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Harkness BM, Chen S, Kim K, Reddy AP, McFarland TJ, Hegarty DM, Everist SJ, Saugstad JA, Lapidus J, Galor A, Aicher SA. Tear Proteins Altered in Patients with Persistent Eye Pain after Refractive Surgery: Biomarker Candidate Discovery. J Proteome Res 2024; 23:2629-2640. [PMID: 38885176 DOI: 10.1021/acs.jproteome.4c00339] [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] [Indexed: 06/20/2024]
Abstract
Some patients develop persistent eye pain after refractive surgery, but factors that cause or sustain pain are unknown. We tested whether tear proteins of patients with pain 3 months after surgery differ from those of patients without pain. Patients undergoing refractive surgery (laser in situ keratomileusis or photorefractive keratectomy ) were recruited from 2 clinics, and tears were collected 3 months after surgery. Participants rated their eye pain using a numerical rating scale (NRS, 0-10; no pain-worst pain) at baseline, 1 day, and 3 months after surgery. Using tandem mass tag proteomic analysis, we examined tears from patients with pain [NRS ≥ 3 at 3 months (n = 16)] and patients with no pain [NRS ≤ 1 at 3 months (n = 32)] after surgery. A subset of proteins (83 of 2748 detected, 3.0%) were associated with pain 3 months after surgery. High-dimensional statistical models showed that the magnitude of differential expression was not the only important factor in classifying tear samples from pain patients. Models utilizing 3 or 4 proteins had better classification performance than single proteins and represented differences in both directions (higher or lower in pain). Thus, patterns of protein differences may serve as biomarkers of postsurgical eye pain as well as potential therapeutic targets.
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Affiliation(s)
- Brooke M Harkness
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon 97239-4197, United States
| | - Siting Chen
- School of Public Health, Oregon Health & Science University-Portland State University, Portland, Oregon 97239-4197, United States
- Biostatistics & Design Program, Oregon Health & Science University, Portland, Oregon 97239-4197, United States
| | - Kilsun Kim
- Proteomics Shared Resource, Oregon Health & Science University, Portland, Oregon 97239-4197, United States
| | - Ashok P Reddy
- Proteomics Shared Resource, Oregon Health & Science University, Portland, Oregon 97239-4197, United States
| | - Trevor J McFarland
- Department of Anesthesiology & Perioperative Medicine, Oregon Health & Science University, Portland, Oregon 97239-4197, United States
| | - Deborah M Hegarty
- Department of Chemical Physiology & Biochemistry, Oregon Health & Science University, Portland, Oregon 97239-4197, United States
| | - Steven J Everist
- Department of Chemical Physiology & Biochemistry, Oregon Health & Science University, Portland, Oregon 97239-4197, United States
| | - Julie A Saugstad
- Department of Anesthesiology & Perioperative Medicine, Oregon Health & Science University, Portland, Oregon 97239-4197, United States
| | - Jodi Lapidus
- School of Public Health, Oregon Health & Science University-Portland State University, Portland, Oregon 97239-4197, United States
- Biostatistics & Design Program, Oregon Health & Science University, Portland, Oregon 97239-4197, United States
| | - Anat Galor
- Bascom Palmer Eye Institute, University of Miami Health System, Miami, Florida 33146, United States
- Miami Veterans Affairs Hospital, Miami, Florida 33125-1624, United States
| | - Sue A Aicher
- Department of Chemical Physiology & Biochemistry, Oregon Health & Science University, Portland, Oregon 97239-4197, United States
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Haghayegh F, Norouziazad A, Haghani E, Feygin AA, Rahimi RH, Ghavamabadi HA, Sadighbayan D, Madhoun F, Papagelis M, Felfeli T, Salahandish R. Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2400595. [PMID: 38958517 DOI: 10.1002/advs.202400595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/19/2024] [Indexed: 07/04/2024]
Abstract
Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.
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Affiliation(s)
- Fatemeh Haghayegh
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Alireza Norouziazad
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Elnaz Haghani
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Ariel Avraham Feygin
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Reza Hamed Rahimi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Hamidreza Akbari Ghavamabadi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Deniz Sadighbayan
- Department of Biology, Faculty of Science, York University, Toronto, ON, M3J 1P3, Canada
| | - Faress Madhoun
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Manos Papagelis
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Tina Felfeli
- Department of Ophthalmology and Vision Sciences, University of Toronto, Ontario, M5T 3A9, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Ontario, M5T 3M6, Canada
| | - Razieh Salahandish
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
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Lyu C, Joehanes R, Huan T, Levy D, Li Y, Wang M, Liu X, Liu C, Ma J. Enhancing selection of alcohol consumption-associated genes by random forest. Br J Nutr 2024; 131:2058-2067. [PMID: 38606596 PMCID: PMC11216877 DOI: 10.1017/s0007114524000795] [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] [Indexed: 04/13/2024]
Abstract
Machine learning methods have been used in identifying omics markers for a variety of phenotypes. We aimed to examine whether a supervised machine learning algorithm can improve identification of alcohol-associated transcriptomic markers. In this study, we analysed array-based, whole-blood derived expression data for 17 873 gene transcripts in 5508 Framingham Heart Study participants. By using the Boruta algorithm, a supervised random forest (RF)-based feature selection method, we selected twenty-five alcohol-associated transcripts. In a testing set (30 % of entire study participants), AUC (area under the receiver operating characteristics curve) of these twenty-five transcripts were 0·73, 0·69 and 0·66 for non-drinkers v. moderate drinkers, non-drinkers v. heavy drinkers and moderate drinkers v. heavy drinkers, respectively. The AUC of the selected transcripts by the Boruta method were comparable to those identified using conventional linear regression models, for example, AUC of 1958 transcripts identified by conventional linear regression models (false discovery rate < 0·2) were 0·74, 0·66 and 0·65, respectively. With Bonferroni correction for the twenty-five Boruta method-selected transcripts and three CVD risk factors (i.e. at P < 6·7e-4), we observed thirteen transcripts were associated with obesity, three transcripts with type 2 diabetes and one transcript with hypertension. For example, we observed that alcohol consumption was inversely associated with the expression of DOCK4, IL4R, and SORT1, and DOCK4 and SORT1 were positively associated with obesity, and IL4R was inversely associated with hypertension. In conclusion, using a supervised machine learning method, the RF-based Boruta algorithm, we identified novel alcohol-associated gene transcripts.
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Affiliation(s)
- Chenglin Lyu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA
| | - Roby Joehanes
- Framingham Heart Study and Population Sciences Branch, NHLBI, Framingham, MA
| | - Tianxiao Huan
- Framingham Heart Study and Population Sciences Branch, NHLBI, Framingham, MA
| | - Daniel Levy
- Framingham Heart Study and Population Sciences Branch, NHLBI, Framingham, MA
| | - Yi Li
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Mengyao Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Xue Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Jiantao Ma
- Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA
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Acharjee A, Wijesinghe SN, Russ D, Gkoutos G, Jones SW. Cross-species transcriptomics identifies obesity associated genes between human and mouse studies. J Transl Med 2024; 22:592. [PMID: 38918843 PMCID: PMC11197204 DOI: 10.1186/s12967-024-05414-1] [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: 12/05/2023] [Accepted: 06/19/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND Fundamentally defined by an imbalance in energy consumption and energy expenditure, obesity is a significant risk factor of several musculoskeletal conditions including osteoarthritis (OA). High-fat diets and sedentary lifestyle leads to increased adiposity resulting in systemic inflammation due to the endocrine properties of adipose tissue producing inflammatory cytokines and adipokines. We previously showed serum levels of specific adipokines are associated with biomarkers of bone remodelling and cartilage volume loss in knee OA patients. Whilst more recently we find the metabolic consequence of obesity drives the enrichment of pro-inflammatory fibroblast subsets within joint synovial tissues in obese individuals compared to those of BMI defined 'health weight'. As such this present study identifies obesity-associated genes in OA joint tissues which are conserved across species and conditions. METHODS The study utilised 6 publicly available bulk and single-cell transcriptomic datasets from human and mice studies downloaded from Gene Expression Omnibus (GEO). Machine learning models were employed to model and statistically test datasets for conserved gene expression profiles. Identified genes were validated in OA tissues from obese and healthy weight individuals using quantitative PCR method (N = 38). Obese and healthy-weight patients were categorised by BMI > 30 and BMI between 18 and 24.9 respectively. Informed consent was obtained from all study participants who were scheduled to undergo elective arthroplasty. RESULTS Principal component analysis (PCA) was used to investigate the variations between classes of mouse and human data which confirmed variation between obese and healthy populations. Differential gene expression analysis filtered on adjusted p-values of p < 0.05, identified differentially expressed genes (DEGs) in mouse and human datasets. DEGs were analysed further using area under curve (AUC) which identified 12 genes. Pathway enrichment analysis suggests these genes were involved in the biosynthesis and elongation of fatty acids and the transport, oxidation, and catabolic processing of lipids. qPCR validation found the majority of genes showed a tendency to be upregulated in joint tissues from obese participants. Three validated genes, IGFBP2 (p = 0.0363), DOK6 (0.0451) and CASP1 (0.0412) were found to be significantly different in obese joint tissues compared to lean-weight joint tissues. CONCLUSIONS The present study has employed machine learning models across several published obesity datasets to identify obesity-associated genes which are validated in joint tissues from OA. These results suggest obesity-associated genes are conserved across conditions and may be fundamental in accelerating disease in obese individuals. Whilst further validations and additional conditions remain to be tested in this model, identifying obesity-associated genes in this way may serve as a global aid for patient stratification giving rise to the potential of targeted therapeutic interventions in such patient subpopulations.
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Affiliation(s)
- Animesh Acharjee
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
- MRC Health Data Research UK (HDR UK), Birmingham, UK.
- Institute of Translational Medicine, Foundation Trust, University Hospitals Birmingham NHS, Birmingham, B15 2TT, UK.
- Centre for Health Data Research, University of Birmingham, Birmingham, B15 2TT, UK.
| | - Susanne N Wijesinghe
- Institute of Inflammation and Ageing, MRC Versus Arthritis Centre for Musculoskeletal Ageing Research, University of Birmingham, Birmingham, UK
| | - Dominic Russ
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK
- MRC Health Data Research UK (HDR UK), Birmingham, UK
- Institute of Translational Medicine, Foundation Trust, University Hospitals Birmingham NHS, Birmingham, B15 2TT, UK
- Centre for Health Data Research, University of Birmingham, Birmingham, B15 2TT, UK
| | - Georgios Gkoutos
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK
- MRC Health Data Research UK (HDR UK), Birmingham, UK
- Institute of Translational Medicine, Foundation Trust, University Hospitals Birmingham NHS, Birmingham, B15 2TT, UK
- Centre for Health Data Research, University of Birmingham, Birmingham, B15 2TT, UK
| | - Simon W Jones
- Institute of Inflammation and Ageing, MRC Versus Arthritis Centre for Musculoskeletal Ageing Research, University of Birmingham, Birmingham, UK
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Yang H, Zhao L, Li D, An C, Fang X, Chen Y, Liu J, Xiao T, Wang Z. Subtype-WGME enables whole-genome-wide multi-omics cancer subtyping. CELL REPORTS METHODS 2024; 4:100781. [PMID: 38761803 PMCID: PMC11228280 DOI: 10.1016/j.crmeth.2024.100781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 01/05/2024] [Accepted: 04/26/2024] [Indexed: 05/20/2024]
Abstract
We present an innovative strategy for integrating whole-genome-wide multi-omics data, which facilitates adaptive amalgamation by leveraging hidden layer features derived from high-dimensional omics data through a multi-task encoder. Empirical evaluations on eight benchmark cancer datasets substantiated that our proposed framework outstripped the comparative algorithms in cancer subtyping, delivering superior subtyping outcomes. Building upon these subtyping results, we establish a robust pipeline for identifying whole-genome-wide biomarkers, unearthing 195 significant biomarkers. Furthermore, we conduct an exhaustive analysis to assess the importance of each omic and non-coding region features at the whole-genome-wide level during cancer subtyping. Our investigation shows that both omics and non-coding region features substantially impact cancer development and survival prognosis. This study emphasizes the potential and practical implications of integrating genome-wide data in cancer research, demonstrating the potency of comprehensive genomic characterization. Additionally, our findings offer insightful perspectives for multi-omics analysis employing deep learning methodologies.
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Affiliation(s)
- Hai Yang
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Liang Zhao
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Dongdong Li
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Congcong An
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Xiaoyang Fang
- Cornell Tech, Cornell University, New York, NY 14853, USA
| | - Yiwen Chen
- Center for Continuing and Lifelong Education, National University of Singapore, Singapore 119077, Singapore
| | - Jingping Liu
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Ting Xiao
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Zhe Wang
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
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Zhang M, Du J, Nie B, Luo J, Liu M, Yuan Y. Hybrid mRMR and multi-objective particle swarm feature selection methods and application to metabolomics of traditional Chinese medicine. PeerJ Comput Sci 2024; 10:e2073. [PMID: 38855250 PMCID: PMC11157565 DOI: 10.7717/peerj-cs.2073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
Metabolomics data has high-dimensional features and a small sample size, which is typical of high-dimensional small sample (HDSS) data. Too high a dimensionality leads to the curse of dimensionality, and too small a sample size tends to trigger overfitting, which poses a challenge to deeper mining in metabolomics. Feature selection is a valuable technique for effectively handling the challenges HDSS data poses. For the feature selection problem of HDSS data in metabolomics, a hybrid Max-Relevance and Min-Redundancy (mRMR) and multi-objective particle swarm feature selection method (MCMOPSO) is proposed. Experimental results using metabolomics data and various University of California, Irvine (UCI) public datasets demonstrate the effectiveness of MCMOPSO in selecting feature subsets with a limited number of high-quality features. MCMOPSO achieves this by efficiently eliminating irrelevant and redundant features, showcasing its efficacy. Therefore, MCMOPSO is a powerful approach for selecting features from high-dimensional metabolomics data with limited sample sizes.
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Affiliation(s)
- Mengting Zhang
- School of Computer Science, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Jianqiang Du
- School of Computer Science, Jiangxi University of Chinese Medicine, Nanchang, China
- Key Laboratory of Artificial Intelligence in Chinese Medicine, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Bin Nie
- School of Computer Science, Jiangxi University of Chinese Medicine, Nanchang, China
- Key Laboratory of Artificial Intelligence in Chinese Medicine, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Jigen Luo
- School of Computer Science, Jiangxi University of Chinese Medicine, Nanchang, China
- Key Laboratory of Artificial Intelligence in Chinese Medicine, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Ming Liu
- School of Computer Science, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Yang Yuan
- School of Computer Science, Jiangxi University of Chinese Medicine, Nanchang, China
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Chen B, Wang Y, Zhang J, Han Y, Benhammouda H, Bian J, Kang R, Shang X. Specific feature recognition on group specific networks (SFR-GSN): a biomarker identification model for cancer stages. Front Genet 2024; 15:1407072. [PMID: 38846963 PMCID: PMC11153737 DOI: 10.3389/fgene.2024.1407072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 05/01/2024] [Indexed: 06/09/2024] Open
Abstract
Background and Objective Accurate identification of cancer stages is challenging due to the complexity and heterogeneity of the disease. Current clinical diagnosis methods primarily rely on phenotypic observations, which may not capture early molecular-level changes accurately. Methods In this study, a novel biomarker recognition method was proposed tailored for cancer stages by considering the change of gene expression relationships. Utilizing the sample-specific information and protein-protein interaction networks, the group specific networks were constructed to address the limited specificity of potential biomarkers. Then, a specific feature recognition method was proposed based on these group specific networks, which employed the random forest algorithm for initial screening followed by a recursive feature elimination process to identify the optimal biomarker subset. During exploring optimal results, a strategy termed the Cost-Benefit Ratio, was devised to facilitate the identification of stage-specific biomarkers. Results Comparative experiments were conducted on lung adenocarcinoma and breast cancer datasets to validate the method's efficacy and generalizability. The results showed that the identified biomarkers were highly stage-specific, and the F1 scores for predicting cancer stages were significantly improved. For the lung adenocarcinoma dataset, the F1 score reached 97.68%, and for the breast cancer dataset, it achieved 96.87%. These results significantly surpassed those of three conventional methods in terms of F1 scores. Moreover, from the perspective of biological functions, the biomarkers were proved playing an important role in cancer stage-evolution. Conclusion The proposed method demonstrated its effectiveness in identifying stage-related biomarkers. By using these biomarkers as features, accurate prediction of cancer stages was achieved. Furthermore, the method exhibited potential for biomarker identification in subtype analyses, offering novel perspectives for cancer prognosis.
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Affiliation(s)
- Bolin Chen
- School of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, China
- Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an, Shaanxi, China
| | - Yuxin Wang
- School of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Jinlei Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Yourui Han
- School of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Hamza Benhammouda
- School of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Jun Bian
- Department of General Surgery, Xi’an Children’s Hosptial, Xi’an Jiaotong University Affiliated Children’s Hosptial, Xi’an, China
| | - Ruiming Kang
- Rewise (Hangzhou) Information Technology Co., Ltd, Hangzhou, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, China
- Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an, Shaanxi, China
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Ramírez Medina CR, Ali I, Baricevic-Jones I, Saleem MA, Whetton AD, Kalra PA, Geifman N. Evaluation of a proteomic signature coupled with the kidney failure risk equation in predicting end stage kidney disease in a chronic kidney disease cohort. Clin Proteomics 2024; 21:34. [PMID: 38762513 PMCID: PMC11102163 DOI: 10.1186/s12014-024-09486-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 04/25/2024] [Indexed: 05/20/2024] Open
Abstract
BACKGROUND The early identification of patients at high-risk for end-stage renal disease (ESRD) is essential for providing optimal care and implementing targeted prevention strategies. While the Kidney Failure Risk Equation (KFRE) offers a more accurate prediction of ESRD risk compared to static eGFR-based thresholds, it does not provide insights into the patient-specific biological mechanisms that drive ESRD. This study focused on evaluating the effectiveness of KFRE in a UK-based advanced chronic kidney disease (CKD) cohort and investigating whether the integration of a proteomic signature could enhance 5-year ESRD prediction. METHODS Using the Salford Kidney Study biobank, a UK-based prospective cohort of over 3000 non-dialysis CKD patients, 433 patients met our inclusion criteria: a minimum of four eGFR measurements over a two-year period and a linear eGFR trajectory. Plasma samples were obtained and analysed for novel proteomic signals using SWATH-Mass-Spectrometry. The 4-variable UK-calibrated KFRE was calculated for each patient based on their baseline clinical characteristics. Boruta machine learning algorithm was used for the selection of proteins most contributing to differentiation between patient groups. Logistic regression was employed for estimation of ESRD prediction by (1) proteomic features; (2) KFRE; and (3) proteomic features alongside KFRE. RESULTS SWATH maps with 943 quantified proteins were generated and investigated in tandem with available clinical data to identify potential progression biomarkers. We identified a set of proteins (SPTA1, MYL6 and C6) that, when used alongside the 4-variable UK-KFRE, improved the prediction of 5-year risk of ESRD (AUC = 0.75 vs AUC = 0.70). Functional enrichment analysis revealed Rho GTPases and regulation of the actin cytoskeleton pathways to be statistically significant, inferring their role in kidney function and the pathogenesis of renal disease. CONCLUSIONS Proteins SPTA1, MYL6 and C6, when used alongside the 4-variable UK-KFRE achieve an improved performance when predicting a 5-year risk of ESRD. Specific pathways implicated in the pathogenesis of podocyte dysfunction were also identified, which could serve as potential therapeutic targets. The findings of our study carry implications for comprehending the involvement of the Rho family GTPases in the pathophysiology of kidney disease, advancing our understanding of the proteomic factors influencing susceptibility to renal damage.
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Affiliation(s)
- Carlos Raúl Ramírez Medina
- Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
| | - Ibrahim Ali
- Salford Royal Hospital, Northern Care Alliance Foundation NHS Trust, Salford, UK
- Division of Cardiovascular Sciences, The University of Manchester, Manchester, UK
| | - Ivona Baricevic-Jones
- Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Salford Royal Hospital, Northern Care Alliance Foundation NHS Trust, Salford, UK
| | - Moin A Saleem
- Bristol Renal and Children's Renal Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Anthony D Whetton
- Veterinary Health Innovation Engine (vHive), Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Philip A Kalra
- Salford Royal Hospital, Northern Care Alliance Foundation NHS Trust, Salford, UK
| | - Nophar Geifman
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
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Nguyen PN. Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways. BMC Bioinformatics 2024; 25:149. [PMID: 38609844 PMCID: PMC11265126 DOI: 10.1186/s12859-024-05755-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 03/20/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery from genetic data. METHOD We propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware. RESULTS We demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation. CONCLUSION The model indicates new genetic biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks .
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Affiliation(s)
- Phuong-Nam Nguyen
- Faculty of Computer Science, PHENIKAA University, Yen Nghia, Ha Dong, Hanoi, 12116, Vietnam.
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10
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Hadish JA, Hargarten HL, Zhang H, Mattheis JP, Honaas LA, Ficklin SP. Towards identification of postharvest fruit quality transcriptomic markers in Malus domestica. PLoS One 2024; 19:e0297015. [PMID: 38446822 PMCID: PMC10917293 DOI: 10.1371/journal.pone.0297015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/27/2023] [Indexed: 03/08/2024] Open
Abstract
Gene expression is highly impacted by the environment and can be reflective of past events that affected developmental processes. It is therefore expected that gene expression can serve as a signal of a current or future phenotypic traits. In this paper we identify sets of genes, which we call Prognostic Transcriptomic Biomarkers (PTBs), that can predict firmness in Malus domestica (apple) fruits. In apples, all individuals of a cultivar are clones, and differences in fruit quality are due to the environment. The apples transcriptome responds to these differences in environment, which makes PTBs an attractive predictor of future fruit quality. PTBs have the potential to enhance supply chain efficiency, reduce crop loss, and provide higher and more consistent quality for consumers. However, several questions must be addressed. In this paper we answer the question of which of two common modeling approaches, Random Forest or ElasticNet, outperforms the other. We answer if PTBs with few genes are efficient at predicting traits. This is important because we need few genes to perform qPCR, and we answer the question if qPCR is a cost-effective assay as input for PTBs modeled using high-throughput RNA-seq. To do this, we conducted a pilot study using fruit texture in the 'Gala' variety of apples across several postharvest storage regiments. Fruit texture in 'Gala' apples is highly controllable by post-harvest treatments and is therefore a good candidate to explore the use of PTBs. We find that the RandomForest model is more consistent than an ElasticNet model and is predictive of firmness (r2 = 0.78) with as few as 15 genes. We also show that qPCR is reasonably consistent with RNA-seq in a follow up experiment. Results are promising for PTBs, yet more work is needed to ensure that PTBs are robust across various environmental conditions and storage treatments.
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Affiliation(s)
- John A. Hadish
- Molecular Plant Science Department, Washington State University, Pullman, Washington, United States of America
- Department of Horticulture, Washington State University, Pullman, Washington, United States of America
| | - Heidi L. Hargarten
- USDA Agricultural Research Service Physiology and Pathology of Tree Fruits Research, Wenatchee, Washington, United States of America
| | - Huiting Zhang
- Department of Horticulture, Washington State University, Pullman, Washington, United States of America
| | - James P. Mattheis
- USDA Agricultural Research Service Physiology and Pathology of Tree Fruits Research, Wenatchee, Washington, United States of America
| | - Loren A. Honaas
- USDA Agricultural Research Service Physiology and Pathology of Tree Fruits Research, Wenatchee, Washington, United States of America
| | - Stephen P. Ficklin
- Molecular Plant Science Department, Washington State University, Pullman, Washington, United States of America
- Department of Horticulture, Washington State University, Pullman, Washington, United States of America
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11
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Szakmany T, Fitzgerald E, Garlant HN, Whitehouse T, Molnar T, Shah S, Tong D, Hall JE, Ball GR, Kempsell KE. The 'analysis of gene expression and biomarkers for point-of-care decision support in Sepsis' study; temporal clinical parameter analysis and validation of early diagnostic biomarker signatures for severe inflammation andsepsis-SIRS discrimination. Front Immunol 2024; 14:1308530. [PMID: 38332914 PMCID: PMC10850284 DOI: 10.3389/fimmu.2023.1308530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 12/26/2023] [Indexed: 02/10/2024] Open
Abstract
Introduction Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. We describe identification of mRNA biomarkers from peripheral blood leukocytes, able to identify severe, systemic inflammation (irrespective of origin) and differentiate Sepsis from SIRS, in adult patients within a multi-center clinical study. Methods Participants were recruited in Intensive Care Units (ICUs) from multiple UK hospitals, including fifty-nine patients with abdominal sepsis, eighty-four patients with pulmonary sepsis, forty-two SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), sampled at four time points, in addition to thirty healthy control donors. Multiple clinical parameters were measured, including SOFA score, with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools. Results Nineteen high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed 'indicators of inflammation' (I°I), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty entities, termed 'SIRS or Sepsis' (S°S) biomarkers, were differentially expressed between sepsis and SIRS (FC>2·0, p-value<0.05). Discussion The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758). The I°I and S°S signatures performed variably in other independent gene expression datasets, this may be due to technical variation in the study/assay platform.
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Affiliation(s)
- Tamas Szakmany
- Department of Anaesthesia, Intensive Care and Pain Medicine, Division of Population Medicine, Cardiff University, Cardiff, United Kingdom
- Anaesthesia, Critical Care and Theatres Directorate, Cwm Taf Morgannwg University Health Board, Royal Glamorgan Hospital, Llantrisant, United Kingdom
| | | | | | - Tony Whitehouse
- NIHR Surgical Reconstruction and Microbiology Research Centre, Queen Elizabeth Hospital, Mindelsohn Way Edgbaston, Birmingham, United Kingdom
| | - Tamas Molnar
- Critical Care Directorate, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, United Kingdom
| | - Sanjoy Shah
- Critical Care Directorate, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, United Kingdom
| | - Dong Ling Tong
- Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia
| | - Judith E. Hall
- Department of Anaesthesia, Intensive Care and Pain Medicine, Division of Population Medicine, Cardiff University, Cardiff, United Kingdom
| | - Graham R. Ball
- Medical Technology Research Facility, Anglia Ruskin University, Essex, United Kingdom
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12
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Su X, Cheung CYY, Zhong J, Ru Y, Fong CHY, Lee CH, Liu Y, Cheung CKY, Lam KSL, Xu A, Cai Z. Ten metabolites-based algorithm predicts the future development of type 2 diabetes in Chinese. J Adv Res 2023:S2090-1232(23)00365-X. [PMID: 38030128 DOI: 10.1016/j.jare.2023.11.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/10/2023] [Accepted: 11/21/2023] [Indexed: 12/01/2023] Open
Abstract
INTRODUCTION Type 2 diabetes (T2D) is a heterogeneous metabolic disease with large variations in the relative contributions of insulin resistance and β-cell dysfunction across different glucose tolerance subgroups and ethnicities. A more precise yet feasible approach to categorize risk preceding T2D onset is urgently needed. This study aimed to identify potential metabolic biomarkers that could contribute to the development of T2D and investigate whether their impact on T2D is mediated through insulin resistance and β-cell dysfunction. METHODS A non-targeted metabolomic analysis was performed in plasma samples of 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from a long-term prospective Chinese community-based cohort with a follow-up period of ∼ 16 years. RESULTS Metabolic profiles revealed profound perturbation of metabolomes before T2D onset. Overall metabolic shifts were strongly associated with insulin resistance rather than β-cell dysfunction. In addition, 188 out of the 578 annotated metabolites were associated with insulin resistance. Bi-directional mediation analysis revealed putative causal relationships among the metabolites, insulin resistance and T2D risk. We built a machine-learning based prediction model, integrating the conventional clinical risk factors (age, BMI, TyG index and 2hG) and 10 metabolites (acetyl-tryptophan, kynurenine, γ-glutamyl-phenylalanine, DG(18:2/22:6), DG(38:7), LPI(18:2), LPC(P-16:0), LPC(P-18:1), LPC(P-20:0) and LPE(P-20:0)) (AUROC = 0.894, 5.6% improvement comparing to the conventional clinical risk model), that successfully predicts the development of T2D. CONCLUSIONS Our findings support the notion that the metabolic changes resulting from insulin resistance, rather than β-cell dysfunction, are the primary drivers of T2D in Chinese adults. Metabolomes as a valuable phenotype hold potential clinical utility in the prediction of T2D.
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Affiliation(s)
- Xiuli Su
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China
| | - Chloe Y Y Cheung
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Junda Zhong
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Yi Ru
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China
| | - Carol H Y Fong
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Chi-Ho Lee
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Yan Liu
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Cynthia K Y Cheung
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Karen S L Lam
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China.
| | - Aimin Xu
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China; Department of Pharmacology & Pharmacy, The University of Hong Kong, Hong Kong, China.
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China.
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Kawalec A, Stojanowski J, Mazurkiewicz P, Choma A, Gaik M, Pluta M, Szymański M, Bruciak A, Gołębiowski T, Musiał K. Systemic Immune Inflammation Index as a Key Predictor of Dialysis in Pediatric Chronic Kidney Disease with the Use of Random Forest Classifier. J Clin Med 2023; 12:6911. [PMID: 37959376 PMCID: PMC10647735 DOI: 10.3390/jcm12216911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 10/25/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Low-grade inflammation is a significant component of chronic kidney disease (CKD). Systemic immune inflammation index (SII), a newly defined ratio combining neutrophil, lymphocyte, and platelet counts, has not yet been evaluated in the pediatric CKD population nor in the context of CKD progression or dialysis. Thus, this study aimed to analyze the complete blood cell count (CBC)-driven parameters, including SII, in children with CKD and to assess their potential usefulness in the prediction of the need for chronic dialysis. METHODS A single-center, retrospective study was conducted on 27 predialysis children with CKD stages 4-5 and 39 children on chronic dialysis. The data were analyzed with the artificial intelligence tools. RESULTS The Random Forest Classifier (RFC) model with the input variables of neutrophil count, mean platelet volume (MPV), and SII turned out to be the best predictor of the progression of pediatric CKD into end-stage kidney disease (ESKD) requiring dialysis. Out of these variables, SII showed the largest share in the prediction of the need for renal replacement therapy. CONCLUSIONS Chronic inflammation plays a pivotal role in the progression of CKD into ESKD. Among CBC-driven ratios, SII seems to be the most useful predictor of the need for chronic dialysis in CKD children.
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Affiliation(s)
- Anna Kawalec
- Department of Pediatric Nephrology, Wroclaw Medical University, Borowska 213, 50-556 Wroclaw, Poland
| | - Jakub Stojanowski
- Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Borowska 213, 50-556 Wroclaw, Poland
| | - Paulina Mazurkiewicz
- Clinic of Pediatric Nephrology, University Clinical Hospital, Borowska 213, 50-556 Wroclaw, Poland
| | - Anna Choma
- Students’ Scientific Association, Department of Pediatric Nephrology, Wroclaw Medical University, Borowska 213, 50-556 Wroclaw, Poland
| | - Magdalena Gaik
- Students’ Scientific Association, Department of Pediatric Nephrology, Wroclaw Medical University, Borowska 213, 50-556 Wroclaw, Poland
| | - Mateusz Pluta
- Students’ Scientific Association, Department of Pediatric Nephrology, Wroclaw Medical University, Borowska 213, 50-556 Wroclaw, Poland
| | - Michał Szymański
- Students’ Scientific Association, Department of Pediatric Nephrology, Wroclaw Medical University, Borowska 213, 50-556 Wroclaw, Poland
| | - Aleksandra Bruciak
- Students’ Scientific Association, Department of Pediatric Nephrology, Wroclaw Medical University, Borowska 213, 50-556 Wroclaw, Poland
| | - Tomasz Gołębiowski
- Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Borowska 213, 50-556 Wroclaw, Poland
| | - Kinga Musiał
- Department of Pediatric Nephrology, Wroclaw Medical University, Borowska 213, 50-556 Wroclaw, Poland
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Acharjee A. Explainable AI for gut microbiome-based diagnostics: colorectal cancer as a case study. Diagnosis (Berl) 2023; 10:448-449. [PMID: 37328267 DOI: 10.1515/dx-2023-0062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 06/04/2023] [Indexed: 06/18/2023]
Affiliation(s)
- Animesh Acharjee
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Institute of Translational Medicine, University of Birmingham, Birmingham, UK
- MRC Health Data Research UK (HDR UK), Midlands Site, Birmingham, UK
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15
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Musiał K, Stojanowski J, Miśkiewicz-Bujna J, Kałwak K, Ussowicz M. KIM-1, IL-18, and NGAL, in the Machine Learning Prediction of Kidney Injury among Children Undergoing Hematopoietic Stem Cell Transplantation-A Pilot Study. Int J Mol Sci 2023; 24:15791. [PMID: 37958774 PMCID: PMC10648899 DOI: 10.3390/ijms242115791] [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: 09/27/2023] [Revised: 10/26/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023] Open
Abstract
Children undergoing allogeneic hematopoietic stem cell transplantation (HSCT) are prone to developing acute kidney injury (AKI). Markers of kidney damage: kidney injury molecule (KIM)-1, interleukin (IL)-18, and neutrophil gelatinase-associated lipocalin (NGAL) may ease early diagnosis of AKI. The aim of this study was to assess serum concentrations of KIM-1, IL-18, and NGAL in children undergoing HSCT in relation to classical markers of kidney function (creatinine, cystatin C, estimated glomerular filtration rate (eGFR)) and to analyze their usefulness as predictors of kidney damage with the use of artificial intelligence tools. Serum concentrations of KIM-1, IL-18, NGAL, and cystatin C were assessed by ELISA in 27 children undergoing HSCT before transplantation and up to 4 weeks after the procedure. The data was used to build a Random Forest Classifier (RFC) model of renal injury prediction. The RFC model established on the basis of 3 input variables, KIM-1, IL-18, and NGAL concentrations in the serum of children before HSCT, was able to effectively assess the rate of patients with hyperfiltration, a surrogate marker of kidney injury 4 weeks after the procedure. With the use of the RFC model, serum KIM-1, IL-18, and NGAL may serve as markers of incipient renal dysfunction in children after HSCT.
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Affiliation(s)
- Kinga Musiał
- Department of Pediatric Nephrology, Wrocław Medical University, Borowska 213, 50-556 Wrocław, Poland
| | - Jakub Stojanowski
- Department of Nephrology and Transplantation Medicine, Wrocław Medical University, 50-556 Wrocław, Poland
| | - Justyna Miśkiewicz-Bujna
- Clinical Department of Pediatric Oncology and Hematology, Mother and Child Health Center, Karol Marcinkowski University Hospital, 65-046 Zielona Góra, Poland
| | - Krzysztof Kałwak
- Department of Pediatric Bone Marrow Transplantation, Oncology and Hematology, Wrocław Medical University, 50-556 Wrocław, Poland
| | - Marek Ussowicz
- Department of Pediatric Bone Marrow Transplantation, Oncology and Hematology, Wrocław Medical University, 50-556 Wrocław, Poland
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Tobin NH, Murphy A, Li F, Brummel SS, Fowler MG, Mcintyre JA, Currier JS, Chipato T, Flynn PM, Gadama LA, Saidi F, Nakabiito C, Koos BJ, Aldrovandi GM. Metabolomic profiling of preterm birth in pregnant women living with HIV. Metabolomics 2023; 19:91. [PMID: 37880481 PMCID: PMC10600291 DOI: 10.1007/s11306-023-02055-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 09/20/2023] [Indexed: 10/27/2023]
Abstract
BACKGROUND Preterm birth is a leading cause of death in children under the age of five. The risk of preterm birth is increased by maternal HIV infection as well as by certain antiretroviral regimens, leading to a disproportionate burden on low- and medium-income settings where HIV is most prevalent. Despite decades of research, the mechanisms underlying spontaneous preterm birth, particularly in resource limited areas with high HIV infection rates, are still poorly understood and accurate prediction and therapeutic intervention remain elusive. OBJECTIVES Metabolomics was utilized to identify profiles of preterm birth among pregnant women living with HIV on two different antiretroviral therapy (ART) regimens. METHODS This pilot study comprised 100 mother-infant dyads prior to antiretroviral initiation, on zidovudine monotherapy or on protease inhibitor-based antiretroviral therapy. Pregnancies that resulted in preterm births were matched 1:1 with controls by gestational age at time of sample collection. Maternal plasma and blood spots at 23-35 weeks gestation and infant dried blood spots at birth, were assayed using an untargeted metabolomics method. Linear regression and random forests classification models were used to identify shared and treatment-specific markers of preterm birth. RESULTS Classification models for preterm birth achieved accuracies of 95.5%, 95.7%, and 80.7% in the untreated, zidovudine monotherapy, and protease inhibitor-based treatment groups, respectively. Urate, methionine sulfone, cortisone, and 17α-hydroxypregnanolone glucuronide were identified as shared markers of preterm birth. Other compounds including hippurate and N-acetyl-1-methylhistidine were found to be significantly altered in a treatment-specific context. CONCLUSION This study identified previously known as well as novel metabolomic features of preterm birth in pregnant women living with HIV. Validation of these models in a larger, independent cohort is necessary to ascertain whether they can be utilized to predict preterm birth during a stage of gestation that allows for therapeutic intervention or more effective resource allocation.
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Affiliation(s)
- Nicole H Tobin
- Division of Infectious Diseases, Department of Pediatrics, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA
| | - Aisling Murphy
- Department of Obstetrics and Gynecology, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA
| | - Fan Li
- Division of Infectious Diseases, Department of Pediatrics, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA
| | - Sean S Brummel
- Center for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Mary Glenn Fowler
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - James A Mcintyre
- Anova Health Institute, Johannesburg, South Africa
- School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa
| | - Judith S Currier
- Division of Infectious Diseases, Department of Internal Medicine, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA
| | - Tsungai Chipato
- University of Zimbabwe College of Health Sciences, Harare, Zimbabwe
| | - Patricia M Flynn
- Department of Infectious Diseases, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Luis A Gadama
- Department of Obstetrics and Gynecology, Johns Hopkins Research Project, Kamuzu University of Health Sciences, Blantyre, Malawi
| | - Friday Saidi
- University of North Carolina Project Malawi, Lilongwe, Malawi
| | | | - Brian J Koos
- Department of Obstetrics and Gynecology, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA
| | - Grace M Aldrovandi
- Division of Infectious Diseases, Department of Pediatrics, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA.
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Will I, Attardo GM, de Bekker C. Multiomic interpretation of fungus-infected ant metabolomes during manipulated summit disease. Sci Rep 2023; 13:14363. [PMID: 37658067 PMCID: PMC10474057 DOI: 10.1038/s41598-023-40065-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/03/2023] [Indexed: 09/03/2023] Open
Abstract
Camponotus floridanus ants show altered behaviors followed by a fatal summiting phenotype when infected with manipulating Ophiocordyceps camponoti-floridani fungi. Host summiting as a strategy to increase transmission is also observed with parasite taxa beyond fungi, including aquatic and terrestrial helminths and baculoviruses. The drastic phenotypic changes can sometimes reflect significant molecular changes in gene expression and metabolite concentrations measured in manipulated hosts. Nevertheless, the underlying mechanisms still need to be fully characterized. To investigate the small molecules producing summiting behavior, we infected C. floridanus ants with O. camponoti-floridani and sampled their heads for LC-MS/MS when we observed the characteristic summiting phenotype. We link this metabolomic data with our previous genomic and transcriptomic data to propose mechanisms that underlie manipulated summiting behavior in "zombie ants." This "multiomic" evidence points toward the dysregulation of neurotransmitter levels and neuronal signaling. We propose that these processes are altered during infection and manipulation based on (1) differential expression of neurotransmitter synthesis and receptor genes, (2) altered abundance of metabolites and neurotransmitters (or their precursors) with known behavioral effects in ants and other insects, and (3) possible suppression of a connected immunity pathway. We additionally report signals for metabolic activity during manipulation related to primary metabolism, detoxification, and anti-stress protectants. Taken together, these findings suggest that host manipulation is likely a multi-faceted phenomenon, with key processes changing at multiple levels of molecular organization.
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Affiliation(s)
- I Will
- Biology Department, University of Central Florida, Orlando, USA.
| | - G M Attardo
- Entomology and Nematology Department, University of California-Davis, Davis, USA
| | - C de Bekker
- Biology Department, University of Central Florida, Orlando, USA.
- Biology Department, Utrecht University, Utrecht, The Netherlands.
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18
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Liu C, Mokashi NV, Darville T, Sun X, O’Connell CM, Hufnagel K, Waterboer T, Zheng X. A Machine Learning-Based Analytic Pipeline Applied to Clinical and Serum IgG Immunoproteome Data To Predict Chlamydia trachomatis Genital Tract Ascension and Incident Infection in Women. Microbiol Spectr 2023; 11:e0468922. [PMID: 37318345 PMCID: PMC10434056 DOI: 10.1128/spectrum.04689-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 06/01/2023] [Indexed: 06/16/2023] Open
Abstract
We developed a reusable and open-source machine learning (ML) pipeline that can provide an analytical framework for rigorous biomarker discovery. We implemented the ML pipeline to determine the predictive potential of clinical and immunoproteome antibody data for outcomes associated with Chlamydia trachomatis (Ct) infection collected from 222 cis-gender females with high Ct exposure. We compared the predictive performance of 4 ML algorithms (naive Bayes, random forest, extreme gradient boosting with linear booster [xgbLinear], and k-nearest neighbors [KNN]), screened from 215 ML methods, in combination with two different feature selection strategies, Boruta and recursive feature elimination. Recursive feature elimination performed better than Boruta in this study. In prediction of Ct ascending infection, naive Bayes yielded a slightly higher median value of are under the receiver operating characteristic curve (AUROC) 0.57 (95% confidence interval [CI], 0.54 to 0.59) than other methods and provided biological interpretability. For prediction of incident infection among women uninfected at enrollment, KNN performed slightly better than other algorithms, with a median AUROC of 0.61 (95% CI, 0.49 to 0.70). In contrast, xgbLinear and random forest had higher predictive performances, with median AUROC of 0.63 (95% CI, 0.58 to 0.67) and 0.62 (95% CI, 0.58 to 0.64), respectively, for women infected at enrollment. Our findings suggest that clinical factors and serum anti-Ct protein IgGs are inadequate biomarkers for ascension or incident Ct infection. Nevertheless, our analysis highlights the utility of a pipeline that searches for biomarkers and evaluates prediction performance and interpretability. IMPORTANCE Biomarker discovery to aid early diagnosis and treatment using machine learning (ML) approaches is a rapidly developing area in host-microbe studies. However, lack of reproducibility and interpretability of ML-driven biomarker analysis hinders selection of robust biomarkers that can be applied in clinical practice. We thus developed a rigorous ML analytical framework and provide recommendations for enhancing reproducibility of biomarkers. We emphasize the importance of robustness in selection of ML methods, evaluation of performance, and interpretability of biomarkers. Our ML pipeline is reusable and open-source and can be used not only to identify host-pathogen interaction biomarkers but also in microbiome studies and ecological and environmental microbiology research.
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Affiliation(s)
- Chuwen Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Neha Vivek Mokashi
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Toni Darville
- Department of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Xuejun Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Catherine M. O’Connell
- Department of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Katrin Hufnagel
- Infections and Cancer Epidemiology, German Cancer Research Center (Deutsches Krebsforschungszentrum), Heidelberg, Germany
| | - Tim Waterboer
- Infections and Cancer Epidemiology, German Cancer Research Center (Deutsches Krebsforschungszentrum), Heidelberg, Germany
| | - Xiaojing Zheng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Hamidi F, Gilani N, Arabi Belaghi R, Yaghoobi H, Babaei E, Sarbakhsh P, Malakouti J. Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta. Front Digit Health 2023; 5:1187578. [PMID: 37621964 PMCID: PMC10445490 DOI: 10.3389/fdgth.2023.1187578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 07/20/2023] [Indexed: 08/26/2023] Open
Abstract
Introduction In gynecologic oncology, ovarian cancer is a great clinical challenge. Because of the lack of typical symptoms and effective biomarkers for noninvasive screening, most patients develop advanced-stage ovarian cancer by the time of diagnosis. MicroRNAs (miRNAs) are a type of non-coding RNA molecule that has been linked to human cancers. Specifying diagnostic biomarkers to determine non-cancer and cancer samples is difficult. Methods By using Boruta, a novel random forest-based feature selection in the machine-learning techniques, we aimed to identify biomarkers associated with ovarian cancer using cancerous and non-cancer samples from the Gene Expression Omnibus (GEO) database: GSE106817. In this study, we used two independent GEO data sets as external validation, including GSE113486 and GSE113740. We utilized five state-of-the-art machine-learning algorithms for classification: logistic regression, random forest, decision trees, artificial neural networks, and XGBoost. Results Four models discovered in GSE113486 had an AUC of 100%, three in GSE113740 with AUC of over 94%, and four in GSE113486 with AUC of over 94%. We identified 10 miRNAs to distinguish ovarian cancer cases from normal controls: hsa-miR-1290, hsa-miR-1233-5p, hsa-miR-1914-5p, hsa-miR-1469, hsa-miR-4675, hsa-miR-1228-5p, hsa-miR-3184-5p, hsa-miR-6784-5p, hsa-miR-6800-5p, and hsa-miR-5100. Our findings suggest that miRNAs could be used as possible biomarkers for ovarian cancer screening, for possible intervention.
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Affiliation(s)
- Farzaneh Hamidi
- Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Neda Gilani
- Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran
- Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Reza Arabi Belaghi
- Department of Mathematics, Applied Mathematics and Statistics, Uppsala University, Uppsala, Sweden
- Department of Statistics, Faculty of Mathematical Science, University of Tabriz, Tabriz, Iran
- Department of Energy and Technology, Swedish Agricultural University, Uppsala, Sweden
| | - Hanif Yaghoobi
- Department of Biological Sciences, School of Natural Sciences, University of Tabriz, Tabriz, Iran
| | - Esmaeil Babaei
- Department of Biological Sciences, School of Natural Sciences, University of Tabriz, Tabriz, Iran
- Interfaculty Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
| | - Parvin Sarbakhsh
- Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Jamileh Malakouti
- Department of Midwifery, Faculty of Nursing and Midwifery, Tabriz University of Medical Science, Tabriz, Iran
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Yang C, Pan Y, Yu H, Hu X, Li X, Deng C. Hollow Crystallization COF Capsuled MOF Hybrids Depict Serum Metabolic Profiling for Precise Early Diagnosis and Risk Stratification of Acute Coronary Syndrome. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302109. [PMID: 37340584 PMCID: PMC10460873 DOI: 10.1002/advs.202302109] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Indexed: 06/22/2023]
Abstract
Acute coronary syndrome (ACS), comprising unstable angina (UA) and acute myocardial infarction (AMI), is the leading cause of death worldwide. Currently, lacking effective strategies for classifying ACS hinders the prognosis improvement of ACS patients. Disclosing the nature of metabolic disorders holds the potential to reflect disease progress and high-throughput mass spectrometry-based metabolic analysis is a promising tool for large-scale screening. Herein, a hollow crystallization COF capsuled MOF hybrids (UiO-66@HCOF) assisted serum metabolic analysis is developed for the early diagnosis and risk stratification of ACS. UiO-66@HCOF exhibits unrivaled chemical and structural stability as well as endowing satisfying desorption/ionization efficiency in the detection of metabolites. Paired with machine learning algorithms, early diagnosis of ACS is achieved with the area under the curve (AUC) value of 0.945 for validation sets. Besides, a comprehensive ACS risk stratification method is established, and the AUC value for the discrimination of ACS from healthy controls, and AMI from UA are 0.890, and 0.928. Moreover, the AUC value of the subtyping of AMI is 0.964. Finally, the potential biomarkers exhibit high sensitivity and specificity. This study makes metabolic molecular diagnosis a reality and provided new insight into the progress of ACS.
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Affiliation(s)
- Chenjie Yang
- Department of ChemistryFudan UniversityShanghai200433China
| | - Yilong Pan
- Department of CardiologyShengjing Hospital of China Medical UniversityNO.36 Sanhao Street, Heping DistrictShenyang110004China
| | - Hailong Yu
- Department of ChemistryFudan UniversityShanghai200433China
| | - Xufang Hu
- School of Chemical Science and TechnologyYunnan UniversityNo. 2 North Cuihu RoadKunming650091P. R. China
| | - Xiaodong Li
- Department of CardiologyShengjing Hospital of China Medical UniversityNO.36 Sanhao Street, Heping DistrictShenyang110004China
| | - Chunhui Deng
- Department of ChemistryFudan UniversityShanghai200433China
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21
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Gerhards C, Haselmann V, Schaible SF, Ast V, Kittel M, Thiel M, Hertel A, Schoenberg SO, Neumaier M, Froelich MF. Exploring the Synergistic Potential of Radiomics and Laboratory Biomarkers for Enhanced Identification of Vulnerable COVID-19 Patients. Microorganisms 2023; 11:1740. [PMID: 37512912 PMCID: PMC10384842 DOI: 10.3390/microorganisms11071740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 06/23/2023] [Accepted: 06/29/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Severe courses and high hospitalization rates were ubiquitous during the first pandemic SARS-CoV-2 waves. Thus, we aimed to examine whether integrative diagnostics may aid in identifying vulnerable patients using crucial data and materials obtained from COVID-19 patients hospitalized between 2020 and 2021 (n = 52). Accordingly, we investigated the potential of laboratory biomarkers, specifically the dynamic cell decay marker cell-free DNA and radiomics features extracted from chest CT. METHODS Separate forward and backward feature selection was conducted for linear regression with the Intensive-Care-Unit (ICU) period as the initial target. Three-fold cross-validation was performed, and collinear parameters were reduced. The model was adapted to a logistic regression approach and verified in a validation naïve subset to avoid overfitting. RESULTS The adapted integrated model classifying patients into "ICU/no ICU demand" comprises six radiomics and seven laboratory biomarkers. The models' accuracy was 0.54 for radiomics, 0.47 for cfDNA, 0.74 for routine laboratory, and 0.87 for the combined model with an AUC of 0.91. CONCLUSION The combined model performed superior to the individual models. Thus, integrating radiomics and laboratory data shows synergistic potential to aid clinic decision-making in COVID-19 patients. Under the need for evaluation in larger cohorts, including patients with other SARS-CoV-2 variants, the identified parameters might contribute to the triage of COVID-19 patients.
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Affiliation(s)
- Catharina Gerhards
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
| | - Verena Haselmann
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
| | - Samuel F Schaible
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Volker Ast
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
| | - Maximilian Kittel
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
| | - Manfred Thiel
- Department of Anaesthesiology and Surgical Intensive Care Medicine, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Alexander Hertel
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Stefan O Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Michael Neumaier
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
| | - Matthias F Froelich
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
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22
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Ramírez Medina CR, Ali I, Baricevic-Jones I, Odudu A, Saleem MA, Whetton AD, Kalra PA, Geifman N. Proteomic signature associated with chronic kidney disease (CKD) progression identified by data-independent acquisition mass spectrometry. Clin Proteomics 2023; 20:19. [PMID: 37076799 PMCID: PMC10116780 DOI: 10.1186/s12014-023-09405-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 03/14/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND Halting progression of chronic kidney disease (CKD) to established end stage kidney disease is a major goal of global health research. The mechanism of CKD progression involves pro-inflammatory, pro-fibrotic, and vascular pathways, but pathophysiological differentiation is currently lacking. METHODS Plasma samples of 414 non-dialysis CKD patients, 170 fast progressors (with ∂ eGFR-3 ml/min/1.73 m2/year or worse) and 244 stable patients (∂ eGFR of - 0.5 to + 1 ml/min/1.73 m2/year) with a broad range of kidney disease aetiologies, were obtained and interrogated for proteomic signals with SWATH-MS. We applied a machine learning approach to feature selection of proteins quantifiable in at least 20% of the samples, using the Boruta algorithm. Biological pathways enriched by these proteins were identified using ClueGo pathway analyses. RESULTS The resulting digitised proteomic maps inclusive of 626 proteins were investigated in tandem with available clinical data to identify biomarkers of progression. The machine learning model using Boruta Feature Selection identified 25 biomarkers as being important to progression type classification (Area Under the Curve = 0.81, Accuracy = 0.72). Our functional enrichment analysis revealed associations with the complement cascade pathway, which is relevant to CKD as the kidney is particularly vulnerable to complement overactivation. This provides further evidence to target complement inhibition as a potential approach to modulating the progression of diabetic nephropathy. Proteins involved in the ubiquitin-proteasome pathway, a crucial protein degradation system, were also found to be significantly enriched. CONCLUSIONS The in-depth proteomic characterisation of this large-scale CKD cohort is a step toward generating mechanism-based hypotheses that might lend themselves to future drug targeting. Candidate biomarkers will be validated in samples from selected patients in other large non-dialysis CKD cohorts using a targeted mass spectrometric analysis.
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Affiliation(s)
- Carlos R Ramírez Medina
- Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
| | - Ibrahim Ali
- Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Ivona Baricevic-Jones
- Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Aghogho Odudu
- Division of Cardiovascular Sciences, The University of Manchester, Manchester, UK
| | - Moin A Saleem
- Bristol Renal and Children's Renal Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Anthony D Whetton
- Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK
| | - Philip A Kalra
- Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Nophar Geifman
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK
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Borch A, Bjerregaard AM, Araujo Barbosa de Lima V, Østrup O, Yde CW, Eklund AC, Mau-Sørensen M, Barra C, Svane IM, Nielsen FC, Funt SA, Lassen U, Hadrup SR. Neoepitope load, T cell signatures and PD-L2 as combined biomarker strategy for response to checkpoint inhibition immunotherapy. Front Genet 2023; 14:1058605. [PMID: 37035751 PMCID: PMC10076713 DOI: 10.3389/fgene.2023.1058605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 03/13/2023] [Indexed: 04/11/2023] Open
Abstract
Immune checkpoint inhibition for the treatment of cancer has provided a breakthrough in oncology, and several new checkpoint inhibition pathways are currently being investigated regarding their potential to provide additional clinical benefit. However, only a fraction of patients respond to such treatment modalities, and there is an urgent need to identify biomarkers to rationally select patients that will benefit from treatment. In this study, we explore different tumor associated characteristics for their association with favorable clinical outcome in a diverse cohort of cancer patients treated with checkpoint inhibitors. We studied 29 patients in a basket trial comprising 12 different tumor types, treated with 10 different checkpoint inhibition regimens. Our analysis revealed that even across this diverse cohort, patients achieving clinical benefit had significantly higher neoepitope load, higher expression of T cell signatures, and higher PD-L2 expression, which also correlated with improved progression-free and overall survival. Importantly, the combination of biomarkers serves as a better predictor than each of the biomarkers alone. Basket trials are frequently used in modern immunotherapy trial design, and here we identify a set of biomarkers of potential relevance across multiple cancer types, allowing for the selection of patients that most likely will benefit from immune checkpoint inhibition.
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Affiliation(s)
- Annie Borch
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Anne-Mette Bjerregaard
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
- Department of Bioinformatics and Datamining, Novo Nordisk, Bagsvaerd, Denmark
| | | | - Olga Østrup
- Center for Genomic Medicine, Rigshospitalet, Copenhagen, Denmark
| | | | | | | | - Carolina Barra
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, Lyngby, Denmark
| | - Inge Marie Svane
- National Center for Cancer Immune Therapy, Copenhagen University Hospital, Herlev, Denmark
| | - Finn Cilius Nielsen
- Center for Genomic Medicine, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Samuel A. Funt
- Weill Cornell Medical College, New York, NY, United States
| | - Ulrik Lassen
- Department of Oncology, Phase 1 Unit, Rigshospitalet, Copenhagen, Denmark
| | - Sine Reker Hadrup
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
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24
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Gerhards C, Kittel M, Ast V, Bugert P, Froelich MF, Hetjens M, Haselmann V, Neumaier M, Thiaucourt M. Humoral SARS-CoV-2 Immune Response in COVID-19 Recovered Vaccinated and Unvaccinated Individuals Related to Post-COVID-Syndrome. Viruses 2023; 15:v15020454. [PMID: 36851668 PMCID: PMC9966735 DOI: 10.3390/v15020454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/01/2023] [Accepted: 02/03/2023] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND The duration of anti-SARS-CoV-2-antibody detectability up to 12 months was examined in individuals after either single convalescence or convalescence and vaccination. Moreover, variables that might influence an anti-RBD/S1 antibody decline and the existence of a post-COVID-syndrome (PCS) were addressed. METHODS Forty-nine SARS-CoV-2-qRT-PCR-confirmed participants completed a 12-month examination of anti-SARS-CoV-2-antibody levels and PCS-associated long-term sequelae. Overall, 324 samples were collected. Cell-free DNA (cfDNA) was isolated and quantified from EDTA-plasma. As cfDNA is released into the bloodstream from dying cells, it might provide information on organ damage in the late recovery of COIVD-19. Therefore, we evaluated cfDNA concentrations as a biomarker for a PCS. In the context of antibody dynamics, a random forest-based logistic regression with antibody decline as the target was performed and internally validated. RESULTS The mean percentage dynamic related to the maximum measured value was 96 (±38)% for anti-RBD/S1 antibodies and 30 (±26)% for anti-N antibodies. Anti-RBD/S1 antibodies decreased in 37%, whereas anti-SARS-CoV-2-anti-N antibodies decreased in 86% of the subjects. Clinical anti-RBD/S1 antibody decline prediction models, including vascular and other diseases, were cross-validated (highest AUC 0.74). Long-term follow-up revealed no significant reduction in PCS prevalence but an increase in cognitive impairment, with no indication for cfDNA as a marker for a PCS. CONCLUSION Long-term anti-RBD/S1-antibody positivity was confirmed, and clinical parameters associated with declining titers were presented. A fulminant decrease in anti-SARS-CoV-2-anti-N antibodies was observed (mean change to maximum value 30 (±26)%). Anti-RBD/S1 antibody titers of SARS-CoV-2 recovered subjects boosted with a vaccine exceeded the maximum values measured after single infection by 235 ± 382-fold, with no influence on preexisting PCS. PCS long-term prevalence was 38.6%, with an increase in cognitive impairment compromising the quality of life. Quantified cfDNA measured in the early post-COVID-19 phase might not be an effective marker for PCS identification.
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Affiliation(s)
- Catharina Gerhards
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
- Correspondence:
| | - Maximilian Kittel
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
| | - Volker Ast
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
| | - Peter Bugert
- Institute of Transfusion Medicine and Immunology, Heidelberg University, 68167 Mannheim, Germany
- Medical Faculty Mannheim, European Center for Angioscience (ECAS), Heidelberg University, 68167 Mannheim, Germany
| | - Matthias F. Froelich
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Michael Hetjens
- Department of Biomedical Informatics, Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
| | - Verena Haselmann
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
| | - Michael Neumaier
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
| | - Margot Thiaucourt
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
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Bahcivanci B, Shafiha R, Gkoutos GV, Acharjee A. Associating transcriptomics data with inflammatory markers to understand tumour microenvironment in hepatocellular carcinoma. Cancer Med 2023; 12:696-711. [PMID: 35715992 PMCID: PMC9844659 DOI: 10.1002/cam4.4941] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 04/25/2022] [Accepted: 06/03/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Liver cancer is the fourth leading cause of cancer-related death globally which is estimated to reach more than 1 million deaths a year by 2030. Among liver cancer types, hepatocellular carcinoma (HCC) accounts for approximately 90% of the cases and is known to have a tumour promoting inflammation regardless of its underlying aetiology. However, current promising treatment approaches, such as immunotherapy, are partially effective for most of the patients due to the immunosuppressive nature of the tumour microenvironment (TME). Therefore, there is an urgent need to fully understand TME in HCC and discover new immune markers to eliminate resistance to immunotherapy. METHODS We analyse three microarray datasets, using unsupervised and supervised methods, in an effort to discover signature genes. First, univariate, and multivariate, feature selection methods, such as the Boruta algorithm, are applied. Subsequently, an optimisation procedure, which utilises random forest algorithm with three dataset pairs combinations, is performed. The resulting optimal gene sets are then combined and further subjected to network analysis and pathway enrichment analysis so as to obtain information related to their biological relevance. The microarray datasets were analysed via the MCP-counter, CIBERSORT, TIMER, EPIC, and quanTIseq deconvolution methods and an estimation of cell type abundances for each dataset sample were identified. The differences in the cell type abundances, between the adjacent and tumour sample groups, were then assessed using a Wilcoxon Rank Sum test (p-value < 0.05). RESULTS The optimal gene signature sets, derived from each of the data pairs combination, achieved AUC values ranging from 0.959 to 0.988 in external validation sets using Random Forest model. CLEC1B and PTTG1 genes are retrieved across each optimal set. Among the signature genes, PTTG1, AURKA, and UBE2C genes are found to be involved in the regulation of mitotic sister chromatid separation and anaphase-promoting complex (APC) dependent catabolic process (adjusted p-value < 0.001). Additionally, the application of deconvolution algorithms revealed significant changes in cell type abundances of Regulatory T (Treg) cells, M0 and M1 macrophages, and T CD8+ cells between adjacent and tumour samples. CONCLUSION We identified ECM1 gene as a potential immune-related marker acting through immune cell migration and macrophage polarisation. Our results indicate that macrophages, such as M0 macrophage and M1 macrophage cells, undergo significant changes in HCC TME. Moreover, our immune deconvolution approach revealed significant infiltration of Treg cells and M0 macrophages, and a significant decrease in T CD8+ cells and M1 macrophages in tumour samples.
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Affiliation(s)
- Basak Bahcivanci
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational BiologyUniversity of BirminghamBirminghamUK
| | - Roshan Shafiha
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational BiologyUniversity of BirminghamBirminghamUK
| | - Georgios V. Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational BiologyUniversity of BirminghamBirminghamUK
- Institute of Translational MedicineUniversity Hospitals Birmingham NHS Foundation TrustBirminghamUK
- NIHR Surgical Reconstruction and Microbiology Research CentreUniversity Hospital BirminghamBirminghamUK
- MRC Health Data Research UK (HDR UK)BirminghamUK
- NIHR Experimental Cancer Medicine CentreBirminghamUK
| | - Animesh Acharjee
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational BiologyUniversity of BirminghamBirminghamUK
- Institute of Translational MedicineUniversity Hospitals Birmingham NHS Foundation TrustBirminghamUK
- NIHR Surgical Reconstruction and Microbiology Research CentreUniversity Hospital BirminghamBirminghamUK
- MRC Health Data Research UK (HDR UK)BirminghamUK
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Lourenço J, McNaughton AL, Pley C, Obolski U, Gupta S, Matthews PC. Polymorphisms predicting phylogeny in hepatitis B virus. Virus Evol 2022; 9:veac116. [PMID: 36628296 PMCID: PMC9825179 DOI: 10.1093/ve/veac116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 11/30/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
Hepatitis B viruses (HBVs) are compact viruses with circular genomes of ∼3.2 kb in length. Four genes (HBx, Core, Surface, and Polymerase) generating seven products are encoded on overlapping reading frames. Ten HBV genotypes have been characterised (A-J), which may account for differences in transmission, outcomes of infection, and treatment response. However, HBV genotyping is rarely undertaken, and sequencing remains inaccessible in many settings. We set out to assess which amino acid (aa) sites in the HBV genome are most informative for determining genotype, using a machine learning approach based on random forest algorithms (RFA). We downloaded 5,496 genome-length HBV sequences from a public database, excluding recombinant sequences, regions with conserved indels, and genotypes I and J. Each gene was separately translated into aa, and the proteins concatenated into a single sequence (length 1,614 aa). Using RFA, we searched for aa sites predictive of genotype and assessed covariation among the sites with a mutual information-based method. We were able to discriminate confidently between genotypes A-H using ten aa sites. Half of these sites (5/10) sites were identified in Polymerase (Pol), of which 4/5 were in the spacer domain and one in reverse transcriptase. A further 4/10 sites were located in Surface protein and a single site in HBx. There were no informative sites in Core. Properties of the aa were generally not conserved between genotypes at informative sites. Among the highest co-varying pairs of sites, there were fifty-five pairs that included one of these 'top ten' sites. Overall, we have shown that RFA analysis is a powerful tool for identifying aa sites that predict the HBV lineage, with an unexpectedly high number of such sites in the spacer domain, which has conventionally been viewed as unimportant for structure or function. Our results improve ease of genotype prediction from limited regions of HBV sequences and may have future applications in understanding HBV evolution.
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Affiliation(s)
| | | | - Caitlin Pley
- Guy’s and St Thomas’ NHS Foundation Trust, Westminster Bridge Rd, London SE1 7EH, UK
| | - Uri Obolski
- School of Public Health, Tel Aviv University, Tel Aviv 6997801, Israel,Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Sunetra Gupta
- Department of Zoology, University of Oxford, Medawar Building for Pathogen Research, South Parks Road, Oxford OX1 3SY, UK
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Zhong J, Cheung CYY, Su X, Lee CH, Ru Y, Fong CHY, Liu Y, Cheung CKY, Lam KSL, Cai Z, Xu A. Specific triacylglycerol, diacylglycerol, and lyso-phosphatidylcholine species for the prediction of type 2 diabetes: a ~ 16-year prospective study in Chinese. Cardiovasc Diabetol 2022; 21:234. [PMCID: PMC9637304 DOI: 10.1186/s12933-022-01677-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 10/22/2022] [Indexed: 11/07/2022] Open
Abstract
Background Bioactive lipids play an important role in insulin secretion and sensitivity, contributing to the pathophysiology of type 2 diabetes (T2D). This study aimed to identify novel lipid species associated with incident T2D in a nested case–control study within a long-term prospective Chinese community-based cohort with a median follow-up of ~ 16 years. Methods Plasma samples from 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from the Hong Kong Cardiovascular Risk Factor Prevalence Study (CRISPS) were first analyzed using untargeted lipidomics. Potential predictive lipid species selected by the Boruta analysis were then verified by targeted lipidomics. The associations between these lipid species and incident T2D were assessed. Effects of novel lipid species on insulin secretion in mouse islets were investigated. Results Boruta analysis identified 16 potential lipid species. After adjustment for body mass index (BMI), triacylglycerol/high-density lipoprotein (TG/HDL) ratio and the presence of prediabetes, triacylglycerol (TG) 12:0_18:2_22:6, TG 16:0_11:1_18:2, TG 49:0, TG 51:1 and diacylglycerol (DG) 18:2_22:6 were independently associated with increased T2D risk, whereas lyso-phosphatidylcholine (LPC) O-16:0, LPC P-16:0, LPC O-18:0 and LPC 18:1 were independently associated with decreased T2D risk. Addition of the identified lipid species to the clinical prediction model, comprised of BMI, TG/HDL ratio and the presence of prediabetes, achieved a 3.8% improvement in the area under the receiver operating characteristics curve (AUROC) (p = 0.0026). Further functional study revealed that, LPC O-16:0 and LPC O-18:0 significantly potentiated glucose induced insulin secretion (GSIS) in a dose-dependent manner, whereas neither DG 18:2_22:6 nor TG 12:0_18:2_22:6 had any effect on GSIS. Conclusions Addition of the lipid species substantially improved the prediction of T2D beyond the model based on clinical risk factors. Decreased levels of LPC O-16:0 and LPC O-18:0 may contribute to the development of T2D via reduced insulin secretion. Supplementary Information The online version contains supplementary material available at 10.1186/s12933-022-01677-4.
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Affiliation(s)
- Junda Zhong
- grid.194645.b0000000121742757Department of Medicine, The University of Hong Kong, Hong Kong, China ,grid.194645.b0000000121742757State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Chloe Y. Y. Cheung
- grid.194645.b0000000121742757Department of Medicine, The University of Hong Kong, Hong Kong, China ,grid.194645.b0000000121742757State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Xiuli Su
- grid.221309.b0000 0004 1764 5980State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
| | - Chi-Ho Lee
- grid.194645.b0000000121742757Department of Medicine, The University of Hong Kong, Hong Kong, China ,grid.194645.b0000000121742757State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Yi Ru
- grid.221309.b0000 0004 1764 5980State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
| | - Carol H. Y. Fong
- grid.194645.b0000000121742757Department of Medicine, The University of Hong Kong, Hong Kong, China ,grid.194645.b0000000121742757State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Yan Liu
- grid.194645.b0000000121742757Department of Medicine, The University of Hong Kong, Hong Kong, China ,grid.194645.b0000000121742757State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Cynthia K. Y. Cheung
- grid.194645.b0000000121742757Department of Medicine, The University of Hong Kong, Hong Kong, China ,grid.194645.b0000000121742757State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Karen S. L. Lam
- grid.194645.b0000000121742757Department of Medicine, The University of Hong Kong, Hong Kong, China ,grid.194645.b0000000121742757State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Zongwei Cai
- grid.221309.b0000 0004 1764 5980State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
| | - Aimin Xu
- grid.194645.b0000000121742757Department of Medicine, The University of Hong Kong, Hong Kong, China ,grid.194645.b0000000121742757State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China ,grid.194645.b0000000121742757Department of Pharmacology & Pharmacy, The University of Hong Kong, Hong Kong, China
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Guryleva MV, Penzar DD, Chistyakov DV, Mironov AA, Favorov AV, Sergeeva MG. Investigation of the Role of PUFA Metabolism in Breast Cancer Using a Rank-Based Random Forest Algorithm. Cancers (Basel) 2022; 14:cancers14194663. [PMID: 36230586 PMCID: PMC9562210 DOI: 10.3390/cancers14194663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 09/15/2022] [Accepted: 09/21/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Polyunsaturated fatty acids (PUFAs) and their derivatives, oxylipins, are a constant focus of cancer research due to the relationship between cancer and processes of energy metabolism and inflammation, where a PUFA system is an active player. Only recently have methods been developed that allow for studying such complex systems. Using the Rank-based Random Forest (RF) model, we show that PUFA metabolism genes are critical for the pathogenesis of breast cancer (BC); BC subtypes differ in PUFA metabolism gene expression. The enrichment of BC subtypes with various genes associated with oxylipin signaling pathways indicates a different contribution of these compounds to the biology of subtypes. Abstract Polyunsaturated fatty acid (PUFA) metabolism is currently a focus in cancer research due to PUFAs functioning as structural components of the membrane matrix, as fuel sources for energy production, and as sources of secondary messengers, so called oxylipins, important players of inflammatory processes. Although breast cancer (BC) is the leading cause of cancer death among women worldwide, no systematic study of PUFA metabolism as a system of interrelated processes in this disease has been carried out. Here, we implemented a Boruta-based feature selection algorithm to determine the list of most important PUFA metabolism genes altered in breast cancer tissues compared with in normal tissues. A rank-based Random Forest (RF) model was built on the selected gene list (33 genes) and applied to predict the cancer phenotype to ascertain the PUFA genes involved in cancerogenesis. It showed high-performance of dichotomic classification (balanced accuracy of 0.94, ROC AUC 0.99) We also retrieved a list of the important PUFA genes (46 genes) that differed between molecular subtypes at the level of breast cancer molecular subtypes. The balanced accuracy of the classification model built on the specified genes was 0.82, while the ROC AUC for the sensitivity analysis was 0.85. Specific patterns of PUFA metabolic changes were obtained for each molecular subtype of breast cancer. These results show evidence that (1) PUFA metabolism genes are critical for the pathogenesis of breast cancer; (2) BC subtypes differ in PUFA metabolism genes expression; and (3) the lists of genes selected in the models are enriched with genes involved in the metabolism of signaling lipids.
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Affiliation(s)
- Mariia V. Guryleva
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119234 Moscow, Russia
| | - Dmitry D. Penzar
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119234 Moscow, Russia
- Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Dmitry V. Chistyakov
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119992 Moscow, Russia
- Correspondence: ; Tel.: +7-495-939-4332
| | - Andrey A. Mironov
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119234 Moscow, Russia
- Kharkevich Institute of Information Transmission Problems, Russian Academy of Sciences, 127051 Moscow, Russia
| | - Alexander V. Favorov
- Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991 Moscow, Russia
- School of Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Marina G. Sergeeva
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119992 Moscow, Russia
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Malik A, Mahajan N, Dar TA, Kim CB. C10Pred: A First Machine Learning Based Tool to Predict C10 Family Cysteine Peptidases Using Sequence-Derived Features. Int J Mol Sci 2022; 23:ijms23179518. [PMID: 36076915 PMCID: PMC9455582 DOI: 10.3390/ijms23179518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/17/2022] [Accepted: 08/20/2022] [Indexed: 12/02/2022] Open
Abstract
Streptococcus pyogenes, or group A Streptococcus (GAS), a gram-positive bacterium, is implicated in a wide range of clinical manifestations and life-threatening diseases. One of the key virulence factors of GAS is streptopain, a C10 family cysteine peptidase. Since its discovery, various homologs of streptopain have been reported from other bacterial species. With the increased affordability of sequencing, a significant increase in the number of potential C10 family-like sequences in the public databases is anticipated, posing a challenge in classifying such sequences. Sequence-similarity-based tools are the methods of choice to identify such streptopain-like sequences. However, these methods depend on some level of sequence similarity between the existing C10 family and the target sequences. Therefore, in this work, we propose a novel predictor, C10Pred, for the prediction of C10 peptidases using sequence-derived optimal features. C10Pred is a support vector machine (SVM) based model which is efficient in predicting C10 enzymes with an overall accuracy of 92.7% and Matthews’ correlation coefficient (MCC) value of 0.855 when tested on an independent dataset. We anticipate that C10Pred will serve as a handy tool to classify novel streptopain-like proteins belonging to the C10 family and offer essential information.
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Affiliation(s)
- Adeel Malik
- Institute of Intelligence Informatics Technology, Sangmyung University, Seoul 03016, Korea
- Correspondence: (A.M.); (C.-B.K.)
| | - Nitin Mahajan
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Tanveer Ali Dar
- Department of Clinical Biochemistry, University of Kashmir, Srinagar 190006, India
| | - Chang-Bae Kim
- Department of Biotechnology, Sangmyung University, Seoul 03016, Korea
- Correspondence: (A.M.); (C.-B.K.)
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Chienwichai P, Nogrado K, Tipthara P, Tarning J, Limpanont Y, Chusongsang P, Chusongsang Y, Tanasarnprasert K, Adisakwattana P, Reamtong O. Untargeted serum metabolomic profiling for early detection of Schistosoma mekongi infection in mouse model. Front Cell Infect Microbiol 2022; 12:910177. [PMID: 36061860 PMCID: PMC9433908 DOI: 10.3389/fcimb.2022.910177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/29/2022] [Indexed: 11/13/2022] Open
Abstract
Mekong schistosomiasis is a parasitic disease caused by blood flukes in the Lao People’s Democratic Republic and in Cambodia. The standard method for diagnosis of schistosomiasis is detection of parasite eggs from patient samples. However, this method is not sufficient to detect asymptomatic patients, low egg numbers, or early infection. Therefore, diagnostic methods with higher sensitivity at the early stage of the disease are needed to fill this gap. The aim of this study was to identify potential biomarkers of early schistosomiasis using an untargeted metabolomics approach. Serum of uninfected and S. mekongi-infected mice was collected at 2, 4, and 8 weeks post-infection. Samples were extracted for metabolites and analyzed with a liquid chromatography-tandem mass spectrometer. Metabolites were annotated with the MS-DIAL platform and analyzed with Metaboanalyst bioinformatic tools. Multivariate analysis distinguished between metabolites from the different experimental conditions. Biomarker screening was performed using three methods: correlation coefficient analysis; feature important detection with a random forest algorithm; and receiver operating characteristic (ROC) curve analysis. Three compounds were identified as potential biomarkers at the early stage of the disease: heptadecanoyl ethanolamide; picrotin; and theophylline. The levels of these three compounds changed significantly during early-stage infection, and therefore these molecules may be promising schistosomiasis markers. These findings may help to improve early diagnosis of schistosomiasis, thus reducing the burden on patients and limiting spread of the disease in endemic areas.
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Affiliation(s)
- Peerut Chienwichai
- Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Kathyleen Nogrado
- Department of Molecular Tropical Medicine and Genetics, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Phornpimon Tipthara
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Joel Tarning
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Yanin Limpanont
- Department of Social and Environmental Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Phiraphol Chusongsang
- Department of Social and Environmental Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Yupa Chusongsang
- Department of Social and Environmental Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Kanthi Tanasarnprasert
- Department of Social and Environmental Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Poom Adisakwattana
- Department of Helminthology, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Onrapak Reamtong
- Department of Molecular Tropical Medicine and Genetics, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- *Correspondence: Onrapak Reamtong,
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Forecasting Staphylococcus aureus Infections Using Genome-Wide Association Studies, Machine Learning, and Transcriptomic Approaches. mSystems 2022; 7:e0037822. [PMID: 35862809 PMCID: PMC9426533 DOI: 10.1128/msystems.00378-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Staphylococcus aureus is a major human and animal pathogen, colonizing diverse ecological niches within its hosts. Predicting whether an isolate will infect a specific host and its subsequent clinical fate remains unknown. In this study, we investigated the S. aureus pangenome using a curated set of 356 strains, spanning a wide range of hosts, origins, and clinical display and antibiotic resistance profiles. We used genome-wide association study (GWAS) and random forest (RF) algorithms to discriminate strains based on their origins and clinical sources. Here, we show that the presence of sak and scn can discriminate strains based on their host specificity, while other genes such as mecA are often associated with virulent outcomes. Both GWAS and RF indicated the importance of intergenic regions (IGRs) and coding DNA sequence (CDS) but not sRNAs in forecasting an outcome. Additional transcriptomic analyses performed on the most prevalent clonal complex 8 (CC8) clonal types, in media mimicking nasal colonization or bacteremia, indicated three RNAs as potential RNA markers to forecast infection, followed by 30 others that could serve as infection severity predictors. Our report shows that genetic association and transcriptomics are complementary approaches that will be combined in a single analytical framework to improve our understanding of bacterial pathogenesis and ultimately identify potential predictive molecular markers. IMPORTANCE Predicting the outcome of bacterial colonization and infections, based on extensive genomic and transcriptomic data from a given pathogen, would be of substantial help for clinicians in treating and curing patients. In this report, genome-wide association studies and random forest algorithms have defined gene combinations that differentiate human from animal strains, colonization from diseases, and nonsevere from severe diseases, while it revealed the importance of IGRs and CDS, but not small RNAs (sRNAs), in anticipating an outcome. In addition, transcriptomic analyses performed on the most prevalent clonal types, in media mimicking either nasal colonization or bacteremia, revealed significant differences and therefore potent RNA markers. Overall, the use of both genomic and transcriptomic data in a single analytical framework can enhance our understanding of bacterial pathogenesis.
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Jungbauer F, Gerhards C, Thiaucourt M, Behnes M, Rotter N, Schell A, Haselmann V, Neumaier M, Kittel M. Anosmia Testing as Early Detection of SARS-CoV-2 Positivity; A Prospective Study under Screening Conditions. LIFE (BASEL, SWITZERLAND) 2022; 12:life12070968. [PMID: 35888058 PMCID: PMC9319241 DOI: 10.3390/life12070968] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 12/11/2022]
Abstract
Sudden onset of anosmia is a phenomenon often associated with developing COVID-19 disease and has even been described as an initial isolated symptom in individual cases. In this case-control study, we investigated the feasibility of this condition as a suitable screening test in a population at risk. We performed a prospective study with a total of 313 subjects with suspected SARS-CoV-2 infection. In parallel to routine PCR analysis, a modified commercial scent test was performed to objectify the presence of potential anosmia as a predictor of SARS-CoV-2 positivity. Furthermore, a structured interview assessment of the participants was conducted. A total of 12.1% of the study participants had molecular genetic detection of SARS-CoV-2 infection in the nasopharyngeal swab. It could be demonstrated that these subjects had a significantly weaker olfactory identification performance of the scents. Further analysis of the collected data from the scent test and medical history via random forest (Boruta) algorithm showed that no improvement of the prediction power was achieved by this design. The assay investigated in this study may be suitable for screening general olfactory function. For the screening of COVID-19, it seems to be affected by too many external and internal biases and requires too elaborate and selective pre-test screening.
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Affiliation(s)
- Frederic Jungbauer
- Department for Otorhinolaryngology, Head- and Neck-Surgery, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (F.J.); (N.R.); (A.S.)
| | - Catharina Gerhards
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (C.G.); (M.T.); (V.H.); (M.N.)
| | - Margot Thiaucourt
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (C.G.); (M.T.); (V.H.); (M.N.)
| | - Michael Behnes
- German Center for Cardiovascular Research, First Department of Medicine, Faculty of Medicine Mannheim, University Medical Centre Mannheim (UMM), University of Heidelberg, DZHK, Partner Site Heidelberg/Mannheim, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany;
| | - Nicole Rotter
- Department for Otorhinolaryngology, Head- and Neck-Surgery, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (F.J.); (N.R.); (A.S.)
| | - Angela Schell
- Department for Otorhinolaryngology, Head- and Neck-Surgery, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (F.J.); (N.R.); (A.S.)
| | - Verena Haselmann
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (C.G.); (M.T.); (V.H.); (M.N.)
| | - Michael Neumaier
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (C.G.); (M.T.); (V.H.); (M.N.)
| | - Maximilian Kittel
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (C.G.); (M.T.); (V.H.); (M.N.)
- Correspondence: ; Tel.: +49-621-383-8417
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McCorkindale AN, Mundell HD, Guennewig B, Sutherland GT. Vascular Dysfunction Is Central to Alzheimer's Disease Pathogenesis in APOE e4 Carriers. Int J Mol Sci 2022; 23:7106. [PMID: 35806110 PMCID: PMC9266739 DOI: 10.3390/ijms23137106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 06/23/2022] [Accepted: 06/23/2022] [Indexed: 11/16/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia and the leading risk factor, after age, is possession of the apolipoprotein E epsilon 4 allele (APOE4). Approximately 50% of AD patients carry one or two copies of APOE4 but the mechanisms by which it confers risk are still unknown. APOE4 carriers are reported to demonstrate changes in brain structure, cognition, and neuropathology, but findings have been inconsistent across studies. In the present study, we used multi-modal data to characterise the effects of APOE4 on the brain, to investigate whether AD pathology manifests differently in APOE4 carriers, and to determine if AD pathomechanisms are different between carriers and non-carriers. Brain structural differences in APOE4 carriers were characterised by applying machine learning to over 2000 brain MRI measurements from 33,384 non-demented UK biobank study participants. APOE4 carriers showed brain changes consistent with vascular dysfunction, such as reduced white matter integrity in posterior brain regions. The relationship between APOE4 and AD pathology was explored among the 1260 individuals from the Religious Orders Study and Memory and Aging Project (ROSMAP). APOE4 status had a greater effect on amyloid than tau load, particularly amyloid in the posterior cortical regions. APOE status was also highly correlated with cerebral amyloid angiopathy (CAA). Bulk tissue brain transcriptomic data from ROSMAP and a similar dataset from the Mount Sinai Brain Bank showed that differentially expressed genes between the dementia and non-dementia groups were enriched for vascular-related processes (e.g., "angiogenesis") in APOE4 carriers only. Immune-related transcripts were more strongly correlated with AD pathology in APOE4 carriers with some transcripts such as TREM2 and positively correlated with pathology severity in APOE4 carriers, but negatively in non-carriers. Overall, cumulative evidence from the largest neuroimaging, pathology, and transcriptomic studies available suggests that vascular dysfunction is key to the development of AD in APOE4 carriers. However, further studies are required to tease out non-APOE4-specific mechanisms.
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Affiliation(s)
- Andrew N. McCorkindale
- Charles Perkins Centre and School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia; (A.N.M.); (H.D.M.)
| | - Hamish D. Mundell
- Charles Perkins Centre and School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia; (A.N.M.); (H.D.M.)
- Brain and Mind Centre and School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia
| | - Boris Guennewig
- Brain and Mind Centre and School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia
| | - Greg T. Sutherland
- Charles Perkins Centre and School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia; (A.N.M.); (H.D.M.)
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Chen JW, Dhahbi J. Identification of four serum miRNAs as potential markers to screen for thirteen cancer types. PLoS One 2022; 17:e0269554. [PMID: 35687572 PMCID: PMC9187062 DOI: 10.1371/journal.pone.0269554] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 05/23/2022] [Indexed: 02/07/2023] Open
Abstract
Introduction Cancer consistently remains one of the top causes of death in the United States every year, with many cancer deaths preventable if detected early. Circulating serum miRNAs are a promising, minimally invasive supplement or even an alternative to many current screening procedures. Many studies have shown that different serum miRNAs can discriminate healthy individuals from those with certain types of cancer. Although many of those miRNAs are often reported to be significant in one cancer type, they are also altered in other cancer types. Currently, very few studies have investigated serum miRNA biomarkers for multiple cancer types for general cancer screening purposes. Method To identify serum miRNAs that would be useful in screening multiple types of cancers, microarray cancer datasets were curated, yielding 13 different types of cancer with a total of 3352 cancer samples and 2809 non-cancer samples. The samples were divided into training and validation sets. One hundred random forest models were built using the training set to select candidate miRNAs. The selected miRNAs were then used in the validation set to see how well they differentiate cancer from normal samples in an independent dataset. Furthermore, the interactions between these miRNAs and their target mRNAs were investigated. Result The random forest models achieved an average of 97% accuracy in the training set with 95% bootstrap confidence interval of 0.9544 to 0.9778. The selected miRNAs were hsa-miR-663a, hsa-miR-6802-5p, hsa-miR-6784-5p, hsa-miR-3184-5p, and hsa-miR-8073. Each miRNA exhibited high area under the curve (AUC) value using receiver operating characteristic analysis. Moreover, the combination of four out of five miRNAs achieved the highest AUC value of 0.9815 with high sensitivity of 0.9773, indicating that these miRNAs have a high potential for cancer screening. miRNA-mRNA and protein-protein interaction analysis provided insights into how these miRNAs play a role in cancer.
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Affiliation(s)
- Joe W. Chen
- California University of Science and Medicine, Colton, CA, United States of America
| | - Joseph Dhahbi
- California University of Science and Medicine, Colton, CA, United States of America
- * E-mail:
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Hooshmand K, Halliday GM, Pineda SS, Sutherland GT, Guennewig B. Overlap between Central and Peripheral Transcriptomes in Parkinson’s Disease but Not Alzheimer’s Disease. Int J Mol Sci 2022; 23:ijms23095200. [PMID: 35563596 PMCID: PMC9104085 DOI: 10.3390/ijms23095200] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 12/20/2022] Open
Abstract
Most neurodegenerative disorders take decades to develop, and their early detection is challenged by confounding non-pathological ageing processes. Therefore, the discovery of genes and molecular pathways in both peripheral and brain tissues that are highly predictive of disease evolution is necessary. To find genes that influence Alzheimer’s disease (AD) and Parkinson’s disease (PD) pathogenesis, human RNA-Seq transcriptomic data from Brodmann Area 9 (BA9) of the dorsolateral prefrontal cortex (DLPFC), whole blood (WB), and peripheral blood mononuclear cells (PBMC) were analysed using a combination of differential gene expression and a random forest-based machine learning algorithm. The results suggest that there is little overlap between PD and AD, and the AD brain signature is unique mainly compared to blood-based samples. Moreover, the AD-BA9 was characterised by changes in ‘nervous system development’ with Myocyte-specific enhancer factor 2C (Mef2C), encoding a transcription factor that induces microglia activation, a prominent feature. The peripheral AD transcriptome was associated with alterations in ‘viral process’, and FYN, which has been previously shown to link amyloid-beta and tau, was the prominent feature. However, in the absence of any overlap with the central transcriptome, it is unclear whether peripheral FYN levels reflect AD severity or progression. In PD, central and peripheral signatures are characterised by anomalies in ‘exocytosis’ and specific genes related to the SNARE complex, including Vesicle-associated membrane protein 2 (VAMP2), Syntaxin 1A (STX1A), and p21-activated kinase 1 (PAK1). This is consistent with our current understanding of the physiological role of alpha-synuclein and how alpha-synuclein oligomers compromise vesicle docking and neurotransmission. Overall, the results describe distinct disease-specific pathomechanisms, both within the brain and peripherally, for the two most common neurodegenerative disorders.
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Affiliation(s)
- Kosar Hooshmand
- Brain and Mind Centre, Faculty of Medicine and Health, School of Medical Sciences, University of Sydney, Camperdown, NSW 2050, Australia; (K.H.); (G.M.H.); (S.S.P.)
| | - Glenda M. Halliday
- Brain and Mind Centre, Faculty of Medicine and Health, School of Medical Sciences, University of Sydney, Camperdown, NSW 2050, Australia; (K.H.); (G.M.H.); (S.S.P.)
| | - Sandy S. Pineda
- Brain and Mind Centre, Faculty of Medicine and Health, School of Medical Sciences, University of Sydney, Camperdown, NSW 2050, Australia; (K.H.); (G.M.H.); (S.S.P.)
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
| | - Greg T. Sutherland
- Charles Perkins Centre, Faculty of Medicine and Health, School of Medical Sciences, University of Sydney, Camperdown, NSW 2050, Australia;
| | - Boris Guennewig
- Brain and Mind Centre, Faculty of Medicine and Health, School of Medical Sciences, University of Sydney, Camperdown, NSW 2050, Australia; (K.H.); (G.M.H.); (S.S.P.)
- Correspondence:
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Salie MT, Yang J, Ramírez Medina CR, Zühlke LJ, Chishala C, Ntsekhe M, Gitura B, Ogendo S, Okello E, Lwabi P, Musuku J, Mtaja A, Hugo-Hamman C, El-Sayed A, Damasceno A, Mocumbi A, Bode-Thomas F, Yilgwan C, Amusa GA, Nkereuwem E, Shaboodien G, Da Silva R, Lee DCH, Frain S, Geifman N, Whetton AD, Keavney B, Engel ME. Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases. Clin Proteomics 2022; 19:7. [PMID: 35317720 PMCID: PMC8939134 DOI: 10.1186/s12014-022-09345-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 02/28/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Rheumatic heart disease (RHD) remains a major source of morbidity and mortality in developing countries. A deeper insight into the pathogenetic mechanisms underlying RHD could provide opportunities for drug repurposing, guide recommendations for secondary penicillin prophylaxis, and/or inform development of near-patient diagnostics. METHODS We performed quantitative proteomics using Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectrometry (SWATH-MS) to screen protein expression in 215 African patients with severe RHD, and 230 controls. We applied a machine learning (ML) approach to feature selection among the 366 proteins quantifiable in at least 40% of samples, using the Boruta wrapper algorithm. The case-control differences and contribution to Area Under the Receiver Operating Curve (AUC) for each of the 56 proteins identified by the Boruta algorithm were calculated by Logistic Regression adjusted for age, sex and BMI. Biological pathways and functions enriched for proteins were identified using ClueGo pathway analyses. RESULTS Adiponectin, complement component C7 and fibulin-1, a component of heart valve matrix, were significantly higher in cases when compared with controls. Ficolin-3, a protein with calcium-independent lectin activity that activates the complement pathway, was lower in cases than controls. The top six biomarkers from the Boruta analyses conferred an AUC of 0.90 indicating excellent discriminatory capacity between RHD cases and controls. CONCLUSIONS These results support the presence of an ongoing inflammatory response in RHD, at a time when severe valve disease has developed, and distant from previous episodes of acute rheumatic fever. This biomarker signature could have potential utility in recognizing different degrees of ongoing inflammation in RHD patients, which may, in turn, be related to prognostic severity.
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Affiliation(s)
- M Taariq Salie
- AFROStrep Research Group, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Jing Yang
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Carlos R Ramírez Medina
- Division of Informatics, Imaging, and Data Sciences, University of Manchester, Manchester , UK
| | - Liesl J Zühlke
- Division of Paediatric Cardiology, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital and University of Cape Town, Cape Town, South Africa
| | - Chishala Chishala
- Division of Cardiology, University of Cape Town & Groote Schuur Hospital, Cape Town, South Africa
| | - Mpiko Ntsekhe
- Division of Cardiology, University of Cape Town & Groote Schuur Hospital, Cape Town, South Africa
| | - Bernard Gitura
- Cardiology Department of Medicine, Kenyatta National Hospital, University of Nairobi, Nairobi, Kenya
| | - Stephen Ogendo
- Department of Surgery, University of Nairobi, Nairobi, Kenya
| | - Emmy Okello
- Departments of Adult and Pediatric Cardiology, Uganda Heart Institute, Kampala, Uganda
| | - Peter Lwabi
- Departments of Adult and Pediatric Cardiology, Uganda Heart Institute, Kampala, Uganda
| | - John Musuku
- University Teaching Hospital-Children's Hospital, University of Zambia, Lusaka, Zambia
| | - Agnes Mtaja
- University Teaching Hospital-Children's Hospital, University of Zambia, Lusaka, Zambia
| | - Christopher Hugo-Hamman
- Division of Paediatric Cardiology, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital and University of Cape Town, Cape Town, South Africa
- Rheumatic Heart Disease Clinic, Windhoek Central Hospital, Windhoek, Namibia
| | - Ahmed El-Sayed
- Department of Cardiothoracic Surgery, Alshaab Teaching Hospital, Alazhari Health Research Center, Alzaiem Alazhari University, Khartoum, Sudan
| | - Albertino Damasceno
- Faculty of Medicine, Eduardo Mondlane University/Nucleo de Investigaçao, Departamento de Medicina, Hospital Central de Maputo, Maputo, Mozambique
| | - Ana Mocumbi
- Faculdade de Medicina, Universidade Eduardo Mondlane, Maputo, Mozambique
- Division of Non Communicable Diseases, Instituto Nacional de Saude, Vila de Marracuene, Mozambique
| | - Fidelia Bode-Thomas
- Departments of Paediatrics, Jos University Teaching Hospital, Jos, Plateau State, Nigeria
| | - Christopher Yilgwan
- Departments of Paediatrics, Jos University Teaching Hospital, Jos, Plateau State, Nigeria
| | - Ganiyu A Amusa
- Department of Medicine, University of Jos and Jos University Teaching Hospital, Jos, Nigeria
| | - Esin Nkereuwem
- Departments of Paediatrics, Jos University Teaching Hospital, Jos, Plateau State, Nigeria
| | - Gasnat Shaboodien
- Department of Medicine and Cape Heart Institute (CHI), University of Cape Town, Cape Town, South Africa
| | - Rachael Da Silva
- Stoller Biomarker Discovery Institute, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Dave Chi Hoo Lee
- Stoller Biomarker Discovery Institute, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Simon Frain
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Nophar Geifman
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Anthony D Whetton
- Faculty of Biosciences and Medicine, University of Surrey, Guildford, UK
| | - Bernard Keavney
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Manchester Heart Institute, Manchester University NHS Foundation Trust, Manchester, UK
| | - Mark E Engel
- AFROStrep Research Group, Department of Medicine, University of Cape Town, Cape Town, South Africa.
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Koppad S, Basava A, Nash K, Gkoutos GV, Acharjee A. Machine Learning-Based Identification of Colon Cancer Candidate Diagnostics Genes. BIOLOGY 2022; 11:biology11030365. [PMID: 35336739 PMCID: PMC8944988 DOI: 10.3390/biology11030365] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/16/2022] [Accepted: 02/23/2022] [Indexed: 01/27/2023]
Abstract
Simple Summary We developed a predictive approach using different machine learning methods to identify a number of genes that can potentially serve as novel diagnostic colon cancer biomarkers. Abstract Background: Colorectal cancer (CRC) is the third leading cause of cancer-related death and the fourth most commonly diagnosed cancer worldwide. Due to a lack of diagnostic biomarkers and understanding of the underlying molecular mechanisms, CRC’s mortality rate continues to grow. CRC occurrence and progression are dynamic processes. The expression levels of specific molecules vary at various stages of CRC, rendering its early detection and diagnosis challenging and the need for identifying accurate and meaningful CRC biomarkers more pressing. The advances in high-throughput sequencing technologies have been used to explore novel gene expression, targeted treatments, and colon cancer pathogenesis. Such approaches are routinely being applied and result in large datasets whose analysis is increasingly becoming dependent on machine learning (ML) algorithms that have been demonstrated to be computationally efficient platforms for the identification of variables across such high-dimensional datasets. Methods: We developed a novel ML-based experimental design to study CRC gene associations. Six different machine learning methods were employed as classifiers to identify genes that can be used as diagnostics for CRC using gene expression and clinical datasets. The accuracy, sensitivity, specificity, F1 score, and area under receiver operating characteristic (AUROC) curve were derived to explore the differentially expressed genes (DEGs) for CRC diagnosis. Gene ontology enrichment analyses of these DEGs were performed and predicted gene signatures were linked with miRNAs. Results: We evaluated six machine learning classification methods (Adaboost, ExtraTrees, logistic regression, naïve Bayes classifier, random forest, and XGBoost) across different combinations of training and test datasets over GEO datasets. The accuracy and the AUROC of each combination of training and test data with different algorithms were used as comparison metrics. Random forest (RF) models consistently performed better than other models. In total, 34 genes were identified and used for pathway and gene set enrichment analysis. Further mapping of the 34 genes with miRNA identified interesting miRNA hubs genes. Conclusions: We identified 34 genes with high accuracy that can be used as a diagnostics panel for CRC.
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Affiliation(s)
- Saraswati Koppad
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Mangalore 575025, India; (S.K.); (A.B.)
| | - Annappa Basava
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Mangalore 575025, India; (S.K.); (A.B.)
| | - Katrina Nash
- College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK;
| | - Georgios V. Gkoutos
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK;
- Institute of Translational Medicine, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham B15 2WB, UK
- MRC Health Data Research UK (HDR UK), Midlands Site, Birmingham B15 2TT, UK
- NIHR Experimental Cancer Medicine Centre, Birmingham B15 2TT, UK
- NIHR Biomedical Research Centre, University Hospital Birmingham, Birmingham B15 2TT, UK
| | - Animesh Acharjee
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK;
- Institute of Translational Medicine, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham B15 2WB, UK
- Correspondence: ; Tel.: +44-07403642022
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Liu D, Wang X, Li L, Jiang Q, Li X, Liu M, Wang W, Shi E, Zhang C, Wang Y, Zhang Y, Wang L. Machine Learning-Based Model for the Prognosis of Postoperative Gastric Cancer. Cancer Manag Res 2022; 14:135-155. [PMID: 35027848 PMCID: PMC8752070 DOI: 10.2147/cmar.s342352] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/23/2021] [Indexed: 12/11/2022] Open
Abstract
Background The use of machine learning (ML) in predicting disease prognosis has increased, and scientists have adopted different methods for cancer classification to optimize the early screening of cancer to determine its prognosis in advance. In this study, we aimed at improving the prediction accuracy of gastric cancer in postoperation patients by constructing a highly effective prognostic model. Methods The study used postoperative gastric cancer patient data from the SEER database. The LASSO regression method was used to construct a clinical prognostic model, and four machine learning methods (Boruta algorithm, neural network, support vector machine, and random forest) were used to screen and recombine the features to construct an ML prognostic model. Clinical information on 955 postoperative gastric cancer patients collected from the Affiliated Tumor Hospital of Harbin Medical University was used for external verification. Results Experimental results showed that the AUC values of 1, 3 and 5 years in the training set, validation set and external validation set of clinical prognosis model and ML prognosis model directly established by LASSO regression are all around 0.8. Conclusion Both models can accurately evaluate the prognosis of postoperative patients with gastric cancer, which may be helpful for accurate and personalized treatment of postoperative patients with gastric cancer.
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Affiliation(s)
- Donghui Liu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang Province, People’s Republic of China
- Department of Oncology, Heilongjiang Provincial Hospital, Harbin, Heilongjiang Province, People’s Republic of China
| | - Xuyao Wang
- Department of Pharmacy, Harbin Second Hospital, Harbin, Heilongjiang Province, People’s Republic of China
| | - Long Li
- Department of General Surgery, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, People’s Republic of China
| | - Qingxin Jiang
- Department of General Surgery, Harbin 242 Hospital of Genertec Medical, Harbin, Heilongjiang Province, People’s Republic of China
| | - Xiaoxue Li
- Department of Oncology, Heilongjiang Provincial Hospital, Harbin, Heilongjiang Province, People’s Republic of China
| | - Menglin Liu
- Department of Oncology, Heilongjiang Provincial Hospital, Harbin, Heilongjiang Province, People’s Republic of China
| | - Wenxin Wang
- Department of Oncology, Heilongjiang Provincial Hospital, Harbin, Heilongjiang Province, People’s Republic of China
| | - Enhong Shi
- Department of Oncology, Heilongjiang Provincial Hospital, Harbin, Heilongjiang Province, People’s Republic of China
| | - Chenyao Zhang
- Department of Oncology, Heilongjiang Provincial Hospital, Harbin, Heilongjiang Province, People’s Republic of China
| | - Yinghui Wang
- Department of Oncology, Heilongjiang Provincial Hospital, Harbin, Heilongjiang Province, People’s Republic of China
| | - Yan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang Province, People’s Republic of China
- Correspondence: Yan Zhang School of Life Science and Technology, Harbin Institute of Technology, No. 92 Xidazhi Street, Nangang District, Harbin, Heilongjiang, People’s Republic of ChinaTel +86 13936253249 Email
| | - Liru Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang Province, People’s Republic of China
- Department of Oncology, Heilongjiang Provincial Hospital, Harbin, Heilongjiang Province, People’s Republic of China
- Liru Wang Department of Oncology, Heilongjiang Provincial Hospital, No. 82 Zhongshan Road, Xiangfang District, Harbin, Heilongjiang, People’s Republic of China, Tel +86 13633609001 Email
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Smith BJ, Silva-Costa LC, Martins-de-Souza D. Human disease biomarker panels through systems biology. Biophys Rev 2021; 13:1179-1190. [PMID: 35059036 PMCID: PMC8724340 DOI: 10.1007/s12551-021-00849-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 10/01/2021] [Indexed: 12/23/2022] Open
Abstract
As more uses for biomarkers are sought after for an increasing number of disease targets, single-target biomarkers are slowly giving way for biomarker panels. These panels incorporate various sources of biomolecular and clinical data to guarantee a higher robustness and power of separation for a clinical test. Multifactorial diseases such as psychiatric disorders show great potential for clinical use, assisting medical professionals during the analysis of risk and predisposition, disease diagnosis and prognosis, and treatment applicability and efficacy. More specific tests are also being developed to assist in ruling out, distinguishing between, and confirming suspicions of multifactorial diseases, as well as to predict which therapy option may be the best option for a given patient's biochemical profile. As more complex datasets are entering the field, involving multi-omic approaches, systems biology has stepped in to facilitate the discovery and validation steps during biomarker panel generation. Filtering biomolecules and clinical data, pre-validating and cross-validating potential biomarkers, generating final biomarker panels, and testing the robustness and applicability of those panels are all beginning to rely on machine learning and systems biology and research in this area will only benefit from advances in these approaches.
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Affiliation(s)
- Bradley J. Smith
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Licia C. Silva-Costa
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Daniel Martins-de-Souza
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
- Instituto Nacional de Biomarcadores Em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Científico E Tecnológico, Sao Paulo, Brazil
- Experimental Medicine Research Cluster (EMRC), University of Campinas, Campinas, Brazil
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Use of Multi-Temporal LiDAR to Quantify Fertilization Effects on Stand Volume and Biomass in Late-Rotation Coastal Douglas-Fir Forests. FORESTS 2021. [DOI: 10.3390/f12050517] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Forest fertilization is common in coastal British Columbia as a means to increase wood production and potentially enhance carbon sequestration. Generally, the effects of fertilization are determined by measuring sample plots pre- and post-treatment, resulting in fertilization effects being determined for a limited portion of the treatment area. Applications of remote sensing-based enhanced forest inventories have allowed for estimations to expand to the wider forested area. However, these applications have not focused on monitoring the effects of silvicultural treatments. The objective of this research was to examine if a multi-temporal application of the LiDAR area-based method can be used to detect the fertilization effects on volume, biomass, and height in a second-growth Douglas-fir (Pseudotsuga menziesii) stand. The study area on Vancouver Island was fertilized in January 2007, and sample plots were established in 2011. LiDAR acquisitions were made in 2004, prior to fertilization, and in 2008, 2011, and 2016, covering both treated and untreated areas. A total of 29 paired LiDAR blocks, comprised of four 20 m resolution raster cells, were selected on either side of the fertilization boundary for analysis of the effects across several different stand types differing in the percentage of Douglas-fir, site index, and age. Random forest (RF) plot-level models were developed to estimate total stem volume and total stem biomass for each year of LiDAR acquisition using an area-based approach. Plot level results showed an increase in stem volume by 13% fertilized over control from 2005 to 2011, which was similar to a 14% increase in above-ground carbon stocks estimated using a tree-ring stand reconstruction approach. Plot-level RF models showed R2 values of 0.86 (volume) and 0.92 (biomass) with relative cross-validated root mean square errors of 12.5% (volume) and 11.9% (biomass). For both the sample plots and LiDAR blocks, statistical results indicated no significant differences in volume or biomass between treatments. However, significant differences in height increments were detected between treatments in LiDAR blocks. The results from this research highlight the promising potential for the use of enhanced forest inventory methods to rapidly expand the assessment of treatment effects beyond sample plots to the stand, block, or landscape level.
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