Organization of Enzyme Concentration across the Metabolic Network in Cancer Cells

Rapid advances in mass spectrometry have allowed for estimates of absolute concentrations across entire proteomes, permitting the interrogation of many important biological questions. Here, we focus on a quantitative aspect of human cancer cell metabolism that has been limited by a paucity of available data on the abundance of metabolic enzymes. We integrate data from recent measurements of absolute protein concentration to analyze the statistics of protein abundance across the human metabolic network. At a global level, we find that the enzymes in glycolysis comprise approximately half of the total amount of metabolic proteins and can constitute up to 10% of the entire proteome. We then use this analysis to investigate several outstanding problems in cancer metabolism, including the diversion of glycolytic flux for biosynthesis, the relative contribution of nitrogen assimilating pathways, and the origin of cellular redox potential. We find many consistencies with current models, identify several inconsistencies, and find generalities that extend beyond current understanding. Together our results demonstrate that a relatively simple analysis of the abundance of metabolic enzymes was able to reveal many insights into the organization of the human cancer cell metabolic network.     

Rapid Transepithelial Transport of Prions following Inhalation

Prion infection and pathogenesis are dependent on the agent crossing an epithelial barrier to gain access to the recipient nervous system. Several routes of infection have been identified, but the mechanism(s) and timing of in vivo prion transport across an epithelium have not been determined. The hamster model of nasal cavity infection was used to determine the temporal and spatial parameters of prion-infected brain homogenate uptake following inhalation and to test the hypothesis that prions cross the nasal mucosa via M cells. A small drop of infected or uninfected brain homogenate was placed below each nostril, where it was immediately inhaled into the nasal cavity. Regularly spaced tissue sections through the entire extent of the nasal cavity were processed immunohistochemically to identify brain homogenate and the disease-associated isoform of the prion protein (PrP^d). Infected or uninfected brain homogenate was identified adhering to M cells, passing between cells of the nasal mucosa, and within lymphatic vessels of the nasal cavity at all time points examined. PrP^d was identified within a limited number of M cells 15 to 180 min following inoculation, but not in the adjacent nasal mucosa-associated lymphoid tissue (NALT). While these results support M cell transport of prions, larger amounts of infected brain homogenate were transported paracellularly across the respiratory, olfactory, and follicle-associated epithelia of the nasal cavity. These results indicate that prions can immediately cross the nasal mucosa via multiple routes and quickly enter lymphatics, where they can spread systemically via lymph draining the nasal cavity.     

CLAN, a Novel Human CED-4-like Gene

Proteins governing cell death form the basis of many normal processes and contribute to the pathogenesis of many diseases when dysregulated. Here we report the cloning of a novel human CED-4-like gene, CLAN, and several of its alternatively spliced isoforms. These caspase-associated recruitment domain (CARD)-containing proteins are expressed at varying degrees in normal human tissues and may contribute to a number of intracellular processes including apoptosis, cytokine processing, and NF-κB activation. The CARD of the CLAN proteins binds a number of other CARD-containing proteins including caspase-1, BCL10, NOD2, and NAC. Once their physiologic functions are uncovered, CLAN proteins may prove to be valuable therapeutic targets.     

Experience Does Not Equal Expertise in Recognizing Infrequent Incoming Gunfire: Neural Markers for Experience and Task Expertise at Peak Behavioral Performance

For a soldier, decisions to use force can happen rapidly and sometimes lead to undesired consequences. In many of these situations, there is a rapid assessment by the shooter that recognizes a threat and responds to it with return fire. But the neural processes underlying these rapid decisions are largely unknown, especially amongst those with extensive weapons experience and expertise. In this paper, we investigate differences in weapons experts and non-experts during an incoming gunfire detection task. Specifically, we analyzed the electroencephalography (EEG) of eleven expert marksmen/soldiers and eleven non-experts while they listened to an audio scene consisting of a sequence of incoming and non-incoming gunfire events. Subjects were tasked with identifying each event as quickly as possible and committing their choice via a motor response. Contrary to our hypothesis, experts did not have significantly better behavioral performance or faster response time than novices. Rather, novices indicated trends of better behavioral performance than experts. These group differences were more dramatic in the EEG correlates of incoming gunfire detection. Using machine learning, we found condition-discriminating EEG activity among novices showing greater magnitude and covering longer periods than those found in experts. We also compared group-level source reconstruction on the maximum discriminating neural correlates and found that each group uses different neural structures to perform the task. From condition-discriminating EEG and source localization, we found that experts perceive more categorical overlap between incoming and non-incoming gunfire. Consequently, the experts did not perform as well behaviorally as the novices. We explain these unexpected group differences as a consequence of experience with gunfire not being equivalent to expertise in recognizing incoming gunfire.     

Landscapes for Energy and Wildlife: Conservation Prioritization for Golden Eagles across Large Spatial Scales

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.     

Evidence that conserved residues Cys-62 and Cys-154 within the Azotobacter vinelandii nitrogenase MoFe protein α-subunit are essential for nitrogenase activity but conserved residues His-83 and Cys-88 are not

Metallocluster extrusion requirements, interspecies MoFe-protein primary sequence comparisons and comparison of the primary sequences of the MoFe-protein subunits with each other have been used to assign potential P-cluster (Fe-S cluster) domains within the MoFe protein. In each alpha-beta unit of the MoFe protein, alpha-subunit domains, which include potential Fe-S cluster ligands Cys-62, His-83, Cys-88 and Cys-154, and beta-subunit domains, which include potential Fe-S cluster ligands Cys-70, His-90, Cys-95 and Cys-153, are proposed to comprise nearly equivalent P-cluster environments located adjacent to each other in the native protein. As an approach to test this model and to probe the functional properties of the P clusters, amino acid residue substitutions were placed at the alpha-subunit Cys-62, His-83, Cys-88 and Cys-154 positions by site-directed mutagenesis of the Azotobacter vinelandii nifD gene. The diazotrophic growth rates, MoFe-protein acetylene-reduction activities, and whole-cell S = 3/2 electron paramagnetic resonance spectra of these mutants were examined. Results of these experiments show that MoFe-protein alpha-subunit residues, Cys-62 and Cys-154, are probably essential for MoFe-protein activity but that His-83 and Cys-88 residues are not. These results indicate either that His-83 and Cys-88 do not provide essential P-cluster ligands or that a new cluster-ligand arrangement is formed in their absence.