Secretome profiling of oral squamous cell carcinoma-associated fibroblasts reveals organization and disassembly of extracellular matrix and collagen metabolic process signatures

An important role has been attributed to cancer-associated fibroblasts (CAFs) in the tumorigenesis of oral squamous cell carcinoma (OSCC), the most common tumor of the oral cavity. Previous studies demonstrated that CAF-secreted molecules promote the proliferation and invasion of OSCC cells, inducing a more aggressive phenotype. In this study, we searched for differences in the secretome of CAFs and normal oral fibroblasts (NOF) using mass spectrometry-based proteomics and biological network analysis. Comparison of the secretome profiles revealed that upregulated proteins involved mainly in extracellular matrix organization and disassembly and collagen metabolism. Among the upregulated proteins were fibronectin type III domain-containing 1 (FNDC1), serpin peptidase inhibitor type 1 (SERPINE1), and stanniocalcin 2 (STC2), the upregulation of which was validated by quantitative PCR and ELISA in an independent set of CAF cell lines. The transition of transforming growth factor beta 1 (TGF-beta 1)-mediating NOFs into CAFs was accompanied by significant upregulation of FNDC1, SERPINE1, and STC2, confirming the participation of these proteins in the CAF-derived secretome. Type I collagen, the main constituent of the connective tissue, was also associated with several upregulated biological processes. The immunoexpression of type I collagen N-terminal propeptide (PINP) was significantly correlated in vivo with CAFs in the tumor front and was associated with significantly shortened survival of OSCC patients. Presence of CAFs in the tumor stroma was also an independent prognostic factor for OSCC disease-free survival. These results demonstrate the value of secretome profiling for evaluating the role of CAFs in the tumor microenvironment and identify potential novel therapeutic targets such as FNDC1, SERPINE1, and STC2. Furthermore, type I collagen expression by CAFs, represented by PINP levels, may be a prognostic marker of OSCC outcome.Peer reviewe

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost

Extracellular vesicles from oral squamous carcinoma cells display pro- and antiangiogenic properties

Abstract Background: A new intercellular communication mode established by neoplastic cells and tumor microenvironment components is based on extracellular vesicles (EVs). However, the biological effects of the EVs released by tumor cells on angiogenesis are not completed understood. Here we aimed to understand the biological effects of EVs isolated from two cell lines of oral squamous cell carcinoma (OSCC) (SCC15 and HSC3) on endothelial cell tubulogenesis. Methods: OSCC-derived EVs were isolated with a polymer-based precipitation method, quantified using nanoparticle tracking analysis and verified for EV markers by dot-blot. Functional assays were performed to assess the angiogenic potential of the OSCC-derived EVs. Results: The results showed that EVs derived from both cell lines displayed typical spherical-shaped morphology and expressed the EV markers CD63 and Annexin II. Although the average particle concentration and size were quite similar, SCC15-derived EVs promoted a pronounced tubular formation associated with significant migration and apoptosis rates of the endothelial cells, whereas EVs derived from HSC3 cells inhibited significantly endothelial cell tubulogenesis and proliferation. Conclusions: The findings of this study reveal that EVs derived from different OSCC cell lines by a polymer-based precipitation method promote pro- or antiangiogenic effects

Tenascin-C and fibronectin expression divide early stage tongue cancer into low- and high-risk groups

Abstract Background: Oral tongue squamous cell carcinoma (OTSCC) metastasises early, especially to regional lymph nodes. There is an ongoing debate on which early stage (T1-T2N0) patients should be treated with elective neck dissection. We need prognosticators for early stage tongue cancer. Methods: Mice immunisation with human mesenchymal stromal cells resulted in production of antibodies against tenascin-C (TNC) and fibronectin (FN), which were used to stain 178 (98 early stage), oral tongue squamous cell carcinoma samples. Tenascin-C and FN expression in the stroma (negative, moderate or abundant) and tumour cells (negative or positive) were assessed. Similar staining was obtained using corresponding commercial antibodies. Results: Expression of TNC and FN in the stroma, but not in the tumour cells, proved to be excellent prognosticators both in all stages and in early stage cases. Among early stages, when stromal TNC was negative, the 5-year survival rate was 88%. Correspondingly, when FN was negative, no cancer deaths were observed. Five-year survival rates for abundant expression of TNC and FN were 43% and 25%, respectively. Conclusions: Stromal TNC and, especially, FN expressions differentiate patients into low- and high-risk groups. Surgery alone of early stage primary tumours might be adequate when stromal FN is negative. Aggressive treatments should be considered when both TNC and FN are abundant