Horst Joachim Schirra

The Three-dimensional Solution Structure of NaD1, a New Floral Defensin from Nicotiana alata and its Application to a Homology Model of the Crop Defense Protein alfAFP

NMR spectroscopy and simulated annealing calculations have been used to determine the three-dimensional structure of NaD1, a novel antifungal and insecticidal protein isolated from the flowers of Nicotiana alata. NaD1 is a basic, cysteine-rich protein of 47 residues and is the first example of a plant defensin from flowers to be characterized structurally. Its three-dimensional structure consists of an alpha-helix and a triple-stranded antiparallel beta-sheet that are stabilized by four intramolecular disulfide bonds. NaD1 features all the characteristics of the cysteine-stabilized alphabeta motif that has been described for a variety of proteins of differing functions ranging from antibacterial insect defensins and ion channel-perturbing scorpion toxins to an elicitor of the sweet taste response. The protein is biologically active against insect pests, which makes it a potential candidate for use in crop protection. NaD1 shares 31% sequence identity with alfAFP, an antifungal protein from alfalfa that confers resistance to a fungal pathogen in transgenic potatoes. The structure of NaD1 was used to obtain a homology model of alfAFP, since NaD1 has the highest level of sequence identity with alfAFP of any structurally characterized antifungal defensin. The structures of NaD1 and alfAFP were used in conjunction with structure-activity data for the radish defensin Rs-AFP2 to provide an insight into structure-function relationships. In particular, a putative effector site was identified in the structure of NaD1 and in the corresponding homology model of alfAFP.

Crystal Structures of Flax Rust Avirulence Proteins AvrL567-A and -D Reveal Details of the Structural Basis for Flax Disease Resistance Specificity

The gene-for-gene mechanism of plant disease resistance involves direct or indirect recognition of pathogen avirulence (Avr) proteins by plant resistance (R) proteins. Flax rust (Melampsora lini) AvrL567 avirulence proteins and the corresponding flax (Linum usitatissimum) L5, L6, and L7 resistance proteins interact directly. We determined the three-dimensional structures of two members of the AvrL567 family, AvrL567-A and AvrL567-D, at 1.4- and 2.3-Å resolution, respectively. The structures of both proteins are very similar and reveal a β-sandwich fold with no close known structural homologs. The polymorphic residues in the AvrL567 family map to the surface of the protein, and polymorphisms in residues associated with recognition differences for the R proteins lead to significant changes in surface chemical properties. Analysis of single amino acid substitutions in AvrL567 proteins confirm the role of individual residues in conferring differences in recognition and suggest that the specificity results from the cumulative effects of multiple amino acid contacts. The structures also provide insights into possible pathogen-associated functions of AvrL567 proteins, with nucleic acid binding activity demonstrated in vitro. Our studies provide some of the first structural information on avirulence proteins that bind directly to the corresponding resistance proteins, allowing an examination of the molecular basis of the interaction with the resistance proteins as a step toward designing new resistance specificities.

Investigating Potential Mechanisms of Obesity by Metabolomics

Obesity is a serious health problem with an increased risk of several common diseases including diabetes, cardiovascular disease, and cancer. Metabolomics is an emerging analytical technique for systemic determination of metabolite profiles, which is useful for understanding the biochemical changes in obesity or related diseases both in individual organs and at the organism level. Increasingly, this technology has been applied to the study of obesity, complementing transcriptomics and/or proteomics analyses. Indeed, the alterations of metabolites in biofluids/tissues are direct indicators of variations in physiology or pathology. In this paper, we will examine the obesity-related alterations in significant metabolites that have been identified by metabolomics as well as their metabolic pathway associations. Issues concerning the screening of biologically significant metabolites related to obesity will also be discussed.

Sunflower Trypsin Inhibitor-1

SFTI-1 is a bicyclic 14 amino acid peptide that was originally isolated from the seeds of the sunflower Helianthus annuus. It is a potent inhibitor of trypsin, with a sub-nanomolar K(i) value and is homologous to the active site region of the well-known family of serine protease inhibitors known as the Bowman-Birk trypsin inhibitors. It has a cyclic backbone that is cross-braced by a single disulfide bridge and a network of hydrogen bonds that result in a well-defined structure. SFTI-1 is amenable to chemical synthesis, allowing for the creation of synthetic variants. Alterations to the structure such as linearising the backbone or removing the disulfide bridge do not reduce the potency of SFTI-1 significantly, and minimising the peptide to as few as nine residues results in only a small decrease in reactivity. The creation of linear variants of SFTI-1 also provides a tool for investigating putative linear precursor peptides. The mechanism of biosynthesis of SFTI-1 is not yet known but it seems likely that it is a gene-coded product that has arisen from a precursor protein that may be evolutionarily related to classic Bowman-Birk inhibitors.

A Core Metabolic Enzyme Mediates Resistance to Phosphine Gas

Dissecting Phosphine Resistance Worldwide populations of pest insects—such as the lesser grain borer, Rhyzopertha dominica, and the rust-red flour beetle, Tribolium castaneum—have become highly resistant to the fumigant phosphine, providing a potential threat to global food security. The nematode, Caenorhabditis elegans is vulnerable to phosphine, but phosphine-resistant strains are known. Schlipalius et al. (p. 807) show that mutations in the delta-1-pyrroline-5-carboxylate dehydrogenase and dihydrolipoamide dehydrogenase (dld-1) genes both give rise to phosphine resistance in C. elegans. Phosphine resistance mutants in R. dominica, and T. castaneum also map to the dld-1 gene, which codes for a core metabolic enzyme. These mutants are, however, hypersensitive to arsenic, mimics of which might thus synergize with phosphine. Mutations in a lipoic acid metabolism enzyme confer resistance to phosphine but also result in sensitivity to arsenite. Phosphine is a small redox-active gas that is used to protect global grain reserves, which are threatened by the emergence of phosphine resistance in pest insects. We find that polymorphisms responsible for genetic resistance cluster around the redox-active catalytic disulfide or the dimerization interface of dihydrolipoamide dehydrogenase (DLD) in insects (Rhyzopertha dominica and Tribolium castaneum) and nematodes (Caenorhabditis elegans). DLD is a core metabolic enzyme representing a new class of resistance factor for a redox-active metabolic toxin. It participates in four key steps of core metabolism, and metabolite profiles indicate that phosphine exposure in mutant and wild-type animals affects these steps differently. Mutation of DLD in C. elegans increases arsenite sensitivity. This specific vulnerability may be exploited to control phosphine-resistant insects and safeguard food security.

Metabolomics: A Novel Approach to Early and Noninvasive Prostate Cancer Detection

Prostate cancer (PCa) is the most commonly diagnosed visceral cancer in men and is responsible for the second highest cancer-related male mortality rate in Western countries, with increasing rates being reported in Korea, Japan, and China. Considering the low sensitivity of prostate-specific antigen (PSA) testing, it is widely agreed that reliable, age-independent markers of the presence, nature, and progression of PCa are required to facilitate diagnosis and timely treatment. Metabolomics or metabonomics has recently emerged as a novel method of PCa detection owing to its ability to monitor changes in the metabolic signature, within biofluids or tissue, that reflect changes in phenotype and function. This review outlines the physiology of prostate tissue and prostatic fluid in health and in malignancy in relation to metabolomics as well as the principles underlying the methods of metabolomic quantification. Promising metabolites, metabolic profiles, and their correlation with the presence and stage of PCa are summarized. Application of metabolomics to biofluids and in vivo quantification as well as the direction of current research in supplementing and improving current methods of detection are discussed. The current debate in the urology literature on sarcosine as a potential biomarker for PCa is reviewed and discussed. Metabolomics promises to be a valuable tool in the early detection of PCa that may enable earlier treatment and improved clinical outcomes.

Altered metabolism of growth hormone receptor mutant mice: a combined NMR metabonomics and microarray study

Background Growth hormone is an important regulator of post-natal growth and metabolism. We have investigated the metabolic consequences of altered growth hormone signalling in mutant mice that have truncations at position 569 and 391 of the intracellular domain of the growth hormone receptor, and thus exhibit either low (around 30% maximum) or no growth hormone-dependent STAT5 signalling respectively. These mutations result in altered liver metabolism, obesity and insulin resistance. Methodology/Principal Findings The analysis of metabolic changes was performed using microarray analysis of liver tissue and NMR metabonomics of urine and liver tissue. Data were analyzed using multivariate statistics and Gene Ontology tools. The metabolic profiles characteristic for each of the two mutant groups and wild-type mice were identified with NMR metabonomics. We found decreased urinary levels of taurine, citrate and 2-oxoglutarate, and increased levels of trimethylamine, creatine and creatinine when compared to wild-type mice. These results indicate significant changes in lipid and choline metabolism, and were coupled with increased fat deposition, leading to obesity. The microarray analysis identified changes in expression of metabolic enzymes correlating with alterations in metabolite concentration both in urine and liver. Similarity of mutant 569 to the wild-type was seen in young mice, but the pattern of metabolites shifted to that of the 391 mutant as the 569 mice became obese after six months age. Conclusions/Significance The metabonomic observations were consistent with the parallel analysis of gene expression and pathway mapping using microarray data, identifying metabolites and gene transcripts involved in hepatic metabolism, especially for taurine, choline and creatinine metabolism. The systems biology approach applied in this study provides a coherent picture of metabolic changes resulting from impaired STAT5 signalling by the growth hormone receptor, and supports a potentially important role for taurine in enhancing β-oxidation.

Comparative Genomics of Serial Isolates of Cryptococcus neoformans Reveals Gene Associated With Carbon Utilization and Virulence

The opportunistic fungal pathogen Cryptococcus neoformans is a leading cause of mortality among the human immunodeficiency virus/acquired immunodeficiency syndrome population and is known for frequently causing life-threatening relapses. To investigate the potential contribution of in-host microevolution to persistence and relapse, we have analyzed two serial isolates obtained from a patient with acquired immunodeficiency syndrome who suffered an initial and relapse episode of cryptococcal meningoencephalitis. Despite being identical by multilocus sequence typing, the isolates differ phenotypically, exhibiting changes in key virulence factors, nutrient acquisition, metabolic profiles, and the ability to disseminate in an animal model. Whole-genome sequencing uncovered a clonal relationship, with only a few unique differences. Of these, two key changes are expected to explain the phenotypic differences observed in the relapse isolate: loss of a predicted AT-rich interaction domain protein and changes in copy number of the left and right arms of chromosome 12. Gene deletion of the predicted transcriptional regulator produced changes in melanin, capsule, carbon source use, and dissemination in the host, consistent with the phenotype of the relapse isolate. In addition, the deletion mutant displayed altered virulence in the murine model. The observed differences suggest the relapse isolate evolved subsequent to penetration of the central nervous system and may have gained dominance following the administration of antifungal therapy. These data reveal the first molecular insights into how the Cryptococcus neoformans genome changes during infection of humans and the manner in which microevolution progresses in this deadly fungal pathogen.

Markers for detection of prostate cancer

Early detection of prostate cancer is problematic, not just because of uncertainly whether a diagnosis will benefit an individual patient, but also as a result of the imprecise and invasive nature of establishing a diagnosis by biopsy. Despite its low sensitivity and specificity for identifying patients harbouring prostate cancer, serum prostate specific antigen (PSA) has become established as the most reliable and widely-used diagnostic marker for this condition. In its wake, many other markers have been described and evaluated. This review focuses on the supporting evidence for the most prominent of these for detection and also for predicting outcome in prostate cancer.

Performance Evaluation of Algorithms for the Classification of Metabolic 1H NMR Fingerprints

Nontargeted metabolite fingerprinting is increasingly applied to biomedical classification. The choice of classification algorithm may have a considerable impact on outcome. In this study, employing nested cross-validation for assessing predictive performance, six binary classification algorithms in combination with different strategies for data-driven feature selection were systematically compared on five data sets of urine, serum, plasma, and milk one-dimensional fingerprints obtained by proton nuclear magnetic resonance (NMR) spectroscopy. Support Vector Machines and Random Forests combined with t-score-based feature filtering performed well on most data sets, whereas the performance of the other tested methods varied between data sets.