Jeong-Hoon Kim's research while affiliated with University of California, San Diego and other places

The potential role of the ghrelin receptor, also known as the growth hormone secretagogue receptor (GHSR), within the nucleus accumbens (NAcc) in regulating drug addiction and feeding has been documented; however, the pattern of its expression in this site remains elusive. In this study, we characterized the expression patterns of GHSR1a and 1b, two subtypes of GHSRs, within the NAcc of the rat brain by immunohistochemistry. We visually detected GHSR signals, for the first time, at the protein level in the NAcc in which they were mostly expressed in neurons including both medium spiny neurons (MSNs) and non-MSNs. Furthermore, GHSR1a was found expressed as localized near the cellular membrane or some in the cytoplasm, whereas GHSR1b expressed solely throughout the large cytoplasmic area. The existence and subcellular expression pattern of GHSRs in the NAcc identified in this study will contribute to improving our understanding about the role of GHSR-mediated neurosignaling in feeding and drug addiction.

Zinc dry electrodes were fabricated and investigated for wearable electrophysiology recording. Results from electrochemical impedance spectroscopy and electromyography functionality testing show that zinc electrodes are suitable for use in electrophysiology. Two electrode configurations were tested: a standard disc and a custom tripolar concentric ring configuration. However, no functional benefit was observed with the tripolar concentric ring electrodes as compared to the disc electrodes.

CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.

The integration of big data analytics with cancer research is catalyzing a transformative approach in cancer treatment, primarily focusing on the discovery of novel and efficacious anticancer targets. Our study presents an advanced algorithm, specifically crafted to exploit the extensive data available in the field of oncology. We started with the genomic and clinical information of 8,864 patients with 33 different cancers (TCGA). Then we implemented the following algorithm to discover anti-cancer targets by analyzing the clinical significance (cBioPortal), drug development status (Cortellis), and oncogenicity (DepMap) of candidate genes: Candidate genes = {gene | gene ∈ Genes, [Frequency(gene) > 50, Drug(gene) ∈ {'biological testing', 'preclinical stage'}, Association(gene) ≥ 0.4, Publication(gene) ≤ 200] ∨ [Publication(gene) ≥ 200 ∧ Boolean(gene)]}. We employed this algorithm to analyze fusion genes, which represent promising anti-cancer targets known for their potential to elicit substantial clinical responses, but there is a high demand for new ones. We identified four druggable therapeutic targets out of a total of 15,291 fusion genes through the algorithm: frame-shifted FGFR3-TACC3, in-framed DLK1-RPS11, frame-shifted CHP1-RAD51B, and in-framed TBC1D22A-SMYD3. We conducted in vitro validation studies of these fusion genes in NIH3T3 cell lines, and it confirmed that all of the fusion genes not only produce mRNA and protein levels but also induce oncogenic effects on cellular behavior. In the case of FGFR3-TACC3, the introduced fusion gene induced mRNA (p < 0.05) and protein expression (p < 0.05) even when frame-shifted. In addition, the proliferation rate of transformed cells increased more than 4-fold on day 10 (p < 0.0001) and colony formation increased more than 5-fold on day 21 (p < 0.01) compared to wild-type cells. These results demonstrate the tumorigenicity of the fusion genes. Taken together, this study emphasizes the crucial role of big data in propelling oncology research forward. The algorithm we developed can offer a new pathway for creating innovative cancer treatment, marking a significant advancement in the realm of personalized cancer therapy. Citation Format: Dooho Kim, Jong Woo Park, Jung-Ae Kim, Jeong-Hoon Kim, Tae Sub Park, Joonghoon Park. Big data-driven discovery of novel oncogenic fusion genes for anticancer therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5954.

Molecular glue degraders (MGDs) are emerging as innovative therapeutic modalities, owing to their efficacy in targeting previously undruggable targets. MGDs induce close proximity between the target protein and E3 ligase, resulting in the degradation of the target protein. In the wake of the remarkable success of PD-1/PD-L1 inhibitors, various approaches to modulate the activity of regulatory T cells (Tregs) activity have been explored. Tregs represent the primary immune suppressor and significantly impede antitumor immune responses. IKZF2, a marker of stable suppressive Treg, is essential for maintaining the stable Treg cell phenotype. Depletion of IKZF2 can induce a transition from Treg to effector T cell (Teff) phenotypes, thereby enhancing anti-tumor responses. Leveraging an Immunomodulatory drug (IMiD), Novartis has developed the IKZF2 degrader DKY709, which is currently undergoing Phase I clinical trials, both as a monotherapy and in combination with a PD-1 immune checkpoint inhibitor. In this presentation, we present the preclinical results of the best-in-class IKZF2 degrader, PRT-101. PRT-101 elicits rapid and robust degradation of IKZF2, demonstrating subnanomolar DC50 and achieving 100% Dmax in a proteasome- and CRBN-dependent manner. However, PRT-101 does not induce the degradation of well-known neosubstrates, including IKZF1, IKZF3, SALL4, and CK1a. Global proteomic analysis reveals that PRT-101 exclusively facilitates IKZF2 degradation. The degradation of IKZF2 by PRT-101results in an increase in IL-2 secretion, a marker of T effector function. Furthermore, oral administration of PRT-101 exhibits excellent pharmacokinetics, concurrent with IKZF2 degradation, yielding superior anti-tumor effects compared to DKY709 in the MC38 mouse syngeneic model. In conclusion, our findings underscore the potential of the novel IKZF2 degrader as a promising immuno-oncology target for the treatment of solid tumors. Citation Format: Seulki Park, Jeong Hee Moon, Gaseul Lee, Hyun Jin Kim, Jeong-Hoon Kim, Jong Yeon Hwang. Discovery of highly potent, selective, and orally bioavailable IKZF2 degrader and its anti-tumor activity in syngeneic mouse models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4512.

A fully depleted silicon‐on‐insulator (FDSOI) metal‐oxide‐semiconductor field‐effect transistors (MOSFETs) device is investigated as for an electronic synapse emulating the synaptic functions of the human brain with stable characteristics. Gate‐last processed FDSOI MOSFET with a high‐k/metal gate stack features a memory window of 10³. Synaptic conductance is stably regulated by utilizing the FDSOI MOSFET, which offers the advantage of mitigating leakage current when compared to bulk Si MOSFET. Short‐ and long‐term plasticity are investigated by applying engineered pulse, verifying the long‐term synaptic properties of pattern recognition processes. With controllable synaptic conductance, the trade‐off between conductance change and linearity regarding the recognition rate is evaluated, attaining a recognition rate of 0.83. To verify the pre‐ and post‐synaptic weights within a real hardware‐based neuromorphic system, 5 × 6 FDSOI field‐effect transistor (FET) synapse array is interconnected to 10 × 10 leaky integrate‐and‐fire (LIF) neuron array. The synaptic plasticity of FDSOI MOSFET in post‐neurons following neuron firing in the neuron device is successfully demonstrated. These results indicate that FDSOI MOSFET devices could be applicable as synapse devices due to controllability and capability to realize signal transmissions and self‐learning processes simultaneously and used to mimic a synapse neuron network system by configuring a hardware system interconnected with the LIF neuron.

Optically transparent neural microelectrodes have facilitated simultaneous electrophysiological recordings from the brain surface with the optical imaging and stimulation of neural activity. A remaining challenge is to scale down the electrode dimensions to the single-cell size and increase the density to record neural activity with high spatial resolution across large areas to capture nonlinear neural dynamics. Here we developed transparent graphene microelectrodes with ultrasmall openings and a large, transparent recording area without any gold extensions in the field of view with high-density microelectrode arrays up to 256 channels. We used platinum nanoparticles to overcome the quantum capacitance limit of graphene and to scale down the microelectrode diameter to 20 µm. An interlayer-doped double-layer graphene was introduced to prevent open-circuit failures. We conducted multimodal experiments, combining the recordings of cortical potentials of microelectrode arrays with two-photon calcium imaging of the mouse visual cortex. Our results revealed that visually evoked responses are spatially localized for high-frequency bands, particularly for the multiunit activity band. The multiunit activity power was found to be correlated with cellular calcium activity. Leveraging this, we employed dimensionality reduction techniques and neural networks to demonstrate that single-cell and average calcium activities can be decoded from surface potentials recorded by high-density transparent graphene arrays.