Transcriptional profiling upon T cell stimulation reveals down-regulation of inflammatory pathways in T and B cells in SLE versus Sjögren's syndrome

Systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS) share clinical as well as pathogenic similarities. Although previous studies suggest various abnormalities in different immune cell compartments, dedicated cell-type specific transcriptomic signatures are often masked by patient heterogeneity. Here, we performed transcriptional profiling of isolated CD4, CD8, CD16 and CD19 lymphocytes from pSS and SLE patients upon T cell stimulation, in addition to a steady-state condition directly after blood drawing, in total comprising 581 sequencing samples. T cell stimulation, which induced a pronounced inflammatory response in all four cell types, gave rise to substantial re-modulation of lymphocyte subsets in the two autoimmune diseases compared to healthy controls, far exceeding the transcriptomic differences detected at steady-state. In particular, we detected cell-type and disease-specific down-regulation of a range of pro-inflammatory cytokine and chemokine pathways. Such differences between SLE and pSS patients are instrumental for selective immune targeting by future therapies.

Distribution modeling quantifies collective TH cell decision circuits in chronic inflammation

Immune responses are tightly regulated by a diverse set of interacting immune cell populations. Alongside decision-making processes such as differentiation into specific effector cell types, immune cells initiate proliferation at the beginning of an inflammation, forming two layers of complexity. Here, we developed a general mathematical framework for the data-driven analysis of collective immune cell dynamics. We identified qualitative and quantitative properties of generic network motifs, and we specified differentiation dynamics by analysis of kinetic transcriptome data. Furthermore, we derived a specific, data-driven mathematical model for T helper 1 versus T follicular helper cell-fate decision dynamics in acute and chronic lymphocytic choriomeningitis virus infections in mice. The model recapitulates important dynamical properties without model fitting and solely by using measured response-time distributions. Model simulations predict different windows of opportunity for perturbation in acute and chronic infection scenarios, with potential implications for optimization of targeted immunotherapy.

Dissecting the dynamic transcriptional landscape of early T helper cell differentiation into Th1, Th2, and 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 at 3-h time intervals in the first hours after stimulation. 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 associated with the transcription factors STAT1 and STAT4. Overall, our results emphasize the key role of high-resolution longitudinal data for the characterization of cellular phenotypes.

Data-Driven Mathematical Model of Apoptosis Regulation in Memory Plasma Cells

Memory plasma cells constitutively produce copious amounts of antibodies, imposing a critical risk factor for autoimmune disease. We previously found that plasma cell survival requires secreted factors such as APRIL and direct contact to stromal cells, which act in concert to activate NF-κB- and PI3K-dependent signaling pathways to prevent cell death. However, the regulatory properties of the underlying biochemical 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 survival signals APRIL and contact to the stromal cell line ST2, we generated a quantitative model of plasma cell survival. Our model emphasizes the non-redundant nature of the two plasma cell survival signals APRIL and stromal cell contact, and highlights a requirement for differential regulation of individual caspases. 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.

Resident memory CD4+ T lymphocytes mobilize from bone marrow to contribute to a systemic secondary immune reaction

Resident memory T lymphocytes (T_RM ) of epithelial tissues and the bone marrow protect their host tissue. To what extent these cells are mobilized and contribute to systemic immune reactions is less clear. Here we show that in secondary immune reactions to the measles-mumps-rubella (MMR) vaccine, CD4⁺ T_RM are mobilized into the blood within 16 to 48 hours after immunization in humans. This mobilization of T_RM is cognate: T_RM recognizing other antigens are not mobilized, unless they cross-react with the vaccine. We also demonstrate through methylome analyses that T_RM are mobilized from the bone marrow. These mobilized cells make significant contribution to the systemic immune reaction, as evidenced by their T-cell receptor Vβ clonotypes represented among the newly generated circulating memory T-cells, 14 days after vaccination. Thus, T_RM of the bone marrow confer not only local, but also systemic immune memory. This article is protected by copyright. All rights reserved.

Peptide-specific recognition of human cytomegalovirus strains controls adaptive natural killer cells

Natural killer (NK) cells are innate lymphocytes that lack antigen-specific rearranged receptors, a hallmark of adaptive lymphocytes. In some people infected with human cytomegalovirus (HCMV), an NK cell subset expressing the activating receptor NKG2C undergoes clonal-like expansion that partially resembles anti-viral adaptive responses. However, the viral ligand that drives the activation and differentiation of adaptive NKG2C⁺ NK cells has remained unclear. Here we found that adaptive NKG2C⁺ NK cells differentially recognized distinct HCMV strains encoding variable UL40 peptides that, in combination with pro-inflammatory signals, controlled the population expansion and differentiation of adaptive NKG2C⁺ NK cells. Thus, we propose that polymorphic HCMV peptides contribute to shaping of the heterogeneity of adaptive NKG2C⁺ NK cell populations among HCMV-seropositive people.

Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions

Cell-to-cell communication networks have critical roles in coordinating diverse organismal processes, such as tissue development or immune cell response. However, compared with intracellular signal transduction networks, the function and engineering principles of cell-to-cell communication networks are far less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. Here, we make use of a framework that treats intracellular signal transduction networks as "black boxes" with characterized input-to-output response relationships. We study simple cell-to-cell communication circuit motifs and find conditions that generate bimodal responses in time, as well as mechanisms for independently controlling synchronization and delay of cell-population responses. We apply our modeling approach to explain otherwise puzzling data on cytokine secretion onset times in T cells. Our approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps.

Membrane Tension Acts Through PLD2 and mTORC2 to Limit Actin Network Assembly During Neutrophil Migration

For efficient polarity and migration, cells need to regulate the magnitude and spatial distribution of actin assembly. This process is coordinated by reciprocal interactions between the actin cytoskeleton and mechanical forces. Actin polymerization-based protrusion increases tension in the plasma membrane, which in turn acts as a long-range inhibitor of actin assembly. These interactions form a negative feedback circuit that limits the magnitude of membrane tension in neutrophils and prevents expansion of the existing front and the formation of secondary fronts. It has been suggested that the plasma membrane directly inhibits actin assembly by serving as a physical barrier that opposes protrusion. Here we show that efficient control of actin polymerization-based protrusion requires an additional mechanosensory feedback cascade that indirectly links membrane tension with actin assembly. Specifically, elevated membrane tension acts through phospholipase D2 (PLD2) and the mammalian target of rapamycin complex 2 (mTORC2) to limit actin nucleation. In the absence of this pathway, neutrophils exhibit larger leading edges, higher membrane tension, and profoundly defective chemotaxis. Mathematical modeling suggests roles for both the direct (mechanical) and indirect (biochemical via PLD2 and mTORC2) feedback loops in organizing cell polarity and motility—the indirect loop is better suited to enable competition between fronts, whereas the direct loop helps spatially organize actin nucleation for efficient leading edge formation and cell movement. This circuit is essential for polarity, motility, and the control of membrane tension.

A mechanosensory biochemical cascade involving phospholipase D2 and mTORC2 coordinates physical forces and cytoskeletal rearrangements to allow efficient polarization and migration of neutrophils.

How cells regulate the size and number of their protrusions for efficient polarity and motility is a fundamental question in cell biology. We recently found that immune cells known as neutrophils use physical forces to regulate this process. Actin polymerization-based protrusion stretches the plasma membrane, and this increased membrane tension acts as a long-range inhibitor of actin-based protrusions elsewhere in the cell. Here we investigate how membrane tension limits protrusion. We demonstrate that the magnitude of actin network assembly in neutrophils is determined by a mechanosensory biochemical cascade that converts increases in membrane tension into decreases in protrusion. Specifically, we show that increasing plasma membrane tension acts through a pathway containing the phospholipase D2 (PLD2) and the mammalian target of rapamycin complex 2 (mTORC2) to limit actin network assembly. Without this negative feedback pathway, neutrophils exhibit larger leading edges, higher membrane tension, and profoundly defective chemotaxis. Mathematical modeling indicates that this feedback circuit is a favorable topology to enable competition between protrusions during neutrophil polarization. Our work shows how biochemical signals, physical forces, and the cytoskeleton can collaborate to generate large-scale cellular organization.

Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells

Cancer therapy has traditionally focused on eliminating fast-growing populations of cells. Yet, an increasing body of evidence suggests that small subpopulations of cancer cells can evade strong selective drug pressure by entering a 'persister' state of negligible growth. This drug-tolerant state has been hypothesized to be part of an initial strategy towards eventual acquisition of bona fide drug-resistance mechanisms. However, the diversity of drug-resistance mechanisms that can expand from a persister bottleneck is unknown. Here we compare persister-derived, erlotinib-resistant colonies that arose from a single, EGFR-addicted lung cancer cell. We find, using a combination of large-scale drug screening and whole-exome sequencing, that our erlotinib-resistant colonies acquired diverse resistance mechanisms, including the most commonly observed clinical resistance mechanisms. Thus, the drug-tolerant persister state does not limit--and may even provide a latent reservoir of cells for--the emergence of heterogeneous drug-resistance mechanisms.

Three-Dimensional Gradients of Cytokine Signaling between T Cells

Immune responses are regulated by diffusible mediators, the cytokines, which act at sub-nanomolar concentrations. The spatial range of cytokine communication is a crucial, yet poorly understood, functional property. Both containment of cytokine action in narrow junctions between immune cells (immunological synapses) and global signaling throughout entire lymph nodes have been proposed, but the conditions under which they might occur are not clear. Here we analyze spatially three-dimensional reaction-diffusion models for the dynamics of cytokine signaling at two successive scales: in immunological synapses and in dense multicellular environments. For realistic parameter values, we observe local spatial gradients, with the cytokine concentration around secreting cells decaying sharply across only a few cell diameters. Focusing on the well-characterized T-cell cytokine interleukin-2, we show how cytokine secretion and competitive uptake determine this signaling range. Uptake is shaped locally by the geometry of the immunological synapse. However, even for narrow synapses, which favor intrasynaptic cytokine consumption, escape fluxes into the extrasynaptic space are expected to be substantial (≥20% of secretion). Hence paracrine signaling will generally extend beyond the synapse but can be limited to cellular microenvironments through uptake by target cells or strong competitors, such as regulatory T cells. By contrast, long-range cytokine signaling requires a high density of cytokine producers or weak consumption (e.g., by sparsely distributed target cells). Thus in a physiological setting, cytokine gradients between cells, and not bulk-phase concentrations, are crucial for cell-to-cell communication, emphasizing the need for spatially resolved data on cytokine signaling.

The adaptive immune system fights pathogens through the activation of immune cell clones that specifically recognize a particular pathogen. Tight contacts, so-called immunological synapses, of immune cells with cells that present ‘digested’ pathogen molecules are pivotal for ensuring specificity. The discovery that immune responses are regulated by small diffusible proteins – the cytokines – has been surprising because cytokine diffusion to ‘bystander’ cells might compromise specificity. It has therefore been argued that cytokines are trapped in immunological synapses, whereas other authors have found that cytokines act on a larger scale through entire lymph nodes. Measurements of cytokine concentrations with fine spatial resolution have not been achieved. Here, we study the spatio-temporal dynamics of cytokines through mathematical analysis and three-dimensional numerical simulation and identify key parameters that control signaling range. We predict that even tight immunological synapses leak a substantial portion of the secreted cytokines. Nevertheless, rapid cellular uptake will render cytokine signals short-range and thus incidental activation of bystander cells can be limited. Long-range signals will only occur with multiple secreting cells or/and slow consumption by sparse target cells. Thus our study identifies key determinants of the spatial range of cytokine communication in realistic multicellular geometries.

Rhythmic degradation explains and unifies circadian transcriptome and proteome data

The rich mammalian cellular circadian output affects thousands of genes in many cell types and has been the subject of genome-wide transcriptome and proteome studies. The results have been enigmatic because transcript peak abundances do not always follow the peaks of gene-expression activity in time. We posited that circadian degradation of mRNAs and proteins plays a pivotal role in setting their peak times. To establish guiding principles, we derived a theoretical framework that fully describes the amplitudes and phases of biomolecules with circadian half-lives. We were able to explain the circadian transcriptome and proteome studies with the same unifying theory, including cases in which transcripts or proteins appeared before the onset of increased production rates. Furthermore, we estimate that 30% of the circadian transcripts in mouse liver and Drosophila heads are affected by rhythmic posttranscriptional regulation.

Reliable encoding of stimulus intensities within random sequences of intracellular Ca2+ spikes

Ca(2+) is a ubiquitous intracellular messenger that regulates diverse cellular activities. Extracellular stimuli often evoke sequences of intracellular Ca(2+) spikes, and spike frequency may encode stimulus intensity. However, the timing of spikes within a cell is random because each interspike interval has a large stochastic component. In human embryonic kidney (HEK) 293 cells and rat primary hepatocytes, we found that the average interspike interval also varied between individual cells. To evaluate how individual cells reliably encoded stimuli when Ca(2+) spikes exhibited such unpredictability, we combined Ca(2+) imaging of single cells with mathematical analyses of the Ca(2+) spikes evoked by receptors that stimulate formation of inositol 1,4,5-trisphosphate (IP3). This analysis revealed that signal-to-noise ratios were improved by slow recovery from feedback inhibition of Ca(2+) spiking operating at the whole-cell level and that they were robust against perturbations of the signaling pathway. Despite variability in the frequency of Ca(2+) spikes between cells, steps in stimulus intensity caused the stochastic period of the interspike interval to change by the same factor in all cells. These fold changes reliably encoded changes in stimulus intensity, and they resulted in an exponential dependence of average interspike interval on stimulation strength. We conclude that Ca(2+) spikes enable reliable signaling in a cell population despite randomness and cell-to-cell variability, because global feedback reduces noise, and changes in stimulus intensity are represented by fold changes in the stochastic period of the interspike interval.

Timescales of IP(3)-evoked Ca(2+) spikes emerge from Ca(2+) puffs only at the cellular level

The behavior of biological systems is determined by the properties of their component molecules, but the interactions are usually too complex to understand fully how molecular behavior generates cellular behavior. Ca(2+) signaling by inositol trisphosphate receptors (IP(3)R) offers an opportunity to understand this relationship because the cellular behavior is defined largely by Ca(2+)-mediated interactions between IP(3)R. Ca(2+) released by a cluster of IP(3)R (giving a local Ca(2+) puff) diffuses and ignites the behavior of neighboring clusters (to give repetitive global Ca(2+) spikes). We use total internal reflection fluorescence microscopy of two mammalian cell lines to define the temporal relationships between Ca(2+) puffs (interpuff intervals, IPI) and Ca(2+) spikes (interspike intervals) evoked by flash photolysis of caged IP(3). We find that IPI are much shorter than interspike intervals, that puff activity is stochastic with a recovery time that is much shorter than the refractory period of the cell, and that IPI are not periodic. We conclude that Ca(2+) spikes do not arise from oscillatory dynamics of IP(3)R clusters, but that repetitive Ca(2+) spiking with its longer timescales is an emergent property of the dynamics of the whole cluster array.

Calcium signaling: from single channels to pathways

Ca(2+) is not only one of the most versatile and ubiquitous second messengers but also a well-established representative example of cell signaling. The identification of most key elements involved in Ca(2+) signaling enables a mechanistic and quantitative understanding of this particular pathway. Cellular behavior relies in general on the orchestration of molecular behavior leading to reliable cellular responses that allow for regulation and adaptation. Ca(2+) signaling uses a hierarchical organization to transform single molecule behavior into cell wide signals. We have recently shown experimentally that this organization carries single channel signatures onto the whole cell level and renders Ca(2+) oscillations stochastic. Here, we briefly review the co-evolution of experimental and theoretical studies in Ca(2+) -signaling and show how dynamic bottom-up modeling can be used to address -biological questions and illuminate biological principles of cell signaling.

Toward a predictive model of Ca2+ puffs

We investigate the key characteristics of Ca(2+) puffs in deterministic and stochastic frameworks that all incorporate the cellular morphology of IP(3) receptor channel clusters. In the first step, we numerically study the Ca(2+) liberation in a three-dimensional representation of a cluster environment with reaction-diffusion dynamics in both the cytosol and the lumen. These simulations reveal that Ca(2+) concentrations at a releasing cluster range from 80 to 170 microM and equilibrate almost instantaneously on the time scale of the release duration. These highly elevated Ca(2+) concentrations eliminate Ca(2+) oscillations in a deterministic model of an IP(3)R channel cluster at physiological parameter values as revealed by a linear stability analysis. The reason lies in the saturation of all feedback processes in the IP(3)R gating dynamics, so that only fluctuations can restore experimentally observed Ca(2+) oscillations. In this spirit, we derive master equations that allow us to analytically quantify the onset of Ca(2+) puffs and hence the stochastic time scale of intracellular Ca(2+) dynamics. Moving up the spatial scale, we suggest to formulate cellular dynamics in terms of waiting time distribution functions. This approach prevents the state space explosion that is typical for the description of cellular dynamics based on channel states and still contains information on molecular fluctuations. We illustrate this method by studying global Ca(2+) oscillations.

Mitochondrial energetic metabolism: a simplified model of TCA cycle with ATP production

Mitochondria play a central role in cellular energetic metabolism. The essential parts of this metabolism are the tricarboxylic acid (TCA) cycle, the respiratory chain and the adenosine triphosphate (ATP) synthesis machinery. Here a simplified model of these three metabolic components with a limited set of differential equations is presented. The existence of a steady state is demonstrated and results of numerical simulations are presented. The relevance of a simple model to represent actual in vivo behavior is discussed.

Electron-microscopic observations on whole human mitotic chromosomes

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