Wnt/β-Catenin Signaling Pathway Is a Direct Enhancer of Thyroid Transcription Factor-1 in Human Papillary Thyroid Carcinoma Cells

The Wnt/β-catenin signaling pathway is involved in the normal development of thyroid gland, but its disregulation provokes the appearance of several types of cancers, including papillary thyroid carcinomas (PTC) which are the most common thyroid tumours. The follow-up of PTC patients is based on the monitoring of serum thyroglobulin levels which is regulated by the thyroid transcription factor 1 (TTF-1): a tissue-specific transcription factor essential for the differentiation of the thyroid. We investigated whether the Wnt/β-catenin pathway might regulate TTF-1 expression in a human PTC model and examined the molecular mechanisms underlying this regulation. Immunofluorescence analysis, real time RT-PCR and Western blot studies revealed that TTF-1 as well as the major Wnt pathway components are co-expressed in TPC-1 cells and human PTC tumours. Knocking-down the Wnt/β-catenin components by siRNAs inhibited both TTF-1 transcript and protein expression, while mimicking the activation of Wnt signaling by lithium chloride induced TTF-1 gene and protein expression. Functional promoter studies and ChIP analysis showed that the Wnt/β-catenin pathway exerts its effect by means of the binding of β-catenin to TCF/LEF transcription factors on the level of an active TCF/LEF response element at [−798, −792 bp] in TTF-1 promoter. In conclusion, we demonstrated that the Wnt/β-catenin pathway is a direct and forward driver of the TTF-1 expression. The localization of TCF-4 and TTF-1 in the same area of PTC tissues might be of clinical relevance, and justifies further examination of these factors in the papillary thyroid cancers follow-up

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio