CD4+ T Cell Differentiation and Plasticity
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ABSTRACT:
OTHER RELATED OMICS DATASETS IN: BIOMD0000000592BIOMD0000000593
SUBMITTER: Shannyn Bird
PROVIDER: 5025 | Cell Collective |
REPOSITORIES: Cell Collective
Publications
A Minimal Regulatory Network of Extrinsic and Intrinsic Factors Recovers Observed Patterns of CD4+ T Cell Differentiation and Plasticity.
Martinez-Sanchez Mariana Esther ME Mendoza Luis L Villarreal Carlos C Alvarez-Buylla Elena R ER
PLoS computational biology 20150619 6
CD4+ T cells orchestrate the adaptive immune response in vertebrates. While both experimental and modeling work has been conducted to understand the molecular genetic mechanisms involved in CD4+ T cell responses and fate attainment, the dynamic role of intrinsic (produced by CD4+ T lymphocytes) versus extrinsic (produced by other cells) components remains unclear, and the mechanistic and dynamic understanding of the plastic responses of these cells remains incomplete. In this work, we studied a ...[more]
Similar Datasets
Project description:Mariana Esther Martinez-Sanchez, Luis Mendoza, Carlos Villarreal & Elena R. Alvarez-Buylla. A Minimal Regulatory Network of Extrinsic and Intrinsic Factors Recovers Observed Patterns of CD4+ T Cell Differentiation and Plasticity. PLOS Computational Biology 11, 6 (2015).
CD4+ T cells orchestrate the adaptive immune response in vertebrates. While both experimental and modeling work has been conducted to understand the molecular genetic mechanisms involved in CD4+ T cell responses and fate attainment, the dynamic role of intrinsic (produced by CD4+ T lymphocytes) versus extrinsic (produced by other cells) components remains unclear, and the mechanistic and dynamic understanding of the plastic responses of these cells remains incomplete. In this work, we studied a regulatory network for the core transcription factors involved in CD4+ T cell-fate attainment. We first show that this core is not sufficient to recover common CD4+ T phenotypes. We thus postulate a minimal Boolean regulatory network model derived from a larger and more comprehensive network that is based on experimental data. The minimal network integrates transcriptional regulation, signaling pathways and the micro-environment. This network model recovers reported configurations of most of the characterized cell types (Th0, Th1, Th2, Th17, Tfh, Th9, iTreg, and Foxp3-independent T regulatory cells). This transcriptional-signaling regulatory network is robust and recovers mutant configurations that have been reported experimentally. Additionally, this model recovers many of the plasticity patterns documented for different T CD4+ cell types, as summarized in a cell-fate map. We tested the effects of various micro-environments and transient perturbations on such transitions among CD4+ T cell types. Interestingly, most cell-fate transitions were induced by transient activations, with the opposite behavior associated with transient inhibitions. Finally, we used a novel methodology was used to establish that T-bet, TGF-β and suppressors of cytokine signaling proteins are keys to recovering observed CD4+ T cell plastic responses. In conclusion, the observed CD4+ T cell-types and transition patterns emerge from the feedback between the intrinsic or intracellular regulatory core and the micro-environment. We discuss the broader use of this approach for other plastic systems and possible therapeutic interventions.
2024-09-02 | BIOMD0000000592 | BioModels
Project description:Martinez-Sanchez2015 - T CD4+ lymphocyte
transcriptional-signaling regulatory network
This model is described in the article:
A Minimal Regulatory Network
of Extrinsic and Intrinsic Factors Recovers Observed Patterns
of CD4+ T Cell Differentiation and Plasticity.
Martinez-Sanchez ME, Mendoza L,
Villarreal C, Alvarez-Buylla ER.
PLoS Comput. Biol. 2015 Jun; 11(6):
e1004324
Abstract:
CD4+ T cells orchestrate the adaptive immune response in
vertebrates. While both experimental and modeling work has been
conducted to understand the molecular genetic mechanisms
involved in CD4+ T cell responses and fate attainment, the
dynamic role of intrinsic (produced by CD4+ T lymphocytes)
versus extrinsic (produced by other cells) components remains
unclear, and the mechanistic and dynamic understanding of the
plastic responses of these cells remains incomplete. In this
work, we studied a regulatory network for the core
transcription factors involved in CD4+ T cell-fate attainment.
We first show that this core is not sufficient to recover
common CD4+ T phenotypes. We thus postulate a minimal Boolean
regulatory network model derived from a larger and more
comprehensive network that is based on experimental data. The
minimal network integrates transcriptional regulation,
signaling pathways and the micro-environment. This network
model recovers reported configurations of most of the
characterized cell types (Th0, Th1, Th2, Th17, Tfh, Th9, iTreg,
and Foxp3-independent T regulatory cells). This
transcriptional-signaling regulatory network is robust and
recovers mutant configurations that have been reported
experimentally. Additionally, this model recovers many of the
plasticity patterns documented for different T CD4+ cell types,
as summarized in a cell-fate map. We tested the effects of
various micro-environments and transient perturbations on such
transitions among CD4+ T cell types. Interestingly, most
cell-fate transitions were induced by transient activations,
with the opposite behavior associated with transient
inhibitions. Finally, we used a novel methodology was used to
establish that T-bet, TGF-? and suppressors of cytokine
signaling proteins are keys to recovering observed CD4+ T cell
plastic responses. In conclusion, the observed CD4+ T
cell-types and transition patterns emerge from the feedback
between the intrinsic or intracellular regulatory core and the
micro-environment. We discuss the broader use of this approach
for other plastic systems and possible therapeutic
interventions.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000593.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000593 | BioModels
Project description:Hamey2017 - Blood stem cell regulatory
network (LMPP network)
This model is described in the article:
Reconstructing blood stem
cell regulatory network models from single-cell molecular
profiles
Fiona K. Hamey, Sonia Nestorowa,
Sarah J. Kinston, David G. Kent, Nicola K. Wilson, and Berthold
Göttgens
Proceedings of the National Academy of
Sciences of the United States of America
Abstract:
Adult blood contains a mixture of mature cell types, each
with specialized functions. Single hematopoietic stem cells
(HSCs) have been functionally shown to generate all mature cell
types for the lifetime of the organism. Differentiation of HSCs
toward alternative lineages must be balanced at the population
level by the fate decisions made by individual cells.
Transcription factors play a key role in regulating these
decisions and operate within organized regulatory programs that
can be modeled as transcriptional regulatory networks. As
dysregulation of single HSC fate decisions is linked to fatal
malignancies such as leukemia, it is important to understand
how these decisions are controlled on a cell-by-cell basis.
Here we developed and applied a network inference method,
exploiting the ability to infer dynamic information from
single-cell snapshot expression data based on expression
profiles of 48 genes in 2,167 blood stem and progenitor cells.
This approach allowed us to infer transcriptional regulatory
network models that recapitulated differentiation of HSCs into
progenitor cell types, focusing on trajectories toward
megakaryocyte–erythrocyte progenitors and lymphoid-primed
multipotent progenitors. By comparing these two models, we
identified and subsequently experimentally validated a
difference in the regulation of nuclear factor, erythroid 2
(Nfe2) and core-binding factor, runt domain, alpha subunit 2,
translocated to, 3 homolog (Cbfa2t3h) by the transcription
factor Gata2. Our approach confirms known aspects of
hematopoiesis, provides hypotheses about regulation of HSC
differentiation, and is widely applicable to other hierarchical
biological systems to uncover regulatory relationships.
This model is hosted on
BioModels Database
and identified by:
MODEL1610060001.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-06-09 | MODEL1610060001 | BioModels
Project description:Hamey2017 - Blood stem cell regulatory
network
This model is described in the article:
Reconstructing blood stem
cell regulatory network models from single-cell molecular
profiles
Fiona K. Hamey, Sonia Nestorowa,
Sarah J. Kinston, David G. Kent, Nicola K. Wilson, and Berthold
Göttgens
Proceedings of the National Academy of
Sciences of the United States of America
Abstract:
Adult blood contains a mixture of mature cell types, each
with specialized functions. Single hematopoietic stem cells
(HSCs) have been functionally shown to generate all mature cell
types for the lifetime of the organism. Differentiation of HSCs
toward alternative lineages must be balanced at the population
level by the fate decisions made by individual cells.
Transcription factors play a key role in regulating these
decisions and operate within organized regulatory programs that
can be modeled as transcriptional regulatory networks. As
dysregulation of single HSC fate decisions is linked to fatal
malignancies such as leukemia, it is important to understand
how these decisions are controlled on a cell-by-cell basis.
Here we developed and applied a network inference method,
exploiting the ability to infer dynamic information from
single-cell snapshot expression data based on expression
profiles of 48 genes in 2,167 blood stem and progenitor cells.
This approach allowed us to infer transcriptional regulatory
network models that recapitulated differentiation of HSCs into
progenitor cell types, focusing on trajectories toward
megakaryocyte–erythrocyte progenitors and lymphoid-primed
multipotent progenitors. By comparing these two models, we
identified and subsequently experimentally validated a
difference in the regulation of nuclear factor, erythroid 2
(Nfe2) and core-binding factor, runt domain, alpha subunit 2,
translocated to, 3 homolog (Cbfa2t3h) by the transcription
factor Gata2. Our approach confirms known aspects of
hematopoiesis, provides hypotheses about regulation of HSC
differentiation, and is widely applicable to other hierarchical
biological systems to uncover regulatory relationships.
This model is hosted on
BioModels Database
and identified by:
MODEL1610060000.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-06-09 | MODEL1610060000 | BioModels
Project description:Sanchez2017 - Inflammatory responses during acute hyperinsulinemia
This model is described in the article:
The CD4+ T cell regulatory
network mediates inflammatory responses during acute
hyperinsulinemia: a simulation study
Mariana E. Martinez-Sanchez, Marcia
Hiriart, Elena R. Alvarez-Buylla
BMC Systems Biology
Abstract:
Obesity is frequently linked to insulin resistance, high
insulin levels, chronic inflammation, and alterations in the
behaviour of CD4+ T cells. Despite the biomedical importance of
this condition, the system-level mechanisms that alter CD4+ T
cell differentiation and plasticity are not well understood. We
model how hyperinsulinemia alters the dynamics of the CD4+ T
regulatory network, and this, in turn, modulates cell
differentiation and plasticity. Different polarizing
microenvironments are simulated under basal and high levels of
insulin to assess impacts on cell-fate attainment and
robustness in response to transient perturbations. In the
presence of high levels of insulin Th1 and Th17 become more
stable to transient perturbations, and their basin sizes are
augmented, Tr1 cells become less stable or disappear, while
TGF? producing cells remain unaltered. Hence, the model
provides a dynamic system-level framework and explanation to
further understand the documented and apparently paradoxical
role of TGF? in both inflammation and regulation of immune
responses, as well as the emergence of the adipose Treg
phenotype. Furthermore, our simulations provide new predictions
on the impact of the microenvironment in the coexistence of the
different cell types, suggesting that in pro-Th1, pro-Th2 and
pro-Th17 environments effector and regulatory cells can
coexist, but that high levels of insulin severely diminish
regulatory cells, especially in a pro-Th17 environment. This
work provides a first step towards a system-level formal and
dynamic framework to integrate further experimental data in the
study of complex inflammatory diseases.
This model is hosted on
BioModels Database
and identified by:
MODEL1606020000.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-06-27 | MODEL1606020000 | BioModels
Project description:<p>Defining the number, proportion, or lineage of distinct cell types in the developing human brain is an important goal of modern brain research. We produced single cell transcriptomic profiles for 40,000 cells at mid-gestation to define deep expression profiles corresponding to all known major cell types at this developmental period and compare this with bulk tissue profiles. We identified multiple transcription factors (TFs) and co-factors expressed in specific cell types, including multiple new cell-type-specific relationships, providing an unprecedented resource for understanding human neocortical development and evolution. This includes the first single-cell characterization of human subplate neurons and subtypes of developing glutamatergic and GABAergic neurons. We also used these data to deconvolute single cell regulatory networks that connect regulatory elements and transcriptional drivers to single cell gene expression programs in the developing CNS. We characterized major developmental trajectories that tie cell cycle progression with early cell fate decisions during early neurogenesis. Remarkably, we found that differentiation occurs on a transcriptomic continuum, so that differentiating cells not only express the few key TFs that drive cell fates, but express broad, mixed cell-type transcriptomes prior to telophase. Finally, we mapped neuropsychiatric disease genes to specific cell types, implicating dysregulation of specific cell types in ASD, ID, and epilepsy, as the mechanistic underpinnings of several neurodevelopmental disorders. Together these results provide an extensive catalog of cell types in human neocortex and extend our understanding of early cortical development, human brain evolution and the cellular basis of neuropsychiatric disease.</p>
| phs001836 | dbGaP
Project description:Carbo2013 - Cytokine driven CD4+ T Cell differentiation and phenotype plasticity
CD4+ T cells can differentiate into different phenotypes
depending on the cytokine milieu. Here a computational and mathematical model with sixty ordinary differential equations representing a CD4+ T cell differentiating into either Th1, Th2, Th17 or iTreg cells, has been constructed.
The model includes cytokines,
nuclear receptors and transcription factors that define fate
and function of CD4+ T cells. Computational simulations
illustrate how a proinflammatory Th17 cell can undergo
reprogramming into an anti-inflammatory iTreg phenotype
following PPARc activation.
This model is described in the article:
Systems Modeling of Molecular Mechanisms Controlling Cytokine-driven CD4+ T Cell Differentiation and Phenotype Plasticity.
Carbo A, Hontecillas R, Kronsteiner B, Viladomiu M, Pedragosa M, Lu P, Philipson CW, Hoops S, Marathe M, Eubank S, Bisset K, Wendelsdorf K, Jarrah A, Mei Y, Bassaganya-Riera J
PLoS Computational Biology [2013, 9(4):e1003027]
Abstract:
Differentiation of CD4+ T cells into effector or regulatory phenotypes is tightly controlled by the cytokine milieu, complex intracellular signaling networks and numerous transcriptional regulators. We combined experimental approaches and computational modeling to investigate the mechanisms controlling differentiation and plasticity of CD4+ T cells in the gut of mice. Our computational model encompasses the major intracellular pathways involved in CD4+ T cell differentiation into T helper 1 (Th1), Th2, Th17 and induced regulatory T cells (iTreg). Our modeling efforts predicted a critical role for peroxisome proliferator-activated receptor gamma (PPARγ) in modulating plasticity between Th17 and iTreg cells. PPARγ regulates differentiation, activation and cytokine production, thereby controlling the induction of effector and regulatory responses, and is a promising therapeutic target for dysregulated immune responses and inflammation. Our modeling efforts predict that following PPARγ activation, Th17 cells undergo phenotype switch and become iTreg cells. This prediction was validated by results of adoptive transfer studies showing an increase of colonic iTreg and a decrease of Th17 cells in the gut mucosa of mice with colitis following pharmacological activation of PPARγ. Deletion of PPARγ in CD4+ T cells impaired mucosal iTreg and enhanced colitogenic Th17 responses in mice with CD4+ T cell-induced colitis. Thus, for the first time we provide novel molecular evidence in vivo demonstrating that PPARγ in addition to regulating CD4+ T cell differentiation also plays a major role controlling Th17 and iTreg plasticity in the gut mucosa.
Author's comment:
CD4+ T cell computational model (Version 1.4)
Steady state corrected. There was a problem in the internalization of IL-17 in its mathematical function.
This model is hosted on BioModels Database
and identified
by: MODEL1304230001
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource
for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to the public
domain worldwide. Please refer to CC0 Public Domain
Dedication
for more information.
2024-09-02 | BIOMD0000000451 | BioModels
Project description:CD4+ T cells differentiate into phenotypically distinct T-helper cells upon antigenic stimulation. Regulation of plasticity between these CD4+ T-cell lineages is critical for immune homeostasis and prevention of autoimmune diseases. However, the factors that regulate lineage stability are largely unknown. Here we investigate a role for retinoic acid (RA) in the regulation of lineage stability using T helper 1 (Th1) cells, traditionally considered the most phenotypically stable Th subset. We found that RA, through its receptor RARa, sustains stable expression of Th1 lineage specifying genes as well as repressing genes that instruct Th17 cell fate. RA signaling is essential for limiting Th1 cell conversion into Th17 effectors and for preventing pathogenic Th17 responses in vivo. Our studies identify RA-RARa as a key component of the regulatory network governing Th1 cell fate and define a new paradigm for the development of pathogenic Th17 cells. These findings have important implications for autoimmune diseases in which dysregulated Th1-Th17 responses are observed. IFN-gamma+ Th1 cells from IFNgeYFP reporter mouse; comparing dnRARa to WT
2015-03-10 | E-GEOD-60354 | biostudies-arrayexpress
Project description:CD4+ T cells differentiate into phenotypically distinct T-helper cells upon antigenic stimulation. Regulation of plasticity between these CD4+ T-cell lineages is critical for immune homeostasis and prevention of autoimmune diseases. However, the factors that regulate lineage stability are largely unknown. Here we investigate a role for retinoic acid (RA) in the regulation of lineage stability using T helper 1 (Th1) cells, traditionally considered the most phenotypically stable Th subset. We found that RA, through its receptor RARa, sustains stable expression of Th1 lineage specifying genes as well as repressing genes that instruct Th17 cell fate. RA signaling is essential for limiting Th1 cell conversion into Th17 effectors and for preventing pathogenic Th17 responses in vivo. Our studies identify RA-RARa as a key component of the regulatory network governing Th1 cell fate and define a new paradigm for the development of pathogenic Th17 cells. These findings have important implications for autoimmune diseases in which dysregulated Th1-Th17 responses are observed. Identification of RARa binding in wild-type Th1 cells and mapping of enhancers using chromatin IP against p300, H3k4me1, H3k4me3, and H3k27ac in wild-type and dnRara Th1 cells.
2015-03-10 | E-GEOD-60353 | biostudies-arrayexpress
Project description:<p>We use next generation sequencing to investigate the different transcriptomes of closely related CD4+ T-cells from healthy human donors to elucidate the genetic programs that underlie their specialized immune functions. Six cell types were included: Regulatory T-cells (CD25hiCD127low/neg with >95% FOXP3+ purity), regulatory T-cells activated using PMA/ionomycin, CD25-CD45RA+ ('naive' helper T-cells), CD25-CD45RO+ ('memory' helper T-cells), activated Th17 cells (>98% IL17A+ purity) and activated IL17-CD4+ T-cells (called 'ThPI'). Poly-T capture beads were used to isolate mRNA from total RNA, and fragment sizes of ~200 were sequenced from both ends on Illumina's genome analyzer. We confirm many of the canonical signature genes of T-cell populations, but also discover new genes whose expression is limited to specific CD4 T-cell lineages, including long non-coding RNAs. Additionally, we find that genes encoded at loci linked to multiple human autoimmune diseases are enriched for preferential expression upon T-cell activation, suggesting that an aberrant response to T-cell activation is fundamental to pathogenesis.</p>
| phs000626 | dbGaP