Biology subjects

Zanghellini, J.

Publications and source records attributed to Zanghellini, J..

26 records · Page 2Linked to original sources

bioRxiv2022

Probabilistic quotient's work and pharmacokinetics' contribution: countering size effect in metabolic time series measurements

Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, many sampling methods suffer from unknown sample sizes commonly known as size effects. This prevents absolute quantification of biomarkers. Recently, several mathematical post acquisition normalization methods have been developed to overcome these problems either by exploiting already known pharmacokinetic information or by statistical means. Here we present an improved normalization method, MIX, that combines the advantages of both approaches. It couples two normalization terms, one based on a pharmacokinetic model (PKM) and the other representing a popular statistical approach, probabilistic quotient normalization (PQN), in a single model. To test the performance of MIX, we generated synthetic data closely resembling real finger sweat metabolome measurements. We show that MIX normalization successfully tackles key weaknesses of the individual strategies: it (i) reduces the risk of overfitting with PKM, and (ii) contrary to PQN, it allows to compute sample volumes. Finally, we validate MIX by using real finger sweat as well as blood plasma metabolome data and demonstrate that MIX allows to better and more robustly correct for size effects. In conclusion, the MIX method improves the reliability and robustness of quantitative biomarker detection in finger sweat and other biofluids, paving the way for biomarker discovery and hypothesis generation from metabolomic time course data.

bioinformatics↗

bioRxiv2021

Elementary vectors and autocatalytic sets for computational models of cellular growth

Traditional (genome-scale) metabolic models of cellular growth involve an approximate biomass "reaction", which specifies biomass composition in terms of precursor metabolites (such as amino acids and nucleotides). On the one hand, biomass composition is often not known exactly and may vary drastically between conditions and strains. On the other hand, the predictions of computational models crucially depend on biomass. Also elementary flux modes (EFMs), which generate the flux cone, depend on the biomass reaction. To better understand cellular phenotypes across growth conditions, we introduce and analyze new classes of elementary vectors for comprehensive (next-generation) metabolic models, involving explicit synthesis reactions for all macromolecules. Elementary growth modes (EGMs) are given by stoichiometry and generate the growth cone. Unlike EFMs, they are not support-minimal, in general, but cannot be decomposed "without cancellations". In models with additional (capacity) constraints, elementary growth vectors (EGVs) generate a growth polyhedron and depend also on growth rate. However, EGMs/EGVs do not depend on the biomass composition. In fact, they cover all possible biomass compositions and can be seen as unbiased versions of elementary flux modes/vectors (EFMs/EFVs) used in traditional models. To relate the new concepts to other branches of theory, we consider autocatalytic sets of reactions. Further, we illustrate our results in a small model of a self-fabricating cell, involving glucose and ammonium uptake, amino acid and lipid synthesis, and the expression of all enzymes and the ribosome itself. In particular, we study the variation of biomass composition as a function of growth rate. In agreement with experimental data, low nitrogen uptake correlates with high carbon (lipid) storage. Author summaryNext-generation, genome-scale metabolic models allow to study the reallocation of cellular resources upon changing environmental conditions, by not only modeling flux distributions, but also expression profiles of the catalyzing proteome. In particular, they do no longer assume a fixed biomass composition. Methods to identify optimal solutions in such comprehensive models exist, however, an unbiased understanding of all feasible allocations is missing so far. Here we develop new concepts, called elementary growth modes and vectors, that provide a generalized definition of minimal pathways, thereby extending classical elementary flux modes (used in traditional models with a fixed biomass composition). The new concepts provide an understanding of all possible flux distributions and of all possible biomass compositions. In other words, elementary growth modes and vectors are the unique functional units in any comprehensive model of cellular growth. As an example, we show that lipid accumulation upon nitrogen starvation is a consequence of resource allocation and does not require active regulation. Our work puts current approaches on a theoretical basis and allows to seamlessly transfer existing workflows (e.g. for the design of cell factories) to next-generation metabolic models.

systems biology↗

bioRxiv2021

PeakBot: Machine learning based chromatographic peak picking

MotivationChromatographic peak picking is among the first steps in data processing workflows of raw LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Random noise, non-baseline separated compounds and unspecific background signals complicate this task. ResultsA machine-learning framework entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention-time vs. m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBots architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box. In training and independent validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (accuracy of 0.99). For training the machine-learning model a minimum of 100 reference features are needed to learn their characteristics to achieve high-quality peak-picking results for detecting such chromatographic peaks in an untargeted fashion. PeakBot is implemented in python (3.8) and uses the TensorFlow (2.5.0) package for machine-learning related tasks. It has been tested on Linux and Windows OSs. AvailabilityThe package is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/PeakBot. Contactchristoph.bueschl@univie.ac.at Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Thermodynamic genome-scale metabolic modeling of metallodrug resistance in colorectal cancer

BackgroundMass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge. MethodsMeasuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively. ResultsNormalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways. ConclusionsHere we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.

cancer biology↗

bioRxiv2020

Inclusion of maintenance energy improves the intracellular flux predictions of CHO

Chinese hamster ovary (CHO) cells are the leading platform for the production of biopharmaceuticals with human-like glycosylation. The standard practice for cell line generation relies on trial and error approaches such as adaptive evolution and high-throughput screening, which typically take several months. Metabolic modeling could aid in designing better producer cell lines and thus shorten development times. The genome-scale metabolic model (GSMM) of CHO can accurately predict growth rates. However, in order to predict rational engineering strategies it also needs to accurately predict intracellular fluxes. In this work we evaluated the agreement between the fluxes predicted by pFBA using the CHO GSMM and a wide range of 13C metabolic flux data from literature. While glycolytic fluxes were predicted relatively well, the fluxes of tricarboxylic acid (TCA) cycle were vastly underestimated due to too low energy demand. Inclusion of computationally estimated maintenance energy significantly improved the overall accuracy of intracellular flux predictions. Maintenance energy was therefore determined experimentally by running continuous cultures at different growth rates and evaluating their respective energy consumption. The experimentally and computationally determined maintenance energy were in good agreement. Additionally, we compared alternative objective functions (minimization of uptake rates of seven nonessential metabolites) to the biomass objective. While the predictions of the uptake rates were quite inaccurate for most objectives, the predictions of the intracellular fluxes were comparable to the biomass objective function.

systems biology↗

bioRxiv2020

Finger Sweat Analysis Enables Short Interval Metabolic Biomonitoring in Humans

Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues and are impractical for performing time-course studies. The analysis of the minute amounts of sweat sampled from the fingertip enables a solution to this challenge. Sweat sampling from the fingertip is non-invasive and robust and can be accomplished repeatedly by untrained personnel. This matrix represents a rich source for metabolomic phenotyping, which is exemplified by the detection of roughly 50000 features per sample. Moreover, the determined limits of detection demonstrate that the ingestion of 200 g of a xenobiotic may be sufficient for its detection in sweat from the fingertip. The feasibility of short interval sampling of sweat from the fingertips was confirmed in three time-course studies after coffee consumption or ingestion of a caffeine capsule, successfully monitoring all known caffeine metabolites. Fluctuations in the rate of sweat production were accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. Biomonitoring using sweat from the fingertip has far reaching implications for personalised medical diagnostics and biomarker discovery.

biochemistry↗

bioRxiv2020

Environmental flexibility does not explain metabolic robustness

Cells show remarkable resilience against genetic and environmental perturbations. However, its evolutionary origin remains obscure. In order to leverage methods of systems biology for examining cellular robustness, a computationally accessible way of quantification is needed. Here, we present an unbiased metric of structural robustness in genome-scale metabolic models based on concepts prevalent in reliability engineering and fault analysis. The probability of failure (PoF) is defined as the (weighted) portion of all possible combinations of loss-of-function mutations that disable network functionality. It can be exactly determined, if all essential reactions, synthetic lethal pairs of reactions, synthetic lethal triplets of reactions etc., are known. In theory, these minimal cut sets (MCSs) can be calculated for any network, but for large models the problem remains computationally intractable. Herein, we demonstrate that even at the genome scale only the lowest-cardinality MCSs are required to efficiently approximate the PoF with reasonable accuracy. We analysed the robustness of 489 E. coli, Shigella, Salmonella, and fungal genome-scale metabolic models (GSMMs). In contrast to the popular "congruence theory", which explains the origin of genetic robustness as a byproduct of selection for environmental flexibility, we found no correlation between network robustness and the diversity of growth-supporting environments. On the contrary, our analysis indicates that amino acid synthesis rather than carbon metabolism dominates metabolic robustness.

systems biology↗

bioRxiv2019

A prospective role for the rumen in generating antibiotic resistance

Antibiotics were a revolutionary discovery of the 20th century, but the ability of bacteria to spread the genetic determinants of resistance via horizontal gene transfer (HGT) has quickly endangered their use1. Indeed, there is a global network of microbial gene exchange, the analysis of which has revealed particularly frequent transfer of resistance determinants between farm animals and human-associated bacteria2. Here, we leverage the recent release of a rumen microbial genome reference set and show that the wide-spread resistance gene cluster aadE-sat4-aphA-3 is harboured in ruminal Bacteroidetes. While this cluster appears to have been recently transferred between commensal bacteria in the rumen and many diverse animal and human pathogens, comparative analysis suggests that the cluster stabilized in the pathogens. Then, focusing on streptomycin resistance, it was found that homologues from the rumen span much of the known diversity of aminoglycoside O-nucleotidyltransferases (AadEs) and that distinct variants of the enzyme are present in a single rumen bacterial genome. Notably, a second variant of AadE has also been recently transferred, albeit more often as a single gene, throughout a different set of animal and human associated bacteria. By examining the synteny of AadE orthologues in various bacterial genomes and analyzing corresponding gene trees in an environmental context, we speculate that the ruminant associated microbiome has a salient role in the emergence of specific resistance variants and clusters. In light of the recent literature on the evolutionary origin of antibiotic resistance, we further suggest that the rumen provides a possible route of dissemination of resistance genes from soil resistomes, throughout the farm, and to human pathogens3.

microbiology↗