Neuhaus, F. | bioRxiv Science

bioRxiv2024

Nfib regulates progenitor competence in maturation of GABAergic neurons

Diverse types of GABAergic projection neurons and interneurons of the telencephalon derive from progenitors in a ventral germinal zone, called the ganglionic eminence. Using single-cell transcriptomics, chromatin accessibility profiling, lineage tracing, birthdating, heterochronic transplantation, and perturbation sequencing in mouse embryos, we investigated how progenitor competence influences the maturation and differentiation of these neurons. We found that the progression of neurogenesis over developmental time shapes maturation competence in ganglionic eminence progenitors, influencing how they progress into mature states. In contrast, differentiation competence, which defines the ability to produce diverse transcriptomic identities, remains largely unaffected by the stages of neurogenesis. Chromatin remodeling alongside a NFIB-driven regulatory gene module influences maturation competence in late-born neurons. These findings provide key insights into how transcriptional programs and chromatin accessibility govern neuronal maturation and the diversification of GABAergic neuron subtypes during neurodevelopment.

neuroscience↗

bioRxiv2024

Transcription factor prediction using protein 3D structures

MotivationTranscription factors (TFs) are DNA-binding proteins that regulate gene expression. Traditional methods predict a protein as a TF if the protein contains any DNA-binding domains (DBDs) of known TFs. However, this approach fails to identify a novel TF that does not contain any known DBDs. Recently proposed TF prediction methods do not rely on DBDs. Such methods use features of protein sequences to train a machine learning model, and then use the trained model to predict whether a protein is a TF or not. Because the 3-dimensional (3D) structure of a protein captures more information than its sequence, using 3D protein structures will likely allow for more accurate prediction of novel TFs. ResultsWe propose a deep learning-based TF prediction method (StrucTFactor), which is the first method to utilize 3D secondary structural information of proteins. We compare StrucTFactor with recent state-of-the-art TF prediction methods based on[~] 525 000 proteins across 12 datasets, capturing different aspects of data bias (including sequence redundancy) possibly influencing a methods performance. We find that StrucTFactor significantly (p-value < 0.001) outperforms the existing TF prediction methods, improving the performance over its closest competitor by up to 17% based on Matthews correlation coefficient. AvailabilityData and source code are available at https://github.com/lieboldj/StrucTFactor and on our website at https://apps.cosy.bio/StrucTFactor/ Contactkhalique.newaz@uni-hamburg.de Supplementary informationIncluded

bioinformatics↗