Biology subjects

Kim, B.-C.

Publications and source records attributed to Kim, B.-C..

4 recordsLinked to original sources

bioRxiv2025

Keratin degradation reflects a starvation survival strategy in Fervidobacterium islandicum AW-1

Keratin is a highly cross-linked, disulfide-rich protein that resists proteolysis, which poses a major challenge for microbial degradation. Here, we show that Fervidobacterium islandicum AW-1 initiates a starvation-induced keratinolytic program involving membrane-associated proteases and redox-mediated sulfitolysis. Multi-omics integration reveals that nutrient limitation triggers global metabolic reprogramming, promoting sulfur assimilation, biofilm formation, and chemotaxis-linked persister-like adaptation. Substrate-specific transcriptomics identified a temporally regulated protease repertoire tightly coordinated with sulfitolytic activity, facilitating efficient feather decomposition under starvation. Protein-protein interaction networks uncovered stress-responsive transcriptional regulators that govern this process. Time-resolved gene expression analysis and metabolomic profiling further revealed that cyclic-di-GMP signaling, stringent response, and flagella assembly mediate transitions between motility and sessile growth, contributing to surface colonization and persistence. Together, our findings establish a starvation-responsive survival mechanism that couples keratin degradation to stress adaptation in extreme environments, offering insights into microbial persistence and potential strategies for keratin valorization.

microbiology↗

bioRxiv2025

Distinct Acetate Utilization Strategies Differentiate Butyrate and Octanoate Producing Chain-Elongating Bacteria

Chain elongating bacteria (CEB) are a unique guild of anaerobes that upcycle organic waste into valuable short- and medium-chain carboxylic acids (MCCAs), enabling a circular bioeconomy. However, the metabolic rules that determine product chain length have remained elusive. Here, we combine 13C isotope tracing, proteomics, enzyme assays, and metabolic modelling to show that distinct acetate utilization strategies underlie the divergence between butyrate- and MCCA-producing CEB. MCCA-producing strains recycle acetate to maximize lactate use under acetate limitation, but at the cost of slower growth. In contrast, butyrate-producing strains grow faster by favoring acetate assimilation, at the cost of restricted lactate utilization when acetate is scarce. These physiological trade-offs are encoded in the substrate specificity of coenzyme A transferase, the terminal enzyme in reverse {beta}-oxidation. Our findings uncover a fundamental constraint shaping chain-length selectivity in CEB and offer new strategies to optimize MCCA production from organic waste streams.

microbiology↗

bioRxiv2023

Evaluation and optimization of sequence-based gene regulatory deep learning models

Neural networks have emerged as immensely powerful tools in predicting functional genomic regions, notably evidenced by recent successes in deciphering gene regulatory logic. However, a systematic evaluation of how model architectures and training strategies impact genomics model performance is lacking. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast, to best capture the relationship between regulatory DNA and gene expression. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. While some benchmarks produced similar results across the top-performing models, others differed substantially. All top-performing models used neural networks, but diverged in architectures and novel training strategies, tailored to genomics sequence data. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide any given model into logically equivalent building blocks. We tested all possible combinations for the top three models and observed performance improvements for each. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets. Overall, we demonstrate that high-quality gold-standard genomics datasets can drive significant progress in model development.

genomics↗

bioRxiv2023

Proformer: a hybrid macaron transformer model predicts expression values from promoter sequences

The breakthrough high-throughput measurement of the cis-regulatory activity of millions of randomly generated promoters provides an unprecedented opportunity to systematically decode the cis-regulatory logic that determines the expression values. We developed an end-to-end transformer encoder architecture named Proformer to predict the expression values from DNA sequences. Proformer used a Macaron-like Transformer encoder architecture, where two half-step feed forward (FFN) layers were placed at the beginning and the end of each encoder block, and a separable 1D convolution layer was inserted after the first FFN layer and in front of the multi-head attention layer. The sliding k-mers from one-hot encoded sequences were mapped onto a continuous embedding, combined with the learned positional embedding and strand embedding (forward strand vs. reverse complemented strand) as the sequence input. Moreover, Proformer introduced multiple expression heads with mask filling to prevent the transformer models from collapsing when training on relatively small amount of data. We empirically determined that this design had significantly better performance than the conventional design such as using the global pooling layer as the output layer for the regression task. These analyses support the notion that Proformer provides a novel method of learning and enhances our understanding of how cis-regulatory sequences determine the expression values.

bioinformatics↗