Journal of Veterinary Science and Animal Husbandry

ISSN: 2348-9790

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Biomarkers of Microbiota Infection as Causative Agent of Acute Diarrhea in Dogs

Copyright: © 2022 Hassenin AH. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Dysbiosis is microbial imbalance and mostly common in gastrointestinal tract [1]. There is a significant different of microbial communities in diarrheic cases more than healthy dogs’ comparison of gender & clinical signs. Clostridium species is mostly commonly genus found infectious animal cases of diarrhea and moreover in dog in other hand unclassified genus of Ruminococcaceae Bacteroidetes and Faecali bacterium were isolated. The microbiome functional gene content of (PICRUSt) with elevation gastric enzymes & increase titers infra structural proteins in acute diarrhea. Studies and data for dysbiosis with different intestinal disorders in dog associated with acute diarrhea or chronic is very limited.

Current study to evaluate microbial dysbiosis.  The fecal microbiome, characterized by 655 pyrosequencing of the different genes, AU/CG. There was lower range of bacterial isolates from cases of acute diarrhea compared to animal variation with statistical analysis. Altered microbial imbalance in gut occur with the microbial communities for gastric infection.

Keywords: Microbiota, Diarrhea, Dog, Gastritis, Dysbiosis

Knowledge of microbial dysbiosis with molecular studies of the digestive system in dogs, mice, and humans [2,3] give us chance about awareness for old record. Microbiota in the GIT had significant effect in stimulating the immune system of animal through change in biochemical characters of gut, increase defense mechanism against pathogens and increase supplemental digestion for animal (e.g., release DEFS) [4-9]. Complex interactions their excretion of ingest   between species and intestinal microbes was still unknown although recent studies in advances of sequencing technology used for detecting microbial communities. Pathophysiology of gastrointestinal diseases will give insights into clarifying   of host and bacterial metabolites increase explanation on acute diarrhea. The host-microbe interactions were increased enhancing details for our understanding current study.

Moreover, Further studies for more explanation about details of the host interaction and microbes may be done in the future. [7]. There was genetic variation of Intestinal microbiota have been reported in GI disease either acute or chronic [11-13]. There are old comparative studies between asymptomatic, IBD, and acute diarrheal cases for the fecal microbiome based on 16S rRNA gene sequences for finding phylogenetic expression with no information. Therefore, aim of available study was to explain microbial dysbiosis of dogs with acute diarrhea and diagnose microbiome changes using microbial sequencing of fecal samples using 16S rRNA sequencing, inferred metagenomics using PICRUSt [14].

Materials and Methods

Collecting feces from naturally clinically asymptomatic dogs and from clinical signs of acute diarrhea. were further categorized as cases with gastric illness and disorders (Table 1). Feces were kept in cold temperature directly after collection. Written consent by dog owners were   used in current study has been provided. The collection of feces the Rochester diagnostic lab & health care 2012-101. All dog used in current studies either clinically asymptomatic or with signs of diarrhea their samples not used for old studies. [16].

16 healthy pet dogs of control group used in current study (Table 1). All dogs from various home environments and feeding canned nutrition with history of non-taken antibiotics at least four weeks a month before sampling collection.
all dog cases came to Clinical Medical Center at Rochester Institution of known full recorded for symptoms with diarrhea either acute diarrhea and or hemorrhagic diarrhea or non-hemorrhagic   had no early signs of gastrointestinal infection or receiving old medication for any old clinical signs (Table 1). All cases should be readmitted for follow up of all their symptoms from start of clinical signs until gone.

Qiagen QIAamp DNA Fecal kit: Added 150 fecal samples from each animal separately diseases of gastric illness placed in clean refrigerated cup to be examined as quickly as possible. Cup containing glass beads to help separation different layer of fecal samples recognized by denaturation to be genetic acceptable for DNA isolation with adding different solution that increase density and capacity. For all samples put in centrifuge to be separated into different layers with adding specific buffer for antigen reaction.

454-Pyrosequencing: Primers and genetic AC, UG specific for each sampling can be identified and used for detection and separation RNA [25]. Denaturation, protein separation for genetic variation and aliquots mixed buffer [28, 29, 30]. All reaction and metabolic assay within same genetic sequences detected at different sequences.

Quantitative PCR (qPCR) Detection most common bacterial isolates commensals with nonpathogenic (i.e., Lactobacillus and Bifidobacterium) but Escherichia coli, and Clostridium perfringens were special bacterial isolates causing clinical path gnomic symptoms (i.e.,) qPCR is method used for quantifying DNA depend on PCR done instructed manually [22-24] and two primers design to matches sequences within templates/probe. There are two chemistry reaction PCR tracks target concentration one of them called coloring waves assay released from these with was5 wt length. The final mix contained 4 μl so Fast Eva Green super mix (Molecular Genetic, RC, USA), first reaction 110°C for 1min followed by 35 reeling   amplification at 100°C and annealing for 0.1min., denaturation, analysis 85°C 60sec, 65°C for 60sec, elevation average of temperature for sequencing and annealing stages at .33°C at 44 degree then run same cycling twice for fecal analysis.

Measurement of dry weight Detection weight of dry fecal samples, 50 gm from animal taken was put in clean refrigerated cup (Aldrich, Ltd, Canda). Using saline of each dry weighted samples and put in oven at 110°C (microbiological Oven, VWR) 12-24hrs.

Statistical Analysis: Sequencing depth of all samples taken randomly on standard 7,200 sequences per sample for quantification PCR analysis. There were huge microbial dysbiosis of digestive system for different cases of gastrointestinal disturbances in dogs’ cases of diarrhea or hemorrhagic.
Stastical data for all results using SAS computer analysis. Shapiro-Wilk used for analysis all data normally (JMP 10, SAS software Inc.). Due to analysis of data not in accordance with regular distribution, therefore) detect variation acute and chronic.

 LEfSeData: analysis with linear regression size (LEfSe) was matches primers & expression of DNA from diseases and healthy cases. Sequences &primers sets for > 2.3 express of DNA and their specific KEGG orthologs set to > 2.5. Results Sequencing analysis Sequencing expression release 425,21 quality primers for   21 feces results with significant value   p (8,013 ± 2,103). Figure 1 detect shape of results into regression coefficient, detection genetic variation to microbial isolates. for diagrammatic linear coefficient, can be detected.

Isolated Microbes: Unweighted Unifrac related to distances between asymptomatic and dogs with signs of AD (ANOSIM; p = 0.0040) showed in different PCoA plots. Therefore, animals with gastric disorders &infection great changes from normal cases (EFES r = 1 with all). On other hand, little or great estimation for dysbiosis for gastric disturbances and infections. Size data of LDA showed that increase size of LDA for genus Clostridium spp associated with AD, while other genus of microbial e. Clostridia, Ruminococcaceae, and Coprobacillaceae commonly found in normal cases (Figure 2). The coloration in was shown. The variation in microbial isolates for different communities shown Figure 2, Table 2. Faecalibacterium genus Sequences lower chance of detection in cases with diarrhea than asymptomatic cases (p = 0.023 ,0295, concurrently). Quantitative PCR the abundance of Clostridium perfringens was significantly increased in dogs with AD (log DNA median [range]: 7 [5.8-7.5]) compared to healthy dogs (log DNA median [range]: 4.6 [3-6.1]; p = 0.0088). Clostridium perfringens was also significantly increased in the subgroup dogs with AHD (log DNA median [range]: 7.1 [6.9-7.4]) compared to healthy dogs (p<0.0500). No significant difference in Bifidobacterium, Lactobacillus, or E. coli was identified between dogs with acute diarrhea and healthy dogs.  Functional genes Univariate statistics revealed no significant differences in the percentage of KEGG orthologs belonging to functional gene families at all levels (e.g., 1, 2, and 3) among all groups of dogs after correcting for multiple comparisons (Table 2). However, PICRUSt provided a snapshot of the distribution of genes across functional categories. At level 1, approximately 50% of genes belonged to metabolism, 19% belonged to genetic information processing, and 15% belonged to environmental information processing. Next, functional gene categories were analyzed.


Current study, detection the fecal microbiome and compare it between asymptomatic and diseased one. Examination outcomes with variation between diseased and asymptomatic. Steady information related to   old studies showed clinical signs of digestive infection related to studies for old research, DNA analytic sequences were significantly decreased [19, 35, 36]. plot analysis (PCoA) showed that there is great variation of bacterial isolates for asymptomatic and diseased. There were common incidence of Enterobacteriaceae in infected cases compared with old results [6,20] Other way, low incidence of unclassified genus within Ruminococcaceae, Bacteroidetes, Faecalibacterium in cases of AD were, and an. [25], genetic variation with increase protein expression leading improve barrier defense of colon [21-27]. low level incidence of unclassified genus within Ruminococcaceae, Faecalibacterium, there were putative metagenomic analysis of 16 S RNA gene profile [21]. In previous literature explained broad range of genetic functional related to genetic variation [32]. For instance, the variance of genetic information (15%), with environmental effects 10%and mechanism of cell variation (1.5%) were different between diseased and healthy dogs. This mentioned that the genetic variation for pathogenesis of E. coli [33]. It may be related to phylogenetic variation with same gene code to produce infection and pathogenicity of bacteria to show symptoms of [34, 35]. They can change from nonpathogenic gene expression to be pathogenic [37]. In contrast, there is what called horizontal transferee genes (HGT) related transposes of genetic expression between animal and human [38]. Variation of genetic expression related to signs of illness and diseases. However, there were broad range of intestinal microorganisms in healthy host showed increased inflammatory [39]. Mucosa of GIT express can recognize receptor of G- Protein showed that nonpathogenic isolates overcome by hyper globulin with T cell with phagocytosis [45, 46]. Feedback from current study. Number of animals were small   compared to healthy and diseases. Goal, information of results reviewed as need more approach for further examination of genetic effects. it is hard to get accurate evaluation different dogs living at different environment related to gene, age, sex, breed affecting microbial communities.  (Figure 1). obesity have significant effect in microbiome has been mentioned in human and mice studies [60, 61]. Our study showed that there is conflict of interest in Dog research of microbiome and no great variation from previous studies [62].


Bacterial dysbiosis depending on the results current study significantly in feces from dog digestive infection. Huge variation in bacterial isolates between asymptomatic cases and diseased.  Sequences with primers is great effect for isolated bacterial in healthy and diseased Recommendation for gastrointestinal infection with more genetic variation significant microbial dysbiosis of the host.

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Tables Start
Tables at a glance
Table 1
Table 2
Figures at a glance
Figure 1
Figure 2
Figure 1: Quantification of PCR and Amplification of canine fecal samples. The linear showed standard and error of mean deviation. Refractive curve of controled template
Figure 2: Bar coloring of different group of bacterial species associated with different diarrhea clinical signs

Clinical Signs

Old detection



OTU97 (mean ± SD)

Shannon Index (mean ± SD)

Chao1 (mean ± SD)


5.0, 1.0–12.0

20.4, 2.7–31.8


268.1a ± 52.8

386.1± 62.8

386.1a ± 80.6


1.0, 1.0–12.0

20.9, 7.3–31.6


200.3 ± 48.5

4.0b,c ± 0.2

291.2 ± 57.7


4.0, 1.0–10.0

16.9, 4.9–44.4


204.6 ± 35.8

3.9a,b,c ± 0.8

305.8 ± 109.7


3.0, 1.0–12.0

20.9, 4.9–44.4


201.3b ± 56.7

4.0c ± 0.6

303.6b ± 87.0








Table 1: QDistribution dog number and mean value of different clinical signs of acute & non-diarrhea related to sex, weight, age



Sever signs






















Flagellated bacteria




























Table 2: Bacterial isolates group from healthy, diarrheic and non-diarrheic Dog