Biology subjects

Huang, E. C.

Publications and source records attributed to Huang, E. C..

6 recordsLinked to original sources

bioRxiv2026

snputils: A High-Performance Python Library for Genetic Variation and Population Structure

The increasing size and resolution of genomic and population genetic datasets offer unprecedented opportunities to study population structure and uncover the genetic basis of complex traits and diseases. The collection of existing analytical tools, however, is characterized by format incompatibilities, limited functionality, and computational inefficiencies, forcing researchers to construct fragile pipelines that chain together fragmented command-line utilities and ad hoc scripts. These are difficult to maintain, scale, and reproduce. To address such limitations, we present snputils, a Python library that unifies high-performance I/O, transformation, and analysis of genotype, ancestry, and phenotypic information within a single framework suitable for biobank-scale research. The library provides efficient tools for essential operations, including querying, cleaning, merging, and statistical analysis. In addition, it offers classical population genetic statistics with optional ancestry-specific masking. An identity-by-descent module supports reading of multiple formats, filtering and ancestry-restricted segment trimming for relatedness and demographic inference. snputils also incorporates ancestry-masking and multi-array functionalities for dimensionality reduction methods, as well as efficient implementations of admixture simulation, admixture mapping, and advanced visualization capabilities. With support for the most commonly used file formats, snputils integrates smoothly with existing tools and clinical databases. At the same time, its modular and optimized design reduces technical overhead, facilitating reproducible workflows that accelerate discoveries in population genetics, genomic research, and precision medicine. Benchmarking demonstrates a significant reduction in genotype data loading speed compared to existing Python libraries. The open-source library is available at https://github.com/AI-sandbox/snputils, with full documentation and tutorials at snputils.org.

bioinformatics↗

bioRxiv2025

Fault-tolerant pedigree reconstruction from pairwise kinship relations

Pedigrees reconstructed from biologically related ancient genomes have revealed many insights into (pre)history. To our knowledge, all reported ancient pedigrees have been manually reconstructed, as existing pedigree reconstruction methods are ill-suited for the quality and nature of ancient DNA data. Here, we introduce repare, an open-source software method to automatically reconstruct pedigrees from inferred pairwise kinship relations, which are readily obtainable from ancient genomes. This method reconstructs pedigrees by iteratively incorporating pairwise kinship relations into a set of candidate pedigrees, with pruning and sampling to reduce its search space. It optionally considers supporting information such as haplogroups and skeletal age-at-death estimates. We evaluate this method on a variety of simulated pedigrees with varying error rates and missingness. We also use this method to reconstruct several published pedigrees that were originally manually reconstructed; for one, we present a potential alternative topology. repare optionally incorporates user-inferred pedigree constraints, enabling "human-in-the-loop" reconstruction workflows. Especially when used with these user-inferred constraints, we find that repare represents a powerful and flexible tool for ancient pedigree reconstruction.

bioinformatics↗

bioRxiv2025

An integrated single-cell and spatial transcriptomic atlas of thyroid cancer progression identifies prognostic fibroblast subpopulations

Thyroid cancer progression from curable well-differentiated thyroid carcinoma to highly lethal anaplastic thyroid carcinoma is distinguished by tumor cell de-differentiation and recruitment of a robust stromal infiltrate. Combining an integrated thyroid cancer single-cell sequencing atlas with spatial transcriptomics and bulk RNA-sequencing, we define stromal cell subpopulations and tumor-stromal cross-talk occurring across the histologic and mutational spectrum of thyroid cancer. We identify distinct inflammatory and myofibroblastic cancer-associated fibroblast (iCAF and myCAF) populations and perivascular-like populations. The myCAF population is only found in malignant samples and is associated with tumor cell invasion, BRAFV600E mutation, lymph node metastasis, and disease progression. Tumor-adjacent myCAFs abut invasive tumor cells with a partial epithelial-to-mesenchymal phenotype. Tumor-distant iCAFs infiltrate inflammatory autoimmune thyroid lesions and anaplastic tumors. In summary, our study provides an integrated atlas of thyroid cancer fibroblast subtypes and spatial characterization at sites of tumor invasion and de-differentiation, defining the stromal reorganization central to disease progression.

cancer biology↗

bioRxiv2024

Tenascin-C potentiates Wnt signaling in thyroid cancer

Tenascin-C (TNC) is a secreted extracellular matrix protein that is highly expressed during embryonic development and re-expressed during wound healing, inflammation, and neoplasia. Studies in developmental models suggest that TNC may regulate the Wnt signaling pathway. Our lab has shown high levels of Wnt signaling and TNC expression in anaplastic thyroid cancer (ATC), a highly lethal cancer with an abysmal [~]3-5 month median survival. Here, we investigated the role of TNC in facilitating ligand-dependent Wnt signaling in thyroid cancer. We utilized bulk RNA-sequencing from three independent multi-institutional thyroid cancer patient cohorts. TNC expression was spatially localized in patient tumors with RNA in situ hybridization. The role of TNC was investigated in vitro using Wnt reporter assays and in vivo with a NOD.PrkdcscidIl2rg-/- mouse ATC xenograft tumor model. TNC expression was associated with aggressive thyroid cancer behavior, including anaplastic histology, extrathyroidal extension, and metastasis. Spatial localization of TNC in patient tissue demonstrated a dramatic increase in expression within cancer cells along the invasive edge, adjacent to Wnt ligand-producing fibroblasts. TNC expression was also increased in areas of intravascular invasion. In vitro, TNC bound Wnt ligands and potentiated Wnt signaling. Finally, in an ATC mouse model, TNC increased Wnt signaling, tumor burden, invasion, and metastasis. Altogether, TNC potentiated ligand driven Wnt signaling and promotes cancer cell invasion and metastasis in a mouse model of thyroid cancer. Understanding the role of TNC and its interaction with Wnt ligands could lead to the development of novel biomarkers and targeted therapeutics for thyroid cancer.

cancer biology↗

bioRxiv2022

Gut Microbial Trimethylamine is Elevated in Alcohol-Associated Hepatitis and Contributes to Ethanol-Induced Liver Injury in Mice

BACKGROUNDThere is mounting evidence that microbes resident in the human intestine contribute to diverse alcohol-associated liver diseases (ALD) including the most deadly form known as alcohol-associated hepatitis (AH). However, mechanisms by which gut microbes synergize with excessive alcohol intake to promote liver injury are poorly understood. Furthermore, whether drugs that selectively target gut microbial metabolism can improve ALD has never been tested. METHODSWe used liquid chromatography tandem mass spectrometry to quantify the levels of microbe and host choline co-metabolites in healthy controls and AH patients, finding elevated levels of the microbial metabolite trimethylamine (TMA) in AH. In subsequent studies, we treated mice with non-lethal bacterial choline TMA lyase (CutC/D) inhibitors to blunt gut microbe-dependent production of TMA in the context of chronic ethanol administration. Indices of liver injury were quantified by complementary RNA sequencing, biochemical, and histological approaches. In addition, we examined the impact of ethanol consumption and TMA lyase inhibition on gut microbiome structure via 16S rRNA sequencing. RESULTSWe show the gut microbial choline metabolite trimethylamine (TMA) is elevated in AH patients and correlates with reduced hepatic expression of the TMA oxygenase flavin-containing monooxygenase 3 (FMO3). Provocatively, we find that small molecule inhibition of gut microbial CutC/D activity protects mice from ethanol-induced liver injury. CutC/D inhibitor-driven improvement in ethanol-induced liver injury is associated with distinct reorganization of the gut microbiome and host liver transcriptome. CONCLUSIONSThe microbial metabolite TMA is elevated in patients with AH, and inhibition of TMA production from gut microbes can protect mice from ethanol-induced liver injury.

biochemistry↗

bioRxiv2018

Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

Cancer patients with advanced disease exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates sequencing data with functional assay data to develop patient-specific combination cancer treatments. This computational modeling approach addresses three major challenges in personalized cancer therapy, which we validate across multiple species via computationally-designed personalized synergistic drug combination predictions, identification of unifying therapeutic targets to overcome intra-tumor heterogeneity, and mitigation of cancer cell resistance and rewiring mechanisms. These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

bioinformatics↗