Derrick W. Crook

Bio: Derrick W. Crook is an academic researcher from University of Oxford. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 92, co-authored 474 publications receiving 29885 citations. Previous affiliations of Derrick W. Crook include Oxford Brookes University & The Chinese University of Hong Kong.

Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study

Abstract: Summary Background Tuberculosis incidence in the UK has risen in the past decade. Disease control depends on epidemiological data, which can be difficult to obtain. Whole-genome sequencing can detect microevolution within Mycobacterium tuberculosis strains. We aimed to estimate the genetic diversity of related M tuberculosis strains in the UK Midlands and to investigate how this measurement might be used to investigate community outbreaks. Methods In a retrospective observational study, we used Illumina technology to sequence M tuberculosis genomes from an archive of frozen cultures. We characterised isolates into four groups: cross-sectional, longitudinal, household, and community. We measured pairwise nucleotide differences within hosts and between hosts in household outbreaks and estimated the rate of change in DNA sequences. We used the findings to interpret network diagrams constructed from 11 community clusters derived from mycobacterial interspersed repetitive-unit–variable-number tandem-repeat data. Findings We sequenced 390 separate isolates from 254 patients, including representatives from all five major lineages of M tuberculosis . The estimated rate of change in DNA sequences was 0·5 single nucleotide polymorphisms (SNPs) per genome per year (95% CI 0·3–0·7) in longitudinal isolates from 30 individuals and 25 families. Divergence is rarely higher than five SNPs in 3 years. 109 (96%) of 114 paired isolates from individuals and households differed by five or fewer SNPs. More than five SNPs separated isolates from none of 69 epidemiologically linked patients, two (15%) of 13 possibly linked patients, and 13 (17%) of 75 epidemiologically unlinked patients (three-way comparison exact p Interpretation Whole-genome sequencing can delineate outbreaks of tuberculosis and allows inference about direction of transmission between cases. The technique could identify super-spreaders and predict the existence of undiagnosed cases, potentially leading to early treatment of infectious patients and their contacts. Funding Medical Research Council, Wellcome Trust, National Institute for Health Research, and the Health Protection Agency.

Genomic islands: tools of bacterial horizontal gene transfer and evolution.

Abstract: Bacterial genomes evolve through mutations, rearrangements or horizontal gene transfer. Besides the core genes encoding essential metabolic functions, bacterial genomes also harbour a number of accessory genes acquired by horizontal gene transfer that might be beneficial under certain environmental conditions. The horizontal gene transfer contributes to the diversification and adaptation of microorganisms, thus having an impact on the genome plasticity. A significant part of the horizontal gene transfer is or has been facilitated by genomic islands (GEIs). GEIs are discrete DNA segments, some of which are mobile and others which are not, or are no longer mobile, which differ among closely related strains. A number of GEIs are capable of integration into the chromosome of the host, excision, and transfer to a new host by transformation, conjugation or transduction. GEIs play a crucial role in the evolution of a broad spectrum of bacteria as they are involved in the dissemination of variable genes, including antibiotic resistance and virulence genes leading to generation of hospital 'superbugs', as well as catabolic genes leading to formation of new metabolic pathways. Depending on the composition of gene modules, the same type of GEIs can promote survival of pathogenic as well as environmental bacteria.

Prospective evaluation of criteria for microbiological diagnosis of prosthetic-joint infection at revision arthroplasty. The OSIRIS Collaborative Study Group.

Abstract: A prospective study was performed to establish criteria for the microbiological diagnosis of prosthetic joint infection at elective revision arthroplasty. Patients were treated in a multidisciplinary unit dedicated to the management and study of musculoskeletal infection. Standard multiple samples of periprosthetic tissue were obtained at surgery, Gram stained, and cultured by direct and enrichment methods. With reference to histology as the criterion standard, sensitivities, specificities, and likelihood ratios (LRs) were calculated by using different cutoffs for the diagnosis of infection. We performed revisions on 334 patients over a 17-month period, of whom 297 were evaluable. The remaining 37 were excluded because histology results were unavailable or could not be interpreted due to underlying inflammatory joint disease. There were 41 infections, with only 65% of all samples sent from infected patients being culture positive, suggesting low numbers of bacteria in the samples taken. The isolation of an indistinguishable microorganism from three or more independent specimens was highly predictive of infection (sensitivity, 65%; specificity, 99.6%; LR, 168.6), while Gram staining was less useful (sensitivity, 12%; specificity, 98%; LR, 10). A simple mathematical model was developed to predict the performance of the diagnostic test. We recommend that five or six specimens be sent, that the cutoff for a definite diagnosis of infection be three or more operative specimens that yield an indistinguishable organism, and that because of its low level of sensitivity, Gram staining should be abandoned as a diagnostic tool at elective revision arthroplasty.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

Nosocomial Bloodstream Infections in US Hospitals: Analysis of 24,179 Cases from a Prospective Nationwide Surveillance Study

Abstract: Background. Nosocomial bloodstream infections (BSIs) are important causes of morbidity and mortality in the United States. Methods. Data from a nationwide, concurrent surveillance study (Surveillance and Control of Pathogens of Epidemiological Importance [SCOPE]) were used to examine the secular trends in the epidemiology and microbiology of nosocomial BSIs. Results. Our study detected 24,179 cases of nosocomial BSI in 49 US hospitals over a 7-year period from March 1995 through September 2002 (60 cases per 10,000 hospital admissions). Eighty-seven percent of BSIs were monomicrobial. Gram-positive organisms caused 65% of these BSIs, gram-negative organisms caused 25%, and fungi caused 9.5%. The crude mortality rate was 27%. The most-common organisms causing BSIs were coagulasenegative staphylococci (CoNS) (31% of isolates), Staphylococcus aureus (20%), enterococci (9%), and Candida species (9%). The mean interval between admission and infection was 13 days for infection with Escherichia coli, 16 days for S. aureus, 22 days for Candida species and Klebsiella species, 23 days for enterococci, and 26 days for Acinetobacter species. CoNS, Pseudomonas species, Enterobacter species, Serratia species, and Acinetobacter species were more likely to cause infections in patients in intensive care units ( ). In neutropenic patients, infections P ! .001 with Candida species, enterococci, and viridans group streptococci were significantly more common. The proportion of S. aureus isolates with methicillin resistance increased from 22% in 1995 to 57% in 2001 ( , trend P ! .001 analysis). Vancomycin resistance was seen in 2% of Enterococcus faecalis isolates and in 60% of Enterococcus faecium isolates. Conclusion. In this study, one of the largest multicenter studies performed to date, we found that the proportion of nosocomial BSIs due to antibiotic-resistant organisms is increasing in US hospitals.