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)

SUMMARY Biotic and abiotic surfaces in marine waters are rapidly colonized by microorganisms. Surface colonization and subsequent biofilm formation and development provide numerous advantages to these organisms and support critical ecological and biogeochemical functions in the changing marine environment. Microbial surface association also contributes to deleterious effects such as biofouling, biocorrosion, and the persistence and transmission of harmful or pathogenic microorganisms and their genetic determinants. The processes and mechanisms of colonization as well as key players among the surface-associated microbiota have been studied for several decades. Accumulating evidence indicates that specific cell-surface, cell-cell, and interpopulation interactions shape the composition, structure, spatiotemporal dynamics, and functions of surface-associated microbial communities. Several key microbial processes and mechanisms, including (i) surface, population, and community sensing and signaling, (ii) intraspecies and interspecies communication and interaction, and (iii) the regulatory balance between cooperation and competition, have been identified as critical for the microbial surface association lifestyle. In this review, recent progress in the study of marine microbial surface colonization and biofilm development is synthesized and discussed. Major gaps in our knowledge remain. We pose questions for targeted investigation of surface-specific community-level microbial features, answers to which would advance our understanding of surface-associated microbial community ecology and the biogeochemical functions of these communities at levels from molecular mechanistic details through systems biological integration.

Microbial Surface Colonization and Biofilm Development in Marine Environments

Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.

/pdf/bigg-models-a-platform-for-integrating-standardizing-and-u0w58cydh8.pdf

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models.

Summary
By the time the complete genome sequence of the soil bacterium Pseudomonas putida KT2440 was published in 2002 (Nelson et al., 2002) this bacterium was considered a potential agent for environmental bioremediation of industrial waste and a good colonizer of the rhizosphere. However, neither the annotation tools available at that time nor the scarcely available omics data—let alone metabolic modeling and other nowadays common systems biology approaches—allowed them to anticipate the astonishing capacities that are encoded in the genetic complement of this unique microorganism. In this work we have adopted a suite of state-of-the-art genomic analysis tools to revisit the functional and metabolic information encoded in the chromosomal sequence of strain KT2440. We identified 242 new protein-coding genes and re-annotated the functions of 1548 genes, which are linked to almost 4900 PubMed references. Catabolic pathways for 92 compounds (carbon, nitrogen and phosphorus sources) that could not be accommodated by the previously constructed metabolic models were also predicted. The resulting examination not only accounts for some of the known stress tolerance traits known in P. putida but also recognizes the capacity of this bacterium to perform difficult redox reactions, thereby multiplying its value as a platform microorganism for industrial biotechnology.

The revisited genome of Pseudomonas putida KT2440 enlightens its value as a robust metabolic chassis.

Genome-scale models (GEMs) of metabolism were constructed for 55 fully sequenced Escherichia coli and Shigella strains. The GEMs enable a systems approach to characterizing the pan and core metabolic capabilities of the E. coli species. The majority of pan metabolic content was found to consist of alternate catabolic pathways for unique nutrient sources. The GEMs were then used to systematically analyze growth capabilities in more than 650 different growth-supporting environments. The results show that unique strain-specific metabolic capabilities correspond to pathotypes and environmental niches. Twelve of the GEMs were used to predict growth on six differentiating nutrients, and the predictions were found to agree with 80% of experimental outcomes. Additionally, GEMs were used to predict strain-specific auxotrophies. Twelve of the strains modeled were predicted to be auxotrophic for vitamins niacin (vitamin B3), thiamin (vitamin B1), or folate (vitamin B9). Six of the strains modeled have lost biosynthetic pathways for essential amino acids methionine, tryptophan, or leucine. Genome-scale analysis of multiple strains of a species can thus be used to define the metabolic essence of a microbial species and delineate growth differences that shed light on the adaptation process to a particular microenvironment.

https://www.pnas.org/content/pnas/110/50/20338.full.pdf

Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments

Determination of the precise composition and variation of microbiota in cystic fibrosis lungs is crucial since chronic inflammation due to microorganisms leads to lung damage and ultimately, death. However, this constitutes a major technical challenge. Culturing of microorganisms does not provide a complete representation of a microbiota, even when using culturomics (high-throughput culture). So far, only PCR-based metagenomics have been investigated. However, these methods are biased towards certain microbial groups, and suffer from uncertain quantification of the different microbial domains. We have explored whole genome sequencing (WGS) using the Illumina high-throughput technology applied directly to DNA extracted from sputa obtained from two cystic fibrosis patients. To detect all microorganism groups, we used four procedures for DNA extraction, each with a different lysis protocol. We avoided biases due to whole DNA amplification thanks to the high efficiency of current Illumina technology. Phylogenomic classification of the reads by three different methods produced similar results. Our results suggest that WGS provides, in a single analysis, a better qualitative and quantitative assessment of microbiota compositions than cultures and PCRs. WGS identified a high quantity of Haemophilus spp. (patient 1) or Staphylococcus spp. plus Streptococcus spp. (patient 2) together with low amounts of anaerobic (Veillonella, Prevotella, Fusobacterium) and aerobic bacteria (Gemella, Moraxella, Granulicatella). WGS suggested that fungal members represented very low proportions of the microbiota, which were detected by cultures and PCRs because of their selectivity. The future increase of reads’ sizes and decrease in cost should ensure the usefulness of WGS for the characterisation of microbiota.

/pdf/microbiota-present-in-cystic-fibrosis-lungs-as-revealed-by-3m4tiswryg.pdf

Microbiota present in cystic fibrosis lungs as revealed by whole genome sequencing

Summary: MetaNetX.org is a website for accessing, analysing and manipulating genome-scale metabolic networks (GSMs) as well as biochemical pathways. It consistently integrates data from various public resources and makes the data accessible in a standardized format using a common namespace. Currently, it provides access to hundreds of GSMs and pathways that can be interactively compared (two or more), analysed (e.g. detection of dead-end metabolites and reactions, flux balance analysis or simulation of reaction and gene knockouts), manipulated and exported. Users can also upload their own metabolic models, choose to automatically map them into the common namespace and subsequently make use of the website’s functionality.

Availability and implementation: MetaNetX.org is available at http://metanetx.org.

Contact: [email protected]

https://serval.unil.ch/resource/serval:BIB_364863E29541.P001/REF.pdf

MetaNetX.org

Genome-scale metabolic network reconstructions are now routinely used in the study of metabolic pathways, their evolution and design. The development of such reconstructions involves the integration of information on reactions and metabolites from the scientific literature as well as public databases and existing genome-scale metabolic models. The reconciliation of discrepancies between data from these sources generally requires significant manual curation, which constitutes a major obstacle in efforts to develop and apply genome-scale metabolic network reconstructions. In this work, we discuss some of the major difficulties encountered in the mapping and reconciliation of metabolic resources and review three recent initiatives that aim to accelerate this process, namely BKM-react, MetRxn and MNXref (presented in this article). Each of these resources provides a pre-compiled reconciliation of many of the most commonly used metabolic resources. By reducing the time required for manual curation of metabolite and reaction discrepancies, these resources aim to accelerate the development and application of high-quality genome-scale metabolic network reconstructions and models.

https://serval.unil.ch/resource/serval:BIB_6FC073DA2CF5.P001/REF.pdf

Reconciliation of metabolites and biochemical reactions for metabolic networks.

Biochemical and structural characterization of the complex agarolytic enzyme system from the marine bacterium Zobellia galactanivorans.

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Microbiota present in cystic fibrosis lungs as revealed by whole genome sequencing

MetaNetX.org

Reconciliation of metabolites and biochemical reactions for metabolic networks.

Biochemical and structural characterization of the complex agarolytic enzyme system from the marine bacterium Zobellia galactanivorans.