Biology subjects

Jeong, E.

Publications and source records attributed to Jeong, E..

8 recordsLinked to original sources

bioRxiv2026

Isocaloric restriction normalizes body weight but not relative adiposity, adipose inflammation, or gut microbial diversity in high-fat diet-fed mice

Background/Objectives: The separate contributions of excess energy intake and diet composition to high-fat diet (HFD)-induced adiposity, adipose inflammation, and gut microbial changes are unclear. We tested whether restricting HFD intake to the energy intake of low-fat (LF)-fed mice (isocaloric restriction) prevents or reverses these outcomes. Subjects/Methods: Male C57BL/6 mice (n = 8/group) received for 12 weeks an LF diet (LF/LF) or HFD (HF/HF) ad libitum, HFD ad libitum then isocaloric HFD from week 7 (HF/IHF), or isocaloric HFD throughout (IHF/IHF). Adiposity, plasma markers, adipose and ileal gene expression, and fecal microbiota (16S rRNA sequencing, n = 4/group) were assessed. Results: Final body weight in IHF/IHF did not differ from LF/LF (difference 0.1 g, 95% CI -6.5 to 6.7; P > 0.99) and was lower than in HF/HF (-8.2 g, -14.7 to -1.6; P = 0.011). Switching to isocaloric feeding reduced weight gain during weeks 6-12 (HF/IHF vs HF/HF, -6.4 g, -9.8 to -3.1; P < 0.001). IHF/IHF mice had lower glucose (P = 0.031) and cholesterol (P = 0.005) than HF/HF mice. However, visceral fat relative to body weight remained higher than in LF/LF in IHF/IHF (+1.5 percentage points, 0.04 to 2.9; P = 0.042) and HF/IHF (+1.8, 0.4 to 3.3; P = 0.008). Adipose Tnf expression remained elevated in HF/IHF (log2 difference 2.2, 0.2 to 4.2; P = 0.025), and fecal phylogenetic diversity was lower in IHF/IHF than in LF/LF (Faith's PD -3.1, -5.9 to -0.4; P = 0.024). Ileal barrier genes and plasma lipopolysaccharide-binding protein were unchanged. Conclusions: Limiting HFD energy intake normalized body weight, glycemia, and plasma lipids but not relative adiposity, adipose Tnf expression, or the loss of gut microbial diversity. Diet composition affects these outcomes independently of energy intake, so normal body weight does not indicate their reversal.

physiology↗

bioRxiv2026

CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction

Graph foundation models have emerged as powerful tools for drug repurposing by enabling the prediction of novel drug-disease indications from large biomedical knowledge graphs. A representative example is TxGNN, which was previously developed and trained on PrimeKG, a comprehensive biomedical knowledge graph covering over 17,000 diseases. While TxGNN demonstrates strong performance, existing biomedical knowledge graphs largely lack fine-grained, cell-type-specific expression context. This limits their ability to capture disease mechanisms driven by dysregulated cellular programs, such as immune cell-specific pathways in autoimmune diseases. Moreover, prior evaluations typically test only randomly selected subsets of diseases, leaving many diseases unexamined and limiting conclusions about model performance across the full disease spectrum. To address these limitations, we first update PrimeKG to PrimeKG-U by incorporating expanded and curated biomedical knowledge and then develop TxGNN-U as a stronger graph-based baseline. Building on this foundation, we introduce CellAwareGNN, a graph foundation model that integrates single-cell genomics into PrimeKG-U. We construct a single-cell-enhanced knowledge graph, scPrimeKG, by incorporating cell-type-specific gene expression signatures from the OneK1K dataset, expanding PrimeKG from approximately 8.1 million edges and 129k nodes to over 14 million edges and 147k nodes. CellAwareGNN is pre-trained on all relation types in scPrimeKG and evaluated on drug indication prediction with explicit coverage of all diseases in the knowledge graph. CellAwareGNN consistently outperforms TxGNN and TxGNN-U. For drug indication prediction, CellAwareGNN achieves an AUPRC of 0.826, representing a 1.2% improvement over TxGNN-U (0.816) and a 3.4% improvement over TxGNN (0.799). Notably, for autoimmune diseases, CellAwareGNN attains an AUPRC of 0.864, improving by 2.0% over TxGNN-U (0.847) and 6.0% over TxGNN (0.815). Importantly, CellAwareGNN prioritizes promising repurposing candidates, including Ocrelizumab for Pemphigus via CD20-expressing B cells, Methotrexate for Pemphigus through DHFR and ATIC activity in T and B cells, and Rosiglitazone for Rheumatoid Arthritis through PPAR-{gamma} activation. These results demonstrate the value of incorporating cell-type-specific expression context to improve both predictive performance and biological interpretability in graph-based drug repurposing. CCS ConceptsO_LIApplied computing [->] Health informatics; Bioinformatics; C_LIO_LIComputing methodologies [->] Knowledge representation and reasoning; Neural networks. C_LI ACM Reference FormatXinmeng Zhang, Eugene Jeong, Chao Yan, Yubo Feng, Linshuoshuo Lyu, Xingyi Guo, and You Chen. 2026. CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD 2026), August 9-13, 2026, Jeju Island, Republic of Korea. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3770855.3819012

bioinformatics↗

bioRxiv2026

NetCrafter: Ontology-derived gene network modeling and interpretation

Understanding the complex nature of multi-functional interactions among genes is crucial for interpreting omics data. We developed NetCrafter, an ontology-driven platform for constructing de novo gene networks that are specific to each input gene list and quantitatively defined by ontology-weighted similarity. By incorporating the probabilistic association of ontology or curated gene sets into a weighted Tanimoto similarity metric, NetCrafter transforms enrichment results into quantitative semantic similarity scores between genes, enabling the creation of context-specific statistical networks. These networks can be further decomposed into optimal sub-networks, facilitating multi-functional interpretation and the identification of gene interaction hotspots. NetCrafter also supports the integration of heterogeneous omics-derived gene lists through consensus ontology scoring. Importantly, this list-specific, quantitative framework reveals functional hotspots and target-biomarker relationships - even in cases where ontology terms alone are not predictive of node-level attributes such as CRISPR efficacy. NetCrafter provides an interactive platform for constructing and interpreting dynamic, context-specific gene networks, leveraging ontology-based functional associations to uncover underlying mechanisms and identify key nodes. It is freely available at https://netcrafter.sookmyung.ac.kr and integrated into Q-omics platform (https://qomics.ai) to enhance the utility of cancer omics data.

bioinformatics↗

bioRxiv2025

The interaction of XPG with TFIIH through p62 and XPD is required for the completion of nucleotide excision repair

Nucleotide excision repair (NER) is the key pathway for the removal of DNA damage induced by UV irradiation and chemotherapeutic reagents. Protein-protein interactions are crucial for the dynamic and coordinated assembly of the proteins involved at DNA lesion. Here we focus on the role of interactions between the multi-subunit helicase/translocase complex TFIIH and the 3 endonuclease XPG. We show that XPG interacts with the p62 and XPD subunits of TFIIH through its long spacer region located in the middle of its split active site. We show the interactions between three acidic regions of XPG with the pleckstrin homology (PH) domain of p62 are of moderate importance for NER, while interactions with XPD are critical for activity. Combining mutations in the p62 and XPD interaction domains leads to additive defects in NER activity. Unexpectedly, we show that these interactions are not required for the recruitment of XPG, but rather for the formation of a catalytically competent NER complex and for triggering the incision 5 to the lesion by ERCC1-XPF. Our studies provide fundamental insights into how interactions between TFIIH and XPG contribute to the NER pathway and more generally how modular protein-protein interactions control each step along the NER reaction coordinate.

biochemistry↗

bioRxiv2024

Autophagy suppression in DNA damaged cells occurs through a newly identified p53-proteasome-LC3 axis

Macroautophagy is thought to have a critical role in shaping and refining cellular proteostasis in eukaryotic cells recovering from DNA damage. Here, we report a mechanism by which autophagy is suppressed in cells exposed to bacterial toxin-, chemical-, or radiation-mediated sources of genotoxicity. Autophagy suppression is directly linked to cellular responses to DNA damage, and specifically the stabilization of the tumor suppressor p53, which is both required and sufficient for regulating the ubiquitination and proteasome-dependent reduction in cellular pools of microtubule-associated protein 1 light chain 3 (LC3A/B), a key precursor of autophagosome biogenesis and maturation, in both epithelial cells and an ex vivo organoid model. Our data indicate that suppression of autophagy, through a newly identified p53-proteasome-LC3 axis, is a conserved cellular response to multiple sources of genotoxicity. Such a mechanism could potentially be important for realigning proteostasis in cells undergoing DNA damage repair.

cell biology↗

bioRxiv2022

Decoding Imagined Musical Pitch from Human Scalp Electroencephalograms

Brain-computer interfaces (BCIs) can restore the functions of communication and control in people with paralysis. In addition to the currently proven functions restored by BCIs, it would enrich life if one could regain a function of musical activity. However, it remains largely unknown whether it is feasible to decode imagined musical information directly from neural activity. Among various musical information, this study aimed to decode pitch information directly from scalp electroencephalography (EEG). Twenty healthy participants performed a task to imagine one of the seven musical pitches (C4 - B4) randomly. To find EEG features for pitch imagination, we took two approaches: exploring multi-band spectral power at individual channels (IC); and exploring power differences between bilaterally symmetric channels (DC). We classified these features into the seven pitch classes using various types of classifiers. The selected spectral power features revealed marked contrasts between left and right hemispheres, between low-, (<13 Hz) and high-frequency (> 13 Hz) bands, and between frontal and parietal areas. The best classification performance for seven pitches was obtained using the IC feature and SVM with the average accuracy of 35.68{+/-}7.47% (max. 50%) and the average information transfer rate (ITR) of 0.37{+/-}0.22 bits/sec. Yet, when we decoded a different number of classes (K = 2 [~] 6) by grouping adjacent pitches, ITR was similar across K as well as between IC and DC features, suggesting efficiency of DC features. This study would be the first to demonstrate the feasibility of decoding imagined musical pitch directly from human EEG.

bioengineering↗

bioRxiv2020

Mutational profiling of micro-dissected pre-malignant lesions from archived specimens

BackgroundSystematic cancer screening has led to the increased detection of pre-malignant lesions (PMLs). The absence of reliable prognostic markers has led mostly to over treatment resulting in potentially unnecessary stress, or potentially insufficient treatment and avoidable progression. Importantly, most mutational profiling studies have relied on PML synchronous to invasive cancer, or performed in patients without outcome information, hence limiting their utility for biomarker discovery. The limitations in comprehensive mutational profiling of PMLs are in large part due to the significant technical and methodological challenges: most PML specimens are small, fixed in formalin and paraffin embedded (FFPE) and lack matching normal DNA. MethodsUsing test DNA from a highly degraded FFPE specimen, multiple targeted sequencing approaches were evaluated, varying DNA input amount (3-200 ng), library preparation strategy (BE: Blunt-End, SS: Single-Strand, AT: A-Tailing) and target size (whole exome vs cancer gene panel). Variants in high-input DNA from FFPE and mirrored frozen specimens were used for PML-specific variant calling training and testing, respectively. The resulting approach was applied to profile and compare multiple regions micro-dissected (mean area 5 mm2) from 3 breast ductal carcinoma in situ (DCIS). ResultsUsing low-input FFPE DNA, BE and SS libraries resulted in 4.9 and 3.7 increase over AT libraries in the fraction of whole exome covered at 20x (BE:87%, SS:63%, AT:17%). Compared to high-confidence somatic mutations from frozen specimens, PML-specific variant filtering increased recall (BE:85%, SS:80%, AT:75%) and precision (BE:93%, SS:91%, AT:84%) to levels expected from sampling variation. Copy number alterations were consistent across all tested approaches and only impacted by the design of the capture probe-set. Applied to DNA extracted from 9 micro-dissected regions (8 PML, 1 normal epithelium), the approach achieved comparable performance, illustrated the data adequacy to identify candidate driver events (GATA3 mutations, ERBB2 or FGFR1 gains, TP53 loss) and measure intra-lesion genetic heterogeneity. ConclusionAlternate experimental and analytical strategies increased the accuracy of DNA sequencing from archived micro-dissected PML regions, supporting the deeper molecular characterization of early cancer lesions and achieving a critical milestone in the development of biology-informed prognostic markers and precision chemo-prevention strategies.

genomics↗

bioRxiv2017

Directed evolution of CRISPR-Cas9 to increase its specificity

The use of CRISPR-Cas9 as a therapeutic reagent is hampered by its off-target effects. Although rationally designed S. pyogenes Cas9 (SpCas9) variants that display higher specificities than the wild-type SpCas9 protein are available, these attenuated Cas9 variants are often poorly efficient in human cells. Here, we have used a directed evolution approach in E. coli to obtain Sniper-Cas9, which shows high specificities without sacrificing on-target activities in human cells.

biochemistry↗