| |
Collapse
|
10
|
Pediatric sepsis biomarkers for prognostic and predictive enrichment. Pediatr Res 2022; 91:283-288. [PMID: 34127800 PMCID: PMC8202042 DOI: 10.1038/s41390-021-01620-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/27/2021] [Accepted: 05/31/2021] [Indexed: 12/29/2022]
Abstract
Sepsis is a major public health problem in children throughout the world. Given that the treatment guidelines emphasize early recognition, there is interest in developing biomarkers of sepsis, and most attention is focused on diagnostic biomarkers. While there is a need for ongoing discovery and development of diagnostic biomarkers for sepsis, this review will focus on less well-known applications of sepsis biomarkers. Among patients with sepsis, the biomarkers can give information regarding the risk of poor outcome from sepsis, risk of sepsis-related organ dysfunction, and subgroups of patients with sepsis who share underlying biological features potentially amenable to targeted therapeutics. These types of biomarkers, beyond the traditional concept of diagnosis, address the important concepts of prognostic and predictive enrichment, which are key components of bringing the promise of precision medicine to the bedside of children with sepsis.
Collapse
|
11
|
Lê Cao KA, Abadi AJ, Davis-Marcisak EF, Hsu L, Arora A, Coullomb A, Deshpande A, Feng Y, Jeganathan P, Loth M, Meng C, Mu W, Pancaldi V, Sankaran K, Righelli D, Singh A, Sodicoff JS, Stein-O’Brien GL, Subramanian A, Welch JD, You Y, Argelaguet R, Carey VJ, Dries R, Greene CS, Holmes S, Love MI, Ritchie ME, Yuan GC, Culhane AC, Fertig E. Community-wide hackathons to identify central themes in single-cell multi-omics. Genome Biol 2021; 22:220. [PMID: 34353350 PMCID: PMC8340473 DOI: 10.1186/s13059-021-02433-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Kim-Anh Lê Cao
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Al J. Abadi
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Emily F. Davis-Marcisak
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Lauren Hsu
- Data Science, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Genetics, UNC, Chapel Hill, NC USA
| | - Arshi Arora
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Alexis Coullomb
- Centre de Recherches en Cancérologie de Toulouse (INSERM), Université Paul Sabatier III, Toulouse, France
| | - Atul Deshpande
- Cancer Convergence Institute and Division of Quantitative Sciences, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Yuzhou Feng
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | | | - Melanie Loth
- Cancer Convergence Institute and Division of Quantitative Sciences, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Chen Meng
- Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Wancen Mu
- Department of Biostatistics, UNC, Chapel Hill, NC USA
| | - Vera Pancaldi
- Centre de Recherches en Cancérologie de Toulouse (INSERM), Université Paul Sabatier III, Toulouse, France
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Kris Sankaran
- Department of Statistics, University of Wisconsin, Madison, WI USA
| | - Dario Righelli
- Department of Statistical Sciences, University of Padova, Padova, PD Italy
| | - Amrit Singh
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC Canada
- PROOF Centre of Excellence, Vancouver, BC Canada
| | - Joshua S. Sodicoff
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI USA
| | - Genevieve L. Stein-O’Brien
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD USA
- Cancer Convergence Institute and Division of Quantitative Sciences, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD USA
| | | | - Joshua D. Welch
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI USA
| | - Yue You
- Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, University of Melbourne, Melbourne, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, Australia
| | | | - Vincent J. Carey
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Ruben Dries
- Department of Hematology and Oncology, Boston Medical Center, Boston, MA USA
- Department of Computational Biomedicine, Boston University School of Medicine, Boston, MA USA
- Center for Regenerative Medicine (CReM), Boston University, Boston, MA USA
| | - Casey S. Greene
- Center for Health AI and Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO USA
| | - Susan Holmes
- Department of Statistics, Stanford University, Stanford, CA USA
| | - Michael I. Love
- Department of Biostatistics, UNC, Chapel Hill, NC USA
- Department of Genetics, UNC, Chapel Hill, NC USA
| | - Matthew E. Ritchie
- Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, University of Melbourne, Melbourne, Australia
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, Australia
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Aedin C. Culhane
- Data Science, Dana-Farber Cancer Institute, Boston, MA USA
- Biostatistics, Harvard TH Chan School of Public Health, Boston, MA USA
| | - Elana Fertig
- Cancer Convergence Institute and Division of Quantitative Sciences, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering, Baltimore, MD USA
| |
Collapse
|
12
|
Circulating biomarkers to assess cardiovascular function in critically ill. Curr Opin Crit Care 2021; 27:261-268. [PMID: 33899816 DOI: 10.1097/mcc.0000000000000829] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
PURPOSE OF REVIEW Circulatory shock is one of the most common reasons for ICU admission. Mortality rates in excess of 40% necessitate the rapid identification of high-risk patients, as well as the early assessment of effects of initiated treatments. There is an unmet medical need for circulating biomarkers that may improve patient stratification, predict responses to treatment interventions and may even be a target for novel therapies, enabling a better biological rationale to personalize therapy. RECENT FINDINGS Apart from established biomarkers such as lactate, ScvO2 or NT-pro-BNP, novel biomarkers, including adrenomedullin, angiopoietins, angiotensin I/II ratios, renin and DPP3 show promise, as they are all associated with well defined, therapeutically addressable molecular pathways that are dysregulated during circulatory shock. Although some of the therapies related to these biomarkers are still in preclinical stages of development, they may represent personalized treatment opportunities for patients in circulatory shock. SUMMARY From a molecular perspective, shock represents a highly heterologous syndrome, in which multiple unique pathways are dysregulated. Assessment of the status of these pathways with circulating biomarkers may provide a unique opportunity to detect specific phenotypes and implement personalized medicine in the treatment of circulatory shock.
Collapse
|
13
|
Vecchié A, Bonaventura A, Meessen J, Novelli D, Minetti S, Elia E, Ferrara D, Ansaldo AM, Scaravilli V, Villa S, Ferla L, Caironi P, Latini R, Carbone F, Montecucco F. PCSK9 is associated with mortality in patients with septic shock: data from the ALBIOS study. J Intern Med 2021; 289:179-192. [PMID: 32686253 DOI: 10.1111/joim.13150] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 05/31/2020] [Accepted: 06/05/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND Pro-protein convertase subtilisin/kexin 9 (PCSK9) is a proenzyme primarily known to regulate low-density lipoprotein receptor re-uptake on hepatocytes. Whether PCSK9 can concurrently trigger inflammation or not remains unclear. Here, we investigated the potential association between circulating levels of PCSK9 and mortality in patients with severe sepsis or septic shock. METHODS Plasma PCSK9 levels at days 1, 2 and 7 were measured in 958 patients with severe sepsis or septic shock previously enrolled in the Albumin Italian Outcome Sepsis (ALBIOS) trial. Correlations between levels of PCSK9 and pentraxin 3 (PTX3), a biomarker of disease severity, were evaluated with ranked Spearman's coefficients. Cox proportional hazards models were used to assess the association of PCSK9 levels at day 1 with 28- and 90-day mortality. RESULTS Median plasma PCSK9 levels were 278 [182-452] ng mL-1 on day 1. PCSK9 correlated positively with PTX3 at the three time-points, and patients with septic shock within the first quartile of PCSK9 showed higher levels of PTX3. Similar mortality rates were observed in patients with severe sepsis across PCSK9 quartiles. Patients with septic shock with lower PCSK9 levels on day 1 (within the first quartile) showed the highest 28- and 90-day mortality rate as compared to other quartiles. CONCLUSION In our sub-analysis of the ALBIOS trial, we found that patients with septic shock presenting with lower plasma PCSK9 levels experienced higher mortality rate. Further studies are warranted to better evaluate the pathophysiological role of PCSK9 in sepsis.
Collapse
Affiliation(s)
- A Vecchié
- From the, First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, Genoa, Italy.,Pauley Heart Center, Division of Cardiology, Department of Internal Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - A Bonaventura
- From the, First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, Genoa, Italy.,Pauley Heart Center, Division of Cardiology, Department of Internal Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - J Meessen
- Department of Cardiovascular Medicine, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - D Novelli
- Department of Cardiovascular Medicine, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - S Minetti
- From the, First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino Genova - Italian Cardiovascular Network, Genoa, Italy
| | - E Elia
- From the, First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, Genoa, Italy
| | - D Ferrara
- From the, First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, Genoa, Italy
| | - A M Ansaldo
- From the, First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, Genoa, Italy
| | - V Scaravilli
- Department of Anesthesia, Critical Care and Emergency, Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Milano, Italy
| | - S Villa
- Dipartimento di Anestesia e Rianimazione, Università degli Studi Milano Bicocca, ASST Monza, Monza, Italy
| | - L Ferla
- Dipartimento Emergenza Urgenza - Rianimazione, Azienda Socio Sanitaria Territoriale - Ovest Milanese, Ospedale di Legnano, Legnano, Italy
| | - P Caironi
- Department of Anesthesia and Critical Care, AOU San Luigi Gonzaga, Department of Oncology, University of Turin, Orbassano, Turin, Italy
| | - R Latini
- Department of Cardiovascular Medicine, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - F Carbone
- From the, First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino Genova - Italian Cardiovascular Network, Genoa, Italy
| | - F Montecucco
- IRCCS Ospedale Policlinico San Martino Genova - Italian Cardiovascular Network, Genoa, Italy.,First Clinic of Internal Medicine, Department of Internal Medicine and Centre of Excellence for Biomedical Research (CEBR), University of Genoa, Genoa, Italy
| | | |
Collapse
|
14
|
Bonaventura A, Carbone F, Vecchié A, Meessen J, Ferraris S, Beck E, Keim R, Minetti S, Elia E, Ferrara D, Ansaldo AM, Novelli D, Caironi P, Latini R, Montecucco F. The role of resistin and myeloperoxidase in severe sepsis and septic shock: Results from the ALBIOS trial. Eur J Clin Invest 2020; 50:e13333. [PMID: 32585739 DOI: 10.1111/eci.13333] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 06/12/2020] [Accepted: 06/14/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND Inflammatory biomarkers are useful in detecting patients with sepsis. The prognostic role of resistin and myeloperoxidase (MPO) has been investigated in sepsis. MATERIALS AND METHODS Plasma resistin and MPO were measured on days 1, 2 and 7 in 957 patients enrolled in the Albumin Italian Outcome Sepsis (ALBIOS) trial. The association between resistin and MPO levels on day 1, 2 and 7 and 90-day mortality was assessed. RESULTS Plasma resistin and MPO concentrations were higher at day 1 and decreased until day 7. Both biomarkers were positively correlated with each other and with physiological parameters. Higher levels of resistin and MPO on day 1 were associated with the development of new organ failures. Patients experiencing death at 90 days showed higher levels of resistin and MPO compared with survivors. At day 1, only MPO in the 4th quartile (Q4), but not resistin, was found to predict 90-day death (adjusted hazard ratio [aHR] 1.55 vs Q1). At day 2, resistin in the Q3 and Q4 predicted a > 40% increase in mortality as also did MPO in the Q4. On day 7, Q4 resistin was able to predict 90-day mortality, while all quartiles of MPO were not. CONCLUSIONS High levels of MPO, but not of resistin, on day 1 were able to predict 90-day mortality. These findings may either suggest that early hyper-activation of neutrophils is detrimental in patients with sepsis or reflect the burden of the inflammatory process caused by sepsis. Further studies are warranted to deepen these aspects (ALBIOS ClinicalTrials.gov Identifier: NCT00707122).
Collapse
Affiliation(s)
- Aldo Bonaventura
- First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, Genoa, Italy.,Pauley Heart Center, Division of Cardiology, Department of Internal Medicine, Virginia Commonwealth University, 1200 E Marshall St, Richmond, VA, 23298, USA
| | - Federico Carbone
- First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino Genova-Italian Cardiovascular Network, Genoa, Italy
| | - Alessandra Vecchié
- First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, Genoa, Italy.,Pauley Heart Center, Division of Cardiology, Department of Internal Medicine, Virginia Commonwealth University, 1200 E Marshall St, Richmond, VA, 23298, USA
| | - Jennifer Meessen
- Department of Cardiovascular Medicine, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | | | | | - Roberto Keim
- UOC Anestesia Rianimazione e Terapia Intensiva - ASST Bergamo Est - Ospedale Bolognini di Seriate, Seriate, Italy
| | - Silvia Minetti
- First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, Genoa, Italy
| | - Edoardo Elia
- First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, Genoa, Italy
| | - Daniele Ferrara
- First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, Genoa, Italy
| | - Anna Maria Ansaldo
- First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, Genoa, Italy
| | - Deborah Novelli
- Department of Cardiovascular Medicine, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Pietro Caironi
- SCDU Anestesia e Rianimazione, Azienda Ospedaliero-Universitaria S. Luigi Gonzaga, Orbassano, Italy.,Dipartimento di Oncologia, Università degli Studi di Torino, Turin, Italy
| | - Roberto Latini
- Department of Cardiovascular Medicine, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Fabrizio Montecucco
- IRCCS Ospedale Policlinico San Martino Genova-Italian Cardiovascular Network, Genoa, Italy.,First Clinic of Internal Medicine, Department of Internal Medicine and Centre of Excellence for Biomedical Research (CEBR), University of Genoa, Genoa, Italy
| |
Collapse
|
15
|
Abbas M, El-Manzalawy Y. Machine learning based refined differential gene expression analysis of pediatric sepsis. BMC Med Genomics 2020; 13:122. [PMID: 32859206 PMCID: PMC7453705 DOI: 10.1186/s12920-020-00771-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 08/19/2020] [Indexed: 12/18/2022] Open
Abstract
Background Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentially expressed between two or more groups. In general, identified differentially expressed genes (DEGs) can be subject to further downstream analysis for obtaining more biological insights such as determining enriched functional pathways or gene ontologies. Furthermore, DEGs are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches. Methods In this work, we present a novel approach for identifying biomarkers from a list of DEGs by re-ranking them according to the Minimum Redundancy Maximum Relevance (MRMR) criteria using repeated cross-validation feature selection procedure. Results Using gene expression profiles for 199 children with sepsis and septic shock, we identify 108 DEGs and propose a 10-gene signature for reliably predicting pediatric sepsis mortality with an estimated Area Under ROC Curve (AUC) score of 0.89. Conclusions Machine learning based refinement of DE analysis is a promising tool for prioritizing DEGs and discovering biomarkers from gene expression profiles. Moreover, our reported 10-gene signature for pediatric sepsis mortality may facilitate the development of reliable diagnosis and prognosis biomarkers for sepsis.
Collapse
Affiliation(s)
- Mostafa Abbas
- Department of Imaging Science and Innovation, Geisinger Health System, Danville, PA, 17822, USA
| | - Yasser El-Manzalawy
- Department of Imaging Science and Innovation, Geisinger Health System, Danville, PA, 17822, USA. .,Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, PA, 17822, USA.
| |
Collapse
|
16
|
Stoppe C, Wendt S, Mehta NM, Compher C, Preiser JC, Heyland DK, Kristof AS. Biomarkers in critical care nutrition. Crit Care 2020; 24:499. [PMID: 32787899 PMCID: PMC7425162 DOI: 10.1186/s13054-020-03208-7] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/27/2020] [Indexed: 02/07/2023] Open
Abstract
The goal of nutrition support is to provide the substrates required to match the bioenergetic needs of the patient and promote the net synthesis of macromolecules required for the preservation of lean mass, organ function, and immunity. Contemporary observational studies have exposed the pervasive undernutrition of critically ill patients and its association with adverse clinical outcomes. The intuitive hypothesis is that optimization of nutrition delivery should improve ICU clinical outcomes. It is therefore surprising that multiple large randomized controlled trials have failed to demonstrate the clinical benefit of restoring or maximizing nutrient intake. This may be in part due to the absence of biological markers that identify patients who are most likely to benefit from nutrition interventions and that monitor the effects of nutrition support. Here, we discuss the need for practical risk stratification tools in critical care nutrition, a proposed rationale for targeted biomarker development, and potential approaches that can be adopted for biomarker identification and validation in the field.
Collapse
Affiliation(s)
- Christian Stoppe
- 3CARE—Cardiovascular Critical Care & Anesthesia Evaluation and Research, Aachen, Germany
- Department of Anesthesiology, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Sebastian Wendt
- 3CARE—Cardiovascular Critical Care & Anesthesia Evaluation and Research, Aachen, Germany
| | - Nilesh M. Mehta
- Department of Anesthesiology, Critical Care and Pain Medicine, Division of Critical Care Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA
| | - Charlene Compher
- Department of Biobehavioral Health Science, University of Pennsylvania and Clinical Nutrition Support Service, Hospital of the University of Pennsylvania, Philadelphia, PA USA
| | - Jean-Charles Preiser
- Erasme University Hospital, Université Libre de Bruxelles, 808 route de Lennik, B-1070 Brussels, Belgium
| | - Daren K. Heyland
- Department of Critical Care Medicine, Queen’s University, Angada 4, Kingston, ON K7L 2V7 Canada
- Clinical Evaluation Research Unit, Kingston General Hospital, Angada 4, Kingston, ON K7L 2V7 Canada
| | - Arnold S. Kristof
- Meakins-Christie Laboratories and Translational Research in Respiratory Diseases Program, Faculty of Medicine, Departments of Medicine and Critical Care, Research Institute of the McGill University Health Centre, 1001 Décarie Blvd., EM3.2219, Montreal, QC H4A 3J1 Canada
| |
Collapse
|