Functional Prediction of Biological Profile During Eutrophication in Marine Environment

Abstract

In the marine environment, coastal nutrient pollution and algal blooms are increasing in many coral reefs and surface waters around the world, leading to higher concentrations of dissolved organic carbon (DOC), nitrogen (N), phosphate (P), and sulfur (S) compounds. The adaptation of the marine microbiota to this stress involves evolutionary processes through mutations that can provide selective phenotypes. The aim of this in silico analysis is to elucidate the potential candidate hub proteins, biological processes, and key metabolic pathways involved in the pathogenicity of bacterioplankton during excess of nutrients. The analysis was carried out on the model organism Escherichia coli K-12, by adopting an analysis pipeline consisting of a set of packages from the Cystoscape platform. The results obtained show that the metabolism of carbon and sugars generally are the 2 driving mechanisms for the expression of virulence factors.

Keywords: Differentially expressed genes; copiotrophic species; functional analysis virulence; metabolic pathways.

Figure 1.

Graphical abstract of the analysis pipeline (step by step), from data collection to hub proteins and metabolic pathway identification with the various packages and databases used.

Figure 2.

Predicted protein-protein interaction networks. Parameters: Score (0.4), no additional nodes; interaction sources used: experimentation, databases, co-expression, co-occurrence, gene fusion, and neighborhood. In the interaction networks, separate lines of different colors are used to show the type of evidence that supports each interaction.

Figure 3.

Topological mapping of the hub proteins obtained in the subselection analysis based on the cutoff value BC <0.02 and node degree >20. The larger circles correspond to the higher degrees and brown to blue color refers to increment of betweenness; the thickness of the lines represents the confidence score of the associations and different colors are used to show the type of evidence that supports each interaction .

Figure 4.

Predicted functional enrichment pie chart for the GO BPs by ClueGO.

Figure 5.

In ClueGO, metabolic pathways were predicted from KEGG as a network with the terms of the enriched pathways visualized using Cytoscape’s ClueGo/CluePedia plugin where several proteins share common functions. The size of the nodes corresponds to the importance of the metabolic pathway.