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PubMed2026

Multistrategy metabolic engineering of Talaromyces pinophilus for α-amylase production from lignocellulosic biomass.

Filamentous fungi are important hosts for industrial enzyme production. Growing demand for α-amylase has increased reliance on food-derived carbon substrates, necessitating fungal strains that efficiently utilize nongrain biomass. In this study, Talaromyces pinophilus Y117 was metabolically engineered to produce α-amylase from lignocellulosic biomass. A strong cellobiohydrolase I gene (cbh1) promoter (Pcbh1Tru) was identified to drive expression. Multiple rounds of multilocus integration of the α-amylase gene were performed using homologous multicopy genomic sequences as recombination arms with a Cre/loxP-based recyclable selection system, yielding the multicopy strain Tp4, which achieved 4124.5 U/mL α-amylase activity in shake-flask fermentation with corncob powder as the sole carbon source. To minimize enzyme degradation, the protease gene 8538 was deleted using the Cre/lox2272 system, generating Tp4Δp. This strain showed a 50% increase in shake-flask α-amylase activity (6208.4 U/mL). In 3-L bioreactor cultivation, Tp4Δp exhibited excellent production performance, achieving 26 712.2 U/mL α-amylase activity. When corncob powder was used as the sole substrate, the cellulose and hemicellulose degradation rates reached 90.00% and 70.01%, respectively, and the enzyme yield reached 213 697.5 U per gram of corncob powder. This engineered strain demonstrates strong potential for industrial applications. The synthesis-degradation synergistic optimization strategy provides a practical approach for engineering filamentous fungal cell factories to produce enzymes directly from lignocellulosic biomass. One sentence summary Metabolic engineering of Talaromyces pinophilus through promoter optimization, multicopy integration, and protease deletion enables efficient α-amylase production from lignocellulosic biomass, achieving 26 712 U/mL in bioreactor fermentation.

Talaromyces↗
PubMed2024

Biochemical insights into the biodegradation mechanism of typical sulfonylureas herbicides and association with active enzymes and physiological response of fungal microbes: A multi-omics approach.

The extensive use of sulfonylurea herbicides has raised major concerns regarding their long-term soil residues and agroecological risks despite their role in agricultural protection. Microbial degradation is an important approach to remove sulfonylureas, whereas understanding the associated biodegradation mechanisms, enzymes, and physiological responses remains incomplete. Based on the rapid biodegradation of nicosulfuron by typical fungal isolate Talaromyces flavus LZM1, the dependency on cellular accumulation and environmental conditions, e.g. pH and nutrient supplies, was shown in the study. The biodegradation of nicosulfuron occurred intracellularly and followed the cascade of reactions including hydrolysis, Smile contraction rearrangement, hydroxylation, and opening of the pyrimidine ring. Besides 2-amino-4,6-dimethoxypyrimidine (ADMP) and 2-aminosulfonyl-N,N-dimethylnicotinamide (ASDM), numerous products and intermediates were newly identified and the structural forms of methoxypyrimidine and sulfonylurea bridge contraction rearrangement are predicted to be more toxic than nicosulfuron. The biodegradation should be enzymatically regulated by glycosylphosphatidylinositol transaminase (GPI-T) and P450s, which were manifested with the significant upregulation in proteomics. It is the first time that the hydrolysis of nicosulfuron into ADMP and ASDM have been associated with GPI-T. The integrated pathways of biodegradation were further elucidated through the involvement of various active enzymes. Except for the enzymatic catalysis, the physiological responses verified by metabolo-proteomics were critical not only to regulate material synthesis, uptake, utilization, and energy transfer but also to maintain antioxidant homeostasis, biodegradability, and tolerance of nicosulfuron by the differentially expressed metabolites, such as acetolactate synthase and 3-isopropylmalate dehydratase. The obtained results would help understand the biodegradation mechanism of sulfonylurea from chemicobiology and enzymology and promote the use of fungal biodegradation in pollution rehabilitation.

Herbicides↗
PubMed2026

Integrating metagenomic next-generation sequencing into a multimodal diagnostic framework for spinal infection: enhancing etiological identification and clinical prediction.

BACKGROUND: Spinal infection (SI) remains diagnostically challenging because of heterogeneous etiologies, nonspecific clinical manifestations, and the limited sensitivity of conventional microbiological approaches, particularly following empirical antimicrobial exposure. Although metagenomic next-generation sequencing (mNGS) enables unbiased pathogen detection, its incremental clinical value beyond pathogen identification and its role within integrated diagnostic strategies remain incompletely established. METHODS: We retrospectively analyzed 208 consecutive patients with suspected SI between August 2022 and August 2025. Final diagnoses were established using a multidisciplinary-adjudicated composite reference standard incorporating clinical, radiological, microbiological, and histopathological evidence. The diagnostic performance of mNGS was compared with conventional culture and histopathology. Furthermore, multimodal predictive models integrating clinical variables and microbiological information were developed using L1-regularized logistic regression. RESULTS: In the comparative cohort, mNGS achieved a significantly higher diagnostic yield than culture (66.5% vs. 27.41%, P < 0.001). Among confirmed SI cases, mNGS demonstrated higher sensitivity than conventional culture (91.67% vs. 40.15%, P < 0.001). mNGS identified a substantially broader pathogen spectrum, ranging from fastidious organisms such as Mycobacterium tuberculosis and Brucella to rare pathogens including Talaromyces marneffei and Coxiella burnetii, and maintained robust sensitivity (98.2%) despite prior antibiotic exposure. While an integrated clinical model achieved an AUC of 0.916, mNGS as a standalone modality provided superior discriminative power (AUC = 0.889) compared to histopathology (AUC = 0.836), the Conventional Biomarker Model (AUC = 0.742), and culture (AUC = 0.693). CONCLUSIONS: mNGS is a high-yield diagnostic tool for spinal infection, particularly in culture-negative and antibiotic-pretreated scenarios. Integrating mNGS into a multimodal clinical framework facilitates etiological clarity and precision antimicrobial therapy.

Humans↗