Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles

Luteinizing Hormone Receptor Mutation (LHRN316S) Causes Abnormal Follicular Development Revealed by Follicle Single-Cell Analysis and CRISPR/Cas9

Abnormal interaction between granulosa cells and oocytes causes disordered development of ovarian follicles. However, the interactions between oocytes and cumulus granulosa cells (CGs), oocytes and mural granulosa cells (MGs), and CGs and MGs remain to be fully explored. Using single-cell RNA-sequencing (scRNA-seq), we determined the transcriptional profiles of oocytes, CGs and MGs in antral follicles. Analysis of scRNA-seq data revealed that CGs may regulate follicular development through the BMP15-KITL-KIT-PI3K-ARF6 pathway with elevated expression of luteinizing hormone receptor (LHR). Because internalization of the LHR is regulated by Arf6, we constructed LHRN316S mice by CRISPR/Cas9 to further explore mechanisms of follicular development and novel treatment strategies for female infertility. Ovaries of LHRN316S mice exhibited reduced numbers of corpora lutea and ovulation. The LHRN316S mice had a reduced rate of oocyte maturation in vitro and decreased serum progesterone levels. Mating LHRN316S female mice with ICR wild type male mice revealed that the infertility rate of LHRN316S mice was 21.4% (3/14). Litter sizes from LHRN316S mice were smaller than those from control wild type female mice. The oocytes from LHRN316S mice had an increased rate of maturation in vitro after progesterone administration in vitro. Furthermore, progesterone treated LHRN316S mice produced offspring numbers per litter equivalent to WT mice. These findings provide key insights into cellular interactions in ovarian follicles and provide important clues for infertility treatment.

Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas

Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not-and sometimes cannot-be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.

Comparative analysis of the hypothalamus transcriptome of laying ducks with different residual feeding intake

Feed costs account for approximately 60 to 70% of the cost of poultry farming, and feed utilization is closely related to the profitability of the poultry industry. To understand the causes of the differences in feeding in Shan Partridge ducks, we compared the hypothalamus transcriptome profiles of 2 groups of ducks using RNA-seq. The 2 groups were: 1) low-residual feed intake (LRFI) group with low feed intake but high feed efficiency, and 2) high-residual feed intake (HRFI) group with high feed intake but low feed efficiency. We found 78 DEGs were enriched in 9 differential Kyoto Encyclopedia of Genes and Genome (KEGG) pathways, including neuroactive ligand-receptor interaction, GABAergic synapse, nitrogen metabolism, cAMP signaling pathway, calcium signaling pathway, nitrogen metabolism, tyrosine metabolism, ovarian steroidogenesis, and gluconeogenesis. To further identify core genes among the 78 DEGs, we performed protein-protein interaction and coexpression network analyses. After comprehensive analysis and experimental validation, 4 core genes, namely, glucagon (GCG), cholecystokinin (CCK), gamma-aminobutyric acid type A receptor subunit delta (GABRD), and gamma-aminobutyric acid type A receptor subunit beta1 (GABRB1), were identified as potential core genes responsible for the difference in residual feeding intake between the 2 breeds. We also investigated the level of cholecystokinin (CCK), neuropeptide Y (NPY), peptide YY (PYY), ghrelin, and glucagon-like peptide1 (GLP-1) hormones in the sera of Shan Partridge ducks at different feeding levels and found that there was a difference between the 2 groups with respect to GLP-1 and NPY levels. The findings will serve as a reference for future research on the feeding efficiency of Shan Partridge ducks and assist in promoting their genetic breeding.

CircRNA0007766 accelerates cancer progression via miR-34c-5p/cyclin D1 axis in adenocarcinoma of the esophagogastric junction (AEG)

Growing empirical evidence shows that circular RNAs (circRNAs) are implicated in tumor pathogenesis. However, little is known about the mechanism by which circRNAs contribute to the progression of adenocarcinoma of the esophagogastric junction (AEG). We conducted RNA high-throughput sequencing and bioinformatic analyses on 22 AEG tissues and their matching healthy gastric mucosal tissues and found that circRNA0007766 may act as a tumor promoter in AEG pathogenesis. BaseScope® in situ hybridization revealed that circRNA0007766 was strongly upregulated in AEG. We then constructed co-expression and ceRNA networks to elucidate the relationships among specific circRNAs, microRNAs (miRNAs), and mRNAs. We also demonstrated that circRNA0007766 acted as the sponge of miR-34c-5p, thereby positively regulating cyclin D1. In vivo and in vitro experiments demonstrated the roles of circRNA0007766 in promoting AEG progression and invasion. AEG tissues are characterized by circRNA0007766 upregulation which is correlated with lymph node metastasis and poor survival. To the best of our knowledge, the present study is one of the first to show that the circRNA0007766/miR-34c-5p/cyclin D1 axis is important in AEG progression. Furthermore, the results of this work imply that circRNA0007766 is potentially a novel AEG biomarker.

Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model

Glioblastoma multiforme (GBM) patients show a variety of signs and symptoms that affect their quality of life (QOL) and self-dependence. Since most existing studies have examined prognostic factors based only on clinical factors, there is a need to consider the value of integrating multi-omics data including gene expression and proteomics with clinical data in identifying significant biomarkers for GBM prognosis. Our research aimed to isolate significant features that differentiate between short-term (≤ 6 months) and long-term (≥ 2 years) GBM survival, and between high Karnofsky performance scores (KPS ≥ 80) and low (KPS ≤ 60), using the iterative random forest (iRF) algorithm. Using the Cancer Genomic Atlas (TCGA) database, we identified 35 molecular features composed of 19 genes and 16 proteins. Our findings propose molecular signatures for predicting GBM prognosis and will improve clinical decisions, GBM management, and drug development.

Hub genes in adenocarcinoma of the esophagogastric junction based on weighted gene co-expression network analysis and immunohistochemistry

BACKGROUND

Gastric cancer (GC) is the fifth most common malignant tumor, and it is usually fatal. Adenocarcinoma of the esophagogastric junction (AEG) accounts for about 50% of all GC cases. However, the systematic co-expression analysis of this tumor does not fully explain its pathogenesis. This study aimed to identify hub genes based on weighted gene co-expression networks and immunohistochemistry analyses.

METHODS

The RNA-seq data of 22 AEG patients were processed using weighted gene co-expression network analysis. We differentiated the modules with clinical tumor markers and performed Gene Ontology and pathway enrichment analysis. We identified the hub genes related to the biological processes of tumorigenesis based on weighted gene co-expression network analysis and immunohistochemistry analysis.

RESULTS

Twenty-five distinct co-expression gene modules were identified; the tumorigenic genes CD93, TRIM28, SLC3A2, CBX4, PATL1, and ZNF473 had high intramodular connectivity. Immunohistochemistry confirmed that these hub genes are upregulated in AEG. Statistical analysis indicated that the expression of CD93 was correlated with the T stage and maximum tumor diameter.

CONCLUSION

Weighted gene co-expression network analysis and immunohistochemistry identified CD93 as a hub gene that might be critical for AEG biology.

Integrated bioinformatics and statistical approach to identify the common molecular mechanisms of obesity that are linked to the development of two psychiatric disorders: Schizophrenia and major depressive disorder

Obesity is a chronic multifactorial disease characterized by the accumulation of body fat and serves as a gateway to a number of metabolic-related diseases. Epidemiologic data indicate that Obesity is acting as a risk factor for neuro-psychiatric disorders such as schizophrenia, major depression disorder and vice versa. However, how obesity may biologically interact with neurodevelopmental or neurological psychiatric conditions influenced by hereditary, environmental, and other factors is entirely unknown. To address this issue, we have developed a pipeline that integrates bioinformatics and statistical approaches such as transcriptomic analysis to identify differentially expressed genes (DEGs) and molecular mechanisms in patients with psychiatric disorders that are also common in obese patients. Biomarker genes expressed in schizophrenia, major depression, and obesity have been used to demonstrate such relationships depending on the previous research studies. The highly expressed genes identify commonly altered signalling pathways, gene ontology pathways, and gene-disease associations across disorders. The proposed method identified 163 significant genes and 134 significant pathways shared between obesity and schizophrenia. Similarly, there are 247 significant genes and 65 significant pathways that are shared by obesity and major depressive disorder. These genes and pathways increase the likelihood that psychiatric disorders and obesity are pathogenic. Thus, this study may help in the development of a restorative approach that will ameliorate the bidirectional relation between obesity and psychiatric disorder. Finally, we also validated our findings using genome-wide association study (GWAS) and whole-genome sequence (WGS) data from SCZ, MDD, and OBE. We confirmed the likely involvement of four significant genes both in transcriptomic and GWAS/WGS data. Moreover, we have performed co-expression cluster analysis of the transcriptomic data and compared it with the results of transcriptomic differential expression analysis and GWAS/WGS.

LncRNA landscape and associated ceRNA network in placental villus of unexplained recurrent spontaneous abortion

BACKGROUND

Unexplained recurrent spontaneous abortion (URSA) is one of the most challenging conditions frustrates women of childbearing age profoundly. The gene expression patterns and biological characteristics of placental villus in patients with URSA remain largely unknown. The aim of our study was to identify potential lncRNAs as well as their action mechanisms in URSA.

METHOD

The ceRNA microarray was used to identify the mRNA and lncRNA expression profiles of URSA patients and normal pregnancy. Functional enrichment analyses for differentially expressed mRNAs in URSA were performed. Protein-protein interaction analysis of differentially expressed mRNAs was performed to identify hub genes and key modules. Subsequently, the co-dysregulated ceRNA network of URSA was established, and the enrichment analyses for the mRNAs in the ceRNA network was implemented. qRT-PCR was performed to validated the expression of key ENST00000429019 and mRNAs in URSA.

RESULTS

We found that URSA placental villus have distinct mRNA and lncRNA expression profiles through ceRNA microarray, with a total of 347 mRNAs and 361 lncRNAs differentially expressed compared with controls. The functional enrichment analysis revealed that ncRNA processing, DNA replication, cell cycle, apoptosis, cytokine-mediated signaling pathway, ECM-receptor interaction were the potentially disrupted pathways in URSA patients. Then we constructed a co-dysregulated ceRNA network and found differentially expressed mRNAs were regulated by a small fraction of hub lncRNAs. Finally, we found a key network of ENST00000429019 and three cell proliferation or apoptosis related key mRNAs (CDCA3, KIFC1, NCAPH), and validated their expression and regulation in tissue and cellular levels.

CONCLUSIONS

This study identified a key ceRNA network, which might take part in URSA and correlate with cell proliferation and apoptosis. Optimistically, this study may deepen our apprehensions about the underlying molecular and biological causes of URSA and provide an important theoretical basis for future therapeutic strategies for patients with URSA.

High-Throughput Sequencing Reveals That Rotundine Inhibits Colorectal Cancer by Regulating Prognosis-Related Genes

Background: Rotundine is an herbal medicine with anti-cancer effects. However, little is known about the anti-cancer effect of rotundine on colorectal cancer. Therefore, our study aimed to investigate the specific molecular mechanism of rotundine inhibition of colorectal cancer. Methods: MTT and cell scratch assay were performed to investigate the effects of rotundine on the viability, migration, and invasion ability of SW480 cells. Changes in cell apoptosis were analyzed by flow cytometry. DEGs were detected by high-throughput sequencing after the action of rotundine on SW480 cells, and the DEGs were subjected to function enrichment analysis. Bioinformatics analyses were performed to screen out prognosis-related DEGs of COAD. Followed by enrichment analysis of prognosis-related DEGs. Furthermore, prognostic models were constructed, including ROC analysis, risk curve analysis, PCA and t-SNE, Nomo analysis, and Kaplan–Meier prognostic analysis. Results: In this study, we showed that rotundine concentrations of 50 μM, 100 μM, 150 μM, and 200 μM inhibited the proliferation, migration, and invasion of SW480 cells in a time- and concentration-dependent manner. Rotundine does not induce SW480 cell apoptosis. Compared to the control group, high-throughput results showed that there were 385 DEGs in the SW480 group. And DEGs were associated with the Hippo signaling pathway. In addition, 16 of the DEGs were significantly associated with poorer prognosis in COAD, with MEF2B, CCDC187, PSD2, RGS16, PLXDC1, HELB, ASIC3, PLCH2, IGF2BP3, CLHC1, DNHD1, SACS, H1-4, ANKRD36, and ZNF117 being highly expressed in COAD and ARV1 being lowly expressed. Prognosis-related DEGs were mainly enriched in cancer-related pathways and biological functions, such as inositol phosphate metabolism, enterobactin transmembrane transporter activity, and enterobactin transport. Prognostic modeling also showed that these 16 DEGs could be used as predictors of overall survival prognosis in COAD patients. Conclusions: Rotundine inhibits the development and progression of colorectal cancer by regulating the expression of these prognosis-related genes. Our findings could further provide new directions for the treatment of colorectal cancer.

Generating weighted and thresholded gene coexpression networks using signed distance correlation

Even within well-studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes or proteins, using a network of gene coexpression data that includes functional annotations. Signed distance correlation has proved useful for the construction of unweighted gene coexpression networks. However, transforming correlation values into unweighted networks may lead to a loss of important biological information related to the intensity of the correlation. Here we introduce a principled method to construct weighted gene coexpression networks using signed distance correlation. These networks contain weighted edges only between those pairs of genes whose correlation value is higher than a given threshold. We analyse data from different organisms and find that networks generated with our method based on signed distance correlation are more stable and capture more biological information compared to networks obtained from Pearson correlation. Moreover, we show that signed distance correlation networks capture more biological information than unweighted networks based on the same metric. While we use biological data sets to illustrate the method, the approach is general and can be used to construct networks in other domains. Code and data are available on https://github.com/javier-pardodiaz/sdcorGCN.

Identification of the Association Between Toll-Like Receptors and T-Cell Activation in Takayasu’s Arteritis

To explore the relationships between Toll-like receptors (TLRs) and the activation and differentiation of T-cells in Takayasu’s arteritis (TAK), using real-time fluorescence quantitative polymerase chain reaction, mRNA abundance of 29 target genes in peripheral blood mononuclear cells (PBMCs) were detected from 27 TAK patients and 10 healthy controls. Compared with the healthy control group, the untreated TAK group and the treated TAK group had an increased mRNA level of TLR2 and TLR4. A sample-to-sample matrix revealed that 80% of healthy controls could be separated from the TAK patients. Correlation analysis showed that the inactive-treated TAK group exhibited a unique pattern of inverse correlations between the TLRs gene clusters (including TLR1/2/4/6/8, BCL6, TIGIT, NR4A1, etc) and the gene cluster associated with T-cell activation and differentiation (including TCR, CD28, T-bet, GATA3, FOXP3, CCL5, etc). The dynamic gene co-expression network indicated the TAK groups had more active communication between TLRs and T-cell activation than healthy controls. BCL6, CCL5, FOXP3, GATA3, CD28, T-bet, TIGIT, IκBα, and NR4A1 were likely to have a close functional relation with TLRs at the inactive stage. The co-expression of TLR4 and TLR6 could serve as a biomarker of disease activity in treated TAK (the area under curve/sensitivity/specificity, 0.919/100%/90.9%). The largest gene co-expression cluster of the inactive-treated TAK group was associated with TLR signaling pathways, while the largest gene co-expression cluster of the active-treated TAK group was associated with the activation and differentiation of T-cells. The miRNA sequencing of the plasma exosomes combining miRDB, DIANA-TarBase, and miRTarBase databases suggested that the miR-548 family miR-584, miR-3613, and miR-335 might play an important role in the cross-talk between TLRs and T-cells at the inactive stage. This study found a novel relation between TLRs and T-cell in the pathogenesis of autoimmune diseases, proposed a new concept of TLR-co-expression signature which might distinguish different disease activity of TAK, and highlighted the miRNA of exosomes in TLR signaling pathway in TAK.

Chordin-like 1 is a novel prognostic biomarker and correlative with immune cell infiltration in lung adenocarcinoma

Chordin-like 1 (CHRDL1), an inhibitor of bone morphogenetic proteins(BMPs), has been recently reported to participate in the progression of numerous tumors, however, its role in lung adenocarcinoma (LUAD) remains unclear. Our study aimed to demonstrate relationship between CHRDL1 and LUAD based on data from The Cancer Genome Atlas (TCGA). Among them, CHRDL1 expression revealed promising power for distinguishing LUAD tissues form normal sample. Low CHRDL1 was correlated with poor clinicopathologic features, including high T stage (OR=0.45, P<0.001), high N stage (OR=0.57, P<0.003), bad treatment effect (OR=0.64, P=0.047), positive tumor status (OR=0.63, P=0.018), and TP53 mutation (OR=0.49, P<0.001). The survival curve illustrated that low CHRDL1 was significantly correlative with a poor overall survival (HR=0.60, P<0.001). At multivariate Cox regression analysis, CHRDL1 remained independently correlative with overall survival. GSEA identified that the CHRDL1 expression was related to cell cycle and immunoregulation. Immune infiltration analysis suggested that CHRDL1 was significantly correlative with 7 kinds of immune cells. Immunohistochemical validation showed that CHRDL1 was abnormally elevated and negatively correlated with Th2 cells in LUAD tissues. In conclusion, CHRDL1 might become a novel prognostic biomarker and therapy target in LUAD. Moreover, CHRDL1 may improve the effectiveness of immunotherapy by regulating immune infiltration.

On the Fourier transform of a quantitative trait: Implications for compressive sensing

This paper explores the genotype-phenotype relationship. It outlines conditions under which the dependence of a quantitative trait on the genome might be predictable, based on measurement of a limited subset of genotypes. It uses the theory of real-valued Boolean functions in a systematic way to translate trait data into the Fourier domain. Important trait features, such as the roughness of the trait landscape or the modularity of a trait have a simple Fourier interpretation. Roughness at a gene location corresponds to high sensitivity to mutation, while a modular organization of gene activity reduces such sensitivity. Traits where rugged loci are rare will naturally compress gene data in the Fourier domain, leading to a sparse representation of trait data, concentrated in identifiable, low-level coefficients. This Fourier representation of a trait organizes epistasis in a form which is isometric to the trait data. As Fourier matrices are known to be maximally incoherent with the standard basis, this permits employing compressive sensing techniques to work from data sets that are relatively small-sometimes even of polynomial size-compared to the exponentially large sets of possible genomes. This theory provides a theoretical underpinning for systematic use of Boolean function machinery to dissect the dependency of a trait on the genome and environment.

HOX cluster and their cofactors showed an altered expression pattern in eutopic and ectopic endometriosis tissues

Endometriosis is major gynecological disease that affects over 10% of women worldwide and 30%-50% of these women have pelvic pain, abnormal uterine bleeding and infertility. The cause of endometriosis is unknown and there is no definite cure mainly because of our limited knowledge about its pathophysiology at the cellular and molecular levels. Therefore, demystifying the molecular mechanisms that underlie endometriosis is essential to develop advanced therapies for this disease. In this regard, HOX genes are remarkable because of their critical role in endometrial development and receptivity during implantation, which is attributed to their ability to mediate some of the sex steroid functions during the reproductive period. Access to the expression profiles of these genes would provide the necessary information to uncover new genes for endometriosis and assist with disease diagnosis and treatment. In this study we demonstrate an altered expression pattern for the HOX clusters (A-D) and their cofactors in both eutopic and ectopic conditions compared to control tissue biopsies. Remarkably, most of the intensive changes occurred in eutopic samples from endometriosis patients compared to control tissue biopsies. Pathway analysis revealed the involvement of differentially expressed genes in cancer that correlate with an association between endometriosis and cancer. Our results suggest critical roles for the HOX cluster and their cofactors in endometriosis pathophysiology.

Aberrant Non-Coding RNA Expressed in Gastric Cancer and Its Diagnostic Value

Gastric cancer (GC) is one of the digestive tract malignancies with high invasion and mortality rates. Recent studies have reported that non-coding RNAs (ncRNAs) seem to play a crucial part in many tumors. Due to their high stability, ncRNAs may used as novel biomarkers to predict the occurrence and prognosis of GC. Here, we measured miRNA, lncRNA and cirRNA expression profiles of GC patients by using microarray and RNA-sequencing data from tissue samples. The diagnosis prediction model based on the ncRNA signatures and clinical features was evaluated by circulating and tissue validation and ROC analysis. Nine miRNAs and eight lncRNAs were obtained from the microarray analysis. Six miRNAs (miR-550a-5p, miRNA-936, miR-1306-3p, miR-3185, miR-6083, miR-6792-3p) and three lncRNAs (lnc-MB21D1-3:5, lnc-PSCA-4:2 and lnc-ABCC5-2:1) were abnormally expressed in circulating and tissue samples compared with normal control (NC), which was closely related to clinical pathology and survival time of GC patients; circRNA sequencing and qRT-PCR revealed four circRNAs (circASHL2, circCCDC9, circNHSL1 and cirMLLT10) were abnormally expressed in GC tissues and parts of them were negative relationship with their predicted binding miRNAs. These ncRNAs might act as promising molecular markers for the diagnosis and prognosis of gastric cancer.

Long Noncoding RNA Expression Profiles of Rat Extrasynaptic and Synaptic Neurons Expressing the N-methyl-D-Aspartate Receptor Revealed by Microarray Analysis

PURPOSE

To study the 24-hour expression of long noncoding RNAs (lncRNAs) in synaptic and extrasynaptic neurons expressing N-methyl-D-aspartate receptor (NMDAR), and normal neuronal cultures, via microarray analysis.

MATERIALS AND METHODS

Cortical neurons from embryonic (day E18) Sprague-Dawley rats were used for primary neuronal culture. NMDAR activation was blocked and the cells were then incubated for 6 hours. Total RNA was extracted, quantified, and analyzed for purity and integrity. Double-stranded cDNA was synthesized, followed by quantile normalization, quantitative polymerase chain reaction validation, and data analysis. The interactions between transcription factors and lncRNAs were analyzed by Pearson correlation.

RESULTS

The lncRNA profiles were obtained after synaptic and extrasynaptic NMDAR activation of rat cortical neuron cultures for 24 hours. In total, 251 lncRNAs were consistently upregulated, and 335 were downregulated, after extrasynaptic NMDAR activation compared with normal neurons. After synaptic NMDAR activation, only 9 lncRNAs were upregulated and 2 were downregulated.

CONCLUSIONS

Differential expression of lncRNAs after synaptic and extrasynaptic NMDAR activation suggests that lncRNAs may be responsible for extrasynaptic NMDAR-induced neurodegeneration.

Luminal A Breast Cancer Co-expression Network: Structural and Functional Alterations

Luminal A is the most common breast cancer molecular subtype in women worldwide. These tumors have characteristic yet heterogeneous alterations at the genomic and transcriptomic level. Gene co-expression networks (GCNs) have contributed to better characterize the cancerous phenotype. We have previously shown an imbalance in the proportion of intra-chromosomal (cis-) over inter-chromosomal (trans-) interactions when comparing cancer and healthy tissue GCNs. In particular, for breast cancer molecular subtypes (Luminal A included), the majority of high co-expression interactions connect gene-pairs in the same chromosome, a phenomenon that we have called loss of trans- co-expression. Despite this phenomenon has been described, the functional implication of this specific network topology has not been studied yet. To understand the biological role that communities of co-expressed genes may have, we constructed GCNs for healthy and Luminal A phenotypes. Network modules were obtained based on their connectivity patterns and they were classified according to their chromosomal homophily (proportion of cis-/trans- interactions). A functional overrepresentation analysis was performed on communities in both networks to observe the significantly enriched processes for each community. We also investigated possible mechanisms for which the loss of trans- co-expression emerges in cancer GCN. To this end we evaluated transcription factor binding sites, CTCF binding sites, differential gene expression and copy number alterations (CNAs) in the cancer GCN. We found that trans- communities in Luminal A present more significantly enriched categories than cis- ones. Processes, such as angiogenesis, cell proliferation, or cell adhesion were found in trans- modules. The differential expression analysis showed that FOXM1, CENPA, and CIITA transcription factors, exert a major regulatory role on their communities by regulating expression of their target genes in other chromosomes. Finally, identification of CNAs, displayed a high enrichment of deletion peaks in cis- communities. With this approach, we demonstrate that network topology determine, to at certain extent, the function in Luminal A breast cancer network. Furthermore, several mechanisms seem to be acting together to avoid trans- co-expression. Since this phenomenon has been observed in other cancer tissues, a remaining question is whether the loss of long distance co-expression is a novel hallmark of cancer.

Comparative analysis of hypothalamus transcriptome between laying hens with different egg-laying rates

Egg-laying performance is one of the most important economic traits in the poultry industry. Commercial layers can lay one egg almost every day during their peak-laying period. However, many Chinese indigenous chicken breeds show a relatively low egg-laying rate, even during their peak-laying period. To understand what makes the difference in egg production, we compared the hypothalamus transcriptome profiles of Lushi blue-shelled-egg chickens (LBS), a Chinese indigenous breed with low egg-laying rate and Rhode Island Red chickens (RIR), a commercial layer with relatively high egg-laying rate using RNA-seq. A total of 753 differentially expressed genes (DEGs) were obtained. Of these DEGs, 38 genes were enriched in 2 Gene Ontology (GO) terms, namely reproduction term and the reproductive process term, and 6 KEGG pathways, namely Wnt signaling pathway, Oocyte meiosis, GnRH signaling pathway, Thyroid hormone signaling pathway, Thyroid hormone synthesis and MAPK signaling pathway, which have been long known to be involved in egg production regulation. To further determine the core genes from the 38 DEGs, protein-protein interaction (PPI) network, co-expression network and transcriptional regulatory network analyses were carried out. After integrated analysis and experimental validation, 4 core genes including RAC1, MRE11A, MAP7 and SOX5 were identified as the potential core genes that are responsible for the laying-rate difference between the 2 breeds. These findings paved the way for future investigating the mechanism of egg-laying regulation and enriched the chicken reproductive regulation theory.

mRNA-lncRNA Co-Expression Network Analysis Reveals the Role of lncRNAs in Immune Dysfunction during Severe SARS-CoV-2 Infection

The recently emerged SARS-CoV-2 virus is responsible for the ongoing COVID-19 pandemic that has rapidly developed into a global public health threat. Patients severely affected with COVID-19 present distinct clinical features, including acute respiratory disorder, neutrophilia, cytokine storm, and sepsis. In addition, multiple pro-inflammatory cytokines are found in the plasma of such patients. Transcriptome sequencing of different specimens obtained from patients suffering from severe episodes of COVID-19 shows dynamics in terms of their immune responses. However, those host factors required for SARS-CoV-2 propagation and the underlying molecular mechanisms responsible for dysfunctional immune responses during COVID-19 infection remain elusive. In the present study, we analyzed the mRNA-long non-coding RNA (lncRNA) co-expression network derived from publicly available SARS-CoV-2-infected transcriptome data of human lung epithelial cell lines and bronchoalveolar lavage fluid (BALF) from COVID-19 patients. Through co-expression network analysis, we identified four differentially expressed lncRNAs strongly correlated with genes involved in various immune-related pathways crucial for cytokine signaling. Our findings suggest that the aberrant expression of these four lncRNAs can be associated with cytokine storms and anti-viral responses during severe SARS-CoV-2 infection of the lungs. Thus, the present study uncovers molecular interactions behind the cytokine storm activation potentially responsible for hyper-inflammatory responses in critical COVID-19 patients.

Identification of novel hub genes and lncRNAs related to the prognosis and progression of pancreatic cancer by microarray and integrated bioinformatics analysis

BACKGROUND

Pancreatic cancer (PC) is one of the most invasive and metastatic neoplasms among the fatal malignancies of the digestive system. Abnormal expression of genes and long non-coding RNAs (lncRNAs) are reportedly linked to multiple cancers. However, the lncRNA-mRNA expression profiles and their molecular mechanisms in PC progression are poorly known. This study aimed to map the hub genes and lncRNAs which might play core roles in the development of PC.

METHODS

This study used microarray expression analysis to screen for both differentially expressed genes (DEGs) and differentially expressed lncRNAs (DElncRNAs) between PC and matched adjacent non-tumor (AN) tissues. In order to clarify the functional classification of DEGs, we conducted GO and KEGG pathway enrichment analyses via the Enrichr database. LncRNA-mRNA co-expressed networks were also constructed to explore the probable core regulating DEGs and DElncRNAs. Subsequently, the hub genes and lncRNAs were validated via the ONCOMINE and GEPIA databases and the co-expressed networks.

RESULTS

By analyzing an mRNA-lncRNA microarray, we identified 943 mRNAs and 1,138 lncRNAs differentially expressed in PC tumors compared with the matched AN tissues. GO analysis confirmed that both up-regulated and down-regulated DEGs were enriched in multiple terms. The KEGG pathways enrichment analyses revealed that DEGs were mostly enriched in the focal adhesion and glutathione metabolism pathways, amongst others. Co-expressed networks were established to reveal the differential interactions between DEGs and DElncRNAs, and to indicate the core regulatory factors located at the core nodes of the co-expressed networks. The expression levels of potential core-regulating DEGs were validated by the GEPIA and ONCOMINE databases, and the relationship between overall survival and tumor stage and the potential core-regulating DEGs was analyzed using the GEPIA database. As a result, five genes and sixteen lncRNAs were finally considered as the hub transcripts in PC.

CONCLUSIONS

This study identified DEGs and DElncRNAs between PC tumors and matched AN tissues, and these transcripts were connected with malignant phenotypes in PC through different BPs and signaling pathways. Furthermore, five hub genes and sixteen lncRNAs were identified, which are expected to represent candidate diagnostic biomarkers or potential therapeutic targets for PC.

MicroRNA-302a is involved in folate deficiency-induced apoptosis through the AKT-FOXO1-BIM pathway in mouse embryonic stem cells

Background

Our previous study had shown that microRNA (miR)-302a played a key role in folate deficiency-induced apoptosis in mouse embryonic stem cells. However, details regarding the mechanism remain unclear. Transcription factors (TFs) and miRNAs are two key elements in gene regulation. The aim of this study is to construct the TF-miRNA gene regulation network and demonstrate its possible mechanism.

Methods

The TF-miRNA gene regulation network was constructed via bioinformatics methods. Chromatin immuno-coprecipitation PCR was selected to confirm the binding between miR-302a and TF. mRNA and protein levels were detected by Real-time quantitative PCR and western blotting. TargetScan prediction and Dual-Luciferase Reporter Assay system were used to confirm whether the miRNA binded directly to the predicted target gene.

Results

FOXO1 and miR-302a were selected as the key TF and miRNA, respectively. FOXO1 was confirmed to bind directly to the upstream promoter region of miR-302a. Real-time quantitative PCR and immunoblotting showed that in folate-free conditions, miR-302a and AKT were down regulated, while FOXO1 and Bim were up-regulated significantly. Additionally, treatment with LY294002 inhibitor revealed the involvement of the Akt/FOXO1/Bim signaling pathway in folate deficiency-induced apoptosis, rather than the ERK pathway. Finally, TargetScan prediction and double luciferase reporting experiments illustrated the ability of miR-302a to target the Bim 3′UTR region.

Conclusion

The involvement of miR-302a in folate deficiency-induced apoptosis through the AKT-FOXO1-BIM pathway in mESCs is a unique demonstration of the regulation mechanism of nutrient expression in embryonic development.

Identification of lncRNA-NR_104160 as a biomarker and construction of a lncRNA-related ceRNA network for essential hypertension

OBJECTIVES

To identify long noncoding RNAs (lncRNAs) and construct a competing endogenous RNA (ceRNA) network for essential hypertension.

METHODS

An RNA microarray and two-step quantitative real-time PCR were applied to identify differentially expressed RNAs (DE-RNAs), and a luciferase assay was performed to explore the binding relationship between RNAs. A generalized linear model and logistic regression model were used to analyze the associations between different RNAs and of RNAs with hypertension. Receiver operating characteristic curve analysis was executed to evaluate the diagnostic performance. Bioinformatics analysis was applied for network construction.

RESULTS

In total, 439 DE-RNAs (387 lncRNAs and 52 mRNAs) were identified in the microarray, and 71 'lncRNA-miRNA-mRNA' loops formed the ceRNA network. The first validation confirmed that five RNAs (NR_104160, lnc-GPR63-8:1, lnc-HPRT1-9:1, ID1 and RSL24D1) were significantly upregulated in hypertensives (P < 0.05). NR_104160 was significantly associated with hypertension (OR = 2.863, 95% CI: 1.143-7.172; P = 0.025) after adjusting for confounding factors. NR_104160 was included in the hypertension diagnostic model, with an area under the curve of 0.852 (95% CI: 0.761-0.944). In the second validation, NR_104160 showed a constant significant difference (P = 0.001). An elevated expression level of NR_104160 was associated with the expression of ID1 (β = 0.2235, P = 0.005). Luciferase assays showed hsa-miR-101-3p stimulation significantly inhibited the reporter gene activation ability of the NR_104160 wild-type plasmid (P < 0.001).

CONCLUSIONS

Our study constructed a ceRNA network to provide hypotheses regarding the mechanism of hypertension development. lncRNA-NR_104160 was identified as a hub element that participates in hypertension transcriptional regulation and as a potential biomarker.

Transcriptome analysis uncovers the diagnostic value of miR-192-5p/HNF1A-AS1/VIL1 panel in cervical adenocarcinoma

Despite the fact that the incidence of cervical squamous cell carcinoma has decreased, there is an increase in the incidence of cervical adenocarcinoma. However, our knowledge on cervical adenocarcinoma is largely unclear. Transcriptome sequencing was conducted to compare 4 cervical adenocarcinoma tissue samples with 4 normal cervical tissue samples. mRNA, lncRNA, and miRNA signatures were identified to discriminate cervical adenocarcinoma from normal cervix. The expression of VIL1, HNF1A-AS1, MIR194-2HG, SSTR5-AS1, miR-192-5p, and miR-194-5p in adenocarcinoma were statistically significantly higher than that in normal control samples. The Receiver Operating Characteristic (ROC) curve analysis indicated that combination of miR-192-5p, HNF1A-AS1, and VIL1 yielded a better performance (AUC = 0.911) than any single molecule -and could serve as potential biomarkers for cervical adenocarcinoma. Of note, the combination model also gave better performance than TCT test for cervical adenocarcinoma diagnosis. However, there was no correlation between miR-192-5p or HNF1A-AS1 and HPV16/18 E6 or E7. VIL1 was weakly correlated with HPV18 E7 expression. In summary, our study has identified miR-192-5p/HNF1A-AS1/VIL1 panel that accurately discriminates adenocarcinoma from normal cervix. Detection of this panel may provide considerable clinical value in the diagnosis of cervical adenocarcinoma.

Comprehensive Transcriptome Profiling of Dairy Goat Mammary Gland Identifies Genes and Networks Crucial for Lactation and Fatty Acid Metabolism

Milk fatty acids secreted by the mammary gland are one of the most important determinants of the nutritional value of goat milk. Unlike cow milk, limited data are available on the transcriptome-wide changes across stages of lactation in dairy goats. In this study, goat mammary gland tissue collected at peak lactation, cessation of milking, and involution were analyzed with digital gene expression (DGE) sequencing to generate longitudinal transcript profiles. A total of 51,299 unigenes were identified and further annotated to 12,763 genes, of which 9,131 were differentially expressed across various stages of lactation. Most abundant genes and differentially expressed genes (DEGs) were functionally classified through clusters of euKaryotic Orthologous Groups (KOG), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. A total of 16 possible expression patterns were uncovered, and 13 genes were deemed novel candidates for regulation of lactation in the goat: POLG, SPTA1, KLC, GIT2, COPS3, PDP, CD31, USP16/29/37, TLL1, NCAPH, ABI2, DNAJC4, and MAPK8IP3. In addition, PLA2, CPT1, PLD, GGA, SRPRB, and AP4S1 are proposed as novel and promising candidates regulating mammary fatty acid metabolism. “Butirosin and neomycin biosynthesis” and “Glyoxylate and dicarboxylate metabolism” were the most impacted pathways, and revealed novel metabolic alterations in lipid metabolism as lactation progressed. Overall, the present study provides new insights into the synthesis and metabolism of fatty acids and lipid species in the mammary gland along with more detailed information on molecular regulation of lactogenesis. The major findings will benefit efforts to further improve milk quality in dairy goats.

Small-world networks of prognostic genes associated with lung adenocarcinoma development

The present study investigates the role of network topology in lung adenocarcinoma (LUAD) development. Analysis of sex- and stage-specific whole-genome expression data revealed that co-expressed and highly connected prognostic genes common to all cancer stages form a small-world network in each stage of LUAD. These small-world networks are present within stage-specific scale-free networks, conserved across the cancer stages, and linked to cancer-specific events. The presence of small-world networks across the cancer stages presents a synchronized system in the disordered environment of cancer, resulting in the evolution of malignancy. Our study reported that these small-world networks are resilient to random and systematic attacks, indicating the least opportunity to introduce perturbations by drugs as a therapeutic intervention. We concluded that highly clustered small-world networks could be controlled through transcriptional modulation for the improved treatment of LUAD.

Construction of a ceRNA coregulatory network and screening of hub biomarkers for salt-sensitive hypertension

Salt-sensitive hypertension (SSH) is an independent risk factor for cardiovascular disease. The regulation of long non-coding RNAs, mRNAs and competing endogenous RNAs (ceRNAs) in the pathogenesis of SSH is uncertain. An RNA microarray was performed to discover SSH-associated differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs), and 296 DElncRNAs and 44 DEmRNAs were identified, and 247 DElncRNAs and 44 DEmRNAs among these RNAs were included in the coexpression network. The coregulatory network included 23 ceRNA loops, and six hub RNAs (lnc-ILK-8:1, lnc-OTX1-7:1, lnc-RCAN1-6:1, GIMAP8, SUV420H1 and PIGV) were identified for further population validation. The ceRNA correlations among lnc-OTX1-7:1, hsa-miR-361-5p and GIMAP8 were confirmed in SSH and SRH patients. A larger-sample validation confirmed that GIMAP8, SUV420H1 and PIGV were differentially expressed between the SSH and SRH groups. In addition, SUV420H1 was included in the SSH screening model, and the area under the curve of the model was 0.720 (95% CI: 0.624-0.816). Our study explored the transcriptome profiles of SSH and constructed a ceRNA network to help elucidate the mechanism of SSH. In addition, SUV420H1 was identified as a hub element that participates in SSH transcriptional regulation and as a potential biomarker for the early diagnosis of SSH.

Identification of candidate lncRNAs and circRNAs regulating WNT3/β-catenin signaling in essential hypertension

Mounting evidence suggests that noncoding RNAs (ncRNAs) contribute to the pathogenesis of cardiovascular diseases. However, their role in essential hypertension (EH) is still unclear. We therefore identified differentially expressed long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) in EH patients from a high-risk population group and constructed a competing endogenous RNA regulatory network that predicts interactions of potential diagnostic and therapeutic relevance between specific lncRNA/circRNA-microRNA-mRNA triplets. Our analysis identified two lncRNAs, transmembrane protein 183A pseudogene (LOC646616) and leucine aminopeptidase 3 pseudogene 2 (LAP3P2), and two circRNAs, hsa_circ_0039388 and hsa_circ_0038648, that are highly co-expressed with both wingless-type MMTV integration site family member 3 (WNT3) and calcium/calmodulin-dependent protein kinase II inhibitor 2 (CAMK2N2) mRNAs and also share common microRNA binding sites with these two transcripts. We also confirmed that a mutually regulated network composed of LOC646616/microRNA-637/WNT3 controls WNT3 expression and influences viability and invasive properties in human arterial smooth muscle cells in vitro. These findings highlight a novel ncRNA-based regulatory mechanism potentially driving WNT/β-catenin activation in EH, and suggest that the identified ncRNAs may represent useful biomarkers and therapeutic targets for this condition.

Long noncoding RNA-mRNA expression profiles and validation in pancreatic neuroendocrine neoplasms

BACKGROUND

Pancreatic neuroendocrine neoplasms (pNENs) are a group of endocrine tumours arising in the pancreas and deemed to be the most common neuroendocrine tumours. The pathogenesis of pNENs remains unknown. Long noncoding RNAs (lncRNAs) are aberrantly expressed in cancers. The functional roles of lncRNAs and lncRNA-mRNA expression profiles in pNENs are undefined. The aim of this study was to identify the lncRNA and mRNA expression profiles and explore the lncRNA-mRNA co-expression networks associated with the pNENs carcinogenesis.

METHOD

Differentially expressed lncRNA and mRNA in pNENs tissues from adjacent tissues were detected using human lncRNA microarray V3.0 containing 30 586 lncRNA and 26 109 coding transcripts. Probable functions for lncRNAs and mRNAs were predicted according to lncRNA-mRNA network.

RESULTS

The microarray identified 2080 lncRNAs and 1771 mRNAs in pNENs tumours differentially expressed compared with the adjacent tissues. The GO terms and KEGG pathway annotation data indicated that cell projection morphogenesis, cell adhesion molecules pathway, PI3K-AKT signalling pathway, focal adhesion pathway, neuroactive ligand-receptor interaction pathways and Ras signalling pathways were significantly associated with the pNENs tumorigenesis. Co-expression network analysis revealed the differential interactions between lncRNAs and mRNAs in pNENs tumours and adjacent tissues. The genes, situated at the important nodes of the co-expression network, include ICOSLG, ENST00000512077, FGF8 and ENST00000511918.

CONCLUSIONS

There were significant differences in lncRNA and mRNA expression between pNEN tumours and adjacent tissues, and these differences were associated with tumorigenesis through multiple biological processes and signalling pathways. These results provided important insights regarding lncRNA in pNENs pathogenesis and provide potential therapeutic targets.

MicroRNA Profile Identifies miR-6165 Could Suppress Gastric Cancer Migration and Invasion by Targeting STRN4

Background

Recent studies showed that aberrant expression of miRNAs causes tumor-suppressing or promoting effects in various cancers including gastric cancer (GC). Our previous studies showed that lots of miRNAs and mRNA expressed differentially in GC and normal tissues. However, the critical miRNAs and mRNA need to be clarified.

Materials and Methods

Microarray sequencing was used to profile the differential expression of miRNAs and mRNA in GC and normal tissues. Bioinformatics analysis and database prediction were used to search the critical miRNAs and mRNA. Real-time quantitative polymerase chain reaction (RT-qPCR), luciferase reporter assay, immunohistochemistry (IHC), wound healing assay and transwell assay were used to clarify the relationship between the target miRNAs and mRNA. Statistical analysis was used to seek their value of diagnosis and prognosis.

Results

We identified microRNA-6165 (miR-6165) as a novel cancer-related miRNA in GC through high-throughput microarray sequencing. By bioinformatics analysis and luciferase reporter assay, we found STRN4 was the target of miR-6165. Via a series of cell experiments, we determined that miR-6165 suppressed GC cells migration and invasion by targeting STRN4. Also, we discovered the potential diagnosis and prognosis value of miR-6165 and STRN4.

Conclusion

It was found that miR-6165 might suppress GC migration and invasion by targeting STRN4 in vitro, and the further research should focus more on the potential diagnosis and prognosis value of miR-6165 and STRN4 in gastric cancer patients.

Dynamic transcriptome profiling exploring cold tolerance in forensically important blow fly, Aldrichina grahami (Diptera: Calliphoridae)

Background

Aldrichina grahami (Diptera: Calliphoridae) is a forensically important fly, which has been widely applied to practical legal investigations. Unlike other necrophagous flies, A. grahami exhibits cold tolerance which helps to maintain its activity during low-temperature months, when other species are usually not active. Hence, A. grahami is considered an important forensic insect especially in cold seasons. In this study, we aim to explore the molecular mechanisms of cold tolerance of A. grahami through transcriptome.

Results

We collected eggs and larvae (first-instar, second-instar and third-instar) at three different temperatures (4 °C, 12 °C and 20 °C) and performed RNA-seq analyses. The differentially expressed genes (DEGs) associated with the cold-tolerance were screened out. The Venn analysis of DEGs from egg to third-instar larvae at three different temperatures showed there were 9 common genes. Candidate biological processes and genes were identified which refer to growth, and development of different temperatures, especially the chitin and cuticle metabolic process. The series-clusters showed crucial and unique trends when the temperature changed. Moreover, by comparing the results of growth and developmental transcriptomes from different temperatures, we found that DEGs belonging to the family of larval cuticle proteins (LCP), pupal cuticle protein (CUP), and heat shock proteins (HSP) have certain differences.

Conclusions

This study identified functional genes and showed differences in the expression pattern of diverse temperatures. The DEGs series-clusters with increasing or decreasing trends were analyzed which may play an important role in cold-tolerance. Moreover, the findings in LCP, CUP and HSP showed more possible modulations in a cold environment. This work will provide valuable information for the future investigation of the molecular mechanism of cold tolerance in A. grahami.

GSTZ1-1 downregulates Wnt/β-catenin signalling in hepatocellular carcinoma cells

Glutathione S-transferase Zeta 1-1 (GSTZ1-1), an enzyme involved in the catabolism of phenylalanine and the detoxification of xenobiotics, plays a tumour suppressor role in hepatocellular carcinoma (HCC), but the underlying mechanism remains largely unknown. Here, we further explored the function of GSTZ1-1 in HCC through transcriptome analysis by RNA sequencing. The analysis revealed that 223 genes were upregulated and 290 genes were downregulated in GSTZ1-1-overexpressing Huh7 cells. Gene Ontology analysis showed that these differentially expressed genes (DEGs) were highly enriched for protein phosphorylation, cell cycle arrest and metabolic processes. Pathway analysis revealed that metabolic pathways were the predominant enriched pathways among the upregulated genes, while the TGF-β and Wnt/β-catenin signalling pathways were prominent in the downregulated clusters. Pathway interaction networks also showed that the Wnt/β-catenin pathway was located in the centre of the cluster. The expression levels of selected DEGs were validated by qRT-PCR, and Wnt/β-catenin involvement was validated by luciferase assays, western blotting and immunohistochemical analysis in vitro and in vivo. These results provide a comprehensive overview of the transcriptome in GSTZ1-1-overexpressing Huh7 cells and indicate that GSTZ1-1 may play a tumour suppressor role by inactivating the Wnt/β-catenin signalling pathway.

A novel network control model for identifying personalized driver genes in cancer

Although existing computational models have identified many common driver genes, it remains challenging to identify the personalized driver genes by using samples of an individual patient. Recently, the methods of exploiting the structure-based control principles of complex networks provide new clues for identifying minimum number of driver nodes to drive the state transition of large-scale complex networks from an initial state to the desired state. However, the structure-based network control methods cannot be directly applied to identify the personalized driver genes due to the unknown network dynamics of the personalized system. Here we proposed the personalized network control model (PNC) to identify the personalized driver genes by employing the structure-based network control principle on genetic data of individual patients. In PNC model, we firstly presented a paired single sample network construction method to construct the personalized state transition network for capturing the phenotype transitions between healthy and disease states. Then, we designed a novel structure-based network control method from the Feedback Vertex Sets-based control perspective to identify the personalized driver genes. The wide experimental results on 13 cancer datasets from The Cancer Genome Atlas firstly showed that PNC model outperforms current state-of-the-art methods, in terms of F-measures for identifying cancer driver genes enriched in the gold-standard cancer driver gene lists. Furthermore, these results showed that personalized driver genes can be explored by their network characteristics even when they are hidden factors in transcription and mutation profiles. Our PNC gives novel insights and useful tools into understanding the tumor heterogeneity in cancer. The PNC package and data resources used in this work can be freely downloaded from https://github.com/NWPU-903PR/PNC.

Notably there may be unique personalized driver genes for an individual patient in cancer. Identifying personalized driver genes that lead to particular cancer initiation and progression of individual patient is one of the biggest challenges in precision medicine. However, most methods for cancer driver genes identification have focused mainly on the cohort information rather than on individual information and fail to identify personalized driver genes. We here proposed personalized network control model (PNC) to identify personalized driver genes by applying the structure based network control principle on personalized data of individual patients. By considering the progression from the healthy state to the disease state as the network control problem, our PNC aims to detect a small number of personalized driver genes that are altered in response to input signals for triggering the state transition in individual patients on expression level. The impetus behind PNC contains two main respects. One is to design a paired single sample network construction method (namely Paired-SSN) for constructing personalized state transition networks to capture the phenotypic transitions between normal and disease attractors. The other one is to develop a novel structure based network control method (namely NCUA) on personalized state transition networks for identifying personalized driver genes which can drive individual patient system state from healthy state to disease state through oncogene activations. Each part of the proposed method has been deeply examined to be efficient. Compared with other existing models, our PNC shows a higher performance in terms of F-measures of the cancer driver genes in the well-known Cancer Census Genes (CCG) and Network of Cancer Genes (NCG). The wide experimental results on multiple cancer datasets highlight that sample specific network theory and structure based network control theory can contribute to identifying personalized driver genes in cancer.

Large-scale analyses identify a cluster of novel long noncoding RNAs as potential competitive endogenous RNAs in progression of hepatocellular carcinoma

The abnormal expression of noncoding RNAs has attracted increasing interest in the field of hepatocellular carcinoma progression. However, the underlying molecular mechanisms mediated by noncoding RNAs in these processes are unclear. Here, we obtained the expression profiles of long noncoding RNAs, microRNAs, and mRNAs from the Gene Expression Omnibus database and identified hepatocarcinogenesis-specific differentially expressed transcripts. Next, we identified significant Gene Ontology and pathway terms that the differentially expressed transcripts involved in. Using functional analysis and target prediction, we constructed a hepatocellular carcinoma-associated deregulated competitive endogenous RNA network to reveal the potential mechanisms underlying tumor progression. By analyzing The Cancer Genome Atlas dataset, six key long noncoding RNAs showed significant association with overall survival as well as strong correlation with some microRNAs and mRNAs in the competitive endogenous RNA network. We further validated the above results and determined their diagnostic and prognostic value in clinical samples. Importantly, by large-scale analyses, we identified a cluster of long noncoding RNAs, GBAP1, MCM3AP-AS1, SLC16A1-AS1, C3P1, DIO3OS, and HNF4A-AS1 as candidate biomarkers for the diagnosis and prognosis of hepatocellular carcinoma, which will improve our understanding of competitive endogenous RNA-mediated regulatory mechanisms underlying hepatocellular carcinoma development and will provide novel therapeutic targets in the future.

Investigation of the underlying hub genes and mechanisms of reperfusion injury in patients undergoing coronary artery bypass graft surgery by integrated bioinformatic analyses

Background

Although coronary artery bypass graft (CABG) surgery is the main method to revascularize the occluded coronary vessels in coronary artery diseases, the full benefits of the operation are mitigated by ischemia-reperfusion (IR) injury. Although many studies have been devoted to reducing IR injury in animal models, the translation of this research into the clinical field has been disappointing. Our study aimed to explore the underlying hub genes and mechanisms of IR injury.

Methods

A weighted gene co-expression network analysis (WGCNA) was executed based on the expression profiles in patients undergoing CABG surgery (GSE29396). Functional annotation and protein-protein interaction (PPI) network construction were executed within the modules of interest. Potential hub genes were predicted, combining both intramodular connectivity (IC) and degrees. Meanwhile, potential transcription factors (TFs) and microRNAs (miRNAs) were predicted by corresponding bioinformatics tools.

Results

A total of 336 differentially expressed genes (DEGs) were identified. DEGs were mainly enriched in neutrophil activity and immune response. Within the modules of interest, 5 upregulated hub genes (IL-6, CXCL8, IL-1β, MYC, PTGS-2) and 6 downregulated hub genes (C3, TIMP1, VSIG4, SERPING1, CD163, and HP) were predicted. Predicted miRNAs (hsa-miR-333-5p, hsa-miR-26b-5p, hsa-miR-124-3p, hsa-miR-16-5p, hsa-miR-98-5p, hsa-miR-17-5p, hsa-miR-93-5p) and TF (STAT1) might have regulated gene expression in the most positively related module, while hsa-miR-333-5p and HSF-1 were predicted to regulate the genes within the most negatively related module.

Conclusions

Our study illustrates an overview of gene expression changes in human atrial samples from patients undergoing CABG surgery and might help translate future research into clinical work.

Identification of key protein-coding genes and lncRNAs in spontaneous neutrophil apoptosis

Polymorphonuclear leukocytes (PMNs) are the most abundant cells of the innate immune system in humans, and spontaneous PMN apoptosis plays crucial roles in maintaining neutrophil homeostasis and resolving inflammation. However, the detailed mechanisms of spontaneous PMN apoptosis remain to be elucidated. By analysis of the public microarray dataset GSE37416, we identified a total of 3050 mRNAs and 220 long non-coding RNAs (lncRNAs) specifically expressed during PMN apoptosis in a time-dependent manner. By short time-series expression miner (STEM) analysis, Gene Ontology analysis, and lncRNA-mRNA co-expression network analyses, we identified some key molecules specifically related to PMN apoptosis. STEM analysis identified 12 gene profiles with statistically significance, including 2 associated with apoptosis. Protein-protein interaction (PPI) network analysis of the genes from 2 profiles and lncRNA-mRNA co-expression network analysis identified a 12-gene hub (including NFκB1 and BIRC3) associated with apoptosis, as well as 2 highly correlated lncRNAs (THAP9-AS1, and AL021707.6). We experimentally examined the expression profiles of two mRNA (NFκB1 and BIRC3) and two lncRNAs (THAP9-AS1 andAL021707.6) by quantitative real-time polymerase chain reaction to confirm their time-dependent expressions. These data altogether demonstrated that these genes are involved in the regulation of spontaneous neutrophil apoptosis and the corresponding gene products could also serve as potential key regulatory molecules for PMN apoptosis and/or therapeutic targets for over-reactive inflammatory response caused by the abnormality in PMN apoptosis.

Applications of molecular networks in biomedicine

Due to the large interdependence between the molecular components of living systems, many phenomena, including those related to pathologies, cannot be explained in terms of a single gene or a small number of genes. Molecular networks, representing different types of relationships between molecular entities, embody these large sets of interdependences in a framework that allow their mining from a systemic point of view to obtain information. These networks, often generated from high-throughput omics datasets, are used to study the complex phenomena of human pathologies from a systemic point of view. Complementing the reductionist approach of molecular biology, based on the detailed study of a small number of genes, systemic approaches to human diseases consider that these are better reflected in large and intricate networks of relationships between genes. These networks, and not the single genes, provide both better markers for diagnosing diseases and targets for treating them. Network approaches are being used to gain insight into the molecular basis of complex diseases and interpret the large datasets associated with them, such as genomic variants. Network formalism is also suitable for integrating large, heterogeneous and multilevel datasets associated with diseases from the molecular level to organismal and epidemiological scales. Many of these approaches are available to nonexpert users through standard software packages.

CeRNA network analysis and functional enrichment of salt sensitivity of blood pressure by weighted-gene co-expression analysis

Background

Salt sensitivity of blood pressure (SSBP) is an independent risk factor for cardiovascular disease. The pathogenic mechanisms of SSBP are still uncertain. This study aimed to construct the co-regulatory network of SSBP and data mining strategy based on the competitive endogenous RNA (ceRNA) theory.

Methods

LncRNA and mRNA microarray was performed to screen for candidate RNAs. Four criteria were used to select the potential differently expressed RNAs. The weighted correlation network analysis (WGCNA) package of R software and target miRNA and mRNA prediction online databases were used to construct the ceRNA co-regulatory network and discover the pathways related to SSBP. Gene ontology enrichment, gene set enrichment analysis (GSEA) and KEGG pathway analysis were performed to explore the functions of hub genes in networks.

Results

There were 274 lncRNAs and 36 mRNAs that differently expressed between salt-sensitive and salt-resistant groups (P < 0.05). Using WGCNA analysis, two modules were identified (blue and turquoise). The blue module had a positive relationship with salt-sensitivity (R = 0.7, P < 0.01), high-density lipoprotein (HDL) (R = 0.53, P = 0.02), and total cholesterol (TC) (R = 0.55, P = 0.01). The turquoise module was positively related with triglyceride (TG) (R = 0.8, P < 0.01) and low-density lipoprotein (LDL) (R = 0.54, P = 0.01). Furthermore, 84 ceRNA loops were identified and one loop may be of great importance for involving in pathogenesis of SSBP. KEGG analysis showed that differently expressed mRNAs were mostly enriched in the SSBP-related pathways. However, the enrichment results of GSEA were mainly focused on basic physical metabolic processes.

Conclusion

The microarray data mining process based on WGCNA co-expression analysis had identified 84 ceRNA loops that closely related with known SSBP pathogenesis. The results of our study provide implications for further understanding of the pathogenesis of SSBP and facilitate the precise diagnosis and therapeutics.

Circular RNA profile of parathyroid neoplasms: analysis of co-expression networks of circular RNAs and mRNAs

Circular RNAs (circRNAs) are a recently identified class of non-coding RNAs that participate in multiple biological processes and tumour progression. However, circRNA expression pattern in parathyroid neoplasms remains unknown. The circRNA profile of 6 parathyroid carcinomas (PCs), 6 parathyroid adenomas (PAs) and 4 normal parathyroid tissues was assessed by a microarray. Bioinformatic analyses were performed to investigate potential core circRNAs via co-expression network. CircRNA and corresponding mRNA expression were validated in a cohort of parathyroid neoplasms by RT-qPCR and fluorescence in situ hybridization (FISH). Compared to normal parathyroid, 5310 and 1055 circRNAs were differentially expressed in PC and PA tissues, respectively. The differential expression of 4 circRNAs (hsa_circRNA_0035563 (p = 0.006), hsa_circRNA_0017545 (p = 0.009), hsa_circRNA_0001687 (p = 0.005) and hsa_circRNA_0075005 (p = 0.001)) and 4 mRNAs (MYC, FSCN1, ANXA2 and AKR1C3) between PC and PA tissues were confirmed by RT-qPCR. In addition, high expression of hsa_circ_0035563 was related to CDC73 mutations (p = 0.022) and recurrence in PC patients (p = 0.042). Furthermore, hsa_circ_0075005 helped distinguish PCs from benign lesions using FISH, and the area under the curve was 0.779 (p = 0.013). Our findings describe the circRNA profile of PC for the first time and suggest that circRNAs and mRNAs interact in parathyroid tumourigenesis. This study demonstrates that hsa_circ_0075005 and MYC mRNA may be used for the differential diagnosis of PC and PA. The expression levels of hsa_circ_0035563 are related to CDC73 mutations and recurrence in malignancy, highlighting the significance of this parameter in prognosis of PC patients.

lncRNA Gm10451 regulates PTIP to facilitate iPSCs-derived β-like cell differentiation by targeting miR-338-3p as a ceRNA

iPSCs-derived insulin-producing cell transplantation is a promising strategy for diabetes therapy. Although there have been many protocols of mature, glucose-responsive β cells induced in vitro over the past few years, many underlying problems remain to be resolved. As a crucial regulator, long noncoding RNAs (lncRNAs) participate in numerous biological processes, including the maintenance of pluripotency, and stem cell differentiation. In this study, we identified a novel lncRNA Gm10451 as a functional regulator for β-like cell differentiation. Localized to the cytoplasm, Gm10451 regulates histone H3K4 methyltransferase complex PTIP to facilitate Insulin⁺/Nkx6.1⁺ β-like cell differentiation by targeting miR-338-3p as a competing endogenous RNA (ceRNA). miR-338-3p has also been shown to suppress Nkx6.1⁺ early-stage β-like cell differentiation by targeting PTIP. Following transplantation into streptozotocin (STZ)-mice, Gm10451 loss in β-like cells prevented the expression of mature β-cell makers, such as Insulin, Nkx6.1, and Mafa. Accordingly, hyperglycemia in the mice was not resolved. Taken together, this study provides an efficient epigenetic target for generating more mature and functional iPSCs-derived β-like cells. We anticipate that pancreatic organoids, which are generated from human stem cells, biological materials, and epigenetic modifications, can be used in the future as a novel diabetes treatment option.

[Role of miR-106b-5p in the regulation of gene profiles in endothelial cells]

OBJECTIVE

To evaluate the role of miR-106b-5p in the regulation of gene expression in endothelial cells.

METHODS

The Taqman low-density microRNAs (miRNAs) array (TLDA) was used to identify miRNA expression profiles in the plasma of patients with atherosclerotic coronary artery disease (CAD) (atherosclerosis group, n=9) and individuals without atherosclerotic CAD disease (control group, n=9). A weighed and undirected miRNA coexpression network analysis was performed to investigate the interactions among miRNAs in the two groups. MiR-106b-5p, whose coexpression pattern in atherosclerosis group was most different from that of control group, was further studied. Human umbilical vein endothelial cells (HUVEC) were transfected with miR-106b-5p mimic or negative control mimic, and Affymetrix GeneChip Human Transcriptome Array 2.0 was used to screen the differential gene expression profiles after transfection. And the signal transduction pathway of differential gene profiles was further analyzed in Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway database. After parsing the whole KEGG database, all differentially expressed genes involved pathways were extracted, and the hypergeometric distribution was used to calculate the pathway enrichment.

RESULTS

The coexpression pattern of the patients with atherosclerosis (140 nodes, 1 154 edges) differed from that of the non-atherosclerosis control group (140 nodes, 612 edges). The analysis of array data with significant analysis of microarray (SAM) identified 746 significantly deregulated genes (fold change ≥ 1.5 and false discovery rate < 0.01) altered by overexpression of miR-106b-5p with miR-106b-5p mimic in HUVEC. By calculating the pathway enrichment, we found that multiple signaling pathways enriched in differential gene profiles were closely related to the process of formation and rupture of atherosclerotic plaque, including phosphatidylinositol-3 kinase (PI3K)/ protein kinase B (PKB, also called Akt), mammalian target of rapamycin (mTOR), transforming growth factor-β (TGF-β), janus kinase / signal transducer and activator of transcription (Jak-STAT), tumor necrosis factor (TNF), toll like receptor (TLR) and hypoxia-inducible factor 1α (HIF-1α) and other signal pathways.

CONCLUSION

The coexpression pattern of miRNAs in plasma of patients with atherosclerosis is more significantly changed than that of individuals without atherosclerotic disease. MiR-106b-5p, which shows the most significant difference between groups, targets multiple signal pathways in vascular endothelial cells, and might play an important role in the regulatory network of atherosclerotic gene expression.

Network Medicine in the Age of Biomedical Big Data

Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.

Topological evolution of coexpression networks by new gene integration maintains the hierarchical and modular structures in human ancestors

We analyze the global structure and evolution of human gene coexpression networks driven by new gene integration. When the Pearson correlation coefficient is greater than or equal to 0.5, we find that the coexpression network consists of 334 small components and one "giant" connected subnet comprising of 6317 interacting genes. This network shows the properties of power-law degree distribution and small-world. The average clustering coefficient of younger genes is larger than that of the elderly genes (0.6685 vs. 0.5762). Particularly, we find that the younger genes with a larger degree also show a property of hierarchical architecture. The younger genes play an important role in the overall pivotability of the network and this network contains few redundant duplicate genes. Moreover, we find that gene duplication and orphan genes are two dominant evolutionary forces in shaping this network. Both the duplicate genes and orphan genes develop new links through a "rich-gets-richer" mechanism. With the gradual integration of new genes into the ancestral network, most of the topological structure features of the network would gradually increase. However, the exponent of degree distribution and modularity coefficient of the whole network do not change significantly, which implies that the evolution of coexpression networks maintains the hierarchical and modular structures in human ancestors.

Changing expression profiles of mRNA, lncRNA, circRNA, and miRNA in lung tissue reveal the pathophysiological of bronchopulmonary dysplasia (BPD) in mouse model

New perinatal care technologies have improved the survival rate of preterm neonates, but the prevalence of bronchopulmonary dysplasia (BPD), one of the most intractable problems in neonatal intensive care unit (NICU), remains unchanged. In present study, high-throughput sequencing (HTS) was performed to detect the expression profiles of long noncoding RNAs (lncRNAs), messenger RNAs (mRNAs), circular RNAs (circRNAs), and microRNAs (miRNAs) in hyperoxia-induced BPD mouse model. Significant differentially expressed RNAs were selected and clustered between the BPD group and the control group. The results revealed that expressions of 1778 lncRNAs, 1240 mRNAs, 97 circRNAs, and 201 miRNAs were significantly altered in the BPD group. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to predict the potential functions of differentially expressed RNAs. lncRNA-mRNA and circRNA-miRNA coexpression networks were constructed to detect their association with the pathogenesis of BPD. Our study provides a systematic perspective on the potential function of RNAs during BPD.

Obesity-associated inflammation triggers an autophagy-lysosomal response in adipocytes and causes degradation of perilipin 1

In obesity, adipocytes exhibit high metabolic activity accompanied by an increase in lipid mobilization. Recent findings indicate that autophagy plays an important role in metabolic homeostasis. However, the role of this process in adipocytes remains controversial. Therefore, we performed an overall analysis of the expression profiles of 322 lysosomal/autophagic genes in the omental adipose tissue of lean and obese individuals, and found that among 35 significantly differentially expressed genes, 34 genes were upregulated. A large number of lysosomal/autophagic genes also were upregulated in murine 3T3-L1 adipocytes challenged with tumor necrosis factor α (TNFα) (within 24 h), which is in accordance with increased autophagy flux in adipocytes. SQSTM1/p62, a selective autophagy receptor that recognizes and binds specifically to ubiquitinated proteins, is transcriptionally upregulated upon TNFα stimulation as well. Perilipin 1 (PLIN1), a crucial lipid droplet protein, can be ubiquitinated and interacts with SQSTM1 directly. Thus, TNFα-induced autophagy is a more selective process that signals through SQSTM1 and can selectively degrade PLIN1. Our study indicates that local proinflammatory cytokines in obese adipose tissue impair triglyceride storage via autophagy induction.

Functional homogeneity and specificity of topological modules in human proteome

BACKGROUND

Functional modules in protein-protein interaction networks (PPIN) are defined by maximal sets of functionally associated proteins and are vital to understanding cellular mechanisms and identifying disease associated proteins. Topological modules of the human proteome have been shown to be related to functional modules of PPIN. However, the effects of the weights of interactions between protein pairs and the integration of physical (direct) interactions with functional (indirect expression-based) interactions have not been investigated in the detection of functional modules of the human proteome.

RESULTS

We investigated functional homogeneity and specificity of topological modules of the human proteome and validated them with known biological and disease pathways. Specifically, we determined the effects on functional homogeneity and heterogeneity of topological modules (i) with both physical and functional protein-protein interactions; and (ii) with incorporation of functional similarities between proteins as weights of interactions. With functional enrichment analyses and a novel measure for functional specificity, we evaluated functional relevance and specificity of topological modules of the human proteome.

CONCLUSIONS

The topological modules ranked using specificity scores show high enrichment with gene sets of known functions. Physical interactions in PPIN contribute to high specificity of the topological modules of the human proteome whereas functional interactions contribute to high homogeneity of the modules. Weighted networks result in more number of topological modules but did not affect their functional propensity. Modules of human proteome are more homogeneous for molecular functions than biological processes.

Differential Coexpression Network Analysis for Gene Expression Data

Gene expression profiling by microarray has been used to uncover molecular variations in many areas. The traditional analysis method to gene expression profiling just focuses on the individual genes, and the interactions among genes are ignored, while genes play their roles not by isolations but by interactions with each other. Consequently, gene-to-gene coexpression analysis emerged as a powerful approach to solve the above problems. Then complementary to the conventional differential expression analysis, the differential coexpression analysis can identify gene markers from the systematic level. There are three aspects for differential coexpression network analysis including the network global topological comparison, differential coexpression module identification, and differential coexpression genes and gene pairs identification. To date, the coexpression network and differential coexpression analysis are widely used in a variety of areas in response to environmental stresses, genetic differences, or disease changes. In this chapter, we reviewed the existing methods for differential coexpression network analysis and discussed the applications to cancer research.

Genome-wide identification of lncRNAs and mRNAs differentially expressed in non-functioning pituitary adenoma and construction of an lncRNA-mRNA co-expression network

The involvement of long non-coding RNAs (lncRNAs) during tumorigenesis is a recent emerging theme. Yet no systematic evaluation of lncRNAs has been previously reported for non-functioning pituitary adenoma (NFPA), a fairly common type of intracranial tumor. Here, we report the first genome-wide expression profile for lncRNAs and mRNAs in NFPA, using formalin-fixed and paraffin-embedded tissue specimens. Using microarray analyses, we identified 113 lncRNAs and 80 mRNAs differentially expressed in NFPA; this list includes lncRNAs previously implicated in a variety of cancers. Using real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) we further confirmed differential expression in NFPA for ten of the 113 lncRNAs. Using these ten doubly confirmed lncRNAs, we constructed an lncRNA-mRNA co-expression network comprising of 130 specific lncRNA-mRNA co-expression relationships. In addition, we conducted GO and KEGG analyses for the 80 mRNAs differentially expressed in NFPA. Our microarray and qRT-PCR analyses provided a working list of lncRNAs that may be functionally relevant to NFPA tumorigenesis. Our co-expression network in turn connected these largely uncharacterized lncRNAs to specific mRNAs, whose roles we further elucidated via GO and KEGG analyses, thus providing specific, testable hypotheses for the functions of these lncRNAs. Together, our study laid the foundation for future investigation of the specific function and mechanism by which lncRNAs are involved in NFPA tumorigenesis.

Summary statement: We identified long non-coding RNAs and mRNAs differentially expressed in non-functioning pituitary adenomas via microarray analyses, and provided working hypotheses for how these RNAs may function via co-expression network analyses.