Atypical Antigen Recognition Mode of a Shark Immunoglobulin New Antigen Receptor (IgNAR) Variable Domain Characterized by Humanization and Structural Analysis

The immunoglobulin new antigen receptors (IgNARs) are a class of Ig-like molecules of the shark immune system that exist as heavy chain-only homodimers and bind antigens by their single domain variable regions (V-NARs). Following shark immunization and/or in vitro selection, V-NARs can be generated as soluble, stable, and specific high affinity monomeric binding proteins of ∼12 kDa. We have previously isolated a V-NAR from an immunized spiny dogfish shark, named E06, that binds specifically and with high affinity to human, mouse, and rat serum albumins. Humanization of E06 was carried out by converting over 60% of non-complementarity-determining region residues to those of a human germ line Vκ1 sequence, DPK9. The resulting huE06 molecules have largely retained the specificity and affinity of antigen binding of the parental V-NAR. Crystal structures of the shark E06 and its humanized variant (huE06 v1.1) in complex with human serum albumin (HSA) were determined at 3- and 2.3-Å resolution, respectively. The huE06 v1.1 molecule retained all but one amino acid residues involved in the binding site for HSA. Structural analysis of these V-NARs has revealed an unusual variable domain-antigen interaction. E06 interacts with HSA in an atypical mode that utilizes extensive framework contacts in addition to complementarity-determining regions that has not been seen previously in V-NARs. On the basis of the structure, the roles of various elements of the molecule are described with respect to antigen binding and V-NAR stability. This information broadens the general understanding of antigen recognition and provides a framework for further design and humanization of shark IgNARs.     

Up-regulation of GABAB Receptor Signaling by Constitutive Assembly with the K+ Channel Tetramerization Domain-containing Protein 12 (KCTD12)

GABA_B receptors are the G-protein coupled receptors (GPCRs) for GABA, the main inhibitory neurotransmitter in the central nervous system. Native GABA_B receptors comprise principle and auxiliary subunits that regulate receptor properties in distinct ways. The principle subunits GABA_B1a, GABA_B1b, and GABA_B2 form fully functional heteromeric GABA_B(1a,2) and GABA_B(1b,2) receptors. Principal subunits regulate forward trafficking of the receptors from the endoplasmic reticulum to the plasma membrane and control receptor distribution to axons and dendrites. The auxiliary subunits KCTD8, -12, -12b, and -16 are cytosolic proteins that influence agonist potency and G-protein signaling of GABA_B(1a,2) and GABA_B(1b,2) receptors. Here, we used transfected cells to study assembly, surface trafficking, and internalization of GABA_B receptors in the presence of the KCTD12 subunit. Using bimolecular fluorescence complementation and metabolic labeling, we show that GABA_B receptors associate with KCTD12 while they reside in the endoplasmic reticulum. Glycosylation experiments support that association with KCTD12 does not influence maturation of the receptor complex. Immunoprecipitation and bioluminescence resonance energy transfer experiments demonstrate that KCTD12 remains associated with the receptor during receptor activity and receptor internalization from the cell surface. We further show that KCTD12 reduces constitutive receptor internalization and thereby increases the magnitude of receptor signaling at the cell surface. Accordingly, knock-out or knockdown of KCTD12 in cultured hippocampal neurons reduces the magnitude of the GABA_B receptor-mediated K⁺ current response. In summary, our experiments support that the up-regulation of functional GABA_B receptors at the neuronal plasma membrane is an additional physiological role of the auxiliary subunit KCTD12.     

Salt-stress induced changes in the leaf proteome of diploid and tetraploid mandarins with contrasting Na and Cl accumulation behaviour

To understand the genotypic variation of citrus to mild salt stress, a proteomic approach has been carried out in parallel on two citrus genotypes (‘Cleopatra’ and ‘Willow leaf’ mandarins), which differ for Na⁺ and Cl⁻ accumulation, and their cognate autotetraploids (4×). Using two-dimensional electrophoresis approximately 910 protein spots were reproducibly detected in control and salt-stressed leaves of all genotypes. Among them, 44 protein spots showing significant variations at least in one genotype were subjected to mass spectrometry analysis for identification. Salt-responsive proteins were involved in several functions, including photosynthetic processes, ROS scavenging, stress defence, and signalling. Genotype factors affect the salt-responsive pattern, especially that of carbon metabolism. The no ion accumulator ‘Cleopatra’ mandarin genotype showed the highest number of salt-responsive proteins, and up-regulation of Calvin cycle-related proteins. Conversely the ion accumulator ‘Willow leaf’ mandarin showed high levels of several photorespiration-related enzymes. A common set of proteins (twelve spots) displayed higher levels in salt-stressed leaves of 2× and 4× ‘Cleopatra’ and 4× ‘Willow leaf’ mandarin. Interestingly, antioxidant enzymes and heat shock proteins showed higher constitutive levels in 4× ‘Cleopatra’ mandarin and 4× ‘Willow leaf’ mandarin compared with the cognate 2× genotype. This work provides for the first time information on the effect of 8 weeks of salt stress on citrus genotypes contrasting for ion accumulation and their cognate autotetraploids. Results underline that genetic factors have a predominant effect on the salt response, although a common stress response independent from genotype was also found.     

Dissecting a complex chemical stress: chemogenomic profiling of plant hydrolysates

The efficient production of biofuels from cellulosic feedstocks will require the efficient fermentation of the sugars in hydrolyzed plant material. Unfortunately, plant hydrolysates also contain many compounds that inhibit microbial growth and fermentation. We used DNA‐barcoded mutant libraries to identify genes that are important for hydrolysate tolerance in both Zymomonas mobilis (44 genes) and Saccharomyces cerevisiae (99 genes). Overexpression of a Z. mobilis tolerance gene of unknown function (ZMO1875) improved its specific ethanol productivity 2.4‐fold in the presence of miscanthus hydrolysate. However, a mixture of 37 hydrolysate‐derived inhibitors was not sufficient to explain the fitness profile of plant hydrolysate. To deconstruct the fitness profile of hydrolysate, we profiled the 37 inhibitors against a library of Z. mobilis mutants and we modeled fitness in hydrolysate as a mixture of fitness in its components. By examining outliers in this model, we identified methylglyoxal as a previously unknown component of hydrolysate. Our work provides a general strategy to dissect how microbes respond to a complex chemical stress and should enable further engineering of hydrolysate tolerance.     

Stand-replacing wildfires alter the community structure of wood-inhabiting fungi in southwestern ponderosa pine forests of the USA

Increases in stand-replacing wildfires in the western USA have widespread implications for ecosystem carbon (C) cycling, in part because the decomposition of trees killed by fire can be a long-term source of CO₂ to the atmosphere. Knowledge of the composition and function of decay fungi communities may be important to understanding how wildfire alters C cycles. We assessed the effects of stand-replacing wildfires on the community structure of wood-inhabiting fungi along a 32-yr wildfire chronosequence. Fire was associated with low species richness for up to 4 yr and altered species composition relative to unburned forest for the length of the chronosequence. A laboratory incubation demonstrated that species varied in their capacity to decompose wood; Hypocrea lixii, an indicator of the most recent burn, caused the lowest decomposition rate. Our results show that stand-replacing wildfires have long-term effects on fungal communities, which may have consequences for wood decomposition and C cycling.     

Using Linkage Analysis to Detect Gene-Gene Interactions. 2. Improved Reliability and Extension to More-Complex Models

Detecting gene-gene interaction in complex diseases has become an important priority for common disease genetics, but most current approaches to detecting interaction start with disease-marker associations. These approaches are based on population allele frequency correlations, not genetic inheritance, and therefore cannot exploit the rich information about inheritance contained within families. They are also hampered by issues of rigorous phenotype definition, multiple test correction, and allelic and locus heterogeneity. We recently developed, tested, and published a powerful gene-gene interaction detection strategy based on conditioning family data on a known disease-causing allele or a disease-associated marker allele⁴. We successfully applied the method to disease data and used computer simulation to exhaustively test the method for some epistatic models. We knew that the statistic we developed to indicate interaction was less reliable when applied to more-complex interaction models. Here, we improve the statistic and expand the testing procedure. We computer-simulated multipoint linkage data for a disease caused by two interacting loci. We examined epistatic as well as additive models and compared them with heterogeneity models. In all our models, the at-risk genotypes are “major” in the sense that among affected individuals, a substantial proportion has a disease-related genotype. One of the loci (A) has a known disease-related allele (as would have been determined from a previous analysis). We removed (pruned) family members who did not carry this allele; the resultant dataset is referred to as “stratified.” This elimination step has the effect of raising the “penetrance” and detectability at the second locus (B). We used the lod scores for the stratified and unstratified data sets to calculate a statistic that either indicated the presence of interaction or indicated that no interaction was detectable. We show that the new method is robust and reliable for a wide range of parameters. Our statistic performs well both with the epistatic models (false negative rates, i.e., failing to detect interaction, ranging from 0 to 2.5%) and with the heterogeneity models (false positive rates, i.e., falsely detecting interaction, ≤1%). It works well with the additive model except when allele frequencies at the two loci differ widely. We explore those features of the additive model that make detecting interaction more difficult. All testing of this method suggests that it provides a reliable approach to detecting gene-gene interaction.     

Detection of silent cells,synchronization and modulatory activity in developing cellular networks

Developing networks in the immature nervous system and in cellular cultures are characterized by waves of synchronous activity in restricted clusters of cells. Synchronized activity in immature networks is proposed to regulate many different developmental processes, from neuron growth and cell migration, to the refinement of synapses, topographic maps, and the mature composition of ion channels. These emergent activity patterns are not present in all cells simultaneously within the network and more immature “silent” cells, potentially correlated with the presence of silent synapses, are prominent in different networks during early developmental periods. Many current network analyses for detection of synchronous cellular activity utilize activity‐based pixel correlations to identify cellular‐based regions of interest (ROIs) and coincident cell activity. However, using activity‐based correlations, these methods first underestimate or ignore the inactive silent cells within the developing network and second, are difficult to apply within cell‐dense regions commonly found in developing brain networks. In addition, previous methods may ignore ROIs within a network that shows transient activity patterns comprising both inactive and active periods. We developed analysis software to semi‐automatically detect cells within developing neuronal networks that were imaged using calcium‐sensitive reporter dyes. Using an iterative threshold, modulation of activity was tracked within individual cells across the network. The distribution pattern of both inactive and active, including synchronous cells, could be determined based on distance measures to neighboring cells and according to different anatomical layers. © 2015 Wiley Periodicals, Inc. Develop Neurobiol 76: 357–374, 2016