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1.
Front Immunol ; 12: 746492, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34737747

RESUMEN

B-cell acute lymphoblastic leukemia (B-ALL) results from the expansion of malignant lymphoid precursors within the bone marrow (BM), where hematopoietic niches and microenvironmental signals provide leukemia-initiating cells (LICs) the conditions to survive, proliferate, initiate disease, and relapse. Normal and malignant lymphopoiesis are highly dependent on the BM microenvironment, particularly on CXCL12-abundant Reticular (CAR) cells, which provide a niche for maintenance of primitive cells. During B-ALL, leukemic cells hijack BM niches, creating a proinflammatory milieu incompetent to support normal hematopoiesis but favoring leukemic proliferation. Although the lack of a phenotypic stem cell hierarchy is apparent in B-ALL, LICs are a rare and quiescent population potentially responsible for chemoresistance and relapse. Here, we developed novel patient-derived leukemia spheroids (PDLS), an ex vivo avatar model, from mesenchymal stromal cells (MSCs) and primary B-ALL cells, to mimic specialized niche structures and cell-to-cell intercommunication promoting normal and malignant hematopoiesis in pediatric B-ALL. 3D MSC spheroids can recapitulate CAR niche-like hypoxic structures that produce high levels of CXCL10 and CXCL11. We found that PDLS were preferentially enriched with leukemia cells displaying functional properties of LICs, such as quiescence, low reactive oxygen species, drug resistance, high engraftment in immunodeficient mice, and long-term leukemogenesis. Moreover, the combination of PDLS and patient-derived xenografts confirmed a microenvironment-driven hierarchy in their leukemic potential. Importantly, transcriptional profiles of MSC derived from primary patient samples revealed two unique signatures (1), a CXCL12low inflammatory and leukemia expansion (ILE)-like niche, that likely supports leukemic burden, and (2) a CXCL11hi immune-suppressive and leukemia-initiating cell (SLIC)-like niche, where LICs are likely sustained. Interestingly, the CXCL11+ hypoxic zones were recapitulated within the PDLS that are capable of supporting LIC functions. Taken together, we have implemented a novel PDLS system that enriches and supports leukemia cells with stem cell features driven by CXCL11+ MSCs within hypoxic microenvironments capable of recapitulating key features, such as tumor reemergence after exposure to chemotherapy and tumor initiation. This system represents a unique opportunity for designing ex vivo personalized avatars for B-ALL patients to evaluate their own LIC pathobiology and drug sensitivity in the context of the tumor microenvironment.


Asunto(s)
Células Madre Neoplásicas/patología , Leucemia-Linfoma Linfoblástico de Células Precursoras/patología , Esferoides Celulares , Nicho de Células Madre , Células Tumorales Cultivadas , Animales , Médula Ósea/patología , Femenino , Xenoinjertos , Humanos , Células Madre Mesenquimatosas/patología , Ratones , Microambiente Tumoral
2.
Methods Mol Biol ; 1819: 357-383, 2018.
Artículo en Inglés | MEDLINE | ID: mdl-30421413

RESUMEN

Computational mechanistic models enable a systems-level understanding of plant development by integrating available molecular experimental data and simulating their collective dynamical behavior. Boolean gene regulatory network dynamical models have been extensively used as a qualitative modeling framework for such purpose. More recently, network modeling protocols have been extended to model the epigenetic landscape associated with gene regulatory networks. In addition to understanding the concerted action of interconnected genes, epigenetic landscape models aim to uncover the patterns of cell state transition events that emerge under diverse genetic and environmental background conditions. In this chapter we present simple protocols that naturally extend gene regulatory network modeling and demonstrate their use in modeling plant developmental processes under the epigenetic landscape framework. We focus on conceptual clarity and practical implementation, providing directions to the corresponding technical literature. The protocols presented here can be applied to any well-characterized gene regulatory network in plants, animals, or human disease.


Asunto(s)
Epigénesis Genética , Regulación de la Expresión Génica de las Plantas , Modelos Genéticos , Desarrollo de la Planta/genética , Plantas/genética , Plantas/metabolismo
3.
Adv Exp Med Biol ; 1069: 1-33, 2018.
Artículo en Inglés | MEDLINE | ID: mdl-30076565

RESUMEN

The aim of this volume is to encourage the use of systems-level methodologies to contribute to the improvement of human-health . We intend to motivate biomedical researchers to complement their current theoretical and empirical practice with up-to-date systems biology conceptual approaches. Our perspective is based on the deep understanding of the key biomolecular regulatory mechanisms that underlie health, as well as the emergence and progression of human-disease . We strongly believe that the contemporary systems biology perspective opens the door to the effective development of novel methodologies to the improvement of prevention . This requires a deeper and integrative understanding of the involved underlying systems-level mechanisms. In order to explain our proposal in a simple way, in this chapter we privilege the conceptual exposition of our chosen framework over formal considerations. The formal exposition of our proposal will be expanded and discussed later in the next chapters.


Asunto(s)
Progresión de la Enfermedad , Biología de Sistemas , Humanos
4.
Adv Exp Med Biol ; 1069: 35-134, 2018.
Artículo en Inglés | MEDLINE | ID: mdl-30076566

RESUMEN

Being concerned by the understanding of the mechanism underlying chronic degenerative diseases , we presented in the previous chapter the medical systems biology conceptual framework that we present for that purpose in this volume. More specifically, we argued there the clear advantages offered by a state-space perspective when applied to the systems-level description of the biomolecular machinery that regulates complex degenerative diseases. We also discussed the importance of the dynamical interplay between the risk factors and the network of interdependencies that characterizes the biochemical, cellular, and tissue-level biomolecular reactions that underlie the physiological processes in health and disease. As we pointed out in the previous chapter, the understanding of this interplay (articulated around cellular phenotypic plasticity properties, regulated by specific kinds of gene regulatory networks) is necessary if prevention is chosen as the human-health improvement strategy (potentially involving the modulation of the patient's lifestyle). In this chapter we provide the medical systems biology mathematical and computational modeling tools required for this task.


Asunto(s)
Redes Reguladoras de Genes , Enfermedades Neurodegenerativas/diagnóstico , Biología de Sistemas , Simulación por Computador , Humanos , Modelos Teóricos
5.
Adv Exp Med Biol ; 1069: 135-209, 2018.
Artículo en Inglés | MEDLINE | ID: mdl-30076567

RESUMEN

The aim of this chapter is to illustrate the modeling procedures discussed in the previous chapter via three well-chosen examples.


Asunto(s)
Redes Reguladoras de Genes , Enfermedades Neurodegenerativas/diagnóstico , Biología de Sistemas , Simulación por Computador , Humanos , Modelos Teóricos
6.
Front Genet ; 6: 160, 2015.
Artículo en Inglés | MEDLINE | ID: mdl-25954305

RESUMEN

Robust temporal and spatial patterns of cell types emerge in the course of normal development in multicellular organisms. The onset of degenerative diseases may result from altered cell fate decisions that give rise to pathological phenotypes. Complex networks of genetic and non-genetic components underlie such normal and altered morphogenetic patterns. Here we focus on the networks of regulatory interactions involved in cell-fate decisions. Such networks modeled as dynamical non-linear systems attain particular stable configurations on gene activity that have been interpreted as cell-fate states. The network structure also restricts the most probable transition patterns among such states. The so-called Epigenetic Landscape (EL), originally proposed by C. H. Waddington, was an early attempt to conceptually explain the emergence of developmental choices as the result of intrinsic constraints (regulatory interactions) shaped during evolution. Thanks to the wealth of molecular genetic and genomic studies, we are now able to postulate gene regulatory networks (GRN) grounded on experimental data, and to derive EL models for specific cases. This, in turn, has motivated several mathematical and computational modeling approaches inspired by the EL concept, that may be useful tools to understand and predict cell-fate decisions and emerging patterns. In order to distinguish between the classical metaphorical EL proposal of Waddington, we refer to the Epigenetic Attractors Landscape (EAL), a proposal that is formally framed in the context of GRNs and dynamical systems theory. In this review we discuss recent EAL modeling strategies, their conceptual basis and their application in studying the emergence of both normal and pathological developmental processes. In addition, we discuss how model predictions can shed light into rational strategies for cell fate regulation, and we point to challenges ahead.

7.
Mol Plant ; 8(5): 796-813, 2015 May.
Artículo en Inglés | MEDLINE | ID: mdl-25636918

RESUMEN

In Arabidopsis thaliana, multiple genes involved in shoot apical meristem (SAM) transitions have been characterized, but the mechanisms required for the dynamic attainment of vegetative, inflorescence, and floral meristem (VM, IM, FM) cell fates during SAM transitions are not well understood. Here we show that a MADS-box gene, XAANTAL2 (XAL2/AGL14), is necessary and sufficient to induce flowering, and its regulation is important in FM maintenance and determinacy. xal2 mutants are late flowering, particularly under short-day (SD) condition, while XAL2 overexpressing plants are early flowering, but their flowers have vegetative traits. Interestingly, inflorescences of the latter plants have higher expression levels of LFY, AP1, and TFL1 than wild-type plants. In addition we found that XAL2 is able to bind the TFL1 regulatory regions. On the other hand, the basipetal carpels of the 35S::XAL2 lines lose determinacy and maintain high levels of WUS expression under SD condition. To provide a mechanistic explanation for the complex roles of XAL2 in SAM transitions and the apparently paradoxical phenotypes of XAL2 and other MADS-box (SOC1, AGL24) overexpressors, we conducted dynamic gene regulatory network (GRN) and epigenetic landscape modeling. We uncovered a GRN module that underlies VM, IM, and FM gene configurations and transition patterns in wild-type plants as well as loss and gain of function lines characterized here and previously. Our approach thus provides a novel mechanistic framework for understanding the complex basis of SAM development.


Asunto(s)
Proteínas de Arabidopsis/metabolismo , Arabidopsis/metabolismo , Regulación del Desarrollo de la Expresión Génica , Redes Reguladoras de Genes , Proteínas de Dominio MADS/metabolismo , Meristema/metabolismo , Brotes de la Planta/crecimiento & desarrollo , Arabidopsis/genética , Arabidopsis/crecimiento & desarrollo , Proteínas de Arabidopsis/genética , Flores/genética , Flores/crecimiento & desarrollo , Flores/metabolismo , Regulación de la Expresión Génica de las Plantas , Proteínas de Dominio MADS/genética , Meristema/genética , Meristema/crecimiento & desarrollo , Brotes de la Planta/genética , Brotes de la Planta/metabolismo
8.
Methods Mol Biol ; 1110: 441-69, 2014.
Artículo en Inglés | MEDLINE | ID: mdl-24395275

RESUMEN

Understanding how genotypes map unto phenotypes implies an integrative understanding of the processes regulating cell differentiation and morphogenesis, which comprise development. Such a task requires the use of theoretical and computational approaches to integrate and follow the concerted action of multiple genetic and nongenetic components that hold highly nonlinear interactions. Gene regulatory network (GRN) models have been proposed to approach such task. GRN models have become very useful to understand how such types of interactions restrict the multi-gene expression patterns that characterize different cell-fates. More recently, such temporal single-cell models have been extended to recover the temporal and spatial components of morphogenesis. Since the complete genomic GRN is still unknown and intractable for any organism, and some clear developmental modules have been identified, we focus here on the analysis of well-curated and experimentally grounded small GRN modules. One of the first experimentally grounded GRN that was proposed and validated corresponds to the regulatory module involved in floral organ determination. In this chapter we use this GRN as an example of the methodologies involved in: (1) formalizing and integrating molecular genetic data into the logical functions (Boolean functions) that rule gene interactions and dynamics in a Boolean GRN; (2) the algorithms and computational approaches used to recover the steady-states that correspond to each cell type, as well as the set of initial GRN configurations that lead to each one of such states (i.e., basins of attraction); (3) the approaches used to validate a GRN model using wild type and mutant or overexpression data, or to test the robustness of the GRN being proposed; (4) some of the methods that have been used to incorporate random fluctuations in the GRN Boolean functions and enable stochastic GRN models to address the temporal sequence with which gene configurations and cell fates are attained; (5) the methodologies used to approximate discrete Boolean GRN to continuous systems and their use in further dynamic analyses. The methodologies explained for the GRN of floral organ determination developed here in detail can be applied to any other functional developmental module.


Asunto(s)
Arabidopsis/crecimiento & desarrollo , Arabidopsis/genética , Flores/crecimiento & desarrollo , Flores/genética , Redes Reguladoras de Genes , Modelos Genéticos , Factores de Tiempo
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