Biology subjects

Chandler, A.

Publications and source records attributed to Chandler, A..

4 recordsLinked to original sources

bioRxiv2026

Climbing fibers encode the gradient of a loss function for the cerebellum

Neurons in the brain are often many synapses away from motoneurons, yet if a movement results in error, each distant neuron needs a teacher that considers its specific contribution to production of that movement. This credit assignment problem is solved in machine learning via gradient descent of a loss function, where the loss defines the subjective cost incurred by error. Does the brain use gradient descent to teach individual neurons? We trained marmosets to make saccades to visual targets and then varied the loss by assigning reward value to each target. The climbing fibers, which are the teachers of Purkinje cells (P-cells) in the cerebellum, used a multiplicative encoding to scale the spatial properties of the error vector with its reward properties, incorporating reward prediction errors. Using spike-triggered suppression of P-cells, we quantified the potent vector that mapped each P-cells output to eye movements and discovered that the climbing fibers were not merely transmitting errors. Rather, they were providing a signal that was, on average, proportional to the dot product of the reward dependent error vector upon the P-cells potent vector. Thus, the climbing fibers solved the credit assignment problem by providing the gradient of a loss function with respect to the output of their individual P-cells.

neuroscience↗

bioRxiv2025

Red Blood Cell Transfusion is a Non-Canonical Immune Stimulus Characterized by the Suboptimal Induction of CD4+ T Cell Help

Red blood cell (RBC) alloimmunization to non-ABO antigens is a major clinical complication for chronically transfused patients. When exposed to transfused RBCs carrying foreign antigens, some patients generate IgG antibodies that target these antigens, creating potential barriers to future transfusions. Interestingly, other patients produce only IgM antibodies against the same non-ABO antigens, which generally have fewer clinical consequences. Despite the stark differences in their impact, the factors regulating IgM versus IgG production in response to transfused RBCs remain poorly understood. This study explores the balance between IgM and IgG production following transfusion, comparing it to the well-characterized antibody response induced by vaccination in mouse models. By directly assessing antibody levels following RBC transfusion versus Alum-adjuvanted vaccination, we demonstrate that transfusion of RBCs expressing a model antigen is a relatively weak inducer of IgG class switching. Additionally, loss-of-function experiments using CD40L blockade and CD4 depletion confirmed that T cell help is essential for class switching after transfusion but has no effect on IgM production. Most notably, providing supra-physiological levels of T cell help enhanced class switching in a dose-dependent manner after transfusion, whereas vaccination-induced class switching remained unaffected. These findings support a model in which the limited IgG class switching following transfusion stems from suboptimal T cell help compared to vaccination. Furthermore, they suggest that transfusion activates T cells through a non-canonical pathway, distinct from the mechanisms driving immune responses to standard Alum vaccination.

immunology↗

bioRxiv2024

Integrated transcriptomic, metabolomic, and lipidomic analyses reveal a unique lipid profile of regulatory T cells upon activation

Regulatory T cells (Tregs) exhibit stable FOXP3 expression and regulate the immune response through suppressive activity. Their unique metabolic properties include reduced glycolysis and increased oxidative phosphorylation. We combined transcriptomic, metabolomic, and lipidomic analyses to dissect the metabolic dynamics of Tregs upon activation. Combined metabolomic and lipidomic analyses showed that freshly isolated and activated Tregs had distinct metabolomic and lipidomic properties, respectively. Activated Tregs contained omega-3 long-chain polyunsaturated fatty acid (PUFA)-rich diglycerides and triglycerides. These were supported by transcriptomics data, showing upregulation of PPAR-alpha and PPAR-gamma. Activated Tregs exhibited increased ceramide production, consistent with the upregulation of ceramide synthase and sphingomyelin synthase. Confocal microscopy revealed that Tregs were enriched in lysosomes and peroxisomes compared with effector T cells upon activation. Our data confirm the unique metabolic properties of Tregs, especially those characterized by omega-3 long-chain PUFA-rich triglycerides and ceramides, together with enriched lysosomes and peroxisomes, which correspond to metabolic alterations.

immunology↗

bioRxiv2020

In silico prediction of ARB resistance: A first step in creating personalized ARB therapy

Angiotensin II type 1 receptor (AT1R) blockers (ARBs) are among the most prescribed drugs. However, ARB effectiveness varies widely, and some patients do not respond to ARB therapy. One reason for the variability between patients is non-synonymous single nucleotide polymorphisms (nsSNPs) within agtr1, the AT1R gene. There are over 100 nsSNPs in the AT1R; therefore, this study embarked on determining which nsSNPs may abrogate the binding of selective ARBs. The crystal structure of olmesartan-bound human AT1R (PDB:4ZUD) served as a template to create an inactive empty AT1R via molecular dynamics simulation (n = 3). All simulations resulted in a smaller ligand-binding pocket than 4ZUD due to the absence of olmesartan in the simulation yet remained inactive with little movement in the receptor core. A single frame representing the average stable AT1R was used as a template to thrice (n = 3) dock each ARB via AutoDock to obtain a predicted affinity from the weighted average of 100 docking simulations. The results were far from known values; thus, an optimization protocol was initiated, resulting in the predicted binding affinities within experimentally determined ranges (n = 6). The empty model AT1R was altered and minimized in Molecular Operating Environment software to represent 103 of the known human AT1R polymorphisms. Each of the eight ARBs was then docked, using the optimized parameters, to each polymorphic AT1R (n = 6). Although each nsSNP has little effect on global AT1R structure, most nsSNPs drastically alter a sub-set of ARBs affinity to the AT1R. Comparisons to previous binding studies suggest that the results have a 60% chance of predicting ARB resistance. Although more biochemical studies and refinement of the model are required to increase the accuracy of the prediction of ARB resistance, personalized ARB therapy based on agtr1 sequence could increase overall ARB effectiveness. Author SummaryThe term "personalized medicine" was coined at the turn of the century, but most medicines are currently prescribed based on disease categories and occasionally racial demographics, but not personalized attributes. In cardiovascular medicine, the personalization of medication is minimal; however, it is accepted that not all patients respond equally to common cardiovascular medications. Here we chose one prominent cardiovascular drug target, the angiotensin receptor, and, using computer modeling, created preliminary models of over 100 known alterations to the angiotensin receptor to determine if the alterations changed the ability of clinically used drugs to interact with the angiotensin receptor. The strength of interaction was compared to the unaltered angiotensin receptor, generating a map predicting which alteration affected each drug. It is expected that in the future, a patients receptors can be sequenced, and maps, such as the one presented here, can be used to select the optimum medication based on the patients genetics. Such a process would allow for the personalization of current medication therapy.

pharmacology and toxicology↗