1. INTRODUCTION
A consistent relationship (symbiosis) between the human body and its natural microbiota starts at delivery. The sustainability of overall health and well-being depends heavily on this relationship, which can be communalistic, mutualistic, or pathogenic (Ogunrinola et al., 2020). The species that make up the microbiota have developed considerably, and they actively respond to their habitats, such as the skin, the mucosa, the gastrointestinal tract, the respiratory tract, the urogenital tract, and the mammary gland within the human body (Whiteside et al., 2015). The human gut alone contains more than 100 trillion bacteria that are affected by various factors, including delivery method, baby feeding practices, lifestyle, medications, food, age, and the host's genetic makeup (Wang et al., 2017). These microbes play significant roles in metabolism, immunity development, and defence against pathogens, all of which have a direct or indirect impact on many human physiological processes (Robinson et al., 2010; Covasa et al., 2019). The training of the host’s immunity system, food digestion, control of gut; endocrine and neurological signals, modification of medication action and metabolism, elimination of toxins and production of various chemicals (Fan and Pedersen 2020) such as bile acids, lipids, amino acids, vitamins, and short-chain fatty acids that have an impact on the host are all key functions of the gut microbiome (Brestoff and Artis 2013; Kho et al., 2018). Changes in the composition and function of intestinal microorganisms, known as dysbiosis, are linked to several diseases, including neurologic, respiratory, metabolic, hepatic, and cardiovascular conditions, as well as more localized gastroenterological ailments (Fan and Pedersen 2020). It is well documented that the gut and respiratory tract have been connected to modulating immune responses at the time of disease development in the respiratory tract, which in some cases progresses to secondary bacterial infections (Fanos et al., 2020; Hanada et al., 2018; Yildiz et al., 2018).
It has been shown that 20% of the respiratory syndrome coronavirus 2 cases had gastrointestinal (GI) symptoms, such as diarrhoea, vomiting or abdominal pain (Huang et al., 2019; Chen et al., 2020; Liang et al., 2020; Cheung et al., 2020) and the presence of SARS-CoV-2 virus in the stools and anal specimens of nearly 50% of COVID-19 cases suggests that the gastrointestinal tract could be an extra-pulmonary site for viral activity and replication (Wölfel et al., 2020).
Recent studies on Chinese COVID-19 patients have found a state of dysbiotic microbiota, decreasing the number of favourable commensals, particularly those that generate short-chain fatty acids (SCFAs), such as those from the Lachnospiraceae and Ruminococcaceae families, and increasing the number of opportunistic pathogens or pathobionts (Gu et al., 2020; Zuo et al., 2020; Chen et al., 2021; Zuo et al., 2021).
Understanding the host microbial perturbations that SARS-CoV-2 causes is urgently needed might be due to its ability to alter the body's response to infection and the effectiveness of different immunological therapies like vaccinations. Consequently, in this study, we aimed to examine how the gut flora of COVID-19 changed over time during the infection and after clearance, using classical and molecular methods.
2. MATERIAL AND METHODS
2.1 Sample collection
Faecal samples were collected from ten COVID-19 patients who were positively confirmed by a local hospital (Central Laboratory of Koya and Shahid Dr Hemn Teaching Hospital) using RT-qPCR. Stool samples were also collected from five healthy persons as a control group, which did not receive any antibiotics during the time of the study (three months). From the COVID-19 patients, faecal samples were collected at three different time points: at the time of infection; after 7 days following the infection; and 50 days after the patient had recovered. The samples were processed within 4 hours in the laboratory for culturing. Individuals of the control group and patients after clearance were subjected to a serological test to measure any trace of the previous infection with SARS-CoV-2 by calculating IgG and IgM levels using a mini-VIDAS device at the REGA specialist laboratory in Sulaymaniyah. Then the stool samples were collected from the control group individuals, who are relatives of the patients once without defining the time frame.
2.2 Classical identification
2.2.1 Isolation: To prepare a pure culture from the samples, a loopful of stool was homogenized in 1ml of sterilized nutrient broth by vertex, and then 50 ml was spread on: Nutrient agar, MacConkey agar, Eosin methylene blue agar, and Mannitol salt agar. All the inoculated plates were incubated overnight at 37°C (Murray et al., 2006). Single different colonies with different characteristics and morphology in each plate set were subjected to a pure culture technique and then stored at -80°C in 25% glycerol for further investigations.
2.2.2 Staining and biochemical tests: The isolated colonies were stained with standard Gram stain and examined under a compound microscope at 100X. The following biochemical tests were performed for the isolate's phenotypic identification: indole, methyl red, Voges Proskauer and citrate utilization (IMVC), triple sugar iron agar (TSI agar), urease, oxidase, catalase, and motility (Atlas et al., 1995, Smith and Hussey 2005 and Cappuccino and Welsh, 2019).
2.3. Molecular identification
2.3.1 Broth culture preparation: To extract genomic DNA from a representative of each bacterial group, a loopful of certain bacteria was inoculated in a 15 ml falcon tube containing 5 ml of nutrient broth, incubated with shaking at 150 rpm for 24 hrs at 37oC.
2.3.2 Genomic DNA extraction: DNA was extracted from nineteen different bacterial samples that were representing their either genus group or stand-alone genus using FavorPrep genomic DNA mini kit (Favorgen) applying the guidelines provided by the manufacturer. Nanodrop (Thermo Scientific NanoDrop 2000. SN. 6113) was used to check the quantity and purity of the extracted DNA, which was subsequently kept at -20°C. The genomic DNA was subjected to further analysis by running 60 ng on 1 % agarose gel for 60 min at 80 V.
2.3.3 Amplification of 16S rDNA by standard PCR: A ~1515 bp of 16S rDNA was amplified using a PCR approach with a final volume of 30 μl reaction including 15 μl of 2X Add Taq Master (Addbio), 5 pmol (1 μl) of each forward (P1F-TGAAGAGTTTGATCATGGCTCAG) and reverse (P1R-TTCCCCTACGGTTACCTTGT) primers, and 20ng (1 μl) genomic DNA. The volume was completed by adding 12μl of nuclease-free water (Figure 1).
Figure 1: Primer’s binding sites on 16S rDNA. P1F and P1R primes provide a ~1515bp PCR amplicon, while P2F and P2R provide a ~265bp amplicon including the V4 region. The figure was generated using data from (Chakravorty et al. 2007)
The PCR was carried out using a BIO-RAD and Corbett thermal cycler and was configured as follows for M1, M2, M4, M6, M7, M8, M9, M10, M11, M12, M13, M14, M15, M16, M17, and M18 samples: Initial denaturation at 95°C for 5 minutes, then 27 cycles of 30 seconds at 95°C for denaturing, 25 seconds for annealing at 58°C, 60 seconds for the extension at 72°C, and 5 minutes for the final extension at 72°C.
However, samples M3, M5, and M19 were amplified with different cycling conditions, which were 33 cycles at 95°C for 40 seconds as the denaturation step, 59.1°C for 40 seconds as the annealing step and 60 seconds at 72°C as the final extension with same initiation denaturation and final extension conditions as mentioned above.
2.3.4 16S rDNA amplicon integrity: To investigate the existence of the V4 region, a total of 30ml PCR reactions was set up containing 1 ml of the PCR amplicons of the 16S rDNA (Section 2.3.3), 1 ml (5 pmol) P2F primer (GTAATACGGAGGGTGCAAGC), 1 ml (5 pmol) P2R primer (TCTAATCCTGTTTGCTCCCCA), 15 μl of 2X Add Taq Master (Addbio), then the volume was completed by adding 12 μl of nuclease-free water (Figure 1). The resulting PCR amplicon must be ~ 263 bp. The PCR was carried out using the BIO-RAD and Corbett thermal cycler and was conducted as follows: Initial denaturation at 95°C for 5 minutes, then 30 cycles of 35 seconds at 95°C for denaturing, 30 seconds for annealing at 58°C, 50 seconds for the extension at 72°C, and 5 minutes for the final extension at 72°C.
2.3.5 Agarose gel electrophoresis: To check the availability of the right PCR amplicons, 2 μl of PCR amplicons were electrophoresed on a 1% agarose gel with 0.07% EtBr along with a 100 bp DNA ladder (Genedrix) and run in 1X TBE buffer at 80 V for 60 minutes to validate that the targeted gene was amplified correctly. Following the run, visualization and photographing of the DNA molecules were done using UV Gel Imager SynGene 1409.
2.3.6 Partial 16S rDNA sequencing: The resulting ~ 1515 bp PCR amplicon was sent out to Macrogen Inc, a South Korean company for sequencing using P2R to include the V4 region.
2.3.7 Quality of the sequenced products: The DNA baser assembler program was used to perform sequence quality, analysis, and editing. The start and end of the sequence were trimmed to assess the quality of the sequence.
2.4 Bacterial identification
To classify the bacterial isolates independently, the EzBioCloud was used to compare the 16S rDNA sequence to previously discovered bacterial DNA sequences (Yoon et al., 2017).
3. RESULTS
Ten verified COVID-19 patients aged (26-52) and five healthy controls aged (25-45) were recruited to be followed up on their most common bacterial alteration. All patients were diagnosed with moderate COVID-19 accompanied by cough and shortness of breath, although only one had a gastrointestinal (GI) symptom of diarrhoea (Patient number 5). None of the patients was suffered from chronic diseases, vaccinated against SARS-CoV-2, and experienced GI problems (Table S1). Triple stool samples were taken from each patient at three different timelines. The first, second and third stool collections were named baseline, illness period and endline, respectively. The patient’s recovery from COVID-19 and the healthy control's history of COVID-19 infection was confirmed and investigated by RT-qPCR and serologically. All the COVID-19 surviving individuals displayed a negative attitude toward the IgM test, which is a sign of full recovery from the infection and a positive attitude against IgG, which is an indication of past infection. On the contrary, the healthy controls serological tests were negative against IgM and IgG (Table S2).
3.1 Standard identification of the bacterial isolates
Eighty-nine different bacterial isolates were isolated from the stool samples including the controls. They were grouped into 6 groups as follows: E. coli (47 isolates from the patients and 8 isolates from the controls), Klebsiella spp. (8 isolates from the patients and 0 isolates from the controls), Shigella spp. (3 isolates from the patients and 0 isolates from controls), Citrobacter spp (only 2 isolates from patients and 0 from controls), Enterobacter spp. (1 isolate from patients and 1 isolate from controls), and Staphylococcus aureus (14 isolates from patients and 2 isolates from controls). However, we could not identify 3 bacterial isolates based on their cultural characteristics and biochemical tests (Tables S3 and S4).
3.2 Bacterial Identification at the molecular level
To assess the extracted DNA from the bacterial samples, gel electrophoresis was employed and Nanodrop was used. No degraded trace was observed in all the genomic DNA samples (Figure 2) which were supported by the Nano-drop results with average purities (A260/A280) of 1.87 (Table S5).
Figure 2: Agarose gel electrophoresis analysis of genomic DNA extracted from the bacterial genera.
The DNA fragments were investigated with 1 % agarose gel. M; 1Kb DNA marker from Genedirex, C-; negative controlcontrol, which is distilled water, C+; positive control in which the genomic DNA extracted from E. coli strain ATCC 25218; lanes (1-19) represent the bacterial sample number in which the genomic DNA was extracted from. The wells contain high molecular weight and genomic DNA with yields of on average 85 µg and average purities of 1.87 at (A260/A280). No degradation was observed in all samples. The negative control result verified that the DNA was pure.
The predicted size of the DNA fragment (~1515 bp) was effectively amplified from bacterial isolate template DNA, and no PCR products were observed in the negative controls (Figure 3). To ensure the amplified PCR products contain the V4 region, P2F and P2R primers were used to target downstream and upstream of the V4 region, respectively. The predicted PCR products of ~ 263 bp were generated for all the samples (Figure 4). To determine the species of each bacterial isolate, the 16S rRNA gene PCR amplicons that have been produced from each isolate using P1F and P1R primers were sequenced using the reverse primer P2R.
Figure 3 Partial amplification of 16S rDNA using P1F and P1R primers. Lanes M, C- and C+ represent a 100bp DNA marker (Genedirex), a negative control that has been run without a DNA template and a positive control that has been run using DNA from E. coli strain ATCC 25218, respectively. Lanes 1 through 19 shows ~1515bp of PCR amplicons generated using a DNA template from M1 through M19, respectively.
Figure 4 16S rDNA verification using P2F and P2R primers. Lanes M, C- and C+ represent a 100bp DNA marker (Genedirex), a negative control that has been run without a DNA template and a positive control that has been run using DNA from E. coli strain ATCC 25218, respectively. Lanes 1 through 19 shows ~263bp of PCR amplicons generated using a PCR template (from section 2.3.3) from M1 through M19, respectively.
3.3 Sequencing and DNA quality
Depending on whether the DNA sequencing was reliable, the 19 investigated samples were considered for further investigation on their high-quality values (QV), which were above 40.
3.4 Bacterial genera
To specify the taxonomic origin of the nineteen 16S rDNA sequences, independent computerized alignments were performed versus accessible prokaryotic sequences of 16S rDNA employing EzBioCloud (Yoon et al., 2017). All nineteen individual sequences were found to be identical to previously cultivated bacteria (Table 1).
Table 1 Identification of selected pure bacterial colonies using classical and molecular approaches.
|
Sample No |
Expectation |
Molecular identification |
Accession Number |
|
M1 |
Shigella spp |
Shigella dysenteriae |
OP808014 |
|
M2 |
Citrobacter spp |
Citrobacter portucalensis |
OP808015 |
|
M3 |
?? |
Comamonas kerstersii |
OP808016 |
|
M4 |
Shigella spp. |
Shigella dysenteriae |
OP808017 |
|
M5 |
?? |
Comamonas kerstersii |
OP808018 |
|
M6 |
?? |
Shigella flexneri |
OP808019 |
|
M7 |
Enterobacter |
Enterobacter cloacae |
OP808020 |
|
M8 |
Shigella spp. |
Shigella dysenteriae |
OP808021 |
|
M9 |
E. coli |
Escherichia fergusonii |
OP808022 |
|
M10 |
Klebsiella spp. |
Klebsiella aerogenes |
OP808023 |
|
M11 |
Klebsiella spp |
Klebsiella pneumoniae |
OP808024 |
|
M12 |
E. coli |
Escherichia fergusonii |
OP808025 |
|
M13 |
E. coli |
Escherichia fergusonii |
OP808026 |
|
M14 |
Enterobacter |
Enterobacter cloacae |
OP808027 |
|
M15 |
Shigella spp. |
Shigella sonnei |
OP808028 |
|
M16 |
E. coli |
Escherichia fergusonii |
OP808029 |
|
M17 |
E. coli |
E. coli |
OP808030 |
|
M18 |
E. coli |
Escherichia fergusonii |
OP808031 |
|
M19 |
S. aureus |
S. aureus |
OP808032 |
Table 1 shows the results of traditional identifications against the molecular method. Even though certain species could not be identified by the classical identification chosen for this study, some classical identifications were successfully comparable to molecular identifications.
Following biochemical tests and molecular confirmation, eleven distinct bacterial species that belong to seven different genera of bacteria were identified; the percentage of bacterium species out of 89 were as follows; 47% Escherichia coli (42 isolates), 18% Staphylococcus aureus (16 isolates), 12% Escherichia fergusonii (11 isolates), 6% Klebsiella pneumoniae (5 isolates), 4% Shigella dysenteriae (4 isolates), 3% Klebsiella aerogenes (3 isolates), 2% Enterobacter cloacae (2 isolates), 2% Citrobacter portucalensis (2 isolates), 2% Comamonas kerstersii (2 isolates), 1% Shigella flexneri (1 isolate), and 1% Shigella Sonni (1 isolate) (Figure 6).
Figure 6 Bacterial species that have been isolated from the patients.
3.5 The faecal bacterial profile associated with COVID-19 infection
To acquire more about the change in the gut bacterial profile in COVID-19 patients, we compared the gut bacteria of faecal samples with a characteristic of COVID-19 infection to those from the control group. The gut bacterial composition of faeces samples from the COVID-19 patients and the five healthy controls was analyzed after faecal culture, conventional identification, and 16S rDNA sequencing and identification. We found that the patients' faeces included the following bacterial species that weren't present in the control group: Escherichia fergusonii, Citrobacter portucalensis, Comamonas kerstersii, Shigella flexneri, Klebsiella pneumoniae, and Klebsiella aerogenes. Among these species, Klebsiella pneumoniae and Klebsiella aerogenes have been linked to bacterial infections that affect the respiratory tract. Compared to the control group, Staphylococcus aureus colonized all of the patients' faeces in large numbers. Thus, these results indicate that COVID-19 affects the bacterial population in the gut.
4. DISCUSSION
The classical and molecular identification were in agreement in determining most bacterial genera, except that the latter identified all the samples down to the species level (Rhoads et al., 2012). In addition, the conventional approach could not identify samples M3, M5, and M6 based on the biochemical tests. The above results suggest that the molecular approach for bacterial identification has an advantage over the biochemical methods. In this work, we sought to determine if alteration of gut bacterial population was linked to infection with COVID-19 throughout the infection and curing period in comparison with some control and after curing. S. aureus was shown to be significantly linked with SARS-CoV-2 faecal samples in most of the patients after seven days of the infection. However, it has been lost in 75% of the patients after recovery from the viral infection. Six bacterial species including Escherichia fergusonii, Klebsiella pneumoniae, Klebsiella aerogenes, Citrobacter portucalensis, Comamonas kerstersii, and Shigella flexneri revealed a considerable increase in the COVID-19 survived patients compared to the healthy group. The presence of Klebsiella spp. in the COVID-19 gut supports the transit or transfer of extra-intestinal microorganisms into the gut. The above result is supported by (Yildiz et al., 2018) and (Groves et al., 2018) studies, which demonstrated that the population of the gut microbiota may be altered by pulmonary viral infections like influenza and respiratory syncytial virus. Considering the baseline abundance of opportunistic pathogens, gut microbiota may have a significant impact on how severe COVID-19 is and the existence of the virus in the gut of the host. Lack of determination of beneficial bacterial species in COVID-19 survived individuals even after clearance, indicating that SARS-CoV-2 viral infection may be linked with a greater long-term harmful impact on the gut microbiome (Liu et al., 2022). All these results point to the possibility that the composition of a patient's most common bacteria may have an impact on how they react to and are susceptible to SARS-CoV-2 infection. The small sample size of this pilot study is a significant drawback. Although larger validation studies are necessary before establishing a correlation link between COVID-19 and gut microbiota, this pilot study provides the first information on the impact of SARS-CoV2 infection on the composition and dynamics of the most common gut bacteria in the Kurdistan region. So, to clarify the significance of microbiome alterations in SARS-CoV-2 infection and survived individuals, additional studies should be conducted with larger size of samples and analysing gut bacterial 16S rRNA genes using a metagenomic approach.
5. CONCLUSION
Our data indicate that COVID-19 patients would experience an alteration in their gut microbiome, which may in turn play a significant effect on how severe the infection is. Therefore, concentrating on the best technique to restore the gut microbiome balance to its healthy state may help COVID-19 patients recover more quickly and effectively. The Faculty of Science and Health's research ethics committee at Koya University approved the research proposal on December 20, 2021.
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