Adenylate kinase 1 gene deletion disrupts muscle energetic economy despite metabolic rearrangement

Efficient cellular energy homeostasis is a critical determinant of muscle performance, providing evolutionary advantages responsible for species survival. Phosphotransfer reactions, which couple ATP production and utilization, are thought to play a central role in this process. Here, we provide evidence that genetic disruption of AK1-catalyzed ss-phosphoryl transfer in mice decreases the potential of myofibers to sustain nucleotide ratios despite up-regulation of high-energy phosphoryl flux through glycolytic, guanylate and creatine kinase phosphotransfer pathways. A maintained contractile performance of AK1-deficient muscles was associated with higher ATP turnover rate and larger amounts of ATP consumed per contraction. Metabolic stress further aggravated the energetic cost in AK1(-/-) muscles. Thus, AK1-catalyzed phosphotransfer is essential in the maintenance of cellular energetic economy, enabling skeletal muscle to perform at the lowest metabolic cost.     

Improved perfusion after subcritical ischemia in muscle flaps treated with vascular endothelial growth factor

Vascular endothelial growth factor (VEGF), a potent endothelial mitogen, is secreted in ischemic tissue and plays a pivotal role in angiogenesis. We studied whether VEGF administered to a rat muscle flap at the time of ischemia induction would increase microcirculatory flow to the flap. The cremaster muscle flap was isolated on its neurovascular pedicle. Ischemia was induced by clamping the vascular pedicle, and 0.2 ml of either VEGF (0.1 microg) or vehicle (phosphate-buffered saline) was immediately infused into the muscle. After 4 or 6 hours, the clamps were released, and the cremaster was placed in a pocket in the medial thigh for 24 hours. The muscle was then dissected, and microcirculatory measurements were made under intravital microscopy. Six animals were used in each of the four groups. All flaps exposed to 6 hours of ischemia, the duration considered to be critical ischemia, had no significant microcirculatory flow, regardless of treatment with VEGF. In the 4-hour ischemia group, or subcritical ischemia group, red blood cell velocity in arterioles was 14 mm/sec in muscles treated with VEGF and 9 mm/sec in controls (p = 0.02), and capillary flow was 7 per high-power field in muscles treated with VEGF versus 2 per high-power field in controls (p = 0.0005). Thus, VEGF did not alter microcirculatory flow in a muscle flap exposed to critical ischemia, but it did enhance flow to a flap exposed to subcritical ischemia.     

Gene ontology improves template selection in comparative protein docking

Structural characterization of protein-protein interactions is essential for our ability to study life processes at the molecular level. Computational modeling of protein complexes (protein docking) is important as the source of their structure and as a way to understand the principles of protein interaction. Rapidly evolving comparative docking approaches utilize target/template similarity metrics, which are often based on the protein structure. Although the structural similarity, generally, yields good performance, other characteristics of the interacting proteins (eg, function, biological process, and localization) may improve the prediction quality, especially in the case of weak target/template structural similarity. For the ranking of a pool of models for each target, we tested scoring functions that quantify similarity of Gene Ontology (GO) terms assigned to target and template proteins in three ontology domains—biological process, molecular function, and cellular component (GO-score). The scoring functions were tested in docking of bound, unbound, and modeled proteins. The results indicate that the combined structural and GO-terms functions improve the scoring, especially in the twilight zone of structural similarity, typical for protein models of limited accuracy.     

Protein models docking benchmark 2

Structural characterization of protein–protein interactions is essential for our ability to understand life processes. However, only a fraction of known proteins have experimentally determined structures. Such structures provide templates for modeling of a large part of the proteome, where individual proteins can be docked by template‐free or template‐based techniques. Still, the sensitivity of the docking methods to the inherent inaccuracies of protein models, as opposed to the experimentally determined high‐resolution structures, remains largely untested, primarily due to the absence of appropriate benchmark set(s). Structures in such a set should have predefined inaccuracy levels and, at the same time, resemble actual protein models in terms of structural motifs/packing. The set should also be large enough to ensure statistical reliability of the benchmarking results. We present a major update of the previously developed benchmark set of protein models. For each interactor, six models were generated with the model‐to‐native C^α RMSD in the 1 to 6 Å range. The models in the set were generated by a new approach, which corresponds to the actual modeling of new protein structures in the “real case scenario,” as opposed to the previous set, where a significant number of structures were model‐like only. In addition, the larger number of complexes (165 vs. 63 in the previous set) increases the statistical reliability of the benchmarking. We estimated the highest accuracy of the predicted complexes (according to CAPRI criteria), which can be attained using the benchmark structures. The set is available at http://dockground.bioinformatics.ku.edu . Proteins 2015; 83:891–897. © 2015 Wiley Periodicals, Inc.     

Text Mining for Protein Docking

The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking). Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from Dockground (http://dockground.compbio.ku.edu). The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features) approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound benchmark set, significantly increasing the docking success rate.     

Comparisons of genetic variability detected among mouse blood proteins using one- and two-dimensional electrophoreses

Comparisons of the sensitivities of one-dimensional (1D) and two-dimensional (2D) electrophoreses to detect genetic variability have generally shown that the 2D approach appears to be two- to five-fold less sensitive than conventional 1D approaches. Concerns about the validity of this conclusion have arisen because such comparisons have involved mainly enzymic proteins in 1D approaches versus a complex mixture of soluble proteins in most 2D analyses. Comparisons involving the absolute number of variants detected, using 1D and 2D sodium dodecyl sulfate (SDS)-polyacrylamide gel electrophoresis (PAGE), denatured mouse blood proteins isolated from C3HeB/FeJ and C57B1/6J inbred strains of mice, and highly sensitive silver staining, indicate that the latter uncovers at least as much variability as the former. Although the relative percentage of variable bands (1D SDS-PAGE) was greater than the relative percentage of variable spots (2D SDS-PAGE) when proteins of intact erythrocytes were surveyed, both techniques uncovered approximately equal percentages of variable proteins when the mouse erythrocyte proteins were partitioned into membrane and lysate components. Therefore, the simpler 1D SDS-PAGE was found to be as effective as 2D SDS-PAGE in detecting protein variability. Since 1D SDS-PAGE separates proteins primarily on the basis of molecular weight and to a lesser degree on other primary protein sequence alterations, much of the variability observed by 2D SDS-PAGE may be due to these same features and unit charge differences may not play a significant role in detecting variability in the proteins studied. This differs from enzymic proteins, where such charge differences appear to be responsible for much of the variability. This study also indicated that decreasing the number of proteins in samples (membranes and lysates vs whole erythrocytes) increased the ability of both of these techniques to resolve differences. Mating studies indicated that most of the differences detected with both techniques were inherited and were not artifacts.     

Defects in mitochondrial dynamics and metabolomic signatures of evolving energetic stress in mouse models of familial Alzheimer's disease

Background

The identification of early mechanisms underlying Alzheimer''s Disease (AD) and associated biomarkers could advance development of new therapies and improve monitoring and predicting of AD progression. Mitochondrial dysfunction has been suggested to underlie AD pathophysiology, however, no comprehensive study exists that evaluates the effect of different familial AD (FAD) mutations on mitochondrial function, dynamics, and brain energetics.

Methods and Findings

We characterized early mitochondrial dysfunction and metabolomic signatures of energetic stress in three commonly used transgenic mouse models of FAD. Assessment of mitochondrial motility, distribution, dynamics, morphology, and metabolomic profiling revealed the specific effect of each FAD mutation on the development of mitochondrial stress and dysfunction. Inhibition of mitochondrial trafficking was characteristic for embryonic neurons from mice expressing mutant human presenilin 1, PS1(M146L) and the double mutation of human amyloid precursor protein APP(Tg2576) and PS1(M146L) contributing to the increased susceptibility of neurons to excitotoxic cell death. Significant changes in mitochondrial morphology were detected in APP and APP/PS1 mice. All three FAD models demonstrated a loss of the integrity of synaptic mitochondria and energy production. Metabolomic profiling revealed mutation-specific changes in the levels of metabolites reflecting altered energy metabolism and mitochondrial dysfunction in brains of FAD mice. Metabolic biomarkers adequately reflected gender differences similar to that reported for AD patients and correlated well with the biomarkers currently used for diagnosis in humans.

Conclusions

Mutation-specific alterations in mitochondrial dynamics, morphology and function in FAD mice occurred prior to the onset of memory and neurological phenotype and before the formation of amyloid deposits. Metabolomic signatures of mitochondrial stress and altered energy metabolism indicated alterations in nucleotide, Krebs cycle, energy transfer, carbohydrate, neurotransmitter, and amino acid metabolic pathways. Mitochondrial dysfunction, therefore, is an underlying event in AD progression, and FAD mouse models provide valuable tools to study early molecular mechanisms implicated in AD.     

Dynamics of oxidized and reduced iron in a northern hardwood forest

Iron (Fe) is ubiquitous in forest ecosystems and its cycle is thought to influence the development of soil, particularly Spodosols (podsolization), and the biogeochemistry of macronutrients such as carbon (C), nitrogen (N), and phosphorus (P), as well as many trace metals. The cycle of Fe in northern hardwood forests remains poorly understood. To address some of these uncertainties, we constructed a biogeochemical budget of Fe for a small catchment at the Hubbard Brook Experimental Forest in the White Mountains of New Hampshire, USA. Horizonal, temporal, and elevational patterns of concentrations and fluxes of oxidized and reduced Fe species were assessed in leaf litter, soil, soil solution, and stream water. The chemistry of dissolved Fe was evaluated in the context of its relationship with dissolved organic carbon, pH, and dissolved oxygen. Soil solution fluxes of Fe were highest in the organic (Oa, 52.5 mol ha^?1 year^?1) horizon and decreased with depth in the mineral (Bh, 50.5 mol ha^?1 year^?1, and Bs, 19.7 mol ha^?1 year^?1) horizons, consistent with podsolization theories predicting immobilization of Fe following downward transport to mineral soils. The export of Fe in stream water (1.8 mol ha^?1 year^?1) was lower than precipitation input (3.5 mol ha^?1 year^?1). The low stream flux indicates most Fe in drainage waters was immobilized in the soil and retained in the watershed. The portion of total Fe as Fe(II) was ~10?C60% in soil solutions, seemingly high for soils that are considered to be well-drained, oxidizing environments. Organic complexes likely stabilized Fe(II) in solution under oxidizing conditions that would otherwise promote considerably higher Fe(III)-to-Fe(II) ratios. Our study indicates that there are organic matter-derived sources of dissolved Fe(II) as well as substantial mobilization of Fe(II), possibly the result of the reduction of Fe-bearing soil minerals.