2.1. Media Optimization for Adenosylcobalamin Production

Medium optimization was carried out by the traditional method of taking one variable at a time and keeping other variables fixed, i.e., varying one factor while keeping all others constant. Using this approach, the important media components were identified, and they were further used to optimize the fermentation media. The effect of varying concentrations of Betaine, Cobalt Nitrate, Beet Molasses, Choline Chloride, and 5,6 DMB on cell growth and adenosylcobalamin biosynthesis, was tested by designing set of experiments using Taguchi’s method [Table 5]. The flasks were incubated at 200 rpm for 7 days at 30°C. 10% (v/v) of 50 % Sucrose feeding was done in a production flask from log 48 to 120 h at 24 h interval.

Table 5: Composition of different sets of Production medium designed using Taguch’s method.

2.2. HPLC Analysis

The samples were analyzed using HPLC as per Singh et al., from 120 h onward up to 168 h, respectively [16].

2.3. LC MS Analysis

The samples were analyzed using LC MS method, executive Plus – Orbitrap MS, column details – Hypersil gold 3 micron 100 × 2.1 mm, solvent used: Solvent A – 0.1% food acid in milliq water, solvent B – 100% acetonitrile, run time – 30.000 [min], syringe type – Hamilton, flow rate – 3.000 mL/min, inner diameter – 2.303 mm, volume – 250 mL, polarity – Positive, In-source CID – 0.0 eV, microscans 1, resolution – 70,000, AGC target – 3e6, Maximum IT – 200 ms, number of scan ranges – 1, scan range – 133.4 to 1800m/z.

2.4. Determination of Dry Cell Weight (DCW)

The liquid culture was centrifuged at 5000 rpm for 10 min after fermentation, followed by three washes with distilled water and pellet was obtained. The biomass was then dried to a persistent weight at 100°C.

2.5. Statistical Analysis

The analysis of the data was done with the help of SPSS 24.0 software (SPSS Inc., Chicago, IL, USA). Primary and secondary variables under study were analyzed and statistical such as like percentages; standard deviation and mean were calculated. Logistic regression was applied considering amount of adenosylcobalamin produced by the isolates as the dependent variable and medium components as an independent variable. P < 0.05 was statistically significant.

3.1. Screening of Isolates and Media Optimization for Adenosylcobalamin Production

A total of 75 isolates were screened for adenosylcobalamin production [Figure 1]. Out of these, eight significant producers were selected based on their performance and were further taken for characterization and optimization studies [Figure 2]. Finally, two highest producing strains were used for further studies.

Figure 1: Adenosylcobalamin production efficiency of different isolates. [Click here to view]

Figure 2: Media optimization for high producers of adenosylcobalamin. [Click here to view]

For media optimization important components such as Betaine, Cobalt Nitrate, 5,6 DMB, Choline Chloride, and Beet molasses were evaluated at various concentrations [Table 6]. It was observed that betaine was a necessary compound for adenosylcobalamin production, although it had a negative impact on cell growth. A maximum DCW of 22 (± 0.01) g/l only was obtained in presence of betaine with the highest adenosylcobalamin titer (5g/L). With the increased concentration of Choline Chloride in fermentation medium, the DCW decreased gradually, confirming that Choline also has an undesirable effect on cell growth. The low yield after addition of Choline Chloride may be due to change in pH during fermentation. This perhaps had a negative effect on activities of enzymes involved in biosynthesis. For the formation of Methionine, Choline Chloride, and betaine are essential and this amino acid is then converted to S-adenosylmethionine by the action of methionine adenosyltransferase enzyme. Betaine, although, has undesirable effect on cell growth, its feeding during fermentation was found effective to enhance production of adenosylcobalamin. The higher synthesis of Vitamin B12 precursors such as methionine, glutamate, and glycine was reported only after addition of betaine to the production media [9,17]. Choline and betaine increased the formation of adenosylcobalamin in Agrobacterium species by, as much as five to six folds [8,18]. As reduction of production cost is an essential aspect, a low cost carbon source, namely, Beet Molasses was screened. The addition of precursors such as DMB, Cobalt ions, or compatible solutes such as Choline and betaine for adenosylcobalamin was found to be beneficial. Under optimal fermentation conditions approximately 28.57 ± 0.26 mg/l of adenosylcobalamin were accumulated in the fermentation medium during 7 days run [Table 6] while, Rhizobium cobalaminogenum was reported as the most active producers of Cyanocobalamin (16.5 mg/l) [2,14]. Margaret et al. screened 70 strains representing six species of Rhizobium, namely, R. trifoli, Rhizobium meliloti, R. japonicum, R. leguminosarum, R. phaseoli, and R. lupine for Vitamin B12 production in which, R. meliloti showed the highest production (1000 mg/ml) of Vitamin B12 under the experimental conditions. Addition of 1 mg/L of Cobalt Chloride in media for R. meliloti and Bradyrhizobium japonicum was found essential for maximum production of Vitamin B12 [13,19].

Table 6: Media optimization for high producers of adenosylcobalamin (AMB and PMT4) and DCW (at 168 h) under four various concentrations of betaine, DMB, beet molasses, choline chloride, and cobalt nitrate.

The adenosylcobalamin produced in the media was evaluated using HPLC and MS. The UV-vis spectra data retention time (11 min) obtained by HPLC analysis showed the peak matching to adenosylcobalamin [Figures 3 and 4]. The LC MS analysis of the isolated compound produced by Rhizobium isolate was carried out. The mass of the ionized peak confirmed the presence of adenosylcobalamin in isolated compound produced by Rhizobium isolate. The mass of adenosylcobalamin is 1579.58m/z and its spectra show that it is doubly charged. Mass of 790 m/z was observed instead of their singly charged mass of 1581 m/z. The mass spectrometry data [Figures 5 and 6] showed that the spectra of the compound produced by Rhizobium isolate matched the values of Vitamin B12 analog, adenosylcobalamin.

Figure 3: HPLC spectrum of the adenosylcobalamin standard. [Click here to view]

Figure 4: HPLC spectrum of the extract from Rhizobium isolates AMB. [Click here to view]

Figure 5: Mass spectrometry spectrum of the standard adenosylcobalamin. [Click here to view]

Figure 6: Mass spectrometry spectrum of the extract from Rhizobium isolate AMB. [Click here to view]

Vitamin B12 is a highly essential and indispensable component for normal functioning of human body. Different producer-strains synthesis pathways for production of Vitamin B12 are under consideration by research institutions. New Technologies and strategies are being developed to increase the potential of the microbes for its production.

In the recent years, a lot of interest on this subject has been shown by the researchers in various parts of the world. In one such metagenomic analysis study, it was observed that less than 10% of soil archaea and bacteria (Predominantly Nitrospirae, Proteobacteria, Thaumarchaeota, Actinobacteria, and Firmicutes) could initiate the coding process of the genetic makeup required for de novo production of the enzyme cofactor, which is required for manufacturing adenosylcobalamin. In the same study, the enrichment of DMB and corresponding DMB synthesis genes, relative to corrin ring synthesis genes, suggests an important role for cobalamin remodelers in terrestrial habitats [20].

In another study, the corrinoid compounds production in Lactobacillus (L.) strains (such as L. reuteri CRL 1098 and L. coryniformis CRL 1001) was increased by adding 5,6-dimethylbenzimidazole and Co2+. Whereas, when L-threonine was added, it only increased the production of corrinoid compounds by CRL 1001 strain [21]. As stated above, the biosynthesis of Vitamin B₁₂ is limited to only few bacteria and archaea and depends as such on microbial fermentation. Current innovations in metabolic engineering and synthetic biology are being involved to efficiently construct many microbial chemical factories [22].

In one more study, genes responsible for biosynthesis of adenosylcobinamide phosphate from Rhodobacter capsulatus were studied in vitro and/or in vivo. The analysis suggests that the biosynthetic steps from co(II)byrinic acid a,c-diamide to adocobalamin are same in both the anaerobic and aerobic pathways. The yield of Vitamin B₁₂ from a genetically engineered, recombinant E. coli strain could be increased by more than ∼250-fold to 307.00?mg?g−1 DCW by metabolic engineering and optimizing the favorable conditions required for fermentation [23].

3.2. Statistical Analysis

Selection of media components plays a key role in adenosylcobalamin production. Sixteen sets of experiments were performed using different combinations of variables – Beet Molasses, 5,6 DMB, Choline Chloride, and Cobalt Nitrate as per Taguchi’s method. Independent T-test was performed and P-value represents that the model was significant [refer in supplementary file Table S1]. Correlation analysis was performed and the relationship between the significant variables and the response was determined. It was observed that the Pearson Correlation was positive and the variable under study had beneficial impact on the adenosylcobalamin production. Negative Pearson Correlation for the variable was also observed which had its beneficial effect at the lower concentrations. The above-mentioned relationship between the variables and the response for adenosylcobalamin production was considered in the next stage for regression analysis. Multiple regression analysis was used to analyze the data for adenosylcobalamin production [refer in supplementary file Table S2 - S5]. The regression model’s goodness of fit was checked by multiple correlation coefficients (R2). The model proved to have accuracy, precision and reliability as the R2 value is close to 1. The P-value of the model revealed that the model was significant.

Table S1

Independent t-test

Table S2

Correlations (PMT4)

Table S3

Correlations (PMT4)

Table S4

Linear regression (For dependent variable: PMT4 isolate)

Table S5

Linear regression (For dependent variable: AMB isolate)