A tool named Iris for versatile high-throughput phenotyping in microorganisms

Kritikos, George; Banzhaf, Manuel; Herrera-Dominguez, Lucía; Koumoutsi, Alexandra; Wartel, Morgane; Zietek, Matylda; Typas, Athanasios

doi:10.1038/nmicrobiol.2017.14

Cited by 75 publications

(84 citation statements)

References 63 publications

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“…Each experiment was replicated once for quality control. After incubation, plates were imaged and colony sizes were extracted using IRIS version v0.9.7 (Kritikos et al , ) with the “Colony growth” profile, which extracts colony size, circularity and opacity from each colony in each plate. Individual strains were scored using the E‐MAP software, which transforms colony sizes into s‐scores (Collins et al , ).…”

Section: Methodsmentioning

confidence: 99%

A resource of variant effect predictions of single nucleotide variants in model organisms

Wagih

Galardini

Busby

et al. 2018

Molecular Systems Biology

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The effect of single nucleotide variants (SNVs) in coding and noncoding regions is of great interest in genetics. Although many computational methods aim to elucidate the effects of SNVs on cellular mechanisms, it is not straightforward to comprehensively cover different molecular effects. To address this, we compiled and benchmarked sequence and structure‐based variant effect predictors and we computed the impact of nearly all possible amino acid and nucleotide variants in the reference genomes of Homo sapiens, Saccharomyces cerevisiae and Escherichia coli. Studied mechanisms include protein stability, interaction interfaces, post‐translational modifications and transcription factor binding sites. We apply this resource to the study of natural and disease coding variants. We also show how variant effects can be aggregated to generate protein complex burden scores that uncover protein complex to phenotype associations based on a set of newly generated growth profiles of 93 sequenced S. cerevisiae strains in 43 conditions. This resource is available through mutfunc (www.mutfunc.com), a tool by which users can query precomputed predictions by providing amino acid or nucleotide‐level variants.

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Section: Methodsmentioning

confidence: 99%

A resource of variant effect predictions of single nucleotide variants in model organisms

Wagih

Galardini

Busby

et al. 2018

Molecular Systems Biology

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“…Plates were incubated at 37°C for 12 h and imaged. Colony size was quantified using the image analysis software Iris (Kritikos et al, 2017).…”

Section: Growth Fitness Assaysmentioning

confidence: 99%

Induced conformational changes activate the peptidoglycan synthase PBP1B

Egan

Maya-Martinez

Ayala

et al. 2018

Molecular Microbiology

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SummaryBacteria surround their cytoplasmic membrane with an essential, stress‐bearing peptidoglycan (PG) layer consisting of glycan chains linked by short peptides into a mesh‐like structure. Growing and dividing cells expand their PG layer using inner‐membrane anchored PG synthases, including Penicillin‐binding proteins (PBPs), which participate in dynamic protein complexes to facilitate cell wall growth. In Escherichia coli, and presumably other Gram‐negative bacteria, growth of the mainly single layered PG is regulated by outer membrane‐anchored lipoproteins. The lipoprotein LpoB is required to activate PBP1B, which is a major, bi‐functional PG synthase with glycan chain polymerising (glycosyltransferase) and peptide cross‐linking (transpeptidase) activities. In this work we show how the binding of LpoB to the regulatory UB2H domain of PBP1B activates both activities. Binding induces structural changes in the UB2H domain, which transduce to the two catalytic domains by distinct allosteric pathways. We also show how an additional regulator protein, CpoB, is able to selectively modulate the TPase activation by LpoB without interfering with GTase activation.

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“…Mutants whose 95% quantile for interaction scores with minimal media conditions fell below zero were classied as auxotrophs. Our auxotrophs had a 83.33% overlap with the [ Kritikos et al, 2017] As an alternative visualization, consider ROC plots [ Figure 4, S5] that assess the ability of matrix linear models to correctly identify auxotrophs found by Nichols et al and Joyce et al To get the TPR and FPR for Figure 4, we took the auxotrophs identied by Nichols et al to be the true auxotrophs. We then obtained TPRs and FPRs by varying cutos for the median minimal media interaction score for the auxotrophs that we identied.…”

Section: Auxotroph Analysismentioning

confidence: 99%

Matrix linear models for high-throughput chemical genetic screens

Liang

Nichols

Sen

2018

Preprint

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We develop a exible and computationally ecient approach for analysing high throughput chemical genetic screens. In such screens, a library of genetic mutants is phenotyped in a large number of stresses. The goal is to detect interactions between genes and stresses. Typically, this is achieved by grouping the mutants and stresses into categories, and performing modied t-tests for each combination. This approach does not have a natural extension if mutants or stresses have quantitative or nonoverlapping annotations (eg. if conditions have doses, or a mutant falls into more than one category simultaneously). We develop a matrix linear model framework that allows us to model relationships between mutants and conditions in a simple, yet exible multivariate framework. It encodes both categorical and continuous relationships to enhance detection of associations. To handle large datasets, we develop a fast estimation approach that takes advantage of the structure of matrix linear models. We evaluate our method's performance in simulations and in an E. coli chemical genetic screen, comparing it with an existing univariate approach based on modied t-tests . We show that matrix linear models perform slightly better than the univariate approach when mutants and conditions are classied in non-overlapping categories, and substantially better when conditions can be ordered in dosage categories. Our approach is much faster computationally and is scalable to larger datasets. It is an attractive alternative to current methods, and provides a natural framework extensible to larger, and more complex chemical genetic screens. A Julia implementation of matrix linear models and the code used for the analysis in this paper can be found at https://bitbucket.org/jwliang/mlm_packages and https://bitbucket.org/jwliang/mlm_gs_supplement, respectively.

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A tool named Iris for versatile high-throughput phenotyping in microorganisms

Cited by 75 publications

References 63 publications

A resource of variant effect predictions of single nucleotide variants in model organisms

A resource of variant effect predictions of single nucleotide variants in model organisms

Induced conformational changes activate the peptidoglycan synthase PBP1B

Matrix linear models for high-throughput chemical genetic screens

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