Frei, S. | bioRxiv Science

bioRxiv2025

Distinct molecular mechanisms of stress habituation in the mouse hippocampus

Chronic stress is a risk factor for neuropsychiatric disorders, making the ability to adapt to repeated stress a crucial determinant of mental health. On a molecular level, it remains unclear whether repeated exposure to stress is characterized by habituation - a decreased responsiveness to the same stimulus - or by the emergence of new, adaptive responses. Here, we explore how the tightly regulated molecular response triggered by acute restraint stress becomes altered after repeated restraint exposure. Transcriptomic sampling of the mouse hippocampus at multiple time points revealed that repeated stress leads to widespread habituation, damping stress-induced gene expression of all stress-responsive genes. However, we find no evidence for the emergence of new response profiles or alterations in baseline gene expression. Using single-cell multi-omics, we show that these findings hold true across cell types, and we reveal cell type specific patterns of habituation. Transcriptomic and chromatin accessibility profiles identify two distinct mechanisms that contribute to the observed habituation patterns: an early cAMP-associated mechanism that is related to blunted transcription after chronic stress, and a late corticosterone-dependent mechanism that is linked to a shortened transcriptional response. These extensive data are integrated, along with our previous work, into an interactive app, providing a uniquely detailed molecular resource that characterizes the acute stress response and the process of habituation across the genome.

neuroscience↗

bioRxiv2024

On the identification of differentially-active transcription factors from ATAC-seq data

ATAC-seq has emerged as a rich epigenome profiling technique, and is commonly used to identify Transcription Factors (TFs) underlying given phenomena. A number of methods can be used to identify differentially-active TFs through the accessibility of their DNA-binding motif, however little is known on the best approaches for doing so. Here we benchmark several such methods using a combination of curated datasets with various forms of short-term perturbations on known TFs, as well as semi-simulations. We include both methods specifically designed for this type of data as well as some that can be repurposed for it. We also investigate variations to these methods, and identify three particularly promising approaches (a chromVAR-limma workflow with critical adjustments, monaLisa and a combination of GC smooth quantile normalization and multivariate modeling). We further investigate the specific use of nucleosome-free fragments, the combination of top methods, and the impact of technical variation. Finally, we illustrate the use of the top methods on a novel dataset to characterize the impact on DNA accessibility of TRAnscription Factor TArgeting Chimeras (TRAFTAC), which can deplete TFs - in our case NFkB - at the protein level. Author summaryTranscription factors regulate gene expression by binding sites in the genome that often harbor a specific DNA motif. The collective accessibility of these motif-matching regions, measured by technologies such as ATAC-seq, can be used to infer the activity of the corresponding transcription factors. Here we use curated datasets of 11 TF-specific perturbations as well as 116 semi-simulated datasets to benchmark various methods for identifying factors that differ in activity between experimental conditions. We investigate important variations in the analysis and make recommendations pertaining to such analysis. Finally, we illustrate the application of the top methods to characterize the effects of a novel method for perturbing transcription factors at the protein level.

bioinformatics↗