SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles

Identification of potential sialic acid binding proteins on cell membranes by proximity chemical labeling

A “protein oxidation of sialic acid environments” (POSE) mapping tool is developed for sialic acid binding protein discovery.

The cell membrane contains a highly interactive glycan surface on a scaffold of proteins and lipids. Sialic acids are negatively charged monosaccharides, and the proteins that bind to sialic acids play an important role in maintaining the integrity and collective functions of this interactive space. Sialic acid binding proteins are not readily identified and have nearly all been discovered empirically. In this research, we developed a proximity labeling method to characterize proteins with oxidation by localized radicals produced in situ. The sites of oxidation were identified and quantified using a standard proteomic workflow. In this method, a clickable probe was synthesized and attached to modified sialic acids on the cell membrane, which functioned as a catalyst for the localized formation of radicals from hydrogen peroxide. The proteins in the sialic acid environment were labeled through amino acid oxidation, and were categorized into three groups including sialylated proteins, non-sialylated proteins with transmembrane domains, and proteins that are associated with the membrane with neither sialylated nor transmembrane domains. The analysis of the last group of proteins showed that they were associated with binding functions including carbohydrate binding, anion binding, and cation binding, thereby revealing the nature of the sialic acid–protein interaction. This new tool identified potential sialic acid-binding proteins in the extracellular space and proteins that were organized around sialylated glycans in cells.

Long non-coding RNA SNHG6 is upregulated in prostate cancer and predicts poor prognosis

Certain long non-coding RNAs (lncRNAs) have been reported to be differentially expressed in various human cancer types, including prostate cancer (PCa). PCa is the most commonly diagnosed cancer type in men and lacks sensitive and accurate biomarkers. Emerging studies have indicated that certain lncRNAs are dysregulated and have crucial roles in PCa progression. The present study reported that the novel lncRNA small nucleolar RNA host gene 6 (SNHG6) is overexpressed in PCa compared with that in normal prostate tissues. In The Cancer Genome Atlas and Taylor datasets, high expression of SNHG6 in PCa tissues was identified to be significantly associated with shorter disease-free survival. In order to reveal the potential mechanisms of the role of SNHG6 in PCa, SNHG6-associated protein-protein interaction networks were constructed. Bioinformatics analysis revealed that these SNHG6-interacting genes were associated with translation, nuclear-transcribed mRNA catabolic process, ribosomal RNA processing and mRNA splicing. Although further functional validation is warranted, the present study suggests that SNHG6 is a potential novel therapeutic target and prognostic biomarker for PCa.

Magic roundabout is an endothelial-specific ohnolog of ROBO1 which neo-functionalized to an essential new role in angiogenesis

BACKGROUND

Magic roundabout (ROBO4) is an unusual endothelial-specific paralog of the family of neuronally-expressed axon guidance receptors called roundabouts. Endothelial cells (ECs), whose uninterrupted sheet delimits the lumen of all vertebrate blood vessels and which are absent from invertebrate species, are a vertebrate-specific evolutionary novelty.

RESULTS

Herein, the evolutionary mechanism of the duplication, retention and divergence of ROBO4 was investigated for the first time. Phylogenetic analyses carried out suggested that ROBO4 is a fast-evolving paralog of ROBO1 formed at the base of vertebrates. The ancestral expression pattern was neuronal. ROBO4 dramatically shifted its expression and became exceptionally specific to ECs. The data-mining of FANTOM5 and ENCODE reveals that ROBO4's endothelial expression arises from a single transcription start site (TSS), conserved in mouse, controlled by a proximal promoter with a complex architecture suggestive of regulatory neo-functionalization. (An analysis of promoter probabilities suggested the architecture was not due to a chance arrangement of TFBSes). Further evidence for the neo-functionalization of ROBO4 comes from the analysis of its protein interactions, the rates of protein evolution, and of positively selected sites.

CONCLUSIONS

The neo-functionalization model explains why ROBO4 protein acquired new context-specific biological functions in the control of angiogenesis. This endothelial-specific roundabout receptor is an illustrative example of the emergence of an essential vertebrate molecular novelty and an endothelial-specific signaling sub-network through 2R-WGD. The emergence of novel cell types, such as ECs, might be a neglected evolutionary force contributing to the high rate of retention of duplicates post-2R-WGD. Crucially, expression neo-functionalization to evolutionarily novel sites of expression conceptually extends the classical model of neo-functionalization.

Coexpression network analysis identified Krüppel-like factor 6 (KLF6) association with chemosensitivity in ovarian cancer

Although most patients with ovarian cancer (OC) are initially sensitive to paclitaxel/carboplatin combination chemotherapy, eventually they develop resistance to chemotherapy drugs and experience disease relapse. OC is the most lethal gynecological malignancy, and the five-year survival rate is extremely low. Thus, research on specific biomarkers and potential targets for chemotherapy-resistant patients with OC is needed. In our study, genes in the top 10% of variance in data set GSE30161 from chemoresistant and chemosensitive OC tissues were determined to conduct a weighted gene coexpression network analysis (WGCNA). The magenta module was most strongly related to OC chemoresponse. Gene ontology enrichment analysis indicated that the function of the magenta module primarily focused on transcription regulation, cell cycle control, and apoptosis modulation. Integration of the WGCN with the protein-protein interaction network identified five candidate genes. These five genes were verified using the GSE51373 test set, and Krüppel-like factor 6 ( KLF6) was identified as tightly linked to OC chemosensitivity. The receiver operating characteristic (ROC) curve showed that KLF6 differentiated chemoresistant from chemosensitive OC tissues. The Kaplan-Meier online database indicated that high KLF6 expression was associated with poor OC prognosis. Gene set enrichment analysis determined that the KLF6 mechanism was potentially associated with cell cycle, mTOR, and DNA-damage repair signaling pathways. In conclusion, KLF6 was identified in association with OC chemoresistance, and the mechanism of KLF6-mediated chemoresistance may involve the cell cycle, mTOR, and DNA-damage repair signaling pathways.

Genetic and genomic analyses of testicular hypoplasia in Nellore cattle

Reproductive performance is a key indicator of the long-term sustainability of any livestock production system. Testicular hypoplasia (TH) is a morphological and functional reproductive disorder that affects bulls around the world and consequently causes major economic losses due to reduced fertility rates. Despite the improvements in management practices to enhance performance of affected animals, the use of hypoplastic animals for reproduction might contribute to expand the prevalence of this disorder. The aim of this study was to identify genomic regions that are associated with TH in Nellore cattle by performing a genome-wide association study (GWAS) and functional analyses. Phenotypic and pedigree data from 47,563 animals and genotypes (500,689 Single Nucleotide Polymorphism, SNPs) from 265 sires were used in this study. TH was evaluated as a binary trait measured at 18 months of age. The estimated breeding values (EBVs) were calculated by fitting a single-trait threshold animal model using a Bayesian approach. The SNP effects were estimated using the Bayes C method and de-regressed EBVs for TH as the response variable (pseudo-phenotype). The top-15 ranking windows (5-adjacent SNPs) that explained the highest proportion of variance were identified for further functional and biological network analyses. The posterior mean (95% highest posterior density) of the heritability for TH was 0.16 (0.08; 0.23). The most important genomic windows were located on BTA1, BTA3, BTA4, BTA5, BTA9, BTA22, BTA23, and BTA25. These windows explained together 22.69% of the total additive genetic variance for TH. Strong candidate genes associated with metabolism and synthesis of steroids, cell survival, spermatogenesis process and sperm motility were identified, which might play an important role in the expression of TH. Our findings contribute to a better biological understanding of TH and future characterization of causal variants might enable improved genomic prediction of this trait in beef cattle.

ACCBN: ant-Colony-clustering-based bipartite network method for predicting long non-coding RNA-protein interactions

BACKGROUND

Long non-coding RNA (lncRNA) studies play an important role in the development, invasion, and metastasis of the tumor. The analysis and screening of the differential expression of lncRNAs in cancer and corresponding paracancerous tissues provides new clues for finding new cancer diagnostic indicators and improving the treatment. Predicting lncRNA-protein interactions is very important in the analysis of lncRNAs. This article proposes an Ant-Colony-Clustering-Based Bipartite Network (ACCBN) method and predicts lncRNA-protein interactions. The ACCBN method combines ant colony clustering and bipartite network inference to predict lncRNA-protein interactions.

RESULTS

A five-fold cross-validation method was used in the experimental test. The results show that the values of the evaluation indicators of ACCBN on the test set are significantly better after comparing the predictive ability of ACCBN with RWR, ProCF, LPIHN, and LPBNI method.

CONCLUSIONS

With the continuous development of biology, besides the research on the cellular process, the research on the interaction function between proteins becomes a new key topic of biology. The studies on protein-protein interactions had important implications for bioinformatics, clinical medicine, and pharmacology. However, there are many kinds of proteins, and their functions of interactions are complicated. Moreover, the experimental methods require time to be confirmed because it is difficult to estimate. Therefore, a viable solution is to predict protein-protein interactions efficiently with computers. The ACCBN method has a good effect on the prediction of protein-protein interactions in terms of sensitivity, precision, accuracy, and F1-score.

ProphTools: general prioritization tools for heterogeneous biological networks

Background

Networks have been proven effective representations for the analysis of biological data. As such, there exist multiple methods to extract knowledge from biological networks. However, these approaches usually limit their scope to a single biological entity type of interest or they lack the flexibility to analyze user-defined data.

Results

We developed ProphTools, a flexible open-source command-line tool that performs prioritization on a heterogeneous network. ProphTools prioritization combines a Flow Propagation algorithm similar to a Random Walk with Restarts and a weighted propagation method. A flexible model for the representation of a heterogeneous network allows the user to define a prioritization problem involving an arbitrary number of entity types and their interconnections. Furthermore, ProphTools provides functionality to perform cross-validation tests, allowing users to select the best network configuration for a given problem. ProphTools core prioritization methodology has already been proven effective in gene-disease prioritization and drug repositioning. Here we make ProphTools available to the scientific community as flexible, open-source software and perform a new proof-of-concept case study on long noncoding RNAs (lncRNAs) to disease prioritization.

Conclusions

ProphTools is robust prioritization software that provides the flexibility not present in other state-of-the-art network analysis approaches, enabling researchers to perform prioritization tasks on any user-defined heterogeneous network. Furthermore, the application to lncRNA-disease prioritization shows that ProphTools can reach the performance levels of ad hoc prioritization tools without losing its generality.

Loss of miR-146b-5p promotes T cell acute lymphoblastic leukemia migration and invasion via the IL-17A pathway

Metastatic disease remains the primary cause of death for individuals with T cell acute lymphoblastic leukemia (T-ALL). microRNAs (miRNAs) play important roles in the pathogenesis of T-ALL by inhibiting gene expression at posttranscriptional levels. The goal of the current project is to identify any significant miRNAs in T-ALL metastasis. We observed miR-146b-5p to be downregulated in T-ALL patients and cell lines, and bioinformatics analysis implicated miR-146b-5p in the hematopoietic system. miR-146b-5p inhibited the migration and invasion in T-ALL cells. Interleukin-17A (IL-17A) was predicted to be a target of miR-146b-5p; this was confirmed by luciferase assays. Interestingly, T-ALL patients and cell lines secreted IL-17A and expressed the IL-17A receptor (IL-17RA). IL-17A/IL-17RA interactions promoted strong T-ALL cell migration and invasion responses. Gene set enrichment analysis (GSEA) and quantitative polymerase chain reaction (qPCR) analysis indicated that matrix metallopeptidase-9 (MMP9), was a potential downstream effector of IL-17A activation, and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling was also implicated in this process. Moreover, IL-17A activation promoted T-ALL cell metastasis to the liver in IL17A ^-/- mouse models. These results indicate that reduced miR-146b-5p expression in T-ALL may lead to the upregulation of IL-17A, which then promotes T-ALL cell migration and invasion by upregulating MMP9 via NF-κB signaling.

Effect of Productive Human Papillomavirus 16 Infection on Global Gene Expression in Cervical Epithelium

Human papillomavirus (HPV) infection is the world's most common sexually transmitted infection and is responsible for most cases of cervical cancer. Previous studies of global gene expression changes induced by HPV infection have focused on the cancerous stages of infection, and therefore, not much is known about global gene expression changes at early preneoplastic stages of infection. We show for the first time the global gene expression changes during early-stage HPV16 infection in cervical tissue using 3-dimensional organotypic raft cultures, which produce high levels of progeny virions. cDNA microarray analysis showed that a total of 594 genes were upregulated and 651 genes were downregulated at least 1.5-fold with HPV16 infection. Gene ontology analysis showed that biological processes including cell cycle progression and DNA metabolism were upregulated, while skin development, immune response, and cell death were downregulated with HPV16 infection in cervical keratinocytes. Individual genes were selected for validation at the transcriptional and translational levels, including UBC, which was central to the protein association network of immune response genes, and top downregulated genes RPTN, SERPINB4, KRT23, and KLK8 In particular, KLK8 and SERPINB4 were shown to be upregulated in cancer, which contrasts with the gene regulation during the productive replication stage. Organotypic raft cultures, which allow full progression of the HPV life cycle, allowed us to identify novel gene modulations and potential therapeutic targets of early-stage HPV infection in cervical tissue. Additionally, our results suggest that early-stage productive infection and cancerous stages of infection are distinct disease states expressing different host transcriptomes.IMPORTANCE Persistent HPV infection is responsible for most cases of cervical cancer. The transition from precancerous to cancerous stages of HPV infection is marked by a significant reduction in virus production. Most global gene expression studies of HPV infection have focused on the cancerous stages. Therefore, little is known about global gene expression changes at precancerous stages. For the first time, we measured global gene expression changes at the precancerous stages of HPV16 infection in human cervical tissue producing high levels of virus. We identified a group of genes that are typically overexpressed in cancerous stages to be significantly downregulated at the precancerous stage. Moreover, we identified significantly modulated genes that have not yet been studied in the context of HPV infection. Studying the role of these genes in HPV infection will help us understand what drives the transition from precancerous to cancerous stages and may lead to the development of new therapeutic targets.

Substrate Specificity Analysis of Dihydrofolate/Dihydromethanopterin Reductase Homologs in Methylotrophic α-Proteobacteria

Methane-producing archaea and methylotrophic bacteria use tetrahydromethanopterin (H₄MPT) and/or tetrahydrofolate (H₄F) as coenzymes in one-carbon (C1) transfer pathways. The α-proteobacterium Methylobacterium extorquens AM1 contains a dihydromethanopterin reductase (DmrA) and two annotated dihydrofolate reductases (DfrA and DfrB). DmrA has been shown to catalyze the final step of H₄MPT biosynthesis; however, the functions of DfrA and DfrB have not been examined biochemically. Moreover, sequence alignment (BLAST) searches have recognized scores of proteins that share up to 99% identity with DmrA but are annotated as diacylglycerol kinases (DAGK). In this work, we used bioinformatics and enzyme assays to provide insight into the phylogeny and substrate specificity of selected Dfr and DmrA homologs. In a phylogenetic tree, DmrA and homologs annotated as DAGKs grouped together in one clade. Purified histidine-tagged versions of the annotated DAGKs from Hyphomicrobium nitrativorans and M. nodulans (respectively, sharing 69 and 84% identity with DmrA) showed only low activity in phosphorylating 1,2-dihexanoyl-sn-glycerol when compared with a commercial DAGK from Escherichia coli. However, the annotated DAGKs successfully reduced a dihydromethanopterin analog (dihydrosarcinapterin, H₂SPT) with kinetic values similar to those determined for M. extorquens AM1 DmrA. DfrA and DfrB showed little or no ability to reduce H₂SPT under the conditions studied; however, both catalyzed the NADPH-dependent reduction of dihydrofolate. These results provide the first evidence that DfrA and DfrB function as authentic dihydrofolate reductases, while DAGKs with greater than 69% identity to DmrA may be misannotated and are likely to function in H₄MPT biosynthesis.

Identifying a Novel Biomarker TOP2A of Clear Cell Renal Cell Carcinoma (ccRCC) Associated with Smoking by Co-Expression Network Analysis

Although it is well known that smoking is one of pathogenesis of clear cell renal cell carcinoma (ccRCC), the underlying molecular mechanism is still unclear. In our study, the microarray dataset GSE46699 is analyzed by weighted gene co-expression network analysis (WGCNA). Then we identify 15 co-expressed gene modules in which the lightcyan module (R² = 0.30) is the most significant. Combined with the protein-protein interaction (PPI) network and WGCNA, two hub genes are identified. Meanwhile, linear regression analyses indicate that TOP2A has a higher connection with smoking in ccRCC, survival analysis proved that overexpression of TOP2A in ccRCC could lead to shorter survival time. Furthermore, bioinformatical analyses based on GSE46699 and GSE2109 as well as qRT-PCR experiment show similar results that TOP2A is significantly up-regulated in smoking ccRCC compared to non-smoking ccRCC samples. In addition, Functional analysis, pathway enrichment analysis and gene set enrichment analysis (GSEA) indicate that high expression of TOP2A is related to cell cycle and p53 signaling pathway in ccRCC samples. Moreover, in vitro experiments revealed that TOP2A induced cell cycle arrest at G2 phase and proliferation inhibition via p53 phosphorylation. Taken together, by using WGCNA, we have identified a novel biomarker named TOP2A, which could affect the development of smoking-related ccRCC by regulating cell cycle and p53 signaling pathway.

Co-expression network analysis identified CDH11 in association with progression and prognosis in gastric cancer

Background and aims

Gastric cancer (GC) is one of the most common cancers worldwide, and its pathogenesis is related to a complex network of gene interactions. The aims of our study were to find hub genes associated with the progression and prognosis of GC and illustrate the underlying mechanisms.

Methods

Weighted gene co-expression network analysis (WGCNA) was conducted using the microarray dataset and clinical data of GC patients from Gene Expression Omnibus (GEO) database to identify significant gene modules and hub genes associated with TNM stage in GC. Functional enrichment analysis and protein-protein interaction network analysis were performed using the significant module genes. We regarded the common hub genes in the co-expression network and protein-protein interaction (PPI) network as "real" hub genes for further analysis. Hub gene was validated in another independent dataset and The Cancer Genome Atlas (TCGA) dataset.

Results

In the significant purple module (R ²=0.35), a total of 12 network hub genes were identified, among which six were also hub nodes in the PPI network of the module genes. Functional annotation revealed that the genes in the purple module focused on the biological processes of system development, biological adhesion, extracellular structure organization and metabolic process. In terms of validation, CDH11 had a higher correlation with the TNM stage than other hub genes and was strongly correlated with biological adhesion based on GO functional enrichment analysis. Data obtained from the Gene Expression Profiling Interactive Analysis (GEPIA) showed that CDH11 expression had a strong positive correlation with GC stages (P<0.0001). In the testing set and Oncomine dataset, CDH11 was highly expressed in GC tissues (P<0.0001). Survival analysis indicated that samples with a high CDH11 expression showed a poor prognosis. Cox regression analysis demonstrated an independent predictor of CDH11 expression in GC prognosis (HR=1.482, 95% CI: 1.015-2.164). Furthermore, gene set enrichment analysis (GSEA) demonstrated that multiple tumor-related pathways, especially focal adhesion, were enriched in CDH11 highly expressed samples.

Conclusion

CDH11 was identified and validated in association with progression and prognosis in GC, probably by regulating biological adhesion and focal adhesion-related pathways.

Muscle-specific regulation of right ventricular transcriptional responses to chronic hypoxia-induced hypertrophy by the muscle ring finger-1 (MuRF1) ubiquitin ligase in mice

BACKGROUND

We recently identified a role for the muscle-specific ubiquitin ligase MuRF1 in right-sided heart failure secondary to pulmonary hypertension induced by chronic hypoxia (CH). MuRF1-/- mice exposed to CH are resistant to right ventricular (RV) dysfunction whereas MuRF1 Tg + mice exhibit impaired function indicative of heart failure. The present study was undertaken to understand the underlying transcriptional alterations in the RV of MuRF1-/- and MuRF1 Tg + mice.

METHODS

Microarray analysis was performed on RNA isolated from the RV of MuRF1-/-, MuRF1 Tg+, and wild-type control mice exposed to CH.

RESULTS

MuRF1-/- RV differentially expressed 590 genes in response to CH. Analysis of the top 66 genes (> 2-fold or < - 2-fold) revealed significant associations with oxidoreductase, transcription regulation, and transmembrane component annotations. The significant genes had promoters enriched for HOXD12, HOXC13, and RREB-1 protein transcription factor binding sites. MuRF1 Tg + RV differentially expressed 150 genes in response to CH. Analysis of the top 45 genes (> 3-fold or < - 3-fold) revealed significant associations with oxidoreductase-metabolic, glycoprotein-transmembrane-integral proteins, and alternative splicing/splice variant annotations. The significant genes were enriched for promoters with ZIC1 protein transcription factor binding sites.

CONCLUSIONS

The differentially expressed genes in MuRF1-/- and MuRF1 Tg + RV after CH have common functional annotations related to oxidoreductase (including antioxidant) and transmembrane component functions. Moreover, the functionally-enhanced MuRF1-/- hearts regulate genes related to transcription, homeobox proteins, and kinases/phosphorylation. These studies also reveal potential indirect effects of MuRF1 through regulating Rreb-1, and they reveal mechanisms by which MuRF1 may transcriptionally regulate anti-oxidant systems in the face of right heart failure.

Identification of key genes and pathways using bioinformatics analysis in septic shock children

Background and hypothesis

Sepsis is still one of the reasons for serious infectious diseases in pediatric intensive care unit patients despite the use of anti-infective therapy and organ support therapy. As it is well-known, the effect of single gene or pathway does not play a role in sepsis. We want to explore the interaction of two more genes or pathways in sepsis patients for future works. We hypothesize that the discovery from the available gene expression data of pediatric sepsis patients could know the process or improve the situation.

Methods and results

The gene expression profile dataset GSE26440 of 98 septic shock samples and 32 normal samples using whole blood-derived RNA samples were generated. A total of 1,108 upregulated and 142 downregulated differentially expressed genes (DEGs) were identified in septic shock children using R software packages. The Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were analyzed using DAVID software; Gene Set Enrichment Analysis method was also used for enrichment analysis of the DEGs. The protein-protein interaction (PPI) network and the top 10 hub genes construction of the DEGs were constructed via plug-in Molecular Complex Detection and cytoHubba of Cytoscape software. From the PPI network, the top 10 hub genes, which are all upregulated DEGs in the septic shock children, were identified as GAPDH, TNF, EGF, MAPK3, IL-10, TLR4, MAPK14, IL-1β, PIK3CB, and TLR2. Some of them were involved in one or more significant inflammatory pathways, such as the enrichment of tumor necrosis factor (TNF) pathway in the activation of mitogen-activated protein kinase activity, toll-like receptor signaling pathway, nuclear factor-κB signaling pathway, PI3K-Akt signaling pathway, and TNF signaling pathway. These findings support future studies on pediatric septic shock.

Cyclic Enterobacterial Common Antigen Maintains the Outer Membrane Permeability Barrier of Escherichia coli in a Manner Controlled by YhdP

Gram-negative bacteria have an outer membrane (OM) impermeable to many toxic compounds that can be further strengthened during stress. In Enterobacteriaceae, the envelope contains enterobacterial common antigen (ECA), a carbohydrate-derived moiety conserved throughout Enterobacteriaceae, the function of which is poorly understood. Previously, we identified several genes in Escherichia coli K-12 responsible for an RpoS-dependent decrease in envelope permeability during carbon-limited stationary phase. For one of these, yhdP, a gene of unknown function, deletion causes high levels of both vancomycin and detergent sensitivity, independent of growth phase. We isolated spontaneous suppressor mutants of yhdP with loss-of-function mutations in the ECA biosynthesis operon. ECA biosynthesis gene deletions suppressed envelope permeability from yhdP deletion independently of envelope stress responses and interactions with other biosynthesis pathways, demonstrating suppression is caused directly by removing ECA. Furthermore, yhdP deletion changed cellular ECA levels and yhdP was found to co-occur phylogenetically with the ECA biosynthesis operon. Cells make three forms of ECA: ECA lipopolysaccharide (LPS), an ECA chain linked to LPS core; ECA phosphatidylglycerol, a surface-exposed ECA chain linked to phosphatidylglycerol; and cyclic ECA, a cyclized soluble ECA molecule found in the periplasm. We determined that the suppression of envelope permeability with yhdP deletion is caused specifically by the loss of cyclic ECA, despite lowered levels of this molecule found with yhdP deletion. Furthermore, removing cyclic ECA from wild-type cells also caused changes to OM permeability. Our data demonstrate cyclic ECA acts to maintain the OM permeability barrier in a manner controlled by YhdP.

Enterobacterial common antigen (ECA) is a surface antigen made by all members of Enterobacteriaceae, including many clinically relevant genera (e.g., Escherichia, Klebsiella, Yersinia). Although this surface-exposed molecule is conserved throughout Enterobacteriaceae, very few functions have been ascribed to it. Here, we have determined that the periplasmic form of ECA, cyclic ECA, plays a role in maintaining the outer membrane permeability barrier. This activity is controlled by a protein of unknown function, YhdP, and deletion of yhdP damages the OM permeability barrier in a cyclic ECA-dependent manner, allowing harmful molecules such as antibiotics into the cell. This role in maintenance of the envelope permeability barrier is the first time a phenotype has been described for cyclic ECA. As the Gram-negative envelope is generally impermeable to antibiotics, understanding the mechanisms through which the barrier is maintained and antibiotics are excluded may lead to improved antibiotic delivery.

Over-expression of TOP2A as a prognostic biomarker in patients with glioma

Topoisomerase (DNA) II alpha (TOP2A), an enzyme that controls and alters the topologic states of DNA during transcription, is aberrantly expressed in many cancers. However, few studies have investigated expression of TOP2A and its clinical significance in glioma. We retrieved six independent investigations from the Oncomine database and found that TOP2A is highly expressed in glioma tissues compared with corresponding normal controls. Similar results were also found in clinical specimens at the protein level. Immunohistochemical analysis indicated that TOP2A over expression was highly correlated with grade stage, KI67 positive percentage, IDH1 mutation, and age, but other clinical parameters such as sex distribution and tumor size were barely associated with high TOP2A gene expression. Meanwhile we used Prognos can to assess the prognostic value of TOP2A expression in glioma patients, and found that high expression was associated with poor prognosis of patients with glioma. Furthermore, we used the Gene-Cloud of Biotechnology Information (GCBI) bioinformatics platform predict the role of TOP2A in glioma. It was not only involved in DNA replication, chromosome condensation, and responses to DNA damage stimuli, but also promoted cancer cell mitotic cell cycle and apoptosis, and phosphatidylinositol-mediated signaling by regulating gene expression. By these approaches we demonstrate that TOP2A may be a reliable prognostic factor or therapeutic target in glioma.

GRMDA: Graph Regression for MiRNA-Disease Association Prediction

Nowadays, as more and more associations between microRNAs (miRNAs) and diseases have been discovered, miRNA has gradually become a hot topic in the biological field. Because of the high consumption of time and money on carrying out biological experiments, computational method which can help scientists choose the most likely associations between miRNAs and diseases for further experimental studies is desperately needed. In this study, we proposed a method of Graph Regression for MiRNA-Disease Association prediction (GRMDA) which combines known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. We used Gaussian interaction profile kernel similarity to supplement the shortage of miRNA functional similarity and disease semantic similarity. Furthermore, the graph regression was synchronously performed in three latent spaces, including association space, miRNA similarity space, and disease similarity space, by using two matrix factorization approaches called Singular Value Decomposition and Partial Least-Squares to extract important related attributes and filter the noise. In the leave-one-out cross validation and five-fold cross validation, GRMDA obtained the AUCs of 0.8272 and 0.8080 ± 0.0024, respectively. Thus, its performance is better than some previous models. In the case study of Lymphoma using the recorded miRNA-disease associations in HMDD V2.0 database, 88% of top 50 predicted miRNAs were verified by experimental literatures. In order to test the performance of GRMDA on new diseases with no known related miRNAs, we took Breast Neoplasms as an example by regarding all the known related miRNAs as unknown ones. We found that 100% of top 50 predicted miRNAs were verified. Moreover, 84% of top 50 predicted miRNAs in case study for Esophageal Neoplasms based on HMDD V1.0 were verified to have known associations. In conclusion, GRMDA is an effective and practical method for miRNA-disease association prediction.

A novel correlation between ATP5A1 gene expression and progression of human clear cell renal cell carcinoma identified by co‑expression analysis

Clear cell renal cell carcinoma (ccRCC) is the most common solid lesion within kidneys, and its prognostic is influenced by the progression covering a complex network of gene interactions. In our study, a weighted gene co‑expression network was constructed to identify gene modules associated with the progression of ccRCC (n=35). In the significant module (R2 = -0.53), a total of 13 network hub genes were identified, and 2 of them were hub nodes in the protein-protein interaction network as well. In validation, ATP5A1 showed a higher correlation with the disease progression than any other hub gene in the hub module (P=0.001219). In the test set (n=202), ATP5A1 was also highly expressed in normal kidney than ccRCC tissues of each grade (P<0.001). Functional and pathway enrichment analysis demonstrated that ATP5A1 is overrepresented in pathway of oxidative phosphorylation, which associated with tumorigenesis and tumor progression. Gene set enrichment analysis (GSEA) also demonstrated that the gene set of 'oxidative phosphorylation' and metabolic pathways were enriched in ccRCC samples with ATP5A1 highly expressed (P<0.05). In conclusion, based on the co‑expression analysis, ATP5A1 was validated to be associated with progression of ccRCC, probably by regulating tumor-related phosphorylation.

Identification of membrane-associated proteins with pathogenic potential expressed by Corynebacterium pseudotuberculosis grown in animal serum

Objective

Previous works defining antigens that might be used as vaccine targets against Corynebacterium pseudotuberculosis, which is the causative agent of sheep and goat caseous lymphadenitis, have focused on secreted proteins produced in a chemically defined culture media. Considering that such antigens might not reflect the repertoire of proteins expressed during infection conditions, this experiment aimed to investigate the membrane-associated proteins with pathogenic potential expressed by C. pseudotuberculosis grown directly in animal serum.

Results

Its membrane-associated proteins have been extracted using an organic solvent enrichment methodology, followed by LC–MS/MS and bioinformatics analysis for protein identification and classification. The results revealed 22 membrane-associated proteins characterized as potentially pathogenic. An interaction network analysis indicated that the four potentially pathogenic proteins ciuA, fagA, OppA4 and OppCD were biologically connected within two distinct network pathways, which were both associated with the ABC Transporters KEGG pathway. These results suggest that C. pseudotuberculosis pathogenesis might be associated with the transport and uptake of nutrients; other seven identified potentially pathogenic membrane proteins also suggest that pathogenesis might involve events of bacterial resistance and adhesion. The proteins herein reported potentially reflect part of the protein repertoire expressed during real infection conditions and might be tested as vaccine antigens.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3180-5) contains supplementary material, which is available to authorized users.

Screening of Potential Genes and Transcription Factors of Postoperative Cognitive Dysfunction via Bioinformatics Methods

Background

The aim of this study was to explore the potential genes and transcription factors involved in postoperative cognitive dysfunction (POCD) via bioinformatics analysis.

Material/Methods

GSE95070 miRNA expression profiles were downloaded from Gene Expression Omnibus database, which included five hippocampal tissues from POCD mice and controls. Moreover, the differentially expressed miRNAs (DEMs) between the two groups were identified. In addition, the target genes of DEMs were predicted using Targetscan 7.1, followed by protein-protein interaction (PPI) network construction, functional enrichment analysis, pathway analysis, and prediction of transcription factors (TFs) targeting the potential targets.

Results

A total of eight DEMs were obtained, and 823 target genes were predicted, including 170 POCD-associated genes. Furthermore, potential key genes in the network were remarkably enriched in focal adhesion, protein digestion and absorption, ECM-receptor interaction, and Wnt and MAPK signaling pathways.

Conclusions

Most potential target genes were involved in the regulation of TFs, including LEF1, SP1, and AP4, which may exert strong impact on the development of POCD.

Co-expression network analysis identified FCER1G in association with progression and prognosis in human clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common solid lesion within kidney, and its prognostic is influenced by the progression covering a complex network of gene interactions. In current study, the microarray data GSE66272 containing ccRCC and adjacent normal tissues was analyzed to identify 4042 differentially expressed genes, on which weighted gene co-expression network analysis was performed. Then 12 co-expressed gene modules were identified. The highest association was found between blue module and pathological stage (r = -0.77) by Pearson's correlation analysis. Functional enrichment analysis revealed that biological processes of blue module focused on inflammatory response, immune response, chemotaxis (all p < 1e-10). In the significant module, a total of 38 network hub genes were identified, FCER1G exhibited the highest correlation (r = 0.95) with ccRCC progression. In addition, FCER1G was hub node in the protein-protein interaction network of the genes in blue module as well. Thus, FCER1G was subsequently selected for validation. In the test set GSE53757 and RNA-sequencing data, FCER1G expression was also positively correlated with four stages of ccRCC progression (p < 0.001). Receiver operating characteristic (ROC) curve indicated that FCER1G could distinguish localized (pathological stage I, II) from non-localized (pathological stage III, IV) ccRCC (AUC=0.74, p < 0.001). Besides, FCER1G could be a prognostic gene in clinical practice as well, revealed by survival analysis based on RNA-sequencing data (p < 0.05). In conclusion, using weighted gene co-expression analysis, FCER1G was identified and validated in association with ccRCC progression and prognosis, which might improve the prognosis by influencing immune-related pathways.

Liver segmentation from CT images using a sparse priori statistical shape model (SP-SSM)

This study proposes a new liver segmentation method based on a sparse a priori statistical shape model (SP-SSM). First, mark points are selected in the liver a priori model and the original image. Then, the a priori shape and its mark points are used to obtain a dictionary for the liver boundary information. Second, the sparse coefficient is calculated based on the correspondence between mark points in the original image and those in the a priori model, and then the sparse statistical model is established by combining the sparse coefficients and the dictionary. Finally, the intensity energy and boundary energy models are built based on the intensity information and the specific boundary information of the original image. Then, the sparse matching constraint model is established based on the sparse coding theory. These models jointly drive the iterative deformation of the sparse statistical model to approximate and accurately extract the liver boundaries. This method can solve the problems of deformation model initialization and a priori method accuracy using the sparse dictionary. The SP-SSM can achieve a mean overlap error of 4.8% and a mean volume difference of 1.8%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 0.8 mm and 1.4 mm, respectively.

Overexpression of Topoisomerase 2-Alpha Confers a Poor Prognosis in Pancreatic Adenocarcinoma Identified by Co-Expression Analysis

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is the fourth most common cause of human cancer-related death in the developed countries. Its progression and prognosis are influenced by a complex network of gene interactions.

AIMS

The purpose of this study is to explore key genes associated with pancreatic ductal adenocarcinoma and to predict the possible mechanisms.

METHODS

A weighted gene co-expression network was constructed to identify gene modules associated with the progression of PDAC.

RESULTS

In the significant module (R ² = 0.30), a total of 20 network hub genes were identified, 6 of which were also hub nodes in the protein-protein interaction network of the module genes. In validation, TOP2A has a higher correlation than other hub genes. Also, in the test set (n = 118), TOP2A was more highly expressed in PDAC than normal pancreas samples (P < 0.001). What is more, gene set enrichment analysis demonstrated that eight gene sets (n = 118), "nucleotide excision repair," "P53 signaling pathway," "proteasome," "mismatch repair," "homologous recombination," "DNA replication," "cell cycle," and "base excision repair," were enriched (FDR < 0.05). In gene ontology analysis, TOP2A in the enriched set was associated with cell cycle and cell division. Furthermore, survival analysis indicated that higher expression of TOP2A resulted in the lower overall survival time as well as disease-free survival time.

CONCLUSION

TOP2A was identified in association with the progression and prognosis of PDAC probably by regulating cell cycle and p53 signaling pathway.

Link prediction based on non-negative matrix factorization

With the rapid expansion of internet, the complex networks has become high-dimensional, sparse and redundant. Besides, the problem of link prediction in such networks has also obatined increasingly attention from different types of domains like information science, anthropology, sociology and computer sciences. It makes requirements for effective link prediction techniques to extract the most essential and relevant information for online users in internet. Therefore, this paper attempts to put forward a link prediction algorithm based on non-negative matrix factorization. In the algorithm, we reconstruct the correlation between different types of matrix through the projection of high-dimensional vector space to a low-dimensional one, and then use the similarity between the column vectors of the weight matrix as the scoring matrix. The experiment results demonstrate that the algorithm not only reduces data storage space but also effectively makes the improvements of the prediction performance during the process of sustaining a low time complexity.

PrePhyloPro: phylogenetic profile-based prediction of whole proteome linkages

Direct and indirect functional links between proteins as well as their interactions as part of larger protein complexes or common signaling pathways may be predicted by analyzing the correlation of their evolutionary patterns. Based on phylogenetic profiling, here we present a highly scalable and time-efficient computational framework for predicting linkages within the whole human proteome. We have validated this method through analysis of 3,697 human pathways and molecular complexes and a comparison of our results with the prediction outcomes of previously published co-occurrency model-based and normalization methods. Here we also introduce PrePhyloPro, a web-based software that uses our method for accurately predicting proteome-wide linkages. We present data on interactions of human mitochondrial proteins, verifying the performance of this software. PrePhyloPro is freely available at http://prephylopro.org/phyloprofile/.

An index-based algorithm for fast on-line query processing of latent semantic analysis

Latent Semantic Analysis (LSA) is widely used for finding the documents whose semantic is similar to the query of keywords. Although LSA yield promising similar results, the existing LSA algorithms involve lots of unnecessary operations in similarity computation and candidate check during on-line query processing, which is expensive in terms of time cost and cannot efficiently response the query request especially when the dataset becomes large. In this paper, we study the efficiency problem of on-line query processing for LSA towards efficiently searching the similar documents to a given query. We rewrite the similarity equation of LSA combined with an intermediate value called partial similarity that is stored in a designed index called partial index. For reducing the searching space, we give an approximate form of similarity equation, and then develop an efficient algorithm for building partial index, which skips the partial similarities lower than a given threshold θ. Based on partial index, we develop an efficient algorithm called ILSA for supporting fast on-line query processing. The given query is transformed into a pseudo document vector, and the similarities between query and candidate documents are computed by accumulating the partial similarities obtained from the index nodes corresponds to non-zero entries in the pseudo document vector. Compared to the LSA algorithm, ILSA reduces the time cost of on-line query processing by pruning the candidate documents that are not promising and skipping the operations that make little contribution to similarity scores. Extensive experiments through comparison with LSA have been done, which demonstrate the efficiency and effectiveness of our proposed algorithm.

Correlated duplications and losses in the evolution of palmitoylation writer and eraser families

Background

Protein post-translational modifications (PTMs) change protein properties. Each PTM type is associated with domain families that apply the modification (writers), remove the modification (erasers) and bind to the modified sites (readers) together called toolkit domains. The evolutionary origin and diversification remains largely understudied, except for tyrosine phosphorylation. Protein palmitoylation entails the addition of a palmitoyl fatty acid to a cysteine residue. This PTM functions as a membrane anchor and is involved in a range of cellular processes. One writer family and two erasers families are known for protein palmitoylation.

Results

In this work we unravel the evolutionary history of these writer and eraser families. We constructed a high-quality profile hidden Markov model (HMM) of each family, searched for protein family members in fully sequenced genomes and subsequently constructed phylogenetic distributions of the families. We constructed Maximum Likelihood phylogenetic trees and using gene tree rearrangement and tree reconciliation inferred their evolutionary histories in terms of duplication and loss events. We identified lineages where the families expanded or contracted and found that the evolutionary histories of the families are correlated. The results show that the erasers were invented first, before the origin of the eukaryotes. The writers first arose in the eukaryotic ancestor. The writers and erasers show co-expansions in several eukaryotic ancestral lineages. These expansions often seem to be followed by contractions in some or all of the lineages further in evolution.

Conclusions

A general pattern of correlated evolution appears between writer and eraser domains. These co-evolution patterns could be used in new methods for interaction prediction based on phylogenies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-017-0932-0) contains supplementary material, which is available to authorized users.

The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible

A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein–protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.

RANKS: a flexible tool for node label ranking and classification in biological networks

RANKS is a flexible software package that can be easily applied to any bioinformatics task formalizable as ranking of nodes with respect to a property given as a label, such as automated protein function prediction, gene disease prioritization and drug repositioning. To this end RANKS provides an efficient and easy-to-use implementation of kernelized score functions, a semi-supervised algorithmic scheme embedding both local and global learning strategies for the analysis of biomolecular networks. To facilitate comparative assessment, baseline network-based methods, e.g. label propagation and random walk algorithms, have also been implemented.

AVAILABILITY AND IMPLEMENTATION

The package is available from CRAN: https://cran.r-project.org/ The package is written in R, except for the most computationally intensive functionalities which are implemented in C.

CONTACT

valentini@di.unimi.it

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.