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The accumulation of senescent cells drives inflammaging and increases morbidity of chronic inflammatory lung diseases. Immune responses are built upon dynamic changes in cell metabolism that supply energy and substrates for cell proliferation, differentiation, and activation. Metabolic changes imposed by environmental stress and inflammation on immune cells and tissue microenvironment are thus chiefly involved in the pathophysiology of allergic and other immune‐driven diseases. Altered cell metabolism is also a hallmark of cell senescence, a condition characterized by loss of proliferative activity in cells that remain metabolically active. Accelerated senescence can be triggered by acute or chronic stress and inflammatory responses. In contrast, replicative senescence occurs as part of the physiological aging process and has protective roles in cancer surveillance and wound healing. Importantly, cell senescence can also change or hamper response to diverse therapeutic treatments. Understanding the metabolic pathways of senescence in immune and structural cells is therefore critical to detect, prevent, or revert detrimental aspects of senescence‐related immunopathology, by developing specific diagnostics and targeted therapies. In this paper, we review the main changes and metabolic alterations occurring in senescent immune cells (macrophages, B cells, T cells). Subsequently, we present the metabolic footprints described in translational studies in patients with chronic asthma and chronic obstructive pulmonary disease (COPD), and review the ongoing preclinical studies and clinical trials of therapeutic approaches aiming at targeting metabolic pathways to antagonize pathological senescence. Because this is a recently emerging field in allergy and clinical immunology, a better understanding of the metabolic profile of the complex landscape of cell senescence is needed. The progress achieved so far is already providing opportunities for new therapies, as well as for strategies aimed at disease prevention and supporting healthy aging. [ABSTRACT FROM AUTHOR]

The article discusses the basophil activation test (BAT) as a diagnostic tool for managing patients with IgE-mediated allergies. The authors emphasize the need for standardization and external quality assurance (EQA) of laboratory protocols and results interpretation. A task force was created to develop a consensus protocol for BAT-EQA, and round robin tests were conducted to train participating laboratories. The article presents a proposed standard operating protocol for BAT and highlights the importance of EQA for routine implementation of the assay. The authors conclude that the consensus protocol provides acceptable inter- and intralaboratory variability and can be implemented across Europe. [Extracted from the article]

A QTL for host response to porcine reproductive and respiratory syndrome (PRRS) was identified in a previous study. The SNP WUR10000125 (WUR) is used as a tag SNP for this QTL. The favorable (B) allele at this SNP is in low frequency in commercial populations, possibly because this allele is unfavorably associated with an important trait under nonchallenging conditions and, therefore, may have been selected against. Therefore, objectives of this study were to estimate the effect of WUR on traits under selection in commercial lines under nonchallenging conditions and to estimate the effect of WUR genotype of parents on performance of crossbred progeny in a commercial-like environment. Data were collected on 4 purebred lines: a Landrace dam line (D1), a Large White dam line (D2), a synthetic sire line (S1), and a Pietrain sire line (S2). Traits analyzed included total number born, number stillborn, farrowing survival, lactation survival, litter mortality, daily feed intake (DFI), backfat, average test daily gain (TGR), average lifetime daily gain (LGR), and Topigs Norsvin selection index (TSI) value, indicative of overall economic value. Deregressed EBV were calculated for each trait (except TSI) and analyzed within line. In the S1 line, AB and BB pigs had significantly lower TGR (P = 0.002) and LGR (P = 0.001) than AA pigs but also lower DFI (P = 0.004). Conversely, AB and BB pigs had significantly higher DFI (P < 0.001) and AB pigs had significantly higher TGR (P = 0.03) than AA pigs in the S2 line. The effect of WUR on TSI was not significant for any line (P ? 0.15). Analyses of phenotypic records collected on crossbred progeny of S1 sires and D1 x D2 F1 females showed no significant effect of parent WUR genotype on DFI, backfat, TGR, or LGR (P ? 0.07). In conclusion, the effect of WUR was nonsignificant for most traits but the magnitude and direction of the effect differed by trait and by line. The favorable allele for host response to PRRS was associated with greater DFI and a tendency for greater TGR in the S2 line, but the opposite direction of effect was detected for the S1 line. Regardless of the effect on individual traits, no significant effect of WUR on TSI was detected for any line. Therefore, selecting for the B allele is expected to result in progeny with increased resistance to PRRS without compromising overall economic value under normal, nonchallenging conditions.

The first attempts of applying marker-assisted selection (MAS) in animal breeding were not very successful because the identification of markers closely linked to QTL using low-density microsatellite panels was difficult. More recently, the use of high-density SNP panels in genome-wide association studies (GWAS) have increased the power and precision of identifying markers linked to QTL, which offer new possibilities for MAS. However, when GWAS started to be performed, the focus of many breeders had already shifted from the use of MAS to the application of genomic selection (using all available markers without any preselection of markers linked to QTL). In this study, we aimed to evaluate the prediction accuracy of a MAS approach that accounts for GWAS findings in the prediction models by including the most significant SNP from GWAS as a fixed effect in the marker-assisted BLUP (MA-BLUP) and marker-assisted genomic BLUP (MA-GBLUP) prediction models. A second aim was to compare the prediction accuracies from the marker-assisted models with those obtained from a Bayesian variable selection (BVS) model. To compare the prediction accuracies of traditional BLUP, MA-BLUP, genomic BLUP (GBLUP), MA-GBLUP, and BVS, we applied these models to the trait onumber of teatso in 4 distinct pig populations, for validation of the results. The most significant SNP in each population was located at approximately 103.50 Mb on chromosome 7. Applying MAS by accounting for the most significant SNP in the prediction models resulted in improved prediction accuracy for number of teats in all evaluated populations compared with BLUP and GBLUP. Using MA-BLUP instead of BLUP, the increase in prediction accuracy ranged from 0.021 to 0.124, whereas using MA-GBLUP instead of GBLUP, the increase in prediction accuracy ranged from 0.003 to 0.043. The BVS model resulted in similar or higher prediction accuracies than MA-GBLUP. For the trait number of teats, BLUP resulted in the lowest prediction accuracies whereas the highest were observed when applying MA-GBLUP or BVS. In the same data set, MA-BLUP can yield similar or superior accuracies compared with GBLUP. The superiority of MA-GBLUP over traditional GBLUP is more pronounced when training populations are smaller and when relationships between training and validation populations are smaller. Marker-assisted GBLUP did not outperform BVS but does have implementation advantages in large-scale evaluations.

As feed costs continue to rise and efficiency during finishing is emphasized, the impact of selecting for more efficient grow/finish pigs on reproductive performance and feed efficiency of sows must be evaluated. Therefore, the objectives of this study were to evaluate correlated responses for sow reproductive performance and lactation feed efficiency to selection for residual feed intake (RFI) during the grow/finish phase of production (RFI.sub.G/F) in 2 selection lines of pigs developed at Iowa State University (Ames, IA) and to estimate heritabilities of these traits. One line was selected over 7 generations for decreased RFIG/F (low RFI [LRFI] line) and the other line was randomly selected for 5 generations and then selected for increased RFI.sub.G/F (high RFI [HRFI] line). After 7 generations of selection, LRFI sows had 1.0 more piglets farrowed (P = 0.11) compared with HRFI sows, 1.3 more pigs born alive (P < 0.05), similar farrowing survival, 0.4 fewer mummies (P < 0.01), and more piglets weaned, both by litter (1.6 more; P < 0.01) and by sow (1.1 more; P < 0.01). Low RFI sows consumed 25 kg less feed and lost 9.8 kg more BW, 7.0 kg more fat mass, and 3.1 mm more backfat than HRFI sows (P < 0.001) during lactation. Although LRFI sows had a greater negative energy balance (?19.8 vs. -8.0 MJ ME/d; P < 0.001), they had better RFI during lactation (-28.6 vs. 8.2 kg; P < 0.0001), and the trend was for LRFI sows to have better lactation efficiency (61.3 vs. 57.8%; P = 0.47) than HRFI sows. Heritabilities for sow weights, sow body composition, sow maintenance requirements (estimated from BW), and piglet birth weight were high (h2 > 0.4, SE < 0.07). Traits pertaining to piglet growth during lactation and mobilization of body tissue of the sow were moderately heritable (0.2 < h2 < 0.4, SE < 0.07). In conclusion, selection for decreased RFIG/F has favorably affected piglet performance and lactation efficiency but has unfavorably affected sow body condition loss and energy balance during lactation. These results indicate that pigs selected for increased efficiency during growufinish are better able to direct resources where needed during other life history phases, that is, reproduction and lactation.

Seasonal infertility is often observed as anestrus and a lower conception rate resulting in a reduced farrowing rate (FR) during late summer and early autumn. This is often regarded as an effect of heat stress; however, we observed a reduction in the FR of sows even after correcting for ambient temperature in our data. Therefore, we added change in photoperiod in the analysis of FR considering its effect on sow fertility. Change in photoperiod was modeled using the cosine of the day of first insemination within a year. On an average, the FR decreased by 2% during early autumn with decreasing daily photoperiod compared with early summer with almost no change in daily photoperiod. It declined 0.2% per degree Celsius of ambient temperature above 19.2[degrees]C. This result is a step forward in disentangling the 2 environmental components responsible for seasonal infertility. Our next aim was to estimate the magnitude of genetic variation in FR in response to change in photoperiod and ambient temperature to explore opportunities for selecting pigs to have a constant FR throughout the year. We used reaction norm models to estimate additive genetic variation in response to change in photoperiod and ambient temperature. The results revealed a larger genetic variation at stressful environments when daily photoperiod decreased and ambient temperatures increased above 19.2[degrees]C compared with neutral environments. Genetic correlations between stressful environments and nonstressful environments ranged from 0.90 ([+ or -]0.03) to 0.46 ([+ or -]0.13) depending on the severity of the stress, indicating changes in expression of FR depending on the environment. The genetic correlation between responses of pigs to changes in photoperiod and to those in ambient temperature were positive, indicating that pigs tolerant to decreasing daily photoperiod are also tolerant to high ambient temperatures. Therefore, selection for tolerance to decreasing daily photoperiod should also increase tolerance to high ambient temperatures or vice versa.