Bioinformatic analysis of chromatin organization and biased expression of duplicated genes between two poplars with a common whole-genome duplication

Abstract

The nonrandom three-dimensional organization of chromatin plays an important role in the regulation of gene expression. However, it remains unclear whether this organization is conserved and whether it is involved in regulating gene expression during speciation after whole-genome duplication (WGD) in plants. In this study, high-resolution interaction maps were generated using high-throughput chromatin conformation capture (Hi-C) techniques for two poplar species, Populus euphratica and Populus alba var. pyramidalis, which diverged ~14 Mya after a common WGD. We examined the similarities and differences in the hierarchical chromatin organization between the two species, including A/B compartment regions and topologically associating domains (TADs), as well as in their DNA methylation and gene expression patterns. We found that chromatin status was strongly associated with epigenetic modifications and gene transcriptional activity, yet the conservation of hierarchical chromatin organization across the two species was low. The divergence of gene expression between WGD-derived paralogs was associated with the strength of chromatin interactions, and colocalized paralogs exhibited strong similarities in epigenetic modifications and expression levels. Thus, the spatial localization of duplicated genes is highly correlated with biased expression during the diploidization process. This study provides new insights into the evolution of chromatin organization and transcriptional regulation during the speciation process of poplars after WGD.

Transcriptional landscape of highly lignified poplar stems at single-cell resolution

“…The raw scRNA-seq reads from wood and bark tissues were separately aligned to the P. alba var. pyramidalis reference genome [ 99 ], using the Cell Ranger pipelines ( https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger ) with default parameters (v2.0, 10x Genomics), and expression matrices for each gene and each cell were generated. The gene-cell matrices were then loaded into the Seurat package (v3.1.0) for further analysis [ 100 ].…”

“…Briefly, total RNA extracted from each tissue was used for library construction and deep sequencing by BGISEQ-500 (BGI-Shenzhen, China). The obtained clean reads were aligned to the reference genome [ 99 ] using HISAT2 (v2.1.0) [ 109 ], and then the expression levels were calculated and normalized by StringTie (v1.3.3b) [ 110 ]. Finally, the Pearson correlations between bulk RNA-seq and scRNA-seq datasets were calculated in R. In addition, the transcriptome data of cryosections from the secondary phloem, vascular cambium, and wood-forming tissues of P. tremula were downloaded [ 15 ].…”

“…For each gene, the cells with at least 1 UMI were assigned a value of 1, and all other cells were assigned a value of 0. To explore functional redundancy between paralogs, we first identified WGD-derived paralogs as previously described [ 99 ], and filtered the gene pairs if one or both copies lacked UMI values. In total, 5143 WGD-derived paralogs were finally retained to compare their Jaccard index values.…”

MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction

“…To overcome these shortcomings, we will employ natural language processing technology 58 , 59 to improve manual update efficiency and use high-performance computing 60 , 61 , 62 to reduce the processing time for similarity computing. Finally, we will make MDCB as a highly integrated web-based MC-related data platform by integrating more advanced bioinformatics applications and algorithms 27 , 63 , 64 , 65 , 66 , 67 , 68 , 69 in the future.…”

Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research

“…However, it is inaccurate to predict the future state of genomics time series with nonlinear complicated interactions because the Lorentz system is not good at processing nonlinear complicated interactions ( Lai et al, 2018 ). Currently, delay embedding theory ( Sauer et al, 1991 ; Holmes et al, 2012 ) is commonly used to transform the spatial information (complicated interactions) into temporal information (the future state of the time series ( Chen et al, 2020 )) for dimensional reduction ( Gao et al, 2017 ; Li et al, 2017 ; Xia et al, 2017 ; Zhang et al, 2019b ; Zhang et al, 2019c ; Wu et al, 2020 ; You et al, 2020 ; Zhang et al, 2021b ), whereas Koopman theory ( Koopman, 1931 ) can switch the nonlinear system into a linear system to reduce computing cost. Therefore, our first research question asks if we can develop such a time series predictive model that integrates the Lorentz system with delay embedding and Koopman theory to accurately predict the future state of genomics time series with chaotic behavior.…”