Additional analyses were further conducted. First, we repeated the main analyses by excluding mortality cases that occurred in the first 3 years of follow-up. Second, we conducted the main analyses using available data before multiple imputations for missing covariates. Third, because severe diseases could confound results, we defined a population that excluded participants with prevalent CVD and cancer at baseline. Fourth, smoking may have potential effect modification because there may be unmeasured confounders between previous and current smokers. Therefore, we repeated the main analyses adjusted for pack-years categories of cigarette smoking (nonsmokers: having smoked zero pack-years; light smokers: fewer than 20 pack-years; and heavy smokers: 20 or more pack-years). Pack-years of cigarette smoking were calculated as the number of cigarettes smoked per day divided by 20 and then multiplied by the number of years of smoking [26]. Fifth, the information on both coffee and tea consumption and potential confounders were collected at the baseline; it is very difficult to assess the role of depression in these associations. We performed main analyses without adjusting for baseline depression. Furthermore, we also investigated coffee and tea consumption interaction on mortality in the above sensitivity analyses. All analyses were performed using R (version 3.6.3, R Foundation for Statistical Computing) and STATA 15 statistical software (StataCorp). The two-sided P
The strengths of this study included the population-based study design with a 15-year follow-up. To the best of our knowledge, the present study is the first to assess the combined associations of coffee and tea consumption with mortality in the general population. More importantly, instead of assuming linearity between them, we explored the dose-response relationship using restricted cubic splines. We also examined the joint effect and interaction between coffee and tea consumption, fully adjusting for confounding factors. Despite the strengths of the present study, several potential limitations should be mentioned. First, data on coffee and tea consumption were collected through self-reported questionnaires, and potential response bias cannot be ruled out. Also, certain baseline information is likely to vary with time during a rather long follow-up period. Second, our conclusions may be affected by reverse causation, although results were similar after excluding the first 3 years of follow-up. Third, as with any observational study design, residual confounding is possible, especially for smoking. Although we have adjusted for pack-years categories of cigarette smoking, we could not avoid the possibility of residual effects due to unmeasured confounders between former and current smokers. Fourth, due to the large sample size in our study, the significance we found such as the interaction between coffee and tea consumption may be attained by chance, although similar results were obtained in multiple sensitivity analyses. Finally, the majority of participants are of European descent in the UK biobank, and they are more health-conscious than the general population [50]. Caution is therefore needed when generalizing our findings to other ethnicities.
K93 Na1 Full Version
Y.W. conceptualized and designed the study. Y.W. and H.Y. had full access to all data. Y.C. and Y.Z. performed the data analysis. Y.C. and Y.Z. drafted the manuscript. M.Z. contributed to analysis and interpretation of data. H.Y. contributed to revision of the manuscript and approved the final draft. All authors contributed to the intellectual content, critical revisions to the drafts of the paper and approved the final version.
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YW and CY performed the analyses and wrote the manuscript. CX, XZ, MZ, YL conducted the study, data analysis, reviewed and edited the manuscript. PZ and PY researched the data, conceived the research, provided overall supervision, and reviewed and edited the manuscript. PZ and PY are the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript.
We next tested whether Gn treatment will induce apoptosis and/or autophagy in cells. We examined PARP cleavage and Caspase-3 activation in two colon cancer cell lines. As shown in Fig. 3a-b, at 10 μM, Gn led to increased PARP cleavage (Fig. 3a), as well as augmented Caspase-3 activation (Fig. 3b); while MP-Gr or DMSO did not show any effect, consistent with our early report [27]. These data indicate that Gn induces apoptosis in colon cancer cell lines. Using a live cell image system, we showed that 20 μM Gn induced autophagy in DLD-1 cells and led to cell death via apoptosis (Additional file 2: Video 1 and Additional file 3: Video 2). One representative view from the video in each treatment was presented in Fig. 3c. The cell treated with 20 μM Gn started to accumulate autophagosome after 14 h, and died via apoptotic cell death (Fig. 3c left panel). In contrast, cells with DMSO control proliferated and covered the whole view at the end of the time lapse (72 h) (Fig. 3c right panel). Additionally, autophagy induction was shown by the LC3 conversion and P62 degradation [72] in Gn treated samples (Fig. 3d). When we pretreated the cells with chloroquine (CQ), an autophagy inhibitor that blocks the fusion of autophagosome with lysosome and lysosomal protein degradation [73], p62 degradation was blocked (Fig. 3d). These data indicate that Gn induces efficient autophagic flux, and leads to apoptotic cell death.
LL, HL, and LX contributed to conception and design of the study. LL, HL, AS, CA, JY, SL, RM and PG acquired and analyzed the data. LL, HL, AS, RM, KL, RG, JK, RND, JA, KN and LX interpreted data. LL and HL drafted the manuscript; LL, HL, AS, JK, RND, JA, KN and LX revised it critically for important intellectual content. All authors gave final approval of the version to be published. LL and HL contributed equally to this work.
In several studies focused on plant-microbe interactions, cell suspension cultures have been utilized instead of whole plants. Cell suspensions have also been successfully used as a model system to study signalling pathways or the effect of exogenous compounds on plant cells. A very popular stable plant cell line maintained in suspension is the tobacco cell line BY-2 [14] that has been used to study plant responses to biotic stress. For example, upon inoculation with zoospores of different P. nicotianae strains it was found that reactive oxygen species (ROS) were produced by the BY-2 cells in an incompatible interaction with a non-pathogenic strain [15, 16]. BY-2 cells were also used to study the potential of a non-pathogenic Streptomyces sp. as biocontrol agent against Pectobacterium spp. [17]. Another example is the use of Arabidopsis cell suspension cultures for transcriptomic analysis with a focus on phosphoinositide-dependent phospholipase C (PI-PLC) regulated gene expression [18]. Tomato cell suspension cultures have been used to study responses to abiotic stress factors. Exposure to low oxygen activated fermentative metabolism and sugar alcohol synthesis while inhibiting the activity of the tricarboxylic acid (TCA) cycle and the biosynthesis of metabolites such as organic acids, amino acids and sugars [19]. Aimé et al. [20] used cell suspensions from the tomato cultivar Montfavet to study expression of genes encoding Pathogenesis-Related (PR) proteins upon inoculation with a pathogenic strain of the soil-borne fungal pathogen Fusarium oxysporum f. sp. lycopersici and a biocontrol strain of F. oxysporum and found a lower PR gene expression in the presence of the biocontrol strain compared to the pathogenic strain. A similar pattern was found in intact tomato plants pointing towards priming as mode of action of the biocontrol strain. Rice cell suspensions have been used to study responses upon treatment with an elicitor from the rice blast fungus Magnaporthe oryzae and this revealed that the elicitor causes metabolic alterations in the rice cells [21].
MsK8 cells are successfully infected by Phytophthora infestans. a Timeline of MsK8 cells infected with P. infestans strain 14-3-GFP. At time point 0 zoospores were added to MsK8 cells. Bars represent 100 μm. b, c Microscopic images (left panels: epifluorescent, right panels: bright field). b Primary infection and penetration of a MsK8 cell at 6 hpi. c Fully developed haustorium in an infected cell and relocation of the nucleus to the penetration point at 16 hpi. ap appressorium; c cyst; h haustorium; hpi hours post inoculation; n nucleus; p penetration peg. Arrows and arrowheads point to secondary infections and sporangia, respectively. Bars represent 50 μm
The most abundant protein secreted by P. infestans is the elicitin INF1, a small protein of 98 amino acids classified as a MAMP that elicits cell death in several Nicotiana spp. [12]. INF2B is larger but shares the canonical elicitin domain with INF1 [38]. In the wild potato species S. microdontum, elicitin recognition is mediated by the receptor-like protein ELR [39]. To study the response of MsK8 cells to elicitins the cells were exposed to the full length INF1 and the elicitin domain of INF2B proteins. The responsiveness was measured by monitoring medium alkalinization. No pH shift was observed upon treatment with INF1 or INF2B (Additional file 5: Figure S3A). On the other hand, tobacco BY-2 cells showed a response to both INF1 and INF2B (Additional file 5: Figure S3B) demonstrating that the protein preparations used do have elicitor activity. To verify the responsiveness of MsK8 cells to other MAMPs, they were treated with the flagellin peptide flg22 and this resulted in a pH shift (Additional file 5: Figure S3C). These findings confirmed previous studies by Felix et al. [28] who also observed medium alkalinization in MsK8 cells upon exposure to flg22. In conclusion, the MsK8 cells do respond to flg22 but not to elicitins and this resembles the response of intact tomato plants to these MAMPs [12, 28]. 2ff7e9595c
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