A plethora of algorithmic assemblers have been proposed for the de novo assembly of genomes, however, no individual assembler guarantees the optimal assembly for diverse species. Optimizing various parameters in an assembler is often performed in order to generate the most optimal assembly. However, few efforts have been pursued to take advantage of multiple assemblies to yield an assembly of high accuracy. In this study, we employ various state-of-the-art assemblers to generate different sets of contigs for bacterial genomes. A tool, named CISA, has been developed to integrate the assemblies into a hybrid set of contigs, resulting in assemblies of superior contiguity and accuracy, compared with the assemblies generated by the state-of-the-art assemblers and the hybrid assemblies merged by existing tools. This tool is implemented in Python and requires MUMmer and BLAST+ to be installed on the local machine. The source code of CISA and examples of its use are available at http://sb.nhri.org.tw/CISA/.

Metagenomics, the application of shotgun sequencing, facilitates the reconstruction of the genomes of individual species from natural environments. A major challenge in the genome recovery domain is to agglomerate or ‘bin’ sequences assembled from metagenomic reads into individual groups. Metagenomic binning without consideration of reference sequences enables the comprehensive discovery of new microbial organisms and aids in the microbial genome reconstruction process. Here we present MyCC, an automated binning tool that combines genomic signatures, marker genes and optional contig coverages within one or multiple samples, in order to visualize the metagenomes and to identify the reconstructed genomic fragments. We demonstrate the superior performance of MyCC compared to other binning tools including CONCOCT, GroopM, MaxBin and MetaBAT on both synthetic and real human gut communities with a small sample size (one to 11 samples), as well as on a large metagenome dataset (over 250 samples). Moreover, we demonstrate the visualization of metagenomes in MyCC to aid in the reconstruction of genomes from distinct bins. MyCC is freely available at http://sourceforge.net/projects/sb2nhri/files/MyCC/.

Background: Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem. Results: Here, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of this reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches. Conclusion: Taken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.

Klebsiella pneumoniae is a Gram-negative bacterium of the family Enterobacteriaceae that possesses diverse metabolic capabilities: many strains are leading causes of hospital-acquired infections that are often refractory to multiple antibiotics, yet other strains are metabolically engineered and used for production of commercially valuable chemicals. To study its metabolism, we constructed a genome-scale metabolic model ( i YL1228) for strain MGH 78578, experimentally determined its biomass composition, experimentally determined its ability to grow on a broad range of carbon, nitrogen, phosphorus and sulfur sources, and assessed the ability of the model to accurately simulate growth versus no growth on these substrates. The model contains 1,228 genes encoding 1,188 enzymes that catalyze 1,970 reactions and accurately simulates growth on 84% of the substrates tested. Furthermore, quantitative comparison of growth rates between the model and experimental data for nine of the substrates also showed good agreement. The genome-scale metabolic reconstruction for K. pneumoniae presented here thus provides an experimentally validated in silico platform for further studies of this important industrial and biomedical organism.

Strengthening the gut epithelial barrier is a potential strategy for management of gut microbiota-associated illnesses. Here, we demonstrate that dual-specificity phosphatase 6 (Dusp6) knockout enhances baseline colon barrier integrity and ameliorates dextran sulfate sodium (DSS)-induced colonic injury. DUSP6 mutation in Caco-2 cells enhances the epithelial feature and increases mitochondrial oxygen consumption, accompanied by altered glucose metabolism and decreased glycolysis. We find that Dusp6-knockout mice are more resistant to DSS-induced dysbiosis, and the cohousing and fecal microbiota transplantation experiments show that the gut/fecal microbiota derived from Dusp6-knockout mice also confers protection against colitis. Further culturomics and mono-colonialization experiments show that one gut microbiota member in the genus Duncaniella confers host protection from DSS-induced injury. We identify Dusp6 deficiency as beneficial for shaping the gut microbiota eubiosis necessary to protect against gut barrier-related diseases.

Motivation: Continual and accumulated mutations in hemagglutinin (HA) protein of influenza A virus generate novel antigenic strains that cause annual epidemics. Results: We propose a model by incorporating scoring and regression methods to predict antigenic variants. Based on collected sequences of influenza A/H3N2 viruses isolated between 1971 and 2002, our model can be used to accurately predict the antigenic variants in 1999–2004 (agreement rate = 91.67%). Twenty amino acid positions identified in our model contribute significantly to antigenic difference and are potential immunodominant positions. Contact: hsiung@nhri.org.tw Supplementary information: The supplementary information includes 62 amino acid sequences of H3N2 viruses and 277 pair-wise antigenic distances.