Reduced Number of Transitional and Naive B Cells in Addition to Decreased BAFF Levels in Response to the T Cell Independent Immunogen Pneumovax?23

Protective immunity against T cell independent (TI) antigens such as Streptococcus pneumoniae is characterized by antibody production of B cells induced by the combined activation of T cell independent type 1 and type 2 antigens in the absence of direct T cell help. In mice, the main players in TI immune responses have been well defined as marginal zone (MZ) B cells and B-1 cells. However, the existence of human equivalents to these B cell subsets and the nature of the human B cell compartment involved in the immune reaction remain elusive. We therefore analyzed the effect of a TI antigen on the B cell compartment through immunization of healthy individuals with the pneumococcal polysaccharide (PnPS)-based vaccine Pneumovax^®23, and subsequent characterization of B cell subpopulations. Our data demonstrates a transient decrease of transitional and naïve B cells, with a concomitant increase of IgA⁺ but not IgM⁺ or IgG⁺ memory B cells and a predominant generation of PnPS-specific IgA⁺ producing plasma cells. No alterations could be detected in T cells, or proposed human B-1 and MZ B cell equivalents. Consistent with the idea of a TI immune response, antigen-specific memory responses could not be observed. Finally, BAFF, which is supposed to drive class switching to IgA, was unexpectedly found to be decreased in serum in response to Pneumovax^®23. Our results demonstrate that a characteristic TI response induced by Pneumovax^®23 is associated with distinct phenotypical and functional changes within the B cell compartment. Those modulations occur in the absence of any modulations of T cells and without the development of a specific memory response.     

Conformational studies on host-guest peptides containing chiral alpha-methyl-alpha-amino acids. Comparison of the helix-inducing potential of alpha-aminoisobutyric acid, (S)-2-ethylalanine and (S)-2-methylserine

The conformational behaviour of host-guest peptides of the type Ac-Ala-Xxx-Ala-Ala-Xxx-Ala-Ala-Xxx-Ala-Ala-NH-PEGM (Xxx = alpha-aminoisobutyric acid (Aib), (S)-2-ethylalanine ((S)-Iva), (S)-2-methylserine ((S)-alpha-MeSer)) has been studied by CD spectroscopy in CF3CH2OH, CH3OH, and water and by i.r. spectroscopy in CHCl3 and in the solid state. In this way the relative helix-inducing potential of the two chiral alpha-methyl-alpha-amino acids (S)-Iva and (S)-alpha-MeSer could be established in comparison to the strong helix-former Aib. The results show that (S)-Iva exerts a comparable helix-inducing effect as Aib, making this amino acid a valuable complementary tool for the stabilization or induction of helices. No significant helix-promoting effect was observed for (S)-alpha-MeSer in polar solvents; however, the i.r.-spectroscopic data in CHCl3 and in the solid state point to a helical conformation under these conditions. Possible reasons for the different behaviour of (S)-Iva and (S)-alpha-MeSer are briefly discussed.     

Customised fragments libraries for protein structure prediction based on structural class annotations

Background

Since experimental techniques are time and cost consuming, in silico protein structure prediction is essential to produce conformations of protein targets. When homologous structures are not available, fragment-based protein structure prediction has become the approach of choice. However, it still has many issues including poor performance when targets’ lengths are above 100 residues, excessive running times and sub-optimal energy functions. Taking advantage of the reliable performance of structural class prediction software, we propose to address some of the limitations of fragment-based methods by integrating structural constraints in their fragment selection process.

Results

Using Rosetta, a state-of-the-art fragment-based protein structure prediction package, we evaluated our proposed pipeline on 70 former CASP targets containing up to 150 amino acids. Using either CATH or SCOP-based structural class annotations, enhancement of structure prediction performance is highly significant in terms of both GDT_TS (at least +2.6, p-values < 0.0005) and RMSD (−0.4, p-values < 0.005). Although CATH and SCOP classifications are different, they perform similarly. Moreover, proteins from all structural classes benefit from the proposed methodology. Further analysis also shows that methods relying on class-based fragments produce conformations which are more relevant to user and converge quicker towards the best model as estimated by GDT_TS (up to 10% in average). This substantiates our hypothesis that usage of structurally relevant templates conducts to not only reducing the size of the conformation space to be explored, but also focusing on a more relevant area.

Conclusions

Since our methodology produces models the quality of which is up to 7% higher in average than those generated by a standard fragment-based predictor, we believe it should be considered before conducting any fragment-based protein structure prediction. Despite such progress, ab initio prediction remains a challenging task, especially for proteins of average and large sizes. Apart from improving search strategies and energy functions, integration of additional constraints seems a promising route, especially if they can be accurately predicted from sequence alone.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0576-2) contains supplementary material, which is available to authorized users.     

Evaluation of a sophisticated SCFG design for RNA secondary structure prediction

Predicting secondary structures of RNA molecules is one of the fundamental problems of and thus a challenging task in computational structural biology. Over the past decades, mainly two different approaches have been considered to compute predictions of RNA secondary structures from a single sequence: the first one relies on physics-based and the other on probabilistic RNA models. Particularly, the free energy minimization (MFE) approach is usually considered the most popular and successful method. Moreover, based on the paradigm-shifting work by McCaskill which proposes the computation of partition functions (PFs) and base pair probabilities based on thermodynamics, several extended partition function algorithms, statistical sampling methods and clustering techniques have been invented over the last years. However, the accuracy of the corresponding algorithms is limited by the quality of underlying physics-based models, which include a vast number of thermodynamic parameters and are still incomplete. The competing probabilistic approach is based on stochastic context-free grammars (SCFGs) or corresponding generalizations, like conditional log-linear models (CLLMs). These methods abstract from free energies and instead try to learn about the structural behavior of the molecules by learning (a manageable number of) probabilistic parameters from trusted RNA structure databases. In this work, we introduce and evaluate a sophisticated SCFG design that mirrors state-of-the-art physics-based RNA structure prediction procedures by distinguishing between all features of RNA that imply different energy rules. This SCFG actually serves as the foundation for a statistical sampling algorithm for RNA secondary structures of a single sequence that represents a probabilistic counterpart to the sampling extension of the PF approach. Furthermore, some new ways to derive meaningful structure predictions from generated sample sets are presented. They are used to compare the predictive accuracy of our model to that of other probabilistic and energy-based prediction methods. Particularly, comparisons to lightweight SCFGs and corresponding CLLMs for RNA structure prediction indicate that more complex SCFG designs might yield higher accuracy but eventually require more comprehensive and pure training sets. Investigations on both the accuracies of predicted foldings and the overall quality of generated sample sets (especially on an abstraction level, called abstract shapes of generated structures, that is relevant for biologists) yield the conclusion that the Boltzmann distribution of the PF sampling approach is more centered than the ensemble distribution induced by the sophisticated SCFG model, which implies a greater structural diversity within generated samples. In general, neither of the two distinct ensemble distributions is more adequate than the other and the corresponding results obtained by statistical sampling can be expected to bare fundamental differences, such that the method to be preferred for a particular input sequence strongly depends on the considered RNA type.     

A fresh look at cancer metabolism in a historical setting

The Institute for Research in Biomedicine (IRB) Barcelona meeting on Cancer Metabolism took place in November 2010. It was organized with the support of the Banco Bilbao Vizcaya Argentaria Foundation and offered an outstanding line-up of acclaimed speakers within an intimate setting that allowed plenty of opportunity for informal interactions.     

Detrimental effects of adenosine signaling in sickle cell disease

Hypoxia can act as an initial trigger to induce erythrocyte sickling and eventual end organ damage in sickle cell disease (SCD). Many factors and metabolites are altered in response to hypoxia and may contribute to the pathogenesis of the disease. Using metabolomic profiling, we found that the steady-state concentration of adenosine in the blood was elevated in a transgenic mouse model of SCD. Adenosine concentrations were similarly elevated in the blood of humans with SCD. Increased adenosine levels promoted sickling, hemolysis and damage to multiple tissues in SCD transgenic mice and promoted sickling of human erythrocytes. Using biochemical, genetic and pharmacological approaches, we showed that adenosine A(2B) receptor (A(2B)R)-mediated induction of 2,3-diphosphoglycerate, an erythrocyte-specific metabolite that decreases the oxygen binding affinity of hemoglobin, underlies the induction of erythrocyte sickling by excess adenosine both in cultured human red blood cells and in SCD transgenic mice. Thus, excessive adenosine signaling through the A(2B)R has a pathological role in SCD. These findings may provide new therapeutic possibilities for this disease.     

Aneuraceae (Metzgeriales) and tulasnelloid fungi (Basidiomycota): a model for early steps in fungal symbiosis

A total of 35 population samples of the liverwort genera Aneura (A. pinguis) and Riccardia (R.?latifrons, R. multifida, and R. palmata) were sampled from diverse habitats and geographical provenances in Germany, Austria, and Switzerland. Light and transmission electron microscopy were used to characterise the morphological features of the associations, and phylogenetic analyses based on internal transcribed spacers (ITS) and the D1/D2 regions of the fungal 28S rDNA were used to address diversity and phylogenetic relationships. By comparing the cellular structures of the plant-fungus interactions, we recognised the following states of fungal colonisation within the thalli: fungus-free, epiphytic, intercellular, and intracellular. Colonising hyphae showed dolipores with imperforate parenthesomes, slime bodies, and multilayered walls. Colonised liverwort cells had pleomorphic nuclei and elongated starch-free chloroplasts with distinctive grana. Our analyses revealed six phylogenetic groups of tulasnelloid fungi associated with liverworts, where major lineages mostly share similar host and/or ecological specialisations. The mode of colonisation of the tulasnelloid mycobionts in Aneura and Riccardia sharing identical fungal sequences is different. Consequently, the mode of colonisation may be host-dependent. Finally, our findings demonstrate that Aneuraceae are model organisms for evolutionary studies of symbiotic associations between liverworts and fungi.