Bacterial communities associated with silage of different forage crops in Malaysian climate analysed using 16S amplicon metagenomics

Abstract

Silage produced in tropical countries is prone to spoilage because of high humidity and temperature. Therefore, determining indigenous bacteria as potential inoculants is important to improve silage quality. This study aimed to determine bacterial community and functional changes associated with ensiling using amplicon metagenomics and to predict potential bacterial additives associated with silage quality in the Malaysian climate. Silages of two forage crops (sweet corn and Napier) were prepared, and their fermentation properties and functional bacterial communities were analysed. After ensiling, both silages were predominated by lactic acid bacteria (LAB), and they exhibited good silage quality with significant increment in lactic acid, reductions in pH and water-soluble carbohydrates, low level of acetic acid and the absence of propionic and butyric acid. LAB consortia consisting of homolactic and heterolactic species were proposed to be the potential bacterial additives for sweet corn and Napier silage fermentation. Tax4fun functional prediction revealed metabolic pathways related to fermentation activities (bacterial division, carbohydrate transport and catabolism, and secondary metabolite production) were enriched in ensiled crops (p < 0.05). These results might suggest active transport and metabolism of plant carbohydrates into a usable form to sustain bacterial reproduction during silage fermentation, yielding metabolic products such as lactic acid. This research has provided a comprehensive understanding of bacterial communities before and after ensiling, which can be useful for desirable silage fermentation in Malaysia.

Figure 1

Rarefaction curve of the observed operational taxonomic units (OTUs) of sweet corn and Napier for fresh forage and silage using unrarefied sequences (S0, Fresh sweet corn; S21, Sweet corn silage; N0, Fresh Napier; N21, Napier silage).

Figure 2

Composition of bacterial communities before and after ensiling using unrarefied sequences at genus level: (a) each sample, (b) group sample, (c) extended error bar comparing fresh sweet corn (S0) and sweet corn silage (S21), (d) extended error bar comparing fresh Napier (N0) and Napier silage (N21) and (e) extended error bar comparing S21 and N21 using t-test at p-value < 0.05.

Figure 2

Composition of bacterial communities before and after ensiling using unrarefied sequences at genus level: (a) each sample, (b) group sample, (c) extended error bar comparing fresh sweet corn (S0) and sweet corn silage (S21), (d) extended error bar comparing fresh Napier (N0) and Napier silage (N21) and (e) extended error bar comparing S21 and N21 using t-test at p-value < 0.05.

Figure 3

Alpha diversity of bacterial communities before and after ensiling for sweet corn and Napier: (a) observed OTU (F-value: 162.3; p-value = 0.00001), (b) Chao1 (F-value: 125.38; p-value = 0.00001), (c) ACE (F-value: 64.905; p-value = 0.00001), (d) Fisher (F-value: 291.78; p-value = 0.00001), (e) Shannon (F-value: 1.4881; p-value = 0.27669) indices measured at feature level (S0, Fresh sweet corn; S21, Sweet corn silage; N0, Fresh Napier; N21, Napier silage).

Figure 4

Principal coordinate analysis (PCoA) derived from the Bray–Curtis distance between fresh forage and silage of sweet corn and Napier. Coloured dots represent different forage crops. PERMANOVA value, F-value: 107.71, R-squared: 0.96998, p-value < 0.001 (S0, Fresh sweet corn; S21, Sweet corn silage; N0, Fresh Napier; N21, Napier silage).

Figure 5

Graphics of linear discriminant analysis (LDA) effect size (LEfSe) between fresh forage and silage, (a) sweet corn and (b) Napier, at genus level. Red indicates fresh forage and blue for silage. The threshold on the logarithmic LDA score for discriminative features was set to 4.0 at p-value < 0.05 (original p-value for sweet corn and FDR-adjusted for Napier) (S0, Fresh sweet corn; S21, Sweet corn silage; N0, Fresh Napier; N21, Napier silage).

Figure 6

Heatmap constructed using differentially abundant taxa from LEFSe analysis at the genus level. Pearson distance was used with Ward clustering algorithm by clustering samples based on the current clustering algorithm. Red and blue indicate a higher and lower abundance, respectively (S0, Fresh sweet corn; S21, Sweet corn silage; N0, Fresh Napier; N21, Napier silage).

Figure 7

Taxa–taxa interaction at species level using SPARCC set at permutation value of 200, p-value < 0.05, correlation > 0.6: (a) sweet corn and (b) Napier. Blue edges represent negative correlation, whereas orange edges represent positive correlation. The proportion of bacterial taxa is shown in green for fresh crops and orange for silage.

Figure 8

Correlation between physicochemical properties and bacterial taxa of fresh forage crops and silage samples by CCA analysis: (a) sweet corn (total variance explained: 98.1%, p < 0.05) and (b) Napier (total variance explained: 99.8%, p < 0.05).

Figure 9

Principal coordinate analysis (PCoA), calculated using the Euclidean distance algorithm between fresh crops and silage for sweet corn and Napier based on KEGG functional profiles predicted by Tax4Fun. Coloured dots represent different forage crops (S0, Fresh sweet corn; S21, Sweet corn silage; N0, Fresh Napier; N21, Napier silage).

Figure 10

Graphics of linear discriminant analysis (LDA) effect size (LEfSe) of the functional prediction profiles using Tax4Fun between fresh forage and silage. The threshold on the logarithmic LDA score for discriminative features was set to 3.0 at FDR-adjusted p-value < 0.05 (S0, Fresh sweet corn; S21, Sweet corn silage; N0, Fresh Napier; N21, Napier silage).

Figure 11

Heatmap constructed using differentially abundant KEGG functional profiles  using Tax4Fun based on LEFSe analysis shown in Fig. 10. Pearson distance was used with the Ward clustering algorithm based on the current clustering algorithm. Red and blue indicate a higher and lower abundance, respectively.

Figure 12

Stacked bar chart and graphic of linear discriminant analysis (LDA) effect size (LEfSe) of COG functional categories across samples, (a) sweet corn and (b) Napier (c) relative abundance of COG functional categories across samples (S0, Fresh sweet corn; S21, Sweet corn silage; N0, Fresh Napier; N21, Napier silage).