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Dissecting dynamic genetic variation that controls temporal gene response in yeast.

Brodt A, Botzman M, David E, Gat-Viks I - PLoS Comput. Biol. (2014)

Bottom Line: Here we develop a computational procedure that captures temporal changes in genetic effects, and apply it to analyze transcription during inhibition of the TOR signaling pathway in segregating yeast cells.We found a high-order coordination of gene modules: sets of genes co-associated with the same genetic variant and sharing a common temporal genetic effect pattern.Our analysis suggests that the same mechanism typically leads to both inter-individual variation and the temporal genetic effect pattern in a module.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell Research and Immunology, Tel Aviv University, Tel Aviv, Israel.

ABSTRACT
Inter-individual variation in regulatory circuits controlling gene expression is a powerful source of functional information. The study of associations among genetic variants and gene expression provides important insights about cell circuitry but cannot specify whether and when potential variants dynamically alter their genetic effect during the course of response. Here we develop a computational procedure that captures temporal changes in genetic effects, and apply it to analyze transcription during inhibition of the TOR signaling pathway in segregating yeast cells. We found a high-order coordination of gene modules: sets of genes co-associated with the same genetic variant and sharing a common temporal genetic effect pattern. The temporal genetic effects of some modules represented a single state-transitioning pattern; for example, at 10-30 minutes following stimulation, genetic effects in the phosphate utilization module attained a characteristic transition to a new steady state. In contrast, another module showed an impulse pattern of genetic effects; for example, in the poor nitrogen sources utilization module, a spike up of a genetic effect at 10-20 minutes following stimulation reflected inter-individual variation in the timing (rather than magnitude) of response. Our analysis suggests that the same mechanism typically leads to both inter-individual variation and the temporal genetic effect pattern in a module. Our methodology provides a quantitative genetic approach to studying the molecular mechanisms that shape dynamic changes in transcriptional responses.

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A catalogue of dynamic, non-linear genetic effects in gene response following rapamycin treatment in yeast.(A) Genetic effect profiles (left) and gene expression profiles (right) at six time points following rapamycin treatment (columns) for all genes identified by DyVER. Genetic effect values are the average increase (red) or decrease (purple) in effect size relative to non-stimulated cells (log-scaled). Gene expression values are the average increase (blue) or decrease (green) in gene expression relative to non-stimulated cells (log-scaled). Cis-associated genes are marked in gray (left color bar). Genes are partitioned into seven groups (C1–C7) based on their temporal two-state model (two state cartoons, shown as Fig. 2c, right; four singleton genes are omitted). (B) Four temporal two-state model groups C1, C2, C5, C7 (top to bottom). Left and middle panels: representative genes in each group. Left: gene expression of a representative gene (y-axis, log-scaled) across time points (x-axis). Each curve represents a different segregant, color coded by the best genetic variant found using DyVER (BY/black, RM/brown). Middle: genetic effect profiles of the representative gene, averaged across strains (log-scaled, y-axis) at each time point (x-axis). Right: shown are mean genetic effects (relative to non-stimulated cells, log-scaled; y-axis) and standard deviation (error bars) across time points (x-axis) for a certain group of genes.
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pcbi-1003984-g004: A catalogue of dynamic, non-linear genetic effects in gene response following rapamycin treatment in yeast.(A) Genetic effect profiles (left) and gene expression profiles (right) at six time points following rapamycin treatment (columns) for all genes identified by DyVER. Genetic effect values are the average increase (red) or decrease (purple) in effect size relative to non-stimulated cells (log-scaled). Gene expression values are the average increase (blue) or decrease (green) in gene expression relative to non-stimulated cells (log-scaled). Cis-associated genes are marked in gray (left color bar). Genes are partitioned into seven groups (C1–C7) based on their temporal two-state model (two state cartoons, shown as Fig. 2c, right; four singleton genes are omitted). (B) Four temporal two-state model groups C1, C2, C5, C7 (top to bottom). Left and middle panels: representative genes in each group. Left: gene expression of a representative gene (y-axis, log-scaled) across time points (x-axis). Each curve represents a different segregant, color coded by the best genetic variant found using DyVER (BY/black, RM/brown). Middle: genetic effect profiles of the representative gene, averaged across strains (log-scaled, y-axis) at each time point (x-axis). Right: shown are mean genetic effects (relative to non-stimulated cells, log-scaled; y-axis) and standard deviation (error bars) across time points (x-axis) for a certain group of genes.

Mentions: We applied DyVER in an unbiased manner (without penalty) to the available dataset of 95 yeast segregants that were stimulated by rapamycin and profiled at six time points (Methods) [27]. DyVER predicted 351 associations to 145 distinct variants (false discovery rate [FDR] 6%). Of these 351 associations, 145 had highly significant dynamic associations (15% FDR, Table S1, Methods) and 105 of them showed non-linear genetic effect patterns (Fig. 4). In agreement with previous findings [2], [11], our results suggest that non-linear associations are prevalent: of the eight previously known causal genes, six were found to have an association with at least one target gene exhibiting a non-linear genetic effect pattern (Table S2). Correlations among genetic effects of consecutive time points were much larger than correlations between non-consecutive time points [P value <10−15 (Wilcoxon test)], justifying our ‘memoryless’ Markov assumption that the next time point is mainly dependent on the current time point (Figure S7). The 105 genes carrying non-linear effect patterns were partitioned into groups based on their predicted two-state pattern (Table S1); seven two-state pattern groups (C1–C7) were created, each including at least two genes (Fig. 4A and B).


Dissecting dynamic genetic variation that controls temporal gene response in yeast.

Brodt A, Botzman M, David E, Gat-Viks I - PLoS Comput. Biol. (2014)

A catalogue of dynamic, non-linear genetic effects in gene response following rapamycin treatment in yeast.(A) Genetic effect profiles (left) and gene expression profiles (right) at six time points following rapamycin treatment (columns) for all genes identified by DyVER. Genetic effect values are the average increase (red) or decrease (purple) in effect size relative to non-stimulated cells (log-scaled). Gene expression values are the average increase (blue) or decrease (green) in gene expression relative to non-stimulated cells (log-scaled). Cis-associated genes are marked in gray (left color bar). Genes are partitioned into seven groups (C1–C7) based on their temporal two-state model (two state cartoons, shown as Fig. 2c, right; four singleton genes are omitted). (B) Four temporal two-state model groups C1, C2, C5, C7 (top to bottom). Left and middle panels: representative genes in each group. Left: gene expression of a representative gene (y-axis, log-scaled) across time points (x-axis). Each curve represents a different segregant, color coded by the best genetic variant found using DyVER (BY/black, RM/brown). Middle: genetic effect profiles of the representative gene, averaged across strains (log-scaled, y-axis) at each time point (x-axis). Right: shown are mean genetic effects (relative to non-stimulated cells, log-scaled; y-axis) and standard deviation (error bars) across time points (x-axis) for a certain group of genes.
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4256076&req=5

pcbi-1003984-g004: A catalogue of dynamic, non-linear genetic effects in gene response following rapamycin treatment in yeast.(A) Genetic effect profiles (left) and gene expression profiles (right) at six time points following rapamycin treatment (columns) for all genes identified by DyVER. Genetic effect values are the average increase (red) or decrease (purple) in effect size relative to non-stimulated cells (log-scaled). Gene expression values are the average increase (blue) or decrease (green) in gene expression relative to non-stimulated cells (log-scaled). Cis-associated genes are marked in gray (left color bar). Genes are partitioned into seven groups (C1–C7) based on their temporal two-state model (two state cartoons, shown as Fig. 2c, right; four singleton genes are omitted). (B) Four temporal two-state model groups C1, C2, C5, C7 (top to bottom). Left and middle panels: representative genes in each group. Left: gene expression of a representative gene (y-axis, log-scaled) across time points (x-axis). Each curve represents a different segregant, color coded by the best genetic variant found using DyVER (BY/black, RM/brown). Middle: genetic effect profiles of the representative gene, averaged across strains (log-scaled, y-axis) at each time point (x-axis). Right: shown are mean genetic effects (relative to non-stimulated cells, log-scaled; y-axis) and standard deviation (error bars) across time points (x-axis) for a certain group of genes.
Mentions: We applied DyVER in an unbiased manner (without penalty) to the available dataset of 95 yeast segregants that were stimulated by rapamycin and profiled at six time points (Methods) [27]. DyVER predicted 351 associations to 145 distinct variants (false discovery rate [FDR] 6%). Of these 351 associations, 145 had highly significant dynamic associations (15% FDR, Table S1, Methods) and 105 of them showed non-linear genetic effect patterns (Fig. 4). In agreement with previous findings [2], [11], our results suggest that non-linear associations are prevalent: of the eight previously known causal genes, six were found to have an association with at least one target gene exhibiting a non-linear genetic effect pattern (Table S2). Correlations among genetic effects of consecutive time points were much larger than correlations between non-consecutive time points [P value <10−15 (Wilcoxon test)], justifying our ‘memoryless’ Markov assumption that the next time point is mainly dependent on the current time point (Figure S7). The 105 genes carrying non-linear effect patterns were partitioned into groups based on their predicted two-state pattern (Table S1); seven two-state pattern groups (C1–C7) were created, each including at least two genes (Fig. 4A and B).

Bottom Line: Here we develop a computational procedure that captures temporal changes in genetic effects, and apply it to analyze transcription during inhibition of the TOR signaling pathway in segregating yeast cells.We found a high-order coordination of gene modules: sets of genes co-associated with the same genetic variant and sharing a common temporal genetic effect pattern.Our analysis suggests that the same mechanism typically leads to both inter-individual variation and the temporal genetic effect pattern in a module.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell Research and Immunology, Tel Aviv University, Tel Aviv, Israel.

ABSTRACT
Inter-individual variation in regulatory circuits controlling gene expression is a powerful source of functional information. The study of associations among genetic variants and gene expression provides important insights about cell circuitry but cannot specify whether and when potential variants dynamically alter their genetic effect during the course of response. Here we develop a computational procedure that captures temporal changes in genetic effects, and apply it to analyze transcription during inhibition of the TOR signaling pathway in segregating yeast cells. We found a high-order coordination of gene modules: sets of genes co-associated with the same genetic variant and sharing a common temporal genetic effect pattern. The temporal genetic effects of some modules represented a single state-transitioning pattern; for example, at 10-30 minutes following stimulation, genetic effects in the phosphate utilization module attained a characteristic transition to a new steady state. In contrast, another module showed an impulse pattern of genetic effects; for example, in the poor nitrogen sources utilization module, a spike up of a genetic effect at 10-20 minutes following stimulation reflected inter-individual variation in the timing (rather than magnitude) of response. Our analysis suggests that the same mechanism typically leads to both inter-individual variation and the temporal genetic effect pattern in a module. Our methodology provides a quantitative genetic approach to studying the molecular mechanisms that shape dynamic changes in transcriptional responses.

Show MeSH