Association mapping for soilborne pathogen resistance in synthetic hexaploid wheat

Soilborne pathogens such as cereal cyst nematode (CCN; Heterodera avenae) and root lesion nematode (Pratylenchus neglectus; PN) cause substantial yield losses in the major cereal-growing regions of the world. Incorporating resistance into wheat cultivars and breeding lines is considered the most cost-effective control measure for reducing nematode populations. To identify loci with molecular markers linked to genes conferring resistance to these pathogens, we employed a genome-wide association approach in which 332 synthetic hexaploid wheat lines previously screened for resistance to CCN and PN were genotyped with 660 Diversity Arrays Technology (DArT) markers. Two sequence-tagged site markers reportedly linked to genes known to confer resistance to CCN were also included in the analysis. Using the mixed linear model corrected for population structure and familial relatedness (Q+K matrices), we were able to confirm previously reported quantitative trait loci (QTL) for resistance to CCN and PN in bi-parental crosses. In addition, we identified other significant markers located in chromosome regions where no CCN and PN resistance genes have been reported. Seventeen DArT marker loci were found to be significantly associated with CCN and twelve to PN resistance. The novel QTL on chromosomes 1D, 4D, 5B, 5D and 7D for resistance to CCN and 4A, 5B and 7B for resistance to PN are suggested to represent new sources of genes which could be deployed in further wheat improvement against these two important root diseases of wheat.     

Global agricultural intensification during climate change: a role for genomics

Agriculture is now facing the ‘perfect storm’ of climate change, increasing costs of fertilizer and rising food demands from a larger and wealthier human population. These factors point to a global food deficit unless the efficiency and resilience of crop production is increased. The intensification of agriculture has focused on improving production under optimized conditions, with significant agronomic inputs. Furthermore, the intensive cultivation of a limited number of crops has drastically narrowed the number of plant species humans rely on. A new agricultural paradigm is required, reducing dependence on high inputs and increasing crop diversity, yield stability and environmental resilience. Genomics offers unprecedented opportunities to increase crop yield, quality and stability of production through advanced breeding strategies, enhancing the resilience of major crops to climate variability, and increasing the productivity and range of minor crops to diversify the food supply. Here we review the state of the art of genomic‐assisted breeding for the most important staples that feed the world, and how to use and adapt such genomic tools to accelerate development of both major and minor crops with desired traits that enhance adaptation to, or mitigate the effects of climate change.     

The cre1 and cre3 nematode resistance genes are located at homeologous loci in the wheat genome

Differential responses in host-nematode pathotype interactions occur in wheat lines carrying different cereal cyst nematode resistance (Cre) genes. Cre1, located on chromosome 2B, confers resistance to most European nematodes and the sole Australian pathotype, while Cre3, present on chromosome 2D, is highly resistant to the Australian pathotype and susceptible to a number of European pathotypes. Genes encoding nucleotide binding site-leucine rich repeat (NBS-LRR) proteins that cosegregate with the Cre3 locus cross hybridize to homologues whose restriction fragment length polymorphism (RFLP) patterns distinguish near-isogenic Cre1 nematode-resistant wheat lines. Genetic mapping showed that the NBS-LRR gene members that distinguished the Cre1 near-isogenic lines were located on chromosome 2BL at a locus, designated Xcsl107, that cosegregates with the Cre1 locus. A haplotype of NBS-LRR genes from the Xcsl107 locus provides a diagnostic marker for the presence of Cre1 nematode resistance in a wide collection of wheat lines and segregating families. Genetic analysis of NBS-LRR haplotypes that cosegregate with Cre1 and Cre3 resistance, together with flanking cDNA markers and other markers from homoeologous group 2 chromosomes, revealed a conserved gene order that suggests Cre1 and Cre3 are homeoloci.     

Effects of carbon and nitrogen sources on rhamnolipid biosurfactant production by <Emphasis Type="Italic">Pseudomonas nitroreducens</Emphasis> isolated from soil

Rhamnolipid biosurfactant production by Pseudomonas nitroreducens isolated from petroleum-contaminated soil was investigated. The effects of carbon, nitrogen and carbon to nitrogen ratio on biosurfactant production were examined using mineral salts medium as the growth medium. The tenso-active properties (surface activity and critical micelle concentrations of the produced biosurfactant were also evaluated. The best carbon source, nitrogen source were glucose and sodium nitrate giving rhamnolipid yields of 5.28 and 4.38 g l⁻¹, respectively. The maximum rhamnolipid production of 5.46 g l⁻¹ was at C/N (glucose/sodium nitrate) of 22. The rhamnolipid biosurfactant reduced the surface tension of water from 72 to ~37 mN/m. It also has critical micelle concentration of ~28 mg l⁻¹. Thus, the results presented in our reports show that the produced rhamnolipid can find wide applications in various bioremediation activities such as enhanced oil recovery and petroleum degradation.     

Reducing the Time Requirement of k-Means Algorithm

Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray data, which have large datasets with large dimension size d. In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k. The problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this work, we develop a novel k-means algorithm, which is simple but more efficient than the traditional k-means and the recent enhanced k-means. Our new algorithm is based on the recently established relationship between principal component analysis and the k-means clustering. We provided the correctness proof for this algorithm. Results obtained from testing the algorithm on three biological data and six non-biological data (three of these data are real, while the other three are simulated) also indicate that our algorithm is empirically faster than other known k-means algorithms. We assessed the quality of our algorithm clusters against the clusters of a known structure using the Hubert-Arabie Adjusted Rand index (ARI_HA). We found that when k is close to d, the quality is good (ARI_HA>0.8) and when k is not close to d, the quality of our new k-means algorithm is excellent (ARI_HA>0.9). In this paper, emphases are on the reduction of the time requirement of the k-means algorithm and its application to microarray data due to the desire to create a tool for clustering and malaria research. However, the new clustering algorithm can be used for other clustering needs as long as an appropriate measure of distance between the centroids and the members is used. This has been demonstrated in this work on six non-biological data.     

Bioremediation of Soil Contaminated with Diesel Using Inorganic Nitrogen Sources: Incorporating nth-Order Algorithm in the Evaluation of Process Kinetics

In the present study, we investigated the effects of inorganic nitrogen sources—(NPK fertilizer, 15:15:15), (urea fertilizer, 46:0:0), (NH₄)₂SO₄ as well as monitored natural attenuation on the bioremediation of diesel-polluted soil. At the end of the 6-week study, the highest degradation was recorded in soil amended with NPK fertilizer (95 ± 2.77%) while the least total petroleum hydrocarbon removal was observed in monitored natural attenuation (89 ± 2.91%). Nth-order kinetics effectively described three of the treatments out of the four treatment plans. These include urea amendment (r² = 0.9925, average relative error (ARE) = 1.45%, root mean square error (RMSE) = 0.038, k_n = (3.57 ± 0.61) × 10^?2, n = 1.33), NPK fertilizer amendment (r² = 0.9751, ARE = 3.241%, RMSE = 0.086, k_n = (8.04 ± 0.23) × 10^?1, n = 0.74), and monitored natural attenuation (r² = 0.9697, ARE = 2.77%, RMSE = 0.073, k_n = (1.57 ± 0.50) × 10^?2, n = 1.16). The values of n from the nth-order kinetics parameter estimation indicated that all the treatments resulted in diesel degradation that followed a first-order kinetics path. Thus, the outcome of kinetic modeling showed that nth-order can be used as validating tool when many kinetic orders are under consideration. The phytotoxicity assay with Zea mays showed that the treatments plans resulted in germination indices of 17–55%.     

Characterization of quantitative trait loci controlling genetic variation for preharvest sprouting in synthetic backcross-derived wheat lines

Aegilops tauschii, the wild relative of wheat, has stronger seed dormancy, a major component of preharvest sprouting resistance (PHSR), than bread wheat. A diploid Ae. tauschii accession (AUS18836) and a tetraploid (Triticum turgidum L. ssp. durum var. Altar84) wheat were used to construct a synthetic wheat (Syn37). The genetic architecture of PHS was investigated in 271 BC(1)F(7) synthetic backcross lines (SBLs) derived from Syn37/2*Janz (resistant/susceptible). The SBLs were evaluated in three environments over 2 years and PHS was assessed by way of three measures: the germination index (GI), which measures grain dormancy, the whole spike assay (SI), which takes into account all spike morphology, and counted visually sprouted seeds out of 200 (VI). Grain color was measured using both Chroma Meter- and NaOH-based approaches. QTL for PHSR and grain color were mapped and their additive and epistatic effects as well as their interactions with environment were estimated by a mixed linear-model approach. Single-locus analysis following composite interval mapping revealed four QTL for GI, two QTL for SI, and four QTL for VI on chromosomes 3DL and 4AL. The locus QPhs.dpiv-3D.1 on chromosome 3DL was tightly linked to the red grain color (RGC) at a distance of 5 cM. The other locus on chromosome 3D, "QPhs.dpiv-3D.2" was independent of RGC locus. Two-locus analysis detected nine QTL with main effects and 18 additive x additive interactions for GI, SI, and VI. Two of the nine main effects QTL and two epistatic QTL showed significant interactions with environments. Both additive and epistatic effects contributed to phenotypic variance in PHSR and the identified markers are potential candidates for marker-assisted selection of favorable alleles at multiple loci. SBLs derived from Ae. tauschii proved to be a promising tool to dissect, introgress, and pyramid different PHSR genes into adapted wheat genetic backgrounds. The enhanced expression of PHS resistance in SBLs enabled us to develop white PHS-resistant wheat germplasm from the red-grained Ae. tauschii accession.