Cardenolide glycosides sensitize gefitinib-induced apoptosis in non-small cell lung cancer: inhibition of Na+/K+-ATPase serving as a switch-on mechanism.

The treatment of non-small cell lung cancer (NSCLC) is known as a significant level of unmet medical need in spite of the progress in targeted therapy and personalized therapy. Overexpression of the Na+/K+-ATPase contributes to NSCLC progression, suggesting its potentiality in antineoplastic approaches. Epi-reevesioside F, purified from Reevesia formosana, showed potent anti-NSCLC activity through inhibiting the Na+/K+-ATPase, leading to internalization of α1- and α3-subunits in Na+/K+-ATPase and suppression of Akt-independent mTOR-p70S6K-4EBP1 axis. Epi-reevesioside F caused a synergistic amplification of apoptosis induced by gefitinib but not cisplatin, docetaxel, etoposide, paclitaxel, or vinorelbine in both NCI-H460 and A549 cells. The synergism was validated by enhanced activation of the caspase cascade. Bax cleavage, tBid formation, and downregulation of Bcl-xL and Bcl-2 contributed to the synergistic apoptosis induced by the combination treatment of epi-reevesioside F and gefitinib. The increase of membrane DR4 and DR5 levels, intracellular Ca2+ concentrations, and active m-calpain expression were responsible for the caspase-8 activation and Bax cleavage. The increased α-tubulin acetylation and activation of MAPK (i.e., p38 MAPK, Erk, and JNK) depending on cell types contributed to the synergistic mechanism under combination treatment. These signaling pathways that converged on profound c-Myc downregulation led to synergistic apoptosis in NSCLC. In conclusion, the data suggest that epi-reevesioside F inhibits the Na+/K+-ATPase and displays potent anti-NSCLC activity. Epi-reevesioside F sensitizes gefitinib-induced apoptosis through multiple pathways that converge on c-Myc downregulation. The data support the inhibition of Na+/K+-ATPase as a switch-on mechanism to sensitize gefitinib-induced anti-NSCLC activity.

Chalcones Display Anti-NLRP3 Inflammasome Activity in Macrophages through Inhibition of Both Priming and Activation Steps-Structure-Activity-Relationship and Mechanism Studies.

Chalcones are responsible for biological activity throughout fruits, vegetables, and medicinal plants in preventing and treating a variety of inflammation-related diseases. However, their structure-activity relationship (SAR) in inhibiting inflammasome activation has not been explored. We synthesized numerous chalcones and determined their SAR on lipopolysaccharide (LPS)-primed ATP-induced NLRP3 inflammasome activation. 11Cha1 displayed good inhibitory activity on release reaction of caspase-1, IL-1ß, and IL-18. It significantly inhibited LPS-induced phosphorylation and proteolytic degradation of IĸB-α and nuclear translocation of NF-ĸB, but had little effect on mitogen-activated protein kinases (MAPKs) activities. Furthermore, 11Cha1 blocked LPS-induced up-regulation of NLRP3, pro-caspase-1, ASC, IL-18, and IL-1ß, indicating the suppression on priming step of inflammasome activation. ASC dimerization and oligomerization are considered to be direct evidence for inflammasome activation. 11Cha1 profoundly inhibited ATP-induced formation of ASC dimers, trimers, and oligomers, and the assembly of ASC, pro-caspase-1, and NLRP3 in inflammasome formation. Decrease of intracellular K+ levels is the common cellular activity elicited by all NLRP3 inflammasome activators. 11Cha1 substantially diminished ATP-mediated K+ efflux, confirming the anti-NLRP3 inflammasome activity of 11Cha1. In summary, the SAR of chalcone derivatives in anti-inflammasome activities was examined. Besides, 11Cha1 inhibited both priming and activation steps of NLRP3 inflammasome activation. It inhibited NF-ĸB activation and subsequently suppressed the up-regulation of NLRP3 inflammasome components including NLRP3, ASC, pro-caspase-1, pro-IL-18, and pro-IL-1ß. Next, 11Cha1 blocked ATP-mediated K+ efflux and suppressed the assembly and activation of NLRP3 inflammasome, leading to the inhibition of caspase-1 activation and proteolytic cleavage, maturation, and secretion of IL-1ß and IL-18.

Discovery of Novel Agents on Spindle Assembly Checkpoint to Sensitize Vinorelbine-Induced Mitotic Cell Death Against Human Non-Small Cell Lung Cancers.

Non-small cell lung cancer (NSCLC) accounts about 80% of all lung cancers. More than two-thirds of NSCLC patients have inoperable, locally advanced or metastatic tumors. Non-toxic agents that synergistically potentiate cancer-killing activities of chemotherapeutic drugs are in high demand. YL-9 was a novel and non-cytotoxic compound with the structure related to sildenafil but showing much less activity against phosphodiesterase type 5 (PDE5). NCI-H460, an NSCLC cell line with low PDE5 expression, was used as the cell model. YL-9 synergistically potentiated vinorelbine-induced anti-proliferative and apoptotic effects in NCI-H460 cells. Vinorelbine induced tubulin acetylation and Bub1-related kinase (BUBR1) phosphorylation, a necessary component in spindle assembly checkpoint. These effects, as well as BUBR1 cleavage, were substantially enhanced in co-treatment with YL-9. Several mitotic arrest signals were enhanced under combinatory treatment of vinorelbine and YL-9, including an increase of mitotic spindle abnormalities, increased cyclin B1 expression, B-cell lymphoma 2 (Bcl-2) phosphorylation and increased phosphoproteins. Moreover, YL-9 also displayed synergistic activity in combining with vinorelbine to induce apoptosis in A549 cells which express PDE5. In conclusion. the data suggest that YL-9 is a novel agent that synergistically amplifies vinorelbine-induced NSCLC apoptosis through activation of spindle assembly checkpoint and increased mitotic arrest of the cell cycle. YL-9 shows the potential for further development in combinatory treatment against NSCLC.

Random sampling process leads to overestimation of ß-diversity of microbial communities.

The site-to-site variability in species composition, known as ß-diversity, is crucial to understanding spatiotemporal patterns of species diversity and the mechanisms controlling community composition and structure. However, quantifying ß-diversity in microbial ecology using sequencing-based technologies is a great challenge because of a high number of sequencing errors, bias, and poor reproducibility and quantification. Herein, based on general sampling theory, a mathematical framework is first developed for simulating the effects of random sampling processes on quantifying ß-diversity when the community size is known or unknown. Also, using an analogous ball example under Poisson sampling with limited sampling efforts, the developed mathematical framework can exactly predict the low reproducibility among technically replicate samples from the same community of a certain species abundance distribution, which provides explicit evidences of random sampling processes as the main factor causing high percentages of technical variations. In addition, the predicted values under Poisson random sampling were highly consistent with the observed low percentages of operational taxonomic unit (OTU) overlap (<30% and <20% for two and three tags, respectively, based on both Jaccard and Bray-Curtis dissimilarity indexes), further supporting the hypothesis that the poor reproducibility among technical replicates is due to the artifacts associated with random sampling processes. Finally, a mathematical framework was developed for predicting sampling efforts to achieve a desired overlap among replicate samples. Our modeling simulations predict that several orders of magnitude more sequencing efforts are needed to achieve desired high technical reproducibility. These results suggest that great caution needs to be taken in quantifying and interpreting ß-diversity for microbial community analysis using next-generation sequencing technologies. IMPORTANCE Due to the vast diversity and uncultivated status of the majority of microorganisms, microbial detection, characterization, and quantitation are of great challenge. Although large-scale metagenome sequencing technology such as PCR-based amplicon sequencing has revolutionized the studies of microbial communities, it suffers from several inherent drawbacks, such as a high number of sequencing errors, biases, poor quantitation, and very high percentages of technical variations, which could greatly overestimate microbial biodiversity. Based on general sampling theory, this study provided the first explicit evidence to demonstrate the importance of random sampling processes in estimating microbial ß-diversity, which has not been adequately recognized and addressed in microbial ecology. Since most ecological studies are involved in random sampling, the conclusions learned from this study should also be applicable to other ecological studies in general. In summary, the results presented in this study should have important implications for examining microbial biodiversity to address both basic theoretical and applied management questions.

Stochastic assembly leads to alternative communities with distinct functions in a bioreactor microbial community.

ABSTRACT The processes and mechanisms of community assembly and its relationships to community functioning are central issues in ecology. Both deterministic and stochastic factors play important roles in shaping community composition and structure, but the connection between community assembly and ecosystem functioning remains elusive, especially in microbial communities. Here, we used microbial electrolysis cell reactors as a model system to examine the roles of stochastic assembly in determining microbial community structure and functions. Under identical environmental conditions with the same source community, ecological drift (i.e., initial stochastic colonization) and subsequent biotic interactions created dramatically different communities with little overlap among 14 identical reactors, indicating that stochastic assembly played dominant roles in determining microbial community structure. Neutral community modeling analysis revealed that deterministic factors also played significant roles in shaping microbial community structure in these reactors. Most importantly, the newly formed communities differed substantially in community functions (e.g., H2 production), which showed strong linkages to community structure. This study is the first to demonstrate that stochastic assembly plays a dominant role in determining not only community structure but also ecosystem functions. Elucidating the links among community assembly, biodiversity, and ecosystem functioning is critical to understanding ecosystem functioning, biodiversity preservation, and ecosystem management. IMPORTANCE Microorganisms are the most diverse group of life known on earth. Although it is well documented that microbial natural biodiversity is extremely high, it is not clear why such high diversity is generated and maintained. Numerous studies have established the roles of niche-based deterministic factors (e.g., pH, temperature, and salt) in shaping microbial biodiversity, the importance of stochastic processes in generating microbial biodiversity is rarely appreciated. Moreover, while microorganisms mediate many ecosystem processes, the relationship between microbial diversity and ecosystem functioning remains largely elusive. Using a well-controlled laboratory system, this study provides empirical support for the dominant role of stochastic assembly in creating variations of microbial diversity and the first explicit evidence for the critical role of community assembly in influencing ecosystem functioning. The results presented in this study represent important contributions to the understanding of the mechanisms, especially stochastic processes, involved in shaping microbial biodiversity.

Molecular ecological network analyses.

BACKGROUND: Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data. RESULTS: Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA). CONCLUSIONS: The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

Reproducibility and quantitation of amplicon sequencing-based detection.

To determine the reproducibility and quantitation of the amplicon sequencing-based detection approach for analyzing microbial community structure, a total of 24 microbial communities from a long-term global change experimental site were examined. Genomic DNA obtained from each community was used to amplify 16S rRNA genes with two or three barcode tags as technical replicates in the presence of a small quantity (0.1% wt/wt) of genomic DNA from Shewanella oneidensis MR-1 as the control. The technical reproducibility of the amplicon sequencing-based detection approach is quite low, with an average operational taxonomic unit (OTU) overlap of 17.2%±2.3% between two technical replicates, and 8.2%±2.3% among three technical replicates, which is most likely due to problems associated with random sampling processes. Such variations in technical replicates could have substantial effects on estimating ß-diversity but less on α-diversity. A high variation was also observed in the control across different samples (for example, 66.7-fold for the forward primer), suggesting that the amplicon sequencing-based detection approach could not be quantitative. In addition, various strategies were examined to improve the comparability of amplicon sequencing data, such as increasing biological replicates, and removing singleton sequences and less-representative OTUs across biological replicates. Finally, as expected, various statistical analyses with preprocessed experimental data revealed clear differences in the composition and structure of microbial communities between warming and non-warming, or between clipping and non-clipping. Taken together, these results suggest that amplicon sequencing-based detection is useful in analyzing microbial community structure even though it is not reproducible and quantitative. However, great caution should be taken in experimental design and data interpretation when the amplicon sequencing-based detection approach is used for quantitative analysis of the ß-diversity of microbial communities.

Patterns and causes of species richness: a general simulation model for macroecology.

Understanding the causes of spatial variation in species richness is a major research focus of biogeography and macroecology. Gridded environmental data and species richness maps have been used in increasingly sophisticated curve-fitting analyses, but these methods have not brought us much closer to a mechanistic understanding of the patterns. During the past two decades, macroecologists have successfully addressed technical problems posed by spatial autocorrelation, intercorrelation of predictor variables and non-linearity. However, curve-fitting approaches are problematic because most theoretical models in macroecology do not make quantitative predictions, and they do not incorporate interactions among multiple forces. As an alternative, we propose a mechanistic modelling approach. We describe computer simulation models of the stochastic origin, spread, and extinction of species' geographical ranges in an environmentally heterogeneous, gridded domain and describe progress to date regarding their implementation. The output from such a general simulation model (GSM) would, at a minimum, consist of the simulated distribution of species ranges on a map, yielding the predicted number of species in each grid cell of the domain. In contrast to curve-fitting analysis, simulation modelling explicitly incorporates the processes believed to be affecting the geographical ranges of species and generates a number of quantitative predictions that can be compared to empirical patterns. We describe three of the 'control knobs' for a GSM that specify simple rules for dispersal, evolutionary origins and environmental gradients. Binary combinations of different knob settings correspond to eight distinct simulation models, five of which are already represented in the literature of macroecology. The output from such a GSM will include the predicted species richness per grid cell, the range size frequency distribution, the simulated phylogeny and simulated geographical ranges of the component species, all of which can be compared to empirical patterns. Challenges to the development of the GSM include the measurement of goodness of fit (GOF) between observed data and model predictions, as well as the estimation, optimization and interpretation of the model parameters. The simulation approach offers new insights into the origin and maintenance of species richness patterns, and may provide a common framework for investigating the effects of contemporary climate, evolutionary history and geometric constraints on global biodiversity gradients. With further development, the GSM has the potential to provide a conceptual bridge between macroecology and historical biogeography.

Resilience of tropical rain forests: tree community reassembly in secondary forests.

Understanding the recovery dynamics of ecosystems presents a major challenge in the human-impacted tropics. We tested whether secondary forests follow equilibrium or non-equilibrium dynamics by evaluating community reassembly over time, across different successional stages, and among multiple life stages. Based on long-term and static data from six 1-ha plots in NE Costa Rica, we show that secondary forests are undergoing reassembly of canopy tree and palm species composition through the successful recruitment of seedlings, saplings, and young trees of mature forest species. Such patterns were observed over time within sites and across successional stages. Floristic reassembly in secondary forests showed a clear convergence with mature forest community composition, supporting an equilibrium model. This resilience stems from three key factors co-occurring locally: high abundance of generalist species in the regional flora, high levels of seed dispersal, and local presence of old-growth forest remnants.

Use of statistical methods to find the polysaccharide structural characteristics and the relationships between monosaccharide composition ratio and macrophage stimulatory activity of regionally different strains of Lentinula edodes.

Multiple linear regression analysis was used to deduce the correlation between the monosaccharide composition ratios of 10 regionally different strains of Lentinula edodes and their in vitro macrophage stimulatory activities. Arabinose, xylose, mannose and galactose were identified as the monosaccharides that could be related to macrophage stimulatory activities. Additional principal component analysis and factor analysis methods were used to treat the same monosaccharide composition ratio data and the compositions of arabinose, xylose, mannose and galactose were found to be important. Interestingly, glucose, although presented in large compositions in all strains presumably forms the backbone of the polysaccharide structures, is not selected as the determinant factor for either structural characteristics or that of the in vitro macrophage stimulatory activities.