Predictive modeling of plant messenger RNA polyadenylation sites

BACKGROUND: One of the essential processing events during pre-mRNA maturation is the post-transcriptional addition of a polyadenine [poly(A)] tail. The 3'-end poly(A) track protects mRNA from unregulated degradation, and indicates the integrity of mRNA through recognition by mRNA export and translation machinery. The position of a poly(A) site is predetermined by signals in the pre-mRNA sequence that are recognized by a complex of polyadenylation factors. These signals are generally tri-part sequence patterns around the cleavage site that serves as the future poly(A) site. In plants, there is little sequence conservation among these signal elements, which makes it difficult to develop an accurate algorithm to predict the poly(A) site of a given gene. We attempted to solve this problem. RESULTS: Based on our current working model and the profile of nucleotide sequence distribution of the poly(A) signals and around poly(A) sites in Arabidopsis, we have devised a Generalized Hidden Markov Model based algorithm to predict potential poly(A) sites. The high specificity and sensitivity of the algorithm were demonstrated by testing several datasets, and at the best combinations, both reach 97%. The accuracy of the program, called poly(A) site sleuth or PASS, has been demonstrated by the prediction of many validated poly(A) sites. PASS also predicted the changes of poly(A) site efficiency in poly(A) signal mutants that were constructed and characterized by traditional genetic experiments. The efficacy of PASS was demonstrated by predicting poly(A) sites within long genomic sequences. CONCLUSION: Based on the features of plant poly(A) signals, a computational model was built to effectively predict the poly(A) sites in Arabidopsis genes. The algorithm will be useful in gene annotation because a poly(A) site signifies the end of the transcript. This algorithm can also be used to predict alternative poly(A) sites in known genes, and will be useful in the design of transgenes for crop genetic engineering by predicting and eliminating undesirable poly(A) sites

Experimental investigation on GaAs/GaAlAs quantum confined stark effect and self electro-optic bistabe effect

A GaAs/GaAlAs Self Electro-optic Effect Device (SEED) with MQW pin structure has been fabricated. The measurement and analysis of the photocurrent spectrum, photocurrent-voltage Characteristics and photocurrent bistability are given. A discussion on the realization of its bistability is also concerned.link_to_subscribed_fulltex

Transcriptomic and Metabolomic Analyses Reveal the Key Genes Related to Shade Tolerance in Soybean

Soybean (Glycine max) is an important crop, rich in proteins, vegetable oils and several other phytochemicals, which is often affected by light during growth. However, the specific regulatory mechanisms of leaf development under shade conditions have yet to be understood. In this study, the transcriptome and metabolome sequencing of leaves from the shade-tolerant soybean ‘Nanxiadou 25′ under natural light (ND1) and 50% shade rate (SHND1) were carried out, respectively. A total of 265 differentially expressed genes (DEGs) were identified, including 144 down-regulated and 121 up-regulated genes. Meanwhile, KEGG enrichment analysis of DEGs was performed and 22 DEGs were significantly enriched in the top five pathways, including histidine metabolism, riboflavin metabolism, vitamin B6 metabolism, glycerolipid metabolism and cutin, suberine and wax biosynthesis. Among all the enrichment pathways, the most DEGs were enriched in plant hormone signaling pathways with 19 DEGs being enriched. Transcription factors were screened out and 34 differentially expressed TFs (DETFs) were identified. Weighted gene co-expression network analysis (WGCNA) was performed and identified 10 core hub genes. Combined analysis of transcriptome and metabolome screened out 36 DEGs, and 12 potential candidate genes were screened out and validated by quantitative real-time polymerase chain reaction (qRT-PCR) assay, which may be related to the mechanism of shade tolerance in soybean, such as ATP phosphoribosyl transferase (ATP-PRT2), phosphocholine phosphatase (PEPC), AUXIN-RESPONSIVE PROTEIN (IAA17), PURPLE ACID PHOSPHATASE (PAP), etc. Our results provide new knowledge for the identification and function of candidate genes regulating soybean shade tolerance and provide valuable resources for the genetic dissection of soybean shade tolerance molecular breeding

QTL Mapping for Seed Quality Traits under Multiple Environments in Soybean (<i>Glycine max</i> L.)

Soybeans are the main source of vegetable protein and edible oil for humans, with an average content of about 40% crude protein and 20% crude fat. Soybean quality traits are mostly quantitative traits controlled by multiple genes. The quantitative trait loci (QTL) for soybean quality traits and mining related candidate genes are of great significance for the molecular breeding of soybean quality traits and understanding the genetic mechanism of protein/fat metabolism. In this study, the F2 population was derived from the high-protein material Changjiang Chun 2 and Jiyu 166. On the basis of a genetic linkage map constructed in our previous study, the QTL of crude protein content, crude oil content and fatty acid fractions were detected using the multiple-QTL model (MQM) mapping method. The results show that a total of 92 QTL were obtained affecting quality traits under three environments, including 14 QTL of crude oil content, 9 QTL of crude protein content, and 20, 20, 11, 10 and 8 QTL for the content of palmitic, stearic, oleic, linoleic and linolenic acids, respectively. Sixteen QTL clusters were identified, among which Loci01.1, Loci06.1 and Loci11.1 were identified as stable QTL clusters with phenotypic contribution rates of 16.5%, 16.4% and 12.1%, respectively, and candidate genes were mined in their regions. A total of 32 candidate genes related to soybean quality were finally screened via GO enrichment and gene annotation. The present study lies the foundations for understanding the genetic mechanism and elite germplasm innovation of seed quality in soybean

Construction of Genetic Map and QTL Mapping for Seed Size and Quality Traits in Soybean (<i>Glycine max</i> L.)

Soybean (Glycine max L.) is the main source of vegetable protein and edible oil for humans, with an average content of about 40% crude protein and 20% crude fat. Soybean yield and quality traits are mostly quantitative traits controlled by multiple genes. The quantitative trait loci (QTL) mapping for yield and quality traits, as well as for the identification of mining-related candidate genes, is of great significance for the molecular breeding and understanding the genetic mechanism. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiangchun 2 and Yushuxian 2 were selected as the mapping population to construct a molecular genetic linkage map. A genetic map containing 445 SSR markers with an average distance of 5.3 cM and a total length of 2375.6 cM was obtained. Based on constructed genetic map, 11 traits including hundred-seed weight (HSW), seed length (SL), seed width (SW), seed length-to-width ratio (SLW), oil content (OIL), protein content (PRO), oleic acid (OA), linoleic acid (LA), linolenic acid (LNA), palmitic acid (PA), stearic acid (SA) of yield and quality were detected by the multiple- d size traits and 113 QTLs related to quality were detected by the multiple QTL model (MQM) mapping method across generations F2, F2:3, F2:4, and F2:5. A total of 71 QTLs related to seed size traits and 113 QTLs related to quality traits were obtained in four generations. With those QTLs, 19 clusters for seed size traits and 20 QTL clusters for quality traits were summarized. Two promising clusters, one related to seed size traits and the other to quality traits, have been identified. The cluster associated with seed size traits spans from position 27876712 to 29009783 on Chromosome 16, while the cluster linked to quality traits spans from position 12575403 to 13875138 on Chromosome 6. Within these intervals, a reference genome of William82 was used for gene searching. A total of 36 candidate genes that may be involved in the regulation of soybean seed size and quality were screened by gene functional annotation and GO enrichment analysis. The results will lay the theoretical and technical foundation for molecularly assisted breeding in soybean