Draft Genome Sequence of
            <i>Sphingobacterium</i>
            sp. CZ-UAM, Isolated from a Methanotrophic Consortium

Steffani-Vallejo, José Luis; Zuñiga, Cristal; Cruz-Morales, Pablo; Lozano, Luis; Morales, Marcia; Licona-Cassani, Cuauhtémoc; Revah, Sergio; Utrilla, José

doi:10.1128/genomea.00792-17

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…To date, systems biology approaches at genome-scale have enabled theoretical identification of the functional or active proteome required to support growth in heterotrophs (O'Brien et al, 2013;Yang et al, 2015;Steffani-Vallejo et al, 2017). Additionally, mechanistic analysis to correlate high-throughput data such as transcriptomics and proteomics with predicted growth rates and flux distributions have been successfully performed (Lewis et al, 2012;Yang et al, 2015;Zielinski et al, 2015;Barenholz et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris

et al. 2017

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Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. Here, we used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealed an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted flux trends and gene expression trends was found for 65% of multisubunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acid metabolism. Furthermore, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of, CZ946, thus increasing our knowledgebase by 10% for this model green alga.

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Section: Discussionmentioning

confidence: 99%

Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris

et al. 2017

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“…In contrast, 6,470scaffolds were produced for Sample C with an N50 of 24,471 bp and highest scaffold length 197,135 bp. Metassembler has been widely used to develop meta-genome assemblies [48] , [49] , [50] , [51] , [52] , [53] , [54] , [55] . The genome size of Sample C was 76.95Mb compared to 72.16Mb for Sample B.…”

Section: Discussionmentioning

confidence: 99%

Comparative analysis of draft genome assemblies developed from whole genome sequences of two Hyaloperonospora brassicae isolate samples differing in field virulence on Brassica napus

You

Akhatar

Mittal

et al. 2021

Biotechnology Reports

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Highlights We report first draft genome assemblies for two isolates Hyaloperonospora brassicae , differing for their virulence. These revealed genome sizes of genome sizes of 72.762 and 76.950Mb and 6,438 and 6,470 scaffolds respectively. In silico annotation allowed understanding of the genome architecture of H. brassicae in terms of genes for pathogenicity and virulence. The observed reduction in virulence or loss of pathogenicity in a larger number of genes in the sample with low virulence in comparison to sample with high virulence may reflect differential rates of mutation and selection during host–parasite co‐evolution. Genomic resources develop will aid in monitoring field prevalence of H. brassicae pathotypes and to detect early any virulence changes within pathogen populations.

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“…Although simplified models are subject to inaccuracies and noises, they can provide valuable insight into the metabolic potential of the entire system (Ji and Nielsen, ). For example, topological analysis of metabolic networks, observing changes in connections and centrality of the nodes (genes or enzymes), can be helpful to identify genes/functions associated with different conditions (Tan et al ., ; Steffani‐Vallejo et al ., ).…”

Section: Whole‐metagenomic Shotgun Sequencingmentioning

confidence: 97%

Elucidation of complexity and prediction of interactions in microbial communities

Zuñiga

Zaramela

Zengler

2017

Microbial Biotechnology

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SummaryMicroorganisms engage in complex interactions with other members of the microbial community, higher organisms as well as their environment. However, determining the exact nature of these interactions can be challenging due to the large number of members in these communities and the manifold of interactions they can engage in. Various omic data, such as 16S rRNA gene sequencing, shotgun metagenomics, metatranscriptomics, metaproteomics and metabolomics, have been deployed to unravel the community structure, interactions and resulting community dynamics in situ. Interpretation of these multi‐omic data often requires advanced computational methods. Modelling approaches are powerful tools to integrate, contextualize and interpret experimental data, thus shedding light on the underlying processes shaping the microbiome. Here, we review current methods and approaches, both experimental and computational, to elucidate interactions in microbial communities and to predict their responses to perturbations.

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Draft Genome Sequence of Sphingobacterium sp. CZ-UAM, Isolated from a Methanotrophic Consortium

Cited by 6 publications

References 14 publications

Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris

Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris

Comparative analysis of draft genome assemblies developed from whole genome sequences of two Hyaloperonospora brassicae isolate samples differing in field virulence on Brassica napus

Elucidation of complexity and prediction of interactions in microbial communities

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