Clonally diverse T cell homeostasis is maintained by a common program of cell-cycle control

Lymphopenia induces T cells to undergo cell divisions as part of a homeostatic response mechanism. The clonal response to lymphopenia is extremely diverse, and it is unknown whether this heterogeneity represents distinct mechanisms of cell-cycle control or whether a common mechanism can account for the diversity. We addressed this question by combining in vivo and mathematical modeling of lymphopenia-induced proliferation (LIP) of two distinct T cell clonotypes. OT-I T cells undergo rapid LIP accompanied by differentiation that superficially resembles Ag-induced proliferation, whereas F5 T cells divide slowly and remain naive. Both F5 and OT-I LIP responses were most accurately described by a single stochastic division model where the rate of cell division was exponentially decreased with increasing cell numbers. The model successfully identified key biological parameters of the response and accurately predicted the homeostatic set point of each clone. Significantly, the model was successful in predicting interclonal competition between OT-I and F5 T cells, consistent with competition for the same resource(s) required for homeostatic proliferation. Our results show that diverse and heterogenous clonal T cell responses can be accounted for by a single common model of homeostasis

Thymic output and CD4 T-cell reconstitution in HIV-infected children on early and interrupted antiretroviral treatment: evidence from the CHER trial.

Objectives: Early treatment of HIV-infected children and adults is important for optimal immune reconstitution. Infants’ immune systems are more plastic and dynamic than older children’s or adults’, and deserve particular attention. This study aimed to understand the response of the HIV-infected infant immune system to early antiretroviral therapy (ART) and planned ART interruption and re-start. Design: We used linear and nonlinear regression and mixed-effects models to describe children’s CD4 trajectories and to identify predictors of CD4 count during early and interrupted ART. Methods: Data from HIV-infected children enrolled CHER trial, starting ART aged between 6 and 12 weeks, was used to explore the effect of ART on immune reconstitution. Results: Early treatment arrested the decline in CD4 count but did not fully restore it to the levels observed in HIV-uninfected children. Treatment interruption at 40 or 96 weeks resulted in a rapid decline in CD4 T-cells, which on retreatment returned to levels observed before interruption. Naïve CD4 T-cell count was an important determinant of overall CD4 levels. A strong correlation was observed between thymic output and the stable CD4 count both before and after treatment interruption. Conclusions: Early identification and treatment of HIV-infected infants is important to stabilize CD4 counts at the highest levels possible. Once stabilized, children’s CD4 counts appear resilient, with good potential for recovery following treatment interruption. The naïve T-cell pool and thymic production of naive cells are key determinants of children’s CD4 levels

Understanding the Slow Depletion of Memory CD4+ T Cells in HIV Infection

Using a simple mathematical model, Andrew Yates and colleagues show that a runaway cycle of T cell activation and infection cannot explain the slow rate of CD4 decline during chronic HIV infection

Dissection of a complex transcriptional response using genome-wide transcriptional modelling

Modern genomics technologies generate huge data sets creating a demand for systems level, experimentally verified, analysis techniques. We examined the transcriptional response to DNA damage in a human T cell line (MOLT4) using microarrays. By measuring both mRNA accumulation and degradation over a short time course, we were able to construct a mechanistic model of the transcriptional response. The model predicted three dominant transcriptional activity profiles—an early response controlled by NFκB and c-Jun, a delayed response controlled by p53, and a late response related to cell cycle re-entry. The method also identified, with defined confidence limits, the transcriptional targets associated with each activity. Experimental inhibition of NFκB, c-Jun and p53 confirmed that target predictions were accurate. Model predictions directly explained 70% of the 200 most significantly upregulated genes in the DNA-damage response. Genome-wide transcriptional modelling (GWTM) requires no prior knowledge of either transcription factors or their targets. GWTM is an economical and effective method for identifying the main transcriptional activators in a complex response and confidently predicting their targets

Distinguishing the Signals of Gingivitis and Periodontitis in Supragingival Plaque: a Cross-Sectional Cohort Study in Malawi.

UNLABELLED: Periodontal disease ranges from gingival inflammation (gingivitis) to the inflammation and loss of tooth-supporting tissues (periodontitis). Previous research has focused mainly on subgingival plaque, but supragingival plaque composition is also known to be associated with disease. Quantitative modeling of bacterial abundances across the natural range of periodontal severities can distinguish which features of disease are associated with particular changes in composition. We assessed a cross-sectional cohort of 962 Malawian women for periodontal disease and used 16S rRNA gene amplicon sequencing (V5 to V7 region) to characterize the bacterial compositions of supragingival plaque samples. Associations between bacterial relative abundances and gingivitis/periodontitis were investigated by using negative binomial models, adjusting for epidemiological factors. We also examined bacterial cooccurrence networks to assess community structure. The main differences in supragingival plaque compositions were associated more with gingivitis than periodontitis, including higher bacterial diversity and a greater abundance of particular species. However, even after controlling for gingivitis, the presence of subgingival periodontitis was associated with an altered supragingival plaque. A small number of species were associated with periodontitis but not gingivitis, including members of Prevotella, Treponema, and Selenomonas, supporting a more complex disease model than a linear progression following gingivitis. Cooccurrence networks of periodontitis-associated taxa clustered according to periodontitis across all gingivitis severities. Species including Filifactor alocis and Fusobacterium nucleatum were central to this network, which supports their role in the coaggregation of periodontal biofilms during disease progression. Our findings confirm that periodontitis cannot be considered simply an advanced stage of gingivitis even when only considering supragingival plaque. IMPORTANCE: Periodontal disease is a major public health problem associated with oral bacteria. While earlier studies focused on a small number of periodontal pathogens, it is now accepted that the whole bacterial community may be important. However, previous high-throughput marker gene sequencing studies of supragingival plaque have largely focused on high-income populations with good oral hygiene without including a range of periodontal disease severities. Our study includes a large number of low-income participants with poor oral hygiene and a wide range of severities, and we were therefore able to quantitatively model bacterial abundances as functions of both gingivitis and periodontitis. A signal associated with periodontitis remains after controlling for gingivitis severity, which supports the concept that, even when only considering supragingival plaque, periodontitis is not simply an advanced stage of gingivitis. This suggests the future possibility of diagnosing periodontitis based on bacterial occurrences in supragingival plaque

Clinical T Cell Receptor Repertoire Deep Sequencing and Analysis: An Application to Monitor Immune Reconstitution Following Cord Blood Transplantation

Spectratyping assays are well recognized as the clinical gold standard for assessing the T cell receptor (TCR) repertoire in haematopoietic stem cell transplant (HSCT) recipients. These assays use length distributions of the hyper variable complementarity-determining region 3 (CDR3) to characterize a patient's T cell immune reconstitution post-transplant. However, whilst useful, TCR spectratyping is notably limited by its resolution, with the technique unable to provide data on the individual clonotypes present in a sample. High-resolution clonotype data are necessary to provide quantitative clinical TCR assessments and to better understand clonotype dynamics during clinically relevant events such as viral infections or GvHD. In this study we developed and applied a CDR3 Next Generation Sequencing (NGS) methodology to assess the TCR repertoire in cord blood transplant (CBT) recipients. Using this, we obtained comprehensive TCR data from 16 CBT patients and 5 control cord samples at Great Ormond Street Hospital (GOSH). These were analyzed to provide a quantitative measurement of the TCR repertoire and its constituents in patients post-CBT. We were able to both recreate and quantify inferences typically drawn from spectratyping data. Additionally, we demonstrate that an NGS approach to TCR assessment can provide novel insights into the recovery of the immune system in these patients. We show that NGS can be used to accurately quantify TCR repertoire diversity and to provide valuable inference on clonotypes detected in a sample. We serially assessed the progress of T cell immune reconstitution demonstrating that there is dramatic variation in TCR diversity immediately following transplantation and that the dynamics of T cell immune reconstitution is perturbed by the presence of GvHD. These findings provide a proof of concept for the adoption of NGS TCR sequencing in clinical practice

Reconstruction of cell population dynamics using CFSE

Background: Quantifying cell division and death is central to many studies in the biological sciences. The fluorescent dye CFSE allows the tracking of cell division in vitro and in vivo and provides a rich source of information with which to test models of cell kinetics. Cell division and death have a stochastic component at the single-cell level, and the probabilities of these occurring in any given time interval may also undergo systematic variation at a population level. This gives rise to heterogeneity in proliferating cell populations. Branching processes provide a natural means of describing this behaviour. Results: We present a likelihood-based method for estimating the parameters of branching process models of cell kinetics using CFSE-labeling experiments, and demonstrate its validity using synthetic and experimental datasets. Performing inference and model comparison with real CFSE data presents some statistical problems and we suggest methods of dealing with them. Conclusion: The approach we describe here can be used to recover the (potentially variable) division and death rates of any cell population for which division tracking information is available

Cell death and the maintenance of immunological memory

Immunological memory is found in diverse populations of a class of lymphocytes called T cells, that are held at roughly constant numbers. Its composition is in continuous flux as we encounter new pathogens and cells are lost. The mechanisms which preserve the memory T cell population in the face of these uncertain factors are largely unknown. We propose a mechanism for homeostasis, driven by density-dependent cell death, that both fits experimental data and naturally preserves the clonal composition of the T cell pool with fluctuating cell numbers. It also provides clues as to the source of differences in diversity between T cell memory subpopulations

Combining cytokine signalling with T-bet and GATA-3 regulation in Th1 and Th2 differentiation: a model for cellular decision-making

Differentiation of uncommitted T cells into Th1 and Th2 subpopulations depends on both intracellular events controlling expression of transcription factors T-bet and GATA-3 and interactions between cells mediated by cytokines, particularly IL4 and IFNγ. A great deal is known about the intracellular and extracellular events involved in Th1 and Th2 (Th) differentiation, but how these are integrated in T-cell populations or indeed why extracellular cytokine control is required after a decision has been made at a transcriptional level is not at all understood. We present a mathematical model of CD4+ T-cell differentiation that describes both intracellular and extracellular processes and the interactions between them. It shows how antigen stimulation in conjunction with cytokines and other extracellular signals gives rise to rapid, reversible and mutually exclusive expression of T-bet or GATA-3 due to feedback between the transcription factors and their signalling pathways. After transient signalling by APC, continued Th1 and Th2 differentiation is shown to require cytokine production by the proliferating T cells. Moreover, intercellular communication by T-cell-derived cytokines lowers the threshold of APC signals required for Th differentiation. This provides an explanation for enhanced Th differentiation by pre-existing memory T cells. The model also predicts that Th differentiation can be reversed at the single cell level before commitment by manipulating the cytokine environment. It suggests a mechanism for switching between Th1 and Th2 in the so-called irreversible state that may be developed as a novel therapeutic means of manipulating Th1 and Th2 responses