Viruses play a major role in controlling the structure and dynamics of microbial communities in anaerobic digesters, ecosystems sensitive to disturbances that inhibit methane production. Here, we studied the interplay between abiotic disturbances, microbiome and virome composition, and process performance, to assess whether provirus induction can be triggered by abiotic stresses known to inhibit anaerobic digestion (ammonium, phenol and sodium chloride). We monitored viral dynamics in batch mesophilic anaerobic digesters fed with biowaste through shotgun metavirome sequencing. The diversity of both prokaryotes and viruses was high, with Clostridiales dominating the prokaryotic community and Caudoviricetes dominating the viromes. We identified 132 viral contigs and 19 host genera that were differentially abundant under disturbed conditions. No significant impact of the tested abiotic stresses on provirus induction was observed under the current experimental and analytical framework. The results were consistent with viruses exerting steady, background-level predation through a putative combination of kill-the-winner dynamics at the sub-genus level and piggyback-the-winner dynamics, rather than stress-triggered, synchronous lytic bursts. A few auxiliary metabolic genes were detected, potentially targeting carbon, sulfur and cofactor metabolism in anaerobic digestion. Temperate viruses were dominant, representing up to 71% of the viral genomes confirmed as complete across all conditions. Electron microscopy analysis revealed diverse virus-like particles, including head-tailed particles typical of Caudoviricetes, but also spherical, rod-shaped and spindle-shaped particles typical of archaeal viruses. Notably, we present a new virus family, Eurekaviridae, of spindle-shaped viruses associated with methanogenic archaea.

Industrial discharges of toxic synthetic dyes, such as anionic Reactive Green 19 (RG-19) and cationic Crystal Violet (CV), threaten aquatic ecosystems. This study investigates the marine brown macroalga Halopteris scoparia as an untreated, mechanically ground biosorbent for removing RG-19 and CV from wastewater. Characterization confirmed a rough surface with a negative charge (-17.8 ± 4.08 mV), favoring cationic dye adsorption. Batch experiments optimized pH and dosage. Optimum removal occurred at pH 5.0 for RG-19 and pH 7.0 for CV. Using a biosorbent dosage of 20.0 g L-1, 95.2% of CV and 31.8% of RG-19 were removed from a 50.0 mg L-1 solution within 120.0 min. The maxımum experımental adsorption capacities were 33.5 and 5.5 mg g-1, respectively. Kinetics followed the pseudo-second-order model, while isotherms fit the Langmuir model for RG-19 and the Freundlich model for CV. Hydroxyl, carbonyl, and polysaccharide groups participated in dye binding. Due to electrostatic attraction, H. scoparia prefers cationic CV over anionic RG-19, proving to be a low-cost, sustainable biosorbent.

The yeast Komagataella phaffii (formerly Pichia pastoris) is a robust host for recombinant protein production, capable of growing at high cell densities, performing post-translational modifications, and efficiently secreting heterologous proteins. However, gene expression still relies mainly on the methanol-inducible AOX1 promoter (PAOX1) and the constitutive GAPDH promoter (PGAP), highlighting the need for alternative promoters with distinct regulatory properties. This study aimed to identify and characterize promoters from the most highly transcribed endogenous genes across diverse culture conditions as strong promoter candidates to expand the genetic toolbox for this yeast. Seven promoters were identified through RNA-seq analysis from genes showing transcript levels comparable to or higher than those of genes regulated by PAOX1 or PGAP under specific culture conditions. These promoters were further transcriptionally characterized by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) under different carbon sources, growth phases, and temperatures through the measurement of transcript levels of their corresponding genes, revealing distinct transcriptional profiles. Two major clusters were identified: one associated with genes upregulated during the stationary phase and the other with genes upregulated during the exponential phase. These transcriptional profiles provided a basis for the rational design of fed-batch strategies tailored to each promoter when driving heterologous gene expression. The 0208 and HSP12 promoters showed particular potential at low specific growth rates, favoring high recombinant protein titers with reduced biomass accumulation. In silico analyses suggested that 0208 encodes a mitochondrial membrane-associated protein with a possible functional relationship to Hsp12. Overall, this work provides well-characterized K. phaffii strong promoter candidates, supporting future biotechnological applications.

Predictive modeling of phototrophic cultures in photobioreactors remains challenging because growth emerges from the coupling between radiative transfer, intracellular bioenergetics, and physiological acclimation. Although existing approaches have progressively integrated light attenuation and reactor-scale heterogeneity, extending knowledge-based formulations to eukaryotic microalgae still raises difficulties, especially regarding the role of respiration under illumination and the dynamic adjustment of pigment content. In this work, we establish a biochemically structured predictive model of photoautotrophic growth for the eukaryotic microalga Chlamydomonas reinhardtii. The model is built from an explicit stoichiometric decomposition of the main metabolic functions involved in biomass synthesis, pigment synthesis, photosynthetic energy conversion, respiration, and ATP-consuming futile processes under redox regulation. Its structure is derived from intracellular conservation relationships and observability analysis, leading to a reduced formulation driven by two variables: the net conversion rate of photochemically productive photons within the biotic phase, q γ , and a dissipative ATP sink. The kinetic formulation explicitly couples growth to radiative transfer and accounts for dynamic pigment acclimation through variable partitioning of biomass formation between residual biomass and pigments. Model parameters were either fixed from previous physiological and bioenergetic analyses or identified from batch-culture experiments performed under different incident photon flux densities in a flat-panel photobioreactor illuminated from one side. The model satisfactorily predicts biomass and pigment dynamics in batch and continuous cultures over a broad range of light conditions and dilution rates, while comparisons with oxygen-exchange measurements under illumination provide additional support for its structural relevance. The proposed framework provides a predictive and mechanistically interpretable description of eukaryotic microalgal growth in photobioreactors.

Atrial fibrillation (AF) is a common cardiac arrhythmia associated with substantial morbidity and mortality worldwide. However, its underlying mechanisms are still not completely understood. Emerging evidence suggests that pyroptosis may contribute to AF development. This study aimed to investigate the involvement of pyroptosis-related genes (PRGs) in AF and to develop a validated diagnostic model for this condition. Three AF-related datasets were obtained from the Gene Expression Omnibus. Data were processed using R packages, including GEOquery, sva for batch effect correction, and limma for normalization. Differentially expressed genes were identified and analyzed using clusterProfiler for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment. Gene set enrichment analysis and gene set variation analysis were performed. A protein-protein interaction network was generated using STRING, immune cell infiltration was assessed with cell-type identification by estimating relative subsets of RNA transcripts, and transcriptional regulatory networks were visualized using ChIPBase and StarBase. A total of 1107 differentially expressed genes (579 upregulated and 528 down-regulated) were identified. Among these, 21 PRGs, including ATP6AP1, PKM, and SPTBN1, showed significant dysregulation in AF. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed enrichment of PRGs in inflammation- and immune-related pathways. A diagnostic model incorporating 15 genes accurately distinguished AF patients from controls (area under the curve> 0.9). Gene set variation analysis further highlighted distinct pathway enrichment between high- and low-risk AF groups, underscoring potential therapeutic targets such as the TGF-β signaling pathway. This study provides new evidence that pyroptotic cell death contributes to AF pathogenesis. The proposed diagnostic model demonstrates strong clinical utility and may guide the future development of targeted therapies aimed at preventing AF onset or protecting against its progression.

Ciguatera poisoning (CP) is the most prevalent marine toxin illness globally and disproportionately affects tropical and subtropical regions that rely on fish as a major food source. It is a global concern due to fish importation, tourism in endemic regions, and changes to ocean temperatures leading to the spread of the causative microorganisms into non-endemic regions. CP is caused by the consumption of fish contaminated with ciguatoxins, a class of lipophilic, ladder-framed polyether marine toxins that cause a combination of gastrointestinal, neurological, and cardiovascular symptoms. A major constraint in ciguatoxin analysis is the lack of reference materials (RMs), especially for Caribbean ciguatoxins. Caribbean ciguatoxin-5 (C-CTX5) was isolated from Gambierdiscus silvae and quantitated by qNMR. The purified C-CTX5 was accurately diluted and used to prepare a small batch calibration solution RM at a concentration of 0.5 µg/mL. The solution was shown to be homogeneous and demonstrated good stability over 28 days at temperatures up to 23 °C. A matrix reference material was prepared using fish tissue (Sphyraena barracuda) naturally incurred with C-CTX1, with concentrations estimated using the C-CTX5 calibration solution. The fish matrix RM was homogeneous, and C-CTX1 remained stable at temperatures up to 4 °C for 28 days. At temperatures above 4 °C, a time-dependent decrease in measured C-CTX1 concentration was observed. Addition of antibiotics and an antioxidant stabilizer was shown to improve both the stability of the fish matrix and measured C-CTX1 content. These successful feasibility studies establish processes for the development of large-scale C-CTX reference materials, which are essential to ensure accurate measurements for both research and regulatory frameworks.

This study challenges the view that glycogen-accumulating organisms (GAOs) solely hinder phosphorus removal in wastewater treatment. We investigated how GAOs influence phosphorus recovery in biofilm sequencing batch reactors (BSBRs) under varying carbon-to-phosphorus (C/P) ratios (20-40 mg-COD/mg-P) and dissolved oxygen (DO) levels (4-6 mg/L). By adjusting C/P and DO, we established systems with GAOs abundances ranging from 21.02 % to 2.49 % and polyphosphate-accumulating organisms (PAOs) abundances from 8.21 % to 25.73 %. Surprisingly, high GAOs abundance (21.02 %) correlated with superior phosphorus recovery (> 80 mg/L) and >95 % removal efficiency, contradicting conventional EBPR models. Metagenomic analysis revealed GAOs enhanced glycogen degradation and PHA synthesis, supporting energy-intensive phosphorus accumulation. Reduced GAOs abundance impaired acetate uptake and PHB polymerization, lowering system performance. GAOs also maintained microbial diversity and stabilized functional gene expression. We conclude that GAOs play a beneficial metabolic role in biofilm systems by optimizing carbon use for phosphorus enrichment, enabling stable recovery even when GAOs outnumber PAOs.

Ionic liquids (ILs) can modulate protein phase behavior, including crystallization and aggregation. However, crystallization outcomes are often difficult to predict because nucleation is stochastic and strongly affected by specific ion effects. Here, we examine the crystallization pathway of lysozyme in ethylammonium nitrate (EAN) using a multi-modal small-angle X-ray scattering (SAXS) strategy that integrates high-throughput 96-well screening, in situ capillary thermal treatment, and time-resolved kinetic monitoring. To address batch-to-batch variability in these soft-matter samples, we implemented a standardized workflow for extracting complementary SAXS descriptors, including a fixed-window pseudo-Rg descriptor of low-q scattering evolution and a crystallinity index based on Bragg-feature intensity. The SAXS data are consistent with a two-step nucleation-like pathway in which EAN-induced association precedes the appearance of long-range crystalline order, while thermal treatment disrupts metastable clusters without restoring a simple native monomeric state. Machine-learning models were used as within-system interpolation and screening-prioritization tools for the measured lysozyme-EAN composition grid. Linear models failed to reproduce the non-linear trends, whereas ensemble methods reproduced the main features of the measured crystallization region. Feature-importance analysis identified EAN concentration as the dominant control variable (approximately 70% contribution). Together, these results provide a SAXS-based descriptor-extraction and data-analysis workflow for mapping composition-dependent crystallization behavior in the lysozyme-EAN system.

The on-column hydrolytic degradation of metamizole to impurity C in aqueous mobile phases represents a major source of analytical errors and false-positive results. Accurate and precise determination of impurity C is critical because this analyte is a direct precursor to a monitored genotoxic nitrosamine impurity. To resolve this issue, a novel isocratic non-aqueous hydrophilic interaction liquid chromatography method was developed on a triazole-bonded stationary phase. The separation was performed on Cosmosil HILIC column column (150 × 3.0 mm, 2.5 µm). The mobile phase was composed of 10 mM ammonium formate and 5 mM formic acid in a mixture of methanol and acetonitrile (3:97, v/v). An isocratic flow rate of 0.8 mL/min was utilized at a column temperature of 10 °C. A short analysis time of 3 min was achieved under optimized conditions. Impurity C exhibited minimal retention (k = 0.4), whereas metamizole was strongly retained on the column (k > 100). Due to this distinct selectivity, on-column artifact formation was completely prevented throughout the chromatographic run. To manage metamizole accumulation on the stationary phase, a periodic column regeneration step was introduced after every 30 sample injections. The method was coupled with mass spectrometric detection in positive electrospray ionization mode. Quadrupole mass analyser was employed in selected ion monitoring mode using a target ion at m/z = 218 as [M+H][+]. A low limit of quantification of 2 ng/mL (2 ppm) and excellent linearity (r = 0.9991) were obtained. The calculated back-fit bias remained between -2.6% and 2.3% across the entire calibrated range. Critical verification parameters demonstrated full compliance with International Council for Harmonisation guidelines. The practical utility of the procedure was confirmed through the analysis of two production batches of metamizole.

Rheumatoid arthritis (RA) is a chronic autoimmune disease driven by dysregulated immune responses, particularly involving CD4 + T cells. Identification of robust and reproducible molecular signatures from peripheral blood CD4 + T cells is critical for advancing biomarker discovery and understanding disease mechanisms. GSE80785 and GSE56649 (both on GPL570 platform) were integrated as the discovery cohort (n = 46; 37 RA and 9 healthy controls) after ComBat batch effect correction. GSE55235 and GSE55457 served as independent validation cohorts. Differentially expressed genes (DEGs) were identified using limma. Weighted gene co-expression network analysis (WGCNA) was performed to identify RA-associated modules. Candidate hub genes were screened by integrating PPI network topology (CytoHubba), LASSO regression, and Random Forest algorithms. External validation assessed expression consistency and diagnostic performance via ROC analysis. In the discovery cohort, 1,672 DEGs were identified (773 upregulated, 899 downregulated). WGCNA revealed 14 modules, with one module showing the strongest association with RA status. Integrative multi-algorithm analysis consistently identified four immune-associated hub genes: POLR2F, CDC42, HNRNPC, and LCN2. These genes exhibited significant correlations with immune-related pathways. In the validation cohorts, the four hub genes showed consistent expression patterns, with the combined model achieving AUC values ranging from 0.750 to 0.913. This multi-cohort study identifies POLR2F, CDC42, HNRNPC, and LCN2 as reproducible immune-associated hub genes in RA peripheral blood CD4 + T cells. The findings provide a solid foundation for further functional studies and potential biomarker development in RA.

Guipi Decoction (GPD) is a traditional Chinese medicine formula used for insomnia and fatigue, but comprehensive quality evaluation of its freeze-dried powder (GDFP) remains challenging due to the lack of a fully validated multi-component analytical method. In this study, an LC-MS/MS method was developed and validated according to ICH Q2(R2) for simultaneous quantification of 11 markers covering seven herbal materials in GDFP. Target analytes were screened by TOF-MS using a literature-curated in-house ion pair database. The method showed excellent linearity (R2 ≥ 0.9935), low LODs (0.026-0.61 ng mL-1), a uniform LOQ of 4 ng mL-1, negligible matrix effects (|ME%| < 15%), and acceptable spike recoveries (86.5-115.1% with RSD < 17.8%). The method was successfully applied to six independently prepared GDFP batches and a commercial Guipi mixture after lyophilization. Principal component analysis (PCA) of eight reliably quantified markers explained 83.2% of total variance, identifying ginsenoside Rg1 (19.3%), ferulic acid (18.6%), and spinosin (17.1%) as the primary contributors to batch-to-batch variation. One-way ANOVA confirmed significant inter-batch differences for ten compounds, with spinosin showing the largest variation (F = 123.46 and p < 0.001). Based on a multi-criteria assessment combining PCA contribution, analytical performance, and reported pharmacological activities, these three compounds are statistically prioritized as candidate quality markers warranting future pharmacodynamic validation. This work provides a chemically robust and statistically validated analytical framework for GDFP quality evaluation.