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Gustafsson, J.

Publications and source records attributed to Gustafsson, J..

2 recordsLinked to original sources

Cellular limitation of enzymatic capacity explains glutamine addiction in cancers

The tumor microenvironment is comprised of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 potential metabolites that could support collaboration between cancer cells and cancer associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme constrained models for accurate prediction of metabolism in cells and tumor microenvironments.

cancer biology↗

Addressing the pooled amplification paradox with unique molecular identifiers in single-cell RNA-seq

The incorporation of unique molecular identifiers (UMIs) in single-cell RNA-seq assays allows for the removal of amplification bias in the estimation of gene abundances. We show that UMIs can also be used to address a problem resulting from incomplete sequencing of amplified molecules in sequencing libraries that can lead to bias in gene abundance estimates. Our method, called BUTTERFLY, is based on a zero truncated negative binomial estimator and is implemented in the kallisto bustools single-cell RNA-seq workflow. We demonstrate its efficacy using a range of datasets and show that it can invert the relative abundance of certain genes in cases of a pooled amplification paradox.

bioinformatics↗