Biology subjects

Kim, K.-J.

Publications and source records attributed to Kim, K.-J..

7 recordsLinked to original sources

bioRxiv2025

VEGFR2 blockade overcomes acquired KRAS G12D inhibitor resistance driven by PI3Kγ activation

KRAS G12D mutation is a key oncogenic driver in many solid tumors, including pancreatic, gastric, and colorectal cancers. While recent studies have characterized features associated with primary and acquired resistance to KRAS inhibitors, strategies to overcome such resistance, particularly in the context of gastrointestinal cancers, remain underexplored. Here, we have generated nine human gastrointestinal cancer models, including three patient-derived organoids (PDOs), with acquired resistance to the KRAS G12D-selective inhibitor MRTX1133. Using single-cell RNA sequencing analysis, we identified the enrichment of angiogenesis, hypoxia, and epithelial-to-mesenchymal transition (EMT) signatures in the resistant model compared to the parental PDO. Across all resistant models, VEGFA expression and VEGFR2 phosphorylation were uniformly elevated, which were driven by AKT activation and SP1 nuclear translocation. Mechanistic investigations uncovered increased PI3K{gamma} activity in MRTX1133-resistant models via complex formation of KRAS with p110{gamma} and p101. This leads to an autocrine VEGFA-VEGFR2 signaling loop formation and EMT induction. Therapeutically, the disruption of VEGFA-VEGFR2 signaling restored MRTX1133 sensitivity and inhibited EMT. Furthermore, cancer-endothelial paracrine signaling amplified angiogenesis, hypoxia, and EMT signatures in cancer cells and simultaneously promoted endothelial cell proliferation, reinforcing an adaptive feedback mechanism. In a mouse model of MRTX1133-resistant tumor xenograft, a combination of anti-VEGFR2 therapy and MRTX1133 more effectively reduced tumor growth, angiogenesis, and proliferation markers than monotherapy without significant body weight change. These findings establish VEGFA-VEGFR2 signaling by PI3K{gamma} activation as a key driver of acquired resistance to KRAS G12D inhibition and provide a rationale for combining VEGFA-VEGFR2 inhibition with KRAS blockade in KRAS-mutant cancers. HighlightO_LIVEGFA-VEGFR2 signaling activation is a common feature of MRTX1133 resistance in KRASG12D cancer cells C_LIO_LINuclear translocation of SP1 by AKT activation promotes VEGFA transcription in MRTX1133-resistant models C_LIO_LIInteraction of p110{gamma}-p101 with KRAS activates PI3K{gamma} in the resistant models C_LIO_LIVEGFA-VEGFR2 inhibition reverses MRTX1133 resistance in vitro and in vivo C_LI

cancer biology↗

bioRxiv2024

Loss of p21-activated kinase 4 (PAK4) suppresses pancreatic tumor progression and metastasis through regulating E-cadherin

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a poor prognosis with early and frequent metastasis. While p21-activated kinase 4 (PAK4) has been implicated in cell migration, and invasion, the molecular mechanisms in PDAC remain unknown. In this study, we found that PAK4 overexpression was correlated with poor survival in PDAC patients through analysis of TCGA data. PAK4-amplified PDAC cells showed enhanced mobility in contrast with wild-type. PAK4 knockdown in PAK4 amplified cells inhibited cell migration, invasion, and displacement by increased and stabilized E-cadherin, which was attributed to decreased activity of Cdc42. PAK4 knock-in in PAK4 wild-type models enhanced cell migration, invasion, and displacement by reduced E-cadherin through elevated Cdc42 activity. PAK4 bounded to E-cadherin, Cdc42, and p120ctn in immunoprecipitation. In confocal imaging, the colocalization of PAK4, E-cadherin, p120ctn, and Cdc42 was also identified. In an orthotopic PDAC mouse model, PAK4 knockdown decreased primary tumor size and occurrence of malignant ascites by activation of E-cadherin. Notably, in patients tissue specimens, inverse correlation on expression of PAK4 and E-cadherin were also shown. In conclusion, our study highlights that PAK4 promotes invasive and metastatic behavior by regulating E-cadherin in PDAC. PAK4 could be a potential therapeutic target for PDAC patients. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/594599v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1755a23org.highwire.dtl.DTLVardef@170b2a1org.highwire.dtl.DTLVardef@1df96edorg.highwire.dtl.DTLVardef@2dc46b_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Landscape profiling of PET depolymerases using a natural sequence cluster framework

Since the demonstration that rapid polyethylene terephthalate (PET) decomposition using enzymes is feasible, a number of efficient depolymerases have been reported with the aim of resolving the plastic pollution issues. However, sporadic studies on enzymes with PET hydrolysis activity hinder the understanding of the distribution of potential PETases hidden in natures repertoire, and subsequently, the identification of potent enzymes. Here, we present the clustering of 1,894 PETase candidates, which include the majority of known PETases, and describe their profiling. An archipelago landscape of 170 lineages shows distribution of 289 representative sequences with features associated with PET-degrading capabilities. A birds-eye view of the landscape identifies three highly promising yet unexplored PETase lineages and two potent PETases, Mipa-P and Kubu-P. The engineered Mipa-PM19 and Kubu-PM12 variants exhibit both high PET hydrolysis activity and thermal stability. In particular, Kubu-PM12 outperformed the engineered benchmarks in terms of PET depolymerization in harsh environments, such as with high substrate load and ethylene glycol as the solvent. Our landscape framework and the identified variants assist in the understanding of how biological processes respond to solid-state and non-natural PET plastics.

systems biology↗

bioRxiv2022

Balance-directed protein engineering of IsPETase enhances both PET hydrolysis activity and thermostability

A mesophilic PETase from Ideonella sakaiensis (IsPETase) has been shown to exhibit high PET hydrolysis activity, but its low thermostability limits its industrial applications. We herein developed an engineering strategy for IsPETase to enhance PET hydrolysis activity, thermostability, and protein folding of the enzyme. Balance-directed Z1-PETase variant outperforms the stability-directed Z2-PETase variant under both mesophilic and thermophilic conditions, although Z2-PETase exhibits higher thermostability than Z1-PETase. The Z1-PETase is also superior to Fast-PETase, Dura-PETase, and LC-CICCG in terms of depolymerization rate regardless of temperature conditions we tested. Thus, maintaining a balance between PET hydrolysis activity and thermostability is essential for the development of high-performance PET hydrolases. In a pH-stat bioreactor, Z1-PETase depolymerized >90% of both transparent and colored post-consumer PET powders within 24 and 8 hours at 40{degrees}C and 55{degrees}C, respectively, demonstrating that the balance-directed IsPETase variant produced herein may be applicable in the bio-recycling of PET.

bioengineering↗

bioRxiv2022

Gallbladder adenocarcinomas undergo subclonal diversification and selection from precancerous lesions to metastatic tumors

We aimed to elucidate the evolutionary trajectories of gallbladder adenocarcinoma (GBAC) using multi-regional and longitudinal tumor samples. Using whole-exome sequencing data, we constructed phylogenetic trees in each patient, and analyzed mutational signatures. A total of 11 patients including 2 rapid autopsy cases were enrolled. The most frequently altered gene in primary tumors was ERBB2 (54.5%), followed by TP53 (45.5%), and FBXW7 (27.3%). Most mutations in frequently altered genes in primary tumors were detectable in concurrent precancerous lesions (biliary intraepithelial neoplasia, BilIN), but some of them were subclonal. Subclonal diversity was common in BilIN (n=4). However, among subclones in BilIN, a certain subclone commonly shrank in concurrent primary tumors. In addition, selected subclones underwent linear and branching evolution, maintaining subclonal diversity. In combined analysis with metastatic tumors (n=11), branching evolution was identified in 9 (81.8%) patients. Of these, 8 patients (88.9%) had a total of 11 subclones expanded at least 7-fold during metastasis. These subclones harbored putative metastasis-driving mutations in tumor suppressor genes such as SMAD4, ROBO1, and DICER1. In mutational signature analysis, 6 mutational signatures were identified: 1, 3, 7, 13, 22, and 24 (cosine similarity >0.9). Signatures 1 (age) and 13 (APOBEC) decreased during metastasis while signatures 22 (aristolochic acid) and 24 (aflatoxin) were relatively highlighted. Subclonal diversity arose early in precancerous lesions and the clonal selection was a common event during malignant transformation in GBAC. However, selected cancer clones continued to evolve and thus maintained subclonal diversity in metastatic tumors.

genetics↗

bioRxiv2018

A network-based model for drug repurposing in Rheumatoid Arthritis

BackgroundThe identification of drug repositioning targets through computational methods has the potential to provide a fast, inexpensive alternative to traditional drug discovery process. Diseases where complicated pathophysiology evolves over time are excellent targets for such methods. One such disease is Rheumatoid Arthritis (RA), a chronic inflammatory autoimmune disease, where the drug survival at 5-years is less than 50% for patients treated with disease-modifying anti-rheumatic drugs (DMARDs).\n\nMethods and FindingsWe have developed a network-based approach for drug repositioning that takes into account the human interactome network, proximity measures between drug targets and disease-associated genes, potential side-effects, genome-wide gene expression and disease modules that emerge through pertinent analysis. We found that all DMARDs, except for hydroxychloroquine (HCQ), were found to be significantly proximal to RA-related genes. Application of the method on anti-diabetic agents, statins and H2 receptor blockers identified anti-diabetic agents - gliclazide, sitagliptin and metformin - that have similar network signatures with the DMARDs. Subsequent in-vitro experiments on mouse fibroblast NIH-3T3 cells validated the findings and the down regulation of six key RA-related inflammatory genes. Our analysis further argues that the combination of HCQ and/or sulfasalazine with methotrexate (MTX) is predicted to have an additive synergistic effect in treatment based on network complementarity. Similarly, leflunomide and tofacitinib were found to be suitable alternatives upon chemoresistance to MTX-based double/triple therapy, given the complementary network signatures and overlapping critical target hubs.\n\nConclusionsOur results corroborate that computational methods that are based on network proximity, among other contextual information can help narrow down the drug candidates for drug repositioning, as well as support decisions for combinatorial drug treatment that is tailored to patients needs.\n\nAuthor summaryThe network-based proximity between drug targets and disease genes can provide novel insights on the repercussion, interplay, and reposition of drugs in the context of disease. Disease-modifying anti-rheumatic drugs (DMARDs) located significantly close to rheumatoid arthritis (RA)-associated genes and RA-relevant pathways. Three anti-diabetic agents were identified to have an anti-inflammatory effect like DMARDs. We built RA disease module encompassing the emerging small-molecule targets and functional neighbors, which better explained the RA pathophysiology. By proximity and network robustness, tofacitinib and tocilizumab were the most potent for RA disease module as a single agent, and this is consistent with clinical observation. Side effects of clinical importance are predictable by measuring network-based proximity between drug targets and side effect protein. Network-based drug-disease proximity offers a novel and clinically actionable information about drugs and opens the new possibility to drug combination and reposition.

systems biology↗

bioRxiv2018

Compendium of Synovial Signatures Identifies Pathologic Characteristics for Predicting Treatment Response in Rheumatoid Arthritis

Treatment of patients with rheumatoid arthritis (RA) is challenging due to clinical heterogeneity and variability. Integration of RA synovial genome-scale transcriptomic profiling of different patient cohorts can provide insights on the causal basis of drug responses. A normalized compendium was built that consists of 256 RA synovial samples that cover an intersection of 11,769 genes from 11 datasets. Differentially expression genes (DEGs) that were identified in three independent methods were fed into functional network analysis, with subsequent grouping of the samples based on a non-negative matrix factorization method. Finally, we built a predictive model for treatment response by using RA-relevant pathway activation scores and four machine learning classification techniques. We identified 876 up-regulated DEGs including 24 known genetic risk factors and 8 drug targets. DEG-based subgrouping revealed 3 distinct RA patient clusters with distinct activity signatures for RA-relevant pathways. In the case of infliximab, we constructed a classifier of drug response that was highly accurate with an AUC/AUPR of 0.92/0.86. The most informative pathways in achieving this performance were the NF{kappa}B-, Fc{varepsilon}RI- TCR-, and TNF signaling pathways. Similarly, the expression of the HMMR, PRPF4B, EVI2A, RAB27A, MALT1, SNX6, and IFIH1 genes contributed in predicting the patient outcome. Construction and analysis of normalized synovial transcriptomic compendia can provide useful insights for understanding RA-related pathway involvement and drug responses for individual patients. The efficacy of a predictive model for personalized drug response has been demonstrated and can be generalized to several drugs, co-morbidities, and other relevant features.

bioinformatics↗