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Biology subjects

Kim, G. D.

Publications and source records attributed to Kim, G. D..

3 recordsLinked to original sources

Single-Cell Analysis of Non-Functioning Gonadotroph Tumors Identifies Lineage Infidelity and Tumor Growth Programs

Non-functioning gonadotroph (NFG) tumors are the most common type of non-functioning pituitary adenomas and can cause significant symptoms due to mass effect. However, the molecular programs underlying NFG tumor growth and their relationship to the normal anterior pituitary gland (APG) are poorly understood. To gain a deeper understanding of NFG tumor biology in the context of the normal APG, we performed single-cell/nucleus RNA-sequencing on 21 NFG tumors and 8 APG samples in human, generating the largest transcriptomic dataset of its kind. Single-cell/nucleus sequencing yielded 77,342, cells from APG samples and 152,649 cells from NFG tumor samples. Differential expression analysis within the APG identified novel marker genes of each neuroendocrine cell type and defined distinct transcriptional signatures of anterior and posterior pituitary stem cell populations. Comparison with tumor transcriptomes revealed that NFG tumor cells most closely resemble gonadotrophs, while also showing significant enrichment of thyrotroph and somatotroph markers. Notably, there was no significant overlap with stem cell markers, suggesting that NFG tumors most likely originate from differentiated gonadotrophs but can take on transcriptional characteristics of other neuroendocrine cell types. Pseudobulk profiling of NFG tumor cells demonstrated that tumor volume is significantly positively correlated with 83 genes including known oncogenes CAD, BRF2, and SOX12, along with 16 zinc finger transcription factors. Lastly, analysis of the tumor microenvironment revealed proportional increases in myeloid, endothelial, and mural cell populations in tumor samples compared to APG samples and highlighted cross talk between tumor and endothelial, mesenchymal, and immune populations via VEGF, PDGF, and MIF signaling pathways respectively. This study identifies novel transcriptomic signatures determining cell type identity in both APG neuroendocrine and NFG tumor cells. Our findings elucidate molecular programs driving NFG lineage infidelity and tumor growth, highlighting candidate predictors of patient outcomes and potential targets for therapeutic intervention.

cancer biology↗

Integrated spatial and single-cell transcriptomic analysis of aggressive glioblastoma growth dynamics.

Glioblastoma (GBM) develops within a complex tumor ecosystem whose temporal dynamics remain poorly understood. Here, we performed longitudinal single-cell RNA sequencing and spatial transcriptomics across multiple timepoints in two widely used murine GBM models - CT2A and GL261 - which differ markedly in aggressiveness and response to immune checkpoint blockade. Tumor cell transcriptomes revealed model-specific programs: CT2A cells progressively upregulated epithelial-mesenchymal transition (EMT), non-classical MHC Class I, and progressively, hypoxia response pathways, resembling the human mesenchymal GBM cell state, while GL261 cells exhibited MHC Class II expression and developmental signatures resembling oligodendrocyte progenitor and astrocytic states. Ligand-receptor interaction analyses identified thrombospondins (Thbs1, Thbs2) and osteopontin (Spp1) as CT2A-specific tumor ligands mediating tumorigenic interactions with immune cells, with downstream targets enriched for EMT and TGF-{beta} pathways. Conversely, the GL261 model presented a differential potential to engage neuronal and perivascular guidance networks, with Glutamate and L1 cell adhesion molecule (L1cam) as lead signaling partners. The CT2A immune compartment exhibited progressive microglia-to-macrophage phenotypic conversion, enhanced macrophage infiltration driven by Spp1, and elevated T cell exhaustion, while GL261 maintained a distinct adaptive immune communication hub via MHC class II-CD4 signaling. Elevated THBS1, THBS2, and SPP1 expression correlated with poor survival in human GBM datasets. Together, these findings reveal divergent tumor-immune ecosystems in CT2A and GL261 that recapitulate distinct aspects of human GBM, with implications for therapeutic targeting.

cancer biology↗

LLM-Assisted Functional Gene Annotation

Functional gene annotation is a highly manual and subjective process that requires analysis of large amounts of statistical and literature results. Here we present Artificial Intelligence Gene Enrichment (AIGE), a careful automation of the current state of the art process used by bioinformatic experts to deter-mine functions enriched in novel or experimentally derived gene lists. In 1206 test cases, AIGE is able to accurately recover 87% of biological functions, path-ways, and cell types, is robust to noise contamination, and accurately assesses its own self-confidence. AIGE reports provide accurate functional annotations and encourage further research in a broad set of contexts.

bioinformatics↗