Biology subjects

Shah, V. V.

Publications and source records attributed to Shah, V. V..

3 recordsLinked to original sources

bioRxiv2026

A Long-lived Avatar for Modeling Age-Related Vascular Disease

BackgroundCurrent microphysiological models do not support long-term investigations into the chronic effects of vascular risk factors and the development of vascular diseases. Prolonged culture frequently leads to cellular senescence and loss of functional integrity, resulting in variability and inconsistency in modeling chronic vascular responses. Here we aimed to develop and sustain a long-term multicellular human vascular avatar, addressing the critical need for long-term disease modeling and drug testing. MethodsTo identify the optimal media for longevity, cell identity and function were assessed by morphology, qPCR, beta-gal staining, ELISA, bulk RNA-seq and single cell RNA-seq analysis. After optimizing the culture media, iPSCs-derived ECs and VSMCs from unaffected and Hutchinson-Gilford Progeria Syndrome (HGPS) donors were grown in Gravitational Lumen Patterning (GLP) Vessel- Chips for 1-6 months to generate a long-lived vascular avatar for the study of vascular aging. ResultsGuided by quantitative morphological analyses and bulk RNAseq profiling, we generated a novel optimized culture media VSL (VEGF, SB431542 as a TGF-{beta} inhibitor, low fetal bovine serum) that enhances the long-term health of vascular endothelial cells (ECs). Furthermore, we modified the VSL formulation (mVSL) by modulating 8Br-cAMP, FGF, PDGF, and a cell viability enhancer HMH1015 levels to enhance EC-VSMC (vascular smooth muscle cell) crosstalk and support long-term cellular viability. Subsequently, we maintained and characterized a human vascular avatar with a three-dimensional extracellular matrix environment and 3D vascular architecture for over 180 days. Finally, we demonstrated that this long-lived human vascular avatar enabled modeling vascular aging using iPSC-derived vascular cells from patients with Hutchinson-Gilford Progeria Syndrome (HGPS). ConclusionsWe have successfully engineered and maintained a human vascular avatar for over 180 days. The vascular avatar provides a robust platform for modeling disease-associated vascular aging and for evaluating therapeutic strategies targeting chronic vascular disorders.

bioengineering↗

bioRxiv2025

Telomerase mRNA Reduces Radiation-induced DNA Damage of human skin

Over four million people undergo radiation therapy annually in the United States. Among these, more than 90% experience varying degrees of radiation-induced skin injury. Despite the enormity of the problem, there is currently no FDA-approved agent to prevent or treat skin damage caused by ionizing radiation. In the current study, ionizing radiation induced dose-dependent genomic and mitochondrial DNA damage, leading to apoptosis in primary cutaneous cells. Prior treatment with mRNA encoding telomerase reverse transcriptase (TERT) substantially reduced radiation-induced DNA damage in human primary skin cells and tissues. Mechanistically, TERT mRNA pretreatment enhances DNA repair, reduces mitochondrial ROS, and decreases apoptosis without extending telomere length during the experimental period, suggesting a non-canonical function of TERT to accelerate the cellular recovery from radiation. These findings highlight a potential therapeutic approach for preventing radiation-induced skin injury. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/636031v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@5bcee4org.highwire.dtl.DTLVardef@16c0c28org.highwire.dtl.DTLVardef@922c5dorg.highwire.dtl.DTLVardef@9eafd0_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2022

A Machine-learning-based Method to Detect Degradation of Motor Control Stability with Applications to Diagnosis of Presymptomatic Parkinson's Disease

Parkinsons disease (PD), a neuro-degenerative disorder, is often detected by onset of its motor symptoms such as rest tremor. Unfortunately, motor symptoms appear only when approximately 40%-60% of the dopaminergic neurons in the substantia nigra are lost. In most cases, by the time PD is clinically diagnosed, the disease may already have started 4 to 6 years beforehand. So there is a need for developing a test for detecting PD before the onset of the motor symptoms. This phase of PD is referred to as Presymptomatic PD (PPD). The motor symptoms of Parkinsons Disease are manifestations of instability in the sensorimotor system that develops gradually due to the neuro-degenerative process. In this paper, based on the above insight, we propose a new method that can potentially be used to detect degradation of motor control stability which can be employed for the detection of PPD. The proposed method tracks the tendency of a feedback control system to transition to an unstable state, and uses machine learning algorithm for its robust detection. This method is explored using simulations of a simple pendulum with PID controller as a conceptual representation for both healthy and PPD individuals. We also propose an example task with physiological measurements that can be used with this method and potentially be employed in a clinical setting. We present representative data collected through such a task, thereby demonstrating the feasibility to generate data for the proposed method. Author summaryParkinsons disease (PD) is a neuro-degenerative disorder that develops and progresses over several years. Currently, one is able to diagnose PD only after the appearance of motor symptoms (symptoms in movements of body parts), which unfortunately may be 4 to 6 years after the neuro-degeneration may have started. It has been shown that there are benefits to diagnosing PD at early stages, motivating the need to explore tools for diagnosing PD in the pre-symptomatic stage referred to as Presymptomatic Parkinsons disease (PPD). In this paper, a novel approach is explored that utilises the insight that the motor symptoms in PD may be seen as an instability in the feedback-control system that controls movements of body parts (sensory-motor loop). The proposed method uses a series of simple movement tasks performed by an individual in a clinic as the input to detect any gradual degradation of movement control that is leading to an instability, but before the instability and consequently the symptoms are manifested. This method is tested through extensive simulations and a potential experimental realisation with preliminary data. While a full-fledged validation will be undertaken as part of future work, initial results show promise and feasibility of further data collection.

bioinformatics↗