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Rijli, F. M.

Publications and source records attributed to Rijli, F. M..

2 recordsLinked to original sources

Genetic identification of novel medullary neurons underlying congenital central hypoventilation syndrome

Congenital Central Hypoventilation Syndrome (CCHS) is a rare, but life-threatening, respiratory disorder that is classically diagnosed in children. This disease is characterized by pronounced alveolar hypoventilation and diminished chemoreflexes, particularly to abnormally high levels of arterial pCO2. Mutations in the transcription factors PHOX2B and LBX1 have been identified in CCHS patients, but the dysfunctional circuit responsible for this disease remains unknown. Here, we show that distinct sets of medullary neurons co-expressing both transcription factors (dB2 neurons) account for specific respiratory functions and phenotypes seen in CCHS. By combining murine intersectional chemogenetics, intersectional labeling, and the selective targeting of the CCHS disease-causing Lbx1FS mutation to specific subgroups of dB2 neurons, we uncovered novel sets of these cells key for i) respiratory tidal volumes and the hypercarbic reflex, ii) neonatal respiratory stability and iii) neonatal survival. These data provide functional evidence for the essential role of dB2 neurons in neonatal respiratory physiology and will be instrumental for the development of therapeutic strategies for the management of CCHS. In summary, our work uncovers new neural components of the central circuit regulating breathing and establishes dB2 neuron dysfunction to be causative of CCHS.

neuroscience↗

monaLisa: an R/Bioconductor package for identifying regulatory motifs

Proteins binding to specific nucleotide sequences, such as transcription factors, play key roles in the regulation of gene expression. Their binding can be indirectly observed via associated changes in transcription, chromatin accessibility, DNA methylation and histone modifications. Identifying candidate factors that are responsible for these observed experimental changes is critical to understand the underlying biological processes. Here we present monaLisa, an R/Bioconductor package that implements approaches to identify relevant transcription factors from experimental data. The package can be easily integrated with other Bioconductor packages and enables seamless motif analyses without any software dependencies outside of R. AvailabilitymonaLisa is implemented in R and available on Bioconductor at https://bioconductor.org/packages/monaLisa with the development version hosted on GitHub at https://github.com/fmicompbio/monaLisa. Contactmichael.stadler@fmi.ch

bioinformatics↗