Characteristics of non-processed and processed RSM
A comprehensive set of 155 plant cell wall glycan-directed monoclonal antibodies was used to screen untreated RSM by a ELISA-based assay (Pattathil et al. 2010; Pattathil et al. 2012), and 34 antibodies reacted with RSM (data not shown). These were subsequently used in the current study to obtain information on the presence and relative abundance of specific epitopes that are characteristic of the different types of polymers in untreated RSM and RSM processed by ALK, CELL, PECT1, and PECT2.
Figure 2 shows that both increases and decreases in epitope recognition occurred in ALK, CELL, PECT1, and PECT2 compared to CON. Samples from after (_A) and before (_B) predigestion clustered together according to each treatment, which indicated _A and _B from the same treatment had similar epitope accessibility. ALK strongly increased binding of non-fucosylated XG mAbs, while CELL, PECT1, and PECT2 led to disappearance of those compared to CON, regardless of _A and _B treatment. All the treatments increased the binding of “Linseed Mucilage RG-I group” directed mAbs, but had little effect on Xylan-2 and RG-Ic group compared to CON. Binding of MAC204 (AG-1), which is binding to gum tragacanth and to lettuce and green tomato RG-I preparations (arabinogalactan), disappeared with ALK_A, ALK_B, and CELL_B, while increased binding of CCRC-M107 (AG-2), which binds to linear and branched arabinans and RG-I preparations from diverse plants but does not bind to larch arabinogalactan (Pattathil et al. 2010), was observed in ALK_A and ALK_B. CELL_A, CELL_B, PECT1_A, PECT1_B, and PECT2_B led to disappearance of the binding of mAbs of “pectic backbone group” and CCRC-M 133, which also binds to linear and branched arabinans and RG-I preparations from diverse plants but do not bind to larch arabinogalactan. ALK, PECT1, and PECT2 increased binding of mAbs directed against the arabinogalactan side chains of RG-I (RG-I/AG).
ALK, CELL, PECT1, and PECT2 significantly changed microbiota composition compared to CON
To determine the changes in composition of the gut microbiota fed with a shot of 5 g CON, ALK, CELL, PECT1, or PECT2, a comparison of microbiota based on sequencing the V3-V4 region of the 16S rRNA gene was performed. Shannon indeces significantly decreased at t4, t6, t8 and t24, compared to that of t0 (Figure S1A). When data from all of the time points were pooled, there were no significant differences among CON, ALK, CELL, PECT1, and PECT2 in Shannon index (Figure S1B). Phylogeny based UniFrac methodology was then used to compare the β-diversity of the microbial communities between microbiota fed with non-processed and processed RSM. Unweighted UniFrac analysis (Fig. 3) shows that samples from processed RSM (ALK, CELL, PECT1, and PECT2) significantly (P = 0.004) separated from non-processed RSM (CON), and samples from different processed method clustered together. Samples from CON, ALK, CELL, PECT1, and PECT2 all clustered together (P = 0.125) with respect to weighted UniFrac (Figure S2).
There were no significant differences between microbiota fed with RSM predigested before or after carbohydrase or ALK treatment with respect to both α-diversity (Figure S3), or β-diversity (data not shown), which indicated predigesting before or after processing RSM had little effect on microbiota composition. Glycome profiling of RSM (Fig. 2) also shows that samples from after and before processing clustered together according to each treatment, which indicated their polysaccharide compositions were similar to each other. Therefore, they were treated as duplicates in the following (microbial relative abundance) analyses.
Relative abundances of taxa within the pig microbiotas fed with non-processed and processed RSM were compared to identify significantly different bacterial taxa. At genus level (Fig. 4), seven genera were significant higher in relative abundance after ALK, CELL, PECT1, and PECT2 treatment compared to CON. These were Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Roseburia, Anaerotruncus, Bifidobacterium, Christensenellaceae R-7 group, and Selenomonas. For genera Christensenellaceae R-7 group and Ruminococcaceae UCG-005, their relative abundances were also higher in ALK and PECT1 compared to CELL. Instead, the relative abundances of Prevotella 7, an unclassified genus from Prevotellaceae, and Prevotellaceae UCG-001 were significantly decreased after feeding ALK, CELL, PECT1, and PECT2 compared to microbiota fed with CON. The relative abundance of Succinivibrionaceae UCG-001 was significant higher in ALK and PECT1 compared to CON.
PICRUSt2 analyses revealed that microbial functional abundances related to carbohydrate metabolism and SCFA production were significantly increased with processed RSM compared to CON
PICRUSt2 was performed to the 16S rRNA gene data to predict metagenomic functional profiles. In this study we focused on carbohydrate metabolism related microbial functions (Fig. 5). The relative abundances of fibre degradation pathways, beta-glucosidase [EC:3.2.1.21], beta-mannosidase [EC:3.2.1.25], cellobiose phosphorylase [EC:2.4.1.20], and sucrose phosphorylase [EC:2.4.1.7], were significant higher in ALK, CELL, PECT1, and PECT2 compared to CON, whereas conversely that of alpha-L-fucosidase [EC:3.2.1.51] was significant higher in CON compared to ALK, CELL, PECT1, and PECT2. For cellobiose phosphorylase [EC:2.4.1.20] and sucrose phosphorylase [EC:2.4.1.7], the relative abundances in ALK were also significant higher than those of CELL, PECT1, and PECT2.
Six microbial pathways related to fermentation were significant higher in relative abundance when microbiotas were fed with ALK, CELL, PECT1, and PECT2 compared to microbiota fed with CON. These pathways were pyruvate dehydrogenase E1 component, short-chain fatty acids transporter, mannose-1-phosphate guanylytransferase, superpathway of glucose and xylose degradation, sucrose degradation IV, and L-lysine fermentation to acetate and butanoate. The relative abundance of lactose/L-arabinose transport system permease protein was significant higher in ALK, PECT1 and PECT2 compared to CON.
Figure 6 shows that the cumulative acetic, propionic, and butyric acid and total SCFA production were higher in ALK, CELL, PECT1, and PECT2 compared to CON. For acetic acid, more than 2 times greater production was observed when the microbiota was fed with ALK, CELL, PECT1, and PECT2 compared to when the microbiota was fed with CON. The production of propionic, butyric acid and total SCFA in ALK, CELL, PECT1, and PECT2 were more than 1.6 times higher than that in CON, except for propionic (1.3 times), and butyric acid (1.4 times) production in ALK.
Glycome profiling shows that binding of mAbs in lumen digests were dynamic during the in vitro fermentation in SLIM
To investigate the dynamic changes of polysaccharides structure in CON, ALK, CELL, PECT1, and PECT2 during in vitro fermentation, a time series of sampling was performed and the set of 34 mAbs was used to screen the lumen digests. Figure 7 shows that no or few binding signals were observed in Non-fucosylated XG, AG-2, and Pectic Backbone mAbs upon feeding CON, ALK, CELL, PECT1, and PECT2 at all time points.
No binding of CCRC-M83 that specifically bind to Linseed Mucilage RG-I was observed at t0 in all treatments (just prior to addition of the fibre shots), and binding signals appeared afterwards. For ALK, binding of CCRC-M83 decreased from t0.5 to t2, increased again at t4, and decreased to the lowest value at t24. For CELL, binding of CCRC-M83 decreased from t0.5 to t2, stabilized from t4 to t8, and decreased to the lowest value at t24. For CON, binding increased from t0.5 to t1, decreased from t2 to t6, increased at t8, and decreased to the lowest value at t24. For PECT1, binding increased from t0.5 to t4, and then decreased progressively until t24. For PECT2, binding increased slightly from t0.5 to t8, and then decreased to the lowest value at t24.
In terms of Xylan-2 recognizing mAb (i.e. CCRC-M105), weak bindings were detected at all time points after t0 with CELL and CON. Increased binding of CCRC-M105 was observed from t0.5 to t1 in ALK and the binding strength decreased from t2 to t4, slightly increased again at t6, and then decreased until t24. For PECT1, increased binding of CCRC-M105 was observed from t0.5 to t6, which decreased afterward until t24. Binding for PECT2 was dynamic from t0.5 to t24, but the lowest binding was observed at t24.
As for the RG-Ic recognizing mAb (i.e. CCRC-M30), binding of CCRC-M30 was lower in ALK, PECT1, and PECT2 according to each time point compared to CELL and CON, but binding over time was dynamic. With respect to the AG-4 mAbs (recognizing arabinogalactans), weak and dynamic binding was observed at each time point in all treatments.
With respect to RG-I/AG mAbs, more active mAbs were observed compared to other groups of mAbs in all treatments. Within RG-I/AG mAbs, binding strengths of CCRC-M25 and CCRC-M60 were stronger than other RG-I/AG mAbs in all treatments, and their binding strengths were fluctuating during the whole fermentation period and still existed at t24 in all treatments.
Correlation between microbiota abundance and SCFA production and mAb binding
Pearson correlation analyses were performed to investigate the relationship between the relative abundance of microbial genera and SCFA production at each time point (Fig. 8). Seven genera (Bifidobacterium, Collinsella, Denitrobacterium, Olsenella, Coriobacteriaceae.1, Bacteroidales S24-7 group.2, and Acetitomaculum) had significant negative correlation with propionic acid, butyric acid, valeric acid and total SCFA production. Within these, Bacteroidales S24-7 group.2, Olsenella, Coriobacteriaceae.1, and Acetitomaculum also significantly negatively correlated with acetic acid. Eight genera (Bacteroidales S24-7 group.1, Prevotella 9, Faecalibacterium, Ruminococcaceae UCG-005, Ruminococcus 2, Selenomonas, Succinivibrio, and Succinivibrionaceae UCG-001) significantly positively correlated with acetic acid, propionic acid, butyric acid, valeric acid and total SCFA production. Within these, Ruminococcus 2 and Succinivibrio also had significant positive correlation with caproic acid. Bacteroidales S24-7 group, Sarcina, and Oribacterium had significant positive correlation with propionic acid, butyric acid, valeric acid and total SCFA production. Roseburia, Ruminococcaceae NK4A214 group and Ruminococcaceae UCG-002 significantly positively correlated with acetic acid, propionic acid, butyric acid and total SCFA production. Prevotella 7 significantly positively correlated with propionic acid, valeric acid, caproic acid, and total SCFA production.
The correlation between binding of mAbs and relative abundance of microbial genera was also analyzed. Within the mAbs that recognizing non-fucocylated XG, CCRC-M93 had significant negative correlation with Bacteroidales S24-7 group.1, Christensenellaceae R-7 group, [Eubacterium] nodatum group, Blautia, Lachnospira, Lachnospiraceae NK3A20 group, Roseburia, Ruminococcaceae UCG-005, Ruminococcus 2, Subdoligranulum, Catenibacterium, Catenisphaera, Succiniclasticum, and Succinivibrionaceae UCG-001. CCRC-M96 significantly negatively correlated with Bacteroidales S24-7 group.2 and Acetitomaculum, while it significantly positively correlated with Anaerotruncus. CCRC-M99 had significant negative correlation with Dorea and Ruminococcus 2, whereas it had significant positive correlation with Megasphaera. CCRC-M104 had significant positive correlation with Prevotellaceae UCG-001, while it negatively correlated with Ruminococcaceae UCG-002.
MH4.2A4 that recognizes linseed mucilage RG-I had significant negative correlations with Bacteroidales S24-7 group.1, Ruminococcaceae NK4A214 group, and Ruminococcaceae UCG-005.
CCRC-M105 that binds to Xylan-2 had significant negative correlations with Prevotellaceae.1, Family XIII UCG-001, Acidaminococcus, and Megasphaera, whereas it positively correlated with Ruminococcus 2 and Subdoligranulum.
CCRC-M30, binding to RG-Ic, had significant positive correlations with Denitrobacterium, Olsenella, Bacteroidales S24-7 group.2, and Acetitomaculum, while it significantly negatively correlated with Roseburia, Faecalibacterium, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Selenomonas, and Succinivibrionaceae UCG-001.
MAC204, that recognizing AG-1, significantly negatively correlated with Denitrobacterium, Olsenella, Bacteroidales S24-7 group.2, Acetitomaculum, and Roseburia, while it had significant positive correlations with Lactobacillus, Sarcina, Roseburia, Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, and Selenomonas.
CCRC-M133, binding to AG-2, significantly negatively correlated with Denitrobacterium, Prevotella 7, Prevotellaceae UCG-001, Acetitomaculum, and Subdoligranulum, while it positively correlated with Ruminococcaceae UCG-002.
CCRC-M131, that binds to pectic backbone, had significant negative correlation with Denitrobacterium, Olsenella, Bacteroidales S24-7 group.2, Prevotellaceae UCG-001, Acetitomaculum, and Subdoligranulum, while it positively correlated with Lactobacillus, Anaerotruncus, and Ruminococcaceae UCG-002.
With respect to mAbs binding to AG-4, CCRC-M78 had significant negative correlation with Bifidobacterium, and Christensenellaceae R-7 group, whereas it significantly positively correlated with Prevotellaceae UCG-001. CCRC-M91 had significant negative correlation with Denitrobacterium, Olsenella, and Acetitomaculum, and CCRC-M91 and CCRC-M92 significantly positively correlated with Lactobacillus. JIM13 had significant negative correlation with Subdoligranulum.
As for mAbs binding to RG-I/AG, they had significant negative correlation with Bacteroidales S24-7 group (CCRC-M32), Bacteroidales S24-7 group.2 (CCRC-M32 and M125), Lactobacillus (CCRC-M24), [Eubacterium] nodatum group (CCRC-M60), Acetitomaculum (CCRC-M42), Blautia (CCRC-M60), Coprococcus 3 (CCRC-M24), Ruminococcaceae NK4A214 group (CCRC-M32, M41, M44, and M60), Ruminococcaceae UCG-002 (CCRC-M44), Ruminococcaceae UCG-005 (CCRC-M32, M44, and M60) Subdoligranulum (CCRC-M 42), and Succiniclasticum (CCRC-M125). Significantly positively correlations were observed with Olsenella (CCRC-M44), Bacteroidales S24-7 group (CCRC-M128), Prevotella 7 (CCRC-M24 and M128), Prevotellaceae UCG-001 (CCRC-M 24 and M128), Prevotellaceae (CCRC-M128), Lactobacillus (CCRC-M42), Lachnospira (CCRC-M128), Ruminococcus 2 (CCRC-M128), Subdoligranulum (CCRC-M128), and Succinivibrio (CCRC-M128).