Xuan, W. | Search bioRxiv

bioRxiv2021

The volatile cedrene from plant beneficial Trichoderma guizhouense modulates Arabidopsis root development through auxin transport and signaling

Rhizosphere microorganisms interact with plant roots by producing chemical signals to regulate root development. However, the involved distinct bioactive compounds and the signal transduction pathways are remaining to be identified. Here, we show that sesquiterpenes (SQTs) are the main volatile compounds produced by plant beneficial Trichoderma guizhouense NJAU 4742, inhibition of SQTs synthesis in this strain indicated their involvement in plant-fungus cross-kingdom signaling. SQTs component analysis further identified the cedrene, a high abundant SQT in strain NJAU 4742, could stimulate plant growth and root development. Genetic analysis and auxin transport inhibition showed that auxin receptor TIR1, AFB2, auxin-responsive protein IAA14, and transcription factor ARF7, ARF19 affect the response of lateral roots to cedrene. Moreover, auxin influx carrier AUX1, efflux carrier PIN2 were also indispensable for cedrene-induced lateral root formation. Confocal imaging showed that cedrene affected the expression of pPIN2:PIN2:GFP and pPIN3:PIN3:GFP, which may be related to the effect of cedrene on root morphology. These results suggest that a novel SQT molecule from plant beneficial T. guizhouense can regulate plant root development through auxin transport and signaling. One-sentence SummaryCedrene, a high- abundance sesquiterpenes produced by plant beneficial Trichoderma guizhouense NJAU 4742, stimulates Arabidopsis lateral root formation and primary root elongation by relying on auxin signaling pathway and auxin transporter PIN2 and AUX1.

microbiology↗

bioRxiv2021

Learning association for single-cell transcriptomics by integrating profiling of gene expression and alternative polyadenylation

Single-cell RNA-sequencing (scRNA-seq) has enabled transcriptome-wide profiling of gene expressions in individual cells. A myriad of computational methods have been proposed to learn cell-cell similarities and/or cluster cells, however, high variability and dropout rate inherent in scRNA-seq confounds reliable quantification of cell-cell associations based on the gene expression profile alone. Lately bioinformatics studies have emerged to capture key transcriptome information on alternative polyadenylation (APA) from standard scRNA-seq and revealed APA dynamics among cell types, suggesting the possibility of discerning cell identities with the APA profile. Complementary information at both layers of APA isoforms and genes creates great potential to develop cost-efficient approaches to dissect cell types based on multiple modalities derived from existing scRNA-seq data without changing experimental technologies. We proposed a toolkit called scLAPA for learning association for single-cell transcriptomics by combing single-cell profiling of gene expression and alternative polyadenylation derived from the same scRNA-seq data. We compared scLAPA with seven similarity metrics and five clustering methods using diverse scRNA-seq datasets. Comparative results showed that scLAPA is more effective and robust for learning cell-cell similarities and clustering cell types than competing methods. Moreover, with scLAPA we found two hidden subpopulations of peripheral blood mononuclear cells that were undetectable using the gene expression data alone. As a comprehensive toolkit, scLAPA provides a unique strategy to learn cell-cell associations, improve cell type clustering and discover novel cell types by augmentation of gene expression profiles with polyadenylation information, which can be incorporated in most existing scRNA-seq pipelines. scLAPA is available at https://github.com/BMILAB/scLAPA.

bioinformatics↗