Biology subjects

Shuai, P.

Publications and source records attributed to Shuai, P..

4 recordsLinked to original sources

bioRxiv2022

Chinese fir genome and the evolution of gymnosperms

Seed plants comprise angiosperms and gymnosperms. The latter includes gnetophytes, cycads, Ginkgo, and conifers. Conifers are distributed worldwide, with 630 species distributed across eight families and 70 genera. Their distinctiveness has triggered much debate on their origin, evolution, and phylogenetic placement among seed plants. To better understand the evolution of gymnosperms and their relation to other seed plants, we report here a high-quality genome sequence for a tree species, Chinese fir (Cunninghamia lanceolata), which has excellent timber quality and high aluminum adaptability and is a member of Cupressaceae with high levels of heterozygosity. We assembled an 11.24 Gb genome with a contig N50 value of 2.15 Mb and anchored the 10.89 Gb sequence to 11 chromosomes. Phylogenomic analyses showed that cycads sister to Ginkgo, which place to sister in all gymnosperm lineages, and Gnetales within conifers sister to Pinaceae. Whole-genome duplication (WGD) analysis showed that the ancestor of seed plants has differentiated into angiosperms and gymnosperms after having experienced a WGD event. The ancestor of extant gymnosperm has experienced a gymnosperm-specific WGD event and the extant angiosperms do not share a common WGD before their most recent common ancestor diverged into existing angiosperms lineages. Analysis of the MADS-box gene family of C. lanceolata revealed the developmental mechanism of the reproductive organs in C. lanceolata, which supported the (A)B(C) model of the development of gymnosperms reproductive organs. In addition, astringent seeds and shedding of whole branches (with withered leaves) might be a strategy of C. lanceolata that evolved during long-term adaptation to an aluminum-rich environment. The findings also reveal the molecular regulation mechanism of shade tolerance in C. lanceolata seedlings. Our results improve the resolution of ancestral genomic features within seed plants and the knowledge of genome evolution and diversification of gymnosperms.

genomics↗

bioRxiv2021

Revised International Staging System (R-ISS) stage-dependent analysis uncovers oncogenes and potential immunotherapeutic targets in multiple myeloma

Multiple myeloma (MM), characterized by high intratumour heterogeneity, accounts for [~]10% of all haematologic malignancies. Stratified by the Revised International Staging System (R-ISS), little is known about R-ISS-related plasma cell (PC) heterogeneity, gene expression modules in cytotoxic T/NK cells and immunoregulatory ligands and receptors. Herein, we constructed a single-cell transcriptome atlas of bone marrow in normal and R-ISS-staged MM patients. Focusing on PCs, we identified and validated a subset of GZMA+ cytotoxic PCs. In addition, a malignant PC population with high proliferation capability (proliferating PCs) was associated with unfavourable prognosis and EBV infection in our collected samples. Ribonucleotide Reductase Regulatory Subunit M2 (RRM2), a specific marker of proliferating PCs, was shown to induce MM cell line proliferation and serve as a detrimental marker in MM. Subsequently, three R-ISS-dependent gene modules in cytotoxic CD8+ T and NKT cells were identified and functionally analysed. Finally, cell-cell communication between neutrophils and proliferating PCs with cytotoxic CD8+ T and NKT cells was investigated, which identified intercellular ligand receptors and potential immunotargets such as SIRPA-CD47 and TIGIT-NECTIN3. Collectively, this study provides an R-ISS-related single-cell MM atlas and reveals the clinical significance of two PC clusters, as well as potential immunotargets in MM progression.

cancer biology↗

bioRxiv2021

Single-cell transcriptomics unveils xylem cell development and evolution

As the most abundant tissue on Earth1, xylem is responsible for lateral growth in plants. Typical xylem has a radial system composed of ray parenchyma cells and an axial system of fusiform cells2. In most angiosperms, fusiform cells are a combination of vessel elements for water transportation and libriform fibers for mechanical support, while both functions are performed together by tracheids in other vascular plants2. However, little is known about the developmental programs and evolutionary relationships of these xylem cell types. Through both single-cell and laser-capture microdissection transcriptomic profiling, here we demonstrate the developmental lineages of ray and fusiform cells in stem-differentiating xylem across four divergent woody angiosperms. Cross-species analyses of single-cell trajectories reveal highly conserved ray, yet variable fusiform, lineages across angiosperms. Core eudicots Populus trichocarpa and Eucalyptus grandis share nearly identical fusiform lineages. The tracheids in the basal eudicot Trochodendron aralioides, an evolutionarily reversed character3, 4, exhibit strong transcriptomic similarity to vessel elements but not libriform fibers, suggesting that water transportation, instead of mechanical support, is the major feature. We also found that the more basal angiosperm Liriodendron chinense has a fusiform lineage distinct from that in core eudicots. This evo-developmental framework provides a comprehensive understanding of the formation of xylem cell lineages across multiple plant species spanning over a hundred million years of evolutionary history5.

plant biology↗

bioRxiv2019

Big data reveals deep associations in physical examination indicators and can help predict overall underlying health status

Because of lacking of the systematic investigation of correlations between the physical examination indicators (PEIs), currently most of them are independently used for disease warning. This results in very limited diagnostic values of general physical examination. Here, we first systematically analyzed the correlations between 221 PEIs in healthy and in 34 unhealthy states in 803,614 peoples in China. We revealed rich relevant between PEIs in healthy physical status (7,662 significant correlations, 31.5% of all). However, in disease conditions, the PEI correlations changed. We further focused on the difference of these PEIs between healthy and 35 unhealthy physical status, 1,239 significant PEI difference were discovered suggesting as candidate disease markers. Finally, we established machine learning algorithms to predict the health status by using 15%-16% PEIs by feature extraction, which reached 66%-99% precision predictions depending on the physical state. This new encyclopedia of PEI correlation provides rich information to chronic disease diagnosis. Our developed machine learning algorithms will have fundamental impact in practice of general physical examination.

pathology↗