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.

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.

Mathematical modeling identifies LAG3 and HAVCR2 as biomarkers of T cell exhaustion in melanoma

Cytotoxic T lymphocytes (CTLs) control tumors via lysis of antigen-presenting targets or through secretion of cytokines such as interferon-γ (IFNG), which inhibit tumor cell proliferation. Improved understanding of CTL interactions within solid tumors will aid the development of immunotherapeutic strategies against cancer. In this study, we take a systems biology approach to compare the importance of cytolytic versus IFNG-mediated cytostatic effects in a murine melanoma model (B16F10) and to dissect the contribution of immune checkpoints HAVCR2, LAG3, and PDCD1/CD274 to CTL exhaustion. We integrated multimodal data to inform an ordinary differential equation (ODE) model of CTL activities inside the tumor. Our model predicted that CTL cytotoxicity played only a minor role in tumor control relative to the cytostatic effects of IFNG. Furthermore, our analysis revealed that within B16F10 melanomas HAVCR2 and LAG3 better characterize the development of a dysfunctional CTL phenotype than does the PDCD1/CD274 axis.

DRAG in situ barcoding reveals an increased number of HSPCs contributing to myelopoiesis with age

Ageing is associated with changes in the cellular composition of the immune system. During ageing, hematopoietic stem and progenitor cells (HSPCs) that produce immune cells are thought to decline in their regenerative capacity. However, HSPC function has been mostly assessed using transplantation assays, and it remains unclear how HSPCs age in the native bone marrow niche. To address this issue, we present an in situ single cell lineage tracing technology to quantify the clonal composition and cell production of single cells in their native niche. Our results demonstrate that a pool of HSPCs with unequal output maintains myelopoiesis through overlapping waves of cell production throughout adult life. During ageing, the increased frequency of myeloid cells is explained by greater numbers of HSPCs contributing to myelopoiesis rather than the increased myeloid output of individual HSPCs. Strikingly, the myeloid output of HSPCs remains constant over time despite accumulating significant transcriptomic changes throughout adulthood. Together, these results show that, unlike emergency myelopoiesis post-transplantation, aged HSPCs in their native microenvironment do not functionally decline in their regenerative capacity.

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.

Bidirectional crosstalk between epithelial-mesenchymal plasticity and IFNγ-induced PD-L1 expression promotes tumour progression

Epithelial-mesenchymal transition (EMT) and immunoevasion through upregulation of programmed death-ligand 1 (PD-L1) are important drivers of cancer progression. While EMT has been proposed to facilitate PD-L1-mediated immunosuppression, molecular mechanisms of their interaction remain obscure. Here, we provide insight into these mechanisms by proposing a mathematical model that describes the crosstalk between EMT and interferon gamma (IFNγ)-induced PD-L1 expression. Our model shows that via interaction with microRNA-200 (miR-200), the multi-stability of the EMT regulatory circuit is mirrored in PD-L1 levels, which are further amplified by IFNγ stimulation. This IFNγ-mediated effect is most prominent for cells in a fully mesenchymal state and less strong for those in an epithelial or partially mesenchymal state. In addition, bidirectional crosstalk between miR-200 and PD-L1 implies that IFNγ stimulation allows cells to undergo EMT for lower amounts of inducing signal, and the presence of IFNγ accelerates EMT and decelerates mesenchymal-epithelial transition (MET). Overall, our model agrees with published findings and provides insight into possible mechanisms behind EMT-mediated immune evasion, and primary, adaptive, or acquired resistance to immunotherapy. Our model can be used as a starting point to explore additional crosstalk mechanisms, as an improved understanding of these mechanisms is indispensable for developing better diagnostic and therapeutic options for cancer patients.

Density-Dependent Migration Characteristics of Cancer Cells Driven by Pseudopod Interaction

The ability of cancer cells to invade neighboring tissue from primary tumors is an important determinant of metastatic behavior. Quantification of cell migration characteristics such as migration speed and persistence helps to understand the requirements for such invasiveness. One factor that may influence invasion is how local tumor cell density shapes cell migration characteristics, which we here investigate with a combined experimental and computational modeling approach. First, we generated and analyzed time-lapse imaging data on two aggressive Triple-Negative Breast Cancer (TNBC) cell lines, HCC38 and Hs578T, during 2D migration assays at various cell densities. HCC38 cells exhibited a counter-intuitive increase in speed and persistence with increasing density, whereas Hs578T did not exhibit such an increase. Moreover, HCC38 cells exhibited strong cluster formation with active pseudopod-driven migration, especially at low densities, whereas Hs578T cells maintained a dispersed positioning. In order to obtain a mechanistic understanding of the density-dependent cell migration characteristics and cluster formation, we developed realistic spatial simulations using a Cellular Potts Model (CPM) with an explicit description of pseudopod dynamics. Model analysis demonstrated that pseudopods exerting a pulling force on the cell and interacting via increased adhesion at pseudopod tips could explain the experimentally observed increase in speed and persistence with increasing density in HCC38 cells. Thus, the density-dependent migratory behavior could be an emergent property of single-cell characteristics without the need for additional mechanisms. This implies that pseudopod dynamics and interaction may play a role in the aggressive nature of cancers through mediating dispersal.

High-content high-throughput imaging reveals distinct connections between mitochondrial morphology and functionality for OXPHOS complex I, III, and V inhibitors

Cells can adjust their mitochondrial morphology by altering the balance between mitochondrial fission and fusion to adapt to stressful conditions. The connection between a chemical perturbation, changes in mitochondrial function, and altered mitochondrial morphology is not well understood. Here, we made use of high-throughput high-content confocal microscopy to assess the effects of distinct classes of oxidative phosphorylation (OXPHOS) complex inhibitors on mitochondrial parameters in a concentration and time resolved manner. Mitochondrial morphology phenotypes were clustered based on machine learning algorithms and mitochondrial integrity patterns were mapped. In parallel, changes in mitochondrial membrane potential (MMP), mitochondrial and cellular ATP levels, and viability were microscopically assessed. We found that inhibition of MMP, mitochondrial ATP production, and oxygen consumption rate (OCR) using sublethal concentrations of complex I and III inhibitors did not trigger mitochondrial fragmentation. Instead, complex V inhibitors that suppressed ATP and OCR but increased MMP provoked a more fragmented mitochondrial morphology. In agreement, complex V but not complex I or III inhibitors triggered proteolytic cleavage of the mitochondrial fusion protein, OPA1. The relation between increased MMP and fragmentation did not extend beyond OXPHOS complex inhibitors: increasing MMP by blocking the mPTP pore did not lead to OPA1 cleavage or mitochondrial fragmentation and the OXPHOS uncoupler FCCP was associated with OPA1 cleavage and MMP reduction. Altogether, our findings connect vital mitochondrial functions and phenotypes in a high-throughput high-content confocal microscopy approach that help understanding of chemical-induced toxicity caused by OXPHOS complex perturbing chemicals.

Quantifying the contribution of transcription factor activity, mutations and microRNAs to CD274 expression in cancer patients

Immune checkpoint inhibitors targeting the programmed cell death protein 1 (PD-1)/programmed cell death protein ligand 1 (PD-L1) axis have been remarkably successful in inducing tumor remissions in several human cancers, yet a substantial number of patients do not respond to treatment. Because this may be partially due to the mechanisms giving rise to high PD-L1 expression within a patient, it is highly relevant to fully understand these mechanisms. In this study, we conduct a bioinformatic analysis to quantify the relative importance of transcription factor (TF) activity, microRNAs (miRNAs) and mutations in determining PD-L1 (CD274) expression at mRNA level based on data from the Cancer Genome Atlas. To predict individual CD274 levels based on TF activity, we developed multiple linear regression models by taking the expression of target genes of the TFs known to directly target PD-L1 as independent variables. This analysis showed that IRF1, STAT1, NFKB and BRD4 are the most important regulators of CD274 expression, explaining its mRNA levels in 90–98% of the patients. Because the remaining patients had high CD274 levels independent of these TFs, we next investigated whether mutations associated with increased CD274 mRNA levels, and low levels of miRNAs associated with negative regulation of CD274 expression could cause high CD274 levels in these patients. We found that mutations or miRNAs offered an explanation for high CD274 levels in 81–100% of the underpredicted patients. Thus, CD274 expression is largely explained by TF activity, and the remaining unexplained cases can largely be explained by mutations or low miRNA abundance.

Mapping the cellular response to electron transport chain inhibitors reveals selective signaling networks triggered by mitochondrial perturbation

Mitochondrial perturbation is a key event in chemical-induced organ toxicities that is incompletely understood. Here, we studied how electron transport chain (ETC) complex I, II, or III (CI, CII and CIII) inhibitors affect mitochondrial functionality, stress response activation, and cell viability using a combination of high-content imaging and TempO-Seq in HepG2 hepatocyte cells. CI and CIII inhibitors perturbed mitochondrial membrane potential (MMP) and mitochondrial and cellular ATP levels in a concentration- and time-dependent fashion and, under conditions preventing a switch to glycolysis attenuated cell viability, whereas CII inhibitors had no effect. TempO-Seq analysis of changes in mRNA expression pointed to a shared cellular response to CI and CIII inhibition. First, to define specific ETC inhibition responses, a gene set responsive toward ETC inhibition (and not to genotoxic, oxidative, or endoplasmic reticulum stress) was identified using targeted TempO-Seq in HepG2. Silencing of one of these genes, NOS3, exacerbated the impact of CI and CIII inhibitors on cell viability, indicating its functional implication in cellular responses to mitochondrial stress. Then by monitoring dynamic responses to ETC inhibition using a HepG2 GFP reporter panel for different classes of stress response pathways and applying pathway and gene network analysis to TempO-Seq data, we looked for downstream cellular events of ETC inhibition and identified the amino acid response (AAR) as being triggered in HepG2 by ETC inhibition. Through in silico approaches we provide evidence indicating that a similar AAR is associated with exposure to mitochondrial toxicants in primary human hepatocytes. Altogether, we (i) unravel quantitative, time- and concentration-resolved cellular responses to mitochondrial perturbation, (ii) identify a gene set associated with adaptation to exposure to active ETC inhibitors, and (iii) show that ER stress and an AAR accompany ETC inhibition in HepG2 and primary hepatocytes.

Dynamic Modeling of Mitochondrial Membrane Potential Upon Exposure to Mitochondrial Inhibitors

Mitochondria are the main bioenergetic organelles of cells. Exposure to chemicals targeting mitochondria therefore generally results in the development of toxicity. The cellular response to perturbations in cellular energy production is a balance between adaptation, by reorganisation and organelle biogenesis, and sacrifice, in the form of cell death. In homeostatic conditions, aerobic mitochondrial energy production requires the maintenance of a mitochondrial membrane potential (MMP). Chemicals can perturb this MMP, and the extent of this perturbation depends both on the pharmacokinetics of the chemicals and on downstream MMP dynamics. Here we obtain a quantitative understanding of mitochondrial adaptation upon exposure to various mitochondrial respiration inhibitors by applying mathematical modeling to partially published high-content imaging time-lapse confocal imaging data, focusing on MMP dynamics in HepG2 cells over a period of 24 h. The MMP was perturbed using a set of 24 compounds, either acting as uncoupler or as mitochondrial complex inhibitor targeting complex I, II, III or V. To characterize the effect of chemical exposure on MMP dynamics, we adapted an existing differential equation model and fitted this model to the observed MMP dynamics. Complex III inhibitor data were better described by the model than complex I data. Incorporation of pharmacokinetic decay into the model was required to obtain a proper fit for the uncoupler FCCP. Furthermore, oligomycin (complex V inhibitor) model fits were improved by either combining pharmacokinetic (PK) decay and ion leakage or a concentration-dependent decay. Subsequent mass spectrometry measurements showed that FCCP had a significant decay in its PK profile as predicted by the model. Moreover, the measured oligomycin PK profile exhibited only a limited decay at high concentration, whereas at low concentrations the compound remained below the detection limit within cells. This is consistent with the hypothesis that oligomycin exhibits a concentration-dependent decay, yet awaits further experimental verification with more sensitive detection methods. Overall, we show that there is a complex interplay between PK and MMP dynamics within mitochondria and that data-driven modeling is a powerful combination to unravel such complexity.

Mathematical Modelling Based on In Vivo Imaging Suggests CD137-Stimulated Cytotoxic T Lymphocytes Exert Superior Tumour Control Due to an Enhanced Antimitotic Effect on Tumour Cells

Simple Summary

Cytotoxic T lymphocytes (CTLs) play an important role in controlling tumours, and an improved understanding of how they accomplish this will benefit immunotherapeutic cancer treatment strategies. Stimulation of CTLs by targeting their CD137 receptor is a strategy currently under investigation for enhancing responses against tumours, yet so far only limited quantitative knowledge regarding the effects of such stimulation upon CTLs has been obtained. Here, we develop mathematical models to describe dynamic in vivo two-photon imaging of tumour infiltrating CTLs, to characterise differences in their function either in the presence or absence of a CD137 agonist antibody. We showed that an increased antiproliferative effect and a more sustained presence of CTLs within the tumour were the most important effects associated with anti-CD137 treatment.

Abstract

Several immunotherapeutic strategies for the treatment of cancer are under development. Two prominent strategies are adoptive cell transfer (ACT) of CTLs and modulation of CTL function with immune checkpoint inhibitors or with costimulatory antibodies. Despite some success with these approaches, there remains a lack of detailed and quantitative descriptions of the events following CTL transfer and the impact of immunomodulation. Here, we have applied ordinary differential equation models to two photon imaging data derived from a B16F10 murine melanoma. Models were parameterised with data from two different treatment conditions: either ACT-only, or ACT with intratumoural costimulation using a CD137 targeted antibody. Model dynamics and best fitting parameters were compared, in order to assess the mode of action of the CTLs and examine how the CD137 antibody influenced their activities. We found that the cytolytic activity of the transferred CTLs was minimal without CD137 costimulation, and that the CD137 targeted antibody did not enhance the per-capita killing ability of the transferred CTLs. Instead, the results of our modelling study suggest that an antiproliferative effect of CTLs exerted upon the tumour likely accounted for the majority of the reduction in tumour growth after CTL transfer. Moreover, we found that CD137 most likely improved tumour control via enhancement of this antiproliferative effect, as well as prolonging the period in which CTLs were inside the tumour, leading to a sustained duration of their antitumour effects following CD137 stimulation.

Stimulation of de novo glutathione synthesis by nitrofurantoin for enhanced resilience of hepatocytes

Toxicity is not only a function of damage mechanisms, but is also determined by cellular resilience factors. Glutathione has been reported as essential element to counteract negative influences. The present work hence pursued the question how intracellular glutathione can be elevated transiently to render cells more resistant toward harmful conditions. The antibiotic nitrofurantoin (NFT) was identified to stimulate de novo synthesis of glutathione in the human hepatoma cell line, HepG2, and in primary human hepatocytes. In intact cells, activation of NFT yielded a radical anion, which subsequently initiated nuclear-factor-erythroid 2-related-factor-2 (Nrf2)-dependent induction of glutamate cysteine ligase (GCL). Application of siRNA-based intervention approaches confirmed the involvement of the Nrf2-GCL axis in the observed elevation of intracellular glutathione levels. Quantitative activation of Nrf2 by NFT, and the subsequent rise in glutathione, were similar as observed with the potent experimental Nrf2 activator diethyl maleate. The elevation of glutathione levels, observed even 48 h after withdrawal of NFT, rendered cells resistant to different stressors such as the mitochondrial inhibitor rotenone, the redox cycler paraquat, the proteasome inhibitors MG-132 or bortezomib, or high concentrations of NFT. Repurpose of the antibiotic NFT as activator of Nrf2 could thus be a promising strategy for a transient and targeted activation of the endogenous antioxidant machinery. Graphical abstract.

A committed tissue-resident memory T cell precursor within the circulating CD8+ effector T cell pool

An increasing body of evidence emphasizes the role of tissue-resident memory T cells (T_RM) in the defense against recurring pathogens and malignant neoplasms. However, little is known with regard to the origin of these cells and their kinship to other CD8⁺ T cell compartments. To address this issue, we followed the antigen-specific progeny of individual naive CD8⁺ T cells to the T effector (T_EFF), T circulating memory (T_CIRCM), and T_RM pools by lineage-tracing and single-cell transcriptome analysis. We demonstrate that a subset of T cell clones possesses a heightened capacity to form T_RM, and that enriched expression of T_RM–fate-associated genes is already apparent in the circulating T_EFF offspring of such clones. In addition, we demonstrate that the capacity to generate T_RM is permanently imprinted at the clonal level, before skin entry. Collectively, these data provide compelling evidence for early stage T_RM fate decisions and the existence of committed T_RM precursor cells in the circulatory T_EFF compartment.

TIL therapy broadens the tumor-reactive CD8(+) T cell compartment in melanoma patients

There is strong evidence that both adoptive T cell transfer and T cell checkpoint blockade can lead to regression of human melanoma. However, little data are available on the effect of these cancer therapies on the tumor-reactive T cell compartment. To address this issue we have profiled therapy-induced T cell reactivity against a panel of 145 melanoma-associated CD8(+) T cell epitopes. Using this approach, we demonstrate that individual tumor-infiltrating lymphocyte cell products from melanoma patients contain unique patterns of reactivity against shared melanoma-associated antigens, and that the combined magnitude of these responses is surprisingly low. Importantly, TIL therapy increases the breadth of the tumor-reactive T cell compartment in vivo, and T cell reactivity observed post-therapy can almost in full be explained by the reactivity observed within the matched cell product. These results establish the value of high-throughput monitoring for the analysis of immuno-active therapeutics and suggest that the clinical efficacy of TIL therapy can be enhanced by the preparation of more defined tumor-reactive T cell products.

Heterogeneous, delayed-onset killing by multiple-hitting T cells: Stochastic simulations to assess methods for analysis of imaging data

Although quantitative insights into the killing behaviour of Cytotoxic T Lymphocytes (CTLs) are necessary for the rational design of immune-based therapies, CTL killing function remains insufficiently characterised. One established model of CTL killing treats CTL cytotoxicity as a Poisson process, based on the assumption that CTLs serially kill antigen-presenting target cells via delivery of lethal hits, each lethal hit corresponding to a single injection of cytotoxic proteins into the target cell cytoplasm. Contradicting this model, a recent in vitro study of individual CTLs killing targets over a 12-hour period found significantly greater heterogeneity in CTL killing performance than predicted by Poisson-based killing. The observed killing process was dynamic and varied between CTLs, with the best performing CTLs exhibiting a marked increase in killing during the final hours of the experiments, along with a "burst killing" kinetic. Despite a search for potential differences between CTLs, no mechanistic explanation for the heterogeneous killing kinetics was found. Here we have used stochastic simulations to assess whether target cells might require multiple hits from CTLs before undergoing apoptosis, in order to verify whether multiple-hitting could explain the late onset, burst killing dynamics observed in vitro. We found that multiple-hitting from CTLs was entirely consistent with the observed killing kinetics. Moreover, the number of available targets and the spatiotemporal kinetics of CTL:target interactions influenced the realised CTL killing rate. We subsequently used realistic, spatial simulations to assess methods for estimating the hitting rate and the number of hits required for target death, to be applied to microscopy data of individual CTLs killing targets. We found that measuring the cumulative duration of individual contacts that targets have with CTLs would substantially improve accuracy when estimating the killing kinetics of CTLs.

ATF6 Is a Critical Determinant of CHOP Dynamics during the Unfolded Protein Response

The unfolded protein response (UPR) pathway senses unfolded proteins and regulates proteostasis and cell fate through activity of the transcription factors ATF4, ATF6, and XBP1 within a complex network of three main branches. Here, we investigated contributions of the three branches to UPR activity in single cells using microscopy-based quantification and dynamic modeling. BAC-GFP HepG2 reporter cell lines were exposed to tunicamycin, and activation of various UPR components was monitored for 24 h. We constructed a dynamic model to describe the adaptive UPR signaling network, for which incorporation of all three branches was required to match the data. Our calibrated model suggested that ATF6 shapes the early dynamics of pro-apoptotic CHOP. We confirmed this hypothesis by measurements beyond 24 h, by perturbing single siRNA knockdowns and by ATF6 measurements. Overall, our work indicates that ATF6 is an important regulator of CHOP, which in turn regulates cell fate decisions.

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A mathematical model of the unfolded protein response describes microscopy data

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Integration of modeling and experimental work offers insights into UPR regulation

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ATF6 shapes the early dynamics of the CHOP response

Contact-Dependent Killing by Cytotoxic T Lymphocytes Is Insufficient for EL4 Tumor Regression In Vivo

Immunotherapies are an emerging strategy for treatment of solid tumors. Improved understanding of the mechanisms employed by cytotoxic T lymphocytes (CTL) to control tumors will aid in the development of immunotherapies. CTLs can directly kill tumor cells in a contact-dependent manner or may exert indirect effects on tumor cells via secretion of cytokines. Here, we aim to quantify the importance of these mechanisms in murine thymoma EL4/EG7 cells. We developed an agent-based model (ABM) and an ordinary differential equation model of tumor regression after adoptive transfer of a population of CTLs. Models were parameterized based on in vivo measurements of CTL infiltration and killing rates applied to EL4/EG7 tumors and OTI T cells. We quantified whether infiltrating CTLs are capable of controlling tumors through only direct, contact-dependent killing. Both models agreed that the low measured killing rate of CTLs in vivo was insufficient to cause tumor regression. In our ABM, we also simulated CTL production of the cytokine IFNγ in order to explore how an antiproliferative effect of IFNγ might aid CTLs in tumor control. In this model, IFNγ substantially reduced tumor growth compared with direct killing alone. Collectively, these data demonstrate that contact-dependent killing is insufficient for EL4 regression in vivo and highlight the potential importance of cytokine-induced antiproliferative effects in T-cell-mediated tumor control. SIGNIFICANCE: Computational modeling highlights the importance of cytokine-induced antiproliferative effects in T-cell-mediated control of tumor progression.

Single-cell imaging of CAR T cell activity in vivo reveals extensive functional and anatomical heterogeneity

Cazaux et al. use intravital imaging to dissect anti-CD19 CAR T cell activity. This study uncovers both anatomical and functional diversity in the outcome of anti-CD19 CAR T cell interactions with tumor cells impacting engraftment, killing dynamics, and tumor immunoediting.

CAR T cells represent a potentially curative strategy for B cell malignancies. However, the outcome and dynamics of CAR T cell interactions in distinct anatomical sites are poorly understood. Using intravital imaging, we tracked interactions established by anti-CD19 CAR T cells in B cell lymphoma–bearing mice. Circulating targets trapped CAR T cells in the lungs, reducing their access to lymphoid organs. In the bone marrow, tumor apoptosis was largely due to CAR T cells that engaged, killed, and detached from their targets within 25 min. Notably, not all CAR T cell contacts elicited calcium signaling or killing while interacting with tumors, uncovering extensive functional heterogeneity. Mathematical modeling revealed that direct killing was sufficient for tumor regression. Finally, antigen-loss variants emerged in the bone marrow, but not in lymph nodes, where CAR T cell cytotoxic activity was reduced. Our results identify a previously unappreciated level of diversity in the outcomes of CAR T cell interactions in vivo, with important clinical implications.

Heritable tumor cell division rate heterogeneity induces clonal dominance

Tumors consist of a hierarchical population of cells that differ in their phenotype and genotype. This hierarchical organization of cells means that a few clones (i.e., cells and several generations of offspring) are abundant while most are rare, which is called clonal dominance. Such dominance also occurred in published in vitro iterated growth and passage experiments with tumor cells in which genetic barcodes were used for lineage tracing. A potential source for such heterogeneity is that dominant clones derive from cancer stem cells with an unlimited self-renewal capacity. Furthermore, ongoing evolution and selection within the growing population may also induce clonal dominance. To understand how clonal dominance developed in the iterated growth and passage experiments, we built a computational model that accurately simulates these experiments. The model simulations reproduced the clonal dominance that developed in in vitro iterated growth and passage experiments when the division rates vary between cells, due to a combination of initial variation and of ongoing mutational processes. In contrast, the experimental results can neither be reproduced with a model that considers random growth and passage, nor with a model based on cancer stem cells. Altogether, our model suggests that in vitro clonal dominance develops due to selection of fast-dividing clones.

Tumors consist of numerous cell populations, i.e., clones, that differ with respect to genotype, and potentially with respect to phenotype, and these populations strongly differ in their size. A limited number of clones tend to dominate tumors, but it remains unclear how this clonal dominance arises. Potential driving mechanisms are the presence of cancer stem cells, which either divide indefinitely of differentiate into cells with a limited division potential, and ongoing evolutionary processes within the tumor. Here we use a computational model to understand how clonal dominance developed during in vitro growth and passage experiments with cancer cells. Incorporating cancer stem cells in this model did not result in a match between simulations and in vitro data. In contrast, by considering all cells to divide indefinitely and division rates to evolve due to the combination of division rate variability and selection by passage, our model closely matches the in vitro data.

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.

A Sigmoid Functional Response Emerges When Cytotoxic T Lymphocytes Start Killing Fresh Target Cells

Cytotoxic T lymphocyte (CTL)-mediated killing involves the formation of a synapse with a target cell, followed by delivery of perforin and granzymes. Previously, we derived a general functional response for CTL killing while considering that CTLs form stable synapses (i.e., single-stage) and that the number of conjugates remains at steady state. However, the killing of target cells sometimes requires multiple engagements (i.e., multistage). To study how multistage killing and a lack of steady state influence the functional response, we here analyze a set of differential equations as well as simulations employing the cellular Potts model, in both cases describing CTLs that kill target cells. We find that at steady state the total killing rate (i.e., the number of target cells killed by all CTLs) is well described by the previously derived double saturation function. Compared to single-stage killing, the total killing rate during multistage killing saturates at higher CTL and target cell densities. Importantly, when the killing is measured before the steady state is approached, a qualitatively different functional response emerges for two reasons: First, the killing signal of each CTL gets diluted over several targets and because this dilution effect is strongest at high target cell densities; this can result in a peak in the dependence of the total killing rate on the target cell density. Second, the total killing rate exhibits a sigmoid dependence on the CTL density when killing is a multistage process, because it takes typically more than one CTL to kill a target. In conclusion, a sigmoid dependence of the killing rate on the CTLs during initial phases of killing may be indicative of a multistage killing process. Observation of a sigmoid functional response may thus arise from a dilution effect and is not necessarily due to cooperative behavior of the CTLs.

Abstract 4535: Tumor cell division rate heterogeneity explains in vitro clonal dominance

The cells that make up a tumor exhibit large phenotypic and genotypic variation, which strongly influences the effectiveness of cancer therapy. A typical manifestation of intratumor heterogeneity is the presence of a few clones that represent most of the tumor population, i.e. clonal dominance. Such heterogeneity could potentially be caused by the occurrence of different ratios of cancer stem cells and differentiated cells per clone, or, alternatively, clonal dominance may be caused by heterogeneity of division rates between clones. Previously published work, applying lineage tracing with genetic barcodes to in vitro iterated growth and passage experiments demonstrated that clonal dominance was accompanied by clone loss and partial overlap in dominant clones between biological replicates [1]. To identify the source of these observations, we build a computational model that simulates the iterated growth and passage experiments. By varying the 'division rules' in the model, we can quantitatively predict the evolution of the clone distribution for the aforementioned hypotheses. In this manner, we show that clonal dominance does not appear in simulations where all cells divide at a uniform rate, even when the population is subdivided in cancer stem cells and differentiated cells. However, when the division rates vary between clones and are inherited from parent cells, dominant clones appear. These simulations also closely match the in vitro evolution of clone loss and clonal overlap between biological replicates. Altogether, our findings suggest that tumor cells exhibit a heritable variability in the division rates of individual cells.

References

[1] Porter, S. et al. (2014). Lentiviral and targeted cellular barcoding reveals ongoing clonal dynamics of cell lines in vitro and in vivo. Genome Biology, 15(5), R75.

Citation Format: Margriet M. Palm, Marjet Elemans, Joost B. Beltman. Tumor cell division rate heterogeneity explains in vitro clonal dominance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4535. doi:10.1158/1538-7445.AM2017-4535

Tissue Dimensionality Influences the Functional Response of Cytotoxic T Lymphocyte-Mediated Killing of Targets

Cytotoxic T lymphocyte (CTL)-mediated killing of virus infections and tumors occurs over a wide range of conditions. The spatial environments in which CTLs encounter target cells vary from narrow vessels, to two-dimensional epithelial tissues, to densely populated 3-dimensional (3D) T cell areas within lymphoid tissues. How such spatial environments alter the functional response of CTL-mediated killing, i.e., how the killing efficiency depends on cell densities, is unclear. In this study, we perform cellular Potts model simulations in different spatial configurations to investigate how the dimensionality of the space affects the functional response of CTL-mediated killing. Irrespective of the spatial configuration, the function with separate saturation constants for CTL and for target cell densities that we previously proposed can in all cases describe the response, demonstrating its generality. However, the tissue dimensionality determines at which cell densities the killing rate starts to saturate. We show that saturation in a fully 3D environment is stronger than in a "flat" 3D environment, which is largely due to accompanying differences in the CTL-target encounter rates.

Combination Approaches with Immune-Checkpoint Blockade in Cancer Therapy

In healthy individuals, immune-checkpoint molecules prevent autoimmune responses and limit immune cell-mediated tissue damage. Tumors frequently exploit these molecules to evade eradication by the immune system. Over the past years, immune-checkpoint blockade of cytotoxic T lymphocyte antigen-4 and programed death-1 emerged as promising strategies to activate antitumor cytotoxic T cell responses. Although complete regression and long-term survival is achieved in some patients, not all patients respond. This review describes promising, novel combination approaches involving immune-checkpoint blockade in the context of the cancer-immunity cycle, aimed at increasing response rates to the single treatments. Specifically, we discuss combinations that promote antigen release and presentation, that further amplify T cell activation, that inhibit trafficking of regulatory T cells or MSDCs, that stimulate intratumoral T cell infiltration, that increase cancer recognition by T cells, and that stimulate tumor killing.

Unraveling cellular pathways contributing to drug-induced liver injury by dynamical modeling

INTRODUCTION

Drug-induced liver injury (DILI) is a significant threat to human health and a major problem in drug development. It is hard to predict due to its idiosyncratic nature and which does not show up in animal trials. Hepatic adaptive stress response pathway activation is generally observed in drug-induced liver injury. Dynamical pathway modeling has the potential to foresee adverse effects of drugs before they go in trial. Ordinary differential equation modeling can offer mechanistic insight, and allows us to study the dynamical behavior of stress pathways involved in DILI. Areas covered: This review provides an overview on the progress of the dynamical modeling of stress and death pathways pertinent to DILI, i.e. pathways relevant for oxidative stress, inflammatory stress, DNA damage, unfolded proteins, heat shock and apoptosis. We also discuss the required steps for applying such modeling to the liver. Expert opinion: Despite the strong progress made since the turn of the century, models of stress pathways have only rarely been specifically applied to describe pathway dynamics for DILI. We argue that with minor changes, in some cases only to parameter values, many of these models can be repurposed for application in DILI research. Combining both dynamical models with in vitro testing might offer novel screening methods for the harmful side-effects of drugs.

Reproducibility of Illumina platform deep sequencing errors allows accurate determination of DNA barcodes in cells

Background

Next generation sequencing (NGS) of amplified DNA is a powerful tool to describe genetic heterogeneity within cell populations that can both be used to investigate the clonal structure of cell populations and to perform genetic lineage tracing. For applications in which both abundant and rare sequences are biologically relevant, the relatively high error rate of NGS techniques complicates data analysis, as it is difficult to distinguish rare true sequences from spurious sequences that are generated by PCR or sequencing errors. This issue, for instance, applies to cellular barcoding strategies that aim to follow the amount and type of offspring of single cells, by supplying these with unique heritable DNA tags.

Results

Here, we use genetic barcoding data from the Illumina HiSeq platform to show that straightforward read threshold-based filtering of data is typically insufficient to filter out spurious barcodes. Importantly, we demonstrate that specific sequencing errors occur at an approximately constant rate across different samples that are sequenced in parallel. We exploit this observation by developing a novel approach to filter out spurious sequences.

Conclusions

Application of our new method demonstrates its value in the identification of true sequences amongst spurious sequences in biological data sets.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0999-4) contains supplementary material, which is available to authorized users.

Subtle CXCR3-Dependent Chemotaxis of CTLs within Infected Tissue Allows Efficient Target Localization

It is well established how effector T cells exit the vasculature to enter the peripheral tissues in which an infection is ongoing. However, less is known regarding how CTLs migrate toward infected cells after entry into peripheral organs. Recently, it was shown that the chemokine receptor CXCR3 on T cells has an important role in their ability to localize infected cells and to control vaccinia virus infection. However, the search strategy of T cells for virus-infected targets has not been investigated in detail and could involve chemotaxis toward infected cells, chemokinesis (i.e., increased motility) combined with CTL arrest when targets are detected, or both. In this study, we describe and analyze the migration of CTLs within HSV-1-infected epidermis in vivo. We demonstrate that activated T cells display a subtle distance-dependent chemotaxis toward clusters of infected cells and confirm that this is mediated by CXCR3 and its ligands. Although the chemotactic migration is weak, computer simulations based on short-term experimental data, combined with subsequent long-term imaging indicate that this behavior is crucial for efficient target localization and T cell accumulation at effector sites. Thus, chemotactic migration of effector T cells within peripheral tissue forms an important factor in the speed with which T cells are able to arrive at sites of infection.

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.

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.

Heterogeneous differentiation patterns of individual CD8+ T cells

Upon infection, antigen-specific CD8(+) T lymphocyte responses display a highly reproducible pattern of expansion and contraction that is thought to reflect a uniform behavior of individual cells. We tracked the progeny of individual mouse CD8(+) T cells by in vivo lineage tracing and demonstrated that, even for T cells bearing identical T cell receptors, both clonal expansion and differentiation patterns are heterogeneous. As a consequence, individual naïve T lymphocytes contributed differentially to short- and long-term protection, as revealed by participation of their progeny during primary versus recall infections. The discordance in fate of individual naïve T cells argues against asymmetric division as a singular driver of CD8(+) T cell heterogeneity and demonstrates that reproducibility of CD8(+) T cell responses is achieved through population averaging.

Chemotactic migration of T cells towards dendritic cells promotes the detection of rare antigens

In many immunological processes chemoattraction is thought to play a role in guiding cells to their sites of action. However, based on in vivo two-photon microscopy experiments in the absence of cognate antigen, T cell migration in lymph nodes (LNs) has been roughly described as a random walk. Although it has been shown that dendritic cells (DCs) carrying cognate antigen in some circumstances attract T cells chemotactically, it is currently still unclear whether chemoattraction of T cells towards DCs helps or hampers scanning. Chemoattraction towards DCs could on the one hand help T cells to rapidly find DCs. On the other hand, it could be deleterious if DCs become shielded by a multitude of attracted yet non-specific T cells. Results from a recent simulation study suggested that the deleterious effect dominates. We re-addressed the question whether T cell chemoattraction towards DCs is expected to promote or hamper the detection of rare antigens using the Cellular Potts Model, a formalism that allows for dynamic, flexible cellular shapes and cell migration. Our simulations show that chemoattraction of T cells enhances the DC scanning efficiency, leading to an increased probability that rare antigen-specific T cells find DCs carrying cognate antigen. Desensitization of T cells after contact with a DC further improves the scanning efficiency, yielding an almost threefold enhancement compared to random migration. Moreover, the chemotaxis-driven migration still roughly appears as a random walk, hence fine-tuned analysis of cell tracks will be required to detect chemotaxis within microscopy data.

Analysing immune cell migration

The visualization of the dynamic behaviour of and interactions between immune cells using time-lapse video microscopy has an important role in modern immunology. To draw robust conclusions, quantification of such cell migration is required. However, imaging experiments are associated with various artefacts that can affect the estimated positions of the immune cells under analysis, which form the basis of any subsequent analysis. Here, we describe potential artefacts that could affect the interpretation of data sets on immune cell migration. We propose how these errors can be recognized and corrected, and suggest ways to prevent the data analysis itself leading to biased results.

Towards estimating the true duration of dendritic cell interactions with T cells

To initiate an adaptive immune response, T cells need to interact with dendritic cells (DCs), and the duration of these interactions plays an important role. In vitro and in vivo experiments have generally tried to estimate the required period of opportunity for T cell stimulation rather than the duration of individual T cell-DC interactions. Since the application of multi-photon microscopy (MPM) to living lymphoid tissues, the interactions between immune cells, as well as the duration thereof, can directly be observed in vivo. Indeed, long-lasting interactions between T cells and DCs were shown to be important for the onset of immune responses. However, because MPM imaging is typically restricted to experiments lasting 1 h, and because T cell-DC conjugates frequently move into and out of the imaged volume, it is difficult to estimate the true duration of interactions from MPM contact data. Here, we present a method to properly make such an estimate of (the average of) the distribution of contact durations. We validate the method by applying it to spatially explicit computer simulations where the true distribution of contact duration is known. Finally, we apply our analysis to a large experimental data set of T-DC contacts, and predict an average contact time of about three hours. However, we identify a mismatch between the experimental data and the model predictions, and investigate possible causes of the mismatch, including minor tissue drift during imaging experiments. We discuss in detail how future experiments can be optimized such that MPM contact data will be minimally affected by these factors.

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).

Spatial modelling of brief and long interactions between T cells and dendritic cells

In the early phases of an immune response, T cells of appropriate antigen specificity become activated by antigen-presenting cells in secondary lymphoid organs. Two-photon microscopy imaging experiments have shown that this stimulation occurs in distinct stages during which T cells exhibit different motilities and interactions with dendritic cells (DCs). In this paper, we utilize the Cellular Potts Model, a model formalism that takes cell shapes and cellular interactions explicitly into account, to simulate the dynamics of, and interactions between, T cells and DCs in the lymph node paracortex. Our three-dimensional simulations suggest that the initial decrease in T-cell motility after antigen appearance is due to "stop signals" transmitted by activated DCs to T cells. The long-lived interactions that occur at a later stage can only be explained by the presence of both stop signals and a high adhesion between specific T cells and antigen-bearing DCs. Furthermore, our results indicate that long-lasting contacts with T cells are promoted when DCs retract dendrites that detect a specific contact at lower velocities than other dendrites. Finally, by performing long simulations (after prior fitting to short time scale data) we are able to provide an estimate of the average contact duration between T cells and DCs.

Speciation: more likely through a genetic or through a learned habitat preference?

A problem in understanding sympatric speciation is establishing how reproductive isolation can arise when there is disruptive selection on an ecological trait. One of the solutions that has been proposed is that a habitat preference evolves, and that mates are chosen within the preferred habitat. We present a model where the habitat preference can evolve either by means of a genetic mechanism or by means of learning. Employing an adaptive-dynamical analysis, we show that evolution proceeds either to a single population of specialists with a genetic preference for their optimal habitat, or to a population of generalists without a habitat preference. The generalist population subsequently experiences disruptive selection. Learning promotes speciation because it increases the intensity of disruptive selection. An individual-based version of the model shows that, when loci are completely unlinked and learning confers little cost, the presence of disruptive selection most probably leads to speciation via the simultaneous evolution of a learned habitat preference. For high costs of learning, speciation is most likely to occur via the evolution of a genetic habitat preference. However, the latter only happens when the effect of mutations is large, or when there is linkage between genes coding for the different traits.

Speciation through the learning of habitat features

Learning of environmental features can influence both mating behaviour and the location where young are produced. This may lead to speciation in three steps: (i) colonization of a new habitat, (ii) genetic divergence of the two groups by adaptation to the habitats, and (iii) a decrease of genetic mixing between the lineages (similar to reinforcement). In a previous paper we showed that steps (i) and (ii) occur readily for a wide range of fixed mating and habitat preferences. Here, we study whether this can ultimately lead to speciation through selective changes in these preferences. We show that this indeed occurs, and, furthermore, it is very general: for a large class of models there is selection toward producing young more frequently in the natal habitat. Once habitat preference is strong, there is selection toward stronger assortative mating. Even when steps (i) and (ii) initially fail, genetic divergence may succeed at a later evolutionary stage, after which a decrease of genetic mixing completes speciation. Our results show that speciation by the learning of habitat features is an extremely effective mechanism.

Learning and colonization of new niches: a first step toward speciation

Learning processes potentially play a role in speciation but are often ignored in speciation models. Learning may, for instance, play a role when a new niche is being colonized, because the learning of niche features may cause niche-specific assortative mating and a tendency to produce young in this niche. Several animal species learn about their environmental features that may be important in finding or attracting mates. We use a gene-culture coevolutionary model to look into the effect of such learning on the colonization of new niches and on the genetic divergence between groups using different niches, which are steps necessary in achieving speciation. We assume that density is regulated separately in each of the two niches and that the viability of an individual depends on its genotype as well as on which niche it exploits. Our results show that genetic adaptation to the new niche is enhanced by a high female fecundity and a low viability selection against heterozygotes. Furthermore, when initial colonization (without genetic adaptation) fails, genetic divergence is more difficult when the mating preference is stronger. In contrast, when colonization without genetic adaptation is successful, a stronger mating preference makes genetic divergence easier. An increase in the number of egg-laying mistakes by females can have a positive or negative effect on the success of genetic adaptation depending on other parameters. We show that genetic divergence can be prevented by a niche shift, which can occur only if viabilities in the two niches are asymmetrical.

MHC polymorphism under host-pathogen coevolution

The genes encoding major histocompatibility (MHC) molecules are among the most polymorphic genes known for vertebrates. Since MHC molecules play an important role in the induction of immune responses, the evolution of MHC polymorphism is often explained in terms of increased protection of hosts against pathogens. Two selective pressures that are thought to be involved are (1) selection favoring MHC heterozygous hosts, and (2) selection for rare MHC alleles by host-pathogen coevolution. We have developed a computer simulation of coevolving hosts and pathogens to study the relative impact of these two mechanisms on the evolution of MHC polymorphism. We found that heterozygote advantage per se is insufficient to explain the high degree of polymorphism at the MHC, even in very large host populations. Host-pathogen coevolution, on the other hand, can easily account for realistic polymorphisms of more than 50 alleles per MHC locus. Since evolving pathogens mainly evade presentation by the most common MHC alleles in the host population, they provide a selective pressure for a large variety of rare MHC alleles. Provided that the host population is sufficiently large, a large set of MHC alleles can persist over many host generations under host-pathogen coevolution, despite the fact that allele frequencies continuously change.