Biotechnology and Bioprocess Engineering最新文献

Emerging findings suggest that non-shivering thermogenesis in brown and beige adipocytes may effectively stimulate energy expenditure, thereby contributing to body weight reduction. Our previous report demonstrated that chrysin, a flavone found in honey and propolis, activates uncoupling protein 1 (UCP1)-dependent thermogenesis in brown fat and induces beige adipocytes. However, the effect of chrysin on UCP1-independent thermogenesis remains unexplored. In this study, we examined the effects of chrysin on UCP1-independent thermogenesis in the 3T3-L1 adipocytes and mouse model. This study showed that chrysin elevates the expression of calcium regulatory proteins, including sarcoendoplasmic reticulum Ca²⁺-ATPase, ryanodine receptor 2, voltage-dependent anion channel, mitochondrial calcium uniporter, and Ca²⁺/calmodulin-dependent protein kinase 2 in 3T3-L1 adipocytes as well as in inguinal and epididymal white adipose tissues of mice. Furthermore, our results also showed chrysin increased Ca²⁺ levels in 3T3-L1 adipocytes in a dose-dependent manner. In addition, our study showed chrysin upregulated creatine-mediated thermogenic markers (creatine kinase B and creatine kinase mitochondrial 2) in both in vitro and in vivo models. Mechanistically, we found that chrysin induces UCP1-independent thermogenesis by stimulating creatine- and calcium-mediated ATP-consuming futile cycle through the activation of the α1-adrenergic receptor. Combining the current and previous studies, it can be proposed that chrysin induces both UCP1-dependent and -independent thermogenesis in beige adipocytes, suggesting its possible use for effective intervention for obesity and metabolic disorders.

This study explores the development of biofoundries, emphasizing the integration of synthetic biology with artificial intelligence (AI) and robotics. It outlines critical challenges such as the necessity for interdisciplinary collaboration and the development of hardware, software, and AI specific to biofoundry operations. To address these challenges, we recommend strategies like rapid prototyping, soft integration, and the strategic implementation of AI. We underscore the vital role of synthetic biology researchers in advancing biofoundry capabilities and advocate for collaborative, multidisciplinary efforts to optimize the development and functionality of biofoundries.

The chitosan production process from fishery waste is already established in industrial scale, whereby fungal chitosan is produced in lower amounts. Since fungal chitosan could be isolated from under-valorized vegan streams while exhibiting slightly different characteristics, it has also potential for other applications. Within this publication, we focus on the chitosan production from Aspergillus niger. This study provides a detailed determination of the biomass composition, adapting and comparing different analytical tools, with special focus on the chitin and chitosan content. The major content of the dried biomass is composed of glucans (48.6 ± 1.4%), followed by proteins with an amount of 22.2 ± 0.7%. Chitin and chitosan provide 16.0 ± 0.8% of the biomass. Within our chitosan production studies, we compared the effect of different process strategies including steps as deproteinization (DP), acid extraction (AE), deacetylation (DA), as well as purification. Initially, we obtained poor values (lower than 73.6%) for the chitosan purity. A direct DA step followed by purification resulted in a chitosan purity of up to 89.6%, a recovery of 30.5% and a yield with regard to the biomass of 5.5%. The DA degree of the resulting chitosan is similar to chitosan derived from fishery waste, whereas the molecular weight is lower. The results achieved so far are consistent with the literature, extending beyond, the data emphasized that a chitosan production from residual fungal biomass after fermentation is suitable by direct DA and purification. However, further adaption is necessary so that other matrix compounds could be also obtained.

Anaerobic digestion (AD) is a biological process where bacteria digest various types of organic matter under anaerobic conditions. AD has been particularly used for generating biogas from organic wastes, such as food waste. Despite its practical applications, the mechanistic understanding of the AD process remains elusive, especially complex interactions within a microbial community, and between the organic waste and microbial community. One systematic approach to address this challenge is to deploy genome-scale metabolic models (GEMs) of microorganisms involved in AD. GEM is a computational model that describes an entire metabolic network of a cell, and can be simulated under various conditions of interest. In this review, we discuss recent metabolic studies of AD-related microorganisms by using their GEMs across the four major stages of AD. We also suggest future directions in this field that need to be addressed to further optimize the GEMs and the AD process.

With advancements in DNA amplification research, isothermal amplification technology has emerged as an attractive method for detecting target DNA. Here, we describe primer generation-rolling circle amplification (PG-RCA) as an isothermal amplification method for detecting Escherichia coli O157:H7, Salmonella Typhimurium, Bacillus cereus, and Listeria monocytogenes. To improve PG-RCA sensitivity, the concentrations of the reaction components, dNTPs, phi29 DNA polymerase, and circular probes were optimized; the optimized conditions were applied to detect each target bacterium. A pair of forward and reverse circular probes that hybridized to the sense and anti-sense target genes was used in PG-RCA, exhibiting target selectivity. PG-RCA, which generated additional primers simultaneously with linear RCA and comprised multiple reaction cycles, resulted in higher accumulation of amplified DNA products than did linear RCA within the same reaction period. The threshold time (Tt) for each target gene concentration was determined based on the threshold value set in the amplification plot for PG-RCA, and a linear correlation between the Tt value and genomic DNA concentration was proven for each of the four bacteria. The PG-RCA-based assay could be applied to gene-based detection of various microorganisms and may be a useful isothermal amplification method for replacing traditional PCR methods.

C13-apocarotenoids are volatile organic compounds naturally derived from the oxidative cleavage of carotenoids. These small molecules form unique aromas in flowers, fruits, and plants. They are highly valued compounds in the flavor and fragrance industry. The microbial production of C13-apocarotenoids, such as β-ionone, α-ionone, and pseudoionone, is an emerging and promising approach with inherent advantageousness of scalable output to reach the goals as stated in the United Nations Sustainable Development Goals. Many engineering efforts have been implemented continuously but very few of them proved to be successful in achieving product titer at the grams-per-liter level with the least accumulated amount of precursor carotenoids and byproducts. The efficiency of oxidative cleavage of carotenoids conducted by carotenoid cleavage dioxygenases is suggested to be the critical metabolic node to reconstruct an economically viable C13-apocarotenoids biosynthetic pathway. In this regard, we review recent advances in improving microbial biosynthesis of C13-apocarotenoids by protein and metabolic engineering. The potential strategies that could be implemented further to achieve efficient C13-apocarotenoid production are also discussed.

Flavonoids are a class of polyphenolic compounds found in plants that offer extensive health benefits and have applications in the pharmaceutical, cosmetic, and food industries. Currently, flavonoid production largely depends on plant extraction methods, which face limitations owing to low yields and seasonal and environmental impacts. To address these issues, the potential of microbial fermentation, which leverages advances in metabolic engineering and genetic tools, has been discussed as an innovative alternative to overcome these challenges, thus offering an environmentally friendly and sustainable approach to flavonoid production. However, the integration of complex biosynthesis pathways into microbial systems presents challenges such as the inefficient expression of plant-derived genes, metabolic conflicts, and toxicity or feedback inhibition by accumulated flavonoids within the microbial cells. This comprehensive review highlights recent advancements in engineering strategies to address these challenges, focusing on biotransformation, single-strain fermentation, and co-culture systems, each with its own unique characteristics and potential for optimizing flavonoid production in a cost-effective and scalable manner.

Innovative soft sensor concepts can recalibrate automatically when the prediction performance decreases due to variations in raw materials, biological variability, and changes in process strategies. For automatic recalibration, data sets are selected from a data pool based on distance-based similarity criteria and then used for calibration. Nevertheless, the most appropriate data sets often are not reliably selected due to variances in the location of landmarks and process length of the bioprocesses. This can be overcome by synchronization methods that align the historical data sets with the current process and increase the accuracy of automatic selection and recalibration. This study investigated two different synchronization methods (dynamic time warping and curve registration) as preprocessing for the automatic selection of data sets using a distance-based similarity criterion for soft sensor recalibration. The prediction performance of the two soft sensors without synchronization was compared to the variants with synchronization and evaluated by comparing the normalized root mean squared errors. Curve registration improved the prediction performance on average by 24% (Pichia pastoris) and 9% (Bacillus subtilis). Using dynamic time warping, no substantial improvement in prediction performance could be achieved. A major factor behind this was the loss of information due to singularities caused by the changing process characteristics. The evaluation was performed on two target variables of real bioprocesses: biomass concentration prediction in P. pastoris and product concentration prediction in B. subtilis.

Cisplatin is a widely used, highly effective chemotherapy drug that has a critical nephrotoxic side effect associated with acute kidney injury. Hypoxia pre-treatment is one of the methods used to reduce cisplatin-induced renal toxicity, but the exact cellular process associated with this protective effect is not clearly understood. Hypoxia-inducible factor 1 alpha (HIF-1α), the main transcription factor under hypoxia, may play a crucial role in this protective effect. To verify this, the degree of HIF-1α activation was investigated. Renal proximal tubular epithelial cells (HK-2) were treated with cisplatin following exposure to FG-4592 and CoCl₂, prolyl hydroxylase domain (PHD) inhibitors that stabilize HIF-1α. Roxadustat (FG-4592) is a PHD inhibitor recently approved by the European medicines agency (EMA) for the treatment of anemia. Hypoxia pre-treatment with PHD inhibitors presented a protective effect against cisplatin-induced kidney injury. In addition, hypoxia pre-treatment relieved oxidative stress by hypoxia response genes sufficiently expressed under hypoxic pre-conditions. In conclusion, we investigated the correlation between the degree of HIF-1α pre-activation and the reduction in cisplatin-induced nephrotoxicity using PHD inhibitors. This study extends the applicability of PHD inhibitors as palliators of cisplatin-induced nephrotoxicity and provides valuable insights into overcoming the limitations of cisplatin use.

The use of nanoparticles (NPs) as an alternative to the current generation of conventional antibiotics has exploded in the research community in recent years, as evidence of the superiority of NPs over antibiotics in the treatment of pathogens has been steadily presented. However, therapy with NPs may result in the removal of both multidrug-resistant (MDR) pathogens and commensal bacteria due to the broad-spectrum activity of NPs and the non-specificity of target bacteria. Therefore, the fabrication of MDR-pathogen-targeting NPs is necessary. In this study, biogenic silver nanoparticles (Bio-AgNPs) were synthesized using bacterial cell-free supernatant from three communicating gram-negative bacteria. The size, physical features, and morphology of the AgNPs were characterized by dynamic light scattering (an average size of 158–168 nm), X-ray diffraction (co-ordinate patterns), and transmission electron microscopy (spherical structure). The antibacterial activity of the Bio-AgNPs as minimum inhibitory concentration values was obtained between 0.8 and > 6.4 μg mL⁻¹ for bacterial strains. Mechanistic studies of Bio-AgNPs have revealed that biofilm inhibition, protein leakage, hyperproduction of reactive oxygen species, and physical cell damage are plausible mechanisms underlying the activity of Bio-AgNPs against gram-negative pathogens. Overall, the Bio-AgNPs synthesized in this study may bolster the potential use of Bio-AgNPs as a stand-in for traditional antibiotics, and offer potential specificity against bacterial targets.

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