Gene Regulatory Network Inference Research Articles

A Novel Model Integration Network Inference Algorithm with Clustering and Hub Genes Finding.

Gene regulatory network Inference with high accuracy based on gene expression data sets is one of the most challenging problems in computational biology. To improve the accuracy of gene regulatory network inference and find hub genes, we proposed a novel model integration network inference method with clustering and hub genes finding called MINICHG. The method is divided into three main steps: (1) using single models inference results based on three machine learning algorithms to construct feature matrix; (2) using k-means to cluster gene pairs according to feature matrix; (3) hub genes finding. MINICHG integrates RF(Random Forest), GBDT (Gradient Boosting Decision Tree) and Pearson Correlation results with a novel weighted strategy in a semi-unsupervised way. The designed optimization scheme in MINICHG considering sparse gold standard data characteristics is suitable for most gene regulatory network reconstruction. We evaluated the proposed method on simulated data sets from five Dream4 multifactorial data sets and Dream5 in silico data set and real data set from E.coli. The performance was better than other network inference methods with high accuracy and robustness.

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data.

We present a systematic evaluation of state-of-the-art algorithms for inferring gene regulatory networks (GRNs) from single-cell transcriptional data. As the ground truth for assessing accuracy, we use synthetic networks with predictable trajectories, literature-curated Boolean models, and diverse transcriptional regulatory networks. We develop a strategy to simulate single-cell transcriptional data from synthetic and Boolean networks that avoids pitfalls of previously-used methods. Furthermore, we collect networks from multiple experimental single-cell RNA-seq datasets. We develop an evaluation framework called BEELINE. We find that the AUPRC and early precision of the algorithms are moderate. The methods are better in recovering interactions in synthetic networks than Boolean models. The algorithms with the best early precision values for Boolean models also perform well on experimental datasets. Techniques that do not require pseudotime-ordered cells are generally more accurate. Based on these results, we present recommendations to end users. BEELINE will aid the development of GRN inference algorithms.

Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks

Discovering gene regulatory relationships and reconstructing gene regulatory networks (GRN) based on gene expression data is a classical, long-standing computational challenge in bioinformatics. Computationally inferring a possible regulatory relationship between two genes can be formulated as a link prediction problem between two nodes in a graph. Graph neural network (GNN) provides an opportunity to construct GRN by integrating topological neighbor propagation through the whole gene network. We propose an end-to-end gene regulatory graph neural network (GRGNN) approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi-supervised framework. To get better inductive generalization capability, GRN inference is formulated as a graph classification problem, to distinguish whether a subgraph centered at two nodes contains the link between the two nodes. A linked pair between a transcription factor (TF) and a target gene, and their neighbors are labeled as a positive subgraph, while an unlinked TF and target gene pair and their neighbors are labeled as a negative subgraph. A GNN model is constructed with node features from both explicit gene expression and graph embedding. We demonstrate a noisy starting graph structure built from partial information, such as Pearson’s correlation coefficient and mutual information can help guide the GRN inference through an appropriate ensemble technique. Furthermore, a semi-supervised scheme is implemented to increase the quality of the classifier. When compared with established methods, GRGNN achieved state-of-the-art performance on the DREAM5 GRN inference benchmarks. GRGNN is publicly available at https://github.com/juexinwang/GRGNN.

Utilizing benchmarked dataset and gene regulatory network to investigate hub genes in postmenopausal osteoporosis

The objective of this paper was to investigate hub genes of postmenopausal osteoporosis (PO) utilizing benchmarked dataset and gene regulatory network (GRN). To achieve this goal, the first step was to benchmark the dataset downloaded from the ArrayExpress database by adding local noise and global noise. Second, differentially expressed genes (DEGs) between PO and normal controls were identified using the Linear Models for Microarray Data package based on benchmarked dataset. Third, five kinds of GRN inference methods, which comprised Zscore, GeneNet, context likelihood of relatedness (CLR) algorithm, Partial Correlation coefficient with Information Theory (PCIT), and GEne Network Inference with Ensemble of trees (Genie3), were described and evaluated by receiver operating characteristic (ROC) and precision and recall (PR) curves. Finally, GRN constructed according to the method with best performance was implemented to conduct topological centrality (closeness) for the purpose of investigate hub genes of PO. A total of 236 DEGs were obtained based on benchmarked dataset of 20,554 genes. By assessing Zscore, GeneNet, CLR, PCIT, and Genie3 on the basis of ROC and PR curves, Genie3 had a clear advantage than others and was applied to construct the GRN which was composed of 236 nodes and 27,730 edges. Closeness centrality analysis of GRN was carried out, and we identified 14 hub genes (such as TTN, ACTA1, and MYBPC1) for PO. In conclusion, we have identified 14 hub genes (such as TN, ACTA1, and MYBPC1) based on benchmarked dataset and GRN. These genes might be potential biomarkers and give insights for diagnose and treatment of PO.

A Sparse Bayesian Learning Method for Structural Equation Model-Based Gene Regulatory Network Inference

Gene regulatory networks (GRNs) are underlying networks identified by interactive relationships between genes. Reconstructing GRNs from massive genetic data is important for understanding gene functions and biological mechanism, and can provide effective service for medical treatment and genetic research. A series of artificial intelligence based methods have been proposed to infer GRNs from both gene expression data and genetic perturbations. The accuracy of such algorithms can be better than those models that just consider gene expression data. A structural equation model (SEM), which provides a systematic framework integrating both types of gene data conveniently, is a commonly used model for GRN inference. Considering the sparsity of GRNs, in this paper, we develop a novel sparse Bayesian inference algorithm based on Normal-Equation-Gamma (NEG) type hierarchical prior (BaNEG) to infer GRNs modeled with SEMs more accurately. First, we reparameterize an SEM as a linear type model by integrating the endogenous and exogenous variables; Then, a Bayesian adaptive lasso with a three-level NEG prior is applied to deduce the corresponding posterior mode and estimate the parameters. Simulations on synthetic data are run to compare the performance of BaNEG to some state-of-the-art algorithms, the results demonstrate that the proposed algorithm visibly outperforms the others. What’s more, BaNEG is applied to infer underlying GRNs from a real data set composed of 47 yeast genes from Saccharomyces cerevisiae to discover potential relationships between genes.

A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy

Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previously published results, suggesting that our method can predict radiosensitivity with the highest accuracy. Additionally, our regulatory network analysis identified 24 highly important ‘hub’ genes. The data analysis workflow we propose provides a unified and computational framework to harness the full potential of large-scale integrated cancer genomic data for integrative signature discovery. Furthermore, the regression model, signature genes, and their regulatory network should provide a reliable quantitative reference for optimizing personalized treatment options, and may aid our understanding of cancer progress mechanisms.

Loss of ADAMTS19 causes progressive non-syndromic heart valve disease.

Valvular heart disease is observed in approximately 2% of the general population1. Although the initial observation is often localized (for example, to the aortic or mitral valve), disease manifestations are regularly observed in the other valves and patients frequently require surgery. Despite the high frequency of heart valve disease, only a handful of genes have so far been identified as the monogenic causes of disease2-7. Here we identify two consanguineous families, each with two affected family members presenting with progressive heart valve disease early in life. Whole-exome sequencing revealed homozygous, truncating nonsense alleles in ADAMTS19 in all four affected individuals. Homozygous knockout mice for Adamts19 show aortic valve dysfunction, recapitulating aspects of the human phenotype. Expression analysis using a lacZ reporter and single-cell RNA sequencing highlight Adamts19 as a novel marker for valvular interstitial cells; inference of gene regulatory networks in valvular interstitial cells positions Adamts19 in a highly discriminatory network driven by the transcription factor lymphoid enhancer-binding factor 1 downstream of the Wnt signaling pathway. Upregulation of endocardial Krüppel-like factor 2 in Adamts19 knockout mice precedes hemodynamic perturbation, showing that a tight balance in the Wnt-Adamts19-Klf2 axis is required for proper valve maturation and maintenance.

Reverse engineering gene networks using global-local shrinkage rules.

Inferring gene regulatory networks from high-throughput 'omics' data has proven to be a computationally demanding task of critical importance. Frequently, the classical methods break down owing to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer the potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time-series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology uses global-local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which were shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient Markov chain Monte Carlo via Gibbs sampling in the electronic supplementary material. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

SIN-KNO: A method of gene regulatory network inference using single-cell transcription and gene knockout data.

As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through the average of cellular gene expressions. These methods are difficult to identify the cell heterogeneity of gene expression. Existing methods for inferring GRN using single-cell transcriptional data lack expression information when genes reach steady state, and the high dimensionality of single-cell data leads to high temporal and spatial complexity of the algorithm. In order to solve the problem in traditional GRN inference methods, including the lack of cellular heterogeneity information, single-cell data complexity and lack of steady-state information, we propose a method for GRN inference using single-cell transcription and gene knockout data, called SINgle-cell transcription data-KNOckout data (SIN-KNO), which focuses on combining dynamic and steady-state information of regulatory relationship contained in gene expression. Capturing cell heterogeneity information could help understand the gene expression difference in different cells. So, we could observe gene expression changes more accurately. Gene knockout data could observe the gene expression levels at steady-state of all other genes when one gene is knockout. Classifying the genes before analyzing the single-cell data could determine a large number of non-existent regulation, greatly reducing the number of regulation required for inference. In order to show the efficiency, the proposed method has been compared with several typical methods in this area including GENIE3, JUMP3, and SINCERITIES. The results of the evaluation indicate that the proposed method can analyze the diversified information contained in the two types of data, establish a more accurate gene regulation network, and improve the computational efficiency. The method provides a new thinking for dealing with large datasets and high computational complexity of single-cell data in the GRN inference.

D3GRN: a data driven dynamic network construction method to infer gene regulatory networks

BackgroundTo infer gene regulatory networks (GRNs) from gene-expression data is still a fundamental and challenging problem in systems biology. Several existing algorithms formulate GRNs inference as a regression problem and obtain the network with an ensemble strategy. Recent studies on data driven dynamic network construction provide us a new perspective to solve the regression problem.ResultsIn this study, we propose a data driven dynamic network construction method to infer gene regulatory network (D3GRN), which transforms the regulatory relationship of each target gene into functional decomposition problem and solves each sub problem by using the Algorithm for Revealing Network Interactions (ARNI). To remedy the limitation of ARNI in constructing networks solely from the unit level, a bootstrapping and area based scoring method is taken to infer the final network. On DREAM4 and DREAM5 benchmark datasets, D3GRN performs competitively with the state-of-the-art algorithms in terms of AUPR.ConclusionsWe have proposed a novel data driven dynamic network construction method by combining ARNI with bootstrapping and area based scoring strategy. The proposed method performs well on the benchmark datasets, contributing as a competitive method to infer gene regulatory networks in a new perspective.

Automated inference of gene regulatory networks using explicit regulatory modules

Gene regulatory networks are a popular tool for modelling important biological phenomena, such as cell differentiation or oncogenesis. Efficient identification of the causal connections between genes, their products and regulating transcription factors, is key to understanding how defects in their function may trigger diseases. Modelling approaches should keep up with the ever more detailed descriptions of the biological phenomena at play, as provided by new experimental findings and technical improvements. In recent years, we have seen great improvements in mapping of specific binding sites of many transcription factors to distinct regulatory regions. Recent gene regulatory network models use binding measurements; but usually only to define gene-to-gene interactions, ignoring regulatory module structure. Moreover, current huge amount of transcriptomic data, and exploration of all possible cis-regulatory arrangements which can lead to the same transcriptomic response, makes manual model building both tedious and time-consuming.In our paper, we propose a method to specify possible regulatory connections in a given Boolean network, based on transcription factor binding evidence. This is implemented by an algorithm which expands a regular Boolean network model into a “cis-regulatory” Boolean network model. This expanded model explicitly defines regulatory regions as additional nodes in the network, and adds new, valuable biological insights to the system dynamics. The expanded model can automatically be compared with expression data. And, for each node, a regulatory function, consistent with the experimental data, can be found. The resulting models are usually more constrained (by biologically-motivated metadata), and can then be inspected in in silico simulations.The fully automated method for model identification has been implemented in Python, and the expansion algorithm in R. The method resorts to the Z3 Satisfiability Modulo Theories (SMT) solver, and is similar to the RE:IN application (Yordanov et al., 2016).It is available on https://github.com/regulomics/expansion-network.

Tuxnet: a simple interface to process RNA sequencing data and infer gene regulatory networks.

Predicting gene regulatory networks (GRNs) from expression profiles is a common approach for identifying important biological regulators. Despite the increased use of inference methods, existing computational approaches often do not integrate RNA-sequencing data analysis, are not automated or are restricted to users with bioinformatics backgrounds. To address these limitations, we developed tuxnet, a user-friendly platform that can process raw RNA-sequencing data from any organism with an existing reference genome using a modified tuxedo pipeline (hisat2+cufflinks package) and infer GRNs from these processed data. tuxnet is implemented as a graphical user interface and can mine gene regulations, either by applying a dynamic Bayesian network(DBN) inference algorithm, genist, or a regression tree-based pipeline, rtp-star. We obtained time-course expression data of a PERIANTHIA (PAN) inducible line and inferred a GRN using genist to illustrate the use of tuxnet while gaining insight into the regulations downstream of the Arabidopsis root stem cell regulator PAN. Using rtp-star, we inferred the network of ATHB13, a downstream gene of PAN, for which we obtained wild-type and mutant expression profiles. Additionally, we generated two networks using temporal data from developmental leaf data and spatial data from root cell-type data to highlight the use of tuxnet to form new testable hypotheses from previously explored data. Our case studies feature the versatility of tuxnet when using different types of gene expression data to infer networks and its accessibility as a pipeline for non-bioinformaticians to analyze transcriptome data, predict causal regulations, assess network topology and identify key regulators.

Commensal microbiota drive the functional diversification of colon macrophages

Mononuclear phagocytes are a heterogeneous population of leukocytes essential for immune homeostasis that develop tissue-specific functions due to unique transcriptional programs driven by local microenvironmental cues. Single cell RNA sequencing (scRNA-seq) of colonic myeloid cells from specific pathogen free (SPF) and germ-free (GF) C57BL/6 mice revealed extensive heterogeneity of both colon macrophages (MPs) and dendritic cells (DCs). Modeling of developmental pathways combined with inference of gene regulatory networks indicate two major trajectories from common CCR2+ precursors resulting in colon MP populations with unique transcription factors and downstream target genes. Compared to SPF mice, GF mice had decreased numbers of total colon MPs, as well as selective proportional decreases of two major CD11c+CD206intCD121b+ and CD11c−CD206hiCD121b− colon MP populations, whereas DC numbers and proportions were not different. Importantly, these two major colon MP populations were clearly distinct from other colon MP populations regarding their gene expression profile, localization within the lamina propria (LP) and ability to phagocytose macromolecules from the blood. These data uncover the diversity of intestinal myeloid cell populations at the molecular level and highlight the importance of microbiota on the unique developmental as well as anatomical and functional fates of colon MPs.

A Highly Robust Model for Predicting Outcome of Multiple Myeloma Patients By Inferring Patient-Specific Transcription Factor Activity

Objective: Multiple myeloma (MM) is the second most common blood cancer in the United States. Clinical and pathological indicators are inadequate for accurate assessing prognosis of MM patients1. The objective of this study was to develop a robust computational model for predicting prognosis of MM patients by inferring transcription factor (TF) activities based on global gene expression measurements. Methods: The workflow is illustrated in Figure 1. Gene expression data from MM patients were retrieved from the Multiple Myeloma Research Foundation CoMMpass study2 (MMRF) and Gene Expression Omnibus (GEO). MM-specific TF-target gene relationships were derived from the ENCODE database3 and co-expression analysis was based on MMRF data. The activity for each TF was inferred using a rank-based enrichment approach based on the target gene expression measurements in individual patients4. A prognostic model was developed using Cox regression analysis based on the inferred TF activities that demonstrated the highest association with patient survival. The TF model performance was further evaluated using data from four independent MM cohorts, in which gene expression levels were measured using different platforms. Results: Based on MMRF data (767 patients), the inferred activity of four TFs, BATF, E2F1, MYBL2, and RAD21, showed the strongest association with patient survival. The derived prognostic model was validated in four independent MM data sets with a total of 1501 patients. Importantly, gene expression levels in the validation data sets were measured using microarray, which is different from the RNAseq used in training data sets, and supports the robustness of the TF activity model across different platforms (Figure 2). In addition, the TF activity model was able to predict clinical outcomes of patients that received bortezomib treatment, and it also revealed an association with metastasis in myeloma cell line xenograft experiments5 (Figure 3). Conclusion: We developed a highly robust prognostic model for predicting MM clinical outcomes. The inferred TF activity further improves our understanding of the underlying regulatory basis of MM progression. Reference 1. Rajkumar SV. Multiple myeloma: 2012 update on diagnosis, risk-stratification, and management. Am J Hematol. 2012;87(1):78-88. 2. l, a longitudinal study in multiple myeloma relating clinical outcomes to genomic and immunophenotypic profiles: Am Soc Hematology; 2013. 3. Consortium EP. The ENCODE (ENCyclopedia of DNA elements) project. Science 2004;306(5696):636-640. 4. Moerman T, Aibar Santos S, Bravo González-Blas C, et al. GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks. Bioinformatics. 2018;35(12):2159-2161. 5. Shen Y, Mishima Y, Shi J, et al. Deciphering Clonal Evolution and Dissemination of Multiple Myeloma Cells In Vivo: Am Soc Hematology; 2018. Disclosures Abonour: Celgene: Consultancy, Research Funding; BMS: Consultancy; Takeda: Consultancy, Research Funding; Janssen: Consultancy, Research Funding. Roodman:Amgen: Membership on an entity's Board of Directors or advisory committees.

Transcriptome Analysis of Pediatric AML Reveals Non Protein-Coding RNAs Associated with Poor Survival Outcome and Treatment Resistance

Pediatric AML is characterized by a high rate of relapse of up to ~40% (Im et al. 2016), and nearly half of the patients achieving initial remission experience relapse within 2 years (Alpenc et al. 2016; Rubnitz and Gruber 2018). Due to the lack of recurrently mutated genes associated with relapse as observed through genomic analysis (Farrar et al. 2016; Boluori et al. 2017), we hypothesize that the transcriptome may reveal insights into molecules and mechanisms contributing to treatment resistance. Farrar et al. (2016) provided support for this hypothesis as they observed that somatic mutations across primary and relapse patient samples converged on genes involved in transcriptional regulation. McNeer et al. (2019) showed that a large number of non protein-coding RNAs, such as pseudogenes and long non-coding RNAs, many of which have regulatory roles through interactions with other genes and proteins, were observed to have increased mutational frequency post-induction compared to samples at diagnosis among induction-failure patients. These lines of evidence suggest that regulatory processes and interactions involving RNA molecules may play important roles in treatment resistance. To address our hypothesis, we conducted analysis of rRNA-depleted RNA sequencing data generated from 1325 primary and 396 relapse bone marrow or peripheral blood samples obtained from patients enrolled in the AAML1031 (treatment arms are ADE, ADE+Bortezomib, and ADE +Sorafenib) and AAML0531 (randomized treatment arms chemotherapy with or without Gemtuzumab Ozogamicin) clinical trials. We focused our analysis to malignant cells in 620 primary and 148 relapse samples with >50% blast count. To identify RNAs associated with overall survival, we conducted Cox proportional-hazards regression and generalized linear model via penalized maximum likelihood analyses using RNA expression profiles. We performed transcription factor and regulatory network profiling as adapted from Aibar et al. (2017) to identify significant interactions between RNAs. We used primary samples for 574 patients enrolled in AAML1031 treated with either ADE or ADE+Bortezomib to identify high risk features associated with overall survival. We identified 7 RNAs (AC002401.1, VAV1, RP1-37C10.3, RP11-92C4.3, PRICKLE4, RP11-491H9.3, and NYNRIN) with log hazard ratios >1 (adjusted p-value < 0.000025) and that were assigned positive coefficients as derived from a combinatorial RNA generalized linear model. Such results indicate that these RNAs are significantly associated with low probability of overall survival, and that the expression of these RNAs at diagnosis could be used to identify high-risk patients and to anticipate poor survival outcome. Interestingly, 5 of these genes encode antisense transcripts. RNA expression profiles of 620 primary and 148 relapse samples were compared to identify molecular features and interactions more directly associated with treatment resistance. Though we observed no differences in transcription factor network activities between primary and relapse samples, we identified 14 RNAs that were significantly upregulated at relapse compared to primary samples (log2 fold change >2; BH-adjusted p-value < 0.05), 10 of which were pseudogenes, long intergenic or non protein-coding RNAs. Gene regulatory network inference analysis using the regression tree-based algorithm GENIE3 (Huynh-Thu et al. 2010) identified 1842 genes to have interactions with these 14 genes (3594 interactions total; weight >0.001). Gene set enrichment analysis of the 1856 genes showed the top enriched pathway to be "ribosome, cytoplasmic." These results suggest that RNA processing and translational control could be associated with treatment resistance. Our findings revealed previously uncharacterized molecular features and interactions potentially associated with low probably of overall survival and treatment resistance. Future analyses of these molecules will ideally contribute to deeper insights into the mechanisms driving relapse disease, and extend therapeutic targeting to regulatory RNAs which, through interactions with other molecules, may play important roles in regulating transcription and translation. Disclosures No relevant conflicts of interest to declare.

Differential regulatory network-based quantification and prioritization of key genes underlying cancer drug resistance based on time-course RNA-seq data.

Drug resistance is a major cause for the failure of cancer chemotherapy or targeted therapy. However, the molecular regulatory mechanisms controlling the dynamic evolvement of drug resistance remain poorly understood. Thus, it is important to develop methods for identifying key gene regulatory mechanisms of the resistance to specific drugs. In this study, we developed a data-driven computational framework, DryNetMC, using a differential regulatory network-based modeling and characterization strategy to quantify and prioritize key genes underlying cancer drug resistance. The DryNetMC does not only infer gene regulatory networks (GRNs) via an integrated approach, but also characterizes and quantifies dynamical network properties for measuring node importance. We used time-course RNA-seq data from glioma cells treated with dbcAMP (a cAMP activator) as a realistic case to reconstruct the GRNs for sensitive and resistant cells. Based on a novel node importance index that comprehensively quantifies network topology, network entropy and expression dynamics, the top ranked genes were verified to be predictive of the drug sensitivities of different glioma cell lines, in comparison with other existing methods. The proposed method provides a quantitative approach to gain insights into the dynamic adaptation and regulatory mechanisms of cancer drug resistance and sheds light on the design of novel biomarkers or targets for predicting or overcoming drug resistance.

A role for auxin signaling in the acquisition of longevity during seed maturation.

Seed longevity, the maintenance of viability during dry storage, is a crucial factor to preserve plant genetic resources and seed vigor. Inference of a temporal gene-regulatory network of seed maturation identified auxin signaling as a putative mechanism to induce longevity-related genes. Using auxin-response sensors and tryptophan-dependent auxin biosynthesis mutants of Arabidopsis thaliana L., the role of auxin signaling in longevity was studied during seed maturation. DII and DR5 sensors demonstrated that, concomitant with the acquisition of longevity, auxin signaling input and output increased and underwent a spatiotemporal redistribution, spreading throughout the embryo. Longevity of seeds of single auxin biosynthesis mutants with altered auxin signaling activity was affected in a dose-response manner depending on the level of auxin activity. Longevity-associated genes with promoters enriched in auxin response elements and the master regulator ABSCISIC ACID INSENSITIVE3 were induced by auxin in developing embryos and deregulated in auxin biosynthesis mutants. The beneficial effect of exogenous auxin during seed maturation on seed longevity was abolished in abi3-1 mutants. These data suggest a role for auxin signaling activity in the acquisition of longevity during seed maturation.

Predicting gene regulatory interactions based on spatial gene expression data and deep learning.

Reverse engineering of gene regulatory networks (GRNs) is a central task in systems biology. Most of the existing methods for GRN inference rely on gene co-expression analysis or TF-target binding information, where the determination of co-expression is often unreliable merely based on gene expression levels, and the TF-target binding data from high-throughput experiments may be noisy, leading to a high ratio of false links and missed links, especially for large-scale networks. In recent years, the microscopy images recording spatial gene expression have become a new resource in GRN reconstruction, as the spatial and temporal expression patterns contain much abundant gene interaction information. Till now, the spatial expression resources have been largely underexploited, and only a few traditional image processing methods have been employed in the image-based GRN reconstruction. Moreover, co-expression analysis using conventional measurements based on image similarity may be inaccurate, because it is the local-pattern consistency rather than global-image-similarity that determines gene-gene interactions. Here we present GripDL (Gene regulatory interaction prediction via Deep Learning), which incorporates high-confidence TF-gene regulation knowledge from previous studies, and constructs GRNs for Drosophila eye development based on Drosophila embryonic gene expression images. Benefitting from the powerful representation ability of deep neural networks and the supervision information of known interactions, the new method outperforms traditional methods with a large margin and reveals new intriguing knowledge about Drosophila eye development.

Gene Expression Maps in Plants: Current State and Prospects.

For many years, progress in the identification of gene functions has been based on classical genetic approaches. However, considerable recent omics developments have brought to the fore indirect but high-resolution methods of gene function identification such as transcriptomics, proteomics, and metabolomics. A transcriptome map is a powerful source of functional information and the result of the genome-wide expression analysis of a broad sampling of tissues and/or organs from different developmental stages and/or environmental conditions. In plant science, the application of transcriptome maps extends from the inference of gene regulatory networks to evolutionary studies. However, only some of these data have been integrated into databases, thus enabling analyses to be conducted without raw data; without this integration, extensive data preprocessing is required, which limits data usability. In this review, we summarize the state of plant transcriptome maps, analyze the problems associated with the combined analysis of large-scale data from various studies, and outline possible solutions to these problems.

A sparse and decomposed particle swarm optimization for inferring gene regulatory networks based on fuzzy cognitive maps.

Inferring gene regulatory networks (GRNs) is vital to understand the complex cellular processes and reveal the regulatory mechanisms among genes. Although various methods have been developed, more accurate algorithms which can control the sparseness of GRNs still need to be developed. In this work, we model GRNs by fuzzy cognitive maps (FCMs), and a node in an FCM means a gene. Then, a new sparse and decomposed particle swarm optimization, termed as SDPSOFCM-GRN, is proposed to train FCMs, which employs the least absolute shrinkage and selection operator (Lasso) to control the network sparseness with a decomposed strategy. In the experiments, the performance of SDPSOFCM-GRN is validated on synthetic data and the well-known benchmark DREAM3 and DREAM4. The results show that SDPSOFCM-GRN can well control the sparseness of GRNs, and infer directed GRNs with high accuracy and efficiency.