A Systematic Review and Meta-Analysis of Proteomics Literature on the Response of Human Skeletal Muscle to Obesity/Type 2 Diabetes Mellitus (T2DM) Versus Exercise Training.

We performed a systematic review and meta-analysis of proteomics literature that reports human skeletal muscle responses in the context of either pathological decline associated with obesity/T2DM and physiological adaptations to exercise training. Literature was collected from PubMed and DOAJ databases following PRISMA guidelines using the search terms 'proteom*', and 'skeletal muscle' combined with either 'obesity, insulin resistance, diabetes, impaired glucose tolerance' or 'exercise, training'. Eleven studies were included in the systematic review, and meta-analysis was performed on a sub-set (four studies) of the reviewed literature that reported the necessary primary data. The majority of proteins (n = 73) more abundant in the muscle of obese/T2DM individuals were unique to this group and not reported to be responsive to exercise training. The main response of skeletal muscle to exercise training was a greater abundance of proteins of the mitochondrial electron transport chain, tricarboxylic acid cycle and mitochondrial respiratory chain complex I assembly. In total, five proteins were less abundant in muscle of obese/T2DM individuals and were also reported to be more abundant in the muscle of endurance-trained individuals, suggesting one of the major mechanisms of exercise-induced protection against the deleterious effects of obesity/T2DM occurs at complex I of the electron transport chain

Fractional Synthesis Rates of Individual Proteins in Rat Soleus and Plantaris Muscles.

Differences in the protein composition of fast- and slow-twitch muscle may be maintained by different rates of protein turnover. We investigated protein turnover rates in slow-twitch soleus and fast-twitch plantaris of male Wistar rats (body weight 412 ± 69 g). Animals were assigned to four groups (n = 3, in each), including a control group (0 d) and three groups that received deuterium oxide (D2O) for either 10 days, 20 days or 30 days. D2O administration was initiated by an intraperitoneal injection of 20 μL of 99% D2O-saline per g body weight, and maintained by provision of 4% (v/v) D2O in the drinking water available ad libitum. Soluble proteins from harvested muscles were analysed by liquid chromatography-tandem mass spectrometry and identified against the SwissProt database. The enrichment of D2O and rate constant (k) of protein synthesis was calculated from the abundance of peptide mass isotopomers. The fractional synthesis rate (FSR) of 44 proteins in soleus and 34 proteins in plantaris spanned from 0.58%/day (CO1A1: Collagen alpha-1 chain) to 5.40%/day NDRG2 (N-myc downstream-regulated gene 2 protein). Eight out of 18 proteins identified in both muscles had a different FSR in soleus than in plantaris (p < 0.05)

Reproducible kk-means clustering in galaxy feature data from the GAMA survey

A fundamental bimodality of galaxies in the local Universe is apparent in many of the features used to describe them. Multiple sub-populations exist within this framework, each representing galaxies following distinct evolutionary pathways. Accurately identifying and characterising these sub-populations requires that a large number of galaxy features be analysed simultaneously. Future galaxy surveys such as LSST and Euclid will yield data volumes for which traditional approaches to galaxy classification will become unfeasible. To address this, we apply a robust $k$-means unsupervised clustering method to feature data derived from a sample of 7338 local-Universe galaxies selected from the Galaxy And Mass Assembly (GAMA) survey. This allows us to partition our sample into $k$ clusters without the need for training on pre-labelled data, facilitating a full census of our high dimensionality feature space and guarding against stochastic effects. We find that the local galaxy population natively splits into $2$, $3$, $5$ and a maximum of $6$ sub-populations, with each corresponding to a distinct ongoing evolutionary mechanism. Notably, the impact of the local environment appears strongly linked with the evolution of low-mass ($M_{*} < 10^{10}$ M$_{\odot}$) galaxies, with more massive systems appearing to evolve more passively from the blue cloud onto the red sequence. With a typical run time of $\sim3$ minutes per value of $k$ for our galaxy sample, we show how $k$-means unsupervised clustering is an ideal tool for future analysis of large extragalactic datasets, being scalable, adaptable, and providing crucial insight into the fundamental properties of the local galaxy population

Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites.

The aim of the paper is two-fold. First, we show that structure finding with the PC algorithm can be inherently unstable and requires further operational constraints in order to consistently obtain models that are faithful to the data. We propose a methodology to stabilise the structure finding process, minimising both false positive and false negative error rates. This is demonstrated with synthetic data. Second, to apply the proposed structure finding methodology to a data set comprising single-voxel Magnetic Resonance Spectra of normal brain and three classes of brain tumours, to elucidate the associations between brain tumour types and a range of observed metabolites that are known to be relevant for their characterisation. The data set is bootstrapped in order to maximise the robustness of feature selection for nominated target variables. Specifically, Conditional Independence maps (CI-maps) built from the data and their derived Bayesian networks have been used. A Directed Acyclic Graph (DAG) is built from CI-maps, being a major challenge the minimization of errors in the graph structure. This work presents empirical evidence on how to reduce false positive errors via the False Discovery Rate, and how to identify appropriate parameter settings to improve the False Negative Reduction. In addition, several node ordering policies are investigated that transform the graph into a DAG. The obtained results show that ordering nodes by strength of mutual information can recover a representative DAG in a reasonable time, although a more accurate graph can be recovered using a random order of samples at the expense of increasing the computation time

Quantum clustering in non-spherical data distributions: Finding a suitable number of clusters

Quantum Clustering (QC) provides an alternative approach to clustering algorithms, several of which are based on geometric relationships between data points. Instead, QC makes use of quantum mechanics concepts to find structures (clusters) in data sets by finding the minima of a quantum potential. The starting point of QC is a Parzen estimator with a fixed length scale, which significantly affects the final cluster allocation. This dependence on an adjustable parameter is common to other methods. We propose a framework to find suitable values of the length parameter σ by optimising twin measures of cluster separation and consistency for a given cluster number. This is an extension of the Separation and Concordance framework previously introduced for K-means clustering. Experimental results on two synthetic data sets and three challenging real-world data sets show that optimisation of cluster separation identifies QC solutions with consistently high Jaccard score measured against true-cluster labels while optimisation of cluster consistency provides insights into hierarchical cluster structure. © 2017 Elsevier B.V

Reliability of Protein Abundance and Synthesis Measurements in Human Skeletal Muscle.

We investigated the repeatability of dynamic proteome profiling (DPP), which is a novel technique for measuring the relative abundance (ABD) and fractional synthesis rate (FSR) of proteins in humans. LC-MS analysis was performed on muscle samples taken from male participants (n = 4) that consumed 4 × 50 ml doses of deuterium oxide (2 H2 O) per day for 14 d. ABD was measured by label-free quantitation and FSR was calculated from time-dependent changes in peptide mass isotopomer abundances. One-hundred and one proteins had at least 1 unique peptide and were used in the assessment of protein ABD. Fifty-four of these proteins met more stringent criteria and were used in the assessment of FSR data. The median (M), lower- (Q1 ) and upper-quartile (Q3 ) values for protein FSR (%/d) were M = 1.63, Q1  = 1.07, Q3  = 3.24. The technical CV of ABD data had a median value of 3.6% (Q1 1.7% - Q3 6.7%), whereas the median CV of FSR data was 10.1% (Q1 3.5% - Q3 16.5%). These values compare favorably against other assessments of technical repeatability of proteomics data, which often set a CV of 20% as the upper bound of acceptability. This article is protected by copyright. All rights reserved

People with obesity exhibit losses in muscle proteostasis that are partly improved by exercise training

This pilot experiment examines if a loss in muscle proteostasis occurs in people with obesity and whether endurance exercise positively influences either the abundance profile or turnover rate of proteins in this population. Men with (n = 3) or without (n = 4) obesity were recruited and underwent a 14-d measurement protocol of daily deuterium oxide (D2 O) consumption and serial biopsies of vastus lateralis muscle. Men with obesity then completed 10-weeks of high-intensity interval training (HIIT), encompassing 3 sessions per week of cycle ergometer exercise with 1 min intervals at 100% maximum aerobic power interspersed by 1 min recovery periods. The number of intervals per session progressed from 4 to 8, and during weeks 8-10 the 14-d measurement protocol was repeated. Proteomic analysis detected 352 differences (p < 0.05, false discovery rate < 5%) in protein abundance and 19 (p < 0.05) differences in protein turnover, including components of the ubiquitin-proteasome system. HIIT altered the abundance of 53 proteins and increased the turnover rate of 22 proteins (p < 0.05) and tended to benefit proteostasis by increasing muscle protein turnover rates. Obesity and insulin resistance are associated with compromised muscle proteostasis, which may be partially restored by endurance exercise

Long Term Immune Responses to Pandemic Influenza A/H1N1 Infection in Solid Organ Transplant Recipients

In solid organ transplant (SOT) recipients it is unknown if natural infection with influenza confers protection from re-infection with the same strain during the next influenza season. The purpose of this study was to determine if infection with pandemic influenza A/H1N1 (pH1N1) resulted in a long-term immunologic response. Transplant recipients with microbiologically proven pH1N1 infection in 2009/2010 underwent humoral and cell-mediated immunity (CMI) testing for pH1N1 just prior to the next influenza season. Concurrent testing for A/Brisbane/59/2007 was done to rule-out cross-reacting antibody. We enrolled 22 adult transplant patients after pH1N1 infection. Follow up testing was done at a median of 7.4 months (range 5.8–15.4) after infection. After excluding those with cross-reactive antibody, 7/19 (36.8%) patients were seroprotected. Detectable pH1N1-specific CD4+ and CD8+ interferon-γ producing T-cells were found in 11/22 (50%) and 8/22 (36.4%) patients respectively. Humoral immunity had a significant correlation with a CD4 response. This is the first study in transplant patients to evaluate long-term humoral and cellular response after natural influenza infection. We show that a substantial proportion of SOT recipients with previous pH1N1 infection lack long-term humoral and cellular immune responses to pH1N1. These patients most likely are at risk for re-infection

Early prediction of response to radiotherapy and androgen-deprivation therapy in prostate cancer by repeated functional MRI: a preclinical study

<p>Abstract</p> <p>Background</p> <p>In modern cancer medicine, morphological magnetic resonance imaging (MRI) is routinely used in diagnostics, treatment planning and assessment of therapeutic efficacy. During the past decade, functional imaging techniques like diffusion-weighted (DW) MRI and dynamic contrast-enhanced (DCE) MRI have increasingly been included into imaging protocols, allowing extraction of intratumoral information of underlying vascular, molecular and physiological mechanisms, not available in morphological images. Separately, pre-treatment and early changes in functional parameters obtained from DWMRI and DCEMRI have shown potential in predicting therapy response. We hypothesized that the combination of several functional parameters increased the predictive power.</p> <p>Methods</p> <p>We challenged this hypothesis by using an artificial neural network (ANN) approach, exploiting nonlinear relationships between individual variables, which is particularly suitable in treatment response prediction involving complex cancer data. A clinical scenario was elicited by using 32 mice with human prostate carcinoma xenografts receiving combinations of androgen-deprivation therapy and/or radiotherapy. Pre-radiation and on days 1 and 9 following radiation three repeated DWMRI and DCEMRI acquisitions enabled derivation of the apparent diffusion coefficient (ADC) and the vascular biomarker <it>K</it>^trans, which together with tumor volumes and the established biomarker prostate-specific antigen (PSA), were used as inputs to a back propagation neural network, independently and combined, in order to explore their feasibility of predicting individual treatment response measured as 30 days post-RT tumor volumes.</p> <p>Results</p> <p>ADC, volumes and PSA as inputs to the model revealed a correlation coefficient of 0.54 (p < 0.001) between predicted and measured treatment response, while <it>K</it>^trans, volumes and PSA gave a correlation coefficient of 0.66 (p < 0.001). The combination of all parameters (ADC, <it>K</it>^trans, volumes, PSA) successfully predicted treatment response with a correlation coefficient of 0.85 (p < 0.001).</p> <p>Conclusions</p> <p>We have in a preclinical investigation showed that the combination of early changes in several functional MRI parameters provides additional information about therapy response. If such an approach could be clinically validated, it may become a tool to help identifying non-responding patients early in treatment, allowing these patients to be considered for alternative treatment strategies, and, thus, providing a contribution to the development of individualized cancer therapy.</p