搜索结果：Biochemical engineering journal

Chitosan, a derivative of the abundant biopolymer chitin, holds significant biotechnological potential but is limited by its poor solubility. Its oligomeric forms, chitooligosaccharides (COSs), exhibit superior bioactivity and solubility, driving demand for efficient enzymatic production methods. This study reports the biochemical and functional characterization of a novel chitosanase, SlCsn46A, identified from Streptomyces lydicus M01 and classified into the glycoside hydrolase family 46 (GH46). The enzyme was successfully heterologously expressed in Escherichia coli and purified. SlCsn46A demonstrated high catalytic efficiency, with an optimal activity at 60°C and pH 6.0 and a maximum specific activity of 957.80 U·mg- 1. It exhibited broad pH stability and significantly enhanced activity in the presence of Mn2 + and Tween 80. Kinetic analysis revealed a low Michaelis constant (Km = 0.61 mg·mL- 1), indicating strong substrate affinity. Product analysis confirmed its endo-type action mode, specifically hydrolyzing chitosan to yield chitobiose [(GlcN)2] and chitotriose [(GlcN)3] as the predominant end products. These properties collectively establish SlCsn46A as an efficient and specific biocatalyst, demonstrating great potential for the targeted and efficient synthesis of low-degree-of-polymerization COS for applications in the food, agricultural, and biomedical industries.

The enzymatic regulation of phenylpropanoid metabolism is a critical determinant of flavonoid biosynthesis in medicinal plants. Sageretia thea is valued for its pharmacological properties associated with flavonoid production; however, the molecular mechanisms governing pathway entry remain poorly understood. In this study, we identified and functionally characterized a novel 4-coumarate:CoA ligase (St4CL1) from S. thea. Phylogenetic analysis classified St4CL1 as a Class II isoform, a group typically associated with flavonoid biosynthesis, and multiple sequence alignment revealed the presence of highly conserved functional motifs, including the AMP-binding and catalytic domains. Recombinant St4CL1, heterologously expressed in Escherichia coli, exhibited a strong preference for Mg2+ and optimal catalytic activity at pH 7.0-8.0 and 35°C. Substrate specificity analysis revealed that St4CL1 exhibited the highest relative activity toward p-coumaric acid, supporting its role in directing carbon flux into the flavonoid biosynthetic pathway. Homology modeling and molecular docking revealed a conserved substrate-binding pocket within the inter-domain cleft, and p-coumaric acid was the substrate to form a salt-bridge contact at the carboxylate-binding site, providing a structural rationale consistent with its preferred turnover. Collectively, these findings provide the first molecular evidence of the phenylpropanoid entry step in S. thea and identify St4CL1 as a promising enzymatic target for metabolic engineering to enhance flavonoid production.

Atomic-level control of catalytic selectivity is critical to nanocatalyst design. Here, we report two structurally defined Cu hydride nanoclusters, [Cu25H22(p-FPh3P)12]+ and [Cu25H10(2,4-F2PhS)18]3-, with distinct hydride contents that govern active-site exposure and CO2 electroreduction selectivity. Phosphine ligand dissociation in Cu25H22-P exposed Cu-H sites and favored C2H4 formation, whereas intrinsically exposed Cu-S sites in Cu25H10-S promoted CH4 production. These findings establish hydride ligands as key regulators of active-site structure and product selectivity in Cu nanoclusters.

The plant microbiome refers to the dynamic microbial communities including bacteria, fungi, protists, viruses, and nematodes that colonize diverse plant tissues and coevolve intimately with their host. The primary objective of microbiome engineering is to improve plant performance by enhancing tolerance to biotic and abiotic stresses, increasing plant fitness, and boosting crop productivity. By discovering the modern approaches and plant-microbe interactions, many experts can design artificial microbial consortia and other biotechnological tools suited to specific crops and environmental conditions. Therefore, in current work special attention is given to the goals, applications, and advanced tools-such as genome editing, synthetic biology, metagenomics, and AI-driven modelling used to optimize plant-microbe interactions for sustainable agriculture and ecosystem restoration. Further, recent advances in ecological, biochemical, and molecular approaches have also introduced a new paradigm for addressing microbiome-based challenges in agricultural management. In this context, microbiome engineering has emerged as a promising biotechnological strategy aimed at the targeted addition, removal, or modification of microbial community traits to achieve greater specificity and efficacy.

Diabetic foot ulcers (DFUs) cause one amputation every 20 s globally, driven by ischemia, dysregulated growth factors, recalcitrant biofilms, and oxidative stress that render conventional therapies ineffective. This review examines smart polymer dressings that actively sense pathological cues and respond with spatiotemporal precision. Natural polymers (chitosan, collagen, hyaluronic acid) provide biochemical signals for cell recruitment and angiogenesis, while synthetic polymers (Polylactic glycolic acid, Polycaprolactone, Polyethylene glycol) enable controlled degradation and drug release. Hybrid systems include glucose-responsive hydrogels, VEGF-loaded nanofibers for hypoxic zones, and microneedles that disrupt biofilms while delivering antimicrobials. Selected preclinical studies have reported wound closure rates exceeding 90% in diabetic animal models. However, clinical translation is hindered by GMP manufacturing challenges, regulatory complexity, and cost barriers. This review analyses the state-of-the-art polymer design and engineering transforming passive dressings into active therapeutics and identifies critical gaps between laboratory breakthroughs and clinical implementation.

In this study, we report engineering of three mutations m1, m2, and m3 respectively, in the wild type SOD, cloned form soil metagenome. Expressed proteins from wild type and mutants were purified to homogeneity using Ni-NTA affinity chromatography. Biochemical characterization of mutants demonstrated enhanced functional activity at varying pH and temperature compared to wild type and other mutant proteins. Additionally, it also showed increased specific activity of 185 ± 0.75 U/mg compared to 150 ± 0.042 U/mg and 168 ± 0.25 U/mg respectively for mutant m1, m2 and m3. Altogether, it was observed that the relative enzyme activity of mutant m1, m2 and m3 enhanced ∼ 30 %, 10 % and 17 % respectively compared to wild type. Biophysical investigation carried out employing circular dichroism and intrinsic tryptophan fluorescence also demonstrated conformational stability in the secondary and tertiary structure of mutant m1 compared to the wild type at varying pH and temperature. Interestingly, in silico molecular simulation dynamics studies carried out at 300 ns demonstrated structural stability, reduced flexibility and attainment of stable conformation in this mutant form. Molecular simulation analysis revealed that mutation T71R in m1 tends to introduce β-sheet like secondary structure at protein surface, which might enhance residue-residue interactions within this protein, leading to allover enhancement in the stability and activity of this mutant.

With the increasing frequency and intensity of extreme weather events due to climate change, heat waves have emerged as a significant public health threat. To date, the potential effect of extreme heat wave events, particularly when combined with air pollution, remains poorly understood for pregnancy outcomes among women undergoing assisted reproductive technology (ART). A retrospective study included 15,198 women receiving ART and 7519 fresh embryo transfer cycles between 2020 to 2022 at the Reproductive Center of West China Second University Hospital in Chengdu, China. Heat wave, a climate change indicator for extreme temperature events, was calculated based on daily temperature during the period of 85 days prior to oocyte retrieval. All environmental exposure variables, including weather and pollution, were matched geospatially to day 0 to day 85 before oocyte retrieval. Generalized linear model (GLMM) were used to assess the association between environmental exposures and ART outcomes, with secondary analysis using interaction terms between heat waves and individual pollutants. Exposure to one heat wave event was positively correlated with the likelihood of becoming pregnant (+ 34.9% in univariate model and + 34.5% in multivariate model for heat wave events + 1 time) and this association was more pronounced in women under 35 years of age (+ 53.7% heat wave events + 1 time), while no statistical correlation was observed between exposure to two heat wave events and ART outcomes. Additionally, CO exhibited a significant negative association with biochemical pregnancy for women under 35 years old (-66.2% for CO + 1 mg/m3), and SO2 exhibited a significant negative association on biochemical pregnancy rate for women older than 35 years old (-6.5% for SO2 + 1 μg/m3). Results from the interaction model indicated that concurrent exposure to O3 and two heat wave events was statistical associated with clinical pregnancy (OR = 3.77). Findings from this study suggest that heat waves could be an important climatic indicator that reflects the impact of extreme weather on pregnancy outcomes among women receiving ART treatment. The synergy between exposure to extreme temperatures and air pollution could be further analyzed to provide deeper insight into the environmental impact on reproductive health.

Secondary spinal cord injury (SCI), which includes a cascade of both biophysical and biochemical processes that mitigate neurological recovery, is thought to initiate within minutes of the direct, primary injury. However, previous in vitro and in vivo-based approaches are incapable of determining how quickly and to what extent these processes occur following injury. In contrast, the perfused bovine ex vivo indentation injury model described here represents a new platform to investigate the short-term effects of observed phenomena like altered blood flow and blood-spinal cord barrier disruption following traumatic SCI. The model can assess tissue changes on the scale of minutes to determine how rapidly the spinal cord responds to injury. Indentation tests simultaneously simulate a crush injury and measure the mechanical properties of the tissue using Hertz contact theory. Our results indicate that injured cords exhibit changes to bulk perfusion, as evidenced by laser speckle contrast imaging, in addition to decreased barrier function minutes after injury. Mechanical testing of the cord reveals that the tissue softening observed previously in the days following injury initiates as early as 30 minutes. Moreover, perfusing red blood cells resuspended in saline instead of whole blood mitigates the drop in elastic modulus, highlighting the importance of leukocytes and plasma proteins in mediating the extracellular matrix mechanical response after SCI. Transcriptomic analysis reveals key differences in gene expression within 15 minutes of injury, including those related to inflammation and the immune response. Identifying the timeframe and extent to which biomechanical and biochemical changes occur after injury provides a more complete understanding of secondary injury.

Fourier-transform infrared spectroscopy coupled with attenuated total reflectance (FTIR-ATR) is increasingly recognized as a rapid and non-destructive approach for monitoring biochemical changes in microbial systems. In this study, FTIR-ATR was used to investigate the growth-phase-dependent spectral responses of Escherichia coli exposed to a limited, structurally defined set of formamide-tetrahydroquinoline (THQ) derivatives as model chemical probes. Bacterial cultures were monitored during the lag (LAG) and logarithmic (LOG) growth phases, and spectral variations were analyzed using second-derivative processing, normalization, statistical testing, and principal component analysis (PCA). Pronounced spectral perturbations were observed during the LAG phase, particularly in regions associated with proteins, lipids, nucleic acids, and polysaccharides. In contrast, comparatively minor changes were detected during the LOG phase. These findings highlight the suitability of FTIR-ATR spectroscopy as an exploratory tool for detecting subtle growth-phase-dependent biochemical responses in bacterial systems. The increasing prevalence of antibiotic resistance highlights the urgent need for new strategies to study bacterial adaptations. Formamide-tetrahydroquinoline (THQ) derivatives have shown potential biological activity; however, their spectral responses in bacterial systems and their possible influence on adaptation processes remain unexplored. This study evaluated the effects of tetrahydroquinoline compounds 1a, 1b, and 1c (20 µg mL-1) on E. coli during the lag (LAG) and logarithmic (LOG) growth phases over six hours using Fourier-transform infrared spectroscopy with attenuated total reflectance (FTIR-ATR). Spectral changes were analyzed to identify the biomolecular alterations induced by the compounds. FTIR-ATR revealed significant spectral perturbations during the LAG phase, particularly in protein, nucleic acid, lipid, and polysaccharide regions. Compounds 1b (chlorosubstituted) and 1a (methoxy-substituted) produced the most notable spectral shifts, indicating differential responses in the spectral regions associated with bacterial adaptation. In contrast, minimal perturbations were observed in the LOG phase, underscoring the LAG phase as the stage with spectral differences. Statistical analysis confirmed significant spectral perturbations in regions commonly assigned to amide I/II and phosphate-associated bands. These findings demonstrate the utility of FTIR-ATR as an exploratory tool for monitoring bacterial responses and suggest that THQ derivatives are promising candidates for studying bacterial growth.

Mechanistic models and machine learning methods provide powerful capabilities for simulating and controlling wastewater treatment processes; however, their application to real-time control faces major challenges due to the high computational cost of the former and the limited physical consistency of the latter. Accordingly, this study develops a physics-informed neural network (PINN) framework that integrates a physics-constrained surrogate model with a rolling optimization loop for pollutant prediction and dynamic control. Evaluation against ten critical water quality indicators demonstrates high predictive accuracy, achieving an average R2 of 0.8860. Comparative analysis with the ASM2D model reveals that the proposed PINN not only enhances prediction accuracy but also reduces computational costs by approximately 786-fold, underscoring its strong potential for real-time deployment. Steady-state multi-objective optimization further validates the computational efficiency of the PINN, yielding a distinct Pareto front. Subsequently, the integration of dynamic optimization with the rolling prediction mechanism facilitates rapid control parameter updates (0.83 s). This strategy reduces energy consumption by 40.3% while ensuring compliance with discharge standards, demonstrating significant advantages in energy efficiency. Biochemical kinetic analysis confirms that the superior performance stems from precise regulation of core reaction rates, facilitated by the hybrid structure of the PINN that combines the computational efficiency of machine learning with the interpretability of biochemical kinetics. Overall, this study provides a reliable intelligent decision-support tool for WWTP operation under varying influent conditions within the same plant configuration.

Natural killer cell is a critical cell type in our immune system. Its therapeutic potential in the development of novel cancer immunotherapy has been emerging. Microfluidic technologies capable of manipulating small volumes of fluid through microchannels are emerging tools for advancing our knowledge of natural killer cell biology. However, the rationale behind microfluidic device design, material choice, and the precise engineering of in vitro microenvironments has yet to be systematically assessed through the lens of complex NK cell biology using cancer cells as targets for activation. This comprehensive review assesses microfluidic technologies designed for the study of NK cell phenotype/function at a single cell level, migration dynamics in the presence or absence of biochemical cues, natural killer-target cell interactions, and observation of sophisticated cellular behavior (infiltration/cytotoxicity) in multi-factorial complex microenvironments. Step by step advances in device complexity such as the integration of acellular hydrogels or multicellular co-culture systems highlight the emerging need for higher resolution analysis of natural killer cell properties at the single cell, tissue, and organ levels. The progression of microfluidic technologies for advancing knowledge of natural killer (NK) cell biological mechanisms within the last twenty years. Figure was generated using BioRender.

As a representative physical factor in the extracellular matrix (ECM), ECM stiffness regulates fibroblast behaviors and contributes to physiological homeostasis and pathological processes. This process relies on mechanosensors, including integrin-based adhesion complexes, mechanosensitive ion channels, and discoidin domain receptors (DDRs), which detect changes in stiffness and transduce them into biochemical signals. Through downstream pathways including YAP/TAZ and RhoA/ROCK, ECM stiffness modulates essential cellular behaviors such as adhesion, migration, proliferation, and differentiation, adapting to the requirements of tissue homeostasis. Under pathological conditions, abnormal elevation of ECM stiffness occurs in tumor and fibrotic tissues, forming a vicious cycle of ECM deposition-increased stiffness-abnormal cellular activation. Therefore, deciphering the regulatory mechanisms of ECM stiffness on fibroblasts not only deepens the understanding of the interaction between cells and the mechanical microenvironment but also provides crucial theoretical support and innovative ideas for the targeted therapy of diseases such as tumors and fibrosis, as well as the development of tissue engineering and regenerative medicine.

Accurate assessment of ionized calcium (Ca++) is critical in clinical settings but remains technically and logistically challenging in many healthcare facilities. This study aimed to evaluate the performance of machine learning (ML) models in predicting Ca++ levels measured by blood gas analysis, using routinely available biochemical parameters-total calcium (TotCa), total protein, and albumin-and to compare them with values obtained through direct measurement and established correction formulas. A retrospective analysis was conducted on 84,410 patients aged 20-70 years (43,863 men, 40,547 women). Whole-blood Ca++, serum TotCa, albumin, and total protein levels were retrieved from hospital records. Three ML algorithms-Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB)-were trained and validated using 5-fold cross-validation. Their performance was benchmarked against three conventional correction formulas: Hanna, Zeisler, and Butler. Among the conventional formulas, the Hanna method showed the highest mean absolute error (MAE = 0.3626), while Zeisler (MAE = 0.0719) and Butler (MAE = 0.0988) performed more closely to measured Ca++. The ML models outperformed all formula-based methods, with GB (R2 = 0.6742), SVM (R2 = 0.6732), and RF (R2 = 0.6730) achieving the highest explained variance. In contrast, Butler and Zeisler yielded R2 values of 0.2684 and 0.4879, respectively. ML models demonstrate superior predictive accuracy for Ca++ compared with conventional correction formulas when using routine biochemical parameters. These findings support the potential integration of ML-based tools into clinical decision support systems. Future research should address model interpretability, pH incorporation, and prospective external validation.

Despite their immense promise as renewable bioenergy resources, microalgae continue to face major barriers to industrial deployment due to low biomass output and constrained lipid accumulation. In this regard, the regulatory role of melatonin (MT) in modulating carbon flux allocation and redox homeostasis was investigated in the oleaginous, D. salina using a two-stage cultivation strategy. During Stage I, supplementation with 20 µM MT enhanced biomass accumulation and chlorophyll a content relative to the control. However, during Stage II, MT supplementation triggered pronounced metabolic reprogramming, leading to substantial enrichment of lipids (41.6%) and carbohydrates (17.52%), accompanied by a concomitant decline in protein content under nitrogen starvation. This metabolic shift was coupled with strong activation of the antioxidant defense system, as evidenced by elevated SOD, CAT, and APX activities, while DCFDA-based flow cytometric analysis revealed a tightly regulated ROS profile, indicative of maintained redox homeostasis. In addition, FTIR spectroscopy showed increased absorbance at 2929 cm⁻¹, and 1726 cm⁻¹, corroborating neutral lipid enrichment. Interestingly, untargeted HR-MS-based metabolomics further revealed a pronounced upregulation of fatty acid biosynthesis pathways, accompanied by enrichment of saturated fatty acids and a concomitant reduction in polyunsaturated fatty acids. These findings indicate that MT coordinates a finely tuned redox-metabolic network in which controlled ROS dynamics, together with reinforced antioxidant defense, synergistically redirect carbon flux toward neutral lipid biosynthesis. This study provides mechanistic insights into MT-mediated biochemical engineering and highlights its potential to enhance the commercial feasibility of vehicular grade biofuel production.

Yeast molecular display is a convenient tool for developing and improving recombinant proteins and assessing their functions. Molecular display using Saccharomyces cerevisiae has been applied for screening valuable biochemical molecules, including antigens and antibodies. In this chapter, we describe the genetic construction of a yeast molecular display system for a nanobody and its evaluation using fluorescence microscopy and flow cytometry.

Deep eutectic solvents (DESs) are promising green media for biocatalysis, yet their application in pyruvate decarboxylase (PDC)-mediated phenylacetylcarbinol (PAC) biotransformation with product separation remains limited. This study screened nine DESs using frozen-thawed whole cells (FT-WHC) of Candida magnoliae (C. mag.), followed by evaluation of water content and DES to phosphate buffer ratios. Glycerol-based DES resulted in significantly higher (p ≤ 0.05) volumetric PDC activity and PAC productivity than other DESs. Choline chloride: glycerol (ChCl: Gly (1:2) 20% (v/v) H2O) achieved the highest overall [PAC] of 148.2 ± 0.3 mM. Adding 100% (v/v) water decreased stability of volumetric PDC activity, but doubled PAC productivity, and reduced solvent cost compared with a 20% (v/v) H2O addition. The DES-to-phosphate buffer (Pi buffer) ratio at 1:1 was optimal, while lower ratios reduced PAC production. Spontaneous formation of PAC-enriched droplets (~ 2.5 M) as a top phase indicated simplified downstream recovery. This indicates DES-based self-separation driven by polarity and product partitioning. Overall, ChCl: Gly (1:2) with 100% (v/v) H2O at a 1:1 DES: Pi buffer ratio was selected for future PAC biotransformation. These findings indicate that DESs enhance biocatalytic performance and enable integrated reaction and product separation in a single step, offering a sustainable route to biochemical production.

Accurate monitoring of polynucleotide kinase (PNK) activity is essential for DNA repair studies and early clinical diagnostics. Conventional λ-exonuclease (λ-exo)-based biosensors often suffer from high background signals and false‑positive results, mainly due to nonspecific hydrolysis and unintended phosphorylation. To address these issues, we developed a double- strand gated (DSG) biosensing platform that integrates high-order dimer G-triplex (DG-3) structures as signal reporters, significantly reducing background interference and enabling robust signal amplification. In this DSG strategy, λ-exo cleavage requires both 5'-phosphorylation by PNK and double-stranded DNA (dsDNA) synthesis mediated by the Klenow fragment polymerase. This dual- verification mechanism effectively eliminates false positives resulting from nonenzymatic hydrolysis or phosphatase interference. The DG-3 structure, formed via Klenow-mediated extension, is fully released upon λ-exo cleavage, generating strong fluorescence upon binding with thioflavin T (ThT). Furthermore, DNA nanoflowers (DNFs) fabricated through rolling circle replication exhibit a unique three-dimensional architecture that spatially confines nucleic acid probes via nanotechnology. This configuration increases the local concentration of probes and enzymes, promoting molecular interactions and accelerating enzymatic kinetics. It also protects nucleic acid probes from nuclease degradation, thereby improving biosensing stability and sensitivity. The proposed strategy allows for the detection of PNK activity within a linear range of 10-4-1 U/mL, with a detection limit of 3.15 × 10-5 U/mL. This method has been successfully applied to assess PNK activity in cell extracts and to screen for potential inhibitors. With its high sensitivity, specificity, and biostability, the platform enables rapid and accurate PNK detection, demonstrating considerable potential for biochemical analysis and inhibitor discovery.

Onchocerciasis-associated epilepsy (OAE) clusters where transmission of Onchocerca volvulus is intense, yet the causal steps to epileptogenesis remain unresolved. Rodent models can close this gap by enabling standardized, brain-anchored readouts that are impossible in humans. Here we synthesize current platforms and outline a pragmatic path to high construct validity. Implantation models using Onchocerca ochengi establish feasibility and reveal longitudinal physiological and behavioral effects, but lack natural infection routes and rarely generate sustained skin microfilariae. Gerbil models are informative for systemic physiology and macrofilaricidal stress-tests, whereas humanized NOD scid gamma mouse (NSG) infection introduces bona fide parasite development and a coherent context for anti-Wolbachia and anti-microfilarial probes, even without reliable patency. We propose a hybrid program integrating: (i) an Infection Core (humanized NSG with trickle L3 and doxycycline/ivermectin perturbations; (ii) an mf-centric arm that reconstructs controllable skin microfilaridermia; (iii) a standardized excretory/secretory product (ESP) program under tight biochemical quality control to test mechanistic sufficiency; and (iv) a refined implantation arm using gravid O. ochengi masses at low load with longer follow-up to generate measurable microfilariae in skin. Across all arms, a Core Outcome Set comprising wireless EEG, quantitative neuropathology, a minimal behavior battery, and paired brain-serum cytokine profiling enables cross-study comparability under ARRIVE-aligned bias control. This integrated approach transforms diffuse hypotheses into testable predictions linking infection context, microfilarial density, and parasite products to seizure propensity, advancing OAE toward actionable prevention and therapy.

Biomolecular condensates undergo dynamic maturation, transitioning from liquid-like droplets to gel-like or solid-like assemblies, exhibiting structural heterogeneity in response to biochemical cues. Synthetic coacervate droplets formed via liquid-liquid phase separation (LLPS) have emerged as simplified models of these condensates and of protocells. Yet, the understanding of complex phase behaviors of liquid-like droplets in both biological and chemical contexts remains less explored, limiting our understanding of biological function and the design of droplet-based soft materials and protocells. Here, we introduce a chemically programmable strategy to dynamically modulate droplet phase transitions through controlled polymer-network cross-linking. Reactive cross-linkers selectively engage polymers within liquid-like droplets, progressively increasing internal microviscosity and inducing a liquid-to-solid transition. Beyond uniform phase regulation, network cross-linking drives spatially heterogeneous phase separation via polymer demixing and Ostwald ripening, with reaction-diffusion dynamics critically shaping both thermodynamically stable and metastable droplets. Using orthogonal cleavable cross-linkers, we further demonstrate chemical control over droplet liquefaction and the generation of multiphasic structures with pathway-dependent configurations. Integration of photoresponsive cross-linkers with digital-micromirror device (DMD) technology enables precise spatial photopatterning of droplet networks. This work establishes a versatile framework for elucidating the structural principles of microphase separation within coacervates and provides a blueprint for designing dynamic soft materials and synthetic protocells.