Biology subjects

Kim, Y. M.

Publications and source records attributed to Kim, Y. M..

5 recordsLinked to original sources

bioRxiv2025

Primary Cilia Dysfunction in Brown Fat Results in Fatal Thermogenesis Failure in Neonatal Mice

Primary cilium, microtubule-based sensory organelle that has emerged as a central player in coordinating numerous signaling pathways. Although primary cilia are known to regulate cellular signaling and energy metabolism, the relationship between these roles in the context of ciliopathies and brown adipose tissue (BAT) dysfunction remains poorly understood. To elucidate the role of primary cilia in BAT, we generated Ucp1-Cre;Ift88flox/flox mice with BAT-specific ciliary loss. There were no significant differences in body size, weight, or BAT weight relative to body weight between P0 Ucp1-Cre;Ift88flox/flox and P0 Ucp1-Cre;Ift88+/+ pups. Embryos examined at E14.5 and E18.5 also showed no discernible differences in size, morphology, or histology. However, P0 Ucp1-Cre;Ift88flox/flox pups exhibited neonatal lethality caused by defective thermogenesis, despite preserved Ucp1 expression. These mice displayed markedly reduced ketone body levels in both BAT and serum, accompanied by downregulation of Hmgcs2, a key enzyme in ketogenesis. Loss of primary cilia in BAT suppressed ketogenesis and increased ROS production through HMGCS2 downregulation, ultimately impairing non-shivering thermogenesis. Remarkably, neonatal lethality of Ucp1-Cre;Ift88flox/flox pups was completely rescued by thermoneutral housing or {beta}-hydroxybutyrate supplementation. Our findings identified a previously unrecognized mechanism by which primary cilia regulate non-shivering, UCP1-independent thermogenesis via ketogenesis.

cell biology↗

bioRxiv2025

Redirecting cytotoxic lymphocytes to breast cancer tumors via metabolite-sensing receptors

Insufficient infiltration of cytotoxic lymphocytes to solid tumors limits the efficacy of immunotherapies and cell therapies. Here, we report a programmable mechanism to mobilize Natural Killer (NK) and T cells to breast cancer tumors by engineering these cells to express orphan and metabolite-sensing G protein-coupled receptors (GPCRs). First, in vivo and in vitro CRISPR activation screens in NK-92 cells identified GPR183, GPR84, GPR34, GPR18, FPR3, and LPAR2 as top enhancers of both tumor infiltration and chemotaxis to breast cancer. These genes equip NK and T cells with the ability to sense and migrate to chemoattracting metabolites such as 7,25-dihydroxycholesterol and other factors released from breast cancer. Based on Perturb-seq and functional investigations, GPR183 also enhances effector functions, such that engineering NK and CAR NK cells to express GPR183 enhances their ability to migrate to, infiltrate, and control breast cancer tumors. Our study uncovered metabolite-based tumor immune recruitment mechanisms, opening avenues for spatially targeted cell therapies.

immunology↗

bioRxiv2024

Three-dimensional Label-Free Measurements of Adipocyte Differentiation and Lipid Droplet Dynamics

The visualization and tracking of adipocytes and their lipid droplets (LDs) during differentiation are pivotal in developmental biology and regenerative medicine studies. Traditional staining or labeling methods, however, pose significant challenges due to their labor-intensive sample preparation, potential disruption of intrinsic cellular physiology, and limited observation timeframe. This study introduces a novel method for long-term visualization and quantification of biophysical parameters of LDs in unlabeled adipocytes, utilizing the refractive index (RI) distributions of LDs and cells. We employ low-coherence holotomography (HT) to systematically investigate and quantitatively analyze the 42-day redifferentiation process of fat cells into adipocytes. This technique yields three-dimensional, high-resolution refractive tomograms of adipocytes, enabling precise segmentation of LDs based on their elevated RI values. Subsequent automated analysis quantifies the mean concentration, volume, projected area, and dry mass of individual LDs, revealing a gradual increase corresponding with adipocyte maturation. Our findings demonstrate that HT is a potent tool for non-invasively monitoring live adipocyte differentiation and analyzing LD accumulation. This study, therefore, offers valuable insights into adipogenesis and lipid research, establishing HT and image-based analysis as a promising approach in these fields.

biochemistry↗

bioRxiv2023

Beclin1-Deficient Adipocytes Promote Tumor Progression by YAP/TAZ-dependent Adipocyte Transformation

Adipocytes are crucial components of the tumor microenvironment (TME) that play a prominent role in supporting tumor growth. However, the characteristics of cancer-associated adipocytes (CAAs) that contribute to the pro-tumorigenic niche remain to be fully established. Here, we used adipocyte-specific Beclin1 KO (BaKO) mice to investigate the role of maladaptive adipocytes in promoting tumor progression. BECN1-deficient adipocytes exhibited downregulation of adipogenic markers and activation of YAP/TAZ signaling, similar to the traits observed in CAAs. Thus, we generated adipocyte-specific Becn1/Yap1/Taz KO mice, which exhibit markedly restored phenotypes in adipose tissue, resulting in tumor regression compared to that in BaKO. Further, we observed dysregulation of the BECN1-YAP/TAZ axis in the adipose tissue of mice fed a high-fat diet (HFD). Treatment with the YAP/TAZ inhibitor, verteporfin, suppressed tumor progression in BaKO and HFD-fed mice, highlighting its efficacy against mice with metabolic dysregulation. Our findings provide insights into CAA formation and its significance in determining malignant TME, thereby suggesting a potential dual therapeutic strategy simultaneously targeting adipocyte homeostasis and cancer growth.

biochemistry↗

bioRxiv2020

Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy

Traditional imaging cytometry uses fluorescence markers to identify specific structures, but is limited in throughput by the labeling process. Here we develop a label-free technique that alleviates the physical staining and provides highly multiplexed readouts via a deep learning-augmented digital labeling method. We leverage the rich structural information and superior sensitivity in reflectance microscopy and show that digital labeling predicts highly accurate subcellular features after training on immunofluorescence images. We demonstrate up to 3x improvement in the prediction accuracy over the state-of-the-art. Beyond fluorescence prediction, we demonstrate that single-cell level structural phenotypes of cell cycles are correctly reproduced by the digital multiplexed images, including Golgi twins, Golgi haze during mitosis and DNA synthesis. We further show that the multiplexed readouts enable accurate multi-parametric single-cell profiling across a large cell population. Our method can dramatically improve the throughput for imaging cytometry toward applications for phenotyping, pathology, and high-content screening.

bioengineering↗