Biology subjects

Jung, A. J.

Publications and source records attributed to Jung, A. J..

3 recordsLinked to original sources

bioRxiv2026

Correlation of Live Imaging and Fixed-Expansion Microscopy to study intracellular pathogens

Expansion microscopy (ExM) enables high-resolution fluorescence imaging by physically enlarging biological samples, but its application to intracellular bacteria remains challenging. Here, we adapted the Ten-fold Robust Expansion Microscopy protocol to study host-pathogen interactions. Expansion of Burkholderia thailandensis-infected epithelial cells revealed previously unresolved membrane architectures associated with bacterial cell-to-cell dissemination. While host cells expanded approximately 9-fold, bacterial cells displayed incomplete and heterogeneous expansion. We found that fixation conditions strongly influence bacterial expansion, while presence of peptidoglycan partially limits expansion. To connect the membrane ultrastructures revealed by expansion with infection dynamics, we developed Live-to-Fixed Expansion microscopy (LiFEx), a workflow that correlates dynamic events observed by live-cell imaging with high-resolution cellular structures resolved by ExM. By correlating T6SS-5 assembly with subsequent high-resolution membrane architecture, LiFEx revealed membrane alterations developing within minutes following T6SS-5 assembly. Together, our approach enabled the investigation of dynamic host-pathogen interactions across complementary spatial and temporal scales.

microbiology↗

bioRxiv2025

Enigma: An Efficient Model for Deciphering Regulatory Genomics

Genomic sequence-to-function models have emerged as powerful tools for deciphering cis-regulatory grammar to advance our understanding of disease biology and guide therapeutic development. Recent advances have been driven by multi-task training of large transformer-based models on thousands of genome tracks. However, these performance gains have come at significant computational cost for both training and inference, hindering large-scale applications and slowing future model development. Here, rather than continuing to scale model size and add more training tracks, we focus on architectural efficiency and train on a substantially smaller, curated set of genome tracks. Our model Enigma achieves competitive performance with current state-of-the-art models at single-base resolution while substantially reducing computational cost. On zero-shot variant effect prediction benchmarks, Enigma outperforms the leading open-source model, the Borzoi ensemble, while using 10.9% of its compute and improving resolution from 32 bases to a single base. Compared to AlphaGenome, Enigma achieves 90.4 - 97.3% of its performance using 7.5% of its estimated compute. These improvements in efficiency can facilitate further development of models for regulatory genomics. We demonstrate this by fine-tuning Enigma on predicting three new molecular phenotypes -- ChIP-seq, RNA half-life, and translation efficiency -- achieving or exceeding the performance of state-of-the-art task-specific models. We are providing Enigma for non-commercial use to benefit the broader research field. [Formula]Code: https://github.com/deepgenomics/enigma

genomics↗

bioRxiv2025

FlashRNA: An Efficient Model for Regulatory Genomics

Transformer-based genomic sequence-to-function models effectively capture long-range genomic interactions but incur high computational costs due to the quadratic complexity of their self-attention layers. In this work, we introduce FlashRNA, which significantly improves computational and memory efficiency through FlashAttention, advancements in model architecture, and optimized training setup. FlashRNA achieves comparable or slightly improved predictive performance compared to similar sized Borzoi or Flashzoi models, notably without depending on pre-trained weights - a major limitation of Flashzoi. Remarkably, we trained FlashRNA from scratch in one day on a single GPU, significantly accelerating training and inference speed. These improvements can facilitate further developments in models for regulatory genomics by reducing computational cost. We demonstrate this in two downstream applications: 1) we train a large ensemble of 16 FlashRNA models and distill them into a single model to improve performance while maintaining efficiency, and 2) we fine-tune FlashRNA on three prediction tasks - ChIP-seq, RNA half-life, and translation efficiency - achieving performance matching or exceeding state-of-the-art task-specific models. Code: https://github.com/deepgenomics/flashrna

genomics↗