Quantification of Streptococcus salivarius using the digital polymerase chain reaction as a liver fibrosis marker

Abstract

BACKGROUND

The Streptococcus salivarius (S. salivarius) group, which produces the enzyme urease has been identified as a potential contributor to ammonia production in the gut. Researchers have reported that patients with minimal HE had an increased abundance of the S. salivarius group, which is a specific change in the gut microbiota that distinguishes them from healthy individuals. The correlation between the aggregation of specific bacterial species and fibrosis progression in chronic liver disease (CLD) is yet to be fully elucidated.

AIM

To quantify S. salivarius using digital PCR (dPCR) as a liver fibrosis marker of CLD.

METHODS

This study retrospectively analysed 52 patients with CLD. To quantify S. salivarius in patients with CLD using dPCR, we evaluated the specificity and sensitivity of S. salivarius bacterial load using dPCR for a type strain. Next, we evaluated the clinical usefulness of dPCR for S. salivarius load quantification for detecting liver fibrosis in patients with CLD. The liver fibrosis stage was categorized into mild and advanced fibrosis based on pathological findings.

RESULTS

The dPCR assay revealed that S. salivarius was highly positive for the tnpA gene. The lower limit of quantification for dPCR using the tnpA gene with a 1 μL template comprising 1.28 × 10² CFU/mL was 4.3 copies. After considering the detection range in dPCR, we adjusted the extracted DNA concentration to 5.0 × 10^-4 ng/μL from 200 mg stool samples. The median bacterial loads of S. salivarius in stool sample from patients with mild and advanced fibrosis were 1.9 and 7.4 copies/μL, respectively. The quantification of S. salivarius load was observed more frequently in patients with advanced fibrosis than in those with mild fibrosis (P = 0.032).

CONCLUSION

Quantifying of S. salivarius load using digital PCR is a useful biomarker for liver fibrosis in patients with CLD.

Key Words: Chronic liver disease; Streptococcus salivarius; Digital PCR; Liver fibrosis; Liver cirrhosis; Quantification

Core Tip: This is a retrospective single-centre observational study and the first to quantify Streptococcus salivarius (S. salivarius) load in stool samples as a liver fibrosis marker for patients with chronic liver disease (CLD) Using digital PCR (dPCR). DPCR is a high precision technique for measuring the absolute quantity of the bacterial species. The quantification of S. salivarius using dPCR is proposed as a potentially useful biomarker for liver fibrosis in patients with CLD.

INTRODUCTION

The human microbiome comprises a vast community of microorganisms that inhabit various internal and external parts of the human body[1]. The gut microbiome, which is one of the most influential microbial communities in the human body, plays a crucial role in various physiological functions, including digestion, metabolism, immune system development, and protection against pathogens[2].

Advancements in molecular biology have revealed that an imbalanced gut microbiome can contribute to chronic inflammation in the liver, leading to the development of conditions such as liver fibrosis[3-5]. Changes in the balance and diversity of the gut microbiota, known as dysbiosis, can cause an imbalance in microbial metabolites. Some bacteria produce metabolites and toxins that can be harmful to the host. The portal vein connects the gastrointestinal tract, including the intestines where the gut microbiota resides, to the liver. The influx of harmful substances from the gut into the liver through the portal vein promotes liver inflammation and the progression of chronic liver disease (CLD)[6,7].

Some researchers have identified the ammonia-contributing factors of gut microbiota dysbiosis in patients with CLD[8,9]. It is an important biological reaction commonly associated with certain bacteria, including those that inhabit the gastrointestinal tract. The ability of streptococci to produce urease and amplify its activity is a significant biological function with potential implications for the gut environment. The hydrolysis of urea by urease produces ammonia, which is basic, leading to an increase in local pH. Therefore, Streptococci with amplified urease activity could potentially contribute to alterations in gut pH. Changes in gut pH can impact the growth and survival of different bacterial species. Consequently, dysbiosis of the gut microbiota contributes significantly to liver inflammation, fibrosis, and other CLD-related complications[10]. The association between liver cirrhosis and an increased abundance of Streptococcus salivarius (S. salivarius) in the gut microbiota has been reported, and this particular change distinguishes patients with cirrhosis from those without cirrhosis[11].

Modulation of the gut microbiota, often referred to as microbiota-targeted therapy, holds promise for treating CLD. Interventions to modulate the composition of the gut microbiota, including the control of specific microbial strains such as S. salivarius, offer a novel strategy for treating CLD. However, metagenomic analysis, which involves sequencing the entire gut microbiota genetic material o, is expensive and resource-intensive. This process involves multiple steps including DNA extraction, library preparation, high-throughput sequencing, and extensive computational analyses. Therefore, concentrated efforts have been made to optimize and streamline metagenomic workflows[12,13]. Of these, molecular biology techniques such as digital PCR (dPCR) used for the quantification of specific microbes can reduce the cost and complexity associated with studying the gut microbiome[14-17]. However, the correlation between the aggregation of specific bacterial species of the genus Streptococcus and fibrosis progression in CLD is complex and yet to be fully elucidated using screening approaches such as dPCR. Therefore, the present study aimed to quantify S. salivarius as a marker of liver fibrosis in patients with CLD using dPCR.

MATERIALS AND METHODS

Ethics

This study protocol was approved by the Institutional Review Board Ethics Committees of Kitasato University School of Medicine (approval number: C21-212). Informed consent was obtained from all participants. This study was registered with the UMIN Clinical Trials Registry (UMIN 000048201).

Population

The study retrospectively analysed 70 patients with CLD (diagnosed by liver biopsy) based on faecal samples collected in a prospective cohort study at the Kitasato University School of Medicine between January 2019 and August 2023. Of these, 18 were excluded from the study because of a history of malignancies other than hepatocellular carcinoma (HCC) or antibiotic use within three months. Patients with CLD cases include those with metabolic dysfunction-associated fatty liver disease (MAFLD) and non-MAFLD. The definition of MAFLD is provided in Supplementary material[18].

Study design

To quantify S. salivarius as a marker of liver fibrosis in patients with CLD using dPCR, we evaluated the analytical specificity, sensitivity, and repeatability of the dPCR assay in quantifying the bacterial load of S. salivarius for the type strain obtained from the Japan Collection of Microorganisms (JCM). Analytical specificity was evaluated using primers and probes specific for S. salivarius among 16 common microorganisms (Supplementary Table 1). Sensitivity was evaluated using a limit of quantification (LOQ). Repeatability was evaluated using intra-assay coefficients of variability (CV), which were used to express the relative variability of a set of data points. Furthermore, quantification of the S. salivarius bacterial load using dPCR was validated or cross-verified using quantitative PCR (qPCR) with the same set of primers and probes.

Next, we evaluated the clinical usefulness of t S. salivarius load quantification using dPCR for detecting liver fibrosis in patients with CLD. The pathological assessment of liver fibrosis was conducted by a pathologist at our institution to ensure the accuracy and reliability of fibrosis staging. The fibrosis stages were classified based on steatosis, activity, and fibrosis scoring[19]. Furthermore, these stages were categorised into mild fibrosis or advanced fibrosis groups, depending on the presence or absence of bridging fibrosis, respectively, based on pathological findings. We evaluated the clinical factors influencing of S. salivarius load quantification in patients with CLD, except at the fibrosis stage.

Faecal sample collection and DNA extraction

Faecal samples were collected from the enrolled patients using collection kits. To preserve the microbial community and prevent degradation, the samples were storedat 4 °C (refrigeration) and -80 °C (freezing) for short- and long-term storage, respectively.

Microbial DNA was extracted using the DNeasy PowerSoil Kit (QIAGEN). This kit was designed to efficiently extracts DNA from environmental samples, including soil and faecal samples.

Extraction of DNA from standard strains

The selected strains of the genus Streptococcus were inoculated into Todd-Hewitt broth (Difco, Becton, Dickinson and Company, MD, United States) with 0.3% yeast extract (THY) medium, and the others were inoculated in brain heart infusion (BHI, Difco) medium (Supplementary Table 1). Bifidobacterium bifidum JCM 1255^T, Bacteroides uniformis JCM 5828^T, Enterococcus faecalis JCM 5803^T, Clostridium ramosum JCM 1298^T, Eggerthella lenta JCM 9979^T, and Blautia coccoides JCM 1395^T were cultured overnight at 35 °C under anaerobic conditions, and other bacteria were cultured overnight at 37 °C under aerobic conditions. The cultures were collected and centrifuged, and bacterial DNAs samples were extracted from the pellet using a NucleoSpin^® Microbial DNA Kit (TaKaRa Bio).

Measurement methods of dPCR and qPCR

The primers and probes were designed to target conserved regions within the 16S rRNA for Streptococcus spp. (Stc16S primer set) and tnpA gene specific to S. salivarius (Sal-tnpA primer set)[20-22].

dPCR was performed using a QX200 Droplet Digital PCR system (Bio-Rad, Hercules, CA, United States). The mastermix used in the dPCR assay comprised 1 × dPCR multiplex Supermix for Probes (Bio-Rad), 900 nM target primers, and 250 nM target probes with sample DNA. Subsequent amplification was performed in a Veriti 96-well thermal cycler (Applied Biosystems) under the following PCR conditions: 95 °C for 10 minutes enzyme activation followed by 45 cycles of denaturation at 94 °C for 30 seconds, 59 °C for 2 minutes and enzyme deactivation at 98 °C for 10 minutes[23].

qPCR assays for the target S. salivarius were conducted using LightCycler^® 96 (Roche). The total reaction volume for qPCRs was 20 μL, which included each template DNA, 500 nM target primers, 200 nM target probes, 10 μL of Probe FastStart Essential DNA ProbeMaster (Roche), and 9 μL of PCR grade water. The qPCR conditions were as follows: 95°C for 10 minutes, followed by 40 cycles at 95 °C for 30 seconds and at 60°C.

Validation of qPCR and dPCR using colony forming units

To validate qPCR and dPCR results, the S. salivarius strain was cultivated in Todd-Hewitt broth (Difco, Becton, Dickinson and Company, MD, United States) with 0.3% THY broth overnight at 37 °C. The cultured broth was dispensed into 0.1 mL portions for DNA extraction and colony counting. To extract DNA, the cultured broth was centrifuged for 1 min at 10000 × g, and washed with Tris-EDTA buffer TE (pH = 8.0). The cells were resuspended in TE buffer and heated at 95 °C for 10 minutes. The suspensions were centrifuged, and the supernatants containing the DNA templates were collected. To calculate the colony forming units (CFUs) of the DNA template, 0.1 mL of the cultured broth was diluted with PBS and plated on BHI (Difco) agar medium. The CFU of the DNA template was calculated from the number of colonies grown on the plate.

Statistical analysis

IBM SPSS Statistics software (version 23.0 New York, NY, United States) was used for statistical analyses. P ≤ 0.05 indicated statistical significance. Continuous variables were expressed as the median and interquartile range in statistical reporting. The test statistic of χ² was calculated based on the observed frequencies in the contingency table. The Mann-Whitney U test was used to compare distributions of dPCR quantification between the presence of advanced fibrosis in load.

RESULTS

Analytical specificity of the bacterial load for S. salivarius using dPCR

Figure 1 shows the positive droplets of 16 common microorganisms using the primer sets of Stc16S and Sal-tnpA with channel amplitude signals. The dPCR assay revealed that the S. salivarius group, including S. salivarius, Streptococcus thermophilus (S. thermophilus), and Streptococcus vestibularis (S. vestibularis) type strains, were highly positive for the Sal-tnpA primer set. Streptococcus spp. and Enterococcus faecalis (E. faecalis) were positive for the Stc16S primer set. Clostridium ramosum was mildly positive for the primer sets of Stc16S and Sal-tnpA. dPCR using the Sal-tnpA primer set showed excellent specificity, with no cross-reactivity to 13 different microorganisms.

Figure 1 Specificity of primers and probes for Streptococcus salivarius. Positive droplets of Streptococcus salivarius with channel amplitude signals (Blue signal: tnpA; Green signal: 16S rRNA). A: Digital PCR (dPCR) assay targeting both the tnpA and 16S rRNA gene to 8 kinds of Streptococcus spp.; B: dPCR assay targeting both the tnpA and 16S rRNA gene to 8 kinds of common microorganisms of the genitourinary tract.

Analytical sensitivity and repeatability of the bacterial load for S. salivarius using dPCR

Figure 2 shows a comparison of bacterial load measured using dPCR (copy number/μL) and bacterial CFU measured using the dilution plate method (CFU/mL). The dPCR assay targeting both the tnpA and 16S rRNA genes reflected the change in DNA concentration. A 5-fold dilution of DNA resulted in a 5-fold decrease in the final readouts indicating unbiased detection of S. salivarius (Figure 2, Supplementary Figure 1).

Figure 2 Comparison of bacterial load measured using digital PCR and bacterial titremeasured using plaque assay. A: Profiles of digital PCR (dPCR) assays targeting the tnpA on bacterial suspensions of Streptococcus salivarius (S. salivarius) strain; B: Profiles of dPCR assays targeting 16S rRNA in S. salivarius bacterial suspensions. Fluorescence amplitude plot of dPCR assays on bacterial suspensions at 5-fold diluted concentrations from 2.00 × 10⁶ to 1.28 × 10² colony forming unit/mL. The blue line represents a threshold to distinguish between positive and negative droplets based on the fluorescence signal. Droplets with a fluorescence signal above this threshold are classified as positive, indicating the presence of amplification. dPCR: Digital PCR; CV: Coefficients of variability; CFU: Colony forming unit.

The lower limit of detections with dPCR with the 1 μL template comprising 1.28 × 10² CFU/mL were 6 copies of the 16S rRNA and 4.3 copies of the tnpA gene. However, analysis using substantialdroplets was complex. The upper LOQ for the dPCR assay was about 20000 copies/mL. Beyond this limit, the assay may become less reliable, and the risk of false positives due to overlapping signals or background noise may increase.

The intra-assay CV values of the bacterial load for S. salivarius using the Sal-tnpA primer set in each 5-fold dilution of input microorganisms, were 0.07, 0.07, 0.07, 0.05, 0.16, and 0.16, respectively. The intra-assay CV values of the bacterial load for S. salivarius using the Stc16S primer set in each 5-fold dilution of input microorganisms were 0.07, 0.04, 0.07, 0.06, and 0.16, respectively. The dPCR assay for two different target genes exhibited excellent intra-assay coefficients of CV. A high intra-assay CV in dPCR was observed at template DNA concentration of 8.00 × 10⁴ and 4.00 × 10⁵ CFU/mL.

Comparison of dPCR and qPCR using standard strains

Figure 3 shows a comparison of bacterial load measured using dPCR (copy number/μL) and qPCR (Ct value). A statistically significant correlation was observed between the results obtained using dPCR and qPCR assays targeting the tnpA and 16S rRNA genes.

Figure 3 Comparison of bacterial load measured using digital PCR and quantitative PCR. A: Quantitative PCR (qPCR) can reliably detect bacterial concentrations down to 6.40 × 10² colony forming unit (CFU)/mL using a 1 μL sample volume; B: Digital PCR has a higher sensitivity than qPCR, as it can detect lower concentrations of bacteria (6.40 × 10² CFU/mL) with greater accuracy and precision. dPCR: Digital PCR; qPCR: Quantitative PCR; CFU: Colony forming unit.

The lower LOQs for qPCR using the1 μL template 6.4 × 10² CFU/mL were 31 Ct of the 16S rRNA and 33 Ct of the tnpA gene. dPCR was more sensitive than qPCR in detecting low concentrations of bacteria.

Quantitation of S. salivarius load based on dPCR in patients with CLD

After considering the dPCR range of detection, we adjusted the extracted DNA concentration to 5 × 10^-4 ng/μL from 200 mg stool samples (Supplementary Figure 2). The median bacterial load for S. salivarius in stool samples based on the primer sets of Sal-tnpA and Stc16S were 2.5 (0.5-160) and 32.8 (0.9-1257) copies/mL, respectively. The correlation coefficient of the detected bacterial load of S. salivarius using the primer sets of Sal-tnpA and streptococci using the primer sets of Stc16S was 0.944. These measurements showed strong correlations for all ranges.

When a threshold of > 1 copy/μL load for S. salivarius in stool samples based on the dPCR using tnpA was defined as positive, 36 patients met this criterion. An association between a positive group based on the defined threshold using the primer sets of Sal-tnpA and a higher frequency of advanced fibrosis among the patients (P = 0.023). was observed. Furthermore, the age was higher in the positive group compared to the negative group (P = 0.006). The presence of HCC was higher in the positive group compared to the negative group (P = 0.017). The proportion of patients using gastric acid secretion inhibitors was higher in the positive group than in the negative group; however, this difference was not statistically significant (P = 0.146). No significant differences were observed between the two groups in terms of sex, weight, the frequency of MAFLD, hepatic encephalopathy, ascites, white blood cells, platelets count, aspartate aminotransferase, alanine aminotransferase, and total bilirubin (Table 1).

Table 1 Baseline clinical characteristics.

Clinical characteristics	All patients	Positive (≤ 1 copy/ μL)	Negative (> 1 copy/ μL)	P value
Number of patients	52	36	16
Age (year)	65.0 (24-84)	68.5 (24-84)	55.0 (41-74)	0.006
Gender: Male [n (%)]	31 (54.3)	23 (63.8)	8 (50.0)	0.450
Weight(kg)	64 (41-121)	64 (41-121)	70 (47-92)	0.234
Body mass index (kg/m2)	25.1 (16.1-46.1)	24.9 (16.0-46.1)	25.3 (16.1-32.1)	0.706
MAFLD/non-MAFLD (n)	28/24	19/17	9/7	0.772
Alcohol [n (%)]	4 (7.7)	2 (5.6)	2 (12.5)	0.578
Hepatocellular carcinoma [n (%)]	10 (19.2)	10 (27.8)	0	0.017
Fibrosis stage 0/1/2/3/4 (n)	4/20/9/13/6	1/12/7/11/5	3/8/2/2/1	0.023
Mild/advance fibrosis (n)	33/19	20/16	13/3
Hepatic encephalopathy [n (%)]	2 (3.8)	2 (5.6)	0	0.587
Hepatic ascites [n (%)]	2 (3.8)	2 (5.6)	0	0.587
Gastric acid secretion inhibitor [n (%)]	13 (25.0)	11 (30.6)	2 (12.5)	0.146
White blood cells (/μL)	5400 (2400-12200)	5250 (3100-12200)	6250 (2400-9300)	0.201
Platelets (× 104/μL)	18.7 (6.3-47.0)	17.3 (6.3-40.7)	19.9 (10.6-30.0)	0.204
Prothrombin time (%)	94 (40-107)	99.0 (40-114)	100 (61-107)	0.571
Serum albumin (g/dL)	4.2 (2.5-5.0)	4.2 (2.5-5.0)	4.3 (3.4-4.8)	0.068
Aspartate aminotransferase (IU/L)	36.5 (16-138)	35.5 (16-138)	37.5 (18-127)	0.684
Alanine aminotransferase (IU/L)	46.0 (11-179)	36.5 (11-179)	48.5 (14-146)	0.252
Total bilirubin (mg/dL)	0.7 (0.4-4.9)	0.7 (0.4-4.9)	0.8 (0.4-2.0)	0.114

MAFLD: Metabolic dysfunction-associated fatty liver disease.

Correlation between fibrosis stage and quantitation of S. salivarius load

An association was observed between increasing S. salivarius load based on the dPCR and liver fibrosis progression in patients with CLD. The median bacterial loads of S. salivarius from stool sample using the primer sets of Sal-tnpA in patients with mild and severe fibrosis were 1.9 and 7.4 copies/μL, respectively. The median bacterial loads of streptococci using the primer sets of Stc16S in patients with mild and severe fibrosis were 21.7 and 62.6 copies/μL, respectively. The bacterial load of S. salivarius was observed more frequently in patients with advanced fibrosis than in those with mild fibrosis (Stc16S, P = 0.010 and Sal-tnpA, P = 0.032; Figure 4).

Figure 4 Correlation between the bacterial load. A: Correlation between fibrosis stage and the bacterial load measured using digital PCR (dPCR) with tnpA; B: Correlation between fibrosis stage and the bacterial load measured using dPCR with 16S rRNA; C: Comparisons between mild and severe fibrosis in the bacterial load measured using dPCR with tnpA; D: Comparisons between mild and severe fibrosis in the bacterial load measured using dPCR with 16S rRNA.

Receiver operating characteristic curve analysis

We performed a receiver operating characteristic (ROC) analysis to assess advanced fibrosis in patients with CLD. The cut-off points for advanced fibrosis were estimated using ROC curves of S. salivarius and streptococci bacterial loads using the primer sets Sal-tnpA and Stc16S, respectively, by identifying the threshold value that optimally differentiatedbetween patients with and without advanced fibrosis. Using a cut-off value of 3.9 for the primer sets of Sal-tnpA to, predict the presence of advanced liver fibrosis resulted ina sensitivity of 64.3% and a specificity of 82.1%. Using a cut-off value of 28.5 for the primer sets of Stc16S to, predict the presence of advanced fibrosis resulted in a sensitivity of 71.4% and a specificity of 71.4% (Figure 5).

Figure 5 Severe fibrosis receiver operating characteristic curves for patients with chronic liver disease. AUC: Area under the curve.

DISCUSSION

This is the first report to quantify S. saribarius load in stool samples using dPCR as a liver fibrosis marker for patients with CLD. Certain Streptococcus species, such as S. salivarius, contribute to oral health by competing with pathogenic bacteria and producing antimicrobial substances[24,25]. However, imbalances in the gut microbial community, including an overgrowth of S. salivarius, can disrupt the normal functioning of the microbiota and contribute to CLD progression[26]. Monitoring changes in the S. salivarius in the gut could potentially serve as diagnostic or prognostic markers for liver disease progression. dPCR is an appropriate method for the detection and quantification of infectious disease research and clinical practice.

The dPCR method divides the adjusted sample into 20000 droplets. By dividing the sample into droplets, target DNA and background DNA are randomly distributed in each droplet, and the PCR is initiated in each droplet[27,28]. Counting the number of positive and negative droplets enables absolute quantification of the DNA target. To quantify of S. salivarius load in stool samples, the dPCR assay targeted both the tnpA and 16S rRNA genes, which were previously used to detect and quantify S. salivarius[11]. The present study showed that the dPCR assay targeting the tnpA was only positive for S. salivarius, S. vestibularis, and S. thermophilus (a group of bacteria classified into three genetically similar species). However, the dPCR assay targeting the 16S rRNA was positive for E. faecalis, in addition to the Streptococcus species. E. faecalis and various Streptococcus species are members of the phylum Firmicutes and the class Bacilli, and they are similar regardingtheir gram-positive cell wall structure and certain physiological characteristics[29]. The 16S rRNA gene is commonly used for bacterial identification and phylogenetic studies owing to its conserved nature across bacterial species. Furthermore, dPCR has high sensitivity and is appropriate for determining relative bacterial load[30]. The amplification of individual DNA molecules in separate droplets enhances the sensitivity of dPCR, facilitating the detection of low-abundance targets[31]. The lower LOQs for qPCR and dPCR with a 1 μL template were 6.40 × 10² and 1.28 × 10² CFU/mL, respectively. These results suggested that dPCR is more sensitive than qPCR in detecting low concentrations of bacteria. Contrarily, the upper LOQ for dPCR was observed. The intra-assay CV in dPCR was relatively high for template DNA concentrations over 8.00 × 10⁴ CFU/mL. The risk of false positives may increase as the upper LOQ is exceeded owing to overlapping signals, background noise, or saturation effects in the detection system[32].

After the bacterial load of S. salivarius from the type strain was quantified using the dPCR, we quantified the S. salivarius in the stool using dPCR for patients with CLD. The bacterial load for S. salivarius was more frequently observed in patients with advanced fibrosis compared to those with mild fibrosis suggesting a potential association between the presence of S. salivarius and the progression of CLD. However, the copy numbers of any bacterial species in stool samples can be influenced by various factors related to the host environment at the time of sample collection[33]. The role of pH and age in influencing gut microbiota is notable[34]. The pH level within the gastrointestinal tract is crucial in shaping the gut microbiota. Age-related physiological changes or medication use, including gastric acid secretion inhibitors, can alter the pH environment within the GI tract, potentially leading to dysbiosis and bacterial overgrowth. Therefore, careful consideration and monitoring are essential when prescribing PPIs, particularly in older adults.

Our final objective is to develop microbiome-targeted interventions to mitigate fibrosis progression. By identifying bacteria associated with fibrosis severity, personalized therapeutic strategies can be tailored to individual microbial profiles and disease stages. For example, S. salivarius in oral saliva, which has been linked to fibrosis progression, may serve as a modifiable factor through oral microbiome-targeted interventions. Preliminary evidence suggests that reducing urease-positive S. salivarius via oral care could attenuate fibrosis progression[11]. If validated in clinical trials, this approach could provide a non-invasive, cost-effective strategy for fibrosis management. DPCR enables precise microbial quantification, reducing the cost and complexity of microbiome analysis[35]. Targeting bacteria or microbial pathways involved in fibrosis progression may facilitate the development of novel therapeutics, including probiotics, prebiotics, antimicrobial agents, and other microbiome-modulating interventions.

This study has certain limitations. First, prospective studies aimed at validating dPCR performance in patients with liver cirrhosis diagnosis remain scarce. Second, this was a single-centre prospective study to evaluate the correlation between dPCR and fibrosis progression in patients with CLD. Third, this study had a relatively small sample size and was conducted at a single centre. Therefore, more research is needed to further validate and generalise the findings.

In conclusion, dPCR is a high precision technique to-quantify bacterial species. The quantification of S. salivarius using digital PCR is a potentially useful biomarker for liver fibrosis in patients with CLD.

CONCLUSION

In conclusion, S. salivarius quantification using dPCR is a useful biomarker for liver fibrosis in patients with CLD.

ACKNOWLEDGEMENTS

We thank STATISTA Corporation for assistance with the statistical analyses in this study.