Immunoglobulin G galactosylation and sialylation are associated with pregnancy-induced improvement of rheumatoid arthritis and the postpartum flare: results from a large prospective cohort study

Introduction  

Improvement of rheumatoid arthritis (RA) during pregnancy has been causatively associated with increased galactosylation of immunoglobulin G (IgG) N-glycans. Since previous studies were small, did not include the postpartum flare and did not study sialylation, these issues were addressed in the present study.     

The effect of genomic information on optimal contribution selection in livestock breeding programs

Background

Long-term benefits in animal breeding programs require that increases in genetic merit be balanced with the need to maintain diversity (lost due to inbreeding). This can be achieved by using optimal contribution selection. The availability of high-density DNA marker information enables the incorporation of genomic data into optimal contribution selection but this raises the question about how this information affects the balance between genetic merit and diversity.

Methods

The effect of using genomic information in optimal contribution selection was examined based on simulated and real data on dairy bulls. We compared the genetic merit of selected animals at various levels of co-ancestry restrictions when using estimated breeding values based on parent average, genomic or progeny test information. Furthermore, we estimated the proportion of variation in estimated breeding values that is due to within-family differences.

Results

Optimal selection on genomic estimated breeding values increased genetic gain. Genetic merit was further increased using genomic rather than pedigree-based measures of co-ancestry under an inbreeding restriction policy. Using genomic instead of pedigree relationships to restrict inbreeding had a significant effect only when the population consisted of many large full-sib families; with a half-sib family structure, no difference was observed. In real data from dairy bulls, optimal contribution selection based on genomic estimated breeding values allowed for additional improvements in genetic merit at low to moderate inbreeding levels. Genomic estimated breeding values were more accurate and showed more within-family variation than parent average breeding values; for genomic estimated breeding values, 30 to 40% of the variation was due to within-family differences. Finally, there was no difference between constraining inbreeding via pedigree or genomic relationships in the real data.

Conclusions

The use of genomic estimated breeding values increased genetic gain in optimal contribution selection. Genomic estimated breeding values were more accurate and showed more within-family variation, which led to higher genetic gains for the same restriction on inbreeding. Using genomic relationships to restrict inbreeding provided no additional gain, except in the case of very large full-sib families.     

Comparing genomic prediction accuracy from purebred,crossbred and combined purebred and crossbred reference populations in sheep

Background

The accuracy of genomic prediction depends largely on the number of animals with phenotypes and genotypes. In some industries, such as sheep and beef cattle, data are often available from a mixture of breeds, multiple strains within a breed or from crossbred animals. The objective of this study was to compare the accuracy of genomic prediction for several economically important traits in sheep when using data from purebreds, crossbreds or a combination of those in a reference population.

Methods

The reference populations were purebred Merinos, crossbreds of Border Leicester (BL), Poll Dorset (PD) or White Suffolk (WS) with Merinos and combinations of purebred and crossbred animals. Genomic breeding values (GBV) were calculated based on genomic best linear unbiased prediction (GBLUP), using a genomic relationship matrix calculated based on 48 599 Ovine SNP (single nucleotide polymorphisms) genotypes. The accuracy of GBV was assessed in a group of purebred industry sires based on the correlation coefficient between GBV and accurate estimated breeding values based on progeny records.

Results

The accuracy of GBV for Merino sires increased with a larger purebred Merino reference population, but decreased when a large purebred Merino reference population was augmented with records from crossbred animals. The GBV accuracy for BL, PD and WS breeds based on crossbred data was the same or tended to decrease when more purebred Merinos were added to the crossbred reference population. The prediction accuracy for a particular breed was close to zero when the reference population did not contain any haplotypes of the target breed, except for some low accuracies that were obtained when predicting PD from WS and vice versa.

Conclusions

This study demonstrates that crossbred animals can be used for genomic prediction of purebred animals using 50 k SNP marker density and GBLUP, but crossbred data provided lower accuracy than purebred data. Including data from distant breeds in a reference population had a neutral to slightly negative effect on the accuracy of genomic prediction. Accounting for differences in marker allele frequencies between breeds had only a small effect on the accuracy of genomic prediction from crossbred or combined crossbred and purebred reference populations.     

Bioenergetic properties of human sarcoma cells help define sensitivity to metabolic inhibitors

Sarcomas represent a diverse group of malignancies with distinct molecular and pathological features. A better understanding of the alterations associated with specific sarcoma subtypes is critically important to improve sarcoma treatment. Renewed interest in the metabolic properties of cancer cells has led to an exploration of targeting metabolic dependencies as a therapeutic strategy. In this study, we have characterized key bioenergetic properties of human sarcoma cells in order to identify metabolic vulnerabilities between sarcoma subtypes. We have also investigated the effects of compounds that inhibit glycolysis or mitochondrial respiration, either alone or in combination, and examined relationships between bioenergetic parameters and sensitivity to metabolic inhibitors. Using 2-deoxy-D-glucose (2-DG), a competitive inhibitor of glycolysis, oligomycin, an inhibitor of mitochondrial ATP synthase, and metformin, a widely used anti-diabetes drug and inhibitor of complex I of the mitochondrial respiratory chain, we evaluated the effects of metabolic inhibition on sarcoma cell growth and bioenergetic function. Inhibition of glycolysis by 2-DG effectively reduced the viability of alveolar rhabdomyosarcoma cells vs. embryonal rhabdomyosarcoma, osteosarcoma, and normal cells. Interestingly, inhibitors of mitochondrial respiration did not significantly affect viability, but were able to increase sensitivity of sarcomas to inhibition of glycolysis. Additionally, inhibition of glycolysis significantly reduced intracellular ATP levels, and sensitivity to 2-DG-induced growth inhibition was related to respiratory rates and glycolytic dependency. Our findings demonstrate novel relationships between sarcoma bioenergetics and sensitivity to metabolic inhibitors, and suggest that inhibition of metabolic pathways in sarcomas should be further investigated as a potential therapeutic strategy.     

Autoantibody systems in rheumatoid arthritis: specificity, sensitivity and diagnostic value

This review focuses on the mechanisms of stress response in the synovial tissue of rheumatoid arthritis. The major stress factors, such as heat stress, shear stress, proinflammatory cytokines and oxidative stress, are discussed and reviewed, focusing on their potential to induce a stress response in the synovial tissue. Several pathways of stress signalling molecules are found to be activated in the synovial membrane of rheumatoid arthritis; of these the most important examples are heat shock proteins, mitogen-activated protein kinases, stress-activated protein kinases and molecules involved in the oxidative stress pathways. The expression of these pathways in vitro and in vivo as well as the consequences of stress signalling in the rheumatoid synovium are discussed. Stress signalling is part of a cellular response to potentially harmful stimuli and thus is essentially involved in the process of synovitis. Stress signalling pathways are therefore new and promising targets of future anti-rheumatic therapies.     

Anaerobic degradation of papermill sludge in a two-phase digester containing rumen microorganisms and colonized polyurethane foam

Summary The use of reticulated polyurethane foam as a support material for the immobilization of methanogenic associations and its application to the anaerobic treatment of fine particulate solid wastes was investigated. The colonization of polyurethane support particles in a continuous upflow reactor fed on a mixture of acetate, propionate and butyrate, was both rapid and dense. The combination of rumen microorganisms and colonized support particles in a two-phase digester resulted in an efficient anaerobic decomposition of papermill sludge.     

Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE): advances and perspectives

The recent trend in science is to assay as many biological molecules as possible within a single experiment. This trend is evident in proteomics where the aim is to characterize thousands of proteins within cells, tissues, and organisms. While advances in mass spectrometry have been critical, developments made in two-dimensional PAGE (2D-PAGE) have also played a major role in enabling proteomics. In this review, we discuss and highlight the advances made in 2D-PAGE over the past 25 years that have made it a foundational tool in proteomic research.     

Comparison of 1D and 2D NMR spectroscopy for metabolic profiling

High-resolution, liquid state nuclear magnetic resonance (NMR) spectroscopy is a popular platform for metabolic profiling because the technique is nondestructive, quantitative, reproducible, and the spectra contain a wealth of biochemical information. Because of the large dynamic range of metabolite concentrations in biofluids, statistical analyses of one-dimensional (1D) proton NMR data tend to be biased toward selecting changes in more abundant metabolites. Although two-dimensional (2D) proton-proton experiments can alleviate spectral crowding, they have been mainly used for structural determination. In this study, 2D total correlation spectroscopy NMR was used to compare the global metabolic profiles of urine obtained from wild-type and Abcc6-knockout mice. The 2D data were compared to an improved 1D experiment in which signal contributions from macromolecules and the urea peak have been spectroscopically removed for more accurate quantitation of low-abundance metabolites. Although statistical models from both 1D and 2D data could differentiate samples acquired from the two groups of mice, only the 2D spectra allowed the characterization of statistically relevant changes in the low-abundance metabolites. While acquisition of the 2D data require more time, the data obtained resulted in a more meaningful and comprehensive metabolic profile, aided in metabolite identifications, and minimized ambiguities in peak assignments.     

Comparison of strong cation exchange and SDS-PAGE fractionation for analysis of multiprotein complexes

Affinity purification of protein complexes followed by identification using liquid chromatography/mass spectrometry (LC-MS/MS) is a robust method to study the fundamental process of protein interaction. Although affinity isolation reduces the complexity of the sample, fractionation prior to LC-MS/MS analysis is still necessary to maximize protein coverage. In this study, we compared the protein coverage obtained via LC-MS/MS analysis of protein complexes prefractionated using two commonly employed methods, SDS-PAGE and strong cation exchange chromatography (SCX). The two complexes analyzed focused on the nuclear proteins Bmi-1 and GATA3 that were expressed within the cells at low and high levels, respectively. Prefractionation of the complexes at the peptide level using SCX consistently resulted in the identification of approximately 3-fold more proteins compared to separation at the protein level using SDS-PAGE. The increase in the number of identified proteins was especially pronounced for the Bmi-1 complex, where the target protein was expressed at a low level. The data show that prefractionation of affinity isolated protein complexes using SCX prior to LC-MS/MS analysis significantly increases the number of identified proteins and individual protein coverage, particularly for target proteins expressed at low levels.