Genetic regulatory networks programming hematopoietic stem cells and erythroid lineage specification

Exploring the contribution of Zfp521/ZNF521 on primary hematopoietic stem/progenitor cells and leukemia progression

Hematopoietic stem cells (HSCs) drive cellular turnover in the hematopoietic system by balancing self-renewal and differentiation. In the adult bone marrow (BM), these cells are regulated by a complex cellular microenvironment known as "niche," which involves dynamic interactions between diverse cellular and non-cellular elements. During blood cell maturation, lineage branching is guided by clusters of genes that interact or counteract each other, forming complex networks of lineage-specific transcription factors. Disruptions in these networks can lead to obstacles in differentiation, lineage reprogramming, and ultimately malignant transformation, including acute myeloid leukemia (AML). Zinc Finger Protein 521 (Znf521/Zfp521), a conserved transcription factor enriched in HSCs in both human and murine hematopoiesis, plays a pivotal role in regulating HSC self-renewal and differentiation. Its enforced expression preserves progenitor cell activity, while inhibition promotes differentiation toward the lymphoid and myeloid lineages. Transcriptomic analysis of human AML patient samples has revealed upregulation of ZNF521 in AMLs with the t(9;11) fusion gene MLL-AF9. In vitro studies have shown that ZNF521 collaborates with MLL-AF9 to enhance the growth of transformed leukemic cells, increase colony formation, and activate MLL target genes. Conversely, inhibition of ZNF521 using short-hairpin RNA (shRNA) results in decreased leukemia proliferation, reduced colony formation, and induction of cell cycle arrest in MLL-rearranged AML cell lines. In vivo experiments have demonstrated that mZFP521-deficient mice transduced with MLL-AF9 experience a delay in leukemia development. This review provides an overview of the regulatory network involving ZNF521, which plays a crucial role in controlling both HSC self-renewal and differentiation pathways. Furthermore, we examine the impact of ZNF521 on the leukemic phenotype and consider it a potential marker for MLL-AF9+ AML.

Deciphering Transcriptomic Variations in Hematopoietic Lineages: HSCs, EBs, and MKs

In the realm of hematopoiesis, hematopoietic stem cells (HSCs) serve as pivotal entities responsible for generating various blood cell types, initiating both the myeloid and lymphoid branches within the hematopoietic lineage. This intricate process is marked by genetic variations that underscore the crucial role of genes in regulating cellular functions and interactions. Recognizing the significance of genetic factors in this context, this article delves into a genetic perspective, aiming to unravel the biological factors that govern the transition from one cell's fate to another within the hematopoietic system. To gain deeper insights into the genetic traits of three distinct blood cell types-HSCs, erythroblasts (EBs), and megakaryocytes (MKs)-we conducted a comprehensive transcriptomic analysis. Leveraging diverse hematopoietic cell datasets from healthy individuals, sourced from The BLUEPRINT consortium, our investigation targeted the identification of genetic variants responsible for changes in gene expression levels and epigenetic modifications across the entire human genome in each of these cell types. The total number of normalized expressed transcripts includes 14,233 novel trinity lncRNAs, 13,749 mRNAs, and 3092 lncRNAs. This scrutiny revealed a total of 31,074 transcripts, with a notable revelation that 14,233 of them were previously unidentified or novel lncRNAs, highlighting a substantial reservoir of genetic information yet to be explored. Examining their expression across distinct lineages further unveiled 2845 differentially expressed (DE) mRNAs and 354 DE long noncoding RNAs (lncRNAs) notably enriched among the three distinct blood cell types: HSCs, EBs, and MKs. Our investigation extended beyond mRNA to focus on the dynamic expression of lncRNAs, revealing a well-defined pattern that played a significant role in regulating differentiation and cell-fate specification. This coordination of lncRNA dynamics extended to aberrations in both mRNA and lncRNA transcriptomes within HSCs, EBs, and MKs. We specifically characterized lncRNAs with preferential expression in HSCs, as well as in various downstream differentiated lineage progenitors of EBs and MKs, providing a comprehensive perspective on lncRNAs in human hematopoietic cells. Notably, the expression of lncRNAs exhibited substantial cell-to-cell variation, a phenomenon discernible only through single-cell analysis. The comparative analysis undertaken in this study provides valuable insights into the distinctive genetic signatures guiding the differentiation of these crucial hematopoietic cell types.

Neural-circuit basis of song preference learning in fruit flies

As observed in human language learning and song learning in birds, the fruit fly Drosophila melanogaster changes its auditory behaviors according to prior sound experiences. This phenomenon, known as song preference learning in flies, requires GABAergic input to pC1 neurons in the brain, with these neurons playing a key role in mating behavior. The neural circuit basis of this GABAergic input, however, is not known. Here, we find that GABAergic neurons expressing the sex-determination gene doublesex are necessary for song preference learning. In the brain, only four doublesex-expressing GABAergic neurons exist per hemibrain, identified as pCd-2 neurons. pCd-2 neurons directly, and in many cases mutually, connect with pC1 neurons, suggesting the existence of reciprocal circuits between them. Moreover, GABAergic and dopaminergic inputs to doublesex-expressing GABAergic neurons are necessary for song preference learning. Together, this study provides a neural circuit model that underlies experience-dependent auditory plasticity at a single-cell resolution.

Towards an integrated understanding of inflammatory pathway influence on hematopoietic stem and progenitor cell differentiation

Recent research highlights that inflammatory signaling pathways such as pattern recognition receptor (PRR) signaling and inflammatory cytokine signaling play an important role in both on-demand hematopoiesis as well as steady-state hematopoiesis. Knockout studies have demonstrated the necessity of several distinct pathways in these processes, but often lack information about the contribution of specific cell types to the phenotypes in question. Transplantation studies have increased the resolution to the level of specific cell types by testing the necessity of inflammatory pathways specifically in donor hematopoietic stem and progenitor cells (HSPCs) or in recipient niche cells. Here, we argue that for an integrated understanding of how these processes occur in vivo and to inform the development of therapies that modulate hematopoietic responses, we need studies that knockout inflammatory signaling receptors in a cell-specific manner and compare the phenotypes caused by knockout in individual niche cells versus HSPCs.

A neural network-based model framework for cell-fate decisions and development

Gene regulatory networks (GRNs) fulfill the essential function of maintaining the stability of cellular differentiation states by sustaining lineage-specific gene expression, while driving the progression of development. However, accounting for the relative stability of intermediate differentiation stages and their divergent trajectories remains a major challenge for models of developmental biology. Here, we develop an empirical data-based associative GRN model (AGRN) in which regulatory networks store multilineage stage-specific gene expression profiles as associative memory patterns. These networks are capable of responding to multiple instructive signals and, depending on signal timing and identity, can dynamically drive the differentiation of multipotent cells toward different cell state attractors. The AGRN dynamics can thus generate diverse lineage-committed cell populations in a robust yet flexible manner, providing an attractor-based explanation for signal-driven cell fate decisions during differentiation and offering a readily generalizable modelling tool that can be applied to a wide variety of cell specification systems.

Cell fate decision in erythropoiesis: Insights from multiomics studies

Every second, the body produces 2 million red blood cells through a process called erythropoiesis. Erythropoiesis is hierarchical in that it results from a series of cell fate decisions whereby hematopoietic stem cells progress toward the erythroid lineage. Single-cell transcriptomic and proteomic approaches have revolutionized the way we understand erythropoiesis, revealing it to be a gradual process that underlies a progressive restriction of fate potential driven by quantitative changes in lineage-specifying transcription factors. Despite these major advances, we still know very little about what cell fate decision entails at the molecular level. Novel approaches that simultaneously measure additional properties in single cells, including chromatin accessibility, transcription factor binding, and/or cell surface proteins are being developed at a fast pace, providing the means to exciting new advances in the near future. In this review, we briefly summarize the main findings obtained from single-cell studies of erythropoiesis, highlight outstanding questions, and suggest recent technological advances to address them.

Evolution of hierarchy and irreversibility in theoretical cell differentiation model

The process of cell differentiation in multicellular organisms is characterized by hierarchy and irreversibility in many cases. However, the conditions and selection pressures that give rise to these characteristics remain poorly understood. By using a mathematical model, here we show that the network of differentiation potency (differentiation diagram) becomes necessarily hierarchical and irreversible by increasing the number of terminally differentiated states under certain conditions. The mechanisms generating these characteristics are clarified using geometry in the cell state space. The results demonstrate that the hierarchical organization and irreversibility can manifest independently of direct selection pressures associated with these characteristics, instead they appear to evolve as byproducts of selective forces favoring a diversity of differentiated cell types. The study also provides a new perspective on the structure of gene regulatory networks that produce hierarchical and irreversible differentiation diagrams. These results indicate some constraints on cell differentiation, which are expected to provide a starting point for theoretical discussion of the implicit limits and directions of evolution in multicellular organisms.

The Evolution of Biomineralization through the Co-Option of Organic Scaffold Forming Networks

Biomineralization is the process in which organisms use minerals to generate hard structures like teeth, skeletons and shells. Biomineralization is proposed to have evolved independently in different phyla through the co-option of pre-existing developmental programs. Comparing the gene regulatory networks (GRNs) that drive biomineralization in different species could illuminate the molecular evolution of biomineralization. Skeletogenesis in the sea urchin embryo was extensively studied and the underlying GRN shows high conservation within echinoderms, larval and adult skeletogenesis. The organic scaffold in which the calcite skeletal elements form in echinoderms is a tubular compartment generated by the syncytial skeletogenic cells. This is strictly different than the organic cartilaginous scaffold that vertebrates mineralize with hydroxyapatite to make their bones. Here I compare the GRNs that drive biomineralization and tubulogenesis in echinoderms and in vertebrates. The GRN that drives skeletogenesis in the sea urchin embryo shows little similarity to the GRN that drives bone formation and high resemblance to the GRN that drives vertebrates’ vascular tubulogenesis. On the other hand, vertebrates’ bone-GRNs show high similarity to the GRNs that operate in the cells that generate the cartilage-like tissues of basal chordate and invertebrates that do not produce mineralized tissue. These comparisons suggest that biomineralization in deuterostomes evolved through the phylum specific co-option of GRNs that control distinct organic scaffolds to mineralization.

IκBα is required for full transcriptional induction of some NFκB-regulated genes in response to TNF in MCF-7 cells

Inflammatory stimuli triggers the degradation of three inhibitory κB (IκB) proteins, allowing for nuclear translocation of nuclear factor-κB (NFκB) for transcriptional induction of its target genes. Of these three, IκBα is a well-known negative feedback regulator that limits the duration of NFκB activity. We sought to determine whether IκBα's role in enabling or limiting NFκB activation is important for tumor necrosis factor (TNF)-induced gene expression in human breast cancer cells (MCF-7). Contrary to our expectations, many more TNF-response genes showed reduced induction than enhanced induction in IκBα knockdown cells. Mathematical modeling was used to investigate the underlying mechanism. We found that the reduced activation of some NFκB target genes in IκBα-deficient cells could be explained by the incoherent feedforward loop (IFFL) model. In addition, for a subset of genes, prolonged NFκB activity due to loss of negative feedback control did not prolong their transient activation; this implied a multi-state transcription cycle control of gene induction. Genes encoding key inflammation-related transcription factors, such as JUNB and KLF10, were found to be best represented by a model that contained both the IFFL and the transcription cycle motif. Our analysis sheds light on the regulatory strategies that safeguard inflammatory gene expression from overproduction and repositions the function of IκBα not only as a negative feedback regulator of NFκB but also as an enabler of NFκB-regulated stimulus-responsive inflammatory gene expression. This study indicates the complex involvement of IκBα in the inflammatory response to TNF that is induced by radiation therapy in breast cancer.

Oncogene-Induced Reprogramming in Acute Lymphoblastic Leukemia: Towards Targeted Therapy of Leukemia-Initiating Cells

Simple Summary

Acute lymphoblastic leukemia is a heterogeneous disease characterized by a diversity of genetic alterations, following a sophisticated and controversial organization. In this review, we present and discuss the concepts exploring the cellular, molecular and functional heterogeneity of leukemic cells. We also review the emerging evidence indicating that cell plasticity and oncogene-induced reprogramming should be considered at the biological and clinical levels as critical mechanisms for identifying and targeting leukemia-initiating cells.

Abstract

Our understanding of the hierarchical structure of acute leukemia has yet to be fully translated into therapeutic approaches. Indeed, chemotherapy still has to take into account the possibility that leukemia-initiating cells may have a distinct chemosensitivity profile compared to the bulk of the tumor, and therefore are spared by the current treatment, causing the relapse of the disease. Therefore, the identification of the cell-of-origin of leukemia remains a longstanding question and an exciting challenge in cancer research of the last few decades. With a particular focus on acute lymphoblastic leukemia, we present in this review the previous and current concepts exploring the phenotypic, genetic and functional heterogeneity in patients. We also discuss the benefits of using engineered mouse models to explore the early steps of leukemia development and to identify the biological mechanisms driving the emergence of leukemia-initiating cells. Finally, we describe the major prospects for the discovery of new therapeutic strategies that specifically target their aberrant stem cell-like functions.

Exact Probability Landscapes of Stochastic Phenotype Switching in Feed-Forward Loops: Phase Diagrams of Multimodality

Feed-forward loops (FFLs) are among the most ubiquitously found motifs of reaction networks in nature. However, little is known about their stochastic behavior and the variety of network phenotypes they can exhibit. In this study, we provide full characterizations of the properties of stochastic multimodality of FFLs, and how switching between different network phenotypes are controlled. We have computed the exact steady-state probability landscapes of all eight types of coherent and incoherent FFLs using the finite-butter Accurate Chemical Master Equation (ACME) algorithm, and quantified the exact topological features of their high-dimensional probability landscapes using persistent homology. Through analysis of the degree of multimodality for each of a set of 10,812 probability landscapes, where each landscape resides over 10⁵–10⁶ microstates, we have constructed comprehensive phase diagrams of all relevant behavior of FFL multimodality over broad ranges of input and regulation intensities, as well as different regimes of promoter binding dynamics. In addition, we have quantified the topological sensitivity of the multimodality of the landscapes to regulation intensities. Our results show that with slow binding and unbinding dynamics of transcription factor to promoter, FFLs exhibit strong stochastic behavior that is very different from what would be inferred from deterministic models. In addition, input intensity play major roles in the phenotypes of FFLs: At weak input intensity, FFL exhibit monomodality, but strong input intensity may result in up to 6 stable phenotypes. Furthermore, we found that gene duplication can enlarge stable regions of specific multimodalities and enrich the phenotypic diversity of FFL networks, providing means for cells toward better adaptation to changing environment. Our results are directly applicable to analysis of behavior of FFLs in biological processes such as stem cell differentiation and for design of synthetic networks when certain phenotypic behavior is desired.

Proteomic/transcriptomic analysis of erythropoiesis

PURPOSE OF REVIEW

Erythropoiesis is a hierarchical process by which hematopoietic stem cells give rise to red blood cells through gradual cell fate restriction and maturation. Deciphering this process requires the establishment of dynamic gene regulatory networks (GRNs) that predict the response of hematopoietic cells to signals from the environment. Although GRNs have historically been derived from transcriptomic data, recent proteomic studies have revealed a major role for posttranscriptional mechanisms in regulating gene expression during erythropoiesis. These new findings highlight the need to integrate proteomic data into GRNs for a refined understanding of erythropoiesis.

RECENT FINDINGS

Here, we review recent proteomic studies that have furthered our understanding of erythropoiesis with a focus on quantitative mass spectrometry approaches to measure the abundance of transcription factors and cofactors during differentiation. Furthermore, we highlight challenges that remain in integrating transcriptomic, proteomic, and other omics data into a predictive model of erythropoiesis, and discuss the future prospect of single-cell proteomics.

SUMMARY

Recent proteomic studies have considerably expanded our knowledge of erythropoiesis beyond the traditional transcriptomic-centric perspective. These findings have both opened up new avenues of research to increase our understanding of erythroid differentiation, while at the same time presenting new challenges in integrating multiple layers of information into a comprehensive gene regulatory model.

Profiling the genetic determinants of chromatin accessibility with scalable single-cell CRISPR screens

CRISPR screens have been used to connect genetic perturbations with changes in gene expression and phenotypes. Here we describe a CRISPR-based, single-cell combinatorial indexing assay for transposase-accessible chromatin (CRISPR-sciATAC) to link genetic perturbations to genome-wide chromatin accessibility in a large number of cells. In human myelogenous leukemia cells, we apply CRISPR-sciATAC to target 105 chromatin-related genes, generating chromatin accessibility data for ~30,000 single cells. We correlate the loss of specific chromatin remodelers with changes in accessibility globally and at the binding sites of individual transcription factors (TFs). For example, we show that loss of the H3K27 methyltransferase EZH2 increases accessibility at heterochromatic regions involved in embryonic development and triggers expression of genes in the HOXA and HOXD clusters. At a subset of regulatory sites, we also analyze changes in nucleosome spacing following the loss of chromatin remodelers. CRISPR-sciATAC is a high-throughput, single-cell method for studying the effect of genetic perturbations on chromatin in normal and disease states.

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.

How haematopoiesis research became a fertile ground for regulatory network biology as pioneered by Eric Davidson

PURPOSE OF REVIEW

This historical perspective reviews how work of Eric H. Davidson was a catalyst and exemplar for explaining haematopoietic cell fate determination through gene regulation.

RECENT FINDINGS

Researchers studying blood and immune cells pioneered many of the early mechanistic investigations of mammalian gene regulatory processes. These efforts included the characterization of complex gene regulatory sequences exemplified by the globin and T-cell/B-cell receptor gene loci, as well as the identification of many key regulatory transcription factors through the fine mapping of chromosome translocation breakpoints in leukaemia patients. As the repertoire of known regulators expanded, assembly into gene regulatory network models became increasingly important, not only to account for the truism that regulatory genes do not function in isolation but also to devise new ways of extracting biologically meaningful insights from even more complex information. Here we explore how Eric H. Davidson's pioneering studies of gene regulatory network control in nonvertebrate model organisms have had an important and lasting impact on research into blood and immune cell development.

SUMMARY

The intellectual framework developed by Davidson continues to contribute to haematopoietic research, and his insistence on demonstrating logic and causality still challenges the frontier of research today.

Absolute Quantification of Transcription Factors Reveals Principles of Gene Regulation in Erythropoiesis

Dynamic cellular processes such as differentiation are driven by changes in the abundances of transcription factors (TFs). However, despite years of studies, our knowledge about the protein copy number of TFs in the nucleus is limited. Here, by determining the absolute abundances of 103 TFs and co-factors during the course of human erythropoiesis, we provide a dynamic and quantitative scale for TFs in the nucleus. Furthermore, we establish the first gene regulatory network of cell fate commitment that integrates temporal protein stoichiometry data with mRNA measurements. The model revealed quantitative imbalances in TFs' cross-antagonistic relationships that underlie lineage determination. Finally, we made the surprising discovery that, in the nucleus, co-repressors are dramatically more abundant than co-activators at the protein level, but not at the RNA level, with profound implications for understanding transcriptional regulation. These analyses provide a unique quantitative framework to understand transcriptional regulation of cell differentiation in a dynamic context.

A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks

BACKGROUND

Reverse engineering of transcriptional regulatory networks (TRN) from genomics data has always represented a computational challenge in System Biology. The major issue is modeling the complex crosstalk among transcription factors (TFs) and their target genes, with a method able to handle both the high number of interacting variables and the noise in the available heterogeneous experimental sources of information.

RESULTS

In this work, we propose a data fusion approach that exploits the integration of complementary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-scale transcriptional networks. We develop a hybrid structure-learning algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia to reconstruct TRNs in a genome-wide perspective. Applying our method to high-throughput data, we verified its ability to deal with the complexity of a genomic TRN, providing a snapshot of the synergistic TFs regulatory activity. Given the noisy nature of data-driven prior knowledge, which potentially contains incorrect information, we also tested the method's robustness to false priors on a benchmark dataset, comparing the proposed approach to other regulatory network reconstruction algorithms. We demonstrated the effectiveness of our framework by evaluating structural commonalities of our learned genomic network with other existing networks inferred by different DNA binding information-based methods.

CONCLUSIONS

This Bayesian omics-data fusion based methodology allows to gain a genome-wide picture of the transcriptional interplay, helping to unravel key hierarchical transcriptional interactions, which could be subsequently investigated, and it represents a promising learning approach suitable for multi-layered genomic data integration, given its robustness to noisy sources and its tailored framework for handling high dimensional data.

Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe

Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for "pseudotime"-ordered single-cell data compared with true time-series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as "RNA velocity" restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses highlight a shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and suggest ways of overcoming it.

A Feedforward Circuit Regulates Action Selection of Pre-mating Courtship Behavior in Female Drosophila

In the early phase of courtship, female fruit flies exhibit an acute rejection response to avoid unfavorable mating. This pre-mating rejection response is evolutionarily paralleled across species, but the molecular and neuronal basis of that behavior is unclear. Here, we show that a putative incoherent feedforward circuit comprising ellipsoid body neurons, cholinergic R4d, and its repressor GABAergic R2/R4m neurons regulates the pre-mating rejection response in the virgin female Drosophila melanogaster. Both R4d and R2/R4m are positively regulated, via specific dopamine receptors, by a subset of neurons in the dopaminergic PPM3 cluster. Genetic deprivation of GABAergic signal via GABA_A receptor RNA interference in this circuit induces a massive rejection response, whereas activation of GABAergic R2/R4m or suppression of cholinergic R4d increases receptivity. Moreover, glutamatergic signaling via N-methyl-d-aspartate receptors induces NO-mediated retrograde regulation potentially from R4d to R2/R4m, likely providing flexible control of the behavioral switching from rejection to acceptance. Our study elucidates the molecular and neural mechanisms regulating the behavioral selection process of the pre-mating female.

Synthetic control systems for high performance gene expression in mammalian cells

Tunable induction of gene expression is an essential tool in biology and biotechnology. In spite of that, current induction systems often exhibit unpredictable behavior and performance shortcomings, including high sensitivity to transactivator dosage and plasmid take-up variation, and excessive consumption of cellular resources. To mitigate these limitations, we introduce here a novel family of gene expression control systems of varying complexity with significantly enhanced performance. These include: (i) an incoherent feedforward circuit that exhibits output tunability and robustness to plasmid take-up variation; (ii) a negative feedback circuit that reduces burden and provides robustness to transactivator dosage variability; and (iii) a new hybrid circuit integrating negative feedback and incoherent feedforward that combines the benefits of both. As with endogenous circuits, the complexity of our genetic controllers is not gratuitous, but is the necessary outcome of more stringent performance requirements. We demonstrate the benefits of these controllers in two applications. In a culture of CHO cells for protein manufacturing, the circuits result in up to a 2.6-fold yield improvement over a standard system. In human-induced pluripotent stem cells they enable precisely regulated expression of an otherwise poorly tolerated gene of interest, resulting in a significant increase in the viability of the transfected cells.

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.

Limit-cycle oscillatory coexpression of cross-inhibitory transcription factors: a model mechanism for lineage promiscuity

Lineage switches are genetic regulatory motifs that govern and maintain the commitment of a developing cell to a particular cell fate. A canonical example of a lineage switch is the pair of transcription factors PU.1 and GATA-1, of which the former is affiliated with the myeloid and the latter with the erythroid lineage within the haematopoietic system. On a molecular level, PU.1 and GATA-1 positively regulate themselves and antagonize each other via direct protein-protein interactions. Here we use mathematical modelling to identify a novel type of dynamic behaviour that can be supported by such a regulatory architecture. Guided by the specifics of the PU.1-GATA-1 interaction, we formulate, using the law of mass action, a system of differential equations for the key molecular concentrations. After a series of systematic approximations, the system is reduced to a simpler one, which is tractable to phase-plane and linearization methods. The reduced system formally resembles, and generalizes, a well-known model for competitive species from mathematical ecology. However, in addition to the qualitative regimes exhibited by a pair of competitive species (exclusivity, bistable exclusivity, stable-node coexpression) it also allows for oscillatory limit-cycle coexpression. A key outcome of the model is that, in the context of cell-fate choice, such oscillations could be harnessed by a differentiating cell to prime alternately for opposite outcomes; a bifurcation-theory approach is adopted to characterize this possibility.

Mechanisms of Action of Hematopoietic Transcription Factor PU.1 in Initiation of T-Cell Development

PU.1 is an ETS-family transcription factor that plays a broad range of roles in hematopoiesis. A direct regulator of myeloid, dendritic-cell, and B cell functional programs, and a well-known antagonist of terminal erythroid cell differentiation, it is also expressed in the earliest stages of T-cell development of each cohort of intrathymic pro-T cells. Its expression in this context appears to give T-cell precursors initial, transient access to myeloid and dendritic cell developmental competence and therefore to represent a source of antagonism or delay of T-cell lineage commitment. However, it has remained uncertain until recently why T-cell development is also intensely dependent upon PU.1. Here, we review recent work that sheds light on the molecular biology of PU.1 action across the genome in pro-T cells and identifies the genes that depend on PU.1 for their correct regulation. This work indicates modes of chromatin engagement, pioneering, and cofactor recruitment (“coregulator theft”) by PU.1 as well as gene network interactions that not only affect specific target genes but also have system-wide regulatory consequences, amplifying the impact of PU.1 beyond its own direct binding targets. The genes directly regulated by PU.1 also suggest a far-reaching transformation of cell biology and signaling potential between the early stages of T-cell development when PU.1 is expressed and when it is silenced. These cell-biological functions can be important to distinguish fetal from adult T-cell development and have the potential to illuminate aspects of thymic function that have so far remained the most mysterious.

Specification and formation of the neural crest: Perspectives on lineage segregation

The neural crest is a fascinating embryonic population unique to vertebrates that is endowed with remarkable differentiation capacity. Thought to originate from ectodermal tissue, neural crest cells generate neurons and glia of the peripheral nervous system, and melanocytes throughout the body. However, the neural crest also generates many ectomesenchymal derivatives in the cranial region, including cell types considered to be of mesodermal origin such as cartilage, bone, and adipose tissue. These ectomesenchymal derivatives play a critical role in the formation of the vertebrate head, and are thought to be a key attribute at the center of vertebrate evolution and diversity. Further, aberrant neural crest cell development and differentiation is the root cause of many human pathologies, including cancers, rare syndromes, and birth malformations. In this review, we discuss the current findings of neural crest cell ontogeny, and consider tissue, cell, and molecular contributions toward neural crest formation. We further provide current perspectives into the molecular network involved during the segregation of the neural crest lineage.

PI3K/Akt signaling transduction pathway, erythropoiesis and glycolysis in hypoxia (Review)

The purpose of this review is to summarize the research progress of PI3K/Akt signaling pathway in erythropoiesis and glycolysis. Phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) is activated by numerous genes and leads to protein kinase B (Akt) binding to the cell membrane, with the help of phosphoinositide-dependent kinase, in the PI3K/Akt signal transduction pathway. Threonine and serine phosphorylation contribute to Akt translocation from the cytoplasm to the nucleus and further mediates enzymatic biological effects, including those involved in cell proliferation, apoptosis inhibition, cell migration, vesicle transport and cell cancerous transformation. As a key downstream protein of the PI3K/Akt signaling pathway, hypoxia-inducible factor (HIF)-1 is closely associated with the concentration of oxygen in the environment. Maintaining stable levels of HIF-1 protein is critical under normoxic conditions; however, HIF-1 levels quickly increase under hypoxic conditions. HIF-1α is involved in the acute hypoxic response associated with erythropoietin, whereas HIF-2α is associated with the response to chronic hypoxia. Furthermore, PI3K/Akt can reduce the synthesis of glycogen and increase glycolysis. Inhibition of glycogen synthase kinase 3β activity by phosphorylation of its N-terminal serine increases accumulation of cyclin D1, which promotes the cell cycle and improves cell proliferation through the PI3K/Akt signaling pathway. The PI3K/Akt signaling pathway is closely associated with a variety of enzymatic biological effects and glucose metabolism.

Gfi1aa and Gfi1b set the pace for primitive erythroblast differentiation from hemangioblasts in the zebrafish embryo

The transcriptional repressors Gfi1(a) and Gfi1b are epigenetic regulators with unique and overlapping roles in hematopoiesis. In different contexts, Gfi1 and Gfi1b restrict or promote cell proliferation, prevent apoptosis, influence cell fate decisions, and are essential for terminal differentiation. Here, we show in primitive red blood cells (prRBCs) that they can also set the pace for cellular differentiation. In zebrafish, prRBCs express 2 of 3 zebrafish Gfi1/1b paralogs, Gfi1aa and Gfi1b. The recently identified zebrafish gfi1aa gene trap allele qmc551 drives erythroid green fluorescent protein (GFP) instead of Gfi1aa expression, yet homozygous carriers have normal prRBCs. prRBCs display a maturation defect only after splice morpholino-mediated knockdown of Gfi1b in gfi1aa qmc551 homozygous embryos. To study the transcriptome of the Gfi1aa/1b double-depleted cells, we performed an RNA-Seq experiment on GFP-positive prRBCs sorted from 20-hour-old embryos that were heterozygous or homozygous for gfi1aa qmc551 , as well as wt or morphant for gfi1b We subsequently confirmed and extended these data in whole-mount in situ hybridization experiments on newly generated single- and double-mutant embryos. Combined, the data showed that in the absence of Gfi1aa, the synchronously developing prRBCs were delayed in activating late erythroid differentiation, as they struggled to suppress early erythroid and endothelial transcription programs. The latter highlighted the bipotent nature of the progenitors from which prRBCs arise. In the absence of Gfi1aa, Gfi1b promoted erythroid differentiation as stepwise loss of wt gfi1b copies progressively delayed Gfi1aa-depleted prRBCs even further, showing that Gfi1aa and Gfi1b together set the pace for prRBC differentiation from hemangioblasts.

From haematopoietic stem cells to complex differentiation landscapes

The development of mature blood cells from haematopoietic stem cells has long served as a model for stem-cell research, with the haematopoietic differentiation tree being widely used as a model for the maintenance of hierarchically organized tissues. Recent results and new technologies have challenged the demarcations between stem and progenitor cell populations, the timing of cell-fate choices and the contribution of stem and multipotent progenitor cells to the maintenance of steady-state blood production. These evolving views of haematopoiesis have broad implications for our understanding of the functions of adult stem cells, as well as the development of new therapies for malignant and non-malignant haematopoietic diseases.

Two-Element Transcriptional Regulation in the Canonical Wnt Pathway

The canonical Wnt pathway regulates numerous fundamental processes throughout development and adult physiology and is often disrupted in diseases [1-4]. Signal in the pathway is transduced by β-catenin, which in complex with Tcf/Lef regulates transcription. Despite the many processes that the Wnt pathway governs, β-catenin acts primarily on a single cis element in the DNA, the Wnt-responsive element (WRE), at times potentiated by a nearby Helper site. In this study, working with Xenopus, mouse, and human systems, we identified a cis element, distinct from WRE, upon which β-catenin and Tcf act. The element is 11 bp long, hundreds of bases apart from the WRE, and exhibits a suppressive effect. In Xenopus patterning, loss of the 11-bp negative regulatory elements (11-bp NREs) broadened dorsal expression of siamois. In mouse embryonic stem cells, genomic deletion of the 11-bp NREs in the promoter elevated Brachyury expression. This reveals a previously unappreciated mechanism within the Wnt pathway, where gene response is not only driven by WREs but also tuned by 11-bp NREs. Using electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation (ChIP), we found evidence for the NREs binding to β-catenin and Tcf-suggesting a dual action by β-catenin as a signal and a feedforward sensor. Analyzing β-catenin ChIP sequencing in human cells, we found the 11-bp NREs co-localizing with the WRE in 45%-71% of the peaks, suggesting a widespread role for the mechanism. This study presents an example of a more complex cis regulation by a signaling pathway, where a signal is processed through two distinct cis elements in a gene circuitry.

The multi-output incoherent feedforward loop constituted by the transcriptional regulators LasR and RsaL confers robustness to a subset of quorum sensing genes in Pseudomonas aeruginosa

Thelasmulti-output IFFL-1 splits the QS regulon into two distinct sub-regulons with different robustness with respect to LasR fluctuations.

Dark-induced leaf senescence: new insights into a complex light-dependent regulatory pathway

563 I. 563 II. 564 III. 564 IV. 565 V. 565 VI. 567 VII. 567 568 References 568 SUMMARY: Leaf senescence - the coordinated, active process leading to the organized dismantling of cellular components to remobilize resources - is a fundamental aspect of plant life. Its tight regulation is essential for plant fitness and has crucial implications for the optimization of plant productivity and storage properties. Various investigations have shown light deprivation and light perception via phytochromes as key elements modulating senescence. However, the signalling pathways linking light deprivation and actual senescence processes have long remained obscure. Recent analyses have demonstrated that PHYTOCHROME-INTERACTING FACTORS (PIFs) are major transcription factors orchestrating dark-induced senescence (DIS) by targeting chloroplast maintenance, chlorophyll metabolism, hormone signalling and production, and the expression of senescence master regulators, uncovering potential molecular links to the energy deprivation signalling pathway. PIF-dependent feed-forward regulatory modules might be of critical importance for the highly complex and initially light-reversible DIS induction.

Modular genetic regulatory networks increase organization during pattern formation

Studies have shown that genetic regulatory networks (GRNs) consist of modules that are densely connected subnetworks that function quasi-autonomously. Modules may be recognized motifs that comprise of two or three genes with particular regulatory functions and connectivity or be purely structural and identified through connection density. It is unclear what evolutionary and developmental advantages modular structure and in particular motifs provide that have led to this enrichment. This study seeks to understand how modules within developmental GRNs influence the complexity of multicellular patterns that emerge from the dynamics of the regulatory networks. We apply an algorithmic complexity to measure the organization of the patterns. A computational study was performed by creating Boolean intracellular networks within a simulated epithelial field of embryonic cells, where each cell contains the same network and communicates with adjacent cells using contact-mediated signaling. Intracellular networks with random connectivity were compared to those with modular connectivity and with motifs. Results show that modularity effects network dynamics and pattern organization significantly. In particular: (1) modular connectivity alone increases complexity in network dynamics and patterns; (2) bistable switch motifs simplify both the pattern and network dynamics; (3) all other motifs with feedback loops increase multicellular pattern complexity while simplifying the network dynamics; (4) negative feedback loops affect the dynamics complexity more significantly than positive feedback loops.

Noise propagation with interlinked feed-forward pathways

Functionally similar pathways are often seen in biological systems, forming feed-forward controls. The robustness in network motifs such as feed-forward loops (FFLs) has been reported previously. In this work, we studied noise propagation in a development network that has multiple interlinked FFLs. A FFL has the potential of asymmetric noise-filtering (i.e., it works at either the “ON” or the “OFF” state in the target gene). With multiple, interlinked FFLs, we show that the propagated noises are largely filtered regardless of the states in the input genes. The noise-filtering property of an interlinked FFL can be largely derived from that of the individual FFLs, and with interlinked FFLs, it is possible to filter noises in both “ON” and “OFF” states in the output. We demonstrated the noise filtering effect in the developmental regulatory network of Caenorhabditis elegans that controls the timing of distal tip cell (DTC) migration. The roles of positive feedback loops involving blmp-1 and the degradation regulation of DRE-1 also studied. Our analyses allow for better inference from network structures to noise-filtering properties, and provide insights into the mechanisms behind the precise DTC migration controls in space and time.

Methods of information theory and algorithmic complexity for network biology

We survey and introduce concepts and tools located at the intersection of information theory and network biology. We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. We show examples such as the emergence of giant components in Erdös-Rényi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data. We provide exact theoretical calculations, numerical approximations and error estimations of entropy, algorithmic probability and Kolmogorov complexity for different types of graphs, characterizing their variant and invariant properties. We introduce formal definitions of complexity for both labeled and unlabeled graphs and prove that the Kolmogorov complexity of a labeled graph is a good approximation of its unlabeled Kolmogorov complexity and thus a robust definition of graph complexity.

RUNX1B Expression Is Highly Heterogeneous and Distinguishes Megakaryocytic and Erythroid Lineage Fate in Adult Mouse Hematopoiesis

The Core Binding Factor (CBF) protein RUNX1 is a master regulator of definitive hematopoiesis, crucial for hematopoietic stem cell (HSC) emergence during ontogeny. RUNX1 also plays vital roles in adult mice, in regulating the correct specification of numerous blood lineages. Akin to the other mammalian Runx genes, Runx1 has two promoters P1 (distal) and P2 (proximal) which generate distinct protein isoforms. The activities and specific relevance of these two promoters in adult hematopoiesis remain to be fully elucidated. Utilizing a dual reporter mouse model we demonstrate that the distal P1 promoter is broadly active in adult hematopoietic stem and progenitor cell (HSPC) populations. By contrast the activity of the proximal P2 promoter is more restricted and its upregulation, in both the immature Lineage- Sca1high cKithigh (LSK) and bipotential Pre-Megakaryocytic/Erythroid Progenitor (PreMegE) populations, coincides with a loss of erythroid (Ery) specification. Accordingly the PreMegE population can be prospectively separated into "pro-erythroid" and "pro-megakaryocyte" populations based on Runx1 P2 activity. Comparative gene expression analyses between Runx1 P2+ and P2- populations indicated that levels of CD34 expression could substitute for P2 activity to distinguish these two cell populations in wild type (WT) bone marrow (BM). Prospective isolation of these two populations will enable the further investigation of molecular mechanisms involved in megakaryocytic/erythroid (Mk/Ery) cell fate decisions. Having characterized the extensive activity of P1, we utilized a P1-GFP homozygous mouse model to analyze the impact of the complete absence of Runx1 P1 expression in adult mice and observed strong defects in the T cell lineage. Finally, we investigated how the leukemic fusion protein AML1-ETO9a might influence Runx1 promoter usage. Short-term AML1-ETO9a induction in BM resulted in preferential P2 upregulation, suggesting its expression may be important to establish a pre-leukemic environment.

Computational Modeling of a Transcriptional Switch Underlying B-Lymphocyte Lineage Commitment of Hematopoietic Multipotent Cells

Despite progresses in identifying the cellular mechanisms at the basis of the differentiation of hematopoietic stem/progenitor cells, little is known about the regulatory circuitry at the basis of lineage commitment of hematopoietic multipotent progenitors. To address this issue, we propose a computational approach to give further insights in the comprehension of this genetic mechanism. Differently from T lymphopoiesis, however, there is at present no mathematical model describing lineage restriction of multipotent progenitors to early B-cell precursors. Here, we provide a first model-constructed on the basis of current experimental evidence from literature and of publicly available microarray datasets-of the genetic regulatory network driving the cellular fate determination at the stage of lymphoid lineage commitment, with particular regard to the multipotent-B-cell progenitor transition. By applying multistability analysis methods, we are able to assess the capability of the model to capture the experimentally observed switch-like commitment behavior. These methods allow us to confirm the central role of zinc finger protein 521 (ZNF521) in this process, that we had previously reported, and to identify a novel putative functional interaction for ZNF521, which is essential to realize such characteristic behavior. Moreover, using the devised model, we are able to rigorously analyze the mechanisms underpinning irreversibility of the physiological commitment step and to devise a possible reprogramming strategy, based on the combined modification of the expression of ZNF521 and EBF1.

Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks

Abstract

Conclusions

To validate the role of network motifs in cancer formation, several examples are presented which demonstrated the effectiveness of the present approach. A web-based platform has been set up which can be accessed at: http://ppi.bioinfo.asia.edu.tw/pathway/. It is very likely that our results can supply very specific CMS missing information for certain cancer types, it is an indispensable tool for cancer biology research.

SCL, LMO1 and Notch1 reprogram thymocytes into self-renewing cells

The molecular determinants that render specific populations of normal cells susceptible to oncogenic reprogramming into self-renewing cancer stem cells are poorly understood. Here, we exploit T-cell acute lymphoblastic leukemia (T-ALL) as a model to define the critical initiating events in this disease. First, thymocytes that are reprogrammed by the SCL and LMO1 oncogenic transcription factors into self-renewing pre-leukemic stem cells (pre-LSCs) remain non-malignant, as evidenced by their capacities to generate functional T cells. Second, we provide strong genetic evidence that SCL directly interacts with LMO1 to activate the transcription of a self-renewal program coordinated by LYL1. Moreover, LYL1 can substitute for SCL to reprogram thymocytes in concert with LMO1. In contrast, inhibition of E2A was not sufficient to substitute for SCL, indicating that thymocyte reprogramming requires transcription activation by SCL-LMO1. Third, only a specific subset of normal thymic cells, known as DN3 thymocytes, is susceptible to reprogramming. This is because physiological NOTCH1 signals are highest in DN3 cells compared to other thymocyte subsets. Consistent with this, overexpression of a ligand-independent hyperactive NOTCH1 allele in all immature thymocytes is sufficient to sensitize them to SCL-LMO1, thereby increasing the pool of self-renewing cells. Surprisingly, hyperactive NOTCH1 cannot reprogram thymocytes on its own, despite the fact that NOTCH1 is activated by gain of function mutations in more than 55% of T-ALL cases. Rather, elevating NOTCH1 triggers a parallel pathway involving Hes1 and Myc that dramatically enhances the activity of SCL-LMO1 We conclude that the acquisition of self-renewal and the genesis of pre-LSCs from thymocytes with a finite lifespan represent a critical first event in T-ALL. Finally, LYL1 and LMO1 or LMO2 are co-expressed in most human T-ALL samples, except the cortical T subtype. We therefore anticipate that the self-renewal network described here may be relevant to a majority of human T-ALL.

Deciphering the initiating events in lymphoid leukemia is important for the development of new therapeutic strategies. In this manuscript, we define oncogenic reprogramming as the process through which non-self-renewing progenitors are converted into pre-leukemic stem cells with sustained self-renewal capacities. We provide strong genetic evidence that this step is rate-limiting in leukemogenesis and requires the activation of a self-renewal program by oncogenic transcription factors, as exemplified by SCL and LMO1. Furthermore, NOTCH1 is a pathway that drives cell fate in the thymus. We demonstrate that homeostatic NOTCH1 levels that are highest in specific thymocyte subsets determine their susceptibilities to oncogenic reprogramming by SCL and LMO1. Our data provide novel insight into the acquisition of self-renewal as a critical first step in lymphoid cell transformation, requiring the synergistic interaction of oncogenic transcription factors with a cellular context controlled by high physiological NOTCH1.

Temporal dynamics and transcriptional control using single-cell gene expression analysis

Background

Changes in environmental conditions lead to expression variation that manifest at the level of gene regulatory networks. Despite a strong understanding of the role noise plays in synthetic biological systems, it remains unclear how propagation of expression heterogeneity in an endogenous regulatory network is distributed and utilized by cells transitioning through a key developmental event.

Results

Here we investigate the temporal dynamics of a single-cell transcriptional network of 45 transcription factors in THP-1 human myeloid monocytic leukemia cells undergoing differentiation to macrophages. We systematically measure temporal regulation of expression and variation by profiling 120 single cells at eight distinct time points, and infer highly controlled regulatory modules through which signaling operates with stochastic effects. This reveals dynamic and specific rewiring as a cellular strategy for differentiation. The integration of both positive and negative co-expression networks further identifies the proto-oncogene MYB as a network hinge to modulate both the pro- and anti-differentiation pathways.

Conclusions

Compared to averaged cell populations, temporal single-cell expression profiling provides a much more powerful technique to probe for mechanistic insights underlying cellular differentiation. We believe that our approach will form the basis of novel strategies to study the regulation of transcription at a single-cell level.

Feed-forward transcriptional programming by nuclear receptors: regulatory principles and therapeutic implications

Nuclear receptors (NRs) are widely targeted to treat a range of human diseases. Feed-forward loops are an ancient mechanism through which single cell organisms organize transcriptional programming and modulate gene expression dynamics, but they have not been systematically studied as a regulatory paradigm for NR-mediated transcriptional responses. Here, we provide an overview of the basic properties of feed-forward loops as predicted by mathematical models and validated experimentally in single cell organisms. We review existing evidence implicating feed-forward loops as important in controlling clinically relevant transcriptional responses to estrogens, progestins, and glucocorticoids, among other NR ligands. We propose that feed-forward transcriptional circuits are a major mechanism through which NRs integrate signals, exert temporal control over gene regulation, and compartmentalize client transcriptomes into discrete subunits. Implications for the design and function of novel selective NR ligands are discussed.

Gene expression variability as a unifying element of the pluripotency network

Heterogeneity is a hallmark of stem cell populations, in part due to the molecular differences between cells undergoing self-renewal and those poised to differentiate. We examined phenotypic and molecular heterogeneity in pluripotent stem cell populations, using public gene expression data sets. A high degree of concordance was observed between global gene expression variability and the reported heterogeneity of different human pluripotent lines. Network analysis demonstrated that low-variability genes were the most highly connected, suggesting that these are the most stable elements of the gene regulatory network and are under the highest regulatory constraints. Known drivers of pluripotency were among these, with lowest expression variability of POU5F1 in cells with the highest capacity for self-renewal. Variability of gene expression provides a reliable measure of phenotypic and molecular heterogeneity and predicts those genes with the highest degree of regulatory constraint within the pluripotency network.

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Gene expression variability is highly concordant with population heterogeneity

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Genes within the pluripotency network have distinct variability profiles

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Expression variability is a network property important for pluripotency

Early dynamic fate changes in haemogenic endothelium characterized at the single-cell level

Haematopoietic stem cells (HSCs) are the founding cells of the adult haematopoietic system, born during ontogeny from a specialized subset of endothelium, the haemogenic endothelium (HE) via an endothelial-to-haematopoietic transition (EHT). Although recently imaged in real time, the underlying mechanism of EHT is still poorly understood. We have generated a Runx1 +23 enhancer-reporter transgenic mouse (23GFP) for the prospective isolation of HE throughout embryonic development. Here we perform functional analysis of over 1,800 and transcriptional analysis of 268 single 23GFP(+) HE cells to explore the onset of EHT at the single-cell level. We show that initiation of the haematopoietic programme occurs in cells still embedded in the endothelial layer, and is accompanied by a previously unrecognized early loss of endothelial potential before HSCs emerge. Our data therefore provide important insights on the timeline of early haematopoietic commitment.

Plasticity in the transcriptional and epigenetic circuits regulating dendritic cell lineage specification and function

Dendritic cells (DC) are critical and functionally versatile innate immune sentinels. Here, we coarsely partition the adult DC lineage into three developmental subtypes and argue that pioneer transcription factors and chromatin remodeling are responsible for specification and plasticity between the DC subsets. Subsequently, intricate signaling-dependent transcription factor networks generate different functional states in response to pathogen stimuli within a specified DC subtype. To expand our understanding of lineage heterogeneity and functional activation states, we discuss the use of single cell genomics approaches in the context of a newly emerging systems immunology era, complementing the dichotomous definition of immune cells based solely on their surface marker expression. Rapid developments in single cell genomics are beginning to provide us with robust tools to potentially revise the working models of DC specification and the common hematopoietic tree.

Global transcriptome analyses of human and murine terminal erythroid differentiation

We recently developed fluorescence-activated cell sorting (FACS)-based methods to purify morphologically and functionally discrete populations of cells, each representing specific stages of terminal erythroid differentiation. We used these techniques to obtain pure populations of both human and murine erythroblasts at distinct developmental stages. RNA was prepared from these cells and subjected to RNA sequencing analyses, creating unbiased, stage-specific transcriptomes. Tight clustering of transcriptomes from differing stages, even between biologically different replicates, validated the utility of the FACS-based assays. Bioinformatic analyses revealed that there were marked differences between differentiation stages, with both shared and dissimilar gene expression profiles defining each stage within transcriptional space. There were vast temporal changes in gene expression across the differentiation stages, with each stage exhibiting unique transcriptomes. Clustering and network analyses revealed that varying stage-specific patterns of expression observed across differentiation were enriched for genes of differing function. Numerous differences were present between human and murine transcriptomes, with significant variation in the global patterns of gene expression. These data provide a significant resource for studies of normal and perturbed erythropoiesis, allowing a deeper understanding of mechanisms of erythroid development in various inherited and acquired erythroid disorders.

Transcriptional mechanisms of cell fate decisions revealed by single cell expression profiling

Transcriptional networks regulate cell fate decisions, which occur at the level of individual cells. However, much of what we know about their structure and function comes from studies averaging measurements over large populations of cells, many of which are functionally heterogeneous. Such studies conceal the variability between cells and so prevent us from determining the nature of heterogeneity at the molecular level. In recent years, many protocols and platforms have been developed that allow the high throughput analysis of gene expression in single cells, opening the door to a new era of biology. Here, we discuss the need for single cell gene expression analysis to gain deeper insights into the transcriptional control of cell fate decisions, and consider the insights it has provided so far into transcriptional regulatory networks in development.

Primitive and definitive erythropoiesis in mammals

Red blood cells (RBCs), which constitute the most abundant cell type in the body, come in two distinct flavors- primitive and definitive. Definitive RBCs in mammals circulate as smaller, anucleate cells during fetal and postnatal life, while primitive RBCs circulate transiently in the early embryo as large, nucleated cells before ultimately enucleating. Both cell types are formed from lineage-committed progenitors that generate a series of morphologically identifiable precursors that enucleate to form mature RBCs. While definitive erythroid precursors mature extravascularly in the fetal liver and postnatal marrow in association with macrophage cells, primitive erythroid precursors mature as a semi-synchronous cohort in the embryonic bloodstream. While the cytoskeletal network is critical for the maintenance of cell shape and the deformability of definitive RBCs, little is known about the components and function of the cytoskeleton in primitive erythroblasts. Erythropoietin (EPO) is a critical regulator of late-stage definitive, but not primitive, erythroid progenitor survival. However, recent studies indicate that EPO regulates multiple aspects of terminal maturation of primitive murine and human erythroid precursors, including cell survival, proliferation, and the rate of terminal maturation. Primitive and definitive erythropoiesis share central transcriptional regulators, including Gata1 and Klf1, but are also characterized by the differential expression and function of other regulators, including myb, Sox6, and Bcl11A. Flow cytometry-based methodologies, developed to purify murine and human stage-specific erythroid precursors, have enabled comparative global gene expression studies and are providing new insights into the biology of erythroid maturation.

Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model

Motivation: Combinatorial interactions of transcription factors with cis-regulatory elements control the dynamic progression through successive cellular states and thus underpin all metazoan development. The construction of network models of cis-regulatory elements, therefore, has the potential to generate fundamental insights into cellular fate and differentiation. Haematopoiesis has long served as a model system to study mammalian differentiation, yet modelling based on experimentally informed cis-regulatory interactions has so far been restricted to pairs of interacting factors. Here, we have generated a Boolean network model based on detailed cis-regulatory functional data connecting 11 haematopoietic stem/progenitor cell (HSPC) regulator genes.

Results: Despite its apparent simplicity, the model exhibits surprisingly complex behaviour that we charted using strongly connected components and shortest-path analysis in its Boolean state space. This analysis of our model predicts that HSPCs display heterogeneous expression patterns and possess many intermediate states that can act as ‘stepping stones’ for the HSPC to achieve a final differentiated state. Importantly, an external perturbation or ‘trigger’ is required to exit the stem cell state, with distinct triggers characterizing maturation into the various different lineages. By focusing on intermediate states occurring during erythrocyte differentiation, from our model we predicted a novel negative regulation of Fli1 by Gata1, which we confirmed experimentally thus validating our model. In conclusion, we demonstrate that an advanced mammalian regulatory network model based on experimentally validated cis-regulatory interactions has allowed us to make novel, experimentally testable hypotheses about transcriptional mechanisms that control differentiation of mammalian stem cells.

Contact:j.heringa@vu.nl or ioannis.xenarios@isb-sib.ch or bg200@cam.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

Dynamic analysis of gene expression and genome-wide transcription factor binding during lineage specification of multipotent progenitors

We used the paradigmatic GATA-PU.1 axis to explore, at the systems level, dynamic relationships between transcription factor (TF) binding and global gene expression programs as multipotent cells differentiate. We combined global ChIP-seq of GATA1, GATA2, and PU.1 with expression profiling during differentiation to erythroid and neutrophil lineages. Our analysis reveals (1) differential complexity of sequence motifs bound by GATA1, GATA2, and PU.1; (2) the scope and interplay of GATA1 and GATA2 programs within, and during transitions between, different cell compartments, and the extent of their hard-wiring by DNA motifs; (3) the potential to predict gene expression trajectories based on global associations between TF-binding data and target gene expression; and (4) how dynamic modeling of DNA-binding and gene expression data can be used to infer regulatory logic of TF circuitry. This rubric exemplifies the utility of this cross-platform resource for deconvoluting the complexity of transcriptional programs controlling stem/progenitor cell fate in hematopoiesis.

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Cross-platform resource for TF-network regulation of multipotent blood cell fate

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DNA motif dependence and changing specificity of GATA factors in lineage choice

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Modeling-based inference identifies GATA2 repression of PU.1 in multipotent cells

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Priming, recruitment, and switching modes of GATA interplay during differentiation

The cost of sensitive response and accurate adaptation in networks with an incoherent type-1 feed-forward loop

The incoherent type-1 feed-forward loop (I1-FFL) is ubiquitous in biological regulatory circuits. Although much is known about the functions of the I1-FFL motif, the energy cost incurred in the network and how it affects the performance of the network have not been investigated. Here, we study a generic I1-FFL enzymatic reaction network modelled after the GEF-GAP-Ras pathway responsible for chemosensory adaptation in eukaryotic cells. Our analysis shows that the I1-FFL network always operates out of equilibrium. Continuous energy dissipation is necessary to drive an internal phosphorylation-dephosphorylation cycle that is crucial in achieving strong short-time response and accurate long-time adaptation. In particular, we show quantitatively that the energy dissipated in the I1-FFL network is used (i) to increase the system's initial response to the input signals; (ii) to enhance the adaptation accuracy at steady state; and (iii) to expand the range of such accurate adaptation. Moreover, we find that the energy dissipation rate, the catalytic speed and the maximum adaptation accuracy in the I1-FFL network satisfy the same energy-speed-accuracy relationship as in the negative-feedback-loop (NFL) networks. Because the I1-FFL and NFL are the only two basic network motifs that enable accurate adaptation, our results suggest that a universal cost-performance trade-off principle may underlie all cellular adaptation processes independent of the detailed biochemical circuit architecture.

Impact of natural genetic variation on gene expression dynamics

DNA sequence variation causes changes in gene expression, which in turn has profound effects on cellular states. These variations affect tissue development and may ultimately lead to pathological phenotypes. A genetic locus containing a sequence variation that affects gene expression is called an “expression quantitative trait locus” (eQTL). Whereas the impact of cellular context on expression levels in general is well established, a lot less is known about the cell-state specificity of eQTL. Previous studies differed with respect to how “dynamic eQTL” were defined. Here, we propose a unified framework distinguishing static, conditional and dynamic eQTL and suggest strategies for mapping these eQTL classes. Further, we introduce a new approach to simultaneously infer eQTL from different cell types. By using murine mRNA expression data from four stages of hematopoiesis and 14 related cellular traits, we demonstrate that static, conditional and dynamic eQTL, although derived from the same expression data, represent functionally distinct types of eQTL. While static eQTL affect generic cellular processes, non-static eQTL are more often involved in hematopoiesis and immune response. Our analysis revealed substantial effects of individual genetic variation on cell type-specific expression regulation. Among a total number of 3,941 eQTL we detected 2,729 static eQTL, 1,187 eQTL were conditionally active in one or several cell types, and 70 eQTL affected expression changes during cell type transitions. We also found evidence for feedback control mechanisms reverting the effect of an eQTL specifically in certain cell types. Loci correlated with hematological traits were enriched for conditional eQTL, thus, demonstrating the importance of conditional eQTL for understanding molecular mechanisms underlying physiological trait variation. The classification proposed here has the potential to streamline and unify future analysis of conditional and dynamic eQTL as well as many other kinds of QTL data.

Complex physiological traits are affected through subtle changes of molecular traits like gene expression in the relevant tissues, which in turn are caused by genetic variation. A genetic locus containing a sequence variation affecting gene expression is called an expression quantitative trait locus (eQTL). Understanding the tissue and cell type specificity of eQTL effects is essential for revealing the molecular mechanisms underlying disease phenotypes. However, so far the cell-state dependence of eQTL is poorly understood. In order to systematically assess the importance of cell state-specific eQTL, we propose to distinguish static, conditional and dynamic eQTL and suggest strategies for mapping these eQTL classes. We applied our framework to mouse gene expression data from four hematopoietic stages and related cellular traits. The different eQTL classes, although derived from the same expression data, represent functionally distinct types of eQTL. Importantly, conditional eQTL are well correlated with relevant hematological traits. These findings emphasize the condition specificity of many regulatory relationships, even if the conditions under study are related. This calls for due caution when transferring conclusions about regulatory mechanisms across cell types or tissues. The proposed classification will also help to unravel dynamic behaviors in many other kinds of QTL data.