Locally correlated kinetics of post-replication DNA methylation reveals processivity and region specificity in DNA methylation maintenance.

DNA methylation occurs predominantly on cytosine-phosphate-guanine (CpG) dinucleotides in the mammalian genome, and the methylation landscape is maintained over mitotic cell division. It has been posited that coupling of maintenance methylation activity among neighbouring CpGs is critical to stability over cellular generations; however, the mechanism is unclear. We used mathematical models and stochastic simulation to analyse data from experiments that probe genome-wide methylation of nascent DNA post-replication in cells. We find that DNA methylation maintenance rates on individual CpGs are locally correlated, and the degree of this correlation varies by genomic regional context. By using theory of protein diffusion along DNA, we show that exponential decay of methylation rate correlation with genomic distance is consistent with enzyme processivity. Our results provide quantitative evidence of genome-wide methyltransferase processivity in vivo. We further developed a method to disentangle different mechanistic sources of kinetic correlations. From the experimental data, we estimate that an individual methyltransferase methylates neighbour CpGs processively if they are 36 basepairs apart, on average. But other mechanisms of coupling dominate for longer inter-CpG distances. Our study demonstrates that quantitative insights into enzymatic mechanisms can be obtained from replication-associated, cell-based genome-wide measurements, by combining data-driven statistical analyses with hypothesis-driven mathematical modelling.

Simulation of receptor triggering by kinetic segregation shows role of oligomers and close contacts.

The activation of T cells, key players of the immune system, involves local evacuation of phosphatase CD45 from a region of the T cell's surface, segregating it from the T cell receptor. What drives this evacuation? In the presence of antigen, what ensures evacuation happens in the subsecond timescales necessary to initiate signaling? In the absence of antigen, what mechanisms ensure that evacuation does not happen spontaneously, which could cause signaling errors? Phenomena known to influence spatial organization of CD45 or similar surface molecules include diffusive motion in the lipid bilayer, oligomerization reactions, and mechanical compression against a nearby surface, such as that of the cell presenting the antigen. Computer simulations can investigate hypothesized spatiotemporal mechanisms of T cell signaling. The challenge to computational studies of evacuation is that the base process, spontaneous evacuation by simple diffusion, is in the extreme rare event limit, meaning direct stochastic simulation is unfeasible. Here, we combine particle-based spatial stochastic simulation with the weighted ensemble method for rare events to compute the mean first passage time for cell surface availability by surface reorganization of CD45. We confirm mathematical estimates that, at physiological concentrations, spontaneous evacuation is extremely rare, roughly 300 years. We find that dimerization decreases the time required for evacuation. A weak bimolecular interaction (dissociation constant estimate 460 µM) is sufficient for an order of magnitude reduction of spontaneous evacuation times, and oligomerization to hexamers reduces times to below 1 s. This introduces a mechanism whereby externally induced CD45 oligomerization could significantly modify T cell function. For large regions of close contact, such as those induced by large microvilli, molecular size and compressibility imply a nonzero reentry probability of 60%, decreasing evacuation times. Simulations show that these reduced evacuation times are still unrealistically long (even with a fourfold variation centered around previous estimates of parameters), suggesting that a yet-to-be-described mechanism, besides compressional exclusion at a close contact, drives evacuation.

Plant silicon application alters leaf alkaloid concentrations and impacts parasitoids more adversely than their aphid hosts.

Grasses accumulate large amounts of silicon (Si) which acts as a highly effective physical defence against insect herbivory, however recent evidence shows that Si supplementation also modifies plant secondary metabolite concetrations. Changes in plant secondary metabolites concentrations can have cascading effects on higher trophic levels, such as parasitoids, as they are dependent on the host herbivore for growth and development. However, relatively little is known about how Si application affects higher trophic levels. We examined the effects of Si addition on alkaloid content in leaves of Phalaris aquatica (Poaceae) and the effect on interactions between an aphid (Rhopalosiphum padi) and its parasitoid (Aphidius colemani). Si supplementation had no effect on aphid abundance or parasitism rate. Adult aphids, aphid mummies (parasitised aphids) and the emergent parasitoids were, however, significantly smaller on Si+ plants. Parasitoid traits (size and emergence) were correlated with aphid mummy size. Si addition reduced parasitoid emergence rate and size due to reduced host mummy size, in addition, significantly fewer females emerged from mummies on Si+ plants. Aphid infestation significantly altered alkaloids concentrations, reducing gramine by 80% while increasing tryptamine by 91% in Si- plants. Si addition reduced aphid-induced tryptamine concentrations by 64% and increased 5-MeO-tryptamine by over 800% in control and 142% in aphid infested plants. Our results show that while Si addition has modest impacts on the herbivore, it significantly alters secondary metabolites and has stronger effects on the higher trophic level through changes in the quality of the parasitised host.

Balint-style reflective practice groups in a year 4 undergraduate general practice attachment: experience of the first two years.

BACKGROUND AND AIMS: Many undergraduate medical curricula include reflective practice sessions based on traditional Balint-groups. Those sessions can help students to acknowledge that experiencing 'negative' feelings in relation to patients is normal and may contain important information about the clinical encounter. They may also help to protect students from some of the emotional challenges of studying medicine. The Edinburgh University scheme provides all students in their first clinical year with two dedicated reflective practice sessions. Here we report on experience of the first two years. METHODS: Students' attitudes to the sessions were ascertained using a questionnaire, and views of the group leaders were assessed using a questionnaire and through informal verbal and email discussions. Practical difficulties were recorded as they arose. RESULTS: Students generally rated the sessions positively with regard to exploring relationships and self-reflection, and they found the sessions interesting and helpful. The sessions did not seem to affect career choice. The free-text comments suggested four positive themes and four areas for future modification. CONCLUSION: We have succeeded in providing all undergraduate students with an opportunity to take part in a reflective practice. We have highlighted aspects which have been successful and suggested future improvements.

Stochastic modeling reveals kinetic heterogeneity in post-replication DNA methylation.

DNA methylation is a heritable epigenetic modification that plays an essential role in mammalian development. Genomic methylation patterns are dynamically maintained, with DNA methyltransferases mediating inheritance of methyl marks onto nascent DNA over cycles of replication. A recently developed experimental technique employing immunoprecipitation of bromodeoxyuridine labeled nascent DNA followed by bisulfite sequencing (Repli-BS) measures post-replication temporal evolution of cytosine methylation, thus enabling genome-wide monitoring of methylation maintenance. In this work, we combine statistical analysis and stochastic mathematical modeling to analyze Repli-BS data from human embryonic stem cells. We estimate site-specific kinetic rate constants for the restoration of methyl marks on >10 million uniquely mapped cytosines within the CpG (cytosine-phosphate-guanine) dinucleotide context across the genome using Maximum Likelihood Estimation. We find that post-replication remethylation rate constants span approximately two orders of magnitude, with half-lives of per-site recovery of steady-state methylation levels ranging from shorter than ten minutes to five hours and longer. Furthermore, we find that kinetic constants of maintenance methylation are correlated among neighboring CpG sites. Stochastic mathematical modeling provides insight to the biological mechanisms underlying the inference results, suggesting that enzyme processivity and/or collaboration can produce the observed kinetic correlations. Our combined statistical/mathematical modeling approach expands the utility of genomic datasets and disentangles heterogeneity in methylation patterns arising from replication-associated temporal dynamics versus stable cell-to-cell differences.

New insights into cheddar cheese microbiota-metabolome relationships revealed by integrative analysis of multi-omics data.

Cheese microbiota and metabolites and their inter-relationships that underpin specific cheese quality attributes remain poorly understood. Here we report that multi-omics and integrative data analysis (multiple co-inertia analysis, MCIA) can be used to gain deeper insights into these relationships and identify microbiota and metabolite fingerprints that could be used to monitor product quality and authenticity. Our study into different brands of artisanal and industrial cheddar cheeses showed that Streptococcus, Lactococcus and Lactobacillus were the dominant taxa with overall microbial community structures differing not only between industrial and artisanal cheeses but also among different cheese brands. Metabolome analysis also revealed qualitative and semi-quantitative differences in metabolites between different cheeses. This also included the presence of two compounds (3-hydroxy propanoic acid and O-methoxycatechol-O-sulphate) in artisanal cheese that have not been previously reported in any type of cheese. Integrative analysis of multi-omics datasets revealed that highly similar cheeses, identical in age and appearance, could be distinctively clustered according to cheese type and brand. Furthermore, the analysis detected strong relationships, some previously unknown, which existed between the cheese microbiota and metabolome, and uncovered specific taxa and metabolites that contributed to these relationships. These results highlight the potential of this approach for identifying product specific microbe/metabolite signatures that could be used to monitor and control cheese quality and product authenticity.

Identification and Distribution of Novel Metabolites of Lolitrem B in Mice by High-Resolution Mass Spectrometry.

Lolitrem B is the most potent indole-diterpene mycotoxin produced by Epichloë festucae var. lolii (termed LpTG-1), with severe intoxication cases reported in livestock. To date, there are no in vivo metabolism studies conducted for the mycotoxin. A mouse model assay established for assessing toxicity of indole-diterpenes was used to investigate metabolic products of lolitrem B. Mice were administered lolitrem B at 0.5 and 2.0 mg/kg body weight (b.wt) intraperitoneally before body and brain tissues were collected at 6 h and 24 h post-treatment. Samples were cryoground and subjected to a biphasic or monophasic extraction. The aqueous and lipophilic phases were analysed using liquid chromatography high-resolution mass spectrometry (LC-HRMS); data analysis was performed with Compound Discoverer™ software. A total of 10 novel phase I metabolic products were identified in the lipophilic phase and their distribution in the liver, kidney and various brain regions are described. The biotransformation products of lolitrem B were found to be present in low levels in the brain. Based on structure-activity postulations, six of these may contribute towards the protracted tremors exhibited by lolitrem B-exposed animals.

Tremorgenic effects and functional metabolomics analysis of lolitrem B and its biosynthetic intermediates.

The neuroactive mycotoxin lolitrem B causes a neurological syndrome in grazing livestock resulting in hyperexcitability, muscle tremors, ataxia and, in severe cases, clonic seizures and death. To define the effects of the major toxin lolitrem B in the brain, a functional metabolomic study was undertaken in which motor coordination and tremor were quantified and metabolomic profiling undertaken to determine relative abundance of both toxin and key neurotransmitters in various brain regions in male mice. Marked differences were observed in the duration of tremor and coordination between lolitrem B pathway members, with some showing protracted effects and others none at all. Lolitrem B was identified in liver, kidney, cerebral cortex and thalamus but not in brainstem or cerebellum which were hypothesised previously to be the primary site of action. Metabolomic profiling showed significant variation in specific neurotransmitter and amino acid profiles over time. This study demonstrates accumulation of lolitrem B in the brain, with non-detectable levels of toxin in the brainstem and cerebellum, inducing alterations in metabolites such as tyrosine, suggesting a dynamic catecholaminergic response over time. Temporal characterisation of key pathways in the pathophysiological response of lolitrem B in the brain were also identified.

Hydrodynamics of transient cell-cell contact: The role of membrane permeability and active protrusion length.

In many biological settings, two or more cells come into physical contact to form a cell-cell interface. In some cases, the cell-cell contact must be transient, forming on timescales of seconds. One example is offered by the T cell, an immune cell which must attach to the surface of other cells in order to decipher information about disease. The aspect ratio of these interfaces (tens of nanometers thick and tens of micrometers in diameter) puts them into the thin-layer limit, or "lubrication limit", of fluid dynamics. A key question is how the receptors and ligands on opposing cells come into contact. What are the relative roles of thermal undulations of the plasma membrane and deterministic forces from active filopodia? We use a computational fluid dynamics algorithm capable of simulating 10-nanometer-scale fluid-structure interactions with thermal fluctuations up to seconds- and microns-scales. We use this to simulate two opposing membranes, variously including thermal fluctuations, active forces, and membrane permeability. In some regimes dominated by thermal fluctuations, proximity is a rare event, which we capture by computing mean first-passage times using a Weighted Ensemble rare-event computational method. Our results demonstrate a parameter regime in which the time it takes for an active force to drive local contact actually increases if the cells are being held closer together (e.g., by nonspecific adhesion), a phenomenon we attribute to the thin-layer effect. This leads to an optimal initial cell-cell separation for fastest receptor-ligand binding, which could have relevance for the role of cellular protrusions like microvilli. We reproduce a previous experimental observation that fluctuation spatial scales are largely unaffected, but timescales are dramatically slowed, by the thin-layer effect. We also find that membrane permeability would need to be above physiological levels to abrogate the thin-layer effect.

Analysis of Single-Cell Gene Pair Coexpression Landscapes by Stochastic Kinetic Modeling Reveals Gene-Pair Interactions in Development.

Single-cell transcriptomics is advancing discovery of the molecular determinants of cell identity, while spurring development of novel data analysis methods. Stochastic mathematical models of gene regulatory networks help unravel the dynamic, molecular mechanisms underlying cell-to-cell heterogeneity, and can thus aid interpretation of heterogeneous cell-states revealed by single-cell measurements. However, integrating stochastic gene network models with single cell data is challenging. Here, we present a method for analyzing single-cell gene-pair coexpression patterns, based on biophysical models of stochastic gene expression and interaction dynamics. We first developed a high-computational-throughput approach to stochastic modeling of gene-pair coexpression landscapes, based on numerical solution of gene network Master Equations. We then comprehensively catalogued coexpression patterns arising from tens of thousands of gene-gene interaction models with different biochemical kinetic parameters and regulatory interactions. From the computed landscapes, we obtain a low-dimensional "shape-space" describing distinct types of coexpression patterns. We applied the theoretical results to analysis of published single cell RNA sequencing data and uncovered complex dynamics of coexpression among gene pairs during embryonic development. Our approach provides a generalizable framework for inferring evolution of gene-gene interactions during critical cell-state transitions.

Rare-event sampling of epigenetic landscapes and phenotype transitions.

Stochastic simulation has been a powerful tool for studying the dynamics of gene regulatory networks, particularly in terms of understanding how cell-phenotype stability and fate-transitions are impacted by noisy gene expression. However, gene networks often have dynamics characterized by multiple attractors. Stochastic simulation is often inefficient for such systems, because most of the simulation time is spent waiting for rare, barrier-crossing events to occur. We present a rare-event simulation-based method for computing epigenetic landscapes and phenotype-transitions in metastable gene networks. Our computational pipeline was inspired by studies of metastability and barrier-crossing in protein folding, and provides an automated means of computing and visualizing essential stationary and dynamic information that is generally inaccessible to conventional simulation. Applied to a network model of pluripotency in Embryonic Stem Cells, our simulations revealed rare phenotypes and approximately Markovian transitions among phenotype-states, occurring with a broad range of timescales. The relative probabilities of phenotypes and the transition paths linking pluripotency and differentiation are sensitive to global kinetic parameters governing transcription factor-DNA binding kinetics. Our approach significantly expands the capability of stochastic simulation to investigate gene regulatory network dynamics, which may help guide rational cell reprogramming strategies. Our approach is also generalizable to other types of molecular networks and stochastic dynamics frameworks.

Preclinical studies for a phase 1 clinical trial of autologous hematopoietic stem cell gene therapy for sickle cell disease.

BACKGROUND AIMS: Gene therapy by autologous hematopoietic stem cell transplantation (HSCT) represents a new approach to treat sickle cell disease (SCD). Optimization of the manufacture, characterization and testing of the transduced hematopoietic stem cell final cell product (FCP), as well as an in depth in vivo toxicology study, are critical for advancing this approach to clinical trials. METHODS: Data are shown to evaluate and establish the feasibility of isolating, transducing with the Lenti/ßAS3-FB vector and cryopreserving CD34+ cells from human bone marrow (BM) at clinical scale. In vitro and in vivo characterization of the FCP was performed, showing that all the release criteria were successfully met. In vivo toxicology studies were conducted to evaluate potential toxicity of the Lenti/ßAS3-FB LV in the context of a murine BM transplant. RESULTS: Primary and secondary transplantation did not reveal any toxicity from the lentiviral vector. Additionally, vector integration site analysis of murine and human BM cells did not show any clonal skewing caused by insertion of the Lenti/ßAS3-FB vector in cells from primary and secondary transplanted mice. CONCLUSIONS: We present here a complete protocol, thoroughly optimized to manufacture, characterize and establish safety of a FCP for gene therapy of SCD.

Markov State Models of gene regulatory networks.

BACKGROUND: Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understanding of network dynamics have the potential to guide cell-reprogramming strategies. Networks are often modeled by the stochastic Chemical Master Equation, but methods for systematic identification of key properties of the global dynamics are currently lacking. RESULTS: The method identifies the number, phenotypes, and lifetimes of long-lived states for a set of common gene regulatory network models. Application of transition path theory to the constructed Markov State Model decomposes global dynamics into a set of dominant transition paths and associated relative probabilities for stochastic state-switching. CONCLUSIONS: In this proof-of-concept study, we found that the Markov State Model provides a general framework for analyzing and visualizing stochastic multistability and state-transitions in gene networks. Our results suggest that this framework-adopted from the field of atomistic Molecular Dynamics-can be a useful tool for quantitative Systems Biology at the network scale.

Solving Immunology?

Emergent responses of the immune system result from the integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for the systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the National Institute of Allergy and Infectious Disease (NIAID) workshop 'Complex Systems Science, Modeling and Immunity' and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies.

Current Understanding of Acute Bovine Liver Disease in Australia.

Acute bovine liver disease (ABLD) is a hepatotoxicity principally of cattle which occurs in southern regions of Australia. Severely affected animals undergo rapid clinical progression with mortalities often occurring prior to the recognition of clinical signs. Less severely affected animals develop photosensitization and a proportion can develop liver failure. The characteristic histopathological lesion in acute fatal cases is severe, with acute necrosis of periportal hepatocytes with hemorrhage into the necrotic areas. Currently there are a small number of toxins that are known to cause periportal necrosis in cattle, although none of these have so far been linked to ABLD. Furthermore, ABLD has frequently been associated with the presence of rough dog's tail grass (Cynosurus echinatus) and Drechslera spp. fungi in the pasture system, but it is currently unknown if these are etiological factors. Much of the knowledge about ABLD is contained within case reports, with very little experimental research investigating the specific cause(s). This review provides an overview of the current and most recently published knowledge of ABLD. It also draws on wider research and unpublished reports to suggest possible fungi and mycotoxins that may give rise to ABLD.

Regulation of macrophage polarization and plasticity by complex activation signals.

Macrophages are versatile cells of the immune system that play an important role in both advancing and resolving inflammation. Macrophage activation has been described as a continuum, and different stimuli lead to M1, M2, or mixed phenotypes. In addition, macrophages expressing markers associated with both M1 and M2 function are observed in vivo. Using flow cytometry, we examine how macrophage populations respond to combined M1 and M2 activation signals, presented either simultaneously or sequentially. We demonstrate that macrophages exposed to a combination of LPS, IFN-γ, IL-4, and IL-13 acquire a mixed activation state, with individual cells expressing both M1 marker CD86 and M2 marker CD206 instead of polarizing to discrete phenotypes. Over time, co-stimulated macrophages lose expression of CD86 and display increased expression of CD206. In addition, we find that exposure to LPS/IFN-γ potentiates the subsequent response to IL-4/IL-13, whereas pre-polarization with IL-4/IL-13 inhibits the response to LPS/IFN-γ. Mathematical modeling of candidate regulatory networks indicates that a complex inter-dependence of M1- and M2-associated pathways underlies macrophage activation. Specifically, a mutual inhibition motif was not by itself sufficient to reproduce the temporal marker expression data; incoherent feed-forward of M1 activation as well as both inhibition and activation of M2 by M1 were required. Together these results corroborate a continuum model of macrophage activation and demonstrate that phenotypic markers evolve with time and with exposure to complex signals.

DNA-Binding Kinetics Determines the Mechanism of Noise-Induced Switching in Gene Networks.

Gene regulatory networks are multistable dynamical systems in which attractor states represent cell phenotypes. Spontaneous, noise-induced transitions between these states are thought to underlie critical cellular processes, including cell developmental fate decisions, phenotypic plasticity in fluctuating environments, and carcinogenesis. As such, there is increasing interest in the development of theoretical and computational approaches that can shed light on the dynamics of these stochastic state transitions in multistable gene networks. We applied a numerical rare-event sampling algorithm to study transition paths of spontaneous noise-induced switching for a ubiquitous gene regulatory network motif, the bistable toggle switch, in which two mutually repressive genes compete for dominant expression. We find that the method can efficiently uncover detailed switching mechanisms that involve fluctuations both in occupancies of DNA regulatory sites and copy numbers of protein products. In addition, we show that the rate parameters governing binding and unbinding of regulatory proteins to DNA strongly influence the switching mechanism. In a regime of slow DNA-binding/unbinding kinetics, spontaneous switching occurs relatively frequently and is driven primarily by fluctuations in DNA-site occupancies. In contrast, in a regime of fast DNA-binding/unbinding kinetics, switching occurs rarely and is driven by fluctuations in levels of expressed protein. Our results demonstrate how spontaneous cell phenotype transitions involve collective behavior of both regulatory proteins and DNA. Computational approaches capable of simulating dynamics over many system variables are thus well suited to exploring dynamic mechanisms in gene networks.

Use of multiplexed tandem PCR to estimate the prevalence and intensity of Theileria orientalis infections in cattle.

This study employed a semi-quantitative, multiplexed tandem PCR (MT-PCR) to assess the prevalence and infection intensity of four genotypes (buffeli, chitose, ikeda and type 5) of Theileria orientalis in cattle in Australia. Genomic DNA samples from blood samples (n=448) collected from 27 to 32 dairy cows from each of 15 dairy herds with a history of recent theileriosis outbreaks (Group 1), and from blood samples available from 24 cows with or without oriental theileriosis (Group 2) were tested using MT-PCR. Results revealed that all four genotypes were present in Group 1 cattle; genotype buffeli had the highest prevalence (80.5%), followed by genotypes ikeda (71.4%), chitose (38.6%) and type 5 (20.3%). Genotype ikeda had the highest average infection intensity in the cattle (relating to 55,277 DNA copies), followed by buffeli, chitose and type 5 (6354-51,648 copies). For Group 2, results indicated that genotype ikeda had a significantly higher average intensity of infection than buffeli in symptomatic cattle (P<0.001), and symptomatic cattle had a higher intensity of ikeda than asymptomatic cattle (P=0.004). Future studies should assess the utility of the present MT-PCR assay as a diagnostic and epidemiological tool in other parts of Australasia and the world.

Differences in the phenotype, cytokine gene expression profiles, and in vivo alloreactivity of T cells mobilized with plerixafor compared with G-CSF.

Plerixafor (Mozobil) is a CXCR4 antagonist that rapidly mobilizes CD34(+) cells into circulation. Recently, plerixafor has been used as a single agent to mobilize peripheral blood stem cells for allogeneic hematopoietic cell transplantation. Although G-CSF mobilization is known to alter the phenotype and cytokine polarization of transplanted T cells, the effects of plerixafor mobilization on T cells have not been well characterized. In this study, we show that alterations in the T cell phenotype and cytokine gene expression profiles characteristic of G-CSF mobilization do not occur after mobilization with plerixafor. Compared with nonmobilized T cells, plerixafor-mobilized T cells had similar phenotype, mixed lymphocyte reactivity, and Foxp3 gene expression levels in CD4(+) T cells, and did not undergo a change in expression levels of 84 genes associated with Th1/Th2/Th3 pathways. In contrast with plerixafor, G-CSF mobilization decreased CD62L expression on both CD4 and CD8(+) T cells and altered expression levels of 16 cytokine-associated genes in CD3(+) T cells. To assess the clinical relevance of these findings, we explored a murine model of graft-versus-host disease in which transplant recipients received plerixafor or G-CSF mobilized allograft from MHC-matched, minor histocompatibility-mismatched donors; recipients of plerixafor mobilized peripheral blood stem cells had a significantly higher incidence of skin graft-versus-host disease compared with mice receiving G-CSF mobilized transplants (100 versus 50%, respectively, p = 0.02). These preclinical data show plerixafor, in contrast with G-CSF, does not alter the phenotype and cytokine polarization of T cells, which raises the possibility that T cell-mediated immune sequelae of allogeneic transplantation in humans may differ when donor allografts are mobilized with plerixafor compared with G-CSF.