Construction Of Gene Regulatory Networks Research Articles

Cell Type- and Age-Specific Expression of lncRNAs across Kidney Cell Types.

Accumulated evidence demonstrates that long non-coding RNAs (lncRNAs) regulate cell differentiation and homeostasis, influencing kidney aging and disease. Despite their versatility, the function of lncRNA remains poorly understood due to the lack of a reference map of lncRNA transcriptome in various cell types. In this study, we employed a targeted single-cell RNA sequencing (scRNA-seq) method to enrich and characterize lncRNAs in individual cells. We applied this method to various mouse tissues, including normal and aged kidneys. Through tissue-specific clustering analysis, we identified cell type-specific lncRNAs that showed a high correlation with known cell-type marker genes. Furthermore, we constructed gene regulatory networks (GRNs) to explore the functional roles of differentially expressed lncRNAs in each cell type. In the kidney, we observed dynamic expression changes of lncRNAs during aging, with specific changes in glomerular cells. These cell type- and age-specific expression patterns of lncRNAs provide insights into their potential roles in regulating cellular processes, such as immune response and energy metabolism, during kidney aging. Our study sheds light on the comprehensive landscape of lncRNA expression and function and provides a valuable resource for future analysis of lncRNAs (https://gist-fgl.github.io/sc-lncrna-atlas/).

Gene regulatory network analysis identifies MYL1, MDH2, GLS, and TRIM28 as the principal proteins in the response of mesenchymal stem cells to Mg2+ ions

Magnesium (Mg)-based implants have emerged as a promising alternative for orthopedic applications, owing to their bioactive properties and biodegradability. As the implants degrade, Mg2+ ions are released, influencing all surrounding cell types, especially mesenchymal stem cells (MSCs). MSCs are vital for bone tissue regeneration, therefore, it is essential to understand their molecular response to Mg2+ ions in order to maximize the potential of Mg-based biomaterials. In this study, we conducted a gene regulatory network (GRN) analysis to examine the molecular responses of MSCs to Mg2+ ions. We used time-series proteomics data collected at 11 time points across a 21-day period for the GRN construction. We studied the impact of Mg2+ ions on the resulting networks and identified the key proteins and protein interactions affected by the application of Mg2+ ions. Our analysis highlights MYL1, MDH2, GLS, and TRIM28 as the primary targets of Mg2+ ions in the response of MSCs during 1–21 days phase. Our results also identify MDH2-MYL1, MDH2-RPS26, TRIM28-AK1, TRIM28-SOD2, and GLS-AK1 as the critical protein relationships affected by Mg2+ ions. By offering a comprehensive understanding of the regulatory role of Mg2+ ions on MSCs, our study contributes valuable insights into the molecular response of MSCs to Mg-based materials, thereby facilitating the development of innovative therapeutic strategies for orthopedic applications.

Constructing eRNA-mediated gene regulatory networks to explore the genetic basis of muscle and fat-relevant traits in pigs

BackgroundEnhancer RNAs (eRNAs) play a crucial role in transcriptional regulation. While significant progress has been made in understanding epigenetic regulation mediated by eRNAs, research on the construction of eRNA-mediated gene regulatory networks (eGRN) and the identification of critical network components that influence complex traits is lacking.ResultsHere, employing the pig as a model, we conducted a comprehensive study using H3K27ac histone ChIP-seq and RNA-seq data to construct eRNA expression profiles from multiple tissues of two distinct pig breeds, namely Enshi Black (ES) and Duroc. In addition to revealing the regulatory landscape of eRNAs at the tissue level, we developed an innovative network construction and refinement method by integrating RNA-seq, ChIP-seq, genome-wide association study (GWAS) signals and enhancer-modulating effects of single nucleotide polymorphisms (SNPs) measured by self-transcribing active regulatory region sequencing (STARR-seq) experiments. Using this approach, we unraveled eGRN that significantly influence the growth and development of muscle and fat tissues, and identified several novel genes that affect adipocyte differentiation in a cell line model.ConclusionsOur work not only provides novel insights into the genetic basis of economic pig traits, but also offers a generalizable approach to elucidate the eRNA-mediated transcriptional regulation underlying a wide spectrum of complex traits for diverse organisms.

ICARUS v3, a massively scalable web server for single-cell RNA-seq analysis of millions of cells.

In recent years, improvements in throughput of single-cell RNA-seq have resulted in a significant increase in the number of cells profiled. The generation of single-cell RNA-seq datasets comprising >1 million cells is becoming increasingly common, giving rise to demands for more efficient computational workflows. We present an update to our single-cell RNA-seq analysis web server application, ICARUS (available at https://launch.icarus-scrnaseq.cloud.edu.au) that allows effective analysis of large-scale single-cell RNA-seq datasets. ICARUS v3 utilizes the geometric cell sketching method to subsample cells from the overall dataset for dimensionality reduction and clustering that can be then projected to the large dataset. We then extend this functionality to select a representative subset of cells for downstream data analysis applications including differential expression analysis, gene co-expression network construction, gene regulatory network construction, trajectory analysis, cell-cell communication inference, and cell cluster associations to GWAS traits. We demonstrate analysis of single-cell RNA-seq datasets using ICARUS v3 of 1.3 million cells completed within the hour. ICARUS is available at https://launch.icarus-scrnaseq.cloud.edu.au.

Single-cell RNA-seq data analysis reveals functionally relevant biomarkers of early brain development and their regulatory footprints in human embryonic stem cells (hESCs).

The complicated process of neuronal development is initiated early in life, with the genetic mechanisms governing this process yet to be fully elucidated. Single-cell RNA sequencing (scRNA-seq) is a potent instrument for pinpointing biomarkers that exhibit differential expression across various cell types and developmental stages. By employing scRNA-seq on human embryonic stem cells, we aim to identify differentially expressed genes (DEGs) crucial for early-stage neuronal development. Our focus extends beyond simply identifying DEGs. We strive to investigate the functional roles of these genes through enrichment analysis and construct gene regulatory networks to understand their interactions. Ultimately, this comprehensive approach aspires to illuminate the molecular mechanisms and transcriptional dynamics governing early human brain development. By uncovering potential links between these DEGs and intelligence, mental disorders, and neurodevelopmental disorders, we hope to shed light on human neurological health and disease. In this study, we have used scRNA-seq to identify DEGs involved in early-stage neuronal development in hESCs. The scRNA-seq data, collected on days 26 (D26) and 54 (D54), of the in vitro differentiation of hESCs to neurons were analyzed. Our analysis identified 539 DEGs between D26 and D54. Functional enrichment of those DEG biomarkers indicated that the up-regulated DEGs participated in neurogenesis, while the down-regulated DEGs were linked to synapse regulation. The Reactome pathway analysis revealed that down-regulated DEGs were involved in the interactions between proteins located in synapse pathways. We also discovered interactions between DEGs and miRNA, transcriptional factors (TFs) and DEGs, and between TF and miRNA. Our study identified 20 significant transcription factors, shedding light on early brain development genetics. The identified DEGs and gene regulatory networks are valuable resources for future research into human brain development and neurodevelopmental disorders.

Abstract 4947: Uncovering the molecular basis for clinically relevant sphingolipidomic subtypes in acute myeloid leukemia

Abstract Acute myeloid leukemia (AML) has been extensively studied at the genomic level, resulting in various genomic classifications. Despite the progress, three key challenges remain: 1) a significant proportion of patients lack identified genomic features, 2) a discrepancy exists between genomic risk classification and clinical outcomes, and 3) many mutated targets remain non-druggable. There is an urgent need for an enhanced AML risk classification that goes beyond genomic aberrations to guide effective therapeutics. Recent research, including our own, has increasingly linked AML pathogenesis and therapeutic resistance with dysfunctional sphingolipid metabolism, a family of bioactive molecules crucial for cellular functions. In a recent manuscript, we identified two robust sphingolipidomic clusters in AML, characterized by reciprocal abundances of hexosylceramide (Hex) and sphingomyelin (SM) species (HexloSMhi, HexhiSMlo). These clusters correlate with latent transcriptional states and stratify patient groups in multiple independent datasets such as TCGA, and BeatAML, with the HexloSMhi cluster representing a high-risk subgroup associated with poor clinical outcomes. These findings underscore the role of sphingolipids in refining AML risk assessment and creating avenues for discovery and therapeutic intervention. However, the signaling states driving these sphingolipidomic clusters and the mechanisms elevating risk in the HexloSMhi cluster require further investigation. To identify regulatory genes for the AML subtypes, we constructed gene regulatory networks using differentially expressed genes. Focusing on the high-risk subtype, we identified highly connected genes ranked by connectivity, including KDM1A, GSK3β, LCK, and STAT5A. We found that higher STAT5A abundances in the HexloSMhi subtype, particularly correlated with pro-survival Bcl-xL proteins. Furthermore, we identified STAT5 protein abundance as a key variable associated with drug sensitivity from pharmacological screening with sphingolipid pathway inhibitors and AML therapeutics 30 AML cell lines and patient samples. This is encouraging as STAT5 is druggable as evidenced in the NIH-Pharos druggable genome database, and several small-molecule inhibitors are currently in clinical trials. Analysis of gene expression data suggest that STAT5 inhibition, through genetic or pharmacological means, downregulates cell cycle-related genes and significantly alters sphingolipid gene expression. Recent work by others indicates that sphingolipid-catalyzing enzymes regulate STAT5 activity in leukemias, with ceramides acting as a second messenger in cells. Taken together, this suggests a possibility of a positive feedback loop between sphingolipid homeostasis and activity of STAT5; the details of this interaction require further investigation. Citation Format: B Bishal Paudel, Su-Fern Tan, Johnson Ung, David Claxton, David J. Feith, Thomas P. Loughran, Kevin A. Janes. Uncovering the molecular basis for clinically relevant sphingolipidomic subtypes in acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4947.

Abstract 6217: Differential genome-wide gene regulatory networks for lung cancer

Abstract Background: Lung cancer remains the leading cause of cancer death worldwide due to the fact that most cases are diagnosed at distant stages, and treatment is less effective. During the past two decades, much effort has been devoted to understanding tumor biology and developing targeted therapeutics, which have significantly improved patient survival. With the increasingly available multi-omics data from healthy controls and lung cancer patients, we constructed gene regulatory networks for the two main subtypes of non-small-cell lung cancer (NSCLC), the most common type of lung cancer. In addition, differential networks were constructed by integrative analysis of data from lung cancer tissue and tissues from healthy lungs. Methods: Transcriptomics and genomic data were obtained from the patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tissue available in The Cancer Genome Atlas (TCGA) project, and both types of omics data of normal lung tissue were downloaded from The Genotype-Tissue Expression (GTEx) consortium. The networks for each subtype and the differential networks were constructed by utilizing our newly developed machine learning tools, two-stage penalized least square (2SPLS) approach, and analysis of variance of directed networks (NetANOVA), respectively. Results: For the top three identified subnetworks of LUAD, we identified some well-known genes associated with lung cancer, such as EGFR, IRF1, KRAS, ERBB2, PIK3CA, MYC, at bootstrap frequency >90%. Based on the top three subnetworks with a bootstrap frequency of 100%, results from the Ingenuity Pathway Analysis (IPA) indicated several significant pathways, primary immunodeficiency signaling and communication between innate and adaptive immune cells. GO enrichment analysis showed statistically significant biological processes, including immune response, extracellular matrix organization, and significant KEGG pathways such as Hematopoietic cell lineage. The differential networks between LUAD, LUSC, and healthy lungs showed the same regulations across all three groups, such as the regulation between EEF2 and RACK1, the perturbations between LUAD and healthy (two-way regulations between HIGD2A and ARL10), and LUSC and healthy (GRB14 regulates EIF3E) respectively. In addition, we identified the differences between LUAD and LUSC, such as bi-directional regulations between CLTB and ARL10. Conclusion: Although all groups share most regulations, we identified differential gene regulations between healthy lung tissue and lung cancer tissue and between LUAD and LUSC of NSCLC. Using multi-omics data generated from lung tissues, coupled with advanced machine learning methods, the causal relationships between genome-wide genes from the analysis will help us understand the molecular mechanism of lung cancer and facilitate the development of personalized treatment strategies. Citation Format: Min Zhang, Zhongli Jiang, Dabao Zhang. Differential genome-wide gene regulatory networks for lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6217.

Dissecting the molecular basis of spike traits by integrating gene regulatory networks and genetic variation in wheat

Spike architecture influences both grain weight and grain number per spike, which are the two major components of grain yield in bread wheat (Triticum aestivum L.). However, the complex wheat genome and the influence of various environmental factors pose challenges in mapping the causal genes that affect spike traits. Here, we systematically identified genes involved in spike trait formation by integrating information on genomic variation and gene regulatory networks controlling young spike development in wheat. We identified 170 loci that are responsible for variations in spike length, spikelet number per spike, and grain number per spike through genome-wide association study and meta-QTL analyses. We constructed gene regulatory networks for young inflorescences at the double ridge stage and the floret primordium stage, in which the spikelet meristem and the floret meristem are predominant, respectively, by integrating transcriptome, histone modification, chromatin accessibility, eQTL, and protein–protein interactome data. From these networks, we identified 169 hub genes located in 76 of the 170 QTL regions whose polymorphisms are significantly associated with variation in spike traits. The functions of TaZF-B1, VRT-B2, and TaSPL15-A/D in establishment of wheat spike architecture were verified. This study provides valuable molecular resources for understanding spike traits and demonstrates that combining genetic analysis and developmental regulatory networks is a robust approach for dissection of complex traits.

Dynamic Model Inference of Gene Regulatory Network based on Hybrid Parallel Genetic Algorithm and Threshold Qualification Method

Gene regulation is the process by which various substances in cells regulate the behaviour of gene expression, thereby controlling almost all cellular activities. Therefore, studying gene regulation not only helps to uncover the internal laws governing life processes but also plays a crucial role in predicting, diagnosing, treating, and designing drugs for genetic diseases. By utilizing multi-source biological information such as gene expression profiles, transcription factor information, and protein interaction data, a network model can be developed to depict the regulatory relationships between genes, facilitating further research. To address the limitations of traditional gene regulatory network construction methods, a novel dynamic model has been created by combining hybrid genetics and threshold restriction. This model comprises two parts: solution space reduction and parameter fitting. During solution space reduction, singular value decomposition is employed to define a mathematically feasible gene regulatory network, reducing unnecessary calculations. Subsequently, the control genes of each gene are constrained within a certain range using threshold limitation, enhancing computational efficiency while adhering to bioinformatics principles. In the parameter fitting phase, parallel genetic algorithms are utilized to expediently optimize the entire solution space. The mountain climbing method is then applied to solve problems meticulously within a limited scope, improving calculation accuracy. In this study, this approach was applied to establish genetic regulatory systems for complex skin melanoma and type 2 diabetes. Through comparison with actual networks, the validity of the method was confirmed. Compared to traditional genetic and particle swarm optimization methods, the effectiveness of the proposed method was verified. This paper models the intricate mechanism of gene regulation and elucidates the regulatory process involving genes, proteins, and small biological molecules in greater detail than other models, aligning more closely with intracellular dynamics laws.

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

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

Phenomic and transcriptomic analyses reveal the sequential synthesis of Fe3O4 nanoparticles in Acidithiobacillus ferrooxidans BYM.

Understanding the molecular mechanism of magnetite (Fe3O4) nanoparticle synthesis in Acidithiobacillus ferrooxidans BYM is particularly important for the commercial development of biogenic Fe3O4 nanoparticles. The phenomic parameters such as intracellular iron content and number and size of Fe3O4 nanoparticles were significantly affected by different treatment conditions, i.e., FeSO4·7H2O concentrations (0, 40, and 80 g/L), growth times (12, 36, and 50 h), and magnetic field intensities (0.05, 3.5, and 15 mT) (P < 0.01). Transcriptome analysis revealed that 2,164, 1,587, and 1,061 differentially expressed genes (DEGs) were accordingly detected, and 24 significant expression profiles were identified in A. ferrooxidans BYM under the three treatment conditions. The construction of gene regulatory networks for Fe3O4 nanoparticle synthesis indicated that DEGs mainly enrich ion transport, oxidation-reduction process, membrane structure, signal transduction, and quorum sensing. The four modules were found to be significantly associated with Fe3O4 nanoparticle phenomic parameters using a weighted gene co-expression network. Ten hub genes significantly correlated with Fe3O4 nanoparticle phenomic parameters (P < 0.01) were finally selected from 24 eigengenes related to iron metabolism screened from these models. On the basis of the previous research results and the present study findings, we provide a hypothetical molecular model for Fe3O4 nanoparticle synthesis mediated by these hub genes in A. ferrooxidans BYM comprising membrane formation, iron uptake and transport, iron redox, and crystal maturity. Our results will enable in-depth studies of Fe3O4 nanoparticle synthesis in non-magnetotactic magnetosome-producing bacteria. IMPORTANCE As the most important non-magnetotactic magnetosome-producing bacteria, Acidithiobacillus ferrooxidans only requires very mild conditions to produce Fe3O4 nanoparticles, thus conferring greater flexibility and potential application in biomagnetic nanoparticle production. However, the available information cannot explain the mechanism of Fe3O4 nanoparticle formation in A. ferrooxidans. In this study, we applied phenomic and transcriptomic analyses to reveal this mechanism. We found that different treatment condition factors notably affect the phenomic data of Fe3O4 nanoparticle in A. ferrooxidans. Using transcriptomic analyses, the gene network controlling/regulating Fe3O4 nanoparticle biogenesis in A. ferrooxidans was proposed, excavating the candidate hub genes for Fe3O4 nanoparticle formation in A. ferrooxidans. Based on this information, a sequential model for Fe3O4 nanoparticle synthesis in A. ferrooxidans was hypothesized. It lays the groundwork for further clarifying the feature of Fe3O4 nanoparticle synthesis.

A Gene Correlation Measurement Method for Spatial Transcriptome Data Based on Partitioning and Distribution.

Spatial transcriptome (ST) technology provides both the spatial location and transcriptional profile of spots, as well as tissue images. ST data can be utilized to construct gene regulatory networks, which can help identify gene modules that facilitate the understanding of biological processes such as cell communication. Correlation measurement is the core basis for constructing a gene regulatory network. However, due to the high noise and sparsity in ST data, common correlation measurement methods such as the Pearson correlation coefficient (PCC) and Spearman correlation coefficient (SPCC) are not suitable. In this work, a new gene correlation measurement method called STgcor is proposed. STgcor defines vertexes as spots in a two-dimensional coordinate plane consisting of axes X and Y from the gene pair (X and Y). The joint probability density of Gaussian distribution of the gene pair (X and Y) is calculated to identify and eliminate outliers. To overcome sparsity, the degree, trend, and location of the distribution of vertexes are used to measure the correlation between gene pairs (X, Y). To validate the performance of the STgcor method, it is compared with the PCC and SPCC in a weighted coexpression network analysis method using two ST datasets of breast cancer and prostate cancer. The gene modules identified by these methods are then compared and analyzed. The results show that the STgcor method detects some special gene modules and cancer-related pathways that cannot be detected by the other two methods.

A single-cell atlas of bovine skeletal muscle reveals mechanisms regulating intramuscular adipogenesis and fibrogenesis.

Intramuscular fat (IMF) and intramuscular connective tissue (IMC) are often seen in human myopathies and are central to beef quality. The mechanisms regulating their accumulation remain poorly understood. Here, we explored the possibility of using beef cattle as a novel model for mechanistic studies of intramuscular adipogenesis and fibrogenesis. Skeletal muscle single-cell RNAseq was performed on three cattle breeds, including Wagyu (high IMF), Brahman (abundant IMC but scarce IMF), and Wagyu/Brahman cross. Sophisticated bioinformatics analyses, including clustering analysis, gene set enrichment analyses, gene regulatory network construction, RNA velocity, pseudotime analysis, and cell-cell communication analysis, were performed to elucidate heterogeneities and differentiation processes of individual cell types and differences between cattle breeds. Experiments were conducted to validate the function and specificity of identified key regulatory and marker genes. Integrated analysis with multiple published human and non-human primate datasets was performed to identify common mechanisms. A total of 32708 cells and 21 clusters were identified, including fibro/adipogenic progenitor (FAP) and other resident and infiltrating cell types. We identified an endomysial adipogenic FAP subpopulation enriched for COL4A1 and CFD (log2FC=3.19 and 1.92, respectively; P<0.0001) and a perimysial fibrogenic FAP subpopulation enriched for COL1A1 and POSTN (log2FC=1.83 and 0.87, respectively; P<0.0001), both of which were likely derived from an unspecified subpopulation. Further analysis revealed more progressed adipogenic programming of Wagyu FAPs and more advanced fibrogenic programming of Brahman FAPs. Mechanistically, NAB2 drives CFD expression, which in turn promotes adipogenesis. CFD expression in FAPs of young cattle before the onset of intramuscular adipogenesis was predictive of IMF contents in adulthood (R2 =0.885, P<0.01). Similar adipogenic and fibrogenic FAPs were identified in humans and monkeys. In aged humans with metabolic syndrome and progressed Duchenne muscular dystrophy (DMD) patients, increased CFD expression was observed (P<0.05 and P<0.0001, respectively), which was positively correlated with adipogenic marker expression, including ADIPOQ (R2 =0.303, P<0.01; and R2 =0.348, P<0.01, respectively). The specificity of Postn/POSTN as a fibrogenic FAP marker was validated using a lineage-tracing mouse line. POSTN expression was elevated in Brahman FAPs (P<0.0001) and DMD patients (P<0.01) but not in aged humans. Strong interactions between vascular cells and FAPs were also identified. Our study demonstrates the feasibility of beef cattle as a model for studying IMF and IMC. We illustrate the FAP programming during intramuscular adipogenesis and fibrogenesis and reveal the reliability of CFD as a predictor and biomarker of IMF accumulation in cattle and humans.

Regulon analysis identifies protective FXR and CREB5 in proximal tubules in early diabetic kidney disease

Diabetic kidney disease (DKD) is the most common complication of diabetes mellitus and a leading cause of kidney failure worldwide. Despite its prevalence, the mechanisms underlying early kidney damage in DKD remain poorly understood. In this study, we used single nucleus RNA-seq to construct gene regulatory networks (GRNs) in the kidney cortex of patients with early DKD. By comparing these networks with those of healthy controls, we identify cell type-specific changes in genetic regulation associated with diabetic status. The regulon activities of FXR (NR1H4) and CREB5 were found to be upregulated in kidney proximal convoluted tubule epithelial cells (PCTs), which were validated using immunofluorescence staining in kidney biopsies from DKD patients. In vitro experiments using cultured HK2 cells showed that FXR and CREB5 protected cells from apoptosis and epithelial–mesenchymal transition. Our findings suggest that FXR and CREB5 may be promising targets for early intervention in patients with DKD.

Variable combinatorial gap-filling method for single-cell RNA-seq data

Abstract With the increasing development of single-cell RNA sequencing technology, a huge amount of sequencing data has emerged. The use of computational methods to fill in the gene expression information in scRNA-seq data is not only an important guide for gene regulatory network construction, embryonic development, and neurological research in the brain but also provides an important basis for drug development and clinical medicine. In this paper, we propose a variable combination of single-cell gap-filling algorithms with high gap-filling accuracy and fast computation speed through the comprehensive study and analysis of image repair technology and single-cell gap-filling algorithm. The experiments demonstrate that the U-net-based gap-filling method proposed in this paper has high accuracy in recovering gene expression values, can reduce the analysis errors caused by dropout events, and applies to large-scale data sets. In summary, the variable combinatorial gap-filling method for single-cell RNA-seq data proposed in this paper can effectively improve the results of downstream analysis and promote the development of research in the field of RNA sequencing data.

ScPlant: A versatile framework for single-cell transcriptomic data analysis in plants

Single-cell transcriptomics has been fully embraced in plant biological research and is revolutionizing our understanding of plant growth, development, and responses to external stimuli. However, single-cell transcriptomic data analysis in plants is not trivial, given that there is currently no end-to-end solution and that integration of various bioinformatics tools involves a large number of required dependencies. Here, we present scPlant, a versatile framework for exploring plant single-cell atlases with minimum input data provided by users. The scPlant pipeline is implemented with numerous functions for diverse analytical tasks, ranging from basic data processing to advanced demands such as cell-type annotation and deconvolution, trajectory inference, cross-species data integration, and cell-type-specific gene regulatory network construction. In addition, a variety of visualization tools are bundled in a built-in Shiny application, enabling exploration of single-cell transcriptomic data on the fly.

A Holistic Approach from Systems Biology Reveals the Direct Influence of the Quorum-Sensing Phenomenon on Pseudomonas aeruginosa Metabolism to Pyoverdine Biosynthesis.

Computational modeling and simulation of biological systems have become valuable tools for understanding and predicting cellular performance and phenotype generation. This work aimed to construct, model, and dynamically simulate the virulence factor pyoverdine (PVD) biosynthesis in Pseudomonas aeruginosa through a systemic approach, considering that the metabolic pathway of PVD synthesis is regulated by the quorum-sensing (QS) phenomenon. The methodology comprised three main stages: (i) Construction, modeling, and validation of the QS gene regulatory network that controls PVD synthesis in P. aeruginosa strain PAO1; (ii) construction, curating, and modeling of the metabolic network of P. aeruginosa using the flux balance analysis (FBA) approach; (iii) integration and modeling of these two networks into an integrative model using the dynamic flux balance analysis (DFBA) approximation, followed, finally, by an in vitro validation of the integrated model for PVD synthesis in P. aeruginosa as a function of QS signaling. The QS gene network, constructed using the standard System Biology Markup Language, comprised 114 chemical species and 103 reactions and was modeled as a deterministic system following the kinetic based on mass action law. This model showed that the higher the bacterial growth, the higher the extracellular concentration of QS signal molecules, thus emulating the natural behavior of P. aeruginosa PAO1. The P. aeruginosa metabolic network model was constructed based on the iMO1056 model, the P. aeruginosa PAO1 strain genomic annotation, and the metabolic pathway of PVD synthesis. The metabolic network model included the PVD synthesis, transport, exchange reactions, and the QS signal molecules. This metabolic network model was curated and then modeled under the FBA approximation, using biomass maximization as the objective function (optimization problem, a term borrowed from the engineering field). Next, chemical reactions shared by both network models were chosen to combine them into an integrative model. To this end, the fluxes of these reactions, obtained from the QS network model, were fixed in the metabolic network model as constraints of the optimization problem using the DFBA approximation. Finally, simulations of the integrative model (CCBM1146, comprising 1123 reactions and 880 metabolites) were run using the DFBA approximation to get (i) the flux profile for each reaction, (ii) the bacterial growth profile, (iii) the biomass profile, and (iv) the concentration profiles of metabolites of interest such as glucose, PVD, and QS signal molecules. The CCBM1146 model showed that the QS phenomenon directly influences the P. aeruginosa metabolism to PVD biosynthesis as a function of the change in QS signal intensity. The CCBM1146 model made it possible to characterize and explain the complex and emergent behavior generated by the interactions between the two networks, which would have been impossible to do by studying each system's individual components or scales separately. This work is the first in silico report of an integrative model comprising the QS gene regulatory network and the metabolic network of P. aeruginosa.

Single-cell chromatin accessibility of developing murine pancreas identifies cell state-specific gene regulatory programs

Numerous studies have characterized the existence of cell subtypes, along with their corresponding transcriptional profiles, within the developing mouse pancreas. The upstream mechanisms that initiate and maintain gene expression programs across cell states, however, remain largely unknown. Here, we generate single-nucleus ATAC-Sequencing data of developing murine pancreas and perform an integrated, multi-omic analysis of both chromatin accessibility and RNA expression to describe the chromatin landscape of the developing pancreas at both E14.5 and E17.5 at single-cell resolution. We identify candidate transcription factors regulating cell fate and construct gene regulatory networks of active transcription factor binding to regulatory regions of downstream target genes. This work serves as a valuable resource for the field of pancreatic biology in general and contributes to our understanding of lineage plasticity among endocrine cell types. In addition, these data identify which epigenetic states should be represented in the differentiation of stem cells to the pancreatic beta cell fate to best recapitulate in vitro the gene regulatory networks that are critical for progression along the beta cell lineage in vivo.

P-CSN: single-cell RNA sequencing data analysis by partial cell-specific network.

Although many single-cell computational methods proposed use gene expression as input, recent studies show that replacing 'unstable' gene expression with 'stable' gene-gene associations can greatly improve the performance of downstream analysis. To obtain accurate gene-gene associations, conditional cell-specific network method (c-CSN) filters out the indirect associations of cell-specific network method (CSN) based on the conditional independence of statistics. However, when there are strong connections in networks, the c-CSN suffers from false negative problem in network construction. To overcome this problem, a new partial cell-specific network method (p-CSN) based on the partial independence of statistics is proposed in this paper, which eliminates the singularity of the c-CSN by implicitly including direct associations among estimated variables. Based on the p-CSN, single-cell network entropy (scNEntropy) is further proposed to quantify cell state. The superiorities of our method are verified on several datasets. (i) Compared with traditional gene regulatory network construction methods, the p-CSN constructs partial cell-specific networks, namely, one cell to one network. (ii) When there are strong connections in networks, the p-CSN reduces the false negative probability of the c-CSN. (iii) The input of more accurate gene-gene associations further optimizes the performance of downstream analyses. (iv) The scNEntropy effectively quantifies cell state and reconstructs cell pseudo-time.

Abstract 3176: Modeling the complex multi-cellular ecosystem of the tumor microenvironment in colorectal cancer using multi-omics data

Abstract The high inter-tumoral phenotypic heterogeneity of colorectal cancer exists within the tumor microenvironment (TME), which ultimately contributes to overall tumor survival. Tumor cells, as the center of the tumor microenvironment, employ sophisticated signaling networks to alter the cell fate decisions between cancerous and non-cancerous cells. A complex network of interactions is the source of this intercellular communication, resulting in tumor growth and progression, drug resistance, and ineffective treatment response. Recent experimental developments in colorectal cancer (CRC) have shown that understanding various underlying mechanisms of tumor growth and progression requires an in-depth understanding of the complex interactions between tumor cells and their surrounding microenvironment. Here, we analyze single-cell RNA sequencing data in CRC and construct gene regulatory networks to define interactions and identify cell states within the TME. The gene regulatory networks are incorporated as a multi-cellular ecosystem representing an experiment-based computational model of phenotypic crosstalk within the TME of CRC. The model simulates and predicts the multi-cellular responses to colorectal cancer to probe and identify mechanisms of cell state evolution and decisions resulting from chemo-agents, used to target TME components. This research will further our understanding of the multi-cellular crosstalk underlying the mechanisms within the TME and facilitate the development and optimization of cancer therapies that inhibit tumor growth in CRC. Citation Format: Geena Ildefonso, Stacey Finley. Modeling the complex multi-cellular ecosystem of the tumor microenvironment in colorectal cancer using multi-omics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3176.