SEAS: a system for SEED-based pathway enrichment analysis

Proteomic analysis of anti-angiogenic effects by conbercept in the mice with oxygen induced retinopathy

AIM

To analyze the retinal proteomes with and without conbercept treatments in mice with oxygen-induced retinopathy (OIR) and identify proteins involved in the molecular mechanisms mediated by conbercept.

METHODS

OIR was induced in fifty-six C57BL/6J mouse pups and randomly divided into four groups. Group 1: Normal17 (n=7), mice without OIR and treated with normal air. Group 2: OIR12/EXP1 (n=14), mice received 75% oxygen from postnatal day (P) 7 to 12. Group 3: OIR17/Control (n=14), mice received 75% oxygen from P7 to P12 and then normal air to P17. Group 4: Lang17/EXP2 (n=21), mice received 75% oxygen from P7 to P12 with intravitreal injection of 1 µL conbercept at the concentration of 10 mg/mL at P12, and then normal air from P12 to P17. Liquid Chromatography-Mass Spectrometry (LC-MS)/MS data were reviewed to find proteins that were up-regulated after the conbercept treatment. Gene ontology (GO) analysis was performed of conbercept-mediated changes in proteins involved in single-organism processes, biological regulation, cellular processes, immune responses, metabolic processes, locomotion and multiple-organism processes.

RESULTS

Conbercept induced a reversal of hypoxia-inducible factor 1 signaling pathway as revealed by the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and also induced down-regulation of proteins involved in blood coagulation and fibrin clot formation as demonstrated by the Database for Annotation, Visualization and Integrated Discovery (DAVID) and the stimulation of interferon genes studies. These appear to be risk factors of retinal fibrosis. Additional conbercept-specific fibrosis risk factors were also identified and may serve as therapeutic targets for fibrosis.

CONCLUSION

Our studies reveal that many novel proteins are differentially regulated by conbercept. The new insights may warrant a valuable resource for conbercept treatment.

Bioinformatic analysis of retinal gene function and expression in diabetic rats

The aim of the present study was to investigate the changes in retinal gene expression at three time points and assess the underlying molecular mechanisms of diabetic retinopathy (DR) in a streptozotocin (STZ)-induced diabetes rat model using bioinformatics analysis. The gene expression profile of GSE28831 was extracted from the Gene Expression Omnibus database and differentially expressed genes (DEGs) were identified at three different time points (1, 4 and 12 weeks) using the limma package in R language. Gene ontology (GO) enrichment analysis of DEGs was performed followed by a principal component and pathway enrichment analysis of the selected DEGs along with protein-protein interaction network construction at the three time points. A total of 402, 105 and 213 DEGs were screened at 1, 4 and 12 weeks, respectively. In addition, the expression of 8 genes was identified to be significantly different at different time points, including cytochrome P450 2B2 (CYP2B2; downregulated gene; P=0.048; at 1 week), mannan binding lectin-associated serine protease-2 (MASP2; downregulated gene; P=0.044), lecithin retinol acyltransferase (LRAT; downregulated gene; P=0.015), retinal pigment epithelium (RPE)-specific protein 65 kDa (RPE65; downregulated gene; P=0.025), 11-cis-retinoldehydrogenase (RDH5; downregulated gene; P=0.04; at 4 weeks), mitogen-activated protein kinase 13 (MAPK13; upregulated gene; P=0.036), LRAT (downregulated gene; P=0.01) and RPE65 (downregulated gene; P=0.009; at 12 weeks). Furthermore, pathway enrichment and GO enrichment analyses revealed that DEGs at 4 weeks were primarily enriched in retinol metabolism and processes associated with visual functions, including ‘visual perception’ and ‘retinol metabolism’. DEGs, including CYP2B2, MASP2, LRAT, RPE65, RDH5 and MAPK13 may be potential targets for the diagnosis and treatment of DR. Thus, the current study demonstrated that abnormal visual functions occur at 4 weeks in STZ-induced diabetic rats. This may provide a scientific basis for the diagnosis and treatment of DR because DEGs may be used to facilitate the development of novel therapeutic strategies to diagnose and treat DR.

Metagenomic Characterisation of the Viral Community of Lough Neagh, the Largest Freshwater Lake in Ireland

Lough Neagh is the largest and the most economically important lake in Ireland. It is also one of the most nutrient rich amongst the world’s major lakes. In this study, 16S rRNA analysis of total metagenomic DNA from the water column of Lough Neagh has revealed a high proportion of Cyanobacteria and low levels of Actinobacteria, Acidobacteria, Chloroflexi, and Firmicutes. The planktonic virome of Lough Neagh has been sequenced and 2,298,791 2×300 bp Illumina reads analysed. Comparison with previously characterised lakes demonstrates that the Lough Neagh viral community has the highest level of sequence diversity. Only about 15% of reads had homologs in the RefSeq database and tailed bacteriophages (Caudovirales) were identified as a major grouping. Within the Caudovirales, the Podoviridae and Siphoviridae were the two most dominant families (34.3% and 32.8% of the reads with sequence homology to the RefSeq database), while ssDNA bacteriophages constituted less than 1% of the virome. Putative cyanophages were found to be abundant. 66,450 viral contigs were assembled with the largest one being 58,805 bp; its existence, and that of another 34,467 bp contig, in the water column was confirmed. Analysis of the contigs confirmed the high abundance of cyanophages in the water column.

Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways

Background

The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling.

Results

We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed.

Conclusions

A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.

Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data

Background

Statistical analyses of whole genome expression data require functional information about genes in order to yield meaningful biological conclusions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) are common sources of functionally grouped gene sets. For bacteria, the SEED and MicrobesOnline provide alternative, complementary sources of gene sets. To date, no comprehensive evaluation of the data obtained from these resources has been performed.

Results

We define a series of gene set consistency metrics directly related to the most common classes of statistical analyses for gene expression data, and then perform a comprehensive analysis of 3581 Affymetrix® gene expression arrays across 17 diverse bacteria. We find that gene sets obtained from GO and KEGG demonstrate lower consistency than those obtained from the SEED and MicrobesOnline, regardless of gene set size.

Conclusions

Despite the widespread use of GO and KEGG gene sets in bacterial gene expression data analysis, the SEED and MicrobesOnline provide more consistent sets for a wide variety of statistical analyses. Increased use of the SEED and MicrobesOnline gene sets in the analysis of bacterial gene expression data may improve statistical power and utility of expression data.