QTL mapping of yield components and kernel traits in wheat cultivars TAM 112 and Duster.

In the Southern Great Plains, wheat cultivars have been selected for a combination of outstanding yield and drought tolerance as a long-term breeding goal. To understand the underlying genetic mechanisms, this study aimed to dissect the quantitative trait loci (QTL) associated with yield components and kernel traits in two wheat cultivars `TAM 112' and `Duster' under both irrigated and dryland environments. A set of 182 recombined inbred lines (RIL) derived from the cross of TAM 112/Duster were planted in 13 diverse environments for evaluation of 18 yield and kernel related traits. High-density genetic linkage map was constructed using 5,081 single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing (GBS). QTL mapping analysis detected 134 QTL regions on all 21 wheat chromosomes, including 30 pleiotropic QTL regions and 21 consistent QTL regions, with 10 QTL regions in common. Three major pleiotropic QTL on the short arms of chromosomes 2B (57.5 - 61.6 Mbps), 2D (37.1 - 38.7 Mbps), and 7D (66.0 - 69.2 Mbps) colocalized with genes Ppd-B1, Ppd-D1, and FT-D1, respectively. And four consistent QTL associated with kernel length (KLEN), thousand kernel weight (TKW), plot grain yield (YLD), and kernel spike-1 (KPS) (Qklen.tamu.1A.325, Qtkw.tamu.2B.137, Qyld.tamu.2D.3, and Qkps.tamu.6A.113) explained more than 5% of the phenotypic variation. QTL Qklen.tamu.1A.325 is a novel QTL with consistent effects under all tested environments. Marker haplotype analysis indicated the QTL combinations significantly increased yield and kernel traits. QTL and the linked markers identified in this study will facilitate future marker-assisted selection (MAS) for pyramiding the favorable alleles and QTL map-based cloning.

Comparative analysis reveals chromosome number reductions in the evolution of African bermudagrass (Cynodon transvaalensis Burtt-Davy).

African bermudagrass (Cynodon transvaalensis Burtt-Davy) (2n = 2x = 18) belongs to the genus Cynodon, tribe Cynodonteae, subfamily Chloridoideae in the grass family Poaceae. The species is frequently crossed with common bermudagrass (Cynodon dactylon Pers.) in developing high-quality hybrid turf cultivars. Molecular resources for C. transvaalensis are scarce; thus, its genomic evolution is unknown. Recently, a linkage map consisting of 1278 markers provided a powerful tool for African bermudagrass genomic research. The objective of this study was to investigate chromosome number reduction events that resulted in the nine haploid chromosomes in this species. Tag sequences of mapped single nucleotide polymorphism markers in C. transvaalensis were compared against genome sequences of Oropetium thomaeum (L.f.) Trin. (2n = 2x = 20), a genomic model in the Cynodonteae tribe. The comparative genomic analyses revealed broad collinearity between the genomes of these two species. The analyses further revealed that two major interchromosomal rearrangements of the paleochromosome ρ12 (ρ1-ρ12-ρ1 and ρ6-ρ12-ρ6) resulted in nine chromosomes in the genome of C. transvaalensis. The findings provide novel information regarding the formation of the initial diploid species in the Cynodon genus.

Genetic and QTL mapping in African bermudagrass.

Cynodon transvaalensis Burtt-Davy is frequently used to cross with C. dactylon Pers. in the creation of F1 hybrid cultivars that are some of the most widely used in the worldwide turf industry. However, molecular resource development in this species is limited. Accordingly, the objectives of this study were to construct a high-density genetic map, and to identify genomic regions associated with establishment rate. In this study, we constructed the first high-density linkage map for African bermudagrass using a genotyping by sequencing approach based on 109 S1 progenies. A total of 1,246 single nucleotide polymorphisms and 32 simple sequence repeat markers were integrated in the linkage map. The total length of nine linkage groups was 882.3 cM, with an average distance of 0.69 cM per interval. Four genomic regions were identified to be associated with sod establishment rate. The results provide important genetic resources towards understanding the genome as well as marker-assisted selection for improving the establishment rate in bermudagrass breeding.

Sequence-based genetic mapping of Cynodon dactylon Pers. reveals new insights into genome evolution in Poaceae.

Bermudagrass (Cynodon dactylon Pers.) is an important warm-season perennial used extensively for turf, forage, soil conservation and remediation worldwide. However, limited genomic information has hindered the application of molecular tools towards understanding genome evolution and in breeding new cultivars. We genotype a first-generation selfed population derived from the tetraploid (4x = 36) 'A12359' using genotyping-by-sequencing. A high-density genetic map of 18 linkage groups (LGs) is constructed with 3,544 markers. Comparative genomic analyses reveal that each of nine homeologous LG pairs of C. dactylon corresponds to one of the first nine chromosomes of Oropetium thomaeum. Two nested paleo-ancestor chromosome fusions (ρ6-ρ9-ρ6, ρ2-ρ10-ρ2) may have resulted in a 12-to-10 chromosome reduction. A segmental dissemination of the paleo-chromosome ρ12 (ρ1-ρ12-ρ1, ρ6-ρ12-ρ6) leads to the 10-to-9 chromosome reduction in C. dactylon genome. The genetic map will assist in an ongoing whole genome sequence assembly and facilitate marker-assisted selection (MAS) in developing new cultivars.

Causal co-expression method with module analysis to screen drugs with specific target.

The considerable increase of investment in research and development by the pharmaceutical industry over the past three decades has not added the number of approved new drugs. An important issue ignored by drug discovery practice is the multi-dimensional interaction network between drugs and their targets. Thus, it is essential to view drug actions through the lens of network biology. In the current study, based on the co-expression network of transcription factors and their downstream genes, we proposed a novel approach, called causal co-expression method with module analysis, to screen drugs with specific target and fewer side effects. We presented a causal co-expression method with module analysis and it could be used in analyzing the microarray data of different drug candidates. At first, the differential wiring value (DW) was calculated to find some causal transcription factors (TFs) by combining with differential expression genes in the regulated networks. After the discovery of the causal TFs, co-expression module analysis method was applied to mine molecular pharmacology pathways around these causal TFs at molecular level. We applied our methods to two drug candidates, Argyrin A and Bortezomib, both with anti-cancer activities. We first obtained some differentially expressed transcription factors of cells treated with Argyrin A or Bortezomib. Nearly all these transcription factors are associated with the tumor suppressor protein p27kip1. Furthermore, module analysis showed that Bortezomib inhibited tumor growth not specifically by cell cycle and cell proliferation pathway, but through many basic metabolic processes which result in cell toxicity. In contrast, Argyrin A had influence on cell cycle, and was involved in DNA damage repair at the same time, showing that Argyrin A was a more suitable drug for anti-cancer treatment. Our study revealed that the causal co-expression method with module analysis was effective and can be used as a tool to evaluate drug candidates.

Combining ZHENG Theory and High-Throughput Expression Data to Predict New Effects of Chinese Herbal Formulae.

ZHENG is the key theory in traditional Chinese medicine (TCM) and it is very important to find the molecular pharmacology of traditional Chinese herbal formulae. One ZHENG is related to many diseases and the herbal formulae are aiming to ZHENG. Therefore, many herbal formulae whose effects on a certain disease have been confirmed might also treat other diseases with the same ZHENG. In this study, the microarrays collected from patients with QiXuXueYu ZHENG (Qi-deficiency and Blood-stasis syndrome) before treatment and after being treated with Fuzheng Huayu Capsule were analyzed by a high-throughput gene microarrays-based drug similarity comparison method, which could find the small molecules which had similar effects with Fuzheng Huayu Capsule. Besides getting the results of anti-inflammatory and anti-fibrosis drugs which embody the known effect of Fuzheng Huayu Capsule, many other small molecules were screened out and could reflect other types of effects of this formula in treating QiXuXueYu ZHENG, including anti-hyperglycemic, anti-hyperlipidemic, hyposenstive effect. Then we integrated this information to display the effect of Fuzheng Huayu Capsule and its potential multiple-target molecular pharmacology. Moreover, through using clinical blood-tested data to verify our prediction, Fuzheng Huayu Capsule was proved to have effects on diabetes and dyslipidemia.

Molecular Mechanisms of Same TCM Syndrome for Different Diseases and Different TCM Syndrome for Same Disease in Chronic Hepatitis B and Liver Cirrhosis.

Traditional Chinese medicine (TCM) treatment is based on the traditional diagnose method to distinguish the TCM syndrome, not the disease. So there is a phenomenon in the relationship between TCM syndrome and disease, called Same TCM Syndrome for Different Diseases and Different TCM Syndrome for Same Disease. In this study, we demonstrated the molecular mechanisms of this phenomenon using the microarray samples of liver-gallbladder dampness-heat syndrome (LGDHS) and liver depression and spleen deficiency syndrome (LDSDS) in the chronic hepatitis B (CHB) and liver cirrhosis (LC). The results showed that the difference between CHB and LC was gene expression level and the difference between LGDHS and LDSDS was gene coexpression in the G-protein-coupled receptor protein-signaling pathway. Therein genes GPER, PTHR1, GPR173, and SSTR1 were coexpressed in LDSDS, but not in LGDHS. Either CHB or LC was divided into the alternative LGDHS and LDSDS by the gene correlation, which reveals the molecular feature of Different TCM Syndrome for Same Disease. The alternatives LGDHS and LDSDS were divided into either CHB or LC by the gene expression level, which reveals the molecular feature of Same TCM Syndrome for Different Diseases.

A cross-species analysis method to analyze animal models' similarity to human's disease state.

BACKGROUND: Animal models are indispensable tools in studying the cause of human diseases and searching for the treatments. The scientific value of an animal model depends on the accurate mimicry of human diseases. The primary goal of the current study was to develop a cross-species method by using the animal models' expression data to evaluate the similarity to human diseases' and assess drug molecules' efficiency in drug research. Therefore, we hoped to reveal that it is feasible and useful to compare gene expression profiles across species in the studies of pathology, toxicology, drug repositioning, and drug action mechanism. RESULTS: We developed a cross-species analysis method to analyze animal models' similarity to human diseases and effectiveness in drug research by utilizing the existing animal gene expression data in the public database, and mined some meaningful information to help drug research, such as potential drug candidates, possible drug repositioning, side effects and analysis in pharmacology. New animal models could be evaluated by our method before they are used in drug discovery. We applied the method to several cases of known animal model expression profiles and obtained some useful information to help drug research. We found that trichostatin A and some other HDACs could have very similar response across cell lines and species at gene expression level. Mouse hypoxia model could accurately mimic the human hypoxia, while mouse diabetes drug model might have some limitation. The transgenic mouse of Alzheimer was a useful model and we deeply analyzed the biological mechanisms of some drugs in this case. In addition, all the cases could provide some ideas for drug discovery and drug repositioning. CONCLUSIONS: We developed a new cross-species gene expression module comparison method to use animal models' expression data to analyse the effectiveness of animal models in drug research. Moreover, through data integration, our method could be applied for drug research, such as potential drug candidates, possible drug repositioning, side effects and information about pharmacology.