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Gan T, Qu LX, Qu S, Qi YY, Zhang YM, Wang YN, Li Y, Liu LJ, Shi SF, Lv JC, Zhang H, Peng YJ, Zhou XJ. Unveiling biomarkers and therapeutic targets in IgA nephropathy through large-scale blood transcriptome analysis. Int Immunopharmacol 2024; 132:111905. [PMID: 38552291 DOI: 10.1016/j.intimp.2024.111905] [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: 03/04/2024] [Revised: 03/12/2024] [Accepted: 03/18/2024] [Indexed: 05/01/2024]
Abstract
INTRODUCTION IgA nephropathy (IgAN) is the most prevalent form of glomerulonephritis. Unfortunately, molecular biomarkers for IgAN derived from omics studies are still lacking. This research aims to identify critical genes associated with IgAN through large-scale blood transcriptome analysis. METHODS We constructed novel blood transcriptome profiles from peripheral blood mononuclear cells (PBMCs) of 53 Chinese IgAN patients and 28 healthy individuals. Our analysis included GO, KEGG, and GSEA for biological pathways. We analyzed immune cell profiles with CIBERSORT and constructed PPI networks with STRING, visualized in Cytoscape. Key differentially expressed genes (DEGs) were identified using CytoHubba and MCODE. We assessed the correlation between gene expressions and clinical data to evaluate clinical significance and identified hub genes through machine learning, validated with an open-access dataset. Potential drugs were explored using the CMap database. RESULTS We identified 333 DEGs between IgAN patients and healthy controls, mainly related to immune response and inflammation. Key pathways included NK cell mediated cytotoxicity, complement and coagulation cascades, antigen processing, and B cell receptor signaling. Cytoscape revealed 16 clinically significant genes (including KIR2DL1, KIR2DL3, VISIG4, C1QB, and C1QC, associated with sub-phenotype and prognosis). Machine learning identified two hub genes (KLRC1 and C1QB) for a diagnostic model of IgAN with 0.92 accuracy, validated at 1.00 against the GSE125818 dataset. Sirolimus, calcifediol, and efaproxiral were suggested as potential therapeutic agents. CONCLUSION Key DEGs, particularly VISIG4, KLRC1, and C1QB, emerge as potential specific markers for IgAN, paving the way for future targeted personalized treatment options.
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Affiliation(s)
- Ting Gan
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Lu-Xi Qu
- Guanghua School of Management, Peking University, Beijing 100871, China
| | - Shu Qu
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Yuan-Yuan Qi
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Yue-Miao Zhang
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Yan-Na Wang
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Yang Li
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Li-Jun Liu
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Su-Fang Shi
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Ji-Cheng Lv
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Hong Zhang
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Yi-Jie Peng
- National Institute of Health Data Science, Peking University, Beijing 100191, China; Xiangjiang Laboratory, Changsha 410205, China.
| | - Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China.
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Vafaei Sadr A, Bülow R, von Stillfried S, Schmitz NEJ, Pilva P, Hölscher DL, Ha PP, Schweiker M, Boor P. Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study. Lancet Digit Health 2024; 6:e58-e69. [PMID: 37996339 PMCID: PMC10728828 DOI: 10.1016/s2589-7500(23)00219-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 10/04/2023] [Accepted: 10/16/2023] [Indexed: 11/25/2023]
Abstract
BACKGROUND Deep learning is a promising way to improve health care. Image-processing medical disciplines, such as pathology, are expected to be transformed by deep learning. The first clinically applicable deep-learning diagnostic support tools are already available in cancer pathology, and their number is increasing. However, data on the environmental sustainability of these tools are scarce. We aimed to conduct an environmental-sustainability analysis of a theoretical implementation of deep learning in patient-care pathology. METHODS For this modelling study, we first assembled and calculated relevant data and parameters of a digital-pathology workflow. Data were breast and prostate specimens from the university clinic at the Institute of Pathology of the Rheinisch-Westfälische Technische Hochschule Aachen (Aachen, Germany), for which commercially available deep learning was already available. Only specimens collected between Jan 1 and Dec 31, 2019 were used, to omit potential biases due to the COVID-19 pandemic. Our final selection was based on 2 representative weeks outside holidays, covering different types of specimens. To calculate carbon dioxide (CO2) or CO2 equivalent (CO2 eq) emissions of deep learning in pathology, we gathered relevant data for exact numbers and sizes of whole-slide images (WSIs), which were generated by scanning histopathology samples of prostate and breast specimens. We also evaluated different data input scenarios (including all slide tiles, only tiles containing tissue, or only tiles containing regions of interest). To convert estimated energy consumption from kWh to CO2 eq, we used the internet protocol address of the computational server and the Electricity Maps database to obtain information on the sources of the local electricity grid (ie, renewable vs non-renewable), and estimated the number of trees and proportion of the local and world's forests needed to sequester the CO2 eq emissions. We calculated the computational requirements and CO2 eq emissions of 30 deep-learning models that varied in task and size. The first scenario represented the use of one commercially available deep-learning model for one task in one case (1-task), the second scenario considered two deep-learning models for two tasks per case (2-task), the third scenario represented a future, potentially automated workflow that could handle 7 tasks per case (7-task), and the fourth scenario represented the use of a single potential, large, computer-vision model that could conduct multiple tasks (multitask). We also compared the performance (ie, accuracy) and CO2 eq emissions of different deep-learning models for the classification of renal cell carcinoma on WSIs, also from Rheinisch-Westfälische Technische Hochschule Aachen. We also tested other approaches to reducing CO2 eq emissions, including model pruning and an alternative method for histopathology analysis (pathomics). FINDINGS The pathology database contained 35 552 specimens (237 179 slides), 6420 of which were prostate specimens (10 115 slides) and 11 801 of which were breast specimens (19 763 slides). We selected and subsequently digitised 140 slides from eight breast-cancer cases and 223 slides from five prostate-cancer cases. Applying large deep-learning models on all WSI tiles of prostate and breast pathology cases would result in yearly CO2 eq emissions of 7·65 metric tons (t; 95% CI 7·62-7·68) with the use of a single deep-learning model per case; yearly CO2 eq emissions were up to 100·56 t (100·21-100·99) with the use of seven deep-learning models per case. CO2 eq emissions for different deep-learning model scenarios, data inputs, and deep-learning model sizes for all slides varied from 3·61 t (3·59-3·63) to 2795·30 t (1177·51-6482·13. For the estimated number of overall pathology cases worldwide, the yearly CO2 eq emissions varied, reaching up to 16 megatons (Mt) of CO2 eq, requiring up to 86 590 km2 (0·22%) of world forest to sequester the CO2 eq emissions. Use of the 7-task scenario and small deep-learning models on slides containing tissue only could substantially reduce CO2 eq emissions worldwide by up to 141 times (0·1 Mt, 95% CI 0·1-0·1). Considering the local environment in Aachen, Germany, the maximum CO2 eq emission from the use of deep learning in digital pathology only would require 32·8% (95% CI 13·8-76·6) of the local forest to sequester the CO2 eq emissions. A single pathomics run on a tissue could provide information that was comparable to or even better than the output of multitask deep-learning models, but with 147 times reduced CO2 eq emissions. INTERPRETATION Our findings suggest that widespread use of deep learning in pathology might have considerable global-warming potential. The medical community, policy decision makers, and the public should be aware of this potential and encourage the use of CO2 eq emissions reduction strategies where possible. FUNDING German Research Foundation, European Research Council, German Federal Ministry of Education and Research, Health, Economic Affairs and Climate Action, and the Innovation Fund of the Federal Joint Committee.
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Affiliation(s)
- Alireza Vafaei Sadr
- Institute of Pathology, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany; Department of Public Health Sciences, College of Medicine, Pennsylvania State University, Hershey, PA, USA
| | - Roman Bülow
- Institute of Pathology, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Saskia von Stillfried
- Institute of Pathology, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Nikolas E J Schmitz
- Institute of Pathology, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Pourya Pilva
- Institute of Pathology, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - David L Hölscher
- Institute of Pathology, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Peiman Pilehchi Ha
- Healthy Living Spaces Lab, Institute for Occupational, Social and Environmental Medicine, Medical Faculty, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Marcel Schweiker
- Healthy Living Spaces Lab, Institute for Occupational, Social and Environmental Medicine, Medical Faculty, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Peter Boor
- Institute of Pathology, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany; Department of Nephrology and Immunology, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany.
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Haaskjold YL, Lura NG, Bjørneklett R, Bostad LS, Knoop T, Bostad L. Long-term follow-up of IgA nephropathy: clinicopathological features and predictors of outcomes. Clin Kidney J 2023; 16:2514-2522. [PMID: 38046027 PMCID: PMC10689167 DOI: 10.1093/ckj/sfad154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Indexed: 12/05/2023] Open
Abstract
Background The establishment of the Oxford classification and newly developed prediction models have improved the prognostic information for immunoglobulin A nephropathy (IgAN). Considering new treatment options, optimizing prognostic information and improving existing prediction models are favorable. Methods We used random forest survival analysis to select possible predictors of end-stage kidney disease among 37 candidate variables in a cohort of 232 patients with biopsy-proven IgAN retrieved from the Norwegian Kidney Biopsy Registry. The predictive value of variables with relative importance >5% was assessed using concordance statistics and the Akaike information criterion. Pearson's correlation coefficient was used to identify correlations between the selected variables. Results The median follow-up period was 13.7 years. An isolated analysis of histological variables identified six variables with relative importance >5%: T %, segmental glomerular sclerosis without characteristics associated with other subtypes (not otherwise specified, NOS), normal glomeruli, global sclerotic glomeruli, segmental adherence and perihilar glomerular sclerosis. When histopathological and clinical variables were combined, estimated glomerular filtration rate (eGFR), proteinuria and serum albumin were added to the list. T % showed a better prognostic value than tubular atrophy/interstitial fibrosis (T) lesions with C-indices at 0.74 and 0.67 and was highly correlated with eGFR. Analysis of the subtypes of segmental glomerulosclerosis (S) lesions revealed that NOS and perihilar glomerular sclerosis were associated with adverse outcomes. Conclusions Reporting T lesions as a continuous variable, normal glomeruli and subtypes of S lesions could provide clinicians with additional prognostic information and contribute to the improved performance of the Oxford classification and prognostic tools.
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Affiliation(s)
- Yngvar Lunde Haaskjold
- Department of Medicine, Haukeland University Hospital, Bergen, Norway
- Renal Research Group, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Njål Gjærde Lura
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Rune Bjørneklett
- Renal Research Group, Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Emergency Care Clinic, Haukeland University Hospital, Bergen, Norway
| | - Lars Sigurd Bostad
- Renal Research Group, Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Emergency Care Clinic, Haukeland University Hospital, Bergen, Norway
| | - Thomas Knoop
- Department of Medicine, Haukeland University Hospital, Bergen, Norway
- Renal Research Group, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Leif Bostad
- Renal Research Group, Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Pathology, Haukeland University Hospital, Bergen, Norway
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Cattran DC, Floege J, Coppo R. Evaluating Progression Risk in Patients With Immunoglobulin A Nephropathy. Kidney Int Rep 2023; 8:2515-2528. [PMID: 38106572 PMCID: PMC10719597 DOI: 10.1016/j.ekir.2023.09.020] [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: 06/06/2023] [Revised: 09/05/2023] [Accepted: 09/08/2023] [Indexed: 12/19/2023] Open
Abstract
The highly variable rate of decline in kidney function in patients with immunoglobulin A nephropathy (IgAN) provides a major clinical challenge. Predicting which patients will progress to kidney failure, and how quickly, is difficult. Multiple novel therapies are likely to be approved in the short-term, but clinicians lack the tools to identify patients most likely to benefit from specific treatments at the right time. Noninvasive and validated markers for selecting at-risk patients and longitudinal monitoring are urgently needed. This review summarizes what is known about demographic, clinical, and histopathologic prognostic markers in the clinician's toolkit, including the International IgAN Prediction Tool. We also briefly review what is known on these topics in children and adolescents with IgAN. Although helpful, currently used markers leave clinicians heavily reliant on histologic features from the diagnostic kidney biopsy and standard clinical data to guide treatment choice, and very few noninvasive markers reflect treatment efficacy over time. Novel prognostic and predictive markers are under clinical investigation, with considerable progress being made in markers of complement activation. Other areas of research are the interplay between gut microbiota and galactose-deficient IgA1 expression; microRNAs; imaging; artificial intelligence; and markers of fibrosis. Given the rate of therapeutic advancement, the remaining gaps in biomarker research need to be addressed. We finish by describing our route to clinical utility of predictive and prognostic markers in IgAN. This route will provide us with the chance to improve IgAN prognosis by using robust, clinically practical markers to inform patient care.
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Affiliation(s)
| | - Jürgen Floege
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany
| | - Rosanna Coppo
- Fondazione Ricerca Molinette, Regina Margherita Hospital, Turin, Italy
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Bülow RD, Hölscher DL, Costa IG, Boor P. Extending the landscape of omics technologies by pathomics. NPJ Syst Biol Appl 2023; 9:38. [PMID: 37550386 PMCID: PMC10406938 DOI: 10.1038/s41540-023-00301-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/31/2023] [Indexed: 08/09/2023] Open
Affiliation(s)
- Roman D Bülow
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
| | - David L Hölscher
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
| | - Ivan G Costa
- Institute for Computational Genomics, RWTH Aachen University Clinic, Aachen, Germany
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany.
- Department of Nephrology and Immunology, RWTH Aachen University Clinic, Aachen, Germany.
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Yu K, Ding L, An X, Yang Y, Zhang X, Li L, Wang C, Bai F, Yang X. APOC1 exacerbates renal fibrosis through the activation of the NF-κB signaling pathway in IgAN. Front Pharmacol 2023; 14:1181435. [PMID: 37305534 PMCID: PMC10248024 DOI: 10.3389/fphar.2023.1181435] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 05/02/2023] [Indexed: 06/13/2023] Open
Abstract
Introduction: IgA nephropathy (IgAN) is the most common disease leading to end-stage renal disease, and tubular fibrosis represents an important risk factor for disease progression. However, research on early molecular diagnostic indicators of tubular fibrosis and the mechanisms underlying disease progression is still lacking. Methods: The GSE93798 dataset was downloaded from the GEO database. DEGs were screened and analyzed for GO and KEGG enrichment in IgAN. The least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithms were applied to screen for hub secretory genes. The expression and diagnostic efficacy of hub genes were confirmed by the GSE35487 dataset. ELISA was applied to detect the expression of APOC1 in serum. The expression and localization of hub genes in IgAN were verified by the expression of IHC and IF in human kidney tissues, and the correlation of expression with clinical data was verified in the Nephroseq database. Finally, cellular experiments clarified the role of hub genes in the signaling pathway. Results: A total of 339 DEGs were identified in IgAN, of which 237 were upregulated and 102 downregulated. The KEGG signaling pathway is enriched in the ECM-receptor interaction and AGE-RAGE signaling pathway. APOC1, ALB, CCL8, CXCL2, SRPX2, and TGFBI identified six hub secretory genes using the LASSO and SVM-RFE algorithms. In vivo and in vitro experiments demonstrated that APOC1 expression was elevated in IgAN. The serum concentration of APOC1 was 1.232 ± 0.1812 μg/ml in IgAN patients, whereas it was 0.3956 ± 0.1233 μg/ml in healthy individuals. APOC1 exhibited high diagnostic efficacy for IgAN (AUC of 99.091%, specificity of 95.455%, and sensitivity of 99.141%) in the GSE93798 dataset. APOC1 expression negatively correlated with eGFR (R 2 = 0.2285, p = 0.0385) and positively correlated with serum creatinine (R 2 = 0.41, p = 0.000567) in IgAN. APOC1 exacerbated renal fibrosis, possibly in part by activating the NF-κB pathway in IgAN. Conclusion: APOC1 was identified as the core secretory gene of IgAN, which was closely associated with blood creatinine and eGFR and had significant efficacy in the diagnosis of IgAN. Mechanistic studies revealed that the knockdown of APOC1 could improve IgAN renal fibrosis by inhibiting the NF pathway, which may be a potential therapeutic target for improving renal fibrosis in IgAN.
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Affiliation(s)
- Kuipeng Yu
- Department of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Department of Blood Purification, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Laboratory of Basic Medical Sciences, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Lin Ding
- Department of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xin An
- Department of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Yanjiang Yang
- Department of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xiaoning Zhang
- Department of Nephrology, Shengli Oilfield Central Hospital, Dongying, Shandong, China
| | - Luyao Li
- Department of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Chunjie Wang
- Department of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Fang Bai
- Department of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xiangdong Yang
- Department of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Department of Blood Purification, Qilu Hospital of Shandong University, Jinan, Shandong, China
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Hölscher DL, Bouteldja N, Joodaki M, Russo ML, Lan YC, Sadr AV, Cheng M, Tesar V, Stillfried SV, Klinkhammer BM, Barratt J, Floege J, Roberts ISD, Coppo R, Costa IG, Bülow RD, Boor P. Next-Generation Morphometry for pathomics-data mining in histopathology. Nat Commun 2023; 14:470. [PMID: 36709324 PMCID: PMC9884209 DOI: 10.1038/s41467-023-36173-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 01/16/2023] [Indexed: 01/29/2023] Open
Abstract
Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretable, quantitative, morphometric features in non-tumour kidney histology. We use two internal and three external, multi-centre cohorts to analyse over 1000 kidney biopsies and nephrectomies. By associating morphometric features with clinical parameters, we confirm previous concepts and reveal unexpected relations. We show that the extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy. We introduce single-structure morphometric analysis by applying techniques from single-cell transcriptomics, identifying distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression. Our study provides a concept for Next-generation Morphometry (NGM), enabling comprehensive quantitative pathology data mining, i.e., pathomics.
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Affiliation(s)
- David L Hölscher
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
| | - Nassim Bouteldja
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
| | - Mehdi Joodaki
- Institute for Computational Genomics, RWTH Aachen University Clinic, Aachen, Germany
| | | | - Yu-Chia Lan
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
| | | | - Mingbo Cheng
- Institute for Computational Genomics, RWTH Aachen University Clinic, Aachen, Germany
| | - Vladimir Tesar
- Department of Nephrology, 1st Faculty of Medicine and General University Hospital, Charles University, Prague, Czech Republic
| | | | | | - Jonathan Barratt
- John Walls Renal Unit, University Hospital of Leicester National Health Service Trust, Leicester, United Kingdom
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | - Jürgen Floege
- Department of Nephrology and Immunology, RWTH Aachen University Clinic, Aachen, Germany
| | - Ian S D Roberts
- Department of Cellular Pathology, Oxford University Hospitals National Health Services Foundation Trust, Oxford, United Kingdom
| | - Rosanna Coppo
- Fondazione Ricerca Molinette, Torino, Italy
- Regina Margherita Children's University Hospital, Torino, Italy
| | - Ivan G Costa
- Institute for Computational Genomics, RWTH Aachen University Clinic, Aachen, Germany
| | - Roman D Bülow
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany.
- Department of Nephrology and Immunology, RWTH Aachen University Clinic, Aachen, Germany.
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He B, Zhan Y, Cai C, Yu D, Wei Q, Quan L, Huang D, Liu Y, Li Z, Liu L, Pan X. Common molecular mechanism and immune infiltration patterns of thoracic and abdominal aortic aneurysms. Front Immunol 2022; 13:1030976. [PMID: 36341412 PMCID: PMC9633949 DOI: 10.3389/fimmu.2022.1030976] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/10/2022] [Indexed: 01/02/2024] Open
Abstract
BACKGROUND Aortic disease (aortic aneurysm (AA), dissection (AD)) is a serious threat to patient lives. Little is currently known about the molecular mechanisms and immune infiltration patterns underlying the development and progression of thoracic and abdominal aortic aneurysms (TAA and AAA), warranting further research. METHODS We downloaded AA (includes TAA and AAA) datasets from the GEO database. The potential biomarkers in TAA and AAA were identified using differential expression analysis and two machine-learning algorithms. The discrimination power of the potential biomarkers and their diagnostic accuracy was assessed in validation datasets using ROC curve analysis. Then, GSEA, KEGG, GO and DO analyses were conducted. Furthermore, two immuno-infiltration analysis algorithms were utilized to analyze the common immune infiltration patterns in TAA and AAA. Finally, a retrospective clinical study was performed on 78 patients with AD, and the serum from 6 patients was used for whole exome sequencing (WES). RESULTS The intersection of TAA and AAA datasets yielded 82 differentially expressed genes (DEGs). Subsequently, the biomarkers (CX3CR1 and HBB) were acquired by screening using two machine-learning algorithms and ROC curve analysis. The functional analysis of DEGs showed significant enrichment in inflammation and regulation of angiogenic pathways. Immune cell infiltration analysis revealed that adaptive and innate immune responses were closely linked to AA progression. However, neither CX3CR1 nor HBB was associated with B cell-mediated humoral immunity. CX3CR1 expression was correlated with macrophages and HBB with eosinophils. Finally, our retrospective clinical study revealed a hyperinflammatory environment in aortic disease. The WES study identified disease biomarkers and gene variants, some of which may be druggable. CONCLUSION The genes CX3CR1 and HBB can be used as common biomarkers in TAA and AAA. Large numbers of innate and adaptive immune cells are infiltrated in AA and are closely linked to the development and progression of AA. Moreover, CX3CR1 and HBB are highly correlated with the infiltration of immune cells and may be potential targets of immunotherapeutic drugs. Gene mutation research is a promising direction for the treatment of aortic disease.
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Affiliation(s)
- Bin He
- Graduate School of Youjiang Medical University for Nationalities, Baise, China
| | - Ya Zhan
- The Third Hospital of MianYang, Sichuan Mental Health Center, MianYang, China
| | - Chunyu Cai
- Graduate School of Youjiang Medical University for Nationalities, Baise, China
| | - Dianyou Yu
- Graduate School of Youjiang Medical University for Nationalities, Baise, China
| | - Qinjiang Wei
- Department of Cardiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Liping Quan
- Graduate School of Youjiang Medical University for Nationalities, Baise, China
| | - Da Huang
- Department of Cardiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Yan Liu
- Department of Cardiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Zhile Li
- Department of Cardiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Li Liu
- Department of Cardiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
- College of Clinical Medicine, Youjiang Medical University for Nationalities, Baise, China
| | - Xingshou Pan
- Department of Cardiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
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9
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Cummins TD, Korte EA, Bhayana S, Merchant ML, Barati MT, Smoyer WE, Klein JB. Advances in proteomic profiling of pediatric kidney diseases. Pediatr Nephrol 2022; 37:2255-2265. [PMID: 35220505 PMCID: PMC9398920 DOI: 10.1007/s00467-022-05497-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 02/03/2022] [Accepted: 02/04/2022] [Indexed: 01/22/2023]
Abstract
Chronic kidney disease (CKD) can progress to kidney failure and require dialysis or transplantation, while early diagnosis can alter the course of disease and lead to better outcomes in both pediatric and adult patients. Significant CKD comorbidities include the manifestation of cardiovascular disease, heart failure, coronary disease, and hypertension. The pathogenesis of chronic kidney diseases can present as subtle and especially difficult to distinguish between different glomerular pathologies. Early detection of adult and pediatric CKD and detailed mechanistic understanding of the kidney damage can be helpful in delaying or curtailing disease progression via precise intervention toward diagnosis and prognosis. Clinically, serum creatinine and albumin levels can be indicative of CKD, but often are a lagging indicator only significantly affected once kidney function has severely diminished. The evolution of proteomics and mass spectrometry technologies has begun to provide a powerful research tool in defining these mechanisms and identifying novel biomarkers of CKD. Many of the same challenges and advances in proteomics apply to adult and pediatric patient populations. Additionally, proteomic analysis of adult CKD patients can be transferred directly toward advancing our knowledge of pediatric CKD as well. In this review, we highlight applications of proteomics that have yielded such biomarkers as PLA2R, SEMA3B, and other markers of membranous nephropathy as well as KIM-1, MCP-1, and NGAL in lupus nephritis among other potential diagnostic and prognostic markers. The potential for improving the clinical toolkit toward better treatment of pediatric kidney diseases is significantly aided by current and future development of proteomic applications.
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Affiliation(s)
- Timothy D Cummins
- Division of Nephrology and Hypertension, Clinical Proteomics Center, University of Louisville School of Medicine, 570 S. Preston St, Louisville, KY, 40202, USA.
| | - Erik A Korte
- Bluewater Diagnostics, Mount Washington, KY, USA
| | - Sagar Bhayana
- Nationwide Children's Hospital, The Ohio State University, Columbus, OH, USA
| | - Michael L Merchant
- Division of Nephrology and Hypertension, Clinical Proteomics Center, University of Louisville School of Medicine, 570 S. Preston St, Louisville, KY, 40202, USA
| | - Michelle T Barati
- Division of Nephrology and Hypertension, Clinical Proteomics Center, University of Louisville School of Medicine, 570 S. Preston St, Louisville, KY, 40202, USA
| | - William E Smoyer
- Nationwide Children's Hospital, The Ohio State University, Columbus, OH, USA
| | - Jon B Klein
- Division of Nephrology and Hypertension, Clinical Proteomics Center, University of Louisville School of Medicine, 570 S. Preston St, Louisville, KY, 40202, USA
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10
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Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects. J Clin Med 2022; 11:jcm11164918. [PMID: 36013157 PMCID: PMC9410196 DOI: 10.3390/jcm11164918] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/30/2022] [Accepted: 08/11/2022] [Indexed: 11/17/2022] Open
Abstract
Digital imaging and advanced microscopy play a pivotal role in the diagnosis of kidney diseases. In recent years, great achievements have been made in digital imaging, providing novel approaches for precise quantitative assessments of nephropathology and relieving burdens of renal pathologists. Developing novel methods of artificial intelligence (AI)-assisted technology through multidisciplinary interaction among computer engineers, renal specialists, and nephropathologists could prove beneficial for renal pathology diagnoses. An increasing number of publications has demonstrated the rapid growth of AI-based technology in nephrology. In this review, we offer an overview of AI-assisted renal pathology, including AI concepts and the workflow of processing digital image data, focusing on the impressive advances of AI application in disease-specific backgrounds. In particular, this review describes the applied computer vision algorithms for the segmentation of kidney structures, diagnosis of specific pathological changes, and prognosis prediction based on images. Lastly, we discuss challenges and prospects to provide an objective view of this topic.
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11
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Haaskjold YL, Lura NG, Bjørneklett R, Bostad L, Bostad LS, Knoop T. Validation of two IgA nephropathy risk-prediction tools using a cohort with a long follow-up. Nephrol Dial Transplant 2022; 38:1183-1191. [PMID: 35904322 PMCID: PMC10157756 DOI: 10.1093/ndt/gfac225] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Recently, two immunoglobulin A nephropathy prediction tools were developed that combine clinical and histopathological parameters. The International IgAN Prediction Tool predicts the risk for 50% declines in the estimated glomerular filtration rate or end-stage renal disease up to 80 months after diagnosis. The IgA Nephropathy Clinical Decision Support System uses artificial neural networks to estimate the risk for end-stage renal disease. We aimed to externally validate both prediction tools using a Norwegian cohort with a long-term follow-up. METHODS We included 306 patients with biopsy-proven primary immunoglobulin A nephropathy in this study. Histopathologic samples were retrieved from the Norwegian Kidney Biopsy Registry and reclassified according to the Oxford classification. We used discrimination and calibration as principles for externally validating the prognostic models. RESULTS The median patient follow-up was 17.1 years. A cumulative dynamic time-dependent receiver operating characteristic analysis showed area under the curve values of ranging from 0.90 at 5 years to 0.83 at 20 years for the International IgAN Prediction Tool, while time-naive analysis showed an area under the curve value at 0.83 for the IgA Nephropathy Clinical Decision Support System. The International IgAN Prediction Tool was well calibrated, while the IgA Nephropathy Clinical Decision Support System tends to underestimate risk for patients with higher risk, and overestimates risk in the lower risk categories. CONCLUSIONS We have externally validated two prediction tools for IgA nephropathy. The International IgAN Prediction Tool performed well, while the IgA Nephropathy Clinical Decision Support System has some limitations.
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Affiliation(s)
- Yngvar Lunde Haaskjold
- Department of Medicine, Haukeland University Hospital, Bergen, Norway.,Renal Research Group, Department of Clinical Medicine, University of Bergen, Norway
| | - Njål Gjærde Lura
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Rune Bjørneklett
- Renal Research Group, Department of Clinical Medicine, University of Bergen, Norway.,Emergency Care Clinic, Haukeland University Hospital, Bergen, Norway
| | - Leif Bostad
- Renal Research Group, Department of Clinical Medicine, University of Bergen, Norway.,Department of Pathology, Haukeland University Hospital, Bergen, Norway
| | - Lars Sigurd Bostad
- Renal Research Group, Department of Clinical Medicine, University of Bergen, Norway.,Emergency Care Clinic, Haukeland University Hospital, Bergen, Norway
| | - Thomas Knoop
- Department of Medicine, Haukeland University Hospital, Bergen, Norway.,Renal Research Group, Department of Clinical Medicine, University of Bergen, Norway
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12
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Bülow RD, Marsh JN, Swamidass SJ, Gaut JP, Boor P. The potential of artificial intelligence-based applications in kidney pathology. Curr Opin Nephrol Hypertens 2022; 31:251-257. [PMID: 35165248 PMCID: PMC9035059 DOI: 10.1097/mnh.0000000000000784] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW The field of pathology is currently undergoing a significant transformation from traditional glass slides to a digital format dependent on whole slide imaging. Transitioning from glass to digital has opened the field to development and application of image analysis technology, commonly deep learning methods (artificial intelligence [AI]) to assist pathologists with tissue examination. Nephropathology is poised to leverage this technology to improve precision, accuracy, and efficiency in clinical practice. RECENT FINDINGS Through a multidisciplinary approach, nephropathologists, and computer scientists have made significant recent advances in developing AI technology to identify histological structures within whole slide images (segmentation), quantification of histologic structures, prediction of clinical outcomes, and classifying disease. Virtual staining of tissue and automation of electron microscopy imaging are emerging applications with particular significance for nephropathology. SUMMARY AI applied to image analysis in nephropathology has potential to transform the field by improving diagnostic accuracy and reproducibility, efficiency, and prognostic power. Reimbursement, demonstration of clinical utility, and seamless workflow integration are essential to widespread adoption.
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Affiliation(s)
- Roman D. Bülow
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Jon N. Marsh
- Washington University School of Medicine in St. Louis, Department of Pathology and Immunology
| | - S. Joshua Swamidass
- Washington University School of Medicine in St. Louis, Department of Pathology and Immunology
| | - Joseph P. Gaut
- Washington University School of Medicine in St. Louis, Department of Pathology and Immunology
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
- Department of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany
- Electron Microscopy Facility, RWTH Aachen University Hospital, Aachen, Germany
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13
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Lohia S, Vlahou A, Zoidakis J. Microbiome in Chronic Kidney Disease (CKD): An Omics Perspective. Toxins (Basel) 2022; 14:toxins14030176. [PMID: 35324673 PMCID: PMC8951538 DOI: 10.3390/toxins14030176] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/14/2022] [Accepted: 02/23/2022] [Indexed: 12/25/2022] Open
Abstract
Chronic kidney disease (CKD) is predominant in 10% of the world’s adult population, and is increasingly considered a silent epidemic. Gut microbiota plays an essential role in maintaining host energy homeostasis and gut epithelial integrity. Alterations in gut microbiota composition, functions and, specifically, production of metabolites causing uremic toxicity are often associated with CKD onset and progression. Here, we present the latest omics (transcriptomics, proteomics and metabolomics) studies that explore the connection between CKD and gut microbiome. A review of the available literature using PubMed was performed using the keywords “microb*”, “kidney”, “proteom”, “metabolom” and “transcript” for the last 10 years, yielding a total of 155 publications. Following selection of the relevant studies (focusing on microbiome in CKD), a predominance of metabolomics (n = 12) over transcriptomics (n = 1) and proteomics (n = 6) analyses was observed. A consensus arises supporting the idea that the uremic toxins produced in the gut cause oxidative stress, inflammation and fibrosis in the kidney leading to CKD. Collectively, findings include an observed enrichment of Eggerthella lenta, Enterobacteriaceae and Clostridium spp., and a depletion in Bacteroides eggerthii, Roseburia faecis and Prevotella spp. occurring in CKD models. Bacterial species involved in butyrate production, indole synthesis and mucin degradation were also related to CKD. Consequently, strong links between CKD and gut microbial dysbiosis suggest potential therapeutic strategies to prevent CKD progression and portray the gut as a promising therapeutic target.
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Affiliation(s)
- Sonnal Lohia
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece; (S.L.); (A.V.)
- Institute for Molecular Cardiovascular Research, RWTH Aachen University Hospital, 52074 Aachen, Germany
| | - Antonia Vlahou
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece; (S.L.); (A.V.)
| | - Jerome Zoidakis
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece; (S.L.); (A.V.)
- Correspondence:
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14
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Glassock RJ. Precision medicine for the treatment of glomerulonephritis: A bold goal but not yet a transformative achievement. Clin Kidney J 2021; 15:657-662. [PMID: 35371458 PMCID: PMC8967540 DOI: 10.1093/ckj/sfab270] [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: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
Abstract
The revolution in our ability to recognize the alterations in fundamental biology brought about by disease has fostered a renewed interest in precision or personalized medicine (“the right treatment, or diagnostic test, for the right patient at the right time”). This nascent field has been led by oncology, immune-hematology and infectious disease, but nephrology is catching up, and quickly. Specific forms of glomerulonephritis thought to represent specific “diseases” have been “downgraded” to “patterns of injury”. New entities have emerged through application of sophisticated molecular technologies; often embraced by the term “multi-omics”. Kidney biopsies are now interpreted by next generation imaging and machine learning. Many opportunities are manifest that will translate these remarkable developments into novel safe and effective treatment regimens for specific pathogenic pathways evoking glomerulonephritis and its progression to kidney failure. A few successes emboldens a positive look to the future. A sustained and highly collaborative engagement with this new paradigm will be required for this field, full of hope and high expectations, to realize its goal of transforming glomerular therapeutics from “one size fits all (or many)” to a true individualized management principle.
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Affiliation(s)
- Richard J Glassock
- Emeritus Professor, Department of Medicine, Geffen School of Medicine. Los Angeles, CA, USA
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15
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Floege J, Barratt J. IgA nephropathy: a perspective for 2021. Semin Immunopathol 2021; 43:625-626. [PMID: 34705062 PMCID: PMC8551095 DOI: 10.1007/s00281-021-00890-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
- Jürgen Floege
- Nephrology and Clinical Immunology, University Hospital, RWTH Aachen, Aachen, Germany.
| | - Jonathan Barratt
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
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