Sepsis target validation for repurposing and combining complement and immune checkpoint inhibition therapeutics

Introduction: Sepsis is a disease that occurs due to an adverse immune response to infection by bacteria, viruses and fungi and is the leading pathway to death by infection. The hallmarks for maladapted immune reactions in severe sepsis, which contribute to multiple organ failure and death, are bookended by the exacerbated activation of the complement system to protracted T-cell dysfunction states orchestrated by immune checkpoint control. Despite major advances in our understanding of the condition, there remains to be either a definitive test or an effective therapeutic intervention. Areas covered: The authors consider a combinational drug therapy approach using new biologics, and mathematical modeling for predicting patient responses, in targeting innate and adaptive immune mediators underlying sepsis. Special consideration is given for emerging complement and immune checkpoint inhibitors that may be repurposed for sepsis treatment. Expert opinion: In order to overcome the challenges inherent to finding new therapies for the complex dysregulated host response to infection that drives sepsis, it is necessary to move away from monotherapy and promote precision for personalized combinatory therapies. Notably, combinatory therapy should be guided by predictive systems models of the immune-metabolic characteristics of an individual’s disease progression

Quantifying the errors in animal contacts recorded by proximity loggers

Automated contact detection by means of proximity loggers permits the measurement of encounters between individuals (animal-animal contacts) and the time spent by individuals in the proximity of a focal resource of interest (animal-fixed logger contacts). The ecological inference derived from contact detection is intrinsically associated with the distance at which the contact occurred. But no proximity loggers currently exist that record this distance and therefore all distance estimations are associated with error. Here we applied a probabilistic approach to model the relationship between contact detection and inter-logger distance, and quantify the associated error, on free-ranging animals in semi-controlled settings. The probability of recording a contact declined with the distance between loggers, and this decline was steeper for weaker radio transmission powers. Even when proximity loggers were adjacent, contact detection was not guaranteed, irrespective of the radio transmission power. Accordingly, the precision and sensitivity of the system varied as a function of inter-logger distance, radio transmission power, and experimental setting (e.g., depending on animal body mass and fine-scale movements). By accounting for these relationships, we were able to estimate the probability that a detected contact occurred at a certain distance, and the probability that contacts were missed (i.e., false negatives). These calibration exercises have the potential to improve the predictability of the study and enhance the applicability of proximity loggers to key wildlife management issues such as disease transmission rates or wildlife use of landscape features and resources

A mathematical insight into cell labelling experiments for clonal analysis

Studying the progression of the proliferative and differentiative patterns of neural stem cells at the individual cell-level is crucial to the understanding of cortex development and how the disruption of such patterns can lead to malformations and neurodevelopmental diseases. However, our understanding of the precise lineage progression program at single cell resolution is still incomplete due to the technical variations in lineage tracing approaches. One of the key challenges involves developing a robust theoretical framework in which we can integrate experimental observations and introduce correction factors to obtain a reliable and representative description of the temporal modulation of proliferation and differentiation. In order to obtain more conclusive insights we carry out virtual clonal analysis using mathematical modelling and compare our results against experimental data. Using a dataset obtained with Mosaic Analysis with Double Markers, we illustrate how the theoretical description can be exploited to interpret and reconcile the disparity between virtual and experimental results

Mathematical Modeling of Cortical Neurogenesis Reveals that the Founder Population does not Necessarily Scale with Neurogenic Output

The mammalian cerebral neocortex has a unique structure, composed of layers of different neuron types, interconnected in a stereotyped fashion. While the overall developmental program seems to be conserved, there are divergent developmental factors generating cortical diversity amongst species. In terms of cortical neuronal numbers some of the determining factors are the size of the founder population, the duration of cortical neurogenesis, the proportion of different progenitor types, and the fine-tuned balance between self-renewing and differentiative divisions. We develop a mathematical model of neurogenesis that, accounting for these factors, aims at explaining the high diversity in neuronal numbers found across species. By framing our hypotheses in rigorous mathematical terms, we are able to identify paths of neurogenesis that match experimentally observed patterns in mouse, macaque and human. Additionally, we use our model to identify key parameters that would particularly benefit from accurate experimental investigation. We find that the timing of a switch in favor of symmetric neurogenic divisions produces the highest variation in cortical neuronal numbers. Surprisingly, assuming similar cell cycle lengths in primate 3 progenitors, the increase in cortical neuronal numbers does not reflect a larger size of founder population, a prediction that identified a specific need for experimental quantifications

Tumour-stromal interactions in cancer progression and drug resistance

The typical response of cancer patients to treatment is only temporary, and is often followed by relapse. The failure of various therapeutic strategies is commonly attributed to the emergence of drug resistance. The response patterns for patients under such treatments indicate that complex dynamics regulate the response of the tumour to the therapy. The environment in which the tumour lives (the stroma) is known to be a modulator of multiple mechanisms that lead to drug resistance and seems to be a likely candidate for explaining some of this complexity. Understanding the role of stromal cells in the promotion of drug resistance is critical for the design of optimal treatment strategies, and for the development of novel therapies that selectively target both the tumour and the stroma. In this thesis we design two novel mathematical models that describe cancer growth within its environment and the evolution of drug resistance within spatially complex and temporally dynamic tumours. A compartment model captures clinically observed dynamics and allows direct comparison with experimental data, facilitating model parametrisation and the understanding of inter-tumour heterogeneity. An individual cell-based model highlights the key role of local interactions, determining heterogeneity at the tissue scale, that will eventually determine treatment outcome. A non-spatial approximation of this second model allows us to nd analytic guidelines for the design of eective therapy. These tools allow the simulation of a range of treatment strategies (including combination of dierent drugs and variation of schedule) as well as the investigation of therapy response based on patient- or organ-specic parameters. The work developed in this dissertation is based on the paradigmatic biology of melanoma and non-small cell lung cancer. Its results are therefore applicable to a variety of cancer treatments that target similar processes, and whose therapeutic failure can be attributed to environment-mediated drug resistance.</p

Tumour-stromal interactions in cancer progression and drug resistance

The typical response of cancer patients to treatment is only temporary, and is often followed by relapse. The failure of various therapeutic strategies is commonly attributed to the emergence of drug resistance. The response patterns for patients under such treatments indicate that complex dynamics regulate the response of the tumour to the therapy. The environment in which the tumour lives (the stroma) is known to be a modulator of multiple mechanisms that lead to drug resistance and seems to be a likely candidate for explaining some of this complexity. Understanding the role of stromal cells in the promotion of drug resistance is critical for the design of optimal treatment strategies, and for the development of novel therapies that selectively target both the tumour and the stroma. In this thesis we design two novel mathematical models that describe cancer growth within its environment and the evolution of drug resistance within spatially complex and temporally dynamic tumours. A compartment model captures clinically observed dynamics and allows direct comparison with experimental data, facilitating model parametrisation and the understanding of inter-tumour heterogeneity. An individual cell-based model highlights the key role of local interactions, determining heterogeneity at the tissue scale, that will eventually determine treatment outcome. A non-spatial approximation of this second model allows us to nd analytic guidelines for the design of eective therapy. These tools allow the simulation of a range of treatment strategies (including combination of dierent drugs and variation of schedule) as well as the investigation of therapy response based on patient- or organ-specic parameters. The work developed in this dissertation is based on the paradigmatic biology of melanoma and non-small cell lung cancer. Its results are therefore applicable to a variety of cancer treatments that target similar processes, and whose therapeutic failure can be attributed to environment-mediated drug resistance.</p

Time to change your mind? Modelling transient properties of cortex formation highlights the importance of evolving cell division strategies

The successful development of the mammalian cerebral neocortex is linked to numerous cognitive functions such as language, voluntary movement, and episodic memory. Neocortex development occurs when neural progenitor cells divide and produce neurons. Critically, although the progenitor cells are able to self-renew they do not reproduce themselves endlessly. Hence, to fully understand the development of the neocortex we are faced with the challenge of understanding temporal changes in cell division strategy. Our approach to modelling neuronal production uses non-autonomous ordinary differential equations and allows us to use a ternary coordinate system in order to define a strategy space, through which we can visualise evolving cell division strategies. Using this strategy space, we fit the known data and use approximate Bayesian computation to predict the founding progenitor population sizes, currently unavailable in the experimental literature. Counter-intuitively, we show that humans can generate a larger number of neurons than a macaque's even when starting with a smaller number of progenitor cells. Accompanying the article is a self-contained piece of software, which provides the reader with immediate simulated results that will aid their intuition. The software can be found at www.dpag.ox.ac.uk/team/noemi-picco

MOESM1 of Understanding and geo-referencing animal contacts: proximity sensor networks integrated with GPS-based telemetry

Additional file 1. WildScope proximity loggers. This additional file describes in detail the technical components (hardware and software) of WildScope GPS-based geo-referencing proximity loggers