Gene Deletion Algorithms for Minimum Reaction Network Design by Mixed-Integer Linear Programming for Metabolite Production in Constraint-Based Models: gDel_minRN.

Genome-scale constraint-based metabolic networks play an important role in the simulation of growth-coupled production, which means that cell growth and target metabolite production are simultaneously achieved. For growth-coupled production, a minimal reaction-network-based design is known to be effective. However, the obtained reaction networks often fail to be realized by gene deletions due to conflicts with gene-protein-reaction (GPR) relations. Here, we developed gDel_minRN that determines gene deletion strategies using mixed-integer linear programming to achieve growth-coupled production by repressing the maximum number of reactions via GPR relations. The results of computational experiments showed that gDel_minRN could determine the core parts, which include only 30% to 55% of whole genes, for stoichiometrically feasible growth-coupled production for many target metabolites, which include useful vitamins such as biotin (vitamin B7), riboflavin (vitamin B2), and pantothenate (vitamin B5). Since gDel_minRN calculates a constraint-based model of the minimum number of gene-associated reactions without conflict with GPR relations, it helps biological analysis of the core parts essential for growth-coupled production for each target metabolite. The source codes, implemented in MATLAB using CPLEX and COBRA Toolbox, are available on https://github.com/MetNetComp/gDel-minRN.

Evaluation of PCR conditions for characterizing bacterial communities with full-length 16S rRNA genes using a portable nanopore sequencer.

MinION (Oxford Nanopore Technologies), a portable nanopore sequencer, was introduced in 2014 as a new DNA sequencing technology. MinION is now widely used because of its low initial start-up costs relative to existing DNA sequencers, good portability, easy-handling, real-time analysis and long-read output. However, differences in the experimental conditions used for 16S rRNA-based PCR can bias bacterial community assessments in samples. Therefore, basic knowledge about reliable experimental conditions is needed to ensure the appropriate use of this technology. Our study concerns the reliability of techniques for obtaining accurate and quantitative full-length 16S rRNA amplicon sequencing data for bacterial community structure assessment using MinION. We compared five PCR conditions using three independent mock microbial community standard DNAs and established appropriate, standardized, better PCR conditions among the trials. We then sequenced two mock communities and six environmental samples using Illumina MiSeq for comparison. Modifying the PCR conditions improved the sequencing quality; the optimized conditions were 35 cycles of 95 °C for 1 min, 60 °C for 1 min and 68 °C for 3 min. Our results provide important information for researchers to determine bacterial community using MinION accurately.

Data integration aids understanding of butterfly-host plant networks.

Although host-plant selection is a central topic in ecology, its general underpinnings are poorly understood. Here, we performed a case study focusing on the publicly available data on Japanese butterflies. A combined statistical analysis of plant-herbivore relationships and taxonomy revealed that some butterfly subfamilies in different families feed on the same plant families, and the occurrence of this phenomenon more than just by chance, thus indicating the independent acquisition of adaptive phenotypes to the same hosts. We consequently integrated plant-herbivore and plant-compound relationship data and conducted a statistical analysis to identify compounds unique to host plants of specific butterfly families. Some of the identified plant compounds are known to attract certain butterfly groups while repelling others. The additional incorporation of insect-compound relationship data revealed potential metabolic processes that are related to host plant selection. Our results demonstrate that data integration enables the computational detection of compounds putatively involved in particular interspecies interactions and that further data enrichment and integration of genomic and transcriptomic data facilitates the unveiling of the molecular mechanisms involved in host plant selection.