Investigation of premature termination codon recognition in nonsense-mediated mRNA decay

Nonsense-mediated mRNA decay (NMD) is best known for its role in quality control of mRNAs, where it recognizes premature translation termination codons (PTCs) and rapidly degrades the corresponding mRNA. The basic mechanism of NMD appears to be conserved among eukaryotes: aberrant translation termination triggers NMD. According to the current working model, correct termination requires the interaction of the ribosome with the poly(A)-binding protein (PABPC1) mediated through the eukaryotic release factors 1 (eRF1) and 3 (eRF3). The model predicts that in the absence of this interaction, the NMD core factor UPF1 binds to eRF3 instead and initiates the events ultimately leading to mRNA degradation. However, the exact mechanism of how the decision between proper and aberrant (i.e. NMD-inducing) translation termination occurs is not yet well understood. We address this question using a tethering approach in which proteins of interest are bound to a reporter transcript into the vicinity of a PTC. Subsequently, the ability of the tethered proteins to inhibit NMD and thus stabilize the reporter transcript is assessed. Our results revealed that the C-terminal domain interacting with eRF3 seems not to be necessary for tethered PABPC1 to suppress NMD. In contrast, the N-terminal part of PABPC1, consisting of 4 RNA recognition motifs (RRMs) and interacting with eukaryotic initiation factor 4G (eIF4G), retains the ability to inhibit NMD. We find that eIF4G is able to inhibit NMD in a similar manner as PABPC1 when tethered to the reporter mRNA. This stabilization by eIF4G depends on two key interactions. One of these interactions is to PABPC1, the other is to eukaryotic initiation factor 3 (eIF3). These results confirm the importance of PABPC1 in inhibiting NMD but additionally reveal a role of translation initiation factors in the distinction between bona fide termination codons and PTCs.

New nucleoside analogues for the recognition of pyrimidine-purine inversion sites

A new nucleoside designed to enhance triplex stability has been synthesised in 15 steps starting from sugar 2. This pathway contains the sugar derivative 9 which is a useful intermediate for the introduction of other natural and unnatural bases into the 2'-aminoethoxy nucleoside containing scaffold

DNA with Hydrophobic Base Substitutes: A Stable, Zipperlike Recognition Motif Based On Interstrand-Stacking Interactions

No Abstract

Evidence for C−H⋅⋅⋅O Hydrogen Bond Assisted Recognition of a Pyrimidine Base in the Parallel DNA Triple-Helical Motif

The efficient recognition of the pyrimidine base uracil by hypoxanthine or thymine in the parallel DNA triplex motif is based on the interplay of a conventional N−H⋅⋅⋅O and an unconventional C−H⋅⋅⋅O hydrogen bond.

Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers

Activities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data. METHODS: A wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL. RESULTS: Out of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB. CONCLUSIONS: The performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.