Biology subjects

Zhou, L. Y.

Publications and source records attributed to Zhou, L. Y..

8 recordsLinked to original sources

bioRxiv2026

Intravital imaging of age-related conjunctival lymphatic changes on the ocular surface

PurposeLymphatic system plays a critical role in fluid regulation and immune response. The conjunctiva of the ocular surface is endowed with a rich lymphatic network, and it provides an ideal site to study lymphatic structure and function. The purpose of this study is to investigate potential morphological and functional changes of conjunctival lymphatics with aging, a time-dependent biological deterioration process. MethodsYoung and aged fluorescently labeled transgenic mice of Prox-1, the master control gene for lymphatic determination, were used in the study. For morphological assessment, conjunctival lymphatic vessels were examined in vivo by our advanced live imaging system. For functional analysis, lymphatic drainage efficiencies were measured by fluorescently labeled tracer injection. ResultsCompared to young mice, both vascular branching points and intraluminal valves were significantly reduced in conjunctival lymphatic vessels of aged mice. Moreover, lymphatic functional deterioration and drainage deficiencies, such as fluid leakage and reflux, were also detected in the aged condition. ConclusionsConjunctival lymphatic system undergoes morphological as well as functional changes with aging. Further investigation into this phenomenon may provide novel insights into lymphatic and age-related diseases inside and outside the eye.

immunology↗

bioRxiv2025

An Efficient Lasso Framework for Admixture-Aware Polygenic Scores

Polygenic scores (PGS) have promising clinical applications for risk stratification, disease screening, and personalized medicine. However, most PGS are trained on predominantly European ancestry cohorts and have limited portability to external populations. While cross-population PGS methods have demonstrated greater generalizability than single-ancestry PGS, they fail to properly account for individuals with recent admixture between continental ancestry groups. GAUDI is a recently proposed PGS method which overcomes this gap by leveraging local ancestry to estimate ancestry-specific effects, penalizing but allowing ancestry-differential effects. However, the modified fused LASSO approach used by GAUDI is computationally expensive and does not readily accommodate more than two-way admixture. To address these limitations, we introduce HAUDI, an efficient LASSO framework for admixed PGS construction. HAUDI re-parameterizes the GAUDI model as a standard LASSO problem, allowing for extension to multi-way admixture settings and far superior computational speed than GAUDI. In extensive simulations, HAUDI compares favorably to GAUDI while dramatically reducing computation time. In real data applications, HAUDI uniformly out-performs GAUDI across 18 clinical phenotypes, including total triglycerides (TG), C-reactive protein (CRP), and mean corpuscular hemoglobin concentration (MCHC), and shows substantial benefits over an ancestry-agnostic PGS for white blood cell count (WBC) and chronic kidney disease (CKD).

genomics↗

bioRxiv2025

Old vs. New Local Ancestry Inference in HCHS/SOL: A Comparative Study

Hispanic/Latino populations are admixed, with genetic contributions from multiple ancestral populations. Studies of genetic association in these admixed populations often use methods such as admixture mapping, which relies on inferred counts of "local" ancestry, i.e., of the source ancestral population at a locus. Local ancestries are inferred using external reference panels that represent ancestral populations, making the choice of inference method and reference panel critical. This study used a dataset of Hispanic/Latino individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) to evaluate the "old" local ancestry inference performed using the state-of-the-art inference method, RFMix, alongside "new" inferences performed using Fast Local Ancestry Estimation (FLARE), which also used an updated reference panel. We compared their performance in terms of global and local ancestry correlations, as well as admixture mapping-based associations. Overall, the old RFMix and new FLARE inferences were highly similar for both global and local ancestries, with FLARE-inferred datasets yielding admixture mapping results consistent with those computed from RFMix. However, in some genomic regions the old and new local ancestries have relatively lower correlations (Pearson R < 0.9). Most of these genomic regions (86.42%) were mapped to either ENCODE blacklist regions, or to gene clusters, compared to 7.67% of randomly-matched regions with high correlations (Pearson R > 0.97) between old and new local ancestries.

genetics↗

bioRxiv2025

Variational Autoencoder-based Model Improves Polygenic Prediction in Blood Cell Traits

Genetic prediction of complex traits, enabled by large-scale genomic studies, has created new measures to understand individual genetic predisposition. Polygenic Risk Scores (PRS) offer a way to aggregate information across the genome, enabling personalized risk prediction for complex traits and diseases. However, conventional PRS calculation methods that rely on linear models are limited in their ability to capture complex patterns and interaction effects in high-dimensional genomic data. In this study, we seek to improve the predictive power of PRS through applying advanced deep learning techniques. We show that the Variational AutoEncoder-based model for PRS construction (VAE-PRS) outperforms currently state-of-the-art methods for biobank-level data in 14 out of 16 blood cell traits, while being computationally efficient. Through comprehensive experiments, we found that the VAE-PRS model offers the ability to capture interaction effects in high-dimensional data and shows robust performance across different pre-screened variant sets. Furthermore, VAE-PRS is easily interpretable via assessing the contribution of each individual marker to the final prediction score through the SHapley Additive exPlanations (SHAP) method, providing potential new insights in identifying trait-associated genetic variants. In summary, VAE-PRS presents a novel measure to genetic risk prediction by harnessing the power of deep learning methods, which could further facilitate the development of personalized medicine and genetic research.

bioinformatics↗

bioRxiv2024

Genetic Architecture and Analysis Practices of Circulating Metabolites in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program

Circulating metabolite levels partly reflect the state of human health and diseases and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single-study analyses. Leveraging the rich metabolomics resources generated by the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally diverse samples. We provided a set of reasonable strategies for outlier and imputation handling to process metabolite data. Following the practical analysis framework, we further performed a genome-wide association analysis on 1,135 selected metabolites using whole genome sequencing data from 16,359 individuals passing the quality control filters, and discovered 1,778 independent loci associated with 667 metabolites. Among 108 novel locus-metabolite pairs, we detected not only novel loci within previously implicated metabolite associated genes but also novel genes (such as GAB3 and VSIG4 located in the X chromosome) that have putative roles in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, including well-known metabolic genes such as FADS2, D2HGDH, SUGP1, UTG2B17, strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

bioinformatics↗

bioRxiv2022

Hedgehog signaling acts as cell fate determinant during adult tissue repair

Successful muscle regeneration relies on the interplay of multiple cell populations. However, the signals required for this coordinated intercellular crosstalk remain largely unknown. Here, we describe how the Hedgehog (Hh) signaling pathway controls the fate of fibro/adipogenic progenitors (FAPs), the cellular origin of intramuscular fat (IMAT) and fibrotic scar tissue. Using conditional mutagenesis and pharmacological Hh modulators in vivo and in vitro, we identify DHH as the key ligand that acts as a potent adipogenic brake by preventing the adipogenic differentiation of FAPs. Hh signaling also impacts muscle regeneration, albeit indirectly through induction of myogenic factors in FAPs. Our results also indicate that ectopic and sustained Hh activation forces FAPs to adopt a fibrogenic fate resulting in widespread fibrosis. In this work, we reveal novel post-developmental functions of Hh signaling in balancing tissue regeneration and fatty fibrosis. Moreover, they provide the exciting possibility that mis-regulation of the Hh pathway with age and disease could be a major driver of pathological IMAT formation.

developmental biology↗

bioRxiv2022

A guide to examining intramuscular fat formation and its cellular origin in skeletal muscle

Fibro-adipogenic progenitors (FAPs) are mesenchymal stromal cells that play a crucial role during skeletal muscle homeostasis and regeneration. FAPs build and maintain the extracellular matrix that acts as a molecular myofiber scaffold. In addition, FAPs are indispensable for myofiber regeneration as they secrete a multitude of beneficial factors sensed by the muscle stem cells (MuSCs). In diseased states, however, FAPs are the cellular origin of intramuscular fat and fibrotic scar tissue. This fatty fibrosis is a hallmark of sarcopenia and neuromuscular diseases, such as Duchenne Muscular Dystrophy. One significant barrier in determining why and how FAPs differentiate into intramuscular fat is effective preservation of adipocytes, especially in frozen tissue sections. Conventional methods of skeletal muscle tissue processing, such as snap-freezing, do not properly preserve the morphology of individual adipocytes, thereby preventing accurate visualization and quantification. Here, we describe a protocol that provides robust preservation of adipocyte morphology in skeletal muscle sections allowing visualization, imaging, and quantification of intramuscular fat. We also outline how to process a portion of muscle tissue for RT-qPCR, enabling users to confirm observed changes in fat formation by viewing differences in expression of adipogenic genes. Additionally, we will describe how our protocol can be adopted to visualize adipocytes by whole mount immunofluorescence of muscle samples. Finally, we will outline how to combine this protocol with genetic lineage tracing of Pdgfr-expressing FAPs to study the adipogenic conversion of FAPs. Our protocol consistently yields high-resolution and morphologically accurate immunofluorescent images of adipocytes that, along with confirmation by RT-qPCR, allows for robust, rigorous, and reproducible visualization and quantification of intramuscular fat. Together, our analysis pipeline is the first step to improve our understanding of how FAPs differentiate into intramuscular fat and provides a framework to validate novel interventions to prevent fat formation.

developmental biology↗

bioRxiv2021

Prioritizing candidate peptides for cancer vaccines by PEPPRMINT: a statistical model to predict peptide presentation by HLA-I proteins

Recent development of cancer immunotherapy has opened unprecedented avenues to eliminate tumor cells using the human immune system. Cancer vaccines composed of neoantigens, or peptides unique to tumor cells due to somatic mutations, have emerged as a promising approach to activate or strengthen the immune response against cancer. A key step to identifying neoantigens is computationally predicting which somatically mutated peptides are presented on the cell surface by a human leukocyte antigen (HLA). Computational prediction relies on large amounts of high-quality training data, such as mass spectrometry data of peptides presented by one of several HLAs in living cells. We developed a complete pipeline to prioritize neoantigens for cancer vaccines. A key step of our pipeline is PEPPRMINT (PEPtide PResentation using a MIxture model and Neural neTwork), a model designed to exploit mass spectrometry data to predict peptide presentation by HLAs. We applied our pipeline to DNA sequencing data of 60 melanoma patients and identified a group of neoantigens that were more immunogenic in tumor cells than in normal cells. Additionally, the neoantigen burden estimated by PEPPRMINT was significantly associated with activity of the immune system, suggesting these neoantigens could induce an immune response.

genomics↗