Abscisic acid (ABA) is a plant hormone that mediates abiotic stress tolerance and regulates growth and development. ABA binds to members of the PYL/RCAR ABA receptor family that initiate signal transduction inhibiting type 2C protein phosphatases. Although crosstalk between ABA and the hormone Jasmonic Acid (JA) has been shown, the molecular entities that mediate this interaction have yet to be fully elucidated. We report a link between ABA and JA signaling through a direct interaction of the ABA receptor PYL6 (RCAR9) with the basic helix-loop-helix transcription factor MYC2. PYL6 and MYC2 interact in yeast two hybrid assays and the interaction is enhanced in the presence of ABA. PYL6 and MYC2 interact in planta based on bimolecular fluorescence complementation and co-immunoprecipitation of the proteins. Furthermore, PYL6 was able to modify transcription driven by MYC2 using JAZ6 and JAZ8 DNA promoter elements in yeast one hybrid assays. Finally, pyl6 T-DNA mutant plants show an increased sensitivity to the addition of JA along with ABA in cotyledon expansion experiments. Overall, the present study identifies a direct mechanism for transcriptional modulation mediated by an ABA receptor different from the core ABA signaling pathway, and a putative mechanistic link connecting ABA and JA signaling pathways.
Neuromodulators are critical controllers of neural states, with dysfunctions linked to various neuropsychiatric disorders. Although many biological aspects of neuromodulation have been studied, the computational principles underlying how neuromodulation of distributed neural populations controls brain states remain unclear. In contrast to external contextual inputs, neuromodulation can act as a single scalar signal that is broadcast to a vast population of neurons. We model the modulation of synaptic weight in a recurrent neural network model and show that neuromodulators can dramatically alter the function of a network, even when highly simplified. We find that under structural constraints like those in brains, this provides a fundamental mechanism that can increase the computational capability and flexibility of a neural network. Diffuse synaptic weight modulation enables storage of multiple memories using a common set of synapses that are able to generate diverse, even diametrically opposed, behaviors. Our findings help explain how neuromodulators unlock specific behaviors by creating task-specific hyperchannels in neural activity space and motivate more flexible, compact and capable machine learning architectures.
Spatiotemporal flows of neural activity, such as traveling waves, have been observed throughout the brain since the earliest recordings; yet there is still little consensus on their functional role. Recent experiments and models have linked traveling waves to visual and physical motion, but these observations have been difficult to reconcile with standard accounts of topographically organized selectivity and feedforward receptive fields. Here, we introduce a theoretical framework that formalizes and generalizes the connection between 'motion' and flowing neural dynamics in the language of equivariant neural network theory. We consider 'motion' not only in physical or visual spaces, but also in more abstract representational spaces, and we argue that recurrent traveling-wave-like dynamics are not just useful but necessary for accurate and stable processing of any signal undergoing such motion. Formally, we show that for any non-trivial recurrent neural network to process a sequence undergoing a flow transformation (such as visual motion) in a structured equivariant manner, its hidden state dynamics must actively realize a homomorphic representation of the same flow through recurrent connectivity. In this "spatiotemporal perspective on dynamical computation", traveling waves and related flows are best understood as faithful dynamic representations of stimulus flows; and consequently the natural inclination of biological systems towards such dynamics may be viewed as an innate inductive bias towards efficiency and generalization in the spatiotemporally-structured dynamical world they inhabit.
The mechanisms regulating transcriptional changes during brain aging remain poorly understood. Here, we use single-cell epigenomics to profile chromatin accessibility and gene expression across eight mouse brain regions at 2, 9, and 18 months of age. In addition to a marked decline in progenitor populations involved in neurogenesis and myelination, we observe widespread and concordant age-associated changes in transcription and chromatin accessibility across both neuronal and glial cell types. These alterations are accompanied by dysregulation of master transcription factors and a shift toward stress-response programs driven by activator protein 1 (AP-1), indicating progressive drift in cellular identity with aging. We further identify region- and cell-type-specific heterochromatin loss, characterized by increased accessibility at H3K9me3-marked domains, activation of transposable elements, and upregulation of long noncoding RNAs, particularly in glutamatergic neurons. Together, these findings reveal age-related disruption of heterochromatin maintenance and transcriptional regulation, highlighting vulnerable brain regions, cell types, and molecular pathways in brain aging.
Aging is a major risk factor for neurodegenerative diseases, yet the underlying epigenetic mechanisms remain unclear. Here, we generated a comprehensive single-nucleus cell atlas of brain aging across multiple brain regions, comprising 132,551 single-cell methylomes and 72,666 joint chromatin conformation-methylome nuclei. Integration with companion transcriptomic and chromatin accessibility data yielded a cross-modality taxonomy of 36 major cell types. We observed that transposable element (TE) methylation alone distinguished age groups, showing cell-type-specific genome-wide demethylation. Chromatin conformation analysis demonstrated age-related increases in topologically associated domain (TAD) boundary strength with enhanced accessibility at CCCTC-binding factor (CTCF) binding sites. Spatial transcriptomics across 895,296 cells revealed regional heterogeneity during aging within identical cell types. Finally, we developed deep-learning models that reliably predict age-related gene expression changes using multi-modal epigenetic features, providing mechanistic insights into gene regulation. Age-related comparisons use a 2-month baseline reflecting the late-adolescent/early-young adult stage. This dataset advances our understanding of brain aging and offers potential translational applications.
