Whole Genome Analysis of Gene Expression Reveals Coordinated Activation of Signaling and Metabolic Pathways during Pollen-Pistil Interactions in Arabidopsis

Plant reproduction depends on the concerted activation of many genes to ensure correct communication between pollen and pistil. Here, we queried the whole transcriptome of Arabidopsis (Arabidopsis thaliana) in order to identify genes with specific reproductive functions. We used the Affymetrix ATH1 whole genome array to profile wild-type unpollinated pistils and unfertilized ovules. By comparing the expression profile of pistils at 0.5, 3.5, and 8.0 h after pollination and applying a number of statistical and bioinformatics criteria, we found 1,373 genes differentially regulated during pollen-pistil interactions. Robust clustering analysis grouped these genes in 16 time-course clusters representing distinct patterns of regulation. Coregulation within each cluster suggests the presence of distinct genetic pathways, which might be under the control of specific transcriptional regulators. A total of 78% of the regulated genes were expressed initially in unpollinated pistil and/or ovules, 15% were initially detected in the pollen data sets as enriched or preferentially expressed, and 7% were induced upon pollination. Among those, we found a particular enrichment for unknown transcripts predicted to encode secreted proteins or representing signaling and cell wall-related proteins, which may function by remodeling the extracellular matrix or as extracellular signaling molecules. A strict regulatory control in various metabolic pathways suggests that fine-tuning of the biochemical and physiological cellular environment is crucial for reproductive success. Our study provides a unique and detailed temporal and spatial gene expression profile of in vivo pollen-pistil interactions, providing a framework to better understand the basis of the molecular mechanisms operating during the reproductive process in higher plants.

Assessment of fight outcome is needed to activate socially driven transcriptional changes in the zebrafish brain

Group living animals must be able to express different behavior profiles depending on their social status. Therefore, the same genotype may translate into different behavioral phenotypes through socially driven differential gene expression. However, how social information is translated into a neurogenomic response and what are the specific cues in a social interaction that signal a change in social status are questions that have remained unanswered. Here, we show for the first time, to our knowledge, that the switch between status-specific neurogenomic states relies on the assessment of fight outcome rather than just on self- or opponent-only assessment of fighting ability. For this purpose, we manipulated the perception of fight outcome in male zebrafish and measured its impact on the brain transcriptome using a zebrafish whole genome gene chip. Males fought either a real opponent, and a winner and a loser were identified, or their own image on a mirror, in which case, despite expressing aggressive behavior, males did not experience either a victory or a defeat. Massive changes in the brain transcriptome were observed in real opponent fighters, with losers displaying both a higher number of differentially expressed genes and of coexpressed gene modules than winners. In contrast, mirror fighters expressed a neurogenomic state similar to that of noninteracting fish. The genes that responded to fight outcome included immediate early genes and genes involved in neuroplasticity and epigenetic modifications. These results indicate that, even in cognitively simple organisms such as zebrafish, neurogenomic responses underlying changes in social status rely on mutual assessment of fighting ability.

Macrophage adaptation leads to parallel evolution of genetically diverseEscherichia colismall-colony variants with increased fitness in vivo and antibiotic collateral sensitivity

Small-colony variants (SCVs) are commonly observed in evolution experiments and clinical isolates, being associated with antibiotic resistance and persistent infections. We recently observed the repeated emergence of Escherichia coli SCVs during adaptation to the interaction with macrophages. To identify the genetic targets underlying the emergence of this clinically relevant morphotype, we performed whole-genome sequencing of independently evolved SCV clones. We uncovered novel mutational targets, not previously associated with SCVs (e.g. cydA, pepP) and observed widespread functional parallelism. All SCV clones had mutations in genes related to the electron-transport chain. As SCVs emerged during adaptation to macrophages, and often show increased antibiotic resistance, we measured SCV fitness inside macrophages and measured their antibiotic resistance profiles. SCVs had a fitness advantage inside macrophages and showed increased aminoglycoside resistance in vitro, but had collateral sensitivity to other antibiotics (e.g. tetracycline). Importantly, we observed similar results in vivo. SCVs had a fitness advantage upon colonization of the mouse gut, which could be tuned by antibiotic treatment: kanamycin (aminoglycoside) increased SCV fitness, but tetracycline strongly reduced it. Our results highlight the power of using experimental evolution as the basis for identifying the causes and consequences of adaptation during host-microbe interactions.