A systematic method to identify modulation of transcriptional regulation via chromatin activity reveals regulatory network during mESC differentiation

Learning Causes of Functional Dynamic Targets: Screening and Local Methods

This paper addresses the challenge of identifying causes for functional dynamic targets, which are functions of various variables over time. We develop screening and local learning methods to learn the direct causes of the target, as well as all indirect causes up to a given distance. We first discuss the modeling of the functional dynamic target. Then, we propose a screening method to select the variables that are significantly correlated with the target. On this basis, we introduce an algorithm that combines screening and structural learning techniques to uncover the causal structure among the target and its causes. To tackle the distance effect, where long causal paths weaken correlation, we propose a local method to discover the direct causes of the target in these significant variables and further sequentially find all indirect causes up to a given distance. We show theoretically that our proposed methods can learn the causes correctly under some regular assumptions. Experiments based on synthetic data also show that the proposed methods perform well in learning the causes of the target.

MIMIC: an optimization method to identify cell type-specific marker panel for cell sorting

Multi-omics data allow us to select a small set of informative markers for the discrimination of specific cell types and study of cellular heterogeneity. However, it is often challenging to choose an optimal marker panel from the high-dimensional molecular profiles for a large amount of cell types. Here, we propose a method called Mixed Integer programming Model to Identify Cell type-specific marker panel (MIMIC). MIMIC maintains the hierarchical topology among different cell types and simultaneously maximizes the specificity of a fixed number of selected markers. MIMIC was benchmarked on the mouse ENCODE RNA-seq dataset, with 29 diverse tissues, for 43 surface markers (SMs) and 1345 transcription factors (TFs). MIMIC could select biologically meaningful markers and is robust for different accuracy criteria. It shows advantages over the standard single gene-based approaches and widely used dimensional reduction methods, such as multidimensional scaling and t-SNE, both in accuracy and in biological interpretation. Furthermore, the combination of SMs and TFs achieves better specificity than SMs or TFs alone. Applying MIMIC to a large collection of 641 RNA-seq samples covering 231 cell types identifies a panel of TFs and SMs that reveal the modularity of cell type association networks. Finally, the scalability of MIMIC is demonstrated by selecting enhancer markers from mouse ENCODE data. MIMIC is freely available at https://github.com/MengZou1/MIMIC.

Targeted epigenetic modulation using a DNA-based histone deacetylase inhibitor enhances cardiomyogenesis in mouse embryonic stem cells

The epigenome has an essential role in orchestrating transcriptional activation and modulating key developmental processes. Previously, we developed a library of pyrrole-imidazole polyamides (PIPs) conjugated with suberoylanilide hydroxamic acid (SAHA), a histone deacetylase (HDAC) inhibitor, for the purpose of sequence-specific modification of epigenetics. Based on the gene expression profile of SAHA-PIPs and screening studies using the α-myosin heavy chain promoter-driven reporter and SAHA-PIP library, we identified that SAHA-PIP G activates cardiac-related genes. Studies in mouse ES cells showed that SAHA-PIP G could enhance the generation of spontaneous beating cells, which is consistent with upregulation of several cardiac-related genes. Moreover, ChIP-seq results confirmed that the upregulation of cardiac-related genes is highly correlated with epigenetic activation, relevant to the sequence-specific binding of SAHA-PIP G. This proof-of-concept study demonstrating the applicability of SAHA-PIP not only improves our understanding of epigenetic alterations involved in cardiomyogenesis but also provides a novel chemical-based strategy for stem cell differentiation.

Hierarchical graphical model reveals HFR1 bridging circadian rhythm and flower development in Arabidopsis thaliana

To study systems-level properties of the cell, it is necessary to go beyond individual regulators and target genes to study the regulatory network among transcription factors (TFs). However, it is difficult to directly dissect the TFs mediated genome-wide gene regulatory network (GRN) by experiment. Here, we proposed a hierarchical graphical model to estimate TF activity from mRNA expression by building TF complexes with protein cofactors and inferring TF's downstream regulatory network simultaneously. Then we applied our model on flower development and circadian rhythm processes in Arabidopsis thaliana. The computational results show that the sequence specific bHLH family TF HFR1 recruits the chromatin regulator HAC1 to flower development master regulator TF AG and further activates AG's expression by histone acetylation. Both independent data and experimental results supported this discovery. We also found a flower tissue specific H3K27ac ChIP-seq peak at AG gene body and a HFR1 motif in the center of this H3K27ac peak. Furthermore, we verified that HFR1 physically interacts with HAC1 by yeast two-hybrid experiment. This HFR1-HAC1-AG triplet relationship may imply that flower development and circadian rhythm are bridged by epigenetic regulation and enrich the classical ABC model in flower development. In addition, our TF activity network can serve as a general method to elucidate molecular mechanisms on other complex biological regulatory processes.

Explore mediated co-varying dynamics in microbial community using integrated local similarity and liquid association analysis

BACKGROUND

Discovering the key microbial species and environmental factors of microbial community and characterizing their relationships with other members are critical to ecosystem studies. The microbial co-occurrence patterns across a variety of environmental settings have been extensively characterized. However, previous studies were limited by their restriction toward pairwise relationships, while there was ample evidence of third-party mediated co-occurrence in microbial communities.

METHODS

We implemented and applied the triplet-based liquid association analysis in combination with the local similarity analysis procedure to microbial ecology data. We developed an intuitive scheme to visualize those complex triplet associations along with pairwise correlations. Using a time series from the marine microbial ecosystem as example, we identified pairs of operational taxonomic units (OTUs) where the strength of their associations appeared to relate to the values of a third "mediator" variable. These "mediator" variables appear to modulate the associations between pairs of bacteria.

RESULTS

Using this analysis, we were able to assess the OTUs' ability to regulate its functional partners in the community, typically not manifested in the pairwise correlation patterns. For example, we identified Flavobacteria as a multifaceted player in the marine microbial ecosystem, and its clades were involved in mediating other OTU pairs. By contrast, SAR11 clades were not active mediators of the community, despite being abundant and highly correlated with other OTUs. Our results suggested that Flavobacteria are more likely to respond to situations where particles and unusual sources of dissolved organic material are prevalent, such as after a plankton bloom. On the other hand, SAR11s are oligotrophic chemoheterotrophs with inflexible metabolisms, and their relationships with other organisms may be less governed by environmental or biological factors.

CONCLUSIONS

By integrating liquid association with local similarity analysis to explore the mediated co-varying dynamics, we presented a novel perspective and a useful toolkit to analyze and interpret time series data from microbial community. Our augmented association network analysis is thus more representative of the true underlying dynamic structure of the microbial community. The analytic software in this study was implemented as new functionalities of the ELSA (Extended local similarity analysis) tool, which is available for free download ( http://bitbucket.org/charade/elsa ).

Sequential transfection of RUNX2/SP7 and ATF4 coated onto dexamethasone-loaded nanospheresenhances osteogenesis

The timing of gene transfection greatly influences stem cell differentiation. Sequential transfection is crucial for regulation of cell behavior. When transfected several days after differentiation initiation, genes expressed at the late stage of differentiation can regulate cell behaviors and functions. To determine the optimal timing of key gene delivery, we sequentially transfected human mesenchymal stem cells (hMSCs). This method can easily control osteogenesis of stem cells. hMSCs were first transfected with RUNX2 and SP7 using poly(lactic-co-glycolic acid) nanoparticles to induce osteogenesis, and then with ATF4 after 5, 7, and 14 days. Prior to transfecting hMSCs with all three genes, each gene was individually transfected and its expression was monitored. Transfection of these genes was confirmed by RT-PCR, Western blotting, and confocal microscopy. The pDNAs entered the nuclei of hMSCs, and RUNX2 and SP7 proteins were translated and triggered osteogenesis. Second, the ATF4 gene was delivered when cells were at the pre-osteoblasts stage. To induce the osteogenesis of hMSCs, the optimal timing of ATF4 gene delivery was 14 days after RUNX2/SP7 transfection. Experiments in 2- and 3-dimensional culture systems confirmed that transfection of ATF4 at 14 days after RUNX2/SP7 promoted osteogenic differentiation of hMSCs.

Accelerated parallel algorithm for gene network reverse engineering

Background

The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) represents one of the most effective tools to reconstruct gene regulatory networks from large-scale molecular profile datasets. However, previous implementations require intensive computing resources and, in some cases, restrict the number of samples that can be used. These issues can be addressed elegantly in a GPU computing framework, where repeated mathematical computation can be done efficiently, but requires extensive redesign to apply parallel computing techniques to the original serial algorithm, involving detailed optimization efforts based on a deep understanding of both hardware and software architecture.

Result

Here, we present an accelerated parallel implementation of ARACNE (GPU-ARACNE). By taking advantage of multi-level parallelism and the Compute Unified Device Architecture (CUDA) parallel kernel-call library, GPU-ARACNE successfully parallelizes a serial algorithm and simplifies the user experience from multi-step operations to one step. Using public datasets on comparable hardware configurations, we showed that GPU-ARACNE is faster than previous implementations and is able to reconstruct equally valid gene regulatory networks.

Conclusion

Given that previous versions of ARACNE are extremely resource demanding, either in computational time or in hardware investment, GPU-ARACNE is remarkably valuable for researchers who need to build complex regulatory networks from large expression datasets, but with limited budget on computational resources. In addition, our GPU-centered optimization of adaptive partitioning for Mutual Information (MI) estimation provides lessons that are applicable to other domains.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0458-5) contains supplementary material, which is available to authorized users.

Valproic Acid Induces Decreased Expression of H19 Promoting Cell Apoptosis in A549 Cells

It has been suggested that the imprinted gene, H19, plays a crucial role in the development of cancer. In the present study, we attempted to treat the abnormal expression and methylation status of H19 in A549 cells using valproic acid (VPA), ascorbic acid (Vc), and 5-aza-Cytidine (5-Aza). The results suggested that VPA administration could alter the expression pattern of H19, while the hypomethylation status of H19 DMR was unchanged. Furthermore, overexpression of HDAC1 and DNMT1 was associated with decreased expression of H19 in VPA-treated cells. Western blot results showed that the expression of p53 protein was increased following treatment with VPA. In addition, we also investigated cellular apoptosis and the cell cycle of treated cells. Flow cytometry data indicated that VPA could increase the occurrence of cell apoptosis in A549 cells. Taken together, our results suggest that H19 expression was suppressed by VPA through HDAC1 and DNMT1 and decreased H19 expression correlated with cell apoptosis in A549 cells.

Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation

Background

Linking computational models of signaling pathways to predicted cellular responses such as gene expression regulation is a major challenge in computational systems biology. In this work, we present Sig2GRN, a Cytoscape plugin that is able to simulate time-course gene expression data given the user-defined external stimuli to the signaling pathways.

Methods

A generalized logical model is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict the downstream changes in gene expression based on the simulated dynamics of transcription factors in signaling pathways.

Results

Our empirical case studies show that the simulation of Sig2GRN can predict changes in gene expression patterns induced by DNA damage signals and drug treatments.

Conclusions

As a software tool for modeling cellular dynamics, Sig2GRN can facilitate studies in systems biology by hypotheses generation and wet-lab experimental design.

Availability: http://histone.scse.ntu.edu.sg/Sig2GRN/