Single-Molecule Imaging Reveals Aβ42:Aβ40 Ratio-Dependent Oligomer Growth on Neuronal Processes

AbstractSoluble oligomers of the amyloid-β peptide have been implicated as proximal neurotoxins in Alzheimer’s disease. However, the identity of the neurotoxic aggregate(s) and the mechanisms by which these species induce neuronal dysfunction remain uncertain. Physiologically relevant experimentation is hindered by the low endogenous concentrations of the peptide, the metastability of Aβ oligomers, and the wide range of observed interactions between Aβ and biological membranes. Single-molecule microscopy represents one avenue for overcoming these challenges. Using this technique, we find that Aβ binds to primary rat hippocampal neurons at physiological concentrations. Although amyloid-β(1–40) as well as amyloid-β(1–42) initially form larger oligomers on neurites than on glass slides, a 1:1 mix of the two peptides result in smaller neurite-bound oligomers than those detected on-slide or for either peptide alone. With 1 nM peptide in solution, Aβ40 oligomers do not grow over the course of 48 h, Aβ42 oligomers grow slightly, and oligomers of a 1:1 mix grow substantially. Evidently, small Aβ oligomers are capable of binding to neurons at physiological concentrations and grow at rates dependent on local Aβ42:Aβ40 ratios. These results are intriguing in light of the increased Aβ42:Aβ40 ratios shown to correlate with familial Alzheimer’s disease mutations

Direct Observation of Single Amyloid-β(1-40) Oligomers on Live Cells: Binding and Growth at Physiological Concentrations

Understanding how amyloid-β peptide interacts with living cells on a molecular level is critical to development of targeted treatments for Alzheimer's disease. Evidence that oligomeric Aβ interacts with neuronal cell membranes has been provided, but the mechanism by which membrane binding occurs and the exact stoichiometry of the neurotoxic aggregates remain elusive. Physiologically relevant experimentation is hindered by the high Aβ concentrations required for most biochemical analyses, the metastable nature of Aβ aggregates, and the complex variety of Aβ species present under physiological conditions. Here we use single molecule microscopy to overcome these challenges, presenting direct optical evidence that small Aβ(1-40) oligomers bind to living neuroblastoma cells at physiological Aβ concentrations. Single particle fluorescence intensity measurements indicate that cell-bound Aβ species range in size from monomers to hexamers and greater, with the majority of bound oligomers falling in the dimer-to-tetramer range. Furthermore, while low-molecular weight oligomeric species do form in solution, the membrane-bound oligomer size distribution is shifted towards larger aggregates, indicating either that bound Aβ oligomers can rapidly increase in size or that these oligomers cluster at specific sites on the membrane. Calcium indicator studies demonstrate that small oligomer binding at physiological concentrations induces only mild, sporadic calcium leakage. These findings support the hypothesis that small oligomers are the primary Aβ species that interact with neurons at physiological concentrations

Allelic analysis of sheath blight resistance with association mapping in rice

Citation: Jia, Limeng, Wengui Yan, Chengsong Zhu, Hesham A. Agrama, Aaron Jackson, Kathleen Yeater, Xiaobai Li, et al. “Allelic Analysis of Sheath Blight Resistance with Association Mapping in Rice.” PLOS ONE 7, no. 3 (March 12, 2012): e32703. https://doi.org/10.1371/journal.pone.0032703.Sheath blight (ShB) caused by the soil-borne pathogen Rhizoctonia solani is one of the most devastating diseases in rice world-wide. Global attention has focused on examining individual mapping populations for quantitative trait loci (QTLs) for ShB resistance, but to date no study has taken advantage of association mapping to examine hundreds of lines for potentially novel QTLs. Our objective was to identify ShB QTLs via association mapping in rice using 217 sub-core entries from the USDA rice core collection, which were phenotyped with a micro-chamber screening method and genotyped with 155 genome-wide markers. Structure analysis divided the mapping panel into five groups, and model comparison revealed that PCA5 with genomic control was the best model for association mapping of ShB. Ten marker loci on seven chromosomes were significantly associated with response to the ShB pathogen. Among multiple alleles in each identified loci, the allele contributing the greatest effect to ShB resistance was named the putative resistant allele. Among 217 entries, entry GSOR 310389 contained the most putative resistant alleles, eight out of ten. The number of putative resistant alleles presented in an entry was highly and significantly correlated with the decrease of ShB rating (r =20.535) or the increase of ShB resistance. Majority of the resistant entries that contained a large number of the putative resistant alleles belonged to indica, which is consistent with a general observation that most ShB resistant accessions are of indica origin. These findings demonstrate the potential to improve breeding efficiency by using marker-assisted selection to pyramid putative resistant alleles from various loci in a cultivar for enhanced ShB resistance in rice

Multisectoral climate impact hotspots in a warming world

The impacts of global climate change on different aspects of humanity’s diverse life-support systems are complex and often difficult to predict. To facilitate policy decisions on mitigation and adaptation strategies, it is necessary to understand, quantify, and synthesize these climate-change impacts, taking into account their uncertainties. Crucial to these decisions is an understanding of how impacts in different sectors overlap, as overlapping impacts increase exposure, lead to interactions of impacts, and are likely to raise adaptation pressure. As a first step we develop herein a framework to study coinciding impacts and identify regional exposure hotspots. This framework can then be used as a starting point for regional case studies on vulnerability and multifaceted adaptation strategies. We consider impacts related to water, agriculture, ecosystems, and malaria at different levels of global warming. Multisectoral overlap starts to be seen robustly at a mean global warming of 3 °C above the 1980–2010 mean, with 11% of the world population subject to severe impacts in at least two of the four impact sectors at 4 °C. Despite these general conclusions, we find that uncertainty arising from the impact models is considerable, and larger than that from the climate models. In a low probability-high impact worst-case assessment, almost the whole inhabited world is at risk for multisectoral pressures. Hence, there is a pressing need for an increased research effort to develop a more comprehensive understanding of impacts, as well as for the development of policy measures under existing uncertainty

The relevance of uncertainty in future crop production for mitigation strategy planning

In order to achieve climate change mitigation, long-term decisions are required that must be reconciled with other societal goals that draw on the same resources. For example, ensuring food security for a growing population may require an expansion of crop land, thereby reducing natural carbon sinks or the area available for bio-energy production. Here, we show that current impact-model uncertainties pose an important challenge to long-term mitigation planning and propose a new risk-assessment and decision framework that accounts for competing interests. Based on cross-sectorally consistent simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) we discuss potential gains and limitations of additional irrigation and trade-offs of the expansion of agricultural land as two possible response measures to climate change and growing food demand. We describe an illustrative example in which the combination of both measures may close the supply demand gap while leading to a loss of approximately half of all natural carbon sinks. We highlight current limitations of available simulations and additional steps required for a comprehensive risk assessment