Biology subjects

Ekambaram, S.

Publications and source records attributed to Ekambaram, S..

4 recordsLinked to original sources

bioRxiv2024

Comprehensive mapping of the Interaction of levodopa and iron metabolism in Parkinson's disease

Levodopa remains the primary treatment for Parkinsons disease (PD), yet its long-term use has been associated with iron accumulation in the brain, a phenomenon linked to neurodegeneration. We utilize deep machine learning to determine plausible molecular mechanisms that may underlie the effects of levodopa on iron metabolism. Using the DRIFT platform, we performed a proteome-wide target identification of levodopa and uncovered significant interactions potentially involved in cellular iron transport. Pathway analysis revealed that levodopa may influence critical iron-related pathways, including the response of EIF2AK1 to heme deficiency, heme signaling, and ABC-family protein-mediated transport. These findings suggest that levodopa may contribute to iron dysregulation in PD by interacting with iron transporters and modulating iron-related pathways. Because levodopa is used at relatively high doses in PD, our findings provide new insight into secondary effects unrelated to being a precursor of dopamine. This highlights the need for careful consideration of its effects on iron metabolism as a consequence of use in the long-term management of PD. Further experimental validation is required to confirm these interactions, and also to explore potential strategies to mitigate iron-related side effects while preserving therapeutic efficacy.

bioinformatics↗

bioRxiv2024

Chronic β3 adrenergic agonist treatment improves brain microvascular endothelial function and cognition in aged mice

Microvascular endothelial dysfunction, characterized by impaired neurovascular coupling, reduced glucose uptake, blood-brain barrier disruption, and microvascular rarefaction, plays a critical role in the pathogenesis of age-related vascular cognitive impairment (VCI). Emerging evidence points to non-cell autonomous mechanisms mediated by adverse circulating milieu (an increased ratio of pro-geronic to anti-geronic circulating factors) in the pathogenesis of endothelial dysfunction leading to impaired cerebral blood flow and cognitive decline in the aging population. In particular, age-related adipose dysfunction contributes, at least in part, to an unfavorable systemic milieu characterized by chronic hyperglycemia, hyperinsulinemia, dyslipidemia, and altered adipokine profile, which together contribute to microvascular endothelial dysfunction. Hence, in the present study, we aimed to test whether thermogenic stimulation, an intervention known to improve adipose and systemic metabolism by increasing cellular energy expenditure, could mitigate brain endothelial dysfunction and improve cognition in the aging population. Eighteen-month-old old C57BL/6J mice were treated with saline or CL ({beta}3-adrenergic agonist) for 6 weeks followed by functional analysis to assess endothelial function and cognition. CL treatment improved neurovascular coupling responses and rescued brain glucose uptake in aged animals. In addition, CL treatment also attenuated blood-brain barrier leakage and associated neuroinflammation in the cortex of aged animals. More importantly, these beneficial changes in microvascular function translated to improved cognitive performance in radial arm water maze and Y-maze tests. Our results suggest that {beta}3-adrenergic agonist treatment improves multiple aspects of brain microvascular endothelial function and can be potentially repurposed for treating age-associated cognitive decline.

neuroscience↗

bioRxiv2024

The metabolic benefits of thermogenic stimulation are preserved in aging

Adipose thermogenesis has been actively investigated as a therapeutic target for improving metabolic dysfunction in obesity. However, its applicability to middle-aged and older populations, which bear the highest obesity prevalence in the US (approximately 40%), remains uncertain due to age-related decline in thermogenic responses. In this study, we investigated the effects of chronic thermogenic stimulation using the {beta}3-adrenergic (AR) agonist CL316,243 (CL) on systemic metabolism and adipose function in aged (18-month-old) C57BL/6JN mice. Sustained {beta}3-AR treatment resulted in reduced fat mass, increased energy expenditure, increased fatty acid oxidation and mitochondrial activity in adipose depots, improved glucose homeostasis, and a favorable adipokine profile. At the cellular level, CL treatment increased uncoupling protein 1 (UCP1)-dependent thermogenesis in brown adipose tissue (BAT). However, in white adipose tissue (WAT) depots, CL treatment increased glycerol and lipid de novo lipogenesis (DNL) and turnover suggesting the activation of the futile substrate cycle of lipolysis and reesterification in a UCP1-independent manner. Increased lipid turnover was also associated with the simultaneous upregulation of proteins involved in glycerol metabolism, fatty acid oxidation, and reesterification in WAT. Further, a dose-dependent impact of CL treatment on inflammation was observed, particularly in subcutaneous WAT, suggesting a potential mismatch between fatty acid supply and oxidation. These findings indicate that chronic {beta}3-AR stimulation activates distinct cellular mechanisms that increase energy expenditure in BAT and WAT to improve systemic metabolism in aged mice. Our study provides foundational evidence for targeting adipose thermogenesis to improve age-related metabolic dysfunction.

pharmacology and toxicology↗

bioRxiv2024

Application of Quantum Tensor Networks for Protein Classification

Computational methods in drug discovery significantly reduce both time and experimental costs. Nonetheless, certain computational tasks in drug discovery can be daunting with classical computing techniques which can be potentially overcome using quantum computing. A crucial task within this domain involves the functional classification of proteins. However, a challenge lies in adequately representing lengthy protein sequences given the limited number of qubits available in existing noisy quantum computers. We show that protein sequences can be thought of as sentences in natural language processing and can be parsed using the existing Quantum Natural Language framework into parameterized quantum circuits of reasonable qubits, which can be trained to solve various proteinrelated machine-learning problems. We classify proteins based on their sub-cellular locations--a pivotal task in bioinformatics that is key to understanding biological processes and disease mechanisms. Leveraging the quantum-enhanced processing capabilities, we demonstrate that Quantum Tensor Networks (QTN) can effectively handle the complexity and diversity of protein sequences. We present a detailed methodology that adapts QTN architectures to the nuanced requirements of protein data, supported by comprehensive experimental results. We demonstrate two distinct QTNs, inspired by classical recurrent neural networks (RNN) and convolutional neural networks (CNN), to solve the binary classification task mentioned above. Our top-performing quantum model has achieved a 94% accuracy rate, which is comparable to the performance of a classical model that uses the ESM2 protein language model embeddings. Its noteworthy that the ESM2 model is extremely large, containing 8 million parameters in its smallest configuration, whereas our best quantum model requires only around 800 parameters. We demonstrate that these hybrid models exhibit promising performance, showcasing their potential to compete with classical models of similar complexity.

bioinformatics↗