Tumor-mediated immunosuppression and cytokine spreading affects the relation between EMT and PD-L1 status

Epithelial-mesenchymal transition (EMT) and immune resistance mediated by Programmed Death-Ligand 1 (PD-L1) upregulation are established drivers of tumor progression. Their bi-directional crosstalk has been proposed to facilitate tumor immunoevasion, yet the impact of immunosuppression and spatial heterogeneity on the interplay between these processes remains to be characterized. Here we study the role of these factors using mathematical and spatial models. We first designed models incorporating immunosuppressive effects on T cells mediated via PD-L1 and the EMT-inducing cytokine Transforming Growth Factor beta (TGFβ). Our models predict that PD-L1-mediated immunosuppression merely reduces the difference in PD-L1 levels between EMT states, while TGFβ-mediated suppression also causes PD-L1 expression to correlate negatively with TGFβ within each EMT phenotype. We subsequently embedded the models in multi-scale spatial simulations to explicitly describe heterogeneity in cytokine levels and intratumoral heterogeneity. Our multi-scale models show that Interferon gamma (IFNγ)-induced partial EMT of a tumor cell subpopulation can provide some, albeit limited protection to bystander tumor cells. Moreover, our simulations show that the true relationship between EMT status and PD-L1 expression may be hidden at the population level, highlighting the importance of studying EMT and PD-L1 status at the single-cell level. Our findings deepen the understanding of the interactions between EMT and the immune response, which is crucial for developing novel diagnostics and therapeutics for cancer patients

Lymph node topology dictates T cell migration behavior

Adaptive immunity is initiated by T cell recognition of foreign peptides presented on dendritic cells (DCs) by major histocompatibility molecules. These interactions take place in secondary lymphoid tissues, such as lymph nodes (LNs) and spleen, and hence the anatomical structure of these tissues plays a crucial role in the development of immune responses. Two-photon microscopy (2PM) imaging in LNs suggests that T cells walk in a consistent direction for several minutes, pause briefly with a regular period, and then take off in a new, random direction. Here, we construct a spatially explicit model of T cell and DC migration in LNs and show that all dynamical properties of T cells could be a consequence of the densely packed LN environment. By means of 2PM experiments, we confirm that the large velocity fluctuations of T cells are indeed environmentally determined rather than resulting from an intrinsic motility program. Our simulations further predict that T cells self-organize into microscopically small, highly dynamic streams. We present experimental evidence for the presence of such turbulent streams in LNs. Finally, the model allows us to estimate the scanning rates of DCs (2,000 different T cells per hour) and T cells (100 different DCs per hour)

Random Migration and Signal Integration Promote Rapid and Robust T Cell Recruitment

To fight infections, rare T cells must quickly home to appropriate lymph nodes (LNs), and reliably localize the antigen (Ag) within them. The first challenge calls for rapid trafficking between LNs, whereas the second may require extensive search within each LN. Here we combine simulations and experimental data to investigate which features of random T cell migration within and between LNs allow meeting these two conflicting demands. Our model indicates that integrating signals from multiple random encounters with Ag-presenting cells permits reliable detection of even low-dose Ag, and predicts a kinetic feature of cognate T cell arrest in LNs that we confirm using intravital two-photon data. Furthermore, we obtain the most reliable retention if T cells transit through LNs stochastically, which may explain the long and widely distributed LN dwell times observed in vivo. Finally, we demonstrate that random migration, both between and within LNs, allows recruiting the majority of cognate precursors within a few days for various realistic infection scenarios. Thus, the combination of two-scale stochastic migration and signal integration is an efficient and robust strategy for T cell immune surveillance

Application of AOPs to assist regulatory assessment of chemical risks - Case studies, needs and recommendations

While human regulatory risk assessment (RA) still largely relies on animal studies, new approach methodologies (NAMs) based on in vitro, in silico or non-mammalian alternative models are increasingly used to evaluate chemical hazards. Moreover, human epidemiological studies with biomarkers of effect (BoE) also play an invaluable role in identifying health effects associated with chemical exposures. To move towards the next generation risk assessment (NGRA), it is therefore crucial to establish bridges between NAMs and standard approaches, and to establish processes for increasing mechanistically-based biological plausibility in human studies. The Adverse Outcome Pathway (AOP) framework constitutes an important tool to address these needs but, despite a significant increase in knowledge and awareness, the use of AOPs in chemical RA remains limited. The objective of this paper is to address issues related to using AOPs in a regulatory context from various perspectives as it was discussed in a workshop organized within the European Union partnerships HBM4EU and PARC in spring 2022. The paper presents examples where the AOP framework has been proven useful for the human RA process, particularly in hazard prioritization and characterization, in integrated approaches to testing and assessment (IATA), and in the identification and validation of BoE in epidemiological studies. Nevertheless, several limitations were identified that hinder the optimal usability and acceptance of AOPs by the regulatory community including the lack of quantitative information on response-response relationships and of efficient ways to map chemical data (exposure and toxicity) onto AOPs. The paper summarizes suggestions, ongoing initiatives and third-party tools that may help to overcome these obstacles and thus assure better implementation of AOPs in the NGRA

The impact of large scale biomass production on ozone air pollution in Europe

Tropospheric ozone contributes to the removal of air pollutants from the atmosphere but is itself a pollutant that is harmful to human health and vegetation. Biogenic isoprene emissions are important ozone precursors, and therefore future changes in land use that change isoprene emissions are likely to affect atmospheric ozone concentrations. Here, we use the chemical transport model LOTOS-EUROS (dedicated to the regional modeling of trace gases in Europe) to study a scenario in which 5% of the crop- and grass-land in Europe is converted into poplar plantations to be used for biofuel production. Although this scenario is rather conservative, our simulations project that isoprene emissions are substantially increased by an average of 45% over the simulated domain. As a consequence, ozone peak values are expected to increase by up to 6%, and ozone indicators for damage to human health and vegetation by up to 25% and 40%, respectively. Finally, we show that after the change in land use NOx emission reductions of 15 e20% in Europe would be required to restore the ozone levels to current values. Because biomass production is expected to increase throughout Europe in the coming decades, we conclude that careful consideration of the tree types and regions to be used is required to constrain the concomitant air pollution to a minimum

Horizontal and vertical noise tolerance of binocular correlation in random dot stereograms

Although our eyes are separated horizontally and binocular disparities are therefore mainly horizontal, binocular correlation tolerates substantial vertical disparities. To study the size of the vertical disparity range for binocular correlation, we measured the tolerance for both horizontal and vertical disparity noise, in detecting sinusoidal depth gratings in random dot patterns. We used dense, dynamic random dot stereograms and Gaussian distributed disparity noise. Trials consisted of two 0.8 s intervals, one containing the depth corrugation (stimulus), the other containing the same disparity values randomly distributed across the window (reference). For different grating parameters tolerance for vertical disparity noise was at least as large as for horizontal disparity noise. Moreover, the effects of horizontal and vertical noise added linearly, suggesting a horizontal-vertical isotropy. To find out whether these resultss indeed reflect the size of horizontal and vertical ranges for resolvable disparities, we performed a parametric model analysis for binocular correlation. The model was presented with random dot stereograms of sinusoidal depth gratings, similar to those in the psychophysical measurements, and solved the correspondence problem by determining all possible matches of a pixel in one eye within an ellipsoid correlation area around the corresponding point in the other eye. An arbitrary, but highly efficient algorithm determined whether the stimulus or reference presentation provided the best match to a sinusoidal depth corrugation. A comparison of horizontal and vertical noise tolerance for the human observer and for the model revealed upper and lower limits for the vertical disparity range. However, psychophysical results could be reproduced with different combinations of horizontal and vertical disparity range, and therefore do not reflect a low level horizontal-vertical isotropy for binocular correlation.</p

Development of a Physiologically Based Pharmacokinetic Model for Nitrofurantoin in Rabbits, Rats, and Humans

Nitrofurantoin (NFT) is a commonly used antibiotic for the treatment of urinary tract infections that can cause liver toxicity. Despite reports of hepatic adverse events associated with NFT exposure, there is still limited understanding of the interplay between NFT exposure, its disposition, and the risk of developing liver toxicity. In this study, we aim to develop a physiologically based pharmacokinetic (PBPK) model for NFT in three different species (rabbits, rats, and humans) that can be used as a standard tool for predicting drug-induced liver injury (DILI). We created several versions of the PBPK model using previously published kinetics data from rabbits, and integrated enterohepatic recirculation (EHR) using rat data. Our model showed that active tubular secretion and reabsorption in the kidney are critical in explaining the non-linear renal clearance and urine kinetics of NFT. We subsequently extrapolated the PBPK model to humans. Adapting the physiology to humans led to predictions consistent with human kinetics data, considering a low amount of NFT to be excreted into bile. Model simulations predicted that the liver of individuals with a moderate-to-severe glomerular filtration rate (GFR) is exposed to two-to-three-fold higher concentrations of NFT than individuals with a normal GFR, which coincided with a substantial reduction in the NFT urinary concentration. In conclusion, people with renal insufficiency may be at a higher risk of developing DILI due to NFT exposure, while at the same time having a suboptimal therapeutic effect with a high risk of drug resistance. Our PBPK model can in the future be used to predict NFT kinetics in individual patients on the basis of characteristics like age and GFR

What do mathematical models tell us about killing rates during HIV-1 infection?

Over the past few decades the extent to which cytotoxic T lymphocytes (CTLs) control human immunodeficiency virus (HIV) replication has been studied extensively, yet their role and mode of action remain controversial. In some studies, CTLs were found to kill a large fraction of the productively infected cells relative to the viral cytopathicity, whereas in others CTLs were suggested to kill only a small fraction of infected cells. In this review, we compile published estimates of CTL-mediated death rates, and examine whether these studies permit determining the rate at which CTLs kill HIV-1 infected cells. We highlight potential misinterpretations of the CTL-killing rates from the escape rates of mutants, and from perturbations of the steady state viral load during chronic infection. Our major conclusion is that CTL-mediated killing rates remain unknown. But contrary to current consensus, we argue that killing rates higher than one per day are perfectly consistent with the experimental data, which would imply that the majority of the productively infected cells could still die from CTL-mediated killing rather than from viral cytopathicity

Deciphering Epithelial–Mesenchymal Transition Regulatory Networks in Cancer through Computational Approaches

Epithelial–mesenchymal transition (EMT), the process by which epithelial cells can convert into motile mesenchymal cells, plays an important role in development and wound healing but is also involved in cancer progression. It is increasingly recognized that EMT is a dynamic process involving multiple intermediate or “hybrid” phenotypes rather than an “all-or-none” process. However, the role of EMT in various cancer hallmarks, including metastasis, is debated. Given the complexity of EMT regulation, computational modeling has proven to be an invaluable tool for cancer research, i.e., to resolve apparent conflicts in experimental data and to guide experiments by generating testable hypotheses. In this review, we provide an overview of computational modeling efforts that have been applied to regulation of EMT in the context of cancer progression and its associated tumor characteristics. Moreover, we identify possibilities to bridge different modeling approaches and point out outstanding questions in which computational modeling can contribute to advance our understanding of pathological EMT