Background. WRKY proteins, which comprise one of the largest transcription factor (TF) families in the plant kingdom, play crucial roles in plant development and stress responses. Despite several studies on WRKYs in wheat (Triticum aestivum L.), functional annotation information about wheat WRKYs is limited. Results. Here, 171 TaWRKY TFs were identified from the whole wheat genome and compared with proteins from 19 other species representing nine major plant lineages. A phylogenetic analysis, coupled with gene structure analysis and motif determination, divided these TaWRKYs into seven subgroups (Group I, IIa-e, and III). Chromosomal location showed that most TaWRKY genes were enriched on four chromosomes, especially on chromosome 3B. In addition, 85 (49.7%) genes were either tandem (5) or segmental duplication (80), which suggested that though tandem duplication has contributed to the expansion of TaWRKY family, segmental duplication probably played a more pivotal role. Analysis of cis-acting elements revealed putative functions of WRKYs in wheat during development as well as under numerous biotic and abiotic stresses. Finally, the expression of TaWRKY genes in flag leaves, glumes, and lemmas under water-deficit condition were analyzed. Results showed that different TaWRKY genes preferentially express in specific tissue during the grain-filling stage. Conclusion. Our results provide a more extensive insight on WRKY gene family in wheat, and also contribute to the screening of more candidate genes for further investigation on function characterization of WRKYs under various stresses.

Background Clustering the metagenomic contigs into potential genomes is a key step to investigate the functional roles of microbial populations. Existing algorithms have achieved considerable success with simulated or real sequencing datasets. However, accurately classifying contigs from complex metagenomes is still a challenge. Results We introduced a novel clustering algorithm, MetaDecoder, which can classify metagenomic contigs based on the frequencies of k-mers and coverages. MetaDecoder was built as a two-layer model with the first layer being a GPU-based modified Dirichlet process Gaussian mixture model (DPGMM), which controls the weight of each DPGMM cluster to avoid over-segmentation by dynamically dissolving contigs in small clusters and reassigning them to the remaining clusters. The second layer comprises a semi-supervised k-mer frequency probabilistic model and a modified Gaussian mixture model for modeling the coverage based on single copy marker genes. Benchmarks on simulated and real-world datasets demonstrated that MetaDecoder can be served as a promising approach for effectively clustering metagenomic contigs. Conclusions In conclusion, we developed the GPU-based MetaDecoder for effectively clustering metagenomic contigs and reconstructing microbial communities from microbial data. Applying MetaDecoder on both simulated and real-world datasets demonstrated that it could generate more complete clusters with lower contamination. Using MetaDecoder, we identified novel high-quality genomes and expanded the existing catalog of bacterial genomes.

More than 90% of autoimmune-associated variants are located in noncoding regions, leading to challenges in deciphering the underlying causal roles of functional variants and genes and biological mechanisms. Therefore, to reduce the gap between traditional genetic findings and mechanistic understanding of disease etiologies and clinical drug development, it is important to translate systematically the regulatory mechanisms underlying noncoding variants. Here, we prioritized functional noncoding SNPs with regulatory gene targets associated with 19 autoimmune diseases by incorporating hundreds of immune cell–specific multiomics data. The prioritized SNPs are associated with transcription factor (TF) binding, histone modification, or chromatin accessibility, indicating their allele-specific regulatory roles. Their target genes are significantly enriched in immunologically related pathways and other known immunologically related functions. We found that 90.1% of target genes are regulated by distal SNPs involving several TFs (e.g., the DNA-binding protein CCCTC-binding factor [CTCF]), suggesting the importance of long-range chromatin interaction in autoimmune diseases. Moreover, we predicted potential drug targets for autoimmune diseases, including 2 genes ( NFKB1 and SH2B3 ) with known drug indications on other diseases, highlighting their potential drug repurposing opportunities. Taken together, these findings may provide useful information for future experimental follow-up and drug applications on autoimmune diseases.

No abstract

No abstract