Gene Regulatory Network Analysis Research Articles

Comparative transcriptome analysis identifies candidate genes related to seed coat color in rapeseed.

Yellow seed coat in rapeseed (Brassica napus) is a desirable trait that can be targeted to improve the quality of this oilseed crop. To better understand the inheritance mechanism of the yellow-seeded trait, we performed transcriptome profiling of developing seeds in yellow- and black-seeded rapeseed with different backgrounds. The differentially expressed genes (DEGs) during seed development showed significant characteristics, these genes were mainly enriched for the Gene Ontology (GO) terms carbohydrate metabolic process, lipid metabolic process, photosynthesis, and embryo development. Moreover, 1206 and 276 DEGs, which represent candidates to be involved in seed coat color, were identified between yellow- and black-seeded rapeseed during the middle and late stages of seed development, respectively. Based on gene annotation, GO enrichment analysis, and protein-protein interaction network analysis, the downregulated DEGs were primarily enriched for the phenylpropanoid and flavonoid biosynthesis pathways. Notably, 25 transcription factors (TFs) involved in regulating flavonoid biosynthesis pathway, including known (e.g., KNAT7, NAC2, TTG2 and STK) and predicted TFs (e.g., C2H2-like, bZIP44, SHP1, and GBF6), were identified using integrated gene regulatory network (iGRN) and weight gene co-expression networks analysis (WGCNA). These candidate TF genes had differential expression profiles between yellow- and black-seeded rapeseed, suggesting they might function in seed color formation by regulating genes in the flavonoid biosynthesis pathway. Thus, our results provide in-depth insights that facilitate the exploration of candidate gene function in seed development. In addition, our data lay the foundation for revealing the roles of genes involved in the yellow-seeded trait in rapeseed.

ScANANSE gene regulatory network and motif analysis of single-cell clusters.

The recent development of single-cell techniques is essential to unravel complex biological systems. By measuring the transcriptome and the accessible genome on a single-cell level, cellular heterogeneity in a biological environment can be deciphered. Transcription factors act as key regulators activating and repressing downstream target genes, and together they constitute gene regulatory networks that govern cell morphology and identity. Dissecting these gene regulatory networks is crucial for understanding molecular mechanisms and disease, especially within highly complex biological systems. The gene regulatory network analysis software ANANSE and the motif enrichment software GimmeMotifs were both developed to analyse bulk datasets. We developed scANANSE, a software pipeline for gene regulatory network analysis and motif enrichment using single-cell RNA and ATAC datasets. The scANANSE pipeline can be run from either R or Python. First, it exports data from standard single-cell objects. Next, it automatically runs multiple comparisons of cell cluster data. Finally, it imports the results back to the single-cell object, where the result can be further visualised, integrated, and interpreted. Here, we demonstrate our scANANSE pipeline on a publicly available PBMC multi-omics dataset. It identifies well-known cell type-specific hematopoietic factors. Importantly, we also demonstrated that scANANSE combined with GimmeMotifs is able to predict transcription factors with both activating and repressing roles in gene regulation.

Spatiotemporal regulation of cholangiocarcinoma growth and dissemination by peritumoral myofibroblasts in a Vcam1-dependent manner

Intrahepatic cholangiocarcinoma (iCCA) is characterized by its highly desmoplastic stroma. Myofibroblasts (MFs) are present both within the tumor mass (intratumoral MFs, iMFs) and at the tumor border (peritumoral MFs, pMFs). Using a spheroid-based coculture system, we show that the initial iCCA-pMF contact is growth suppressive to the tumor cells. However, prolonged iCCA-pMF interaction elicits significant tumor cell invasion and dissemination. We find that vascular cell adhesion molecule-1 (Vcam1) level is elevated in tumor cells in contact with pMFs but low in disseminated tumor cells both in vitro and in vivo. A gene regulatory network analysis of mouse and patient iCCA tumors and Vcam1 knockout (Vcam1KO) demonstrate a heavy involvement of Vcam1 in epithelial-to-mesenchymal transition. While Vcam1KO has only a limited impact on tumor cell growth in their monoculture, Vcam1KO spheroids exhibit instant dissemination and a severe growth defect when cocultured with pMFs. When transplanted into the liver, Vcam1KO iCCA cells show a similar increase in dissemination but a significant defect in establishing primary and metastatic tumors. Incomplete blocking of Vcam1 in vivo reduces the size but increase the number of metastatic lesions. Overall, our study shows a spatiotemporal regulation of iCCA growth and dissemination by pMFs in a Vcam1-dependent manner.

RNA-Sequencing Reveals the Involvement of Sesquiterpene Biosynthesis Genes and Transcription Factors during an Early Response to Mechanical Wounding of Aquilaria sinensis.

Plants respond to wounding by reprogramming the expression of genes involved in secondary metabolism. Aquilaria trees produce many bioactive secondary metabolites in response to wounding, but the regulatory mechanism of agarwood formation in the early response to mechanical wounding has remained unclear. To gain insights into the process of transcriptome changes and to determine the regulatory networks of Aquilaria sinensis to an early response (15 days) to mechanical wounding, we collected A. sinensis samples from the untreated (Asc1) and treated (Asf1) xylem tissues and performed RNA sequencing (RNA-seq). This generated 49,102,523 (Asc1) and 45,180,981 (Asf1) clean reads, which corresponded to 18,927 (Asc1) and 19,258 (Asf1) genes, respectively. A total of 1596 differentially expressed genes (DEGs) were detected in Asf1 vs. Asc1 (|log2 (fold change)| ≥ 1, Padj ≤ 0.05), of which 1088 were up-regulated and 508 genes were down-regulated. GO and KEGG enrichment analysis of DEGs showed that flavonoid biosynthesis, phenylpropanoid biosynthesis, and sesquiterpenoid and triterpenoid biosynthesis pathways might play important roles in wound-induced agarwood formation. Based on the transcription factor (TF)-gene regulatory network analysis, we inferred that the bHLH TF family could regulate all DEGs encoding for farnesyl diphosphate synthase, sesquiterpene synthase, and 1-deoxy-D-xylulose-5-phosphate synthase (DXS), which contribute to the biosynthesis and accumulation of agarwood sesquiterpenes. This study provides insight into the molecular mechanism regulating agarwood formation in A. sinensis, and will be helpful in selecting candidate genes for improving the yield and quality of agarwood.

Genome-wide analysis of Catalase gene family reveal insights into abiotic stress response mechanism in Brassica juncea and B. rapa.

Environmental stresses affect the yield and productivity of Brassica crops. Catalases are important antioxidant enzymes involved in reducing excess hydrogen peroxide produced by environmental stresses. In the present study, nine and seven CAT family members in two oilseed Brassica species (B. juncea and B. rapa) were identified with complete characterization based on gene and protein structure. Phylogenetic classification categorized CAT proteins into three classes and differentiated the monocot and dicot-specific CAT proteins. Further, the gene and protein characterizations revealed a high degree of conservation across the CAT family members. Differences were observed in the CAT-HEME binding affinity in CAT1, CAT2, and CAT3 isozymes, which could suggest their differential enzyme activities in different conditions. Furthermore, protein-protein interaction with other antioxidant proteins suggested their coordinated role in ROS scavenging mechanisms. Notably, the differential gene expression of BjuCATs and BraCATs and CAT enzyme activities suggested their crucial roles in major abiotic stresses faced by Brassica species. Promoter analysis in BjuCATs and BraCATs suggested the presence of abiotic-stress responsive cis-regulatory elements. Gene regulatory network analysis suggested miRNA and TF mediated stress response in BjuCATs and BraCATs. CAT family screening and characterization in Brassica sp. has established a basic ground for further functional validation in abiotic and heavy-metal stresses which can help in developing stress tolerant crops.

Bioinformatic analysis of single-cell RNA sequencing dataset dissects cellular heterogeneity of triple-negative breast cancer in transcriptional profile, splicing event and crosstalk network.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with high tumoral heterogeneity, while the detailed regulatory network is not well known. Via single-cell RNA-sequencing (scRNA-seq) data analysis, we comprehensively investigated the transcriptional profile of different subtypes of TNBC epithelial cells with gene regulatory network (GRN) and alternative splicing (AS) event analysis, as well as the crosstalk between epithelial and non-epithelial cells. Of note, we found that luminal progenitor subtype exhibited the most complex GRN and splicing events. Besides, hnRNPs negatively regulates AS events in luminal progenitor subtype. In addition, we explored the cellular crosstalk among endothelial cells, stromal cells and immune cells in TNBC and discovered that NOTCH4 was a key receptor and prognostic marker in endothelial cells, which provide potential biomarker and target for TNBC intervention. In summary, our study elaborates on the cellular heterogeneity of TNBC, revealing that NOTCH4 in endothelial cells was critical for TNBC intervention. This in-depth understanding of epithelial cell and non-epithelial cell network would provide theoretical basis for the development of new drugs targeting this sophisticated network in TNBC.

Single-cell analysis reveals the COL11A1+ fibroblasts are cancer-specific fibroblasts that promote tumor progression.

Background: Cancer-associated fibroblasts (CAFs) promote tumor progression through extracellular matrix (ECM) remodeling and extensive communication with other cells in tumor microenvironment. However, most CAF-targeting strategies failed in clinical trials due to the heterogeneity of CAFs. Hence, we aimed to identify the cluster of tumor-promoting CAFs, elucidate their function and determine their specific membrane markers to ensure precise targeting. Methods: We integrated multiple single-cell RNA sequencing (scRNA-seq) datasets across different tumors and adjacent normal tissues to identify the tumor-promoting CAF cluster. We analyzed the origin of these CAFs by pseudotime analysis, and tried to elucidate the function of these CAFs by gene regulatory network analysis and cell-cell communication analysis. We also performed cell-type deconvolution analysis to examine the association between the proportion of these CAFs and patients' prognosis in TCGA cancer cohorts, and validated that through IHC staining in clinical tumor tissues. In addition, we analyzed the membrane molecules in different fibroblast clusters, trying to identify the membrane molecules that were specifically expressed on these CAFs. Results: We found that COL11A1+ fibroblasts specifically exist in tumor tissues but not in normal tissues and named them cancer-specific fibroblasts (CSFs). We revealed that these CSFs were transformed from normal fibroblasts. CSFs represented a more activated CAF cluster and may promote tumor progression through the regulation on ECM remodeling and antitumor immune responses. High CSF proportion was associated with poor prognosis in bladder cancer (BCa) and lung adenocarcinoma (LUAD), and IHC staining of COL11A1 confirmed their specific expression in tumor stroma in clinical BCa samples. We also identified that CSFs specifically express the membrane molecules LRRC15, ITGA11, SPHK1 and FAP, which could distinguish CSFs from other fibroblasts. Conclusion: We identified that CSFs is a tumor specific cluster of fibroblasts, which are in active state, may promote tumor progression through the regulation on ECM remodeling and antitumor immune responses. Membrane molecules LRRC15, ITGA11, SPHK1 and FAP could be used as therapeutic targets for CSF-targeting cancer treatment.

Analysis of co-expression and gene regulatory networks associated with sterile lemma development in rice

BackgroundThe sterile lemma is a unique organ of the rice (Oryza sativa L.) spikelet. However, the characteristics and origin of the rice sterile lemma have not been determined unequivocally, so it is important to elucidate the molecular mechanism of the development of the sterile lemma.ResultsIn the paper, we outline the regulatory mechanism of sterile lemma development by LONG STERILE LEMMA1 (G1), which has been identified as the gene controlling sterile lemma development. Based on the comprehensive analyses of transcriptome dynamics during sterile lemma development with G1 alleles between wild-type (WT) and mutant (MT) in rice, we obtained co-expression data and regulatory networks related to sterile lemma development. Co-transfection assays of rice protoplasts confirmed that G1 affects the expression of various phytohormone-related genes by regulating a number of critical transcription factors, such as OsLBD37 and OSH1. The hormone levels in sterile lemmas from WT and MT of rice supports the hypotheses that lower auxin, lower gibberellin, and higher cytokinin concentrations are required to maintain a normal phenotype of sterile lemmas.ConclusionThe regulatory networks have considerable reference value, and some of the regulatory relationships exhibiting strong correlations are worthy of further study. Taken together, these work provided a detailed guide for further studies into the molecular mechanism of sterile lemma development.

Identification of the Genetic Influence of SARS-CoV-2 Infections on IgA Nephropathy Based on Bioinformatics Method

Introduction: Coronavirus disease-2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. It was initially detected in Wuhan, China, in December 2019. In March 2020, the World Health Organization (WHO) declared COVID-19 a global pandemic. Compared to healthy individuals, patients with IgA nephropathy (IgAN) are at a higher risk of SARS-CoV-2 infection. However, the potential mechanisms remain unclear. This study explores the underlying molecular mechanisms and therapeutic agents for the management of IgAN and COVID-19 using the bioinformatics and system biology way. Methods: We first downloaded GSE73953 and GSE164805 from the Gene Expression Omnibus (GEO) database to obtain common differentially expressed genes (DEGs). Then, we performed the functional enrichment analysis, pathway analysis, protein-protein interaction (PPI) analysis, gene regulatory networks analysis, and potential drug analysis on these common DEGs. Results: We acquired 312 common DEGs from the IgAN and COVID-19 datasets and used various bioinformatics tools and statistical analyses to construct the PPI network to extract hub genes. Besides, we performed gene ontology (GO) and pathway analyses to reveal the common correlation between IgAN and COVID-19. Finally, on the basis of common DEGs, we determined the interactions between DEGs-miRNAs, the transcription factor-genes (TFs-genes), protein-drug, and gene-disease networks. Conclusion: We successfully identified hub genes that may act as biomarkers of COVID-19 and IgAN and also screened out some potential drugs to provide new ideas for COVID-19 and IgAN treatment.

Single-cell RNA sequencing reveals resident progenitor and vascularization-associated cell subpopulations in rat annulus fibrosus.

One of the main causes of low back pain is intervertebral disc degeneration (IDD). Annulus fibrosus (AF) is important for the integrity and functions of the intervertebral disc (IVD). However, the resident functional cell components such as progenitors and vascularization-associated cells in AF are yet to be fully identified. Identification of functional AF cell subpopulations including resident progenitors and vascularization-associated cells. In this study, the single-cell RNA sequencing data of rat IVDs from a public database were analyzed using Seurat for cell clustering, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional analysis, StemID for stem cell identification, Monocle and RNA velocity for pseudotime differentiation trajectory validation, single-cell regulatory network inference and clustering (SCENIC) for gene regulatory network (GRN) analysis, and CellChat for cell-cell interaction analysis. Immunostaining on normal and degenerated rat IVDs, as well as human AF, was used for validations. From the data analysis, seven AF cell clusters were identified, including two newly discovered functional clusters, the Grem1 + subpopulation and the Lum +​subpopulation. The Grem1 + subpopulation had progenitor characteristics, while the Lum +​subpopulation was associated with vascularization during IDD. The GRN analysis showed that Sox9 and Id1 were among the key regulators in the Grem1 + subpopulation, and Nr2f2 and Creb5 could be responsible for the vascularization function in the Lum +​subpopulation. Cell-cell interaction analysis revealed highly regulated cellular communications between these cells, and multiple signaling networks including PDGF and MIF signaling pathways were involved in the interactions. Our results revealed two new functional AF cell subpopulations, with stemness and vascularization induction potential, respectively. These findings complement our knowledge about IVDs, especially the AF, and in return provide potential cell source and regulation targets for IDD treatment and tissue repair. The existence of the cell subpopulations was also validated in human AF, which strengthen the clinical relevance of the findings.

Epididymis cell atlas in a patient with a sex development disorder and a novel NR5A1 gene mutation.

This study aims to characterize the cell atlas of the epididymis derived from a 46,XY disorders of sex development (DSD) patient with a novel heterozygous mutation of the nuclear receptor subfamily 5 group A member 1 (NR5A1) gene. Next-generation sequencing found a heterozygous c.124C>G mutation in NR5A1 that resulted in a p.Q42E missense mutation in the conserved DNA-binding domain of NR5A1. The patient demonstrated feminization of external genitalia and Tanner stage 1 breast development. The surgical procedure revealed a morphologically normal epididymis and vas deferens but a dysplastic testis. Microfluidic-based single-cell RNA sequencing (scRNA-seq) analysis found that the fibroblast cells were significantly increased (approximately 46.5%), whereas the number of main epididymal epithelial cells (approximately 9.2%), such as principal cells and basal cells, was dramatically decreased. Bioinformatics analysis of cell-cell communications and gene regulatory networks at the single-cell level inferred that epididymal epithelial cell loss and fibroblast occupation are associated with the epithelial-to-mesenchymal transition (EMT) process. The present study provides a cell atlas of the epididymis of a patient with 46,XY DSD and serves as an important resource for understanding the pathophysiology of DSD.

Characterization of Gene Regulatory Networks in Plants Using New Methods and Data Types.

A major question in plant biology is to understand how plant growth, development, and environmental responses are controlled and coordinated by the activities of regulatory factors. Gene regulatory network (GRN) analyses require integrated approaches that combine experimental approaches with computational analyses. A wide range of experimental approaches and tools are now available, such as targeted perturbation of gene activities, quantitative and cell-type specific measurements of dynamic gene activities, and systematic analysis of the molecular 'hard-wiring' of the systems. At the computational level, different tools and databases are available to study regulatory sequences, including intuitive visualizations to explore data-driven gene regulatory networks in different plant species. Furthermore, advanced data integration approaches have recently been developed to efficiently leverage complementary regulatory data types and learn context-specific networks.

Cell cycle protein BORA is associated with colorectal cancer progression by AURORA-PLK1 cascades: a bioinformatics analysis.

Colorectal cancer (CRC) is the third most diagnosed cancer in the world. A better understanding of the molecular mechanism of CRC is essential for making novel strategies for the CRC management and its prevention. The present study aims to explore the molecular mechanism through integrated bioinformatics analysis by analyzing genes and their co-expression pattern in normal and CRC states. GSE110223, GSE110224 and GSE113513 gene expression profiles were analyzed in this study. The co-expression networks for normal and tumor samples were constructed separately and analyzed to identify the modules, sub-networks and key genes. Gene regulatory network analysis was done to understand the regulatory mechanism of selected genes. Survival analysis was performed for the identified sub-networks and key genes to understand their role in CRC progression. A total of seven modules were detected and the KEGG pathway analysis revealed these modules were mainly enriched with cell cycle, metabolism and signaling-related pathways. E2F6 and ETV4 transcription factors regulating the activity of multiple genes of identified modules were found to be up-regulated in CRC. Six Sub-networks and seven key genes, BORA, CCT7, DTL, RUVBL1, RUVBL2, THEM6 and TMEM97 associated with the CRC progression were identified. Disease-gene association analysis identified a novel association of the BORA gene with CRC that activates and regulates the AURORA-PLK1 cascades in the cell cycle. Survival analysis indicates that the overexpressed BORA is associated with unfavourable overall survival in CRC. The mechanistic role of BORA in the regulation of cell cycle progression suggests that BORA might act as a potential therapeutic target for CRC.

Transcriptome and Gene Regulatory Network Analyses Reveal New Transcription Factors in Mature Fruit Associated with Harvest Date in Prunus persica.

Harvest date is a critical parameter for producers and consumers regarding agro-industrial performance. It involves a pleiotropic effect controlling the development of other fruit quality traits through finely controlling regulatory mechanisms. Fruit ripening is a process in which various signals and biological events co-occur and are regulated by hormone signaling that produces the accumulation/degradation of multiple compounds. However, the regulatory mechanisms that control the hormone signaling involved in fruit development and ripening are still unclear. To investigate the issue, we used individuals with early, middle and late harvest dates from a peach segregating population to identify regulatory candidate genes controlling fruit quality traits at the harvest stage and validate them in contrasting peach varieties for this trait. We identified 467 and 654 differentially expressed genes for early and late harvest through a transcriptomic approach. In addition, using the Arabidopsis DAP-seq database and network analysis, six transcription factors were selected. Our results suggest significant hormonal balance and cell wall composition/structure differences between early and late harvest samples. Thus, we propose that higher expression levels of the transcription factors HB7, ERF017 and WRKY70 in early harvest individuals would induce the expression of genes associated with the jasmonic acid pathway, photosynthesis and gibberellins inhibition. While on the other hand, the high expression levels of LHY, CDF3 and NAC083 in late harvest individuals would promote the induction of genes associated with abscisic acid biosynthesis, auxins and cell wall remodeling.

Integrated bioinformatics analysis of the transcription factor-mediated gene regulatory networks in the formation of spermatogonial stem cells.

Background: In vitro induction of spermatogonial stem cells (SSCs) from embryonic stem cells (ESCs) provides a promising tool for the treatment of male infertility. A variety of molecules are involved in this complex process, which needs to be further clarified. Undoubtedly, the increased knowledge of SSC formation will be beneficial to facilitate the currently complex induction process. Methods: Based on ATAC-seq, DNase-seq, RNA-seq, and microarray data from GEO datasets, chromatin property data (ATAC-seq, DNase-seq) and gene expression data (RNA-seq, microarray data) were combined to search for SSC-specific transcription factors (TFs) and hub SSC-specific genes by using the WGCNA method. Then, we applied RNA-seq and microarray data screening for key SSC-specific TFs and constructed key SSC-specific TF-mediated gene regulatory networks (GRNs) using ChIP-seq data. Results: First, after analysis of the ATAC-seq and DNase-seq data of mouse ESCs, primordial germ cells (PGCs), and SSCs, 33 SSC-specific TFs and 958 targeting genes were obtained. RNA-seq and WGCNA revealed that the key modules (turquoise and red) were the most significantly related to 958 SSC-specific genes, and a total of 10 hub SSC-specific genes were identified. Next, when compared with the cell-specific TFs in human ESCs, PGCs, and SSCs, we obtained five overlapping SSC-specific TF motifs, including the NF1 family TF motifs (NFIA, NFIB, NFIC, and NFIX), GRE, Fox:Ebox, PGR, and ARE. Among these, Nfib and Nfix exhibited abnormally high expression levels relative to mouse ESCs and PGCs. Moreover, Nfib and Nfix were upregulated in the testis sample with impaired spermatogenesis when compared with the normal group. Finally, the ChIP-seq data results showed that NFIB most likely targeted the hub SSC-specific genes of the turquoise module (Rpl36al, Rps27, Rps21, Nedd8, and Sec61b) and the red module (Vcam1 and Ccl2). Conclusion: Our findings preliminarily revealed cell-specific TFs and cell-specific TF-mediated GRNs in the process of SSC formation. The hub SSC-specific genes and the key SSC-specific TFs were identified and suggested complex network regulation, which may play key roles in optimizing the induction efficiency of the differentiation of ESCs into SSCs in vitro.

Gene expression profile analysis to discover molecular signatures for early diagnosis and therapies of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is one of the most lethal subtypes of breast cancer (BC), and it accounts for approximately 10%-20% of all invasive BCs diagnosed worldwide. The survival rate of TNBC in stages III and IV is very low, and a large number of patients are diagnosed in these stages. Therefore, the purpose of this study was to identify TNBC-causing molecular signatures and anti-TNBC drug agents for early diagnosis and therapies. Five microarray datasets that contained 304 TNBC and 109 control samples were collected from the Gene Expression Omnibus (GEO) database, and RNA-Seq data with 116 tumor and 124 normal samples were collected from TCGA database to identify differentially expressed genes (DEGs) between TNBC and control samples. A total of 64 DEGs were identified, of which 29 were upregulated and 35 were downregulated, by using the statistical limma R-package. Among them, seven key genes (KGs) were commonly selected from microarray and RNA-Seq data based on the high degree of connectivity through PPI (protein-protein interaction) and module analysis. Out of these seven KGs, sixKGs (TOP2A, BIRC5, AURKB, ACTB, ASPM, and BUB1B) were upregulated and one (EGFR) was downregulated. We also investigated their differential expression patterns with different subtypes and progression stages of BC by the independent datasets of RNA-seq profiles from UALCAN database, which indicated that they may be potential biomarkers for early diagnosis. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses with the proposed DEGs were performed using the online Enrichr database to investigate the pathogenetic processes of TNBC highlighting KGs. Then, we performed gene regulatory network analysis and identified three transcriptional (SOX2, E2F4, and KDM5B) and three post-transcriptional (hsa-mir-1-3p, hsa-mir-124-3p, and hsa-mir-34a-5p) regulators of KGs. Finally, we proposed five KG-guided repurposable drug molecules (imatinib, regorafenib, pazopanib, teniposide, and dexrazoxane) for TNBC through network pharmacology and molecular docking analyses. These drug molecules also showed significant binding performance with some cancer-related PTM-sites (phosphorylation, succinylation, and ubiquitination) of top-ranked four key proteins (EGFR, AURKB, BIRC5, and TOP2A). Therefore, the findings of this computational study may play a vital role in early diagnosis and therapies against TNBC by wet-lab validation.

Identification of the Transcriptional Regulatory Role of RUNX2 by Network Analysis in Lung Cancer Cells.

The use of a new bioinformatics pipeline allowed the identification of deregulated transcription factors (TFs) coexpressed in lung cancer that could become biomarkers of tumor establishment and progression. A gene regulatory network (GRN) of lung cancer was created with the normalized gene expression levels of differentially expressed genes (DEGs) from the microarray dataset GSE19804. Moreover, coregulatory and transcriptional regulatory network (TRN) analyses were performed for the main regulators identified in the GRN analysis. The gene targets and binding motifs of all potentially implicated regulators were identified in the TRN and with multiple alignments of the TFs' target gene sequences. Six transcription factors (E2F3, FHL2, ETS1, KAT6B, TWIST1, and RUNX2) were identified in the GRN as essential regulators of gene expression in non-small-cell lung cancer (NSCLC) and related to the lung tumoral process. Our findings indicate that RUNX2 could be an important regulator of the lung cancer GRN through the formation of coregulatory complexes with other TFs related to the establishment and progression of lung cancer. Therefore, RUNX2 could become an essential biomarker for developing diagnostic tools and specific treatments against tumoral diseases in the lung after the experimental validation of its regulatory function.

Identification of temporally distributed candidate genes for high iron (Fe) and zinc (Zn) content in wheat (Triticum aestivum L.)

Micronutrient deficiency causes a major global public health issues. Increasing micronutrient content through biofortification can be achieved by exploring molecular regulatory networks for micronutrients metabolism in cereal crops. The present study provides regulatory gene networks for micronutrients homeostasis through transcriptome analysis at two seed developmental stages (14 and 28 days after anthesis) in three micronutrients variant wheat genotypes, ‘FAR 4’, ‘HD 3226’ and ‘K 8027’ at Illumina Hiseq-2500 platform. Differential gene expression (DGE) with gene ontology and KEGG pathway analysis, revealed the role of iron binding protein ferritin, nicotianamine synthase, and Yellow Stripe-Like transporters in maintaining higher grain micronutrient contents in ‘FAR 4’ genotype. The gene regulatory network analysis suggested the possible interactions between the protein and associated transcription factors and revealed NAC TFs, as the major regulatory factor in ‘FAR 4’ responsible for micronutrient homeostasis. Further, 11 F-box and 12 NBS-LRR disease resistance protein genes were abundant transcriptionally across the two seed development stages in all these wheat genotypes. The potential candidate genes may be further exploited to increase the micronutrient contents in cereal grains making them more nutrition for human consumption.

Plasma metabolomics and gene regulatory networks analysis reveal the role of nonstructural SARS-CoV-2 viral proteins in metabolic dysregulation in COVID-19 patients

Metabolomic analysis of blood plasma samples from COVID-19 patients is a promising approach allowing for the evaluation of disease progression. We performed the metabolomic analysis of plasma samples of 30 COVID-19 patients and the 19 controls using the high-performance liquid chromatography (HPLC) coupled with tandem mass spectrometric detection (LC–MS/MS). In our analysis, we identified 103 metabolites enriched in KEGG metabolic pathways such as amino acid metabolism and the biosynthesis of aminoacyl-tRNAs, which differed significantly between the COVID-19 patients and the controls. Using ANDSystem software, we performed the reconstruction of gene networks describing the potential genetic regulation of metabolic pathways perturbed in COVID-19 patients by SARS-CoV-2 proteins. The nonstructural proteins of SARS-CoV-2 (orf8 and nsp5) and structural protein E were involved in the greater number of regulatory pathways. The reconstructed gene networks suggest the hypotheses on the molecular mechanisms of virus-host interactions in COVID-19 pathology and provide a basis for the further experimental and computer studies of the regulation of metabolic pathways by SARS-CoV-2 proteins. Our metabolomic analysis suggests the need for nonstructural protein-based vaccines and the control strategy to reduce the disease progression of COVID-19.

Computational Tactics for Precision Cancer Network Biology.

Network biology has garnered tremendous attention in understanding complex systems of cancer, because the mechanisms underlying cancer involve the perturbations in the specific function of molecular networks, rather than a disorder of a single gene. In this article, we review the various computational tactics for gene regulatory network analysis, focused especially on personalized anti-cancer therapy. This paper covers three major topics: (1) cell line's (or patient's) cancer characteristics specific gene regulatory network estimation, which enables us to reveal molecular interplays under varying conditions of cancer characteristics of cell lines (or patient); (2) computational approaches to interpret the multitudinous and massive networks; (3) network-based application to uncover molecular mechanisms of cancer and related marker identification. We expect that this review will help readers understand personalized computational network biology that plays a significant role in precision cancer medicine.