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Biology subjects

Komkova, D.

Publications and source records attributed to Komkova, D..

3 recordsLinked to original sources

Cell-of-Origin, not Oncogenic Effect, Determines esmoplastic Immune Exclusion in KRAS-Driven Liver Cancer

Intrahepatic cholangiocarcinoma (iCCA) and hepatocellular carcinoma (HCC) are the two most common primary liver cancers and share common risk factors. Yet they exhibit distinct oncogenic driver landscapes and fundamentally different tumor microenvironments (TME), with iCCA characterised by dense desmoplastic stroma that limits therapeutic efficacy. Whether these differences reflect oncogenic context or the developmental lineage of the cancer cell has remained unresolved. Here, using syngeneic orthotopic murine models derived from CRISPR-engineered cholangiocyte and hepatocyte organoids each carrying Trp53 deletion and KrasG12D mutation, we show that cell-of-origin, not oncogenic pathway activation, is the dominant determinant of TME architecture. Spatial proteomics of [~]390,000 cells reveals that cholangiocyte-derived tumors develop a stromal barrier of peripherally enriched SMA+ cancer-associated fibroblasts (CAFs) that physically excludes immune cells and elevates PD-1/PD-L1 engagement, whereas hepatocyte derived tumors permit broader immune infiltration. Transcriptional variance partitioning confirms lineage as the primary source of gene expression divergence. Integrating murine and human transcriptomic and secretomic datasets, we identify LAMC2 and uPA as cholangiocyte lineage-specific secreted factors that trigger CAF activation. Genetic deletion of either factor markedly impairs iCCA formation in vivo. These findings establish that lineage-encoded secretory programmes create a desmoplastic and immune-excluded stroma and identify LAMC2 and uPA as functionally relevant modulators of TME in KRAS-driven iCCA.

cancer biology↗

Contextualizing Models: Deriving a Kinetic Model of Cancer Metabolism including Growth via Stoichiometric Reduction

Genome-scale metabolic models (GEMs) offer unprecedented possibilities to study human metabolism, including alterations in cancers. Yet, analyses of GEMs still entail several disadvantages. In particular, constraint-based methods, such as flux balance analysis, are typically restricted to analyse steady-state flux distributions. In contrast, kinetic models based on ordinary differential equations allow assessment of regulatory properties and dynamics. Building kinetic models, however, is still hampered by the lack of knowledge about kinetic parameters and is typically focused on individual pathways. Here, we present an approach to derive kinetic models of metabolism augmented by coarse-grained overall reactions that represent the remaining cellular metabolism and biosynthetic processes. Using algorithmic network reduction, we derive coarse-grained reactions that preserve the correct stoichiometry of precursors, energy, and redox equivalents required for cellular growth. Analysis of the GEM-embedded kinetic model uses Monte Carlo sampling to address parameter uncertainty. We exemplify our approach by constructing a kinetic model of cancer metabolism that includes an explicit description of cellular growth. We show that the GEM-embedded kinetic model differs in its control properties from the corresponding model without growth, with implications for understanding regulatory hotspots and drug target identification.

systems biology↗

A quantitative description of light-limited cyanobacterial growth using flux balance analysis

The metabolism of phototrophic cyanobacterial is an integral part of global biogeochemical cycles, and the capability of cyanobacteria to assimilate atmospheric CO2 into organic carbon has manifold potential applications for a sustainable biotechnology. To elucidate the properties of cyanobacterial metabolism and growth, computational reconstructions of the genome-scale metabolic networks play an increasingly important role. Here, we present an updated reconstruction of the metabolic network of the cyanobacterium Synechocystis sp. PCC 6803 and its analysis using flux balance analysis (FBA). To overcome limitations of conventional FBA, and to allow for the integration of quantitative experimental analyses, we develop a novel approach to describe light absorption and light utilization. Our approach incorporates photoinhibition and a variable quantum yield into the constraint-based description of light-limited phototrophic growth. We show that the resulting model is capable to predict quantitative properties of cyanobacterial growth, including photosynthetic oxygen evolution and the ATP/NADPH ratio required for growth and cellular maintenance. Our approach retains the computational and conceptual simplicity of FBA and is readily applicable to other phototropic microorganisms.

systems biology↗