Non-communicable diseases are a growing public health challenge, shaped not only by biological predispositions but also by geo-demographic, socioeconomic, psychological, and lifestyle factors. A comprehensive understanding of these determinants is essential for developing targeted public health strategies. This study aimed to examine the multifactorial determinants of individual health status by analyzing geo-demographic, socio-economic, behavioral, psychological, and lifestyle variables. Data were collected from 4,010 participants (age: 37.2 ± 15.4 years; 59.5% female) across 10 Mediterranean and neighboring countries using the multinational MEDIET4ALL e-survey. Health status was categorized as healthy, at-risk, or with diseases. Multinomial logistic regression, Quade's Rank ANCOVA and series of multiple regression models were conducted. Collectively, around 25% of respondents declared to be at-risk of or with known disease. BMI emerged as the strongest negative predictor of health status (β = -0.145), with both obesity and underweight significantly increasing the odds of being at risk (OR = 1.8 and 5.2, respectively) and having diseases (OR = 2.2 and 11.9, respectively). Other significant negative predictors included psychological distress (notably anxiety, β = -0.091), insomnia (β = -0.084), alcohol consumption (β = -0.053), and prolonged sitting time (β = -0.037). Conversely, life satisfaction was the strongest significant protective factor (β = 0.066), followed by higher education, better sleep quality, and adherence to the Mediterranean Diet and lifestyle (β = 0.034 to 0.050). Socio-economic disparities, including employment status (β = -0.045) and living environment (β = -0.031), also significantly influenced health outcomes with rural environment and employed individual showing lower odd ratios of being at-risk and/or having diseases (p < 0.001). Furthermore, individuals residing in Mediterranean regions, females, married or cohabiting individuals, and non-smokers exhibited significantly lower odds of being at-risk or having diseases (p < 0.05). While gender remained a significant predictor in the final refined comprehensive regression model (β = -0.049), marital status lost significance, suggesting that its protective effect may be mediated by psychological well-being and health-related behaviors. These findings highlight the complex interplay of lifestyle, mental health, and socio-environmental factors in determining health outcomes, while emphasizing the urgent need for multi-level public health interventions, including policies promoting physical activity, healthy eating, mental well-being, and equitable healthcare access. Future research should employ longitudinal designs to establish causal relationships and guides preventive strategies.
The main goal of this work was to establish the differences in the physical properties and nutritional quality of chan seeds (Hyptis suaveolens (L.) Poit) as well as to determine their influence on the structural characteristics. In this study, three sites along the eastern coast of Mexico were selected for sample collection since they are the areas in which chan is produced. Its protein content is comparable to pseudocereal (17.92%-19.63%). For their part, in fats (17.81%-18.38%), ῳ-6 (74.67%-78.19%) and ῳ-9 (9.31%-11.32%) acids mainly predominate. The high crude fiber content (18.79%-20.85%) allows the prebiotic potential of the seeds to be considered. Physical properties showed variations between crops: length (3.275-3.559 mm), width (2.443-2.635 mm), perimeter (10.139-12.202 mm), and weight (5.161-5.651 g/1000 seeds), occupying a volume of 4.913-5.174 mm3. The true density ranged between 1.056 and 1.088 g/cm3; bulk density was 0.616-0.621 g/cm3; the static (28.31°-29.73°) and dynamic (35.21°-40.58°) angle of repose also varied between crops. The surface, thickness, arithmetic and geometric diameter, percentage of sphericity, and porosity were determined and the volume of PDI was calculated, which ranged between 5.649 and 6.226 mm3. Pearson correlation coefficient determined the relationship between the properties studied, with n = 120 determinations per parameter (p < 0.05). In the microstructure of the exocarp of the seeds, there is an areolar zone of globular cells and a predominant zone (mixocarpy) which is characterized by being formed by intermingled mucilaginous and non-mucilaginous cells of isodiametric and angular shape. This study provides knowledge for modernizing sowing and post-harvest for intensive cultivation. This study contributes essential knowledge for the modernization of sowing and post harvest practices in intensive cultivation systems. It also provides the basis for establishing standardized procedures and designing equipment for the efficient handling, processing, storage, and distribution of agricultural products.
The toxic effects of polyethylene (PE) MPs on different-sex zebrafish and the possibility of recovery in MPs-free water remain unclear. In this study, adult male and female zebrafish were exposed to PE-MPs (1-4 μm) with 0, 10 and 100 μg/L for 21 days. PE-MPs exposure significantly decreased body weight and condition factor in both sexes. The 16S rRNA sequencing results indicated that exposure to MPs markedly altered the gut microbiota composition and reduced alpha diversity. At the phylum level, MPs induced a significant increase in Fusobacteriota and a decrease in Proteobacteria in both males and females. Hepatic physiological parameters and glycolipid metabolism genes were also perturbed after PE-MPs exposure. Furthermore, hepatic untargeted metabolomic analysis identified 61 and 194 differential metabolites in male and female zebrafish, respectively, predominantly belonging to the lipid and lipid-like molecules superclass. Females in the MPs-100 group exhibited a great number of differential phospholipids than males, particularly phosphatidylcholine and lysophosphatidylcholine. After exposure, zebrafish from the control and MPs-100 groups were transferred to MPs-free water for a 7-day recovery. Male zebrafish body weight recovered to levels comparable to those of the control group, whereas female body weight did not. Most phylum-level gut microbiota composition and hepatic physiological parameters returned toward the control group levels. These results suggested that female zebrafish were more sensitive to MPs toxicity than males, possibly due to alterations in phospholipid metabolites. In addition, recovery in MPs-free conditions ameliorated the toxic effects caused by prior MPs exposure and female zebrafish exhibited a slower recovery process than males.
Terrestrial hydrothermal systems provide a window for studying the biogeochemical interactions that occur in hot and gas-rich ecosystems resembling the conditions found in early life on Earth. The biogeochemical dynamics of the Andean hydrothermal systems in the Atacama Desert area are still understudied. Thus, we aimed to characterize the taxonomic composition and genomic potential of nitrogen transformations in a microbial community inhabiting a high-altitude hydrothermal system on the Altiplano Plateau of the Chilean Andes. Specifically, we sampled sediment and microbial mats in three ponds with water temperatures ranging from 42 to 64 °C. We found a high prevalence of photoheterotrophs, with differences in taxonomic composition and gene abundance between the microbial communities found in the sediment and microbial mats. Changes in physicochemical conditions, such as temperature and pH, and the concentrations of CO2, CH4 and Mn accounted for the variability in the microbial community structure. Our results indicated an enrichment of N-related genes associated with nitrate reduction, denitrification, and ammonia assimilation, suggesting a metabolically versatile community using nitrate, nitrite, and gaseous nitrogen species to assimilate ammonia into their biomass. This study contributes to our understanding of the taxonomy and functional microbial dynamics in a high-altitude thermal system, where ammonia assimilation is potentially critical for biomass formation, and particular environmental conditions favor adaptations to maintain biogeochemical cycles.
Solid tumors like advanced-stage prostate carcinoma pose significant hindrances to therapeutic approaches due to a rich tumor microenvironment (TME) that establishes strong physical and immune resistance to treatment. The TME is defined by a dense, fibrotic stroma and non-functional vasculature, which produce high mechanical stress, high interstitial fluid pressure (IFP), and hypoxia. These properties critically inhibit therapeutic agent penetration and suppress anti-tumor immunity. This review details the mechanistic foundation of such hindrances and discusses the nascent paradigm of TME normalization. This review critically evaluates approaches aimed at stromal normalization, which include anti-cancer stroma cell targeting and extracellular matrix (ECM) degradation, as well as vascular normalization, which involves anti-angiogenic therapy for vessel repair. The unifying hypothesis of this review is that therapeutic efficacy in advanced solid tumors can be significantly enhanced by first restoring key abnormalities of the tumor microenvironment, including vascular dysfunction and stromal stiffness, thereby improving perfusion, reducing hypoxia, and facilitating immune cell and nanoparticle penetration. This microenvironmental normalization creates a transient therapeutic window that can be strategically exploited through nano-immunotherapy. This strategy enhances the penetration of nanoparticles, streamlines immune cell migration, and counteracts immune suppression. Lastly, key clinical issues and future directions include optimal scheduling, predictive markers, and next-generation nanocarriers. This overall strategy offers a combined method to overcome cancer resistance while improving outcomes in difficult-to-treat tumors.
The cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway serves as a central innate immune signaling axis in host defense against DNA virus infections, and RNA viruses have also evolved diverse strategies to counteract this pathway. Encephalomyocarditis virus (EMCV), a zoonotic RNA virus, utilizes its 2C protein to antagonize RIG-I-like receptor-mediated type I interferon signaling and induce autophagic degradation of calcium binding and coiled-coil domain 2, thereby evading host antiviral immunity. However, the precise molecular mechanism by which EMCV 2C protein modulates the cGAS-STING pathway remains incompletely understood. Herein, we show that EMCV infection reduces the expression of cGAS and STING proteins, and its 2C protein significantly suppresses the production of IFN-β triggered by poly(dA:dT) or viral infection, as well as the mRNA expression of interferon-stimulated genes. Mechanistically, 2C protein binds to STING via its ATPase domain and facilitates K48-linked polyubiquitination and proteasomal degradation of STING, while dominantly interfering STING translocation to the Golgi apparatus and the formation of STING-TBK1-IRF3 complex, thereby blocking STING-mediated IFN-β signal transduction at multiple levels. This study reveals a novel mechanism by which the EMCV 2C protein suppresses the host antiviral response by targeting STING and promoting its ubiquitination and degradation. This finding deepens understanding of the immune evasion mechanism of EMCV and provides a theoretical foundation for the development of antiviral therapies targeting the 2C protein of picornaviruses.
The growing demand for biopharmaceutical products reflects their effectiveness in medical treatments. However, developing new biopharmaceuticals remains a major bottleneck, often taking up to a decade before market approval. Machine learning (ML) models have the potential to accelerate this process, but their success depends on access to large and diverse data sets for training. Multi-fidelity ML techniques offer a promising solution by integrating abundant, low-cost, and less accurate low-fidelity (LF) data with limited, expensive, and more accurate high-fidelity (HF) data. In this framework, LF data capture global system trends, while HF data refine and align model predictions with the available ground truth. Such integration can substantially reduce development costs and timelines by minimizing the need to acquire HF data, for example, through extensive experimental campaigns. This work reviews developments in surrogate modeling within the biopharmaceutical context, including Gaussian processes, neural networks, and physics-informed approaches. It also provides practical recommendations for identifying appropriate LF and HF data. Existing research has primarily focused on upstream processing and drug discovery, highlighting opportunities to extend these methods to other stages, like downstream processing. While Gaussian processes and neural networks remain the most frequently used models, emerging architectures such as Transformer and diffusion models present promising directions for future research.
Phthalate esters (PAEs) are ubiquitous in the environment, and their microbial metabolism is associated with transport proteins. In this study, multiple novel membrane proteins were identified, revealing their pivotal role in PAEs degradation. Based on whole-genome and transcriptomic analyses, approximately 20 potential PAEs transport membrane proteins were predicted to be involved in PAEs degradation by Rhodococcus sp. AH-ZY2. Among them, four protein genes (0620, 3572, 4497, and 5299) exhibited increased transcription levels (Log2F(c)>2.8) in response to di-n-octyl-phthalate (DnOP) as the sole carbon source, instead of fructose, were selected for functional verification. Gene knockout and complementation experiments showed that these four transport protein genes, belonging to the MFS family (0620, 3572, and 5299) and the ABC family (4497), respectively, exhibited the function to transport multiple PAEs into cells. Specifically, membrane protein 5299 could transport the most diverse kinds of PAEs. Molecular docking results also showed that Ser and Arg play a key role in the PAEs transport process of membrane protein 5299, and the PAEs-binding pocket size of 5299 in the transition region was larger than that of 0620, 3572, and 4497. Then, the expression of the genes encoding 0620, 3572, 5299, and 4497 was enhanced after they were cloned into the plasmid pNV18 and transformed into the strain AH-ZY2. The enhancement of 5299 expression exhibited a better improvement in PAEs degradation than the other three genes. This study provides four novel transport proteins and a new strategy for effective bioremediation of PAEs via enhanced membrane transport. Phthalate esters (PAEs) are widely present in the environment, with carcinogenic, teratogenic, and mutagenic toxicity to the human body. The efficient microbial degradation of PAEs is urgent for eco-friendly bioremediation. In addition to PAE esterases, membrane proteins for PAE transport are also important for the microbial degradation of PAEs. However, few experimental reports on the membrane proteins involved in PAE transport, and specifically no studies regarding their underlying transport mechanisms, have been published. Therefore, investigation of the PAE transport mechanisms is crucial for understanding how PAEs enter and exit cells, and it contributes to identifying the rate-limiting steps in PAE degradation. It is conducive to revealing the role of membrane proteins in PAEs degradation, for improving PAE degradation efficiency via membrane protein engineering, or endowing other chassis cells with PAEs degradation capability via constructing membrane protein-esterase co-expressing strains.
The widespread use of herbicides glyphosate (Gly) and glufosinate (Glu) has raised increasing concerns about their environmental toxicity. In aquatic systems, herbicide residues can form complexes or chelate with dissolved substances in water, thereby altering their bioavailability. This study examined the acute toxicity of Gly and Glu, as well as the modulatory effects of heavy metals (Cu and Cd) on Daphnia magna, a freshwater cladoceran. The study further investigated the combined effects of these compounds on D. magna, assessing endpoints including pollution stress response, heart rate, beat frequency, cellular apoptosis, and gene expression levels. Results indicate that the 48-h EC50 values for Gly and Glu on D. magna are 198 ± 1.29 and 1193 ± 89.2 mg/L, respectively, classifying them as low-toxicity pesticides. Co-exposure significantly reduced the heart rate and hopping frequency of D. magna, induced apoptosis, and upregulated related gene expression.Predictive analyses using CA and IA models indicated that low concentrations of Gly exhibited an antagonistic effect with both heavy metals, whereas the interaction between Cu and Gly in the IA model showed this trend but was not significant. Low concentrations of Glu and Cu, as well as high concentrations of Glu and Cd, all exhibited antagonistic effects. Overall, the addition of low-concentration herbicides to the binary mixture of heavy metals reduced the toxicity to D. magna. Nevertheless, as the concentration of the herbicide increased, the weakening of the toxicity effect observed in the low-concentration group was no longer significant.
In light of climate change and growing resource scarcity, microbial production of organic acids offers a sustainable alternative to fossil-based chemical synthesis. In this study, malic acid production by Aspergillus oryzae was optimized through cultivation temperature adjustment and biotin supplementation, while organic acid formation from various carbon sources was systematically characterized. The process conditions applied during substrate screening, including pH control using Na2CO3 and NaOH, Zn2+ supplementation and hypoxia, were based on previously established strategies to stimulate malic acid production. Cultivation at 35°C increased respiratory activity compared to 32°C, resulting in an average productivity of 0.17 g L-1 h-1. Biotin supplementation enhanced productivity by 20% and increased the carbon yield, defined as the proportion of consumed carbon recovered in malic acid, by 5%. Under optimized cultivation conditions, the highest malic acid productivity was achieved in cultivation with glucose as substrate and Na2CO3 as pH-neutralizing agent, reaching 57.57 g L-1 malic acid with a yield of 0.66 g g-1 and an overall productivity of 0.24 g L-1 h-1, while fructose and glycerol resulted in substantially lower productivities. Furthermore, we demonstrate the ability to perform carbon balancing even in the presence of carbonate-based neutralizing agents. This is achieved by quantifying and subtracting the CO2 generated during neutralization reactions from the total emissions, enabling precise determination of microbial CO2 production and calculation of carbon yields. By systematically combining optimization strategies reported in previous studies, this work achieves productivity and carbon efficiency exceeding those of the individual approaches reported so far.
Reliable prediction of the structural and functional characteristics of plant proteins remains a formidable challenge, largely due to a lack of appropriate template structures and the complex effects of environmental stressors on protein stability and function. Existing deep learning techniques, such as AlphaFold2, have shown promising geometric accuracy; however, most methods rely solely on sequence-based structural inference and do not explicitly model environmental and stress-associated biological context. To overcome this limitation, we propose DeepStruct, a multimodal deep learning framework that fuses omics-derived stress signatures and an energy-based scoring module to achieve biological, contextually informed protein structure modelling in plants.DeepStruct combines sequence embeddings, evolutionary profiles (PSSMs), physicochemical descriptors, structural prior and abiotic stress-responsive omic features in a hybrid CNN--BLSTM--Transformer system. An energy-based scoring module is introduced to increase structural plausibility through residue-level functional weights. The model was first pre-trained on UniRef90 and the BFD corpora, then fine-tuned on non-redundant (less than 30% identity) PDB and Swiss-Prot annotations, using temporally controlled data splits to reduce dataset leakage. When tested on Capsicum annuum under abiotic stress, DeepStruct was able to achieve an average TM-score of 0.78 ± 0.02, 95% (CI: 0.76-0.80), and RMSD of 2.6 + -0.3 Angstrom (Å) in the same controlled benchmarking framework, achieving in the precision region above 80% for long-range contacts. The incorporation of stress-responsive omics features produced statistically significant improvements in TM-score, with a maximum 12% gain in the evaluated settings, with adjusted p < 0.05 compared with sequence-only configurations with the same input constraints. These results show that contextual biological signal integration can complement sequence-driven structural prediction, especially for proteins that are conditioned by environmental stress.
A chitinase-producing bacterium was isolated from a dumping ground soil sample and identified as Bacillus cereus V5331 based on 16S rRNA gene sequencing. The production of chitinase was enhanced through the application of solid-state fermentation (SSF). The effect of variables like pH, temperature, swollen chitin (SC) concentration, moisture content, inoculum size and incubation period were assessed by using a one-variable-at-a-time (OVAT) approach. A central composite design (CCD) using response surface methodology (RSM) was subsequently employed with the chitin concentration, moisture ratio, incubation period and inoculum size as found most effective independent variables. Optimization resulted in a 1.84 fold increase in chitinase production by meeting the parameters: 0.2% chitin, 1:1 moisture ratio, 1% inoculum and 120 h of incubation at 37 °C and pH 7 ± 0.2. The scanning electron microscopy (SEM) studies revealed that chitinase showed significant antifungal action against Candida albicans, causing cell wall disintegration, cell wall inhibition (1.5 cm) and substantial biofilm degradation (53.56%). Chitin breakdown was further confirmed by Fourier transform infrared spectroscopic (FTIR) analysis and p-DMAB assay of the enzyme-treated yeast, which showed the release of 211 µg mL-1 of N-acetylglucosamine (NAG). The current study demonstrates a newly isolated bacterium, which is non-hemolytic (gamma- hemolytic) and producing chitinase constitutively as well as in the presence of chitin in cost-effective locally available media. Further, its anti-fungal potential against C. albicans makes it a promising candidate for future studies to develop an antifungal topical therapeutic agent.
Anaplastic thyroid carcinoma (ATC) is one of the most aggressive and lethal malignancies, with limited treatment options and poor clinical outcomes. KU-57788, a selective inhibitor of DNA-dependent protein kinase catalytic subunit (DNA-PKcs), has shown promise in cancer therapy when combined with radiotherapy and chemotherapy. However, its therapeutic potential and underlying mechanisms in ATC remain unclear. In this study, we demonstrate that KU-57788 exerts potent anti-ATC activity both in vitro and in vivo by inducing DNA damage and triggering mitotic catastrophe. Unexpectedly, we identify a novel mechanism whereby KU-57788 directly binds to and activates dynamin-related protein 1 (DRP1), leading to excessive mitochondrial fission and fragmentation. This process is accompanied by the protective activation of the NRF2/SLC7A11/GSH axis, which mitigates the cytotoxic effects of KU-57788. Notably, pharmacological induction of ferroptosis or cystine depletion effectively synergizes with ATC cells to KU-57788, overcoming resistance and promoting ferroptosis. Collectively, our findings highlight the therapeutic potential of KU-57788 in ATC while revealing an intrinsic resistance mechanism mediated by DRP1 activation and the potential involvement of the NRF2/SLC7A11/GSH axis. More importantly, we provide strong evidence that combining KU-57788 with ferroptosis inducers significantly enhances its anticancer efficacy, offering a promising therapeutic strategy for ATC.
Cancer cells undergo various adaptive measures to survive the high level of oxidative stress that threatens genomic integrity and survival. This oxidative stress is enhanced during cancer chemotherapy and undermines the drug efficacy due to the altered cellular signalling, which promotes cell survival and cancer progression. While oxidative stress induces autophagy, the ensuing genotoxic stress activates the DNA damage response (DDR) in cancer cells. Cancer cells show high dependency on these mechanisms for survival, which also indicates the likelihood of increased cooperativity between them under stress. In this study, through co-immunoprecipitation and immunofluorescence assays, we have demonstrated strong interaction between MDC1 and Beclin-1 in H2O2 and doxorubicin treated HeLa cells. Furthermore, we observed that in Hela cells depleted of MDC1, the translocation of Beclin-1 to the nucleus was abrogated, which adversely affected both the DDR and autophagy response in these cells. Additionally, we have analyzed the effect of CHK2 kinase activity on this interaction, and the presence of phospho-CHK2 probably supports the nuclear activity of Beclin-1 through its phosphorylation, but is dispensable for the nuclear translocation of Beclin-1. In conclusion, our findings contribute to the understanding of the crosstalk between DDR and autophagy for cell survival under genotoxic stress, with potential implications on the efficacy of drug treatment.
Food allergies, particularly to crustaceans, are a growing health concern with limited management options. We heterologously expressed and purified crab tropomyosin and then evaluated the beneficial effects of tuna oil on crab TM-induced food allergy in mice. The results demonstrated that tuna oil dose-dependently alleviated allergic symptoms and reduced serum levels of TM-specific IgE, histamine, and cytokines. It also improved intestinal dendritic cell imbalance by reducing CD11c+CD80+/CD86+ cells and restoring CD103+ subsets. In addition, tuna oil enhanced the richness and diversity of gut microbiota, increased beneficial Ligilactobacillus, and decreased allergy-associated taxa. Proteomics further revealed that tuna oil partially reversed TM-induced colonic proteomic alterations, including the downregulation of Tpsab1 and the modulation of lipid metabolism pathways. In conclusion, tuna oil alleviates crab TM-induced allergy through integrated regulation of intestinal immunity, gut microbiota, and colonic proteome, supporting its potential as a dietary strategy against crustacean allergy.
Brain aging is accompanied by cognitive decline and an increased risk of neurodegenerative disease, with neuronal aging being a key causative factor. Studies have shown that the earliest damage to blood-brain barrier (BBB) integrity occurs in the hippocampus, leading to the abnormal accumulation of Fe²⁺;however, the mechanisms underlying subsequent neuronal aging remain unclear. Using single-cell and spatial transcriptomic analyses, this study focuses on the phospholipid flippase ATP11B. We found that ATP11B deficiency facilitates the transport of Fe²⁺ from ependymal cells to hippocampal neurons, activating the Hippo signaling pathway and inducing mitochondrial respiratory dysfunction and dynamic imbalance, which results in neuronal ferroptosis and exacerbation of aging phenotypes. Mechanistically, ATP11B blocks mitochondrial respiratory function by regulating the chromatin accessibility of KLF4 to mitochondrial respiratory chain complex genes. Simultaneously, it impairs the mitochondrial quality control system, resulting in elevated levels of reactive oxygen species(ROS) and enhanced neuronal aging. The mitochondria-associated metabolite, lactate, facilitates histone lactylation of ferroptosis and the key aging-related genes Acsl4, Trp53 and Cdkn1a via the TEAD-YAP complex, thereby promoting transcription. This research uncovers the molecular mechanism through which ATP11B mediates neuronal aging: regulating the iron transport-mitochondrial plasticity axis. This provides a novel avenue for targeting iron homeostasis to intervene in cognitive decline and neurodegenerative disease.
The Bamberger rearrangement is a key route to functionalized aminophenols that are widely used in pharmaceutical synthesis. Hydroxylaminobenzene mutase (HabM) catalyzes a Bamberger-type isomerization in the biosynthesis of the Pranlukast intermediate 3-amino-2-hydroxyacetophenone (3AHAP), but its low catalytic efficiency and unknown structure have limited rational improvement. Here, we combine AI-assisted phylogenetic mining, structural elucidation, protein engineering, and metabolic coordination to enhance 3AHAP biosynthesis. Deep learning-guided screening identified the NRBh-HabMEo pair as the most effective combination, substantially outperforming previously reported systems. Using an integrated dry-wet strategy involving homology template search, spectroscopy, mutagenesis, Size-Exclusion-Chromatography analysis, and AlphaFold3-assisted modeling, we reveal that HabMEo is a Fe-dependent tetramer. Rational mutagenesis supported by molecular dynamics simulations yielded a synergistic triple mutant with improved pocket dynamics, optimized Fe-substrate positioning, and markedly enhanced catalytic efficiency. To alleviate reductive limitations, NAD kinase was introduced to strengthen NADPH cycling; however, increased upstream flux led to intermediate accumulation and by-product formation. This was overcome by implementing a RIAD/RIDD-based scaffold to spatially organize NR, HabM, GDH, and NADK, thereby promoting intermediate channeling and suppressing over-reduction. Overall, this study elucidates the structure of HabM and established a successful paradigm for optimizing complex multi-enzyme cascades for sustainable production of high-value biopharmaceutical intermediates.
Chitosan-cellulose hydrogels have emerged as versatile stimuli-responsive biomaterials for biomedical therapeutics, combining the antimicrobial, mucoadhesive, and pH-sensitive properties of chitosan with the mechanical strength, high water retention, and biodegradability of cellulose. These hybrid systems have demonstrated significant potential in controlled drug delivery, tissue engineering, and regenerative medicine, enabling sustained and localized therapeutic release while reducing systemic toxicity in cancer and chronic disease management. Despite these advantages, important challenges remain regarding long-term in vivo biocompatibility, immune responses, and the biological fate of degradation products, which may influence systemic inflammation and organ function. Furthermore, precise control over multi-stimuli responsiveness, mechanical stability, and degradation kinetics remains critical for clinical translation. Recent advances in nanocomposite strategies, including integration with graphene-based materials, magnetic nanoparticles, and other functional nanomaterials, have introduced next-generation hybrid platforms with enhanced mechanical, electrical, and targeted therapeutic capabilities. Although several preclinical studies have demonstrated promising outcomes, further systematic in vivo investigations and translational research are required to ensure safety, reproducibility, and clinical applicability. This review provides a comprehensive overview of recent advances in chitosan-cellulose hydrogels, critically discusses current limitations, and highlights emerging hybrid systems and future directions toward next-generation biomedical applications.
Lactate is a major byproduct of Chinese hamster ovary (CHO) cell metabolism, typically accumulating during the exponential growth phase and being consumed later during the production phase. Although commonly viewed as a waste product, recent studies suggest that lactate may play a broader role in cellular regulation. To investigate this, we developed a system to modulate intracellular lactate levels by co-expressing lactate oxidase (LOX) and catalase (CAT) in specific cellular compartments, including the cytosol, nucleus, and mitochondria. Using CHO cells secreting a bispecific antibody, this approach enabled assessment of how compartment-specific reduction of intracellular lactate influences cell performance. Reduction of nuclear lactate levels resulted in the most pronounced improvements, including approximately 40% higher viable cell density, 35%-40% increased protein titer, and reduced oxidative stress in fed-batch cultures. In contrast, reduction of mitochondrial lactate levels had minimal impact, indicating that the functional role of lactate is highly dependent on its subcellular localization. Further analysis demonstrated that intracellular lactate reduction was associated with decreased histone acetylation and histone lactylation, a recently described epigenetic modification linked to lactate metabolism. These epigenetic changes correlated with reduced markers of DNA damage and repair activity, suggesting improved genome stability. Overall, our findings indicate that lactate functions as more than a metabolic byproduct and may act as a regulatory metabolite influencing epigenetic state and cellular performance. Targeted modulation of intracellular lactate therefore represents a promising strategy to enhance productivity in CHO cell cultures.
Tomato yellow leaf curl virus (TYLCV) is among the most devastating pathogens affecting tomato production worldwide, with emerging virulent strains increasingly overcoming genetic resistance and triggering severe outbreaks. Traditional field diagnosis, reliant on visual inspection or image-based AI models, remains constrained by symptom dependence, environmental variability, and poor strain-level interpretability. To address these challenges, we introduce DeepTYLCV, a novel deep learning framework for accurate virulence prediction directly from viral genome-derived open reading frame (ORF) sequences. We first constructed a comprehensive dataset of globally sourced TYLCV sequences, curated by virulence annotations. DeepTYLCV integrates protein language model (PLM)-based embeddings with optimal concatenated conventional descriptors (optCCDs) using a hybrid architecture composed of a Transformer encoder and a multi-scale convolutional neural network (CNN), enabling effective extraction of both global and local sequence features. Benchmark analyses demonstrate that DeepTYLCV significantly outperforms our previously developed IML-TYLCV model, which was trained on Korean isolates and lacked global generalizability. Importantly, blind predictions on 15 uncharacterized or representative TYLCV isolates were experimentally validated in tomato plants, achieving 100% concordance between model predictions and observed symptom severity. Furthermore, 1D-Grad-CAM++-based interpretability analyses revealed that the model consistently focused on relevant sequence motifs associated with severe strains, offering mechanistic insights into symptom severity. DeepTYLCV is publicly available at https://balalab-skku.org/DeepTYLCV/, represents a powerful, interpretable, and globally scalable platform for early TYLCV surveillance, resistance monitoring, and strategic disease management in tomato cultivation.