Integrated urine proteomics and renal single-cell genomics identify an IFN-γ response gradient in lupus nephritis

Fava, Andrea; Buyon, Jill P.; Mohan, Chandra; Zhang, Ting; Belmont, H. Michael; Izmirly, Peter; Clancy, Robert M.; Monroy‐Trujillo, Jose M.; Fine, Derek M.; Zhang, Yuji; Magder, Laurence S.; Rao, Deepak A.; Arazi, Arnon; Berthier, Céline C.; Davidson, Anne; Diamond, Betty; Hacohen, Nir; Wofsy, David; Apruzzese, William; Raychaudhuri, Soumya; Petri, Michelle

doi:10.1172/jci.insight.138345

Cited by 71 publications

(62 citation statements)

References 49 publications

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“…Kidney tissues analysis revealed chemokine secretion in the urine was related to intrarenal chemokine production within tubular cells. While transcriptomic analysis found IFN‐γ, was found to be the most abundant cytokine produced by infiltrating CD8 + T cells in the kidney tissue across all classes of LN 53 . Proteomic analysis of neutrophil subsets has shown differential expression of interferon regulated within SLE patients 61 .…”

Section: The Application Of “Omics” Technologies At the Frontline Of Precision Medicine For Slementioning

confidence: 99%

Understand SLE heterogeneity in the era of omics, big data, and artificial intelligence

Puri

Jiang

Yang

et al. 2021

Rheumatology & Autoimmunity

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Systemic lupus erythematosus (SLE) is a systemic autoimmune disease characterized by extraordinary heterogeneity, due to the complex pathogenesis and diverse manifestations. Stratification of patients for therapy and prognosis represents a major challenge to manage SLE. Conventional biomarkers for disease diagnosis and activity assessment provide very limited insight into immunological pathogenesis and therapeutic response rates. The advancement of “omics” technologies including genomics, transcriptomics, proteomics, and metabolomics has constituted an unprecedented opportunity to characterize the immunopathological landscape in individual patients with SLE. Indeed, genomic studies reveal a subset of SLE patients carrying one or more functional single nucleotide polymorphisms (SNPs) underlying immune dysregulation while transcriptomic studies have revealed subgroups in SLE patients showing distinct signatures for Type I interferon (TI‐IFN) pathway activation or aberrant differentiation of B cells into plasma cells. This review will summarize results from the latest studies using omics technology to understand SLE heterogeneity. In addition, we propose that the application of artificial intelligence, such as by machine learning‐based nonlinear dimensionality reduction method uniform manifold approximation and projection (UMAP) can further strengthen the analysis of omics big data. The combination of new technology and novel analysis pipeline can lead to breakthroughs in stratifying SLE patients for a better monitoring of disease activity and more precise design of treatment regime, not only for conventional immunosuppression but also novel immunotherapies targeting B‐cell activating factor (BAFF), TI‐IFN, and interleukin 2 (IL‐2).

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Section: The Application Of “Omics” Technologies At the Frontline Of Precision Medicine For Slementioning

confidence: 99%

Understand SLE heterogeneity in the era of omics, big data, and artificial intelligence

Puri

Jiang

Yang

et al. 2021

Rheumatology & Autoimmunity

View full text Add to dashboard Cite

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“…Advances in proteomic techniques allow for screening a large number of proteins simultaneously with the aim of discovering novel biomarkers. A recent high throughput urine proteome study integrated with single-cell transcriptomic of lupus kidneys detected a chemokine profile, inducible by IFN-γ, which is produced mainly by CD8 + T cells in active LN patients (65). Although this study was limited by the sample size, it is an encouraging example of the potential of urine proteome profiling to predict active immune pathways in the kidney.…”

Section: Lupus Nephritis and Application Of Cutting Edge Technologiesmentioning

confidence: 84%

Current Status of the Evaluation and Management of Lupus Patients and Future Prospects

Yavuz

Lipsky

2021

Front. Med.

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The vastly diverse nature of systemic lupus erythematosus (SLE) poses great challenges to clinicians and patients, as well as to research and drug development efforts. Precise management of lupus patients would be advanced by the ability to identify specific abnormalities operative in individual patients at the time of encounter with the clinician. Advances in new technologies and bioinformatics have greatly improved the understanding of the pathophysiology of SLE. Recent research has focused on the discovery and classification of sensitive and specific markers that could aid early accurate diagnosis, better monitoring of disease and identification of appropriate therapy choices based on specific dysregulated molecular pathways. Here, we summarize some of the advances and discuss the challenges in moving toward precise patient-centric management modalities in SLE.

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“…The tumour microenvironment is crucial for tumourigenesis and tumour development, and the CD8 + T cell-mediated anti-tumour response has been shown to be significantly increased through the production of cytokines such as INF-γ [ 38 ]. Accumulating evidence has shown that immune cells, such as CD8 + T cells, can facilitate the upregulation of immune checkpoints and enhance anti-tumour immune responses [ 39 ]. To annotate IFN-γ and T helper cell-related genes, we performed cluster analysis and found that the immune response has a crucial relationship with IFN-γ-related regulatory pathways (Fig.…”

Section: Resultsmentioning

confidence: 99%

Immune classifier-based signatures provide good prognostic stratification and predict the clinical benefits of immune-based therapies for hepatocellular carcinoma

Xue

2021

Cancer Cell Int

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Background Hepatocellular carcinoma (HCC) is an aggressive cancer with a high rate of death globally. The use of bioinformatics may help to identify immune cell-related genes both as targets for potential immunotherapies and for their value associated with predicting therapy responses. Methods In this study, mRNA expression profiles of HCC samples from The Cancer Genome Atlas (TCGA) database were subjected to gene enrichment, cell type abundance, immune cell infiltration, and pathway enrichment analyses to determine immune cell gene features, cell type abundance, and functional annotation characteristics. We also evaluated their prognostic values using Cox regression and Kaplan–Meier analyses and assessed potential responses to chemotherapy. Four subgroups (Groups 1–4) were identified. Group 4 was associated with advanced clinical characteristics, high immune cell enrichment scores, and the poorest outcomes. Results Differentially expressed genes (DEGs) in the HCC samples were enriched in the following pathways: antigen binding, cell surface receptor signal transduction of the immune response, and cell surface activated receptor signal transduction of the immune response. Highly expressed genes in Group 4 were enriched in elements of the WNT signalling pathway. We identified five immune-related genes (SEMA3A, TNFRSF11B, GUCA2A, SAA1, and CALCR) that were significantly related to HCC prognosis. A prognostic model based on these five genes exhibited good predictive value, with 1-year and 5-year area under the curve (AUC) values of > 0.66. Group 4 was also potentially more sensitive to EHT 1864, FH535, and lapatinib chemotherapies than the other groups. Conclusions We identified and validated four HCC subgroups based on immune system-related genes and identified five genes that may be used for an immune-based prognostic model for HCC treatment.

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Integrated urine proteomics and renal single-cell genomics identify an IFN-γ response gradient in lupus nephritis

Cited by 71 publications

References 49 publications

Understand SLE heterogeneity in the era of omics, big data, and artificial intelligence

Understand SLE heterogeneity in the era of omics, big data, and artificial intelligence

Current Status of the Evaluation and Management of Lupus Patients and Future Prospects

Immune classifier-based signatures provide good prognostic stratification and predict the clinical benefits of immune-based therapies for hepatocellular carcinoma

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