GPX-GI is a cytosolic tetrameric Se-dependent glutathione peroxidase, similar in properties to GPX-1. Unlike the almost ubiquitous GPX-1, GPX-GI is mainly expressed in the epithelium of gastrointestinal tract. GPX-GI ...GPX-GI is a cytosolic tetrameric Se-dependent glutathione peroxidase, similar in properties to GPX-1. Unlike the almost ubiquitous GPX-1, GPX-GI is mainly expressed in the epithelium of gastrointestinal tract. GPX-GI contributes to at least fifty percent of GPX activity in rodent small intestmal epithelium. The total GPX activity consists of at least 70% of selenium-dependent GPX activity in this compartment.By analyzing a panel of mouse mterspecies DNA from the Jackson Laboratory's backcross resource,we mapped Gpx2 gene to mouse chromosome 12 between D12Mit4 and D12Mit5, near the Ccs1 locus which contains a colon cancer susceptibility gene. A pseudogene, Gpx2-ps is mapped to mouse chromosome 7.Comparison of Gpx2 gene expression in three pairs of C57BL/6Ha and ICR/Ha mice which are respectively resistant and sensitive to dimethylhydrazine-induced colon cancer, we found a higher Gpx2 mRNA level in C57BL/6Ha colon than ICR/Ha colon. Interestingly, a lower level of GPX activity is found in the resistant strain of mice. Because GPX-1 has three times higher specific activity than GPX GI, our data suggest that the decreased GPX activity may result from a higher level of Gpx2 gene expression in those cells co-express GPx1 gene展开更多
Single-cell transcriptome sequencing technology has been applied to decode the cell types and functional states of immune cells,revealing their tissue-specific gene expression patterns and functions in cancer immunity...Single-cell transcriptome sequencing technology has been applied to decode the cell types and functional states of immune cells,revealing their tissue-specific gene expression patterns and functions in cancer immunity.Comprehensive assessments of immune cells within and across tis-sues will provide us with a deeper understanding of the tumor immune system in general.Here,we present Cross-tissue Immune cell type or state Enrichment analysis of gene lists for Cancer(ClEC),the first web-based application that integrates database and enrichment analysis to estimate the cross-tissue immune cell types or states.CiEC version 1.0 consists of 480 samples covering primary tumor,adjacent normal tis-sue,lymph node,metastasis tissue,and peripheral blood from 323 cancer patients.By applying integrative analysis,we constructed an immune cell type/state map for each context,and adopted our previously developed Kyoto Encyclopedia of Genes and Genomes(KEGG)Orthology Based Annotation System(KOBAS)algorithm to estimate the enrichment for context-specific immune cell types/states.In addition,CIEC also provides an easy-to-use online interface for users to comprehensively analyze the immune cell characteristics mapped across multiple tissues,including expression map,correlation,similar gene detection,signature score,and expression comparison.We believe that ClEC will be a valu-able resource for exploring the intrinsic characteristics of immune cells in cancer patients and for potentially guiding novel cancer-immune bio-marker development and immunotherapy strategies.ClEC is freely accessible at http://ciec.gene.ac/.展开更多
OBJECTIVE： To construct a protein-protein interaction（PPI） network in hypertension patients with blood-stasis syndrome（BSS） by using digital gene expression（DGE） sequencing and database mining techniques.METHOD...OBJECTIVE： To construct a protein-protein interaction（PPI） network in hypertension patients with blood-stasis syndrome（BSS） by using digital gene expression（DGE） sequencing and database mining techniques.METHODS： DGE analysis based on the Solexa Genome Analyzer platform was performed on vascular endothelial cells incubated with serum of hypertension patients with BSS. The differentially expressed genes were f iltered by comparing the expression levels between the different experimental groups. Then functional categories and e nriched pathways of the unique genes for BSS were analyzed using Database for Annotation, Visualization and Integrated Discovery（DAVID） to select those in the enrichment pathways. I nterologous Interaction Database（I2D） was used to construct PPI networks with the selected genes for hypertension patients with BSS. The potential candidate genes related to BSS were identif ied by comparing the number of relationships among genes. Confi rmed by quantitative reverse transcription-polymerase chain reaction（q RTPCR）, gene ontology（GO） analysis was used to infer the functional annotations of the potential candidate genes for BSS.RESULTS： With gene enrichment analysis using DAVID, a list of 58 genes was chosen from the unique genes. The selected 58 genes were analyzed using I2 D, and a PPI network was constructed. Based on the network analysis results, candidate genes for BSS were identifi ed：DDIT3, JUN, HSPA8, NFIL3, HSPA5, HIST2H2 BE, H3F3 B, CEBPB, SAT1 and GADD45 A. Verif ied through qRT-PCR and analyzed by GO, the functional annotations of the potential candidate genes were explored.CONCLUSION： Compared with previous methodologies reported in the literature, the present DGE analysis and data mining method have shown a great improvement in analyzing BSS.展开更多
Spatial statistics are crucial for analyzing clustering patterns in various spaces,such as the distribution of trees in a forest or stars in the sky.Advances in spatial biology,such as single-cell spatial transcriptom...Spatial statistics are crucial for analyzing clustering patterns in various spaces,such as the distribution of trees in a forest or stars in the sky.Advances in spatial biology,such as single-cell spatial transcriptomics,enable researchers to map gene expression patterns within tissues,offering unprecedented insights into cellular functions and disease pathology.Common methods for deriving spatial relationships include density-based methods(quadrat analysis,kernel density estimators)and distance-based methods(nearest-neighbor distance[NND],Ripley’s K function).While density-based methods are effective for visualization,they struggle with quantification due to sensitivity to parameters and complex significance tests.In contrast,distance-based methods offer robust frameworks for hypothesis testing,quantifying spatial clustering or dispersion,and facilitating comparisons with models such as uniform random distributions or Poisson processes[1,2].展开更多
Background:Genome-wide association studies(GWASs)have identified thousands of genetic variants that are associated with many complex traits.However,their biological mechanisms remain largely unknown.Transcriptome-wide...Background:Genome-wide association studies(GWASs)have identified thousands of genetic variants that are associated with many complex traits.However,their biological mechanisms remain largely unknown.Transcriptome-wide association studies(TWAS)have been recently proposed as an invaluable tool for investigating the potential gene regulatory mechanisms underlying variant-trait associations.Specifically,TWAS integrate GWAS with expression mapping studies based on a common set of variants and aim to identify genes whose GReX is associated with the phenotype.Various methods have been developed for performing TWAS and/or similar integrative analysis.Each such method has a different modeling assumption and many were initially developed to answer different biological questions.Consequently,it is not straightforward to understand their modeling property from a theoretical perspective.Results:We present a technical review on thirteen TWAS methods.Importantly,we show that these methods can all be viewed as two-sample Mendelian randomization(MR)analysis,which has been widely applied in GWASs for examining the causal effects of exposure on outcome.Viewing different TWAS methods from an MR perspective provides us a unique angle for understanding their benefits and pitfalls.We systematically introduce the MR analysis framework,explain how features of the GWAS and expression data influence the adaptation of MR for TWAS,and re-interpret the modeling assumptions made in different TWAS methods from an MR angle.We finally describe future directions for TWAS methodology development.Conclusions:We hope that this review would serve as a useful reference for both methodologists who develop TWAS methods and practitioners who perform TWAS analysis.展开更多
文摘GPX-GI is a cytosolic tetrameric Se-dependent glutathione peroxidase, similar in properties to GPX-1. Unlike the almost ubiquitous GPX-1, GPX-GI is mainly expressed in the epithelium of gastrointestinal tract. GPX-GI contributes to at least fifty percent of GPX activity in rodent small intestmal epithelium. The total GPX activity consists of at least 70% of selenium-dependent GPX activity in this compartment.By analyzing a panel of mouse mterspecies DNA from the Jackson Laboratory's backcross resource,we mapped Gpx2 gene to mouse chromosome 12 between D12Mit4 and D12Mit5, near the Ccs1 locus which contains a colon cancer susceptibility gene. A pseudogene, Gpx2-ps is mapped to mouse chromosome 7.Comparison of Gpx2 gene expression in three pairs of C57BL/6Ha and ICR/Ha mice which are respectively resistant and sensitive to dimethylhydrazine-induced colon cancer, we found a higher Gpx2 mRNA level in C57BL/6Ha colon than ICR/Ha colon. Interestingly, a lower level of GPX activity is found in the resistant strain of mice. Because GPX-1 has three times higher specific activity than GPX GI, our data suggest that the decreased GPX activity may result from a higher level of Gpx2 gene expression in those cells co-express GPx1 gene
基金supported by the National Key R&D Program of China(Grant No.2022YFF1203303)the Basic and Applied Basic Research Foundation of Science and Technology Research Project of Guangdong Province(Grant Nos.A2024164,A2024270,A2023216,A2022524,and A2020304)+4 种基金the Guangdong Basic and Applied Basic Research Foundation(Grant No.2022A1515220217)the Science and Technology Program of Guangzhou(Grant Nos.202201010840,202201010810,202102080532,202002030032,202002020023,and 20211A011116)the Health Commission Program of Guangzhou(Grant Nos.20212A010025 and 20201A010085)the Science and Technology Project of Panyu,Guangzhou(Grant Nos.2022-Z04-009,2022-Z04-090,2022-Z04-072,and 2021-Z04-053)the Scientific Research Project of Guangzhou Panyu Central Hospital(Grant Nos.PY-2023-001,PY-2023-002,PY-2023-003,PY-2023-004,PY-2023-005,2022Y002,2021Y004,and 2021Y002),China.
文摘Single-cell transcriptome sequencing technology has been applied to decode the cell types and functional states of immune cells,revealing their tissue-specific gene expression patterns and functions in cancer immunity.Comprehensive assessments of immune cells within and across tis-sues will provide us with a deeper understanding of the tumor immune system in general.Here,we present Cross-tissue Immune cell type or state Enrichment analysis of gene lists for Cancer(ClEC),the first web-based application that integrates database and enrichment analysis to estimate the cross-tissue immune cell types or states.CiEC version 1.0 consists of 480 samples covering primary tumor,adjacent normal tis-sue,lymph node,metastasis tissue,and peripheral blood from 323 cancer patients.By applying integrative analysis,we constructed an immune cell type/state map for each context,and adopted our previously developed Kyoto Encyclopedia of Genes and Genomes(KEGG)Orthology Based Annotation System(KOBAS)algorithm to estimate the enrichment for context-specific immune cell types/states.In addition,CIEC also provides an easy-to-use online interface for users to comprehensively analyze the immune cell characteristics mapped across multiple tissues,including expression map,correlation,similar gene detection,signature score,and expression comparison.We believe that ClEC will be a valu-able resource for exploring the intrinsic characteristics of immune cells in cancer patients and for potentially guiding novel cancer-immune bio-marker development and immunotherapy strategies.ClEC is freely accessible at http://ciec.gene.ac/.
基金supported by the National Natural Science Foundation of China (No. 81173157)the Guangdong Natural Science Foundation (No. 10151063201000045)
文摘OBJECTIVE： To construct a protein-protein interaction（PPI） network in hypertension patients with blood-stasis syndrome（BSS） by using digital gene expression（DGE） sequencing and database mining techniques.METHODS： DGE analysis based on the Solexa Genome Analyzer platform was performed on vascular endothelial cells incubated with serum of hypertension patients with BSS. The differentially expressed genes were f iltered by comparing the expression levels between the different experimental groups. Then functional categories and e nriched pathways of the unique genes for BSS were analyzed using Database for Annotation, Visualization and Integrated Discovery（DAVID） to select those in the enrichment pathways. I nterologous Interaction Database（I2D） was used to construct PPI networks with the selected genes for hypertension patients with BSS. The potential candidate genes related to BSS were identif ied by comparing the number of relationships among genes. Confi rmed by quantitative reverse transcription-polymerase chain reaction（q RTPCR）, gene ontology（GO） analysis was used to infer the functional annotations of the potential candidate genes for BSS.RESULTS： With gene enrichment analysis using DAVID, a list of 58 genes was chosen from the unique genes. The selected 58 genes were analyzed using I2 D, and a PPI network was constructed. Based on the network analysis results, candidate genes for BSS were identifi ed：DDIT3, JUN, HSPA8, NFIL3, HSPA5, HIST2H2 BE, H3F3 B, CEBPB, SAT1 and GADD45 A. Verif ied through qRT-PCR and analyzed by GO, the functional annotations of the potential candidate genes were explored.CONCLUSION： Compared with previous methodologies reported in the literature, the present DGE analysis and data mining method have shown a great improvement in analyzing BSS.
基金Daniel Shafiee Kermany,Ju Young Ahn,Matthew Vasquez,Lin Wang,Kai Liu,Raksha Raghunathan,Jianting Sheng,Hong Zhao,and Stephen Tin Chi Wong are supported by NCI U01CA252553,NCI R01CA238727,NCI R01CA177909,NCI R01CA244413John S.Dunn Research Foundation,and Ting Tsung and Wei Fong Chao Foundation+3 种基金Xiang Hong-Fei Zhang,Zhan Xu,Xiaoxin Hao,Weijie Zhang are supported by US Department of Defense DAMD W81XWH-16-1-0073(Era of Hope Scholarship)NCI R01CA183878,NCI R01CA251950,NCI U01CA252553,DAMD W81XWH-20-1-0375Breast Cancer Research Foundation,and McNair Medical Institute.Vahid Afshar-Kharghan,Min Soon Cho,Wendolyn Carlos-AlcaldeHani Lee are supported by NCI R01CA177909,NCI R01CA016672,NCI R01CA275762,and NCI P50CA217685.
文摘Spatial statistics are crucial for analyzing clustering patterns in various spaces,such as the distribution of trees in a forest or stars in the sky.Advances in spatial biology,such as single-cell spatial transcriptomics,enable researchers to map gene expression patterns within tissues,offering unprecedented insights into cellular functions and disease pathology.Common methods for deriving spatial relationships include density-based methods(quadrat analysis,kernel density estimators)and distance-based methods(nearest-neighbor distance[NND],Ripley’s K function).While density-based methods are effective for visualization,they struggle with quantification due to sensitivity to parameters and complex significance tests.In contrast,distance-based methods offer robust frameworks for hypothesis testing,quantifying spatial clustering or dispersion,and facilitating comparisons with models such as uniform random distributions or Poisson processes[1,2].
基金the National Institutes of Health(NIH)Grants RO1HG009124 and the National Science Foundation(NSF)Grant DMS1712933.
文摘Background:Genome-wide association studies(GWASs)have identified thousands of genetic variants that are associated with many complex traits.However,their biological mechanisms remain largely unknown.Transcriptome-wide association studies(TWAS)have been recently proposed as an invaluable tool for investigating the potential gene regulatory mechanisms underlying variant-trait associations.Specifically,TWAS integrate GWAS with expression mapping studies based on a common set of variants and aim to identify genes whose GReX is associated with the phenotype.Various methods have been developed for performing TWAS and/or similar integrative analysis.Each such method has a different modeling assumption and many were initially developed to answer different biological questions.Consequently,it is not straightforward to understand their modeling property from a theoretical perspective.Results:We present a technical review on thirteen TWAS methods.Importantly,we show that these methods can all be viewed as two-sample Mendelian randomization(MR)analysis,which has been widely applied in GWASs for examining the causal effects of exposure on outcome.Viewing different TWAS methods from an MR perspective provides us a unique angle for understanding their benefits and pitfalls.We systematically introduce the MR analysis framework,explain how features of the GWAS and expression data influence the adaptation of MR for TWAS,and re-interpret the modeling assumptions made in different TWAS methods from an MR angle.We finally describe future directions for TWAS methodology development.Conclusions:We hope that this review would serve as a useful reference for both methodologists who develop TWAS methods and practitioners who perform TWAS analysis.
