Biology subjects

Chao, J. T.

Publications and source records attributed to Chao, J. T..

4 recordsLinked to original sources

bioRxiv2026

A Cell-Based Assay to Categorize Variants of Human Succinate Semialdehyde Dehydrogenase Associated with Autism Spectrum Disorder

Many genes are implicated in autism spectrum disorder (ASD) as a result of sequencing the genomes of individuals with ASD, however in most cases it remains unclear which genes are playing causative roles. ALDH5A1, which encodes the enzyme succinate semialdehyde dehydrogenase (SSADH), an important regulator of GABA metabolism, is causative in the syndrome SSADH deficiency and is also implicated in ASD. However, it is unknown how variants found in ASD affect SSADH function. We developed a yeast growth assay that models SSADH deficiency to quantify the functional impact of seven ALDH5A1 variants found in ASD. In this assay, expression of human ALDH5A1 partially complemented the growth defect caused by deletion of the ALDH5A1 ortholog UGA2. Using growth rate measurements, we calculated functional scores for 27 variants divided into calibration and ASD test variant groups. Functional scores for benign and pathogenic calibration variants segregated accordingly, validating the assay, while ASD variants displayed a range of activities from complete loss to normal function. Comparisons with published enzymatic assays and computational predictions showed broad agreement, while also identifying some limitations of these approaches. SUMMARY STATEMENTWe introduce a simple yeast assay to measure how autism-linked gene variants in ALDH5A1, which encodes the enzyme succinate semialdehyde dehydrogenase, affect its function, helping clarifying its role in autism.

neuroscience↗

bioRxiv2020

Functional assessments of PTEN variants using machine-assisted phenotype scoring

Genetic testing is widely used in evaluating a patients predisposition for developing a malignancy. In the case of cancer, when a functionally impactful inherited mutation (i.e. genetic variant) is identified in a disease-relevant gene, the patient is at elevated risk of developing a lesion in their lifetime. Unfortunately, as the rate and coverage of genetic testing has accelerated, our ability to make informed assessments regarding the functional status of the variants has lagged. Currently, there are two main strategies for assessing variant functions: in silico predictions or in vitro testing. The first approach is to build generalist computational prediction software using theoretical parameters such as amino acid conservation as feature inputs. These types of software can classify any variant of any gene. Although versatile, their non-specific design and theoretical assumptions result in different models frequently producing conflicting classifications. The second approach is to develop gene-specific assays. Although each assay is tailored to the physiological function of the gene, this approach requires significant investments. Therefore, there is an urgent need for more practical, streamlined and cost-effective methods. To directly address these issues, we designed a new approach of using alterations in protein subcellular localization as a key indicator of loss of function. Thus, new variants can be rapidly functionalized by using high-content microscopy. To facilitate the analysis of large amounts of image data, we developed a new software, named MAPS for machine-assisted phenotype scoring, that utilizes deep learning (DL) techniques to extract and classify cell-level phenotypes. This new Python-based toolkit helps users leverage commercial cloud-based DL services that are easy to train and deploy to fit varying experimental conditions. Model training is entirely code-free and can be done with limited number of images. Users simply input the trained endpoints into MAPS to accomplish cell detection, phenotype discovery and phenotype classification. Thus, MAPS allows cell biologists to easily apply DL to accelerate their image analysis workflow.

bioinformatics↗

bioRxiv2020

Sentinel Interaction Mapping (SIM) - A generic approach for the functional analysis of human disease gene variants using yeast

Advances in sequencing technology have led to an explosion in the number of known genetic variants of human genes. A major challenge is to now determine which of these variants contribute to diseases as a result of their effect on gene function. Here we describe a generic approach using the yeast Saccharomyces cerevisiae to quickly develop gene-specific in vivo assays that can be used to quantify the level of function of a genetic variant. Using Synthetic Dosage Lethality screening, "sentinel" yeast strains are identified that are sensitive to overexpression of a human disease gene. Variants of the gene can then be functionalized in high-throughput fashion through simple growth assays using either solid or liquid media. Sentinel Interaction Mapping (SIM) has the potential to create functional assays for the large majority of human disease genes that do not have a yeast orthologue. Using the tumour suppressor gene PTEN as an example, we show that SIM assays can provide a fast and economical means to screen a large number of genetic variants.

genomics↗

bioRxiv2019

Transfer of Septin Rings to Cytokinetic Remnants Directs Age-Sensitive ER stress Surveillance Cell Cycle Re-entry

During cell division, cells must actively pass on organelles. Previously, we discovered the endoplasmic reticulum (ER) stress surveillance (ERSU) pathway that ensures the inheritance of functional ER. Activation of the ERSU causes the septin ring to mislocalize, which blocks ER inheritance and cytokinesis. Here, we found that the septin ring mislocalizes to previously utilized cell division sites called cytokinetic remnants (CRMs). The transfer of the septin ring to CRMs requires Nba1, a negative polarity component that normally prevents septin ring formation at CRMs. Furthermore, septin ring movement to CRMs relies on the ERSU component Slt2, which is recruited by binding Bem1. During ER stress, Bem1 also binds the GTP exchange factor Cdc24, without activating Cdc42, a GTPase that normally establishes polarized growth. Failure to translocate septin rings to CRMs delays the cells ability to re-enter cell division when ER homeostasis is re-established. Thus, ER stress considers the history of previous cell cycle for future cell cycle re-entry upon ER stress recovery.

cell biology↗