Bacterial colonization of the respiratory tract in COVID-19 patients: potential source of bacterial infection

Abstract

Background

Secondary bacterial infections of the respiratory system are one of the biggest medical concerns in patients undergoing hospitalization with a diagnosis of COVID-19. Opportunistic upper respiratory tract bacterial colonization or carriage typically precedes the majority of lower respiratory tract infections and pneumonia. Few studies have examined the relationship between the SARS-CoV-2 virus and bacterial colonization in COVID-19 patients.

Aim

This study looked into the link between COVID-19 patients who have SARS-CoV-2 illness and respiratory system bacterial colonization and how this colonization could impact on COVID-19 clinical presentation.

Methods

This research thus examines this relationship by conducting an analytical, cross-sectional study with 210 COVID-19 patients. The patients are categorized into mild and moderate-to-severe cases, which will be compared with 50 non-COVID-19 controls. All participants provided sputum samples that were subjected to microbiological culture analysis. To identify specific microbes, a VITEK Compact automatic microbiology analyzer was employed.

Result

The findings of the research highlighted that, as the severity of COVID-19 infection increased, significant changes in bacterial colonization patterns emerged. A normal oral flora was found to be present in the control group, while a notable increase in pathogenic bacteria was evident in those with moderate-severe COVID-19. Furthermore, severe cases presented with Gram-positive bacteria, particularly Staphylococcus aureus. Additionally, Gram-negative bacteria (i.e., Klebsiella pneumoniae, Pseudomonas aeruginosa, and Haemophilus influenzae) were commonly identified in moderate-severe cases, indicating more extensive bacterial colonization.

Conclusion

The need to develop more comprehensive approaches to diagnosing COVID-19 and creating targeted treatment plans is highlighted through the increasing presence of Gram-negative bacteria and polymicrobial colonization in COVID-19 cases at admission. Thus, this research highlights the importance of monitoring bacterial colonization in the upper respiratory tracts of individuals diagnosed with COVID-19.

Clinical trial number

Not applicable

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-026-12579-1.

Background

The viral pathogen that is known to cause COVID-19 is SARS-CoV-2. This virus was first discovered in December 2019 in Wuhan (China), and ultimately lead to the global COVID-19 pandemic [1]. Figures published in early April 2025 suggested that over 220 countries had been impacted by the pandemic, with approximately 779 million cases and seven million deaths being reported worldwide. Moreover, SARS-CoV-2 infection can lead to long-term sequelae, although the mechanisms underlying this process remain unclear at present [2].

COVID-19 has a range of clinical severity from asymptomatic infection to adverse respiratory problems with general symptoms such as cough, fever, dyspnea and some gastrointestinal manifestation [3, 4]. The pathogenesis of SARS-CoV-2 is considered to occur by cytopathic damage to ACE2-expressing respiratory epithelial cells [5]. This results in epithelial desquamation, intense inflammatory cell infiltration as well as hyaline membrane formation in in severe cases. Additionally, the disease has a connection with profound immune dysregulation and excessive inflammatory responses, which is generally known as a ‘cytokine storm.’ These changes have an impact upon mucociliary clearance and decrease the effectiveness of host defense mechanisms within the respiratory tract, which expose patients to secondary bacterial colonization [6].

Patients severe disease may develop pneumonia and acute respiratory distress syndrome (ARDS). Such conditions are correlated with significant epithelial damage and impaired pulmonary defenses. Individuals that have prior health issues or immunosuppressive states have higher risk of severe complication, which means that immune dysregulation is central in the progression of the disease [7]. This negative respiratory and immunological environment may predispose COVID-19 patients to secondary bacterial colonization of the respiratory tract [3]. There is evidence to suggest COVID-19 is correlated with ARDS and pulmonary edema that results in an adverse impact on mucosal defenses, predisposing patients to be infected with organisms including Pseudomonas aeruginosa, Klebsiella spp., and Staphylococcus aureus [8].

Secondary bacterial infections often associated with respiratory viral infections, particularly influenza [9]. This has been noted in prior outbreaks of Severe Acute Respiratory Syndrome (SARS) and Middle Eastern Respiratory Syndrome (MERS) [10–13]. Secondary bacterial infections often occurs in COVID-19 cases, particularly those that are more severe in nature [14–16]. Research conducted by Zhang, Hu [17] revealed that, out of 221 patients, 7.7% were experiencing bacterial pneumonia, with Acinetobacter baumannii being the most commonly identified bacteria. Furthermore, a study conducted by Contou, Claudinon [18] in France revealed that 28% of secondary bacterial infections were caused by common pathogens such as Staphylococcus aureus, Haemophilus influenza, and Streptococcus pneumonia. Meanwhile, studies examining cases of co-infection with the influenza virus have identified Legionella pneumophila, Neisseria meningitidis, Moraxella catarrhalis as prominent infective bacteria [19]. All of these pathogens, as well as other opportunistic invaders of the respiratory tract required asymptomatic bacterial colonization, or carriage, to precede local and systemic disease [20, 21].

As a result of the dysbiosis produced by the SARS-CoV-2 infection, the bacterial co-infections mentioned above can emerge, which impairs lung function and ultimately increases the risk of disease progression [22]. COVID-19 patients were found to have a reduced prevalence of the healthy bacterial taxa in comparison to the controls [23]. The respiratory microbiome can predispose individuals to bacterial respiratory tract infections. It has also been found to be related to the severity of symptoms and clinical outcomes [24]. As SARS-CoV-2 enters the lungs, fewer common respiratory components of the microbiota may be present. This is likely due to the excessive growth of competing bacteria, or an over-zealous immune response to the viral infection [25].

There is an intricate relationship between COVID-19 and bacterial respiratory colonization. The SARS-CoV-2 infection interferes with pulmonary immune defenses, causes damage to the respiratory tract’s epithelial lining, and establishes a pro-inflammatory environment. In turn, this creates an environment conducive to bacterial growth and colonization and can even lead to extension to the lower respiratory tract [26]. Moreover, prolonged and invasive medical interventions (i.e., mechanical ventilation and endotracheal intubation) can increase the chances of developing secondary infections through microbial invasion. Many researchers have identified a correlation between secondary infections in COVID-19 patients and poorer clinical outcomes, including increased sepsis rates, longer stays in intensive care units, and higher mortality rates [15, 27, 28].

Even though the significance of bacterial colonization in COVID-19 patients is well documented, comprehensive data pertaining to the frequency, clinical implications, and microbiological profiles of such colonizing bacteria are lacking. Previous studies tend to focus on secondary bacterial infections, whilst omitting to examining bacterial colonization during the early stages of infection. The present work thus aims to identify any differences in respiratory colonization between different levels of COVID-19 severity by analyzing the distribution, characteristics, and differences in pathogens isolated from sputum samples provided by those infected with COVID-19. It is important to understand trends in bacterial colonization in COVID-19 patients in order to enhance antimicrobial treatment, inform the development of infection control measures, and enhance clinical outcomes.

Methods

Ethical approval

Ethical approval for this study was sought and received from the ethics committees at Hashemite University and Prince Hamza Hospital (ref. no. 5/3/2020/2021). All participants were required to provide informed consent in writing before being recruited for the study. The research process followed all relevant guidelines and legislation, whilst all research methods also adhered to relevant guidelines, regulations and in compliance with the Helsinki Declaration.

Research design

This analytical study incorporated a cross-sectional design. Altogether, 210 patients admitted to a single tertiary care hospital in Amman, Jordan, as well as 50 individuals without COVID-19 infection (i.e., the control group) took part in this research. The research process was approved by the hospital’s institutional review board. After contracting COVID-19, patients were hospitalized at Prince Hamza Hospital for isolation and treatment.

Research population

The research sample in this study included 260 participants, 210 of whom were in-patients from the New Prince Hamza teaching Hospital and 50 of whom were individuals not infected with COVID-19. The control group consisted of adult undergoing standard physical evaluations at primary care facilities, who exhibited no COVID-19 symptoms and tested negative for COVID-19 via a routine real-time polymerase chain reaction (RT-PCR) testing. The participants in the control groups had no recent infections and no prior history of anti-microbial, probiotic, chemotherapy, or other drug use in the 14 days prior to the research. SARs-CoV-2 infection was verified in the experimental group using two consecutive throat swabs and a RT-PCR test. Individuals were excluded from the study if they demonstrated any indications of respiratory failure or need to receive mechanical ventilation, shock treatment, or visceral impairments that complicated the infection Furthermore, any patients who had been administered antibiotics prior to the sputum test were excluded from the study. To be included in the experimental group, all patients had to have been diagnosed with COVID-19 and receive treatment in general hospital wards.

Patient cohort classification

The COVID-19 patients included in this study (n = 210) were assigned to two groups, the first of which was the mild COVID-19 groups, and the second was the moderate to severe ill COVID-19 patients, based on the National Institute of Health’s Guidance for Corona Virus Disease 2019 [29]. The two severity-based disease categories can be defined as:

• Mild illness: Patients who demonstrate any signs or symptoms of COVID-19 (i.e., fever, sore throat, cough, headache, muscular pains, vomiting, diarrhea, malaise, loss of taste and smell but do not exhibit problems such as dyspnea or shortness of breath, and do not produce abnormal chest X-rays or CT scans.

• Moderate to severe illness: people who exhibit signs of infection in the lower respiratory tract in clinical assessments or receive abnormal radiological results on a CT scan or chest X-ray.

Collection of sputum samples

All potential participants were given an information sheet by nurses, who helped those deciding to participate in the study to complete the consent form by guiding them on how to mark each item to indicate consent. The participants and relevant nurses were then asked to print their names on the consent form and provide their signatures.

Once they had completed the consent form, participants were asked to complete a newly designed questionnaire to obtain demographic information (Supplementary material), which was verified using patient medical records. As well as seeking demographic information, the questionnaire also asks about the patients’ medical and surgical histories. Patients were required to indicate whether they had previously experienced any comorbidities such as lung disease, heart disease, diabetes mellitus, gastrointestinal disease, chronic renal disease, and thyroid disorders. Moreover, they were questioned about their use of alcohol and tobacco. To calculate the body mass index (BMI) for each patient, self-reported height and weight data were sought.

After being admitted to hospital, each COVID-19 patient was asked to provide a sputum sample, which were collected in sterile containers. Protective equipment was worn by all clinical staff involved, which included eye protection, solid front-wraparound gowns, and N95 respirators, in line with the recommended biosafety guidelines.

To ensure that the findings are reliable and accurate, the sampling process used to collect sputum samples from participants was carefully created. Guided by the research coordinator, a demonstration of sputum collection was performed by a nurse so that the participants would know how to collect their samples. Participants were also asked to rinse their teeth and remove braces or dentures. They were then provided with a sterile container marked with the date and participant number, and they were instructed to expectorate deep cough sputum (after fasting overnight) so that the sputum samples could be collected. To improve the quality of the samples and ensure that the sample was representative of the lungs, participants were told not to eat or drink anything during the time leading up to sample collection. Sputum samples of approximately 2 milliliters per patient were collected either on the day of hospital admission or the following day.

Sputum samples microbial study

Microbiological cultures were performed on the sputum samples using standard approaches. To conduct the bacterial cultures, 10 µl of homogenized sputum was. The media employed for the study were Columbia blood agar containing 5% horse blood, chocolate agar containing 70 mg/L bacitracin and MacConkey medium (Zhengzhou Renfa có Sai Biotechnology, Henan, China). As per standard procedures, plates were incubated. The chocolate medium was left for 18–24 h in a setting at 35 °C in a 5% carbon dioxide incubator for culturing, while the blood and MacConkey media were cultured at 35 °C for 18–24 h in the incubator.

The plates were checked for signs of different colonial microbial growth each day. Every morphological variation was subcultured, identified, and stored in 10% glycerol skim milk at -20 °C. Furthermore, microbial identification testing was conducted utilizing a VITEK^®2 Compact automatic microbiological analyzer (Meyrié Diagnostic Products, Shanghai, China), a process that took between 18 and 24 h.

Two physicians that are specialist in infectious diseases reviewed the culture information. Within the sputum cultures, normal respiratory flora is deemed as a component of the commensal population thus excluded from the analysis. Normal flora, in sputum microbiology context means non-pathogenic microorganisms that are normally present in the upper respiratory tract (e.g., oropharynx and nasopharynx). These have the potential to exist within sputum samples as a result of contamination during expectoration. Such bacteria include, Diphtheroids (Corynebacterium spp.), Viridans group streptococci, Enterococcus and Micrococcus species, Coagulase-negative staphylococci.

Meanwhile, contamination of a sputum specimen is defined its quantity of salivary or oropharyngeal secretions and the existence of predominant normal oral flora rather than pathogenic bacteria. The sample’s appearance is similar to saliva or watery instead of being thick or purulent. This shows that the sample has poor quality and means that the sample was mixed with normal oral flora instead of the lower respiratory tract secretions.

Statistical analysis

GraphPad InStat 6.0 software was employed to perform the data analysis. Calculations were performed to determine the absolute (n) and relative (%) frequencies of categorical variables and the proportions were evaluated through Chi-square and Fisher’s exact tests. Additionally, the t-test results (unpaired, independent samples) were used to compare the mean and standard deviation of continuous variables with a normal distribution. A multivariable logistic regression model was performed to determine adjusted associations between patient characteristics and moderate disease severity. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported. To determine the normality of the available data, a Shapiro–Wilk test was conducted, while results were deemed to be statistically significant if they met the 0.05 threshold.

Results

Participants’ characteristics

Altogether, 210 patients with COVID-19 and 50 individuals without COVID-19 participated in this research. In Table 1, the clinical characteristics of the research participants can be seen. The COVID-19 sample has a mean age of 60.7 ± 11.8 years, varies between 25 and 85 years, and contains 51.4% (108/210) males and 48.6% (102/210) females. Meanwhile, with regard to the control group, the mean age was 51.2 ± 13.5 years, which is considered to be a statistically significant difference from the COVID-19 patient group (t = 17.94, p < 0.0001). The ages of the control group participants varied between 18 and 83 years, with 54.0% (27/50) being male and 46.0% (23/50) being female.

Table 1.

Demographic features of the research cohort

Characteristic	All patients (n = 210)	Control (n = 50)
Patient age: mean ± SD (range)	60.7 ± 11.8 (25–85)	51.2 + 13.5 (18–83)
Age group
18–28	4 (1.9%)	1 (2.0%)
29–39	8 (3.8%)	3 (6.0%)
40–50	37 (17.9%)	9 (18.0%)
51–61	55 (26.2%)	12 (24.0%)
62–72	61 (29.0%)	13 (26.0%)
73–83	43 (20.5%)	8 (16.0%)
> 83	2 (1.0%)	0 (0.0%)
Gender
Male	108 (51.4%)	27 (54.0%)
Female	102 (48.6%)	23 (46.0%)
BMI, kg/m2, mean ± SD	29.0 ± 4.5	27.4 ± 3.7
Smokers	67 (31.9)	13 (26%)
Alcoholic	17 (8.0%)	3 (6%)
Comorbidity
Coronary artery disease	39 (18.5%)	2 (4.0%)
Congestive heart failure	24 (11.4%)	1 (2.0%)
Cardiac arrhythmia	8 (3.8%)	0 (0.0%)
Hypertension	57 (27.1%)	6 (12.0%)
Hyperlipidemia	38 (18.1%)	4 (8.0%)
Diabetes	51 (24.3%)	5 (10.0%)
Cerebrovascular accident	11 (5.2%)	0(0.0%)
Pulmonary disorders	9(4.2%)	2(4.0%)
Chronic renal insufficiency	19(9.0%)	0(0.0%)
Thyroid disorders	11(5.2%)	1(4.7%)
Irritable bowel syndrome	22 (10%)	3(6.0%)
Inflammatory bowel disease	6 (2.9%)	1 (2.0%)
Other GI disorders	3 (1.4%)	2 (4.0%)

Most of the COVID-19 patients who participated in this research were categorized as overweight or obese with a body mass index (BMI) mean of 29.0 ± 4.5. Meanwhile, the control group demonstrated a mean BMI of 27.4 ± 3.7.

Over 50% of the research participants were non-smokers, including 141 of the COVID-19 patients and 37 of the control group. Hypertension was the most commonly reported comorbidity, followed by diabetes mellitus and cardiovascular disease. Similar patterns were also evident in the control group. Nonetheless, there was a significant difference (χ² = 270.80, p < 0.0001) between the COVID-19 and control groups in terms of the prevalence of such comorbidities, with the COVID-19 patient group demonstrating a higher prevalence of comorbidities such as hypertension (27.1% vs. 12.0%), diabetes (24.3% vs. 10.0%), and coronary heart disease (18.5% vs. 4.0%).

Severity of COVID-19 infection

The COVID-19 patient sample was sub-categorized based on the severity of the infection that they were experiencing. Altogether, 116 (55.2%) patients were categorized using the criteria for classification of the patient cohort, a total of 116 (55.2%) as having mild COVID-19 infection, while 94 (31.9%) were classed as moderate to severe COVID-19 cases. In Table 2, the clinical characteristics of these patients can be seen. With regard to comorbidity and demography, there were no statistically significant differences between the groups.

Table 2.

Demographic features of the COVID patients participating in this study based on the severity of their representation

Characteristic	Mild COVID-1 patient (N = 116, 55.2%)	Moderate COVID-19 patients (N = 94,31.9%)
Patient age: mean ± SD (range)	60 ± 10.6 (25–80)	61.2 ± 13.3 (35–85)	P = 0.1473
Age group
18–28	4 (3.4%)	0 (0.0%)	P = 0.61
29–39	9 (7.8%)	4 (4.5%)	P = 0.40
40–50	15 (12.9%)	18 (20.9%)	P = 0.45
51–61	34 (29.3%)	22 (23.9%)	P = 0.70
62–72	28 (24.1%)	27 (28.4%)	P = 0.30
73–83	25 (21.6%)	23 (22.4%)	P = 0.91
> 83	1 (0.8%)	1 (0.0%)	P = 0.21
Gender
Male	54 (46.6%)	54(58.2%)	p = 0.31
Female	62 (53.4)	40 (41.8%)
BMI, kg/m2, mean ± SD	26.8 ± 4.6	31.2 ± 5.1	P = 0.15
Smokers	36 (31.0%)	31(26.8%)	P = 0.22
Alcoholic	9 (7.7%)	8 (8.9%)	P = 0.40
Underlying medical conditions
Coronary artery disease	20 (18.1%)	19 (19.4%)	P = 0.30
Congestive heart failure	12 (10.3%)	12 (13.4%)	P = 0.61
Cardiac arrhythmia	5 (4.3%)	3 (3.0%)	P = 0.50
Hypertension	29 (25.0%)	28 (28.4%)	P = 0.75
Hyperlipidemia	18 (15.5%)	20(22.4%)	P = 0.40
Diabetes	26 (22.4%)	25 (26.9%)	P = 0.52
Cerebrovascular accident	5 (4.3%)	6 (6.0%)	P = 0.50
Pulmonary disorders	2 (1.7%)	7 (4.5%)	P = 0.17
Chronic renal insufficiency	10 (8.6%)	9 (10.4%)	P = 0.87
Thyroid disorders	5 (4.3%)	6 (7.5%)	P = 0.42
Irritable bowel syndrome	10 (8.6%)	12 (11.9)	P = 0.60
Inflammatory bowel disease	4 (3.4%)	2 (3.0%)	P = 0.27
Other GI disorders	2(1.7%)	1(1.5%)	P = 0.35

Table 3 presents the results of the multivariable logistic regression model. By making adjustment of potential confounders, anyone that is at or over the age of 40 has a significant correlation with moderate severity due to COVID-19 in comparison to those aged less than 40. Adjustment made are OR [aOR] = 4.34; 95% CI: 1.22–15.44; p = 0.024. BMI played the role of predictor independently where every increase of 1 kg/m² is correlated with an increase of 19.6% in the odds of moderate disease with the following: aOR = 1.20; 95% CI: 1.12–1.28; p < 0.001.

Table 3.

Multivariable logistic regression with adjusted a odds ratio estimates

Predictor	Adjusted OR	95% CI	p-value	Reference
Age ≥ 40 years	4.34	1.22–15.44	0.024	< 40 years
Male sex	1.54	0.85–2.81	0.156	Female
Smoker	1.00	0.53–1.88	0.988	Non-smoker
Any comorbidity	1.11	0.53–2.31	0.791	No comorbidity
BMI (per kg/m²)	1.20	1.12–1.28	< 0.001	-

Male patients demonstrated an increased possibility of moderate severity despite the correlation haveing no statistical relevance at aOR = 1.54; 95% CI: 0.85–2.81; p = 0.156. Smoking activity (aOR = 1.00; 95% CI: 0.53–1.88; p = 0.988) and prior comorbidity (aOR = 1.11; 95% CI: 0.53–2.31; p = 0.791) had no significant correlation with the severity of the disease.

Bacterial pathogens

The culture information was reviewed by two physicians who were specially trained in infectious diseases. In the sputum cultures, Enterococcus and Streptococcus species (except S pneumoniae) were considered to be normal flora. Meanwhile, Coagulase-negative Staphylococcus were excluded from sputum because they are typically regarded as commensals or contaminants. Additionally, when a mixed co-infection was identified in a sample, presenting with a number of different infectious agents and/or subspecies, the case was labeled as contaminated.

In Fig. 1, three pie charts are presented. These charts compare patterns in bacterial growth between the Control, Mild, and Moderate to Severe infection groups. A significant number of participants in the control group (42%) manifested no bacterial growth, with 32% of bacteria being classified as single Gram-positive pathogens and 10% as single Gram-negative pathogens. Moreover, smaller traces of gram-positive and negative pathogen combinations (12%), as well as a combination of two gram-positive pathogens (2%) were detected. An increase in bacterial growth was identified in the mild infection group, with no bacterial growth decreasing to 37%. Meanwhile, a higher number of single gram-positive pathogens (22%) and gram-positive and negative combinations (21%) were identified. Furthermore, there was an increase in single gram-negative pathogens to 12%, while combinations of two gram-positive pathogens constituted 9%. In the moderate to severe group, the presence of bacteria was found to be more diverse, with no bacterial growth declining further to 20%. There is a relative balance between the number of single gram-positive (18%) and single gram-negative pathogens (22%), although a clear increase can be seen in gram-positive and negative pathogen combinations (15%). Interestingly, combinations of two gram-positive pathogens were consistently low (2%), while the presence of normal flora (19%) indicates that some microbial equilibrium may have been retained.

Fig. 1.

Sputum culture results for the control group, mild infection group, and moderate-severe infection group

The findings of the statistical analysis indicate that there are significant differences in bacterial distribution between the control, mild infection, and moderate-severe infection groups (χ² = 20.70, p < 0.0001). Table 4; Fig. 2 shows that the control group (n = 50) had a lower prevalence of pathogens than the infection groups, with gram-positive bacteria (i.e., Methicillin sensitive S aureus (MSSA) (4%) and Methicillin Resistant S aureus (MRSA (2%)) occurring at low frequencies. On the other hand, there was a significant increase in bacterial presence in the mild infection group (n = 116), with the most common gram-positive bacteria in this case being MSSA (11.2%) and S pneumoniae (4.3%). There was also an evident increase in the incidence of gram-negative bacteria in this group, including K pneumoniae (4.3%) and H influenzae (4.3%). In the moderate to severe infection group (n = 94), the greatest bacterial diversity was found, with substantial rises in single gram-negative pathogens (i.e., P aeruginosa (5.3%) and K pneumoniae (5.3%), as well as combinations of gram-positive and gram-negative pathogens.

Table 4.

Pathogenic bacterial distribution across patient group based on their severity of their presentation

Pathogenic bacteria	Control group (N = 50)	Mild COVID-1 patient (N = 116, 55.2%)	Moderate to severe COVID-19 patients (N = 94,31.9%)
Single Gram-positive pathogen
Methicillin sensitive S. aureus (MSSA)	2(4%)	13 (11.2%)	9 (9.5%)
Methicillin Resistant S. aureus (MRSA)	1 (2%)	4 (3.4%)	4 (4.2%)
S. pyogenes	1 (2%)	1 (0.8%)	1 (1.1%)
S. pneumoniae	-	5(4.3%)	4 (4.2%)
Combination of 2 Gram-positive pathogen
S. pyogenes, MSSA	1 (2%)		2 (2.1%)
Single Gram-negative
P. aeruginosa	1 (2%)	3 (2.5%)	5 (5.3%)
Escherichia coli	-	-	2(2.1%)
P. mirabilis	-	-	2 (2.1%)
K. pneumoniae	0	5 (4.3%)	5 (5.3%)
H. influenzae	4 (8%)	5 (4.3%)	6 (6.4%)
M. catarrhalis	-	-	-
A. baumannii	-	-	1(1.1%)
Combination of Gram positive and negative pathogen
MSSA, K. pneumoniae	-	2 (1.7%)	1(1.1%)
MSSA, P. mirabilis	-	2 (1.7%)	2(2.1%)
MSSA, P. aeruginosa	1(2%)	2 (1.7%)	3(3.2%)
MRSA, Enterobacter, P. aeruginosa	-	1 (0.8%)	1(2.1%)
MRSA, H. influenzae, P. aeruginosa	-	3 (2.5%)	5(5.3%)
MRSA, P. aeruginosa	-	-	2 (2.1)
Combination of more than one-gram negative pathogen
P. aeruginosa, Enterobacter	-	-	1 (1.1%)
P. aeruginosa, K. pneumoniae	-	-	2 (2.1%)

Fig. 2.

A stacked bar graph presenting the bacterial distributions across severity groups, demonstrating a gradual change from non-pathogenic flora in the control and mild disease groups to a predominance of pathogenic bacteria in the moderate-severe infection group

Furthermore, moderate to severe patients had a significantly higher prevalence of polymicrobial infections, especially those involving a combination of Gram-positive and Gram-negative bacteria. MRSA with H influenzae and P aeruginosa (5.5%), MSSA with Proteus mirabilis (2.1%), and MSSA with P aeruginosa (3.2%) were noteworthy combinations. Multiple pathogens in one infection site can make the infection harder to treat because different bacteria have different resistance profiles. The correlation between severity and complicated bacterial colonization was further supported by the fact that only cases in the moderate to severe infection group demonstrated combinations of multiple gram-negative pathogens (e.g., P aeruginosa with Enterobacter species or K pneumoniae).

Discussion

Reduced mucociliary clearance can result from respiratory virus infections that harm and obstruct the healing of respiratory epithelial cells [30]. In turn, pneumonia is often caused by microbes that colonize the upper respiratory system and invade the lungs [31, 32]. Viral compromise of the innate immune system is a common cause of secondary bacterial infection. These include the production of type I interferons and desensitization to Toll-like receptor ligands, which degrade and deplete resident alveolar macrophages and neutrophils, which are essential in eliminating harmful pathogens [8, 33]. Furthermore, COVID-19 hyperinflammation engages complement system activation and the formation of neutrophil extracellular traps within lung vasculature resulting in damage to the lung. Respiratory tissue damage along with dysregulated opsonization increase the possibility of bacteria adhesion/overgrowth on damaged mucosa [8].

Secondary bacterial infections represent a serious complication of severe respiratory virus infections [10, 34]. Such infections contracted during the COVID-19 pandemic and have been particularly problematic, increasing hospital stays, mortality rates, and antimicrobial-resistant infections [35]. According to earlier studies, the prevalence of bacterial infections varies greatly and is frequently recorded on admission to the hospital [36, 37]. The coexistence of bacterial and viral illnesses at admission can increase the risk of mortality [37]. The purpose of this work is to examine bacterial colonization in COVID-19 patients when they were admitted to hospital. It also aimed to explore how this colonization affected their clinical presentation. Altogether, 210 patients diagnosed with COVID-19 were included in the study.

We have found an evident decline in normal flora in both the mild and moderate/severe infection groups, alongside an increase in the diversity of pathogens. This is consistent with prior research on microbial dysbiosis, which indicates that a loss of normal bacterial communities can lead to opportunistic infections [38, 39]. Disturbances in the normal microbial balance have been shown in numerous studies to foster an environment that is conducive to opportunistic infections, especially in the respiratory systems [40, 41]. As losing beneficial bacteria could facilitate the multiplication of harmful species, this is particularly important in cases COVID-19 infections well as other viral infection.

Moreover, a significant finding of this work was a rise in the number of gram-positive pathogens, particularly S aureus (both MSSA and MRSA), in the moderate to severe infection group. Relevant literature demonstrates that S aureus is a leading cause of pneumonia [42]. In more severe cases, the presence of MRSA is especially worrying, with many studies finding correlations between MRSA infections and longer hospital stays, increased morbidity, and higher mortality rates. This is because the pathogen is resistant to common beta-lactam antibiotics [43]. Research carried out by Yamamoto, Saito [44] highlighted differences in the microbiome between COVID-19 patients and healthy controls, with increased incidences of Staphylococcus, Streptococcus, and Enterobacterales species identified in the infection group. Meanwhile, Sharov [45] analyzed 3382 cases of bacterial infections at the beginning of the pandemic and determined that S. pneumoniae, S. aureus and H. influenzae were the most common pathogens related to bacterial pneumonia [45]. Likewise, in the present study, S. pneumoniae was responsible for the respiratory infections experienced by 34.1% of patients, followed by MSSA (21.6%) and MRSA (17.0%) [45]. These findings support our finding, highlighting the importance of early detection and targeted antimicrobial therapy to address bacterial colonization and ultimately prevent respiratory infections from progressing to pneumonia.

In more severe cases of COVID-19, the presence of pathogenic bacteria tends to be more prominent. This was revealed when analyzing gram-negative bacteria, which were primarily discovered in the moderate to severe infection group. The most commonly isolated pathogens in the moderate-severe infection group were P aeruginosa (5.3%), K pneumoniae (4.3%), and H influenzae (4.3%). Although K pneumoniae and P mirabilis could not be identified in the control group, they were found to be present in the COVID-19 groups, indicating an association between their presence and increasing severity of infection. The mild infection group presented with Escherichia coli and Acinetobacter species, which further demonstrates the wide variety of bacterial pathogens that can cause infection. Prior studies have revealed that gram-negative pathogens are critical components of COVID-19 secondary bacterial infections [27]. For instance, P aeruginosa is widely documented as being an opportunistic pathogen that possesses an intrinsic resistance to many different antibiotics [46]. Similarly, research has shown that K pneumoniae (a critical pathogen in nosocomial infections) is related to severe bloodstream infections and pneumonia, particularly in COVID-19 patients [47]. One Italian study found that broncho-alveolar lavage from 24 critically ill COVID-19 patients bred gram-negative bacteria that were resistant to multiple drugs, while more commensal flora was identified in 24 matched non-COVID-19 controls (such as streptococci) [48]. This is in line with the findings of our own study.

The rising rate of colonization being detected in COVID-19 patients that have severe symptoms within this study has a plausible relationship with immunopathological changes caused by SARS-CoV-2. The virus is increasingly cytopathic to lung epithelial and alveolar cells resulting in the destruction of tissue characterized by infiltration of lymphocytes and macrophages, hyaline membrane formation, desquamative pneumocytes and interstitial inflammation especially for cases involving ARDS [6]. The damage to such structure has an adverse impact upon the mucociliary clearance and epithelial integrity, resulting in a feasible environmental for bacteria that is opportunistic for adherence and colonization [6].

Additionally, SARS-CoV-2 infection results in excessive inflammation and a cytokine storm that includes increased levels of IL-6, TNF-α, and IL-1β, which play a role in the immune dysregulation and impaired host defense mechanisms [6]. Increasingly, aggressive and difficult to treat infections may arise from the rising gram-positive and negative pathogen combinations we observed in severe cases.

It has been found that viral inflammation in conjunction with weakened immune defense mechanisms especially in patients with comorbidities results in a microenvironment that is suitable for the colonization of opportunistic bacteria such as Klebsiella pneumoniae, Pseudomonas aeruginosa and Staphylococcus aureus [3]. This is in accordance with the findings from prior studies such as by Mohammadi, Omidi [49], which found higher frequency of bacteria colonization within the moderate to severe COVID-19 patients.More aggressive and challenging-to-treat infections can emerge from increases in gram-positive and negative pathogen combinations in severe cases, which highlights the need to establish more focused screening and treatment approaches.

These alterations may promote the growth of respiratory pathogens such as Pseudomonas aeruginosa, Klebsiella spp., and Staphylococcus aureus, especially in patients with moderate-to-severe disease who experience prolonged viral activity and alveolar injury. Consequently, the findings of this study align with previous data suggesting that COVID-19–induced immune dysfunction and lung damage significantly predispose patients to secondary bacterial colonization and infection.”

To conclude, the findings of this investigation show that patterns of bacterial colonization change substantially as infection severity levels rise, demonstrating a noticeable shift from healthy oral flora to harmful bacteria. To enhance patient outcomes, comprehensive screening, diagnosis and treatment approaches are required, as evidenced by the increasing prevalence of Gram-negative bacteria and polymicrobial infections in moderate-severe COVID-19 cases. Azizian, Mamishi [50] revealed that molecular diagnostic and point-of-care tests (POCT) may have a primary role in diagnosing bacterial agents in the respiratory tract such as Staphylococcus aureus and Klebsiella spp. To further understand the causes of these microbiological alterations and their clinical implications, it is recommended that future studies focus on examining host immunological responses and antibiotic resistance patterns. Our results highlight the importance of early and precise microbial identification in patients with severe clinical presentation from a clinical standpoint. To determine the best course of treatment, it is important to integrate routine bacterial cultures screening, antibiotic resistance tests, and molecular diagnostics into routine clinical practice. In conjunction with the tissue degradation this virus causes, SARS-CoV-2 can help bacteria colonize and adhere to the host’s respiratory tissues, resulting in a variety of illnesses [51].

As far as the researcher is aware, this study is one of very few that have explored the variations in bacterial colonization between SARS-CoV-2 RT-qPCR positive and negative participants. Nonetheless, this study as well as our previous study [23] employed 16 s rRNA sequencing and metagenomics for the groups with and without COVID-19, could significantly enhance our understanding of global changes in the upper respiratory tract’s microbiome that occur during SARS-CoV-2 infection. However, several important limitations must be acknowledged that could influence the interpretation of our results.

It should be noted that the limitations of this research pertain to the limited sample sizes. Therefore, future studies should investigate the potential correlation between pathogens upon a broader and diverse population. It is also significant to understand limitations including the bacterial quantification through applying standard culture techniques, limited availability of procalcitonin values and limited diagnostic tools that assessed atypical bacterial infections such as mycoplasma. Furthermore, focusing only upon general culture-based techniques may have resulted in a lower detection of pathogens that grow at slower rate, fastidious or intracellular along with organisms that exist with minimal quantity. Therefore, future research should meticulously consider microbial communities’ composition from a broader view. Additional attention should also be paid on metagenomics techniques or deep sequencing of ribosomal markers.

Additionally, it was very common to administer empiric antibiotic during the COVID-19 pandemic. In spite of limited validated data, they were administered prophylactically or when possible bacterial superinfection was suspected. Antimicrobial exposure may play a role upon the findings of this work and decrease the detection of some bacterial species. Another key consideration is the immune status of the host. COVID-19 patients have a diverse baseline immunity associated with underlying comorbidities including chronic lung disease, diabetes, immunosuppressive therapy or malignancy. This may have an impact upon the results of bacterial colonization. This study did not measure or adjust for the immune status and use of immunomodulatory treatments. Therefore, there is a possibility of inflation or misattributed colonization.

Recommendations for future studies should include the collection of in-depth antibiotic exposure data prior or during hospitalization. Other factors that should be included in future studies are baseline immune status markers and collection of information on immunomodulatory therapy, comprehensive comorbidity and underlying lung-disease, standardized timing of respiratory sampling in relation to COVID-19 onset and therapies. Ideally, long term monitoring to investigate the evolution of colonization into infection, including techniques involving microbiome and metagenomics analysis could help to differentiate between transient colonization and establishment of pathogenic organisms. The inclusion of the aforementioned confounders improve the understanding if the colonization of respiratory tract bacterial within COVID-19 patients plays the role of true reservoir or precursor to bacteria infection instead of playing a simple role of a marker of illness severity or impact on treatments.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

BMI

Body mass index

MERS

Middle eastern respiratory syndrome

MSSA

Methicillin sensitive S aureus

MRSA

Methicillin resistant S aureus

RT-PCR

Real time-polymerase chain reaction

SARS

Severe acute respiratory syndrome

Author contributions

Conceptualization: H. Al-Momani, O.E and C.W.Resources and data curation: I.A, A.I.K and H.T.Methodology: H. Al-Momani, A.A and A.M.Z.Patient sampling: H.A and D.A.Writing (original draft preparation): H. Al-Momani, C.W and J.P.Writing (review and editing): H-Al-Momani, A.N, C.W and J.P.Supervision: H. Al-Momani.Project administration: H-Al-Momani and C.W.All authors have read and agreed to the published version of the manuscript.

Funding

The work in the H. Al-Momani laboratory was supported by a grant provided by the Deanship of Scientific Research at Hashemite University (grant no. 593/61/2020).

Data availability

All data generated or analysed during this study are included in this published article and its supplementary information files.

Declarations

Ethics approval and consent to participate

Ethical approval for this study was sought and received from the ethics committees at Hashemite University and Prince Hamza Hospital (ref. no. 5/3/2020/2021). All participants were required to provide informed consent in writing before being recruited for the study. The research process followed all relevant guidelines and legislation, whilst all research methods also adhered to relevant guidelines, regulations and in compliance with the Helsinki Declaration.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.