Synapse-associated proteins (SAPs) are constituents of the pre- and postsynaptic submembraneous cytomatrix. Here, we present SAP102, a novel 102kDa SAP detected in dendritic shafts and spines of asymmetric type 1 synapses. SAP102 is enriched in preparations of synaptic junctions, where it biochemically behaves as a component of the cortical cytoskeleton. Antibodies directed against NMDA receptors coimmunoprecipitate SAP102 from rat brain synaptosomes. Recombinant proteins containing the carboxy-terminal tail of NMDA receptor subunit NR2B interact with SAP102 from rat brain homogenates. All three PDZ domains in SAP102 bind the cytoplasmic tail of NR2B in vitro. These data represent direct evidence that in vivo SAP102 is involved in linking NMDA receptors to the submembraneous cytomatrix associated with postsynaptic densities at excitatory synapses.

Microscopic studies of superconductors and their vortices play a pivotal role in understanding the mechanisms underlying superconductivity. Local measurements of penetration depths or magnetic stray fields enable access to fundamental aspects such as nanoscale variations in superfluid densities or the order parameter symmetry of superconductors. However, experimental tools that offer quantitative, nanoscale magnetometry and operate over large ranges of temperature and magnetic fields are still lacking. Here, we demonstrate the first operation of a cryogenic scanning quantum sensor in the form of a single nitrogen-vacancy electronic spin in diamond, which is capable of overcoming these existing limitations. To demonstrate the power of our approach, we perform quantitative, nanoscale magnetic imaging of Pearl vortices in the cuprate superconductor YBa2Cu3O7-δ. With a sensor-to-sample distance of ∼10 nm, we observe striking deviations from the prevalent monopole approximation in our vortex stray-field images, and find excellent quantitative agreement with Pearl's analytic model. Our experiments provide a non-invasive and unambiguous determination of the system's local penetration depth and are readily extended to higher temperatures and magnetic fields. These results demonstrate the potential of quantitative quantum sensors in benchmarking microscopic models of complex electronic systems and open the door for further exploration of strongly correlated electron physics using scanning nitrogen-vacancy magnetometry.

Methane (CH4) is produced as an end product from feed fermentation in the rumen. Yield of CH4 varies between individuals despite identical feeding conditions. To get a better understanding of factors behind the individual variation, 73 dairy cows given the same feed but differing in CH4 emissions were investigated with focus on fiber digestion, fermentation end products and bacterial and archaeal composition. In total 21 cows (12 Holstein, 9 Swedish Red) identified as persistent low, medium or high CH4 emitters over a 3 month period were furthermore chosen for analysis of microbial community structure in rumen fluid. This was assessed by sequencing the V4 region of 16S rRNA gene and by quantitative qPCR of targeted Methanobrevibacter groups. The results showed a positive correlation between low CH4 emitters and higher abundance of Methanobrevibacter ruminantium clade. Principal coordinate analysis (PCoA) on operational taxonomic unit (OTU) level of bacteria showed two distinct clusters (P < 0.01) that were related to CH4 production. One cluster was associated with low CH4 production (referred to as cluster L) whereas the other cluster was associated with high CH4 production (cluster H) and the medium emitters occurred in both clusters. The differences between clusters were primarily linked to differential abundances of certain OTUs belonging to Prevotella. Moreover, several OTUs belonging to the family Succinivibrionaceae were dominant in samples belonging to cluster L. Fermentation pattern of volatile fatty acids showed that proportion of propionate was higher in cluster L, while proportion of butyrate was higher in cluster H. No difference was found in milk production or organic matter digestibility between cows. Cows in cluster L had lower CH4/kg energy corrected milk (ECM) compared to cows in cluster H, 8.3 compared to 9.7 g CH4/kg ECM, showing that low CH4 cows utilized the feed more efficient for milk production which might indicate a more efficient microbial population or host genetic differences that is reflected in bacterial and archaeal (or methanogens) populations.

BackgroundSyntrophic acetate oxidation (SAO) is the predominant pathway for methane production in high ammonia anaerobic digestion processes. The bacteria (SAOB) occupying this niche and the metabolic pathway are poorly understood. Phylogenetic diversity and strict cultivation requirements hinder comprehensive research and discovery of novel SAOB. Most SAOB characterised to date are affiliated to the physiological group of acetogens. Formyltetrahydrofolate synthetase is a key enzyme of both acetogenic and SAO metabolism. The encoding fhs gene has therefore been identified as a suitable functional marker, using a newly designed primer pair. In this comparative study, we used a combination of terminal restriction fragment length polymorphism profiling, clone-based comparison, qPCR and Illumina amplicon sequencing to assess the bacterial community and acetogenic sub-community prevailing in high- and low-ammonia laboratory-scale digesters in order to delineate potential SAOB communities. Potential candidates identified were further tracked in a number of low-ammonia and high-ammonia laboratory-scale and large-scale digesters in order to reveal a potential function in SAO.ResultsAll methodical approaches revealed significant changes in the bacterial community composition concurrently with increasing ammonia and predominance of SAO. The acetogenic community under high ammonia conditions was revealed to be generally heterogeneous, but formed distinct phylogenetic clusters. The clusters differed clearly from those found under low-ammonia conditions and represented an acetogenic assemblage unique for biogas processes and recurring in a number of high-ammonia processes, indicating potential involvement in SAO.ConclusionsThe phylogenetic affiliation and population dynamics observed point to a key community, belonging mainly to the Clostridia class, in particular to the orders Clostridiales and Thermoanaerobacterales, which appear to specialise in SAO rather than being metabolically versatile. Overall, the results reported here provide evidence of functional importance of the bacterial families identified in high-ammonia systems and extend existing knowledge of bacterial and acetogenic assemblages at low and high ammonia levels. This information will be of help in monitoring and assessing the impacts on the SAOB community in order to identify characteristics of robust and productive high ammonia biogas processes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-016-0454-9) contains supplementary material, which is available to authorized users.

We develop a quantitative single cell-based mathematical model for multi-cellular tumor spheroids (MCTS) of SK-MES-1 cells, a non-small cell lung cancer (NSCLC) cell line, growing under various nutrient conditions: we confront the simulations performed with this model with data on the growth kinetics and spatial labeling patterns for cell proliferation, extracellular matrix (ECM), cell distribution and cell death. We start with a simple model capturing part of the experimental observations. We then show, by performing a sensitivity analysis at each development stage of the model that its complexity needs to be stepwise increased to account for further experimental growth conditions. We thus ultimately arrive at a model that mimics the MCTS growth under multiple conditions to a great extent. Interestingly, the final model, is a minimal model capable of explaining all data simultaneously in the sense, that the number of mechanisms it contains is sufficient to explain the data and missing out any of its mechanisms did not permit fit between all data and the model within physiological parameter ranges. Nevertheless, compared to earlier models it is quite complex i.e., it includes a wide range of mechanisms discussed in biological literature. In this model, the cells lacking oxygen switch from aerobe to anaerobe glycolysis and produce lactate. Too high concentrations of lactate or too low concentrations of ATP promote cell death. Only if the extracellular matrix density overcomes a certain threshold, cells are able to enter the cell cycle. Dying cells produce a diffusive growth inhibitor. Missing out the spatial information would not permit to infer the mechanisms at work. Our findings suggest that this iterative data integration together with intermediate model sensitivity analysis at each model development stage, provide a promising strategy to infer predictive yet minimal (in the above sense) quantitative models of tumor growth, as prospectively of other tissue organization processes. Importantly, calibrating the model with two nutriment-rich growth conditions, the outcome for two nutriment-poor growth conditions could be predicted. As the final model is however quite complex, incorporating many mechanisms, space, time, and stochastic processes, parameter identification is a challenge. This calls for more efficient strategies of imaging and image analysis, as well as of parameter identification in stochastic agent-based simulations.