Kinetics of Muscle Carnosine Decay after ß-Alanine Supplementation: A 16-wk Washout Study.

PURPOSE: This study aimed to describe the kinetics of carnosine washout in human skeletal muscle over 16 wk. METHODS: Carnosine washout kinetics were studied in 15 young, physically active omnivorous men randomly assigned to take 6.4 g·d-1 of ß-alanine (n = 11) or placebo (n = 4) for 8 wk. Muscle carnosine content (M-Carn) was determined before (PRE), immediately after (POST), and 4, 8, 12, and 16 wk after supplementation. High-intensity exercise tests were performed at these same time points. Linear and exponential models were fitted to the washout data, and the leave-one-out method was used to select the model with the best fit for M-Carn decay data. Repeated-measures correlation analysis was used to assess the association between changes in M-Carn and changes in performance. RESULTS: M-Carn increased from PRE to POST in the ß-alanine group only (+91.1% ± 29.1%; placebo, +0.04% ± 10.1%; P < 0.0001). M-Carn started to decrease after cessation of ß-alanine supplementation and continued to decrease until week 16 (POST4, +59% ± 40%; POST8, +35% ± 39%; POST12, +18% ± 32%; POST16, -3% ± 24% of PRE M-Carn). From week 12 onward, M-Carn was no longer statistically different from PRE. Both linear and exponential models displayed very similar fit and could be used to describe carnosine washout, although the linear model presented a slightly better fit. The decay in M-Carn was mirrored by a similar decay in high-intensity exercise tolerance; M-Carn was moderately and significantly correlated with total mechanical work done (r = 0.505; P = 0.032) and time to exhaustion (r = 0.72; P < 0.001). CONCLUSIONS: Carnosine washout takes 12-16 wk to complete, and it can be described either by linear or exponential curves. Changes in M-Carn seem to be mirrored by changes in high-intensity exercise tolerance. This information can be used to optimize ß-alanine supplementation strategies.

Expression estimation and eQTL mapping for HLA genes with a personalized pipeline.

The HLA (Human Leukocyte Antigens) genes are well-documented targets of balancing selection, and variation at these loci is associated with many disease phenotypes. Variation in expression levels also influences disease susceptibility and resistance, but little information exists about the regulation and population-level patterns of expression. This results from the difficulty in mapping short reads originated from these highly polymorphic loci, and in accounting for the existence of several paralogues. We developed a computational pipeline to accurately estimate expression for HLA genes based on RNA-seq, improving both locus-level and allele-level estimates. First, reads are aligned to all known HLA sequences in order to infer HLA genotypes, then quantification of expression is carried out using a personalized index. We use simulations to show that expression estimates obtained in this way are not biased due to divergence from the reference genome. We applied our pipeline to the GEUVADIS dataset, and compared the quantifications to those obtained with reference transcriptome. Although the personalized pipeline recovers more reads, we found that using the reference transcriptome produces estimates similar to the personalized pipeline (r ≥ 0.87) with the exception of HLA-DQA1. We describe the impact of the HLA-personalized approach on downstream analyses for nine classical HLA loci (HLA-A, HLA-C, HLA-B, HLA-DRA, HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DPA1, HLA-DPB1). Although the influence of the HLA-personalized approach is modest for eQTL mapping, the p-values and the causality of the eQTLs obtained are better than when the reference transcriptome is used. We investigate how the eQTLs we identified explain variation in expression among lineages of HLA alleles. Finally, we discuss possible causes underlying differences between expression estimates obtained using RNA-seq, antibody-based approaches and qPCR.

The Effect of Balancing Selection on Population Differentiation: A Study with HLA Genes.

Balancing selection is defined as a class of selective regimes that maintain polymorphism above what is expected under neutrality. Theory predicts that balancing selection reduces population differentiation, as measured by FST. However, balancing selection regimes in which different sets of alleles are maintained in different populations could increase population differentiation. To tackle the connection between balancing selection and population differentiation, we investigated population differentiation at the HLA genes, which constitute the most striking example of balancing selection in humans. We found that population differentiation of single nucleotide polymorphisms (SNPs) at the HLA genes is on average lower than that of SNPs in other genomic regions. We show that these results require using a computation that accounts for the dependence of FST on allele frequencies. However, in pairs of closely related populations, where genome-wide differentiation is low, differentiation at HLA is higher than in other genomic regions. Such increased population differentiation at HLA genes for recently diverged population pairs was reproduced in simulations of overdominant selection, as long as the fitness of the homozygotes differs between the diverging populations. The results give insight into a possible "divergent overdominance" mechanism for the nature of balancing selection on HLA genes across human populations.

Supervised Variational Relevance Learning, An Analytic Geometric Feature Selection with Applications to Omic Datasets.

We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational method is applied to a cost function that penalizes large intraclass distances and favors small interclass distances. We find analytically the metric tensor that minimizes the cost function. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. Among these datasets, two were tested by the MAQC-II project and, even without the use of further preprocessing, our results improve on their performance.