Identification of novel soybean oil content-related genes using QTL-based collinearity analysis from the collective soybean genome

Soybean is a global principal source of edible plant oil. As more soybean oil-related quantitative trait loci(QTLs) have been located in the collective genome, it is urgent to establish a classification system for these distributed QTLs. A collinear platform may be useful to characterize and identify relationships among QTLs as well as aid in novel gene discovery. In this study, the collinearity MCScan X algorithm and collective soybean genomic information were used to construct collinearity blocks, to which soybean oil-related QTLs were mapped. The results demonstrated that 666 collinearity blocks were detected in the soybean genome across 20 chromosomes, and 521 collinearity relationships existed in 231 of the 242 effective soybean oil-related QTLs. This included 214 inclusion relationships and 307 intersecting relationships. Among them, the collinearity among QTLs that are related to soybean oil content was shown on a maximum of seven chromosomes and minimum of one chromosome, with the majority of QTLs having collinearity on two chromosomes. Using overlapping hotspot regions in the soybean oil QTLs with collinearity, we mined for novel oil content-related genes. Overall, we identified 23 putatively functional genes associated with oil content in soybean and annotated them using a number of annotation databases. Our findings provide a valuable framework for elucidating evolutionary relationships between soybean oil-related QTLs and lay a foundation for functional marker-assisted breeding relating to soybean oil content.

Zinc finger transcription factor ZAT family genes confer multi-tolerances in Gossypium hirsutum L.

ZAT(Zinc Finger of Arabidopsis thaliana)proteins are composed of a plant-specific transcription factor family,which play an important role in plant growth,development,and stress resista nee.To study the potential function of ZAT family in cotton,the whole genome identification,expression,and structure analysis of ZAT gene family were carried out.In this study,our analysis revealed the presenee of 115Z 56,59,and 115 ZAT genes in Gossypium hirsutum,G raimondii,G.arboreum and G barbadense,respectively.According to the number of domains and phylogenetic characteristics,we divided ZAT genes of four Gossypium species into 4 different clades,and further divided them into 11 subfamilies.The results of collinearity an alysis showed that segmental duplicati on was the main method to amplify the cotton ZAT genes family.Analysis of c/s-elements of promoters indicated that most GhZAT genes contained c/5-elements related to plant hormones and abiotic stress.According to heatmap analysis,the expression patterns of GhZAT genes under different stresses indicated that GhZAT genes were significantly involved in the response to cold,heat,salt,and PEG stress,possibly through different mechanisms.Among the highly expressed genes,we cloned a G hirsutum gene GhZAT67.Through virus-induced gene silencing(VIGS),we found that its expression level decreased significantly after being sileneed.Under alkaline treatment,the wilting degree of silenced plants was even greater than the wild type,which proved that GhZAT67 gene was involved in the response to alkaline stress.

Comprehensive cross cancer analyses reveal mutational signature cancer specificity

Mutational signatures refer to distinct patterns of DNA mutations that occur in a specific context or under certain conditions.It is a powerful tool to describe cancer etiology.We conducted a study to show cancer heterogeneity and cancer specificity from the aspect of mutational signatures through collinearity analysis and machine learning techniques.Through thorough training and independent validation,our results show that while the majority of the mutational signatures are distinct,similarities between certain mutational signature pairs can be observed through both mutation patterns and mutational signature abundance.The observation can potentially assist to determine the etiology of yet elusive mutational signatures.Further analysis using machine learning approaches demonstrated moderate mutational signature cancer specificity.Skin cancer among all cancer types demonstrated the strongest mutational signature specificity.