N6-methyladenosine modification of KLF2 may contribute to endothelial-to-mesenchymal transition in pulmonary hypertension.

BACKGROUND: Pulmonary hypertension (PH) is a progressive disease characterized by pulmonary vascular remodeling. Increasing evidence indicates that endothelial-to-mesenchymal transition (EndMT) in pulmonary artery endothelial cells (PAECs) is a pivotal trigger initiating this remodeling. However, the regulatory mechanisms underlying EndMT in PH are still not fully understood. METHODS: Cytokine-induced hPAECs were assessed using RNA methylation quantification, qRT-PCR, and western blotting to determine the involvement of N6-methyladenosine (m6A) methylation in EndMT. Lentivirus-mediated silencing, overexpression, tube formation, and wound healing assays were utilized to investigate the function of METTL3 in EndMT. Endothelial-specific gene knockout, hemodynamic measurement, and immunostaining were performed to explore the roles of METTL3 in pulmonary vascular remodeling and PH. RNA-seq, RNA Immunoprecipitation-based qPCR, mRNA stability assay, m6A mutation, and dual-luciferase assays were employed to elucidate the mechanisms of RNA methylation in EndMT. RESULTS: The global levels of m6A and METTL3 expression were found to decrease in TNF-α- and TGF-ß1-induced EndMT in human PAECs (hPAECs). METTL3 inhibition led to reduced endothelial markers (CD31 and VE-cadherin) and increased mesenchymal markers (SM22 and N-cadherin) as well as EndMT-related transcription factors (Snail, Zeb1, Zeb2, and Slug). The endothelial-specific knockout of Mettl3 promoted EndMT and exacerbated pulmonary vascular remodeling and hypoxia-induced PH (HPH) in mice. Mechanistically, METTL3-mediated m6A modification of kruppel-like factor 2 (KLF2) plays a crucial role in the EndMT process. KLF2 overexpression increased CD31 and VE-cadherin levels while decreasing SM22, N-cadherin, and EndMT-related transcription factors, thereby mitigating EndMT in PH. Mutations in the m6A site of KLF2 mRNA compromise KLF2 expression, subsequently diminishing its protective effect against EndMT. Furthermore, KLF2 modulates SM22 expression through direct binding to its promoter. CONCLUSIONS: Our findings unveil a novel METTL3/KLF2 pathway critical for protecting hPAECs against EndMT, highlighting a promising avenue for therapeutic investigation in PH.

Subitizing endures in sequential rather than simultaneous comparison tasks.

Subitizing is the ability to appraise a number of small quantities (up to four) rapidly and precisely. This system, however, can be impaired by distractors presented along with targets to be enumerated. To better understand whether this limitation arises in perceptual circuits or in the response selection stage, we investigated whether subitizing can endure in simultaneous comparison tasks. Participants were asked to compare the number of dots in two sets on the left and right sides of the screen, presented either simultaneously or sequentially. For comparing within the numerosity range (6-32 dots), both the error rate and reaction time increased steadily as the ratio between the two numbers compared approached "1." Namely, a phenomenon labeled the ratio effect was revealed. For comparison with small numbers (<5), the sequential comparison task was errorless despite the ratio, suggesting the feature of subitizing. Individual efficiency (measured by the inverse efficiency score [IES]) did not correlate between number ranges in sequential comparison, suggesting that distinct mechanisms were involved. However, we found that in simultaneous tasks, error rate and efficiency showed an increase as the ratios of the two numbers compared approached "1." This is similar to the ratio effect revealed in the comparison for moderate numbers. Individual efficiency within these two ranges correlated, indicating that the enumeration within these two ranges was based on a single mechanism. These results suggest that subitizing cannot process sets in parallel, and numerosity takes the job whenever subitizing fails.

Comprehensive characterization of small noncoding RNA profiles in hypoxia-induced pulmonary hypertension (HPH) rat tissues.

Hypoxia-induced pulmonary hypertension (HPH) is a fatal cardiovascular disease characterized by an elevation in pulmonary artery pressure, resulting in right ventricular dysfunction and eventual heart failure. Exploring the pathogenesis of HPH is crucial, and small noncoding RNAs (sncRNAs) are gaining recognition as potential regulators of cellular responses to hypoxia. In this study, we conducted a comprehensive analysis of sncRNA profiles in eight tissues of male HPH rats using high-throughput sequencing. Our study unveiled several sncRNAs, with the brain, kidney, and spleen exhibiting the highest abundance of microRNA (miRNA), tRNA-derived small RNA (tDR), and small nucleolar RNA (snoRNA), respectively. Moreover, we identified numerous tissue-specific and hypoxia-responsive sncRNAs, particularly miRNAs and tDRs. Interestingly, we observed arm switching in miRNAs under hypoxic conditions and a significant increase in the abundance of 5' tRNA-halves among the total tDRs during hypoxia. Overall, our study provides a comprehensive characterization of the sncRNA profiles in HPH rats.

Terminal modifications independent cell-free RNA sequencing enables sensitive early cancer detection and classification.

Cell-free RNAs (cfRNAs) offer an opportunity to detect diseases from a transcriptomic perspective, however, existing techniques have fallen short in generating a comprehensive cell-free transcriptome profile. We develop a sensitive library preparation method that is robust down to 100 µl input plasma to analyze cfRNAs independent of their 5'-end modifications. We show that it outperforms adapter ligation-based method in detecting a greater number of cfRNA species. We perform transcriptome-wide characterizations in 165 lung cancer, 30 breast cancer, 37 colorectal cancer, 55 gastric cancer, 15 liver cancer, and 133 cancer-free participants and demonstrate its ability to identify transcriptomic changes occurring in early-stage tumors. We also leverage machine learning analyses on the differentially expressed cfRNA signatures and reveal their robust performance in cancer detection and classification. Our work sets the stage for in-depth study of the cfRNA repertoire and highlights the value of cfRNAs as cancer biomarkers in clinical applications.