Genome folding is not static but emerges from dynamic processes that control transcription, replication, recombination, and repair. DNA loop extrusion by cohesin is central to genome organization, yet it remains unclear how cells can tune extrusion kinetics to achieve precise and functional chromosome folding patterns. Here, we show that extrusion rate acts as a tunable biophysical parameter in cells, quantitatively dialed by the respective dosage of the cohesin cofactors NIPBL and PDS5. Modulation of extrusion rate can offset changes in cohesin lifetime to buffer steady-state chromosome structure and transcriptional states, even in the face of abnormal extrusion dynamics. These findings provide a long-sought mechanistic basis for the genetic interactions between cohesin cofactors and for the molecular origin of haploinsufficiency in cohesinopathies, such as Cornelia de Lange syndrome.
Animals continuously evaluate their surroundings to decide whether to approach rewarding opportunities or avoid potential threats. Assigning the appropriate importance to environmental stimuli is not only crucial for survival but also underlies complex forms of goal-directed behaviour that are shared across species, including humans1-4. Understanding how the brain translates such sensory cues into motivated behaviours is, therefore, central to neuroscience and psychology. The dorsomedial prefrontal cortex (dmPFC) is a critical structure that bridges relevant environmental stimuli to goal-directed behaviour. Salience, valence and value are key dimensions defining stimulus relevance, but how the dmPFC processes and organizes such dimensions to drive motivated behaviour remains unclear. Here we monitored single-neuron populations in the dmPFC using calcium imaging in freely moving male mice while discriminating between stimuli predicting different reward or punishment outcomes, which enabled an unprecedented dissociation of salience, valence and value information. We found that dmPFC populations primarily encode appetitive and aversive values of learned stimuli and that subpopulations encode valence and salience along orthogonal information axes. Our results highlight a concurrent multifaceted population coding of value, salience and valence of stimuli during associative learning within dmPFC networks, such that the geometry of dmPFC neuronal representations dynamically shapes appetitive and aversive motivated behaviours.
Loss of a sensory modality can enhance performance in the remaining senses. However, the circuit mechanisms by which such cross-modal compensation can improve cognitive functions, including learning, are unknown. Here, we show that compromising olfaction in both larval and adult Drosophila enhances visual associative learning. Using behavioural analysis, functional imaging, and comparative connectomics, we reveal the circuit mechanisms that underlie this improvement. Animals with improved learning ability have enhanced responses to visual stimuli in the higher-order learning circuit. The complementary circuit mechanisms that can enhance these responses are structural reweighting of inputs in the larva, resulting in an increased fraction of synaptic inputs from visual pathways onto neurons in the learning circuit, and a reduction in cross-modal inhibition in the adult. Together, these findings reveal synaptic and disinhibitory circuit mechanisms that enhance learning in higher-order associative networks following sensory loss.
Gene expression in mammalian cells is controlled by enhancers that are often dispersed across large cis-regulatory landscapes around a promoter. How enhancers determine transcription of their target genes and how this depends on their relative position within a cis-regulatory landscape remain unclear. Here we use live-cell imaging to track the activity of a promoter under the control of the same enhancer, but inserted at different positions across a simplified regulatory landscape with minimal complexity. Combined with mathematical modeling, this reveals that RNA production from the promoter occurs in clusters of transcriptional bursts, with enhancer position controlling the frequency at which clusters appear. This results in bursts being more frequent and occurring more uniformly across cells when the enhancer is genomically close to the promoter than when it is located at a large genomic distance. Mathematical modeling further indicates that the enhancer modulates the promoter's ability to transition from its basal transcriptional state to a regime in which clusters of bursts become more frequent. Our results reveal unexplored modes of mammalian promoter operation and show that enhancer position within a cis-regulatory landscape critically controls the timing and variability of transcriptional output in single cells.
Model-based reinforcement learning (MBRL) agents operating in high-dimensional observation spaces, such as Dreamer, rely on learning abstract representations for effective planning and control. Existing approaches typically employ reconstruction-based objectives in the observation space, which can render representations sensitive to task-irrelevant details. Recent alternatives trade reconstruction for auxiliary action prediction heads or view augmentation strategies, but perform worse in the Crafter environment than reconstruction-based methods. We close this gap between Dreamer and reconstruction-free models by introducing a JEPA-style predictor defined on continuous, deterministic representations. Our method matches Dreamer's performance on Crafter, demonstrating effective world model learning on this benchmark without reconstruction objectives.
