Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology

Metabolomic and transcriptomic response to imatinib treatment of gastrointestinal stromal tumour in xenograft-bearing mice

Background Although imatinib is a well-established first-line drug for treating a vast majority of gastrointestinal stromal tumours (GIST), GISTs acquire secondary resistance during therapy. Multi-omics approaches provide an integrated perspective to empower the development of personalised therapies through a better understanding of functional biology underlying the disease and molecular-driven selection of the best-targeted individualised therapy. In this study, we applied integrative metabolomic and transcriptomic analyses to elucidate tumour biochemical processes affected by imatinib treatment. Materials and methods A GIST xenograft mouse model was used in the study, including 10 mice treated with imatinib and 10 non-treated controls. Metabolites in tumour extracts were analysed using gas chromatography coupled with mass spectrometry (GC-MS). RNA sequencing was also performed on the samples subset (n=6). Results Metabolomic analysis revealed 21 differentiating metabolites, whereas next-generation RNA sequencing data analysis resulted in 531 differentially expressed genes. Imatinib significantly changed the profile of metabolites associated mainly with purine and pyrimidine metabolism, butanoate metabolism, as well as alanine, aspartate, and glutamate metabolism. The related changes in transcriptomic profiles included genes involved in kinase activity and immune responses, as well as supported its impact on the purine biosynthesis pathway. Conclusions Our multi-omics study confirmed previously known pathways involved in imatinib anticancer activity as well as correlated imatinib-relevant downregulation of expression of purine biosynthesis pathway genes with the reduction of respectful metabolites. Furthermore, considering the importance of the purine biosynthesis pathway for cancer proliferation, we identified a potentially novel mechanism for the anti-tumour activity of imatinib. Based on the results, we hypothesise metabolic modulations aiming at the reduction in purine and pyrimidine pool may ensure higher imatinib efficacy or re-sensitise imatinib-resistant tumours.publishedVersio

PGE2 inhibits TIL expansion by disrupting IL-2 signalling and mitochondrial function.

Expansion of antigen-experienced CD8+ T cells is critical for the success of tumour-infiltrating lymphocyte (TIL)-adoptive cell therapy (ACT) in patients with cancer1. Interleukin-2 (IL-2) acts as a key regulator of CD8+ cytotoxic T lymphocyte functions by promoting expansion and cytotoxic capability2,3. Therefore, it is essential to comprehend mechanistic barriers to IL-2 sensing in the tumour microenvironment to implement strategies to reinvigorate IL-2 responsiveness and T cell antitumour responses. Here we report that prostaglandin E2 (PGE2), a known negative regulator of immune response in the tumour microenvironment4,5, is present at high concentrations in tumour tissue from patients and leads to impaired IL-2 sensing in human CD8+ TILs via the PGE2 receptors EP2 and EP4. Mechanistically, PGE2 inhibits IL-2 sensing in TILs by downregulating the IL-2Rγc chain, resulting in defective assembly of IL-2Rβ-IL2Rγc membrane dimers. This results in impaired IL-2-mTOR adaptation and PGC1α transcriptional repression, causing oxidative stress and ferroptotic cell death in tumour-reactive TILs. Inhibition of PGE2 signalling to EP2 and EP4 during TIL expansion for ACT resulted in increased IL-2 sensing, leading to enhanced proliferation of tumour-reactive TILs and enhanced tumour control once the cells were transferred in vivo. Our study reveals fundamental features that underlie impairment of human TILs mediated by PGE2 in the tumour microenvironment. These findings have therapeutic implications for cancer immunotherapy and cell therapy, and enable the development of targeted strategies to enhance IL-2 sensing and amplify the IL-2 response in TILs, thereby promoting the expansion of effector T cells with enhanced therapeutic potential

Genome-Scale Metabolic Modeling of Chitin-Degrading Microbial Systems

As a major component of fungal cell walls and exoskeletons of invertebrates, chitin is widespread in soils, constituting the second most abundant biopolymer in nature. Composed of N-acetyl-D-glucosamine chains, it serves as a vital source of nutrients, including both carbon and nitrogen, for the growth of microorganisms. A solid understanding of the microbial degradation of chitin is critical for predicting their impacts on biogeochemical cycling in soil ecosystems. Organisms that degrade biopolymers (degraders) produce energetically expensive extracellular enzymes to break down complex organic carbons into simpler labile forms that are sharable with other species, including those that do not contribute directly to the degradation process (cheaters). Therefore, it impacts not only the metabolic and growth efficiencies of the degraders but also fosters diverse interspecies interactions within microbial communities. The level of complexity in this process necessitates the use of mechanistic metabolic models. However, reconstruction of phenotype-consistent genome-scale metabolic networks is still challenging due to the frequent occurrence of false positives (model prediction of biomass production in media where actual organism cannot grow) when gapfilled using typical sequential gapfilling approaches. In this work, I developed a new iterative gapfilling method to address this issue and applied it to build metabolic networks of chitin-degrading communities and their isolates—using a consortium of Cellvibrio japonicus (degrader) and Escherichia coli (non-degrader) as a model system. This new development revealed previously unknown and interesting findings on how bioenergetic cost on chitin degradation affects degrader’s metabolism and its interactions with non-degraders. The model also provided mechanistic interpretations of the predicted changes in metabolism and interactions based on carbon and nitrogen use efficiencies. Both the methods and findings are reproducible, and may be used in other biopolymer-degrading communities

Enhanced understanding of protein glycosylation in CHO cells through computational tools and experimentation

Chinese hamster ovary (CHO) cells are the workhorse of the multibillion-dollar biopharmaceuticals industry. They have been extensively harnessed for recombinant protein synthesis, as they exhibit high titres and human-like post translational modifications (PTM), such as protein N-linked glycosylation. More specifically, N-linked glycosylation is a crucial PTM that includes the addition of an oligosaccharide in the backbone of the protein and strongly affects therapeutic efficacy and immunogenicity. In addition, the Quality by Design (QbD) paradigm that is broadly applied in academic research, necessitates a comprehensive understanding of the underlying biological relationships between the process parameters and the product quality attributes. To that end, computational tools have been vastly employed to elucidate cellular functions and predict the effect of process parameters on cell growth, product synthesis and quality. This thesis reports several advancements in the use of mathematical models for describing and optimizing bioprocesses. Firstly, a kinetic mathematical model describing CHO cell growth, metabolism, antibody synthesis and N-linked glycosylation was proposed, in order to capture the effect of galactose and uridine supplementation on cell growth and monoclonal antibody (mAb) glycosylation. Subsequently, the model was utilized to optimize galactosylation, a desired quality attribute of therapeutic mAbs. Following the QbD paradigm for ensuring product titre and quality, the kinetic model was subsequently used to identify an in silico Design Space (DS) that was also experimentally verified. An elaborate parameter estimation methodology was also developed in order to adapt the existing model to data from a newly introduced CHO cell line, without altering model structure. In an effort to reduce the burden of parameter estimation, the N-linked glycosylation submodel was replaced with an artificial neural network that was used as a standalone machine learning algorithm to predict the effect of feeding alterations and genetic engineering on the glycan distribution of several therapeutic proteins. In addition, a hybrid model configuration (HyGlycoM) incorporating the ANN-glycosylation model was also formulated to link extracellular process conditions to glycan distribution. The latter was found to outperform its fully kinetic equivalent when compared to experimental data. Finally, a comprehensive investigation of mAb galactosylation bottlenecks was carried out. Five fed-batch experiments with different concentrations of galactose and uridine supplemented throughout the culturing period, were carried out and were found to present similar mAb galactosylation. In order to identify the bottlenecks that limit galactosylation, further experimental analysis, including the investigation of glycans microheterogeneity of CHO host cell proteins (HCPs), was conducted. The experimental results were used to parameterize a novel and significant extension of the kinetic glycosylation model that simultaneously describes the N-linked glycosylation of both HCPs and the mAb product. Flux balance analysis was also used to analyse carbon and nitrogen metabolism using the experimental amino acid concentration profiles. In addition to the expression levels of the beta-1,4-galactosyltransferase enzyme, constraints imposed by the transport of the galactosylation sugar donor in the Golgi compartments and the consumption of resources towards HCPs glycosylation, were found to considerably influence mAb galactosylation.Open Acces