A method to measure lactate recycling in cultured cells by edited 1H nuclear magnetic resonance spectroscopy

http://www.sciencedirect.com/science/article/B6W9V-4PF6B5M-1/1/385b6c0836057ee00a92ea234317f1e

A minimum of two distinct heritable factors are required to explain correlation structures in proliferating lymphocytes

During the adaptive immune response, lymphocyte populations undergo a characteristic three-phase process: expansion through a series of cell divisions; cessation of expansion; and, finally, most of the accumulated lymphocytes die by apoptosis. The data used, thus far, to inform understanding of these processes, both in vitro and in vivo, are taken from flow cytometry experiments. One significant drawback of flow cytometry is that individual cells cannot be tracked, so that it is not possible to investigate interdependencies in the fate of cells within a family tree. This deficit in experimental information has recently been overcome by Hawkins et al. (Hawkins et al. 2009 Proc. Natl Acad. Sci. USA 106, 13 457–13 462 (doi:10.1073/pnas.0905629106)), who reported on time-lapse microscopy experiments in which B-cells were stimulated through the TLR-9 receptor. Cells stimulated in this way do not aggregate, so that data regarding family trees can be recorded. In this article, we further investigate the Hawkins et al. data. Our conclusions are striking: in order to explain the familial correlation structure in division times, death times and propensity to divide, a minimum of two distinct heritable factors are necessary. As the data show that two distinct factors are necessary, we develop a stochastic model that has two heritable factors and demonstrate that it can reproduce the key features of the data. This model shows that two heritable factors are sufficient. These deductions have a clear impact upon biological understanding of the adaptive immune response. They also necessitate changes to the fundamental premises behind the tools developed by statisticians to draw deductions from flow cytometry data. Finally, they affect the mathematical modelling paradigms that are used to study these systems, as these are widely developed based on assumptions of cellular independence that are not accurate

Regulatory T Cells Suppress Effector T Cell Proliferation by Limiting Division Destiny

Understanding how the strength of an effector T cell response is regulated is a fundamental problem in immunology with implications for immunity to pathogens, autoimmunity, and immunotherapy. The initial magnitude of the T cell response is determined by the sum of independent signals from antigen, co-stimulation and cytokines. By applying quantitative methods, the contribution of each signal to the number of divisions T cells undergo (division destiny) can be measured, and the resultant exponential increase in response magnitude accurately calculated. CD4+CD25+Foxp3+ regulatory T cells suppress self-reactive T cell responses and limit pathogen-directed immune responses before bystander damage occurs. Using a quantitative modeling framework to measure T cell signal integration and response, we show that Tregs modulate division destiny, rather than directly increasing the rate of death or delaying interdivision times. The quantitative effect of Tregs could be mimicked by modulating the availability of stimulatory co-stimuli and cytokines or through the addition of inhibitory signals. Thus, our analysis illustrates the primary effect of Tregs on the magnitude of effector T cell responses is mediated by modifying division destiny of responding cell populations

Differential requirement for OBF-1 during antibody-secreting cell differentiation

Resting B cells can be cultured to induce antibody-secreting cell (ASC) differentiation in vitro. A quantitative analysis of cell behavior during such a culture allows the influences of different stimuli and gene products to be measured. The application of this analytical system revealed that the OBF-1 transcriptional coactivator, whose loss impairs antibody production in vivo, has two effects on ASC development. Although OBF-1 represses early T cell–dependent (TD) differentiation, it is also critical for the completion of the final stages of ASC development. Under these conditions, the loss of OBF-1 blocks the genetic program of ASC differentiation so that Blimp-1/prdm1 induction fails, and bcl-6, Pax5, and AID are not repressed as in control ASC. Retroviral complementation confirmed that OBF-1 was the critical entity. Surprisingly, when cells were cultured in lipopolysaccharide to mimic T cell–independent conditions, OBF-1–null B cells differentiated normally to ASC. In the OBF-1−/− ASC generated under either culture regimen, antibody production was normal or only modestly reduced, revealing that Ig genes are not directly dependent on OBF-1 for their expression. The differential requirement for OBF-1 in TD ASC generation was confirmed in vivo. These studies define a new regulatory role for OBF-1 in determining the cell-autonomous capacity of B cells to undergo terminal differentiation in response to different immunological signals

Cyton2:A Model of Immune Cell Population Dynamics That Includes Familial Instructional Inheritance

Lymphocytes are the central actors in adaptive immune responses. When challenged with antigen, a small number of B and T cells have a cognate receptor capable of recognising and responding to the insult. These cells proliferate, building an exponentially growing, differentiating clone army to fight off the threat, before ceasing to divide and dying over a period of weeks, leaving in their wake memory cells that are primed to rapidly respond to any repeated infection. Due to the non-linearity of lymphocyte population dynamics, mathematical models are needed to interrogate data from experimental studies. Due to lack of evidence to the contrary and appealing to arguments based on Occam’s Razor, in these models newly born progeny are typically assumed to behave independently of their predecessors. Recent experimental studies, however, challenge that assumption, making clear that there is substantial inheritance of timed fate changes from each cell by its offspring, calling for a revision to the existing mathematical modelling paradigms used for information extraction. By assessing long-term live-cell imaging of stimulated murine B and T cells in vitro, we distilled the key phenomena of these within-family inheritances and used them to develop a new mathematical model, Cyton2, that encapsulates them. We establish the model’s consistency with these newly observed fine-grained features. Two natural concerns for any model that includes familial correlations would be that it is overparameterised or computationally inefficient in data fitting, but neither is the case for Cyton2. We demonstrate Cyton2’s utility by challenging it with high-throughput flow cytometry data, which confirms the robustness of its parameter estimation as well as its ability to extract biological meaning from complex mixed stimulation experiments. Cyton2, therefore, offers an alternate mathematical model, one that is, more aligned to experimental observation, for drawing inferences on lymphocyte population dynamics

Genomic characterisation of Eμ-Myc mouse lymphomas identifies Bcor as a Myc co-operative tumour-suppressor gene

The Eμ-Myc mouse is an extensively used model of MYC driven malignancy; however to date there has only been partial characterization of MYC co-operative mutations leading to spontaneous lymphomagenesis. Here we sequence spontaneously arising Eμ-Myc lymphomas to define transgene architecture, somatic mutations, and structural alterations. We identify frequent disruptive mutations in the PRC1-like component and BCL6-corepressor gene Bcor. Moreover, we find unexpected concomitant multigenic lesions involving Cdkn2a loss and other cancer genes including Nras, Kras and Bcor. These findings challenge the assumed two-hit model of Eμ-Myc lymphoma and demonstrate a functional in vivo role for Bcor in suppressing tumorigenesis.We acknowledge the following funding agencies: Leukaemia Foundation of Australia, Arrow Bone Marrow Transplant Foundation, National Health and Medical Research Council Australia, Cancer Council Victoria, Victorian Cancer Agency, Australian Cancer Research Foundation, Peter MacCallum Cancer Centre Foundation, National Institutes of Health

A genome-wide association study of bronchodilator response in participants of European and African ancestry from six independent cohorts

Introduction Bronchodilator response (BDR) is a measurement of acute bronchodilation in response to short-acting β2-agonists, with a heritability between 10 and 40%. Identifying genetic variants associated with BDR may lead to a better understanding of its complex pathophysiology. Methods We performed a genome-wide association study (GWAS) of BDR in six adult cohorts with participants of European ancestry (EA) and African ancestry (AA) including community cohorts and cohorts ascertained on the basis of obstructive pulmonary disease. Validation analysis was carried out in two paediatric asthma cohorts. Results A total of 10 623 EA and 3597 AA participants were included in the analyses. No single nucleotide polymorphism (SNP) was associated with BDR at the conventional genome-wide significance threshold (p<5×10−8). Performing fine mapping and using a threshold of p<5×10−6 to identify suggestive variants of interest, we identified three SNPs with possible biological relevance: rs35870000 (within FREM1), which may be involved in IgE- and IL5-induced changes in airway smooth muscle cell responsiveness; rs10426116 (within ZNF284), a zinc finger protein, which has been implicated in asthma and BDR previously; and rs4782614 (near ATP2C2), involved in calcium transmembrane transport. Validation in paediatric cohorts yielded no significant SNPs, possibly due to age–genotype interaction effects. Conclusion Ancestry-stratified and ancestry-combined GWAS meta-analyses of over 14 000 participants did not identify genetic variants associated with BDR at the genome-wide significance threshold, although a less stringent threshold identified three variants showing suggestive evidence of association. A common definition and protocol for measuring BDR in research may improve future efforts to identify variants associated with BDR.publishedVersio

MultiCellDS: a community-developed standard for curating microenvironment-dependent multicellular data

Exchanging and understanding scientific data and their context represents a significant barrier to advancing research, especially with respect to information siloing. Maintaining information provenance and providing data curation and quality control help overcome common concerns and barriers to the effective sharing of scientific data. To address these problems in and the unique challenges of multicellular systems, we assembled a panel composed of investigators from several disciplines to create the MultiCellular Data Standard (MultiCellDS) with a use-case driven development process. The standard includes (1) digital cell lines, which are analogous to traditional biological cell lines, to record metadata, cellular microenvironment, and cellular phenotype variables of a biological cell line, (2) digital snapshots to consistently record simulation, experimental, and clinical data for multicellular systems, and (3) collections that can logically group digital cell lines and snapshots. We have created a MultiCellular DataBase (MultiCellDB) to store digital snapshots and the 200+ digital cell lines we have generated. MultiCellDS, by having a fixed standard, enables discoverability, extensibility, maintainability, searchability, and sustainability of data, creating biological applicability and clinical utility that permits us to identify upcoming challenges to uplift biology and strategies and therapies for improving human health

MultiCellDS: a standard and a community for sharing multicellular data

Cell biology is increasingly focused on cellular heterogeneity and multicellular systems. To make the fullest use of experimental, clinical, and computational efforts, we need standardized data formats, community-curated "public data libraries", and tools to combine and analyze shared data. To address these needs, our multidisciplinary community created MultiCellDS (MultiCellular Data Standard): an extensible standard, a library of digital cell lines and tissue snapshots, and support software. With the help of experimentalists, clinicians, modelers, and data and library scientists, we can grow this seed into a community-owned ecosystem of shared data and tools, to the benefit of basic science, engineering, and human health