Biology subjects

Johnson, W. A.

Publications and source records attributed to Johnson, W. A..

3 recordsLinked to original sources

bioRxiv2026

Drosophila glutathione S-transferase S1 exacerbates phenotypes of the gain-of-function voltage-gated sodium channel mutant paraShu through a hemocyte-mediated mechanism

Drosophila glutathione S-transferase S1 (GstS1) is a unique member of the glutathione S-transferase family and is considered the ortholog of vertebrate hematopoietic prostaglandin D synthase based on amino acid sequence similarity. A previous forward genetic screen revealed that a null mutation in GstS1 dominantly suppresses genetically induced behavioral hyperexcitability in several seizure-prone Drosophila mutants, including paraShu, a gain-of-function mutant of the voltage-gated sodium channel gene paralytic (para), and eas2, a loss-of-function mutant of the ethanolamine kinase gene easily shocked (eas). Here, using the GAL4/UAS system combined with GstS1 RNAi, we show that hemocyte-specific GstS1 knockdown suppresses hyperexcitable phenotypes in both paraShu and eas2. Notably, suppression of adult paraShu phenotypes required GstS1 knockdown before adult eclosion, suggesting that GstS1 modulates adult neuronal excitability by influencing neural development through neuroimmune interactions. Consistent with this model, hemocyte-specific GstS1 knockdown rescued abnormal dendritic morphology in developing class IV dendritic arborization (C4da) neurons of paraShu larvae. Moreover, paraShu and eas2 larvae exhibited increased numbers of sessile hemocytes surrounding C4da neuron somata, a phenotype reversed by either GstS1 knockdown or dietary supplementation with the {omega}-3 polyunsaturated fatty acid -linolenic acid. Together, these findings reveal a previously unrecognized developmental role for GstS1 in promoting nervous system hyperexcitability through hemocyte-mediated neuroimmune interactions, potentially involving bioactive lipid signaling. Our results highlight neuroimmune crosstalk as an important mechanism linking genetic perturbations to altered neural development and seizure-associated hyperexcitability.

neuroscience↗

bioRxiv2026

Temporal AI model predicts drivers of cell state trajectories across human aging

Foundational AI models have recently shown promise for predicting the impact of perturbations on cell states. However, current models typically consider only one cell state at a time, limiting their ability to learn how cellular responses unfold over time, particularly across long trajectories such as diseases of aging. Here, we develop a temporal AI model, MaxToki, trained on nearly 1 trillion gene tokens including cell state trajectories across the human lifespan to generate cell states across long timelapses of human aging. MaxToki generalized to unseen trajectories through in-context learning and predicted novel age-modulating targets that were experimentally verified to influence age-related gene programs and functional decline in vivo. MaxToki represents a promising strategy for temporal modeling to accelerate the discovery of interventions for programming therapeutic cellular trajectories.

bioinformatics↗

bioRxiv2024

CRISPR-GPT: An LLM Agent for Automated Design of Gene-Editing Experiments

Performing effective gene-editing experiments requires a deep understanding of both the CRISPR technology and the biological system involved. Meanwhile, despite their versatility and promise, Large Language Models (LLMs) often lack domain-specific knowledge and struggle to accurately solve biological design problems. We present CRISPR-GPT, an LLM agent system to automate and enhance CRISPR-based gene-editing design and data analysis. CRISPR-GPT leverages the reasoning capabilities of LLMs for complex task decomposition, decision-making, and interactive human-artificial intelligence (AI) collaboration. This system incorporates domain expertise, retrieval techniques, external tools, and a specialized LLM fine-tuned with open-forum discussions among scientists. CRISPR-GPT assists users in selecting CRISPR systems, experiment planning, designing gRNAs, choosing delivery methods, drafting protocols, designing assays, and analyzing data. We showcase the potential of CRISPR-GPT by knocking-out four genes with CRISPR-Cas12a in a human lung adenocarcinoma cell line and epigenetically activating two genes using CRISPR-dCas9 in human melanoma cell line. CRISPR-GPT enables fully AI-guided gene-editing experiment design and analysis across different modalities, validating its effectiveness as an AI co-pilot in genome engineering.

bioinformatics↗