Identification Of Gene Regulatory Networks Research Articles

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.

Identification of Gene Regulatory Networks in B-Cell Progenitor Differentiation and Leukemia.

Pro-B- and pre-B-cells are consecutive entities in early B-cell development, representing cells of origin for B-cell precursor acute lymphoid leukemia (BCP-ALL). Normal B-cell differentiation is critically regulated by specific transcription factors (TFs). Accordingly, TF-encoding genes are frequently deregulated or mutated in BCP-ALL. Recently, we described TF-codes which delineate physiological activities of selected groups of TF-encoding genes in hematopoiesis including B-cell development. Here, we exploited these codes to uncover regulatory connections between particular TFs in pro-B- and pre-B-cells via an analysis of developmental TFs encoded by NKL and TALE homeobox genes and by ETS and T-box genes. Comprehensive expression analyses in BCP-ALL cell lines helped identify validated models to study their mutual regulation in vitro. Knockdown and overexpression experiments and subsequent RNA quantification of TF-encoding genes in selected model cell lines revealed activating, inhibitory or absent connections between nine TFs operating in early B-cell development, including HLX, MSX1, IRX1, MEIS1, ETS2, ERG, SPIB, EOMES, and TBX21. In addition, genomic profiling revealed BCP-ALL subtype-specific copy number alterations of ERG at 21q22, while a deletion of the TGFbeta-receptor gene TGFBR2 at 3p24 resulted in an upregulation of EOMES. Finally, we combined the data to uncover gene regulatory networks which control normal differentiation of early B-cells, collectively endorsing more detailed evaluation of BCP-ALL subtypes.

Identification of gene regulatory networks affected across drug-resistant epilepsies

Epilepsy is a chronic and heterogenous disease characterized by recurrent unprovoked seizures, that are commonly resistant to antiseizure medications. This study applies a transcriptome network-based approach across epilepsies aiming to improve understanding of molecular disease pathobiology, recognize affected biological mechanisms and apply causal reasoning to identify therapeutic hypotheses. This study included the most common drug-resistant epilepsies (DREs), such as temporal lobe epilepsy with hippocampal sclerosis (TLE-HS), and mTOR pathway-related malformations of cortical development (mTORopathies). This systematic comparison characterized the global molecular signature of epilepsies, elucidating the key underlying mechanisms of disease pathology including neurotransmission and synaptic plasticity, brain extracellular matrix and energy metabolism. In addition, specific dysregulations in neuroinflammation and oligodendrocyte function were observed in TLE-HS and mTORopathies, respectively. The aforementioned mechanisms are proposed as molecular hallmarks of DRE with the identified upstream regulators offering opportunities for drug-target discovery and development.

Abstract C061: Therapeutic profiling, patient response prediction, and tumor evolution in pancreatic cancer organoids

Abstract Pancreatic cancer (PC) is characterized by an aggressive biology and high tumor heterogeneity causing considerable variations in therapy response. Patient-derived organoids (PDOs) reflect parental tumor features and represent a powerful preclinical tool to predict drug response and harness personalized treatment. We have derived >100 PDO lines from treatment-naïve and pretreated PC patient primary tumor and metastases with a reliable efficacy and previously developed a pharmacotyping-guided prediction model to prognosticate patient therapy response (Beutel, 2021). Following up our initial feasibility trial, we now validated the model accuracy in real-life in a higher number of PC patients (43 cases). Additionally, a limited set of 7 PDO pairs were derived from the same patients at two distinct timepoints, followed by pharmacotyping and whole exome sequencing (WES). Our model allowed overall a successful drug-response prediction in naïve patients with an accuracy of 85.7% for first and second-line regimens. Prediction power was nevertheless lower in pretreated patients with a precision of 57.1% for subsequent chemotherapy lines. Intriguingly, a trend was observed towards a better performance of our system in prognosticating chemoresponsiveness vs. unresponsiveness (89.5% vs. 68.8%). Retrospective analysis of patient clinical data finally showed that the administration of a regimen predicted to be efficient ultimately translated into a longer progression-free survival. Importantly, the implementation of an automated pipetting system and the subsequent miniaturization of drug screenings enhanced our process capacity, conferring the possibility to extend our panel to a second list of approved targeted substances and significantly reducing the time before pharmacotyping. The access to longitudinal biopsies allowed to conduct WES on 14 PDOs to capture a comprehensive genetic profiling over the time of treatment, notably revealing a CHEK2-mutated patient responding over time upon PARP inhibitor maintenance therapy, in line with our PDO-based prediction, further highlighting the robustness of our method and algorithm. WES revealed a lower mutational burden and amount of nonsynonymous mutations upon chemotherapy, while tracking clonal evolution also revealed therapy-linked changes. Finally, to decipher the molecular mechanisms driving PC chemoresistance with an unprecedented level of accuracy, we took advantage of our phenotypically and therapeutically-profiled PDO living biobank and employed a true single-nucleus multiome approach to fully capture the transcriptome, as well as the chromatin accessibility landscape, of chemotherapy responsive and non-responsive PC PDOs. Overall, we report a robust and clinically-relevant preclinical tool for drug-response prediction, paving the way towards a PDO therapeutic profiling-guided true precision medicine in clinical routine. The identification of gene-regulatory networks and expression signatures driving chemoresistance may dramatically improve patient stratification and thus, foster true precision oncology. Citation Format: Johann Gout, Yazid J. Resheq, Jessica Lindenmayer, Thomas Ettrich, Lukas Perkhofer, Thomas Seufferlein, Alexander Kleger. Therapeutic profiling, patient response prediction, and tumor evolution in pancreatic cancer organoids [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C061.

SCRN: Single-cell Gene Regulatory Network Identification in Alzheimer's Disease.

Alzheimer's disease (AD) is the most common neurodegenerative disease, and it consumes considerable medical resources with increasing number of patients every year. Mounting evidence show that the regulatory disruptions altering the intrinsic activity of genes in brain cells contribute to AD pathogenesis. To gain insights into the underlying gene regulation in AD, we proposed a graph learning method, Single-Cell based Regulatory Network (SCRN), to identify the regulatory mechanisms based on single-cell data. SCRN implements the γ-decaying heuristic link prediction based on graph neural networks and can identify reliable gene regulatory networks using locally closed subgraphs. In this work, we first performed UMAP dimension reduction analysis on single-cell RNA sequencing (scRNA-seq) data of AD and normal samples. Then we used SCRN to construct the gene regulatory network based on three well-recognized AD genes (APOE, CX3CR1, and P2RY12). Enrichment analysis of the regulatory network revealed significant pathways including NGF signaling, ERBB2 signaling, and hemostasis. These findings demonstrate the feasibility of using SCRN to uncover potential biomarkers and therapeutic targets related to AD.

Informative community structure revealed using Arabidopsis time series transcriptome data via partitioned local depth

Abstract Transcriptome studies that provide temporal information about transcript abundance facilitate identification of gene regulatory networks (GRNs). Inferring GRNs from time series data using computational modeling remains a central challenge in systems biology. Commonly employed clustering algorithms identify modules of like-responding genes but do not provide information on how these modules are interconnected. These methods also require users to specify parameters such as cluster number and size, adding complexity to the analysis. To address these challenges, we used a recently developed algorithm, partitioned local depth (PaLD), to generate cohesive networks for 4 time series transcriptome datasets (3 hormone and 1 abiotic stress dataset) from the model plant Arabidopsis thaliana. PaLD provided a cohesive network representation of the data, revealing networks with distinct structures and varying numbers of connections between transcripts. We utilized the networks to make predictions about GRNs by examining local neighborhoods of transcripts with highly similar temporal responses. We also partitioned the networks into groups of like-responding transcripts and identified enriched functional and regulatory features in them. Comparison of groups to clusters generated by commonly used approaches indicated that these methods identified modules of transcripts that have similar temporal and biological features, but also identified unique groups, suggesting that a PaLD-based approach (supplemented with a community detection algorithm) can complement existing methods. These results revealed that PaLD could sort like-responding transcripts into biologically meaningful neighborhoods and groups while requiring minimal user input and producing cohesive network structure, offering an additional tool to the systems biology community to predict GRNs.

Identification of Gene Regulatory Networks Using Variational Bayesian Inference in the Presence of Missing Data.

The identification of gene regulatory networks (GRN) from gene expression time series data is a challenge and open problem in system biology. This paper considers the structure inference of GRN from the incomplete and noisy gene expression data, which is a not well-studied issue for GRN inference. In this paper, the dynamical behavior of the gene expression process is described by a stochastic nonlinear state-space model with unknown noise information. A variational Bayesian (VB) framework are proposed to estimate the parameters and gene expression levels simultaneously. One of the advantages of this method is that it can easily handle the missing observations by generating the prediction values. Considering the sparsity of GRN, the smoothed gene data are modeled by the extreme gradient boosting tree, and the regulatory interactions among genes are identified by the importance scores based on the tree model. The proposed method is tested on the artificial DREAM4 datasets and one real gene expression dataset of yeast. The comparative results show that the proposed method can effectively recover the regulatory interactions of GRN in the presence of missing observations and outperforms the existing methods for GRN identification.

SRGS: sparse partial least squares-based recursive gene selection for gene regulatory network inference

BackgroundThe identification of gene regulatory networks (GRNs) facilitates the understanding of the underlying molecular mechanism of various biological processes and complex diseases. With the availability of single-cell RNA sequencing data, it is essential to infer GRNs from single-cell expression. Although some GRN methods originally developed for bulk expression data can be applicable to single-cell data and several single-cell specific GRN algorithms were developed, recent benchmarking studies have emphasized the need of developing more accurate and robust GRN modeling methods that are compatible for single-cell expression data.ResultsWe present SRGS, SPLS (sparse partial least squares)-based recursive gene selection, to infer GRNs from bulk or single-cell expression data. SRGS recursively selects and scores the genes which may have regulations on the considered target gene based on SPLS. When dealing with gene expression data with dropouts, we randomly scramble samples, set some values in the expression matrix to zeroes, and generate multiple copies of data through multiple iterations to make SRGS more robust. We test SRGS on different kinds of expression data, including simulated bulk data, simulated single-cell data without and with dropouts, and experimental single-cell data, and also compared with the existing GRN methods, including the ones originally developed for bulk data, the ones developed specifically for single-cell data, and even the ones recommended by recent benchmarking studies.ConclusionsIt has been shown that SRGS is competitive with the existing GRN methods and effective in the gene regulatory network inference from bulk or single-cell gene expression data. SRGS is available at: https://github.com/JGuan-lab/SRGS.

PGRNIG: novel parallel gene regulatory network identification algorithm based on GPU.

Molecular biology has revealed that complex life phenomena can be treated as the result of many gene interactions. Investigating these interactions and understanding the intrinsic mechanisms of biological systems using gene expression data have attracted a lot of attention. As a typical gene regulatory network (GRN) inference method, the S-system has been utilized to deal with small-scale network identification. However, it is extremely difficult to optimize it to infer medium-to-large networks. This paper proposes a novel parallel swarm intelligent algorithm, PGRNIG, to optimize the parameters of the S-system. We employed the clone selection strategy to improve the whale optimization algorithm (CWOA). To enhance the time efficiency of CWOA optimization, we utilized a parallel CWOA (PCWOA) based on the compute unified device architecture (CUDA) platform. Decomposition strategy and L1 regularization were utilized to reduce the search space and complexity of GRN inference. We applied the PGRNIG algorithm on three synthetic datasets and two real time-series expression datasets of the species of Escherichia coli and Saccharomyces cerevisiae. Experimental results show that PGRNIG could infer the gene regulatory network more accurately than other state-of-the-art methods with a convincing computational speed-up. Our findings show that CWOA and PCWOA have faster convergence performances than WOA.

Single_cell_GRN: gene regulatory network identification based on supervised learning method and Single-cell RNA-seq data

Single-cell RNA-seq overcomes the shortcomings of conventional transcriptome sequencing technology and could provide a powerful tool for distinguishing the transcriptome characteristics of various cell types in biological tissues, and comprehensively revealing the heterogeneity of gene expression between cells. Many Intelligent Computing methods have been presented to infer gene regulatory network (GRN) with single-cell RNA-seq data. In this paper, we investigate the performances of seven classifiers including support vector machine (SVM), random forest (RF), Naive Bayesian (NB), GBDT, logical regression (LR), decision tree (DT) and K-Nearest Neighbor (KNN) for solving the binary classification problems of GRN inference with single-cell RNA-seq data (Single_cell_GRN). In SVM, three different kernel functions (linear, polynomial and radial basis function) are utilized, respectively. Three real single-cell RNA-seq datasets from mouse and human are utilized. The experiment results prove that in most cases supervised learning methods (SVM, RF, NB, GBDT, LR, DT and KNN) perform better than unsupervised learning method (GENIE3) in terms of AUC. SVM, RF and KNN have the better performances than other four classifiers. In SVM, linear and polynomial kernels are more fit to model single-cell RNA-seq data.

CRISPR-Act3.0-Based Highly Efficient Multiplexed Gene Activation in Plants.

CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein)-mediated genome editing has revolutionized fundamental research and plant breeding. Beyond gene editing, CRISPR/Cas systems have been repurposed as a platform for programmable transcriptional regulation. Catalytically inactive Cas variants (dCas), when fused with transcriptional activation domains, allow for specific activation of any target gene in the genome without inducing DNA double-strand breaks. CRISPR activation enables simultaneous activation of multiple genes, holding great promise in the identification of gene regulatory networks and rewiring of metabolic pathways. Here, we describe a simple protocol for constructing a dCas9-mediated multiplexed gene activation system based on the CRISPR-Act3.0 system. The resulting vectors are tested in rice protoplasts. © 2022 Wiley Periodicals LLC. Basic Protocol 1: sgRNA design and construction of CRISPR-Act3.0 vectors for multiplexed gene activation Basic Protocol 2: Determining the activation efficiency of CRISPR-Act3.0 vectors using rice protoplasts.

An auxin-mediated regulatory framework for wound-induced adventitious root formation in tomato shoot explants.

Adventitious roots (ARs) are produced from non-root tissues in response to different environmental signals, such as abiotic stresses, or after wounding, in a complex developmental process that requires hormonal crosstalk. Here, we characterized AR formation in young seedlings of Solanum lycopersicum cv. 'Micro-Tom' after whole root excision by means of physiological, genetic and molecular approaches. We found that a regulated basipetal auxin transport from the shoot and local auxin biosynthesis triggered by wounding are both required for the re-establishment of internal auxin gradients within the vasculature. This promotes cell proliferation at the distal cambium near the wound in well-defined positions of the basal hypocotyl and during a narrow developmental window. In addition, a pre-established pattern of differential auxin responses along the apical-basal axis of the hypocotyl and an as of yet unknown cell-autonomous inhibitory pathway contribute to the temporal and spatial patterning of the newly formed ARs on isolated hypocotyl explants. Our work provides an experimental outline for the dissection of wound-induced AR formation in tomato, a species that is suitable for molecular identification of gene regulatory networks via forward and reverse genetics approaches.

Identification of Gene Regulatory Networks from Single-Cell Expression Data.

Single-cell RNAseq is an emerging technology that allows the quantification of gene expression in individual cells. In plants, single-cell sequencing technology has been applied to generate root cell expression maps under many experimental conditions. DAP-seq and ATAC-seq have also been used to generate genome-scale maps of protein-DNA interactions and open chromatin regions in plants. In this protocol, we describe a multistep computational pipeline for the integration of single-cell RNAseq data with DAP-seq and ATAC-seq data to predict regulatory networks and key regulatory genes. Our approach utilizes machine learning methods including feature selection and stability selection to identify candidate regulatory genes. The network generated by this pipeline can be used to provide a putative annotation of gene regulatory modules and to identify candidate transcription factors that could play a key role in specific cell types.

Optimal Robust Search for Parameter Values of Qualitative Models of Gene Regulatory Networks.

Computational and mathematical models are a must for the in silico analysis or design of Gene Regulatory Networks (GRN)as they offer a theoretical context to deeply address biological regulation. We have proposed a framework where models of network dynamics are expressed through a class of nonlinear and temporal multiscale Ordinary Differential Equations (ODE). To find out models that disclose network structures underlying an observed or desired network behavior, and parameter values that enable the candidate models to reproduce such behavior, we follow a reasoning cycle that alternates procedures for model selection and parameter refinement. Plausible network models are first selected via qualitative simulation, and next their parameters are given quantitative values such that the ODE model solution reproduces the specified behavior. This paper gives algorithms to tackle the parameter refinement problem formulated as an optimization problem. We search, within the parameter space symbolically expressed, for the largest hypersphere whose points correspond to parameter values such that the ODE solution gives an instance of the given qualitative trajectory. Our approach overcomes the limitation of a previously proposed stochastic approach, namely computational load and very reduced scalability. Its applicability and effectiveness are demonstrated through two benchmark synthetic networks with different complexity.

Parameter identification of genetic regulatory network with time-varying delays via adaptive synchronization method

In this paper, the parameter identification of gene regulatory network with time-varying delay is studied. Firstly, we introduce the differential equation model of gene regulatory network with unknown parameters and time delay. Secondly, for the unknown parameters in the time-varying model, a corresponding system with adaptive parameters and adaptive controller is introduced, and the parameter identification problem of the original model is transformed into the synchronization problem of the two systems. Thirdly, we design an effective adaptive controller and an adaptive law for parameters and construct a Lyapunov functional. Then we give a strict theoretical proof that the adaptive parameters can converge to unknown parameters by Barbalat’s lemma. Finally, a numerical example is given to verify the validity of the theoretical results.

Chromatin dynamics, DNA‐binding transcription factors and lens differentiation

AbstractPurposeLens differentiation is an advantageous model to study how chromatin structure and dynamics (“open” and “closed” chromatin) regulates gene expression of both regulatory factors and proteins needed for intricate lens fiber cell structure and function. Our purpose was to map and explain dynamic changes in local chromatin structure of genes during lens differentiation at genome‐wide level.MethodsATAC‐seq experiments were conducted using epithelia and fibers from E14.5 and newborn (P0.5) mouse lenses. RNA‐seq data from similar samples were included in the analysis.ResultsOpen chromatin changes were defined through identification of differentially accessible regions coupled with differential gene expression during lens differentiation. Lens‐specific regulatory regions are shown using ES cells, forebrain, and liver. Two pathways were identified: epiE14.5 → fibE14.5 → fibP0.5 (path1) and epiE14.5 → epiP0.5 (path2). Unbiased cis‐motif analysis of “open” regions corresponding to promoters and enhancers revealed the cis‐regulatory principles of lens differentiation via known (e.g. AP‐1, Ets, Hsf4, Maf, and Pax6) and novel (e.g. CTCF, Tead, and NF1) motifs.ConclusionsThe current data support a general model of dynamic changes in open chromatin associated with transcriptional activities of functionally related genes that required for lens fiber cell differentiation and epithelial maturation. Together, the data pave the road for detailed studies of lens‐specific enhancers, identification of gene regulatory networks underlying lens morphogenesis, and identification of cataract‐causing mutation in non‐coding regulatory sequences.

An Augmented Lagrangian Approach for Distributed Robust Estimation in Large-Scale Systems

Nonlinear estimation using maximum-likelihood estimation and maximum a posterior probability approaches is frequently employed for large-scale cyber-physical and complex systems in the big data era. Efficient distributed processing and robust estimation algorithms resilient to outliers are of great importance. This paper proposes a novel method for distributed solution of these robust estimation problems with equality constraints based on the augmented Lagrangian method (ALM). Specifically, a novel covariance normalization method and an automatic method for selecting regularization parameter with improved performance are proposed. Under the ALM framework, nonlinear equality constraints and nonsmooth L 1 regularization can be incorporated. The proposed method is illustrated with two emerging applications respectively in robust distributed power system state estimation (DSSE) with nonlinear zero injection constraints and gene regulatory network (GRN) identification. Experimental results in DSSE show that the covariance normalization method improves considerably the convergence speed over the alternating direction method of multipliers algorithm and the robust statistics employed effectively mitigates the adverse effects of extreme outliers. Zero-injection constraints can be effectively incorporated. For GRN identification, putative genes and connectivity for a yeast dataset with 1.57 million variables can be identified via sparsity and piecewise temporal continuity penalties and they generally align well with literature.

VariED: the first integrated database of gene annotation and expression profiles for variants related to human diseases.

Integrated analysis of DNA variants and gene expression profiles may facilitate precise identification of gene regulatory networks involved in disease mechanisms. Despite the widespread availability of public resources, we lack databases that are capable of simultaneously providing gene expression profiles, variant annotations, functional prediction scores and pathogenic analyses. VariED is the first web-based querying system that integrates an annotation database and expression profiles for genetic variants. The database offers a user-friendly platform and locates gene/variant names in the literature by connecting to established online querying tools, biological annotation tools and records from free-text literature. VariED acts as a central hub for organized genome information consisting of gene annotation, variant allele frequency, functional prediction, clinical interpretation and gene expression profiles in three species: human, mouse and zebrafish. VariED also provides a novel scoring scheme to predict the functional impact of a DNA variant. With one single entry, all results regarding queried DNA variants can be downloaded. VariED can potentially serve as an efficient way to obtain comprehensive variant knowledge for clinicians and scientists around the world working on important drug discoveries and precision treatments.

Identification of Gene Regulatory Networks by Integrating Genetic Programming With Particle Filtering

Gene regulatory network can help to analyze and understand the underlying regulatory mechanism and the interaction among genes, and it plays a central role in morphogenesis of complex diseases such as cancer. DNA sequencing technology has efficiently produced a large amount of data for constructing gene regulatory networks. However, measured gene expression data usually contain uncertain noise, and inference of gene regulatory network model under non-Gaussian noise is a challenging issue which needs to be addressed. In this study, a joint algorithm integrating genetic programming and particle filter is presented to infer the ordinary differential equations model of gene regulatory network. The strategy uses genetic programming to identify the terms of ordinary differential equations, and applies particle filtering to estimate the parameters corresponding to each term. We systematically discuss the convergence and complexity of the proposed algorithm, and verify the efficiency and effectiveness of the proposed method compared to the existing approaches. Furthermore, we show the utility of our inference algorithm using a real HeLa dataset. In summary, a novel algorithm is proposed to infer the gene regulatory networks under non-Gaussian noise and the results show that this method can achieve more accurate models compared to the existing inference algorithms based on biological datasets.

Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform

Inference of gene regulatory network (GRN) is crucial to understand intracellular physiological activity and function of biology. The identification of large-scale GRN has been a difficult and hot topic of system biology in recent years. In order to reduce the computation load for large-scale GRN identification, a parallel algorithm based on restricted gene expression programming (RGEP), namely MPRGEP, is proposed to infer instantaneous and time-delayed regulatory relationships between transcription factors and target genes. In MPRGEP, the structure and parameters of time-delayed S-system (TDSS) model are encoded into one chromosome. An original hybrid optimization approach based on genetic algorithm (GA) and gene expression programming (GEP) is proposed to optimize TDSS model with MapReduce framework. Time-delayed GRNs (TDGRN) with hundreds of genes are utilized to test the performance of MPRGEP. The experiment results reveal that MPRGEP could infer more accurately gene regulatory network than other state-of-art methods, and obtain the convincing speedup.