Introduction:While there is evidence that Ebola virus (EBOV) antagonizes the antiviral type I interferon (IFN-I) response, the role of IFN-I for EBOV disease remains controversial, as initially protective responses may contribute to disease pathogenesis later. Methods:We analyzed patient data from the 2014-2016 West Africa epidemic with a combination of machine learning and mathematical modeling to identify predictive immune mediators and reconstruct their temporal dynamics in survivors and non-survivors. Results:Our results suggest that IFN-I response in survivors occurs before symptom onset, while non-survivors mount IFN-I responses 3-4 days later, although with a similar strength. This delayed IFN-I response overlaps in time with IL-12 signals in non-survivors. As optimal T cell activation requires a particular temporal sequence in cytokine signals, this impairs the development of T cell-based cellular immunity. Discussion:The presented patient data analysis helps reconcile the seemingly contradictory role of IFN-I in EBOV disease from a cytokine dynamics perspective and supports the theory of sequential T cell activation, according to which a dysregulated temporal sequence of cytokine signals keeps T cells unresponsive to the pathogen.

Epidemic and pandemic preparedness with rapid outbreak response rely on timely, trustworthy evidence. Mathematical models are crucial for supporting timely and reliable evidence generation for public health decision-making with models spanning approaches from compartmental and metapopulation models to detailed agent-based simulations. Yet, the accompanying software ecosystem remains fragmented across model types, spatial resolutions, and computational targets, making models harder to compare, extend, and deploy at scale. Here we present MEmilio, a modular, high-performance framework for epidemic simulation that harmonizes the specification and execution of diverse dynamic epidemiological models within a unified and harmonized architecture. MEmilio couples an efficient C++ simulation core with coherent model descriptions and a user-friendly Python interface, enabling workflows that run on laptops as well as high-performance computing systems. Standardized representations of space, demography, and mobility support straightforward adaptations in resolution and population size, facilitating systematic inter-model comparisons and ensemble studies. The framework integrates readily with established tools for uncertainty quantification and parameter inference, supporting a broad range of applications from scenario exploration to calibration. Finally, strict software-engineering practices, including extensive unit and continuous integration testing, promote robustness and minimize the risk of errors as the framework evolves. By unifying implementations across modeling paradigms, MEmilio aims to lower barriers to reuse and generalize models, enable principled comparisons of implicit assumptions, and accelerate the development of novel approaches that strengthen modeling-based outbreak preparedness.

An efficacious HIV vaccine will need to generate broadly neutralizing antibodies (bnAbs) against distinct viral epitopes. To facilitate this, immunogens targeting precursor B cells of bnAbs have been developed. With this strategy, individual immunogens can even target multiple lineages, thereby beneficially limiting the number of immunogens needed for a multi-bnAb-generating vaccine. However, it is unclear whether this approach diminishes the responses compared with isolated targeting of lineages with distinct immunogens. Here, we address this using an in silico model of naive B cell activation and affinity maturation in germinal centers. By incorporating the (1) precursor properties and (2) epitope masking by antibodies obtained from germinal center-derived plasma cells, the model recapitulated features of bnAb lineage evolution as seen in pre-clinical mouse models. Our model predicts that under physiologically relevant conditions, priming of multiple bnAb lineages with a single immunogen can be additive, thus having implications for further testing and development of multi-lineage-targeting immunogens.

Pancreatic islet β-cells step-up insulin production and secretion in response to the elevation of blood glucose levels. ICA512/PTPRN is a transmembrane cargo of the insulin secretory granules (SGs). Upon SG exocytosis, its Ca2+/calpain mediated cleavage at the plasma membrane generates an ICA512-cleaved cytosolic fragment (ICA512-CCF) which as a decoy phosphatase prolongs phospho-STAT5/3 activities, thereby enhancing the transcription of mRNAs for SG cargoes, including insulin and its own, to replenish SG stores. In addition, ICA512 positively regulates the expression of F-actin modifier villin . Villin, in turn, modulates the size of actin cages surrounding cortical SGs, hence regulating SG mobility and exocytosis. Here we show that villin controls the number of SG docking sites for exocytosis by directly interacting at low glucose with the t-SNARE SNAP-25, thus restricting in this condition SG access to fusion sites and insulin release. Replacement of ICA512-CCF N-terminal ubiquitin-acceptor lysine 609 (K609) with valine (V) stabilized ICA512-CCF in insulinoma cells and homozygous ICA512K609V mice and enhanced the levels of phospho-STAT5/3 and villin. In ICA512K609V female mice these molecular traits correlated with reduced body weight, improved insulin sensitivity, reduced basal insulin secretion and onset time for glucose stimulated insulin secretion. Taken together, these data demonstrates that SG exocytosis induced ICA512 retrograde signaling acts in concert with the novel SNARE complex regulator villin to plastically adapt β-cell actin cytoskeleton and access to SG docking sites for optimal control of insulin secretion in response to variations in extracellular glucose levels. ![Figure][1] Graphical abstract Model of ICA512 and STAT3/5–dependent regulation of β-cell function. At basal conditions, villin limits secretory granule (SG) docking by interacting with SNAP-25, restricting insulin release (1). Increased metabolic load triggers hyperglycemia-induced ICA512 processing and growth factor–mediated STAT3/5 phosphorylation (2–4), enhancing ISG mRNAs, granule biogenesis, and villin expression (5,6). Villin modulates SNARE protein availability, controlling SG docking and secretion. Together, ICA512 retrograde signaling and villin-mediated SNARE regulation dynamically remodel the β-cell actin cytoskeleton, adjusting SG docking site access to ensure appropriate insulin secretion in response to fluctuating glucose levels. ### Competing Interest Statement The authors have declared no competing interest. German Center for Diabetes Research German Ministry for Education and Research (BMBF) INNODIA and INNODIA HARVEST, 115797 (INNODIA), 945268 (INNODIA HARVEST) IRTG 2251: ICMSD, 288034826 (IRTG 2251: ICMSD; MS, PK) INTERCEPT Project, 1010954433. : CAIMed – Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine, ZN4257 [1]: pending:yes

Infectious diseases remain a major threat to human societies. During the recent COVID-19 pandemic, mathematical modeling and extensive computer simulations proved highly effective in supporting public health experts and decision makers. Despite these advances, the full potential of modern modeling approaches and digital technologies has not yet been realized. Many critical tasks – including expert consultations, model execution, scenario analyses, report preparation, and result communication – still relied heavily on manual, human-driven processes with each manual interaction introducing avoidable delays and limiting responsiveness during rapidly evolving outbreaks. Pandemic preparedness should opt for automated workflows and seamlessly integrated software modules that can improve pandemic mitigation capabilities by substantially reducing response times. For this step, we require robust and flexible computational infrastructure capable of supporting heterogeneous hardware and continuously evolving infectious-disease models. In addition, data sources need to be dynamically integrated. Managing such demands needs infrastructure that supports automated high-performance computing (HPC) workflows. Beyond computational performance, software infrastructure must ensure secure user and data management to comply with data-protection regulations and provide clear, transparent presentation of results to both decision makers and the public. Meeting the aforementioned challenges requires tight integration of state-of-the-art scientific software with modern, scalable infrastructure that can leverage supercomputing resources when necessary. For rapid deployment in future epidemic or pandemic scenarios, adherence to the FAIR principles for research software is critical to ensure reusability and sustainability.

In the course of antibody affinity maturation, germinal centre (GC) B cells mutate their immunoglobulin heavy- and light-chain genes in a process known as somatic hypermutation (SHM) 1–4 . Panels of mutant B cells with different binding affinities for antigens are then selected in a Darwinian manner, which leads to a progressive increase in affinity among the population 5 . As with any Darwinian process, rare gain-of-fitness mutations must be identified and common loss-of-fitness mutations avoided 6 . Progressive acquisition of mutations therefore poses a risk during large proliferative bursts 7 , when GC B cells undergo several cell cycles in the absence of affinity-based selection 8–13 . Using a combination of in vivo mouse experiments and mathematical modelling, here we show that GCs achieve this balance by strongly suppressing SHM during clonal-burst-type expansion, so that a large fraction of the progeny generated by these bursts does not deviate from their ancestral genotype. Intravital imaging and image-based cell sorting of a mouse strain carrying a reporter of cyclin-dependent kinase 2 (CDK2) activity showed that B cells that are actively undergoing proliferative bursts lack the transient CDK2 low ‘G0-like’ phase of the cell cycle in which SHM takes place. We propose a model in which inertially cycling B cells mostly delay SHM until the G0-like phase that follows their final round of division in the GC dark zone, thus maintaining affinity as they clonally expand in the absence of selection.

Agent-based models have proven to be useful tools in supporting decision-making processes in different application domains. The advent of modern computers and supercomputers has enabled these bottom-up approaches to realistically model human mobility and contact behavior. The COVID-19 pandemic showcased the urgent need for detailed and informative models that can answer research questions on transmission dynamics. We present a sophisticated agent-based model to simulate the spread of respiratory diseases. The model is highly modularized and can be used on various scales, from a small collection of buildings up to cities or countries. Although not being the focus of this paper, the model has undergone performance engineering on a single core and provides an efficient intra- and inter-simulation parallelization for time-critical decision-making processes. In order to allow answering research questions on individual level resolution, nonpharmaceutical intervention strategies such as face masks or venue closures can be implemented for particular locations or agents. In particular, we allow for sophisticated testing and isolation strategies to study the effects of minimal-invasive infectious disease mitigation. With realistic human mobility patterns for the region of Brunswick, Germany, we study the effects of different interventions between March 1st and May 30, 2021 in the SARS-CoV-2 pandemic. Our analyses suggest that symptom-independent testing has limited impact on the mitigation of disease dynamics if the dark figure in symptomatic cases is high. Furthermore, we found that quarantine length is more important than quarantine efficiency but that, with sufficient symptomatic control, also short quarantines can have a substantial effect.

Control of cell proliferation is critical for the lymphocyte life cycle. However, little is known about how stage-specific alterations in cell cycle behavior drive proliferation dynamics during T cell development. Here, we employed in vivo dual-nucleoside pulse labeling combined with the determination of DNA replication over time as well as fluorescent ubiquitination-based cell cycle indicator mice to establish a quantitative high-resolution map of cell cycle kinetics of thymocytes. We developed an agent-based mathematical model of T cell developmental dynamics. To generate the capacity for proliferative bursts, cell cycle acceleration followed a “stretch model” characterized by the simultaneous and proportional contraction of both G1 and S phases. Analysis of cell cycle phase dynamics during regeneration showed tailored adjustments of cell cycle phase dynamics. Taken together, our results highlight intrathymic cell cycle regulation as an adjustable system to maintain physiologic tissue homeostasis and foster our understanding of dysregulation of the T cell developmental program.

Immunization strategies are central to pathogen control, where efficacy relies on antigen uptake, distribution, persistence, and inflammatory context. We recently demonstrated that dermal lymphatic capillaries regulate antigen presentation in lymph nodes (LN) by restraining fluid and virion transport following vaccinia virus (VACV) infection by skin scarification. Concurrently, a perifollicular, LN lymphangiogenic response encapsulates expanding B cell follicles. Given the important role of antigen transport and uptake on humoral immunity, we tested the hypothesis that lymphatic remodeling in the skin and LNs regulates germinal center (GC)-dependent antibody responses during infection. Using a model of lymphatic-specific VEGFR2 inhibition, we found that inhibiting viral-induced lymphatic remodeling in skin and LNs prompted significant GC expansion but paradoxically decreases protective VACV-specific class-switched antibodies. While the larger GC responses appeared structurally normal, they failed to support a proliferative burst consistent with clonal selection. Mathematical modeling revealed that this disconnect between GC size and function arises from impaired productive T follicular cell interactions in larger GC volumes and consistent with this finding, the optimal GC size was evolutionarily conserved across diverse mammals. Finally, we found that the presence of virus in the LN initiates these changes in GC function, inhibits LN lymphangiogenesis, increases B cell follicle size, and reduces selection efficiency. Therefore, protecting LN lymphatic vessels from virus-induced interferons rescues perifollicular lymphatic growth and follicle size, indicating that LN lymphangiogenesis directly constrains the follicular response. In summary, this study underscores the central role of lymphatic remodeling in compartmentalizing antigen and inflammatory signals to optimize GC fitness and protective antibody responses.

Emerging infectious diseases and climate change are two of the major challenges in 21st century. Although over the past decades, highly-resolved mathematical models have contributed in understanding dynamics of infectious diseases and are of great aid when it comes to finding suitable intervention measures, they may need substantial computational effort and produce significant CO2 emissions. Two popular modeling approaches for mitigating infectious disease dynamics are agent-based and population-based models. Agent-based models (ABMs) offer a microscopic view and are thus able to capture heterogeneous human contact behavior and mobility patterns. However, insights on individual-level dynamics come with high computational effort that scales with the number of agents. On the other hand, population-based models (PBMs) using e.g. ordinary differential equations (ODEs) are computationally efficient even for large populations due to their complexity being independent of the population size. Yet, population-based models are restricted in their granularity as they assume a (to some extent) homogeneous and well-mixed population. To manage the trade-off between computational complexity and level of detail, we propose spatial- and temporal-hybrid models that use ABMs only in an area or time frame of interest. To account for relevant influences to disease dynamics, e.g., from outside, due to commuting activities, we use population-based models, only adding moderate computational costs. Our hybridization approach demonstrates significant reduction in computational effort by up to 98% - without losing the required depth in information in the focus frame. The hybrid models used in our numerical simulations are based on two recently proposed models, however, any suitable combination of ABM and PBM could be used, too. Concluding, hybrid epidemiological models can provide insights on the individual scale where necessary, using aggregated models where possible, thereby making a contribution to green computing.

IntroductionA protective humoral response to pathogens requires the development of high affinity antibodies in germinal centers (GC). The combination of antigens available during immunization has a strong impact on the strength and breadth of the antibody response. Antigens can display various levels of immunogenicity, and a hierarchy of immunodominance arises when the GC response to an antigen dampens the response to other antigens. Immunodominance is a challenge for the development of vaccines to mutating viruses, and for the development of broadly neutralizing antibodies. The extent by which antigens with different levels of immunogenicity compete for the induction of high affinity antibodies and therefore contribute to immunodominance is not known.MethodsHere, we perform in silico simulations of the GC response, using a structural representation of antigens with complex surface amino acid composition and topology. We generate antigens with complex domains of different levels of immunogenicity and perform simulations with combinations of these domains.ResultsWe found that GC dynamics were driven by the most immunogenic domain and immunodominance arose as affinity maturation to less immunogenic domain was inhibited. However, this inhibition was moderate since the less immunogenic domain exhibited a weak GC response in the absence of the most immunogenic domain. Less immunogenic domains reduced the dominance of GC responses to more immunogenic domains, albeit at a later time point.DiscussionThe simulations suggest that increased vaccine valency may decrease immunodominance of the GC response to strongly immunogenic domains and therefore, act as a potential strategy for the natural induction of broadly neutralizing antibodies in GC reactions.

Identifying immune modulators that impact neutralizing antibody responses against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is of great relevance. We postulated that high serum concentrations of soluble angiotensin-converting enzyme 2 (sACE2) might mask the spike and interfere with antibody maturation toward the SARS-CoV-2-receptor-binding motif (RBM). We tested 717 longitudinal samples from 295 COVID-19 patients and showed a 2- to 10 -fold increase of enzymatically active sACE2 (a-sACE2), with up to 1 m g/mL total sACE2 in moderate and severe patients. Fifty percent of COVID-19 sera inhibited ACE2 activity, in contrast to 1.3% of healthy donors and 4% of non-COVID-19 pneumonia patients. A mild inverse correlation of a-sACE2 with RBM-directed serum antibodies was observed. In silico , we show that sACE2 concentrations measured in COVID-19 sera can disrupt germinal center formation and inhibit timely production of high -affinity antibodies. We suggest that sACE2 is a biomarker for COVID-19 and that soluble receptors may contribute to immune suppression informing vaccine design.

In the realm of infectious disease control, accurate modeling of the transmission dynamics is pivotal. As human mobility and commuting patterns are key components of communicable disease spread, we introduce a novel travel time aware metapopulation model. Our model aims to enhance estimations of disease transmission. By providing more reliable assessments on the efficacy of interventions, curtailing personal rights or human mobility behavior through interventions can be minimized. The proposed model is an advancement over traditional compartmental models, integrating explicit transmission on travel and commute, a factor available in agent-based models but often neglected with metapopulation models. Our approach employs a multi-edge graph ODE-based (Graph-ODE) model, which represents the intricate interplay between mobility and disease spread. This granular modeling is particularly important when assessing the dynamics in densely connected urban areas or when heterogeneous structures across entire countries have to be assessed. The given approach can be coupled with any kind of ODE-based model. In addition, we propose a novel multi-layer waning immunity model that integrates waning of different paces for protection against mild and severe courses of the disease. As this is of particular interest for late-phase epidemic or endemic scenarios, we consider the late-phase of SARS-CoV-2 in Germany. The results of this work show that accounting for resolved mobility significantly influences the pattern of outbreaks. The improved model provides a refined tool for predicting outbreak trajectories and evaluating intervention strategies in relation to mobility by allowing us to assess the transmission that result on traveling. The insights derived from this model can serve as a basis for decisions on the implementation or suspension of interventions, such as mandatory masks on public transportation. Eventually, our model contributes to maintaining mobility as a social good while reducing exuberant disease dynamics potentially driven by travel activities.

Supplementary Methods from The Molecular Basis of Synergism between Carboplatin and ABT-737 Therapy Targeting Ovarian Carcinomas

Germinal centers (GCs) that form within lymphoid follicles during antibody responses are sites of massive cell death. Tingible body macrophages (TBMs) are tasked with apoptotic cell clearance to prevent secondary necrosis and autoimmune activation by intracellular self antigens. We show by multiple redundant and complementary methods that TBMs derive from a lymph node-resident, CD169-lineage, CSF1R-blockade-resistant precursor that is prepositioned in the follicle. Non-migratory TBMs use cytoplasmic processes to chase and capture migrating dead cell fragments using a “lazy” search strategy. Follicular macrophages activated by the presence of nearby apoptotic cells can mature into TBMs in the absence of GCs. Single-cell transcriptomics identified a TBM cell cluster in immunized lymph nodes which upregulated genes involved in apoptotic cell clearance. Thus, apoptotic B cells in early GCs trigger activation and maturation of follicular macrophages into classical TBMs to clear apoptotic debris and prevent antibody-mediated autoimmune diseases.

The magnitude and quality of the germinal center (GC) response decline with age, resulting in poor vaccine-induced immunity in older individuals. A functional GC requires the co-ordination of multiple cell types across time and space, in particular across its two functionally distinct compartments: the light and dark zones. In aged mice, there is CXCR4-mediated mislocalization of T follicular helper (T FH ) cells to the dark zone and a compressed network of follicular dendritic cells (FDCs) in the light zone. Here we show that T FH cell localization is critical for the quality of the antibody response and for the expansion of the FDC network upon immunization. The smaller GC and compressed FDC network in aged mice were corrected by provision of T FH cells that colocalize with FDCs using CXCR5. This demonstrates that the age-dependent defects in the GC response are reversible and shows that T FH cells support stromal cell responses to vaccines.

Visual analytics tools can help illustrate the spread of infectious diseases and enable informed decisions on epidemiological and public health issues. To create visualisation tools that are intuitive, easy to use, and effective in communicating information, continued research and development focusing on user-centric and methodological design models is extremely important. As a contribution to this topic, this paper presents the design and development process of the visual analytics application ESID (Epidemiological Scenarios for Infectious Diseases). ESID is a visual analytics tool aimed at projecting the future developments of infectious disease spread using reported and simulated data based on sound mathematical-epidemiological models. The development process involved a collaborative and participatory design approach with project partners from diverse scientific fields. The findings from these studies, along with the guidelines derived from them, played a pivotal role in shaping the visualisation tool.

Sequencing of B-cell and T-cell immune receptor repertoires helps us to understand the adaptive immune response, although it only provides information about the clonotypes (lineages) and their frequencies and not about, for example, their affinity or antigen (Ag) specificity. To further characterize the identified clones, usually with special attention to the particularly abundant ones (dominant), additional time-consuming or expensive experiments are generally required. Here, we present an extension of a multiscale model of the germinal center (GC) that we previously developed to gain more insight in B-cell repertoires. We compare the extent that these simulated repertoires deviate from experimental repertoires established from single GCs, blood, or tissue. Our simulations show that there is a limited correlation between clonal abundance and affinity and that there is large affinity variability among same-ancestor (same-clone) subclones. Our simulations suggest that low-abundance clones and subclones, might also be of interest since they may have high affinity for the Ag. We show that the fraction of plasma cells (PCs) with high B-cell receptor (BcR) mRNA content in the GC does not significantly affect the number of dominant clones derived from single GCs by sequencing BcR mRNAs. Results from these simulations guide data interpretation and the design of follow-up experiments.

The selection of high-affinity B cells and the production of high-affinity antibodies are mediated by T follicular helper cells (Tfhs) within germinal centres (GCs). Therein, somatic hypermutation and selection enhance B cell affinity but risk the emergence of self-reactive B cell clones. Despite being outnumbered compared to their helper counterpart, the ablation of T follicular regulatory cells (Tfrs) results in enhanced dissemination of self-reactive antibody-secreting cells (ASCs). The specific mechanisms by which Tfrs exert their regulatory action on self-reactive B cells are largely unknown. We developed computer simulations to investigate how Tfrs regulate either selection or differentiation of B cells to prevent auto-reactivity. We observed that Tfr-induced apoptosis of self-reactive B cells during the selection phase impedes self-reactivity with physiological Tfr numbers, especially when Tfrs can access centrocyte-enriched GC areas. While this aided in selecting non-self-reactive B cells by restraining competition, higher Tfr numbers distracted non-self-reactive B cells from receiving survival signals from Tfhs. Thus, the location and number of Tfrs must be regulated to circumvent such Tfr distraction and avoid disrupting GC evolution. In contrast, when Tfrs regulate differentiation of selected centrocytes by promoting recycling to the dark zone phenotype of self-reactive GC resident pre-plasma cells (GCPCs), higher Tfr numbers were required to impede the circulation of self-reactive ASCs (s–ASCs). On the other hand, Tfr-engagement with GCPCs and subsequent apoptosis of s–ASCs can control self-reactivity with low Tfr numbers, but does not confer selection advantage to non-self-reactive B cells. The simulations predict that to restrict auto-reactivity, natural redemption of self-reactive B cells is insufficient and that Tfrs should increase the mutation probability of self-reactive B cells.