The European livestock resistome

Metagenomic sequencing has proven to be a powerful tool in the monitoring of antimicrobial resistance (AMR). Here, we provide a comparative analysis of the resistome from pigs, poultry, veal calves, turkey, and rainbow trout, for a total of 538 herds across nine European countries. We calculated the effects of per-farm management practices and antimicrobial usage (AMU) on the resistome in pigs, broilers, and veal calves. We also provide an in-depth study of the associations between bacterial diversity, resistome diversity, and AMR abundances as well as co-occurrence analysis of bacterial taxa and antimicrobial resistance genes (ARGs) and the universality of the latter. The resistomes of veal calves and pigs clustered together, as did those of avian origin, while the rainbow trout resistome was different. Moreover, we identified clear core resistomes for each specific food-producing animal species. We identified positive associations between bacterial alpha diversity and both resistome alpha diversity and abundance. Network analyses revealed very few taxa-ARG associations in pigs but a large number for the avian species. Using updated reference databases and optimized bioinformatics, previously reported significant associations between AMU, biosecurity, and AMR in pig and poultry farms were validated. AMU is an important driver for AMR; however, our integrated analyses suggest that factors contributing to increased bacterial diversity might also be associated with higher AMR load. We also found that dispersal limitations of ARGs are shaping livestock resistomes, and future efforts to fight AMR should continue to emphasize biosecurity measures.IMPORTANCEUnderstanding the occurrence, diversity, and drivers for antimicrobial resistance (AMR) is important to focus future control efforts. So far, almost all attempts to limit AMR in livestock have addressed antimicrobial consumption. We here performed an integrated analysis of the resistomes of five important farmed animal populations across Europe finding that the resistome and AMR levels are also shaped by factors related to bacterial diversity, as well as dispersal limitations. Thus, future studies and interventions aimed at reducing AMR should not only address antimicrobial usage but also consider other epidemiological and ecological factors.

Improved metagenome binning and assembly using deep variational autoencoders

Despite recent advances in metagenomic binning, reconstruction of microbial species from metagenomics data remains challenging. Here we develop variational autoencoders for metagenomic binning (VAMB), a program that uses deep variational autoencoders to encode sequence coabundance and k-mer distribution information before clustering. We show that a variational autoencoder is able to integrate these two distinct data types without any previous knowledge of the datasets. VAMB outperforms existing state-of-the-art binners, reconstructing 29-98% and 45% more near-complete (NC) genomes on simulated and real data, respectively. Furthermore, VAMB is able to separate closely related strains up to 99.5% average nucleotide identity (ANI), and reconstructed 255 and 91 NC Bacteroides vulgatus and Bacteroides dorei sample-specific genomes as two distinct clusters from a dataset of 1,000 human gut microbiome samples. We use 2,606 NC bins from this dataset to show that species of the human gut microbiome have different geographical distribution patterns. VAMB can be run on standard hardware and is freely available at https://github.com/RasmussenLab/vamb .

Metagenomics-Based Approach to Source-Attribution of Antimicrobial Resistance Determinants - Identification of Reservoir Resistome Signatures

Metagenomics can unveil the genetic content of the total microbiota in different environments, such as food products and the guts of humans and livestock. It is therefore considered of great potential to investigate the transmission of foodborne hazards as part of source-attribution studies. Source-attribution of antimicrobial resistance (AMR) has traditionally relied on pathogen isolation, while metagenomics allows investigating the full span of AMR determinants. In this study, we hypothesized that the relative abundance of fecal resistome components can be associated with specific reservoirs, and that resistomes can be used for AMR source-attribution. We used shotgun-sequences from fecal samples of pigs, broilers, turkeys- and veal calves collected across Europe, and fecal samples from humans occupationally exposed to livestock in one country (pig slaughterhouse workers, pig and broiler farmers). We applied both hierarchical and flat forms of the supervised classification ensemble algorithm Random Forests to classify resistomes into corresponding reservoir classes. We identified country-specific and -independent AMR determinants, and assessed the impact of country-specific determinants when attributing AMR resistance in humans. Additionally, we performed a similarity percentage analysis with the full spectrum of AMR determinants to identify resistome signatures for the different reservoirs. We showed that the number of AMR determinants necessary to attribute a resistome into the correct reservoir increases with a larger reservoir heterogeneity, and that the impact of country-specific resistome signatures on prediction varies between countries. We predicted a higher occupational exposure to AMR determinants among workers exposed to pigs than among those exposed to broilers. Additionally, results suggested that AMR exposure on pig farms was higher than in pig slaughterhouses. Human resistomes were more similar to pig and veal calves’ resistomes than to those of broilers and turkeys, and the majority of these resistome dissimilarities can be explained by a small set of AMR determinants. We identified resistome signatures for each individual reservoir, which include AMR determinants significantly associated with on-farm antimicrobial use. We attributed human resistomes to different livestock reservoirs using Random Forests, which allowed identifying pigs as a potential source of AMR in humans. This study thus demonstrates that it is possible to apply metagenomics in AMR source-attribution.

Data integration for prediction of weight loss in randomized controlled dietary trials

Diet is an important component in weight management strategies, but heterogeneous responses to the same diet make it difficult to foresee individual weight-loss outcomes. Omics-based technologies now allow for analysis of multiple factors for weight loss prediction at the individual level. Here, we classify weight loss responders (N = 106) and non-responders (N = 97) of overweight non-diabetic middle-aged Danes to two earlier reported dietary trials over 8 weeks. Random forest models integrated gut microbiome, host genetics, urine metabolome, measures of physiology and anthropometrics measured prior to any dietary intervention to identify individual predisposing features of weight loss in combination with diet. The most predictive models for weight loss included features of diet, gut bacterial species and urine metabolites (ROC-AUC: 0.84-0.88) compared to a diet-only model (ROC-AUC: 0.62). A model ensemble integrating multi-omics identified 64% of the non-responders with 80% confidence. Such models will be useful to assist in selecting appropriate weight management strategies, as individual predisposition to diet response varies.

Proficiency Testing of Metagenomics-Based Detection of Food-Borne Pathogens Using a Complex Artificial Sequencing Dataset

Metagenomics-based high-throughput sequencing (HTS) enables comprehensive detection of all species comprised in a sample with a single assay and is becoming a standard method for outbreak investigation. However, unlike real-time PCR or serological assays, HTS datasets generated for pathogen detection do not easily provide yes/no answers. Rather, results of the taxonomic read assignment need to be assessed by trained personnel to gain information thereof. Proficiency tests are important instruments of validation, harmonization, and standardization. Within the European Union funded project COMPARE [COllaborative Management Platform for detection and Analyses of (Re-) emerging and foodborne outbreaks in Europe], we conducted a proficiency test to scrutinize the ability to assess diagnostic metagenomics data. An artificial dataset resembling shotgun sequencing of RNA from a sample of contaminated trout was provided to 12 participants with the request to provide a table with per-read taxonomic assignments at species level and a report with a summary and assessment of their findings, considering different categories like pathogen, background, or contaminations. Analysis of the read assignment tables showed that the software used reliably classified the reads taxonomically overall. However, usage of incomplete reference databases or inappropriate data pre-processing caused difficulties. From the combination of the participants' reports with their read assignments, we conclude that, although most species were detected, a number of important taxa were not or not correctly categorized. This implies that knowledge of and awareness for potentially dangerous species and contaminations need to be improved, hence, capacity building for the interpretation of diagnostic metagenomics datasets is necessary.

Proficiency Testing of Virus Diagnostics Based on Bioinformatics Analysis of Simulated In Silico High-Throughput Sequencing Data Sets

Quality management and independent assessment of high-throughput sequencing-based virus diagnostics have not yet been established as a mandatory approach for ensuring comparable results. The sensitivity and specificity of viral high-throughput sequence data analysis are highly affected by bioinformatics processing using publicly available and custom tools and databases and thus differ widely between individuals and institutions. Here we present the results of the COMPARE [Collaborative Management Platform for Detection and Analyses of (Re-)emerging and Foodborne Outbreaks in Europe] in silico virus proficiency test. An artificial, simulated in silico data set of Illumina HiSeq sequences was provided to 13 different European institutes for bioinformatics analysis to identify viral pathogens in high-throughput sequence data. Comparison of the participants' analyses shows that the use of different tools, programs, and databases for bioinformatics analyses can impact the correct identification of viral sequences from a simple data set. The identification of slightly mutated and highly divergent virus genomes has been shown to be most challenging. Furthermore, the interpretation of the results, together with a fictitious case report, by the participants showed that in addition to the bioinformatics analysis, the virological evaluation of the results can be important in clinical settings. External quality assessment and proficiency testing should become an important part of validating high-throughput sequencing-based virus diagnostics and could improve the harmonization, comparability, and reproducibility of results. There is a need for the establishment of international proficiency testing, like that established for conventional laboratory tests such as PCR, for bioinformatics pipelines and the interpretation of such results.

Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage

Antimicrobial resistance (AMR) is a serious threat to global public health, but obtaining representative data on AMR for healthy human populations is difficult. Here, we use metagenomic analysis of untreated sewage to characterize the bacterial resistome from 79 sites in 60 countries. We find systematic differences in abundance and diversity of AMR genes between Europe/North-America/Oceania and Africa/Asia/South-America. Antimicrobial use data and bacterial taxonomy only explains a minor part of the AMR variation that we observe. We find no evidence for cross-selection between antimicrobial classes, or for effect of air travel between sites. However, AMR gene abundance strongly correlates with socio-economic, health and environmental factors, which we use to predict AMR gene abundances in all countries in the world. Our findings suggest that global AMR gene diversity and abundance vary by region, and that improving sanitation and health could potentially limit the global burden of AMR. We propose metagenomic analysis of sewage as an ethically acceptable and economically feasible approach for continuous global surveillance and prediction of AMR.

Norgal: extraction and de novo assembly of mitochondrial DNA from whole-genome sequencing data

BACKGROUND

Whole-genome sequencing (WGS) projects provide short read nucleotide sequences from nuclear and possibly organelle DNA depending on the source of origin. Mitochondrial DNA is present in animals and fungi, while plants contain DNA from both mitochondria and chloroplasts. Current techniques for separating organelle reads from nuclear reads in WGS data require full reference or partial seed sequences for assembling.

RESULTS

Norgal (de Novo ORGAneLle extractor) avoids this requirement by identifying a high frequency subset of k-mers that are predominantly of mitochondrial origin and performing a de novo assembly on a subset of reads that contains these k-mers. The method was applied to WGS data from a panda, brown algae seaweed, butterfly and filamentous fungus. We were able to extract full circular mitochondrial genomes and obtained sequence identities to the reference sequences in the range from 98.5 to 99.5%. We also assembled the chloroplasts of grape vines and cucumbers using Norgal together with seed-based de novo assemblers.

CONCLUSION

Norgal is a pipeline that can extract and assemble full or partial mitochondrial and chloroplast genomes from WGS short reads without prior knowledge. The program is available at: https://bitbucket.org/kosaidtu/norgal .

Meta-genomic analysis of toilet waste from long distance flights; a step towards global surveillance of infectious diseases and antimicrobial resistance

Human populations worldwide are increasingly confronted with infectious diseases and antimicrobial resistance spreading faster and appearing more frequently. Knowledge regarding their occurrence and worldwide transmission is important to control outbreaks and prevent epidemics. Here, we performed shotgun sequencing of toilet waste from 18 international airplanes arriving in Copenhagen, Denmark, from nine cities in three world regions. An average of 18.6 Gb (14.8 to 25.7 Gb) of raw Illumina paired end sequence data was generated, cleaned, trimmed and mapped against reference sequence databases for bacteria and antimicrobial resistance genes. An average of 106,839 (0.06%) reads were assigned to resistance genes with genes encoding resistance to tetracycline, macrolide and beta-lactam resistance genes as the most abundant in all samples. We found significantly higher abundance and diversity of genes encoding antimicrobial resistance, including critical important resistance (e.g. blaCTX-M) carried on airplanes from South Asia compared to North America. Presence of Salmonella enterica and norovirus were also detected in higher amounts from South Asia, whereas Clostridium difficile was most abundant in samples from North America. Our study provides a first step towards a potential novel strategy for global surveillance enabling simultaneous detection of multiple human health threatening genetic elements, infectious agents and resistance genes.

Cofactory: sequence-based prediction of cofactor specificity of Rossmann folds

Obtaining optimal cofactor balance to drive production is a challenge in metabolically engineered microbial production strains. To facilitate identification of heterologous enzymes with desirable altered cofactor requirements from native content, we have developed Cofactory, a method for prediction of enzyme cofactor specificity using only primary amino acid sequence information. The algorithm identifies potential cofactor binding Rossmann folds and predicts the specificity for the cofactors FAD(H2), NAD(H), and NADP(H). The Rossmann fold sequence search is carried out using hidden Markov models whereas artificial neural networks are used for specificity prediction. Training was carried out using experimental data from protein-cofactor structure complexes. The overall performance was benchmarked against an independent evaluation set obtaining Matthews correlation coefficients of 0.94, 0.79, and 0.65 for FAD(H2), NAD(H), and NADP(H), respectively. The Cofactory method is made publicly available at http://www.cbs.dtu.dk/services/Cofactory.

Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP

We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: http://www.cbs.dtu.dk/services/NetDiseaseSNP.

A systematic study of site-specific GalNAc-type O-glycosylation modulating proprotein convertase processing

Site-specific GalNAc-type O-glycosylation is emerging as an important co-regulator of proprotein convertase (PC) processing of proteins. PC processing is crucial in regulating many fundamental biological pathways and O-glycans in or immediately adjacent to processing sites may affect recognition and function of PCs. Thus, we previously demonstrated that deficiency in site-specific O-glycosylation in a PC site of the fibroblast growth factor, FGF23, resulted in marked reduction in secretion of active unprocessed FGF23, which cause familial tumoral calcinosis and hyperostosis hyperphosphatemia. GalNAc-type O-glycosylation is found on serine and threonine amino acids and up to 20 distinct polypeptide GalNAc transferases catalyze the first addition of GalNAc to proteins making this step the most complex and differentially regulated steps in protein glycosylation. There is no reliable prediction model for O-glycosylation especially of isolated sites, but serine and to a lesser extent threonine residues are frequently found adjacent to PC processing sites. In the present study we used in vitro enzyme assays and ex vivo cell models to systematically address the boundaries of the region within site-specific O-glycosylation affect PC processing. The results demonstrate that O-glycans within at least ±3 residues of the RXXR furin cleavage site may affect PC processing suggesting that site-specific O-glycosylation is a major co-regulator of PC processing.

NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features

β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC  = 0.50, Q_total = 82.1%, sensitivity  = 75.6%, PPV  = 68.8% and AUC  = 0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17 – 0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively.

Conclusion

The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences.

CPHmodels-3.0--remote homology modeling using structure-guided sequence profiles

CPHmodels-3.0 is a web server predicting protein 3D structure by use of single template homology modeling. The server employs a hybrid of the scoring functions of CPHmodels-2.0 and a novel remote homology-modeling algorithm. A query sequence is first attempted modeled using the fast CPHmodels-2.0 profile–profile scoring function suitable for close homology modeling. The new computational costly remote homology-modeling algorithm is only engaged provided that no suitable PDB template is identified in the initial search. CPHmodels-3.0 was benchmarked in the CASP8 competition and produced models for 94% of the targets (117 out of 128), 74% were predicted as high reliability models (87 out of 117). These achieved an average RMSD of 4.6 Å when superimposed to the 3D structure. The remaining 26% low reliably models (30 out of 117) could superimpose to the true 3D structure with an average RMSD of 9.3 Å. These performance values place the CPHmodels-3.0 method in the group of high performing 3D prediction tools. Beside its accuracy, one of the important features of the method is its speed. For most queries, the response time of the server is <20 min. The web server is available at http://www.cbs.dtu.dk/services/CPHmodels/.

A generic method for assignment of reliability scores applied to solvent accessibility predictions

BACKGROUND

Estimation of the reliability of specific real value predictions is nontrivial and the efficacy of this is often questionable. It is important to know if you can trust a given prediction and therefore the best methods associate a prediction with a reliability score or index. For discrete qualitative predictions, the reliability is conventionally estimated as the difference between output scores of selected classes. Such an approach is not feasible for methods that predict a biological feature as a single real value rather than a classification. As a solution to this challenge, we have implemented a method that predicts the relative surface accessibility of an amino acid and simultaneously predicts the reliability for each prediction, in the form of a Z-score.

RESULTS

An ensemble of artificial neural networks has been trained on a set of experimentally solved protein structures to predict the relative exposure of the amino acids. The method assigns a reliability score to each surface accessibility prediction as an inherent part of the training process. This is in contrast to the most commonly used procedures where reliabilities are obtained by post-processing the output.

CONCLUSION

The performance of the neural networks was evaluated on a commonly used set of sequences known as the CB513 set. An overall Pearson's correlation coefficient of 0.72 was obtained, which is comparable to the performance of the currently best public available method, Real-SPINE. Both methods associate a reliability score with the individual predictions. However, our implementation of reliability scores in the form of a Z-score is shown to be the more informative measure for discriminating good predictions from bad ones in the entire range from completely buried to fully exposed amino acids. This is evident when comparing the Pearson's correlation coefficient for the upper 20% of predictions sorted according to reliability. For this subset, values of 0.79 and 0.74 are obtained using our and the compared method, respectively. This tendency is true for any selected subset.

Identification of novel genes regulated in the developing human ventral mesencephalon

In the human embryo, from approximately 6 weeks gestational age (GA), dopaminergic (DA) neurons can be found in the ventral mesencephalon (VM). More specifically, the post-mitotic neurons are located in the ventral part of the tegmentum (VT), whereas no mature DA neurons are found in the neighboring dorsal part. We used Affymetrix HG-U133 GeneChip technology to compare genome-wide expression profiles of ventral and dorsal tegmentum from 8 weeks GA human embryos, in order to identify genes involved in specification, differentiation, and survival of mesencephalic DA (mDA) neurons. Known mDA marker genes including ALDH1A1, DAT1, VMAT2, TH, CALB1, NURR1, FOXA1, GIRK2, PITX3, RET, and DRD2 topped the list of 96 genes from HG-U133A with higher expression in VT, validating the experimental set-up. In addition, 28 probes from HG-U133B were identified whereof most are annotated to UniGene clusters with no gene associated or to genes of unknown function. Of these, the fifteen most regulated transcripts, representing changes down to 56% could be verified by quantitative real-time PCR (Q-PCR) on a developmental series of subdissected human embryonic and fetal brain material, resulting in not only a regional but also a temporal expression profile. This revealed a distinct DA-associated profile for in particular a putative transcription factor (FLJ45455) and the uncharacterized transmembrane proteins KIAA1145 and SLC10A4. The data presented here may help to device cell replacement and regenerative therapies for Parkinson's disease (PD).

Structure of porcine pancreatic spasmolytic polypeptide at 1.95 A resolution

The structure of a trigonal crystal form of porcine pancreatic spasmolytic polypeptide (PSP) has been solved by molecular replacement and refined to 1.95 A resolution. Three heavy-atom derivatives were prepared, giving unbiased phase information, which was used in the model building of the protein molecules. The final conventional R value is 19.8% with the inclusion of 183 water molecules. PSP crystallizes as a dimer in space group P3(1)21 with a non-crystallographic twofold axis relating the monomers. The monomer consists of two very similar domains each composed of three loop regions. Two clefts are found in the monomer, one in each domain, that are proposed as possible substrate-binding sites. Important interactions have been identified in the proposed substrate-binding sites, where conserved water molecules probably mimic the hydrophilic positions of the substrate. The estimated cleft size is 9 x 9 x 12 A. Analysis of the charge distribution within the clefts, by an electrostatic potential calculation, shows the clefts to be essentially non-charged.

Prediction of protein secondary structure at 80% accuracy

Secondary structure prediction involving up to 800 neural network predictions has been developed, by use of novel methods such as output expansion and a unique balloting procedure. An overall performance of 77.2%-80.2% (77.9%-80.6% mean per-chain) for three-state (helix, strand, coil) prediction was obtained when evaluated on a commonly used set of 126 protein chains. The method uses profiles made by position-specific scoring matrices as input, while at the output level it predicts on three consecutive residues simultaneously. The predictions arise from tenfold, cross validated training and testing of 1032 protein sequences, using a scheme with primary structure neural networks followed by structure filtering neural networks. With respect to blind prediction, this work is preliminary and awaits evaluation by CASP4.

The crystal structure of rhamnogalacturonase A from Aspergillus aculeatus: a right-handed parallel beta helix

BACKGROUND

Pectic substances are the major polysaccharide components of the middle lamella and primary cell wall of dicotyledonous plants. They consist of homogalacturonan 'smooth' regions and highly rhamnified 'hairy' regions of rhamnogalacturonan. The backbone in rhamnogalacturonan-l (RG-l), which is composed of alternating galacturonic acid and rhamnose residues, is the substrate for a new class of enzymes known as rhamnogalacturnoases (RGases). RGase A is a novel enzyme implicated in the enzymatic degradation of RG-l.

RESULTS

The structure of RGase A from Aspergillus aculeatus has been solved by the single isomorphous replacement method including anomalous scattering (SIRAS method) to 2.0 A resolution. The enzyme folds into a large right-handed parallel beta helix, with a core composed of 13 turns of beta strands. Four parallel beta sheets (PB1, PB1a, PB2 and PB3), formed by the consecutive turns, are typically separated by a residue in the conformation of a left-handed alpha helix. As a consequence of the consecutive turns, 32% of all residues have their sidechains aligned at the surface or in the interior of the parallel beta helix. The aligned residues at the surface are dominated by threonine, aspartic acid and asparagine, whereas valine, leucine and isoleucine are most frequently found in the interior. A very large hydrophobic cavity is found in the interior of the parallel beta helix. The potential active site is a groove, oriented almost perpendicular to the helical axis, containing a cluster of three aspartic acid residues and one glutamic acid residue. The enzyme is highly glycosylated; two N-linked and eighteen O-linked glycosylation sites have been found in the structure.

CONCLUSIONS

Rhamnogalacturonase A from A. aculeatus is the first three-dimensional structure of an enzyme hydrolyzing glycoside bonds within the backbone of RG-l. The large groove, which is the potential active site of RGase A, is also seen in the structures of pectate lyases. Two catalytic aspartic acid residues, which have been proposed to have a catalytic role, reside in this area of RGase A. The distance between the aspartic acid residues is consistent with the inverting mechanism of catalysis. The glycan groups bound to RGase A are important to the stability of the crystal, as the carbohydrate moiety is involved in most of the intermolecular hydrogen bonds.

Crystallization and preliminary X-ray studies of rhamnogalacturonase A from Aspergillus aculeatus

Recombinant rhamnogalacturonase A from Aspergillus aculeatus has been crystallized and X-ray diffraction data has been collected. Crystals were grown by the hanging-drop vapour-diffusion technique, under the conditions 10% PEG 8000, 0.05 M KH(2)PO(4) and 0.1 M sodium acetate buffered at pH 3.5. The crystals diffract beyond 2.0 A resolution and belong to one of the orthorhombic space groups I2(1)2(1)2(1) or I222, with the unit-cell parameters a = 62.9, b = 125.4 and c = 137.0 A. There is one molecule in the asymmetric unit and a solvent content of approximately 54%. The enzyme is highly glycosylated corresponding to 5.9 kDa.

Pancreatic spasmolytic polypeptide: first three-dimensional structure of a member of the mammalian trefoil family of peptides

BACKGROUND

The trefoil peptides are a rapidly growing family of peptides, mainly found in the gastrointestinal tract. There is circumstantial evidence that they stabilize the mucus layer, and may affect the rate of healing of the mucosal epithelium.

RESULTS

We have determined the structure of porcine pancreatic spasmolytic polypeptide (PSP) to 2.5 A resolution. The polypeptide contains two trefoil domains. The domain structure is compact, and is composed of a central short antiparallel beta-sheet with one short helix above and one below it. This is a novel motif. The two domains are related by two-fold symmetry, and each domain contains a cleft.

CONCLUSIONS

The cleft within each domain could accommodate a polysaccharide chain, and may therefore be responsible for binding mucin glycoproteins. We suggest that PSP may cross-link glycoproteins, explaining its ability to stabilize the mucus layer.