The composition and natural variation of the skin microbiota in healthy Australian cattle

doi:10.21203/rs.3.rs-2207709/v1

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The composition and natural variation of the skin microbiota in healthy Australian cattle

https://doi.org/10.21203/rs.3.rs-2207709/v1

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Background

Skin diseases in cattle pose significant animal welfare issues and financial burdens. Microbial communities inhabiting the skin have essential roles in maintaining host health. Microbiota manipulation can be an efficient strategy for improving the productivity and sustainability of skin disease management. However, a lack of information on the skin microbiota of healthy cattle and how environmental and host factors drive its normal variation has limited using skin microbes for diagnosing or treating skin disease and pest infestation in cattle. Here, we profiled the skin microbiota of 1,734 healthy cattle from 25 different herds located in a 187,000 km² area in the northeast to east Australia using 16S rRNA gene amplicon sequencing. The impact of host and climatic conditions on the skin microbial populations was examined.

Results

Our results demonstrate a highly diverse microbiota on the skin of healthy cattle. While the structure and diversity of microbial communities varied between herds, several bacteria were present consistently despite the differences in environmental conditions. For example, bacterial families Moraxellaceae, Sphingomonadaceae, Bacillaceae and Burkholderiaceae were observed in most cattle, however, their relative abundance varied significantly between herds. Solar exposure and rainfall were key factors contributing to the observed variations in skin microbiota between herds, while temperature and cattle breed did not show any statistically significant impact on the composition of cattle skin microbiota.

Conclusions

This is the first report of the composition of the commensal skin microbiota of healthy cattle, specifically herds located in the northeast to east Australia and the impact of environmental and host variables on these microbial communities. Our study provides valuable insight into normal variation in cattle skin microbiota, an essential step for developing skin microbiota-based therapies for skin disease present in cattle.

cattle skin microbiome

livestock microbiome

Australian cattle

The diverse microbiota inhabiting the skin has essential roles in providing protection against invading pathogens, and the development and regulation of the host immune system [1–3]. Perturbation of skin microbial populations has been associated with several dermatological and inflammatory diseases in both humans and animals [1, 4, 5]. Skin diseases in cattle present a significant burden on the dairy and beef industries. In addition to devastating impacts on animal health and welfare, these illnesses cause substantial financial losses due to costs associated with mortality, treatments, and decreased yield in production. As a key contributor to skin health, skin microbiota presents a promising tool to manage the susceptibility, diagnosis, and treatment of dermatological diseases in cattle [6, 7].

External factors including temperature, humidity and exposure to ultraviolet light have been shown to impact the healthy skin microbial composition in people [8]. While these factors could also significantly influence the skin microbiota of cattle, there is a lack of direct examinations on this. In addition, differences in species and breed, host factors such as genetics, age and gender, the influence of geographical location of herds, and ecological and climatic conditions could shape the microbial populations inhabiting healthy cattle skin [6, 9]. Despite the essential roles of environmental and host factors in determining any skin microbiota, how they affect microbes living on cattle skin is yet to be examined.

Profiling the healthy cattle skin microbiota is important not only for understanding how these microbial populations contribute to health and disease but also for developing strategies to effectively manage skin diseases and pathogen infestation in cattle. For example, skin probiotic products intended to ameliorate the cattle skin microbiota and infestations of the Australian cattle tick, Rhipicephalus australis (Boophilus microplus), buffalo fly (Haematobia irritans exigua), acari and insect exo-parasites are already under development [10]. However, for such products to succeed, integration of the probiotic microbes into the commensal skin microbiota of cattle under a range of host and environmental conditions is necessary. Thus far, studies on cattle skin microbiota have been largely limited to specific body sites, such as the digits [11, 12] or teat skin [13, 14] and to how skin microbes associate with particular diseases, mainly bovine digital dermatitis [15–17]. Characterising the commensal microbial communities in healthy cattle hide and understanding how different external factors influence their composition is fundamental to efficiently using microbiota-based therapies, such as probiotics to manage skin disease and pest infestation in cattle.

In this present investigation, we report the skin (hide) microbiota composition of 25 cattle herds, comprising a total of 1,743 healthy cattle, located in a 187,000 km² area from northeast to east Australia where cattle tick infestation is endemic. The impact of the location of farms and climatic conditions on the skin microbiota structure and composition were examined.

Environmental factors shaped the skin microbiota community structure and diversity

To profile the skin microbiota of healthy cattle, skin scurf was sampled from 25 different herds located in northeast to east Australia (Fig. 1 and Table S1) and the 16S rRNA gene amplicons were sequenced from microbial DNA isolated from each sample. The skin microbial community structure was visualised using a Bray-Curtis similarity-based multidimensional scaling (MDS) plot (Fig. 2A). Each cattle herd appeared to have distinct microbial community structures, with herds 19 and 20 being the most divergent to the rest of the herds. These two herds were located at the northernmost sampling site within a proximity of 3 km to each other (Fig. 1).

Associations between environmental variables and microbiota community ordination were examined to determine the factors driving the observed differences in the skin microbiota. This identified significant correlations of the microbial community structure with the location of the farm (altitude, latitude, and longitude) and specific climatic parameters (solar exposure, temperature, and rainfall) (Fig. 2A). For example, altitude and latitude were found to be the strongest parameters driving the microbiota differences in herds 22 and 24, whilst rainfall and temperature (seven-day average prior to sample collection) contributed significantly to shaping the skin microbial community in herd 8. Upon examination of the impact of host factors such as the breed, we observed no significant correlation with the microbiota community structure, suggesting that differences in the microbiota community structure between herds were mainly due to environmental variables.

The microbiota alpha diversity, as determined using a Shannon diversity index, was generally high across most herds, indicating a diverse skin microbiota in healthy cattle (Fig. 2B), this is consistent with previous reports [1]. Herds 3, 19 and 20 had lower alpha diversity indices compared to all other samples. To examine whether this is due to an impact of environmental variables, we conducted pair-wise correlation analyses between Shannon diversity indices and geographic and climatic information. This revealed a significant negative association (Pearson correlation = − 0.43, P < 0.05) between the Shannon diversity indices and latitude of the farms. Other tested environmental variables had no significant association with microbiota diversity. While our results suggest an impact of the location, particularly latitude, on the diversity of cattle skin microbiome, future studies with sampling sites spanning a broader geographical area are essential for confirming this observation.

Some Bacterial Phyla And Families Were Highly Abundant Across All Cattle Herds

Despite the differences in microbiota community structure and diversity, some microbes were consistently identified on the skin of most cattle. For example, upon examination of the relative microbial abundance at the phylum, we identified 35 different bacterial phylum assignments across all skin microbiota samples. Among these, the phyla Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes and Acidobacteria were highly abundant in the skin microbiota of all herds (Figure S1). Microbial abundance at the order and family level was assigned to 210 orders and 378 families (Table S2A), demonstrating a complex and diverse community in the skin microbiota of healthy cattle. Several bacterial families including Moraxellaceae, Sphingomonadaceae, Bacillaceae, Burkholderiaceae and Peptostreptococcaceae were observed on the skin of all 25 herds (Fig. 3 and Table S2B). A total of 7,476 amplicon sequence variants (ASVs) were identified across all 25 cattle herds (Table S3). Of these, 234 ASVs, some of which were in the families Moraxellaceae, Sphingomonadaceae and Bacillaceae, were highly abundant across most herds.

While similar types of bacterial phyla and families were seen in most samples, their relative abundance significantly differed between herds. Notably, a higher abundance of Proteobacteria and a lower abundance of Actinobacteria and Acidobacteria were seen in cattle with a lower microbiota alpha diversity (herds 3, 19 and 20) compared to the herds with a higher microbiota alpha diversity (Fig. 2B and Figure S1). Similar variations in the microbiota composition were observed at the family level (Fig. 3). For example, in herds 3, 4, 19 and 20, more than 50% of the taxa were assigned to the family Moraxellaceae, a member of the phylum Proteobacteria that is commonly observed in both human and animal skin [5] including the cattle skin microbiota [18].

Identifying core microbes that are present on the skin irrespective of the host and environmental variables is vital for developing microbiota-based strategies to manage skin disease and pest infestation in cattle. Our study is the first to investigate this using 25 herds of Australian cattle. The most abundant bacterial phyla (Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes) which were observed across all herds (Figure S1), have been previously reported in healthy bovine digits and teat skin [1, 19, 20]. Moraxellaceae, Sphingomonadaceae, Bacillaceae, Burkholderiaceae and Peptostreptococcaceae are bacterial families generally present on the teat and digit skin of healthy cattle [18, 21–24]. Despite these similarities, investigations on the cattle skin microbiota thus far have been limited to specific body sites. Our study addresses this knowledge gap through profiling the microbial communities living in the skin of cattle.

Location And Climate Had Significant Impacts On The Abundance Of Skin Bacteria

To examine the impact of environmental factors on the observed variations in the relative abundance of skin microbial communities, pair-wise correlation analyses were conducted. These revealed significant associations between specific environmental parameters and the abundance of specific bacterial families (Table 1). Solar exposure and rainfall significantly contributed to the differences in microbiota composition across herds. For example, the relative abundance of Bacillaceae, Planococcaceae, Staphylococcaceae and Micrococcaceae positively correlated with solar exposure. The first three families, which are all in the class Bacilli, contain members that are resistant to desiccation [25–27]. The family Micrococcaceae, a member of the phylum Actinobacteria, is tolerant to heat [28]. The higher abundance of these families in the skin microbiome of cattle located in areas with a higher solar exposure is likely explained by their ability to tolerate physical challenges including desiccation. The abundance of the family Sphingomonadaceae was found to be higher in areas with greater rainfall (Table 1). In agreement with our observations, members of this family have been previously reported in freshwater [29], a potential source introducing this bacterial family onto the cattle skin.

Table 1

Pearson’s correlation coefficient of pair-wise correlation analysis between the relative abundance of bacterial families and environmental variables.*
Bacterial Family	Latitude	Longitude	Altitude	Rainfall	Solar exposure
Bacillaceae					+ 0.40
Staphylococcaceae					+ 0.48
Micrococcaceae					+ 0.41
Planococcaceae					+ 0.50
Sphingomonadaceae	+ 0.45	+ 0.52		+ 0.44
Cellvibrionaceae	+ 0.45	+ 0.42
Chitinophagaceae	+ 0.44
Pseudomonadaceae	+ 0.43	+ 0.45
Rhodopirillaceae	+ 0.43
Weeksellaceae			+ 0.47
*Coordinates of statistically significant (p < 0.05) correlations are shown. + denotes a positive correlation.

The location of the farm (latitude, longitude, and altitude) showed significant positive correlations with the abundance of six bacterial families: Cellvibrionaceae, Chitinophagaceae, Pseudomonadaceae, Rhodosprillaceae, Sphingomonadaceae and Weeksellaceae. However, there were no significant associations between these families (except Sphingomonadaceae) and any of the tested climatic parameters (temperature, rainfall, or solar exposure). Members of the Cellvibrionaceae, Chitinophagaceae and Sphingomonadaceae have been found in soil and water [30, 31], suggesting that observed associations could be related to environmental or host factors not tested in this current study.

The current study examined the overall skin microbiota of cattle (along the backline and flanks). Future investigations on whether microbes on different skin sites respond differently to climatic and environmental changes will be useful for comprehensively understanding the commensal skin microbiota of cattle. The herds in this study were in northeast and east Australia and their skin scurf was sampled once. Sampling herds from a larger geographical area and at multiple times throughout the year will contribute to extending the outcomes of the current study and provide a thorough understanding of the commensal skin microbiota of cattle and how seasonal changes influence it.

Alterations in the skin microbiota have been associated with various skin diseases and inflammatory conditions in both humans and animals [4, 24, 32]. Skin health in cattle is paramount for the wellbeing of animals and profitability in the dairy and beef industries. While there is an increase in the recognition of the role of the skin microbiota in regulating cattle skin health, there is a lack of studies directly examining this in livestock. Elucidating how external factors that impact the skin microbiota of healthy cattle will not only be critical for developing novel microbiome therapies but will also facilitate designing better studies to examine the microbiota alterations in various skin diseases.

Our results demonstrate a very diverse skin microbial community in healthy cattle sampled across 25 herds in the northeast and east Australia. While each herd had a unique microbiota community structure, several bacterial phyla and families were common between herds. Bacterial phyla Proteobacteria, Firmicutes and Actinobacteria, and families Bacillaceae, Burkholderiaceae and Moraxellaceae were the most abundant cattle skin microbes observed across most herds. Environmental factors including the location of the herd and weather conditions (solar exposure and rainfall) were found to be key contributors to shaping the skin microbial communities. This study serves as the first report of the commensal skin microbiota of Australian cattle living across geographically and climactically distinct locations.

Much of the microbiota research has focused on the gastrointestinal tract driven by discovery research and a multi-billion-dollar nutraceutical industry. In contrast, our understanding of the skin microbiota is only emerging; the known roles of the skin microbes in human and animal health have recently stimulated both science and investment. Our study provides the essential groundwork for elucidating the commensal skin microbiota of healthy cattle and determining the host and environmental factors that drive its normal variation. This study highlights the importance of collective efforts in understanding the skin microbiota of farm animals to facilitate the development of microbiota-based strategies for effective and sustainable management of skin disease and pest infestation.

Study area and sampling procedure

Samples for microbiome analysis were obtained from 25 cattle farms, comprising a total of 1,743 cattle, located in an area spanning 187,000 km² in northeast to east Australia, from Queensland 17.4° S, 145.6° E to 26.0° S, 152.1° E, Australia (Fig. 1). Coordinates of each farm and daily weather information 30 days prior to sample collection were obtained for each location (Table S1A). The skins of cattle at each location were gently surface vacuumed using a Makita DVC260Z cordless backpack vacuum cleaner with custom brush head. Each cow in the herd at each farm was vacuumed along the backline and flanks. The vacuumed bag was changed between farms. Detailed information on cattle breed, the number of cattle sampled at each farm are provided in Table S1B. Samples were stored at -80°C prior to microbiota analysis.

16s Rrna Gene Amplicon Library Preparation And Bioinformatic Analysis

Total community DNA was extracted from cattle skin scurf samples using a FastDNA spin kit (MP Biomedicals, Australia) according to the manufacturer’s instructions. The 16S rRNA (V4 region) gene was amplified using 515 (5ʹ-GTGCCAGCMGCCGCGGTAA-3ʹ) forward and 806 (5ʹ-GGACTACHVGGGTWTCTAAT-3ʹ) reverse primers with custom barcodes [33, 34]. The PCR reactions were performed with 30 cycles at 94°C for 45 seconds, 50°C for 60 seconds and 72°C for 90 seconds using a Platinum hot start PCR master mix (Life Technologies, Australia). The resulting amplicons were quantified using Quant-iT™ PicoGreen® (Invitrogen, Australia) and equimolar amounts of barcoded amplicons from each sample were pooled, gel purified (Wizard® SV gel and PCR clean up system, Promega, Australia) and sequenced on an Illumina MiSeq platform (2 × 250 bp) at the Ramaciotti Centre for Genomics, Australia.

Raw sequence data were processed using Quantitative Insights Into Microbial Ecology software (QIIME2 version 2019.7) [35]. Demultiplexed paired-end reads were quality filtered using the q2-demux plugin (median q < 30), followed by denoising with DADA2 [36]. Amplicon sequence variants (ASVs) were aligned with mafft (via q2‐alignment) [37] and used to construct a phylogeny with fasttree2 (via q2‐phylogeny) [38]. The generated ASVs were assigned to taxonomy using the q2‐feature‐classifier [39] classify‐sklearn naïve Bayes taxonomy classifier against the SILVA 132 database [40] of the 515F/R806 region of the 16S rRNA gene at 99% similarity. A total of 951,858 reads (mean 36,609 ± 21,0915) were obtained, after quality filtering, each of the 25 samples was rarefied at 6,264 reads prior to statistical analyses.

Statistical analysis

Statistical analyses of the microbiota sequence data were performed on filtered and rarefied abundance of the ASVs using the R package vegan [41]. Non-metric multidimensional scaling (nMDS) plots were constructed using default parameters to visualize the differences in the microbiota community structure in each sample. The Shannon diversity index for each sample was determined based on the ASV abundance of samples. The microbial relative abundance at the phylum and family levels were determined using QIIME2 software and graphed using GraphPad Prism (Version 7). Correlation between environmental parameters and the relative abundance of bacterial phyla, families and microbiota alpha diversity was determined using Pearson correlation coefficient on GraphPad Prism (Version 7). Only statistically significant (p < 0.05) correlations are presented.

MDS- multidimensional scaling; ASV- amplicon sequence variant; rRNA-ribosomal ribonucleic acid

Ethics approval and consent to participate

Samples were collected as a part of routine animal husbandry by trained cattle property staff using accepted animal handling practices routinely undertaken in rural Australia; approval by an ethical committee was not necessary.

Availability of data and material

The 16S rRNA gene sequence data generated and analysed in this study are available on the Sequence Read Archive under accession number PRJNA894323 https://dataview.ncbi.nlm.nih.gov/object/PRJNA894323?reviewer=rutsb7ed62360npd8kt1hdad2k

Competing interests

EL is the Chief Scientist and DV, SAM are RC are employed at Microbial Screening Technologies Pty Ltd, the industry partner of this project. All other authors declare that they have no competing interests.

Funding

This work was supported under the Australian Government Collaborative Research Centres Project funding scheme (CRC-P58601), through funding of the Probio-TICK Initiative.

Authors' contributions

HKAHG prepared microbiome sequencing libraries, performed bioinformatic analysis, interpreted data and drafted the manuscript; DV, SM and RC assisted sample collection and edited the manuscript; EL designed the study, contributed to data interpretation, and edited the manuscript; AMP and ITP contributed to data interpretation and edited the manuscript.

Acknowledgements

We thank Paul Caton for assisting with sample collection.

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Competing interest reported. EL is the Chief Scientist and DV, SAM are RC are employed at Microbial Screening Technologies Pty Ltd, the industry partner of this project. All other authors declare that they have no competing interests.

FigureS1.png
Figure S1 Phylum-level taxonomic composition of the cattle skin microbial communities collected from each of the 25 sites. Amplicon sequence variants (ASVs) that were not assigned to a phylum are categorised as “Unassigned”. Bacterial phyla with a relative abundance < 1% in all locations are indicated as “Other”.
TableS1.xlsx
Table S1 Information on the location of the farm, climate, and herds. (A) Location coordinates of the farms and climatic information.* (B) The breed and number of cattle sampled at each of the 25 farms.
- Average temperature (maximum and minimum), rainfall and solar exposure in the 24 hours, 7-days and 30-days prior to sample collection are provided. Seven-day and 24-hour (rainfall) average of each environmental parameter was used to examine correlations with the microbiome.
TableS2.xlsx
Table S2 Summary of the taxonomic assignments.(A) The total number of microbial identifications at each taxonomic level observed across all cattle herds. (B) The relative abundance of the skin microbiome of each cattle herd at the family level.
TableS3.xlsx
Table S3 The relative abundance of the cattle skin microbiota at the amplicon sequence variant (ASV) level.* *The relative abundance of ASVs in each cattle herd is provided with taxonomic information.

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The composition and natural variation of the skin microbiota in healthy Australian cattle

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Abstract

Background

Results

Conclusions

Figures

Background

Results And Discussion

Environmental factors shaped the skin microbiota community structure and diversity

Some Bacterial Phyla And Families Were Highly Abundant Across All Cattle Herds

Location And Climate Had Significant Impacts On The Abundance Of Skin Bacteria

Conclusions

Methods

Study area and sampling procedure

16s Rrna Gene Amplicon Library Preparation And Bioinformatic Analysis

Statistical analysis

List Of Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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