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Burt, P.

Publications and source records attributed to Burt, P..

3 recordsLinked to original sources

High-resolution kinetic gene expression analysis of T helper cell differentiation reveals a STAT-dependent, unique transcriptional program in Th1/2 hybrid cells

Selective differentiation of CD4+ T helper (Th) cells into specialized subsets such as Th1 and Th2 cells is a key element of the adaptive immune system driving appropriate immune responses. Besides those canonical Th cell lineages, hybrid phenotypes such as Th1/2 cells arise in vivo, and their generation could be reproduced in vitro. While master-regulator transcription factors like T-bet for Th1 and GATA-3 for Th2 cells drive and maintain differentiation into the canonical lineages, the transcriptional architecture of hybrid phenotypes is less well understood. In particular, it has remained unclear whether a hybrid phenotype implies a mixture of the effects of several canonical lineages for each gene, or rather a bimodal behavior across genes. Th cell differentiation is a dynamic process in which the regulatory factors are modulated over time, but longitudinal studies of Th cell differentiation are sparse. Here, we present a dynamic transcriptome analysis following Th cell differentiation into Th1, Th2 and Th1/2 hybrid cells. We identified an early bifurcation point in gene expression programs, and we found that only a minority of [~]20% of Th cell-specific genes showed mixed effects from both Th1 and Th2 cells on Th1/2 hybrid cells. While most genes followed either Th1 or Th2 cell gene expression, another fraction of [~]20% of genes followed a Th1 and Th2 cell-independent transcriptional program under control of the transcription factors STAT1 and STAT4. Overall, our results emphasize the key role of high-resolution longitudinal data for the characterization of cellular phenotypes.

immunology↗

Mathematical modeling reveals a complex network of signaling and apoptosis pathways in the survival of memory plasma cells

The long-term survival of memory plasma cells is conditional on the signals provided by dedicated survival niches in the bone marrow organized by mesenchymal stromal cells. Recently, we could show that plasma cell survival requires secreted factors such as APRIL and direct contact to stromal cells, which act in concert to activate NF-kB- and PI3K-dependent signaling pathways to prevent cell death. However, the precise dynamics of the underlying regulatory network are confounded by the complexity of potential interaction and cross-regulation pathways. Here, based on flow-cytometric quantification of key signaling proteins in the presence or absence of the required survival signals, we generated a quantitative model of plasma cell survival. Our model emphasizes the non-redundant and essential nature of the two plasma cell survival signals APRIL and stromal cell contact, providing resilience to endoplasmic reticulum stress and mitochondrial stress, respectively. Importantly, the modeling approach allowed us to unify distinct data sets and derive a consistent picture of the intertwined signaling and apoptosis pathways regulating plasma cell survival.

systems biology↗

Mechanisms Underlying the Complex Dynamics of Temperature Entrainment by a Circadian Clock

Autonomously oscillating circadian clocks resonate with daily environmental (zeitgeber) rhythms to organize physiology around the solar day. While entrainment properties and mechanisms have been studied widely and in great detail for light-dark cycles, entrainment to daily temperature rhythms remains poorly understood despite that they are potent zeitgebers. Here we investigate the entrainment of the chronobiological model organism Neurospora crassa, subject to thermocycles of different periods and fractions of warm versus cold phases, mimicking seasonal variations. Depending on the properties of these thermocycles, regularly entrained rhythms, period-doubling (frequency demultiplication) but also irregular aperiodic behavior occurs. We demonstrate that the complex nonlinear phenomena of experimentally observed entrainment dynamics can be understood by molecular mathematical modeling. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/441752v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@17494b0org.highwire.dtl.DTLVardef@cdd47dorg.highwire.dtl.DTLVardef@1395d01org.highwire.dtl.DTLVardef@1092092_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗