Comparison of structural changes in skin and amnion tissue grafts for transplantation induced by gamma and electron beam irradiation for sterilization

Sterilization is an important step in the preparation of biological material for transplantation. The aim of the study is to compare morphological changes in three types of biological tissues induced by different doses of gamma and electron beam radiation. Frozen biological tissues (porcine skin xenografts, human skin allografts and human amnion) were irradiated with different doses of gamma rays (12.5, 25, 35, 50 kGy) and electron beam (15, 25, 50 kGy). Not irradiated specimens served as controls. The tissue samples were then thawn and fixed in 10 % formalin, processed by routine paraffin technique and stained with hematoxylin and eosin, alcian blue at pH 2.5, orcein, periodic acid Schiff reaction, phosphotungstic acid hematoxylin, Sirius red and silver impregnation. The staining with hematoxylin and eosin showed vacuolar cytoplasmic changes of epidermal cells mainly in the samples of xenografts irradiated by the lowest doses of gamma and electron beam radiation. The staining with orcein revealed damage of fine elastic fibers in the xenograft dermis at the dose of 25 kGy of both radiation types. Disintegration of epithelial basement membrane, especially in the xenografts, was induced by the dose of 15 kGy of electron beam radiation. The silver impregnation disclosed nuclear chromatin condensation mainly in human amnion at the lowest doses of both radiation types and disintegration of the fine collagen fibers in the papillary dermis induced by the lowest dose of electron beam and by the higher doses of gamma radiation. Irradiation by both, gamma rays and the electron beam, causes similar changes on cells and extracellular matrix, with significant damage of the basement membrane and of the fine and elastic and collagen fibers in the papillary dermis, the last caused already by low dose electron beam radiation.     

Tight Coupling Between Shoot Level Foliar N and P,Leaf Area,and Shoot Growth in Arctic Dwarf Shrubs Under Simulated Climate Change

Nutrient availability limits productivity of arctic ecosystems, and this constraint means that the amount of nitrogen (N) in plant canopies is an exceptionally strong predictor of vegetation productivity. However, climate change is predicted to increase nutrient availability leading to increases in carbon sequestration and shifts in community structure to more productive species. Despite tight coupling of productivity with canopy nutrients at the vegetation scale, it remains unknown how species/shoot level foliar nutrients couple to growth, or how climate change may influence foliar nutrients–productivity relationships to drive changes in ecosystem carbon gain and community structure. We investigated the influence of climate change on arctic plant growth relationships to shoot level foliar N and phosphorus (P) in three dominant subarctic dwarf shrubs using an 18-year warming and nutrient addition experiment. We found a tight coupling between total leaf N and P per shoot, leaf area and shoot extension. Furthermore, a steeper shoot length-leaf N relationship in deciduous species (Vaccinium myrtillus and Vaccinium uliginosum) under warming manipulations suggests a greater capacity for nitrogen to stimulate growth under warmer conditions in these species. This mechanism may help drive the considerable increases in deciduous shrub cover observed already in some arctic regions. Overall, our work provides the first evidence at the shoot level of tight coupling between foliar N and P, leaf area and growth i.e. consistent across species, and provides mechanistic insight into how interspecific differences in alleviation of nutrient limitation will alter community structure and primary productivity in a warmer Arctic.     

A Complex-based Reconstruction of the Saccharomyces cerevisiae Interactome

Most cellular processes are performed by proteomic units that interact with each other. These units are often stoichiometrically stable complexes comprised of several proteins. To obtain a faithful view of the protein interactome we must view it in terms of these basic units (complexes and proteins) and the interactions between them. This study makes two contributions toward this goal. First, it provides a new algorithm for reconstruction of stable complexes from a variety of heterogeneous biological assays; our approach combines state-of-the-art machine learning methods with a novel hierarchical clustering algorithm that allows clusters to overlap. We demonstrate that our approach constructs over 40% more known complexes than other recent methods and that the complexes it produces are more biologically coherent even compared with the reference set. We provide experimental support for some of our novel predictions, identifying both a new complex involved in nutrient starvation and a new component of the eisosome complex. Second, we provide a high accuracy algorithm for the novel problem of predicting transient interactions involving complexes. We show that our complex level network, which we call ComplexNet, provides novel insights regarding the protein-protein interaction network. In particular, we reinterpret the finding that “hubs” in the network are enriched for being essential, showing instead that essential proteins tend to be clustered together in essential complexes and that these essential complexes tend to be large.Biological processes exhibit a hierarchical structure in which the basic working units, proteins, physically associate to form stoichiometrically stable complexes. Complexes interact with individual proteins or other complexes to form functional modules and pathways that carry out most cellular processes. Such higher level interactions are more transient than those within complexes and are highly dependent on temporal and spatial context. The function of each protein or complex depends on its interaction partners. Therefore, a faithful reconstruction of the entire set of complexes in the cell is essential to identifying the function of individual proteins and complexes as well as serving as a building block for understanding the higher level organization of the cell, such as the interactions of complexes and proteins within cellular pathways. Here we describe a novel method for reconstruction of complexes from a variety of biological assays and a method for predicting the network of interactions relating these core cellular units (complexes and proteins).Our reconstruction effort focuses on the yeast Saccharomyces cerevisiae. Yeast serves as the prototypical case study for the reconstruction of protein-protein interaction networks. Moreover the yeast complexes often have conserved orthologs in other organisms, including human, and are of interest in their own right. Several studies (1–4) using a variety of assays have generated high throughput data that directly measure protein-protein interactions. Most notably, two high quality data sets (3, 4) used tandem affinity purification (TAP)¹ followed by MS to provide a proteome-wide measurement of protein complexes. These data provide the basis for attempting a comprehensive reconstruction of a large fraction of the protein complexes in this organism. Indeed a number of works (5, 6) have attempted such a reconstruction. Generally speaking, all use the same general procedure: one or more data sources are used to estimate a set of affinities between pairs of proteins, essentially measuring the likelihood of that pair to participate together in a complex. These affinities induce a weighted graph whose nodes are proteins and whose edges encode the affinities. A clustering algorithm is then used to construct complexes, sets of proteins that have high affinity in the graph. Although similar at a high level, the different methods differ significantly on the design choices made for the key steps in the process.Recent works (since 2006) all focus on processing the proteome-wide TAP-MS data and using the results to define complexes. Gavin et al. (3), Collins et al. (7), and Hart et al. (5) all use probabilistic models that compare the number of interactions observed between proteins in the data versus the number expected in some null model. Collins et al. (7) and Hart et al. (5) both used all three of the available high throughput data sets (2–4) in an attempt to provide a unified interaction network. The two unified networks resulting from these studies were shown to have large overlap and to achieve comparable agreement with the set of co-complex interactions in the MIPS data set (8) that are collated from previous small scale studies. The interaction graphs resulting from the computed affinity scores are then clustered to produce a set of identified complexes. Gavin et al. (3), Hart et al. (5), and Pu et al. (6) all use a Markov clustering (MCL) (9) procedure; Collins et al. (7) use a hierarchical agglomerative clustering (HAC) procedure but do not suggest a computational procedure for using the resulting dendrogram to produce specific complex predictions.Despite the fairly high quality of these networks and the agreement between them, they still contain many false positives and negatives. False negatives can arise, for example, from the difficulty in detecting interactions involving low abundance proteins or membrane proteins or from cases where the tag added to the bait protein during TAP-MS prevents binding of the bait to its interacting partners. False positives can arise, for example, from complexes that share components or from the contaminants that bind to the bait nonspecifically after cell lysis. Therefore, the set of complexes derived from the protein-protein interaction network alone has limited accuracy. Less than 20% of the MIPS complexes (8), which are derived from reliable small scale experiments, are exactly captured by the predictions of Pu et al. (6) or by those of Hart et al. (5).In this study, we constructed a method that generates a set of complexes with higher sensitivity and coverage by integrating multiple sources of data, including mRNA gene expression data, cellular localization, and yeast two-hybrid data. The data integration approach was used in some early works on predicting protein-protein interactions (10, 11) and more recently by Qiu and Noble (12), but these studies focus only on predicting pairs of proteins in the same complex and not on reconstructing entire complexes. Many recent studies (13–21) have successfully integrated multiple types of data to predict functional linkage between proteins, constructing a graph whose pairwise affinity score summarizes the information from different sources of data. However, because the data integration is not trained toward predicting complexes, the high affinity pairs contain transient binding partners and even protein pairs that never interact directly but merely function in the same pathways. When these graphs are clustered, the clusters correspond to a variety of cellular entities, including pathways, functional modules, or co-expression clusters. We developed a data integration approach that is aimed directly at the problem of predicting stoichiometrically stable complexes.We used a two-phase automated procedure that we trained on a new high quality reference set that we generated from annotations in MIPS and SGD and from manual curation of the literature. In the first phase, we used boosting (22), a state-of-the-art machine learning method, to train an affinity function that is specifically aimed at predicting whether two proteins are co-complexed. Unlike most other learning methods, boosting is capable of inducing useful features by combining different aspects of the raw data, making it particularly well suited to a data integration setting. Once we generated the learned affinity graph over pairs of proteins, we predicted complexes by using a novel clustering algorithm called hierarchical agglomerative clustering with overlap (HACO). The HACO algorithm is a simple and elegant extension of HAC that addresses many of its limitations, such as the irreversible commitment to a possibly incorrect clustering decision. HACO can be applied to any setting where HAC is applied; given the enormous usefulness of HAC for the analysis of biological data sets of many different types (e.g. Refs. 7, 23, and 24), we believe that HACO may be applicable in a broad range of other tasks.To validate our approach, we tested the ability of our methods and other methods to predict reference complexes that were not used in training. By integrating multiple sources of data, we recovered more reference complexes than other state-of-the-art methods (5, 6) when applied to the same set of yeast proteins. We also validated our predicted set of complexes against external data sources that are not used in the training. In all cases, our predictions were shown to be more coherent than other methods and, in many cases, more coherent even than the set of reference complexes.A detailed examination of our predicted complexes suggests that many of them were previously known but not included in our (comprehensive) reference set, suggesting that our complexes form a valuable new set of reference complexes. In several cases, our predicted complexes were not previously characterized. We experimentally validated two of these predictions: a new component in the recently characterized eisosome complex (25), which marks the site of endocytosis in eukaryotes, and a newly characterized six-protein complex, including four phosphatases, that appears to be involved in the response to nutrient starvation and that we named the nutrient starvation complex (NSC).The complex-based view provides a new perspective on the analysis and reconstruction of the protein interaction network. In the past, Jeong et al. (26) have suggested that the degree of a protein in an interaction network is positively correlated with its essentiality and have argued that “hubs” in the network are more likely to be essential because they are involved in more interactions. Our analysis presents a complex-based alternative view: essential proteins tend to cluster together in essential complexes (5), and essential complexes tend to be large; thus, the essential hubs in the network are often members in large complexes comprised mostly of essential proteins. We also reformulate the task of reconstructing the protein interaction network. Rather than considering interactions between individual proteins (27–29), a somewhat confusing network that confounds interactions within complexes and interactions between complexes, we tackle the novel task of predicting a comprehensive protein interaction network that involves both individual proteins and larger complexes. We argue that these entities are the right building blocks in reconstructing cellular processes, providing a view of cellular interaction networks that is both easier to interpret than the complex network of interactions between individual proteins and more faithful to biological reality. Moreover a complex, which is a stable collection of many proteins that act together, provides a more robust basis for predicting interactions as we can combine signals for all its constituent proteins, reducing sensitivity to noise.To accomplish this goal, we constructed a reference set of complex-complex interactions, considering two complexes to interact if they are significantly enriched for reliable interactions between their components. We further augmented this set with a hand-curated list of established complex-complex interactions. We then used a machine learning approach to detect the “signature” of such interactions from a large set of assays that are likely to be indicative. We explored different machine learning methods and showed that a partially supervised naïve Bayes model, where we learned the model from both labeled and unlabeled interactions, provides the best performance. This model was applied both to our predicted complexes and to individual proteins, providing a new, comprehensive reconstruction of the S. cerevisiae interaction network, which can be downloaded from our project Web page.² We showed that entities that are predicted to interact are more likely to share the same functional categories. A detailed investigation of our new predicted interactions presents many that are established in the literature as well as some that are novel but consistent, presenting plausible hypotheses for further investigation.     

Ascorbic acid-independent synthesis of collagen in mice

The mouse has become the most important model organism for the study of human physiology and disease. However, until the recent generation of mice lacking the enzyme gulanolactone oxidase (Gulo), the final enzyme in the ascorbic acid biosynthesis pathway, examination of the role of ascorbic acid in various biochemical processes using this model organism has not been possible. In the mouse, similar to most mammals but unlike humans who carry a mutant copy of this gene, Gulo produces ascorbic acid from glucose. We report here that, although ascorbic acid is essential for survival, its absence does not lead to measurable changes in proline hydroxylation. Vitamin C deficiency had no significant effect on the hydroxylation of proline and collagen production during tumor growth or in angiogenesis associated with tumor or mammary gland growth. This suggests that factors other than ascorbic acid can support proline hydroxylation and collagen synthesis in vivo. Furthermore, the failure of Gulo-/- mice to thrive on a vitamin C-deficient diet therefore suggests that ascorbic acid plays a critical role in survival other than the maintenance of the vasculature.     

ChChd3, an inner mitochondrial membrane protein, is essential for maintaining crista integrity and mitochondrial function

The mitochondrial inner membrane (IM) serves as the site for ATP production by hosting the oxidative phosphorylation complex machinery most notably on the crista membranes. Disruption of the crista structure has been implicated in a variety of cardiovascular and neurodegenerative diseases. Here, we characterize ChChd3, a previously identified PKA substrate of unknown function (Schauble, S., King, C. C., Darshi, M., Koller, A., Shah, K., and Taylor, S. S. (2007) J. Biol. Chem. 282, 14952-14959), and show that it is essential for maintaining crista integrity and mitochondrial function. In the mitochondria, ChChd3 is a peripheral protein of the IM facing the intermembrane space. RNAi knockdown of ChChd3 in HeLa cells resulted in fragmented mitochondria, reduced OPA1 protein levels and impaired fusion, and clustering of the mitochondria around the nucleus along with reduced growth rate. Both the oxygen consumption and glycolytic rates were severely restricted. Ultrastructural analysis of these cells revealed aberrant mitochondrial IM structures with fragmented and tubular cristae or loss of cristae, and reduced crista membrane. Additionally, the crista junction opening diameter was reduced to 50% suggesting remodeling of cristae in the absence of ChChd3. Analysis of the ChChd3-binding proteins revealed that ChChd3 interacts with the IM proteins mitofilin and OPA1, which regulate crista morphology, and the outer membrane protein Sam50, which regulates import and assembly of β-barrel proteins on the outer membrane. Knockdown of ChChd3 led to almost complete loss of both mitofilin and Sam50 proteins and alterations in several mitochondrial proteins, suggesting that ChChd3 is a scaffolding protein that stabilizes protein complexes involved in maintaining crista architecture and protein import and is thus essential for maintaining mitochondrial structure and function.     

<Emphasis Type="Italic">Vr</Emphasis><Subscript><Emphasis Type="Italic">2</Emphasis></Subscript>: a new apple scab resistance gene

Reports from several European countries of the breakdown of the Vf resistance, the most frequently used source of resistance in breeding programs against apple scab, emphasize the urgency of diversifying the basis of apple scab resistance and pyramiding different apple scab resistances with the use of their associated molecular markers. GMAL 2473 is an apple scab resistant selection thought to carry the resistance gene Vr. We report the identification by BSA of three AFLP markers and one RAPD marker associated with the GMAL 2473 resistance gene. SSRs associated with the resistance gene were found by (1) identifying the linkage group carrying the apple scab resistance and (2) testing the SSRs previously mapped in the same region. One such SSR, CH02c02a, mapped on linkage group 2, co-segregates with the resistance gene. GMAL 2473 was tested with molecular markers associated with other apple scab resistance genes, and accessions carrying known apple scab resistance genes were tested with the SSR linked to the resistance gene found in GMAL 2473. The results indicate that GMAL 2473 does not carry Vr, and that a new apple scab resistance gene, named Vr ₂, has been identified.     

Intestinal ion transport in NKCC1-deficient mice

The Na(+)-K(+)-2Cl(-) cotransporter (NKCC1) located on the basolateral membrane of intestinal epithelia has been postulated to be the major basolateral Cl(-) entry pathway. With targeted mutagenesis, mice deficient in the NKCC1 protein were generated. The basal short-circuit current did not differ between normal and NKCC1 -/- jejuna. In the -/- jejuna, the forskolin response (22 microA/cm(2); bumetanide insensitive) was significantly attenuated compared with the bumetanide-sensitive response (52 microA/cm(2)) in normal tissue. Ion-replacement studies demonstrated that the forskolin response in the NKCC1 -/- jejuna was HCO(3)(-) dependent, whereas in the normal jejuna it was independent of the HCO(3)(-) concentration in the buffer. NKCC1 -/- ceca exhibited a forskolin response that did not differ significantly from that of normal ceca, but unlike that of normal ceca, was bumetanide insensitive. Ion-substitution studies suggested that basolateral HCO(3)(-) as well as Cl(-) entry (via non-NKCC1) paths played a role in the NKCC1 -/- secretory response. In contrast to cystic fibrosis mice, which lack both basal and stimulated Cl(-) secretion and exhibit severe intestinal pathology, the absence of intestinal pathology in NKCC1 -/- mice likely reflects the ability of the intestine to secrete HCO(3)(-) and Cl(-) by basolateral entry mechanisms independent of NKCC1.