A network-based method for the identification of putative genes related to infertility

GNG2 acts as a tumor suppressor in breast cancer through stimulating MRAS signaling

G-protein gamma subunit 2 (GNG2) is involved in several cell signaling pathways, and is essential for cell proliferation and angiogenesis. However, the role of GNG2 in tumorigenesis and development remains unclear. In this study, 1321 differentially expressed genes (DEGs) in breast cancer (BC) tissues were screened using the GEO and TCGA databases. KEGG enrichment analysis showed that most of the enriched genes were part of the PI3K-Akt signaling pathway. We identified GNG2 from the first five DEGs, its expression was markedly reduced in all BC subtype tissues. Cox regression analysis showed that GNG2 was independently associated with overall survival in patients with luminal A and triple-negative breast cancers (TNBC). GNG2 over-expression could significantly block the cell cycle, inhibit proliferation, and promote apoptosis in BC cells in vitro. In animal studies, GNG2 over-expression inhibited the growth of BC cells. Further, we found that GNG2 significantly inhibited the activity of ERK and Akt in an MRAS-dependent manner. Importantly, GNG2 and muscle RAS oncogene homolog (MRAS) were co-localized in the cell membrane, and the fluorescence resonance energy transfer (FRET) experiment revealed that they had direct interaction. In conclusion, the interaction between GNG2 and MRAS likely inhibits Akt and ERK activity, promoting apoptosis and suppressing proliferation in BC cells. Increasing GNG2 expression or disrupting the GNG2-MRAS interaction in vivo could therefore be a potential therapeutic strategy to treat BC.

Identification of Therapeutic Targets for the Selective Killing of HBV-Positive Hepatocytes

The hepatitis B virus (HBV) infection is a major risk factor for cirrhosis and hepatocellular carcinoma. Most infected individuals become lifelong carriers of HBV as the drugs currently used to treat the patients can only control the disease, thereby achieving functional cure (loss of the hepatitis B surface antigen) but not complete cure (elimination of infected hepatocytes). Therefore, we aimed to identify the target genes for the selective killing of HBV-positive hepatocytes to develop a novel therapy for the treatment of HBV infection. Our strategy was to recognize the conditionally essential genes that are essential for the survival of HBV-positive hepatocytes, but non-essential for the HBV-negative hepatocytes. Using microarray gene expression data curated from the Gene Expression Omnibus database and the known essential genes from the Online GEne Essentiality database, we used two approaches, comprising the random walk with restart algorithm and the support vector machine approach, to determine the potential targets for the selective killing of HBV-positive hepatocytes. The final candidate genes list obtained using these two approaches consisted of 36 target genes, which may be conditionally essential for the cell survival of HBV-positive hepatocytes; however, this requires further experimental validation. Therefore, the genes identified in this study can be used as potential drug targets to develop novel therapeutic strategies for the treatment of HBV, and may ultimately help in achieving the elusive goal of a complete cure for hepatitis B.

Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing "Omics Data" to Druggability Simulations

Multiple sclerosis (MS) is the most common chronic inflammatory demyelinating disease of the central nervous system. While the drugs currently available for MS provide symptomatic benefit, there is no curative treatment. The emergence of large-scale multiomics data and network theory provide new opportunities for drug discovery in MS, as these are promising strategies for developing novel drugs. In this study, we proposed a computational framework that combined biomolecular network modeling and structural dynamics analysis to facilitate the discovery of new drugs with potential activity in MS. First, we developed a new shortest path-based algorithm that prioritized differentially expressed genes using a newly topological and functional exploration of protein-protein interaction network. Then, pathway enrichment analysis and an assessment of target druggability suggested that TNF-α-induced protein 3 (TNFAIP3), which is involved in NF-κ B signaling, could be a potential therapeutic target for MS. Finally, druggability simulations and mutation enrichment analysis of the TNFAIP3 dimer presented two druggable sites. Follow-up pharmacophore model-based virtual screening of the two sites yielded 30 hit compounds with low energy scores. In summary, this novel method based on analyzing "omics data" and performing druggability simulations, is a systematic approach that unravels disease mechanisms and links them to the chemical space to develop treatments and can be applied to other complex diseases.

Prediction of key gene function in spinal muscular atrophy using guilt by association method based on network and gene ontology

Guilt by association (GBA) algorithm has been widely used to predict gene functions statistically, and a network-based approach may increase the confidence and veracity of identifying molecular signatures for diseases. The aim of the present study was to suggest a gene ontology (GO)-based method by integrating the GBA algorithm and network, to identify key gene functions for spinal muscular atrophy (SMA). The inference of predicting key gene functions was comprised of four steps, preparing gene lists and sets; extracting differentially expressed genes (DEGs) using microarray data [linear models for microarray data (limma)] package; constructing a co-expression matrix on gene lists using the Spearman correlation coefficient method; and predicting gene functions by GBA algorithm. Ultimately, key gene functions were predicted according to the area under the curve (AUC) index for GO terms and the GO terms with AUC >0.7 were determined as the optimal gene functions for SMA. A total of 484 DEGs and 466 background GO terms were regarded as gene lists and sets for the subsequent analyses, respectively. The predicted results obtained from the network-based GBA approach showed 141 gene sets had a good classified performance with AUC >0.5. Most significantly, 3 gene sets with AUC >0.7 were denoted as seed gene functions for SMA, including cell morphogenesis, which is involved in differentiation and ossification. In conclusion, we have predicted 3 key gene functions for SMA compared with control utilizing network-based GBA algorithm. The findings may provide great insights to reveal pathological and molecular mechanism underlying SMA.

Application of Molecular Methods in the Identification of Ingredients in Chinese Herbal Medicines

There are several kinds of Chinese herbal medicines originating from diverse sources. However, the rapid taxonomic identification of large quantities of Chinese herbal medicines is difficult using traditional methods, and the process of identification itself is prone to error. Therefore, the traditional methods of Chinese herbal medicine identification must meet higher standards of accuracy. With the rapid development of bioinformatics, methods relying on bioinformatics strategies offer advantages with respect to the speed and accuracy of the identification of Chinese herbal medicine ingredients. This article reviews the applicability and limitations of biochip and DNA barcoding technology in the identification of Chinese herbal medicines. Furthermore, the future development of the two technologies of interest is discussed.

Metabolic profiling putatively identifies plasma biomarkers of male infertility using UPLC-ESI-IT-TOFMS

Male infertility has become a global health problem. Currently, the diagnosis of male infertility depends on the results of semen quality or requires invasive surgical intervention. The process is complex and time-consuming. Metabolomics is an emerging platform with unique advantages in disease diagnosis and pathological mechanism research. In this study, ultra-performance liquid chromatography-electrospray ionization-ion trap-time of flight mass spectrometry (UPLC-ESI-IT-TOFMS) combined with chemometrics methods was used to discover potential biomarkers of male infertility based on non-targeted plasma metabolomics. Plasma samples from healthy controls (HC, n = 43) and various types of infertile patients, i.e., patients having oligozoospermia (OS, n = 36), asthenospermia (AS, n = 56) and erectile dysfunction (ED, n = 45) were analyzed by UPLC-ESI-IT-TOFMS. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed. The results of OPLS-DA showed that HCs could be discriminated from infertile patients including OS (R² = 0.903, Q² = 0.617, AUC = 0.992), AS (R² = 0.985, Q² = 0.658, AUC = 0.999) or ED (R² = 0.942, Q² = 0.500, AUC = 0.998). Some potential biomarkers were successfully discovered by variable selection methods and variable important in the projection (VIP) in combination with the T-test. Statistical significance was set at p < 0.05; the Benjamini–Hochberg false discovery rate was used to reduce type 1 errors resulting from multiple comparisons. The identified biomarkers were associated with energy consumption, hormone regulation and antioxidant defenses in spermatogenesis. To elucidate the pathophysiology of male infertility, relative metabolic pathways were studied. It was found that male infertility is closely related to disturbed phospholipid metabolism, amino acid metabolism, steroid hormone biosynthesis metabolism, metabolism of fatty acids and products of carnitine acylation, and purine and pyrimidine metabolisms. Plasma metabolomics provides a novel strategy for the diagnosis of male infertility and offers a new insight to study pathogenesis mechanism.

Ultra-performance liquid chromatography-electrospray ionization-ion trap-time of flight mass spectrometry combined with chemometrics methods was used to discover potential biomarkers of male infertility based on untargeted plasma metabolomics.

Prediction of potential drivers connecting different dysfunctional levels in lung adenocarcinoma via a protein-protein interaction network

Lung cancer is a serious disease that threatens an affected individual's life. Its pathogenesis has not yet to be fully described, thereby impeding the development of effective treatments and preventive measures. "Cancer driver" theory considers that tumor initiation can be associated with a number of specific mutations in genes called cancer driver genes. Four omics levels, namely, (1) methylation, (2) microRNA, (3) mutation, and (4) mRNA levels, are utilized to cluster cancer driver genes. In this study, the known dysfunctional genes of these four levels were used to identify novel driver genes of lung adenocarcinoma, a subtype of lung cancer. These genes could contribute to the initiation and progression of lung adenocarcinoma in at least two levels. First, random walk with restart algorithm was performed on a protein-protein interaction (PPI) network constructed with PPI information in STRING by using known dysfunctional genes as seed nodes for each level, thereby yielding four groups of possible genes. Second, these genes were further evaluated in a test strategy to exclude false positives and select the most important ones. Finally, after conducting an intersection operation in any two groups of genes, we obtained several inferred driver genes that contributed to the initiation of lung adenocarcinoma in at least two omics levels. Several genes from these groups could be confirmed according to recently published studies. The inferred genes reported in this study were also different from those described in a previous study, suggesting that they can be used as essential supplementary data for investigations on the initiation of lung adenocarcinoma. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Network-based method for mining novel HPV infection related genes using random walk with restart algorithm

The human papillomavirus (HPV), a common virus that infects the reproductive tract, may lead to malignant changes within the infection area in certain cases and is directly associated with such cancers as cervical cancer, anal cancer, and vaginal cancer. Identification of novel HPV infection related genes can lead to a better understanding of the specific signal pathways and cellular processes related to HPV infection, providing information for the development of more efficient therapies. In this study, several novel HPV infection related genes were predicted by a computation method based on the known genes involved in HPV infection from HPVbase. This method applied the algorithm of random walk with restart (RWR) to a protein-protein interaction (PPI) network. The candidate genes were further filtered by the permutation and association tests. These steps eliminated genes occupying special positions in the PPI network and selected key genes with strong associations to known HPV infection related genes based on the interaction confidence and functional similarity obtained from published databases, such as STRING, gene ontology (GO) terms and KEGG pathways. Our study identified 104 novel HPV infection related genes, a number of which were confirmed to relate to the infection processes and complications of HPV infection, as reported in the literature. These results demonstrate the reliability of our method in identifying HPV infection related genes. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Prioritizing chronic obstructive pulmonary disease (COPD) candidate genes in COPD-related networks

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, which could be caused by many factors, including disturbances of metabolism and protein-protein interactions (PPIs). In this paper, a weighted COPD-related metabolic network and a weighted COPD-related PPI network were constructed base on COPD disease genes and functional information. Candidate genes in these weighted COPD-related networks were prioritized by making use of a gene prioritization method, respectively. Literature review and functional enrichment analysis of the top 100 genes in these two networks suggested the correlation of COPD and these genes. The performance of our gene prioritization method was superior to that of ToppGene and ToppNet for genes from the COPD-related metabolic network or the COPD-related PPI network after assessing using leave-one-out cross-validation, literature validation and functional enrichment analysis. The top-ranked genes prioritized from COPD-related metabolic and PPI networks could promote the better understanding about the molecular mechanism of this disease from different perspectives. The top 100 genes in COPD-related metabolic network or COPD-related PPI network might be potential markers for the diagnosis and treatment of COPD.

A network-based method using a random walk with restart algorithm and screening tests to identify novel genes associated with Menière's disease

As a chronic illness derived from hair cells of the inner ear, Menière’s disease (MD) negatively influences the quality of life of individuals and leads to a number of symptoms, such as dizziness, temporary hearing loss, and tinnitus. The complete identification of novel genes related to MD would help elucidate its underlying pathological mechanisms and improve its diagnosis and treatment. In this study, a network-based method was developed to identify novel MD-related genes based on known MD-related genes. A human protein-protein interaction (PPI) network was constructed using the PPI information reported in the STRING database. A classic ranking algorithm, the random walk with restart (RWR) algorithm, was employed to search for novel genes using known genes as seed nodes. To make the identified genes more reliable, a series of screening tests, including a permutation test, an interaction test and an enrichment test, were designed to select essential genes from those obtained by the RWR algorithm. As a result, several inferred genes, such as CD4, NOTCH2 and IL6, were discovered. Finally, a detailed biological analysis was performed on fifteen of the important inferred genes, which indicated their strong associations with MD.

Identifying novel fruit-related genes in Arabidopsis thaliana based on the random walk with restart algorithm

Fruit is essential for plant reproduction and is responsible for protection and dispersal of seeds. The development and maturation of fruit is tightly regulated by numerous genetic factors that respond to environmental and internal stimulation. In this study, we attempted to identify novel fruit-related genes in a model organism, Arabidopsis thaliana, using a computational method. Based on validated fruit-related genes, the random walk with restart (RWR) algorithm was applied on a protein-protein interaction (PPI) network using these genes as seeds. The identified genes with high probabilities were filtered by the permutation test and linkage tests. In the permutation test, the genes that were selected due to the structure of the PPI network were discarded. In the linkage tests, the importance of each candidate gene was measured from two aspects: (1) its functional associations with validated genes and (2) its similarity with validated genes on gene ontology (GO) terms and KEGG pathways. Finally, 255 inferred genes were obtained, subsequent extensive analysis of important genes revealed that they mainly contribute to ubiquitination (UBQ9, UBQ8, UBQ11, UBQ10), serine hydroxymethyl transfer (SHM7, SHM5, SHM6) or glycol-metabolism (HXKL2_ARATH, CSY5, GAPCP1), suggesting essential roles during the development and maturation of fruit in Arabidopsis thaliana.

Identification of exosomes and its signature miRNAs of male and female Cynoglossus semilaevis

Exosomes are small membrane particles which are widely found in various cell lines and physiological fluids in mammalian. MicroRNAs (miRNAs) enclosed in exosomes have been identified as proper signatures for many diseases and response to therapies. However, the composition of exosomes and enclosed miRNAs in fishes has not been investigated. Cynoglossus semilaevis is an important commercial flatfish with ambiguous distinction between males and females before sex maturation, which leads to screening difficulty in reproduction and cultivation. An effective detection method was required for sex differentiation of C. semilaevis. In this work, we successfully identified exosomes in C. semilaevis serum. The analysis of nucleotide composition showed that miRNA dominated in exosomes. Thereafter the miRNA profiles in exosomes from males and females were sequenced and compared to identify the signature miRNAs corresponding to sex differentiation. The functions of signature miRNAs were analyzed by target matching and annotation. Furthermore, 7 miRNAs with high expression in males were selected from signature miRNAs as the markers for sex identification with their expression profiles verified by real time quantitative PCR. Exosomes were first found in fish serum in this work. Investigation of marker miRNAs supplies an effective index for the filtration of male and female C. semilaevis in cultivation.

Integrative analyses of transcriptome sequencing identify novel functional lncRNAs in esophageal squamous cell carcinoma

Long non-coding RNAs (lncRNAs) have a critical role in cancer initiation and progression, and thus may mediate oncogenic or tumor suppressing effects, as well as be a new class of cancer therapeutic targets. We performed high-throughput sequencing of RNA (RNA-seq) to investigate the expression level of lncRNAs and protein-coding genes in 30 esophageal samples, comprised of 15 esophageal squamous cell carcinoma (ESCC) samples and their 15 paired non-tumor tissues. We further developed an integrative bioinformatics method, denoted URW-LPE, to identify key functional lncRNAs that regulate expression of downstream protein-coding genes in ESCC. A number of known onco-lncRNA and many putative novel ones were effectively identified by URW-LPE. Importantly, we identified lncRNA625 as a novel regulator of ESCC cell proliferation, invasion and migration. ESCC patients with high lncRNA625 expression had significantly shorter survival time than those with low expression. LncRNA625 also showed specific prognostic value for patients with metastatic ESCC. Finally, we identified E1A-binding protein p300 (EP300) as a downstream executor of lncRNA625-induced transcriptional responses. These findings establish a catalog of novel cancer-associated functional lncRNAs, which will promote our understanding of lncRNA-mediated regulation in this malignancy.

A Reconfiguration Strategy of Distribution Networks Considering Node Importance

Node importance degree is a vital index in distribution network reconfiguration because it reflects the robustness of the network structure by evaluating node importance. Since the traditional reconfiguration ignores this index, the reconstructed network structure may be vulnerable which would reduce the security and stability of the distribution systems. This paper presents a novel reconfiguration strategy considering the node importance. The optimization objectives are the improvement of the node importance degree and the reduction of power loss. To balance the objectives, the reconfiguration mathematical model is formulated as a compound objective function with weight coefficients. Then the quantum particle swarm algorithm is employed to address this compound objective optimization problem. The strategy can model different scenarios network reconfiguration by adjusting the weight vector based on the tendencies of the utility decision maker. Illustrative examples verify the effectiveness of the proposed strategy.