al., 1996). CIMMYT bread wheat germplasm bred in Mexico by means of this shuttle is distributed globally Understanding the way different environments differentiate cultiin the Elite Spring Wheat Yield Trial (ESWYT). vars for yield allows the plant breeder to optimize choice of parents, germplasm screening, yield testing, and resource use within the targetIn an attempt to focus the wheat breeding effort at region. To determine the associations among yield testing environ-CIMMYT, the major wheat production zones have been ments, wheat (Triticum aestivum L.) yield data from 963 replicated divided into zones of similar agro-ecological adaptation trials sown across a 20-yr period were analyzed by means of pattern (Calhoun et al., 1994;Rajaram et al., 1994). Abdalla et analysis and the shifted multiplicative model (SHMM) to group sites al. (1997) examined durum (Triticum turgidum L.) within and across years. Pattern analysis identified four primary cluswheat yield trials sown at 40 different locations in 1990ters of sites and four representative locations within these clusters 1991 and concluded that test sites associated on the basis were identified by squared Euclidean distances. Group-1 represented of latitude and similar production constraints. Others primarily Mediterranean and West Asian locations and South Amerihave indicated the importance of identifying and tarcan sites. Group-2 was comprised of generally warmer sites in southern geting wheat germplasm to specific environments (Deand eastern Asia. Group-3 comprised higher rainfall locations in South America and eastern Africa and Group-4 represented cooler sites in Lacy and Lawrence, 1988; DeLacy et al., 1994; Peterson South America and West Asia. The respective key locations for each and Pfeiffer, 1989). These studies examined associations of the four groups were Sakha, Egypt; Quezaltenango, Guatemala; among locations by estimating genotype ϫ environ-Londrina, Brazil; and Pirsabak, Pakistan. The four key sites were ment interactions. then used to examine site clusters within each year by SHMM. The Two models have been used to study the effects of sites at Pirsabak and Sakha associated best across all global wheat-GEI on site groupings without crossover interaction growing regions where a combined total of 700 of 1117 (62%) pos-(COI). These are the shifted multiplicative model sible clusters with other global wheat locations were realized. This (SHMM) (Cornelius et al., 1992; Crossa et al., 1993, compared with 52% for Quezaltenango and 38% for Londrina. Factors 1996) and the site regression model (SREG) (Crossa with a primary influence on site clustering were cropping seasonand Cornelius, 1997). The SHMM has also been used

The inheritance and genetic linkage analysis for seed dormancy and preharvest sprouting (PHS) resistance were carried out in an F8 recombinant inbred lines (RILs) derived from the cross between "CN19055" (white-grained, PHS-resistant) with locally adapted Australian cultivar "Annuello" (white-grained, PHS-susceptible). Seed dormancy was assessed as germination index (GI7) while assessment for preharvest sprouting resistance was based on whole head assay (sprouting index, SI) and visibly sprouted seeds (VI). Segregation analysis of the F2, F3 data from the glasshouse and the RIL population in 2004 and 2005 field data sets indicated that seed dormancy and PHS resistance in CN19055 is controlled by at least two genes. Heritabilities for GI7 and VI were high and moderate for SI. The most accurate method for assessing PHS resistance was achieved using VI and GI7 while SI exhibited large genotype by environment interaction. Two quantitative trait loci (QTLs) QPhs.dpivic.4A.1 and QPhs.dpivic.4A.2 were identified. On pooled data across four environments, the major QTL, QPhs.dpivic.4A.2, explained 45% of phenotypic variation for GI7, 43% for VI and 20% for SI, respectively. On the other hand, QPhs.dpivic.4A.1 which accounted for 31% of the phenotypic variation in GI7 in 2004 Horsham field trial, was not stable across environments. Physical mapping of two SSR markers, Xgwm937 and Xgwm894 linked to the major QTL for PHS resistance, using Chinese Spring deletions lines for chromosome 4AS and 4AL revealed that the markers were located in the deletion bins 4AL-12 and 4AL-13. The newly identified SSR markers (Xgwm937/Xgwm894) showed strong association with seed dormancy and PHS resistance in a range of wheat lines reputed to possess PHS resistance. The results suggest that Xgwm937/Xgwm894 could be used in marker-assisted selection (MAS) for incorporating preharvest sprouting resistance into elite wheat cultivars susceptible to PHS.

and lowland Bolivia and Paraguay) and lowland dry areas (e.g., central and peninsular India, Nigeria and A good understanding of the target environment and the extent of Sudan). The most important disease constraints are Helgenotype ϫ environment (G ϫ E) interaction is essential for all cereal breeding programs. Differential adaptation of bread wheat (Triticum minthosporium Leaf Blight (HLB) caused by Bipolaris aestivum L.) to various heat-stressed environments around the world sorokiniana (Sacc.) Shoemaker and leaf rust caused was analyzed by cumulative cluster analysis of locations and genotypes by Puccinia triticina Eriks. ϭ P. recondita Roberge ex in 9 yr of CIMMYT's High Temperature Wheat Yield Trial (HTWYT). Desmaz. f. sp. tritici (Eriks. & E. Henn.) D.M. Hender-The grouping pattern of yield-testing environments could largely be son (Dubin and van Ginkel, 1990). HLB is mostly conexplained by the temperature at different growth stages and relative fined to the humid tropical areas, whereas leaf rust is humidity at booting. A clear distinction was observed between sites important to all areas (Dubin and Rajaram 1996). Adwith heat stress and more temperate locations, and the heat-stressed vanced breeding lines targeted for heat-stressed areas environments could be grouped into sites experiencing high temperaare annually distributed to international cooperators ture throughout the season and sites with more specific terminal heat through the HTWYT. stress. In addition, dry and humid heat-stressed locations tended to differentiate. The ability of individual locations to predict yield in Crop environments can be characterized in terms of different heat-stressed environments was studied by the shifted multi-the way they influence the relative performance or rank plicative model (SHMM) site clustering method, and identified locaof genotypes. One useful method for this purpose is tions like Tandojam (Pakistan), which associated well with both heatthe SHMM, which identifies subsets of locations with stressed and temperate environments. The good ability of the January minimal internal crossover interaction (Cornelius et al., planting date in Ciudad Obregon (Mexico) to predict yield perfor-1992; Crossa et al., 1993). However, this method requires mance in many heat-stressed environments was also confirmed. Genobalanced data sets where the same locations and genotypes grouped according to their relative performance in different types are repeated over years. To analyze multienvironlocations, and specific adaptation to the various types of heat-stressed ment trials where the composition of genotypes changes environments was apparent. However, a subset of genotypes was idenfrom year to year, DeLacy et al. (1996b) developed a tified that showed stable, and high yield across all types of environments, both heat-stressed and temperate.

The immunoregulatory transcriptional modulators -IFN-regulatory factor (IRF)-3 and IRF-7 -possess similar structural features but distinct gene-regulatory potentials. For example, adenovirus-mediated transduction of the constitutively active form of IRF-3 triggered cell death in primary human MU, whereas expression of active IRF-7 induced a strong anti-tumoral activity in vitro. To further characterize target genes involved in these distinct cellular responses, transcriptional profiles of active IRF-3-or IRF-7-transduced primary human MU were compared and used to direct further mechanistic studies. The pro-apoptotic BH3-only protein Noxa was identified as a primary IRF-3 target gene and an essential regulator of IRF-3, dsRNA and vesicular stomatitis virus-induced cell death. The critical role of IRF-7 and type I IFN production in increasing the immunostimulatory capacity of MU was also evaluated; IRF-7 increased the expression of a broad range of IFNstimulated genes including immunomodulatory cytokines and genes involved in antigen processing and presentation. Furthermore, active IRF-7 augmented the cross-presentation capacity and tumoricidal activity of MU and led to an anti-tumor response against the B16 melanoma model in vivo. Altogether, these data further highlight the respective functions of IRF-3 and IRF-7 to program apoptotic, immune and anti-tumor responses.Key words: Anti-tumor immunotherapy . Apoptosis . Cross-presentation .IFN-regulatory factor . Microarray Supporting Information available online IntroductionType I interferons (IFN-a/b) induce the expression of hundreds of IFN-stimulated genes (ISG), which mediate a broad range of anti-viral, growth-regulatory and immunomodulatory effects. In virus-infected cells, these cytokines inhibit many RNA and DNA viruses at various stages of their replication cycles [1,2]. During tumor-cell development, type I IFN exert direct cytotoxic or antiproliferative functions and negatively regulate angiogenesis [3,4]. Furthermore, direct anti-viral and anti-tumor properties [14,15] leading to their dimerization, nuclear translocation and binding to the promoters of type I IFN genes [16][17][18][19]. Although IRF-3 and IRF-7 share highly related structural and functional characteristics [13,20,21], major biological and functional distinctions can be identified between the two. While IRF-3 is ubiquitously expressed [22], the basal expression of IRF-7 is low in most cells (with the notable exception of plasmacytoid DC [23]) and is strongly increased by type I IFN-mediated signaling [24]. IRF-3 and IRF-7 present both redundant and distinct transcriptional profiles. Qualitative and quantitative differences exist in type I IFN induction capacities of IRF-3 and IRF-7 upon activation: IRF-7 can induce the efficient activation of IFNB genes and multiple IFNA subtypes, whereas IRF-3 is a potent activator of IFNB genes but not of IFNA genes with the exception of human IFNA1 and mouse Ifna4 genes [21,[24][25][26]. This observation may account for the increased vulnerability to ...