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Fungal functional genomics: tunable knockout-knock-in expression and tagging strategies.

ABSTRACT: Strategies for promoting high-efficiency homologous gene replacement have been developed and adopted for many filamentous fungal species. The next generation of analysis requires the ability to manipulate gene expression and to tag genes expressed from their endogenous loci. Here we present a suite of molecular tools that provide versatile solutions for fungal high-throughput functional genomics studies based on locus-specific modification of any target gene. Additionally, case studies illustrate caveats to presumed overexpression constructs. A tunable expression system and different tagging strategies can provide valuable phenotypic information for uncharacterized genes and facilitate the analysis of essential loci, an emerging problem in systematic deletion studies of haploid organisms.

SUBMITTER: Larrondo LF

PROVIDER: S-EPMC2681610 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature

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Fungal functional genomics: tunable knockout-knock-in expression and tagging strategies.

Larrondo Luis F LF Colot Hildur V HV Baker Christopher L CL Loros Jennifer J JJ Dunlap Jay C JC

Eukaryotic cell 20090313 5

Strategies for promoting high-efficiency homologous gene replacement have been developed and adopted for many filamentous fungal species. The next generation of analysis requires the ability to manipulate gene expression and to tag genes expressed from their endogenous loci. Here we present a suite of molecular tools that provide versatile solutions for fungal high-throughput functional genomics studies based on locus-specific modification of any target gene. Additionally, case studies illustrat ...[more]

PMID: 19286985

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Project description:BackgroundThe discovery of breast cancer subtypes and subsequent development of treatments aimed at them has allowed for a great reduction in the mortality of breast cancer. But despite this progress, tumors with similar characteristics that belong to the same subtype continue to respond differently to the same treatment. Five subtypes of breast cancer, namely intrinsic subtypes, have been characterized to date based on their gene expression profiles. Among other characteristics, subtypes vary in their degree of intra-subtype heterogeneity. It is not clear, however, whether this heterogeneity is shared across all tumor traits. It is also unclear whether individual traits can be highly heterogeneous among a majority of homogeneous traits.ResultsWe employ network theory to uncover gene modules and accordingly consider them as tumor traits, which capture shared biological processes among the subtypes. We then use the β-diversity metric from ecology to quantify the heterogeneity in these gene modules. In doing so, we show that breast cancer heterogeneity is contained in gene modules and that this modular heterogeneity increases monotonically across the subtypes. We identify a core of two modules that are shared among all subtypes which contain nucleosome assembly and mammary morphogenesis genes, and a number of modules that are specific to subtypes. This modular heterogeneity, which increases with global heterogeneity, relates to tumor aggressiveness. Indeed, we observe that Luminal A, the most treatable of subtypes, has the lowest modular heterogeneity whereas the Basal-like subtype, which is among the hardest to treat, has the highest. Furthermore, our analysis shows that a higher degree of global heterogeneity does not imply higher heterogeneity for all modules, as Luminal B shows the highest heterogeneity for core modules.ConclusionsOverall, modular heterogeneity provides a framework with which to dissect cancer heterogeneity and better understand its underpinnings, thereby ultimately advancing our knowledge towards a more effective personalized cancer therapy.

Dataset Information

Fungal functional genomics: tunable knockout-knock-in expression and tagging strategies.

Publications

Fungal functional genomics: tunable knockout-knock-in expression and tagging strategies.

Similar Datasets

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