BackgroundSoybean (Glycine max [L.] Merr.) is one of the most important oil and protein crops. Ever-increasing soybean consumption necessitates the improvement of varieties for more efficient production. However, both correlations among different traits and genetic interactions among genes that affect a single trait pose a challenge to soybean breeding.ResultsTo understand the genetic networks underlying phenotypic correlations, we collected 809 soybean accessions worldwide and phenotyped them for two years at three locations for 84 agronomic traits. Genome-wide association studies identified 245 significant genetic loci, among which 95 genetically interacted with other loci. We determined that 14 oil synthesis-related genes are responsible for fatty acid accumulation in soybean and function in line with an additive model. Network analyses demonstrated that 51 traits could be linked through the linkage disequilibrium of 115 associated loci and these links reflect phenotypic correlations. We revealed that 23 loci, including the known Dt1, E2, E1, Ln, Dt2, Fan, and Fap loci, as well as 16 undefined associated loci, have pleiotropic effects on different traits.ConclusionsThis study provides insights into the genetic correlation among complex traits and will facilitate future soybean functional studies and breeding through molecular design.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1289-9) contains supplementary material, which is available to authorized users.

BackgroundDrug repositioning offers the possibility of faster development times and reduced risks in drug discovery. With the rapid development of high-throughput technologies and ever-increasing accumulation of whole genome-level datasets, an increasing number of diseases and drugs can be comprehensively characterized by the changes they induce in gene expression, protein, metabolites and phenotypes.Methodology/Principal FindingsWe performed a systematic, large-scale analysis of genomic expression profiles of human diseases and drugs to create a disease-drug network. A network of 170,027 significant interactions was extracted from the ∼24.5 million comparisons between ∼7,000 publicly available transcriptomic profiles. The network includes 645 disease-disease, 5,008 disease-drug, and 164,374 drug-drug relationships. At least 60% of the disease-disease pairs were in the same disease area as determined by the Medical Subject Headings (MeSH) disease classification tree. The remaining can drive a molecular level nosology by discovering relationships between seemingly unrelated diseases, such as a connection between bipolar disorder and hereditary spastic paraplegia, and a connection between actinic keratosis and cancer. Among the 5,008 disease-drug links, connections with negative scores suggest new indications for existing drugs, such as the use of some antimalaria drugs for Crohn's disease, and a variety of existing drugs for Huntington's disease; while the positive scoring connections can aid in drug side effect identification, such as tamoxifen's undesired carcinogenic property. From the ∼37K drug-drug relationships, we discover relationships that aid in target and pathway deconvolution, such as 1) KCNMA1 as a potential molecular target of lobeline, and 2) both apoptotic DNA fragmentation and G2/M DNA damage checkpoint regulation as potential pathway targets of daunorubicin.Conclusions/SignificanceWe have automatically generated thousands of disease and drug expression profiles using GEO datasets, and constructed a large scale disease-drug network for effective and efficient drug repositioning as well as drug target/pathway identification.

While the androgen receptor (AR) might promote renal cell carcinoma (RCC) initiation and progression, the molecular mechanisms involved remain largely unclear. Here, we discovered the novel LncRNA-SARCC, which was suppressed and associated with better prognosis in RCC. Preclinical studies using multiple RCC cells and in vivo mouse model indicated that LncRNA-SARCC could attenuate RCC cell invasion, migration and proliferation in vitro and in vivo. Mechanistically, LncRNA-SARCC bound and destabilized AR protein with an inhibition of AR function, which led to transcriptionally de-repress miR-143-3p expression, thus inhibition of its downstream signals including AKT, MMP-13, K-RAS and P-ERK. In addition, bisulfite sequencing analysis substantiated that LncRNA-SARCC promoter was highly methylated in renal cancer tissues compared with paired non-cancerous renal tissues. Notably, treating with Sunitinib, the multi-targeted receptor tyrosine kinase inhibitor, increased the expression of LncRNA-SARCC, which decreased RCC cells resistance to Sunitinib. Thus, our study presented a road map for targeting this newly identified LncRNA-SARCC and its pathway, which expands potential therapeutic strategies for RCC treatment.

We have determined, by high resolution x-ray analysis, 10 structures comprising the mRNA cap-specific methyltransferase VP39 or specific mutants thereof in the presence of methylated nucleobase analogs (N1-methyladenine, N3-methyladenine, N1-methylcytosine, N3-methylcytosine) and their unmethylated counterparts, or nucleoside N7-methylguanosine. Together with solution affinity studies and previous crystallographic data for N7-methylguanosine and its phosphorylated derivatives, these data demonstrate that only methylated, The ability of proteins to discriminate alkylated from nonalkylated nucleic acids is of central importance in numerous biological processes (1, 2). VP39, a vaccinia virus protein, is an excellent system with which to study protein interactions with methylated nucleobases and mRNA recognition. In addition to serving as a processivity factor for the vaccinia poly(A) polymerase (3), VP39 acts at the mRNA 5Ј end as a cap 0 [N7-methylguanosine (m 7 G) (5Ј)pppN⅐⅐⅐)]-specific (nucleoside-2Ј-O-)-methyltransferase (4). To perform this latter function, VP39 must specifically recognize the N7-methylguanine (m 7 Gua) moiety of the cap nucleotide while also binding the mRNA transcript in a sequence-nonspecific manner.High resolution structures for VP39 complexed with its S-adenosylmethionine coenzyme product S-adenosylhomocysteine plus capped nucleotides m (5-7). From these studies, three factors emerged as contributors to VP39's specific recognition of the cap's m 7 Gua moiety: (i) van der Waals contacts with the 7-methyl group itself; (ii) electrostatic interactions with guanine-specific polar functionalities, via hydrogen bonds and salt links; and (iii) enhanced stacking interactions arising from partial orbital or charge transfer interactions between the highest occupied molecular orbital of each of the two donor rings (i.e., the aromatic side chains of Tyr 22 and Phe 180) and the lowest unoccupied molecular orbital of the acceptor ring [the N(7)-methylated guanine moiety, which possesses a net positive charge at pH values Ͻ7.5 (8)]. Similar features have more recently been observed in the structure of eIF4E, where m 7 G(5Ј)pp is found sandwiched between two Trp residues (9). The extension of our studies to complexes of wild-type and active mutant VP39 with other methylated, positively charged bases, as described herein, provides evidence that the major determinant of mRNA cap recognition is the enhanced stacking interaction. Additional features of methylated-base binding will also be described.