Identification and molecular evolution of the GLX genes in 21 plant species: a focus on the Gossypium hirsutum.

BACKGROUND: The glyoxalase system includes glyoxalase I (GLXI), glyoxalase II (GLXII) and glyoxalase III (GLXIII), which are responsible for methylglyoxal (MG) detoxification and involved in abiotic stress responses such as drought, salinity and heavy metal. RESULTS: In this study, a total of 620 GLX family genes were identified from 21 different plant species. The results of evolutionary analysis showed that GLX genes exist in all species from lower plants to higher plants, inferring that GLX genes might be important for plants, and GLXI and GLXII account for the majority. In addition, motif showed an expanding trend in the process of evolution. The analysis of cis-acting elements in 21 different plant species showed that the promoter region of the GLX genes were rich in phytohormones and biotic and abiotic stress-related elements, indicating that GLX genes can participate in a variety of life processes. In cotton, GLXs could be divided into two groups and most GLXIs distributed in group I, GLXIIs and GLXIIIs mainly belonged to group II, indicating that there are more similarities between GLXII and GLXIII in cotton evolution. The transcriptome data analysis and quantitative real-time PCR analysis (qRT-PCR) show that some members of GLX family would respond to high temperature treatment in G.hirsutum. The protein interaction network of GLXs in G.hirsutum implied that most members can participate in various life processes through protein interactions. CONCLUSIONS: The results elucidated the evolutionary history of GLX family genes in plants and lay the foundation for their functions analysis in cotton.

New evidence of consistency between phylogeny and morphology for two taxa in ciliated protists, the subclasses Oligotrichia and Choreotrichia (Protista, Ciliophora).

Marine planktonic ciliates are largely oligotrichs and choreotrichs, which are two subclasses of the class Spirotrichea. The current phylogenetic assignments of oligotrichs and choreotrichs are inconsistent with previous results based on morphological features, probably hindered by the limited information from a single gene locus. Here we provide 53 new sequences from small subunit ribosomal RNA (SSU rDNA), ITS1-5.8S rDNA-ITS2, and large subunit ribosomal RNA (LSU rDNA) gene loci in 25 oligotrich and choreotrich species. We also predict RNA secondary structures for the ITS2 regions in 55 species, 48 species of which are reported for the first time. Based on these novel data, we make a more comprehensive phylogenetic reconstruction, revealing consistency between morphological taxonomy and an updated phylogenetic system for oligotrichs and choreotrichs. With the addition of data from ciliature patterns and genes, the phylogenetic analysis of the subclass Oligotrichia suggests three evolutionary trajectories, among which: 1) Novistrombidium asserts an ancestral ciliary pattern in Oligotrichia; 2) the subgenera division of Novistrombidium and Parallelostrombidium are fully supported; 3) the three families (Tontoniidae, Pelagostrombidiidae and Cyrtostrombidiidae) all evolved from the most diverse family Strombidiidae, which explains why strombidiids consistently form polyphyletic clades. In the subclass Choreotrichia, Strombidinopsis likely possesses an ancestral position to other choreotrichs, and both phylogenetic analysis and RNA secondary structure prediction support the hypothesis that tintinnids may have evolved from Strombidinopsis. The results presented here offer an updated hypothesis for the evolutionary history of oligotrichs and choreotrichs based on new evidence obtained by expanding sampling of molecular information across multiple gene loci.

A flexible network-based imputing-and-fusing approach towards the identification of cell types from single-cell RNA-seq data.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) provides an effective tool to investigate the transcriptomic characteristics at the single-cell resolution. Due to the low amounts of transcripts in single cells and the technical biases in experiments, the raw scRNA-seq data usually includes large noise and makes the downstream analyses complicated. Although many methods have been proposed to impute the noisy scRNA-seq data in recent years, few of them take into account the prior associations across genes in imputation and integrate multiple types of imputation data to identify cell types. RESULTS: We present a new framework, NetImpute, towards the identification of cell types from scRNA-seq data by integrating multiple types of biological networks. We employ a statistic method to detect the noise data items in scRNA-seq data and develop a new imputation model to estimate the real values of data noise by integrating the PPI network and gene pathways. Meanwhile, based on the data imputed by multiple types of biological networks, we propose an integrated approach to identify cell types from scRNA-seq data. Comprehensive experiments demonstrate that the proposed network-based imputation model can estimate the real values of noise data items accurately and integrating the imputation data based on multiple types of biological networks can improve the identification of cell types from scRNA-seq data. CONCLUSIONS: Incorporating the prior gene associations in biological networks can potentially help to improve the imputation of noisy scRNA-seq data and integrating multiple types of network-based imputation data can enhance the identification of cell types. The proposed NetImpute provides an open framework for incorporating multiple types of biological network data to identify cell types from scRNA-seq data.