## 3 Results and Discussion
### Biotin and Thiamine Impact on the Wine Metabolome
#### 3.4.1 Biotin Influence on the Wine Metabolome  
ANOVA performed on the extracted features allowed for the isolation of 208 compounds that presented significant differences (\(p\) < 0.01) between both high and low biotin conditions, therefore accounting for less than 10% of the extracted features. Putative annotations were assigned to all significant extracted features according to the KEGG, Metlin, and Oligonet online databases and tools [27]. Consequently, out of those 208 notable features, 164 were assigned a possible chemical formula, and therefore annotated at level 4 [57]; as such, those annotated features were the sole ones to be considered in all further investigations of the impact of biotin on the _S. cerevisiae_ metabolome. Amongst those, a remarkably higher proportion of features appeared to be significantly more intense in high biotin than in low biotin, since 133 of those appeared significant (\(p\) < 0.01) in wines obtained from 3 \(\upmu\)g/L of biotin, against 31 resulting from those associated with the lower doses of the vitamin. Hierarchical clustering of those features led to a clear distinction between wines obtained from higher and lower initial contents of the vitamin, although two low biotin samples appeared less properly discriminated from the high biotin ones (Figure 3B), which strongly highlights the impact of biotin on the yeast metabolism. To further assess the nature of this influence, Van Krevelen diagrams were plotted on the features' O/C and H/C ratios for both biotin conditions, and the elemental compositions of them were investigated (Figure 4). Notable differences in the chemical composition of wines appeared between the features associated with each biotin condition; as such, while high biotin-resulting wines displayed a dominance of CHON-based features, the predominating ones in low biotin wines were the phosphorus-containing features (CHOP, CHONP, and CHONPS). It is, however, relevant to note that those predominant features are, in either biotin condition, found in the lipid (O/C \(<\) 0.6 and H/C \(>\) 1.3) and polyphenol (O/C \(<\) 1.2 and H/C \(<\) 1.3) regions of the Van Krevelen diagram, consistent with their  
Figure 3: Intensity and distribution of the wine extracted metabolomic features after alcoholic fermentation in varying biotin and thiamine concentrations. (**A**) Principal component analysis individual plot according to the initial biotin content in the growth medium. (**B**) Hierarchical clustered heatmap of the one-way ANOVA significantly different (\(p<\) 0.01) level 4 annotated features between high and low biotin fermentation conditions. (**C**) Principal component analysis individual plot according to the initial thiamine content in the growth medium. (**D**) Hierarchical clustered heatmap of the one-way ANOVA significantly different (\(p<\) 0.01) level 4 annotated features between high and low thiamine fermentation conditions. predicted chemical families [58]. In addition, an interesting influence exerted by biotin on the proportion in the CHONS features appeared here, since they dropped from 11.3% in high biotin to 6.5% in low biotin. Subsequently to the features' nature assumption, hypothetical annotations were assigned using the MassTRIX application [26] (Helmholtz Zentrum Munchen, Munchen, Germany), and its associated databases, such as KEGG [27], as accessed on 11 November 2022. A limited number of the specific biomarkers were successfully annotated in the databases, since less than 10% of those were matched, reflecting the high complexity and current low understanding of the wine composition [59; 60]. Those annotations have allowed to identify the metabolic pathways associated with the changes in the exometabolome changes observed as a result of the initial biotin in the medium (Supplementary Materials Table S2, Figure S1).