Biology subjects

Castro, R. I.

Publications and source records attributed to Castro, R. I..

4 recordsLinked to original sources

bioRxiv2026

Surface charge tuning of lipid-polymer hybrid nanoparticles for optimized cilostazol delivery and platelet compatibility

Cardiovascular diseases (CVD) are the leading cause of morbidity and mortality worldwide. This clinical burden is predominantly attributable to atherothrombotic events resulting from platelet hyperactivation, a process integral to the pathophysiology of atherosclerosis. Cilostazol (CLZ) is a clinically established phosphodiesterase-IIIA inhibitor with antiplatelet and vasodilatory activity. However, its extremely low aqueous solubility results in dissolution-limited and variable oral absorption, while dose-dependent adverse effects and cardiovascular safety restrictions may constrain its clinical use. Lipid-polymer hybrid nanoparticles (LPHNPs) represent a highly promising drug delivery system to address these biopharmaceutical limitations. However, their interactions with blood components remain poorly understood, with surface charge among the most controversial physicochemical parameters implicated in platelet toxicity. Here, we applied an integrated ex vivo and in silico screening strategy to design LPHNPs based on poly(lactic acid) and poly(ethylene glycol)-modified lipids displaying positive (amine), nominally neutral (methoxy), and negative (carboxyl) surface charges to systematically identify safe boundaries at the nano-human platelet interface and evaluate their suitability as CLZ carriers. Positively charged nanoparticles precipitated immediately during self-assembly due to uncontrolled ionic pairing and were therefore structurally unviable. Neutral nanoparticles maintained a stable size (~113 nm hydrodynamic diameter) but unexpectedly induced spontaneous platelet aggregation, limiting their applicability. In contrast, negatively charged nanoparticles exhibited exceptional colloidal stability (hydrodynamic size of ~120 nm and zeta potential of -48 mV), achieved significant inhibition of ADP-induced platelet aggregation, and adhered to the platelet surface without inducing activation or membrane disruption. Steered molecular dynamics simulations, performed under identical non-equilibrium pulling conditions, showed the highest interfacial resistance to CLZ extraction for the negatively charged model, qualitatively consistent with the sustained CLZ release profile observed in vitro. These findings highlight the importance of precise surface charge tuning in the development of safe cardiovascular nanomedicines based on LPHNPs, emphasizing the need for comprehensive platelet compatibility testing before further biological evaluation.

pharmacology and toxicology↗

bioRxiv2025

Identifying non-coding variant effects at scale via machine learning models of cis-regulatory reporter assays

The inability to interpret the functional impact of non-coding variants has been a major impediment in the promise of precision medicine. While high-throughput experimental approaches such as Massively Parallel Reporter Assays (MPRAs) have made major progress in identifying causal variants and their underlying molecular mechanisms, these tools cannot exhaustively measure variant effects genome-wide. Here we present MPAC, an ensemble of machine-learning models trained on MPRA data that provides accurate and scalable prediction of the cis-regulatory impact of non-coding variants. Using MPAC we predict allelic effects for 575M single nucleotide variants (SNVs) across diverse applications, including complex trait genetics, clinical and tumor sequencing, evolutionary analyses, and saturation mutagenesis. We find MPAC predictions match the performance of empirical MPRAs in identifying causal complex trait-associated alleles. We demonstrate the utility of MPAC by applying it to ClinVar, identifying non-coding pathogenic variation with higher accuracy than other sequence-to-function models. We also nominate 1,892 candidate non-coding cancer drivers by predicting the functional effects of somatic SNVs in the COSMIC database. Next, we evaluate population-level genetic variation by predicting effects for all 514M non-coding SNVs in gnomAD, quantifying the relationship between regulatory function and evolutionary constraint. Finally, we generate prospective functional maps using in-silico saturation mutagenesis across 18,658 human promoters, observing widespread selection against variants predicted to disrupt promoter activity. Collectively, this study establishes the value of non-coding functional predictions and provides a comprehensive, publicly available resource for variant interpretation.

genomics↗

bioRxiv2024

Functional dissection of complex and molecular trait variants at single nucleotide resolution

Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics. The majority of these variants have individually weak effects and lie in non-coding gene-regulatory elements where we lack a complete understanding of how single nucleotide alterations modulate transcriptional processes to affect human phenotypes. To address this, we measured the activity of 221,412 trait-associated variants that had been statistically fine-mapped using a Massively Parallel Reporter Assay (MPRA) in 5 diverse cell-types. We show that MPRA is able to discriminate between likely causal variants and controls, identifying 12,025 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% can plausibly be explained by the disruption of a known transcription factor (TF) binding motif. We dissect the mechanisms of 136 variants using saturation mutagenesis and assign impacted TFs for 91% of variants without a clear canonical mechanism. Finally, we provide evidence that epistasis is prevalent for variants in close proximity and identify multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants underlying complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.

genetics↗

bioRxiv2023

Machine-guided design of synthetic cell type-specific cis-regulatory elements

Cis-regulatory elements (CREs) control gene expression, orchestrating tissue identity, developmental timing, and stimulus responses, which collectively define the thousands of unique cell types in the body. While there is great potential for strategically incorporating CREs in therapeutic or biotechnology applications that require tissue specificity, there is no guarantee that an optimal CRE for an intended purpose has arisen naturally through evolution. Here, we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell type specificity. We leverage innovations in deep neural network modeling of CRE activity across three cell types, efficient in silico optimization, and massively parallel reporter assays (MPRAs) to design and empirically test thousands of CREs. Through in vitro and in vivo validation, we show that synthetic sequences outperform natural sequences from the human genome in driving cell type-specific expression. Synthetic sequences leverage unique sequence syntax to promote activity in the on-target cell type and simultaneously reduce activity in off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs and demonstrate the required literacy to write regulatory code that is fit-for-purpose in vivo across vertebrates.

synthetic biology↗