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

Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

https://www.zora.uzh.ch/id/eprint/165500/1/30476243.pdf

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.

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 Human Microbiome Project Consortium reports the first results of their analysis of microbial communities from distinct, clinically relevant body habitats in a human cohort; the insights into the microbial communities of a healthy population lay foundations for future exploration of the epidemiology, ecology and translational applications of the human microbiome.

/pdf/structure-function-and-diversity-of-the-healthy-human-4mecpxyl8r.pdf

Structure, function and diversity of the healthy human microbiome

The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

/pdf/string-v10-protein-protein-interaction-networks-integrated-2gg77rxec0.pdf

STRING v10: protein–protein interaction networks, integrated over the tree of life

Pseudomonas species were targeted early for genomic studies since they were noted for their diverse metabolic capacity, ability to inhabit a wide range of environments and hosts, and include notable human and agriculturally relevant pathogens As more genomes are sequenced, the power of genome-scale analyses are increasing and a wide range of analyses are now possible The Pseudomonas Genome database has contributed to this effort by providing peer-reviewed, continually updated annotations of the Pseudomonas aeruginosa PAO1 reference strain genome plus integrated data and analyses of related Pseudomonas species Analyses are now available via multiple resources to facilitate identification and characterization of drug targets, virulence factors, regulatory elements, genomic islands, genome rearrangements, orthologs, single nucleotide polymorphisms, and multiple other gene/protein-based analyses from gene expression to protein structure We describe here how the Pseudomonas Genome Database and other bioinformatics resources can be leveraged to help Pseudomonas researchers "mine" Pseudomonas genomes, and associated genome-scale data, to facilitate new discovery

Mining the Pseudomonas genome.

We report high-quality closed reference genomes for 1 bovine strain and 10 human Shiga toxin (Stx)-producing Escherichia coli (STEC) strains from serogroups O26, O45, O91, O103, O104, O111, O113, O121, O145, and O157. We also report draft assemblies, with standardized metadata, for 360 STEC strains isolated from watersheds, animals, farms, food, and human infections.

https://journals.asm.org/doi/pdf/10.1128/MRA.00625-19

Eleven High-Quality Reference Genome Sequences and 360 Draft Assemblies of Shiga Toxin-Producing Escherichia coli Isolates from Human, Food, Animal, and Environmental Sources in Canada.

A strong relationship between virulence-associated sensor histidine kinases of fungi and those in Streptomyces coelicolor was observed, and phylogenetic analysis suggested that bacterium-to-eukaryote horizontal gene transfer had occurred between ancestors of these organisms. Phylogenetic analysis also identified a group of histidine kinases orthologous to the Streptomyces proteins that includes Pseudomonas aeruginosa GacS. We provide evidence that GacS is important for swarming motility, lipase production, and virulence in mice and had evolved to have partial functional overlaps with PhoQ, a less-related virulence-associated histidine kinase.

Evolutionary Relationships among Virulence-Associated

A statistical approach to high-throughput screening of predicted orthologs

The FoodOn Food Ontology contains standardized terms and a facetbased classification scheme for describing food products, processing and environments. Mapping of foodborne pathogen isolate source information (descriptors of the contaminated materials and locations) to the FoodOn standard can facilitate data sharing and integration between multijurisdictional health and regulatory agencies utilizing disparate software platforms and data dictionaries. Faster and more efficient sharing of information is critical for tracking and controlling outbreaks of foodborne disease at local, national and international levels. This work describes mapping procedures which can be utilized by organizations and software developers to better enable interoperability between foodborne pathogen surveillance and outbreak management systems.

/pdf/foodon-a-semantic-ontology-approach-for-mapping-foodborne-4b4hqkx441.pdf

Fiona S. L. Brinkman

Papers

Mining the Pseudomonas genome.

Eleven High-Quality Reference Genome Sequences and 360 Draft Assemblies of Shiga Toxin-Producing Escherichia coli Isolates from Human, Food, Animal, and Environmental Sources in Canada.

Evolutionary Relationships among Virulence-Associated

A statistical approach to high-throughput screening of predicted orthologs

FoodOn: A Semantic Ontology Approach for Mapping Foodborne Disease Metadata.