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1.
Immunity ; 57(2): 271-286.e13, 2024 Feb 13.
Article in English | MEDLINE | ID: mdl-38301652

ABSTRACT

The immune system encodes information about the severity of a pathogenic threat in the quantity and type of memory cells it forms. This encoding emerges from lymphocyte decisions to maintain or lose self-renewal and memory potential during a challenge. By tracking CD8+ T cells at the single-cell and clonal lineage level using time-resolved transcriptomics, quantitative live imaging, and an acute infection model, we find that T cells will maintain or lose memory potential early after antigen recognition. However, following pathogen clearance, T cells may regain memory potential if initially lost. Mechanistically, this flexibility is implemented by a stochastic cis-epigenetic switch that tunably and reversibly silences the memory regulator, TCF1, in response to stimulation. Mathematical modeling shows how this flexibility allows memory T cell numbers to scale robustly with pathogen virulence and immune response magnitudes. We propose that flexibility and stochasticity in cellular decisions ensure optimal immune responses against diverse threats.


Subject(s)
CD8-Positive T-Lymphocytes , Memory T Cells , Epigenesis, Genetic , Clone Cells , Immunologic Memory , Cell Differentiation
2.
Res Sq ; 2024 Feb 12.
Article in English | MEDLINE | ID: mdl-38410458

ABSTRACT

Virus specific PD-1+ TCF-1+ TOX+ stem-like CD8+ T cells are essential for maintaining T cell responses during chronic infection and are also critical for PD-1 directed immunotherapy. In this study we have used the mouse model of chronic LCMV infection to examine when these virus specific stem-like CD8+ T cells are generated during the course of chronic infection and what is the role of antigen in maintaining the stem-like program. We found that these stem-like CD8+ T cells are generated early (day 5) during chronic infection and that antigen is essential for maintaining their stem-like program. This early generation of stem-like CD8+ T cells suggested that the fate commitment to this cell population was agnostic to the eventual outcome of infection and the immune system prepares a priori for a potential chronic infection. Indeed, we found that an identical virus specific stem-cell like CD8+ T cell population was also generated during acute LCMV infection but these cells were lost once the virus was cleared. To determine the fate of these early PD-1+TCF-1+TOX+ stem-like CD8+ T cells that are generated during both acute and chronic LCMV infection we set up two reciprocal adoptive transfer experiments. In the first experiment we transferred day 5 stem-like CD8+ T cells from chronically infected into acutely infected mice and examined their differentiation after viral clearance. We found that these early stem-like CD8+ T cells downregulated canonical markers of the chronic stem-like CD8+ T cells and expressed markers (CD127 and CD62L) associated with central memory CD8+ T cells. In the second experiment, we transferred day 5 stem-like cells from acutely infected mice into chronically infected mice and found that these CD8+ T cells could function like resource cells after transfer into a chronic environment by generating effector CD8+ T cells in both lymphoid and non-lymphoid tissues while also maintaining the number of stem-like CD8+ T cells. These findings provide insight into the generation and maintenance of virus specific stem-like CD8+ T cells that play a critical role in chronic viral infection. In particular, our study highlights the early generation of stem-like CD8+ T cells and their ability to adapt to either an acute or chronic infection. These findings are of broad significance since these novel stem-like CD8+ T cells play an important role in not only viral infections but also in cancer and autoimmunity.

3.
Proc Natl Acad Sci U S A ; 120(52): e2308366120, 2023 Dec 26.
Article in English | MEDLINE | ID: mdl-38113261

ABSTRACT

Immune system threat detection hinges on T cells' ability to perceive varying peptide-major histocompatibility complex (pMHC) antigens. As the Erk and NFAT pathways link T cell receptor engagement to gene regulation, their signaling dynamics may convey information about pMHC inputs. To test this idea, we developed a dual reporter mouse strain and a quantitative imaging assay that, together, enable simultaneous monitoring of Erk and NFAT dynamics in live T cells over day-long timescales as they respond to varying pMHC inputs. Both pathways initially activate uniformly across various pMHC inputs but diverge only over longer (9+ h) timescales, enabling independent encoding of pMHC affinity and dose. These late signaling dynamics are decoded via multiple temporal and combinatorial mechanisms to generate pMHC-specific transcriptional responses. Our findings underscore the importance of long timescale signaling dynamics in antigen perception and establish a framework for understanding T cell responses under diverse contexts.


Subject(s)
Lymphocyte Activation , T-Lymphocytes , Mice , Animals , Receptors, Antigen, T-Cell , Antigens/metabolism , Histocompatibility Antigens/metabolism , Peptides/metabolism , Major Histocompatibility Complex , Perception , Protein Binding
4.
bioRxiv ; 2023 Jun 05.
Article in English | MEDLINE | ID: mdl-37333368

ABSTRACT

Immune system threat detection hinges on T cells' ability to perceive varying peptide major-histocompatibility complex (pMHC) antigens. As the Erk and NFAT pathways link T cell receptor engagement to gene regulation, their signaling dynamics may convey information about pMHC inputs. To test this idea, we developed a dual reporter mouse strain and a quantitative imaging assay that, together, enable simultaneous monitoring of Erk and NFAT dynamics in live T cells over day-long timescales as they respond to varying pMHC inputs. Both pathways initially activate uniformly across various pMHC inputs, but diverge only over longer (9+ hrs) timescales, enabling independent encoding of pMHC affinity and dose. These late signaling dynamics are decoded via multiple temporal and combinatorial mechanisms to generate pMHC-specific transcriptional responses. Our findings underscore the importance of long timescale signaling dynamics in antigen perception, and establish a framework for understanding T cell responses under diverse contexts. SIGNIFICANCE STATEMENT: To counter diverse pathogens, T cells mount distinct responses to varying peptide-major histocompatibility complex ligands (pMHCs). They perceive the affinity of pMHCs for the T cell receptor (TCR), which reflects its foreignness, as well as pMHC abundance. By tracking signaling responses in single living cells to different pMHCs, we find that T cells can independently perceive pMHC affinity vs dose, and encode this information through the dynamics of Erk and NFAT signaling pathways downstream of the TCR. These dynamics are jointly decoded by gene regulatory mechanisms to produce pMHC-specific activation responses. Our work reveals how T cells can elicit tailored functional responses to diverse threats and how dysregulation of these responses may lead to immune pathologies.

5.
PLoS Comput Biol ; 17(12): e1009626, 2021 12.
Article in English | MEDLINE | ID: mdl-34968384

ABSTRACT

Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source for dynamic cell phenotypic state identification. Here, we describe an end-to-end deep learning platform, UPSIDE (Unsupervised Phenotypic State IDEntification), for discovering cell states and their dynamics from transmitted light movies. UPSIDE uses the variational auto-encoder architecture to learn latent cell representations, which are then clustered for state identification, decoded for feature interpretation, and linked across movie frames for transition rate inference. Using UPSIDE, we identified distinct blood cell types in a heterogeneous dataset. We then analyzed movies of patient-derived acute myeloid leukemia cells, from which we identified stem-cell associated morphological states as well as the transition rates to and from these states. UPSIDE opens up the use of transmitted light movies for systematic exploration of cell state heterogeneity and dynamics in biology and medicine.


Subject(s)
Blood Cells/classification , Blood Cells/cytology , Microscopy/methods , Single-Cell Analysis/methods , Time-Lapse Imaging/methods , Unsupervised Machine Learning , Algorithms , Blood Cells/pathology , Humans , Image Processing, Computer-Assisted/methods , Leukemia, Myeloid, Acute/pathology , Light , Phenotype
6.
J R Soc Interface ; 18(180): 20210109, 2021 07.
Article in English | MEDLINE | ID: mdl-34283940

ABSTRACT

During development, progenitor cells follow timetables for differentiation that span many cell generations. These developmental timetables are robustly encoded by the embryo, yet scalably adjustable by evolution, facilitating variation in organism size and form. Epigenetic switches, involving rate-limiting activation steps at regulatory gene loci, control gene activation timing in diverse contexts, and could profoundly impact the dynamics of gene regulatory networks controlling developmental lineage specification. Here, we develop a mathematical framework to model regulatory networks with genes controlled by epigenetic switches. Using this framework, we show that such epigenetic switching networks uphold developmental timetables that robustly span many cell generations, and enable the generation of differentiated cells in precisely defined numbers and fractions. Changes to epigenetic switching networks can readily alter the timing of developmental events within a timetable, or alter the overall speed at which timetables unfold, enabling scalable control over differentiated population sizes. With their robust, yet flexibly adjustable nature, epigenetic switching networks could represent central targets on which evolution acts to manufacture diversity in organism size and form.


Subject(s)
Gene Expression Regulation, Developmental , Gene Regulatory Networks , Cell Differentiation , Embryo, Mammalian , Epigenesis, Genetic
7.
Nucleic Acids Res ; 49(14): e82, 2021 08 20.
Article in English | MEDLINE | ID: mdl-34048564

ABSTRACT

Proper regulation of genome architecture and activity is essential for the development and function of multicellular organisms. Histone modifications, acting in combination, specify these activity states at individual genomic loci. However, the methods used to study these modifications often require either a large number of cells or are limited to targeting one histone mark at a time. Here, we developed a new method called Single Cell Evaluation of Post-TRanslational Epigenetic Encoding (SCEPTRE) that uses Expansion Microscopy (ExM) to visualize and quantify multiple histone modifications at non-repetitive genomic regions in single cells at a spatial resolution of ∼75 nm. Using SCEPTRE, we distinguished multiple histone modifications at a single housekeeping gene, quantified histone modification levels at multiple developmentally-regulated genes in individual cells, and evaluated the relationship between histone modifications and RNA polymerase II loading at individual loci. We find extensive variability in epigenetic states between individual gene loci hidden from current population-averaged measurements. These findings establish SCEPTRE as a new technique for multiplexed detection of combinatorial chromatin states at single genomic loci in single cells.


Subject(s)
Chromatin/metabolism , Genome, Human/genetics , Histones/metabolism , Microscopy, Confocal/methods , Microscopy, Fluorescence/methods , Single-Cell Analysis/methods , Cell Line , Chromatin/genetics , Epigenesis, Genetic/genetics , Histone Code/genetics , Humans , In Situ Hybridization, Fluorescence/methods , Myosin Light Chains/genetics
8.
Cell Rep ; 34(12): 108888, 2021 03 23.
Article in English | MEDLINE | ID: mdl-33761349

ABSTRACT

During development, progenitors often differentiate many cell generations after receiving signals. These delays must be robust yet tunable for precise population size control. Polycomb repressive mechanisms, involving histone H3 lysine-27 trimethylation (H3K27me3), restrain the expression of lineage-specifying genes in progenitors and may delay their activation and ensuing differentiation. Here, we elucidate an epigenetic switch controlling the T cell commitment gene Bcl11b that holds its locus in a heritable inactive state for multiple cell generations before activation. Integrating experiments and modeling, we identify a mechanism where H3K27me3 levels at Bcl11b, regulated by methyltransferase and demethylase activities, set the time delay at which the locus switches from a compacted, silent state to an extended, active state. This activation delay robustly spans many cell generations, is tunable by chromatin modifiers and transcription factors, and is independent of cell division. With their regulatory flexibility, such timed epigenetic switches may broadly control timing in development.


Subject(s)
Cell Division/genetics , Polycomb-Group Proteins/metabolism , Transcriptional Activation/genetics , Animals , Cell Lineage/genetics , Epigenesis, Genetic , Genetic Loci , Histones/metabolism , Humans , Lysine/metabolism , Methylation , Mice, Inbred C57BL , Protein Conformation , Repressor Proteins/chemistry , Repressor Proteins/metabolism , T-Lymphocytes/cytology , Time Factors , Transcription Factors/metabolism , Tumor Suppressor Proteins/chemistry , Tumor Suppressor Proteins/metabolism
9.
Immunol Rev ; 300(1): 134-151, 2021 03.
Article in English | MEDLINE | ID: mdl-33734444

ABSTRACT

Proper timing of gene expression is central to lymphocyte development and differentiation. Lymphocytes often delay gene activation for hours to days after the onset of signaling components, which act on the order of seconds to minutes. Such delays play a prominent role during the intricate choreography of developmental events and during the execution of an effector response. Though a number of mechanisms are sufficient to explain timing at short timescales, it is not known how timing delays are implemented over long timescales that may span several cell generations. Based on the literature, we propose that a class of cis-regulatory elements, termed "timing enhancers," may explain how timing delays are controlled over these long timescales. By considering chromatin as a kinetic barrier to state switching, the timing enhancer model explains experimentally observed dynamics of gene expression where other models fall short. In this review, we elaborate on features of the timing enhancer model and discuss the evidence for its generality throughout development and differentiation. We then discuss potential molecular mechanisms underlying timing enhancer function. Finally, we explore recent evidence drawing connections between timing enhancers and genetic risk for immunopathology. We argue that the timing enhancer model is a useful framework for understanding how cis-regulatory elements control the central dimension of timing in lymphocyte biology.


Subject(s)
Chromatin , Enhancer Elements, Genetic , Cell Differentiation , Enhancer Elements, Genetic/genetics
10.
Cell Rep ; 31(12): 107804, 2020 06 23.
Article in English | MEDLINE | ID: mdl-32579930

ABSTRACT

Cell proliferation changes concomitantly with fate transitions during reprogramming, differentiation, regeneration, and oncogenesis. Methods to resolve cell cycle length heterogeneity in real time are currently lacking. Here, we describe a genetically encoded fluorescent reporter that captures live-cell cycle speed using a single measurement. This reporter is based on the color-changing fluorescent timer (FT) protein, which emits blue fluorescence when newly synthesized before maturing into a red fluorescent protein. We generated a mouse strain expressing an H2B-FT fusion reporter from a universally active locus and demonstrate that faster cycling cells can be distinguished from slower cycling ones on the basis of the intracellular fluorescence ratio between the FT's blue and red states. Using this reporter, we reveal the native cell cycle speed distributions of fresh hematopoietic cells and demonstrate its utility in analyzing cell proliferation in solid tissues. This system is broadly applicable for dissecting functional heterogeneity associated with cell cycle dynamics in complex tissues.


Subject(s)
Cell Cycle , Genes, Reporter , Animals , Cell Division , Cell Proliferation , Cells, Cultured , Hematopoietic Stem Cells/metabolism , Histones/metabolism , Luminescent Proteins , Mice , Models, Biological , Mouse Embryonic Stem Cells/metabolism , Recombinant Fusion Proteins/metabolism , Red Fluorescent Protein
11.
Curr Opin Syst Biol ; 18: 95-103, 2019 Dec.
Article in English | MEDLINE | ID: mdl-33791444

ABSTRACT

To protect against diverse challenges, the immune system must continuously generate an arsenal of specialized cell types, each of which can mount a myriad of effector responses upon detection of potential threats. To do so, it must generate multiple differentiated cell populations with defined sizes and proportions, often from rare starting precursor cells. Here, we discuss the emerging view that inherently probabilistic mechanisms, involving rare, rate-limiting regulatory events in single cells, control fate decisions and population sizes and fractions during immune development and function. We first review growing evidence that key fate control points are gated by stochastic signaling and gene regulatory events that occur infrequently over decision-making timescales, such that initially homogeneous cells can adopt variable outcomes in response to uniform signals. We next discuss how such stochastic control can provide functional capabilities that are harder to achieve with deterministic control strategies, and may be central to robust immune system function.

12.
Elife ; 72018 11 20.
Article in English | MEDLINE | ID: mdl-30457103

ABSTRACT

Cell fate decisions occur through the switch-like, irreversible activation of fate-specifying genes. These activation events are often assumed to be tightly coupled to changes in upstream transcription factors, but could also be constrained by cis-epigenetic mechanisms at individual gene loci. Here, we studied the activation of Bcl11b, which controls T-cell fate commitment. To disentangle cis and trans effects, we generated mice where two Bcl11b copies are tagged with distinguishable fluorescent proteins. Quantitative live microscopy of progenitors from these mice revealed that Bcl11b turned on after a stochastic delay averaging multiple days, which varied not only between cells but also between Bcl11b alleles within the same cell. Genetic perturbations, together with mathematical modeling, showed that a distal enhancer controls the rate of epigenetic activation, while a parallel Notch-dependent trans-acting step stimulates expression from activated loci. These results show that developmental fate transitions can be controlled by stochastic cis-acting events on individual loci.


Subject(s)
Cell Differentiation , Epigenesis, Genetic , Repressor Proteins/biosynthesis , T-Lymphocytes/physiology , Transcription, Genetic , Tumor Suppressor Proteins/biosynthesis , Animals , Genes, Reporter , Intravital Microscopy , Luminescent Proteins/analysis , Luminescent Proteins/genetics , Mice , Models, Theoretical , Staining and Labeling , Time Factors
13.
Proc Natl Acad Sci U S A ; 114(23): 5800-5807, 2017 06 06.
Article in English | MEDLINE | ID: mdl-28584128

ABSTRACT

T-cell development from hematopoietic progenitors depends on multiple transcription factors, mobilized and modulated by intrathymic Notch signaling. Key aspects of T-cell specification network architecture have been illuminated through recent reports defining roles of transcription factors PU.1, GATA-3, and E2A, their interactions with Notch signaling, and roles of Runx1, TCF-1, and Hes1, providing bases for a comprehensively updated model of the T-cell specification gene regulatory network presented herein. However, the role of lineage commitment factor Bcl11b has been unclear. We use self-organizing maps on 63 RNA-seq datasets from normal and perturbed T-cell development to identify functional targets of Bcl11b during commitment and relate them to other regulomes. We show that both activation and repression target genes can be bound by Bcl11b in vivo, and that Bcl11b effects overlap with E2A-dependent effects. The newly clarified role of Bcl11b distinguishes discrete components of commitment, resolving how innate lymphoid, myeloid, and dendritic, and B-cell fate alternatives are excluded by different mechanisms.


Subject(s)
Cell Differentiation/genetics , Gene Regulatory Networks , Repressor Proteins/physiology , T-Lymphocytes/cytology , Tumor Suppressor Proteins/physiology , Animals , Mice , Mice, Inbred C57BL , Mice, Knockout , Receptors, Notch , Repressor Proteins/genetics , Repressor Proteins/metabolism , Signal Transduction , Tumor Suppressor Proteins/genetics , Tumor Suppressor Proteins/metabolism
14.
Sci Rep ; 6: 36585, 2016 11 04.
Article in English | MEDLINE | ID: mdl-27811996

ABSTRACT

Anti-mitotic drugs constitute a major class of cytotoxic chemotherapeutics used in the clinic, killing cancer cells by inducing prolonged mitotic arrest that activates intrinsic apoptosis. Anti-mitotics-induced apoptosis is known to involve degradation of anti-apoptotic Bcl-2 proteins during mitotic arrest; however, it remains unclear how this mechanism accounts for significant heterogeneity observed in the cell death responses both within and between cancer cell types. To unravel quantitative determinants underlying variability in anti-mitotic drug response, we constructed a single-cell dynamical Bcl-2 network model describing cell death control during mitotic arrest, and constrained the model using experimental data from four representative cancer cell lines. The modeling analysis revealed that, given a variable, slowly accumulating pro-apoptotic signal arising from anti-apoptotic protein degradation, generation of a switch-like apoptotic response requires formation of pro-apoptotic Bak complexes with hundreds of subunits, suggesting a crucial role for high-order cooperativity. Moreover, we found that cell-type variation in susceptibility to drug-induced mitotic death arises primarily from differential expression of the anti-apoptotic proteins Bcl-xL and Mcl-1 relative to Bak. The dependence of anti-mitotic drug response on Bcl-xL and Mcl-1 that we derived from the modeling analysis provides a quantitative measure to predict sensitivity of distinct cancer cells to anti-mitotic drug treatment.


Subject(s)
Antimitotic Agents/pharmacology , Antineoplastic Agents/pharmacology , Mitosis/drug effects , Proto-Oncogene Proteins c-bcl-2/metabolism , A549 Cells , Apoptosis/drug effects , Apoptosis Regulatory Proteins/metabolism , Cell Death/drug effects , Cell Line, Tumor , HeLa Cells , Humans , Myeloid Cell Leukemia Sequence 1 Protein/metabolism , Signal Transduction/drug effects , bcl-X Protein/metabolism
15.
PLoS One ; 11(8): e0161260, 2016.
Article in English | MEDLINE | ID: mdl-27551921

ABSTRACT

BACKGROUND/OBJECTIVES: A cascade of gene activations under the control of Notch signalling is required during T-cell specification, when T-cell precursors gradually lose the potential to undertake other fates and become fully committed to the T-cell lineage. We elucidate how the gene/protein dynamics for a core transcriptional module governs this important process by computational means. METHODS: We first assembled existing knowledge about transcription factors known to be important for T-cell specification to form a minimal core module consisting of TCF-1, GATA-3, BCL11B, and PU.1 aiming at dynamical modeling. Model architecture was based on published experimental measurements of the effects on each factor when each of the others is perturbed. While several studies provided gene expression measurements at different stages of T-cell development, pure time series are not available, thus precluding a straightforward study of the dynamical interactions among these genes. We therefore translate stage dependent data into time series. A feed-forward motif with multiple positive feed-backs can account for the observed delay between BCL11B versus TCF-1 and GATA-3 activation by Notch signalling. With a novel computational approach, all 32 possible interactions among Notch signalling, TCF-1, and GATA-3 are explored by translating combinatorial logic expressions into differential equations for BCL11B production rate. RESULTS: Our analysis reveals that only 3 of 32 possible configurations, where GATA-3 works as a dimer, are able to explain not only the time delay, but very importantly, also give rise to irreversibility. The winning models explain the data within the 95% confidence region and are consistent with regard to decay rates. CONCLUSIONS: This first generation model for early T-cell specification has relatively few players. Yet it explains the gradual transition into a committed state with no return. Encoding logics in a rate equation setting allows determination of binding properties beyond what is possible in a Boolean network.


Subject(s)
Cell Differentiation/genetics , Cell Lineage/genetics , Models, Biological , T-Lymphocytes/cytology , Animals , Computational Biology , Gene Regulatory Networks/genetics , Receptors, Notch/genetics , Signal Transduction , T-Lymphocytes/metabolism
16.
Nat Immunol ; 17(8): 956-65, 2016 08.
Article in English | MEDLINE | ID: mdl-27376470

ABSTRACT

During T cell development, multipotent progenitors relinquish competence for other fates and commit to the T cell lineage by turning on Bcl11b, which encodes a transcription factor. To clarify lineage commitment mechanisms, we followed developing T cells at the single-cell level using Bcl11b knock-in fluorescent reporter mice. Notch signaling and Notch-activated transcription factors collaborate to activate Bcl11b expression irrespectively of Notch-dependent proliferation. These inputs work via three distinct, asynchronous mechanisms: an early locus 'poising' function dependent on TCF-1 and GATA-3, a stochastic-permissivity function dependent on Notch signaling, and a separate amplitude-control function dependent on Runx1, a factor already present in multipotent progenitors. Despite their necessity for Bcl11b expression, these inputs act in a stage-specific manner, providing a multitiered mechanism for developmental gene regulation.


Subject(s)
Core Binding Factor Alpha 2 Subunit/metabolism , GATA3 Transcription Factor/metabolism , Gene Expression Regulation, Developmental , Hepatocyte Nuclear Factor 1-alpha/metabolism , Lymphopoiesis/genetics , Receptors, Notch/metabolism , Repressor Proteins/metabolism , T-Lymphocytes/physiology , Tumor Suppressor Proteins/metabolism , Animals , Cell Differentiation/genetics , Cell Lineage/genetics , Cell Tracking , Cells, Cultured , Core Binding Factor Alpha 2 Subunit/genetics , GATA3 Transcription Factor/genetics , Hepatocyte Nuclear Factor 1-alpha/genetics , Mice , Mice, 129 Strain , Mice, Inbred C57BL , Mice, Transgenic , Repressor Proteins/genetics , Signal Transduction , Single-Cell Analysis , Tumor Suppressor Proteins/genetics
17.
Immunol Rev ; 271(1): 72-97, 2016 May.
Article in English | MEDLINE | ID: mdl-27088908

ABSTRACT

The pathway to generate T cells from hematopoietic stem cells guides progenitors through a succession of fate choices while balancing differentiation progression against proliferation, stage to stage. Many elements of the regulatory system that controls this process are known, but the requirement for multiple, functionally distinct transcription factors needs clarification in terms of gene network architecture. Here, we compare the features of the T-cell specification system with the rule sets underlying two other influential types of gene network models: first, the combinatorial, hierarchical regulatory systems that generate the orderly, synchronized increases in complexity in most invertebrate embryos; second, the dueling 'master regulator' systems that are commonly used to explain bistability in microbial systems and in many fate choices in terminal differentiation. The T-cell specification process shares certain features with each of these prevalent models but differs from both of them in central respects. The T-cell system is highly combinatorial but also highly dose-sensitive in its use of crucial regulatory factors. The roles of these factors are not always T-lineage-specific, but they balance and modulate each other's activities long before any mutually exclusive silencing occurs. T-cell specification may provide a new hybrid model for gene networks in vertebrate developmental systems.


Subject(s)
Cell Differentiation , Hematopoiesis , Hematopoietic Stem Cells/physiology , Immune System/embryology , T-Lymphocytes/physiology , Animals , Cell Lineage , Gene Expression Regulation, Developmental , Gene Regulatory Networks/immunology , Humans , Immune System/growth & development , Models, Biological
18.
Article in English | MEDLINE | ID: mdl-24135716

ABSTRACT

Precursor cell entry into the T-cell developmental pathway can be divided into two phases by the closure of T-lineage commitment. As cells decide against the last alternative options to the T-cell fate, they turn on the transcription factor Bcl11b and silence expression of a group of multipotent progenitor regulatory factors that include hematopoietic transcription factor PU.1. Functional perturbation tests show that Bcl11b is needed for commitment while PU.1 actively participates in keeping open access to alternative fates, until it is silenced; however, PU.1 and Bcl11b both contribute positively to T-cell development. Our recent work reviewed here sheds light on the transcriptional regulatory network that determines the timing and irreversibility of Bcl11b activation, the ways that Notch signaling from the thymic microenvironment restricts the action of PU.1 to prevent it from diverting cells to non-T fates, and the target genes that PU.1 still regulates under the influence of Notch signaling to contribute to T-cell generation. We argue that T-cell development depends on the sequential operation of two interlaced, but mutually antagonistic, gene regulatory networks, one initially supporting expansion before commitment and the other imposing a "terminal" differentiation process on committed cells.


Subject(s)
Cell Lineage , T-Lymphocytes/cytology , Transcription, Genetic , Binding Sites , Cell Differentiation/genetics , Gene Regulatory Networks , Genes, Dominant , Humans , Proto-Oncogene Proteins/metabolism , Receptors, Notch/genetics , Receptors, Notch/metabolism , Signal Transduction/genetics , Stem Cells/cytology , Trans-Activators/metabolism , Transcription Factors/metabolism , Treatment Outcome
19.
Science ; 341(6146): 670-3, 2013 Aug 09.
Article in English | MEDLINE | ID: mdl-23868921

ABSTRACT

Regulatory gene circuits with positive-feedback loops control stem cell differentiation, but several mechanisms can contribute to positive feedback. Here, we dissect feedback mechanisms through which the transcription factor PU.1 controls lymphoid and myeloid differentiation. Quantitative live-cell imaging revealed that developing B cells decrease PU.1 levels by reducing PU.1 transcription, whereas developing macrophages increase PU.1 levels by lengthening their cell cycles, which causes stable PU.1 accumulation. Exogenous PU.1 expression in progenitors increases endogenous PU.1 levels by inducing cell cycle lengthening, implying positive feedback between a regulatory factor and the cell cycle. Mathematical modeling showed that this cell cycle-coupled feedback architecture effectively stabilizes a slow-dividing differentiated state. These results show that cell cycle duration functions as an integral part of a positive autoregulatory circuit to control cell fate.


Subject(s)
Cell Cycle/genetics , Cell Differentiation/genetics , Gene Expression Regulation , Gene Regulatory Networks , Myeloid Cells/cytology , Precursor Cells, B-Lymphoid/cytology , Proto-Oncogene Proteins/physiology , Trans-Activators/physiology , Animals , Cells, Cultured , Feedback, Physiological , Macrophages/cytology , Mice , Mice, Inbred Strains , Proto-Oncogene Proteins/genetics , Trans-Activators/genetics
20.
Blood ; 122(6): 902-11, 2013 Aug 08.
Article in English | MEDLINE | ID: mdl-23741008

ABSTRACT

Bcl11b is a T-cell specific gene in hematopoiesis that begins expression during T-lineage commitment and is required for this process. Aberrant expression of BCL11B or proto-oncogene translocation to the vicinity of BCL11B can be a contributing factor in human T-ALL. To identify the mechanism that controls its distinctive T-lineage expression, we corrected the identified Bcl11b transcription start site and mapped a cell-type-specific differentially methylated region bracketing the Bcl11b promoter. We identified a 1.9-kb region 850 kb downstream of Bcl11b, "Major Peak," distinguished by its dynamic histone marking pattern in development that mirrors the pattern at the Bcl11b promoter. Looping interactions between promoter-proximal elements including the differentially methylated region and downstream elements in the Major Peak are required to recapitulate the T-cell specific expression of Bcl11b in stable reporter assays. Functional dissection of the Major Peak sequence showed distinct subregions, in which TCF-1 sites and a conserved element were required for T-lineage-specific activation and silencing in non-T cells. A bacterial artificial chromosome encompassing the full Bcl11b gene still required the addition of the Major Peak to exhibit T-cell specific expression. Thus, promoter-proximal and Major Peak sequences are cis-regulatory elements that interact over 850 kb to control expression of Bcl11b in hematopoietic cells.


Subject(s)
Enhancer Elements, Genetic , Gene Expression Regulation , Repressor Proteins/genetics , T-Lymphocytes/cytology , Tumor Suppressor Proteins/genetics , Animals , Cell Lineage , CpG Islands , DNA Methylation , Gene Silencing , Genes, Reporter , Hematopoietic Stem Cells , Histones/metabolism , Mice , Promoter Regions, Genetic , Proto-Oncogene Mas , Repressor Proteins/metabolism , T-Lymphocytes/immunology , Transcription Factors/genetics , Tumor Suppressor Proteins/metabolism
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