MPe26-TREnDs: Translational Research in Rare and Endocrine Diseases (Emerging)

The emerging TREnDs (Translational Research in Rare and Endocrine Diseases) group aims to develop high-impact translational research in the field of rare and endocrine diseases, integrating multi-omic, clinical, experimental, and computational approaches. Our mission is to advance the identification of new biomarkers and therapeutic targets, and the characterization of the molecular pathways involved in the development of these pathologies, in order to improve their diagnosis, prognosis, and treatment.

The group applies innovative methodologies, including the use of omics technologies (genomics, transcriptomics, proteomics, metabolomics, microbiome, among others), cellular and molecular functional analysis, integration of clinical and medical imaging data, as well as bioinformatics tools and artificial intelligence and machine learning algorithms. This multidisciplinary approach allows for a precise characterization of the biological and functional bases of the diseases studied.

TREnDs works on a wide range of rare diseases, both endocrine and others, including inherited cardiovascular diseases (such as arrhythmias, sudden cardiac death, and early heart attacks), rare bone disorders (such as hypophosphatasia), sepsis, and complex endocrine disorders, among others. The strategies developed are designed with a clear translational focus, with the aim of facilitating their application in clinical practice and contributing to precision medicine in the field of rare diseases.

1. Identification of new biomarkers and therapeutic targets

We address the identification of diagnostic, prognostic, and therapeutic biomarkers, as well as novel molecular targets, using multi-omic platforms such as genomics, transcriptomics, proteomics, metabolomics, and interactomics, in addition to differential analysis of microRNAs and the microbiome. These approaches allow for a comprehensive study of the mechanisms involved in the pathophysiology of rare and endocrine diseases.

2. Bioinformatics and advanced computational analysis

We employ bioinformatics tools and artificial intelligence and machine learning algorithms for 3D structural modeling of proteins and genetic variants, molecular docking, biological network analysis, functional enrichment, multi-omics data clustering, and phenotypic identification using algorithms such as PGMR. These strategies allow us to extract functional knowledge from large volumes of data and identify relevant clinical-molecular patterns.

3. Functional characterization of genetic variants

We investigate the effects of new genetic variants associated with various pathologies (cardiovascular, bone, endocrine, infectious, and various rare diseases) using a two-pronged approach: (1) at the cellular and molecular level, through expression in cell lines, the use of second-generation lentiviral particles, and virtual structural studies; and (2) at the experimental level, through functional analyses such as measurement of enzymatic activities, viability, cell signaling, and other assays in cell cultures.

4. Development of new technologies with clinical application

We design and validate new tools to improve clinical diagnosis, such as innovative flow cytometry protocols and functionalized nanoparticles. This line is developed in collaboration with biotechnology companies such as Sensactive Technology, facilitating the transfer of results to the clinical and business environments.

5. Integration of multi-scale, clinical and bioinformatics data

We integrate data from multi-omics techniques, functional studies, structural analyses, medical imaging techniques, and computational results using data analysis platforms and artificial intelligence. This integration allows us to characterize new molecular pathways involved in the etiopathogenesis of the diseases studied and identify clinically relevant biomarkers and therapeutic targets with translational potential.