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Paczkó M, Vörös D, Szabó P, Jékely G, Szathmáry E, Szilágyi A. A neural network-based model framework for cell-fate decisions and development. Commun Biol 2024; 7:323. [PMID: 38486083 PMCID: PMC10940658 DOI: 10.1038/s42003-024-05985-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 02/28/2024] [Indexed: 03/18/2024] Open
Abstract
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.
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Affiliation(s)
- Mátyás Paczkó
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
- Doctoral School of Biology, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary
| | - Dániel Vörös
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
- Doctoral School of Biology, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary
| | - Péter Szabó
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
| | - Gáspár Jékely
- Living Systems Institute, University of Exeter, Stocker Road 4QD, EX4, Exeter, UK
| | - Eörs Szathmáry
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary.
- Center for the Conceptual Foundations of Science, Parmenides Foundation, Hindenburgstr. 15, 82343, Pöcking, Germany.
- Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary.
| | - András Szilágyi
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
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2
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Benyoucef A, Haigh JJ, Brand M. Unveiling the complexity of transcription factor networks in hematopoietic stem cells: implications for cell therapy and hematological malignancies. Front Oncol 2023; 13:1151343. [PMID: 37441426 PMCID: PMC10333584 DOI: 10.3389/fonc.2023.1151343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023] Open
Abstract
The functionality and longevity of hematopoietic tissue is ensured by a tightly controlled balance between self-renewal, quiescence, and differentiation of hematopoietic stem cells (HSCs) into the many different blood lineages. Cell fate determination in HSCs is influenced by signals from extrinsic factors (e.g., cytokines, irradiation, reactive oxygen species, O2 concentration) that are translated and integrated by intrinsic factors such as Transcription Factors (TFs) to establish specific gene regulatory programs. TFs also play a central role in the establishment and/or maintenance of hematological malignancies, highlighting the need to understand their functions in multiple contexts. TFs bind to specific DNA sequences and interact with each other to form transcriptional complexes that directly or indirectly control the expression of multiple genes. Over the past decades, significant research efforts have unraveled molecular programs that control HSC function. This, in turn, led to the identification of more than 50 TF proteins that influence HSC fate. However, much remains to be learned about how these proteins interact to form molecular networks in combination with cofactors (e.g. epigenetics factors) and how they control differentiation, expansion, and maintenance of cellular identity. Understanding these processes is critical for future applications particularly in the field of cell therapy, as this would allow for manipulation of cell fate and induction of expansion, differentiation, or reprogramming of HSCs using specific cocktails of TFs. Here, we review recent findings that have unraveled the complexity of molecular networks controlled by TFs in HSCs and point towards possible applications to obtain functional HSCs ex vivo for therapeutic purposes including hematological malignancies. Furthermore, we discuss the challenges and prospects for the derivation and expansion of functional adult HSCs in the near future.
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Affiliation(s)
- Aissa Benyoucef
- Department of Pharmacology and Therapeutics, Rady Faulty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- CancerCare Manitoba Research Institute, Winnipeg, MB, Canada
| | - Jody J. Haigh
- Department of Pharmacology and Therapeutics, Rady Faulty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- CancerCare Manitoba Research Institute, Winnipeg, MB, Canada
| | - Marjorie Brand
- Sprott Center for Stem Cell Research, Ottawa Hospital Research Institute, Ottawa, ON, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
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3
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Kim J, Hopper C, Cho KH. Statistical control of structural networks with limited interventions to minimize cellular phenotypic diversity represented by point attractors. Sci Rep 2023; 13:6275. [PMID: 37072458 PMCID: PMC10113376 DOI: 10.1038/s41598-023-33346-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 04/12/2023] [Indexed: 05/03/2023] Open
Abstract
The underlying genetic networks of cells give rise to diverse behaviors known as phenotypes. Control of this cellular phenotypic diversity (CPD) may reveal key targets that govern differentiation during development or drug resistance in cancer. This work establishes an approach to control CPD that encompasses practical constraints, including model limitations, the number of simultaneous control targets, which targets are viable for control, and the granularity of control. Cellular networks are often limited to the structure of interactions, due to the practical difficulty of modeling interaction dynamics. However, these dynamics are essential to CPD. In response, our statistical control approach infers the CPD directly from the structure of a network, by considering an ensemble average function over all possible Boolean dynamics for each node in the network. These ensemble average functions are combined with an acyclic form of the network to infer the number of point attractors. Our approach is applied to several known biological models and shown to outperform existing approaches. Statistical control of CPD offers a new avenue to contend with systemic processes such as differentiation and cancer, despite practical limitations in the field.
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Affiliation(s)
- Jongwan Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Corbin Hopper
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
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Hérault L, Poplineau M, Duprez E, Remy É. A novel Boolean network inference strategy to model early hematopoiesis aging. Comput Struct Biotechnol J 2022; 21:21-33. [PMID: 36514338 PMCID: PMC9719905 DOI: 10.1016/j.csbj.2022.10.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/24/2022] [Accepted: 10/24/2022] [Indexed: 11/07/2022] Open
Abstract
Hematopoietic stem cell (HSC) aging is a multifactorial event leading to changes in HSC properties and functions, which are intrinsically coordinated and affect the early hematopoiesis. To better understand the mechanisms and factors controlling these changes, we developed an original strategy to construct a Boolean model of HSC differentiation. Based on our previous scRNA-seq data, we exhaustively characterized active transcription modules or regulons along the differentiation trajectory and constructed an influence graph between 15 selected components involved in the dynamics of the process. Then we defined dynamical constraints between observed cellular states along the trajectory and using answer set programming with in silico perturbation analysis, we obtained a Boolean model explaining the early priming of HSCs. Finally, perturbations of the model based on age-related changes revealed important deregulations, such as the overactivation of Egr1 and Junb or the loss of Cebpa activation by Gata2. These new regulatory mechanisms were found to be relevant for the myeloid bias of aged HSC and explain the decreased transcriptional priming of HSCs to all mature cell types except megakaryocytes.
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Affiliation(s)
- Léonard Hérault
- Aix Marseille Université, CNRS, Marseille I2M, France,Epigenetic Factors in Normal and Malignant Hematopoiesis Team, Aix Marseille Université, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
| | - Mathilde Poplineau
- Epigenetic Factors in Normal and Malignant Hematopoiesis Team, Aix Marseille Université, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
| | - Estelle Duprez
- Epigenetic Factors in Normal and Malignant Hematopoiesis Team, Aix Marseille Université, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
| | - Élisabeth Remy
- Aix Marseille Université, CNRS, Marseille I2M, France,Corresponding author.
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5
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Hérault L, Poplineau M, Remy E, Duprez E. Single Cell Transcriptomics to Understand HSC Heterogeneity and Its Evolution upon Aging. Cells 2022; 11:cells11193125. [PMID: 36231086 PMCID: PMC9563410 DOI: 10.3390/cells11193125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/15/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
Abstract
Single-cell transcriptomic technologies enable the uncovering and characterization of cellular heterogeneity and pave the way for studies aiming at understanding the origin and consequences of it. The hematopoietic system is in essence a very well adapted model system to benefit from this technological advance because it is characterized by different cellular states. Each cellular state, and its interconnection, may be defined by a specific location in the global transcriptional landscape sustained by a complex regulatory network. This transcriptomic signature is not fixed and evolved over time to give rise to less efficient hematopoietic stem cells (HSC), leading to a well-documented hematopoietic aging. Here, we review the advance of single-cell transcriptomic approaches for the understanding of HSC heterogeneity to grasp HSC deregulations upon aging. We also discuss the new bioinformatics tools developed for the analysis of the resulting large and complex datasets. Finally, since hematopoiesis is driven by fine-tuned and complex networks that must be interconnected to each other, we highlight how mathematical modeling is beneficial for doing such interconnection between multilayered information and to predict how HSC behave while aging.
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Affiliation(s)
- Léonard Hérault
- I2M, CNRS, Aix Marseille University, 13009 Marseille, France
- Epigenetic Factors in Normal and Malignant Hematopoiesis Lab., CRCM, CNRS, INSERM, Institut Paoli Calmettes, Aix Marseille University, 13009 Marseille, France
| | - Mathilde Poplineau
- Epigenetic Factors in Normal and Malignant Hematopoiesis Lab., CRCM, CNRS, INSERM, Institut Paoli Calmettes, Aix Marseille University, 13009 Marseille, France
- Equipe Labellisée Ligue Nationale Contre le Cancer, 75013 Paris, France
| | - Elisabeth Remy
- I2M, CNRS, Aix Marseille University, 13009 Marseille, France
| | - Estelle Duprez
- Epigenetic Factors in Normal and Malignant Hematopoiesis Lab., CRCM, CNRS, INSERM, Institut Paoli Calmettes, Aix Marseille University, 13009 Marseille, France
- Equipe Labellisée Ligue Nationale Contre le Cancer, 75013 Paris, France
- Correspondence:
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6
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Handzlik JE. Data-driven modeling predicts gene regulatory network dynamics during the differentiation of multipotential hematopoietic progenitors. PLoS Comput Biol 2022; 18:e1009779. [PMID: 35030198 PMCID: PMC8794271 DOI: 10.1371/journal.pcbi.1009779] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 01/27/2022] [Accepted: 12/21/2021] [Indexed: 01/05/2023] Open
Abstract
Cellular differentiation during hematopoiesis is guided by gene regulatory networks (GRNs) comprising transcription factors (TFs) and the effectors of cytokine signaling. Based largely on analyses conducted at steady state, these GRNs are thought to be organized as a hierarchy of bistable switches, with antagonism between Gata1 and PU.1 driving red- and white-blood cell differentiation. Here, we utilize transient gene expression patterns to infer the genetic architecture—the type and strength of regulatory interconnections—and dynamics of a twelve-gene GRN including key TFs and cytokine receptors. We trained gene circuits, dynamical models that learn genetic architecture, on high temporal-resolution gene-expression data from the differentiation of an inducible cell line into erythrocytes and neutrophils. The model is able to predict the consequences of gene knockout, knockdown, and overexpression experiments and the inferred interconnections are largely consistent with prior empirical evidence. The inferred genetic architecture is densely interconnected rather than hierarchical, featuring extensive cross-antagonism between genes from alternative lineages and positive feedback from cytokine receptors. The analysis of the dynamics of gene regulation in the model reveals that PU.1 is one of the last genes to be upregulated in neutrophil conditions and that the upregulation of PU.1 and other neutrophil genes is driven by Cebpa and Gfi1 instead. This model inference is confirmed in an independent single-cell RNA-Seq dataset from mouse bone marrow in which Cebpa and Gfi1 expression precedes the neutrophil-specific upregulation of PU.1 during differentiation. These results demonstrate that full PU.1 upregulation during neutrophil development involves regulatory influences extrinsic to the Gata1-PU.1 bistable switch. Furthermore, although there is extensive cross-antagonism between erythroid and neutrophil genes, it does not have a hierarchical structure. More generally, we show that the combination of high-resolution time series data and data-driven dynamical modeling can uncover the dynamics and causality of developmental events that might otherwise be obscured. The supply of blood cells is replenished by the maturation of hematopoietic progenitor cells into different cell types. Which cell type a progenitor cell develops into is determined by a complex network of genes whose protein products directly or indirectly regulate each others’ expression and that of downstream genes characteristic of the cell type. We inferred the nature and causality of the regulatory connections in a 12-gene network known to affect the decision between erythrocyte and neutrophil cell fates using a predictive machine-learning approach. Our analysis showed that the overall architecture of the network is densely interconnected and not hierarchical. Furthermore, the model inferred that PU.1, considered a master regulator of all white-blood cell lineages, is upregulated during neutrophil development by two other proteins, Cebpa and Gfi1. We validated this prediction by showing that Cebpa and Gfi1 expression precedes that of PU.1 in single-cell gene expression data from mouse bone marrow. These results revise the architecture of the gene network and the causality of regulatory events guiding hematopoiesis. The results also show that combining machine learning approaches with time course data can help resolve causality during development.
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Affiliation(s)
- Joanna E Handzlik
- Department of Biology, University of North Dakota, Grand Forks, North Dakota, United States of America
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7
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Karanam A, Rappel WJ. Boolean modelling in plant biology. QUANTITATIVE PLANT BIOLOGY 2022; 3:e29. [PMID: 37077966 PMCID: PMC10095905 DOI: 10.1017/qpb.2022.26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 10/24/2022] [Accepted: 11/16/2022] [Indexed: 05/03/2023]
Abstract
Signalling and genetic networks underlie most biological processes and are often complex, containing many highly connected components. Modelling these networks can provide insight into mechanisms but is challenging given that rate parameters are often not well defined. Boolean modelling, in which components can only take on a binary value with connections encoded by logic equations, is able to circumvent some of these challenges, and has emerged as a viable tool to probe these complex networks. In this review, we will give an overview of Boolean modelling, with a specific emphasis on its use in plant biology. We review how Boolean modelling can be used to describe biological networks and then discuss examples of its applications in plant genetics and plant signalling.
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Affiliation(s)
- Aravind Karanam
- Department of Physics, University of California, San Diego, La Jolla, California92093, USA
| | - Wouter-Jan Rappel
- Department of Physics, University of California, San Diego, La Jolla, California92093, USA
- Author for correspondence: W.-J. Rappel, E-mail:
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8
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Rozum JC, Gómez Tejeda Zañudo J, Gan X, Deritei D, Albert R. Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks. SCIENCE ADVANCES 2021; 7:eabf8124. [PMID: 34272246 PMCID: PMC8284893 DOI: 10.1126/sciadv.abf8124] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 06/03/2021] [Indexed: 05/14/2023]
Abstract
We present new applications of parity inversion and time reversal to the emergence of complex behavior from simple dynamical rules in stochastic discrete models. Our parity-based encoding of causal relationships and time-reversal construction efficiently reveal discrete analogs of stable and unstable manifolds. We demonstrate their predictive power by studying decision-making in systems biology and statistical physics models. These applications underpin a novel attractor identification algorithm implemented for Boolean networks under stochastic dynamics. Its speed enables resolving a long-standing open question of how attractor count in critical random Boolean networks scales with network size and whether the scaling matches biological observations. Via 80-fold improvement in probed network size (N = 16,384), we find the unexpectedly low scaling exponent of 0.12 ± 0.05, approximately one-tenth the analytical upper bound. We demonstrate a general principle: A system's relationship to its time reversal and state-space inversion constrains its repertoire of emergent behaviors.
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Affiliation(s)
- Jordan C Rozum
- Department of Physics, The Pennsylvania State University, University Park, PA 16802, USA.
| | - Jorge Gómez Tejeda Zañudo
- Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Xiao Gan
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Dávid Deritei
- Department of Molecular Biology, Semmelweis University, Budapest, Hungary
| | - Réka Albert
- Department of Physics, The Pennsylvania State University, University Park, PA 16802, USA.
- Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA
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9
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Teschendorff AE, Feinberg AP. Statistical mechanics meets single-cell biology. Nat Rev Genet 2021; 22:459-476. [PMID: 33875884 PMCID: PMC10152720 DOI: 10.1038/s41576-021-00341-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2021] [Indexed: 02/07/2023]
Abstract
Single-cell omics is transforming our understanding of cell biology and disease, yet the systems-level analysis and interpretation of single-cell data faces many challenges. In this Perspective, we describe the impact that fundamental concepts from statistical mechanics, notably entropy, stochastic processes and critical phenomena, are having on single-cell data analysis. We further advocate the need for more bottom-up modelling of single-cell data and to embrace a statistical mechanics analysis paradigm to help attain a deeper understanding of single-cell systems biology.
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Affiliation(s)
- Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. .,UCL Cancer Institute, University College London, London, UK.
| | - Andrew P Feinberg
- Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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10
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Zanin M, Aitya NA, Basilio J, Baumbach J, Benis A, Behera CK, Bucholc M, Castiglione F, Chouvarda I, Comte B, Dao TT, Ding X, Pujos-Guillot E, Filipovic N, Finn DP, Glass DH, Harel N, Iesmantas T, Ivanoska I, Joshi A, Boudjeltia KZ, Kaoui B, Kaur D, Maguire LP, McClean PL, McCombe N, de Miranda JL, Moisescu MA, Pappalardo F, Polster A, Prasad G, Rozman D, Sacala I, Sanchez-Bornot JM, Schmid JA, Sharp T, Solé-Casals J, Spiwok V, Spyrou GM, Stalidzans E, Stres B, Sustersic T, Symeonidis I, Tieri P, Todd S, Van Steen K, Veneva M, Wang DH, Wang H, Wang H, Watterson S, Wong-Lin K, Yang S, Zou X, Schmidt HH. An Early Stage Researcher's Primer on Systems Medicine Terminology. NETWORK AND SYSTEMS MEDICINE 2021; 4:2-50. [PMID: 33659919 PMCID: PMC7919422 DOI: 10.1089/nsm.2020.0003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/27/2020] [Indexed: 12/19/2022] Open
Abstract
Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.
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Affiliation(s)
- Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Nadim A.A. Aitya
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - José Basilio
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Jan Baumbach
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Arriel Benis
- Faculty of Technology Management, Holon Institute of Technology (HIT), Holon, Israel
| | - Chandan K. Behera
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Magda Bucholc
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Ioanna Chouvarda
- Lab of Computing, Medical Informatics, and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Blandine Comte
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Tien-Tuan Dao
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Xuemei Ding
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Estelle Pujos-Guillot
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Nenad Filipovic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - David P. Finn
- Pharmacology and Therapeutics, School of Medicine, Galway Neuroscience Centre, National University of Ireland, Galway, Republic of Ireland
| | - David H. Glass
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Nissim Harel
- Faculty of Sciences, Holon Institute of Technology (HIT), Holon, Israel
| | - Tomas Iesmantas
- Department of Mathematics and Natural Sciences, Kaunas University of Technology, Kaunas, Lithuania
| | - Ilinka Ivanoska
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, Macedonia
| | - Alok Joshi
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Karim Zouaoui Boudjeltia
- Laboratory of Experimental Medicine (ULB 222), Medicine Faculty, Université libre de Bruxelles, CHU de Charleroi, Charleroi, Belgium
| | - Badr Kaoui
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Daman Kaur
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Liam P. Maguire
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Paula L. McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Niamh McCombe
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - João Luís de Miranda
- Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre, Portalegre, Portugal
- Centro de Recursos Naturais e Ambiente (CERENA), Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | | | | | - Annikka Polster
- Centre for Molecular Medicine Norway (NCMM), Forskningparken, Oslo, Norway
| | - Girijesh Prasad
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Ioan Sacala
- Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Jose M. Sanchez-Bornot
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Johannes A. Schmid
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Trevor Sharp
- Department of Pharmacology, University of Oxford, Oxford, United Kingdom
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic–Central University of Catalonia, Vic, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- College of Artificial Intelligence, Nankai University, Tianjin, China
| | - Vojtěch Spiwok
- Department of Biochemistry and Microbiology, University of Chemistry and Technology, Prague, Czech Republic
| | - George M. Spyrou
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Egils Stalidzans
- Computational Systems Biology Group, Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Blaž Stres
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Tijana Sustersic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - Ioannis Symeonidis
- Center for Research and Technology Hellas, Hellenic Institute of Transport, Thessaloniki, Greece
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Stephen Todd
- Altnagelvin Area Hospital, Western Health and Social Care Trust, Altnagelvin, United Kingdom
| | - Kristel Van Steen
- BIO3-Systems Genetics, GIGA-R, University of Liege, Liege, Belgium
- BIO3-Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Da-Hui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, and School of Systems Science, Beijing Normal University, Beijing, China
| | - Haiying Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Hui Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry, United Kingdom
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Su Yang
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Xin Zou
- Shanghai Centre for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Harald H.H.W. Schmidt
- Faculty of Health, Medicine & Life Science, Maastricht University, Maastricht, The Netherlands
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11
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Hérault L, Poplineau M, Mazuel A, Platet N, Remy É, Duprez E. Single-cell RNA-seq reveals a concomitant delay in differentiation and cell cycle of aged hematopoietic stem cells. BMC Biol 2021; 19:19. [PMID: 33526011 PMCID: PMC7851934 DOI: 10.1186/s12915-021-00955-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 01/08/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Hematopoietic stem cells (HSCs) are the guarantor of the proper functioning of hematopoiesis due to their incredible diversity of potential. During aging, heterogeneity of HSCs changes, contributing to the deterioration of the immune system. In this study, we revisited mouse HSC compartment and its transcriptional plasticity during aging at unicellular scale. RESULTS Through the analysis of 15,000 young and aged transcriptomes, we identified 15 groups of HSCs revealing rare and new specific HSC abilities that change with age. The implantation of new trajectories complemented with the analysis of transcription factor activities pointed consecutive states of HSC differentiation that were delayed by aging and explained the bias in differentiation of older HSCs. Moreover, reassigning cell cycle phases for each HSC clearly highlighted an imbalance of the cell cycle regulators of very immature aged HSCs that may contribute to their accumulation in an undifferentiated state. CONCLUSIONS Our results establish a new reference map of HSC differentiation in young and aged mice and reveal a potential mechanism that delays the differentiation of aged HSCs and could promote the emergence of age-related hematologic diseases.
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Affiliation(s)
- Léonard Hérault
- Epigenetic Factors in Normal and Malignant Hematopoiesis Team, Aix Marseille Université, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
- Aix Marseille Université, CNRS, Centrale Marseille, I2M, Marseille, France
| | - Mathilde Poplineau
- Epigenetic Factors in Normal and Malignant Hematopoiesis Team, Aix Marseille Université, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
| | - Adrien Mazuel
- Epigenetic Factors in Normal and Malignant Hematopoiesis Team, Aix Marseille Université, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
| | - Nadine Platet
- Epigenetic Factors in Normal and Malignant Hematopoiesis Team, Aix Marseille Université, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
| | - Élisabeth Remy
- Aix Marseille Université, CNRS, Centrale Marseille, I2M, Marseille, France
| | - Estelle Duprez
- Epigenetic Factors in Normal and Malignant Hematopoiesis Team, Aix Marseille Université, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France.
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12
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Abstract
Diverse cellular phenotypes are determined by groups of transcription factors (TFs) and other regulators that influence each others' gene expression, forming transcriptional gene regulatory networks (GRNs). In many biological contexts, especially in development and associated diseases, the expression of the genes in GRNs is not static but evolves in time. Modeling the dynamics of GRN state is an important approach for understanding diverse cellular phenomena such as cell-fate specification, pluripotency and cell-fate reprogramming, oncogenesis, and tissue regeneration. In this protocol, we describe how to model GRNs using a data-driven dynamic modeling methodology, gene circuits. Gene circuits do not require knowledge of the GRN topology and connectivity but instead learn them from training data, making them very general and applicable to diverse biological contexts. We utilize the MATLAB-based gene circuit modeling software Fast Inference of Gene Regulation (FIGR) for training the model on quantitative gene expression data and simulating the GRN. We describe all the steps in the modeling life cycle, from formulating the model, training the model using FIGR, simulating the GRN, to analyzing and interpreting the model output. This protocol highlights these steps with the example of a dynamical model of the gap gene GRN involved in Drosophila segmentation and includes example MATLAB statements for each step.
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Affiliation(s)
- Joanna E Handzlik
- Department of Biology, University of North Dakota, Grand Forks, ND, 58202, USA
| | - Yen Lee Loh
- Department of Physics and Astrophysics, University of North Dakota, Grand Forks, ND, 58202, USA
| | - Manu
- Department of Biology, University of North Dakota, Grand Forks, ND, 58202, USA.
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13
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An S, Cho SY, Kang J, Lee S, Kim HS, Min DJ, Son E, Cho KH. Inhibition of 3-phosphoinositide-dependent protein kinase 1 (PDK1) can revert cellular senescence in human dermal fibroblasts. Proc Natl Acad Sci U S A 2020; 117:31535-31546. [PMID: 33229519 PMCID: PMC7733858 DOI: 10.1073/pnas.1920338117] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Cellular senescence is defined as a stable, persistent arrest of cell proliferation. Here, we examine whether senescent cells can lose senescence hallmarks and reenter a reversible state of cell-cycle arrest (quiescence). We constructed a molecular regulatory network of cellular senescence based on previous experimental evidence. To infer the regulatory logic of the network, we performed phosphoprotein array experiments with normal human dermal fibroblasts and used the data to optimize the regulatory relationships between molecules with an evolutionary algorithm. From ensemble analysis of network models, we identified 3-phosphoinositide-dependent protein kinase 1 (PDK1) as a promising target for inhibitors to convert the senescent state to the quiescent state. We showed that inhibition of PDK1 in senescent human dermal fibroblasts eradicates senescence hallmarks and restores entry into the cell cycle by suppressing both nuclear factor κB and mTOR signaling, resulting in restored skin regeneration capacity. Our findings provide insight into a potential therapeutic strategy to treat age-related diseases associated with the accumulation of senescent cells.
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Affiliation(s)
- Sugyun An
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Si-Young Cho
- R&D Unit, Amorepacific Corporation, 17074 Gyeonggi-do, Republic of Korea
| | - Junsoo Kang
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Soobeom Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Hyung-Su Kim
- R&D Unit, Amorepacific Corporation, 17074 Gyeonggi-do, Republic of Korea
| | - Dae-Jin Min
- R&D Unit, Amorepacific Corporation, 17074 Gyeonggi-do, Republic of Korea
| | - EuiDong Son
- R&D Unit, Amorepacific Corporation, 17074 Gyeonggi-do, Republic of Korea
| | - Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea;
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14
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Jacobsen A, Ivanova O, Amini S, Heringa J, Kemmeren P, Feenstra KA. A framework for exhaustive modelling of genetic interaction patterns using Petri nets. Bioinformatics 2020; 36:2142-2149. [PMID: 31845959 DOI: 10.1093/bioinformatics/btz917] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Revised: 07/09/2019] [Accepted: 12/13/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Genetic interaction (GI) patterns are characterized by the phenotypes of interacting single and double mutated gene pairs. Uncovering the regulatory mechanisms of GIs would provide a better understanding of their role in biological processes, diseases and drug response. Computational analyses can provide insights into the underpinning mechanisms of GIs. RESULTS In this study, we present a framework for exhaustive modelling of GI patterns using Petri nets (PN). Four-node models were defined and generated on three levels with restrictions, to enable an exhaustive approach. Simulations suggest ∼5 million models of GIs. Generalizing these we propose putative mechanisms for the GI patterns, inversion and suppression. We demonstrate that exhaustive PN modelling enables reasoning about mechanisms of GIs when only the phenotypes of gene pairs are known. The framework can be applied to other GI or genetic regulatory datasets. AVAILABILITY AND IMPLEMENTATION The framework is available at http://www.ibi.vu.nl/programs/ExhMod. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Annika Jacobsen
- Department of Computer Science, Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
| | - Olga Ivanova
- Department of Computer Science, Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
| | - Saman Amini
- Princess Máxima Center for Pediatric Oncology, 3584 CS Utrecht, Netherlands.,Divison of Biomedical Genetics, Center for Molecular Medicine, University Medical Centre Utrecht, 3584 CX Utrecht, Netherlands
| | - Jaap Heringa
- Department of Computer Science, Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
| | - Patrick Kemmeren
- Princess Máxima Center for Pediatric Oncology, 3584 CS Utrecht, Netherlands.,Divison of Biomedical Genetics, Center for Molecular Medicine, University Medical Centre Utrecht, 3584 CX Utrecht, Netherlands
| | - K Anton Feenstra
- Department of Computer Science, Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
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15
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Hall BA, Fisher J. Constructing and Analyzing Computational Models of Cell Signaling with BioModelAnalyzer. CURRENT PROTOCOLS IN BIOINFORMATICS 2020; 69:e95. [PMID: 32078258 DOI: 10.1002/cpbi.95] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BioModelAnalyzer (BMA) is an open-source graphical tool for the development of executable models of protein and gene networks within cells. Based upon the Qualitative Networks formalism, the user can rapidly construct large networks, either manually or by connecting motifs selected from a built-in library. After the appropriate functions for each variable are defined, the user has access to three analysis engines to test the model. In addition to standard simulation tools, BMA includes an interface to the stability-testing algorithm and to a graphical Linear Temporal Logic (LTL) editor and analysis tool. Alongside this, we have developed a novel ChatBot to aid users constructing LTL queries and to explain the interface and run through tutorials. Here we present worked examples of model construction and testing via the interface. As an initial example, we discuss fate decisions in Dictyostelium discoidum and cAMP signaling. We go on to describe the workflow leading to the construction of a published model of the germline of C. elegans. Finally, we demonstrate how to construct simple models from the built-in network motif library. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Modeling the signaling network of Dictyostelium discoidum Basic Protocol 2: Modeling the germline progression of Caenorhabditis elegans Basic Protocol 3: Constructing a model of the cell cycle using motifs.
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Affiliation(s)
- Benjamin A Hall
- MRC Cancer Unit, University of Cambridge, Cambridge, United Kingdom
| | - Jasmin Fisher
- UCL Cancer Institute, University College London, London, United Kingdom
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16
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Classification-Based Inference of Dynamical Models of Gene Regulatory Networks. G3-GENES GENOMES GENETICS 2019; 9:4183-4195. [PMID: 31624138 PMCID: PMC6893186 DOI: 10.1534/g3.119.400603] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Cell-fate decisions during development are controlled by densely interconnected gene regulatory networks (GRNs) consisting of many genes. Inferring and predictively modeling these GRNs is crucial for understanding development and other physiological processes. Gene circuits, coupled differential equations that represent gene product synthesis with a switch-like function, provide a biologically realistic framework for modeling the time evolution of gene expression. However, their use has been limited to smaller networks due to the computational expense of inferring model parameters from gene expression data using global non-linear optimization. Here we show that the switch-like nature of gene regulation can be exploited to break the gene circuit inference problem into two simpler optimization problems that are amenable to computationally efficient supervised learning techniques. We present FIGR (Fast Inference of Gene Regulation), a novel classification-based inference approach to determining gene circuit parameters. We demonstrate FIGR’s effectiveness on synthetic data generated from random gene circuits of up to 50 genes as well as experimental data from the gap gene system of Drosophila melanogaster, a benchmark for inferring dynamical GRN models. FIGR is faster than global non-linear optimization by a factor of 600 and its computational complexity scales much better with GRN size. On a practical level, FIGR can accurately infer the biologically realistic gap gene network in under a minute on desktop-class hardware instead of requiring hours of parallel computing. We anticipate that FIGR would enable the inference of much larger biologically realistic GRNs than was possible before.
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17
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Executable pathway analysis using ensemble discrete-state modeling for large-scale data. PLoS Comput Biol 2019; 15:e1007317. [PMID: 31479446 PMCID: PMC6743792 DOI: 10.1371/journal.pcbi.1007317] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 09/13/2019] [Accepted: 08/01/2019] [Indexed: 12/15/2022] Open
Abstract
Pathway analysis is widely used to gain mechanistic insights from high-throughput omics data. However, most existing methods do not consider signal integration represented by pathway topology, resulting in enrichment of convergent pathways when downstream genes are modulated. Incorporation of signal flow and integration in pathway analysis could rank the pathways based on modulation in key regulatory genes. This implementation can be facilitated for large-scale data by discrete state network modeling due to simplicity in parameterization. Here, we model cellular heterogeneity using discrete state dynamics and measure pathway activities in cross-sectional data. We introduce a new algorithm, Boolean Omics Network Invariant-Time Analysis (BONITA), for signal propagation, signal integration, and pathway analysis. Our signal propagation approach models heterogeneity in transcriptomic data as arising from intercellular heterogeneity rather than intracellular stochasticity, and propagates binary signals repeatedly across networks. Logic rules defining signal integration are inferred by genetic algorithm and are refined by local search. The rules determine the impact of each node in a pathway, which is used to score the probability of the pathway's modulation by chance. We have comprehensively tested BONITA for application to transcriptomics data from translational studies. Comparison with state-of-the-art pathway analysis methods shows that BONITA has higher sensitivity at lower levels of source node modulation and similar sensitivity at higher levels of source node modulation. Application of BONITA pathway analysis to previously validated RNA-sequencing studies identifies additional relevant pathways in in-vitro human cell line experiments and in-vivo infant studies. Additionally, BONITA successfully detected modulation of disease specific pathways when comparing relevant RNA-sequencing data with healthy controls. Most interestingly, the two highest impact score nodes identified by BONITA included known drug targets. Thus, BONITA is a powerful approach to prioritize not only pathways but also specific mechanistic role of genes compared to existing methods. BONITA is available at: https://github.com/thakar-lab/BONITA.
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18
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Enciso J, Pelayo R, Villarreal C. From Discrete to Continuous Modeling of Lymphocyte Development and Plasticity in Chronic Diseases. Front Immunol 2019; 10:1927. [PMID: 31481957 PMCID: PMC6710364 DOI: 10.3389/fimmu.2019.01927] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 07/30/2019] [Indexed: 12/12/2022] Open
Abstract
The molecular events leading to differentiation, development, and plasticity of lymphoid cells have been subject of intense research due to their key roles in multiple pathologies, such as lymphoproliferative disorders, tumor growth maintenance and chronic diseases. The emergent roles of lymphoid cells and the use of high-throughput technologies have led to an extensive accumulation of experimental data allowing the reconstruction of gene regulatory networks (GRN) by integrating biochemical signals provided by the microenvironment with transcriptional modules of lineage-specific genes. Computational modeling of GRN has been useful for the identification of molecular switches involved in lymphoid specification, prediction of microenvironment-dependent cell plasticity, and analyses of signaling events occurring downstream the activation of antigen recognition receptors. Among most common modeling strategies to analyze the dynamical behavior of GRN, discrete dynamic models are widely used for their capacity to capture molecular interactions when a limited knowledge of kinetic parameters is present. However, they are less powerful when modeling complex systems sensitive to biochemical gradients. To compensate it, discrete models may be transformed into regulatory networks that includes state variables and parameters varying within a continuous range. This approach is based on a system of differential equations dynamics with regulatory interactions described by fuzzy logic propositions. Here, we discuss the applicability of this method on modeling of development and plasticity processes of adaptive lymphocytes, and its potential implications in the study of pathological landscapes associated to chronic diseases.
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Affiliation(s)
- Jennifer Enciso
- Centro de Investigación Biomédica de Oriente, Instituto Mexicano del Seguro Social, Mexico City, Mexico
- Programa de Doctorado en Ciencias Biomédicas, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Rosana Pelayo
- Centro de Investigación Biomédica de Oriente, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Carlos Villarreal
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Departamento de Física Cuántica y Fotónica, Instituto de Física, Universidad Nacional Autónoma de México, Mexico City, Mexico
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19
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Amini S, Jacobsen A, Ivanova O, Lijnzaad P, Heringa J, Holstege FCP, Feenstra KA, Kemmeren P. The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern. PLoS Comput Biol 2019; 15:e1007061. [PMID: 31083661 PMCID: PMC6532943 DOI: 10.1371/journal.pcbi.1007061] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 05/23/2019] [Accepted: 04/30/2019] [Indexed: 12/21/2022] Open
Abstract
Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker's yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.
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Affiliation(s)
- Saman Amini
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
- Center for Molecular Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Annika Jacobsen
- Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Olga Ivanova
- Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Philip Lijnzaad
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Jaap Heringa
- Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - K. Anton Feenstra
- Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Patrick Kemmeren
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
- Center for Molecular Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands
- * E-mail:
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20
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Abstract
Hematopoietic stem cells (HSCs) reside at the apex of the hematopoietic hierarchy, possessing the ability to self-renew and differentiate toward all mature blood lineages. Along with more specialized progenitor cells, HSCs have an essential role in maintaining a healthy blood system. Incorrect regulation of cell fate decisions in stem/progenitor cells can lead to an imbalance of mature blood cell populations-a situation seen in diseases such as leukemia. Transcription factors, acting as part of complex regulatory networks, are known to play an important role in regulating hematopoietic cell fate decisions. Yet, discovering the interactions present in these networks remains a big challenge. Here, we discuss a computational method that uses single-cell gene expression data to reconstruct Boolean gene regulatory network models and show how this technique can be applied to enhance our understanding of transcriptional regulation in hematopoiesis.
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Affiliation(s)
- Fiona K Hamey
- Department of Haematology, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Berthold Göttgens
- Department of Haematology, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
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21
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Chen L, Kulasiri D, Samarasinghe S. A Novel Data-Driven Boolean Model for Genetic Regulatory Networks. Front Physiol 2018; 9:1328. [PMID: 30319440 PMCID: PMC6167558 DOI: 10.3389/fphys.2018.01328] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 09/03/2018] [Indexed: 11/30/2022] Open
Abstract
A Boolean model is a simple, discrete and dynamic model without the need to consider the effects at the intermediate levels. However, little effort has been made into constructing activation, inhibition, and protein decay networks, which could indicate the direct roles of a gene (or its synthesized protein) as an activator or inhibitor of a target gene. Therefore, we propose to focus on the general Boolean functions at the subfunction level taking into account the effectiveness of protein decay, and further split the subfunctions into the activation and inhibition domains. As a consequence, we developed a novel data-driven Boolean model; namely, the Fundamental Boolean Model (FBM), to draw insights into gene activation, inhibition, and protein decay. This novel Boolean model provides an intuitive definition of activation and inhibition pathways and includes mechanisms to handle protein decay issues. To prove the concept of the novel model, we implemented a platform using R language, called FBNNet. Our experimental results show that the proposed FBM could explicitly display the internal connections of the mammalian cell cycle between genes separated into the connection types of activation, inhibition and protein decay. Moreover, the method we proposed to infer the gene regulatory networks for the novel Boolean model can be run in parallel and; hence, the computation cost is affordable. Finally, the novel Boolean model and related Fundamental Boolean Networks (FBNs) could show significant trajectories in genes to reveal how genes regulated each other over a given period. This new feature could facilitate further research on drug interventions to detect the side effects of a newly-proposed drug.
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Affiliation(s)
- Leshi Chen
- Computational Systems Biology Laboratory, Centre for Advanced Computational Solutions, Lincoln University, Lincoln, New Zealand
| | - Don Kulasiri
- Computational Systems Biology Laboratory, Centre for Advanced Computational Solutions, Lincoln University, Lincoln, New Zealand
| | - Sandhya Samarasinghe
- Integrated Systems Modelling Group, Centre for Advanced Computational Solutions, Lincoln University, Lincoln, New Zealand
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22
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Woodhouse S, Piterman N, Wintersteiger CM, Göttgens B, Fisher J. SCNS: a graphical tool for reconstructing executable regulatory networks from single-cell genomic data. BMC SYSTEMS BIOLOGY 2018; 12:59. [PMID: 29801503 PMCID: PMC5970485 DOI: 10.1186/s12918-018-0581-y] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2018] [Accepted: 04/10/2018] [Indexed: 11/25/2022]
Abstract
Background Reconstruction of executable mechanistic models from single-cell gene expression data represents a powerful approach to understanding developmental and disease processes. New ambitious efforts like the Human Cell Atlas will soon lead to an explosion of data with potential for uncovering and understanding the regulatory networks which underlie the behaviour of all human cells. In order to take advantage of this data, however, there is a need for general-purpose, user-friendly and efficient computational tools that can be readily used by biologists who do not have specialist computer science knowledge. Results The Single Cell Network Synthesis toolkit (SCNS) is a general-purpose computational tool for the reconstruction and analysis of executable models from single-cell gene expression data. Through a graphical user interface, SCNS takes single-cell qPCR or RNA-sequencing data taken across a time course, and searches for logical rules that drive transitions from early cell states towards late cell states. Because the resulting reconstructed models are executable, they can be used to make predictions about the effect of specific gene perturbations on the generation of specific lineages. Conclusions SCNS should be of broad interest to the growing number of researchers working in single-cell genomics and will help further facilitate the generation of valuable mechanistic insights into developmental, homeostatic and disease processes. Electronic supplementary material The online version of this article (10.1186/s12918-018-0581-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Steven Woodhouse
- Department of Hematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.,Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK.,Microsoft Research Cambridge, 21 Station Road, Cambridge, CB1 2FB, UK
| | - Nir Piterman
- Department of Informatics, University of Leicester, University Road, Leicester, LE1 7RH, UK
| | | | - Berthold Göttgens
- Department of Hematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK. .,Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK.
| | - Jasmin Fisher
- Microsoft Research Cambridge, 21 Station Road, Cambridge, CB1 2FB, UK. .,Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, UK.
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23
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Logical modeling of lymphoid and myeloid cell specification and transdifferentiation. Proc Natl Acad Sci U S A 2018; 114:5792-5799. [PMID: 28584084 DOI: 10.1073/pnas.1610622114] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Blood cells are derived from a common set of hematopoietic stem cells, which differentiate into more specific progenitors of the myeloid and lymphoid lineages, ultimately leading to differentiated cells. This developmental process is controlled by a complex regulatory network involving cytokines and their receptors, transcription factors, and chromatin remodelers. Using public data and data from our own molecular genetic experiments (quantitative PCR, Western blot, EMSA) or genome-wide assays (RNA-sequencing, ChIP-sequencing), we have assembled a comprehensive regulatory network encompassing the main transcription factors and signaling components involved in myeloid and lymphoid development. Focusing on B-cell and macrophage development, we defined a qualitative dynamical model recapitulating cytokine-induced differentiation of common progenitors, the effect of various reported gene knockdowns, and the reprogramming of pre-B cells into macrophages induced by the ectopic expression of specific transcription factors. The resulting network model can be used as a template for the integration of new hematopoietic differentiation and transdifferentiation data to foster our understanding of lymphoid/myeloid cell-fate decisions.
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Olariu V, Peterson C. Kinetic models of hematopoietic differentiation. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2018; 11:e1424. [PMID: 29660842 PMCID: PMC6191385 DOI: 10.1002/wsbm.1424] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 02/13/2018] [Accepted: 03/16/2018] [Indexed: 01/02/2023]
Abstract
As cell and molecular biology is becoming increasingly quantitative, there is an upsurge of interest in mechanistic modeling at different levels of resolution. Such models mostly concern kinetics and include gene and protein interactions as well as cell population dynamics. The final goal of these models is to provide experimental predictions, which is now taking on. However, even without matured predictions, kinetic models serve the purpose of compressing a plurality of experimental results into something that can empower the data interpretation, and importantly, suggesting new experiments by turning "knobs" in silico. Once formulated, kinetic models can be executed in terms of molecular rate equations for concentrations or by stochastic simulations when only a limited number of copies are involved. Developmental processes, in particular those of stem and progenitor cell commitments, are not only topical but also particularly suitable for kinetic modeling due to the finite number of key genes involved in cellular decisions. Stem and progenitor cell commitment processes have been subject to intense experimental studies over the last decade with some emphasis on embryonic and hematopoietic stem cells. Gene and protein interactions governing these processes can be modeled by binary Boolean rules or by continuous-valued models with interactions set by binding strengths. Conceptual insights along with tested predictions have emerged from such kinetic models. Here we review kinetic modeling efforts applied to stem cell developmental systems with focus on hematopoiesis. We highlight the future challenges including multi-scale models integrating cell dynamical and transcriptional models. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Developmental Biology > Stem Cell Biology and Regeneration.
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Affiliation(s)
- Victor Olariu
- Department of Computational Biology, Lund University, Lund, Sweden
| | - Carsten Peterson
- Department of Computational Biology, Lund University, Lund, Sweden
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25
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Bergiers I, Andrews T, Vargel Bölükbaşı Ö, Buness A, Janosz E, Lopez-Anguita N, Ganter K, Kosim K, Celen C, Itır Perçin G, Collier P, Baying B, Benes V, Hemberg M, Lancrin C. Single-cell transcriptomics reveals a new dynamical function of transcription factors during embryonic hematopoiesis. eLife 2018; 7:29312. [PMID: 29555020 PMCID: PMC5860872 DOI: 10.7554/elife.29312] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 02/15/2018] [Indexed: 11/22/2022] Open
Abstract
Recent advances in single-cell transcriptomics techniques have opened the door to the study of gene regulatory networks (GRNs) at the single-cell level. Here, we studied the GRNs controlling the emergence of hematopoietic stem and progenitor cells from mouse embryonic endothelium using a combination of single-cell transcriptome assays. We found that a heptad of transcription factors (Runx1, Gata2, Tal1, Fli1, Lyl1, Erg and Lmo2) is specifically co-expressed in an intermediate population expressing both endothelial and hematopoietic markers. Within the heptad, we identified two sets of factors of opposing functions: one (Erg/Fli1) promoting the endothelial cell fate, the other (Runx1/Gata2) promoting the hematopoietic fate. Surprisingly, our data suggest that even though Fli1 initially supports the endothelial cell fate, it acquires a pro-hematopoietic role when co-expressed with Runx1. This work demonstrates the power of single-cell RNA-sequencing for characterizing complex transcription factor dynamics.
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Affiliation(s)
- Isabelle Bergiers
- European Molecular Biology Laboratory, EMBL Rome, Monterotondo, Italy
| | | | | | - Andreas Buness
- European Molecular Biology Laboratory, EMBL Rome, Monterotondo, Italy
| | - Ewa Janosz
- European Molecular Biology Laboratory, EMBL Rome, Monterotondo, Italy
| | | | - Kerstin Ganter
- European Molecular Biology Laboratory, EMBL Rome, Monterotondo, Italy
| | - Kinga Kosim
- European Molecular Biology Laboratory, EMBL Rome, Monterotondo, Italy
| | - Cemre Celen
- European Molecular Biology Laboratory, EMBL Rome, Monterotondo, Italy
| | - Gülce Itır Perçin
- European Molecular Biology Laboratory, EMBL Rome, Monterotondo, Italy
| | - Paul Collier
- Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Bianka Baying
- Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Vladimir Benes
- Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Martin Hemberg
- Wellcome Trust Sanger Institute, Hinxton, United Kingdom
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26
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Yu P, Nie Q, Tang C, Zhang L. Nanog induced intermediate state in regulating stem cell differentiation and reprogramming. BMC SYSTEMS BIOLOGY 2018; 12:22. [PMID: 29486740 PMCID: PMC6389130 DOI: 10.1186/s12918-018-0552-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 02/21/2018] [Indexed: 01/18/2023]
Abstract
Background Heterogeneous gene expressions of cells are widely observed in self-renewing pluripotent stem cells, suggesting possible coexistence of multiple cellular states with distinct characteristics. Though the elements regulating cellular states have been identified, the underlying dynamic mechanisms and the significance of such cellular heterogeneity remain elusive. Results We present a gene regulatory network model to investigate the bimodal Nanog distribution in stem cells. Our model reveals a novel role of dynamic conversion between the cellular states of high and low Nanog levels. Model simulations demonstrate that the low-Nanog state benefits cell differentiation through serving as an intermediate state to reduce the barrier of transition. Interestingly, the existence of low-Nanog state dynamically slows down the reprogramming process, and additional Nanog activation is found to be essential to quickly attaining the fully reprogrammed cell state. Conclusions Nanog has been recognized as a critical pluripotency gene in stem cell regulation. Our modeling results quantitatively show a dual role of Nanog during stem cell differentiation and reprogramming, and the importance of the intermediate state during cell state transitions. Our approach offers a general method for analyzing key regulatory factors controlling cell differentiation and reprogramming. Electronic supplementary material The online version of this article (10.1186/s12918-018-0552-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Peijia Yu
- Center for Quantitative Biology, Peking University, Beijing, 100871, China
| | - Qing Nie
- Department of Mathematics and Departmentof Developmental and Cell Biology, University of California Irvine, Irvine, CA, 92697, USA.
| | - Chao Tang
- Center for Quantitative Biology, Peking University, Beijing, 100871, China. .,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
| | - Lei Zhang
- Center for Quantitative Biology, Peking University, Beijing, 100871, China. .,Beijing International Center for Mathematical Research, Peking University, Beijing, 100871, China.
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27
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Ramírez C, Mendoza L. Phenotypic stability and plasticity in GMP-derived cells as determined by their underlying regulatory network. Bioinformatics 2017; 34:1174-1182. [DOI: 10.1093/bioinformatics/btx736] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 11/23/2017] [Indexed: 12/30/2022] Open
Affiliation(s)
- Carlos Ramírez
- Programa de Doctorado en Ciencias Biomédicas, Universidad Nacional Autónoma de México
- Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Cd. Mx., México
| | - Luis Mendoza
- Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Cd. Mx., México
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28
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Wilkinson AC, Nakauchi H, Göttgens B. Mammalian Transcription Factor Networks: Recent Advances in Interrogating Biological Complexity. Cell Syst 2017; 5:319-331. [PMID: 29073372 PMCID: PMC5928788 DOI: 10.1016/j.cels.2017.07.004] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 06/29/2017] [Accepted: 07/20/2017] [Indexed: 12/11/2022]
Abstract
Transcription factor (TF) networks are a key determinant of cell fate decisions in mammalian development and adult tissue homeostasis and are frequently corrupted in disease. However, our inability to experimentally resolve and interrogate the complexity of mammalian TF networks has hampered the progress in this field. Recent technological advances, in particular large-scale genome-wide approaches, single-cell methodologies, live-cell imaging, and genome editing, are emerging as important technologies in TF network biology. Several recent studies even suggest a need to re-evaluate established models of mammalian TF networks. Here, we provide a brief overview of current and emerging methods to define mammalian TF networks. We also discuss how these emerging technologies facilitate new ways to interrogate complex TF networks, consider the current open questions in the field, and comment on potential future directions and biomedical applications.
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Affiliation(s)
- Adam C Wilkinson
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, 265 Campus Drive, Stanford, CA 94305, USA
| | - Hiromitsu Nakauchi
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, 265 Campus Drive, Stanford, CA 94305, USA; Division of Stem Cell Therapy, Center for Stem Cell Biology and Regenerative Medicine, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Berthold Göttgens
- Department of Haematology, Cambridge Institute for Medical Research and Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0XY, UK.
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29
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Rothenberg EV. Fitting structure to function in gene regulatory networks. HISTORY AND PHILOSOPHY OF THE LIFE SCIENCES 2017; 39:37. [PMID: 29038942 PMCID: PMC5660880 DOI: 10.1007/s40656-017-0164-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Cascades of transcriptional regulation are the common source of the forward drive in all developmental systems. Increases in complexity and specificity of gene expression at successive stages are based on the collaboration of varied combinations of transcription factors already expressed in the cells to turn on new genes, and the logical relationships between the transcription factors acting and becoming newly expressed from stage to stage are best visualized as gene regulatory networks. However, gene regulatory networks used in different developmental contexts underlie processes that actually operate through different sets of rules, which affect the kinetics, synchronicity, and logical properties of individual network nodes. Contrasting early embryonic development in flies and sea urchins with adult mammalian hematopoietic development from stem cells, major differences are seen in transcription factor dosage dependence, the silencing or damping impacts of repression, and the impact of cellular regulatory history on the parts of the genome that are accessible to transcription factor action in a given cell type. These different features not only affect the kinds of models that can illuminate developmental mechanisms in the respective biological systems, but also reflect the evolutionary needs of these biological systems to optimize different aspects of development.
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Affiliation(s)
- Ellen V Rothenberg
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.
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30
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Haydarlou R, Jacobsen A, Bonzanni N, Feenstra KA, Abeln S, Heringa J. BioASF: a framework for automatically generating executable pathway models specified in BioPAX. Bioinformatics 2017; 32:i60-i69. [PMID: 27307645 PMCID: PMC4908334 DOI: 10.1093/bioinformatics/btw250] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Motivation: Biological pathways play a key role in most cellular functions. To better understand these functions, diverse computational and cell biology researchers use biological pathway data for various analysis and modeling purposes. For specifying these biological pathways, a community of researchers has defined BioPAX and provided various tools for creating, validating and visualizing BioPAX models. However, a generic software framework for simulating BioPAX models is missing. Here, we attempt to fill this gap by introducing a generic simulation framework for BioPAX. The framework explicitly separates the execution model from the model structure as provided by BioPAX, with the advantage that the modelling process becomes more reproducible and intrinsically more modular; this ensures natural biological constraints are satisfied upon execution. The framework is based on the principles of discrete event systems and multi-agent systems, and is capable of automatically generating a hierarchical multi-agent system for a given BioPAX model. Results: To demonstrate the applicability of the framework, we simulated two types of biological network models: a gene regulatory network modeling the haematopoietic stem cell regulators and a signal transduction network modeling the Wnt/β-catenin signaling pathway. We observed that the results of the simulations performed using our framework were entirely consistent with the simulation results reported by the researchers who developed the original models in a proprietary language. Availability and Implementation: The framework, implemented in Java, is open source and its source code, documentation and tutorial are available at http://www.ibi.vu.nl/programs/BioASF. Contact:j.heringa@vu.nl
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Affiliation(s)
- Reza Haydarlou
- Centre for Integrative Bioinformatics (IBIVU) & Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1081, Amsterdam, The Netherlands
| | - Annika Jacobsen
- Centre for Integrative Bioinformatics (IBIVU) & Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1081, Amsterdam, The Netherlands
| | - Nicola Bonzanni
- Centre for Integrative Bioinformatics (IBIVU) & Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1081, Amsterdam, The Netherlands NKI-AVL, The Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam, The Netherlands
| | - K Anton Feenstra
- Centre for Integrative Bioinformatics (IBIVU) & Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1081, Amsterdam, The Netherlands
| | - Sanne Abeln
- Centre for Integrative Bioinformatics (IBIVU) & Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1081, Amsterdam, The Netherlands
| | - Jaap Heringa
- Centre for Integrative Bioinformatics (IBIVU) & Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1081, Amsterdam, The Netherlands
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31
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Traynard P, Tobalina L, Eduati F, Calzone L, Saez-Rodriguez J. Logic Modeling in Quantitative Systems Pharmacology. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 6:499-511. [PMID: 28681552 PMCID: PMC5572374 DOI: 10.1002/psp4.12225] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 06/01/2017] [Accepted: 06/15/2017] [Indexed: 12/12/2022]
Abstract
Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).
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Affiliation(s)
- Pauline Traynard
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Luis Tobalina
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany
| | - Federica Eduati
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Laurence Calzone
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Julio Saez-Rodriguez
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany.,European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
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32
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Hamey FK, Nestorowa S, Kinston SJ, Kent DG, Wilson NK, Göttgens B. Reconstructing blood stem cell regulatory network models from single-cell molecular profiles. Proc Natl Acad Sci U S A 2017; 114:5822-5829. [PMID: 28584094 PMCID: PMC5468644 DOI: 10.1073/pnas.1610609114] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Adult blood contains a mixture of mature cell types, each with specialized functions. Single hematopoietic stem cells (HSCs) have been functionally shown to generate all mature cell types for the lifetime of the organism. Differentiation of HSCs toward alternative lineages must be balanced at the population level by the fate decisions made by individual cells. Transcription factors play a key role in regulating these decisions and operate within organized regulatory programs that can be modeled as transcriptional regulatory networks. As dysregulation of single HSC fate decisions is linked to fatal malignancies such as leukemia, it is important to understand how these decisions are controlled on a cell-by-cell basis. Here we developed and applied a network inference method, exploiting the ability to infer dynamic information from single-cell snapshot expression data based on expression profiles of 48 genes in 2,167 blood stem and progenitor cells. This approach allowed us to infer transcriptional regulatory network models that recapitulated differentiation of HSCs into progenitor cell types, focusing on trajectories toward megakaryocyte-erythrocyte progenitors and lymphoid-primed multipotent progenitors. By comparing these two models, we identified and subsequently experimentally validated a difference in the regulation of nuclear factor, erythroid 2 (Nfe2) and core-binding factor, runt domain, alpha subunit 2, translocated to, 3 homolog (Cbfa2t3h) by the transcription factor Gata2. Our approach confirms known aspects of hematopoiesis, provides hypotheses about regulation of HSC differentiation, and is widely applicable to other hierarchical biological systems to uncover regulatory relationships.
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Affiliation(s)
- Fiona K Hamey
- Department of Haematology, Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge Institute for Medical Research, Cambridge CB2 0XY, United Kingdom
| | - Sonia Nestorowa
- Department of Haematology, Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge Institute for Medical Research, Cambridge CB2 0XY, United Kingdom
| | - Sarah J Kinston
- Department of Haematology, Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge Institute for Medical Research, Cambridge CB2 0XY, United Kingdom
| | - David G Kent
- Department of Haematology, Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge Institute for Medical Research, Cambridge CB2 0XY, United Kingdom
| | - Nicola K Wilson
- Department of Haematology, Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge Institute for Medical Research, Cambridge CB2 0XY, United Kingdom
| | - Berthold Göttgens
- Department of Haematology, Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge Institute for Medical Research, Cambridge CB2 0XY, United Kingdom
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33
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Lim CY, Wang H, Woodhouse S, Piterman N, Wernisch L, Fisher J, Göttgens B. BTR: training asynchronous Boolean models using single-cell expression data. BMC Bioinformatics 2016; 17:355. [PMID: 27600248 PMCID: PMC5012073 DOI: 10.1186/s12859-016-1235-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 09/01/2016] [Indexed: 12/25/2022] Open
Abstract
Background Rapid technological innovation for the generation of single-cell genomics data presents new challenges and opportunities for bioinformatics analysis. One such area lies in the development of new ways to train gene regulatory networks. The use of single-cell expression profiling technique allows the profiling of the expression states of hundreds of cells, but these expression states are typically noisier due to the presence of technical artefacts such as drop-outs. While many algorithms exist to infer a gene regulatory network, very few of them are able to harness the extra expression states present in single-cell expression data without getting adversely affected by the substantial technical noise present. Results Here we introduce BTR, an algorithm for training asynchronous Boolean models with single-cell expression data using a novel Boolean state space scoring function. BTR is capable of refining existing Boolean models and reconstructing new Boolean models by improving the match between model prediction and expression data. We demonstrate that the Boolean scoring function performed favourably against the BIC scoring function for Bayesian networks. In addition, we show that BTR outperforms many other network inference algorithms in both bulk and single-cell synthetic expression data. Lastly, we introduce two case studies, in which we use BTR to improve published Boolean models in order to generate potentially new biological insights. Conclusions BTR provides a novel way to refine or reconstruct Boolean models using single-cell expression data. Boolean model is particularly useful for network reconstruction using single-cell data because it is more robust to the effect of drop-outs. In addition, BTR does not assume any relationship in the expression states among cells, it is useful for reconstructing a gene regulatory network with as few assumptions as possible. Given the simplicity of Boolean models and the rapid adoption of single-cell genomics by biologists, BTR has the potential to make an impact across many fields of biomedical research. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1235-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chee Yee Lim
- Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge, CB2 0XY, UK
| | - Huange Wang
- Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge, CB2 0XY, UK
| | - Steven Woodhouse
- Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge, CB2 0XY, UK
| | - Nir Piterman
- Department of Computer Science, University of Leicester, Leicester, UK
| | | | - Jasmin Fisher
- Microsoft Research Cambridge, Cambridge, UK.,Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Berthold Göttgens
- Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge, CB2 0XY, UK.
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Cho KH, Joo JI, Shin D, Kim D, Park SM. The reverse control of irreversible biological processes. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2016; 8:366-77. [PMID: 27327189 PMCID: PMC5094504 DOI: 10.1002/wsbm.1346] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Revised: 04/16/2016] [Accepted: 04/28/2016] [Indexed: 12/17/2022]
Abstract
Most biological processes have been considered to be irreversible for a long time, but some recent studies have shown the possibility of their reversion at a cellular level. How can we then understand the reversion of such biological processes? We introduce a unified conceptual framework based on the attractor landscape, a molecular phase portrait describing the dynamics of a molecular regulatory network, and the phenotype landscape, a map of phenotypes determined by the steady states of particular output molecules in the attractor landscape. In this framework, irreversible processes involve reshaping of the phenotype landscape, and the landscape reshaping causes the irreversibility of processes. We suggest reverse control by network rewiring which changes network dynamics with constant perturbation, resulting in the restoration of the original phenotype landscape. The proposed framework provides a conceptual basis for the reverse control of irreversible biological processes through network rewiring. WIREs Syst Biol Med 2016, 8:366–377. doi: 10.1002/wsbm.1346 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Jae Il Joo
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Dongkwan Shin
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Dongsan Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Sang-Min Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
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35
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Abou-Jaoudé W, Traynard P, Monteiro PT, Saez-Rodriguez J, Helikar T, Thieffry D, Chaouiya C. Logical Modeling and Dynamical Analysis of Cellular Networks. Front Genet 2016; 7:94. [PMID: 27303434 PMCID: PMC4885885 DOI: 10.3389/fgene.2016.00094] [Citation(s) in RCA: 125] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2016] [Accepted: 05/12/2016] [Indexed: 12/28/2022] Open
Abstract
The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.
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Affiliation(s)
- Wassim Abou-Jaoudé
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, PSL Research UniversityParis, France
| | - Pauline Traynard
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, PSL Research UniversityParis, France
| | - Pedro T. Monteiro
- INESC-ID/Instituto Superior Técnico, University of LisbonLisbon, Portugal
- Instituto Gulbenkian de CiênciaOeiras, Portugal
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen UniversityAachen, Germany
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-LincolnLincoln, NE, USA
| | - Denis Thieffry
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, PSL Research UniversityParis, France
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36
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Construction and Experimental Validation of a Petri Net Model of Wnt/β-Catenin Signaling. PLoS One 2016; 11:e0155743. [PMID: 27218469 PMCID: PMC4878796 DOI: 10.1371/journal.pone.0155743] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Accepted: 05/03/2016] [Indexed: 11/19/2022] Open
Abstract
The Wnt/β-catenin signaling pathway is important for multiple developmental processes and tissue maintenance in adults. Consequently, deregulated signaling is involved in a range of human diseases including cancer and developmental defects. A better understanding of the intricate regulatory mechanism and effect of physiological (active) and pathophysiological (hyperactive) WNT signaling is important for predicting treatment response and developing novel therapies. The constitutively expressed CTNNB1 (commonly and hereafter referred to as β-catenin) is degraded by a destruction complex, composed of amongst others AXIN1 and GSK3. The destruction complex is inhibited during active WNT signaling, leading to β-catenin stabilization and induction of β-catenin/TCF target genes. In this study we investigated the mechanism and effect of β-catenin stabilization during active and hyperactive WNT signaling in a combined in silico and in vitro approach. We constructed a Petri net model of Wnt/β-catenin signaling including main players from the plasma membrane (WNT ligands and receptors), cytoplasmic effectors and the downstream negative feedback target gene AXIN2. We validated that our model can be used to simulate both active (WNT stimulation) and hyperactive (GSK3 inhibition) signaling by comparing our simulation and experimental data. We used this experimentally validated model to get further insights into the effect of the negative feedback regulator AXIN2 upon WNT stimulation and observed an attenuated β-catenin stabilization. We furthermore simulated the effect of APC inactivating mutations, yielding a stabilization of β-catenin levels comparable to the Wnt-pathway activities observed in colorectal and breast cancer. Our model can be used for further investigation and viable predictions of the role of Wnt/β-catenin signaling in oncogenesis and development.
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Schütte J, Wang H, Antoniou S, Jarratt A, Wilson NK, Riepsaame J, Calero-Nieto FJ, Moignard V, Basilico S, Kinston SJ, Hannah RL, Chan MC, Nürnberg ST, Ouwehand WH, Bonzanni N, de Bruijn MF, Göttgens B. An experimentally validated network of nine haematopoietic transcription factors reveals mechanisms of cell state stability. eLife 2016; 5:e11469. [PMID: 26901438 PMCID: PMC4798972 DOI: 10.7554/elife.11469] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2015] [Accepted: 02/12/2016] [Indexed: 12/12/2022] Open
Abstract
Transcription factor (TF) networks determine cell-type identity by establishing and maintaining lineage-specific expression profiles, yet reconstruction of mammalian regulatory network models has been hampered by a lack of comprehensive functional validation of regulatory interactions. Here, we report comprehensive ChIP-Seq, transgenic and reporter gene experimental data that have allowed us to construct an experimentally validated regulatory network model for haematopoietic stem/progenitor cells (HSPCs). Model simulation coupled with subsequent experimental validation using single cell expression profiling revealed potential mechanisms for cell state stabilisation, and also how a leukaemogenic TF fusion protein perturbs key HSPC regulators. The approach presented here should help to improve our understanding of both normal physiological and disease processes.
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Affiliation(s)
- Judith Schütte
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.,Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
| | - Huange Wang
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.,Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
| | - Stella Antoniou
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Andrew Jarratt
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Nicola K Wilson
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.,Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
| | - Joey Riepsaame
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Fernando J Calero-Nieto
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.,Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
| | - Victoria Moignard
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.,Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
| | - Silvia Basilico
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.,Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
| | - Sarah J Kinston
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.,Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
| | - Rebecca L Hannah
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.,Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
| | - Mun Chiang Chan
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Sylvia T Nürnberg
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom.,NHS Blood and Transplant, Cambridge, United Kingdom
| | - Willem H Ouwehand
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom.,NHS Blood and Transplant, Cambridge, United Kingdom
| | - Nicola Bonzanni
- IBIVU Centre for Integrative Bioinformatics, VU University Amsterdam, Amsterdam, Netherlands.,Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Marella Ftr de Bruijn
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Berthold Göttgens
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.,Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
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38
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Göttgens B. Regulatory network control of blood stem cells. Blood 2015; 125:2614-20. [PMID: 25762179 DOI: 10.1182/blood-2014-08-570226] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Accepted: 10/06/2014] [Indexed: 12/13/2022] Open
Abstract
Hematopoietic stem cells (HSCs) are characterized by their ability to execute a wide range of cell fate choices, including self-renewal, quiescence, and differentiation into the many different mature blood lineages. Cell fate decision making in HSCs, as indeed in other cell types, is driven by the interplay of external stimuli and intracellular regulatory programs. Given the pivotal nature of HSC decision making for both normal and aberrant hematopoiesis, substantial research efforts have been invested over the last few decades into deciphering some of the underlying mechanisms. Central to the intracellular decision making processes are transcription factor proteins and their interactions within gene regulatory networks. More than 50 transcription factors have been shown to affect the functionality of HSCs. However, much remains to be learned about the way in which individual factors are connected within wider regulatory networks, and how the topology of HSC regulatory networks might affect HSC function. Nevertheless, important progress has been made in recent years, and new emerging technologies suggest that the pace of progress is likely to accelerate. This review will introduce key concepts, provide an integrated view of selected recent studies, and conclude with an outlook on possible future directions for this field.
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Affiliation(s)
- Berthold Göttgens
- Department of Haematology, Cambridge Institute for Medical Research and Wellcome Trust & Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
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39
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Abstract
Behaviours of complex biomolecular systems are often irreducible to the elementary properties of their individual components. Explanatory and predictive mathematical models are therefore useful for fully understanding and precisely engineering cellular functions. The development and analyses of these models require their adaptation to the problems that need to be solved and the type and amount of available genetic or molecular data. Quantitative and logic modelling are among the main methods currently used to model molecular and gene networks. Each approach comes with inherent advantages and weaknesses. Recent developments show that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms.
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Affiliation(s)
- Nicolas Le Novère
- Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
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40
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Moignard V, Woodhouse S, Haghverdi L, Lilly AJ, Tanaka Y, Wilkinson AC, Buettner F, Macaulay IC, Jawaid W, Diamanti E, Nishikawa SI, Piterman N, Kouskoff V, Theis FJ, Fisher J, Göttgens B. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat Biotechnol 2015; 33:269-276. [PMID: 25664528 PMCID: PMC4374163 DOI: 10.1038/nbt.3154] [Citation(s) in RCA: 288] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2014] [Accepted: 01/16/2015] [Indexed: 11/16/2022]
Abstract
Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.
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Affiliation(s)
- Victoria Moignard
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, UK
- Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Steven Woodhouse
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, UK
- Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Laleh Haghverdi
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Mathematics, Technische Universität München, Garching, Germany
| | - Andrew J. Lilly
- Cancer Research UK Stem Cell Haematopoiesis Group, Paterson Institute for Cancer Research, University of Manchester, Manchester, UK
| | - Yosuke Tanaka
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, UK
- Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Laboratory for Stem Cell Biology, RIKEN Center for Developmental Biology, Chuo-ku, Kobe, Japan
| | - Adam C. Wilkinson
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, UK
- Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Florian Buettner
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Iain C. Macaulay
- Sanger Institute-EBI Single Cell Genomics Centre, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | - Wajid Jawaid
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, UK
| | - Evangelia Diamanti
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, UK
- Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Shin-Ichi Nishikawa
- Laboratory for Stem Cell Biology, RIKEN Center for Developmental Biology, Chuo-ku, Kobe, Japan
| | - Nir Piterman
- Department of Computer Science, University of Leicester, Leicester, UK
| | - Valerie Kouskoff
- Cancer Research UK Stem Cell Haematopoiesis Group, Paterson Institute for Cancer Research, University of Manchester, Manchester, UK
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Mathematics, Technische Universität München, Garching, Germany
| | - Jasmin Fisher
- Microsoft Research Cambridge, Cambridge, UK
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Berthold Göttgens
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, UK
- Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
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41
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Ebina W, Rossi DJ. Transcription factor-mediated reprogramming toward hematopoietic stem cells. EMBO J 2015; 34:694-709. [PMID: 25712209 DOI: 10.15252/embj.201490804] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
De novo generation of human hematopoietic stem cells (HSCs) from renewable cell types has been a long sought-after but elusive goal in regenerative medicine. Paralleling efforts to guide pluripotent stem cell differentiation by manipulating developmental cues, substantial progress has been made recently toward HSC generation via combinatorial transcription factor (TF)-mediated fate conversion, a paradigm established by Yamanaka's induction of pluripotency in somatic cells by mere four TFs. This review will integrate the recently reported strategies to directly convert a variety of starting cell types toward HSCs in the context of hematopoietic transcriptional regulation and discuss how these findings could be further developed toward the ultimate generation of therapeutic human HSCs.
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Affiliation(s)
- Wataru Ebina
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA Program in Cellular and Molecular Medicine, Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
| | - Derrick J Rossi
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA Program in Cellular and Molecular Medicine, Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA Department of Pediatrics, Harvard Medical School, Boston, MA, USA Harvard Stem Cell Institute, Cambridge, MA, USA
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42
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Abou-Jaoudé W, Monteiro PT, Naldi A, Grandclaudon M, Soumelis V, Chaouiya C, Thieffry D. Model checking to assess T-helper cell plasticity. Front Bioeng Biotechnol 2015; 2:86. [PMID: 25674559 PMCID: PMC4309205 DOI: 10.3389/fbioe.2014.00086] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2014] [Accepted: 12/20/2014] [Indexed: 12/03/2022] Open
Abstract
Computational modeling constitutes a crucial step toward the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g., stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity. In this article, we present a multivalued logical framework along with computational methods recently developed to efficiently analyze large models. We mainly focus on a symbolic model checking approach to investigate switches between attractors subsequent to changes of input conditions. As a case study, we consider the cellular network regulating the differentiation of T-helper (Th) cells, which orchestrate many physiological and pathological immune responses. To account for novel cellular subtypes, we present an extended version of a published model of Th cell differentiation. We then use symbolic model checking to analyze reachability properties between Th subtypes upon changes of environmental cues. This allows for the construction of a synthetic view of Th cell plasticity in terms of a graph connecting subtypes with arcs labeled by input conditions. Finally, we explore novel strategies enabling specific Th cell polarizing or reprograming events.
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Affiliation(s)
- Wassim Abou-Jaoudé
- Institut de Biologie de l’Ecole Normale Supérieure, Paris, France
- UMR CNRS 8197, Paris, France
- INSERM U1024, Paris, France
- Laboratoire d’Informatique de l’Ecole Normale Supérieure, Paris, France
| | - Pedro T. Monteiro
- INESC-ID, Lisboa, Portugal
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
| | - Aurélien Naldi
- Centre Intégratif de Génomique, Université de Lausanne, Lausanne, Switzerland
| | | | - Vassili Soumelis
- Laboratoire d’Immunologie Clinique, Institut Curie, Paris, France
- INSERM U932, Paris, France
| | | | - Denis Thieffry
- Institut de Biologie de l’Ecole Normale Supérieure, Paris, France
- UMR CNRS 8197, Paris, France
- INSERM U1024, Paris, France
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43
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Fisher J, Piterman N, Bodik R. Toward synthesizing executable models in biology. Front Bioeng Biotechnol 2014; 2:75. [PMID: 25566538 PMCID: PMC4271700 DOI: 10.3389/fbioe.2014.00075] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2014] [Accepted: 12/05/2014] [Indexed: 12/30/2022] Open
Abstract
Over the last decade, executable models of biological behaviors have repeatedly provided new scientific discoveries, uncovered novel insights, and directed new experimental avenues. These models are computer programs whose execution mechanistically simulates aspects of the cell’s behaviors. If the observed behavior of the program agrees with the observed biological behavior, then the program explains the phenomena. This approach has proven beneficial for gaining new biological insights and directing new experimental avenues. One advantage of this approach is that techniques for analysis of computer programs can be applied to the analysis of executable models. For example, one can confirm that a model agrees with experiments for all possible executions of the model (corresponding to all environmental conditions), even if there are a huge number of executions. Various formal methods have been adapted for this context, for example, model checking or symbolic analysis of state spaces. To avoid manual construction of executable models, one can apply synthesis, a method to produce programs automatically from high-level specifications. In the context of biological modeling, synthesis would correspond to extracting executable models from experimental data. We survey recent results about the usage of the techniques underlying synthesis of computer programs for the inference of biological models from experimental data. We describe synthesis of biological models from curated mutation experiment data, inferring network connectivity models from phosphoproteomic data, and synthesis of Boolean networks from gene expression data. While much work has been done on automated analysis of similar datasets using machine learning and artificial intelligence, using synthesis techniques provides new opportunities such as efficient computation of disambiguating experiments, as well as the ability to produce different kinds of models automatically from biological data.
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Affiliation(s)
- Jasmin Fisher
- Microsoft Research , Cambridge , UK ; Department of Biochemistry, University of Cambridge , Cambridge , UK
| | - Nir Piterman
- Department of Computer Science, University of Leicester , Leicester , UK
| | - Rastislav Bodik
- Electrical Engineering and Computer Science, University of California Berkeley , Berkeley, CA , USA
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44
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Chen H, Guo J, Mishra SK, Robson P, Niranjan M, Zheng J. Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development. ACTA ACUST UNITED AC 2014; 31:1060-6. [PMID: 25416748 DOI: 10.1093/bioinformatics/btu777] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Accepted: 11/17/2014] [Indexed: 11/12/2022]
Abstract
MOTIVATION Transcriptional regulatory networks controlling cell fate decisions in mammalian embryonic development remain elusive despite a long time of research. The recent emergence of single-cell RNA profiling technology raises hope for new discovery. Although experimental works have obtained intriguing insights into the mouse early development, a holistic and systematic view is still missing. Mathematical models of cell fates tend to be concept-based, not designed to learn from real data. To elucidate the regulatory mechanisms behind cell fate decisions, it is highly desirable to synthesize the data-driven and knowledge-driven modeling approaches. RESULTS We propose a novel method that integrates the structure of a cell lineage tree with transcriptional patterns from single-cell data. This method adopts probabilistic Boolean network (PBN) for network modeling, and genetic algorithm as search strategy. Guided by the 'directionality' of cell development along branches of the cell lineage tree, our method is able to accurately infer the regulatory circuits from single-cell gene expression data, in a holistic way. Applied on the single-cell transcriptional data of mouse preimplantation development, our algorithm outperforms conventional methods of network inference. Given the network topology, our method can also identify the operational interactions in the gene regulatory network (GRN), corresponding to specific cell fate determination. This is one of the first attempts to infer GRNs from single-cell transcriptional data, incorporating dynamics of cell development along a cell lineage tree. AVAILABILITY AND IMPLEMENTATION Implementation of our algorithm is available from the authors upon request. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Haifen Chen
- School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Genome Institute of Singapore, Biopolis, Singapore 138672, Singapore and School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
| | - Jing Guo
- School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Genome Institute of Singapore, Biopolis, Singapore 138672, Singapore and School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
| | - Shital K Mishra
- School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Genome Institute of Singapore, Biopolis, Singapore 138672, Singapore and School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
| | - Paul Robson
- School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Genome Institute of Singapore, Biopolis, Singapore 138672, Singapore and School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
| | - Mahesan Niranjan
- School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Genome Institute of Singapore, Biopolis, Singapore 138672, Singapore and School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
| | - Jie Zheng
- School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Genome Institute of Singapore, Biopolis, Singapore 138672, Singapore and School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Genome Institute of Singapore, Biopolis, Singapore 138672, Singapore and School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
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45
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Pinho R, Garcia V, Irimia M, Feldman MW. Stability depends on positive autoregulation in Boolean gene regulatory networks. PLoS Comput Biol 2014; 10:e1003916. [PMID: 25375153 PMCID: PMC4222607 DOI: 10.1371/journal.pcbi.1003916] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Accepted: 09/10/2014] [Indexed: 01/21/2023] Open
Abstract
Network motifs have been identified as building blocks of regulatory networks, including gene regulatory networks (GRNs). The most basic motif, autoregulation, has been associated with bistability (when positive) and with homeostasis and robustness to noise (when negative), but its general importance in network behavior is poorly understood. Moreover, how specific autoregulatory motifs are selected during evolution and how this relates to robustness is largely unknown. Here, we used a class of GRN models, Boolean networks, to investigate the relationship between autoregulation and network stability and robustness under various conditions. We ran evolutionary simulation experiments for different models of selection, including mutation and recombination. Each generation simulated the development of a population of organisms modeled by GRNs. We found that stability and robustness positively correlate with autoregulation; in all investigated scenarios, stable networks had mostly positive autoregulation. Assuming biological networks correspond to stable networks, these results suggest that biological networks should often be dominated by positive autoregulatory loops. This seems to be the case for most studied eukaryotic transcription factor networks, including those in yeast, flies and mammals. Multicellular organisms show an incredible diversity of cell types in their different tissues. Functional classes of cells can be attributed to the activation and repression of genes, which enable each cell type to support different functions within the organism. These patterns of activity have been studied by means of gene regulatory networks (GRNs). How these gene networks generate stable phenotypic states is thought to underlie the development and evolution of organisms. The pathways to these states are influenced by the autoregulatory properties of these networks. The stability and robustness of gene networks are used to investigate how such states are maintained. This study sheds light on how these properties relate to one another. By simulating the evolution of these networks, we show that genes depend on positive self-regulation to remain stable and robust when faced with random mutations or environmental perturbations. Assuming biological networks correspond to stable networks, our results suggest that biological networks should often be dominated by positive autoregulatory loops. This seems to be the case for most studied eukaryotic transcription factor networks, including those in yeast, flies and mammals.
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Affiliation(s)
- Ricardo Pinho
- Department of Biology, Stanford University, Stanford, California, United States of America
- PhD Program in Computational Biology, Instituto Gulbenkian de Ciência, Oeiras, Portugal
- * E-mail: (RP); (MI)
| | - Victor Garcia
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Manuel Irimia
- EMBL/CRG Research Unit in Systems Biology, Centre for Genomic Regulation (CRG), Barcelona, Spain
- * E-mail: (RP); (MI)
| | - Marcus W. Feldman
- Department of Biology, Stanford University, Stanford, California, United States of America
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46
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Sive JI, Göttgens B. Transcriptional network control of normal and leukaemic haematopoiesis. Exp Cell Res 2014; 329:255-64. [PMID: 25014893 PMCID: PMC4261078 DOI: 10.1016/j.yexcr.2014.06.021] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Revised: 06/26/2014] [Accepted: 06/28/2014] [Indexed: 12/23/2022]
Abstract
Transcription factors (TFs) play a key role in determining the gene expression profiles of stem/progenitor cells, and defining their potential to differentiate into mature cell lineages. TF interactions within gene-regulatory networks are vital to these processes, and dysregulation of these networks by TF overexpression, deletion or abnormal gene fusions have been shown to cause malignancy. While investigation of these processes remains a challenge, advances in genome-wide technologies and growing interactions between laboratory and computational science are starting to produce increasingly accurate network models. The haematopoietic system provides an attractive experimental system to elucidate gene regulatory mechanisms, and allows experimental investigation of both normal and dysregulated networks. In this review we examine the principles of TF-controlled gene regulatory networks and the key experimental techniques used to investigate them. We look in detail at examples of how these approaches can be used to dissect out the regulatory mechanisms controlling normal haematopoiesis, as well as the dysregulated networks associated with haematological malignancies.
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Affiliation(s)
- Jonathan I Sive
- Department of Haematology, Cambridge Institute for Medical Research and Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
| | - Berthold Göttgens
- Department of Haematology, Cambridge Institute for Medical Research and Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
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47
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Park SJ, Umemoto T, Saito-Adachi M, Shiratsuchi Y, Yamato M, Nakai K. Computational promoter modeling identifies the modes of transcriptional regulation in hematopoietic stem cells. PLoS One 2014; 9:e93853. [PMID: 24710559 PMCID: PMC3977923 DOI: 10.1371/journal.pone.0093853] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2013] [Accepted: 03/07/2014] [Indexed: 01/19/2023] Open
Abstract
Extrinsic and intrinsic regulators are responsible for the tight control of hematopoietic stem cells (HSCs), which differentiate into all blood cell lineages. To understand the fundamental basis of HSC biology, we focused on differentially expressed genes (DEGs) in long-term and short-term HSCs, which are closely related in terms of cell development but substantially differ in their stem cell capacity. To analyze the transcriptional regulation of the DEGs identified in the novel transcriptome profiles obtained by our RNA-seq analysis, we developed a computational method to model the linear relationship between gene expression and the features of putative regulatory elements. The transcriptional regulation modes characterized here suggest the importance of transcription factors (TFs) that are expressed at steady state or at low levels. Remarkably, we found that 24 differentially expressed TFs targeting 21 putative TF-binding sites contributed significantly to transcriptional regulation. These TFs tended to be modulated by other nondifferentially expressed TFs, suggesting that HSCs can achieve flexible and rapid responses via the control of nondifferentially expressed TFs through a highly complex regulatory network. Our novel transcriptome profiles and new method are powerful tools for studying the mechanistic basis of cell fate decisions.
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Affiliation(s)
- Sung-Joon Park
- Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, Japan
| | - Terumasa Umemoto
- Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, Tokyo, Japan
| | - Mihoko Saito-Adachi
- Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, Japan
| | - Yoshiko Shiratsuchi
- Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, Tokyo, Japan
| | - Masayuki Yamato
- Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, Tokyo, Japan
| | - Kenta Nakai
- Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, Japan
- * E-mail:
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48
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Moignard V, Göttgens B. Transcriptional mechanisms of cell fate decisions revealed by single cell expression profiling. Bioessays 2014; 36:419-26. [PMID: 24470343 PMCID: PMC3992849 DOI: 10.1002/bies.201300102] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
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.
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Affiliation(s)
- Victoria Moignard
- Department of Haematology, University of Cambridge, Cambridge, UK; Wellcome Trust - Medical Research Council, Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK; Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
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49
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Peterson H, Abu Dawud R, Garg A, Wang Y, Vilo J, Xenarios I, Adjaye J. Qualitative modeling identifies IL-11 as a novel regulator in maintaining self-renewal in human pluripotent stem cells. Front Physiol 2013; 4:303. [PMID: 24194720 PMCID: PMC3809568 DOI: 10.3389/fphys.2013.00303] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2013] [Accepted: 10/02/2013] [Indexed: 11/13/2022] Open
Abstract
Pluripotency in human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) is regulated by three transcription factors—OCT3/4, SOX2, and NANOG. To fully exploit the therapeutic potential of these cells it is essential to have a good mechanistic understanding of the maintenance of self-renewal and pluripotency. In this study, we demonstrate a powerful systems biology approach in which we first expand literature-based network encompassing the core regulators of pluripotency by assessing the behavior of genes targeted by perturbation experiments. We focused our attention on highly regulated genes encoding cell surface and secreted proteins as these can be more easily manipulated by the use of inhibitors or recombinant proteins. Qualitative modeling based on combining boolean networks and in silico perturbation experiments were employed to identify novel pluripotency-regulating genes. We validated Interleukin-11 (IL-11) and demonstrate that this cytokine is a novel pluripotency-associated factor capable of supporting self-renewal in the absence of exogenously added bFGF in culture. To date, the various protocols for hESCs maintenance require supplementation with bFGF to activate the Activin/Nodal branch of the TGFβ signaling pathway. Additional evidence supporting our findings is that IL-11 belongs to the same protein family as LIF, which is known to be necessary for maintaining pluripotency in mouse but not in human ESCs. These cytokines operate through the same gp130 receptor which interacts with Janus kinases. Our finding might explain why mESCs are in a more naïve cell state compared to hESCs and how to convert primed hESCs back to the naïve state. Taken together, our integrative modeling approach has identified novel genes as putative candidates to be incorporated into the expansion of the current gene regulatory network responsible for inducing and maintaining pluripotency.
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Affiliation(s)
- Hedi Peterson
- Quretec Ltd. Tartu, Estonia ; Faculty of Mathematics and Computer Science, Institute of Computer Science, University of Tartu Tartu, Estonia ; Faculty of Science and Technology, Institute of Molecular and Cellular Biology, University of Tartu Tartu, Estonia
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50
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Diez D, Hutchins AP, Miranda-Saavedra D. Systematic identification of transcriptional regulatory modules from protein-protein interaction networks. Nucleic Acids Res 2013; 42:e6. [PMID: 24137002 PMCID: PMC3874207 DOI: 10.1093/nar/gkt913] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Transcription factors (TFs) combine with co-factors to form transcriptional regulatory modules (TRMs) that regulate gene expression programs with spatiotemporal specificity. Here we present a novel and generic method (rTRM) for the reconstruction of TRMs that integrates genomic information from TF binding, cell type-specific gene expression and protein–protein interactions. rTRM was applied to reconstruct the TRMs specific for embryonic stem cells (ESC) and hematopoietic stem cells (HSC), neural progenitor cells, trophoblast stem cells and distinct types of terminally differentiated CD4+ T cells. The ESC and HSC TRM predictions were highly precise, yielding 77 and 96 proteins, of which ∼75% have been independently shown to be involved in the regulation of these cell types. Furthermore, rTRM successfully identified a large number of bridging proteins with known roles in ESCs and HSCs, which could not have been identified using genomic approaches alone, as they lack the ability to bind specific DNA sequences. This highlights the advantage of rTRM over other methods that ignore PPI information, as proteins need to interact with other proteins to form complexes and perform specific functions. The prediction and experimental validation of the co-factors that endow master regulatory TFs with the capacity to select specific genomic sites, modulate the local epigenetic profile and integrate multiple signals will provide important mechanistic insights not only into how such TFs operate, but also into abnormal transcriptional states leading to disease.
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Affiliation(s)
- Diego Diez
- World Premier International (WPI) Immunology Frontier Research Center (IFReC), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Osaka, Japan, South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, 190 Kaiyuan Ave, Guangzhou 510663, China and Fibrosis Laboratories, Institute of Cellular Medicine, Newcastle University Medical School, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom
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