Diligent Design Enables Antibody-ASO Conjugates with Optimal Pharmacokinetic Properties.

Antisense-oligonucleotides (ASOs) are a promising drug modality for the treatment of neurological disorders, but the currently established route of administration via intrathecal delivery is a major limitation to its broader clinical application. An attractive alternative is the conjugation of the ASO to an antibody that facilitates access to the central nervous system (CNS) after peripheral application and target engagement at the blood-brain barrier, followed by transcytosis. Here, we show that the diligent conjugate design of Brainshuttle-ASO conjugates is the key to generating promising delivery vehicles and thereby establishing design principles to create optimized molecules with drug-like properties. An innovative site-specific transglutaminase-based conjugation technology was chosen and optimized in a stepwise process to identify the best-suited conjugation site, tags, reaction conditions, and linker design. The overall conjugation performance was found to be specifically governed by the choice of buffer conditions and the structure of the linker. The combination of the peptide tags YRYRQ and RYESK was chosen, showing high conjugation fidelity. Elaborate conjugate analysis revealed that one leading differentiating factor was hydrophobicity. The increase of hydrophobicity by the ASO payload could be mitigated by the appropriate choice of conjugation site and the heavy chain position 297 proved to be the most optimal. Evaluating the properties of the linker suggested a short bicyclo[6.1.0]nonyne (BCN) unit as best suited with regards to conjugation performance and potency. Promising in vitro activity and in vivo pharmacokinetic behavior of optimized Brainshuttle-ASO conjugates, based on a microtubule-associated protein tau (MAPT) targeting oligonucleotide, suggest that such designs have the potential to serve as a blueprint for peripherally delivered ASO-based drugs for the CNS in the future.

Mathematical modelling reveals cellular dynamics within tumour spheroids.

Tumour spheroids are widely used as an in vitro assay for characterising the dynamics and response to treatment of different cancer cell lines. Their popularity is largely due to the reproducible manner in which spheroids grow: the diffusion of nutrients and oxygen from the surrounding culture medium, and their consumption by tumour cells, causes proliferation to be localised at the spheroid boundary. As the spheroid grows, cells at the spheroid centre may become hypoxic and die, forming a necrotic core. The pressure created by the localisation of tumour cell proliferation and death generates an cellular flow of tumour cells from the spheroid rim towards its core. Experiments by Dorie et al. showed that this flow causes inert microspheres to infiltrate into tumour spheroids via advection from the spheroid surface, by adding microbeads to the surface of tumour spheroids and observing the distribution over time. We use an off-lattice hybrid agent-based model to re-assess these experiments and establish the extent to which the spatio-temporal data generated by microspheres can be used to infer kinetic parameters associated with the tumour spheroids that they infiltrate. Variation in these parameters, such as the rate of tumour cell proliferation or sensitivity to hypoxia, can produce spheroids with similar bulk growth dynamics but differing internal compositions (the proportion of the tumour which is proliferating, hypoxic/quiescent and necrotic/nutrient-deficient). We use this model to show that the types of experiment conducted by Dorie et al. could be used to infer spheroid composition and parameters associated with tumour cell lines such as their sensitivity to hypoxia or average rate of proliferation, and note that these observations cannot be conducted within previous continuum models of microbead infiltration into tumour spheroids as they rely on resolving the trajectories of individual microbeads.

Synergism of shrew-1's signal peptide and transmembrane segment required for plasma membrane localization.

Signal peptides (SP) and transmembrane segments (TMS) ensure proper subcellular targeting and localization of proteins. Thus, understanding the molecular variability of this targeting information is essential. In this study, we functionally analyzed the predicted SP and the TMS of adherens junction protein, shrew-1 (Bharti et al. Novel membrane protein shrew-1 targets to cadherin-mediated junctions in polarized epithelial cells. Mol Biol Cell 2004:15:397). We used human secreted alkaline phosphatase (SEAP) as reporter protein. The SP of shrew-1 was able to functionally substitute for SEAP's intrinsic SP and was cleaved, indicating that it acts as a start-transfer signal and not a signal anchor. In turn, the TMS of shrew-1 functions as stop-transfer signal. Notably, clearly detectable plasma membrane localization is only achieved when the fusion protein contains both the SP and the TMS of shrew-1. In combination with the intrinsic SP from SEAP, the shrew-1 TMS is unable to promote stable plasma membrane localization. Hence, it may be assumed that this synergism between an SP and a TMS to mediate plasma membrane localization is essential for structural and/or functional integrity of shrew-1.

Combining multiple spatial statistics enhances the description of immune cell localisation within tumours.

Digital pathology enables computational analysis algorithms to be applied at scale to histological images. An example is the identification of immune cells within solid tumours. Image analysis algorithms can extract precise cell locations from immunohistochemistry slides, but the resulting spatial coordinates, or point patterns, can be difficult to interpret. Since localisation of immune cells within tumours may reflect their functional status and correlates with patient prognosis, novel descriptors of their spatial distributions are of biological and clinical interest. A range of spatial statistics have been used to analyse such point patterns but, individually, these approaches only partially describe complex immune cell distributions. In this study, we apply three spatial statistics to locations of CD68+ macrophages within human head and neck tumours, and show that images grouped semi-quantitatively by a pathologist share similar statistics. We generate a synthetic dataset which emulates human samples and use it to demonstrate that combining multiple spatial statistics with a maximum likelihood approach better predicts human classifications than any single statistic. We can also estimate the error associated with our classifications. Importantly, this methodology is adaptable and can be extended to other histological investigations or applied to point patterns outside of histology.

An integrated approach to quantitative modelling in angiogenesis research.

Angiogenesis, the process by which new vessels form from existing ones, plays an important role in many developmental processes and pathological conditions. We study angiogenesis in the context of a highly controllable experimental environment: the cornea micropocket assay. Using a multidisciplinary approach that combines experiments, image processing and analysis, and mathematical modelling, we aim to provide mechanistic insight into the action of two angiogenic factors, vascular endothelial growth factor A (VEGF-A) and basic fibroblast growth factor (bFGF). We use image analysis techniques to extract quantitative data, which are both spatially and temporally resolved, from experimental images, and we develop a mathematical model, in which the corneal vasculature evolves in response to both VEGF-A and bFGF. The experimental data are used for model parametrization, while the mathematical model is used to assess the utility of the cornea micropocket assay and to characterize proposed synergies between VEGF-A and bFGF.

Semantically linking in silico cancer models.

Multiscale models are commonplace in cancer modeling, where individual models acting on different biological scales are combined within a single, cohesive modeling framework. However, model composition gives rise to challenges in understanding interfaces and interactions between them. Based on specific domain expertise, typically these computational models are developed by separate research groups using different methodologies, programming languages, and parameters. This paper introduces a graph-based model for semantically linking computational cancer models via domain graphs that can help us better understand and explore combinations of models spanning multiple biological scales. We take the data model encoded by TumorML, an XML-based markup language for storing cancer models in online repositories, and transpose its model description elements into a graph-based representation. By taking such an approach, we can link domain models, such as controlled vocabularies, taxonomic schemes, and ontologies, with cancer model descriptions to better understand and explore relationships between models. The union of these graphs creates a connected property graph that links cancer models by categorizations, by computational compatibility, and by semantic interoperability, yielding a framework in which opportunities for exploration and discovery of combinations of models become possible.

Model-driven experimental analysis of the function of SHP-2 in IL-6-induced Jak/STAT signaling.

Misregulated interleukin-6 (IL-6)-induced Jak/STAT signaling contributes to many diseases. Under non-pathological conditions Jak/STAT signaling is tightly regulated by a complex network of regulators. One of these regulators is the protein tyrosine phosphatase SH2-containing phosphatase 2 (SHP2). Although SHP2 is known to be a negative regulator of IL-6-induced Jak/STAT signaling, its exact molecular function is not entirely understood. To elucidate the function of SHP2 in IL-6 signaling we followed a systems biology approach, in which modeling, stepwise model refinement, and experimental analysis are closely linked. We come up with an identifiable model of early Jak/STAT signaling that describes the data and proves to be predictive. The model-based analysis implies that (1) the stepwise association of IL-6 with gp80 and gp130 and (2) STAT3 dimerization at the receptor are essential for the dynamics of early pathway activation, and (3) phosphorylation of SHP2 is nonlinear. Furthermore, the modeling results indicate that SHP2 does not act as a feedback inhibitor in an early phase of IL-6-induced Jak/STAT signaling. However, experimental data reveal that SHP2 exhibits a basal repressory function.

A simple work flow for biologically inspired model reduction--application to early JAK-STAT signaling.

BACKGROUND: Modeling of biological pathways is a key issue in systems biology. When constructing a model, it is tempting to incorporate all known interactions of pathway species, which results in models with a large number of unknown parameters. Fortunately, unknown parameters need not necessarily be measured directly, but some parameter values can be estimated indirectly by fitting the model to experimental data. However, parameter fitting, or, more precisely, maximum likelihood parameter estimation, only provides valid results, if the complexity of the model is in balance with the amount and quality of the experimental data. If this is the case the model is said to be identifiable for the given data. If a model turns out to be unidentifiable, two steps can be taken. Either additional experiments need to be conducted, or the model has to be simplified. RESULTS: We propose a systematic procedure for model simplification, which consists of the following steps: estimate the parameters of the model, create an identifiability ranking for the estimated parameters, and simplify the model based on the identifiability analysis results. These steps need to be applied iteratively until the resulting model is identifiable, or equivalently, until parameter variances are small. We choose parameter variances as stopping criterion, since they are concise and easy to interpret. For both, the parameter estimation and the calculation of parameter variances, multi-start parameter estimations are run on a parallel cluster. In contrast to related work in systems biology, we do not suggest simplifying a model by fixing some of its parameters, but change the structure of the model. CONCLUSIONS: We apply the proposed approach to a model of early signaling events in the JAK-STAT pathway. The resulting model is not only identifiable with small parameter variances, but also shows the best trade-off between goodness of fit and model complexity.

Systematic identifiability testing for unambiguous mechanistic modeling--application to JAK-STAT, MAP kinase, and NF-kappaB signaling pathway models.

BACKGROUND: When creating mechanistic mathematical models for biological signaling processes it is tempting to include as many known biochemical interactions into one large model as possible. For the JAK-STAT, MAP kinase, and NF-kappaB pathways a lot of biological insight is available, and as a consequence, large mathematical models have emerged. For large models the question arises whether unknown model parameters can uniquely be determined by parameter estimation from measured data. Systematic approaches to answering this question are indispensable since the uniqueness of model parameter values is essential for predictive mechanistic modeling. RESULTS: We propose an eigenvalue based method for efficiently testing identifiability of large ordinary differential models and compare this approach to three existing ones. The methods are benchmarked by applying them to models of the signaling pathways mentioned above. In all cases the eigenvalue method proposed here and the orthogonal method find the largest set of identifiable parameters, thus clearly outperforming the other approaches. The identifiability analysis shows that the pathway models are not identifiable, even under the strong assumption that all system state variables are measurable. We demonstrate how the results of the identifiability analysis can be used for model simplification. CONCLUSION: While it has undoubtedly contributed to recent advances in systems biology, mechanistic modeling by itself does not guarantee unambiguous descriptions of biological processes. We show that some recent signal transduction pathway models have reached a level of detail that is not warranted. Rigorous identifiability tests reveal that even if highly idealized experiments could be carried out to measure all state variables of these signaling pathways, some unknown parameters could still not be estimated. The identifiability tests therefore show that the level of detail of the investigated models is too high in principle, not just because too little experimental information is available. We demonstrate how the proposed method can be combined with biological insight, however, to simplify these models.

Kinases and G proteins join the Wnt receptor complex.

Wnt proteins form a family of secreted signaling proteins that play a key role in various developmental events such as cell differentiation, cell migration, cell polarity and cell proliferation. It is currently thought that Wnt proteins activate at least three different signaling pathways by binding to seven transmembrane receptors of the Frizzled family and the co-receptor LRP6. Despite our growing knowledge of intracellular components that mediate a Wnt signal, the molecular events at the membrane have remained rather unclear. Now several publications(1-4) indicate that Frizzled receptors are G-protein coupled and kinases were identified that phosphorylate the co-receptor LRP6. These data deepen our understanding of Wnt-mediated signal transduction and provide more insight into how specificity may be achieved.