Biology subjects

Oh, E. J.

Publications and source records attributed to Oh, E. J..

5 recordsLinked to original sources

bioRxiv2026

Super Learner Ensemble Modeling of CPTAC Proteomic Data for Survival Prediction in Head and Neck Squamous Cell Carcinoma

Survival analysis in head and neck squamous cell carcinoma (HNSCC) is traditionally performed using Cox proportional hazards models, alongside some exploration into black-box machine learning methods. The Super Learner (SL) algorithm addresses this model selection dilemma by combining diverse candidate algorithms into a weighted ensemble to perform comparably to the best candidate method. This study evaluates the performance of SL in HNSCC. Proteomic features as well as clinical covariates from 96 CPTAC HNSCC samples were modeled with three candidate algorithms (Cox LASSO, Cox Ridge, and Random Survival Forest) as well as the ensemble SL method. Models were optimized via Unos time-dependent Concordance Index (C-index) and tested at 1- and 3-year time horizons using 2000 bootstrap resamples. The Cox Ridge regression model achieved the highest predictive accuracy among the four total methods. However, the SL demonstrated stable performance over both time horizons (1-year C-index: 0.985; 3-year C-index: 0.960). Variable importance analysis of the Cox Ridge model successfully identified malignant proteins (ATR, MAML1, MIEN1) alongside novel potential prognostic indicators (ZNF800, KERA). This analysis emphasizes the statistical necessity for larger cohorts for ensemble learning, while providing a benchmark of proteomic indicators in HNSCC.

bioinformatics↗

bioRxiv2026

Integrating Spatially Adjusted Protein Summaries for Survival Prediction in Spatial Proteomics

Recent advances in spatial proteomics, particularly imaging mass cytometry, enable the measurement of protein expression at the single-cell level while preserving a spatial context. Conventional survival analyses, however, typically rely on patient-level averages of protein intensities and therefore overlook spatial heterogeneity and tissue architecture. To address this limitation, we introduce a framework that incorporates spatial information into survival modeling by generating spatially adjusted protein summaries (SAPS). In this approach, cell-level protein intensities within each patient are modeled using spatial spline regression to capture spatial trends. From these models, we extract two complementary features: a spatially adjusted mean expression and a residual variance that reflects cell-to-cell variability unexplained by spatial effects. These summaries are then incorporated into Cox proportional hazards models in combination with clinical covariates. We further show that our estimator is asymptotically equivalent to an oracle estimator under mild regularity conditions. In simulation studies, our proposed framework achieved improved predictive performance compared to other alternative methods. The application of the method to breast cancer imaging mass cytometry data indicate that spatially adjusted summaries may enhance survival prediction and reveal biologically interpretable spatial protein patterns, suggesting high translational potential. This methodology offers an efficient means of translating complex spatial proteomics data into patient-level features, providing both improved survival prediction and new insights into the role of spatial heterogeneity in cancer outcomes. R package is available on the Comprehensive R Archive Network repository at https://cran.r-project.org/web/packages/SurvSPro/index.html

bioinformatics↗

bioRxiv2025

Structural mechanism of insulin receptor activation by a dimeric aptamer agonist

Insulin binding to the insulin receptor (IR) triggers signaling pathways that regulate glucose uptake and cell growth. In previous work, we identified a DNA aptamer, A62, which partially activates the IR. During engineering aptamers for improved in vivo stability, we discovered that crosslinking two A62 aptamers with linkers of varying lengths led to full phosphorylation of the IR, though activation remained selective to the AKT pathway. To elucidate the mechanism behind this aptamer-induced full activation of the IR, we determined the structure of the IR in complex with a dimeric form of A62 (A62D) linked by an 8-nucleotide connector. We identified three distinct conformations of the IR: arrowhead-shaped, pseudo-arrowhead-shaped, and pseudo-gamma-shaped. The pseudo-gamma-shaped conformation closely resembles the structure of a fully active IR bound by a single insulin molecule. In these configurations, only one A62 monomer (A62M) within the A62D dimer binds to the IR dimer. This binding brings the IR monomers into close proximity, promoting intermolecular trans-phosphorylation. Our findings provide valuable structural insights for the development of novel therapeutic strategies targeting the IR.

biochemistry↗

bioRxiv2025

Acetate metabolism during xylose fermentation enhances 3-hydroxypropionic acid production in engineered acid-tolerant Issatchenkia orientalis

Efficient bioconversion of acetate-rich lignocellulosic biomass into value-added chemicals remains a major challenge due to the toxicity of acetic acid. In this study, we engineered an acid-tolerant Issatchenkia orientalis strain (IoDY01H) capable of producing 3-hydroxypropionic acid (3-HP), a key bioplastic precursor, from glucose, xylose, and acetate. Using a Cas9-based genome editing system with a hygromycin B resistance marker, we introduced heterologous genes encoding xylose utilization and {beta}-alanine-based 3-HP biosynthetic pathways into the I. orientalis genome. Metabolomic analysis revealed that acetate supplementation redirected metabolic flux toward amino acid and lipid metabolism while reducing TCA cycle intermediates. Acetate enhanced 3-HP production by promoting accumulation of {beta}-alanine, but also revealed {beta}-alanine-pyruvate aminotransferase as a metabolic bottleneck under acidic conditions. Using pretreated hemp stalk hydrolysate as a feedstock, the engineered strain achieved a 3-HP titer of 8.7 g/L via separate hydrolysis and fermentation (SHF), outperforming simultaneous saccharification and fermentation (SSF). These findings demonstrate the feasibility of producing 3-HP from acetate-rich biomass using engineered non-conventional yeast and highlight I. orientalis as a promising microbial chassis for industrial bioconversion. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/649204v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@c04442org.highwire.dtl.DTLVardef@c16b4aorg.highwire.dtl.DTLVardef@6af6d9org.highwire.dtl.DTLVardef@743029_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIEngineered I. orientalis co-utilized xylose and acetate to produce 3-HP. C_LIO_LIAcetate addition enhanced 3-HP production during xylose fermentation. C_LIO_LIAcetate redirected metabolic flux toward 3-HP biosynthesis pathways. C_LIO_LIEngineered I. orientalis achieved 8.7 g/L 3-HP from hemp stalk hydrolysate via SHF. C_LI

synthetic biology↗

bioRxiv2018

Isolation of Genomic Deoxyxylulose Phosphate Reductoisomerase (DXR) Mutations Conferring Resistance to Fosmidomycin

Sequence to activity mapping technologies are rapidly developing, enabling the isolation of mutations that confer novel phenotypes. Here we used the CRISPR EnAbled Trackable genome Engineering (CREATE) technology to investigate the inhibition of the essential IspC gene in Escherichia coli. IspC gene product, Deoxyxylulose Phosphate Reductoisomerase (DXR), converts 1-deoxy-D-xylulose 5-phosphate to 2-C-methyl-D-erythritol 4-phosphate in the DXP pathway. Since this pathway is shared with many pathogenic bacteria and protozoa and is missing in humans, it is an appealing target for inhibition. We created a full saturation library of 33 sites proximal to ligand binding and other sites and challenged it with the DXR-specific inhibitor, fosmidomycin. We identified several mutations that confer fosmidomycin resistance. All sites are highly conserved and also exist in pathogens including the malaria-inducing Plasmodium falciparum. These findings may have general implications on the isolation of resistance-conferring mutations and specifically, may affect the design of future generations of fosmidomycin-based drugs.\n\nSignificanceThe emergence of acquired drug resistance is a natural process that is likely to occur under most circumstances. Recently-developed technologies allow to map relative fitness contribution of multiple mutations in parallel. Such approaches may be used to predict which mutations are most likely to confer resistance, instead of waiting for them to evolve spontaneously. In this study, a rationally-designed IspC mutant library was generated genomically in E. coli. Mutants resistant to fosmidomycin, an antimalarial drug were identified, and most were in the highly conserved proline at position 274. These results may have implications on next-generation fosmidomycin drug design, and more broadly, this approach may be used for predicting mutational acquired resistance.

microbiology↗