Changes in the Sheep Foot Microbiome During the Development of CODD
Foot swab samples from 6 sheep in the study whose feet developed typical, progressive CODD lesions were selected for the analysis of changes in foot microbiome composition during the development of CODD lesions. These sheep were selected on the basis that the foot had undergone the previously described clinical progression, namely from a healthy foot to interdigital dermatitis to footrot and then to CODD. Samples included were those from: Sheep 2 (n = 16 samples), sheep 5 (n = 22 samples), sheep 8 (n = 17 samples), sheep 9 (n = 15 samples), sheep 12 (n = 19 samples) and sheep 14 (n = 16 samples). As CODD developed at different rates and a different time points in the study for each sheep, a different number of samples for each disease stage was available for each sheep, providing 105 samples. Based on the associated clinical metadata, these samples were classed as A_Healthy (samples collected from healthy feet at least 15 days prior to lesion development) (n = 23); B_Healthy, (samples collected from healthy feet at 0–14 days prior to lesion development)(n = 18); C_ID (samples collected from feet classed as affected by interdigital dermatitis )(n = 20); D_Footrot, (samples collected from sheep’s feet classed as affected by footrot) (n = 20); E_CODD (samples collected from sheep’s feet classed as affected by CODD) (n = 24).
Changes in Bacterial Diversity of Sheep Foot Microbiome (Alpha and Beta) During the Development of CODD
Changes in microbial community diversity for disease stages observed during the development of the CODD lesions were determined by examining the number of observed ASVs at each stage. Generally, when compared with the healthy state (up to 2 weeks before disease onset), as CODD developed there was a reduction in the number of observed ASVs, consistent with dominance of disease associated bacterial species in the lesions across all foot disease states (p < 0.05) (Fig. 2). Pairwise comparisons were then used to identify differences in diversity between the different disease states observed as the clinical lesions progressed. Significant reductions in ASV numbers (p < 0.05) were observed when the foot moved from the B_ Healthy state (2 weeks before disease onset) to the ID stage and then to the FR stage. However, there was no difference in number of ASVs between the FR and CODD stages (Table 1).
Distinct clustering patterns of microbiota for each phenotypic stage observed in the development of CODD lesions were also observed in the beta diversity analysis (Fig. 3). When measured with a weighted Uni Frac metric, beta diversity in terms of location and spread was significantly affected by disease state overall (ANOSIM R statistic = 0.64329, p = 0.001). Furthermore, pairwise ANOSIM tests demonstrated significant differences (p = 0.001) between microbiota at each phenotypic lesion stage of CODD development, in terms of microbiota location, dispersion and correlation structure (Table 2).
Changes in Composition of Sheep Foot Microbiome During the Development of CODD (Gneiss Analysis)
1) Identification of Balances Significantly Associated with Changes in Disease State
The relative abundance of bacterial taxa in microbiota of sheep’s feet at different stages of CODD lesion development differed. The overall linear regression model fit was R2 = 0.503. B_Healthy samples accounted for 3.31% of variance with ID, Footrot and CODD samples accounting for 8.44%, 23.8% and 20.4% of variance respectively. Investigation of balance log ratios which were significantly different (p < 0.05) between disease states and inspection of the dendogram heatmap (Figs. 4 and 5) allow a description of the significant changes in microbiome composition as CODD developed.
Inspection of the dendogram heat map and examination of log ratio of balances clearly show y0 was significantly lower in A_Healthy than ID (β = 29.9, p < 0.001), Footrot (β = 74.0, p < 0.001) and CODD (β = 63.8, p < 0.001) samples (Fig. 5A) but was not significantly different from B_Healthy samples (β = 1.98, p = 0.9). The dendogram heatmap shows that broadly, y0denominator ASVs were more abundant in healthy samples while y0numerator were more abundant in diseased samples with ID samples acting as an intermediate.
Subdivisions of y0denominator ASVs reveal further differences between A_Healthy, B_Healthy and ID samples; these stages represent the earlier stages of the CODD disease development process and therefore examination of microbial compositional changes here give an indication of earlier changes in the bacterial community. The log ratio of balance y1 was significantly lower in A_Healthy samples compared to B_Healthy (β = 16.5, p < 0.001) and ID (β = 18.6, p < 0.001) samples (Fig. 5B). The dendogram heatmap shows that this was due to a higher abundance of some y1denominator ASVs in A_Healthy samples, specifically those further identified by balance y3denominator (Fig. 5D). In addition, there was a higher abundance of some y1numerator ASVs in B_Healthy and ID samples, the most abundant taxa identified by y4denominator (Fig. 5E).
Balance y2 (Fig. 5C) is useful to describe differences in microbial composition as the foot lesions develop from ID stage as this balance was significantly lower in ID samples compared to Footrot (t = 10.25, p < 0.001) and CODD (t = 6.4, p < 0.001) samples. The dendogram heatmap shows that this was due to a higher abundance of some y2denominator ASVs in ID samples, specifically y5numerator ASVs as demonstrated by a significantly higher log ratio in ID samples when compared to other groups (A_Healthy: t = 8.34, p < 0.001; B_Healthy: t = 3.25, p = 0.002; Footrot: t = 6.12, p < 0.001; CODD: t = 8.40, p < 0.001; Fig. 5F).
Furthermore, balance y6 log ratio was significantly higher in ID samples compared to Footrot (t = 3.16, p = 0.003) and CODD (t = 8.17, p < 0.001) due to a higher abundance of y6denomiantor ASVs in footrot and CODD samples (Fig. 5G).
Differences in microbiome composition between footrot and CODD are observed in balance y14 whose log ratio was significantly higher in ID and footrot samples compared to CODD samples (t = 7.96, p < 0.001 and t = 13.3, p < 0.001 respectively) due to an increased abundance of y14numerator ASVs in these samples (Fig. 5H).
2) Bacterial Taxa Associations with Changes in Disease State
The Gneiss analysis results were then used to classify the ASVs into four groups as follows:
1. Healthy ASVs - ASVs with higher abundance in A_Healthy samples (y3denominator) and ASVs with higher abundance in A_Healthy, B_Healthy and ID samples (y3numerator and y4numerator);
2. Intermediate ASVs - ASVs with higher abundance in B_Healthy and ID samples (y4denominator) and ASVs with higher abundance in ID samples (y5numerator);
3. Diseased ASVs - ASVs with higher abundance in ID and Footrot samples (y14numerator), ASVs with higher abundance in ID, Footrot and CODD samples (y14denominator) and ASVs with higher abundance in Footrot and CODD samples (y6denominator and y11denominator);
4. ASVs not differentially abundant between sample groups (y11numerator).
The families of ASVs identified by Gneiss as more abundant between disease states were plotted in a taxa bar plot to compare the relative abundance of the most abundant 10 families from both the Healthy and Diseased ASV groups (Fig. 6). The most abundant families in the healthy group were Moraxellaceae, Corynebacteriaceae Pseudomonaceae Saccharimonadaceae Acholeplasmataceae, Flavobacteriaceae, Ruminococcaceae Carnobacteriaceae Aerococcaceae, Family XI. Whilst in the diseased sheep the most abundant 10 families were Porphyromonadaceae, Family XI, Bacterioida Peptostreptococcaceae Fusobacteriaceae Lachnospiraceae, Wohlfahrtiimonadaceae, Ruminococcaceae Veillonellaceae, Acidaminococcaceae
Examination of the distribution of ASVs in these families between Healthy, Intermediate and Diseased ASV groups demonstrated significant associations (p < 0.05) between specific taxa, namely Porphyromonadaceae, Family XI, Veillonellaceae and Fusobacteriaceae and the Diseased ASV group (Table 3). Furthermore, the taxa plot shows the relative abundance of these four families increasing from Healthy samples to ID, footrot and CODD samples (Fig. 6), although an increased abundance of Veillonellaceae and Fusobacteriaceae was most associated with ID and footrot rather than CODD.
No other families were significantly differently distributed between Healthy, Intermediate and Diseased groups. This may be an artefact of small numbers of ASVs assigned to these families. For example, Bacteroidia was not identified as significantly differently distributed between groups even though 4 of 5 ASVs were assigned to the Diseased ASV group. The taxa plot (Fig. 6) shows that the relative abundance of Bacteroidia was especially increased in footrot samples. Equally, the majority of ASVs assigned to Corynebacterium, Pseudomonadaceae, Saccharimonadaceae, Acholeplasmataceae, Flavobacteriaceae, Carnobacteriaceae and Aerococcaceae were assigned to the Healthy ASV group with these families featuring prominently in the taxa plots of Healthy samples. Ruminococcaceae and Lachnospiraceae, families commonly associated with the intestinal microbiome, were more divided between Healthy and Diseased ASV groups. The taxa plot shows that a different profile of Ruminococcaceae and Lachnospiraceae was found in Healthy samples compared to footrot and CODD samples.
Pathogens previously identified as important in the pathogenesis of ID, footrot and CODD were found within the dataset. One ASV assigned to Dichelobacter nodosus was classified as an Intermediate ASV and was found mainly in ID samples. Three ASVs assigned to Fusobacterium necrophorum were classified as Diseased ASVs and were found mainly in ID and footrot samples. Six ASVs were identified as Treponema with all six classified as Diseased ASVs which were present in ID and footrot samples. When these six treponeme sequences were compared with a wide range of relevant, previously isolated treponeme 16S rRNA gene sequences (39), three of the sequences had 100% sequence identity to the key DD associated treponemes, specifically Treponema medium-phylogroup strain T19, Treponema phagedenis phylogroup strain T320A and Treponema pedis strain T3552BT. The further three treponeme sequences identified all shared 98.4–99.6% nucleotide sequence identity with the Treponema medium phylogroup strain T19.
Changes in the Sheep Foot Microbiome Following Antibiotic Treatment of CODD
Foot swab samples from five sheep in the study whose feet developed progressive CODD lesions and were subsequently treated (2 doses of long acting amoxicillin every 48 hours), were selected for the analysis of changes in foot microbiome composition following treatment. Antibiotic treatment of sheep was effective in achieving a clinical cure (CODD lesion stage grade 5 (20)) in all sheep within 7 days of initial treatment. Based on clinical presentation foot lesions were classified as A_Healthy (samples collected from sheep feet classed as healthy upon entry to study (n = 5)); B_CODD (samples collected from sheep’s feet clinically assessed as having active CODD lesions (n = 21)); C_Treat (samples collected from sheep’s feet 2 weeks post treatment and classed as healed, grade 5 CODD lesions (n = 5)).
Changes in Bacterial Diversity of Sheep Foot Microbiome (Alpha and Beta) Following Treatment of CODD
Changes in microbial community diversity for A_Healthy, B_CODD and C_Treated feet were measured by examining the number of observed ASVs (Fig. 7). Pairwise comparisons found significant reductions in observed ASV numbers between B_CODD feet and both A_Healthy feet (p < 0.01) and C_Treated feet (p < 0.001). However, no differences were observed in diversity of samples from A_Healthy feet and C_Treated feet (p = 0.62) indicating similar bacterial diversity in the microbiomes of the healthy and treated feet.
Similarly, when measured with a weighted UniFrac metric, the ANOSIM tests show beta diversity was significantly different between all three groups B_CODD and both A_Healthy, C_Treated feet (R test statistic 0.825846, p = 0.001)(Table 5). However, the PCoA plot shows that the A_Healthy and C_Treated feet tend to cluster together suggesting that following treatment the community clustering tends to return towards the healthy state (Fig. 8).
Changes in Composition of Sheep Foot Microbiome Following Treatment of CODD (Gneiss Analysis)
1) Identification of Balances Significantly Associated with Changes in Disease State
The overall linear regression model fit was R2 = 0.468 with covariate “Treatment” as the only variable. B_CODD accounted for 17.44% of variance with C_Treat samples accounting for 37.83% of variance. The dendogram heat map (Fig. 9), in conjunction with balance analysis demonstrates a clear pattern of microbiome composition change as foot lesions move from healthy to CODD state and then healed state. There was a strong tendency for the microbiome of the healed state to return to that of the healthy microbiome. However, 3 log ratio balances y6 (B=-39.18, p < 0.001), y11(B = 7.74, p = 0.004) and y14 (B = 28.78, P < 0.001) showed significant differences in log ratios between the groups (Fig. 10A-C).
2) Bacterial Taxa Associations with Changes in Disease State
The log ratio of balance y6 (Fig. 10A) was significantly lower in C_Treated feet compared with the healthy feet due to higher abundance of some y6 denominator ASV in the treated feet compared to healthy feet. These ASVs were mainly assigned to Ruminococcaceae, Lachnospiraceae, Carnobacteriaceae, Staphylococcaceae, Pseudomonadaceae. The log ratio balance of y11 (Fig. 10B) was significantly higher in C_Treated feet compared to the healthy state due to higher abundance of some y11 numerator ASVs which were assigned to Family XI, Corynebacteriaceae, Acholeplasmataceae, Staphylococcaceae, and Rhodobacteraceae. The log ratio balance of y14 (Fig. 10C) was significantly higher in C Treated feet compared to healthy state. The heatmap demonstrates here a clear decrease in y14 denominator ASVs in the treated feet which were assigned to Moraxellaceae, Saccharinonadaceae, Micrococcaceae, Microbacteriaceae and Pseudomonadaceae.
Differences in Healthy Foot Microbial Communities between Animals that Did and Did Not Develop CODD Lesions
To investigate associations between the healthy foot microbiome of the sheep and disease outcome, the differences in the microbial community diversity and composition of the feet of animals that did (case n = 8) or did not (control n = 8) go on to develop CODD lesions at the start of the study were compared using alpha and beta diversity metrics and Gneiss analysis.
Kruskall Wallis pairwise comparisons found no significant differences in ASV numbers between the foot microbiomes of the two groups (p = 0.207) (Fig. 10) and pairwise ANOSIM tests found no difference in the beta diversity metrics (R test statistic = -803, p = 0.803) (Fig. 11). Neither the dendogram heat map (Fig. 12) or Gneiss analysis of log ratio balances (y0-y100) revealed any significant microbiome compositional differences between sheep’s feet which did or did not go onto to develop CODD at the start of the study.