Adaptability of metabolic networks in evolution and disease

There are 114.101 small molecule metabolites currently annotated in the Human Metabolome Database, which are highly connected amongst each other, with a few metabolites exhibiting an estimated number of more than 103 connections. Redundancy and plasticity are essential features of metabolic networks enabling cells to respond to fluctuating environments, presence of toxic molecules, or genetic perturbations like mutations. These system-level properties are inevitably linked to all aspects of biological systems ensuring cell viability by enabling processes like adaption and differentiation. To this end, the ability to interrogate molecular changes at omics level has opened new opportunities to study the cell at its different layers from the epigenome and transcriptome to its proteome and metabolome. In this thesis, I tackled the question how redundancy and plasticity shape adaptation in metabolic networks in evolutionary and disease contexts. I utilize a multi-omics approach to study comprehensively the metabolic state of a cell and its regulation at the transcriptional and proteomic level. One of the challenges with multi-omics approaches is the integration and interpretation of multi-layered data sets. To approach this challenge, I use genome scale metabolic models as a knowledge-based scaffold to overlay omics data and thereby to enable biological interpretation beyond statistical correlation. This integrative methodology has been applied to two different projects, namely the evolutionary adaptation towards a nutrient source in yeast and the metabolic adaptations following disease progression. For the latter, I also curated a current human genome-scale metabolic model and made it more suitable for flux predictions. In the yeast case study, I investigate the metabolic network adaptations enabling yeast to grow on an alternative carbon source – glycerol. I could show that network redundancy is one of the key features of fast adaptation of the yeast metabolic network to the new nutrient environment. Genomics, transcriptomics, proteomics, metabolomics and metabolic modeling together revealed a shift of the organism’s redox-balance under glycerol consumption as a driving force of adaption, which can be linked to the causal mutation in the enzyme Kgd1. On the other hand, the limitations of metabolic network adaptation also became apparent since all evolved and adapted strains exhibited metabolic trade-offs in other environmental conditions than the adaptation niche. Either an impaired diauxic shift (as in the case of the glycerol mutant) or an increased sensitivity towards osmotic stress (caused by mutations in the HOG pathway) was coupled with efficient use of glycerol. In the second project, the molecular phenotype of regressed breast cancer cells was studied to identify what differentiates these cells from healthy breast tissue and to characterize the potential source of tumor recurrence. Using a breast cancer mouse model with inducible oncogenes, transcriptomics together with an extensive set of different types of metabolomics (targeted and untargeted metabolomics, lipidomics and fluxomics) could show that regressed cancer cells, albeit their apparently normal morphology, possess a highly altered molecular phenotype with an oncogenic memory. While in cancer redundancy and plasticity enable the adaptation towards a proliferative state, in regressed cells, on the contrary, prolonged oncogenic signaling leads to a loss of metabolic network regulation and the entering of an irreversible metabolic state. This state appears to be insensitive to adaptation mechanisms as transcripts and metabolites reciprocally enhance each other to maintain the tumor-like metabolic phenotype. In conclusion, this work demonstrates how genome scale metabolic models can help identifying functional mechanisms from complex and multi-layered omics data. Appropriate genome scale metabolic models combined with metabolite measurements have proven particularly useful in this context. The comprehensive understanding of all integrated aspects of a cell’s physiology is a challenging endeavor and the results of this thesis might stimulate further research towards this goal

Metabolic memory underlying minimal residual disease in breast cancer.

Funder: European Molecular Biology LaboratoryFunder: European Molecular Biology Laboratory (EMBL)Tumor relapse from treatment-resistant cells (minimal residual disease, MRD) underlies most breast cancer-related deaths. Yet, the molecular characteristics defining their malignancy have largely remained elusive. Here, we integrated multi-omics data from a tractable organoid system with a metabolic modeling approach to uncover the metabolic and regulatory idiosyncrasies of the MRD. We find that the resistant cells, despite their non-proliferative phenotype and the absence of oncogenic signaling, feature increased glycolysis and activity of certain urea cycle enzyme reminiscent of the tumor. This metabolic distinctiveness was also evident in a mouse model and in transcriptomic data from patients following neo-adjuvant therapy. We further identified a marked similarity in DNA methylation profiles between tumor and residual cells. Taken together, our data reveal a metabolic and epigenetic memory of the treatment-resistant cells. We further demonstrate that the memorized elevated glycolysis in MRD is crucial for their survival and can be targeted using a small-molecule inhibitor without impacting normal cells. The metabolic aberrances of MRD thus offer new therapeutic opportunities for post-treatment care to prevent breast tumor recurrence

Single-cell transcriptomics identifies CD44 as a marker and regulator of endothelial to haematopoietic transition.

The endothelial to haematopoietic transition (EHT) is the process whereby haemogenic endothelium differentiates into haematopoietic stem and progenitor cells (HSPCs). The intermediary steps of this process are unclear, in particular the identity of endothelial cells that give rise to HSPCs is unknown. Using single-cell transcriptome analysis and antibody screening, we identify CD44 as a marker of EHT enabling us to isolate robustly the different stages of EHT in the aorta-gonad-mesonephros (AGM) region. This allows us to provide a detailed phenotypical and transcriptional profile of CD44-positive arterial endothelial cells from which HSPCs emerge. They are characterized with high expression of genes related to Notch signalling, TGFbeta/BMP antagonists, a downregulation of genes related to glycolysis and the TCA cycle, and a lower rate of cell cycle. Moreover, we demonstrate that by inhibiting the interaction between CD44 and its ligand hyaluronan, we can block EHT, identifying an additional regulator of HSPC development

Single-cell transcriptomics identifies CD44 as a marker and regulator of endothelial to haematopoietic transition

The endothelial to haematopoietic transition (EHT) is the process whereby haemogenic endothelium differentiates into haematopoietic stem and progenitor cells (HSPCs). The intermediary steps of this process are unclear, in particular the identity of endothelial cells that give rise to HSPCs is unknown. Using single-cell transcriptome analysis and antibody screening we identified CD44 as a new marker of EHT enabling us to isolate robustly the different stages of EHT in the aorta gonad mesonephros (AGM) region. This allowed us to provide a very detailed phenotypical and transcriptional profile for haemogenic endothelial cells, characterising them with high expression of genes related to Notch signalling, TGFbeta/BMP antagonists (Smad6, Smad7 and Bmper) and a downregulation of genes related to glycolysis and the TCA cycle. Moreover, we demonstrated that by inhibiting the interaction between CD44 and its ligand hyaluronan we could block EHT, identifying a new regulator of HSPC development

Integrating knowledge and omics to decipher mechanisms via large‐scale models of signaling networks

Abstract Signal transduction governs cellular behavior, and its dysregulation often leads to human disease. To understand this process, we can use network models based on prior knowledge, where nodes represent biomolecules, usually proteins, and edges indicate interactions between them. Several computational methods combine untargeted omics data with prior knowledge to estimate the state of signaling networks in specific biological scenarios. Here, we review, compare, and classify recent network approaches according to their characteristics in terms of input omics data, prior knowledge and underlying methodologies. We highlight existing challenges in the field, such as the general lack of ground truth and the limitations of prior knowledge. We also point out new omics developments that may have a profound impact, such as single‐cell proteomics or large‐scale profiling of protein conformational changes. We provide both an introduction for interested users seeking strategies to study cell signaling on a large scale and an update for seasoned modelers

Reliable sensing platform for plasmonic enzyme-linked immunosorbent assays based on automatic flow-based methodology

[eng] Plasmonic enzyme-linked immunosorbent assays (ELISA) using the localized surface plasmon resonance (LSPR) of metal nanoparticles has emerged as an appealing alternative to conventional ELISA counterparts for ultrasensitive naked-eye detection of biomolecules and small contaminants. However, batchwise plasmonic ELISA involving end-point detection lacks ruggedness inasmuch as the generation or etching of NP is greatly dependent on every experimental parameter of the analytical workflow. To tackle the above shortcomings, this paper reports on an automatic flow methodology as a reliable detection scheme of hydrogen peroxide related enzymatic bioassays for ultrasensitive detection of small molecules. Here, a competitive ELISA is combined with the in-line generation of plasmonic gold nanoparticles (AuNPs) followed by the real-time monitoring of the NP nucleation and growth rates and size distribution using a USB miniaturized photometer. Glucose oxidase was labeled to the secondary antibody and yielded hydrogen peroxide that acted as the measurand and the reducing agent of the Au(III)/citrate system in the flow network. High-throughput plasmonic assays were feasible by assembling a hybrid flow system composed of two microsyringe pumps, a perfluoroalkoxy alkane reaction coil, and a 26-port multiposition valve and operated under computer-controllable flow conditions. The ultratrace determination of diclofenac in high matrix samples, e.g., seawater, without any prior sample treatment was selected as a proof-of-concept application of the flow-based platform for determination of emerging contaminants via plasmonic ELISA. The detection limit (0.001 μg L-1) was 1 order of magnitude lower than that endorsed by the first EU Watch List for diclofenac as a potentially emerging contaminant in seawater and also than that of a conventional colorimetric ELISA, which in turn is inappropriate for determination of diclofenac in seawater at the levels endorsed by the EU regulation. The proposed automatic fluidic approach is characterized by the reproducible timing in AuNPs nucleation and growth along with the unsupervised LSPR absorbance detection of AuNPs with a dynamic range for diclofenac spanning from 0.01 to 10 μg L-1. Repeatability and intermediate precision (given as normalized signal readouts) in seawater were <4% and <14%, respectively, as compared to RSDs as high as 30% as obtained with the batchwise plasmonic ELISA counterpart

Single-cell transcriptomics identifies CD44 as a marker and regulator of endothelial to haematopoietic transition.

The endothelial to haematopoietic transition (EHT) is the process whereby haemogenic endothelium differentiates into haematopoietic stem and progenitor cells (HSPCs). The intermediary steps of this process are unclear, in particular the identity of endothelial cells that give rise to HSPCs is unknown. Using single-cell transcriptome analysis and antibody screening, we identify CD44 as a marker of EHT enabling us to isolate robustly the different stages of EHT in the aorta-gonad-mesonephros (AGM) region. This allows us to provide a detailed phenotypical and transcriptional profile of CD44-positive arterial endothelial cells from which HSPCs emerge. They are characterized with high expression of genes related to Notch signalling, TGFbeta/BMP antagonists, a downregulation of genes related to glycolysis and the TCA cycle, and a lower rate of cell cycle. Moreover, we demonstrate that by inhibiting the interaction between CD44 and its ligand hyaluronan, we can block EHT, identifying an additional regulator of HSPC development