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PubMed2004

Mining the structural genomics pipeline: identification of protein properties that affect high-throughput experimental analysis.

Structural genomics projects represent major undertakings that will change our understanding of proteins. They generate unique datasets that, for the first time, present a standardized view of proteins in terms of their physical and chemical properties. By analyzing these datasets here, we are able to discover correlations between a protein's characteristics and its progress through each stage of the structural genomics pipeline, from cloning, expression, purification, and ultimately to structural determination. First, we use tree-based analyses (decision trees and random forest algorithms) to discover the most significant protein features that influence a protein's amenability to high-throughput experimentation. Based on this, we identify potential bottlenecks in various stages of the structural genomics process through specialized "pipeline schematics". We find that the properties of a protein that are most significant are: (i.) whether it is conserved across many organisms; (ii). the percentage composition of charged residues; (iii). the occurrence of hydrophobic patches; (iv). the number of binding partners it has; and (v). its length. Conversely, a number of other properties that might have been thought to be important, such as nuclear localization signals, are not significant. Thus, using our tree-based analyses, we are able to identify combinations of features that best differentiate the small group of proteins for which a structure has been determined from all the currently selected targets. This information may prove useful in optimizing high-throughput experimentation. Further information is available from http://mining.nesg.org/.

Algorithms↗

PubMed2004

3DCoffee: combining protein sequences and structures within multiple sequence alignments.

Most bioinformatics analyses require the assembly of a multiple sequence alignment. It has long been suspected that structural information can help to improve the quality of these alignments, yet the effect of combining sequences and structures has not been evaluated systematically. We developed 3DCoffee, a novel method for combining protein sequences and structures in order to generate high-quality multiple sequence alignments. 3DCoffee is based on TCoffee version 2.00, and uses a mixture of pairwise sequence alignments and pairwise structure comparison methods to generate multiple sequence alignments. We benchmarked 3DCoffee using a subset of HOMSTRAD, the collection of reference structural alignments. We found that combining TCoffee with the threading program Fugue makes it possible to improve the accuracy of our HOMSTRAD dataset by four percentage points when using one structure only per dataset. Using two structures yields an improvement of ten percentage points. The measures carried out on HOM39, a HOMSTRAD subset composed of distantly related sequences, show a linear correlation between multiple sequence alignment accuracy and the ratio of number of provided structure to total number of sequences. Our results suggest that in the case of distantly related sequences, a single structure may not be enough for computing an accurate multiple sequence alignment.

Protein Conformation↗

PubMed2005

Maximum-likelihood multi-reference refinement for electron microscopy images.

A maximum-likelihood approach to multi-reference image refinement is presented. In contrast to conventional cross-correlation refinement, the new approach includes a formal description of the noise, implying that it is especially suited to cases with low signal-to-noise ratios. Application of this approach to a cryo-electron microscopy dataset revealed two major classes for projections of simian virus 40 large T-antigen in complex with an asymmetric DNA-probe, containing the origin of simian virus 40 replication. Strongly bent projections of dodecamers showed density that may be attributed to the complexed double-stranded DNA, while almost straight projections revealed a twist in the relative orientation of the hexameric subunits. This new level of detail for large T-antigen projections was not detected using conventional techniques. For a negative stain dataset, maximum-likelihood refinement yielded results that were practically identical to those obtained using conventional multi-reference refinement. Results obtained using simulated data suggest that the efficiency of the maximum-likelihood approach may be further enhanced by explicitly incorporating the microscope contrast transfer function in the image formation model.

Algorithms↗

PubMed2005

Armadillo: domain boundary prediction by amino acid composition.

The identification and annotation of protein domains provides a critical step in the accurate determination of molecular function. Both computational and experimental methods of protein structure determination may be deterred by large multi-domain proteins or flexible linker regions. Knowledge of domains and their boundaries may reduce the experimental cost of protein structure determination by allowing researchers to work on a set of smaller and possibly more successful alternatives. Current domain prediction methods often rely on sequence similarity to conserved domains and as such are poorly suited to detect domain structure in poorly conserved or orphan proteins. We present here a simple computational method to identify protein domain linkers and their boundaries from sequence information alone. Our domain predictor, Armadillo (http://armadillo.blueprint.org), uses any amino acid index to convert a protein sequence to a smoothed numeric profile from which domains and domain boundaries may be predicted. We derived an amino acid index called the domain linker propensity index (DLI) from the amino acid composition of domain linkers using a non-redundant structure dataset. The index indicates that Pro and Gly show a propensity for linker residues while small hydrophobic residues do not. Armadillo predicts domain linker boundaries from Z-score distributions and obtains 35% sensitivity with DLI in a two-domain, single-linker dataset (within +/-20 residues from linker). The combination of DLI and an entropy-based amino acid index increases the overall Armadillo sensitivity to 56% for two domain proteins. Moreover, Armadillo achieves 37% sensitivity for multi-domain proteins, surpassing most other prediction methods. Armadillo provides a simple, but effective method by which prediction of domain boundaries can be obtained with reasonable sensitivity. Armadillo should prove to be a valuable tool for rapidly delineating protein domains in poorly conserved proteins or those with no sequence neighbors. As a first-line predictor, domain meta-predictors could yield improved results with Armadillo predictions.

Amino Acid Sequence↗

PubMed2005

Looking at enzymes from the inside out: the proximity of catalytic residues to the molecular centroid can be used for detection of active sites and enzyme-ligand interfaces.

Analysis of the distances of the exposed residues in 175 enzymes from the centroids of the molecules indicates that catalytic residues are very often found among the 5% of residues closest to the enzyme centroid. This property of catalytic residues is implemented in a new prediction algorithm (named EnSite) for locating the active sites of enzymes and in a new scheme for re-ranking enzyme-ligand docking solutions. EnSite examines only 5% of the molecular surface (represented by surface dots) that is closest to the centroid, identifying continuous surface segments and ranking them by their area size. EnSite ranks the correct prediction 1-4 in 97% of the cases in a dataset of 65 monomeric enzymes (rank 1 for 89% of the cases) and in 86% of the cases in a dataset of 176 monomeric and multimeric enzymes from all six top-level enzyme classifications (rank 1 in 74% of the cases). Importantly, identification of buried or flat active sites is straightforward because EnSite "looks" at the molecular surface from the inside out. Detailed examination of the results indicates that the proximity of the catalytic residues to the centroid is a property of the functional unit, defined as the assembly of domains or chains that form the active site (in most cases the functional unit corresponds to a single whole polypeptide chain). Using the functional unit in the prediction further improves the results. The new property of active sites is also used for re-evaluating enzyme-inhibitor unbound docking results. Sorting the docking solutions by the distance of the interface to the centroid of the enzyme improves remarkably the ranks of nearly correct solutions compared to ranks based on geometric-electrostatic-hydrophobic complementarity scores.

Algorithms↗

PubMed2005

Contribution of electrostatic interactions, compactness and quaternary structure to protein thermostability: lessons from structural genomics of Thermotoga maritima.

Studies of the structural basis of protein thermostability have produced a confusing picture. Small sets of proteins have been analyzed from a variety of thermophilic species, suggesting different structural features as responsible for protein thermostability. Taking advantage of the recent advances in structural genomics, we have compiled a relatively large protein structure dataset, which was constructed very carefully and selectively; that is, the dataset contains only experimentally determined structures of proteins from one specific organism, the hyperthermophilic bacterium Thermotoga maritima, and those of close homologs from mesophilic bacteria. In contrast to the conclusions of previous studies, our analyses show that oligomerization order, hydrogen bonds, and secondary structure play minor roles in adaptation to hyperthermophily in bacteria. On the other hand, the data exhibit very significant increases in the density of salt-bridges and in compactness for proteins from T.maritima. The latter effect can be measured by contact order or solvent accessibility, and network analysis shows a specific increase in highly connected residues in this thermophile. These features account for changes in 96% of the protein pairs studied. Our results provide a clear picture of protein thermostability in one species, and a framework for future studies of thermal adaptation.

Bacterial Proteins↗

PubMed2006

Insights into protein-protein interfaces using a Bayesian network prediction method.

Identifying the interface between two interacting proteins provides important clues to the function of a protein, and is becoming increasing relevant to drug discovery. Here, surface patch analysis was combined with a Bayesian network to predict protein-protein binding sites with a success rate of 82% on a benchmark dataset of 180 proteins, improving by 6% on previous work and well above the 36% that would be achieved by a random method. A comparable success rate was achieved even when evolutionary information was missing, a further improvement on our previous method which was unable to handle incomplete data automatically. In a case study of the Mog1p family, we showed that our Bayesian network method can aid the prediction of previously uncharacterised binding sites and provide important clues to protein function. On Mog1p itself a putative binding site involved in the SLN1-SKN7 signal transduction pathway was detected, as was a Ran binding site, previously characterized solely by conservation studies, even though our automated method operated without using homologous proteins. On the remaining members of the family (two structural genomics targets, and a protein involved in the photosystem II complex in higher plants) we identified novel binding sites with little correspondence to those on Mog1p. These results suggest that members of the Mog1p family bind to different proteins and probably have different functions despite sharing the same overall fold. We also demonstrated the applicability of our method to drug discovery efforts by successfully locating a number of binding sites involved in the protein-protein interaction network of papilloma virus infection. In a separate study, we attempted to distinguish between the two types of binding site, obligate and non-obligate, within our dataset using a second Bayesian network. This proved difficult although some separation was achieved on the basis of patch size, electrostatic potential and conservation. Such was the similarity between the two interacting patch types, we were able to use obligate binding site properties to predict the location of non-obligate binding sites and vice versa.

Animals↗

PubMed2006

Spike sorting based upon machine learning algorithms (SOMA).

We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.

Action Potentials↗

PubMed2004

Association of common T cell activation gene polymorphisms with multiple sclerosis in Australian patients.

Susceptibility to multiple sclerosis (MS) may be influenced by the interaction of several genes within a biological pathway. T cell activation and costimulation may be potentially important in MS pathogenesis. We have therefore investigated associations between MS and polymorphisms in the CD152 (CTLA-4), CD28, CD80 and CD86 genes in Australian patients. We found no significant MS association with CTLA-4 exon 1 +49 alleles, and meta-analysis showed no significant association across nine comparable datasets (OR=1.04, p=0.54), nor with primary progressive MS across seven datasets (OR=1.19, p=0.21). Haplotype analysis showed a trend towards a decrease of the CTLA-4-1722C, -1577G, +49G haplotype in +49 G positive MS patients compared with controls (p=0.06). Screening of CD28, CD80 and CD86 genes identified novel polymorphisms in the putative promoter regions of CD28 (-372 G/A) and CD86 (exon 2 -359 deletionAAG). There was a significant increase of the CD28 -372 G allele frequency in MS patients vs. controls (p=0.045) and a trend towards a significant interaction between this allele and the CTLA-4 +49 G allele (OR=4.00, p=0.058). Our results suggest that the CTLA-4 +49 alone is not associated with overall susceptibility to MS, but may be important in clinical subsets of patients and/or may interact epistatically with other gene polymorphisms.

Antigens, CD↗

PubMed2026

Single-organ proteomics in Drosophila melanogaster larva.

The combination of genetic accessibility, organ complexity, evolutionary conservation, and cost-efficiency makes Drosophila melanogaster (Dm) a well-known model system for biomedical and fundamental biological research. Proteomic analysis of single organs enables the identification and quantification of proteins expressed in specific organs. This will help to uncover specific biological functions and unique protein profiles that are not detectable in whole-organism analyses. In this study we have isolated single organs form Dm larvae, and we have performed a deep proteomics mapping by following a minimal manipulation preparation procedure. The combined dataset across all organs comprised 9132 identified proteins. As anticipated, principal component analysis (PCA) revealed clear separation between the proteomes of most organs, confirming distinct protein profiles. These findings demonstrate the applicability of the sample preparation strategy for high-resolution proteomic characterization of individual organs in Drosophila. Given the extensive genetic tools available for this model organism, our approach has the potential to open new avenues for proteomic studies in Drosophila melanogaster and any other biological systems where the sample amount is limiting. SIGNIFICANCE STATEMENT: Drosophila melanogaster is a well-known model system for biomedical and fundamental biological research that serves as a valuable in vivo model organism due to its high degree of evolutionary conservation with higher vertebrates, tractable genetics, and logistical efficiency. However, the proteome of Drosophila at single organ level has been elusive to date, due to several factors like low sensitivity of previous generation mass spectrometers and sample preparation procedures, difficult isolation of some organs. In this study we have applied a compilation of advanced methods including minimal sample manipulation together with simple, straightforward and efficient protein extraction and digestion methods. Obtained peptides were minimally handled to be analyzed by applying specific and sensitive nLC methods coupled on-line to state-of-the-art MS/MS system. Altogether, the applied strategy allowed us to get the first single organ study to date for this animal. These datasets represent a significative resource for future genomic, transcriptomic and proteomic studies in Drosophila, as multi-omic integration requires deep proteomics to translate data into functional biochemistry, and serves as a critical bridge and an indispensable standalone resource across the genomic, transcriptomic, and proteomic landscapes.

Animals↗

PubMedDate not supplied

A two step approach for semi-automated particle selection from low contrast cryo-electron micrographs.

Over recent years advances in cryo-electron microscopy for the study of macromolecular structure have resulted in resolutions in the range 10-15 A becoming routine. With this drive for increased resolution comes the need to collect larger datasets, commonly >10,000 particle images. Manual selection of particles from micrographs is often difficult and with such large numbers of particles now involved it is also laborious and a common bottleneck. Automated methods do exist but are normally restricted to specific samples or data, i.e., spherical particles, no aggregation, high contrast, and low noise. A two step approach has been developed that remains general and can be applied to low contrast, high noise micrographs of small molecules. Specifically, application of the approach is presented using micrographs of Escherichia coli RNA polymerase, which due to low contrast and the relatively small size of the molecule prove difficult to pick manually. To test the automated approach, independent reconstructions of RNA polymerase were carried out using manual and automatically picked data. The two reconstructions are shown to be comparable and the reconstruction from the automatically picked dataset is at a higher resolution, due to an increase in the number of particles picked.

Algorithms↗

PubMed2006

Unsupervised classification of single particles by cluster tracking in multi-dimensional space.

In cryo-electron microscopy (cryo-EM) single-particle reconstruction, the heterogeneity of two-dimensional projection image data resulting from the co-existence of different conformational or ligand binding states of a macromolecular complex remains a major obstacle as it impairs the validity of reconstructed density maps and limits the progress toward higher resolution. Classification of cryo-EM data according to the different conformations is difficult because of the coexistence of multiple orientations in a single dataset. Here, we present an unsupervised classification method, termed cluster tracking, which utilizes the continuity in multi-dimensional space induced by angular adjacency of projections in large datasets. In a proof of concept, the testing of cluster tracking on simulated projection data, which were generated from multiple conformations and orientations of an existing volume, produced clusters that are consistent with the conformational identity of the data. The application of the method to experimental cryo-EM projection data is found to result in a partition similar to the one generated by supervised classification.

Algorithms↗

PubMed2005

Predicting enzyme family classes by hybridizing gene product composition and pseudo-amino acid composition.

A new method has been developed to predict the enzymatic attribute of proteins by hybridizing the gene product composition and pseudo amino acid composition. As a demonstration, a working dataset was generated with a cutoff of 60% sequence identity to avoid redundancy and bias in statistical prediction. The dataset thus constructed contains 39989 protein sequences, of which 27469 are non-enzymes and 12520 enzymes that were further classified into 6 enzyme family classes according to their 6 main EC (Enzyme Commission) numbers (2314 are oxidoreductases, 3653 transferases, 3246 hydrolases, 1307 lyases, 676 isomerases, and 1324 ligases). The overall success rate by the jackknife test for the identification between enzyme and non-enzyme was 94%, and that for the identification among the 6 enzyme family classes was 98%. It is anticipated that, with the rapid increase of protein sequences entering into databanks, the current method will become a useful automated tool in identifying the enzymatic attribute of a newly found protein sequence.

Algorithms↗

PubMed2006

Internal validation of risk models in lung resection surgery: bootstrap versus training-and-test sampling.

OBJECTIVE: The objective of the present analysis was to compare the performance of a lung resection mortality model developed by means of logistic regression and bootstrap analysis with that of multiple mortality models developed by using the traditional training-and-test method from the same dataset. METHODS: Eleven mortality models (1 developed by means of logistic regression and bootstrap validation and the other 10 developed by means of the traditional training-and-test random splitting of the dataset) were generated by the data of unit A (571 patients submitted to major lung resection). The performances of each of the 11 mortality models were then evaluated by assessing the distribution of the respective c-statistics in 1000 bootstrap samples derived from unit B (224 patients). RESULTS: The first model (logistic regression and bootstrap analysis) had good discrimination among the 1000 bootstrap external samples (c-statistics >0.7 in 80% of samples and >0.8 in 38% of samples). Among the 10 training-and-test models, only one model had a similar performance, whereas the others had a poorer discrimination. CONCLUSIONS: The traditional training-and-test method for risk model building proved to be unreliable across multiple external populations and was generally inferior to bootstrap analysis for variable selection in regression analysis. Therefore the use of bootstrap analysis must be recommended for every future model-building process.

Aged↗

PubMed2004

A statewide experience with endovascular abdominal aortic aneurysm repair: rapid diffusion with excellent early results.

OBJECTIVE: The purpose of this study was to compare survival and outcomes of endovascular versus open repair of abdominal aortic aneurysms (AAAs) in New York State (NYS). METHODS: We used the NYS discharge dataset Statewide Planning and Research Cooperative System (SPARCS) to analyze the outcomes of elective admission for nonruptured (International Classification of Diseases-9th revision [ICD-9] 441.4) open aneurysm repair (38.44) and endovascular aneurysm repair (39.71) during the years 2000-2002. The ICD-9 code for endovascular repair was introduced in late 2000, thus capturing 3 months of empiric data for 2000. RESULTS: There has been a significant increase in the number of AAA procedures performed in NYS (comparing before and after 2000: average, 1419 vs 1701; P =.0001), temporally coinciding with the implementation of training programs after US Food and Drug Administration approval of endovascular grafts and the new payment code. From 2000 to 2002 the number of NYS hospitals performing endovascular repairs increased from 24 to 60. By 2002 there were more endovascular repairs being performed than open repairs (871 vs 783). The target population for these surgical interventions showed interesting differences. In 2002, women had a 43% chance of receiving an endograft, whereas men had a 55% probability. The use of endovascular repair over the observation period was relatively constant in patients younger than 65 years. In patients older than 65 years, and especially those older than 75 years, endovascular use increased substantially, so that by 2002 older patients were more likely to undergo endovascular repair than open repair. Patients who underwent endovascular repair had significantly more hypertension, coronary artery disease, diabetes, and hyperlipidemia than did patients who underwent open repair. Yet the mean length of stay for endovascular procedures was approximately 3.6 days, and for open procedures was about 10.3 days, across all 3 years (P = <.0001). Moreover, patients who underwent endovascular repair had statistically fewer postoperative complications and significantly lower mortality. In-hospital mortality in 2001 was 3.55% for open repair and 1.14% for endovascular repair (P =.0018), and in 2002 these rates were 4.21% versus 0.8% (P <.0001), respectively. CONCLUSION: This dataset suggests that endovascular AAA repairs are being performed in a patient population with a higher frequency of comorbidities. However, endovascular repairs still are associated with significantly lower in-hospital mortality, fewer postoperative complications, and a dramatically shorter length of stay. These results suggest that, despite the rapid diffusion of this new technique, early perioperative outcomes may be superior to those with conventional open repair. However, prospective clinical studies are needed to confirm these insights, and such studies may require the infrastructure of consortia of hospitals or society-based registries.

Adolescent↗

PubMed2026

Convergence and global molecular epidemiology of Klebsiella pneumoniae plasmids harbouring the iuc3 virulence locus: a population genomic analysis.

BACKGROUND: Klebsiella pneumoniae is an important pathogen of humans and animals. In the past five years, increasing reports of convergent strains that carry both virulence factors and antimicrobial resistance genes (ARGs) have raised serious public health concerns. The aim of this study is to describe the global diversity of plasmids carrying iuc3 (a key virulence factor in K pneumoniae associated with pigs and clinical isolates) from diverse settings, and their role in the emergence of convergent strains through hybridisation with plasmids carrying ARGs. METHODS: This population genomic analysis study was designed to describe both the global and local diversity of iuc3-carrying plasmids from diverse sources, and the co-occurrence of iuc3 with ARGs. We used all 4148 Klebsiella spp isolates from two large One-Health studies (SpARK, Italy, and OH-DART, Thailand), including 191 Klebsiella isolates from pigs, 635 from clinical isolates, 1040 from hospital and community carriage, and 2282 from other sources. Short-read sequencing of Klebsiella isolates was performed as part of the SpARK study. We sequenced Klebsiella isolates from the OH-DART (MicrobesNG, Birmingham, UK; HiSeq and NovaSeq, Illumina San Diego, CA, USA; GridION, Oxford Nanopore Technologies, Oxford, UK) and SpARK (MinION or GridION, Oxford Nanopore Technologies, Oxford, UK) studies. We also retrieved plasmid sequences carrying iuc3 from the National Centre for Biotechnology Information (NCBI). To ascertain the degree of diversity, evolutionary dynamics, and structuring across ecological and geographical axes, we detected ARGs and virulence loci, analysed clustering patterns and generated approximate maximum-likelihood phylogenetic trees. FINDINGS: We identified 48 K pneumoniae isolates with iuc3 in the SpARK data and 79 in the OH-DART data. Three (2·4%) of these 127 isolates were from clinical sources, 73 (57·5%) were from pig or pork meat. iuc3 isolates corresponded to multiple (n=47) host sequence types (STs), with ST35, ST45, ST881, ST25, and ST967 harbouring iuc3 in both datasets. We generated hybrid assemblies for 44 (SpARK) and 36 (OH-DART) isolates, plus a single iuc3 isolate from Germany. 53 (65·4%) of these isolates were from pigs, three (3·7%) from clinical sources, and 25 (30·9%) from other sources. There were an additional 48 iuc3 positive isolates from our collections for which only short read data was available. A single iuc3-positive Klebsiella oxytoca isolate from a pig farm was detected in the SpARK data, which was also sequenced. We identified 330 iuc3-positive isolates and 58 iuc3-carrying plasmid assemblies from NCBI, of which 83 (21·4%) were from clinical sources, 120 from pigs (30·9%), and 185 (47·7%) from other sources or of unknown provenance. These isolates were from K pneumoniae except two isolates of Klebsiella quasipneumoniae subsp similipneumoniae and one of Enterobacter hormaechei. The combined dataset of 517 iuc3 plasmids ranged in size from 110 375 bp to 365 580 bp and mostly corresponded to multiple IncFIB(K) and IncFII replicon types. We found seven convergent K pneumoniae plasmids in the Thai data: six from fresh markets and one from a neighbouring hospital. These plasmids emerged through the hybridisation of cocirculating iuc3 plasmids and plasmids encoding extended-spectrum β-lactamases (ESBLs), although none of these seven plasmids carried genes encoding carbapenemases. We also identified putative cocirculating parental plasmids carrying iuc3 and ESBL-encoding genes. Clustering and phylogenetic analysis resolved the iuc3 plasmid sequences into three groups, which were consistent using both complete plasmid sequences (n=139) and short-read data (n=517). In the complete plasmid sequence data, 66 strains contained group 1 plasmids, 38 strains contained group 2 plasmids, and 35 strains contained group 3 plasmids. Group 3 plasmids are mostly carried by isolates circulating in hospitals throughout Asia, with occasional examples in Europe and elsewhere, and carry multiple ARGs and potential virulence factors. By contrast, group 1 plasmids are commonly carried by porcine isolates in Europe, and group 2 are a heterogeneous mixture of geographical and ecological sources. INTERPRETATION: Plasmid hybridisation occurs frequently outside of the health-care environment and can lead to the convergence of resistance and virulence traits. Generating complete plasmid sequences from regional population-scale samples facilitates the identification of convergent plasmids and their putative parental plasmids. Three robust groups of iuc3 plasmids were resolved, which show both epidemiological and geographical differences; one of these groups was associated with clinical isolates in Asia and warrants targeted plasmid surveillance. FUNDING: UKRI, JPIAMR, Evolution Education Trust, and a Schlumberger Foundation Fellowship.

Plasmids↗

PubMed2005

Rapid underway profiling of water quality in Queensland estuaries.

We present an overview of a portable underway water quality monitoring system (RUM-Rapid Underway Monitoring), developed by integrating several off-the-shelf water quality instruments to provide rapid, comprehensive, and spatially referenced 'snapshots' of water quality conditions. We demonstrate the utility of the system from studies in the Northern Great Barrier Reef (Daintree River) and the Moreton Bay region. The Brisbane dataset highlights RUM's utility in characterising plumes as well as its ability to identify the smaller scale structure of large areas. RUM is shown to be particularly useful when measuring indicators with large small-scale variability such as turbidity and chlorophyll-a. Additionally, the Daintree dataset shows the ability to integrate other technologies, resulting in a more comprehensive analysis, whilst sampling offshore highlights some of the analytical issues required for sampling low concentration data. RUM is a low cost, highly flexible solution that can be modified for use in any water type, on most vessels and is only limited by the available monitoring technologies.

Animals↗

PubMed2007

Marine debris accumulation in the Northwestern Hawaiian Islands: an examination of rates and processes.

Large amounts of derelict fishing gear accumulate and cause damage to shallow coral reefs of the Northwestern Hawaiian Islands (NWHI). To facilitate maintenance of reefs cleaned during 1996-2005 removal efforts, we identify likely high-density debris areas by assessing reef characteristics (depth, benthic habitat type, and energy regime) that influence sub-regional debris accumulation. Previously cleaned backreef and lagoonal reefs at two NWHI locations were resurveyed for accumulated debris using two survey methods. Accumulated debris densities and weights were found to be greater in lagoonal reef areas. Sample weight-based debris densities are extrapolated to similar habitats throughout the NWHI using a spatial 'net habitat' dataset created by generalizing IKONOS satellite derivatives for depth and habitat classification. Prediction accuracy for this dataset is tested using historical debris point data. Annual NWHI debris accumulation is estimated to be 52.0 metric tonnes. For planning purposes, individual NWHI atolls/reefs are allotted a proportion of this total.

Ecosystem↗