Biology subjects

Wagner, M. J.

Publications and source records attributed to Wagner, M. J..

8 recordsLinked to original sources

bioRxiv2026

Granule cells reorient cortical manifolds to separate contexts but preserve their geometry

To learn effectively, animals must generalize across yet distinguish between related contexts. Generalization relies on low-dimensional neural manifolds found throughout the neocortex1,2, which accelerate learning by constraining neural activity to task-relevant axes3. Conversely, context separation is attributed to neural expansion layers that can project information into high-dimensional feature spaces4,5, most famously cerebellar granule cells (GrCs)6-8. To investigate the generalization-separation tradeoff, we simultaneously imaged key nodes in the universal cortico-cerebellar pathway9--premotor layer 5 pyramidal tract (L5PT) and GrCs--during parallel learning of two distinct skills with shared temporal structure. Rather than expanding the cortical representations, GrCs retained their low-rank encoding of each task. Across contexts, despite stable cortico-cerebellar coupling, L5PT activity patterns generalized while GrC patterns temporally remapped. But rather than independently scrambling, GrC populations remapped coherently: their low-dimensional trajectories "rotated" apart between tasks, separating the contexts while preserving the cortical geometry of each. Moreover, GrC trajectories diverged most strongly in expert animals. This suggests a fundamental architectural division of labor: the cortex provides invariant dynamic primitives for smooth generalization, while cerebellar activity reconfigures them to drive context-specific output.

neuroscience↗

bioRxiv2026

Cerebellar circuits anticipate dopamine rewards

Learning motivated behaviors requires both anticipating rewards and reinforcing actions that yield them. Although cerebellar activity encodes natural rewards like water and food, it also coordinates physical movements like eating and drinking. To disentangle reward from consummatory movements, we trained mice to push for delayed dopamine rewards delivered directly into the brain. Here we show that cerebellar input streams use both predictive and instructive codes for dopamine reward. Two-photon imaging revealed that many cerebellar granule cells (GrCs) predictively encoded dopamine rewards with sustained activity that "stretched" to match 1- or 2-s delay intervals before terminating upon reward receipt. By contrast, most cerebellar climbing fibers (CFs) spiked just after dopamine delivery. In mice also trained with water rewards, encoding strength for dopamine matched or exceeded that for water. Both cell types contributed causally: chronic GrC inhibition disrupted self- stimulation learning, and CF self-stimulation "rewards" drove moderate operant learning in naive animals. Thus, cerebellar encoding of dopamine reward helps drive motivated behavior, suggesting deeper cerebellar integration in brain reward prediction networks.

neuroscience↗

bioRxiv2022

An unbiased screen identified the Hsp70-BAG3 complex as a regulator of myosin binding protein C3 stability.

Structured AbstractO_ST_ABSObjectiveC_ST_ABSWe aim to identify regulators of myosin binding protein C3 (MyBP-C) protein homeostasis. BackgroundVariants in myosin binding protein C3 (MYBPC3) account for approximately 50% of familial hypertrophic cardiomyopathy (HCM). Most pathogenic variants in MYBPC3 are truncating variants that lead to reduced total levels of MyBP-C protein. Elucidation of the pathways that regulate MyBP-C protein homeostasis could uncover new therapeutic strategies that restore normal protein levels. MethodWe developed a high-throughput screen to identify compounds that can increase or decrease steady-state levels of MyBP-C in an induced pluripotent stem cell cardiomyocyte (iPSC-CM) model derived from a patient with HCM. To normalize results, we also monitored effects on myosin heavy chain (MYH) and focused on those molecules that selectively modulated MyBP-C levels. ResultsScreening a library of 2,426 known biologically active compounds, we identified compounds which either decreased (241/2426, 9.9%) or increased (29/2426, 1.2%) MyBP-C/MYH levels. After a rigorous validation process, including a counter screen for cellular toxicity, two compounds (JG98 and parthenolide) were confirmed as decreasing MyBP-C levels and no compounds were confirmed to increase MyBP-C levels. For further studies, we focused on JG98, which is an allosteric modulator of heat shock protein 70 (Hsp70), inhibiting its interaction with BAG domain co-chaperones. We found that genetic reduction of BAG3 phenocopies treatment with JG98 by reducing MyBP-C protein levels. ConclusionAn unbiased compound screen identified the Hsp70-BAG3 complex as a regulator of MyBP-C stability. Thus, approaches that stimulate this complexs function may be beneficial in the treatment of HCM. Highlights- Hypertrophic cardiomyopathy (HCM) is commonly caused by pathogenic MYBPC3 variants that reduce total wild-type MyBP-C (the protein encoded by MYBPC3). - It is critical to understand the regulators of MyBP-C protein homeostasis to uncover novel therapeutic strategies. - We developed and executed a high-throughput chemical screen in iPSC-CMs to identify compounds which alter steady-state levels of MyBP-C protein, revealing two compounds, JG98 and parthenolide, that significantly reduced MyBP-C levels. - Validation studies suggest that the complex between heat shock protein 70 (Hsp70) and its co-chaperone BAG3 is a dynamic regulator of MyBP-C stability, suggesting that this axis could be a new therapeutic target for HCM.

cell biology↗

bioRxiv2022

Oncogenic role of a developmentally regulated NTRK2 splice variant

Temporally-regulated alternative splicing choices are vital for proper development yet the wrong splice choice may be detrimental. Here we highlight a novel role for the neurotrophin receptor splice variant TrkB.T1 in neurodevelopment, embryogenesis, transformation, and oncogenesis across multiple tumor types in both humans and mice. TrkB.T1 is the predominant NTRK2 isoform across embryonic organogenesis and forced over-expression of this embryonic pattern causes multiple solid and nonsolid tumors in mice in the context of tumor suppressor loss. TrkB.T1 also emerges the predominant NTRK isoform expressed in a wide range of adult and pediatric tumors, including those harboring TRK fusions. Affinity purification-mass spectrometry (AP-MS) proteomic analysis reveals TrkB.T1 has distinct interactors with known developmental and oncogenic signaling pathways such as Wnt, TGF-{beta}, Hedgehog, and Ras. From alterations in splicing factors to changes in gene expression, the discovery of isoform specific oncogenes with embryonic ancestry has the potential to shape the way we think about developmental systems and oncology.

cancer biology↗

bioRxiv2021

Fast and statistically robust cell extraction from large-scale neural calcium imaging datasets

State-of-the-art Ca2+ imaging studies that monitor large-scale neural dynamics can produce video datasets that tally up to [~]100 TB in size ([~]10 days transfer over 1 Gbit/s ethernet). Processing such data volumes requires automated, general-purpose and fast computational methods for cell identification that are robust to a wide variety of noise sources. We present EXTRACT, an algorithm that is based on robust estimation theory and uses graphical processing units (GPUs) to extract neural dynamics from a typical Ca2+ video in computing times up to [~]10-times faster than imaging durations. We extensively validated EXTRACT on simulated and experimental data and processed 199 public datasets ([~]12 TB) from the Allen Institute in a day. Showcasing its superiority over past cell extraction methods at removing noise contaminants, neural activity traces from EXTRACT allow more accurate decoding of animal behavior. Overall, EXTRACT is a powerful computational tool matched to the present challenges of neural Ca2+ imaging studies in behaving animals.

neuroscience↗

bioRxiv2020

Differential encoding in prefrontal cortex projection neuron classes across cognitive tasks

Single-cell transcriptomics has been widely applied to classify neurons in the mammalian brain, while systems neuroscience has historically analyzed the encoding properties of cortical neurons without considering cell types. Here we examine how specific transcriptomic types of mouse prefrontal cortex (PFC) projection neurons relate to axonal projections and encoding properties across multiple cognitive tasks. We found that most types projected to multiple targets, and most targets received projections from multiple types, except PFC[->]PAG (periaqueductal gray). By comparing Ca2+-activity of the molecularly homogeneous PFC[->]PAG type against two heterogeneous classes in several two-alternative choice tasks in freely-moving mice, we found that all task-related signals assayed were qualitatively present in all examined classes. However, PAG-projecting neurons most potently encoded choice in cued tasks, whereas contralateral PFC-projecting neurons most potently encoded reward context in an uncued task. Thus, task signals are organized redundantly, but with clear quantitative biases across cells of specific molecular-anatomical characteristics.

neuroscience↗

bioRxiv2018

Identifying individual risk rare variants using protein structure-guided local tests (POINT)

Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set. To perform inference on a localized level, additional information, e.g., biological annotation, is often needed to boost the information content of a rare variant. Following the observation that important variants are likely to cluster together on functional domains, we propose a protein structure guided local test (POINT) to provide variant-specific association information using structure-guided aggregation of signal. Constructed under a kernel machine framework, POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion. Besides merely providing a list of promising variants, POINT assigns each variant a p-value to permit variant ranking and prioritization. We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9 associated with low-density lipoprotein in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data.\n\nAuthor summaryWhile it is known that rare variants play an important role in understanding associations between genotype and complex diseases, pinpointing individual rare variants likely to be responsible for association is still a daunting task. Due to their low frequency in the population and reduced signal, localizing causal rare variants often requires additional information, such as type of DNA change or location of variant along the sequence, to be incorporated in a biologically meaningful fashion that does not overpower the genotype data. In this paper, we use the observation that important variants tend to cluster together on functional domains to propose a new approach for prioritizing rare variants: the protein structure guided local test (POINT). POINT uses a genes 3-dimensional protein folding structure to guide aggregation of information from neighboring variants in the protein in a robust manner. We show how POINT improves selection performance over single variant tests and sliding window approaches. We further illustrate how it can be used to prioritize individual rare variants using the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data, finding five promising variants within PCSK9 in association with low-density lipoprotein, including three new mutations near the PCSK9-LDLR binding domain.

bioinformatics↗

bioRxiv2017

Low affinity membrane transporters can increase net substrate uptake rate by reducing efflux

Cells require membrane-located transporter proteins to import nutrients from the environment. Many organisms have several similar transporters for the same nutrient, which differ in their affinity. Typically, high affinity transporters are expressed when substrate is scarce and low affinity ones when substrate is more abundant. The benefit of using low affinity transporters when high affinity ones are available has so far remained unclear. Here, we investigate two hypotheses. First, it was previously hypothesized that a trade-off between the affinity and the maximal catalytic rate explains this phenomenon. We find some theoretical and experimental support for this hypothesis, but no conclusive evidence. Secondly, we propose a new hypothesis: for uptake by facilitated diffusion, at saturating extracellular substrate concentrations, lowering the affinity enhances the net uptake rate by reducing the substrate efflux rate. As a consequence, there exists an optimal, external substrate concentration dependent transporter affinity. An in silico analysis of glycolysis in Saccharomyces cerevisiae shows that using the low affinity HXT3 transporter instead of the high affinity HXT6 enhances the steady-state flux by 36%. We tried to test this hypothesis using yeast strains expressing a single glucose transporter that was modified to have either a high or a low affinity. Due to the intimate and reciprocal link between glucose perception and metabolism, direct experimental proof for this hypothesis remained inconclusive in our hands. Still, our theoretical results provide a novel reason for the presence of low affinity transport systems which might have more general implications for enzyme catalyzed conversions.

microbiology↗