Biology subjects

Theorell, A.

Publications and source records attributed to Theorell, A..

4 recordsLinked to original sources

bioRxiv2024

Scalable enumeration and sampling of minimal metabolic pathways for organisms and communities

Many interactions in microbial consortia or tissues of multicellular organisms rely on networks of metabolite exchanges. To predict community function and composition beyond statistical correlations, one can use genome-scale metabolic models. However, comprehensive model analysis via metabolic pathways is a major challenge because pathway counts grow combinatorially with model size. Here, we define minimal pathways that yield compact representations of metabolic network capabilities. They generalize existing pathway concepts by allowing inhomogeneous constraints and targeted analysis of subnetworks, and we show how to enumerate and sample them efficiently via iterative minimization and pathway graphs. This enables applications such as assessing quantitative gene essentiality in the central metabolism of Escherichia coli, predicting metabolite exchanges associated with homeostasis and health in a host-microbe model of the human gut, and designing butyrate-producing microbial communities. Minimal pathways enable scalable analysis of metabolic subnetworks such as metabolite exchanges in uni- and multicellular systems.

systems biology↗

bioRxiv2022

Bayesian multi-model-based 13C15N-metabolic flux analysis quantifies carbon-nitrogen metabolism in mycobacteria

Metabolic flux is the final output of cellular regulation and has been extensively studied for carbon but much less is known about nitrogen, which is another important building block for living organisms. For the pathogen Mycobacterium tuberculosis (Mtb), this is particularly important in informing the development of effective drugs targeting Mtbs metabolism. Here we performed 13C15N dual isotopic labelling of mycobacterial steady state cultures and quantified intracellular carbon-nitrogen (CN) and nitrogen (N) fluxes in addition to carbon (C) fluxes and inferred their reaction bidirectionalities. The combination of 13C15N-MFA with a Bayesian multi-model approach allowed us to resolve C and N fluxes simultaneously which was not possible with classical 13C-MFA. We quantified CN fluxes for amino acid and, for the first time, nucleotide biosynthesis. Our analysis identified glutamate as the central CN and N node in mycobacteria, and improved resolution of the anaplerotic node. Our study describes a powerful platform to measure carbon and nitrogen metabolism in any biological system with statistical rigor.

systems biology↗

bioRxiv2019

A mixed nitrogen diet and compartmentalized utilization for Mycobacterium tuberculosis replicating in host cells: results of a systems-based analysis

Nitrogen metabolism of Mycobacterium tuberculosis (Mtb) is crucial for the survival and virulence of this pathogen inside host macrophages but little is known about the nitrogen sources acquired from the host or their route of assimilation. Here we performed a systems-based analysis of nitrogen metabolism in intracellullar Mtb and developed 15N-Flux Spectral Ratio Analysis (FSRA) to probe the metabolic cross-talk between the host cell and Mtb. We demonstrate that intracellular Mtb acquires nitrogen from multiple amino acids in the macrophage including glutamate, glutamine, aspartate, alanine, glycine and valine, with glutamine being the predominant nitrogen donor. Each nitrogen source is uniquely assimilated into specific intracellular pools indicating compartmentalised metabolism. This was not observed for in vitro-grown Mtb indicating that there is a switch in nitrogen metabolism when the pathogen enters the intracellular environment. These results provide clues about the potential metabolic targets for development of innovative anti-tuberculosis therapies.

microbiology↗

bioRxiv2018

Determination of essential phenotypic elements of clusters in high-dimensional entities - DEPECHE

Technological advances have facilitated an exponential increase in the amount of information that can be derived from single cells, necessitating new computational tools that can make this highly complex data interpretable. Here, we introduce DEPECHE, a rapid, parameter free, sparse k-means-based algorithm for clustering of multi-and megavariate single-cell data. In a number of computational benchmarks aimed at evaluating the capacity to form biologically relevant clusters, including flow/mass-cytometry and single cell RNA sequencing data sets with manually curated gold standard solutions, DEPECHE clusters as well or better as the best performing state-of-the-art clustering algorithms. However, the main advantage of DEPECHE, compared to the state-of-the-art, is its unique ability to enhance interpretability of the formed clusters, in that it only retains variables relevant for cluster separation, thereby facilitating computational efficient analyses as well as understanding of complex datasets. An open source R implementation of DEPECHE is available at https://github.com/theorell/DepecheR.\n\nAuthor summaryDEPECHE-a data-mining algorithm for mega-variate data\n\nModern experimental technologies facilitate an array of single cells measurements, e.g. at the RNA-level, generating enormous datasets with thousands of annotated biological markers for each of thousands of cells. To analyze such datasets, researchers routinely apply automated or semi-automated techniques to order the cells into biologically relevant groups. However, even after such groups have been generated, it is often difficult to interpret the biological meaning of these groups since the definition of each group often dependends on thousands of biological markers. Therefore, in this article, we introduce DEPECHE, an algorithm designed to simultaneously group cells and enhance interpretability of the formed groups. DEPECHE defines groups only with respect to biological markers that contribute significantly to differentiate the cells in the group from the rest of the cells, yielding more succinct group definitions. Using the open source R software DepecheR on RNA sequencing data and mass cytometry data, the number of defining markers were reduced up to 1000-fold, thereby increasing interpretability vastly, while maintaining or improving the biological relevance of the groups formed compared to state-of-the-art algorithms.

bioinformatics↗