Summary:
For this study, metagenomics was utilized to assess the genetic material of a group of canine fecal specimen.
To begin the metagenomics project, fecal material from 184 unique species of Labrador retrievers was obtained.
The total fecal microbial complement was determined by isolation, amplification of the 16S rDNA, and sequencing.
The entire project lasted 8 weeks. Weeks 5-7 were practice for isolation and amplification of soil samples. Week 7 involved the extraction of gDNA from the canine fecal samples. For week 8, PCR reactions were set up to amplify the 16S rDNA gene sequence from the isolated fecal gDNA. Finally, the amplified genomic DNA samples were run on agarose gel electrophoresis. Samples were sent to Anschutz Medical Campus to be sequenced.
The documentation of the data was provided on an online based platform known as QIIME.
Analysis on the findings was based on the success of the sequenced fecal samples, phylogenetic diversity, abundance of species, and correlations between gut microbiota and existing health conditions.
Abstract:
Gut microbiota is diverse as well as complex and plays a substantial role in the body of its host. This study was designed to test the microbial community structure of the GI tract of those canines that were treated with filtered and unfiltered well water and its effects on health. Bacterial species of the gastrointestinal tract was analyzed by using a combination of molecular techniques. Microbial community composition of the GI tract was assessed by extracting genomic DNA from canine fecal matter. The extracted gDNA was then run onto a gel made of a substance called agarose and the 16S rDNA was later amplified. The DNA obtained provided results in bacterial composition, diversity of taxa, and the types of microbial species. Microbiome profiles revealed no reported links between the composition of gut microbiota and the influence of water quality in canine fecal samples. Further research is needed to understand how water type impacts dispersal of microbiota in the gastrointestinal tract of canines and the effects on health.
Introduction:
Complete genome sequencing provides a comprehensive and detailed overview relative to an organisms composition such as structure and function (Bello et al. 2012). Much research has been conducted relative to the types of microorganisms present in the gastrointestinal tract and the overall effects on the hosts health and behavior. Microorganisms inhabiting the gastrointestinal tract that of a dog plays an integral role that facilitates in nutriment, immune defense, and susceptibility to local and systemic disease. The ever-diverse microbial community with its high population density, form a myriad of networks inside a closely integrated ecologic unit in organ systems.
Organisms across all spectrums are exposed to various sorts of bacteria that can influence the genetic and metabolic diversity situated in the gut (Caporaso et al. 2014). Research suggests that ecological factors and host activities are important indicators of gut health immunity (Kirchoff et al. 2019). In this case, water content plays an important part in the overall concentration and dispersal of microbiota. Fecal pollutants vary depending on water filtration measures. Ingestion of water contaminated with several impurities may lead to a distressed system and cause various waterborne linked diseases. Pathogenic strains can challenge the immune system by colonizing the space and disrupt cell functions. Enzymes produced by microbes can influence the rate of reactions required for the breakdown of complex substrates (Bird and Conlon 2015). However, distinctions between health associated microbiota and harmful pathogenic microbiota are not always clear. Depending on the bacterial species, some may be induced with certain strains which promote health or cause disease (Browne et al. 2017).
Given these observations, I thereby hypothesized that water type and quality influences composition of the canine gut microbiome between different speciess communities. So far, no studies have explored a potential link between the gut microbiome and the effects of water quality that dogs are exposed to. Similar works point out a potential interaction between the dispersal of fecal bacteria in sea creatures and water type. For example, harmful bacteria found in aquatic environments such as metals, or toxins can disrupt several of the hosts health systems and cause proliferation of certain microbial communities (Hintz et al. 2005).
The objective of this exploratory analysis is to examine variability in the composition of bacterial communities between canines treated with unfiltered and filtered well water along the gastrointestinal tract and to measure the effects on health.
Materials and Methods:
Fecal Sampling
A total of 206 fecal samples were obtained from 184 pure bred Labrador retrievers along with complete medical and behavioral histories from the Morris Animal Foundation. Out of the 206 samples, 11 were repeated 3 times.
DNA Extraction
Total genomic DNA was extracted using the DNeasy Powersoil Kit made by a company called Qiagen per manufacturers instructions. To release bacterial DNA effectively from the fecal matter, chemical lysis was performed. Fecal samples were placed in PowerBead tubes already containing C1 solution and incubated at 60?C for 10 min, followed by a vigorous bead beating process with a vortex adapter, until thoroughly homogenized. The DNA in the tubes were then pelleted and transferred into a centrifuge and spun for a minute at 10,000 x g. The remaining solution (supernatant) was collected and pipetted into a clean 2 ml collection tube, whereby the pellet containing non soluble debris was discarded. The rest of the protocol was followed as recommended by the manufacturer. All samples were eluted in 50 ul of buffer and stored at -20 C until analyzed. The integrity of the nucleic acids was determined visually using the Lonza FlashGel ® System stained with agarose. The DNA concentration was measured using ultraviolet light illumination.
PCR and 16S rDNA Sequencing
PCR was performed from each sample to produce a fragment of the 16S rDNA gene and obtain profiles of the type and abundance of bacterial communities present in the canine fecal genomic DNA. Different pairs of barcoded primers were used to distinguish individual student samples. To analyze the PCR products, the amplified samples were run though gel electrophoresis stained with agarose using the Lonza system with modifications to input volume to prevent overloading the column. Those reactions that worked were combined into one pool of DNA. Subsequently, DNA was rid of its reagents and purified using the DNA Clean & Concentrator -5 (DCC-5) method by Zymo Research Corporation. Samples were then collected for each section and sent to Anschutz Medical Campus for Illumina Sequencing. Data was provided through an online platform named QIIME to determine whether associations between gut microbial community composition and type of water (unfiltered vs filtered) affected the dispersal of certain microbiota.
Results:
The yield of the extracted DNA from the Labrador retriever fecal sample failed to produce genomic DNA (gDNA). Upon visualizing the DNA onto an electric field and running it into a gel matrix, there seems to have not been any distinct fragments of nucleic acids appearing as bands at the top of the gel. As for my PCR products, the amplification of the 16S rDNA gene did not produce any results, because there was an inadequate separation of PCR products in my lane. Based on the results of the gDNA and PCR amplification, I would not have expected my sequencing to work because of factors such as, contaminants left over from the PCR reaction. On reviewing the data for my fecal sample, there were no sequencing reads generated. After pooling all the results from every section, I would conclude that the experiment worked. Many samples involving the isolation of genomic DNA and amplification of PCR products were successful, hence several reads were produced. The quality and detailed assessment of the data derived from the metagenomic analysis could only be as good as the procedures followed for the metagenomics experiment. Bacterial composition and profile in most samples exhibited successful product yield and little to no error during the experiment. As for the sampling threshold, 143,000 samples passed a sampling depth threshold of 1000, 402,000 samples passed a sampling depth threshold of 3000, and 635,000 samples passed a sampling depth threshold of 5000 (Fig 1).
B
A
Sampling depth of 1000
Sampling depth of 3000
Fig 1. Threshold depths of sequenced fecal samples according to GB sections
C
Sampling depth of 5000
To identify sequences of the collected samples, operational taxonomic units (OTUs) was examined. A total of 1263 phylogenetic sequences were obtained with the average length being 237 base pairs long. Bacterium clones were grouped together according to their sequence similarity percentile. Upon reviewing the 3 random sequences on the BLAST database, three different bacteria types were located to be in the gut microflora of mammals. The first one termed Lachnospiraceae was observed to be in the phylum class of Firmicutes. The second bacterial species is called Clostridium also under the phylum of Firmicutes. The last bacterial species was named Bacteroides eggerthii and labeled under the phylum known as Proteobacteria. The sequences generated were consistent with the samples of fecal matter based on the metadata provided.
There were some replicates that were included in this experiment. Replication would reduce the variability of several factors in experimental results and increase confidence in the data. Replications can lower some amount of error in the experiment. Applicable conclusions can be drawn by several trials. With the gut microbiota of the fecal samples, I would expect to see similar but slightly different results based on the consistency of the experiment and sources of variability. A further investigation of the specific replicant data would represent legitimate outcomes of the study and increase the accuracy of the estimate.
Since my sample didnt generate any reads, I chose a different sample to report the top most abundant species by percent. The first species was Prevotella copri, having the most abundance at 34.85%. The second species was Eubacterium biforme, having a relative abundance of 5.71%. The last species was Catenibacterium, having a relative abundance of 5.6%.
Advanced Analysis:
The bacterial composition and profile of dog fecal matter to the type of water (untreated/treated well/municipality) was evaluated. Upon reviewing the principal coordinate plot, there were clusters of various dots pertaining to that specific sample. The blue dots indicated treated well water, the red indicated municipal water, and the orange dot indicated untreated well water. The direction with most variation was toward principal component 1 followed by PC 2 and then PC 3. The most abundant variable displayed was those samples treated with filtered well water. There was one sample that showed unfiltered well water and a few others that were tended with municipal water. The species assemblage in water poor sites versus in water rich sites did not show much clustering between the two. The samples that clustered together were of the same variable. There were no compositional differences between either of the samples. Therefore, no correlation was drawn between the treatment type of water and the differences within microbial communities in the gastrointestinal tract. Figure 2 displays two different angles of the principal component plot of sample dispersion.
B
A
Fig 2. PCA biplot of water treatment levels (unfiltered, filtered, and municipal) in fecal samples
Discussion:
In this study, we analyzed the bacterial composition in the gastrointestinal tract of a population of Labrador retriever dogs treated with three different water types using a non-cultured based approach known as metagenomics (Caporaso et al. 2014). The emphasis was placed on the composition of microbial communities present in each fecal sample and its association with health. The quality of the DNA was derived from various fecal specimen using different molecular methods and the following data was extrapolated.
In this study, water type was not found to be a potential factor in overall function and dispersal of microbes in the gut of canines. Differences in the microbial communities at different taxonomic levels was observed. The two dominant phyla spotted was Firmicutes and Proteobacteria. Many of these individual subjects displayedbetween sample variability in its relative community composition across all the taxonomic spectra. In particular, the phylotype level was sample-specific, thereby revealing the integrity of the bacterial lineages within several individuals. Such variability was also present in the water tested samples of canines. PCA plots showed the assemblage of fecal metabolome. There was little to no specific trends in water sampling when the fecal specimen was categorized to the treatment type. Moreover, in the PCA plot, there were no pronounced differences in beta diversity. The PCA plot displayed closeness between the samples from their estimated compositions. With this observation, there was no similarity between any of the subjects. Another observation was that the well water treated samples was closest to that of its own group. These results confirm that the gut microbiota is quite elaborate, and its complexity harbors the richness and diversity of many bacterial species, much of which have yet to be identified. Hence, the results of this study suggest that the bacterial composition in fecal samples is not representative of the microbial communities situated in the GI tract.
Further research is needed to assess the composition of gut microbiota of canine species in water treatment types and its overall impact in shaping the GI tract. From a clinical point of view, it is important to obtain accurate information about various bacterial species in order to develop effective reagents to combat health implications. Moreover, as advancements in sequencing technologies continue to expand, discovery of complex bacterial habitats can be investigated as well as characterization of various microorganisms can allow for a broader insight in future metagenomic studies.
Cited References
Bello MGD, Contreras M, Manary MJ, Rey FE, Trehan I, Yatsunenko T. 2012. Human gut microbiome viewed across age and geography. Nature. 486(7402): 222.
Bird AR, Conlon MA. 2015. The impact of diet and lifestyle on gut microbiota and human health. PMC. 7(1): 17-44.
Browne HP, Forster SC , Lawley TD, Neville BA. 2017. Transmission of the gut microbiota: spreading of health. PMC. 15(9): 531-543.
Caporaso JG, Goodrich JK, Knight R, Koren O, Ley RE, Poole AC. Rienzi SCD, Walters WA. 2014. Conducting a microbiome study. Cell. 158(2): 250 262.
Cookson WOCM, Cox MJ, Moffatt MF. 2013. Sequencing the human microbiome in health and disease. Hum Mol Genet. 22(R1): R88 R94.
Hintz RJ, Lambertsen RH, Lancaster WC, Rasmussen KJ. 2005. Functional morphology of the mouth of the bowhead whale and its implications for conservation. JSTOR. 86(2): 342-352.
Kirchoff NS, Udell MAR, Sharpton TJ. 2019. The gut microbiome correlates with conspecific aggression in a small population of rescued dogs (Canis familiaris). PeerJ. 6: e6103.
Pinto AJ, Xi C, Raskin L. 2012. Bacterial community structure in the drinking water microbiome is governed by filtration processes. Environ Sci Technol. 46(16): 8851 -8859.
Get Help With Your Essay
If you need assistance with writing your essay, our professional essay writing service is here to help!
Find out more
