Biochemical Engineering Research Articles

The Neuroprotective Role of Cyanobacteria with Focus on the Anti-Inflammatory and Antioxidant Potential: Current Status and Perspectives.

Neurodegenerative diseases are linked to the process of neurodegeneration. This can be caused by several mechanisms, including inflammation and accumulation of reactive oxygen species. Despite their high incidence, there is still no effective treatment or cure for these diseases. Cyanobacteria have been seen as a possible source for new compounds with anti-inflammatory and antioxidant potential, such as polysaccharides (sacran), phycobiliproteins (phycocyanin) and lipopeptides (honaucins and malyngamides), which can be interesting to combat neurodegeneration. As a promising case of success, Arthrospira (formerly Spirulina) has revealed a high potential for preventing neurodegeneration. Additionally, advantageous culture conditions and sustainable production of cyanobacteria, which are allied to the development of genetic, metabolic, and biochemical engineering, are promising. The aim of this review is to compile and highlight research on the anti-inflammatory and antioxidant potential of cyanobacteria with focus on the application as neuroprotective agents. Also, a major goal is to address essential features that brand cyanobacteria as an ecoefficient and economically viable option, linking health to sustainability.

Treatment of bio-treated coking wastewater in a 3DEF system with Fe-loaded needle coke particle electrodes

As one of the most representative hard-to-biodegrade industrial wastewater, coking wastewater is poor in biodegradability and has a high concentration of organic matter. Consequently, even after treatment using biochemical technology, the discharge standards cannot be reached. We used the self-developed Fe-loaded needle coke electrodes (Fe-NCPEs) as particle electrodes and established a three-dimensional electro-Fenton (3DEF) system for the treatment of bio-treated coking wastewater (BTCW) in this study. The 3DEF system's conditions for treating BTCW were optimized using Response Surface Method (RSM). When the initial pH was 4.69, the applied voltage was 11.1 V, the Fe-NCPE dosage was 11.63 g L−1, and the COD was reduced from 387 to 57.3 mg L−1 and the colour removal rate reached 99 % after 3 h of reaction, meeting local coking wastewater discharge standards. The power consumption is only 15 kWh per ton of BTCW, indicating that this system can treat the BTCW with high efficiency and low energy consumption. UV-Vis, FTIR, and GC-MS analysis illustrated that cyclic aromatic compounds, esters, and ethers can be efficiently degraded to alkanes and olefins by the 3DEF system. The ·OH quenching experiments showed that in the 3DEF system, the ·OH is the main reactive group, which can oxidize the large hard-to-degrade organic compounds in BTCW to small organic compounds, and even to CO2 and H2O. Dynamic experiments showed that the 3DEF system can successfully remove pollutants from BTCW under continuous flow conditions with an HRT of 3 h, allowing it to meet emission standards.

Process Model and Life Cycle Assessment of Biorefinery Concept Using Agricultural and Industrial Residues for Biohydrogen Production

Sustainable waste management strategies are urgently needed due to an increasing global population and increased waste production. In this context, biorefineries have recently emerged as a promising approach to valorize waste streams and supply a broad range of products. This study presents the process model and life cycle assessment (LCA) of a biorefinery concept using a novel biochemical method, a so-called “dark photosynthesis” conversion. This process is coupled to a photo-fermentation using microalgae. Overall, the biorefinery concept can produce hydrogen, lutein, β-carotene, and proteins for animal feed. Apple pomace from apple juice production is used as feedstock for the primary conversion step. A process model was created with the process simulation software Aspen Plus® using experimental and literature data. Results from this model were then used in an LCA. The environmental impacts of the proposed biorefinery concept are relatively high, showing the need for process optimization in several areas. Energy system integration, stream recycling, and higher hydrogen yields are recognized as especially important for improving the environmental performance of this concept. Despite these findings, the model shows the feasibility of implementing the biochemical conversion technologies in a biorefinery concept for effectively utilizing residue streams.

Elemental Profiling with Reference to Metal Index to Study Pollutant Load in Northern River of India

Present light was to determine elemental load in Sirsa river ecosystem to know the concentration of toxicants in this Sutlej river’s tributary that act as a gutter for industrial effluents of Baddi region. The inductively coupled plasma mass spectroscopy was used to detect concentration of elements in the department of biochemical engineering and biotechnology at IIT Delhi and the collected data was further analyzed statistically using one way ANOVA at (p≤0.05) followed by post hoc Duncan’s test to predict degree of variations and comparative profile in Table 1. The application of metal index was also performed to estimate water quality and co-related it with health hazards. Variations in concentration of elements was reported near common effluent treatment plant on pollutant Nallah (S2) in deceasing order read as Mg>Na>Ca>B>Zn>Cu>Mn>Fe>Mo>Al>Tl>Se in ppb. The concentration of elements reported at S1, S2, S3 were within permissible limit of WHO and Bureau of Indian Standards, except the value of Boron, Magnesium and Thallium were exceeded these guidelines constantly at site S2 of study area.The metal index values at S1, S2 and S3 were 1.55, 5.80 and 2.57 respectively. The site S2 was affected strongly due to presence of elemental dust and un-treated waste water discharged by chemical industrial units of Baddi region. This site (S2) was nearer to common effluent treatment plant on the bank of Sirsa river and found to be unsuitable at all with more value of metal index. It was also viewed to adopt such scale indices to estimate drinking water quality with respect to concentration of elements after equal interval of time; as there may be seasonal change in the load of debris due to rate of pollutants dumping in the riverine ecosystem which is directly proportional to the content of elements. The load of pollutants in water system was underlined to know the impact of inorganic waste discharges on water quality of river in this region of Himachal Pradesh along industrial belt.

Catalytic Redundancies and Conformational Plasticity Drives Selectivity and Promiscuity in Quorum Quenching Lactonases.

Several enzymes from the metallo-β-lactamase-like family of lactonases (MLLs) degrade N-acyl L-homoserine lactones (AHLs). They play a role in a microbial communication system known as quorum sensing, which contributes to pathogenicity and biofilm formation. Designing quorum quenching (QQ) enzymes that can interfere with this communication allows them to be used in a range of industrial and biomedical applications. However, tailoring these enzymes for specific communication signals requires a thorough understanding of their mechanisms and the physicochemical properties that determine their substrate specificities. We present here a detailed biochemical, computational, and structural study of GcL, which is a highly proficient and thermostable MLL with broad substrate specificity. We show that GcL not only accepts a broad range of substrates but also hydrolyzes these substrates through at least two different mechanisms. Further, the preferred mechanism appears to depend on both the substrate structure and/or the nature of the residues lining the active site. We demonstrate that other lactonases, such as AiiA and AaL, show similar mechanistic promiscuity, suggesting that this is a shared feature among MLLs. Mechanistic promiscuity has been seen previously in the lactonase/paraoxonase PON1, as well as with protein tyrosine phosphatases that operate via a dual general acid mechanism. The apparent prevalence of this phenomenon is significant from both a biochemical and protein engineering perspective: in addition to optimizing for specific substrates, it may be possible to optimize for specific mechanisms, opening new doors not just for the design of novel quorum quenching enzymes but also of other mechanistically promiscuous enzymes.

Biomass of invasive plants as a resource for the development of the bioeconomy

The global spread of invasive plants is an important environmental problem and a real threat to biological diversity, with significant impacts on agriculture, forestry, and human and animal health. Invasive plant eradication produces large amounts of plant biomass, which should be safely utilized. The study reviews possibilities for using biomass of invasive plant species in the bioeconomy to safely convert them to items of value. Invasive plant biomass can be used as fuel or for energy production applying either biochemical or thermochemical processing technologies. The biomass of invasive plants also can be used for energy production or isolation of biologically active components. Invasive plants contain many groups of substances providing their defense potential against predators; these substances participate in metabolic regulation processes and others. Amongst the substances of interest for bioeconomy are lipids, polyphenols, alkaloids, carbohydrates, plant fibers, and essential oils. In the development of invasive plant biomass utilization strategies, the bio-based value pyramid and the waste hierarchy should be considered. Scientific sound strategies of invasive plant management will limit their spread and provide economic benefits via their eradication.

Opportunities of artificial intelligence in valorisation of biodiversity, biomass and bioresidues – towards advanced bio-economy, circular engineering, and sustainability

Artificial intelligence (AI) has rapidly gained notoriety due to fast advances in generative AI. However, this field encompasses a broader set of already mature tools and methods. Here, we explore its broad impact on the valorization of assets directly derived from living organisms, biomass, produced in high quantities and used in non-traditional applications at an industrial scale. For this review, we have explored the trends in scientific publications as well as in patents to measure the current state of the art and the potential for commercial applications. The number of publications and patents is rapidly increasing, showing the penetration of these technologies into chemical and biochemical engineering processes. The ethical considerations of such rapid advances need to be addressed to maximize the benefits and minimize the unintentional collateral negative social impact. Considering AI’s current limitations, biases, and economic impacts will facilitate a better transition to the broad implementation of these new technologies. The valorization of biomass and bioresidues, along with the sustainable use of biodiversity, faces important challenges and obstacles that AI tools are helping to overcome, accelerating basic research and optimizing industrial processes in the development of sustainable energy, and high value-added bioproducts and biomaterials. The application of AI in these fields promises industrial innovation, enhanced efficiency, cost reduction and increased product yields for a global growing market; and thereby promotes Circular Engineering and Advanced Bioeconomy to achieve United Nations Sustainable Development goals in the near future.

Thermo-responsive aqueous two-phase system for two-level compartmentalization

Hierarchical compartmentalization responding to changes in intracellular and extracellular environments is ubiquitous in living eukaryotic cells but remains a formidable task in synthetic systems. Here we report a two-level compartmentalization approach based on a thermo-responsive aqueous two-phase system (TR-ATPS) comprising poly(N-isopropylacrylamide) (PNIPAM) and dextran (DEX). Liquid membraneless compartments enriched in PNIPAM are phase-separated from the continuous DEX solution via liquid-liquid phase separation at 25 °C and shrink dramatically with small second-level compartments generated at the interface, resembling the structure of colloidosome, by increasing the temperature to 35 °C. The TR-ATPS can store biomolecules, program the spatial distribution of enzymes, and accelerate the overall biochemical reaction efficiency by nearly 7-fold. The TR-ATPS inspires on-demand, stimulus-triggered spatiotemporal enrichment of biomolecules via two-level compartmentalization, creating opportunities in synthetic biology and biochemical engineering.

Purification of his-tagged proteins using printed monolith adsorption columns

Bio-separation is a crucial process in biotechnology and biochemical engineering for separating biological macromolecules, and the field has long relied on bead-based and expanded bed chromatography. Printed monolith adsorption (PMA) is a new alternative to which uses a 3D-printed monolithic structure containing self-supporting, ordered flow channels. PMA allows for direct purification of biological molecules from crude cell lysates and cell cultures, and like the other technologies, can functionalized to specifically target a molecule and enable affinity chromatography. Here we have combined PMA technology with an immobilized metal affinity ligand (iminodiacetic acid) to provide selectivity of binding to polyhistidine-tagged proteins during PMA chromatography. Two different PMA structures were created and tested for both static and dynamic protein-binding capacity. At comparative linear flow rates, the dynamic binding capacity of both columns was ≈3 mg/mL, while static capacity was shown to differentiate based on column voidage. We show that a polyhistidine-tagged protein can be directly purified from crude lysate with comparable results to the available commercial providers of IMAC, and with a substantially reduced purification time.

Production of Biofuels from Agricultural Waste

Agricultural waste represents a largely untapped resource that could be utilized for the production of biofuels through various conversion pathways. As the global demand for renewable and sustainable energy grows, biofuels offer solutions to mitigate climate change impacts while improving waste management. This review analyzes using agricultural residues and by-products as feedstocks for biofuel production through biological, thermochemical and chemical conversion processes. The different types of lignocellulosic biomass available from agricultural activities are discussed, along with their compositions. While agricultural waste has advantages like wide availability and low cost, challenges relating to heterogeneous composition, pre-existing contamination and seasonal availability must be addressed. Fermentation, anaerobic digestion, pyrolysis and gasification are examined as established routes for converting agricultural waste into liquid biofuels and biogas. Pretreatment methods, enzyme production pathways and synthesis of fuels like ethanol, butanol and diesel substitutes are outlined. Environmental benefits of biofuels from waste, including greenhouse gas mitigation and recycling of soil nutrients, are evaluated against fossil fuel alternatives. Case studies on operational plants and feasibility studies provide insights into technical and economic viability at scale. Challenges regarding feedstock logistics, conversion efficiency, commercial scale-up and sustainability assessment are identified for future research focus. In conclusion, the review finds that agricultural waste is a promising renewable resource for biofuel production when integrated with appropriate thermochemical, biochemical or anaerobic digestion technologies. While the field is advancing, further improvements in areas such as feedstock supply, pretreatment technologies, and demonstration of sustainability will be critical to realize the full potential of this emerging bioeconomy sector. The review recommends steps to accelerate commercialization and policy frameworks to incentivize waste-to-energy solutions.

Living Bacteriophage Engineering for Functional Material Synthesis, Bioanalytical Sensing and Disease Theranostics

Facing the increasingly global crisis of antibiotic resistance, it is urgent to develop new antibacterial agents and methods. Simultaneously, as research progresses, the occurrence, development, and treatment of diseases, especially some malignant cancers, are found to be closely associated with the bacterial microenvironment, prompting us to reconsider the efficiency of existing antibacterial strategies for disease treatments. Bacteriophages have been employed as antibacterial agents for an extended period owing to their high biocompatibility and particular targetability toward the host bacterial strains. Nonetheless, they are almost neglected due to their slow and limited efficacy in antibacterial practice, especially in acute and severe infectious cases. In recent years, fantastic advancements in various biochemical technologies, such as bacteriophage display technology, genetic engineering, and chemical molecular engineering, have enabled scientists to conduct a broader range of modifications and transformations on the existing bacteriophages with inherited unique characteristics of themselves. As a result, a series of novel bacteriophage platforms are designed and fabricated with significantly enhanced properties and multiplied functionalities. These offer new avenues for combating infections caused by drug-resistant bacteria and treatment of malignancies that are associated with bacterial infections, holding great significance and potential in the innovative theranostic applications.

A physiological model for iohexol plasma clearance supporting diagnostics of kidney function

BackgroundThere are several shortcomings in present methods for estimation of GFR from plasma clearance. The aim of the present study was therefore to develop a physiologically based method for calculation of plasma clearance of iohexol. MethodsA mechanistic model founded on classical biochemical engineering principles where in- and outgoing molecular flows of iohexol between plasma and surrounding tissues were balanced over time. After intravenous injections of iohexol, plasma samples were taken from the investigated subjects until complete elimination of iohexol. After tuning of the model parameters, the clearance value was calculated from the injected dose and the integral of the iohexol concentrations over the investigated period. ResultsThe mass balance model was able to predict the time course of iohexol distribution and elimination after parameterization of mass balance and kinetic equations. Four model structures were evaluated, all based on model parameters derived from published data and from internal tests, each complied at varying physiological conditions. Iohexol clearance was assessed through the model and compared with calculations from previously practiced methods. When testing the mass balance model on ten healthy subjects, clearance was estimated accurately. ConclusionsThe physiological and mechanistic character of the mass balance model may suggest that its derived clearance comes closer to actual in vivo conditions than data derived from previously practiced calculation methods. Although here, only verified with the clearance marker iohexol, the mass balance model should be applicable also to other renal clearance markers.

Machine learning: an advancement in biochemical engineering.

One of the most remarkable techniques recently introduced into the field of bioprocess engineering is machine learning. Bioprocess engineering has drawn much attention due to its vast application in different domains like biopharmaceuticals, fossil fuel alternatives, environmental remediation, and food and beverage industry, etc. However, due to their unpredictable mechanisms, they are very often challenging to optimize. Furthermore, biological systems are extremely complicated; hence, machine learning algorithms could potentially be utilized to improve and build new biotechnological processes. Gaining insight into the fundamental mathematical understanding of commonly used machine learning algorithms, including Support Vector Machine, Principal Component Analysis, Partial Least Squares and Reinforcement Learning, the present study aims to discuss various case studies related to the application of machine learning in bioprocess engineering. Recent advancements as well as challenges posed in this area along with their potential solutions are also presented.

Preface: 3rd International Conference on Biochemical Engineering and Environmentally Friendly Material (BEEFM 2024)

Preface: 3rd International Conference on Biochemical Engineering and Environmentally Friendly Material (BEEFM 2024)

Immunosuppression with cyclosporine versus tacrolimus shows distinctive nephrotoxicity profiles within renal compartments.

Calcineurin inhibitors (CNIs) are the backbone for immunosuppression after solid organ transplantation. Although successful in preventing kidney transplant rejection, their nephrotoxic side effects contribute to allograft injury. Renal parenchymal lesions occur for cyclosporine A (CsA) as well as for the currently favored tacrolimus (Tac). We aimed to study whether chronic CsA and Tac exposures, before reaching irreversible nephrotoxic damage, affect renal compartments differentially and whether related pathogenic mechanisms can be identified. CsA and Tac were administered chronically in wild type Wistar rats using osmotic minipumps over 4 weeks. Functional parameters were controlled. Electron microscopy, confocal, and 3D-structured illumination microscopy were used for histopathology. Clinical translatability was tested in human renal biopsies. Standard biochemical, RNA-seq, and proteomic technologies were applied to identify implicated molecular pathways. Both drugs caused significant albeit differential damage in vasculature and nephron. The glomerular filtration barrier was more affected by Tac than by CsA, showing prominent deteriorations in endothelium and podocytes along with impaired VEGF/VEGFR2 signaling and podocyte-specific gene expression. By contrast, proximal tubule epithelia were more severely affected by CsA than by Tac, revealing lysosomal dysfunction, enhanced apoptosis, impaired proteostasis and oxidative stress. Lesion characteristics were confirmed in human renal biopsies. We conclude that pathogenetic alterations in the renal compartments are specific for either treatment. Considering translation to the clinical setting, CNI choice should reflect individual risk factors for renal vasculature and tubular epithelia. As a step in this direction, we share protein signatures identified from multiomics with potential pathognomonic relevance.

Microbial cell factory for butyl butyrate production: Knowledges and perspectives.

Butyl butyrate is a short-chain fatty acid ester (C8) with a fruity aroma. It has broad prospects in the fields of foods, cosmetics and biofuels. At present, butyl butyrate is produced by chemical synthesis in the industry, but it is highly dependent on petroleum-based products. The growing concerns regarding the future scarcity of fossil fuels have been strongly promoted the transition from traditional fossil fuels and products to renewable bioenergy and biochemicals. Therefore, it is necessary to develop a green biochemical technology to replace traditional petroleum-based materials. In recent years, microorganisms such as Escherichia coli and Clostridium have been engineered to serve as cell factories for the sustainable one-pot production of short-chain fatty acid esters, including butyl butyrate. This opinion highlights the recent development in the use of lipases and alcohol acyltransferases (AATs) for butyl butyrate production in microbial fermentation, as well as future perspectives.

Ultrasonic reactor set-ups and applications: A review

Sonochemistry contributes to green science as it uses less hazardous solvents and methods to carry out a reaction. In this review, different reactor designs are discussed in detail providing the necessary knowledge for implementing various processes. The main characteristics of ultrasonic batch systems are their low cost and enhanced mixing; however, they still have immense drawbacks such as their scalability. Continuous flow reactors offer enhanced production yields as the limited cognition which governs the design of these sonoreactors, renders them unusable in industry. In addition, microstructured sonoreactors show improved heat and mass transfer phenomena due to their small size but suffer though from clogging. The optimisation of various conditions of regulations, such as temperature, frequency of ultrasound, intensity of irradiation, sonication time, pressure amplitude and reactor design, it is also discussed to maximise the production rates and yields of reactions taking place in sonoreactors. The optimisation of operating parameters and the selection of the reactor system must be considered to each application’s requirements. A plethora of different applications that ultrasound waves can be implemented are in the biochemical and petrochemical engineering, the chemical synthesis of materials, the crystallisation of organic and inorganic substances, the wastewater treatment, the extraction processes and in medicine. Sonochemistry must overcome challenges that consider the scalability of processes and its embodiment into commercial applications, through extensive studies for understanding the designs and the development of computational tools to implement timesaving and efficient theoretical studies.

Upcycling Food Waste into Biomaterials Applicable to Medical Products

Globally, an estimated 1.3 billion tons of food are wasted each year, according to a report from the Food and Agriculture Organization of the United Nations. A variety of waste streams constantly generate large amounts of food waste that end up in landfills. As food waste is left to naturally decay in landfills, it emits greenhouse gases that pollute the environment and induce climate change. However, most types of food waste contain valuable components that can be extracted to manufacture industrial products. Therefore, instead of abandoning food waste to decay and harm the environment, there is an alternative to upcycle it as a new raw materials supply source. This review provides a comprehensive update on how environmental sustainability can be improved using diverse types of food waste as sources to generate biomaterials for fabricating medical products, including lignin, cellulose, chitosan, pectin, collagen, hydroxyapatite, and biodegradable polymers. The review also highlights biochemical technologies applied for extracting useful components from food waste and details the current advances for developing medical products, including wound dressings and nanoparticles for tissue engineering and drug delivery.

Insights into the fluid dynamics of the Bach impeller

The Bach impeller is a novel impeller that was purposefully designed to reduce power consumption as well as shear stress levels in stirred bioreactors. Contrary to conventional impellers where stirring is achieved by the pushing action of the blades, the Bach impeller relies on a central spiral element which induces an up-pumping central vortex and discharges the flow in the tangential direction through vanes. This feature is new and relevant to the field of biochemical engineering where cell culture and microbial fermentation often make use of shear-sensitive, living organisms to produce biopharmaceuticals and medicines. This article offers a thorough characterisation of the fluid mechanics of this impeller by means of 2D phased- and time-resolved particle image velocimetry (PIV) experiments. Key flow dynamics parameters including velocity magnitudes, kinetic energy, and shear stresses were estimated for different combinations of impeller size, clearance, and speed. The standard impeller size, D/T=0.52, performed most promisingly in combination with an off-bottom clearance of C=0.6 T, as the elevated impeller height allowed to pull high momentum fluid to the upper part of the tank and therefore improved overall transport phenomena in the entire reactor volume. For this impeller diameter-clearance combination peak ensemble-averaged shear stresses at the vessel base were lower due to the impeller greater distance from the bottom. A distinctive feature of the Bach impeller was its "smooth operation" mode, where velocity fluctuations due to the vane passages are nearly negligible (∼5 % of ensemble-averaged kinetic energy). These aspects make of the Bach impeller an appealing option in the context of stem cell bioprocess applications.

A hybrid approach for rice crop disease detection in agricultural IoT system

Agriculture is an essential sector that plays a necessary role in the economic improvement of a country. Prediction of plant diseases at the earliest stage may result in better yield and sustainable for growing population. The conventional method necessitates highly skilled inspectors to identify the phenotypic expression of different diseases. Alternatively, biochemical technologies offer more precise means of obtaining crop disease information by analyzing susceptible rice. However, these methods are time-consuming, expensive, reliant on laboratories, and require skilled professionals, rendering them unaffordable for most farmers. The paper aims to propose a solution to prevent infection at the earliest stage for the benefit of farmers. A novel crop disease detection model deploying a deep convolutional generative adversarial network (DC-GAN) and with multidimensional feature compensation Residual Neural Network (MDFC-ResNet) and named as DC-GAN-MDFC–ResNet, which aims at fine grained disease identification system detects from three aspects, bacterial leaf blight, leaf streak and panicle blight. Initially the input data undergone preprocessing using the several processes like data improvement, data normalization, and Singular value decomposition (SVD) to reduce the negative influence that the data set has on the training of the model. When compared to traditional convolution models, the suggested DC-GAN-MDFC–ResNet architecture exhibits in terms of highest classification accuracy, Segmentation free methodology and training stability. The experiments done in this work using Plant Village dataset which show the proposed technique offering improved recognition with the rate of 95.99% accuracy and generating higher quality samples compared to other well-known deep learning models.