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-2Rß-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.

Rational strain design with minimal phenotype perturbation.

Devising genetic interventions for desired cellular phenotypes remains challenging regarding time and resources. Kinetic models can accelerate this task by simulating metabolic responses to genetic perturbations. However, exhaustive design evaluations with kinetic models are computationally impractical, especially when targeting multiple enzymes. Here, we introduce a framework for efficiently scouting the design space while respecting cellular physiological requirements. The framework employs mixed-integer linear programming and nonlinear simulations with large-scale nonlinear kinetic models to devise genetic interventions while accounting for the network effects of these perturbations. Importantly, it ensures the engineered strain's robustness by maintaining its phenotype close to that of the reference strain. The framework, applied to improve the anthranilate production in E. coli, devises designs for experimental implementation, including eight previously experimentally validated targets. We expect this framework to play a crucial role in future design-build-test-learn cycles, significantly expediting the strain design compared to exhaustive design enumeration.

Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models.

MOTIVATION: Large-scale kinetic models are an invaluable tool to understand the dynamic and adaptive responses of biological systems. The development and application of these models have been limited by the availability of computational tools to build and analyze large-scale models efficiently. The toolbox presented here provides the means to implement, parameterize and analyze large-scale kinetic models intuitively and efficiently. RESULTS: We present a Python package (SKiMpy) bridging this gap by implementing an efficient kinetic modeling toolbox for the semiautomatic generation and analysis of large-scale kinetic models for various biological domains such as signaling, gene expression and metabolism. Furthermore, we demonstrate how this toolbox is used to parameterize kinetic models around a steady-state reference efficiently. Finally, we show how SKiMpy can implement multispecies bioreactor simulations to assess biotechnological processes. AVAILABILITY AND IMPLEMENTATION: The software is available as a Python 3 package on GitHub: https://github.com/EPFL-LCSB/SKiMpy, along with adequate documentation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dynamic partitioning of branched-chain amino acids-derived nitrogen supports renal cancer progression.

Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we integrate multimodal analyses of primary and metastatic clonally-related clear cell renal cancer cells (ccRCC) grown in physiological media to identify key stage-specific metabolic vulnerabilities. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism sustains the de novo biosynthesis of aspartate and arginine enabling tumor cells with the flexibility of partitioning the nitrogen of the amino acids depending on their needs. Importantly, we identify the epigenetic reactivation of argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, as a crucial event for metastatic renal cancer cells to acquire the capability to generate arginine, invade in vitro and metastasize in vivo. Overall, our study uncovers a mechanism of metabolic flexibility occurring during ccRCC progression, paving the way for the development of novel stage-specific therapies.

A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics.

Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.

Author Correction: Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN.

Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the consistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the thermodynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells.

A patient care program for adjusting the autoinjector needle depth according to subcutaneous tissue thickness in patients with multiple sclerosis receiving subcutaneous injections of glatiramer acetate.

BACKGROUND: The perceived pain on injection site caused by subcutaneous (SC) self-injection may negatively affect acceptance and adherence to treatment in patients with multiple sclerosis (MS). Pain on injection may be caused by inaccurate injection technique, inadequate needle length adjustment, or repeated use of the same injection body area. However, information is lacking concerning the optimal needle depth to minimize the injection pain. OBJECTIVE: The purpose of this program was to characterize the perceived injection-site pain associated with the use of various injection depths of the autoinjector of glatiramer acetate (GA) based on SC tissue thickness (SCT) of the injection site. METHODS: This was a pilot program performed by MS-specialized nurses in patients with MS new to GA. Patients were trained by MS nurses on the preparation and administration of SC injection and on an eight-site rotation (left and right arms, thighs, abdomen, and upper quadrant of the buttock). The needle length setting was selected based on SCT measures as follows: 4 or 6 mm for SCT < 25 mm, 6 or 8 mm for SCT between 25 and 50 mm, and 8 or 10 mm for SCT > 50 mm. Injection pain was rated using a visual analog scale (VAS) at 5- and 40-minute postinjection and during two 24-day treatment periods. RESULTS: Thirty-eight patients with MS were evaluated. The mean SCT ranged from 15.5 mm in the upper outer quadrant of the buttocks to 29.2 mm in the thighs. The mean perceived pain on injection was below 3 for all the injection sites, at both time points (5 and 40 minutes) and during both 24-day evaluation periods. The mean VAS scores were significantly greater after 5 minutes of injection compared with that reported 40-minute postinjection during both 24-day treatment periods and for all the injection areas. Mean VAS measures at 5- and 40-minute postinjection significantly decreased during the second 24-day treatment period with respect to that reported during the first 24 SC injections for all injection sites. CONCLUSIONS: Our findings suggest that the adjustment of injection depth of SC GA autoinjector according to SCT of body injection areas is suitable to maintain a low degree of postinjection pain. Moreover, our results also may indicate that the use of needle lengths of 6 mm or shorter is appropriate with regard to injection pain for adult patients with MS with SCT < 50 mm.