Transcriptional patterns in both host and bacterium underlie a daily rhythm of anatomical and metabolic change in a beneficial symbiosis.

Mechanisms for controlling symbiont populations are critical for maintaining the associations that exist between a host and its microbial partners. We describe here the transcriptional, metabolic, and ultrastructural characteristics of a diel rhythm that occurs in the symbiosis between the squid Euprymna scolopes and the luminous bacterium Vibrio fischeri. The rhythm is driven by the host's expulsion from its light-emitting organ of most of the symbiont population each day at dawn. The transcriptomes of both the host epithelium that supports the symbionts and the symbiont population itself were characterized and compared at four times over this daily cycle. The greatest fluctuation in gene expression of both partners occurred as the day began. Most notable was an up-regulation in the host of >50 cytoskeleton-related genes just before dawn and their subsequent down-regulation within 6 h. Examination of the epithelium by TEM revealed a corresponding restructuring, characterized by effacement and blebbing of its apical surface. After the dawn expulsion, the epithelium reestablished its polarity, and the residual symbionts began growing, repopulating the light organ. Analysis of the symbiont transcriptome suggested that the bacteria respond to the effacement by up-regulating genes associated with anaerobic respiration of glycerol; supporting this finding, lipid analysis of the symbionts' membranes indicated a direct incorporation of host-derived fatty acids. After 12 h, the metabolic signature of the symbiont population shifted to one characteristic of chitin fermentation, which continued until the following dawn. Thus, the persistent maintenance of the squid-vibrio symbiosis is tied to a dynamic diel rhythm that involves both partners.

Effects of colonization, luminescence, and autoinducer on host transcription during development of the squid-vibrio association.

The light-organ symbiosis between the squid Euprymna scolopes and the luminous bacterium Vibrio fischeri offers the opportunity to decipher the hour-by-hour events that occur during the natural colonization of an animal's epithelial surface by its microbial partners. To determine the genetic basis of these events, a glass-slide microarray was used to characterize the light-organ transcriptome of juvenile squid in response to the initiation of symbiosis. Patterns of gene expression were compared between animals not exposed to the symbiont, exposed to the wild-type symbiont, or exposed to a mutant symbiont defective in either of two key characters of this association: bacterial luminescence or autoinducer (AI) production. Hundreds of genes were differentially regulated as a result of symbiosis initiation, and a hierarchy existed in the magnitude of the host's response to three symbiont features: bacterial presence > luminescence > AI production. Putative host receptors for bacterial surface molecules known to induce squid development are up-regulated by symbiont light production, suggesting that bioluminescence plays a key role in preparing the host for bacteria-induced development. Further, because the transcriptional response of tissues exposed to AI in the natural context (i.e., with the symbionts) differed from that to AI alone, the presence of the bacteria potentiates the role of quorum signals in symbiosis. Comparison of these microarray data with those from other symbioses, such as germ-free/conventionalized mice and zebrafish, revealed a set of shared genes that may represent a core set of ancient host responses conserved throughout animal evolution.

Robust semiparametric microarray normalization and significance analysis.

Microarray technology allows the monitoring of expression levels of thousands of genes simultaneously. A semiparametric location and scale model is proposed to model gene expression levels for normalization and significance analysis purposes. Robust estimation based on weighted least absolute deviation regression and significance analysis based on the weighted bootstrap are investigated. The proposed approach naturally combines normalization and significance analysis, and incorporates the variations due to normalization into the significance analysis properly. A small simulation study is used to compare finite sample performance of the proposed approach with alternatives. We also demonstrate the proposed method with a real dataset.

A robust two-way semi-linear model for normalization of cDNA microarray data.

BACKGROUND: Normalization is a basic step in microarray data analysis. A proper normalization procedure ensures that the intensity ratios provide meaningful measures of relative expression values. METHODS: We propose a robust semiparametric method in a two-way semi-linear model (TW-SLM) for normalization of cDNA microarray data. This method does not make the usual assumptions underlying some of the existing methods. For example, it does not assume that: (i) the percentage of differentially expressed genes is small; or (ii) the numbers of up- and down-regulated genes are about the same, as required in the LOWESS normalization method. We conduct simulation studies to evaluate the proposed method and use a real data set from a specially designed microarray experiment to compare the performance of the proposed method with that of the LOWESS normalization approach. RESULTS: The simulation results show that the proposed method performs better than the LOWESS normalization method in terms of mean square errors for estimated gene effects. The results of analysis of the real data set also show that the proposed method yields more consistent results between the direct and the indirect comparisons and also can detect more differentially expressed genes than the LOWESS method. CONCLUSIONS: Our simulation studies and the real data example indicate that the proposed robust TW-SLM method works at least as well as the LOWESS method and works better when the underlying assumptions for the LOWESS method are not satisfied. Therefore, it is a powerful alternative to the existing normalization methods.