Maximizing target protein ablation by integration of RNAi and protein knockout.
Ontology highlight
ABSTRACT:
SUBMITTER: Hannah J
PROVIDER: S-EPMC3129447 | biostudies-literature | 2011 Jul
REPOSITORIES: biostudies-literature
Publications
Maximizing target protein ablation by integration of RNAi and protein knockout.
Hannah Jeffrey J Zhou Pengbo P
Cell research 20110524 7
Similar Datasets
Project description:PurposeTo compare an algorithm of gradually ramped-up power to a full-power-level technique to determine which technical parameters maximized tissue coagulation by using a saline-perfused electrode.Materials and methodsInstitutional review board approval was not necessary and animal committee approval was unnecessary because an ex vivo bovine liver model was used and the animals were not specifically killed for this study. This four-part experiment utilized multiple ablations of ex vivo bovine liver with a standard radiofrequency (RF) generator and an internally cooled needle. First, 10 RF ablations were performed at 20-60 W for 12 minutes. Second, ablation volumes obtained from an algorithm of eight ablations performed at 50 W were compared with those obtained from an algorithm of eight ablations that were gradually ramped-up to 50 W, until full impedance. Third, volumes obtained from 10 ablations performed at impedance control power levels were compared with those obtained from 10 ablations performed with a gradual ramp-up of power that started at 50 W, terminating at full impedance. Last, the third part was repeated, but with 11 ablations continuing past full impedance for 12 minutes each.ResultsIn the first part, maximum measurements of tissue coagulation seemed to plateau from 40 to 60 W. The second part produced significantly larger measurements of tissue coagulation than did the use of a constant power level of 50 W. The third and final parts produced larger measurements of tissue coagulation than did utilizing full power for 12 minutes. Larger measurements and volumes were obtained from repeat ablations after the generator reached impedance level than were obtained from ablations stopped at maximum impedance.ConclusionA gradual ramp-up of power and repeating ablations after power impedance level is reached are the two methods that increased tissue ablation in this ex vivo experiment.
| S-EPMC2909435 | biostudies-literature
Project description:Nucleic acid-directed self-assembly provides an attractive method to fabricate prerequisite nanoscale structures for a wide range of technological applications due to the remarkable programmability of DNA/RNA molecules. In this study, exquisite RNAi-AuNP nanoconstructs with various geometries were developed by utilizing anti-VEGF siRNA molecules as RNAi-based therapeutics in addition to their role as building blocks for programmed self-assembly. In particular, the anti-VEGF siRNA-functionalized AuNP nanoconstructs can take additional advantage of gold-nanoclusters for photothermal cancer therapeutic agent. A noticeable technical aspect of self-assembled RNAi-AuNP nanoconstructs in this study is the precise conjugation and separation of designated numbers of therapeutic siRNA onto AuNP to develop highly sophisticated RNA-based building blocks capable of creating various geometries of RNAi-AuNP nano-assemblies. The therapeutic potential of RNAi-AuNP nanoconstructs was validated in vivo as well as in vitro by combining heat generation capability of AuNP and anti-angiogenesis mechanism of siRNA. This strategy of combining anti-VEGF mechanism for depleting angiogenesis process at initial tumor progression and complete ablation of residual tumors with photothermal activity of AuNP at later tumor stage showed effective tumor growth inhibition and tumor ablation with PC-3 tumor bearing mice.
| S-EPMC5196881 | biostudies-literature
Project description:RNA interference (RNAi) technology is considered as an alternative for control of pests. However, RNAi has not been used in field conditions yet, since delivering exogenous ds/siRNA to target pests is very difficult. The laboratory methods of introducing the ds/siRNA into insects through feeding, micro feeding / dripping and injecting cannot be used in fields. Transgenic crop is perhaps the most effective application of RNAi for pest control, but it needs long-time basic researches in order to reduce the cost and evaluate the safety. Therefore, transgenic microbe is maybe a better choice. Entomopathogenic fungi generally invade the host insects through cuticle like chemical insecticides contact insect to control sucking sap pests. Isaria fumosorosea is a common fungal entomopathogen in whitefly, Bemisia tabaci. We constructed a recombinant strain of I. fumosorosea expressing specific dsRNA of whitefly's TLR7 gene. It could silence the TLR7 gene and improve the virulence against whitefly. Transgenic fungal entomopathogen has shown great potential to attain the application of RNAi technology for pests control in fields. In the future, the research interests should be focused on the selection of susceptible target pests and their vital genes, and optimizing the methods for screening genes and recombinants as well.
| S-EPMC5153893 | biostudies-other
Project description:The rapid advancement in targeted genome editing using engineered nucleases such as ZFNs, TALENs, and CRISPR/Cas9 systems has resulted in a suite of powerful methods that allows researchers to target any genomic locus of interest. A complementary set of design tools has been developed to aid researchers with nuclease design, target site selection, and experimental validation. Here, we review the various tools available for target selection in designing engineered nucleases, and for quantifying nuclease activity and specificity, including web-based search tools and experimental methods. We also elucidate challenges in target selection, especially in predicting off-target effects, and discuss future directions in precision genome editing and its applications.
| S-EPMC4786925 | biostudies-other
Project description:Loss-of-function genetic tools are widely applied for validating therapeutic targets, but their utility remains limited by incomplete on- and uncontrolled off-target effects. We describe artificial RNA interference (ARTi) based on synthetic, ultra-potent, off-target-free shRNAs that enable efficient and inducible suppression of any gene upon introduction of a synthetic target sequence into non-coding transcript regions. ARTi establishes a scalable loss-of-function tool with full control over on- and off-target effects.
| S-EPMC10409502 | biostudies-literature
Project description:Rumen microbes produce cellular protein inefficiently partly because they do not direct all ATP toward growth. They direct some ATP toward maintenance functions, as long-recognized, but they also direct ATP toward reserve carbohydrate synthesis and energy spilling (futile cycles that dissipate heat). Rumen microbes expend ATP by vacillating between (1) accumulation of reserve carbohydrate after feeding (during carbohydrate excess) and (2) mobilization of that carbohydrate thereafter (during carbohydrate limitation). Protozoa account for most accumulation of reserve carbohydrate, and in competition experiments, protozoa accumulated nearly 35-fold more reserve carbohydrate than bacteria. Some pure cultures of bacteria spill energy, but only recently have mixed rumen communities been recognized as capable of the same. When these communities were dosed glucose in vitro, energy spilling could account for nearly 40% of heat production. We suspect that cycling of glycogen (a major reserve carbohydrate) is a major mechanism of spilling; such cycling has already been observed in single-species cultures of protozoa and bacteria. Interconversions of short-chain fatty acids (SCFA) may also expend ATP and depress efficiency of microbial protein production. These interconversions may involve extensive cycling of intermediates, such as cycling of acetate during butyrate production in certain butyrivibrios. We speculate this cycling may expend ATP directly or indirectly. By further quantifying the impact of reserve carbohydrate accumulation, energy spilling, and SCFA interconversions on growth efficiency, we can improve prediction of microbial protein production and guide efforts to improve efficiency of microbial protein production in the rumen.
| S-EPMC4432691 | biostudies-literature
Project description:RNA interference (RNAi) mediated by small interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs) has become a powerful tool for gene knockdown studies. However, the levels of knockdown vary greatly. Here, we examine the effect of target disruption energy, a novel measure of target accessibility, along with other parameters that may affect RNAi efficiency. Based on target secondary structures predicted by the Sfold program, the target disruption energy represents the free energy cost for local alteration of the target structure to allow target binding by the siRNA guide strand. In analyses of 100 siRNAs and 101 shRNAs targeted to 103 endogenous human genes, we find that the disruption energy is an important determinant of RNAi activity and the asymmetry of siRNA duplex asymmetry is important for facilitating the assembly of the RNA-induced silencing complex (RISC). We estimate that target accessibility and duplex asymmetry can improve the target knockdown level significantly by nearly 40% and 26%, respectively. In the RNAi pathway, RISC assembly precedes target binding by the siRNA guide strand. Thus, our findings suggest that duplex asymmetry has significant upstream effect on RISC assembly and target accessibility has strong downstream effect on target recognition. The results of the analyses suggest criteria for improving the design of siRNAs and shRNAs.
| S-EPMC1986803 | biostudies-literature
Project description:MotivationMolecular subtyping by integrative modeling of multi-omics and clinical data can help the identification of robust and clinically actionable disease subgroups; an essential step in developing precision medicine approaches.ResultsWe developed a novel outcome-guided molecular subgrouping framework, called Deep Multi-Omics Integrative Subtyping by Maximizing Correlation (DeepMOIS-MC), for integrative learning from multi-omics data by maximizing correlation between all input -omics views. DeepMOIS-MC consists of two parts: clustering and classification. In the clustering part, the preprocessed high-dimensional multi-omics views are input into two-layer fully connected neural networks. The outputs of individual networks are subjected to Generalized Canonical Correlation Analysis loss to learn the shared representation. Next, the learned representation is filtered by a regression model to select features that are related to a covariate clinical variable, for example, a survival/outcome. The filtered features are used for clustering to determine the optimal cluster assignments. In the classification stage, the original feature matrix of one of the -omics view is scaled and discretized based on equal frequency binning, and then subjected to feature selection using RandomForest. Using these selected features, classification models (for example, XGBoost model) are built to predict the molecular subgroups that were identified at clustering stage. We applied DeepMOIS-MC on lung and liver cancers, using TCGA datasets. In comparative analysis, we found that DeepMOIS-MC outperformed traditional approaches in patient stratification. Finally, we validated the robustness and generalizability of the classification models on independent datasets. We anticipate that the DeepMOIS-MC can be adopted to many multi-omics integrative analyses tasks.Availability and implementationSource codes for PyTorch implementation of DGCCA and other DeepMOIS-MC modules are available at GitHub (https://github.com/duttaprat/DeepMOIS-MC).Supplementary informationSupplementary data are available at Bioinformatics Advances online.
| S-EPMC10328436 | biostudies-literature
Project description:The discovery of RNA interference (RNAi) gave rise to the development of new nucleic acid-based technologies as powerful investigational tools and potential therapeutics. Mechanistic key details of RNAi in humans need to be deciphered yet, before such approaches take root in biomedicine and molecular therapy. We developed and validated an in silico-based model of siRNA-mediated RNAi in human cells in order to link in vitro-derived pre-steady state kinetic data with a quantitative and time-resolved understanding of RNAi on the cellular level. The observation that product release by Argonaute 2 is accelerated in the presence of an excess of target RNA in vitro inspired us to suggest an associative mechanism for the RNA slicer reaction where incoming target mRNAs actively promote dissociation of cleaved mRNA fragments. This novel associative model is compatible with high multiple turnover rates of RNAi-based gene silencing in living cells and accounts for target mRNA concentration-dependent enhancement of the RNAi machinery.
| S-EPMC4678823 | biostudies-other
Project description:Synthetic in vivo molecular 'computers' could rewire biological processes by establishing programmable, non-native pathways between molecular signals and biological responses. Multiple molecular computer prototypes have been shown to work in simple buffered solutions. Many of those prototypes were made of DNA strands and performed computations using cycles of annealing-digestion or strand displacement. We have previously introduced RNA interference (RNAi)-based computing as a way of implementing complex molecular logic in vivo. Because it also relies on nucleic acids for its operation, RNAi computing could benefit from the tools developed for DNA systems. However, these tools must be harnessed to produce bioactive components and be adapted for harsh operating environments that reflect in vivo conditions. In a step toward this goal, we report the construction and implementation of biosensors that 'transduce' mRNA levels into bioactive, small interfering RNA molecules via RNA strand exchange in a cell-free Drosophila embryo lysate, a step beyond simple buffered environments. We further integrate the sensors with our RNAi 'computational' module to evaluate two-input logic functions on mRNA concentrations. Our results show how RNA strand exchange can expand the utility of RNAi computing and point toward the possibility of using strand exchange in a native biological setting.
| S-EPMC2860122 | biostudies-literature