ESCAPE: database for integrating high-content published data collected from human and mouse embryonic stem cells

GAEDGRN: reconstruction of gene regulatory networks based on gravity-inspired graph autoencoders

Reconstructing high-resolution gene regulatory networks (GRNs) based on single-cell RNA sequencing data provides an opportunity to gain insight into disease pathogenesis. At present, there are a large number of GRN reconstruction methods based on graph neural networks, and they can obtain excellent performance in GRN inference by extracting network structure features. However, most of these methods fail to fully exploit the directional characteristics or even ignore them when extracting network structural features. To this end, a novel framework called GAEDGRN is proposed based on gravity-inspired graph autoencoder (GIGAE) to infer potential causal relationships between genes. Among them, GIGAE can help us capture the complex directed network topology in GRN. Additionally, due to the uneven distribution of the latent vectors generated by the graph autoencoder, a random walk-based method is used to regularize the latent vectors learnt by the encoder. Furthermore, considering that some genes in GRN usually have a significant impact on biological functions, GAEDGRN designs a gene importance score calculation method and pays attention to genes with high importance in the process of GRN reconstruction. Experimental results on seven cell types of three GRN types show that GAEDGRN achieves high accuracy and strong robustness. Moreover, a case study on human embryonic stem cells demonstrates that GAEDGRN can help identify important genes.

Inferring gene regulatory networks by hypergraph generative model

We present hypergraph variational autoencoder (HyperG-VAE), a Bayesian deep generative model that leverages hypergraph representation to model single-cell RNA sequencing (scRNA-seq) data. The model features a cell encoder with a structural equation model to account for cellular heterogeneity and construct gene regulatory networks (GRNs) alongside a gene encoder using hypergraph self-attention to identify gene modules. The synergistic optimization of encoders via a decoder improves GRN inference, single-cell clustering, and data visualization, as validated by benchmarks. HyperG-VAE effectively uncovers gene regulation patterns and demonstrates robustness in downstream analyses, as shown in B cell development data from bone marrow. Gene set enrichment analysis of overlapping genes in predicted GRNs confirms the gene encoder's role in refining GRN inference. Offering an efficient solution for scRNA-seq analysis and GRN construction, HyperG-VAE also holds the potential for extending GRN modeling to temporal and multimodal single-cell omics.

GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data

BACKGROUND

A gene regulatory network (GRN) is a graph-level representation that describes the regulatory relationships between transcription factors and target genes in cells. The reconstruction of GRNs can help investigate cellular dynamics, drug design, and metabolic systems, and the rapid development of single-cell RNA sequencing (scRNA-seq) technology provides important opportunities while posing significant challenges for reconstructing GRNs. A number of methods for inferring GRNs have been proposed in recent years based on traditional machine learning and deep learning algorithms. However, inferring the GRN from scRNA-seq data remains challenging owing to cellular heterogeneity, measurement noise, and data dropout.

RESULTS

In this study, we propose a deep learning model called graph representational learning GRN (GRLGRN) to infer the latent regulatory dependencies between genes based on a prior GRN and data on the profiles of single-cell gene expressions. GRLGRN uses a graph transformer network to extract implicit links from the prior GRN, and encodes the features of genes by using both an adjacency matrix of implicit links and a matrix of the profile of gene expression. Moreover, it uses attention mechanisms to improve feature extraction, and feeds the refined gene embeddings into an output module to infer gene regulatory relationships. To evaluate the performance of GRLGRN, we compared it with prevalent models and performed ablation experiments on seven cell-line datasets with three ground-truth networks. The results showed that GRLGRN achieved the best predictions in AUROC and AUPRC on 78.6% and 80.9% of the datasets, and achieved an average improvement of 7.3% in AUROC and 30.7% in AUPRC. The interpretation discussion and the network visualization were conducted.

CONCLUSIONS

The experimental results and case studies illustrate the considerable performance of GRLGRN in predicting gene interactions and provide interpretability for the prediction tasks, such as identifying hub genes in the network and uncovering implicit links.

Unravelling Convergent Signaling Mechanisms Underlying the Aging-Disease Nexus Using Computational Language Analysis

Multiple lines of evidence suggest that multiple pathological conditions and diseases that account for the majority of human mortality are driven by the molecular aging process. At the cellular level, aging can largely be conceptualized to comprise the progressive accumulation of molecular damage, leading to resultant cellular dysfunction. As many diseases, e.g., cancer, coronary heart disease, Chronic obstructive pulmonary disease, Type II diabetes mellitus, or chronic kidney disease, potentially share a common molecular etiology, then the identification of such mechanisms may represent an ideal locus to develop targeted prophylactic agents that can mitigate this disease-driving mechanism. Here, using the input of artificial intelligence systems to generate unbiased disease and aging mechanism profiles, we have aimed to identify key signaling mechanisms that may represent new disease-preventing signaling pathways that are ideal for the creation of disease-preventing chemical interventions. Using a combinatorial informatics approach, we have identified a potential critical mechanism involving the recently identified kinase, Dual specificity tyrosine-phosphorylation-regulated kinase 3 (DYRK3) and the epidermal growth factor receptor (EGFR) that may function as a regulator of the pathological transition of health into disease via the control of cellular fate in response to stressful insults.

CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data

Gene regulatory network (GRN) inference based on single-cell RNA sequencing data (scRNAseq) plays a crucial role in understanding the regulatory mechanisms between genes. Various computational methods have been employed for GRN inference, but their performance in terms of network accuracy and model generalization is not satisfactory, and their poor performance is caused by high-dimensional data and network sparsity. In this paper, we propose a self-supervised method for gene regulatory network inference using single-cell RNA sequencing data (CVGAE). CVGAE uses graph neural network for inductive representation learning, which merges gene expression data and observed topology into a low-dimensional vector space. The well-trained vectors will be used to calculate mathematical distance of each gene, and further predict interactions between genes. In overall framework, FastICA is implemented to relief computational complexity caused by high dimensional data, and CVGAE adopts multi-stacked GraphSAGE layers as an encoder and an improved decoder to overcome network sparsity. CVGAE is evaluated on several single cell datasets containing four related ground-truth networks, and the result shows that CVGAE achieve better performance than comparative methods. To validate learning and generalization capabilities, CVGAE is applied in few-shot environment by change the ratio of train set and test set. In condition of few-shot, CVGAE obtains comparable or superior performance.

iGTP: Learning interpretable cellular embedding for inferring biological mechanisms underlying single-cell transcriptomics

Deep-learning models like Variational AutoEncoder have enabled low dimensional cellular embedding representation for large-scale single-cell transcriptomes and shown great flexibility in downstream tasks. However, biologically meaningful latent space is usually missing if no specific structure is designed. Here, we engineered a novel interpretable generative transcriptional program (iGTP) framework that could model the importance of transcriptional program (TP) space and protein-protein interactions (PPI) between different biological states. We demonstrated the performance of iGTP in a diverse biological context using gene ontology, canonical pathway, and different PPI curation. iGTP not only elucidated the ground truth of cellular responses but also surpassed other deep learning models and traditional bioinformatics methods in functional enrichment tasks. By integrating the latent layer with a graph neural network framework, iGTP could effectively infer cellular responses to perturbations. Lastly, we applied iGTP TP embeddings with a latent diffusion model to accurately generate cell embeddings for specific cell types and states. We anticipate that iGTP will offer insights at both PPI and TP levels and holds promise for predicting responses to novel perturbations.

Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods

Gene regulatory networks are powerful tools for modeling genetic interactions that control the expression of genes driving cell differentiation, and single-cell sequencing offers a unique opportunity to build these networks with high-resolution genomic data. There are many proposed computational methods to build these networks using single-cell data, and different approaches are used to benchmark these methods. However, a comprehensive discussion specifically focusing on benchmarking approaches is missing. In this article, we lay the GRN terminology, present an overview of common gold-standard studies and data sets, and define the performance metrics for benchmarking network construction methodologies. We also point out the advantages and limitations of different benchmarking approaches, suggest alternative ground truth data sets that can be used for benchmarking, and specify additional considerations in this context.

Deep Learning in Gene Regulatory Network Inference: A Survey

Understanding the intricate regulatory relationships among genes is crucial for comprehending the development, differentiation, and cellular response in living systems. Consequently, inferring gene regulatory networks (GRNs) based on observed data has gained significant attention as a fundamental goal in biological applications. The proliferation and diversification of available data present both opportunities and challenges in accurately inferring GRNs. Deep learning, a highly successful technique in various domains, holds promise in aiding GRN inference. Several GRN inference methods employing deep learning models have been proposed; however, the selection of an appropriate method remains a challenge for life scientists. In this survey, we provide a comprehensive analysis of 12 GRN inference methods that leverage deep learning models. We trace the evolution of these major methods and categorize them based on the types of applicable data. We delve into the core concepts and specific steps of each method, offering a detailed evaluation of their effectiveness and scalability across different scenarios. These insights enable us to make informed recommendations. Moreover, we explore the challenges faced by GRN inference methods utilizing deep learning and discuss future directions, providing valuable suggestions for the advancement of data scientists in this field.

Scalable identification of lineage-specific gene regulatory networks from metacells with NetID

The identification of gene regulatory networks (GRNs) is crucial for understanding cellular differentiation. Single-cell RNA sequencing data encode gene-level covariations at high resolution, yet data sparsity and high dimensionality hamper accurate and scalable GRN reconstruction. To overcome these challenges, we introduce NetID leveraging homogenous metacells while avoiding spurious gene-gene correlations. Benchmarking demonstrates superior performance of NetID compared to imputation-based methods. By incorporating cell fate probability information, NetID facilitates the prediction of lineage-specific GRNs and recovers known network motifs governing bone marrow hematopoiesis, making it a powerful toolkit for deciphering gene regulatory control of cellular differentiation from large-scale single-cell transcriptome data.

scMGATGRN: a multiview graph attention network-based method for inferring gene regulatory networks from single-cell transcriptomic data

The gene regulatory network (GRN) plays a vital role in understanding the structure and dynamics of cellular systems, revealing complex regulatory relationships, and exploring disease mechanisms. Recently, deep learning (DL)-based methods have been proposed to infer GRNs from single-cell transcriptomic data and achieved impressive performance. However, these methods do not fully utilize graph topological information and high-order neighbor information from multiple receptive fields. To overcome those limitations, we propose a novel model based on multiview graph attention network, namely, scMGATGRN, to infer GRNs. scMGATGRN mainly consists of GAT, multiview, and view-level attention mechanism. GAT can extract essential features of the gene regulatory network. The multiview model can simultaneously utilize local feature information and high-order neighbor feature information of nodes in the gene regulatory network. The view-level attention mechanism dynamically adjusts the relative importance of node embedding representations and efficiently aggregates node embedding representations from two views. To verify the effectiveness of scMGATGRN, we compared its performance with 10 methods (five shallow learning algorithms and five state-of-the-art DL-based methods) on seven benchmark single-cell RNA sequencing (scRNA-seq) datasets from five cell lines (two in human and three in mouse) with four different kinds of ground-truth networks. The experimental results not only show that scMGATGRN outperforms competing methods but also demonstrate the potential of this model in inferring GRNs. The code and data of scMGATGRN are made freely available on GitHub (https://github.com/nathanyl/scMGATGRN).

Inference of Gene Regulatory Networks Based on Multi-view Hierarchical Hypergraphs

Since gene regulation is a complex process in which multiple genes act simultaneously, accurately inferring gene regulatory networks (GRNs) is a long-standing challenge in systems biology. Although graph neural networks can formally describe intricate gene expression mechanisms, current GRN inference methods based on graph learning regard only transcription factor (TF)-target gene interactions as pairwise relationships, and cannot model the many-to-many high-order regulatory patterns that prevail among genes. Moreover, these methods often rely on limited prior regulatory knowledge, ignoring the structural information of GRNs in gene expression profiles. Therefore, we propose a multi-view hierarchical hypergraphs GRN (MHHGRN) inference model. Specifically, multiple heterogeneous biological information is integrated to construct multi-view hierarchical hypergraphs of TFs and target genes, using hypergraph convolution networks to model higher order complex regulatory relationships. Meanwhile, the coupled information diffusion mechanism and the cross-domain messaging mechanism facilitate the information sharing between genes to optimise gene embedding representations. Finally, a unique channel attention mechanism is used to adaptively learn feature representations from multiple views for GRN inference. Experimental results show that MHHGRN achieves better results than the baseline methods on the E. coli and S. cerevisiae benchmark datasets of the DREAM5 challenge, and it has excellent cross-species generalization, achieving comparable or better performance on scRNA-seq datasets from five mouse and two human cell lines.

GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks

We introduce GRouNdGAN, a gene regulatory network (GRN)-guided reference-based causal implicit generative model for simulating single-cell RNA-seq data, in silico perturbation experiments, and benchmarking GRN inference methods. Through the imposition of a user-defined GRN in its architecture, GRouNdGAN simulates steady-state and transient-state single-cell datasets where genes are causally expressed under the control of their regulating transcription factors (TFs). Training on six experimental reference datasets, we show that our model captures non-linear TF-gene dependencies and preserves gene identities, cell trajectories, pseudo-time ordering, and technical and biological noise, with no user manipulation and only implicit parameterization. GRouNdGAN can synthesize cells under new conditions to perform in silico TF knockout experiments. Benchmarking various GRN inference algorithms reveals that GRouNdGAN effectively bridges the existing gap between simulated and biological data benchmarks of GRN inference algorithms, providing gold standard ground truth GRNs and realistic cells corresponding to the biological system of interest.

Inferring gene regulatory networks from single-cell transcriptomics based on graph embedding

MOTIVATION

Gene regulatory networks (GRNs) encode gene regulation in living organisms, and have become a critical tool to understand complex biological processes. However, due to the dynamic and complex nature of gene regulation, inferring GRNs from scRNA-seq data is still a challenging task. Existing computational methods usually focus on the close connections between genes, and ignore the global structure and distal regulatory relationships.

RESULTS

In this study, we develop a supervised deep learning framework, IGEGRNS, to infer GRNs from scRNA-seq data based on graph embedding. In the framework, contextual information of genes is captured by GraphSAGE, which aggregates gene features and neighborhood structures to generate low-dimensional embedding for genes. Then, the k most influential nodes in the whole graph are filtered through Top-k pooling. Finally, potential regulatory relationships between genes are predicted by stacking CNNs. Compared with nine competing supervised and unsupervised methods, our method achieves better performance on six time-series scRNA-seq datasets.

AVAILABILITY AND IMPLEMENTATION

Our method IGEGRNS is implemented in Python using the Pytorch machine learning library, and it is freely available at https://github.com/DHUDBlab/IGEGRNS.

scGREAT: Transformer-based deep-language model for gene regulatory network inference from single-cell transcriptomics

Gene regulatory networks (GRNs) involve complex and multi-layer regulatory interactions between regulators and their target genes. Precise knowledge of GRNs is important in understanding cellular processes and molecular functions. Recent breakthroughs in single-cell sequencing technology made it possible to infer GRNs at single-cell level. Existing methods, however, are limited by expensive computations, and sometimes simplistic assumptions. To overcome these obstacles, we propose scGREAT, a framework to infer GRN using gene embeddings and transformer from single-cell transcriptomics. scGREAT starts by constructing gene expression and gene biotext dictionaries from scRNA-seq data and gene text information. The representation of TF gene pairs is learned through optimizing embedding space by transformer-based engine. Results illustrated scGREAT outperformed other contemporary methods on benchmarks. Besides, gene representations from scGREAT provide valuable gene regulation insights, and external validation on spatial transcriptomics illuminated the mechanism behind scGREAT annotation. Moreover, scGREAT identified several TF target regulations corroborated in studies.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON

Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.

Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions

Gene regulatory networks (GRNs) drive organism structure and functions, so the discovery and characterization of GRNs is a major goal in biological research. However, accurate identification of causal regulatory connections and inference of GRNs using gene expression datasets, more recently from single-cell RNA-seq (scRNA-seq), has been challenging. Here we employ the innovative method of Causal Inference Using Composition of Transactions (CICT) to uncover GRNs from scRNA-seq data. The basis of CICT is that if all gene expressions were random, a non-random regulatory gene should induce its targets at levels different from the background random process, resulting in distinct patterns in the whole relevance network of gene-gene associations. CICT proposes novel network features derived from a relevance network, which enable any machine learning algorithm to predict causal regulatory edges and infer GRNs. We evaluated CICT using simulated and experimental scRNA-seq data in a well-established benchmarking pipeline and showed that CICT outperformed existing network inference methods representing diverse approaches with many-fold higher accuracy. Furthermore, we demonstrated that GRN inference with CICT was robust to different levels of sparsity in scRNA-seq data, the characteristics of data and ground truth, the choice of association measure and the complexity of the supervised machine learning algorithm. Our results suggest aiming at directly predicting causality to recover regulatory relationships in complex biological networks substantially improves accuracy in GRN inference.

Identifying Cancer Type-Specific Transcriptional Programs through Network Analysis

Identifying cancer type-specific genes that define cell states is important to develop effective therapies for patients and methods for detection, early diagnosis, and prevention. While molecular mechanisms that drive malignancy have been identified for various cancers, the identification of cell-type defining transcription factors (TFs) that distinguish normal cells from cancer cells has not been fully elucidated. Here, we utilized a network biology framework, which assesses the fidelity of cell fate conversions, to identify cancer type-specific gene regulatory networks (GRN) for 17 types of cancer. Through an integrative analysis of a compendium of expression data, we elucidated core TFs and GRNs for multiple cancer types. Moreover, by comparing normal tissues and cells to cancer type-specific GRNs, we found that the expression of key network-influencing TFs can be utilized as a survival prognostic indicator for a diverse cohort of cancer patients. These findings offer a valuable resource for exploring cancer type-specific networks across a broad range of cancer types.

Integrative modeling of lncRNA-chromatin interaction maps reveals diverse mechanisms of nuclear retention

BACKGROUND

Many long non-coding RNAs, known to be involved in transcriptional regulation, are enriched in the nucleus and interact with chromatin. However, their mechanisms of chromatin interaction and the served cellular functions are poorly understood. We sought to characterize the mechanisms of lncRNA nuclear retention by systematically mapping the sequence and chromatin features that distinguish lncRNA-interacting genomic segments.

RESULTS

We found DNA 5-mer frequencies to be predictive of chromatin interactions for all lncRNAs, suggesting sequence-specificity as a global theme in the interactome. Sequence features representing protein-DNA and protein-RNA binding motifs revealed potential mechanisms for specific lncRNAs. Complementary to these global themes, transcription-related features and DNA-RNA triplex formation potential were noted to be highly predictive for two mutually exclusive sets of lncRNAs. DNA methylation was also noted to be a significant predictor, but only when combined with other epigenomic features.

CONCLUSIONS

Taken together, our statistical findings suggest that a group of lncRNAs interacts with transcriptionally inactive chromatin through triplex formation, whereas another group interacts with transcriptionally active regions and is involved in DNA Damage Response (DDR) through formation of R-loops. Curiously, we observed a strong pattern of enrichment of 5-mers in four potentially interacting entities: lncRNA-bound DNA tiles, lncRNAs, miRNA seed sequences, and repeat elements. This finding points to a broad sequence-based network of interactions that may underlie regulation of fundamental cellular functions. Overall, this study reveals diverse sequence and chromatin features related to lncRNA-chromatin interactions, suggesting potential mechanisms of nuclear retention and regulatory function.

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.

Chemoproteomic and Transcriptomic Analysis Reveals that O-GlcNAc Regulates Mouse Embryonic Stem Cell Fate through the Pluripotency Network

Self-renewal and differentiation of embryonic stem cells (ESCs) are influenced by protein O-linked β-N-acetylglucosamine (O-GlcNAc) modification, but the underlying mechanism remains incompletely understood. Herein, we report the identification of 979 O-GlcNAcylated proteins and 1340 modification sites in mouse ESCs (mESCs) by using a chemoproteomics method. In addition to OCT4 and SOX2, the third core pluripotency transcription factor (PTF) NANOG was found to be modified and functionally regulated by O-GlcNAc. Upon differentiation along the neuronal lineage, the O-GlcNAc stoichiometry at 123 sites of 83 proteins-several of which were PTFs-was found to decline. Transcriptomic profiling reveals 2456 differentially expressed genes responsive to OGT inhibition during differentiation, of which 901 are target genes of core PTFs. By acting on the core PTF network, suppression of O-GlcNAcylation upregulates neuron-related genes, thus contributing to mESC fate determination.

STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data

MOTIVATION

Single-cell RNA-sequencing (scRNA-seq) technologies provide an opportunity to infer cell-specific gene regulatory networks (GRNs), which is an important challenge in systems biology. Although numerous methods have been developed for inferring GRNs from scRNA-seq data, it is still a challenge to deal with cellular heterogeneity.

RESULTS

To address this challenge, we developed an interpretable transformer-based method namely STGRNS for inferring GRNs from scRNA-seq data. In this algorithm, gene expression motif technique was proposed to convert gene pairs into contiguous sub-vectors, which can be used as input for the transformer encoder. By avoiding missing phase-specific regulations in a network, gene expression motif can improve the accuracy of GRN inference for different types of scRNA-seq data. To assess the performance of STGRNS, we implemented the comparative experiments with some popular methods on extensive benchmark datasets including 21 static and 27 time-series scRNA-seq dataset. All the results show that STGRNS is superior to other comparative methods. In addition, STGRNS was also proved to be more interpretable than "black box" deep learning methods, which are well-known for the difficulty to explain the predictions clearly.

AVAILABILITY AND IMPLEMENTATION

The source code and data are available at https://github.com/zhanglab-wbgcas/STGRNS.

Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning

The inference of gene regulatory networks (GRNs) is of great importance for understanding the complex regulatory mechanisms within cells. The emergence of single-cell RNA-sequencing (scRNA-seq) technologies enables the measure of gene expression levels for individual cells, which promotes the reconstruction of GRNs at single-cell resolution. However, existing network inference methods are mainly designed for data collected from a single data source, which ignores the information provided by multiple related data sources. In this paper, we propose a multi-view contrastive learning (DeepMCL) model to infer GRNs from scRNA-seq data collected from multiple data sources or time points. We first represent each gene pair as a set of histogram images, and then introduce a deep Siamese convolutional neural network with contrastive loss to learn the low-dimensional embedding for each gene pair. Moreover, an attention mechanism is introduced to integrate the embeddings extracted from different data sources and different neighbor gene pairs. Experimental results on synthetic and real-world datasets validate the effectiveness of our contrastive learning and attention mechanisms, demonstrating the effectiveness of our model in integrating multiple data sources for GRN inference.

Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing data

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) data provides unprecedented opportunities to reconstruct gene regulatory networks (GRNs) at fine-grained resolution. Numerous unsupervised or self-supervised models have been proposed to infer GRN from bulk RNA-seq data, but few of them are appropriate for scRNA-seq data under the circumstance of low signal-to-noise ratio and dropout. Fortunately, the surging of TF-DNA binding data (e.g. ChIP-seq) makes supervised GRN inference possible. We regard supervised GRN inference as a graph-based link prediction problem that expects to learn gene low-dimensional vectorized representations to predict potential regulatory interactions.

RESULTS

In this paper, we present GENELink to infer latent interactions between transcription factors (TFs) and target genes in GRN using graph attention network. GENELink projects the single-cell gene expression with observed TF-gene pairs to a low-dimensional space. Then, the specific gene representations are learned to serve for downstream similarity measurement or causal inference of pairwise genes by optimizing the embedding space. Compared to eight existing GRN reconstruction methods, GENELink achieves comparable or better performance on seven scRNA-seq datasets with four types of ground-truth networks. We further apply GENELink on scRNA-seq of human breast cancer metastasis and reveal regulatory heterogeneity of Notch and Wnt signalling pathways between primary tumour and lung metastasis. Moreover, the ontology enrichment results of unique lung metastasis GRN indicate that mitochondrial oxidative phosphorylation (OXPHOS) is functionally important during the seeding step of the cancer metastatic cascade, which is validated by pharmacological assays.

AVAILABILITY AND IMPLEMENTATION

The code and data are available at https://github.com/zpliulab/GENELink.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CELLoGeNe - An energy landscape framework for logical networks controlling cell decisions

Experimental and computational efforts are constantly made to elucidate mechanisms controlling cell fate decisions during development and reprogramming. One powerful computational method is to consider cell commitment and reprogramming as movements in an energy landscape. Here, we develop Computation of Energy Landscapes of Logical Gene Networks (CELLoGeNe), which maps Boolean implementation of gene regulatory networks (GRNs) into energy landscapes. CELLoGeNe removes inadvertent symmetries in the energy landscapes normally arising from standard Boolean operators. Furthermore, CELLoGeNe provides tools to visualize and stochastically analyze the shapes of multi-dimensional energy landscapes corresponding to epigenetic landscapes for development and reprogramming. We demonstrate CELLoGeNe on two GRNs governing different aspects of induced pluripotent stem cells, identifying experimentally validated attractors and revealing potential reprogramming roadblocks. CELLoGeNe is a general framework that can be applied to various biological systems offering a broad picture of intracellular dynamics otherwise inaccessible with existing methods.

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CELLoGeNe – Computation of Energy Landscapes of Logical Gene Networks

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Cell states as landscape attractors

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Maintenance and acquisition of cell pluripotency applications

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Single cell stochastic landscape navigation and visualization tool

Dynamic regulatory module networks for inference of cell type-specific transcriptional networks

Changes in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled bulk multi-omic data sets with parallel transcriptomic and epigenomic measurements at different stages of a developmental process. However, integrating these data to infer cell type-specific regulatory networks is a major challenge. We present dynamic regulatory module networks (DRMNs), a novel approach to infer cell type-specific cis-regulatory networks and their dynamics. DRMN integrates expression, chromatin state, and accessibility to predict cis-regulators of context-specific expression, where context can be cell type, developmental stage, or time point, and uses multitask learning to capture network dynamics across linearly and hierarchically related contexts. We applied DRMNs to study regulatory network dynamics in three developmental processes, each showing different temporal relationships and measuring a different combination of regulatory genomic data sets: cellular reprogramming, liver dedifferentiation, and forward differentiation. DRMN identified known and novel regulators driving cell type-specific expression patterns, showing its broad applicability to examine dynamics of gene regulatory networks from linearly and hierarchically related multi-omic data sets.

Network inference with Granger causality ensembles on single-cell transcriptomics

Cellular gene expression changes throughout a dynamic biological process, such as differentiation. Pseudotimes estimate cells' progress along a dynamic process based on their individual gene expression states. Ordering the expression data by pseudotime provides information about the underlying regulator-gene interactions. Because the pseudotime distribution is not uniform, many standard mathematical methods are inapplicable for analyzing the ordered gene expression states. Here we present single-cell inference of networks using Granger ensembles (SINGE), an algorithm for gene regulatory network inference from ordered single-cell gene expression data. SINGE uses kernel-based Granger causality regression to smooth irregular pseudotimes and missing expression values. It aggregates predictions from an ensemble of regression analyses to compile a ranked list of candidate interactions between transcriptional regulators and target genes. In two mouse embryonic stem cell differentiation datasets, SINGE outperforms other contemporary algorithms. However, a more detailed examination reveals caveats about poor performance for individual regulators and uninformative pseudotimes.

Three topological features of regulatory networks control life-essential and specialized subsystems

Gene regulatory networks (GRNs) play key roles in development, phenotype plasticity, and evolution. Although graph theory has been used to explore GRNs, associations amongst topological features, transcription factors (TFs), and systems essentiality are poorly understood. Here we sought the relationship amongst the main GRN topological features that influence the control of essential and specific subsystems. We found that the K_nn, page rank, and degree are the most relevant GRN features: the ones are conserved along the evolution and are also relevant in pluripotent cells. Interestingly, life-essential subsystems are governed mainly by TFs with intermediary K_nn and high page rank or degree, whereas specialized subsystems are mainly regulated by TFs with low K_nn. Hence, we suggest that the high probability of TFs be toured by a random signal, and the high probability of the signal propagation to target genes ensures the life-essential subsystems' robustness. Gene/genome duplication is the main evolutionary process to rise K_nn as the most relevant feature. Herein, we shed light on unexplored topological GRN features to assess how they are related to subsystems and how the duplications shaped the regulatory systems along the evolution. The classification model generated can be found here: https://github.com/ivanrwolf/NoC/ .

Efficient induction and sustenance of pluripotent stem cells from bovine somatic cells

Although derivation of naïve bovine embryonic stem cells is unachieved, the possibility for generation of bovine induced pluripotent stem cells (biPSCs) has been generally reported. However, attempts to sustain biPSCs by promoting self-renewal have not been successful. Methods established for maintaining murine and human induced pluripotent stem cells (iPSCs) do not support self-renewal of iPSCs for any bovid species. In this study, we examined methods to enhance complete reprogramming and concurrently investigated signaling relevant to pluripotency of the bovine blastocyst inner cell mass (ICM). First, we identified that forced expression of SV40 large T antigen together with the reprogramming genes (OCT4, SOX2, KLF4 and MYC) substantially enhanced the reprogramming efficacy of bovine fibroblasts to biPSCs. Second, we uncovered that TGFβ signaling is actively perturbed in the ICM. Inhibition of ALK4/5/7 to block TGFβ/activin/nodal signaling together with GSK3β and MEK1/2 supported robust in vitro self-renewal of naïve biPSCs with unvarying colony morphology, steady expansion, expected pluripotency gene expression and committed differentiation plasticity. Core similarities between biPSCs and stem cells of the 16-cell-stage bovine embryo indicated a stable ground state of pluripotency; this allowed us to reliably gain predictive understanding of signaling in bovine pluripotency using systems biology approaches. Beyond defining a high-fidelity platform for advancing biPSC-based biotechnologies that have not been previously practicable, these findings also represent a significant step towards understanding corollaries and divergent aspects of bovine pluripotency.

This article has an associated First Person interview with the joint first authors of the paper.

Summary: Pluripotency reprogramming by overcoming the stable epigenome of bovine cells, and uncovering precise early embryo self-renewal mechanisms enables sustenance and expansion of authentic induced pluripotent stem cells in vitro.

[Identification of potential regulatory genes for embryonic stem cell self-renewal and pluripotency by random forest]

OBJECTIVE

To identify novel genes associated with self-renewal and pluripotency of mouse embryonic stem cells(mESCs)by integrating multiomics data based on machine learning methods.

METHODS

We integrated multiomics information of mESCs involving transcriptome, histone modifications, chromatin accessibility, transcription factor binding and architectural protein binding, and compared the signal differences between known stem cell self-renewal and pluripotency genes and other genes.By integrating these multiomics data, we established prediction models based on several machine learning classifiers including random forests and performed 5-fold cross validations.The model was trained using the training dataset containing two thirds of the input samples, and the remaining one third of the input samples were used as the test dataset to assess the performance of the model in independent tests.Finally, the results predicted by the model were validated through gene function annotation and cell function experiments including cell viability assay, colony formation assay and cell cycle analysis.

RESULTS

Compared with the random genes, the genes known to be associated with self-renewal and pluripotency of mESCs in the multiomics data showed significantly different features.Random forest outperformed the other machine learning algorithms tested on these multiomics data, with an area under the curve (AUC) of 0.883±0.018 for cross validation and an AUC of 0.880±0.028 for independent test.Based on this model, we identified 893 potential regulatory genes associated wwith self-renewal and pluripotency of mESCs, which were similar to the known genes in functional annotation.Known-down of the predicted novel regulator gene Cct6a resulted in significant decreases in the cell viability of mESCs (P < 0.0001) and the number of cell clones (P < 0.01), significantly increased the number of cells in G1 phase (P < 0.01) and decreasedthe number of S phase cells (P < 0.05).Knockdown of Cct6a also led to failure of positive alkaline phosphatase staining of the mESCs.

CONCLUSION

Machine learning model based on multiomics data can be used to predict potential self-renewal and pluripotency regulators with high performance.By using this model, we predicted potential self-renewal and pluripotency regulatory genes including Cct6a and applied experimental validation.This model provides new insights into the regulatory mechanism of mESCs and contribute to stem cell research.

Modeling gene regulatory networks using neural network architectures

Gene regulatory networks (GRNs) encode the complex molecular interactions that govern cell identity. Here we propose DeepSEM, a deep generative model that can jointly infer GRNs and biologically meaningful representation of single-cell RNA sequencing (scRNA-seq) data. In particular, we developed a neural network version of the structural equation model (SEM) to explicitly model the regulatory relationships among genes. Benchmark results show that DeepSEM achieves comparable or better performance on a variety of single-cell computational tasks, such as GRN inference, scRNA-seq data visualization, clustering and simulation, compared with the state-of-the-art methods. In addition, the gene regulations predicted by DeepSEM on cell-type marker genes in the mouse cortex can be validated by epigenetic data, which further demonstrates the accuracy and efficiency of our method. DeepSEM can provide a useful and powerful tool to analyze scRNA-seq data and infer a GRN.

A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data

Gene regulatory network is a complicated set of interactions between genetic materials, which dictates how cells develop in living organisms and react to their surrounding environment. Robust comprehension of these interactions would help explain how cells function as well as predict their reactions to external factors. This knowledge can benefit both developmental biology and clinical research such as drug development or epidemiology research. Recently, the rapid advance of single-cell sequencing technologies, which pushed the limit of transcriptomic profiling to the individual cell level, opens up an entirely new area for regulatory network research. To exploit this new abundant source of data and take advantage of data in single-cell resolution, a number of computational methods have been proposed to uncover the interactions hidden by the averaging process in standard bulk sequencing. In this article, we review 15 such network inference methods developed for single-cell data. We discuss their underlying assumptions, inference techniques, usability, and pros and cons. In an extensive analysis using simulation, we also assess the methods' performance, sensitivity to dropout and time complexity. The main objective of this survey is to assist not only life scientists in selecting suitable methods for their data and analysis purposes but also computational scientists in developing new methods by highlighting outstanding challenges in the field that remain to be addressed in the future development.

An Esrrb and Nanog Cell Fate Regulatory Module Controlled by Feed Forward Loop Interactions

Cell fate decisions during development are governed by multi-factorial regulatory mechanisms including chromatin remodeling, DNA methylation, binding of transcription factors to specific loci, RNA transcription and protein synthesis. However, the mechanisms by which such regulatory “dimensions” coordinate cell fate decisions are currently poorly understood. Here we quantified the multi-dimensional molecular changes that occur in mouse embryonic stem cells (mESCs) upon depletion of Estrogen related receptor beta (Esrrb), a key pluripotency regulator. Comparative analyses of expression changes subsequent to depletion of Esrrb or Nanog, indicated that a system of interlocked feed-forward loops involving both factors, plays a central part in regulating the timing of mESC fate decisions. Taken together, our meta-analyses support a hierarchical model in which pluripotency is maintained by an Oct4-Sox2 regulatory module, while the timing of differentiation is regulated by a Nanog-Esrrb module.

TINC- A Method to Dissect Regulatory Complexes at Single-Locus Resolution- Reveals an Extensive Protein Complex at the Nanog Promoter

Cellular identity is ultimately dictated by the interaction of transcription factors with regulatory elements (REs) to control gene expression. Advances in epigenome profiling techniques have significantly increased our understanding of cell-specific utilization of REs. However, it remains difficult to dissect the majority of factors that interact with these REs due to the lack of appropriate techniques. Therefore, we developed TINC: TALE-mediated isolation of nuclear chromatin. Using this new method, we interrogated the protein complex formed at the Nanog promoter in embryonic stem cells (ESCs) and identified many known and previously unknown interactors, including RCOR2. Further interrogation of the role of RCOR2 in ESCs revealed its involvement in the repression of lineage genes and the fine-tuning of pluripotency genes. Consequently, using the Nanog promoter as a paradigm, we demonstrated the power of TINC to provide insight into the molecular makeup of specific transcriptional complexes at individual REs as well as into cellular identity control in general.

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TINC allows the isolation of a specific locus for molecular analyses

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TINC identified hundreds of proteins at the Nanog promoter

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RCOR2 is a component of the pluripotency network in embryonic stem cells

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RCOR2 is required for efficient differentiation

Functional role of Tet-mediated RNA hydroxymethylcytosine in mouse ES cells and during differentiation

Tet-enzyme-mediated 5-hydroxymethylation of cytosines in DNA plays a crucial role in mouse embryonic stem cells (ESCs). In RNA also, 5-hydroxymethylcytosine (5hmC) has recently been evidenced, but its physiological roles are still largely unknown. Here we show the contribution and function of this mark in mouse ESCs and differentiating embryoid bodies. Transcriptome-wide mapping in ESCs reveals hundreds of messenger RNAs marked by 5hmC at sites characterized by a defined unique consensus sequence and particular features. During differentiation a large number of transcripts, including many encoding key pluripotency-related factors (such as Eed and Jarid2), show decreased cytosine hydroxymethylation. Using Tet-knockout ESCs, we find Tet enzymes to be partly responsible for deposition of 5hmC in mRNA. A transcriptome-wide search further reveals mRNA targets to which Tet1 and Tet2 bind, at sites showing a topology similar to that of 5hmC sites. Tet-mediated RNA hydroxymethylation is found to reduce the stability of crucial pluripotency-promoting transcripts. We propose that RNA cytosine 5-hydroxymethylation by Tets is a mark of transcriptome flexibility, inextricably linked to the balance between pluripotency and lineage commitment.

TET mediated RNA-hydroxymethylation (5hmC) has been detected in mammals, but its physiological role remains unclear. Here the authors map 5hmC during embryonic stem cell (ESC) differentiation and find that Tet-mediated RNA hydroxymethylation reduces the stability of crucial pluripotency related transcripts.

The Contribution of lincRNAs at the Interface between Cell Cycle Regulation and Cell State Maintenance

Cell cycle progression is controlled by the interplay of established cell cycle regulators. Changes in these regulators' activity underpin differences in cell cycle kinetics between cell types. We investigated whether long intergenic noncoding RNAs (lincRNAs) contribute to embryonic stem cell cycle adaptations. Using single-cell RNA sequencing data for mouse embryonic stem cells (mESCs) staged as G1, S, or G2/M we found differentially expressed lincRNAs are enriched among cell cycle-regulated genes. These lincRNAs (CC-lincRNAs) are co-expressed with genes involved in cell cycle regulation. We tested the impact of two CC-lincRNA candidates and show using CRISPR activation that increasing their expression is associated with deregulated cell cycle progression. Interestingly, CC-lincRNAs are often differentially expressed between G1 and S, their promoters are enriched in pluripotency transcription factor (TF) binding sites, and their transcripts are frequently co-regulated with genes involved in the maintenance of pluripotency, suggesting a contribution of CC-lincRNAs to mESC cell cycle adaptations.

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Genes differentially expressed between mESC cell cycle stages are enriched in lincRNAs

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CC-lincRNAs are co-expressed with cell cycle and pluripotency genes

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CC-lincRNAs are often mESC specific and their promoters enriched in pluripotency TFs

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Upregulation of two CC-lincRNAs results in deregulated mESC cell cycle progression

Splicing of enhancer-associated lincRNAs contributes to enhancer activity

Transcription is common at active mammalian enhancers sometimes giving rise to stable enhancer-associated long intergenic noncoding RNAs (elincRNAs). Expression of elincRNA is associated with changes in neighboring gene product abundance and local chromosomal topology, suggesting that transcription at these loci contributes to gene expression regulation in cis Despite the lack of evidence supporting sequence-dependent functions for most elincRNAs, splicing of these transcripts is unexpectedly common. Whether elincRNA splicing is a mere consequence of cognate enhancer activity or if it directly impacts enhancer function remains unresolved. Here, we investigate the association between elincRNA splicing and enhancer activity in mouse embryonic stem cells. We show that multi-exonic elincRNAs are enriched at conserved enhancers, and the efficient processing of elincRNAs is strongly associated with their cognate enhancer activity. This association is supported by their enrichment in enhancer-specific chromatin signatures; elevated binding of co-transcriptional regulators; increased local intra-chromosomal DNA contacts; and strengthened cis-regulation on target gene expression. Our results support the role of efficient RNA processing of enhancer-associated transcripts to cognate enhancer activity.

Informatics Approaches for Harmonized Intelligent Integration of Stem Cell Research

As biomedical data integration and analytics play an increasing role in the field of stem cell research, it becomes important to develop ways to standardize, aggregate, and share data among researchers. For this reason, many databases have been developed in recent years in an attempt to systematically warehouse data from different stem cell projects and experiments at the same time. However, these databases vary widely in their implementation and structure. The aim of this scoping review is to characterize the main features of available stem cell databases in order to identify specifications useful for implementation in future stem cell databases. We conducted a scoping review of peer-reviewed literature and online resources to identify and review available stem cell databases. To identify the relevant databases, we performed a PubMed search using relevant MeSH terms followed by a web search for databases which may not have an associated journal article. In total, we identified 16 databases to include in this review. The data elements reported in these databases represented a broad spectrum of parameters from basic socio-demographic variables to various cells characteristics, cell surface markers expression, and clinical trial results. Three broad sets of functional features that provide utility for future stem cell research and facilitate bioinformatics workflows were identified. These features consisted of the following: common data elements, data visualization and analysis tools, and biomedical ontologies for data integration. Stem cell bioinformatics is a quickly evolving field that generates a growing number of heterogeneous data sets. Further progress in the stem cell research may be greatly facilitated by development of applications for intelligent stem cell data aggregation, sharing and collaboration process.

PSCRIdb: A database of regulatory interactions and networks of pluripotent stem cell lines

Pluripotency in stem cells is regulated by a complex network between the transcription factors, signaling molecules, mRNAs, and epigenetic regulators like non-coding RNAs. Different pluripotent stem cell (PSC) lines were isolated and characterized to study the regulatory network topology to understand the mechanism that control developmental potential of pluripotent cells. PSCRIdb is a manually curated database of regulatory interactions including protein-protein, protein-DNA, gene-gene, and miRNA-mRNA interactions in mouse and human pluripotent stem cells including embryonic stem cells and embryonic carcinoma cells. At present, 22 different mouse and human pluripotent stem-cell-line-specific regulatory interactions are compiled in the database. Detailed information of the four types of interaction data are presented in tabular format and graphical network view in Cytoscape layout. The database is available at http://bicresources.jcbose.ac.in/ ssaha4/pscridb. The database contains 3037 entries of experimentally validated molecular interactions that can be useful for systematic study of pluripotency integrating multi-omics data. In summary, the database can be a useful resource for identification of regulatory networks present in different pluripotent stem cell lines.

Integrative and perturbation-based analysis of the transcriptional dynamics of TGFβ/BMP system components in transition from embryonic stem cells to neural progenitors

Cooperative actions of extrinsic signals and cell‐intrinsic transcription factors alter gene regulatory networks enabling cells to respond appropriately to environmental cues. Signaling by transforming growth factor type β (TGFβ) family ligands (eg, bone morphogenetic proteins [BMPs] and Activin/Nodal) exerts cell‐type specific and context‐dependent transcriptional changes, thereby steering cellular transitions throughout embryogenesis. Little is known about coordinated regulation and transcriptional interplay of the TGFβ system. To understand intrafamily transcriptional regulation as part of this system's actions during development, we selected 95 of its components and investigated their mRNA‐expression dynamics, gene‐gene interactions, and single‐cell expression heterogeneity in mouse embryonic stem cells transiting to neural progenitors. Interrogation at 24 hour intervals identified four types of temporal gene transcription profiles that capture all stages, that is, pluripotency, epiblast formation, and neural commitment. Then, between each stage we performed esiRNA‐based perturbation of each individual component and documented the effect on steady‐state mRNA levels of the remaining 94 components. This exposed an intricate system of multilevel regulation whereby the majority of gene‐gene interactions display a marked cell‐stage specific behavior. Furthermore, single‐cell RNA‐profiling at individual stages demonstrated the presence of detailed co‐expression modules and subpopulations showing stable co‐expression modules such as that of the core pluripotency genes at all stages. Our combinatorial experimental approach demonstrates how intrinsically complex transcriptional regulation within a given pathway is during cell fate/state transitions.

The pluripotency transcription factor Nanog represses glutathione reductase gene expression in mouse embryonic stem cells

OBJECTIVE

Redox homeostasis maintenance is essential to bring about cellular functions. Particularly, embryonic stem cells (ESCs) have high fidelity mechanisms for DNA repair, high activity of different antioxidant enzymes and low levels of oxidative stress. Although the expression and activity of antioxidant enzymes are reduced throughout the differentiation, the knowledge about the transcriptional regulation of genes involved in defense against oxidative stress is yet restricted. Since glutathione is a central component of a complex system involved in preserving cellular redox status, we aimed to study whether the expression of the glutathione reductase (Gsr) gene, which encodes an essential enzyme for cellular redox homeostasis, is modulated by the transcription factors critical for self-renewal and pluripotency of ESCs.

RESULTS

We found that Gsr gene is expressed in ESCs during the pluripotent state and it was upregulated when these cells were induced to differentiate, concomitantly with Nanog decreased expression. Moreover, we found an increase in Gsr mRNA levels when Nanog was downregulated by a specific shRNA targeting this transcription factor in ESCs. Our results suggest that Nanog represses Gsr gene expression in ESCs, evidencing a role of this crucial pluripotency transcription factor in preservation of redox homeostasis in stem cells.

Potential Effect of SOX2 on the Cell Cycle of Wharton's Jelly Stem Cells (WJSCs)

The connective tissue of the umbilical cord contains stem cells called Wharton's jelly cells. These cells express core transcription factors (NANOG, OCT4, and SOX2). The protein product of the SOX2 gene controls the cell cycle by interacting with cyclin D (directly and indirectly) and cycle inhibitors—p21 and p27, as well as two E2f3 protein isoforms. The aim of the study was to analyze the effect of SOX2 on the cell cycle of stem cells of Wharton's jelly. The material for the study was the stem cells of Wharton's jelly isolated from 20 umbilical cords collected during childbirth. The stem cells collected were subjected to cytometric analysis, cell culture, and RNA isolation. cDNA was the starting material for the analysis of gene expression: SOX2, CCND1, CDK4, and CDKN1B. The studies indicate a high proliferative potential of the Wharton's jelly stem cells and the inhibitory effect of SOX2 on the expression of the CCND1 and CDK4 gene.

The gene regulatory network of mESC differentiation: a benchmark for reverse engineering methods

A large body of data have accumulated that characterize the gene regulatory network of stem cells. Yet, a comprehensive and integrative understanding of this complex network is lacking. Network reverse engineering methods that use transcriptome data to derive these networks may help to uncover the topology in an unbiased way. Many methods exist that use co-expression to reconstruct networks. However, it remains unclear how these methods perform in the context of stem cell differentiation, as most systematic assessments have been made for regulatory networks of unicellular organisms. Here, we report a systematic benchmark of different reverse engineering methods against functional data. We show that network pruning is critical for reconstruction performance. We also find that performance is similar for algorithms that use different co-expression measures, i.e. mutual information or correlation. In addition, different methods yield very different network topologies, highlighting the challenge of interpreting these resulting networks as a whole.This article is part of the theme issue 'Designer human tissue: coming to a lab near you'.

Causal Transcription Regulatory Network Inference Using Enhancer Activity as a Causal Anchor

Transcription control plays a crucial role in establishing a unique gene expression signature for each of the hundreds of mammalian cell types. Though gene expression data have been widely used to infer cellular regulatory networks, existing methods mainly infer correlations rather than causality. We developed statistical models and likelihood-ratio tests to infer causal gene regulatory networks using enhancer RNA (eRNA) expression information as a causal anchor and applied the framework to eRNA and transcript expression data from the FANTOM Consortium. Predicted causal targets of transcription factors (TFs) in mouse embryonic stem cells, macrophages and erythroblastic leukaemia overlapped significantly with experimentally-validated targets from ChIP-seq and perturbation data. We further improved the model by taking into account that some TFs might act in a quantitative, dosage-dependent manner, whereas others might act predominantly in a binary on/off fashion. We predicted TF targets from concerted variation of eRNA and TF and target promoter expression levels within a single cell type, as well as across multiple cell types. Importantly, TFs with high-confidence predictions were largely different between these two analyses, demonstrating that variability within a cell type is highly relevant for target prediction of cell type-specific factors. Finally, we generated a compendium of high-confidence TF targets across diverse human cell and tissue types.

Prediction of novel pluripotent proteins involved in reprogramming of male Germline stem cells (GSCs) into multipotent adult Germline stem cells (maGSCs) by network analysis

Germline stem cells (GSCs) are known to transmit genetic information from parents to offspring. These GSCs can undergo reprogramming to transform themselves into pluripotent stem cells, called as Multipotent adult Germline stem cells (maGSCs). The mechanism of the reprogramming of GSCs to maGSCs is elusive. To investigate novel factors that may govern the process of reprogramming, the RNA-seq data of both GSCs and maGSCs were retrieved and subjected to Tuxedo protocol using Galaxy server. Total 1558 differentially expressed genes were identified from the analysis. Protein sequence in the FASTA format of all 1558 differentially expressed genes was retrieved and submitted to Pluripred web server to predict whether the proteins were pluripotent or not. A total of 232 proteins were predicted as pluripotent, and to identify the novel proteins, these were submitted to STRING database to obtain an interaction map. The obtained interaction map was submitted to Cytoscape, and various apps such as MCODE and Centiscape were used to identify the clusters and centrality measures between the nodes of the generated network. Five clusters were identified and ranked according to their score. Novel pluripotent proteins like cadherin related cdh5, cdh10 were predicted. Phox2b, Nrp2, Dll1, Shh, Gbx2, Nodal, Lefty1, Wnt7b, Pitx2, fgf4, Pou5f1, Nanog, Tet1, trim8, alx2, Dppa2, Prdm14,Sox11, Esrrb were predicted to be involved in the stem cell development. Dppa2, Sox11, Sox2, Bmp4, Shh, and Otp were predicted to be involved in positive regulation of the stem cell proliferation. Pathway analysis further revealed that signaling pathways such as Wnt, Jak-Stat and PI3K may play important role in the pluripotency of the maGSCs. Novel proteins involved in pluripotency, which were predicted by our findings, can be experimentally researched in future.

Oct4 regulates DNA methyltransferase 1 transcription by direct binding of the regulatory element

Background

The transcription factor Oct4 plays a pivotal role in the pre-implantation development of the mouse embryo. DNA methyltransferase 1 (Dnmt1) maintains the changes in DNA methylation during mammalian early embryonic development. Little is known of the role of Oct4 in DNA methylation in mice. In this study, Kunming white mice were used as an animal model to reveal any correlation between DNA methylation and Oct4 during mammalian embryonic development.

Results

The expressions of Dnmt1 and Oct4 were initially studied using real-time PCR. They exhibited different patterns during the pre-implantation stage. Moreover, by using a promoter assay and ChIP analysis, we found that the transcriptional activities of Dnmt1 in mouse NIH/3 T3 cells and CCE cells were regulated by Oct4 through direct binding to the - 554 to - 294 fragment of the upstream regulation element of Dnmt1. The downregulation of Dnmt1 expression and enzyme activity by mouse Oct4 were further confirmed by transfecting Oct4 siRNA into mouse CCE cells.

Conclusion

Our results indicate that Oct4 is involved in DNA methylation through the regulation of Dnmt1 transcription, especially during the early stages of mouse pre-implantation embryo development.

Neural induction by the node and placode induction by head mesoderm share an initial state resembling neural plate border and ES cells

Around the time of gastrulation in higher vertebrate embryos, inductive interactions direct cells to form central nervous system (neural plate) or sensory placodes. Grafts of different tissues into the periphery of a chicken embryo elicit different responses: Hensen's node induces a neural plate whereas the head mesoderm induces placodes. How different are these processes? Transcriptome analysis in time course reveals that both processes start by induction of a common set of genes, which later diverge. These genes are remarkably similar to those induced by an extraembryonic tissue, the hypoblast, and are normally expressed in the pregastrulation stage epiblast. Explants of this epiblast grown in the absence of further signals develop as neural plate border derivatives and eventually express lens markers. We designate this state as "preborder"; its transcriptome resembles embryonic stem cells. Finally, using sequential transplantation experiments, we show that the node, head mesoderm, and hypoblast are interchangeable to begin any of these inductions while the final outcome depends on the tissue emitting the later signals.

Sex chromosomes drive gene expression and regulatory dimorphisms in mouse embryonic stem cells

BACKGROUND

Pre-implantation embryos exhibit sexual dimorphisms in both primates and rodents. To determine whether these differences reflected sex-biased expression patterns, we generated transcriptome profiles for six 40,XX, six 40,XY, and two 39,X mouse embryonic stem (ES) cells by RNA sequencing.

RESULTS

We found hundreds of coding and non-coding RNAs that were differentially expressed between male and female cells. Surprisingly, the majority of these were autosomal and included RNA encoding transcription and epigenetic and chromatin remodeling factors. We showed differential Prdm14-responsive enhancer activity in male and female cells, correlating with the sex-specific levels of Prdm14 expression. This is the first time sex-specific enhancer activity in ES cells has been reported. Evaluation of X-linked gene expression patterns between our XX and XY lines revealed four distinct categories: (1) genes showing 2-fold greater expression in the female cells; (2) a set of genes with expression levels well above 2-fold in female cells; (3) genes with equivalent RNA levels in male and female cells; and strikingly, (4) a small number of genes with higher expression in the XY lines. Further evaluation of autosomal gene expression revealed differential expression of imprinted loci, despite appropriate parent-of-origin patterns. The 39,X lines aligned closely with the XY cells and provided insights into potential regulation of genes associated with Turner syndrome in humans. Moreover, inclusion of the 39,X lines permitted three-way comparisons, delineating X and Y chromosome-dependent patterns.

CONCLUSIONS

Overall, our results support the role of the sex chromosomes in establishing sex-specific networks early in embryonic development and provide insights into effects of sex chromosome aneuploidies originating at those stages.

Myst2/Kat7 histone acetyltransferase interaction proteomics reveals tumour-suppressor Niam as a novel binding partner in embryonic stem cells

MYST histone acetyltransferases have crucial functions in transcription, replication and DNA repair and are hence implicated in development and cancer. Here we characterise Myst2/Kat7/Hbo1 protein interactions in mouse embryonic stem cells by affinity purification coupled to mass spectrometry. This study confirms that in embryonic stem cells Myst2 is part of H3 and H4 histone acetylation complexes similar to those described in somatic cells. We identify a novel Myst2-associated protein, the tumour suppressor protein Niam (Nuclear Interactor of ARF and Mdm2). Human NIAM is involved in chromosome segregation, p53 regulation and cell proliferation in somatic cells, but its role in embryonic stem cells is unknown. We describe the first Niam embryonic stem cell interactome, which includes proteins with roles in DNA replication and repair, transcription, splicing and ribosome biogenesis. Many of Myst2 and Niam binding partners are required for correct embryonic development, implicating Myst2 and Niam in the cooperative regulation of this process and suggesting a novel role for Niam in embryonic biology. The data provides a useful resource for exploring Myst2 and Niam essential cellular functions and should contribute to deeper understanding of organism early development and survival as well as cancer. Data are available via ProteomeXchange with identifier PXD005987.