Molecular fingerprinting of carbohydrate structure phenotypes of three porifera proteoglycan-like glyconectins

Glyconectins (GNs) represent a new class of proteoglycan-like cell adhesion and recognition molecules found in several Porifera species. Physico-chemical properties of GN carbohydrate moieties, such as size, composition, and resistance to most glycosaminoglycan-degrading enzymes, distinguish them from any other type of known glycoproteins. The molecular mechanism of GN-mediated self/non-self discrimination function is based on highly species-specific and Ca(2+)-dependent GN to GN associations that approach the selectivity of the evolutionarily advanced immunoglobulin superfamily. Carbohydrates of glyconectins 1, 2, and 3 are essential for species-specific auto-aggregation properties in three respective Porifera species. To obtain a structural insight into the molecular mechanisms, we performed carbohydrate structural analyses of glyconectins isolated from the three sponge model systems, Microciona prolifera (GN1), Halichondria panicea (GN2), and Cliona celata (GN3). The glycan content of all three GNs ranged between 40 and 60% of their total mass. Our approach using sequential and selective chemical degradation of GN glycans and subsequent mass spectrometric and NMR analyses revealed that each glyconectin presents novel and highly species-specific carbohydrate sequences. All three GNs include distinct acid-resistant and acid-labile carbohydrate domains, the latter composed of novel repetitive units. We have sequenced four short sulfated and one pyruvilated unit in GN1, eight larger and branched pyruvilated oligosaccharides in GN2, which represent a heterogeneous but related family of structures, and four sulfated units in GN3.     

Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor

Despite important advances from Genome Wide Association Studies (GWAS), for most complex human traits and diseases, a sizable proportion of genetic variance remains unexplained and prediction accuracy (PA) is usually low. Evidence suggests that PA can be improved using Whole-Genome Regression (WGR) models where phenotypes are regressed on hundreds of thousands of variants simultaneously. The Genomic Best Linear Unbiased Prediction (G-BLUP, a ridge-regression type method) is a commonly used WGR method and has shown good predictive performance when applied to plant and animal breeding populations. However, breeding and human populations differ greatly in a number of factors that can affect the predictive performance of G-BLUP. Using theory, simulations, and real data analysis, we study the performance of G-BLUP when applied to data from related and unrelated human subjects. Under perfect linkage disequilibrium (LD) between markers and QTL, the prediction R-squared (R²) of G-BLUP reaches trait-heritability, asymptotically. However, under imperfect LD between markers and QTL, prediction R² based on G-BLUP has a much lower upper bound. We show that the minimum decrease in prediction accuracy caused by imperfect LD between markers and QTL is given by (1−b)², where b is the regression of marker-derived genomic relationships on those realized at causal loci. For pairs of related individuals, due to within-family disequilibrium, the patterns of realized genomic similarity are similar across the genome; therefore b is close to one inducing small decrease in R². However, with distantly related individuals b reaches very low values imposing a very low upper bound on prediction R². Our simulations suggest that for the analysis of data from unrelated individuals, the asymptotic upper bound on R² may be of the order of 20% of the trait heritability. We show how PA can be enhanced with use of variable selection or differential shrinkage of estimates of marker effects.     

Differential myogenicity of satellite cells isolated from extensor digitorum longus (EDL) and soleus rat muscles revealed in vitro

Following muscle damage, fast- and slow-contracting fibers regenerate, owing to the activation of their satellite cells. In rats, crush-induced regeneration of extensor digitorum longus (EDL, a fast muscle) and soleus (a slow muscle) present different characteristics, suggesting that intrinsic differences exist among their satellite cells. An in vitro comparative study of the proliferation and differentiation capacities of satellite cells isolated from these muscles is presented there. We observed several differences between soleus and EDL satellite cell cultures plated at high density on gelatin-coated dishes. Soleus satellite cells proliferated more actively and fused into myotubes less efficiently than EDL cells. The rate of muscular creatine kinase enzyme appeared slightly lower in soleus than in EDL cultures at day 11 after plating, when many myotubes were formed, although the levels of muscular creatine kinase mRNA were similar in both cultures. In addition, soleus cultures expressed higher levels of MyoD and myogenin mRNA and of MyoD protein than EDL satellite cell cultures at day 12. A clonal analysis was also carried out on both cell populations in order to determine if distinct lineage features could be detected among satellite cells derived from EDL and soleus muscles. When plated on gelatin at clonal density, cells from both muscles yielded clones within 2 weeks, which stemmed from 3–15 mitotic cycles and were classified into three classes according to their sizes. Myotubes resulting from spontaneous fusion of cells from the progeny of one single cell were seen regardless of the clone size in the standard culture medium we used. The proportion of clones showing myotubes in each class depended on the muscle origin of the cells and was greater in EDL- than in soleus-cell cultures. In addition, soleus cells were shown to improve their differentiation capacity upon changes in the culture condition. Indeed, the proportions of clones showing myotubes, or of cells fusing into myotubes in clones, were increased by treatments with a myotube-conditioned medium, with phorbol ester, and by growth on extra-cellular matrix components (Matrigel). These results, showing differences among satellite cells from fast and slow muscles, might be of importance to muscle repair after trauma and in pathological situations.     

External validation of Adjuvant! Online breast cancer prognosis tool. Prioritising recommendations for improvement

Background

Adjuvant! Online is a web-based application designed to provide 10 years survival probability of patients with breast cancer. Several predictors have not been assessed in the original Adjuvant! Online study. We provide the validation of Adjuvant! Online algorithm on two breast cancer datasets, and we determined whether the accuracy of Adjuvant! Online is improved with other well-known prognostic factors.

Patients and Methods

The French data set is composed of 456 women with early breast cancer. The Dutch data set is composed of 295 women less than 52 years of age. Agreement between observation and Adjuvant! Online prediction was checked, and logistic models were performed to estimate the prognostic information added by risk factors to Adjuvant! Online prediction.

Results

Adjuvant! Online prediction was overall well-calibrated in the French data set but failed in some subgroups of such high grade and HER2 positive patients. HER2 status, Mitotic Index and Ki67 added significant information to Adjuvant! Online prediction. In the Dutch data set, the overall 10-year survival was overestimated by Adjuvant! Online, particularly in patients less than 40 years old.

Conclusion

Adjuvant! Online needs to be updated to adjust overoptimistic results in young and high grade patients, and should consider new predictors such as Ki67, HER2 and Mitotic Index.