scholarly journals Integrative Genomic–Epigenomic Analysis of Clozapine-Treated Patients with Refractory Psychosis

2021 ◽  
Vol 14 (2) ◽  
pp. 118
Author(s):  
Yerye Gibrán Mayén-Lobo ◽  
José Jaime Martínez-Magaña ◽  
Blanca Estela Pérez-Aldana ◽  
Alberto Ortega-Vázquez ◽  
Alma Delia Genis-Mendoza ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Mood Stabilizer ◽  
Metabolic Ratio ◽  
P Value ◽  
Polygenic Risk Score ◽  
Gene Products ◽  
Protein Protein Interactions ◽  
Gabaergic Synapse ◽  
Future Studies ◽  
Larger Sample

Clozapine (CLZ) is the only antipsychotic drug that has been proven to be effective in patients with refractory psychosis, but it has also been proposed as an effective mood stabilizer; however, the complex mechanisms of action of CLZ are not yet fully known. To find predictors of CLZ-associated phenotypes (i.e., the metabolic ratio, dosage, and response), we explore the genomic and epigenomic characteristics of 44 patients with refractory psychosis who receive CLZ treatment based on the integration of polygenic risk score (PRS) analyses in simultaneous methylome profiles. Surprisingly, the PRS for bipolar disorder (BD-PRS) was associated with the CLZ metabolic ratio (pseudo-R2 = 0.2080, adjusted p-value = 0.0189). To better explain our findings in a biological context, we assess the protein–protein interactions between gene products with high impact variants in the top enriched pathways and those exhibiting differentially methylated sites. The GABAergic synapse pathway was found to be enriched in BD-PRS and was associated with the CLZ metabolic ratio. Such interplay supports the use of CLZ as a mood stabilizer and not just as an antipsychotic. Future studies with larger sample sizes should be pursued to confirm the findings of this study.

scholarly journals Serum MicroRNAs as Biomarkers of Sepsis and Resuscitation

2021 ◽  
Vol 11 (23) ◽  
pp. 11549
Author(s):  
Lorena Oteiza ◽  
Antonio Ferruelo ◽  
Nicolás Nín ◽  
Mario Arenillas ◽  
Marta de Paula ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Sus Scrofa ◽  
P Value ◽  
Toll Like Receptor ◽  
Protein Protein Interactions ◽  
E Coli ◽  
Receptor Signaling Pathway ◽  
Sus Scrofa Domesticus ◽  
Septic Group ◽  
Serum Micrornas

There is a lack of biomarkers of sepsis and the resuscitation status. Our objective was to prove that the serum expression of certain microribonucleic acids (miRNAs) is differentially regulated in sepsis and is sensitive to different resuscitation regimes. Anesthetized pigs (Sus scrofa domesticus) received no treatment (n = 15) or intravenous live E. coli (n = 24). The septic animals received 0.9% saline at 4 mL/kg/h (n = 8) (low resuscitation group (LoR)) or 10–17 mL/kg/h (high resuscitation group (HiR)) (n = 8 each group). Blood samples were obtained at the end of the experiment for measurement of seven different miRNAs (RT-qPCR, Qiagen, Hilden, Germany). The serum expression of miR-146a-5p and miR-34a-5p increased significantly in the septic group, and miR-146a-5p was significantly lower in the HiR group than in the LoR group. The toll-like receptor signaling pathway involving 22 target proteins was significantly (adjusted p = 3.87 × 10−4) regulated by these two microRNAs (KEGG). Highly significant (p value = 2.22 × 10−16) protein–protein interactions (STRING) were revealed for these 22 hits. MiR-146a-5p and miR-34a-5p were identified as biomarkers of sepsis, and miRNA146a-5p seemed to be a biomarker of the intensity of the resuscitation.

scholarly journals Identification of hub genes and pathways in nonalcoholic steatohepatitis by integrated bioinformatics analysis

2021 ◽  
Author(s):  
Pegah Einaliyan ◽  
Ali Owfi ◽  
Mohammadamin Mahmanzar ◽  
Taha Aghajanzadeh ◽  
Morteza Hadizadeh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Expression Profiles ◽  
Principal Component ◽  
Gene Expression Omnibus ◽  
P Value ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Alcoholic Fatty Liver ◽  
Critical Pathways

AbstractBackgroundCurrently, non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases in the world. Forecasting the short-term, up to 2025, NASH due to fibrosis is one of the leading causes of liver transplantation. Cohort studies revealed that non-alcoholic steatohepatitis (NASH) has a higher risk of fibrosis progression among NAFLD patients. Identifying differentially expressed genes helps to determine NASH pathogenic pathways, make more accurate diagnoses, and prescribe appropriate treatment.Methods and ResultsIn this study, we found 11 NASH datasets by searching in the Gene Expression Omnibus (GEO) database. Subsequently, NASH datasets with low-quality control scores were excluded. Four datasets were analyzed with packages of R/Bioconductor. Then, all integrated genes were Imported into Cytoscape to illustrate the protein-protein interactions network. All hubs and nodes degree has been calculated to determine the hub genes with critical roles in networks.Possible correlations between expression profiles of mutual DEGs were identified employing Principal Component Analysis (PCA). Primary analyzed data were filtered based on gene expression (logFC > 1, logFC < −1) and adj-P-value (<0.05). Ultimately, among 379 DEGs, we selected the top 10 genes (MYC, JUN, EGR1, FOS, CCL2, IL1B, CXCL8, PTGS2, IL6, SERPINE1) as candidates among up and down regulated genes, and critical pathways such as IL-6, IL-17, TGF β, and TNFα were identified.ConclusionThe present study suggests an important DEGs, biological processes, and critical pathways involved in the pathogenesis of NASH disease. Further investigations are needed to clarify the exact mechanisms underlying the development and progression of NASH disease.

scholarly journals Maternal proteomic profiling reveals alterations in lipid metabolism in late-onset fetal growth restriction

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Cristina Paules ◽  
Lina Youssef ◽  
Jezid Miranda ◽  
Francesca Crovetto ◽  
Josep Maria Estanyol ◽  
...  
Keyword(s):  
Fetal Growth ◽  
Protein Interactions ◽  
Fetal Growth Restriction ◽  
Late Onset ◽  
Growth Restriction ◽  
Growth Potential ◽  
P Value ◽  
Biological Processes ◽  
Protein Protein Interactions

AbstractFetal growth restriction defined as the failure to achieve the fetal genetic growth potential is a major cause of perinatal morbidity and mortality. The role of maternal adaptations to placental insufficiency in this disorder is still not fully understood. We aimed to investigate the biological processes and protein–protein interactions involved in late-onset fetal growth restriction in particular. We applied 2D nano LC–MS/MS proteomics analysis on maternal blood samples collected at the time of delivery from 5 singleton pregnancies with late-onset fetal growth restriction and 5 uncomplicated pregnancies. Data were analyzed using R package “limma” and Ingenuity Pathway Analysis. 25 proteins showed significant changes in their relative abundance in late-onset fetal growth restriction (p value < 0.05). Direct protein–protein interactions network demonstrated that Neurogenic locus notch homolog protein 1 (NOTCH1) was the most significant putative upstream regulator of the observed profile. Gene ontology analysis of these proteins revealed the involvement of 14 canonical pathways. The most significant biological processes were efflux of cholesterol, efflux of phospholipids, adhesion of blood cells, fatty acid metabolism and dyslipidemia. Future studies are warranted to validate the potential role of the detected altered proteins as potential therapeutic targets in the late-onset form of fetal growth restriction.

scholarly journals Ontology-based validation and identification of regulatory phenotypes

2018 ◽  
Author(s):  
Maxat Kulmanov ◽  
Paul N Schofield ◽  
Georgios V Gkoutos ◽  
Robert Hoehndorf
Keyword(s):  
Protein Interactions ◽  
Research Group ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Computational Method ◽  
Gene Products ◽  
Protein Protein Interactions ◽  
Functional Annotations ◽  
Disease Associations ◽  
Annotation Quality

AbstractMotivationFunction annotations of gene products, and phenotype annotations of genotypes, provide valuable information about molecular mechanisms that can be utilized by computational methods to identify functional and phenotypic relatedness, improve our understanding of disease and pathobiology, and lead to discovery of drug targets. Identifying functions and phenotypes commonly requires experiments which are time-consuming and expensive to carry out; creating the annotations additionally requires a curator to make an assertion based on reported evidence. Support to validate the mutual consistency of functional and phenotype annotations as well as a computational method to predict phenotypes from function annotations, would greatly improve the utility of function annotations.ResultsWe developed a novel ontology-based method to validate the mutual consistency of function and phenotype annotations. We apply our method to mouse and human annotations, and identify several inconsistencies that can be resolved to improve overall annotation quality. Our method can also be applied to the rule-based prediction of phenotypes from functions. We show that the predicted phenotypes can be utilized for identification of protein-protein interactions and gene-disease associations. Based on experimental functional annotations, we predict phenotypes for 1,986 genes in mouse and 7,301 genes in human for which no experimental phenotypes have yet been determined.Availabilityhttps://github.com/bio-ontology-research-group/[email protected]

scholarly journals Discovery–Versus Hypothesis–Driven Detection of Protein–Protein Interactions and Complexes

2021 ◽  
Vol 22 (9) ◽  
pp. 4450
Author(s):  
Isabell Bludau
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Protein Interactions ◽  
Prior Information ◽  
Protein Complexes ◽  
Functional Modules ◽  
Protein Protein Interactions ◽  
Future Studies ◽  
Protein Protein Interaction ◽  
Selection Of

Protein complexes are the main functional modules in the cell that coordinate and perform the vast majority of molecular functions. The main approaches to identify and quantify the interactome to date are based on mass spectrometry (MS). Here I summarize the benefits and limitations of different MS-based interactome screens, with a focus on untargeted interactome acquisition, such as co-fractionation MS. Specific emphasis is given to the discussion of discovery- versus hypothesis-driven data analysis concepts and their applicability to large, proteome-wide interactome screens. Hypothesis-driven analysis approaches, i.e., complex- or network-centric, are highlighted as promising strategies for comparative studies. While these approaches require prior information from public databases, also reviewed herein, the available wealth of interactomic data continuously increases, thereby providing more exhaustive information for future studies. Finally, guidance on the selection of interactome acquisition and analysis methods is provided to aid the reader in the design of protein-protein interaction studies.

scholarly journals Why FRET over genomics?

1999 ◽  
Vol 1 (2) ◽  
pp. 93-99 ◽  
Author(s):  
DINO A. DE ANGELIS
Keyword(s):  
Protein Interactions ◽  
Complex Traits ◽  
High Capacity ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Regulatory Sequences ◽  
Gene Products ◽  
Nucleotide Polymorphisms ◽  
Protein Protein Interactions ◽  
Individual Gene

De Angelis, Dino A. Why FRET over genomics? Physiol. Genomics 1: 93–99, 1999.—Genetic information is being uncovered quickly and in vast amounts through the largely automated sequencing of genomes from all kinds of organisms. As this information becomes available, enormous challenges are emerging on three levels: first, functions will have to be assigned to individual gene products; second, factors that influence the expression level of these gene products will have to be identified; and third, allelic variants that act alone or in combination to give rise to complex traits will have to be characterized. Because of the sheer size of genomes, methods that can streamline or automate these processes are highly desirable. Fluorescence is an attractive readout for such high-throughput tasks because of the availability of equipment designed to detect light-emitting compounds with great speed and high capacity. The following is an overview of the achievements and potential of fluorescence resonance energy transfer (FRET) as applied in three areas of genomics: the identification of single-nucleotide polymorphisms, the detection of protein-protein interactions, and the genomewide analysis of regulatory sequences.

scholarly journals Identification of Significant Proteins in Coronavirus Disease 2019 Protein-Protein Interaction Using Principal Component Analysis and ClusterONE

2021 ◽  
Vol 3 (2) ◽  
pp. 25-34
Author(s):  
Hilmi Farhan Ramadhani ◽  
Annisa ◽  
Wisnu Ananta Kusuma
Keyword(s):  
Principal Component Analysis ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Topological Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Centrality Measures ◽  
P Value ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction

Coronavirus Disease 2019 (COVID-19) will cause disease complications and organ damage due to excessive inflammatory reactions if left untreated. Computational analysis of protein-protein interactions can be carried out in various ways, including topological analysis and clustering of protein-protein interaction networks. Topological analysis can identify significant proteins by measuring the most important nodes with centrality measurements. By using Principal Component Analysis (PCA), the types of centrality measures were extracted into the overall centrality value. The study aimed to found significant proteins in COVID-19 protein-protein interactions using PCA and ClusterONE. This study used 57 proteins associated with COVID-19 to obtain protein networks. All of these proteins are homo sapiens organism. The number of proteins and the number of interactions from 57 proteins were 357 proteins and 1686 interactions. The results of this study consisted of two clusters; the best cluster was the first cluster with a lower p-value but had an average overall centrality value that closed to the second clus-ter. There are twenty important proteins in that cluster, and all of these proteins are related to COVID-19. These proteins are expected to be used in the process of discovering medicinal compounds in COVID-19

Distinct subunit functions and cell cycle regulated phosphorylation of 20S APC/cyclosome required for anaphase in fission yeast

1997 ◽  
Vol 110 (15) ◽  
pp. 1793-1804 ◽  
Author(s):  
H. Yamada ◽  
K. Kumada ◽  
M. Yanagida
Keyword(s):  
Cell Cycle ◽  
Fission Yeast ◽  
Protein Interactions ◽  
Budding Yeast ◽  
Gene Products ◽  
Anaphase Promoting Complex ◽  
Protein Protein Interactions ◽  
Yeast Gene ◽  
Ubiquitin Ligase Activity ◽  
Distinct Subunit

We show here that the fission yeast gene products Cut9 and Nuc2 are the subunits of the 20S complex, the putative APC (anaphase promoting complex)/cyclosome which contains ubiquitin ligase activity required for cyclin and Cut2 destruction. The assembly of Cut9 into the 20S complex requires functional Nuc2, and vice versa. The size of fission yeast APC/cyclosome is similar to that of higher eukaryotes, but differs greatly from that (36S) of budding yeast. The 20S complex is present in cells arrested at different stages of the cell cycle, and becomes slightly heavier in mitosis than interphase. Cut9 in the 20S complex is hyperphosphorylated specifically at the time of metaphase. The truncated forms of Cut9 block entry into mitosis, however. The 20S assembly impaired in the cut9 mutant can be restored by elevating the level of a novel gene product Hcnl, similar to budding yeast Cdc26. Furthermore, deletion of protein kinase PKA (Pkal) suppresses the phenotype of the cut9 mutation and reduces phosphorylation of Cut9. In contrast, PP1 (Dis2) phosphatase mutation shows the reverse effect on the phenotype of cut9. The Cut9 subunit is likely to be a target for regulating APC/ cyclosome function through protein-protein interactions and phosphorylation.

scholarly journals PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions

2021 ◽  
Vol 17 (8) ◽  
pp. e1008844
Author(s):  
Seyed Ziaeddin Alborzi ◽  
Amina Ahmed Nacer ◽  
Hiba Najjar ◽  
David W. Ritchie ◽  
Marie-Dominique Devignes
Keyword(s):  
Protein Interactions ◽  
Gold Standard ◽  
Statistical Significance ◽  
Building Blocks ◽  
P Value ◽  
Protein Protein Interactions ◽  
Multiple Sources ◽  
Ppi Networks ◽  
Domain Interactions ◽  
Computational Discovery

Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing. We describe a new computational approach called “PPIDM” (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described “CODAC” (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as “Gold-Standard” a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84, 552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9, 175 DDIs), Silver (24, 934 DDIs) and Bronze (50, 443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains 10, 229 DDIs that are consistent with more than 13, 300 PPIs extracted from the IMEx database, and nearly 23, 300 DDIs (27.5%) that are consistent with more than 214, 000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than 10 PPIs in the IMEx database are provided. Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/.

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