Research Notes : United States : Genotype-race interactions in relation to phytophthora rot of soybean

Many of the widely grown soybean cultivars in the Mid-Atlautic region are susceptible to phytophthora rot caused by Phytophthora megasperma f. sp. glycinea Kuan and E?: Win (Pmg). There is limited information available on tolerance to Pmg in these popular soybean cultivars. Tolerance or field resistance to Pmg in soybean is characterized as race nonspecific resistance involving root resistance to Pmg infection, whereas resistance to Pmg in soybeans is characterized as race specific resistance (Schmitthenner, 1985

Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Data description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed

MicroRNA Expression Variability in Human Cervical Tissues

MicroRNAs (miRNAs) are short (∼22 nt) non-coding regulatory RNAs that control gene expression at the post-transcriptional level. Deregulation of miRNA expression has been discovered in a wide variety of tumours and it is now clear that they contribute to cancer development and progression. Cervical cancer is one of the most common cancers in women worldwide and there is a strong need for a non-invasive, fast and efficient method to diagnose the disease. We investigated miRNA expression profiles in cervical cancer using a microarray platform containing probes for mature miRNAs. We have evaluated miRNA expression profiles of a heterogeneous set of cervical tissues from 25 different patients. This set included 19 normal cervical tissues, 4 squamous cell carcinoma, 5 high-grade squamous intraepithelial lesion (HSIL) and 9 low-grade squamous intraepithelial lesion (LSIL) samples. We observed high variability in miRNA expression especially among normal cervical samples, which prevented us from obtaining a unique miRNA expression signature for this tumour type. However, deregulated miRNAs were identified in malignant and pre-malignant cervical tissues after tackling the high expression variability observed. We were also able to identify putative target genes of relevant candidate miRNAs. Our results show that miRNA expression shows natural variability among human samples, which complicates miRNA data profiling analysis. However, such expression noise can be filtered and does not prevent the identification of deregulated miRNAs that play a role in the malignant transformation of cervical squamous cells. Deregulated miRNAs highlight new candidate gene targets allowing for a better understanding of the molecular mechanism underlying the development of this tumour type