Extracellular matrix in skin diseases: the road to new therapies

"Article in Press"Background: The extracellular matrix (ECM) is a vital structure with a dynamic and complex organization that plays an essential role in tissue homeostasis. In the skin, the ECM is arranged into two types of com- partments: interstitial dermal matrix and basement membrane (BM). All evidence in the literature sup- ports the notion that direct dysregulation of the composition, abundance or structure of one of these types of ECM, or indirect modifications in proteins that interact with them is linked to a wide range of human skin pathologies, including hereditary, autoimmune, and neoplastic diseases. Even though the ECMâ s key role in these pathologies has been widely documented, its potential as a therapeutic target has been overlooked. Aim of review: This review discusses the molecular mechanisms involved in three groups of skin ECM- related diseases - genetic, autoimmune, and neoplastic â and the recent therapeutic progress and oppor- tunities targeting ECM. Key scientific concepts of review: This article describes the implications of alterations in ECM components and in BM-associated molecules that are determinant for guaranteeing its function in different skin dis- orders. Also, ongoing clinical trials on ECM-targeted therapies are discussed together with future oppor- tunities that may open new avenues for treating ECM-associated skin diseases.This work was supported by ERC Consolidator Grant – ECM_INK (ERC-2016-COG-726061) (A. P. Marques and FCT with grant SFRH/ BD/137766/2018 (M. D. Malta) and contract from Norte-01-0145- FEDER-02219015 (M. T. Cerqueira)

Dermal extracellular matrix extracts for wound healing: a pleiotropic trigger

Apresentação efetuada no "Tissue Engineering and Regenerative Medicine International Society (TERMIS) European Chapter Meeting" , em Manchester, Reino Unido, 2023INTRODUCTION: Extracellular matrix (ECM) role is defined by direct cell‐ECM interactions and biomechanics and also by its capacity to store biochemical cues that are vital in tissue's repair. With this in mind, an in‐house method was devised to obtain extracts comprised of structural ECM components (strECM) and enriched in soluble ECM‐derived factors (sECM). Herein we hypothesised that each ECM fraction may trigger different biological functions in multiple cell types, objectively confering them therapeutic and biomimetic potential. METHODS: To prove the concept, we used human dermal fibroblasts (hdFBs) to obtain the ECM extracts, prepared by fractioning cultured cells' own ECM. Extracts were analysed by mass spectrometry to obtain a proteomic profile and then regarding their in vitro functionality. Human dermal endothelial cells (hDMECs), keratinocytes (hKCs) and dFBs, were used to confirm the features identified by the proteomic profiling. The effect of the extracts over cell adhesion (focal adhesion formation) was analysed. A Matrigel assay was used to evaluate a potential angiogenic effect of the extracts. Moreover, hKCs migration and ability to differentiate and form a stratified epidermis was assessed. Finally, matrix (Collagen, elastin, GAGs) deposition by hdFBs and metalloproteinases (MMP 1, 2, 9)secretion and activity were measured. RESULTS: Proteomic analysis revealed that strECM and sECM complement each other, preserving the native ECM protein profile. The GO accessions linked to each fraction allowed pinpointing the specific cues provided by either of them. strECM was mainly comprised of components that were able to promote cell adhesion and spatial organization. On the other hand sECM proteomic profile revealed factors that play a role in wound healing such as angiogenesis, ECM remodeling and re‐epithelialization. A dose‐dependent response was observed regarding the formation of tubular structures in the angiogenic assay. The presence of sECM leads to a significant increase in the migration and proliferative ability (Ki67) of hKCs while maintaining their phenotype. Finally, sECM led to enhanced collagen, elastin and GAGs deposition by hDFbs while increasing the expression of MMPs. DISCUSSION & CONCLUSIONS: Our results validate the hypothesis that each ECM fraction effectively triggers different biological functions in multiple cell types. Overall the presence of sECM boosted the major cellular mechanisms that lead to successful wound healing, while strECM provides cues for cellular adhesion and organization. This study supports the use of ECM extracts as a wound healing enhancer, which might aid in the development of future therapies or improve the biomimicry of ECM‐based 3D tissue models

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

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 matrix distinct signature among dystrophic epidermolysis bullosa variants

Introduction & objectives: Mutations in the COL7A1 gene, which encodes collagen VII protein, the major component of the anchoring fibrils in the dermal-epidermal junction, cause all forms of dystrophic epidermolysis bullosa (DEB). Different clinical variants have been described with both dominant and recessive inheritance. However, information regarding the consequences of different COL7A1 mutations in the cell microenvironment, particularly on extracellular matrix (ECM), is still scarce. Moreover, several studies found the spectrum of biologic and clinical phenotypes of DEB to be wider than initially anticipated. Hence, this work aims to unravel the main differences in ECM composition between DEB patients and healthy individuals, as well as between representative variants of the disease. Materials & methods: Healthy primary fibroblasts and immortalized cell lines of three DEB variants (generalized DDEB, generalized intermediate RDEB and generalized severe RDEB). The cells were seeded at a density of 50x103 cells per cm2 for 14 days with 50μg/mL ascorbic acid, in order to promote maximum ECM deposition. Mass spectrometry-based label-free quantification was used to assess changes in the ECM deposited by the different cell populations. Then a combination of western blot, quantitative real-time PCR and histological methods were used to confirm the proteomic results and investigate the biological pathways linked to the obtained results. Results: Analysis of the extracellular proteome revealed that fibroblasts from each DEB variant have their own proteomic signature. Independently of the DEB variant - and its associated clinical aggressiveness - the different COL7A1 mutations studied impacted dermal ECM organization through the down-regulation of major ECM players such as collagen XII, decorin, biglycan and lysyl oxidase homolog 2. Furthermore, ECM organization-associated proteins were found to be differently expressed between DEB variants. For the phenotypes associated to increased severity of disease, a down-regulation of proteins linked to ECM structure and remodelling, namely collagens I, III and V and matrix metalloproteinases 1 and 2, was observed. Conclusions: Our results corroborate previous studies showing that total loss of collagen VII has an enormous impact on dermal ECM dynamics. Additionally, our results also demonstrated that a partial loss of type VII collagen impacts cell microenvironment, affecting mostly the ECM structural proteins. Overall, our work contributes to the generation of further knowledge on DEB variants molecular features.The authors would like to acknowledge FCT for grant SFRH/BD/137766/2018 (MDM) and contract CEECIND/00695/2017 (MTC), the ERC Consolidator Grant â ECM_INK (ERC-2016-COG-726061) the European Union for The Discoveries Centre for Regenerative and Precision Medicine (H2020-WIDESPREAD-2014-1-739572)

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field