Richard Carl Van der Wath

Hematopoietic Stem Cells Reversibly Switch from Dormancy to Self-Renewal during Homeostasis and Repair

Bone marrow hematopoietic stem cells (HSCs) are crucial to maintain lifelong production of all blood cells. Although HSCs divide infrequently, it is thought that the entire HSC pool turns over every few weeks, suggesting that HSCs regularly enter and exit cell cycle. Here, we combine flow cytometry with label-retaining assays (BrdU and histone H2B-GFP) to identify a population of dormant mouse HSCs (d-HSCs) within the lin(-)Sca1+cKit+CD150+CD48(-)CD34(-) population. Computational modeling suggests that d-HSCs divide about every 145 days, or five times per lifetime. d-HSCs harbor the vast majority of multilineage long-term self-renewal activity. While they form a silent reservoir of the most potent HSCs during homeostasis, they are efficiently activated to self-renew in response to bone marrow injury or G-CSF stimulation. After re-establishment of homeostasis, activated HSCs return to dormancy, suggesting that HSCs are not stochastically entering the cell cycle but reversibly switch from dormancy to self-renewal under conditions of hematopoietic stress.

Estimating dormant and active hematopoietic stem cell kinetics through extensive modeling of bromodeoxyuridine label-retaining cell dynamics

Bone marrow hematopoietic stem cells (HSCs) are responsible for both lifelong daily maintenance of all blood cells and for repair after cell loss. Until recently the cellular mechanisms by which HSCs accomplish these two very different tasks remained an open question. Biological evidence has now been found for the existence of two related mouse HSC populations. First, a dormant HSC (d-HSC) population which harbors the highest self-renewal potential of all blood cells but is only induced into active self-renewal in response to hematopoietic stress. And second, an active HSC (a-HSC) subset that by and large produces the progenitors and mature cells required for maintenance of day-to-day hematopoiesis. Here we present computational analyses further supporting the d-HSC concept through extensive modeling of experimental DNA label-retaining cell (LRC) data. Our conclusion that the presence of a slowly dividing subpopulation of HSCs is the most likely explanation (amongst the various possible causes including stochastic cellular variation) of the observed long term Bromodeoxyuridine (BrdU) retention, is confirmed by the deterministic and stochastic models presented here. Moreover, modeling both HSC BrdU uptake and dilution in three stages and careful treatment of the BrdU detection sensitivity permitted improved estimates of HSC turnover rates. This analysis predicts that d-HSCs cycle about once every 149–193 days and a-HSCs about once every 28–36 days. We further predict that, using LRC assays, a 75%–92.5% purification of d-HSCs can be achieved after 59–130 days of chase. Interestingly, the d-HSC proportion is now estimated to be around 30–45% of total HSCs - more than twice that of our previous estimate.

Cell Organisation in the Colonic Crypt: A Theoretical Comparison of the Pedigree and Niche Concepts

The intestinal mucosa is a monolayer of rapidly self-renewing epithelial cells which is not only responsible for absorption of water and nutrients into the bloodstream but also acts as a protective barrier against harmful microbes entering the body. New functional epithelial cells are produced from stem cells, and their proliferating progeny. These stem cells are found within millions of crypts (tubular pits) spaced along the intestinal tract. The entire intestinal epithelium is replaced every 2–3 days in mice (3–5 days in humans) and hence cell production, differentiation, migration and turnover need to be tightly regulated. Malfunctions in this regulation are strongly linked to inflammatory bowel diseases and to the formation of adenomas and ultimately cancerous tumours. Despite a great deal of biological experimentation and observation, precisely how colonic crypts are regulated to produce mature colonocytes remains unclear. To assist in understanding how cell organisation in crypts is achieved, two very different conceptual models of cell behaviour are developed here, referred to as the ‘pedigree’ and the ‘niche’ models. The pedigree model proposes that crypt cells are largely preprogrammed and receive minimal prompting from the environment as they move through a routine of cell differentiation and proliferation to become mature colonocytes. The niche model proposes that crypt cells are primarily influenced by the local microenvironments along the crypt, and that predetermined cell behaviour plays a negligible role in their development. In this paper we present a computational model of colonic crypts in the mouse, which enables a comparison of the quality and controllability of mature coloncyte production by crypts operating under these two contrasting conceptual models of crypt regulation.

Bayesian Phylogeny on Grid

Grid computing defines the combination of computers or clusters of computers across networks, like the internet, to form a distributed supercomputer. This infrastructure allows scientists to process complex and time consuming computations in parallel on demand. Phylogenetic inference for large data sets of DNA/protein sequences is known to be computationally intensive and could greatly benefit from this parallel supercomputing approach. Bayesian algorithms allows the estimation of important parameters on species divergence modus and time but at the price of running repetitive long series of MonteCarlo simulations. As part of the BioinfoGrid project, we ported parallel MrBayes to the EGEE (Enabling Grids for E-sciencE) grid infrastructure. As case study we investigate both a challenging dataset of arthropod phylogeny and the most appropriate model of amino acid replacement for that data set. Our aim is to resolve the position of basal hexapod lineages with respect to Insecta and Crustacea. In this effort, a new matrix of protein change was derived from the dataset itself, and its performance compared with other currently used models.

A Stochastic Single Cell Based Model of BrdU Measured Hematopoietic Stem Cell Kinetics

The therapeutic potential of stem cells due to their ability to build and maintain tissues and organs is widely recognised. Much can be learned by studying stem cell turnover dynamics and Bromodeoxyuridine (BrdU) is often used for this purpose. Good computational models are however needed for a full understanding of BrdU data and in this paper we present such a model. Our approach is to model single cells as well as their chromosomes as agents which make probabilistic decisions over fixed intervals of time. We demonstrate the power of our model by comparing its performance to a deterministic BrdU model used in a recently published study on asymmetric chromosome segregation in Hematopoietic stem cells.

Identifying regulatory sites using neighborhood species

The annotation of transcription binding sites in new sequenced genomes is an important and challenging problem. We have previously shown how a regression model that linearly relates gene expression levels to the matching scores of nucleotide patterns allows us to identify DNA-binding sites from a collection of co-regulated genes and their nearby non-coding DNA sequences. Our methodology uses Bayesian models and stochastic search techniques to select transcription factor binding site candidates. Here we show that this methodology allows us to identify binding sites in nearby species. We present examples of annotation crossing from Schizosaccharomyces pombe to Schizosaccharomyces japonicus. We found that the eng1 motif is also regulating a set of 9 genes in S. japonicus. Our framework may have an effective interest in conveying information in the annotation process of a new species. Finally we discuss a number of statistical and biological issues related to the identification of binding sites through covariates of genes expression and sequences.

Substitution Matrices and Mutual Information Approaches to Modeling Evolution

Substitution matrices are at the heart of Bioinformatics: sequence alignment, database search, phylogenetic inference, protein family classification are all based on BLOSUM, PAM, JTT, mtREV24 and other matrices. These matrices provide means of computing models of evolution and assessing the statistical relationships amongst sequences. This paper reports two results; first we show how Bayesian and grid settings can be used to derive novel specific substitution matrices for fish and insects and we discuss their performances with respect to standard amino acid replacement matrices. Then we discuss a novel application of these matrices: a refinement of the mutual information formula applied to amino acid alignments by incorporating a substitution matrix into the calculation of the mutual information. We show that different substitution matrices provide qualitatively different mutual information results and that the new algorithm allows the derivation of better estimates of the similarity along a sequence alignment. We thus express an interesting procedure: generating ad hoc substitution matrices from a collection of sequences and combining the substitution matrices and mutual information for the detection of sequence patterns.

Combining Experimental Evidences from Replicates and Nearby Species Data for Annotating Novel Genomes

For several years now, there has been an exponential growth of the amount of life science data (e.g., sequenced complete genomes, 3D structures, DNA chips, Mass spectroscopy data) generated by high throughput experiments. Carrying out analyses of complex, voluminous, and heterogeneous data and guiding the analysis of data using a statistical and mathematical sound methodology is thus of paramount importance. Here we make and justify the observation that experimental replicates and phylogenetic data may be combined to strength the evidences on identifying transcriptional motifs, which seems to be quite difficult using other currently used methods. We present a case study considering sequences and microarray data from fungi species. Although we show that our methodology can result of immediate practical utility to bioinformaticians and biologists for annotating new genomes, here the focus is also on discussing the dependent interesting mathematical problems that high throughput data integration poses.

Combining replicates and nearby species data: a Bayesian approach

Here we discuss the biological high-throughput data dilemma: how to integrate replicated experiments and nearby species data? Should we consider each species as a monadic source of data when replicated experiments are available or, viceversa, should we try to collect information from the large number of nearby species analyzed in the different laboratories? In this paper we make and justify the observation that experimental replicates and phylogenetic data may be combined to strength the evidences on identifying transcriptional motifs and identify networks, which seems to be quite difficult using other currently used methods. In particular we discuss the use of phylogenetic inference and the potentiality of the Bayesian variable selection procedure in data integration. In order to illustrate the proposed approach we present a case study considering sequences and microarray data from fungi species. We also focus on the interpretation of the results with respect to the problem of experimental and biological noise.

Parallel Hematopoietic Stem Cell Division Rate Estimation Using an Agent-Based Model on the Grid

In previous work [1], [2] we presented supportive computational analysis on newly found biological evidence which indicates the existence of a dormant Hematopoietic Stem Cell (HSC) population [3]. Through extensive modelling of experimental DNA label-retaining cell data we showed that an ordinary differential equation (ODE) model can successfully capture a heterogeneous HSC population structure. The ODE models analytical tractability made it especially suitable for parameter estimation in contrast to an earlier agent-based model we developed on a related but independent Bromodeoxyuridine (BrdU) dataset [4]. For this current study we explore the predictive power of the same agent-based model on the larger more elaborate BrdU dataset [3] by comparing the BrdU detection threshold (BDT) estimates of both the ODE (continuous BDT implementation) and agent-based (discrete BDT implementation) models. We therefore re-estimate the HSC division parameters using the agent-based model which entailed a brute-force search approach. In order to cover a worthwhile region of the parameter hyperspace within a reasonable amount of time we executed our search algorithm in parallel over the EGEE Grid environment. Our results indicate that the agent-based model more or less supports the same conclusions as the ODE model. However, actual cell division rate estimates as well as model prediction differ slightly for the same set of parameters. The estimates for the dormant HSC proportion and BDT, on the other hand, are in strong agreement with the ODE estimates. For the BDT in particular, this is an encouraging result as the two models have a very different approach in their implementation of the BDT.