For glycemic control in patients with diabetes on peritoneal dialysis (PD), the level of glycated albumin (GA) associated with mortality is unclear. Accordingly, we examined the difference in the association of GA and glycated hemoglobin (HbA1c) with 2-year mortality in a Japanese Society for Dialysis Therapy cohort. We examined 1601 patients with prevalent diabetes who were on PD. Of these, 1282 had HbA1c (HbA1c cohort) and 725 had GA (GA cohort) measured. We followed them for 2 years from 2013 to 2015 and used Cox regression to calculate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for 2-year mortality after adjusting for potential confounders in each cohort. No significant association was found between HbA1c levels and all-cause death HRs before and after adjustment for confounders in the HbA1c cohort. In contrast, the adjusted all-cause death HRs and 95% CIs for GAs < 12.0%, 12.0–13.9%, 16.0–17.9%, 18.0–19.9%, 20.0–21.9%, and ≥22.0%, compared with 14.0–15.9% (reference), were 1.56 (0.32–7.45), 1.24 (0.32–4.83), 1.32 (0.36–4.77), 2.02 (0.54–7.53), 4.36 (1.10–17.0), and 4.10 (1.20–14.0), respectively. In the GA cohort, GA ≥ 20.0% was significantly associated with a higher death HR compared with the reference GA. Thus, GA ≥ 20.0% appears to be associated with a decrease in survival in diabetic patients on PD. There were no associations between HbA1c levels and 2-year mortality in PD patients. Thus, GA could be a more robust indicator of glycemic control than HbA1c in patients with diabetes on HD. Even so, the 2012 Kidney Disease Outcomes Quality Initiative (KDOQI) Clinical Practice Guideline for Diabetes does not recommend GA as a first-line tool for assessing glycemic control5. In patients with no history of cardiovascular events, survival was reported to be significantly longer in those with GA < 20.0% than that in those with GA 20.0–24.5 or >24.5%6. Based on these results, the practice guidelines (2012) published by the Japanese Society for Dialysis Therapy (JSDT) suggests GA levels < 20.0% as a potential target level for glycemic control in patients with diabetes on HD without a history of cardiovascular disease (CVD) and <24.0% for those with such a history7.Erythrocytes have a shorter life span than normal in dialysis patients, and blood loss and bleeding may also occur during hemodialysis (HD). This can lead to anemia requiring treatment with erythropoiesis-stimulating agents (ESAs), which falsely lowers the glycated hemoglobin (HbA1c) level by raising the proportion of young erythrocytes. As a result, HbA1c levels tend to be low in dialysis patients and glycemic control may be overestimated. In contrast, glycated albumin (GA) level is not significantly associated with erythrocyte life span, hemoglobin level, or ESA dose in patients with diabetes undergoing HD9, and, to our knowledge, no studies have investigated GA levels and mortality in PD patients. Accordingly, to establish the association between glycemic control and mortality—focusing on the ability to predict mortality with GA—we studied a nationwide registry of PD patients in Japan.The issue of glycemic control may be particularly important in PD patients. However, few studies have examined the association between HbA1c and clinical outcomes in PD patients11. The standard analysis file prepared by the JRDR Committee for the present study included data for 314,438 patients who underwent dialysis at 4,268 facilities in the 2013 survey12, 320,448 patients dialyzed at 4,330 facilities in 2014, and 324,986 patients dialyzed at 4,321 centers in 201513. Those diagnosed with diabetes and/or receiving diabetes medications and undergoing PD on 31 December 2013 were followed for 2 years. We excluded patients who had undergone HD, those aged <20 years, those whose records for date of birth, initiation of dialysis, or outcome were incomplete, and those who were considered outliers. In total, 315,631 patients on maintenance dialysis were registered at the end of 2013. After exclusions, 1,601 patients on PD were included in this study . As described previously, all surveys were conducted by volunteersThe following demographic and clinical data were collected: age, sex, duration of dialysis, primary cause of end-stage kidney disease (ESKD), body mass index (BMI), smoking status, use of antihypertensive agents, and history of complications of cardiovascular disease (CVD)—including cerebral infarction, cerebral hemorrhage, myocardial infarction, and limb amputation. Blood samples were collected and assayed at each dialysis facility or hospital, typically within 24 h of collection; and the latest values at the time of the survey were collated including serum albumin, hemoglobin, calcium, phosphate, intact parathyroid hormone (i-PTH), total cholesterol, high-density lipoprotein (HDL) cholesterol, and C-reactive protein (CRP). Using residual renal function  and peritoneal dialysis (PD Kt/V), weekly Kt/V urea values were recorded. The sum of renal Kt/V and PD Kt/V was considered as total Kt/V. The dialysate to plasma creatinine ratio (D/P Cr) was obtained using the peritoneal equilibration test. The type of PD fluid used  and whether or not an automated PD system was used were also recorded. Furthermore, the type of treatment was recorded because PD + HD combination therapy, that is, HD treatment 1 day a week and PD on other days, has been approved in Japan since 2010.We defined three categories of antidiabetic therapy: no medication, insulin therapy, and oral antidiabetic medication only. The no medication group comprised patients managed with diet modification therapy only or those for whom no antidiabetic medication was prescribed. Insulin therapy included insulin injection therapy only or a combination of insulin with an oral antidiabetic agent. Cause of death was classified as cardiovascular death, infection-related death, or other. We defined cardiovascular death as that due to chronic heart failure, pulmonary edema, ischemic heart disease , arrhythmia, valvular heart disease, endocarditis, other cardiac disease, subarachnoid hemorrhage, intracerebral hemorrhage, cerebral infarction, or other brain disease.The data were expressed as proportions, using the mean ± standard deviation or median (interquartile range) as appropriate. We analyzed categorical variables using the chi-square test and continuous variables using the Student’s t-test. Categorical data were compared between groups with repeated-measures analysis of variance (ANOVA) and Tukey’s honestly significant difference test or Kruskal-Wallis test, as appropriate. Patients who were switched to HD during follow-up were censored from the outcome analysis. We defined reference ranges for the laboratory data such that patients with measured values outside of the following ranges were considered outliers and were excluded from the analysis: height 120–200 cm, body weight 20–150 kg, serum albumin 1.0–5.0 g/dL, CRP < 30 mg/dL, hemoglobin 5.0–20.0 g/dL, i-PTH < 3000 pg/mL, HbA1c 4.0–15.0%, and GA 10.0–50.0%. the conventional method for multivariate regression was used to imputed missing covariate data as appropriate.a priori categories defined by HbA1c values between <5.0% and ≥7.5% at increments of 0.5%  and seven a priori categories defined by GA values of <12.0% and ≥22% at increments of 2% . We used the 5.5–5.9% HbA1c category as the reference based on previous studies14. We used the 14.0–15.9% GA category as the reference because the standard value of GA was between 12.0 and 16.0%16. Survival analyses with Cox proportional hazards regression were used to examine whether basic factors at baseline—including age, sex, PD duration, cardiovascular comorbidity, PD-related factors, and nutritional and inflammatory factors—predicted survival for up to 2 years of follow-up. To examine the relationship between dialysis duration and risk of death, we grouped the patients into six a priori categories by duration of dialysis . Additional analyses were performed with adjustment for PD-related factors, including residual renal function , total Kt/V, use of 2.5% glucose or icodextrin dialysate, and type of treatment . Further analyses were performed with adjustments for nutrition-related and inflammation-related factors, including BMI and levels of hemoglobin, albumin, total cholesterol, HDL-cholesterol, calcium, phosphate, i-PTH, and CRP. Age, hemoglobin, and CRP levels were analyzed as continuous variables. We performed unadjusted and adjusted analyses that included all variables for all-cause mortality.We examined the clinical and demographic measures in each cohort by dividing the patients into 7 ® version 13.0 . P-values less than 0.05 were considered statistically significant.The study was conducted according to the principles of the Declaration of Helsinki, Japanese privacy protection laws, and Ethical Guidelines for Medical and Health Research Involving Human Subjects published by the Ministry of Education, Science and Culture, and the Ministry of Health, Labour and Welfare in 2015. The study protocol was approved by the Medicine Ethics Committee of the Japanese Society for Dialysis Therapy. The study was registered with the University Hospital Medical Information Network (UMIN000018641). The need for informed consent was waived because the study used de-identified information. All analysis was performed using JMPTable Table a priori categories according to baseline HbA1c. Higher HbA1c levels were noted to be associated with higher rates of type 1 diabetes and insulin use, 2.5% glucose dialysate, a lower proportion of males, and higher hemoglobin and CRP levels. The rate of transfer from PD to HD was not associated with the a priori HbA1c categories.Table Unadjusted all-cause death HRs and 95% CIs for HbA1c < 5.0%, 5.0–5.4%, 6.0–6.4%, 6.5–6.9%, 7.0–7.4%, and ≥ 7.5%, compared with 5.5–5.9% (reference), were 0.69 (0.31–1.53), 1.10 (0.67–1.81), 1.12 (0.68–1.84), 1.12 (0.65–1.92), 1.36 (0.72–2.55), and 1.51 (0.85–2.66), respectively.Figure Table a priori categories according to baseline GA. Higher GA levels were noted to be associated with older age, type 1 diabetes, insulin use, 2.5% glucose dialysate, and higher CRP levels. The rate of transfer from PD to HD was not associated with a priori GA categories. A higher GA level was associated with higher mortality, especially infection-related death.Table Unadjusted all-cause death HRs and 95% CIs for GAs < 12.0%, 12.0–13.9%, 16.0–17.9%, 18.0–19.9%, 20.0–21.9%, and ≥22.0%, compared with the reference value, were 1.14 (0.42–3.09), 1.17 (0.58–2.38), 1.50 (0.79–2.86), 1.68 (0.84–3.37), 1.22 (0.51–2.94), and 3.09 (1.66–5.77), respectively. Patients with GA ≥ 22.0% had a significantly higher death HR when compared with the reference GA (P = 0.0003). Figure In this study, we first identified the predictors of 2-year mortality in PD patients. Because survival outcome among dialysis patients might be determined by additional multiple confounding factors—dialysis-related or non-dialysis related—investigations into the control of these factors are difficult to perform. However, we compared mortality rates among glycemic control categories, adjusting for multiple potentially confounding factors. After fully adjusting for these factors, there were no significant differences for death HRs among the HbA1c categories compared with the HbA1c reference value of 5.5–5.9%. However, in this large-scale contemporary cohort of 1,601 PD patients, GA ≥ 20.0% was associated with higher all-cause mortality. This is the first study to suggest that mortality risk for PD patients might differ according to GA level.17. A further study from Canada reported 1- and 2-year survival rates of 94% and 89% for patients without diabetes, respectively, and 91% and 76% for patients with diabetes18. Data from single centers in Asia and Europe also showed that survival rates were significantly lower in patients with diabetes on PD than in those without diabetes on PD19. In the present study, the 1- and 2-year survival rates were 88.3% and 84.0%, respectively, which are higher than those reported previously. Older age, longer duration of PD, use of 2.5% glucose dialysate, anuric state, and lower albumin levels and BMI at baseline might have contributed to lower survival rates in this cohort.Data from the US Renal Data System for 2010 revealed that the overall 1- and 2-year survival rates of patients on PD were 83% and 67.2%, respectively, whereas those of a diabetes subgroup were 80.3% and 61.7%22. In the largest of the previous studies, which involved 2,798 patients, time-averaged HbA1c ≥ 8% was found to be associated with the highest risk of all-cause mortality9. Another study found that glycemic control was more important in patients who were less than 60 years old20. Infection was a notable cause of death among these patients, particularly those with HbA1c ≥ 8%23. These findings suggest that in PD patients, poor glycemic control adversely impacts survival due to an increased risk of infection. Similar results were obtained in a prospective observational study of diabetes patients on PD; non-cardiovascular mortality primarily due to infection was highest in patients in the highest HbA1c tertile24. The present study revealed no significant differences in the rates of cardiovascular death and infection-related death among the HbA1c categories. However, infection-related death occurred most frequently in patients in the highest GA category.Relatively few studies have investigated PD patients with diabetes25. The Dialysis Outcomes and Practice Patterns Study demonstrated that the relationship between low HbA1c and mortality appeared to be even stronger in patients with indicators of poor nutritional status, including low serum albumin, low BMI, or presence of cachexia26. These data suggest that not only high HbA1c levels but also low HbA1c levels related to malnutrition or anemia are associated with increased mortality in HD patients. Potential contributors to these phenomena include reduced kidney gluconeogenesis, decreased kidney and hepatic insulin clearance, diminished food intake, deficient catecholamine release, protein-energy wasting, and the hypoglycemic effects of HD. Furthermore, patients on dialysis have low HbA1c levels due to renal anemia and use of ESAs. Thus, HbA1c levels could overestimate glycemic control in HD patients and PD patients as well, because of renal anemia and ESA use in PD patients. In contrast, GA is not affected by hemoglobin levels and ESA4. Understanding factors associated with mortality remains a priority in clinical care for patients with diabetes on dialysis. Glycemic control is definitely the most important factor for diabetes patients, including those with ESKD. Recent reports have questioned the utility of HbA1c and GA as glycemic markers in HD patients28. Although GA may be a more accurate index of glycemic control than HbA1c in HD patients, there is limited evidence of an association between GA and mortality in PD patients. Therefore, concluding that GA is superior to HbA1c as a mortality marker in these patients is premature, although some data suggest that GA is less affected by anemia and serum albumin levels than HbA1c is. Nevertheless, the present study does provide some evidence for the utility of GA in predicting 2-year mortality in a large cohort of PD patients.A meta-analysis reported that high levels of HbA1c (>8.5%) and very low HbA1c levels (<5.4%) were associated with an increased mortality risk31. Remarkably, no significant association was seen between mean HbA1c levels and mortality in these patients. In addition, it was reported that GA, not HbA1c, accurately predicted the risk of death and hospitalization in patients with diabetes on HD (n = 401) or PD (n = 43)32. GA level is a useful index of glycemic control in HD patients, there is little evidence on the relationship between GA levels and the risk of cardiovascular events or prognosis in PD patients.According to numerous studies, higher GA levels are associated with higher all-cause or cardiovascular mortality rates in HD patients with diabetes27; an adequate sample size was estimated to be 856 and 1905 patients in the GA cohort and HbA1c cohort, respectively. It must be noted that GA is not a widely available index, and there are limited outcome studies that used GA. Further studies are thus needed to confirm reproducibility. Second, as with any annual survey, our database involves a yearly one-point estimate of glycemic control, rendering analyses with time-averaged GA levels within a year impossible. However, some previous large-database reports have shown similar mortality among initial and time-averaged HbA1c groups33. Third, we lacked information about the use of ESA. However, because GA is reported to be unaffected by the life-span of erythrocytes or ESA administration35, the effect of ESA in the GA-mortality association may be small and hence may not change our results. Finally, blood glucose level data were not available and many patients in this study had either HbA1c or GA measured, not both. Therefore, we were unable to compare GA, HbA1c, and plasma glucose levels. Thus, in the future, prospective studies are warranted to elucidate target GA levels, given that several recommendations for the treatment of diabetes in PD patients are based on longer-term studies of HbA1c levels.This study has several limitations. First, as in any annual survey or observational cohort study, sample size and power were limited in the present study. The number of patients who had higher HbA1c or GA categories was small and mortality was low compared with previous reportsIn conclusion, the findings of this study show that a GA > 20.0% was associated with decreased survival, especially infection-related death, in patients with diabetes on PD. No associations were evident between the HbA1c levels and 2-year mortality in PD patients. These results underscore the need for further research on the factors that influence patient outcomes, so as to identify alternative interventions that would improve the outlook for patients undergoing PD.


Different ratios of Bacillus subtilis TKSP 24 (B), Aspergillus oryzae complex (A), Rhizopus nigricans  (R), and Mucor racemosus 15 (M15) were selected as meju starter cultures to produce doenjang with improved quality. Microbial strain combinations (B: A: R and B: M15: R) were mixed separately at three different ratios  to prepare BAR-1, BAR-2, BAR-3, BM15R-1, BM15R-2, and BM15R-3 doenjang samples. Quantitative analyses included free amino acids, free sugar, volatile and non-volatile organic acids, cellular antioxidant activity along with the presence of biogenic amines and aflatoxins, and microbial counts. Total free amino acids responsible for the sweet taste of doenjang were highest in BAR-2 (322.50 mg/100 g) and BM15R-3 (320.07 mg/100 g). Total volatile organic acid was highest in BAR-1 compared to other preparations. All doenjang samples had biogenic amines, especially histamine, below the toxicity level (500 mg/kg). Also, the aflatoxin and hazardous microbial count in the tested doenjang samples were below the level of toxicity. The findings suggest that use of multiple microbial strains in combination with R. nigricans as a starter culture could be a novel and effective approach to improve the nutrition and safety of fermented soybean food products of doenjang.We aimed to develop a consortium of starter culture of effective microorganisms to prepare  Doenjang is a protein-rich traditional food product and flavouring ingredient in Korea that is produced by the fermentation of soybeans. Traditionally, in the preparation of starter culture (meju) for doenjang, soybeans are fermented by Bacillus species in the early stage, followed by the secondary microflora of Aspergillus species such as Aspergillus oryzae and Aspergillus niger1. Processing of traditional doenjang differs depending on its region of origin1. In addition, traditional meju can be made from various salt solutions and is fermented for more than 2 months2. The taste and colour of doenjang are considered as the main factors responsible for its overall acceptability, which is highly influenced by the starter culture and the presence of oxygen1. A standardised starter culture would be advantageous to ensure the rapid production of doenjang with improved quality and safety.Rhizopus species, Bacillus species, and lactic acid bacteria are commonly found during the fermentation of soybeans3. Rhizopus species have an especially vital role in the food industry due to their ability to produce thermostable polygalacturonases, which hydrolyses α-1,4 glycosidic bonds between galacturonic acid residues and thus affect the plant cell wall4. In particular, R. oligosporus is used to ferment soybeans to make tempeh, which has many health benefits including potent anti-radical activity5. R. nigricans  ferments the ground nut flour, which improves its nutritional value6. R. nigricans has received increased interest in the fermentation industry because of its ability to produce fumaric acid abundantly, which has acidifying and gelling properties8.Several microorganisms, mainly filamentous fungi including doenjang include microbial contamination, formation of biogenic amines, and production of aflatoxins, which depend on various factors, such as raw materials, microbial flora, fermentation conditions, and salt content9. Several studies have been carried out to produce commercially modified traditional Korean doenjang1. However, no studies have so far been carried out on commercially modified meju prepared by the inoculation of a mixture of various starter cultures, especially Rhizopus strains. The present study sought to develop a novel starter culture in combination with R. nigricans for the preparation of doenjang. Prepared doenjang samples were evaluated for their nutritional and safety parameters. Additionally, the cellular antioxidant activity of the selected doenjang sample was carried out to demonstrate its potential against oxidative stress induced by various reactive oxygen species (ROS).Safety concerns with All chemicals and solvents were of the analytical and chromatographic grade. Standard biogenic amines , dansyl chloride, and acetone were purchased from Sigma-Aldrich . Sodium hydroxide, sodium hydrogen carbonate, ammonium hydroxide, and perchloric acid were purchased from Junsei Chemicals . High-performance liquid chromatography (HPLC) grade acetonitrile and ammonium acetate were obtained from Merck . Nutrient agar was purchased from Difco . API kits were purchased from BioMerieux .Bacillus subtilis TKSP 24, A. oryzae complex, Mucor racemosus 15, and R. nigricans were isolated from traditional meju and identified using 16 S rRNA and internal transcribed spacer gene sequence analyses. All microorganisms were grown individually under aseptic conditions and used as a starter culture. The microbial strains were deposited in National Center for Biotechnology Information (NCBI) GenBank and Korean Collection for Type Cultures (KCTC), Republic of Korea under the following accession numbers: B. subtilis TKSP 24 , A. oryzae complex , M. racemosus 15 , R. nigricans (R. stolonifera) .A. oryzae complex, M. racemosus 15, and R. nigricans seeded soybeans were incubated for 3 days at 28 °C. They were designated A meju, M15 meju, and R meju, respectively. Soybeans seeded with B. subtilis TKSP 24 were incubated at 30 °C for 2 days and named as B meju. Six different doenjang samples were prepared by mixing these four starter cultures in different proportions. Doenjang samples, BAR-1, BAR-2, and BAR-3, were prepared by mixing B. subtilis meju, A. oryzae complex meju, and R. nigricans meju at 1:1:1 (w/w), 1:0.5:1.5 (w/w), and 1:1.5:0.5 (w/w), respectively. Similarly, doenjang samples, BM15R-1, BM15R-2, and BM15R-3, were prepared by mixing B. subtilis meju, M. racemosus 15 meju, and R. nigricans meju at 1:1:1 (w/w), 1:0.5:1.5 (w/w), and 1:1.5:0.5 (w/w), respectively  on a stainless-steel square plate (36 cm × 32 cm × 6 cm). At 70% relative humidity, doenjang samples was determined as previously described method2 using a pH meter .The pH levels of doenjang samples was determined by using a chromameter . Doenjang samples were placed on a white surface and the Hunter L*, a*, and b* colour values were measured as previously described method11.The colour values of 12 was adopted for the extraction of free amino acids, free sugars, and organic acids (volatile and non-volatile) from doenjang samples. Briefly, 100 g of doenjang was refluxed with 150 ml of 85% ethanol at 65 °C for 1 h, followed by filtration. The residual was re-extracted twice using same conditions. The collected filtrates were pooled and centrifuged at 4000 × g for 20 min at 4 °C. The supernatant was collected and vacuum-dried.A previously described methoddoenjang samples on ion exchange column as previously described12. Amberlite IR-120 and amberlite IRA-400 columns were used as cation and anion exchangers for chromatographic separation, respectively. Quantitative analysis of the eluted free sugars was carried out by HPLC using a model 600E HPLC unit , equipped with an RI-Model 410 refractive index detector. Separation was achieved using a Sugar-Pak I column  using Ca-EDTA buffer solution (50 mg of Ca-EDTA per litre of distilled water) as the mobile phase at a constant flow rate of 0.5 ml/min. Free sugars were identified and quantified in doenjang samples by comparison with standard sugar samples of known strength.Free sugars were separated by passing the extracted doenjang samples were carried out by the method adopted by Shukla et al.12. Briefly, the amberlite IR-120 and IRA-400 columns used earlier for free sugar collection were washed with 2 N NH4OH and 2 N (NH4)2CO3 for the recovery of bound free amino acids and non-volatile organic acids. Non-volatile organic acids were quantified by HPLC using an RS Pak KC-811column  at a constant column temperature of 40 °C. H3PO4 solution (0.1% in distilled water) was used as a mobile phase with a constant flow rate of 1 ml/min.Quantification of free amino acids and non-volatile organic acids in et al.12.Separation and quantification of amino acids was made using an amino acid analyser , equipped with a cation exchange resin in a column  at a temperature of 30 °C to 70 °C as a method adopted by Shukla 13. In brief, 2 µl of each extracted doenjang sample and standard were separately injected into a gas chromatography system . Separation was carried out in a glass column filled with 10% polyethylene glycol 6000  using helium as a carrier gas at a constant flow rate of 0.9 ml/min. The column was maintained at 170 °C throughout the operation process.Extraction and quantification of volatile organic acids were carried as previously described methoddoenjang sample was homogenized with 10 ml of 0.4 M perchloric acid, followed by centrifugation at 4000 × g for 10 min. The supernatant was collected, and the pellet was re-extracted twice with 10 ml of 0.4 M perchloric acid. All the collected supernatant was pooled and processed for derivatisation before HPLC analysis.For extraction of biogenic amines, 5 g of doenjang sample with 0.2 ml of 2 M NaOH, 0.3 ml of saturated NaHCO3, and 2 ml of 10 mg/ml dansyl chloride, followed by 45 min incubation at 40 °C. After incubation, the reaction was stop by adding 100 µl of 25% NH4OH, and the volume was adjusted to 5 ml by acetonitrile, followed by centrifugation at 2,500 × g for 5 min. Finally, samples were filtered through a 0.2 µm syringe filter  and processed for HPLC analysis.Derivatisation of biogenic amines was carried out by mixing 1 ml of extracted Separation of biogenic amines was carried out using HPLC , equipped with an ultraviolet-visible spectrophotometer as a detector. A 20 µl volume of derivatised extracted biogenic amines sample was injected into an Agilent C-18 column . Separation was carried by gradient elution using 0.1 M ammonium acetate and acetonitrile mobile phase at a constant flow rate of 1 ml/min. Biogenic amines were analysed at 254 nm. Quantification and identification of biogenic amines in samples were made by comparison with reference standards.doenjang samples, cellular antioxidant activity was carried out using BAR-1 as a representative candidate. For the evaluation of cellular antioxidant activity, 10 g of BAR-1 doenjang was extracted with 100 ml distilled water, followed by filtration and freeze-drying to obtain the dry powder.To evaluate the functional property of 2 incubator at 37 °C.NIH 3T3 mouse fibroblast cells were purchased from American Type Culture Collection . Cells were grown and maintained in Dulbecco’s modified Eagle’s medium [10% (v/v) fetal calf serum and 1% (v/v) penicillin-streptomycin mix] in a 5% COdoenjang on the cell viability of NIH 3T3 cells was evaluated by the 3--2,5-diphenyltetrazolium bromide (MTT) assay14 using different concentrations of BAR-1 (0–10 mg/ml).Effect of BAR-1 15. In brief, NIH 3T3 cells were grown in 24-well culture plates, followed by the treatment with various concentrations of BAR-1 . After 24 h incubation, oxidative stress in the cells was induced by treatment with 0.25 mM hydrogen peroxide (H2O2) for 2 h. Finally, cells were washed twice with 0.1 M phosphate buffered saline , followed by the addition of DCFDA (20 µM). After 30 min of incubation, the stained cells were washed with 0.1 M PBS and immediately visualised by fluorescence microscopy . The fluorescence intensity was quantified using Image J software .Cellular ROS were determined by 2′,7′-dichlorofluorescein diacetate (DCFDA) assaydoenjang samples (BAR-1 and BM15R-1) were selected as representatives and analysed for microbial food pathogens. Pathogenic microbes were analysed as previously described methods16. The analysis was carried out for the presence or absence of foodborne pathogens, including total coliforms, Staphylococcus aureus, Escherichia coli O157, and Salmonella species, using selective culture-dependent methods. Isolates showing typical pathogen morphologies were further identified using a microbial pathogen detection API kit.Based on profiling of amino acids, volatile and non-volatile organic acids, and sugar derivatives, two doenjang samples of BAR-1 and BM15R-1 was performed according to the instructions of the manufacturer of the quantitative aflatoxin detection kit . Aflatoxin was extracted by homogenizing 10 g of each sample in 50 ml of 70% methanol, followed by filtration. Filtered samples were transferred into wells of an ELISA plate pre-coated with an antibody against aflatoxin. After sufficient antigen (aflatoxin)-antibody interaction, a chromogenic substrate was added, and the intensity of the colour was measured at 650 nm using ELISA reader . Quantification of aflatoxin was made using Veratox data reduction software (Neogen Corp.) by converting absorbance into concentration against reference standards.Analysis of aflatoxins in P < 0.05 was considered to be significantly different.Results were presented as mean ± standard deviation (SD) of three independent experiments. Statistical analyses involved one-way ANOVA, followed by Duncan test for post-hoc analysis using IBM SPSS 19 program . doenjang samples were prepared using meju starter culture, formed by four different types microorganisms , 1:0.5:1.5 (w/w), and 1:1.5:0.5 (w/w), respectively. Similarly, doenjang samples, BM15R-1, BM15R-2, and BM15R-3, were prepared by blending B. subtilis meju, M. racemosus 15 meju, and R. nigricans meju at a ratio of 1:1:1 (w/w), 1:0.5:1.5 (w/w), and 1:1.5:0.5 (w/w), respectively.Six different kinds of ms Table . The doedoenjang samples are shown in Table doenjang samples displayed a slightly acidic pH, ranging from 4.97 ± 0.01 to 5.60 ± 0.11. The pH was the highest in control (5.60 ± 0.11) and the lowest in BAR-1 (4.97 ± 0.01). In fermented foods, a pH below 4 to 5 is usually considered to be safe, as most microbial pathogens are unable to survive in such a low pH17. The results were consistent with our previous findings in which doenjang prepared using different meju starter cultures displayed an acidic pH10.pH is one of the most important parameters influencing proper fermentation and many biochemical events in fermented foods. The pH values of all the prepared doenjang samples showed the yellow surface colour. The maximum yellow surface colour was observed for BAR-1 (24.70 ± 1.40) and minimum for BAR-3 (10.74 ± 0.44). The inner region of all doenjang samples displayed a highly intense yellow colour, compared to the outer surface  of 25.64%, 88.64%, and 80.07% was observed in BAR-1, BAR-2, and BAR-3, respectively, compared to the surface colour value. A similar trend was observed in doenjang samples BM15R-1, BM15R-2, and BM15R-3, where the inner colour value was enhanced by 25.55%, 36.20%, and 27.85%, respectively, compared to that of the surface. Both the major preparations, BAR and BM15R, displayed inner colour changes as compared to the surface, however, the most prominent changes were observed in doenjang prepared by BAR meju. These findings suggested an important role of A. oryzae complex in combination with B. subtilis and R. nigricans in yellow colour development. These results agreed well with the published report, indicating the different colours of doenjang samples, based on the starter culture used12.Colour is an important physical characteristic of food that catches the attention of the consumer and promotes acceptance. Presently, all six doenjang samples are listed in Table 19. Apart from imparting the flavour to food, amino acids are vital nutrients that have a role as growth factors and stabilizer of nucleic acid20. In this study, traditional Korean doenjang (control) displayed the highest content of free amino acids responsible for sweet taste (390.20 mg/100 g), followed by BAR-2 (322.50 mg/100 g), and BM15R-3 (320.07 mg/100 g). Of the doenjang samples tested for free amino acids related to savoury taste, BAR-2 showed a higher score (285.65 mg/100 g) than traditional doenjang (263.20 mg/100 g), followed by BAR-3 (234.49 mg/100 g) and BAR-1 (228.64 mg/100 g). For free amino acids related to bitter taste, BAR-2 (221.73 mg/100 g) showed the highest contents among all tested samples of doenjang, followed by BM15R-3 (220.46 mg/100 g) and traditional doenjang (209.50 mg/100 g). BM15R-1 displayed the lowest amino acid contents related to bitter taste (125.71 mg/100 g). The highest amount of amino acids related to other tastes was in BAR-2 (226.42 mg/100 g), followed by BAR-1 (196.68 mg/100 g), with lowest content in BM15R-1 (113.63 mg/100 g). The results agreed well with the previous findings of the varied amount of free amino acids in doenjang samples and other fermented products21. The amount of free amino acids is strongly dependent on the raw material and the microorganisms that participate in fermentation. The variation in the content of free amino acids in different doenjang samples can be explained by the fact that they use a different meju as a starter culture, and thus the involvement of diverse microorganisms results in consequently varied free amino acids. The results are consistent with our previous report that demonstrated diversification in the free amino acids in different doenjang samples with the difference of meju as a starter culture12.The contents of individual and total free amino acids of doenjang, various microbial enzymes act on the complex food material and convert it into simple biomolecules such as amino acids, sugar, organic acids, and fatty acids, which are precursors of various volatile compounds22. Free sugar content is not only responsible for the nutritive value of food but also important for flavour characteristics. Figure doenjang samples. BM15R-1 and BAR-1 displayed the highest total free sugar contents . Among the five tested sugars, fructose was present in all the doenjang samples. However, the sugar sorbitol was present only in BAR-2. We speculate that the variations in free sugar contents were due to differences in the meju starter culture as a result of the involvement of different microorganisms and the enzyme produced by them23. Many reports have demonstrated that the varied amount of free sugars in fermented foods are associated with the involvement of microorganisms. One such a study conducted by our group demonstrated the influence of different meju starter culture on the variation in sugar and other metabolites in doenjang12.During fermentation of 24. Knowing the importance of organic acids in fermented foods, we quantified organic acids in the doenjang samples, and results are presented in Fig. doenjang sample. However, glutaric acid was abundant in the control doenjang sample.During fermentation, microorganisms convert simple organic biomolecules into various useful products such as organic acids, which impart characteristic tastes, aromas, and functional properties to foods. Organic acids (citric acid and lactic acid) are well known for the improvement of the taste of soy saucedoenjang samples with a significantly high amount in BAR-1. In contrast, a high amount of butyric acid was present in BAR-2. Overall, the total volatile organic acids were highest and lowest in BAR-1 and BM15R-1, respectively. These findings indicate the influence of the meju starter culture on the production of organic acids, consistent with a previous report concerning the role of starter culture on volatile and non-volatile organic acids in doenjang12.Among the four volatile organic acids that were tested, acetic acid was present in all the 25, which are formed by the decarboxylation of amino acids and serve as a good indicator of food quality26. Some biogenic amines, such as putrescine, cadaverine, and agmatine, react with nitrites and are converted into a potential carcinogenic (nitrosamine) and thus pose a serious threat to health26. Other than this, aromatic biogenic amines, such as tyramine and 2-phenylethylamine, are the main agents of a dietary-induced migraine and hypertensive crisis26. Knowing the importance of biogenic amines in the food, we quantified them in the doenjang samples. The results are provided in Table doenjang samples varied in the amount of biogenic amines. The maximum amount of total biogenic amines was observed in BAR-2 (6.79 mg/100 g), followed by BAR-1 (5.27 mg/100 g). Biogenic amines were lowest in BM15R-3 (0.80 mg/100 g). A negative correlation  between the amount of biogenic amines and the addition of A. oryzae complex meju was established in doenjang samples  to 0.37 ± 0.02 mg/100 g (BM15R-1). Intermediate values were 0.17 ± 0.03 mg/100 g (BAR-3) and 0.13 ± 0.01 mg/100 g (BM15R-2). The amount of histamine in all the doenjang samples was well below the toxicity level26, suggesting that the preparations were safe to consume. Biogenic amines in all the samples were also well below the established toxic level26. The results are markedly lower than the reported content of biogenic amines in doenjang27. The present results are consistent with our earlier report of a similar profile of biogenic amines in doenjang samples prepared by the combination of different meju12. The present findings also reveal that none of the biogenic amines in the doenjang samples were present in high amounts, which might be due to the use of specific starter cultures that have an impact on the inhibition of biogenic amines formation. Results are well supported by the findings of Zaman et al.28, demonstrating the role of starter culture on the inhibition of biogenic amines.The most common contamination of soybean-based fermented foods is biogenic amines2O2. A dose-dependent decreased fluorescent intensity of 97.53 ± 7.40%, 86.71 ± 6.40%, and 77.61 ± 4.10% was observed in the cells treated with 0.5 mg/ml, 1.0 mg/ml, and 2.5 mg/ml of BAR-1 respectively, as compared to 100% in only H2O2 stimulated cells , as described in the Veratox total aflatoxin test kit instructions  in food samples is considered hazardousication < µg/kg, aR. nigricans-based starter culture, featuring the combined inoculation of bacteria and fungus to improve the quality of doenjang. Six different types of doenjang samples were manufactured and analysed for their taste and safety characteristics. Furthermore, BAR-1 produced substantial ROS inhibition at the cellular level and was effective in mitigating the harmful effect exerted by ROS. None of the doenjang samples contained the biogenic amines above their safety limits. Therefore, it can be concluded that selection of appropriate microorganisms in the proper ratio with R. nigricans could be used as a novel starter culture for the production of doenjang with enhanced levels of nutritional and functional components and quality characteristics.In summary, this study focused on the use of


The current way to assess the risk of cardiovascular disease (CVD) is to measure conventional lipid and lipoprotein cholesterol fractions. Despite the success of statin treatment, residual cardiovascular risk remains high. Therefore, the value of extensive serum apolipoprotein (apo) profiling to assess the risk of ST-segment elevation myocardial infarction (STEMI) and of major adverse cardiac events (MACE) in patients with STEMI was investigated in a case–control design.Serum apo levels were measured using liquid chromatography and mass spectrometry in 299 healthy individuals and 220 patients with STEMI. First, the association of apo profiles in baseline samples with risk of STEMI was examined, and second, the association of apo profiles at baseline with risk of recurrent MACE in patients with STEMI in a longitudinal study design was studied. High baseline (> 1.25 g/L) apoA1 levels were associated with a decreased risk of STEMI , whereas high apoB (> 1.00 g/L) levels  and apoB/apoA1 ratio (OR per 1 SD (OR/SD): 2.16; 95% CI 1.76–2.65) were associated with an increased risk. Very-low-density-lipoprotein (VLDL)-associated apos gave conflicting results. Neither conventional lipid levels nor apo levels were associated with MACE in the STEMI group.In conclusion, apoA1, apoB, and apoB/apoA1 were strongly associated with risk of STEMI. No clear relation between VLDL-associated apos and the risk of STEMI was found. Neither baseline serum apos nor lipids predicted MACE in statin-treated patients during long-term follow-up after a first STEMI. Identification of patients at high risk for developing ST-segment elevation myocardial infarction (STEMI) is essential. Dyslipidemia in these patients is one of the factors that impair their long-term clinical outcome –4. The cThe measurement of functional and structural protein components of lipoproteins, i.e., apolipoprotein (apos), is suggested to have additional value for coronary artery disease (CAD) risk assessment –15. It hIn the current study, the lipid and apo profiles in patients with STEMI from the MISSION! Intervention Trial  were comn = 11), prior PCI (n = 3), and/or prior coronary artery bypass grafting (CABG) (n = 1) were excluded. A total of 5 patients had no available frozen serum samples for quantitative serum apo profiling. Since in the MEGA study, only controls were included who were < 70 years, MISSION patients who were > 70 years were excluded in this logistic regression analysis (n = 57). Therefore, in total, 220 cases were included. During the study, all patients were treated according to the institutional MISSION! protocol . If TG exceeded 4.52 mmol/L, LDLc was not calculated. In the MEGA study, the apo profile was determined similarly as in the MISSION! cohort. Remnant cholesterol concentration was calculated by TC − LDLc − HDLc.The definitions of Nordestgaard et al. for abnormal lipid or apo levels in non-fasting state were used: TC 5.0 mmol/L, LDLc 3.0 mmol/L, HDLc 1.0 mmol/L, TG 2.0 mmol/L, remnant cholesterol 0.9 mmol/L, non-HDLc 3.9 mmol/L, apoA1 1.25 g/L, and apoB 1.0 g/L  which waU test was used to test differences between two groups of not-normally distributed data. Lipid/apo profiles were log-transformed if not-normally distributed. Categorical variables are expressed as numbers and percentages.Continuous variables with a normal distribution are presented as mean ± standard deviation (SD). Not-normally distributed data are presented as medians and interquartile range (IQR). The Mann–Whitney To study determinants of conventional lipid and apo profiles in the general population, simple and multiple linear regression analyses were performed in the group of healthy individuals. To determine the association of various lipid and apo profiles with (1) risk of STEMI and (2) risk of MACE in patients with STEMI, odds ratios (ORs) with 95% confidence intervals (CIs) were calculated, and were adjusted for age, sex, and statin use by logistic regression methods. Model 1 in Table n = 40), as statin use do affect conventional lipid levels and apo profiles. To evaluate changes over time in lipid levels within patients a paired T test or a Wilcoxon signed rank test was performed when appropriate. All statistical tests were 2-tailed, p values < 0.05 were considered statistically significant. All statistical analyses were performed with SPSS for Windows, version 24.0 .Duration of follow-up was counted from time of first STEMI to end of follow-up, defined as 10-year follow-up, the date of a MACE, or lost to follow-up, whichever occurred first. Incidence rates of MACE or death were estimated as the number of events over the accumulated follow-up time. Cox-proportional hazards models were used to evaluate risks between groups, and were progressively adjusted for age, sex, and statin use. In all regression analyses, a preplanned sensitivity analysis was performed in which statin users were excluded at the time of blood draw (p < 0.001) and apoB was significantly higher in the STEMI group than in the control group . All baseline characteristics are summarized in Table In total, 220 STEMI patients and 299 control subjects were eligible for this study. As expected, patients in the STEMI group were older (mean age 55.0 ± 9.37 years) than RDD controls (mean age 47.5 ± 13.1 years) in the control group. Patients with STEMI were more often men as compared with the control group . Overall, the STEMI group had a lipid profile consistent with an increased cardiovascular risk. Whereas TC and LDLc levels did not differ between the two groups, HDLc was significantly lower and TG and remnant cholesterol were significantly higher in the STEMI group. ApoA1 was significantly lower in the STEMI group than in the control group . Per SD increase of apoA1 (0.31 g/L) the OR was 0.24 (95% CI 0.18–0.33). These results were similar in the group without statin users. High apoB levels were also associated with risk of STEMI with an OR of 2.17 (95% CI 1.40–3.36). This effect appeared to be even stronger in the group without statin users . Per SD increase of apoB (0.29 g/L), the OR of STEMI was 1.27 (95% CI 1.02–1.57) in the total group, and 1.36 (95% CI 1.08–1.72) in the group without statin users. High baseline apoB/apoA1 ratios were also associated with STEMI risk. The OR per SD increase (0.25) was 2.16 (95% CI 1.76–2.65) in the total group and 2.29 (95% CI 1.84–2.85) in the group without statin users.High apoCI levels were associated with risk of STEMI with an OR of 0.32 (95% CI 0.18–0.57) of the highest quartile versus the lowest quartile. Per SD increase of apoCI (5.33 mg/L), the OR was 0.76 (95% CI 0.62–0.93). The VLDL-associated apos gave conflicting results. No association of apoCII and apoE with the risk of STEMI was found, whereas higher apoCIII was associated with lower risk of STEMI in the groups divided by quartiles. The highest quartile of apoCIII versus its lowest quartile was associated with an OR of 0.41 (CI 95% 0.24–0.72) for risk of STEMI.p < 0.001). Mean LDLc was 2.33 ± 0.75 mmol/L compared to 3.48 ± 0.94 at baseline, which is a 27% reduction (p < 0.001). HDLc raised from 1.27 ± 0.36 mmol/L to 1.40 ± 0.36 which is an increase of 12% (p < 0.001) Median triglyceride level was 1.31 mmol/L (1.00–1.80) at baseline compared to 1.53 mmol/L (1.01–2.22) after 1 year which is a 20% reduction (p < 0.001). According to medical record review 7 patients (3.2%) were not on statin therapy after 1 year due to pharmacological side effects.At discharge, 100% of the STEMI patients were on statin therapy. The fast majority was on rosuvastatin 10 mg daily. Values of continuation samples of conventional lipoproteins were available in STEMI patients after 1 year. Mean TC levels were reduced from 5.63 ± 1.09 mmol/L to 3.96 ± 0.73 mmol/L which is a 27% reduction (In the patients with STEMI 83 (38%) events were observed after a mean follow-up duration of 8.94 years. For each baseline lipid and apo species the hazard ratio for MACE was calculated after adjustment for age, gender, and statin use. Neither conventional lipid levels nor apo levels were associated with a recurrent event in the STEMI group  apoA1, apoB and the apoB/apoA1 ratio were strongly associated with the risk of developing a STEMI; (2) remnant cholesterol was significantly associated with risk of STEMI; (3) apoCII, apoCIII and apo E were not clearly associated with risk of STEMI; and (4) no significant association of serum lipids or serum apos with MACE in STEMI patients during follow-up who were all treated with statins during follow-up was found.Despite current standards of care aimed at achieving targets for LDLc and other traditional risk factors, STEMI patients remain at high risk of new cardiovascular events , 13. TheIn recent years, it has been demonstrated that apoA1, apoB and apoB/apoA1 ratio can predict CVD better than LDLc , 26–28. ApoB concentration represents the total number of atherogenic particles including VLDL, intermediate-density lipoprotein (IDL), IDL remnants, LDL and lipoprotein (a), as each of these particles carries one molecule of apoB. With the routine measurement of apoB, a considerable number of events could be prevented on top of LDLc . The cliSo far, only serum apo A1 and B were measured in medical laboratories, whereas the apos CI, CII, CIII and E are not routinely measured. To determine the full panel of serum apos in a multiplexed and immunoassay independent way, van den Broek et al.  recentlyRemnant cholesterol is the cholesterol content of triglyceride-rich lipoproteins, composed of mainly VLDL and IDL , 37. TheThe VLDL-associated apos apoCII, apoE and—to a large extent—apoCIII have recently been identified as potentially important new risk factors , 18. TheRecently, Pechlaner et al.  showed tThe relation of apoE with CVD risk is more controversial. Although Pechlaner et al. found a strong association, a meta-analysis with almost 10,000 individuals and 1400 events recently performed by Sofat et al. found no association of apoE with CVD [In STEMI patients, no association of baseline serum lipids or serum apos with MACE during long-term follow-up was found. Although earlier results from the TNT study  suggest The primary goal in STEMI patients is to reduce the residual risk as much as possible. Statin therapy has shown to reduce the residual risk substantially by reducing LDLc levels,  but the r2 of 0.89 and 0.93, respectively, but the effect of thawing on apoC and apoE levels is unknown. Furthermore, the models for risk of STEMI and MACE were adjusted for age, gender and statin use. An important factor modifying the association between (apo) lipoproteins and outcome is inflammation. Since C-reactive protein was measured in the acute phase in the STEMI cohort, and in the non-acute phase in the MEGA cohort, we were not able to reliably adjust the apo (lipoproteins) for inflammation.Some potential limitations deserve a comment. First, the controls of the MEGA population were < 70 years; so, results from the case–control study only apply to individuals < 70 years. Second, apoC and E levels were measured in MEGA controls in serum that was thawed twice. However, Bland–Altman plots and scatterplots suggest that this did not lead to measurement error as apoA1 and apoB levels that were measured on both once and twice thawed serum showed equivalent levels with In conclusion, apoA1, apoB, and apoB/apoA1 ratio and remnant cholesterol were strongly associated with risk of STEMI, the apoB/apoA1 ratio being superior to LDLc and non-HDLc. Second, no clear relation of apoCI, apoCII, apoCIII and apoE with the risk of STEMI as compared with a population-based control group was found. Neither serum lipids nor serum apos predicted death, re-infarction or revascularization in statin-treated patients during follow-up after a first STEMI. Valuable effort should be made to further reduce residual cardiovascular risk by intensive life style modification, by testing new powerful cholesterol-lowering agents and using additional more discriminating and more refined treatments targets like apoB and apoB/apoA1 ratio.


FCGR3A and FCGR2A high‐affinity polymorphisms predict clinical outcomes to alemtuzumab therapy in 85 patients with relapsing‐remitting multiple sclerosis. No differences in clinical and MRI‐based efficacy parameters, the development of severe infusion‐associated reactions and secondary autoimmune diseases during a 2 year follow‐up was observed based on FCGR3A or FCGR2A polymorphisms. This study does not support the use of FCGR genetic variants to predict clinical outcomes to alemtuzumab.Allelic variants of genes encoding for the Fc gamma receptors IIIA and IIA have been associated with the clinical response to cell‐depleting antibodies in lymphoma patients. Here, we tested the hypothesis that  Alemtuzumab  is a lymphocyte‐depleting humanized monoclonal IgG1 kappa antibody that selectively targets CD52, an antigen highly expressed on T and B cells. It is currently approved for highly active relapsing‐remitting multiple sclerosis (RR‐MS) in over 50 countries, and data from initial treatment cohorts as well as extension studies of phase III clinical trials provided evidence that alemtuzumab can induce prolonged disease remission with no evidence for disease activity in more than 50% of treated patients.Lymphocyte‐depleting antibodies such as alemtuzumab lyse target cells through IgG Fc‐mediated effector functions such as antibody‐dependent cell‐mediated cytotoxicity (ADCC), antibody‐dependent cellular phagocytosis (ADCP), complement‐dependent cytotoxicity (CDC), and by induction of apoptosis. ADCC and ADCP require engagement and cross‐linking of activating FcγR expressed by immune effector cells. In preclinical models, FcγR‐mediated cytotoxicity contributes substantially to the efficacy of depleting antibodies such as the anti‐HER2 antibody trastuzumab, rituximab, and alemtuzumab,FCGR3A results in tighter binding of the FcγRIIIA to IgG1 and IgG3, whereas the low‐affinity F allele with phenylalanine at this position is associated with decreased binding to IgG, resulting in less efficient ADCC.FCGR2A results in greater affinity of FcγRIIA for IgG2, whereas the low‐affinity R allele correlates with decreased IgG binding. Here we investigated whether FcγRIIIA and FcγRIIA polymorphisms are associated with clinical outcomes to alemtuzumab treatment in patients with MS.The clinical efficacy of alemtuzumab to induce durable disease remission in MS is believed to involve the initial elimination of pathogenic lymphocytes.n = 85 – x for some characteristics. These dropouts included: NEDA‐3, one patient (F/F group); MRI stability, two patients (F/F and V/V groups) for FcγRIIIA and NEDA‐3, one patient (R/R group); MRI stability, two patients (H/R and R/R groups) for FcγRIIA. PBMCs were isolated from ethylenediaminetetraacetic acid blood derived from these patients during blood draws between 2014 – 2017 and cryopreserved as previously described.All patients were recruited in the Department of Neurology at the University Hospital Münster, Germany. 85 patients with RR‐MS on alemtuzumab treatment Table  were incThis study was performed according to the Declaration of Helsinki and approved by the local ethics committee (2014‐398‐f‐S). All patients gave written informed consent.DNeasy Blood & Tissue Kit (Cat No.: 69506) according to the manufacturer’s recommendation.DNA was isolated using Qiagen’s FCGR2A (NP_067674.2 position 166) and FCGR3A . Due to high homology of FCGR3A, primers were designed in a region allowing the specific amplification of the FCGR3A gene. The PCRs were tested on a gradient and the optimal annealing temperature was chosen.PCR primers were designed to amplify the region of the two SNPs of interest in FCGR2A and FCGR3A genotypes recruited at the University Hospital Erlangen, Germany. The results of the test runs are shown in Table The analytical procedure was tested using six blood samples derived from donors with known 2, 200 µmol/L of each dNTP, 0.02 U µl‐1 DNA polymerase , and 25 ng DNA of the sample. PCR cycle conditions are given in Table PCR products were amplified in a 25 µL reaction volume containing 0.3 µmol/L of each primer, 1.5 µmol/L MgClFCGR2A. In case of the FCGR3A gene an internal forward primer (FCGR3A_For02: GGGGTGTCTGTGTCTTTCAG) as well as the reverse PCR primer were used for the Sanger sequencing. The sequences were aligned against the reference sequences derived from GenBank (and any SNPs were recorded with Soft Genetics Mutation Surveyor v4.0.9 software).The PCR products were purified, and bi‐directionally Sanger sequenced at Microsynth  using the two PCR primers for p‐values (Bonferroni correction) were calculated with the p.adjust function of the “stats” package (version 3.6.0) with R (http://www.R-project.org). A p‐value < .05 was considered statistically significant.The distributions of positive versus negative clinical and radiological outcomes for patients in different genotypic groups were compared using the two‐sided Fisher’s exact test via Prism version 7.0a for Mac OSX  and corrected n = 28), fingolimod (n = 18), IFNβ (n = 9), dimethylfumarat (n = 6), glatirameracetat (n = 4), teriflunomide (n = 2), mitoxantrone (n = 1), azathioprine (n = 1), and siponimod (n = 1). Fifteen patients did not receive any pretreatment  were female. The mean age of patients at initial treatment with alemtuzumab was 35.8 years (SD 9.7). Patients received 12 mg of alemtuzumab administered intravenously daily for five consecutive days and again one year later for three consecutive days. The median expanded disability status scale (EDSS) at baseline was 2.8 (interquartile range [IQR] 2). Mean relapse frequency prior to treatment was 2.6 relapses in the last two years before commencement of alemtuzumab (SD 1.8). The mean number of treatments prior to alemtuzumab was 2.3 (SD 1.7) and included natalizumab (Common Terminology Criteria for Adverse Events (CTCAE), death, life‐threatening consequences, fetal damage, and need for urgent hospitalization and were observed in 30 patients (35%). A total of 24 (28%) patients developed SAD. Observed SAD entities included Graves’ disease (n = 16), idiopathic thrombocytopenic purpura (ITP) (n = 2), vitiligo (n = 2), autoimmune hepatitis (n = 1), autoimmune thyroid disease (AITD) (n = 5), and hemophagocytic syndrome (n = 1). Two patients developed more than one SAD . The mean time point of SAD manifestation was at 22 months . Six patients (7%) developed acute acalculous cholecystitis (AAC).The total number of patients who experienced at least one relapse during the first two years of the follow‐up period was 34 (40%). Twenty patients (24%) met the definition for confirmed disease progression (CDP). NEDA‐3, as defined by absence of clinical relapse, no confirmed EDSS disability progression, no new gadolinium enhancing lesions and no new or enlarging T2 lesions, was observed in 41 (49%) of patients. Severe IAR included according to the FCGR3A and FCGR2A genotypes. Among the 85 RR‐MS patients investigated, 11 (13%) carried the high‐affinity FCGR3A 158 V/V genotype, 41 (48%) were heterozygous carriers (FCGR3A 158 V/F), and 33 (39%) were homozygous for FCGR3A 158 F/F. With respect to the FCGR2A 131 polymorphism, 21 patients were (25%) genotyped as FCGR2A 131 H/H, 36 (42%) were heterozygous FCGR2A 131 R/H carriers, and 28 (33%) were homozygous for FCGR2A 131 R/R , 69% (F/F) and 66% (F/V) respectively showed MRI stability as defined by no new or enlarging T2 or gadolinium‐enhancing lesion. NEDA‐3 criteria were met by 18% of the V/V group versus 59% (F/F) and 49% (F/V). Severe infusion‐associated reactions were experienced by 36% of patients with V/V, 21% with F/F and 46% with F/V genotypes. In 55% of V/V secondary autoimmune diseases occurred as opposed to 30% of F/F and 20% of F/V , 68% (R/R) and 86% (H/R) of carriers, respectively. 52% (H/H), 56% (R/R) 83% (H/R) depicted stable MRI activity and NEDA‐3 status was achieved in 38% (H/H), 41% (H/R) and 61% (R/R) of study participants. Severe IAR occurrence was distributed as follows: 33% (H/H), 29% (R/R) and 42% (H/R). 24% (H/H), 36% (R/R), and 25% (H/R) developed SADs  as compared to patients homozygous for both low‐affinity FcγRs   and clearly require validation in larger cohorts. Moreover, we cannot exclude potentially confounding effects of neutralizing antibodies to alemtuzumab which may occur in individual patients within the first 2 years after treatment initiation.Our study was motivated by epidemiological investigations that reported an association between the low affinity in vivo mechanisms of therapeutic antibodies in MS.Given the clinical efficacy and increasing use of lymphocyte‐depleting antibodies in the therapeutic landscape of MS and other neurological diseases, our initial assessment provides incentive to conduct larger prospective studies in order to evaluate the impact of FcγR polymorphisms on clinical outcomes and Christian W. Keller: Study concept and design, acquisition of data, analysis and interpretation, writing of the manuscript. Tobias Ruck: Study concept and design, acquisition of data, analysis and interpretation, critical revision of the manuscript for important intellectual content. Donal McHugh: Study concept and design, acquisition of data, analysis and interpretation, critical revision of the manuscript for important intellectual content. Steffen Pfeuffer: Acquisition of data, critical revision of the manuscript for important intellectual content. Catharina C. Gross: Acquisition of data, critical revision of the manuscript for important intellectual content. Catharina Korsukewitz: Acquisition of data, critical revision of the manuscript for important intellectual content. Nico Melzer: Acquisition of data, critical revision of the manuscript for important intellectual content. Luisa Klotz: Acquisition of data, critical revision of the manuscript for important intellectual content. Sven G. Meuth: Critical revision of the manuscript for important intellectual content. Christian Münz: Critical revision of the manuscript for important intellectual content. Falk Nimmerjahn: Critical revision of the manuscript for important intellectual content. Heinz Wiendl: Critical revision of the manuscript for important intellectual content. Jan D. Lünemann: Study concept and design, analysis and interpretation, writing of the manuscript, study supervision.C.W.K. received research support from the German Research Foundation (DFG) and the University of Zurich and travel expenses from Boehringer Ingelheim, the National Institute of Diabetes and Digestive and Kidney Diseases/NIH, and the National Institute on Aging/NIH. T.R. received travel expenses and financial research support from Genzyme and Novartis, and received honoraria for lecturing from Roche, Merck, Genzyme, Biogen, and Teva. S.P. received travel grants from Sanofi Genzyme and Merck Serono, lecturing honoraria from Sanofi Genzyme, Mylan Healthcare and Biogen and research support from Diamed and Merck Serono. C.C.G. received speaker honoraria, travel expenses for attending meetings, and research support from Biogen, Euroimmun, Genzyme, Mylan, Novartis, and Bayer Health Care. N. M. has received honoraria for lecturing and travel expenses for attending meetings from Biogen Idec, GlaxoSmith Kline, Teva, Novartis Pharma, Bayer Healthcare, Genzyme, Alexion Pharamceuticals, Fresenius Medical Care, Diamed, and BIAL, and has received financial research support from Euroimmun, Fresenius Medical Care, Diamed, Alexion Pharamceuticals, and Novartis Pharma. L.K. received compensation for serving on scientific advisory boards ; speaker honoraria and travel support ; research support . S.G.M. received honoraria for lecturing, and travel expenses for attending meetings from Almirall, Amicus Therapeutics Germany, Bayer Health Care, Biogen, Celgene, Diamed, Genzyme, MedDay Pharmaceuticals, Merck Serono, Novartis, Novo Nordisk, ONO Pharma, Roche, Sanofi‐Aventis, Chugai Pharma, QuintilesIMS and Teva. His research is funded by Almirall, Amicus Therapeutics Germany, Biogen, Diamed, Fresenius Medical Care, Genzyme, Merck Serono, Novartis, ONO Pharma, Roche, and Teva. H.W. received honoraria for acting as a member of Scientific Advisory Boards for Biogen, Evgen, Genzyme, MedDay Pharmaceuticals, Merck Serono, Novartis, Roche Pharma AG, and Sanofi‐Aventis as well as speaker honoraria and travel support from Alexion, Biogen, Cognomed, F. Hoffmann‐La Roche Ltd., Gemeinnützige Hertie‐Stiftung, Merck Serono, Novartis, Roche Pharma AG, Genzyme, TEVA, and WebMD Global. He is acting as a paid consultant for Abbvie, Actelion, Biogen, IGES, Johnson & Johnson, Novartis, Roche, Sanofi‐Aventis, and the Swiss Multiple Sclerosis Society. J.D.L. received grant support, consultant and lecture fees from Novartis, Merck, Sanofi‐Genzyme, Bayer Pharma, Pfizer.


Astragalus membranaceus. We tested the probiotics Enterococcus faecium, Lactobacillus plantarum and Enterococcus faecium + Lactobacillus plantarum and applied PacBio single molecule, real-time sequencing technology (SMRT) to evaluate the quality of Astragalus fermentation. We found that the production rates of acetic acid, methylacetic acid, aethyl acetic acid and lactic acid using E. faecium + L. plantarum were 1866.24 mg/kg on day 15, 203.80 mg/kg on day 30, 996.04 mg/kg on day 15, and 3081.99 mg/kg on day 20, respectively. Other production rates were: polysaccharides, 9.43%, 8.51%, and 7.59% on day 10; saponins, 19.6912 mg/g, 21.6630 mg/g and 20.2084 mg/g on day 15; and flavonoids, 1.9032 mg/g, 2.0835 mg/g, and 1.7086 mg/g on day 20 using E. faecium, L. plantarum and E. faecium + L. plantarum, respectively. SMRT was used to analyze microbial composition, and we found that E. faecium and L. plantarum were the most prevalent species after fermentation for 3 days. E. faecium + L. plantarum gave more positive effects than single strains in the Astragalus solid state fermentation process. Our data demonstrated that the SMRT sequencing platform is applicable to quality assessment of Astragalus fermentation.We investigated if fermentation with probiotic cultures could improve the production of health-promoting biological compounds in  Astragalus root, a well-known traditional herbal medicine, has been widely used in humans, poultry, and other livestock in China and East Asia. It contains polysaccharides, saponins, flavonoids, anthraquinones, alkaloids, amino acids, β-sitosterol, and metallic elements3. Astragalus has anti-inflammatory4, immunostimulant5, antioxidative6 and antiviral activities7. Fermentation is often used for various foods, including fruits. However, accumulating evidence has shown that some herbs can also be fermented, for example Flos Lonicera and Rhizoma atractylodis9.10. SSF has several advantages, including microbial culture on water-insoluble substrates, higher product concentration and stability, higher fermentation productivity, lower catabolic repression, and less need for sterility10.Solid state fermentation (SSF) is the process by which microorganisms are cultured on a solid moist substrate. It is considered superior to submerged fermentation technologyAstragalus is normally based on changes in microbial composition and physiological parameters, such as pH and water content11. However, little is known about organic acid content, yield of active substances, and microbial composition after lactic acid bacterial (LAB) fermentation. Although culture-dependent methods and quantitative real-time polymerase chain reaction (PCR) methods have been used to study microbial composition, these methods are time-consuming and the results are sometimes inaccurate, especially the target bacterial counts12.In the literature, quality assessment of fermented 13, which is a high-throughput platform based on the Roche GS20 454 sequencer14, Illumina GA, and MiSeq and HiSeq platforms15. Since the DNA sequencing techniques can determine only partial sequences of 16 S rRNA genes, these methods have limited taxonomic resolution16. Third NextGen, Pacific Biosciences’ (PacBio) single molecule, real-time sequencing (SMRT) technology is now available, which is faster and provides more informative sequencing. PacBio offers long DNA sequence reads that are able to depict the bacterial profiles of target samples to the species level17.Recently, there has been a revolution in next-generation sequencing platforms, such as the Sanger sequencing method16. In the present study, we specifically focused on the detection and comparison of the bacterial composition of Astragalus fermented by Enterococcus faecium and Lactobacillus plantarum using the PacBio SMRT method.The PacBio SMRT method has been used to evaluate the quality of silage production successfullyAstragalus are shown in Fig. L. plantarum, E. faecium, L. plantarum + E. faecium) resulted in varying degrees of fermentative pH changes. After fermentation, pH decreases to below 5.0 were significant from days 6 to 30 in the L. plantarum, E. faecium and L. plantarum + E. faecium groups.The changes in pH of fermented L. plantarum group, E. faecium group, and L. plantarum + E. faecium group, reaching peaks of 1723.01 mg/kg, 1329.61 mg/kg, and 1866.24 mg/kg on day 15, respectively, as shown in Fig. L. plantarum and E. faecium promoted the production of acetic acid.There were significant rises in acetic acid content in the L. plantarum group, E. faecium group, and L. plantarum + E. faecium group, reaching peaks of 173.29 mg/kg, 123.88 mg/kg, and 203.80 mg/kg on day 30, respectively, as shown in Fig. L. plantarum and E. faecium promoted the production of methylacetic acid.There was a gradual rise in methylacetic acid in the L. plantarum group, E. faecium group, and L. plantarum + E. faecium group, reaching peaks of 616.07 mg/kg, 445.74 mg/kg, and 996.04 mg/kg on day 25, respectively, as shown in Fig. L. plantarum and E. faecium promoted the production of aethyl acetic acid.There were significant rises in aethyl acetic acid in the L. plantarum + E. faecium group and L. plantarum group, reaching peaks of 3081.99 mg/kg on day 20 and 1946.17 mg/kg on day 15, respectively, as shown in Fig. E. faecium group exhibited no significant change compared to the control group. These results indicated that L. plantarum + E. faecium promoted the production of lactic acid.There were gradual rises in lactic acid in the L. plantarum fermentation than in the control group as shown in Fig. L. plantarum fermentation (p < 0.05).The polysaccharide yield was higher in the L. plantarum + E. faecium group sharply increased on day 15 and reached 21.6630 mg/g as shown in Fig. L. plantarum + E. faecium was superior to either L. plantarum or E. faecium alone (p < 0.05).The total yield of saponins in the E. faecium group sharply increased and reached 2.0835 mg/g as shown in Fig. E. faecium fermentation (p < 0.05).The flavonoid yield in the Astragalus samples at the species level. A total of 2,945,166 sequence reads were obtained from nine Astragalus samples, with an average of 8,888 reads for each sample. The ACE, Chao 1, Shannon and Simpson indices were calculated, and a different richness for each of the nine groups was observed , 1,866 in the B group (L. plantarum fermentation), and 1,853 in the C group (E. faecium + L. plantarum fermentation). As shown in Fig. The total OTUs obtained were as follows: 1,505 in the A group  was most abundant in the A2 sample, L. plantarum (71.0%) in the B2 sample, and E. faecium + L. plantarum (98.7%) in the C2 sample. However, microbial compositions in the A3, B3, and C3 samples tended to be more consistent at day 30. These results showed that the genus Stanieria spp. was the most abundant in the A1, B1, and C1 samples before fermentation, whereas after fermentation on day 3, Enterococcus and Lactobacillus were the most abundant genera in the A2, B2, and C2 samples.As shown in Fig. E. faecium exhibited dynamic changes in the A group, and its proportion was 44.8%. L. plantarum displayed slight changes in the B group, and its proportion was 35.3%. E. faecium + L. plantarum underwent great changes in the C group, and its proportion was 47.35%. Clearly, the prevalent species that existed in fermented Astragalus were highly dependent on the original bacterial composition.At the species level, the relative abundances of microbes in the three groups are shown in Fig. Enterococcus and Lactobacillus were more abundant in the A2, B2, and C2 samples than in the other samples. However, with time the fermentation bacteria were reduced in numbers and other naturally occurring bacteria commenced growth.As shown in Fig. Astragalus fermentation.On day 30 of the fermentation, we concluded that the fermentation bacteria were dominant on day 3 of the fermentation, and this indicated that 3 days may be the optimum length of time for A. membranaceus  has been widely used as a dietary supplement in Asia18. In this study, E. faecium and L. plantarum were added to assess the nutritional value and organic content of Astragalus fermented with probiotics. The full 16 S rRNA gene-SMRT sequencing method was applied to monitor the quality of Astragalus fermentation, as traditional methods, including culture-dependent methods, can be inaccurate and the results may be ambiguous and difficult to interpret.The Chinese herb Astragalus with E. faecium and L. plantarum additives caused a decrease in pH due to the production of organic acids during the fermentation process. In general, the decrease in pH value was mainly due to the production of lactic acid during fermentation. The low pH is advantageous because it creates a more stable fermentation. The organic acid production of fermented Astragalus is dependent upon the type of bacteria used. L. plantarum is a well-known homo-fermentative LAB19, which efficiently produces lactic acid from fermented Astragalus. Moreover, the fermentation conducted with combined E. faecium + L. plantarum was enhanced compared to single-strain fermentation. It has been reported that there is a natural synergy between different probiotics when they are present in certain proportions21. It is important to determine the appropriate ratios of bacteria because unsuitable proportions can reduce the rates of production of organic acids and perhaps even inhibit the fermentation process22. Also, combining E. faecium + L. plantarum in the fermentation may improve the odor and provide an acidic flavor16.In this study, we found that the fermentation of Astragalus produces polysaccharides, flavonoids and saponins, which increased gradually during fermentation and were found in higher concentrations than in the control group during the entire fermentation process when E. faecium and L. plantarum were applied to SSF. The reason may be that free polysaccharides and extracellular polysaccharides were produced due to the degradation of cellular wall cellulose by digestive enzymes during fermentation23. At the later stage of fermentation, the yield of active substances decreased, which may be due to their utilization by E. faecium and L. plantarum, their transformation into other compounds, or the generation of secondary glycosides.Moreover, the current study demonstrated that Astragalus. The current study focused on the microbial composition during fermentation of Astragalus powder using E. faecium and L. plantarum. The results showed that Astragalus may promote the growth of E. faecium and L. plantarum because of the high content of organic acids, carbon, inorganic salts, and polysaccharides25. The preparation of traditional Chinese medicines mostly incorporates extraction and prefractionation processes26 that can be accompanied by an irritating odor27 which is not conducive to animal feeds. Thus, the fermentation of herbal medicines by probiotics may be an effective processing method to remove odor28. Moreover, Astragalus has a sour taste after fermentation which is more conducive to animal feeds.The microbial profile is another indicator that reflects the quality of fermented Astragalus samples showed that the major bacterial species depended on the original bacterial composition. After day 3 of fermentation, the original bacteria namely E. faecium and L. plantarum, were dominant. Moreover, the microbial diversity was greater when E. faecium + L. plantarum were used for fermentation compared to a single species. These results confirmed that multiple strains have positive effects on Astragalus fermentation. In conclusion, it appears that Astragalus promoted the growth of bacteria to exert prebiotic-like effects that selectively stimulated the growth of synergistic beneficial bacteria29.SMRT analysis of the microbial composition of nine fermented 31. By 30 days, bacteria naturally associated with Astragalus had begun to grow and multiply. Compared with the traditional method of colony counting32, SMRT analysis can accurately reflect microbial composition during Astragalus fermentation.However, microbial composition tended to remain constant after fermentation for 30 days. The reason is that lactic acid bacteria are relatively fragile, with a short growth periodE. faecium and L. plantarum have positive effects on the fermentation of Astragalus. Although only nine of the samples were analyzed using the SMRT sequencing technology, our data show that this is a promising method for assessing the quality of fermented Astragalus.In summary, Astragalus, the dried root of Astragalus membranaceus (Fisch.) Bge. var. mongholicus was obtained from Gansu Hui Sen Pharmaceutical Co., Ltd. . Astragalus fermentation was performed following our laboratory-optimized procedure. Briefly, Astragalus was ground into a powder using a 100-mesh screen. Then, the dried powder  was divided into three groups: A, B, and C. The A group was inoculated with 106 colony-forming units (CFU)/g of E. faecium (CGMCC 1.130), the B group was inoculated with 106 colony-forming units (CFU)/g of L. plantarum (CGMCC 1.557), and the C group was inoculated with 106 CFU/g E. faecium + L. plantarum. The two strains were isolated and deposited in the China General Microbiological Culture Collection Center . The control group did not add any probiotics. Fermentation was conducted in 35 × 45-mm plastic film bags , and the bags were evacuated and sealed using a vacuum packing machine. Subsequently, the mixtures were incubated for 30 days at 37 °C under anaerobic conditions to produce Astragalus fermentation. The three groups were sampled at days 0, 3, and 30 and labeled A1, A2, A3, B1, B2, B2, C1, C2, and C3, respectively.g for 20 min. Ten milliliters of supernatant were filtered through a 0.45-μm membrane before chromatographic analysis. Separations by high performance liquid chromatography (HPLC) were performed on an Agilent 1260 Series LC system with a preparative XB-C18 column . Solvent A was phosphate buffer solution (pH 2.70), and solvent B was methanol solution. Elution was performed with a gradient of 97:3, while the analytical column temperature was 20 °C, and the flow rate was 0.80 mL/min. Absorbance was detected at 210 nm. Acetic acid, methylacetic acid, aethyl acetic acid, and lactic acid concentrations were determined. For pH measurement, 25 g fermented Astragalus from the three groups was dissolved in 225 mL of deionized water. After vortex mixing for 30 min, measurements were taken using a pH meter .To perform organic acid analysis, 5 g of day 5, 10, 15, 20, 25 and 30 fermented sampls from A group, B group, C group and control group were mixed with 60 mL of deionized water, followed by heating in a water bath for 20 min. Then, the filtrate was centrifuged at 80,000 × Astragalus root was kept in distilled water (at a ratio of 1:8) for 24 h and extracted 3 times with distilled water in a boiling water bath. The extract was collected by centrifugation  at 5, 000 × g for 15 min, and the supernatant was concentrated by rotary evaporation . Then, 95% ethanol (3-fold volume) was added to the concentrated solution and the mixture was stored at 4 °C for 24 h and then centrifuged at 5, 000 × g for 20 min. The precipitate was dried at 60 °C and ground into a powder. The amounts of polysaccharides in the extracts were determined using the phenol-sulfuric acid method3.Fermented dried Astragalus methyl glucoside reference substance  (5 mg) was added to a 25-mL volumetric flask, and methanol was added, diluted to scale, and the mixture was shaken well and used as a reference solution. There were 0.1-, 0.2-, 0.4-, 0.6-, 0.8-, 1.0-, and 1.2-mL aliquots of the reference solution that were placed in 10-mL calibrated test tubes, dried in a water bath, and cooled. Then, 0.2 mL of freshly prepared 5% vanillin glacial acetic acid solution was added to 0.8 mL of perchloric acid and shaken well. After that, the mixture was heated at 70 °C in a water bath for 20 min, cooled with ice water for 5 min, and followed by the addition of 5 mL of glacial acetic acid. The mixture was shaken well and retained as a blank control. A standard curve was constructed with absorbance at 580 nm as the ordinate (Y) and the concentration of reference solution as the ordinate (X). The production of glycosides from Astragalus could be calculated according to the standard curve.A 10 mg sample of a rutin reference solution (Suolaibao Co.) was mixed with 60% ethanol to a final volume of 50 mL in a volumetric flask and used as the reference solution. 0-, 2-, 4-, 6-, 8-, 10-, and 12-mL aliquots of the reference solution were placed in 25-mL volumetric flasks, and then 1.0 mL of 5% sodium nitrite solution was added and shaken well for 6 min. After that, 1.0 mL of 10% aluminum nitrate solution was added and shaken well for 6 min, then 10 mL of 4% sodium hydroxide solution in 60% ethanol was added and shaken for 15 min. A standard curve was constructed with absorbance at 510 nm as the ordinate (Y) and concentration of the reference substance as the ordinate (X), and the production of astragaloside IV was calculated according to the standard curve.Flavonoids yield = [flavonoid concentration (mg/mL) × flavonoid solution volume (mL)]/sample quality (g) × 100%.Astragalus from each group was utilized for DNA isolation. DNA samples were quantified using a Qubit 2.0 Fluorometer . The quality of extracted DNA was assessed by 0.8% agarose gel electrophoresis and spectrophotometry . All of the extracted DNA samples were stored at −20 °C prior to further analysis.A total of nine samples, A1, A2, A3, B1, B2, B3, C1, C2, and C3, were collected. The samples were immediately frozen at −196 °C until DNA extraction. A total of 200 mg of fermented Bacterial 16 S rRNA was amplified by PCR for barcoded SMRT sequencing with forward 27 F (5′-AGAGTTTGATCMTGGCTCAG-3′) and reverse 1492 R (5′-ACCTTGTTACGACTT-3′) primers. The PCR program was as follows: 95 °C for 2 min; 30 cycles of 95 °C for 30 s, 55 °C for 30 s and 72 °C for 30 s with a final extension of 72 °C for 5 min.http://qiime.org/acripts/pick_oyus.html), and they were clustered into operational taxonomic units (OTUs). All of the sequences were compared against the Greengenes reference database . Taxa summarization, alpha diversity, and a taxon differential distribution analysis were performed using all of the available sequences for each sample.The entire 16 S rRNA lengths of the community were sequenced by the Pacbio Sequel platform at Personalbio, Inc. . The raw data were taken for Circular Consensus Sequencing and corrected so that the correctness of the forecast was not less than 90%. The extraction of high-quality sequences was performed with the QIIME package , and statistical significance was tested using analysis of variance. The chemical composition of each sample was tested three times, and the results are expressed as the mean ± standard deviation.16. Using R software, a partial least squares discriminant analysis (PLS-DA) discriminant model was constructed based on the species abundance matrix and the sample packet data.Sequences were rarefied prior to the calculation of alpha and microbial comparative analysis. Alpha diversity indices were calculated in QIIME from rarefied samples using the Shannon index for diversity and the Chao 1 index for richness