Human PRPF39 is an alternative splicing factor recruiting U1 snRNP to weak 5' splice sites

Differences in syncytia formation by SARS-CoV-2 variants modify host chromatin accessibility and cellular senescence via TP53

Coronavirus disease 2019 (COVID-19) remains a significant public health threat due to the ability of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants to evade the immune system and cause breakthrough infections. Although pathogenic coronaviruses such as SARS-CoV-2 and Middle East respiratory syndrome (MERS)-CoV lead to severe respiratory infections, how these viruses affect the chromatin proteomic composition upon infection remains largely uncharacterized. Here, we use our recently developed integrative DNA And Protein Tagging methodology to identify changes in host chromatin accessibility states and chromatin proteomic composition upon infection with pathogenic coronaviruses. SARS-CoV-2 infection induces TP53 stabilization on chromatin, which contributes to its host cytopathic effect. We mapped this TP53 stabilization to the SARS-CoV-2 spike and its propensity to form syncytia, a consequence of cell-cell fusion. Differences in SARS-CoV-2 spike variant-induced syncytia formation modify chromatin accessibility, cellular senescence, and inflammatory cytokine release via TP53. Our findings suggest that differences in syncytia formation alter senescence-associated inflammation, which varies among SARS-CoV-2 variants.

Sequence variations affect the 5' splice site selection of plant introns

Introns are noncoding sequences spliced out of pre-mRNAs by the spliceosome to produce mature mRNAs. The 5' ends of introns mostly begin with GU and have a conserved sequence motif of AG/GUAAGU that could base-pair with the core sequence of U1 snRNA of the spliceosome. Intriguingly, ∼ 1% of introns in various eukaryotic species begin with GC. This occurrence could cause misannotation of genes; however, the underlying splicing mechanism is unclear. We analyzed the sequences around the intron 5' splice site (ss) in Arabidopsis (Arabidopsis thaliana) and found sequences at the GC intron ss are much more stringent than those of GT introns. Mutational analysis at various positions of the intron 5' ss revealed that although mutations impair base pairing, different mutations at the same site can have different effects, suggesting that steric hindrance also affects splicing. Moreover, mutations of 5' ss often activate a hidden ss nearby. Our data suggest that the 5' ss is selected via a competition between the major ss and the nearby minor ss. This work not only provides insights into the splicing mechanism of intron 5' ss but also improves the accuracy of gene annotation and the study of the evolution of intron 5' ss.

Differences in syncytia formation by SARS-CoV-2 variants modify host chromatin accessibility and cellular senescence via TP53

COVID-19 remains a significant public health threat due to the ability of SARS-CoV-2 variants to evade the immune system and cause breakthrough infections. Although pathogenic coronaviruses such as SARS-CoV-2 and MERS-CoV lead to severe respiratory infections, how these viruses affect the chromatin proteomic composition upon infection remains largely uncharacterized. Here we used our recently developed integrative DNA And Protein Tagging (iDAPT) methodology to identify changes in host chromatin accessibility states and chromatin proteomic composition upon infection with pathogenic coronaviruses. SARS-CoV-2 infection induces TP53 stabilization on chromatin, which contributes to its host cytopathic effect. We mapped this TP53 stabilization to the SARS-CoV-2 spike and its propensity to form syncytia, a consequence of cell-cell fusion. Differences in SARS-CoV-2 spike variant-induced syncytia formation modify chromatin accessibility, cellular senescence, and inflammatory cytokine release via TP53. Our findings suggest that differences in syncytia formation alter senescence-associated inflammation, which varies among SARS-CoV-2 variants.

Identification of copper death-associated molecular clusters and immunological profiles in rheumatoid arthritis

Objective

An analysis of the relationship between rheumatoid arthritis (RA) and copper death-related genes (CRG) was explored based on the GEO dataset.

Methods

Based on the differential gene expression profiles in the GSE93272 dataset, their relationship to CRG and immune signature were analysed. Using 232 RA samples, molecular clusters with CRG were delineated and analysed for expression and immune infiltration. Genes specific to the CRGcluster were identified by the WGCNA algorithm. Four machine learning models were then built and validated after selecting the optimal model to obtain the significant predicted genes, and validated by constructing RA rat models.

Results

The location of the 13 CRGs on the chromosome was determined and, except for GCSH. LIPT1, FDX1, DLD, DBT, LIAS and ATP7A were expressed at significantly higher levels in RA samples than in non-RA, and DLST was significantly lower. RA samples were significantly expressed in immune cells such as B cells memory and differentially expressed genes such as LIPT1 were also strongly associated with the presence of immune infiltration. Two copper death-related molecular clusters were identified in RA samples. A higher level of immune infiltration and expression of CRGcluster C2 was found in the RA population. There were 314 crossover genes between the 2 molecular clusters, which were further divided into two molecular clusters. A significant difference in immune infiltration and expression levels was found between the two. Based on the five genes obtained from the RF model (AUC = 0.843), the Nomogram model, calibration curve and DCA also demonstrated their accuracy in predicting RA subtypes. The expression levels of the five genes were significantly higher in RA samples than in non-RA, and the ROC curves demonstrated their better predictive effect. Identification of predictive genes by RA animal model experiments was also confirmed.

Conclusion

This study provides some insight into the correlation between rheumatoid arthritis and copper mortality, as well as a predictive model that is expected to support the development of targeted treatment options in the future.