Large-scale transgenic Drosophila resource collections for loss- and gain-of-function studies

ABSTRACTThe Transgenic RNAi Project (TRiP), a Drosophila functional genomics platform at Harvard Medical School, was initiated in 2008 to generate and distribute a genome-scale collection of RNAi fly stocks. To date, the TRiP has generated >15,000 RNAi fly stocks. As this covers most Drosophila genes, we have largely transitioned to development of new resources based on CRISPR technology. Here, we present an update on our libraries of publicly available RNAi and CRISPR fly stocks, and focus on the TRiP-CRISPR overexpression (TRiP-OE) and TRiP-CRISPR knockout (TRiP-KO) collections. TRiP-OE stocks express sgRNAs targeting upstream of a gene transcription start site. Gene activation is triggered by co-expression of catalytically dead Cas9 (dCas9) fused to an activator domain, either VP64-p65-Rta (VPR) or Synergistic Activation Mediator (SAM). TRiP-KO stocks express one or two sgRNAs targeting the coding sequence of a gene or genes, allowing for generation of indels in both germline and somatic tissue. To date, we have generated more than 5,000 CRISPR-OE or -KO stocks for the community. These resources provide versatile, transformative tools for gene activation, gene repression, and genome engineering.

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Large-Scale Transgenic Drosophila Resource Collections for Loss- and Gain-of-Function Studies

Genetics ◽

10.1534/genetics.119.302964 ◽

2020 ◽

Vol 214 (4) ◽

pp. 755-767 ◽

Cited By ~ 10

Author(s):

Jonathan Zirin ◽

Yanhui Hu ◽

Luping Liu ◽

Donghui Yang-Zhou ◽

Ryan Colbeth ◽

...

Keyword(s):

Large Scale ◽

Genome Engineering ◽

Gene Activation ◽

Somatic Tissue ◽

Guide Rnas ◽

Synergistic Activation ◽

A Genome ◽

Or Genes ◽

Genome Scale ◽

Transgenic Drosophila

The Transgenic RNAi Project (TRiP), a Drosophila melanogaster functional genomics platform at Harvard Medical School, was initiated in 2008 to generate and distribute a genome-scale collection of RNA interference (RNAi) fly stocks. To date, it has generated >15,000 RNAi fly stocks. As this covers most Drosophila genes, we have largely transitioned to development of new resources based on CRISPR technology. Here, we present an update on our libraries of publicly available RNAi and CRISPR fly stocks, and focus on the TRiP-CRISPR overexpression (TRiP-OE) and TRiP-CRISPR knockout (TRiP-KO) collections. TRiP-OE stocks express single guide RNAs targeting upstream of a gene transcription start site. Gene activation is triggered by coexpression of catalytically dead Cas9 fused to an activator domain, either VP64-p65-Rta or Synergistic Activation Mediator. TRiP-KO stocks express one or two single guide RNAs targeting the coding sequence of a gene or genes. Cutting is triggered by coexpression of Cas9, allowing for generation of indels in both germline and somatic tissue. To date, we have generated >5000 TRiP-OE or TRiP-KO stocks for the community. These resources provide versatile, transformative tools for gene activation, gene repression, and genome engineering.

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

Genetics ◽

10.1093/genetics/159.4.1765 ◽

2001 ◽

Vol 159 (4) ◽

pp. 1765-1778

Author(s):

Gregory J Budziszewski ◽

Sharon Potter Lewis ◽

Lyn Wegrich Glover ◽

Jennifer Reineke ◽

Gary Jones ◽

...

Keyword(s):

Large Scale ◽

Protein Translocation ◽

Gene Families ◽

Mutant Phenotype ◽

Lethal Mutant ◽

A Genome ◽

Genes Encoding ◽

High Level ◽

Mutant Lines ◽

Genome Scale

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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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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Improved sgRNA design in bacteria via genome-wide activity profiling

10.1101/272377 ◽

2018 ◽

Cited By ~ 1

Author(s):

Jiahui Guo ◽

Tianmin Wang ◽

Changge Guan ◽

Bing Liu ◽

Cheng Luo ◽

...

Keyword(s):

Dna Cleavage ◽

Large Scale ◽

Genome Engineering ◽

Prediction Models ◽

Prokaryotic Genome ◽

Model Organism ◽

Bacterial Cells ◽

Highly Active ◽

Promising Tool ◽

A Genome

AbstractCRISPR/Cas9 is a promising tool in prokaryotic genome engineering, but its success is limited by the widely varying on-target activity of single guide RNAs (sgRNAs). Based on the association of CRISPR/Cas9-induced DNA cleavage with cellular lethality, we systematically profiled sgRNA activity by co-expressing a genome-scale library (~70,000 sgRNAs) with Cas9 or its specificity-improved mutant in E. coli. Based on this large-scale dataset, we constructed a comprehensive and high-density sgRNA activity map, which enables selecting highly active sgRNAs for any locus across the genome in this model organism. We also identified ‘resistant’ genomic loci with respect to CRISPR/Cas9 activity, notwithstanding the highly accessible DNA in bacterial cells. Moreover, we found that previous sgRNA activity prediction models that were trained on mammalian cell datasets were inadequate when coping with our results, highlighting the key limitations and biases of previous models. We hence developed an integrated algorithm to accurately predict highly effective sgRNAs, aiming to facilitate the design of CRISPR/Cas9-based genome engineering or screenings in bacteria. We also isolated the important sgRNA features that contribute to DNA cleavage and characterized their key differences among wild type Cas9 and its mutant, shedding light on the biophysical mechanisms of the CRISPR/Cas9 system.

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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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Construction of a minimal genome as a chassis for synthetic biology

Essays in Biochemistry ◽

10.1042/ebc20160024 ◽

2016 ◽

Vol 60 (4) ◽

pp. 337-346 ◽

Cited By ~ 10

Author(s):

Bong Hyun Sung ◽

Donghui Choe ◽

Sun Chang Kim ◽

Byung-Kwan Cho

Keyword(s):

Synthetic Biology ◽

Genome Stability ◽

Large Scale ◽

Genome Engineering ◽

Essential Gene ◽

Industrial Applications ◽

Microbial Genomes ◽

Gene Set ◽

Minimal Genome ◽

A Genome

Microbial diversity and complexity pose challenges in understanding the voluminous genetic information produced from whole-genome sequences, bioinformatics and high-throughput ‘-omics’ research. These challenges can be overcome by a core blueprint of a genome drawn with a minimal gene set, which is essential for life. Systems biology and large-scale gene inactivation studies have estimated the number of essential genes to be ∼300–500 in many microbial genomes. On the basis of the essential gene set information, minimal-genome strains have been generated using sophisticated genome engineering techniques, such as genome reduction and chemical genome synthesis. Current size-reduced genomes are not perfect minimal genomes, but chemically synthesized genomes have just been constructed. Some minimal genomes provide various desirable functions for bioindustry, such as improved genome stability, increased transformation efficacy and improved production of biomaterials. The minimal genome as a chassis genome for synthetic biology can be used to construct custom-designed genomes for various practical and industrial applications.

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Scalable metabolic pathway analysis

10.1101/2020.07.31.230177 ◽

2020 ◽

Author(s):

Ove Øyås ◽

Jörg Stelling

Keyword(s):

Pathway Analysis ◽

Metabolic Pathway ◽

Large Scale ◽

Metabolic Networks ◽

Central Carbon Metabolism ◽

Graph Representation ◽

Metabolic Pathway Analysis ◽

A Genome ◽

Scope Of Application ◽

Genome Scale

The scope of application of genome-scale constraint-based models (CBMs) of metabolic networks rapidly expands toward multicellular systems. However, comprehensive analysis of CBMs through metabolic pathway analysis remains a major computational challenge because pathway numbers grow combinatorially with model sizes. Here, we define the minimal pathways (MPs) of a metabolic (sub)network as a subset of its elementary flux vectors. We enumerate or sample them efficiently using iterative minimization and a simple graph representation of MPs. These methods outperform the state of the art and they allow scalable pathway analysis for microbial and mammalian CBMs. Sampling random MPs from Escherichia coli’s central carbon metabolism in the context of a genome-scale CBM improves predictions of gene importance, and enumerating all minimal exchanges in a host-microbe model of the human gut predicts exchanges of metabolites associated with host-microbiota homeostasis and human health. MPs thereby open up new possibilities for the detailed analysis of large-scale metabolic networks.

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