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MOTIVATION: Investigating cell differentiation under a genetic disorder offers the potential for improving current gene therapy strategies. Clonal tracking provides a basis for mathematical modelling of population stem cell dynamics that sustain the blood cell formation, a process known as haematopoiesis. However, many clonal tracking protocols rely on a subset of cell types for the characterization of the stem cell output, and the data generated are subject to measurement errors and noise. RESULTS: We propose a stochastic framework to infer dynamic models of cell differentiation from clonal tracking data. A state-space formulation combines a stochastic quasi-reaction network, describing cell differentiation, with a Gaussian measurement model accounting for data errors and noise. We developed an inference algorithm based on an extended Kalman filter, a nonlinear optimization, and a Rauch-Tung-Striebel smoother. Simulations show that our proposed method outperforms the state-of-the-art and scales to complex structures of cell differentiations in terms of nodes size and network depth. The application of our method to five in vivo gene therapy studies reveals different dynamics of cell differentiation. Our tool can provide statistical support to biologists and clinicians to better understand cell differentiation and haematopoietic reconstitution after a gene therapy treatment. The equations of the state-space model can be modified to infer other dynamics besides cell differentiation. AVAILABILITY AND IMPLEMENTATION: The stochastic framework is implemented in the R package Karen which is available for download at https://cran.r-project.org/package=Karen. The code that supports the findings of this study is openly available at https://github.com/delcore-luca/CellDifferentiationNetworks.

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We develop a generalized linear mixed model (GLMM) for bivariate count responses for statistically analyzing dragonfly population data from the Northern Netherlands. The populations of the threatened dragonfly species Aeshna viridis were counted in the years 2015-2018 at 17 different locations (ponds and ditches). Two different widely applied population size measures were used to quantify the population sizes, namely the number of found exoskeletons ('exuviae') and the number of spotted egg-laying females were counted. Since both measures (responses) led to many zero counts but also feature very large counts, our GLMM model builds on a zero-inflated bivariate geometric (ZIBGe) distribution, for which we show that it can be easily parameterized in terms of a correlation parameter and its two marginal medians. We model the medians with linear combinations of fixed (environmental covariates) and random (location-specific intercepts) effects. Modeling the medians yields a decreased sensitivity to overly large counts; in particular, in light of growing marginal zero inflation rates. Because of the relatively small sample size (n = 114) we follow a Bayesian modeling approach and use Metropolis-Hastings Markov Chain Monte Carlo (MCMC) simulations for generating posterior samples.

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BACKGROUND: Mathematical models of haematopoiesis can provide insights on abnormal cell expansions (clonal dominance), and in turn can guide safety monitoring in gene therapy clinical applications. Clonal tracking is a recent high-throughput technology that can be used to quantify cells arising from a single haematopoietic stem cell ancestor after a gene therapy treatment. Thus, clonal tracking data can be used to calibrate the stochastic differential equations describing clonal population dynamics and hierarchical relationships in vivo. RESULTS: In this work we propose a random-effects stochastic framework that allows to investigate the presence of events of clonal dominance from high-dimensional clonal tracking data. Our framework is based on the combination between stochastic reaction networks and mixed-effects generalized linear models. Starting from the Kramers-Moyal approximated Master equation, the dynamics of cells duplication, death and differentiation at clonal level, can be described by a local linear approximation. The parameters of this formulation, which are inferred using a maximum likelihood approach, are assumed to be shared across the clones and are not sufficient to describe situation in which clones exhibit heterogeneity in their fitness that can lead to clonal dominance. In order to overcome this limitation, we extend the base model by introducing random-effects for the clonal parameters. This extended formulation is calibrated to the clonal data using a tailor-made expectation-maximization algorithm. We also provide the companion  package RestoreNet, publicly available for download at https://cran.r-project.org/package=RestoreNet . CONCLUSIONS: Simulation studies show that our proposed method outperforms the state-of-the-art. The application of our method in two in-vivo studies unveils the dynamics of clonal dominance. Our tool can provide statistical support to biologists in gene therapy safety analyses.

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BACKGROUND: The potential role of individual plasma biomarkers in the pathogenesis of type 2 diabetes (T2D) has been broadly studied, but the impact of biomarkers interaction remains underexplored. Recently, the Mahalanobis distance (MD) of plasma biomarkers has been proposed as a proxy of physiological dysregulation. Here we aimed to investigate whether the MD calculated from circulating biomarkers is prospectively associated with development of T2D. METHODS: We calculated the MD of the Principal Components (PCs) integrating the information of 32 circulating biomarkers (comprising inflammation, glycemic, lipid, microbiome and one-carbon metabolism) measured in 6247 participants of the PREVEND study without T2D at baseline. Cox proportional-hazards regression analyses were performed to study the association of MD with T2D development. FINDINGS: After a median follow-up of 7·3 years, 312 subjects developed T2D. The overall MD (mean (SD)) was higher in subjects who developed T2D compared to those who did not: 35·65 (26·67) and 30.75 (27·57), respectively (P = 0·002). The highest hazard ratio (HR) was obtained using the MD calculated from the first 31 PCs (per 1 log-unit increment) (1·72 (95% CI 1·42,2·07), P < 0·001). Such associations remained after the adjustment for age, sex, plasma glucose, parental history of T2D, lipids, blood pressure medication, and BMI (HRadj 1·37 (95% CI 1·11,1·70), P = 0·004). INTERPRETATION: Our results are in line with the premise that MD represents an estimate of homeostasis loss. This study suggests that MD is able to provide information about physiological dysregulation also in the pathogenesis of T2D. FUNDING: The Dutch Kidney Foundation (Grant E.033).

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