Methyl-accepting chemotaxis proteins (MCPs), also termed transducer-like proteins (Tlps), serve as sensors in bacterial chemotactic signalling, and detect attractants and promote bacterial movement towards suitable sites for colonization. Campylobacter jejuni is a leading cause of human enteritis, but the mechanisms responsible for bacterial chemotaxis and early colonization in the jejunum of hosts are poorly understood. In the present study, we identified several types of bile and sodium deoxycholate (SDC) acting as chemotactic attractants of C. jejuni strain NCTC 11168-O in vitro, in which SDC was the most efficient chemoattractant. In mice with bile duct ligation, the wild-type strain displayed a markedly attenuated ability for colonization. Blockage of Tlp3 or Tlp4 protein with antibody or disruption of the tlp3 or tlp4 gene (Dtlp3 or Dtlp4) caused a significant inhibition of SDC-induced chemotaxis and attenuation for colonization on jejunal mucosa in mice of the bacterium. Disruption of both the genes (Dtlp3/Dtlp4) resulted in the absence of bacterial chemotaxis and colonization, while the tlp-gene-complemented mutants (CDtlp3 and CDtlp4) reacquired these abilities. The results indicate that SDC is an effective chemoattractant for C. jejuni, and Tlp3 and Tlp4 are the SDC-specific sensor proteins responsible for the bacterial chemoattraction.

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Patients with acute kidney injury (AKI) show high morbidity and mortality, and a lack of effective biomarkers increases difficulty in its early detection. Weighted gene co-expression network analysis (WGCNA) detected a total of 22 gene modules and 6 miRNA modules, of which 4 gene modules and 3 miRNA modules were phenotypically co-related. Functional analysis revealed that these modules were related to different molecular pathways, which mainly involved PI3K-Akt signaling pathway and ECM-receptor interaction. The brown modules related to transplantation mainly involved immune-related pathways. Finally, five genes with the highest AUC were used to establish a diagnosis and prediction model of AKI. The model showed a high area under curve (AUC) in the training set and validation set, and their prediction accuracy for AKI was as high as 100%. Similarly, the prediction accuracy of AKI after 24 h in the 0 h transplant sample was 100%. This study may provide new features for the diagnosis and prediction of AKI after kidney transplantation, and facilitate the diagnosis and drug development of AKI in kidney transplant patients.

Objectives: To examine the association between smoking cessation and risk of type 2 diabetes with emphasis on post-cessation weight gain.Methods: In total, 8,951 participants from the China Health and Retirement Longitudinal Study at the baseline (2011) were included. Diabetes incidence was accessed at the third survey (2015). Current smokers were treated as the reference and odds ratios (OR) of type 2 diabetes for never smokers, recent, and long-term quitters were computed using multivariable logistic regression. Stratified analysis was further conducted by weight gain after smoking cessation.Results: There were 712 cases of type 2 diabetes identified. Compared with current smokers, the fully multivariable-adjusted ORs were 1.55 (1.02, 2.36) for recent quitters, 0.88 (0.61, 1.28) for long-term quitters, and 0.75 (0.59, 0.95) for never smokers. Stratified analysis showed recent quitters with weight gain of ≥2.0 kg had a significantly higher odds of type 2 diabetes [2.25 (1.02, 4.95)].Conclusion: The present study of the Chinese population suggested recent quitters with weight gain of ≥2.0 kg, compared with current smokers, had a significantly increased odds of type 2 diabetes.

ObjectiveThe present study aimed to screen and find alkyl hydroperoxide reductase (AhpC) B cell dominant epitope of Campylobacter jejuni (C. jejuni).Materials and methodsBio-informatic algorithms were used to predict B cell epitopes of AhpC. The AhpC protein and chemically synthesized antigenic epitopes of C. jejuni were considered as antigens, and the AhpC antibody was used as the primary antibody, ELISA and dot blot were used to analyze and screen the dominant epitope. The specific IgG of mice serum and IL-4 in splenocyte culture supernatant were detected by ELISA. The protective efficacy was evaluated by animal disease index and tissue histopathological staining of the jejunum.ResultsSeven epitopes of AhpC were predicted, one epitope (AhpC4–16) was found to recognize the antibodies of AhpC and had strong antigenicity by ELISA and dot blot analysis. In epitope AhpC4–16 immunized mice, specific IgG of serum and IL-4 in splenocyte culture supernatant were significantly higher. The illness index decreased significantly, the protective rate was 66.67%. Histopathology displayed that the jejunum morphology was better than the control group.ConclusionsThese findings suggested that epitope AhpC4–16 showed effective protective role against C. jejuni and is a candidate epitope of vaccine against this pathogen.

Background Coronavirus disease 2019 (COVID-19) greatly affects cancer patients, especially those with lung cancer. This study aimed to identify potential drug targets for lung adenocarcinoma (LUAD) patients with COVID-19. Methods LUAD samples were obtained from public databases. Differentially expressed genes (DEGs) related to COVID-19 were screened. Protein–protein interactions among COVID-19-related genes, the traditional Chinese medicine (TCM) and TCM target genes were analyzed by CytoScape. The correlation between tumor microenvironment and COVID-19 target genes were assessed by Pearson correlation analysis. Unsupervised consensus clustering was conducted to categorize molecular subtypes. Results We filtered 26 COVID-19 target genes related to TCM for LUAD. Interleukin (IL)-17 signaling pathway and tumor necrosis factor (TNF) signaling pathway were significantly enriched in these 26 genes. A strong correlation was found between COVID-19 target genes and tumor microenvironment (TME), cell death. Importantly, interleukin-1beta (IL1B) was identified as a core gene in the protein–protein interactions (PPI) network. Based on the 26 target genes, two molecular subtypes showing distinct overall survival, TME and response to target therapy were developed. Conclusions This study explored 26 COVID-19 target genes, which could serve as potential therapeutic drug targets for LUAD. IL1B was verified as a critical target for developing new molecular drugs. Furthermore, two novel molecular subtypes showed the potential to guide personalized therapies in clinical practice.

Background: Immune-related genes possess promising prognostic potential in multiple cancer types. Here, we describe the development of an immune-related prognostic signature for predicting prostate cancer recurrence. Methods: Prostate cancer gene expression profiles for 477 prostate cases, as well as accompanying follow-up information were downloaded from The Cancer Genome Atlas (TCGA) and GEO. The samples were divided into 3 groups and immune gene sets that significantly associated with prognosis identified by evaluating the relationship between the expression of 1039 immune gene and prognosis in the training set. Relative expression levels of these genes were used to identify prognostic gene pairs. LASSO was used for feature selection and robust biomarkers selected. Finally, the identified immune prognostic markers were validated using dataset and GEO validation dataset and their performance compared with existing prognostic models. Results: In total, 87 immune genes, significantly associated with prognosis were identified and 2447 immune gene pairs (IRGPs) established. Univariate survival analysis identified 641 prognosis-associated immune gene pairs. 8 IRGPs were obtained via LASSO feature selection and an 8-IRGPs signature established. The 8-IRGPs signature exhibited independent prognosis value in prostate cancer the training set, test set, and external validation set (p = <0.001). The 5-year survival AUC in both the training set and the validation set was >0.7. The 8-IRGPs outperformed clinical tumor classification features, including T,,N, radiation therapy (RT) and targeted molecular therapy (TMT) (p <0.01). In addition, we compared the prognostic characteristics of 8-IRGPs with 3 reported prostate cancers and found that 8-IRGPs achieved a high C index (0.85) and had the highest predictive performance within 10 years of follow-up (HR: 10.5). Finally, we integrated T, N, RT, TMT, and 8-IRGPs and generated a novel alignment chart to aid the prediction of prostate cancer recurrence in individual patients (p <0.01). Conclusion: Here, we identified an 8-IRGP novel prognostic signature for the prediction of prostate cancer recurrence.

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