An Efficient Root Transformation System for Recalcitrant Vicia sativa

Common vetch (Vicia sativa) is a multi-purpose legume widely used in pasture and crop rotation systems. Vetch seeds have desirable nutritional characteristics and are often used to feed ruminant animals. Although transcriptomes are available for vetch, problems with genetic transformation and plant regeneration hinder functional gene studies in this legume species. Therefore, the aim of this study was to develop a simple, efficient and rapid hairy root transformation system for common vetch to facilitate functional gene analysis. At first, we infected the hypocotyls of 5-day-old in vitro or in vivo, soil-grown seedlings with Rhizobium rhizogenes K599 using a stabbing method and produced transgenic hairy roots after 24 days at 19 and 50% efficiency, respectively. We later improved the hairy root transformation in vitro by infecting different explants (seedling, hypocotyl-epicotyl, and shoot) with R. rhizogenes. We observed hairy root formation at the highest efficiency in shoot and hypocotyl-epicotyl explants with 100 and 93% efficiency, respectively. In both cases, an average of four hairy roots per explant were obtained, and about 73 and 91% of hairy roots from shoot and hypocotyl-epicotyl, respectively, showed stable expression of a co-transformed marker β-glucuronidase (GUS). In summary, we developed a rapid, highly efficient, hairy root transformation method by using R. rhizogenes on vetch explants, which could facilitate functional gene analysis in common vetch.

A computational pipeline for functional gene discovery

Many computational pipelines exist for the detection of differentially expressed genes. However, computational pipelines for functional gene detection rarely exist. We developed a new computational pipeline for functional gene identification from transcriptome profiling data. Key features of the pipeline include batch effect correction, clustering optimization by gap statistics, gene ontology analysis of clustered genes, and literature analysis for functional gene discovery. By leveraging this pipeline on RNA-seq datasets from two mouse retinal development studies, we identified 7 candidate genes involved in the formation of the photoreceptor outer segment. The expression of top three candidate genes (Pde8b, Laptm4b, and Nr1h4) in the outer segment of the developing mouse retina were experimentally validated by immunohistochemical analysis. This computational pipeline can accurately predict novel functional gene for a specific biological process, e.g., development of the outer segment and synapses of the photoreceptor cells in the mouse retina. This pipeline can also be useful to discover functional genes for other biological processes and in other organs and tissues.

A Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study

Critical in revealing cell heterogeneity and identifying new cell subtypes, cell clustering based on single-cell RNA sequencing (scRNA-seq) is challenging. Due to the high noise, sparsity, and poor annotation of scRNA-seq data, existing state-of-the-art cell clustering methods usually ignore gene functions and gene interactions. In this study, we propose a feature extraction method, named FEGFS, to analyze scRNA-seq data, taking advantage of known gene functions. Specifically, we first derive the functional gene sets based on Gene Ontology (GO) terms and reduce their redundancy by semantic similarity analysis and gene repetitive rate reduction. Then, we apply the kernel principal component analysis to select features on each non-redundant functional gene set, and we combine the selected features (for each functional gene set) together for subsequent clustering analysis. To test the performance of FEGFS, we apply agglomerative hierarchical clustering based on FEGFS and compared it with seven state-of-the-art clustering methods on six real scRNA-seq datasets. For small datasets like Pollen and Goolam, FEGFS outperforms all methods on all four evaluation metrics including adjusted Rand index (ARI), normalized mutual information (NMI), homogeneity score (HOM), and completeness score (COM). For example, the ARIs of FEGFS are 0.955 and 0.910, respectively, on Pollen and Goolam; and those of the second-best method are only 0.938 and 0.910, respectively. For large datasets, FEGFS also outperforms most methods. For example, the ARIs of FEGFS are 0.781 on both Klein and Zeisel, which are higher than those of all other methods but slight lower than those of SC3 (0.798 and 0.807, respectively). Moreover, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation and in inferring cell lineage trajectories. As for application, take glioma as an example; we demonstrated that our clustering methods could identify important cell clusters related to glioma and also inferred key marker genes related to these cell clusters.

Metagenomic Analysis of The Effects of Salinity On Microbial Diversity and Functional Gene Diversity in Kongsfjorden Estuary

Due to the inflow of meltwater from the Midre Lovénbreen glacier upstream of Kongsfjorden, the salinity of Kongsfjorden increases from the estuary to the interior of the fjord. Our goal was to determine which bacterial taxa and metabolism-related gene abundance were affected by changes in salinity, and whether salinity is correlated with genes related to nitrogen and sulfur cycling in fjord ecosystem using metagenomic analysis. Our data indicate that changes in salinity may affect some bacterial taxa, such as the relative abundance of Alphaproteobacteria and Deltaproteobacteria is higher at high salinity sites, while the relative abundance of Gammaproteobacteria and Betaproteobacteria is more dominant at low salinity sites. In addition, the relative abundance of some bacteria at the high and low salinity sites was different at the family level. For example, Rhodobacteraceae, Pseudoalteromonadaceae, Flavobacteriaceae, Vibrionaceae at the high salinity site Colwelliaceae, Chromatiaceae and Alteromonadaceae at the low salinity site are affected by salinity. In terms of functional gene diversity, our study proved that salinity could affect the relative abundance of related genes by affecting the metabolic mechanism of microorganisms. In addition to salinity, functional attributes of microorganisms themselves were also important factors affecting the relative abundance of metabolism-related genes. In addition, salinity has a certain effect on the relative abundance of genes related to nitrogen and sulfur cycling.