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Mastrangelo, A.

Publications and source records attributed to Mastrangelo, A..

2 recordsLinked to original sources

HIF-1α and HIF-2α transcription factors differentially regulate lung alveolar macrophage function

Alveolar Macrophages (AMs) reside in the alveoli, where oxygen pressure is high, therefore maintaining an active degradation of hypoxia-inducible transcription factors (HIF) mediated by the Von Hippel-Lindau protein (pVHL) ubiquitin ligase complex. We previously found that Vhl-deficient AMs not sensing high oxygen pressure are immature and functionally impaired. Here we investigated the specific roles of HIF-1 and HIF-2 isoforms in the regulation of AM functions. With this aim, we combined deletion of Vhl with single or double deletion of Hif1a and/or Hif2a in AMs under the control of the CD11c promoter using a Cre-Lox system. Our work demonstrates that in Vhl-deficient macrophages, both HIF-1 and HIF-2 contribute to AM defective self-renewal, while HIF-2 plays a central role in regulating the impaired AM maturation associated with pVHL loss. HIF-1 promotes a glycolytic shift in alveolar macrophages, while HIF-2 hinders lipid oxidation and surfactant clearance. Thus, HIF-2 raises as a selective critical factor restraining the therapeutic potential of AMs to degrade surfactant excess in mice that have developed pulmonary alveolar proteinosis (PAP). Overall, regulation of both HIF-1 and HIF-2 isoforms is required for an optimal AM function, highlighting HIF-2 as a potential pharmacological target for secondary PAP.

physiology↗

TurbOmics: a web-based platform for the analysis of metabolomics data using a multi-omics integrative approach

In recent years, multi-omics integration has proven highly effective for the holistic characterization of biological systems, with the development of numerous bioinformatic platforms. However, these tools face limitations when incorporating metabolomics data, including absence of support for untargeted metabolomics annotation and dependence on predefined knowledge bases. Furthermore, the advanced algorithms required for multi-omics integration typically demand programming skills and statistical background, restricting their use to specialized users. To advance towards resolving these challenges, we present TurbOmics, a user-friendly web-based platform that enables researchers with diverse backgrounds to analyze metabolomics, proteomics, and transcriptomics data using advanced algorithms for multi-omics integration, while addressing key challenges associated with metabolomics data. Users can upload quantitative data and include additional information, such as metabolite identification or lipid classes, that streamline the interpretation of complex results. TurbOmics can be used sequentially with our previously published tool, TurboPutative, to simplify, reduce and prioritize the list of putative annotations from untargeted metabolomics datasets. Thanks to its flexible and interactive interface, researchers can perform exploratory data analysis and multi-omics integration using the Multi-Omics Factor Analysis, Pathway Integrative Analysis and Enrichment Analysis modules. We believe that TurbOmics will make multi-omics analysis more accessible to the research community. The platform is freely available at https://proteomics.cnic.es/TurboPutative/TurbOmicsApp.html. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=149 SRC="FIGDIR/small/653072v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@26a2e6org.highwire.dtl.DTLVardef@90bfa8org.highwire.dtl.DTLVardef@116d6e4org.highwire.dtl.DTLVardef@76134b_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACTC_FLOATNO C_FIG

bioinformatics↗