Biology subjects

Wit, E. C.

Publications and source records attributed to Wit, E. C..

5 recordsLinked to original sources

bioRxiv2022

Stochastic modelling of cell differentiation networks from partially-observed clonal tracking data

MotivationClarifying how hematopoietic stem cells differentiate into mature cell types is important for understanding how they attain specific functions and offers the potential for therapeutic manipulation. Over the past decades, clonal tracking has proven to be capable of unveiling population dynamics and hierarchical relationships in vivo. For this reason, clonal tracking studies are required for safety and long-term efficacy assessment in gene therapy. However, many standard clonal tracking studies consider only a subset of cell-types and are subject to noise. ResultsIn this work, we propose a stochastic framework that investigates the dynamics of cell differentiation from typical clonal tracking data subject to measurement noise, false-negative errors, and systematically unobserved cell types. Our framework is based on stochastic reaction networks combined with extended Kalman filtering and Rauch-Tung-Striebel smoothing. Our tool can provide statistical support to biologists in gene therapy clonal tracking studies to better understand clonal reconstitution dynamics. AvailabilityThe stochastic framework is implemented in the [Formula] package Karen which is available for download at https://github.com/delcore-luca/Karen. The code that supports the findings of this study is openly available at https://github.com/delcore-luca/CellDifferentiationNetworks. Contactl.del.core@rug.nl

systems biology↗

bioRxiv2022

TRACKING HEMATOPOIETIC STEM CELL EVOLUTION IN A WISKOTT-ALDRICH CLINICAL TRIAL

Hematopoietic Stem Cells (HSC) are the cells that give rise to all other blood cells and, as such, they are crucial in the healthy development of individuals. Wiskott-Aldrich Syndrome (WAS) is a severe disorder affecting the regulation of hematopoietic cells and is caused by mutations in the WASP gene. We consider data from a revolutionary gene therapy clinical trial, where HSC harvested from 3 WAS patients bone marrow have been edited and corrected using viral vectors. Upon re-infusion into the patient, the HSC multiply and differentiate into other cell types. The aim is to unravel the cell multiplication and cell differentiation process, which has until now remained elusive. This paper models the replenishment of blood lineages resulting from corrected HSC via a multivariate, density-dependent Markov process and develops an inferential procedure to estimate the dynamic parameters given a set of temporally sparsely observed trajectories. Starting from the master equation, we derive a system of non-linear differential equations for the evolution of the first- and second-order moments over time. We use these moment equations in a generalized method-of-moments framework to perform inference. The performance of our proposal has been evaluated by considering different sampling scenarios and measurement errors of various strengths using a simulation study. We also compared it to another state-of-the-art approach and found that our method is statistically more efficient. By applying our method to the Wiskott-Aldrich Syndrome gene therapy data we found strong evidence for a myeloid-based developmental pathway of hematopoietic cells where fates of lymphoid and myeloid cells remain coupled even after the loss of erythroid potential. All code used in this manuscript can be found in the online Supplement, and the latest version of the code is available at github. com/dp3ll1n/SLCDP_v1.0.

bioinformatics↗

bioRxiv2022

Stochastic inference of clonal dominance in gene therapy studies

Clonal dominance is a wake-up-call for adverse events in gene therapy applications. This phenomenon has mainly been observed as a consequence of a malignancy progression, and, in some rare cases, also during normal haematopoiesis. We propose here a random-effects stochastic model that allows for a quick detection of clonal expansions that possibly occur during a gene therapy treatment. Starting from the Ito-type equation, the dynamics of cells duplication, death and differentiation at clonal level without clonal dominance can be described by a local linear approximation. The parameters of the base model, which are inferred using a maximum likelihood approach, are assumed to be shared across the clones. In order to incorporate the possibility of clonal dominance, we extend the base model by introducing random effects for the clonal parameters. This extended model is estimated using a tailor-made expectation maximization algorithm. The main idea of this paper is to compare the base and the extended models in high dimensional clonal tracking datasets by means of Akaike Information Criterion in order to detect the presence of clonal dominance. The method is evaluated using a simulation study, and is applied to investigating the dynamics of clonal expansion in a in-vivo model of rhesus macaque hematopoiesis. Author summaryPreventing or quickly detecting clonal dominance is an important aspect in gene therapy applications. Over the past decades, clonal tracking has proven to be a cutting-edge analysis capable to unveil population dynamics and hierarchical relationships in vivo. For this reason, clonal tracking studies are required for safety and long-term efficacy assessment in preclinical and clinical studies. In this work we propose a random-effects stochastic framework that allows to investigate events of clonal dominance using high-dimensional clonal tracking data. Our framework is based on the combination between stochastic reaction networks and mixed-effects generalized linear models. We have shown in a simulation study and in a real world application that our method is able to detect the presence of clonal expansions. Our tool can provide statistical support to biologists in gene therapy surveillance analyses.

systems biology↗

bioRxiv2021

DETECTING PHYLODIVERSITY-DEPENDENT DIVERSIFICATION WITH A GENERAL PHYLOGENETIC INFERENCE FRAMEWORK

AO_SCPLOWBSTRACTC_SCPLOWDiversity-dependent diversification models have been extensively used to study the effect of ecological limits and feedback of community structure on species diversification processes, such as speciation and extinction. Current diversity-dependent diversification models characterise ecological limits by carrying capacities for species richness. Such ecological limits have been justified by niche filling arguments: as species diversity increases, the number of available niches for diversification decreases. However, as species diversify they may diverge from one another phenotypically, which may open new niches for new species. Alternatively, this phenotypic divergence may not affect the species diversification process or even inhibit further diversification. Hence, it seems natural to explore the consequences of phylogenetic diversity-dependent (or phylodiversity-dependent) diversification. Current likelihood methods for estimating diversity-dependent diversification parameters cannot be used for this, as phylodiversity is continuously changing as time progresses and species form and become extinct. Here, we present a new method based on Monte Carlo Expectation-Maximization (MCEM), designed to perform statistical inference on a general class of species diversification models and implemented in the R package emphasis. We use the method to fit phylodiversity-dependent diversification models to 14 phylogenies, and compare the results to the fit of a richness-dependent diversification model. We find that in a number of phylogenies, phylogenetic divergence indeed spurs speciation even though species richness reduces it. Not only do we thus shine a new light on diversity-dependent diversification, we also argue that our inference framework can handle a large class of diversification models for which currently no inference method exists.

evolutionary biology↗

bioRxiv2019

MAGMA: inference of sparse microbial association networks

Microorganisms often live in symbiotic relationship with their environment and they play a central role in many biological processes. They form a complex system of interacting species. Within the gut micro-biota these interaction patterns have been shown to be involved in obesity, diabetes and mental disease. Understanding the mechanisms that govern this ecosystem is therefore an important scientific challenge. Recently, the acquisition of large samples of microbiota data through metabarcoding or metagenomics has become easier. Until now correlation-based network analysis and graphical modelling have been used to identify the putative interaction networks formed by the species of microorganisms, but these methods do not take into account all features of microbiota data. Indeed, correlation-based network cannot distinguish between direct and indirect correlations and simple graphical models cannot include covariates as environmental factors that shape the microbiota abundance. Furthermore, the compositional nature of the microbiota data is often ignored or existing normalizations are often based on log-transformations, which is somewhat arbitrary and therefore affects the results in unknown ways. We have developed a novel method, called MAGMA, for detecting interactions between microbiota that takes into account the noisy structure of the microbiota data, involving an excess of zero counts, overdispersion, compositionality and possible covariate inclusion. The method is based on Copula Gaus-sian graphical models whereby we model the marginals with zero-inflated negative binomial generalized linear models. The inference is based on an efficient median imputation procedure combined with the graphical lasso. We show that our method beats all existing methods in recovering microbial association networks in an extensive simulation study. Moreover, the analysis of two 16S microbial data studies with our method reveals interesting new biology. MAGMA is implemented as an R-package and is freely available at https://gitlab.com/arcgl/rmagma, which also includes the scripts used to prepare the material in this paper.

bioinformatics↗