Richard W. Titball

Bio: Richard W. Titball is an academic researcher from University of Exeter. The author has contributed to research in topics: Burkholderia pseudomallei & Clostridium perfringens. The author has an hindex of 79, co-authored 410 publications receiving 22484 citations. Previous affiliations of Richard W. Titball include Imperial College London & United States Army Medical Research Institute of Infectious Diseases.

Genome sequence of Yersinia pestis , the causative agent of plague

Abstract: The Gram-negative bacterium Yersinia pestis is the causative agent of the systemic invasive infectious disease classically referred to as plague, and has been responsible for three human pandemics: the Justinian plague (sixth to eighth centuries), the Black Death (fourteenth to nineteenth centuries) and modern plague (nineteenth century to the present day). The recent identification of strains resistant to multiple drugs and the potential use of Y. pestis as an agent of biological warfare mean that plague still poses a threat to human health. Here we report the complete genome sequence of Y. pestis strain CO92, consisting of a 4.65-megabase (Mb) chromosome and three plasmids of 96.2 kilobases (kb), 70.3 kb and 9.6 kb. The genome is unusually rich in insertion sequences and displays anomalies in GC base-composition bias, indicating frequent intragenomic recombination. Many genes seem to have been acquired from other bacteria and viruses (including adhesins, secretion systems and insecticidal toxins). The genome contains around 150 pseudogenes, many of which are remnants of a redundant enteropathogenic lifestyle. The evidence of ongoing genome fluidity, expansion and decay suggests Y. pestis is a pathogen that has undergone large-scale genetic flux and provides a unique insight into the ways in which new and highly virulent pathogens evolve.

Genomic plasticity of the causative agent of melioidosis, Burkholderia pseudomallei

Abstract: Burkholderia pseudomallei is a recognized biothreat agent and the causative agent of melioidosis. This Gram-negative bacterium exists as a soil saprophyte in melioidosis-endemic areas of the world and accounts for 20% of community-acquired septicaemias in northeastern Thailand where half of those affected die. Here we report the complete genome of B. pseudomallei, which is composed of two chromosomes of 4.07 megabase pairs and 3.17 megabase pairs, showing significant functional partitioning of genes between them. The large chromosome encodes many of the core functions associated with central metabolism and cell growth, whereas the small chromosome carries more accessory functions associated with adaptation and survival in different niches. Genomic comparisons with closely and more distantly related bacteria revealed a greater level of gene order conservation and a greater number of orthologous genes on the large chromosome, suggesting that the two replicons have distinct evolutionary origins. A striking feature of the genome was the presence of 16 genomic islands (GIs) that together made up 6.1% of the genome. Further analysis revealed these islands to be variably present in a collection of invasive and soil isolates but entirely absent from the clonally related organism B. mallei. We propose that variable horizontal gene acquisition by B. pseudomallei is an important feature of recent genetic evolution and that this has resulted in a genetically diverse pathogenic species.

Tularaemia: bioterrorism defence renews interest in Francisella tularensis

Abstract: Francisella tularensis is a highly infectious aerosolizable intracellular pathogen that is capable of causing a debilitating or fatal disease with doses as low as 25 colony-forming units. There is no licensed vaccine available. Since the 1950s there has been concern that F. tularensis could be used as a biological threat agent, and it has received renewed attention recently owing to concerns about bioterrorism. The International Conference on Tularaemia in 2003 attracted more than 200 delegates, twice the number of participants as previous meetings. This is a reflection of the increased funding of research on this pathogen, particularly in the United States.

The complete genome sequence of Francisella tularensis, the causative agent of tularemia.

Abstract: Francisella tularensis is one of the most infectious human pathogens known. In the past, both the former Soviet Union and the US had programs to develop weapons containing the bacterium. We report the complete genome sequence of a highly virulent isolate of F. tularensis (1,892,819 bp). The sequence uncovers previously uncharacterized genes encoding type IV pili, a surface polysaccharide and iron-acquisition systems. Several virulence-associated genes were located in a putative pathogenicity island, which was duplicated in the genome. More than 10% of the putative coding sequences contained insertion-deletion or substitution mutations and seemed to be deteriorating. The genome is rich in IS elements, including IS630 Tc-1 mariner family transposons, which are not expected in a prokaryote. We used a computational method for predicting metabolic pathways and found an unexpectedly high proportion of disrupted pathways, explaining the fastidious nutritional requirements of the bacterium. The loss of biosynthetic pathways indicates that F. tularensis is an obligate host-dependent bacterium in its natural life cycle. Our results have implications for our understanding of how highly virulent human pathogens evolve and will expedite strategies to combat them.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

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.

The COG database: an updated version includes eukaryotes

Abstract: The availability of multiple, essentially complete genome sequences of prokaryotes and eukaryotes spurred both the demand and the opportunity for the construction of an evolutionary classification of genes from these genomes. Such a classification system based on orthologous relationships between genes appears to be a natural framework for comparative genomics and should facilitate both functional annotation of genomes and large-scale evolutionary studies. We describe here a major update of the previously developed system for delineation of Clusters of Orthologous Groups of proteins (COGs) from the sequenced genomes of prokaryotes and unicellular eukaryotes and the construction of clusters of predicted orthologs for 7 eukaryotic genomes, which we named KOGs after euk aryotic o rthologous g roups. The COG collection currently consists of 138,458 proteins, which form 4873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms. The euk aryotic o rthologous g roups (KOGs) include proteins from 7 eukaryotic genomes: three animals (the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster and Homo sapiens), one plant, Arabidopsis thaliana, two fungi (Saccharomyces cerevisiae and Schizosaccharomyces pombe), and the intracellular microsporidian parasite Encephalitozoon cuniculi. The current KOG set consists of 4852 clusters of orthologs, which include 59,838 proteins, or ~54% of the analyzed eukaryotic 110,655 gene products. Compared to the coverage of the prokaryotic genomes with COGs, a considerably smaller fraction of eukaryotic genes could be included into the KOGs; addition of new eukaryotic genomes is expected to result in substantial increase in the coverage of eukaryotic genomes with KOGs. Examination of the phyletic patterns of KOGs reveals a conserved core represented in all analyzed species and consisting of ~20% of the KOG set. This conserved portion of the KOG set is much greater than the ubiquitous portion of the COG set (~1% of the COGs). In part, this difference is probably due to the small number of included eukaryotic genomes, but it could also reflect the relative compactness of eukaryotes as a clade and the greater evolutionary stability of eukaryotic genomes. The updated collection of orthologous protein sets for prokaryotes and eukaryotes is expected to be a useful platform for functional annotation of newly sequenced genomes, including those of complex eukaryotes, and genome-wide evolutionary studies.