Arabidopsis Genes Essential for Seedling Viability: Isolation of Insertional Mutants and Molecular Cloning

Abstract We have undertaken a large-scale genetic screen to identify genes with a seedling-lethal mutant phenotype. From screening ~38,000 insertional mutant lines, we identified >500 seedling-lethal mutants, completed cosegregation analysis of the insertion and the lethal phenotype for >200 mutants, molecularly characterized 54 mutants, and provided a detailed description for 22 of them. Most of the seedling-lethal mutants seem to affect chloroplast function because they display altered pigmentation and affect genes encoding proteins predicted to have chloroplast localization. Although a high level of functional redundancy in Arabidopsis might be expected because 65% of genes are members of gene families, we found that 41% of the essential genes found in this study are members of Arabidopsis gene families. In addition, we isolated several interesting classes of mutants and genes. We found three mutants in the recently discovered nonmevalonate isoprenoid biosynthetic pathway and mutants disrupting genes similar to Tic40 and tatC, which are likely to be involved in chloroplast protein translocation. Finally, we directly compared T-DNA and Ac/Ds transposon mutagenesis methods in Arabidopsis on a genome scale. In each population, we found only about one-third of the insertion mutations cosegregated with a mutant phenotype.

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Phylogenomic Reconstruction of the Oomycete Phylogeny Derived from 37 Genomes

mSphere ◽

10.1128/msphere.00095-17 ◽

2017 ◽

Vol 2 (2) ◽

Cited By ~ 32

Author(s):

Charley G. P. McCarthy ◽

David A. Fitzpatrick

Keyword(s):

Large Scale ◽

Plant Pathogens ◽

Single Gene ◽

Genomic Data ◽

Gene Families ◽

Phylogenomic Analysis ◽

Phylogenetic Studies ◽

A Genome ◽

Supertree Methods ◽

Genome Scale

ABSTRACT The oomycetes are a class of eukaryotes and include ecologically significant animal and plant pathogens. Single-gene and multigene phylogenetic studies of individual oomycete genera and of members of the larger classes have resulted in conflicting conclusions concerning interspecies relationships among these species, particularly for the Phytophthora genus. The onset of next-generation sequencing techniques now means that a wealth of oomycete genomic data is available. For the first time, we have used genome-scale phylogenetic methods to resolve oomycete phylogenetic relationships. We used supertree methods to generate single-gene and multigene species phylogenies. Overall, our supertree analyses utilized phylogenetic data from 8,355 oomycete gene families. We have also complemented our analyses with superalignment phylogenies derived from 131 single-copy ubiquitous gene families. Our results show that a genome-scale approach to oomycete phylogeny resolves oomycete classes and clades. Our analysis represents an important first step in large-scale phylogenomic analysis of the oomycetes. The oomycetes are a class of microscopic, filamentous eukaryotes within the Stramenopiles-Alveolata-Rhizaria (SAR) supergroup which includes ecologically significant animal and plant pathogens, most infamously the causative agent of potato blight Phytophthora infestans. Single-gene and concatenated phylogenetic studies both of individual oomycete genera and of members of the larger class have resulted in conflicting conclusions concerning species phylogenies within the oomycetes, particularly for the large Phytophthora genus. Genome-scale phylogenetic studies have successfully resolved many eukaryotic relationships by using supertree methods, which combine large numbers of potentially disparate trees to determine evolutionary relationships that cannot be inferred from individual phylogenies alone. With a sufficient amount of genomic data now available, we have undertaken the first whole-genome phylogenetic analysis of the oomycetes using data from 37 oomycete species and 6 SAR species. In our analysis, we used established supertree methods to generate phylogenies from 8,355 homologous oomycete and SAR gene families and have complemented those analyses with both phylogenomic network and concatenated supermatrix analyses. Our results show that a genome-scale approach to oomycete phylogeny resolves oomycete classes and individual clades within the problematic Phytophthora genus. Support for the resolution of the inferred relationships between individual Phytophthora clades varies depending on the methodology used. Our analysis represents an important first step in large-scale phylogenomic analysis of the oomycetes. IMPORTANCE The oomycetes are a class of eukaryotes and include ecologically significant animal and plant pathogens. Single-gene and multigene phylogenetic studies of individual oomycete genera and of members of the larger classes have resulted in conflicting conclusions concerning interspecies relationships among these species, particularly for the Phytophthora genus. The onset of next-generation sequencing techniques now means that a wealth of oomycete genomic data is available. For the first time, we have used genome-scale phylogenetic methods to resolve oomycete phylogenetic relationships. We used supertree methods to generate single-gene and multigene species phylogenies. Overall, our supertree analyses utilized phylogenetic data from 8,355 oomycete gene families. We have also complemented our analyses with superalignment phylogenies derived from 131 single-copy ubiquitous gene families. Our results show that a genome-scale approach to oomycete phylogeny resolves oomycete classes and clades. Our analysis represents an important first step in large-scale phylogenomic analysis of the oomycetes.

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Systematic Detection of Large-Scale Multi-Gene Horizontal Transfer in Prokaryotes

Molecular Biology and Evolution ◽

10.1093/molbev/msab043 ◽

2021 ◽

Author(s):

Lina Kloub ◽

Sean Gosselin ◽

Matthew Fullmer ◽

Joerg Graf ◽

J Peter Gogarten ◽

...

Keyword(s):

Gene Transfer ◽

Large Scale ◽

Single Gene ◽

Gene Families ◽

Microbial Evolution ◽

Phylogenetic Distance ◽

Secretion Systems ◽

Type Iii Secretion Systems ◽

A Genome ◽

Conserved Gene

Abstract Horizontal gene transfer (HGT) is central to prokaryotic evolution. However, little is known about the “scale” of individual HGT events. In this work, we introduce the first computational framework to help answer the following fundamental question: How often does more than one gene get horizontally transferred in a single HGT event? Our method, called HoMer, uses phylogenetic reconciliation to infer single-gene HGT events across a given set of species/strains, employs several techniques to account for inference error and uncertainty, combines that information with gene order information from extant genomes, and uses statistical analysis to identify candidate horizontal multi-gene transfers (HMGTs) in both extant and ancestral species/strains. HoMer is highly scalable and can be easily used to infer HMGTs across hundreds of genomes. We apply HoMer to a genome-scale dataset of over 22000 gene families from 103 Aeromonas genomes and identify a large number of plausible HMGTs of various scales at both small and large phylogenetic distances. Analysis of these HMGTs reveals interesting relationships between gene function, phylogenetic distance, and frequency of multi-gene transfer. Among other insights, we find that (i) the observed relative frequency of HMGT increases as divergence between genomes increases, (ii) HMGTs often have conserved gene functions, and (iii) rare genes are frequently acquired through HMGT. We also analyze in detail HMGTs involving the zonula occludens toxin and type III secretion systems. By enabling the systematic inference of HMGTs on a large scale, HoMer will facilitate a more accurate and more complete understanding of HGT and microbial evolution.

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Advances in metabolic flux analysis toward genome-scale profiling of higher organisms

Bioscience Reports ◽

10.1042/bsr20170224 ◽

2018 ◽

Vol 38 (6) ◽

Cited By ~ 11

Author(s):

Georg Basler ◽

Alisdair R. Fernie ◽

Zoran Nikoloski

Keyword(s):

Large Scale ◽

Metabolic Flux ◽

Cell Types ◽

Flux Analysis ◽

Metabolic Labeling ◽

Modeling Framework ◽

Metabolomics Data ◽

Technological Advances ◽

A Genome ◽

Genome Scale

Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.

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Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

PLANT PHYSIOLOGY ◽

10.1104/pp.114.252361 ◽

2015 ◽

Vol 167 (4) ◽

pp. 1685-1698 ◽

Cited By ~ 18

Author(s):

Taehyong Kim ◽

Kate Dreher ◽

Ricardo Nilo-Poyanco ◽

Insuk Lee ◽

Oliver Fiehn ◽

...

Keyword(s):

Metabolic Network ◽

Large Scale ◽

A Genome ◽

Genome Scale ◽

Gene Perturbations

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A genome-scale phylogeny of Fungi; insights into early evolution, radiations, and the relationship between taxonomy and phylogeny

10.1101/2020.08.23.262857 ◽

2020 ◽

Cited By ~ 3

Author(s):

Yuanning Li ◽

Jacob L. Steenwyk ◽

Ying Chang ◽

Yan Wang ◽

Timothy Y. James ◽

...

Keyword(s):

Large Scale ◽

Sequence Divergence ◽

Fungal Species ◽

Data Matrix ◽

Evolutionary Divergence ◽

A Genome ◽

Taxonomic Groups ◽

Taxonomic Studies ◽

Genome Scale ◽

Data Matrices

AbstractPhylogenomic studies based on genome-scale amounts of data have greatly improved understanding of the tree of life. Despite their diversity, ecological significance, and biomedical and industrial importance, large-scale phylogenomic studies of Fungi are lacking. Furthermore, several evolutionary relationships among major fungal lineages remain controversial, especially those at the base of the fungal phylogeny. To begin filling these gaps and assess progress toward a genome-scale phylogeny of the entire fungal kingdom, we compiled a phylogenomic data matrix of 290 genes from the genomes of 1,644 fungal species that includes representatives from most major fungal lineages; we also compiled 11 additional data matrices by subsampling genes or taxa based on filtering criteria previously shown to improve phylogenomic inference. Analyses of these 12 data matrices using concatenation- and coalescent-based approaches yielded a robust phylogeny of the kingdom in which ∼85% of internal branches were congruent across data matrices and approaches used. We found support for several relationships that have been historically contentious (e.g., for the placement of Wallemiomycotina (Basidiomycota), as sister to Agaricomycotina), as well as evidence for polytomies likely stemming from episodes of ancient diversification (e.g., at the base of Basidiomycota). By examining the relative evolutionary divergence of taxonomic groups of equivalent rank, we found that fungal taxonomy is broadly aligned with genome sequence divergence, but also identified lineages, such as the subphylum Saccharomycotina, where current taxonomic circumscription does not fully account for their high levels of evolutionary divergence. Our results provide a robust phylogenomic framework to explore the tempo and mode of fungal evolution and directions for future fungal phylogenetic and taxonomic studies.

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Reconstruction and analysis of a genome-scale metabolic model of Methylovorus sp. MP688, a high-level pyrroloquinolone quinone producer

Biosystems ◽

10.1016/j.biosystems.2018.07.009 ◽

2018 ◽

Vol 172 ◽

pp. 37-42 ◽

Cited By ~ 3

Author(s):

Wei Zou ◽

Xianghua Xiong ◽

Jing Zhang ◽

Kaizheng Zhang ◽

Xingxiu Zhao ◽

...

Keyword(s):

Metabolic Model ◽

A Genome ◽

High Level ◽

Genome Scale

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ChiMera: An easy to use pipeline for Bacterial Genome Based Metabolic Network Reconstruction, Evaluation and Visualization

10.1101/2021.11.30.470608 ◽

2021 ◽

Author(s):

Gustavo Tamasco ◽

Ricardo Roberto da Silva ◽

Rafael Silva-Rocha

Keyword(s):

Genetic Engineering ◽

Bacterial Genome ◽

Draft Genome ◽

Scale Model ◽

Metabolic Reconstruction ◽

Acid Oxidation ◽

Model Driven ◽

A Genome ◽

High Level ◽

Genome Scale

Several genome scale metabolic reconstruction tools have been developed in the last decades. They have helped to construct many metabolic models, which have contributed to a variety of fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a higher level of bioinformatic skills, and most of them are not scalable for multiple genomes. Moreover, the functionalities required to build models are generally scattered through multiple tools, requiring knowledge of their utilization. Here, we present ChiMera, which combines the most efficient tools in model reconstruction, prediction, and visualization. ChiMera uses CarveMe top-down approach based on genomic evidence to prune a global model with a high level of curation, generating a draft genome able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules of visualization implemented, predefined and universal. The first generates maps for the most important pathways, e.g., core-metabolism, fatty acid oxidation and biosynthesis, nucleotides and amino acids biosynthesis, glycolysis, and others. The second module produces a genome-wide metabolic map, which can be used to harvest KEGG pathway information for each compound in the model. A module of gene essentiality and knockout is also present. Overall, ChiMera combines model creation, gap-filling, FBA and metabolic visualization to create a simulation ready genome-scale model, helping genetic engineering projects, prediction of phenotypes, and other model-driven discoveries in a friendly manner.

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A Genome-Wide Identification Study Reveals That HmoCYP76AD1, HmoDODAα1 and HmocDOPA5GT Involved in Betalain Biosynthesis in Hylocereus

Genes ◽

10.3390/genes12121858 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1858

Author(s):

Qingzhu Hua ◽

Canbin Chen ◽

Fangfang Xie ◽

Zhike Zhang ◽

Rong Zhang ◽

...

Keyword(s):

Large Scale ◽

Water Soluble ◽

Soluble Nitrogen ◽

Genome Data ◽

Genome Wide ◽

A Genome ◽

Red Color ◽

Hylocereus Undatus ◽

High Level ◽

Cytochrome P 450

Betalains are water-soluble nitrogen-containing pigments with multiple bioactivities. Pitayas are the only at large-scale commercially grown fruit containing abundant betalains for consumers. Currently, the key genes involved in betalain biosynthesis remain to be fully elucidated. Moreover, genome-wide analyses of these genes in betalain biosynthesis are not available in betalain-producing plant species. In this study, totally 53 genes related to betalain biosynthesis were identified from the genome data of Hylocereus undatus. Four candidate genes i.e., one cytochrome P-450 R gene (HmoCYP76AD1), two L-DOPA 4,5-dioxygenase genes (HmoDODAα1 and HmoDODAα2), and one cyclo-DOPA 5-O glucosyltransferase gene (HmocDOPA5GT) were initially screened according to bioinformatics and qRT-PCR analyses. Silencing HmoCYP76AD1, HmoDODAα1, HmoDODAα2 or HmocDOPA5GT resulted in loss of red pigment. HmoDODAα1 displayed a high level of L-DOPA 4,5-dioxygenase activity to produce betalamic acid and formed yellow betaxanthin. Co-expression of HmoCYP76AD1, HmoDODAα1 and HmocDOPA5GT in Nicotiana benthamiana and yeast resulted in high abundance of betalain pigments with a red color. These results suggested that HmoCYP76AD1, HmoDODAα1, and HmocDOPA5GT play key roles in betalain biosynthesis in Hylocereus. The results of the present study provide novel genes for molecular breeding programs of pitaya.

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Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview

Metabolites ◽

10.3390/metabo9020022 ◽

2019 ◽

Vol 9 (2) ◽

pp. 22 ◽

Cited By ~ 25

Author(s):

Partho Sen ◽

Matej Orešič

Keyword(s):

Gut Microbiome ◽

Large Scale ◽

Metabolic Modeling ◽

Metagenomic Data ◽

Shotgun Metagenomics ◽

A Genome ◽

Functional Profiling ◽

Host Microbe Interactions ◽

Genome Scale ◽

Host Metabolism

There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet–microbiome, microbe–microbe and host–microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.

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Median Absolute Deviation to Improve Hit Selection for Genome-Scale RNAi Screens

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057107312035 ◽

2008 ◽

Vol 13 (2) ◽

pp. 149-158 ◽

Cited By ~ 110

Author(s):

Namjin Chung ◽

Xiaohua Douglas Zhang ◽

Anthony Kreamer ◽

Louis Locco ◽

Pei-Fen Kuan ◽

...

Keyword(s):

High Throughput Screening ◽

Large Scale ◽

Selection Method ◽

Rnai Screen ◽

Median Absolute Deviation ◽

Cellular Heterogeneity ◽

Selection Strategy ◽

Absolute Deviation ◽

A Genome ◽

Genome Scale

High-throughput screening (HTS) of large-scale RNA interference (RNAi) libraries has become an increasingly popular method of functional genomics in recent years. Cell-based assays used for RNAi screening often produce small dynamic ranges and significant variability because of the combination of cellular heterogeneity, transfection efficiency, and the intrinsic nature of the genes being targeted. These properties make reliable hit selection in the RNAi screen a difficult task. The use of robust methods based on median and median absolute deviation (MAD) has been suggested to improve hit selection in such cases, but mean and standard deviation (SD)—based methods are still predominantly used in many RNAi HTS. In an experimental approach to compare these 2 methods, a genome-scale small interfering RNA (siRNA) screen was performed, in which the identification of novel targets increasing the therapeutic index of the chemotherapeutic agent mitomycin C (MMC) was sought. MAD values were resistant to the presence of outliers, and the hits selected by the MAD-based method included all the hits that would be selected by SD-based method as well as a significant number of additional hits. When retested in triplicate, a similar percentage of these siRNAs were shown to genuinely sensitize cells to MMC compared with the hits shared between SD- and MAD-based methods. Confirmed hits were enriched with the genes involved in the DNA damage response and cell cycle regulation, validating the overall hit selection strategy. Finally, computer simulations showed the superiority and generality of the MAD-based method in various RNAi HTS data models. In conclusion, the authors demonstrate that the MAD-based hit selection method rescued physiologically relevant false negatives that would have been missed in the SD-based method, and they believe it to be the desirable 1st-choice hit selection method for RNAi screen results. ( Journal of Biomolecular Screening 2008:149-158)

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