Biology subjects

Hsu, P.-K.

Publications and source records attributed to Hsu, P.-K..

5 recordsLinked to original sources

bioRxiv2024

HyperGen: Compact and Efficient Genome Sketching using Hyperdimensional Vectors

MotivationGenomic distance estimation is a critical workload since exact computation for whole-genome similarity metrics such as Average Nucleotide Identity (ANI) incurs prohibitive runtime overhead. Genome sketching is a fast and memory-efficient solution to estimate ANI similarity by distilling representative k-mers from the original sequences. In this work, we present HyperGen that improves accuracy, runtime performance, and memory efficiency for large-scale ANI estimation. Unlike existing genome sketching algorithms that convert large genome files into discrete k-mer hashes, HyperGen leverages the emerging hyperdimensional computing (HDC) to encode genomes into quasi-orthogonal vectors (Hypervector, HV) in high-dimensional space. HV is compact and can preserve more information, allowing for accurate ANI estimation while reducing required sketch sizes. In particular, the HV sketch representation in HyperGen allows efficient ANI estimation using vector multiplication, which naturally benefits from highly optimized general matrix multiply (GEMM) routines. As a result, HyperGen enables the efficient sketching and ANI estimation for massive genome collections. ResultsWe evaluate HyperGens sketching and database search performance using several genome datasets at various scales. HyperGen is able to achieve comparable or superior ANI estimation error and linearity compared to other sketch-based counterparts. The measurement results show that HyperGen is one of the fastest tools for both genome sketching and database search. Meanwhile, HyperGen produces memory-efficient sketch files while ensuring high ANI estimation accuracy. AvailabilityA Rust implementation of HyperGen is freely available under the MIT license as an open-source software project at https://github.com/wh-xu/Hyper-Gen. The scripts to reproduce the experimental results can be accessed at https://github.com/wh-xu/experiment-hyper-gen. Contactwexu@ucsd.edu

genomics↗

bioRxiv2023

Cell specificity of adeno-associated virus (AAV) serotypes in human cortical organoids

Human-derived cortical organoids (hCOs) recapitulate cell diversity and 3D structure found in the human brain and offer a promising model for discovery of new gene therapies targeting neurological disorders. Adeno-associated viruses (AAVs) are the most promising vehicles for non-invasive gene delivery to the central nervous system (CNS), but reliable and reproducible in vitro models to assess their clinical potential are lacking. hCOs can take on these issues as they are a physiologically relevant model to assess AAV transduction efficiency, cellular tropism, and biodistribution within the tissue parenchyma, all of which could significantly modulate therapeutic efficacy. Here, we examine a variety of naturally occurring AAV serotypes and measure their ability to transduce neurons and glia in hCOs from multiple donors. We demonstrate cell tropism driven by AAV serotype and hCO donor and quantify fractions of neurons and astrocytes transduced with GFP as well as overall hCO health.

neuroscience↗

bioRxiv2021

BoolSim, a Graphical Interface for Open Access Boolean Network Simulations and Use in Guard Cell CO2 Signaling

Signaling networks are at the heart of almost all biological processes. Most of these networks contain a large number of components and often the connections between these components are either not known, or the rate equations that govern the dynamics of soluble signaling components are not quantified. This uncertainty in network topology and parameters can make it challenging to formulate detailed mathematical models. Boolean networks, in which all components are either on or off, have emerged as viable alternatives to more detailed mathematical models but can be difficult to implement. Therefore, open source format of such models for community use is desirable. Here we present BoolSim, a freely available graphical user interface (GUI) that allows users to easily construct and analyze Boolean networks. BoolSim can be applied to any Boolean network. We demonstrate BoolSims application using a previously published network for abscisic acid-driven stomatal closure in Arabidopsis. We also show how BoolSim can be used to generate testable predictions by extending the network to include CO2 regulation of stomatal movements. Predictions of the model were experimentally tested and the model was iteratively modified based on experiments showing that ABA closes stomata even at near zero CO2 concentrations (1.5 ppm CO2). One Sentence SummaryThis study presents an open-source, graphical interface for the simulation of Boolean networks and applies it to an abscisic acid signaling network in guard cells, extended to include input from CO2.

plant biology↗

bioRxiv2020

Optimization and scaling of patient-derived brain organoids uncovers deep phenotypes of disease

Cerebral organoids provide unparalleled access to human brain development in vitro. However, variability induced by current culture methodologies precludes using organoids as robust disease models. To address this, we developed an automated Organoid Culture and Assay (ORCA) system to support longitudinal unbiased phenotyping of organoids at scale across multiple patient lines. We then characterized organoid variability using novel machine learning methods and found that the contribution of donor, clone, and batch is significant and remarkably consistent over gene expression, morphology, and cell-type composition. Next, we performed multi-factorial protocol optimization, producing a directed forebrain protocol compatible with 96-well culture that exhibits low variability while preserving tissue complexity. Finally, we used ORCA to study tuberous sclerosis, a disease with known genetics but poorly representative animal models. For the first time, we report highly reproducible early morphological and molecular signatures of disease in heterozygous TSC+/- forebrain organoids, demonstrating the benefit of a scaled organoid system for phenotype discovery in human disease models.

neuroscience↗

bioRxiv2018

Cryo-EM Structure of OSCA1.2 from Oryza sativa: Mechanical basis of hyperosmolality-gating in Plants

Sensing and responding to environmental water deficiency and osmotic stresses is essential for the growth, development and survival of plants. Recently, an osmolality-sensing ion channel called OSCA1 was discovered that functions in sensing hyperosmolality in Arabidopsis. Here, we report the cryo-EM structure and function of an ion channel from rice (Oryza sativa; OsOSCA1.2), showing how it mediates hyperosmolality sensing and ion permeability. The structure reveals a dimer; the molecular architecture of each subunit consists of eleven transmembrane helices and a cytosolic soluble domain that has homology to RNA recognition proteins. The transmembrane domain is structurally related to the TMEM16 family of calcium dependent ion channels and scramblases. The cytosolic soluble domain possesses a distinct structural feature in the form of extended intracellular helical arms that is parallel to the plasma membrane. These helical arms are well positioned to sense lateral tension on the inner leaflet of the lipid bilayer caused by changes in turgor pressure. Computational dynamic analysis suggests how this domain couples to the transmembrane portion of the molecule to open the channel. Hydrogen-deuterium exchange mass spectrometry (HDXMS) experimentally confirms the conformational dynamics of these coupled domains. The structure provides a framework to understand the structural basis of hyperosmolality sensing in an important crop plant, extends our knowledge of the anoctamin superfamily important for plants and fungi, and provides a structural mechanism for translating membrane stress to ion transport regulation.

biophysics↗