A Constitutively Mannose-Sensitive Agglutinating Salmonella enterica subsp. enterica Serovar Typhimurium Strain, Carrying a Transposon in the Fimbrial Usher Gene stbC, Exhibits Multidrug Resistance and Flagellated Phenotypes

Static broth culture favors Salmonella enterica subsp. enterica serovar Typhimurium to produce type 1 fimbriae, while solid agar inhibits its expression. A transposon inserted in stbC, which would encode an usher for Stb fimbriae of a non-flagellar Salmonella enterica subsp. enterica serovar Typhimurium LB5010 strain, conferred it to agglutinate yeast cells on both cultures. RT-PCR revealed that the expression of the fimbrial subunit gene fimA, and fimZ, a regulatory gene of fimA, were both increased in the stbC mutant when grown on LB agar; fimW, a repressor gene of fimA, exhibited lower expression. Flagella were observed in the stbC mutant and this phenotype was correlated with the motile phenotype. Microarray data and RT-PCR indicated that the expression of three genes, motA, motB, and cheM, was enhanced in the stbC mutant. The stbC mutant was resistant to several antibiotics, consistent with the finding that expression of yhcQ and ramA was enhanced. A complementation test revealed that transforming a recombinant plasmid possessing the stbC restored the mannose-sensitive agglutination phenotype to the stbC mutant much as that in the parental Salmonella enterica subsp. enterica serovar Typhimurium LB5010 strain, indicating the possibility of an interplay of different fimbrial systems in coordinating their expression

Molecular subtype identification and signature construction based on Golgi apparatus-related genes for better prediction prognosis and immunotherapy response in hepatocellular carcinoma

IntroductionThe Golgi apparatus (GA) is the center of protein and lipid synthesis and modification in normal cells and is involved in regulating various cellular process as a signaling hub, the dysfunction of which can lead to the development of various pathological conditions, including tumors. Mutations in Golgi apparatus-related genes (GARGs) are prevalent in most tumors, and their mutations can make them pro-tumor metastatic. The aim of this study was to analyze the predictive role of GARGs in the prognosis and immunotherapeutic outcome of hepatocellular carcinoma.MethodsWe used TCGA, GEO and ICGC databases to classify hepatocellular carcinoma samples into two molecular subtypes based on the expression of GARGs. Signature construction was then performed using GARGs, and signature genes were selected for expression validation and tumor phenotype experiments to determine the role of GARGs in the prognosis of hepatocellular carcinoma.ResultsUsing the TCGA, GEO and ICGC databases, two major subtypes of molecular heterogeneity among hepatocellular carcinoma tumors were identified based on the expression of GARGs, C1 as a high-risk subtype (low survival) and C2 as a low-risk subtype (high survival). The high-risk subtype had lower StromalScore, ImmuneScore, ESTIMATEScore and higher TumorPurity, indicating poorer treatment outcome for ICI. Meanwhile, we constructed a new risk assessment profile for hepatocellular carcinoma based on GARGs, and we found that the high-risk group had a worse prognosis, a higher risk of immune escape, and a higher TP53 mutation rate. Meanwhile, TME analysis showed higher tumor purity TumorPurity and lower ESTIMATEScore, ImmuneScore and StromalScore in the high-risk group. We also found that the high-risk group responded more strongly to a variety of anticancer drugs, which is useful for guiding clinical drug use. Meanwhile, the expression of BSG was experimentally found to be associated with poor prognosis of HCC. After interfering with the expression of BSG in HCC cells SMMC-7721, the proliferation and migration ability of HCC cells were significantly restricted.DiscussionThe signature we constructed using GARGs can well predict the prognosis and immunotherapy effect of hepatocellular carcinoma, providing new ideas and strategies for the treatment of hepatocellular carcinoma

Case report: Sarcomatoid urothelial carcinoma of the renal pelvis masquerading as a renal abscess

Sarcomatoid urothelial carcinoma (SUC), a rare tumor of the urinary tract epithelium, exhibits a high degree of malignancy and therefore a poor prognosis. Due to the absence of specific clinical presentations and imaging findings, SUC of the renal pelvis masquerades as a renal abscess is frequently under-recognized or misdiagnosed as benign inflammatory disease, resulting in delayed or erroneous treatment. Here, we report a patient with SUC of the renal pelvis who presented with a renal abscess. Repeated anti-inflammatory treatment was ineffective. Unexpectedly, cancerous cells were detected in subsequent exfoliative cytology of nephrostomy drainage fluid. In accordance with this, radical surgery and postoperative chemotherapy were conducted. Fortunately, neither recurrence nor metastasis occurred during a one-year follow-up

Identification of cuproptosis-related biomarkers and analysis of immune infiltration in allograft lung ischemia-reperfusion injury

Background: Allograft lung ischemia-reperfusion injury (ALIRI) is a major cause of early primary graft dysfunction and poor long-term survival after lung transplantation (LTx); however, its pathogenesis has not been fully elucidated. Cell death is a mechanism underlying ALIRI. Cuproptosis is a recently discovered form of programmed cell death. To date, no studies have been conducted on the mechanisms by which cuproptosis-related genes (CRGs) regulate ALIRI. Therefore, we explored the potential biomarkers related to cuproptosis to provide new insights into the treatment of ALIRI.Materials and methods: Datasets containing pre- and post-LTx lung biopsy samples and CRGs were obtained from the GEO database and previous studies. We identified differentially expressed CRGs (DE-CRGs) and performed functional analyses. Biomarker genes were selected using three machine learning algorithms. The ROC curve and logistic regression model (LRM) of these biomarkers were constructed. CIBERSORT was used to calculate the number of infiltrating immune cells pre- and post-LTx, and the correlation between these biomarkers and immune cells was analyzed. A competing endogenous RNA network was constructed using these biomarkers. Finally, the biomarkers were verified in a validation set and a rat LTx model using qRT-PCR and Western blotting.Results: Fifteen DE-CRGs were identified. GO analysis revealed that DE-CRGs were significantly enriched in the mitochondrial acetyl-CoA biosynthetic process from pyruvate, protein lipoylation, the tricarboxylic acid (TCA) cycle, and copper-transporting ATPase activity. KEGG enrichment analysis showed that the DE-CRGs were mainly enriched in metabolic pathways, carbon metabolism, and the TCA cycle. NFE2L2, NLRP3, LIPT1, and MTF1 were identified as potential biomarker genes. The AUC of the ROC curve for each biomarker was greater than 0.8, and the LRM provided an excellent classifier with an AUC of 0.96. These biomarkers were validated in another dataset and a rat LTx model, which exhibited good performance. In the CIBERSORT analysis, differentially expressed immune cells were identified, and the biomarkers were associated with the immune cells.Conclusion:NFE2L2, NLRP3, LIPT1, and MTF1 may serve as predictors of cuproptosis and play an important role in the pathogenesis of cuproptosis in ALIRI

Highly efficient preparation of Ce0.8Sm0.2O2-δ–SrCo0.9Nb0.1O3-δ dual-phase four-channel hollow fiber membrane via one-step thermal processing approach

Fabricating dual-phase hollow-fiber membranes via a one-step thermal processing (OSTP) approach is challenging, because of complex sintering kinetics and the subsequent impacts on membrane morphology, phase stability, and permeation properties. In this study, we have demonstrated that Ce0.8Sm0.2O2-δ-SrCo0.9Nb0.1O3-δ (SDC-SCN) four-channel hollow fiber membrane can be manufactured via a single high-temperature sintering process, by using metal oxides and carbonates directly as membrane materials (sources of metal ions). It has been found that use of a low ramping rate reduces grain sizes, increases grain and forming cobalt oxide nanoparticles, a key step to promoting surface exchange process followed by enhancing oxygen permeation. While the grain boundary interface region can be limited to approximately 20–30 nm. At 1173 K oxygen permeation of the SDC-SCN four-channel hollow fiber membrane was measured at approximately 1.2 mL cm−2·min−1 using helium as the sweep gas. Meanwhile, the dual-phase membrane shows a good tolerance to carbon dioxide, with the oxygen permeation flux fully recovered after long-term exposure to carbon dioxide (more than 100 h). This will enable further application of the OSTP approach for preparing dual-phase multi-channel hollow fiber membranes for applications of oxyfuel combustion, catalytic membrane reactors and carbon dioxide capture

Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis

IntroductionRecent studies in human brain connectomics with multimodal magnetic resonance imaging (MRI) data have widely reported abnormalities in brain structure, function and connectivity associated with schizophrenia (SZ). However, most previous discriminative studies of SZ patients were based on MRI features of brain regions, ignoring the complex relationships within brain networks.MethodsWe applied a graph convolutional network (GCN) to discriminating SZ patients using the features of brain region and connectivity derived from a combined multimodal MRI and connectomics analysis. Structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 140 SZ patients and 205 normal controls. Eighteen types of brain graphs were constructed for each subject using 3 types of node features, 3 types of edge features, and 2 brain atlases. We investigated the performance of 18 brain graphs and used the TopK pooling layers to highlight salient brain regions (nodes in the graph).ResultsThe GCN model, which used functional connectivity as edge features and multimodal features (sMRI + fMRI) of brain regions as node features, obtained the highest average accuracy of 95.8%, and outperformed other existing classification studies in SZ patients. In the explainability analysis, we reported that the top 10 salient brain regions, predominantly distributed in the prefrontal and occipital cortices, were mainly involved in the systems of emotion and visual processing.DiscussionOur findings demonstrated that GCN with a combined multimodal MRI and connectomics analysis can effectively improve the classification of SZ at an individual level, indicating a promising direction for the diagnosis of SZ patients. The code is available at https://github.com/CXY-scut/GCN-SZ.git