scholarly journals Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christopher Pries ◽  
Zahra Razaghi-Moghadam ◽  
Joachim Kopka ◽  
Zoran Nikoloski
Keyword(s):  
Arabidopsis Thaliana ◽  
Ribosome Biogenesis ◽  
Time Course ◽  
Metabolic Effects ◽  
Plant Metabolism ◽  
Wild Type ◽  
Scale Level ◽  
A Genome ◽  
Time Course Data ◽  
Genome Scale

AbstractRibosome biogenesis is tightly associated to plant metabolism due to the usage of ribosomes in the synthesis of proteins necessary to drive metabolic pathways. Given the central role of ribosome biogenesis in cell physiology, it is important to characterize the impact of different components involved in this process on plant metabolism. Double mutants of the Arabidopsis thaliana cytosolic 60S maturation factors REIL1 and REIL2 do not resume growth after shift to moderate 10 $$^{\circ }\hbox {C}$$ ∘ C chilling conditions. To gain mechanistic insights into the metabolic effects of this ribosome biogenesis defect on metabolism, we developed TC-iReMet2, a constraint-based modelling approach that integrates relative metabolomics and transcriptomics time-course data to predict differential fluxes on a genome-scale level. We employed TC-iReMet2 with metabolomics and transcriptomics data from the Arabidopsis Columbia 0 wild type and the reil1-1 reil2-1 double mutant before and after cold shift. We identified reactions and pathways that are highly altered in a mutant relative to the wild type. These pathways include the Calvin–Benson cycle, photorespiration, gluconeogenesis, and glycolysis. Our findings also indicated differential NAD(P)/NAD(P)H ratios after cold shift. TC-iReMet2 allows for mechanistic hypothesis generation and interpretation of system biology experiments related to metabolic fluxes on a genome-scale level.

scholarly journals Metabolic Network Analysis of Pseudomonas aeruginosa during Chronic Cystic Fibrosis Lung Infection

2010 ◽  
Vol 192 (20) ◽  
pp. 5534-5548 ◽  
Author(s):  
Matthew A. Oberhardt ◽  
Joanna B. Goldberg ◽  
Michael Hogardt ◽  
Jason A. Papin
Keyword(s):  
Cystic Fibrosis ◽  
Disease Progression ◽  
Time Course ◽  
Lung Infection ◽  
System Level ◽  
Metabolic Reconstruction ◽  
Metabolic Network Analysis ◽  
A Genome ◽  
Future Drug ◽  
Genome Scale

ABSTRACT System-level modeling is beginning to be used to decipher high throughput data in the context of disease. In this study, we present an integration of expression microarray data with a genome-scale metabolic reconstruction of P seudomonas aeruginosa in the context of a chronic cystic fibrosis (CF) lung infection. A genome-scale reconstruction of P. aeruginosa metabolism was tailored to represent the metabolic states of two clonally related lineages of P. aeruginosa isolated from the lungs of a CF patient at different points over a 44-month time course, giving a mechanistic glimpse into how the bacterial metabolism adapts over time in the CF lung. Metabolic capacities were analyzed to determine how tradeoffs between growth and other important cellular processes shift during disease progression. Genes whose knockouts were either significantly growth reducing or lethal in silico were also identified for each time point and serve as hypotheses for future drug targeting efforts specific to the stages of disease progression.

scholarly journals Genome-wide identification of tomato xylem sap fitness factors for Ralstonia pseudosolanacearum and Ralstonia syzygii

2020 ◽  
Author(s):  
Stratton Georgoulis ◽  
Katie E. Shalvarjian ◽  
Tyler C. Helmann ◽  
Corri D. Hamilton ◽  
Hans K. Carlson ◽  
...  
Keyword(s):  
Stress Responses ◽  
Culture Media ◽  
Xylem Sap ◽  
Genetic Screens ◽  
Rich Medium ◽  
Wild Type ◽  
Bacterial Membranes ◽  
A Genome ◽  
Ralstonia Pseudosolanacearum ◽  
Genome Scale

AbstractPlant pathogenic Ralstonia spp. colonize plant xylem and cause wilt diseases on a broad range of host plants. To identify genes that promote growth of diverse Ralstonia strains in xylem sap from tomato plants, we performed genome-scale genetic screens (random barcoded transposon mutant sequencing screens; RB-TnSeq) in Ralstonia pseudosolanacearum GMI1000 and R. syzygii PSI07. Contrasting mutant fitness phenotypes in culture media versus in xylem sap suggest that Ralstonia strains are adapted to sap and that culture media impose foreign selective pressures. Although wild-type Ralstonia grew in sap and in rich medium with similar doubling times and to a similar carrying capacity, more genes were essential for growth in sap than in rich medium. Multiple mutants lacking amino acid biosynthesis and central metabolism functions had fitness defects in xylem sap and minimal medium. Our screen identified > 26 genes in each strain that contributed to growth in xylem sap but were dispensable for growth in culture media. Many sap-specific fitness factors are associated with bacterial stress responses: envelope remodeling and repair processes such as peptidoglycan peptide formation (murI and RSc1177), LPS O-antigen biosynthesis (RSc0684), periplasmic protein folding (dsbA), drug efflux (tolA and tolR), and stress responses (cspD3). Our genome-scale genetic screen identified Ralstonia fitness factors that promote growth in xylem sap, an ecologically relevant condition.ImportanceTraditional transposon mutagenesis genetic screens pioneered molecular plant pathology and identified core virulence traits like the type III secretion system. TnSeq approaches that leverage next-generation sequencing to rapidly quantify transposon mutant phenotypes are ushering in a new wave of biological discovery. Here we have adapted a genome-scale approach, random barcoded transposon mutant sequencing (RB-TnSeq), to discover fitness factors that promote growth of two related bacterial strains in a common niche, tomato xylem sap. Fitness of wild-type and mutants show that Ralstonia spp. are adapted to grow well in xylem sap from their natural host plant, tomato. Our screen identified multiple sap-specific fitness factors with roles in maintaining the bacterial envelope. These factors are putative adaptations to resist plant defenses, including antimicrobial proteins and specialized metabolites that damage bacterial membranes.

scholarly journals C4GEM, a Genome-Scale Metabolic Model to Study C4 Plant Metabolism

2010 ◽  
Vol 154 (4) ◽  
pp. 1871-1885 ◽  
Author(s):  
Cristiana Gomes de Oliveira Dal’Molin ◽  
Lake-Ee Quek ◽  
Robin William Palfreyman ◽  
Stevens Michael Brumbley ◽  
Lars Keld Nielsen
Keyword(s):  
Metabolic Model ◽  
Plant Metabolism ◽  
C4 Plant ◽  
A Genome ◽  
Genome Scale

scholarly journals Predicting stimulation-dependent enhancer-promoter interactions from ChIP-Seq time course data

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3742 ◽  
Author(s):  
Tomasz Dzida ◽  
Mudassar Iqbal ◽  
Iryna Charapitsa ◽  
George Reid ◽  
Henk Stunnenberg ◽  
...  
Keyword(s):  
Estrogen Receptor Alpha ◽  
Time Course ◽  
Target Genes ◽  
Mcf7 Cells ◽  
Genome Wide ◽  
A Genome ◽  
Machine Learning Approach ◽  
Protein Occupancy ◽  
Time Course Data ◽  
Over Time

We have developed a machine learning approach to predict stimulation-dependent enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα), RNA polymerase (Pol II) and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.

scholarly journals Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Daqi Wang ◽  
Chengdong Zhang ◽  
Bei Wang ◽  
Bin Li ◽  
Qiang Wang ◽  
...  
Keyword(s):  
Design Tool ◽  
High Fidelity ◽  
Design Tools ◽  
Wild Type ◽  
Activity Prediction ◽  
Guide Rna ◽  
Biological Features ◽  
A Genome ◽  
Rna Design ◽  
Genome Scale

Abstract Highly specific Cas9 nucleases derived from SpCas9 are valuable tools for genome editing, but their wide applications are hampered by a lack of knowledge governing guide RNA (gRNA) activity. Here, we perform a genome-scale screen to measure gRNA activity for two highly specific SpCas9 variants (eSpCas9(1.1) and SpCas9-HF1) and wild-type SpCas9 (WT-SpCas9) in human cells, and obtain indel rates of over 50,000 gRNAs for each nuclease, covering ~20,000 genes. We evaluate the contribution of 1,031 features to gRNA activity and develope models for activity prediction. Our data reveals that a combination of RNN with important biological features outperforms other models for activity prediction. We further demonstrate that our model outperforms other popular gRNA design tools. Finally, we develop an online design tool DeepHF for the three Cas9 nucleases. The database, as well as the designer tool, is freely accessible via a web server, http://www.DeepHF.com/.

scholarly journals Drought Stress Responses in Context-Specific Genome-Scale Metabolic Models of Arabidopsis thaliana

Metabolites ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 159
Author(s):  
Ratklao Siriwach ◽  
Fumio Matsuda ◽  
Kentaro Yano ◽  
Masami Yokota Hirai
Keyword(s):  
Arabidopsis Thaliana ◽  
Drought Stress ◽  
Stress Responses ◽  
In Silico ◽  
Metabolic Model ◽  
Metabolic Reaction ◽  
A Genome ◽  
Balance Analysis ◽  
Context Specific ◽  
Genome Scale

Drought perturbs metabolism in plants and limits their growth. Because drought stress on crops affects their yields, understanding the complex adaptation mechanisms evolved by plants against drought will facilitate the development of drought-tolerant crops for agricultural use. In this study, we examined the metabolic pathways of Arabidopsis thaliana which respond to drought stress by omics-based in silico analyses. We proposed an analysis pipeline to understand metabolism under specific conditions based on a genome-scale metabolic model (GEM). Context-specific GEMs under drought and well-watered control conditions were reconstructed using transcriptome data and examined using metabolome data. The metabolic fluxes throughout the metabolic network were estimated by flux balance analysis using the context-specific GEMs. We used in silico methods to identify an important reaction contributing to biomass production and clarified metabolic reaction responses under drought stress by comparative analysis between drought and control conditions. This proposed pipeline can be applied in other studies to understand metabolic changes under specific conditions using Arabidopsis GEM or other available plant GEMs.

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