Dataset Information

Lymphopoiesis Regulatory Network

ABSTRACT: Due to the large number of diseases associated to a malfunction of the hematopoietic system, there is an interest in knowing the molecular mechanisms controlling the differentiation of blood cell lineages. However, the structure and dynamical properties of the underlying regulatory network controlling this process is not well understood. This manuscript presents a regulatory network of 81 nodes, representing several types of molecules that regulate each other during the process of lymphopoiesis. The regulatory interactions were inferred mostly from published experimental data. However, 15 out of 159 regulatory interactions are predictions arising from the present study. The network is modelled as a continuous dynamical system, in the form of a set of differential equations. The dynamical behaviour of the model describes the differentiation process from the common lymphocyte precursor (CLP) to several mature B and T cell types; namely, plasma cell (PC), cytotoxic T lymphocyte (CTL), T helper 1 (Th1), Th2, Th17, and T regulatory (Treg) cells. The model qualitatively recapitulates key cellular differentiation events, being able to represent the directional and branched nature of lymphopoiesis, going from a multipotent progenitor to fully differentiated cell types.

SUBMITTER: Erica Pribil

PROVIDER: 8080 | Cell Collective |

REPOSITORIES: Cell Collective

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A dynamical model of the regulatory network controlling lymphopoiesis.

Mendoza Luis L Méndez Akram A

Bio Systems 20150925

Due to the large number of diseases associated to a malfunction of the hematopoietic system, there is an interest in knowing the molecular mechanisms controlling the differentiation of blood cell lineages. However, the structure and dynamical properties of the underlying regulatory network controlling this process is not well understood. This manuscript presents a regulatory network of 81 nodes, representing several types of molecules that regulate each other during the process of lymphopoiesis. ...[more]

PMID: 26408858

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Project description:The main goal of the project is to develop a new generation of bioinformatics resources for the integrative analysis of multiple types of 'omics data. These resources include both novel statistical methodologies as well as user-friendly software implementations. STATegra methods address many aspects of the 'omics data integration problem such as the design of multi-omics experiments, integrative transcriptional and regulatory networks, integrative variable selection, data fusion, integration of public domain data, and integrative pathway analysis. To support method development STATegra uses a model biological system, namely the differentiation process of mouse pre-B-cells. The STATegra consortium generated data focused on a critical step in the differentiation of B lymphocytes, which are key components of the adaptive immune system. Transcription factors of the Ikaros family are central to the normal differentiation of B cell progenitors and their expression increases in response to developmental stage-specific signals to terminate the proliferation of B cell progenitors and to initiate their differentiation. In particular, a novel biological system that models the transition from the pre-BI stage to the pre-BII subsequent stage, where B cell progenitors undergo growth arrest and differentiation, was used. The approach involves a pre-B cell line, B3, and an inducible version of the Ikaros transcription factor, Ikaros-ERt2. Ikaros factors act to down-regulate genes that drive proliferation and to simultaneously up-regulate the expression of genes that promote the differentiation of B cell progenitors. Hence, in the B3 system, before induction of Ikaros, cells proliferate and their gene expression pattern is similar to proliferating B cell progenitors in vivo. Following Ikaros induction, B3 cells undergo gene expression changes that resemble those that occur in vivo during the transition from cycling to resting pre-B cells, followed by a marked reduction in cellular proliferation and by G1 arrest. On this system the consortium has created a high-quality data collection consisting of a replicated time course using seven different 'omics platforms: RNA-seq, miRNA-seq, ChIP-seq, DNase-seq, Methyl-seq, proteomics and metabolomics, which is used to assess and to validate STATegra methods. The STATegra experimental design consists of a 6 point time course that captures the differentiation of B3 cells containing Ikaros-ERt2 upon Ikaros induction within a 24 hr period. The process was sampled at 0, 2, 6, 12, 18, and 24 hrs respectively after Ikaros induction by Tamoxifen. As control, B3 cells transfected with an empty vector were treated and sampled in the same way as the inducible line. Eight different 'omic technologies were measured on this system: RNA-seq, miRNA-seq, DNase-seq, RRBS-seq, ChIP-seq, scRNA-seq, Proteomics and Metabolomics. Generally, three biological replicates were obtained per platform and were processed as independent batches. Metabolomics analysis was performed using 4 biological batches (7, 8, 9 and 10) and at times 0, 2, 6, 12, 18 and 24 hrs respectively. Each sample was analyzed using gas chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS).

Dataset Information

Lymphopoiesis Regulatory Network

Publications

A dynamical model of the regulatory network controlling lymphopoiesis.

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