The Impact of Drinking Water Sources on Gut Microbial Diversity in Canines

Abstract

The presence of gut microflora in the gastrointestinal track (GIT) has a tremendous impact on antiviral capabilities, digestion, and many other physiological factors. In canines, changes in gastrointestinal tract are linked to variations in diet and these changes can be seen through amplification of the 16s rDNA gene and analysis of the types of bacteria present in the GIT.

gDNA was extracted from each canine fecal sample and amplified using PCR. The amplified samples were then sequenced using the Illumina system at the Anschutz Medical Campus and returned to the lab for analysis. The F19_67 canine sample was studied along with approximately 450 other canine samples to observe the presence of gut microbial communities in the GIT.

Through this analysis, a relationship between the variations in gastrointestinal track microbial communities and changes in water sources was established, showing that canines with exposure to rural communities of water had a more diverse gut microbial community, and canines with exposure to regulated urban water sources had a less diverse community due to urban water processing to remove the presence of bacteria in drinking water.

Introduction

There are millions of microbes that live in the gastrointestinal tracts (GIT) of all mammals on earth. It is estimated that there are over 1010 or more individual microbes present in the gastrointestinal tract, making it one of the most numerous environments for microbial communities in the body (Gootenberg and Turnbaugh 2011). They are some of the smallest life forms in the world and contribute to psychological, nutritional, antiviral, and many more factors of the human and animal anatomy (Kil and Swanson 2011).

In the GIT, Microbes pose as an antiviral barrier that protects the tract from viruses and aids in processes such as digestion and nutrient extraction along with hundreds of other bodily functions. (Suchodolski 2011). Changes in the community of microbes present in the GIT can be linked to factors such as diet, geographical location, environmental exposure, and these factors can help identify relationships between animals and their GIT microbial communities and how they affect animal behavior and health.

Differences in animal and human diet can cause drastic changes in the diversity of gut microbial communities (Scott et al. 2013), and differentiation between water sources plays a large role. Animals who consistently drink more rural sources of water are expected to have a more diverse microbial community in the GIT due to the lack of water filtration techniques typically used in urban drinking water sources.

This also implies that animals that consistently drink out of urban water sources are expected to have a less diverse microbial community in the GIT due to the presence of chemicals such as chlorine and fluorine that are commonly introduced into municipal drinking water. This study takes a closer look into influences on the GIT microbial community due to different types of drinking water by extracting gDNA from canines exposed to different water sources. and amplifying the 16s rDNA gene to analyze the specific types of bacteria present.

Methods

Canine Fecal Matter gDNA Extraction

gDNA was extracted and isolated from canine fecal samples using the DNeasy PowerSoil Kit by Qiagen which used Inhibitor Removal Technology (IRT) to remove inhibitors and isolate genomic DNA in preparation for a Polymerase Chain Reaction (PCR) to amplify the 16S rDNA Gene. The DNeasy PowerSoil Kit procedure was followed to perform the extraction and isolation. During the extraction, cells were lysed using 6 different types of C solutions that allowed the gDNA to adhere to a synthetic membrane. The gDNA present on the membrane was then eluted and used in the PCR reaction.

Agarose Gel of Canine Fecal gDNA

Agarose Gel Electrophoresis was used to determine if gDNA was isolated. In order to view the isolated gDNA, The Lonza FlashGel Electrophoresis system was used, and its protocol was followed.  A small portion of the gDNA solution was placed in an Eppendorf tube along water and a sample buffer that allowed the DNA to be seen under UV light. The solution was mixed and placed into a corresponding well in the gel apparatus. Current was run through the gel which contained conductive fluid. This caused the gDNA to travel through the charged gel towards the positive end, therefore separating the gDNA.

PCR Amplification of 16S rDNA gene

A Polymerase Chain Reaction (PCR) was used to amplify the 16s rDNA gene in order to determine the presence of bacteria in the canine fecal gDNA sample. The double stranded helixes of DNA were separated in the denaturation phase using a water bath at a temperature of 96^◦C. The pre-selected primers were then bound to the target DNA in the upstream direction during the annealing phase at a temperature of 54^◦C. During the Replication phase at a temperature of 72^◦C, taq-polymerase filled the remaining gaps with nucleotides creating duplicated double strand of DNA. The PCR process caused specific variable regions on the 16s rDNA gene to be amplified which allowed them to be viewed in an agarose gel.

Agarose Gel of PCR using Canine Fecal gDNA

Agarose Gel Electrophoresis was used to determine if the 16s rDNA gene in the canine fecal sample was isolated. In order to view the isolated gene, The Lonza FlashGel Electrophoresis system was used, and its protocol was followed.  A small portion of the PCR product solution was placed in an Eppendorf tube along water and a sample buffer that allowed the DNA to be seen seen under UV light. The solution was mixed and placed into a corresponding well in the gel apparatus. Current was run through the gel which contained conductive fluid. This caused the gDNA to travel through the charged gel towards the positive end, therefore separating the gDNA.

Canine Fecal Matter gDNA Sample Purification

Canine Fecal Matter gDNA sample was purified using the DNA Clean & Concentrator™-5 Kit. The DNA Clean & Concentrator™-5 Kit procedure was followed in order to purify the DNA by removing reactants leftover from the PCR procedure. Without this step, leftover reactants would have interfered with DNA sequencing.

Canine Fecal Matter gDNA Sample Pooling

Canine Fecal Matter gDNA sample was pooled into one tube to be sent to Anschutz Medical Campus for sequencing using the Illumina Next-Generation Sequencing Technology. Each gDNA sample is unique which allowed it to be combined instead of sequencing each individual sample, therefore lowering the overall cost of the study.

Sequencing of Canine Fecal Matter gDNA Sample

The fecal matter sample was sequenced at the Anschutz Medical Campus and returned to the lab for further analysis. The Illumina Next-Generation Sequencing used clonal amplification and sequencing by synthesis, meaning the DNA sequences were identified and placed into a nucleic acid chain. Each DNA had its own unique fluorescent signal that allows it to be identified in the nucleic acid chain. These signals assisted in determining the type and order of the DNA sequence.

Analysis of Sequenced Canine Fecal Matter gDNA Sample

Sequenced DNA was returned to the lab and analyzed to determine correlation between canine gut microflora and canine health.

Results

Presence of gDNA in the canine fecal matter sample was confirmed visually by the presence of banding on the gel in the apparatus. The presence of bands indicated that gDNA was present in the sample and the number of bands present and their corresponding base pair length was recorded from the gel.

Figure 1.0 – Agarose Gel of PCR products demonstrating that PCR was successful, and the 16s rDNA gene was amplified.

A successful PCR was observed by the presence of banding on the gel in the apparatus. The presence of bands indicated that the target gene (16s rDNA) was successfully amplified and the number of bands and their corresponding base pair length was recorded from the gel. The PCR reaction was expected to work as there was gDNA in the sample that was able to be amplified through PCR.

Figure 2.0 – Graph demonstrating the samples present in the study that passed the initial sampling depth threshold of zero.

Sequencing was successful due to the indicated number of reads for the sample as seen on the graph. The experiment was “successful” overall as there was 479 samples present in the experiment meaning a good proportion of samples passed the initial sampling depth threshold value of zero. When filtered by sampling depth threshold, there were 335 samples that passes the first sampling depth threshold of 1000, 258 samples that passed the second sampling depth threshold of 3000, and 194 samples that passes the third sampling depth threshold of 5000.

These values demonstrate moderate results as a decent number of samples passed the first threshold, however a significantly decreased number of samples passed the second and third depth threshold criteria.

The PCR sequencing produced 21791 quality reads for the F19_67 sample that were filtered from the overall number of reads recorded by the Illumina Sequencing System. The overall experiment produced 846 different phylogenetic sequences, and a blast of these sequences produced several different types of bacteria that are commonly found in the GIT.

Three examples of bacteria found in the samples include: the Prevotellaceae family which is commonly found in the human gastrointestinal tract and can be cultured from the rumen and hind gut of sheep and cattle, the Faecalibacterium genus which is a commensal bacteria that is commonly found in the gut microbiota and is responsible for the production of butyrate through the process of fermentation, and the Lachnospiraceae family which is commonly found in the rumen and gut microbiota, and is responsible for the fermentation of plant polysaccharides.

The presence of these bacteria prove that they originated from a sample taken from the gastrointestinal tract and therefore be assumes that the sequences generated are consistent with gut bacteria. In the study, sample replications were included to analyze replication consistency and errors between samples. Replications showed that there was a systematic error in the procedure that caused variation between replicant samples. The variation between samples causes skepticism in the data due to the significance of the variation between the sample. 

For the F19_67 fecal sample, the most abundant species was found in the Fusobacteriaceae family of unknown genus and species with 5256 reads, making up 24.1% of the sample. The second most abundant species was found in the Bacteroides genus of unknown species with 1726 reads making up 7.9% of the sample. The third most abundant species was the Clostridium hiranonis with 1234 reads, making up 5.7% of the sample.

Figure 3.0 – Bray Curtis PCoA plot comparing dissimilarity between gut microbial communities against water source type where blue plots represent canines who drank from treated well water samples, red plots represent canines who drank from municipal water samples, and orange plots represent canines who drank from untreated well water samples.

Figure 4.0 – Shannon Index Box Plot comparing richness and evenness of gut microbial communities against water source type.

When the samples were compared to water source type, there was no visible trend in the data and therefore no correlation between the variable and the results. The Bray Curtis PCoA plot (Figure 3.0) compared dissimilarity between samples, and the lack of clustered data points in the figure shows that there were no sample trends seen when organized by water source. The Shannon Index Box Plot (Figure 4.0) compared both richness of bacterium and representation and the overlap of lower and upper quartiles, and the virtually unchanged mean line further demonstrates that no trends were found when the samples were organized by water source.

Discussion

The lack of trends represented in the data show that there is no correlation between water type the diversity and richness of gut microbial communities. The randomly dispersed data points found in the Bray Curtis Dissimilarity graph (Figure 3.0), and the unchanged median values on the Shannon Index Graph (Figure 4.0) demonstrate the lack of trends in the data.

This could be caused by the small sample size of approximately 450 canines in the study which provides a miniscule data pool to analyze trending patterns. Sources of error could include lack of gDNA presence in the sample or lack of PCR product which would prevent samples from being synthesized and would not allow for trend analysis after synthesis. In the future, a larger sample pool with more data to analyze may produce a more distinguishable trend in the data.

Literature Cited

Gootenberg D, Turnbaugh P. 2011. Humanized animal models of the microbiome. Paper presented at: COMPANION ANIMALS SYMPOSIUM; College Station, TX.

Kil D, Swanson K. 2011. Role of microbes in canine and feline health. Paper presented at: COMPANION ANIMALS SYMPOSIUM; Champaign, IL.

Scott K, Gratz S, Sheridan P, Flint H, Duncan S. 2013. The influence of diet on the gut microbial community. Pharm Research [Internet]. [cited 19 Nov 2019]; 69(1):52-60. Available from: https://www.sciencedirect.com/science/article/pii/S1043661812002071

Suchodolski J. 2011. Microbial and gastrointestinal health of dogs and cats. Paper presented at: COMPANION ANIMALS SYMPOSIUM; College Station, TX.