Search

Cognitive abilities are crucial for survival and adaptation, enabling animals to navigate their environment, recognize predators and remember the location of food resources. However, underlying factors related to learning and memory can be energetically demanding and thus may vary depending on an individual's metabolism or aerobic capacity, potentially affecting individuals' cognitive performance. In this study, we explored the link between cognitive performance and whole-body metabolic traits, including aerobic scope (AS), maximum metabolic rate (MMR) and standard metabolic rate (SMR). European minnows, Phoxinus phoxinus , were trained over 20 days to locate a food reward in a maze. Individuals were trained in either a simple (two-door) or a complex (four-door) maze. Fish in the simpler maze had consistently higher success and a lower latency to reach the reward, suggesting the two-door maze was less cognitively challenging. We found a correlation between metabolic traits and cognitive performance traits (i.e. success and latency to reach the reward) at the end of training. However, this relationship varied depending on maze complexity. In the two-door maze, individuals with higher MMR and SMR had higher success and a lower latency to reach the reward. However, in the more complex maze, fish with lower metabolic rates (MMR and SMR) had higher success and lower latency to reach the reward. AS followed similar patterns but mostly affected the success to reach the reward. In simpler environments, having a higher metabolism may be more beneficial for cognitive performance, whereas in complex environments, having a lower metabolism may be more beneficial as it could be associated with a slower but more thorough exploration and learning process. • Fish in two-door mazes had higher success and lower latency to reach a reward. • Fish with higher metabolic rates (MMR, SMR) performed better in two-door mazes. • Fish with lower MMR and SMR performed better in four-door mazes. • Aerobic scope followed similar patterns but mostly affected success to reach a reward. • Metabolic traits and cognitive performance were correlated at the end of training. [ABSTRACT FROM AUTHOR]

Background: Exposure to environmental endocrine disrupting chemicals (EDCs) may play an important role in the epidemic of metabolic diseases. Epigenetic alterations may functionally link EDCs with gene expression and metabolic traits. Objectives: We aimed to evaluate metabolic-related effects of the exposure to endocrine disruptors including five parabens, three bisphenols, and 13 metabolites of nine phthalates as measured in 24-hour urine on epigenome-wide DNA methylation. Methods: A blood-based epigenome-wide association study was performed in 622 participants from the Lifelines DEEP cohort using Illumina Infinium HumanMethylation450 methylation data and EDC excretions in 24-hour urine. Out of the 21 EDCs, 13 compounds were detected in >75% of the samples and, together with bisphenol F, were included in these analyses. Furthermore, we explored the putative function of identified methylation markers and their correlations with metabolic traits. Results: We found 20 differentially methylated cytosine-phosphate-guanines (CpGs) associated with 10 EDCs at suggestive p-value

In animal breeding, body components and metabolic traits always fall behind body weights in genetic improvement, which leads to the decline in standards and qualities of animal products. Phenotypically, the relative growth of multiple body components and metabolic traits relative to body weights are characterized by using joint allometric scaling models, and then random regression models (RRMs) are constructed to map quantitative trait loci (QTLs) for relative grwoth allometries of body compositions and metabolic traits in chicken. Referred to as real joint allometric scaling models, statistical utility of the so-called LASSO-RRM mapping method is given a demonstration by computer simulation analysis. Using the F2 population by crossing broiler × Fayoumi, we formulated optimal joint allometric scaling models of fat, shank weight (shank-w) and liver as well as thyroxine (T4) and glucose (GLC) to body weights. For body compositions, a total of 9 QTLs, including 4 additive and 5 dominant QTLs, were detected to control the allometric scalings of fat, shank-w, and liver to body weights; while a total of 10 QTLs of which 6 were dominant, were mapped to govern the allometries of T4 and GLC to body weights. We characterized relative growths of body compositions and metabolic traits to body weights in broilers with joint allometric scaling models and detected QTLs for the allometry scalings of the relative growths by using RRMs. The identified QTLs, including their highly linked genetic markers, could be used to order relative growths of the body components or metabolic traits to body weights in marker-assisted breeding programs for improving the standard and quality of broiler meat products.

Background Plasminogen activator inhibitor-1 (PAI-I), encoded by SERPINE1 gene, is a member of the serine protease inhibitor superfamily, and polymorphisms in SERPINE1 have been reported to be associated with type 2 diabetes (T2D). This study investigated whether the polymorphism in PAI-I contribute to the risk for T2D. Methods A 1:1 case-control study was conducted to investigate the association of rs6092 in SERPINE1 with T2D and diabetes-related metabolic traits, including body mass index, waist circumference (WC), triglyceride (TG), total cholesterol (TC), high density lipoprotein cholesterol, low density lipoprotein cholesterol, fasting plasma glucose and glycosylated hemoglobin (HbA1c) in a Chinese population, with a total of 1572 subjects (786 T2D patients and 786 healthy controls). The polymorphism was genotyped based on MassARRAY genotyping system. Results The AA genotype and A allele of rs6092 exerted a protective effect on T2D risk (odds ratio (OR) = 0.431 and 0.630, respectively). In a recessive model, we also observed the protective association of rs6092 with T2D (OR = 0.195). The above associations were only observed in men. In female patients, there was a significant difference in HbA1c level between the AA homozygotes and GG homozygotes, as well as between the AA homozygotes and combined GG and GA genotypes. In male patients, the WC level in the subjects carrying AA genotype was lower than those in the subjects with GG genotype ( P  = 0.000), and the association was also significant in a recessive model ( P  = 0.000). Additionally, there was a significant difference in TG level between the AA homozygotes and GG homozygotes ( P  = 0.017), as well as the AA homozygotes and combined GG and GA genotypes ( P  = 0.032). Conclusions Our study suggests that the A allele and AA genotype of rs6092 may protect against T2D, and have a protective effect on WC, but a negative effect on TG in men, while may contribute to a lower HbA1c level in women. [ABSTRACT FROM AUTHOR]

The mule duck, an interspecific hybrid obtained by crossing common duck (Anas platyrhynchos) females with Muscovy (Cairina moschata) drakes, is widely used for fatty liver production. The purpose of the present study was to detect and map single and pleiotropic QTL that segregate in the common duck species, and influence the expression of traits in their overfed mule duck offspring. To this end, we generated a common duck backcross (BC) population by crossing Kaiya and heavy Pekin experimental lines, which differ notably in regard to the BW and overfeeding ability of their mule progeny. The BC females were mated to Muscovy drakes and, on average, 4 male mule ducks hatched per BC female (1600 in total) and were measured for growth, metabolism during growth and the overfeeding period, overfeeding ability, and the quality of their breast meat and fatty liver. The phenotypic value of BC females was estimated for each trait by assigning to each female the mean value of the phenotypes of her offspring. Estimations allowed for variance, which depended on the number of male offspring per BC and the heritability of the trait considered. The genetic map used for QTL detection consisted of 91 microsatellite markers aggregated into 16 linkage groups (LG) covering a total of 778 cM. Twenty-two QTL were found to be significant at the 1% chromosome-wide threshold level using the single-trait detection option of the QTLMap software. Most of the QTL detected were related to the quality of breast meat and fatty liver: QTL for meat pH 20 min post mortem were mapped to LG4 (at the 1% genome-wide significance level), and QTL for meat lipid content and cooking losses were mapped to LG2a. The QTL related to fatty liver weight and liver protein and lipid content were for the most part detected on LG2c and LG9. Multitrait analysis highlighted the pleiotropic effects of QTL in these chromosome regions. Apart from the strong QTL for plasma triglyceride content at the end of the overfeeding period mapped to chromosome Z using single-trait analysis, all metabolic trait QTL were detected with the multitrait approach: the QTL mapped to LG14 and LG21 affected the plasma cholesterol and triglyceride contents, whereas the QTL mapped to LG2a seemed to impact glycemia and the basal plasma corticosterone content. A greater density genetic map will be needed to further fine map the QTL. [ABSTRACT FROM AUTHOR]

Type 2 diabetes mellitus (T2DM) is a metabolic disorder that is characterized by insulin resistance and hyperglycemia. Leptin inhibits the glucose-stimulated insulin secretion, and leptin receptors are present on β cells as well as on fat cells, thus enabling leptin to modulate both insulin secretion and action. Therefore, leptin ( LEP) and leptin receptor ( LEPR) genes could play a role in the regulation of glucose and insulin after an oral glucose load. For the association study of LEP and LEPR with T2DM and metabolic traits, 752 women from Seoul National University Hospital (SNUH data) and 532 women from the Korean Health and Genome Study (KHGS data) were selected. Using the SNUH data, we identified that LEP−632G>A and +4998A>C polymorphisms were marginally associated with T2DM, LEP+ 4950G>A was significantly associated with several metabolic traits, and LEPR+5193G>A, +7187A>C, +27265G>A, +35861T>C, and +52289A>G showed strongly significant association with body mass index (BMI). We observed reproducibility of these results using the KHGS data; LEP+4950G>A and +4998A>C were significantly associated with systolic blood pressure and low-density lipoprotein cholesterol level, respectively. In conclusion, we observed that several polymorphisms in LEPR that had previous reports of association with BMI were significantly replicated in our samples and newly found that some variations of LEP were associated with T2DM and metabolic traits. [ABSTRACT FROM AUTHOR]

The current study is a comprehensive genome analysis to detect QTL affecting metabolic traits in chickens. Two unique F2 crosses generated from a commercial broiler male line and 2 genetically distinct inbred lines (Leghorn and Fayoumi) were used in the present study. The plasma glucagon, insulin, lactate, glucose, triiodothyronine, thyroxine, insulin-like growth factor I, and insulin-like growth factor II concentrations at 8 wk were measured in the 2 F2 crosses. Birds were genotyped for 269 microsatellite markers across the entire genome. The program QTL Express was used for QTL detection. Significance levels were obtained using the permutation test. For the 10 traits, a total of 6 and 9 significant QTL were detected at a 1% chromosome-wise significance level, of which 1 and 6 were significant at the 5% genome-wise level for the broiler-Leghorn cross and broiler-Fayoumi cross, respectively. Most QTL for metabolic traits in the present study were detected in Gga 2, 6, 8, 9, 13, and Z for the broiler-Leghorn cross and Gga 1, 2, 4, 7, 8, 13, 17, and E47 for the broiler-Fayoumi cross. Phenotypic variation for each trait explained by all QTL across genome ranged from 2.73 to 14.08% in the broiler-Leghorn cross and from 6.93 to 21.15% in the broiler-Fayoumi cross. Several positional candidate genes within the QTL region for metabolic traits at the 1% chromosome-wise significance level are biologically associated with the regulation of metabolic pathways of insulin, triiodothyronine, and thyroxine. [ABSTRACT FROM AUTHOR]

Simple Summary: The relative growth of body components and metabolic traits relative to body weights are phenotypically characterized using joint allometric scaling models, and random regression models (RRMs) are constructed to map quantitative trait loci (QTLs) for allometries of body compositions and metabolic traits in broilers. Prior to statistically inferring the QTLs for the allometric scalings, the QTL candidates in RRMs are obtained by rapidly shrinking most of marker genetic effects to zero with the LASSO technique. Referred to as real joint allometric scaling models, statistical utility of the so-called LASSO-RRM mapping method is demonstrated by computer simulation analysis. Using the F2 population by crossing broiler × Fayoumi, we formulate optimal joint allometric scaling models of fat, shank weight (shank-w) and liver as well as thyroxine (T4) and glucose (GLC) to body weights. For body compositions, a total of 9 QTLs, including 4 additive and 5 dominant, were detected to control the allometric scalings of fat, shank-w and liver to body weights; while for metabolic traits, total 10 QTLs, were mapped to govern the allometries of T4 and GLC to body weights, among which 6 QTLs were of dominant genetic effect. The detected QTLs or highly linked markers can be used to regulate relative growths for meat quality traits to body weight in marker-assisted breeding of broilers. In animal breeding, body components and metabolic traits always fall behind body weights in genetic improvement, which leads to the decline in standards and qualities of animal products. Phenotypically, the relative growth of multiple body components and metabolic traits relative to body weights are characterized by using joint allometric scaling models, and then random regression models (RRMs) are constructed to map quantitative trait loci (QTLs) for relative grwoth allometries of body compositions and metabolic traits in chicken. Referred to as real joint allometric scaling models, statistical utility of the so-called LASSO-RRM mapping method is given a demonstration by computer simulation analysis. Using the F2 population by crossing broiler × Fayoumi, we formulated optimal joint allometric scaling models of fat, shank weight (shank-w) and liver as well as thyroxine (T4) and glucose (GLC) to body weights. For body compositions, a total of 9 QTLs, including 4 additive and 5 dominant QTLs, were detected to control the allometric scalings of fat, shank-w, and liver to body weights; while a total of 10 QTLs of which 6 were dominant, were mapped to govern the allometries of T4 and GLC to body weights. We characterized relative growths of body compositions and metabolic traits to body weights in broilers with joint allometric scaling models and detected QTLs for the allometry scalings of the relative growths by using RRMs. The identified QTLs, including their highly linked genetic markers, could be used to order relative growths of the body components or metabolic traits to body weights in marker-assisted breeding programs for improving the standard and quality of broiler meat products. [ABSTRACT FROM AUTHOR]

Nonalcoholic fatty liver disease (NAFLD) generally co-occurs with metabolic disorders, but it is unclear which genes have a pleiotripic effect on NAFLD and metabolic traits. We performed a large-scale cross-trait association analysis to identify the overlapping genes between NAFLD and nine metabolic traits. Among all the metabolic traits, we found that obesity and type II diabetes are associated with NAFLD. Then, a multitrait association analysis among NAFLD, obesity and type II diabetes was conducted to improve the overall statistical power. We identified 792 significant variants by a cross-trait meta-analysis involving 100 pleiotripic genes. Moreover, we detected another two common genes by a genome-wide gene test. The results from the pathway enrichment analysis show that the 102 shared risk genes are enriched in cancer, diabetes, insulin secretion, and other related pathways. This study can help us understand the molecular mechanisms underlying comorbid NAFLD and metabolic disorders. [ABSTRACT FROM AUTHOR]

• Unique study of association of multiple EDCs with genome-wide DNA methylation. • EDC excretions in 24-hour urine were associated with 20 DNA methylation markers. • EDC-associated CpGs were associated with glucose, HbA1c, lipids and blood pressure. • EDC-associated CpGs may be functional markers for assessing metabolic alterations. Exposure to environmental endocrine disrupting chemicals (EDCs) may play an important role in the epidemic of metabolic diseases. Epigenetic alterations may functionally link EDCs with gene expression and metabolic traits. We aimed to evaluate metabolic-related effects of the exposure to endocrine disruptors including five parabens, three bisphenols, and 13 metabolites of nine phthalates as measured in 24-hour urine on epigenome-wide DNA methylation. A blood-based epigenome-wide association study was performed in 622 participants from the Lifelines DEEP cohort using Illumina Infinium HumanMethylation450 methylation data and EDC excretions in 24-hour urine. Out of the 21 EDCs, 13 compounds were detected in >75% of the samples and, together with bisphenol F, were included in these analyses. Furthermore, we explored the putative function of identified methylation markers and their correlations with metabolic traits. We found 20 differentially methylated cytosine-phosphate-guanines (CpGs) associated with 10 EDCs at suggestive p -value < 1 × 10−6, of which four, associated with MEHP and MEHHP, were genome-wide significant (Bonferroni-corrected p -value < 1.19 × 10−7). Nine out of 20 CpGs were significantly associated with at least one of the tested metabolic traits, such as fasting glucose, glycated hemoglobin, blood lipids, and/or blood pressure. 18 out of 20 EDC-associated CpGs were annotated to genes functionally related to metabolic syndrome, hypertension, obesity, type 2 diabetes, insulin resistance and glycemic traits. The identified DNA methylation markers for exposure to the most common EDCs provide suggestive mechanism underlying the contributions of EDCs to metabolic health. Follow-up studies are needed to unravel the causality of EDC-induced methylation changes in metabolic alterations. [ABSTRACT FROM AUTHOR]

Selection of plant material with desired traits from different populations can be difficult, if not impossible, when evaluation methods are not standardized. Discerning comparable fruit postharvest traits among populations is particularly problematic because techniques and reporting protocols are often unique or non-existent for those crucial to quality and storability. Moreover, difficulties evaluating postharvest traits may be exacerbated by the dynamic nature of fruit ripening, introducing error even into intrapopulation comparisons. With the advent of biochemical phenotyping of fruit quality, opportunities to standardize evaluation of these and other important fruit postharvest traits are materializing. Standardized trait evaluation among breeding programs and, most importantly, germplasm collections is expected to allow more precise comparison between populations, expediting integration of economically important fruit quality, traits into new populations as well as facilitating marker discovery. [ABSTRACT FROM AUTHOR]