scholarly journals Identification of cardiomyopathy-related core genes through human metabolic networks and expression data

BMC Genomics ◽  
2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Zherou Rong ◽  
Hongwei Chen ◽  
Zihan Zhang ◽  
Yue Zhang ◽  
Luanfeng Ge ◽  
...  
Keyword(s):  
Metabolic Network ◽  
Metabolic Networks ◽  
Pearson Correlation ◽  
Expression Data ◽  
Complex Type ◽  
Omics Integration ◽  
Integration Approach ◽  
Different Types ◽  
Core Genes ◽  
Potential Biomarkers

Abstract Background Cardiomyopathy is a complex type of myocardial disease, and its incidence has increased significantly in recent years. Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common and indistinguishable types of cardiomyopathy. Results Here, a systematic multi-omics integration approach was proposed to identify cardiomyopathy-related core genes that could distinguish normal, DCM and ICM samples using cardiomyopathy expression profile data based on a human metabolic network. First, according to the differentially expressed genes between different states (DCM/ICM and normal, or DCM and ICM) of samples, three sets of initial modules were obtained from the human metabolic network. Two permutation tests were used to evaluate the significance of the Pearson correlation coefficient difference score of the initial modules, and three candidate modules were screened out. Then, a cardiomyopathy risk module that was significantly related to DCM and ICM was determined according to the significance of the module score based on Markov random field. Finally, based on the shortest path between cardiomyopathy known genes, 13 core genes related to cardiomyopathy were identified. These core genes were enriched in pathways and functions significantly related to cardiomyopathy and could distinguish between samples of different states. Conclusion The identified core genes might serve as potential biomarkers of cardiomyopathy. This research will contribute to identifying potential biomarkers of cardiomyopathy and to distinguishing different types of cardiomyopathy.

scholarly journals MiRNA Expression in Neuroendocrine Neoplasms of Frequent Localizations

2021 ◽  
Vol 7 (3) ◽  
pp. 38
Author(s):  
Alexandra Korotaeva ◽  
Danzan Mansorunov ◽  
Natalya Apanovich ◽  
Anna Kuzevanova ◽  
Alexander Karpukhin
Keyword(s):  
Mirna Expression ◽  
Malignant Tumors ◽  
Neuroendocrine Neoplasms ◽  
Specific Expression ◽  
Clinical Biomarkers ◽  
Different Types ◽  
Functional Features ◽  
First Time ◽  
Potential Biomarkers

Neuroendocrine neoplasms (NEN) are infrequent malignant tumors of a neuroendocrine nature that arise in various organs. They occur most frequently in the lungs, intestines, stomach and pancreas. Molecular diagnostics and prognosis of NEN development are highly relevant. The role of clinical biomarkers can be played by microRNAs (miRNAs). This work is devoted to the analysis of data on miRNA expression in NENs. For the first time, a search for specificity or a community of their functional characteristics in different types of NEN was carried out. Their properties as biomarkers were also analyzed. To date, more than 100 miRNAs have been characterized as differentially expressed and significant for the development of NEN tumors. Only about 10% of the studied miRNAs are expressed in several types of NEN; differential expression of the remaining 90% was found only in tumors of specific localizations. A significant number of miRNAs have been identified as potential biomarkers. However, only a few miRNAs have values that characterized their quality as markers. The analysis demonstrates the predominant specific expression of miRNA in each studied type of NEN. This indicates that miRNA’s functional features are predominantly influenced by the tissue in which they are formed.

Systematic Investigation of Mouse Models of Parkinson’s Disease by Transcriptome Mapping on a Brain-Specific Genome-Scale Metabolic Network

2021 ◽  
Author(s):  
Ecehan Abdik ◽  
Tunahan Cakir
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Metabolic Network ◽  
Mouse Models ◽  
Mus Musculus ◽  
Metabolic Networks ◽  
Systematic Investigation ◽  
Omics Data ◽  
Metabolic Alterations ◽  
Genome Scale

Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model...

scholarly journals DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks

2020 ◽  
Author(s):  
Pablo Rodríguez-Mier ◽  
Nathalie Poupin ◽  
Carlo de Blasio ◽  
Laurent Le Cam ◽  
Fabien Jourdan
Keyword(s):  
Metabolic Network ◽  
Metabolic Networks ◽  
Relevant Information ◽  
Experimental Information ◽  
Correct Identification ◽  
Good Representation ◽  
Metabolic State ◽  
Whole Space ◽  
Context Specific ◽  
Optimal Networks

AbstractThe correct identification of metabolic activity in tissues or cells under different environmental or genetic conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific organism and condition, containing only the reactions predicted to be active in such context. A major limitation of this approach is that the available information does not generally allow for an unambiguous characterization of the corresponding optimal metabolic sub-network, i.e., there are usually many different sub-network that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic state. In this work, we formalize the problem of enumeration of optimal metabolic networks, we implement a set of techniques that can be used to enumerate optimal networks, and we introduce DEXOM, a novel strategy for diversity-based extraction of optimal metabolic networks. Instead of enumerating the whole space of optimal metabolic networks, which can be computationally intractable, DEXOM samples solutions from the set of optimal metabolic sub-networks maximizing diversity in order to obtain a good representation of the possible metabolic state. We evaluate the solution diversity of the different techniques using simulated and real datasets, and we show how this method can be used to improve in-silico gene essentiality predictions in Saccharomyces Cerevisiae using diversity-based metabolic network ensembles. Both the code and the data used for this research are publicly available on GitHub1.

scholarly journals Identification of Hub Genes Associated With Melanoma Development by Comprehensive Bioinformatics Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Jie Jiang ◽  
Chong Liu ◽  
Guoyong Xu ◽  
Tuo Liang ◽  
Chaojie Yu ◽  
...  
Keyword(s):  
Target Gene ◽  
Enrichment Analysis ◽  
High Expression ◽  
Expression Data ◽  
Hub Genes ◽  
Gene Therapies ◽  
Protein Levels ◽  
Melanoma Diagnosis ◽  
Melanoma Patients ◽  
Core Genes

IntroductionThis study aimed to identify important genes associated with melanoma to further develop new target gene therapies and analyze their significance concerning prognosis.Materials and methodsGene expression data for melanoma and normal tissue were downloaded from three databases. Differentially co-expressed genes were identified by WGCNA and DEGs analysis. These genes were subjected to GO, and KEGG enrichment analysis and construction of the PPI visualized with Cytoscape and screened for the top 10 Hub genes using CytoHubba. We validated the Hub gene’s protein levels with an immunohistochemical assay to confirm the accuracy of our analysis.ResultsA total of 435 differentially co-expressed genes were obtained. Survival curves showed that high expression of FOXM1,\ EXO1, KIF20A, TPX2, and CDC20 in melanoma patients with 5 of the top 10 hub genes was associated with reduced overall survival (OS). Immunohistochemistry showed that all five genes were expressed at higher protein levels in melanoma than in paracancerous tissues.ConclusionFOXM1, EXO1, KIF20A, TPX2, and CDC20 are prognosis-associated core genes of melanoma, and their high expression correlates with the low prognosis of melanoma patients and can be used as biomarkers for melanoma diagnosis, treatment, and prognosis prediction.

Finding Minimum Reaction Cuts of Metabolic Networks Under a Boolean Model Using Integer Programming and Feedback Vertex Sets

2012 ◽  
pp. 774-791
Author(s):  
Takeyuki Tamura ◽  
Kazuhiro Takemoto ◽  
Tatsuya Akutsu
Keyword(s):  
Integer Programming ◽  
Metabolic Network ◽  
Drug Targets ◽  
Metabolic Networks ◽  
Boolean Model ◽  
Pentose Phosphate ◽  
Minimum Size ◽  
E Coli ◽  
Potential Applications ◽  
Vertex Set

In this paper, the authors consider the problem of, given a metabolic network, a set of source compounds and a set of target compounds, finding a minimum size reaction cut, where a Boolean model is used as a model of metabolic networks. The problem has potential applications to measurement of structural robustness of metabolic networks and detection of drug targets. They develop an integer programming-based method for this optimization problem. In order to cope with cycles and reversible reactions, they further develop a novel integer programming (IP) formalization method using a feedback vertex set (FVS). When applied to an E. coli metabolic network consisting of Glycolysis/Glyconeogenesis, Citrate cycle and Pentose phosphate pathway obtained from KEGG database, the FVS-based method can find an optimal set of reactions to be inactivated much faster than a naive IP-based method and several times faster than a flux balance-based method. The authors also confirm that our proposed method works even for large networks and discuss the biological meaning of our results.

IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks

Nanoscale ◽  
2021 ◽  
Author(s):  
Bernabé Ortega-Tenezaca ◽  
Humberto González-Díaz
Keyword(s):  
Machine Learning ◽  
Antibacterial Activity ◽  
Metabolic Network ◽  
Network Structure ◽  
Metabolic Networks ◽  
Antibacterial Nanoparticles

Machine learning mapping of antibacterial nanoparticles vs. bacteria metabolic network structure.

scholarly journals Investigating Serum and Tissue Expression Identified a Cytokine/Chemokine Signature as a Highly Effective Melanoma Marker

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3680
Author(s):  
Marco Cesati ◽  
Francesca Scatozza ◽  
Daniela D’Arcangelo ◽  
Gian Carlo Antonini-Cappellini ◽  
Stefania Rossi ◽  
...  
Keyword(s):  
Single Molecule ◽  
Learning Algorithm ◽  
Profile Analysis ◽  
Pearson Correlation ◽  
Tissue Expression ◽  
Support Vector ◽  
Expression Data ◽  
Serum Samples ◽  
Tissue Samples ◽  
Mortality And Morbidity

The identification of reliable and quantitative melanoma biomarkers may help an early diagnosis and may directly affect melanoma mortality and morbidity. The aim of the present study was to identify effective biomarkers by investigating the expression of 27 cytokines/chemokines in melanoma compared to healthy controls, both in serum and in tissue samples. Serum samples were from 232 patients recruited at the IDI-IRCCS hospital. Expression was quantified by xMAP technology, on 27 cytokines/chemokines, compared to the control sera. RNA expression data of the same 27 molecules were obtained from 511 melanoma- and healthy-tissue samples, from the GENT2 database. Statistical analysis involved a 3-step approach: analysis of the single-molecules by Mann–Whitney analysis; analysis of paired-molecules by Pearson correlation; and profile analysis by the machine learning algorithm Support Vector Machine (SVM). Single-molecule analysis of serum expression identified IL-1b, IL-6, IP-10, PDGF-BB, and RANTES differently expressed in melanoma (p < 0.05). Expression of IL-8, GM-CSF, MCP-1, and TNF-α was found to be significantly correlated with Breslow thickness. Eotaxin and MCP-1 were found differentially expressed in male vs. female patients. Tissue expression analysis identified very effective marker/predictor genes, namely, IL-1Ra, IL-7, MIP-1a, and MIP-1b, with individual AUC values of 0.88, 0.86, 0.93, 0.87, respectively. SVM analysis of the tissue expression data identified the combination of these four molecules as the most effective signature to discriminate melanoma patients (AUC = 0.98). Validation, using the GEPIA2 database on an additional 1019 independent samples, fully confirmed these observations. The present study demonstrates, for the first time, that the IL-1Ra, IL-7, MIP-1a, and MIP-1b gene signature discriminates melanoma from control tissues with extremely high efficacy. We therefore propose this 4-molecule combination as an effective melanoma marker.

Graph methods for the investigation of metabolic networks in parasitology

Parasitology ◽  
2010 ◽  
Vol 137 (9) ◽  
pp. 1393-1407 ◽  
Author(s):  
LUDOVIC COTTRET ◽  
FABIEN JOURDAN
Keyword(s):  
Metabolic Network ◽  
Large Scale ◽  
Metabolic Networks ◽  
Graph Model ◽  
New Drugs ◽  
Mathematical Methods ◽  
Graph Models ◽  
Metabolic Network Reconstruction ◽  
Network Analyses ◽  
Genome Scale

SUMMARYRecently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

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