Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles

Physiological and molecular response to drought in somatic plants from Pinus radiata embryonal masses induced at high temperatures

Drought and heat are among the major abiotic stresses in forest trees and are directly related with the consequences of climatic change. Many responses to abiotic stresses in plants have been associated with plant memory but mechanisms underlying this phenomenon remain unclear. Somatic embryogenesis, which is considered one of the most important methods for large-scale vegetative propagation of plants, is also used for stress induction and study the mechanisms involved in adaptation to abiotic stress. Specifically, heat stress during initiation stage of somatic embryogenesis has shown to have an impact in differential expression of stress related genes in pines. Modifications caused by a previous stress could eventually influence the stress tolerance of somatic plants years later. In this study we analysed the response to drought in 2-year-old radiata pine somatic plants, derived from embryonal masses initiated at 60 °C, at physiological, transcriptomic and amino acid accumulation level. Our results showed a more pronounce response to drought in plants coming from 60 °C treatment, which presented lower values in several physiological parameters as well as higher proline and tyrosine levels. Additionally, the transcriptomic response to drought was stronger in heat primed plants compared to control plants, suggesting a memory acquired two years before.

Inferring gene-pathway associations from consolidated transcriptome datasets: an interactive gene network explorer for Tetrahymena thermophila

Although an established model organism, Tetrahymena thermophila remains comparatively inaccessible to high throughput screens, and alternative bioinformatic approaches still rely on unconnected datasets and outdated algorithms. Here, we report a new approach to consolidating RNA-seq and microarray data based on a systematic exploration of parameters and computational controls, enabling us to infer functional gene associations from their co-expression patterns. To illustrate the power of this approach, we took advantage of new data regarding a previously studied pathway, the biogenesis of a secretory organelle called the mucocyst. Our untargeted clustering approach recovered over 80% of the genes that were previously verified to play a role in mucocyst biogenesis. Furthermore, we tested four new genes that we predicted to be mucocyst-associated based on their co-expression and found that knocking out each of them results in mucocyst secretion defects. We also found that our approach succeeds in clustering genes associated with several other cellular pathways that we evaluated based on prior literature. We present the Tetrahymena Gene Network Explorer (TGNE) as an interactive tool for genetic hypothesis generation and functional annotation in this organism and as a framework for building similar tools for other systems.

Rice master regulator 'HYR' enhances growth and defense mechanisms with consequences for fall armyworm growth and host selection

Rice (Oryza sativa L.), the staple food for half of the world's population, suffers heavy damage by insect herbivores, especially the emerging fall armyworm (FAW), Spodoptera frugiperda. HIGHER YIELD RICE (HYR), a master regulator of multiple biological pathways with an established gene regulatory network, has been found to improve rice yield to ∼29% and tolerance to environmental stress. However, its impact on defense has not been explored. We hypothesized that, FAW would target HYR plants because of its vigorous growth and lead to trade-offs for defense. Through a series of experiments with HYR and its wild type (WT), we show that HYR plants have enhanced below-ground growth, physiological traits, and direct and indirect defense traits including leaf trichomes, wax, and volatile organic compounds. To test possible phytohormone-mediated defense signaling, we focussed on jasmonic acid and salicylic acid gene expression panel and found that most of these genes are highly expressed in HYR when compared to its WT counterpart. Bioassays examining developmental milestones also revealed that HYR plants effectively deter FAW, and when force-fed, caused negative effects. Collectively, our findings suggest that the master regulator HYR (Higher Yield Rice expressing) plants enhance growth and physiological traits, as well as physical and chemical defense mechanisms through co-ordinated defense gene expression, which deter herbivore feeding, growth, development, and host selection.

The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification

Understanding interaction mechanisms within cells, tissues, and organisms is crucial for driving developments across biology and medicine. Mathematical modeling is an essential tool for simulating such biological systems. Building on experiments, mechanistic models are widely used to describe small-scale intracellular networks. The development of sequencing techniques and computational tools has recently enabled multiscale models. Combining such larger scale network modeling with mechanistic modeling provides us with an opportunity to reveal previously unknown disease mechanisms and pharmacological interventions. Here, we review systems biology models from mechanistic models to multiscale models that integrate multiple layers of cellular networks and discuss how they can be used to shed light on disease states and even wellness-related states. Additionally, we introduce several methods that increase the certainty and accuracy of model predictions. Thus, combining mechanistic models with emerging mathematical and computational techniques can provide us with increasingly powerful tools to understand disease states and inspire drug discoveries.

Shining light on Arabidopsis regulatory networks integrating nitrogen use and photosynthesis

Nitrogen and light availability are well-known to influence photosynthesis, having both individual and synergistic effects. However, the regulatory interactions between these signaling pathways, especially the transcription factors (TFs) that perceive and integrate these cues, remain to be elucidated. Arabidopsis grown in a matrix of nitrogen and light treatments exhibited distinct physiological and transcriptomic responses. Notably, the effect of nitrogen dose on biomass, nitrogen use efficiency, carbon-to-nitrogen ratio, and gene expression was highly dependent on light intensity. Genes differentially expressed across the treatments were enriched for photosynthetic processes, including the pentose-phosphate cycle, light-harvesting, and chlorophyll biosynthesis. TFs coordinating photosynthesis, carbon-to-nitrogen balance, and nitrogen uptake were identified based on motif enrichment, validated binding data, and gene regulatory network analysis. Dynamic light-by-nitrogen responses were found for TFs previously linked to either nitrogen or light signaling, which now emerge as regulatory hubs that integrate these signals. Among these TFs, we identified bZIP and MYB-related family transcription factors as pivotal players in harmonizing photosynthesis, nitrogen assimilation, and light responses. The transcription factors unveiled in this study have the potential to unlock new strategies for optimizing photosynthetic activity and nutrient-use efficiency in plants.

Network-based analysis of Alzheimer's Disease genes using multi-omics network integration with graph diffusion

Alzheimer's Disease (AD) is a complex neurodegenerative disorder affecting millions worldwide. Despite extensive research, the mechanisms behind AD remain elusive. Many studies suggest that disease-responsible genes often act as hub genes in biological networks. However, this assumption requires further investigation in the context of AD. To examine the network characteristics of known AD genes, it is crucial to construct a highly confident network, which is challenging to achieve using a single data source. This work integrates multi-omics networks inferred from microarray, single-cell RNA sequencing, and single-nuclei RNA sequencing expression data, weighted with protein interaction and gene ontology information. We generate a high-quality integrated network by utilizing various inference methods and combining them through a graph diffusion-based integration approach. This network is then analyzed to investigate the properties of known AD-specific genes. Our findings reveal that AD genes are not always high-degree or central hub nodes in the network. Instead, these genes are distributed across different quartiles of degree centrality while maintaining significant interconnections for effective regulation. Furthermore, our study highlights that peripheral genes, often overlooked, also play crucial roles by connecting to relevant disease nodes and hub genes. These findings challenge the conventional understanding that AD-responsible genes are primarily the hub genes in the network, offering new insights into the complex regulatory mechanisms of AD and suggesting novel directions for future research.

Testing and overcoming the limitations of modular response analysis

Modular response analysis (MRA) is an effective method to infer biological networks from perturbation data. However, it has several limitations such as strong sensitivity to noise, need of performing independent perturbations that hit a single node at a time, and linear approximation of dependencies within the network. Previously, we addressed the sensitivity of MRA to noise by reinterpreting MRA as a multilinear regression problem. We demonstrated the advantages of this approach over the conventional MRA and other known inference methods, particularly in handling noise measurements and nonlinear networks. Here, we provide new contributions to complement this theory. First, we overcome the need of perturbations to be independent, thereby augmenting MRA applicability. Second, using analysis of variance and lack-of-fit tests, we can now assess MRA compatibility with the data and identify the primary source of errors. In cases where nonlinearity prevails, we propose extending the model to a second-order polynomial. Third, we demonstrate how to effectively use prior knowledge about a network. We validated these results using 4 networks with known dynamics (3, 4, and 6 nodes) and 40 simulated networks, ranging from 10 to 200 nodes. Finally, we incorporated these innovations into our R software package MRARegress to offer a comprehensive, extended theory for MRA and to facilitate its use by the community. Mathematical aspects, tests details, and scripts are provided as Supplementary Information (see 'Data Availability Statement').

Deep transcriptome and metabolome analysis to dissect untapped spatial dynamics of specialized metabolism in Saussurea costus (Falc.) Lipsch

Saussurea costus (Falc.) is an endangered medicinal plant possessing diverse phytochemical compounds with clinical significance and used to treat numerous human ailments. Despite the source of enriched phytochemicals, molecular insights into spatialized metabolism are poorly understood in S. costus. This study investigated the dynamics of organ-specific secondary metabolite biosynthesis using deep transcriptome sequencing and high-throughput UHPLC-QTOF based untargeted metabolomic profiling. A de novo assembly from quality reads fetched 59,725 transcripts with structural (53.02%) and functional (66.13%) annotations of non-redundant transcripts. Of the 7,683 predicted gene families, 3,211 were categorized as 'single gene families'. Interestingly, out of the 4,664 core gene families within the Asterids, 4,560 families were captured in S. costus. Organ-specific differential gene expression analysis revealed significant variations between leaves vs. stems (23,102 transcripts), leaves vs. roots (30,590 transcripts), and roots vs. stems (21,759 transcripts). Like-wise, putative metabolites (PMs) were recorded with significant differences in leaves vs. roots (250 PMs), leaves vs. stem (350 PMs), and roots vs. stem (107 PMs). The integrative transcriptomic and metabolomic analysis identified organ-specific differences in the accumulation of important metabolites, including secologanin, menthofuran, taraxerol, lupeol, acetyleugenol, scopoletin, costunolide, and dehydrocostus lactone. Furthermore, a global gene co-expression network (GCN) identified putative regulators controlling the expression of key target genes of secondary metabolite pathways including terpenoid, phenylpropanoid, and flavonoid. The comprehensive functionally relevant genomic resource created here provides beneficial insights for upscaling targeted metabolite biosynthesis through genetic engineering, and for expediting association mapping efforts to elucidate the casual genetic elements controlling specific bioactive metabolites.

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data

Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunities to reconstruct GRNs at fine-grained resolution. However, the inference of GRNs is hindered by insufficient single omic profiles due to the characteristic high loss rate of single-cell sequencing data. In this study, we developed scMultiomeGRN, a deep learning framework to infer transcription factor (TF) regulatory networks via unique integration of single-cell genomic (single-cell RNA sequencing) and epigenomic (single-cell ATAC sequencing) data. We create scMultiomeGRN to elucidate these networks by conceptualizing TF network graph structures. Specifically, we build modality-specific neighbor aggregators and cross-modal attention modules to learn latent representations of TFs from single-cell multi-omics. We demonstrate that scMultiomeGRN outperforms state-of-the-art models on multiple benchmark datasets involved in diseases and health. Via scMultiomeGRN, we identified Alzheimer's disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data.

Diffusion-based generation of gene regulatory networks from scRNA-seq data with DigNet

A gene regulatory network (GRN) intricately encodes the interconnectedness of identities and functionalities of genes within cells, ultimately shaping cellular specificity. Despite decades of endeavors, reverse engineering of GRNs from gene expression profiling data remains a profound challenge, particularly when it comes to reconstructing cell-specific GRNs that are tailored to precise cellular and genetic contexts. Here, we propose a discrete diffusion generation model, called DigNet, capable of generating corresponding GRNs from high-throughput single-cell RNA sequencing (scRNA-seq) data. DigNet embeds the network generation process into a multistep recovery procedure with Markov properties. Each intermediate step has a specific model to recover a portion of the gene regulatory architectures. It thus can ensure compatibility between global network structures and regulatory modules through the unique multistep diffusion procedure. Furthermore, through iMetacell integration and non-Euclidean discrete space modeling, DigNet is robust to the presence of noise in scRNA-seq data and the sparsity of GRNs. Benchmark evaluation results against more than a dozen state-of-the-art network inference methods demonstrate that DigNet achieves superior performance across various single-cell GRN reconstruction experiments. Furthermore, DigNet provides unique insights into the immune response in breast cancer, derived from differential gene regulation identified in T cells. As an open-source software, DigNet offers a powerful and effective tool for generating cell-specific GRNs from scRNA-seq data.

Time-series transcriptome analysis reveals the cascade mechanism of biological processes following the perturbation of the MVA pathway in Salvia miltiorrhiza

Various biological processes are interconnected in plants. Transcription factors (TFs) often act as regulatory hubs to regulate plant growth and responses to stress by integrating various biological pathways. Despite extensive studies on TFs functions in various plant species, our understanding of the details of TFs regulation remains limited. In this study, clonal seedlings of Salvia miltiorrhiza were exposed to specific inhibitors for 12 h. Time-series transcriptome data, sampled hourly, were used to construct co-expression networks and gene regulatory networks (GRNs). Transcriptome dynamic analysis was utilized to capture the gene expression dynamics of various biological processes and decipher the potential molecular mechanisms that regulate these processes. The perturbation results showed the growth and development processes of S.miltiorrhiza were primarily affected at the early stage, whereas stress response-related biological processes were mainly influenced at the later stage. And there was a correlation between the series of key differentially expressed genes in terpenoid biosynthesis pathways and the topological distribution of these pathways. Furthermore, the GRNs based on TFs indicate that TFs play a crucial role in connecting various biological processes. In the cytoplasmic lysate gene regulatory module, SmWRKY48-SmTCP4-SmWRKY28 constituted a regulation hub regulating S.miltiorrhiza responses to perturbation of the MVA pathway. The regulation hub mediated various pathways, including pyruvate metabolism, glycolysis/gluconeogenesis, amino acid metabolism, and ubiquinone and other terpenoid-quinone biosynthesis.Our findings suggest that perturbation of a key biological pathway in S.miltiorrhiza has time-dependent effects on other biological processes. And SmWRKY48-SmTCP4-SmWRKY28 constitutes the regulatory hub in S.miltiorrhiza responses to perturbation of MVA pathway.

Maturation and persistence of CAR T cells derived from human pluripotent stem cells via chemical inhibition of G9a/GLP

Elucidating mechanisms of T cell development can guide in vitro T cell differentiation from induced pluripotent stem cells (iPSCs) and facilitate off-the-shelf T cell-based immunotherapies. Using a stroma-free human iPSC-T cell differentiation platform, we screened for epigenetic modulators that influence T cell specification and identified the H3K9-directed histone methyltransferases G9a/GLP as repressors of T cell fate. We show that G9a/GLP inhibition during specific time windows of differentiation of hematopoietic stem and progenitor cells (HSPCs) skews cell fates toward lymphoid lineages. Inhibition of G9a/GLP promotes the production of lymphoid cells during zebrafish embryonic hematopoiesis, demonstrating the evolutionary conservation of G9a/GLP function. Importantly, chemical inhibition of G9a/GLP facilitates the generation of mature iPSC-T cells that bear transcriptional similarity to peripheral blood αβ T cells. When engineered to express chimeric antigen receptors, the epigenetically engineered iPSC-T cells exhibit enhanced effector functions in vitro and durable, persistent antitumor activity in a xenograft tumor-rechallenge model.

Inferring gene-pathway associations from consolidated transcriptome datasets: an interactive gene network explorer for Tetrahymena thermophila

Although an established model organism, Tetrahymena thermophila remains comparatively inaccessible to high throughput screens, and alternative bioinformatic approaches still rely on unconnected datasets and outdated algorithms. Here, we report a new approach to consolidating RNA-seq and microarray data based on a systematic exploration of parameters and computational controls, enabling us to infer functional gene associations from their co-expression patterns. To illustrate the power of this approach, we took advantage of new data regarding a previously studied pathway, the biogenesis of a secretory organelle called the mucocyst. Our untargeted clustering approach recovered over 80% of the genes that were previously verified to play a role in mucocyst biogenesis. Furthermore, we tested four new genes that we predicted to be mucocyst-associated based on their co-expression and found that knocking out each of them results in mucocyst secretion defects. We also found that our approach succeeds in clustering genes associated with several other cellular pathways that we evaluated based on prior literature. We present the Tetrahymena Gene Network Explorer (TGNE) as an interactive tool for genetic hypothesis generation and functional annotation in this organism and as a framework for building similar tools for other systems.

Integrated analysis of immunometabolic interactions in Down syndrome

Down syndrome (DS), caused by trisomy 21 (T21), results in immune and metabolic dysregulation. People with DS experience co-occurring conditions at higher rates than the euploid population. However, the interplay between immune and metabolic alterations and the clinical manifestations of DS are poorly understood. Here, we report an integrated analysis of immunometabolic pathways in DS. Using multi-omics data, we infered cytokine-metabolite relationships mediated by specific transcriptional programs. We observed increased mediation of immunometabolic interactions in those with DS compared to euploid controls by genes in interferon response, heme metabolism, and oxidative phosphorylation. Unsupervised clustering of immunometabolic relationships in people with DS revealed subgroups with different frequencies of co-occurring conditions. Across the subgroups, we observed distinct mediation by DNA repair, Hedgehog signaling, and angiogenesis. The molecular stratification associates with the clinical heterogeneity observed in DS, suggesting that integrating multiple omic profiles reveals axes of coordinated dysregulation specific to DS co-occurring conditions.

Gene regulatory network inference based on novel ensemble method

Gene regulatory networks (GRNs) contribute toward understanding the function of genes and the development of cancer or the impact of key genes on diseases. Hence, this study proposes an ensemble method based on 13 basic classification methods and a flexible neural tree (FNT) to improve GRN identification accuracy. The primary classification methods contain ridge classification, stochastic gradient descent, Gaussian process classification, Bernoulli Naive Bayes, adaptive boosting, gradient boosting decision tree, hist gradient boosting classification, eXtreme gradient boosting (XGBoost), multilayer perceptron, light gradient boosting machine, random forest, support vector machine, and k-nearest neighbor algorithm, which are regarded as the input variable set of FNT model. Additionally, a hybrid evolutionary algorithm based on a gene programming variant and particle swarm optimization is developed to search for the optimal FNT model. Experiments on three simulation datasets and three real single-cell RNA-seq datasets demonstrate that the proposed ensemble feature outperforms 13 supervised algorithms, seven unsupervised algorithms (ARACNE, CLR, GENIE3, MRNET, PCACMI, GENECI, and EPCACMI) and four single cell-specific methods (SCODE, BiRGRN, LEAP, and BiGBoost) based on the area under the receiver operating characteristic curve, area under the precision-recall curve, and F1 metrics.

Identification of SPP1 + macrophages as an immune suppressor in hepatocellular carcinoma using single-cell and bulk transcriptomics

Introduction

Macrophages and T cells play crucial roles in liver physiology, but their functional diversity in hepatocellular carcinoma (HCC) remains largely unknown.

Methods

Two bulk RNA-sequencing (RNA-seq) cohorts for HCC were analyzed using gene co-expression network analysis. Key gene modules and networks were mapped to single-cell RNA-sequencing (scRNA-seq) data of HCC. Cell type fraction of bulk RNA-seq data was estimated by deconvolution approach using single-cell RNA-sequencing data as a reference. Survival analysis was carried out to estimate the prognosis of different immune cell types in bulk RNA-seq cohorts. Cell-cell interaction analysis was performed to identify potential links between immune cell types in HCC.

Results

In this study, we analyzed RNA-seq data from two large-scale HCC cohorts, revealing a major and consensus gene co-expression cluster with significant implications for immunosuppression. Notably, these genes exhibited higher enrichment in liver macrophages than T cells, as confirmed by scRNA-seq data from HCC patients. Integrative analysis of bulk and single-cell RNA-seq data pinpointed SPP1 + macrophages as an unfavorable cell type, while VCAN + macrophages, C1QA + macrophages, and CD8 + T cells were associated with a more favorable prognosis for HCC patients. Subsequent scRNA-seq investigations and in vitro experiments elucidated that SPP1, predominantly secreted by SPP1 + macrophages, inhibits CD8 + T cell proliferation. Finally, targeting SPP1 in tumor-associated macrophages through inhibition led to a shift towards a favorable phenotype.

Discussion

This study underpins the potential of SPP1 as a translational target in immunotherapy for HCC.

Dynamic heterogeneity in an E. coli stress response regulon mediates gene activation and antimicrobial peptide tolerance

The bacterial stress response is an intricately regulated system that plays a critical role in cellular resistance to drug treatment. The complexity of this response is further complicated by cell-to-cell heterogeneity in the expression of bacterial stress response genes. These genes are often organized into networks comprising one or more transcriptional regulators that control expression of a suite of downstream genes. While the expression heterogeneity of many of these upstream regulators has been characterized, the way in which this variability affects the larger downstream stress response remains hard to predict, prompting two key questions. First, how does heterogeneity and expression noise in stress response regulators propagate to the diverse downstream genes in their regulons. Second, when expression levels vary, how do multiple downstream genes act together to protect cells from stress. To address these questions, we focus on the transcription factor PhoP, a critical virulence regulator which coordinates pathogenicity in several gram-negative species. We use optogenetic stimulation to precisely control PhoP expression levels and examine how variations in PhoP affect the downstream activation of genes in the PhoP regulon. We find that these downstream genes exhibit differences both in mean expression level and sensitivity to increasing levels of PhoP. These response functions can also vary between individual cells, increasing heterogeneity in the population. We tie these variations to cell survival when bacteria are exposed to a clinically-relevant antimicrobial peptide, showing that high expression of the PhoP-regulon gene pmrD provides a protective effect against Polymyxin B. Overall, we demonstrate that even subtle heterogeneity in expression of a stress response regulator can have clear consequences for enabling bacteria to survive stress.

Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

Over the past decade, single-cell genomics technologies have allowed scalable profiling of cell-type-specific features, which has substantially increased our ability to study cellular diversity and transcriptional programs in heterogeneous tissues. Yet our understanding of mechanisms of gene regulation or the rules that govern interactions between cell types is still limited. The advent of new computational pipelines and technologies, such as single-cell epigenomics and spatially resolved transcriptomics, has created opportunities to explore two new axes of biological variation: cell-intrinsic regulation of cell states and expression programs and interactions between cells. Here, we summarize the most promising and robust technologies in these areas, discuss their strengths and limitations and discuss key computational approaches for analysis of these complex datasets. We highlight how data sharing and integration, documentation, visualization and benchmarking of results contribute to transparency, reproducibility, collaboration and democratization in neuroscience, and discuss needs and opportunities for future technology development and analysis.

GRNMOPT: Inference of gene regulatory networks based on a multi-objective optimization approach

BACKGROUND AND OBJECTIVE

The reconstruction of gene regulatory networks (GRNs) stands as a vital approach in deciphering complex biological processes. The application of nonlinear ordinary differential equations (ODEs) models has demonstrated considerable efficacy in predicting GRNs. Notably, the decay rate and time delay are pivotal in authentic gene regulation, yet their systematic determination in ODEs models remains underexplored. The development of a comprehensive optimization framework for the effective estimation of these key parameters is essential for accurate GRN inference.

METHOD

This study introduces GRNMOPT, an innovative methodology for inferring GRNs from time-series and steady-state data. GRNMOPT employs a combined use of decay rate and time delay in constructing ODEs models to authentically represent gene regulatory processes. It incorporates a multi-objective optimization approach, optimizing decay rate and time delay concurrently to derive Pareto optimal sets for these factors, thereby maximizing accuracy metrics such as AUROC (Area Under the Receiver Operating Characteristic curve) and AUPR (Area Under the Precision-Recall curve). Additionally, the use of XGBoost for calculating feature importance aids in identifying potential regulatory gene links.

RESULTS

Comprehensive experimental evaluations on two simulated datasets from DREAM4 and three real gene expression datasets (Yeast, In vivo Reverse-engineering and Modeling Assessment [IRMA], and Escherichia coli [E. coli]) reveal that GRNMOPT performs commendably across varying network scales. Furthermore, cross-validation experiments substantiate the robustness of GRNMOPT.

CONCLUSION

We propose a novel approach called GRNMOPT to infer GRNs based on a multi-objective optimization framework, which effectively improves inference accuracy and provides a powerful tool for GRNs inference.

LBF-MI: Limited Boolean Functions and Mutual Information to Infer a Gene Regulatory Network from Time-Series Gene Expression Data

BACKGROUND

In the realm of system biology, it is a challenging endeavor to infer a gene regulatory network from time-series gene expression data. Numerous Boolean network inference techniques have emerged for reconstructing a gene regulatory network from a time-series gene expression dataset. However, most of these techniques pose scalability concerns given their capability to consider only two to three regulatory genes over a specific target gene.

METHODS

To overcome this limitation, a novel inference method, LBF-MI, has been proposed in this research. This two-phase method utilizes limited Boolean functions and multivariate mutual information to reconstruct a Boolean gene regulatory network from time-series gene expression data. Initially, Boolean functions are applied to determine the optimum solutions. In case of failure, multivariate mutual information is applied to obtain the optimum solutions.

RESULTS

This research conducted a performance-comparison experiment between LBF-MI and three other methods: mutual information-based Boolean network inference, context likelihood relatedness, and relevance network. When examined on artificial as well as real-time-series gene expression data, the outcomes exhibited that the proposed LBF-MI method outperformed mutual information-based Boolean network inference, context likelihood relatedness, and relevance network on artificial datasets, and two real Escherichia coli datasets (E. coli gene regulatory network, and SOS response of E. coli regulatory network).

CONCLUSIONS

LBF-MI's superior performance in gene regulatory network inference enables researchers to uncover the regulatory mechanisms and cellular behaviors of various organisms.

Transcriptome-wide investigation and functional characterization reveal a terpene synthase involved in γ-terpinene biosynthesis in Monarda citriodora

Monarda citriodora Cerv. ex Lag. is a rich source of industrially important compounds like γ-terpinene, carvacrol, thymol and thymoquinone. Understanding the regulation of γ-terpinene biosynthesis, a precursor for other monoterpenes, could facilitate upscaling of these metabolites in M. citriodora. Therefore, the present study aimed to unravel and characterize the terpene synthase (TPS) involved in γ-terpinene biosynthesis. Homology searches revealed 33 TPS members in the transcriptome assembly of M. citriodora. Based on the correlation of expression patterns and phytochemical profile, McTPS22 emerged as the putative TPS for γ-terpinene biosynthesis. Molecular docking suggested geranyl diphosphate (GPP) as a potential substrate for McTPS22. Heterologous expression in Escherichia coli and Nicotiana benthamiana confirmed the role of McTPS22 in γ-terpinene biosynthesis. Both in-silico prediction and confocal microscopy indicated plastidial localization of the McTPS22. Gene co-expression network analysis revealed 507 genes interacting with McTPS22, including 80 transcription factors (TFs). Of these, 46 TFs had binding sites in the McTPS22 promoter, and 36 showed significant correlations with γ-terpinene accumulation, suggesting they may be potential regulators. Promoter analysis indicated regulation by phytohormones and abiotic factors, confirmed by phytohormone elicitation and QRT-PCR. The histochemical GUS staining suggested that McTPS22 is primarily active in the glandular trichomes of M. citriodora. The present work provides insights into the molecular regulation of biosynthesis of γ-terpinene in M. citriodora.

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.

From Noise to Knowledge: Diffusion Probabilistic Model-Based Neural Inference of Gene Regulatory Networks

Understanding gene regulatory networks (GRNs) is crucial for elucidating cellular mechanisms and advancing therapeutic interventions. Original methods for GRN inference from bulk expression data often struggled with the high dimensionality and inherent noise in the data. Here we introduce RegDiffusion, a new class of Denoising Diffusion Probabilistic Models focusing on the regulatory effects among feature variables. RegDiffusion introduces Gaussian noise to the input data following a diffusion schedule and uses a neural network with a parameterized adjacency matrix to predict the added noise. We show that using this process, GRNs can be learned effectively with a surprisingly simple model architecture. In our benchmark experiments, RegDiffusion shows superior performance compared to several baseline methods in multiple datasets. We also demonstrate that RegDiffusion can infer biologically meaningful regulatory networks from real-world single-cell data sets with over 15,000 genes in under 5 minutes. This work not only introduces a fresh perspective on GRN inference but also highlights the promising capacity of diffusion-based models in the area of single-cell analysis. The RegDiffusion software package and experiment data are available at https://github.com/TuftsBCB/RegDiffusion.

k- Strong Inference Algorithm: A Hybrid Information Theory Based Gene Network Inference Algorithm

Gene networks allow researchers to understand the underlying mechanisms between diseases and genes while reducing the need for wet lab experiments. Numerous gene network inference (GNI) algorithms have been presented in the literature to infer accurate gene networks. We proposed a hybrid GNI algorithm, k-Strong Inference Algorithm (ksia), to infer more reliable and robust gene networks from omics datasets. To increase reliability, ksia integrates Pearson correlation coefficient (PCC) and Spearman rank correlation coefficient (SCC) scores to determine mutual information scores between molecules to increase diversity of relation predictions. To infer a more robust gene network, ksia applies three different elimination steps to remove redundant and spurious relations between genes. The performance of ksia was evaluated on microbe microarrays database in the overlap analysis with other GNI algorithms, namely ARACNE, C3NET, CLR, and MRNET. Ksia inferred less number of relations due to its strict elimination steps. However, ksia generally performed better on Escherichia coli (E.coli) and Saccharomyces cerevisiae (yeast) gene expression datasets due to F- measure and precision values. The integration of association estimator scores and three elimination stages slightly increases the performance of ksia based gene networks. Users can access ksia R package and user manual of package via https://github.com/ozgurcingiz/ksia .

Predicting bacterial fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis disease, the greatest source of global mortality by a bacterial pathogen. Mtb adapts and responds to diverse stresses such as antibiotics by inducing transcriptional stress-response regulatory programs. Understanding how and when these mycobacterial regulatory programs are activated could enable novel treatment strategies for potentiating the efficacy of new and existing drugs. Here we sought to define and analyze Mtb regulatory programs that modulate bacterial fitness. We assembled a large Mtb RNA expression compendium and applied these to infer a comprehensive Mtb transcriptional regulatory network and compute condition-specific transcription factor activity profiles. We utilized transcriptomic and functional genomics data to train an interpretable machine learning model that can predict Mtb fitness from transcription factor activity profiles. We demonstrated that this transcription factor activity-based model can successfully predict Mtb growth arrest and growth resumption under hypoxia and reaeration using only RNA-seq expression data as a starting point. These integrative network modeling and machine learning analyses thus enable the prediction of mycobacterial fitness under different environmental and genetic contexts. We envision these models can potentially inform the future design of prognostic assays and therapeutic intervention that can cripple Mtb growth and survival to cure tuberculosis disease.

StableMate: a statistical method to select stable predictors in omics data

Identifying statistical associations between biological variables is crucial to understanding molecular mechanisms. Most association studies are based on correlation or linear regression analyses, but the identified associations often lack reproducibility and interpretability due to the complexity and variability of omics datasets, making it difficult to translate associations into meaningful biological hypotheses. We developed StableMate, a regression framework, to address these challenges through a process of variable selection across heterogeneous datasets. Given datasets from different environments, such as experimental batches, StableMate selects environment-agnostic (stable) and environment-specific predictors in predicting the response of interest. Stable predictors represent robust functional dependencies with the response, and can be used to build regression models that make generalizable predictions in unseen environments. We applied StableMate to (i) RNA sequencing data of breast cancer to discover genes that consistently predict estrogen receptor expression across disease status; (ii) metagenomics data to identify microbial signatures that show persistent association with colon cancer across study cohorts; and (iii) single-cell RNA sequencing data of glioblastoma to discern signature genes associated with the development of pro-tumour microglia regardless of cell location. Our case studies demonstrate that StableMate is adaptable to regression and classification analyses and achieves comprehensive characterization of biological systems for different omics data types.

Systems genetic analysis of lignin biosynthesis in Populus tremula

The genetic control underlying natural variation in lignin content and composition in trees is not fully understood. We performed a systems genetic analysis to uncover the genetic regulation of lignin biosynthesis in a natural 'SwAsp' population of aspen (Populus tremula) trees. We analyzed gene expression by RNA sequencing (RNA-seq) in differentiating xylem tissues, and lignin content and composition using Pyrolysis-GC-MS in mature wood of 268 trees from 99 genotypes. Abundant variation was observed for lignin content and composition, and genome-wide association study identified proteins in the pentose phosphate pathway and arabinogalactan protein glycosylation among the top-ranked genes that are associated with these traits. Variation in gene expression and the associated genetic polymorphism was revealed through the identification of 312 705 local and 292 003 distant expression quantitative trait loci (eQTL). A co-expression network analysis suggested modularization of lignin biosynthesis and novel functions for the lignin-biosynthetic CINNAMYL ALCOHOL DEHYDROGENASE 2 and CAFFEOYL-CoA O-METHYLTRANSFERASE 3. PHENYLALANINE AMMONIA LYASE 3 was co-expressed with HOMEOBOX PROTEIN 5 (HB5), and the role of HB5 in stimulating lignification was demonstrated in transgenic trees. The systems genetic approach allowed linking natural variation in lignin biosynthesis to trees´ responses to external cues such as mechanical stimulus and nutrient availability.

A resource of identified and annotated lincRNAs expressed during somatic embryogenesis development in Norway spruce

Long non-coding RNAs (lncRNAs) have emerged as important regulators of many biological processes, although their regulatory roles remain poorly characterized in woody plants, especially in gymnosperms. A major challenge of working with lncRNAs is to assign functional annotations, since they have a low coding potential and low cross-species conservation. We utilised an existing RNA-Sequencing resource and performed short RNA sequencing of somatic embryogenesis developmental stages in Norway spruce (Picea abies L. Karst). We implemented a pipeline to identify lncRNAs located within the intergenic space (lincRNAs) and generated a co-expression network including protein coding, lincRNA and miRNA genes. To assign putative functional annotation, we employed a guilt-by-association approach using the co-expression network and integrated these results with annotation assigned using semantic similarity and co-expression. Moreover, we evaluated the relationship between lincRNAs and miRNAs, and identified which lincRNAs are conserved in other species. We identified lincRNAs with clear evidence of differential expression during somatic embryogenesis and used network connectivity to identify those with the greatest regulatory potential. This work provides the most comprehensive view of lincRNAs in Norway spruce and is the first study to perform global identification of lincRNAs during somatic embryogenesis in conifers. The data have been integrated into the expression visualisation tools at the PlantGenIE.org web resource to enable easy access to the community. This will facilitate the use of the data to address novel questions about the role of lincRNAs in the regulation of embryogenesis and facilitate future comparative genomics studies.

Multi-objective context-guided consensus of a massive array of techniques for the inference of Gene Regulatory Networks

BACKGROUND AND OBJECTIVE

Gene Regulatory Network (GRN) inference is a fundamental task in biology and medicine, as it enables a deeper understanding of the intricate mechanisms of gene expression present in organisms. This bioinformatics problem has been addressed in the literature through multiple computational approaches. Techniques developed for inferring from expression data have employed Bayesian networks, ordinary differential equations (ODEs), machine learning, information theory measures and neural networks, among others. The diversity of implementations and their respective customization have led to the emergence of many tools and multiple specialized domains derived from them, understood as subsets of networks with specific characteristics that are challenging to detect a priori. This specialization has introduced significant uncertainty when choosing the most appropriate technique for a particular dataset. This proposal, named MO-GENECI, builds upon the basic idea of the previous proposal GENECI and optimizes consensus among different inference techniques, through a carefully refined multi-objective evolutionary algorithm guided by various objective functions, linked to the biological context at hand.

METHODS

MO-GENECI has been tested on an extensive and diverse academic benchmark of 106 gene regulatory networks from multiple sources and sizes. The evaluation of MO-GENECI compared its performance to individual techniques using key metrics (AUROC and AUPR) for gene regulatory network inference. Friedman's statistical ranking provided an ordered classification, followed by non-parametric Holm tests to determine statistical significance.

RESULTS

MO-GENECI's Pareto front approximation facilitates easy selection of an appropriate solution based on generic input data characteristics. The best solution consistently emerged as the winner in all statistical tests, and in many cases, the median precision solution showed no statistically significant difference compared to the winner.

CONCLUSIONS

MO-GENECI has not only demonstrated achieving more accurate results than individual techniques, but has also overcome the uncertainty associated with the initial choice due to its flexibility and adaptability. It is shown intelligently to select the most suitable techniques for each case. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/MO-GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a Python package available at PyPI: https://pypi.org/project/geneci/.

Transcriptome data are insufficient to control false discoveries in regulatory network inference

Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Availability and implementation

Not applicable.

SLIVER: Unveiling large scale gene regulatory networks of single-cell transcriptomic data through causal structure learning and modules aggregation

Prevalent Gene Regulatory Network (GRN) construction methods rely on generalized correlation analysis. However, in biological systems, regulation is essentially a causal relationship that cannot be adequately captured solely through correlation. Therefore, it is more reasonable to infer GRNs from a causal perspective. Existing causal discovery algorithms typically rely on Directed Acyclic Graphs (DAGs) to model causal relationships, but it often requires traversing the entire network, which result in computational demands skyrocketing as the number of nodes grows and make causal discovery algorithms only suitable for small networks with one or two hundred nodes or fewer. In this study, we propose the SLIVER (cauSaL dIscovery Via dimEnsionality Reduction) algorithm which integrates causal structural equation model and graph decomposition. SLIVER introduces a set of factor nodes, serving as abstractions of different functional modules to integrate the regulatory relationships between genes based on their respective functions or pathways, thus reducing the GRN to the product of two low-dimensional matrices. Subsequently, we employ the structural causal model (SCM) to learn the GRN within the gene node space, enforce the DAG constraint in the low-dimensional space, and guide each factor to aggregate various functions through cosine similarity. We evaluate the performance of the SLIVER algorithm on 12 real single cell transcriptomic datasets, and demonstrate it outperforms other 12 widely used methods both in GRN inference performance and computational resource usage. The analysis of the gene information integrated by factor nodes also demonstrate the biological explanation of factor nodes in GRNs. We apply it to scRNA-seq of Type 2 diabetes mellitus to capture the transcriptional regulatory structural changes of β cells under high insulin demand.

A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data

Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes. In this review, we highlight the key components for successful (e)GRN inference from (sc)RNA-seq and (sc)ATAC-seq data exemplified by state-of-the-art methods as well as open challenges and future developments. Moreover, we address preprocessing strategies, metacell generation and computational omics pairing, transcription factor binding site detection, and linear and three-dimensional approaches to identify chromatin interactions as well as dynamic and causal eGRN inference. We believe that the integration of transcriptomics together with epigenomics data at a single-cell level is the new standard for mechanistic network inference, and that it can be further advanced with integrating additional omics layers and spatiotemporal data, as well as with shifting the focus towards more quantitative and causal modeling strategies.

Refining computational inference of gene regulatory networks: integrating knockout data within a multi-task framework

Constructing accurate gene regulatory network s (GRNs), which reflect the dynamic governing process between genes, is critical to understanding the diverse cellular process and unveiling the complexities in biological systems. With the development of computer sciences, computational-based approaches have been applied to the GRNs inference task. However, current methodologies face challenges in effectively utilizing existing topological information and prior knowledge of gene regulatory relationships, hindering the comprehensive understanding and accurate reconstruction of GRNs. In response, we propose a novel graph neural network (GNN)-based Multi-Task Learning framework for GRN reconstruction, namely MTLGRN. Specifically, we first encode the gene promoter sequences and the gene biological features and concatenate the corresponding feature representations. Then, we construct a multi-task learning framework including GRN reconstruction, Gene knockout predict, and Gene expression matrix reconstruction. With joint training, MTLGRN can optimize the gene latent representations by integrating gene knockout information, promoter characteristics, and other biological attributes. Extensive experimental results demonstrate superior performance compared with state-of-the-art baselines on the GRN reconstruction task, efficiently leveraging biological knowledge and comprehensively understanding the gene regulatory relationships. MTLGRN also pioneered attempts to simulate gene knockouts on bulk data by incorporating gene knockout information.

Targeted co-expression networks for the study of traits

Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used approach for the generation of gene co-expression networks. However, networks generated with this tool usually create large modules with a large set of functional annotations hard to decipher. We have developed TGCN, a new method to create Targeted Gene Co-expression Networks. This method identifies the transcripts that best predict the trait of interest based on gene expression using a refinement of the LASSO regression. Then, it builds the co-expression modules around those transcripts. Algorithm properties were characterized using the expression of 13 brain regions from the Genotype-Tissue Expression project. When comparing our method with WGCNA, TGCN networks lead to more precise modules that have more specific and yet rich biological meaning. Then, we illustrate its applicability by creating an APP-TGCN on The Religious Orders Study and Memory and Aging Project dataset, aiming to identify the molecular pathways specifically associated with APP role in Alzheimer's disease. Main biological findings were further validated in two independent cohorts. In conclusion, we provide a new framework that serves to create targeted networks that are smaller, biologically relevant and useful in high throughput hypothesis driven research. The TGCN R package is available on Github: https://github.com/aliciagp/TGCN .

EIEPCF: accurate inference of functional gene regulatory networks by eliminating indirect effects from confounding factors

Reconstructing functional gene regulatory networks (GRNs) is a primary prerequisite for understanding pathogenic mechanisms and curing diseases in animals, and it also provides an important foundation for cultivating vegetable and fruit varieties that are resistant to diseases and corrosion in plants. Many computational methods have been developed to infer GRNs, but most of the regulatory relationships between genes obtained by these methods are biased. Eliminating indirect effects in GRNs remains a significant challenge for researchers. In this work, we propose a novel approach for inferring functional GRNs, named EIEPCF (eliminating indirect effects produced by confounding factors), which eliminates indirect effects caused by confounding factors. This method eliminates the influence of confounding factors on regulatory factors and target genes by measuring the similarity between their residuals. The validation results of the EIEPCF method on simulation studies, the gold-standard networks provided by the DREAM3 Challenge and the real gene networks of Escherichia coli demonstrate that it achieves significantly higher accuracy compared to other popular computational methods for inferring GRNs. As a case study, we utilized the EIEPCF method to reconstruct the cold-resistant specific GRN from gene expression data of cold-resistant in Arabidopsis thaliana. The source code and data are available at https://github.com/zhanglab-wbgcas/EIEPCF.

Exploring gene regulation and biological processes in insects: Insights from omics data using gene regulatory network models

Gene regulatory network (GRN) comprises complicated yet intertwined gene-regulator relationships. Understanding the GRN dynamics will unravel the complexity behind the observed gene expressions. Insect gene regulation is often complicated due to their complex life cycles and diverse ecological adaptations. The main interest of this review is to have an update on the current mathematical modelling methods of GRNs to explain insect science. Several popular GRN architecture models are discussed, together with examples of applications in insect science. In the last part of this review, each model is compared from different aspects, including network scalability, computation complexity, robustness to noise and biological relevancy.

Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks

MOTIVATION

Gene regulatory networks (GRNs) are vital tools for delineating regulatory relationships between transcription factors and their target genes. The boom in computational biology and various biotechnologies has made inferring GRNs from multi-omics data a hot topic. However, when networks are constructed from gene expression data, they often suffer from false-positive problem due to the transitive effects of correlation. The presence of spurious noise edges obscures the real gene interactions, which makes downstream analyses, such as detecting gene function modules and predicting disease-related genes, difficult and inefficient. Therefore, there is an urgent and compelling need to develop network denoising methods to improve the accuracy of GRN inference.

RESULTS

In this study, we proposed a novel network denoising method named REverse Network Diffusion On Random walks (RENDOR). RENDOR is designed to enhance the accuracy of GRNs afflicted by indirect effects. RENDOR takes noisy networks as input, models higher-order indirect interactions between genes by transitive closure, eliminates false-positive effects using the inverse network diffusion method, and produces refined networks as output. We conducted a comparative assessment of GRN inference accuracy before and after denoising on simulated networks and real GRNs. Our results emphasized that the network derived from RENDOR more accurately and effectively captures gene interactions. This study demonstrates the significance of removing network indirect noise and highlights the effectiveness of the proposed method in enhancing the signal-to-noise ratio of noisy networks.

AVAILABILITY AND IMPLEMENTATION

The R package RENDOR is provided at https://github.com/Wu-Lab/RENDOR and other source code and data are available at https://github.com/Wu-Lab/RENDOR-reproduce.

SFINN: inferring gene regulatory network from single-cell and spatial transcriptomic data with shared factor neighborhood and integrated neural network

MOTIVATION

The rise of single-cell RNA sequencing (scRNA-seq) technology presents new opportunities for constructing detailed cell type-specific gene regulatory networks (GRNs) to study cell heterogeneity. However, challenges caused by noises, technical errors, and dropout phenomena in scRNA-seq data pose significant obstacles to GRN inference, making the design of accurate GRN inference algorithms still essential. The recent growth of both single-cell and spatial transcriptomic sequencing data enables the development of supervised deep learning methods to infer GRNs on these diverse single-cell datasets.

RESULTS

In this study, we introduce a novel deep learning framework based on shared factor neighborhood and integrated neural network (SFINN) for inferring potential interactions and causalities between transcription factors and target genes from single-cell and spatial transcriptomic data. SFINN utilizes shared factor neighborhood to construct cellular neighborhood network based on gene expression data and additionally integrates cellular network generated from spatial location information. Subsequently, the cell adjacency matrix and gene pair expression are fed into an integrated neural network framework consisting of a graph convolutional neural network and a fully-connected neural network to determine whether the genes interact. Performance evaluation in the tasks of gene interaction and causality prediction against the existing GRN reconstruction algorithms demonstrates the usability and competitiveness of SFINN across different kinds of data. SFINN can be applied to infer GRNs from conventional single-cell sequencing data and spatial transcriptomic data.

AVAILABILITY AND IMPLEMENTATION

SFINN can be accessed at GitHub: https://github.com/JGuan-lab/SFINN.

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights

Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.

Molecular cascade reveals sequential milestones underlying hippocampal neural stem cell development into an adult state

Quiescent adult neural stem cells (NSCs) in the mammalian brain arise from proliferating NSCs during development. Beyond acquisition of quiescence, an adult NSC hallmark, little is known about the process, milestones, and mechanisms underlying the transition of developmental NSCs to an adult NSC state. Here, we performed targeted single-cell RNA-seq analysis to reveal the molecular cascade underlying NSC development in the early postnatal mouse dentate gyrus. We identified two sequential steps, first a transition to quiescence followed by further maturation, each of which involved distinct changes in metabolic gene expression. Direct metabolic analysis uncovered distinct milestones, including an autophagy burst before NSC quiescence acquisition and cellular reactive oxygen species level elevation along NSC maturation. Functionally, autophagy is important for the NSC transition to quiescence during early postnatal development. Together, our study reveals a multi-step process with defined milestones underlying establishment of the adult NSC pool in the mammalian brain.

Comparative transcriptomic analysis of the nodulation-competent zone and inference of transcription regulatory network in silicon applied Glycine max [L.]-Merr. Roots

KEY MESSAGE

The study unveils Si's regulatory influence by regulating DEGs, TFs, and TRs. Further bHLH subfamily and auxin transporter pathway elucidates the mechanisms enhancing root development and nodulation. Soybean is a globally important crop serving as a primary source of vegetable protein for millions of individuals. The roots of these plants harbour essential nitrogen fixing structures called nodules. This study investigates the multifaceted impact of silicon (Si) application on soybean, with a focus on root development, and nodulation employing comprehensive transcriptomic analyses and gene regulatory network. RNA sequence analysis was utilised to examine the change in gene expression and identify the noteworthy differentially expressed genes (DEGs) linked to the enhancement of soybean root nodulation and root development. A set of 316 genes involved in diverse biological and molecular pathways are identified, with emphasis on transcription factors (TFs) and transcriptional regulators (TRs). The study uncovers TF and TR genes, categorized into 68 distinct families, highlighting the intricate regulatory landscape influenced by Si in soybeans. Upregulated most important bHLH subfamily and the involvement of the auxin transporter pathway underscore the molecular mechanisms contributing to enhanced root development and nodulation. The study bridges insights from other research, reinforcing Si's impact on stress-response pathways and phenylpropanoid biosynthesis crucial for nodulation. The study reveals significant alterations in gene expression patterns associated with cellular component functions, root development, and nodulation in response to Si.

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.

Operant Training for Highly Palatable Food Alters Translating Messenger RNA in Nucleus Accumbens D2 Neurons and Reveals a Modulatory Role of Ncdn

BACKGROUND

Highly palatable food triggers behavioral responses including strong motivation. These effects involve the reward system and dopamine neurons, which modulate neurons in the nucleus accumbens (NAc). The molecular mechanisms underlying the long-lasting effects of highly palatable food on feeding behavior are poorly understood.

METHODS

We studied the effects of 2-week operant conditioning of mice with standard or isocaloric highly palatable food. We investigated the behavioral responses and dendritic spine modifications in the NAc. We compared the translating messenger RNA in NAc neurons identified by the type of dopamine receptors they express, depending on the kind of food and training. We tested the consequences of invalidation of an abundant downregulated gene, Ncdn.

RESULTS

Operant conditioning for highly palatable food increased motivation for food even in well-fed mice. In wild-type mice, free choice between regular and highly palatable food increased weight compared with access to regular food only. Highly palatable food increased spine density in the NAc. In animals trained for highly palatable food, translating messenger RNAs were modified in NAc neurons expressing dopamine D2 receptors, mostly corresponding to striatal projection neurons, but not in neurons expressing D1 receptors. Knockout of Ncdn, an abundant downregulated gene, opposed the conditioning-induced changes in satiety-sensitive feeding behavior and apparent motivation for highly palatable food, suggesting that downregulation may be a compensatory mechanism.

CONCLUSIONS

Our results emphasize the importance of messenger RNA alterations in D2 striatal projection neurons in the NAc in the behavioral consequences of highly palatable food conditioning and suggest a modulatory contribution of Ncdn downregulation.

Network depth affects inference of gene sets from bacterial transcriptomes using denoising autoencoders

Summary

The increasing number of publicly available bacterial gene expression data sets provides an unprecedented resource for the study of gene regulation in diverse conditions, but emphasizes the need for self-supervised methods for the automated generation of new hypotheses. One approach for inferring coordinated regulation from bacterial expression data is through neural networks known as denoising autoencoders (DAEs) which encode large datasets in a reduced bottleneck layer. We have generalized this application of DAEs to include deep networks and explore the effects of network architecture on gene set inference using deep learning. We developed a DAE-based pipeline to extract gene sets from transcriptomic data in Escherichia coli, validate our method by comparing inferred gene sets with known pathways, and have used this pipeline to explore how the choice of network architecture impacts gene set recovery. We find that increasing network depth leads the DAEs to explain gene expression in terms of fewer, more concisely defined gene sets, and that adjusting the width results in a tradeoff between generalizability and biological inference. Finally, leveraging our understanding of the impact of DAE architecture, we apply our pipeline to an independent uropathogenic E.coli dataset to identify genes uniquely induced during human colonization.

Availability and implementation

https://github.com/BarquistLab/DAE_architecture_exploration.

Bioactive exometabolites drive maintenance competition in simple bacterial communities

During prolonged resource limitation, bacterial cells can persist in metabolically active states of non-growth. These maintenance periods, such as those experienced in stationary phase, can include upregulation of secondary metabolism and release of exometabolites into the local environment. As resource limitation is common in many environmental microbial habitats, we hypothesized that neighboring bacterial populations employ exometabolites to compete or cooperate during maintenance and that these exometabolite-facilitated interactions can drive community outcomes. Here, we evaluated the consequences of exometabolite interactions over the stationary phase among three environmental strains: Burkholderia thailandensis E264, Chromobacterium subtsugae ATCC 31532, and Pseudomonas syringae pv. tomato DC3000. We assembled them into synthetic communities that only permitted chemical interactions. We compared the responses (transcripts) and outputs (exometabolites) of each member with and without neighbors. We found that transcriptional dynamics were changed with different neighbors and that some of these changes were coordinated between members. The dominant competitor B. thailandensis consistently upregulated biosynthetic gene clusters to produce bioactive exometabolites for both exploitative and interference competition. These results demonstrate that competition strategies during maintenance can contribute to community-level outcomes. It also suggests that the traditional concept of defining competitiveness by growth outcomes may be narrow and that maintenance competition could be an additional or alternative measure.

IMPORTANCE

Free-living microbial populations often persist and engage in environments that offer few or inconsistently available resources. Thus, it is important to investigate microbial interactions in this common and ecologically relevant condition of non-growth. This work investigates the consequences of resource limitation for community metabolic output and for population interactions in simple synthetic bacterial communities. Despite non-growth, we observed active, exometabolite-mediated competition among the bacterial populations. Many of these interactions and produced exometabolites were dependent on the community composition but we also observed that one dominant competitor consistently produced interfering exometabolites regardless. These results are important for predicting and understanding microbial interactions in resource-limited environments.

Automatic design of gene regulatory mechanisms for spatial pattern formation

Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target spatial pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.

Decoding chromatin states by proteomic profiling of nucleosome readers

DNA and histone modifications combine into characteristic patterns that demarcate functional regions of the genome1,2. While many 'readers' of individual modifications have been described3-5, how chromatin states comprising composite modification signatures, histone variants and internucleosomal linker DNA are interpreted is a major open question. Here we use a multidimensional proteomics strategy to systematically examine the interaction of around 2,000 nuclear proteins with over 80 modified dinucleosomes representing promoter, enhancer and heterochromatin states. By deconvoluting complex nucleosome-binding profiles into networks of co-regulated proteins and distinct nucleosomal features driving protein recruitment or exclusion, we show comprehensively how chromatin states are decoded by chromatin readers. We find highly distinctive binding responses to different features, many factors that recognize multiple features, and that nucleosomal modifications and linker DNA operate largely independently in regulating protein binding to chromatin. Our online resource, the Modification Atlas of Regulation by Chromatin States (MARCS), provides in-depth analysis tools to engage with our results and advance the discovery of fundamental principles of genome regulation by chromatin states.

Integrated Metabolome and Transcriptome Analysis of Gibberellins Mediated the Circadian Rhythm of Leaf Elongation by Regulating Lignin Synthesis in Maize

Plant growth exhibits rhythmic characteristics, and gibberellins (GAs) are involved in regulating cell growth, but it is still unclear how GAs crosstalk with circadian rhythm to regulate cell elongation. The study analyzed growth characteristics of wild-type (WT), zmga3ox and zmga3ox with GA3 seedlings. We integrated metabolomes and transcriptomes to study the interaction between GAs and circadian rhythm in mediating leaf elongation. The rates of leaf growth were higher in WT than zmga3ox, and zmga3ox cell length was shorter when proliferated in darkness than light, and GA3 restored zmga3ox leaf growth. The differentially expressed genes (DEGs) between WT and zmga3ox were mainly enriched in hormone signaling and cell wall synthesis, while DEGs in zmga3ox were restored to WT by GA3. Moreover, the number of circadian DEGs that reached the peak expression in darkness was more than light, and the upregulated circadian DEGs were mainly enriched in cell wall synthesis. The differentially accumulated metabolites (DAMs) were mainly attributed to flavonoids and phenolic acid. Twenty-two DAMs showed rhythmic accumulation, especially enriched in lignin synthesis. The circadian DEGs ZmMYBr41/87 and ZmHB34/70 were identified as regulators of ZmHCT8 and ZmBM1, which were enzymes in lignin synthesis. Furthermore, GAs regulated ZmMYBr41/87 and ZmHB34/70 to modulate lignin biosynthesis for mediating leaf rhythmic growth.