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Drees, Becky; Thórsson, Vésteinn; Carter, Gregory W.; Rives, Alexander; Raymond, Marisa Z; Avila-Campillo, Iliana; Shannon, Paul; Galitski, Timothy

doi:10.1186/gb-2005-6-4-r38

Cited by 114 publications

(101 citation statements)

References 29 publications

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“…Optical density measurements (St Onge et al, 2007), biomass quantification analysis termed flux balance analysis (FBA) (Segre et al, 2005), quantitative phenotype (Drees et al, 2005) and gene expression data (Van Driessche et al, 2005) have also been employed to map GIs in specific biological processes. However, these studies remain restricted in terms of genome coverage.…”

Section: Mapping Genetic Interactomes In Model Organismsmentioning

confidence: 99%

Genetic interaction networks: better understand to better predict

Boucher

Jenna

2013

Front. Genet.

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A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances.

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Section: Mapping Genetic Interactomes In Model Organismsmentioning

confidence: 99%

Genetic interaction networks: better understand to better predict

Boucher

Jenna

2013

Front. Genet.

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“…Other models such as the log-multiplicative model 61 for studying epistasis can be applied to evaluate the genetic interactions between miRNAs. For further analysis, our data are available upon request.…”

Section: Methodsmentioning

confidence: 99%

Massively parallel high-order combinatorial genetics in human cells

et al. 2015

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The systematic functional analysis of combinatorial genetics has been limited by the throughput that can be achieved and the order of complexity that can be studied. To enable massively parallel characterization of genetic combinations in human cells, we developed a technology for rapid, scalable assembly of high-order barcoded combinatorial genetic libraries that can be quantified with high-throughput sequencing. We applied this technology, combinatorial genetics en masse (CombiGEM), to create high-coverage libraries of 1,521 two-wise and 51,770 three-wise barcoded combinations of 39 human microRNA (miRNA) precursors. We identified miRNA combinations that synergistically sensitize drug-resistant cancer cells to chemotherapy and/or inhibit cancer cell proliferation, providing insights into complex miRNA networks. More broadly, our method will enable high-throughput profiling of multifactorial genetic combinations that regulate phenotypes of relevance to biomedicine, biotechnology and basic science.Complex biological traits are regulated by the concerted action of combinatorial gene sets 1 . For example, multiple genetic factors are needed to reprogram somatic cells into induced pluripotent stem cells or distinct cell types 2 . Combinatorial drug therapies can achieve enhanced efficacy over conventional monotherapies, because targeting multiple pathways can be synergistic 3 . Furthermore, although genomewide association studies have implicated multiple individual loci in multifactorial human diseases, these loci can explain only a minor Reprints and permissions information is available online at

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“…288 homozygous diploid offspring were generated by sporulation followed by autodiploidization. Traits assessed include: A) resistance to the antifungal drug fluconazole (MIC 50 as determined in microtiter plates); B) a measure of colony biofilm complexity (Granek et al, 2013); C) a measure of invasive growth on agar substrates (logarithm of the ratio of post-wash to pre-wash colony density; (Drees et al, 2005)); and D) a measure of growth at high temperature on agar plates (square root of the mean spot density for three replicates of each segregant grown at 42°C). The red arrow in each subfigure indicates the phenotype of the parental strain YJM311.…”

Section: Figmentioning

confidence: 99%

Revisiting Mortimer’s Genome Renewal Hypothesis: Heterozygosity, Homothallism, and the Potential for Adaptation in Yeast

Magwene

2013

Advances in Experimental Medicine and Biology

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In diploid organisms, the frequency and nature of sexual cycles have a major impact on genome-wide patterns of heterozygosity. Recent population genomic surveys in the budding yeast, Saccharomyces cerevisiae, have revealed surprising levels of genomic heterozygosity in what has been traditionally considered a highly inbred organism. I review evidence and hypotheses regarding the generation, maintenance, and evolutionary consequences of genomic heterozygosity in S. cerevisiae. I propose that high levels of heterozygosity in S. cerevisiae, arising from population admixture due to human domestication, coupled with selfing during rare sexual cycles, can facilitate rapid adaptation to novel environments.

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Untitled

Cited by 114 publications

References 29 publications

Genetic interaction networks: better understand to better predict

Genetic interaction networks: better understand to better predict

Massively parallel high-order combinatorial genetics in human cells

Revisiting Mortimer’s Genome Renewal Hypothesis: Heterozygosity, Homothallism, and the Potential for Adaptation in Yeast

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