Prokaryotic Expression, Purification, and Antibacterial Activity of the Hepcidin Peptide of Crescent Sweetlips (Plectorhinchus cinctus)

The hepcidin peptide of crescent sweetlips (Plectorhinchus cinctus) is a cysteine-rich, cationic antimicrobial peptide that plays a crucial role in the innate immune system's defense against invading microbes. The aim of this study was to identify the optimal parameters for prokaryotic expression and purification of this hepcidin peptide and characterize its antibacterial activity. The recombinant hepcidin peptides were expressed in Escherichia coli strain Arctic Express (DE3), with culture and induction conditions optimized using response surface methodology (RSM). The obtained hepcidin peptides were then purified before tag cleavage, and their antibacterial activity was determined. The obtained results revealed that induction temperature had the most significant impact on the production of soluble recombinant peptides. The optimum induction conditions were determined to be an isopropylthio-β-galactoside (IPTG) concentration of 0.21 mmol/L, induction temperature of 18.81 °C, and an induction time of 16.01 h. Subsequently, the recombinant hepcidin peptide was successfully purified using Ni-IDA affinity chromatography followed by SUMO protease cleavage. The obtained hepcidin peptide (without His-SUMO tag) demonstrated strong antimicrobial activity in vitro against V. parahaemolyticus, E. coli, and S. aureus. The results showed prokaryotic (E. coli) expression is a feasible way to produce the hepcidin peptide of crescent sweetlips in a cost-effective way, which has great potential to be used as an antimicrobial agent in aquaculture.

PERISCOPE-Opt: Machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in Escherichia coli

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The ensemble model considered both fermentation conditions and protein properties.

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Optimal fermentation conditions and periplasmic recombinant protein yield can be predicted.

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Predictor’s accuracy and Pearson correlation coefficient are 75% and 0.91, respectively.

Optimization of the fermentation process for recombinant protein production (RPP) is often resource-intensive. Machine learning (ML) approaches are helpful in minimizing the experimentations and find vast applications in RPP. However, these ML-based tools primarily focus on features with respect to amino-acid-sequence, ruling out the influence of fermentation process conditions. The present study combines the features derived from fermentation process conditions with that from amino acid-sequence to construct an ML-based model that predicts the maximal protein yields and the corresponding fermentation conditions for the expression of target recombinant protein in the Escherichia coli periplasm. Two sets of XGBoost classifiers were employed in the first stage to classify the expression levels of the target protein as high (>50 mg/L), medium (between 0.5 and 50 mg/L), or low (<0.5 mg/L). The second-stage framework consisted of three regression models involving support vector machines and random forest to predict the expression yields corresponding to each expression-level-class. Independent tests showed that the predictor achieved an overall average accuracy of 75% and a Pearson coefficient correlation of 0.91 for the correctly classified instances. Therefore, our model offers a reliable substitution of numerous trial-and-error experiments to identify the optimal fermentation conditions and yield for RPP. It is also implemented as an open-access webserver, PERISCOPE-Opt (http://periscope-opt.erc.monash.edu).

Soluble expression, purification, and secondary structure determination of human PDX1 transcription factor

The transcription factor PDX1 is a master regulator essential for proper development of the pancreas, duodenum and antrum. Furthermore, it is an indispensable reprogramming factor for the derivation of human β-cells, and recently, it has been identified as a tumor suppressor protein in gastric cancer. Here, we report the soluble expression and purification of the full-length human PDX1 protein from a heterologous system. To achieve this, the 849 bp coding sequence of the PDX1 gene was first codon-optimized for expression in Escherichia coli (E. coli). This codon-optimized gene sequence was fused to a protein transduction domain, a nuclear localization sequence, and a His-tag, and this insert was cloned into the protein expression vector for expression in E. coli strain BL21(DE3). Next, screening and identification of the suitable gene construct and optimal expression conditions to obtain this recombinant fusion protein in a soluble form was performed. Further, we have purified this recombinant fusion protein to homogeneity under native conditions. Importantly, the secondary structure of the protein was retained after purification. Further, this recombinant PDX1 fusion protein was applied to human cells and showed the ability to enter the cells as well as translocate to the nucleus. This recombinant tool can be used as a safe tool and can potentially replace its genetic and viral forms in the reprogramming process to induce a β-cell-specific transcriptional profile in an integration-free manner. Additionally, it can also be used to elucidate its role in cellular processes and for structural and biochemical studies.

Improved Production of Recombinant Human β-NGF in Escherichia coli - a Bioreactor Scale Study

Human nerve growth factor β (β-NGF) is considered a major therapeutic agent for treatment of neurodegenerative diseases. We have previously reported the optimized conditions for β-NGF overproduction in Escherichia coli in a shake-flask culture. In this study the optimal %DO (dissolved oxygen) and post induction temperature values for improved production of β-NGF were found in the bioreactor scale using response surface methodology (RSM) as the most common statistical method. Also, for further enhancement of the yield, different post-induction periods of time were selected for testing. In all experiments, the productivity level and bacterial cell growth were evaluated by western blotting technique and monitoring of absorbance at 600 nm, respectively. Our results indicated that %DO, the post-induction time and temperature have significant effects on the production of β-NGF. After 2 hours of induction, the low post induction temperature of 32°C and 20% DO were used to increase the production of β-NGF in a 5-l bioreactor. Another important result obtained in this study was that the improved β-NGF production was not achieved at highest dry cell weigh or highest cell growth. These results are definitely of importance for industrial β-NGF production.

Optimization of recombinant β-NGF expression in Escherichia coli using response surface methodology

Human nerve growth factor a member of the neurotrophin family can be used to treat neurodegenerative diseases. As it has disulfide bonds in its structure, periplasmic expression of it using appropriate signal sequence is beneficial. Therefore, in this work β-nerve growth factor (β-NGF) was expressed in Escherichia coli using pET39b expression vector containing DsbA signal sequence. In an initial step, the effect of isopropyl β-D-1-thiogalactopyranoside (IPTG) and lactose concentration as inducer on protein production was investigated using response surface methodology. Then the effect of different postinduction time and temperature on protein production was studied. Our results indicated that the highest β-NGF production was achieved with 1 mM IPTG and low concentrations of lactose (0-2% w/v), low cultivation temperature of 25°C and postinduction time of 2 hr. Also following β-NGF purification, bioassay test using PC12 cell line was done. The biological activity of the purified β-NGF showed a similar cell proliferation activity with the standard recombinant human β-NGF. In conclusion, the results indicated an optimized upstream process to obtain high yields of biologically active β-NGF.

Application of Statistically Based Experimental Designs to Optimize Cellulase Production and Identification of Gene

A natural bacterial strain identified as Bacillus amyloliquefaciens MBAA3 using 16S rDNA partial genome sequencing has been studied for optimization of cellulase production. Statistical screening of media components for production of cellulase by B. amyloliquefaciens MBAA3 was carried out by Plackett-Burman design. Plackett-Burman design showed CMC, MgSO4 and pH as significant components influencing the cellulase production from the media components screened by Plackett-Burman fractional factorial design. The optimum concentrations of these significant parameters were determined employing the response surface central composite design, involving three factors and five levels was adopted to acquire the best medium for the production of cellulase enzyme revealed concentration of CMC (1.84 g), MgSO4 (0.275 g), and pH (8.5) in media for highest enzyme production. Response surface counter plots revealed that middle level of MgSO4 and middle level of CMC, higher level of CMC and lower level of pH and higher level of MgSO4 with lower level of pH increase the production of cellulase. After optimization cellulase activity increased by 6.81 fold. Presence of cellulase gene in MBAA3 was conformed by the amplification of genomic DNA of MBAA3. A PCR product of cellulase gene of 1500 bp was successfully amplified. The amplified gene was conformed by sequencing the amplified product and sequence was deposited in the gene bank under the accession number KF929416. Response surface graph showing interaction effects between concentration of a CMC and MgSO4. b pH and CMC. c MgSO4 and pH.

Comparison of the estimation capabilities of response surface methodology and artificial neural network for the optimization of recombinant lipase production by E. coli BL21

Response surface methodology (RSM) and artificial neural network (ANN) were used to optimize the effect of four independent variables, viz. glucose, sodium chloride (NaCl), temperature and induction time, on lipase production by a recombinant Escherichia coli BL21. The optimization and prediction capabilities of RSM and ANN were then compared. RSM predicted the dependent variable with a good coefficient of correlation determination (R2) and adjusted R2 values for the model. Although the R2 value showed a good fit, absolute average deviation (AAD) and root mean square error (RMSE) values did not support the accuracy of the model and this was due to the inferiority in predicting the values towards the edges of the design points. On the other hand, ANN-predicted values were closer to the observed values with better R2, adjusted R2, AAD and RMSE values and this was due to the capability of predicting the values throughout the selected range of the design points. Similar to RSM, ANN could also be used to rank the effect of variables. However, ANN could not predict the interactive effect between the variables as performed by RSM. The optimum levels for glucose, NaCl, temperature and induction time predicted by RSM are 32 g/L, 5 g/L, 32°C and 2.12 h, and those by ANN are 25 g/L, 3 g/L, 30°C and 2 h, respectively. The ANN-predicted optimal levels gave higher lipase activity (55.8 IU/mL) as compared to RSM-predicted levels (50.2 IU/mL) and the predicted lipase activity was also closer to the observed data at these levels, suggesting that ANN is a better optimization method than RSM for lipase production by the recombinant strain.