2 CHAPTER TWO: CHARACTERIZATION AND COMPARISON OF THE
2.4 Discussion
This study identified bacteria from 38 phyla and 1665 genera on the skin of horses. This is similar to other companion animals and humans.2,3 The cutaneous microbiota of healthy horses is more diverse than previously described by culture methods alone.23 Four phyla (Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes) made up 82-100% median relative abundance across all anatomical sites sampled. Proteobacteria predominated with a median relative abundance of 37-60%, followed by Firmicutes (27-38%). Actinobacteria and Bacteroidetes were found in lower numbers (median relative abundance of 6-9% and 3-6% respectively). Proteobacteria has also been identified as the most abundant phylum on healthy canine24 and feline4 skin and the same top four phyla predominate on healthy human skin, though in a different order. In humans, Actinobacteria and Firmicutes are most prevalent, followed by lower proportions of Bacteroidetes and Proteobacteria.9
At the genus level, nine genera made up the highest median relative abundance across all sites, accounting for a total of 14.68-27.69% of the sequences identified at the four sites. These included Psychrobacter, Macrococcus, Pseudomonas, Acinetobacter, Planomicrobium, Arthrobacter, Carnobacterium, Desemzia, and Corynebacterium.
54
These results are in contrast to Kamus et al. 201817, where the most common bacterium isolated in healthy control horses could not be identified to the genus level but belonged to the Acidobacteria phylum. The sampling technique varied significantly in that study, with 16S rRNA gene sequencing being performed on skin biopsy samples as opposed to skin swabs. In addition, samples were limited to the thorax and a different DNA extraction kit and RNA primers were used, possibly explaining the differences seen.
In humans and dogs, the main factor influencing microbial populations appears to be the individual.8,24In this cohort of horses, the individual did not appear to be an important driving factor for cutaneous microbial composition, with similar bacterial
genera being identified across all individuals. In addition, no significant differences were seen in alpha diversity and AMOVA analysis of both community membership and
structure found no significant difference between inter-individual variability. Subjects were intentionally sampled from the same farm to standardize the environment as much as possible, possibly explaining this finding. Horses from the same farm are known to share a similar gut microbiota25 and humans and dogs living in the same household are more likely to share similar cutaneous microbial populations to those from a different household.26,27 Further studies comparing the cutaneous microbiota of healthy horses from different farms to those from within the same farm, would be warranted to further evaluate inter-farm variation effects and the importance of the individual in equine cutaneous microbiota.
Bacterial diversity, richness and evenness (alpha diversity) were found to be the same across the different anatomical locations evaluated in this cohort of horses. This is in
55
contrast to what has been seen in humans and companion animals. In humans, three different skin micro-environments are; dry, sebaceous and moist.8 In animals, no specific niche-associated microbiota have been identified but in a study evaluating the skin of 12 dogs, mucosal surfaces were less diverse than haired skin.6 Another study found diversity to be highest on the inner pinnae and lowest in the perianal area.24 In cats, the pre-aural space had the highest bacterial richness, whereas the mucosa and ear canal had the lowest.4 An explanation for the lack of significant differences in alpha diversity parameters in this study could be that the microenvironments evaluated were too similar. In comparison to humans, skin of horses is covered in dense hair, and apocrine and sebaceous glands are distributed relatively uniformly. In a small study of four horses, the thorax was found to be more diverse than the distal limbs17 but the sampling technique was very different in that study. In addition, the sample size was very small, decreasing statistical power. Future studies looking at the microbial
communities of healthy horses at mucus membranes, mucocutaneous junctions or the ear canal may be considered to determine if, similar to companion animals, diversity is lower in these areas.
When evaluating beta diversity parameters this study identified that cutaneous microbial communities in healthy horses were influenced by anatomical site with differences identified in both community membership and structure. This is similar to the findings in humans.1,8 Macrococcus was significantly more abundant on the dorsum than all other sites and linear discriminant analysis effect size (LEfSe) analysis showed Macrococcus was consistently overrepresented on the dorsum, regardless of month evaluated.
56
Several other OTUs were found to be overrepresented at different anatomical locations but these differences varied depending on season. Several species of Macrococcus have previously been identified on equine skin.28 They are gram-positive bacteria that are typically arranged in pairs or tetrads and are closely related to Staphylococcus.28 The cause for their increased relative abundance on the dorsum in this cohort of horses is unknown but may be due to this anatomical location being less commonly in contact with soil and soil-associated bacteria.
Despite significant differences in community structure and membership identified based on AMOVA calculations, NMDS plots of these beta diversity parameters revealed only weak clustering based on body site. In a study of twenty different skin sites in humans, Grice et al. (2009) found that physiologically similar sites had similar microbial
communities.8 The lack of clustering based on skin site in our study may be because the locations chosen were too similar and further studies evaluating more topographical locations are warranted.
Ten OTUs (Psychrobacter, Pseudomonas, Macrococcus, Acinetobacter, Carnobacterium, and Desemzia as well as two OTUs each from the genera
Planomicrobium and Arthrobacter) were found to be present in all samples, regardless of individual, skin site or month sampled. These may represent a ‘core cutaneous microbiota’ present in all healthy horses and a marker of health. Alternatively, they may represent environmental contamination as they were also present in all null exposure samples.Different methods to subtract lineages present in negative control samples have been described but this may lead to elimination of taxa that are authentic
57
members of the microbiota.29 This is of particular concern with skin samples. The skin is in constant contact with the external environment, making it difficult to distinguish
between bacteria that are genuine resident members of the cutaneous microbiota, transient microbes or environmental contaminants. Transient bacteria are considered to be bacteria that are present on skin for hours to days and can be easily removed with soap and water.30 While microbiota studies of amphibians have included a rinsing step to remove environmental microbes,31 this is not common practice in mammalian studies and was not performed in this study. Unlike fecal samples, where bacterial DNA
abundance is very high, the microbial load in skin samples is much lower. Low DNA biomass increases concerns about contamination because contaminants could influence overall results more prominently, unlike high DNA biomass samples where contaminant sequence numbers would be overwhelmed by sequences from the sample.
To assess for contamination from laboratory reagents, negative controls were
performed at multiple steps along the pipeline and did not extract measurable DNA but contamination of swabs from the general environment is largely unavoidable. Despite the fact that many of the bacteria with the highest relative abundance detected on skin were also isolated in high numbers in our null exposure samples, beta diversity
assessment revealed that skin samples were more similar to one another than they were to the null exposure swabs. Despite these differences, many of the bacteria with the highest relative abundance detected on skin were also isolated in high numbers in our null exposure samples, suggesting that samples likely contained some
environmental contaminants and that the difference in molecular variance was likely due
58
to ‘other’ taxa. Future studies comparing different sampling protocols, including an
‘environmental decontamination’ step, may prove insightful. Alternatively, pairing 16S rRNA gene sequencing with culture-based analysis may help identify viable/living microbial colonies versus DNA fragments but given some bacterial species do not grow readily in culture,32 the importance of some taxa may still be underestimated with this method, while others may be overestimated. Further study comparing different
populations and different environments is needed to truly characterize the cutaneous microbiota in healthy horses and get a better idea of the role of environmental microbes as ‘bone fide’ members of the cutaneous ecosystem versus contaminants.
59 2.5 References
1. Costello EK, Lauber CL, Hamady M et al. Bacterial community variation in human body habitats across space and time. Science 2009; 326: 1694-1697
2. Rodrigues Hoffmann A. The cutaneous ecosystem: the roles of the skin microbiome in health and its association with inflammatory skin conditions in humans and animals. Vet Dermatol 2017; 28:
60-e15.
3. Weese S. The canine and feline skin microbiome in health and disease. Vet Dermatol 2013;
24:137-e31.
4. Older CE, Diesel A, Patterson AP et al. The feline skin microbiota: The bacteria inhabiting the skin of healthy and allergic cats. PLoS One 2017; 12:e0178555.
5. Korbelik J, Singh A, Rousseau J et al. Characterization of the otic bacterial microbiota in dogs with otitis externa compared to healthy individuals. Vet Dermatol 2019; 30: 228-e70.
6. Rodrigues Hoffmann A, Patterson AP, Diesel A et al. The skin microbiome in healthy and allergic dogs. PLoS One 2014; 9: e83197.
7. Sanford JA and Gallo. Functions of the skin microbiota in health and disease. Seminars in Immunol. 2013; 25: 370-377
8. Grice EA, Kong HH, Conlan S et al. Topographical and temporal diversity of the human skin microbiome. Science 2009; 324: 1,190–1,192
9. Grice EA, Kong HH, Renaud G, Young AC, Bouffard GG, Blakesley RW, Wolfsberg TG, Turner ML, Segre JA. A diversity profile of the human skin microbiota. Genome Res 2008; 18: 1,043–
1,050.
10. Kong HH, Oh J, Deming C et al. Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis. Genome Res 2012; 22: 850–859.
11. Ross AA, Müller KM, Weese SJ et al. Comprehensive skin microbiome analysis reveals the uniqueness of human skin and evidence for phylosymbiosis within the class Mammalia. Proc Natl Acad Sci 2018: e201801302
12. Kobayashi T, Glatz M, Horiuchi K et al. Dysbiosis and Staphylococcus aureus colonization drives inflammation in atopic dermatitis. Immunity 2015; 42: 756–766.
13. Bradley CW, Morris DO, Rankin SC et al. Longitudinal evaluation of the skin microbiome and association with microenvironment and treatment in canine atopic dermatitis. J Invest Dermatol 2016; 136: 1,182–1,190.
14. Pierezan F, Olivry T, Paps JS et al. The skin microbiome in allergen-induced canine atopic dermatitis. Vet Dermatol 2016; 27: 332-e82.
15. Scott DW, Miller WH. Equine Dermatology, 2nd edition. Maryland Heights, MO: Saunders Elsevier, 2011; p 50-51
16. Sangiorgio DB, Epper P, Kaiser-Thom S, Ramseyer AA, Overesch G, Jores J, Perreten V, Hilty M, Gerber V. The microbiome in horses with pastern dermatitis differs according to lesional scores. Original Abstract. ESVD/ECVD Congress Proceedings 2018
17. Kamus LJ, Theoret C, Costa MC. Use of next generation sequencing to investigate the microbiota of experimentally induced wounds and the effect of bandaging in horses. PLoS One 2018; 13:
e0206989.
18. Walters W, Hyde ER, Berg-Lyons D et al. Improved bacterial 16S rRNA gene (V4 and V4-V5) and fungal internal transcribed spacer marker gene primers for microbial community surveys.
mSystems. 2016; 1 (1): e00009-15
19. Schloss PD, Westcott SL, Ryabin T et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl
Environ Microbiol 2009; 75: 7,537–7,541
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20. Quast C, Pruesse E, Yilmaz P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucl Acids Res 2013; 41: D590-596.
21. Rognes T, Flouri T, Nichols B, et al. VSEARCH: a versatile open source tool for metagenomics.
PeerJ 2016; 4: e2584 doi: 10.7717/peerj.2584
22. Wang Q, Garrity GM, Tiedje JM et al. Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy. Appl Environ Microbiol 2007; 73:5261-5267 23. Scott DW, Miller WH. Equine Dermatology, 2nd edition. Maryland Heights, MO: Saunders
Elsevier, 2011; p 17
24. Cuscó A, Sanchez A, Altet L et al. Individual signatures define canine skin microbiota composition and variability. Front Vet Sci 2017; 4: 6.
25. Kaiser-Thom S, Hilty M, Gerber V. Effects of hypersensitivity disorders and environmental factors on the equine intestinal microbiota. Veterinary Quarterly 2020; 40: 97-107.
26. Song SJ, Lauber C, Costello EK et al. Cohabiting family members share microbiota with one another and with their dogs. Elife 2013; 2: e00458
27. Ngo J, Taminiau B, Fall PA et al. Ear canal microbiota – a comparison between healthy dogs and atopic dogs without clinical signs of otitis externa. Vet Dermatol 2018; 29:425-e140
28. Kloos WE, Ballard DN, George CG et al. Delimiting the genus Staphylococcus through
description of Macrococcus caseolyticus gen. nov., comb. Nov. and Macrococcus equipercicus sp. nov., Macrococcus bovicus sp. nov. and Macrococcus carouselicus sp. nov. Int J of Sys Bacteriol 1998; 48: 859-877.
29. Kim D, Hofstaedter CE, Zhao C et al. Optimizing methods and dodging pitfalls in microbiome research. Microbiome 2017; 5: 52.
30. Lilly HA, Lowbury EJ. Transient skin flora: Their removal by cleansing or disinfection in relation to their mode of deposition. J Clin Pathol 1987; 31:919–922
31. Culp CE, Falkinham JO, Belden LK. Identification of the natural bacterial microflora on the skin of eastern newts, bullfrog tadpoles and redback salamanders. Herpetologica 2007; 63:66–71 32. Wade WG. Unculturable bacteria--the uncharacterized organisms that cause oral infections. J of
the Royal Soc of Med 2002; 95(2):81–83
61 2.6 Tables
Table 2-1: Signalment of the study population
Horse
62
Table 2-2: Alpha diversity: Median observed richness (sobs); estimated richness (chao1); estimated diversity (inverse Simpson’s diversity index) (diversity); and estimated evenness (Shannon’s evenness index) (evenness) and their interquartile ranges (IQR) per site measured at 48,591 sequences per sample across all months
Abdomen 861.5
315.75-2817.75 1799.49
698.52-4894.64 6.09
3.33-15.70 0.42
0.344-0.55
Dorsum 981.5
396-2461 2066.11
850.20-3980.5 4.45
2.71-6.45 0.36 0.29-0.43
Groin 972
235.5-2947.5 1919.63
507.82-5005.17 5.7
3.17-14.03 0.4 0.3-0.55
Pastern 1192.5 415-2636
Table 2-3: Beta diversity: Analysis of molecular variance (AMOVA) of community membership (Jaccard’s index) and structure (Yue & Clayton measure of dissimilarity) of the four sites A=abdomen, D=dorsum, P=Pastern, G=Groin) sampled. Calculations were done in mothur. P values were adjusted for false discovery rate using the Benjamini-Hochberg method in R. * indicates a significant difference
Sites
compared Jaccard’s index Adjusted P value Yue&Clayton measure Adjusted P value
Beta diversity between sites in the Winter
A-D-P-G 0.004* 0.002*
A-D 0.004* 0.002*
A-P 0.009* 0.002*
A-G 0.07 0.06
63
D-G 0.07 0.06
D-P 0.03* 0.005*
G-P 0.03* 0.006*
Beta diversity between sites in the Spring
A-D-P-G 0.007* 0.063
A-D 0.03* 0.063
A-G 0.06 0.063
A-P 0.35 0.063
G-P 0.19 0.62
D-G 0.007* 0.12
D-P 0.06 0.14
Beta diversity between sites in the Summer
A-D-P-G 0.002* 0.001*
A-D 0.003* 0.556
A-G 0.049* 0.001*
A-P 0.045* 0.001*
D-G 0.002* 0.004*
64
D-P 0.002* 0.001*
G-P 0.002* 0.001*
Beta diversity between sites in the Fall
A-D-P-G 0.004* 0.14
A-P 0.01* 0.06
D-G 0.005* 0.19
D-P 0.01* 0.64
G-P 0.004* 0.46
A-D 0.06 0.19
A-G 0.117 0.48
Table 2-4: Median relative abundances and interquartile ranges (IQR) of the most abundant phyla and genera (based on total median relative abundance) identified at each body site (abdomen, dorsum, groin, pastern) sampled. Phyla and genera with a total median relative abundance of <0.5% are pooled in
‘Others’.
Level Taxon Abdomen Dorsum Groin Pastern
Phylum Proteobacteria 0.60
(0.21-0.78) 0.36 (0.1-0.69) 0.41
(0.16-0.83) 0.58 (0.20-0.79)
Phylum Firmicutes 0.27
(0.13-0.48) 0.38
(0.23-0.64) 0.34
(0.12-0.50) 0.31 (0.15-0.51)
Phylum Actinobacteria 0.07
(0.02-0.14) 0.057
(0.02-0.13) 0.09
(0.01-0.13) 0.065 (0.019-0.13)
65
Phylum Bacteroidetes 0.056
(0.02-0.09) 0.02
(0.01-0.05) 0.047
(0.02-0.09) 0.032 (0.014-0.06)
Phylum Other 0.01
(0.001-0.08) 0.04
(0.003-0.12) 0.008
(0.0006-0.08) 0.02 (0.002-0.06)
Genus Pseudomonas 0.019 (0.006-0.21)
Genus Psychrobacter 0.073
(0.004-0.24) 0.043
(0.003-0.30) 0.035
(0.002-0.29) 0.03 (0.006-0.17)
Genus Macrococcus 0.007 (0.001-0.016)
Genus Acinetobacter 0.04
(0.009-0.19) 0.008
(0.002-0.03) 0.01
(0.002-0.08) 0.049 (0.01-0.21)
Genus Planomicrobium 0.02
(0.004-0.11) 0.016
(0.004-0.03) 0.013
(0.004-0.11) 0.04 (0.004-0.19)
Genus Arthrobacter 0.02
(0.007-0.042) 0.011
(0.006-0.02) 0.011
(0.007-0.02) 0.019 (0.006-0.03)
Genus Carnobacterium 0.02 (0.006-0.04)
Genus Desemzia 0.01
(0.004-0.033) 0.003
(0.001-0.008) 0.006
(0.002-0.03) 0.012 (0.002-0.04)
Genus Corynebacterium 0.01 (0.0007-0.031)
66
Table 2-5: Taxa of the most abundant phyla and genera (based on total median abundance) that were significantly different between body site sampled. P-values were adjusted in R using the Benjamini-Hochberg method
Level Taxon More abundant Less
abundant
P-value Adjusted P-value
Genus Macrococcus Dorsum
Dorsum
Genus Acinetobacter Pastern
Pastern
Genus Desemzia Pastern
Abdomen
Genus Corynebacterium Groin Dorsum 0.0059 0.0354
67 2.7 Figures
Figure 2-1: Alpha diversity measurements in horses by anatomical location sampled. (a) Observed richness; (b) estimated richness (Chao1); (c) estimated diversity (inverse Simpson’s diversity index); and (d) estimated evenness (Shannon’s evenness index)
68
Figure 2-2: Alpha diversity measurements in horses by individual sampled. (a) Observed richness;
(b) estimated richness (Chao1); (c) estimated diversity (inverse Simpson’s diversity index); and (d) estimated evenness (Shannon’s evenness index)
69
Figure 2-3: Non-metric multidimensional scaling plots generated in JMP® based on Jaccard’s index (which evaluates the number of shared genera between communities) calculated in MOTHUR comparing community structure between body site (abdomen, dorsum, groin and pastern) in the Winter (a), Spring (b), Summer (c) and Fall (d). Ellipsoid coverage is 90%.
70
Figure 2-4: Non-metric multidimensional scaling plots generated in JMP® based on Yue & Clayton measure of dissimilarity (which evaluates the number of shared genera between communities as well as their relative abundances) calculated in MOTHUR comparing community structure between body site (abdomen, dorsum, groin and pastern) in the Winter (a), Spring (b), Summer (c) and Fall (d). Ellipsoid coverage is 90%.
71
Figure 2-5: Operational taxonomic units (OTUs) identified by linear discriminant analysis effect size (LEfSe) as significantly overrepresented (P<0.05) at the four body sites (abdomen, dorsum, groin and pastern) evaluated in the Winter (a), Spring (b), Summer (c) and Fall (d). Only OTUs with a linear discriminant analysis (LDA) >3.5 are depicted
72
Figure 2-6: Median relative abundance of the most common phyla identified at each body site (abdomen, dorsum, groin, pastern). Phyla with a total median relative abundance of <0.5% are pooled in
‘Other’.
Abdomen Dorsum Groin Pastern 0.0
0.2 0.4 0.6 0.8 1.0
Proteobacteria
Firmicutes
Actinobacteria
Bacteroidetes
'Other' phyla
73
Figure 2-7: Median relative abundance of the most common genera identified for each individual (Horses 1-12) sampled. Genera with a total median relative abundance of <0.5% are pooled in ‘Other’.
Figure 2-8: Median relative abundance of the most common genera identified for each body site sampled. Genera with a total median relative abundance of <0.5% are pooled in ‘Other’.
Horse
74
Figure 2-9: Median relative abundance of the most common genera identified on the skin of twelve healthy horses (Horse) and five null exposure samples (NULL) based on total median abundance.
Genera with a total median relative abundance of <0.5% are pooled in ‘Other’
Horse Null
0.0 0.2 0.4 0.6 0.8 1.0
Pseudomonas Psychrobacter Acinetobacter Macrococcus Planomicrobium Carnobacterium Desemzia Arthrobacter Corynebacterium Other
75
3 CHAPTER THREE: EVALUATION OF THE EFFECT OF TIME OF YEAR ON THE CUTANEOUS MICROBIOTA IN HEALTHY HORSES
3.1 Introduction
The skin is inhabited by millions of microorganisms, including bacteria, that interact with host cells and play a crucial role in maintaining homeostasis and health.1 These
microbes make up the cutaneous microbiota.
In healthy adult humans, the skin microbiota appears to be relatively stable over several months to years,2 however it has a higher degree of variability over time when
compared to the gut and oral microbiota.3 In dogs, a study of 40 healthy individuals from 20 different households found cutaneous microbial community structure clustered
significantly by season but not by skin site or individual.4 Environmental or extrinsic factors are known to play an important role in microbial composition. This is
demonstrated by the fact that humans and dogs living in the same household are more likely to share a similar microbiota than individuals from a different household.4,5
Longitudinal studies on the cutaneous microbiota in horses are lacking. Thus, the stability of the cutaneous microbiota over time remains unknown.
Horses in temperate latitudes such as Ontario, Canada shed their Winter coat in the Spring (May/June). Hair growth increases again in the Fall with the Winter coat typically lasting from September to May.6 The effect of these changes on the cutaneous
microbiota is unknown. Understanding temporal changes and natural variations to the
76
‘normal’ microbiota of healthy individuals is important for future study design, as well as to understand disruptions in disease states.
The objective of this study was to evaluate the effect of season on taxonomic
distributions, as well as alpha and beta diversity of the cutaneous microbiota of healthy horses by sampling the same individuals at four timepoints (Winter, Spring, Summer and Fall) within the same calendar year.
3.2 Materials and Methods 3.2.1 Study population
Fifteen horses were recruited from the same hunter-jumper farm. The decision to only include horses from the same farm was deliberate and done to reduce the variability that location and housing environments may play on the microbiota. Adult horses of any breed between the ages of 2 to 20 years with no prior history of skin or endocrine
disease were included. Horses that received systemic or topical antibiotic therapy within the preceding 6 months, corticosteroids within 4 weeks, antihistamines within 2 weeks, bathing within the preceding 7 days and traveled off-farm within the preceding 4 weeks were excluded. Samples were not collected within 24 hours of precipitation. The horses were housed in a variety of large outdoor paddocks year-round, with some of the horses being housed in the same paddock as one another, and others being in a different paddock on the same farm. Horses in different paddocks still came in contact with one another during riding lessons throughout the year. The ground in the outdoor paddocks was composed of grass and bare earth. None of the horses shared tack or saddles.
None of the horses were blanketed during the Spring and Summer collection dates,