UBE2L3 regulates TLR7-induced B cell autoreactivity in Systemic Lupus Erythematosus

Mauro, Daniele; Manou-Stathopoulou, Sotiria; Rivellese, Felice; Sciacca, Elisabetta; Goldmann, Katriona; Tsang, Victoria; Lucey-Clayton, Isabelle; Pagani, Sara; Alam, Farah; Pyne, Debasish; Rajakariar, Ravindra; Gordon, Patrick; Whiteford, James R.; Bombardieri, Michèle; Pitzalis, Costantino; Lewis, Myles

doi:10.1016/j.jaut.2023.103023

Cited by 10 publications

(9 citation statements)

References 56 publications

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“…IRAK1, a mediator of TLR 43 and IFNα/β signaling 44 resides on the X-chromosome 45 , thus providing a connection to XCI escape. An SLE risk-allele of UBE2L3 increases its expression in B cells and monocytes and drives greater NF-κB mediated immune activity 46 , and inhibition of UBE2L3 activity suppressed the response to TLR7 activation in ex-vivo human B cells 47 . Taken together, these observations suggest that dysregulated IRAK1 and UBE2L3 activity may contribute to the emergence of a self-reinforcing signaling feedback loop thought to underlie SLE pathogenesis (Supplementary Fig.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised generative AI discovers pan-leukocyte dysregulated pathways in single-cell lupus data

Bolouri,

Cerosaletti,

Lacy-Hulbert

2024

Preprint

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Comparisons of single-cell RNA-sequencing (scRNA-seq) data from healthy and diseased tissues are widely used to identify cell-type-specific dysregulated genes, pathways, and processes. Accordingly, many sophisticated methods have been developed for this purpose. However, such tools generally require considerable user expertise for optimal performance. Here, we show that unsupervised application of a linearly-decoded Variational Auto Encoder (a generative AI model) to scRNA-seq data recapitulates and extends findings from a seminal recent lupus study and leads to new insights. Thanks to existing software libraries, our approach is straightforward to implement, computationally efficient, methodologically robust, and produces consistent and reproducible results.

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Section: Discussionmentioning

confidence: 99%

Unsupervised generative AI discovers pan-leukocyte dysregulated pathways in single-cell lupus data

Bolouri,

Cerosaletti,

Lacy-Hulbert

2024

Preprint

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“…The altered expression of CX3CR1 [599], S100A12 [600], PF4 [601], MPO (myeloperoxidase) [602], RXFP1 [603], S100A8 [604], VEGFD (vascular endothelial growth factor D) [605], CXCL11 [606], IL1A [607], BPI (bactericidal permeability increasing protein) [608], SLC6A4 [609], CXCL10 [610], FCGR3B [611], S100A9 [612], IL1B [613], CXCR2 [614], CTNND2 [615], CD36 [616], PCSK9 [617], FGF2 [618], SHH (sonic hedgehog signaling molecule) [619], KL (klotho) [620], BMPR2 [621], TLR8 [622], GATA3 [623], CCR2 [624], TLR7 [625], CAV1 [626], TFPI (tissue factor pathway inhibitor) [627] and SPAG17 [628] have been identified in systemic sclerosis. A previous study found that CX3CR1 [629], S100A12 [630], MPO (myeloperoxidase) [631], CD5L [632], F11 [633], S100A8 [634], BPI (bactericidal permeability increasing protein) [635], AQP4 [636], MME (membrane metalloendopeptidase) [637], BDNF (brain derived neurotrophic factor) [638], CXCL10 [639], RNASE2 [640], FCGR3B [641], S100A9 [642], GPIHBP1 [643], AFF3 [644], APOH (apolipoprotein H) [645], FCN3 [646], PCSK9 [308], LILRA2 [647], APOA1 [648], LRRK2 [649], CD244 [650], TLR3 [651], TLR8 [652], GATA3 [653], CCR2 [654], IFIT3 [655], NEK7 [656], TLR7 [657], CAV1 [658], CR1 [659], TFPI (tissue factor pathway inhibitor) [660], GPER1 [661], SIGLEC14 [662], FOXJ1 [663], GABRP (gamma-aminobutyric acid type A receptor subunit pi) [664] and TSGA10 [665] are positively correlated with the severity of systemic lupus erythematosus, suggesting its potential as a biomarker ...…”

Section: Discussionmentioning

confidence: 99%

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

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Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analysis of DEGs were performed. Then, a protein protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst database was used to construct the targeted microRNAs (miRNAs) and transcription factors (TFs) of the hub genes. Finally receiver operating characteristic (ROC) curve analysis was used to validates the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8 and EFHC2 were selected. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

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“…Moreover, BG-12 appears to suppress platelet function and thrombus formation (Chu et al, 2023). Several other reports suggest a potential ameliorative role for dimethyl fumarate in Alzheimer's disease, ischemic stroke, systemic lupus erythematosus, traumatic brain injury, among others (Cheng et al, 2023;Mauro et al, 2023;Owjfard et al, 2023). On the other hand, Oltipraz is currently in phase III trial for the treatment of nonalcoholic fatty liver disease (Robledinos-Antón et al, 2019) and is being proposed as potential agent for the management of Thoracic aortic aneurysm and dissection and osteoporosis (Wang et al, 2022;Yang et al, 2022).…”

Section: Redox Signalingmentioning

confidence: 99%

Biochemical and cellular basis of oxidative stress: Implications for disease onset

Aramouni

Assaf

Shaito

et al. 2023

Journal Cellular Physiology

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Cellular oxidation–reduction (redox) systems, which encompass pro‐ and antioxidant molecules, are integral components of a plethora of essential cellular processes. Any dysregulation of these systems can cause molecular imbalances between the pro‐ and antioxidant moieties, leading to a state of oxidative stress. Long‐lasting oxidative stress can manifest clinically as a variety of chronic illnesses including cancers, neurodegenerative disorders, cardiovascular disease, and metabolic diseases like diabetes. As such, this review investigates the impact of oxidative stress on the human body with emphasis on the underlying oxidants, mechanisms, and pathways. It also discusses the available antioxidant defense mechanisms. The cellular monitoring and regulatory systems that ensure a balanced oxidative cellular environment are detailed. We critically discuss the notion of oxidants as a double‐edged sword, being signaling messengers at low physiological concentrations but causative agents of oxidative stress when overproduced. In this regard, the review also presents strategies employed by oxidants including redox signaling and activation of transcriptional programs such as those mediated by the Nrf2/Keap1 and NFk signaling. Likewise, redox molecular switches of peroxiredoxin and DJ‐1 and the proteins they regulate are presented. The review concludes that a thorough comprehension of cellular redox systems is essential to develop the evolving field of redox medicine.

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UBE2L3 regulates TLR7-induced B cell autoreactivity in Systemic Lupus Erythematosus

Cited by 10 publications

References 56 publications

Unsupervised generative AI discovers pan-leukocyte dysregulated pathways in single-cell lupus data

Unsupervised generative AI discovers pan-leukocyte dysregulated pathways in single-cell lupus data

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Biochemical and cellular basis of oxidative stress: Implications for disease onset

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