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Multi-omics joint analysis revealed the metabolic profile of retroperitoneal liposarcoma
Frontiers of Medicine doi: 10.1007/s11684-023-1020-z
Keywords: RLPS proteomics metabolomics lipidomics metabolism
Sepsis biomarkers: an omics perspective
Xiao Liu, Hui Ren, Daizhi Peng
Frontiers of Medicine 2014, Volume 8, Issue 1, Pages 58-67 doi: 10.1007/s11684-014-0318-2
Keywords: sepsis biomarker genomics transcriptomics proteomics metabolomics
Hudan Pan, Yanfang Zheng, Zhongqiu Liu, Zhongwen Yuan, Rutong Ren, Hua Zhou, Ying Xie, Liang Liu
Frontiers of Medicine 2019, Volume 13, Issue 5, Pages 564-574 doi: 10.1007/s11684-018-0676-2
Keywords: arthritis traditional Chinese medicine pharmacological mechanism metabolism adjuvant-induced arthritis omics
Frontiers of Medicine 2022, Volume 16, Issue 4, Pages 596-609 doi: 10.1007/s11684-021-0868-z
Keywords: innate immune checkpoint Siglec10 kidney renal clear cell carcinoma
Applying TCM Constitution Theory to Infertility via Omics
Qi Wang
Engineering 2023, Volume 30, Issue 11, Pages 30-31 doi: 10.1016/j.eng.2023.05.005
Hong LI,Ziding ZHANG
Frontiers of Agricultural Science and Engineering 2016, Volume 3, Issue 2, Pages 102-112 doi: 10.15302/J-FASE-2016100
Keywords: plant–pathogen interactions systems biology omics plant immunity protein–protein interaction network
Technology and Equipment of Big Data on Crop Phenomics
Wen Weiliang , Guo Xinyu, Zhang Ying , Gu Shenghao, Zhao Chunjiang
Strategic Study of CAE 2023, Volume 25, Issue 4, Pages 227-238 doi: 10.15302/J-SSCAE-2023.04.015
Automatic equipment and information technologies make it possible to acquire multi-scale and multi-source heterogeneous data of crops under different growth conditions, forming big data on crop phenomics. This will greatly promote the research progress of crop functional genomics, digital breeding, and smart cultivation. In this paper, the demand for and industrial development of technology and equipment of big data on crop phenomics are analyzed. Then, the current situation of research and development in this area is summarized from five aspects: data acquisition hardware, data transmission, data analysis, knowledge formation, and applications. The problems and developmental trends of relevant technologies, equipment, and industrial application in China are analyzed from the perspectives of high-throughput acquisition and intelligent analysis of big data on crop phenomics. At last, the following suggestions are proposed: achieving breakthroughs regarding key crop phenotyping sensor technologies from the underlying chip level, forming an autonomous phenotyping extraction technology system on the basis of controllable open source, strengthening the standards system construction for big data on crop phenomics, creating a new model of genotype‒phenotype‒environment big data-driven digital breeding and smart cultivation, and building a talent pool and collaborative network for crop phenomics.
Keywords: crop phenomics phenotyping big data technology and equipment for phenotyping multi-omics
The Application of Artificial Intelligence Accelerates G Protein-Coupled Receptor Ligand Discovery Review
Wei Chen,Chi Song,Liang Leng,Sanyin Zhang,Shilin Chen
Engineering 2024, Volume 32, Issue 1, Pages 19-29 doi: 10.1016/j.eng.2023.09.011
Keywords: G protein-coupled receptor Ligand Artificial intelligence Multi-omics Drug discovery
Tao Rui,Xueyou Zhang,Shi Feng,Haitao Huang,Shaowei Zhan,Haiyang Xie,Lin Zhou,Shusen Zheng,Qi Ling,
Engineering 2022, Volume 16, Issue 9, Pages 162-175 doi: 10.1016/j.eng.2021.07.020
Keywords: Hepatocellular carcinoma miRNA cluster Exosome Multi-omics
Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research Article
Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang
Engineering 2021, Volume 7, Issue 12, Pages 1725-1731 doi: 10.1016/j.eng.2020.05.028
Keywords: Multivariate regression methods Reduced rank regression Sparsity Dimensionality reduction Variable selection
Multi-Omics-Guided Discovery of Omicsynins Produced by Streptomyces sp. 1647: Pseudo-Tetrapeptides Article
Hongmin Sun, Xingxing Li, Minghua Chen, Ming Zhong, Yihua Li, Kun Wang, Yu Du, Xin Zhen, Rongmei Gao, Yexiang Wu, Yuanyuan Shi, Liyan Yu, Yongsheng Che, Yuhuan Li, Jian-Dong Jiang, Bin Hong, Shuyi Si
Engineering 2022, Volume 16, Issue 9, Pages 176-186 doi: 10.1016/j.eng.2021.05.010
Keywords: Multi-omics Anti-influenza A virus Anti-coronavirus Streptomyces sp. 1647 Pseudo-tetrapeptides
Yifan Liu, Jing Chen, Zhonglin Liu, Zhaowei Hou, Bo Liang, Liying Wang, Lei Zhou, Libin Shou, Dandan Lin, Shizhong Yang, Jinfeng Liu, Xiaolin Wu, Jidong Gu, Bozhong Mu
Engineering 2023, Volume 24, Issue 5, Pages 265-276 doi: 10.1016/j.eng.2021.08.027
The microbial conversion of alkanes to methane in hydrocarbon contaminated environments is an intrinsic bioremediation strategy under anoxic conditions. However, the mechanism of microbial methanogenic alkane degradation is currently unclear. Under ten-years of continuous efforts, we obtained a methanogenic n-alkane-degrading (C15–C20) enrichment culture that exhibited sustained improvements in the kinetic properties of methane production. The integrated metagenomic and metatranscriptomic analyses revealed that n-alkanes were mainly attacked by members of Desulfosarcinaceae, Firmicutes, and Synergistetes using the fumarate addition strategy, and were then further degraded in a common effort by Tepidiphilus members. Meanwhile, the abundant members of Anaerolineaceae were mainly responsible for cell debris recycling. However, according to the metatranscriptomic analyses, methane was predicted to be produced mainly via H2-dependent methylotrophic methanogenesis, primarily from necromass-derived trimethylamine mediated by Methanomethyliaceae within the candidate phylum Verstraetearchaeota. These findings reveal that H2-dependent methylotrophic methanogens, as well as methylotrophic methanogens, may play important ecological roles in the carbon cycle of hydrocarbon enriched subsurface ecosystems.
Keywords: Methanogenic hydrocarbon degradation Oily sludge Bioremediation Alkanes
Zhengyi Jiang, Zeyu Sun, Xiaoxi Ouyang, Yalei Zhao, Menghao Zhou, Baohong Wang, Qirui Li, Linxiao Fan, Sainan Zhang, Lanjuan Li
Engineering 2020, Volume 6, Issue 11, Pages 1302-1314 doi: 10.1016/j.eng.2020.02.011
Primary hepatocytes (PHCs) are widely used in various fields, but the progressive deterioration of liver-specific features in vitro significantly limits their application. While the transcriptional regulation and whole cell proteome (WCP) of PHCs have been extensively studied, only a small number of studies have addressed the role of posttranslational modifications in this process. To elucidate the underlying mechanisms that induce dedifferentiation, we carried out parallel quantifications of the transcriptome, WCP, ubiquitinome, and phosphoproteome of rat PHCs after 0, 6, 12, 24, and 48 h of in vitro culture. Our data constitute a detailed proteomic analysis of dedifferentiated PHCs including 2196 proteins, 2056 ubiquitinated sites, and 4932 phosphorylated peptides. We revealed a low correlation between the transcriptome and WCP during dedifferentiation. A combined analysis of the ubiquitinome with the corresponding WCP indicated that the dedifferentiation of PHCs led to an increase in nondegradative K27 ubiquitination. Functional analysis of the altered phosphoproteins suggested a significant enrichment in ferroptosis. In all, 404 proteins with both ubiquitination and phosphorylation were identified to be involved in critical metabolic events. Furthermore, Ptbp1, Hnrpd, Hnrnpu, and Srrm2 were identified as hub genes. Taken together, our data provide new insights into proteome dynamics during PHC dedifferentiation and potential targets to inhibit the dedifferentiation process.
Keywords: Ubiquitination Phosphoproteome Proteome Dedifferentiation Primary hepatocytes
Pei Han, Li-Sha Li, Zi-Xi Wang, Lin Xi, Hang Yu, Lin Cong, Zheng-Wei Zhang, Jie Fu, Ran Peng, Li-Bin Pan, Shu-Rong Ma, Xue-Yan Wang, Hong-Tian Wang, Xiang-Dong Wang, Yan Wang, Jin-Lyu Sun, Jian-Dong Jiang
Engineering 2022, Volume 15, Issue 8, Pages 115-125 doi: 10.1016/j.eng.2021.03.013
Due to the worldwide epidemic of allergic disease and a cure nowhere in sight, there is a crucial need to explore its pathophysiological mechanisms. As allergic disease has been associated with gut dysbiosis, we searched for a possible mechanism from the perspective of the molecular interface between host and microbiota with concurrent metabolomics and microbiome composition analysis. Sprague-Dawley rats were injected with Artemisia pollen extract to stimulate a hyper reaction to pollen. This hyper reaction decreased the circulation of valine, isoleucine, aspartate, glutamate, glutamine, indole-propionate (IPA), and myo-inositol, and reduced short-chain fatty acids (SCFAs) in feces. Several beneficial genera belonging to Ruminococcaceae, Lachnospiraceae, and Clostridiales declined in the model group, whereas Helicobacter and Akkermansia were only expressed in the model group. Furthermore, the expression of intestinal claudin-3 and liver fatty acid binding protein was downregulated in the model group and associated with metabolic changes and bacteria. Our results suggest that alterations in amino acids as well as their derivatives (especially valine, and IPA which is the reductive product of tryptophan) , SCFAs, and the gut microbiome (specifically Akkermansia and Helicobacter) may disrupt the intestinal barrier function by inhibiting the expression of claudin proteins and affecting the mucus layer, which further results in hay fever.
Keywords: Metabolome Gut microbiota Hay fever Allergic diseases Intestinal barrier dysfunction
Title Author Date Type Operation
Multi-omics joint analysis revealed the metabolic profile of retroperitoneal liposarcoma
Journal Article
Deciphering the pharmacological mechanism of Guan-Jie-Kang in treating rat adjuvant-induced arthritis using omics
Hudan Pan, Yanfang Zheng, Zhongqiu Liu, Zhongwen Yuan, Rutong Ren, Hua Zhou, Ying Xie, Liang Liu
Journal Article
Innate immune checkpoint Siglec10 in cancers: mining of comprehensive omics data and validation in patient
Journal Article
Systems understanding of plant–pathogen interactions through genome-wide protein–protein interaction networks
Hong LI,Ziding ZHANG
Journal Article
Technology and Equipment of Big Data on Crop Phenomics
Wen Weiliang , Guo Xinyu, Zhang Ying , Gu Shenghao, Zhao Chunjiang
Journal Article
The Application of Artificial Intelligence Accelerates G Protein-Coupled Receptor Ligand Discovery
Wei Chen,Chi Song,Liang Leng,Sanyin Zhang,Shilin Chen
Journal Article
MiR-516a-3p is a Novel Mediator of Hepatocellular Carcinoma Oncogenic Activity and Cellular Metabolism
Tao Rui,Xueyou Zhang,Shi Feng,Haitao Huang,Shaowei Zhan,Haiyang Xie,Lin Zhou,Shusen Zheng,Qi Ling,
Journal Article
Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research
Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang
Journal Article
Multi-Omics-Guided Discovery of Omicsynins Produced by Streptomyces sp. 1647: Pseudo-Tetrapeptides
Hongmin Sun, Xingxing Li, Minghua Chen, Ming Zhong, Yihua Li, Kun Wang, Yu Du, Xin Zhen, Rongmei Gao, Yexiang Wu, Yuanyuan Shi, Liyan Yu, Yongsheng Che, Yuhuan Li, Jian-Dong Jiang, Bin Hong, Shuyi Si
Journal Article
Pan Hudan: Discovery of Anti-Arthritis Mechanism of Traditional Chinese Medicine Compound based on Omics
23 Oct 2021
Conference Videos
Long-Term Cultivation and Meta-Omics Reveal Methylotrophic Methanogenesis in Hydrocarbon-Impacted Habitats
Yifan Liu, Jing Chen, Zhonglin Liu, Zhaowei Hou, Bo Liang, Liying Wang, Lei Zhou, Libin Shou, Dandan Lin, Shizhong Yang, Jinfeng Liu, Xiaolin Wu, Jidong Gu, Bozhong Mu
Journal Article
Temporal Integrative Omics Reveals an Increase in Nondegradative Ubiquitylation during Primary Hepatocyte
Zhengyi Jiang, Zeyu Sun, Xiaoxi Ouyang, Yalei Zhao, Menghao Zhou, Baohong Wang, Qirui Li, Linxiao Fan, Sainan Zhang, Lanjuan Li
Journal Article
Multi-Omics Analysis Provides Insight into the Possible Molecular Mechanism of Hay Fever Based on Gut
Pei Han, Li-Sha Li, Zi-Xi Wang, Lin Xi, Hang Yu, Lin Cong, Zheng-Wei Zhang, Jie Fu, Ran Peng, Li-Bin Pan, Shu-Rong Ma, Xue-Yan Wang, Hong-Tian Wang, Xiang-Dong Wang, Yan Wang, Jin-Lyu Sun, Jian-Dong Jiang
Journal Article