Biology subjects

Choi, J. M.

Publications and source records attributed to Choi, J. M..

10 recordsLinked to original sources

bioRxiv2026

Comparative proteomic quantification reveals novel longevity regulators

Proteomics is a powerful tool for profiling the protein landscape underlying cellular and physiological processes. Advances in mass spectrometry-based methods, such as tandem-mass-tags (TMT) isobaric labeling-based proteomics at both MS2 and MS3, label-free quantification (LFQ) with data-dependent-acquisition (DDA) or data-independent acquisition (DIA)-based proteomics, and stable isotope labeling with amino acid (SILAC) spike-in-assisted proteomics, enable quantitative proteome comparisons across biological conditions. These five quantitative proteomic methods offer distinct advantages in coverage, throughput, and quantitative reproducibility. Here, we treated them as orthogonal approaches, retaining protein candidates with significant, consistent-direction changes supported across multiple independently instrumented methods to identify high-confidence protein-level changes associated with longevity. Insulin/insulin-like growth factor signaling (IIS) is a highly conserved longevity regulatory pathway from Caenorhabditis elegans to humans. By quantitatively profiling IIS mutants in C. elegans and performing genetic screening, we obtained a systems-view of proteomic changes linked to longevity and identified new longevity regulators. This systematic cross-method comparison also serves as a valuable resource for guiding the selection of quantitative proteomics approaches.

molecular biology↗

bioRxiv2025

Decrypting cellular engagement and recruitment from spatially resolved transcriptomics

The recruitment of the various types of cells into the tissue microenvironment and how these cells engage with other cells in the tissue sites play critical biological roles. However, it is difficult to study these processes on a genome-wide scale using traditional low-throughput experiments. To address this gap, we developed a fully interpretable multi-instance deep learner, coined "spacer". Spacer digests the transcriptomics and spatial modalities of spatially resolved transcriptomics (SRT) data in alignment with the biological principles of the tissue spatial architecture. We deployed spacer to a panel of 17 high definition and 20 low definition SRT datasets, for studying how stromal and immune cells were recruited into tumors and heart during myocarditis. Spacer discovered novel biological insights not affordable by prior spatial data analysis tools, which were validated by orthogonal immuno-peptidomics, spatial T cell receptor (TCR) sequencing, and single cell sequencing experiments. We discovered genes that encode more immunogenic peptides and that are involved in developmental pathways are more potent in recruiting T cells to local tumor sites, while the recruitment of B cells and macrophages is stipulated by a different molecular program. On the other hand, expression of mucins in the tumor cells was found to repel T cell localization. For the engaging cell side, we also uncovered T cell-intrinsic features that determine their localization, validated by spatial-TCR-seq data. Spacer also unexpectedly revealed that CD4+ T cells, though fewer in numbers, are more responsive than CD8+ T cells in the heart during myocarditis. Collectively, this study establishes a new spatially resolved paradigm for studying cellular localization mechanisms in situ and shifts the paradigm from descriptive mapping to mechanistic discovery.

bioinformatics↗

bioRxiv2025

CancerSubminer: an integrated framework for cancer subtyping using supervised and unsupervised learning on DNA methylation profiles

Human cancer is highly heterogeneous, resulting in variable drug resistance and clinical outcomes. This complexity hinders accurate prognosis prediction and the development of targeted therapies. Molecular subtyping addresses these challenges by grouping cancers into more homogeneous subsets based on molecular characteristics, enabling subtype-specific treatment strategies. Subtyping is crucial for early diagnosis, personalized therapy, and improved survival by capturing differential therapeutic responses. Existing approaches to cancer subtyping fall into supervised and unsupervised categories. Supervised methods, often trained on The Cancer Genome Atlas (TCGA), rely on predefined subtype annotations but face limitations in generalizability and novel subtype discovery. Unsupervised methods, while capable of identifying new subtypes, may overlook widely recognized ones, hindering consistency with established classifications. Multi-omics approaches improve accuracy but are constrained by costs and data collection. We propose CancerSubminer, a hybrid subtyping framework that integrates supervised and unsupervised learning. A subtype classifier is first trained on labeled data, after which clustering is applied to extracted features, with low-confidence samples reassigned to refine subtype boundaries. Model is retrained with the refined subtypes, and adversarial training corrects batch effects and learns domain-invariant features across labeled TCGA and unlabeled external datasets. A subsequent semi-supervised fine-tuning phase aligns subtypes between datasets and designates low-confidence samples as potential novel candidates. CancerSubminer was evaluated on five cancer types, including breast, bladder, brain, kidney, and thyroid cancers, using TCGA methylation data with annotated subtypes and unlabeled datasets from the Gene Expression Omnibus. The framework outperformed state-of-the-art subtyping models (iClusterPlus, iClusterBayes, NEMO) and clustering methods (Spectral, K-means). Kaplan-Meier survival analysis demonstrated significant prognostic separation (p < 0.05) for all cancers, including thyroid cancer where predefined subtypes showed no significance but CancerSubminer-derived subtypes did. These findings highlight CancerSubminers ability to identify distinct prognostic subtypes, mitigate batch effects, and improve prognostic stratification across heterogeneous datasets. CancerSubminer is publicly available at https://github.com/joungmin-choi/CancerSubminer.

bioinformatics↗

bioRxiv2025

Leveraging AI for facioscapulohumeral muscular dystrophy prediction and omics biomarker identification

Facioscapulohumeral muscular dystrophy (FSHD) is an autosomal dominant muscle disorder characterized by a complex genetic etiology, variable prognosis, and a lack of effective therapies. Previous studies have identified candidate protein and miRNA biomarkers using various profiling techniques, underscoring their potential for monitoring FSHD, assessing prognosis, and evaluating pharmacodynamic responses. However, the feasibility of applying machine learning (ML) models to predict FSHD using these molecular signatures has not been explored. In this study, we developed ML models to predict FSHD using a multi-omics dataset comprising protein abundance and miRNA expression profiles. Key predictive features were identified using Random Forest and the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) methods. Performance evaluations demonstrated the robustness of the ML classifiers, with logistic regression consistently achieving the robust predictive accuracy in distinguishing FSHD from healthy conditions. Additionally, we assessed the predictive power of the identified features by comparing them with biomarker sets reported in previous studies. Our findings highlight the potential of AI to improve prediction accuracy and facilitate the cost- and time-efficient strategy for identifying FSHD biomarker candidates, even with limited sample sizes in the context of rare diseases.

bioinformatics↗

bioRxiv2025

Nutrient-Sensing Nuclear Receptor PPARα Controls Liver Proteostasis

Peroxisome proliferator-activated receptor alpha (PPAR) is a nuclear receptor that orchestrates metabolic adaptation to fasting by regulating hepatic lipid and glucose metabolism. While these functions are well established, its role in protein homeostasis, another energy-intensive process, has remained unclear. Here, we performed multi-omics analyses in wild-type and PPAR knockout mice treated with the PPAR agonist fenofibrate to define its broader role in proteostasis. Interestingly, chronic PPAR activation suppressed hepatic secretory pathways, reducing secretome gene expression and circulating serum proteins, and downregulated genes involved in endoplasmic reticulum (ER) translocation, glycosylation, folding, and trafficking. Concurrently, PPAR activation enhanced proteasome activity, which was associated with selective induction of proteasome 26S subunit non-ATPase (PSMD) family members. In addition, hepatic protein synthesis was strongly attenuated. This was associated with an increase in the inhibitory phosphorylation of the eukaryotic translation initiation factor 2 (eIF2), that in turn was linked to disrupted iron homeostasis. Together, these findings identify PPAR as a regulator of proteostasis, suppressing protein synthesis and secretion while promoting protein degradation pathways. Beyond its canonical role in lipid and glucose metabolism, we conclude that PPAR exerts an additional energy-conserving function by coordinating proteostasis, expanding our understanding of its systemic metabolic role.

physiology↗

bioRxiv2025

ARGfore: A multivariate framework for forecasting antibiotic resistance gene abundances using time-series metagenomic datasets

BackgroundThe global spread of antibiotic resistance presents a significant threat to human, animal, and plant health. Metagenomic sequencing is increasingly being utilized to profile antibiotic resistance genes (ARGs) in various environments, but presently a mechanism for predicting future trends in ARG occurrence patterns is lacking. Capability of forecasting ARG abundance trends could be extremely valuable towards informing policy and practice aimed at mitigating the evolution and spread of ARGs. ResultsHere we propose ARGfore, a multivariate forecasting model for predicting ARG abundances from time-series metagenomic data. ARGfore extracts features that capture inherent relationships among ARGs and is trained to recognize patterns in ARG trends and seasonality. ConclusionARGfore outperformed standard time-series forecasting methods, such as N-HiTS, LSTM, and ARIMA, exhibiting the lowest mean absolute percentage error when applied to different wastewater datasets. Additionally, ARGfore demonstrated enhanced computational efficiency, making it a promising candidate for a variety of ARG surveillance applications. The rapid prediction of future trends can facilitate early detection and deployment of mitigation efforts if necessary. ARGfore is publicly available at https://github.com/joungmin-choi/ARGfore.

bioinformatics↗

bioRxiv2024

CIWARS: a web server for waterborne antibiotic resistance surveillance using longitudinal metagenomic data

The rise of antibiotic resistance (AR) is a major global health crisis, exacerbated by the overuse and misuse of antibiotics, leading to the rapid spread of antibiotic resistance genes (ARGs) in bacterial pathogens. This phenomenon poses significant threats to human and animal health, food security, and economic stability. Water bodies, particularly wastewater treatment plants (WWTPs), serve as critical reservoirs for ARGs, creating environments that favor the proliferation of resistant bacteria. Wastewater-based surveillance (WBS) has emerged as a cost-effective strategy for monitoring AR at the population level, providing real-time data to guide public health and policy decisions. Despite advancements in WBS, there are no comprehensive online analytical platforms for continuous environmental AR surveillance. This paper introduces CIWARS, a web server designed for AR analyses of longitudinal metagenomic data. CIWARS offers comprehensive ARG profiling, taxonomic annotation, and anomalous AR risk points detection. We demonstrate its capabilities through an interactive temporal data visualization, showcasing its potential for enhancing AR risk monitoring and guiding effective mitigation strategies. CIWARS is broadly applicable to longitudinal metagenomic data generated from any environment and aims to support global efforts in addressing the AR crisis by providing cyberinfrastructure for continuous AR surveillance. The web server is freely available at https://ciwars.cs.vt.edu/.

bioinformatics↗

bioRxiv2024

CAMKV Kinase Signaling Is a Novel Therapeutic Avenue with Prognostic Relevance in Neuroblastoma

Neuroblastoma (NB) can be a highly aggressive malignancy in children. However, the precise mechanisms driving NB tumorigenesis remain elusive. This study revealed the critical role of CREB phosphorylation in NB cell proliferation. By employing a CRISPR-Cas9 knockout screen targeting calcium/calmodulin-dependent protein kinase (CaMK) family members, we identified the CaM kinase-like vesicle-associated (CAMKV) protein as a kinase that mediates direct phosphorylation of CREB to promote NB cell proliferation. CAMKV was found to be a transcriptional target of MYCN/MYC in NB cells. CAMKV knockout and knockdown effectively suppressed NB cell proliferation and tumor growth both in vitro and in vivo. Bioinformatic analysis revealed that high CAMKV expression is significantly correlated with poor patient survival. High-risk NB frequently had high CAMKV protein levels by Immunohistochemical staining. Integrated transcriptomic and proteomic analyses of CAMKV knockdown cells unveiled downstream targets involved in CAMKV-regulated phosphorylation and signaling pathways, many of which are linked to neural development and cancer progression. We identified small molecule inhibitors targeting CAMKV and further demonstrated the efficacy of one inhibitor in suppressing NB tumor growth and prolonging the survival of mice bearing xenografted tumors. These findings reveal a critical role for CAMKV kinase signaling in NB growth and identified CAMKV kinase as a potential therapeutic target and prognostic marker for patients with NB.

cancer biology↗

bioRxiv2018

MiR-146a wild-type 3’ sequence identity is dispensable for proper innate immune function in vivo

The prevailing model of microRNA function is that the \"seed\" region (nucleotides 2-8) is typically sufficient to mediate target recognition and repression. However, numerous recent studies have challenged this model, either by demonstrating extensive 3 pairing between physically defined miRNA-mRNA pairs or by showing in C. elegans that disrupted 3 pairing can result in impaired function in vivo. To test the importance of miRNA 3 pairing in a mammalian system in vivo, we engineered a mutant murine mir-146a allele in which the 5 half of the mature microRNA retains its wild-type sequence, but the 3 half has been altered to be anti-complementary. Mice homozygous or hemizygous for this mutant allele are phenotypically indistinguishable from wild-type controls and do not recapitulate any of the immunopathology previously described for mir-146a-null mice. Our results indicate that 3 pairing is dispensable for the established myeloid function of this key mammalian microRNA.\n\nSummary Blurb3 sequence identity is dispensable for the established function of a mammalian miRNA in vivo.

genetics↗

bioRxiv2018

Genetic predisposition to hypouricemia on whole-exome sequencing analysis and its utilities in primary screening purposes

Differentiating between inherited renal hypouricemia and transient hypouricemia is challenging. Here, we aimed to describe the genetic predisposition of hypouricemia patients using whole-exome sequencing (WES) and assess the feasibility for genetic diagnosis in primary screening. WES was performed for the discovery of diagnostic markers in discovery cohorts (N=31). Two known genetic markers SLC22A12 c.774G>A (p.Trp258*) and SLC22A12 c.269G>A (p.Arg90His) were identified, We genotyped for the 2 SLC22A12 SNPs among screened 50 hypouricemia subjects for the replication cohorts; 47 carried known SLC22A12 markers; three unexplained hypouricemic cases were analyzed by using WES. We used 46 healthy internal controls for the variant discovery. Four novel variants of SLC22A12, c.408C>A (p.Asn136Lys), c.674C>A (p.Thr225Lys), c.851G>A (p.Arg284Gln), and c.1285G>A (p.Glu429Lys), and one novel variant of SLC2A9, c. 376A>G (p.Met155Val), were identified. After filtering out known genes (SLC22A12 and SLC2A9), the p.Arg78His variant in ASB12 was overlapped in two unexplained conditions. This is the first attempt to investigate the effectiveness of integrating exome sequencing and genotype into the clinical care for hypouricemia and determine the value of genetic diagnostic screening for hypouricemia in the clinical setting. Screening of just two SNPs (p.Trp258* and p.Arg90His) identified 87.7% (71/81) of patients with hypouricemia. Early identification and intervention of hypouricemia is feasible using genetic screening to prevent acute kidney injury, especially for soldiers and athletics.

genetics↗