Biology subjects

Gowda, S.

Publications and source records attributed to Gowda, S..

5 recordsLinked to original sources

bioRxiv2026

Zinc preserves translational proteostasis to counteract aging and proteotoxicity in Caenorhabditis elegans

The progressive collapse of protein homeostasis is a defining feature of aging, yet upstream events deteriorating protein synthesis remain poorly defined. Zinc is an essential micronutrient and structural cofactor, but its role in the aging ribosomal proteome is poorly understood. Here, using Caenorhabditis elegans, we show that the whole-body zinc-to-sulfur ratio declines by ~90% between day 1 and day 13 of adulthood. Supplementing aged animals with a low dose of ZnCl2 (10 M) restored organismal zinc levels and improved healthspan, thermotolerance, and oxidative stress resistance, whereas higher doses were toxic. Quantitative proteomics revealed that structural constituents of the ribosome were the most enriched functional category altered by zinc, accompanied by robust induction of metallothionein MTL-1. Zinc-supplemented animals exhibited increased protein synthesis and a transient increase in ribosomal proteins, while 45S pre-rRNA levels remained unaffected, indicating that zinc acts on ribosome structure rather than biogenesis. Zinc supplementation coordinately reversed the age-associated decline of antioxidant, chaperone, proteasome, and energy-metabolism factors. Furthermore, zinc conferred protection in two proteotoxicity models, suppressing polyglutamine aggregation and delaying amyloid-beta-induced paralysis. Our findings establish that zinc preserves translational proteostasis, identifying late-life zinc supplementation as a translatable micronutrient intervention against age-related proteinopathies.

molecular biology↗

bioRxiv2024

KAT3 mutations impair neural crest migration through EMT regulators snai1b and snai2 in Rubinstein Taybi Syndrome

Rubinstein Taybi syndrome, a rare congenital disease is caused by mutation in KAT3 genes, EP300 and CREBBP. A subset of tissues affected in RSTS have their origin in neural crest cells, prompting our exploration into the role of KAT3 in neural crest development. Our zebrafish RSTS models generated by knocking down or mutating ep300a and cbpa genes, reveal defects in neural crest migration and its derived tissues when KAT3 genes are perturbed. We also demonstrate that the effects on neural crest can be reversed by HDAC inhibition in in morphant embryos. KAT3 knockdown causes downregulation of EMT regulators, snai1b and snai2. Snai2 is known to repress cdh6b in neural crest cells facilitating their delamination from neural tube and migration. We generated RSTS patient-derived iPSC line and differentiated them into neural crest cells in vitro. We show that role of KAT3 proteins in neural crest migration is conserved in human iPSC derived neural crest cells. Our findings make a case for classifying RSTS as a neurocristopathy. HighlightsO_LIPerturbation of KAT3 gene expression in zebrafish recapitulates the Rubinstein Taybi patient defects C_LIO_LIThe zebrafish model of Rubinstein Taybi model reveals defects in neural crest cell migration C_LIO_LIKAT3 proteins regulate snai2, snai1b and cdh6, genes important for neural crest migration C_LIO_LIThe neural crest migration defects in the zebrafish model can be partially rescued by modulating the global acetylation levels C_LIO_LIStudy of RSTS patient-derived neural crest cells reveals that the role of KAT3 in neural crest migration is conserved across vertebrates C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/593474v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1761fceorg.highwire.dtl.DTLVardef@220e21org.highwire.dtl.DTLVardef@1cdd311org.highwire.dtl.DTLVardef@1dc0e53_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2022

Catalyst: Fast Biochemical Modeling with Julia

We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). Catalyst supports simulating stochastic chemical kinetics (jump process), chemical Langevin equation (stochastic differential equation), and reaction rate equation (ordinary differential equation) representations for CRNs. Through comprehensive benchmarks, we demonstrate that Catalyst simulation runtimes are often one to two orders of magnitude faster than other popular tools. More broadly, Catalyst acts as both a domain-specific language and an intermediate representation for symbolically encoding CRN models as Julia-native objects. This enables a pipeline of symbolically specifying, analyzing, and modifying CRNs; converting Catalyst models to symbolic representations of concrete mathematical models; and generating compiled code for numerical solvers. Leveraging ModelingToolkit.jl and Symbolics.jl, Catalyst models can be analyzed, simplified, and compiled into optimized representations for use in numerical solvers. Finally, we demonstrate Catalysts broad extensibility and composability by highlighting how it can compose with a variety of Julia libraries, and how existing open-source biological modeling projects have extended its intermediate representation.

systems biology↗

bioRxiv2021

The brain uses invariant dynamics to generalize outputs across movements

It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it re-uses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, where dynamics refer to how the instantaneous spatial pattern of population activity changes in time. Here we test whether invariant dynamics of neural populations are actually used to issue the commands that direct movement. Using a brain-machine interface that transformed rhesus macaques motor cortex activity into commands for a neuroprosthetic cursor, we discovered that the same command is issued with different neural activity patterns in different movements. However, these different patterns were predictable, as we found that the transitions between activity patterns are governed by the same dynamics across movements. These invariant dynamics are low-dimensional, and critically, they align with the brain-machine interface, so that they predict the specific component of neural activity that actually issues the next command. We introduce a model of optimal feedback control that shows that invariant dynamics can help transform movement feedback into commands, reducing the input that the neural population needs to control movement. Altogether our results demonstrate that invariant dynamics drive commands to control a variety of movements, and show how feedback can be integrated with invariant dynamics to issue generalizable commands.

neuroscience↗

bioRxiv2019

Flexible Modulation of Neural Variance Facilitates Neuroprosthetic Skill Learning

One hallmark of natural motor control is the brains ability to adapt to perturbations ranging from temporary visual-motor rotations to paresis caused by stroke. These adaptations require modifications of learned neural patterns that can span the time-course of minutes to months. Previous work with brain-machine interfaces (BMI) has shown that over learning, neurons consolidate firing activity onto low-dimensional neural subspaces, and additional studies have shown that neurons require longer timescales to adapt to task perturbations that require neural activity outside of these subspaces. However, it is unclear how the motor cortex adapts alongside task changes that do not require modifications of the existing neural subspace over learning. To answer this question, five nonhuman primates were used in three BMI experiments, which allowed us to track how specific populations of neurons changed firing patterns as task performance improved. In each experiment, neural activity was transformed into cursor kinematics using decoding algorithms that were periodically readapted based on natural arm movements or visual feedback. We found that decoder changes caused neurons to increase exploratory-like patterns on within-day timescales without hindering previously consolidated patterns regardless of task performance. The flexible modulation of these exploratory patterns in contrast to relatively stable consolidated activity suggests a simultaneous exploration-exploitation strategy that adapts existing neural patterns during learning.

neuroscience↗