Defining Functions of Mannoproteins in Saccharomyces cerevisiae by High-Dimensional Morphological Phenotyping

Ghanegolmohammadi, Farzan; Okada, Hiroki; Liu, Yaxuan; Itto-Nakama, Kaori; Ohnuki, Shinsuke; Savchenko, A.S.; Bi, Erfei; Yoshida, Satoshi; Ohya, Yoshikazu

doi:10.3390/jof7090769

Cited by 10 publications

(9 citation statements)

References 55 publications

(82 reference statements)

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“…4B ). In line with this, the function of a gene with unknown function was predicted (Ghanegolmohammadi et al 2021 ). Like the morphological phenotypes of mutant strains caused by loss of gene function, the morphological phenotypes of cells treated with exogenous compounds are caused by loss of gene function induced by these agents (Young et al.…”

Section: Selection Of Mutant Strains With Similar Morphologymentioning

confidence: 91%

Application of unimodal probability distribution models for morphological phenotyping of budding yeast

Ohya,

Ghanegolmohammadi,

Itto-Nakama

2024

FEMS Yeast Research

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Morphological phenotyping of the budding yeast Saccharomyces cerevisiae has helped to greatly clarify the functions of genes and increase our understanding of cellular functional networks. It is necessary to understand cell morphology and perform quantitative morphological analysis (QMA) but assigning precise values to morphological phenotypes has been challenging. We recently developed the Unimodal Morphological Data image analysis pipeline for this purpose. All true values can be estimated theoretically by applying an appropriate probability distribution if the distribution of experimental values follows a unimodal pattern. This reliable pipeline allows several downstream analyses, including detection of subtle morphological differences, selection of mutant strains with similar morphology, clustering based on morphology, and study of morphological diversity. In addition to basic research, morphological analyses of yeast cells can also be used in applied research to monitor breeding and fermentation processes and control the fermentation activity of yeast cells.

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Section: Selection Of Mutant Strains With Similar Morphologymentioning

confidence: 91%

Application of unimodal probability distribution models for morphological phenotyping of budding yeast

Ohya,

Ghanegolmohammadi,

Itto-Nakama

2024

FEMS Yeast Research

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“…All new constructs were validated by sequencing performed at the DNA Sequencing Facility, University of Pennsylvania. Plasmids pAG25 (Goldstein and McCusker, 1999), YIp128-CDC3-GFP (Gao et al, 2007), pFA6a-link-yoEGFP-SpHIS5 and pFA6a-link-yoTagRFP-T-CaURA3 (Lee et al, 2013), pFA6a-link-ymScarlet-I-CaURA (Marquardt et al, 2020), pMAL-MYO1-mTD1 (Fang et al, 2010), pRG205MX (Gnugge et al, 2016), proHIS3-ymScarlet-I-TUB1-tTUB1-HPH (Ghanegolmohammadi et al, 2021), pRS316-N-MYO1-GFP (Caviston et al, 2003), and pUG36-GFP-Ecm25-xACT(536–588aa) (Duan et al, 2021) were described previously.…”

Section: Methodsmentioning

confidence: 99%

Bni5 tethers myosin-II to septins to enhance retrograde actin flow and the robustness of cytokinesis

Okada,

Chen,

Wang

et al. 2023

Preprint

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The collaboration between septins and myosin-II in driving processes outside of cytokinesis remains largely uncharted. Here, we demonstrate that Bni5 in the budding yeastS. cerevisiaeinteracts with myosin-II, septin filaments, and the septin-associated kinase Elm1 via distinct domains at its N- and C-termini, thereby tethering the mobile myosin-II to the stable septin hourglass at the division site from bud emergence to the onset of cytokinesis. The septin and Elm1-binding domains, together with a central disordered region, of Bni5 control timely remodeling of the septin hourglass into a double ring, enabling the actomyosin ring constriction. The Bni5-tethered myosin-II enhances retrograde actin cable flow, which contributes to the asymmetric inheritance of mitochondria-associated protein aggregates during cell division, and also strengthens cytokinesis against various perturbations. Thus, we have established a biochemical pathway involving septin-Bni5-myosin-II interactions at the division site, which can inform mechanistic understanding of the role of myosin-II in other retrograde flow systems.SummaryOkada et al. have determined the molecular mechanism underlying the Bni5 interactions with septins and myosin-II at the cell division site and uncovered its roles in promoting retrograde actin flow and the robustness of cytokinesis in budding yeast.

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“…Morphological features are extracted using various image analysis tools [ 10 , 11 ]. For example, CellProfiler is used for analyzing images of the tissues and cells of humans, fruit flies, worms, and yeast [ 12 ], while CalMorph is used for analyzing images of Saccharomyces cerevisiae cells [ 9 , 13 – 15 ]. Increased reproducibility and reduced artifacts are often achieved using image quality control systems associated with the image analysis tools [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Assignment of unimodal probability distribution models for quantitative morphological phenotyping

2022

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Background Cell morphology is a complex and integrative readout, and therefore, an attractive measurement for assessing the effects of genetic and chemical perturbations to cells. Microscopic images provide rich information on cell morphology; therefore, subjective morphological features are frequently extracted from digital images. However, measured datasets are fundamentally noisy; thus, estimation of the true values is an ultimate goal in quantitative morphological phenotyping. Ideal image analyses require precision, such as proper probability distribution analyses to detect subtle morphological changes, recall to minimize artifacts due to experimental error, and reproducibility to confirm the results. Results Here, we present UNIMO (UNImodal MOrphological data), a reliable pipeline for precise detection of subtle morphological changes by assigning unimodal probability distributions to morphological features of the budding yeast cells. By defining the data type, followed by validation using the model selection method, examination of 33 probability distributions revealed nine best-fitting probability distributions. The modality of the distribution was then clarified for each morphological feature using a probabilistic mixture model. Using a reliable and detailed set of experimental log data of wild-type morphological replicates, we considered the effects of confounding factors. As a result, most of the yeast morphological parameters exhibited unimodal distributions that can be used as basic tools for powerful downstream parametric analyses. The power of the proposed pipeline was confirmed by reanalyzing morphological changes in non-essential yeast mutants and detecting 1284 more mutants with morphological defects compared with a conventional approach (Box–Cox transformation). Furthermore, the combined use of canonical correlation analysis permitted global views on the cellular network as well as new insights into possible gene functions. Conclusions Based on statistical principles, we showed that UNIMO offers better predictions of the true values of morphological measurements. We also demonstrated how these concepts can provide biologically important information. This study draws attention to the necessity of employing a proper approach to do more with less.

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Defining Functions of Mannoproteins in Saccharomyces cerevisiae by High-Dimensional Morphological Phenotyping

Cited by 10 publications

References 55 publications

Application of unimodal probability distribution models for morphological phenotyping of budding yeast

Application of unimodal probability distribution models for morphological phenotyping of budding yeast

Bni5 tethers myosin-II to septins to enhance retrograde actin flow and the robustness of cytokinesis

Assignment of unimodal probability distribution models for quantitative morphological phenotyping

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