Virtual reality 3D echocardiography in the assessment of tricuspid valve function after surgical closure of ventricular septal defect

Background

Chronic right ventricular apical pacing may have detrimental effect on left ventricular function and may promote to heart failure in adult patients with left ventricular dysfunction.

Methods

A group of 99 pediatric patients with previously implanted pacemaker was studied retrospectively. Forty-three patients (21 males) had isolated congenital complete or advanced atrioventricular block. The remaining 56 patients (34 males) had pacing indication in the presence of structural heart disease. Thirty-two of them (21 males) had isolated structural heart disease and the remaining 24 (13 males) had complex congenital heart disease. Patients were followed up for an average of 53 ± 41.4 months with 12-lead electrocardiogram and transthoracic echocardiography. Left ventricular shortening fraction was used as a marker of ventricular function. QRS duration was assessed using leads V₅ or II on standard 12-lead electrocardiogram.

Results

Left ventricular shortening fraction did not change significantly after pacemaker implantation compared to preimplant values overall and in subgroups. In patients with complex congenital heart malformations shortening fraction decreased significantly during the follow up period. (0.45 ± 0.07 vs 0.35 ± 0.06, p = 0.015). The correlation between the change in left ventricular shortening fraction and the mean increase of paced QRS duration was not significant. Six patients developed dilated cardiomyopathy, which was diagnosed 2 months to 9 years after pacemaker implantation.

Conclusion

Chronic right ventricular pacing in pediatric patients with or without structural heart disease does not necessarily result in decline of left ventricular function. In patients with complex congenital heart malformations left ventricular shortening fraction shows significant decrease.     

M-LDH serves as a sarcolemmal K(ATP) channel subunit essential for cell protection against ischemia

ATP-sensitive K(+) (K(ATP)) channels in the heart are normally closed by high intracellular ATP, but are activated during ischemia to promote cellular survival. These channels are heteromultimers composed of Kir6.2 subunit, an inwardly rectifying K(+) channel core, and SUR2A, a regulatory subunit implicated in ligand-dependent regulation of channel gating. Here, we have shown that the muscle form (M-LDH), but not heart form (H-LDH), of lactate dehydrogenase is directly physically associated with the sarcolemmal K(ATP) channel by interacting with the Kir6.2 subunit via its N-terminus and with the SUR2A subunit via its C-terminus. The species of LDH bound to the channel regulated the channel activity despite millimolar concentration of intracellular ATP. The presence of M-LDH in the channel protein complex was required for opening of K(ATP) channels during ischemia and ischemia-resistant cellular phenotype. We conclude that M-LDH is an integral part of the sarcolemmal K(ATP) channel protein complex in vivo, where, by virtue of its catalytic activity, it couples the metabolic status of the cell with the K(ATP) channels activity that is essential for cell protection against ischemia.     

An efficient variance component approach implementing an average information REML suitable for combined LD and linkage mapping with a general complex pedigree

Variance component (VC) approaches based on restricted maximum likelihood (REML) have been used as an attractive method for positioning of quantitative trait loci (QTL). Linkage disequilibrium (LD) information can be easily implemented in the covariance structure among QTL effects (e.g. genotype relationship matrix) and mapping resolution appears to be high. Because of the use of LD information, the covariance structure becomes much richer and denser compared to the use of linkage information alone. This makes an average information (AI) REML algorithm based on mixed model equations and sparse matrix techniques less useful. In addition, (near-) singularity problems often occur with high marker densities, which is common in fine-mapping, causing numerical problems in AIREML based on mixed model equations. The present study investigates the direct use of the variance covariance matrix of all observations in AIREML for LD mapping with a general complex pedigree. The method presented is more efficient than the usual approach based on mixed model equations and robust to numerical problems caused by near-singularity due to closely linked markers. It is also feasible to fit multiple QTL simultaneously in the proposed method whereas this would drastically increase computing time when using mixed model equation-based methods.     

A novel tetravalent bispecific TandAb (CD30/CD16A) efficiently recruits NK cells for the lysis of CD30+ tumor cells

To improve recruitment and activation of natural killer (NK) cells to lyse tumor cells, we isolated a human anti-CD16A antibody with similar affinity for the CD16A 158F/V allotypes, but no binding to the CD16B isoform. Using CD16A-targeting Fv domains, we constructed a tetravalent bispecific CD30/CD16A tandem diabody (TandAb®) consisting solely of Fv domains. This TandAb has two binding sites for CD16A and two for CD30, the antigen identifying Hodgkin lymphoma cells. The binding and cytotoxicity of the TandAb were compared with antibodies with identical anti-CD30 domains: (1) a native IgG, (2) an IgG optimized for binding to Fc receptors, and (3) a bivalent bispecific CD30/CD16A diabody. Due to its CD16A-bivalency and reduced k_off, the TandAb was retained longer on the surface of NK cells than the IgGs or the diabody. This contributed to the higher potency and efficacy of the TandAb relative to those of the other anti-CD30 antibodies. TandAb cytotoxicity was independent of the CD16A allotype, whereas the anti-CD30 IgGs were substantially less cytotoxic when NK cells with low affinity CD16A allotype were employed. TandAb activation of NK cells was strictly dependent on the presence of CD30⁺ target cells. Therefore, the CD30/CD16A TandAb may represent a promising therapeutic for the treatment of Hodgkin’s lymphoma; further, anti-CD16A TandAbs may function as potent immunotherapeutics that specifically recruit NK cells to destroy cancer cells.     

A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation

Background

Efficient, robust, and accurate genotype imputation algorithms make large-scale application of genomic selection cost effective. An algorithm that imputes alleles or allele probabilities for all animals in the pedigree and for all genotyped single nucleotide polymorphisms (SNP) provides a framework to combine all pedigree, genomic, and phenotypic information into a single-stage genomic evaluation.

Methods

An algorithm was developed for imputation of genotypes in pedigreed populations that allows imputation for completely ungenotyped animals and for low-density genotyped animals, accommodates a wide variety of pedigree structures for genotyped animals, imputes unmapped SNP, and works for large datasets. The method involves simple phasing rules, long-range phasing and haplotype library imputation and segregation analysis.

Results

Imputation accuracy was high and computational cost was feasible for datasets with pedigrees of up to 25 000 animals. The resulting single-stage genomic evaluation increased the accuracy of estimated genomic breeding values compared to a scenario in which phenotypes on relatives that were not genotyped were ignored.

Conclusions

The developed imputation algorithm and software and the resulting single-stage genomic evaluation method provide powerful new ways to exploit imputation and to obtain more accurate genetic evaluations.     

The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes

Background

The theory of genomic selection is based on the prediction of the effects of genetic markers in linkage disequilibrium with quantitative trait loci. However, genomic selection also relies on relationships between individuals to accurately predict genetic value. This study aimed to examine the importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values.

Methods

Simulated and real data were used to examine the effects of various degrees of relationship on the accuracy of genomic selection. Genomic Best Linear Unbiased Prediction (gBLUP) was compared to two pedigree based BLUP methods, one with a shallow one generation pedigree and the other with a deep ten generation pedigree. The accuracy of estimated breeding values for different groups of selection candidates that had varying degrees of relationships to a reference data set of 1750 animals was investigated.

Results

The gBLUP method predicted breeding values more accurately than BLUP. The most accurate breeding values were estimated using gBLUP for closely related animals. Similarly, the pedigree based BLUP methods were also accurate for closely related animals, however when the pedigree based BLUP methods were used to predict unrelated animals, the accuracy was close to zero. In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy.

Conclusions

An animal''s relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. However a baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.     

Accuracy of pedigree and genomic predictions of carcass and novel meat quality traits in multi-breed sheep data assessed by cross-validation

Background

Genomic predictions can be applied early in life without impacting selection candidates. This is especially useful for meat quality traits in sheep. Carcass and novel meat quality traits were predicted in a multi-breed sheep population that included Merino, Border Leicester, Polled Dorset and White Suffolk sheep and their crosses.

Methods

Prediction of breeding values by best linear unbiased prediction (BLUP) based on pedigree information was compared to prediction based on genomic BLUP (GBLUP) and a Bayesian prediction method (BayesR). Cross-validation of predictions across sire families was used to evaluate the accuracy of predictions based on the correlation of predicted and observed values and the regression of observed on predicted values was used to evaluate bias of methods. Accuracies and regression coefficients were calculated using either phenotypes or adjusted phenotypes as observed variables.

Results and conclusions

Genomic methods increased the accuracy of predicted breeding values to on average 0.2 across traits (range 0.07 to 0.31), compared to an average accuracy of 0.09 for pedigree-based BLUP. However, for some traits with smaller reference population size, there was no increase in accuracy or it was small. No clear differences in accuracy were observed between GBLUP and BayesR. The regression of phenotypes on breeding values was close to 1 for all methods, indicating little bias, except for GBLUP and adjusted phenotypes (regression = 0.78). Accuracies calculated with adjusted (for fixed effects) phenotypes were less variable than accuracies based on unadjusted phenotypes, indicating that fixed effects influence the latter. Increasing the reference population size increased accuracy, indicating that adding more records will be beneficial. For the Merino, Polled Dorset and White Suffolk breeds, accuracies were greater than for the Border Leicester breed due to the smaller sample size and limited across-breed prediction. BayesR detected only a few large marker effects but one region on chromosome 6 was associated with large effects for several traits. Cross-validation produced very similar variability of accuracy and regression coefficients for BLUP, GBLUP and BayesR, showing that this variability is not a property of genomic methods alone. Our results show that genomic selection for novel difficult-to-measure traits is a feasible strategy to achieve increased genetic gain.     

Differential regulation of macrophage CCAAT-enhancer binding protein isoforms by lipopolysaccharide and cytokines

The regulation of the C/EBP family in macrophages by LPS and cytokines is of potentially crucial importance in several pathophysiological conditions. The action of LPS and three cytokines on the expression of C/EBP mRNA, protein and functional DNA binding activity in the murine J774.2 cell line was therefore studied. Exposure of the cells to LPS, IL-1, IFN-gamma and TNF-alpha produced a reduction of C/EBP alpha mRNA levels and a corresponding increase in the expression of C/EBP beta and C/EBP delta. EMSA showed time-dependent changes in the DNA binding activity of individual C/EBP isoforms and demonstrated the participation of heterodimers between the different members in DNA-protein interactions. Additionally, mediator-specific changes in the kinetics and magnitude of C/EBP mRNA expression pattern and profile of DNA-protein interactions were observed. These studies provide novel insights into the potential mechanisms that may be responsible for the mediator-specific regulation of macrophage gene expression through the C/EBP family.     

Computational investigation of epithelial cell dynamic phenotype in vitro

Background  

When grown in three-dimensional (3D) cultures, epithelial cells typically form cystic organoids that recapitulate cardinal features of in vivo epithelial structures. Characterizing essential cell actions and their roles, which constitute the system's dynamic phenotype, is critical to gaining deeper insight into the cystogenesis phenomena.