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Xie MJ, Cromie GA, Owens K, Timour MS, Tang M, Kutz JN, El-Hattab AW, McLaughlin RN, Dudley AM. Constructing and interpreting a large-scale variant effect map for an ultrarare disease gene: Comprehensive prediction of the functional impact of PSAT1 genotypes. PLoS Genet 2023; 19:e1010972. [PMID: 37812589 PMCID: PMC10561871 DOI: 10.1371/journal.pgen.1010972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/13/2023] [Indexed: 10/11/2023] Open
Abstract
Reduced activity of the enzymes encoded by PHGDH, PSAT1, and PSPH causes a set of ultrarare, autosomal recessive diseases known as serine biosynthesis defects. These diseases present in a broad phenotypic spectrum: at the severe end is Neu-Laxova syndrome, in the intermediate range are infantile serine biosynthesis defects with severe neurological manifestations and growth deficiency, and at the mild end is childhood disease with intellectual disability. However, L-serine supplementation, especially if started early, can ameliorate and in some cases even prevent symptoms. Therefore, knowledge of pathogenic variants can improve clinical outcomes. Here, we use a yeast-based assay to individually measure the functional impact of 1,914 SNV-accessible amino acid substitutions in PSAT. Results of our assay agree well with clinical interpretations and protein structure-function relationships, supporting the inclusion of our data as functional evidence as part of the ACMG variant interpretation guidelines. We use existing ClinVar variants, disease alleles reported in the literature and variants present as homozygotes in the primAD database to define assay ranges that could aid clinical variant interpretation for up to 98% of the tested variants. In addition to measuring the functional impact of individual variants in yeast haploid cells, we also assay pairwise combinations of PSAT1 alleles that recapitulate human genotypes, including compound heterozygotes, in yeast diploids. Results from our diploid assay successfully distinguish the genotypes of affected individuals from those of healthy carriers and agree well with disease severity. Finally, we present a linear model that uses individual allele measurements to predict the biallelic function of ~1.8 million allele combinations corresponding to potential human genotypes. Taken together, our work provides an example of how large-scale functional assays in model systems can be powerfully applied to the study of ultrarare diseases.
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Affiliation(s)
- Michael J. Xie
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
- Molecular Engineering Graduate Program, University of Washington, Seattle, Washington, United States of America
| | - Gareth A. Cromie
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
| | - Katherine Owens
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
| | - Martin S. Timour
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
| | - Michelle Tang
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
| | - J. Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
| | - Ayman W. El-Hattab
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | | | - Aimée M. Dudley
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
- Molecular Engineering Graduate Program, University of Washington, Seattle, Washington, United States of America
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Lo RS, Cromie GA, Tang M, Teng K, Owens K, Sirr A, Kutz JN, Morizono H, Caldovic L, Ah Mew N, Gropman A, Dudley AM. The functional impact of 1,570 individual amino acid substitutions in human OTC. Am J Hum Genet 2023; 110:863-879. [PMID: 37146589 PMCID: PMC10183466 DOI: 10.1016/j.ajhg.2023.03.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/30/2023] [Indexed: 05/07/2023] Open
Abstract
Deleterious mutations in the X-linked gene encoding ornithine transcarbamylase (OTC) cause the most common urea cycle disorder, OTC deficiency. This rare but highly actionable disease can present with severe neonatal onset in males or with later onset in either sex. Individuals with neonatal onset appear normal at birth but rapidly develop hyperammonemia, which can progress to cerebral edema, coma, and death, outcomes ameliorated by rapid diagnosis and treatment. Here, we develop a high-throughput functional assay for human OTC and individually measure the impact of 1,570 variants, 84% of all SNV-accessible missense mutations. Comparison to existing clinical significance calls, demonstrated that our assay distinguishes known benign from pathogenic variants and variants with neonatal onset from late-onset disease presentation. This functional stratification allowed us to identify score ranges corresponding to clinically relevant levels of impairment of OTC activity. Examining the results of our assay in the context of protein structure further allowed us to identify a 13 amino acid domain, the SMG loop, whose function appears to be required in human cells but not in yeast. Finally, inclusion of our data as PS3 evidence under the current ACMG guidelines, in a pilot reclassification of 34 variants with complete loss of activity, would change the classification of 22 from variants of unknown significance to clinically actionable likely pathogenic variants. These results illustrate how large-scale functional assays are especially powerful when applied to rare genetic diseases.
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Affiliation(s)
- Russell S Lo
- Pacific Northwest Research Institute, Seattle, WA, USA
| | | | - Michelle Tang
- Pacific Northwest Research Institute, Seattle, WA, USA
| | - Kevin Teng
- Pacific Northwest Research Institute, Seattle, WA, USA
| | - Katherine Owens
- Pacific Northwest Research Institute, Seattle, WA, USA; Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Amy Sirr
- Pacific Northwest Research Institute, Seattle, WA, USA
| | - J Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Hiroki Morizono
- Center for Genetic Medicine Research, Children's National Research Institute, Children's National Hospital, Washington, DC, USA; Department of Genomics and Precision Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, USA
| | - Ljubica Caldovic
- Center for Genetic Medicine Research, Children's National Research Institute, Children's National Hospital, Washington, DC, USA; Department of Genomics and Precision Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, USA
| | - Nicholas Ah Mew
- Center for Genetic Medicine Research, Children's National Research Institute, Children's National Hospital, Washington, DC, USA; Department of Genomics and Precision Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, USA
| | - Andrea Gropman
- Center for Genetic Medicine Research, Children's National Research Institute, Children's National Hospital, Washington, DC, USA; Department of Genomics and Precision Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, USA; Department of Neurology, Division of Neurogenetics and Neurodevelopmental Disabilities, Children's National Hospital, Washington, DC, USA; Center for Neuroscience Research, Children's National Research Institute, Children's National Hospital, Washington, DC, USA
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Xie MJ, Cromie GA, Owens K, Timour MS, Tang M, Kutz JN, El-Hattab AW, McLaughlin RN, Dudley AM. Predicting the functional effect of compound heterozygous genotypes from large scale variant effect maps. bioRxiv 2023:2023.01.11.523651. [PMID: 36711904 PMCID: PMC9882023 DOI: 10.1101/2023.01.11.523651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Background Pathogenic variants in PHGDH, PSAT1 , and PSPH cause a set of rare, autosomal recessive diseases known as serine biosynthesis defects. Serine biosynthesis defects present in a broad phenotypic spectrum that includes, at the severe end, Neu-Laxova syndrome, a lethal multiple congenital anomaly disease, intermediately in the form of infantile serine biosynthesis defects with severe neurological manifestations and growth deficiency, and at the mild end, as childhood disease with intellectual disability. However, because L-serine supplementation, especially if started early, can ameliorate and in some cases even prevent symptoms, knowledge of pathogenic variants is highly actionable. Methods Recently, our laboratory established a yeast-based assay for human PSAT1 function. We have now applied it at scale to assay the functional impact of 1,914 SNV-accessible amino acid substitutions. In addition to assaying the functional impact of individual variants in yeast haploid cells, we can assay pairwise combinations of PSAT1 alleles that recapitulate human genotypes, including compound heterozygotes, in yeast diploids. Results Results of our assays of individual variants (in haploid yeast cells) agree well with clinical interpretations and protein structure-function relationships, supporting the use of our data as functional evidence under the ACMG interpretation guidelines. Results from our diploid assay successfully distinguish patient genotypes from those of healthy carriers and agree well with disease severity. Finally, we present a linear model that uses individual allele measurements (in haploid yeast cells) to accurately predict the biallelic function (in diploid yeast cells) of ~ 1.8 million allele combinations corresponding to potential human genotypes. Conclusions Taken together, our work provides an example of how large-scale functional assays in model systems can be powerfully applied to the study of a rare disease.
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Sirr A, Timour MS, Cromie GA, Tang M, Dudley AM. A method for quantifying sporulation efficiency and isolating meiotic progeny in non-GMO strains of Saccharomyces cerevisiae. Yeast 2022; 39:354-362. [PMID: 35706372 DOI: 10.1002/yea.3802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/25/2022] [Accepted: 06/03/2022] [Indexed: 11/07/2022] Open
Abstract
Meiotic mapping, a linkage-based method for analyzing the recombinant progeny of a cross, has long been a cornerstone of genetic research. The yeast Saccharomyces cerevisiae is a powerful system because it is possible to isolate and cultivate the four products (spores) of a single meiotic event. However, the throughput of this process has historically been limited by the process of identifying tetrads in a heterogeneous population of vegetative cells, tetrads, and dyads followed by manual separation (dissection) of the spores contained in a tetrad. To date, methods that facilitate high throughput characterization and isolation of meiotic progeny have relied on genetic engineering. Here, we characterize the ability of the fluorescent dye DiBAC4 (5) to stain yeast tetrads and dyads as well as to adhere to spores following bulk tetrad disruption. Applications include quantitative assays of sporulation rates and efficiency by flow cytometry as well as enrichment of intact tetrads, dyads, or disrupted spores by fluorescence-activated cell sorting in strains that have not been genetically modified.
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Affiliation(s)
- Amy Sirr
- Pacific Northwest Research Institute, Seattle, Washington, USA
| | - Martin S Timour
- Pacific Northwest Research Institute, Seattle, Washington, USA
| | - Gareth A Cromie
- Pacific Northwest Research Institute, Seattle, Washington, USA
| | - Michelle Tang
- Pacific Northwest Research Institute, Seattle, Washington, USA
| | - Aimée M Dudley
- Pacific Northwest Research Institute, Seattle, Washington, USA
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Maddirevula S, Shamseldin HE, Sirr A, AlAbdi L, Lo RS, Ewida N, Al-Qahtani M, Hashem M, Abdulwahab F, Aboyousef O, Kaya N, Monies D, Salem MH, Al Harbi N, Aldhalaan HM, Alzaidan H, Almanea HM, Alsalamah AK, Al Mutairi F, Ismail S, Abdel-Salam GMH, Alhashem A, Asery A, Faqeih E, AlQassmi A, Al-Hamoudi W, Algoufi T, Shagrani M, Dudley AM, Alkuraya FS. Exploiting the Autozygome to Support Previously Published Mendelian Gene-Disease Associations: An Update. Front Genet 2020; 11:580484. [PMID: 33456446 PMCID: PMC7806527 DOI: 10.3389/fgene.2020.580484] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 11/30/2020] [Indexed: 01/06/2023] Open
Abstract
There is a growing interest in standardizing gene-disease associations for the purpose of facilitating the proper classification of variants in the context of Mendelian diseases. One key line of evidence is the independent observation of pathogenic variants in unrelated individuals with similar phenotypes. Here, we expand on our previous effort to exploit the power of autozygosity to produce homozygous pathogenic variants that are otherwise very difficult to encounter in the homozygous state due to their rarity. The identification of such variants in genes with only tentative associations to Mendelian diseases can add to the existing evidence when observed in the context of compatible phenotypes. In this study, we report 20 homozygous variants in 18 genes (ADAMTS18, ARNT2, ASTN1, C3, DMBX1, DUT, GABRB3, GM2A, KIF12, LOXL3, NUP160, PTRHD1, RAP1GDS1, RHOBTB2, SIGMAR1, SPAST, TENM3, and WASHC5) that satisfy the ACMG classification for pathogenic/likely pathogenic if the involved genes had confirmed rather than tentative links to diseases. These variants were selected because they were truncating, founder with compelling segregation or supported by robust functional assays as with the DUT variant that we present its validation using yeast model. Our findings support the previously reported disease associations for these genes and represent a step toward their confirmation.
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Affiliation(s)
- Sateesh Maddirevula
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Hanan E Shamseldin
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Amy Sirr
- Pacific Northwest Research Institute, Seattle, WA, United States
| | - Lama AlAbdi
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.,Department of Zoology, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Russell S Lo
- Pacific Northwest Research Institute, Seattle, WA, United States
| | - Nour Ewida
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Mashael Al-Qahtani
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Mais Hashem
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Firdous Abdulwahab
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Omar Aboyousef
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Namik Kaya
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Dorota Monies
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - May H Salem
- Pediatric Nephrology Service, Department of Pediatrics, King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia
| | - Naffaa Al Harbi
- Pediatric Nephrology Service, Department of Pediatrics, King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia
| | - Hesham M Aldhalaan
- Department of Neuroscience, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Hamad Alzaidan
- Department of Medical Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.,College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Hadeel M Almanea
- Anatomic Pathology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Abrar K Alsalamah
- Vitreoretinal and Uveitis Divisions, King Khaled Eye Specialist Hospital, Riyadh, Saudi Arabia
| | - Fuad Al Mutairi
- Medical Genetics Division, Department of Pediatrics, King Abdullah International Medical Research Centre, King Abdulaziz Medical City, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Samira Ismail
- Human Genetics & Genome Research Division, Clinical Genetics Department, Center of Excellence of Human Genetics, National Research Centre, Cairo, Egypt
| | - Ghada M H Abdel-Salam
- Human Genetics & Genome Research Division, Clinical Genetics Department, Center of Excellence of Human Genetics, National Research Centre, Cairo, Egypt
| | - Amal Alhashem
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.,Department of Pediatric, Prince Sultan Medical Military City, Riyadh, Saudi Arabia
| | - Ali Asery
- Section of Pediatric Gastroenterology, Children's Specialist Hospital, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Eissa Faqeih
- Department of Pediatric Subspecialties, Children's Hospital, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Amal AlQassmi
- Pediatric Neurology, King Saud Medical City, Riyadh, Saudi Arabia
| | - Waleed Al-Hamoudi
- Department of Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Talal Algoufi
- King Faisal Specialist Hospital and Research Center, Organ Transplant Centre, Riyadh, Saudi Arabia
| | - Mohammad Shagrani
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.,King Faisal Specialist Hospital and Research Center, Organ Transplant Centre, Riyadh, Saudi Arabia
| | - Aimée M Dudley
- Pacific Northwest Research Institute, Seattle, WA, United States
| | - Fowzan S Alkuraya
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.,College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
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Sirr A, Lo RS, Cromie GA, Scott AC, Ashmead J, Heyesus M, Dudley AM. A yeast-based complementation assay elucidates the functional impact of 200 missense variants in human PSAT1. J Inherit Metab Dis 2020; 43:758-769. [PMID: 32077105 PMCID: PMC7444316 DOI: 10.1002/jimd.12227] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 02/12/2020] [Accepted: 02/17/2020] [Indexed: 11/10/2022]
Abstract
Defects in serine biosynthesis resulting from loss of function mutations in PHGDH, PSAT1, and PSPH cause a set of rare, autosomal recessive diseases known as Neu-Laxova syndrome (NLS) or serine-deficiency disorders. The diseases present with a broad range of phenotypes including lethality, severe neurological manifestations, seizures, and intellectual disability. However, because L-serine supplementation, especially if started prenatally, can ameliorate and in some cases even prevent symptoms, knowledge of pathogenic variants is medically actionable. Here, we describe a functional assay that leverages the evolutionary conservation of an enzyme in the serine biosynthesis pathway, phosphoserine aminotransferase, and the ability of the human protein-coding sequence (PSAT1) to functionally replace its yeast ortholog (SER1). Results from our quantitative, yeast-based assay agree well with clinical annotations and expectations based on the disease literature. Using this assay, we have measured the functional impact of the 199 PSAT1 variants currently listed in ClinVar, gnomAD, and the literature. We anticipate that the assay could be used to comprehensively assess the functional impact of all SNP-accessible amino acid substitution mutations in PSAT1, a resource that could aid variant interpretation and identify potential NLS carriers.
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Affiliation(s)
- Amy Sirr
- Pacific Northwest Research Institute, Seattle, Washington, USA
| | - Russell S. Lo
- Pacific Northwest Research Institute, Seattle, Washington, USA
| | | | - Adrian C. Scott
- Pacific Northwest Research Institute, Seattle, Washington, USA
| | - Julee Ashmead
- Pacific Northwest Research Institute, Seattle, Washington, USA
| | - Mirutse Heyesus
- Pacific Northwest Research Institute, Seattle, Washington, USA
| | - Aimée M. Dudley
- Pacific Northwest Research Institute, Seattle, Washington, USA
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Intosalmi J, Scott AC, Hays M, Flann N, Yli-Harja O, Lähdesmäki H, Dudley AM, Skupin A. Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies. BMC Mol Cell Biol 2019; 20:59. [PMID: 31856706 PMCID: PMC6923950 DOI: 10.1186/s12860-019-0234-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 10/24/2019] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell-cell and metabolic coupling lead to functionally optimized structures is still limited. RESULTS Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this purpose, we first developed and parameterized a dynamic cell state and growth model for yeast based on on experimental data from homogeneous liquid media conditions. The inferred model is subsequently used in a spatially coarse-grained model for colony development to investigate the effect of metabolic coupling by calibrating spatial parameters from experimental time-course data of colony growth using state-of-the-art statistical techniques for model uncertainty and parameter estimations. The model is finally validated by independent experimental data of an alternative yeast strain with distinct metabolic characteristics and illustrates the impact of metabolic coupling for structure formation. CONCLUSIONS We introduce a novel model for yeast colony formation, present a statistical methodology for model calibration in a data-driven manner, and demonstrate how the established model can be used to generate predictions across scales by validation against independent measurements of genetically distinct yeast strains.
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Affiliation(s)
- Jukka Intosalmi
- Department of Computer Science, Aalto University, P.O.Box 15400, Aalto, FI-00076, Finland.
| | - Adrian C Scott
- Pacific Northwest Research Institute, 720 Broadway, Seattle, WA, 98122, USA
| | - Michelle Hays
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, 98195, USA
| | - Nicholas Flann
- Department of Computer Science, Utah State University, 4205 Old Main Hill, Logan, UT, 84322, USA
| | - Olli Yli-Harja
- BioMediTech and Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, P.O.Box 553, Tampere, 33101, Finland
- Institute for Systems Biology, 1441N 34th Street, Seattle, WA, 98103-8904, USA
| | - Harri Lähdesmäki
- Department of Computer Science, Aalto University, P.O.Box 15400, Aalto, FI-00076, Finland
| | - Aimée M Dudley
- Pacific Northwest Research Institute, 720 Broadway, Seattle, WA, 98122, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, 98195, USA
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 2, avenue de l'Université, Esch-sur-Alzette, L-4365, Luxembourg.
- University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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Sakhanenko NA, Cromie GA, Dudley AM, Galas DJ. Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies. G3 (Bethesda) 2019; 9:2071-2088. [PMID: 31109921 PMCID: PMC6643883 DOI: 10.1534/g3.119.400166] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 05/12/2019] [Indexed: 11/23/2022]
Abstract
We describe an information-theory-based method and associated software for computationally identifying sister spores derived from the same meiotic tetrad. The method exploits specific DNA sequence features of tetrads that result from meiotic centromere and allele segregation patterns. Because the method uses only the genomic sequence, it alleviates the need for tetrad-specific barcodes or other genetic modifications to the strains. Using this method, strains derived from randomly arrayed spores can be efficiently grouped back into tetrads.
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Affiliation(s)
| | | | | | - David J Galas
- Pacific Northwest Research Institute, Seattle, WA 98122
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Baliga NS, Björkegren JLM, Boeke JD, Boutros M, Crawford NPS, Dudley AM, Farber CR, Jones A, Levey AI, Lusis AJ, Mak HC, Nadeau JH, Noyes MB, Petretto E, Seyfried NT, Steinmetz LM, Vonesch SC. The State of Systems Genetics in 2017. Cell Syst 2019; 4:7-15. [PMID: 28125793 DOI: 10.1016/j.cels.2017.01.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Cell Systems invited 16 experts to share their views on the field of systems genetics. In questions repeated in the headings, we asked them to define systems genetics, highlight its relevance to researchers outside the field, discuss what makes a strong systems genetics paper, and paint a picture of where the field is heading in the coming years. Their responses, ordered by the journal but otherwise unedited, make it clear that deciphering genotype to phenotype relationships is a central challenge of systems genetics and will require understanding how networks and higher-order properties of biological systems underlie complex traits. In addition, our experts illuminate the applications and relevance of systems genetics to human disease, the gut microbiome, development of tools that connect the global research community, sustainability, drug discovery, patient-specific disease and network models, and personalized treatments. Finally, a table of suggested reading provides a sample of influential work in the field.
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Affiliation(s)
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029-6574, USA; Department of Medical Biochemistry and Biophysics, Vascular Biology Unit, Karolinska Institutet, Stockholm, Sweden; Department of Physiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Estonia
| | - Jef D Boeke
- Institute for Systems Genetics, NYU Langone Medical Center, New York, NY, USA
| | - Michael Boutros
- German Cancer Research Center (DKFZ) and Heidelberg University, Germany
| | | | - Aimée M Dudley
- Pacific Northwest Research Institute, Seattle, Washington, USA
| | - Charles R Farber
- Departments of Public Health Sciences and Biochemistry and Molecular Genetics, Center for Public Health Genomics, University of Virginia, Charlottesville VA, 22908, USA
| | - Allan Jones
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany
| | - Allan I Levey
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Aldons J Lusis
- Department of Medicine; Department of Microbiology, Immunology and Molecular Genetics; Department of Human Genetics, University of California Los Angeles, California, USA
| | - H Craig Mak
- Cell Systems, Cell Press, Cambridge, MA, USA.
| | - Joseph H Nadeau
- Pacific Northwest Research Institute, Seattle, Washington, USA
| | - Marcus B Noyes
- Institute for Systems Genetics, NYU Langone Medical Center, New York, NY, USA
| | - Enrico Petretto
- Cardiovascular & Metabolic Disorders Program and Centre for Computational Biology, Duke-National University of Singapore (NUS) Medical School, Singapore
| | - Nicholas T Seyfried
- Department of Biochemistry and Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Lars M Steinmetz
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany; Stanford Genome Technology Center, Stanford University, Palo Alto, CA 94304, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sibylle C Vonesch
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany
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Fay JC, Liu P, Ong GT, Dunham MJ, Cromie GA, Jeffery EW, Ludlow CL, Dudley AM. A polyploid admixed origin of beer yeasts derived from European and Asian wine populations. PLoS Biol 2019; 17:e3000147. [PMID: 30835725 PMCID: PMC6400334 DOI: 10.1371/journal.pbio.3000147] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 01/30/2019] [Indexed: 11/18/2022] Open
Abstract
Strains of Saccharomyces cerevisiae used to make beer, bread, and wine are genetically and phenotypically distinct from wild populations associated with trees. The origins of these domesticated populations are not always clear; human-associated migration and admixture with wild populations have had a strong impact on S. cerevisiae population structure. We examined the population genetic history of beer strains and found that ale strains and the S. cerevisiae portion of allotetraploid lager strains were derived from admixture between populations closely related to European grape wine strains and Asian rice wine strains. Similar to both lager and baking strains, ale strains are polyploid, providing them with a passive means of remaining isolated from other populations and providing us with a living relic of their ancestral hybridization. To reconstruct their polyploid origin, we phased the genomes of two ale strains and found ale haplotypes to both be recombinants between European and Asian alleles and to also contain novel alleles derived from extinct or as yet uncharacterized populations. We conclude that modern beer strains are the product of a historical melting pot of fermentation technology.
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Affiliation(s)
- Justin C. Fay
- Department of Biology, University of Rochester, Rochester, New York, United States of America
- Department of Genetics, Washington University, St. Louis, Missouri, United States of America
- * E-mail:
| | - Ping Liu
- Department of Genetics, Washington University, St. Louis, Missouri, United States of America
| | - Giang T. Ong
- Department of Genome Sciences, Seattle, Washington, United States of America
| | - Maitreya J. Dunham
- Department of Genome Sciences, Seattle, Washington, United States of America
| | - Gareth A. Cromie
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
| | - Eric W. Jeffery
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
| | - Catherine L. Ludlow
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
| | - Aimée M. Dudley
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
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11
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Jarosz DF, Dudley AM. Meeting Report on Experimental Approaches to Evolution and Ecology Using Yeast and Other Model Systems. G3 (Bethesda) 2017; 7:g3.300124.2017. [PMID: 28814445 PMCID: PMC5633374 DOI: 10.1534/g3.117.300124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 08/01/2017] [Indexed: 11/18/2022]
Abstract
The fourth EMBO-sponsored conference on Experimental Approaches to Evolution and Ecology Using Yeast and Other Model Systems (https://www.embl.de/training/events/2016/EAE16-01/), was held at the EMBL in Heidelberg, Germany, October 19-23, 2016. The conference was organized by Judith Berman (Tel Aviv University), Maitreya Dunham (University of Washington), Jun-Yi Leu (Academia Sinica), and Lars Steinmetz (EMBL Heidelberg and Stanford University). The meeting attracted ~120 researchers from 28 countries and covered a wide range of topics in the fields of genetics, evolutionary biology, and ecology with a unifying focus on yeast as a model system. Attendees enjoyed the Keith Haring inspired yeast florescence microscopy artwork (Figure 1), a unique feature of the meeting since its inception, and the one-minute flash talks that catalyzed discussions at two vibrant poster sessions. The meeting coincided with the 20th anniversary of the publication describing the sequence of the first eukaryotic genome, Saccharomyces cerevisiae (Goffeau et al. 1996). Many of the conference talks focused on important questions about what is contained in the genome, how genomes evolve, and the architecture and behavior of communities of phenotypically and genotypically diverse microorganisms. Here, we summarize highlights of the research talks around these themes. Nearly all presentations focused on novel findings, and we refer the reader to relevant manuscripts that have subsequently been published.
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Affiliation(s)
- Daniel F. Jarosz
- Department of Chemical and Systems Biology and
- Department of Developmental Biology, Stanford University, California 94305 and
| | - Aimée M. Dudley
- Pacific Northwest Research Institute, Seattle, Washington 98122
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12
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Cromie GA, Tan Z, Hays M, Sirr A, Jeffery EW, Dudley AM. Transcriptional Profiling of Biofilm Regulators Identified by an Overexpression Screen in Saccharomyces cerevisiae. G3 (Bethesda) 2017; 7:2845-2854. [PMID: 28673928 PMCID: PMC5555487 DOI: 10.1534/g3.117.042440] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Accepted: 06/27/2017] [Indexed: 12/25/2022]
Abstract
Biofilm formation by microorganisms is a major cause of recurring infections and removal of biofilms has proven to be extremely difficult given their inherent drug resistance . Understanding the biological processes that underlie biofilm formation is thus extremely important and could lead to the development of more effective drug therapies, resulting in better infection outcomes. Using the yeast Saccharomyces cerevisiae as a biofilm model, overexpression screens identified DIG1, SFL1, HEK2, TOS8, SAN1, and ROF1/YHR177W as regulators of biofilm formation. Subsequent RNA-seq analysis of biofilm and nonbiofilm-forming strains revealed that all of the overexpression strains, other than DIG1 and TOS8, were adopting a single differential expression profile, although induced to varying degrees. TOS8 adopted a separate profile, while the expression profile of DIG1 reflected the common pattern seen in most of the strains, plus substantial DIG1-specific expression changes. We interpret the existence of the common transcriptional pattern seen across multiple, unrelated overexpression strains as reflecting a transcriptional state, that the yeast cell can access through regulatory signaling mechanisms, allowing an adaptive morphological change between biofilm-forming and nonbiofilm states.
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Affiliation(s)
- Gareth A Cromie
- Pacific Northwest Research Institute, Seattle, Washington 98122
| | - Zhihao Tan
- Pacific Northwest Research Institute, Seattle, Washington 98122
- Institute of Medical Biology, Agency for Science, Technology and Research, Singapore 138648
| | - Michelle Hays
- Molecular and Cellular Biology Program, University of Washington, Seattle, Washington 98195
| | - Amy Sirr
- Pacific Northwest Research Institute, Seattle, Washington 98122
| | - Eric W Jeffery
- Pacific Northwest Research Institute, Seattle, Washington 98122
| | - Aimée M Dudley
- Pacific Northwest Research Institute, Seattle, Washington 98122
- Molecular and Cellular Biology Program, University of Washington, Seattle, Washington 98195
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13
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Ludlow CL, Cromie GA, Garmendia-Torres C, Sirr A, Hays M, Field C, Jeffery EW, Fay JC, Dudley AM. Independent Origins of Yeast Associated with Coffee and Cacao Fermentation. Curr Biol 2016; 26:965-71. [PMID: 27020745 DOI: 10.1016/j.cub.2016.02.012] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Revised: 01/30/2016] [Accepted: 02/03/2016] [Indexed: 01/01/2023]
Abstract
Modern transportation networks have facilitated the migration and mingling of previously isolated populations of plants, animals, and insects. Human activities can also influence the global distribution of microorganisms. The best-understood example is yeasts associated with winemaking. Humans began making wine in the Middle East over 9,000 years ago [1, 2]. Selecting favorable fermentation products created specialized strains of Saccharomyces cerevisiae [3, 4] that were transported along with grapevines. Today, S. cerevisiae strains residing in vineyards around the world are genetically similar, and their population structure suggests a common origin that followed the path of human migration [3-7]. Like wine, coffee and cacao depend on microbial fermentation [8, 9] and have been globally dispersed by humans. Theobroma cacao originated in the Amazon and Orinoco basins of Colombia and Venezuela [10], was cultivated in Central America by Mesoamerican peoples, and was introduced to Europeans by Hernán Cortés in 1530 [11]. Coffea, native to Ethiopia, was disseminated by Arab traders throughout the Middle East and North Africa in the 6(th) century and was introduced to European consumers in the 17(th) century [12]. Here, we tested whether the yeasts associated with coffee and cacao are genetically similar, crop-specific populations or genetically diverse, geography-specific populations. Our results uncovered populations that, while defined by niche and geography, also bear signatures of admixture between major populations in events independent of the transport of the plants. Thus, human-associated fermentation and migration may have affected the distribution of yeast involved in the production of coffee and chocolate.
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Affiliation(s)
| | - Gareth A Cromie
- Pacific Northwest Diabetes Research Institute, Seattle, WA 98122, USA
| | | | - Amy Sirr
- Pacific Northwest Diabetes Research Institute, Seattle, WA 98122, USA
| | - Michelle Hays
- Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Molecular and Cellular Biology Program, University of Washington, Seattle, WA 98195, USA
| | | | - Eric W Jeffery
- Pacific Northwest Diabetes Research Institute, Seattle, WA 98122, USA
| | - Justin C Fay
- Department of Genetics, Washington University, St. Louis, MO 63110, USA; Center for Genome Sciences and Systems Biology, Washington University, St. Louis, MO 63110, USA
| | - Aimée M Dudley
- Pacific Northwest Diabetes Research Institute, Seattle, WA 98122, USA; Molecular and Cellular Biology Program, University of Washington, Seattle, WA 98195, USA.
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Abstract
Individuals, and cells, vary in their ability to tolerate aneuploidy, an unbalanced chromosome complement. Tolerance mechanisms can be karyotype-specific or general. General tolerance mechanisms may allow cells to benefit from the phenotypic plasticity conferred by access to multiple aneuploid states.
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Affiliation(s)
- Gareth A Cromie
- Pacific Northwest Diabetes Research Institute, Seattle, WA 98122, USA
| | - Aimée M Dudley
- Pacific Northwest Diabetes Research Institute, Seattle, WA 98122, USA.
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15
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Sirr A, Cromie GA, Jeffery EW, Gilbert TL, Ludlow CL, Scott AC, Dudley AM. Allelic variation, aneuploidy, and nongenetic mechanisms suppress a monogenic trait in yeast. Genetics 2015. [PMID: 25398792 DOI: 10.1534/genetics.114.170563/-/dc1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Clinically relevant features of monogenic diseases, including severity of symptoms and age of onset, can vary widely in response to environmental differences as well as to the presence of genetic modifiers affecting the trait's penetrance and expressivity. While a better understanding of modifier loci could lead to treatments for Mendelian diseases, the rarity of individuals harboring both a disease-causing allele and a modifying genotype hinders their study in human populations. We examined the genetic architecture of monogenic trait modifiers using a well-characterized yeast model of the human Mendelian disease classic galactosemia. Yeast strains with loss-of-function mutations in the yeast ortholog (GAL7) of the human disease gene (GALT) fail to grow in the presence of even small amounts of galactose due to accumulation of the same toxic intermediates that poison human cells. To isolate and individually genotype large numbers of the very rare (∼0.1%) galactose-tolerant recombinant progeny from a cross between two gal7Δ parents, we developed a new method, called "FACS-QTL." FACS-QTL improves upon the currently used approaches of bulk segregant analysis and extreme QTL mapping by requiring less genome engineering and strain manipulation as well as maintaining individual genotype information. Our results identified multiple distinct solutions by which the monogenic trait could be suppressed, including genetic and nongenetic mechanisms as well as frequent aneuploidy. Taken together, our results imply that the modifiers of monogenic traits are likely to be genetically complex and heterogeneous.
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Affiliation(s)
- Amy Sirr
- Pacific Northwest Diabetes Research Institute, Seattle, Washington 98122
| | - Gareth A Cromie
- Pacific Northwest Diabetes Research Institute, Seattle, Washington 98122
| | - Eric W Jeffery
- Pacific Northwest Diabetes Research Institute, Seattle, Washington 98122
| | - Teresa L Gilbert
- Pacific Northwest Diabetes Research Institute, Seattle, Washington 98122
| | - Catherine L Ludlow
- Pacific Northwest Diabetes Research Institute, Seattle, Washington 98122
| | - Adrian C Scott
- Pacific Northwest Diabetes Research Institute, Seattle, Washington 98122
| | - Aimée M Dudley
- Pacific Northwest Diabetes Research Institute, Seattle, Washington 98122
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16
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Ruusuvuori P, Lin J, Scott AC, Tan Z, Sorsa S, Kallio A, Nykter M, Yli-Harja O, Shmulevich I, Dudley AM. Quantitative analysis of colony morphology in yeast. Biotechniques 2014; 56:18-27. [PMID: 24447135 DOI: 10.2144/000114123] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2013] [Accepted: 11/19/2013] [Indexed: 11/23/2022] Open
Abstract
Microorganisms often form multicellular structures such as biofilms and structured colonies that can influence the organism's virulence, drug resistance, and adherence to medical devices. Phenotypic classification of these structures has traditionally relied on qualitative scoring systems that limit detailed phenotypic comparisons between strains. Automated imaging and quantitative analysis have the potential to improve the speed and accuracy of experiments designed to study the genetic and molecular networks underlying different morphological traits. For this reason, we have developed a platform that uses automated image analysis and pattern recognition to quantify phenotypic signatures of yeast colonies. Our strategy enables quantitative analysis of individual colonies, measured at a single time point or over a series of time-lapse images, as well as the classification of distinct colony shapes based on image-derived features. Phenotypic changes in colony morphology can be expressed as changes in feature space trajectories over time, thereby enabling the visualization and quantitative analysis of morphological development. To facilitate data exploration, results are plotted dynamically through an interactive Yeast Image Analysis web application (YIMAA; http://yimaa.cs.tut.fi) that integrates the raw and processed images across all time points, allowing exploration of the image-based features and principal components associated with morphological development.
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Affiliation(s)
- Pekka Ruusuvuori
- Department of Signal Processing, Tampere University of Technology, Tampere, Finland; Institute for Systems Biology, Seattle, WA
| | - Jake Lin
- Department of Signal Processing, Tampere University of Technology, Tampere, Finland; Institute for Systems Biology, Seattle, WA; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg
| | - Adrian C Scott
- Pacific Northwest Diabetes Research Institute, Seattle, WA
| | - Zhihao Tan
- Pacific Northwest Diabetes Research Institute, Seattle, WA; Molecular and Cellular Biology Program, University of Washington, Seattle, WA
| | - Saija Sorsa
- Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Aleksi Kallio
- Institute of Biomedical Technology, University of Tampere, Tampere, Finland
| | - Matti Nykter
- Institute of Biomedical Technology, University of Tampere, Tampere, Finland
| | - Olli Yli-Harja
- Department of Signal Processing, Tampere University of Technology, Tampere, Finland; Institute for Systems Biology, Seattle, WA
| | - Ilya Shmulevich
- Department of Signal Processing, Tampere University of Technology, Tampere, Finland; Institute for Systems Biology, Seattle, WA
| | - Aimée M Dudley
- Pacific Northwest Diabetes Research Institute, Seattle, WA; Molecular and Cellular Biology Program, University of Washington, Seattle, WA
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17
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Garmendia-Torres C, Skupin A, Michael SA, Ruusuvuori P, Kuwada NJ, Falconnet D, Cary GA, Hansen C, Wiggins PA, Dudley AM. Unidirectional P-body transport during the yeast cell cycle. PLoS One 2014; 9:e99428. [PMID: 24918601 PMCID: PMC4053424 DOI: 10.1371/journal.pone.0099428] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2014] [Accepted: 05/12/2014] [Indexed: 12/29/2022] Open
Abstract
P-bodies belong to a large family of RNA granules that are associated with post-transcriptional gene regulation, conserved from yeast to mammals, and influence biological processes ranging from germ cell development to neuronal plasticity. RNA granules can also transport RNAs to specific locations. Germ granules transport maternal RNAs to the embryo, and neuronal granules transport RNAs long distances to the synaptic dendrites. Here we combine microfluidic-based fluorescent microscopy of single cells and automated image analysis to follow p-body dynamics during cell division in yeast. Our results demonstrate that these highly dynamic granules undergo a unidirectional transport from the mother to the daughter cell during mitosis as well as a constrained “hovering” near the bud site half an hour before the bud is observable. Both behaviors are dependent on the Myo4p/She2p RNA transport machinery. Furthermore, single cell analysis of cell size suggests that PBs play an important role in daughter cell growth under nutrient limiting conditions.
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Affiliation(s)
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- National Center for Microscopy and Imaging Research, University of California San Diego, La Jolla, California, United States of America
| | - Sean A. Michael
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Pekka Ruusuvuori
- Tampere University of Technology, Pori, Finland
- BioMediTech, University of Tampere, Tampere, Finland
| | - Nathan J. Kuwada
- Physics and Bioengineering, University of Washington, Seattle, Washington, United States of America
| | - Didier Falconnet
- Centre for High-Throughput Biology, Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Gregory A. Cary
- Institute for Systems Biology, Seattle, Washington, United States of America
- Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, United States of America
| | - Carl Hansen
- Centre for High-Throughput Biology, Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Paul A. Wiggins
- Physics and Bioengineering, University of Washington, Seattle, Washington, United States of America
| | - Aimée M. Dudley
- Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, United States of America
- Pacific Northwest Diabetes Research Institute, Seattle, Washington, United States of America
- * E-mail:
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Abstract
Tetrad analysis is a valuable tool for yeast genetics, but the laborious manual nature of the process has hindered its application on large scales. Barcode Enabled Sequencing of Tetrads (BEST)1 replaces the manual processes of isolating, disrupting and spacing tetrads. BEST isolates tetrads by virtue of a sporulation-specific GFP fusion protein that permits fluorescence-activated cell sorting of tetrads directly onto agar plates, where the ascus is enzymatically digested and the spores are disrupted and randomly arrayed by glass bead plating. The haploid colonies are then assigned sister spore relationships, i.e. information about which spores originated from the same tetrad, using molecular barcodes read during genotyping. By removing the bottleneck of manual dissection, hundreds or even thousands of tetrads can be isolated in minutes. Here we present a detailed description of the experimental procedures required to perform BEST in the yeast Saccharomyces cerevisiae, starting with a heterozygous diploid strain through the isolation of colonies derived from the haploid meiotic progeny.
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Cary GA, Yoon SH, Torres CG, Wang K, Hays M, Ludlow C, Goodlett DR, Dudley AM. Identification and characterization of a drug-sensitive strain enables puromycin-based translational assays in Saccharomyces cerevisiae. Yeast 2014; 31:167-78. [PMID: 24610064 DOI: 10.1002/yea.3007] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2013] [Revised: 02/27/2014] [Accepted: 03/03/2014] [Indexed: 11/11/2022] Open
Abstract
Puromycin is an aminonucleoside antibiotic with structural similarity to aminoacyl tRNA. This structure allows the drug to bind the ribosomal A site and incorporate into nascent polypeptides, causing chain termination, ribosomal subunit dissociation and widespread translational arrest at high concentrations. In contrast, at sufficiently low concentrations, puromycin incorporates primarily at the C-terminus of proteins. While a number of techniques utilize puromycin incorporation as a tool for probing translational activity in vivo, these methods cannot be applied in yeasts that are insensitive to puromycin. Here, we describe a mutant strain of the yeast Saccharomyces cerevisiae that is sensitive to puromycin and characterize the cellular response to the drug. Puromycin inhibits the growth of yeast cells mutant for erg6∆, pdr1∆ and pdr3∆ (EPP) on both solid and liquid media. Puromycin also induces the aggregation of the cytoplasmic processing body component Edc3 in the mutant strain. We establish that puromycin is rapidly incorporated into yeast proteins and test the effects of puromycin on translation in vivo. This study establishes the EPP strain as a valuable tool for implementing puromycin-based assays in yeast, which will enable new avenues of inquiry into protein production and maturation.
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Affiliation(s)
- Gregory A Cary
- Institute for Systems Biology, Seattle, WA, USA; Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
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20
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Lin J, Kreisberg R, Kallio A, Dudley AM, Nykter M, Shmulevich I, May P, Autio R. POMO--Plotting Omics analysis results for Multiple Organisms. BMC Genomics 2013; 14:918. [PMID: 24365393 PMCID: PMC3880012 DOI: 10.1186/1471-2164-14-918] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2013] [Accepted: 12/18/2013] [Indexed: 12/15/2022] Open
Abstract
Background Systems biology experiments studying different topics and organisms produce thousands of data values across different types of genomic data. Further, data mining analyses are yielding ranked and heterogeneous results and association networks distributed over the entire genome. The visualization of these results is often difficult and standalone web tools allowing for custom inputs and dynamic filtering are limited. Results We have developed POMO (http://pomo.cs.tut.fi), an interactive web-based application to visually explore omics data analysis results and associations in circular, network and grid views. The circular graph represents the chromosome lengths as perimeter segments, as a reference outer ring, such as cytoband for human. The inner arcs between nodes represent the uploaded network. Further, multiple annotation rings, for example depiction of gene copy number changes, can be uploaded as text files and represented as bar, histogram or heatmap rings. POMO has built-in references for human, mouse, nematode, fly, yeast, zebrafish, rice, tomato, Arabidopsis, and Escherichia coli. In addition, POMO provides custom options that allow integrated plotting of unsupported strains or closely related species associations, such as human and mouse orthologs or two yeast wild types, studied together within a single analysis. The web application also supports interactive label and weight filtering. Every iterative filtered result in POMO can be exported as image file and text file for sharing or direct future input. Conclusions The POMO web application is a unique tool for omics data analysis, which can be used to visualize and filter the genome-wide networks in the context of chromosomal locations as well as multiple network layouts. With the several illustration and filtering options the tool supports the analysis and visualization of any heterogeneous omics data analysis association results for many organisms. POMO is freely available and does not require any installation or registration.
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Affiliation(s)
- Jake Lin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg, Luxembourg.
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21
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Affiliation(s)
- Joseph H Nadeau
- Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA.
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22
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Šípová H, Zhang S, Dudley AM, Galas D, Wang K, Homola J. Surface plasmon resonance biosensor for rapid label-free detection of microribonucleic acid at subfemtomole level. Anal Chem 2010; 82:10110-5. [PMID: 21090631 PMCID: PMC3021551 DOI: 10.1021/ac102131s] [Citation(s) in RCA: 164] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Microribonucleic acids (miRNAs) have been linked with various regulatory functions and disorders, such as cancers and heart diseases. They, therefore, present an important target for detection technologies for future medical diagnostics. We report here a novel method for rapid and sensitive miRNA detection and quantitation using surface plasmon resonance (SPR) sensor technology and a DNA*RNA antibody-based assay. The approach takes advantage of a novel high-performance portable SPR sensor instrument for spectroscopy of surface plasmons based on a special diffraction grating called a surface plasmon coupler and disperser (SPRCD). The surface of the grating is functionalized with thiolated DNA oligonucleotides which specifically capture miRNA from a liquid sample without amplification. Subsequently, an antibody that recognizes DNA*RNA hybrids is introduced to bind to the DNA*RNA complex and enhance sensor response to the captured miRNA. This approach allows detection of miRNA in less than 30 min at concentrations down to 2 pM with an absolute amount at high attomoles. The methodology is evaluated for analysis of miRNA from mouse liver tissues and is found to yield results which agree well with those provided by the quantitative polymerase chain reaction (qPCR).
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Affiliation(s)
- Hana Šípová
- Institute of Photonics and Electronics, Academy of Sciences of the Czech Republic, Chaberská 57, 182 51 Prague, Czech Republic
| | - Shile Zhang
- Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA
| | - Aimée M. Dudley
- Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA
| | - David Galas
- Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA
| | - Kai Wang
- Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA
| | - Jiří Homola
- Institute of Photonics and Electronics, Academy of Sciences of the Czech Republic, Chaberská 57, 182 51 Prague, Czech Republic
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23
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Ruusuvuori P, Aijö T, Chowdhury S, Garmendia-Torres C, Selinummi J, Birbaumer M, Dudley AM, Pelkmans L, Yli-Harja O. Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images. BMC Bioinformatics 2010; 11:248. [PMID: 20465797 PMCID: PMC3098061 DOI: 10.1186/1471-2105-11-248] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2009] [Accepted: 05/13/2010] [Indexed: 11/30/2022] Open
Abstract
Background Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed. Results To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies. Conclusions These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.
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Affiliation(s)
- Pekka Ruusuvuori
- Department of Signal Processing, Tampere University of Technology, Tampere, 33101, Finland.
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24
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Lee SI, Dudley AM, Drubin D, Silver PA, Krogan NJ, Pe'er D, Koller D. Learning a prior on regulatory potential from eQTL data. PLoS Genet 2009; 5:e1000358. [PMID: 19180192 PMCID: PMC2627940 DOI: 10.1371/journal.pgen.1000358] [Citation(s) in RCA: 144] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2008] [Accepted: 12/29/2008] [Indexed: 11/19/2022] Open
Abstract
Genome-wide RNA expression data provide a detailed view of an organism's biological state; hence, a dataset measuring expression variation between genetically diverse individuals (eQTL data) may provide important insights into the genetics of complex traits. However, with data from a relatively small number of individuals, it is difficult to distinguish true causal polymorphisms from the large number of possibilities. The problem is particularly challenging in populations with significant linkage disequilibrium, where traits are often linked to large chromosomal regions containing many genes. Here, we present a novel method, Lirnet, that automatically learns a regulatory potential for each sequence polymorphism, estimating how likely it is to have a significant effect on gene expression. This regulatory potential is defined in terms of “regulatory features”—including the function of the gene and the conservation, type, and position of genetic polymorphisms—that are available for any organism. The extent to which the different features influence the regulatory potential is learned automatically, making Lirnet readily applicable to different datasets, organisms, and feature sets. We apply Lirnet both to the human HapMap eQTL dataset and to a yeast eQTL dataset and provide statistical and biological results demonstrating that Lirnet produces significantly better regulatory programs than other recent approaches. We demonstrate in the yeast data that Lirnet can correctly suggest a specific causal sequence variation within a large, linked chromosomal region. In one example, Lirnet uncovered a novel, experimentally validated connection between Puf3—a sequence-specific RNA binding protein—and P-bodies—cytoplasmic structures that regulate translation and RNA stability—as well as the particular causative polymorphism, a SNP in Mkt1, that induces the variation in the pathway. Gene expression data of genetically diverse individuals (eQTL data) provide a unique perspective on the effect of genetic variation on cellular pathways. However, the burden of multiple hypotheses, combined with the challenges of linkage disequilibrium, makes it difficult to correctly identify causal polymorphisms. Researchers traditionally apply heuristics for selecting among plausible hypotheses, favoring polymorphisms that are more conserved, that lead to significant amino acid change, or that reside in genes whose function is related to that of the targets. But how do we know how much weight to attribute to different regulatory features? We describe Lirnet, which learns from eQTL data how to weight regulatory features and induce a regulatory potential for sequence variations. Lirnet assesses these weights simultaneously to learning a regulatory network, finding weights that lead to a more predictive network. We show that Lirnet constructs high-accuracy regulatory programs and demonstrate its ability to correctly identify causative polymorphisms. Lirnet can flexibly use any regulatory features, including sequence features that are available for any sequenced organism, and automatically learn their weights in a dataset-specific way. This feature makes it especially advantageous for mammalian systems, where many forms of prior knowledge used in simple model organisms are incomplete or unavailable.
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Affiliation(s)
- Su-In Lee
- Computer Science Department, Stanford University, Stanford, California, United States of America
| | - Aimée M. Dudley
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - David Drubin
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Pamela A. Silver
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Nevan J. Krogan
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California, United States of America
| | - Dana Pe'er
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
| | - Daphne Koller
- Computer Science Department, Stanford University, Stanford, California, United States of America
- * E-mail:
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Snitkin ES, Dudley AM, Janse DM, Wong K, Church GM, Segrè D. Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions. Genome Biol 2008; 9:R140. [PMID: 18808699 PMCID: PMC2592718 DOI: 10.1186/gb-2008-9-9-r140] [Citation(s) in RCA: 45] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2008] [Revised: 09/01/2008] [Accepted: 09/22/2008] [Indexed: 11/22/2022] Open
Abstract
An iterative approach that integrates high-throughput measurements of yeast deletion mutants and flux balance model predictions improves understanding of both experimental and computational results. Background Understanding the response of complex biochemical networks to genetic perturbations and environmental variability is a fundamental challenge in biology. Integration of high-throughput experimental assays and genome-scale computational methods is likely to produce insight otherwise unreachable, but specific examples of such integration have only begun to be explored. Results In this study, we measured growth phenotypes of 465 Saccharomyces cerevisiae gene deletion mutants under 16 metabolically relevant conditions and integrated them with the corresponding flux balance model predictions. We first used discordance between experimental results and model predictions to guide a stage of experimental refinement, which resulted in a significant improvement in the quality of the experimental data. Next, we used discordance still present in the refined experimental data to assess the reliability of yeast metabolism models under different conditions. In addition to estimating predictive capacity based on growth phenotypes, we sought to explain these discordances by examining predicted flux distributions visualized through a new, freely available platform. This analysis led to insight into the glycerol utilization pathway and the potential effects of metabolic shortcuts on model results. Finally, we used model predictions and experimental data to discriminate between alternative raffinose catabolism routes. Conclusions Our study demonstrates how a new level of integration between high throughput measurements and flux balance model predictions can improve understanding of both experimental and computational results. The added value of a joint analysis is a more reliable platform for specific testing of biological hypotheses, such as the catabolic routes of different carbon sources.
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Affiliation(s)
- Evan S Snitkin
- Bioinformatics graduate Program, Boston University, Boston, MA 02215, USA.
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Lee SI, Pe'er D, Dudley AM, Church GM, Koller D. Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification. Proc Natl Acad Sci U S A 2006; 103:14062-7. [PMID: 16968785 PMCID: PMC1599912 DOI: 10.1073/pnas.0601852103] [Citation(s) in RCA: 115] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Sequence polymorphisms affect gene expression by perturbing the complex network of regulatory interactions. We propose a probabilistic method, called Geronemo, which directly aims to identify the mechanism by which genetic changes perturb the regulatory network. Geronemo automatically constructs a set of coregulated genes (modules), whose regulation can involve both sequence variations and expression of regulators. By exploiting the modularity of genetic regulatory systems, Geronemo reveals regulatory relationships that are indiscernible when genes are considered in isolation, allowing the recovery of intricate combinatorial regulation. By incorporating both expression and genotype of regulators, Geronemo captures cases where the effect of sequence variation on its targets is indirect. We applied Geronemo to a data set from the progeny generated by a cross between laboratory BY4716 (BY) and wild RM11-1a (RM) isolates of Saccharomyces cerevisiae. Geronemo produced previously undescribed hypotheses regarding genetic perturbations in the yeast regulatory network, including transcriptional regulation, signal transduction, and chromatin modification. In particular, we find a large number of modules that have both chromosomal characteristics and are regulated by chromatin modification proteins. Indeed, a large fraction of the variance in the expression can be explained by a small number of markers associated with chromatin modifiers. Additional analysis reveals positive selection for sequence evolution of elements in the Swi/Snf chromatin remodeling complex. Overall, our results suggest that a significant part of individual expression variation in yeast arises from evolution of a small number of chromatin structure modifiers.
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Affiliation(s)
- Su-In Lee
- *Department of Computer Science, Stanford University, Stanford, CA 94305-9010; and
| | - Dana Pe'er
- Department of Genetics, Harvard Medical School, Boston, MA 02115
| | - Aimée M. Dudley
- Department of Genetics, Harvard Medical School, Boston, MA 02115
| | - George M. Church
- Department of Genetics, Harvard Medical School, Boston, MA 02115
| | - Daphne Koller
- *Department of Computer Science, Stanford University, Stanford, CA 94305-9010; and
- To whom correspondence should be addressed. E-mail:
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Abstract
MOTIVATION Predicting the outcome of specific experiments (such as the growth of a particular mutant strain in a particular medium) has the potential to allow researchers to devote resources to experiments with higher expected numbers of 'hits'. RESULTS We use decision trees to predict phenotypes associated with Saccharomyces cerevisiae genes on the basis of Gene Ontology (GO) functional annotations from the Saccharomyces Genome Database (SGD) and other phenotypic annotations from the Yeast Phenotype Catalog at the Munich Information Center for Protein Sequences (MIPS). We assess the methodology in three ways: (1) we use cross-validation on the phenotypic annotations listed in MIPS, and show ROC curves indicating the tradeoff between true-positive rate and false-positive rate; (2) we do a literature-search for 100 of the predicted gene-phenotype associations that are not listed in MIPS, and find evidence for 43 of them; (3) we use deletion strains to experimentally assess 61 predicted gene-phenotype associations not listed in MIPS; significantly more of these deletion strains show abnormal growth than would be expected by chance.
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Affiliation(s)
- Oliver D King
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue, Boston, Massachusetts, 02115, USA
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Dudley AM, Aach J, Steffen MA, Church GM. Measuring absolute expression with microarrays with a calibrated reference sample and an extended signal intensity range. Proc Natl Acad Sci U S A 2002; 99:7554-9. [PMID: 12032321 PMCID: PMC124281 DOI: 10.1073/pnas.112683499] [Citation(s) in RCA: 199] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Gene expression ratios derived from spotted-glass microarray experiments have become invaluable to researchers by providing sensitive and comprehensive indicators of the molecular underpinnings of cell behaviors and states. However, several drawbacks to this form of data have been noted, including the inability to relate ratios to absolute expression levels or to compare experimental conditions not measured with the same control. In this study we demonstrate a method for overcoming these obstacles. First, instead of cohybridizing labeled experimental and control samples, we hybridize each sample against labeled oligos complementary to every microarray feature. Ratios between sample intensities and intensities of the oligo reference measure sample RNA levels on a scale that relates to their absolute abundance, instead of to the variable and unknown abundances of a cDNA reference. We demonstrate that results from this type of hybridization are accurate and retain absolute abundance information far better than conventional microarray ratios. Next, to ensure the accurate measurement of sample and oligo reference intensities, which may differ by several orders of magnitude, we use a linear regression algorithm, implemented in a freely available PERL script, to combine the linear ranges of multiple scans taken at different scanner sensitivity settings onto an extended linear scale. We discuss future applications of our method to measure RNA expression on the absolute scale of number of transcripts per cell from any organism for which oligo-based spotted-glass microarrays are available.
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Affiliation(s)
- Aimée M Dudley
- Department of Genetics, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
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Abstract
The SAGA complex of Saccharomyces cerevisiae is required for the transcription of many RNA polymerase II-dependent genes. Previous studies have demonstrated that SAGA possesses histone acetyltransferase activity, catalyzed by the SAGA component Gcn5. However, the transcription of many genes, although SAGA dependent, is Gcn5 independent, suggesting the existence of distinct SAGA activities. We have studied the in vivo role of two other SAGA components, Spt3 and Spt20, at the well-characterized GAL1 promoter. Our results demonstrate that both Spt3 and Spt20 are required for the binding of TATA-binding protein but not of the activator Gal4 and that this role is Gcn5 independent. These results suggest a coactivator role for Spt3 and Spt20 in the recruitment of TBP.
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Affiliation(s)
- A M Dudley
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
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Dudley AM, Gansheroff LJ, Winston F. Specific components of the SAGA complex are required for Gcn4- and Gcr1-mediated activation of the his4-912delta promoter in Saccharomyces cerevisiae. Genetics 1999; 151:1365-78. [PMID: 10101163 PMCID: PMC1460567 DOI: 10.1093/genetics/151.4.1365] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Mutations selected as suppressors of Ty or solo delta insertion mutations in Saccharomyces cerevisiae have identified several genes, SPT3, SPT7, SPT8, and SPT20, that encode components of the SAGA complex. However, the mechanism by which SAGA activates transcription of specific RNA polymerase II-dependent genes is unknown. We have conducted a fine-structure mutagenesis of one widely used SAGA-dependent promoter, the delta element of his4-912delta, to identify sequence elements important for its promoter activity. Our analysis has characterized three delta regions necessary for full promoter activity and accurate start site selection: an upstream activating sequence, a TATA region, and an initiator region. In addition, we have shown that factors present at the adjacent UASHIS4 (Gcn4, Bas1, and Pho2) also activate the delta promoter in his4-912delta. Our results suggest a model in which the delta promoter in his4-912delta is primarily activated by two factors: Gcr1 acting at the UASdelta and Gcn4 acting at the UASHIS4. Finally, we tested whether activation by either of these factors is dependent on components of the SAGA complex. Our results demonstrate that Spt3 and Spt20 are required for full delta promoter activity, but that Gcn5, another member of SAGA, is not required. Spt3 appears to be partially required for activation of his4-912delta by both Gcr1 and Gcn4. Thus, our work suggests that SAGA exerts a large effect on delta promoter activity through a combination of smaller effects on multiple factors.
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Affiliation(s)
- A M Dudley
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
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Madison JM, Dudley AM, Winston F. Identification and analysis of Mot3, a zinc finger protein that binds to the retrotransposon Ty long terminal repeat (delta) in Saccharomyces cerevisiae. Mol Cell Biol 1998; 18:1879-90. [PMID: 9528759 PMCID: PMC121417 DOI: 10.1128/mcb.18.4.1879] [Citation(s) in RCA: 22] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/1997] [Accepted: 01/06/1998] [Indexed: 02/07/2023] Open
Abstract
Spt3 and Mot1 are two transcription factors of Saccharomyces cerevisiae that are thought to act in a related fashion to control the function of TATA-binding protein (TBP). Current models suggest that while Spt3 and Mot1 do not directly interact, they do function in a related fashion to stabilize the TBP-TATA interaction at particular promoters. Consistent with this model, certain combinations of spt3 and mot1 mutations are inviable. To identify additional proteins related to Spt3 and Mot1 functions, we screened for high-copy-number suppressors of the mot1 spt3 inviability. This screen identified a previously unstudied gene, MOT3, that encodes a zinc finger protein. We show that Mot3 binds in vitro to three sites within the retrotransposon Ty long terminal repeat (delta) sequence. One of these sites is immediately 5' of the delta TATA region. Although a mot3 null mutation causes no strong phenotypes, it does cause some mild phenotypes, including a very modest increase in Ty mRNA levels, partial suppression of transcriptional defects caused by a mot1 mutation, and partial suppression of an spt3 mutation. These results, in conjunction with those of an independent study of Mot3 (A. Grishin, M. Rothenberg, M. A. Downs, and K. J. Blumer, Genetics, in press), suggest that this protein plays a varied role in gene expression that may be largely redundant with other factors.
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Affiliation(s)
- J M Madison
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
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Abstract
An in vivo expression system has been developed for controlling the transcription of individual genes in the mitochondrial genome of Saccharomyces cerevisiae. The bacteriophage T7 RNA polymerase (T7Pol), fused to the COXIV mitchondrial import peptide and expressed under the control of either the GAL1 or the ADH1 promoter, efficiently transcribes a target gene, T7-COX2, in the mitochondrial genome. Cells bearing the T7-COX2 gene, but lacking wild-type COX2, require T7Pol for respiration. Functional expression of T7-COX2 is completely dependent on the COX2-specific translational activator Pet111p, despite additional nucleotides at the 5' end of the T7-COX2 transcript. Expression of mitochondrion-targeted T7Pol at high levels from the GAL1 promoter has no detectable effect on mitochondrial function in rho+ cells lacking the T7-COX2 target gene, but in cells with T7-COX2 integrated into the mitochondrial genome, an equivalent level of T7Pol expression causes severe respiratory deficiency. In comparison with wild-type COX2 expression, steady-state levels of T7-COX2 mRNA increase fivefold when transcription is driven by T7Pol expressed from the ADH1 promoter, yet COXII protein levels and cellular respiration rates decrease by about 50%. This discoordinate expression of mRNA and protein provides additional evidence for posttranscriptional control of COX2 expression.
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Affiliation(s)
- J L Pinkham
- Department of Biochemistry and Molecular Biology, University of Massachusetts, Amherst 01003-4505
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