Regulatory Interaction Networks Research Articles

Ultrafine mapping of chromosome conformation at hundred basepair resolution reveals regulatory genome architecture.

The resolution limit of chromatin conformation capture methodologies (3Cs) has restrained their application in detection of fine-level chromatin structure mediated by cis-regulatory elements (CREs). Here, we report two 3C-derived methods, Tri-4C and Tri-HiC, which utilize multirestriction enzyme digestions for ultrafine mapping of targeted and genome-wide chromatin interaction, respectively, at up to one hundred basepair resolution. Tri-4C identified CRE loop interaction networks and quantitatively revealed their alterations underlying dynamic gene control. Tri-HiC uncovered global fine-gauge regulatory interaction networks, identifying >20-fold more enhancer:promoter (E:P) loops than in situ Hi-C. In addition to vastly improved identification of subkilobase-sized E:P loops, Tri-HiC also uncovered interaction stripes and contact domain insulation from promoters and enhancers, revealing their loop extrusion behaviors resembling the topologically associating domain boundaries. Tri-4C and Tri-HiC provide robust approaches to achieve the high-resolution interactome maps required for characterizing fine-gauge regulatory chromatin interactions in analysis of development, homeostasis, and disease.

Bioinformatic analysis of the ABC transporter protein family and their function in Penicillium digitatum

ATP-binding cassette transporter (ABC transporter) has prominent effects on the normal growth, development, substance transport and drug detoxification of organisms. However, the function of most of the ABC family members in filamentous fungi remains unknown. In the present research, 171 ABCs were identified from fruit postharvest pathogenic Penicillium spp. fungi including P. digitatum, P. expansum and P. italicum, of which 45 were from P. digitatum. Bioinformatics analysis showed that all 45 PdABCs had ABC_tran domain, belonging to seven subfamilies and containing 1–12 introns, distributed on six chromosomes of P. digitatum. The transcriptome data of spore germination, infection and sodium dehydroacetate (SD) inhibition showed that 43 PdABCs were detected as expressed in at least one stage, 6 PdABCs were all significantly differenially expressed during spore germination, infection or SD repression, five, eight and six PdABCs were significantly differentially expressed at the spore germination, infection and SD repression stages, respectively. RT-qPCR experiments further validated these six co-differentially expressed PdABCs. Cis-acting element (CAE) analysis showed that C2H2, C6 and Heat shock factors family-specific binding elements were highly present in their promoters, and the interaction regulatory network analysis indicated that there were interactions between PdABCs and PdABCs, major facilitator superfamily (MFS) transporter proteins, cytochrome P450 proteins, TFs (PdRING1、PdHeat1、PdbZIP1、PdzincC6). This study contributes to the screening of key ABCs and provides foundations for further investigation into the function of PdABCs and the creation of novel antifungal drugs to combat citrus green mold.

Identification of target hub genes and construction of a novel miRNA regulatory network in autism spectrum disorder by integrated analysis.

The incidence of autism spectrum disorder (ASD) is increasing year by year in children. The aim of the study was to find possible biomarkers for ASD diagnosis as well as examine MicroRNA (miRNA) signatures and crucial pathways. We conducted a two-stage study to explore potential target genes and functional miRNAs. Peripheral blood samples of children with ASD were enrolled and performed RNA sequencing analysis. The overlapped candidate genes were further screened in combination with differentially expressed genes (DEGs) of GSE77103 datasets. STRING established a protein-protein interaction network comprising DEGs. The hub genes were filtered out using the CytoHubba. Then, we set up a miRNA-mRNA regulatory network. Correlational analyses between hub genes and immune cells associated with ASD were carried out using the CIBERSORT software to assess the diversity of immune cell types in ASD. RNA-sequencing analysis was used to confirm the differential expression of 3 hub genes. Briefly, after blood samples were sequenced interrogating 867 differential genes in our internal screening dataset. After screening GEO databases, 551 DEGs obtained from GSE77103. Fourteen common genes were overlapped through DEGs of GEO datasets and internal screening dataset. Among protein-protein interaction network, 10 hub genes with high degree algorithm were screened out and 3 hub genes of them - ADIPOR1, LGALS3, and GZMB - that were thought to be most associated with the emergence of ASD. Then, we developed a network of miRNA-mRNA regulatory interactions by screening miRNAs (such as hsa-miR-20b-5p, hsa-miR-17-5p, and hsa-miR-216b-5p) that were closely associated to 3 hub genes. Additionally, we discovered 18 different immune cell types associated with ASD using the CIBERSORT algorithm, and we discovered that mononuclear macrophages differed considerably between the 2 groups. Overall, 3 hub genes (ADIPOR1, LGALS3, and GZMB) and 15 candidates miRNAs-target 3 genes regulatory pathways representing potentially novel biomarkers of ASD diseases were revealed. These findings could enhance our knowledge of ASD and offer possible therapeutic targets of ASD patients in the future.

ScKINETICS: inference of regulatory velocity with single-cell transcriptomics data.

Transcriptional dynamics are governed by the action of regulatory proteins and are fundamental to systems ranging from normal development to disease. RNA velocity methods for tracking phenotypic dynamics ignore information on the regulatory drivers of gene expression variability through time. We introduce scKINETICS (Key regulatory Interaction NETwork for Inferring Cell Speed), a dynamical model of gene expression change which is fit with the simultaneous learning of per-cell transcriptional velocities and a governing gene regulatory network. Fitting is accomplished through an expectation-maximization approach designed to learn the impact of each regulator on its target genes, leveraging biologically motivated priors from epigenetic data, gene-gene coexpression, and constraints on cells' future states imposed by the phenotypic manifold. Applying this approach to an acute pancreatitis dataset recapitulates a well-studied axis of acinar-to-ductal transdifferentiation whilst proposing novel regulators of this process, including factors with previously appreciated roles in driving pancreatic tumorigenesis. In benchmarking experiments, we show that scKINETICS successfully extends and improves existing velocity approaches to generate interpretable, mechanistic models of gene regulatory dynamics. All python code and an accompanying Jupyter notebook with demonstrations are available at http://github.com/dpeerlab/scKINETICS.

Dynamics-Based Regulatory Switches of Type II Antitoxins: Insights into New Antimicrobial Discovery.

Type II toxin-antitoxin (TA) modules are prevalent in prokaryotes and are involved in cell maintenance and survival under harsh environmental conditions, including nutrient deficiency, antibiotic treatment, and human immune responses. Typically, the type II TA system consists of two protein components: a toxin that inhibits an essential cellular process and an antitoxin that neutralizes its toxicity. Antitoxins of type II TA modules typically contain the structured DNA-binding domain responsible for TA transcription repression and an intrinsically disordered region (IDR) at the C-terminus that directly binds to and neutralizes the toxin. Recently accumulated data have suggested that the antitoxin's IDRs exhibit variable degrees of preexisting helical conformations that stabilize upon binding to the corresponding toxin or operator DNA and function as a central hub in regulatory protein interaction networks of the type II TA system. However, the biological and pathogenic functions of the antitoxin's IDRs have not been well discussed compared with those of IDRs from the eukaryotic proteome. Here, we focus on the current state of knowledge about the versatile roles of IDRs of type II antitoxins in TA regulation and provide insights into the discovery of new antibiotic candidates that induce toxin activation/reactivation and cell death by modulating the regulatory dynamics or allostery of the antitoxin.

Identification of anoikis-related genes classification patterns and immune infiltration characterization in ischemic stroke based on machine learning.

Ischemic stroke (IS) is a type of stroke that leads to high mortality and disability. Anoikis is a form of programmed cell death. When cells detach from the correct extracellular matrix, anoikis disrupts integrin junctions, thus preventing abnormal proliferating cells from growing or attaching to an inappropriate matrix. Although there is growing evidence that anoikis regulates the immune response, which makes a great contribution to the development of IS, the role of anoikis in the pathogenesis of IS is rarely explored. First, we downloaded GSE58294 set and GSE16561 set from the NCBI GEO database. And 35 anoikis-related genes (ARGs) were obtained from GSEA website. The CIBERSORT algorithm was used to estimate the relative proportions of 22 infiltrating immune cell types. Next, consensus clustering method was used to classify ischemic stroke samples. In addition, we used least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms to screen the key ARGs in ischemic stroke. Next, we performed receiver operating characteristics (ROC) analysis to assess the accuracy of each diagnostic gene. At the same time, the nomogram was constructed to diagnose IS by integrating trait genes. Then, we analyzed the correlation between gene expression and immune cell infiltration of the diagnostic genes in the combined database. And gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on these genes to explore differential signaling pathways and potential functions, as well as the construction and visualization of regulatory networks using NetworkAnalyst and Cytoscape. Finally, we investigated the expression pattern of ARGs in IS patients across age or gender. Our study comprehensively analyzed the role of ARGs in IS for the first time. We revealed the expression profile of ARGs in IS and the correlation with infiltrating immune cells. And The results of consensus clustering analysis suggested that we can classify IS patients into two clusters. The machine learning analysis screened five signature genes, including AKT1, BRMS1, PTRH2, TFDP1 and TLE1. We also constructed nomogram models based on the five risk genes and evaluated the immune infiltration correlation, gene-miRNA, gene-TF and drug-gene interaction regulatory networks of these signature genes. The expression of ARGs did not differ by sex or age. This study may provide a beneficial reference for further elucidating the pathogenesis of IS, and render new ideas for drug screening, individualized therapy and immunotherapy of IS.

Potential biomarkers for the prognosis and treatment of HCC immunotherapy.

The liver is a unique organ containing large populations of immune cells. Immunotherapy for liver cancer is a promising yet particularly challenging method. Therefore, it harbors great significance for the identification of immune-related subtypes and the potential therapeutic targets for hepatocellular carcinoma (HCC). Firstly, we classified the HCC samples downloaded from the dataset of Cancer Genome Atlas (TCGA) into two clusters based on the immune cell infiltration. Thereafter, we identified the significant module and regulatory factors using the weighted gene co-expression network analysis (WGCNA). The immune competence of the regulatory factors was delineated through the ESTIMATE algorithm, the analysis of the tumor microenvironment, and pan-cancer analysis. In the single-cell RNA sequencing analysis, we further explored the immune competence of regulatory factors. We also collected the potential drugs targeting the regulatory factors. In addition, we constructed lncRNA-miRNA-mRNA interaction regulatory networks. Finally, western blot and quantitative real-time polymerase chain reaction (qRT-PCR) were conducted to verify the protein expression of regulatory genes in HCC cell lines and tissues. According to the immune cell infiltration, two immune-related subtypes-cluster 1 and cluster 2-were found. Patients in cluster 2 had a more significant immune infiltration than in cluster 1. Afterward, six significant regulatory genes were identified through WGCNA, and the expression in cluster 2 was high in cluster 1. We performed a comprehensive analysis to clarify the immune signature. The results showed that the six genes had significant immunological competence. Moreover, the expression of the six genes was similar to the subtypes' classification. In the analysis of the prognosis value, patients in cluster 2 had a better prognosis. In addition, the lncRNA in the lncRNA-miRNA-mRNA interaction regulatory networks was located in the nucleus and cytoplasm. In the single-cell RNA sequencing analysis, the six genes were related to the immune cell. We also identified potential drugs for CD6 and CLEC12A, which may provide potential therapeutic drugs. Finally, the regulatory genes were verified in the western blot and quantitative real-time polymerase chain reaction. The classification into two clusters based on the immune cell infiltration may provide a promising prospect for HCC through immunotherapy. The six regulatory genes may be potential therapeutic targets in the treatment of HCC.

Cell-Type-Specific Gene Regulatory Networks of Pro-Inflammatory and Pro-Resolving Lipid Mediator Biosynthesis in the Immune System.

Lipid mediators are important regulators in inflammatory responses, and their biosynthetic pathways are targeted by commonly used anti-inflammatory drugs. Switching from pro-inflammatory lipid mediators (PIMs) to specialized pro-resolving (SPMs) is a critical step toward acute inflammation resolution and preventing chronic inflammation. Although the biosynthetic pathways and enzymes for PIMs and SPMs have now been largely identified, the actual transcriptional profiles underlying the immune cell type-specific transcriptional profiles of these mediators are still unknown. Using the Atlas of Inflammation Resolution, we created a large network of gene regulatory interactions linked to the biosynthesis of SPMs and PIMs. By mapping single-cell sequencing data, we identified cell type-specific gene regulatory networks of the lipid mediator biosynthesis. Using machine learning approaches combined with network features, we identified cell clusters of similar transcriptional regulation and demonstrated how specific immune cell activation affects PIM and SPM profiles. We found substantial differences in regulatory networks in related cells, accounting for network-based preprocessing in functional single-cell analyses. Our results not only provide further insight into the gene regulation of lipid mediators in the immune response but also shed light on the contribution of selected cell types in their biosynthesis.

Phylogeny, transcriptional profile, and auxin-induced phosphorylation modification characteristics of conserved PIN proteins in Moso bamboo (Phyllostachys edulis)

Auxin polar transport is an important way for auxin to exercise its function, and auxin plays an irreplaceable role in the rapid growth of Moso bamboo. We identified and performed the structural analysis of PIN-FORMED auxin efflux carriers in Moso bamboo and obtained a total of 23 PhePIN genes from five gene subfamilies. We also performed chromosome localization and intra- and inter-species synthesis analysis. Phylogenetic analyses of 216 PIN genes showed that PIN genes are relatively conserved in the evolution of the Bambusoideae and have undergone intra-family segment replication in Moso bamboo. The PIN genes' transcriptional patterns showed that the PIN1 subfamily plays a major regulatory role. PIN genes and auxin biosynthesis maintain a high degree of consistency in spatial and temporal distribution. Phosphoproteomics analysis identified many phosphorylated protein kinases that respond to auxin regulation through autophosphorylation and phosphorylation of PIN proteins. The protein interaction network showed that there is a plant hormone interaction regulatory network with PIN protein as the core. We provide a comprehensive PIN protein analysis that complements the auxin regulatory pathway in Moso bamboo and paves the way for further auxin regulatory studies in bamboo.

PIEZO1-Related Physiological and Pathological Processes in CNS: Focus on the Gliomas.

PIEZO1 is ubiquitously expressed in cells in different kinds of tissues throughout the body, which can sense physical or mechanical stimuli and translate them into intracellular electrochemical signals to regulate organism functions. In particular, PIEZO1 appears in complex interactive regulatory networks as a central node, governing normal and pathological functions in the body. However, the effect and mechanism of the activation or expression of PIEZO1 in diseases of the central nervous system (CNS) remain unclear. On one hand, in CNS diseases, pathophysiological processes in neurons and glial are often accompanied by variations in the mechanical properties of the cellular and extracellular matrix stiffness. The expression of PIEZO1 can therefore be upregulated, in responding to mechanical stimulation, to drive the biological process in cells, which in turns indirectly affects the cellular microenvironment, resulting in alterations of the cellular status. On the other hand, it may have contradictory effects with the change of active patterns and/or subcellular location. This review highlights the biological processes involved with PIEZO1 in CNS cells, with special emphasis on its multiple roles in glioma-associated phenotypes. In conclusion, PIEZO1 can be used as an indicator to assess the malignancy and prognosis of patients with gliomas, as well as a therapeutic target for clinical application following fully exploring the potential mechanism of PIEZO1 in CNS diseases.

Bioinformatics Identification of miRNA-mRNA Regulatory Network Contributing Primary Lung Cancer

Introduction: In clinical practice, distinguishing invasive lung tumors from primary tumors remains a challenge. With recent advances in understanding biological alterations of tumorigenesis and molecular analytic technologies, using these molecular alterations can be sensitive and tumor-specific as biomarker for the stratification of patients. In this study, the molecular network of miRNA-mRNA contributing to primary lung cancer has been assessed by bioinformatics approaches.
 Methods: In this analytical-observational study gene expression profiles of patients with primary lung cancer were collected from the RNASeq data of the Cancer Genome Atlas (TCGA) database by TCGAbiolinks package. With edgeR and limma packages in R, non-specific expression genes were filtered and the significant differentially expressed mRNAs and miRNAs between tumor tissues and normal tissues were saved and their targets were predicted by 2 databases; miRWalk, and Targetscan. Subsequently, the interaction regulatory network of miRNA-mRNA was visualized using Cytoscape software.
 Results: By miRNA-mRNA network analysis revealed that, 7 miRNAs included; hsa-miR-373-3p, hsa-let-7a-5p, hsa-miR-23b-3p, hsa-miR-152-3p, hsa-miR -216a-3p, hsa-miR-106-5p, hsa-let-7i-5p and 6 miRNAs including; has-miR-107, has-miR-17-5p, has-185-5p, has-miR-34a- 5p, has-miR-130a-5p and has-96-5p, mediated regulation of up-regulated and down-regulated mRNAs in primary lung cancer patients, respectively.
 Conclusion: This bioinformatics study proposes a miRNA–mRNA network associated with primary lung cancer, which may help to screening and new therapeutic targets for primary lung cancer as prognostic marker.

Identifying novel regulators of placental development using time-series transcriptome data.

The placenta serves as a connection between the mother and the fetus during pregnancy, providing the fetus with oxygen, nutrients, and growth hormones. However, the regulatory mechanisms and dynamic gene interaction networks underlying early placental development are understudied. Here, we generated RNA-sequencing data from mouse fetal placenta at embryonic days 7.5, 8.5, and 9.5 to identify genes with timepoint-specific expression, then inferred gene interaction networks to analyze highly connected network modules. We determined that timepoint-specific gene network modules were associated with distinct developmental processes, and with similar expression profiles to specific human placental cell populations. From each module, we identified hub genes and their direct neighboring genes, which were predicted to govern placental functions. We confirmed that four novel candidate regulators identified through our analyses regulate cell migration in the HTR-8/SVneo cell line. Overall, we predicted several novel regulators of placental development expressed in specific placental cell types using network analysis of bulk RNA-sequencing data. Our findings and analysis approaches will be valuable for future studies investigating the transcriptional landscape of early development.

CircRNA screening and ceRNA network construction for milk fat metabolism in dairy cows.

Milk fat is one of the main reference elements for evaluating milk quality and is a primary objective trait in dairy cattle breeding. In recent years, circular RNAs (circRNAs) have been found to play crucial roles in many biological processes. However, the function and expression profiles of circRNAs in milk fat synthesis in cows are not completely understood. We performed RNA sequencing to analyze the genome-wide expression of circRNA transcripts in bovine mammary epithelial cells (BMECs) from cows with extreme differences in milk fat percentage. We identified candidate differential circRNAs associated with milk fat metabolism using functional enrichment analysis and constructed a lipid metabolism-related competing endogenous RNA (ceRNA) interactive regulatory network. A total of 290 circRNAs were significantly differentially expressed (DE-circRNAs) in high milk fat percentage (HMF) cows compared to that in low milk fat percentage (LMF) cows. Of the 290 circRNAs, 142 were significantly upregulated and 148 were significantly downregulated. Enrichment analysis (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) identified four DE-circRNAs (circ_0001122, circ_0007367, circ_0018269, and circ_0015179) that potentially regulate milk fat metabolism. Among them, circ_0001122, circ_0007367, and circ_0015179 had relatively high expression levels in cow mammary gland tissue compared to other tissues (heart, liver, kidney, uterus, ovaries, and small intestine) of cows. The regulatory networks circ_0001122:miR-12043:LIPG, circ_0007367:miR-331-3p:CIDEA/PML, and circ_0018269:miR-11989:RORC/HPX are potential networks to explore the mechanism of milk fat regulation. These results reveal the possible role of circRNAs in milk fat metabolism in dairy cows. Several important circRNAs and ceRNAs affecting milk fat synthesis were identified, providing insights into the complex biology of milk fat synthesis as well as a novel theoretical perspective for future research on lactation, milk quality, and breed improvement in dairy cows.

Exploring the nucleotide molecular mechanism of compound kushen injection for lung adenocarcinoma based on network pharmacology and molecular docking.

Compound kushen injection is an effective traditional Chinese medicine for the treatment of lung cancer. However, its influence on the survival and prognosis of patients with lung adenocarcinoma patients was less studied; especially its pharmacological mechanism remains to be further elucidated. In the present study, we adopted a network pharmacology (NP)-based approach to screening effective compounds, screening and predicting target genes, analyzing biological functions and pathways, constructing a regulatory network and protein interaction network, and screening the key targets. Moreover, mass survival analysis and molecular docking were conducted. In the end, 35 key compounds and four possible central target genes were screened out, which could be used for the treatment of lung adenocarcinoma and affected the survival and prognosis of patients with lung adenocarcinoma. In addition, their key compounds had good docking affinity. Enrichment analysis showed that CKI might affect the treatment and prognosis of lung adenocarcinoma patients by regulating the PI3K-Akt signaling pathway, TNF signaling pathway, non-small cell lung cancer, Hepatitis C, etc. We discussed the pharmacological mechanisms and potential therapeutic targets of CKI in the treatment of lung adenocarcinoma, which verified the effect of CKI on the prognosis and survival of patients. The present study might promote the further clinical application of CKI and provide a theoretical basis for further experimental studies.

CircRNAs Related to Breast Muscle Development and Their Interaction Regulatory Network in Gushi Chicken.

Circular RNAs (circRNAs) play a significant regulatory role during skeletal muscle development. To identify circRNAs during postnatal skeletal muscle development in chickens, we constructed 12 cDNA libraries from breast muscle tissues of Chinese Gushi chickens at 6, 14, 22, and 30 weeks and performed RNA sequencing. In total, 2112 circRNAs were identified, and among them 79.92% were derived from exons. CircRNAs are distributed on all chromosomes of chickens, especially chromosomes 1-9 and Z. Bioinformatics analysis showed that each circRNA had an average of 38 miRNA binding sites, 61.32% of which have internal ribosomal entry site (IRES) elements. Furthermore, in total 543 differentially expressed circRNAs (DE-circRNAs) were identified. Functional enrichment analysis revealed that DE-circRNAs source genes are engaged in biological processes and muscle development-related pathways; for example, cell differentiation, sarcomere, and myofibril formation, mTOR signaling pathway, and TGF-β signaling pathway, etc. We also established a competitive endogenous RNA (ceRNA) regulatory network associated with skeletal muscle development. The results in this report indicate that circRNAs can mediate the development of chicken skeletal muscle by means of a complex ceRNA network among circRNAs, miRNAs, genes, and pathways. The findings of this study might help increase the number of known circRNAs in skeletal muscle tissue and offer a worthwhile resource to further investigate the function of circRNAs in chicken skeletal muscle development.

Clinical relevance of CERK and SPHK1 in breast cancer and their association with metastasis and drug resistance

Despite numerous reports on the altered sphingolipids metabolism in human cancers, their clinical significance in breast cancer remains obscure. Previously, we identified the high levels of sphingolipids, ceramide phosphates and sphingosine phosphates, and the genes involved in their synthesis, CERK and SPHK1, in breast cancer patients. The present study aimed to determine the correlations of CERK and SPHK1 with clinical outcomes as well as metastasis and drug resistance markers. Both local and TCGA cohorts were analysed. High-confidence regulatory interaction network was constructed to find association of target genes with metastasis and drug resistance. Furthermore, correlations of CERK and SPHK1 with selected metastasis and drug resistance markers were validated in both cohorts. Overexpression of CERK and SPHK1 was associated with nodal metastasis, late tumor stage and high proliferation potency. In addition, increased CERK expression was also indicative of poor patient survival. Computational network analysis revealed the association of CERK and SPHK1 with known metastasis markers MMP-2 and MMP-9 and drug resistance markers ABCC1 and ABCG2. Correlation analysis confirmed the associations of target genes with these markers in both local as well as TCGA cohort. The above findings suggest clinical utility of CERK and SPHK1 as potential biomarkers in breast cancer patients and thus could provide novel leads in the development of therapeutics.

Identification of critical lncRNAs for milk fat metabolism in dairy cows using WGCNA and the construction of a ceRNAs network.

As key regulators, long non-coding RNAs (lncRNAs) play a crucial role in the ruminant mammary gland. However, the function of lncRNAs in milk fat synthesis from dairy cows is largely unknown. In this study, we used the weighted gene co-expression network analysis (WGCNA) to comprehensive analyze the expression profile data of lncRNAs from the group's previous Illumina PE150 sequencing results based on bovine mammary epithelial cells from high- and low-milk-fat-percentage (MFP) cows, and identify core_lncRNAs significantly associated with MFP by module membership (MM) and gene significance (GS). Functional enrichment analysis (Gene Ontology, Kyoto Encyclopedia of Genes and Genomes) of core_lncRNA target genes (co-localization and co-expression) was performed to screen potential lncRNAs regulating milk fat metabolism and further construct an interactive regulatory network of lipid metabolism-related competing endogenous RNAs (ceRNAs). A total of 4876 lncRNAs were used to construct the WGCNA. The MEdarkturquoise module among the 19 modules obtained was significantly associated with MFP (r=0.78, p-value <0.05) and contained 64 core_lncRNAs (MM > 0.8, GS > 0.4). Twenty-four lipid metabolism-related lncRNAs were identified by core_lncRNA target gene enrichment analysis. TCONS_00054233, TCONS_00152292, TCONS_00048619, TCONS_00033839, TCONS_00153791 and TCONS_00074642 were key candidate lncRNAs for regulating milk fat synthesis. The 22 ceRNAs most likely to be involved in milk fat metabolism were constructed by interaction network analysis, and TCONS_00133813 and bta-miR-2454-5p were located at the network's core. TCONS_00133813_bta-miR-2454-5p_TNFAIP3, TCONS_00133813_bta-miR-2454-5p_ARRB1 and TCONS_00133813_bta-miR-2454-5p_PIK3R1 are key candidate ceRNAs associated with milk fat metabolism. This study provides a framework for the co-expression module of MFP-related lncRNAs in ruminants, identifies several major lncRNAs and ceRNAs that influence milk fat synthesis, and provides a new understanding of the complex biology of milk fat synthesis in dairy cows.

Bioinformatics and network-based screening and discovery of potential molecular targets and small molecular drugs for breast cancer.

Accurate identification of molecular targets of disease plays an important role in diagnosis, prognosis, and therapies. Breast cancer (BC) is one of the most common malignant cancers in women worldwide. Thus, the objective of this study was to accurately identify a set of molecular targets and small molecular drugs that might be effective for BC diagnosis, prognosis, and therapies, by using existing bioinformatics and network-based approaches. Nine gene expression profiles (GSE54002, GSE29431, GSE124646, GSE42568, GSE45827, GSE10810, GSE65216, GSE36295, and GSE109169) collected from the Gene Expression Omnibus (GEO) database were used for bioinformatics analysis in this study. Two packages, LIMMA and clusterProfiler, in R were used to identify overlapping differential expressed genes (oDEGs) and significant GO and KEGG enrichment terms. We constructed a PPI (protein–protein interaction) network through the STRING database and identified eight key genes (KGs) EGFR, FN1, EZH2, MET, CDK1, AURKA, TOP2A, and BIRC5 by using six topological measures, betweenness, closeness, eccentricity, degree, MCC, and MNC, in the Analyze Network tool in Cytoscape. Three online databases GSCALite, Network Analyst, and GEPIA were used to analyze drug enrichment, regulatory interaction networks, and gene expression levels of KGs. We checked the prognostic power of KGs through the prediction model using the popular machine learning algorithm support vector machine (SVM). We suggested four TFs (TP63, MYC, SOX2, and KDM5B) and four miRNAs (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, and hsa-mir-23b-3p) as key transcriptional and posttranscriptional regulators of KGs. Finally, we proposed 16 candidate repurposing drugs YM201636, masitinib, SB590885, GSK1070916, GSK2126458, ZSTK474, dasatinib, fedratinib, dabrafenib, methotrexate, trametinib, tubastatin A, BIX02189, CP466722, afatinib, and belinostat for BC through molecular docking analysis. Using BC cell lines, we validated that masitinib inhibits the mTOR signaling pathway and induces apoptotic cell death. Therefore, the proposed results might play an effective role in the treatment of BC patients.

Network pharmacology and molecular docking reveal zedoary turmeric-trisomes in Inflammatory bowel disease with intestinal fibrosis.

BACKGROUNDInflammatory bowel disease (IBD) is a complex chronic IBD that is closely associated with risk factors such as environment, diet, medications and lifestyle that may influence the host microbiome or immune response to antigens. At present, with the increasing incidence of IBD worldwide, it is of great significance to further study the pathogenesis of IBD and seek new therapeutic targets. Traditional Chinese medicine (TCM) treatment of diseases is characterized by multiple approaches and multiple targets and has a long history of clinical application in China. The mechanism underlying the effect of zedoary turmeric-trisomes on inducing mucosal healing in IBD is not clear.AIMTo explore the effective components and potential mechanism of zedoary turmeric-trisomes in the treatment of IBD with intestinal fibrosis using network pharmacology and molecular docking techniques.METHODSThe chemical constituents and targets of Rhizoma zedoary and Rhizoma sanarum were screened using the TCMSP database. The GeneCards database was searched to identify targets associated with intestinal fibrosis in IBD. The intersection of chemical component targets and disease targets was obtained using the Venny 2.1 online analysis platform, and the common targets were imported into the STRING 11.0 database to construct a protein interaction regulatory network. A “zedoary turmeric-trisomes-chemical composition-target-disease” network diagram was subsequently constructed using Cytoscape 3.7.2 software, and the topological properties of the network were analyzed using the “Network Analysis” plug-in. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the common targets were performed using the DAVID 6.8 database to elucidate the mechanism of zedoary turmeric-trisomes in the treatment of IBD. Subsequently, molecular docking of the compounds and targets with the highest intermediate values in the “zedoary turmeric-trisomes-chemical composition-target-disease” network was performed using Sybyl-x 2.1.1 software.RESULTSA total of 5 chemical components with 60 targets were identified, as well as 3153 targets related to IBD and 44 common targets. The protein-protein interaction network showed that the core therapeutic targets included JUN, MAPK14, CASP3, AR, and PTGS2. The GO enrichment analysis identified 759 items, and the KEGG enrichment analysis yielded 52 items, including the cancer pathway, neuroactive ligand-receptor interaction, hepatitis B, and the calcium signaling pathway, reflecting the complex biological processes of the multicomponent, multitarget and multipathway treatment of diseases with zedoary turmeric-trisomes. Molecular docking showed that the compound bonded with the target through hydrogen bond interactions and exhibited good docking activity.CONCLUSIONThis study identified the potential mechanism of action of zedoary turmeric-trisomes in the treatment of inflammatory bowel fibrosis using network pharmacology and molecular docking technology, providing a scientific basis for further expansion of their clinical use.

Peeling Back the Layers of Lymph Gland Structure and Regulation.

During the past 60 years, the fruit fly, Drosophila melanogaster, has proven to be an excellent model to study the regulation of hematopoiesis. This is not only due to the evolutionarily conserved signalling pathways and transcription factors contributing to blood cell fate, but also to convergent evolution that led to functional similarities in distinct species. An example of convergence is the compartmentalization of blood cells, which ensures the quiescence of hematopoietic stem cells and allows for the rapid reaction of the immune system upon challenges. The lymph gland, a widely studied hematopoietic organ of the Drosophila larva, represents a microenvironment with similar features and functions to classical hematopoietic stem cell niches of vertebrates. Lymph gland studies were effectively supported by the unparalleled toolkit developed in Drosophila, which enabled the high-resolution investigation of the cellular composition and regulatory interaction networks of the lymph gland. In this review, we summarize how our understanding of lymph gland structure and hematopoietic cell-to-cell communication evolved during the past decades and compare their analogous features to those of the vertebrate hematopoietic stem cell niche.