Bcl11b and combinatorial resolution of cell fate in the T-cell gene regulatory network

BCL11b interacts with RNA and proteins involved in RNA processing and developmental diseases

BCL11b is a transcription regulator and a tumor suppressor involved in lymphomagenesis, central nervous system (CNS) and immune system developments. BCL11b favors persistence of HIV latency and contributes to control cell cycle, differentiation and apoptosis in multiple organisms and cell models. Although BCL11b recruits the non-coding RNA 7SK and epigenetic enzymes to regulate gene expression, BCL11b-associated ribonucleoprotein complexes are unknown. Thanks to CLIP-seq and quantitative LC-MS/MS mass spectrometry approaches complemented with systems biology validations, we show that BCL11b interacts with RNA splicing and non-sense-mediated decay proteins, including FUS, SMN1, UPF1 and Drosha, which may contribute in isoform selection of protein-coding RNA isoforms from noncoding-RNAs isoforms (retained introns or nonsense mediated RNA). Interestingly, BCL11b binds to RNA transcripts and proteins encoded by the same genes (FUS, ESWR1, CHD and Tubulin). Our study highlights that BCL11b targets RNA processing and splicing proteins, and RNAs that implicate cell cycle, development, neurodegenerative, and cancer pathways. These findings will help future mechanistic understanding of developmental disorders. IMPORTANCE: BCL11b-protein and RNA interactomes reveal BLC11b association with specific nucleoprotein complexes involved in the regulation of genes expression. BCL11b interacts with RNA processing and splicing proteins.

Transcriptional network dynamics in early T cell development

The rate at which cells enter the T cell pathway depends not only on the immigration of hematopoietic precursors into the strong Notch signaling environment of the thymus but also on the kinetics with which each individual precursor cell reaches T-lineage commitment once it arrives. Notch triggers a complex, multistep gene regulatory network in the cells in which the steps are stereotyped but the transition speeds between steps are variable. Progenitor-associated transcription factors delay T-lineage differentiation even while Notch-induced transcription factors within the same cells push differentiation forward. Progress depends on regulator cross-repression, on breaching chromatin barriers, and on shifting, competitive collaborations between stage-specific and stably expressed transcription factors, as reviewed here.

Transcriptional rewiring in CD8+ T cells: implications for CAR-T cell therapy against solid tumours

T cells engineered to express chimeric-antigen receptors (CAR-T cells) can effectively control relapsed and refractory haematological malignancies in the clinic. However, the successes of CAR-T cell therapy have not been recapitulated in solid tumours due to a range of barriers such as immunosuppression, poor infiltration, and tumour heterogeneity. Numerous strategies are being developed to overcome these barriers, which include improving culture conditions and manufacturing protocols, implementing novel CAR designs, and novel approaches to engineering the T cell phenotype. In this review, we describe the various emerging strategies to improve CAR T cell therapy for solid tumours. We specifically focus on new strategies to modulate cell function and fate that have precipitated from the growing knowledge of transcriptional circuits driving T cell differentiation, with the ultimate goal of driving more productive anti-tumour T cell immunity. Evidence shows that enrichment of particular phenotypic subsets of T cells in the initial cell product correlates to improved therapeutic responses and clinical outcomes. Furthermore, T cell exhaustion and poor persistence are major factors limiting therapeutic efficacy. The latest preclinical work shows that targeting specific master regulators and transcription factors can overcome these key barriers, resulting in superior T cell therapeutic products. This can be achieved by targeting key transcriptional circuits promoting memory-like phenotypes or sustaining key effector functions within the hostile tumour microenvironment. Additional discussion points include emerging considerations for the field such as (i) targeting permutations of transcription factors, (ii) transient expression systems, (iii) tissue specificity, and (iv) expanding this strategy beyond CAR-T cell therapy and cancer.

Functional overlap of inborn errors of immunity and metabolism genes defines T cell metabolic vulnerabilities

Inborn errors of metabolism (IEMs) and immunity (IEIs) are Mendelian diseases in which complex phenotypes and patient rarity have limited clinical understanding. Whereas few genes have been annotated as contributing to both IEMs and IEIs, immunometabolic demands suggested greater functional overlap. Here, CRISPR screens tested IEM genes for immunologic roles and IEI genes for metabolic effects and found considerable previously unappreciated crossover. Analysis of IEMs showed that N-linked glycosylation and the hexosamine pathway enzyme Gfpt1 are critical for T cell expansion and function. Further, T helper (TH1) cells synthesized uridine diphosphate N-acetylglucosamine more rapidly and were more impaired by Gfpt1 deficiency than TH17 cells. Screening IEI genes found that Bcl11b promotes the CD4 T cell mitochondrial activity and Mcl1 expression necessary to prevent metabolic stress. Thus, a high degree of functional overlap exists between IEM and IEI genes, and immunometabolic mechanisms may underlie a previously underappreciated intersection of these disorders.

C2H2-type zinc-finger protein BCL11B suppresses avian Leukosis virus subgroup J replication by regulating apoptosis

Apoptosis plays a crucial role in host antiviral defense. The avian leukosis virus subgroup J (ALV-J), an avian oncogenic retrovirus, has been shown to suppress apoptosis while promoting its own replication. ALV-J induces myeloid tumors and hemangiomas in chickens resulting in significant economic losses for commercial layer and meat-type chicken production. B-cell lymphoma/leukemia 11B (Bcl11b) encodes a C2H2-type zinc finger protein-BCL11B, that exerts critical functions in cell proliferation, differentiation, and plays an essential role in the immune system. Previous study has been shown that Bcl11b is associated with ALV-J infection. In this study, we further investigated the pathological changes in ALV-J infected cells and examined the role and expression regulation of chicken Bcl11b. Our results demonstrate that Bcl11b, as an interferon-stimulated gene (ISG), encodes C2H2-type zinc finger protein BCL11B that promotes apoptosis to inhibit ALV-J infection. Additionally, gga-miR-1612 and gga-miR-6701-3p regulate apoptosis and are involved in ALV-J infection by targeting Bcl11b, thus revealing immune response strategies between the host and ALV-J. Although the underlying mechanisms require further validation, Bcl11b and its regulatory miRNAs are the first to demonstrate inhibition of ALV-J replication via apoptosis. BCL11B can a valuable target for treating diseases triggered by ALV-J infection.

Netrin G1 Ligand is a new stromal immunomodulator that promotes pancreatic cancer

Understanding pancreatic cancer biology is fundamental for identifying new targets and for developing more effective therapies. In particular, the contribution of the stromal microenvironment to pancreatic cancer tumorigenesis requires further exploration. Here, we report the stromal roles of the synaptic protein Netrin G1 Ligand (NGL-1) in pancreatic cancer, uncovering its pro-tumor functions in cancer-associated fibroblasts and in immune cells. We observed that the stromal expression of NGL-1 inversely correlated with patients' overall survival. Moreover, germline knockout (KO) mice for NGL-1 presented decreased tumor burden, with a microenvironment that is less supportive of tumor growth. Of note, tumors from NGL-1 KO mice produced less immunosuppressive cytokines and displayed an increased percentage of CD8 + T cells than those from control mice, while preserving the physical structure of the tumor microenvironment. These effects were shown to be mediated by NGL-1 in both immune cells and in the local stroma, in a TGF-β-dependent manner. While myeloid cells lacking NGL-1 decreased the production of immunosuppressive cytokines, NGL-1 KO T cells showed increased proliferation rates and overall polyfunctionality compared to control T cells. CAFs lacking NGL-1 were less immunosuppressive than controls, with overall decreased production of pro-tumor cytokines and compromised ability to inhibit CD8 + T cells activation. Mechanistically, these CAFs downregulated components of the TGF-β pathway, AP-1 and NFAT transcription factor families, resulting in a less tumor-supportive phenotype. Finally, targeting NGL-1 genetically or using a functionally antagonistic small peptide phenocopied the effects of chemotherapy, while modulating the immunosuppressive tumor microenvironment (TME), rather than eliminating it. We propose NGL-1 as a new local stroma and immunomodulatory molecule, with pro-tumor roles in pancreatic cancer.

Statement of Significance

Here we uncovered the pro-tumor roles of the synaptic protein NGL-1 in the tumor microenvironment of pancreatic cancer, defining a new target that simultaneously modulates tumor cell, fibroblast, and immune cell functions. This study reports a new pathway where NGL-1 controls TGF-β, AP-1 transcription factor members and NFAT1, modulating the immunosuppressive microenvironment in pancreatic cancer. Our findings highlight NGL-1 as a new stromal immunomodulator in pancreatic cancer.

Inferring Gene Regulatory Networks and Predicting the Effect of Gene Perturbations via IQCELL

IQCELL is a platform that infers Boolean gene regulatory networks from single-cell RNA sequencing data. Boolean networks can be simulated under normal and perturbed conditions. In this chapter, we provide a detailed guideline for implementing IQCELL from a raw dataset. The steps include processing data, inferring informative genes, inferring gene regulatory network, and simulating the resulted network under normal and perturbed conditions.

scMHNN: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data

Technological advances have now made it possible to simultaneously profile the changes of epigenomic, transcriptomic and proteomic at the single cell level, allowing a more unified view of cellular phenotypes and heterogeneities. However, current computational tools for single-cell multi-omics data integration are mainly tailored for bi-modality data, so new tools are urgently needed to integrate tri-modality data with complex associations. To this end, we develop scMHNN to integrate single-cell multi-omics data based on hypergraph neural network. After modeling the complex data associations among various modalities, scMHNN performs message passing process on the multi-omics hypergraph, which can capture the high-order data relationships and integrate the multiple heterogeneous features. Followingly, scMHNN learns discriminative cell representation via a dual-contrastive loss in self-supervised manner. Based on the pretrained hypergraph encoder, we further introduce the pre-training and fine-tuning paradigm, which allows more accurate cell-type annotation with only a small number of labeled cells as reference. Benchmarking results on real and simulated single-cell tri-modality datasets indicate that scMHNN outperforms other competing methods on both cell clustering and cell-type annotation tasks. In addition, we also demonstrate scMHNN facilitates various downstream tasks, such as cell marker detection and enrichment analysis.

PTEN regulates hematopoietic lineage plasticity via PU.1-dependent chromatin accessibility

PTEN loss in fetal liver hematopoietic stem cells (HSCs) leads to alterations in myeloid, T-, and B-lineage potentials and T-lineage acute lymphoblastic leukemia (T-ALL) development. To explore the mechanism underlying PTEN-regulated hematopoietic lineage choices, we carry out integrated assay for transposase-accessible chromatin using sequencing (ATAC-seq), single-cell RNA-seq, and in vitro culture analyses using in vivo-isolated mouse pre-leukemic HSCs and progenitors. We find that PTEN loss alters chromatin accessibility of key lineage transcription factor (TF) binding sites at the prepro-B stage, corresponding to increased myeloid and T-lineage potentials and reduced B-lineage potential. Importantly, we find that PU.1 is an essential TF downstream of PTEN and that altering PU.1 levels can reprogram the chromatin accessibility landscape and myeloid, T-, and B-lineage potentials in Ptennull prepro-B cells. Our study discovers prepro-B as the key developmental stage underlying PTEN-regulated hematopoietic lineage choices and suggests a critical role of PU.1 in modulating the epigenetic state and lineage plasticity of prepro-B progenitors.

Multi-modular structure of the gene regulatory network for specification and commitment of murine T cells

T cells develop from multipotent progenitors by a gradual process dependent on intrathymic Notch signaling and coupled with extensive proliferation. The stages leading them to T-cell lineage commitment are well characterized by single-cell and bulk RNA analyses of sorted populations and by direct measurements of precursor-product relationships. This process depends not only on Notch signaling but also on multiple transcription factors, some associated with stemness and multipotency, some with alternative lineages, and others associated with T-cell fate. These factors interact in opposing or semi-independent T cell gene regulatory network (GRN) subcircuits that are increasingly well defined. A newly comprehensive picture of this network has emerged. Importantly, because key factors in the GRN can bind to markedly different genomic sites at one stage than they do at other stages, the genes they significantly regulate are also stage-specific. Global transcriptome analyses of perturbations have revealed an underlying modular structure to the T-cell commitment GRN, separating decisions to lose "stem-ness" from decisions to block alternative fates. Finally, the updated network sheds light on the intimate relationship between the T-cell program, which depends on the thymus, and the innate lymphoid cell (ILC) program, which does not.

Functional Overlap of Inborn Errors of Immunity and Metabolism Genes Define T Cell Immunometabolic Vulnerabilities

Inborn Errors of Metabolism (IEM) and Immunity (IEI) are Mendelian diseases in which complex phenotypes and patient rarity can limit clinical annotations. Few genes are assigned to both IEM and IEI, but immunometabolic demands suggest functional overlap is underestimated. We applied CRISPR screens to test IEM genes for immunologic roles and IEI genes for metabolic effects and found considerable crossover. Analysis of IEM showed N-linked glycosylation and the de novo hexosamine synthesis enzyme, Gfpt1 , are critical for T cell expansion and function. Interestingly, Gfpt1 -deficient T H 1 cells were more affected than T H 17 cells, which had increased Nagk for salvage UDP-GlcNAc synthesis. Screening IEI genes showed the transcription factor Bcl11b promotes CD4 + T cell mitochondrial activity and Mcl1 expression necessary to prevent metabolic stress. These data illustrate a high degree of functional overlap of IEM and IEI genes and point to potential immunometabolic mechanisms for a previously unappreciated set of these disorders.

HIGHLIGHTS

Inborn errors of immunity and metabolism have greater overlap than previously known Gfpt1 deficiency causes an IEM but also selectively regulates T cell subset fate Loss of Bcl11b causes a T cell deficiency IEI but also harms mitochondrial function Many IEM may have immune defects and IEI may be driven by metabolic mechanisms.

BCL11B depletion induces the development of highly cytotoxic innate T cells out of IL-15 stimulated peripheral blood αβ CD8+ T cells

BCL11B, an essential transcription factor for thymopoiesis, regulates also vital processes in post-thymic lymphocytes. Increased expression of BCL11B was recently correlated with the maturation of NK cells, whereas reduced BCL11B levels were observed in native and induced T cell subsets displaying NK cell features. We show that BCL11B-depleted CD8+ T cells stimulated with IL-15 acquired remarkable innate characteristics. These induced innate CD8+ (iiT8) cells expressed multiple innate receptors like NKp30, CD161, and CD16 as well as factors regulating migration and tissue homing while maintaining their T cell phenotype. The iiT8 cells effectively killed leukemic cells spontaneously and neuroblastoma spheroids in the presence of a tumor-specific monoclonal antibody mediated by CD16 receptor activation. These iiT8 cells integrate the innate natural killer cell activity with adaptive T cell longevity, promising an interesting therapeutic potential. Our study demonstrates that innate T cells, albeit of limited clinical applicability given their low frequency, can be efficiently generated from peripheral blood and applied for adoptive transfer, CAR therapy, or combined with therapeutic antibodies.

Decoding the IGF1 signaling gene regulatory network behind alveologenesis from a mouse model of bronchopulmonary dysplasia

Lung development is precisely controlled by underlying gene regulatory networks (GRN). Disruption of genes in the network can interrupt normal development and cause diseases such as bronchopulmonary dysplasia (BPD) - a chronic lung disease in preterm infants with morbid and sometimes lethal consequences characterized by lung immaturity and reduced alveolarization. Here, we generated a transgenic mouse exhibiting a moderate severity BPD phenotype by blocking IGF1 signaling in secondary crest myofibroblasts (SCMF) at the onset of alveologenesis. Using approaches mirroring the construction of the model GRN in sea urchin's development, we constructed the IGF1 signaling network underlying alveologenesis using this mouse model that phenocopies BPD. The constructed GRN, consisting of 43 genes, provides a bird's eye view of how the genes downstream of IGF1 are regulatorily connected. The GRN also reveals a mechanistic interpretation of how the effects of IGF1 signaling are transduced within SCMF from its specification genes to its effector genes and then from SCMF to its neighboring alveolar epithelial cells with WNT5A and FGF10 signaling as the bridge. Consistently, blocking WNT5A signaling in mice phenocopies BPD as inferred by the network. A comparative study on human samples suggests that a GRN of similar components and wiring underlies human BPD. Our network view of alveologenesis is transforming our perspective to understand and treat BPD. This new perspective calls for the construction of the full signaling GRN underlying alveologenesis, upon which targeted therapies for this neonatal chronic lung disease can be viably developed.

Functional inference of gene regulation using single-cell multi-omics

Cells require coordinated control over gene expression when responding to environmental stimuli. Here we apply scATAC-seq and single-cell RNA sequencing (scRNA-seq) in resting and stimulated human blood cells. Collectively, we generate ~91,000 single-cell profiles, allowing us to probe the cis-regulatory landscape of the immunological response across cell types, stimuli, and time. Advancing tools to integrate multi-omics data, we develop functional inference of gene regulation (FigR), a framework to computationally pair scA-TAC-seq with scRNA-seq cells, connect distal cis-regulatory elements to genes, and infer gene-regulatory networks (GRNs) to identify candidate transcription factor (TF) regulators. Utilizing these paired multi-omics data, we define domains of regulatory chromatin (DORCs) of immune stimulation and find that cells alter chromatin accessibility and gene expression at timescales of minutes. Construction of the stimulation GRN elucidates TF activity at disease-associated DORCs. Overall, FigR enables elucidation of regulatory interactions across single-cell data, providing new opportunities to understand the function of cells within tissues.

Signaling networks controlling ID and E protein activity in T cell differentiation and function

E and inhibitor of DNA binding (ID) proteins are involved in various cellular developmental processes and effector activities in T cells. Recent findings indicate that E and ID proteins are not only responsible for regulating thymic T cell development but also modulate the differentiation, function, and fate of peripheral T cells in multiple immune compartments. Based on the well-established E and ID protein axis (E-ID axis), it has been recognized that ID proteins interfere with the dimerization of E proteins, thus restricting their transcriptional activities. Given this close molecular relationship, the extent of expression or stability of these two protein families can dynamically affect the expression of specific target genes involved in multiple aspects of T cell biology. Therefore, it is essential to understand the endogenous proteins or extrinsic signaling pathways that can influence the dynamics of the E-ID axis in a cell-specific and context-dependent manner. Here, we provide an overview of E and ID proteins and the functional outcomes of the E-ID axis in the activation and function of multiple peripheral T cell subsets, including effector and memory T cell populations. Further, we review the mechanisms by which endogenous proteins and signaling pathways alter the E-ID axis in various T cell subsets influencing T cell function and fate at steady-state and in pathological settings. A comprehensive understanding of the functions of E and ID proteins in T cell biology can be instrumental in T cell-specific targeting of the E-ID axis to develop novel therapeutic modalities in the context of autoimmunity and cancer.

Realization of the T Lineage Program Involves GATA-3 Induction of Bcl11b and Repression of Cdkn2b Expression

The zinc-finger transcription factor GATA-3 plays a crucial role during early T cell development and also dictates later T cell differentiation outcomes. However, its role and collaboration with the Notch signaling pathway in the induction of T lineage specification and commitment have not been fully elucidated. We show that GATA-3 deficiency in mouse hematopoietic progenitors results in an early block in T cell development despite the presence of Notch signals, with a failure to upregulate Bcl11b expression, leading to a diversion along a myeloid, but not a B cell, lineage fate. GATA-3 deficiency in the presence of Notch signaling results in the apoptosis of early T lineage cells, as seen with inhibition of CDK4/6 (cyclin-dependent kinases 4 and 6) function, and dysregulated cyclin-dependent kinase inhibitor 2b (Cdkn2b) expression. We also show that GATA-3 induces Bcl11b, and together with Bcl11b represses Cdkn2b expression; however, loss of Cdkn2b failed to rescue the developmental block of GATA-3-deficient T cell progenitor. Our findings provide a signaling and transcriptional network by which the T lineage program in response to Notch signals is realized.

Single-cell deletion analyses show control of pro-T cell developmental speed and pathways by Tcf7, Spi1, Gata3, Bcl11a, Erg, and Bcl11b

As early T cell precursors transition from multipotentiality to T lineage commitment, they change expression of multiple transcription factors. It is unclear whether individual transcription factors directly control choices between T cell identity and some alternative fate or whether these factors mostly affect proliferation or survival during the normal commitment process. Here, we unraveled the impacts of deleting individual transcription factors at two stages in early T cell development, using synchronized in vitro differentiation systems, single-cell RNA-seq with batch indexing, and controlled gene-disruption strategies. First, using a customized method for single-cell CRISPR disruption, we defined how the early-acting transcription factors Bcl11a, Erg, Spi1 (PU.1), Gata3, and Tcf7 (TCF1) function before commitment. The results revealed a kinetic tug of war within individual cells between T cell factors Tcf7 and Gata3 and progenitor factors Spi1 and Bcl11a, with an unexpected guidance role for Erg. Second, we tested how activation of transcription factor Bcl11b during commitment altered ongoing cellular programs. In knockout cells where Bcl11b expression was prevented, the cells did not undergo developmental arrest, instead following an alternative path as T lineage commitment was blocked. A stepwise, time-dependent regulatory cascade began with immediate-early transcription factor activation and E protein inhibition, finally leading Bcl11b knockout cells toward exit from the T cell pathway. Last, gene regulatory networks of transcription factor cross-regulation were extracted from the single-cell transcriptome results, characterizing the specification network operating before T lineage commitment and revealing its links to both the Bcl11b knockout alternative network and the network consolidating T cell identity during commitment.

Helix-Loop-Helix Proteins in Adaptive Immune Development

The E/ID protein axis is instrumental for defining the developmental progression and functions of hematopoietic cells. The E proteins are dimeric transcription factors that activate gene expression programs and coordinate changes in chromatin organization. Id proteins are antagonists of E protein activity. Relative levels of E/Id proteins are modulated throughout hematopoietic development to enable the progression of hematopoietic stem cells into multiple adaptive and innate immune lineages including natural killer cells, B cells and T cells. In early progenitors, the E proteins promote commitment to the T and B cell lineages by orchestrating lineage specific programs of gene expression and regulating VDJ recombination of antigen receptor loci. In mature B cells, the E/Id protein axis functions to promote class switch recombination and somatic hypermutation. E protein activity further regulates differentiation into distinct CD4+ and CD8+ T cells subsets and instructs mature T cell immune responses. In this review, we discuss how the E/Id proteins define the adaptive immune system lineages, focusing on their role in directing developmental gene programs.

Langley M, Fisher CL, Aguilar-Hidalgo D, Shukla S, Yachie-Kinoshita A, Hughes M, M. McNagny K, Zandstra PW. IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data

The increasing availability of single-cell RNA-sequencing (scRNA-seq) data from various developmental systems provides the opportunity to infer gene regulatory networks (GRNs) directly from data. Herein we describe IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from scRNA-seq data. Such executable GRNs allow simulation of fundamental hypotheses governing developmental programs and help accelerate the design of strategies to control stem cell fate. We first describe the architecture of IQCELL. Next, we apply IQCELL to scRNA-seq datasets from early mouse T-cell and red blood cell development, and show that the platform can infer overall over 74% of causal gene interactions previously reported from decades of research. We will also show that dynamic simulations of the generated GRN qualitatively recapitulate the effects of known gene perturbations. Finally, we implement an IQCELL gene selection pipeline that allows us to identify candidate genes, without prior knowledge. We demonstrate that GRN simulations based on the inferred set yield results similar to the original curated lists. In summary, the IQCELL platform offers a versatile tool to infer, simulate, and study executable GRNs in dynamic biological systems.

Human Cytomegalovirus Infection Promotes Expansion of a Functionally Superior Cytoplasmic CD3+ NK Cell Subset with a Bcl11b-Regulated T Cell Signature

Human CMV (HCMV) is a ubiquitous pathogen that indelibly shapes the NK cell repertoire. Using transcriptomic, epigenomic, and proteomic approaches to evaluate peripheral blood NK cells from healthy human volunteers, we find that prior HCMV infection promotes NK cells with a T cell-like gene profile, including the canonical markers CD3ε, CD5, and CD8β, as well as the T cell lineage-commitment transcription factor Bcl11b. Although Bcl11b expression is upregulated during NK maturation from CD56bright to CD56dim, we find a Bcl11b-mediated signature at the protein level for FcεRIγ, PLZF, IL-2Rβ, CD3γ, CD3δ, and CD3ε in later-stage, HCMV-induced NK cells. BCL11B is targeted by Notch signaling in T cell development, and culture of NK cells with Notch ligand increases cytoplasmic CD3ε expression. The Bcl11b-mediated gain of CD3ε, physically associated with CD16 signaling molecules Lck and CD247 in NK cells is correlated with increased Ab-dependent effector function, including against HCMV-infected cells, identifying a potential mechanism for their prevalence in HCMV-infected individuals and their prospective clinical use in Ab-based therapies.

Identification, visualization, statistical analysis and mathematical modeling of high-feedback loops in gene regulatory networks

Background

Feedback loops in gene regulatory networks play pivotal roles in governing functional dynamics of cells. Systems approaches demonstrated characteristic dynamical features, including multistability and oscillation, of positive and negative feedback loops. Recent experiments and theories have implicated highly interconnected feedback loops (high-feedback loops) in additional nonintuitive functions, such as controlling cell differentiation rate and multistep cell lineage progression. However, it remains challenging to identify and visualize high-feedback loops in complex gene regulatory networks due to the myriad of ways in which the loops can be combined. Furthermore, it is unclear whether the high-feedback loop structures with these potential functions are widespread in biological systems. Finally, it remains challenging to understand diverse dynamical features, such as high-order multistability and oscillation, generated by individual networks containing high-feedback loops. To address these problems, we developed HiLoop, a toolkit that enables discovery, visualization, and analysis of several types of high-feedback loops in large biological networks.

Results

HiLoop not only extracts high-feedback structures and visualize them in intuitive ways, but also quantifies the enrichment of overrepresented structures. Through random parameterization of mathematical models derived from target networks, HiLoop presents characteristic features of the underlying systems, including complex multistability and oscillations, in a unifying framework. Using HiLoop, we were able to analyze realistic gene regulatory networks containing dozens to hundreds of genes, and to identify many small high-feedback systems. We found more than a 100 human transcription factors involved in high-feedback loops that were not studied previously. In addition, HiLoop enabled the discovery of an enrichment of high feedback in pathways related to epithelial-mesenchymal transition.

Conclusions

HiLoop makes the study of complex networks accessible without significant computational demands. It can serve as a hypothesis generator through identification and modeling of high-feedback subnetworks, or as a quantification method for motif enrichment analysis. As an example of discovery, we found that multistep cell lineage progression may be driven by either specific instances of high-feedback loops with sparse appearances, or generally enriched topologies in gene regulatory networks. We expect HiLoop’s usefulness to increase as experimental data of regulatory networks accumulate. Code is freely available for use or extension at https://github.com/BenNordick/HiLoop.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04405-z.

Stage-specific action of Runx1 and GATA3 controls silencing of PU.1 expression in mouse pro-T cells

PU.1 (encoded by Spi1), an ETS-family transcription factor with many hematopoietic roles, is highly expressed in the earliest intrathymic T cell progenitors but must be down-regulated during T lineage commitment. The transcription factors Runx1 and GATA3 have been implicated in this Spi1 repression, but the basis of the timing was unknown. We show that increasing Runx1 and/or GATA3 down-regulates Spi1 expression in pro-T cells, while deletion of these factors after Spi1 down-regulation reactivates its expression. Leveraging the stage specificities of repression and transcription factor binding revealed an unconventional but functional site in Spi1 intron 2. Acute Cas9-mediated deletion or disruption of the Runx and GATA motifs in this element reactivates silenced Spi1 expression in a pro-T cell line, substantially more than disruption of other candidate elements, and counteracts the repression of Spi1 in primary pro-T cells during commitment. Thus, Runx1 and GATA3 work stage specifically through an intronic silencing element in mouse Spi1 to control strength and maintenance of Spi1 repression during T lineage commitment.

New insights into TCR β-selection

T cell receptor (TCR) β-selection (herein referred to as β-selection) is a pivotal checkpoint in mammalian T cell development when immature CD4^-CD8^- T-cells (thymocytes) express pre-TCR following successful Tcrb gene rearrangement. At this stage, αβ T cell lineage commitment and allelic exclusion to restrict one β-chain per cell take place and thymocytes undergo a proliferative burst. β-selection is known to be crucially dependent upon synchronized Notch and pre-TCR signaling; however, other necessary inputs have been identified over the past decade, expanding our knowledge and understanding of the β-selection process. In this review, we discuss recent mechanistic findings that have enabled a more detailed decoding of the molecular dynamics of the β-selection checkpoint and have helped to elucidate its role in early T cell development.

SATB1-mediated chromatin landscape in T cells

The regulatory circuits that define developmental decisions of thymocytes are still incompletely resolved. SATB1 protein is predominantly expressed at the CD4⁺CD8⁺cell stage exerting its broad transcription regulation potential with both activatory and repressive roles. A series of post-translational modifications and the presence of potential SATB1 protein isoforms indicate the complexity of its regulatory potential. The most apparent mechanism of its involvement in gene expression regulation is via the orchestration of long-range chromatin loops between genes and their regulatory elements. Multiple SATB1 perturbations in mice uncovered a link to autoimmune diseases while clinical investigations on cancer research uncovered that SATB1 has a promoting role in several types of cancer and can be used as a prognostic biomarker. SATB1 is a multivalent tissue-specific factor with a broad and yet undetermined regulatory potential. Future investigations on this protein could further uncover T cell-specific regulatory pathways and link them to (patho)physiology.

Advances in Genomics Research of Blood Pressure Responses to Dietary Sodium and Potassium Intakes

More than half of US adults have hypertension by 40 years of age and a subsequent increase in atherosclerotic cardiovascular disease risk. Dietary sodium and potassium are intricately linked to the pathophysiology of hypertension. However, blood pressure responses to dietary sodium and potassium, phenomena known as salt and potassium sensitivity of blood pressure, respectively, are heterogenous and normally distributed in the general population. Like blood pressure, salt and potassium sensitivity are complex phenotypes, and previous research has shown that up to 75% of individuals experience a blood pressure change in response to such dietary minerals. Previous research has also implicated both high salt sensitivity and low salt sensitivity (or salt resistance) of blood pressure to an increased risk of hypertension and potentially atherosclerotic cardiovascular disease risk. Given the clinical challenges required to accurately measure the sodium and potassium response phenotypes, genomic characterization of these traits has become of interest for hypertension prevention initiatives on both the individual and population levels. Here, we review advances in human genomics research of blood pressure responses to dietary sodium and potassium by focusing on 3 main areas, including the phenotypic characterization of salt sensitivity and resistance, clinical challenges in diagnosing such phenotypes, and the genomic mechanisms that may help to explain salt and potassium sensitivity and resistance. Through this process, we hope to further underline the value of leveraging genomics and broader multiomics for characterizing the blood pressure response to sodium and potassium to improve precision in lifestyle approaches for primordial and primary atherosclerotic cardiovascular disease prevention.

The Route of Early T Cell Development: Crosstalk between Epigenetic and Transcription Factors

Hematopoietic multipotent progenitors seed the thymus and then follow consecutive developmental stages until the formation of mature T cells. During this process, phenotypic changes of T cells entail stage-specific transcriptional programs that underlie the dynamic progression towards mature lymphocytes. Lineage-specific transcription factors are key drivers of T cell specification and act in conjunction with epigenetic regulators that have also been elucidated as crucial players in the establishment of regulatory networks necessary for proper T cell development. In this review, we summarize the activity of transcription factors and epigenetic regulators that together orchestrate the intricacies of early T cell development with a focus on regulation of T cell lineage commitment.

Epigenetic Dynamics in the Function of T-Lineage Regulatory Factor Bcl11b

The transcription factor Bcl11b is critically required to support the development of diverse cell types, including T lymphocytes, type 2 innate lymphoid cells, neurons, craniofacial mesenchyme and keratinocytes. Although in T cell development its onset of expression is tightly linked to T-lymphoid lineage commitment, the Bcl11b protein in fact regulates substantially different sets of genes in different lymphocyte populations, playing strongly context-dependent roles. Somewhat unusually for lineage-defining transcription factors with site-specific DNA binding activity, much of the reported chromatin binding of Bcl11b appears to be indirect, or guided in large part by interactions with other transcription factors. We describe evidence suggesting that a further way in which Bcl11b exerts such distinct stage-dependent functions is by nucleating changes in regional suites of epigenetic modifications through recruitment of multiple families of chromatin-modifying enzyme complexes. Herein we explore what is - and what remains to be - understood of the roles of Bcl11b, its cofactors, and how it modifies the epigenetic state of the cell to enforce its diverse set of context-specific transcriptional and developmental programs.

Logic and lineage impacts on functional transcription factor deployment for T-cell fate commitment

Transcription factors are the major agents that read the regulatory sequence information in the genome to initiate changes in expression of specific genes, both in development and in physiological activation responses. Their actions depend on site-specific DNA binding and are largely guided by their individual DNA target sequence specificities. However, their action is far more conditional in a real developmental context than would be expected for simple reading of local genomic DNA sequence, which is common to all cells in the organism. They are constrained by slow-changing chromatin states and by interactions with other transcription factors, which affect their occupancy patterns of potential sites across the genome. These mechanisms lead to emergent discontinuities in function even for transcription factors with minimally changing expression. This is well revealed by diverse lineages of blood cells developing throughout life from hematopoietic stem cells, which use overlapping combinations of transcription factors to drive strongly divergent gene regulation programs. Here, using development of T lymphocytes from hematopoietic multipotent progenitor cells as a focus, recent evidence is reviewed on how binding specificity and dynamics, transcription factor cooperativity, and chromatin state changes impact the effective regulatory functions of key transcription factors including PU.1, Runx1, Notch-RBPJ, and Bcl11b.

A natural killer's hike through epigenetic landscapes

The transcription factor BCL11B guides NK cells along their differentiation trajectory (see the related Research Article by Holmes et al.).

How transcription factors drive choice of the T cell fate

Recent evidence has elucidated how multipotent blood progenitors transform their identities in the thymus and undergo commitment to become T cells. Together with environmental signals, a core group of transcription factors have essential roles in this process by directly activating and repressing specific genes. Many of these transcription factors also function in later T cell development, but control different genes. Here, we review how these transcription factors work to change the activities of specific genomic loci during early intrathymic development to establish T cell lineage identity. We introduce the key regulators and highlight newly emergent insights into the rules that govern their actions. Whole-genome deep sequencing-based analysis has revealed unexpectedly rich relationships between inherited epigenetic states, transcription factor-DNA binding affinity thresholds and influences of given transcription factors on the activities of other factors in the same cells. Together, these mechanisms determine T cell identity and make the lineage choice irreversible.

RORα is a critical checkpoint for T cell and ILC2 commitment in the embryonic thymus

Type 2 innate lymphoid cells (ILC2) contribute to immune homeostasis, protective immunity and tissue repair. Here we demonstrate that functional ILC2 cells can arise in the embryonic thymus from shared T cell precursors, preceding the emergence of CD4⁺CD8⁺ (double-positive) T cells. Thymic ILC2 cells migrated to mucosal tissues, with colonization of the intestinal lamina propria. Expression of the transcription factor RORα repressed T cell development while promoting ILC2 development in the thymus. From RNA-seq, assay for transposase-accessible chromatin sequencing (ATAC-seq) and chromatin immunoprecipitation followed by sequencing (ChIP-seq) data, we propose a revised transcriptional circuit to explain the co-development of T cells and ILC2 cells from common progenitors in the thymus. When Notch signaling is present, BCL11B dampens Nfil3 and Id2 expression, permitting E protein-directed T cell commitment. However, concomitant expression of RORα overrides the repression of Nfil3 and Id2 repression, allowing ID2 to repress E proteins and promote ILC2 differentiation. Thus, we demonstrate that RORα expression represents a critical checkpoint at the bifurcation of the T cell and ILC2 lineages in the embryonic thymus.

Multi-scale Dynamical Modeling of T Cell Development from an Early Thymic Progenitor State to Lineage Commitment

Intrathymic development of committed progenitor (pro)-T cells from multipotent hematopoietic precursors offers an opportunity to dissect the molecular circuitry establishing cell identity in response to environmental signals. This transition encompasses programmed shutoff of stem/progenitor genes, upregulation of T cell specification genes, proliferation, and ultimately commitment. To explain these features in light of reported cis-acting chromatin effects and experimental kinetic data, we develop a three-level dynamic model of commitment based upon regulation of the commitment-linked gene Bcl11b. The levels are (1) a core gene regulatory network (GRN) architecture from transcription factor (TF) perturbation data, (2) a stochastically controlled chromatin-state gate, and (3) a single-cell proliferation model validated by experimental clonal growth and commitment kinetic assays. Using RNA fluorescence in situ hybridization (FISH) measurements of genes encoding key TFs and measured bulk population dynamics, this single-cell model predicts state-switching kinetics validated by measured clonal proliferation and commitment times. The resulting multi-scale model provides a mechanistic framework for dissecting commitment dynamics.

Bcl11b/Ctip2 in Skin, Tooth, and Craniofacial System

Ctip2/Bcl11b is a zinc finger transcription factor with dual action (repression/activation) that couples epigenetic regulation to gene transcription during the development of various tissues. It is involved in a variety of physiological responses under healthy and pathological conditions. Its role and mechanisms of action are best characterized in the immune and nervous systems. Furthermore, its implication in the development and homeostasis of other various tissues has also been reported. In the present review, we describe its role in skin development, adipogenesis, tooth formation and cranial suture ossification. Experimental data from several studies demonstrate the involvement of Bcl11b in the control of the balance between cell proliferation and differentiation during organ formation and repair, and more specifically in the context of stem cell self-renewal and fate determination. The impact of mutations in the coding sequences of Bcl11b on the development of diseases such as craniosynostosis is also presented. Finally, we discuss genome-wide association studies that suggest a potential influence of single nucleotide polymorphisms found in the 3’ regulatory region of Bcl11b on the homeostasis of the cardiovascular system.

Deep learning of immune cell differentiation

Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular atlas of chromatin accessibility across 81 immune cell types, we asked if a convolutional neural network (CNN) could learn to infer cell type-specific chromatin accessibility solely from regulatory DNA sequences. With a tailored architecture and an ensemble approach to CNN parameter interpretation, we show that our trained network ("AI-TAC") does so by rediscovering ab initio the binding motifs for known regulators and some unknown ones. Motifs whose importance is learned virtually as functionally important overlap strikingly well with positions determined by chromatin immunoprecipitation for several TFs. AI-TAC establishes a hierarchy of TFs and their interactions that drives lineage specification and also identifies stage-specific interactions, like Pax5/Ebf1 vs. Pax5/Prdm1, or the role of different NF-κB dimers in different cell types. AI-TAC assigns Spi1/Cebp and Pax5/Ebf1 as the drivers necessary for myeloid and B lineage fates, respectively, but no factors seemed as dominantly required for T cell differentiation, which may represent a fall-back pathway. Mouse-trained AI-TAC can parse human DNA, revealing a strikingly similar ranking of influential TFs and providing additional support that AI-TAC is a generalizable regulatory sequence decoder. Thus, deep learning can reveal the regulatory syntax predictive of the full differentiative complexity of the immune system.

SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks

A common approach to benchmarking of single-cell transcriptomics tools is to generate synthetic datasets that statistically resemble experimental data. However, most existing single-cell simulators do not incorporate transcription factor-gene regulatory interactions that underlie expression dynamics. Here, we present SERGIO, a simulator of single-cell gene expression data that models the stochastic nature of transcription as well as regulation of genes by multiple transcription factors according to a user-provided gene regulatory network. SERGIO can simulate any number of cell types in steady state or cells differentiating to multiple fates. We show that datasets generated by SERGIO are statistically comparable to experimental data generated by Illumina HiSeq2000, Drop-seq, Illumina 10X chromium, and Smart-seq. We use SERGIO to benchmark several single-cell analysis tools, including GRN inference methods, and identify Tcf7, Gata3, and Bcl11b as key drivers of T cell differentiation by performing in silico knockout experiments. SERGIO is freely available for download here: https://github.com/PayamDiba/SERGIO.

Occupancy maps of 208 chromatin-associated proteins in one human cell type

Transcription factors are DNA-binding proteins that have key roles in gene regulation^1,2. Genome-wide occupancy maps of transcriptional regulators are important for understanding gene regulation and its effects on diverse biological processes^3–6. However, only a minority of the more than 1,600 transcription factors encoded in the human genome has been assayed. Here we present, as part of the ENCODE (Encyclopedia of DNA Elements) project, data and analyses from chromatin immunoprecipitation followed by high-throughput sequencing (ChIP–seq) experiments using the human HepG2 cell line for 208 chromatin-associated proteins (CAPs). These comprise 171 transcription factors and 37 transcriptional cofactors and chromatin regulator proteins, and represent nearly one-quarter of CAPs expressed in HepG2 cells. The binding profiles of these CAPs form major groups associated predominantly with promoters or enhancers, or with both. We confirm and expand the current catalogue of DNA sequence motifs for transcription factors, and describe motifs that correspond to other transcription factors that are co-enriched with the primary ChIP target. For example, FOX family motifs are enriched in ChIP–seq peaks of 37 other CAPs. We show that motif content and occupancy patterns can distinguish between promoters and enhancers. This catalogue reveals high-occupancy target regions at which many CAPs associate, although each contains motifs for only a minority of the numerous associated transcription factors. These analyses provide a more complete overview of the gene regulatory networks that define this cell type, and demonstrate the usefulness of the large-scale production efforts of the ENCODE Consortium.

ChIP–seq and CETCh–seq data are used to analyse binding maps for 208 transcription factors and other chromatin-associated proteins in a single human cell type, providing a comprehensive catalogue of the transcription factor landscape and gene regulatory networks in these cells.

ZEB1 Represses Neural Differentiation and Cooperates with CTBP2 to Dynamically Regulate Cell Migration during Neocortex Development

Zinc-finger E-box binding homeobox 1 (Zeb1) is a key regulator of epithelial-mesenchymal transition and cancer metastasis. Mutation of ZEB1 is associated with human diseases and defective brain development. Here we show that downregulation of Zeb1 expression in embryonic cortical neural progenitor cells (NPCs) is necessary for proper neuronal differentiation and migration. Overexpression of Zeb1 during neuronal differentiation, when its expression normally declines, blocks NPC lineage progression and disrupts multipolar-to-bipolar transition of differentiating neurons, leading to severe migration defects and subcortical heterotopia bands at postnatal stages. ZEB1 regulates a cohort of genes involved in cell differentiation and migration, including Neurod1 and Pard6b. The interaction between ZEB1 and CTBP2 in the embryonic cerebral cortex is required for ZEB1 to elicit its effect on the multipolar-to-bipolar transition, but not its suppression of Neurod1. These findings provide insights into understanding the complexity of transcriptional regulation during neuronal differentiation.

Cell type-specific actions of Bcl11b in early T-lineage and group 2 innate lymphoid cells

Bcl11b binds to distinctive genomic regions with different partners and regulates completely different target genes in pro-T and ILC2 cells. Despite these divergences in Bcl11b function, a shared enhancer supports initial Bcl11b locus opening in both pro-T and ILC2 lineages.

The zinc finger transcription factor, Bcl11b, is expressed in T cells and group 2 innate lymphoid cells (ILC2s) among hematopoietic cells. In early T-lineage cells, Bcl11b directly binds and represses the gene encoding the E protein antagonist, Id2, preventing pro-T cells from adopting innate-like fates. In contrast, ILC2s co-express both Bcl11b and Id2. To address this contradiction, we have directly compared Bcl11b action mechanisms in pro-T cells and ILC2s. We found that Bcl11b binding to regions across the genome shows distinct cell type–specific motif preferences. Bcl11b occupies functionally different sites in lineage-specific patterns and controls totally different sets of target genes in these cell types. In addition, Bcl11b bears cell type–specific post-translational modifications and organizes different cell type–specific protein complexes. However, both cell types use the same distal enhancer region to control timing of Bcl11b activation. Therefore, although pro-T cells and ILC2s both need Bcl11b for optimal development and function, Bcl11b works substantially differently in these two cell types.

Functional definition of a transcription factor hierarchy regulating T cell lineage commitment

T cell factor 1 (Tcf1) is the first T cell-specific protein induced by Notch signaling in the thymus, leading to the activation of two major target genes, Gata3 and Bcl11b. Tcf1 deficiency results in partial arrests in T cell development, high apoptosis, and increased development of B and myeloid cells. Phenotypically, seemingly fully T cell-committed thymocytes with Tcf1 deficiency have promiscuous gene expression and an altered epigenetic profile and can dedifferentiate into more immature thymocytes and non-T cells. Restoring Bcl11b expression in Tcf1-deficient cells rescues T cell development but does not strongly suppress the development of non-T cells; in contrast, expressing Gata3 suppresses their development but does not rescue T cell development. Thus, T cell development is controlled by a minimal transcription factor network involving Notch signaling, Tcf1, and the subsequent division of labor between Bcl11b and Gata3, thereby ensuring a properly regulated T cell gene expression program.

Advancing Stem Cell Research through Multimodal Single-Cell Analysis

Technological advances play a key role in furthering our understanding of stem cell biology, and advancing the prospects of regenerative therapies. Highly parallelized methods, developed in the last decade, can profile DNA, RNA, or proteins in thousands of cells and even capture data across two or more modalities (multiomics). This allows unbiased and precise definition of molecular cell states, thus allowing classification of cell types, tracking of differentiation trajectories, and discovery of underlying mechanisms. Despite being based on destructive techniques, novel experimental and bioinformatic approaches enable embedding and extraction of temporal information, which is essential for deconvolution of complex data and establishing cause and effect relationships. Here, we provide an overview of recent studies pertinent to stem cell biology, followed by an outlook on how further advances in single-cell molecular profiling and computational analysis have the potential to shape the future of both basic and translational research.

Characterization of a novel lncRNA (SETD3-OT) in turbot (Scophthalmus maximus L.)

LncRNAs have been demonstrated to play pivotal roles in virous biological processes, especially the gene expression regulation, including transcriptional regulation, posttranscriptional control and epigenetic processes. However, most of the current studies of lncRNAs are still limited in mammalian species, the investigations of functional roles of lncRNAs in teleost species are still lacking. In current study, we identified a novel lncRNA (SETD3-OT) in turbot, with 2,504 bp full-length obtained by 5' and 3' RACE, located in turbot chromosome 17, ranged from 20,933,835 to 20,936,302 bp. In addition, 8 neighboring genes of SETD3-OT were identified within 100 kbp in genome location. From the annotation of the neighboring adjacent genes, SETD3-OT might involve in regulation of cell apoptosis and cycle, the immune cell development, and the immune response against infection, and its expression pattern is similar to majority of the neighboring genes following Aeromonas salmonicida challenge. Intriguingly, SETD3-OT showed significant high expression levels in mucosal surfaces (intestine, gill and skin), and was dramatically down-regulated in these mucosal tissues following Vibrio anguillarum challenge, especially in gill and skin. In addition, SETD3-OT was distributed in nucleus, it might regulate the neighboring genes in cis or in trans. Taken together, our results provide insights for lncRNA in fish innate immunity, further studies should be conduct to explore the detailed molecular mechanism of the gene regulation between SETD3-OT and its neighboring genes.

Dynamic control of the T-cell specification gene regulatory network

Specification of multipotent blood precursor cells in postnatal mice to become committed T-cell precursors involves a gene regulatory network of several interacting but functionally distinct modules. Many links of this network have been defined by perturbation tests and by functional genomics. However, using the network model to predict real-life kinetics of the commitment process is still difficult, partly due to the tenacity of repressive chromatin states, and to the ability of transcription factors to affect each other's binding site choices through competitive recruitment to alternative sites ("coregulator theft"). To predict kinetics, future models will need to incorporate mechanistic information about chromatin state change dynamics and more sophisticated understanding of the proteomics and cooperative DNA site choices of transcription factor complexes.

Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps

Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq regions with scRNA-seq genes that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of heterogeneous data.

Programming for T-lymphocyte fates: modularity and mechanisms

In this review, Rothenburg discusses the gene regulatory network and chromatin-based kinetic constraints that determine activities of transcription factors in the primary establishment of T-cell identity.

T-cell development in mammals is a model for lineage choice and differentiation from multipotent stem cells. Although T-cell fate choice is promoted by signaling in the thymus through one dominant pathway, the Notch pathway, it entails a complex set of gene regulatory network and chromatin state changes even before the cells begin to express their signature feature, the clonal-specific T-cell receptors (TCRs) for antigen. This review distinguishes three developmental modules for T-cell development, which correspond to cell type specification, TCR expression and selection, and the assignment of cells to different effector types. The first is based on transcriptional regulatory network events, the second is dominated by somatic gene rearrangement and mutation and cell selection, and the third corresponds to establishing a poised state of latent regulator priming through an unknown mechanism. Interestingly, in different lineages, the third module can be deployed at variable times relative to the completion of the first two modules. This review focuses on the gene regulatory network and chromatin-based kinetic constraints that determine activities of transcription factors TCF1, GATA3, PU.1, Bcl11b, Runx1, and E proteins in the primary establishment of T-cell identity.

Single-Cell Analysis Reveals Regulatory Gene Expression Dynamics Leading to Lineage Commitment in Early T Cell Development

Intrathymic T cell development converts multipotent precursors to committed pro-T cells, silencing progenitor genes while inducing T cell genes, but the underlying steps have remained obscure. Single-cell profiling was used to define the order of regulatory changes, employing single-cell RNA sequencing (scRNA-seq) for full-transcriptome analysis, plus sequential multiplexed single-molecule fluorescent in situ hybridization (seqFISH) to quantitate functionally important transcripts in intrathymic precursors. Single-cell cloning verified high T cell precursor frequency among the immunophenotypically defined "early T cell precursor" (ETP) population; a discrete committed granulocyte precursor subset was also distinguished. We established regulatory phenotypes of sequential ETP subsets, confirmed initial co-expression of progenitor with T cell specification genes, defined stage-specific relationships between cell cycle and differentiation, and generated a pseudotime model from ETP to T lineage commitment, supported by RNA velocity and transcription factor perturbations. This model was validated by developmental kinetics of ETP subsets at population and clonal levels. The results imply that multilineage priming is integral to T cell specification.

Molecular mechanisms of lineage decisions in metabolite-specific T cells

Mucosal-associated invariant T cells (MAIT cells) recognize the microbial metabolite 5-(2-oxopropylideneamino)-6-D-ribitylaminouracil (5-OP-RU) presented by the MHC class Ib molecule, MR1. MAIT cells acquire effector functions during thymic development, but the mechanisms involved are unclear. Here we used single-cell RNA-sequencing to characterize the developmental path of 5-OP-RU-specific thymocytes. In addition to the known MAIT1 and MAIT17 effector subsets selected on bone-marrow-derived hematopoietic cells, we identified 5-OP-RU-specific thymocytes that were selected on thymic epithelial cells and differentiated into CD44^- naive T cells. MAIT cell positive selection required signaling through the adapter, SAP, that controlled the expression of the transcription factor, ZBTB16. Pseudotemporal ordering of single cells revealed transcriptional trajectories of 5-OP-RU-specific thymocytes selected on either thymic epithelial cells or hematopoietic cells. The resulting model illustrates T cell lineage decisions.

Causal Gene Regulatory Network Modeling and Genomics: Second-Generation Challenges

Gene regulatory network modeling has played a major role in advancing the understanding of developmental systems, by crystallizing structures of relevant extant information, by formally posing hypothetical functional relationships between network elements, and by offering clear predictive tests to improve understanding of the mechanisms driving developmental progression. Both ordinary differential equation (ODE)-based and Boolean models have also been highly successful in explaining dynamics within subcircuits of more complex processes. In a very small number of cases, gene regulatory network models of much more global scope have been proposed that successfully predict the dynamics of the processes establishing most of an embryonic body plan. Can such successes be expanded to very different developmental systems, including post-embryonic mammalian systems? This perspective discusses several problems that must be solved in more quantitative and predictive theoretical terms, to make this possible. These problems include: the effects of cellular history on chromatin state and how these affect gene accessibility; the dose dependence of activities of many transcription factors (a problem for Boolean models); stochasticity of some transcriptional outputs (a problem for simple ODE models); response timing delays due to epigenetic remodeling requirements; functionally different kinds of repression; and the regulatory syntax that governs responses of genes with multiple enhancers.

An enriched network motif family regulates multistep cell fate transitions with restricted reversibility

Multistep cell fate transitions with stepwise changes of transcriptional profiles are common to many developmental, regenerative and pathological processes. The multiple intermediate cell lineage states can serve as differentiation checkpoints or branching points for channeling cells to more than one lineages. However, mechanisms underlying these transitions remain elusive. Here, we explored gene regulatory circuits that can generate multiple intermediate cellular states with stepwise modulations of transcription factors. With unbiased searching in the network topology space, we found a motif family containing a large set of networks can give rise to four attractors with the stepwise regulations of transcription factors, which limit the reversibility of three consecutive steps of the lineage transition. We found that there is an enrichment of these motifs in a transcriptional network controlling the early T cell development, and a mathematical model based on this network recapitulates multistep transitions in the early T cell lineage commitment. By calculating the energy landscape and minimum action paths for the T cell model, we quantified the stochastic dynamics of the critical factors in response to the differentiation signal with fluctuations. These results are in good agreement with experimental observations and they suggest the stable characteristics of the intermediate states in the T cell differentiation. These dynamical features may help to direct the cells to correct lineages during development. Our findings provide general design principles for multistep cell linage transitions and new insights into the early T cell development. The network motifs containing a large family of topologies can be useful for analyzing diverse biological systems with multistep transitions.

The functions of cells are dynamically controlled in many biological processes including development, regeneration and disease progression. Cell fate transition, or the switch of cellular functions, often involves multiple steps. The intermediate stages of the transition provide the biological systems with the opportunities to regulate the transitions in a precise manner. These transitions are controlled by key regulatory genes of which the expression shows stepwise patterns, but how the interactions of these genes can determine the multistep processes was unclear. Here, we present a comprehensive analysis on the design principles of gene circuits that govern multistep cell fate transition. We found a large network family with common structural features that can generate systems with the ability to control three consecutive steps of the transition. We found that this type of networks is enriched in a gene circuit controlling the development of T lymphocyte, a crucial type of immune cells. We performed mathematical modeling using this gene circuit and we recapitulated the stepwise and irreversible loss of stem cell properties of the developing T lymphocytes. Our findings can be useful to analyze a wide range of gene regulatory networks controlling multistep cell fate transitions.