Selective genotyping strategies for a sib test scheme of a broiler breeder program.

BACKGROUND: In broiler breeding, genotype-by-environment interaction is known to result in a genetic correlation between body weight measured in bio-secure and commercial environments that is substantially less than 1. Thus, measuring body weights on sibs of selection candidates in a commercial environment and genotyping them could increase genetic progress. Using real data, the aim of this study was to evaluate which genotyping strategy and which proportion of sibs placed in the commercial environment should be genotyped to optimize a sib-testing breeding program in broilers. Phenotypic body weight and genomic information were collected on all sibs raised in a commercial environment, which allowed to retrospectively analyze different sampling strategies and genotyping proportions. RESULTS: Accuracies of genomic estimated breeding values (GEBV) obtained with the different genotyping strategies were assessed by computing their correlation with GEBV obtained when all sibs in the commercial environment were genotyped. Results showed that, compared to random sampling (RND), genotyping sibs with extreme phenotypes (EXT) resulted in higher GEBV accuracy across all genotyping proportions, especially for genotyping proportions of 12.5% or 25%, which resulted in correlations of 0.91 vs 0.88 for 12.5% and 0.94 vs 0.91 for 25% genotyped. Including pedigree on birds with phenotype in the commercial environment that were not genotyped increased accuracy at lower genotyping proportions, especially for the RND strategy (correlations of 0.88 vs 0.65 at 12.5% and 0.91 vs 0.80 at 25%), and a smaller but still substantial increase in accuracy for the EXT strategy (0.91 vs 0.79 for 12.5% and 0.94 vs 0.88 for 25% genotyped). Dispersion bias was virtually absent for RND if 25% or more birds were genotyped. However, GEBV were considerably inflated for EXT, especially when the proportion genotyped was low, which was further exacerbated if the pedigree of non-genotyped sibs was excluded. CONCLUSIONS: When less than 75% of all animals placed in a commercial environment are genotyped, it is recommended to use the EXT strategy, because it yields the highest accuracy. However, caution should be taken when interpreting the resulting GEBV because they will be over-dispersed. When 75% or more of the animals are genotyped, random sampling is recommended because it yields virtually no bias of GEBV and results in similar accuracies as the EXT strategy.

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm.

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner-Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.

Phenotypic variation in milk fatty acid composition and its association with stearoyl-CoA desaturase 1 (SCD1) gene polymorphisms in Gir cows.

Individual variation in milk fatty acid (FA) composition has been partially attributed to stearoyl-CoA desaturase 1 (SCD1) gene polymorphisms in taurine breeds, but much less is known for Zebu breeds. This study investigated the phenotypic variation in milk FA composition, and the influence of SCD1 variants on this trait and on milk fat desaturase indices (DI) in Gir cows. The functional impact of SCD1 variants was predicted using bioinformatics tools. Milk and blood samples were collected from 312 cows distributed in 10 herds from five states of Brazil. SCD1 variants were identified through target sequencing, and milk FA composition was determined by gas chromatography. Phenotypic variation in milk FA composition fell within the range reported for taurine breeds, with SCD18 index showing the lowest variation among the DI. Fourteen SCD1 variants were identified, six of which not previously described. Regarding the A293V polymorphism, all cows were homozygous for the C allele (coding for alanine), whereas all genotypes were detected for the second SNP affecting the 293 codon (G > A), with compelling evidence for functional effects. Significant associations (based on raw p-values) were found between this SNP and C12:0, cis-9, trans-11 CLA and short-chain FA, and between another SNP (rs523411937) and C15:0 and odd-chain linear FA. A new SNP on Chr26:21277069 was associated with trans-11 C18:1, cis-9, trans-11 CLA, C18:3 n-3 and n-3 FA. These findings indicate that SCD1 polymorphisms also contributes to the phenotypic variation in milk FA composition of Gir cows, with potential use in their breeding programmes.

Strategic Bidding of Retailers in Wholesale Markets: Continuous Intraday Markets and Hybrid Forecast Methods.

The deregulation process of the electricity sector has led to competition in wholesale and retail markets. In particular, retailers submit bids to wholesale markets to satisfy the energy needs associated with portfolios of end-use customers. This paper describes a strategic process for retailers bidding in a wholesale market composed of a day-ahead market, an intraday market, and a balancing market. It considers a market design that involves a hybrid model for the intraday market, based on daily auctions and a continuous procedure. The paper also presents a computational study to illustrate and test both the market design and the strategic bidding process of retailers. The results confirm the advantages of considering a continuous intraday market, show that bidding in short-term markets is more beneficial than bidding in medium-term markets, and indicate important aspects to consider when selecting customers to add to the portfolios of retailers.

Phylogenomic Discordance in the Eared Seals is best explained by Incomplete Lineage Sorting following Explosive Radiation in the Southern Hemisphere.

The phylogeny and systematics of fur seals and sea lions (Otariidae) have long been studied with diverse data types, including an increasing amount of molecular data. However, only a few phylogenetic relationships have reached acceptance because of strong gene-tree species tree discordance. Divergence times estimates in the group also vary largely between studies. These uncertainties impeded the understanding of the biogeographical history of the group, such as when and how trans-equatorial dispersal and subsequent speciation events occurred. Here, we used high-coverage genome-wide sequencing for 14 of the 15 species of Otariidae to elucidate the phylogeny of the family and its bearing on the taxonomy and biogeographical history. Despite extreme topological discordance among gene trees, we found a fully supported species tree that agrees with the few well-accepted relationships and establishes monophyly of the genus Arctocephalus. Our data support a relatively recent trans-hemispheric dispersal at the base of a southern clade, which rapidly diversified into six major lineages between 3 and 2.5 Ma. Otaria diverged first, followed by Phocarctos and then four major lineages within Arctocephalus. However, we found Zalophus to be nonmonophyletic, with California (Zalophus californianus) and Steller sea lions (Eumetopias jubatus) grouping closer than the Galapagos sea lion (Zalophus wollebaeki) with evidence for introgression between the two genera. Overall, the high degree of genealogical discordance was best explained by incomplete lineage sorting resulting from quasi-simultaneous speciation within the southern clade with introgresssion playing a subordinate role in explaining the incongruence among and within prior phylogenetic studies of the family. [Hybridization; ILS; phylogenomics; Pleistocene; Pliocene; monophyly.].

Multisource and temporal variability in Portuguese hospital administrative datasets: Data quality implications.

BACKGROUND: Unexpected variability across healthcare datasets may indicate data quality issues and thereby affect the credibility of these data for reutilization. No gold-standard reference dataset or methods for variability assessment are usually available for these datasets. In this study, we aim to describe the process of discovering data quality implications by applying a set of methods for assessing variability between sources and over time in a large hospital database. METHODS: We described and applied a set of multisource and temporal variability assessment methods in a large Portuguese hospitalization database, in which variation in condition-specific hospitalization ratios derived from clinically coded data were assessed between hospitals (sources) and over time. We identified condition-specific admissions using the Clinical Classification Software (CCS), developed by the Agency of Health Care Research and Quality. A Statistical Process Control (SPC) approach based on funnel plots of condition-specific standardized hospitalization ratios (SHR) was used to assess multisource variability, whereas temporal heat maps and Information-Geometric Temporal (IGT) plots were used to assess temporal variability by displaying temporal abrupt changes in data distributions. Results were presented for the 15 most common inpatient conditions (CCS) in Portugal. MAIN FINDINGS: Funnel plot assessment allowed the detection of several outlying hospitals whose SHRs were much lower or higher than expected. Adjusting SHR for hospital characteristics, beyond age and sex, considerably affected the degree of multisource variability for most diseases. Overall, probability distributions changed over time for most diseases, although heterogeneously. Abrupt temporal changes in data distributions for acute myocardial infarction and congestive heart failure coincided with the periods comprising the transition to the International Classification of Diseases, 10th revision, Clinical Modification, whereas changes in the Diagnosis-Related Groups software seem to have driven changes in data distributions for both acute myocardial infarction and liveborn admissions. The analysis of heat maps also allowed the detection of several discontinuities at hospital level over time, in some cases also coinciding with the aforementioned factors. CONCLUSIONS: This paper described the successful application of a set of reproducible, generalizable and systematic methods for variability assessment, including visualization tools that can be useful for detecting abnormal patterns in healthcare data, also addressing some limitations of common approaches. The presented method for multisource variability assessment is based on SPC, which is an advantage considering the lack of gold standard for such process. Properly controlling for hospital characteristics and differences in case-mix for estimating SHR is critical for isolating data quality-related variability among data sources. The use of IGT plots provides an advantage over common methods for temporal variability assessment due its suitability for multitype and multimodal data, which are common characteristics of healthcare data. The novelty of this work is the use of a set of methods to discover new data quality insights in healthcare data.

Potential of human helminth therapy for resolution of inflammatory bowel disease: The future ahead.

Inflammatory bowel disease (IBD) is associated with a dysregulated mucosal immune response in the gastrointestinal tract. The number of patients with IBD has increased worldwide, especially in highly industrialized western societies. The population of patients with IBD in North America is forecasted to reach about four million by 2030; meanwhile, there is no definitive therapy for IBD. Current anti-inflammatory, immunosuppressive, or biological treatment may induce and maintain remission, but not all patients respond to these treatments. Recent studies explored parasitic helminths as a novel modality of therapy due to their potent immunoregulatory properties in humans. Research using IBD animal models infected with a helminth or administered helminth-derived products such as excretory-secretory products has been promising, and helminth-microbiota interactions exert their anti-inflammatory effects by modulating the host immunity. Recent studies also indicate that evidence that helminth-derived metabolites may play a role in anticolitic effects. Thus, the helminth shows a potential benefit for treatment against IBD. Here we review the current feasibility of "helminth therapy" from the laboratory for application in IBD management.

Economic values of reproductive, growth, feed efficiency and carcass traits in Nellore cattle.

A bioeconomic model was developed to calculate the economic value (ev) of reproductive and growth performance, feed efficiency and carcass traits of a seedstock Nellore herd. Data from a full-cycle cattle operation (1,436 dams) located in the Brazilian Cerrado were assessed. The ev was calculated by the difference in profit before and after one-unit improvement in the trait, with others remaining unchanged. The ev was standardized by the phenotypic standard deviation of each trait. Preweaning average daily gain (ADG) was the most economically important trait evaluated (R$ 58.04/animal/year), followed by age at first calving (R$ 44.35), postweaning ADG (R$ 31.43), weight at 450 days (R$ 25.36), accumulated productivity (R$ 21.43), ribeye area (R$ 21.35), calving interval (R$ 19.97), feed efficiency (R$ 15.24), carcass dressing per cent (R$ 8.27), weight at 120 days (R$ 6.22), weight at 365 days (R$ 6.06), weight at weaning (210 days, R$ 5.82), stayability (R$ 5.70) and the probability of early calving (R$ 0.32). The effects of all traits on profits are evidence that their selection may result in the economic and genetic progress of the herd if there is genetic variability.

All Patient Refined-Diagnosis Related Groups' (APR-DRGs) Severity of Illness and Risk of Mortality as predictors of in-hospital mortality.

The aims of this study were to assess All-Patient Refined Diagnosis-Related Groups' (APR-DRG) Severity of Illness (SOI) and Risk of Mortality (ROM) as predictors of in-hospital mortality, comparing with Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) scores. We performed a retrospective observational study using mainland Portuguese public hospitalizations of adult patients from 2011 to 2016. Model discrimination (C-statistic/ area under the curve) and goodness-of-fit (R-squared) were calculated. Our results comprised 4,176,142 hospitalizations with 5.9% in-hospital deaths. Compared to the CCI and ECI models, the model considering SOI, age and sex showed a statistically significantly higher discrimination in 49.6% (132 out of 266) of APR-DRGs, while in the model with ROM that happened in 33.5% of APR-DRGs. Between these two models, SOI was the best performer for nearly 20% of APR-DRGs. Some particular APR-DRGs have showed good discrimination (e.g. related to burns, viral meningitis or specific transplants). In conclusion, SOI or ROM, combined with age and sex, perform better than more widely used comorbidity indices. Despite ROM being the only score specifically designed for in-hospital mortality prediction, SOI performed better. These findings can be helpful for hospital or organizational models benchmarking or epidemiological analysis.

Sperm DNA damage and seminal antioxidant activity in subfertile men.

Supraphysiological ROS levels can lead to apoptosis, lipid peroxidation, and DNA and protein damage. This pilot study aimed to investigate the sperm oxidative damage in subfertile men, to describe the relationship between the antioxidant system and ROS. Sixty-four semen samples were categorised according to the evaluated routine parameters (WHO, WHO laboratory manual for the examination and processing of human semen, 2010). Results were cross-referenced with the DNA damage [Comet (n = 53) and TUNEL (n = 49) assays], antioxidant enzyme activity [SOD (n = 51), CAT (n = 48) and GST (n = 48)], and content of total thiols (n = 36), lipid hydroperoxides (n = 35) and MDA (n = 31). Compared to pathospermic samples, normozoospermic presented 40%-45% fewer spermatozoa with fragmented DNA, 19% fewer hydroperoxides, and slightly higher total thiols and MDA levels. Asthenozoospermic/asthenoteratozoospermic samples had the lowest GST activity. SOD and CAT showed a similar trend. Our results evidenced significant positive correlations between DNA damage and immotile spermatozoa; SOD and CAT, GST and total thiols; CAT and GST; total thiols and sperm concentration; and MDA levels and head/midpiece abnormalities and hydroperoxides. This work contributes to the existing body of knowledge by showing that the oxidative status correlates with the classic sperm analysis parameters. Oxidative stress and DNA damage evaluation might be a valuable diagnostic and prognostic tool in cases of idiopathic male subfertility.

Weighted single-step genome-wide association study and pathway analyses for feed efficiency traits in Nellore cattle.

The aim was to conduct a weighted single-step genome-wide association study to detect genomic regions and putative candidate genes related to residual feed intake, dry matter intake, feed efficiency (FE), feed conversion ratio, residual body weight gain, residual intake and weight gain in Nellore cattle. Several protein-coding genes were identified within the genomic regions that explain more than 0.5% of the additive genetic variance for these traits. These genes were associated with insulin, leptin, glucose, protein and lipid metabolisms; energy balance; heat and oxidative stress; bile secretion; satiety; feed behaviour; salivation; digestion; and nutrient absorption. Enrichment analysis revealed functional pathways (p-value < .05) such as neuropeptide signalling (GO:0007218), negative regulation of canonical Wingless/Int-1 (Wnt) signalling (GO:0090090), bitter taste receptor activity (GO:0033038), neuropeptide hormone activity (GO:0005184), bile secretion (bta04976), taste transduction (bta0742) and glucagon signalling pathway (bta04922). The identification of these genes, pathways and their respective functions should contribute to a better understanding of the genetic and physiological mechanisms regulating Nellore FE-related traits.

Differentially expressed genes identified through RNA-seq with extreme values of principal components for beef fatty acid in Nelore cattle.

The aim of this study was to identify differentially expressed genes (DEG) in the Longissimus thoracis muscle of Nelore cattle related to fatty acid (FA) profile through RNA sequencing and principal component analysis (PCA). Two groups of 10 animals each were selected containing PC1 and PC2 extreme DEG values (HIGH × LOW) for each FA group. The intramuscular fat (IMF) was compared between cluster groups by ANOVA, and only the sum of monounsaturated FA (MUFA) and ω3 showed significant differences (p < .05). Interestingly, the highest percentage (95%) of phenotypic variation explained by the sum of the first two PC was observed for ω3, which also displayed the lowest number of DEG (n = 1). The lowest percentage (59%) was observed for MUFA, which also revealed the largest number of DEG (n = 66). Since only MUFA and ω3 exhibited significant differences between cluster groups, we can conclude that the differences observed for the remaining groups are not due to the percentage of IMF. Several genes that have been previously associated with meat quality and FA traits were identified as DEG in this study. The functional analysis revealed one KEGG pathway and eight GO terms as significant (p < .05), in which we highlighted the purine metabolism, glycolytic process, adenosine triphosphate binding and bone development. These results strongly contribute to the knowledge of the biological mechanisms involved in meat FA profile of Nelore cattle.

A completely digital workflow for an interim implant-supported crown: A clinical report.

This article describes a completely digital workflow for the diagnostic phase, surgical and prosthetic planning, extraction, immediate single implant placement by guided surgery, and interim implant-supported crown. From a virtual impression, zirconia abutments and a polymethylmethacrylate (PMMA) interim crown was planned in a computer-aided design (CAD) software program. This workflow shortened the time required for chairside placement of an interim restoration with enhanced function and esthetics while restoring an anterior mandibular tooth lost after trauma.

Partially replacing sorghum silage with cactus (Opuntia stricta) cladodes in a soybean oil-supplemented diet markedly increases trans-11 18:1, cis-9, trans-11 CLA and 18:2 n-6 contents in cow milk.

Based on low 18:0 contents observed in milk fat of cows fed cactus cladodes (CC), we hypothesized that including Opuntia stricta cladodes in a soybean oil (SO)-supplemented diet would promote incomplete rumen biohydrogenation of supplemental PUFA, leading to increased trans-11 18:1 and cis-9, trans-11 CLA contents in milk. Twelve Holstein cows were used in a two-period study: (a) Baseline: all cows received a total mixed ration (TMR) composed of sorghum silage (SS) and a concentrate containing no SO for 14 days; (b) Treatment: cows received one of the following SO-supplemented diets for 21 days: (1) SS-TMR: a TMR composed of SS and a SO-enriched concentrate, (2) CC-TMR: a TMR containing CC as a partial substitute for SS plus the SO-enriched concentrate, and (3) CC-PMR: same diet as in treatment 2, but CC were mixed with the SO-enriched concentrate and fed as a partial mixed ration (PMR). Both CC diets increased relative abundances of trans-11 18:1, cis-9, trans-11 CLA, and 18:2 n-6 in milk fat, whereas opposite effects were observed on 18:0 and cis-9 18:1. Proportion of 18:2 n-6 increased, and cis-9, trans-11 CLA tended to increase with CC-PMR as compared to CC-TMR, whereas 18:3 n-3 was higher with CC-PMR than with SS-TMR. Proportions of several odd- and branched-chain fatty acids, certain 18:1 isomers, and trans-9, cis-11 CLA changed with CC diets, notably with CC-PMR. Milk yield and intake of most nutrients (except fibre) increased or tended to increase with the CC diets, whereas gross milk composition was unaltered. Stearoyl-CoA desaturase-1 index for C18 (SCD18 ) was higher with CC-PMR than with SS-TMR, and milk n-6:n-3 FA ratio and apparent transfer of 18:2 n-6 to milk increased with CC diets. These results indicate that Opuntia stricta cladodes can be a valuable feed ingredient for improving the nutraceutical value of milk fat.

Analysis of the Columellar-Labial Angle in Perioral Aging.

BACKGROUND: The young face presents a convex and rounded aspect; with aging, this changes to a more concave and flattened aspect, with the angles of the face undergoing changes through soft tissue repositioning and bone remodeling. The columellar-labial angle is one of the features that change. OBJECTIVES: The objective of the present study was to analyze the columellar-labial angle at different stages of adult life and assess its contribution to facial changes during aging. METHODS: We analyzed a database (December 2017-March 2018) of 722 female patients, aged 21 to 88 years, and obtained anthropometric measurements of the columellar-labial angle with a Canfield Vectra 3D image analysis system. Our database originates from a private clinic where all patients were registered and photographed. RESULTS: Our analysis showed a decrease in the columellar-labial angle, mainly observed from patients aged 60 years and older. This decrease can be explained by the absorption of body structures that occurs during aging. CONCLUSIONS: The columellar-labial angle decreases with age, and this finding may provide a better understanding of the transformation that occurs with aging. Obtaining a clearer view of the changes that occur in the face will improve treatments for facial rejuvenation, either conservative or surgical, and provide a basis for future studies and knowledge expansion.

Would large dataset sample size unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? The case for body weight in broilers.

BACKGROUND: Deep neural networks (DNN) are a particular case of artificial neural networks (ANN) composed by multiple hidden layers, and have recently gained attention in genome-enabled prediction of complex traits. Yet, few studies in genome-enabled prediction have assessed the performance of DNN compared to traditional regression models. Strikingly, no clear superiority of DNN has been reported so far, and results seem highly dependent on the species and traits of application. Nevertheless, the relatively small datasets used in previous studies, most with fewer than 5000 observations may have precluded the full potential of DNN. Therefore, the objective of this study was to investigate the impact of the dataset sample size on the performance of DNN compared to Bayesian regression models for genome-enable prediction of body weight in broilers by sub-sampling 63,526 observations of the training set. RESULTS: Predictive performance of DNN improved as sample size increased, reaching a plateau at about 0.32 of prediction correlation when 60% of the entire training set size was used (i.e., 39,510 observations). Interestingly, DNN showed superior prediction correlation using up to 3% of training set, but poorer prediction correlation after that compared to Bayesian Ridge Regression (BRR) and Bayes Cπ. Regardless of the amount of data used to train the predictive machines, DNN displayed the lowest mean square error of prediction compared to all other approaches. The predictive bias was lower for DNN compared to Bayesian models, across all dataset sizes, with estimates close to one with larger sample sizes. CONCLUSIONS: DNN had worse prediction correlation compared to BRR and Bayes Cπ, but improved mean square error of prediction and bias relative to both Bayesian models for genome-enabled prediction of body weight in broilers. Such findings, highlights advantages and disadvantages between predictive approaches depending on the criterion used for comparison. Furthermore, the inclusion of more data per se is not a guarantee for the DNN to outperform the Bayesian regression methods commonly used for genome-enabled prediction. Nonetheless, further analysis is necessary to detect scenarios where DNN can clearly outperform Bayesian benchmark models.

Macrophages treated with antigen from the tapeworm Hymenolepis diminuta condition CD25+ T cells to suppress colitis.

Macrophages play central roles in immunity as early effectors and modulating adaptive immune reponses; we implicated macrophages in the anticolitic effect of infection with the tapeworm Hymenolepis diminuta. Here, gene arrays revealed that H. diminuta antigen (HdAg) evoked a program in murine macrophages distinct from that elicited by IL-4. Further, HdAg suppressed LPS-evoked release of TNF-α and IL-1ß from macrophages via autocrine IL-10 signaling. In assessing the ability of macrophages treated in vitro with an extract of H. diminuta [M(HdAg)] to affect disease, intravenous, but not peritoneal, injection of M(HdAg) protected wild-type but not RAG1-/- mice from dinitrobenzene sulphonic acid (DNBS)-induced colitis. Administration of splenic CD4+ T cells from in vitro cocultures with M(HdAg), but not those cocultured with M(IL-4) cells, inhibited DNBS-induced colitis; fractionation of the T-cell population indicated that the CD4+CD25+ T cells from cocultures with M(HdAg) drove the suppression of DNBS-induced colitis. Use of IL-4-/- or IL-10-/- CD4+ T cells revealed that neither cytokine alone from the donor cells was essential for the anticolitic effect. These data illustrate that HdAg evokes a unique regulatory program in macrophages, identifies HdAg-evoked IL-10 suppression of macrophage activation, and reveals the ability of HdAg-treated macrophages to educate ( i.e., condition) and mobilize CD4+CD25+ T cells, which could be deployed to treat colonic inflammation.-Reyes, J. L., Lopes, F., Leung, G., Jayme, T. S., Matisz, C. E., Shute, A., Burkhard, R., Carneiro, M., Workentine, M. L., Wang, A., Petri, B., Beck, P. L., Geuking, M. B., McKay, D. M., Macrophages treated with antigen from the tapeworm Hymenolepis diminuta condition CD25+ T cells to suppress colitis.

Brain TNF drives post-inflammation depression-like behavior and persistent pain in experimental arthritis.

Patients with rheumatoid arthritis experience chronic pain, depression and fatigue, even when inflammation of the joints is well controlled. To study the relationship between arthritis, depression, and sustained pain when articular inflammation is no longer observed, we tested the hypothesis that brain TNF drives post-inflammation depression-like behavior and persistent pain in experimental arthritis. The murine model of antigen-induced arthritis (AIA) was used to evaluate the effects of knee inflammation on sustained pain and depression-like behavior. We measured joint pain using an automated dynamic plantar algesiometer and depression-like behavior with the tail suspension test. Cytokines were measured by Luminex assay and ELISA. TNF in the brain was blocked by intracerebroventricular injection of anti-TNF antibodies. Histological damage and elevated levels of cytokines were observed in the knee 24 h after antigen treatment, but not at 13 days. Reduced pain thresholds were seen 24 h and 13 days after treatment. Depression-like behavior was observed on day 13. Treatment with the antidepressant imipramine reduced both depression-like behavior and persistent pain. However, blocking joint pain with the analgesic dipyrone did not alter depression-like behavior. Elevated levels of TNF, CCL2, and CXCL-1 were observed in the hippocampus 24 h after treatment, with TNF remaining elevated at day 13. Intracerebroventricular infusion of an anti-TNF antibody blocked depression-like behavior and reduced persistent pain. We have demonstrated that depression-like behavior and pain is sustained in AIA mice after the resolution of inflammation. These changes are associated with elevated levels of TNF in the hippocampus and are dependent upon brain TNF. The findings reveal an important mechanistic link between the expression of chronic pain and depression in experimental arthritis. Furthermore, they suggest treating depression in rheumatoid arthritis may positively impact other debilitating features of this condition.

Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls.

This study evaluated the use of multiomics data for classification accuracy of rheumatoid arthritis (RA). Three approaches were used and compared in terms of prediction accuracy: (1) whole-genome prediction (WGP) using SNP marker information only, (2) whole-methylome prediction (WMP) using methylation profiles only, and (3) whole-genome/methylome prediction (WGMP) with combining both omics layers. The number of SNP and of methylation sites varied in each scenario, with either 1, 10, or 50% of these preselected based on four approaches: randomly, evenly spaced, lowest p value (genome-wide association or epigenome-wide association study), and estimated effect size using a Bayesian ridge regression (BRR) model. To remove effects of high levels of pairwise linkage disequilibrium (LD), SNPs were also preselected with an LD-pruning method. Five Bayesian regression models were studied for classification, including BRR, Bayes-A, Bayes-B, Bayes-C, and the Bayesian LASSO. Adjusting methylation profiles for cellular heterogeneity within whole blood samples had a detrimental effect on the classification ability of the models. Overall, WGMP using Bayes-B model has the best performance. In particular, selecting SNPs based on LD-pruning with 1% of the methylation sites selected based on BRR included in the model, and fitting the most significant SNP as a fixed effect was the best method for predicting disease risk with a classification accuracy of 0.975. Our results showed that multiomics data can be used to effectively predict the risk of RA and identify cases in early stages to prevent or alter disease progression via appropriate interventions.

Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.

The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non-autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10-6 ), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree-based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle.