Biology subjects

Paik, H.

Publications and source records attributed to Paik, H..

9 recordsLinked to original sources

bioRxiv2026

GENOAR: Global Engine for Navigating the Omnicell space via Agent-based Research

Although generative models have revolutionized many fields, including genomics, building a scalable dataset compatible with artificial intelligence (AI) is essential yet underexplored. Here, we developed a Global Engine for navigating the Omnicell space via Agent-based Research (GENOAR) that covers approximately 54 million cells for diverse downstream tasks using AI agents and parallel computing. GENOAR outperforms in three parts. Parallel computations accelerated the preprocessing of single-cell transcriptomic data; the other components enabled agent-assisted data storage and exploration for both AI models and humans. GENOAR involves two AI agents: an AI nomenclaturist ordered the unstructured labels of the single-cell transcriptomic profiles, and an AI librarian retrieved data. GENOAR presents transcriptomic profiles from more than 54 million cells from humans and mice in an analysis-ready format for humans and AI agents. AI agents in GENOAR exhibited successful retrieval performance compared with human experts. Using GENOAR, we elucidated progenitor cell-associated transcriptional signatures in native retinoblastoma and organoid environments. GENOAR was released and validated for the on-demand construction of single-cell atlases for metazoa. In conclusion, using GENOAR, the development of a single-cell transcriptomic atlas compatible with both AI systems and human researchers is feasible, scalable, and reproducible.

bioinformatics↗

bioRxiv2026

GOLDEN: Gene expression and allele-informed decomposition for genetic heterogeneity in spatial transcriptomic data

Motivation Spatial transcriptomics (ST) enables exploration of cellular interactions, including immune responses. However, current methods rely on pathologists, with limitations in addressing genetic heterogeneity, where niches harboring nonclonal cells, including placental or allograft tissues, are present. Deciphering these interindividual mixtures remains challenging. Results For deciphering interindividual heterogeneity in ST, we developed GOLDEN (Gene expressiOn and aLlele-informed DEcomposition for geNetic heterogeneity), which involves an allele-aware multitask network that encodes spot-level expressions into shared latent representations. Incorporating allele features, dual heads classify spot identity (target, nontarget, or mixed) and estimate target fractions. We utilized synthetic ST data with spot-level ground truth using semiallogeneic single-cell profiles. For these synthetic benchmarks, GOLDEN achieved reliable deconvolution, with high correlation (Pearson's r = 0.977) and low error (mean absolute error (MAE) = 0.045) in fraction estimation and consistent predictions across real-world STs. These predictions were genetically validated. Using GOLDEN, resolving spot-level genetic heterogeneity is feasible. Availability and implementation GOLDEN and the simulation code for ST are available at https://github.com/othertics/GOLDEN.

bioinformatics↗

bioRxiv2026

ELiAH2: update of the atlas of E3 ligases in human tissues by integrating tissue-specificity and docking properties of ligands for targeted protein degradation

Targeted protein degradation (TPD), including proteolysis-targeting chimeras (PROTACs), requires coordinated selection of E3 ligases, target proteins, and ligands to achieve efficient and tissue-selective degradation. However, current TPD resources provide limited support for integrating tissue-specific transcriptomic evidence with structural compatibility during early candidate prioritization. Here, we present ELiAH2, an expanded database for transcriptome- and structure-guided prioritization of TPD candidates. ELiAH2 integrates GTEx RNA-seq profiles across 24 human tissues to characterize tissue-specific expression patterns of E3 ligases and target genes. Compared with the previous version, ELiAH, current update presented over 200% of expansion for the number of covered tissues and samples. Previously, E3 ligase-target gene associations were inferred using mutual information-based network analysis, enabling users to explore transcriptomic relationships. To further support structural evaluation, ELiAH2 provides large-scale docking datasets comprising 827,385 E3 ligase-target protein pairs and 7,800,521 protein-ligand docking models, including E3 ligase-ligand and target protein-ligand interactions. Docking scores, ranked interaction tables, gene and ligand identifiers, and downloadable PDB structures are incorporated into the web interface. By combining tissue specificity, E3-target association, protein-protein docking, and protein-ligand docking, ELiAH2 provides a comprehensive resource for narrowing the TPD search space and supporting rational degrader design. ELiAH2 is freely available at https://eliahdb.org.

bioinformatics↗

bioRxiv2025

Unfolded protein response signaling promotes myeloid cell production and cooperates with oncogenic mutation

Unfolded protein response (UPR) promotes protein homeostasis under endoplasmic reticulum stress. UPR signaling has numerous functions in metabolism, cancer, immunology, and neurodegenerative diseases. Recent studies also showed that UPR signaling has important roles in hematopoietic stem and progenitor cell biology. However, whether UPR signaling regulates hematopoietic lineage fate decision remains elusive. Here, we found that Fc{gamma}R- MPP3 generates erythroid lineage and Jak2V617F mutation leads to overproduction of erythroid cells by expanding Fc{gamma}R- MPP3. We showed that UPR signaling increases myeloid cell production through promoting Fc{gamma}R- MPP3 transition to granulocyte/macrophage progenitor producing Fc{gamma}R+ MPP3 at the expense of erythroid lineage via the XBP1 pathway. Under a disease condition, UPR signaling cooperates with Jak2V617F mutation and exacerbates disease phenotype in a mouse model of polycythemia vera (PV) through the ATF4 pathway. Activation of UPR signaling also increased myeloid output in healthy donor bone marrow MPP cells while skewing the output towards erythroid lineage in PV patient bone marrow MPP cells. Together, our results identify a novel function of UPR signaling in hematopoietic lineage specification and provide critical insights into targeting UPR signaling in hematological malignancies. Key pointsO_LIUPR signaling promotes myeloid cell production at the expense of erythroid lineage in steady state. C_LIO_LIUPR signaling collaborates with Jak2V617F mutation and increases red blood cell production. C_LI

cell biology↗

bioRxiv2025

Machine learning-driven decoding of maternal immune signatures in repeated pregnancy loss

BackgroundRepeated pregnancy loss (RPL) is a multifactorial condition in which the underlying immunological mechanisms remain incompletely understood. Although immune tolerance at the maternal-fetal interface is critical for successful pregnancy, the immune disruptions that contribute to RPL, independent of fetal aneuploidy, remain poorly characterized. MethodsWe performed single-cell RNA sequencing of decidual tissues from RPL patients and gestational age-matched controls. We employed single-cell transcriptomics coupled with genotype-based origin analysis to dissect immune dysregulation in RPL. To identify cell type-specific RPL signatures, we mutually applied a supervised machine learning model and foundation model-based analysis, followed by pathway enrichment and network analysis. In addition, we prioritized drug repurposing candidates based on the drug response data, which reversed the RPL-associated transcriptional profiles. ResultsIn normal pregnancy, fetal immune cells were nearly absent but increased in RPL, whereas fetal trophoblasts were reduced, suggesting hindered placental development despite the absence of chromosomal abnormalities in fetal cells. Interestingly, by showing a strong common rejection module score, RPL immune cells resemble the transcriptional signatures of acute transplant rejection, indicating the contribution of the maternal immune system. Our machine learning and transformer-based models mutually identified T-cell-derived transcriptomic signatures that distinguished RPL immune cells. By examining biological confounding factors, including fetal-origin signatures, we prioritized CXCR4 and JUN in maternal T cells as RPL-associated signatures. For clinical application, we explored reversed transcriptional signatures from drug response data, and three compounds were highlighted as candidates for drug repurposing. ConclusionsTogether, our approach identifies maternal immune signatures such as those of CXCR4 and JUN and links them to potential drug repurposing candidates, thereby providing both mechanistic insights and therapeutic opportunities for RPL.

bioinformatics↗

bioRxiv2025

Searching for influencers among placental immune cells in preeclampsia.

Cells in maternal and fetal immune systems may communicate, leading to immune tolerance during pregnancy; however, this hypothesis remains controversial. Here, we profiled single-cell transcriptional signatures in placental layers comprising the maternal-fetal interface and deep placenta, then searched for genes associated with preeclampsia. To investigate the underlying principle of the failure of immune tolerance, we started by clarifying the systemic framework, comprising models of immune interaction frequency (IIF) and specific triggers (i.e., influencers) of tolerance (IT). We generated single-cell transcriptional profiles of normal term (Norms) and preeclampsia preterm (PePT) parturitions. Fetal and maternal cells are admixed across the placenta, for both Norms and PePTs, rejecting the IIF model of immune failure during pregnancy posed by excessive interactions between fetomaternal cells. Whereas placental layers are well mixed with maternal cells, we identified a conserved gradual immune transition of fetal T-cells in both PePT and Norm, disproving the IIF model. To search for influencers of PePT in the IT model, we established and validated a classification model for PePT and Norm immune cells, including T-cells, and then prioritized major contributors to the classifier model, which are highly enriched in ligands and receptors (p = 5.98e-5). Among the prioritized ligand receptors, SPP1 and CD44 are suggested as influencers of inflammation signatures and were experimentally validated by the exclusive colocalization of SPP1- and CD44-expressing cells in the PePT placentas. Different interleukin-4 and interferon-{psi} levels in the serum and urine of PePTs further support the contribution of SPP1 to associated pathways, including allograft rejection. Our findings provide insight into the influence of specific immune interactions between cells in the human placenta and their influencer-derived impact on PePT.

bioinformatics↗

bioRxiv2021

Secretory MPP3 reinforce myeloid differentiation trajectory and amplify myeloid cell production

Recent lineage tracing analyses revealed multipotent progenitors (MPP) to be major functional contributors to steady-state hematopoiesis (1-6). However, we are still lacking a precise resolution of myeloid differentiation trajectories and cellular heterogeneity in MPPs. Here, we found that myeloid-biased MPP3 (2, 3) are functionally and molecularly heterogeneous, with a distinct subset of myeloid-primed secretory cells with high endoplasmic reticulum (ER) volume and Fc{gamma}R expression. We show that Fc{gamma}R+/ERhigh MPP3 are a transitional population for rapid production of granulocyte/macrophage progenitors (GMP), which directly amplify myelopoiesis through inflammation-triggered secretion of cytokines in the local bone marrow (BM) microenvironment. Our results identify a novel regulatory function for a subset of secretory MPP3 that controls myeloid differentiation through lineage-priming and cytokine production, and act as a self-reinforcing amplification compartment in stress and disease conditions. One-Sentence SummaryA secretory subset of multipotent hematopoietic progenitors augment myelopoiesis in stress and diseases conditions.

immunology↗

bioRxiv2018

Tracing diagnosis trajectories over millions of inpatients reveal an unexpected association between schizophrenia and rhabdomyolysis

While it has been technically feasible to create longitudinal representations of individual health at a nationwide scale, the use of these techniques to identify novel disease associations for the risk stratification of patients has had limited success. Here, we created a large-scale US longitudinal disease network of traced readmission patterns (i.e., disease trajectories), merging data from over 10.4 million inpatients from 350 California hospitals through the Healthcare Cost and Utilization Project between 1980 and 2010. We were able to create longitudinal representations of disease progression mapping over 300 common diseases, including the well-known complication of heart failure after acute myocardial infarction. Surprisingly, out of these generated disease trajectories, we discovered an unknown association between schizophrenia, a chronic mental disorder, and rhabdomyolysis, a rare disease of muscle breakdown. It was found that 92 of 3674 patients (2.5%) with schizophrenia were readmitted for rhabdomyolysis (relative risk, 2.21 [1.80-2.71, confidence interval = 0.95] P-value 9.54E-15), which has a general population incidence of 1 in 10,000. We validated this association using independent electronic health records from over 830,000 patients treated over seven years at the University of California, San Francisco (UCSF) medical center. A case review of 29 patients at UCSF who were treated for schizophrenia and who went on to develop rhabdomyolysis demonstrated that the majority of cases (62%) are idiopathic, which suggests a biological connection between these two diseases. Together, these findings demonstrate the power of using public disease registries in combination with electronic medical records to discover novel disease associations.\n\nOne Sentence SummaryBased on the longitudinal health records from millions of California inpatient discharges, we created a temporal network that enabled us to understand statewide patterns of hospital readmissions, which led to the novel finding that hospitalization for schizophrenia is significantly associated with rehospitalization for rhabdomyolysis.

bioinformatics↗

bioRxiv2017

Creating ethnicity-specific reference intervals for lab tests from EHR data

The results of clinical lab tests are an essential component of medical decision-making. To guide interpretation, test results are returned with reference intervals defined by the range in which 95% of values occur in healthy individuals. Clinical laboratories often set their own reference intervals to accommodate local population and instruments variations. This approach is costly and can be biased. We describe a novel data-driven method for using electronic health record data to extract healthy patients information to define reference intervals. We found that the distributions of many clinical lab tests differ among self-identified racial and ethnic groups (SIREs) in healthy patients. Finally, we derived SIRE-specific reference intervals and provide evidence that these intervals have clinical prognostic value. Specifically, we show that for two lab tests, serum creatinine level and hemoglobin A1C, SIRE-specific reference intervals are more predictive for need for dialysis and development type 2 diabetes than existing reference intervals.\n\nOne Sentence SummaryA novel method for defining population-specific reference intervals of common clinical laboratory tests from electronical health records has better prognostic value than existing reference intervals.

bioinformatics↗