Color variation within populations of the pea aphid influences relative susceptibility to predators and parasites. We have discovered that infection with a facultative endosymbiont of the genus Rickettsiella changes the insects' body color from red to green in natural populations. Approximately 8% of pea aphids collected in Western Europe carried the Rickettsiella infection. The infection increased amounts of blue-green polycyclic quinones, whereas it had less of an effect on yellow-red carotenoid pigments. The effect of the endosymbiont on body color is expected to influence prey-predator interactions, as well as interactions with other endosymbionts.

Despite the relative ease of isolating microsatellites, their development still requires substantial inputs of time, money and expertise. For this reason there is considerable interest in using existing microsatellites on species from which markers were not cloned. We tested cross‐species amplification of 48 existing aphid loci in species of the following genera: Aphidinae: Aphidini: Aphis and Rhopalosiphum; Aphidinae: Macrosiphini: Acyrthosiphum, Brevicoryne, Diuraphis, Illinoia, Macrosiphoniella, Macrosiphum, Metopeurum, Metapolophium, Myzus, Phorodon, Sitobion and Uroleucon and Neuquenaphidinae: Neuquenaphis. Our results show cross‐species application of known microsatellite loci is a highly promising source of codominant markers for population genetic and evolutionary studies in aphids.

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Insulin-like growth factors (IGFs) stimulate growth rate in a number of animal species and are likely to contribute to genetic variations of growth potential. The present study was designed to link levels of IGF-I and IGF-II mRNA and peptides with growth rate in divergently selected genotypes of chickens with high (HG) or low (LG) growth rates. Circulating IGF-I and -II and hepatic mRNA levels were measured under ad libitum feeding conditions from 1 to 12 weeks of age, and at 6 weeks of age under three different nutritional conditions (fed, fasted for 16 or 48 h, re-fed for 4 or 24 h after a 48-h fast). IGF binding proteins (IGFBPs) were also measured. Circulating IGFs increased with age and were higher in HG chickens from 1 to 6 weeks. They decreased with fasting and only IGF-II was fully restored after 24 h of re-feeding, while IGF-I remained low. A significant decrease in steady state IGF-I mRNA levels was also observed with fasting. Across the nutritional study, hepatic IGF-I mRNAs were significantly higher in HG chickens. Variations of IGF-II mRNA levels with nutritional state or genotype exhibited a similar trend. IGFBP (28, 34 and 40 kDa) levels increased with age, while only faint differences were observed between genotypes. IGFBP-28 transiently increased with fasting and was inversely related to blood glucose and insulin levels, suggesting that it is equivalent to mammalian IGFBP-1. In HG chickens, IGFBP-28 and IGFBP-34 levels decreased markedly following refeeding. Therefore, high and low growth rates were respectively associated with high and low IGF-I and -II levels, supporting the hypothesis of a stimulatory role for both IGFs during post-hatching growth of chickens.

Since the sequencing of the genome and the development of high-throughput tools for the exploration of functional elements of the genome, the chicken has reached model organism status. Functional genomics focuses on understanding the function and regulation of genes and gene products on a global or genome-wide scale. Systems biology attempts to integrate functional information derived from multiple high-content data sets into a holistic view of all biological processes within a cell or organism. Generation of a large collection ( approximately 600K) of chicken expressed sequence tags, representing most tissues and developmental stages, has enabled the construction of high-density microarrays for transcriptional profiling. Comprehensive analysis of this large expressed sequence tag collection and a set of approximately 20K full-length cDNA sequences indicate that the transcriptome of the chicken represents approximately 20,000 genes. Furthermore, comparative analyses of these sequences have facilitated functional annotation of the genome and the creation of several bioinformatic resources for the chicken. Recently, about 20 papers have been published on transcriptional profiling with DNA microarrays in chicken tissues under various conditions. Proteomics is another powerful high-throughput tool currently used for examining the dynamics of protein expression in chicken tissues and fluids. Computational analyses of the chicken genome are providing new insight into the evolution of gene families in birds and other organisms. Abundant functional genomic resources now support large-scale analyses in the chicken and will facilitate identification of transcriptional mechanisms, gene networks, and metabolic or regulatory pathways that will ultimately determine the phenotype of the bird. New technologies such as marker-assisted selection, transgenics, and RNA interference offer the opportunity to modify the phenotype of the chicken to fit defined production goals. This review focuses on functional genomics in the chicken and provides a road map for large-scale exploration of the chicken genome.