Transcriptional Regulatory Networks Research Articles

IModulonDB 2.0: dynamic tools to facilitate knowledge-mining and user-enabled analyses of curated transcriptomic datasets.

iModulons-sets of co-expressed genes identified through independent component analysis (ICA) of high-quality transcriptomic datasets-provide an unbiased, modular view of an organism's transcriptional regulatory network. Established in 2020, iModulonDB (iModulonDB.org) serves as a centralized repository of curated iModulon sets, enabling users to explore iModulons and download the associated transcriptomic data. This update reflects a significant expansion of the database-19 new ICA decompositions (+633%) spanning 8925 expression profiles (+1370%), 503 studies (+2290%)and 12 additional organisms (+400%)-and introduces new features to help scientists decipher the mechanisms governing prokaryotic transcriptional regulation. To facilitate comprehension of the underlying expression profiles, the updated user-interface displays essential information about each data-generating study (e.g. the experimental conditions and publication abstract). Dashboards now include condition-specific coloring and highlight data generated from genetically perturbed strains, enabling users to rapidly interpret disruptions in transcriptional regulation. New interactive graphs rapidly convey omics-derived indicators (e.g. the explained variance of ICA decompositions, genetic overlap between iModulons and regulons). Direct links to operon diagrams (BioCyc) and protein-protein interaction networks (STRING) provide users with seamless access to external resources for further assessment of iModulons. Lastly, a new suite of search-driven and species-wide analysis tools promotes user-engagement with iModulons, reinforcing iModulonDB's role as a dynamic, interactive knowledgebase of prokaryotic transcriptional regulation.

Regulation mechanism of nitrite degradation in Lactobacillus plantarum WU14 mediated by Fnr.

Fumarate and nitrate reduction regulatory protein (Fnr)-a global transcriptional regulator-can directly or indirectly regulate many genes in different metabolic pathways at the top of the bacterial transcription regulation network. The present study explored the regulatory mechanism of Fnr-mediated nitrite degradation in Lactobacillus plantarum WU14 through gene transcription and expression analysis of oxygen sensing and nir operon expression regulation by Fnr. The interaction and the mechanism of transcriptional regulation between Fnr and GlnR were also examined under nitrite stress. After Fnr and GlnR purification by glutathione S-transferase tags, they were successfully expressed in Escherichia coli by constructing an expression vector. The results of electrophoresis mobility shift assay and qRT-PCR indicated that Fnr specifically bound to the PglnR and Pnir promoters and regulated the expression of nitrite reductase (Nir) and GlnR. After 6-12h of culture, the expressions of fnr and nir under anaerobic conditions were higher than under aerobic conditions; the expression of these two genes increased with sodium nitrite (NaNO2) addition during aerobic culture. Overall, the present study indicated that Fnr not only directly participated in the expression of Nir and GlnR but also indirectly regulated the expression of Nir through GlnR regulation.

Unveiling the novel regulatory roles of RpoD-family sigma factors in Salmonella Typhimurium heat shock response through systems biology approaches

Three RpoD-family sigma factors, RpoD, RpoS, and RpoH, play critical roles in transcriptional regulation in Salmonella enterica serovar Typhimurium under heat shock conditions. However, the genome-wide regulatory mechanisms of these sigma factors in response to heat stress have remained elusive. In this study, we comprehensively identified 2,319, 2,226, and 213 genome-wide binding sites for RpoD, RpoS, and RpoH, respectively, under sublethal heat shock conditions (42°C). Machine learning-based transcriptome analysis was employed to infer the relative activity of iModulons, providing valuable insights into the transcriptional impact of heat shock. Integrative data analysis enabled the reconstruction of the transcriptional regulatory network of sigma factors, revealing how they modulate gene expression to adapt to heat stress, including responses to anaerobic and oxidative stresses. Notably, we observed a significant expansion of the RpoS sigmulon from 97 to 301 genes in response to heat shock, underscoring the crucial role of RpoS in regulating various metabolic processes. Moreover, we uncovered a competition mechanism between RpoD and RpoS within RpoS sigmulons, where RpoS significantly increases its binding within promoter regions shared with RpoD under heat shock conditions. These findings illuminate how three RpoD-family sigma factors coordinate multiple cellular processes to orchestrate the overall response of S. Typhimurium to heat stress.

Time-Course Transcriptome Analysis Reveals Distinct Transcriptional Regulatory Networks in Resistant and Susceptible Grapevine Genotypes in Response to White Rot

Grapevine (Vitis vinifera L.) is a globally significant economic crop. However, its widely cultivated varieties are highly susceptible to white rot disease. To elucidate the mechanisms of resistance in grapevine against this disease, we utilized time-ordered gene co-expression network (TO-GCN) analysis to investigate the molecular responses in the grapevine varieties ‘Guifeimeigui’ (GF) and ‘Red Globe’ (RG). An assessment of their resistance demonstrated that GF is highly resistant to white rot, whereas RG is highly susceptible. We conducted transcriptome sequencing and a TO-GCN analysis on leaf samples from GF and RG at seven time points post-infection. Although a significant portion of the differentially expressed genes related to disease resistance were shared between GF and RG, the GF variety rapidly activated its defense mechanisms through the regulation of transcription factors during the early stages of infection. Notably, the gene VvLOX3, which is a key enzyme in the jasmonic acid biosynthetic pathway, was significantly upregulated in GF. Its upstream regulator, Vitvi08g01752, encoding a HD-ZIP family transcription factor, was identified through TO-GCN and yeast one-hybrid analyses. This study provides new molecular insights into the mechanisms of grapevine disease resistance and offers a foundation for breeding strategies aimed at enhancing resistance.

Exploration of transcriptional regulation network between buffalo oocytes and granulosa cells and its impact on different diameter follicles

BackgroundBuffalo is a globally important livestock species, but its reproductive performance is relatively low than cattles. At present, dominant follicle development specific process and mechanistic role of follicular growth related genes in water buffaloes are not well understood. Therefore, we comprehensively performed transcriptomics of granulosa cells and oocytes from different-sized follicles in water buffalo to identify key candidate genes that influence follicle development and diameter, and further explored the potential regulatory mechanisms of granulosa cells and oocytes in the process of water buffalo follicle development.ResultsIn this study, we found918 granulosa cell transcripts and 1401 oocyte transcripts were correlated in follicles of different diameters, and the expression differences were significant. Subsequent enrichment analysis of the co-expressed differentially expressed transcripts identified several genes targeted by long non-coding RNAs (lncRNAs) and associated with follicular development. Notably, the upregulation of BUB1 regulated by MSTRG.41325.4 and interactive action of SMAD2 and SMAD7 might have key regulatory role in follicular development. Additionally, we also detected key differentially expressed genes that potentially influence follicular hormone metabolism and growth, like ID2, CHRD, TGIF2 and MAD2L1, and constructed an interaction network between lncRNA transcripts and mRNAs.ConclusionsIn summary, this study preliminarily revealed the differences in gene expression patterns among buffalo follicles of different sizes and their potential molecular regulatory mechanisms. It provides a new perspective for exploring the mechanism of buffalo follicular dominance and improving buffalo reproductive performance.

IModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia.

Public gene expression databases are a rapidly expanding resource of organism responses to diverse perturbations, presenting both an opportunity and a challenge for bioinformatics workflows to extract actionable knowledge of transcription regulatory network function. Here, we introduce a five-step computational pipeline, called iModulonMiner, to compile, process, curate, analyze, and characterize the totality of RNA-seq data for a given organism or cell type. This workflow is centered around the data-driven computation of co-regulated gene sets using Independent Component Analysis, called iModulons, which have been shown to have broad applications. As a demonstration, we applied this workflow to generate the iModulon structure of Bacillus subtilis using all high-quality, publicly-available RNA-seq data. Using this structure, we predicted regulatory interactions for multiple transcription factors, identified groups of co-expressed genes that are putatively regulated by undiscovered transcription factors, and predicted properties of a recently discovered single-subunit phage RNA polymerase. We also present a Python package, PyModulon, with functions to characterize, visualize, and explore computed iModulons. The pipeline, available at https://github.com/SBRG/iModulonMiner, can be readily applied to diverse organisms to gain a rapid understanding of their transcriptional regulatory network structure and condition-specific activity.

Isoform-level expression of the constitutive androstane receptor (CAR or NR1I3) transcription factor better predicts the mRNA expression of the cytochrome P450s in human liver samples.

Many factors cause inter-person variability in the activity and expression of liver cytochrome P450 (CYP) drug-metabolizing enzymes, leading to variable drug exposure and treatment outcomes. Several liver-enriched transcription factors (TFs) are associated with CYP expression, with estrogen receptor alpha (ESR1) and constitutive androstane receptor (CAR or NR1I3) being the two top factors. ESR1 and NR1I3 undergo extensive alternative splicing that results in numerous splice isoforms, but how these splice isoforms associate with CYP expression is unknown. Here, we quantified 18 NR1I3 splice isoforms and the three most abundant ESR1 isoforms in 260 liver samples derived from African Americans (AA, n=125) and European Americans (EA, n=135). Our results showed variable splice isoform populations in the liver for both NR1I3 and ESR1. Multiple linear regression analyses revealed that, compared to gene-level NR1I3, isoform-level NR1I3 expression better predicted the mRNA expression of most CYPs and three UDP-glucuronosyltransferases (UGTs), while ESR1 isoforms improved predictive models for the UGTs and CYP2D6, but not for most CYPs. Also, different NR1I3 isoforms were associated with different CYPs, and the associations varied depending on sample ancestry. Surprisingly, non-canonical NR1I3 isoforms having retained introns (introns 2 or 6) were abundantly expressed and associated with the expression of most CYPs and UGTs, whereas the reference isoform (NR1I3-205) only associated with CYP2D6. Moreover, NR1I3 isoform diversity increased during the differentiation of induced pluripotent stem cells to hepatocytes, paralleling increasing CYP expression. These results suggest that isoform-level TF expression may help to explain variation in CYP or UGT expression between individuals. Significance Statement We quantified 18 NR1I3 splice isoforms and three ESR1 splice isoforms in 260 liver samples derived from AA and EA donors and found variable NR1I3 and ESR1 splice isoform expression in the liver. Multiple linear regression analysis showed that, compared to gene-level expression, isoform-level expression of NR1I3 and ESR1 better predicted the mRNA expression of some CYPs and UGTs, highlighting the importance of isoform-level analyses to enhance our understanding of gene transcriptional regulatory networks controlling the expression of drug-metabolizing enzymes.

The MaNAP1-MaMADS1 transcription factor module mediates ethylene-regulated peel softening and ripening in banana.

The banana (Musa spp.) peel undergoes rapid softening during ripening, leading to finger drop and a shortened shelf life. The regulatory mechanism behind this process remains to be elucidated. In this study, we confirmed the role of peel softening in banana finger drop and uncovered the underlying transcriptional regulatory network. Cell wall-related (CWR) genes were substantially upregulated in both the peel and finger drop zone during ethylene-induced ripening. Transcriptome analysis and genome-wide profiling of chromatin accessibility and transcription factor (TF) binding revealed that two key regulators of fruit ripening, Musa acuminata NAC-like, Activated by apetala3/Pistillata1 (MaNAP1) and MaMADS1, regulate CWR genes by directly binding to their promoters or by targeting other ripening-related TFs to form a hierarchical regulatory network. Notably, MaNAP1 and MaMADS1 were directly targeted by ETHYLENE INSENSITIVE3 (MaEIN3), and MaNAP1 and MaMADS1 associated with tissue-specific histone modifications, enabling them to integrate MaEIN3-mediated ethylene signaling and undergo epigenetic regulation. Overexpression of MaNAP1, MaMADS1 or other identified regulatory TFs upregulated CWR genes and promoted peel softening. Our findings unveil a MaNAP1-MaMADS1-centered regulatory cascade governing banana peel softening and finger drop, offering potential targets for enhancing banana texture and shelf life.

The Critical Role of Phenylpropanoid Biosynthesis Pathway in Lily Resistance Against Gray Mold

Gray mold caused by Botrytis elliptica is one of the most determinative factors of lily growth and has become a major threat to lily productivity. However, the nature of the lily B. elliptica interaction remains largely unknown. Here, comparative transcriptomic and metabolomic were used to investigate the defense responses of resistant (‘Sorbonne’) and susceptible (‘Tresor’) lily cultivars to B. elliptica infection at 24 hpi. In total, 1326 metabolites were identified in ‘Sorbonne’ and ‘Tresor’ after infection, including a large number of phenylpropanoids. Specifically, the accumulation of four phenylpropanes, including eriodictyol, hesperetin, ferulic acid, and sinapyl alcohol, was significantly upregulated in the B. elliptica-infected ‘Sorbonne’ compared with the infected ‘Tresor’, and these phenylpropanes could significantly inhibit B. elliptica growth. At the transcript level, higher expression levels of F3′M, COMT, and CAD led to a higher content of resistance-related phenylpropanes (eriodictyol, ferulic acid, and sinapyl alcohol) in ‘Sorbonne’ following B. elliptica infection. It can be assumed that these phenylpropanes cause the resistance difference between ‘Sorbonne’ and ‘Tresor’, and could be the potential marker metabolites for gray mold resistance in the lily. Further transcriptional regulatory network analysis suggested that members of the AP2/ERF, WRKY, Trihelix, and MADS-M-type families positively regulated the biosynthesis of resistance-related phenylpropanes. Additionally, the expression patterns of genes involved in phenylpropanoid biosynthesis were confirmed using qRT-PCR. Therefore, we speculate that the degree of gray mold resistance in the lily is closely related to the contents of phenylpropanes and the transcript levels of the genes in the phenylpropanoid biosynthesis pathway. Our results not only improve our understanding of the lily’s resistance mechanisms against B. elliptica, but also facilitate the genetic improvement of lily cultivars with gray mold resistance.

Transcriptomic dynamics of ABA response in Brassica napus guard cells

Drought has a significant, negative impact on crop production; and these effects are poised to increase with climate change. Plants acclimate to drought and water stress through diverse physiological responses, primarily mediated by the hormone abscisic acid (ABA). Because plants lose the majority of their water through stomatal pores on aerial surfaces of plants, stomatal closure is one of the rapid responses mediated by ABA to reduce transpirational water loss. The dynamic changes in the transcriptome of stomatal guard cells in response to ABA have been investigated in the model plant Arabidopsis thaliana. However, guard cell transcriptomes have not been analyzed in agronomically valuable crops such as a major oilseed crop, rapeseed. In this study, we investigated the dynamics of ABA-regulated transcriptomes in stomatal guard cells of Brassica napus and conducted comparison analysis with the transcriptomes of A. thaliana. We discovered changes in gene expression indicating alterations in a host of physiological processes, including stomatal movement, metabolic reprogramming, and light responses. Our results suggest the existence of both immediate and delayed responses to ABA in Brassica guard cells. Furthermore, the transcription factors and regulatory networks mediating these responses are compared to those identified in Arabidopsis. Our results imply the continuing evolution of ABA responses in Brassica since its divergence from a common ancestor, involving both protein-coding and non-coding nucleotide sequences. Together, our results will provide a basis for developing strategies for molecular manipulation of drought tolerance in crop plants.

SMARCD1 is an essential expression-restricted metastasis modifier

Breast cancer is the most frequently diagnosed cancer worldwide, constituting 15% of cases in 2023. The predominant cause of breast cancer-related mortality is metastasis, and a lack of metastasis-targeted therapies perpetuates dismal outcomes for late-stage patients. By using meiotic genetics to study inherited transcriptional network regulation, we have identified, to the best of our knowledge, a new class of “essential expression-restricted” genes as potential candidates for metastasis-targeted therapeutics. Building upon previous work implicating the CCR4-NOT RNA deadenylase complex in metastasis, we demonstrate that RNA-binding proteins NANOS1, PUM2, and CPSF4 also regulate metastatic potential. Using various models and clinical data, we pinpoint Smarcd1 mRNA as a target of all three RNA-BPs. Strikingly, both high and low expression of Smarcd1 correlate with positive clinical outcomes, while intermediate expression significantly reduces the probability of survival. Applying the theory of “essential genes” from evolution, we identify 50 additional genes that require precise expression levels for metastasis to occur. Specifically, small perturbations in Smarcd1 expression significantly reduce metastasis in mouse models and alter splicing programs relevant to the ER+/HER2-enriched breast cancer. Identification subtype-specific essential expression-restricted metastasis modifiers introduces a novel class of genes that, when therapeutically “nudged” in either direction, may significantly improve late-stage breast cancer patients.

Integrated multi-omics reveals contrasting epigenetic patterns in leaf and root morphogenesis in Aegilops speltoides

Aegilops speltoides, the closest ancestor of the wheat B subgenome, has been well studied genomically. However, the epigenetic landscape of Ae. speltoides and the effects of epigenetics on its growth and development remain poorly understood. Here, we present a comprehensive multi-omics atlas of leaves and roots in Ae. speltoides, encompassing transcriptome, DNA methylation, histone modifications, and small RNA profiling. Divergent DNA methylation levels were detected between leaves and roots, and were associated with differences in accumulated 24-nt siRNAs. DNA methylation changes in promoters and gene bodies showed strong connections with altered expression between leaves and roots. Transcriptional regulatory networks (TRN) reconstructed between leaves and roots were driven by tissue-specific TF families. DNA methylation and histone modification act together as switches that shape root and leaf morphogenesis by modulating the binding of tissue-specific TFs to their target genes. The TRNs in leaves and roots reshaped during wheat polyploidization were associated with alterations in epigenetic modifications. Collectively, these results not only shed light on the critical contribution of epigenetic regulation in the morphogenesis of leaves and roots in Ae. speltoides but also provide new insights for future investigations into the complex interplay of genetic and epigenetic factors in the developmental biology of common wheat.

A Controllability Reinforcement Learning Method for Pancreatic Cancer Biomarker Identification.

Pancreatic cancer is one of the most malignant cancers with rapid progression and poor prognosis. The use of transcriptional data can be effective in finding new biomarkers for pancreatic cancer. Many network-based methods used to identify cancer biomarkers are proposed, among which the combination of network controllability appears. However, most of the existing methods do not study RNA, rely on priori and mutations information, or can only achieve classification tasks. In this study, we propose a method combined Relational Graph Convolutional Network and Deep Q-Network called RDDriver to identify pancreatic cancer biomarkers based on multi-layer heterogeneous transcriptional regulation network. Firstly, we construct a regulation network containing long non-coding RNA, microRNA, and messenger RNA. Secondly, Relational Graph Convolutional Network is used to learn the node representation. Finally, we use the idea of Deep Q-Network to build a model, which score and prioritize each RNA with the Popov-Belevitch-Hautus criterion. We train RDDriver on three small simulated networks, and calculate the average score after applying the model parameters to the regulation networks separately. To demonstrate the effectiveness of the method, we perform experiments for comparison between RDDriver and other eight methods based on the approximate benchmark of three types cancer drivers RNAs.

Identification of Grape Laccase Genes and Their Potential Role in Secondary Metabolite Synthesis.

Laccase, a copper-containing oxidoreductase, has close links with secondary metabolite biosynthesis in plants. Its activity can affect the synthesis and accumulation of secondary metabolites, thereby influencing plant growth, development, and stress resistance. This study aims to identify the grape laccases (VviLAC) gene family members in grape (Vitis vinifera L.) and explore the transcriptional regulatory network in berry development. Here, 115 VviLACs were identified and divided into seven (Type I-VII) classes. These were distributed on 17 chromosomes and out of 47 VviLACs on chromosome 18, 34 (72.34%) were involved in tandem duplication events. VviLAC1, VviLAC2, VviLAC3, and VviLAC62 were highly expressed before fruit color development, while VviLAC4, VviLAC12, VviLAC16, VviLAC18, VviLAC20, VviLAC53, VviLAC60 and VviLAC105 were highly expressed after fruit color transformation. Notably, VviLAC105 showed a significant positive correlation with important metabolites including resveratrol, resveratrol dimer, and peonidin-3-glucoside. Analysis of the transcriptional regulatory network predicted that the 12 different transcription factors target VviLACs genes. Specifically, WRKY and ERF were identified as potential transcriptional regulatory factors for VviLAC105, while Dof and MYB were identified as potential transcriptional regulatory factors for VviLAC51. This study identifies and provides basic information on the grape LAC gene family members and, in combination with transcriptome and metabolome data, predicts the upstream transcriptional regulatory network of VviLACs.

Crosstalk between BER and NHEJ in XRCC4-Deficient Cells Depending on hTERT Overexpression.

Targeting DNA repair pathways is an important strategy in anticancer therapy. However, the unrevealed interactions between different DNA repair systems may interfere with the desired therapeutic effect. Among DNA repair systems, BER and NHEJ protect genome integrity through the entire cell cycle. BER is involved in the repair of DNA base lesions and DNA single-strand breaks (SSBs), while NHEJ is responsible for the repair of DNA double-strand breaks (DSBs). Previously, we showed that BER deficiency leads to downregulation of NHEJ gene expression. Here, we studied BER's response to NHEJ deficiency induced by knockdown of NHEJ scaffold protein XRCC4 and compared the knockdown effects in normal (TIG-1) and hTERT-modified cells (NBE1). We investigated the expression of the XRCC1, LIG3, and APE1 genes of BER and LIG4; the Ku70/Ku80 genes of NHEJ at the mRNA and protein levels; as well as p53, Sp1 and PARP1. We found that, in both cell lines, XRCC4 knockdown leads to a decrease in the mRNA levels of both BER and NHEJ genes, though the effect on protein level is not uniform. XRCC4 knockdown caused an increase in p53 and Sp1 proteins, but caused G1/S delay only in normal cells. Despite the increased p53 protein, p21 did not significantly increase in NBE1 cells with overexpressed hTERT, and this correlated with the absence of G1/S delay in these cells. The data highlight the regulatory function of the XRCC4 scaffold protein and imply its connection to a transcriptional regulatory network or mRNA metabolism.

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.

HATZFS predicts pancreatic cancer driver biomarkers by hierarchical reinforcement learning and zero-forcing set

The search for cancer biomarkers is of great significance for the early diagnosis and clinical treatment planning of pancreatic cancer. RNA transcription networks can effectively represent the interactions between RNA molecules, and the irregular perturbations in these interactions can lead to pathological states, making them commonly used for identifying cancer biomarkers. However, traditional network-based methods for identifying cancer biomarkers often rely on prior information and mutation data. Furthermore, most network controllability-based methods face the limitation of high computational complexity, preventing their integration with deep learning methods. In this paper, we propose a novel deep learning framework, called HATZFS, for identifying driver biomarkers in pancreatic cancer using RNA differential expression data. The proposed model combines hierarchical deep Q-network (HDQN), graph attention network (GAT), and zero-forcing set (ZFS). Firstly, a pancreatic cancer RNA transcription regulatory network is constructed to embed the complex relationship among Long non-coding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA). Then, we transform it into multiple subgraphs using graph sampling method, and apply GAT to learn the node features of the subgraphs. Finally, the HATZFS performs HDQN to select subgraphs and identify important RNA as driver nodes, and determining the controllability of the subgraphs based on ZFS. More important, the paper theoretically proves that when all subgraphs are controllable, the original graph is also controllable, and the union of the driver node sets of all subgraphs is equal to the driver node set of the original graph. To confirm the effectiveness of the proposed model, comprehensive experiments are conducted on the benchmark dataset consisting of 14 databases, comparing it with eight other algorithms. The experimental results demonstrate that the proposed model outperforms other methods. Overall, HATZFS not only provides the importance of all RNA molecules in the pancreatic cancer RNA regulatory network, but also identifies the driver node set of the network as driver biomarkers. Source code can be accessed at https://github.com/HongJieTongXue/HATZFS.

Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators.

The transcriptional regulatory network (TRN) is a graph framework that helps understand the complex transcriptional regulation mechanisms in the transcription process. Identifying the phenotype-specific transcription regulators is vital to reveal the functional roles of transcription elements in associating the specific phenotypes. Although many methods have been developed towards detecting the phenotype-specific transcription elements based on the static TRN in the past decade, most of them are not satisfactory for elucidating the phenotype-related functional roles of transcription regulators in multiple levels, as the dynamic characteristics of transcription regulators are usually ignored in static models. In this study, we introduce a novel framework called DTGN to identify the phenotype-specific transcription factors (TFs) and pathways by constructing dynamic TRNs. We first design a graph autoencoder model to integrate the phenotype-oriented time-series gene expression data and static TRN to learn the temporal representations of genes. Then, based on the learned temporal representations of genes, we develop a statistical method to construct a series of dynamic TRNs associated with the development of specific phenotypes. Finally, we identify the phenotype-specific TFs and pathways from the constructed dynamic TRNs. Results from multiple phenotypic datasets show that the proposed DTGN framework outperforms most existing methods in identifying phenotype-specific TFs and pathways. Our framework offers a new approach to exploring the functional roles of transcription regulators that associate with specific phenotypes in a dynamic model.

In Silico Analysis of the Effect of LINC00525 Knockdown on mRNA Profile in A549 Lung Adenocarcinoma Cells

Background: High expression of lncRNA LINC00525 is associated with the progressive development of various cancers, including lung adenocarcinoma (LUAD). LncRNAs play regulatory roles in transcription and gene expression. Objectives: This study aimed to investigate, for the first time, the effect of LINC00525 knockdown on the transcriptome and signaling pathways of A549 LUAD cells. Methods: Differentially expressed genes (DEGs) were identified after LINC00525 silencing in A549 cells using the gene expression profile GSE171460. R packages were employed to identify enriched pathways. Hub genes were identified using a PPI network based on the STRING database. The RNAInter and TRRUST databases were used to predict transcription factors (TFs) that interact with LINC00525 and regulate DEGs. Results: LINC00525 knockdown significantly altered the expression of approximately 3,093 genes. The most significantly enriched pathways were the cell cycle and cytokine signaling pathways. All identified hub genes were upregulated, many of which were related to the cytokine signaling pathway, including STAT1, IL6, ISG15, IRF1, IRF7, IFIH1, and IFIT3. According to the GEPIA database, decreased LINC00525 expression showed a positive correlation with increased expression of the hub genes IL6, DDX58, IFIH1, OAS1, OAS2, and IFIT3, supporting the findings of this study. Based on the transcriptional regulatory network, some TFs of DEGs and hub genes may interact with LINC00525. Conclusions: These findings support the positive association between LINC00525 overexpression and tumor growth. LINC00525 may serve as a potential therapeutic target for LUAD. Further research is needed to explore other functional mechanisms of LINC00525.

Integrated transcriptomic and metabolomic analyses reveal transcriptional regulatory network for phenolic acid biosynthesis in potato tubers

The phenolic acid profile and concentration in potatoes signify their nutritional significance and functional value. In this study, a total of 398 metabolites, including 52 phenolic acids, were identified from three colored potato clones at three developmental stages using widely targeted metabolomics. Red and purple-fleshed potatoes exhibit higher levels of most phenolic acids compared to yellow-fleshed potatoes, particularly notable in their chlorogenic acid content. We concentrated on the distribution of differentially expressed transcription factors (TFs) among these clones, pinpointing the ERF (ethylene response factor), bHLH (basic helix-loop-helix), and MYB (myeloblastosis) as the predominant TFs via RNA-Seq analysis. Based on integrated transcriptomic and widely targeted metabolomic analysis, 23 pathway genes that might be involved in the biosynthesis of phenolic acid were identified. Furthermore, three central modules and several hub genes, encompassing 7 TFs, were identified. Notably, bHLH1 was identified as a key transcriptional regulator within this network, specifically activating the transcription of StCCoAOMT, a key enzyme catalyzing the methylation of caffeoyl-CoA to feruloyl-CoA. This study may provide new insights into the molecular mechanisms of phenolic acid biosynthesis in potatoes and potentially advances the development of functional food products derived from these potatoes.