Belowground net primary productivity and biomass allocation of a grassland in Inner Mongolia is affected by grazing intensity

The root system of permanent grasslands is of outstanding importance for resource acquisition. Particularly under semi-arid conditions, the acquisition of water and nutrients is highly variable during the vegetation growth period and between years. Additionally, grazing is repeatedly disturbing the functional equilibrium between the root system and the transpiring leaf canopy. However, very few data is available considering grazing effects on belowground net primary productivity (BNPP) and root-shoot dry mass allocation in natural grassland systems. We hypothesise that grazing significantly reduces BNPP due to carbon reallocation to shoot growth. Root biomass and BNPP were estimated by soil coring in 2004, 2005 and 2006 and from ingrowth cores in 2005 and 2006 at one site which has been protected from grazing since 1979 (UG79), at one winter grazing (WG), and one heavily grazed (HG) site. BNPP was estimated from the summation of significant increments of total and live root biomass and from accumulated root biomass of ingrowth cores. Belowground biomass varied from 1,490–2,670 g m^?2 and was significantly lower under heavy grazing than at site UG79. Root turnover varied from 0.23 to 0.33 year^?1 and was not significantly different between sites. Heavy grazing significantly decreased live root biomass and BNPP compared to site UG79. Taking BNPP estimates from live root biomass dynamics and ingrowth cores as the most reliable values, the portion of dry mass allocated belowground relative to total net primary productivity (BNPP/NPP) varied between 0.50–0.66 and was reduced under heavy grazing in 2005, but not in 2006. The positive correlation between cumulative root length density of ingrowth cores and leaf dry matter suggests that the ingrowth core method is suitable for studying BNPP in this semi-arid steppe system. Grazing effects on BNPP and BNPP/NPP should be considered in regional carbon models and estimates of belowground nutrient cycling.     

Effects of nitrogen supply on growth, yield and yield components of safflower and sunflower

Safflower (Carthamus tinctorius L.) represents an important oil crop internationally and may have a certain production potential under low input conditions, particularly in organic farming systems, where the putatively low nutrient requirement would be an advantage. However, little is known about the nutrient requirements of safflower. This study was undertaken to determine the growth and yield response of safflower as compared to sunflower (Helianthus annuus L.) under different N supplies. Safflower and sunflower plants were grown in Mitscherlich pots containing equal volumes of sand, nutrient-poor limed soil, and perlite. Nitrogen supply was the same for both species (0.25, 0.5, 1.0, 1.5, and 2.0 g per pot) in the first year, but was raised for sunflower (0.5, 1.0, 2.0, 3.0, and 4.0 g per pot) in the second. Increased N supply enhanced plant growth and yield for both species. Growth and yield of safflower increased up to 1.0 g pot⁻¹, while the optimum for sunflower was 2.0 g pot⁻¹. Safflower out-yielded sunflower at low N supply, while at high N level the opposite occurred. Functional analysis according to Michaelis–Menten revealed that – in terms of yield formation – safflower is superior to sunflower under N-limited conditions. Safflower is generally more efficient than sunflower in concentrating N in their shoots. N concentration in the photosynthetically active youngest mature blade of both species exhibited the same functional relationship with the N supply, but safflower required a much lower leaf N concentration to produce optimal yield as compared to sunflower, indicating the higher efficiency of the former in terms of NUE. Yield components analysis revealed that in safflower, yield is tightly correlated with the number of capitula per plant and the mass per achene, both being strongly correlated, too. On the contrary, sunflower yield was merely determined by the number of achenes per capitulum, followed by the mass per achene. Path coefficient analysis showed that in safflower, the direct effects of the achene and leaf N content as well as the leaf dry matter on oil yield are small, and mediated principally via indirect effects on the number of achenes per capitulum, while for sunflower the number of achenes per capitulum exerts a strong direct effect. Dedicated to the late Prof. Dr. Burkhard Sattelmacher, who passed away on November 21, 2005.     

Poultry-Associated Salmonella enterica subsp. enterica Serovar 4,12:d:? Reveals High Clonality and a Distinct Pathogenicity Gene Repertoire

A European baseline survey during the years 2005 and 2006 has revealed that the monophasic Salmonella enterica subsp. enterica serovar 4,12:d:− was, with a prevalence of 23.6%, the most frequently isolated serovar in German broiler flocks. In Denmark and the United Kingdom, its serovar prevalences were 15.15% and 2.8%, respectively. Although poultry is a major source of human salmonellosis, serovar 4,12:d:− is rarely isolated in humans (approximately 0.09% per year). Molecular typing studies using pulsed-field gel electrophoresis and DNA microarray analysis show that the serovar is highly clonal and lacks genes with known contributions to pathogenicity. In contrast to other poultry-associated serovars, all strains were susceptible to 17 antimicrobial agents tested and did not encode any resistance determinant. Furthermore, serovar 4,12:d:− lacked the genes involved in galactonate metabolism and in the glycolysis and glyconeogenesis important for energy production in the cells. The conclusion of the study is that serovar 4,12:d:− seems to be primarily adapted to broilers and therefore causes only rare infections in humans.Salmonella spp. are major zoonotic food-borne pathogens which cause outbreaks and sporadic cases of gastroenteritis in humans worldwide (12). Depending on the serovar, cases of salmonellosis can differ substantially in severity (13). The primary sources of salmonellosis are food-producing animals, such as poultry, pigs, and cattle (30). The pathogen is spread by trade in animals and nonheated animal food products (10).A European baseline survey on the prevalence of Salmonella in commercial broiler flocks of Gallus gallus in 2005 and 2006 showed that in the European Union (EU), 23.7% of the broiler flocks were Salmonella positive (8). However, the Salmonella prevalences and serovar distribution varied widely among the EU member states. The five most frequently isolated Salmonella enterica serovars in Europe were those classically observed, like serovar Enteritidis (33.8%), serovar Infantis (22.0%), serovar Mbandaka (8.1%), serovar Hadar (3.7%), and serovar Typhimurium (3.0%). In Germany, the flock prevalence of Salmonella was 15.0% among the 377 broiler flocks investigated. In contrast to the well-known serovars described above, the predominating serovar was monophasic serovar 4,12:d:−, with a prevalence of 23.6%. This serovar was also isolated in Denmark and the United Kingdom, with prevalences of 15.2% and 2.8%, respectively.The German Salmonella National Reference Laboratory (NRL-Salmonella) has received 818 isolates of this serovar between 1998 and 2007, with peaks in 2001 (240 isolates) and 2004 (160 isolates), for diagnosis. Since 2005, the number has doubled annually, and the serovar obviously established itself well in poultry production lines. These isolates were found mostly in broilers (78%), occasionally in turkeys (11.6%) and feedstuff (8.4%), and rarely in pigs (1.3%) and cattle (0.6%). In contrast, infections in humans are only sporadic. During the last 10 years (1998 to 2007), the National Reference Centre for Salmonellae and Other Enterics located at the Robert-Koch Institute, Wernigerode branch, has received 55 strains of this serovar from sporadic human cases of salmonellosis and carriers in Germany (W. Rabsch, personal communication). Similarly, in Denmark, only two isolates in 1993 and in 2002 were isolated from humans (E. Møller Nielsen, Statens Serum Institut, Copenhagen, Denmark, personal communication).Subtyping food-borne pathogens is an approach often applied to facilitate the epidemiological investigation of outbreaks of gastrointestinal disease and to identify the source of entry into the food chain. Several molecular-based tools have been developed to type bacteria genotypically. Pulsed-field gel electrophoresis (PFGE) is currently the method of choice for the molecular subtyping of Salmonella serovars. It has been proven to be a useful discriminatory method which was standardized by the PulseNet Consortium (9). However, although this approach is certainly valuable, it does not reveal data on the gene repertoire and biological properties of a strain. To overcome this weakness, whole-genome DNA microarrays have successfully been applied in comparative genomic hybridizations for Salmonella (7, 24, 25). However, whole-genome arrays reflect only one genome of one strain. Because of many serovar or strain genome variations described for Salmonella, thematic arrays were developed, such as arrays specially targeting genes involved in resistance profiles (2, 17, 32), phage types (23), or serovars (33, 35). A condensed selection of 109 various Salmonella genetic markers comprising the detection of flagellar and somatic antigen-encoding genes, important virulence genes, phage-associated genes, and antibiotic resistance determinants have been used to show the usefulness of DNA microarrays for the discriminative characterization of Salmonella serovars (18).In this study, we elucidate the contradicting situation between the high prevalence in broilers and source attribution of broiler meat for humans and the low infection rates in humans of the serovar 4,12:d:− by genotypic characterization using PFGE and DNA microarray to determine the clonality, the pathogenic gene repertoire, and resistance determinants. These data give basic information to discuss the hazard potential of this serovar for humans. For that purpose, a new prototype of a Salmonella DNA microarray comprising 281 60-mer oligonucleotide probes was developed and validated in house.     

Using genetic algorithms to select most predictive protein features

Many important characteristics of proteins such as biochemical activity and subcellular localization present a challenge to machine-learning methods: it is often difficult to encode the appropriate input features at the residue level for the purpose of making a prediction for the entire protein. The problem is usually that the biophysics of the connection between a machine-learning method's input (sequence feature) and its output (observed phenomenon to be predicted) remains unknown; in other words, we may only know that a certain protein is an enzyme (output) without knowing which region may contain the active site residues (input). The goal then becomes to dissect a protein into a vast set of sequence-derived features and to correlate those features with the desired output. We introduce a framework that begins with a set of global sequence features and then vastly expands the feature space by generically encoding the coexistence of residue-based features. It is this combination of individual features, that is the step from the fractions of serine and buried (input space 20 + 2) to the fraction of buried serine (input space 20 * 2) that implicitly shifts the search space from global feature inputs to features that can capture very local evidence such as a the individual residues of a catalytic triad. The vast feature space created is explored by a genetic algorithm (GA) paired with neural networks and support vector machines. We find that the GA is critical for selecting combinations of features that are neither too general resulting in poor performance, nor too specific, leading to overtraining. The final framework manages to effectively sample a feature space that is far too large for exhaustive enumeration. We demonstrate the power of the concept by applying it to prediction of protein enzymatic activity.     

Cell cycle kinases predicted from conserved biophysical properties

Machine-learning techniques can classify functionally related proteins where homology-transfer as well as sequence and structure motifs fail. Here, we present a method that aimed at complementing homology-transfer in the identification of cell cycle control kinases from sequence alone. First, we identified functionally significant residues in cell cycle proteins through their high sequence conservation and biophysical properties. We then incorporated these residues and their features into support vector machines (SVM) to identify new kinases and more specifically to differentiate cell cycle kinases from other kinases and other proteins. As expected, the most informative residues tend to be highly conserved and tend to localize in the ATP binding regions of the kinases. Another observation confirmed that ATP binding regions are typically not found on the surface but in partially buried sites, and that this fact is correctly captured by accessibility predictions. Using these highly conserved, semi-buried residues and their biophysical properties, we could distinguish cell cycle S/T kinases from other kinase families at levels around 70-80% accuracy and 62-81% coverage. An application to the entire human proteome predicted at least 97 human proteins with limited previous annotations to be candidates for cell cycle kinases.     

Relaxation-optimised Hartmann–Hahn transfer using a specifically Tailored MOCCA-XY16 mixing sequence for carbonyl–carbonyl correlation spectroscopy in <Superscript>13</Superscript>C direct detection NMR experiments

Isotropic mixing sequences are one of the key methods to achieve efficient coherence transfer. Among them, the MOCCA-XY16, which keeps the magnetization longitudinal for a significant amount of time, is characterised by favourable relaxation properties. We show here that its adapted version is particularly suited for carbonyl–carbonyl correlations in ¹³C direct detection NMR experiments.     

Soluble HLA-DQ2 expressed in S2 cells copurifies with a high affinity insect cell derived protein

We here describe that soluble HLA-DQ2 (sDQ2) molecules, when expressed in Drosophila melanogaster S2 insect cells without a covalently tethered peptide, associate tightly with the D. melanogaster calcium binding protein DCB-45. The interaction between the proteins is stable in S2 cell culture and during affinity purification, which is done at high salt concentrations and pH 11.5. After affinity purification, the sDQ2/DCB-45 complex exists in substantial quantities next to a small amount of free heterodimeric sDQ2 and large amounts of aggregated sDQ2 free of DCB-45. Motivated by the stable complex formation and our interest in the development of reagents which inhibit HLA-DQ2 peptide binding, we have further characterized the sDQ2/DCB-45 interaction. Several lines of evidence indicate that an N-terminal fragment of DCB-45 is involved in the interaction with the peptide binding groove of sDQ2. Further mapping of this fragment of 54 residues identified a pentadecapeptide with high affinity for sDQ2 which may serve as a lead compound for the design of HLA-DQ2 blockers.     

Varying survival of motoneurons and activation of distinct molecular mechanism in response to altered peripheral myelin protein 22 gene dosage

Alteration in the expression level of peripheral myelin protein 22 (PMP22) is the most frequent cause for demyelinating neuropathies of Charcot-Marie-Tooth type. Here, we demonstrate a loss of motoneurons (MNs) in the spinal cords from transgenic mice over-expressing Pmp22 ( Pmp22 ^tg ) while mice lacking Pmp22 [ Pmp22 ^ko ; knockout (ko)] exhibited normal MN numbers at the symptomatic age of 60 days. In order to describe the molecular changes in affected MNs, these cells were isolated from lumbar spinal cords by laser-capture microdissection. Remarkably, the MNs of the Pmp22 ^ko and Pmp22 ^tg mice showed different expression profiles because of the altered Pmp22 expression. The changes in the expression profile of MNs from Pmp22 ^ko mice resemble those described in MNs from mice after nerve injury and included genes that had been described in neuronal growth and regeneration like Gap 43 and Sprr 11a. The changes detected in the expression pattern of MNs from Pmp22 ^tg mice exhibited fewer similarities to other expression patterns. The specific expression pattern in the MNs of the Pmp22 ^ko mice might contribute to the better survival of the MNs. Our study also revealed induction of genes like brain-expressed X-linked 1 ( Bex 1) and desmoplakin ( Dsp ) that had recently been found up-regulated in MNs of human amyotrophic lateral sclerosis patients.     

Structural genomics is the largest contributor of novel structural leverage

The Protein Structural Initiative (PSI) at the US National Institutes of Health (NIH) is funding four large-scale centers for structural genomics (SG). These centers systematically target many large families without structural coverage, as well as very large families with inadequate structural coverage. Here, we report a few simple metrics that demonstrate how successfully these efforts optimize structural coverage: while the PSI-2 (2005-now) contributed more than 8% of all structures deposited into the PDB, it contributed over 20% of all novel structures (i.e. structures for protein sequences with no structural representative in the PDB on the date of deposition). The structural coverage of the protein universe represented by today’s UniProt (v12.8) has increased linearly from 1992 to 2008; structural genomics has contributed significantly to the maintenance of this growth rate. Success in increasing novel leverage (defined in Liu et al. in Nat Biotechnol 25:849–851, 2007) has resulted from systematic targeting of large families. PSI’s per structure contribution to novel leverage was over 4-fold higher than that for non-PSI structural biology efforts during the past 8 years. If the success of the PSI continues, it may just take another ~15 years to cover most sequences in the current UniProt database.