Mycobacterium tuberculosis and M. smegmatis form drug-tolerant biofilms through dedicated genetic programs. In support of a stepwise process regulating biofilm production in mycobacteria, it was ...shown elsewhere that lsr2 participates in intercellular aggregation, while groEL1 was required for biofilm maturation in M. smegmatis. Here, by means of RNA-Seq, we monitored the early steps of biofilm production in M. bovis BCG, to distinguish intercellular aggregation from attachment to a surface. Genes encoding for the transcriptional regulators dosR and BCG0114 (Rv0081) were significantly regulated and responded differently to intercellular aggregation and surface attachment. Moreover, a M. tuberculosis H37Rv deletion mutant in the Rv3134c-dosS-dosR regulon, formed less biofilm than wild type M. tuberculosis, a phenotype reverted upon reintroduction of this operon into the mutant. Combining RT-qPCR with microbiological assays (colony and surface pellicle morphologies, biofilm quantification, Ziehl-Neelsen staining, growth curve and replication of planktonic cells), we found that BCG0642c affected biofilm production and replication of planktonic BCG, whereas ethR affected only phenotypes linked to planktonic cells despite its downregulation at the intercellular aggregation step. Our results provide evidence for a stage-dependent expression of genes that contribute to biofilm production in slow-growing mycobacteria.
Biofilm formed in vitro by mycobacteria has been associated with increased antibiotic tolerance as compared with planktonic cells. Cellulose has been identified as a component of DTT-exposed biofilms ...formed by M. tuberculosis. The celA1 gene of M. tuberculosis encodes a cellulase, which could affect the formation of biofilm by slow-growing mycobacteria. In this work, the celA1 gene of M. tuberculosis was cloned into the integrative pMV361 plasmid and then transformed into M. bovis BCG Pasteur to produce BCG:celA1, to have celA1 expressed from the strong promoter hsp60. We compared planktonic and biofilm growth, possible presence of CelA1 in whole protein extracts, quantitated biofilm, presence of monosaccharides, and bacillary burden in lungs after aerosol infection in BALB/c mice. Differences in the appearance of the surface pellicle and of the biofilm attached to the substrate were observed. In biofilms, we observed a significant decrease of glucosamine in BCG:celA1 compared with BCG:pMV361. Finally, BCG:celA1 had lower viable bacteria than the BCG:pMV361 strain after 24 h and 3 weeks post-infection, but no difference was found at 9 weeks post-infection.
Various studies have emphasized the importance of identifying the optimal Trigger Timing (TT) for the trigger shot in In Vitro Fertilization (IVF), which is crucial for the successful maturation and ...release of oocytes, especially in minimal ovarian stimulation treatments. Despite its significance for the ultimate success of IVF, determining the precise TT remains a complex challenge for physicians due to the involvement of multiple variables. This study aims to enhance TT by developing a machine learning multi-output model that predicts the expected number of retrieved oocytes, mature oocytes (MII), fertilized oocytes (2 PN), and useable blastocysts within a 48-h window after the trigger shot in minimal stimulation cycles. By utilizing this model, physicians can identify patients with possible early, late, or on-time trigger shots. The study found that approximately 27 % of treatments administered the trigger shot on a suboptimal day, but optimizing the TT using the developed Artificial Intelligence (AI) model can potentially increase useable blastocyst production by 46 %. These findings highlight the potential of predictive models as a supplementary tool for optimizing trigger shot timing and improving IVF outcomes, particularly in minimal ovarian stimulation. The experimental results underwent statistical validation, demonstrating the accuracy and performance of the model. Overall, this study emphasizes the value of AI prediction models in enhancing TT and making the IVF process safer and more efficient.
•AI-optimized trigger timing significantly increases the number of useable blastocysts.•Oocyte quality and quantity are crucial for successful blastocyst formation.•Hyperparameter optimization in ANN model significantly improves trigger timing.•Architecture ANN analysis identifies key variables impacting blastocyst numbers.
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