Protein factors involved in the biogenesis of the mitochondrial ribosome

The mammalian mitochondria contain their own genome which encodes thirteen polypeptide components of the oxidative phosphorylation (OxPhos) system, and the mitochondrial (mt-) rRNAs and tRNAs required for their translation. The maturation of the mitochondrial ribosome requires both mt-rRNAs to undergo post-transcriptional chemical modifications, folding of the rRNA and assembly of the protein components assisted by numerous biogenesis factors. The post-transcriptional modifications of the mt-rRNAs include base methylations, 2’-O-ribose methylations and pseudouridylation. However, the exact function of these modifications is unknown. Many mitoribosome biogenesis factors still remain to be identified and characterised. This work aims to broaden our understanding of two proteins involved in mitoribosome biogenesis through the study of the function of an rRNA methyltransferase and a novel biogenesis factor. Firstly, we characterised MRM1 (mitochondrial rRNA methyltransferase 1), a highly conserved 2’-O-ribose methyltransferase. We confirmed that MRM1 modifies a guanine in the peptidyl (P) transferase region of the 16S mt-rRNA that specifically interacts with the 3’ end of the tRNA at the ribosomal P-site. In bacteria, the modification is dispensable for ribosomal biogenesis and cell viability under standard conditions. However, in yeast mitochondria, Mrm1p is vital for ribosomal assembly and function. We generated knockout cells lines using programmable nuclease technology, and characterised the possible effects of MRM1 depletion on mitochondrial translation and mitoribosome biogenesis. We demonstrated that neither the enzyme nor the modification is required for human mitoribosomal assembly and translation in our experimental setup. Secondly, we identified a novel mitochondrially-targeted putative RNA endonuclease, YbeY. Using YbeY knockout cell lines, we showed that depletion of YbeY leads to loss of cell viability and OxPhos function as a consequence of a severe decrease in mitochondrial translation. Northern blotting and transcriptomic analysis using next generation RNA-Seq revealed transcript-specific changes to steady state levels. This analysis identified mt-tRNASer as a potential target of YbeY. We investigated the effect of YbeY deficiency on mitoribosomal assembly by quantitative sucrose gradient fractionation and mass spectrometry. This analysis showed that the mt-SSU is depleted in YbeY knockout cells. Further, immunoaffinity purification identified MRPS11 as a key interactor of YbeY. We propose that YbeY is a multifunctional protein that performs endonucleolytic functions in the mitochondria and also acts as a mitochondrial ribosome biogenesis factor, assisting small subunit assembly through its interaction with MRPS11.Medical Research Counci

Mitochondrial Transcription and Translation: Overview

Mitochondria are the major source of ATP in the cell. Five multi-subunit complexes in the inner membrane of the organelle are involved in the oxidative phosphorylation required for ATP production. Thirteen subunits of these complexes are encoded by the mitochondrial genome often referred to as mtDNA. For this reason, the expression of mtDNA is vital for the assembly and functioning of the oxidative phosphorylation complexes. Defects of the mechanisms regulating mtDNA gene expression have been associated with deficiencies in assembly of these complexes, resulting in mitochondrial diseases. Recently, numerous factors involved in these processes have been identified and characterised leading to a deeper understanding of the mechanisms that underlie mitochondrial diseases

The Bayesian Backfitting Relevance Vector Machine

Traditional non-parametric statistical learning techniques are often computationally attractive, but lack the same generalization and model selection abilities as state-of-the-art Bayesian algorithms which, however, are usually computationally prohibitive. This paper makes several important contributions that allow Bayesian learning to scale to more complex, real-world learning scenarios. Firstly, we show that back tting | a traditional non-parametric, yet highly e cient regression tool | can be derived in a novel formulation within an expectation maximization (EM) framework and thus can nally be given a probabilistic interpretation. Secondly, we show that the general framework of sparse Bayesian learning and in particular the relevance vector machine (RVM), can be derived as a highly e cient algorithm using a Bayesian version of back tting at its core. As we demonstrate on several regression and classi cation benchmarks, Bayesian back tting o ers a compelling alternative to current regression methods, especially when the size and dimensionality of the data challenge computational resources

LWPR: A Scalable Method for Incremental Online Learning in High Dimensions

Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear func- tion approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its core, it employs nonparametric regression with locally linear models. In order to stay computa- tionally efficient and numerically robust, each local model performs the regression analysis with a small number of univariate regressions in selected directions in input space in the spirit of partial least squares regression. We discuss when and how local learning techniques can successfully work in high dimensional spaces and compare various techniques for local dimensionality reduction before finally deriving the LWPR algorithm. The properties of LWPR are that it i) learns rapidly with second order learning methods based on incremental training, ii) uses statistically sound stochastic leave-one-out cross validation for learning without the need to memorize training data, iii) adjusts its weighting kernels based only on local information in order to minimize the danger of negative interference of incremental learning, iv) has a computational complexity that is linear in the num- ber of inputs, and v) can deal with a large number of - possibly redundant - inputs, as shown in various empirical evaluations with up to 50 dimensional data sets. For a probabilistic interpreta- tion, predictive variance and confidence intervals are derived. To our knowledge, LWPR is the first truly incremental spatially localized learning method that can successfully and efficiently operate in very high dimensional spaces

Incremental Online Learning in High Dimensions

Locally weighted projection regression (LWPR) is a new algorithm for incremental non-linear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its cor

Incremental online learning in high dimensions

this article, however, is problematic, as it requires a careful selection of initial ridge regression parameters to stabilize the highly rank-deficient full covariance matrix of the input data, and it is easy to create too much bias or too little numerical stabilization initially, which can trap the local distance metric adaptation in local minima.While the LWPR algorithm just computes about a factor 10 times longer for the 20D experiment in comparison to the 2D experiment, RFWR requires a 1000-fold increase of computation time, thus rendering this algorithm unsuitable for high-dimensional regression. In order to compare LWPR's results to other popular regression methods, we evaluated the 2D, 10D, and 20D cross data sets with gaussian process regression (GP) and support vector (SVM) regression in addition to our LWPR method. It should be noted that neither SVM nor GP methods is an incremental method, although they can be considered state-of-the-art for batch regression under relatively small numbers of training data and reasonable input dimensionality. The computational complexity of these methods is prohibitively high for real-time applications. The GP algorithm (Gibbs & MacKay, 1997) used a generic covariance function and optimized over the hyperparameters. The SVM regression was performed using a standard available package (Saunders et al., 1998) and optimized for kernel choices. Figure 6 compares the performance of LWPR and gaussian processes for the above-mentioned data sets using 100, 300, and 500 training data point

Automatic License plate Recognition System

Intelligence surveillance is an important commodity in traffic-based systems. Automatic License Plate Recognition (ALPR) is a challenging area of research. This work deals with problems related to artificial intelligence, neural networks and machine vision in the construction of an automatic license plate recognition (ALPR) system. This is done using mathematical principles and algorithms. These intelligent systems help in traffic monitoring during rush hours, road safety, commercial applications like in car parking lots and law enforcement. In this paper, a license plate recognition system is proposed which uses captured digital images of the rear or front of a vehicle and can be easily applied to commercial car park systems for access to parking spaces and also to prevent car theft issues

Dose-dependent increases in p70S6K phosphorylation and intramuscular branched-chain amino acids in older men following resistance exercise and protein intake.

Resistance exercise and whey protein supplementation are effective strategies to activate muscle cell anabolic signaling and ultimately promote increases in muscle mass and strength. In the current study, 46 healthy older men aged 60–75 (69.0 ± 0.55 years, 85.9 ± 1.8 kg, 176.8 ± 1.0 cm) performed a single bout of unaccustomed lower body resistance exercise immediately followed by ingestion of a noncaloric placebo beverage or supplement containing 10, 20, 30, or 40 g of whey protein concentrate (WPC). Intramuscular amino acid levels in muscle biopsy samples were measured by Gas Chromatography–Mass Spectrometry (GC-MS) at baseline (before exercise and WPC supplementation) plus at 2 h and 4 h post exercise. Additionally, the extent of p70S6K phosphorylation at Thr389 in muscle biopsy homogenates was assessed by western blot. Resistance exercise alone reduced intramuscular branch chain amino acid (BCAA; leucine, isoleucine, and valine) content. Supplementation with increasing doses of whey protein prevented this fall in muscle BCAAs during postexercise recovery and larger doses (30 g and 40 g) significantly augmented postexercise muscle BCAA content above that observed following placebo ingestion. Additionally, the fold change in the phosphorylation of p70S6K (Thr389) at 2 h post exercise was correlated with the dose of whey protein consumed (r = 0.51, P < 001) and was found to be significantly correlated with intramuscular leucine content (r = 0.32, P = 0.026). Intramuscular BCAAs, and leucine in particular, appear to be important regulators of anabolic signaling in aged human muscle during postexercise recovery via reversal of exercise-induced declines in intramuscular BCAAs

Communication and Citizenship: Reflections on Classroom Practice

This essay reflects on a semester-length classroom activity designed to give students an opportunity to practice their citizenship skills. We approach the problem of lack of citizen participation as a communication challenge and present our adaptation of Deliberative Polling to provide students with opportunities to: 1) research alternatives on an issue related to citizenship, 2) hone their research and critical thinking skills, and 3) participate in communication on issues related to citizenship with focused reflection on the communication processes involved. Because the topic is citizenship, students discuss issues related to political participation (e.g. voting) and are asked to reflect on their own practice of citizenship throughout the process. The activity allowed students to experience an alternative to the “either/or debate” perception of politics and gave them tools to participate in politics differently, and in more satisfying ways

YbeY is required for ribosome small subunit assembly and tRNA processing in human mitochondria.

Mitochondria contain their own translation apparatus which enables them to produce the polypeptides encoded in their genome. The mitochondrially-encoded RNA components of the mitochondrial ribosome require various post-transcriptional processing steps. Additional protein factors are required to facilitate the biogenesis of the functional mitoribosome. We have characterized a mitochondrially-localized protein, YbeY, which interacts with the assembling mitoribosome through the small subunit. Loss of YbeY leads to a severe reduction in mitochondrial translation and a loss of cell viability, associated with less accurate mitochondrial tRNASer(AGY) processing from the primary transcript and a defect in the maturation of the mitoribosomal small subunit. Our results suggest that YbeY performs a dual, likely independent, function in mitochondria being involved in precursor RNA processing and mitoribosome biogenesis. Issue Section: Nucleic Acid Enzymes