Biology subjects

Hatzikirou, H.

Publications and source records attributed to Hatzikirou, H..

At least 19 recordsLinked to original sources

bioRxiv2026

A population-scale landscape of the subgingival microbiome reveals divergent routes to periodontal dysbiosis

Periodontitis is an archetypical mucosal inflammatory disease in which microbiome dysbiosis at the tooth-epithelial interface interacts with host genetic and behavioral risk factors to drive immune-mediated tissue destruction. Although subgingival microbiome compositional shifts are thought to parallel disease severity, microbiome variation at the population-level and its relationship to periodontal clinical phenotypes and disease-modifying factors remain poorly defined. Here, we use unsupervised manifold learning to map the compositional landscape of the subgingival microbiome in 1,355 adults spanning periodontal health to severe periodontitis. We identified eight latent microbiome states organized along a branching continuum from eubiosis to dysbiosis. An intermediate microbial configuration marked ecological destabilization and bifurcation into two distinct periodontitis-associated dysbiotic trajectories, distinguished by links to gingival inflammation and smoking. Although the microbiome trajectories broadly tracked periodontal destruction, a minority of individuals showed discordant microbiome-clinical phenotypes, with some individuals with periodontitis retaining otherwise eubiotic microbiomes enriched for low-abundance pathobionts, while some cases of health or mild disease had highly dysbiotic communities, suggesting distinct host susceptibility. Together, these findings define a population-scale ecological landscape of the subgingival microbiome, reveal divergent trajectories to periodontal dysbiosis, and highlight heterogeneity in the relationship between microbial community structure and clinical disease expression.

microbiology↗

bioRxiv2026

Regulatory memory and growth-coupled inheritance shape nutrient-dependent flagella number variation in Salmonella

Bacteria must balance the advantage of movement against the cost of building flagella, yet how nutrient availability shapes variation in flagellar number across single cells remains unclear. Here, we combine time-resolved basal-body measurements in Salmonella enterica with a mechanistically constrained stochastic model of flagellar remodeling. The model separates two routes from nutrient availability to flagellar number: an RflP-dependent regulatory memory that sets a synthesis target via a latent sensing-memory variable, and a physical inheritance process in which synthesis, binomial partitioning, and division reshape the flagellar-number distribution. Coarse-graining this process yields leaky-integrator dynamics in which the mean flagellar number tracks a regulatory target, while the latent correlation follows acquisition-decay dynamics. In wild-type cells, nutrient-dependent acquisition raises the target and increases flagellar investment; in {Delta}rflP cells, loss of acquisition produces a transient overshoot that isolates the intrinsic decay (memory) timescale of the regulatory state. The fitted model predicts a held-out nutrient condition and reveals which parameter combinations are identifiable from the data. Analysis of the fitted dynamics suggests that precision is tuned primarily by sensing-dependent signal amplitude, rather than integration time, with an apparent [~]1.7-fold increase in effective wild-type noise amplitude after mean normalization, consistent with a precision cost of active regulation relative to the mutant. A model-free, information-geometric (Cramer-Rao) speed limit further shows that active remodeling approaches the statistical speed allowed by the observed distributional variability. Together, these results reveal how Salmonella cells couple regulatory memory with growth-dependent inheritance to record recent nutrient history in their flagellar number.

systems biology↗

bioRxiv2026

Decoupling T Cell Cytotoxicity: A CCL21+ICAM1-Based Synthetic Immune Niche Enhances Tumor Elimination by Accelerating Lytic Hit Delivery

Preserving T cell cytotoxic function during ex vivo expansion remains a major challenge for adoptive cancer immunotherapy. A synthetic immune niche (SIN) composed of immobilized CCL21 and ICAM1 was shown to improve T cell expansion while preserving cytotoxicity; however, it remains unclear which specific step of the T cell killing process is enhanced by the SIN stimulation. Here, we combined advanced imaging based on time-lapse microscopy with a mean-field model to analyze the distinct stages in killing of B16 melanoma cells by CD8+ T cells. This framework enabled us to resolve the tumor cell killing process into discrete steps throughout target cell engagement with SIN-treated T cells and lytic hit delivery. We found that tumor cell death is best explained by a multi-hit process, requiring approximately four discrete hits to trigger cell death. Model-based analysis identified an increase in the lytic hit delivery as the parameter that best accounts for the enhanced cytotoxicity of SIN-treated T cells, a difference not explained by changes in target encounter frequency or conjugate stability. Global sensitivity analysis further showed that tumor control is more strongly improved by enhancing lytic hit delivery than by increasing target encounter rates. These findings fundamentally reorient our understanding of optimized T cell manufacturing, suggesting that the lytic execution step may be the primary rate-limiting bottleneck in this in-vitro system, and that engineering strategies targeting granule polarization or discharge warrant prioritization alongside affinity-enhancement approaches.

immunology↗

bioRxiv2025

Ecological and Stochastic Determinants of the Growth and Persistence of the Oral Pathogen Porphyromonas gingivalis

Population density plays a critical role in microbial fitness, yet its influence on pathogen colonization and persistence remains incompletely understood. Porphyromonas gingivalis (Pg) exhibits Allee-type growth, requiring a quorum threshold to replicate, yet is frequently detected at low abundance in vivo. We integrate quantitative growth experiments with mathematical modeling to identify ecological and stochastic determinants of Pg persistence. A cubic Allee-effect model quantifies a quorum threshold below which populations collapse, while conditioned medium from Veillonella parvula (Vp) lowers this threshold, indicating early-colonizer facilitation. Stochastic extensions and Fokker-Planck analysis show that microenvironmental noise enables escapes across the Allee barrier, consistent with long-term subthreshold experiments yielding a stationary, powerlaw-like distribution and under-threshold survival. Pg-Vp co-cultures further demonstrate replicate rescue outcomes for subcritical inocula. Critically, Vp reliably saturates to capacity, constraining terminal phases within the experimental horizon to coexistence (Pg persists with Vp at capacity) or Pg extinction. A two-species replicator model maps these outcomes onto a ({beta}, {gamma}) plane, restricting accessible regions once Vp is established and suggesting interventions that reduce facilitation or variability to restore eubiosis and limit Pg-associated inflammation.

systems biology↗

bioRxiv2025

In Silico Investigation of the Role of Local and Global Inflammation-Driven Feedback in Myelopoiesis and Clonal Cell Expansion

Chronic inflammation perturbs hematopoietic homeostasis, promoting aberrant myelopoiesis and clonal expansion of mutated stem cells. Here, we develop a mathematical model that integrates both local (bone marrow-intrinsic) and global (systemic/peripheral) inflammation-driven feedback mechanisms to investigate their roles in hematopoietic regulation and disease progression. Our model captures the nonlinear interplay between self-renewal, progenitor proliferation, and inflammatory cues, enabling classification of healthy, myelodysplastic, and leukemic states based on stem cell population dynamics. We show that global inflammatory feedback enhances the resilience of hematopoiesis, while excessive feedback on progenitor cells under chronic inflammation drives instability and clonal dominance. Using sensitivity analysis and parameter space mapping, we identify critical feedback thresholds governing transitions between hematopoietic states and reveal how mutated clones exploit inflammation to outcompete wild-type cells. This systems-level framework offers mechanistic insights into the emergence of myeloid malignancies and provides a computational platform for exploring potential anti-inflammatory therapeutic strategies.

systems biology↗

bioRxiv2025

On the onset of multicellular invasive behavior in hierarchical lineage: the role of inhibitory feedback and local fluctuations

The emergence of multicellular invasive behavior is a key characteristic of various biological processes, including wound healing, development, and tissue regeneration. In this study, we develop a lattice-gas cellular automaton (LGCA) model to explore the role of inhibitory feedback in the invasive behavior of a hierarchical lineage composed of stem cells and differentiated cells. We consider both non-spatial and spatial stochastic models to investigate how spatial interactions influence invasion dynamics. Our findings suggest that inhibitory feedback from differentiated cells significantly impacts the invasive potential of stem cells. In addition, local fluctuations induce unstable fronts that move with relatively low speed. Finally, we explore the implications of our work for understanding the regulation of multicellular dynamics in various pathophysiological contexts.

cancer biology↗

bioRxiv2025

Data-driven identification of biomedical systems using multi-scale analysis

Biomedical systems inherently exhibit multi-scale dynamics, making accurate system identification particularly challenging due to the complexity of capturing a wide time scale spectrum. Traditional methods capable of addressing this issue rely on explicit equations, limiting their applicability in cases where only observational data are available. To overcome this limitation, we propose a data-driven framework that integrates the Sparse Identification of Nonlinear Dynamics (SINDy) method, the multi scale analysis algorithm Computational Singular Perturbation (CSP) and neural networks (NNs). This framework allows the partition of the available dataset in subsets characterized by similar dynamics, so that system identification can proceed within these subsets without facing a wide time scale spectrum. Accordingly, when the full dataset does not allow SINDy to identify the proper model, CSP is employed for the generation of subsets of similar dynamics, which are then fed into SINDy. CSP requires the availability of the gradient of the vector field, which is estimated by the NNs. The framework is tested on the Michaelis-Menten model, for which various reduced models in analytic form exist at different parts of the phase space. It is demonstrated that the CSP-based data subsets allow SINDy to identify the proper reduced model in cases where the full dataset does not. In addition, it is demonstrated that the framework succeeds even in the cases where the available data set originates from stochastic versions of the Michaelis-Menten model. This framework is algorithmic, so system identification is not hindered by the dimensions of the dataset. Author summaryBiomedical systems often evolve across multiple time scales, posing major challenges for constructing accurate models directly from data. Traditional model reduction techniques require explicit equations and thus cannot be applied when only observational data are available. To address this, we developed a data-driven framework that combines Sparse Identification of Nonlinear Dynamics (SINDy), Computational Singular Perturbation (CSP) and neural networks (NNs). Our approach automatically partitions a dataset into subsets characterized by similar dynamics, allowing valid reduced models to be identified in each region. When SINDy fails to recover a global model from the full dataset, CSP -leveraging Jacobian estimates from NNs-successfully isolates dynamical regimes where SINDy can be applied locally. We validated this framework using the Michaelis-Menten biochemical model, which is known to admit multiple reduced models in different regions of the phase space. Our method consistently identified the appropriate reduced dynamics, even when the data originated from stochastic simulations. Because our approach is algorithmic and equation-free, it is scalable to high-dimensional systems and robust to noise, offering a promising solution for data-driven model discovery in complex biomedical systems.

systems biology↗

bioRxiv2025

On the Impact of Smoking on The Oral-brain Axis: The Microbiome-Metabolism-Brain Interactions

Systemic interactions between distant organs, such as the oral-brain axis, remain largely unknown. This study aimed to shed light on a specific aspect of these oral-brain interactions, by focusing on the brain, in the form of psychological traits, the oral microbiome, and metabolism (BMM) with respect to the influence of smoking on the oral microbiota. We analyzed three datasets covering microbiome composition, psychological traits, and metabolic pathways in smokers and non-smokers. We used Canonical Correlation Analysis (CCA) to reduce the dimensionality of the three different datasets into scalar variables, and network analysis to identify the interactions between important features of the BMM system. Nonlinear regression revealed steady state interactions, showing tight correlations between metabolic pathways and microbiome composition. Smoking weakens the directed interaction between metabolism and psychological traits. Sensitivity analysis demonstrated the systems robustness to small perturbations, but large disruptions could destabilize it, potentially leading to oral pathologies or psychological conditions. These findings highlight smokings complex role in BMM interactions and its implications for managing psychological conditions in smokers.

systems biology↗

bioRxiv2024

The Role of Biopsy Position and Tumor-Associated Macrophages for Predictions on Recurrence of Malignant Gliomas: An In Silico Study

Predicting the biological behavior and time to recurrence (TTR) of high-grade diffuse gliomas (HGG) after the maximum safe neurosurgical resection and combined radiation and chemotherapy plays a pivotal role in planning the clinical follow-up, the choice of potentially necessary second-line treatment, and the quality of life of patients faced with the diagnosis of a malignant brain tumor. The current standard-of-care (SoC) for HGG includes follow-up neuroradiological imaging to detect recurrence as early as possible and several clinical, neuropathological, and radiological prognostic factors with limited accuracy toward predicting TTR. Herein, using an in-silico analysis, we aim to improve predictive power towards TTR considering the role of (i) prognostically relevant information available by diagnostics used in current SoC, (ii) advanced image-based information that is currently not part of the standard diagnostic workup, such as interface of tumor and normal tissue (edge) features and quantitative data specific for the position of biopsies within the tumor, and (iii) information on tumor-associated macrophages. In particular, we introduce a state-of-the-art spatio-temporal model of tumor-immune interactions, emphasizing the interplay between macrophages and glioma cells. This model serves as a synthetic reality for assessing the predictive value of various features. We generate a cohort of virtual patients based on our mathematical model. Each patients dataset includes simulated T1 and FLAIR MRI volumes, and simulated results on macrophage density and proliferative activity either in a specified part of the tumor, namely tumor core or edge ("localized"), or unspecified ("non-localized"). We impose different levels of noise to enhance the realism of our synthetic data. Our findings reveal that macrophage density at the tumor edge contributes to a high predictive value of feature importance for the selected regression model. Moreover, there is a lower MSE and higher R2 for the "localized" biopsy in prediction accuracy toward recurrence post-resection compared with "non-localized" specimens. In conclusion, the results show us that localized biopsies can bring more information about the tumor behavior, especially at the interface of tumor and normal tissue (Edge).

systems biology↗

bioRxiv2024

Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy

Cancer is a significant global health issue, with treatment challenges arising from intratumor heterogeneity. This heterogeneity stems mainly from somatic evolution, causing genetic diversity within the tumor, and phenotypic plasticity of tumor cells leading to reversible phenotypic changes. However, the interplay of both factors has not been rigorously investigated. Here, we examine the complex relationship between somatic evolution and phenotypic plasticity, explicitly focusing on the interplay between cell migration and proliferation. This type of phenotypic plasticity is essential in glioblastoma, the most aggressive form of brain tumor. We propose that somatic evolution alters the regulation of phenotypic plasticity in tumor cells, specifically the reaction to changes in the microenvironment. We study this hypothesis using a novel, spatially explicit model that tracks individual cells phenotypic and genetic states. We assume cells change between migratory and proliferative states controlled by inherited and mutation-driven genotypes and the cells microenvironment. We observe that cells at the tumor edge evolve to favor migration over proliferation and vice versa in the tumor bulk. Notably, different genetic configurations can result in this pattern of phenotypic heterogeneity. We analytically predict the outcome of the evolutionary process, showing that it depends on the tumor microenvironment. Synthetic tumors display varying levels of genetic and phenotypic heterogeneity, which we show are predictors of tumor recurrence time after treatment. Interestingly, higher phenotypic heterogeneity predicts poor treatment outcomes, unlike genetic heterogeneity. Our research offers a novel explanation for heterogeneous patterns of tumor recurrence in glioblastoma patients. Author summaryIntratumor heterogeneity presents a significant barrier to effective cancer therapy. This heterogeneity stems from the evolution of cancer cells and their capability for phenotypic plasticity. However, the interplay between these two factors still needs to be fully understood. This study examines the interaction between cancer cell evolution and phenotypic plasticity, focusing on the phenotypic switch between migration and proliferation. Such plasticity is particularly relevant to glioblastoma, the most aggressive form of brain tumor. By employing a novel model, we explore how tumor cell evolution, influenced by both genotype and microenvironment, contributes to tumor heterogeneity. We observe that cells at the tumor periphery tend to migrate, while those within the tumor are more inclined to proliferate. Interestingly, our analysis reveals that distinct genetic configurations of the tumor can lead to this observed pattern. Further, we delve into the implications for cancer treatment and discover that it is phenotypic, rather than genetic, heterogeneity that more accurately predicts tumor recurrence following therapy. Our findings offer insights into the significant variability observed in glioblastoma recurrence times post-treatment.

cancer biology↗

bioRxiv2024

Comparative Analysis of Biomarkers in Type 2 Diabetes Patients with and without Comorbidities: Insights into the Role of Hypertension and Cardiovascular Disease

BackgroundType 2 diabetes mellitus (T2DM) are 90% of diabetes cases, and its prevalence and incidence, including comorbidities, are rising worldwide. Clinically, diabetes and associated comorbidities are identified by biochemical and physical characteristics including glycaemia, glycated hemoglobin (HbA1c), and tests for cardiovascular, eye and kidney disease. ObjectivesDiabetes may have a common etiology based on inflammation and oxidative stress that may provide additional information about disease progression and treatment options. Thus, identifying high-risk individuals can delay or prevent diabetes and its complications. DesignIn patients with or without hypertension and cardiovascular disease, as part of progression from no diabetes to T2DM, this research studied the changes in biomarkers between control and prediabetes, prediabetes to T2DM, and control to T2DM, and classified patients based on first-attendance data. Control patients and patients with hypertension, cardiovascular, and with both hypertension and cardiovascular diseases are 156, 148, 61, and 216, respectively. MethodsLinear discriminant analysis is used for classification method and feature importance, This study examined the relationship between Humanin and mitochondrial protein (MOTSc), mitochondrial peptides associated with oxidative stress, diabetes progression, and associated complications. ResultsMOTSc, reduced glutathione and glutathione disulfide ratio (GSH/GSSG), interleukin-1{beta} (IL-1{beta}), and 8-isoprostane were significant (p<0.05) for the transition from prediabetes to T2DM, highlighting the importance of mitochondrial involvement. Complement component 5a (C5a) is a biomarker associated with disease progression and comorbidities, with GSH/GSSG, monocyte chemoattractant protein-1 (MCP-1), and 8-isoprostane being the most important biomarkers. ConclusionsComorbidities affect the hypothesized biomarkers as diabetes progresses. Mitochondrial oxidative stress indicators, coagulation, and inflammatory markers help assess diabetes disease development and provide appropriate medications. Future studies will examine longitudinal biomarker evolution.

bioinformatics↗

bioRxiv2022

Lemaitre's law and the spatial distributions of the cone photoreceptors in the retinas of vertebrates

Cone photoreceptor cells are wavelength-sensitive neurons in the retinas of vertebrate eyes and are responsible for color vision. The spatial distribution of these nerve cells is commonly referred to as cone photoreceptor mosaic. By applying the principle of maximum entropy, we demonstrate the universality of retinal cone mosaics in vertebrate eyes by examining various species, namely, rodent, dog, monkey, human, fish, and bird. We introduce a parameter called retinal temperature, which is conserved across the retinas of vertebrates. The virial equation of state for two-dimensional cellular networks, known as Lemaitres law, is also obtained as a special case of our formalism. We investigate the behavior of several artificially generated networks and the natural one of the retina concerning this universal, topological law.

biophysics↗

bioRxiv2021

Lattice-based microenvironmental uncertainty driven phenotypic decision-making: a comparison with Notch-Delta-Jagged signaling

Phenotypic decision-making is a process of determining important phenotypes in accordance with the available microenvironmental information. Although phenotypic decision at the level of a single cell has been precisely studied, but the knowledge is still imperceptible at the multicellular level. How cells sense their environment and adapt? How single cells change their phenotype in a multicellular complex environment (without knowing the interactions among the cells), is still a rheotorical question. To unravel the fragmental story of multicellular decision-making, Least microEnvironmental Uncertainty Principle (LEUP) was refined and applied in this context. To address this set of questions, we use variational principle to grasp the role of sensitivity, build a LEUP driven agent-based model on a lattice which solely hinges on microenvironmental information and investigate the parallels in a well-known biological system, viz., Notch-Delta-Jagged signaling pathway. The analyses of this model led us to interesting spatiotemporal patterns in a population of cells, responsive to the sensitivity parameter and the radius of interaction. This resembles the tissue-level pattern of a population of cells interacting via Notch-Delta-Jagged signaling pathway in some parameter regimes.

biophysics↗

bioRxiv2021

Close to optimal cell sensing ensures the robustness of tissue differentiation process: the avian photoreceptor mosaic case

The way that progenitor cell fate decisions and the associated environmental sensing are regulated to ensure the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue still remains elusive. Here, we investigate how cells regulate their sensing intensity and radius to guarantee the required thermodynamic robustness of a differentiated tissue. In particular, we are interested in finding the conditions where dedifferentiation at cell level is possible (microscopic reversibility) but tissue maintains its spatial order and differentiation integrity (macroscopic irreversibility). In order to tackle this, we exploit the recently postulated Least microEnvironmental Uncertainty Principle (LEUP) to develop a theory of stochastic thermodynamics for cell differentiation. To assess the predictive and explanatory power of our theory, we challenge it against the avian photoreceptor mosaic data. By calibrating a single parameter, the LEUP can predict the cone color spatial distribution in the avian retina and, at the same time, suggest that such a spatial pattern is associated with quasi-optimal cell sensing. By means of the stochastic thermodynamics formalism, we find out that thermodynamic robustness of differentiated tissues depends on cell metabolism and cell sensing properties. In turn, we calculate the limits of the cell sensing radius that ensure the robustness of differentiated tissue spatial order. Finally, we further constrain our model predictions to the avian photoreceptor mosaic.

biophysics↗

bioRxiv2020

BIO-LGCA: a cellular automaton modelling class for analysing collective cell migration

1Collective dynamics in multicellular systems such as biological organs and tissues plays a key role in biological development, regeneration, and pathological conditions. Collective tissue dynamics - understood as population behaviour arising from the interplay of the constituting discrete cells - can be studied with on- and off-lattice agent-based models. However, classical on-lattice agent-based models, also known as cellular automata, fail to replicate key aspects of collective migration, which is a central instance of collective behaviour in multicellular systems. To overcome drawbacks of classical on-lattice models, we introduce an on-lattice, agent-based modelling class for collective cell migration, which we call biological lattice-gas cellular automaton (BIO-LGCA). The BIO-LGCA is characterised by synchronous time updates, and the explicit consideration of individual cell velocities. While rules in classical cellular automata are typically chosen ad hoc, rules for cell-cell and cell-environment interactions in the BIO-LGCA can also be derived from experimental cell migration data or biophysical laws for individual cell migration. We introduce elementary BIO-LGCA models of fundamental cell interactions, which may be combined in a modular fashion to model complex multicellular phenomena. We exemplify the mathematical mean-field analysis of specific BIO-LGCA models, which allows to explain collective behaviour. The first example predicts the formation of clusters in adhesively interacting cells. The second example is based on a novel BIO-LGCA combining adhesive interactions and alignment. For this model, our analysis clarifies the nature of the recently discovered invasion plasticity of breast cancer cells in heterogeneous environments. A Python package which implements various interaction rules and visualisations of BIO-LGCA model simulations we have developed is available at https://github.com/sisyga/BIO-LGCA. 2 Author summaryPattern formation during embryonic development and pathological tissue dynamics, such as cancer invasion, emerge from individual inter-cellular interactions. In order to study the impact of single cell dynamics and cell-cell interactions on tissue behaviour, one needs to develop space-time-dependent on- or off-lattice agent-based models (ABMs), which consider the behaviour of individual cells. However, classical on-lattice agent-based models also known as cellular automata fail to replicate key aspects of collective migration, which is a central instance of collective behaviour in multicellular systems. Here, we present the rule- and lattice-based BIO-LGCA modelling class which allows for (i) rigorous derivation of rules from biophysical laws and/or experimental data, (ii) mathematical analysis of collective migration, and (iii) computationally efficient simulations.

bioinformatics↗

bioRxiv2020

Bayesian combination of mechanistic modeling and machine learning (BaM3): improving clinical tumor growth predictions

In clinical practice, a plethora of medical examinations are conducted to assess the state of a patients pathology producing a variety of clinical data. However, exploiting these data faces the following challenges: (C1) we lack the knowledge of the mechanisms involved in regulating these data variables, and (C2) data collection is sparse in time since it relies on patients clinical presentation. (C1) implies that only a small subset of the relevant variables can be modeled by virtue of mathematical modeling. This limitation allows models to be effective in analyzing the qualitative dynamics of the system, but limits their predictive accuracy. On the other hand, statistical learning methods are well-suited for quantitative reproduction of data, but they do not provide mechanistic understanding of the investigated problem. Moreover, due to (C2) any algorithm is challenged in learning the corresponding disease dynamics. Herein, we propose a novel method, based on the Bayesian coupling of mathematical modeling and machine learning (BaM3), aiming at improving individualized predictions by addressing the aforementioned challenges. As a proof of concept, we evaluate the proposed method on a synthetic dataset for brain tumor growth and analyze its performance in predicting two major clinical outputs, namely tumor burden and infiltration. The BaM3 method results in improved predictions in almost all simulated patients, especially for those with a late clinical presentation. In addition, we test the proposed methodology in two settings dealing with real patient cohorts. In both cases, namely cancer growth in chronic lymphocytic leukemia and ovarian cancer, BaM3 predictions show excellent agreement with reported clinical data.

bioinformatics↗

bioRxiv2020

Murine Staphylococcus aureus chronic infection is cured by theory-driven therapy

Staphylococcus aureus is considered a dangerous pathogen due to its ability to evade the immune system and resist multiple antibiotics. These evasive strategies lead to difficult-to-treat chronic infections and abscesses in internal organs including kidneys, which are associated with the expansion of myeloid-derived suppressor cells (MDSCs) and their suppressive effect on T cells. Here, we developed a mathematical model of chronic S. aureus infection that incorporates the T-cell suppression by MDSCs and suggests therapeutic strategies to eradicate S. aureus. We quantified in silico a therapeutic protocol with heat-killed S. aureus (HKSA), which we tested in vivo. Contrary to conventional administration of heat-killed bacteria as vaccination prior to infection, we administered HKSA as treatment, when the hosts were already chronically infected. Our treatment cured all chronically S. aureus-infected mice, reduced MDSCs, and reversed T-cell dysfunction by inducing acute inflammation during ongoing, chronic infection without any use of standard treatments that involve antibiotics, MDSC-targeting drugs (chemotherapy), or procedures such as abscess drainage. This study is a proof-of-principle for a treatment protocol against chronic S. aureus infection and renal abscesses by repurposing heat-killed treatments, guided and quantified by mathematical modelling. Our mathematical model further explains why previous treatment with inactivated S. aureus administered to long-term infected human patients has not led to cure. Overall, our results can have direct relevance to the design of human therapeutics against chronic S. aureus infections. In briefA theory-driven treatment protocol with heat-killed S. aureus eradicates S. aureus, reduces MDSCs, and reverses T-cell dysfunction in vivo. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=157 HEIGHT=200 SRC="FIGDIR/small/910786v3_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@1e2d0c9org.highwire.dtl.DTLVardef@1f97374org.highwire.dtl.DTLVardef@b0235org.highwire.dtl.DTLVardef@bdf2ee_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2019

Modeling bacteria-based therapy in tumor spheroids

Tumor-targeting bacteria elicit anticancer effects by infiltrating hypoxic regions, releasing toxic agents and inducing immune responses. Although current research has largely focused on the influence of chemical and immunological aspects on the mechanisms of bacterial therapy, the impact of physical effects is still elusive. Here, we propose a mathematical model for the anti-tumor activity of bacteria in avascular tumors that takes into account the relevant chemo-mechanical effects. We consider a time-dependent administration of bacteria and analyze the impact of bacterial chemotaxis and killing rate. We show that active bacterial migration towards tumor hypoxic regions provides optimal infiltration and that high killing rates combined with high chemotactic values provide the smallest tumor volumes at the end of the treatment. We highlight the emergence of steady states in which a small population of bacteria is able to constrain tumor growth. Finally, we show that bacteria treatment works best in the case of tumors with high cellular proliferation and low oxygen consumption.

biophysics↗