Urzua, C. | Search bioRxiv

bioRxiv2025

An Amygdalar Oscillatory Switch Governs Valence Assignment

Reward pursuit and punishment avoidance are among the most fundamental behaviors necessary for survival. The binary valuation of an experience as either positive or negative - "valence assignment" - is imperative for successful navigation of a regularly updating environment. Mounting evidence highlights the critical role of valence responsive basolateral amygdala (BLA) ensembles in coding valence information. However, how BLA ensembles are recruited to drive real-time valence assignment remains elusive. Here, we show locus coeruleus (LC)-derived norepinephrine coordinates this neural computational process via modulatory control over network-organizing BLA parvalbumin-expressing (PV) interneuron activity. Specifically, optogenetic activation of LC to BLA noradrenergic terminals (LC-BLANE) drives real-time negative valence assignment and suppression of BLA fast gamma oscillatory activity via BLA interneuronal 1a adrenergic receptor signaling. Conversely, positive valence assignment also requires BLA PV interneuron activity but is associated with an enhancement of local fast gamma power. Together, these converging data highlight a PV-driven amygdalar oscillatory switch that governs valence assignment.

neuroscience↗

bioRxiv2024

The regulatory grammar of human promoters uncovered by MPRA-trained deep learning

One of the major challenges in genomics is to build computational models that accurately predict genome-wide gene expression from the sequences of regulatory elements. At the heart of gene regulation are promoters, yet their regulatory logic is still incompletely understood. Here, we report PARM, a cell-type specific deep learning model trained on specially designed massively parallel reporter assays that query human promoter sequences. PARM requires [~]1,000 times less computational power than state-of-the-art technology, and reliably predicts autonomous promoter activity throughout the genome from DNA sequence alone, in multiple cell types. PARM can even design purely synthetic strong promoters. We leveraged PARM to systematically identify binding sites of transcription factors (TFs) that are likely to contribute to the activity of each natural human promoter. We uncovered and experimentally confirmed striking positional preferences of TFs that differ between activating and repressive regulatory functions, as well as a complex grammar of motif-motif interactions. For example, many, but not all, TFs act as repressors when their binding motif is located near or just downstream of the transcription start site. Our approach lays the foundation towards a deep understanding of the regulation of human promoters by TFs. HighlightsO_LICausality-trained deep learning model PARM captures regulatory grammar of human promoters C_LIO_LIPARM is highly economical, both experimentally and computationally C_LIO_LITranscription factors have different preferred positions for their regulatory activity C_LIO_LIMany (but not all) transcription factors act as repressors when binding downstream of transcription start sites C_LI

genetics↗