Bioinformatics analysis of the pathogenic link between Epstein-Barr virus infection, systemic lupus erythematosus and diffuse large B cell lymphoma

Zhu, Qianying

doi:10.1038/s41598-023-33585-2

Cited by 7 publications

References 84 publications

(88 reference statements)

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Regulation of classical zinc fingers for neuronal signaling in the central nervous system

Hwang,

Park,

Kim

et al. 2024

Bulletin Korean Chem Soc

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Zinc finger (ZF) proteins are well‐known for their regulatory functions in the central dogma, and their structural domains serve as promising scaffolds for the study of neurodegenerative diseases. These proteins often contain multiple ZF domains, enabling interactions with target molecules that regulate transcription and translation. The Cys2His2 (C2H2) type ZF domains, found in the brain, are associated with long‐ and short‐term memory, neuronal differentiation and development, and other physiological processes. The classical C‐X2‐C‐X12‐H‐X3‐H type ZF domains have been detected in studies of Parkinson's disease (PD) and are closely linked to biological pathways involved in a wide range of neurodegenerative diseases. In this review, we introduce three ZF proteins expressed in the brain: Parkin‐interacting substrate (PARIS), zinc finger and BTB domain‐containing 20 (ZBTB20), and zinc finger protein 18 (ZNF18). We explore the structural and functional roles of these ZF proteins in the brain. Each of these proteins contains more than four ZF domains, as well as functional domains such as KRAB, BTB, and SCAN, which perform modular roles independently of the ZF domains. Biophysical studies of PARIS have demonstrated that its classical three‐ZF domain, PARIS(ZF2–4), forms hydrogen bonds with insulin response sequences (IRSs) with high specificity (Kd = 38.9 ± 2.4 nM). Metal coordination studies showed that PARIS binds Co2+ with high affinity (Kd = 49.1 ± 7.7 nM), more strongly than other ZF domains, and it also coordinates with other xenobiotic metal ions such as Fe2+ and Ni2+. Although Zn2+–PARIS(ZF2–4) binds specifically to IRSs, Fe2+–, Fe3+– or Co2+–PARIS(ZF2–4) cannot, due to distortions in the ZF domain structure that disrupt hydrogen bonding. These brain‐specific ZF domains exhibit common patterns, with similar numbers of ZF domains and sequence homology at the C‐terminus, whereas both the ZF domains and N‐terminal protein–protein interaction domains contribute to their functional versatility. Elucidating the structure and function of these classical ZF proteins offers promising avenues for the treatment of diverse brain disorders, including Alzheimer's disease, PD, and autism spectrum disorder.

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Regulation of classical zinc fingers for neuronal signaling in the central nervous system

Hwang,

Park,

Kim

et al. 2024

Bulletin Korean Chem Soc

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A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer

Gao,

McClellan,

Barbeira

et al. 2024

The American Journal of Human Genetics

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Exploring genetic and immune cell dynamics in systemic lupus erythematosus patients with Epstein–Barr virus infection via machine learning

Gui,

Fang,

et al. 2024

Rheumatology

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Objectives Epstein-Barr Virus (EBV) is a widespread virus implicated in various diseases, including Systemic Lupus Erythematosus (SLE). However, the specific genes and pathways altered in SLE patients with EBV infection remain unclear. We aimed to identify key genes and immune cells in SLE patients with EBV infection. Methods The datasets of SLE (GSE50772 and GSE81622) or EBV infection (GSE85599 and GSE45918) were obtained from the Gene Expression Omnibus (GEO) database. Next, differential gene expression (DEGs) analysis were conducted to identify overlapping DEGs and then enrichment analysis was performed. Machine learning was applied to identify key genes. Validation was conducted using ROC curve analysis and expression level verification in test datasets and single-cell RNA sequencing. Immune cell infiltration patterns were analyzed using CIBERSORTx, and clinical data were reviewed for SLE patients. Results We identified 58 overlapping DEGs enriched in interferon-related pathways. Five overlapping DEGs (IFI27, TXK, RAPGEF6, PIK3IP1, PSENEN) were selected as key genes by machine learning algorithms, with IFI27 showing the highest diagnostic performance. The expression level of IFI27 was found higher in CD4 CTL, CD8 naïve and various B cell subsets of SLE patients with EBV infection. IFI27 showed significant correlation with B intermediate and CD4 CT. Clinical data showed lower CD4 T cell proportions in SLE patients with EBV infection. Conclusion This study identifies IFI27 as a key gene for SLE patients with EBV infection, influencing CD4 CTL and B cell subtypes. These findings enhance the understanding of the molecular mechanisms linking SLE and EBV infection, providing potential targets for diagnostic and therapeutic strategies.

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Bioinformatics analysis of the pathogenic link between Epstein-Barr virus infection, systemic lupus erythematosus and diffuse large B cell lymphoma

Cited by 7 publications

References 84 publications

Regulation of classical zinc fingers for neuronal signaling in the central nervous system

Regulation of classical zinc fingers for neuronal signaling in the central nervous system

A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer

Exploring genetic and immune cell dynamics in systemic lupus erythematosus patients with Epstein–Barr virus infection via machine learning

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