Next Article in Journal
Optimizing Palliative Pelvic Radiotherapy in Gynecological Cancers: A Systematic Review and Analysis
Previous Article in Journal
On the Dependence of the Critical Success Index (CSI) on Prevalence
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 14
Issue 5
10.3390/diagnostics14050546
diagnostics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Enterobacterales Biofilm-Specific Genes and Antimicrobial and Anti-Inflammatory Biomarkers in the Blood of Patients with Ischemic Heart Disease

by
Agne Giedraitiene
1,*,
Vacis Tatarunas
2,
Kornelija Kaminskaite
3,
Ugne Meskauskaite
3,
Svitlana Boieva
2,
Yu Ajima
3,4,
Ieva Ciapiene
2,
Audrone Veikutiene
2,
Vaidotas Zvikas
5,
Nora Kupstyte-Kristapone
3,
Valdas Jakstas
5,
Dalia Luksiene
2,
Abdonas Tamosiunas
2 and
Vaiva Lesauskaite
2
1
Institute of Microbiology and Virology, Lithuanian University of Health Sciences, Eiveniu 4, LT 50161 Kaunas, Lithuania
2
Institute of Cardiology, Lithuanian University of Health Sciences, Sukileliu 15, LT 50103 Kaunas, Lithuania
3
Medical Academy, Lithuanian University of Health Sciences, A. Mickeviciaus 9, LT 44307 Kaunas, Lithuania
4
School of Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8553, Japan
5
Institute of Pharmaceutical Technologies, Lithuanian University of Health Sciences, Sukileliu 13, LT 50161 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Diagnostics 2024, 14(5), 546; https://doi.org/10.3390/diagnostics14050546
Submission received: 3 February 2024 / Revised: 29 February 2024 / Accepted: 1 March 2024 / Published: 5 March 2024
(This article belongs to the Section Diagnostic Microbiology and Infectious Disease)
Browse Figures
Versions Notes

Abstract

:
Background: Ischemic heart disease (IHD) is the most prevalent type of cardiovascular disease. The main cause of IHD is atherosclerosis, which is a multifactorial inflammatory disease of blood vessels. Studies show that bacteria might have a significant impact on the pathogenesis of atherosclerosis and plaque rupture. This study aimed to evaluate the complexity of interactions between bacteria and the human body concerning metabolites and bacterial genes in patients with ischemic heart disease. Methods: Bacterial 16S rDNA and wcaF, papC, and sdhC genes were detected in whole blood using a real-time PCR methodology. An enzyme-linked immunosorbent assay was used to measure the concentration of the LL-37 protein. An analysis of ARA in blood plasma was performed. Results: Bacterial 16S rDNA was detected in 31% of the study patients, and the genes wcaF and sdhC in 20%. Enterobacterales genes were detected more frequently in patients younger than 65 years than in patients aged 65 years and older (p = 0.018) and in patients with type 2 diabetes (p = 0.048). Concentrations of the human antimicrobial peptide LL-37 and 12S-HETE concentrations were determined to be higher if patients had 16S rDNA and biofilm-specific genes. Conclusions: The results of this study enhance the understanding that Enterobacterales bacteria may participate in the pathogenesis of atherosclerosis and IHD. Bacterial DNA and host metabolites in higher concentrations appear to be detected.

1. Introduction

Cardiovascular diseases (CVD) remain the main cause of death in Western countries, and ischemic heart disease (IHD) is the most prevalent among CVD. It was estimated that around 126 million people, who make up approximately 1.7% of the world’s population, have IHD [1]. In Central and Eastern European countries, an increase in IHD morbidity, compared to Northern, Southern, and Western Europe, is observed [2]. The standardized rate of deaths from IHD of 5362 deaths per million inhabitants made Lithuania a leader with the highest mortality rate among the European Union (EU) Member States in 2017 [3].
The main cause of IHD is atherosclerosis. Atherosclerosis is an inflammatory lipoprotein-driven multifactorial disease causing atherosclerotic plaque development and coronary blood flow reduction. If a rupture or erosion of a plaque’s fibrous cap occurs in coronary arteries, it might result in unstable angina or myocardial infarction. The complexity of this condition has led to great concern in finding novel tools for the early diagnosis or prognosis of atherosclerosis and IHD [4,5,6,7].
One of the possible factors for an early diagnosis of IHD could be a biomarker of atherosclerosis progression, the genetic material of certain species of bacteria [8]. Numerous studies have shown that various bacteria and viruses have a direct impact on the vascular endothelium [9,10]. Pathogens are found in the atherosclerotic plaque, and they are harbored in the latent state [11]. A lipopolysaccharide of Escherichia coli (E. coli) was detected in atherosclerotic plaques, and possibly it is related to the damage and rupture of plaques via Toll-Like Receptor 4 (TLR4)-mediated oxidative stress [12]. Zdimal and Davies, 2022, demonstrated that if a biofilm is exposed to an elevated concentration of free iron, it may undergo dispersion contributing to the weakening of arterial tissues and destabilization of the atherosclerotic plaque [13].
The study demonstrated that bacteria might form a biofilm within human carotid arterial plaques [14]. Microbial cells within biofilms tolerate up to 100–1000 times higher concentrations of antibiotics and enhance bacterial survival in the human body, including the bloodstream [15,16]. More than 100 genes have been encountered encoding biofilm in E. coli [17,18]. The wcaF gene encodes an acetyltransferase associated with the polysaccharide colanic acid’s synthesis. Colanic acid is an extracellular polysaccharide needed for the formation of the complex three-dimensional structure of E. coli biofilms [19]. The P-fimbriae, encoded by the pap operon, is responsible for adhesion and is found in many UTI E. coli strains. The papC-encoded protein forms pores in the outer membrane of E. coli and is responsible for the transportation of protein pilin subunits [20]. The sdhC gene encodes one of the four subunits of the inner membrane protein succinate dehydrogenase, which is involved in the Krebs cycle [21]. All three genes wcaF, papC, and sdhC are located on the E. coli chromosome and encode proteins directly involved in biofilm formation.
The 16S rRNA gene is a part of the prokaryotic ribosome 30S subunit and it is coded in the genomes of all bacteria. The 16S rRNA gene sequencing has been used for the identification, classification, and quantitation of microbes for decades [22]. It may also allow the identification of new and non-cultured bacteria [23], as 16S rRNA is highly conserved and specific. It plays a central role in phylogenetic research, as it is sustained between different species of bacteria and archaea [24].
The detection of bacteria in the blood flow is usually associated with a low sensitivity in the presence of extremely low concentrations and time-consuming methodology [25]. The inhibition of PCR amplification and interference with the detection of fluorescence slow down bacterial detection and identification [26]. New enzymes used in the polymerase chain reaction, which have been developed during recent years, allow researchers to overcome these weak points of research and ensure the successful performance of the bacterial gene amplification directly from patient blood (without the extraction of DNA before amplification in classic PCR).
LL-37 is a cathelicidin-related antimicrobial peptide (CRAMP). It plays a crucial role in the immune response to bacterial infections and is actively involved in regulating inflammatory processes. CRAMP enhances the chemotactic responsiveness of bone marrow and progenitor cells [27]. A study shows that decreased LL-37 might be associated with myocardial infarction [28,29], while a high LL-37 level predicts lower major adverse cardiovascular events after ST-segment elevation myocardial infarction [30,31].
Stimulated immune cells may produce significant amounts of arachidonic acid metabolites such as 12-HETE, 15-HETE, PGE2, and LTB4. These compounds could be an early inflammatory signal for the initiation of the immune system [32].
Thus, this study aimed to assess the complexity of interactions between bacteria and the human body concerning metabolites and bacterial genes in patients with ischemic heart disease. Moreover, concentrations of the protein LL-37 and arachidonic acid (ARA) metabolites produced by a host in the immune response to ischemic heart disease were measured, and associations between all parameters were determined.

2. Materials and Methods

2.1. Study Population and Inclusion Criteria

The study included 75 randomly selected patients with ischemic heart disease (39 men and 36 women), and 75 healthy persons (39 men and 36 women) as a control group. All patients were hospitalized due to ischemic heart disease at the Department of Cardiology at the Hospital of Lithuanian University of Health Sciences Kaunas Clinics in Kaunas, Lithuania, from 2013 to 2017, and were treated with dual antiplatelet therapy (DAPT) of clopidogrel, or ticagrelor and aspirin. Hormone anti-inflammatory medications were not prescribed to the patients.
The exclusion criteria of patients were conditions leading to an increased activity of coagulation (malignant neoplasia and severe inflammation [C-reactive protein level > 100 mg/L] or patients who had undergone antibiotic therapy due to infection, patients diagnosed with atrial fibrillation or pericardial diseases, significant structural heart disease such as valvular heart disease), cardiogenic shock or hypovolemia, and the refusal of a patient to take part in this study.
The patient population’s clinical data and blood samples were collected during the SEN-09/2015 study [33,34].
The inclusion criteria for the control group: (1) Control subjects were followed up for 10 years for CVD mortality events and health conditions/diseases after inclusion in the international study Health, Alcohol and Psychosocial Factors in Eastern Europe (HAPIEE), conducted in Kaunas (Lithuania) [35]; (2) None of the participants had a cardiovascular disease, stroke, or diabetes during the follow-up period. The mean age of healthy subjects was 67.7 years (minimum age—45, maximum age—72).
The study design is presented in Figure 1.

2.2. Preparation of Blood Samples

Venous blood sampling of patients was carried out by a routine venipuncture procedure using plastic tubes containing 3.2% sodium citrate anticoagulant (BD Vacutainer, Franklin Lakes, NJ, USA). All blood samples were stored at −20 °C. It is known that PCR-inhibitory compounds in human blood can considerably reduce the sensitivity of the PCR assay [36]. The blood protein hemoglobin adversely affects DNA polymerase activity and inhibits amplification and fluorescence signaling; immunoglobulin G disrupts amplification of the first PCR cycles by binding to single-stranded DNA [37]. Red blood cells naturally fluoresce across multiple wavelengths, which span the emission and excitation spectra of many commonly used fluorescent reporters and dyes such as SYBR Green I, and Midori Green, making the endogenous fluorescence difficult to distinguish [38]. One microliter of whole blood was taken for the assay. The mixture of blood and a buffer, the composition of which was developed in our laboratory, were gently mixed and heated for 5 min at 98 °C, then centrifuged for 2 to 4 s (2700 rpm). The amplification and detection of bacterial genes were performed using a QuantStudio 3 Real-Time System instrument (Thermo Fisher Scientific, Waltham, MA, USA). A blood direct PCR (BD-PCR) using genomic DNA without its extraction was performed directly from 1 µL of treated whole blood using specific primers. A mix of primers was prepared to amplify bacterial 16S rDNA. Primer sequences (Table 1), the primer mixture (Table 2), and reaction parameters (Table 3) were adopted from Barghouthi S. A. et al., 2011 [39], And oligonucleotide primers used for the amplification of wcaF, papC, and sdhC were adopted from the following references [31,32,40] (Table 4). The reaction was performed in standard 96-well real-time PCR plates. Thermal cycling program parameters used in blood direct PCR are shown in Table 3 and Table 5. Amplicon size was verified in 2% agarose gel. Bacterial DNA from selected strains was used as a positive control.

2.3. Extraction of Bacterial DNA

A reference E. coli ATCC 25922 strain was used to prepare a bacterial suspension. A few colonies of one-day E. coli bacteria were mixed with 700 µL nuclease-free water, then the suspension was vortexed and centrifuged for 2–4 s (2700 rpm). Bacterial cell wall structures were disrupted mechanically, and high-fidelity DNA polymerase (Thermo Fisher Scientific, Vilnius, Lithuania) was used for the gene amplification; no further DNA purification was required.

2.4. Confirmation of Bacterial DNA with Sanger Sequencing

The purification of randomly selected PCR products for bacterial DNA sequencing from TBE-buffered agarose gel was performed using the Zymoclean™ Gel DNA Recovery Kit (Zymo Research, Irvin, CA, USA). DNA sequencing was conducted by using the Sanger sequencing method. The interpretation of sequencing chromatograms was carried out with Chromas (Technelysium, Brisbane, Queensland, Australia).

2.5. Detection of LL-37

The sandwich enzyme immunoassay (Enzyme-linked Immunosorbent Assay Kit “Human Antibacterial Peptide LL-37 ELISA Kit” (Cusabio Technology LLC, Houston, TX, USA) was used to measure LL-37 in human plasma. All reactions were performed following the manufacturer’s instructions. LL-37 was detected by measuring optical density at 450 nm using a microplate reader Stat Fax 4200 (Awareness Technologies, Palm City, FL, USA). The concentration of the LL-37 peptide was calculated using a standard calibration curve. The calibration curve was performed using Curve Expert 1.4 (Hyams Development, Huntsville, AL, USA).

2.6. Detection of ARA Metabolites in Blood Plasma

The concentrations of ARA metabolites in blood plasma were measured as described above [41]. The extraction and preparation of the samples for analysis were conducted at the Laboratory of Molecular Cardiology. The analysis of ARA metabolites in blood plasma was performed at the Institute of Pharmaceutical Technologies of the Lithuanian University of Health Sciences.

2.7. Statistical Analysis

Statistical analysis was performed with IBM SPSS Statistics V27 software (IBM Corp., Foster City, CA, USA). The Shapiro–Wilk test was used to check the normality of the variable’s distribution. Pearson’s χ2 or Fisher′s exact test was used for categorical variables. The results were considered statistically significant when p < 0.05.

3. Results

Bacterial 16S rDNA was detected in 31% (n = 23/75) and genes specific for Enterobacterales wcaF and sdhC but not the papC were detected in 20% (n = 15/75) of the whole-blood samples of the study patients. The highest percentage of Enterobacterales-specific genes detected in the analyzed samples was 13.3% of the gene wcaF (n = 10/75), followed by 8% of the gene sdhC (n = 6/75). One of the patients had both the wcaF and sdhC genes. The differences in gene prevalence by sex were not significant due to the small sample size (Table 6). The 16S rRNA gene, a molecular marker for the identification of bacterial species, was not detected in any of the control subjects. Further analysis of the healthy group was not performed.

3.1. Bacterial DNA Sequencing Results

Sanger sequencing was performed for the PCR product of a blood sample with the wcaF gene positive. Analysis of the resulting sequence in the BLAST program corresponded to Escherichia spp. bacterium by 93.8%. Sequencing of the PCR product of a blood sample with the sdhC gene detected by RT-PCR showed the sdhC gene encoded by bacteria of the genus Serratia or Escherichia (90.5% and 88.6%, respectively).

3.2. Enterobacterales Genes in Patient Blood

The distribution of Enterobacterales genes may vary according to the patient’s gender and age. Bacterial genes tended to be detected more frequently in the blood of men than women (p = 0.086) (Appendix A). Patients younger than 65 years old more frequently had bacterial genes detected in their blood than patients aged 65 years and older (p = 0.018). Additionally, bacterial genes were more frequently detected in patients with type 2 diabetes (p = 0.048) (Appendix A). The results with 16S rDNA were not significant.

3.3. Protein LL-37 Concentration and Bacterial Genes

The concentrations of LL-37 were higher in patient blood with 16S rDNA and biofilm-specific genes (Table 7).

3.4. Concentration of Arachidonic Acid Metabolites and Presence of Enterobacterales Genes in Blood Samples

Patients with Enterobacterales genes in their blood had a higher concentration of 12S-HETE and 5S-HETE than patients without bacterial genes (respectively, p = 0.046 and p = 0.077) (Table 8). The results with 16S rDNA were not significant.

3.5. LL-37 and Arachidonic acid Metabolites

A positive correlation of LL-37 was observed with 5S-HETE (r = 0.367, p = 0.015) when a trend was determined between LL-37 and 12S-HETE (r = 0.307, p = 0.048) in blood plasma.

4. Discussion

This study reports that bacterial 16S rDNA was detected in one-third of the venous blood samples of patients with IHD (30.5%). The presence of 16S rDNA was not significantly associated with IHD, diabetes, or the other factors or conditions analyzed in our study. The healthy population sample tested negative for bacterial 16S rDNA and biofilm-encoding genes. The presence of DNA, a marker of bacterial translocation and a potential biomarker, was reported by other studies in patients with cirrhosis [42], cardiovascular disease [43], type 2 diabetes [44], psoriatic arthritis [45], and atherosclerosis [46]. Bacteria might play a role in the development and progression of atherosclerosis by the formation of biofilms within arterial plaques and the rupture of unstable plaques [14,47]. A plaque’s rupture leads to a life-threatening atherothrombotic lesion, occlusion of the coronary artery, and myocardial ischemia. However, the mechanism of IHD pathogenesis which involves the formation of bacterial biofilms is still not fully understood. There is a big shortage of knowledge on how bacterial fragments accumulate in different parts of the host’s body. One of the possible ways is bacterial biofilm formation [48]. We determined that biofilm-specific genes were more prevalent among patients younger than 65 years old and patients with diabetes. Anhe et al., 2020, determined that plasma samples of individuals with diabetes were enriched with Enterobacterales [49]. Possibly, the increase in the bacterial count of some specific genera might be related to the use of antidiabetic drugs, as E. coli proliferation is increased due to insulin administration under in vitro conditions. Insulin may serve as a signal molecule for the formation of bacterial biofilms [50,51]. About 17% of the study patients had Enterobacterales genes in their blood: 13.3% had the gene wcaF, followed by 8% with the gene sdhC. One of the patients had both the wcaF and sdhC genes. The gene wcaF encodes an acetyltransferase associated with the polysaccharide colanic acid’s synthesis [19]. Prospective studies have shown that exopolysaccharides’ (alginate and colanic acid) synthesis is induced upon attachment of bacteria to the surface [52], but at the same time, the biofilm formation-associated gene wcaF might be regulated by the quorum sensing E. coli regulators B and C [40]. Thus, by regulating exopolysaccharides, the gene wcaF allows control of the E. coli biofilm formation [53]. Importantly, the P-fimbriae, encoded by the pap operon, is responsible for adhesion and is found in many UTI E. coli strains [20,54]. It is worth noting that the papC gene might be associated with amoxicillin resistance [20]. Moreover, the sdhC gene encodes one of the four inner membrane protein subunits of succinate dehydrogenase, which is involved in the Krebs cycle [21]. A lower prevalence of the papC gene was determined in ciprofloxacin-resistant uropathogenic E. coli than in their susceptible counterparts [55].
It is known that the bacterial cell wall components peptidoglycan and lipopolysaccharides (LPS) alter immune and glucose homeostasis in the host, and bacterial translocation possibly influences the host’s metabolism [56]. Also, the production of immune response signals (such as the synthesis of CRAMP peptides or arachidonic acid derivatives) takes place during the invasion of the host organism [32,57]. This research showed that patients with bacterial genes in blood plasma tend to have higher 12S-HETE and LL-37 concentrations than patients with no Enterobacterales genes detected. Certain host-derived metabolites of lipids could have antimicrobial activity via membrane interactions. It was determined that arachidonic acid (ARA) might reduce the virulence of certain strains of E. coli [58], be toxic to certain bacteria, and be able to kill Staphylococcus aureus [59]. The inhibition of the production of cyclooxygenase-derived compounds of ARA induces the accumulation of precursors, especially ARA [56]. The results of our study showed that the concentration of 12S-HETE, a derivative of ARA, was higher in patients with detected biofilm-forming genes than in patients with no bacterial genes. The bio-active molecular 12S-HETE participates in the host metabolism of glucose [60] and inflammation [61]. In addition, our results showed that the cathelicidin LL-37 concentration was higher in patients with 16S rDNA and biofilm-specific genes in their blood than in patients without these bacterial genes. The antimicrobial host defense peptide cathelicidin LL-37 is a part of the innate immune response in humans. It is expressed during infection and kills bacteria via membrane interactions [62]. Studies have shown that expression of LL-37 is increased up to six-fold in atherosclerotic lesions [63]. The peptide LL-37 is produced by cells of the immune system: neutrophils, monocytes, macrophages, and epithelial cells [64]. Hypothetical mechanisms of action of bacteria and the host metabolites in the pathogenesis of IHD are shown in Figure 2.
Assessing bacterial genes such as biofilm-encoding genes or 16S ribosomal-ribonucleic acid might be useful in diagnostics or the prediction of non-infectious diseases such as cardiovascular diseases. Rigorous referral pathways to perform molecular tests may result in significant savings [65]. Genetic screening for some specific genes and testing individuals in advance even if they are asymptomatic, but have a family history of cardiovascular diseases, might help to reduce healthcare costs, personalize treatment, and reduce the number of interventions. Overall, it might improve the patient’s quality of life and survivability [66]. To sum up, it is essential to consider introducing molecular methods for bacterial gene detection/determination in clinical practice that would allow clinicians to obtain more detailed information about the disease and more accurately diagnose it.

5. Conclusions

Bacterial biofilm-specific DNA was more prevalent in IHD patients younger than 65 years of age and in patients with diabetes. Concentrations of the human antimicrobial peptide LL-37 in blood were determined to be higher if patients had 16S rDNA and biofilm-specific genes. Additionally, the concentration of the ARA metabolite 5S-HETE correlated with the LL-37 protein concentration. Thus, the results of this study enhance our understanding that Enterobacterales bacteria may participate in the pathogenesis of atherosclerosis and IHD. The described biomarkers might be useful for the identification of patients who might be at risk of atherosclerotic plaque rupture and IHD. Nevertheless, further studies are needed to elucidate the specific role of the host and microorganism metabolites in the progression and development of atherosclerosis.

Author Contributions

Conceptualization, A.G., V.T. and A.V.; methodology, A.G., K.K., V.Z., V.J., I.C., S.B. and Y.A.; validation, K.K., V.T., A.G., S.B., I.C., V.J. and Y.A.; formal analysis, K.K., S.B., I.C., V.Z., Y.A., A.T. and D.L.; investigation, K.K., N.K.-K., V.Z. and S.B.; resources, A.G., V.T., A.V., V.J. and V.L.; writing—original draft preparation, A.G., I.C., K.K., V.T., V.L., V.J., V.Z., Y.A., A.T. and D.L.; writing—review and editing, A.G., V.T., V.J. and V.L.; visualization, I.C. and U.M.; supervision, A.G., V.T. and V.L; project administration, V.T.; funding acquisition, A.G. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the funds of the Medical Academy, the Lithuanian University of Health Sciences.

Institutional Review Board Statement

This study was approved by the Kaunas Regional Ethics Committee for Biomedical Research (Permission No. BE 2-42) and Bioethics Center of Lithuanian University of Health Sciences (Permission No. BEC-MVG(B), approved on the 16 January 2019). Written informed consent was obtained from all participants prior to inclusion in the study.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available, due to the next work.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Patient baseline characteristics according to the detection of bacterial genes.
Table A1. Patient baseline characteristics according to the detection of bacterial genes.
FactorEnterobacterales Genes DetectedEnterobacterales Genes Not DetectedPearson χ2
p-Value or p-Value between Medians		Total
Sex
Males n (%)11 (28.2)28 (71.8)3.419
p = 0.086		39 (100)
Females n (%)4 (11.1)32 (88.9)36 (100)
Antropometric parameters
Less than 65 years old n (%)10 (34.5)19 (65.5)6.198
p = 0.018		29 (100)
65 years and older n (%)5 (10.9)41 (89.1)46 (100)
Age in years median (min–max)64.3 (44.5–79.3)70.1 (42.5–87.5)p = 0.03968.9 (42.5–87.5)
Patient weight in kg median (min–max)88 (53–113)78 (45–134)p = 0.37880 (45–134)
Body weight index in kg/m2 median (min–max)26.9 (18.1–45.3)27.7 (17.3–49.8)p = 0.62427.3 (17.3–49.8)
Waist circumference in cm median (min–max)92 (67–110)92 (70–132)p = 0.88692 (67–132)
Left ventricle ejection fraction
Left ventricle ejection fraction in % median (min–max)45 (20–55)50 (25–55)p = 0.668 49 (20–55)
ST-elevation MI
STEMI n (%)4 (14.3)24 (85.7)0.912
p = 0.388		28 (100)
NSTEMI n (%)11 (23.4)36 (76.6)47 (100)
MI in anamnesis
First n (%)11 (19.3)46 (80.7)0.073
p = 0.747		57 (100)
Recurrent n (%)4 (22.2)14 (77.8)18 (100)
Hypertension
Yes n (%)7 (15.2)39 (84.8)1.701
p = 0.241		46 (100)
No n (%)8 (27.6)21 (72.4)29 (100)
Diabetes
Yes n (%)7 (36.8)12 (63.2)4.511
p = 0.048		19 (100)
No n (%)8 (14.3)48 (85.7)56 (100)
Smoking
Smokers n (%)3 (16.7)15 (83.3)0.079
p = 1		18 (100)
Non-smokers n (%)11 (19.6)45 (80.4)56 (100)
Concomitant drug users, n (%)
Antidiabetic drugs6 (35.3)11 (64.7)3.214
p = 0.091		17 (100)
Insulin3 (42.9)4 (57.1)2.521
p = 0.138		7 (100)
ACE inhibitors15 (21.4)55 (78.6)1.339
p = 0.576		70 (100)
Angiotensin II receptor antagonists1 (25)3 (75)0.066
p = 1		4 (100)
Beta adrenoblockers15 (21.1)56 (78.9)1.056
p = 0.578		71 (100)
Statins15 (20.3)59 (79.7)0.253
p = 1		74 (100)
Calcium Channel Blockers1 (20)4 (80)0.000
p = 1		5 (100)
Diuretics4 (25)12 (75)0.318
p = 0.725		16 (100)
Proton pump inhibitors2 (50)2 (50)2.377
p = 0.176		4 (100)
Blood test parameters
Platelet count (×109/L) median (min–max)224 (94–926)220 (112–1000)p = 0.632222 (94–926)
WBC (×109/L) median (min–max)9.43 (5.3–19.9)8.67 (4.34–16.9)p = 0.2518.77 (4.34–19.9)
C-reactive protein (mg/L) median (min–max)1.68 (1–249.5)6.33 (1–275.6)p = 0.1855.88 (1–275.6)
Creatinine (µmol/L) median (min–max)89 (73–303)86.5 (54–262)p = 0.67287 (54–303)
Hemoglobin (g/L) median (min–max)134 (107–153)130.5 (89–160)p = 0.543132 (89–160)
Platelet aggregation after induction with ADP (%)18 (6–59)18.5 (6–68)p = 0.79118 (6–68)
MI—myocardial infarction, STEMI—ST-elevation myocardial infarction, NSTEMI—a non-ST-elevation myocardial infarction, ACE inhibitors—angiotensin-converting enzyme inhibitors, WBC—white blood cells, ADP—adenosine diphosphate.

References

  1. Khan, M.A.; Hashim, M.J.; Mustafa, H.; Baniyas, M.Y.; Al Suwaidi, S.K.B.M.; AlKatheeri, R.; Alblooshi, F.M.K.; Almatrooshi, M.E.A.H.; Alzaabi, M.E.H.; Al Darmaki, R.S.; et al. Global Epidemiology of Ischemic Heart Disease: Results from the Global Burden of Disease Study. Cureus 2020, 12, e9349. [Google Scholar] [CrossRef]
  2. European Cardiovascular Disease Statistics. 2017. Available online: https://ehnheart.org/cvd-statistics/cvd-statistics-2017.html (accessed on 15 March 2022).
  3. An Official Website of the European Union. Available online: https://ec.europa.eu/eurostat/web/products-eurostat-news/-/edn-20200928-1 (accessed on 23 April 2022).
  4. Ou, J.S.; Li, H.M.; Shi, M.M.; Ou, Z.J. Ischemic Heart Disease. In Encyclopedia of Gerontology and Population Aging; Gu, D., Dupre, M., Eds.; Springer: Cham, Switzerland, 2020. [Google Scholar]
  5. Pahwa, R.; Jialal, I. Atherosclerosis. [Updated 8 August 2022]. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, January 2022; Available online: https://www.ncbi.nlm.nih.gov/books/NBK507799/ (accessed on 1 March 2022).
  6. Severino, P.; D’Amato, A.; Pucci, M.; Infusino, F.; Adamo, F.; Birtolo, L.I.; Netti, L.; Montefusco, G.; Chimenti, C.; Lavalle, C.; et al. Ischemic Heart Disease Pathophysiology Paradigms Overview: From Plaque Activation to Microvascular Dysfunction. Int. J. Mol. Sci. 2020, 21, 8118. [Google Scholar] [CrossRef]
  7. Buja, L.M.; Vander Heide, R.S. Pathobiology of Ischemic Heart Disease: Past, Present and Future. Cardiovasc. Pathol. 2016, 25, 214–220. [Google Scholar] [CrossRef]
  8. Jonsson, A.; Bäckhed, F. Role of gut microbiota in atherosclerosis. Nat. Rev. Cardiol. 2017, 14, 79–87. [Google Scholar] [CrossRef]
  9. Six, I.; Guillaume, N.; Jacob, V.; Mentaverri, R.; Kamel, S.; Boullier, A.; Slama, M. The Endothelium and COVID-19: An Increasingly Clear Link Brief Title: Endotheliopathy in COVID-19. Int. J. Mol. Sci. 2022, 23, 6196. [Google Scholar] [CrossRef]
  10. Xu, S.; Jin, T.; Weng, J. Endothelial Cells as a Key Cell Type for Innate Immunity: A Focused Review on RIG-I Signaling Pathway. Front. Immunol. 2022, 13, 951614. [Google Scholar] [CrossRef] [PubMed]
  11. Pothineni, N.V.K.; Subramany, S.; Kuriakose, K.; Shirazi, L.F.; Romeo, F.; Shah, P.K.; Mehta, J.L. Infections, atherosclerosis, and coronary heart disease. Eur. Heart J. 2017, 38, 3195–3201. [Google Scholar] [CrossRef]
  12. Carnevale, R.; Nocella, C.; Petrozza, V.; Cammisotto, V.; Pacini, L.; Sorrentino, V.; Martinelli, O.; Irace, L.; Sciarretta, S.; Frati, G.; et al. Localization of lipopolysaccharide from Escherichia Coli into human atherosclerotic plaque. Sci. Rep. 2018, 8, 3598. [Google Scholar] [CrossRef] [PubMed]
  13. Zdimal, A.M.; Davies, D.G. Laboratory Grown Biofilms of Bacteria Associated with Human Atherosclerotic Carotid Arteries Release Collagenases and Gelatinases during Iron-Induced Dispersion. Microbiol. Spectr. 2022, 10, e0100121. [Google Scholar] [CrossRef]
  14. Lanter, B.B.; Sauer, K.; Davies, D.G.; Cassone, A. Bacteria Present in Carotid Arterial Plaques Are Found as Biofilm Deposits Which May Contribute to Enhanced Risk of Plaque Rupture. mBio 2014, 5, e01206-14. [Google Scholar] [CrossRef]
  15. Macia, M.D.; Rojo-Molinero, E.; Oliver, A. Antimicrobial susceptibility testing in biofilm-growing bacteria. CMI 2014, 20, P981–P990. [Google Scholar] [CrossRef] [PubMed]
  16. Ceri, H.; Olson, M.E.; Stremick, C.; Read, R.R.; Morck, D.; Buret, A. The Calgary Biofilm Device: New technology for rapid determination of antibiotic susceptibilities of bacterial biofilms. J. Clin. Microbiol. 1999, 37, 1771–1776. [Google Scholar] [CrossRef] [PubMed]
  17. Niba, E.T.; Naka, Y.; Nagase, M.; Mori, H.; Kitakawa, M. A genome-wide approach to identify the genes involved in biofilm formation in E. coli. DNA Res. 2007, 14, 237–246. [Google Scholar] [CrossRef]
  18. Beloin, C.; Roux, A.; Ghigo, J.M. Escherichia coli biofilms. Curr. Top Microbiol. Immunol. 2008, 322, 249–289. [Google Scholar] [PubMed]
  19. Scott, P.M.; Erickson, K.M.; Troutman, J.M. Identification of the Functional Roles of Six Key Proteins in the Biosynthesis of Enterobacteriaceae Colanic Acid. Biochemistry 2019, 58, 1818–1830. [Google Scholar] [CrossRef] [PubMed]
  20. Yazdanpour, Z.; Tadjrobehkar, O.; Shahkhah, M. Significant association between genes encoding virulence factors with antibiotic resistance and phylogenetic groups in community acquired uropathogenic Escherichia coli isolates. BMC Microbiol. 2020, 20, 241. [Google Scholar] [CrossRef]
  21. Qin, Y.; He, Y.; She, Q.; Larese-Casanova, P.; Li, P.; Chai, Y. Heterogeneity in respiratory electron transfer and adaptive iron utilization in a bacterial biofilm. Nat. Commun. 2019, 10, 3702. [Google Scholar] [CrossRef]
  22. Johnson, J.S.; Spakowicz, D.J.; Hong, B.Y.; Petersen, L.M.; Demkowicz, P.; Chen, L.; Leopold, S.R.; Hanson, B.M.; Agresta, H.O.; Gerstein, M.; et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun. 2019, 10, 5029. [Google Scholar] [CrossRef]
  23. Fida, M.; Wolf, M.J.; Hamdi, A.; Vijayvargiya, P.; Esquer Garrigos, Z.; Khalil, S.; Greenwood-Quaintance, K.E.; Thoendel, M.J.; Patel, R. Detection of Pathogenic Bacteria from Septic Patients Using 16S Ribosomal RNA Gene-Targeted Metagenomic Sequencing. Clin. Infect. Dis. 2021, 73, 1165–1172. [Google Scholar] [CrossRef]
  24. Tsukuda, M.; Kitahara, K.; Miyazaki, K. Comparative RNA function analysis reveals high functional similarity between distantly related bacterial 16 S rRNAs. Sci. Rep. 2017, 7, 9993. [Google Scholar] [CrossRef]
  25. Narayana Iyengar, S.; Dietvorst, J.; Ferrer-Vilanova, A.; Guirado, G.; Muñoz-Berbel, X.; Russom, A. Toward Rapid Detection of Viable Bacteria in Whole Blood for Early Sepsis Diagnostics and Susceptibility Testing. ACS Sens. 2021, 6, 3357–3366. [Google Scholar] [CrossRef]
  26. Liu, C.F.; Shi, X.P.; Chen, Y.; Jin, Y.; Zhang, B. Rapid diagnosis of sepsis with TaqMan-Based multiplex real-time PCR. J. Clin. Lab Anal. 2018, 32, e22256. [Google Scholar] [CrossRef]
  27. Klyachkin, Y.M.; Idris, A.; Rodell, C.B.; Tripathi, H.; Ye, S.; Nagareddy, P.; Asfour, A.; Gao, E.; Annabathula, R.; Ratajczak, M.; et al. Cathelicidin Related Antimicrobial Peptide (CRAMP) Enhances Bone Marrow Cell Retention and Attenuates Cardiac Dysfunction in a Mouse Model of Myocardial Infarction. Stem. Cell Rev. Rep. 2018, 14, 702–714. [Google Scholar] [CrossRef]
  28. Zhao, H.; Yan, H.; Yamashita, S.; Li, W.; Liu, C.; Chen, Y.; Zhou, P.; Chi, Y.; Wang, S.; Zhao, B.; et al. Acute ST-segment elevation myocardial infarction is associated with decreased human antimicrobial peptide LL-37 and increased human neutrophil peptide-1 to 3 in plasma. J. Atheroscler. Thromb. 2012, 19, 357–368. [Google Scholar] [CrossRef] [PubMed]
  29. Chen, R.; Zhao, H.; Zhou, J.; Wang, Y.; Li, J.; Zhao, X.; Li, N.; Liu, C.; Zhou, P.; Chen, Y.; et al. Prognostic Impacts of LL-37 in Relation to Lipid Profiles of Patients with Myocardial Infarction: A Prospective Cohort Study. Biomolecules 2022, 12, 1482. [Google Scholar] [CrossRef]
  30. Bei, Y.; Pan, L.L.; Zhou, Q.; Zhao, C.; Xie, Y.; Wu, C.; Meng, X.; Gu, H.; Xu, J.; Zhou, L.; et al. Cathelicidin-related antimicrobial peptide protects against myocardial ischemia/reperfusion injury. BMC Med. 2019, 17, 42. [Google Scholar] [CrossRef] [PubMed]
  31. Zhao, H.; Sheng, Z.; Tan, Y.; Chen, R.; Zhou, J.; Li, J.; Zhao, Q.; Wang, Y.; Zhao, X.; Chen, Y.; et al. High Human Antimicrobial Peptide LL-37 Level Predicts Lower Major Adverse Cardiovascular Events after an Acute ST-Segment Elevation Myocardial Infarction. J. Atheroscler. Thromb. 2022, 29, 1499–1510. [Google Scholar] [CrossRef]
  32. Eberhard, J.; Jepsen, S.; Pohl, L.; Albers, H.K.; Açil, Y. Bacterial challenge stimulates formation of arachidonic acid metabolites by human keratinocytes and neutrophils in vitro. Clin. Diagn. Lab Immunol. 2002, 9, 132–137. [Google Scholar] [CrossRef] [PubMed]
  33. Tatarunas, V.; Kupstyte-Kristapone, N.; Zvikas, V.; Jakstas, V.; Zaliunas, R.; Lesauskaite, V. Factors associated with platelet reactivity during dual antiplatelet therapy in patients with diabetes after acute coronary syndrome. Sci. Rep. 2020, 10, 3175. [Google Scholar] [CrossRef]
  34. Tatarunas, V.; Kupstyte-Kristapone, N.; Norvilaite, R.; Tamakauskas, V.; Skipskis, V.; Veikutiene, A.; Jurgaityte, J.; Stuoka, M.; Lesauskaite, V. The impact of CYP2C19 and CYP4F2 variants and clinical factors on treatment outcomes during antiplatelet therapy. Pharmacogenomics 2019, 20, 483–492. [Google Scholar] [CrossRef]
  35. Peasey, A.; Bobak, M.; Kubinova, R.; Malyutina, S.; Pajak, A.; Tamosiunas, A.; Pikhart, H.; Nicholson, A.; Marmot, M. Determinants of cardiovascular disease and other non-communicable diseases in Central and Eastern Europe: Rationale and design of the HAPIEE study. BMC Public Health 2006, 6, 255. [Google Scholar] [CrossRef]
  36. Sidstedt, M.; Hedman, J.; Romsos, E.L.; Waitara, L.; Wadsö, L.; Steffen, C.R.; Vallone, P.M.; Rådström, P. Inhibition mechanisms of hemoglobin, immunoglobulin G, and whole blood in digital and real-time PCR. Anal. Bioanal. Chem. 2018, 410, 2569–2583. [Google Scholar] [CrossRef] [PubMed]
  37. Trung, N.T.; Hien, T.T.T.; Huyen, T.T.T.; Quyen, D.T.; Van Son, T.; Hoan, P.Q.; Phuong, N.T.K.; Lien, T.T.; Binh, M.T.; Van Tong, H.; et al. Enrichment of bacterial DNA for the diagnosis of blood stream infections. BMC Infect. Dis. 2016, 16, 235. [Google Scholar] [CrossRef] [PubMed]
  38. Shrirao, A.B.; Schloss, R.S.; Fritz, Z.; Shrirao, M.V.; Rosen, R.; Yarmush, M.L. Autofluorescence of blood and its application in biomedical and clinical research. Biotechnol. Bioeng. 2021, 118, 4550–4576. [Google Scholar] [CrossRef] [PubMed]
  39. Barghouthi, S.A. A Universal Method for the Identification of Bacteria Based on General PCR Primers. Indian J. Microbiol. 2011, 51, 430–444. [Google Scholar] [CrossRef] [PubMed]
  40. Li, W.; Xue, M.; Yu, L.; Qi, K.; Ni, J.; Chen, X.; Deng, R.; Shang, F.; Xue, T. QseBC is involved in the biofilm formation and antibiotic resistance in Escherichia coli isolated from bovine mastitis. Peer J. 2020, 8, e8833. [Google Scholar] [CrossRef]
  41. Tatarunas, V.; Kupstyte, N.; Giedraitiene, A.; Skipskis, V.; Jakstas, V.; Zvikas, V.; Lesauskaite, V. The impact of CYP2C19*2, CYP4F2*3, and clinical factors on platelet aggregation, CYP4F2 enzyme activity, and 20-hydroxyeicosatetraenoic acid concentration in patients treated with dual antiplatelet therapy. Blood Coagul. Fibrinolysis 2017, 28, 658–664. [Google Scholar] [CrossRef]
  42. Bellot, P.; García-Pagán, J.C.; Francés, R.; Abraldes, J.G.; Navasa, M.; Pérez-Mateo, M.; Such, J.; Bosch, J. Bacterial DNA translocation is associated with systemic circulatory abnormalities and intrahepatic endothelial dysfunction in patients with cirrhosis. Hepatology 2010, 52, 2044–2052. [Google Scholar] [CrossRef] [PubMed]
  43. Dinakaran, V.; Rathinavel, A.; Pushpanathan, M.; Sivakumar, R.; Gunasekaran, P.; Rajendhran, J. Elevated levels of circulating DNA in cardiovascular disease patients: Metagenomic profiling of microbiome in the circulation. PLoS ONE 2014, 9, e105221. [Google Scholar] [CrossRef]
  44. Amar, J.; Lange, C.; Payros, G.; Garret, C.; Chabo, C.; Lantieri, O.; Courtney, M.; Marre, M.; Charles, M.A.; Balkau, B.; et al. Blood microbiota dysbiosis is associated with the onset of cardiovascular events in a large general population: The D.E.S.I.R. study. PLoS ONE 2013, 8, e54461. [Google Scholar] [CrossRef]
  45. Hammad, D.B.M.; Hider, S.L.; Liyanapathirana, V.C.; Tonge, D.P. Molecular Characterization of Circulating Microbiome Signatures in Rheumatoid Arthritis. Front. Cell Infect Microbiol. 2020, 9, 440. [Google Scholar] [CrossRef]
  46. Sato, J.; Kanazawa, A.; Ikeda, F.; Yoshihara, T.; Goto, H.; Abe, H.; Komiya, K.; Kawaguchi, M.; Shimizu, T.; Ogihara, T.; et al. Gut dysbiosis and detection of "live gut bacteria" in blood of Japanese patients with type 2 diabetes. Diabetes Care 2014, 37, 2343–2350. [Google Scholar] [CrossRef]
  47. Snow, D.E.; Everett, J.; Mayer, G.; Cox, S.B.; Miller, B.; Rumbaugh, K.; Wolcott, R.A.; Wolcott, R.D. The presence of biofilm structures in atherosclerotic plaques of arteries from legs amputated as a complication of diabetic foot ulcers. J. Wound Care 2016, 25, S16–S22. [Google Scholar] [CrossRef]
  48. Vestby, L.K.; Grønseth, T.; Simm, R.; Nesse, L.L. Bacterial Biofilm and its Role in the Pathogenesis of Disease. Antibiotics 2020, 9, 59. [Google Scholar] [CrossRef] [PubMed]
  49. Anhê, F.F.; Jensen, B.A.H.; Varin, T.V.; Servant, F.; Van Blerk, S.; Richard, D.; Marceau, S.; Surette, M.; Biertho, L.; Lelouvier, B.; et al. Type 2 diabetes influences bacterial tissue compartmentalisation in human obesity. Nat. Metab. 2020, 2, 233–242. [Google Scholar] [CrossRef] [PubMed]
  50. Madacki-Todorović, K.; Eminović, I.; Mehmedinović, N.I.; Ibrišimović, M. Insulin Acts as Stimulatory Agent in Diabetes-Related Escherichia Coli Pathogenesis. Int. J. Diabetes Clin. Res. 2018, 5, 098. [Google Scholar]
  51. Patel, N.; Curtis, J.C.; Plotkin, B.J. Insulin Regulation of Escherichia coli Abiotic Biofilm Formation: Effect of Nutrients and Growth Conditions. Antibiotics 2021, 10, 1349. [Google Scholar] [CrossRef]
  52. Danese, P.N.; Pratt, L.A.; Kolter, R. Exopolysaccharide production is required for the development of Escherichia coli K-12 biofilm architecture. J. Bacteriol. 2000, 182, 3593–3596. [Google Scholar] [CrossRef]
  53. Zhang, J.; Poh, C.L. Regulating exopolysaccharide gene wcaF allows control of Escherichia coli biofilm formation. Sci. Rep. 2018, 8, 13127. [Google Scholar] [CrossRef]
  54. Norgren, M.; Båga, M.; Tennent, J.M.; Normark, S. Nucleotide sequence, regulation and functional analysis of the papC gene required for cell surface localization of Pap pili of uropathogenic Escherichia coli. Mol. Microbiol. 1987, 1, 169–178. [Google Scholar] [CrossRef]
  55. Harwalkar, A.; Gupta, S.; Rao, A.; Srinivasa, H. Lower prevalence of hlyD, papC and cnf-1 genes in ciprofloxacin-resistant uropathogenic Escherichia coli than their susceptible counterparts isolated from southern India. J. Infect. Public Health. 2014, 7, 413–419. [Google Scholar] [CrossRef]
  56. Das, U.N. Arachidonic acid and other unsaturated fatty acids and some of their metabolites function as endogenous antimicrobial molecules: A review. J. Adv. Res. 2018, 11, 57–66. [Google Scholar] [CrossRef]
  57. Blasco-Baque, V.; Garidou, L.; Pomié, C.; Escoula, Q.; Loubieres, P.; Le Gall-David, S.; Lemaitre, M.; Nicolas, S.; Klopp, P.; Waget, A.; et al. Periodontitis induced by Porphyromonas gingivalis drives periodontal microbiota dysbiosis and insulin resistance via an impaired adaptive immune response. Gut 2016, 66, 872–885. [Google Scholar] [CrossRef]
  58. Ellermann, M.; Jimenez, A.G.; Pifer, R.; Ruiz, N.; Sperandio, V. The Canonical Long-Chain Fatty Acid Sensing Machinery Processes Arachidonic Acid to Inhibit Virulence in Enterohemorrhagic Escherichia coli. mBio 2021, 12, e03247-20. [Google Scholar] [CrossRef]
  59. Beavers, W.N.; Monteith, A.J.; Amarnath, V.; Mernaugh, R.L.; Roberts, L.J.; Chazin, W.J.; Davies, S.S.; Skaar, E.P. Arachidonic Acid Kills Staphylococcus aureus through a Lipid Peroxidation Mechanism. mBio 2019, 10, e01333-19. [Google Scholar] [CrossRef]
  60. Abot, A.; Wemelle, E.; Laurens, C.; Paquot, A.; Pomie, N.; Carper, D.; Bessac, A.; Orea, X.M.; Fremez, C.; Fontanie, M.; et al. Identification of new enterosynes using prebiotics: Roles of bioactive lipids and mu-opioid receptor signalling in humans and mice. Gut 2021, 70, 1078–1087. [Google Scholar] [CrossRef]
  61. Kulkarni, A.; Nadler, J.L.; Mirmira, R.G.; Casimiro, I. Regulation of Tissue Inflammation by 12-Lipoxygenases. Biomolecules 2021, 11, 717. [Google Scholar] [CrossRef]
  62. Ridyard, K.E.; Overhage, J. The Potential of Human Peptide LL-37 as an Antimicrobial and Anti-Biofilm Agent. Antibiotics 2021, 10, 650. [Google Scholar] [CrossRef]
  63. Linde, A.; Lushington, G.H.; Abello, J.; Melgarejo, T. Clinical Relevance of Cathelicidin in Infectious Disease. J. Clin. Cell Immunol. 2013, S13. [Google Scholar] [CrossRef]
  64. Pahar, B.; Madonna, S.; Das, A.; Albanesi, C.; Girolomoni, G. Immunomodulatory Role of the Antimicrobial LL-37 Peptide in Autoimmune Diseases and Viral Infections. Vaccines 2020, 8, 517. [Google Scholar] [CrossRef] [PubMed]
  65. Aggarwal, D.; Kanitkar, T.; Narouz, M.; Azadian, B.S.; Moore, L.S.; Mughal, N. Clinical utility and cost-effectiveness of bacterial 16S rRNA and targeted PCR based diagnostic testing in a UK microbiology laboratory network. Sci. Rep. 2020, 10, 7965. [Google Scholar] [CrossRef] [PubMed]
  66. Catchpool, M.; Ramchand, J.; Martyn, M.; Hare, D.L.; James, P.A.; Trainer, A.H.; Knight, J.; Goranitis, I. A cost-effectiveness model of genetic testing and periodical clinical screening for the evaluation of families with dilated cardiomyopathy. Genet. Med. 2019, 21, 2815–2822. [Google Scholar] [CrossRef] [PubMed]
Diagnostics 14 00546 g001
Figure 1. Flowchart. Study design including exclusion and inclusion criteria and performed procedures.
Figure 1. Flowchart. Study design including exclusion and inclusion criteria and performed procedures.
Diagnostics 14 00546 g001
Diagnostics 14 00546 g002
Figure 2. The scheme shows possible mechanisms of interaction between bacteria and host metabolites arachidonic acid (ARA) and LL-37. (A) ARA and LL-37 attack bacterial membranes; (B) Atherosclerosis in blood vessels, bacteria in biofilms, high ARA metabolites produced by COX, high LL-37 levels.
Figure 2. The scheme shows possible mechanisms of interaction between bacteria and host metabolites arachidonic acid (ARA) and LL-37. (A) ARA and LL-37 attack bacterial membranes; (B) Atherosclerosis in blood vessels, bacteria in biofilms, high ARA metabolites produced by COX, high LL-37 levels.
Diagnostics 14 00546 g002
Table 1. 16S rDNA primers used in blood-direct PCR.
Table 1. 16S rDNA primers used in blood-direct PCR.
16S rDNA PrimerPrimer SequenceOligomer Location
F3 (Forward)5′-GATACCCTGGTAGTCCA-3′753–769
R3 (Reverse)5′-TGGACTACCAGGGTATC-3′769–752
F4 (Forward)5′-CCGCCTGGGGAGTACG-3′840–856
R4 (Reverse)5′-CGTACTCCCCAGGCGG-3′856–840
F5 (Forward)5′-CCTACGGGAGGCAGCAG-3′326–343
F6 (Forward)5′-GCAGCCGCGGTAATAC-3′481–497
R1b (Reverse)5′-TACCTTGTTACGACTTC-3′1468–1451
Table 2. Primer mixtures used for 16S rDNA blood direct PCR.
Table 2. Primer mixtures used for 16S rDNA blood direct PCR.
General MixturesOligomers Used in Mixtures
Reaction oneF5, F6, R1b
Reaction twoF5, F6, R3
Reaction two (b)F5, F6, R3, R4
Reaction threeF3, F4, R1b
Table 3. PCR program for 16S rDNA.
Table 3. PCR program for 16S rDNA.
StepTemperature (°C)TimeCycles
Initialization955 min1
Denaturation9530 s35
Annealing4830 s
Elongation5030 s
Table 4. Oligonucleotide primers used for the amplification of wcaF, papC, and sdhC.
Table 4. Oligonucleotide primers used for the amplification of wcaF, papC, and sdhC.
GenePrimerPrimer SequenceAmplification Product Size (bp)Reference
wcaFForward5′-TCTCGGTGCCGAAAGGGTTC-3′236[40]
Reverse5′-ATTGACGTCATCGCCGACCC-3′
papCForward5′-GACGGCTGTACTGCAGGGTGTGGCG-3′328[31]
Reverse5′-ATATCCTTTCTGCAGGGATGCAATA-3′
sdhCForward5′-CGCCAGCCGCCCAGCACAG-3′285[32]
Reverse5′-GGTATGGAAGGTCTGTTCCGTCA
GATTGGTATTTACAGCCC-3′
Table 5. PCR program for wcaF, papC, and sdhC amplification.
Table 5. PCR program for wcaF, papC, and sdhC amplification.
StepTemperature (°C)TimeCycles
Initialization985 min1
Denaturation985 s40
Annealing5815 s
Elongation7230 s
Table 6. Prevalence of 16S rDNA and genes responsible for biofilm formation in blood samples of women (n = 36) and men (n = 39).
Table 6. Prevalence of 16S rDNA and genes responsible for biofilm formation in blood samples of women (n = 36) and men (n = 39).
GenesGene DetectionBlood Samples from Female Patients, n (%)Blood Samples from Male Patients,
(n) %		p-Value
16S rDNADetected8 (22.2)15 (38.5)0.142
Not detected28 (77.8)24 (61.5)
wcaFDetected3 (8.3)7 (17.9)0.176
Not detected33 (91.7)39 (82.1)
papCDetected0 (0)0 (0)–
Not detected36 (100)39 (100)
sdhCDetected1 (2.8)5 (12.8)0.089
Not detected35 (97.2)34 (87.2)
Note: p—significance level.
Table 7. Presence of bacterial genes and concentration of LL-37 in patient blood.
Table 7. Presence of bacterial genes and concentration of LL-37 in patient blood.
n (%)LL-37 Concentration (ng/mL)
Median (Min–Max)		p
16S rDNA
Present18 (30.5)4 (1.3–11.8)0.014
Absent41 (69.5)2.8 (0.8–9.2)
Total59 (100)2.9 (0.8–11.8)
Biofilm-associated genes
Present10 (16.9)4.4 (1.3–8.4)0.03
Absent49 (83.1)2.8 (0.8–11.8)
Total59 (100)2.9 (0.8–11.8)
Table 8. The concentration of arachidonic acid metabolites in patients with Enterobacterales genes in their blood.
Table 8. The concentration of arachidonic acid metabolites in patients with Enterobacterales genes in their blood.
VariableEnterobacterales Genes DetectedEnterobacterales Genes Not Detectedp-ValueTotal
5S-HETE (ng/mL) (min–max)21 (6.4–68.1)12.7 (1.5–57)0.07713.7 (1.5–68.1)
9-HETE (ng/mL) (min–max)3.9 (1.6–14)3.2 (0.1–20.7)0.2073.3 (0.1–20.7)
12S-HETE (ng/mL) (min–max)6.4 (1–20.9)1.9 (0.2–30.5)0.0463 (0.2–30.5)
15S-HETE (ng/mL) (min–max)5.3 (3.4–80.9)8.6 (3–289)0.4467.2 (3–289)
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Giedraitiene, A.; Tatarunas, V.; Kaminskaite, K.; Meskauskaite, U.; Boieva, S.; Ajima, Y.; Ciapiene, I.; Veikutiene, A.; Zvikas, V.; Kupstyte-Kristapone, N.; et al. Enterobacterales Biofilm-Specific Genes and Antimicrobial and Anti-Inflammatory Biomarkers in the Blood of Patients with Ischemic Heart Disease. Diagnostics 2024, 14, 546. https://doi.org/10.3390/diagnostics14050546

AMA Style

Giedraitiene A, Tatarunas V, Kaminskaite K, Meskauskaite U, Boieva S, Ajima Y, Ciapiene I, Veikutiene A, Zvikas V, Kupstyte-Kristapone N, et al. Enterobacterales Biofilm-Specific Genes and Antimicrobial and Anti-Inflammatory Biomarkers in the Blood of Patients with Ischemic Heart Disease. Diagnostics. 2024; 14(5):546. https://doi.org/10.3390/diagnostics14050546

Chicago/Turabian Style

Giedraitiene, Agne, Vacis Tatarunas, Kornelija Kaminskaite, Ugne Meskauskaite, Svitlana Boieva, Yu Ajima, Ieva Ciapiene, Audrone Veikutiene, Vaidotas Zvikas, Nora Kupstyte-Kristapone, and et al. 2024. "Enterobacterales Biofilm-Specific Genes and Antimicrobial and Anti-Inflammatory Biomarkers in the Blood of Patients with Ischemic Heart Disease" Diagnostics 14, no. 5: 546. https://doi.org/10.3390/diagnostics14050546

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop