Candida albicans represents the predominant opportunistic fungal pathogen in clinical contexts, with its virulence contingent upon multilayered regulatory networks. CaLrp1 (orf19.5067) encodes a conserved Sas10/C1D domain-containing protein, though its precise biological role has remained uncharacterized. CaLrp1 deleted and complemented strains were constructed to systematically investigate its function in C. albicans. Abrogation of CaLrp1 resulted in delayed hyphal induction, a loss of rugose colony morphology, and a pronounced reduction in invasive growth on solid media. Furthermore, the mutant demonstrated impaired epithelial translocation, diminished capacity to damage Caco-2 cells, and enhanced susceptibility to macrophage phagocytosis in vitro. In murine models of gastrointestinal colonization and systemic infection, CaLrp1-deficient strains displayed restricted proliferative capacity, reduced tissue pathology, and significantly attenuated virulence. Transcriptomic profiling via RNA-seq revealed that CaLrp1 deletion induced extensive transcriptional reprogramming, marked by the downregulation of genes implicated in ribosome biogenesis, rRNA processing, energy metabolism, and multiple virulence-associated pathways. Notably, key hyphal development and adhesion genes-including Als1, Ece1, and Hwp1-were significantly suppressed, a finding consistent with the phenotypic defects observed on solid substrates. Our results establish CaLrp1 as a critical regulator of C. albicans morphogenesis and pathogenicity, likely operating via the maintenance of cellular RNA-processing and protein-biosynthetic fidelity.

The prolonged cold season on the Qinghai-Tibet Plateau poses substantial challenges for most animals, including limited access to natural pasture, reduced appetite, and subsequent weight loss. The polysaccharides that are contained in yeast cell wall (YCW) act as prebiotics, promoting the action and development of beneficial gut microbiota while inhibiting the proliferation of pathogens, thereby maintaining normal gastrointestinal function in animals. This research aimed to examine how incorporating yeast cell wall (YCW) into the diet influences rumen fermentation, the composition of microbiota, and liver metabolism in Tibetan sheep. A total of 30 one-year-old Tibetan sheep, with an mean weight of 30.51 ± 7.07kg, was randomly assigned to groups: a control group and a supplementation of 0.3% YCW group. Each group consisted of 15 sheep. The experimental period lasted for 98 days. The research showed that the addition of YCW increased Dry matter digestibility and average daily gain (ADG) of Tibetan sheep significantly (P < 0.05); the concentrations of propionic acid, acetic acid, total volatile fatty acids, and ammonia nitrogen in the rumen were significantly increased (P < 0.05); In the liver, the mRNA expression of genes associated with gluconeogenesis, including Glucose-6-phosphatase, catalytic subunit (G6PC), Phosphoenolpyruvate carboxykinase 1(PEPCK1), and Fructose-1,6-bisphosphatase (FBP), were significantly increased following YCW supplementation (P < 0.05); the mRNA expression level of Sterol regulatory element-binding protein 1c (SREBP1c), which is involved in lipid metabolism, was significantly decreased (P < 0.05); The inclusion of YCW in the diet reduced the relative abundance of Desulfobacterota and Firmicutes significantly, and increased Prevotella's abundance significantly in the rumen (P < 0.05). Liver metabolites were substantially enriched in the glycerolipid metabolism, glycolysis/gluconeogenesis, and the AMP-activated protein kinase (AMPK) signaling pathway (P < 0.05). YCW supplementation in feed may advance the growth and gain of Tibetan sheep and improve gluconeogenesis. This effect may be ascribed to modifications in the rumen microbiota facilitating propionic acid fermentation, subsequently regulating liver lipid metabolism.

Understanding how biostimulants modulate plant-associated microbiomes is critical for advancing sustainable agriculture. Here, we investigated the effects of Bacillus-derived bacillin 20 on the root and rhizosphere microbiomes of soybean (Glycine max) using amplicon-based profiling, community ecology, and network analysis. Microbial community assembly was driven primarily by plant compartmentalization, with higher bacterial richness in the rhizosphere and stronger host filtering in root-associated microbiomes. PERMANOVA analysis indicated that compartment explained most of the variation in community structure, whereas bacillin 20 treatment had no statistically significant effect. Despite no meaningful shifts in alpha diversity, bacillin 20 was associated with subtle, non-significant compositional trends in specific taxa across treatments. Indicator species and core microbiome analyses revealed compartment-specific taxa with potential roles in nutrient cycling, stress tolerance, and plant growth promotion. Bacillin 20 was associated with changes in cross-domain microbial co-occurrence patterns, including differences in network connectivity, particularly in the rhizosphere, where several fungal ASVs (e.g., ASV33, ASV5, ASV8, and ASV88) exhibited high centrality. These findings indicate that bacillin 20 is associated with changes in microbial interaction patterns while maintaining overall community diversity. Overall, treatment effects were minor relative to compartment-driven structuring of the microbiome. Together, our results suggest that microbiome-informed approaches, including the use of targeted biostimulants, may contribute to the management of plant-microbe interactions in agricultural systems. Future studies integrating multi-omics approaches will be required to elucidate the underlying mechanisms of these interactions.

Antibiotic-resistant tuberculosis is a worldwide health threat responsible for hundreds of thousands of deaths each year. The continuous escalation of multidrug resistance demands efforts to identify effective novel therapy approaches against mycobacteria, where selective pressure on the pathogen is not the only treatment goal. In this perspective, Sertoli cells (SCs), originally considered as "sustentacular cells" in the testis, have been elevated to cells of the immune system owing to their capacity to modulate the immune response. As such, they express immunoregulatory factors with a remarkable phagocytic capacity and anti-bacterial activity. The aim of the present study was to explore whether SCs could play a role in the treatment of mycobacteria infections that, to the best of our knowledge, has never been reported before. Neonatal porcine SCs monolayer were co-cultured with three different concentrations of Mycobacterium bovis Bacillus Calmette and Guerin for 48h. The phagocytic activity of SCs and the interaction between mycobacteria and SCs have been investigated by the uptake experiment and electron microscopy analysis, respectively. The intra- and extracellular antibacterial activities of SCs have been evaluated by measuring their killing performances. SCs were shown capable of phagocyting mycobacteria while remaining viable and maintaining ultrastructure integrity. Even at higher mycobacterial concentrations, SCs demonstrated an intracellular killing activity with a Logarithmic reduction of 0.4 Log10 CFU/ml up to 7 days and an extracellular killing activity with a Logarithmic reduction of 1 Log10 CFU/ml up to 10 days post-infection. This study provided evidence that SCs could be a potentially valuable therapeutic tool for the treatment of mycobacterial infections, such as tuberculosis, by exerting a direct and indirect antibacterial action.

Implant-associated infections, particularly those caused by Staphylococcus epidermidis, pose a significant challenge in orthopaedic surgery due to the ability of this organism to form antibiotic-resistant biofilms on titanium surfaces. Although povidone-iodine (PVP-I) is widely used as an intraoperative antiseptic, its effectiveness against biofilms at different stages of development remains unclear. This in vitro study aimed to evaluate the bactericidal effects of 0.35% diluted PVP-I on S. epidermidis biofilms on mirror-polished titanium alloy (ASTM F136) discs. Irrigation with PVP-I or saline (control) was applied at three critical stages: (1) irrigation at the pre-exposure stage; (2) irrigation after the bacterial adhesion stage; and (3) irrigation at the early biofilm stage (6h after adhesion). Viable bacterial counts were quantified by colony-forming unit (CFU) assays, and biofilm structure was assessed using biofilm coverage rate (BCR) measured by crystal violet staining and image analysis. Irrigation at the pre-exposure stage did not significantly reduce the number of adherent viable bacteria compared to controls (P = 0.55, d = 0.21). However, irrigation after the bacterial adhesion stage resulted in no viable bacteria being detected in the PVP-I group under the assay conditions (0 CFU/mL; P < 0.0001, d = 2.56). Irrigation at the early biofilm stage with PVP-I achieved a 98% reduction in viable bacteria compared to controls (P < 0.0001, d = 45.3). The relative BCR remained at 102.3% (P = 0.30, d = 0.24), likely due to matrix protein fixation by iodine rather than increased formation. The timing of antiseptic irrigation is critical for effective reduction of viable bacteria from titanium surfaces. Irrigation after the bacterial adhesion stage with diluted PVP-I appears most effective in preventing biofilm establishment, while irrigation at the pre-exposure stage is insufficient, and chemical irrigation alone cannot fully remove established biofilm matrix structures. These findings highlight the importance of integrating mechanical debridement with antiseptic irrigation for optimal management of implant-associated infections.

Type 1 Diabetes Mellitus (T1D) has been increasingly associated with alterations in the gut microbiome. However, the impact of taxonomic resolution, feature selection strategies, and machine learning methods on microbiome-based prediction remains incompletely understood. We analyzed publicly available 16S rRNA gene sequencing datasets from two geographic cohorts to evaluate microbiome-based prediction of T1D. Microbial features were constructed at multiple taxonomic levels and as full hierarchical taxonomic paths preserving phylogenetic structure. Machine learning models were trained using stratified cross-validation and cross-cohort validation frameworks. Feature selection was performed using Binary Particle Swarm Optimization (BPSO) to identify compact and predictive microbial signatures. Model performance was evaluated using AUC, Accuracy, F1 score, and Matthews Correlation Coefficient. Differential abundance analysis using the LinDA framework was used to support biological interpretation of selected taxa. Tree-based models, particularly Random Forest and XGBoost, achieved the strongest predictive performance across taxonomic representations. Taxonomic resolution influenced model behavior, with family-level features providing strong performance with compact feature sets, while higher-resolution representations did not consistently improve performance despite increased complexity. BPSO identified consistently selected taxa across validation frameworks, suggesting stable predictive signatures. Several of these taxa have been linked to inflammatory or metabolically altered gut environments. Cross-cohort validation showed reduced performance compared with within-study models, highlighting challenges in generalization. Machine learning combined with BPSO-based feature selection provides an effective framework for identifying predictive microbial signatures associated with T1D. Our findings highlight the importance of taxonomic resolution, feature stability, and cross-cohort validation in microbiome-based predictive modeling. Integrating evolutionary feature selection with machine learning and biological validation may improve the robustness and interpretability of candidate microbial signatures.

Balanced fertilization with nitrogen (N), phosphorus (P), and potassium (K) plays a pivotal role in sustaining maize productivity and shaping soil-microbe interactions. However, the integrated effects of long-term fertilization on crop performance, soil nutrient dynamics, and rhizosphere bacterial communities in purple soils remain unclear. In a 10-year field experiment in purple soil, conducted under a randomized complete block design (RCBD) with three blocks, we evaluated the effects of different fertilization regimes on maize growth and yield, soil physicochemical properties, and bacterial community composition using 16S rRNA gene sequencing. Nitrogen-containing fertilizer treatments (NPK, NK, NP) markedly enhanced grain yield, shoot and root biomass, and root morphological traits compared with unfertilized (CK) and PK treatments, with balanced NPK fertilization delivering the highest performance. Fertilization significantly influenced soil nutrient availability, with NPK maintaining more stable nutrient levels over time. Microbial analyses revealed that rhizosphere communities were more responsive to fertilization than bulk soil communities, with N availability driving diversity shifts and community separation. Key taxa, including Variovorax, Microscillaceae, Lysobacter, and Dyadobacter, were enriched in N-fertilized soils and positively correlated with grain yield and N uptake. Collectively, these findings demonstrate that balanced NPK fertilization, particularly nitrogen input, enhances maize productivity by simultaneously improving soil fertility and fostering beneficial rhizosphere microbial assemblages, offering a basis of sustainable nutrient and microbiome management in purple soil.

Microbes play a vital role in healthcare, clinical microbiology, biotechnology, and environmental health, making their accurate detection and classification essential in these fields. The accurate detection of bacteria enables investigators to distinguish various organisms and group them according to requirements of the relevant individuals. The recent advancements in deep learning-enabled models have enhanced image-based microbial analysis with higher accuracy as well as efficiency. In this study, we expound a hybrid ensemble model i.e. DeepBACT, which coalesce three fine-tuned pre-trained models such as VGG19, ResNet50, and InceptionV3 to capture highly discriminative features from microbial images for accurate detection. Our approach is efficient in terms training overhead mainly due to the concept of transfer learning-based hybrid ensemble. The proposed approach employs principal component analysis (PCA)for feature selection and efficient classification via support vector machine (SVM). The approach is trained and tested on a self- created novel dataset i.e. Bacterial Images (BACTI) that contains 320 samples of four types of bacterial images i.e. Priestia aryabhattai strain D5, Bacillus cereus strain NTFB22, Bacillus thuringiensis strain NTFB5, and Bacillus cereus strain NTFB9. The approach is evaluated using a randomized train-test split along with the 5-cross validation that demonstrates efficacy of DeepBACT with an F1-score of 97% and accuracy of 98.61%. The experimental results clearly shows that our method outperforms similar state-of-the-art methods in terms of accuracy as well as efficiency. The proposed approach is effective in classifying the unseen samples of microbe images with promising performance and suitable for real-time deployment.

Extended-spectrum beta-lactamases (ESBLs) of Escherichia coli give rise to resistance to broad-spectrum beta-lactam antibiotics, making treatment difficult clinically and complicating environmental management. Understanding hospital effluent as a reservoir and dissemination route for these resistant enzymes is therefore very vital. The crude beta-lactamase enzyme extracts were partially purified by ammonium sulfate precipitation and Sephadex G-100 gel filtration chromatography, enzyme activity was assessed for observed degradation of ceftriaxone by UV spectrophotometry. Protein content was quantified by the Lowry method, and enzyme purity analyzed by SDS-PAGE. The ability of the partially purified enzymes to degrade ceftriaxone in hospital wastewater was also assessed. Crude enzyme extracts showed appreciable ceftriaxone degradation activity. Partial purification enhanced the specific activity of the enzyme while significantly reducing contaminating proteins. SDS-PAGE indicated a reduction in protein bands after purification. Partially purified enzymes showed remarkable ceftriaxone degradation in buffer and remained active, albeit to a lesser degree, in hospital wastewater. This study demonstrates the partial purification and characterization of CTX-type ESBL beta-lactamases from E. coli isolates obtained from wastewater. Partially purified enzymes remain active and have potential for application in reducing -beta-lactam antibiotics in hospital wastewater, thus proposing an environmental approach for curbing antibiotic resistance.

The hospital microbiome significantly influences patient recovery and clinical outcomes. However, the dynamics of microbial colonization and transmission following initial patient occupancy remain poorly understood. Here, we employed 16S rRNA gene amplicon sequencing of the V3-V4 region (Illumina platform) to investigate bacterial community dynamics on surfaces within neurosurgery ward and patients as a new hospital became operational. Our results showed that bacterial colonization in hospital wards follows distinct site-specific patterns, after hospital opening, alpha diversity was significantly increased on floors and drawer handles but decreased on bedrails and faucet handles compared to preopening. Beta diversity analysis showed that surfaces frequently contacted by patients exhibited the greatest compositional turnover, such as bedrails, drawer handles, and faucet handles, bacterial communities in after-opening were more homogeneous across sites than preopening, indicating potential bacterial transmission. Moreover, we found that following patient admission, patient hand-derived microbiomes exert a significant influence on the bacterial communities in hospital wards, with a particularly pronounced impact on bedrails. Additionally, the potential pathogenic potential of the microbial community at the taxonomic level of bedrails in post-opening was significantly higher than preopening, which does not reflect direct clinical infection risk. Taken together, these findings underscore the critical role of human contact in shaping hospital microbiomes and highlight the importance of targeted infection control strategies to mitigate potential pathogen transfer.