Disulfide bond formation and redox regulation in the Golgi apparatus

Reznik, Nava; Fass, Deborah

doi:10.1002/1873-3468.14510

Cited by 8 publications

(4 citation statements)

References 121 publications

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“…We found that within MSC subpopulations, Tead2 was enriched in clusters 1, 2, 3, 4, 8, and 9, while Atf3 was weakly expressed in clusters 3, 4, and 9. Qsox1 encodes an enzyme that catalyzes disulfide bond formation and is involved in redox regulation (Reznik and Fass, 2022). In our analysis, Qsox1 was expressed in MSC clusters 1, 2, 3, 4, 8, 9, and 10; Runx2 in cluster 1, 2, 3, 4, 7, 8, and 9; and Maf in clusters 1, 2, 3, 4, 8, and 10.…”

Section: Single-cell Rna Expression Profile Of Interacting Partners O...mentioning

confidence: 75%

Single-cell transcriptome profiling reveals distinct expression patterns among genes in the mouse incisor dental pulp

Enkhmandakh¹,

Bayarsaihan²

2023

Int. J. Dev. Biol.

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SOX transcription factors play key roles in cell differentiation and cell fate determination during development. Using single-cell RNA-sequencing data, we examined the expression profiles of Sox genes in the mouse incisor dental pulp. Our analysis showed that Sox4, Sox5, Sox9, Sox11, and Sox12 are mainly expressed in mesenchymal stem/stromal cells (MSCs) representing osteogenic cells at different stages of differentiation. We found that in several MSCs, Sox genes co-expressed with regulatory genes such as Sp7, Satb2, Msx1, Snai2, Dlx1, Twist2, and Tfap2a. In addition, Sox family genes colocalized with Runx2 and Lef1, which are highly enriched in MSCs undergoing osteoblast differentiation. A protein interaction network analysis uncovered that CREBBP, CEBPB, TLE1, TWIST1, and members of the HDAC and SMAD families are interacting partners of RUNX2 and LEF1 during skeletal development. Collectively, the distinct expression patterns of the SOX transcription factors suggest that they play essential regulatory roles in directing lineage-specific gene expression during differentiation of MSCs.

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Section: Single-cell Rna Expression Profile Of Interacting Partners O...mentioning

confidence: 75%

Single-cell transcriptome profiling reveals distinct expression patterns among genes in the mouse incisor dental pulp

Enkhmandakh¹,

Bayarsaihan²

2023

Int. J. Dev. Biol.

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“…Moreover, the gene-based scoring system can forecast NAFLD progression, facilitating early interventions for patients at risk of advancing to severe disease stages.Previous studies have explored the relationship between QSOX1, IL1RAP, and NAFLD or steatosis(16, 61). QSOX1 has been suggested as a potential diagnostic biomarker for NAFLD, playing a significant role in lipid metabolism as an enzyme expressed in various tissues, particularly in quiescent fibroblasts (18, 62, 63). IL1RAP is localized in vesicles and cytosol, and it is secreted into the bloodstream.…”

Section: Discussionmentioning

confidence: 99%

A gene-based clustering approach reveals QSOX1/IL1RAP as promising biomarkers for the severity of non-alcoholic fatty liver disease

Huang

Cai

et al. 2023

Preprint

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Background and Aims: Non-alcoholic fatty liver disease (NAFLD) is a progressive liver disease that ranges from simple steatosis to inflammation, fibrosis, and cirrhosis. To address the unmet need for new NAFLD biomarkers, we aimed to identify candidate biomarkers using publicly available RNA sequencing (RNA-seq) and proteomics data. Methods: A novel approach involving unsupervised gene clustering was performed using homogeneously processed and integrated RNA-seq data of 625 liver specimens to screen for NAFLD biomarkers, in combination with public proteomics data from healthy controls and NAFLD patients. Additionally, we validated the results in the NAFLD and healthy cohorts using enzyme-linked immunosorbent assay (ELISA) of plasma and immunohistochemical staining (IHC) of liver samples. Results: We generated a database (https://dreamapp.biomed.au.dk/NAFLD/) for exploring gene expression changes along NAFLD progression to facilitate the identification of genes and pathways involved in the disease's progression. Through cross-analysis of the gene and protein clusters, we identified 38 genes as potential biomarkers for NAFLD severity. Up-regulation of Quiescin sulfhydryl oxidase 1 (QSOX1) and down-regulation of Interleukin-1 receptor accessory protein (IL1RAP) were associated with increasing NAFLD severity in RNA-seq and proteomics data. Particularly, the QSOX1/IL1RAP ratio in plasma demonstrated effectiveness in diagnosing NAFLD, with an area under the receiver operating characteristic (AUROC) of up to 0.95 as quantified by proteomics profiling, and an AUROC of 0.82 with ELISA. Conclusions: We discovered a significant association between the levels of QSOX1/IL1RAP and NAFLD severity. Furthermore, the QSOX1/IL1RAP ratio shows promise as a non-invasive biomarker for diagnosing NAFLD and assessing its severity. (ChiCTR2300073940).

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“…Furthermore, through an analysis of the integrated large-scale scRNA and snRNA sequencing data to interpret potential biomarkers, we shed light on previously undiscovered facets of NAFLD pathogenesis and intercellular crosstalk. QSOX1 and its less common variant, QSOX2, are unique among sulfhydryl oxidases because they are specifically found in the Golgi apparatus of animal cells (28).…”

Section: Discussionmentioning

confidence: 99%

“…QSOX1 and its less common variant, QSOX2, are unique among sulfhydryl oxidases because they are specifically found in the Golgi apparatus of animal cells (28). Studies found elevated expression of QSOX1 in quiescent fibroblasts, and it is also released into the bloodstream (29, 30).…”

Section: Discussionmentioning

confidence: 99%

Unveiling the pathogenesis of non-alcoholic fatty liver disease by decoding biomarkers through integrated single-cell and single-nucleus profiles

Ma,

Zhong,

Cai

et al. 2023

Preprint

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Background and AimsNon-alcoholic fatty liver disease (NAFLD) is a heterogenous liver disease encompassing pathological changes ranging from simple steatosis, inflammation and fibrosis to cirrhosis. To further unravel NAFLD pathogenesis, we aimed to decode the candidate NAFLD biomarkers associated with NAFLD severity using publicly available single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) data.MethodsSeurat v5 and anchor-based reciprocal principal components analysis (RPCA) integration were performed to integrate and analyze the scRNA-seq and snRNA-seq data of 82 liver and Peripheral Blood Mononuclear Cell (PBMC) specimens from NAFLD patients and healthy controls to decode the candidate NAFLD biomarkers generated previously. Using the ‘CellChat’ R package, we analyzed ligand-receptor interactions of our candidate biomarkers from secreted genes to understand their signaling crosstalk and implications in NAFLD’s biological processes.ResultsWe generated a database (https://dreamapp.biomed.au.dk/NAFLD-scRNA-seq/) to present the NAFLD pathogenesis by analyzing integrated scRNA-seq and snRNA-seq data. Through cell-level decoding, we discovered the expression distribution of the candidate biomarkers associated with NAFLD severity. The analysis of ligand-receptor pairs in NAFLD liver and PBMC data suggests that the IL1B-(IL1R1+IL1RAP) interaction between liver monocytes and hepatocytes/cholangiocytes may explain the correlation between NAFLD severity and IL1RAP down-regulation.ConclusionsWe confirmed a strong correlation between liver QSOX1/IL1RAP concentrations and NAFLD severity at the cellular level. Additionally, our analysis of comprehensive data unveiled new aspects of NAFLD pathogenesis and intercellular communication through the use of scRNA and snRNA sequencing data. (ChiCTR2300073940).Lay SummaryWe integrated single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) data to unravel non-alcoholic fatty liver disease (NAFLD) pathogenesis. We decoded candidate biomarkers associated with NAFLD progression, which were previously screened from RNA sequencing (RNA-seq) data of 625 liver samples with a novel gene clustering method. A new version of the R package ‘’Seurat v5’ and anchor-based reciprocal principal components analysis (RPCA) integration were performed to process and integrate scRNA-seq and snRNA-seq data of 82 liver and Peripheral Blood Mononuclear Cell (PBMC) specimens from NAFLD patients and healthy controls. The research delved deeper into the cellular expression patterns of the candidate biomarkers and examined the intercellular communication of their secreted signaling.Graphical abstractHighlightsIntegrated single-cell and single-nucleus profiles from 82 liver and PBMC specimens comprising NAFLD patients and healthy controls with increasing severity were utilized to unveil the NAFLD pathogenesis through decoding candidate biomarkers of NAFLD.In cell-level observations, we decoded 16 up-regulated and 22 down-regulated secreting genes previously identified as associated with increasing NAFLD severity in the liver RNA-seq and plasma proteomics data.QSOX1, enriched in fibroblasts, and IL1RAP, enriched in hepatocytes, have been further validated and interpreted in integrated single-cell and single-nucleus profiles for their potential to predict NAFLD severity.The analysis of intercellular crosstalk, focusing on secreted signaling from our previously identified candidate biomarkers sourced from secreted genes, highlighted the IL1B-(IL1R1+IL1RAP) pathway between liver monocytes and hepatocytes/cholangiocytes. This suggests that this pathway might be a potential reason for the observed downregulation of IL1RAP in NAFLD liver.

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Disulfide bond formation and redox regulation in the Golgi apparatus

Cited by 8 publications

References 121 publications

Single-cell transcriptome profiling reveals distinct expression patterns among genes in the mouse incisor dental pulp

Single-cell transcriptome profiling reveals distinct expression patterns among genes in the mouse incisor dental pulp

A gene-based clustering approach reveals QSOX1/IL1RAP as promising biomarkers for the severity of non-alcoholic fatty liver disease

Unveiling the pathogenesis of non-alcoholic fatty liver disease by decoding biomarkers through integrated single-cell and single-nucleus profiles

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