Dayu ZOU, Shenzhen University,2021.01.11

Description

Metagenomic raw data of the bottom water layers and surface sediments of the Changjiang River Estuary and the Eastern China Sea.

OEP001524

Shen Rong, Nanfang hospital,2022.07.15

Description

Single-cell transcriptomics of human bronchoalveolar lavage fluid from immunosuppression in late sepsis patients and healthy controls

OEP003526

Wancheng Chen, Shanghai University of Traditional Chinese Medicine,2023.07.14

Description

The genus Salvia L. (Lamiaceae) comprises myriad distinct medicinal herbs, with terpenoids as one of their major active chemical groups. Abietane-type diterpenoids (ATDs), such as tanshinones and carnosic acids, are specific to Salvia and exhibit taxonomic chemical diversity among lineages. To elucidate how ATD chemical diversity evolved, we carried out large-scale metabolic and phylogenetic analyses of 71 Salvia species, combined with enzyme function, ancestral sequence and chemical trait reconstruction, and comparative genomics experiments. This integrated approach showed that the lineage-wide ATD diversities in Salvia were induced by differences in the oxidation of the terpenoid skeleton at C-20, which was caused by the functional divergence of the cytochrome P450 subfamily CYP76AK. These findings present a unique pattern of chemical diversity in plants that was shaped by the loss of enzyme activity and associated catalytic pathways.

OEP004258

sui weikang, shanghai jiaotong univerisity,2022.08.18

Description

The sedimentary dissolved organic matter (DOM) comprises mainly humic-like compounds regarded as refractory DOM for molecular complexity. However, their roles in supporting the subsurface microbial communities remain poorly explored. Here, we revealed a contrasting relationship between humic-like DOM and microbes in the coastal and deep-sea sediments. In coastal sediments, the majority of the microbial groups were found significantly correlated to labile DOM, while specific microbial groups were found enriched in the deep layers co-varying with humic-like DOM, a net production of which was likely resulted from labile DOM transformation. On the contrary, in the deep-sea sediments, over 70% of the microbial groups were found closely correlated with humic-like DOM, a net consumption of which was demonstrated. The refractory humic-like DOM could serve as an important carbon and energy source for the microbial communities in the energy-limiting sediments, which would have a long-lasting influence on the oceanic refractory DOM cycling.

OEP003598

Sijia Wang, Shanghai Institute of Nutrition and Health，Chinese Academy of Sciences,2021.11.16

Description

This project has included individual-level genotype data (SNP chip, methy_3523_qc0.01.bed,bim,fam) and DNA methylation data (Illumina EPIC, methy_3523.rdata) of 3523 Han Chinese (NSPT), and also the individual-level genotype data (SNP chip, 200505_960Sps_passed.vcf) and DNAm data (Illumina EPIC, beta_norm.rda) of additional 798 individuals (CGZ), which are in controlled access. The individual-level genotype data is not available because of IRB restriction due to privacy concern. The individual-level DNA methylation data can be available with reasonable requests. The project also included mQTL summary statistics (cis1_10.zip, cis11_22.zip, lcis.zip, trans.zip) with threshold of 1e-10 in 3523 Han Chinese (NSPT), the replication for these mQTLs (CGZ_mQTL.rep2.csv) in the CGZ cohort, and blood cell-lineage (lymphoid- and myeloid-) mQTLs estimated by using CELLDMC (cts_mQTL_info.Rd). These data are available for download. Data usage shall be in full compliance with the Regulations on Management of Human Genetic Resources in China. Use of this data requires a citation of our article. For controlled access data, requests are normally processed within two weeks. A template of the data sharing and confidentiality agreement will be sent to the applicant, and the data can be used within the permitted scope after the data sharing and confidentiality agreement has been signed.

OEP002902

Peiwei Chai, ,2023.07.15

Description

OEP004259

Chunye Liu, SIBCB,2023.07.14

Description

For the primary mammary gland scRNA-seq, Wild type (C57BL/6) mice were used. After preparation of primary mammary single cell suspension, we specifically sorted 2000 Procr+ MaSCs (CD31-, CD45-, Ter119-, CD24+, CD29hi) and also enrich 20000 mammary epithelial cells (CD31-, CD45-, Ter119-, CD24+), 30000 mammary immune cells (CD45+) and enrich 10000 stromal cells which contain endothelial cells and mesenchymal cells (CD45-, Ter119-, CD24-) and resuspended total cells at a density of 1000 cells per μl. And then 13,000 single cells were loaded for 10x single cell genomics RNA seq. Filtering, alignment to the mm10 database and unique molecular identifier (UMI)-collapsing were performed using the Cell Ranger (v2) pipeline with default mapping arguments (10′ Genomics). Cell filtering: we used the Seurat 3 R package for data integration, analysis and visualization. To create Seurat object, genes which expressed in at least 3 cells and cells which had at least 200 detected genes, 1500 detected UMIs and no more than 5000 detected UMIs were selected. PC selection: the differentially expressed genes were found by “vst” method and the top 2000 differentially expressed genes were selected for PCA analysis. PCs selection was based on an elbow plot. 20 PCs were used for analysis. Dimensional reduction, cell clustering and data

OEP004257

Chunye Liu, Shanghai Institutes for Biological Sciences,2023.04.14

Description

For the primary mammary gland scRNA-seq, Wild type (C57BL/6) mice were used. After preparation of primary mammary single cell suspension, we enriched 2000 Procr+ basal cells, 30000 mammary epithelial cells and 30000 mammary immune cells and resuspended total cells at a density of 1000 cells per μl. And then 13,000 single cells were loaded for 10x single cell genomics RNA seq.

OEP004107

Zheng-Jiang Zhu, Interdisciplinary Research Center on Biology and Chemistry (IRCBC), Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences,2022.04.06

Description

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a grand challenge in untargeted metabolomics. Here, we developed an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrated three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we applied KGMN in an in-vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites were validated with in-silico MS/MS tools. Finally, we successfully validated 5 unknown metabolites through the repository-mining and the syntheses of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites towards deciphering dark matters in untargeted metabolomics.

OEP003284

Zheng-Jiang Zhu, Interdisciplinary Research Center on Biology and Chemistry (IRCBC), Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences,2022.02.16

Description

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a grand challenge in untargeted metabolomics. Here, we developed an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrated three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we applied KGMN in an in-vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites were validated with in-silico MS/MS tools. Finally, we successfully validated 5 unknown metabolites through the repository-mining and the syntheses of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites towards deciphering dark matters in untargeted metabolomics.

OEP003157