Elevated Membrane Potential as a Tetracycline Resistance Mechanism in Escherichia coli.

The metabolic environment is responsible for antibiotic resistance, which highlights the way in which the antibiotic resistance mechanism works. Here, GC-MS-based metabolomics with iTRAQ-based proteomics was used to characterize a metabolic state in tetracycline-resistant Escherichia coli K12 (E. coli-RTET) compared with tetracycline-sensitive E. coli K12. The repressed pyruvate cycle against the elevation of the proton motive force (PMF) and ATP constructed the most characteristic feature as a consequence of tetracycline resistance. To understand the role of the elevated PMF in tetracycline resistance, PMF inhibitor carbonyl cyanide 3-chlorophenylhydrazone (CCCP) and the pH gradient were used to investigate how the elevation influences bacterial viability and intracellular antibiotic concentration. A strong synergy was detected between CCCP and tetracycline to the viability, which was consistent with increasing intracellular drug and decreasing external pH. Furthermore, E. coli-RTET and E. coli-RGEN with high and low PMF concentrations were susceptible to gentamicin and tetracycline, respectively. The elevated PMF in E. coli-RTET was attributed to the activation of other metabolic pathways, except for the pyruvate cycle, including a malate-oxaloacetate-phosphoenolpyruvate-pyruvate-malate cycle. These results not only revealed a PMF-dependent mechanism for tetracycline resistance but also provided a solution to tetracycline-resistant pathogens by aminoglycosides and aminoglycoside-resistant bacteria by tetracyclines.

Elevated proton motive force is a tetracycline resistance mechanism that leads to the sensitivity to gentamicin in Edwardsiella tarda.

Tetracycline is a commonly used human and veterinary antibiotic that is mostly discharged into environment and thereby tetracycline-resistant bacteria are widely isolated. To combat these resistant bacteria, further understanding for tetracycline resistance mechanisms is needed. Here, GC-MS based untargeted metabolomics with biochemistry and molecular biology techniques was used to explore tetracycline resistance mechanisms of Edwardsiella tarda. Tetracycline-resistant E. tarda (LTB4-RTET ) exhibited a globally repressed metabolism against elevated proton motive force (PMF) as the most characteristic feature. The elevated PMF contributed to the resistance, which was supported by the three results: (i) viability was decreased with increasing PMF inhibitor carbonylcyanide-3-chlorophenylhydrazone; (ii) survival is related to PMF regulated by pH; (iii) LTB4-RTET were sensitive to gentamicin, an antibiotic that is dependent upon PMF to kill bacteria. Meanwhile, gentamicin-resistant E. tarda with low PMF are sensitive to tetracycline is also demonstrated. These results together indicate that the combination of tetracycline with gentamycin will effectively kill both gentamycin and tetracycline resistant bacteria. Therefore, the present study reveals a PMF-enhanced tetracycline resistance mechanism in LTB4-RTET and provides an effective approach to combat resistant bacteria.

Effects of the Nonstructural Protein-Nucleolar and Coiled-Body Phosphoprotein 1 Protein Interaction on rRNA Synthesis Through Telomeric Repeat-Binding Factor 2 Regulation Under Nucleolar Stress.

To investigate the effects and underlying molecular mechanisms of the interaction between the non-structural protein 1 (NS1) and nucleolar and coiled-body phosphoprotein 1 (NOLC1) on rRNA synthesis through nucleolar telomeric repeat-binding factor 2 (TRF2) under nucleolar stress in avian influenza A virus infection. The analysis of TRF2 ties into the exploration of ribosomal protein L11 (RPL11) and mouse double minute 2 (MDM2) because TRF2 has been found to interact with NOLC1, and the RPL11-MDM2 pathway plays an important role in nucleolar regulation and cellular processes. Both human embryonic kidney 293T cells and human lung adenocarcinoma A549 cells were transfected with the plasmids pCAGGS-HA and pCAGGS-HA-NS1, respectively. In addition, A549 cells were transfected with the plasmids pEGFP-N1, pEGFP-N1-NS1, and pDsRed2-N1-TRF2. The cell cycle was detected by flow cytometry, and coimmunoprecipitation was applied to examine the interactions between different proteins. The effect of NS1 on TRF2 was detected by immunoprecipitation, and the colocalization of NOLC1 and TRF2 or NS1 and TRF2 was visualized by immunofluorescence. Quantitative real-time PCR was conducted to detect the expression of the TRF2 and p21. There is a strong interaction between NOLC1 and TRF2, and the colocalization of NOLC1 and TRF2 in the nucleus. The protein expression of NOLC1 in A549-HA-NS1 cells was lower than that in A549-HA cells, which was accompanied by the upregulated protein expression of p53 in A549-HA-NS1 cells (all p < .05). TRF2 was scattered throughout the nucleus without clear nucleolar aggregation. RPL11 specifically interacted with MDM2 in the NS1 group, and expression of the p21 gene was significantly increased in the HA-NS1 group compared with the HA group (p < .01). NS1 protein can lead to the reduced aggregation of TRF2 in the nucleolus, inhibition of rRNA expression, and cell cycle blockade by interfering with the NOLC1 protein and generating nucleolar stress.

Study on the transport and internalisation mechanism of dietary supplement nattokinase in the small intestine using animal and Caco-2 cell monolayer models.

Maintaining proper blood flow is critical to promoting good health. Nattokinase is a serine protease from Bacillus subtilis that has significant in vitro thrombolytic activity, but its mechanism as a dietary supplement to prevent thrombosis through intestinal absorption and transport is still unclear.The purpose of this study is to study the transport and internalisation mechanism of NK in the small intestine using animal models and Caco-2 cell monolayer models.This study first evaluated the preventive effect of supplementing low dose (4000 FU (Fibrin Unit)/kg, n = 6), medium dose (8000 FU/kg, n = 6), and high dose (12000 FU/kg, n = 6) of nattokinase on carrageenan induced thrombosis in mice. Subsequently, we used the rat gut sac model, ligated intestinal loop model, and Caco-2 cell uptake model to study the intestinal transport mechanism of NK.Results indicate that NK is a moderately absorbed biomolecule whose transport through enterocytes is energy- and time-dependent. Chlorpromazine, nystatin and EIPA all inhibited the endocytosis of NK to varying degrees, indicating that the endocytosis of NK in Caco-2 cells involves macropinocytosis, clathrin-mediated and caveolae-mediated pathway. These findings offer a theoretical basis for investigating the mechanism of oral NK supplementation in greater depth.

Interaction of influenza A virus NS1 and cytoskeleton scaffolding protein α-actinin 4.

NS1 (Non-structural protein 1) is a non-structural protein that can highly express when the avian influenza virus infects the host cells. NS1 can interact with various proteins to alter the intracellular distribution of host proteins and regulate the virulence and pathogenicity of the avian influenza virus. To further study the role of NS1 protein in replication and pathogenesis of avian influenza virus, Glutathione S-transferase (GST) Pull-down was used for screening more proteins interacting with NS1 in human lung adenocarcinoma cell line A549. By mass spectrometry, a potential interacted protein is identified as α-actinin 4 and its interaction with NS1 has not been reported yet. The interaction between NS1 and α-actinin 4 in vitro was confirmed by enzyme-linked immunosorbent assay experiments, and the results showed that the absorbance value of OD450nm in the experimental group was positively correlated with the concentration of NS1-GST protein compared to the negative control group. The co-immunoprecipitation and immunofluorescence results further confirmed the interaction between NS1 and α-actinin 4 at the cellular level. The interaction between NS1 and α-actinin 4 provided a new target for pathogenic mechanism studying and drug screening.

Ensemble Learning Models Based on Noninvasive Features for Type 2 Diabetes Screening: Model Development and Validation.

BACKGROUND: Early diabetes screening can effectively reduce the burden of disease. However, natural population-based screening projects require a large number of resources. With the emergence and development of machine learning, researchers have started to pursue more flexible and efficient methods to screen or predict type 2 diabetes. OBJECTIVE: The aim of this study was to build prediction models based on the ensemble learning method for diabetes screening to further improve the health status of the population in a noninvasive and inexpensive manner. METHODS: The dataset for building and evaluating the diabetes prediction model was extracted from the National Health and Nutrition Examination Survey from 2011-2016. After data cleaning and feature selection, the dataset was split into a training set (80%, 2011-2014), test set (20%, 2011-2014) and validation set (2015-2016). Three simple machine learning methods (linear discriminant analysis, support vector machine, and random forest) and easy ensemble methods were used to build diabetes prediction models. The performance of the models was evaluated through 5-fold cross-validation and external validation. The Delong test (2-sided) was used to test the performance differences between the models. RESULTS: We selected 8057 observations and 12 attributes from the database. In the 5-fold cross-validation, the three simple methods yielded highly predictive performance models with areas under the curve (AUCs) over 0.800, wherein the ensemble methods significantly outperformed the simple methods. When we evaluated the models in the test set and validation set, the same trends were observed. The ensemble model of linear discriminant analysis yielded the best performance, with an AUC of 0.849, an accuracy of 0.730, a sensitivity of 0.819, and a specificity of 0.709 in the validation set. CONCLUSIONS: This study indicates that efficient screening using machine learning methods with noninvasive tests can be applied to a large population and achieve the objective of secondary prevention.

Imaging: Gear up for mechano-immunology.

Immune cells including B and T lymphocytes have a remarkable ability to sense the physical perturbations through their surface expressed receptors. At the advent of modern imaging technologies paired with biophysical methods, we have gained the understanding of mechanical forces exerted by immune cells to perform their functions. This review will go over the imaging techniques already being used to study mechanical forces in immune cells. We will also discuss the dire need for new modern technologies for future work.

B cell mechanosensing: A mechanistic overview.

B cells are essential to the adaptive immune system for providing the humoral immunity against cohorts of pathogens. The presentation of antigen to the B cell receptor (BCR) leads to the initiation of B cell activation, which is a process sensitive to the stiffness features of the substrates presenting the antigens. Mechanosensing of the B cells, potentiated through BCR signaling and the adhesion molecules, efficiently regulates B cell activation, proliferation and subsequent antibody responses. Defects in sensing of the antigen-presenting substrates can lead to the activation of autoreactive B cells in autoimmune diseases. The use of high-resolution, high-speed live-cell imaging along with the sophisticated biophysical materials, has uncovered the mechanisms underlying the initiation of B cell activation within seconds of its engagement with the antigen presenting substrates. In this chapter, we reviewed studies that have contributed to uncover the molecular mechanisms of B cell mechanosensing during the initiation of B cell activation.

Profiling the origin, dynamics, and function of traction force in B cell activation.

B lymphocytes use B cell receptors (BCRs) to recognize membrane-bound antigens to further initiate cell spreading and contraction responses during B cell activation. We combined traction force microscopy and live-cell imaging to profile the origin, dynamics, and function of traction force generation in these responses. We showed that B cell activation required the generation of 10 to 20 nN of traction force when encountering antigens presented by substrates with stiffness values from 0.5 to 1 kPa, which mimic the rigidity of antigen-presenting cells in vivo. Perturbation experiments revealed that F-actin remodeling and myosin- and dynein-mediated contractility contributed to traction force generation and B cell activation. Moreover, membrane-proximal BCR signaling molecules (including Lyn, Syk, Btk, PLC-γ2, BLNK, and Vav3) and adaptor molecules (Grb2, Cbl, and Dok-3) linking BCR microclusters and motor proteins were also required for the sustained generation of these traction forces. We found a positive correlation between the strength of the traction force and the mean fluorescence intensity of the BCR microclusters. Furthermore, we demonstrated that isotype-switched memory B cells expressing immunoglobulin G (IgG)-BCRs generated greater traction forces than did mature naïve B cells expressing IgM-BCRs during B cell activation. Last, we observed that primary B cells from patients with rheumatoid arthritis generated greater traction forces than did B cells from healthy donors in response to antigen stimulation. Together, these data delineate the origin, dynamics, and function of traction force during B cell activation.

Substrate stiffness governs the initiation of B cell activation by the concerted signaling of PKCß and focal adhesion kinase.

The mechanosensing ability of lymphocytes regulates their activation in response to antigen stimulation, but the underlying mechanism remains unexplored. Here, we report that B cell mechanosensing-governed activation requires BCR signaling molecules. PMA-induced activation of PKCß can bypass the Btk and PLC-γ2 signaling molecules that are usually required for B cells to discriminate substrate stiffness. Instead, PKCß-dependent activation of FAK is required, leading to FAK-mediated potentiation of B cell spreading and adhesion responses. FAK inactivation or deficiency impaired B cell discrimination of substrate stiffness. Conversely, adhesion molecules greatly enhanced this capability of B cells. Lastly, B cells derived from rheumatoid arthritis (RA) patients exhibited an altered BCR response to substrate stiffness in comparison with healthy controls. These results provide a molecular explanation of how initiation of B cell activation discriminates substrate stiffness through a PKCß-mediated FAK activation dependent manner.

TLE3 represses colorectal cancer proliferation by inhibiting MAPK and AKT signaling pathways.

BACKGROUND: Transducin-like enhancer of Split3 (TLE3) serves as a transcriptional corepressor during cell differentiation and shows multiple roles in different kinds of cancers. Recently, TLE3 together with many other genes involved in Wnt/ß-catenin pathway were detected hyper-methylated in colorectal cancer (CRC). However, the potential role and the underlying mechanism of TLE3 in CRC progression remain scarce. METHODS: Gene expression profiles were analyzed in The Cancer Genome Atlas (TCGA) microarray dataset of 41 normal colorectal intestine tissues and 465 CRC tissues. Western blot and Real-time Quantitative PCR (RT-qPCR) were respectively performed to detect protein and mRNA expression in 8 pairs of CRC tissue and matched adjacent normal mucosa. Immunohistochemistry (IHC) was conducted to evaluate TLE3 protein expression in 105 paraffin-embedded, archived human CRC tissues from patients, whose survival data were analyzed with Kaplan-Meier method. In vitro experiments including MTT assay, colony formation assay, and soft agar formation assay were used to investigate the effects of TLE3 on CRC cell growth and proliferation. Additionally, subcutaneous tumorigenesis assay was performed in nude mice to confirm the effects of TLE3 in vivo. Furthermore, gene set enrichment analysis (GSEA) was run to explore potential mechanism of TLE3 in CRC, and then we measured the distribution of CRC cell cycle phases and apoptosis by flow cytometry, as well as the impacts of TLE3 on MAPK and AKT signaling pathways by Western blot and RT-qPCR. RESULTS: TLE3 was significantly down-regulated in 465 CRC tissues compared with 41 normal tissues. Both protein and mRNA expressions of TLE3 were down-regulated in CRC compared with matched adjacent normal mucosa. Lower expression of TLE3 was significantly associated with poorer survival of patients with CRC. Besides, knock down of TLE3 promoted CRC cell growth and proliferation, while overexpression of TLE3 showed suppressive effects. Furthermore, overexpression of TLE3 caused G1-S phase transition arrest, inhibition of MAPK and AKT pathways, and up-regulation of p21Cip1/WAF1 and p27Kip1. CONCLUSION: This study indicated that TLE3 repressed CRC proliferation partly through inhibition of MAPK and AKT signaling pathways, suggesting the possibility of TLE3 as a biomarker for CRC prognosis.

How B cells remember? A sophisticated cytoplasmic tail of mIgG is pivotal for the enhanced transmembrane signaling of IgG-switched memory B cells.

Antibody memory is critical for protection against many human infectious diseases and is the basis for nearly all current human vaccines. Isotype switched immunoglobulin (Ig) G-expressing memory B cells are considered as one of the fundaments for the rapid, high affinity and high-titered memory antibody response. The detailed molecular mechanism of the enhanced activation of IgG-switched memory B cells upon BCR engagement with antigens has been an elusive question in immunology. In this review, we tried to discuss all the exciting new advances revealing the molecular mechanisms of the transmembrane signaling through mIgG cytoplasmic tail in IgG-switched memory B cells.

Substrate stiffness regulates B-cell activation, proliferation, class switch, and T-cell-independent antibody responses in vivo.

B cells use B-cell receptors (BCRs) to sense antigens that are usually presented on substrates with different stiffness. However, it is not known how substrate stiffness affects B-cell proliferation, class switch, and in vivo antibody responses. We addressed these questions using polydimethylsiloxane (PDMS) substrates with different stiffness (20 or 1100 kPa). Live cell imaging experiments suggested that antigens on stiffer substrates more efficiently trigger the synaptic accumulation of BCR and phospho-Syk molecules compared with antigens on softer substrates. In vitro expansion of mouse primary B cells shows different preferences for substrate stiffness when stimulated by different expansion stimuli. LPS equally drives B-cell proliferation on stiffer or softer substrates. Anti-CD40 antibodies enhance B-cell proliferation on stiffer substrates, while antigens enhance B-cell proliferation on softer substrates through a mechanism involving the enhanced phosphorylation of PI3K, Akt, and FoxO1. In vitro class switch differentiation of B cells prefers softer substrates. Lastly, NP67-Ficoll on softer substrates accounted for an enhanced antibody response in vivo. Thus, substrate stiffness regulates B-cell activation, proliferation, class switch, and T cell independent antibody responses in vivo, suggesting its broad application in manipulating the fate of B cells in vitro and in vivo.