Direct characterization of target podocyte antigens and auto-antibodies in human membranous glomerulonephritis: Alfa-enolase and borderline antigens

The identification of glomerular auto-antigens in idiopathic human membranous glomerulonephritis (MGN) is a crucial step towards the definition of the mechanisms of the disease. Recent 'in vivo' studies demonstrated a heterogeneous composition of glomerular immune-deposits in MGN biopsies only a part of which have been characterized. We studied with a proteomical approach IgGs eluted from laser capture microdissected glomeruli of 8 MGN patients and showed the existence of other three immune proteins in MGN glomeruli (α-enolase, elongation factor 2 and Glycyl Aminoacyl-tRNA Synthetase). One of these, i.e. α-enolase, fulfilled all criteria for being considered an auto-antigen. Specific IgG? and IgG? reacting with podocyte α-enolase were, in fact, eluted from microdissected glomeruli and Confocal- and Immuno Electron-Microscopy showed co-localization of α-enolase with IgG? and C5b-9 in immune-deposits. Serum levels of anti a-enolase IgG4 were determined in 131 MGN patients and were found elevated in 25% of cases. Overall, our data demonstrate that glomerular α-enolase is a target antigen of autoimmunity in human MGN. Circulating anti α-enolase auto-antibodies can be detected in sera of a significant quota of MGN patients. Like other auto-antigens, α-enolase may be implicated in the pathogenesis of human MGN.     

Recognition of morphometric vertebral fractures by artificial neural networks: analysis from GISMO Lombardia Database

Background

It is known that bone mineral density (BMD) predicts the fracture''s risk only partially and the severity and number of vertebral fractures are predictive of subsequent osteoporotic fractures (OF). Spinal deformity index (SDI) integrates the severity and number of morphometric vertebral fractures. Nowadays, there is interest in developing algorithms that use traditional statistics for predicting OF. Some studies suggest their poor sensitivity. Artificial Neural Networks (ANNs) could represent an alternative. So far, no study investigated ANNs ability in predicting OF and SDI. The aim of the present study is to compare ANNs and Logistic Regression (LR) in recognising, on the basis of osteoporotic risk-factors and other clinical information, patients with SDI≥1 and SDI≥5 from those with SDI = 0.

Methodology

We compared ANNs prognostic performance with that of LR in identifying SDI≥1/SDI≥5 in 372 women with postmenopausal-osteoporosis (SDI≥1, n = 176; SDI = 0, n = 196; SDI≥5, n = 51), using 45 variables (44 clinical parameters plus BMD). ANNs were allowed to choose relevant input data automatically (TWIST-system-Semeion). Among 45 variables, 17 and 25 were selected by TWIST-system-Semeion, in SDI≥1 vs SDI = 0 (first) and SDI≥5 vs SDI = 0 (second) analysis. In the first analysis sensitivity of LR and ANNs was 35.8% and 72.5%, specificity 76.5% and 78.5% and accuracy 56.2% and 75.5%, respectively. In the second analysis, sensitivity of LR and ANNs was 37.3% and 74.8%, specificity 90.3% and 87.8%, and accuracy 63.8% and 81.3%, respectively.

Conclusions

ANNs showed a better performance in identifying both SDI≥1 and SDI≥5, with a higher sensitivity, suggesting its promising role in the development of algorithm for predicting OF.     

Mitochondrial DNA backgrounds might modulate diabetes complications rather than T2DM as a whole

Mitochondrial dysfunction has been implicated in rare and common forms of type 2 diabetes (T2DM). Additionally, rare mitochondrial DNA (mtDNA) mutations have been shown to be causal for T2DM pathogenesis. So far, many studies have investigated the possibility that mtDNA variation might affect the risk of T2DM, however, when found, haplogroup association has been rarely replicated, even in related populations, possibly due to an inadequate level of haplogroup resolution. Effects of mtDNA variation on diabetes complications have also been proposed. However, additional studies evaluating the mitochondrial role on both T2DM and related complications are badly needed. To test the hypothesis of a mitochondrial genome effect on diabetes and its complications, we genotyped the mtDNAs of 466 T2DM patients and 438 controls from a regional population of central Italy (Marche). Based on the most updated mtDNA phylogeny, all 904 samples were classified into 57 different mitochondrial sub-haplogroups, thus reaching an unprecedented level of resolution. We then evaluated whether the susceptibility of developing T2DM or its complications differed among the identified haplogroups, considering also the potential effects of phenotypical and clinical variables. MtDNA backgrounds, even when based on a refined haplogroup classification, do not appear to play a role in developing T2DM despite a possible protective effect for the common European haplogroup H1, which harbors the G3010A transition in the MTRNR2 gene. In contrast, our data indicate that different mitochondrial haplogroups are significantly associated with an increased risk of specific diabetes complications: H (the most frequent European haplogroup) with retinopathy, H3 with neuropathy, U3 with nephropathy, and V with renal failure.     

A topological framework for the computation of the HOMFLY polynomial and its application to proteins

Polymers can be modeled as open polygonal paths and their closure generates knots. Knotted proteins detection is currently achieved via high-throughput methods based on a common framework insensitive to the handedness of knots. Here we propose a topological framework for the computation of the HOMFLY polynomial, an handedness-sensitive invariant. Our approach couples a multi-component reduction scheme with the polynomial computation. After validation on tabulated knots and links the framework was applied to the entire Protein Data Bank along with a set of selected topological checks that allowed to discard artificially entangled structures. This led to an up-to-date table of knotted proteins that also includes two newly detected right-handed trefoil knots in recently deposited protein structures. The application range of our framework is not limited to proteins and it can be extended to the topological analysis of biological and synthetic polymers and more generally to arbitrary polygonal paths.     

Surface localization of glucosylceramide during Cryptococcus neoformans infection allows targeting as a potential antifungal

Cryptococcus neoformans (Cn) is a significant human pathogen that, despite current treatments, continues to have a high morbidity rate especially in sub-Saharan Africa. The need for more tolerable and specific therapies has been clearly shown. In the search for novel drug targets, the gene for glucosylceramide synthase (GCS1) was deleted in Cn, resulting in a strain (Δgcs1) that does not produce glucosylceramide (GlcCer) and is avirulent in mouse models of infection. To understand the biology behind the connection between virulence and GlcCer, the production and localization of GlcCer must be characterized in conditions that are prohibitive to the growth of Δgcs1 (neutral pH and high CO(2)). These prohibitive conditions are physiologically similar to those found in the extracellular spaces of the lung during infection. Here, using immunofluorescence, we have shown that GlcCer localization to the cell surface is significantly increased during growth in these conditions and during infection. We further seek to exploit this localization by treatment with Cerezyme (Cz), a recombinant enzyme that metabolizes GlcCer, as a potential treatment for Cn. Cz treatment was found to reduce the amount of GlcCer in vitro, in cultures, and in Cn cells inhabiting the mouse lung. Treatment with Cz induced a membrane integrity defect in wild type Cn cells similar to Δgcs1. Cz treatment also reduced the in vitro growth of Cn in a dose and condition dependent manner. Finally, Cz treatment was shown to have a protective effect on survival in mice infected with Cn. Taken together, these studies have established the legitimacy of targeting the GlcCer and other related sphingolipid systems in the development of novel therapeutics.     

A signature inferred from Drosophila mitotic genes predicts survival of breast cancer patients

Introduction

The classification of breast cancer patients into risk groups provides a powerful tool for the identification of patients who will benefit from aggressive systemic therapy. The analysis of microarray data has generated several gene expression signatures that improve diagnosis and allow risk assessment. There is also evidence that cell proliferation-related genes have a high predictive power within these signatures.

Methods

We thus constructed a gene expression signature (the DM signature) using the human orthologues of 108 Drosophila melanogaster genes required for either the maintenance of chromosome integrity (36 genes) or mitotic division (72 genes).

Results

The DM signature has minimal overlap with the extant signatures and is highly predictive of survival in 5 large breast cancer datasets. In addition, we show that the DM signature outperforms many widely used breast cancer signatures in predictive power, and performs comparably to other proliferation-based signatures. For most genes of the DM signature, an increased expression is negatively correlated with patient survival. The genes that provide the highest contribution to the predictive power of the DM signature are those involved in cytokinesis.

Conclusion

This finding highlights cytokinesis as an important marker in breast cancer prognosis and as a possible target for antimitotic therapies.     

Species mixing boosts root yield in mangrove trees

Enhanced species richness can stimulate the productivity of plant communities; however, its effect on the belowground production of forests has scarcely been tested, despite the role of tree roots in carbon storage and ecosystem processes. Therefore, we tested for the effects of tree species richness on mangrove root biomass: thirty-two 6 m by 6 m plots were planted with zero (control), one, two or three species treatments of six-month-old Avicennia marina (A), Bruguiera gymnorrhiza (B) and Ceriops tagal (C). A monoculture of each species and the four possible combinations of the three species were used, with four replicate plots per treatment. Above- and belowground biomass was measured after three and four years’ growth. In both years, the all-species mix (ABC) had significant overyielding of roots, suggesting complementarity mediated by differences in rhizosphere use amongst species. In year four, there was higher belowground than aboveground biomass in all but one treatment. Belowground biomass was strongly influenced by the presence of the most vigorously growing species, A. marina. These results demonstrate the potential for complementarity between fast- and slow-growing species to enhance belowground growth in mangrove forests, with implications for forest productivity and the potential for belowground carbon sequestration.