The landscape and diagnostic potential of T and B cell repertoire in Immunoglobulin A Nephropathy

Huang, Chen; Li, Xuemei; Wu, Jinghua; Zhang, Wei; Sun, Shiren; Lin, Liya; Wang, Xie; Li, Hongmei; Wu, Xiaolei; Zhang, Peng; Xu, Guoshuang; Wan, Hanmin; Liu, Hongbao; Liu, Yuzhen; Chen, Dapeng; Li, Zhuo; Yang, Huanming; Wang, Jian; Liu, Xiao

doi:10.1101/316216

Cited by 9 publications

(12 citation statements)

References 45 publications

(29 reference statements)

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“…In addition to measuring serum levels of gd-IgA1, and the “Hit 2”autoantibodies discussed below, a number of investigators have examined peripheral blood lymphocyte and mononuclear cell subsets in an effort to identify changes in the key immune cells responsible for gd-IgA1and autoantibody synthesis, as well as those mediating inflammation in IgAN ( 139 – 143 ). Results of these studies are inconsistent and none have been validated and therefore their utility as biomarkers in IgAN is uncertain.…”

Section: The Four Hit Hypothesis Of Iganmentioning

confidence: 99%

Monitoring Immune Responses in IgA Nephropathy: Biomarkers to Guide Management

et al. 2020

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IgA nephropathy (IgAN) is the commonest biopsy-reported primary glomerulonephritis worldwide. It has an incidence which peaks among young adults, and 30 to 40% of patients’ progress to end stage kidney disease within twenty years of diagnosis. Ten-year kidney survival rates have been reported to be as low as 35% in some parts of the world. The successful management of IgAN is limited by an incomplete understanding of the pathophysiology of IgAN and a poor understanding of how pathophysiology may vary both from patient to patient and between patient groups, particularly across races. This is compounded by a lack of rigorously designed and delivered clinical trials in IgAN. This is slowly changing, with a number of Phase 2 and 3 clinical trials of novel therapies targeting a number of different putative pathogenic pathways in IgAN due to report in the next 5 years. From our current, albeit limited, understanding of the pathophysiology of IgAN it is unlikely a single therapy will be effective in all patients with IgAN. The successful management of IgAN in the future is, therefore, likely to be reliant on targeted therapies, carefully selected based on an individualized understanding of a patient’s risk of progression and underlying pathophysiology. The potential role of biomarkers to facilitate personalization of prognostication and treatment of IgAN is immense. Here we review the progress made over the past decade in identifying and validating new biomarkers, with a particular focus on those that reflect immunological responses in IgAN.

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Section: The Four Hit Hypothesis Of Iganmentioning

confidence: 99%

Monitoring Immune Responses in IgA Nephropathy: Biomarkers to Guide Management

et al. 2020

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“…The STAR detector is a multi-purpose detector designed with large, uniform acceptance in 0 < φ < 2π and |η| < 1. The relevant STAR subsystems used for this study are the Time Projection Chamber (TPC), the Magnet System, the Time-of-Flight (TOF) detector, and the Muon Telescope detector (MTD) [1][2][3].…”

Section: Star Detectormentioning

confidence: 99%

“…The performance of the neural network based classifiers are compared using modified receiver operating characteristic (ROC) curves in Fig. 9 by plotting the back- A more thorough look at using likelihood ratios for muon identification with the MTD can be found in [3]. The BDT classifier was trained with N T rees = 250 and M axDepth = 5 with all other parameters set to the defaults.…”

Section: Comparison Of Multivariate Classifiersmentioning

confidence: 99%

Muon Identification using Neural Networks With the Muon Telescope Detector at STAR

Brandenburg

2019

Nuclear Physics A

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The installation of the muon telescope detector opened new possibilities for studying dimuon production at STAR. However, backgrounds from hadron punch-through and weak decays of pions and kaons make the identification of primary muons challenging. In this paper we present a study of shallow and deep neural networks trained as classifiers for the purpose of muon identification using information from the muon telescope detector at STAR. The performance of shallow neural networks is presented as a function of the number of neurons in their hidden layer. A hyperparameter optimization for determining the optimal deep neural network classifier architecture is presented. The optimized deep neural network is compared with shallow neural networks, boosted decision trees, likelihood ratios, and traditional cut-based PID techniques. The superiority of the deep neural network based muon identification technique is demonstrated and compared with traditional PID through the measurement of the φ meson and the ψ(2S) in p+p collisions at √ s = 200 GeV. The deep neural network based PID simultaneously provides higher signal efficiency, signal-tobackground ratio, and significance of the φ peak compared to traditional PIDtechniques. Finally, a deep neural network assisted technique for measuring the muon purity in data is presented and discussed.

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“…The application of HTS in TCR repertoire analysis (TCR-seq) began 10 years ago ( 4 – 6 ) and has been widely used in basic and translational research ( 2 , 7 – 9 ) due to its high resolution and throughput. The technology has been used in fields such as investigating the functional heterogeneity of the human T cell ( 10 ), comparing TCR repertoire of different tissues ( 11 , 12 ), analyzing the effect of genetic background on TCR repertoire ( 13 ), identifying autoimmune-disease-related T cells ( 14 – 16 ), quantifying minimal residual disease in blood cancer ( 17 ), and predicting the responses of vaccination ( 18 ) or immunotherapy ( 19 – 21 ).…”

Section: Introductionmentioning

confidence: 99%

Developing an Unbiased Multiplex PCR System to Enrich the TRB Repertoire Toward Accurate Detection in Leukemia

Wang

Lin

et al. 2020

Front. Immunol.

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Accurate T cell receptor repertoire profiling has provided novel biological and clinical insights in widespread immunological settings; however, there is a lack of reference materials in the community that can be used to calibrate and optimize the various experimental systems in different laboratories. In this study, we designed and synthesized 611 T cell receptor (TCR) beta chain (TRB) templates and used them as reference materials to optimize the multiplex PCR experimental system to enrich the TRB repertoire. We assessed the stability of the optimized system by repeating the experiments in different batches and by remixing the TRB templates in different ratios. These TRB reference materials could be used as independent positive controls to assess the accuracy of the experimental system, and they can also be used as spike-in materials to calibrate the residual biases of the experimental system. We then used the optimized system to detect the minimal residual disease of T cell acute lymphoblastic leukemia and showed a higher sensitivity compared with flow cytometry. We also interrogated how chemotherapy affected the TCR repertoire of patients with B-cell acute lymphoblastic leukemia. Our result shows that high-avidity T cells, such as those targeting known pathogens, are largely selected during chemotherapy, despite the global immunosuppression. These T cells were stimulated and emerged at the time of induction treatment and further expanded during consolidation treatment, possibly to fight against infections. These data demonstrate that accurate immune repertoire information can improve our understanding of the adaptive immunity in leukemia and lead to better treatment management of the patients.

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The landscape and diagnostic potential of T and B cell repertoire in Immunoglobulin A Nephropathy

Cited by 9 publications

References 45 publications

Monitoring Immune Responses in IgA Nephropathy: Biomarkers to Guide Management

Monitoring Immune Responses in IgA Nephropathy: Biomarkers to Guide Management

Muon Identification using Neural Networks With the Muon Telescope Detector at STAR

Developing an Unbiased Multiplex PCR System to Enrich the TRB Repertoire Toward Accurate Detection in Leukemia

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