Citrullination of TNF-α by peptidylarginine deiminases reduces its capacity to stimulate the production of inflammatory chemokines

Citrullination, a posttranslational modification (PTM) recently discovered on inflammatory chemokines such as interleukin-8 (IL-8/CXCL8) and interferon-γ-inducible protein-10 (IP-10/CXCL10), seriously influences their biological activity. Citrullination or the deimination of arginine to citrulline is dependent on peptidylarginine deiminases (PADs) and has been linked to autoimmune diseases such as multiple sclerosis (MS) and rheumatoid arthritis (RA). Chemokines are to date the first identified PAD substrates with receptor-mediated biological activity. We investigated whether cytokines that play a crucial role in RA, like interleukin-1β (IL-1β) and tumor necrosis factor-alpha (TNF-α), may be citrullinated by PAD and whether such a PTM influences the biological activity of these cytokines. IL-1β and TNF-α were first incubated with PAD in vitro and the occurrence of citrullination was examined by Edman degradation and a recently developed detection method for citrullinated proteins. Both techniques confirmed that human TNF-α, but not IL-1β, was citrullinated by PAD. Citrullination of TNF-α reduced its potency to stimulate chemokine production in vitro on human primary fibroblasts. Concentrations of the inflammatory chemokines CXCL8, CXCL10 and monocyte chemotactic protein-1 (MCP-1/CCL2) were significantly lower in supernatants of fibroblasts induced with citrullinated TNF-α compared to unmodified TNF-α. However, upon citrullination TNF-α retained its capacity to induce apoptosis/necrosis of mononuclear cells, its binding potency to Infliximab and its ability to recruit neutrophils to the peritoneal cavity of mice.     

Role of histidine for charge regulation of unstructured peptides at interfaces and in bulk

Histidine‐rich, unstructured peptides adsorb to charged interfaces such as mineral surfaces and microbial cell membranes. At a molecular level, we investigate the adsorption mechanism as a function of pH, salt, and multivalent ions showing that (1) proton charge fluctuations are—in contrast to the majority of proteins—optimal at neutral pH, promoting electrostatic interactions with anionic surfaces through charge regulation and (2) specific zinc(II)‐histidine binding competes with protons and ensures an unusually constant charge distribution over a broad pH interval. In turn, this further enhances surface adsorption. Our analysis is based on atomistic molecular dynamics simulations, coarse grained Metropolis Monte Carlo, and classical polymer density functional theory. This multiscale modeling provides a consistent picture in good agreement with experimental data on Histatin 5, an antimicrobial salivary peptide. Biological function is discussed and we suggest that charge regulation is a significant driving force for the remarkably robust activity of histidine‐rich antimicrobial peptides. Proteins 2014; 82:657–667. © 2013 Wiley Periodicals, Inc.     

Land Use and Land Cover Change Dynamics across the Brazilian Amazon: Insights from Extensive Time-Series Analysis of Remote Sensing Data

Throughout the Amazon region, the age of forests regenerating on previously deforested land is determined, in part, by the periods of active land use prior to abandonment and the frequency of reclearance of regrowth, both of which can be quantified by comparing time-series of Landsat sensor data. Using these time-series of near annual data from 1973–2011 for an area north of Manaus (in Amazonas state), from 1984–2010 for south of Santarém (Pará state) and 1984–2011 near Machadinho d’Oeste (Rondônia state), the changes in the area of primary forest, non-forest and secondary forest were documented from which the age of regenerating forests, periods of active land use and the frequency of forest reclearance were derived. At Manaus, and at the end of the time-series, over 50% of regenerating forests were older than 16 years, whilst at Santarém and Machadinho d’Oeste, 57% and 41% of forests respectively were aged 6–15 years, with the remainder being mostly younger forests. These differences were attributed to the time since deforestation commenced but also the greater frequencies of reclearance of forests at the latter two sites with short periods of use in the intervening periods. The majority of clearance for agriculture was also found outside of protected areas. The study suggested that a) the history of clearance and land use should be taken into account when protecting deforested land for the purpose of restoring both tree species diversity and biomass through natural regeneration and b) a greater proportion of the forested landscape should be placed under protection, including areas of regrowth.     

High-performance data mining with intelligent SSD

An intuitive way to process the big data efficiently is to reduce the volume of data transferred over the storage interface to a host system. This is the reason that the notion of intelligent SSD (iSSD) was proposed to give processing power to SSD. There is rich literature on iSSD, however, its real implementation has not been provided to the public yet. Most prior work aims to quantify the benefits of iSSD with analytical modeling. In this paper, we first develop on iSSD simulator and present the potential of iSSD in data mining through the iSSD simulator. Our iSSD simulator performs on top of the gem 5 simulator and fully simulates all the processes of data mining algorithms running in iSSD with cycle-level accuracy. Then, we further addresse how to exploit all the computing resources for efficient processing of data mining algorithms. These days, CPU, GPU, and SSD are recently equipped together in most computing environment. If SSD is replaced with iSSD later on, we have a new computing environment where the three computing resources collaborate one another to process big data quite effectively. For this, scheduling is required to decide which computing resource is going to run for which function at which time. In our heterogeneous scheduling, types of computing resources, memory sizes in computing resources, and inter-processor communication times including IO time in SSD are considered. Our scheduling results show that processing in the collaborative environment outperforms that in the traditional one by up to about 10 times.     

Genome-wide promoter methylation analysis in neuroblastoma identifies prognostic methylation biomarkers

Background

Accurate outcome prediction in neuroblastoma, which is necessary to enable the optimal choice of risk-related therapy, remains a challenge. To improve neuroblastoma patient stratification, this study aimed to identify prognostic tumor DNA methylation biomarkers.

Results

To identify genes silenced by promoter methylation, we first applied two independent genome-wide methylation screening methodologies to eight neuroblastoma cell lines. Specifically, we used re-expression profiling upon 5-aza-2''-deoxycytidine (DAC) treatment and massively parallel sequencing after capturing with a methyl-CpG-binding domain (MBD-seq). Putative methylation markers were selected from DAC-upregulated genes through a literature search and an upfront methylation-specific PCR on 20 primary neuroblastoma tumors, as well as through MBD- seq in combination with publicly available neuroblastoma tumor gene expression data. This yielded 43 candidate biomarkers that were subsequently tested by high-throughput methylation-specific PCR on an independent cohort of 89 primary neuroblastoma tumors that had been selected for risk classification and survival. Based on this analysis, methylation of KRT19, FAS, PRPH, CNR1, QPCT, HIST1H3C, ACSS3 and GRB10 was found to be associated with at least one of the classical risk factors, namely age, stage or MYCN status. Importantly, HIST1H3C and GNAS methylation was associated with overall and/or event-free survival.

Conclusions

This study combines two genome-wide methylation discovery methodologies and is the most extensive validation study in neuroblastoma performed thus far. We identified several novel prognostic DNA methylation markers and provide a basis for the development of a DNA methylation-based prognostic classifier in neuroblastoma.     

Influence of Soil Physical Properties on Plants of the Mussununga Ecosystem, Brazil

Distribution ranges of plant species are related to physical variables of ecosystems that limit plant growth. Therefore, each plant species response to physical factors builds up the functional diversity of an ecosystem. The higher the species richness of an ecosystem, the larger the probability of maintaining functions and the higher the potential number of plant functional groups (FGs). Thus, the richness potentially increases the number of functions of the highly diverse Atlantic Rainforest domain in Brazil. Severe plant growth limitations caused by stress, however, decrease species richness. In the Spodosols of the Mussununga, an associated ecosystem of Atlantic Rainforest, the percentage of fine sand is directly related to water retention. Moreover, the depth of the cementation layer in the Mussununga??s sandy soil is a physical factor that can affect the plants?? stress gradients. When a shallow cementation layer depth is combined with low water retention in soils and with low fine sand percentage, the double stresses of flooding in the rainy season and water scarcity in the dry season result. This study aimed to identify FGs among Mussununga plant species responding to water stress gradients of soil and to verify the effects of the gradients on plant species richness of the Mussununga. A canonical correspondence analysis (CCA) of species abundance and soil texture variables was performed on 18 plots in six physiognomies of the Mussununga. Species richness rarefactions were calculated for each vegetation form to compare diversity. The two main axes of the CCA showed two FGs responding to soil texture and cementation layer depth: stress tolerator species and mesic species. Physical variables affect plant diversity, with species richness rising as the fine sand proportion also rises in the Mussununga. The effect of the cementation layer is not significantly related to species richness variation.     

QTL-seq identifies an early flowering QTL located near Flowering Locus T in cucumber

Key message

Next-generation sequencing enabled a fast discovery of a major QTL controlling early flowering in cucumber, corresponding to the FT gene conditioning flowering time in Arabidopsis.

Abstract

Next-generation sequencing technologies are making it faster and more efficient to establish the association of agronomic traits with molecular markers or candidate genes, which is the requirement for marker-assisted selection in molecular breeding. Early flowering is an important agronomic trait in cucumber (Cucumis sativus L.), but the underlying genetic mechanism is unknown. In this study, we identified a candidate gene for early flowering QTL, Ef1.1 through QTL-seq. Segregation analysis in F₂ and BC₁ populations derived from a cross between two inbred lines “Muromskij” (early flowering) and “9930” (late flowering) suggested quantitative nature of flowering time in cucumber. Genome-wide comparison of SNP profiles between the early and late-flowering bulks constructed from F₂ plants identified a major QTL, designated Ef1.1 on cucumber chromosome 1 for early flowering in Muromskij, which was confirmed by microsatellite marker-based classical QTL mapping in the F₂ population. Joint QTL-seq and traditional QTL analysis delimited Ef1.1 to an 890 kb genomic region. A cucumber gene, Csa1G651710, was identified in this region, which is a homolog of the FLOWERING LOCUS T (FT), the main flowering switch gene in Arabidopsis. Quantitative RT-PCR study of the expression level of Csa1G651710 revealed significantly higher expression in early flowering genotypes. Data presented here provide support for Csa1G651710 as a possible candidate gene for early flowering in the cucumber line Muromskij.     

Stability of the Transthyretin Molecule as a Key Factor in the Interaction with A-Beta Peptide - Relevance in Alzheimer's Disease

Transthyretin (TTR) protects against A-Beta toxicity by binding the peptide thus inhibiting its aggregation. Previous work showed different TTR mutations interact differently with A-Beta, with increasing affinities correlating with decreasing amyloidogenecity of the TTR mutant; this did not impact on the levels of inhibition of A-Beta aggregation, as assessed by transmission electron microscopy. Our work aimed at probing differences in binding to A-Beta by WT, T119M and L55P TTR using quantitative assays, and at identifying factors affecting this interaction. We addressed the impact of such factors in TTR ability to degrade A-Beta. Using a dot blot approach with the anti-oligomeric antibody A11, we showed that A-Beta formed oligomers transiently, indicating aggregation and fibril formation, whereas in the presence of WT and T119M TTR the oligomers persisted longer, indicative that these variants avoided further aggregation into fibrils. In contrast, L55PTTR was not able to inhibit oligomerization or to prevent evolution to aggregates and fibrils. Furthermore, apoptosis assessment showed WT and T119M TTR were able to protect against A-Beta toxicity. Because the amyloidogenic potential of TTR is inversely correlated with its stability, the use of drugs able to stabilize TTR tetrameric fold could result in increased TTR/A-Beta binding. Here we showed that iododiflunisal, 3-dinitrophenol, resveratrol, [2-(3,5-dichlorophenyl)amino] (DCPA) and [4-(3,5-difluorophenyl)] (DFPB) were able to increase TTR binding to A-Beta; however only DCPA and DFPB improved TTR proteolytic activity. Thyroxine, a TTR ligand, did not influence TTR/A-Beta interaction and A-Beta degradation by TTR, whereas RBP, another TTR ligand, not only obstructed the interaction but also inhibited TTR proteolytic activity. Our results showed differences between WT and T119M TTR, and L55PTTR mutant regarding their interaction with A-Beta and prompt the stability of TTR as a key factor in this interaction, which may be relevant in AD pathogenesis and for the design of therapeutic TTR-based therapies.