Crosstalk between glial and glioblastoma cells triggers the “go-or-grow” phenotype of tumor cells

Background

Glioblastoma (GBM), the most malignant primary brain tumor, leads to poor and unpredictable clinical outcomes. Recent studies showed the tumor microenvironment has a critical role in regulating tumor growth by establishing a complex network of interactions with tumor cells. In this context, we investigated how GBM cells modulate resident glial cells, particularly their paracrine activity, and how this modulation can influence back on the malignant phenotype of GBM cells.

Methods

Conditioned media (CM) of primary mouse glial cultures unexposed (unprimed) or exposed (primed) to the secretome of GL261 GBM cells were analyzed by proteomic analysis. Additionally, these CM were used in GBM cells to evaluate their impact in glioma cell viability, migration capacity and activation of tumor-related intracellular pathways.

Results

The proteomic analysis revealed that the pre-exposure of glial cells to CM from GBM cells led to the upregulation of several proteins related to inflammatory response, cell adhesion and extracellular structure organization within the secretome of primed glial cells. At the functional levels, CM derived from unprimed glial cells favored an increase in GBM cell migration capacity, while CM from primed glial cells promoted cells viability. These effects on GBM cells were accompanied by activation of particular intracellular cancer-related pathways, mainly the MAPK/ERK pathway, which is a known regulator of cell proliferation.

Conclusions

Together, our results suggest that glial cells can impact on the pathophysiology of GBM tumors, and that the secretome of GBM cells is able to modulate the secretome of neighboring glial cells, in a way that regulates the “go-or-grow” phenotypic switch of GBM cells.

     

Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study

Background

The growing field of metabolomics has opened up new opportunities for prediction of type 2 diabetes (T2D) going beyond the classical biochemistry assays.

Objectives

We aimed to identify markers from different pathways which represent early metabolic changes and test their predictive performance for T2D, as compared to the performance of traditional risk factors (TRF).

Methods

We analyzed 2776 participants from the Erasmus Rucphen Family study from which 1571 disease free individuals were followed up to 14-years. The targeted metabolomics measurements at baseline were performed by three different platforms using either nuclear magnetic resonance spectroscopy or mass spectrometry. We selected 24 T2D markers by using Least Absolute Shrinkage and Selection operator (LASSO) regression and tested their association to incidence of disease during follow-up.

Results

The 24 markers i.e. high-density, low-density and very low-density lipoprotein sub-fractions, certain triglycerides, amino acids, and small intermediate compounds predicted future T2D with an area under the curve (AUC) of 0.81. The performance of the metabolic markers compared to glucose was significantly higher among the young (age?<?50 years) (0.86 vs. 0.77, p-value <0.0001), the female (0.88 vs. 0.84, p-value =0.009), and the lean (BMI?<?25 kg/m²) (0.85 vs. 0.80, p-value =0.003). The full model with fasting glucose, TRFs, and metabolic markers yielded the best prediction model (AUC?=?0.89).

Conclusions

Our novel prediction model increases the long-term prediction performance in combination with classical measurements, brings a higher resolution over the complexity of the lipoprotein component, increasing the specificity for individuals in the low risk group.

     

Integrated analysis of microRNA-target interactions with clinical outcomes for cancers

Background

Clinical statement alone is not enough to predict the progression of disease. Instead, the gene expression profiles have been widely used to forecast clinical outcomes. Many genes related to survival have been identified, and recently miRNA expression signatures predicting patient survival have been also investigated for several cancers. However, miRNAs and their target genes associated with clinical outcomes have remained largely unexplored.

Methods

Here, we demonstrate a survival analysis based on the regulatory relationships of miRNAs and their target genes. The patient survivals for the two major cancers, ovarian cancer and glioblastoma multiforme (GBM), are investigated through the integrated analysis of miRNA-mRNA interaction pairs.

Results

We found that there is a larger survival difference between two patient groups with an inversely correlated expression profile of miRNA and mRNA. It supports the idea that signatures of miRNAs and their targets related to cancer progression can be detected via this approach.

Conclusions

This integrated analysis can help to discover coordinated expression signatures of miRNAs and their target mRNAs that can be employed for therapeutics in human cancers.

     

Hot spot prediction in protein-protein interactions by an ensemble system

Background

Hot spot residues are functional sites in protein interaction interfaces. The identification of hot spot residues is time-consuming and laborious using experimental methods. In order to address the issue, many computational methods have been developed to predict hot spot residues. Moreover, most prediction methods are based on structural features, sequence characteristics, and/or other protein features.

Results

This paper proposed an ensemble learning method to predict hot spot residues that only uses sequence features and the relative accessible surface area of amino acid sequences. In this work, a novel feature selection technique was developed, an auto-correlation function combined with a sliding window technique was applied to obtain the characteristics of amino acid residues in protein sequence, and an ensemble classifier with SVM and KNN base classifiers was built to achieve the best classification performance.

Conclusion

The experimental results showed that our model yields the highest F1 score of 0.92 and an MCC value of 0.87 on ASEdb dataset. Compared with other machine learning methods, our model achieves a big improvement in hot spot prediction.

Availability

http://deeplearner.ahu.edu.cn/web/HotspotEL.htm.

     

Crop metabolomics: from diagnostics to assisted breeding

Background

Until recently, plant metabolomics have provided a deep understanding on the metabolic regulation in individual plants as experimental units. The application of these techniques to agricultural systems subjected to more complex interactions is a step towards the implementation of translational metabolomics in crop breeding.

Aim of Review

We present here a review paper discussing advances in the knowledge reached in the last years derived from the application of metabolomic techniques that evolved from biomarker discovery to improve crop yield and quality.

Key Scientific Concepts of Review

Translational metabolomics applied to crop breeding programs.

     

Nucleotide binding domain 1 pharmacophore modeling for visualization and analysis of P-glycoprotein–flavonoid molecular interactions

Background

P-glycoprotein (P-gp) is a 170-kDa membrane protein. It provides a barrier function and help to excrete toxins from the body as a transporter. Some bioflavonoids have been shown to block P-gp activity.

Objective

To evaluate the important amino acid residues within nucleotide binding domain 1 (NBD1) of P-gp that play a key role in molecular interactions with flavonoids using structure-based pharmacophore model.

Methods

In the molecular docking with NBD1 models, a putative binding site of flavonoids was proposed and compared with the site for ATP. The binding modes for ligands were achieved using LigandScout to generate the P-gp–flavonoid pharmacophore models.

Results

The binding pocket for flavonoids was investigated and found these inhibitors compete with the ATP for binding site in NBD1 including the NBD1 amino acid residues identified by the in silico techniques to be involved in the hydrogen bonding and van der Waals (hydrophobic) interactions with flavonoids.

Conclusion

These flavonoids occupy with the same binding site of ATP in NBD1 proffering that they may act as an ATP competitive inhibitor.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Chaperone–substrate interactions monitored via a robust TEM-1 β-lactamase fragment complementation assay

Objective

To investigate the application of the TEM-1 β-lactamase protein fragment complementation assay (PCA) in detecting weak and unstable protein–protein interactions as typically observed during chaperone-assisted protein folding in the periplasm of Escherichia coli.

Results

The TEM-1 β-lactamase PCA system effectively captured the interactions of three pairs of chaperones and substrates. Moreover, the strength of the interactions can be quantitatively analyzed by comparing different levels of penicillin resistance, and the assay can be performed under 0.5% butanol, a stress condition thought to be physiologically relevant.

Conclusions

The β-lactamase PCA system faithfully reports chaperone-substrate interactions in the bacterial cell envelope, and therefore this system has the potential to map the complex protein homeostasis network under a fluctuating environment.

     

Estimating survival time of patients with glioblastoma multiforme and characterization of the identified microRNA signatures

Background

Though glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy in adults, clinical treatment still faces challenges due to poor prognoses and tumor relapses. Recently, microRNAs (miRNAs) have been extensively used with the aim of developing accurate molecular therapies, because of their emerging role in the regulation of cancer-related genes. This work aims to identify the miRNA signatures related to survival of GBM patients for developing molecular therapies.

Results

This work proposes a support vector regression (SVR)-based estimator, called SVR-GBM, to estimate the survival time in patients with GBM using their miRNA expression profiles. SVR-GBM identified 24 out of 470 miRNAs that were significantly associated with survival of GBM patients. SVR-GBM had a mean absolute error of 0.63 years and a correlation coefficient of 0.76 between the real and predicted survival time. The 10 top-ranked miRNAs according to prediction contribution are as follows: hsa-miR-222, hsa-miR-345, hsa-miR-587, hsa-miR-526a, hsa-miR-335, hsa-miR-122, hsa-miR-24, hsa-miR-433, hsa-miR-574 and hsa-miR-320. Biological analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway on the identified miRNAs revealed their influence in GBM cancer.

Conclusion

The proposed SVR-GBM using an optimal feature selection algorithm and an optimized SVR to identify the 24 miRNA signatures associated with survival of GBM patients. These miRNA signatures are helpful to uncover the individual role of miRNAs in GBM prognosis and develop miRNA-based therapies.

     

BioNetBuilder2.0: bringing systems biology to chicken and other model organisms

Background

Systems Biology research tools, such as Cytoscape, have greatly extended the reach of genomic research. By providing platforms to integrate data with molecular interaction networks, researchers can more rapidly begin interpretation of large data sets collected for a system of interest. BioNetBuilder is an open-source client-server Cytoscape plugin that automatically integrates molecular interactions from all major public interaction databases and serves them directly to the user's Cytoscape environment. Until recently however, chicken and other eukaryotic model systems had little interaction data available.

Results

Version 2.0 of BioNetBuilder includes a redesigned synonyms resolution engine that enables transfer and integration of interactions across species; this engine translates between alternate gene names as well as between orthologs in multiple species. Additionally, BioNetBuilder is now implemented to be part of the Gaggle, thereby allowing seamless communication of interaction data to any software implementing the widely used Gaggle software. Using BioNetBuilder, we constructed a chicken interactome possessing 72,000 interactions among 8,140 genes directly in the Cytoscape environment. In this paper, we present a tutorial on how to do so and analysis of a specific use case.

Conclusion

BioNetBuilder 2.0 provides numerous user-friendly systems biology tools that were otherwise inaccessible to researchers in chicken genomics, as well as other model systems. We provide a detailed tutorial spanning all required steps in the analysis. BioNetBuilder 2.0, the tools for maintaining its data bases, standard operating procedures for creating local copies of its back-end data bases, as well as all of the Gaggle and Cytoscape codes required, are open-source and freely available at http://err.bio.nyu.edu/cytoscape/bionetbuilder/.

     

Improving the specificity of exon prediction using comparative genomics

Background

Computational gene prediction tools routinely generate large volumes of predicted coding exons (putative exons). One common limitation of these tools is the relatively low specificity due to the large amount of non-coding regions.

Methods

A statistical approach is developed that largely improves the gene prediction specificity. The key idea is to utilize the evolutionary conservation principle relative to the coding exons. By first exploiting the homology between genomes of two related species, a probability model for the evolutionary conservation pattern of codons across different genomes is developed. A probability model for the dependency between adjacent codons/triplets is added to differentiate coding exons and random sequences. Finally, the log odds ratio is developed to classify putative exons into the group of coding exons and the group of non-coding regions.

Results

The method was tested on pre-aligned human-mouse sequences where the putative exons are predicted by GENSCAN and TWINSCAN. The proposed method is able to improve the exon specificity by 73% and 32% respectively, while the loss of the sensitivity ≤ 1%. The method also keeps 98% of RefSeq gene structures that are correctly predicted by TWINSCAN when removing 26% of predicted genes that are in non-coding regions. The estimated number of true exons in TWINSCAN's predictions is 157,070. The results and the executable codes can be downloaded from http://www.stat.purdue.edu/~jingwu/codon/

Conclusion

The proposed method demonstrates an application of the evolutionary conservation principle to coding exons. It is a complementary method which can be used as an additional criteria to refine many existing gene predictions.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Modeling EphB4-EphrinB2 protein–protein interaction using flexible docking of a short linear motif

Background

Many protein–protein interactions are mediated by a short linear motif. Usually, amino acid sequences of those motifs are known or can be predicted. It is much harder to experimentally characterize or predict their structure in the bound form. In this work, we test a possibility of using flexible docking of a short linear motif to predict the interaction interface of the EphB4-EphrinB2 complex (a system extensively studied for its significance in tumor progression).

Methods

In the modeling, we only use knowledge about the motif sequence and experimental structures of EphB4-EphrinB2 complex partners. The proposed protocol enables efficient modeling of significant conformational changes in the short linear motif fragment during molecular docking simulation. For the docking simulations, we use the CABS-dock method for docking fully flexible peptides to flexible protein receptors (available as a server at http://biocomp.chem.uw.edu.pl/CABSdock/). Based on the docking result, the protein–protein complex is reconstructed and refined.

Results

Using this novel protocol, we obtained an accurate EphB4-EphrinB2 interaction model.

Conclusions

The results show that the CABS-dock method may be useful as the primary docking tool in specific protein–protein docking cases similar to EphB4-EphrinB2 complex—that is, where a short linear motif fragment can be identified.

     

A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles

Introduction

It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.

Objectives

This work simplifies this process.

Methods

A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.

Results

The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.

Conclusion

This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.

     

KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

Identification of protein complexes from multi-relationship protein interaction networks

Background

Protein complexes play an important role in biological processes. Recent developments in experiments have resulted in the publication of many high-quality, large-scale protein-protein interaction (PPI) datasets, which provide abundant data for computational approaches to the prediction of protein complexes. However, the precision of protein complex prediction still needs to be improved due to the incompletion and noise in PPI networks.

Results

There exist complex and diverse relationships among proteins after integrating multiple sources of biological information. Considering that the influences of different types of interactions are not the same weight for protein complex prediction, we construct a multi-relationship protein interaction network (MPIN) by integrating PPI network topology with gene ontology annotation information. Then, we design a novel algorithm named MINE (identifying protein complexes based on Multi-relationship protein Interaction NEtwork) to predict protein complexes with high cohesion and low coupling from MPIN.

Conclusions

The experiments on yeast data show that MINE outperforms the current methods in terms of both accuracy and statistical significance.

     

Pseudo current density maps of electrophysiological heart,nerve or brain function and their physical basis

Background

In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.

Methods

The physical basis of these intuitive maps is clarified by means of analytically solvable problems.

Results

Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.

Conclusion

Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.