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。

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

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.).

Machine learning

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.

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

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.

/pdf/the-cog-database-an-updated-version-includes-eukaryotes-520q3dh3w8.pdf

The COG database: an updated version includes eukaryotes

Evolution of Protein Molecules

Francisella tularensis, the etiological agent of tularemia, is one of the most infectious pathogens known. Human cases of the disease occur infrequently in the northern hemisphere, mainly in some parts of Scandanavia and in Russia.(1) It is probably the high infectivity, ease of culture, and low levels of natural immunity to the bacterium that originally attracted interest in F. tularensis as a bioweapon.(2) During the 1930s and 1940s the bacterium was evaluated by Japanese germ warfare units. Later, both the former Soviet Union (fSU) and the USA reportedly produced weapons capable of disseminating the bacterium.(2) The programme to develop biological weapons in the USA was abandoned in 1969. In other countries the status of the programme is not clear, and there are some suggestions that strains which are resistant to commonly available antibiotics have been developed for use as bioweapons.(2) The severity of disease caused by F. tularensis is highly dependent on the causative strain and the route of entry of the bacterium into the host. Currently, there are four accepted subspecies (Table I), and F. tularensis subsp. tularensis is the most virulent in humans. Most naturally acquired cases of disease in humans are the consequence of a bite from an arthropod vector that has previously fed on an infected animal.(1) Ulceroglandular tularemia is the usual form of disease

Tularemia Pathogenesis and Immunity

We have developed a method to analyze the functionality of putative TA loci by expressing them in Escherichia coli. Here, we describe the procedure for cloning recombinant TA genes into inducible plasmids and expressing these in E. coli. Following expression, toxicity, resuscitation of growth, and changes in persister cell formation are assayed. This can confirm whether predicted TA loci are active in E. coli and whether expression can affect persister cell formation.

Functional Analysis of the Role of Toxin–Antitoxin (TA) Loci in Bacterial Persistence

A method for the detection of Francisella tularensis in clinical samples based on a nested polymerase chain reaction (PCR) for the FopA gene using primers selected from the nucleotide sequences FNA8L (sequence GAGGAGTCTCAATGTACTAAGGTTTGCCC), FNB2L (sequence CACCATTATCCTGGATATTACCAGTGTCAT), FNA7L (sequence CTTGAGTCTTATGTTTCGGCATGTGAATAG) and FNB1L (sequence CCAACTAATTGGTTGTACTGTACAGCGAAG).

Detection of francisella tularensis using oligonucleotide probes

A method of killing or controling the growth of Gram negative bacteria which comprises interrupting the branched chain amino acid biosynthesis pathway by either (a) applying to the Gram negative bacteria or to the environment thereof a chemical inhibitor of an enzyme active in said pathway or (b) downregulating expression of a gene which encodes an enzyme active in said pathway. The methods can be used in treatment or prophylaxis of bacterial infection.

Antibacterial methods and screens

Publisher Summary
This chapter focuses on the physical and biological properties of Clostridium perfringens β-toxin. The differential production of the major toxins is used to classify the bacterium into five toxigenic types, A, B, C, D and E. It is now recognized as the major virulence determinant in enterotoxaemia and necrotic enteritis of a variety of animal species, particularly sheep, lambs, calves, piglets, and fowl. It is also known to cause a similar disease in humans called enteritis necroticans or pig-bel, which is endemic in the Highlands of Papua New Guinea. The conditions leading to the onset of the human disease are curious, and are associated with the consumption of under-cooked meat contaminated with C. perfringens type C spores by individuals who have a reduced level of intestinal proteases resulting from malnourishment, or who consume other foods containing protease inhibitors. The diseases in both humans and animals can be controlled effectively by the administration of β-toxoid, prepared from C. perfringens type C culture filtrates.

Richard W. Titball

Papers

Tularemia Pathogenesis and Immunity

Functional Analysis of the Role of Toxin–Antitoxin (TA) Loci in Bacterial Persistence

Detection of francisella tularensis using oligonucleotide probes

Antibacterial methods and screens

Chapter 15 – The Clostridium perfringens β-toxin