CD69 is a TGF-β/1α,25-dihydroxyvitamin D3 target gene in monocytes

CD69 is a transmembrane lectin that can be expressed on most hematopoietic cells. In monocytes, it has been functionally linked to the 5-lipoxygenase pathway in which the leukotrienes, a class of highly potent inflammatory mediators, are produced. However, regarding CD69 gene expression and its regulatory mechanisms in monocytes, only scarce data are available. Here, we report that CD69 mRNA expression, analogous to that of 5-lipoxygenase, is induced by the physiologic stimuli transforming growth factor-β (TGF-β) and 1α,25-dihydroxyvitamin D3 (1α,25(OH)2D3) in monocytic cells. Comparison with T- and B-cell lines showed that the effect was specific for monocytes. CD69 expression levels were increased in a concentration-dependent manner, and kinetic analysis revealed a rapid onset of mRNA expression, indicating that CD69 is a primary TGF-β/1α,25(OH)2D3 target gene. PCR analysis of different regions of the CD69 mRNA revealed that de novo transcription was initiated and proximal and distal parts were induced concomitantly. In common with 5-lipoxygenase, no activation of 0.7 kb or ~2.3 kb promoter fragments by TGF-β and 1α,25(OH)2D3 could be observed in transient reporter assays for CD69. Analysis of mRNA stability using a transcription inhibitor and a 3′UTR reporter construct showed that TGF-β and 1α,25(OH)2D3 do not influence CD69 mRNA stability. Functional knockdown of Smad3 clearly demonstrated that upregulation of CD69 mRNA, in contrast to 5-LO, depends on Smad3. Comparative studies with different inhibitors for mitogen activated protein kinases (MAPKs) revealed that MAPK signalling is involved in CD69 gene regulation, whereas 5-lipoxygenase gene expression was only partly affected. Mechanistically, we found evidence that CD69 gene upregulation depends on TAK1-mediated p38 activation. In summary, our data indicate that CD69 gene expression, conforming with 5-lipoxygenase, is regulated monocyte-specifically by the physiologic stimuli TGF-β and 1α,25(OH)2D3 on mRNA level, although different mechanisms account for the upregulation of each gene

GazPNE2: A general place name extractor for microblogs fusing gazetteers and pretrained transformer models

The concept of ‘human as sensors’ defines a new sensing model, in which humans act as sensors by contributing their observations, perceptions, and sensations. This is crucial for the development of social Internet of Things, which is an integral part of Cyber-Physical-Social systems. Online social media platforms, as the most active places where users act as social sensors, are responsive to real-world events and are useful for gathering situational information in real-time. Unfortunately, posts rarely contain structured geographic information, thus hindering their usage for contributing to various challenges, such as emergency response. We address this limitation by introducing a general approach for extracting place names from tweets, named GazPNE2. It combines global gazetteers (i.e., OpenStreetMap and GeoNames), deep learning, and pretrained transformer models (i.e., BERT and BERTweet), which requires no manually annotated data. It can extract place names at both coarse (e.g., city) and fine-grained (e.g., street and POI) levels and place names with abbreviations. To fully evaluate GazPNE2 and compare it with 11 competing approaches, we use 19 public tweet datasets, containing 38,802 tweets and 22,197 places across the world. The results show GazPNE2 achieves much higher F1 (0.8) than the other approaches. Furthermore, we apply GazPNE2 to three large unannotated tweet datasets related to over 20 crisis events (e.g., COVID-19), containing 560,040 tweets. An F1 of 0.84 is achieved on 3,000 tweets, which are randomly selected from the three datasets and then manually annotated. Code and data are available on GitHub page: https://github.com/uhuohuy/GazPNE2