Machine learning and molecular subtype analyses provide insights into PANoptosis-associated genes in rheumatoid arthritis.

BACKGROUND: PANoptosis represents a newly identified form of programmed cell death that plays a significant role in the autoimmune diseases. Rheumatoid arthritis (RA) is characterized by the presence of autoantibodies. Nevertheless, the specific biomarkers and molecular mechanisms responsible for the apoptotic characteristics of RA remain largely uninvestigated. METHODS: We utilized 8 synovial tissue RA datasets. We selected genes associated with PANoptosis from the GeneCard database. By employing the limma, WGCNA, and machine learning algorithms we identified core genes. We utilized consensus clustering analysis to identify distinct PANoptosis subtypes of RA. Boruta algorithm was employed to construct a PANoptosis signature score. The sensitivity of distinct subtypes to drug treatment was verified using an independent dataset. RESULTS: The SPP1 emerged as the significant gene, with its elevated expression in RA patients. We identified two PANoptosis RA subtypes. Cluster 1 showed high expression of Tregs, resting dendritic cells, and resting mast cells. Cluster 2 exhibited high expression of CD4 memory T cells and follicular helper T cells. Cluster 2 exhibited a higher degree of sensitivity towards immune checkpoint therapy. Employing the Boruta algorithm, a subtype score was devised for 37 PANoptosis genes, successfully discerning the subtypes (AUC = 0.794), wherein patients with elevated scores demonstrated enhanced responsiveness to Rituximab treatment. CONCLUSION: Our analysis revealed that SPP1 holds potential biomarker for the diagnosis of RA. Cluster 2 exhibited enhanced sensitivity to immune checkpoint therapy, higher PANoptosis scores, and improved responsiveness to drug treatment. This study offers potential implications in the realm of diagnosis and treatment.

Metabolomic and transcriptomic analyses reveal the biosynthetic mechanisms of pigments and main taste compounds in an albino tea cultivar.

Significant variations in leaf colours, pigment contents, and main taste compounds in young shoots from albino tea plants (Camellia sinensis) influence tea flavour. However, the seasonal metabolic pattern and molecular regulatory mechanism of these metabolites remain largely elusive. Herein, we conducted morphological, biochemical, metabolomic and transcriptomic analyses between an albino tea cultivar 'Zhonghuang 3' ('ZH3') and a green strain 'Tai cha 15' ('TC15') at four-time points (April 12, May 31, July 14, and August 17) to elucidate dynamic changes in these compounds and predict the relationships among transcription factors (TFs), target genes (TGs), and metabolite abundance. Generally, leaf colours and pigment contents were significantly lighter and lower, respectively, in 'ZH3' than in 'TC15' from spring to summer, but were subsequently similar. Compared to 'TC15', 'ZH3' had a lower and broader phenol/ammonia ratio as well as stable caffeine content and showed more significantly different metabolites and differentially expressed genes. The relationship between pigments, main taste compounds, and their biosynthetic genes, as well as TFs and their TGs, had genetic specificity. These results suggested that the biosynthesis of these compounds was probably both season- and variety-dependent. In total, 12 models of the TF-TG-metabolite regulatory network were proposed to uncover the biosynthetic and regulatory mechanisms of these metabolites in tea plants. A high correlation was observed between some structural genes and TFs with the accumulation of these metabolites. These findings provide novel insights into the regulatory mechanisms underlying accumulation of pigments and main taste compounds in tea plants.

The chronergy of recombinant streptokinase thrombolysis in acute myocardial infarction.

The aim of this study was to explore the chronergy of intravenous recombinant streptokinase (r-SK) in patients with acute myocardial infarction (AMI). A total of 114 patients were divided into two groups according to the time of AMI onset: the morning onset (6:01-12:00, n=53) and non-morning onset (12:01-06:00, n=61) groups. The recanalization rate was recorded, as well as anticoagulant and fibrinolytic indices. Statistical analysis was performed to evaluate the recanalization rate following thrombolysis, as well as the anticoagulant and fibrinolytic activities. The recanalization rates following thrombolysis in the morning onset and non-morning onset groups were 60.4 and 82.0%, respectively (P<0.05). The level of plasminogen activator inhibitor-1 (PAI-1) antigen was significantly higher in the morning onset group compared with that in the non-morning onset group (P<0.05). This indicated a resistance to r-SK thrombolysis in the morning at the early stage of AMI, which possibly correlates with increased PAI-1 antigen levels and activity.