Pheno2Geno - High-throughput generation of genetic markers and maps from molecular phenotypes for crosses between inbred strains

Background

Genetic markers and maps are instrumental in quantitative trait locus (QTL) mapping in segregating populations. The resolution of QTL localization depends on the number of informative recombinations in the population and how well they are tagged by markers. Larger populations and denser marker maps are better for detecting and locating QTLs. Marker maps that are initially too sparse can be saturated or derived de novo from high-throughput omics data, (e.g. gene expression, protein or metabolite abundance). If these molecular phenotypes are affected by genetic variation due to a major QTL they will show a clear multimodal distribution. Using this information, phenotypes can be converted into genetic markers.

Results

The Pheno2Geno tool uses mixture modeling to select phenotypes and transform them into genetic markers suitable for construction and/or saturation of a genetic map. Pheno2Geno excludes candidate genetic markers that show evidence for multiple possibly epistatically interacting QTL and/or interaction with the environment, in order to provide a set of robust markers for follow-up QTL mapping.We demonstrate the use of Pheno2Geno on gene expression data of 370,000 probes in 148 A. thaliana recombinant inbred lines. Pheno2Geno is able to saturate the existing genetic map, decreasing the average distance between markers from 7.1 cM to 0.89 cM, close to the theoretical limit of 0.68 cM (with 148 individuals we expect a recombination every 100/148=0.68 cM); this pinpointed almost all of the informative recombinations in the population.

Conclusion

The Pheno2Geno package makes use of genome-wide molecular profiling and provides a tool for high-throughput de novo map construction and saturation of existing genetic maps. Processing of the showcase dataset takes less than 30 minutes on an average desktop PC. Pheno2Geno improves QTL mapping results at no additional laboratory cost and with minimum computational effort. Its results are formatted for direct use in R/qtl, the leading R package for QTL studies. Pheno2Geno is freely available on CRAN under “GNU GPL v3”. The Pheno2Geno package as well as the tutorial can also be found at: http://pheno2geno.nl.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0475-6) contains supplementary material, which is available to authorized users.     

Relationship of aerobic and anaerobic parameters with 400 m front crawl swimming performance

The aims of the present study were to investigate the relationship of aerobic and anaerobic parameters with 400 m performance, and establish which variable better explains long distance performance in swimming. Twenty-two swimmers (19.1±1.5 years, height 173.9±10.0 cm, body mass 71.2±10.2 kg; 76.6±5.3% of 400 m world record) underwent a lactate minimum test to determine lactate minimum speed (LMS) (i.e., aerobic capacity index). Moreover, the swimmers performed a 400 m maximal effort to determine mean speed (S400m), peak oxygen uptake (V.O2PEAK) and total anaerobic contribution (C_ANA). The C_ANA was assumed as the sum of alactic and lactic contributions. Physiological parameters of 400 m were determined using the backward extrapolation technique (V.O2PEAK and alactic contributions of C_ANA) and blood lactate concentration analysis (lactic anaerobic contributions of C_ANA). The Pearson correlation test and backward multiple regression analysis were used to verify the possible correlations between the physiological indices (predictor factors) and S400m (independent variable) (p < 0.05). Values are presented as mean ± standard deviation. Significant correlations were observed between S400m (1.4±0.1 m·s^-1) and LMS (1.3±0.1 m·s^-1; r = 0.80), V.O2PEAK (4.5±3.9 L·min^-1; r = 0.72) and C_ANA (4.7±1.5 L·O₂; r= 0.44). The best model constructed using multiple regression analysis demonstrated that LMS and V.O2PEAK explained 85% of the 400 m performance variance. When backward multiple regression analysis was performed, C_ANA lost significance. Thus, the results demonstrated that both aerobic parameters (capacity and power) can be used to predict 400 m swimming performance.     

Use of polyethyleneimine polymer in cell culture as attachment factor and lipofection enhancer

Background  

Several cell lines and primary cultures benefit from the use of positively charged extracellular matrix proteins or polymers that enhance their ability to attach to culture plates. Polyethyleneimine is a positively charged polymer that has gained recent attention as a transfection reagent. A less known use of this cationic polymer as an attachment factor was explored with several cell lines.     

Neutrophil fatty acid composition: effect of a single session of exercise and glutamine supplementation

The fatty acid composition of immune cells appears to contribute to variations of cell function. The independent and combined effects of a single session of exercise (SSE) and glutamine supplementation (GS) on neutrophil fatty acid composition were investigated. Compared to control (no treatment given--i.e. neither SSE or GS), single session of exercise decreased myristic, palmitic and eicosapentaenoic (EPA) acids, and increased lauric, oleic, linoleic, arachidonic (AA) and docosahexaenoic (DHA) acids whereas glutamine supplementation combined with SSE (GS+SSE) increased oleic acid. Polyunsaturated/saturated fatty acid ratio and Unsaturation index were higher in neutrophils from the SSE and GS groups as compared with control. These findings support the proposition that SSE and GS may modulate neutrophil function through alterations in fatty acid composition.     

An AFLP-based genetic linkage map of Plasmodium chabaudi chabaudi

Background

Plasmodium chabaudi chabaudi can be considered as a rodent model of human malaria parasites in the genetic analysis of important characters such as drug resistance and immunity. Despite the availability of some genome sequence data, an extensive genetic linkage map is needed for mapping the genes involved in certain traits.

Methods

The inheritance of 672 Amplified Fragment Length Polymorphism (AFLP) markers from two parental clones (AS and AJ) of P. c. chabaudi was determined in 28 independent recombinant progeny clones. These, AFLP markers and 42 previously mapped Restriction Fragment Length Polymorphism (RFLP) markers (used as chromosomal anchors) were organized into linkage groups using Map Manager software.

Results

614 AFLP markers formed linkage groups assigned to 10 of 14 chromosomes, and 12 other linkage groups not assigned to known chromosomes. The genetic length of the genome was estimated to be about 1676 centiMorgans (cM). The mean map unit size was estimated to be 13.7 kb/cM. This was slightly less then previous estimates for the human malaria parasite, Plasmodium falciparum

Conclusion

The P. c. chabaudi genetic linkage map presented here is the most extensive and highly resolved so far available for this species. It can be used in conjunction with the genome databases of P. c chabaudi, P. falciparum and Plasmodium yoelii to identify genes underlying important phenotypes such as drug resistance and strain-specific immunity.     

Ischemic postconditioning during reperfusion activates Akt and ERK without protecting against lethal myocardial ischemia-reperfusion injury in pigs

Transient episodes of ischemic preconditioning (PC) render myocardium protected against subsequent lethal injury after ischemia and reperfusion. Recent studies indicate that application of short, repetitive ischemia only during the onset of reperfusion after the lethal ischemic event may obtain equivalent protection. We assessed whether such ischemic postconditioning (Postcon) is cardioprotective in pigs by limiting lethal injury. Pentobarbital sodium-anesthetized, open-chest pigs underwent 30 min of complete occlusion of the left anterior descending coronary artery and 3-h reflow. PC was elicited by two cycles of 5-min occlusion plus 10-min reperfusion before the 30-min occlusion period. Postcon was elicited by three cycles of 30-s reperfusion, followed by 30-s reocclusion, after the 30-min occlusion period and before the 3-h reflow. Infarct size (%area-at-risk using triphenyltetrazolium chloride macrochemistry; means +/- SE) after 30 min of ischemia was 26.5 +/- 5.2% (n = 7 hearts/treatment group). PC markedly limited myocardial infarct size (2.8 +/- 1.2%, n = 7 hearts/treatment group, P < 0.05 vs. controls). However, Postcon had no effect on infarct size (37.8 +/- 5.1%, n = 7 hearts/treatment group). Within the subendocardium, Postcon increased phosphorylation of Akt (74 +/- 12%) and ERK1/2 (56 +/- 10%) compared with control hearts subjected only to 30-min occlusion and 15-min reperfusion (P < or = 0.05), and these changes were not different from the response triggered by PC (n = 5 hearts/treatment group). Phosphorylation of downstream p70S6K was also equivalent in PC and Postcon groups. These data do not support the hypothesis that application of 30-s cycles of repetitive ischemia during reperfusion exerts a protective effect on pig hearts subjected to lethal ischemia, but this is not due to a failure to phosphorylate ERK and Akt during early reperfusion.     

Molecular mechanisms of glutamine action

Glutamine is the most abundant free amino acid in the body and is known to play a regulatory role in several cell specific processes including metabolism (e.g., oxidative fuel, gluconeogenic precursor, and lipogenic precursor), cell integrity (apoptosis, cell proliferation), protein synthesis, and degradation, contractile protein mass, redox potential, respiratory burst, insulin resistance, insulin secretion, and extracellular matrix (ECM) synthesis. Glutamine has been shown to regulate the expression of many genes related to metabolism, signal transduction, cell defense and repair, and to activate intracellular signaling pathways. Thus, the function of glutamine goes beyond that of a simple metabolic fuel or protein precursor as previously assumed. In this review, we have attempted to identify some of the common mechanisms underlying the regulation of glutamine dependent cellular functions.