Gut microbiota dysbiosis contributes to choline unavailability and NAFLD development

Abstract

Objectives

Non-alcoholic fatty Liver Disease (NAFLD) poses a growing global health concern, yet its complex aetiology remains incompletely understood. Emerging evidence implicates the gut microbiome and choline metabolism in NAFLD pathogenesis. This study aims to elucidate the association of choline-consuming bacteria in gut microbiome with choline level.

Methods

A population comprising 85 NAFLD patients and 30 healthy controls was selected. DNA extraction from stool samples was conducted using the FavorPrep™ Stool DNA Isolation Mini Kit, followed by polymerase chain reaction (PCR) detection of choline-consuming bacterial strains and quantitative PCR (qPCR) for Cut C gene expression. Choline content measurement was performed using fluorescence high-performance liquid chromatography (FL-HPLC).

Results

Our findings revealed a significant reduction in choline levels among NAFLD patients compared to healthy controls. ROC curve analysis demonstrated choline levels and Cut C expression as a promising diagnostic tool for NAFLD, with high sensitivity and specificity. The microbial analysis identified specific choline-consuming bacteria enriched in NAFLD patients, notably Anarococcus Hydrogenalis and Clostridium asparagiforme. This was consistent with higher Cut C gene expression in patients compared to healthy individuals, which is responsible for encoding an enzyme to consume choline by these bacteria.

Conclusion

The current study gives a possible association between gut microbiota and the development of NAFLD, possibly due to an alteration in choline bioavailability. Further research is required to determine whether gut bacteria alter in the context of NAFLD or a change in their composition might lead to NAFLD progression, possibly via alternation in choline bioavailability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-024-01511-6.

Introduction

NAFLD has a sophisticated pathogenesis, starting with fat accumulation and ending up with cirrhosis, fibrosis and hepatocellular carcinoma [1]. Its pathogenesis is complex and multifactorial, involving various genetic, metabolic, and environmental factors [2, 3]. In recent years, scholars have investigated the role of the gut-liver axis in NAFLD pathophysiology. It is widely acknowledged that disruptions in the balance of intestinal microorganisms, known as dysbacteriosis, significantly contribute to the development of non-alcoholic fatty liver disease (NAFLD) and its associated metabolic dysregulations [4]. In our previous work, we showed an elevated level of Trimethylamine N-Oxid (TMAO)- a gut-liver metabolite in NAFLD patients [5]. Choline, a precursor of TMAO, is catabolized by certain gut bacteria. It undergoes microbial conversion within the gastrointestinal tract to form trimethylamine (TMA) Subsequently, TMA is enzymatically metabolized to TMAO. In NAFLD, the choline conversion rate is elevated into TMA/TMAO, resulting in a choline deficiency [6, 7].

Choline is an essential nutrient involved in lipid metabolism, cell membrane integrity, and the synthesis of phosphatidylcholine, a major component of very low-density lipoproteins (VLDL) [8]. Choline is primarily obtained through dietary sources. It can also be produced endogenously via the hepatic phosphatidylethanolamine N-methyltransferase (PEMT) pathway [9]. Several studies have suggested a potential association between choline deficiency and the development or progression of liver steatosis [10, 11]. Choline deficiency leads to impaired VLDL secretion, resulting in lipid accumulation in the liver. Furthermore, alterations in choline metabolism and genetic polymorphisms in the PEMT gene have been implicated in NAFLD pathogenesis [12]. Specific bacterial strains of the gut have been identified as choline consumers, contributing to the choline depletion. These choline-consuming bacterial strains include Anaerococcus hydrogenalis, Clostridium asparagiforme, Prevotella copri, Clostridium hathewayi, and Proteus penneri [13, 14]. The relationship between serum choline levels, gut microbiota composition, and NAFLD development and progression still needs to be better understood.

The CutC gene is associated with choline utilization in certain bacteria [15]. Specifically, CutC is part of the microbial pathway responsible for the conversion of choline to trimethylamine (TMA). The process involves several steps, and CutC acts as a choline-TMA lyase, catalyzing the conversion of choline to trimethylamine [16]. Hence, we assumed that in the case of sufficient choline or elevated choline uptake in NAFLD patients, increased Cut C expression may be responsible for choline deficiency in patients with NAFLD. This study aimed to investigate the correlation of between serum choline levels and the frequency of choline-consuming bacterial strains which abundantly expressed the Cut C gene with NAFLD. To address this, we assessed serum choline levels and evaluated the prevalence of choline-consuming bacterial strains and Cut C expression in individuals with NAFLD and healthy individuals.

Materials and methods

Study population

This study encompassed a selected cohort of 85 individuals diagnosed with non-alcoholic fatty liver disease (NAFLD) and 30 healthy individuals who served as controls [5]. Sample size calculation was determine using two-sample comparisons of means [17]. In short, the calculation was based on the requirement of detecting a clinically significant difference in plasma choline levels between subjects with NAFLD and healthy controls. We assumed a population standard deviation of 20 µM, as obtained from previous studies and our preliminary data, and a clinically relevant difference of 15 µM. Indeed, using an appropriate power of 80% (β = 0.20) and a significance level of 0.05 (α = 0.05, two-tailed), the calculation formula resulted in a needed sample size of at least 28 participants for each group. In this study, the control group consisted of more than the required sample size of 30 participants. The patient group consisted of 85 participants, which also added more power to make robust statistical comparisons. A formula to compare two independent means:

$$\text{N}=\frac{2\times {\sigma }^2\times {({\text{Z}}_{\alpha \times 2}+{\text{Z}}_{\beta })}^2}{{\mathrm{\Delta }}^2}$$

where:

1. (σ): The estimated standard deviation of the population plasma choline levels. We assumed that this was 20 µM based on prior studies and preliminary data.

2. (Δ): Clinically meaningful difference in plasma choline levels, estimated at 15 µM based on literature and clinical judgment.

3. (Zα × 2): Z-score corresponding to a significance level of 0.05 (two-tailed), which is 1.96.

4. (Zβ): The corresponding Z-score for a power of 80% (β = 0.20) is equal to 0.84.

Putting these values in the formula:

$$\text{N}=\frac{2\times {(20)}^2\times {(1.96+0.84)}^2}{{15}^2}\approx 28$$

The diagnosis of NAFLD in patients was conducted by skilled subspecialist gastroenterologists, utilizing liver ultrasound or Fibroscan in conjunction with laboratory tests, ensuring a definitive diagnosis of fatty liver disease grade 3. The control group consisted of 30 healthy individuals, matched to the patient group in terms of age, sex, and demographic characteristics, and selected from the general population to ensure unbiased comparisons. All participants were from the Kurdish population of Kurdistan province, Sanandaj, Iran. Inclusion and exclusion criteria were carefully applied: exclusion criteria included the presence of cardiovascular diseases (such as atherosclerosis or history of heart attack or stroke), diabetes, kidney problems, multiple sclerosis, HIV infection, and a long history of alcohol consumption. Other exclusions in this study included the presence of blood in stool, history of the use of metformin, and yo-yo dieting that could alter choline metabolism and the gut microbiota. Dietary habits and medication history were reviewed to exclude those factors that may interfere with choline levels. Those subjects who took multivitamins or supplements known to affect liver function and choline levels were excluded. All participants entered the study with full knowledge of the research project and provided informed written consent. Ethical standards were strictly observed, and the study protocol was approved by the Medical Ethics Committee of Kurdistan University of Medical Sciences.

Laboratory tests

Due to the close association of laboratory tests in this study with the pathophysiology of non-alcoholic fatty liver disease, the measurement of these tests was examined. All laboratory tests were performed in a single laboratory. Laboratory tests related to liver function or non-alcoholic fatty liver disease, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), cholesterol (Chol), triglycerides (TG), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) were measured using standard laboratory methods in an automated chemistry analyzer machine.

Assays for choline-consuming bacterial strains

DNA extraction

DNA was extracted from the fresh stool samples of patients and healthy individuals using the FavorPrep™ Stool DNA Isolation Mini Kit (FASTI 001–1, Favorgen, TAIWAN). The protocol was followed according to the manufacturer's instructions. In brief, approximately 200 mg of stool sample were transferred into 1.5 ml bead-beating tubes. Subsequently, 300 µL of 1SDE solution was added to each tube. The samples underwent bead-beating at 4000 rpm for 30 s, followed by 30 s of incubation on ice, and this cycle was repeated five times. Afterwards, 20 µL of Proteinase K enzyme was introduced to facilitate the hydrolysis of peptide bonds. A vigorous vortex for 5 min ensured proper mixing of the enzyme, followed by incubation at 60 °C for 20 min with intermittent vortex. Another 5-min incubation at 95° C followed, and the tube was then placed on ice for 30 s. 100 µL of 2SDE solution was then added and mixed vigorously for 5 min, followed by incubation on ice. Subsequently, the tube was centrifuged at 16,000 g for 7 min, and the supernatant containing the liquid phase was carefully transferred to fresh 1.5 ml tubes without disturbing the pellet. Then, 250 µL of 3SDE solution was added to the transferred supernatant and vortexed thoroughly. After a 2-min incubation at room temperature, the tube was centrifuged at 16,000 g for 3 min. The remaining liquid was transferred to a fresh 1.5 ml tube, and 250 µl of 4SDE solution, and 250 µL of 100% ethanol were added. The entire contents were vortexed for 1 min. Finally, the tube was centrifuged at 16,000 g for 2 min at 4 °C, and the supernatant was discarded. The remaining material is the DNA product.

PCR detection of choline-consuming bacterial strains

In the present study, we selected Anaerococcus hydrogenalis, Clostridium asparagiforme, Clostridium hathewayi, Providencia rettgeri, and Proteus penneri for further study based on two important criteria. First, these bacteria are able to metabolize choline at rates far higher than any of the previously described choline-utilizing bacteria [18] and, as such, are particularly relevant to investigate the potential relationship between reduced choline bioavailability and NAFLD risk. Second, these bacteria are known to express the CutC gene encoding a key enzyme responsible for the conversion of choline into TMA. Since the CutC-catalyzed metabolism of choline represented a critical part of our goals, focusing on those bacteria allowed us specifically to address the impact of CutC on the bioavailability of choline in relationship to the induction of NAFLD.

For this purpose, the extracted DNAs were applied in a simple polymerase chain reaction (PCR) assay using a Taq DNA Polymerase Master Mix (180301, Amplyqon, Denmark) and the strain specific primers for Anaerococcus hydrogenalis, Clostridium asparagiforme, Clostridium hathewayi, Providencia rettgeri, and Proteus penneri in a thermal cycler machine (FlexCycler, BYQ602101D-1546, Germany). The characteristic of the primers used for Cut. C gene amplification is given in Table 1. The amplified fragments of targeted genes were confirmed using 2% agarose gel electrophoresis (Supplementary file). Finally, the frequency of bacteria with Cut.C gene in two groups was expressed as a percentage of all samples.

Table 1.

Specific primers for the PCR amplification of bacterial strains

Bacteria	Primer Sequence	Product Size	Annealing Temperature
Anaerococcus hydrogenalis	Forward: 5'-CCTGTACTTGACGCTACACAG-3' Reverse: 5'-GGCCTTCAACTCCTTTCGAC-3'	118	60
Clostridium asparagiforme	Forward: 5'-ATCCTGTTTGTTGCCTGCTG-3' Reverse: 5'-CTTCCACATTTCCAGCGACA-3'	339	60
Clostridium hathewayi	Forward: 5'-TGCTTCTGCTGAGTGCTGTG-3' Reverse: 5'-CAGGTTTCTCGGAGGCATTT-3'	269	61
Providencia rettgeri	Forward: 5'-GGTGGAGAAGGTGTTTCCTG-3' Reverse: 5'-TGCGTTCTGTTCCTGCTTTC-3'	249	60
Proteus penneri	Forward: 5'-TTAACGATTGCACCACCAGC-3' Reverse: 5'-GATCAGCCAACGTGTTCACC-3'	111	60

Quantitative PCR (qPCR) for total Cut. C assay

To evaluate and compare the total expression of Cut. C in patients and healthy controls, a qPCR assay was performed on the extracted DNAs using a Mix qPCR-HS Blue SYBR kit (SinaColon, IRAN) and the degenerate primers (Table 2) in a Corbett Rotor-Gene 6000 instrument. The Cut. C expression was normalized against 16 s rRNA as the internal reference gene. The quality of the qPCR assay was also checked by using appropriate melting curves and gel electrophoresis of qPCR products (Supplementary file). Finally, LinRegPCR ver. 2013 software was used to calculate the relative level of the Cut. C.

Table 2.

Degenerated primer for cutC gene and 16 s rRNA in choline- consuming bacteria

Gene Name	Primer Sequence	Product Size	Annealing Temperature
cutC	Forward: 5'-TTYGCIGGITAYCARCCNTT-3' Reverse: 5'-TGNGGYTCIACRCAICCCAT-3'	314	57
s16 rRNA	Forward: 5'-GGAGGCAGCAGTRRGGAAT-3' Reverse: 5'-CTACCRGGGTATCTAATCC-3'	500	60

FL-HPLC determination of choline content measurement

In both patients and healthy individuals, the circulatory (Serum) level of choline was measured after a derivatization reaction of the hydroxyl group of choline with 1-naphthyl isocyanate (Analytical grade, 170518-5G, sigma-aldrich) to form a stable cationic compound that can be detected by high-performance liquid chromatography device connected to fluorescence detector (FL-HPLC). To prepare the calibration curve, the several serial concentrations of choline (Analytical standard, C7017, sigma-aldrich) in the range of 0.05–50 μM were prepared and derivatizated. They were finally injected into the HPLC system separately (Fig. 3). Using different known standard of choline, the standard of choline was drowned (3). The results of this variable were also reported as SD ± Mean.

Fig. 3.

A Bar plot illustrating the quantitative measurement of the cutC gene using real-time PCR (qPCR) in patient and healthy groups. The average expression of the cutC gene was significantly higher in patients compared to healthy individuals. B ROC curve analysis showed significant sensitivity and specificity in differentiation patients from healthy individuals. *** = P value < 0.0001

Instrumentation

An HPLC system with the following components was used (KNAUER, Germany): HPLC pump K-1001 (KNAUER, Germany), Degasser 5000 (KNAUER, Germany), FL-Detector XL A-10RF (KNAUER, Ex: 220 nm, Em: 350 nm), dynamic mixing chamber, column thermostat 5–85 0C, temperature 25 0C, mobile phase (methanol/ buffer 15:85), isocratic run for 15 min, flow rate 1 ml/min, column (SXC- A100, 250 mm, 4.6 mm, 5 μm, Italy), and a personal computer running software chrome gate. The mobile phase was prepared by mixing 10 μL of trimethylamine hydroxide (TMAH) solution (1 M) with 20 mL of aqueous glycolic acid solution. Then, 120 mL of HPLC-grade water was added, followed by the addition of acetonitrile to a final volume of 1 L. The mixture was thoroughly mixed to remove impurities and excess substances using an ultrasonic filter.

Sample/standard preparation and derivatization for choline assay

First, 1 mL of acetonitrile was mixed with 20 μL of serum sample or choline standard solution (for standard curve preparation) in a 1.5 mL microcentrifuge tube. The mixture was vortexed, and then 80 mg of magnesium oxide was added. Next, 20 μL of 1-naphthyl isothiocyanate solution (131 μM) was added, and the tube was shaken for 15 min at room temperature. After centrifugation at 13,000 rpm for 5 min, 400 μL of the supernatant was transferred to a fresh microcentrifuge tube and evaporated under a gentle stream of nitrogen gas. Finally, the residue was reconstituted in 200 μL of mobile phase, vortexed, and then filtered through a 0.22 μm syringe filter prior to injection into the HPLC system. Finally, 100 μl of this solution was injected into the HPLC system. For quality assurance, we used to several standard concentrations of choline comprise of 1.25, 2.5, 5, 10 and 50 µM (shown in Fig. 1).

Fig. 1.

Standard Curve for Choline Measurement using HPLC. As part of quality control for HPLC method, Different concentrations of choline (1.25, 2.5, 5, 10 and 50 µM) were used to draw the standard curve and specify the accuracy of the method

Statistical analysis

Statistical analysis was performed using R version 4.3.0 (2023–04–21 ucrt). For analyzing bacteria’ frequency in library(openxlsx), chi-square Test was used. For relative gene expression of Cut C gene in library(dplyr), One sample Wilcoxon test applied as well as Pearson correlation was used to correlation between Cut C expression, choline level and bacteria frequency. ROC curve analysis was used to calculated sensitivity and specificity. All graphs were drawn using library(ggplot2). A p-value less than 0.05 was considered statistically significant.

Results

Laboratory test results

Based on theresults, the mean levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) enzymes were significantly higher in the patient group compared to the control group. However, no significant difference was found in the plasma LDL levels between the two groups in evaluating of the lipid profile. There was a significant difference in the plasma HDL levels between the two groups. Moreover, the plasma cholesterol and triglyceride levels were higher in the patient group than in the healthy individuals (Table 3).

Table 3.

The result of Laboratory Tests

Tests	Patients (Mean ± SD, n = 85)	Healthy Individuals (Mean ± SD, n = 30)	P value
ALT(U/L)	49.84 ± 30	23.86 ± 4	P < 0.0001
AST(U/L)	33.74 ± 18	22.13 ± 3	P < 0.0001
TG (mg/dl)	200.77 ± 97	112.10 ± 21	P < 0.0001
Cholesterol(mg/dl)	192.10 ± 41	156.66 ± 32	P < 0.0001
LDL (mg/dl)	109.08 ± 25	102.50 ± 23	P = 0.136
HDL (mg/dl)	44.50 ± 12	53.13 ± 13	P = 0.002

The tests include alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglycerides (TG), total cholesterol, low-density lipoprotein (LDL), and high-density lipoprotein (HDL). P-values were calculated using the student’s t-test for normally distributed data and the Mann–Whitney U test for non-normally distributed data. Statistically significant results are highlighted for ALT, AST, TG, cholesterol, and HDL, with no significant difference in LDL between the two groups

Results related to the analysis of intestinal microbiota in the study groups

The presence or absence of the studied bacteria in the study groups was reported as positive (present) or negative (absent) using specific primers targeting gene. The frequency of the targeted bacteria's presence and the difference between the two groups were examined using Chi-square test between the patient and healthy groups. Interestingly, Anarococcus hydrogenalis DSM7454 (p = 0.003), Clostridium asparagiform DSM15981 (p = 0.007) were significantly abundant in patients in comparison with healthy individuals. While others, Providencia rettgeri DSM 1131 (p = 0.637), Clostridium hathewayi DSM 13749 (p = 0.296), and Proteus penneri DSM 35198 (p = 0.130) did not show significant differences between study groups (shown in Fig. 2).

Fig. 2.

Bar Plot depicting the P-values for the abundance of different bacterial species in patients compared to healthy individuals. Notably, Anarococcus hydrogenalis (p = 0.003) and Clostridium asparagiform (p = 0.007) exhibited significantly higher abundance in patients, whereas Providencia rettgeri (p = 0.637), Clostridium hathewayi (p = 0.296), and Proteus penneri (p = 0.130) did not demonstrate significant differences between the study groups

Results related to quantitative measurement of the cutc gene

The quantitative measurement of the cutC gene in the patient and healthy groups was performed using the real-time PCR (qPCR) method. The average expression of the cutC gene in patients was higher than in healthy individuals. Statistical analysis indicated that there was a significant difference between patients and healthy individuals (p < 0.0001) (shown in Fig. 3A). Receiver Operating Characteristic (ROC) curve analysis was conducted to evaluate the discriminatory power of Cut C expression in distinguishing patients with non-alcoholic fatty liver disease (NAFLD) from healthy individuals. The results revealed that Cut C expression exhibited a high sensitivity of 76.63% and specificity of 75.23% (P value < 0.0001, Fig. 3B). These findings underscore the potential utility of Cut C expression as a biomarker for identifying individuals with NAFLD, highlighting its diagnostic significance in clinical settings.

Results related to choline

Our study showed significant differences in plasma choline levels between patients and healthy individuals (p < 0.0001, shown in Fig. 4A). The mean of plasma level of choline was lower in patients compared to healthy individuals. Receiver Operating Characteristic (ROC) curve analysis was performed to assess the discriminatory capacity of choline levels in distinguishing patients with non-alcoholic fatty liver disease (NAFLD) from healthy individuals. The results revealed that choline levels exhibited a sensitivity of 82.33% and a specificity of 65.54% (P value < 0.0001, Fig. 4B). These findings suggest that choline levels hold promise as a potential biomarker for detecting NAFLD, indicating its potential value in clinical diagnosis and management.

Fig. 4.

A Bar plot illustrating the significant differences in plasma choline levels between patients and healthy individuals (p < 0.0001). The mean plasma level of choline (µM) was higher in healthy individuals compared to patients. B ROC curve analysis showed significant sensitivity and specificity in differentiation patients from healthy individuals. *** = P value < 0.0001

Correlation analysis

Correlation analysis was used to determine the associations among choline levels, Cut C gene expression, and various characteristics of the study population, including age, sex, BMI, and dietary habits. Specifically, Pearson correlation coefficients were calculated to assess the strength and direction of relationships between choline levels, Cut C expression, bacterial frequency, dietary sources, and potential influencing factors.

Correlation of choline level and Cut c gene expression with characteristics of study population

In this study, the dietary habits of the population, including the consumption of red meat, chicken meat, fish meat, and eggs as sources of choline, alongside age, sex and BMI were examined for their correlation with choline levels. Notably, age exhibited a negative correlation with serum level of choline (P value < 0.01). There was no significant correlation between choline level and its dietary sources (P value > 0.05) (Fig. 5).

Fig. 5.

Pearson correlation analysis was conducted to examine the relationships between dietary habits, demographics with choline level and Cut C expression. * = P value < 0.05. age was only factor which had a significant negative correlation with choline level in NAFLD patients

Investigating the relationship between choline levels, Cut C expression, and bacterial frequency

Given the role of the Cut C gene in choline consumption within the studied bacteria, correlation analysis was undertaken to explore the associations among choline levels, Cut C expression, and bacterial frequency. Statistical analysis revealed choline level negatively had a significant correlation with Anarococcus Hydrogenalis and Clostridium asparagiforme which were significantly abundant in patients. Interestingly, Cut C expression also showed a significant negative correlation with choline level, indicating higher expression of Cut C in Anarococcus Hydrogenalis and Clostridium asparagiforme result in choline shortage in patients. Furthermore, Cut C expression positively was correlated with Anarococcus Hydrogenalis and Clostridium asparagiforme and Providencia Rettgeri (P value < 0.05), adding further evidence this gene is highly expressed in these bacterial species which might contribute higher choline consumption by these bacteria (Fig. 6).

Fig. 6.

Pearson correlation analysis was applied to see the relationship between Cut c expression, Choline level and the studied bacterial species. Choline level was negatively correlated with Cut C expression, and abundance of Anarococcus hydrogenalis and Clostridium asparagiforme. While a positive correlation was found between Cut C expression and Anarococcus hydrogenalis and Clostridium asparagiforme and Providencia Rettgeri. * = P value < 0.01, *** = P value < 0.001 and *** = P value < 0.0001

Discussion

NAFLD is a rapidly growing health problem worldwide, yet its understanding and implications are still not fully comprehended. Evidence regarding the primary role of the microbiome in different diseases such as obesity and related disorders, NAFLD and metabolic syndrome is increasing. this evidence initially emerged from animal studies using high-fat diets in susceptible mouse models of NAFLD and glucose homeostasis, which mimic the effects of choline-deficient diets [19]. The availability of choline and hepatic steatosis may be rooted in the necessity of choline availability for the secretion of VLDL from the liver. Insufficient available choline leads to a reduction and disruption in VLDL synthesis, resulting in the accumulation of triglycerides (TG) in the liver. The role of dysbiosis (imbalanced gut bacterial composition) in the development of NAFLD has been identified through several possible mechanisms, including alterations in short-chain fatty acid (SCFA) metabolism, increased gut permeability, activation of toll-like receptors (TLR) and inflammation, endogenous ethanol production, reduced choline availability, and trimethylamine (TMA) production [20, 21]. Human gut bacteria actively metabolize dietary choline precursors and convert them into trimethylamine (TMA). This alteration reduces the choline bioavailability and may predispose the body to choline deficiency. On the other hand, TMA is further converted to TMAO in the liver cells to worsen the adverse conditions in liver diseases and cardiovascular disorders [22]. Our study aimed to explore the prevalence of selected choline-consuming bacteria within NAFLD patients and healthy individuals in conjunction with assessing circulating plasma choline levels. Additionally, we quantified the relative expression of the Cut C gene, responsible for choline conversion to trimethylamine (TMA), within these bacteria.

First, we confirmed a significant reduction of choline in patients with NAFLD compared to healthy individuals, as previously existing literature reported [23, 24]. Furthermore, employing ROC curve analysis, we demonstrated that choline levels exhibit significant potential as a diagnostic test for distinguishing patients with NAFLD from healthy individuals. The analysis yielded a sensitivity of 82.33% and a specificity of 65.54%, with a statistically significant p-value of less than 0.0001 (Fig. 4B). These findings signify that the measurement of choline levels effectively identifies individuals afflicted with NAFLD with an accuracy of 82.33% while also enabling the discrimination of those without the condition with a specificity of 65.54%.

Our investigation into the microbial composition, Enterococcus hydrolyticus, Clostridium asparagiforme, Clostridium hathewayi, Providencia rettgeri, and Proteus penneri, known for their significant contribution to choline conversion to trimethylamine (TMA), has yielded interesting results. The study aimed to validate our initial hypothesis regarding the association of choline deficiency and gut bacteria in NAFLD patients compared to healthy individuals. Remarkably, our findings indicated a deficiency of choline in NAFLD patients, which may be attributable to two specific gut bacteria strains- Enterococcus hydrolyticus and Clostridium asparagiforme, which were predominant in NAFLD patients compared to the control group. It appears that these specific gut bacteria strains engage in competition with the host for choline consumption, a phenomenon accentuated by the increased population of these bacteria in patients with NAFLD. Our finding is consistent with Haung et al., revealing how Clostridium asparagiforme significantly consumes choline from dietary sources and affects its bioavailability for the host [25]. This is supported by the findings of Goh et al., which indicated how Cut C overexpression is remarkably associated with choline consumption by selected gut microbiota [26]. While based on existent literature, choline deficiency is the major cause of NAFLD [23, 27], our results provide the possible basis for choline deficiency in NAFLD patients.

Furthermore, our analysis of the relationship between choline levels, Cut C expression, and bacterial frequency yielded a new avenue in the pathogenesis of NAFLD. We found a significant negative correlation between choline levels and the abundance of Anarococcus hydrogenalis and Clostridium asparagiforme, notably enriched in NAFLD patients. Additionally, Cut C expression exhibited a significant negative correlation with choline levels, indicating that higher expression of Cut C in these choline-consuming bacteria may contribute to choline shortage in patients. Conversely, Cut C expression positively correlated with Anarococcus hydrogenalis, Clostridium asparagiforme, and Providencia rettgeri, suggesting heightened gene expression in these bacterial species, potentially leading to increased choline consumption (Fig. 6).

Additionally, we speculated that the Cut C gene which encodes a lyase enzyme that converts choline to TMA in these specific gut bacteria can be differentially expressed in patients compared to healthy people. RT-qPCR results showed that the Cut C gene was highly expressed in patients compared to the control group (Fig. 3) which conveys a rise in Cut C gene expression may associated with a higher abundance of Anarococcus hydrolyticus and Clostridium asparagiforme in patients. Interestingly, we also showed that despite the fact that various factors affect Cut C expression, it could serve as a reliable test alongside other methods to differentiate NAFLD patients from normal people (Fig. 3B). This is important since the change at the transcriptomics level is usually occures in the early stage of the disease, suggesting the Cut C expression may have the potential to be a biomarker for early detection of NAFLD.

In addition, we suggested that choline level could serve as a screening test for diagnosing of NAFLD patients since it showed 82.33% sensitivity, followed by Cut C expression, which showed 75.23% specificity. These findings underscore the importance of integrating multiple biomarkers, such as choline levels and Cut C expression, into screening and diagnostic protocols for NAFLD. By combining the sensitivity of choline levels with the specificity of Cut C expression, clinicians can enhance the accuracy of NAFLD diagnosis, facilitating early intervention and treatment initiation. Moreover, these biomarkers offer non-invasive and cost-effective alternatives to liver biopsy, enabling widespread screening and surveillance of NAFLD in at-risk populations.

Indeed, inflammation, oxidative stress, and insulin resistance are well-accepted pivotal contributors to NAFLD pathogenesis, associated differentially with liver dysfunction and metabolic imbalance. Previous studies, including ours [5], have underlined that the level of TMAO is increased in NAFLD patients-a product of microbial choline metabolism associated with systemic inflammation, oxidative stress, and insulin resistance. We already showed that high levels of TMAO activates inflammatory pathways such as the TLR4 signaling pathway, which mediates hepatic inflammation and orchestrates NAFLD in a worse direction [28].

Furthermore, TMAO has been suggested to promote the generation of ROS [29], to induce oxidative stress and, hence, promoting damage to hepatocyte function. TMAO changes the insulin signaling pathway to induce insulin resistance [30]. insulin resistance impairs hepatic metabolism and lipid transport, thus creating a sort of vicious cycle by which choline deficiency is perpetuated. of the choline deficiency documented in this study and upregulation of CutC gene expression in choline-consuming bacteria likely contribute to lower choline levels. our findings support reduced levels of choline May produced through disturbance or dysbiosis of gut microbiota to play a central role in the initiation, promotion or development of info NAFLD and its adverse consequences. Correlation analysis showed that age emerged as the sole demographic factor with a negative correlation with choline levels, indicating a potential age-related impact on choline metabolism in NAFLD. However, other demographic features such as sex and BMI did not demonstrate significant correlations with choline levels, suggesting that factors beyond traditional demographics may play a more substantial role in influencing choline metabolism in NAFLD. Moreover, our investigation into dietary sources of choline, specifically red meat, chicken meat, fish meat, and eggs, did not yield significant correlations with choline levels. This result implies that the gut microbiome may serve as a critical mediator in determining choline metabolism and availability in the context of NAFLD, potentially shaping disease progression through microbial interactions with choline-utilizing pathways.

This was quite a cumbersome sample collection from NAFLD patients and healthy individuals because of strict inclusion and exclusion criteria, to get a patient group free from comorbid conditions like diabetes and hypertension are generally seen in NAFLD. Similarly, finding healthy controls matching all the criteria involved much time consumption and ultimately took more than two years in sample collection. Another limitation is providing the cultured choline-consuming bacteria as positive controls for in-vitro and other experimental studies. Our study provides novel molecular insights into the potential role of choline-consuming bacteria and their contribution to choline deficiency in NAFLD. Moreover, investigating CutC gene expression may have diagnostic potential as a molecular biomarker. Comprehensive and extensive studies with larger populations are required to detect more choline-consuming bacteria and other genes related to the pathogenesis of these meta organismal pathways in NAFLD patients.

In conclusion, our findings indicate a significant correlation between choline-consuming bacteria and reduced choline levels in NAFLD patients and point to important questions regarding the contribution of microbial dysbiosis to the development of NAFLD. Although our results highlight the importance of choline bioavailability in the context of NAFLD, they cannot support a causal role. Further investigation will be required to establish whether intestinal microbiota represent a contributor to NAFLD pathogenesis or whether microbial dysbiosis is a consequence of NAFLD itself, subsequent to a mediation in choline bioavailability.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

ZV conceived and led the project, contributing to study design, supervising experimental stages, and providing critical insights during manuscript writing and editing. MM conducted experimental stages, statistical analysis using R, generated the final report, and prepared the initial draft of the manuscript. DG participated in the execution of experimental stages. MA participated in study design and provided valuable input. FSE conducted the validation of NAFLD patients. KhR validated the statistical analyses. All authors critically reviewed and approved the final version of the manuscript.

Funding

This study was supported financially by vice chancellor in research of Kurdistan University of Medical Sciences [grant numbers: IR.MUK.REC.1397/253].

Data availability

Data will be avialabile from the corresponding author upon reseanable request.

Declarations

Ethical approval

The research protocol was subjected to rigorous ethical scrutiny and received approval from the Ethical Committee of Kurdistan University of Medical Science (Approval No. IR.MUK.REC.1397/253).

Consent to participate

Written informed consent was obtained from all participants prior to their inclusion in the study.

Conflict of interest

The authors declare no conflicts of interest related to this study.