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LASSIM -a network inference toolbox for genome-wide mechanistic modeling [USF2, KLF6/COPEB, CBL and HIS1 siRNA]]

ABSTRACT: We present LASSIM, which is a toolbox built to build and infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces the time for the computation to finish.

ORGANISM(S): Homo sapiens

PROVIDER: GSE95508 | GEO | 2017/06/12

SECONDARY ACCESSION(S): PRJNA377298

REPOSITORIES: GEO

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LASSIM -a network inference toolbox for genome-wide mechanistic modeling

Project description:We present LASSIM, which is a toolbox built to build and infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces the time for the computation to finish.

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LASSIM -a network inference toolbox for genome-wide mechanistic modeling [Time-series polarization: CD4+ T cells toward Th2]

Project description:Recent and ongoing revolutions in measurement technologies imply completely new possibilities for genome research: today, time-resolved, quantitative, and systems-level data are available. Nevertheless, without a corresponding revolution in methods for data analysis, these new data tend to drown researchers and doctors, rather than provide clear and useful insights. Such new methods are developed within the field of systems biology. Systems biology has two main approaches: mechanistically detailed and well-determined simulation models for small subsystems, and more approximative statistical models for the entire genome. However, there are few, if any, methods that combine the strengths of these two approaches. Herein, we present LASSIM, a new simulation-based approach, which can be applied to systems of the size of the entire genome. The superior performance of LASSIM is demonstrated in three examples: i) an example with simulated data shows that unlike traditional large-scale methods, LASSIM correctly identifies the true behavior between measured data-points, ii) LASSIM outperforms the winner of a previous DREAM challenge, the most competitive benchmarking approach available, iii) based on new data from TH2 differentiation, LASSIM identifies a first mechanistic model for the entire genome. The key predictions of this model are typically enriched for DNA bindings, which suggests that most predicted interactions are direct. Moreover, in silico knockdowns were experimentally validated. In summary, LASSIM opens the door to a new type of model-based data analysis: to models that combine the strengths of reliable mechanistic models with truly systems-level data.

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LASSIM -a network inference toolbox for genome-wide mechanistic modeling

Project description:LASSIM -a network inference toolbox for genome-wide mechanistic modeling

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Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis.

Project description:BackgroundTo further understand the development of the spinal cord, an exploration of the patterns and transcriptional features of spinal cord development in newborn mice at the cellular transcriptome level was carried out.MethodsThe mouse single-cell sequencing (scRNA-seq) dataset was downloaded from the GSE108788 dataset. Single-cell RNA-Seq (scRNA-Seq) was conducted on cervical and lumbar spinal V2a interneurons from 2 P0 neonates. Single-cell analysis using the Seurat package was completed, and marker mRNAs were identified for each cluster. Then, pseudotemporal analysis was used to analyze the transcription changes of marker mRNAs in different clusters over time. Finally, the functions of these marker mRNAs were assessed by enrichment analysis and protein-protein interaction (PPI) networks. A transcriptional regulatory network was then constructed using the TRRUST dataset.ResultsA total of 949 cells were screened. Single-cell analysis was conducted based on marker mRNAs of each cluster, which revealed the heterogeneity of neonatal mouse spinal cord neuronal cells. Functional analysis of pseudotemporal trajectory-related marker mRNAs suggested that pregnancy-specific glycoproteins (PSGs) and carcinoembryonic antigen cell adhesion molecules (CEACAMs) were the core mRNAs in cluster 3. GSVA analysis then demonstrated that the different clusters had differences in pathway activity. By constructing a transcriptional regulatory network, USF2 was identified to be a transcriptional regulator of CEACAM1 and CEACAM5, while KLF6 was identified to be a transcriptional regulator of PSG3 and PSG5. This conclusion was then validated using the Genotype-Tissue Expression (GTEx) spinal cord transcriptome dataset.ConclusionsThis study completed an integrated analysis of a single-cell dataset with the utilization of marker mRNAs. USF2/CEACAM1&5 and KLF6/PSG3&5 transcriptional regulatory networks were identified by spinal cord single-cell analysis.

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Identification of miRNAs that specifically target tumor suppressive KLF6-FL rather than oncogenic KLF6-SV1 isoform.

Project description:The Krüppel like factor 6 (KLF6) gene encodes multiple protein isoforms derived from alternative mRNA splicing, most of which are intimately involved in hepatocarcinogenesis and tumor progression. Recent bioinformatics analysis shows that alternative mRNA splicing of the KLF6 gene produces around 16 alternatively spliced variants with divergent or even opposing functions. Intriguingly, the full-length KLF6 (KLF6-FL) is a tumor suppressor gene frequently inactivated in liver cancer, whereas KLF6 splice variant 1 (KLF6-SV1) is an oncogenic isoform with antagonistic function against KLF6-FL. Compelling evidence indicates that miRNA, the small endogenous non-coding RNA (ncRNA), acts as a vital player in modulating a variety of cellular biological processes through targeting different mRNA regions of protein-coding genes. To identify the potential miRNAs specifically targeting KLF6-FL, we utilized bioinformatics analysis in combination with the luciferase reporter assays and screened out two miRNAs, namely miR-210 and miR-1301, specifically targeted the tumor suppressive KLF6-FL rather than the oncogenic KLF6-SV1. Our in vitro experiments demonstrated that stable expression of KLF6-FL inhibited cell proliferation, migration and angiogenesis while overexpression of miR-1301 promoted cell migration and angiogenesis. Further experiments demonstrated that miR-1301 was highly expressed in liver cancer cell lines as well as clinical specimens and we also identified the potential methylation and histone acetylation for miR-1301 gene. To sum up, our findings unveiled a novel molecular mechanism that specific miRNAs promoted tumorigenesis by targeting the tumor suppressive isoform KLF6-FL rather than its oncogenic isoform KLF6-SV1.

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