CRISPR/Cas systems in archaea: What array spacers can teach us about parasitism and gene exchange in the 3rd domain of life

CRISPR (Clustered, Regularly, Interspaced, Short, Palindromic Repeats) loci have been shown to provide prokaryotes with an adaptive immunity against viruses and plasmids. CRISPR arrays are transcribed and processed into small CRISPR RNA molecules, which base-pair with invading DNA or RNA and lead to its degradation by CRISPR-associated (Cas) protein complexes. New spacers can be acquired by active CRISPR/Cas systems, and thus the sequences of these spacers provide a record of the past “infection history” of the organism. Recently we used spacer sequences from archaeal genomes to infer gene exchange events among archaeal species and genera and to demonstrate that at least in this domain of life CRISPR indeed has an anti-viral role

POEM: Identifying Joint Additive Effects on Regulatory Circuits

Motivation: Expression Quantitative Trait Locus (eQTL) mapping tackles the problem of identifying variation in DNA sequence that have an effect on the transcriptional regulatory network. Major computational efforts are aimed at characterizing the joint effects of several eQTLs acting in concert to govern the expression of the same genes. Yet, progress towards a comprehensive prediction of such joint effects is limited. For example, existing eQTL methods commonly discover interacting loci affecting the expression levels of a module of co-regulated genes. Such ‘modularization’ approaches, however, are focused on epistatic relations and thus have limited utility for the case of additive (non-epistatic) effects.Results: Here we present POEM (Pairwise effect On Expression Modules), a methodology for identifying pairwise eQTL effects on gene modules. POEM is specifically designed to achieve high performance in the case of additive joint effects. We applied POEM to transcription profiles measured in bone marrow-derived dendritic cells across a population of genotyped mice. Our study reveals widespread additive, trans-acting pairwise effects on gene modules, characterizes their organizational principles, and highlights high-order interconnections between modules within the immune signaling network. These analyses elucidate the central role of additive pairwise effect in regulatory circuits, and provide computational tools for future investigations into the interplay between eQTLs.Availability: The software described in this article is available at csgi.tau.ac.il/POEM/

Origin and evolution of the octoploid strawberry genome.

Cultivated strawberry emerged from the hybridization of two wild octoploid species, both descendants from the merger of four diploid progenitor species into a single nucleus more than 1 million years ago. Here we report a near-complete chromosome-scale assembly for cultivated octoploid strawberry (Fragaria × ananassa) and uncovered the origin and evolutionary processes that shaped this complex allopolyploid. We identified the extant relatives of each diploid progenitor species and provide support for the North American origin of octoploid strawberry. We examined the dynamics among the four subgenomes in octoploid strawberry and uncovered the presence of a single dominant subgenome with significantly greater gene content, gene expression abundance, and biased exchanges between homoeologous chromosomes, as compared with the other subgenomes. Pathway analysis showed that certain metabolomic and disease-resistance traits are largely controlled by the dominant subgenome. These findings and the reference genome should serve as a powerful platform for future evolutionary studies and enable molecular breeding in strawberry

Dissecting Dynamic Genetic Variation That Controls Temporal Gene Response in Yeast

<div><p>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.</p></div

Co-associated genes typically share a similar pattern of genetic effects over time.

<p>(<b>A</b>) Six gene modules (column 1), constructed on the basis of a shared <i>trans-</i>associated genetic variant (a genomic interval; column 2), are listed together with their known causal gene, if available (column 3; ^†<i>-cis</i>-associated causal gene, references are in parentheses) and the number of associated genes in a module (column 4). Significant enrichments in biological processes are detailed in column 5. Significant enrichments of temporal two-state patterns in each module are presented together with the description of these enriched patterns (columns 6 and 7, respectively). (<b>B−E</b>) Gene expression and genetic effects in modules nos. 1 (left), 3 (middle) and 4 (right). Gene expression (<b>B</b>) and genetic effect (<b>C</b>) of representative genes, as well as genetic effects of an entire module (<b>D</b>); plots are shown as in <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003984#pcbi-1003984-g004" target="_blank">Fig. 4B</a></b>. (<b>E</b>) Average gene expression (<i>y</i>-axis) at six time points (<i>x</i>-axis) for the known causal gene of each module. For <i>cis</i>-associated causal genes (modules nos. 3 and 4), brown and black indicate strains carrying the RM and BY alleles, respectively. The plots demonstrate the good match between the timing of abrupt changes in causal genes (<b>E</b>) and the timing of alterations in the observed genetic effects of their associated target genes (<b>D</b>).</p

A catalogue of dynamic, non-linear genetic effects in gene response following rapamycin treatment in yeast.

<p>(<b>A</b>) 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). <i>Cis</i>-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 <b>Fig. 2c</b>, right; four singleton genes are omitted). (<b>B</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 (<i>y</i>-axis, log-scaled) across time points (<i>x</i>-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, <i>y</i>-axis) at each time point (<i>x</i>-axis). Right: shown are mean genetic effects (relative to non-stimulated cells, log-scaled; <i>y</i>-axis) and standard deviation (error bars) across time points (<i>x</i>-axis) for a certain group of genes.</p

Comparative performance analysis on synthetic data.

<p>Shown is the accuracy measure (scatter plots, left) and an example (histograms, right) across compared methods and different synthetic data parameters. Left: The accuracy measure (<i>y</i>-axis) using different patterns of genetic effects (impulse, single state-transitioning (sustained), linear, and complex sub-panels). Results are shown over genes that were measured in different numbers of time points (measures were averaged over effect sizes; <i>x</i>-axis, <b>A</b>), or over genes of different effect sizes (averaged over time points; <i>x</i>-axis <b>B</b>). Plots depict six alternative mapping methods (color coded). Right: Examples of performance (<i>y</i>-axis) using the four different dynamic effect patterns (color coded) across various methods (<i>x-</i>axis) for nine time points (<b>A</b>) or for genetic effect size 0.5 (<b>B</b>). The plots indicate that for non-linear genetic effect patterns, DyVER has an advantage over existing methods.</p

Temporal genetic effect patterns.

<p>Schematic view of gene expression patterns (top) and the relevant temporal genetic effects for these genes (bottom). The cartoons demonstrate a <i>non-dynamic genetic effect pattern</i> (<b>A</b>), a <i>dynamic, linear genetic effect pattern</i> (<b>B</b>), and a <i>dynamic</i>, <i>non-linear genetic effect pattern</i> (<b>C</b>). Top: shown are gene expression levels (<i>y</i>-axis) during a response to stimulation (<i>x-</i>axis). Each curve represents measurements in a different homozygous animal strain (segregants), where brown or black indicates whether the genotype of the associated genetic variant is or , respectively, in each strain. Bottom: shown are <i>genetic effects</i> (that is, the change in gene expression between the -carrying and -carrying strains, <i>y</i>-axis) during a response to stimulation (<i>x-</i>axis). (<b>C</b>) Examples of non-linear genetic effect patterns, which are the focus of this study, including (left to right) a <i>single state-transitioning</i> pattern, which may be followed by a sustained new level of genetic effect, a <i>single-pulse</i> (<i>impulse</i>) pattern, and a multiple-pulse (<i>complex</i>) genetic effect pattern.</p