bioRxiv Science⌕ Search

Biology subjects

Domingo, D.

Publications and source records attributed to Domingo, D..

2 recordsLinked to original sources

Hyaluronic acid dynamics are regulated by neural crest cells and NEDD4 in the developing cardiac outflow tract valves

The extracellular matrix (ECM) is essential for cardiac valve development, with hyaluronic acid (HA) playing a critical role in cardiac cushion formation. While HA is required for atrioventricular valve development, its role in semilunar valve formation remains poorly understood. The ubiquitin ligase NEDD4 is essential for cardiac outflow tract development, particularly within cardiac neural crest cells, however its role in semilunar valve development has been unexplored. Here, we demonstrate that neural crest-specific deletion of NEDD4 in mice disrupts outflow tract valve development, resulting in abnormal leaflet number and morphology. Analysis of valve ECM revealed a specific reduction in HA within the neural crest-derived valve mesenchyme, while other ECM components such as fibronectin and versican remained unaffected. In contrast, deletion of NEDD4 in endocardium-derived valve mesenchyme did not alter HA abundance. Using NEDD4-deficient neural crest cells, we further demonstrate a cell-autonomous requirement for NEDD4 in regulating HA abundance, with in vitro evidence supporting impaired HA synthesis rather than increased turnover of HA in the ECM. Our findings identify a previously unrecognised role for NEDD4 in regulating HA synthesis in cardiac neural crest cells, revealing a cell-type-specific mechanism governing semilunar valve development and highlighting distinct cellular requirements for ECM regulation across cardiac valve populations.

developmental biology↗

Identification of Mycobacterium abscessus subspecies by MALDI-TOF Mass Spectrometry and Machine Learning

Mycobacterium abscessus complex is one of the most common and pathogenic nontuberculous mycobacteria (NTM) isolated in clinical laboratories. It consists of three subspecies: M. abscessus subsp. abscessus, M. abscessus subsp. bolletii and M. abscessus subsp. massiliense. Due to their different antibiotic susceptibility pattern, a rapid and accurate identification method is necessary for their differentiation. Although matrix assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) has proven useful for NTM identification, the differentiation of M. abscessus subspecies is challenging. In this study, a collection of 244 clinical isolates of M. abscessus complex was used for MALDI-TOF MS analysis and for the development of machine learning predictive models. Overall, using a Random Forest model with several confidence criteria (samples by triplicate and similarity values >60%), a total of 95.8% of isolates were correctly identified at subspecies level. In addition, differences in culture media, colony morphology and geographic origin of the strains were evaluated, showing that the latter most affected the mass spectra of isolates. Finally, after studying all protein peaks previously reported for this complex, two novel peaks with potential for subspecies differentiation were found. Therefore, machine learning methodology has proven to be a promising approach for rapid and accurate identification of subspecies of the M. abscessus complex using MALDI-TOF MS.

microbiology↗