## 2 Results and Discussion
### Acertainment of Affected Bacterial Pathways  
MetaboAnalyst is an online platform that offers several tools for data analysis on metabolomics. Pathway analysis (PA) allows us to determine the main metabolic paths affected by a studied event, using as input a list of metabolites that were found significantly altered. The entries are then related to a repository containing pathways already fully described by the literature, for a given organism (in this case, KEGG pathway database resource). MetaboAnalyst PA module reports measurements of pathway impact and node importance. The importance can be expressed in terms of number of shortest paths passing through a specific node (betweenness centrality), in this manner, compounds with potential to regulate a greater number of processes are considered more relevant. In addition, pathway impact value is given by the sum of the importance of the matched entries normalized by the sum of the importance of the total number of metabolites contained in a referred pathway [70]. Figure 7 presents a weighted plot that uses as input \(p\)-values and pathway impact calculated by MetaboAnalyst PA tool. Most of the discriminating metabolites that matched to the database source were those detected in _E. coli_ and _E. faecalis_ species. Tricarboxylic acid (TCA) cycle, propanoate metabolism and sulfur metabolism configured as the most affected pathways in _E. coli_. Similar results were verified for _E. faecalis_ (with effect on butanoate instead of propanoate metabolism), suggesting that bio-AgNPs action was analogous for these species. For _K. pneumoniae_, propanoate metabolism was reported as notably disturbed. _P. mirabilis_ glycolysis and phenylalanine metabolism appear influenced by bio-AgNPs supplementation; however, the impact calculated for these pathways was low. In general, this PA method denoted that main dysregulated processes present to be those regarding releasing of energy for the cell and the processing of amino acids. Nevertheless, it is important to highlight that such a PA method is limited to the data available in the used repository that are given for model organisms, and it comprehends solely mechanisms that have been fully elucidated and registered. More specific processes take place in the bacterial metabolism, reflecting on the observed variety of compounds. In this sense, another approach to study affected pathways was applied. For this purpose, we considered compounds that appeared significantly altered when the control and all bio-AgNPs-treated cultures were compared--it means, compounds that configured as consistently altered in treated samples, regardless of applied concentration of agent. The approach consisted of assigning the found discriminating compounds to general metabolic processes reported by the previous literature. Figure 8 is a network exhibiting the connections between volatiles and the metabolic mechanisms that possibly originate them, for each of the investigated species. Mechanisms belonging to the same path appear interconnected. The extension of the influence on a pathway can be inferred from the frequency that their addressed metabolites appear altered. In this manner, pathway processes with greater frequency values are those that were more disturbed by the action of added nanoparticles. Figure 7: Pathway topology analysis based on KEGG-described paths. EC = _E. coli_, EF = _E. faecalis_, KP = _K. pneumoniae_, PM = _P. mirabilis_. The metabolism of S-containing amino acids appears as the most downregulated process in _E. coli_, _K. pneumoniae_ and _P. mirabilis_. Regarding the fatty acids' pathway, only the reduction of fatty acyl-ACP, resulting in fatty aldehydes, appeared as possibly enhanced mechanism. However, metabolites of this latter path overlap with lipid peroxidation process. Negative modulation of the aldoxime-nitrile pathway was evidenced for the studied bacteria, with exception of _E. faecalis_. This route provides source of carbon, nitrogen and, eventually, bioactive molecules.