Bioinformatics Analysis to Identify RNA–Protein Interactions in Oogenesis

2015 ◽  
pp. 231-241
Author(s):  
Ravinder Singh
Keyword(s):  
Protein Interactions ◽  
Bioinformatics Analysis

scholarly journals Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Bojun Xu ◽  
Lei Wang ◽  
Huakui Zhan ◽  
Liangbin Zhao ◽  
Yuehan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetic Nephropathy ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Topological Analysis ◽  
Gene Expression Omnibus ◽  
Complement Cascade ◽  
Hub Genes ◽  
Novel Biomarkers

Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

scholarly journals Proteome-Wide Analysis of Lysine 2-Hydroxyisobutyrylation in Aspergillus niger in Peanuts

2021 ◽  
Vol 12 ◽  
Author(s):  
Manlin Xu ◽  
Xia Zhang ◽  
Jing Yu ◽  
Zhiqing Guo ◽  
Ying Li ◽  
...  
Keyword(s):  
Aspergillus Niger ◽  
Protein Interactions ◽  
Root Rot ◽  
Bioinformatics Analysis ◽  
Biological Processes ◽  
Post Translational Modification ◽  
Functional Studies ◽  
Novel Proteins ◽  
Proteasome Subunits ◽  
Cellular Compartments

Aspergillus niger is a very destructive pathogen causing severe peanut root rot, especially in the seeding stage of peanuts (Arachis hypogaea), and often leading to the death of the plant. Protein lysine 2-hydroxyisobutyrylation (Khib) is a newly detected post-translational modification identified in several species. In this study, we identified 5041 Khib sites on 1,453 modified proteins in A. niger. Compared with five other species, A. niger has conserved and novel proteins. Bioinformatics analysis showed that Khib proteins are widely distributed in A. niger and are involved in many biological processes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that Khib proteins were significantly enriched in many cellular compartments and pathways, such as ribosomes and proteasome subunits. A total of 223 Khib proteins were part of the PPI network, thus, suggesting that Khib proteins are associated with a large range of protein interactions and diverse pathways in the life processes of A. niger. Several identified proteins are involved in pathogenesis regulation. Our research provides the first comprehensive report of Khib and an extensive database for potential functional studies on Khib proteins in this economically important fungus.

scholarly journals Autoantibodies as biomarkers for colorectal cancer: A systematic review, meta-analysis, and bioinformatics analysis

2019 ◽  
Vol 34 (4) ◽  
pp. 334-347 ◽  
Author(s):  
Hejing Wang ◽  
Xiaojin Li ◽  
Donghu Zhou ◽  
Jian Huang
Keyword(s):  
Colorectal Cancer ◽  
Protein Interactions ◽  
Odds Ratio ◽  
Bioinformatics Analysis ◽  
Early Stage ◽  
Meta Analysis ◽  
Cyclin B1 ◽  
Diagnostic Odds Ratio ◽  
Summary Receiver Operating Characteristic ◽  
Bioinformatics Analyses

Colorectal cancer is a very common cancer worldwide. Serum tumor-associated autoantibodies (TAAbs), especially the anti-p53 autoantibody, may be promising biomarkers to detect early-stage colorectal cancer. This study aimed to identify all known autoantibodies and their value in colorectal cancer diagnosis, as well as exploring the underlying connections and mechanisms through a bioinformatics analysis. Databases were used to select available articles of TAAbs in colorectal cancer. In a meta-analysis of the anti-p53 autoantibody, the diagnostic odds ratio and area under the curve (AUC) of the summary receiver-operating characteristic (SROC) curve were calculated using Stata 12.0 and Meta-Disc 1.4. We identified 73 articles including 199 single autoantibodies and 42 multiple autoantibodies. The maximum value of Youden’s index was 0.76, combining c-MYC, p53, cyclin B1, p62, Koc, IMP1, and survivin. The diagnostic odds ratio for anti-p53 autoantibody at all stages was 10.86 (95% CI 8.40, 14.06) with low heterogeneity (I2 = 40.3%) and the AUC of the SROC curve was 0.82. For the anti-p53 autoantibody in early-stage colorectal cancer, the diagnostic odds ratio was 4.82 (95% CI 2.95, 7.87) with heterogeneity (I2 = 7.9%) and the AUC of the SROC curve was 0.72. Eighty-seven autoantibodies were selected for bioinformatics analyses. We found that the most enriched functional terms and protein–protein interactions may relate to the mechanism of autoantibody generation. In summary, our study summarized the diagnostic value of TAAbs in colorectal cancer, either as single molecules or in combination. Bioinformatics analyses may be a new approach to explore the mechanism of autoantibody generation.

Characterization of microtubules by dark-field STEM and replication

1991 ◽  
Vol 49 ◽  
pp. 152-153
Author(s):  
S.B. Andrews ◽  
R.D. Leapman ◽  
P.E. Gallant ◽  
T.S. Reese
Keyword(s):  
Protein Interactions ◽  
Low Dose ◽  
Unit Length ◽  
Dark Field ◽  
Carbon Films ◽  
Microtubule Associated Proteins ◽  
Molecular Weights ◽  
Freeze Dried ◽  
Protein Motors ◽  
Associated Proteins

As part of a study on protein interactions involved in microtubule (MT)-based transport, we used the VG HB501 field-emission STEM to obtain low-dose dark-field mass maps of isolated, taxol-stabilized MTs and correlated these micrographs with detailed stereo images from replicas of the same MTs. This approach promises to be useful for determining how protein motors interact with MTs. MTs prepared from bovine and squid brain tubulin were purified and free from microtubule-associated proteins (MAPs). These MTs (0.1-1 mg/ml tubulin) were adsorbed to 3-nm evaporated carbon films supported over Formvar nets on 600-m copper grids. Following adsorption, the grids were washed twice in buffer and then in either distilled water or in isotonic or hypotonic ammonium acetate, blotted, and plunge-frozen in ethane/propane cryogen (ca. -185 C). After cryotransfer into the STEM, specimens were freeze-dried and recooled to ca.-160 C for low-dose (<3000 e/nm2) dark-field mapping. The molecular weights per unit length of MT were determined relative to tobacco mosaic virus standards from elastic scattering intensities. Parallel grids were freeze-dried and rotary shadowed with Pt/C at 14°.

Role of small nuclear RNAs in eukaryotic gene expression

2013 ◽  
Vol 54 ◽  
pp. 79-90 ◽  
Author(s):  
Saba Valadkhan ◽  
Lalith S. Gunawardane
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Rna Stability ◽  
Splice Sites ◽  
Branch Site ◽  
Small Nuclear Rnas ◽  
Eukaryotic Gene Expression ◽  
Eukaryotic Gene ◽  
Rna Biogenesis

Eukaryotic cells contain small, highly abundant, nuclear-localized non-coding RNAs [snRNAs (small nuclear RNAs)] which play important roles in splicing of introns from primary genomic transcripts. Through a combination of RNA–RNA and RNA–protein interactions, two of the snRNPs, U1 and U2, recognize the splice sites and the branch site of introns. A complex remodelling of RNA–RNA and protein-based interactions follows, resulting in the assembly of catalytically competent spliceosomes, in which the snRNAs and their bound proteins play central roles. This process involves formation of extensive base-pairing interactions between U2 and U6, U6 and the 5′ splice site, and U5 and the exonic sequences immediately adjacent to the 5′ and 3′ splice sites. Thus RNA–RNA interactions involving U2, U5 and U6 help position the reacting groups of the first and second steps of splicing. In addition, U6 is also thought to participate in formation of the spliceosomal active site. Furthermore, emerging evidence suggests additional roles for snRNAs in regulation of various aspects of RNA biogenesis, from transcription to polyadenylation and RNA stability. These snRNP-mediated regulatory roles probably serve to ensure the co-ordination of the different processes involved in biogenesis of RNAs and point to the central importance of snRNAs in eukaryotic gene expression.

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