The effect of mTOR-siRNA on the levels of mTOR mRNA and protein.

<p>HLE B3 cells were treated with mTOR-siRNA, non-silencing siRNA or control (transfection reagent only), and harvested after 24 h, 48 h and 72 h of transfection. (<b>A</b>) MTOR mRNA was quantified by real-time PCR. The values of mTOR were normalized to Actin and then normalized to control relative value. (<b>B</b>) Representative agarose gel images of the real-time PCR products. (<b>C</b>) MTOR protein levels were examined by Western blot. (Data = Mean ± SEM, *<i>p</i><0.05, **<i>p</i><0.01, compared with the control groups).</p

MTOR-siRNA eliminated the protein interaction of Rictor and mTOR protein and inhibited the phosphorylation of AKT.

<p>(<b>A</b>) HLE B3 cells were treated with mTOR-siRNA, non-silencing siRNA or control and harvested after 24, 48 and 72 hours of transfection. The levels of mRNA were determined by quantitative real-time PCR. The values of AKT mRNA were normalized to Actin mRNA and then normalized to the control value. (<b>B</b>) The phosphorylation of AKT was reduced by mTOR-siRNA after 72 hours of transfection. (<b>C</b>) The interaction of Rictor and mTOR proteins was assessed by co-immunoprecipitation using an anti-mTOR antibody. The precipitates were examined by western blot with anti-Rictor antibody. In the mTOR antibody precipitates, Rictor was undetectable in the samples treated with mTOR-siRNA. (Data = Mean ± SEM, **<i>p</i><0.01, compared with the non-silencing siRNA).</p

MTOR-siRNA blocked EMT induced by TGF-β.

<p>HLE B3 cells were transfected with mTOR-siRNA and 24 hours later cells were treated with TGF-β for 48 hours. Cells were then lysed and subjected to Western blot.</p

Primer sequences employed for real-time PCR.

<p>Primer sequences employed for real-time PCR.</p

The HLE B3 cells growth curve in the presence of mTOR-siRNA and rapamycin.

<p>mTOR-siRNA inhibited cell growth at 48 hours of transfection and this effect was dramatically enlarged at 72 hours of transfection. Rapamycin significantly reduced cell growth at 72 hours of transfection. (Data = Mean ± SEM, *<i>p</i> < 0.05).</p

The effect of mTOR-siRNA on HLE B3 proliferation.

<p>CCK8 assay was used to determine HLE B3 proliferation. MTOR-siRNA significantly reduced cell proliferation at 48 hours of transfection and this effect was enhanced at 72 hours of transfection. (Data = Mean ± SEM, *<i>p</i><0.05, compared with the control groups).</p

The inhibition of cell migration by mTOR-siRNA.

<p>(<b>A</b>) Representative images were taken from Scratch assay. (<b>B</b>) TGF-β-induced cell migration was blocked by mTOR-siRNA. The Gap closure was reduced by mTOR-siRNA. (<b>C</b>) Cell migration was assessed using the Byoden chamber in the absence of TGF-β. The mTOR-siRNA transfected HLE B3 cells were seeded into the Boyden chamber and incubated for 48 hours. The cell migration was significantly reduced by mTOR-siRNA at 48 hours. (Data = Mean ± SEM, *<i>p</i> < 0.05).</p

Table_1_Urogenital microbiota-driven virulence factor genes associated with recurrent urinary tract infection.XLSX

Urinary tract infections (UTIs) are a common health issue affecting individuals worldwide. Recurrent urinary tract infections (rUTI) pose a significant clinical challenge, with limited understanding of the underlying mechanisms. Recent research suggests that the urobiome, the microbial community residing in the urinary tract, may play a crucial role in the development and recurrence of urinary tract infections. However, the specific virulence factor genes (VFGs) driven by urobiome contributing to infection recurrence remain poorly understood. Our study aimed to investigate the relationship between urobiome driven VFGs and recurrent urinary tract infections. By analyzing the VFGs composition of the urinary microbiome in patients with rUTI compared to a control group, we found higher alpha diversity in rUTI patients compared with healthy control. And then, we sought to identify specific VFGs features associated with infection recurrence. Specifically, we observed an increased abundance of certain VGFs in the recurrent infection group. We also associated VFGs and clinical data. We then developed a diagnostic model based on the levels of these VFGs using random forest and support vector machine analysis to distinguish healthy control and rUIT, rUTI relapse and rUTI remission. The diagnostic accuracy of the model was assessed using receiver operating characteristic curve analysis, and the area under the ROC curve were 0.83 and 0.75. These findings provide valuable insights into the complex interplay between the VFGs of urobiome and recurrent urinary tract infections, highlighting potential targets for therapeutic interventions to prevent infection recurrence.</p

Rarefaction curves of the OTU number at 97% similarity for each sample

<p>Rarefaction curves of the OTU number at 97% similarity for each sample</p

The pairwise comparison of the alpha diversity of seed and sieve-through of each wheat sample.

<p>a) OTU number, b) Shannon index c) ACE Estimator and d) Chao1 Estimator.</p