Cervical Cancer Correlates with the Differential Expression of Nicotinic Acetylcholine Receptors and Reveals Therapeutic Targets

Nicotinic acetylcholine receptors (nAChRs) are associated with various cancers, but the relation between nAChRs and cervical cancer remains unclear. Therefore, this study investigated the differential expression of nAChR subunits in human cervical cancer cell lines (SiHa, HeLa, and CaSki) and in normal ectocervical cell lines (Ect1/E6E7) at mRNA and protein levels. Two specific nAChR subtype blockers, αO-conotoxin GeXIVA and α-conotoxin TxID, were then selected to treat different human cervical cancer cell lines with specific nAChR subtype overexpression. The results showed that α3, α9, α10, and β4 nAChR subunits were overexpressed in SiHa cells compared with that in normal cells. α9 and α10 nAChR subunits were overexpressed in CaSki cells. α*-conotoxins that targeted either α9α10 or α3β4 nAChR were able to significantly inhibit cervical cancer cell proliferation. These findings may provide a basis for new targets for cervical cancer targeted therapy

αO-Conotoxin GeXIVA Inhibits the Growth of Breast Cancer Cells via Interaction with α9 Nicotine Acetylcholine Receptors

The α9-containing nicotinic acetylcholine receptor (nAChR) is increasingly emerging as a new tumor target owing to its high expression specificity in breast cancer. αO-Conotoxin GeXIVA is a potent antagonist of α9α10 nAChR. Nevertheless, the anti-tumor effect of GeXIVA on breast cancer cells remains unclear. Cell Counting Kit-8 assay was used to study the cell viability of breast cancer MDA-MD-157 cells and human normal breast epithelial cells, which were exposed to different doses of GeXIVA. Flow cytometry was adopted to detect the cell cycle arrest and apoptosis of GeXIVA in breast cancer cells. Migration ability was analyzed by wound healing assay. Western blot (WB), quantitative real-time PCR (QRT-PCR) and flow cytometry were used to determine expression of α9-nAChR. Stable MDA-MB-157 breast cancer cell line, with the α9-nAChR subunit knocked out (KO), was established using the CRISPR/Cas9 technique. GeXIVA was able to significantly inhibit the proliferation and promote apoptosis of breast cancer MDA-MB-157 cells. Furthermore, the proliferation of breast cancer MDA-MB-157 cells was inhibited by GeXIVA, which caused cell cycle arrest through downregulating α9-nAChR. GeXIVA could suppress MDA-MB-157 cell migration as well. This demonstrates that GeXIVA induced a downregulation of α9-nAChR expression, and the growth of MDA-MB-157 α9-nAChR KO cell line was inhibited as well, due to α9-nAChR deletion. GeXIVA inhibits the growth of breast cancer cell MDA-MB-157 cells in vitro and may occur in a mechanism abolishing α9-nAChR

Cell-based immunotherapy with cytokine-induced killer (CIK) cells: From preparation and testing to clinical application

Cell-based immunotherapy holds promise in the quest for the treatment of cancer, having potential synergy with surgery, chemotherapy and radiotherapy. As a novel approach for adoptive cell-based immunotherapy, cytokine-induced killer (CIK) cells have moved from the ‘bench to bedside’. CIK cells are a heterogeneous subset of ex-vitro expanded, polyclonal T-effector cells with both natural killer (NK) and T-cell properties, which present potent non-major histocompatibility complex-restricted cytotoxicity against a variety of tumor target cells. Initial clinical studies on CIK cell therapy have provided encouraging results and revealed synergistic antitumor effects when combined with standard therapeutic procedures. At the same time, issues such as inadequate quality control and quantity of CIK cells as well as exaggerated propaganda were continuously emerging. Thus, the Ministry of Health in China stopped CIK cell therapy in May 2016, which was a major setback for the innovation of CIK cell-based immunotherapy. Thus, it is very important to modify technical criteria to develop a standardized operation procedure (SOP) and standardized system for evaluating antitumor efficacy in a safe way

Effects of BmCPV Infection on Silkworm Bombyx mori Intestinal Bacteria.

The gut microbiota has a crucial role in the growth, development and environmental adaptation in the host insect. The objective of our work was to investigate the microbiota of the healthy silkworm Bombyx mori gut and changes after the infection of B. mori cypovirus (BmCPV). Intestinal contents of the infected and healthy larvae of B. mori of fifth instar were collected at 24, 72 and 144 h post infection with BmCPV. The gut bacteria were analyzed by pyrosequencing of the 16S rRNA gene. 147(135) and 113(103) genera were found in the gut content of the healthy control female (male) larvae and BmCPV-infected female (male) larvae, respectively. In general, the microbial communities in the gut content of healthy larvae were dominated by Enterococcus, Delftia, Pelomonas, Ralstonia and Staphylococcus, however the abundance change of each genus was depended on the developmental stage and gender. Microbial diversity reached minimum at 144 h of fifth instar larvae. The abundance of Enterococcus in the females was substantially lower and the abundance of Delftia, Aurantimonas and Staphylococcus was substantially higher compared to the males. Bacterial diversity in the intestinal contents decreased after post infection with BmCPV, whereas the abundance of both Enterococcus and Staphylococcus which belongs to Gram-positive were increased. Therefore, our findings suggested that observed changes in relative abundance was related to the immune response of silkworm to BmCPV infection. Relevance analysis of plenty of the predominant genera showed the abundance of the Enterococcus genus was in negative correlation with the abundance of the most predominant genera. These results provided insight into the relationship between the gut microbiota and development of the BmCPV-infected silkworm

Phylogenetic trees of predominant genera in different samples.

<p>The phylogenetic tree was inferred using the neighbor-joining method with MEGA6.0 and the bootstrap value was 1000 replications. Only the branch with bootstrap value >500 are shown in the tree. (a) for CK-144-F, (b) for CK-144-M, (c) for CPV-144-F, and (d) for CPV-144-M; the numbers in parentheses represented the percentage of a predominant genus. Serial numbers from 1–12 represented the <i>Delftia</i>, <i>Pelomonas</i>, <i>Tepidimona</i>, <i>Ralstonia</i>, <i>Aspromonas</i>, <i>Pseudomonas</i>, <i>Enterococcus</i>, <i>Methylobacterium</i>, <i>Staphylococcus</i>, <i>Tepidimonas</i>, <i>Corynebacterium</i> and <i>Brevibacterium</i> genera, respectively. Samples CK (CPV)-144-F (M) were mentioned in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146313#pone.0146313.t001" target="_blank">Table 1</a>.</p

Heatmap and sorting analysis of the different samples.

<p>(a) Heatmap based on the hierarchical clustering solution (Bray–Curtis distance metric and complete clustering method) of the 12 samples. Rows and columns all represent the 12 samples, the similarity represented by the values in the heatmap and the heatmap of bacterial microbiota in different samples, unweighted UniFrac distance metric between samples using Unifrac was calculated to make the heatmap, the lower number represents greater similarity in bacterial microbiota between samples in the heatmap. (b) Sample sorting analysis, A PCoA plot was used to visualize the data based on β-diversity metrics of weighted UniFrac. Scatterplot of PCA-score depicting variance of fingerprints derived from different bacterial community. Principal components (PCs) 1, 2 and 3 explained 51.74%, 33.34% and 6.53% of the variance, respectively.</p

Additional file 1 of Enolase of Streptococcus suis serotype 2 promotes biomolecular condensation of ribosomal protein SA for HBMECs apoptosis

Additional file 1: Fig. S1. RPSA forms spherical condensations. (A) By use of appropriate antibodies in an immunofluorescence experiment, RPSA condensates are shown to localize in HCMEC/D3 cells. A representative image is displayed (scale bar = 40 μm) and magnified. (B) Representative confocal images of RPSA condensates formed by in vitro reconstitution using purified EGFP-RPSAWT. EGFP-ENO was used as a control. A representative image is displayed (scale bar = 10 μm) and magnified (scale bar = 5 μm). Fig. S2. RPSA is associated with intermediate filament-related proteins. (A) After SS2 infection for 1 h, cell lysates were used for pull-down analysis by using antibody against RPSA, followed by SDS-PAGE and silver staining analysis. (B) The strips obtained from result (A) were sent for mass spectrometry sequencing and quantification (QL Bio, Beijing). Data visualization of protein abundance was performed by a heat map (http://mev.tm4.org/). (C) Proteins from result (B) were subjected to a Gene Ontology (GO) biological pathway (BP) analysis using the online Metascape software (http://metascape.org/). Fig. S3. Multiple fluorescence immunohistochemistry analyses of RPSA and VIM proteins from brain tissue of piglets. Changes in RPSA and VIM expression levels in piglet brain tissues before and after SS2 infection. The brain tissue is labeled with the indicated antibodies (scale bar = 3 mm). Quantitative analysis of images by using HALO software. Fig. S4. SS2 infection or ENO stimulation can damage host cell mitochondria. (A) After SS2 infection of HCMCE/D3 cells for the indicated times, VIM and mitochondria were observed by immunofluorescence using the antibodies against VIM and UQCRC1. (B) Representative confocal images are shown (scale bar = 40 μm) and magnified (scale bar = 20 μm). HCMEC/D3 cells were stimulated for the indicated times using the indicated final concentration of ENO protein. Mitochondrial activity was detected and analyzed by immunofluorescence. Representative images are shown (scale bar = 50 μm). (C and D) The indicated serum and SS2 were mixed and added together to the HCMEC/D3 cells. After 2 h, mitochondria potential (C) or reactive oxygen species level (D) was then detected. Data represent the mean ± SD (n = 4 biologically independent samples). NS for not significant, * for P < 0.05, *** for P < 0.001; one-way ANOVA with Tukey’s test. Fig. S5. Ca2+ promotes ENO to induce apoptosis. (A) Cells were stimulated for the indicated time using a final concentration of 30 μg/mL of ENO protein. Flow cytometry analysis of the apoptosis level of cells. In the specified circumstances, the ratio of dead cells to total cells was examined by FlowJo. (B) The HCMEC/D3 cells were given a final concentration of 200 μM Ca2+, 30 μg/mL ENO, or a combination of the two. After 12 h, flow cytometry was used to analyze the death level of cells. The ratio of dead to total cells in the indicated conditions was quantitatively analyzed by FlowJo as mean ± SD (n ≥ 2 biologically independent samples). NS for not significant, ** for P < 0.01; one-way ANOVA with Tukey’s tes